{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Síntesis de redes no disipativas doblemente cargadas\n",
" \n",
"\n",
"#### Por Mariano Llamedo Soria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resumen \n",
"\n",
"En este notebook aplicarán los conceptos de síntesis de inmitancias para la síntesis de un filtro pasabanda cargado tanto en su entrada como la salida. Se presentarán los parámetros scattering necesarios para la síntesis y las de dichos parámetros para redes no disipativas. \n",
"\n",
"Se usarán algunas funciones de PyTC2 para realizar los cálculos y presentar los resultados.\n",
"\n",
"* Funciones de dibujo de redes: [dibujar_puerto_entrada](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_entrada), [dibujar_funcion_exc_abajo](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_funcion_exc_abajo), [dibujar_elemento_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_derivacion), [dibujar_espacio_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_espacio_derivacion), [dibujar_elemento_serie](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_serie)\n",
"* Funciones de síntesis de dipolos: [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [modsq2mod_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.modsq2mod_s), [trim_func_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.trim_func_s)\n",
"* Funciones para presentación de markdown y latex: [print_subtitle](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_subtitle), [print_latex](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_latex), [a_equal_b_latex_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.a_equal_b_latex_s)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introducción\n",
"\n",
"La síntesis de redes eléctricas no disipativas doblemente cargadas es de especial utilidad para el diseño de filtros de alta frecuencia y/o potencia. Este método fue originalmente propuesto por [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) en 1939, permitiendo que un filtro cumpla con una respuesta prescrita de pérdidas por inserción, como las de Butterworth, Chebyshev y Bessel. El gran aporte de Darlington fue relacionar las pérdidas de inserción con las de retorno, y en consecuencia con la impedancia de entrada de la red, siempre que la red sea no disipativa. Con el tiempo el método se adaptó a los **parámetros de dispersión** (S ó scattering), que están íntimamente relacionados con las pérdidas de un filtro.\n",
"\n",
"Los **parámetros S** son útiles en el análisis de redes cuando se tienen cargas en ambos puertos. Estos parámetros relacionan las ondas normalizadas incidentes ($a$) y reflejadas ($b$) en cada plano de referencia (puerto en éste contexto) del cuadripolo:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se plantea el modelo lineal para el cuadripolo \n",
"\n",
"\n",
"$$ b = S \\cdot a $$\n",
"\n",
"o de forma explícita para cada puerto\n",
"\n",
"$$\n",
"\\begin{pmatrix} b_1 \\\\ b_2 \\end{pmatrix} = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} \\begin{pmatrix} a_1 \\\\ a_2 \\end{pmatrix}\n",
"$$\n",
"\n",
"Habrá dos tipos de parámetros:\n",
"\n",
"Un coeficiente de reflexión por cada puerto $x$, denominado $S_{xx}$ mientras el puerto restante está cargado con la resistencia característica $ R_{G1} = R_{G2} = R_0 $. Para un filtro de dos puertos se tiene entonces:\n",
"\n",
" $$\n",
" S_{11} = \\frac{b_1}{a_1} \\Bigg|_{a_2 = 0} = \\frac{Z_1 - R_0}{Z_1 + R_0}\n",
" $$\n",
" $$\n",
" S_{22} = \\frac{b_2}{a_2} \\Bigg|_{a_1 = 0} = \\frac{Z_2 - R_0}{Z_2 + R_0}\n",
" $$\n",
" \n",
"Siendo $Z_x$ la impedancia vista desde el plano $x$ hacia el cuadripolo cuando se conecta en el otro puerto $R_0$.\n",
"\n",
"También habrá un coeficiente de transmisión para modelar el flujo de potencia del puerto $x$ al puerto $y$, denominado $ S_{yx} $. Circuitalmente se modela con un generador $V_{gx}$ con resistencia interna $R_{Gx}$ y una carga $R_{Gy}$ en el puerto $y$. Resulta entonces\n",
"\n",
" $$\n",
" S_{21} = \\frac{b_2}{a_1} \\Bigg|_{a_2 = 0} = \\frac{V_2}{V_{G1}/2} \\cdot \\sqrt{\\frac{R_{G1}}{R_{G2}}}\n",
" $$\n",
" $$\n",
" S_{12} = \\frac{b_1}{a_2} \\Bigg|_{a_1 = 0} = \\frac{V_1}{V_{G2}/2} \\cdot \\sqrt{\\frac{R_{G2}}{R_{G1}}}\n",
" $$\n",
"\n",
"Para un sistema pasivo y no disipativo, se cumple que la matriz de parámetros $S$ es **unitaria**. Esto significa que:\n",
"\n",
"$$ S^H \\cdot S = U $$\n",
"\n",
"siendo $ S^H $ la matriz conjugada y transpuesta, o *Hermitiana*, y $U$ la matriz unitaria. Entonces los parámetros $S$ cumplen que\n",
"\n",
"$$\n",
"S_{11}^* S_{11} + S_{21}^* S_{21} =|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"$$\n",
"|S_{22}|^2 + |S_{12}|^2 = 1\n",
"$$\n",
"$$\n",
"S_{11} S_{12}^* + S_{21} S_{22}^* = 0\n",
"$$\n",
"donde el operador $*$ indica el conjugado complejo. La primera ecuación es de vital importancia, porque indica que toda la potencia disponible en el puerto 1 que no se transmita, necesariamente debe reflejarse al generador ya que el cuadripolo no podrá disiparla. La tercera ecuación también será importante más adelante cuando se generalice el enfoque. Retomando, el parámetro $|S_{11}|^2$ estará condicionado por\n",
"\n",
"$$\n",
"|S_{11}|^2 = 1 - |S_{21}|^2\n",
"$$\n",
"\n",
"que a su vez\n",
"\n",
"$$\n",
"|S_{11}|^2 = S_{11}^* S_{11} = S_{11}(-s) S_{11}(s)\n",
"$$\n",
"\n",
"es decir que podemos hallar el factor $S_{11}(s)$ a partir de una restricción en el ratio de potencias $|S_{21}|^2$. Como ya se anticipó, [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) propuso un método de síntesis muy elegante, en el que a partir de dicha restricción se condiciona la impedancia de entrada al cuadripolo, sabiendo que\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{ Z_1 - R_0}{ Z_1 + R_0}\n",
"$$\n",
"\n",
"Notar que el signo de $S_{11}$ es un grado de libertad con el que se cuenta a la hora de sintetizar. Entonces la impedancia en el puerto de entrada será, dependiendo la elección del signo de $S_{11}$\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}}\n",
"$$\n",
"\n",
"Cabe destacar que si bien $Z_1$ contempla la impedancia vista hacia un cuadripolo no disipativo, debe recordarse que la condición de medición de $S_{11}$ asume que el puerto 2 esté cargado con $R_{G2} = R_0$. Por este motivo, $Z_1$ será una impedancia RLC **muy particular**, dado que estará compuesta por una inmitancia no disipativa seguida de la carga $R_0$ al final. Esto permite que todos los métodos de síntesis vistos hasta el momento sean aplicables para diseñar el cuadripolo no disipativo, con la particularidad de que el último componente será $R_0$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Un enfoque alternativo\n",
"\n",
"A esta altura está claro que los parámetros S de un filtro no disipativo cargado en ambos extremos está condicionado por la ecuación de complementaridad\n",
"\n",
"$$\n",
"|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"\n",
"De la misma podemos expresar, siguiendo el enfoque propuesto por *Cameron et. al.*\n",
"\n",
"$$ S_{21} = \\frac{P(s)}{\\epsilon \\cdot E(s)} $$\n",
"\n",
"donde $\\epsilon$ será un factor de escala para que ambos polinomios $P$ y $E$ sean *mónicos*. Entonces, simplificando la dependencia de $s$\n",
"\n",
"$$ |S_{21}|^2 = \\frac{P . P^*}{\\epsilon^2 \\cdot (E . E^*)} $$\n",
"\n",
"y en consecuencia\n",
"\n",
"$$\n",
"|S_{11}|^2 = \\frac{\\epsilon^2 \\cdot (E . E^*) - P . P^*}{E . E^*} = \\frac{F . F^*}{\\epsilon_R^2 \\cdot (E . E^*)} \n",
"$$\n",
"\n",
"es decir que del mismo modo que con $ S_{21}$, también se podrá expresar\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{F(s)}{\\epsilon_R \\cdot E(s)} \n",
"$$\n",
"\n",
"y es claro que todos los parámetros compartirán los mismos polos definidos por $E(s)$. Es decir que la matriz S se puede expresar como\n",
"\n",
"$$\n",
"S = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} = \\frac{1}{E(s)} \\begin{pmatrix} \\frac{F(s)}{\\epsilon_R} & \\frac{P(s)}{\\epsilon} \\\\ \\frac{P(s)}{\\epsilon} & \\frac{F(s)}{\\epsilon_R} \\end{pmatrix} \n",
"$$\n",
"\n",
"Se simplifican algunos detalles para facilitar el ejemplo que se presentará a continuación (Ver *Cogollos* cap 2. ó *Cameron et. al.* cap. 6 para un tratamiento general).\n",
"\n",
"Finalmente podemos expresar la impedancia de entrada al filtro como\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}} = R_0 \\cdot \\frac{\\epsilon_R.E \\pm F}{\\epsilon_R.E \\mp F}\n",
"$$\n",
"\n",
"Resta por aclarar cómo se calculan las constantes $\\epsilon$ y $\\epsilon_R$, que permitirán fijar las pérdidas de transmisión y retorno en frecuencias señaladas dependiendo de las características del filtro, pero eso lo dejaremos para más adelante por ahora.\n",
"\n",
"### Referencias\n",
"\n",
"1. [Richard J. Cameron, Chandra M. Kudsia, Raafat R. Mansour. *Microwave Filters for Communication Systems Fundamentals, Design and Applications. (2007). John Wiley & Sons](https://drive.google.com/file/d/1Fdr5WT8e7g9W5j4YM5Xd0-Za1BhrlQQ_/view?usp=drive_link)\n",
"2. [Cogollos Borrás, Santiago. *Fundamentos de la Teoría de Filtros*. (2016). Editorial de la Universitat Politècnica de València.](https://drive.google.com/file/d/1g4pgNS1m9dF43ZyR6LdkE-HAntfswhwh/view?usp=drive_link)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import sympy as sp\n",
"import numpy as np\n",
"\n",
"# Ahora importamos las funciones de PyTC2\n",
"\n",
"from pytc2.remociones import remover_polo_dc, remover_polo_infinito, modsq2mod_s, trim_func_s\n",
"\n",
"from pytc2.general import print_latex, print_subtitle, a_equal_b_latex_s\n",
"from IPython.display import display, Markdown\n",
"\n",
"# Importante importar símbolos de variables \n",
"from pytc2.general import s, w, simplify_n_monic\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ejemplo 1: Pasabanda Butterworth\n",
"\n",
"Implemente un filtro pasabanda Butterworth de 4º orden con un $Q = 5$.\n",
"\n",
"Aprovechando que este método nos permite un grado de detalle tan preciso para establecer la función transferencia $S_{21}$, se procede a hallar la función transferencia que satisface dicho requerimiento. Para ello se retomamará el análisis que hicimos en el notebook [núcleo de transformación pasabanda](ejnb_transformacion_pasabanda.ipynb). En resumen, a partir de una transferencia pasabajos *Butterworth* de segundo orden, se llega a \n",
"\n",
"$$ T_{bp}(s) = \\frac{s^2 \\cdot 0.04}{s^4 + s^3 \\cdot 0.2828+s^2 \\cdot 2.04+ s \\cdot 0.2828 + 1} $$ \n",
"\n",
"luego simplemente se asume que $ S_{21} = T_{bp} $. Debido a la complejidad de la expresión, el ejercicio demandará cierta destreza algebraica que puede suplirse gracias a las capacidades simbólicas de PyTC2 y SymPy.\n",
"\n",
"Comenzamos buscando la expresión de $Z_1$ a partir de $S_{11}$, no sin antes definir $S_{21}$."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}=\\frac{s^{2}}{25 \\left(s^{4} + \\frac{\\sqrt{2} s^{3}}{5} + \\frac{51 s^{2}}{25} + \\frac{\\sqrt{2} s}{5} + 1\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Corroboramos ganancia $\\le 1$ el centro de la banda de paso"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}(\\omega = 1)=1.0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Resolución simbólica\n",
"\n",
"# Q de la transformación\n",
"Q_bp = sp.Rational(5)\n",
"Q_lp = sp.sqrt(2)/2\n",
"\n",
"# frecuencias de corte del pasabanda\n",
"w1, w2 = np.sort(np.abs(np.roots([1, -1/Q_bp.evalf(), -1])))\n",
"\n",
"# nucleo de transformación pasabanda\n",
"Kbp = Q_bp * (s**2 + 1) / s\n",
"\n",
"\n",
"T2proto = 1/(s**2 + s * 1/Q_lp + 1)\n",
"\n",
"H2bp = sp.simplify(sp.expand(T2proto.subs(s, Kbp)))\n",
"\n",
"S21 = H2bp\n",
"\n",
"ks21, Ps, Es = simplify_n_monic(S21)\n",
"\n",
"S21 = ks21 * Ps / Es\n",
"\n",
"print_latex(a_equal_b_latex_s('S_{21}', S21))\n",
"\n",
"print_subtitle('Corroboramos ganancia $\\le 1$ el centro de la banda de paso')\n",
"print_latex(a_equal_b_latex_s('S_{21}(\\omega = 1)', sp.simplify(sp.expand(sp.Abs(S21.subs(s, sp.I*1)))).evalf(4) ))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Luego se procede a hallar el parámetro $S_{11}$, que será su complementario en potencia."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def plt_Sparams(S21sq, S11sq, bDb = True ):\n",
" \n",
" # Convertir las funciones simbólicas a funciones numéricas para evaluar\n",
" mod_S21_squared_func = sp.lambdify(w, S21sq.subs(s, sp.I*w), modules='numpy')\n",
" mod_S11_squared_func = sp.lambdify(w, S11sq.subs(s, sp.I*w), modules='numpy')\n",
" \n",
" # Crear un rango de frecuencias para graficar\n",
" frequencies = np.logspace(-1, 1, 1000)\n",
" \n",
" # Evaluar las funciones en el rango\n",
" S21_vals = mod_S21_squared_func(frequencies)\n",
" S11_vals = mod_S11_squared_func(frequencies)\n",
" \n",
" if bDb:\n",
" # Convertir a decibelios (10 * log10(valor))\n",
" S21_dB = 10 * np.log10(S21_vals)\n",
" S11_dB = 10 * np.log10(S11_vals)\n",
" s21_lbl = r'$|S_{21}|^2$ (dB)'\n",
" s11_lbl = r'$|S_{11}|^2$ (dB)'\n",
" ylbl = 'Módulo al cuadrado [dB]'\n",
" \n",
" else:\n",
" S21_dB = S21_vals\n",
" S11_dB = S11_vals\n",
" s21_lbl = r'$|S_{21}|^2$'\n",
" s11_lbl = r'$|S_{11}|^2$'\n",
" ylbl = 'Módulo al cuadrado'\n",
" \n",
" \n",
" # Graficar en escala log-log\n",
" plt.figure(1, figsize=(8, 5))\n",
" plt.semilogx(frequencies, S21_dB, label=s21_lbl, color='blue')\n",
" plt.semilogx(frequencies, S11_dB, label=s11_lbl, color='red', linestyle='--')\n",
" plt.xlabel('Frecuencia (f) [Hz]')\n",
" plt.ylabel(ylbl)\n",
" plt.title(r'Complementariedad de $|S_{21}|^2$ y $|S_{11}|^2$')\n",
" plt.legend()\n",
" plt.grid(which='both', linestyle='--', linewidth=0.5)\n",
"\n",
"\n",
"S21sq = sp.simplify(sp.expand(S21 * S21.subs(s, -s)))\n",
"\n",
"S11sq = sp.simplify(sp.expand(1 - S21sq))\n",
"\n",
"plt_Sparams(S21sq, S11sq, bDb = True)\n",
"\n",
"plt.plot( [w1, w1], [-40, 3], 'k--', label = 'banda de paso' )\n",
"plt.plot( [w2, w2], [-40, 3], 'k--' )\n",
"plt.fill([w1, w1, w2, w2], [-40, -20, -20, -40], 'lightgrey', alpha=0.4, lw=1, ls='--', ec='k', label = '$R_L$ objetivo')\n",
"\n",
"plt.ylim([-40, 3])\n",
"plt.xlim([.6, 2])\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Es interesante notar que $|S_{11}| \\leq$ 20 dB solo en una pequeña parte de la banda pasante del filtro. Es decir que podemos considerar al filtro **adaptado al generador y carga** sólo en una pequeña parte del ancho de banda. Recordar que las pérdidas por retorno ($R_L$) están estrechamente relacionadas al $|S_{11}|$ son inferiores a\n",
"\n",
"$$\n",
"R_L = -20.\\log|S_{11}|\n",
"$$\n",
"\n",
"mientras que las pérdidas por insersión $I_L$\n",
"\n",
"$$\n",
"I_L = -20.\\log|S_{21}|\n",
"$$\n",
"\n",
"También se aprecia la complementaridad de ambos parámetros $S_{21}$ y $S_{11}$, aunque es conveniente visualizarlos en unidades lineales para poder tener certeza que suman 1 a cualquier frecuencia."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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UcYTPP/+c/Px8Jk+e7LRjNKe1c9WjHIRwJzLoTwghhMs6cOAAb7/9NuvXr6dr164EBQURFBTEjz/+CKhxAf7+/rz99ts89dRT+Pv7O63vb1paGm+99RazZ89m+PDhjB071inHaUlr59qR5SCEO5IWZiGEEC7LshpdS+bNm9dhXQxef/11PvzwQ4YNG8Z7773XIcdsrLVz7chyEMIdSR9mIYQQQgghWiFdMoQQQgghhGiFJMxCCCGEEEK0QhJmIYQQQgghWtHpBv2ZzWYOHz5McHAwJpNJ73CEEEIIIcRpNE3j5MmT9OzZEw8P/dt3O13CfPjwYaKjo/UOQwghhBBCnEVeXh69e/fWO4zOlzAHBwcDqgJCQkJ0jsY1paenk5qaqncYhtEZy8Ndz9ldz0sIIVxNaWkp0dHR1rxNb50uYbZ0wwgJCZGEuY169uwpZddIZywPdz1ndz0vIYRwVUbpPqt/pxDhcvr06aN3CIbSGcvDXc/ZXc9LCCFE+0jCLOy2bds2vUMwlM5YHu56zu56XkIIIdpHEmYhhBBCCCFaIQmzsFtMTIzeIRhKZywPdz1ndz0vIYQQ7SMJs7BbXV2d3iEYSmcsD3c9Z3c9LyGEEO0jCbOw28GDB/UOwVA6Y3m46zm763kJIYRoH0mYhRBCCCGEaIVJ0zRN7yA6UmlpKaGhoZSUlMh8q21UW1uLt7e33mEYRmcsD3c9Z3c9LyGEcDVGy9ekhVnYbdeuXXqHYCidsTzc9Zzd9byEEEK0jyTMwm4VFRV6h2AonbE83PWc3fW8hBBCtI+uCfP//vc/Jk6cSM+ePTGZTHz++ednfc+aNWtISUnB19eX+Ph43nvvPafHKZoKCgrSOwRD6Yzl4a7n7K7nJYQQon10TZjLy8sZOnQor776qk3bZ2dnc/nll/Ob3/yGjIwMZs2axR133ME333zj5EhFY3FxcXqHYCidsTzc9Zzd9byEEEK0j64J82WXXcaTTz7J1VdfbdP2ixYtIjY2lueff56kpCRmzJjBddddx4svvujkSEVjW7Zs0TsEQ+mM5eGu5+yu5yWEEKJ9vPQOwB7r1q3j4osvbvLa+PHjmTVrlj4BCSFcgqbBoUOQlwcnTkBJCdTXg9nc8Gg2w7593UhPBw8P8PICT0/14+sLERHQtSvExUFoqN5nJIQwhNO/RE7/CQwEHx+1bWUlFBer55rW8GP5d0SE2h6gvByOHTtzG8tPVBSEhanXy8rgwIGmv2/8np49oVu3hm137Wp52+ho6N27YdtNm848tuWnb1+Ij2+I96efGsrl9AnY+vaFpCT1vKoKvv++5W2jo2HoUFtKv0O5VMJ89OhRoqKimrwWFRVFaWkplZWV+Pv7n/Ge6upqqqurrf8uLS09c8cTJ6qKtvx19PBoeAwMhJ07G7a991748cfmt/X0hLVrwWRS2z79dMN+Lb9v/Pzdd8HPT2370UeQng7e3s3/3HVXw3+kDRtg796Wtx0xomG/RUXqP6mfX8OPR/tuLPTp06dd73c3nbE8XOGcDxyAZcvgP/+BLVtUknx2sTbtu08fGD0arrsOLr+84b+mEG7LbFZ/2yx/306cgNJSqKlp/uc3v1FXnQA//6z+jlp+V1fX8FNfD7NnQ3Cw2nb5cli9Wr1u+X3jbV98EXr0UNu+9x58/HHT/TV+z5IlkJiotl20CF54oeXE9vPPYdSohm3nzDlzG01TjytXwiWXqG3ffhv++MeWy235crDcRf/sM/jDH1re9oMP4JZb1PNVq+Cqq1redtGihuOuWweXXtryti+8AA88oJ5v3Qrnndfytk88AX/9q3q+bx+MHdvytnPmwPz56vnhw/C737W87f33w8KF6nlREVxxRcvbTpumytVgXCphbov58+fz+OOPn/F6eno6gYGBpKSkUH34MEGWq77TmAMC2JCWBsCwYcOo2bqV4G3bWjxe2vr1AAwZMoTatWsJ+fbbFrfd8Mc/YvbzIzk5mbqlSwn94osWt904cCB1YWEkJSVhfuklwj7+uMVtNy9fTk3PngwYMAD+8hfC3nqr6Tl5eaH5+WHy92fbyy9TFRNDQkIC5rfewmfJEsw+PoRFRVFUWUmtnx/eYWEERkWRecEF1ERFERYWhrZnD6WbNlHv709iaipZR45Q4eFBUPfuRA8YwLbt2wGIiYmhrq7OuoJaSkoKu3btoqKigqCgIOLi4qy3wS1JWG5uLgBDhw5l3759lJWVERAQQGJiIptOXe327t0bLy8vcnJyABg8eDC5ubmUlJTgd6pM09PTAejRowcBAQHs27cPgEGDBnH48GGKiorw9vYmJSWFtFN1HBUVRUhICHv27AEgKSmJ48ePc+LECTw9PUlNTWXDhg2YzWYiIyOJiIggKyuL3Nxc+vfvT1FREfn5+ZhMJkaNGsXGjRupq6sjIiKCqKgodp66+IqPj6esrIyjR48CMGrUKDIyMqipqSEsLIzevXuz/VQZ9uvXj6qqKg4fPgzAiBEjyMzMpKqqipCQEGJiYti6dSsAffv2pb6+3lrew4cPJysri/LycoKCgoiPjycjIwOA6OhoPDw8OHDgAKA+s9nZ2Zw8eRJ/f3+SkpKs5d2rVy98fHzIzs62ltPmzZtJSUkB1IXojh07AOjevTuBgYHW8h44cCBHjx6lsLDwjPLu1q0boaGh1vJOTEykoKCAgoICPDw8GDlypLW8u3btSteuXa1TvyUkJFBSUsLx48cBGD16NJs2bWLfPk/eeCOO779vOoDP01OjW7dqQkPr6NUrkMrKk0A9Pj7eBAX5cfJkKZpWT2hoMHV1ZsrKqjGbISgolBMnKigq8qC42IcTJ7zIzYXcXFi6FAICzNx002FuueUIY8YMYe/evZSVlREYGEj//v3ZvHmz9TPr6enZpLxzcnIoLS3Fz8+PQYMGsXHjRgB69uyJn58f+/fvByA5OZmDBw9SXFyMj48Pw4YNY/2p75ru3bsTFBTE3r17rZ/Z7Oxshg0bBkBZWRmZmZlomkZkZCTh4eFkZWUBMGDAAAoLC8nPz7eWd3p6OvX19XTp0oVu3bpZP7MJCQmUlpZy7NixJuVdW1tLeHg4PXv2JDMzE1B9wSsqKjhy5AgAqampbN++naqqKkJDQ+nTpw/bTn2PdobviN27dwM49zuiooKj+/bhWVXFkEsvtX5HRGVl0bO6msO7d+NRUUGEnx9aeTnlJ05gqqsjfMkS63dEv7feosvmzVQXFmKqrsa7thZTVRVUVuJRX09lcTHZhw5x8uRJ+v/974SvXElLKvLyyCsvp7i4mLgXX6Trp5+2uG3ZDTdwxNOTwsJCYpYvJ+qjj1rc9uSf/sTxigoKCgqI/vFHen73XYvblhw5QqGvL8ePH6fn1q1En6qz5hQfO0bJgQOqYS4ri5hWrrCLCwspO3iQQ4cOEZmTQ78Wt4SiEyeoPHyYvLw8IvbvJ97Do+Gi49QFiKV9tbi4mJqjRzlw4ABh+/bRPzAQs6X11WTCw9OTerMZgNKyMurz89m/fz/Be/eS1KUL9ZqGpmlgMuHl7U1tXR2YTJysqoITJ9i7dy8Be/YwKDqa+vp6zGYzeHjg6+tLVU0NAGX19XgWFZGVlYVfTg7JCQnUm83U19WBhwf+/v5UVlWhmUyUe3jgW1rKzp078Tl2jMGDB1NvNlNXWwtAYFAQFRUVaJpGha8v/qe+i7yKixkybBhms5na2lowmQgMCKCyshKzplEZFERgRYX1/6dRGGbhEpPJxGeffcZVrVxRXXjhhaSkpPDSSy9ZX/vnP//JrFmzKGnhw91cC3N0dHTTibAPHlS3E06/R1tfr36fmtqww4wMKChofluzGa69tmHb1ashJ6fh96dvO2NGwxX40qWwcSPU1qor49rapj9vvtnQjPXSS/Dll2duY/n58Ufo3l1t++c/q6u6urrmC3XPnoZbKo880nC12JxNm2D4cNLS0hj9ww9q++aYTOrcLVemK1bA+++r203h4eqx8fOhQxtuLbmgtLQ0Ro8erXcYHSotLY3k5GTrrBKWBFFPtbWqYeTFF9VzkwkuvBAmTVKP/furrhWtsaUui4pg2zb46iv497/hVE5Lr17w2mtw5ZUOOqF2sFwggTHqRtihqkrdii8ogMJC1ZJbUwOTJzds8+c/wy+/qA9jaSmcPKlun2saBAWpf1tcdhl8/XXLx6uvb7jreP316m9RS06eVPsHuPNO1brr49Pw4+vb8Pz776FLF7Xt66+rGHx8Gu6EWvo8eXmpVk3LtitXqnPz8mr4sWzn5aVaaCMi1LYZGZCZ2XSbxo/nnAOWv/N5eeoq18Oj+Z+4uIZzKyqC/PyWt42IaLiLW12t7uK2tK2nZ0OCLOxitIVLXCph/stf/sLKlSutLRMAN998M4WFhXzd2hdCI0argA5TV6e+iBv/VFY2zSIyM2HHjqa/Ly9XX8RlZfDww9C9u0oqtmxRt4Qsv7N8YVts3gynWriYP7/l5Brghx/U7TtQt9CefVYl/D16NH3s2ROSkxtu3RmEJMz6J2Xl5epvvaXBa8IEeO65hi5ztrK3LjUNPv0UHnxQXRubTPDyy6rnlp4kYTag6mqVtB04AEePqu/YadMafj9pkkoyi4rOfG9wsEqMLVpLgr281LEsSfDf/gbr16vEMSgIAgLA37/h8cEHGxpuNmxQiWLj31seLR3529mlTwhbGS1f0zVhLisrs95GHD58OC+88AK/+c1viIiIoE+fPjz88MMcOnSI999/H1DTyiUnJ3Pvvfdy++2388MPP3D//fezYsUKxo8fb9MxjVYBrqiqqgo/y9V1Y2azSrJPnlStBZYlhjdtUn25CwvVH4PCwqbPP/0UBg1S2z75pPqCb8k33zT01Vq1SrWGxMRAbCwMGKB+AgIcer5n02J5uLGqqirq6+sNkZSdOKH6Eaelqb/rH3zQ9EaPPdpal1VVMGsWvPGG+vcjj6iPsl4NS5Iw60DTVFLbeEToo4+qcS05OaqPZ+M/t6cnwZdf3nDF5+OjRph26aKS1C5d1HedJVldvVoNHgsPV8cLDlY/ISGq5VNaNIUbMFq+pmsf5vT0dH5jaVkEZs+eDcCUKVN47733OHLkiLWvGkBsbCwrVqzggQceYOHChfTu3Zu3337b5mRZOMa+ffsYZElwG7MMkjz9j3NKivqxxa23wvDhcOSIaoU5erTh+cGDKjG2+OUX1VWlMZOpYTTuggUNifipfl3O0GJ5uLF9+/YRExOjdxjU18ONN6pkOSIC/vtfGDOm7ftra136+am7zr17q+u9p59WAwNbGwskXJRlypWtW9VAtp071awDO3eq35040fBds3WrGvBm4e+vvp969lR3zerqGlp3n39efWd1764S4da+rxr93RRCdAxdE+Zx48bRWgN3c6v4jRs3zjqIRuijrHHXC0fr21f92OKii1TGdOCAmjVk1y71xyonR/006uvOc8+pUbeW5H3ECDUi2gEruzm1PAzKKOf85JPqRkNAgGp0GzKkfftrz3mZTKoPtZeX6r00c6b6iA0f3r6YhM6OH2+YkgvgmmvUjArNMZnUd1DXrurfM2eqRoCYGPUTGdlyImyZzUEIYUhuP0uGcLyADu7y0KLzz1c/jeXnN7T29Gs0dnnjRsjKUj+ffKJe8/RU/azPP181C1oGndjJMOXRgYxwzqtWgWUCnEWL2p8sg2PO66GH1M2PL79U3VI3bpR5m12GpqlxHD/+qLqR/fyzuiAvKmqoxLg49d0xYAAMHKjuZll++vdv2iXstHUDhBCuyzCD/jqK0frEuKLa2lq8Lf2TXUV+vhqIuGmTymDWr1cjpkH1FywpaRj1/PXX6vm55zZMON8KlyyPdqqtraW+vp5rT3UW/vTTTzu0H3dFhcpNDh2CO+6A02ZObDNH1WVhobqRceCA6paxaJEDgrNDVVWVbnXjkn78Uc2L/+23qq9xYx4eqh+y5eK8qEh9PzQz778QwnGMlq9Jwizs5jazQuTlqRakvDw1Utxi+HA1XVFQkJqIfdKkVlencJvysIPe52yZeKVvX3UzwVG5iyPPa+1aGDdONUZu3y533A0lN1e1GFtajV9/HaZPV8/9/dXFsuUO1ujRhpuZR4jOwGj5mswPIzqv6Gg1YqxxslxXB4MHqz6LZWVqqbgbblB9DydNUis1CV0VFMAzz6jnTz1l3Ia+sWPVIqL19apPs9DZ4cNqxbMxY9SV1r/+1fC7CRPU3MbffaduD6xaBfPmqS4VkiwLIZA+zKINelvWmXdHXl5qkRWzWXXfWLZMTee0f796bjI1LHN6iluXRwv0POcnn1SzcQ0fDjfd5Nh9O/q8nnlGrdvz+efqZkZrK9IKJzCbVTeLN95Qncoti1GZTGrRJou+fdUMFUII0QJpYRZ28/LqBNdZHh5qhcdnnlEzcKSnw1/+oka9W+zeDRddRODKlQ1/iDsJLy8vysvLCQwMJDAwkPLy8g457oEDajU9UOvbOHoNBUd/tgcOhNtvV8/nzHHorlulR90YTmWlGph32WXqiqW+Xl2xvPKK6vz+/PN6RyiEcCGSMAu75eTk6B1CxzKZ1DR0zzzTtInwzTdh9WrC7rpLZUbvv9/yEuRuxvIZqKiooKKiosOO+/rratnriy5yzgQEzvhsz5un1vD56Sc17rSjdHTdGELj8/X3VyNDw8Lg/vvVSqY//aSWYezRQ7cQhRCuSRJmIdpq5kz461+pDQlR09VNmaJatN55B2pq9I7O7VRXq4kMAO67T99Y7NGrV8PKg6+/rm8sbuvIEfjTn1RhN1rsikWLVGvywoXqolYIIdpIEmZht8GDB+sdgjH06QNPPEFtVhb84x9qYOD+/Wqes5QUt25t1uMzsHy5mh2wVy+44grnHMNZ53X33erx44/VDIbCQcrK1Bzq/fqpAX3FxaqQLaKjm86LLIQQbSQJs7Bb4+XKBeQWFanVKrKz1R/t7t3VamBu3Ndbj8+AZS7jO+90XtE667wuvFA1cJaXw4cfOuUQnYumwUcfqS4XTz4JVVVq9ouvv1ZjDYQQwsEkYRZ2K5Emsias5REYCA88oEbfNx7htWGDWjO5slKfAJ2goz8DmZnwv/+pOY3vuMN5x3HWeZlMDa3Mr7+u8j3RRmazmq/vlltUV4y4ODXd488/w/jxLS89LYQQ7SAJs7CbrBrW1BnlERTUcBu4vh7uuktNGDxihJqqzg109GfAspLflVeqLhnO4szzuvVW9bHIzIR165x2GPfn4QFDh4Kvr/p/lZkJV10libIQwqkkYRZ2S05O1jsEQ2m1PDw8VB/L7t3VknSjR6vZNszmjgvQCZKTk/Hw8GDs2LGMHTsWD0fP79aI2aymwIaGKdqcxZmf7bAw1VMHGs7HWTqqbjrM8eOwb1/Dv//6V7V84iOPqMRZCCGczA2+SUVHS09P1zsEQ2m1PEwmlSVt26Ye6+rUsm8TJ6oVxVxUeno6/v7+rFmzhjVr1uDvxOX20tPVRAfBwc6ZSq7psZz72bbMlrF8uXO7ZXRU3XSIdevUINobbmiYfcbfH+Lj9Y1LCNGpSMIsREfo2lU1K779Nvj5wcqVqotGdrbekRne8uXqccIEVXSu7NJLVbeMAwc6dk5ml7VokVpj/NAhNSPG8eN6RySE6KQkYRZ26yGT/jdhc3mYTDBtGvzyi5oGq18/53bIdaKO+gxoGnz6qXpu6c7gTM4+r4AAtfAcNFwIiGaYzWpe5XvuUSvVXHedGjzbCZehF0IYgyTMwm4BMq9pE3aXx/Dhqp/Bp5+Cj49zgnKygIAAysvLiYyMJDIy0mnLL2dmqpXJfX0bEk1n6ojPtiXx/+wz5x2jI+rGaaqqYNIkNUUjqGnj/v1v1SdHCCF0IgmzsNu+xoNvRNvKIzxcjQID1Yz60EPw0kuODMupLOdcUFBAQUGB045jaYW99NKOyZc64rN9+eVqqewdO2DXLucdx9l14zQzZqiK9/FRi5A8+qjMgCGE0J0kzELobdUqWLBAzeE8f77e0RiKJWHuiO4YHSU0FH77W/VcumU049FHITFRLUJy0016RyOEEIAkzKINBg0apHcIhtLu8rj4Ypg3Tz1/5BF47rl2x+RsHfEZOHQItmxRjYsTJzr9cEDHfbavvlo9fvVVhxzOtcTGqinjfvMbvSMRQggrSZiF3Q4fPqx3CIbS7vIwmWDuXLUIA8CDD6rZNAysIz4Dq1erxxEjoEsXpx8O6LjP9iWXqMdff1XLZXdqlkF9X37Z8Jqnp37xCCFEMyRhFnYrKirSOwRDcVh5PPKI6ssManVAA9+v74jPwPffq8eLLnL6oaw66rMdGwsxMWpa7h9/7JBDGpOmwdSpagDsH/7g0nOTCyHcmyTMwm7e3t56h2AoDi2PZ55RybKmqbWUjxxx3L4dyNmfAU2DH35Qzy39fTtCR362LRcClvPslJ54Aj76CLy84JNPICJC74iEEKJZXnoHIFxPSkqK3iEYikPLw2SCV19VCzRccw0YdM7rlJQUKisrSU1NBXD48sv790NurppN4rzzHLrrVnXkZ/u3v4V3321oSXckDw8Pp9WNwyxdqroigVqgZMIEfeMRQohWGPSbVBhZWlqa3iEYisPLw8tLdce49VbH7teB0tLS8Pf3Z8OGDWzYsMHhyy9bWl3POQcCAx2661Z15GfbMqZt82bH90RwZt04REYGTJminj/wgFrQRwghDEwSZiGMqPG8s8ePw1tv6ReLDiytrh3ZHaOj9egBSUmq+8natXpH04FOnoTrr4fKSrUazYIFekckhBBnJQmzsFtUVJTeIRiKU8ujpEStDHjXXc5dGs5Ozjznxv2XO3LAH3T8Z9tyfs7olmFY3t5qmpDoaPjwQ5kRQwjhEiRhFnYLCQnROwRDcWp5hIbCzTer57ffDgcOOO9YdggJCaGiooKYmBhiYmKoqKhw2L4zMyE/HwICYPRoh+3WJh392ba0oDt64J+z6sYh/PxUP/0tW2SQnxDCZUjCLOy2Z88evUMwFKeXx9NPq8yxuFj19dQ05x7PBnv27EHTNA4cOMCBAwfQHBiTpXvC+eer1ZE7Ukd/tseOVb1vdu5UPW8cxVl10y7FxWA2N/w7PFy3UIQQwl6SMAthdN7e8MEH4O+v7t2/8YbeETnVr7+qx3PP1TeOjhARofoxA7j1WFqzGa69Vq1qaZC7JEIIYQ9JmIXdkix/4QXQQeWRkKBamkGtBJiT4/xjtsKZ52xJHM85x2mHaJEen21LtxO3TpjffFP1O/n1V7WynxBCuBhJmIXdjjvy3rEb6LDyuP9+1U+hrAwefrhjjtkCZ53ziRNg6RUxapRTDtEqPT7blgsDS8u62zl0SF3kgVqYJz5e33iEEKINJGEWdjtx4oTeIRhKh5WHh4da6WLmTHj99Y45Zgucdc6WVtYBA/Tp4qrHZ9vSwrx+PdTXd/jhne/Pf1YXeWPGwIwZekcjhBBtIgmzsJunTAPVRIeWR0ICvPQShIV13DGb4axz1rM7Bujz2R40SC3OcvIk7NrV4Yd3rjVr1JLXHh7w2mvqUQghXJB8ewm7WZbcFYpu5aFpag42HaSmpmIymRg4cCADBw7E1HihlXawdEvo6OnkLPSoSy8vGDlSPXdUtwxn1I3damsbWpTvvhuGDdMnDiGEcABJmIXdNmzYoHcIhqJLeZSWqkl8R4zQZdaBDRs2EBAQQGZmJpmZmQQEBLR7n2az/i3Men22LRcIjkqYHV03bZKbC3V10LUrPPGEPjEIIYSDSMIs7GZuPJeq0Kc8goNVC3N1NTz+eIcf3hnnnJWlFjb094fBgx2+e5vo9dm2XCC41UwZcXGwfTv8+KMsUCKEcHmSMAu7RUZG6h2CoehSHiaTmnEAYPFi2LGjQw/vjHO2tK6mpqpuCnrQ67NtaWHevl31ZXYbXl6QmKh3FEII0W6SMAu7RUhrURO6lcfo0XD11aovw6OPduihIyIiqKioYNCgQQwaNMghyy/r3X8Z9KvLHj2gTx9108ARvUIcXTd2KSiAhQuhqqpjjyuEEE4kCbOw2+7du/UOwVB0LY+nnlIzD3z+eYdO5Lt79240TWPHjh3s2LHDIcsvWxJFPRNmPevSct6OSJgdXTd2mT8fZs2C66/v2OMKIYQTScIshCtLSoIpU9RzFx5YVVuruiMApKToG4teLOedkaFrGO2Tn98wR/j06frGIoQQDiQJs7Bb//799Q7BUHQvj0ceUa3Me/d2WAdYR5/zrl1QUwMhIRAT49Bd20XPurTMuubSCfPChVBZqTqijx+vdzRCCOEwkjALuxUVFekdgqHoXh7x8Womgh071OwZHcDR52xJEocO1XdtCz3r0pIw794N5eW6hdF2paXwyivq+SOPqIGpQgjhJiRhFnbLz8/XOwRDMUR5nHsudOAqdY4+Z0vCrPfaFnrWZffuEBWlBv5Zuqe4lNdfV/MCJiXB73+vdzRCCOFQkjALu+m2cphBGao8qqpg7VqnH8bR52yUhFnvunTZbhmVlfDii+r5X/4iS2ALIdyOTrOdClc2atQovUMwFMOUx7FjMHw4FBbCwYNqhTUnGTVqFBUVFfTt2xdoX6KpacZJmPWuy2HD4Jtv2p8wm0wmh9SNzU6cUP2WMzPh5pudfzwhhOhg0gwg7LZx40a9QzAUw5RHt27Qs6da/e+dd5x6qI0bNxIQEEBOTg45OTntWn754EGV43t5waBBDgyyDfSuy+HD1WN7E2ZH1Y3NeveG//5X9SXx9nb+8YQQooNJwizsVldXp3cIhmKY8jCZYMYM9fy116C+3mmHcuQ5W5LDgQPB19dhu20TvevS0sK+datTq895AgP1jkAIIZxCEmZhN1nprylDlccNN0CXLpCbq1r8nMSR57x5s3rUuzsG6F+X8fEQEAAVFbBnj66h2O6TT9TnTQgh3JgkzMJuUVFReodgKIYqD39/uOMO9fzll512mKioKCorKxk5ciQjR46ksrKyzfsySv9l0L8uPT1hyBD1vD3dMhxVN2d15AjceivExsL+/c47jhBC6EwSZmG3nTt36h2CoRiuPO65R81S8P33akUQJ9i5cydms5n09HTS09Mxm81t3peREmYj1KUjZspwVN2c1dtvQ12dWte7Xz/nHUcIIXQmCbMQ7qZvX7j8cvX822/1jeUsSkogO1s9HzpU31iMwpIwW7qqGJbZDP/8p3p+zz36xiKEEE4mCbOwW3x8vN4hGIohy+Opp9QUX/ff75TdO+qcLQt09O4NRugKboS6tHTJMPziJf/7n7raCQ6Ga6/VOxohhHAqSZiF3crKyvQOwVAMWR6DB6tpJ5zEUeecmake9Z5OzsIIdWmptsOHobhY11Ba99576vGGG9RIRSGEcGOSMAu7HT16VO8QDMXw5eGEQV+OOmejJcxGqMvQUNXiDg3lYzgnT8LSper51Kn6xiKEEB1AEmYh3FVlJdx4I3TvrlZiM6AdO9SjURJmo7CUh6V8DCcjQ8373b8/jBmjdzRCCOF0kjALu+m9fLDRGLY8/P1h924oLYV//cuhu7acc9euXenajiW4jdbCbJS6tJRHe1qY21s3rbrgAjWl3LJlKnEWQgg3JwmzsFtGe9ftdTOGLo8pU9Tjxx87dLcZGRkEBgaSn59Pfn4+gW1Y4a2oSOVc4NTu1nYxSl22N2Fub93YJDhY9ZUXQohOQBJmYbeamhq9QzAUQ5fHpEmqBXDdOoeuxuaIc7Ykg336qNzLCIxSl45oYXaakyf1jkAIITqc7gnzq6++SkxMDH5+fowePZr169e3uv1LL73EgAED8Pf3Jzo6mgceeICqqqoOilYAhIWF6R2CoRi6PHr1gvPPV8+XLXPYbh1xzkbrjgHGqUtLi/uRI6ol3lAuuUTNfZeernckQgjRYXRNmJcsWcLs2bOZO3cumzZtYujQoYwfP57jx483u/3HH3/MnDlzmDt3Ljt37uSdd95hyZIlPPLIIx0ceefW2zKEXwAuUB433KAelyxx2C579+5NZWUl48aNY9y4cW1aftmSMBulOwYYpy6DgyE6Wj1vSytze+umRTk5kJamgjJIWQkhREfQNWF+4YUXuPPOO5k6dSoDBw5k0aJFBAQE8O677za7/S+//MJ5553HzTffTExMDJdeeik33XTTWVulhWNtN/yKCh3L8OVx7bWqW8b69SrhcYDt27djNptZu3Yta9eubdPyy0acIcNIddmemTLaWzctskwlN3asmn1FCCE6Cd0S5pqaGjZu3MjFF1/cEIyHBxdffDHr1q1r9j3nnnsuGzdutCbI+/fvZ+XKlUyYMKFDYhbCJXXvDtOnw4IFxuksjDG7ZBiJIfsxW+5SXH+9vnEIIUQH89LrwAUFBdTX1xMVFdXk9aioKHbt2tXse26++WYKCgo4//zz0TSNuro67r777la7ZFRXV1NdXW39d2lpqWNOoBPr16+f3iEYikuUxyuvOHR37T3nwkKwrBFipC4ZRqpLwyXM+/bBxo3g4QHXXKN3NEII0aF0S5jbYs2aNTz99NO89tprjB49mr179zJz5kyeeOIJ/va3vzX7nvnz5/P444+f8Xp6ejqBgYGkpKSwc+dOKisrCQ4OJjY2lq1btwLQt29fzGYzeXl5AAwbNoy9e/dSVlZGYGAg/fv3Z/PmzYDq++jp6cmBAwcAGDJkCDk5OZSWluLn58egQYPYuHEjAD179sTPz4/9+/cDkJyczMGDBykuLsbHx4dhw4ZZW9G7d+9OUFAQe/fuBSApKYljx45RWFiIl5cXI0aMYP369WiaRmRkJOHh4WRlZQEwYMAACgsLyc/Px8PDg5EjR5Kenk59fT1dunShW7du7Ny5E4CEhARKS0s5duwYAKNHj2bTpk3U1tYSHh5Oz549yTz1lzs4OJjKykqOnJoTLDU1le3bt1NVVUVoaCh9+vRh27ZtAMTExFBXV8fBgwcBSElJYdeuXVRUVBAUFERcXBxbtmwBoE+fPgDknprNYejQoezbt4+ysjICAgJITExk06ZN1vL28vIi51QXg8GDB5Obm0tJSQl+fn4kJyeTfmpQUo8ePQgICGDfvn0ADBo0iMOHD1NUVIS3tzcpKSmkpaUB6oItJCSEPXv2WMv7+PHjnDhxAk9PT1JTU9mwYQNms5nIyEgiIiLIzMzE39+f/v37U1RURH5+PiaTiVGjRrFx40bq6uqIiIggKirKWt7x8fGUlZVZV5YbNWoUGRkZ1NTUEBYWRu/eva3dA/r160dVVRWHDx8GYMSIEWRmZlJVVUVISAgxMTFNPrP19fXW8h4+fDhZWVmUl5cTFBREfHy8deq06OhoPDw8mnxms7OzOXnyJP7+/iQlJVnLu1evXvj4+JCdnQ1Aly5drMcA1QXAUobdu3cnMDDQWt4DBw7k6NGjFBYWWst76dIdwEB69aqlpuYkaWmqvBMTEykoKKCgoMD6mbWUt2VeYcsFdUJCAiUlJdYxD40/sxEREXTv3p0dp/ozxMXFUV5ebi3vkSNHsnXrVqqrqwkLCyM6Oppt27ZRWVnJwIEDqamp4dChQ9bPrB7fET17BgHhZGTUUF5ea9d3hKWeLBzxHTH4228JAEpSUtiVnc3obt1a/I6Ii4ujoqJCviNOfUfs3r0boFN9RwwePJi8vDyKi4vx9fVlyJAhbNiwwfqZPdt3hKW8u3XrRmhoqLW89f6OAIiNjTXEd4Qr5xG2fEekG2xgsUnTNE2PA9fU1BAQEMCyZcu46qqrrK9PmTKF4uJivvjiizPec8EFF3DOOeewYMEC62sffvghd911F2VlZXh4nNnDpLkW5ujoaEpKSggJCXHsSXUSaWlpjB49Wu8wDMNlyuPECVi5EqKi4NJL27WrtLQ0kpOTCQoKArB++dtq0SK45x6YMAFWrGhXKA5lpLosK2voQXPiBERE2P5eS/Kj9mNf3bRo3DhYuxYWLoT772///oQQohWlpaWEhoYaJl/TrQ+zj48PI0aM4Pvvv7e+Zjab+f777xnTwlKrFRUVZyTFnp6eALSU9/v6+hISEtLkR4hO6e23YfJkePFFvSPhVIOEobpjGE1QkJqjGhrKS1ezZqmFcCZO1DsSIYTocLrOkjF79mzeeustFi9ezM6dO7nnnnsoLy9n6tSpAEyePJmHH37Yuv3EiRN5/fXX+eSTT8jOzua7777jb3/7GxMnTrQmzsL5RowYoXcIhuIy5WFJdH74QTVftoPlnAMCAggICLD7/afuUDNgQLvCcDij1aWlfCzlZY+21k2LrroK3nsPYmMdt08hhHARuvZhvuGGG8jPz+exxx7j6NGjDBs2jK+//to6EDA3N7dJi/Jf//pXTCYTf/3rXzl06BCRkZFMnDiRp556Sq9T6JQyMzMZOnSo3mEYhsuUR1ISxMWpwVvffQdXX93mXVnOuby8vE3vt4zrTUxscwhOYbS6TExUVWVvwhwYGNjmuhFCCHEm3Qf9zZgxgxkzZjT7uzVr1jT5t5eXF3PnzmXu3LkdEJloiays2JTLlIfJpFqZX3oJ/vOfdiXM7TnnysqGVbqN1sJstLq0lE8LEwd1jNpaeOYZ1eE8JUV9joQQopPRfWls4XqkH3hTLlUelm4ZK1ZAfX2bd9Oec96zBzQNwsOha9c278YpjFaX7emS4TD/+x889hhcdhk4chEUIYRwIZIwC7vFxMToHYKhuFR5XHABhIZCfr5a+a+NYmJiqKqq4vLLL+fyyy+3q2W2cXcMozVWGq0uLV1W9u1TDb22amvdNOvLL9XjFVeAjBURQnRSkjALu1nmlxSKS5WHtzeMH6+en5rPsy22bt1KfX09K1euZOXKldTb0Vpt1AF/YLy67NULAgOhrg5OTbdqk7bWTbO++UY9Xn55+/YjhBAuTBJmITqbp59Wy+y1MHbA2YycMBuNyQT9+6vnunTLyMtTtwQ8POC3v9UhACGEMAZJmIXd+vbtq3cIhuJy5REXpxYvaYf2nLOlS4YRE2Yj1qWu/Zi/+049jhwJYWE6BCCEEMYgCbOwW7tv8bqZzlgebT1nTWtI/Iw2pRwYsy4t5aRrwtzOlSGFEMLVScIs7Hbw4EG9QzAUlyyP775Tt9hnz27T29t6zkeOqDVTPD1VQ7fRGLEudZtaTtMaBoZeckkHH1wIIYxFEmYhOqPKSrXin2UGhA5iSfpiY8HHp0MP7bJ065JhMqkK+/FHGD26gw8uhBDGIgmzsNvw4cP1DsFQXLI8xo1Tzbx790J2tt1vb+s5G7k7BhizLi2D/goK4MSJDj64tzecf75c3QghOj1JmIXdsrKy9A7BUFyyPEJCYMwY9dzST9UOWVlZBAYGomkamqYRGBho0/uMPkOGEesyMBCio9VzW1uZ21I3QgghWiYJs7BbeXm53iEYisuWh6VfahsS5raes5FnyADj1mWHd8uorITkZJg+XT0XQohOThJmYbegoCC9QzAUly2Piy5Sj2vXqgFedmjrORu9hdmoddnhCXNaGmRmwhdfgJ9fBx1UCCGMSxJmYbf4+Hi9QzAUly2PkSNVMpSfb/cUDPHx8VRVVTFp0iQmTZpk0/LL1dWQm6ueW/rlGo1R69JSXnv32ra9vXVzhv/9Tz2OHWu89cuFEEIHkjALu2VkZOgdgqG4bHn4+qr5dS+7DOxMqjIyMqivr2fZsmUsW7bMpvmLc3LAbFZ9ctu5borTGLUuLXn8nj22bW9v3Zxh7Vr1eOGF9r9XCCHckJfeAQghdPTFFx12qH371GNcnDRa2ishQT3u3at6zzi1/GpqYN069VwSZiGEAKSFWbRBtGXIvgA6Z3m05Zwt3QkM2usBMG5d9u2rZgGsqICjR518sPR0NdCva1dISnLywYQQwjVIwizs5uEhH5vG3KI8Dh2CkhKbN2/LObtCwmzUuvTxUUkz2N4to80s/ZcvvFBuBQghxCnG/OsgDO3AgQN6h2AoLl8eN9wAvXvD8uU2v6Ut5+wKCbOR67JxtwynCghQlSTdMYQQwkoSZiE6u7g49WhpWXQSV0iYjcxSbk5PmO+/XzVj33efkw8khBCuQxJmYbchQ4boHYKhuHx5WFoS7UiY7T3nurqGFbiNnDAbuS7tnSmj3QzaPUUIIfQg34jCbtmWzEcAblAe552nkqP9+1VfZhtkZ2cTEBBAWVkZZWVlBAQEtLp9bq5Kmn19oVcvRwTtHEauS3u6ZNhTN00UFkJbpqETQgg3JwmzsNvJkyf1DsFQXL48goPB0rL66682veXkyZOYTCYCAwMJDAzEdJbBYZYp5fr1M3bDpZHrsnGXjLMtzGhP3TRxzz0QFgYfftjmOIUQwh0Z+E+XMCp/f3+9QzAUtyiPc85RjzYmzPaes6v0XzZyXcbGqouNsjI4dsxJB1m3Th3AyLcBhBBCB5IwC7slydysTbhFeVgSZsuCFWeRlJREdXU1t912G7fddhvV1dWtbu8qCbOR67Lx1HJn65ZhT91YHToEeXkqKx85sn3BCiGEm5GEWdht06ZNeodgKG5RHhdeCPfeCzNn2rT5pk2bqKurY/HixSxevJi6urpWt3eVhNnodWnrwD976sYqLU09Dh4MQUFtD1IIIdyQLI0thFD3+195xWm7d5WE2eji4+G775w0tZzl7oLlboMQQggraWEWdusl/Rub6IzlYc85m80Ng/6MnjAbvS6duniJpf/6mDFO2LkQQrg2SZiF3Xx8fPQOwVDcpjyqquDnn2HFirNuas85HzoE1dXg5QV9+rQnQOczel06bS7mmhpIT1fPpYVZCCHOIAmzsJuR56rVg9uUx48/wvnn27TCmz3nbGkNjY1VSbORGb0u7Zlazi7V1fDww3DttdC/vwN3LIQQ7sHgf76EEB1m1CgwmdSSfMeOQVSUQ3Zr6Y5hWYFbtF2/fqqKTp6E/Hzo1s1BOw4Ohscec9DOhBDC/UgLs7Db4MGD9Q7BUNymPEJDYeBA9dwyY0IL7DlnVxrwZ/S69PVt6NbSYUtkCyGEkIRZ2C8vL0/vEAzFrcrD0n/1LAlzXl4eAQEBHD9+nOPHj7e6/LIrJcyuUJeNu2W0xNa6sfruO9XZ3KH9PIQQwn20qUtGfX09n3/+OTt37gRg0KBBXHnllXh6ejo0OGFMxcXFeodgKG5VHqmp8M47sHFjq5sVFxdjMpmIjIw86y5dKWF2hbpMSIDvv289Yba1bgAoLoZLL1XPT5yAiIh2xyiEEO7G7oR57969XH755Rw8eJABAwYAMH/+fKKjo1mxYgVx0lHR7fn6+uodgqG4VXmMGKEe09NVa6PJ1Oxmtp6zprlWwuwKdenwmTIsi7XExEiyLIQQLbC7S8b9999Pv379yMvLY9OmTWzatInc3FxiY2O5//77nRGjMJghQ4boHYKhuFV5DB6sprI4cQJyc1vcbMiQIVRXV3Pvvfdy7733trj88rFjUF6uVluOiXFSzA7kCnVpS5cMW+rGynI3wXKxJIQQ4gx2J8xr167l2WefJaJRS0SXLl145plnWLt2rUODE8a0YcMGvUMwFLcqDz8/eO89+Okn6N69xc02bNhAXV0dr732Gq+99lqLyy9bkro+fdSANaNzhbpsvHhJS12ObakbK0vCnJrquCCFEMLN2N0lw9fXl5MnT57xellZmeEn/RdC2OAPf3DYrlxlhT9XYplarqQECgrA1q7KLZIWZiGEOCu7W5ivuOIK7rrrLtLS0tA0DU3T+PXXX7n77ru58sornRGjMJjurbQ8dkadsTxsPWdLC7OrDG1whbr084PevdXzdi+RXVzcsJOUlHbuTAgh3JfdCfP//d//ERcXx5gxY/Dz88PPz4/zzjuP+Ph4Fi5c6IwYhcEEBgbqHYKhuF15VFaqbhkPPtjiPX9bz9mVBvyB69Rl424Z7dJ4wF+XLu3cmRBCuC+7u2SEhYXxxRdfsGfPHnbt2gVAUlIS8a7yF1G02759++jataveYRiG25WHyQR33gl1dTBjBvTte8Ym+/btIzk5+ay7crWE2VXqMj4efvjBATNlJCfDBx9Aba1D4hJCCHfV5qWxExISSLA0cwgh3Iefn5otY/Nm1b+1mYTZFprWkNC5SsLsKmyZKcMm3brBLbe0Ox4hhHB3NiXMs2fPtnmHL7zwQpuDEa5hoGX5ZAG4aXmMGKES5vR0uOaaM35tyzkXFqqBaaAGqrkCV6lLh3XJEEIIYRObEubNmzc3+femTZuoq6uzLlySlZWFp6cnI2SUdadw9OhRgoOD9Q7DMNyyPEaMgLffbnHFv6NHjxIXF0d2djYA/v7+Z2xjSeZ69QJbVmc2Alepy8aLlzS3voy/v3+rdQOoq5l33lHTyV14oROjFUII12dTwrx69Wrr8xdeeIHg4GAWL15MeHg4AEVFRUydOpULLrjAOVEKQyksLNQ7BENxy/KwXPxu3txsRlZYWEhCQgIxraxG4opTyrlKXVpa7IuLVUv+6eP1PDw8Wq0bQNXtn/6kBvydSq6FEEI0z+5ZMp5//nnmz59vTZYBwsPDefLJJ3n++ecdGpwwJm9vb71DMBS3LI/kZLU8X34+HD16xq9tOWdXm1IOXKcuAwIappZr88C/LVvU49ChDolJCCHcmd0Jc2lpKfn5+We8np+f3+yCJsL9pMh8rU24ZXn4+0P//ur59u1n/DolJYWamhoefPBBHnzwQWpqas7YxtVmyADXqktLuVpa8hs7W90AkjALIYQd7E6Yr776aqZOncry5cs5ePAgBw8e5NNPP2XatGlc08zgIOF+0tLS9A7BUNy2PJYsUa3Ll1xyxq/S0tKora3lueee47nnnqO2mWnJXDFhdqW6bG2mjLPVDSAJsxBC2MHuaeUWLVrEn//8Z26++WbrF7GXlxfTpk1jwYIFDg9QCKGTIUPa9XZXTJhdiaWrS5tmyqirg8xM9VwSZiGEOCu7E+aAgABee+01FixYwL5T9wLj4uJcZoUs0X7dunXTOwRD6YzlcbZzLilR3Z/Btfowu1Jdtmsu5qwsqK6GoCCIjXVoXEII4Y7avHBJYGAgQ9rZAiVcU2hoqN4hGIrblkdNDcybB9u2wdKlakGTU852zpZ+td26QUiIE2N0MFeqy3YlzJbuGEOGqMGdQgghWtWmhDk9PZ1///vf5ObmnjGgZPny5Q4JTBjXnj17GD16tN5hGIbbloe3N7zxhpq3bMcOaDQgbs+ePa0uje2q3TFcqS4tLfcFBapF365c//e/h3XrZElsIYSwkd1NC5988gnnnnsuO3fu5LPPPqO2tpbMzEx++OEHl2qdEUKchcnU0L/V0iJpI1ecg9nVBAerFnxofqaMVgUEwDnngMydL4QQNrE7YX766ad58cUX+fLLL/Hx8WHhwoXs2rWL66+/nj59+jgjRmEwiYmJeodgKG5dHi0kzGc7Z1ecgxlcry7b1S1DCCGEzexOmPft28fll18OgI+PD+Xl5ZhMJh544AHefPNNhwcojKegoEDvEAzFrcujhYS5oKAAf39/tm/fzvbt289YftlVu2S4Wl22lDC3VjecOAH33QfvvtsxQQohhBuwO2EODw+3LlDSq1cvtp9a1KC4uJiKigrHRicMydWSCmdz6/JonDBrmvXlgoICPDw8GDRoEIMGDcLjtIFjkjB3jJYWL2mtbti8GV55BebP75gghRDCDdidMF944YV89913AEyaNImZM2dy5513ctNNN/Hb3/7W4QEK4znjD3An59blMXAgeHlBUREcPGh9ubVzLi+Hw4fVc1dLmF2tLts0F7MsWCKEEHaze5aMV155haqqKgAeffRRvL29+eWXX7j22mv561//6vAAhfGMHDlS7xAMxa3Lw9cXBgyAAwcgNxeiowF1zjU1NTz99NMAPPLII/j4+ACwf796a3g4REToEnWbuVpdttQlo6W6AdQ0gdDuhWmEEKIzMWlao/usZ1FXV8fHH3/M+PHjiYqKcmZcTlNaWkpoaCglJSWEuNIEsQayYcMGl0ssnMnty+PECZX9Nmp93bBhAwMHDiQoKAiAsrIy6+JFn30G11wDI0fC+vW6RNxmrlaXhYXQpYt6Xl6uJr9Qz8ubrRtAVUx6Onz6qaooIc5C0zTq6uqor6/XOxThZry9vfH09Gz2d0bL1+xqYfby8uLuu+9m586dzopHuACz2ax3CIbi9uVhycgaae2cXXlKOVery4gIdS1TVKRa9luZGlsxm9Wc2gCDBjk9PuH6ampqOHLkiIxREk5hMpno3bu39QLfyOzukjFq1CgyMjLo27evQwJ49dVXWbBgAUePHmXo0KG8/PLLjBo1qsXti4uLefTRR1m+fDmFhYX07duXl156iQkTJjgkHnF2Xbt21TsEQ+mM5dHaObvqgD9wzbqMj4cNG1S5nzVhzsmBigrw8XG9Of9EhzObzWRnZ+Pp6UnPnj3x8fHBZDLpHZZwE5qmkZ+fz8GDB0lISGixpdko7E6Yp0+fzuzZs8nLy2PEiBFNb/WBXctlL1myhNmzZ7No0SJGjx7NSy+9xPjx49m9ezfdLDPyN1JTU8Mll1xCt27dWLZsGb169eLAgQOEhYXZexqiHVwxqXAmty+PoiK46y6VkW3cCB4eNiXMrpiPuWJdxsU1JMxntXu3ekxMVIM5hWhFTU0NZrOZ6OhoAiz9fYRwoMjISHJycqitrXW/hPnGG28E4P7777e+ZjKZ0DQNk8lkVx+nF154gTvvvJOpU6cCsGjRIlasWMG7777LnDlzztj+3XffpbCwkF9++QVvb28AYmJi7D0F0U67du1ymeWDO4Lbl0dICHz5JVRXQ3Y2xMWxa9euFpfGduUWZlesS7sWL7nsMjh+XK2nLYSNXG32GOE6XOmOhd0Jc3Z2tkMOXFNTw8aNG3n44Yetr3l4eHDxxRezbt26Zt/zn//8hzFjxnDvvffyxRdfEBkZyc0338xf/vIXw1+ZCOGyPD1Vi+SWLZCZ2WrTcXW1mkwDXDNhdkUtzcXcoshI9SOEEMJmdifMjuq7XFBQQH19/RmzbURFRbFr165m37N//35++OEH/vCHP7By5Ur27t3L9OnTqa2tZe7cuc2+p7q6murqauu/S0tLHRJ/Z5aQkKB3CIbSKcpj0KCGhPnKK1s85+xstb5JUBA006vK8FyxLmV5bCGEcD6bEub//Oc/Nu/wyiuvbHMwZ2M2m+nWrRtvvvkmnp6ejBgxgkOHDrFgwYIWE+b58+fz+OOPn/F6eno6gYGBpKSksHPnTiorKwkODiY2NpatW7cC6uLAbDaTl5cHwLBhw9i7d691mqb+/fuzefNmAHr37o2npycHDhwAVF/unJwcSktL8fPzY9CgQWzcuBGAnj174ufnx/5TE9YmJydz8OBBiouL8fHxYdiwYaw/NR9X9+7dCQoKYu+pv4ZJSUkcO3aMwsJCvLy8GDFiBOvXr0fTNCIjIwkPDycrKwuAAQMGUFhYSH5+Ph4eHowcOZL09HTq6+vp0qUL3bp1s854kpCQQGlpKceOHQNg9OjRbNq0idraWsLDw+nZsyeZmZkABAQEUFZWxpEjRwBITU1l+/btVFVVERoaSp8+fdh2aq7XmJgY6urqOHhq0YuUlBR27dpFRUUFQUFBxMXFseXUQgp9+vQBIPdUE+XQoUPZt28fZWVlBAQEkJiYyKZNm6zl7eXlRU5ODgCDBw8mNzeXkpIS/Pz8SE5OJj09HYAePXoQEBDAvlNNcIMGDeLw4cMUFRXh7e1NSkoKaWlpgLpgCwkJYc+ePdbyPn78OCdOnMDT05PU1FQ2bNiA2WwmMjKSiIgIMjIyrJ+HoqIi8vPzMZlMjBo1io0bN1JXV0dERARRUVHW8o6Pj6esrIyjR48CDYNpa2pqCAsLo3fv3tZVNPv160dVVRWHT60GMmLECDIzM6mqqiIkJISYmJgmn9n6+npreQ8fPpysrCzrNGPx8fFkZGQAEB0djYeHR5PPbHZ2NidPnsTf35+kpCRreSdGRxMKFKxdy76LLiIsLIz6+nreffddfHx88PHxIS0tjZ9+CgMGEBNTx/r16vM+cOBAjh49SmFh4Rnl3a1bN0JDQ63lnZiYSEFBgXUlwZEjR1rLu2vXrnTt2tV6QZ2QkEBJSQnHjx8/4zMbERFB9+7d2XFqRoi4uDjKy8ut5T1y5Ei2bt1KdXU1YWFhREdHs23bNsrLy0lOTqampoZDhw5ZP7NG/o7w8ekJRJObq/HjjxsYOjSRw4cP8+677+Lp6Ymfn5/6jqirI3nuXDyTkth2zTWY/f2d9h0RFxdHRUWFfEec+o7YfarvuKt9R/To0YP6+nrKy8upq6sjICCA6upq6uvr8fDwwM/Pzzp7hmVAoKWByt/fn9raWurq6vDw8MDf35/y8nJATSXm4eHR7LYmk4nAwEDKysqs23p6elrXf/Dz86Ourq7JtuXl5WiahpeXF15eXk22ra+vp7a2FoCgoKAm23p7e1NZWQmAr68vZrPZum1gYCCVlZWYzeZmt9U0jZqaGkD9TayqqsJsNuPp6Ymvr6+1XJrb1lKGp297ehmeXt6Ny9De8m5chs2Vt6VcmitvSxk2V96Ny6Ut5V1eXm49xy1btjT5jrD8/zQKm+ZhPr3/kqXPcuN/W9jah7mmpoaAgACWLVvGVVddZX19ypQpFBcX88UXX5zxnrFjx+Lt7c2qVausr3311VdMmDCB6urqppPzn9JcC3N0dLRh5vVzRWlpaS7Xz9OZOkV5/Oc/8Pvfq9XhMjJaPOeFC2HWLLjuOli6tOPDbC9XrEtNg+BgNQ/zrl1qnZlmZWWpX/r7Q1lZk3m1hWhOVVUV2dnZxMbG4ufnp3c4LiMvL49bb72V48eP4+Xlxd/+9jcmTZqkd1iG1NpnzGjzMNv0jWk2m60/3377LcOGDeOrr76iuLiY4uJiVq5cSUpKCl9//bXNB/bx8WHEiBF8//33TY7z/fffM2bMmGbfc95557F3794mc6VmZWXRo0ePZpNlUFcxISEhTX6EEHayzNm7axe0clHsygP+XJXJZGO3jFOtvyQlSbIshBN5eXnx0ksvsWPHDr799ltmzZplbe0Vrsvub81Zs2axcOFCxo8fb01Ax48fzwsvvNBk5gxbzJ49m7feeovFixezc+dO7rnnHsrLy62zZkyePLnJoMB77rmHwsJCZs6cSVZWFitWrODpp5/m3nvvtfc0RDu4Wgucs3WK8oiNVStkJCZCQQGjR4+mpqaGBQsWsGDBAuvtRleeUg5cty5PH/jXXN1w6vb92SdrFsI9rFmzpkNm0jr9OD169GDYsGGA6lbZtWtXCgsLnR6HcC67E+Z9+/Y1O+9xaGiota+YrW644Qaee+45HnvsMYYNG0ZGRgZff/21dSBgbm6utQ8cqD6X33zzDRs2bGDIkCHcf//9zJw5s9kp6ITzWPoICqVTlIeHh5qKLCMDoqKsfVcfeughHnroIWufNVdvYXbVurRcoFjKv7m6sbYwywp/ohM7ePAgd999N/Hx8fj5+REVFcWll15q7VPvaBs3bqS+vp7o6Gin7F90HLtnyRg5ciSzZ8/mgw8+sCa2x44d48EHH2x1hb6WzJgxgxkzZjT7uzVr1pzx2pgxY/j111/tPo5wHOsfYAF0ovJodBu/uXOurVULyYHrJsyuWpd2dcmQhFl0Ujk5OYwcOZJx48bxwQcf0KNHD/Ly8vj000/x9fV1+PEKCwuZPHkyb731lsP3LTqe3Qnzu+++y9VXX02fPn2sV0x5eXkkJCTw+eefOzo+YUARERF6h2AonbE8mjvn3FyoqwM/P+jZU4egHMBV6/KsCXNdXcMqf5Iwi07q5ZdfJjAwkCVLllgnM4iJieGCCy5w+LGqq6u56qqrmDNnDueee67D9y86nt0Jc3x8PFu3buW7776zTu+UlJTExRdf7FIrtoi26969u94hGEqnKY9Nm2DaNAgIoHszA3wb91921TFlrlqXloQ5J0flxmc4ckRdyXh5wamp2YRoC02DU7OgdbiAADXIta2KioqoqqoiNzfXqX2bNU3jtttu46KLLuLWW2912nFEx7I7YQY1jdyll17KpZde6uh4hAvYsWOHyw6OcoZOUx7BwaoPs78/O7ZvJ3nIkCa/dvX+y+C6ddmrF/j6qpUW8/KaWTQmOhpKSlQ/dFe9mhGGUFGhFibSQ1kZBAa2/f0zZszg+++/p1+/fowYMYLf/va3TJ48mYEDBzouSODnn39myZIlDBkyxHrn/YMPPmDw4MEOPY7oWG1KmMvLy1m7di25ubkNI7BPsXemDCGEi4iNBR8fqKzE59TiFY1ZZmhw5YTZVXl4QL9+sHOnunBpdpVFk0mWxBadWkpKCvv37+enn37i22+/ZenSpTz//PMsX76ciRMntjp/8tVXX82aNWv47W9/y7Jly1o9zvnnn99k+lvhHuxOmDdv3syECROoqKigvLyciIgICgoKCAgIoFu3bpIwdwJxrjpnmJN0mvLw8oKEBMjMpF+jxYAs3KGF2ZXrMj6+IWGWLpPCWQICVEuvXsduL09PT8aOHcvYsWOZN28eMTExfPzxx0ycONE6f/KwYcM4evQoI0aMYMKECQQGBjJz5kxuv/12Fi9e3P4ghEuyO2F+4IEHmDhxIosWLSI0NJRff/0Vb29vbrnlFmbOnOmMGIXBlJeX07VrV73DMIxOVR6JiZCZSX1mJkHXXcfq1asBtSSqq8/BDK5dl43nYvbz82tSN9x8M1RWwt//DnJbWLSDydS+bhFGYjabqa6uJvLUnZcePXrQo0cPoOn8yYGBgYwbN67ZmbtE52F3wpyRkcEbb7yBh4cHnp6eVFdX069fP5599lmmTJnCNddc44w4hYEcPXqUvn376h2GYXSq8khKAqB2+3Y8PT0ZN24coBb/c4cuGa5cl43nYm5cN2gafPUVFBerhFmITujWW29l4MCBXHTRRURFRbF//36efvppQC3IdjqZP1mczu7RH97e3tbpWLp160Zubi6gFi7Jy8tzbHRCCGNJTATA/7RFig4dgpoa8PZW48tEx2txarnjx1Wy7OGhutQI0QmlpKTw3//+lyuuuIKkpCTuvvtuEhMTycjIoF+/fk22tcyf/Oabb+oUrTAiu1uYhw8fzoYNG0hISGDs2LE89thjFBQU8MEHH5AsS652CiNHjtQ7BEPpVOUxaBAkJRE8fDi1tbXWPygJCXcB3sTGqq7OrsqV67Jxl4zq6lreflvVzV0DBuANatCmn59u8QmhpwceeIAHHnjgrNvJ/MmiJXa3MD/99NPWPj5PPfUU4eHh3HPPPeTn58vVWCexdetWvUMwlE5VHsOGwY4dbLnvPmpqaqwrde7erWbLceX+y+Daddm3r7pYqaqC3NyGuqmxrPB36u6AEKJ5Mn+yaI3dbUGpqanW5926dePrZhYwEO6tupkZEjqzzlgep5+zpRtA//46BONArlyXXl4qad63r6E/OQBZWepREmYhWtXa/MkXX3wxW7Zsoby8nN69e7N06VLGjBmjb8CiQ7nwzVOhl7CwML1DMJTOWB5hoaFqlYxTLAmzq3eRdfW6jI9XyfL+/Y1ePLUiq2XAphCdRUxMTLMD+lrS2vzJq1atcthxhGuyO2GOjY1tdQns/U2+qYU7klHDTXW68njuOfr//e9UTJ1qfcnSounqCbOr12V8PHzzzWktzN7e4O8vLcyi0+moRFYS5s7B7oT59A9FbW0tmzdv5uuvv+bBBx90VFzCwLZt2+aSywc7S6crj8BATCdPwu7d1pcs18mu3iXD1evSMvAvO7vRi59+qhJmTdMlJiGEcAd2J8wtLU7y6quvkp6e3u6AhBAGZ7m1b+kbC9TWqlWzXbyB1uVZBl02aWEGNaWcEEKINnPYt+hll13Gp59+6qjdCQOLjY3VOwRD6XTlMWCAejxtLua4OPD07PhwHMnV69LSwiw944QQwrEcljAvW7aMiIgIR+1OGFhNTY3eIRhKpyuP7t0xBwbiq2n89/XXufvu/wK+Lt9/GVy/LmNj1dLFZWW+fPjhf/nvlCn4jhkDr7+ud2hCCOHS2rRwSeNBf5qmcfToUfLz83nttdccGpwwpkOHDtG7d2+9wzCMTlceJhOVvXoRmJXF5T178u3OywHX778Mrl+Xfn6qW0xurhexsZdzbs1HsG0bnDypd2hCCOHS7E6Yr7rqqib/9vDwIDIyknHjxpEoo7CF6BSqoqMJzMqCPXvYs0e95g4tzO4gPh5yc1U/5nMt/czd4WpGCCF0ZHfCPHfuXGfEIVxISkqK3iEYSmcsj7ArrqDW15eP9u9n48b3gD+QkOCtd1jt5g51GRcHP/xQy7JlH1KfmckfAG9Lv3MhhBBtYncf5pUrV/LNN9+c8fo333zDV1995ZCghLHt3LlT7xAMpTOWR+ZFF1Hz738z9bXXOH58KlDjFo2Y7lCXauBfDf/5z+1MraqixmSCfv30DksIIVya3QnznDlzqK+vP+N1TdOYM2eOQ4ISxlZZWal3CIbSGcvj9HP294eePXUKxoHcoS4tM2VY9e0Lvr66xCKEEO7C7oR5z549DBw48IzXExMT2WtZH1e4teDgYL1DMJTOWB7BwcFNFsKIi1OzM7g6d6jLMxLmM14QQghhL7sT5tDQ0GaXv967dy+BgYEOCUoYm6vPVetonbE8YmNjYfBg678tC2a4OneoyzN6XwwapEscQgjhTuxOmH//+98za9Ys9jVaSmrv3r386U9/4sorr3RocMKYtm7dqncIhtIZy2Pr1q3QpYv13+7SiOkOdRkUBN26NXrhqad0i0WIzigvL49x48YxcOBAhgwZwtKlS/UOSTiA3Qnzs88+S2BgIImJicTGxhIbG0tSUhJdunThueeec0aMQggjapQlu0vC7C7cpcVfCFfk5eXFSy+9xI4dO/j222+ZNWsW5eXleocl2qlNXTJ++eUXVqxYwfTp0/nTn/7E999/zw8//EBYWJgTQhRG07dvX71DMJTOWB59+/ZtkiW7S4LmLnXZL1Y7+0ZCuLk1a9YQExPT4cfp0aMHw4YNA6B79+507dqVwsJCp8chnMvueZgBTCYTl156KZdeeqmj4xEuwGw26x2CoXTG8jCbzZj6JfJvYAeJDBrkHrMwuEtdnhN1iEvxozi4D74+PnqHI4RhHDx4kCeffJJVq1Zx8OBBQkNDGTp0KM8//zyDG43LcJSNGzdSX19PdHS0w/ctOpbdLcxC5OXl6R2CoXTG8sjLy+NIUBKTgLtNRfTo0aZrb8Nxl7ocHrCfyVQxoc4HL2/XX1BGCEfIyclh+PDhnDhxgg8++IBdu3axbNkyBg4ciK8Tpl4sLCxk8uTJvPnmmw7ft+h47vFXTgjR4XbWJRAHRGnH4GQphIToHZI4pV+dWhI7s24AcZp7TPknRHu9/PLLBAYGsmTJEjw8VHthTEwMF1xwgcOPVV1dzVVXXcWcOXM499xzHb5/0fEkYRZ2s/TNEkpnLI9hw4bx4mpPvuNcAnuE8veKCrzcIGF2l7oMP7GLpcDy2npGHa1zmzsAwkBaG8Tm6Ql+frZt6+GhVj4627YOmLa2qKiIqqoqcnNzndq3WdM0brvtNi666CJuvfVWpx1HdCzpkiHsJgvUNNUZy2Pv3r3s2lXN//EL8498RbUbLPgB7lOX9ft2cz3wCZ+TmVmtdzjCHQUFtfxz7bVNt+3WreVtL7us6bYxMc1v5wAzZszA19eXfv36MXLkSObMmcOOHTscsu/Gfv75Z5YsWcLnn3/OsGHDGDZsGNu2bXP4cUTHsilhLi0ttflHuL+ysjK9QzCUzlgeZWVlNJqK3W24TV02WlzKTa4BhGi3lJQU9u/fz+rVq7n00ktZvnw5Q4cO5csvv7Ruc/XVVxMeHs51113X5L0tvd6c888/H7PZTEZGhvXHGQMKRcey6T5dWFgYprN0gtM0DZPJRH19vUMCE8YlKzo21RnLIzAwsGkilpPjFivKuUVd1tRAbq71n3v26BiLcF+tXVx6ejb99/HjLW/rcVq7XU5Om0OyhaenJ2PHjmXs2LHMmzePmJgYPv74YyZOnAjAzJkzuf3221m8eHGT97X0uug8bEqYV69e7ew4hAvp37+/3iEYSmcsj549+3P0aG3DC1Onwvr1+gXkIG5RlydPwu9+B199BUjCLJzEnotLZ23bTmazmerqaiIjI62vjRs3jjVr1pyxbUuvi87DpoR57Nixzo5DuJDNmzczevRovcMwjM5YHitW7Ab6NbzgJvf93aIuu3SBpUut/T7dpGqEaJdbb72VgQMHctFFFxEVFcX+/ft5+umnAZg1a5a+wQmX0Oah0xUVFeTm5lJTU9Pk9SFDhrQ7KCGEseXl+TV9oagICgshIkKfgESL9u2D+voz75IL0ZmkpKSwbNkyXnjhBcrKyoiOjubSSy/lvffeo3fv3nqHJ1yA3Qlzfn4+U6dO5atTt/tOJ32Y3Z98uTTVGcujpKTbmS/u2QMu3jrrFnVZWQlaw9LYNTWQl6cmHxCis3rggQd44IEH9A5DuDC7p5WbNWsWxcXFpKWl4e/vz9dff83ixYtJSEjgP//5jzNiFAbjKU1VTXTG8sjJ8QF8uPrqf/LPAQPwAcjK0jmq9nOLurzuOnx69eKf995Lz57/BHzcoWqEEEJXdifMP/zwAy+88AKpqal4eHjQt29fbrnlFp599lnmz5/vjBiFwRw4cEDvEAylM5bHzp11gDc33HAbt40dize4xegyt6jLvXvxLi3ltmuuYdSo2wBvSZiFsNHFF1/MpEmTWLlyJb1792bdunWtvi46D7u7ZJSXl9Otm7odGx4eTn5+Pv3792fw4MFs2rTJ4QEKIYzn4EHVhzkhAchLUC9KVqa/+nrIzlbP4+OxTPohVSM6o5iYGLsH9K1atcqu19t6HOF67G5hHjBgALt37wZg6NChvPHGGxw6dIhFixbRo0cPhwcojEcGdjbV2cqjuBiKiryBOvbtW8EKoO6ee+Dqq3WOrP1cvi7z8qC2ljofH1Zs2UJV1QqgjlNf2UJ0Kh2VyErC3DnY3cI8c+ZMjhw5AsDcuXP53e9+x0cffYSPjw/vvfeeo+MTBpSTk0NSUpLeYRhGZysPS/LVrVs1119/BaBWyPNyg0U/XL4uT80hVx0TwxVXXnnqxTJ2727zhEhCCCFoQ8J8yy23WJ+PGDGCAwcOsGvXLvr06UPXrl0dGpwwJlkCvanOVh67dqnHxMTWF/ByRS5fl5ZJl/v1a9IP48ABKC/v0DUhhBDCrdjdJeN0AQEBpKSkSLLcifj5+Z19o06ks5XHzp3qccCARi+WlsLGjS6fQbt8Xe7bpx7j4qwvdemiHqUfsxBCtF27E2bR+QwaNEjvEAyls5WHpYW5ySrSt9wCqanw3//qEpOjuHxdJibC5ZfDiBHWlyz1ZKk3IYQQ9pOEWdht48aNeodgKJ2tPCyJV5MWZkuLpotPLefydTltmrpouf5660uWhNlyZ0AIIYT9JGEWQtispqahm2yThDk+Xj26eMLsjiz1JC3MQgjRdpIwC7v17NlT7xAMpTOVx759aqrfwEAzTU7bkjBbsmkX5dJ1WV0NhYVnvGxJmKWFWQgh2q5Ncw0VFxfzzjvvsPPUN/CgQYO4/fbbCQ0NdWhwwphcfmCUg3Wm8rC0UiYk1OPr68Mrr7wCgI/lvv/evaBpYDLpFGH7uHRdpqXB2LGQkoLPr79a6yY52QdQg/7q68EdVv8WQoiOZncLc3p6OnFxcbz44osUFhZSWFjICy+8QFxcnKz010ns379f7xAMpTOVh6WVsnv3Ery9vbn33nu599578U5IAA8PNXfZsWP6BtkOLl2Xltb9rl2b1E1cnDd+fqo7jWURQCHc3bhx43RbTGTevHkMGzbM4fu97bbbuOqqqxy+X2EbuxPmBx54gCuvvJKcnByWL1/O8uXLyc7O5oorrpCVboRwc5YW5piYyqa/8PWFPn3UcxfvluGyLOVu6R5ziqenzJQhhBDtZXeXjPT0dN566y28vBre6uXlxUMPPURqaqpDgxPGlJycrHcIhtKZysPSwnzBBZHU19fz448/nvr3BXjOmqXu+VsSZxfk0nVpmYM5Pv6MuklK8mTrVpUwX3GFjjEKIYSLsruFOSQkhNzc3DNez8vLIzg42CFBCWM7ePCg3iEYSmcpD01raKEMCztCVVUVv/nNb/jNb35DVVUVzJwJs2e7dMLs0nXZqIX59LpJTFS/koF/ojOpq6tjxowZhIaG0rVrV/72t7+haZr19x988AGpqakEBwfTvXt3br75Zo43WnxpzZo1mEwmvv/+e1JTUwkICODcc89l9+7dTY7zzDPPEBUVRXBwMNOmTVPfh41s2LCBSy65hK5duxIaGsrYsWPP2oW1vr6e2bNnExYWRpcuXXjooYeaxA5gNpuZP38+sbGx+Pv7M3ToUJYtW9bqfmNiYnjiiSe46aabCAwMpFevXrz66qtNtnnhhRcYPHgwgYGBREdHM336dMrKyqy/P3DgABMnTiQ8PJzAwEAGDRrEypUrrb9fu3Yto0aNwtfXlx49ejBnzhzq6upajcsV2J0w33DDDUybNo0lS5aQl5dHXl4en3zyCXfccQc33XSTM2IUBlNcXKx3CIbSWcrj8GEoK1O3+ENC8vUOxylcti41rSFhbrTKn4UlYZYuGcJRysvLW/w5PWFsbdvKykqbtm2LxYsX4+Xlxfr161m4cCEvvPACb7/9tvX3tbW1PPHEE2zZsoXPP/+cnJwcbrvttjP28+ijj/L888+Tnp6Ol5cXt99+u/V3//73v5k3bx5PP/006enp9OjRg9dee63J+0+ePMmUKVP46aef+PXXX0lISGDChAmcPHmyxdiff/553nvvPd59911++uknCgsL+eyzz5psM3/+fN5//30WLVpEZmYmDzzwALfccgtr165ttVwWLFjA0KFD2bx5M3PmzGHmzJl899131t97eHjwf//3f2RmZrJ48WJ++OEHHnroIevv7733Xqqrq/nf//7Htm3b+Mc//kFQUBAAhw4dYsKECYwcOZItW7bw+uuv88477/Dkk0+2GpNL0OxUXV2t3X///ZqPj4/m4eGheXh4aL6+vtqsWbO0qqoqe3fX4UpKSjRAKykp0TsUl7Vp0ya9QzCUzlIe332naaBpAwaocy4rK9MADdDKyso0rapK0zZv1rRvv9U71DZz2bo8flxVjsmkaZWVZ9RNRob6dXi4ppnNegcrXEVlZaW2Y8cOrbKy8ozfWT5fzf1MmDChybYBAQEtbjt27Ngm23bt2rXZ7ew1duxYLSkpSTM3+sD/5S9/0ZKSklp8z4YNGzRAO3nypKZpmrZ69WoN0FatWmXdZsWKFRpgLZMxY8Zo06dPb7Kf0aNHa0OHDm3xOPX19VpwcLD25ZdftrhNjx49tGeffdb679raWq13797a73//e03TNK2qqkoLCAjQfvnllybvmzZtmnbTTTe1uN++fftqv/vd75q8dsMNN2iXXXZZi+9ZunSp1qVLF+u/Bw8erM2bN6/ZbR955BFtwIABTcr91Vdf1YKCgrT6+voztm/tM2a0fM3uFmYfHx8WLlxIUVERGRkZZGRkUFhYyIsvvoivr2+bE3fhOpwx+teVdZbysLROJia2cM579sDw4TBpkmrxdEEuW5dmM8yYATffDM1Mjde/v5rpr6gIGt1xFsKtnXPOOZgaTXE5ZswY9uzZQ319PaBW9pw4cSJ9+vQhODiYsWPHApzR7XTIkCHW5z169ACwdt3YuXMno0ePbrL9mDFjmvz72LFj3HnnnSQkJBAaGkpISAhlZWXNdm8FKCkp4ciRI0326+Xl1WSc2N69e6moqOCSSy4hKCjI+vP++++zzzKeoQWnxzdmzBjrNMEAq1at4re//S29evUiODiYW2+9lRMnTlBRUQHA/fffz5NPPsl5553H3Llz2bp1q/W9O3fuZMyYMU3K/bzzzqOsrMy1u7zRxnmYAQICAhg8eLAjYxEuYv369Wd8QXRmnaU8duxQj0lJ6pzPGCDXr596LCmBEyega9eODdABXLYuo6Lg5Zdb/LW/P8TEqGnldu5UmwvRHo37tJ7O87TJvo+3cpXm4dG03S4nJ6ddcdmqvLyc8ePHM378eD766CMiIyPJzc1l/Pjx1NTUNNnW29vb+tySCJrNZpuPNWXKFE6cOMHChQvp27cvvr6+jBkz5ozj2MNS/itWrKBXr15NfteexsucnByuuOIK7rnnHp566ikiIiL46aefmDZtGjU1NQQEBHDHHXcwfvx4VqxYwbfffsv8+fN5/vnnue+++9p8XFdgU8J8zTXX2LzD5cuXtzkYIYRxbd+uHgcNamGDgADo1QsOHVIzNrhgwuzOkpNVwrx9O4wbp3c0wtUFBgbqvu3ZpKWlNfm3pf+wp6cnu3bt4sSJEzzzzDNER0cDahYweyUlJZGWlsbkyZObHKexn3/+mddee40JEyYAapKEgoKCFvcZGhpKjx49SEtL48ILLwTUAMaNGzeSkpICwMCBA/H19SU3N9faMm6r0+P79ddfSUpKAlSru9ls5vnnn7dezPz73/8+Yx/R0dHcfffd3H333Tz88MO89dZb3HfffSQlJfHpp5+iaZr14uLnn38mODiY3r172xWn0djUJSM0NNTmn7Z49dVXiYmJwc/Pj9GjR7N+/Xqb3vfJJ59gMplkIu8O1r17d71DMJTOUB6aBpmZ6nlycivn7OJLZLtsXebkwFkGLFoudCz1KIS7y83NZfbs2ezevZt//etfvPzyy8ycOROAPn364OPjw8svv8z+/fv5z3/+wxNPPGH3MWbOnMm7777LP//5T7Kyspg7dy6Zp/0nS0hI4IMPPmDnzp2kpaXxhz/8AX9//7Pu95lnnuHzzz9n165dTJ8+vcmg5ODgYP785z/zwAMPsHjxYvbt28emTZt4+eWXWbx4cav7/vnnn3n22WfJysri1VdfZenSpdZyiY+Pp7a21louH3zwAYsWLWry/lmzZvHNN9+QnZ3Npk2bWL16tTXhnj59Onl5edx3333s2rWLL774grlz5zJ79uwz7ia4HL07UX/yySeaj4+P9u6772qZmZnanXfeqYWFhWnHjh1r9X3Z2dlar169tAsuuMDaCd4WRutE7ooKCgr0DsFQOkN5HDmiBo15eGhaZaU65+rqau3ZZ5/Vnn32Wa26ulptOG2a2rCFASFG57J1ec45qtw/+0zTNK3ZuvngA7XJBRfoGKdwKa0NyDK6sWPHatOnT9fuvvtuLSQkRAsPD9ceeeSRJoPRPv74Yy0mJkbz9fXVxowZo/3nP//RAG3z5s2apjUM+isqKrK+Z/PmzRqgZWdnW1976qmntK5du2pBQUHalClTtIceeqjJoL9NmzZpqampmp+fn5aQkKAtXbpU69u3r/biiy+2GH9tba02c+ZMLSQkRAsLC9Nmz56tTZ48uUm+YzabtZdeekkbMGCA5u3trUVGRmrjx4/X1q5d2+J++/btqz3++OPapEmTtICAAK179+7awoULm2zzwgsvaD169ND8/f218ePHa++//36TcpgxY4YWFxen+fr6apGRkdqtt97a5LtzzZo12siRIzUfHx+te/fu2l/+8hettra22XhcadCfSdP0HZ0zevRoRo4cySuvvAKofkHR0dHcd999zJkzp9n31NfXc+GFF3L77bfz448/UlxczOeff27T8UpLSwkNDaWkpISQkBBHnUankpaW5pr9PJ2kM5THqlVwySWQkABZWa2c8zPPwMMPwy23wAcfdHyg7eSydRkZCQUFkJEBQ4c2u0lGhhqTGR6uupg3GpMjRLOqqqrIzs4mNjYWv2YGkwrXExMTw6xZswyzMnNrnzGj5Wt2D/qLjY1tMvrxdPv377d5XzU1NWzcuJGHH37Y+pqHhwcXX3wx69ata/F9f//73+nWrRvTpk2zrmYlhHCext0xWmXpknGWUdrCgYqLVbIMDQMvm5GYCB4eaqaMo0fh1GB/IYQQNrA7YT79qqS2tpbNmzfz9ddf8+CDD9q1r4KCAurr64k6bch2VFQUu1qYYf+nn37inXfeISMjw6ZjVFdXU11dbf13aWmpXTGKM1n6KgmlM5SHJWG29INNSkqivr7eulpVSkqKGhk/ciQsWGBDZm1MLlmXlouTqCg4tdpqc3Xj56fWNNmzR9WnJMxCCGE7uxNmS8fw07366qttGmFqj5MnT3Lrrbfy1ltv0dXGEfjz58/n8ccfP+P19PR0AgMDSUlJYefOnVRWVhIcHExsbKx1TsG+fftiNpvJy8sD1Byte/fupaysjMDAQPr378/mzZsB6N27N56enhw4cABQ8zbm5ORQWlqKn58fgwYNYuPGjQD07NkTPz8/a2t8cnIyBw8epLi4GB8fH4YNG2Yd+Ni9e3eCgoLYe2oQVVJSEseOHaOwsBAvLy9GjBjB+vXr0TSNyMhIwsPDycrKAmDAgAEUFhaSn5+Ph4cHI0eOJD09nfr6erp06UK3bt2scy8mJCRQWlrKsWPHANVVZtOmTdTW1hIeHk7Pnj2tAxl8fX2JiIjgyJEjAKSmprJ9+3aqqqoIDQ2lT58+bNu2DVC3f+rq6qzzL6akpLBr1y4qKioICgoiLi6OLVu2AGoQBjTMgTl06FD27dtHWVkZAQEBJCYmWpOA3r174+XlZZ2CaPDgweTm5lJSUoKfnx/JycnWz2OPHj0ICAiwzk05aNAgDh8+TFFREd7e3qSkpFhHU0dFRRESEsKePXus5X38+HFOnDiBp6cnqampbNiwAbPZTGRkJBEREWzcuJGgoCD69+9PUVER+fn5mEwmRo0axcaNG6mrqyMiIoKoqChrecfHx1NWVsbRo0cBGDVqFBkZGdTU1BAWFkbv3r3Zfmpain79+lFVVcXhw4cBGDFiBJmZmVRVVRESEkJMTEyTz2x9fb21vIcPH05WVhbl5eUEBQURHx9vvdiMjo7Gw8OjyWc2OzubkydP4u/vT1JSkrW8N28eBvji67uHtLRCgoKCqKmpYdSoUYC6EN1xat657pMmERgYyL5TZTpw4ECOHj1KYWHhGeXdrVs3QkNDreWdmJhIQUEBBQUF1s+spby7du1K165drRfTCQkJlJSUWKesavyZjYiIoHv37taY4uLiKC8vt5b3yJEj2bp1K9XV1YSFhREdHc22bdsoKytj8ODB1NTUcOjQIetn1sjfEf02bCASOBkVxY60NJKSksjOzrbWTVlZGZmZmWiaRmxsMnv2BLJyZQ7Bwcec9h0RFxdHRUWFfEec+o6wLKXsat8RPXr0oL6+nvLycurq6ggICKC6upr6+no8PDzw8/Ozzs3r4+ODyWSyNlD5+/tTW1tLXV0dHh4e+Pv7W1fs8/b2xsPDo9ltTSYTgYGB1mnTvL298fT0tK4e6OfnR11dXZNty8vL0TQNLy8vvLy8mmxbX19PbW0tAEFBQU229fb2tq406Ovri9lstm4bGBhIZWUlZrO52W01TbNOCxcQEEBVVRVmsxlPT098fX2t5dLctpYyPH3b08vw9PJuXIb2lrelDLdv346/vz9VVVXNlmFz5W0pw+bKu3G5tKW8y8vLree4ZcuWJt8Rzs4p7eWwPsz79+9n2LBhdrXgWub0W7ZsWZOZLqZMmUJxcTFffPFFk+0zMjIYPnx4kzkeLXMhenh4sHv3buJOWxa2uRbm6Ohow/SJcUUu28/TSdy9PDQNwsKgtBS2boXBg9U5JycnW5dDtSSIrs4l6/Lpp+HRR2HKFHjvPQBr8gNN6+Zvf4Mnn4Q77oC33tIrYOEqpA+zcDa37sPckmXLlhEREWHXe3x8fBgxYgTff/+9NWE2m818//33zJgx44ztExMTra0SFn/96185efIkCxcutM6l2Jivr6+sQOhgXl4O+9i4BXcvj0OHVLLs5QUDBqjXWj3nPXtg2za1womLdXFwybq0TOF3WmNBc2RqOSGEaBu7/zoMHz68yaA/TdM4evQo+fn5vPbaa3YHMHv2bKZMmUJqaiqjRo3ipZdeory8nKlTpwIwefJkevXqxfz586230RoLCwsDOHPVMeE0I0aM0DsEQ3H38rAkVwkJ4OOjno8YMcJ6u+8MTz0FixerRxdLmF2yLi+7TC3ld/75Z93U8jWZmanuHMhMGcIWOk+mJdyYK3227E6YT18kxMPDg8jISMaNG0diYqLdAdxwww3k5+fz2GOPcfToUYYNG8bXX39tHQiYm5vr+pNdu5n169db+0cK9y+P5lb4W79+PYNaWvLP0tLpgouXuGRdTpqkfmzQv7+6U1BaCgcPQjM35YSwsiwJXVFRcdaFNoRoC0vf7tOXUzciuxPmuXPnOjyIGTNmNNsFA2DNmjWtvve9U332RMdxpSvCjuDu5dHclHKtnrMLr/bn7nXp46PuFOzcqepVEmbRGk9PT8LCwqwDawMCAlqdVlYIe5jNZvLz8wkICHCJ7nA2RWjPQD4jdMwWzhUZGal3CIbi7uVx+pRycJZzduGE2eXqsrgYsrNVq76N373JyQ0J8+9+59zwhOuzLBdvSZqFcCQPDw/69OnjEhdiNiXMYWFhNp9MfX19uwISxhceHq53CIbizuVRX9/QJaNxC3N4eDje3t7WO06WW7dAQ8J85AiUl4MLzZ7hcnW5ejVcc42a//rUNHNAy3WDqselS9WMJ0KcjclkokePHnTr1s06XZgQjuLj4+My3W5tSphXr15tfZ6Tk8OcOXO47bbbGDNmDADr1q1j8eLFzJ8/3zlRCkPJyspyvam3nMidy2PfPqioUGPKEhIaXrec87x58858U3i4+ikqUjsYMqTD4m0vl6tLy6IllouUU3x8fJqvGxpWzj41tbEQNvH09HSJfqZCOItNCfPYsWOtz//+97/zwgsvcNNNN1lfu/LKKxk8eDBvvvkmU6ZMcXyUQghdWFohk5PBrr+V8fGwYYPLJcwux9Lt5bSEuTWWhHnHDqithdMaoIUQQjTD7nbwdevWkZqaesbrqamp1pWnhHsbYJmMVwDuXR6WVsjTc94BAwZgNpvJzMwkMzPTuoCQ1V//CsuWwam7UK7C5eqyhTmYW6ubvn1Vd+faWji1aKIQQoizsDthjo6O5q1mloh6++23m104RLifwsJCvUMwFHcuD0vCbGmVtCgsLKSyspLk5GSSk5Oty51aXXklXHstnBow5Cpcri5baGFurW5MpoYLIOmWIYQQtrF7Ho8XX3yRa6+9lq+++sra12/9+vXs2bOHTz/91OEBCuPJz8+nX79+eodhGO5cHi0lzPn5+da50t2JS9VldTXk5qrndnTJAFWfP/2k6veWW5wQmxBCuBm7W5gnTJjAnj17mDhxIoWFhRQWFjJx4kSysrKYMGGCM2IUBuMqI1o7iruWR1FRQz52epeMs55zRQV88QUsWuSc4JzEpeoyJ0ct1xcUBN262fVWGfgnhBD2MWnuPlP/aUpLSwkNDaWkpETmjBaiFWvXwrhxqs9rTs6Zvy8vLycoKAiAsrIyAhtPH3f8OERFqfv/lZXg69shMXcqR4/Chx+q8v3b35r8qtW6Qc1AN3q0yrOPHeuwiIUQwmZGy9fa1JxSXFzM888/zx133MEdd9zBiy++SElJiaNjEwaVnp6udwiG4q7l0VJ3DLDhnCMjIThYtYBmZzs+OCdxqbrs3h3+/OczkmVbJCeDh4e6rjl61AmxCSGEmzlrwrx///4m/05PTycuLo4XX3zR2iXjhRdeIC4ujk2bNjktUGEcsjhNU+5aHq0lzGc9Z5PJJVf8c9e6PF1AQMO82tItQwghzu6sCfMnn3zCtGnTrFMTPfDAA1x55ZXk5OSwfPlyli9fTnZ2NldccQWzZs1ydrzCALp06aJ3CIbiruXRWsJs0zm7YMLsUnX5889qouyqqja9XfoxCyGE7c6aMP/pT3/C09PTOqAvPT2dv/zlL3h5NUyw4eXlxUMPPeRatzNFm3Wzc4CRu3PH8qira1gSu7l1R7p164a3tzd//vOf+fOf/3zG8suASybMLlWXt92mst5168741VnrBplaTggh7HHWhNnX15c333yTyZMnAxASEkKuZeh8I3l5eQQHBzs+QmE4O3fu1DsEQ3HH8sjKUrOWBQaesSYGoM7Zx8eHBQsWsGDBAnx8fM7cyPJGF0qYXaYua2sbRmI2XrP8lLPWDdLCLIQQ9rB50N/NN98MwA033MC0adNYsmQJeXl55OXl8cknn3DHHXc0WS5bCOG6Nm9Wj0OGqMFhbeKCLcwuIzdX3Qbw84OePdu0i2HD1OOuXWoWQCGEEC2ze+GS5557DpPJxOTJk6mrqwPU7b977rmHZ555xuEBCuNJaKZFqzNzx/Kw9K5KTW3+9wkJCZjNZuvdpj59+pw5h/HQobBkSbMtoEblMnXZeIW/Zq5ozlo3QK9eaua/Y8dUK7OLrWIuhBAdyu62Ix8fHxYuXEhRUREZGRlkZGRQWFjIiy++iK/MtdoplJaW6h2CobhjeWzcqB5HjGj+96WlpVRWVhIbG0tsbOyZS2MDhIXB9dfD8OFOi9PRXKYu9+xRjy2s8HfWukFNZGKpX0t9CyGEaF6bl7UKCAhg8ODBDB48mICAAEfGJAzumKx00IS7lYfZ3NAlo6WE2d3O2cJlzqtxC3M7SMIshBC2sblLxu23327Tdu+++26bgxFC6C8rC8rK1Fy9iYnt3NmGDfDTT5CSAmPHOiQ+gcMSZkuXG0mYhRCidTYnzO+99x59+/Zl+PDhdLLVtMVpRo8erXcIhuJu5WHpvzxsGHi18A0xevRoysvLz76zJUvg+edh1iyXSJhdpi5nzoQLL1Q/7WBpYc7MVAP/5GahEEI0z+aE+Z577uFf//oX2dnZTJ06lVtuuYWIiAhnxiYMatOmTaSkpOgdhmG4W3mcrf8yqHMeMGDA2XfmYjNluExdXnKJ+mmnnj1l4J8QQtjC5j7Mr776KkeOHOGhhx7iyy+/JDo6muuvv55vvvlGWpw7mdraWr1DMBR3Kw9bEmabz9nFEmZ3q8uzkYF/QghhG7sG/fn6+nLTTTfx3XffsWPHDgYNGsT06dOJiYmhrKzMWTEKgwkPD9c7BENxp/Kor28Y8NfSlHJgxzlbEub9+9XODc4l6jI7Gz7/vGGmjHaSfsxCCHF2ds/DbOHh4YHJZELTNOpd4A+hcJyebVwowV25U3nYOuCvZ8+eeHl5MX36dAC8WursHB0N3t5QUwOHDkGfPk6I2nFcoi6/+gruvReuvBK++KLZTWyqm1MsLcyWvutCCCHOZFcLc3V1Nf/617+45JJL6N+/P9u2beOVV14hNzeXoKAgZ8UoDCYzM1PvEAzFncrD0so4bBh4era8XWZmJr6+vrz66qu8+uqrLc/B7ukJ/fqp5y7QLcMl6vIsczADttXNKZaEeccOWfFPCCFaYnPCPH36dHr06MEzzzzDFVdcQV5eHkuXLmXChAnNriIlhHA9tvRftpuL9WM2PEs5OmhVQsvAP7MZMjIcskshhHA7NnfJWLRoEX369KFfv36sXbuWtWvXNrvd8uXLHRacMKa4uDi9QzAUdyqPtDT1OHJk69vFxcWhaRoFBQUAdO3aFZPJ1PzGf/87zJsHtsyqoTOXqEsbWphtrhvUwL9Ro+DLL2H9ejj3XIdGK4QQbsHmhHny5MmtfumKzqNC7ts24S7lUV3d0MJ8tunFKioq8Pf3p1u3bgCUlZURGBjY/MauME3bKYavy/p6NYASWk2YKyoqbKubU845RyXM69apKbOFEEI0ZdfCJUIAHDlyhD4GH7zVkdylPDIy1Ni8rl3hbA2tR44coUuXLh0SV0cyfF3m5UFtLfj4qAGVDmK5QPr1V4ftUggh3Ip0PhZCAA3J0jnnqNv0DlNZCQsXqqZLmbO9fSzdMfr1a31Upp1GjgQPD8jNhcOHHbZbIYRwG5IwC7ultjZBbyfkLuWxbp16tGW1N7vO2dMT/vQnlTQfPdq24DqI4ety6FD497/h8ccdutugIBg8WD2XVmYhhDiTJMzCbtu3b9c7BENxl/Jo3MJ8Nnads48P9O2rnht8pgzD12W3bjBpElx/vcN3bal3SZiFEOJMkjALu1VVVekdgqG4Q3kcOQIHDqjb8mebIQPacM6WTtEGT5jdoS7bShJmIYRomSTMwm6hoaF6h2Ao7lAeliQpORmCg8++vd3n7CJzMRu+Lt97T01nUVbm8F1buuKkp6txhUIIIRq0eWls0XkZehYBHbhDedjTHQPUOXt6ejJlyhTg7Msvu0rCbOi6NJvh7rvV/H/796uOxy3w8vKyvW5OSUiA8HAoKoKtWx28eI0QQrg4aWEWdtu2bZveIRiKO5SHJWG2ZcAfqHP29fXlvffe47333jvr8suukjAbui4PHlTJsrf3WaeUs6tuTvHwkG4ZQgjREkmYhejkamthwwb1fPRoJx3EkjDv2ydTy7WVZUq52FiwsdXYXpaE+ZdfnLJ7IYRwWdIlQ9gtJiZG7xAMxdXLIz1dTZXctSskJtr2npiYGDRNs66MFxAQ0PpKoPHxqtmyldXpjMDQdWlpnU9IOOumdtVNI+efrx7/9z91XSOLuwohhCItzMJudXV1eodgKK5eHv/7n3q84ALbE6S6ujoqKioICgoiKCjo7EtK+/io5usuXQydhRm6Li0Jsw0XHXbVTSPnnKN6fBw8CDk5bYxTCCHckCTMwm4HDx7UOwRDcfXyWLtWPY4da/t7XP2cW2Lo87J0ybChhbmtAgIaphW0XEgJIYSQhFmITq2+Hn76ST2/8EInH2z1arU89scfO/lAbsqOFub2sHwOLBdSQgghJGEWbZCSkqJ3CIbiyuWRkQEnT0JoKAwZYvv72nTO69er5bFXrrT/vR3E0HX54YfwySfg5OW7LQmztDALIUQDSZiF3Xbt2qV3CIbiyuVhSYrOPx88PW1/X5vO2QWmljN0XQ4bBjfcoPqBO9F556kp5vbtg0OHnHooIYRwGZIwC7vZM4ioM3Dl8rDcdre3O0abztkFEmZXrktHCQmB4cPVc2llFkIIRRJmYbegVlYY64xctTzMZvjxR/Xc3oS5TeccF6ceT5yA4mL7398BDFuXP/+surOsX98hh5NuGUII0ZQkzMJucZbERwCuWx47dkBhoZoZwd5lkOPi4vD09OS6667juuuuw9OW/hxBQdC9u3pu0FZmw9blF1+oAZMffWTT5nbXzWksM6bIwD8hhFAkYRZ227Jli94hGIqrlsfq1erx3HPV3Lv22LJlC35+fixdupSlS5fi5+dn2xv791ePu3fbd8AOYti6tJTXgAE2bd6mumnk/PPVdNk7d8LRo3a/XQgh3I4kzEJ0Ut9+qx4vvrgDD2pJ+Pbv78CDugHLYEQbE+b26tKloR/zqlUdckghhDA0SZiF3fr06aN3CIbiiuVRWwtr1qjnl1xi//vbfM7z5sHx4/DXv7bt/U5myLqsrW24wOighBkaPhfffddhhxRCCMOShFmITujXX6GsDLp2VbOVtUV5eTkmkwmTyUR5ebltb+rZEyIjDb08tuHs3w91dRAYCL162fSWNtXNaRonzJrWpl0IIYTbkIRZ2C03N1fvEAzFFcvD0mp48cVqzl17ueI528KQ59W4O0YHXmicdx74+8ORI5CZ2WGHFUIIQ5KEWYhOyNJ/+dJLdTj4o4/CxImQl6fDwV2QnQP+HMXPr2F6OemWIYTo7CRhFnYbOnSo3iEYiquVR1ERbNignrel/zK085w/+wz++181r53BGLIu77kH0tJgzpwOP7T0YxZCCEUSZmG3ffv26R2CobhaeaxerRYtSUyE3r3bto92nXNiono04DLUhqzL4GAYNQqGDOnwQ1vuQKxZA9XVHX54IYQwDEmYhd3Kysr0DsFQXK08LK2F7emO0a5ztnQtMOBczK5Wl86WnKzWmqmsVIsNCiFEZyUJs7BbQECA3iEYiiuVh6bBypXqeXsS5nads6WF2YAJs+HqsqgI7r8fXntNl6kqTKaGbhlffdXhhxdCCMOQhFnYLdGS8AjAtcpj61bIzVWzH1x0Udv3k5iYiKenJxMmTGDChAn2Lb9s4BZmw9Xlzp3w8svwzDN2zZDR5rppxsSJ6vE//2nXboQQwqVJwizstmnTJr1DMBRXKo8vv1SPl1yikua22rRpE35+fqxYsYIVK1bYt/yyJWE+dAhOnmx7EE5guLps4wwZba6bZowfr5ZOz8pSP0II0RlJwixEJ2JJmC2throID4du3dRgtoMHdQzEBVgGRurY8h0SAmPHqueWz48QQnQ2hkiYX331VWJiYvDz82P06NGsX7++xW3feustLrjgAsLDwwkPD+fiiy9udXvheL3bOrWCm3KV8jhyBCz/VS6/vH37avc579gBJSWQlNS+/TiY4epSpzmYTyfdMoQQnZ3uCfOSJUuYPXs2c+fOZdOmTQwdOpTx48dz/PjxZrdfs2YNN910E6tXr2bdunVER0dz6aWXcujQoQ6OvPPy8vLSOwRDcZXyWLFCPY4aBT16tG9fXl5elJeXExgYSGBgoP3LL3fpYsjlsQ1Xl5aE2c4W5nbVTTMsCfPPP0NhYbt3J4QQLkf3hPmFF17gzjvvZOrUqQwcOJBFixYREBDAu+++2+z2H330EdOnT2fYsGEkJiby9ttvYzab+f777zs48s4rJydH7xAMxVXKw5HdMSznXFFRQUVFRft3aBCGqsuaGti7Vz1vQwuzI+smNlZNMVdfL7NlCCE6J10T5pqaGjZu3MjFF19sfc3Dw4OLL76YdevW2bSPiooKamtriYiIcFaYQri8ysqG+Zd17b9ssXcvXH11+/uGuLN9+6CuTnUiNkBXEemWIYTozHRNmAsKCqivrycqKqrJ61FRURw9etSmffzlL3+hZ8+eTZLuxqqrqyktLW3yI9pn8ODBeodgKK5QHitXqqS5b1/HLBjX7nP29YXPP4dvv1UtqQZhqLpMSoITJ9QyewbovvL736vHFSvAjW4qCCGETQzWYc8+zzzzDJ988glr1qxpceqk+fPn8/jjj5/xenp6OoGBgaSkpLBz504qKysJDg4mNjaWrVu3AtC3b1/MZjN5eXkADBs2jL1791JWVkZgYCD9+/dn8+bNgBos5OnpyYEDBwAYMmQIOTk5lJaW4ufnx6BBg9i4cSMAPXv2xM/Pj/379wOQnJzMwYMHKS4uxsfHh2HDhlkHMnbv3p2goCD2nro1m5SUxLFjxygsLMTLy4sRI0awfv16NE0jMjKS8PBwsk7N/TRgwAAKCwvJz8/Hw8ODkSNHkp6eTn19PV26dKFbt27s3LkTgISEBEpLSzl27BgAo0ePZtOmTdTW1hIeHk7Pnj3JzMwEwNvbm65du3LkyBEAUlNT2b59O1VVVYSGhtKnTx+2bdsGQExMDHV1dRw8NRtCSkoKu3btoqKigqCgIOLi4tiyZQsAffr0ASA3NxeAoUOHsm/fPsrKyggICCAxMdE67Vfv3r3x8vKy3kIfPHgwubm5lJSU4OfnR3JyMunp6QD06NGDgIAA67LHgwYN4vDhwxQVFeHt7U1KSgppaWmAulgLCQlhz5491vI+fvw4J06cwNPTk9TUVDZs2IDZbCYyMpKIiAjS09MJDg6mf//+FBUVkZ+fj8lkYtSoUWzcuJG6ujoiIiKIioqylnd8fDxlZWXWC8NRo0aRkZFBTU0NYWFh9O7dm+3btwPQr18/qqqqOHz4MAAjRowgMzOTqqoqQkJCiImJafKZra+vt5b38OHDycrK4vXXewBduPbaOtavV5/D6OhoPDw8mnxms7OzOXnyJP7+/iQlJVnLu1evXvj4+JCdnQ2oBT7q6uqs/5/MZrO1DLt3705gYKC1vAcOHMjRo0cpLCxsKO9DhxgRGIhXeTkl6ensOjVXcGJiIgUFBRQUFFg/s5by7tq1K127dmXXqZkjEhISKCkpsY53aPyZjYiIoHv37uzYsQOAuLg4ysvLreU9cuRItm7dSnV1NWFhYURHR7Nt2zZOnjzJkCFDqKmpsY6LMMR3RHm5Xd8RlnqycMR3RHx8Ar16hXDokBcLF2bx8MP9W/yOiIuLo6KiQr4jTn1H7D7VF93I3xHl5eUEBQURHx9PRkYG0L7viMGDB5OXl0dxcTG+vr4MGTKEDRs2WD+zZ/2OOFXe3bp1IzQ01Freen9HAMTGxhrvO8LF8ghbviMs/z+NwqRpOiwfdUpNTQ0BAQEsW7aMq666yvr6lClTKC4u5osvvmjxvc899xxPPvkkq1atIjU1tcXtqqurqa6utv67tLSU6OhoSkpKCAkJcch5dDZpaWmMHj1a7zAMw+jlUV6uZnGrqIANG6CV/y42S0tLIzk5maCgIADrl79dxoyBX3+FJUvg+uvbH5QDGL0ubWVJfqCNddOChx6CBQtUdS1Z4pBdCiFEs0pLSwkNDTVMvqZrlwwfHx9GjBjRZMCeZQDfmDFjWnzfs88+yxNPPMHXX3/darIM4OvrS0hISJMf0T7tXQjB3Ri9PCy30Pv1gxEjHLNPh5zzoEHq8VSLgxEYqi7vvBNmz4ZTrYZGYLmu+e9/1YWYEEJ0Frp3yZg9ezZTpkwhNTWVUaNG8dJLL1FeXs7UqVMBmDx5Mr169WL+/PkA/OMf/+Cxxx7j448/JiYmxnr7JCgoyNqiIpwrOTlZ7xAMxejl8e9/q8frr3dcV9jk5GRqamoYe2pFCw+PNlx7DxyoHg2UMBumLqur4Z//VNNS/OlPdr/dw8OjfXXTghEj1IXX/v3qQswgNwaEEMLpdJ9W7oYbbuC5557jscceY9iwYWRkZPD1119bBwLm5uZa+7gAvP7669TU1HDdddfRo0cP689zzz2n1yl0OkbrV6Q3I5fHyZMN8y87MrlJT0/H39+fNWvWsGbNGvzbss62AVuYDVOXWVkqWQ4JgZ497X57u+umBSZTw+fIciEmhBCdge4tzAAzZsxgxowZzf5uzZo1Tf5tqHlShTC4L7+EqiqIj4dhw/SO5jSDBoGfn0oKzWZwYEuoy7NcRAwaZIgZMhq7/np45hl1IXbypFrhXAgh3J38hRJ269HeZeLcjJHL44MP1OONNzo273LIOffqBWVlkJZmmGTZMHXZOGE2mGHD1DoqVVWwbJne0QghRMcwxl8p4VICAgL0DsFQjFoehw6paY4Bpkxx7L4DAgIoLy8nMjKSyMjIti2/bDLBqenkjMIwddnOhLndddMKk6nh8/Teew7dtRBCGJYkzMJulrkzhWLU8nj/fdXT4YILVJcMR7Kcs2U+VHdhmLp0QAuzM+vm1lvVTYH//U8tSCiEEO5OEmYh3JCmNbT+3XabnpGcxVdfqaUHb75Z70iMo64OiorUcwN2yQC1Uvcll6jn0soshOgMJGEWdhtk0D/iejFieaxbpyZaCAiASZMcv3+HnbOXF2zbBqdWC9ObIerSywuOHVPzLxulT3UzTs38yeLFakIPIYRwZ5IwC7sdNtBCCkZgxPL45z/V46RJzpnFwGHnbElQ9+yBykrH7LMdDFOXJpNKlg02Q0Zjv/89hIVBXh788IPe0QghhHNJwizsVmS5XSwA45VHSQn861/quaUV0NEcds49ekBkpOpsvX27Y/bZDkarSyPz84ObblLP33xT31iEEMLZJGEWdvP29tY7BEMxWnm8/75atjgpCS680DnHcNg5m0wNE0RnZDhmn+1giLqcPBmuusow3VRac8896vGzz+DgQX1jEUIIZ5KEWdgtJSVF7xAMxUjlYTbDK6+o5zNmOO+OfkpKCh4eHqSmppKamtq+5ZeHD1ePBkiYda9LTYOVK+GLL9TzNnJY3ZzF4MEwdqzqw7xokdMOI4QQupOEWdgtLS1N7xAMxUjlsWqVGuwXHKym/nKWtLQ0/P392bBhAxs2bGjf8ssGamHWvS4PHYITJ9T81O0YgOiwurGBZZHWN9+E6mqnHkoIIXQjCbMQbsTSujx1qgstWTx8OCQmqj4knZ3loiEpSXUSdgG//71atDE/H5Yu1TsaIYRwDkmYhd2ioqL0DsFQjFIe+/fDf/+rnk+f7txjOfScExNh5054+23H7bONdK9LS8JsaXV3Ad7ecPfd6vn//V+7epIIIYRhScIs7BYSEqJ3CIZilPJYsEAlK+PHw4ABzj1WSEgIFRUVxMTEEBMTQ0VFhXMP2EF0r0sHJcwdXTd33aUaxDdsgDVrnH44IYTocJIwC7vt2bNH7xAMxQjlceQIvPuuev7ww84/3p49e9A0jQMHDnDgwAE0RzQrms0NK9zpRPe6dFDC7PC6OYtu3eD229Xzp592+uGEEKLDScIshBt48UWoqYFzz3XeVHJOtXw5hITAH/6gdyT6qa1Vmae/v0t1ybB48EE1VnHVKkhP1zsaIYRwLEmYhd2SZHBWE3qXR1ERvP66ev7wwx2zOJzDz7lnTzV5tM4zZehal97e8MsvcPIkdOmiXxxtFBMDN9+sns+fr2soQgjhcJIwC7sdP35c7xAMRe/yePllKCuDIUPg8ss75pgOP+fBg1Wmf+QIHDvm2H3bQe+6BFQzrYuaM0c9Ll8OmZn6xiKEEI4kCbOw24kTJ/QOwVD0LI+CAnjuOfX8kUc6pnUZnHDOgYHQv796vnmzY/dtB10/27W1+h3bQQYOhGuuUc//9jd9YxFCCEeShFnYzdOFW8CcQc/ymD9f3cEfNgwmTeq44zrlnEeMUI8bNjh+3zbS9bOdmqpa2g2wgEt7PPEEeHio5bL1XgdGCCEcRRJmYbfU1FS9QzAUvcojNxdefVU9nz9fJSkdJTU1FZPJxMCBAxk4cCAmRzRtjx6tHn/9tf37aiPdPtulpbBtG2zfDt27t3t3Dq8bOwwcCFOmqOdz5si8zEII9yAJs7DbBh1bAI1Ir/J4/HG1FPHYsWru5Y60YcMGAgICyMzMJDMzk4CAgPbv9Jxz1GNamm5Zlm6f7fR0dc59+zokYXZ43dhp3jzw9VVzMn/7bYcfXgghHE4SZmE3s9msdwiGokd5bNoE//ynev7MMx3Xd9nCKec8dKgatXjffepKQAe6fbYtreqWiwYX16cP3Huvev7nP7tF92whRCcnCbOwW2RkpN4hGEpHl4fZDDNmqAbJG2/UJ8dyyjn7+qq1vefOVcvG6UC3z7abJcwAjz6qZsfbvr2h65AQQrgqSZiF3f6/vTsPi+q6+wD+HfYdRMIqiwoa3ABZ3OMSo9FEq3F7jYnUNE3fVKrW+GrNWzVtE7fUxiQmWqKpNq+mxhi1tTGJQbRxRxQVFRAUUWQV2feZ+/5xZAAZkZGZucPw/TzPfebOnTv3nnPmcvndc889x9XVVe4kGBVDl8cXXwCnTomOJRp6yDA0V1dXVFZWom/fvujbt6/JDI0ty7EtSY1PxzW0424nY/htXF3F3Q9AXAPl5sqSDCIinWDATFpLTU2VOwlGxZDlUVwMLF0q5leuBHx8DLbrZlJTUyFJEq5evYqrV6/qdvjl3FwgPl5329OCLMd2ZiaQny8GLgkL08km9fbbaOm114DISPFM47JlsiWDiKjdGDATdSBLlojYqlcvYNEiuVOjB1lZgJcXMG4cUFUld2oMQ6kE5s4Fpk6VrSmKvpiZAZs2iTb2f/87HwAkoo6LATNprVfDABMEwHDl8d13wLZtYv6zzwArK4PsViO95dnXF/DwAOrrZRnARJZjOzAQ2LED2L3b8Ps2gKgo0eYeAF5/HSgpkTc9RERPggEzae3+/ftyJ8GoGKI8SkqAX/5SzC9YADzzjN532Sq95VmhaGzHK8OoFzy29WPNGqBnT+D2beB//kfu1BARaY8BM2mtoKBA7iQYFUOUx8KFwJ07IuhYvVrvu3ssvea5oaeIU6f0t49HMPixXVMj+ghUKg27XwOztwc+/1zMf/YZ8O238qaHiEhbDJhJa4YeOczY6bs8vvhC3LE3MxN9L9vb63V3baLXPI8YIV6PHjX4ACYGP7ZPnhRDgoeEGHa/MnjmmcZ299HRQHa2rMkhItIKA2bSWlRUlNxJMCr6LI+UFODNN8X8qlWNsaTcoqKioFAo4O/vD39/f90GmlFRgJ0dUFAgOvE1IIMf20eOiNfQUJ1uVm+/TTutXQsMHAgUFgKzZ4um6kREHQEDZtJaYmKi3EkwKvoqj/JyYOZMoKICGDNGDARhLBITE2FnZ4fMzExkZmbqdvhlK6vGK4OGgNJADH5sx8WJ1zFjdLpZvf027WRtLZ5tdHQEfvoJWLFC7hQREbUNA2bSWj2rhZrRR3moVMCrrwKXL4tOI3buBMzNdb6bJ6b3Y2DhQtEP2axZ+t3PQwx6bJeVAWfPivlnnzXcfmUWGAhs3Srm164Fdu2SNz1ERG1hIXcCqOPhSH/N6aM8fv97YP9+Udm6bx/g6anzXbSL3o+BCRP0u/1HMOix/dNP4mG/Hj0Af3/D7dcIzJwpeg1cu1YMbtKjh0mNCk5EJog1zKQ1Dw8PuZNgVHRdHp9/LrrhAkS/y0OG6HTzOuHh4YGqqipERkYiMjISVSYyyIhBj+2G5hh6qF3uCL/Ne+8BU6aIjkJ+9jMgPV3uFBERPRoDZtLatWvX5E6CUdFleXzzTWN/y2+/Dbzyis42rVPXrl2DSqXCuXPncO7cOahUKt3v5Pp14M9/Bvbs0f22H8Ggx3ZD+2wdt18GoP/fRgfMzEQPMKGhYvTK555jzxlEZLwYMBMZiR9/FD0HqFTAL34BvPuu3CmS2fffi1EuYmPlTol+bNwonnrTQ8DcUTg4AIcOiXbNmZliRPR79+ROFRFRSwyYSWuBgYFyJ8Go6KI8vv8emDQJqK0Fpk8H/vpXMeidsTLIMdAQSB4/DhioWYFBj+2RI4E//hFwdzfcPo2Qpydw+DDg7Q1cvQqMHi1qnImIjAkDZtJaeXm53EkwKu0tjwMHgMmTgepq4MUXgf/7P+PqEUMTgxwDwcFAt26iYH78Uf/7A49tuQQEiJ/Y01P0DPPMM2yeQUTGhQEzaS03N1fuJBiV9pTH1q3AtGmiZnnGDGDvXtFXrbEzyDGgUABTp4r5ffv0vz8YKF+1tcD8+WJ8aBMfElsbwcHAf/4D+PoCqanAsGGixpmIyBgwYCaSgUoFLF8uHvBTKsVQwbt2iW7kqImXXhKvBw6YzrBw8fHAp5+KhurG3O5GBkFBore9oCDg1i1g6NDGzkSIiOTEgJm0xqGxm9O2PEpKRBy4dq14v2oV8Le/ARYdqFf0hjy7ubnBzc1NfzsaPhxwcwOKikT1o54Z5Nj+5hvxOmWK6CpCT/T+2+iJvz9w6pT46UtKgOefBz76CJAkuVNGRJ0ZA2bSWlJSktxJMCralMfly0BEhKgwtbICtm8H3nmn41U0JiUlwd7eHgUFBSgoKIC9vb1+dmRhITrptbMDbtzQzz6a0PuxrVSKEWmAxuYmemCQ30aPunYVDwK+/LK4sbBwITBnjhgunohIDgyYSWu1tbVyJ8GotKU8VCrgww+ByEgxQIOfn+j8ITraAAnUA4MeA++9BxQWAq+/rvdd6T1fJ0+KLiBcXIBRo/S7rw7OxkY8APvBB+K66csvgbAw4MwZuVNGRJ0RA2bSmouLi9xJMCqPK49bt8Rt5UWLxKhmEycC58+L4LmjMugx4OEB2NoaZFd6z1fDw4uTJrHBehsoFOLvJj5edJiSni4eBly5UvwtEREZCgNm0lq3bt3kToJReVR51NcDGzYAffqI28u2tuJZr4MHxS3njqxbt26oqqrCqFGjMGrUKMMMvyxJ4upDj/R6bNfViWpSoPFhRj0x+G+jZ8OHA5cuiSYaSiXwpz+JEQIN0KydiAgAA2Z6AsnJyXInwahoKo/Dh8Xt4yVLgMpK0a/shQvAm292vPbKmiQnJ0OlUuHYsWM4duyY/odfzskRBdq/v14bsur12L59W1wpeXiI2wx6ZNDfxkC6dAF27gS++koUYUqKGPtlzhxRtERE+sSAmUiHEhKAF14QQ/wmJ4t/8lu3ilvKvXvLnboOzMNDXHmUlYn+9zqiHj3EU58JCWyO0Q4zZgDXrgFvvCEuPnftAnr1An73O6CgQO7UEZGpYsBMWuvRo4fcSTAqPXr0wKlTwIQJQFSUGI/CwkI82Z+eLrrb1WPvYbIw+DFgZgb86ldifssWvfUxpvd8KRRiZA5qly5dxPDx584BI0aIwSDXrRMjBi5ZAnBsJSLSNRP7N06GUF1dLXcSjIJKJZpevPSSI4YOBb77TgxpPXcucOUKsHEj4Ooqdyr1Q5Zj4Oc/F8MgXrgAnD2rl13oLV9nzgAVFfrZdic2cCBw7JjopjEiQtyE2LAB6N4dWLAAyMiQO4VEZCoYMJPW7t69K3cSZFVQAKxfL24DjxsHHD9uAwsLUZOcmgrs2CE+M2WyHANduwKzZon5Tz/Vyy70kq/KSnH7wdubYz3rgUIBTJ4srqG+/RYYPFjUOH/8MRAYKP5G9+4Vz1wSET0pBsxEbVBbK/4Zz54turdatkzUXjk5AdOn5+L6ddFWuWdPuVNq4ubPF687d4qrk47gww+B+/dFwM+G7HqjUIjrkpMnxZ2f558Xyw4fBqZPF32fv/226G2DowYSkbYUktS5Th2lpaVwdnZGSUkJnJyc5E5Oh1RfXw+LjjSO8xOqrQV+/BHYs0cMzlZc3PhZZKRoUvtf/wVYW3eO8miqvr4eNTU1cHd3BwDk5+cbbkS5yZOBf/0LWL4cWL1ap5vW+bFdWCiuokpLgS++AF55RXfbbkVFRYU8v42RuXkT+Owz4PPPgby8xuW9e4uHB2fOBPr1M42ea4hMjbHFawyYSWsXL15ESEiI3MnQi6ws4PvvxfTjj0BJSeNnXl7AtGnAa6+JHs4amHJ5PIqseU5JEU0bpk7VeaSj83wtWiRqmENDgcRE03v6s4OoqxPtnHfuBA4daj7oSWAgMH68mEaPBhwc5EsnETUytnitc1WLkU6Y0kN/2dnAiRNimOrDh0Us1pSnp7idO2OGGGHM3LzlNkypPNpK1jw//bSY9ECn+bpxo7Gt9fr1DJZlZGkp/o6nTxeV/QcPiv6cv/tO9GSTng588olYb9gwETgPHw4MGgR00sp5InoIA2bSmjFc6T2J8nLg4kUxLPWZMyJQzsxsvo6ZmXhoqKHGKSJCc5DcVEctj/Ywmjzn5QGnTwM/+5lONqezfNXXi6dA6+qA554TExkFJycxYuDLL4tuvY8cabyrdOMGcPSomADxtx8aKoLoyEjRK0fv3o8/JxCR6WGTDNJaVVUVbG1t5U7GI9XViRqjlBQxwMGVKyJITk1t+bCPmRkQEiJqk555Bnj2WdHHqzaMvTz0oaqqCgqFAtOmTQMA7N27FzY2NoZNxJ07ouPrwkJx9RMZ2e5N6uy3rK0FFi8WXaacO2fwh/2qq6vl/W06qPR04IcfxB2n48c1jyBoZyfOGaGhQHBw4+TtzbbQRLpkbPEaA2bS2pkzZzBo0CBZ01BbK/6ZZWaKKSNDBMfXron5+nrN3/P2FrVE4eGi1mjwYMDRsX1pMYbyMLQzZ86gX79+cHjQ4LO8vNzwD5ZJkmhUvm+fGLHi/Hntr3YeovPf8s4d0a2KgVVUVMj725iIrCxxLXbqlDi8kpIe3Z22o6NoKRQcLK6PuncXh2VAgBioki1yiLRjbPEam2SQUZEk0XQiJ0eM1tX0NTu7MUDOzm69aygHh8amrsHB4iG9sDDRJplMhEIhuj+4eFHcSx83Dvj3v4EHvUPI4vhxUf3YcBUmQ7BMuuPnJ6bZs8V7pRK4fl2MnXPxYuNdrIwM0bwjIUFMD7OxAfz9RfDs7y8eIPbyEuejhldPT46YTmTMjCJg/uSTT/D+++8jNzcXISEh+PjjjxEVFfXI9ffs2YMVK1YgMzMTQUFBWLduHSZOnGjAFHdu/v7+bVqvIfgtKhLT/fua54uKRFPUhsC4srJt6bCxaazBCQhoDI6Dg0WcYqjbo20tD1NiNHl2cQG++QYYO1Y0fRg6VDRGfcIOsduVr127xGiEI0eKwJ3Rj8kxN2+8EG8IogHR60bTZmBpac0v7qurRZOwx3Ud7uraGEC7u4v3rq6iC29N8y4uQCfr0ZJINrL/qe3evRuLFy/Gli1bMGjQIGzcuBHjx49Hamqquh/Rpk6ePInZs2djzZo1ePHFF7Fr1y5MmTIF58+fR79+/WTIQccnSaKJQ3V186mqStx+LC8XU1mZeM3OtoW5eeP7hz8vLxd9Ft+//+imEY/j6Ni89sXLSzSnaAiO/f3FPxRjaDOoVCrlToLBGVWeQ0LEffPx40VVX1gYEB8v2t1o6YnydesWsHSp6HYBEBFN52rp1ulZWwN9+4rpYXV1omVOQwB961bLu2e5uWK9hgqEK1favm97e3G+dHQUDzQ2fdW0zNZWTHZ2ml8bJgbiRM3J3oZ50KBBiIyMxKZNmwAAKpUKvr6++M1vfoPf/e53LdafNWsWKioqcPDgQfWywYMHIzQ0FFu2bHns/trTJkaSxC05pRJQqZq/PskypVKcJNsy1de3fd2GSVMQ/KhJn0eBlVVjjYirq2hm2nS+SxfRxq/prcmO1Bcq2zAbSTvZnBzRprm4WNwvt7QUy7Oz2/xEVpt/y6aj2vzjH+KPSKEAliwB1q6VvcEq2zB3LJIkAuWGADonRzzL2hBA37vXcr60VL9psrRsHkRbW4tzecNr06kty5q+t7AQtfUWFi3nW/tM23kzs8bJGCpXSDtsw9xEbW0tEhMTsXz5cvUyMzMzjB07FqdOndL4nVOnTmHx4sXNlo0fPx779+/Xat8NldHaBLidhY1N4+Tg0HxydASqqvLRvbt7s2UPr+fi0hgY29ryZEUG4OUl2hDn5DQGy9XVoo2OmZn4o+/ZU9QAd+ki/qOGh4u2zwBQWQmP3btFI1SlUlylNpwEqqtFzfWUKWLd1FTghRca9z1qFLBxo6jtJtKSQiEOy65dNddSa1JXJ+7ilZWJqbS09fnSUnH3r6pKNHtr+tow37Qb8ro6MXBT08GbOrqmAfTDwfSjPmvL1JbvKxQtJ0Dz8tY+03a5ob7TdHmD9s43HWDIGMgaMBcWFkKpVMLDw6PZcg8PD6Q8PILEA7m5uRrXz83N1bh+TU0NapqUeumDy3JN3QXpgkIhrnDNzMRr03lNyywtdTtZWDTOW1mJYNXGpvH1cZOV1eOD29paFzbPbCKs6bB/nURYWBjq6urkTkZLZmaAj0/j+4sXRW1wTY1otnHiRPP1Y2IaA+ayMgR88MGjtz1vXmPA3Ls34Osr+n+eOVP0S8irQjIgS0vRLE2Xz7iqVI3N8R4OrGtrm081NU+2rKam8Xq04ZpUV/NtyZ9Kpbvyos7F5FsprVmzBn/4wx9aLP/446twcHBAnz69cevWTdTWVsPR0Q7+/t2QlpYCc3PA19cHgBJ5eXdhZgYMGNAXt27dRFVVORwd7dCrVyAuX06CmRng5+cDS0tzZGXdAgAMGDAAmZmZKC0thY2NDfr27YvExEQAgLe3N2xsbHDjxg0AQL9+/XDnzh0UFxfDysoKoaGhOHv2LADA09MTDg4OSE9PBwAEBwcjLy8PRUVFsLCwQHh4OM6ePQtJkvDUU0+hS5cuSEtLAwD07t0bRUVFKCgogJmZGSIjI3Hu3DkolUrY23eFu7s7rl27BgAICgpCTk4p8vLyAIimMufPn0ddXR26dOkCb29vXHnQsM7MzAweHh7IyckBAERERCA5ORnV1dVwdnaGn58fLl++DAAICAhAfX097ty5AwAYOHAgUlJSUFlZCQcHB/Ts2RMXL14EAPj5+QEAsrKyAAAhISHIyMhAeXk57Ozs8PTTT+P8+fMAgG7dusHCwgKZD0Ye6d+/P7KyslBSUgIbGxv069cP586dAwB4eXnBzs4OGRkZAIC+ffvi7t27uH//PiwtLTFw4ECcOXMGgLj4cnJywvXr19XlnZ+fj3v37sHc3BwRERFISEiASqXCU089BVdXVyQkJMDJyQm9evXC/fv3UVBQAIVCgaioKCQmJqK+vh6urq7w8PBQl3dgYCDKy8vVF3pRUVFISkpCbW0tXFxc0K1bNyQnJwMAevTogerqaty9excAEB4ejitXrqC6uhpOTk4ICAjApUuXAIiH1pRKpbq8w8LCkJaWpr5FHxgYiKSkJACAr68vzMzMcOtW4zF78+ZNlJWVwdbWFsHBwery9vHxgZWVFW7evAkAsLKygp2dHU6fPg1ra2vY2tqqy9DT0xP29vbq8u7Tpw9yc3NRVFTUorzd3d3h7OysLu+nn34ahYWFKCwsVB+zDeXt5uYGNzc39cV0UFAQSkpKkJ+f3+KYdXV1haenJ64CUBw+jCBJQn1SEqpSU2FRUgIvW1sUFRbi/lNPQZmaCl9fX1y9fBndRo2Ci5MTVAAqa2shmZmhq7s7iiorUezvj9qrV9G9e3dR3l99Bf+AAKhUKtx+8PcaGhqK9PR0dTOIXr164cKFC+pj1tzcvFl56/Mccfr0aVhYWMDe3v6JzhFdu7Y8R5SWtu0c0bNnT1RWVvIc8eAckfrgab+Oco5IT288Rzg5iXOEjQ0QFdX2c0T//v1x+/ZtFBcXw9raGgMGDEDCgy5E9HGOUCpVcHV1g4uLG65dS0N9vQI9evREcXEp8vMLoVIBYWHhuHjxMmpr6+Hk5IKuXZ9CWlo6VCrA19cPFRVVyM8vhCQp0KdPP6SkpKGmpg729o5wd/dEWlo6JEkBT09v1NTUqdft2TMImZlZqKmphbW1LdzdPXHjRiZUKsDNzR1KpYSCgnuQJMDfPwA5Obmorq6GtbUNPD29cPNmJiQJ6NLFFQqFGQoL7z04hn1RUFCIqqoqWFhYwcvLG5mZ4vzh7OwCCwtLFBQUQpIALy9v3L9fjMrKSpibW8DHpxsyM29BkgBHRydYWVmjoKDwQZl6oLS0DJWVlVAozOHj0w1ZWbchSRLs7e1hY2On3q6bmxsqKqpQWVkBwAze3j7Izr4LlUoFGxs72NnZo7CwUJ3+mpoaVDzoe9Hb2we5ublQKlWwsbGBg4ODOg0uLi6oq6tHeXn5g78rTxQWFqK+vh7W1tZwdHTC3bu52LMHRkPWNsy1tbWws7PD119/jSkNNTcAoqOjUVxcjAMHDrT4jp+fHxYvXoxFixapl61atQr79+9Xn1Sb0lTD7OvrazRtYjqizthmtzWdsTxMNc+mmi8ioo7G2Nowy/pkipWVFcLDwxEXF6deplKpEBcXhyFDhmj8zpAhQ5qtDwCHDx9+5PrW1tZwcnJqNlH7OHSkJ/IMoDOWh6nm2VTzRURE7SN7k4zFixcjOjoaERERiIqKwsaNG1FRUYF58+YBAObOnQsfHx+sWbMGALBw4UKMHDkSGzZswAsvvIB//OMfOHfuHGJjY+XMRqcSGBgodxKMSmcsD1PNs6nmi4iI2kf2wTpnzZqFP//5z1i5ciVCQ0ORlJSE7777Tv1gX1ZWlrodHAAMHToUu3btQmxsLEJCQvD1119j//797IPZgBrawJLQGcvDVPNsqvkiIqL2kb2GGQBiYmIQExOj8bOjR4+2WDZjxgzMmDFDz6kiIiIiIjKCGmbqeHx9feVOglHpjOVhqnk21XwREVH7MGAmrZnJPIqZsemM5WGqeTbVfBERUfvwvwNpraEfWRI6Y3mYap5NNV9ERNQ+DJiJiIiIiFrBgJm0NmDAALmTYFQ6Y3mYap5NNV9ERNQ+DJhJaw1Dn5LQGcvDVPNsqvkiIqL2YcBMWisrK5M7CUalM5aHqebZVPNFRETtw4CZtGZrayt3EoxKZywPU82zqeaLiIjaRyFJkiR3IgyptLQUzs7OKCkpgZOTk9zJ6ZDq6upgaWkpdzKMRmcsD1PNs6nmi4ioozG2eI01zKS18+fPy50Eo9IZy8NU82yq+SIiovYxiqGxDamhQr20tFTmlHRcFRUVLL8mOmN5mGqeTTVfREQdTcO52FgaQnS6gLnhoR4OgUtERERk3MrKyuDs7Cx3MjpfG2aVSoVevXohMTERCoVC7uR0SJGRkUhISJA7GUajM5aHKea5tLQUvr6+uH37tlG0lyMi6swkSUJ4eDjS0tJgZiZ/C+JOV8NsZmYGKysro7ha6ajMzc0ZUDTRGcvDlPPs5ORksnkjIupIrKysjCJYBjrpQ3/z58+XOwkdGsuvuc5YHp0xz0REZFjG9L+m0zXJICLSxNi6MCIiIuPRKWuYiYgeZm1tjVWrVsHa2lrupBARkZFhDTMRERERUStYw0xERERE1AoGzERERERErWDATERERETUCgbMejB16lR06dIF06dPlzspRERERATg4MGD6N27N4KCgrB161atvsuH/vTg6NGjKCsrw44dO/D111/LnRwiIiKiTq2+vh59+vRBfHw8nJ2dER4ejpMnT6Jr165t+j5rmPVg1KhRcHR0lDsZRKRD7amZICIieZ09exZ9+/aFj48PHBwcMGHCBPzwww9t/r5RBczZ2dl45ZVX0LVrV9ja2qJ///44d+6czrb/n//8B5MmTYK3tzcUCgX279+vcb1PPvkEAQEBsLGxwaBBg3D27FmdpYGIOp76+nosXrwYR44cwYULF/D+++/j3r17cieLiEh2a9asQWRkJBwdHeHu7o4pU6YgNTVVp/vQRfx29+5d+Pj4qN/7+PggOzu7zWkwmoD5/v37GDZsGCwtLXHo0CFcvXoVGzZsQJcuXTSuf+LECdTV1bVYfvXqVeTl5Wn8TkVFBUJCQvDJJ588Mh27d+/G4sWLsWrVKpw/fx4hISEYP3488vPz1euEhoaiX79+Laa7d+9qmWsi6gjaWzNBRGSqjh07hvnz5+P06dM4fPgw6urqMG7cOFRUVGhcX874rV0kI7Fs2TJp+PDhbVpXqVRKISEh0vTp06X6+nr18pSUFMnDw0Nat27dY7cBQNq3b1+L5VFRUdL8+fOb7cvb21tas2ZNm9LWID4+Xpo2bZpW3yEi/Th27Jj04osvSl5eXo/829+0aZPk7+8vWVtbS1FRUdKZM2fUn+3Zs6fZeWH9+vXS+++/b4ikExF1KPn5+RIA6dixYy0+kzN+O3HihDRlyhT15wsXLpR27tzZ5nwZTQ3zP//5T0RERGDGjBlwd3dHWFgYPvvsM43rmpmZ4dtvv8WFCxcwd+5cqFQqZGRkYMyYMZgyZQqWLl36RGmora1FYmIixo4d22xfY8eOxalTp55om0Qkv8fVTui9ZoKIqJMoKSkBALi6urb4TM74LSoqCsnJycjOzkZ5eTkOHTqE8ePHt3kfRhMw37hxA5s3b0ZQUBC+//57vPnmm1iwYAF27NihcX1vb28cOXIEx48fx8svv4wxY8Zg7Nix2Lx58xOnobCwEEqlEh4eHs2We3h4IDc3t83bGTt2LGbMmIFvv/0W3bp1Y7BNJLMJEybg3XffxdSpUzV+/pe//AW//OUvMW/ePPTp0wdbtmyBnZ0dPv/8cwDifNO0rVt2dja8vb0NknYioo5CpVJh0aJFGDZsGPr166dxHbniNwsLC2zYsAGjR49GaGgo3nrrrTb3kAEAFk+cOh1TqVSIiIjA6tWrAQBhYWFITk7Gli1bEB0drfE7fn5++OKLLzBy5Ej06NED27Ztg0KhMGSyNfrxxx/lTgIRtVFDzcTy5cvVy1qrmXB2dsahQ4ewYsUKuZJMRGSU5s+fj+TkZBw/frzV9eSK3yZPnozJkyc/0XeNpobZy8sLffr0abYsODgYWVlZj/xOXl4e3njjDUyaNAmVlZX47W9/2640uLm5wdzcvEWj87y8PHh6erZr20RknAxRM0FEZOpiYmJw8OBBxMfHo1u3bq2u2xHjN6MJmIcNG9aiG5K0tDT4+/trXL+wsBDPPvssgoOD8c033yAuLg67d+/GkiVLnjgNVlZWCA8PR1xcnHqZSqVCXFwchgwZ8sTbJaKOb/LkyUhLS0N6ejreeOMNuZNDRGQUJElCTEwM9u3bhyNHjqB79+6trt9R4zejaZLx29/+FkOHDsXq1asxc+ZMnD17FrGxsYiNjW2xrkqlwoQJE+Dv74/du3fDwsICffr0weHDhzFmzBj4+PhovFopLy9Henq6+v3NmzeRlJQEV1dX+Pn5AQAWL16M6OhoREREICoqChs3bkRFRQXmzZunv8wTkWx4Z4mI6MnNnz8fu3btwoEDB+Do6Ki+M+fs7AxbW9tm63bo+K3N/WkYwL/+9S+pX79+krW1tfT0009LsbGxj1z3hx9+kKqqqlosP3/+vHT79m2N34mPj5cAtJiio6Obrffxxx9Lfn5+kpWVlRQVFSWdPn26XfkiIuMBDV0SRUVFSTExMer3SqVS8vHx0bo7SSKizkZTXAVA+tvf/qZx/Y4avykkSZJ0E3oTERmnprUTYWFh+Mtf/oLRo0erayd2796N6Oho/PWvf1XXTHz11VdISUlp0baZiIg6HwbMRGTyjh49itGjR7dYHh0dje3btwMANm3ahPfffx+5ubkIDQ3FRx99hEGDBhk4pUREZIwYMBMRERERtcJoeskgIiIiIjJGDJiJiIiIiFrBgJmIiIiIqBUMmImIiIiIWsGAmYiIiIioFQyYiYiIiIhawYCZiIiIiKgVDJiJiIiIiFrBgJmIqIN75513EBoa2u7t1NbWIjAwECdPnlQvS0lJweDBg2FjY4PQ0FAUFhbC3d0dd+7caXVb27dvh0KhgEKhwKJFi9qdtoc1bNvFxUXn2yYiehgDZiIyGT//+c/VgVTTKT09Xe6k6dWSJUsQFxfX7u1s2bIF3bt3x9ChQ9XLVq1aBXt7e6SmpiIuLg5ubm6YO3cuVq1a9djtOTk5IScnB3/605/Uy0aNGqUxgN6+fbtWwW9OTg42btzY5vWJiNqDATMRmZTnn38eOTk5zabu3bu3WK+2tlaG1OmHg4MDunbt2q5tSJKETZs24Re/+EWz5RkZGRg+fDj8/f3V+5g3bx527tyJoqKiVrepUCjg6ekJR0fHdqVNE09PTzg7O+t8u0REmjBgJiKTYm1tDU9Pz2aTubk5Ro0ahZiYGCxatAhubm4YP348ACA5ORkTJkyAg4MDPDw88Oqrr6KwsFC9PZVKhfXr1yMwMBDW1tbw8/PDe++9BwA4evQoFAoFiouL1esnJSVBoVAgMzNTvez48eMYMWIEbG1t4evriwULFqCiokL9eUBAAFavXo3XXnsNjo6O8PPzQ2xsbLN83blzB7Nnz4arqyvs7e0RERGBM2fOAGjZJCMhIQHPPfcc3Nzc4OzsjJEjR+L8+fOtlltiYiIyMjLwwgsvqJcpFAokJibij3/8IxQKBd555x0AQN++feHt7Y19+/Y9/gd5QgEBARrvFhARyYEBMxF1Gjt27ICVlRVOnDiBLVu2oLi4GGPGjEFYWBjOnTuH7777Dnl5eZg5c6b6O8uXL8fatWuxYsUKXL16Fbt27YKHh0eb95mRkYHnn38e06ZNw6VLl7B7924cP34cMTExzdbbsGEDIiIicOHCBfz617/Gm2++idTUVABAeXk5Ro4ciezsbPzzn//ExYsXsXTpUqhUKo37LCsrQ3R0NI4fP47Tp08jKCgIEydORFlZ2SPT+dNPP6FXr17NaoNzcnLQt29fvPXWW8jJycGSJUvUn0VFReGnn35qczloKyEhQX2H4M6dOxg8eDBGjBiht/0REbXGQu4EEBHp0sGDB+Hg4KB+P2HCBOzZswcAEBQUhPXr16s/e/fddxEWFobVq1erl33++efw9fVFWloavLy88OGHH2LTpk2Ijo4GAPTs2RPDhw9vc3rWrFmDOXPmqNvtBgUF4aOPPsLIkSOxefNm2NjYAAAmTpyIX//61wCAZcuW4YMPPkB8fDx69+6NXbt2oaCgAAkJCXB1dQUABAYGPnKfY8aMafY+NjYWLi4uOHbsGF588UWN37l16xa8vb2bLfP09ISFhQUcHBzg6enZ7DNvb29cuHChzeXQ1KeffoqtW7c2W1ZfX68uCwB46qmn1PMLFy5ETk4OEhISnmh/RETtxYCZiEzK6NGjsXnzZvV7e3t79Xx4eHizdS9evIj4+PhmAXaDjIwMFBcXo6amBs8+++wTp+fixYu4dOkSdu7cqV4mSRJUKhVu3ryJ4OBgAMCAAQPUnze0/c3PzwcgmnmEhYWpg+XHycvLw+9//3scPXoU+fn5UCqVqKysRFZW1iO/U1VV1SxgfRxbW1tUVla2ef2m5syZg//93/9ttuybb75pduHSIDY2Ftu2bcPJkyebBdFERIbEgJmITIq9vf0ja1+bBs+AaOowadIkrFu3rsW6Xl5euHHjRqv7MjMTrdokSVIvq6ura7GPX/3qV1iwYEGL7/v5+annLS0tm32mUCjUTS5sbW1bTcfDoqOjce/ePXz44Yfw9/eHtbU1hgwZ0uqDjm5ubrh8+XKb91FUVPTEAayzs3OL38jd3b3FevHx8fjNb36DL7/8stkFBRGRoTFgJqJOa+DAgdi7dy8CAgJgYdHydBgUFARbW1vExcXh9ddfb/F5Q8CYk5ODLl26ABC1wQ/v4+rVq602oXicAQMGYOvWrSgqKmpTLfOJEyfw6aefYuLEiQCA27dvN3uQUZOwsDBs3rwZkiS16eG65ORkjBo1qk3pfxLp6emYPn063n77bbz00kt62w8RUVvwoT8i6rTmz5+PoqIizJ49GwkJCcjIyMD333+PefPmQalUwsbGBsuWLcPSpUvx97//HRkZGTh9+jS2bdsGQLQj9vX1xTvvvIPr16/j3//+NzZs2NBsH8uWLcPJkycRExODpKQkXL9+HQcOHGjx0F9rZs+eDU9PT0yZMgUnTpzAjRs3sHfvXpw6dUrj+kFBQfjiiy9w7do1nDlzBnPmzHlsLfXo0aNRXl6OK1euPDY9lZWVSExMxLhx49qcB21UVVVh0qRJCAsLwxtvvIHc3Fz1REQkBwbMRNRpeXt748SJE1AqlRg3bhz69++PRYsWwcXFRd3cYsWKFXjrrbewcuVKBAcHY9asWeq2xZaWlvjyyy+RkpKCAQMGYN26dXj33Xeb7WPAgAE4duwY0tLSMGLECISFhWHlypUtHrBrjZWVFX744Qe4u7tj4sSJ6N+/P9auXQtzc3ON62/btg3379/HwIED8eqrr2LBggUamzw01bVrV0ydOrVZW+tHOXDgAPz8/PTWa0VeXh5SUlIQFxcHb29veHl5qSciIjkopKaN74iIqNO6dOkSnnvuOWRkZGh8ELLB4MGDsWDBArz88suPXGf79u1YtGhRsz6qdc0Q+yAiAljDTEREDzTUkt+8efOR6xQWFuKll17C7NmzH7u9kpISODg4YNmyZbpMJgAxuuF///d/63y7RESasIaZiIh0rqysDHl5eQAAFxcXuLm56XT76enpAABzc3ONQ58TEekSA2YiIiIiolawSQYRERERUSsYMBMRERERtYIBMxERERFRKxgwExERERG1ggEzEREREVErGDATEREREbWCATMRERERUSsYMBMRERERtYIBMxERERFRK/4fNtaBbrbb3acAAAAASUVORK5CYII=",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt_Sparams(S21sq, S11sq, bDb = False)\n",
"\n",
"plt.plot( [w1, w1], [0, 1], 'k--', label = 'banda de paso' )\n",
"plt.plot( [w2, w2], [0, 1], 'k--' )\n",
"plt.xlim([.6, 2])\n",
"plt.legend()\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Asegurar que un filtro mantenga en su banda de paso las $R_L$ por debajo de 20 dB es equivalente a asegurar la adaptación al generador y a la carga, ya que el módulo de $|S_{11}| \\approx 0$ . Para ello se buscará la relación entre las $R_L$ y el parámetro $\\epsilon$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Entonces volviendo a la ecuación de complementaridad\n",
"\n",
"$$\n",
"|S_{11}|^2 + |S_{21}|^2 = 1 \n",
"$$\n",
"$$\n",
"1 + \\frac{|S_{21}|^2}{|S_{11}|^2} = \\frac{1}{|S_{11}|^2} = 10^{\\frac{R_L}{10}}\n",
"$$\n",
"tenemos\n",
"$$\n",
"1 + \\frac{\\epsilon_R^2}{\\epsilon^2} \\cdot \\left|\\frac{P(s)}{F(s)}\\right|^2 = 10^{\\frac{R_L}{10}}\n",
"$$\n",
"\n",
"Como el filtro que estamos analizando no tiene ceros de transmisión en frecuencias finitas, debe cumplir la condición de unitariedad en los extremos, donde $|S_{21}|=0$, luego\n",
"\n",
"$$\n",
"\\lim_{\\omega \\to \\infty} |S_{11}(s)|^2 + 0 = 1\n",
"$$\n",
"\n",
"$$\n",
"\\lim_{\\omega \\to \\infty} \\left| \\frac{F(s)}{\\epsilon_R.E(s)} \\right| = 1\n",
"$$\n",
"\n",
"recordando que ambos polinomios son mónicos, entonces $\\epsilon_R = 1$, y en consecuencia\n",
"\n",
"$$\n",
"\\epsilon = \\frac{1}{\\sqrt{10^{R_L/10} - 1}} \\cdot \\left|\\frac{P(s)}{F(s)}\\right| \n",
"$$\n",
"\n",
"En el caso de nuestro pasabanda, partimos de un *prototipo pasabajo* donde aseguraremos $R_L \\leq$ 20 dB en la banda de paso. Para ello sabemos que $P(s)=1$ para el caso de un pasabajo *Butterworth*. Del mismo modo $F(s)= s^2$ y para $s = j.1$ r/s tendremos $\\left|F(s)\\right| = 1$. Luego simplemente resta calcular\n",
"\n",
"$$\n",
"\\epsilon = \\frac{1}{\\sqrt{10^{R_L/10} - 1}}\n",
"$$\n",
"\n",
"para $R_L = 20$ db en el límite de la banda de paso."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"RL = 20 # dB\n",
"eps_sq = sp.Rational(1)/sp.Rational(10**(RL/10)-1)\n",
"\n",
"S21sq_proto = 1/(1+ sp.Rational(eps_sq) * (s/sp.I)**4)\n",
"\n",
"S11sq_proto = sp.simplify(sp.expand(1 - S21sq_proto))\n",
"\n",
"plt_Sparams(S21sq_proto, S11sq_proto, bDb = True)\n",
"\n",
"plt.title('Prototipo pasabajo')\n",
"\n",
"plt.plot( [1., 1.], [-40, 3], 'k--', label = 'banda de paso' )\n",
"plt.fill([.1, .1, 1., 1.], [-40, -20, -20, -40], 'lightgrey', alpha=0.4, lw=1, ls='--', ec='k', label = '$R_L$ objetivo')\n",
"\n",
"plt.ylim([-40, 3])\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se verifica entonces que el prototipo pasabajo cumple con el requerimiento de $R_L \\leq$ 20 dB para $\\omega \\leq$ 1 r/s. Luego se procede a calcular el filtro pasabanda usando este prototipo"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S21sq = sp.simplify(sp.expand(S21sq_proto.subs(s, Kbp)))\n",
"\n",
"S11sq = sp.simplify(sp.expand(1 - S21sq))\n",
"\n",
"plt_Sparams(S21sq, S11sq, bDb = True)\n",
"\n",
"plt.plot( [w1, w1], [-40, 3], 'k--', label = 'banda de paso' )\n",
"plt.plot( [w2, w2], [-40, 3], 'k--' )\n",
"plt.fill([w1, w1, w2, w2], [-40, -20, -20, -40], 'lightgrey', alpha=0.4, lw=1, ls='--', ec='k', label = '$R_L$ objetivo')\n",
"plt.ylim([-40, 3])\n",
"plt.xlim([.6, 2])\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En este caso verificamos que el pasabanda también cumple el requerimiento de $R_L$ en el ancho de banda especificado por $Q=5$.\n",
"\n",
"Ahora procedemos a calcular $S_{21}$ y $S_{11}$."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"99*s**4\n",
"\n",
"{0: 4}\n",
"\n",
"625*s**8 + 2500*s**6 + 3750*s**4 + 2500*s**2 + 625\n",
"\n",
"{-I: 4, I: 4}\n"
]
},
{
"data": {
"text/latex": [
"$\\displaystyle S_{11}=\\frac{1.0 s^{4} + 2.0 s^{2} + 1.0}{1.0 s^{4} + 0.8922 s^{3} + 2.398 s^{2} + 0.8922 s + 1.0}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}=\\frac{0.398 s^{2}}{1.0 s^{4} + 0.8922 s^{3} + 2.398 s^{2} + 0.8922 s + 1.0}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# A veces puede tener sentido forzar el signo para hallar la red\n",
"# dual.\n",
"S11_sign = 1\n",
"# s11_sign = -1\n",
"\n",
"# Cuando se demora mucho en factorizar se puede probar bTryNumeric=True\n",
"S21 = modsq2mod_s(S21sq, bTryNumeric=True)\n",
"S11 = S11_sign * modsq2mod_s(S11sq, bTryNumeric=True)\n",
"\n",
"# Truquito, S21 y S11 comparten polos\n",
"ks21, Ps, Es = simplify_n_monic(S21)\n",
"ks11, Fs, _ = simplify_n_monic(S11)\n",
"\n",
"S11 = ks11 * Fs / Es\n",
"S21 = ks21 * Ps / Es\n",
"\n",
"print_latex(a_equal_b_latex_s('S_{11}', S11.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('S_{21}', S21.evalf(4)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculamos entonces $Z_1$ a partir de $S_{11}$"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle Z_1=\\frac{2.0 s^{4} + 0.8922 s^{3} + 4.398 s^{2} + 0.8922 s + 2.0}{s \\left(0.8922 s^{2} + 0.398 s + 0.8922\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Corroboramos $Z_1$ en el centro de la banda de paso"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_1(\\omega = 1)=1.0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"Z1 = sp.simplify(sp.expand(Es + Fs) / sp.expand(Es - Fs))\n",
"\n",
"print_latex(a_equal_b_latex_s('Z_1', Z1.evalf(4)))\n",
"print_subtitle('Corroboramos $Z_1$ en el centro de la banda de paso')\n",
"print_latex(a_equal_b_latex_s('Z_1(\\omega = 1)', sp.simplify(sp.expand(sp.Abs(Z1.subs(s, sp.I*1)))).evalf(4) ))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora se procede con la síntesis de $Z_1$ siguiendo los ceros de la transferencia. En este caso tendremos dos ceros en cada extremo de banda."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"#### 1º Remoción en $\\infty$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle L_1=2.242$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_2=\\frac{2.688 \\left(4.674 \\cdot 10^{-16} s^{4} + 1.169 \\cdot 10^{-16} s^{3} + 1.0 s^{2} + 0.3721 s + 0.834\\right)}{s \\left(1.0 s^{2} + 0.4461 s + 1.0\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Se descartarán los coeficientes poco significativos"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_2=\\frac{2.688 s^{2} + 1.0 s + 2.242}{1.0 s^{3} + 0.4461 s^{2} + 1.0 s}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('1º Remoción en $\\infty$')\n",
"\n",
"Z2, ZL1 = remover_polo_infinito(Z1)\n",
"\n",
"# ZL1 es la impedancia removida\n",
"# extraigo L1\n",
"L1 = ZL1/s\n",
"\n",
"print_latex(a_equal_b_latex_s('L_1', L1.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Z_2', Z2.evalf(4)))\n",
"\n",
"print_subtitle('Se descartarán los coeficientes poco significativos')\n",
"\n",
"Z2 = trim_func_s(Z2)\n",
"print_latex(a_equal_b_latex_s('Z_2', Z2.evalf(4)))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Como puede apreciarse, las funciones de remoción fallan al aproximar el valor de los residuos y en consecuencia la remoción total nunca sucede. Para ello hace falta un poco de práctica y conocimiento del método de síntesis para proceder a la cancelación de aquellos términos residuales."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/markdown": [
"#### 2º Remoción en $0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle C_1=0.4461$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_4=\\frac{0.4461 \\left(1.0 s^{2} - 1.751 \\cdot 10^{-15} s - 2.102 \\cdot 10^{-15}\\right)}{s \\left(1.0 s^{2} + 0.4461 s + 1.0\\right)}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Poda de coeficientes y ..."
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Z_4=\\frac{0.4461 s^{2}}{1.0 s^{3} + 0.4461 s^{2} + 1.0 s}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('2º Remoción en $0$')\n",
"\n",
"Z4, ZC1 = remover_polo_dc(Z2)\n",
"\n",
"# ZC1 es la impedancia removida\n",
"# extraigo C1\n",
"C1 = 1/ZC1/s\n",
"\n",
"print_latex(a_equal_b_latex_s('C_1', C1.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Z_4', Z4.evalf(4)))\n",
"\n",
"print_subtitle('Poda de coeficientes y ...')\n",
"\n",
"Z4 = trim_func_s(Z4)\n",
"print_latex(a_equal_b_latex_s('Z_4', Z4.evalf(4)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"luego de haber removido en ambos extremos de impedancia, procedemos a remover en admitancia para finalizar la síntesis."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"#### 3º Remoción en $\\infty$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle C_2=2.242$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Y_6=\\frac{1.0 s + 2.242}{s}$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('3º Remoción en $\\infty$')\n",
"\n",
"Y6, YC2 = remover_polo_infinito(1/Z4)\n",
"\n",
"# ZL1 es la admitancia removida\n",
"# extraigo C2\n",
"C2 = YC2/s\n",
"\n",
"Y6 = trim_func_s(Y6)\n",
"\n",
"print_latex(a_equal_b_latex_s('C_2', C2.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Y_6', Y6.evalf(4)))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"en el otro extremo removeremos el último componente"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"#### 4º Remoción en $0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle L_2=0.4461$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/latex": [
"$\\displaystyle Y_8=1.0$"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_subtitle('4º Remoción en $0$')\n",
"\n",
"Y8, YL2 = remover_polo_dc(Y6)\n",
"\n",
"# YL2 es la admitancia removida\n",
"# extraigo L2\n",
"L2 = 1/YL2/s\n",
"\n",
"Y8 = trim_func_s(Y8)\n",
"\n",
"print_latex(a_equal_b_latex_s('L_2', L2.evalf(4)))\n",
"print_latex(a_equal_b_latex_s('Y_8', Y8.evalf(4)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y finalmente el remanente $Y_8$ es la resistencia de carga en el puerto de salida del filtro. Con esto finalizamos la síntesis y procedemos a visualizar la red resultante"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pytc2.dibujar import dibujar_puerto_entrada, dibujar_puerto_salida, dibujar_elemento_serie, dibujar_elemento_derivacion, dibujar_tanque_serie, dibujar_funcion_exc_abajo, dibujar_espacio_derivacion\n",
"from schemdraw import Drawing\n",
"\n",
"d = dibujar_puerto_entrada( Drawing(unit=4),\n",
" voltage_lbl = ('+', '$V1$', '-'), \n",
" current_lbl = '$I1$')\n",
"\n",
"d = dibujar_funcion_exc_abajo(d, 'Z_1', \n",
" Z1.evalf(4), \n",
" hacia_salida = True,\n",
" k_gap_width = 0.5)\n",
"\n",
"d = dibujar_elemento_serie(d, 'L', L1.evalf(4))\n",
"\n",
"d = dibujar_elemento_serie(d, 'C', C1.evalf(4))\n",
"\n",
"d = dibujar_elemento_derivacion(d, 'L', L2.evalf(4))\n",
"\n",
"d = dibujar_espacio_derivacion(d)\n",
"\n",
"d = dibujar_elemento_derivacion(d, 'C', C2.evalf(4))\n",
"\n",
"d = dibujar_puerto_salida(d,\n",
" voltage_lbl = ('+', '$V2$', '-'), \n",
" current_lbl = '$I2$')\n",
"\n",
"display(d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finalizamos el ejemplo con una simulación circuital de los parámetros del filtro diseñado.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Con este ejemplo se finaliza el documento."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
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"varInspector": {
"cols": {
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"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
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"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
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},
"types_to_exclude": [
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}
![](\"./img/logo_UTN.svg\"){kind=logo}
\n",
"\n",
"#### Por Mariano Llamedo Soria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resumen \n",
"\n",
"En este notebook aplicarán los conceptos de síntesis de inmitancias para la síntesis de un filtro pasabanda cargado tanto en su entrada como la salida. Se presentarán los parámetros scattering necesarios para la síntesis y las de dichos parámetros para redes no disipativas. \n",
"\n",
"Se usarán algunas funciones de PyTC2 para realizar los cálculos y presentar los resultados.\n",
"\n",
"* Funciones de dibujo de redes: [dibujar_puerto_entrada](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_puerto_entrada), [dibujar_funcion_exc_abajo](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_funcion_exc_abajo), [dibujar_elemento_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_derivacion), [dibujar_espacio_derivacion](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_espacio_derivacion), [dibujar_elemento_serie](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/dibujar/index.html#pytc2.dibujar.dibujar_elemento_serie)\n",
"* Funciones de síntesis de dipolos: [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [remover_polo_infinito](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.remover_polo_infinito), [modsq2mod_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.modsq2mod_s), [trim_func_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/remociones/index.html#pytc2.remociones.trim_func_s)\n",
"* Funciones para presentación de markdown y latex: [print_subtitle](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_subtitle), [print_latex](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.print_latex), [a_equal_b_latex_s](https://pytc2.readthedocs.io/en/latest/autoapi/pytc2/general/index.html#pytc2.general.a_equal_b_latex_s)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introducción\n",
"\n",
"La síntesis de redes eléctricas no disipativas doblemente cargadas es de especial utilidad para el diseño de filtros de alta frecuencia y/o potencia. Este método fue originalmente propuesto por [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) en 1939, permitiendo que un filtro cumpla con una respuesta prescrita de pérdidas por inserción, como las de Butterworth, Chebyshev y Bessel. El gran aporte de Darlington fue relacionar las pérdidas de inserción con las de retorno, y en consecuencia con la impedancia de entrada de la red, siempre que la red sea no disipativa. Con el tiempo el método se adaptó a los **parámetros de dispersión** (S ó scattering), que están íntimamente relacionados con las pérdidas de un filtro.\n",
"\n",
"Los **parámetros S** son útiles en el análisis de redes cuando se tienen cargas en ambos puertos. Estos parámetros relacionan las ondas normalizadas incidentes ($a$) y reflejadas ($b$) en cada plano de referencia (puerto en éste contexto) del cuadripolo:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se plantea el modelo lineal para el cuadripolo \n",
"\n",
"\n",
"$$ b = S \\cdot a $$\n",
"\n",
"o de forma explícita para cada puerto\n",
"\n",
"$$\n",
"\\begin{pmatrix} b_1 \\\\ b_2 \\end{pmatrix} = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} \\begin{pmatrix} a_1 \\\\ a_2 \\end{pmatrix}\n",
"$$\n",
"\n",
"Habrá dos tipos de parámetros:\n",
"\n",
"Un coeficiente de reflexión por cada puerto $x$, denominado $S_{xx}$ mientras el puerto restante está cargado con la resistencia característica $ R_{G1} = R_{G2} = R_0 $. Para un filtro de dos puertos se tiene entonces:\n",
"\n",
" $$\n",
" S_{11} = \\frac{b_1}{a_1} \\Bigg|_{a_2 = 0} = \\frac{Z_1 - R_0}{Z_1 + R_0}\n",
" $$\n",
" $$\n",
" S_{22} = \\frac{b_2}{a_2} \\Bigg|_{a_1 = 0} = \\frac{Z_2 - R_0}{Z_2 + R_0}\n",
" $$\n",
" \n",
"Siendo $Z_x$ la impedancia vista desde el plano $x$ hacia el cuadripolo cuando se conecta en el otro puerto $R_0$.\n",
"\n",
"También habrá un coeficiente de transmisión para modelar el flujo de potencia del puerto $x$ al puerto $y$, denominado $ S_{yx} $. Circuitalmente se modela con un generador $V_{gx}$ con resistencia interna $R_{Gx}$ y una carga $R_{Gy}$ en el puerto $y$. Resulta entonces\n",
"\n",
" $$\n",
" S_{21} = \\frac{b_2}{a_1} \\Bigg|_{a_2 = 0} = \\frac{V_2}{V_{G1}/2} \\cdot \\sqrt{\\frac{R_{G1}}{R_{G2}}}\n",
" $$\n",
" $$\n",
" S_{12} = \\frac{b_1}{a_2} \\Bigg|_{a_1 = 0} = \\frac{V_1}{V_{G2}/2} \\cdot \\sqrt{\\frac{R_{G2}}{R_{G1}}}\n",
" $$\n",
"\n",
"Para un sistema pasivo y no disipativo, se cumple que la matriz de parámetros $S$ es **unitaria**. Esto significa que:\n",
"\n",
"$$ S^H \\cdot S = U $$\n",
"\n",
"siendo $ S^H $ la matriz conjugada y transpuesta, o *Hermitiana*, y $U$ la matriz unitaria. Entonces los parámetros $S$ cumplen que\n",
"\n",
"$$\n",
"S_{11}^* S_{11} + S_{21}^* S_{21} =|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"$$\n",
"|S_{22}|^2 + |S_{12}|^2 = 1\n",
"$$\n",
"$$\n",
"S_{11} S_{12}^* + S_{21} S_{22}^* = 0\n",
"$$\n",
"donde el operador $*$ indica el conjugado complejo. La primera ecuación es de vital importancia, porque indica que toda la potencia disponible en el puerto 1 que no se transmita, necesariamente debe reflejarse al generador ya que el cuadripolo no podrá disiparla. La tercera ecuación también será importante más adelante cuando se generalice el enfoque. Retomando, el parámetro $|S_{11}|^2$ estará condicionado por\n",
"\n",
"$$\n",
"|S_{11}|^2 = 1 - |S_{21}|^2\n",
"$$\n",
"\n",
"que a su vez\n",
"\n",
"$$\n",
"|S_{11}|^2 = S_{11}^* S_{11} = S_{11}(-s) S_{11}(s)\n",
"$$\n",
"\n",
"es decir que podemos hallar el factor $S_{11}(s)$ a partir de una restricción en el ratio de potencias $|S_{21}|^2$. Como ya se anticipó, [Sidney Darlington](https://en.wikipedia.org/wiki/Sidney_Darlington) propuso un método de síntesis muy elegante, en el que a partir de dicha restricción se condiciona la impedancia de entrada al cuadripolo, sabiendo que\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{ Z_1 - R_0}{ Z_1 + R_0}\n",
"$$\n",
"\n",
"Notar que el signo de $S_{11}$ es un grado de libertad con el que se cuenta a la hora de sintetizar. Entonces la impedancia en el puerto de entrada será, dependiendo la elección del signo de $S_{11}$\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}}\n",
"$$\n",
"\n",
"Cabe destacar que si bien $Z_1$ contempla la impedancia vista hacia un cuadripolo no disipativo, debe recordarse que la condición de medición de $S_{11}$ asume que el puerto 2 esté cargado con $R_{G2} = R_0$. Por este motivo, $Z_1$ será una impedancia RLC **muy particular**, dado que estará compuesta por una inmitancia no disipativa seguida de la carga $R_0$ al final. Esto permite que todos los métodos de síntesis vistos hasta el momento sean aplicables para diseñar el cuadripolo no disipativo, con la particularidad de que el último componente será $R_0$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Un enfoque alternativo\n",
"\n",
"A esta altura está claro que los parámetros S de un filtro no disipativo cargado en ambos extremos está condicionado por la ecuación de complementaridad\n",
"\n",
"$$\n",
"|S_{11}|^2 + |S_{21}|^2 = 1\n",
"$$\n",
"\n",
"De la misma podemos expresar, siguiendo el enfoque propuesto por *Cameron et. al.*\n",
"\n",
"$$ S_{21} = \\frac{P(s)}{\\epsilon \\cdot E(s)} $$\n",
"\n",
"donde $\\epsilon$ será un factor de escala para que ambos polinomios $P$ y $E$ sean *mónicos*. Entonces, simplificando la dependencia de $s$\n",
"\n",
"$$ |S_{21}|^2 = \\frac{P . P^*}{\\epsilon^2 \\cdot (E . E^*)} $$\n",
"\n",
"y en consecuencia\n",
"\n",
"$$\n",
"|S_{11}|^2 = \\frac{\\epsilon^2 \\cdot (E . E^*) - P . P^*}{E . E^*} = \\frac{F . F^*}{\\epsilon_R^2 \\cdot (E . E^*)} \n",
"$$\n",
"\n",
"es decir que del mismo modo que con $ S_{21}$, también se podrá expresar\n",
"\n",
"$$\n",
"S_{11} = \\pm \\frac{F(s)}{\\epsilon_R \\cdot E(s)} \n",
"$$\n",
"\n",
"y es claro que todos los parámetros compartirán los mismos polos definidos por $E(s)$. Es decir que la matriz S se puede expresar como\n",
"\n",
"$$\n",
"S = \\begin{pmatrix} S_{11} & S_{12} \\\\ S_{21} & S_{22} \\end{pmatrix} = \\frac{1}{E(s)} \\begin{pmatrix} \\frac{F(s)}{\\epsilon_R} & \\frac{P(s)}{\\epsilon} \\\\ \\frac{P(s)}{\\epsilon} & \\frac{F(s)}{\\epsilon_R} \\end{pmatrix} \n",
"$$\n",
"\n",
"Se simplifican algunos detalles para facilitar el ejemplo que se presentará a continuación (Ver *Cogollos* cap 2. ó *Cameron et. al.* cap. 6 para un tratamiento general).\n",
"\n",
"Finalmente podemos expresar la impedancia de entrada al filtro como\n",
"\n",
"$$\n",
"Z_1 = R_0 \\cdot \\frac{1 \\pm S_{11}}{1 \\mp S_{11}} = R_0 \\cdot \\frac{\\epsilon_R.E \\pm F}{\\epsilon_R.E \\mp F}\n",
"$$\n",
"\n",
"Resta por aclarar cómo se calculan las constantes $\\epsilon$ y $\\epsilon_R$, que permitirán fijar las pérdidas de transmisión y retorno en frecuencias señaladas dependiendo de las características del filtro, pero eso lo dejaremos para más adelante por ahora.\n",
"\n",
"### Referencias\n",
"\n",
"1. [Richard J. Cameron, Chandra M. Kudsia, Raafat R. Mansour. *Microwave Filters for Communication Systems Fundamentals, Design and Applications. (2007). John Wiley & Sons](https://drive.google.com/file/d/1Fdr5WT8e7g9W5j4YM5Xd0-Za1BhrlQQ_/view?usp=drive_link)\n",
"2. [Cogollos Borrás, Santiago. *Fundamentos de la Teoría de Filtros*. (2016). Editorial de la Universitat Politècnica de València.](https://drive.google.com/file/d/1g4pgNS1m9dF43ZyR6LdkE-HAntfswhwh/view?usp=drive_link)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import sympy as sp\n",
"import numpy as np\n",
"\n",
"# Ahora importamos las funciones de PyTC2\n",
"\n",
"from pytc2.remociones import remover_polo_dc, remover_polo_infinito, modsq2mod_s, trim_func_s\n",
"\n",
"from pytc2.general import print_latex, print_subtitle, a_equal_b_latex_s\n",
"from IPython.display import display, Markdown\n",
"\n",
"# Importante importar símbolos de variables \n",
"from pytc2.general import s, w, simplify_n_monic\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ejemplo 1: Pasabanda Butterworth\n",
"\n",
"Implemente un filtro pasabanda Butterworth de 4º orden con un $Q = 5$.\n",
"\n",
"Aprovechando que este método nos permite un grado de detalle tan preciso para establecer la función transferencia $S_{21}$, se procede a hallar la función transferencia que satisface dicho requerimiento. Para ello se retomamará el análisis que hicimos en el notebook [núcleo de transformación pasabanda](ejnb_transformacion_pasabanda.ipynb). En resumen, a partir de una transferencia pasabajos *Butterworth* de segundo orden, se llega a \n",
"\n",
"$$ T_{bp}(s) = \\frac{s^2 \\cdot 0.04}{s^4 + s^3 \\cdot 0.2828+s^2 \\cdot 2.04+ s \\cdot 0.2828 + 1} $$ \n",
"\n",
"luego simplemente se asume que $ S_{21} = T_{bp} $. Debido a la complejidad de la expresión, el ejercicio demandará cierta destreza algebraica que puede suplirse gracias a las capacidades simbólicas de PyTC2 y SymPy.\n",
"\n",
"Comenzamos buscando la expresión de $Z_1$ a partir de $S_{11}$, no sin antes definir $S_{21}$."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$\\displaystyle S_{21}=\\frac{s^{2}}{25 \\left(s^{4} + \\frac{\\sqrt{2} s^{3}}{5} + \\frac{51 s^{2}}{25} + \\frac{\\sqrt{2} s}{5} + 1\\right)}$"
],
"text/plain": [
"