## 4 Literature Review on DAS+PRS
### Experimental Procedure
#### 4.3.1 2-Class Classification  
People, train, threat, and water are the most common classes to detect by the works in the literature in the 2-class classification systems. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Number of Classes**} & \multirow{2}{*}{**Fiber Optic Length**} & \multirow{2}{*}{**Distance to the Sensor**} & \multirow{2}{*}{**Acc. **} & \multirow{2}{*}{**TDR**} & \multirow{2}{*}{**FAR**} \\  & **2** & & & & & & \\ \hline
[3] & 2 & & 44 m & 0 m & 100\% & - & - \\ \hline
[28] & 2 & & 5 m & 0 m & 100\% & - & - \\ \hline
[29] & 2 & & 8.5 km & 3.3–8.4 km & 59\% & - & - \\ \hline
[31] & 2 & & 12 km & & 100\% & - & - \\  & & (sensed segment 44 m) & 2 km & 100\% & - & - \\ \hline
[24] & 2 & 8 & 45 km & 22–34 km & 55\% & 81\% & 35\% \\ \hline
[17] & 3 & & 12 km & & & & \\  & & (sensed segment 44 m) & 50 f & - & - & - \\ \hline
[18] & 3 & - & - & - & - & - \\ \hline
[26] & 3 & 220 km & - & 96\% & - & - \\ \hline
[19] & 3 & & 20.6 km & & & & \\  & & (sensed segment 20 m) & 14.1 km & - & 94\% & 6\% \\ \hline
[21] & 3 & 20 km & - & 98\% & - & - \\ \hline
[46] & 3 & 50 km & 20 km & 93\% & - & - \\ \hline
[27] & 3 & & 23.7 km & 13 km & 89\% & 86\% & 1.75\% \\  & & (sensed segment 1 km) & & & & \\ \hline
[23] & 2 & 8 & 45 km & 22–34 km & 45\% & 80\% & 40\% \\ \hline
[12] & 2 & & 200 m & 0 m & 100\% & - & - \\ \hline
[13] & 2 & & 200 m & 0 m & 100\% & - & - \\ \hline
[14] & & 6 & 1 km & - & 100\% & - & - \\ \hline
[15] & & 9 & 1 km & -For example, the proposals in [3; 28; 29; 31] aimed to detect a person walking along the fiber. In [3], the length of the optical fiber cable was 44 m, and the signals were recorded near the sensor. 12 s of signal recordings were employed for testing. Classification accuracy was 100%. In [28], the length of the optical fiber cable was 5 m, and the signals were also recorded near the sensor. Classification accuracy was 100%. In [29], the length of the optical fiber cable was 8.4 km, and the signals were recorded between 3.3 km and 8.4 km far from the sensor. Classification accuracy was 59%. Finally, in [31] the length of the optical fiber cable was 12 km, although only 44 m were sensed for the experiments. The signal recordings were carried out 2 km far from the sensor. Classification accuracy was 100%. The works presented in [12; 13] aimed to detect water along the fiber. In [12], the length of the sensed fiber segment is 200 m. The sensor is 250 m far from the beginning of the optical fiber cable and signal recordings were carried out near the sensor. 24 h of data (presence and absence of water) were recorded. A set of 5 data was used for testing (4 data with presence of water and 1 data with absence of water), and the rest for ANN training. A classification accuracy of 100% was obtained. In [13], the length of the sensed fiber section is 200 m. Two sensors were employed for signal recording. The first one is 250 m far from the beginning of the optical fiber cable and the second one is 290 m far from the beginning of the optical fiber cable. Signal recordings were carried out near the sensors. 24 h of data (presence and absence of water) were recorded. A set of 5 data was used for testing (4 data with presence of water and 1 data with absence of water), and a set of 40 data was employed for ANN training. A classification accuracy of 100% was obtained for both sensors. It must be noted that all these systems only aim to detect one target. However, since the target has to be distinguished from _nothing_, these works belong to the 2-class category for classification. The work presented in [22] aimed to classify threats and non-threats occurring in a long pipeline. The length of the fiber cable is 45 km, and the signal recording was carried out in different locations that spread different optical fiber positions (from 22.24 km far from the sensor to 34.27 km far from the sensor) and different soil/weather conditions. Experiments were run by 5-fold CV on a location basis for GMM training and testing.