Moved pgf plots into own tex files

tex/chapters/3_ftm.tex

@@ -76,7 +76,7 @@ The \docLogDistance{} model can be reformulated to compute the distance $d_i$ ba
 \end{equation}
 
 Since $\mathcal{X}_{\sigma_i}$ is a Gaussian random variable, the logarithm of $d_i$ is normally distributed as well.
-Consequently, the distance $d_i$ follows a log-normal distribution, $\ln{d_i} \sim \mathcal{N}(d_i^*, \sigma_{i,d}^2)$, where $d_i=\ln(10) \frac{P_0 - P_i}{10\mPLE}$ is the expected distance and $\sigma_{i,d}^2=\left( \frac{\ln(10)\sigma_i}{10\mPLE} \right)^2$ is the variance of the distance.
+Consequently, the distance $d_i$ follows a log-normal distribution, \ie $\ln\left(d_i\right) \sim \mathcal{N}\left(d_i^*, \ln\left(\sigma_{i}^2\right)\right)$, where $d_i^*=\ln(10) \frac{P_0 - P_i}{10\mPLE}$ is the expected distance and $\ln\left(\sigma_{i}^2\right)=\left( \frac{\ln(10)\sigma_i}{10\mPLE} \right)^2$ is the variance of the distance.
 
 In free space the value of the path loss exponent is $\mPLE=2$.
 In indoor scenarios $\mPLE$ accounts for the architecture around the AP, thus a single global factor is chosen for the whole building.