{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Source file for the documentation in the [jupyter notebook](SoundTransmissionThroughGaps.ipynb) and converted to markdown by `jupyter nbconvert SoundTransmissionThroughGaps.ipynb --to=markdown --output=README`.\n", "All source files of this example can be downloaded from the [userdocu source-code repository](https://gitlab.com/openCFS/userdocu/-/archive/master/userdocu-master.zip?path=docs/Applications/Singlefield/Acoustics/SoundTransmissionThroughGaps).\n", "\n", "Sound Transmission Trough Gaps\n", "============================\n", "\n", "## Problem Definition\n", "\n", "This example illustrates the use of openCFS to evaluate an acoustic wave passing through a periodic interface. The goal is to extract the reflection and transmission coefficients dependent on the incoming wave's frequency and angle of incidence.\n", "\n", "The problem is simulated in a 2D domain. The simulated domain represents only a small section of the real domain in y-Direction. Therefore a periodic boundary condition is used on the top and bottom boundary. This leads to the following domain, which has to be simulated. As a soundhard interface, several discs are choosen in this first simulation.\n", "\n", "![full_domain](Files/full_domain.png)\n", "\n", "The transmission coefficient is defined as\n", "\n", "$$ \\tau=\\frac{P_{trans}}{P_{inc}}, $$\n", "\n", "and the reflection coefficient as\n", "\n", "$$ r=\\frac{P_{refl}}{P_{inc}}. $$\n", "\n", "Therefore the calculation of the Transmittet Acoustic Power $P_{trans}$, the Reflected Acoustic Power $P_{refl}$ and the Incoming Acoustic Power $P_{inc}$ has to be performed to obtain both coefficients.\n", "\n", "$P_{trans}$ can be evaluated by calculating the power after the interface in the domain. Using openCFS post-processing, the Power can be calculated as a Surface Region Result `acouPower`.\n", "\n", "For calculating $P_{inc}$, a second domain, the \"excitation domain\" is simulated, which only represents the excitation side of the full problem, thus allowing to obtain only the incoming acoustic field. The acoustic power calculated within this domain represents the incoming power. Here the power is also calculated as a Surface Region Result `acouPower`.
\n", "However, since the incoming wave is a plane acoustic wave and the relation $v_{\\mathrm{a}}=\\frac{p_{\\mathrm{a}}}{\\rho \\cdot c}$ holds, this power can easily be calculated analytically. With the definition of the acoustic power $P_{\\mathrm{a}}=\\int_{\\Gamma} \\mathbf{I}_{\\mathrm{a}} \\cdot \\mathbf{n} \\mathrm{d} \\Gamma^{\\prime}$. This leads to the equation for the power of the incoming as\n", "\n", "$$ P_{inc}=\\frac{p_{\\mathrm{a}}^2}{2 \\cdot \\rho \\cdot c}\\cdot cos(\\alpha) \\cdot L. $$\n", "\n", "Here $p_{\\mathrm{a}}$ is the amplitude of the applied acoustic pressure, $\\rho$ is the density of air, $c$ the speed of sound, $\\alpha$ the angle of the applied wave field and $L$ the heigth of the simulated domain.\n", "\n", "![incoming_domain](Files/incoming_domain.png)\n", "\n", "Obtaining $P_{refl}$ is the most difficult part, since it can't be done directly via openCFS. When simulating the entire domain, the acoustic field on the incoming side of the domain represents the reflected wave superimposed with the incoming wave. Therefore, the wavefield obtained by simulating the excitation domain has to be subtracted from the full simulations' wavefield. This provides the reflected acoustic field which susequently allows the calculation of the reflected power.
\n", "With the acoustic fields for both domains obtained via openCFS, the following substraction and calculation of $P_{refl}$ is done in Python.\n", "\n", "\n", "## Meshing\n", "\n", "The mesh input file is generated with [gmsh](https://gmsh.info/) and subsequently, the mesh file is produced with the command \n", "```XML\n", "gmsh mesh_input.geo -2 -v 0 -format msh2 -o mesh.msh\n", "```\n", "The provided files are:
\n", "- The mesh input file for the full domain [geometry_total.geo](Files/geometry_discs.geo)
\n", "- The mesh input file for the excitation domain [geometry_excitation.geo](Files/geometry_incoming.geo)
\n", "\n", "## XML Simulation Files\n", "\n", "Now two XML files for harmonic acoustic analysis are set up. One for simulating the incoming acoustic field, hence the excitation domain, provided [here](Files/incoming_simulation.xml), and one for simulating the entire domain with the interface, provided [here](Files/full_simulation.xml). In addition to that, the XML [material file](Files/mat_accou.xml) is required.\n", "\n", "The angle of incidence $\\alpha$ can be chosen in the XML file by adjusting this value\n", "\n", "```XML\n", "\n", "```\n", "\n", "The simulated frequencies for the harmonic analysis are calculated within the XML file. Due to the periodic boundary condition, the frequencies for the harmonic analysis can not be chosen arbitrarily. They are calculated, depending on the angle of incidence $\\alpha$, the height of the domain and the speed of sound $c$. With these frequencies, a plane wave field can be produced in combination with the periodicity of the domain's boundary. Additionally an if-condition is implemented when calculating the frequencies, to directly set the frequencies in the case of $\\alpha=0°$.\n", "\n", "```XML\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "```\n", "\n", "For the subtraction of the incoming wave field from the entire wave field, it suffices to only execute this for one line in the domain and then calculate the power along this line. Therefore, in both simulated domains, a sensor array is set up at the same position to calculate the acoustic pressure and the velocity along a vertical line in the domain. \n", "\n", "```XML\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "```\n", "\n", "The power of the incoming acoustic wave is directly calculated by using the openCFS post-processing in the excitation domain. By the same approach, the transmitted acoustic power is calculated using the entire domain.\n", "\n", "## Python Post-processing\n", "\n", "The calculation of acoustic power reflected by the interface is carried out in Python. Note that much of the coding is done using pandas dataframes. This allows for a comprehensible code without the excessive use of comments. This code is provided [here](Files/Calculate_Coefficients.ipynb).\n", "\n", "First, some libraries are imported." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions\n", "\n", "Now the functions, which are defined [functions.py](Files/functions.py) are imported.\n", "\n", "The first function, `calculate_power`, reads the text files, created by the openCFS sensor arrays. Therefore the function needs the information of the angle of incidence and the name of the simulation in order to find the right files. Moreover the number of frequencies which were simulated in openCFS have to be provided. For each frequency the sensor array creates a separate file, hence this information is needed to know how many files have to be read. After reading the files, the velocities and the pressures of the excitation domain are subtracted from the entire domain's to obtain only the values for the reflected acoustic field. With these resulting values the acoustic intensity and subsequently the reflected acoustic power are calculated and returned.\n", "\n", "The function `read_inc_power` reads the power of the incoming acoustic field, which is calculated with openCFS directly by using the excitation domain and stored in a text file. The angle of incidence has to be passed over to this function as well, to choose the right file to read the data from. In addition to the power, the frequency for each simulation is stored in the same file, which is read and saved by the function as well.\n", "\n", "The last function, `read_trans_power`, similar to the second function, is created to read and store the power transmitted through the interface." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from functions import calculate_power\n", "from functions import read_inc_power\n", "from functions import read_trans_power" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Main Program\n", "\n", "Now all functions are defined and the main part of the code starts. First, the information on the performed simulations has to be defined. Simulations for five angles of incidence (60°, 30°, 0°, -30°, 60°) with ten simulated frequencies per angle, have been carried out. The performed simulations were stored by the name \"Discs\"." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# defining which simulation to process\n", "simulation=\"Discs\" # for getting the coefficients of the original domain (discs as iterface)\n", "angles_str = [\"60\", \"30\", \"00\", \"m30\", \"m60\"] # angles of incidence which were simulated (m for minus)\n", "freq_num=10 # number of frequencies which were simulated" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the next step some empty dataframes and arrays are defined, needed in the following parts of the code." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# vector for storing dataframe of the calculation for reflected power\n", "results_dataframes = [None] * len(angles_str) \n", "\n", "# empty dataframes for power calculation \n", "df_Power_ref = pd.DataFrame()\n", "df_Power_inc = pd.DataFrame()\n", "df_Power_trans = pd.DataFrame()\n", "df_Power_balance = pd.DataFrame()\n", "df_Coefficients = pd.DataFrame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following this, a for-loop over all angles of incidence is created. In this loop, the reflected, transmitted and incoming powers are calculated with the predefined functions and stored in the dataframes.\n", "\n", "With the determined powers, a power balance around the interface is set up. This step is not necessary for the calculation of the coefficients, but is a good way of validating the data obtained up until now. Since the interface in the performed simulation is soundhard, the power balance $P_{inc}=P_{refl}+P_{trans}$ has to be fulfilled. The relative error of this balance is calculated and used for validating the obtained powers.\n", "\n", "The last step performed in the loop is the calculation of the transmission coefficient and the reflection coefficient." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# for loop over all simulated incidence angles\n", "for index, angle in enumerate(angles_str):\n", " \n", " # calcualting power reflected from interface\n", " [df,Power_ref]=calculate_power(angle,simulation,freq_num) \n", " results_dataframes[index]=df \n", " df_Power_ref[angle]=np.concatenate(Power_ref)\n", " \n", " # reading the incoming power\n", " [Power_inc,frequency]=read_inc_power(angle)\n", " df_Power_inc['freq_'+angle]=frequency\n", " df_Power_inc[angle]=Power_inc\n", " \n", " # reading the transmitted power\n", " [Power_trans]=read_trans_power(angle,simulation)\n", " df_Power_trans['freq_'+angle]=frequency\n", " df_Power_trans[angle]=Power_trans\n", "\n", " # check the power balance (for soundhard interface: P_inc=P_refl+P_trans)\n", " df_Power_balance['balance_'+angle]=df_Power_inc[angle]-df_Power_trans[angle]-df_Power_ref[angle]\n", " df_Power_balance['Error_in_%_'+angle]=abs(df_Power_balance['balance_'+angle]/df_Power_inc[angle])*100 \n", "\n", " # calculate transmission and reflection coefficients\n", " df_Coefficients['Trans_coef_'+angle]=df_Power_trans[angle]/df_Power_inc[angle]\n", " df_Coefficients['Refl_coef_'+angle]=df_Power_ref[angle]/df_Power_inc[angle]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Coefficients\n", "\n", "For showing the obtained coefficients two plots are created. The first shows the **transmission coefficients** depending on the angle of incidence and the excitation frequency.\n", "\n", "This plot shows, that the calculated coefficients are nearly the same when simply the sign of the incidence angle is switched. Considering the interface being symmetrical along the x-axis, this is an expected result. In fact, the transmission coefficients should be exactly the same.
\n", "The encountered small deviations might be due to the postprocessing procedure and indicate the overall error that can be expected from it.
\n", "\n", "By simply looking at the interface, one can assume, that the transmitted power has a maximum by an incoming wavefront parallel to the interface and that this transmitted power decreases with a rising angle of incidence. This presumption is also confirmed by the calculated transmission coefficient, which on average has a maximum at an angle of incidence of $\\alpha=0°$." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Transmission Coefficients\n", "colors = ['red', 'blue', 'green', 'orange', 'purple']\n", "line_styles = ['--', '--', ':', '-.', '-.']\n", "for index, angle in enumerate(angles_str):\n", " plt.plot(df_Power_trans['freq_'+angle], df_Coefficients['Trans_coef_'+angle], \n", " linestyle=line_styles[index], color=colors[index], label=angle+'deg')\n", "plt.xlabel('Frequency')\n", "plt.ylabel('Transmission Coefficient')\n", "plt.ylim(0, 1.1) \n", "plt.grid(True)\n", "plt.legend()\n", "plt.title('Transmission Coefficient vs Frequency')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the same manner, the **reflection coefficients** are plotted.\n", "\n", "With the interface being soundhard no acoustic power is absorbed by it. Knowing the transmission coefficient, the reflection coefficient can be simply calculated, using $r=1-\\tau$ as well. Similar to the power balance, this equation can be used to verify the obtained coefficients.
\n", "Looking at the plot, it can be seen, that the reflection coefficients for $\\alpha=30°$ and $\\alpha=-30°$ almost perfectly coincide, contrary to the transmission coefficients. This indicates that there are some small inconsistencies when calculating the transmitted power." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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vXr2a+Pj4PBtO36ZNGypUqMDUqVNp2bJlpiO1/v77b+rWrYuFhQXVq1dnzZo1zJw5Uzd0WavV8uuvv1KsWDFKly4NQMuWLdm0aRMlS5bM9TxJuVG6dGlKlizJwoULGTp0qK6N/0tu3tvcyMlVtefZ2trStm1bli5dSs2aNQkLC0t3O+vfihQpQocOHbh37x6DBw8mJCQk13MS/Rdzc3MaNGjAqVOnqFixIsbGxlnWbdCgAdOnT+f06dPpbmstX748Q11PT0/OnDmTruzKlStcvnw53c9My5YtmTJlCvb29plePcotMzOzHP18QlpS9umnn/LDDz9w8OBBKleuTO3atV86BlEwScIjXgu2traMGjWKESNGsHz5cj766CPmz59P8+bNadasGd27d8fNzY2IiAguXrzIyZMncz2s1NLSktmzZ9OtWzciIiLo0KEDjo6OPHr0iNOnT/Po0SPmzp0LQGBgIHXq1KF69eqMHDmSUqVK8eDBA9avX8/8+fOxsrLSJRM//fQTVlZWmJqa4uXllekl+SZNmtCsWTO++OILYmJiqF27NmfOnOHrr7/Gz88v0+HML8LAwIC1a9fStGlTatasSd++fWnQoAEWFhbcunWLVatWsWHDBiIjIwGYOnUqTZo0oUGDBgwfPhxjY2PmzJnDuXPn+O2333RXCyZMmMC2bduoVasWgwYNwsfHh8TEREJCQti0aRPz5s3Ls1uAP/74I61ataJGjRoMGTKE4sWLc/v2bf7++2+WLVuW6T65eW9zo0KFCuzevZsNGzbg4uKClZXVf15Z7NGjBytXrmTAgAEUK1aMxo0bp9veqlUr3TxURYsW5datWwQFBeHh4YG3t3euY8yJ7777jjp16lC3bl369u2Lp6cnsbGxXLt2jQ0bNuj6mA0ePJiFCxfyzjvvMGnSJJycnFi2bBmXLl3KcMwuXbrw0Ucf0a9fP959911u3brF9OnTM9y2HTx4MKtXr6ZevXoMGTKEihUrotVquX37Nlu3bmXYsGHp+iDlRE5+Pp/q168f06dP58SJE/z8888v0HritaHvXtNCPC+rYemKkjYSqXjx4oq3t7duKOnp06eVjh07Ko6OjoqRkZHi7OysNGzYUJk3b55uv5yO0npqz549yjvvvKPY2dkpRkZGipubm/LOO+8of/zxR7p6Fy5cUN577z3F3t5eMTY2VooXL65079493QiooKAgxcvLSzEwMEg3XP7fo7Sevr4vvvhC8fDwUIyMjBQXFxelb9++SmRkZLp6WY1y+vfol+xERUUpEydOVKpUqaJYWloqRkZGSvHixZWPPvpIOXDgQLq6+/btUxo2bKhYWFgoZmZmSo0aNZQNGzZkOOajR4+UQYMGKV5eXoqRkZFiZ2enVK1aVRkzZowSFxf3n/GTw1FaipI2JLx58+aKjY2NYmJiopQsWVIZMmSIbvvLvLdPR2k9evQo3b6ZHTM4OFipXbu2Ym5urgA5an+NRqO4u7srgDJmzJgM27/99lulVq1aioODg+5z1bNnTyUkJCTb42b1OX9eZm38/P49evRQ3NzcFCMjI6Vo0aJKrVq1lEmTJqWrd+HCBaVJkyaKqampYmdnp/Ts2VP5888/M7xPWq1WmT59ulKiRAnF1NRU8ff3V3bu3Jnp5zQuLk758ssvFR8fH8XY2Fg3RH7IkCFKWFjYf8af2YiwnPx8PlW/fn3Fzs5OSUhIyLLtxOtPpSiKooc8SwghRCGxe/duGjRowK5du7KcfLGgevjwIR4eHgwcOJDp06frOxyRj+SWlhBCiDfO3bt3uXHjBjNmzECtVuttbTvx6sg8PEIIId44P//8M/Xr1+f8+fMsW7YMNzc3fYck8pnc0hJCCCFEoSdXeIQQQghR6EnCI4QQQohCTxIeIYQQQhR6b9woLa1Wy/3797GyssowxboQQgghCiZFUYiNjcXV1RW1OvfXa964hOf+/fu4u7vrOwwhhBBCvIA7d+680Mztb1zC83RK8Tt37mBtba3naF5eSkoKW7dupWnTplmuNvwmk/bJnrRP9qR9siftkz1pn+zltn1iYmJwd3dPtzRIbrxxCc/T21jW1taFJuExNzfH2tpafqAyIe2TPWmf7En7ZE/aJ3vSPtl70fZ50e4o0mlZCCGEEIWeJDxCCCGEKPQk4RFCCCFEoffG9eERQgjxZtBqtSQnJ+vt/CkpKRgaGpKYmIhGo9FbHAVVZu1jbGz8QkPOc0ISHiGEEIVOcnIyN2/eRKvV6i0GRVFwdnbmzp07Mu9bJjJrH7VajZeXF8bGxnl+Pkl4hBBCFCqKohAaGoqBgQHu7u75dsXgv2i1WuLi4rC0tNRbDAXZv9vn6cTAoaGhFC9ePM+TREl4hBBCFCqpqakkJCTg6uqKubm53uJ4ekvN1NRUEp5MZNY+RYsW5f79+6Smpub5UH55B4QQQhQqz/cHEa+Xp+9ZfvR5koRHCCFEoST9Zl4/+fmeScIjhBBCiEJPEh4hhBDiDdO9e3fatm2r7zBeKUl4hBBCiALi3r17fPTRR9jb22Nubk7lypU5ceKEbruiKIwbNw5XV1fMzMyoX78+58+f12PErw9JeIQQQogCIDIyktq1a2NkZMTmzZu5cOEC3377LUWKFNHVmT59OoGBgfzwww8cO3YMZ2dnmjRpQmxsrP4Cf01IwiOEEEIUANOmTcPd3Z1FixZRrVo1PD09adSoESVLlgTSru4EBQUxZswY2rdvT/ny5VmyZAkJCQksX748y+NqNBqGDh1KkSJFsLe3Z8SIESiKkq6OoihMnz6dEiVKYGZmRqVKlVi1alW6OuvXr8fb2xszMzMaNGjAkiVLUKlUREVF5Xlb5AdJeIQQQrwZ4uOzfiQm5rzukyc5q5tL69evx9/fn/feew9HR0f8/Pz43//+p9t+8+ZNwsLCaNq0qa7MxMSEgIAADh48mOVxv/32WxYuXMiCBQvYv38/ERERrF27Nl2dL7/8kkWLFjF37lzOnz/PkCFD+Oijj9izZw8AISEhdOjQgbZt2xIcHEzv3r0ZM2ZMrl+jPsnEg0IIId4MlpZZb2vRAjZufPbc0RESEjKvGxAAu3c/e+7pCeHhGevlci6ZGzduMHfuXIYOHcro0aM5evQogwYNwsTEhK5duxIWFgaAk5NTuv2cnJy4detWlscNCgpi1KhRvPvuuwDMmzePv//+W7c9Pj6ewMBAdu7cSc2aNQEoUaIE+/fvZ/78+QQEBDBv3jx8fHyYMWMGAD4+Ppw7d47Jkyfn6jXqkyQ8QgghRAGg1Wrx9/dnypQpAPj5+XH+/Hnmzp1L165ddfX+PVeNoihZzl8THR1NaGioLpEBMDQ0xN/fX3db68KFCyQmJtKkSZN0+yYnJ+Pn5wfA5cuXeeutt9Jtr1at2gu+Uv2QhEcIIcSbIS4u620GBumfP3yYdd1/LxMREvLCIT3PxcWFsmXLpivz9fVl9erVADg7OwMQFhaGi4vLc6E+zHDVJzeeLrC6ceNG3Nzc0m0zMTEBMk+q/t0PqKCThEcIIcSbwcLi1dbN5UrttWvX5vLly+nKrly5goeHBwBeXl44Ozuzbds23ZWX5ORk9uzZw7Rp0zI9po2NDS4uLhw+fJh69eoBaWuNnThxgipVqgBQtmxZTExMuH37NgEBAZkep0yZMmzatCld2fHjx3P1+vRNEh4hhBCiABgyZAi1atViypQpdOzYkaNHj/LTTz/x008/AWm3sgYPHsyUKVPw9vbG29ubKVOmYG5uTufOnbM87meffcY333yDt7c3vr6+BAYGphtZZWVlxfDhwxkyZAharZY6deoQExPDwYMHsbS0pFu3bvTu3ZvAwEC++OILevbsSXBwMIsXL9bF9TqQhEcIIYQoAN566y3Wrl3LqFGjmDBhAl5eXgQFBfHhhx/q6owYMYInT57Qr18/IiMjqV69Olu3bsXKyirL4w4bNozQ0FC6d++OWq2mR48etGvXjujoaF2diRMn4ujoyNSpU7lx4wZFihShSpUqjB49Gki7urRq1SqGDRvGd999R82aNRkzZgx9+/bV3fYq6CThEUIIIQqIli1b0rJlyyy3q1Qqxo0bx7hx43J8TENDQ4KCgggKCsr2uIMGDWLQoEFZ1mndujWtW7fWPZ88eTLFihXD1NQ0x7HokyQ8QgghhPhPc+bM4a233sLe3p4DBw4wY8YMBgwYoO+wckwSHiGEEEL8p6tXrzJp0iQiIiIoXrw4w4YNY9SoUfoOK8ck4RFCCCHEf5o1axazZs3SdxgvTJaWEEIIIUShp9eEZ+/evbRq1QpXV1dUKhXr1q37z3327NlD1apVMTU1pUSJEsybNy//AxVCCCHEa02vCU98fDyVKlXihx9+yFH9mzdv0qJFC+rWrcupU6cYPXo0gwYN0s1CKYQQQgiRGb324WnevDnNmzfPcf158+ZRvHhx3dA6X19fjh8/zsyZM3WLogkhhBBC/Ntr1Yfn0KFDNG3aNF1Zs2bNOH78OCkpKXqKSgghhBAF3Ws1SissLCzDAmlOTk6kpqYSHh6ebjG1p5KSkkhKStI9j4mJASAlJaVQJElPX0NheC35Qdone9I+2ZP2yV5BbZ+UlBQURUGr1eoWxtSHp4trPo1FpJdZ+2i1WhRFISUlBYN/Lej6sp+z1yrhgYxrdjxtsKzW8pg6dSrjx4/PUL5161bMzc3zPkA92bZtm75DKNCkfbIn7ZM9aZ/sFbT2MTQ0xNnZmbi4OJKTk/UdDrGxsfoOIYN+/foRHR3NsmXL9B1KuvZJTk7myZMn7N27l9TU1HT1EhISXuo8r1XC4+zsTFhYWLqyhw8fYmhoiL29fab7jBo1iqFDh+qex8TE4O7uTtOmTbG2ts7XeF+FlJQUtm3bRpMmTTAyMtJ3OAWOtE/2pH2yJ+2TvYLaPomJidy5cwdLS0u9LnugKAqxsbFYWVnlaIHNuXPnMn/+fEJCQgAoV64cX375Zbq+roqiMGHCBP73v//p1tKaPXs25cqVy1VsRkZGGBoa6vV7MLP2SUxMxMzMjHr16mV4757eoXlRr1XCU7NmTTZs2JCubOvWrfj7+2f5w2ZiYpLpwmZGRkYF6gf0ZRW215PXpH2yJ+2TPWmf7BW09tFoNKhUKtRqNWq1/rqqPr1N8zSW/1K8eHG++eYbSpUqBcCSJUto164dp06d0iU006ZNY9asWSxevJjSpUszadIkmjVrxuXLl7NdQPTfVCpVjuPKL5m1j1qtRqVSZfqZetnPmF47LcfFxREcHExwcDCQNuw8ODiY27dvA2lXZ7p27aqr36dPH27dusXQoUO5ePEiCxcuZMGCBQwfPlwf4QshhBB5plWrVrRo0YLSpUtTunRpJk+ejKWlJYcPHwbSrogEBQUxZswY2rdvT/ny5VmyZAkJCQksX748y+NqNBqGDh1KkSJFsLe3Z8SIEbruIE8pisL06dMpUaIEZmZmVKpUiVWrVqWrs379ery9vTEzM6NBgwYsWbIElUpFVFRUnrdFftBrwnP8+HH8/Pzw8/MDYOjQofj5+TF27FgAQkNDdckPpC1Pv2nTJnbv3k3lypWZOHEi33//vQxJF0II8Z/i47N+JCbmvO6TJzmr+zI0Gg0rVqwgPj6emjVrAmkXBcLCwtKNVjYxMSEgIICDBw9meaxvv/1Wd4Fg//79REREsHbt2nR1vvzySxYtWsTcuXM5f/48Q4YM4aOPPmLPnj0AhISE0KFDB9q2bUtwcDC9e/dmzJgxL/ciXzG93tKqX79+hizzeYsXL85QFhAQwMmTJ/MxKiGEEIWRpWXW21q0gI0bnz13dISs+sgGBMDu3c+ee3pCeHjGehpN7mM8e/YsNWvWJDExEUtLS9auXUvZsmUBdH1YMxutfOvWrSyPGRQUxKhRo3QXB+bNm8fff/+t2x4fH09gYCA7d+7UJVclSpRg//79zJ8/n4CAAObNm4ePjw8zZswAwMfHh3PnzjF58uTcv0g9ea368AghhBCFmY+PD8HBwURFRbF69Wq6devGnj17dEkPZD5aOatO0dHR0YSGhuoSGUgbxebv76+74HDhwgUSExNp0qRJun2Tk5N1d2AuX77MW2+9lW57tWrVXvyF6oEkPEIIId4IcXFZb/vXlC88fJh13X/38/1nUFWeMDY21nVa9vf359ixY3z33XfMnz8fZ2dnIO1Kz/Pzzj18+DDDVZ/ceNp5eOPGjbi5uaXb9nTQT2ZJVXZ3aAoiSXiEEEK8ESwsXm3dvJhrUFEU3eS5Xl5eODs7s23bNt2Vl+TkZPbs2cO0adMy3d/GxgYXFxcOHz5MvXr1AEhNTeXEiRNUqVIFgLJly2JiYsLt27cJCAjI9DhlypRh06ZN6cqOHz/+8i/wFZKERwghhCgARo8eTfPmzXF3dyc2NpYVK1awe/dutmzZAqTdyho8eDBTpkzB29sbb29vpkyZgrm5OZ07d87yuJ999hnffPMN3t7e+Pr6EhgYmG5klZWVFcOHD2fIkCFotVrq1KlDTEwMBw8exNLSkm7dutG7d28CAwP54osv6NmzJ8HBwbp+tjmZY6ggkIRHCCGEKAAePHhAly5dCA0NxcbGhooVK7Jly5Z0fWtGjBjBkydP6Nevn27iwa1bt2Y7B8+wYcMIDQ2le/fuqNVqevToQbt27YiOjtbVmThxIo6OjkydOpUbN25QpEgRqlSpwujRo4G0q0urVq1i2LBhfPfdd9SsWZMxY8bQt2/fTOe6K4gk4RFCCCEKgAULFvxnHZVKxbhx4xg3blyOj2toaEhQUBBBQUHZHnfQoEEMGjQoyzqtW7emdevWuueTJ0+mWLFiep3NOjck4RFCCCHEf5ozZw5vvfUW9vb2HDhwgBkzZjBgwAB9h5VjkvAIIYQQ4j9dvXqVSZMmERERQfHixRk2bBijRo3Sd1g5JgmPEEIIIf7TrFmzmDVrlr7DeGF6XVpCCCGEEOJVkIRHCCGEEIWeJDxCCCGEKPQk4RFCCCFEoScJjxBCCCEKPUl4hBBCCFHoScIjhBBCvGG6d+9O27Zt9R3GKyUJjxBCCFFAzJkzBy8vL0xNTalatSr79u1Lt11RFMaNG4erqytmZmbUr1+f8+fP6yna14skPEIIIUQBsHLlSgYPHsyYMWM4deoUdevWpXnz5ty+fVtXZ/r06QQGBvLDDz9w7NgxnJ2dadKkCbGxsXqM/PUgCY8QQghRAAQGBtKzZ0969eqFr68vQUFBuLu7M3fuXCDt6k5QUBBjxoyhffv2lC9fniVLlpCQkMDy5cuzPK5Go2Ho0KEUKVIEe3t7RowYgaIo6eooisL06dMpUaIEZmZmVKpUiVWrVqWrs379ery9vTEzM6NBgwYsWbIElUpFVFRUnrdFfpCERwghxBshPjme+OT4dF/2yZpk4pPjSUpNyrSuVtHqylI0KcQnx5OYmpijurmRnJzMiRMnaNq0abrypk2bcvDgQQBu3rxJWFhYujomJiYEBATo6mTm22+/ZeHChSxYsID9+/cTERHB2rVr09X58ssvWbRoEXPnzuX8+fMMGTKEjz76iD179gAQEhJChw4daNu2LcHBwfTu3ZsxY8bk6jXqmyQ8Qggh3giWUy2xnGpJeEK4rmzGgRlYTrVkwKb0q347znTEcqolt6Of3U768diPWE61pOf6nunqen7nieVUSy4+uqgrWxy8OFexhYeHo9FocHJySlfu5OREWFgYgO7f7OpkJigoiFGjRvHuu+/i6+vLvHnzsLGx0W2Pj48nMDCQhQsX0qxZM0qUKEH37t356KOPmD9/PgDz5s3Dx8eHGTNm4OPjwwcffED37t1z9Rr1TRYPFUIIIQoIlUqV7rmiKBnKclLnqejoaEJDQ6lZs6auzNDQEH9/f92VrgsXLpCYmEiTJk3S7ZucnIyfnx8Aly9f5q233kq3vVq1arl4ZfonCY8QQog3QtyoOADMjcx1ZZ/X/pzBNQZjqE7/dfhw+EMAzIzMdGX93+rPJ1U+wUBtkK5uyGchGep2r9w9V7E5ODhgYGCQ4UrNw4cPdVd0nJ2dgbQrPS4uLpnWeRFabdqtuI0bN+Lm5pZum4mJCZB5UvXvfkAFndzSEkII8UawMLbAwtgi3Re3sYExFsYWmBiaZFpXrXr2NWlkYISFsQWmhqY5qpsbxsbGVK1alW3btqUr37ZtG7Vq1QLAy8sLZ2fndHWSk5PZs2ePrs6/2djY4OLiwuHDh3VlqampnDhxQve8bNmymJiYcPv2bUqVKpXu4e7uDkCZMmU4duxYumMfP348V69R3+QKjxBCCFEADB06lC5duuDv70/NmjX56aefuH37Nn369AHSbmUNHjyYKVOm4O3tjbe3N1OmTMHc3JzOnTtnedzPPvuMb775Bm9vb3x9fQkMDEw3ssrKyorhw4czZMgQtFotderUISYmhoMHD2JpaUm3bt3o3bs3gYGBfPHFF/Ts2ZPg4GAWL16si+t1IAmPEEIIUQC8//77PH78mAkTJhAaGkr58uXZtGkTHh4eujojRozgyZMn9OvXj8jISKpXr87WrVuxsrLK8rjDhg0jNDSU7t27o1ar6dGjB+3atSM6OlpXZ+LEiTg6OjJ16lRu3LhBkSJFqFKlCqNHjwbSri6tWrWKYcOG8d1331GzZk3GjBlD3759dbe9CjpJeIQQQogCol+/fvTr1y/L7SqVinHjxjFu3LgcH9PQ0JCgoCCCgoKyPe6gQYMYNGhQlnVat25N69atdc8nT55MsWLFMDU1zXKfgkQSHiGEEEL8pzlz5vDWW29hb2/PgQMHmDFjBgMGDPjvHQsISXiEEEII8Z+uXr3KpEmTiIiIoHjx4gwbNoxRo0bpO6wck4RHCCGEEP9p1qxZzJo1S99hvDAZli6EEEKIQk8SHiGEEEIUepLwCCGEEKLQk4RHCCGEEIWeJDxCCCGEKPQk4RFCCCFEoScJjxBCCCH+U0hICCqViuDgYH2H8kIk4RFCCCFeU48fP+btt9/G1dUVExMT3N3dGTBgADExMenqnT17loCAAMzMzHBzc2PChAkoiqKnqPVDJh4UQgghXlNqtZo2bdowadIkihYtyrVr1+jfvz8REREsX74cgJiYGJo0aUKDBg04duwYV65coXv37lhYWDBs2DA9v4JXR67wCCGEEAVA/fr1GThwIIMHD8bW1hYnJyd++ukn4uPj+fjjj7GysqJkyZJs3rxZt4+trS19+/bF398fDw8PGjVqRL9+/di3b5+uzrJly0hMTGTx4sWUL1+e9u3bM3r0aAIDA7O9ynP06FH8/PwwNTXF39+fU6dOZahz4cIFWrRogaWlJU5OTnTp0oXw8HDd9tjYWD788EMsLCxwcXFh1qxZ1K9fn8GDB+dNo+WCJDxCCCHeDKnxuX9oU5/tr039p/xJzo77ApYsWYKDgwNHjx5l4MCB9O3bl/fee49atWpx8uRJmjVrRpcuXUhISMh0//v377NmzRoCAgJ0ZYcOHSIgIAATExNdWbNmzbh//z4hISGZHic+Pp6WLVvi4+PDiRMnGDduHMOHD09XJzQ0lICAACpXrszx48fZsmULDx48oGPHjro6Q4cO5cCBA6xfv55t27axb98+Tp48+UJt87LklpYQQog3w++Wud+nzu9Q/L20/99dC/s7gmMANN79rM6fnpAUnnHfDzS5Pl2lSpX48ssvARg1ahTffPMNDg4OfPLJJwCMHTuWuXPncubMGWrUqKHbr1OnTvz55588efKEVq1a8fPPP+u2hYWF4enpme48Tk5Oum1eXl4Z4li2bBkajYaFCxdibm5OuXLluHv3Ln379tXVmTt3LlWqVGHKlCm6soULF+Lu7s6VK1dwcXFhyZIlLF++nEaNGgGwaNEiXF1dc90ueUGu8AghhBAFRMWKFXX/NzAwwN7engoVKujKniYqDx8+TLffrFmzOHnyJOvWreP69esMHTo03XaVSpXu+dNbWf8uf+rixYtUqlQJc3NzXVnNmjXT1Tlx4gS7du3C0tJS9yhTpgwA169f58aNG6SkpFCtWjXdPjY2Nvj4+GTfCPlErvAIIYR4M3SMy/0+6me3gSjW7p9j/OtaQZuQl4kqHSMjo3TPVSpVurKnCYpWq01Xz9nZGWdnZ8qUKYO9vT1169blq6++wsXFBWdnZ8LCwtLVf5owPU2g/i0nI7i0Wi2tWrVi2rRpGba5uLhw9erVdDHn5tj5Qa7wCCGEeDMYWuT+oX7uuoDa8J9ys5wdV0+eJhRJSUlA2pWZvXv3kpycrKuzdetWXF1dM9zqeqps2bKcPn2aJ0+e9Vc6fPhwujpVqlTh/PnzeHp6UqpUqXQPCwsLSpYsiZGREUePHtXtExMTo0uEXjVJeIQQQojX1KZNm1i0aBHnzp0jJCSETZs20bdvX2rXrq1LZjp37oyJiQndu3fn3LlzrF27lilTpjB06NAsb2l17twZtVpNz549uXDhAps2bWLmzJnp6jwd/t6pUyeOHj3KjRs32Lp1Kz169ECj0WBlZUW3bt34/PPP2bVrF+fPn6dHjx6o1eosz5ufJOERQgghXlNmZmb873//o06dOvj6+jJ48GBatmzJX3/9patjY2PDtm3buHv3Lv7+/vTr14+hQ4dm6OfzPEtLSzZs2MCFCxfw8/NjzJgxGW5dubq6cuDAATQaDc2aNaN8+fJ89tln2NjYoFanpReBgYHUrFmTli1b0rhxY2rXro2vry+mpqb50yDZkD48QgghRAGwe/fuDGWZDRt/vg9MgwYNOHjw4H8eu0KFCuzduzdX8dSoUSPDMhL/7n/j7e3NmjVrsjyGlZUVy5Yt0z2Pj49n/PjxfPrpp7mKJS9IwiOEEEKIfHHq1CkuXbpEtWrViI6OZsKECQC0adPmlcciCY8QQggh8s3MmTO5fPkyxsbGVK1alX379uHg4JBhpFl+k4RHCCGEEPnCz8+PEydO6DsMQDotCyGEEOINIAmPEEIIIQo9SXiEEEIIUehJwiOEEEKIQk8SHiGEEEIUepLwCCGEEKLQ03vCM2fOHLy8vDA1NdWNz8/OsmXLdEvWu7i48PHHH/P48eNXFK0QQgjxZgoJCUGlUmWYffl1odeEZ+XKlQwePJgxY8Zw6tQp6tatS/Pmzbl9+3am9ffv30/Xrl3p2bMn58+f548//uDYsWP06tXrFUcuhBBCFByLFy+mYsWKmJqa4uzszIABA9JtP3v2LAEBAZiZmeHm5saECRMyLBNR2Ol14sHAwEB69uypS1iCgoL4+++/mTt3LlOnTs1Q//Dhw3h6ejJo0CAAvLy86N27N9OnT3+lcQshhBAFRWBgIN9++y0zZsygevXqJCYmcuPGDd32mJgYmjRpQoMGDTh27BhXrlyhe/fuWFhYMGzYMD1G/mrpLeFJTk7mxIkTjBw5Ml1506ZNs1wIrVatWowZM4ZNmzbRvHlzHj58yKpVq3jnnXeyPE9SUhJJSUm65zExMQCkpKSQkpKSB69Ev56+hsLwWvKDtE/2pH2yJ+2TvYLaPikpKSiKglarfeXLFzzv6RWUp7H8l4YNG1K+fHkMDAxYunQpxsbGjB8/ng8//JCBAweyevVqHB0d+f7772nevDkAkZGRfPnll/z55580atRIdyxfX1/dOX/55RcSExNZuHAhJiYmlC1bllGjRhEYGMjgwYNRqVSZxnP06FH69u3LxYsXKV++PKNGjQJI164XLlzg888/Z9++fVhYWNCkSRMCAwNxcHAAIDY2lr59+/Lnn39ibW3N559/zvr166lUqRKBgYEZ2ker1aIoCikpKRgYGKSL52U/Z3pLeMLDw9FoNDg5OaUrd3JyIiwsLNN9atWqxbJly3j//fdJTEwkNTWV1q1bM3v27CzPM3XqVMaPH5+hfOvWrZibm7/ciyhAtm3bpu8QCjRpn+xJ+2RP2id7Ba19DA0NcXZ2Ji4ujuTkZF15SnzuvzANTAxQG6b1/tCmatEkaVCpVRiaPfv6zOq4RhZGQNqXfk6kpqaydOlSBg0axPbt21m7di39+/dn9erVtGzZkoEDBzJnzhy6du3K2bNnMTc3Z/369Wi1Wq5fv86AAQOIi4ujWrVqTJw4kWLFigGwb98+atWqle4CQO3atRk9ejTnzp3Dw8MjQyzx8fG0atWKunXrMmfOHG7dusXw4cN122JiYggLC6N+/fp07dqV8ePHk5iYyLhx4+jQoQPr168H4LPPPmP//v0sX76cokWLMnXqVE6ePImvr6+uXZ5vn+TkZJ48ecLevXtJTU1NF1NCQkKO2jErel9L69+ZpaIoWWabFy5cYNCgQYwdO5ZmzZoRGhrK559/Tp8+fViwYEGm+4waNYqhQ4fqnsfExODu7k7Tpk2xtrbOuxeiJykpKWzbto0mTZpgZGSk73AKHGmf7En7ZE/aJ3sFtX0SExO5c+cOlpaWmJqa6son2k7M9bHeXfEuZd8rC8CFPy6w+oPVeAR40HVnV12db72/JSE845fxl6lfEhsbi5WVVZbfa88zNDSkUqVKuhXFK1asSFBQEM7OzgwcODDtNUycyMKFCwkJCaFGjRqEhYWh1WoJCgoiKCgIGxsbxo4dS4cOHQgODsbY2JjHjx/j6emZ7juvRIkSAMTFxWX6XbhixQq0Wi1Lly7F3Nyc6tWrExERQf/+/bGwsMDa2ppvv/2WKlWqMHPmTN1+ixcvxsPDg7CwMFxcXPjtt9/49ddfadWqFQBLly6lWLFiGBsbY2VllaF9EhMTMTMzo169euneO3h2h+ZF6S3hcXBwwMDAIMPVnIcPH2a46vPU1KlTqV27Np9//jmQ9mGwsLCgbt26TJo0CRcXlwz7mJiYYGJikqHcyMioQP2AvqzC9nrymrRP9qR9siftk72C1j4ajQaVSoVarUatfrmxOSq1SncMlfpZ0pKT4z79En8aS05UrFhRV1etVmNvb5+u7On3XHh4OGq1Wnf75/vvv6dp06YA/Pbbbzg7O7Nnzx6aNWuWaVs8jc3AwCDT2C5fvkylSpWwtLTUldWuXVsXl1qt5uTJk+zevTvThOnmzZskJSWRkpJCjRo1dOewtbXFx8cHlUqVafuo1WpUKlWmn6mX/YzpLeF5ukz8tm3baNeuna5827ZttGnTJtN9EhISMDRMH/LTe3xvWm9zIYQQuTMqblSu9zE0efad49vOl1Fxo9IlPgCfhXz20rE99e8v9adf/s8/B3R9Xp4mQGXLltXVKVq0KA4ODroRz87OzpleXACyvMCQk+9UrVZLq1atmDZtWoZtLi4uXL16NV3MuTl2ftDrLa2hQ4fSpUsX/P39qVmzJj/99BO3b9+mT58+QNrtqHv37rF06VIAWrVqxSeffMLcuXN1t7QGDx5MtWrVcHV11edLEUIIUcAZWxi/1P5qQzXGhhmPkdVxX0WH6adXXS5fvqzrsxMREUF4eLiub07NmjUZPXo0ycnJGBunxbp161ZcXV3x9PTM9Lhly5bll19+4cmTJ5iZmQFpI6WfV6VKFVavXo2np2eGixEAJUuWxMjIiKNHj+Lu7g6k3Za6evUqAQEBL//ic0mv8/C8//77BAUFMWHCBCpXrszevXvZtGmT7k0KDQ1NNydP9+7dCQwM5IcffqB8+fK89957+Pj4sGbNGn29BCGEEEJvSpcuTZs2bfjss884ePAg586do1u3bpQpU4YGDRoA0LlzZ0xMTOjevTvnzp1j7dq1TJkyhaFDh2bZt6hz586o1Wp69uzJhQsX2LRpU7q+OgD9+/cnIiKCTp06cfToUW7cuMHWrVvp0aMHGo0GKysrunXrxueff86uXbs4f/48PXr00N22etX0PtNyv379CAkJISkpiRMnTlCvXj3dtsWLF7N79+509QcOHMj58+dJSEjg/v37/Prrr7i5ub3iqIUQQoiCYenSpVSvXp133nmHgIAAjIyM2LJli+5WmI2NDdu2bePu3bv4+/vTr18/hg4dmm5Az79ZWlqyYcMGLly4gJ+fH2PGjMlw68rV1ZUDBw6g0Who1qwZ5cuX57PPPsPGxkbXJycwMJCaNWvSsmVLGjduTO3atfH19c3QIflV0PsoLSGEEEKQ4Q98SFvO4d/+3QfG2tqaBQsWZDlaGaBChQrs3bs3V/HUqFEjwzIS/z63t7d3tndZrKysWLZsme55fHw848eP59NPP81VLHlBEh4hhBBC5ItTp05x6dIlqlWrRnR0tG7IfVaDk/KTJDxCCCGEyDczZ87k8uXLutHZ+/btw8HB4ZXPgp3rhOf27du4u7tnOszszp07FC9ePM+CE0IIIcTry8/PjxMnTug7DOAFOi17eXnx6NGjDOURERF4eXnlSVBCCCGEEHkp1wlPVks/xMXF6aXXtRBCCJEZmZD29ZOf71mOb2k9Hb6mUqn46quv0i28qdFoOHLkCJUrV87zAIUQQojceDoDf3Jysm7SPPF6eLrY679XSs8LOU54Tp06BaRlX2fPntXN1ghpy0RUqlRJt5KqEEIIoS+GhoaYm5vz6NEjjIyMXno9rRel1WpJTk4mMTFRbzEUZP9uH61Wy6NHjzA3N8905uaXleMj7tq1C4CPP/6Y7777rlCsNC6EEKLwUalUuLi4cPPmTW7duqW3OBRF0S3NoI+ZhQu6zNpHrVZTvHjxfGmvXKdQixYtyvMghBBCiLxkbGyMt7e37haJPqSkpLB3717q1atXoFaTLygyax9jY+N8uxqW64QnPj6eb775hh07dvDw4cMM4+hv3LiRZ8EJIYQQL0qtVut1MI2BgQGpqamYmppKwpOJV90+uU54evXqxZ49e+jSpQsuLi5ymU4IIYQQBV6uE57NmzezceNG3ZL0QgghhBAFXa5vlNna2mJnZ5cfsQghhBBC5ItcJzwTJ05k7NixJCQk5Ec8QgghhBB5Lte3tL799luuX7+Ok5MTnp6eGToanTx5Ms+CE0IIIYTIC7lOeNq2bZsPYQghhBBC5J9cJzxff/11fsQhhBBCCJFvXmh2n6ioKH7++WdGjRpFREQEkHYr6969e3kanBBCCCFEXsj1FZ4zZ87QuHFjbGxsCAkJ4ZNPPsHOzo61a9dy69Ytli5dmh9xCiGEEEK8sFxf4Rk6dCjdu3fn6tWr6WawbN68OXv37s3T4IQQQggh8kKuE55jx47Ru3fvDOVubm6EhYXlSVBCCCGEEHkp1wmPqakpMTExGcovX75M0aJF8yQoIYQQQoi8lOuEp02bNkyYMIGUlBQAVCoVt2/fZuTIkbz77rt5HqAQQgghxMvKdcIzc+ZMHj16hKOjI0+ePCEgIIBSpUphZWXF5MmT8yNGIYQQQoiXkutRWtbW1uzfv5+dO3dy8uRJtFotVapUoXHjxvkRnxBCCCHES8t1wvNUw4YNadiwYV7GIoQQQgiRL3KU8Hz//fd8+umnmJqa8v3332dbd9CgQXkSmBBCCCFEXslRwjNr1iw+/PBDTE1NmTVrVpb1VCqVJDxCCCGEKHBylPDcvHkz0/8LIYQQQrwOXmgtLSGEEEKI10muE54OHTrwzTffZCifMWMG7733Xp4EJYQQQgiRl3Kd8OzZs4d33nknQ/nbb78ta2kJIYQQokDKdcITFxeHsbFxhnIjI6NMl5wQQgghhNC3XCc85cuXZ+XKlRnKV6xYQdmyZfMkKCGEEEKIvJTriQe/+uor3n33Xa5fv66beHDHjh389ttv/PHHH3keoBBCCCHEy8p1wtO6dWvWrVvHlClTWLVqFWZmZlSsWJHt27cTEBCQHzEKIYQQQryUF1pa4p133sm047IQQgghREEk8/AIIYQQotDL0RUeOzs7rly5goODA7a2tqhUqizrRkRE5FlwQgghhBB5IcdraVlZWQEQFBSUn/EIIYQQQuS5HCU8p0+fpkOHDpiYmODl5UWtWrUwNHyh7j9CCCGEEK9cjvrwzJ49m7i4OAAaNGggt62EEEII8VrJ0WUaT09Pvv/+e5o2bYqiKBw6dAhbW9tM69arVy9PAxRCCCGEeFk5SnhmzJhBnz59mDp1KiqVinbt2mVaT6VSodFo8jRAIYQQQoiXlaOEp23btrRt25a4uDisra25fPkyjo6O+R2bEEIIIUSeyFHCM3ToUCZOnIilpSW7du3Cy8tLOi0LIYTIGa0WnjxJ/yhRAp5+j5w7B5cvZ6zz9NG6NdSurd/XIF57OcpaZs+ezRdffIGFhQUNGzYkNDRUrvAIIURhEBYGjx+nJRaJiRmTjS5dwMAgre7vv8ORI7ptBvHxVLt1C4Mffkjbd+NGsLZOqzt8OPz8M5qEJJJSVKgAM57oTvt491lSbYuiSdGgnbkEzYrf0aJGiwEa1On+b+/3Di6KkjYHXEoKGBm9+nYSrz3ptCyEEG+Y2/tvc/6P81T9tCqO4wfBH39wE0+O8xbOhFGXfbq6SxdrSU7Uok3Vor1xC01kEloM0WKDBju0eKBFjQYDtE7fo01VaL2wNZWSkyE6mmuUZgWdceMuvfg5LVkxM2PpB5uJCUv45yyWQI+sA+60C+dpF2k4uSHeKyfB7dvQvz+0aSPJj8gx6bQshBBvisePudhsMKtOe6NNVSjZpCSOtrZgb0+01osLkeVItrKHsslgZgZmZtzdG0ZKfMo/BzAGimZ9/MS03//aFC2MGgX9+6M+/Bi6b0NTqQqcSNVdLbLw/wktatSGatRGagyMDDL9v0qt4t6Re4QFh5ESGQurVkFCAsruPahcXeDTT9MeLi7523bitSedloUQ4k1w9y4XavRg9b0aaFFwr+2OXSk7mD8f5s+n2KVwmu+4QRHPIvBOad1u7f+8BJAxITFSo0XLwcMHCWgQgLGZsa7czNYMzI3AxYVS3gpffVQDlVoFzy1L9OnxT3McesLjBM4uP4tPx0pQ7xLMn8++oJPcvO9IvXGL8Zo0Cdq3h6FDoXr1PGsyUbjkquexdFoWQojX0NWrnK/1CavDA1BQU7GVB23WdkVt8GzuWYcyDjiUcciwa5k2ZbI8bEpKCmZhZjj4OmCUxa0llVpFWg+eF2dub071gf8kMu7uKBMncurX74iKj8bPOw6uhqT1L6pcWRIekaVcr5YeEBDArVu3+PLLL+nUqRMPHz4EYMuWLZw/fz7PAxRCCPESgoM579/tWbLTvmSGZOd1o1Kp6L7vY+qPr0/ZMysgOBh69+aw5i1Wtl/Jje03UNaug4ED4eJFPUcrCopcf+L37NlDhQoVOHLkCGvWrNEtOXHmzBm+/vrrPA9QCCHEC9q3j3O1PmF1TBMU1FR634c2v3d+rZOdp2zcbQgYG4ChqSFUqoQydy7Hllzk0tpL/NLkF3788CCHfjjOk7JVoGFDWL0aUlP1HbbQo1zflxo5ciSTJk1i6NChuhXUIW2Nre+++y5PgxPitdK7NyxaBAYGLEnpzD2NM+9WvobPqZUAXPnrCts+WICBJhkDtYKBAc/+NQADK3MMqvtjYGyQ9jh5DIOkBJrViU8biWJkxKWbJkTGGlKyig2O04YBEPcgjtszfsfgSSwGJoYYmBj9868hBqZGGFhbYBBQ59lxQ65jkJqEUfnSYGGhzxYT+UlRONvnB9Y+aYGCmsoflqXVkncLRbKTGZVKxfvr3uf43OOcXnqax7GwlbfZSSPK7zrLW7sG4er2WdrP6SefgLOzvkMWr1iuE56zZ8+yfPnyDOVFixbl8ePHeRKUEK+b8P+tochPCzBEAykppKAmBWNUCQm6Ok8inhAebw6YZ36QR8CNc88VGGCAGc3OfasrOUUnruBDq+C/dQlPWHAYf3x75z8iPJWh5H2TT/H+vBWG5crl7EWK18rZ386x9lI5FBQqd61A60Xt0joOF2KO5Rxp8UMLGk1txNnlZzk+5zgPzjwgmCoEUwXXe/d4a+waym3dhdG+nfoOV7xiuU54ihQpQmhoKF5eXunKT506hZubW64DmDNnDjNmzCA0NJRy5coRFBRE3bp1s6yflJTEhAkT+PXXXwkLC6NYsWKMGTOGHj2ymcNBiHyUEhnLsn6HUNOPDzqrKTplCB+ExpASm4iF8we6eiWblaTb7KpoYhPQJKWiSUpBk5j6z/9T0RiZoPEtjyZZk/Y4fAziEyBgXNpkaykpeBxWYRKWhF2NmrrjmlibULyYBk18IhoNaDQqNFrQaFVpDwzQmFqkHTMpbdiwq+EDfJJOo5p0msY2NiRdu4/R5/3BxORVN5/IaydPcuaCIeu6rUPRKvj18qPV/FaFPtl5nomVCf69/an6aVXuHrrLsTnHuPDHBe4nu/Enbvx9Sk3lYX/j38cfe1slbaj7Rx+BpaW+Qxf5KNcJT+fOnfniiy/4448/UKlUaLVaDhw4wPDhw+natWuujrVy5UoGDx7MnDlzqF27NvPnz6d58+ZcuHCB4sWLZ7pPx44defDgAQsWLKBUqVI8fPiQVLkvK/TowNR9RKVaYq0Gm5kjwcUeS4+M9SydLLEc0DIXR844iWetTGq513Tn4zsTcnRERVFQNAqa5FRUGxugjB4N124z76tQPKb25p2f2mD+YebzbIkCTlFgyhSSv5zANusvUbQKVT6pQst5Ld+oZOd5KpUK91ruuNdyp9msZpxaeIoT804QFRLF4cDDnF95nsEDUlCPGglffAHdukG/flAm65Fp4vWV65u5kydPpnjx4ri5uREXF0fZsmWpV68etWrV4ssvv8zVsQIDA+nZsye9evXC19eXoKAg3N3dmTt3bqb1t2zZwp49e9i0aRONGzfG09OTatWqUatWZl8DQuS/yBuR7P/+JABN57fH2MVezxFlT6VSoTZUY2RuDO+9R+rp0+xo1o94LAhLsMaEJH2HKF6EVgvDhsGXX2JMMl06p1JrRK03Otn5N4uiFtT5og4Drw2k01+d8H7Hm6q9q6J2cwVvb7QxcRyYfYJY37egcWNYs0Y6ORcyub7CY2RkxLJly5gwYQKnTp1Cq9Xi5+eHt7d3ro6TnJzMiRMnGDlyZLrypk2bcvDgwUz3Wb9+Pf7+/kyfPp1ffvkFCwsLWrduzcSJEzEzM8t0n6SkJJKSnv0Sj4mJAdLmj0hJScl0n9fJ09dQGF5Lfsjv9tk8aDOaJA2eDT3x7lr1tXsfUoCUvvXpPsoH7fq/0XZohzYlBU2yhjNDf6HiB2UwqFND32HqzWvx85WaikGfPjxZugoLQDNzJraDBlEfSNWkQj5Ofv9atE8mvJp64dXUC0VRSFHVgo4duTJ5NdsnXuUwNRmyIxD1jh0oJUuSevo0GBu/0Hle1/Z5VXLbPi/bjipFUZQX3fnpripV7v+CuH//Pm5ubhw4cCDdFZopU6awZMkSLl++nGGft99+m927d9O4cWPGjh1LeHg4/fr1o2HDhixcuDDT84wbN47x48dnKF++fDnm5ll0HhUiB9S/neDkSgNUBuATVAZTd1N9h5RnHq+9z50lDylCJFW8b5E0qBHx7u76Dkv8izo5Gf9vvyXsSDJbeJuq7ybzpEvWfSBF1mLPxBL2Wxg2HioCLA7hsX07DypWYnvFbhSpVQQDCwOsb94kxtMz3YzR4tVJSEigc+fOREdHY/10kdpceKHpkpcuXcqMGTO4evUqAKVLl+bzzz+nS5cuuT7Wv5Ml5emKuJnQarWoVCqWLVuGjY0NkHZbrEOHDvz444+ZXuUZNWoUQ4cO1T2PiYnB3d2dpk2bvlCDFTQpKSls27aNJk2aZDnT6Zssv9onNTKW/3XYB1hRo2oKDXq3z7Njv0pZtc/5+4eJ/H0rUU9s2XnVFreBp2nyznlcf/gCXmBwwuuqQP98paZi8M47qI4cYae6C8laE7Qe9WjRotErC6FAt09utQBGgjZVi9pwECQl8eTP89z5aAthi8Io97Yrvmum4VShKJq+fVE++OA/OzkXqvbJB7ltn6d3aF5UrhOewMBAvvrqKwYMGEDt2rVRFIUDBw7Qp08fwsPDGTJkSI6O4+DggIGBAWFhYenKHz58iJOTU6b7uLi44Obmpkt2AHx9fVEUhbt372Z6W83ExASTTEaeGBkZFaoPYGF7PXktr9vn4AeziUy1wkodT8DaL177tv93+1TuW5eyXatz6Is/OTDvLPc0xVi8EXw3j6JxTw/spn0BtrZ6jPjVKpA/X0ZG0KwZHD/O+6u6E3zTmqqfVn2hK+4vH0oBbJ8X9fRlGBmhtrOjaLmiPDr/iOA1twimD8XO3sG/3zzKfTEGw4+7pHVy9vHJ/pCFqX3yQU7b52XbMNedlmfPns3cuXOZNm0arVu3pk2bNkyfPp05c+bw/fff5/g4xsbGVK1alW3btqUr37ZtW5adkGvXrs39+/d1szsDXLlyBbVaTbFixXL7UoR4IVF7z7B/V9q95KYDvDFxLdgdlV+UsYUxAT+8x8A7n1OllRsqFC5qy/Dj/4zZ0v8vEh4n/PdBRL65c+gOyogRcOEChk0b4t/bXy/JTmHm3dybvmf70n1Pd8q9Xw61kZq7uLOO9gTGfsK27y8QWaYmNGkCd+/qO1zxH3Kd8ISGhmaakNSqVYvQ0NBcHWvo0KH8/PPPLFy4kIsXLzJkyBBu375Nnz59gLTbUc8Pde/cuTP29vZ8/PHHXLhwgb179/L555/To0ePLDstC5HX/u64kFSM8LSNotysnvoOJ99ZuVjRan0v+pzpS6mq1mgx4MhvN/i+5PccnHmQ1N37ZTTLq3LlCrz7LseD9rOw1kK2jdiG8gbdYtQHlUqFRz0POqzowJDbQ2gwqQHW7tY8wZyD1OF7BrFsrztXjkWj1Wj1Ha7IRq4TnlKlSvH7779nKF+5cmWuR2q9//77BAUFMWHCBCpXrszevXvZtGkTHh5pk5iEhoZy+/ZtXX1LS0u2bdtGVFQU/v7+fPjhh7Rq1SpXV5aEeBnXxv3KpQe2qNHQfMkHqNSFc5r+zDhWcOLD40P4aOtHOFV0Iik6iW2fb+PHBqu54NEc1q5NmwtG5I9Tp6BOHY6tucPGITsAULTS3q+SpbMl9cbU47Mbn/HBnx9QsllJQMW1ZA9+a7+KH31/RJOSj8PixEvJdR+e8ePH8/7777N3715q166NSqVi//797NixI9NE6L/069ePfv36Zbpt8eLFGcrKlCmT4TaYEK9CalIqm6edASyo9paCY6vq+g5JL0o2KYnXSS/O/HKGncM3EfW4CA/vp1C2fXuoUQO++QYCAvQdZuGybx+0bMnRmDJspgUANYfXpMn0JnIbSw/Uhmp8Wvvg09qHiGsRHJ9/nOCFwbhWdcXAyEDf4Yks5PrP03fffZcjR47g4ODAunXrWLNmDQ4ODhw9epR27WSGVlF4qVQq/L5oip2dQv0/c9Y5v7BSG6ip3L0yA29/TuMJdag1oi6Ym8Phw4TV/4DHAe3h9Gl9h1k4bNwITZtyJMZXl+zUGlFLkp0Cwq6UHU1nNGXI3SE0m9VM3+GIbLzQsPSqVavy66+/5nUsQhRoBsYG1BnXmFpfNkRt+ObcysqOkbkRtb9qBDSCIf1RJkxk/VwND/Y68m7NnpR9uFvWJ3oZy5dDt24cTq3K3zQHoPbI2jSa0kiSnQLGyMwIIzMZiVWQ5fi39v379xk+fHim4+Cjo6P5/PPPefDgQZ4GJ0SBoChot2zV9U+RZCcLzs4kTp6JZUBVjIzAY2Cr9MlOVJTeQnstxcfDiBEcSvXXJTt1RteRZEeIF5Tj39yBgYHExMRkOlmfjY0NsbGxBAYG5mlwQhQE18f/yrzmfxLi/17amkUiS2a2ZnTe/Ql9rw/D4puxuvL1LeZx1rkJytfjITZWjxG+RiwsONTpe7byNgB1v6xLw0kNJdkR4gXlOOHZsmVLtquhd+3alb/++itPghKiwEhIYM83h3iEI5cMysIbNCrrZdi42+im37+x/QanNj9gTVJLfp5wj1vudWD2bEhO1nOUBZBWC+fOAXBw5kG2zjwLQL2x9WgwoYEkO0K8hBz/9r558ybFixfPcnuxYsUICQnJi5iEKDimTKFT0mJqWZ154zsqvyj3Wu40nNQAY1M193FjcXR7Vgw6SHjJ6ml9VOSqWZrUVOjRA956i4P9fmXb52mjUQPGBdBgvCQ7QrysHCc8ZmZm2SY0ISEhMvmfKFyuXIEZMzDjCU2WdsHU5c1ZSiEvGZkbUXdMPQaGDMG/dxVUarhMGebcbc2mD5cR3/oDfYeof4mJ0KEDLFkCKSnYGMajMlBRf3x96n9dX9/RCVEo5DjhqV69Or/88kuW25cuXUq1atXyJCgh9E5RuNNlFEpyMjRvDm3a6Dui156lkyXvzGtF33P9KN2iJApqjlGN2TsrsP+b/aQ8SXkzJy6MjYUWLeDPP8HEBNasodz3vel7pi8BY2U+IyHySo4TnuHDh7No0SKGDx+ebjTWgwcPGDZsGIsXL2b48OH5EqQQr9qNcUtZeLQiy1Vd0M4K0vVHES+vqG9ROm38iK47u+JSqShJT7TsGLWDH3x+4MzHQSjvdoDLl/Ud5qsRHg4NG8KuXRw1qUv00nXQujUARcsW1W9sQhQyOU54GjRowI8//sgPP/yAq6srtra22NnZ4erqyo8//sjs2bNp2LBhfsYqxCuhSdaweckjAOz8vVD7lNZzRIWTVwMvPjnZl3a/tMPa3ZqYOzGsXRLDz2vsSClbCT79FO7d03eY+efRI6hbF44f54hFQzYnNWLJ6CskxSbpOzIhCqVcTTzYu3dvWrZsye+//861a9dQFIXSpUvToUMHWa1cFBqHvztM+K14LBwtaLDhE32HU6ip1CoqflQR33d9OfLdEfZN2oN9ETOM7iXB//4Hv/wCn30GffqAh0fhutJmZwcVKkB8PGUWT+fIJweo8kkVTKxM9B2ZEIVSrmdadnNzY8gQGa0iCqeYO9HsGb8HgMbTGmPqZKPniN4MRmZG1BlZB7+efmhTtXCjI4wcSeT+cxyadpaa0/ywDRqXlvxA2pB2tRoMX2iy+ILBwAB+/RXCw7FxdaV3cHlJdoTIRzKpiBBPKQpba4wlJT6FYtVdqdS1kr4jeuNYFLXAysUKateGvXvZWXc8x6jGAVVdqFIFAK1GS8pvf4CtLTRpAuPHw/btr8eEhvv2Qf/+KBoNu8ft5vyfV8HVFUCSHSHy2Wv855EQeevm14s5f98OFVpajPFDpS5Et09eRyoVVSe2Jn7iXkr3bQc1ywJw9/Bdful5FU9NG0ptv4r39u+xY1zaFZ/KldOSpc8/B3d3vYafwcaN0KEDSmIiu64VZ9/WRNSGalz8XLArZafv6IQo9CThEQLQRESxeWowYEfVt9S4tPLXd0gC8AzwxDPAM11ZyO4QUjUqruHNNbzZAtgZxlAq9SKlTl7F8+RcjEaMeLbDhg1w9y7UqQPlyulntuxly6B7d5TUVHaW6s3+rYkANJ7eWJIdIV4RSXiEAI68O51HqXaYqxNpuG7Ef+8g9Kbu6Lr4tPbh2uZrXNt8jdv7bxORas1RqnOU6hgaKnh+sodSzUtRqnkp7OfNg02b0na2sYGaNdOSn9q1oVo1MDfP34B//BEGDEABdpT7jAPn0yawbBbUjBqf1cjfcwshdF444UlOTubhw4do/zUtfHbLTwhREMXuPsGe3Wn/bzygDGauMqNyQaZSqXCq4IRTBSdqj6hNUkwSN3bc0CVAMXdjuLblGte2XIPPwNa+OtVKO1Dj/hqIjoYtW9IekJYAPX6c1oEY0lYot7DIm0AVBSZNgrFjUYDtVb7g4Mm02ejf/v5tqg+snjfnEULkSK4TnqtXr9KjRw8OHjyYrlxRFFQqFRqNJs+CEyLfKQrbPlhAMk642cZTeVbWC+SKgsnE2gTfdr74tvNFURQenX/E1c1XdVd/Ih9rSRz4MYxZAGfOkLh9H6dXXKLUvb3Yl3V8luxA2hWfpKS0qz+1a0P16i++1teVKzBxIgqwrebXHDqU1ies+Q/NqdZfZqUX4lXLdcLTvXt3DA0N+euvv3BxcZEF7cRrLWTJHs4+cAIUWizuKB2VX3MqlQrH8o44lnek9ue1SYpN4ubOmziWd0wbwl6lCjdumLLlVBT2Pn0ZsKG7bl/t40jUly6lJTjXr8PSpRgBzS0tMahXD959N21xz5zy8UFZvpytP17j8O60yQRb/NiCt/q9lbcvWgiRI7lOeIKDgzlx4gRlypTJj3iEeKUcmvtTuf0dDGMjcG0tHZULGxMrE8q0Sf+7ytjKGK+GXrj4u4ClJQCpSakE+vwP1wYLKFUKvFXXsLt4AI4ewTguLq0PkJPTs4QnNRW++gpq1IBataDoP8tAJCZCaCh4eaEoCn8fsOTIP8nOO/Pewb+3fMaE0JdcJzxly5YlPDw8P2IR4pWzdLKkzeouKNo3cNHKN1SpZqUo1axUurK7h+7y5PETru+4xfUd8DdG2JZoR4muQ0hOvkCLUgaYVq/ybIczZ+Cbb5499/FJuwV25QrcuoWybx9bAi9w9PujALT8qSVVP6n6Kl6eECILuU54pk2bxogRI5gyZQoVKlTAyMgo3XZra+s8C06I/KI5cx6DJ3FpfTRAbmW94TwCPOh3vp+u78+tvbeIvBHJifmRAFwwMcAzIJRSZw+njfwyNUX1ySewfz9cvJi22OnTBU+trLj820mOfn8GVNDqf62o0rNKNmcXQrwKuU54GjduDECjRo3SlUunZfHaUBTWN5lN0sNo3p5UhyJj+us7IqFnKpWKomWLUrRsUWoNq0VyXDI3d97k8sbLnFt7jpRHKVzfep3rW6/z95C/KeJVhNKt2vH2+fmoIiLg0KG05OfaNRg9Gh8/P2o+tqCob1H8evjp++UJIXiBhGfXrl35EYcQr0z090s499ARLc7UrVCDIvoOSBQ4xpbG+LT2oUTzEmiba6leojoh20N0V3+ibkbx8MzDtEEb9vbQsiWnI91x+9gNBx8HVEDTGU31/TKEEM/JdcITEBCQH3EI8WpER2PzzSj6kMqNloNway39KkT2VCoVDr4OuFR0oebQmmlXf3bdxND02a/P+EfxrOu6Dq+GXnTe2DndNiFEwfBCP5VRUVEsWLCAixcvolKpKFu2LD169MDGRlaWFgXcuHEQFkbR0qUpukpmVBa5Z2xpjE8rn3RlTx4/oUSTEjw8+5Db+29TonEJPUUnhMhKrhOe48eP06xZM8zMzKhWrRqKohAYGMjkyZPZunUrVapI5zxRMMXvOkbs97/jDDB7NpjI6tQibziUcaDL1i76DkMIkY1cJzxDhgyhdevW/O9//8PQMG331NRUevXqxeDBg9m7d2+eBynES1MUtn/wM6e1vWhS8QE1m0r/CiGEeJO80BWe55MdAENDQ0aMGIG/v0yqJQqmuwfvEPzQFQD3iZ/oORohhBCvmjq3O1hbW3P79u0M5Xfu3MHKyipPghIiLykahb8HbwPA7+NKFJOOykII8cbJdcLz/vvv07NnT1auXMmdO3e4e/cuK1asoFevXnTq1Ck/YhTipTze/IgHpx9gWsSURtOa6DscIYQQepDrW1ozZ85EpVLRtWtXUlNTATAyMqJv37588/xU60IUAPE7D/NowQ3AlIaTG2JR1ELfIQkhhNCDXCc8xsbGfPfdd0ydOpXr16+jKAqlSpXC3Nw8P+IT4sVptezpspQkpRjORZ5QtbfcyhJCiDfVC8+OZW5uToUKFfIyFiHy1N2xPxH8qBgATRe0R22Q6zu4QgghCokcJTzt27dn8eLFWFtb0759+2zrrlmzJk8CE+JlaMMfs+mbs4AjHqViKdZK1jMSQog3WY4SHhsbm7Q1Y0gbpfX0/0IUVCffnUKoxhETdTJWo97SdzhCCCH0LEcJz6JFi3T/X7x4cX7FIkSeSNhxiB17jQAI6OvDQ3tTPUckhBBC33LdqaFhw4ZERUVlKI+JiaFhw4Z5EZMQL2XHiK0kYoZTkSSqzHhf3+EIIYQoAHKd8OzevZvk5OQM5YmJiezbty9PgiqMru3fzvG5/UFR9B1KoffWgt64l7OixaIOqA2lo7IQQohcjNI6c+aM7v8XLlwgLCxM91yj0bBlyxbc3NzyNrpC4kFIKO7X36GUTTKn1tbAr70sMpifnCs78/HZIahUKlJSUvQdjhBCiAIgxwlP5cqVUalUqFSqTG9dmZmZMXv27DwNrrBw8nRh86qvae46hhKRA4m42wC7YsX0HVahkzL3Z4xaNwc3N+lYL4QQIp0cX++/efOmbqLBo0ePcvPmTd3j3r17xMTE0KNHj/yM9bXWoN8Iztyrho1ZNLf/6ImilVtbeSlh2wG+73eJbV69Sbl5V9/hCJEvjtw9wv7b+/UdhhCvpRxf4fHw8ABAq9XmWzCFmam5IeraS3hyxY/KTls5uuJ/VOv8qb7DKhw0Gs73mkUcFbhmUZFG7q76jkiIPLf+8nrarmhLs1LN2NBpA4bqF543Vog3Uq57dE6dOpWFCxdmKF+4cCHTpk3Lk6AKq/K1yrA3egoAZZOG8uD6DT1HVEj8/DNv3V5NJ7O1tPylo3RUFoXS1cdXUVBoX6a9JDtCvIBcfzPMnz+fMmXKZCgvV64c8+bNy5OgCrOGfT/jxN16WJrEE7+zByhyxeylhIfDqFEAlP6mB+4tK+s3HiHyybBaw9jeZTvdK3fXdyhCvJZynfCEhYXh4uKSobxo0aKEhobmSVCFmZGxGusmi0jWWlDCYg9clo7eL+NW96+Jj0yGihWhXz99hyNEnkpISUCj1eieNyrRCCMDIzRaDZP3TmZJ8BI9RifE6yXXCY+7uzsHDhzIUH7gwAFcXaXvRE54+5XAuPrMtCenR0LMZf0G9Jp6snUfv2+0YDYDCRsyDQzlMr8oPDRaDR3/6EjblW2JS45Lt23Z2WV8uetL+m/qT2is/KEpRE7k+huiV69eDB48mJSUFN3w9B07djBixAiGDRuW5wEWWqV6w501ELaN0LXdcfpwH2r5ws6Vnasek4AFRW1TKPphE32HI0SeOv3gNDtu7gDgyuMrVHGpotv2YYUPWX1xNR18O+Bs6ayvEIV4reT6G3bEiBFERETQr18/3YzLpqamfPHFF4z6py+FyAGVCu1bC4j/owIupofZv3AmdT4dqe+oXhuhJ0M5sSBtMswWq3tiYGSg54iEyFtVXKqwu9tuQuNC0yU7AAZqA9a9v07mmxIiF3Kd8KhUKqZNm8ZXX33FxYsXMTMzw9vbGxMTk/yIr1BTW7lzTPmeGkl9+H2tDQ71IJP+4OJflLh4NvXfiKJVKP9BeTwbeOk7JCHyjKIoukSmerHqWdZ7PtlJSk3idvRtvO298z0+IV5XLzx+NywsjIiICEqWLImJiQmKrBGFotUS8/dBEk+cR3v7LsTEgEaT7T4NPu7CJ+uvMHtLX7p1g9TUVxTsayy4+UjuHr6HsbkhTWbKrSxReASHBVNnUR3uRN/J8T4hUSFU/7k6TX5pQnRidD5GJ8TrLddXeB4/fkzHjh3ZtWsXKpWKq1evUqJECXr16kWRIkX49ttv8yPO10JqZCyz3t6me25AKsYkY0QKxgYajO0sMSrrjbGlMcYWRhgd3oexiYqR3qZsNB7H0aNmTGuwkdYlL1KkkifuQzrojhW9+SBGtpYYOdhgaG+NytISjIz08TL16smWPWzfbwZAvY+9sHaz1nNEQuQNRVH4ZMMnHL9/nJE7RrKs/bIc7WdnZkdMUgzxKfFceXyFt9zeyudIhXg95TrhGTJkCEZGRty+fRtfX19d+fvvv8+QIUPe6IQnJTIOFVqUfy6caTDkCYY8SXsCj4A9t57bwx6A0leX8gP3mO/Tm8pOP7JmSXV8nA/wwdOER1H4vsUWtKT1U1GhTUuiSMFInYrKzIClFcPTEilLY4xPHsHIUEuVckm4ehqDhQVRKRaE3DfGxtcVry8/1EWgxMamJU+vQ1+A1FR2dVlIAiVwsEmhRuD7+o5IiDyjUqlY3XE1w7YO48cWP+Z4P2sTa9Z9sA5HC0fpwCxENnKd8GzdupW///6bYv9a/NLb25tbt25lsdebwbyUG19px6FJ1pASl0zy4ziSw6NIDo8l5XEMyVpDki1tSYlPITkqgeT9R0mOTcKu4sd8aBSPB1MomnyTGx7eOJVy1B1Xm5iMGoWnUxQqqEnGhGRMQAvEQ8Sh59ePsgGgxPXfceUCAPcox5+8h4fN8XQJzw+2X6NotdiaJ2Nrr8K2mAW2Je2wLe+GbS1fTOv453Or5VzYlz9wPNwTgOYL3sXAWDoqi8KluE1x/njvj1zvV9GpYj5EI0ThkuuEJz4+HnNz8wzl4eHh0nGZtL/SDE0MMTQxxMzeHEo7Zl15aO10T33vPeb44ol02Dsel+I2unK1mQljlIloNdq0ZCkynpRHUSSHx5DwIJITJ89SpnYDtEnatERr1wGSY5NwLNMZzBMgLg7zq6mUPBWHU8kiuuNq4hOJ1FihoCYyHogHbgMHk4AbwA1MbXdgW8I27XHhALYOBpSsWZQifiXA0xM8PMDRMd+vEClhYWyaeQEFN8pVNaHEu375ej4hXpXxu8fT3Ls51dyq5cnxjtw9wpzjc1jYeiEGavmjQIincp3w1KtXj6VLlzJx4kQg7Qteq9UyY8YMGjRokOcBvkkc3Ox5e0xQltvVBmpMrE0wsTYBDzsAUlJSOFskhjItymD0tE9P34z38L3+eaQ7nrkJg6/2J/LIZSJPhhB56QGRt2KIfJBMZLSa+BQTEiMTCT0RSuiJUMAWgPf3LKYIaZMlXqMkh9R1KVHRktqnns0anbRsFSYl3NKSIicnUL/c+lZn3pvEHY0bRupUmq4e9FLHEqKgWHZmGeP2jGP6welcG3gNF6uMs9jnRmxSLM2XNScyMRI/Zz8G1xicN4EKUQjkOuGZMWMG9evX5/jx4yQnJzNixAjOnz9PREREpjMw/5c5c+YwY8YMQkNDKVeuHEFBQdStW/c/9ztw4AABAQGUL1+e4ODgXJ+3wFMUTq9fhneDdphbW+TLKVQqFdalHLEu5YjHhxnbPDk+maibUUTeiCTi0kMitxwh6nYsDmVrQmRRCAnhwV1nbmg9sYiO1e2nTUhk2kdnMeE4tkRiq47G1kaLrbMJtl5FsG1UBeuBXZ/NnaPVZpsQJT+KZtthKwDq9S2LtYdt3jaEEHrS2qc1LbxbUMe9zksnOwBWJlYEvR3ElmtbZM0tIf4l1wlP2bJlOXPmDHPnzsXAwID4+Hjat29P//79M11jKzsrV65k8ODBzJkzh9q1azN//nyaN2/OhQsXKF68eJb7RUdH07VrVxo1asSDBw9y+xJeC8d+/IS37Baw5+cBBAzVz3pbxhbGOJZ3xLG8I7T2gREZk6Iy50Kx2HwWG1dLXVnM5fsoqEnEjFDMCNW6QiRpj4vAptuoRkzGprhNWgK0Zx22Nloq+qZg7ePy7FaZpyd4e2Pk7EyLZR9yYvoOagZ2fEWvXoj8Z2VixfoP1qNWvdwV0Od1qdiFLhW7yKSEQvzLC61l4OzszPjx41/65IGBgfTs2ZNevXoBEBQUxN9//83cuXOZOnVqlvv17t2bzp07Y2BgwLp16146joJI7dkRYhYQ4PwDpza3xa95I32HlCn78i7Yl0+f6BbxK8GouFFEhUQReSWcyOAQIs/eJ/JGBJH3nhAVoyI1WSHqZhRRN6O4SWWIgFIH5mF9YDMAx/HnJFWoWNWIGsd/oGzH8pTtWF4Pr1CIvHU/9j5H7h6hnW87gDzvZ/PvROf8w/OUcyyXp+cQ4nWUo4TnzJkzOT5gxYo5Gy2QnJzMiRMnGDky/XIKTZs25eDBg1nut2jRIq5fv86vv/7KpEmT/vM8SUlJJCUl6Z7HxMQAaX1fUlJSchSrPlRs1oDdQX2o7zaPojd68DjsJNb2GeecefoaCtprURmrsC1ti21pW2iZfvZXRasQFxaXlvBcjyDy9G2iLz3Aqu0kNA/voLp1iwfbjAm950oJM+WlXltBbZ+CQtone3ndPgkpCbRc3pJTYaeY12IePSr3yJPjZkZRFEbuHMmsI7NY2X4l7cq0y/NzyOcne9I+2ctt+7xsO+Yo4alcuTIqleo/Z1NWqVRo/mNm4afCw8PRaDQ4OTmlK3dyciIsLCzTfa5evcrIkSPZt28fhjlcaHPq1KmZXo3aunVrpqPNCpKUYg0JefQ3ng432TS3PylVOmVZd9u2bVluK9AcgEYGGDRyZTuAmwP4+ZFUMwmvW4mEOxmzadOmlz7Na9s+r4i+2ycqJYrN4ZupYl0FHwsfvcaSmbxqH42iwS3VjesG1+EmbLr/8p/t7Ny6nzZVyB8H/sDkRv6NotX356egk/bJXk7bJyEh4aXOk6Os4ebNmy91kuz8+/Lr8+vIPE+j0dC5c2fGjx9P6dKlc3z8UaNGMXToUN3zmJgY3N3dadq0KdbWBX+W3rM7HCge3ogWPis5mNKZt9o0T7c9JSWFbdu20aRJk2ejtISOtE/2Ckr79NvUj5UPVpJUJIkhLYYAkJiaSMSTCFytXPUWV360TytaERYX9komCWysaUzvO71p4Jk/I2gLyuenoJL2yV5u2+fpHZoXlaOEp127duzYsQNbW1smTJjA8OHDX/rqiIODAwYGBhmu5jx8+DDDVR+A2NhYjh8/zqlTpxgwYAAAWq0WRVEwNDRk69atNGzYMMN+JiYmmc4PZGRk9Fp8AKu83YA9QUMIcAykZHhvkmLPY2lnl6He6/J69EXaJ3uvsn20ipaNVzZS1bWqLpkZXHMw58LP0blCZ10cX+35innH5/FTq5/oWE6/ndWzap+UFLh5E4oXB1PTrPc/GXoSP2c/3R9z7rbu+RVqOkZGRjT1bvpKziM/X1mT9sleTtvnZdswR0MDLl68SHx8PADjx48nLi7upU4KYGxsTNWqVTNcytq2bRu1atXKUN/a2pqzZ88SHByse/Tp0wcfHx+Cg4OpXj3rVYVfd9U/mcStqDI4WYdheXFghu2ycKt4nXRd25XWK1rzw9EfdGXlHMtxqOch3iv3HgCp2lT23tpLdFI0JgYFb0LTK5cVRn6hpVgx8PGBMsXvMLv3JL4ZvJ0nT9LX3XJtC9X+V42P//yYVK3+VgeOTYql1/penA47rbcYhNCnHPfh+fjjj6lTpw6KojBz5kwsLS0zrTt27Ngcn3zo0KF06dIFf39/atasyU8//cTt27fp06cPkHY76t69eyxduhS1Wk358ulH6Tg6OmJqapqhvLAxtTDDo+MS2FYTbi0H93ZQPG2dLUVR+P7295zad4pxDcbJUFRR4NyKuoWzpTMmhmmJy7u+77Lx6kYsjLKeX8pQbcjej/ey6eomWvu01pXfi7mHi5VLng7jzpXEh6yd9Qs+hgsI2TuWhw8/wMgIannvY2DAVxy9WRNT08a66r+Nn8OBuMs4WCvcugW3bhpQooR+lq77YvsXLDi1gKP3jhLcJ1h/bSiEnuQo4Vm8eDFff/01f/31FyqVis2bN2faaVilUuUq4Xn//fd5/PgxEyZMIDQ0lPLly7Np0yY8PDwACA0N5fbt2zk+XqHmUA3KjoLzk9Ec6UsEdSla3ImdITvZFbmLvfv30r5seyo5V9J3pELofLb5M3489iMLWi+gW+VuQNpke3eG3MHSOPM/mp4yVBumS3YSUhIIWByAu407v7b7FTdrt3yN/amQmxYkXN2MzeMlcHc97TzSrtIMbf8rnUZ+QIsWEHnZjXsXOmPkUf5ZMpMaT8eSA+mk1vIDcDdiOyfmtGXF/apEG1TF2rMqX056dYt9jq8/nlNhp5jeeLokO+KNlKOEx8fHhxUrVgCgVqvZsWMHjo7ZrBGVC/369aNfv36Zblu8eHG2+44bN45x48blSRyvhfJjibm0AeuUM1z7rQ8On6+hkVcj+rn3o1y5cpLsCL1L1aZiqH72a8XFygWNouHY/WO6hMdAbfCfyU5mjt8/TmhcKEmapBfaPzdiYuCvFTdIOLeQd0svxib4nm6bxrYaMQ49qfbeB/BPlwLH8gFQPoCnKVhCSgIGieFEW3bCMPYERdSXKWZ3j2J292hTdf2zE611BbuqYFeVqfOrEJpUFU9fVypXhkqVwN4+715TUYuiHOxxUK4CizdWrice1Gq1/11J5A8DYx6WXIrphbfwtDzMikWhdOhalKb2TWlRpYWu2qP4R9yOvk1V16p6DFa8aYIOB/HtoW/5470/qFGsBgC9q/amhXeLPFnNu55HPc72PUtYXBg2ps8W130Y/xBHi5f/A0xR4PCBRM5sXIOP4QI6++6EtJdBfKo9FuW6QMmeGBQpT3aLm2gVLR+t+YjwhHDWvr8WO3N7SImFyGA0j04Qc+sEqsgT2KguoXpyH+7dh3sbGPVP18VyI85x4V7aRIG1Kt7Eu4wx1eu70rfvyycqzyc7j+IfAWmJUGHwqka+5ae7d1Kx2lMWY1U0D11n4NGgKwCp4WdJPjkRQzMrjMysUBlZwdOHYVb/WoKRtX7unxZQLzTT8i+//MK8efO4efMmhw4dwsPDg1mzZlGiRAnatGmT1zGK55Tyr8Taw3/Qa1QdkrHHv176iZiSNcm0/709x+8fZ2WHleluCQiRn04/OM3dmLv8fPJnXcJja2aLrVnerX1WwrYEJWxL6J5vv7GdVr+1YmKDiQyvNfyFj5sa94BV4ybSzGcZNStEAaBVVNxKasxto6rU6jgGTHN2VenK4yvsuLmDxNRErjy+Qk3zmmlfQI51MXCsi+3TSY9T4iDqNEScQPPoBPF3T2CSepuyNXxIPAU3bkDvWl/Tte4vrLj8DfAFAJqkeD7qGImjpxuVK6uoXBnKloVMBqNmae+tvby/6n2qulRlQ6cNr/1Vnx+P/sjwbcNfv995Wg1K2HYO3WzKd9+rWL3akIkd3mVU6284dFjB45/ZBCLu3MYx/I9cH/6nyChMrWywtoZKqq9wTN1Ikudg7KqmJVLE34FLs55LkJ5LmDIkT//8+xrfDs11wjN37lzGjh3L4MGDmTx5sm6iQVtbW4KCgiTheQVa921D2ZWwfz988okBn332bFuyJhkbExuMDYzxtvPO+iBCvCBFUdhxcwezj85m3jvzdItefl7rcwI8AuhUPusJMvPa7+d/JzE1kVtRt3K1n1YLJ09o8X8r7Ze3obEJ7SotwMQgkfAnHiQ4f4x7QHeKmbhyZtMmyMVIsTIOZTjY4yCXH1+mpnvNrCsaWULR2lC0NgY+YA2gSeKPj9J+LUdHQ8rOOLQJakr7P1saIvTUdn57vy0Poh05cbEqf22qyqTbVYk1qopLyWJ06KCiVavsY7Q1tSXySSQhUSGEJ4S/9ld5bkXfIjE1kd/P//56JDwpsWiuLiL+1PdYq67z9dStbD/XBIAdoSO5tb4z9Zo9m38qmrJM/fU7zI1jsTSNw8o0Nu1hlvZv6RKxeLjGpR03ORZtYixGhqn0HWiJ9p9BvL8NuMoHNU+x4a9IPvjn4n/sg9tYXZ6Vq9BTseBI0UuY2BbDygqcY+dhEf0XBiU6ofL6MK2SNhXUL3Q9JV/lOqLZs2fzv//9j7Zt2/LNN9/oyv39/Rk+/MX/whI5Z2AAixdDxYoKxVKXE3HiFrRMu6VlaWzJnx/8yeXHl/Et6qvfQEWhpFKp+Hr31xy8c5BKTpWY0GACAGWLlqVs0bKvNJb5LedT37N+ui+52KRYTA1NMTLIOGfHvXuw5tdbuD8eg53pba7Z7qVUKcC4CHHe35Lq4I1DyUbP/orNxVT2z0+aWs6x3IutX/VcYmVjA7RbA6kJVFE9W2/LzuQ2WsUAJ5uHtKi8mRaVN+u2PYwuSnRYVTid1i8oLLkqvYe6664EVa6ctiZvBacKbPloC9XdqmNmZJb7OAuAFE2K7j2e2GAiFRwr8FHFj/Qc1X+IC4Ers+H6zxikxGCtgsj4IngUvU+PHjBoEFSqZANUSLebt58XgRsHkZgIsbFpfcye/zfWE/hnsHLEI5g8WeFJfBLt3zXQ1fvt7Fg2XepO5XrPZjKPTXFmzoYRuuTJ0iROl0RZmcXibB+LnVUspMaCktadxZB4WrUzJzJtphrm9gimT6ONrNxdjfcn/nNgRcML3kDKV7mO6ObNm/j5+WUoNzEx0c3VI/JfyZKwKuhPmlt0IfaJJTfOfoBPlZJAWqfQ5794Lj66SL9N/VjadinuNq9mwjNReITFhbHw1EKG1xqOsYExACNqjWD7je10rdRVr7GpVCo6V+icrmzg5oEEhwWztN1SKjpVJDUVNm9M5X8LDNm4EewszLn3w+8YG6aw48wFSpVK+1mxr5H54ImcOPfwHF3XdmX5u8sp41DmpV5TBobpJ3k19xsIFXrpbocpj0+Q8uAEhgnncbR5hCNb4PwWAJyBn5s7sDH4Hdq3XwykJVKVKkHlyvUx/xCqVcvbcPNbdGI0g7YMIiElgd87/I5KpcLE0IQulbroO7TMKQqq8ANEHP2BInFrUav+6QdrVZpDkYM58Lgr3/xugYND9odRqcDMLO2R3ZihokUhKEgF/HsmzLL/PJ6rW6Ik706cli6BehgL1/55Xt4FmjdPew0R4Yn06ZV29cjNswiWMWn1l+7rzvEb/hSv9FxeUEBve+U64fHy8iI4OFg3dPypzZs3U7bsq/3r7k33do9WnJzViLMPK9OmXebT7yuKQq8NvTh45yBD/h7Cqo6rXnGUBYdWC7//eADt7bUkFO1MrxFV0soTHvDH/85jbFkEM5siWNgWwdreGls7Q2xtwdLyze33p1W01FxQk5CoEDxsPPiwYtol6zZl2tCmTMG7ff0w/iF/XfmLiCcRPEmK4ea+tVz7ewGWxLNhwy4AfCsX5YRqNn4BVWjkmjdXQQdtHsSpsFOM3D6SdR+sy5NjZsvQDBxqgEMNVIAxQOoTiDoDESd0DyX6PEWtw6nkG4mfH5w7B9HRCnNbluNuRDHuXFlEtWpurDi3gmM3rlA6dCzvvst/fvnq0/XI6yw/uxytoiU4LBg/l4x/gBcImmQ013/D79E0DHddxA5ABfeVJrjWHwyub1NTpSabm575zsiItCuc/0Wlwq6oGb//aQakz7YUpQYJCTVIN55JXTBnlc51wvP555/Tv39/EhMTURSFo0eP8ttvvzF16lR+/vnn/IhRZEFlYECZvhu5s30LFlaZv5UqlYpl7ZcxaPMg5r4z9xVHWHBERkLfT58wu8l7uPqFMu98bSAt4UkI2c37RT9Iq5gMPEh7xDyxIjK+CBHGRfAoWQSMbFCMivDX1iJo1EXYET4BOzsVtrbgZXceW+tEHDxLULZy3nXSfdU0Wg37bu+jvmd9ANQqNb38erHx6sbXYgSMtYEjW+uvwzRqKmWPvwuJD/H65++wSaNu827X4pQpA9A7T8+7osMKhm0dRlCzoDw9bq4YmoFD9bTHP1SaRIg6i59KzcnPIDkZrp+5i++Vi/i4XuF2dTtO3D9Bp9WdGGADcZcu0j5oAObFa9LxfTXt2oFtAfs4V3Gpwg/Nf6CcY7mCmewkRxF3ag7ayz9ibXif4haQmGzCskNduG74GZ36lMf11Uwh9UqoVGCR9RyiBUquE56PP/6Y1NRURowYQUJCAp07d8bNzY3vvvuODz74ID9iFNkwMn526VCbksTj0AiKFndJV8eziCfrO61PV3b47mGquVV7IyYgu37dhiFDDLl504j4kF+ZO2ga9Vo9uxqpqEy4G1sOU4MozI2iMDdKuzVrbRaLtVkscAfSRvCiAlr5QFyiBe3GTdQd46/hI6jnt4m5yxZStvLHAGju7yBsbTdik4qQkFKERG0RUihCqroIipEN9s5F8KtWBIyLgFERLt1Iu8Jk5VISmyJqDJ5123glUjQpVJpXiYvhFzn2yTH8Xf0BGFlnJGPqjXm1weTSxTNxBG/4nRLKAqqXOPhsg6kzt0zfo/Pl36hZ/3s8S00i46X+l+do4cgv7X7J8+O+NANTsH9L99TYGHyrOIPXMQziruPlYYYXVenn34/PH/9B8XorGFZvBaGRzvx5oA0fzm6PsXt93n3PmPfey369sPxy4dEFPtvyGUvaLtGtvdbbP28T1rz04F4s9lfGYmio4X6kCwv398bItzfdJziTyTKR4hV6oV5Fn3zyCZ988gnh4eFotVrdJIT37t3Dza0Qpa6vkQdXLpC86yNin1hiPWAfJmZZv7U7b+6k6S9NaVm6JSs7rNRN+V/YKAr8uegUe36L4eZNFV5eMGVhQ7yqpF9k1sq3LVa+bZ8VaFMgORolOYqk2Cg0iVFYGEVBchTJ8VFcOB1NYqqW0aPTrhxFRICJtR0PYothXfTZvYAnkeG42d4D7pGlo8/++7Tnh3HlJFK1xtjYwKyPBlHXewdHE76k08h/Rj/F3eTYip9RWbhj5VISx5IlKeJaHJVB7n+cY5KerT5sZGCEv6s/YXFhXI+4rkt4DNSvOPPKobhYhV2rDqNcW0CDEivx9Upb4y9VY0BK0RaYle8Jri3YfHIBB4+FE3t9K1MaTcmz80/cM5Fa7rVoVKJRnh3zlVAbgb1/2uMfPzSfjepeY7izBs2dDbjYhtGn0Xz6NJpPZHwRNh9tBTXagVczMDRHUV7NbV5FUej9V2/2397PkL+HsLLDyvw/aW4oWjR3txJ27jBuzccB4OTlzi9zRnP1QWm8G7WnzLvbadPGHlk7VP9eqhu1wz83esPCwpg8eTI///wzT/69cp54JQxNrbA2u00Juxi2zJ3J20NHZln3UfwjDNWGWJtY6zqhFjqKwv29P9PSeCBN+xgzfPtRps0pk7PL82ojMHVAZeqAqXX6TcZA5X8mtK6RbkvaX/cfPldiVqIZdzXHiY+KJjEmLXlKfRKFNjEKkqNwcYiipHva/zWJUYTdicJQlUyKJu09iYqCoqbXKelwga2nnv1caaKu8Jb5FFCA+2mPFI0hoTEeRCSXxMCmJBVqlgTLkmBVklTTEhiapr/mnKJJIfBQINMOTGNWyWfDUqc3mc7cd+ZiYVywr1EfWDIfu0ff08r1AvwzGOp+bCliHXtSqklXzCyf9Wnr49+HYtbFcLVy1X3eFUUhMTXxhUcorbu0jrG7x2KkNuLSgEvp5gZ6HanU6rQ1+tzboU5N4s61X3CPOU5qyDpsLR7QueYvcOwXOGkGrs0Z/3M7bqW2pHkbS3K4BvWLxaVS8b9W/2P0jtH6vV2YiceP4feFIfR2aYGbWuHR9U4ULZk2Aqrt2AlYWUFKSgqbNsnizgVFjhOeqKgo+vfvz9atWzEyMmLkyJEMGDCAcePGMXPmTMqVK8fChQvzM1aRDfvi7py2+g7/1I9pYP81p/e+Q6V6FTKt+3759yltXxrfor6v/YRjmUpNgGP9cLu3BIzhwsOazP7JDhOrVxuGgVkRilXM2WzXBqBbliCpZ1qyExEBTx4EcjhqONXbltbVTTFyY0fIACwIoajZdYrb3sDUOInittcpzvW0SqeeHdsQeBjrTNfVN3H3MKVECajieYzjj38mNTmSI9FH6EzaSKeC2k8nMkJDSooaR6e0z6uX9XFcjS7wJMWM68kdcKnTE9cy9bK87NCydMt0z5efXc5Xu77i59Y/09CrYab7ZKd5qeZ0rtAZbzvv1z7ZeV5sUiydVndid8hugvsEU8r/Rwg/BHfXwp01EB8Cd9YwrtkaUlINKT/4HLcjm7NunZpOnaBp09xNgJiZVRdWkZiaqBtiXsahDGveX/PyLy4vJNzlzvE9TPjlQ379FRITS2DVtzNxqY5UcLSiaNpAWaxe8e8akTM5TnhGjx7N3r176datG1u2bGHIkCFs2bKFxMRENm/eTEBAQH7GKXLA/71unAxaQxWnDRge60ZClcOYW2Z+Beffnf2+2PYFZYuW1a139LrauvoqAeoOmDw5Ayo1mvITuHO9LBVMC1jPy2wYG6cNO3V0BMr4AD7ptps6lafp6Nm6508StFy9ep/wkOvEP7iOp/11Sjldh7jraGKuY5AaiSYV/t6TAskmgIpto8byR/lrjH/Qk0r2TQHQhJ9m36/LUVuXxMKpJA5eJXEt5Y6RsX5uaSkK7N0LYTunUsthLqserGbIpLT+KM71BnDtaFVKNuxEeROb/zjSv4+rEHg4kJtRNzl059ALJTwmhib82u7XXO9X0JkbmROXHEeqNpWToScpZVcKHOukPfxmQmQwyp01PLm6luTkaBKNSpGYaMBvv4FbzAwOLzLAt0VnPur1Yonzxisbee+P97AwsqC2e228bL3y+BW+oPCjxBybhcXjP3BFYeu6OiQmeuDnB5rqv9L9ff30bxK5k+OEZ+PGjSxatIjGjRvTr18/SpUqRenSpQkKCsrH8ESuqFSU6PQTkRvKUc7lFFvmTObtEeP/c7et17cy/eB0IG0ERAWnzK8MFWQpKbBs2hraOX+MiXkMiokjqjor0NrVgRub9B1evjIzV+NdqRjelYoB6f/wUCsQdieS9cd+xear0tRP/QbrG93QGjtxP6oYTsZdUakiAYi6fpj6jmmfA6KBYEg+bsTNKE8eJaYlQeWqp90q01qUJFpTAluHvJ+07kFoMouXGrNgAVy9Cr/0PY+7zx08wn4B0hIetX0lSjV/scVyVSoVu7vtZvbR2Xxe63NdeVJqUrb92R7EPWDr9a26+V4K49VRA7UBv7T7hajEqIy/B1QqsPNDZeeHeaWJmCc95konLUFBBwi9V52R5adjbxnOIfPKpM0ABCE3krhx05iA+qocdcJ/u9TbNPJqRM1iNSlmXSzPX1+uaFNR7qxFdXkWhB9KmwlbDbsv1Oe9NtG07Q61a7+5U1a8jnKc8Ny/f183z06JEiUwNTWlV69e+RaYeDFFnJ057TQH27gPaOwymWN/t+atZtnfVmlcojFj643FQG3wWiY79++msOf7UXSv/C0AN+Lq4N5yJUbWrrmaKbcwUqnAubgt0XeeEH0ujEfFf2Lt111RqZYB0CM5hc2b0xJCrVU59oQNxFxzHXuT67jZ3MTEKBkvh6t4cTXtgCfT/lEDtsC5uxXp/vtpSpSAEiWgtvcunFwtKF6xPM5u5hkDyk7kaXb+72cqF1nGrz/u4eqdClhaQojpUK45taRdx7Z50iYAViZWjK47WvdcURTarGiDk6UTs5rNws7MLl39pNQk2qxow5F7R3gQ/+Cl1u0q6Nxt3HM2QamJPeqUFMqUiWToZ8kY3BxL+MVtVH27nq7K/b9G4Ja8hR+XtyexaHtqtvCndh0V6n+6/SSlJrEoeBGfVv0UtUqNgdqAvz/6W78d5ZOjeHDwfxjdmI2d6Z20MrUxeHRi+93PKP2pHzOL6y888eJynPBotVqMnutmbmBggMXrMvj+DVOp9fscD1qDv+PvlI/rCpoTacNTs6BWqRnfIP2VoNikWB4/eYxnEc98jvblHNxxH/Wh9+lUeT8AVwyGUbrX1AI78dWrkqpNJfJJpG6NpCE1hmBmaMYnVT9Jd2Xi+b9Oi5atQ0DZOrrn2lQND27f4+GN68SGXser6HVcLK9D7HVSo65jqEQTn2jKiRNw4kTaPle//YRSZtf5aeEuPv2qPgARF3dy5M9tqCxLYuZYEjuPkriXLkYRWzV3bkThlvIb6psLIOIEDf/5o37ouytRKlSgY0ewtKzC0zmT8sux+8fYen0rJoYmjKk7JkPCY2xgzDve73A14urrsVZTHgmJCmH0jtHMfWduuhXqMzAwRe07EAffgemKfaw3YW98DR/Xb4BvuHO6GItXtCPBoT3+zWsz/GIjDtw5QHxyPMNqDUs7lJ6SHW30VW5v/Q6nhMU4GcWDadpSHYp3X5xq9wUzZxrrJTKRV3Kc8CiKQvfu3TH5p0daYmIiffr0yZD0rFlTQDqXveHKdvsRZecezJIuwJmx4Dc9x/tqtBo+XPMhB+8cZO37a6nrUTcfI30xWi2s+O7/7d15WFRl/wbwexiGYUcB2ZRVEFFcwUxDERdcyCVLyYXlRUtTS6Pc8vWyfCu11NBMbFH0/VVG5ZKapWiI5pqAW7lDriC5AYKs8/z+4PXkBI6iwMCZ+3NdXFfznOec85xvON6e7dmFnuoX4eR1DfnF1shvlYAWnYfqe2h6dyLnBCI2RMDWzBY7InZAoVBApVTh1c6vPnzl+xgZK+Ho5QZHLzcAIVrLjIVAwe2baJSRix/aV8zsnZEhcAfuuJZ3F41cm0t975xLQn+3/827VwbgPFB8ygRnbniiWeOLMFL/7wk0IxUKbQcjx2oM/vVin9p8+KeSp5o+hb0xe3Hmxhm0sPv7BvEyTRmAistXs4Nn45VOr8DevB6/hrgGCSEwNHEo0rPTYaGywOeDPq/2NuxGpaLs0k/4K3U9Gt/9Ea52lxET9DGAj3H9uD0+d/bFf+5Yo0Uj94duq7bcuZSGnB1z4KH6ER5GAlABxy62we5rU9B20Eh062Fa8QIuavAeOfBERWnfzDp6dD2fpM3AmTe2Bzp/BuweDJxciOImQ6Bu1vWR1s0rzkPWnSzcKblTbx9bL7t5CuFNekFppMGl/Dawf24dXBw4OzwAmBmb4dT1UzBXmSPjVgaa2zZ/+ErVpVDAorEdfAPs4CtdMVUA2AkAGH5fV2PnIBy8OBGmpefRWHUeTlZ/Qq0qQQun0wCAnJLWcHh6DOAxGuamTeBR86N9JF1cu2jNbp5xKwPNlzbHxy3/vkHcUMIOUBHylg1Yhhk7Zmhd/qsWlTWMvcLh7BUOlBfh6qm1yDu2FU2Lf4G95XXY51/H1/aAOBGD5PXf45rJUPj3DUPrthZ1dm/M4b030UO9BRqNAj8dG4gLZlPQNyIEk7yYcuTmkQNPQkJCbY6DakOzQdB4RMHozzW4uj4K1uFHYOf48MuQjc0aIyU6BalXU9G5WeeH9tcHE/uWyG86HtmXC+AdsxwKVTXvF5ERIQTO3TwHH7uKwNfctjm+G/YdOjftLF3S0ieXwDC4BIb93aApR8H1S7h27jzMGjWGs1+Hennn54d7PwQA/N/V/0NUWZTWJX1D0dW1K1KiU2rkBu3ki/sxdEssvG29sS/6MnDj3uPuG6C4ewUhXokAEuHb/xSUjXwRHg6ED9egpV/NnerTFF5DxrblKCo1g//wineVPTO0F1a/NRfqFi9i0GyfBjNNAlWf/OcVMHAl/nHIymsGT/tzSFq25JHXM1eZa13KupR7CZEbIpFblFsbw3woIYANK9Ox8D/ZUptV8FL4jE4w6LBTUFKAIYlD0HZFW5y/eV5qf7bFs/Ui7FTJSAkLBw94de0F51Yd62XYAYAl/Zfgw94fopVlK6iV8nwb+aO4P+ycvXEW5Zryx9pOC7sWUPzv2tCN4lzAqScQ+DEw5CIKux3AH5iO364+iz9v+OLkSeDtt4H0T0bh4Pu9cGDLgSc6hjt3gOXLgYkvHoL33blwzV+AsrsVb+ZWmSgQvXA2RrzMsCN3T/SmZar/TK0bId9vFRKWp+DtdW9C4Q+Eh1dvG0IIjFg3Ansv7UVRWRG+HfZt7Qz2AQoLgVVvr8PYVqOwr6gr9u3djq7PGAP1dMqDumSuMkdhaSHKNeXYd2lf7Vy+MlAmShNMfmoytl7fKstH0Kvri7Qv8OpPr2J299mPfInrSt4VNLWueKVmU+umSIlOQUv7llAp7ztbpjCCuWtntBpZcTY55yXghx+A778txsCOm2FpWoBU9d9/VV09fRaAAi6+D5nmW1OO7LQt+GVbHiZ8GIHcXEChCEOv5i8iv9FQPF9sCuuaf6sC1WM8w2MAWgT3QXHLd1FaboIJE4CsrOqtr1AosLT/UnR07ogP+3xYO4N8gLNngS5dgGVftkZpuQpuXhZ4OtCwpy85mn0UJeUlACr+33z67KdIH5cuvR+GqDaojFQoKivCgcsHIITu6RLKNeWYun0qmi9tjrSsNKm9jWMb7bBTBRsbIDIS2LRFjbLQYzhQvgztQ/5+tcaFrfPgkuqDc0vb4uDnc5Bz5mjFKeB7SvOAU0tw+6sWcDozBL1s30RRYRF8fIClS43Qd+5a/GvOMFg34r/3DQ3/jxuIWbOAzZuB48dK8cU7P+Hf8YOqdSWho3NHHH7psNa/dDNuZdTqa/U3rctHRIwV8vIAB4eW+MP1IDr3aQkYwAzvDzI3ZS7eSXkH7/V8DzOCKu5BkNPUBlR/RbaLhL25PQb4DHjoGS8jhREyb2eiuLwYP575ER2dH++1Ao2aeeHpiIlabZrSuygtM4a3/XF44zhweC6u7PTCNdVz8G5eDuu/VgJl+WikBG7eaYxdl2KweWMJevUzld7/Q4aJgcdAmJgA/7e6BIWbgtDJ6zdsX/MjQqMHVGsb93/JHb56GN0SumFsh7H4qN9HMDaq2V+ltR9sQKjNWLR3WQ9N22AkJgIuLq1qdB8NkUcjD2iEBmdunNH3UMjAKBQKhLUIe+ByjdCgXFMOlVIlnXmMaheFgb4Da3Qcz0xbi6wLn+DYT1tgdn09OjXbhqY2GWiKRcC9s9fWLQHfKchTRCDc23Dv8SNtDDwGpHVbE/y2Pwg375xDuaLkibZ18PJBFJUVIeN2hnQjYo3QlAFHZ2JEs4UAgI8nfQy/l4NRnQdkhBBSOBNC4OSdkwguCUYjVaOaG2cduZJ3BbeLbqO1Q8WU4BFtI+Bj66P1+DRRXSvTlOH9Pe+jlV0rqKDCpbxLeOnHlxDoHIgFfRYAAOzM7Wo87Nzj7G4L5/GRACJxKbMAaT/+DJOcDTBRFqPHmDFQNg0FFEZ6e8UB1U88wWdgOka/h9yg39H/pSFPtJ2JT03ET6N+wtrn19bYm1ELb2YBO3sCJyvCTo5tLNqM/xrF4g5uF92W+gkhsGjfIszcMRMFJQVS+7JDy2C7wBYTfpwgtSkUCsw5Pwd2i+zw25Xf/t5XaaH0Urn6Kul8Evw+8cPI9SNRWl4xRYZCoWDYIb37LPUzzNk1B+O2jkN+WT7Ss9PxS+YvWH54Of4q+KtOx+LqaYHBk55H/7lfotec76Bs1s+gL3vTg/G3wsAo1WbwbOUsfRYazWNvq593P1irraXPH+3/CEnnkx66Xl5xHv746w/8eftPABVvTf7yo2Tkb/QG/toDYWwFBH0Ph36L8N7eD2A1zwrTk6ZL6ysUCszZNQfz985H9p2/H1M3UhjhVtEt5BTm/H18QqCZacV8BX5N/KT25b8th/U8a8zaOUtrbA+7GbMutXdqDxOlCcxV5rheeF3fwyGSjO04Fj09e2Jxn8WwMrbCoBaDsKD3AqS+nFp/X4dABo+XtAzYqR2boPx9FhS9dsLb3+GJtrUzYydit8dCqVDi6PijaO3QGuWacrzw3Qv4q+Av/DjyR2kunrgDcZizaw5e6vgSFjzzKTYt+ACj27wFpZEGx4sB627r4O7aBwCkOY1uFd3S2t+YDmOgUChgavz3HGHDWg1DiEcIHC0dpTaFQoHFvosRGBwISxNLqf3otaO4W3ZXK7AVlBSg+dLm6OjcEd8P/x7mdfx+n4KSAuzI2IHBLQcDAJpYNMHemL3wsfOBEf/FSvWIidIEOyJ2oKysDFsvVkw+O+2ZaXoeFZFuDDyGSlMGk1Oz4dXkBHZtGAcP3/UwVj3+vThBbkGIbBcJG7WNdL+J0kiJHRk7cKfkDnIKcqTA42jhCFszW5Tc1ODwR0MQ1W4TAGDX3Q446jsMo+w6SNuNbh+NqHZRlcLHkv6VX6LYxKLJA/916WChHejWDFmDWd1mwUb994SI6dnpuFZwDUevHdXa39u73sap66cwodMEdHfvjtpwu+g2OnzaARduX8DBsQfRqWknAICvvW+t7I/oSfHdRNTQMPAYKiNjmPdag9LDndCj+UZsiv8Kg157/PnR1MZqrB68GhqhfYksPiwepsamWoFjXOA4OBzrjHa3noeXXwaKy0yQ3exj9Ah5CT3+8SVaW2dZjBRGaGnfUqvtqaZP4dDYQ8gpyNFq33xmM9Ky0vBCqxektvM3z2P+r/PR3b17jbz/ppFpIwS5BUEjNCgoLXj4CkREVC0MPAbMya890g/PQQfMRjezV/H74RC0Dmz62NtTKBRQKrRvYB7dtnKISl65Gv2Ur8DMoQjX7njALPR7uHsEVOpX10yUJtKZlfst7LMQBy4fwDOuz0htv178FV+kf4HTN05rBZ4Vh1egsWljhDYPRWOzxg/cl0ZokJCegOGth8NKbQUAWNpvKUyUJrAw4fvtiYhqGgOPgWs/cgbOLN+EFna/4fefx6KkzVaYqGv3VHWXgFsw/aMI5wrD4BXxXxj97z6d+irEMwQhniFabe2c2mHGMzPg0chDatMIDWbsmIHc4lykvZwmBZ7T108j41YGOjXtJM22HbEhAl8f/xrHc44jrl8cAOgMSERE9GR4J6SBUyiNYRu2BkWlagR5/Ywfl66slf0c2C+kt7+btpuC0s7fwXvMpnofdh6kvVN7zOs9D+MCx0ltd0vvYnTb0ejq2hX+Dv5S+1fHv8KArwdgatJUqS2qXRSsTKzgY+tTp+MmIjJUDDwEey8/nDF9DwDQ1+F1aPL+rLFtl5UBq9/9AUjqguVL8ysaFQqomr8gu3dlWJhYYNmAZdgbs1drviBrtTV87XzRuWlnqS20eSguTLmAiU9NrGpTRERUw+T1Nw49trbDpuC2KgjmqjswOvQvQDz++3nuycoC+vW5i57Wr+Jp74Pw1SyqgZE2PG92fROnJp3CuIBxWu28hEVEVHcYeKiCkRKN+q0GlOZAzi7gzCdPtLmUFKBDB2DnLjP8a+W3OKOMRe/Jsx6+oozxMV4iIv1h4KG/WTUHOlZM61B8aDoOJFV/gkohgK/idiN+xre4dg1o0waI/+5ptAhfBBhVY0IsIiKiGsTAQ9q8x+Nsfm+olXdhkhqFvNzyR19XCFxL/hDhdj2x6uVozHzldxw4ALRoUXvDJSIiehQMPKRNoYDzkJW4etsVH//0MmLfeMRfkZLbwJ6hcMqeBmNlOa4YPY/3lnjAvG5nZyAiIqoS38NDlVg6uiG99Tms2WMCsRt47jkgLOzB/TcmHEV/q+ehLjkPGJkAAUvg4z0O4D0rRERUT/AMD1WpW7AJXn+94r9jJ93Gjb9KK/UpLARWzUpAX8XTUJech8bMHeizF/AZz7BDRET1CgMPPdC77wIxA3Zg5xv+SF72vtay82fu4ud3xiKmdQzMTIpwrrA/0C8VsAvU02iJiIgejIGHHsjMDHjrjetoZnsFbWwSkXWlBACwbV0GCjZ0xdB2K6HRKHDe4j/wHrMFRmZ2eh4xERFR1XgPD+nUPCQcv31bCI+R4Whsb4KE/2zCc06RaOSai1t37VHeeS2a+/fW9zCJiIh0YuAh3RQKdAqPAQCU3TyFKK8hMFIIZN7pgmbh30Jl00zPAyQiIno4Bh56ZMa2LXHHfRqyrxTBe8wHgNJE30MiIiJ6JAw8VC2Wz8yDN5/AIiKiBoY3LVP1MOwQEVEDxMBDREREssfAQ0RERLLHwENERESyx8BDREREssfAQ0RERLLHwENERESyx8BDREREssfAQ0RERLLHwENERESyp/fAs3z5cnh6esLU1BQBAQHYs2fPA/uuX78effr0QZMmTWBtbY0uXbpg27ZtdThaIiIiaoj0GngSExMxZcoUzJo1C+np6ejWrRv69++PixcvVtl/9+7d6NOnD7Zu3YrU1FSEhIRg4MCBSE9Pr+ORExERUUOi18CzePFijBkzBmPHjoWfnx/i4uLg6uqK+Pj4KvvHxcVh2rRp6NSpE3x8fPD+++/Dx8cHmzdvruORExERUUOit8BTUlKC1NRUhIaGarWHhoZi3759j7QNjUaD/Px82Nra1sYQiYiISCaM9bXj69evo7y8HI6Ojlrtjo6OyM7OfqRtLFq0CAUFBRg+fPgD+xQXF6O4uFj6nJeXBwAoLS1FaWnpY4y8frl3DHI4ltrA+ujG+ujG+ujG+ujG+uhW3fo8aR31FnjuUSgUWp+FEJXaqrJ27Vq8/fbb+OGHH+Dg4PDAfvPmzcM777xTqX379u0wNzev/oDrqaSkJH0PoV5jfXRjfXRjfXRjfXRjfXR71PoUFhY+0X70Fnjs7e2hVCornc3JycmpdNbnnxITEzFmzBh899136N27t86+M2fORGxsrPQ5Ly8Prq6uCA0NhbW19eMfQD1RWlqKpKQk9OnTByqVSt/DqXdYH91YH91YH91YH91YH92qW597V2gel94Cj4mJCQICApCUlITnnntOak9KSsLgwYMfuN7atWsRExODtWvXIiws7KH7UavVUKvVldpVKpWsfgHldjw1jfXRjfXRjfXRjfXRjfXR7VHr86Q11OslrdjYWERERCAwMBBdunTBZ599hosXL2L8+PEAKs7OXLlyBf/9738BVISdyMhILFmyBE8//bR0dsjMzAw2NjZ6Ow4iIiKq3/QaeMLDw3Hjxg3MnTsXWVlZ8Pf3x9atW+Hu7g4AyMrK0nonz6effoqysjJMnDgREydOlNqjoqKwevXquh4+ERERNRB6v2l5woQJmDBhQpXL/hlidu3aVfsDIiIiItnR+9QSRERERLWNgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgYeIiIhkT++BZ/ny5fD09ISpqSkCAgKwZ88enf1TUlIQEBAAU1NTeHl5YcWKFXU0UiIiImqo9Bp4EhMTMWXKFMyaNQvp6eno1q0b+vfvj4sXL1bZPzMzEwMGDEC3bt2Qnp6Ot956C6+99hrWrVtXxyMnIiKihkSvgWfx4sUYM2YMxo4dCz8/P8TFxcHV1RXx8fFV9l+xYgXc3NwQFxcHPz8/jB07FjExMVi4cGEdj5yIiIgaEr0FnpKSEqSmpiI0NFSrPTQ0FPv27atynf3791fq37dvXxw+fBilpaW1NlYiIiJq2Iz1tePr16+jvLwcjo6OWu2Ojo7Izs6ucp3s7Owq+5eVleH69etwdnautE5xcTGKi4ulz7m5uQCAmzdvyiIklZaWorCwEDdu3IBKpdL3cOod1kc31kc31kc31kc31ke36tYnPz8fACCEeKz96S3w3KNQKLQ+CyEqtT2sf1Xt98ybNw/vvPNOpXZPT8/qDpWIiIj0LD8/HzY2NtVeT2+Bx97eHkqlstLZnJycnEpnce5xcnKqsr+xsTHs7OyqXGfmzJmIjY2VPms0Gty8eRN2dnY6g1VDkZeXB1dXV1y6dAnW1tb6Hk69w/roxvroxvroxvroxvroVt36CCGQn58PFxeXx9qf3gKPiYkJAgICkJSUhOeee05qT0pKwuDBg6tcp0uXLti8ebNW2/bt2xEYGPjA02FqtRpqtVqrrVGjRk82+HrI2tqaf6B0YH10Y310Y310Y310Y310q059HufMzj16fUorNjYWX3zxBVatWoWTJ0/i9ddfx8WLFzF+/HgAFWdnIiMjpf7jx4/HhQsXEBsbi5MnT2LVqlVYuXIl3nzzTX0dAhERETUAer2HJzw8HDdu3MDcuXORlZUFf39/bN26Fe7u7gCArKwsrXfyeHp6YuvWrXj99dfxySefwMXFBUuXLsXzzz+vr0MgIiKiBkDvNy1PmDABEyZMqHLZ6tWrK7UFBwcjLS2tlkfVcKjVasyZM6fSZTuqwProxvroxvroxvroxvroVtf1UYjHfb6LiIiIqIHQ+1xaRERERLWNgYeIiIhkj4GHiIiIZI+Bh4iIiGSPgacemjdvHjp16gQrKys4ODhgyJAhOH36tFYfIQTefvttuLi4wMzMDD169MDvv/+u1ae4uBivvvoq7O3tYWFhgUGDBuHy5ct1eSi1bt68eVAoFJgyZYrUxtoAV65cwejRo2FnZwdzc3O0b98eqamp0nJDrlFZWRn+/e9/w9PTE2ZmZvDy8sLcuXOh0WikPoZUn927d2PgwIFwcXGBQqHAxo0btZbXVC1u3bqFiIgI2NjYwMbGBhEREbh9+3YtH92T01Wf0tJSTJ8+HW3atIGFhQVcXFwQGRmJq1evam3DUOvzT+PGjYNCoUBcXJxWe53VR1C907dvX5GQkCBOnDghjhw5IsLCwoSbm5u4c+eO1Gf+/PnCyspKrFu3Thw/flyEh4cLZ2dnkZeXJ/UZP368aNq0qUhKShJpaWkiJCREtGvXTpSVlenjsGrcoUOHhIeHh2jbtq2YPHmy1G7otbl586Zwd3cX0dHR4uDBgyIzM1Ps2LFDnDt3TupjyDV69913hZ2dndiyZYvIzMwU3333nbC0tBRxcXFSH0Oqz9atW8WsWbPEunXrBACxYcMGreU1VYt+/foJf39/sW/fPrFv3z7h7+8vnn322bo6zMemqz63b98WvXv3FomJieLUqVNi//79onPnziIgIEBrG4Zan/tt2LBBtGvXTri4uIiPPvpIa1ld1YeBpwHIyckRAERKSooQQgiNRiOcnJzE/PnzpT5FRUXCxsZGrFixQghR8QdRpVKJb775Rupz5coVYWRkJH7++ee6PYBakJ+fL3x8fERSUpIIDg6WAg9rI8T06dNFUFDQA5cbeo3CwsJETEyMVtvQoUPF6NGjhRCGXZ9//oVVU7X4448/BABx4MABqc/+/fsFAHHq1KlaPqqao+sv9HsOHTokAIgLFy4IIVgfIYS4fPmyaNq0qThx4oRwd3fXCjx1WR9e0moAcnNzAQC2trYAgMzMTGRnZyM0NFTqo1arERwcjH379gEAUlNTUVpaqtXHxcUF/v7+Up+GbOLEiQgLC0Pv3r212lkbYNOmTQgMDMSwYcPg4OCADh064PPPP5eWG3qNgoKCsHPnTpw5cwYAcPToUfz6668YMGAAANbnfjVVi/3798PGxgadO3eW+jz99NOwsbGRVb2Aiu9rhUIhzdlo6PXRaDSIiIjA1KlT0bp160rL67I+en/TMukmhEBsbCyCgoLg7+8PANKM8f+cVd7R0REXLlyQ+piYmKBx48aV+vxzxvmG5ptvvkFaWhp+++23SssMvTYAkJGRgfj4eMTGxuKtt97CoUOH8Nprr0GtViMyMtLgazR9+nTk5uaiZcuWUCqVKC8vx3vvvYcRI0YA4O/Q/WqqFtnZ2XBwcKi0fQcHB1nVq6ioCDNmzMDIkSOlyTANvT4LFiyAsbExXnvttSqX12V9GHjquUmTJuHYsWP49ddfKy1TKBRan4UQldr+6VH61GeXLl3C5MmTsX37dpiamj6wnyHW5h6NRoPAwEC8//77AIAOHTrg999/R3x8vNZkvIZao8TERHz55Zf4+uuv0bp1axw5cgRTpkyBi4sLoqKipH6GWp+q1EQtquovp3qVlpbixRdfhEajwfLlyx/a3xDqk5qaiiVLliAtLa3ax1Eb9eElrXrs1VdfxaZNm5CcnIxmzZpJ7U5OTgBQKdnm5ORI/xJzcnJCSUkJbt269cA+DVFqaipycnIQEBAAY2NjGBsbIyUlBUuXLoWxsbF0bIZYm3ucnZ3RqlUrrTY/Pz9pIl5D/v0BgKlTp2LGjBl48cUX0aZNG0REROD111/HvHnzALA+96upWjg5OeHatWuVtv/XX3/Jol6lpaUYPnw4MjMzkZSUJJ3dAQy7Pnv27EFOTg7c3Nyk7+sLFy7gjTfegIeHB4C6rQ8DTz0khMCkSZOwfv16/PLLL/D09NRa7unpCScnJyQlJUltJSUlSElJQdeuXQEAAQEBUKlUWn2ysrJw4sQJqU9D1KtXLxw/fhxHjhyRfgIDAzFq1CgcOXIEXl5eBlube5555plKrzE4c+YM3N3dARj27w8AFBYWwshI+6tPqVRKj6Uben3uV1O16NKlC3Jzc3Ho0CGpz8GDB5Gbm9vg63Uv7Jw9exY7duyAnZ2d1nJDrk9ERASOHTum9X3t4uKCqVOnYtu2bQDquD6PfHsz1ZlXXnlF2NjYiF27domsrCzpp7CwUOozf/58YWNjI9avXy+OHz8uRowYUeWjos2aNRM7duwQaWlpomfPng3ysdmHuf8pLSFYm0OHDgljY2Px3nvvibNnz4qvvvpKmJubiy+//FLqY8g1ioqKEk2bNpUeS1+/fr2wt7cX06ZNk/oYUn3y8/NFenq6SE9PFwDE4sWLRXp6uvSUUU3Vol+/fqJt27Zi//79Yv/+/aJNmzYN4rFrXfUpLS0VgwYNEs2aNRNHjhzR+r4uLi6WtmGo9anKP5/SEqLu6sPAUw8BqPInISFB6qPRaMScOXOEk5OTUKvVonv37uL48eNa27l7966YNGmSsLW1FWZmZuLZZ58VFy9erOOjqX3/DDysjRCbN28W/v7+Qq1Wi5YtW4rPPvtMa7kh1ygvL09MnjxZuLm5CVNTU+Hl5SVmzZql9ReUIdUnOTm5yu+bqKgoIUTN1eLGjRti1KhRwsrKSlhZWYlRo0aJW7du1dFRPj5d9cnMzHzg93VycrK0DUOtT1WqCjx1VR+FEEI8+vkgIiIiooaH9/AQERGR7DHwEBERkewx8BAREZHsMfAQERGR7DHwEBERkewx8BAREZHsMfAQERGR7DHwEBERkewx8BBRjYiOjoZCoaj0c+7cOX0PjYgIxvoeABHJR79+/ZCQkKDV1qRJE63PJSUlMDExqcthERHxDA8R1Ry1Wg0nJyetn169emHSpEmIjY2Fvb09+vTpAwD4448/MGDAAFhaWsLR0RERERG4fv26tK2CggJERkbC0tISzs7OWLRoEXr06IEpU6ZIfRQKBTZu3Kg1hkaNGmH16tXS5ytXriA8PByNGzeGnZ0dBg8ejD///FNaHh0djSFDhmDhwoVwdnaGnZ0dJk6ciNLSUqlPcXExpk2bBldXV6jVavj4+GDlypUQQsDb2xsLFy7UGsOJEydgZGSE8+fPP3lRiahGMPAQUa1bs2YNjI2NsXfvXnz66afIyspCcHAw2rdvj8OHD+Pnn3/GtWvXMHz4cGmdqVOnIjk5GRs2bMD27duxa9cupKamVmu/hYWFCAkJgaWlJXbv3o1ff/0VlpaW6NevH0pKSqR+ycnJOH/+PJKTk7FmzRqsXr1aKzRFRkbim2++wdKlS3Hy5EmsWLEClpaWUCgUiImJqXRWa9WqVejWrRuaN2/+eAUjoppXralGiYgeICoqSiiVSmFhYSH9vPDCCyI4OFi0b99eq+/s2bNFaGioVtulS5cEAHH69GmRn58vTExMxDfffCMtv3HjhjAzMxOTJ0+W2gCIDRs2aG3HxsZGJCQkCCGEWLlypfD19RUajUZaXlxcLMzMzMS2bdukcbu7u4uysjKpz7Bhw0R4eLgQQojTp08LACIpKanK47569apQKpXi4MGDQgghSkpKRJMmTcTq1asfoWpEVFd4Dw8R1ZiQkBDEx8dLny0sLDBixAgEBgZq9UtNTUVycjIsLS0rbeP8+fO4e/cuSkpK0KVLF6nd1tYWvr6+1RpPamoqzp07BysrK632oqIirctNrVu3hlKplD47Ozvj+PHjAIAjR45AqVQiODi4yn04OzsjLCwMq1atwlNPPYUtW7agqKgIw4YNq9ZYiah2MfAQUY2xsLCAt7d3le3302g0GDhwIBYsWFCpr7OzM86ePftI+1MoFBBCaLXdf++NRqNBQEAAvvrqq0rr3n8ztUqlqrRdjUYDADAzM3voOMaOHYuIiAh89NFHSEhIQHh4OMzNzR/pGIiobjDwEFGd69ixI9atWwcPDw8YG1f+GvL29oZKpcKBAwfg5uYGALh16xbOnDmjdaalSZMmyMrKkj6fPXsWhYWFWvtJTEyEg4MDrK2tH2usbdq0gUajQUpKCnr37l1lnwEDBsDCwgLx8fH46aefsHv37sfaFxHVHt60TER1buLEibh58yZGjBiBQ4cOISMjA9u3b0dMTAzKy8thaWmJMWPGYOrUqdi5cydOnDiB6OhoGBlpf2X17NkTy5YtQ1paGg4fPozx48drna0ZNWoU7O3tMXjwYOzZsweZmZlISUnB5MmTcfny5Ucaq4eHB6KiohATE4ONGzciMzMTu3btwrfffiv1USqViI6OxsyZM+Ht7a11KY6I6gcGHiKqcy4uLti7dy/Ky8vRt29f+Pv7Y/LkybCxsZFCzYcffoju3btj0KBB6N27N4KCghAQEKC1nUWLFsHV1RXdu3fHyJEj8eabb2pdSjI3N8fu3bvh5uaGoUOHws/PDzExMbh79261zvjEx8fjhRdewIQJE9CyZUu89NJLKCgo0OozZswYlJSUICYm5gkqQ0S1RSH+eQGciKie6tGjB9q3b4+4uDh9D6WSvXv3okePHrh8+TIcHR31PRwi+gfew0NE9ASKi4tx6dIlzJ49G8OHD2fYIaqneEmLiOgJrF27Fr6+vsjNzcUHH3yg7+EQ0QPwkhYRERHJHs/wEBERkewx8BAREZHsMfAQERGR7DHwEBERkewx8BAREZHsMfAQERGR7DHwEBERkewx8BAREZHsMfAQERGR7P0/6uULOYXgCGwAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Reflection Coefficients\n", "colors = ['red', 'blue', 'green', 'orange', 'purple','black']\n", "line_styles = ['--', '--', ':', '-.', '-.']\n", "for index, angle in enumerate(angles_str):\n", " plt.plot(df_Power_trans['freq_'+angle], df_Coefficients['Refl_coef_'+angle], \n", " linestyle=line_styles[index], color=colors[index], label=angle+' deg')\n", "plt.xlabel('Frequency')\n", "plt.ylabel('Reflection Coefficient')\n", "plt.ylim(0, 1.1)\n", "plt.grid(True)\n", "plt.legend()\n", "plt.title('Reflection Coefficient vs Frequency')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interface with Tilted Slits\n", "\n", "For further testing of the implemented methods, a second interface geometry is set up and simulated. The interface is still soundhard but it is not symmetrical around the x-axis.\n", "\n", "![full_domain_new](Files/full_domain_new.png)\n", "\n", "Here the [mesh input file](Files/geometry_tilted_slits.geo) for the new domain is provided.\n", "\n", "### Coefficients\n", "Using the XML files and Python script given above, the transmission and reflection coefficients are obtained as shown in the following plots.\n", "\n", "The plot shows a big decrease in the transmission coefficient at $\\alpha=-60°$. This is the angle, at which the incoming soundwaves are parallel to the interface's fins, hence this outcome is expected.
\n", "At an incidence angle of $\\alpha=30°$, the soundwaves are orthogonal to the fins, and the highest transmission coefficient is expected. The plot shows, that on average the coefficient at this angle is slightly above the other transmission coefficients. Yet, also for other angles of incidence, similarly high transmission coefficients are reached.
\n", "Overall, the plots show that because of the asymmetric interface, both coefficients don't have symmetric behaviour. Moreover, the relation $r=1-\\tau$ holds, with some small error, for this simulation as well. \n", "\n", "![trans_coef_new](Files/trans_coef.png)\n", "![refl_coef_new](Files/refl_coef.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Animation\n", "\n", "Although the transmission coefficient at $\\alpha=-60°$ is on average very low, there are very high fluctuations of the transmission coefficient depending on the frequencies. To understand this behaviour better, an animation of the acoustic pressure field at different frequencies is observed. At $f=237Hz$, the transmission coefficient $\\tau=0,93$, meaning that almost all acoustic power passes through the interface. The following gif, displaying the acoustic pressure in the domain, shows that at this frequency the acoustic waves are still able to pass through the interface without losing much energy.\n", "\n", "![gif_237Hz](Files/gif_237Hz.gif)\n", "\n", "\n", "In contrast, the acoustic pressure field at $f=356Hz$ is shown in the next gif. Here $\\tau=0,15$, which means only a fraction of the acoustic power is transmitted through the interface. Looking at the animation confirms this. A big portion of the incoming acoustic wave is reflected, leading to a highly reduced pressure on the outgoing side of the interface.\n", "\n", "![gif_356Hz](Files/gif_356Hz.gif)\n", "\n", "## Script File\n", "To easily run the simulations yourself, a shell script [run.sh](Files/run.sh) is provided. Executing this script in the same folder as the provided `.geo` and `.xml` files will automatically run and store all the necessary openCFS simulations. \n", "\n", "After the simulations are done, the reflection and transmission coefficients can be calculated by opening and running the provided jupyter notebook `Calculate_Coefficients.ipynb` in the same folder. Within the notebook, it is possible to choose whether to calculate the coefficients for the original interface (discs) or for the interface with tilted slits." ] } ], "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 4 }