## 4 Literature Review on DAS+PRS
### Experimental Procedure
#### 4.3.2 Multi-Class Classification  
People carrying out certain activities, vehicle/machinery, gas leakage, and interferences are the most common classes in the 3-class classification systems. For example, for people and vehicle/machinery-related activities, the authors of [17] aimed to classify humans on foot, vehicle traffic, and construction-like vehicle activity. The fiber cable length is 12-km long, but the sensed area is restricted to 44 m, and the recording scenario is just a few feet (50 or less) from the optical fiber sensor. This work did not report any results in terms of classification accuracy. The proposal in [18] aimed to classify climbing up the wall by a person, kicking at the wall by a person, and water. This work did not report either any results in terms of classification accuracy. The work described in [21] aimed to classify vehicles, digging, and walking. The fiber cable length is 20 km. 100 different signals were recorded for each event, and 5-fold CV was applied for the experiments. A classification accuracy of 98% was obtained. Among works aiming to classify people activities and interferences, the proposal in [19] classified background noises, sound interferences for simulating the effects of air movement, and hand perturbation. The fiber cable length is 20.6 km, and the sensed area is 20-m long, located 14.1 km far from the optical fiber sensor. Results showed a classification accuracy of 94% for 165 feature vectors that correspond to 55 signals per class. The authors of [26] aimed to classify intrusions, environmental interferences, and background noises along the optical fiber cable of 220-km length. Results showed an accuracy of 96% for a set of 1200 signal recordings. The target of [27] aimed to classify background noise, hand perturbation, and hand clapping. The length of the optical fiber cable is 1 km and the testing point is about 13 km far from the sensor. The signal recordings comprise a set of 92 training acoustic signals, which were divided into 35 background noise, 35 hand perturbation, and 22 hand clapping signals, and a set of 148 testing acoustic signals, which were divided into 65 background noise, 65 hand perturbation, and 18 hand clapping signals. Results showed an accuracy of 89%. Finally, the work presented in [20] aimed to classify gas leakage, digging, and human walk. 150 m of optical fiber cable were employed, and the sensor was located near the recording scenario. 20 signals of each class were employed for SVM training, and 30 signals of each class were used for testing. The classification accuracy was 97%. The work described in [46] aimed to classify five different events: stable state, walk on the lawn and fence exposed to wind, shake the fence, walk on the lawn, and vibration exceter. An optical fiber cable of 50-km length was employed for the experiments, and recordings were carried out 20 km far from the sensor. 1300 signals corresponding to the five events were recorded in total. Half of them were used for SVM training and the other half for testing. The classification accuracy was 93%. Among the works that employ more than 5 classes in their system, different works have been proposed to detect different chemical products. For example, the proposal in [14] aimed to classify the following combinations of air, ethanol, and water from three different sensors: air+ethanol+water, water+air+ethanol, ethanol+water+air, air+water+ethanol, water+ethanol+air, and ethanol+air+water. The length of the optical fiber cable is 1 km, and the sensors were placed at 665 m, 756 m, and 857 m. ANN training was carried out with 270 recorded signals (45 for each class), and the testing was done with 6 signals for each of the 6 combinations, which results in 36 testing signals in total. The classification accuracy was 100%. The authors of [15] aimed to classify air, ethanol, and water in each of the two sensors integrated in the system, whose combination derives in 9 different classes.