Added(A)/Deleted(D) following books

A Microwave_engineering__by_D.M.Pozar_/Chapter_10_ACTIVE_MICROWAVE_CIRCUITS_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_12_INTRODUCTION_TO_MICROWAVE_SYSTEMS_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_1_ELECTROMAGNETIC_THEORY_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_2_TRANSMISSION_LINE_THEORY_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_3_TRANSMISSION_LINE_AND_WAVEGUIDES_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_4_MICROWAVE_NETWORK_ANALYSIS_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_5_IMPEDENCE_MATCHING_AND_TUNNING_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_6_MICROWAVE_RESONATORS_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_7_POWER_DIVIDERS_DIRECTIONAL_COUPLERS_AND_HYBRIDS_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_8_MICROWAVE_FILTERS_3.ipynb A Microwave_engineering__by_D.M.Pozar_/Chapter_9_THEORY_AND_DESIGN_OF_FERRIMAGNETIC_COMPONENTS_3.ipynb A Microwave_engineering__by_D.M.Pozar_/screenshots/chap_1.png A Microwave_engineering__by_D.M.Pozar_/screenshots/chap_2.png A Microwave_engineering__by_D.M.Pozar_/screenshots/chap_3.png
author: Trupti Kini 2016-07-10 23:30:35 +0600
committer: Trupti Kini 2016-07-10 23:30:35 +0600
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tree: 6314cda453043b792b2e40fcbb1cd12146ef18a6 /Microwave_engineering__by_D.M.Pozar_
parent: 9c2e3ab9e98db0de17a5def2f9c59635aa462895 (diff)
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diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_10_ACTIVE_MICROWAVE_CIRCUITS_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_10_ACTIVE_MICROWAVE_CIRCUITS_3.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 10 ACTIVE MICROWAVE CIRCUITS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:10.1 page.no:554"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the equivalent noise temperature in kelwin =  170.074792027\n",
+      "the total noise power out of the amplifier in dBm will be =  -60.6767683732\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to determine the equivalent noise temperature of the amplifier .\n",
+    "from math import log10\n",
+    "\n",
+    "T1=290;P1=-62;G=100;B=10**9;k=1.38*10**-23;\n",
+    "T2 =77; P2 = -64.7; Ts =450;\n",
+    "Y=P1-P2; # Yfactor in db.\n",
+    "Y=10**0.27;\n",
+    "Te=(T1-Y*T2)/(Y-1);\n",
+    "Po=G*k*B*(Ts+Te);\n",
+    "Po=10*log10(Po/0.001); #/ converting in to dBm.\n",
+    "print \"the equivalent noise temperature in kelwin = \",Te\n",
+    "print \"the total noise power out of the amplifier in dBm will be = \",Po"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:10.2 page.no:557"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the dynamic range in dB = 67.467\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the dynamic range of the amplifier .\n",
+    "from math import log10\n",
+    "\n",
+    "G=20;F=3.5; # in db.\n",
+    "k=1.38*10**-23;To=290;B=2*10**9;\n",
+    "# output noise power => No=G⇤F⇤k⇤To⇤B.so in dbm it will be\n",
+    "No=20+3.5+10*log10((k*To*B)/0.001);\n",
+    "DR=10-No;\n",
+    "print \"the dynamic range in dB = %.3f\"%DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:10.3 page.no:558"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the noise figure of the cascade in dB =  2.0\n",
+      "the noise figure of the amplifier in dB =  0.0\n",
+      "the noise figure of the line in dB =  2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to calculate the noise figure ig anteena is replaced by amplifier .\n",
+    "from math import log10\n",
+    "\n",
+    "L=10**0.2;T=300;To=290;Te=150;\n",
+    "Fl=1+(L-1.)*(T/To);\n",
+    "Fld=10.*log10(Fl); # converting in to dBm.\n",
+    "Fa=1.+(Te/To)\n",
+    "Fad=10.*log10(Fa);# converting in to dBm.\n",
+    "Fcas=Fl+L*(Fa-1.);\n",
+    "Fcasd=10*log10(Fcas);# converting in to dBm.\n",
+    "print \"the noise figure of the cascade in dB = \",Fcasd\n",
+    "print \"the noise figure of the amplifier in dB = \",Fad\n",
+    "print \"the noise figure of the line in dB = \",Fld"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:10.4 page.no:562"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "junction resistance for Io=0, in ohm =  250000.0\n",
+      "junction resistance for Io=0, in ohm = 415.973\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to calculate the impedence of the diode. 70\n",
+    "\n",
+    "Cp=0.1e-12;Lp=2e-9;Cj=0.15e-12;Rs=10.;Is =0.1e-6;\n",
+    "Io1=0.;Io2=60e-6;alpha=0.04e3;\n",
+    "R1j=1./(alpha*(Io1+Is)); # for Io=0.\n",
+    "R2j=1./(alpha*(Io2+Is)); # for Io=60 mA.\n",
+    "print \"junction resistance for Io=0, in ohm = \",R1j \n",
+    "print \"junction resistance for Io=0, in ohm = %.3f\"%R2j"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example:10.5 page.no:579"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "for series circuit = 0.49\n",
+      "for series circuit = 10.16\n",
+      "for shunt circuit = 0.03\n",
+      "for shunt circuit = 7.62\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to obtain the greatest ratio of off to on attenuation .\n",
+    "from math import pi,log10\n",
+    "from sympy import I\n",
+    "\n",
+    "Cj=0.1*10**-12;Rr=1;Rf=5;\n",
+    "Li=0.4*10**-9;f=5*10**9;Zo=50;\n",
+    "w=2*pi*f;\n",
+    "Zr=Rr+I*((w*Li)-(1/(w*Cj)));\n",
+    "Zf=Rf+(I*w*Li);\n",
+    "# for series circuit .\n",
+    "ILon=-20*log10(abs((2*Zo)/(2*Zo+Zf)));\n",
+    "ILoff=-20*log10(abs((2*Zo)/(2*Zo+Zr)));\n",
+    "# for shunt circuit .\n",
+    "ILon1=-20*log10(abs((2*Zr)/(2*Zr+Zo)));\n",
+    "ILoff1=-20*log10(abs((2*Zf)/(2*Zf+Zo)));\n",
+    "print \"for series circuit = %.2f\"%ILon\n",
+    "print \"for series circuit = %.2f\"%ILoff\n",
+    "print \"for shunt circuit = %.2f\"%ILon1\n",
+    "print \"for shunt circuit = %.2f\"%ILoff1"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_12_INTRODUCTION_TO_MICROWAVE_SYSTEMS_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_12_INTRODUCTION_TO_MICROWAVE_SYSTEMS_3.ipynb
new file mode 100644
index 00000000..bd76d16b
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_12_INTRODUCTION_TO_MICROWAVE_SYSTEMS_3.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 12 INTRODUCTION TO MICROWAVE SYSTEMS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 12.1 page.no:660"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the radiation intensity is given by =  Io*ko*l*no*r*exp(-I*ko*r)*exp(I*conjugate(ko)*conjugate(r))*sin(theta)*sin(conjugate(theta))*conjugate(Io)*conjugate(ko)*conjugate(l)/(16*pi**2*conjugate(r))\n",
+      "directivity is given by =  3/2\n",
+      "the effective area of the dipole =  3*lamda**2/(8*pi)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to compute directivity , radiation intensity,F,the effective area.\n",
+    "from sympy import symbols,conjugate,integrate,sin,I,exp,pi\n",
+    "\n",
+    "Etheta,Hphi,ko,no,Io,l,r,theta,C,phi,lamda=symbols('Etheta,Hphi,ko,no,Io,l,r,theta,C,phi,lamda')\n",
+    "Etheta=((I*ko*no*Io*l)/(4*pi*r))*sin(theta)*exp(-I*ko*r);\n",
+    "Hphi=((I*ko*Io*l)/(4*pi*r))*sin(theta)*exp(-I*ko*r);\n",
+    "F=(r**2)*(Etheta*conjugate(Hphi));\n",
+    "Prad=C*integrate(integrate(sin(theta)**3,(theta,0,pi)),(phi,0,2*pi));\n",
+    "Prad=8*pi*C/3; \n",
+    "D=4*pi*C/Prad;\n",
+    "Ac=((lamda**2)*D)/(4*pi);\n",
+    "print \"the radiation intensity is given by = \",F\n",
+    "print \"directivity is given by = \",D\n",
+    "print \"the effective area of the dipole = \",Ac"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 12.2 page.no:666"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "received power in dBm will be = -75.31\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the reactive power in dbm.\n",
+    "from math import pi,log10\n",
+    "\n",
+    "Pt=120;f=6*10**9;\n",
+    "Gt =10**4.2; Gr =10**3.1;\n",
+    "lamda=0.05;R=3.59*10**7;\n",
+    "Pr=(Pt*Gt*Gr*(lamda**2))/((4*pi*R)**2);\n",
+    "Pr=10*log10(Pr/0.001);\n",
+    "print \"received power in dBm will be = %.2f\"%Pr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 12.3 page.no:669"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input SNR in dB = 35.38\n",
+      "output SNR in dB = 29.78\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to calculate the input and output SNR.\n",
+    "from math import log10\n",
+    "\n",
+    "f=4*10**9;B=1*10**6;Grf=10**2;Gif=10**3;\n",
+    "Lt=10**0.15;Lm=10**0.6;To=290;\n",
+    "Fm=10**0.7;Tm=(Fm-1)*To;Tp=300;Tb=200;eta=0.9;\n",
+    "Frf=10**0.3;Fif=10**0.11;k=1.38*10**-23;\n",
+    "Trf=(Frf-1)*To;\n",
+    "Tif=(Fif-1)*To;\n",
+    "Trec=Trf+(Tm/Grf)+((Tif*Lm)/Grf);\n",
+    "Ttl=(Lt-1)*Tp;\n",
+    "Ta=eta*Tb+(1-eta)*Tp;\n",
+    "Ni=k*B*Ta;\n",
+    "Ni=10*log10(Ni/0.001); # converting in to dBm.\n",
+    "si=-80; # in dBm.\n",
+    "SNRi=si-Ni; # input SNR.\n",
+    "Tsys=Ta+Ttl+Lt*Trec;\n",
+    "SNRo=si-10*log10((k*B*Tsys)/0.001);\n",
+    "print \"input SNR in dB = %.2f\"%SNRi\n",
+    "print \"output SNR in dB = %.2f\"%SNRo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 12.4 page.no:675"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the maximum range of the radar in meter = 8113.46\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the maximum range of radar.\n",
+    "from math import pi\n",
+    "\n",
+    "G=10**2.8;Pt=2000;sigma=12;\n",
+    "Pmin =10**-12; lamda =0.03;\n",
+    "Rmax=((Pt*(G**2)*sigma*(lamda**2))/(((4*pi)**3)*Pmin))**(0.25);\n",
+    "print \"the maximum range of the radar in meter = %.2f\"%Rmax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 12.5 page.no:693"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "THE J/S ration for the SSJ case in dB is = 16.50\n",
+      "THE J/S ratio for the SOJ case in dB is = 0.01\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the J/S ratio.\n",
+    "from math import pi,log10\n",
+    "\n",
+    "Gr=3162;Pj=1000.;R=3000.;Br=1e6;Bj=20e6;\n",
+    "Gj=10.;lamda=0.03;Pt=1e5;sigma=4.;Rj=10000.;\n",
+    "x=(Pj/Pt)*((4.*pi*(R**2)*Gj)/(sigma*Gr))*(Br/Bj); #x=J/S\n",
+    "x=10.*log10(x);\n",
+    "Grsl=10**(3.5-2);# radar anteena gain in its sidelobe region .\n",
+    "x1=(Pj/Pt)*(((R**4)*Gj*Grsl)/((Gr**2)*(Rj**2)))*(Br/Bj) ;\n",
+    "#x1=10*log10(x1);\n",
+    "print \"THE J/S ration for the SSJ case in dB is = %.2f\"%x\n",
+    "print \"THE J/S ratio for the SOJ case in dB is = %.2f\"%x1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example 12.6 page.no:695"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the power density in the main beam of the anteena at a distance of 20 m in mw/cmˆ2 = 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to calculate the power density of 20 m from the anteena .\n",
+    "from math import pi\n",
+    "\n",
+    "G=10**4;Pin=5;R=20;\n",
+    "S=(Pin*G)/(4*pi*(R**2))*0.1;\n",
+    "print \"the power density in the main beam of the anteena at a distance of 20 m in mw/cmˆ2 = %.1f\"%S"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_1_ELECTROMAGNETIC_THEORY_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_1_ELECTROMAGNETIC_THEORY_3.ipynb
new file mode 100644
index 00000000..2d841a1c
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_1_ELECTROMAGNETIC_THEORY_3.ipynb
@@ -0,0 +1,298 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1 ELECTROMAGNETIC THEORY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example 1.1,page no.17"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "phase velocity in meter per second= 6.54e+07 \n",
+      "wavelength in meter= 2.18e-02 \n",
+      "wave impedence in ohm= 2.47e+02 \n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to calculate wavelength , phase velocity and wave impedence .\n",
+    "from math import pi,sqrt\n",
+    "\n",
+    "f=3*10**9;\n",
+    "mur =3;\n",
+    "muo =4*pi*10**-7;\n",
+    "eipsilao =8.854*10**-12;\n",
+    "eipsilar =7;\n",
+    "mue=muo*mur;\n",
+    "eipsila=eipsilao*eipsilar;\n",
+    "Vp=sqrt(1/(mue*eipsila));\n",
+    "lamda=Vp/f;\n",
+    "eta=sqrt(mue/eipsila);\n",
+    "#Result\n",
+    "print\"phase velocity in meter per second= %.2e \"%Vp;\n",
+    "# phase velocity .\n",
+    "print\"wavelength in meter= %.2e \"%lamda # wavelength.\n",
+    "print\"wave impedence in ohm= %.2e \"%eta # wave impedence ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:−1.2  page no.−20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skin depth of aluminium in meter=8.15e-07 \n",
+      "skin depth of copper in meter= 6.60e-07\n",
+      "skin depth of gold in meter= 7.86e-07\n",
+      "skin depth of silver in meter= 6.41e-07\n"
+     ]
+    }
+   ],
+   "source": [
+    "# progarm to find out skin depth of aluminium, copper , gold and silver at frequency 10GHZ.\n",
+    "from math import sqrt\n",
+    "\n",
+    "f=10*10**9;\n",
+    "muo=4*pi*10**-7; # permeability in free space.\n",
+    "omega=2*pi*f;\n",
+    "sigma_aluminium =3.816*10**7;\n",
+    "sigma_copper =5.813*10**7;\n",
+    "sigma_gold =4.098*10**7;\n",
+    "sigma_silver =6.173*10**7;\n",
+    "delta1=sqrt(2/(omega*muo*sigma_aluminium));\n",
+    "delta2=sqrt(2/(omega*muo*sigma_copper));\n",
+    "delta3=sqrt(2/(omega*muo*sigma_gold));\n",
+    "delta4=sqrt(2/(omega*muo*sigma_silver));\n",
+    "#result\n",
+    "print\"skin depth of aluminium in meter=%.2e \"%delta1; # skin depth of aluminium\n",
+    "print\"skin depth of copper in meter= %.2e\"%delta2; # skin depth of copper .\n",
+    "print\"skin depth of gold in meter= %.2e\"%delta3; #skin depth of gold .\n",
+    "print\"skin depth of silver in meter= %.2e\"%delta4; # skin depth of silver ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:−1.3 page no.−24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "for z<0, E1= A*N*x*exp(I*ko*z)\n",
+      "for z<0, H1= -A*N*y*exp(I*ko*z)\n",
+      "for z>0, E2= B*N*x*exp(-I*ko*z)\n",
+      "for z>0, H2= B*N*y*exp(-I*ko*z)\n",
+      "Matrix([[-Jo/2], [-Jo/2]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to find the resulting fields by assuming plane waves on either side of the current sheet and enforcing the boundary conditions.\n",
+    "from sympy import symbols,Matrix,exp,I\n",
+    "\n",
+    "E,x,E1,E2,H1,H2,z,Jo,A,B,N,n,ko,y,l,m = symbols('E,x,E1,E2,H1,H2,z,Jo,A,B,N,n,ko,y,l,m');\n",
+    "E1=A*N*exp(I*ko*z)*x; # x component of elec tricfield (region z<0).\n",
+    "H1=A*N*exp(I*ko*z)*(-y); # y component of magnetic field (region z<0).\n",
+    "E2=B*N*exp(-I*ko*z)*x;# x component of electric field (region z>0).\n",
+    "H2=B*N*exp(-I*ko*z)*y;  # y component of electric field (region z>0).\n",
+    "print \"for z<0, E1=\",E1\n",
+    "print \"for z<0, H1=\",H1\n",
+    "print \"for z>0, E2=\",E2\n",
+    "print \"for z>0, H2=\",H2\n",
+    "#from boundary conditions\n",
+    "c=Matrix([[-1,-1],[1,-1]]);\n",
+    "d=Matrix([[A],[B]]);\n",
+    "d=c.inv()*Matrix([[Jo],[0]]);\n",
+    "print d;\n",
+    "#result\n",
+    "# A=−Jo/2; B=−Jo/2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:−1.4 page no.−38."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "skin depth in meter= 2.087e-06\n",
+      "propagation comstant = 479049.101381194 + 479049.101381194*I\n",
+      "intrinsic impedence in ohm= 0.00824099606711155 + 0.00824099606711155*I\n",
+      "reflection coefficient= (-376.991759003933 + 0.00824099606711155*I)/(377.008240996067 + 0.00824099606711155*I)\n",
+      "transmission coefficient=  (0.0164819921342231 + 0.0164819921342231*I)/(377.008240996067 + 0.00824099606711155*I)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to compute propagation constan , impedence , skin depth , reflection and transmission coefficient\n",
+    "from sympy import I\n",
+    "from numpy import pi,sqrt\n",
+    "\n",
+    "f=1*10**9;\n",
+    "omega=2*pi*f;\n",
+    "sigma=5.813*10**7; # for copper .\n",
+    "mue=4*pi*10**-7; # permeability in free space.\n",
+    "delta=sqrt(2/(mue*sigma*omega)); # skin depth .\n",
+    "gama=((1+I)/delta); # propagation constant .\n",
+    "eta=gama/sigma; # impedence\n",
+    "etao=377; # intrinsic impedence in free space .\n",
+    "tao=((eta-etao)/(eta+etao)); # reflection coefficient .\n",
+    "t=(2*eta)/(eta+etao); # transmission coefficient\n",
+    "# result\n",
+    "print \"skin depth in meter= %.3e\"%delta\n",
+    "print \"propagation comstant =\",gama\n",
+    "print \"intrinsic impedence in ohm=\",eta\n",
+    "print \"reflection coefficient=\",tao\n",
+    "print \"transmission coefficient= \",t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:−1.5 page no.−42."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The wave impendaces are 377 ohm , 236 ohm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ljmHY1cy2y9geFi1hdZyy4Y5tTrlZsCCsQrrnntrKzhaHOJ2jRZI2b96QtBmw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\nfZK6maeegk03rVyjUCQjgS0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kt6IynQju769F230k/SX6fpekPykk7Hhf0pEZsi9USObx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\nfzl2Kjb4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b2c4d10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# example:−2.7.page no.−50.\n",
+    "# program to plot the reflection coefficients for parallel and perpendicular polarized plane waves incident from free space on to a dielectric region with Er=2.55,versus incidence angle.\n",
+    "\n",
+    "%matplotlib inline\n",
+    "from pylab import plot,subplot,title,xlabel,ylabel\n",
+    "from sympy import acos,asin\n",
+    "import numpy as n\n",
+    "import math as m\n",
+    "\n",
+    "Er=2.55; # relaitve permittivity of dielectric medium .\n",
+    "N1=377.; # intrinsic impedence\n",
+    "N2=N1/m.sqrt(Er); # intrinsic impedence of dielectric medium.\n",
+    "xb=asin(m.sqrt(1./(1.+1/2.55)));# brewster angle valid only in case of parallel polarization.\n",
+    "xt=acos(m.sqrt(1.-(1./Er)**2.*m.sin(xb))); # angle of transmission .\n",
+    "xi=n.arange(0,m.pi/2,0.05); # incidence\n",
+    "print \"The wave impendaces are %d ohm , %d ohm\"%(N1,N2)\n",
+    "# for parallel polarization angle .\n",
+    "N2=N2*m.cos(xt);\n",
+    "N1=N1*n.cos(xi);\n",
+    "Tpar=(N2-N1)/(N2+N1);\n",
+    "w=abs(Tpar);\n",
+    "\n",
+    "# result\n",
+    "subplot(121)\n",
+    "title ('parallel polarization');\n",
+    "xlabel('xi(incidence angle)');\n",
+    "ylabel('Tpar(reflection coefficient)');\n",
+    "plot(xi,w)\n",
+    "# for perpendicular polarization .\n",
+    "#NOTE:− in case of this polarization . there is no brewster angle .\n",
+    "xt=acos(n.sqrt(1-(1/Er)**2.*m.sin(xb)));\n",
+    "n1=377.*m.cos(xt);\n",
+    "n2=(377/m.sqrt(Er))*n.cos(xi);\n",
+    "Tper=(n2-n1)/(n1+n2);\n",
+    "z=abs(Tper);\n",
+    "#result\n",
+    "subplot(122)\n",
+    "\n",
+    "title ('perpendicular polarization');\n",
+    "xlabel('xi(incidence angle)');\n",
+    "#ylabel('Tper(reflection coefficient)');\n",
+    "plot(xi,z);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_2_TRANSMISSION_LINE_THEORY_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_2_TRANSMISSION_LINE_THEORY_3.ipynb
new file mode 100644
index 00000000..3d028800
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_2_TRANSMISSION_LINE_THEORY_3.ipynb
@@ -0,0 +1,496 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2 TRANSMISSION LINE THEORY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example:2.1 page.no:61"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "self inductance in H/m = mue*(-log(a) + log(b))/(4*pi)\n",
+      "capacitance in F/m = 2*pi*eipsila/(-log(a) + log(b))\n",
+      "resistance in Ohm/m = Rs*(2*pi/b + 2*pi/a)/(4*pi**2)\n",
+      "shunt conductance in S/m = 2*pi*eipsila*w/(-log(a) + log(b))\n"
+     ]
+    }
+   ],
+   "source": [
+    "#example:3.1,page no.72.\n",
+    "# program to determine transmission line parameters\n",
+    "\n",
+    "from sympy import symbols,I,conjugate,log,exp,integrate,pi\n",
+    "\n",
+    "E,H,Vo,P,a,b,Io,mue,y,z,Q,p,i,L,eipsila,G,C,R,Rs,w=symbols('E,H,Vo,P,a,b,Io,mue,y,z,Q,p,i,L,eipsila,G,C,R,Rs,w');\n",
+    "E=(Vo/(P*log(b/a)))*exp(-I*y*z); # in radial direction .\n",
+    "H=(Io/(2*pi*P))*exp(-I*y*z); # in phi direction .\n",
+    "H=H*conjugate(H)*P;\n",
+    "Io=2*pi\n",
+    "E=E*conjugate(E)*P;\n",
+    "Vo=log(b)-log(a)\n",
+    "E=1/P**2\n",
+    "L=(mue/((Io)**2))*integrate(integrate((1/P),(P,a,b)),(Q,pi,2*pi));# surface integral in culindrical coordinate system\n",
+    "C=(eipsila/(Vo**2))*integrate(integrate(E*P,(P,a,b)),(Q,0,2*pi)); # surface integral in culindrical coordinate systemR=\n",
+    "R=(Rs/(Io**2))*(integrate((1/a),(Q,0,2*pi))+integrate((1/b),(Q,0,2*pi)))\n",
+    "G=((w*eipsila)/(Vo**2))*integrate(integrate(1/P,(P,a,b)),(Q,0,2*pi)); # surface integral in culindrical coordinate system\n",
+    "# result\n",
+    "print \"self inductance in H/m =\",L\n",
+    "print \"capacitance in F/m =\",C\n",
+    "print \"resistance in Ohm/m =\",R\n",
+    "print \"shunt conductance in S/m =\",G"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:2.3 page no:77"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reflection coefficient =  0.367607311047\n",
+      "standing wave ratio =  2.1625919068\n",
+      "return loss in dB =  8.69231719731\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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h1Sqivn0VCufMGY6vmJqynI0bK857HPKY7La2okrnXlD0l6Lk6MgKXf1VR0Rw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\nitggAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107827250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# functions\n",
+    "%matplotlib inline\n",
+    "import pylab as plb\n",
+    "import numpy as npy\n",
+    "from matplotlib.patches import Circle\n",
+    "\n",
+    "def smith(smithR=1, chart_type = 'z',ax=None):\n",
+    "    if ax == None:\n",
+    "        ax1 = plb.gca()\n",
+    "    else:\n",
+    "        ax1 = ax\n",
+    "\n",
+    "    contour = []\n",
+    "    rHeavyList = [0,1]\n",
+    "    xHeavyList = [1,-1]\n",
+    "\n",
+    "    rLightList = plb.logspace(3,-5,30,base=.5)\n",
+    "    xLightList = plb.hstack([plb.logspace(2,-5,15,base=.5), -1*plb.logspace(2,-5,15,base=.5)]) \n",
+    "    if smithR > 1:\n",
+    "        rMax = (1.+smithR)/(1.-smithR)\n",
+    "        rLightList = plb.hstack([ plb.linspace(0,rMax,11)  , rLightList ])\n",
+    "    if chart_type is 'y':\n",
+    "        y_flip_sign = -1\n",
+    "    else:\n",
+    "        y_flip_sign = 1\n",
+    "    for r in rLightList:\n",
+    "        center = (r/(1.+r)*y_flip_sign,0 ) \n",
+    "        radius = 1./(1+r)\n",
+    "        contour.append( Circle( center, radius, ec='blue',fc = 'none'))\n",
+    "    for x in xLightList:\n",
+    "        center = (1*y_flip_sign,1./x)\n",
+    "        radius = 1./x\n",
+    "        contour.append( Circle( center, radius, ec='green',fc = 'none'))\n",
+    "    for r in rHeavyList:\n",
+    "        center = (r/(1.+r)*y_flip_sign,0 )\n",
+    "        radius = 1./(1+r)\n",
+    "        contour.append( Circle( center, radius, ec= 'blue', fc = 'none'))\n",
+    "    for x in xHeavyList:\n",
+    "        center = (1*y_flip_sign,1./x)\n",
+    "        radius = 1./x\t\n",
+    "        contour.append( Circle( center, radius, ec='green',fc = 'none'))\n",
+    "    ax1.axhline(0, color='red')\n",
+    "    ax1.axvline(1*y_flip_sign, color='red')\n",
+    "    ax1.grid(0)\n",
+    "    ax1.axis('equal')\n",
+    "    ax1.axis(smithR*npy.array([-1., 1., -1., 1.]))\n",
+    "    plb.title('Smith chart')\n",
+    "    plb.xlabel('Tao real')\n",
+    "    plb.ylabel('Tao imaginary')    \n",
+    "    for currentContour in contour:\n",
+    "        ax1.add_patch(currentContour)\n",
+    "        \n",
+    "#”Tao Real”,”Tao Imaginary””Tao Real”,”Tao Imaginary” \n",
+    "# function for reflection coefficient .\n",
+    "def reflection_coefficient(Zl,Zo): \n",
+    "    tao=(Zl-Zo)/(Zl+Zo);\n",
+    "    return tao;\n",
+    "\n",
+    "def VSWR(tao):\n",
+    "    SWR=(1+tao)/(1-tao)\n",
+    "    return SWR;\n",
+    "\n",
+    "# program to find out return loss in dB,SWR and reflection coefficient .\n",
+    "from math import log10\n",
+    "\n",
+    "Zl=80-40j; # load impedence .\n",
+    "Zo=50; # characteristic impedence .\n",
+    "z=Zl/Zo; # normalized impedence .\n",
+    "tao=reflection_coefficient(Zl,Zo);\n",
+    "SWR=VSWR(abs(tao));\n",
+    "Rl=-20*log10(abs(tao));\n",
+    "print \"reflection coefficient = \",abs(tao)\n",
+    "print \"standing wave ratio = \",SWR\n",
+    "print \"return loss in dB = \",Rl\n",
+    "smith();\n",
+    "# when analyse with the help of smith chart . see the angle from x=0 axis i.e Tao real axis.if it is above this axis take angle anticlockwise and if it is below this axis . take angle clockwise from Tao  real axis below ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:2.4 page no:80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load impedence =  50*(1 + 0.2*exp(2.48*I*pi))/(1 - 0.2*exp(2.48*I*pi))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LjnTrgxRTUteuQPnyQNSzujDJ/QLOjz3x119yQCwtzuLkyJ4lO4Ij9HtMOXNm\nibJZsAB4/Bg4cQKIzH8eQZEBOPXrdCxfDgQG6rXLJIlrdlKrgUd+j9BhZwd029sNTYs2xcsRL7Gs\n9TKUz1s+WTlpSc9x5IiY4ExMxAbeoHAD7Oi8A65DXVHQoiDqrK+DQYcGpZj0zt1dviM7dgD16qVu\nLMkxt/lc3Ht7T+/fB02ERIbAPHP6O8nPvziPcnnLIW/21NeINSqJL5weFXpgdO3RaLmtpUFKHmYy\nyYTlrZdjzKkxCAgL0Kvsjx+BpUuBsmXlxHDjxnJoa90aU3Qp3x77Hm/Xa3+aiMmjk14oikzUvsUW\noZR1KaxZkgt//y3O5MGDgfvpF+QUy+jRQEDYB9Sf2x+N/myE+oXqw2O4B4ZUH4KcWbVLbJgWJXH2\nrKQOSUi+HPkwseFEeAz3gIWZBSqsrICJ5yYiPCo8UduAAAmfnTkz/VKM1y1UF9mzZMeyG8vSp4M4\neAdlzEn/7S7b0al0GrMpprTV+Cc88C81N8Xll1O/sObamgwODzZI/0MOD2G/A/30IsvdnRw4kLS0\nJLt1Iy9c+Gy3j+G653UWXVQ00eEffXPd8zodVzumax8kaTnLkr+f/T327zdvxGeRP7/4WnbvFnNM\neuDk5sS8s+1p1nEY77kHpErGxo3ib0lECr+9qCjxJ/lotnbE4/XH1+ywswPLryjPm29uxpPx1Vfk\njz/qNubU0GJzC1ZYUSFd+1Cr1TSbZsaQCD051pLAL8SPlrMs+S7kXZJtYPRJ/HtQq9Xsu78vW21t\nxYioiAzvPyg8iEUXFU2Ts83TkxwwQGzkkyeT3t5Jt1Wr1XRc7cijHkdT3Z82vAp4Rfv59unaxxP/\nJ8Rk0D/EP9FrERFyErl6dbJyZfLYscQKM7UEhgWy977eLLa4GM8/P89Zs8hWrVIn68oVGWMiUvjt\n3bpFliunfT9qtZrb7m9j3rl5OeHMBEapojh6NNm0qTji05sDrgeYaUomqtJLY5N8H/qeFjMt0k1+\nDHMuzWHf/X2TbWNUEv8yIqIi+L9t/2Of/X0yzJEcl7PPzrLAggI6R6W8f0+OHStRP2PHkv6J50qN\nrL+9nm22t0nFSLUnIiqCpn+Yxovm0TeDDw9mvnn5km2jVouyKFWKbNRIIrjSwrP3z1h+RXl+d+C7\n2N1neDhZtKjuqStI8t072REk+tql8NubO5ccOlT3/ryDvNl0U1NWmN2SRUp/4LukF8N6RaVS0fQP\nU+5y2aX6vnXOAAAgAElEQVTTdWq1KLHQUDIwUKLvwsM17w5dfFxYZlkZPY1YMyq1ikUXFeV1z+vJ\nttNGSRh9Ev8gMmfKjN1dd8PD3wNjTo2JUZAZRuOijdHOoR1GnhipVfvQUGDWLLFnf/gg9vfZs4Hc\nGlM0JqZ7+e64+voqXgS8SP2gUyBzpsywMrOCX6hfuvVx5PERtCjWItk2iiKFeFxcpEhO167yt6ur\n7v2df3EetdfXxsCqA7Gu3Tpkz5IdgJwDmToVGDdO95QdefJINTw/Hd+m8+d1T5UBiL9iconjeHLN\nAehfE++o58pHSWBiYoIKeStgrfPa2OfUasDNTU53L14s71/fvkDz5hJokTu3OOXNzKQuuJ0dYG0N\nZM8u75m5uWTTrV8f+Ppr4Pc53oh4b4fNm+XMT2io/u/jxJMTyJ0tN6rbp+IYegKMSuIfhnlmcxz5\n5ghOPzuNCWcnZLiimN18Ni68uICjj48m227PHlEOzs7ApUvAmjWaD5glh3lmc/Sp1Aerb+kxrakG\n7HPap2s5yTeBb7ROb2JqCvTvL07i2rWBhg3Fwa1tNNQul13otrcbtnbaiuE1hyc6tNajB/Dpk+Q5\n0pUSJYAnT7RvHxUln33Dhrr39eYN0K2rKXZ9uxgTGo9Fgz8bZEiWZLUaqGrZHDde3seoUTJ2S0ug\nVStg61aJVsuVSxTfzz9LCVkPD0k7EhUFhIVJttvQUEmvrlZLXYvjx6UueadOgJmNF7KG2+PECUkp\nYm0NVKwoB0KXLQOuXZPPKC2suLUCQ6oP0U9qn5S2Gv+EB/4j5qa4+Ab7svyK8px0blKG952c2cnH\nh+zSRcwmly+nvS/3d+7MOzcvwyLD0i4sCVpubclD7kmlOU0bbn5uxGQwUpU6g/qHD2T//mShQnKI\nLDm23NtCu3l2vPf2XrLtjhwhS5fW3cbfsSO5Z0+CJ5P57d24QZYvr1sfpJhsqlUjZ8z4/Nxh98O0\nmWPDy6/08KVKQEAAuXMn+c03pJUVaVf9CjHJhNOnq3jyJPVu6kp4gDMsjLx5k1y1Snx2VauS5uZi\ndlywgHzyRDf5zz88Z57ZebRyjMNobvr3YpPdBmf6nMGeR3sw9cLUDO27cdHG6Fi6IwYcGhBvJ7Nn\nj6yIihYF7tzRT4lKhzwOqGRbCXsf7U27sKT6yO0AV79U2HW0YO+jvbA0s4SpSeoy4FhaAmvXyk7s\n+++lJKemXcWeh3sw5tQYnO5zGhVtKyYrs1UrIG9eYLOOda7s7CSHlbak1tQ0eLDsWsbFyUf3P4f/\nYUvHLeiwswNuvrmpu9AEPH8OLFkiobkFCwJbtsiuwcUF8LxWE4pCtOh3G82bi6lNnzx69wgOeRxi\n/86aVVK6DBokn7Ozs5j1Ro0Sc2O9ekC5clJ98MoV2bUkx+pbq9GnUh/9ncNISYv8Ex74D+4kYvAO\n8mappaU4/e/pGdrvp8hPrLyqMlfeXElf38+7h7Q6XDWx33U/66yvo3/B0Wy6u4nd9nRLF9nNNzdn\ntdXV9CIrIEDzruLSy0u0mWPDu953tZZ15YqcBA8N1b7/adPIceMSPJnMb691a3HG68KBA3JKPTiJ\nSO+DbgeZb14+vgx4qZtgyop961ayTh05tf3tt+T+/Zr7sp5jzfFnxuvchzaUW16Ot700HV/XjEpF\nXr9O/vabnHzPn1/Cp728ErcNiwxj3rl5tT7FDmN003+DN4FvWGppKf5x/o8M7df9nTtzTbVm7rJ3\nOWaMbhOOLkSqIllgQQHe8b6TLvJdfFxYYkmJdJFtP9+ew48O16vM48dlgh84kHTzfkm7eXapCk1u\n356cN0/79uvXk337Jngyid9eZCSZKxfp56e9/HfvSDs78u+/k2837/I8Vl5VWeszQ8+fi3LLm5ds\n3lwUQ0qmtrrr67LBhgbaDVwHgsODaT7dnOFR4amWce8e+cMPcs6oa1eJVouJOtt6byubb26utSyj\nkvgP4R3kzTLLynDi2YkZEh6rUsnKJk/jrSw0x4FB4Zqyv+mPGX/PYI+9PdJFdpQqitmnZ2fAp9Qd\nNEuOrFOzcu/DvXqXGxBAdusVwmwjqvDXw3NSJcPFRVbUAVre9rFjGirLJfHbu35dVr260LMn+dNP\nKbdTq9Xss78Pu+zukuR3Xa0mT58m27Qh8+SRPFru2qeI4rAjw1hgQQHtL9CSSy8vsdoa/ewsP34k\nly0jy5SRx5KlKlZcUYlObk5ay9BGSRh9Ev8S8uXIh/P9zmOf2750j3oKDAQ6dgT+/ht4tLMnmjnU\nw9CjQ9OtPwAYXnM4zr04h7tv9ViQIJpMJplQOV9l3Pa+rXfZEaqIFH0EqcHCAsjTYyxKWZfCxgE/\n49Il3WWUKwf873+S7lwb7OykpoY2nD8vaVa05eBBieqZPj3ltoqiYHWb1XgR8AKrnRNHvt28Kb6G\nwYOBDh2AV68kj5aDgwZhSVAidwkEhus/wZaztzMc7Rz1IitXLomOevhQ6ptvvLkTbi5m8LvcJkW/\nhU6kpEX+CQ8YdxKx+IX4sdLKShx5fGS6pLR48kRO0A4cKIeFSNlCl1lWhn/e+VPv/cVl6fWlbLm1\nZbrI/vHoj5x7ea5eZfqH+BOTkS6nd889P8f88/Pzfeh7HjsmO4J163SX8/KlHHLUZN9OiI+PrMrj\nkcRvr1Urct8+7cagrZkpIQ99H9J6jjWff3hOknRzE9+YvT25erWcZk8tJ5+cpOkfpqkXkAR99vfh\nmltr9C43pjb90kPnWL8+Wbas+HdSMirAaG76b+If6s866+uw175eek3hcfq02HWXL0/85Xvg84DW\nc6x1csjpSswP4dzzc3qXvenuJnbf212vMs89P8dMUzLpVSb5OUVK3LBdNzfSwYEcPlz30NZRo8jB\ng1Nup1KRmTOLAzgWDb89tVoUT3JpV+KirZlJE7MuzmK9NU3Zf4Ca1tbkrFn6qTXiHeSdLgq+3PJy\ndPZy1qtMMv4CSq2WMOeKFcnatSU3WlJ88UoCwAYAPgAeJNNmCYDHAO4BqJJEm9S8r/9qQiJC2GZ7\nG3615Su9+AuWLydtbckzZ5Jus9tlNwstLMS3QW/T3F9SbLu/jTXX1tS73+WR7yMWWVREr3KX31jO\nHDNy6E1eDGNPjmXvfb0TPf/hgyTCa9pU/q8tfn4yqb/UImCoQAHyxYs4T2j47T1/Lqt5bThwgCxR\nInUTu1pNrloTSdPB1fm/Xzfz/XvdZSQHJiNVUVRJEfApgNmnZ0+T01oTQeFBzDcvX6LADpWK3LJF\nouG+/16z70kbJWFon8RGAElWvFcUpTWAEiRLAhgIYGVGDeyfjnlmc+zvth/5c+ZH081N05RmfPp0\nYOFCidFu0iTpdl3LdUXfSn3ReXdnRKgiUt1fcnQv3x1hUWE44JaKI8PJUNq6NNRUw/Wd/s5LPP/w\nHDmzaJeKW1s8Az2x7s46zGo2K9FrlpZSu6FMGUmn7e+vnUxra0kFsmZNym3t7VM+K+HsLCVlU8Lf\nX/wGGzboXnb21SugZUtgzSpTrOu6AC55f4d5zsQpxtNCZpPMeOirv1rkx58cR8MiDZElUxa9yQSA\nhVcXonGRxqicL34dVxMToFcv4MEDSQ9SoYLUNtEVgyoJkhcBfEimSTsAm6LbXgdgqSiKbUaM7d+A\nqYkp1rVbh2ZFm6Huhro650Aigd9/B7ZtEyd1sWIpXzO50WTYZLfB0CNDY3Z5esVEMcHMpjMx/ux4\nRKmj9CZXURS0c2gHJ3cnvckMiQzR+4Qw5fwUDKw6EPY57TW+nimTHBJr3lwcxz4+2skdPBhYtw6I\nSEG3a+O8vnVLOyUxcqTkMqpfX7sxAvKdXLdO5DdoIM7uvo3roYJtBay6tUp7QVqQySQTgiKC9CbP\nycMJ7Rza6U0eAPiF+GHx9cWY2jjpA7W5cgGrV4syHjRI0r58/Kh9H4beSaREfgCv4/ztCaCAgcby\nj0RRFExvOh3Dqg9DvQ31cN9Huyo3pJx4dXKSSBU7LeujmCgm2NxhM669uYblN5enfuDJ0LJES9hm\nt8XmezoeGU6BdqXSriTUasDTU6qo+bwFVGHZ4OoKvHyZ8gScEh7+HjjgfgBj645Ntp2iSHGezp3l\nxPNbLWoqlSkjxZ/27Uu+nb098NorAh/DPsInWDRQUHgQIlWRsW202Uk4OwOnT2sXzRSDl5ecFl+5\nUgoZ/fabVAEEgBlNZmDGpRl6rSqnQEFElH52xJGqSBx7fAxtHNroRV4MMy/NRPfy3VE8d/EU2zZr\nJkk2Y3YVp09r10fqcgVkLAkzVGlcnk6ePDn2/40aNUKj1OQD+BczvOZw5M2eF802N8Per/eiQeEG\nybafNEmSkp09q3tagpxZc8KpuxNqr6+NMtZl0LSYfkuJKYqCWc1m4es9X6NH+R7IljmbXuQ2LNIQ\nru9c4RPsA9scSW9YPT0lI+i5c5Lewd9fwoI/ffqcMkFRADpWAAp9RLnxn7OumphIGoacOSV7aP78\nkr6kUyegcuUkuwQALLuxDD84/gCrbFYp3ouiyGdoYiKmp/PnUy4BO3SoZDnt3v3zc2+D38LZyxnO\n3vI4Z3sLIe988fvCbDAzNYMvAPsF9giLCkNhi8JwtHPEJVbDgHyOCAirCkszS419/fqr7FKzZ0/x\nVgAA16+L0uvfP75yiKGCbQXULVgX2x9sx0DHgdoJTQFFURCpjky5oRZcenUJxXMXR/5cOma5TIaX\nAS+x6d4mPByivUns9u3zsLM7j4YNJYxdK1JyWqT3A0ARJOG4BrAKQPc4f7sBsNXQTkdXz3+XU09P\n0WaODf96lHS+hOnT5XCOr2/a+jr77Czzzs1LFx+XtAlKgg47O3D2pdl6ldl1d1euv70+9m+Vijx0\niGzXThyAmTOLrzZHDjks1qmTRAetWCFRJHFrZQxwGsBii4rF/h0SIknv/vyTHD+e7NGDrFlTnMaK\nQpqYSPRYo0aS7C08jn8zKDyIuWfn5uuPr3W+p/HjpaBRSo7diAhxOF+85c95l+fRYakDrWZZsdnm\nZvzl1C/c83APZ6x8xn7fxnHuR//2olRRfOj7kPNPb6Z5559Yb0M95piRg9XXVOeG2xsYGvH5OP7p\n0+Ks1jZEddMmCfF1SuGM2Kmnp1hxZUW9BR9km5aNW+9v1YusEcdG6D0jQp/9fTjhzIRUX//y5T8g\nuokpK4nWAI5G/78WgGtJtEv1G/VfxNnLmfnn5+e0C9MS/aBWrCBLltQubl4btt7bygILCug1SiQG\nj3cezDM7Dx/7P9abzC33trDNlo5ctIh0dBSlkCmTKM3+/SX2/9Mn7WQNOTKEhRcW1qqtSkVevCgK\np0YN0sxMFEeJElKoac6p9ey4s2Oq7kmtFrk1ayafOuWG5w1WntyPWSZasNe+Xrz86nKi78eRI2TL\nuEdVEvz29uwh27aV/0eponjY/TBbb2tN6znWHH1iND3ePWa1apJ1NSWiosiff5ZcTi5arDNUahUd\nljroLVOs2VQz7nmYMO2t7qjVahZbXEyn3FopceLJCRZaWIgfwz6mSc4XryQA7ADgBSAC4nv4DsAg\nAIPitFkG4AkkBLZqEnLS9Eb9F3kT+IY11tZgtz3dYlMKnzkjYa5Pn+q3rwVXFrD0stLJ1tpNi+z6\nG+rr5eDgkSNk+cqfiGzvmM08io0akdu3p7729LhT42g71zbV47l4kezcWXYayBxM6yLeXLgwdeNR\nq6WeeK9eic+4uPq5sv6G+iyyqAjHH5lFC3tffkxi7rl9W+LvY0nw2xs3TkrTJuTp+6cce3Isc021\noeWAr+kdmHzR6w8fRBk1bap9JUNS8jp9e+Bb7S9IBtM/THnmWTIx31ri4uPCQgsL6W2H8zHsIwst\nLMQTT06kWdYXryT09TAqidTxKfITe+/rzSqrqvDve6+YNy959mz69DX25FjWWldL66Rs2hKlimLd\n9XW5+NriVF0fEiKrVSsrMffUq0fWnt0vnskptWy4vYHm083TLOdN4BvmmliYXbpGMWtWMmtWSez2\nWkfLU0iI1CqYE53qKUoVxbmX5zLP7Dxcen1pbAnXLl3kXIwm3r4V008sCX57zZuLeU4TkZFkiTKh\n/HrNL7Sda5tkiVBvbznVP2yY7qemn71/Rps5NjqVo42KkgOCkZGfFahKpSImg34hOmQoTIKRx0fy\nl1O/pFlODAOcBnCA0wC9yDIqCSMpolarOe3sXJr+YsdRiy6laz999vfh/7b9T6+nwMnUmZ0+fpQi\nOiYmkq10+HAyKPrM4RGPI6y6umqaV37XXl+jyRSTNMkgydW3VscmN1SpyIUL5VCboohJzM1Ne1mv\nXonfYdVeV9ZaV4sNNzbk0/fxt45nz8okren2Y05dx/pL4vz2Yk5aJ2WqXLOGbNJE2l17fY2ll5Vm\nl91d6BP8eVfh6Sknx/9Ig/m+wooKsSanyEjy/n1y40byl1/I3r3JZs3k/qys5D1UlM9mRUAK/hSs\n+IyYBH79tZwGX7xYdnZBOp5LDYkIYZ7ZeWJTh6QVfZmZYjAqCSMpolKJU7bl8CO0mWOjlxV0UkRE\nRbDV1lbsu7+v3vNKaWt2Cg+X6l+ZMomTePv2xG2iVFFaFZFPCm9v8vBhcs6CEGIS2KN3eOzEVKSI\nTPB2dpLuu2hRqcLWti05aJCYataulbocMf6D/237H3c82JGon5s3pfKboohZRts0GJP2bqPySx5O\ncFqm8f1Sq6VyXVLpHOztRdmQjKcknj+X+9JEaKjUQbge5y39FPmJY0+Ope1cW15+dZmvXon/YU7q\nktqSlNoQXVf+xqpjf2GtWjLhOzhIkMC0aaIsjh+XdNt+frKLiItKJQuIlWcO0nRKFm7fLunUBw2S\nzylbNvFP9e4tSi8l39362+vZZnub1N9QHPRpZorBqCSMpMhvv5H168vk6ernypJLSvLHoz+mutxm\nSgSHB7PO+joccniIXlNgaGN2mjRJTDU5c5JLlyYvb/al2ex3oF/K/UaRly5JEZi2bWUCtbKSFXPf\nviQmKWw34B779hWfQIsWUvSmZk2ZdGrUkH9r1yYbNiQbNybr1pXJ0syMLF9BxcwTc3Lucr/46TDi\ncPo0Wbiw7Iq6dk0+xcXyG8uZf35+Tln5gCVLJh3xtGSJjFcT1arFmezj/Pb27JHU3JqYPVv8K5o4\n/vg488yyoX39E5w/P+mxJ4WXl0zYbdrIZ1ul4wUW/KMGz59nkr6VlJh9aTYtZ1kmej48XPwya9eS\n3btLTYcaNUQB3b+fePdVbU01HvU4mrpBJECfZqYYjErCSLLs2ycr27ihru9D3/OrLV+x2eZm6eJo\nJiWHTY21NfjTsZ/0qiiSMju5uMgqNksWCQfVxvHrF+JHy1mWGt+DoCB57/r1E/t8xYpikvjlF3Lo\nUMmAmjevvGbyezbWGbyJo0eTv/8uZpQJE8gffxSb+6BBklG3Tx+ZdDp3loputWuTFhZkbgdXZhlT\nlJUqiVmsQgWRc/Nm4vvYvl3MPdmyaahFTXLR1UUsuqhorHlpxAgZq6aPICBAJkBNu5O2baVwD8l4\nSuL33+XeEhISQlpbk66umt9rHx+yUL1LzD45r9YTqkolO4J27UQp9+gh9//+PRkYFkjz6eZpMmv2\nP9ifRRYVSbFdeLgo6Z9+EkVdubIorOBgiRYruqioXnbN+jYzkVLFThsl8aWfuDaSTvj5AUOGADt2\nxD9kZZXNCoe/OYwq+aqgyuoquPQqFYUKUsDCzAInep3ApVeX8PPJn2MUfZopmackfqv/G747+B3U\nVAOQ1A8VK8qhNR8fOeFrosW33trcGu1KtcOfd/+Mfe72bWDAAKBAAcnfX7w4MGIEUKiQpDy4fl3y\nJ3XsCEybJm2zRuaD8/tzWLZM6igfPy51iyMjAVNTIEcOeVhaAmZmQHAw8Po18PgxEBoKZC3ijFwh\njrCxkVrMz54Bu3cDbdtKmpR58z7naOrRQz7Xbt0k3UXTpiIDADbe2Yj5V+fjXN9zKGYl+VXmzpX2\n69Ylvn8LC6B9e6lbnpCk8jd5eAClSyd+fudOoFYtza99+gS0aQP0aVQXp747gD4H+uDiy4tJfi4f\nPgDz50ttiF9/Bdq1k/dr+3a5fysrOcxZyKIQHvk9SlJOStz2vo3SeTQMOAFZssj7vGiRfDazZ0v+\nrEKFgL7LV6BL4cEwUdI2zQaGB2LAoQFY23YtcmXNlSZZMYRHhaPLni7aNU5Ji/wTHjDuJHSma1dy\nzJjk2xx2P0zbubacdmGaTtEi2uIf6k/H1Y563VHEmJ3G7VkVu3tITZ0FUpyrxRYV55+bolirlhym\nmzpVTA2NG8tKu3t3ybR5+DA5erScMcmbV3YDv/9O1lzQgcXmlePBg2KS+OEHWf1Wry6+iVy5xG5u\nZiYH9HLnFpt3kyYiu8yIkaw0dCY7dCArVZI2lSrJitXMTGRkzy47kRs34oz9mqzes2UjJ625wXzz\n8tHVL/FS3sVF2mnKALt/vzh5EzJ5cpwdQ5zfXpUq8cdAyi6lalXyqIYNglotacK7d/+8m4k57JnQ\n0RsSQs6YIWPt1Ut8Nsl9ZXr+1TNN/rVcM3Ol6aDmXfd3NJtkSetCfuzXj0maCrWh/8H+ejUzfYr8\nxNbbWrPTrk5Gc5MRzezeTZYqpV1N6tcfX7PBxgZstrkZvYO09IzqwIdPH1hjbQ0OPTJUb4pi7qrX\nRM7XLF3jlU4ps+MSFUVu3Khm1sF1We6bzdy4USZ9e3vx4ezYIRPV999LIZ4qVcTn4ewsZTLnzRMb\nea6my4nx2dmihZijli8XU9W1a9Luzh0xVxw6RP71F7lrlyi1xYvJBQvI0pPbsdHgv1i+vCiTypVl\n4m7QQJRUkSKf/SCWlhKCeu+e3INKRfbsK+c+Wn+XdJ2PGTPkuoRvf3Cw2PgTpphes0bum2SsklCr\nRVklfL+vXxfnvCYT3+zZokAS+lBmXZzFppuaUq1WMyKCXLlS7vHrr7UvQTrl/BT+duY37RonIFIV\nSUwGn/g/SdX1pIR8Dzo0iAEBolBz55YSqrrU/CbJdc7r6LDUQW9mptCIUH615St+vedrRkRFGJWE\nkcT4+pL58skEpy2RqkhOPDuRdvPsePLJSb2PKeBTAGuvq83+B/un2WE+ZIhE+3w9/A7t5tnxxQfd\nlnBqNXnwoEQi1a1Lzt5xkbkmFqGldRgHD/6cVqNGDdlZzJghq8QbN+RkdOnSEuEzaBC5dy9545EU\nrzl2KoTz5ontvFw5mdCzZhU7ds2asnNo0UIeTZqIczh/flIZWJ0W5a6ycmVZQY8aJb6MevVkF1Kn\njoTB5swpuxgzM5msu3Ujnz0jhx4ZykZ//EZTUxlzuIZSBpGR0t8aDQXTWrdOfDr60CF5nmSskvD0\nlB1UQvr21RytdORIgiipuONRRbLG2hocvWMlS5USpXjzZnKfWmLWOq/ldwe+0+2imLF5HElTVbrX\nH18z9+zcfBP4JvY5b2/xV+XJIwsFbfxil15eYt65eenmp0OMczIEhgWy6aam/Oavb2J/Z0YlYSQR\nXbvK4bHUcObZGdrPt+f40+P1Hv0UGBbI5pubs8PODvwUqWXeiziEh5O1apGmpjI5kxIWW2llJa0P\n8D15Imak8uXF8Tt1qvyoC437HyceXcyFC2UibNlS8ggFBsqq39FRVssTJshkFhUlq/mpU2VixoQs\nLNlhJ4cNEwVz546U7FSpZJXu6iqn3bdtE3mrVkn5zQ0byDzTCnDjvhc8eFCK3g8YIKtvc3NRFN98\nI7sACwuZ6HPnlkWAmRmZtfRZ5pyUn28D3tPNTRSTjY1m01JSZqdVq6SPuNy6JTsakrFK4uxZ2WHF\n5d076TPh6vnRIxnHlSuaP4eQELLPqEc0+cWaq3c91+qzS8hh98OpLnU7+PBgFlhQIFXXkhKFlNTh\nOVdX+Z42bixKPCleBryk3Tw7Hnt8LNXjiItPsA+rrq7KQYcGxTMdG5WEkXjoYmZKirdBb9liSwvW\n21CPrwI0LAPTQHhUOHvs7cH6G+rzwyft7UT+/jIxWljEP1imVqvZ70A/dt7VOdkIE5VKQj7z5CHn\nzxdbfP78shp3dSWnrrlHk7G2bNkukPfvy6T388/Svk0bsberVLKj+PVX2R0ULSoRL2fOkKWXlmbb\n7e3o5ib+ixEjZELNmVMeDg6S1K97d/K770QRDBhA9u2npjIxM6vVCmOBAnLgq1AhskMHcuJEMW/1\n7i2pVBwcZMVtayvhszlyB9FkZFFmLneYRYuKGSwkRKKjsmTRPEFrMjt5eoriiXvy+c0b6YdkrJJY\ntSqOCSqauXPFVxKX4GAZ64YNmj+LixclX9U335ATT8xik01NUmWGdPZyZqWVlXS+jiRLLinJzruS\niNdNATc/N1rPseb70KSzKUZFye4qqV1FcHgwK6+qzHmX56VqDAl5+v4pSywpwUnnJiV6L41Kwkgs\nISEy8V3Sw6FqlVrFmRdnMu/cvNx+f7tew1hVahV/OvYTK6yoEG+7nhQ+PmKPL1xY8/mAsMgw1l5X\nm1POT9F4/dOncj6hTh3xE/TqJZPshQvyd4UKYnZqsaoXx5+YzClTZNIcPFgOj6lU5LFjEhaaO7co\ngHv3ZKKNiJAVds1fJlEZb8ECBWQnN2uW+CHi5iRSq2Xl/eABeeKE+C227wpnpsmm3L+fPH9eFNad\nO2L+GTNGVqM5c4qCGT5cwmctLUUBWX61hOb9OtHcXMxaZmbiEwkPJ9u3lx3XuXPx34vISNkhJAyf\nrVYtftvQUJFJMlZJjBwpPobYz1FFFism72FcfvxR3uOEqNVyfb58n8NrI1WRLLu8LM8+0z5XTGSk\nnJs46ezGwvNL0t1dPuOPH5N3dMcQHhlOZbLCiy8vat1nXDrv6sxZF2dp1dbVVXaa7dvLrpSUhU3X\n3V3ZZ38fvfyubnvdpv18e668uVLj60YlYSSWWbOSPsyUWm543mDZ5WXZcWdHvda1VqvVnHVxFoss\nKh3ojOkAACAASURBVJKsPdbHR+zyJUtqtrXH4B3kzYILCnLfo33xnj96VMwe8+bJxGRvL5PYu3fi\nX7C1FQd1VBQ5bdkzKr/kYafePnzyRCaco0c/RxqtWyerZFLMVj//LOabatXIcZP9icmgu58HScki\ne/EiuWiRTJhlysika2n5ObKpQweyfZcQmkzMynbtxLRUvLhEK+XIIZPL4MGStXfxYlnF29rKeLp+\nrWbmEaVZqP4FWlvLxJs9u/RRoYK8b927y6nz06fjv1cnT8pKPzKONfGPP8jhIz/x2utrXH5jOb89\n8B3RqyUbbmhEAmy9rTULDx7CYes38N7be4xURfLYMTGLxZ3nLlyQ9zhhwr7QUIlyqlo1sY9i+Y3l\nGlf1796JMp0xQ3JNVa0q92lqKibBghWf0HR0UZYoIQuI7NnFRFeihDj9hwwh168n796Nv0tadXMV\nzaaZJf1lSobrnteZf37+2ISZ2hAeLtmFy5cXZTbtwjTWXFszVSbXhJx5doY2c2y49+HeJNsYlYQR\nknLAKLnDTGkhLDKMv57+NV12FRtub6DtXFteeZXYNuLvLzuIkiXjT2hJcevNLdrMseG9t/eoVosp\nxM6O/PtvOXVeuLBMYtevy0TdubNMps+eyUq9Vi2yx+YfOfTIUF65IhNNmTKiXNRqeRw7Jv4Ka2tZ\n6T+JExyTd04+Npr3Azt2FMVWrZpM8uvWye5A0y4oNDyMWaZmob+/mLiCguRe37+XcS9cKErGwUEm\nyO+/FxNUjW5nmGlYeTZtpqatrTjKzc1FwZiaijK6fVsm5kyZ4u8u1WpRUqtXy67uqMdRNljZhsqE\nbKy8qjK/P/g9V9xYweyVD/PAvTMkQCc3J1q3nc//bfiGpZaWYvbp2VlgeB+OX34t9vsQHCxK7uDB\n+Pfo6SnvRffumt+DwLBAWs2y4qsPnrx6Vcx55crJe9iwoTjyt20TX5Cn5+fvgsc7D5ZYUiK+rECJ\njjpzRiLHevaUQIMcOWQ1v24dWXVFTdZdXzflL1QC1Go1G25syDW3NHj/U7xWMgBY1DxAmxkF6BWY\n9jz9Ox/spM0cG557fi7ZdkYlYYSkmBn690/fPtJrV3HEQ3JK/Xnnz9jnwsNltVi4cPI7iIRsv7+d\nhRcWYde+vqxShXz4UCaH+vVFIaxeLTuLnTtl97BsmdiN586Vv595v6P573a0cbzI9es/T0hXroiM\nMmXEMR3j84l7Kjhzl37MNr4gN21K7MgNCxNn8KpVnx3T2bKRMIkiJim0sFTTykomekWRib5kSZlY\n580TU9Dt2zLxNWpEZu7ZmdWHrGC/fmICa9BA/DX58omCyJxZ/BLbtknRJFNTcSbHcOVaBC2+WsCi\nC4ux6uqqXOe8nvmLBvPhw89tChSIdnIDjIiQXUpYmLz24KkfszWdG3v9tvvbOGy4OpGZ6dEjkTNj\nRtKmoKdPycq/DWH2/01i+fKiJK5eTTk66KHvQ5ZeVjr5RtH4+5Nbt0qILSZkYcmvN3D79s/3ow2r\nb61mtTXVUh3Qcf/tfVpMs2bu8je4NQ11jlRqFSecmcBCCwtpVb/CqCSMxDoePT3Tv6/02lU89H3I\nEktKcNTxUYxURbJaNdlFJJejSBOBgWTBb39lnrG1eeNuEMuVk5QYwcESnli6NOnhIfbrNm0kNDVm\n93X8uCTka/HjfhZbVIIhESF8/FiUTMGC4oiNSRb36ZOYgEqU+Jym4dbLR1QmK3zx4QWjoiQ6Kq5C\nqFBB0nwsXSpKJzBQJsKEqUHUapm8HjwQhTR8uPhTsmcXxdGulyezTbbiqF8DaW8v99CypSiHUqXE\nJGVmJorB1FQisGJSgAQFkffe3mOVVVVoO/orDp1xPfYzHDpUTJYxlCkTXQgIoLu7+B9iWLVKVukx\nO5ES8yoz63et+eDl5y/hvXuitDZvTvw5qdXyfrduLbuyvmMfMO9se53SbJx/fp71NtTTuj0pi4hM\nUzJx5+5INm0q79X48SknTnzx4QWt51inugLjy4CXLLKoCLfe28qHD8V3mJoDoIFhgWy/oz3rbagX\nL7NuchiVhBEOGCD29YwkPXYV/qH+bLa5GQt8PYemmaPo4aHb9QEBMhl+31/Fzn/2Z5aBjTh/SQj9\n/MQB3Lq1tHn8WCbAIUPEVh0WJqekCxcmT50SWT32fsOGM0cwTx5xtsZUqpMDeBKB1LatTPZx9WT+\neYVYbkonFi4sPoXFi6WNJmUXY3O3mVyG3YY/4Lffys6hUyf5t18/MVdNnSq+kTdvRHH0W7qaBX/q\nRSsruYcFC0QBVaki91+woDi7s2UTU5OJiew0bW3VtKl0k9ZzrLn+9nq6uqppbf05AeCJE6KMYqhR\nI9opDdDJKX61urhnKyIjyVJlIth95WTazLHhprub+OCBKIhdGspJXL4su7KyZePvyqqsqqJTxbkd\nD3aw6+6uWrcnyVJLS7HZps9HzN3cRAnnzi0myYSHCkkxMzXf3Jwz/p6hU18xvAl8wxJLSnDBlQWx\nz3l4yOe0XocD40/fP2X5FeXZ/2B/hkdpv702Kon/OG5ushLTpbKXvoi7q1h/e71ekpytXhtJZAmi\n/c+tdTpgFBQkE9yQIeKotLWLYt15vdhoXQuWrfiJo0bJBH/qlJixVkYHgrx9K9d16vQ5m+jjx2St\nxu+Y+Vc7br/0OQLmwgWxlderl9jGf+mShHSaNVhKk0lZeONG/Pfi06fPTthOnUQhxdjcC05owqHz\nT3LdOjEP7dkj/65fL+awsWMlTbilpZhuCg8byP9NWcpNm2Tyz5dP5AwbJnIrVZK2+fKJsjA1JZEp\nnCV+7kfF6hnbfv35yxJ3gREWJrsNn+gFapMm0UoT4Lx54vCPea/jntJet076V6vJu953WWS+A3O0\n/Z3btsXfZT59+rmO+MaNiVN4Dzo0iEuuLdH6M19wZQF/OvaT1u1ffHhBZbLCe2/vJXrt5cvPyRwX\nL44/trSYmXyCfVhmWRmNCsbdXZz8mlLZJ+Tss7O0nWvLpdeX6rx7NyqJ/zgDB2ouJZmROHs5s9a6\nWqy5tiZvvbmVajl37siqd8IEydFvM8eGTm5OKV4XFiY7hf79xTxiZycRS688I5nzu64sNbktwyLD\nuWuXKIjz56PH7SwT1qRJn+3fhw6J0l2wgPzr4X6WWFKCvh9C+OOP8oPety/+zuHMGZmUS5aUa969\nUzHr1Kyx8e/PnskkbGMjymX0aJkU3N0/99l7X+/YHEQxTmsvL5lUnzwRM6K/v9znkydksVmO7Dnu\nChs1khXwsGHi4G7YUO69bVsx1ZUtK/eSPWck0b0D0b0dv+0fRkWRMyOkRBpZWX0Oz2zX7vPqv317\nuV8CHDGCsSm+9+37nO8pNFQUV8zpfn9/skg5HxacXpnjTo0jKfcZ4/uZOTPp+uFrbq1hn/19Yt+H\njx9FYb18Kbsof3/pL+b9H3l8pE65lzrt7MRCCwsl28bFRfw7derIAiwtZqZ3Ie9YYUUFTjw7Mdn+\nbGySDltXq9Vccm0JbefaprrMql6UBIAFAMql1M6QD6OSSExMqueUiqJkBCq1KjZS6YdDP9A/VLet\njUolE3jdOEEnV15dYaGFhTjy+Mgkt9dqtRxO69BBDroVKEBu2iSpScqWJadMjWD7He3pOLc9be3D\neDfaz3fsmEygMecF1GqZwOzt46czabH6G1p0G8FeveLv1gIDxURVoAB54EB8J2u33d2Ze3p+tm4t\nE+Po0bI7SUhM3YIuC+ay/JjhsQVvYnYBRYrIgT07O5nIzczI8pXDmGliNi5YGsKrV2UiGz9e3rum\nTWUir1VLookqVCCLFFXz/+xddVxUadu+hu4OAcUAu1DsWLFW11y7V3ftbkVdAxvs7naNtXXt7sRC\n1wCxQEBAOgZmzvX98TDMDCHo8u37+u5e/M6POec858yZM2fu+7mvu/Q79aGsV3PK9OQE1KGxoRkp\nKu3bizBbUoTCqiyLnj3FvSRE9zbVjPfnn9VKZsECce9JIdibNBHRSJFJkSy3qhx/Pb4oM3Ist8i7\nxEQRLjx2oT8tJ5dnuXLC8W5mJr6jwoXVDnlDQ6EYmzYlXac25OTNJxkcnHd+hFKppMFMA664nUeT\nEaoTL21sJZaa1ZRzLn85zRSZFMkqa6tw/Jnxec78T5wQ33HWTPhPyZ/Ybk87Vl1X9S/VmCooJdEf\nwHUAdwAMAmCZ1zH5XQA0B/AcQCCAiTns9wIQB+BBxvJrLuf56pv0v4rly0W0xn8TPiV/4tDjQ+mw\nwIHr763PNwXVv78QgllbR0YnR7PN7jastr5ajj+UZcuEMPz4UZTOmD9f8P+enuoqprv2ymn4UzvW\nX/sDk9OSeeSImL1dz6C/5XJBFVWrpu3837yZtC0cRbs52vkX588LAf7LL9rF7lQz5sJlQonpMo5c\ndSxb5nt8vKC6atUSCqFcOfL7ARdYfFYdXrumzsPICamp5PZz9+g8uwL79RM+CGNjYcnMmSOcyfXq\nCadoq1ZCmFbusZs6I8rS2CKJ+voickpHR1x/2bLivOfOiRh+SRL1lho3FtsHDcrogw2wfn0RYaVQ\niHv3+rX47Pb2zIyIGjVK1KVSRYTtPfmOson2HDnvQTZq6e1b4QMoV058hmrVyP6D5DTwMeb1O4mf\njWj78IE8clRJwxmW/L7dRzo7i8/aurUIFsj6XqQoxmc825jK/BRUysCYQ7NpNrIW23VI/+z3khXh\nCeGssLoCvc9655saWrBAfJ8q39WNdzdYdElRjjw5kqnpXxCClQMKlG4CUAbAfADvAOwC0DC/x+Zy\nPl0AQQCKAdAH8BBA2SxjvAAczce5/tKN+l9DXu0n/9N4EPaAdTbVYfX11Xkn5M5nx/r7C+GVGzcr\nSRKX3VpGez977n2i9oSePStmmMHBoqhe9+5CUHfpImbBkiQEoIMDefd+Grvu78rKixvT3jkxs5hc\nSgrZsqWYDasEenq6yC52dxchnHdDhbP35qvHmdZD1rLYgYFCQNerJ/IwWuxsQedFzpn7AwKEv8Ta\nWszcT51SK4TYlFiazjHN5LxTU0VOwIYNorSDj4+wFqZNIzvO3kLPuT146pQIs01NFbPwPn3ETLtX\nLxFN1LQpWbpqOPW8HVi83m2am4vwWj09IRHMzIQ14esr7lPp0iIvIzxcXKMkiTyQ+fNJAnR3FxTZ\n9euiARMprunnn8XrLVuo1QVv9Wpx3yf8to2V1lSiXCGnUin8Mm3bCqE+YoT4nJoKwWOtR57PCyly\nJDSpow8fhBO8Zk3hl5k7V+1bSUlPocEsg1wz8nPC4WeH6bLIha8+hrJ3b6GI81MKPCQuhKVXlKbP\nJZ8v8h1IkvjuOndRcv5VXzoscOCR50fyPjAPHH1+tOCURIZA/xHAEQD+ACYCOAZgb36Oz+WctQGc\n0lj3BuCdZYwXgGP5ONdfvmH/S/hcI/v/FiglJbc+2MpCCwux/9H+jEzKXkNZRTN9913e57sXeo9u\ny9w48NhAvgxOzvQvzJsnZqLJyUI41KghhH9QkDj3hYyKD5euKGjYpQ89ltVnXGocU1NFxE6XLuqM\n3NRUwcs3aaLd9nPmod+oN7Y4u/eLzGY9LF0qaKWlS9W0U3RSNHV9dDl4/UZ+952gE6ZNI9+/z/65\nnj8nC80qw7aD72ZaBxUriuqq48YJi2jWLOE7aTJ5OStOHEYvL+H4dnUl27UTfSwOHBBC381NhOW6\njm9Po9YT2by5cEhbW4tz6+gIqeDoqKadli0TNBQplOCrV4J6mjKFJEATE2EFTZyojgKythYWQXCw\n+PxPnojncdQoYSEEBgoF33x7SzadO00rXDi3mbnXVq98lejY+mArO/7eMefn5J6w8iwtxcSh9cY+\ntJxnmW8rIiAigPZ+9pn9zyVJ+JsKFRL0YG54E/OGJZaV+OoeFW+jImg2qBlLzqvLt7E5VGj8AgRF\nB7Hlby1ZakWpAqOblmTM+NcDqJFl34u8jv/MeTsC2KCx3hPAiixjGgCIBvAIwAkA5XI511+6af9r\n6Ngxgwr4BhCTEsPhJ4bT1teWsy/P1qrYOnp0zjRTbohLjWO3/d1oOqEsB868w2vXxI83JERQDS4u\nQujFxQlBpbpHb9+KcX8cV3LIH0NYaXUlNu38mh06qOmRlBShNDp21J7dnjsnaJX2q73ZYEuDzFh+\nTesha7ju+/ek89CfqDPZinv2KLXKQpBCKe3bJxzujo5k+dFj+YPvdN68mT1cNj1dOJifPydHbtnM\nn1Yv5vPngvL580/hpB87VijKIkWEQpm95S4NvV1ZsUoK69QREwonJzGDNzAQlhsghLuHh6COVK1M\n27Yld+9RcMrCIHYf80AoCad3lCSJZcsKS0nVH1upFMl9fn7i9aBBYjavUqSXL5NFK4RQf6oVT13+\nlOOkJiVF/VnqrG7OlaeP8+VL8T3mNgnqsLcDtzzY8tln5dMnctaCaGKaLptM3JCvopdRSVEssawE\ndzzakW3f/v1i0pG16RIpmle5LHL5bP/1z+Hws8N0WujEX3ZOpp1D+lf7GcMTwjns+DDa+Npw3tV5\n+W5fmpcglwGYBsA0l/1Web3BZ87dIR9KwhyAScbrHwC8zOVcnD59euZyMWvlsn8QQkPFLO5rG8D/\npxAYHciu+7vSaaETV91ZxZh4OfX1tYvG5QcbNkgs1mo37f0caN3Jm3v3pzI8XPyAVXkLbdqIfg+S\nJGatHh6C9yXF7LbRpKU0mFSI5wIFXyeXC9qpc2ftEiCHDgkFcfmy6IjXalcrDv5jMLdvz249iHML\nX4adHfnrjBTqzzTgrMuzMveHhopoNM3GRnI5efnNZVZZW4UKhaCmNm8Ws2B3d3X2tKEhqWsRTh3L\nDzQwENt1dYVibNFCzHb37BGzaIOOv7DSkLkcO1ZcZ/v24plxcBAzbJU1AQiFcfUq2W3QW3r5TKXr\njHrUn25Gm1mutJ5ciQSoO7EQreba0KBvUy64toglK0Xz8mWhhGvWFPds6FCRpxEfL+65KiLsyBGy\n+4HuXHJzCVNShJJZvVqUGPHwENaNq6ugvCwGtqZr08N0dxfXamUlQnEnTBBRV0FBZHJaCi3mWfBj\nokbj9lzQ8reWdPRzYteuorTJ9c+kYaQp0thwa0NOOJN70tGRI+K6HjxQb9v2cFu+I/GyIjo5mj0O\n9KD7cndeeXOFpLAcW7f+MpYgLjWOUy9MpY2vDTv4duC4SeMyZWVBKYkneZ3kaxYAtbLQTZNycl5n\nOeY1AJsctuf/jv2Pw8/v/78Ex/8n/D/48/sd39O09RSaOoZ9UX7Fu3dCAD9+TA4YE87CY9ux3Kpy\n9Op+h5MmiTFr14pZtVwufmhdugi+V/Wj275dZA/vv3+GDgscuPbuOv7yi/hhas74Dx4Us/x7GlG9\ncalxdJxZltZNV2uVsCCFNdOihRB8qiiq6RemU2+mHkM+RdDHRwjqwYPF9Wvi9dt0GjX2o7lFOo2M\nhPC3tRWzdB8fUZTP358cf2ghf94zmgEBgs/fsUNkPpcunaFEdEkDQyX1vvPj6CmRLFVKhHPWqSN4\ndVWkkJGR2prQt4ykSbkLtJxjQ8uuwzl71zl+1+wT9+wRFW0J8LvvyMUbPvC7/kfZZHUP6ky2ZOed\nfWldOFyUWp8lggViY4VCdXOjVkTYtovXaPlrSVpaKVm5slBkq1cLhaEZEtt0e1OeCjyVuR4RIfw/\ns2YJC8fFhSzsdZLFZ9XNsyPh5deXKZsh44mXwoF04ID47GPG5FxKf+jxoWzxW4s82/ju2yfO8ypY\nwXGnx9FtmdtXhcgefnaYzoucOerkKK2CgXK5oBtzylTPitT0VC6+sZgOCxz406GfsrWEJVlgdNO2\nrDRTQSwA9AC8ynBcG+TiuHYEIMt4XQPAm1zOlfcd+4egXr2c+wl/S4iIIGX6qSwx5id6rPXgycCT\n+XL0tWolBMbVq4I+iYyUOGztbup6O3DcKW++eJVKOzt1xM327eIHpxJEd+4IyyAgQKy/jHpJx5ll\naNNrCD/FqjXE2bNinL+/+r0lSQhsV49A2vsW0qq8efy4GO/jw2zUkotfCRqNqsQfftD2Sagc640b\nCwGvU+IC3bus5cmT2VuEqrDy9ir22jOYT5+Ka7t3T3zWd++ERfP2LdnSeycNy5+kjo6IYOrWTdBM\n3boJJeXsLGboOnoKoo4fMbIooZ/IHbuTWLiwcGBbW4ue3s2bkwTYtauwFBYtEnTc/BUfWXrYeJrN\ncOSYzXvoUlhiaKhwZqusB7lcWEr165NOzhIdZ1TivrsXP/v91t9cn5ff5B6NIUlk4zVdWXXwMlpZ\niclSTn6CdGU6reZb8YedP2htj4xkplWhSRHOvjyb5VaVY2xKDinXOWDWghiaD27OBpsbaZVUyQ9y\nsh6y4s4d8Xzn5rtRKBXc+mAriy4pyta7WvNx+OOcB7LglMQLAEoAwQACMpbHeR2XnyWDQnqR4fOY\nlLFtIICBGa+HAniSoUBuAKiVy3k+c9v/Ofj4UTgsc0tI+lbw/fdiVitJEg/8eYClV5Sm11Yv3nyf\ne8/VixeFBZCcLAT/vn1C2Tg4kKeuhbPdnnY0HV+OA+YIIRMaKgS3SogkJYkZ7n6NqspnzpAOrrFs\ntLElvbZ6MTIpkrduqSkmTcydK/wcYWEiesthgQOPPj/K3bvVVJcm0tKE0rAuEUzZDB0uubE0c9/j\nxyI6zcRECOwpU8gzjx+wyOIiWpm9ISGCZpkwQdAuJp4HadCnJcuUESGTVaqI86jyCBo1Isv82oVD\n1m3j778LC0JHR1BMpUuL0FtnZ9KpSDJ1e7Uk+jQgrIIJiM8wZIiIaHJxEQK+Xj2SAEePFsfu3y8U\nyMWL4jybT9+m7ogy7Lx5JPv2U7JGDSGIT5wQ/pFGjcQxaWnk+DPjtai3nFB0SVG+iMq9yXVYQhit\n5lsxJiWG4eEi9LdIETF50Axf7rqvK03nmFKennMs7fr14vofPSIXXl/IkstL5rsy64uoFyy9ojTL\njB7O9h3T8k0LSZLEPQF76LzImSNPjsyz3HiXLiIgIes5jjw/wvKryrPuprr56olRUEqiWE5LXsf9\nncu/SkJg61YRzfItIzpaUB2a1lC6Mp0b/Tey8OLCbLajGS+9vqRlWUiS4L5/+01YB3XqiG1Dh4ru\ncCS5caNEt9b7WGRxEfY40INN2n3g1Knq9xg5UlAzKkRECDrp0iUxM5t4diKLLi5Ox4qPs5W7Xr1a\nKChVAhop6leZz7SnTY1T2eijkBBR2E9lPUy9MDWTdpo+XfDwFhYiWU3TB1J7Y20e/PMwT5xQ5zq0\nbSsijY4fJ+8FvmWhhYVytLrCw8U9tZnhzkZdntLGRvhZdu8WFoGOjlCuljZyGvzSjAbdulHXIC3T\nNwEIZVSnjvDpzJ8vKCoCnD9fKLRffxXO6SZNhPPa3Z1ctSmGthNqs3D/4Xz7VuLPP4s8jPNZEoT3\nPtnLH/f8mOtzEZUURYt5Fp+lH2dfns3+R/trbZPLReSXvb34fVwKFjTT8ZfHcz0PKbo4mjVaSWff\n4vnuwHgq8FRmDlBKingmlyzJ+7inH5+y0bZGrLSmEq+9zV9XsMBAQTlGRYnnc9/TffRc58kKqyvw\n2Itj+Q6xLRAlQbUgdgDgqlrye9zfsfyrJAQ6dBAx6d8y+vYVP+ickJqeyg3+G+i+3J21N9bm0edH\nqZSUPHhQCKzkZCGArlwRTkxbWzFzTUoSM8M7d8gEeQLbrvCm7iRb+l1dzDRFGq9cEeZ7VAYzIEni\nXk7M0qa4wbCdNJpqx0PPDmVuO3JEzKxfvdIe+9tvpF3Va7SZZ89jL45lbg8KEpnS8+ZpOx8Lz6lI\n/e6daWKqZPPmQqhrIiGB7Dp3B40GebFqVVG7KWukkyRJtPOz47k7Idy6VQj1YcNEMuKQIeSQMTE0\n9DHj9ZsKRkeLc1StKq6nTx8h6PVtQ6jXeiQdCqXT1FT4MVRKwt1dKK8JE8Q5S5QgCXDePGGJVK0q\nKKeSJUVhvO7dRfvSxi1jWWx+JVo1Xs8hQ9RlPjQRFB302b7Sp4NO02urV677U9JTWHhxYd7/kHMc\n6oMHZPkqCdT71YKNN7XI9TwqLLm5hA5zi9LWLZiPspdz0oIkSVx0YxGdFjppzd5Vgjy3YpTxqfEc\ne3os7fzsuPzW8i+u/9R/kJzfT9zE0itKs+aGmjz87PAX10grKEuiDURGdFKG41gC8DSv4/7O5V8l\nISgmCwtBOX3LMDUVM7/PQaFUcO+TvfRY68EKqyrQuflOHjuezmXLhHOYFAJq5kzxev58wZWTYmbp\n5kZuPfac3+/4nuVWlmfhepcyW2aSIgqobFlt2u7ECZGIdfHlHbouceXAYwP5OjSehQplj4o5dkzQ\nMwEBIvzRYYEDD/x5gM+eCWW1dq32+O3bSWOrWGK8PWss7JiNorh4UQjyjp3T6epXiqcDz2jtj4gQ\ndFedOqRu72Z08jqSSUcsWybeb8UKctDcq7SfVJOVKwvndOXKIm9h3z5BXzjWuEz91iMo01HSzCx7\npBMgrJ9RowTV5OBAEuD48SLh0NZWRDBNny4orv79RSmVMWNIZ48AWs62yzXGX5Ikms01y7W3+Zwr\nczj29Ngc95GioF+b3W1y3U+SZVaUpel0R9o5pPPqZ5iYuVfm0m2ZG9/GvuXeveI7y03Qh8SFsMVv\nLVh1XdUcP9vSpeIeaGZ6S5LEXY930WWRC/sc7vPFlZIT5YlccnMJnRYUpt7PTXng/oWvLstfUEri\nMQA7AA8y1hsC2JzXcX/n8q+SEPWGNGsbfYvYulVk/ea3kZAkSZyx8yTNh3/HYkuK06LRat72T+HD\nh0JIJSSou/I9zygau2qV8Hmojh+w5ACNvF3ZZV8XBkYHMiZG0EyavZljYoSPRNXqMzYlln2P9KXJ\n5KLsOOGs1jWpLBjN4+9/uE+7+Y60brQ5m6W3YoVQjGXLkgf8L1LHR4d+1/xIiusfOlRYKscy9Zip\nhQAAIABJREFUjJHfn/xOz3WeVCiVmdVlLS1FRNC5c+SY45OzFY2TJDF733XnBL02NmNMjHB63rpF\nentn1HZqoqDN9JK0qX2EjRsL5WBsLMJrNRVFlSpCUdjYkEYm6STAbt2E5dWypbhP7u7iusuXF0l/\nNWoIGnHOlTlsu7ttrt+nyyKXXKmd9nvbc9fjnNPu41Lj6LDAgQERAbmeu+u+rjScZcj3ce95+rR4\nJk6e1B6jlJT0PuvNMivLaPVXX7s2+6RBkiRuebCF9n72nHFxRq71w5RK4ZxfuVKs3w65zQZbGtBj\nrccXlT4nhVPb55IP7f3s2WFvB94NvctffhFW6deioJSEf8b/RwB0M14XiOO6oJZ/lUT2pjDfIkqW\n1O5LkB/88INQLjM2Xafd8FYstLAQqwyfx+m+gjuaMkUdEpyYKGglVVSSioa6cjORsy7Poq2vLatO\nHcKu/bS7zPz8s+DaNbF/P+nS4CQLLyrCgccGMj41nkqlqLa6cKH22MhIsqjnn7SbqW6cRIpZprk5\nWb26urT2ohuLKJsh45pT51m8uKBrNLO7JUlixRWerNRtD93dRf6DZnHBo8+Pss6Gejx4UHRx+/57\nobRMTEjz6odp0Ls1LSxE6Y26dUW+wsaN5KDFx2g2ugZLlhTC3tFRKAd9fQ0lYRBPGEdTp/f3xARb\nYprYoT+xMK0GtWXZrtvYvFUKGzYU79m2rUgIVNFLyWnJtPOzy7UgXfGlxXPcJ1fI6bjAka8+vcrh\nKPLX87+y96HeOe7TvKealVKvXxfKUaUoPtewR5JELom3KFybaT1UXlOZD8IeMC88ekTaln7GNjvb\n02WRC9ffW59nKK0m3sS84djTY2k935o/H/6ZzyLV1RDv3RMWbk41qfKDglIS5zKS2lYC2ANgOYAb\neR33dy7/KgnBB2eNoPmWEBkpnsYnXxBSHhQkZoTJyULgHThAXnr2iPqdf6LlPCv23N+b1hVu80VG\nQMzixWraiRQUTSeNvjQBryJp2HY0rebZcMr5KYxNic2kmTR59MhIZtJMKqui6JKiHLroLGvXzv6D\n7dRJZI+rGic129GMK9cl0sxM5Gxk9S00XN2F+NWQK3YGa22XJBF5Y1nxKs1nODM8Trua7osX5PBR\naZSNc2LtNk84fbrINFc51E8GnmSzHc1ICuvo/HmRV9OpE6nX+wfWHLiVM2YI4dm2raAvZTJSpqsg\n6s8hJlgR5u9pXfkKHdxCaWZGEqChUxANq+2ibu/mlE1woHv77axVW2LdutnDNMedHsfxZ8bn+H26\nLHLh+7jstUn2Ptmbqz8iICKAdn52uVogZ4LOaFlnmrhxQ/i/9p55xfKryn+2YU94OOngKHHqgbyt\nB028j3vPfkf60XCqHZtM92VyWj5SuymsmpOBJ9l6V2va+Npw1MlRuVJ1NWqI7/lrUFBKwgyidpM+\ngD4ARgCwzeu4v3P5pyuJ1FRBDXxpO8//JkyeLMI0vwTjxonl4UNByaSnC6HXp48ox9x1hS+NvYuz\n2vpq3Oi/mW5lkjL9BykpQkBolqgeNEic723sW/Y53If2fva0a72QR09oxxR36SJKXWhi05WT1Blb\nhF12CKtChd9/F2GoKqoiXZnOTouWUGb2kSU8g7N1O7twgbSxU7LUQk+azjHNFJrR0WSzZiIpLSCA\nHHFiBHseFE2j370TyX4ODsJ6GHFoGoceH5p5zoQE4cwf7nedtt7V2bKlKPLXsqWgg3yXJNJwpjFn\nz09mqVKi6mvDhiLT2cQmmjD5SDQdS1i+ISCsn+LFhbVAgE5OwnFdoQJZoak/9UdUpGXfLgyPzF6h\n9HH4Y7ovd8+2XSkpaTbXLMdchAZbGvD3J79n256uTGe19dW4/t76bPtIkTCn66PLPof75LifJP32\nXaDOBEfOOv35hj0hcSGssqAFDUdW5q03eVsP0cnRHH9mPG18beh91psPnn2ijY26sGBuiEqK4oLr\nC+i2zI1V1lbhRv+NWqVqcsLWreLZ+BoUaHTTf/PyT1cS9+6JH+i3jFKl1L0H8oP0dCHkAwOFcPfx\nEfxv8eLq+jl16pD7Dyp4/OVx1lzWkrqTbDn61Bi+jHrJHTvUvglSWAdWVuK/CusOPaHFgLZ0WuhE\n32u+jEuN4/nzgnPXzMpVKkURwlkL1FbF2Vdn+fGjsDg0e1AkJwvLxKjsOdrOt8/M+CVFhrGdnVAU\nSqWSFVZVoPlcc94PfM+KFYXDWJWMlyhPpNsyNw5dcZh2dsJJnZohk9/Hvaf1fGsePRXP9u0FtVSz\nJtlvaBwNfEx4+Gg6T58Wfo7168m2w6/RZJQnzcxE6ZGpUzP8FC1jqTO0EnWqbiUgaTmwixUTn01V\nBbZwYfE+FhakpW0qm25szza722SjVRRKBU3nmGZTBi+iXrDokqLZvucnEU/otNApx/7Wc67MYdPt\nTXMU7tfeXqPeTL1cW5hKksRVd1aJbOQZ59iiRc6lLjR9D9MvzuCPHeSZtFNO+BD/IVM5DDg6QMu3\nMWhQzkEZkiTxdsht9j7Um5bzLPnToZ946/2tfDujk5PFfdd8dvOLgrIkOmREN8UDSMhY4vM67u9c\n/ulKYt06MSP8VpGeLnjvrLHzn8OVK6LEhUIhBNqrVyKnoXJlsf/RIyG4VHkGP/5IzlkVzIlnJ9Le\nz56Ww76n99ZDmcLH11dd2lqFtm3FvX0Y9pBd93elra8tSw+ewgWrtUPINm6kFs10MvAkXZe40mVM\nO/4y4U+tsaNHi4SzDRuEIHNa6MTpF6czLCItMxs5874o01lqWVnKpphy9Iw3WkIsNpas2ekq9bwd\neeKmNld/9Chp1rc9ndus4Zo12lRZ6RWleeTmY+7cqS4z3nzactaZN4CnTgl/StmywjJwGPAzTbr1\npamZlFmmQ7VYWAh/DgE6OoqscHt7oaT9/IQfod7melx0Y1G2767OpjrZqrnueryLHfZ2yDZ26PGh\nOXZvu/j6Ih0XOOZIwZx7dY66Prpsv6d9tn2koAj7HO7DcqvKMSg6iHK5eG6yBhVceXOFdTbVocda\nj0zfgyp/JmvuS1B0EAceG0jr+dYcfmI438S8yfa+AQHinqkUfYI8gZvub6LnOk8WX1qcvtd8c6yG\nnB+0a5fRBOoLUVBK4lXWchn/bcs/XUkMGCCiZL5VbNwohMyXYNw4MeO9cUNtRY0Zo27XOm2aGEOK\nGZalpbqa7K17KbTx2sE6G+vSzs+Og48NYaHqN3jnjloKv30rhLlmBdorTwNp0GEAredbc8SJEXwb\n+5aSJKJ4LmjLPO7al0z7tn6097Nn3yN9+S72Ha9fF7NtLy/1rDUkLoQ/7PyB1t4e7D7modY5EhLI\nylWUdJzuQePZxrwbKhpdfPwoooyGDCGX31zF8qvKMz41ntHRoiaSmxu5YN9Fui1zY1JaEiVJWCm9\ne5N6XbrTrskWduwonPqzZpHfzfiV1cb6ZHa/q12b7DT+EnXHFmWtBvG0tBRRTppKAkZRtKl8nQSo\nY/mBFhYirLZ6dbWyDIwOpK2vbTY/Q6ffO3FPwB6tbWNPj83W6/lt7Fva+NpkOz74UzAdFzjy3Ktz\n2Z6LPQF7qOujy+77u2fbR4qEtyKLi3DA0QFatOCDB0LJhYSQj8IfseVvLVlsaTFuf7g9mzU0Z44I\n7yXF2G77u9HW15ZTzk/Js7Bg/QbpnLz5BLsf6E7LeZZstasVj788/kWO7JywZYuIMPtSFJSSuJ7X\nmP/08k9XEp6e37bT+rvvBB3yJShdWhSx8/YWPLwkCeGoKrPh4SGsDVLMsNprTCpVx5BC4Py0cTaN\nxpZhiWUlOPXCVD6PfM4pU0RCmCamThXJaaHxoRx3ehyt51uzyZrudK17nUqldgZ41aqivlFMSgwn\nnZtE69lOtHCKoJlNfLZWlIcOSXT4fjPt/Ozpc8kn07oZPFgUH1QolGy2oxl1fHS44tpGVqkiPoMk\nCapi4LGB/G5NGzq7KDlihNpZ3G1/N3bdOorVq4sZvq8v6XdhbeaMPSxMXKPXbG82mjGHs2cL+mnZ\nMtJ++I80/W4ta9dWR0fJ9NJEhFPDyYTrZeoOrEUCxERr6vRoRf1yJ/j0T+1krmHHh3Hqhala27of\n6M6dj3Zq3C+JZVaW0co2liSJTbc3zaY44lPjWWF1hRxbjY4+NZqyGTKOOjkq2z7NAIOzr85m20+S\nI2cE02VYTzoscODSm0tz7foW+kFB0yrH2HhLcy0qMjeo6KThJ4bTcpYDLcbU4srbK/NVqTa/+NqS\nPAWlJJYB2AugWwb11AFA+7yO+zuXf7KS+F9wWtvZCdojv3jxQpjtSqWol3Tzpug3IOo9CUeura2a\nasqaiV6unHYew4AB5MKFEu+F3uPoU6NZaEEh6g2uzkmHl2YmOsnlgoPXrO76KfkTPQYvpv1Md1Ze\nU5nr7q1jgjyBt28LoaxZJnzJmk80LvKUpj62nHNlTqYzMipKhOVeuSJ8CT/s/IEeaz24/shDFimi\nXcxv8rlfiekylpvYX4t6unRVTv1+Ddlkeb/MjNvUVHLkpCjqjHfir+uvZF7L2/A4mvhY071KKK2t\nRQmN2t4zWXfaJE6aJPIu3MrHEJPM+WPnBFapIhSyiX0E0b86Ufg6YR9AQKKREYWS0E+irOpm2kyp\nyA57O2hF8DwOf5zN19B6V2utlq8Xgi+w/KryWhz8unvrWG19Na0s5KS0JHpt9eKgY4O0xqYr01l/\nc33q+ujmmEuhaT3kJMwjEiM4/MRw2sy3oUXb6bx8K4eU8Ixx867OY9ElRWk7sQa7+21lSnruUjkw\nOpAzLs6g+3J3llxekj6XfBgQGkhzc3V2f0Gibl3R1fBLUFBKYmvGskVzyeu4v3P5JyuJx4/V/Yi/\nRSiVIsxS1TI0P1i3TuQPhIQIZaBUin4QgweL/atWif2kEJaWlupM9MBAwSmrhKZSKYS0Zkbtzl3p\nrNz+DH869BOt5lux2Y5mHLhuI+s01c6MDQsTzu5PMUqeDjrNtrvb0sbXhmVGD+e4+c+0xnp4iKS5\n+29fssu+LnRa6MTVd1Zz6Ig0DhumHidJElff2Eydifbsvn6aVmTLypVkqTaHqTdTj57rPJmSLhIH\nHRzIg38ksPbG2hz8x2B+ilGyXj0R8bT5xiG6L3dnQmoSV64USXDuIwfxl20+lCRxf6bu3seyM1uz\nVy+hUBv3O0+XqXXZu7eIGivrEUedIZWp03RSNgc2AcpkonxH4OtUdvy9I3/c82MmfSJJEq3mW2nl\nHhRfWlwr1r/T75246o66S9abmDe087PTKrGdlJbEptubsseBHlrUTGhcKJ0WOtF8rjkDwrWT6SIS\nIz5rPcSlxnHahWm08bXhiBMjGJEYwblzRWkYze/j+rvr7HGgB63mW/GXw7/wbuhdXr0qgi2y+paD\nooO4+MZi1tpYiw4LHDjixAjeDrmtpdR+/DF/pb6/FJMn512tICv+jW76B+DkSe0onW8NN24Ip/UX\n9KDngAGigNzhw+rkuw4dRL0kUkToqH6E58+LCqUqLFmiLQTu3BEhqpro3Fk09iGFcNodsJv2Q7rQ\ndKYVa22sxTlX5jAgIoAzZ0ocMED72Iev39Lwhym093Vk/c31ue7eOp6/GkdLS7USI0W71UZbmlJn\njCsnHJurJUTHjCE793vPzvs603mRM9fcXcPngWm0tRUhu0HRQbT1taXJHFM6NzzCHRmN0uJS41hr\nfV3a9f2Zg4amZd7Tdju70aX/MNaqJY5/FP6IhRY4c4x3Ih0cyKqNg2k6zZmbN4uQ3R7LVrLS5IFs\n0EAolUK9xlO/0080NNJ0YCsI0zBhScgUmXShXCFnrY21uO2h2otad1NdXnp9iaQIDTWfa55p8QR/\nCqb1fOvMGb5CqWDjbY21aKbYlFh6bfVir4O9tCyLRTcWUW+mHsuuLMu4FLWFEJ8az+kXp9PG14Yj\nT47MZj0ERQdx/JnxtPOzY8+DPRn8SZ2PEhEhFH/A2/f0vebLiqsr0n25OxffWMzoZHVeiiQJX9jp\nswreeHeD3me9WW5VOToucGS/I/144uWJHCOySPFsaebrFBQOHBBhzV+Cv6QkkNEACMCKHJbleZ34\n71z+yUpi06ZvO7JpwoSMGkBfAFXi4NSpGX2WKUIyVaU3SpQQ9BMpstBHaVDUmsqEFPWdsuY8uLlp\n00opKYLSi02Q8+yrsxx+YjiLLilKvbHF2W37SJ4PPp8pEBYsEFaMXCHn4WeH2fH3jtSvtp26Vbdz\nyan9WvTEunVkg253+cvhX2g134o9DvTg2efXaWUtZfaWuBd6j023N6XxRHf2mLcnU7gqlUqWndKd\nmC5jsx3NmJKeQkkiW7VPYJGJLdhwa0NGJUUxJIQsUT6KNtNLccG1RZQkUV7cqFtPVpwwgi9ekEql\nRLt5zmzR5wmtrUlZnUWUNR9FExOyUtVk6k+xo5lrEM3MSJhGEKUPE/aPiarrhJIY5creG+dk0kxH\nnx9lzQ1qJ1OT7U14Oug0SeFYbrq9KUkxS2+8rTHnX52fuf7L4V/YZHuTTGWgKr09/MTwTAsiLCGM\nFVZVoK6PLmdcnJH5PqnpqVx2axkdFziy18FeWsJfoVTw6POjbL6zOe387Dju9Lhs2d0xKTHc6L+R\njhMa0sTHmv2O9OPF1xezFc1LlCfy0LNDrDX/Zxr+as+Kqyty8rnJvPX+Vr4K7EVECP/Bl0yM8gNV\nG94vwV9VEq0z/vfJYemd14n/zuWfrCRmz1Y7Yb9F1K0rHNf5haYPpkUL0SEuKkr9o/v0SUQQqSJs\nOnUid6p9pFrKhBRlr3/XyNPKejwprI1KlbSvIzRUokXJR5x5aRZrbKhB6/nW7Lq/K11b7eCBsyFa\n5zM2kVii7W9suLUhredbs++Rvjz36jwrVk7nmYxafdHJ0aKL2KyStJyo9m+QIg/GoeY5eq6rxqrr\nqvJM0Bnevp3Rl/vRNdr42tB0jilHrT3MihXJ5BQFJ5yZwOJLSrBErQD6+opIIdclrmw+eQNLlSJP\nX4mm8yJn/rrxMitVIm3aT2O9uUP59i05aN1mlv+1J/v1I6v9spNmg5rR0pK0cn1PjCxBNBlHQJlJ\nN+kV8WebXaKkRVJaEhVKBV2XuGaGjXqs9eDtkNsktRPj1txdwxobajBdmU5Jkjj61GjW2lgr83Of\nDjqdWXpbhYXXF1Jvph7dl7tnhpkqlApuf7idxZYWY8vfWvJRuLpsa0RiBOdemcuiS4qy5oaa3PZw\nm5bPJCktiYeeHWLH3zvSYp4F2+1px9kH9tOtdEqmEJckiS+jXnLN3TVs+VtLms81Z+NtjTnl6DI6\nl9fOis8vihfXTuQsCEiS8O9plqzPC//STf8ADB0qqJdvFa6uIpQzv/D3V4e8OjoKJ/WZM6JmEimK\n3NWvrx5fooT6x6ipTFRwdiaDNX7n585lL5S4Zk32HIpjx0TWsgof4j9w5c311OnWjjbzbVhyeUn2\nP9qfo5dcoaNzCjduFOPex72n3zU/ll5UlTqTbNh9fw/ufbKXsSmxlCSyYiUl5+87wx/3/EgbXxsO\nPzGcHQb+yblzhbDa93QfS60oRdtx9dlvyV6mKdKoVCrZbqewKootLJMZKttkzA4aTrXlytsrqZSU\nHDb9BfUnFOHc88uoVJLtvI9Qf2wJHjoRS//A9zSabk3HYtH0bHWf9jPKccwYsuLIKSzaayZNTUmj\ngY2o6zVH0E0yBSGTkwBr1EqnUlKyx4EeHHFiBEmy877O3B2wm6npqTSebczktGTe/3A/MzEuMDqQ\ndn52/PPjn1RKSg46Noie6zz5KfkTU9JTOPHsRBZaWCizO9vhZ4dZaGEhLeshPjWea+6uYblV5Vhn\nU53MsZIk8drba5lhpr8c/oX3QtV9ZsMSwrjBfwNb72pN87nm9NrqxXX31mXSSUqlRKfygZx9YgO7\nH+hO50XOdFnkwp4He3J3wO7MSrVK5dcnsXXsqD15KSg0a6YuBpkf5EdJ6CEPyGSyYwAI0e8aGa/j\nANwDsI5kal7n+Bf/f/jwAWjY8D99FV+PuDigZMn8jw8OFuMTEoDERKBwYeDgQaBCBbH/8WPAw0N9\n7o8fgVKlxPr9+0CVKoCOjlgPDwdSU4FixdTn9/cHqlXTfk9/f8DTM/s2zXFO5k6oJuuPSs/6w/83\nCU8+PsGlN5ewaks4Ioovx+zYzbj7hxe8inmhV+VeSDk/HuFSCCoV/QNbH25Fv6P9UNa8FsKLtEHn\nRq0x0eYQ3se9x7Lr63HQohFCbNxheKsd2pRug5Otn6By58N4MXA1ii4dhX5V+6HIc1/0iJ2JQJfu\nqLGhBspa1ED08d24fPwaRl36GZtu7sf7XZtw/fgVdD/VFEfOh4OXfdB73FnMetUaEUNOwb1TT7hM\nGonvYjZhamwE/rjxCmmV5KDSHPouT5Fi8Qw6t04CtRcDOnLg+gQAwIvmxXH4+TL4NvFFxTUVMbvR\nbBjqGkKukONs8Fl4FPKAvq4++h/rDx8vH0QmR6LZzmaY3XA2XCxc0P1Ad4QnhuNC7wt4HvUcfQ73\nQTn7cng06BECowPhvtwdwTHBaF2qNba124bQ+FAMOzEMuwJ2oWHxhljefDkaFmuIhxEP4XPJBwee\nHUCqIhVDqg/Byh9WwsrICk8jn2Lu1bk4+uIoXkS/QDO3ZuhSvgu2/rgV1kbWCI4JxqFnh3Dp7SVc\nfH0RMT8SJ581xM9ejTDTayZKWJeATCbTegZ0dMTz5O8PNGuW/2cYEM+Ovz/Qo8eXHZcXihUD3r0r\n2HPmqSQgekjYAdgNoSi6AEgEUArABgC9CvaS/sWXICwMcHL6T1/F1yM5GShdOv/jw8IAZ2ehHJ2d\nAZlMvHZxEftDQ4EiRcTrd+8AV1e1UggK0n6vp0+BihXFOVTw9wdatdJ+T39/oG9f7W337gF9+mQf\nV60aoCPTQSXHSqjkWAlH44E3dyXs39sMV99fxJ4nezDk+BCkpziiWqFaKC9Vw7QG0+Bm7YYJq64h\nvsJR1Nw0E9bG1vAq6gW88cIPIbcwuF8Ajr44igU3FkCRYI1SDdtgXpPZsDC0wJq767FWUQmNK3th\nTqM5sDCwQKMVvZHUzQ3eVxtgadOVaDX8IuQ/1cDx0GEYaHoKM6KGwa1nTbRy2Yyd+xfBbXBrrGn6\nGxrurIfUpBNoUrIPXldbjfDnDjB1CoFumT8ge9YJelV2QlF1I7DtDFTzxg3NfseQ4+1wsMtBVHGq\ngitvr+B9/HvYmthi5Z2VGOg5EL7XfGFrYotmbs3QaFsj9K3SF8Wti6Pimopo5tYMcxvNxcSzE3Ho\n+SEsa74MFgYWaLK9CZ58fIK6ReridM/T8A/zx497fsTL6JfoX7U/7va/i8BPgTjw7AD6HOkDIz0j\ntC3dFit+WAEncydceXsFw08Ox6U3l6Cvq4/WpVpjdqPZqOxYGU8+PoF/mD+GnhiK6++uQyEp0LB4\nQ3gV9cL0BtOxd40bYj7I0Lfq559HlbD/UiVRqRKwZMmXHZMfODuL30hBIj9Kog5JzbnVUZlMdo9k\nNZlM9rRgL+dffClUwvJbRXq6ENT5xYcPQimq/gPiR1GunHp/lSrq7Zr3Juu90lQuKvj7A9Onq9dT\nU4Fnz4DKlbOPW7FCe9u9e0CNGup1UmyrUEEHni6V4elSGaNqjYJSUsLJIwCN/W7jUYQ/Nj/cjGeR\nz2CY5I7ydp4YXP9XWBtZIzwxHKtv/I6PZYbi1RkHeBXzwqLvF2HKWCtU/ukmhpwYgrCEMFQwaInS\nwSvR+scojDk9BrHJSVC++BFzRv2MnU+3otaW6jAo54IJdQYjMDoIe1/WxYC63qhczhgdDjZFi6rD\nYOysj0abWqFfmTnY+WkA9PZtRFrzvqjgsAX+Totglz4ApimWiK/mC1zwATp3BrZcBQiseTEFE+pO\nwMIbC2FlZIU3sW8QEBGARHkigmOCAQCr763G8ubLUX9rfQyoOgCvY15jnf86LG22FHc/3EX1jdXR\nq1IvdCrXCYOPD0ZsaixquNTA0mZL8STyCepurouy9mXRq3IvgMCJoBPwXO+JCg4V0LpUa2xsvRHv\n497j8rvL6HmoJyRKaFisIWq41EBz9+b4EP8B98PvY/DxwQhLCINHIQ94OnniB/cf4OPlg5I2JbUs\nhWrVgPnz834ePT2B/fvzHpcVLi7i+StoODkBt24V7DnzoyRMZTJZUZJvAUAmkxUFYJqxL+2vvLlM\nJmsOYClEldmNJH1zGLMcwA8AkgH0Ifngr7zn/xqiowFb2//0VXwdVD+SL1FyYWHAd99pK4CsCkP1\nWnO7al/NmtrrmvtTUoCQEG1r488/AXd3wNhY+zi5HChaVPva/P2BwYPV6+/fA5IE1KqlPS4qUheK\nEA9MaeaRacXIFXKU/i4ADSf648lHf/iH+eNZ5DMgzQ0NirVGKVcrJMgTsO7eRryp8hgnw/Xh6eyJ\nhsUa4tzlFMgrrMWUCw9Qr0g9GEe6wbRcJH5/fh4RSRGwjWwL6yJh8LvhB0qAQUJN+Mt3Y++Zd7B8\n1w3xpS7hakAsKtrWxG+xQyE9awepdT/Qvwdel58M/YgKSNeLRLrDG8A8FKi+FvhYAdBRAErAysgK\nD8Mf4u6HuzDQNYCZvhnalG6D0WdGo2fFnvA+740mxZtg2Ilh6FKhC9bfX48aLjXQtERT/HzkZ3gU\n8oCbtRtW3FkBE30TVHeuDkNdQ1x/fx17nu5BGbsy6FC2Ax5/fIwxp8egduHacLN2Q69KvfAs6hl8\nr/vCRN8EFR0qorBlYbQv0x7hieG4HXobh58fRuVClVHNqRpalGyBqd9NRRm7MtDV0f3sc+bpKehJ\nSVJbormNmzTps6fKESpruKDh5PSfsSTGArgqk8mCM9ZLABgik8lMAWz72jeWyWS6ED0qmgAIBXBX\nJpMdJflMY0wLAO4kS8pkspoA1gColeMJ/6FQKAB9/f/0VXwdXr4E9PQ+/yPMirAwoFDJciEMAAAg\nAElEQVQhMbsvVEi9LSfFkFUJ5KQ0ChdWrycnA6amgK6G/IiJAezts1+Dq6s2TQUAr15lp7PMzdU+\nEhUePBDWjubxaSmGiHxUDdNbVoNexq8yIVkO+4oBaLnzAd4kvEBkSiTeRIUChgnQ03VAaHwowhPD\nEaxIgkz2Aca6hohIisDrxGiYWMcj7lMIzA0sEKF8A2vjZOik6UA31QVwfIa7H6IB6sKs1O+4HRIP\neVwZRJU8jdRwI9hUOo+Yj7bQq7wTsZEuMLYLQZwUAOqkASBQ+AZg/xQ4tRQAcP39dejKdGGkZwQd\nmQ6OvTwGfV19OJg64Pc/f4euTBf+Yf5Ik9Jw5tUZmBmY4VTQKRjrGSNBnoBr767BzsQORSyKICo5\nChGJEdDT1YO+rj6eRT1DnDwOZvpmkCvkAAH/MH+ExIfAWN8YcoUcBroG+JTyCaEJoTA1MIW9rT1a\nlmqJaQ2m5Ush5AQ7O/EsaNKXOcHV9euEsq0tkJQkLFUjoy8/Pjf8R5QEyRMymawUgDIQTusXGs7q\npX/hvWsACCL5BgBkMtkeAG0BPNMY0wYZiojkbZlMZiWTyRxJRvyF9/2fwresJJKTv0xBAGK2b2Ym\nfmBmZmJbUpIQxoBwZltaitdxcerXABAbC1hbq9ejo7VppJx+sLlt07QsAEEtpaQAJibqbUlJQhFo\nXgMAfPoEODhob1MpND2NX+THMEM4SdUwrLaa7d20Cbj0JB3TRr7By+iXeBH9EhNPvkCtli8QGPsM\nAR8DoNQzg0ymBwMdA0QnR4EOUfiYbAxDfUPEpb+FoY4eTGVmSExNRpIsEpKSgMMDhMTrQjKSEJNu\nhDSLNCgkQ8D2BRIlAroZsStSxgVGlgUU4sMqJSWSFEn4lPIJEiXIZDJIcgmxKbHQ19GHXClHdHI0\n0pRpiE2NhUQJOtBBcnoy9HT1YKxnjJiUGBCEqb4p3se/R3J6MvR19GGib4KPSR8RogxBcnoyXMxd\nUNa+LErZlEIpW/VSxLIIdGRf+DDlAQsL8R1+DoaGgjLNy+LICpkMsLERz0JB0sUmJuL5LEjkx5IA\ngJIASgMwAlBZJpOB5Pa/+N4uAN5rrIcAqJmPMYUB/KskIASTUvnlgva/BSkp2WfjeSE9XQhShUIt\nUFXbsr7OqkDT07OvGxhoX09WhZCSkl0h5DQuPV18D5pCPiVF/M/vOXPapql0AKEErS31UdK2JEra\nlsT36S0x8Q/g6qGMY9JTYFk4DAduhCFaHoZT1z/gwoNglKn7Em9i3iHuUzgk/XgkKlMBHUACMmMX\nJSgBGZAmpYh9TBYn1ZyI66YJf3Wxq0CLQcAJIDolGjmCQJokGGmFUpG5DQAkSACBdGU60pRpMNQ1\nhLWRNQqZFUIxq2IoZVMKxayLwdncGU5mTnA2d4ajmSMMdA1yfq//BxgZqb/D3CCTCUWRmpr9u8oL\n+vriGS1I6OmJZ7EgIROhsp8ZIJPNANAAQHkAxyH8A9dIdvxLbyyTdQDQnGT/jPWeAGqSHK4x5hiA\n+SSvZ6yfAzCB5P0s56KGrxFeGcu/+Bf/4l/8CzUuZSwq+AAg+dmpWn4siY4AKgO4T/JnmUzmCOC3\nr7xGTYQC0GT7ikBYCp8bUzhjWzbMyEPZ/a9CR0fMRr5Fa+LoUaBTJ+EEzi/q1QPmzQMuXBAmvo+P\n4IWvXRP/nZyEw9HJCZg4UZj0EyeKY2vVApYuVTuSe/YUoYu9MoK4374F6tfXjjM/ckRQPEePqrdd\nuQJMmQJcvareJkliFqdUqq2jPXuAceOAZcuADh3UY7dvB86eBXbsUG979gxo1w54/ly97cULoHVr\n4btRYfVqICAAWLNGrMenJsDKLRDbT7zAq5iXCPwUiN0n38Kl7HtEpXyEXJEGKvVhYECkKdNApQ70\n9XWgkBSgpANdHUBJpXYWlAyZM35AB8iY9asHyaCTUgjSghAQOjCYqQ+FpIAMMjDjTxypIyyGXKAL\nXVBGgICejh6UVIIkDPQMAAJypRymBqZwNHVEUcuiKGZVDO427ihtVxqlbEvB3cYdRnoFSOhnQfny\nwN696hyc3KCnJyyOL6V9XVyAO3eyR9j9Fbx4AbRpI/7nBC9oT6B98mHK50dJpJBUymQyhUwmswTw\nEdqC+2txD0BJmUxWDMAHiPyLblnGHAUwDMAemUxWC0Dsv/4IbaioF4O/zwovMBgaCsrsS6CvD6Sl\nif/x8WKbgYGah1WZ/qrXmnSBsbE2x2xqKugbzf1Z+Vxj4+yUQ07jdHTEdcjlanpJRR9lPT7r+wLC\nb/Hpk/Y2e3uR8CdPT8ebuGC8jH4Jf8OXOI2XeLb1BV5EvUCsPBa63R0x7ZwxoCfHx6SPYCFCLrdF\nEcsiSE1Lw7tP4TDRN4GjqSPefYqELnRhZmSGmOQEKKGAsZ4ZUtLS4Gxpjw8RClhbKxGTmAJbo0KI\nSY0EQejQEJJCD6QSMP4EKVntaDHUM4S5rjmsjayhpBIKSYHktGTIlXLYm9pn+husDa2RqkxFbGos\ndHV0kZyeDAMdEQ2VokiBiZ4JzA3MEZsaC5JwtnCGnkwPiWmJuPbuGvzD/GFnYgc9HT0kpSfhY+JH\nOFs4C79Eho9CpUCKWBT5Koe1JnKiALNCRRd9jV8wMfHLKaq8kJamTXkWBPJzursymcwaInHuHoAk\nADf+6huTVMhksmEATkOwnptIPpPJZAMz9q/LcJq3kMlkQRnv+/Nffd//NXzLSsLF5cs5WXt7IDJS\nOH5fvRLbHBzUmdWOjkBEBFC8uIh+evxYfayTkxC6muuakSA5cdDGxtkFurm5iHrKCjs7cb7ixcV6\nkSLiR6u6ThXKlBGRT5pwchLWyOv3ckTrBuDeh3vw/+APeR9/WPo+Q2ELFxS3Kg4dXRNEyOUolJYI\nuVIOKyMrAGVgK5nDzDIKMakxMJdZwTq9HMrY6SMsIQzvP0XB1agcJIN46OiFQa5QoIiVE3TSbJAo\nT0brct/h4P2LcNarCBrFIik+Ho5GukgKc4K+XRz0QutBR49IMH4MmEYAyfZATHHAMB6QAy3cWyA0\nIRRvYt8gKT0JZe3KoqhlUVx/fx32JvZwtXTFq+hXKGVXCklpSQhPDEc152o4G3wWDqYOSFOkIVYe\nC5lMhkJmheBg6oCgT0FwNnOGnakdktKS8DjiMXRkOihhXQKWhpaITolGYloiUtJTEJcah1cxr/Au\n7h0OPDuAVzGvEJMSg8qFKsPTyVMszp4oY1cGejr5k6BpaeJZUUXQ5YaYGHXQxJcgOVm8h5XVlx/7\nOURFieewIJEnSUFyCMkYkmsBfA/gJ5IFIqxJniRZmqQ7yXkZ29aRXKcxZljG/spZfRH/QkT4JCT8\np6/i61CmjLAkkpPzf4xKsKsS6jS3fe61al0zNj3rfnNzoXQ/flRvK11aUECaFo+7uxAgKktGhapV\nRa6EChUqiDG3b2f/3GFhwMdoOe6G3sXae2sx4Fh/pP1cFWW2WKPv0b64E3oHpW1Lo7S8JxqZD4aN\nsQ1uhtxEuiwBRlE10anYYIytPRaVHCshxuwGwqOT0LpUa4ypNQaO5nYIkt+CmYEZarjUgLV+ITyK\nuYF4eTyGVB0OWXg16NAQPo0nQwqujzvv76N38V9xP+w+WldsAEluCqtYL5jbx0J2dQrSnK7AOKkU\nDIJ/BKgL3BgH2D4H5EI6XnxzER3LdYSDqQNqF66NpiWa4kX0C7Qq2QqlbUvD3MAcNQrXgLO5M8KT\nwjGy5khcfnsZfTz6YGj1oUhnOlqVbIWfKv6E17GvcT/sPkpYl0CtwrWglJS4FXIL5e3LY3St0fAq\n5oWwxDDcD7uPxsUbY1ydcehXtR/K25dHeFI47n24B3cbdwyvMRydynaCs7kzTr06hfZ728NqvhXq\nbq6LESdHYNvDbXjy8QkUUs6zlCdPADc3YfV9Dg8fiuzpL4XqGf7SwI288P+RXJsvJlsmk1WWyWRt\nAVSBoIjaF+xl/Iuvxf9HXPTfBVWOREBA/o9RlR3QLD+Q9bVmkp7mvclrXSbLLugdHIQi1rQG9PRE\nlviDLGmdnp7axxoYCOvm7l2xHi+Px/GXx+F9fjzYvzpcV1uj37F+uBN6Bx6FPNDWYAW6SsfRwr0F\nnkY+xYzLM5Docgyx4Zbwa+qHUz1PoZ5rPeiVP4LZt7wR+CkQAz0HYk/LU/gY5IpZV2bhSeQTrPhx\nHixv+eHY8xNYc28NKrgUh+mh0yhlWRl/BB9EQ7PBKPJqBubc/BX1PVwg/8MXh2Nmob/1bvx29ANq\nu5eHnaIKPn4wQuXSlrBJrgMdhRmMdU0h+7MLYP0a2Pc7IBP+hkPtT+N44HH08RClMSo4VkCcPA7d\nKnbD5beX4V3PG5HJkShnXw6T6k2C7w1f7O24F+/j32PNvTXY2W4n6hetjwPPD6BD2Q441PUQdGQ6\nWHRzER5HPMbejnsxuPpgnAk+g+W3l6NB0Qa48NMFtC7VGjdDbmLsmbG4FXoLHct2xOU+lzGhzgQo\nqcSuJ7sw+8psRCRGoI9HH5ztdRazvGbB1dJVS3F4bfXCjEszcOnNJaQqBI947172el05Iae6XvlB\n1pydgsL/R5mePJWETCbbAmATgPYAWgNolfH/X/wX4FtWEoCgeP78M//jNUtyaFoSOb12dhYZ1CoU\nKQK8eaNeL1FC2ykMZBf0f2VbgjwBLuXeIdbxCKqsrgHnRc5YdHMRLAwt0AyLMN0oGjd+uYE2pdvA\nP8wfR43b4WDySOjIdODXxA9RE6KwvNFmPHmRgh4He6D/sf6QK+SYXn0VXA++RdMS32PxzcUYdqUz\n3Oxd4G3+J/pU7oO+x/og5rsBMA9rgdgJcfipahewfXe8vl4Nfw5+hrJej3FF3xvtlb9jauc2iGsw\nAEaHjqBMxSSYlr2OoOUrcF93Fbq7TkSKcRBMY2vAOKE8Em2vwuT6fMD1GlD0KkAhPnru6wsTfRP0\nrdIXN0NuoqJDRVRzroaQ+BAMqzEMmx5swvYft8Pvuh/qFqmLBU0XoMfBHpjfeD58vHzQZX8XWBtZ\n4+XwlyCJESdHYG7jufgw9gPcrN3QcldLzLw8E4u+X4TLfS4jKT0JTXc2xcHnBzGg6gCEjQ3D5HqT\nERwTjDZ72mD06dGwN7HHiR4n8GHsB4ytPRbhieHodqAb+h7ri/dx79GvSj88HfIUoWNC4V3PGynp\nKZh4biLs/OzQcFtDrH8xE5aVrogEvs/ga5XEy5fi+StoZC1FUyDIq0wsgD+RESr737rgH1wqvE8f\nZpah/hbh6Eh6e+d//Pnzov+EQiH6SiQkkLt2iWZCJLlnD9munXitVIrWpapSzs+fi34SKqSni5ai\nsbHqbXv2iPaSmpg1ixw/Xnvb5s2iH7Qmgt4l0azKSU44M5E1N9Sk6RxTluqzkGbuDzh316XMhkMf\nEz+y7+p1dBglehM02d6Ey24t45OQYNrYkMGvFTz+8jhb/taStr62dOo9hiv3ii5KSWlJnHtlHnUn\nOtBzWWMe/PMg05Xp3PLHE+qNKkPZDBmb72jO0JhIlvX8yIq+zVh1XVXeC3nEqp4KVp3RlzU31OSj\nl9EsVjGEJtPteSboHDdukVN3XGFWaHWJS7e8o/ksO/b8ScGi/cbTtfs8upVMo2ycE3Vd7xJ6KYRV\nEKGbQgKs0+cYFUoFtz7YSq+tXiTJngd7ctvDbfwQ/4GW8ywpSRJX3VnFRtsakSRX31nN/2PvqsOi\nXJv3LIodxC4lJSIGNnbrMRG79djdeFSwwUIJFRBbsbE78KDYimKggN1iooKgxAL73r8/hi12KfX7\nvuPveF/Xe7G8+7zPG7s788zMPTOVAiohJT0Fjz49gtVyKyy9shQAEPIkBJbLLTHyyEikpKfgXuw9\n1FpTCyJ3ERpvbIyPSR/xTfoN62+uR401NVDOrxw2RWxChiwDgiAgLCYMgw8Nht4SPQw8OBBhMWEQ\nBAGCICDyfSQWnl+IuuvrQm+JHvrs64OgyCBFm9iE1AQcf3Qcxn9OQ6VldVDCowRabmmJ+efm4+LL\ni2qd8YDv7wsxZgx3SfzZ6NVLvalWbqCf1ON6CxHZ5zbuf7n9m5XEzJksxH5VVKmiFOp5wcePyp4Q\ndeoAFy+qC//Hj7lHhRwtWiibw8tk0GhC36ABcPas8v/HjwELC/VznjgBtGypvi8yEqhQQbM3QaFR\nTTDxgBvOPj+LlPQUREfz9Q4dxW1QO+zogNKLS6P7zt4oXm8Xoh4rNdTHpI9o5OIJPbeycFjrgMBb\ngUhKS8LWrUDrdmlYc30NzJaaoceeHpi5/C7++APIyJDBOdgZOvN0YDDDAX9O4OYYt9/dhqmnFYp2\nnI6IyDTIBBn67ByCoqNb4K8ZiUhLE9Aq0BFWg9xgYwP089iFRuta4MABoFqvg9Ad3B4FCgCFW3qh\nZJ8xqFABKOywE/SXOcj4llqPa0OxgGMPj0HsJUb463AAQJPAJjjx6AQAwGKZBR5/foy0jDSY+pgi\n+kM0BEFA993d4XrKFQDw6ssr2K2wUyiKhNQE9NrbC3XX18WbRO6ic+nlJVgss4DufF2sCl8FgHtH\nXHhxAY0DG6Pyyso4dP+Qop/0p6RP8L7sDRs/G9RcUxPrb65HUlqS4nm/TXyLdTfWof329ii9uDT6\n7++P4MfBeP02HaVLc2OrLylfcOzhMUz9eypqrqkJA08D9N/PPUCevfmCkiW/r8Nc3brAhQv5Py43\nVKwI3L6d9/E/S0k0J6JEInpERFGZW2Rux/03t3+zklixQr138q+G/v25XWh+YGkJPHoEjB4N+Pqq\nC39BULcepk4FFi1SHtu0KRTd4ABg/HhuOSqH/PgPypbTir7HggDFanTBuUXQGVkPpRfroffe3tgR\nuQOfkz9jyBBumQpwx7SQJyEwsH0M6t4Xf2xqi213tik6r02aBMyYySvfAQcGQG+JHrpuHgyDKuGQ\nSuXXI2B7xB4UcC6P2itaKoRwejpQuVk09OaXQaEFhbDh5gbExQFlygCzd+yD2EuMnVE7sX07YGom\n4M/tzmiwoQGevf6GP/4AynbZDLtl1ZGaLsXVq4Cxa1MUq70XHTsC9Sb5oorLePTpA5hWeoYCMwxh\nZ58EAwNAp/oOUPdeoGLvQZJIgAg00gEWPmVx6eUlAMCjT48g8ZIoLKcWm1vg9NPTAIC5Z+Zi7DHu\nMvXh2wcYexvjasxVAKworJZbYcPNDYp7X3h+IcosLaPobAcALiEu0Jmng1prauFj0kfF2KMPj6LK\nqiposKEBzr84rxgvE2QIfhyMjkEdYeBpAOdgZzz4qNKeMPNaVlxbwRaguxHsp0xE+OtwhcKRIyYh\nBquvr0b77e1RdH5J6E1oBf+r/nge/1z7l1ULpFKlFfwzkZgIFCsGpGlvra0VP0tJPCWuoWRDRNby\nLbfj/pvbv1lJ7N+v6R75lbB9O1C4cP6O6doV2LkTWL8eGDCA9zVrBvzNbZTVrIedO9UtlalTAXd3\n5f9793I3L1W0aMHWgxxpGWkwbnAa/bZNhLWvNax9rTHxxEQ0H3oKS32laseGhwNlqjzDtBBXmPqY\nova62ug3NxgSk1SsWqUcJwgCfE/tRcFxNVHW1wbel73xKYlNnJYtuXf56aen4bDWAbXW1sL0dSGo\nbC8gNZWP/+vvvyBy10GB0XVx52G8Yl7noADoTDHHxmBlJ7aRqwJRYII9fNfGQRCA+OQvKDFPDEP7\nCLRoAawK/IJiC4vjzbs07N0L9Fy2HHXdJmLcOO7SpzuoAwxarUepUoBIBBBlsJKocJCVRPnjGDNW\nuZx2DnZWWAgA8MeWP3Dq6SkA3LPaarmV4r1dUbtQbXU1hTB+9OkRjL2NERYTphhz6P4hiL3EOPn4\npGKf3EWlO18Xa2+sVexXbWXquMNRrZUpADyPf44Zp2fAyNsIrba2UnSzUxyfAZjYP8LIIDfY+tvC\nboUdPC95KpSRKrr1+Yqh3gcw+NBgSLy41/Ws0Fm49vpajr2uQ0OB2rWzffu7ceECUK9e7uNU8bOU\nRFhuY/7X279ZSVy5wqbrr4qvX/lbmJ8WkAsXsrC/dQuoVIn3/fWX0u02fToweza/fvMG0NdXrq5C\nQ9lNJUdiIlshiYnKfe7uwJixMlx8eRGjjo6CoachyrjVQ71pCxH5PlIh0C5cYJeTILBwkscRCs40\nROfVk3EvluMIKSlsiZQvz2NPPz2N2utqo+aamqje4xg2b1EXKKcuf0Lhfv1gtcwGu6N3QybIIAi8\nGHCZKUXddXVRcH5BbLi5Ad7e3H87Lg7wDfNFWd+y2HzoGcRi4PBhXvmKvcTYd/E2HByAVq2ACdtW\noNeeXpBKOQbTbNBZFB7bAIULsxVVsPpuFB3cBe3acQ/wEhXDoOMqQWHL2wo3k6q7iYhXxqmpLNBN\nfUwRkxCjuB9bf1vcjb0LgFf1pReXVihEQRBQeWVlnH1+VjF+T/QeVAyoqLBEAHY1SbwkOP7ouNqz\ncglxgchdhAH7B6jtT01Phf9Vfxh5G8H1lKvaXPL3N0Vs0uiLfegQUL8+FNd25dUVDDo4SCO+8e4d\nf6bxmfo5Q5aBy68uw/WUKyoGVITFMgu4nnJF1IcoZMWkSf8ZF/Hy5flrBQz8PCWxioiCiLOhu2du\n3XI77r+5/ZuVhKqP/ldFsWLA2rW5j5Pj9GleMaWlsQJ48wYIDlb2pr50CaheXTm+dm1l3EH1GDna\ntAH27ePX92LvYfzBmdCZbI1KK+yx+OJivIh/gZgYPk5VmQgCULHWR4zY7Imyvso4wpqNSXB0VL/m\nadOAUqbv4ODXCrb+ttgVtQsyQYbz59lFFBfH4w7ePwhTH1PUmeMMp65JUPV23Hj0EiIXCUos1FO4\nSwQBcHYGrLqvgcVSK7yIfwGAFw9W1gLKuLTHjJPzFPe+cqWAQpMrwbLpObi6Anv2AK6HlmHQnnG4\nf5/7I09Z9BhFZpZBaT0BLVqwMCxVfy9omgQkiWZLgoCCBQEQQSQCdIp8RW8/Hxh7GytcYgAQlxyH\nEh4lkCHLUOxrtqkZQp4ofX4B1wLQc09PtefVc09PTAtRZwuExYRB4iVRuK7kOPn4JArNL4Sqq6oi\nSZqk9t6Hbx/QfXd3VAqopOaykiM1PRV+V/1g7G2MPw/8icYdn2HbNo1hGvGNzvPWY8jIJM2BmYh8\nHwnXU64wX2aOaqurweuSF2ISYiAIHOy+cyfbQ78bffowoSI/+FlKYnPmtkl1y+24/+b2b1YSgNJH\n/6uiYkWgW7e8j5dKecX77h3Qty8rmNRUVpaxsewyEIuBFywvMX8+MHmy8nj5MXIs8n+LWmOXodba\nWjBbaoapf09Fy763sWqVuj+6Wzdg9Wp+ff/jfQw5NARF5+nBauJgNcGYnMznf/qU/3/46SEcA8ZB\nVOwTzB23IUWq7jQePx7oNfgT+u3vB1t/W1x8eRGpqYC9vZKpEvIkBIUWFIK1VxUYmibh5k3l8ccf\nnkDxuaYoV/sJbt1S7l91JRDiWTVgWiYNixYB798DZ5+fReWVlXH+vIB589hS0O8+CyU6zIelJQfy\nx4wVoDenAqo4XkD58oCZGSsKMg8DFYkDdRwGKv4ORRpsZCXRYTzI1QBFBnVF5JuHave2KnwVuu1W\n/3B77e2FnVE7Ff8npCZAb4meIkANMAMsq9sJAM6/OA+Jl0RhpcnxIv4FxF5i6C/Rx6NP6j8GQRCw\nK2oXjL2NtVoVAJCYmojBm90gmm6AMUcn4MO3DxpjALaEjj0IRpEhnVB6kQGmhUzD28S3WsfKx597\nfg7DDw+H/hJ91A1oCcM/AhGf/CXbY74HaWmAgQHw+nX+jvspSuJX2P7tSkLuo/9V4ewMSCT5O6Zn\nT6b+7twJdOjA+7p3BzZt4teDBnFQHwAiIgAbGyhW5bt3A81bJ2Pr7a1ovbU19Bbro1DPITgQcVqx\n4j11CqhaFWor+dBQoFyT6+i2qxskXhLMOzcPzz98gr6+5o/TxQXoPfwNRh0dBbGXGB4XPDDHXQp9\nfcDTU31s0K2DKOBiik6rnNXYN9ev83OZfWA9RO4i9NvHnNuDBwEjI+DyZVZWEi8JLr28hK1befzc\nucCLz28h8ZLg9rvbuHULGD6cBb3N2Ilou2AxQkJYiX74AIw/NANjdy7EunXAyJFs2Vj38YWxc2eI\nxYCpKVCiBJRupuLvQRaXUKTPIFYSzRagoMErlCoFzJihvK+0jDRUDKiosfLvu68vtt/Zrrav//7+\nWHdjndq+PdF7UGFFBUgz1OM+gbcCYetvi7jkOLX90nQpaq+tDd35uooguiref32Pbru7abUqvn1j\nAsWWfR8w8cREGHgaYO6ZuUhITdCY5+BBoGFDjm9MODEB+kv0MeroKDyNe6oxVhUp6Slo47wPFd27\notTiUui5pyeOPDiiZmV9L86e/b44xw8pCSJyzfy7Qsvmn9vE/83t364k5D76XxUvX/I38d27vB+z\nbRvQuTP7hEuW5B/5li3KIPWBAxyABljQV6jAdNnHnx/D+cQUiFzFaLq2PXZH70ZyWjIGDlRnOclk\ngJ0dHyMIAkKfhaLVllYoOM0cE3b4Knj1APuBVZ9/cloyppyYCZ3pBuixdprC/56ezpTf4sWBe/fY\n1TH00FDY+tvC/9BFmJmxJaSK4etWgNxEGLvbXW1/cDBgYBYPo/l2WHdDmSjz5g3g5AQYdJ+NJovH\nqeWAxMUBdksaoce0UDRrBpibA4aGQAnHhRD3dcGgQczMmjYNsC6fjCLT7KDXYB9MTNi1pBqL0NVl\nCwNEKFmSrbgSJdglF55pVLmfdYfjDkcNhpDjDkccun9IbZ/fVT+MOjpK43NutqkZdkXt0tjvHOyM\n1ltba+QtAEDXXV1RYF4BhD4L1XhP1apYeH6h4tomTWKmnRzP4p5hwIEBMPI2QsC1AEUgWiYDGjdm\nwoUcH759wKzQWTD0NETffX01guVyJCQoXZ2fkz9j7Y21qLe+HiyXW8Ljgke21nj9NbEAACAASURB\nVEte4OzMFnN+8aNKomPm38FENEhlG0xEg3Kb+L+5/duVRHCwUiD+qjAw4NV3XvH5MyuHpCQOxu7e\nzfv09DhOk5rKiXoPHnBQcbj3YZhMawuJlwQuIS4YPeMpnJ2V8129ytaGamxn2TIBzUYcR931dWG3\nwg6BtwKx1FeqSNyT4+1bXsFfvw5cfnUZdivs0GtvLwQdi4GFhXqyXlQUC1P7uu9Rb3199NjTQ6Fw\nZs0CHBxYmACA/1V/iNxF6LPCE8bGPL8qOm8eAOOho9G8udK1BQCp6VIYeJig/YB70NMDhgxhq+vG\nrQyU8CiB+BQlG0oqBZYePwT7xe0wYAA/v169WNGIa1yBjqsEpWzvaASsy5RRKgkbG76n4sX5mVeq\nBOy8vR/G3sZ4naDp/zBfZo4nn5+o7bv86jJqr9NcCu+9uxdNNzXV2J8uS0fzzc2x5OISjfcAoN++\nftkqCgB4k/gGddfXRa+9vRByNglmZvz9yYo77++g0cZGaBLYBI8/P8bq1UwUSdfUTUhITYDnJU+Y\n+Jigw44Oai5IAFi5ki3grLj59iaGHx4OvSV66Le/Hy69vKShWHOCPM4REZHnQxT47W76lyA29tcP\nXnfvDtja5u8YR0cO1O3cqUx2GzQI8PLi15NmfkBDVw9YLrdEnTX1Uaz+Vrx4zf7oFy9YMX3LNAgE\ngX/8e/bw/5dfXUaDdU2gM6Ey1l3ao3AJfP3KiXuqFFkA2LglGYb9psDE2wT77u5T7B85kl09qhg9\n7TVEVhfRdPYCNWEgCJz70bQp4H+JXUxel/hmDh/mOMfKlfw5H3lwBDZ+NkhI/gYfH7YIpk4Fnjxh\nWmmLzbxqePcO8PMD/vwTsKkXDdFEW5iYcBzLxIRZSXa1Y1BkjgSLFwtwc2MLo317VgLixgdANiEg\nyV2FgihYkBWqkREAIpQty4qhQgWgeo0MGHb0QbG5xrjx5gay4sO3D9BboqchBJPSklB0YVEN11Ja\nRhrMlpppZQk9j38OQ09DBXMqK+SK4sqrK1rfT0lPQe9dA1B4Yk1s2PtS6xiAFxnLriyD/mJDFG/p\ni+i7Of/QUtJTsDJ8pSLx8cHHB5BK+ZldvJj9cXHJcfAN84XdCjtUW10Na66vUeTU5ITwcFYS+dAr\nCvxWEv8i/OrB64sXmYMvleY+Vo5jx9gPK5XyCvbePeDqVQGmdS+h775+KOWhh8I9huPSU47yDh/O\nrjk5unUDfHyU/4eEAFZ1otFxRydYLLNA4K1ATJmWgd691c8bGsqCVE5/lFsPZhN7Y/IsdS5vQgJg\nZaXM29gRuQOGi41Rpf47lCzJyYCqyMgA2o0/AXITweW4u9p79+8zPbNx688w9jTDuefnFO89f85u\nIrEY0P+rKf7asBevX6sLjpAnIWge+AfevuXxb9+y6+PvvwWUmGWD4nbh6N2bg9kWFuxuK1YMEBX5\nAhpTBdTXCVT0I0zNBFhYsNUBIhQtChiI01Gq/n4UHFMXuiOawrTyE61lJwJvBaJjUEetn6ext7HW\nILDbWTdFAl5WrApfhbrr62p1OwHsetKdr4tncc803vv2DWjYSEATV2+Y+pji4svsJbhMBtR3fATr\n+UqrIjckpSVh8cXFEHuJ0XDxCLTolLeosiAIOP30NLru6gr9JfoYf3y8RqBeFYMHKxM484vfSuJf\nhG7d1P2kvyKKF1fmN+QFGRm8qg8PB2bOksFpyhE02NAAhaeVw8jA5YhLjkOnTkpGUnQ0r37l7p/o\naHYTffnC7JhBBwdBd6YEPZYuVTBgkpNZWO7bp37u0aOBAUOTMeXvKTDxYevh7VueP6tb6NQpwMQs\nAyN3T0dZ37KIfB+JpCRWcCVKKK8P4OvQna8L+1mDUK6cZumGjAygwcIxKNxtLBYt0swvufEqGnoL\nzNC6XRoMDdla6NCB2V09Zx2B7dwOmDqVXUrlyvH5GzUCWs9bgnoeQ2BhwUrC2pqVgCIWUTAZVGMj\nqPAX0ERbFBzYATodxwBEKDKyJXRm6KHY+Mao2GMnevaSwdqaFc369erXV3tdbY1cBzmsfa21Bn9f\nJ7yG/hJ9JKYmarwnE2Rovrk5Voav1DonAEU5DVVWU3IyU58HD2YFcOLRCUi8JIps76yQu5lSpWxV\nGHoawjfMN8ekOTlexn5G8c4uKL3IAK6nXDUC7jkhJiEGc87MgbG3MTrt7KTB9vr0iZl+WWNZecVv\nJfEvwpo1mgXnfjUMH84r4fxg0eJ0NBm3HRX8qqDAuBrYfH03AjdloHlzXkVfvcqr/uRkHj9okLoi\n6j/kGxrMmQ4DTwPMDp2NM5e/wMxMGRcAmEVkYqL+Qzz14AoKTq6Apv691bJxt2/n4LT8fAAgzZCi\nlmc3FB7VDLcfKcd++QLUqsWCevFiZueIvcSovpqTPA4dYmbRpEkcewE48G7oaYiwO58weDAL8gED\n+D4FAVgethxjjnGdFkFgt9r+/Wwx/Tn/KMq7O8LLC9i6Fbh7F3j4kN1UBuaxKDhbD469X8PYmN1X\nhQrxtXGWNW8mJgIqN3iJAlUOQKdeAECEOn2D0arzB3TuzKwfAwMmFQwcyBau3Fo6/+I8yvqWzZbN\nY7ncMtvyFk03NVVkbWfFzbc3YepjqkYmUEVKegoMPA1Qa00tAPzZNmvGVGjV2MKDjw9gt8IO009N\nV3OHPXnC38t7Kov5R58eqcUqcsLs2XyumIQYjDgyAmIvMfyv+mdr/WhDcloyAq4FwGq5FZpvbo6/\nn/wNQRD4c/0zz9No4GflSVgQ0UEi+pi57Sci89yO+29uv5UEUzBVM4t/RXz+zAIpVHusUQ0p6SlY\nFb4KlsvKouDwptgaFox+/Zn7n57O/nG5i6drV2WcQh6LePdOwJ7oPTDztkChPv1x/YGSoz98OMcS\nVDFlCq++pRlSTAuZBhMfE8zdtVfN7QSwYB4wgFfvaWl8nR12dECXXV2wxDsV5cszm0uOb984BlGi\nBGAwrQH0l6iveD99YuVva8vPpc/ePlhwfoHa+97e7O+2sQGsJvdHT4+NCA1lpZaSwtcklQKH7pyG\ng38LrFwJDB3KCYdiMSuJbdsAg25uMBzdG6VKCShalBVFwYLqSsLRkZ+PvT1/30CE8eM5IFulCltS\n48YxmaB8ecDNjS2xiVOSYedfAXvv7s32M5V4SfDuq3aK2+STk7H44uJsj+29tzcWnl+Y7fvP4p5B\nd74uemwdhlq1uN6Zthjex6SPqLmmJpyDnSEIAt6+ZYtL1dqTQx6rMPQ0xIprK7QGmyMi2FpVpUhH\nf4hGi80tUH11da1U3ZyQlpGGrbe3ovLKyqi1phaMW+zFxcvfT6H9WUriNHHbUN3MbTARncrtuP/m\n9ltJMBwcgDNn/tdX8WOoXTtnvndCagKWXFwCEx8TdAzqyKUQXFlwPX3Kgi02lt1DNWqwILh3j4Wh\nXJgP/Os+ysxohSqrquDc83Nwd2fhJ/+NJyTwCli1EGByMmBTNRaVvZrBKcgJsd/YrBg9GujRg91A\ncqSlAR07Aj36JqHVltbovbc30jJYey9fznM/VMk5S00Fqo7yBk2yRmnLFwgO1rzvgwcByzp3UNDV\nFH6rvmkUh5PJ2DIwXVQRfSffRqNGLMQLF4aCslrc/Al0XcwxbBiwahVw7hwQEMDKwtYWaNw0DVT8\nA0qVi0aZMmylqCqIokVZYE6fzgrTwgIAEebNYwtCImFl2qMHz+ntzWP8/YFy4yaj1LBeGq44OeJT\n4jUys1Wx/c529NjTI7uvhcLCUmVuZX0+w72OgtxE6Oe5Jccgb1xyHOqtr4fB+0bDvopMLY6lDY8+\nPULNNTUx8OBANQUvlfJz2LxZ8xhBELAzaifKLC2DgQcH4v3X9zmfJOv9CDJMXHkYJZ3rw26FHTbe\n2qgR9M8LfpaSuJOXff/L7beSYMybBzVa56+I0FAWTFnpiPEp8ZgdOhuGnobov78/It9HKt6Li2Ml\ncP8+Zy87O7PAr1NHmWQ4dCgwYcpXuIS4wGCJIUq3XY7TZ1hwp6WxQpEn4gFshVhaKuMXd97fgekS\naxTvOBPv3iuXoMnJ7LoYNUo9SPw5IRl6k/5A2b8GICVVXfAFBrL76uBB/v/m25sQuYvgtnM/zM05\nWNyvnzp1FgCGHR6OIYGL0KULK4Bx4zh4L88vSUxNRLFFxRQKSQ75dQmCgNIeethx6APGj2eLqksX\nYNkynq9IEcC29jMUcDVCiZrHUbSoUkGIRFz2oUwZfpazZgGVKwMggrc3W0LyAKqpKWeKSyT8eZZq\nsxxGC22xZttHGBlxeXt5oULF5/4sFI02Nsr6dVDg/sf7KOtbNtv3AaDf/n7wDfPV2P/4Mec2NG4M\nDAiagMILCmerTOR4+joBxSc0QvVZoyGT5U4bSkpLQq+9vVBvfT1F5vicOWxR5qSQElMTMfXvqRB7\nieF31S/PLqiUFP5+Xrwo4Ozzs2izrQ3Ml5nD76ofUtNTc58gEz9LSZwhogFEVICIChLRn0QUmttx\nucxpQESnMsuPhxCRXjbjXhBRJBFFEFF4DvPl+aH8f0ZEBK/0vocK90+Cqakyizo5LRlel7wg8ZJg\n6KGh2Wa1LlnCNNr371n4PX/OVpWVFddbCgo/AZ0pFmi39k+8TXyLI0fYPSOnwMrdAjHKunSYMIEt\njL3R+yH2EiMoMgizZ3MAU7WGU0ICWz8uLpmunQwpHHc4oseuPmjbLgOdOvGPWhUXL7I7pk8/GcSe\nRmgS2ITvN5kVXbFinAcydSpfU3xKPPSW6CkSrl694kVBq1Ys4MuUARr1vQAzt7pYtowthVWrWAHM\nncsxgjJlgILDWqJ6jxNwc2MFWrcuoKPDpVEMDLgmlmH1MIimGUFUYzOIBBDxmHnz2E1TowYHpOvV\nA0CEWbPYEtm8mRVHQAAHhT2902HSZy4svMvBrNIL+PqyQuvShd1Vu3Yp2Wxel7ww8cTEbL8TMkGG\nkh4l8TlZSzJDJi69vAS7FXYKt8+bN3zvhobKkvIymQxmS81Qf339bOeJieHn4TInAfXW11O4nnKD\nvLS5+TJzeAeFo0wZZpDlBXdj76Lllpaovro6It7lnvCwbBlbq6q48eYGnIKcYLXcCltub8lTJvfP\nUhLWRHRUJSZxmIgsczsulzm9iMgl87UrES3JZtxzIjLIw3y5Pox/AwSBzfu72mnjvwwOHwZEIgGL\nT2yB+TJzdNvdDfc/5tz+KymJheDFi0xzbdeOn0e/ofGo6DIUVsutMCvwFCpWVArsAQNYIMuxcCG7\nTeSr3FSpDNaD3VFyroWC8y8IwLBhnJehKvg/fuQyHpOnpKPrrq7ouqsr0jLSIJWya6ZhQw6AZr3m\nKjNGgGYXQdBedf/RtWt8D4ULcwDZttt2tAzop3UBIAjsahu5dh2qzx0CZ2d2g40axVbV7Nmc//H0\nKTD5uCuaTvWHWKxUDjVq8NaypZLVVMgiEjTWHtSrC3R0pRgyhFlQhw+z2+noUeCPPwAQYehQoHdv\nznivXJkLMFrUuouKPnVh4vIHOvZ7i+fPWSmOG8e5JocOcQKoiQlfX7tNXbH19tYcP+O66+vi8qvL\n2b4vCAKqra4GnwOn0bMn38fo0WxJqCLqfRRE7iIE3tKshhcayr8hOTU6PiUeNdfUxIzTMzTGZgff\nkwchchVj3oH80Q0FQcCmiE0Qe4nhdtYtW/dRQgLHfqI0U0cAABdfXkTDjQ1RZVUVHH14NEcF949l\nNxHRAyIyznxtQkQPshn3nIgM8zBftg/h34axYwEPj//1VXw/BEHAvrv7oFttH0o13qZoSJMX7N/P\ngujLF6BmTcA54ATMfMxRvPcoHDnJS/8ePZSZ3XFxLBDkbh+ZjIPcw4YBialf0X13d9ReXR+Wld9h\nq4r8ysjgQG2nTupuk8+fAZNBzjCb3hrJKgkfGRm88hOL2T8vD5jK3Uxz9wahfHlWJlmzZt++ZSFa\noNR7FC79BXp6QPPmXM789Gll9ViAS4Wrrsbl7KZt27jkhJ0dUKBoIqjEe3TvIcP06XxNffqw0KlT\nh/NNSpQAChQAqEAqqNoWkPg+Buwai+Jlo3DhAvvZFX1MiNC+PRMDJk4S4Lz8PCwm90aJBQYwdVqD\nuDgBdeuyFRIXx4rZxobjIQAvaIZNfA/RdD04dovHypWsILNaXgDQcktLjTpQANefOnGCz2HcMQCl\nhvRDQIA6Qy0rJpxQdzslJrJCMTfXTJT8mPQRFVZUwPqb67XMpHktNjaAx4ZIlPUtC5cQl3zXZnqd\n8BqOOxyztSrGjWOXX04QBAFHHhyB/Up7NNrYKNsckJ9lSRQlovHEJcMD5Vtux+UyZ7zKa5Hq/1nG\nPct0Nd0gohE5zJfzE/sX4dw5zn79FV1Ooc9CUWddHdRcUxPLDpyDSCTku9RAnz7AmMnx6LJpKHT+\nssKOsFM4cULpdvrwgd1Z8uBweDgLysjMEMfXr0CFes9htqAaBh8ajNT0VERHs/A8pFJuSCplhdKm\njdJltfHWRpTzK4+mbePQrp1m0b+HD9miaNqUrQqr5VZouKEhALYqPDxYaTVowIJdLihfxL+AeLEJ\n9u3PQP/+rAh1dTmGUKAAr5jLlAEMav8NvfoHFdnUhQrx+wUK8PsdOnA8QDytEYrXOowePdhdZWfH\nq/oyZZhzX6gQ1LKrx039hLaL3VF0lhnE821gPa0nuvkuRN1xTIE17j0XDVY4QXe6Kez8K6Joc3/c\nf/4FXbtywT85Q8jPj+/nyBE+14QJ/OwWXViEwftHIDCQFXSNGmytVK/OwnDhQmDpUsDewxEjfY7C\ny4v9/Z07s1DX02OrxsUF2HPyNQw8DTTiMlkhk8lg4mOCNlvbIDSULaihQ9WZamqf3aeHkHhJcky4\n+/SJrUk3N/7/Y9JHNN/cHI47HPElJX9VX+VWhcRLAvez7gqr4uxZ9dLyuSFDloEtt7fAarkVnIKc\n1GJ5wM9TEvuIaEGmwB6UGUvItcBf5rgoLVunrEqBiOKymcM086+EiG4TUZNsxsHNzU2xnVVtWvwv\ngyCwkviVHsHtd7fRemtrlPMrh51ROxUJSg4O7A7JD/bfOg2dqebovH4UZs1LRNu2vHIfOZJjFjIZ\nu6QkEq7rBHBuQ9my7DK69PISJJ4mKNl6OfbtU2raGzd4tX34sPJc6emcd9GwIRB8l5vi3P94H1Ip\nr/QlEvbTqypsuVVRot4uiNx18PyjSmOLzDkPH2blI5EArq7AX7tW4M99g9XGZWRwMmBgIK+A+/UD\nKv9xA7bNr6BPHxZ4y5Zxv+N79ziGULs2C8OeC7eiskcbGBuzoDU2ZsEskWRmWGcymgwM2KpIT2fF\ndO5CBrqPuo9BS7ejlacL7F04ma6Y02zsityHkpbP8eaNgNGjeVX//j0/s/BwtmisrZU5E3FxnEdR\ntlwGjDwsceut+mogJYUtilWrWNE4OwNW053gOPkwpk7lWMPu3axssy6Iaq+rrdbEKDvsuXkS5CaC\nSaUnGtaDNpx8fBKmPqZ4+UWzhEdsLD9DeVxKjrSMNIw9NhYVAypq1KvKC1StivDn0bCxYbJCfpGa\nngrfMF/ojdZDtV7V4OzqDDc3tx8u8Fcw8+/tzL+RmX91iehabhPneFJ2N5lkvjbNzt2U5Rg3IpqS\nzXv5f2r/j7FihfZCYv80xKfEY/zx8TDyNsLK8JUaq79nz9hvnrV0hTaky9IxK3QWTH1MMWfz37C1\nZaHfsCGv7FJTeYU+j/vvYMMGXkHLV46urkCVTqch8ZIg+HEwbt1iAXfkiPIc16+zQJX3eABY6Qxx\nfokCLqbwPqQuaSIieDXcoYOmVaG/WAKj8d1hbMyr+5daSgc9esSU0pJjW6NQ9f2oWpUZRCtWcILf\no0ccwI6N5SDtrKPL0H/bJAQF8XHNm7NlYGXF34egILZWLMulQNfFAjW7n0K5chyANjXl+l/yDOsC\nBVhZBAaql02vUYMT9xYvznTbEaFgQWaI9enDyujOHV7tpqXxOStXZqH/4gVbFO7uSkE6fIMvio1p\ngSpVWCEkaiZVK/DHlj/UmhVlh3nn5sE5WDvNT55gOXAgWyB6syqi1uo6Wsdqw9IrS1FjTQ21xL3n\nz/keZ87M3oJfcW0FzJeZ5xpb037NAjbc3IAic8VoMG5jvor/ZUVCagLmnJkDQ09DLL64+IeVxK3M\nv+GZfy8SUdXMVf2z3CbO8aQcuJaXIp+uLXBNRMWIqGTm6+JEdJmI2mQz33c/tP+P+PKFfwBv3uQ+\n9n8BmSBD4K1AmPiYYNTRUYpS2towezYLrg85VFGOSYhB48DGaL21tSIZa9w4LlL3+jW7cPbtY2aN\nhQWXEQeAiRO5v7VUChx5cByFZ0tQp8d5RZzh2jVWFPv3K88VGckr4pkzWUEkpSWh5pqaGLDGCyYm\n3EZVnh0NQKtV4Rvmi4LzC+Kr9Cvu3+eMagMDjnGcPKme5JUuS0fxRcXx4csXXL/OmfUjRrBVYGvL\nwlhefsOw7WroDRiBbt2ARYt4rthY7lL3559QVIRdvBjQr30SRaZbQd8kAba2PIc8p0JuRcjbeHbt\nyslkKSnsBkpO5vtfsAAAEUxNmREUFKRk3LRqxdcqCGzBjR7N+9++ZaXUsydwO+YRDD0N8ejTY4SG\n8jh9fY6raQvKNtzYUKMntTZceXUFtdbWUtuXlMQLg1q1OGbg7c3uIXlcKGvF1uwgCAIGHhyIHnt6\nQBAEnDvHzz4vC5lNEZtg6mOqtVhhbjh4EDCpGo2K/vbot7+f1hIl+cHTuKfoGNTxh5VERObf4cSU\n1WaZLqdYIhqd28Q5npTnO01ZKLBEZEZExzNf22S6mG4TUTQRzchhvh96YP8fMWqUctX8T8LNtzdR\nf0N91F1fF9ffZJNZlQU2NryS1YajD4/C2NsYHhc81OropKWxn33aNHYVicXsepG/vnmT3SidOwP1\nBx+EkbcRLr4IQ8+ezCqSl9W4dYuFgJ+fcpUYG8txBaeOAroF9cafB/6EIAj4+JFX0+XL80pfFRER\nvAqvWl2GwvNKYNjB0Wrvf/vGq/AaNVgJDRjAgmdLcBTK+ZXP03M6+fgkmgW2QHg4C/Xhw3mFa2vL\nFOGVK1lA29ryX72Bw1C890iYmbHwl7uZGjRghZGQALW2reHhyrawEyZkCkYiODjwe3FxyvLt169z\nFdmkJJ6nUiVl1nJKCjBwkAzFxzfG3BPq0vX1a7b8TE35WYwcCaxbB9y4IUDiJdFaejwrktKSUGRB\nUew/lAo3t8z+Ggb898QJzUzreuvrwW6FXZ6eMcBZ9PXW14OT93wYGaknXeaGoMggGHsb4+bbm7kP\nzkRkJH9nw8P53kYcGQFbf9t8zZEdflRJvCaiv4hoirYtt4n/m9tvJaEJVZP/n4C45DiMPTYWRt5G\n2HBzQ54Ko8mhze0kzZDir5N/wXK5ZbalDT594ljDtm2cE2BlxULv4EF2G0VFATtu70GhmcZoNeAG\n0tJYcfTrx64aeXDw2TNWUsOGKdlMUinQYnIgCk2sjpOnk9XOu38/FFaFag0nmQwYvckXormFoGeQ\njokTOQFQFYLAn9369bz6Ltt1Ewr07oNKldgamDaNg7YeHmwRzJ3Lbp+hQ4EqdT+CZpRC1WoyDB3K\nSuHAAc61EItZ+U2YwFZDx45AaaMv0PnLCkXqb4aODkuDZs34WcuzhOfMUdKEV69WsmrkPSpABCcn\nZVC/ZUtl3KZnT75GgGmoRkZKVtO0EBfYLGgKQ7EMGzZoumnS0rhXuZ8fu4bsar8CTTWGQ20Bw4Zx\nwcKZM7nRzty5/FwmTGBFYGoKFBhfBfW63MCMGWxF5tTW81ncM4jcRYh4mzeWxNu3QJtub1FwujH2\nXAnL/YAs2H9vP4y8jfJkvXz8yN9hVRcnAOyM2qmoAfUj7qe8KAkdyh4FiKgkEZXIZvuNfzCqVSMq\nW5bo6NH/7XUIEGjjrY1UaWUlAoHuj7tPw2oNIx1RTl89dZQtSzRnDtGUKUTR0UTvv72n5pub0+O4\nx3Rr5C1qZNlI63GGhkRHjhBNnkwkkRCNG0f0xx9E9esT+foSNR1xmCYGT6CLo/6mol8cqEcPovR0\noq1biWrWJKpbl+j+fT7/lStEcXFELVsSvXlDFJv6mqJMXci74RYaNqgoTZhA9O0bn7dbN6KoKKK3\nb4kqVSLy9ib69IlIR4foePxS6mbfke5EFKSSJYmaN+dr2rqV6OFDdvZUq0Y0fDjR6tVETiNu0aJx\nDhQURNSiBZFYTFSwIFFiIlF8PJFIRGRgwNe6wV9MFhI9WrbpKbVqRXTsGNHIkUQyGdGMGURfvhCd\nOUNUrhzfl5BSmgrtCabURtNJqLSX7Oz4uuvXJxo0iCgtjWj9eqIxY/i+bt4kcnDg11+/EpUsya/N\nzIjevePXHTvyMyciWriQaOlSfm62tkQ7dhD17k005fBCOvH4OIX/dYDOhOrQypVE7dsTxcQoPztd\nXaJGjYgmTiTasoVoydYb1LaqA/n7iahOHaIyZYiKFSOSSvm5GhryOQYP5s/qzz8caOjsm+ThQdS9\nO4/P9vulX5YqiCvQlJApOX4PAb6HGjWI6lY2pS19VtCcm4MpJT0lx+OyolulbrSh4wZy2ulEt9/f\nznZcejpRz55EvXoR9eun/l6fKn0obFgYbbmzhfru70vJ6cn5uoZ8ITvtQZnupl9ho9+WhFbs3s3c\n9/8VHfZe7D3U31Af9dbX09qAJr9o0gQopp8Icy8bzDs3L8/WyLlzvJK+cIFXnpUqAduvnkCpBRIY\n2N/AjRtsGfTvz4wqedb1pk0cS5CzSWQypmOKJQKqLmkP97Psz5MzdaysOKFMtY7TtWtQVGvtMDwC\n5C7Ci3hllFoqZSune3d2M5Usya6syZOZddV8nRP2RB7U+AzlRfseP+bM5alT2b1WsH9XGDbbiS5d\nOCFsyhRewbdowQlv+vqcQ2JoyC4mXV2ATCJQwMUE5rWiYGiopN6uX5+ZYdDzOgAAIABJREFUMJcJ\nOzt2vwEcyzlxAgAR3N25TAfAbCNjY6VLZ+RIZV6KTJChlacLCjlXRsQTZSpyWhrHNwwMOAaVtRwJ\nAMwKnYU5Z+bk6fMGgAXnF+QrAW5X1C7ozNNRq72kiitX+HOpWpVdlnL03NMT00Km5fk8qth3dx9M\nfEwQ/SFa472MDCh6e2TkkGaRkp6CAQcGoOaamlpZV7mBfkZM4lfYfisJ7ZDJ2K+btRfCfxoZsgx4\nXvKEoachVoavzJdrKSdsjQiCqMIxmFV+nu8ufKdOscA/exYYuuA0CkyX4MD1Kzh0iBXIvn0seJcs\nYV96WKYX4coV/t/FRSk83Q8HoqhzTbTrkKZWxuPCBfbnV63KGcmqgv3TJ6Dc/JbQda6IWrVYAGct\n0icfFxLCbpru3YFC4x1QwPIadHSYnqqvz303dHSYgWRhwUltCxaw0HY5thDd106FkxMrgokTWYAb\nG7MrSSLhCrmlSnGehUjEc3Ts+xak9wxO64bhY9JHJCdzDoL8OTx4wO5L+T01apTZZY0I27ZxLEYO\ne3tmEAHs5jEwAC5EP0aTwCZoHNgYsxZ+QqVKmmSEFy+YUmxkxHkR35QEIrTb3k6jL3ZO2HhrIwYf\nGpzn8QBQ0qMkXELUe+hGRnLcysKCmV5ZBfaHbx9g7G2s0echr9gRuQNmS83w8JOy4mNGBi86WrXS\nnlSYFYIgwOeyD0x9TPNdVfZHlUSumc7/lO23ksgewcEsFLT15P1P4F7sPdRbXw8tNrfQ2g3se5Ah\ny8D0U9Nh7WuNU3eiULgwZ07nF2fOAKXtw1BqoRgTfM7DzIwDzDdvcrE0NzdWrEePsjCVs3M+fGCB\nXakScORcDMReYtx4fQfu7iwAZ81Srn4FgX3yVapAjdaZkp4CnXk62BW5G8HBHBMoWpQF6sCBnIl9\n+bI6MwoAzJaa4dWXV0hPZ6Xy6RPPl57OW2QkWzzjxjEbqYjdBRSZUhleXgJcXTk20qgRKy9TU75e\niYQD0zo6TEldtoyVxb5DyZh8cjJMfUwxwGM/unRVajkvL67bJEe1apnZ4US4do1ZQ3LMmMHxAvln\n187dD7qzDLH0yjJF9rGbG+fAaGPgRUUxo8rAgJlfN6IS1OpW5QUnHp1Am21t8jweAMYcGwMDTwNI\npWydNWvGz8zHJ2dhvSd6DyqsqIDktOTsB+WAwFuBMF9mjpdfXipiYi1aaH4XckNujZO04YeUxK+0\n/VYS2UMQOAibtUPYz4aq9bAqfNVPsx4SUhPgFOSEZpuaKZr7nD/PAk6e2ZpXvPryCuLFZtCreww+\nPsDx4ywwN27kxK+GDbnD35cvnIDm4MBJbS9f8nPcuVNAoSHt0XDGPEV+xcuX7E6SSFiYyH/YggA1\nWme9yV4oPL+YWhkPqZQV1Nq17JZxcFAqjj59gGHDZRC5FcAUFymmT2eBOWoUM5/q12fLws6OG9os\nXcrKbf16ASVcK6Fk1XMYMICD1cbGXMjP0JCP0dXl51e/PgecdXSYpivHieiLKDCxEir4VsfaG2vx\nVfoVTZqol6soWzazFhUR4uM56U5uZYSFAXY1P2DRBQ9YLbdC08BmqNjoIbZsUf88PDxYSamWTVfF\nixescEq0DIBkbE/4+TGJIC+IeBeBqquyocRpgVQKHApOALmJYGAfgebNud5VXokfP+J2AgDvy96o\ntqoGOnb/hrZt1QkP+YG8cdLEExPzVFH2t5L4DQCa3dl+Nv4T1gMAvEl8g6qrqmL00dEaiXYbNvDq\n19Mzb3MlpSWh1tpa8LzkiRcvmMo5aBDTYsuXZwGcmMirZQsLzjFIS8uMQYhZye6IDEKVgBoYPDQN\nEglz7eXPNDpaufp1dlYXfK9fA6bzqqP0+FYoXZqtoHXr2L+ftad3air7vLdtAwJWS1HAXRdLlnDe\ng7y6a2Agu81iYlhhenvzyrNkSWb3NJ22AkbjesHQkOM4pUuzpWRgwBaEri4rm3Pn2GU1YID6NcyY\nAQwdJkPIkxB02dUFeov1odtzEHwvr8TVmKtISU+BRJLpLsr87YnLJGDv9bPwueyDbru6Q2eGHpzW\nD1XQnOVVdrNaDmvX8v6//9b+uQmCgEoBlbFg21kMGcJjq1Zl5tiOHewG0+Z6fPjpIcr7Z08dTkjg\n+1+6FIpigPXqAaXdLdAxcEC2x2WH2G+xMPY2xq23t/J9LAC8eCFAf9gAWE3tiZSUHwsixqfEo+22\ntmi/vX223frkyIuSEPG4XxsikQj/H+7jP4nu3Zm1Mm3az5tTJshoadhS8rrsRQtaLKBRtUfli7WU\nEx5+ekjtdrSjUQ6jyLWRK4lEIo0xAQHMfvH0zPm+AFDf/X2poE5B2tZ1G4lEIkpKYiZMTAwzeGbN\nInr1imjzZmYiDR9O1KoVs3NiYogGDU2juy0q0pIGm8i5SzO6d4+PuXGDaOZMogEDiEqUIHrxgmjt\nWqLAQKKqVZnN4+gokHVgYdrVfRc1EXenY8eIzp1jttDz50RVqhBVqEBkaqrcSpUiyhAlU8/rhrS/\nVgrFxTGD6N07Zk1FRfH1VqnCjKNy5Yg+fiTauZPIwDSB7raxpuKb7pGVoSk9ecIsqIwMIkEgWrmS\nWVfNmxN16UK0d6/yWb17x3PeuUNkbs77lgfG0NawY+TQ8SbdfHeTHn56SCnx+mRjUZSeOj8lUx8T\niv3ylSoZVKMWFRyotlltenW6Ez2O1KetW5Vzu7vz8zp6lK9HjgsXmMEzfTrRpEnq751/cZ7GHB9D\nd8feJZFIRDIZ0bVrfMzNmzzf5898/6amzLQyNSVKLfaUAtNb0/SizygtjT9T+fN7/Zro/XtmkTk4\nENWpQ9S2LZGJCdG4E+No/7399H7q+3x/ZwPCA+j44+MU3D84X8ddvswspol/pdLB0s2oY4WONLvp\n7HyfXxXpsnQaeWwk3f94n473O06GxQy1jhOJRARA88elity0yK+w0W9LIlfcu8ersLwWBssNT+Oe\nov6G+j/degCAa6+vwcTHRGsp56zw92eLIqfEQY8LHqizro6Gz1gQOEAsFrNlImczubkxP33kSLbA\nNm8GfK+sQHWv9rCxYWaPnOUTFsYWhL4+5xPIy7SnpjK7rG9foLjDAdDcgnBzlyEkRL2h0rdvnA+w\nZQtfy8SJbGm0awe0aJUK0dxCaN+emVdTp/LKd/t2jnts3crZ1yYmUORRtG/PloN48CgUaTMfZcuy\ni6lQIXY7Xb3K1WMLFGBXWFaMHs3nUUX37urd1b6mpEBHLwYPPz4CiPA64TUGD03H2rXKMZ8/8+r8\no7Kld46d2p4/5zjH4MHqAf1ee3thxbUV2X20ADhOc+MGl1BZu5ZdZ8NdH0Fvji2mTuVOet7ebHmc\nOcPWR3Yxumdxz0DupNa3PK+QZkhh42eTp7pRAH//Vq/m75zclfcm8Q3Ml5nnK0if/fwCpp+ajooB\nFbNlPtFvd9NvqGLMGE4I+1Hsjt4NsZcYy8OW/7TYgxzBj4Mh8ZLg6MOjeT5m7VpWFMOHa753+MFh\nlFlaJsdM3Tt3mBbarh1ntXbowAIrNJQZPPWafEVBVxP47bqN1FRuqmNiwuOPHGE2yqtXnHhmYsK+\n/sWLWWEIAvDH5taw8a6GKVOYRlmyJPv0u3dnt4mqALt6lQVeeDhw8XIGRO4irF0nw7x5LMDl9Zgs\nLZnVNGkSl7GoUoUptK1asdKzqBUNmiZBMeO3KFKEn823b+yq0tHRdDEBrPBMTNSVWEoKny82Vrnv\n82feB0DhblqyhO9FFUOGaLoD5W4nbbGFr1/ZBVi2LLvTrsZchbG3cb4rqALcR7pSQKV8HwcAekv0\nMDN05ncduyNyB+qur5trgtvLlxzvqlVLM6Ey/HU4xF5ijYqt34vlYcthvsxcazmQ30riN9SQkMBc\n/pMnv+/45LRkjDo6CuX8yuW5pEZ+sO3ONhh7G+PKqyv5PvbwYa7x5OCg9PNHf4iG2Eucp54UaWmc\nQyEWs+9/+3YOqrZuDYwKmo9mfv1gb8/B7QMHWKBt2cJ+bEtLjhm8fMnnDglhq8LSkkuKFJpbGl2X\nL0BkJK9gZTIWDEFBzBqaPJk58c2acYC5Zk2+j3r1AN1ZBug7LBazZnHGuZ8fHzNkCAv0ihXZp96k\nCdNaHRyYIisWA4XazULhwR1x9iwLrFGjWJnO1CL/kpOZBbd3r/r+4GBmR6ni3j0OmANQKImDBzke\noorr11ngZ6WN+vuzUsuumN/Ro4CZZQr0Z1fC5uu7cv3stOHMszNouqnpdx3rtMMJVVZW+a5jZYIM\n1VdXx4F7B7S+Lwgc3xKLOd6VXWB8+53tKOtb9rssGm0Iigzi0jNZSp3/VhK/oYFTpzgwqy1hKSfc\n/3gfVVdVRe+9vb9rZZcb/K/6w3K5Je7Gfn9bvSdP2O0jFgMR9z/Dxs8GW25vyf1AFURFsbCzsmLW\n02L/WIhcDeHY/ymuX2fB3rAhu6EWLOCigTdu8Erd0JDzUubO5X0yGXDjVjrIndBx4BNUqMCunzp1\n2EU1bhwLio0bmXK5bx9vO3dyYNvdHdCfZY9mve+genVmPlWrxhTJQYOUrqWKFXl/yZJ8XSVKsKKY\nOTcVlQOqYNON7ahdm5XooWy8GNOmcdOjrBg7VtMaCA1lhQZAoSTu3lVRHCqoU4dZZKoQBH5eXbpo\nDzoDwKSjrrCc1g3WZQXs2ZP/hNAdkTvQZ1+f3AdqwcrwlSi+qPh3HQswFbViQEUNdlFEBCcn1qql\n7F+SE1xCXNB8c/Nce2PkFSFPQiDxkuDEIyVN7beS+A2tGDkyf26nzRGbIfYSY92NdT9UJyY7LL2y\nFDZ+NngR/+KH50pJAapVz4Co9Ct09vL+7nkuXGBlYNjfGa2WjVM0BKpfn5lHV6/yc9TTY2G9ejX7\n1S9cYJ++nR2ziWr0PA4dt4LYu5eVmLwq6969vKKePp0Ffq9erDi6dePXQ4dy/kX5+a0weFEwpkxh\n5WBvz0qgYUO2cszNeTMx4fMVK8aCXZ6otv9SBETlTqG0QSoePdJ+r3I3k6pLCWDBbG6u6Q7Zvp1j\nLQAUSiI1lZlTWVfGmzYp+5WrQioFGjdmF11WyN1M77++x6lTLFQdHHiBk1d4X/bG5JOT836ACt59\nfQdyJyRJ85mokAlBENBsUzNFvsKTJ/y8TEy4zHteabUZsgw47nDE2GNjv+s6tCEsJgwSLwmOPOAa\n+L+VxG9oRV7dTl+lXzHw4EBUCqj00/yjWeF5yRPl/Mrh1ZdXP23OOWfmwKyHN0QiAW3b5i1rVRs+\nfI1F8QV6qNvyPcqUYQshMFDZEGjSJHaNBAVx0NjAgAXazJm8Yr9xA2gfMAal3M3h5MRKRleXLY6q\nVXmenj1ZgPTvzy6nLl048c3aOpOu2nMwLLuuRdeuPKZtW1ZM5cuz4CxWjDOhixdnxfL0qfL6XVw4\n/mBS9yJaB3bQypvPzs0EcHC+fHnNVbyXl0r8QeW3V7asZs5DcjJbdtpiEB8+sEtu927lvviUeFQM\nqIhdUUo3k0zGY2xtOeZy/nzulsWEExOw9MrSnAflgEILCmF39O7cB2aDy68uw9zHGqPHZsDQkF2Z\n2jLsc8OXlC+wW2H3Q9eSFeGvw2HkbYSD9w/mSUkU/CGe1W/8kihVimmfw4YxlbJ0ac0xd97fod77\nelNDi4Z0fcR1Kl6o+E+/Dq/LXrTh1gY6P/g8lSmVQwW2fODWu1u05sYauh14m547i6hTJy6KFxRE\n1KlT/ubadDuQelXtSoGzjenOHS645+/P1NjZs4liY4nmzSN6+pQL1Pn7c2G6O3eI1qxhimZcz2uk\nr1uFrK2JGjYkMjbmwnREXHAvNZULuWVkcEG9b9+IrK15f2Ii0TlpdXojRFCxh3wfSUk8LiODCw0a\nGTFtdNAgIn19nvfhQ6I2bZjuGRBANGxkXeq4M52GHxlOgZ0D1WjKc+cyFbRHD837P3KEn1lW9vG7\nd9oL5tnZET16xH/lKFqUr23NGqYqq8LIiOjQIb7WcuWIKlVLJqcgJ2pt05p62fdSjNPRYYps165M\nLR45kqhQIaKxY4n691cWGlRFxPsI6lyhc/Yfbi4oU7IMBT8JVruOvEAmIzp5kmjVqob0zkpCsaVP\n0oMHHUgs/r7rKF2kNG3tspU67+pMza2bk1Fxo++bSAV1ytShE/1OkGOQY94OyE2L/Aob/bYkvgsj\nR/LqNOuqbFfULoi9xNh2Z9t/7NxLryyFrb9tnvoD5BXSDCmqrqqKrbe3KvbJZOymEYnYPZNdD+Os\nyJBlwNrXWiNA/+ULl+to147dPq1bc1xhwQIuxmZszCvntm05Ka2Ie2m0ne+J2bO52N6AAbwarlaN\naau2trySt7fnRkKtWrE1IafBVmh9EQVG14GREY8zNeXaRiNGsPtF1acvk3Ein44OWxmq9NNv0m9o\nuLEhxh4bq2CknTnD15vVzQTwd6JCBc2+GABbPIrS1Sq/vYkTmaKbFY8fs+WVXTLngQOAsXkSGq5u\ng4EHB+bKmBMEpvHKs9nHjOF7kbtxMmQZKOFRAnHJ38/37rKzS56D14LAyZQLF7I1VacOu9nWXtsE\nxx2O330NqnAJcUH33d1/qrv3xpsbv91Nv5Ezvn1jJo2PD/8vE2SYFToLVsutEPEub7X1vwd+V/1g\n42fzU11MALuZnIKctP6QLl1in3CBAhyPyZrpnBXHHh5DnXU5t7VMTORA86BB7PLR1+d+CmPGcCB4\n1ChA5FYAlR3PwsqKcxX09NiVJFcOtrbs+itZkgvuWVqyAqlVizdru6+gmcVQo1YaZs9maqy2YO/S\npexyKlqUs7K1IT4lHg03NsTAgwPx8HE6jI1Z2GrD6dPKlqVZ0bQpC2UAakpi9WpmXWlD9+7ZZ8d/\nSfmCCkuaoGj//rh7P39Fxl6/ZuFcpw4//759AZ/N92C9zCZf82TFzNCZMPIyyvZ9qZQD+JMmsWKw\nsmJGW7hKi4jktGSIvcR4Gvc023nyipT0FFQMqPhT3U4AfiuJ38gdL1/y6nTf0UR02tkJTQKb5KuQ\nWn6x5fYWWC63/ClBalXcfHsTEi8J3iTm3LN11SqmihYurGxBqg2OOxyxKWJTvq7h/Xtm8syfz5VD\nK9nLQG6EgqU+w8KCBVnTpsqtYUOmuVaowPGMggVZ2dSpA0XToKtXgaorq+HyKy1LenA8xNCQjx0/\nPvdCjt+k39ByUxuUGNUeC/3eZjuuWzdlJ7msKF+eE9IAqCmJ8HBWcNpw/z5bWFmTOaM/RKP66uoY\nd3wcNmyUwdxcZe584s0bzpmpOngtCvTqh/LluQaWtzcrtdjY7D/vrNhyewuKLiwKgBcDN28y40y1\nxlbt2mxB3rmTfYxkyt9TNCrLfi/kAf2f+fv8rSR+I0/YHfIEBSbYo+fWEZBm5LLE/gEcf3Qcxt7G\nuBd776fOq83NlBNkMs6qLlKEV9+jR6u7oZ7GPYXYS/zdVT3lePTpEUTuIqSkMPPp8mXOoThxggPe\nJ0/yij0ighVMdgJs5umZmH5quuL/9HRelZuasmupd++8B0VlMqBjFylq/DULRt5G2H5nu4blpdqy\nVBuKF2fyAwA1JZGSws80O7fS8OHM5gK4d7fHBQ8YehpizfU1imvYtInvS1uP67zCKcgJ224HISqK\ns7snTGAygL4+W3OWlqycu3ZlZTxqFDPCRo/ma+zTB6jpeB00VwfFizM5QF6t18+PrdJvOZdEUuDx\n58eQeEmy7VORX/xst9NvJfEbuSL0WSiMvY3RzzcA5e2EPPvs84uwmDCIvcTfXXc/J+TkZsoJUinH\nDQwNOWZRrx4zZ6aFTMOUv6f88HUduHcAhRcU/uF5rsZcReWVlfHsGccrdHVZGPfrp9mTITfMmcPU\nU6kUuP7mOuxX2qPTzk54m6i0KubO5RwObUhMZCWheNRZfns1aih7UGRFTAxbTGeiolFnXR202tpK\nq0UZFMSxkmvX8ndvAFtKJT1KZhuPSElhptXly8zoWreOLaYVK9jKXLeOKb4nT6dweY7P0h9u2tV2\nW9s8L2Byw892O/1jlQQR9SSiu0QkI6JaOYxrR0QPiOgxEbnmMO6nPLB/EwRBQMC1ABh7GyP0WSgA\nDjy2bZtzJ6zvwb3YezD2Nsaxh8d+7sTIu5spN4SEsBtBJBIgsjkDp94fcP0Hk8o9LnhAf4n+D83x\n8iUwcZIMOvb7QDoZsLbmwHl+my4BTCO1tFRXLKnpqZh5eiYkXhIsOL8Ar+LewdSUA7Ha8OABx1EU\nyPLbGz6cy5Zow8NPD+EwyxlF5qpbD9pw5Ai7p7blkztx6P4htNzSMn8HZQOdeTp57nudEw7eP/jd\n2d/a8DPdTv9kJVGRiOyI6Gx2SoK4x/YTIrImIl0iuk1ElbIZ+8MP698EaYYUI4+MhP1Ke7WgWno6\ns2vGj/95LU9jEmJgtdwKmyM2/5wJVZBfN1NecOzOJUj6ToO1NVsXxYpxGe7t2/OfbzH++HhYLLPI\n1zEymZK5Y2DAv1CJBLDruhsjt3rk7wJUEBaWmYmejcyL+hCFEUdGoPh8PUjG9MGFFxe0CvGzZ7kE\niAJZfntZg9fpsnQcvH8Qrbe2hsRLgklHp0PfKibbHhJq1xTFZU1cXPK+cOm8szPW3ViXt8G5oPCC\nwtmW18gPUtJTUGpxqZ9WYgP4eW6nf6ySUJw8ZyXRgIhOqvw/nYimZzP2hx7UvwmJqYlos60NnIKc\nkJiq6XSOj2dWjavrjyuK+JR42K+0h+elPDZ9yCcWX1yMDjs6/FRa4NS/p2LumbkAuIHQ8uX8PAoV\n4l9LiRLM+hkyhN0VqsXwsmLUkVGw9rXO9v2kJGbITJ7Mri4DA1ZMOjq8Wndx4bIfAJdFMfY2/q6Y\n0c2bTJvNWh5DGxq1isfg1X6osKICqqyqApcQF+yO3o2ncU8hCAKCgjgGokCW317YtQzYNYrGlttb\nMPHERFgss0CDDQ2w/c52pKZzxyUPD04izAs+fWIl7eiYeymZl19ewsDTINceCnlFkYVF1JL6fgRd\nd3XNd4mYnJCSnoIKKyrkqxBmVghC3pTEPzmZrgwRxaj8/5qI6v2PruX/BWKTYqlDUAeqYVyDVjut\npoI6mh+/nh5RSAjR/7V33mFRXU8f/x6aFQUsyIK9Kzaa3WCNRsUWNUSjJmIlxiS+iRqTWBNRY2yx\nRE3UiL2j2BUxYrBgRYoNG6CCgIKAlJ33j8Mq4Ja77N296O9+nmcf1917z5lddu/smTPznY4deSHU\n9OlFmytXmQvvXd7wrOGJ79qI2MQij6SMJCz8byFCvghR22uiqOy/uR/+/f0B8KK3r7/mN4D3Idi1\nCzh5kt/8/XkhHGO8iK5MGf7+2dry/8dUc8AL25po3Zof9+IFkJzMC+ZevQKIAHNzoFIl3t9hwgSg\nf39e3FaYBhUbwLmyM3ZH7sYnzp8Ifj3XrwMffcR7XHyko3YqIgK4E26Dk4FfwdJyAk7fP43T909j\n8/XNmHR0El5mvUTFbBeUqlUPk46UQinLUpgD4Nsj3yItKw03Em7g6uOrSHd1wIEoN3hUdUWAdwCa\nV2leYJ6vvgLq1uW9INzctNtUoQJw5AjwzTe8H8q+fQWL9fKzOmw1Pmv6mWiFn2bMDJk5maKM5VXf\nCwHRARjWbJgo45W0KIn5Xedj6omp6FGnB8zNzPU6PzMTGDtW2LFGcxKMsWMAqqh56gci2i9gCL26\nCM2YMeP1fU9PT3h6eupz+ntPTHIMPvT/EIMbD8asjrO0XlgrVACOHeNNaczNeXWxvkw5PgXZudlY\n3H2xqBdxFX5n/NC/QX/Uq6DhilEEohOjkZqVChcHF7XPOznx6uaJE988plTyiuvwcCAqCrh7l1ck\nZ2cDZuYEmPGKYAsLoFEjXk1dvz7QuDH/v5WVcPt83X3hF+KHwY0HC3pPr1/nzXSWLOHNhXSxciUw\napTKJoYPanyAD2p88Pr5J2lPMG52GKh8DBysM5CRncHfl3JOKGVRCt7O3mjh0AKd2thg0qdASw0/\n6cqU4ZXeU6fyz5kuLC155fjq1bxqfeZMYNw4XomtIi0rDWsvrcWpEad0DygBPev2xMTDE5GZk4mS\nFiVFGbN3vd6YFzIPm65vEux8Tp06BX//U9i3j1f/C0LXUsOYN2gPN7VCwXDTVGjYvIYcbtLK5fjL\npFiooD/OadhR1EBcHFGjRlxoTp+IzoYrG6j2ktqU+DJRT0uF8fD5Q7KbZydqtTYRF4Ubs3+MaOPp\nCjfpiy4Z6vyEhfEMoa0CoyWpqTxF9OFD7cd5exfaTFbz3fPx4TUe2sjK4vUWAQHC7FMRGclFFj09\nC+pUzTo1iz7d9al+g+lAzHATEVHbv9rSoVuHRBuPiOj0vdNUfVH116E8bWRk8BCm6nMhNNwkTq9J\nw9D0k+gigLqMsRqMMSsAgwEEmM6s94NT906h28ZuWPzhYvh6+Op1roMDb7MZGAj83//x8Iguzj06\nh0lHJ2HfJ/s0tkw0lJmnZmKUyyjR9J5UBEQHwKu+ngJPWrA0t0SuMle08cyYGeZ2nosfTv6AHGWO\nxuNCQ7mW1MqVvH2qEDZt4itHVctSTcTH88+FNlxduW6VNiwt+cpg3DgeghNKgwbAmTNAz56Ahwdv\nxfokNQFLzi3BLM9ZwgcSABHBylyPpZ4Oetfrjf3RQoIowmlfvT2cKztj1cVVWo87dw5wceGr3mvX\n+OdC8AJflxcxxg1AP/D9hgwAjwEcyntcASAw33E9AESDZzlN1TJeEXzw+8/OGzup0vxKr1Nci0pS\nEv/1Nngw32zVxKPnj8hxoeNrGWJjEJkQSRXnVzRIl0cdz9KfUbm55UQreiISJwW2MCoZak0ZPNu3\n8ywmIZvUKnJzeRMgIVLc9erxpkOvUfPd01Z5XRhfX16kVhRUqwpHn4n0qb+Gwg4DECsFVkXE0why\n+t1JdLn9q4+vUuUFlel55vO3nnvx4u3VQ35Q3LObxLrJTuJtVpzjFqRaAAAgAElEQVRfQYqFCroU\nd0mU8dLTuRy2iwtv1VmYjOwMcl/tTr+eLnqaphD6b+tPfv/6iT5u4M1A6ryhs6hjilVMV5hLcZfe\nqg3JzeWFctWra05z1cTWrbxGRMi1y9q6kEiimu9eRgaXrdBUeZ2f1FSe5qpv2ElFyP1Qsp5pT3ZV\nn5CPD9dyEoOMbF5M9ypbPAUCpVJJlRdUFl2zjIho6O6hr7PyiHh/jyVLuHMYOlRz0aXsJP5HmR08\nm2ovqU23n90WdVylkvcSUCjeVgf1DfQVXaWyMOcenSPHhY70MqtozWC0MevULNE0dlSoZDmMQf4q\n8xcveCV227b6V2BnZfF0WyGriNRUfvEv8CfW8N1r0YI3VxJCUBDXrCqs66QLVfXx1utbKSmJp23b\n2fFfztpSk4VwIfYCmc00M2wQNfTw7yFK7UVh7ibdJbt5dhT3/Alt3MhFJD/6iOtKaUOIkygOexIy\nIkFEmB40HZuvb8a/n/+L2na1RR2fMeC773gvir59gXXr+OO7Inbh4K2DWOu11iiZTAB/bVOOT8H0\nD6ajtGVp0ccPiw+Dq8JV1DFr29YGgZCUniTquADwY4cfcT/lPn4/sRFt2vBeEydP8h4N+vDXXzzj\nqksX3ceq9iOE/Ik7dABOnxZmg6cn7xWhSjUWyvSg6WhcqTEGNR4EW1vAz4/H21NSeAaZnx9PNy4K\nEQkRKGFeomgna8FN4YaweB0bNkWghk1NtLUeiia+c7BiBbBhA99LVJdOrTe6vMi7cIO8kiClUkk/\nHP+BnFc4G1XFVUVkJM9O+fzbGKo0vxKde1QEoR09OHL7CNVbVk9tdzUxcPrdSfSVFxGR+UxzCooJ\nEn1cIqLVAZeJTa5IUxZdLVLhY1oaF9MTKj8SHMx1nwqg4bu3Zw+XeBGKvmGnvZF7yel3J42f9ago\n3gbWzo4L/EXoqSmpSyq8qOyN3Evd/buLNl5yMi/4rFePqJH7E7KebUd3nqlpA6gByCuJ/w2ICJOP\nT0bgrUAEDQ8SpXuVLho0AM6czcZus09Q4sIUlEnxMOp880Pm46cOP6ktADSUJ2lPkJaVhlq2tUQf\nu6xVWZyPPS/qmBkZfEX306jmmNp8KbayPniWkaj3OEuXAu3a6S5oUxEXpzuzSUWHDsDZs7xeRAhl\ny/JVjZBsp/Cn4fDZ74Pdg3Zr/KzXrw9s2wZcucILHDt14redO4XZFPE0wijfI1eFK8LiwlQ/bovM\npUu8pqVmTeDCBd6xL/xcZfi2Go2l55eIZC1HdhLvOESE7499j+N3j+PEsBOoWLqIfRKLwG+Xp6F9\ni0r4qcs38PQE5s7lbTXFJioxCuFPwzGw0UDxBwcPNbk4uBglVFbbtjaCYoJEGy80FGjRArh/nxfL\n/fKJN7ydvfHx9o+RlZsleJykJGDhQmDOHOFzC0l/VWFnx1uS6kqFzY8q7JS/WLEwiemJ6LO1DxZ/\nuBjuju46x6xaFZg1i79fY8YAy5bx8NrUqfy9VCrVn3f1yVW4OQr0nnrgaO0IxhgevXik97n37/Oi\nwtatebi3Zk1ewLlpE9C2LQ8DjnUbi43XNuJl1kvRbJadxDsMEWHqiak4HnMcx4cdN1pdgjoO3TqE\nreFbsa7vOowezRAWBgQF8Q/wjRvizrXq4iqMbDESJSzEjxEDQFhcGNwcxL8gAEBLp5YITwg3eBzV\n6qFvX2D2bGD7di7nAQBzOs1BhdIV4L3LW2v9RH78/IABAzRLXKgjPh5QKIQf7+nJPxP64OcHhITw\n11eYlMwUfOj/IbydvTGk6RC9xrWy4rUBwcFc5oMxwMeHvx4fHy738TLfdTU2NRY96vTQz3gBMMbg\n6uAqaF9CqeSrhJ9+Apo14yu+ixeBH34AYmL4v4WrpqvbVEe7au2wJXyLeEbrike9Czf8D+5JKJVK\nmnZiGjVZ0cRolc2aSHyZSA6/OVDwveBCNnE9/ooVuYibri5pQkh7lUZ28+xE72SXn4+3f0ybr202\nytiBNwPJYpaFQWP89x/vXjdwoPp+1ERc8ruHfw8auH0gZeVkaR1P1dchVk919SFDiDYU1qjT8t3b\nt4+oWzf95iAiunSJf4Yu5cveTkpPIo81HjTx0ERRM+ju3CFavJi3nbW2JurZk2jqnHjCDNCDOPGz\n6Ih4E6mZp2a+9XhmJq8xUSnpOjgQNWjAs7XOnBGuhHv41mFqvqq5oPcJcgrs+8uMoBnUaHkjk2xS\nF8Z7pzd9c/gbjc/fv0/UtStvQHPkiGFqsmvC1pDXFq+iDyCA1mtb07/3/zXK2Nm52YQZKNKmeGws\n75pmb8+L5HSRkZ1BPTf1pN6be2tVQs3fIU4fOnVSkyqr5buXlMQvvFnafZZaVL0vHj8minsRR81X\nNaevD31t1BTrpCReM9Jp8nIy+6kMWVvzVNIBA/iPnsBAnlKakGDYZ3rxmZU02H80nTrFHYKPD08Z\nLlWKFyF+/jnvySFETl0ducpcqr2ktqAGX0KcRHFWgZXRwG9nf8OW8C0IHhFskk3q/OyJ3IOLcRdx\nZewVjcdUq8aX9Lt2cWVTJyceRnDXHUIuABFh+YXl8OvsZ6DV2olLjYNDWYHBdj2xMLNA+RLlsSV8\nC37sIEwpMSUFmDePy1Z88QUP31UQEEksaVESuwfvxqj9o9BuXTvs+2QfqpWvVuCYqChg717g5k39\nX0tMDP/bCsXWFqhTh4dM2rTRb65Bg/ieS7fPLyCpS3+McRuDae2nGS3FGuD2Dh4M+OceQqOUmria\nAty6xfdVwsKAxYv55n18PE+trVKF79EoFPzvY2nJhRzNzXmoKCeH31JT+Tmqc1/VcIBFy0DERnE1\nXFdXHvJq2pQrLxuKGTPDOLdxWHFhBVo5tTJ4PNlJvGNsvLoRS88tRcgXIbAvK1TGURwS0xPhe9AX\nOwbu0FmrwBjw8cdAnz68nqJfPy71/MsvPPNECOdizyH1VSq61u4qgvXqISLEp8XDwdo4TgIAPBw9\nsCNih04nkZHBNyYXLAC8vHhmTtWq+s1lZW6F9X3WY1HoIrRa2wrbB25Hu2rtXj//4498b8PWVr9x\nX73iF7maNfU7r2NHrv+lr5MAgPofb0YUTUSHmNWY9k0/4VpDBnLm4RmMdx8PMzP+Wa1fH/j004LH\nZGYCjx+/ufgnJXGHkJ3N/zU35w5DJSGvciYODkB0qgLjD8bj3zXGew0jmo9AnWV1kJieaHgyi66l\nxrtww/9IuOngzYNUeUFluvH0hiTz6wozaePlSyI/Px5rHjVKvbRHYT7b/Rn9FvJbkeYTSuLLRCo/\nt7xR59gdsZssZllQroaeo69eEa1ZQ+TkRNSvn/45/Zo4dOsQVZpfiX46+RO9ynlFZ8/yymZt+lua\nuHGD5+K/hY7vXkAA73aoD8kZyTRi7wiqtaQW/XfnGjVuzHtQm4K7SXd5b2sRu8gV5uHzh+Twm4PR\nxlcxYu8InQ2/IO9JvD+EPgylivMr0tkHArUORGZ3xG6qu7SuwZIYSUl8I87Wlsd6T5xQH99NeJlA\nNn42Rt+Uv/7kOjX8o6FR58jNzSWLWRa088bOAo8/fMj1lqpUIercmSg0VPy5Y1/EUq/NvajJ8qZU\ns/Ul2rKlaOPs2UPUq5eaJ3R895KTeTe/VwIlkAJvBpLT7040/sB4Sn2VSkR8c9nenrd1NTbjA8eT\n/QJ7o86RlZNFlrMsKSdX5GbyhTj/6DzVXFxT6zxCnIScAvsOEJUYhT5b+2Bdn3VoXbW1yedPzkiG\n70FfrOuzzmBJDFtbHm+/fx/o3JnnxDdsyAu7UlLeHPf35b/Rt0Ffo6f1xqcaN9QEAGZmZmhcqTFW\nXVwFpRI4fvxNB7rkZODECf6YpiY9hqCwViDgkwBUj/0OcZ26I9h6PB6nPdZ7nJs39UuXVWFjw8M1\nFy5oPy4qMQoDtg/Alwe/xD99/8HynstR1qosAKBWLWDzZh7yuXNHfxv0YX/0fnSr1c2oc1iaW8K2\nlC0S0hOMOo+7ozsqlK6Aw7cPGzSO7CSKObEvYtHdvzv8uvihV71ektjwU9BP6FO/D9pWayvamNbW\nvLr22jWuBfXffzzePXo0zwXfdH0TRrYYKdp8mohLjYPCWo/k/yLSp9YQBMecRcOGwKRJvGPcgwe8\nuKtRI+POfeECw4W/huLyqAiUsSyNxisaY9qJaUjOEN7IITpa+F5SYTw9ua6UOh4+fwifAB90WNcB\nrRxb4cb4G+hYs+Nbx3XqxLvZeXkBz58XzQ5dvMh8gUcvHuHb1t8aZ4J8KKwViEuNM/o8I1uMxObw\nzYYNomup8S7c8J6Gm5Izksl5hbNRpLGFcjn+MlVeUJmepRsoqymAx4+JfvmFqHqzGDKbXInGjs+h\nw4d5/rix+P3s7/TVwa9EH1ep5PpW8+ZxddayNhnEppvTzJ3bDEqf1JeMDKKGDQt2qLufcp8+3/s5\n2fjZ0OiA0XQl/orOcdq142qtbyHgu3fsGO/7oEKpVNLJuydpwLYBZONnQ1OOTaHkjGTNA7w+j/ef\naNeO606JzbgD48hunp34A6uh84bOdOyOAOldA4l9EUu2frYaa2cgh5veXTKyM+C1xQtdanbB922/\nl8QGJSnhe9AXczrOgV0pO6PPZ2/Pq0i//XM/+jbqhZrVzTF7Nn/844+Bf/4BEvWXKNJKjjJHtO5j\nOTm8onfSJB6a6dqVh9V++glIfFwSHWt+gC2Pp5ssSwcAZszg4bxBg948Vq18Nfzd529E+kaiavmq\n6LWlF9r+3RZLQpfgbvJdteMUNdwEcB2nyCjCiRtXMTt4NhqvaIwJhyagU81OuP/1fcztMhc2JW10\njsMYD0vWqcNXFBkZRbNHE/7X/OHTwkfcQTVgaW4puDreEBTWCtSxq4MzD84UeQw5BbYYoiQlhu4Z\nCqdyTlj44UKj5oZr45+r/yA7NxsjXYwf9snP/psB8PXwRd8GwPffA0+fAgcP8vz+CRN4rr6r65tb\n8+ZA6SJuleQoc4okGqhU8vi4Kof+4kXg8mWuV+TlxWUlmjcvKKu9sNtCuKx2wYPnD96qXzAG588D\n69cDV6+ql/euUrYKfuzwI6a0m4LDtw9jb9Re/HrmV1QqXQk96vSAu6M73BRusEVNvHzJBOs2AUCu\nMhdRiVEIiw9D6KNQZI8PhPceCwxx88Kfvf5Eu2rtivS5NjMD1q4FPvuM7+vs3QuUEEGtZVv4NrzM\nfomZHWcaPpgALMwskJ0rUP3QQLzqeyEgOkBtGE8IjK843m0YY/Q+vA4V04Om40TMCZwYdsJoekW6\nSMlMQcPlDRHwSYAgITWxeJ75HFUXVUX8pHiUsSrz1vOvXgHh4W8uzmFhQEQEvzirHEbVqjwfXXUr\nWVLzfLODZyMrNwuzO81+6zmlEkhI4Lnwqnz4qKg3DqF8+TeOys2N9xBW6SlpotqiavBw9MDOQTv1\nfWv0IjOT2zN9uvA+1wD/gXI+9jyO3TmGsPgwhMWH4Xl6GpRPGqFHewUcyjpAYa1A+RLlYWFmgVFu\no7H83B9IzkxGXGoc4lLjEJsai8iESDhYO8BN4QY3BzfkRvfAmb0NEbBPnB88OTnAJ5/wuoQdO7g2\nkyE0XN4QirIKnBh+QhT7dNFvWz8MazoM/Rr2M/pcVx9fRb9t/XDnqztvOWbGGIhI6x9FkpUEY2wg\ngBkAGgBwJ6JLGo67B+AFgFwA2URkXD3qYsCOGzuw/up6nPc5L5mDAICfg36GVz0vkzoIADh8+zDa\nV2+v1kEA/Fej6sKsIivrjeO4epU3u1Fd1B8/LljMVLHim8pYCwvgcjlCjhJ4sKFgdWx8PF/BlC9f\nsBCqbl1gyhRhDkEdk1pPwvfHvi/yCkYo6sJMQjBjZmjl1KpApe7y9U+w+040Pm4Yj/i0eMSlxuF+\nyn1kK7MxCly6266UHRpVaoQutbrAoawDGlZqWCCElNQA+OVrHiISo6rYwoJnPA0axIUKd+zQ/mNA\nGzHJMYhOjMaW/iKK4umAgUFJGiRoRaapfVPkUi4iEiLQuHJjvc+XKtx0HUA/AH/qOI4AeBKR+K29\niiGX4y9j/MHxODr0qMmrqfMTlRiFLeFbEOUbZfK5A24GwKuel17nWFnxi7aLy9vPEQHPnr1xGs+e\nvZFLyM4GnqRaIJtlomNTfuEpU+aNQ6hSxfBfqIWZ4DEBPwb9iAkHJ2Blr5XiDp7HuXPaw0z68uSu\nPdpVtcdgZ3XP/iXoddjZ8b/PiRNAL5GS9KysuHMYMoRX9u/ZU7Swo/cub9StUBfNHZqLY5gAcpQ5\nsDS3NMlcjDF41eMhp6I4CamzkoIAuGh5PgZABQHj6L/tX8x4nPqYqi2qRtvCt0ltCvXf1l9npaYx\nyMrJIls/W3r0XKRu9gKYd2YefXf0O5PNR0S0+L/FZDHL4nWxmJioy2YylMGDifz9NTypx3fv9995\ntb3YZGcTDR1K5OnJO9zpw4XYC8RmMDr/6Lz4hmmhh38POnjzoMnmO3r7KLVa2+qtx/EeZDcRgOOM\nsYuMsVFSG2MssnKzMGD7AAxrOgyDGusZHxCZ87Hnce7ROXzp8aXJ5z7z4Axq29WGYzlHk81pyg1E\nFRNbTYRtSVuM2DtC9LGLGmbSRnR00TOb8tO7N7B/v+ZGP0XFwoKvnOrU4fUnz54JP/ezPZ/BTeFm\n8rCqscONhfmgxgeITIjEk7Qnep9rNCfBGDvGGLuu5tZbj2HaElELAD0A+DLG2hvJXMkgIvgG+qJi\n6Yomy6zQZsuU41Mw/YPpBldWF4X9N/ejdz19Ph6GY21ljeevjFSdpYVlPZZhT9QexL0Qr6DqwAFg\n40ZgxQpxwkwAD9fduiWOk6hTh1fcX7xo+FiFMTcH/vyTd2jz8BDW+OrQrUN8L2KA6fYiVDx/9RzW\nJaxNNp+VuRW61e6GwFuBep9rNFdGRAZLdxJRfN6/CYyxPQA8APyr7tgZM2a8vu/p6QlPT09DpzcJ\nyy8sR2hsKM5+cRZmTNqF3bG7xxCbGovPW3wuyfyBtwJN/oVVWCsQnxZv0jkBYLDzYEw+PhkDdwxE\nyMgQg8eLiOCy4vv2vd2tzBDi4nj/6fLlxRnPywsICOAXcrExMwPmzweaNOFV3n/9xedTh1KpxBcB\nX6Brra6obVdbfGN0YKpK//z0rtcba3avwYN9D/Q7UVc8ypg38D0JVw3PlQZgnXe/DIAQAN00HFv0\nYJ2EHL9znOwX2NOdpDtSm0K5ylxqsaoF7bixQ5L5kzOSqeyvZY0uelaYsLgwaraymUnnzD83m8HI\n/5qmgL8wnj0jqlOHaP16kQzLx8mTRO3bazlAz+9eSAhvrGNsQkO54u0vv6gXkJxwcAKVmF2Cnmc8\nN74xhchV5pLlLEt6lSNQ9VAkbj+7TU6/OxV4DMV1T4Ix1o8x9hBAKwCBjLFDeY8rGGOq9VAVAP8y\nxq4AOAfgABEdlcJeYxCXGoehe4Zi84DNqGVbS2pzsOPGDpibmWNAwwGSzH8p/hKa2TeDuZm5Sec1\nlYaOOlwcXODj4gOfAB+kZaUVaYycHF4H4eUFDB8usoEQbz9CRcuWPNPs3j3xxtQ0z7lzvNjO2xtI\nT3/zXPiTcPxx/g+s7LkS5UqWM64hakhMT0T5kuVFq/QXSi3bWkjLSsPTl0/1Ok8SJ0FEe4ioKhGV\nIqIqRNQj7/E4IuqZd/8uETXPuzkT0VwpbDUGucpcDNk9BOPcxqFTzU5SmwMlKTEzeCbmdJwjWXV3\nWFwYXB1cdR8oMpVKV0JyZrLJN69VrOq5CuVKlMNHmz4q0vn/9388Hj9vnsiG5WGIHIc6zM15CmxA\ngHhjasLRkcukWFgA7dsDDx/yMFM3/25o6dhSsrBqfGq80TohaoMxBhcHF4TFhel1XnHPbnovmXN6\nDhgYprWfJrUpAICA6ACUtiyNbrWNK5GsjbD4MLgqTO8kzM3MUblM5SLJZ4uBmZkZDg05hDMPzmDT\n9U16nfv338ChQ8DWrfxCaAzEdhIA1+HaulXcMTVRqhTfzB88mK8uBq//GkkZSTgy9IhpDFCDFPsR\nKlwdXBEWLzuJYs2pe6ewKmwVNvXfZPLQijqICHPPzMXUdlMlW0UA3Em4KdwkmduhrIMkm9cqXBxc\n4OvuixF7R+BOkrCGCSEhvPI7IID3bDAWN28WXSJcE127ct0rY/eGUMEY1wAbteAAdj74A4NLr4Z1\nCdOHmVQYu12uNmQnUcxJeJmAobuHYl2fdZJ9SAoTdC8IzzOfm0RDRhMpmSl4nPYY9SuIfDUSiMJa\ngdgXsZLMrWLZR8vQ3L453Ne4IzMnU+uxDx4AAwcCGzaIfwHPT1YWV7GtJfKWmaUl/2W/Sb+Fk0HE\nJMdg7t3++LjWSNzYPOx1Pw8piH0RK0m4CQBcFa64GKdfDrLsJEyEkpQYvnc4hjYdiu51ukttzmvm\nnpmLyW0nS5p+K9WmtYpGlRrh+tPrksydn5AvQmBuZo5Wa1tpPCY9HejbF/j2W6BHD+PaExMDODmJ\no7JamCFDuJMwhS5nZk4m3Na4oUnlJtgxbA1CQ3mKrKsrb3hlam3Q60+vo1ElI3ea0kBt29pIfZWq\n1+a17CRMxMKzC5GSmYLZHd9WG5WKi3EXEZ0YjSFNh0hqh1Sb1iqKsgQ3BlYWVrg46iIiEiIwfM/b\nqUpEvBbC2Zn3rDA2xtiPUOHhwSuvjVFYV5g2f7UBA3tdj2JhwfuWBAXxArzu3U27qgiLl+7zXpTN\na9lJmIDQR6H47b/fsGXAFpOJegnB74wfJrWeZPJUvMJItWmtwlXhqnfGh7GoblMd+z7Zh43XNmLm\nqYIV+LNn81/3q1eLV1GtDWPsR6hgDBg6FPD3N874Kvpv64/wp+G4MOoCSloUlIl1duZtczt0MN2q\nIjkjGQkvE1CvgpG8rwDcFG56/SiSnYSRSclMgfcub/zZ609Ut6kutTmvuZdyD0H3guDjYppOXNqQ\nctMaAGra1ER6dnqRdG2MQY+6PbC692rMDJ6JBSELAACLFvEL6t69RZfE1hdjriQAHnLato3XehiD\nT3d9iv039yN4RDBq2tZUe4ylJTBtmulWFWHxYWhepbmkSSv6rpxlJ2FkvjnyDbrX7o6+DfpKbUoB\nVoetxrCmwzT2bTAVmTmZePj8oWSb1kC+JXgxCDmp8HHxwdIeSzH5+GT0/W0+li3jMtv6dIczFLEL\n6QpTpw5QowZw/Lj4Y3+661Nsv7EdR4YeQeuqrXUeX3hVsWIFl5IXG6lDqwDvLxH+NFzw8bKTMCKB\nNwMRfC8YC7otkNqUArzKeYW/Lv+FsW5jpTYF8anxsC9rL3k6sJvCrdiEnFR86fElvG2WYl/aFHgt\nnImqVU03NxFv5NSwoXHnMUbIqd/Wfth+YzuOfnZUr2LV/KuKXbu449ixQ9wQlNShVQBwKueEuNQ4\nlaSRTmQnYSSSM5Ix5sAYrPVai7JWZaU2pwC7InehqX1T1K8o3a93FfFp8ZIVFuWnuGxe52fTJuDU\n/C8x22M1ll6biSG7TJdg8OABv2gqjPynGTSIq9emFU2VpABZOVlwW+2GwFuBCB4RXGQ1A2dnvrpZ\ntgyYOxdwdxdvtSPlprUK6xLWYGBIzUoVdLzsJIzEt0e/hVd9r2Ihu1GY5ReWY7zbeKnNAMCrT6XK\nGc9PS6eWOPvwrMlaSupi+3YuuXHsGPDjRz44MvQIdkbuhPMKZ6RnpesewEDCwnjYxdgb5JUrc3nv\nffsMG+d+yn04LnLE3eS7uDH+BtpWa2vQeIwB3brx7KvvvgPGjeNFgGEG/I6IT41HUkaSpJvWKvTR\nLJOdhBFQhZnmd50vtSlvceXxFdxPuY/e9U3bt0ET8anFYyVRrXw12Je1x/nY81Kbgo0bgYkTgSNH\ngEZ56fRda3fFrQm3kPAyAY6LHBGdGG1UGy5eLNhH3JgYGnI6dOsQ6i6rC4W1Ao++eYS6FeqKZpuZ\nGS/8i4jgvbR79+arn5s39R/rwM0D6F6nu+ShVQBwsHZAfKowlQHZSYhMcQ4zAcDKCysxxnWMSbti\naaO4rCQArrcfEG0C5TktrFkDTJ3KN6mbNi34XLXy1fDwG77J77zSGX9d+stodqhWEqbAywsIDeXq\nsPry/bHv0XNzT3g7e+Pq2KsobWWcZlmWlsDYsbwBU4sWQJs2wJgxPCVZKEXp324s9OmjIjsJkSnO\nYab07HRsj9iOkS4jpTblNcVlTwIAvOp7Seok/vgDmDOHb5w20lCQa2VhhVCfUHzV8iuMPjAaLde0\nREpmiqh2EJnWSZQpw3+dr10r/JzoxGhUX1wdi0MXY1WvVdjQb4PxDMxHmTLciUdH8y577u5c1fbg\nQSA3V/N56dnpCL4XXGzUFhzKOsjhJikozmEmgKu9ejh6FJuLMpC3kigmOlYejh5ITE8ULLInJr/9\nxmshgoOBugKiJQu7LcS1sdcQmxoL+9/sRV1VmGrTOj/jx/M6BSE1E5OPTUajFY1QqXQlxE2Kw2jX\n0cY3sBAVKgB+fvy9GjAA+Pln/nebPx9ITHz7+ON3j8Pd0R22pWxNbqs6FNYKOdxkalIyU4p1mAkA\nNl3fhKFNhkptRgHi06TR1leHGTNDr3q9sP/mfpPNqVRyiYjVq7mDqFFD+LmNKzfGo28fwdfdF6MP\njIbrn66iODhTbVrnp1kz/tr3a3nrQx6EoNqialgUugh/9PgDF0dfRMXSFU1mozpKlwY+/5zv4Wzb\nBkRGcmcxbBgPoamyTAOii0+oCchbSaTJKwmTMu3ENPSs27NYhpkArkB7+v7pYlfUJ6W2vjpMGXJ6\n8YKL9YWE8JuTU9HG+f3D33Ft7DWk56Sj7rK66O7fHYnpan7OCsSUoab8jB/Pi9gKE5kQCZc/XdB+\nXXvUsKmBuElxGOc+zvQG6sDdHVi3Drh9mzu9oUP5+7h6jRIB0fuLTbIIIK8kTM7l+MvYGbkTc7sU\n3+Z5229sR8+6PWFdwlpqU17zKucVUl+lokLpClKb8poutcksnekAAAtwSURBVLrgYtxFgy6yQrhz\nh29+KhQ8zbVSJcPGa1y5MSJ9IxH4aSDCn4bD/jd7DNk1pEjpslI5iQEDgGvXeLwfAOJexKHThk5o\nvKIxcpQ5uDb2Gk5/flry1YMuKlTgAow3b/I6i3+C/sOzB5Uw7pNaWL5cOony/DhYC++hIjsJA1GS\nEr4HfTGn4xzYlbKT2hyN+F/3x5Am0qq9FiYpIwl2pewklSkvTGnL0ujboC/WX1lvtDlOnOC1Ab6+\nwKpVgJWI+oo96vbAo28f4W+vv3Ho9iGU8yuHrv90RfgTYTIMRKZNf81PiRLAyJHA1LXH4LbaDU6L\nnHA3+S5ODj+Ja+Ouwdne2fRGGYCZGfDhh0CdQWsws99wjB4NXLjA39sWLYDp0/l7rZSgNKdi6YrC\nfwgRkclvABYAiARwFcBuAOU1HNcdQBSAWwAmaxmPpGLd5XXkscaDcpW5ktmgi9vPblOl+ZUoKydL\nalMKcD/lPjn97iS1GW/x38P/qNaSWqL/TZVKomXLiOztiU6eFHVojWy+tpkaLGtAbAaj6ouq0/Lz\nyyk3V/PrunePqEoVbqsgRPruvcp+RT+c+IFs51YgTGfk/mdLCo4JFmVsKUl8mUg2fjaU8DLh9WM5\nOUT//kv03XdEDRoQKRREY8YQBQYSZWSYxq6UjBQqN7cc5V07tV6vpfoJdxRAYyJqBuAmgKmFD2CM\nmQP4A9xRNALgzRgzspKMfqRkpmDqialY/tFyo/4aPnXqlEHnbw3fioGNBhYrmXIAyFHmIOeOkSRA\nDaClY0uUL1EeR+8cNXgs1d8uK4vn1f/5J3D2LNCxo8FDC8K7iTciv4xEzMQYuDi44OvDX6PkLyXh\nvtodf5z/460uePpuWp8ywLak9CTMCJqBRssbodSvpbD03FIMdh6ID68kYYx5KDrU6GDA6OJg6Hdv\n3ZV18KrvVSBEZm4OtGvHM6EiI3nKc926wLx5PFTl5sY/K2vWAJcu8c+O2FiaWyJHKey7J4mTIKJj\nRK/1D84BULdl5wHgNhHdI6JsAFsB9DGVjUL4OehneNXzMrrMtaEf1ICbAZK2J9VEjjIHWXeM8A0w\nEMYYxruPx4oLanZR9eTUqVN4+hTo3BlISOAOQux2oEKoblMduwfvRua0TKzruw5lrMrg+2Pfo/Qv\npVF7SW1MODgBJ2NO4kKYUq9Q0yk9bMjMycSeyD0Yvmc4FAsVqLCgAhafW4zatrVx8NODSJ2aipW9\nVuLrsTZYvtz0HePUYch3T0lKrLy4UqcETr16fA8jOBh4+hRYupTrR4WEAMOH8x7mKsexejV35IY6\nDgszC2TnCpO5LQ5lt18A2KLmcUcAD/P9/xGAliaxSABXH1/FthvbEDE+QmpTtPI47TFuPruJDtWl\n/1VWmBxlTrHaj8iPt7M3Jh+fjHsp91DDpkaRx3n8mHdhGzYMmDGDx6mlxMzMDEOaDHm9P/Xfw/+w\n5NwS7IzciZUXVyLXUomyFjY4tKYOWju1RlP7pnCu7IwmlZsIrmZOSk/CtafXEJkQiUvxl3A+7jxi\nkmOQmpUKSzNLKKwV8KrnhUltJqmV0OjWje/XnD8PtCw233j9OXrnKGxK2sDD0UPwOWXK8ISGNm3e\nPPbyJXD1KncOZ89y4cE7d4Dq1Xnig4PDm3/z31co+HjqsDCzQLZSYifBGDsGoIqap34gov15x0wD\nkEVEm9UcVwx+R6iHiPDloS8xy3NWscrMUceBmwfwYe0PJe8+p44cZQ6YKZPx9aCMVRl81vQzrA5b\njV87/1qkMXbuBP75B1i/nlcUF0daV239ut8CEWBXPxxfLNyFK0mnsSNiB9ZeXovMnEwoSQkGhhIW\nJVDSoiTMmBnMmTnGA6gwvwJylbnIzMlEVm4WCARzZo5SlqVgV8oOTSo3wfBmwzGg4QBBjbfMzLig\n3vLl77aTWHFhBca7jTf4M67JccTEcCmT+HggLg64d487kfyPWVpyh1G+PL9vYaH61wxoxSDkMstI\nojUdY2wEgFEAOhNRpprnWwGYQUTd8/4/FYCSiOapObbYOhQZGRmZ4gwRafVikoSbGGPdAXwH4AN1\nDiKPiwDqMsZqAIgDMBiAt7oDdb1IGRkZGZmiIVWEdBmAsgCOMcYuM8ZWAABjTMEYCwQAIsoB8CWA\nIwAiAGwjokiJ7JWRkZH5n0SycJOMjIyMTPGneKaW6AljbDZj7Cpj7Apj7ARjzITdgI0PY2wBYywy\n7zXuZoyVl9omMWGMDWSM3WCM5TLGXKS2RywYY90ZY1GMsVuMsclS2yMmjLG/GWNPGGPXpbZFbBhj\nVRljQXmfyXDG2FdS2yQmjLGSjLFzedfLCMaYVj2h92IlwRizJqLUvPsTADQjIh+JzRINxlhXACeI\nSMkY8wMAIpoisVmiwRhrAEAJ4E8Ak4joksQmGUxeMWg0gC4AYgFcAOD9voRMGWPtAaQB+IeImkht\nj5gwxqoAqEJEVxhjZQGEAej7vvztAIAxVpqI0hljFgDOAPg/Ijqj7tj3YiWhchB5lAVgXHU2EyOw\n+PCdhYiiiKgIDSGLNcW+GNQQiOhfAMlS22EMiOgxEV3Ju58GLiFUfKSKRYCIVMqPVgDMASRpOva9\ncBIAwBj7hTH2AMBwAH5S22NEvgBwUGojZHSirhjUUSJbZIpIXnZlC/AfZ+8NjDEzxtgVAE8ABBGR\nxqrg4lBxLQhdxXlENA3ANMbYFACLAHxuUgMNRITiw2KNkNf3nvHux3H/x8kLNe0EMDFvRfHekBeZ\naJ63v3mEMeZJRKfUHfvOOAki6irw0M14B39p63p9ecWHHwHobBKDREaPv9/7QiyA/AkUVcFXEzLv\nAIwxSwC7APgT0V6p7TEWRPQ8r+zADRqkuN6LcBNjLL8ATB8Al6WyxRjkKz7so6X48H3hfSmMfF0M\nyhizAi8GNU3LOxmDYFxH4y8AEUS0WGp7xIYxVpExZpN3vxSArtByzXxfspt2AqgPIBfAHQDjiOip\ntFaJB2PsFvgGk2pz6T8i0i4t+Q7BGOsHYCmAigCeA7hMRD2ktcpwGGM9ACwG3xj8i4iKb+tCPWGM\nbQHwAYAKAJ4C+JmI1klrlTgwxtoBOA3gGt6EDacS0WHprBIPxlgTABvAFwlmADYS0QKNx78PTkJG\nRkZGxji8F+EmGRkZGRnjIDsJGRkZGRmNyE5CRkZGRkYjspOQkZGRkdGI7CRkZGRkZDQiOwkZGRkZ\nGY3ITkJGphCMsQp5zbAuM8biGWOP8u5fylPNNLU9noyx91G6ROYd4J2R5ZCRMRVE9Axc1A2MsekA\nUonodzHGZoxZ5HVdlJF5J5BXEjIyumGMMR/G2Pm8Ri078+QMkCe7cTKvIdRxdQ2vGGMzGGMbGWNn\nAGzIk0XYmTfeecZYm7zjPBhjZ/NWLCGMsXomfp0yMm8hOwkZGWHsJiIPImoO3l9gZN7jywCsI6Jm\nADaBy4uoowGAzkQ0JO+YRUTkAeBjAGvzjokE0J6IXABMB/CrcV6KjIxw5HCTjIwwmjDG5gAoD97Y\nSqXj0wpA37z7/gDmqzmXAAQQ0au8/3cB0JDryAEArBljpQHYAPiHMVYn7xxL0V+FjIyeyE5CRkYY\n68BVeK8zxoaDi9upEKJcm57vPgPQkoiy8h/AGFsB3qa2H2OsOjRIN8vImBI53CQjI4yyAB7n9RkY\nmu/xswA+ybs/BFw9VBdHAXyl+g9jrFne3XIA4vLuv1NNs2TeX2QnISMjjJ/BW1ieAd87UDEBwOeM\nsavgTmKihvPzyy1/BcAtb7P7BoAxeY/PBzCXMXYJXF6cNJwvI2MyZKlwGRkZGRmNyCsJGRkZGRmN\nyE5CRkZGRkYjspOQkZGRkdGI7CRkZGRkZDQiOwkZGRkZGY3ITkJGRkZGRiOyk5CRkZGR0YjsJGRk\nZGRkNPL/f73qscY1aEcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1039f53d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#function:\n",
+    "import pylab as plb\n",
+    "import numpy as npy\n",
+    "from matplotlib.patches import Circle\n",
+    "\n",
+    "def smith(smithR=1, chart_type = 'z',ax=None):\n",
+    "    if ax == None:\n",
+    "        ax1 = plb.gca()\n",
+    "    else:\n",
+    "        ax1 = ax\n",
+    "\n",
+    "    contour = []\n",
+    "    rHeavyList = [0,1]\n",
+    "    xHeavyList = [1,-1]\n",
+    "\n",
+    "    rLightList = plb.logspace(3,-5,30,base=.5)\n",
+    "    xLightList = plb.hstack([plb.logspace(2,-5,15,base=.5), -1*plb.logspace(2,-5,15,base=.5)]) \n",
+    "    if smithR > 1:\n",
+    "        rMax = (1.+smithR)/(1.-smithR)\n",
+    "        rLightList = plb.hstack([ plb.linspace(0,rMax,11)  , rLightList ])\n",
+    "    if chart_type is 'y':\n",
+    "        y_flip_sign = -1\n",
+    "    else:\n",
+    "        y_flip_sign = 1\n",
+    "    for r in rLightList:\n",
+    "        center = (r/(1.+r)*y_flip_sign,0 ) \n",
+    "        radius = 1./(1+r)\n",
+    "        contour.append( Circle( center, radius, ec='blue',fc = 'none'))\n",
+    "    for x in xLightList:\n",
+    "        center = (1*y_flip_sign,1./x)\n",
+    "        radius = 1./x\n",
+    "        contour.append( Circle( center, radius, ec='green',fc = 'none'))\n",
+    "    for r in rHeavyList:\n",
+    "        center = (r/(1.+r)*y_flip_sign,0 )\n",
+    "        radius = 1./(1+r)\n",
+    "        contour.append( Circle( center, radius, ec= 'blue', fc = 'none'))\n",
+    "    for x in xHeavyList:\n",
+    "        center = (1*y_flip_sign,1./x)\n",
+    "        radius = 1./x\t\n",
+    "        contour.append( Circle( center, radius, ec='green',fc = 'none'))\n",
+    "    ax1.axhline(0, color='red')\n",
+    "    ax1.axvline(1*y_flip_sign, color='red')\n",
+    "    ax1.grid(0)\n",
+    "    ax1.axis('equal')\n",
+    "    ax1.axis(smithR*npy.array([-1., 1., -1., 1.]))\n",
+    "    plb.title('Smith chart')\n",
+    "    plb.xlabel('Tao real')\n",
+    "    plb.ylabel('Tao imaginary')    \n",
+    "    for currentContour in contour:\n",
+    "        ax1.add_patch(currentContour)\n",
+    "\n",
+    "\n",
+    "# NOTE:-this example is a method for calculating unknown load impedence of slotted line section. all data are given and preassumed.\n",
+    "# program to determine unknown load impedence.\n",
+    "from sympy import I,oo,pi,exp,symbols\n",
+    "\n",
+    "y=symbols('y')\n",
+    "Zl=0;Zo=50; # for short circuitting the load.\n",
+    "SWR=oo\n",
+    "# short circuit is removed and replace with unknown load .\n",
+    "SWR =1.5; lamda =0.04;\n",
+    "lmin =4.2 -2.72;\n",
+    "tao=(1.5-1)/(1.5+1);\n",
+    "theta=(pi+((4*pi)/4)*1.48);\n",
+    "tao=abs(tao)*exp(I*theta);\n",
+    "Zl=50*((1+tao)/(1-tao));\n",
+    "# result\n",
+    "print \"load impedence = \",Zl\n",
+    "smith(2,y)\n",
+    "# when analyse with the help of smith chart . see angle from x=0 axis i.e Tao real axis.if it is above this axis take angle anticlockwise and if it is below this axis . take angle clockwise from Tao  real axis below ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:2.5 page no:84"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "characteristic impedence of the matching section=  70.7106781187\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x1188c7f90>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Si5VeNDI1Iv5IKpcf5nneTirz2knzI6S7pUeAj5NqAFppuS4RcS6pbcjsvP/eCOyShz1M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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x105cad150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# program to find out characteristic impedence and plot the magnitude of reflection coefficient versus normalized frequency .\n",
+    "%matplotlib inline\n",
+    "from sympy import symbols,I\n",
+    "from pylab import arange,plot,title,xlabel,ylabel,axis\n",
+    "from math import pi\n",
+    "from numpy import sin,cos,sqrt,real,imag\n",
+    "\n",
+    "Zl=100;# load impedence\n",
+    "Zi=50;#impedence of line which is to be matched\n",
+    "#as it is a quarter wave transformer so , Zi=(Zo)ˆ2/zl;\n",
+    "Zo=sqrt(Zi*Zl);\n",
+    "print \"characteristic impedence of the matching section= \",Zo\n",
+    "f,fo,x=symbols('f,fo,x');\n",
+    "x=f/fo;\n",
+    "x=arange(0,4,0.001)\n",
+    "y=(pi/2)*(x);\n",
+    "Zin=Zo*(((Zl*cos(y))+(Zo*I*sin(y)))/((Zo*cos(y))+(Zl*I*sin(y))))\n",
+    "tao=((Zin-Zo)/(Zin+Zo));\n",
+    "tao=abs(tao)\n",
+    "plot(x,tao)\n",
+    "axis([0,4,0,1])\n",
+    "title (\"reflection coefficient versus normalized frequency for quarter wave transformer\")\n",
+    "xlabel(\"f/fo\")\n",
+    "ylabel(\"tao(reflection coefficient)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:2.6 page no:92"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "attenuation constant =  Rs*sqrt(eipsila*pi**2/(mue*log(b/a)**2))*(1/b + 1/a)/(2*pi) + eipsilac*pi*w*sqrt(mue*log(b/a)**2/(eipsila*pi**2))/(2*log(b/a))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to calculate attenuation constant.\n",
+    "from sympy import symbols,sqrt,log\n",
+    "\n",
+    "alpha,R,Rs,L,G,C,eta,a,b,w,pi,eipsila,eipsilac,mue,eta=symbols('alpha,R,Rs,L,G,C,eta,a,b,w,pi,eipsila,eipsilac,mue,eta')\n",
+    "eta=sqrt(mue/eipsila);\n",
+    "L=(mue/(2*pi))*(log(b/a));\n",
+    "C=(2*pi*eipsila)/log(b/a);\n",
+    "R=(Rs/(2*pi))*((1/a)+(1/b));\n",
+    "G=(2*pi*w*eipsilac)/log(b/a);\n",
+    "alpha=(R*sqrt(C/L)+G*sqrt(L/C))/2;\n",
+    "print \"attenuation constant = \",alpha"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:2.7 page no:95"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "power flowing on the lossless line =  Vo**2/(2*Zo) \n",
+      "\n",
+      "conductor loss =  Rs*Vo*conjugate(Vo)*Integral(exp(-I*B*z)*exp(I*conjugate(B)*conjugate(z)), (z, 0, 1))/(4*P*Zo*conjugate(Zo)*conjugate(pi)) \n",
+      "\n",
+      "dielectric loss =  Vo*eipsila*pi*w*conjugate(Vo)*Integral(exp(-I*B*z)*exp(I*conjugate(B)*conjugate(z)), (z, 0, 1))*Integral(1/conjugate(P), (P, a, b))/((log(a) - log(b))*(conjugate(log(a)) - conjugate(log(b)))) \n",
+      "\n",
+      "attenuation constant =  Zo*(Vo*eipsila*pi*w*conjugate(Vo)*Integral(exp(-I*B*z)*exp(I*conjugate(B)*conjugate(z)), (z, 0, 1))*Integral(1/conjugate(P), (P, a, b))/((log(a) - log(b))*(conjugate(log(a)) - conjugate(log(b)))) + Rs*Vo*conjugate(Vo)*Integral(exp(-I*B*z)*exp(I*conjugate(B)*conjugate(z)), (z, 0, 1))/(4*P*Zo*conjugate(Zo)*conjugate(pi)))/Vo**2 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find ht eattenuation constant of coaxial line .\n",
+    "from sympy import symbols,log,I,integrate,conjugate\n",
+    "\n",
+    "E,H,Vo,Zo,P,a,b,B,z,pi,Po,Q,Rs,Plc,alpha,Pld,w,eipsila=symbols('E,H,Vo,Zo,P,a,b,B,z,pi,Po,Q,Rs,Plc,alpha,Pld,w,eipsila')\n",
+    "#Zo=(eta/(2⇤pi))⇤log(b/a);\n",
+    "E=(Vo/(P*(log(b)-log(a))))*exp(-I*B*z);\n",
+    "H=(Vo/(2*pi*P*Zo))*exp(-I*B*z);\n",
+    "H=conjugate(H)*P; # for defining E cross H⇤.\n",
+    "Po=(1/2)*integrate(integrate((E*H),(P,0,2*pi)),(Q,a,b));\n",
+    "Po=Vo**2/(2*Zo)\n",
+    "print \"power flowing on the lossless line = \",Po,\"\\n\"\n",
+    "H=(H*conjugate(H))/P; # for defining |H|ˆ2(;)\n",
+    "Plc=(Rs/2)*integrate(integrate(H,(z,0,1)),(Q,0,2*pi));\n",
+    "print \"conductor loss = \",Plc,\"\\n\"\n",
+    "E=E*conjugate(E)*P;\n",
+    "Pld=((w*eipsila)/2)*integrate(integrate(integrate(E,(P,a,b)),(Q,0,2*pi)),(z,0,1));\n",
+    "print \"dielectric loss = \",Pld,\"\\n\"\n",
+    "alpha=(Pld+Plc)/(2*Po); # attenuation\n",
+    "#B=beta . constant .\n",
+    "print \"attenuation constant = \",alpha,\"\\n\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:2.8 page no:97"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "attenuation due to conductor loss =  Rs*(1/b + 1/a)/(2*eta*pi*log(b/a)) \n",
+      "\n",
+      "attenuation corrected for surface roughness =  Rs*(1 + 2*atan(0.7*sqrt(2)*delta/sqrt(1/(mue*sigma*w)))/pi)*(1/b + 1/a)/(2*eta*pi*log(b/a))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to calculate attenuaton due to conductor loss of a coaxial line using incremental inductance rule .\n",
+    "from sympy import symbols,sqrt,log,diff,atan\n",
+    "\n",
+    "Zo,eta,pi,a,b,Rs,l,alpha,alpha_c,alpha_dash,delta,alpha_c_dash,sigma,w,mue=symbols('Zo,eta,pi,a,b,Rs,l,alpha,alpha_c,alpha_dash,delta,alpha_c_dash,sigma,w,mue')\n",
+    "sd=sqrt(2/(w*mue*sigma))\n",
+    "Zo=(eta*log(b/a))/(2*pi);\n",
+    "alpha_c=(Rs/(4*Zo*pi**2))*(diff(log(b/a),b)-diff(log(b/a),a));\n",
+    "print \"attenuation due to conductor loss = \",alpha_c,\"\\n\"\n",
+    "alpha_c_dash=alpha_c*(1+((2/pi)*atan((1.4*delta)/sd)));\n",
+    "print \"attenuation corrected for surface roughness = \",alpha_c_dash"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_3_TRANSMISSION_LINE_AND_WAVEGUIDES_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_3_TRANSMISSION_LINE_AND_WAVEGUIDES_3.ipynb
new file mode 100644
index 00000000..0c9527a2
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_3_TRANSMISSION_LINE_AND_WAVEGUIDES_3.ipynb
@@ -0,0 +1,367 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 3 TRANSMISSION LINE AND WAVEGUIDES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:3.1 page no.127"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cut off frequency for TE01 mode in GHZ= 14.7637795276\n",
+      "cut off frequency for TE10 mode in GHZ= 6.56167979003\n",
+      "cut off frequency for TE20 mode in GHZ= 13.1233595801\n",
+      "cut off frequency for TE11 mode in GHZ= 16.1562627982\n",
+      "surface resistance in ohm= 0.0260895069422\n",
+      "attenuation constant in dB/m= 0.108405591329\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the cut off frequency fo the first four propagating modes .\n",
+    "\n",
+    "from math import pi,sqrt,log10,e\n",
+    "\n",
+    "a=0.02286;\n",
+    "b=0.01016;\n",
+    "f=10*10**9;\n",
+    "k=209.44;\n",
+    "sigma =5.8*10**7;\n",
+    "mue =4*pi*10**-7;\n",
+    "c=3*10**8;\n",
+    "m=0;n=1;\n",
+    "fc=(c/(pi*2))*sqrt(((pi*m)/a)**2+((pi*n)/b)**2); \n",
+    "fc=fc/(10**9);\n",
+    "print \"cut off frequency for TE01 mode in GHZ=\",fc\n",
+    "m=1;n=0;\n",
+    "fc=(c/(pi*2))*sqrt(((pi*m)/a)**2+((pi*n)/b)**2); \n",
+    "fc=fc/(10**9);\n",
+    "print \"cut off frequency for TE10 mode in GHZ=\",fc \n",
+    "m=2;n=0;\n",
+    "fc=(c/(pi*2))*sqrt(((pi*m)/a)**2+((pi*n)/b)**2); \n",
+    "fc=fc/(10**9);\n",
+    "print \"cut off frequency for TE20 mode in GHZ=\",fc \n",
+    "m=1;n=1;\n",
+    "fc=(c/(pi*2))*sqrt(((pi*m)/a)**2+((pi*n)/b)**2);\n",
+    "fc=fc/(10**9);\n",
+    "print \"cut off frequency for TE11 mode in GHZ=\",fc\n",
+    "B=sqrt(k**2-(pi/a)**2) # for TE10 mode\n",
+    "Rs=sqrt(((2*pi*f)*mue)/(2*sigma)); # surface resistance .\n",
+    "print \"surface resistance in ohm=\",Rs\n",
+    "ac=(Rs/(a**3*b*B*k*377))*((2*b*pi**2)+(a**3*k**2)) # attenuation constant .\n",
+    "ac=-20*(-ac)*log10(e);\n",
+    "print \"attenuation constant in dB/m=\",ac"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:3.2 page no.138."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cut off frequency for TE11 mode in GHZ= 11.7201700093\n",
+      "cut off frequency for TE01 mode in GHZ= 15.3107055254\n",
+      "k in m1= 408.407044967\n",
+      "propagation constant of TE11 mode = 176.706180929\n",
+      "total attenuation factor in dB= 2.37559081601\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to find the cut off frequency of two propagating modes of a circular waveguide .\n",
+    "\n",
+    "from math import pi,sqrt,e,log10\n",
+    "\n",
+    "a=0.005;eipsilar=2.25;f=13*10**9;c=3*10**8;d=0.001; sigma=6.17*01**7;muo=4*pi*10**-7;\n",
+    "m=1;n=1;\n",
+    "p11 =1.841; p01 =2.405;\n",
+    "fc=(p11*c)/(2*pi*a*sqrt(eipsilar));\n",
+    "kc=p11/a;\n",
+    "fc=fc/(10**9);\n",
+    "print \"cut off frequency for TE11 mode in GHZ=\",fc\n",
+    "m=0;n=1;\n",
+    "fc=(p01*c)/(2*pi*a*sqrt(eipsilar));\n",
+    "fc=fc/(10**9);\n",
+    "print \"cut off frequency for TE01 mode in GHZ=\",fc\n",
+    "# so ,TE01 can ’ t be propagating mode. only TE11 will be.\n",
+    "k=(2*pi*f*sqrt(eipsilar))/c;\n",
+    "print \"k in m1=\",k\n",
+    "B=sqrt(k**2-kc**2);\n",
+    "print \"propagation constant of TE11 mode =\",B\n",
+    "ac=(k**2*d)/(2*B);\n",
+    "Rs=sqrt((2*pi*f*muo)/(2*sigma)); # surface resistance .\n",
+    "acm=(Rs/(a*k*377*B))*(kc**2+((k**2)/(p11**2-1)));\n",
+    "a=ac+acm;\n",
+    "a=-20*(-0.547*0.5)*log10(e);\n",
+    "print \"total attenuation factor in dB=\",a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:3.3 page no.145"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "maximum usable frequency in GHZ= 16.1677560714\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to find out the highest usable frequency.\n",
+    "from math import sqrt,pi\n",
+    "\n",
+    "a=0.000889;b=0.0029464;eipsilar=2.2;c=3*10**8;\n",
+    "# here (b/a)=3.3,so for this kc⇤a=0.47\n",
+    "kc=0.47/a;\n",
+    "fc=(c*kc)/(2*pi*sqrt(eipsilar))\n",
+    "fc=fc/(10**9);\n",
+    "fmax=0.95*fc;\n",
+    "print \"maximum usable frequency in GHZ=\",fmax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:3.4 page no.153"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nao3BiZwT3L7wdkL8Qtj50E6aB+q+pOxJWVk+SUkfk5j4PqGhl9C373KCgvqo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\nOVfgRcE551yBFwXnnHMF/wPvwnvVslbmWQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1049112d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# program to calculate and plot the propagation constant of first three propagating surface wave mode .\n",
+    "%matplotlib inline\n",
+    "from pylab import plot,title,xlabel,ylabel,legend,xlim,ylim\n",
+    "from numpy import arange,sqrt,seterr\n",
+    "\n",
+    "old_settings = seterr(all='ignore')\n",
+    "eipsilar=2.55;c=3*10**8; # x=d/lamdao ;\n",
+    "x=arange(0.001,1.2,0.01);\n",
+    "for n in range(0,4):\n",
+    "    y=sqrt(eipsilar -((n**2)/(4.*(x**2)*(eipsilar -1))));# y=beta/lamdao;\n",
+    "    plot(x,y)\n",
+    "x=arange(0.001,1.2,0.01);\n",
+    "for n in range(1,4):\n",
+    "    y=sqrt(eipsilar -((((2.*n)-1)**2)/(16.*(x**2)*(eipsilar -1)))) \n",
+    "    plot(x,y)\n",
+    "title ('plot of propagation constant of first 4 mode');\n",
+    "xlabel('d/lamdao');\n",
+    "ylabel('beta/Ko'); \n",
+    "xlim(0,1)\n",
+    "ylim(1,1.6);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:3.5 page no.157."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width of copper strip line conductor is 0.00266m\n",
+      "wave number= 310.648995795\n",
+      "dielectric aattenuation= 0.155324497897\n",
+      "conductor attenuation= 0.121743652883\n",
+      "total attenuation constant= 2.4065833799\n",
+      "attenuation in dB/lamda= 0.048675545512\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find width of a copper strip line conductor .\n",
+    "from math import pi,e,sqrt,log10\n",
+    "\n",
+    "eipsilar=2.20;\n",
+    "Zo=50;b=0.0032;d=0.001;f=10**10;t =0.00001;\n",
+    "c=3*10**8;Rs=0.026;A=4.74;\n",
+    "x=(30*pi)/(sqrt(eipsilar)*Zo);\n",
+    "x=x-0.441;\n",
+    "w=b*x;\n",
+    "if ((sqrt(eipsilar)*Zo)<120):\n",
+    "    print \"width of copper strip line conductor is 0.00266m\"\n",
+    "K=(2*pi*f*sqrt(eipsilar))/c;\n",
+    "ad=(K*d)/2;\n",
+    "ac=(2.7*(10**-3)*Rs*eipsilar*Zo*A)/(30*pi*(b-t));\n",
+    "a=ac+ad;\n",
+    "a=20*a*log10(e);\n",
+    "lamda=c/(sqrt(eipsilar)*f);\n",
+    "alamda=lamda*a;\n",
+    "print \"wave number=\",K\n",
+    "print \"dielectric aattenuation=\",ad\n",
+    "print \"conductor attenuation=\",ac\n",
+    "print \"total attenuation constant=\",a\n",
+    "print \"attenuation in dB/lamda=\",alamda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:3.7 page no.163"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width in centi meter= 0.391287\n",
+      "length of microstrip in centi meter= 2.19381727238\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to calculate the width and length of microstrip line .\n",
+    "from math import sqrt,pi,e\n",
+    "\n",
+    "eipsilae=1.87;# effective dielectric constant .\n",
+    "Zo=50;q=pi/2;c=3*10**8;\n",
+    "f=2.5*10**9;\n",
+    "ko=(2*pi*f)/c;\n",
+    "d=0.00127;\n",
+    "eipsilar =2.20;\n",
+    "# for w/d>2;\n",
+    "B=7.985;\n",
+    "w=3.081*d*100;\n",
+    "print \"width in centi meter=\",w\n",
+    "l=(q*100)/(sqrt(eipsilae)*ko);\n",
+    "print \"length of microstrip in centi meter=\",l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## example:3.9 page no.173"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "group velocity= c**2*sqrt(-kc**2 + w**2/c**2)/w\n",
+      "phase velocity= w/sqrt(-kc**2 + w**2/c**2)\n",
+      "conclusion:since B<ko,we have that vg<c<vp, which indicates that the phase velocity of a waveguide mode may be greater than the speed of light.but the group velocity will be lesser than the speed of light .\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to calculate the group velocity.\n",
+    "from sympy import symbols,sqrt,diff\n",
+    "\n",
+    "w,c,v,B,ko,kc=symbols('w,c,v,B,ko,kc');\n",
+    "ko=w/c;\n",
+    "B=sqrt(ko**2-kc**2);\n",
+    "v=diff(B,w);\n",
+    "vg=v**(-1);\n",
+    "vg=(c*B)/ko;\n",
+    "vp=w/B;\n",
+    "print \"group velocity=\",vg\n",
+    "print \"phase velocity=\",vp\n",
+    "print \"conclusion:since B<ko,we have that vg<c<vp, which indicates that the phase velocity of a waveguide mode may be greater than the speed of light.but the group velocity will be lesser than the speed of light .\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_4_MICROWAVE_NETWORK_ANALYSIS_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_4_MICROWAVE_NETWORK_ANALYSIS_3.ipynb
new file mode 100644
index 00000000..d2ff5acf
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_4_MICROWAVE_NETWORK_ANALYSIS_3.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 4 MICROWAVE NETWORK ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:4.1 page.no:187"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix([[sqrt(2)*sqrt(a*b)/2], [sqrt(2)*Zte*sqrt(a*b)/2]])\n",
+      "which completes the transmission line equivalence for the TE10 mode \n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to find the equivalent voltages and current .\n",
+    "from sympy import symbols,sqrt,Matrix\n",
+    "\n",
+    "a,b,A,Zte,V,I,C1,C2,P=symbols('a,b,A,Zte,V,I,C1,C2,P');\n",
+    "P=(a*b*A**2)/(4*Zte);\n",
+    "c=(1/2)*V*I;\n",
+    "d=(1/2)*(A**2)*C1*C2;\n",
+    "C1=sqrt((a*b)/2); # on comparision .\n",
+    "C2=sqrt((a*b)/2)*Zte; # on comparision .\n",
+    "c=Matrix([C1,C2]);\n",
+    "print c;\n",
+    "print \"which completes the transmission line equivalence for the TE10 mode \""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:4.2 page.no:188"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reflection coefficient -0.627245765824\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to compute reflection coefficient .\n",
+    "from math import pi,sqrt\n",
+    "\n",
+    "a=0.03485;b=0.01580;eipsilao=8.854*10**-12;muo=4*pi*10** -7;\n",
+    "f=4.5*10**9;\n",
+    "w=2*pi*f; # angular frequency .\n",
+    "# for z<0 region air filled.\n",
+    "eipsilar=2.56; # for z>0 region .\n",
+    "ko=w*sqrt(muo*eipsilao);\n",
+    "k=ko*sqrt(eipsilar);\n",
+    "Ba=sqrt(ko**2-(pi/a)**2); # propagation constant in air region z<0.\n",
+    "Bd=sqrt(k**2-(pi/a)**2); # propagation constant in dielectric region z>0.\n",
+    "Zoa=(ko*377)/Ba;\n",
+    "Zod=(ko*377)/Bd;\n",
+    "tao=(Zod-Zoa)/(Zod+Zoa);\n",
+    "print \"reflection coefficient\",tao"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:4.3 page.no:195"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z-parameter of two port network =  Matrix([[Za + Zc, Zc], [Zc, Zb + Zc]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the z parameter of the two port network .\n",
+    "from sympy import symbols,Matrix\n",
+    "\n",
+    "Z11,Z12,Z22,Z21,Za,Zb,Zc=symbols('Z11,Z12,Z22,Z21,Za,Zb,Zc');\n",
+    "Z11=Za+Zc; # for I2=0.\n",
+    "Z12=(Zc/(Zb+Zc))*(Zb+Zc); #for I1=0.\n",
+    "Z21=(Zc/(Za+Zc))*(Za+Zc); # for I2=0.\n",
+    "Z22=Zb+Zc; #for I1=0.\n",
+    "Z=Matrix([[Z11,Z12],[Z21,Z22]]); # z_parameter matrix.\n",
+    "print \"Z-parameter of two port network = \",Z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:4.4 page.no:198"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "S-parameter of 3db attenuator circuit is =\n",
+      "[[  4.43981086e-05   7.07663252e-01]\n",
+      " [  7.07663252e-01   4.43981086e-05]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the s-parameter of 3-dB attenuator circuit .\n",
+    "from numpy import matrix\n",
+    "\n",
+    "Za=8.56;Zb=8.56;Zc=141.8;Zo=50.;\n",
+    "S11=(((((Zo+Zb)*Zc)/(Zo+Zb+Zc))+Za)-Zo)/(((((Zo+Zb)*Zc)/(Zo+Zb+Zc))+Za)+Zo); # reflection coefficient seen at port 1.\n",
+    "S22=(((((Zo+Za)*Zc)/(Zo+Za+Zc))+Zb)-Zo)/(((((Zo+Za)* Zc)/(Zo+Za+Zc))+Zb)+Zo); # reflection coefficient seen at port 2.\n",
+    "S12=(((1/((((Zo+Za)*Zc)/(Zo+Za+Zc))+Zb))*(((Zo+Za)* Zc)/(Zo+Za+Zc)))*(Zo/(Zo+Za))); # transmission coefficient from port 2 to 1.\n",
+    "S21=(((1/((((Zo+Zb)*Zc)/(Zo+Zb+Zc))+Za))*(((Zo+Zb)* Zc)/(Zo+Zb+Zc)))*(Zo/(Zo+Zb))); # transmission coefficient from port 1 to 2.\n",
+    "S=matrix([[S11,S12],[S21,S22]]); # sparameter matrix.\n",
+    "print \"S-parameter of 3db attenuator circuit is =\"\n",
+    "print S"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:4.5 page.no:202"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the network is reciprocal .\n",
+      "the network is lossy .\n",
+      "return loss at port 1 in dB= 3.967\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to determine the reciprccity and lossless of two port network and find return loss.\n",
+    "from sympy import symbols,I\n",
+    "from numpy import matrix\n",
+    "from math import log10\n",
+    "\n",
+    "Rl,tao=symbols('Rl,tao');\n",
+    "S=matrix([[0.1,0.8*I],[0.8*I,0.2]]); # s-parameter matrix.\n",
+    "if (S[0,1]==S[1,0]):\n",
+    "    print \"the network is reciprocal .\"\n",
+    "else:\n",
+    "    print \"the network is not reciprocal .\"\n",
+    "if (S[0,0]**2+S[0,1]**2==1):\n",
+    "    print \"the network is lossless .\"\n",
+    "else:\n",
+    "    print \"the network is lossy .\"\n",
+    "tao=S[0,0]-(S[0,1]*S[1,0])/(1+S[1,1]); #input reflection coefficient .\n",
+    "Rl=-20*log10(abs(tao)); # return loss in dB.\n",
+    "#result\n",
+    "print \"return loss at port 1 in dB= %.3f\"%Rl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:4.6 page.no:208"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "abcd parameter\n",
+      "Matrix([[1, Z], [0, 1]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to find the ABCD parameter of a two-port network .\n",
+    "from sympy import symbols,Matrix\n",
+    "\n",
+    "A,B,C,D,V1,V2,I1,I2,Z=symbols('A,B,C,D,V1,V2,I1,I2,Z');\n",
+    "#A=V1/V2; #for i2=0;\n",
+    "A=1;\n",
+    "B=V1/(V1/Z);\n",
+    "C=0;\n",
+    "D=I1/I1;\n",
+    "ABCD=Matrix([[A,B],[C,D]]);\n",
+    "#result\n",
+    "print \"abcd parameter\"\n",
+    "print ABCD"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:4.7 page.no:226"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "admittance matrix for bridge-T network=\n",
+      "Matrix([[1/(Z1 + Z2) + 1/Z3 + Z2**2/(Z1*(Z1 + Z2)*(Z1 + 2*Z2)), -1/Z3 - Z2/(Z1*(Z1 + 2*Z2))], [-1/Z3 - Z2/(Z1*(Z1 + 2*Z2)), 1/Z3 + (Z1 + Z2)/(Z1*(Z1 + 2*Z2))]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the admittance matrix for bridge-T network.\n",
+    "from sympy import symbols,Matrix\n",
+    "\n",
+    "Za,Z1,Z2,Z3,Y,Ya,Yb,D=symbols('Za,Z1,Z2,Z3,Y,Ya,Yb,D');\n",
+    "Za=Matrix([[Z1+Z2,Z2],[Z2,Z1+Z2]]);\n",
+    "Yb=Matrix([[1/Z3,-1/Z3],[-1/Z3,1/Z3]]);\n",
+    "Y1=1/Z1;Y2=1/Z2;\n",
+    "Ya=Za**-1\n",
+    "Y=Ya+Yb;\n",
+    "D=((Z2+Z1)**2-Z2**2);\n",
+    "# result\n",
+    "print  \"admittance matrix for bridge-T network=\"\n",
+    "print Y"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_5_IMPEDENCE_MATCHING_AND_TUNNING_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_5_IMPEDENCE_MATCHING_AND_TUNNING_3.ipynb
new file mode 100644
index 00000000..cb7340f4
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_5_IMPEDENCE_MATCHING_AND_TUNNING_3.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 5 IMPEDENCE MATCHING AND TUNNING"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exa 5.1 page no:254"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inductor of first circuit in nH = 38.985\n",
+      "capacitor of the first circuit in pF = 0.923\n",
+      "inductor of second circuit in nH = 46.139\n",
+      "capacitor of the second circuit in pF = 2.599\n",
+      "\"NOTE:−for above specific problem Rl>Zo, positive X implies inductor , negative X implies capacitor , positive B implies capacitor and negative B implies inductor .\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to design an L section matching network\n",
+    "from math import pi,sqrt\n",
+    "from sympy import I\n",
+    "\n",
+    "# program to design an L section matching network to match a series RC load.\n",
+    "Zl=200-I*100; # load impedence .\n",
+    "Rl=200;Xl=-100;f=500*10**6;Zo=100;\n",
+    "B1=(Xl+sqrt(Rl/Zo)*sqrt(Rl**2+Xl**2-(Rl*Zo)))/(Rl**2+Xl**2);\n",
+    "B2=(Xl-sqrt(Rl/Zo)*sqrt(Rl**2+Xl**2-(Rl*Zo)))/(Rl**2+Xl**2);\n",
+    "C1=(B1/(2*pi*f))*10**12;\n",
+    "L2=(-1/(B2*2*pi*f))*10**9;\n",
+    "X1=(1/B1)+((Xl*Zo)/Rl)-(Zo/(B1*Rl));\n",
+    "X2=(1/B2)+((Xl*Zo)/Rl)-(Zo/(B2*Rl));\n",
+    "L1=(X1/(2*pi*f))*10**9;\n",
+    "C2=(-1/(X2*2*pi*f))*10**12;\n",
+    "print\"inductor of first circuit in nH = %.3f\"%L1\n",
+    "print\"capacitor of the first circuit in pF = %.3f\"%C1\n",
+    "print\"inductor of second circuit in nH = %.3f\"%L2\n",
+    "print\"capacitor of the second circuit in pF = %.3f\"%C2 \n",
+    "print\"\\\"NOTE:−for above specific problem Rl>Zo, positive X implies inductor , negative X implies capacitor , positive B implies capacitor and negative B implies inductor .\\\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exa 5.5 page no:275"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "charecteristic impedence of matching section = 22.36\n",
+      " fractional bandwidth = 0.29\n"
+     ]
+    }
+   ],
+   "source": [
+    "#design quarter wave matching transformer\n",
+    "from math import sqrt,pi,acos\n",
+    "\n",
+    "#program to design a single section quarter wave matching transformer .\n",
+    "Zl=10; # load impedence .\n",
+    "Zo=50; # characteristic impedence .\n",
+    "fo=3*10**9;swr=1.5; # maximum limit of swr.\n",
+    "Z1=sqrt(Zo*Zl); # characteristic impedence of the matching section .\n",
+    "taom=(swr-1)/(swr+1);\n",
+    "frac_bw=2-(4/pi)*acos((taom/sqrt(1-taom**2))*(2*sqrt(Zo*Zl)/abs(Zl-Zo))); # fractional bandwidth .\n",
+    "print \"charecteristic impedence of matching section = %.2f\"%Z1\n",
+    "print \" fractional bandwidth = %.2f\"%frac_bw"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exa 5.6 page no:280"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z1 = 91.70\n",
+      "Z2 = 84.09\n",
+      "Z3 = 77.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "#design three section binomial transformer\n",
+    "from math import pi,acos\n",
+    "\n",
+    "Zl=50.;Zo=100.;N=3;taom=0.05;\n",
+    "A=(2**-N)*abs((Zl-Zo)/(Zl+Zo));\n",
+    "frac_bw=2.-(4/pi)*acos(0.5*(taom/A)**2);\n",
+    "c=1.\n",
+    "Z1=Zo*((Zl/Zo)**((2.**-N)*(c**N)));\n",
+    "print \"Z1 = %.2f\"%Z1\n",
+    "c=3.**(1/3)\n",
+    "Z2=Z1*((Zl/Zo)**((2.**-N)*(c**N)));\n",
+    "print \"Z2 = %.2f\"%Z2\n",
+    "c=3.**(1/3)\n",
+    "Z3=Z2*((Zl/Zo)**((2.**-N)*(c**N)));\n",
+    "print \"Z3 = %.2f\"%Z3\n",
+    "# answers may vary due to round off errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exa 5.7 page no:288"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the characteristic impedences are = 57.27 ,69.84 ,87.30\n"
+     ]
+    }
+   ],
+   "source": [
+    "#design three section chebysev transfomer\n",
+    "from math import pi,cosh\n",
+    "from sympy import asec,acosh\n",
+    "\n",
+    "Zl=100.;Zo=50.;taom=0.05;N=3;A=0.05;\n",
+    "thetam=asec(cosh((1/N)*acosh((1/taom)*abs((Zl-Zo)/(Zl+Zo)))))*(180/pi);\n",
+    "x=(cosh((1./N)*acosh((1./taom)*abs((Zl-Zo)/(Zl+Zo)))))\n",
+    "tao_o=A*(x**3)/2;\n",
+    "tao_1=(3*A*(x**3-x))/2; # from symmetry tao 3=tao \n",
+    "Z1=Zo*((1+tao_o)/(1-tao_o));\n",
+    "Z2=Z1*((1+tao_1)/(1-tao_1));\n",
+    "Z3=Zl*((1-tao_o)/(1+tao_o));\n",
+    "print \"the characteristic impedences are = %.2f ,%.2f ,%.2f\"%(Z1,Z2,Z3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_6_MICROWAVE_RESONATORS_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_6_MICROWAVE_RESONATORS_3.ipynb
new file mode 100644
index 00000000..e6a37416
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_6_MICROWAVE_RESONATORS_3.ipynb
@@ -0,0 +1,328 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 6 MICROWAVE RESONATORS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:6.1 page.no:309"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Qair = 2379.5\n",
+      "Qteflon = 1217.7\n",
+      "conclusion: Qair is almost twice that of Qteflon\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to compare the Q of an air filled and teflon filled coaxial line resonator .\n",
+    "from math import pi,sqrt,log\n",
+    "\n",
+    "sigma=5.813*10**7;muo=4*pi*10**-7;f=5*10**9;eta=377;a =1*10**-3;b=4*10**-3;\n",
+    "omega=2*pi*f;ko=104.7;B=104.7;alpha=0.022;\n",
+    "Rs=sqrt((omega*muo)/(2*sigma));\n",
+    "alphaca=(Rs/(2*eta*log(b/a)))*((1/a)+(1/b)); # attenuation due to conductor loss for air filled line .\n",
+    "eipsilar=2.08;tandelta=0.0004; # for teflon filled line .\n",
+    "alphact=((Rs*sqrt(2.08)*0.01)/(2*eta*log(b/a)))*((1/ a)+(1/b)); # attenuation due to conductor loss for teflon filled line .\n",
+    "alphada=0; # for air filled line .\n",
+    "alphadt=ko*(sqrt(eipsilar)/2)*tandelta;\n",
+    "Qair=B/(2*alpha);\n",
+    "B=B*sqrt(eipsilar);\n",
+    "alpha =0.062;\n",
+    "Qteflon=B/(2*alpha);\n",
+    "print \"Qair = %.1f\"%Qair\n",
+    "print \"Qteflon = %.1f\"%Qteflon\n",
+    "print \"conclusion: Qair is almost twice that of Qteflon\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:6.2 page.no:312"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "length of the line in meter = 0.0219\n",
+      "Q of the resonator = 525.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to compute the length of the line for resonance at 5 GHZ and the Q of the resonator .\n",
+    "from math import sqrt,pi\n",
+    "\n",
+    "W=0.0049;c=3*10**8;f=5*10**9;Zo=50;eipsilar=2.2;ko =104.7;tandelta =0.001;\n",
+    "Rs=0.0184; # taken from example 7.1.\n",
+    "eipsilae=1.87; # effective permittivity .\n",
+    "l=c/(2*f*sqrt(eipsilae)); # resonator length .\n",
+    "B=(2*pi*f*sqrt(eipsilae))/c;\n",
+    "alphac=Rs/(Zo*W);\n",
+    "alphad=(ko*eipsilar*(eipsilae -1)*tandelta)/(2*sqrt(eipsilae)*(eipsilar -1));\n",
+    "alpha=alphac+alphad;\n",
+    "Q=B/(2*alpha);\n",
+    "print \"length of the line in meter = %.4f\"%l\n",
+    "print \"Q of the resonator = %.1f\"%Q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:6.3 page.no:317"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "d in meter = 0.0465\n",
+      "Q1 = 1437\n",
+      "Q2 = 1518\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find required length ,d and Q for l=1 and l=2 resonator mode.\n",
+    "from math import sqrt,pi\n",
+    "\n",
+    "a=0.04755;b=0.02215;eipsilar=2.25;tandelta=0.0004;f =5*10**9;c=3*10**8;\n",
+    "k=(2*pi*f*sqrt(eipsilar))/c # wave number .\n",
+    "for l in range(1,2):\n",
+    "    d=(l*pi)/sqrt((k**2)-((pi/b)**2)); # m=1 & n=0 mode .\n",
+    "    print \"d in meter = %.4f\"%d\n",
+    "eta=377/sqrt(eipsilar);\n",
+    "Qc1=3380.;# l=1.\n",
+    "Qc2=3864.;# l=2.\n",
+    "Qd=2500.; # Q due to dielectric loss only .\n",
+    "Q1=((1./Qc1)+(1./Qd))**-1; # for l =1.\n",
+    "Q2=((1./Qc2)+(1./Qd))**-1; # for l =2.\n",
+    "print \"Q1 = %.0f\"%Q1\n",
+    "print \"Q2 = %.0f\"%Q2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:6.4 page.no:323"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a in meter = 0.0395\n",
+      "Qc = 42364.227\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find dimension and Q;\n",
+    "from math import pi,sqrt\n",
+    "\n",
+    "f=5.*10**9;c=3.*10**8;p01=3.832;sigma=5.813*10**7;muo=4.*pi*10** -7;\n",
+    "eipsilar =2.25;\n",
+    "# mode TE011 . and d=2a .\n",
+    "omega=2*pi*f;\n",
+    "eta =377.;\n",
+    "lamda=c/f;\n",
+    "k=(2.*pi)/lamda;\n",
+    "# f=(c/(2⇤pi))⇤sqrt((p01/a)ˆ2+(%pi/(2⇤a))ˆ2); as d=2a given\n",
+    "a=sqrt((p01)**2+(pi/2)**2)/k;\n",
+    "Rs=sqrt((omega*muo)/(2.*sigma))\n",
+    "Qc=(k*a*eta)/(2.*Rs); # for m=l =1,n=0 and d=2a .\n",
+    "print \"a in meter = %.4f\"%a\n",
+    "print \"Qc = %.3f\"%Qc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:6.5 page.no:309"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "f1 in GHZ= 2.853\n",
+      "f2 in GHZ= 27.804\n",
+      "approx. value of Q due to dielectric loss = 1000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the resonant frequency and Q for TE01delta mode .\n",
+    "from math import sqrt,pi,tan\n",
+    "\n",
+    "delta=0.001;eipsilar=95.;a=0.413;L=0.008255;c=3.*10**8;\n",
+    "#tan((B⇤L)/2)=alpha/beta.\n",
+    "ko=2.405\n",
+    "alpha=(sqrt((2.405/a)**2-(ko)**2));\n",
+    "B=sqrt((eipsilar*(ko)**2) -(2.405/a)**2); # beta\n",
+    "f1=((c*2.405)/(2*pi*sqrt(eipsilar)*a))*10**-7;\n",
+    "f2=((c*2.405)/(2*pi*a))*10**-7;\n",
+    "print \"f1 in GHZ= %.3f\"%f1\n",
+    "print \"f2 in GHZ= %.3f\"%f2\n",
+    "Q=1/tan(delta);\n",
+    "print \"approx. value of Q due to dielectric loss = %.0f\"%Q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example:6.6 page.no:336"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coupling capacitor in pF =  433773.991\n",
+      "frequency in GHZ=  3.66602230334e-07\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find the value of the coupling capacitor required for critical coupling .\n",
+    "from math import pi,sqrt,atan\n",
+    "\n",
+    "l=0.02175;Zo=50;eipsilae=1.9;c=3*10^8;\n",
+    "fo=c/(2*l*sqrt(eipsilae)); # first resonant frequency will occur when the resonator ia about l=lamdag/2 in length .\n",
+    "lamdag=c/fo;\n",
+    "alpha=1/8.7; # in Np/m.\n",
+    "Q=pi/(2*l*alpha);\n",
+    "bc=sqrt(pi/(2*Q));\n",
+    "C=bc/(2*pi*fo*Zo)*10**12;\n",
+    "print \"coupling capacitor in pF = \",C\n",
+    "C=bc/(2*pi*fo*Zo);\n",
+    "w1=atan(2*pi*fo*C*Zo)*c/(l*sqrt(eipsilae)); # from equation tan (B⇤l) =bc ;\n",
+    "w1=w1*10**-8;\n",
+    "print \"frequency in GHZ= \",w1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:6.7 page.no:342"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "on taking sin (2⇤pi )=0 ,w becomes=  A**2*a*c*d*t/4\n",
+      "fractional change in resonant frequency=  2*(-A**2*a*b*d*eo/2 + A**2*a*c*d*t/4)/(A**2*a*b*d*eo)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to derive an expression for the change in resonant frequency .\n",
+    "from sympy import symbols,sin,cos,integrate,limit\n",
+    "from math import pi\n",
+    "\n",
+    "Ey,Hx,Hz,A,Zte,n,a,p,i,x,z,d,j,k,t,y,er,eo,c,wo,w,b=symbols('Ey,Hx,Hz,A,Zte,n,a,p,i,x,z,d,j,k,t,y,er,eo,c,wo,w,b')\n",
+    "Ey=A*sin((pi*x)/a)*sin((pi*z)/d);\n",
+    "Hx=((-j*A)/Zte)*sin((pi*x)/a)*cos((pi*z)/d);\n",
+    "Hz=((j*pi*A)/(k*n*a))*cos((pi*x)/a)*sin((pi*z)/d); \n",
+    "Ey=Ey**2; #c=(er1)⇤eo;\n",
+    "w=c*integrate(integrate(integrate(Ey,(z,0,d)),(y,0,t)),(x,0,a));\n",
+    "# as sin (2⇤ pi )=0; then last term of above result will be:\n",
+    "w=(c*A**2*a*t*d)/4;\n",
+    "print \"on taking sin (2⇤pi )=0 ,w becomes= \",w\n",
+    "wo=((a*b*d*eo)/2)*A**2;\n",
+    "deltaw=(w-wo)/wo;\n",
+    "print \"fractional change in resonant frequency= \",deltaw"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_7_POWER_DIVIDERS_DIRECTIONAL_COUPLERS_AND_HYBRIDS_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_7_POWER_DIVIDERS_DIRECTIONAL_COUPLERS_AND_HYBRIDS_3.ipynb
new file mode 100644
index 00000000..d3e4093c
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_7_POWER_DIVIDERS_DIRECTIONAL_COUPLERS_AND_HYBRIDS_3.ipynb
@@ -0,0 +1,393 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 7 POWER DIVIDERS DIRECTIONAL COUPLERS AND HYBRIDS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.1 page.no:360"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "not matched\n",
+      "reflection coefficient looking at 150 ohm  line = -0.71\n",
+      "reflection coefficient looking at 75 ohm line = -0.14\n"
+     ]
+    }
+   ],
+   "source": [
+    "#function:\n",
+    "def parallel_impedence(Z1,Z2):\n",
+    "    return (Z1*Z2)/(Z1+Z2);\n",
+    "\n",
+    "# program to compute the reflection coefficients seen looking in to the output port.\n",
+    "# as the power is divided in to 2:1 ratio. and Pin=(1/2)⇤Voˆ2/Zo;\n",
+    "# so,P1=(1/3)⇤Pin;and P2=(2/3)⇤Pin ............( i)\n",
+    "Zo =50.;\n",
+    "Z1=3.*Zo; # from above condition .............( i)\n",
+    "Z2=(3/2)*Zo;\n",
+    "Zin=parallel_impedence(Z1,Z2); # input impedence to the junction .\n",
+    "if Zin==Zo:\n",
+    "    print \"input is matched to the 50 ohm sources\"\n",
+    "else:\n",
+    "    print \"not matched\"\n",
+    "Zin1=parallel_impedence(Zo,Z2); # looking in to the 150 ohm source.\n",
+    "Zin2=parallel_impedence(Zo,Z1); # looking in to the 75 ohm source.\n",
+    "tao1=(Zin1-Z1)/(Zin1+Z1);\n",
+    "tao2=(Zin2-Z2)/(Zin2+Z2);\n",
+    "print \"reflection coefficient looking at 150 ohm  line = %.2f\"%tao1\n",
+    "print \"reflection coefficient looking at 75 ohm line = %.2f\"%tao2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.2 page.no:365"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the shunt resistance value should be in ohm=  100\n",
+      "the quarter wave transmission line in the divide should have a characteristic impedence in ohm =  70.7106781187\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design an equisplit wilkinson power divider for 50 ohm system impedence .\n",
+    "from math import sqrt\n",
+    "\n",
+    "Zo =50;\n",
+    "Z=sqrt(2)*Zo; # impedence of quarter wave transmission line .\n",
+    "R=2*Zo; # shunt resistor .\n",
+    "print \"the shunt resistance value should be in ohm= \",R\n",
+    "print \"the quarter wave transmission line in the divide should have a characteristic impedence in ohm = \",Z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.3 page.no:372"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the aperture position in mm = 9.72\n",
+      "the aperture size in mm =  1000.0\n",
+      "NOTE:the above shown results completes the design of the betha hole coupler \n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design bethe-hole coupler for x-band wave guide .\n",
+    "from math import pi,asin\n",
+    "\n",
+    "f=9*10**9;C=20;a=0.02286;b=0.01016;\n",
+    "Ko=188.5;B=129;Z10 =550.9;\n",
+    "P10=4.22*10**-7;lamdao=0.0333;\n",
+    "uo=4*pi*10**-7;eo=8.854*10**-12;w=2*pi*f;\n",
+    "s=(a/pi)*asin(lamdao/sqrt(2*(lamdao**2-a**2)))*10**3; \n",
+    "# a=10⇤b;# as C=20db; # take x=a/b; so x=10;\n",
+    "ro=(P10/((10*w)*((((2*eo/3)+(4*uo)/(3*Z10**2))*0.944)-((4*pi**2*uo*0.056)/(3*B**2*a**2*Z10**2)))))**(1/3)*10**3;\n",
+    "print \"the aperture position in mm = %.2f\"%s\n",
+    "print \"the aperture size in mm = \",ro\n",
+    "print \"NOTE:the above shown results completes the design of the betha hole coupler \""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.4 page.no:378"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kf = 394835.06\n",
+      "kb = 345030.82\n",
+      "thetam in degree = 70.59\n",
+      "ro in mm= 3009.49\n",
+      "r1 in mm= 1000.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design a four hole chebysev coupler in xband wave guide using round aperture located at s=a/4.\n",
+    "from math import pi\n",
+    "from mpmath import cosh,acosh,sin,cos,asec\n",
+    "\n",
+    "a=0.02286;b=0.01016;lamdao=0.0333;\n",
+    "ko=188.5;bta=129; Z10=550.9;\n",
+    "P10=4.22*10**-7;f=9*10**9;no=377;N=3;\n",
+    "s=a/4;\n",
+    "kf=((2*ko)/(3*no*P10))*((sin(pi*s/a)**2) -(2*(bta**2)/(ko**2))*((sin(pi*s/a)**2)+((pi**2)/((bta**2)*(a**2)))*(cos(pi*s/a)**2)));\n",
+    "kf=abs(kf)\n",
+    "kb=((2*ko)/(3*no*P10))*((sin(pi*s/a)**2)+(2*(bta**2) /(ko**2))*((sin(pi*s/a)**2)-((pi**2)/((bta**2)*(a **2)))*(cos(pi*s/a)**2)));\n",
+    "kb=abs(kb)\n",
+    "x=cosh(acosh(100)/3); # x=sec(thetam).\n",
+    "thetam=asec(x)*180/pi; # so , thetam=70.6 and at the band edge .\n",
+    "k=10**(-171.94/20);\n",
+    "ro=(((k/2)**(1/3))*x)*1000;\n",
+    "r1=(1.5*k*((x**3)-x))**(1/3)*1000;\n",
+    "print \"kf = %.2f\"%kf\n",
+    "print \"kb = %.2f\"%kb\n",
+    "print \"thetam in degree = %.2f\"%thetam\n",
+    "print \"ro in mm= %.2f\"%ro\n",
+    "print \"r1 in mm= %.2f\"%r1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.5 page.no:382"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the branch line impedence in ohm will be = 35.36\n"
+     ]
+    }
+   ],
+   "source": [
+    "#program to design a 50 ohm branchline quadrature hybrid junction \n",
+    "from math import sqrt\n",
+    "\n",
+    "Zo =50.;\n",
+    "Z=Zo/sqrt(2)\n",
+    "print \"the branch line impedence in ohm will be = %.2f\"%Z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.6 page.no:387"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Zoe =  sqrt(eo*er*uo)/(W*eo*er/(b/2 + s/2) + W*eo*er/(b/2 - s/2))  zoo =  sqrt(eo*er*uo)/(W*eo*er/(b/2 + s/2) + W*eo*er/(b/2 - s/2) + 2*W*eo*er/s)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to determine the even and odd mode characteristic impedence.\n",
+    "from sympy import symbols,sqrt\n",
+    "\n",
+    "C,A,d,W,C11,C12,Ce,Co,v,eo,er,s,b,uo,Zoe,Zoo,eipsila=symbols('C,A,d,W,C11,C12,Ce,Co,v,eo,er,s,b,uo,Zoe,Zoo,eipsila');\n",
+    "C=A*eipsila/d;\n",
+    "C11=(eo*er*W)/((b-s)/2)+(eo*er*W)/((b+s)/2);\n",
+    "C12=er*eo*W/s;\n",
+    "Ce=C11;\n",
+    "Co=C11+2*C12\n",
+    "v=1/sqrt(er*eo*uo);\n",
+    "Zoe=1/(v*C11); # as Ce=C11; \n",
+    "Zoo=1/(v*Co);\n",
+    "print \"Zoe = \",Zoe,\" zoo = \",Zoo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.7 page.no:394"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "conductor width in cm = 0.114\n",
+      "conductor seperation in cm = 0.054\n"
+     ]
+    }
+   ],
+   "source": [
+    "#design a 20 db single section coupled line coupler in stripline .\n",
+    "from math import sqrt\n",
+    "\n",
+    "C=10**(-20/20);f=3*10**9;eipsila=2.56;Zo=50;b=0.00158; \n",
+    "Zoe=Zo*sqrt((1+C)/(1-C));\n",
+    "Zoo=Zo*sqrt((1-C)/(1+C));\n",
+    "Zoe=eipsila*Zoe;\n",
+    "Zoo=eipsila*Zoo; \n",
+    "x=0.72; #x=w/b. \n",
+    "y=0.34; # y=s/b.\n",
+    "w=0.72*b*100;\n",
+    "s=0.34*b*100;\n",
+    "print \"conductor width in cm = %.3f\"%w\n",
+    "print \"conductor seperation in cm = %.3f\"%s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.8 page.no:396"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "for a maximally flat response for a three= section coupler doupble derivative of C will be zero\n",
+      "the even and odd mode characteristic impedences for each section are :\n",
+      "Zoe1 = 50.63 \n",
+      "Zoo1= 49.38 \n",
+      "Zoe2= 56.69 \n",
+      "Zoo2= 56.69 \n",
+      "Zoe3= 50.63 \n",
+      "Zoo3= 49.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "# design a three section 20 db coupler with a binomial response .\n",
+    "from sympy import symbols,sqrt\n",
+    "\n",
+    "Zo=50;f=3*10**9;N=3;\n",
+    "C,C1,C2,theta=symbols('C,C1,C2,theta')\n",
+    "C=10**(-20/20);\n",
+    "print \"for a maximally flat response for a three= section coupler doupble derivative of C will be zero\"\n",
+    "C1=0.0125;C2=0.125;C3=0.0125;\n",
+    "Zoe1=Zo*sqrt((1+C1)/(1-C1));\n",
+    "Zoe3=Zo*sqrt((1+C3)/(1-C3));\n",
+    "Zoo1=Zo*sqrt((1-C1)/(1+C1));\n",
+    "Zoo3=Zo*sqrt((1-C1)/(1+C1));\n",
+    "Zoe2=Zo*sqrt((1+C2)/(1-C2));\n",
+    "Zoo2=Zo*sqrt((1+C2)/(1-C2));\n",
+    "print \"the even and odd mode characteristic impedences for each section are :\"\n",
+    "print \"Zoe1 = %.2f\"%Zoe1,\"\\nZoo1= %.2f\"%Zoo1,\"\\nZoe2= %.2f\"%Zoe2,\"\\nZoo2= %.2f\"%Zoo2,\"\\nZoe3= %.2f\"%Zoe3,\"\\nZoo3= %.2f\"%Zoo3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## exa 7.9 page.no:407"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the characteristic impedence of the ring transmission line in ohm is = 70.71\n"
+     ]
+    }
+   ],
+   "source": [
+    "#design a 180 deg. ring hybrid for a 50 ohm system impedence .\n",
+    "from math import sqrt\n",
+    "\n",
+    "Zo =50;\n",
+    "Z=sqrt(2)*Zo;\n",
+    "print \"the characteristic impedence of the ring transmission line in ohm is = %.2f\"%Z"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_8_MICROWAVE_FILTERS_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_8_MICROWAVE_FILTERS_3.ipynb
new file mode 100644
index 00000000..f0292322
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_8_MICROWAVE_FILTERS_3.ipynb
@@ -0,0 +1,449 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 8 MICROWAVE FILTERS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.1 page.no:429"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bx1Y7NuhwEqKgABo2hCVLwr9wnoigqoHM6g973TaprbJ1u6xGgHPjiMdEqF29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\nfwObl9idx5jvAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x104a20910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# program to compute the propagation constant , phase velocity and bloch impedence .\n",
+    "%matplotlib inline\n",
+    "from pylab import arange,plot,axis,title,xlabel,ylabel,subplot\n",
+    "from numpy import cos,sin,pi,arccos,seterr\n",
+    "from mpmath import acos\n",
+    "\n",
+    "Co=2.666*10**-12;\n",
+    "d=0.01;c=3*10**8;\n",
+    "Zo=50.;f=3*10**9;\n",
+    "p=(Co*Zo*c)/(2*d); # constant of equation given below .\n",
+    "y=arange(0,0.96,0.001)\n",
+    "x=arccos(cos(y)-p*y*sin(y)); # x=ko⇤d; and y=beta⇤d;\n",
+    "subplot(121)\n",
+    "plot(x,y)\n",
+    "plot(-x,y)\n",
+    "axis([-3,3,0,1])\n",
+    "title (\"k-beta diagram for first pass band \")\n",
+    "xlabel (\"beta-d\")\n",
+    "ylabel (\"ko-d\")\n",
+    "y=arange(3,4,0.001)\n",
+    "seterr(all='ignore')\n",
+    "x=arccos(cos(y)-p*y*sin(y)); # x=ko⇤d; and y=beta⇤d ;\n",
+    "subplot(122)\n",
+    "plot(x,y)\n",
+    "axis([0,3,3,4]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.2 page.no:441"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "design parameter for m=0.2195 ==> 1.3101e-06 4.6582e-10 1.2939e-05 \n",
+      "\n",
+      "design parameter for m=0.6 ==> 3.5810e-06 6.3662e-10 6.3662e-06 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design a low pass composite filter with cutoff frequency of 2 MHZ.\n",
+    "from math import sqrt,pi\n",
+    "\n",
+    "fc=2*10**6;f=2.05*10**6;Ro=75;\n",
+    "L=(2*Ro)/(2*pi*fc);\n",
+    "C=2/(Ro*2*pi*fc);\n",
+    "m=sqrt(1-(fc/f)**2)\n",
+    "x=m*L/2;\n",
+    "y=m*C;\n",
+    "z=((1-m**2)/(4*m))*L; # x,y,z are design parameter assumed .\n",
+    "print \"design parameter for m=0.2195 ==> %.4e %.4e %.4e \\n\"%(x,y,z)\n",
+    "m=0.6\n",
+    "x=m*L/2;\n",
+    "y=m*C/2;\n",
+    "z=((1-m**2)/(2*m))*L; # x,y,z are design parameter assumed .\n",
+    "print \"design parameter for m=0.6 ==> %.4e %.4e %.4e \\n\"%(x,y,z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.3 page.no:449"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "from table we see that an attenuation of 20 db at this frequency requires that N>=8 for x =  0.375\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to find out number of filter elements required .\n",
+    "from math import pi\n",
+    "\n",
+    "fc=8*10**9;f=11*10**9;\n",
+    "w=2*pi*f;\n",
+    "wc=2*pi*fc;\n",
+    "x=abs(w/wc)-1;\n",
+    "print \"from table we see that an attenuation of 20 db at this frequency requires that N>=8 for x = \",x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.4 page.no:447"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g1 =  0.618\n",
+      "g2 =  1.618\n",
+      "g3 =  2.0\n",
+      "g4 =  1.618\n",
+      "g5 =  0.618\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design a maximum flat low pass filter with cut off frequency of 2 GHZ.\n",
+    "from math import pi\n",
+    "\n",
+    "fc=2*10**9;f=3*10**9;\n",
+    "w=2*pi*f;\n",
+    "wc=2*pi*fc;\n",
+    "x=abs(w/wc)-1;\n",
+    "# from table we can see that N=5 will be sufficient .\n",
+    "# then prototype element values are:\n",
+    "g1 =0.618; g2 =1.618; g3 =2.000; g4 =1.618; g5 =0.618;\n",
+    "print \"g1 = \",g1\n",
+    "print \"g2 = \",g2\n",
+    "print \"g3 = \",g3\n",
+    "print \"g4 = \",g4\n",
+    "print \"g5 = \",g5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.5 page.no:460"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L1- 1.27e-16\n",
+      "L2- 7.26e-01\n",
+      "L3- 1.27e-16\n",
+      "C1- 1.99e-04\n",
+      "C2- 3.49e-20\n",
+      "C3- 1.99e-04\n"
+     ]
+    }
+   ],
+   "source": [
+    "# design a band pass filter having a 0.5 db equal ripple respnse with N=3.\n",
+    "from math import pi\n",
+    "\n",
+    "N=3;Zo=50;f=1*10**9;delta=1*10**8;\n",
+    "L1 =1.596; L3 =1.5963; C2 =1.0967; Rl =1.000;\n",
+    "L_1=(L1*Zo)/(2*pi*f*delta);\n",
+    "C_1=delta/(2*pi*f*L1*Zo);\n",
+    "L_2=(delta*Zo)/(2*pi*f*C2);\n",
+    "C_2=C2/(2*pi*f*delta*Zo);\n",
+    "L_3=(L3*Zo)/(2*pi*f*delta); \n",
+    "C_3=delta/(2*pi*f*L3*Zo);\n",
+    "print \"L1- %.2e\"%L_1\n",
+    "print \"L2- %.2e\"%L_2\n",
+    "print \"L3- %.2e\"%L_3\n",
+    "print \"C1- %.2e\"%C_1\n",
+    "print \"C2- %.2e\"%C_2\n",
+    "print \"C3- %.2e\"%C_3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.6 page.no:466"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "from table , the normalized low pass prototype element values are = \n",
+      "3.3487\n",
+      "1.0\n",
+      "0.7117\n",
+      "3.3487\n",
+      "1.299\n"
+     ]
+    }
+   ],
+   "source": [
+    "# design a low pass filter for fabrication using micrstrip lines .\n",
+    "print \"from table , the normalized low pass prototype element values are = \"\n",
+    "L1 =3.3487; C2 =0.7117; L3 =3.3487; Rl =1.0000;\n",
+    "n=1+(1/3.3487);\n",
+    "print(L1)\n",
+    "print(Rl)\n",
+    "print(C2)\n",
+    "print(L3)\n",
+    "print \"%.3f\"%n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.7 page.no:472"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.92438360165\n",
+      "27.0054107438\n",
+      "22.1390892039\n",
+      "36.8984820064\n",
+      "16.2032464463\n",
+      "9.87397266942\n"
+     ]
+    }
+   ],
+   "source": [
+    "# design a steppedimpedence low pass filter having a maximally flat response and a cutoff frequency of 2.5 GHZ.\n",
+    "from math import pi\n",
+    "\n",
+    "w=4*10**9;wc=2.5*10**9;Zh=150;Ro=50;Zl=10; 62\n",
+    "C1 =0.517; L2 =1.414; C3 =1.932; L4 =1.932; C5 =1.414; L6 =0.517;\n",
+    "# above values are taken from table.\n",
+    "# for finding electrical lengths .\n",
+    "x1=(C1*Zl/Ro)*(180/pi);\n",
+    "x2=(L2*Ro/Zh)*(180/pi);\n",
+    "x3=(C3*Zl/Ro)*(180/pi);\n",
+    "x4=(L4*Ro/Zh)*(180/pi);\n",
+    "x5=(C5*Zl/Ro)*(180/pi);\n",
+    "x6=(L6*Ro/Zh)*(180/pi);\n",
+    "print(x1)\n",
+    "print(x2)\n",
+    "print(x3)\n",
+    "print(x4)\n",
+    "print(x5)\n",
+    "print(x6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.8 page.no:486"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "attenuation in db =  20\n"
+     ]
+    }
+   ],
+   "source": [
+    "# design a coupled line band pass filter with N=3.\n",
+    "\n",
+    "delta=0.1;f=1.8*10889;fo=2*10889;Zo=50;fc=1;\n",
+    "f=(1/delta)*((f/fo)-(fo/f));\n",
+    "x=abs(f/fc)-1; # the value on the horizontal scale.\n",
+    "attntn=20; # from above values .\n",
+    "print \"attenuation in db = \",attntn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.9 page.no:489"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the cahracteristic impedence of a bandpass filter is = 265.87\n",
+      "for a bandpass filter using short circuited stub resonators , the corresponding result is = 3.69\n"
+     ]
+    }
+   ],
+   "source": [
+    "# design a bandpass filter using three quarter wave open circuit stubs .\n",
+    "from math import pi\n",
+    "\n",
+    "f=2*10**9;delta=0.15;Zo=50;N=3;gn=1.5963; 63\n",
+    "Zon=4*Zo/(pi*gn*delta);\n",
+    "Z_on=(pi*Zo*delta)/(4*gn);\n",
+    "print \"the cahracteristic impedence of a bandpass filter is = %.2f\"%Zon\n",
+    "print \"for a bandpass filter using short circuited stub resonators , the corresponding result is = %.2f\"%Z_on"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:8.10 page.no:429"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "thetao in degree =  155.813419985\n",
+      "the coupling capacitor value in PF =  0.553745271333\n"
+     ]
+    }
+   ],
+   "source": [
+    "# design a bandpass filter using capacitive coupled resonators , with a 0.5 db equal passband haracteristic .\n",
+    "from math import pi,sqrt,atan\n",
+    "\n",
+    "fo=2*10**9;delta=0.1;Zo=50;\n",
+    "f=2.2*10**9;g1=1.5963;\n",
+    "g2 =1.0967;g3=1.5963;g4=1;\n",
+    "f=(1/delta)*((f/fo)-(fo/f));\n",
+    "x=abs(f/fo)-1; # the value on the horizontal scale.\n",
+    "x0=sqrt((pi*delta)/(2*g1))/Zo; # x0=ZoJ1 ;\n",
+    "x1=((pi*delta)/(2*sqrt(g1*g2)))/Zo; # x0=ZoJn ;\n",
+    "B0=x0/(1-(Zo*x0)**2)\n",
+    "B1=x1/(1-(Zo*x1)**2)\n",
+    "theta0=(pi-0.5*(atan(2*Zo*B0)+atan(2*Zo*B1)))*(180/ pi);\n",
+    "C0=(B0/(2*pi*fo))*10**12;\n",
+    "print \"thetao in degree = \",theta0\n",
+    "print \"the coupling capacitor value in PF = \",C0"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/Chapter_9_THEORY_AND_DESIGN_OF_FERRIMAGNETIC_COMPONENTS_3.ipynb b/Microwave_engineering__by_D.M.Pozar_/Chapter_9_THEORY_AND_DESIGN_OF_FERRIMAGNETIC_COMPONENTS_3.ipynb
new file mode 100644
index 00000000..77872af2
--- /dev/null
+++ b/Microwave_engineering__by_D.M.Pozar_/Chapter_9_THEORY_AND_DESIGN_OF_FERRIMAGNETIC_COMPONENTS_3.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 9 THEORY AND DESIGN OF FERRIMAGNETIC COMPONENTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Example:9.2 page.no:525"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "for total reverse attenuation of 20 db,the length of the slab in cm must be =  2.41935483871\n",
+      "for total reverse attenuation to be at least 27 db, alpha in db/cm be >  11.16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design an e plane resonance isolatorin x band waveguide .\n",
+    "\n",
+    "er =13; revatt =30;\n",
+    "deltaH=200;x=1700; # x=4⇤pi⇤Ms.\n",
+    "f=10*10**9;alpha_=12.4; # from graph 10.13.\n",
+    "L=revatt/alpha_;\n",
+    "alpha_1=27/L;\n",
+    "print \"for total reverse attenuation of 20 db,the length of the slab in cm must be = \",L\n",
+    "print \"for total reverse attenuation to be at least 27 db, alpha in db/cm be > \",alpha_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:9.3 page.no:528"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the slab length required for 30db total reverse attenuation in cm =  15\n",
+      "cut-off wave number in m-1 = 137.428\n",
+      "propagation constant = 158.046\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design a resonance isolator using the H-plane ferrite slab geometry in x-band.\n",
+    "from mpmath import atan\n",
+    "from math import pi,sqrt\n",
+    "\n",
+    "f=10*10**9;delta_sbys=0.01;forpims=1700;deltaH=200;\n",
+    "revatt=30;ko=(2*pi*f)/(3*10**8);\n",
+    "Ho=f/(2.8*10**9);\n",
+    "# for x-band waveguide , a=2.286 cm.\n",
+    "a=2.286;\n",
+    "kc=(pi*100)/a;\n",
+    "betao=sqrt(ko**2-kc**2);\n",
+    "x=(1/pi)*atan(kc/betao); # x=c/a .\n",
+    "L=revatt/2;\n",
+    "print \"the slab length required for 30db total reverse attenuation in cm = \",L\n",
+    "print \"cut-off wave number in m-1 = %.3f\"%kc\n",
+    "print \"propagation constant = %.3f\"%betao"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example:9.4 page.no:532"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the ferrite length required for the 180 deg phase shift section in cm =  3.75\n",
+      "the ferrite length required for the 90 deg phase shift section in cm =  1.875\n"
+     ]
+    }
+   ],
+   "source": [
+    "# program to design a two slab remanent phase shifter .\n",
+    "from math import pi\n",
+    "\n",
+    "forpims=1786;er=13;f=10*10**9;\n",
+    "uo=4*pi*10**-7;ko=(2* pi*f)/(3*10**10);\n",
+    "fm=2.8;s=0.1;# s and a in cm.\n",
+    "x=(2*pi*fm*forpims)/(2*pi*f);# x=wm/w = k/uo. 6 a=2.286; # for x-band.\n",
+    "t=.274; #from figure 10.19;\n",
+    "diffphaseshift=0.4*ko*(180/pi); # differential phase shift .\n",
+    "L_1=180/diffphaseshift;\n",
+    "L_2=90/diffphaseshift;\n",
+    "print \"the ferrite length required for the 180 deg phase shift section in cm = \",L_1\n",
+    "print \"the ferrite length required for the 90 deg phase shift section in cm = \",L_2"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Microwave_engineering__by_D.M.Pozar_/screenshots/chap_1.png b/Microwave_engineering__by_D.M.Pozar_/screenshots/chap_1.png
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diff --git a/Microwave_engineering__by_D.M.Pozar_/screenshots/chap_3.png b/Microwave_engineering__by_D.M.Pozar_/screenshots/chap_3.png
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author	Trupti Kini	2016-07-10 23:30:35 +0600
committer	Trupti Kini	2016-07-10 23:30:35 +0600
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tree	6314cda453043b792b2e40fcbb1cd12146ef18a6 /Microwave_engineering__by_D.M.Pozar_
parent	9c2e3ab9e98db0de17a5def2f9c59635aa462895 (diff)
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