## 7 Recommended Practices  
Given the exhaustive literature review and the contributions of the related works, the main recommendations to consider when applying machine learning methods to surveillance tasks in DAS are:  
* The database recordings must cover the broadest possible range of acoustic conditions, which are influenced by:
* Environmental and soil conditions (that will have an impact in the characteristics of the generated signals),
* Geographical conditions (in what respect to the distance to the sensing equipment, which will have an impact in the SNR of the generated signals). This is due to the need of generating robust models that are able to properly generalize when the system faces unseen data (that can be obtained at any location along the fiber trajectory).
* The database labeling must be _accurate_ enough to provide precise time alignment between the labels and the actual activities being recorded. On the one hand, this is important to generate models that actually correspond to the desired activity (otherwise, the models will also contain information of wrong activities). On the other hand, that is also important to provide accurate labels for system evaluation (a wrong label will generate a classification error).
* The database size should be large enough to provide enough data for generating robust models and also to ensure the statistical significance of the results. It is very difficult to provide a recommendation on the actual database size, but according to the experience acquired in the PIT-STOP project [23; 24], and for initial system development purposes, we may initially recommend 30 min per event, per day, with at least five recording days and locations. Also, precise information on the actual duration of the recordings must be provided.
* The training subset must be completely independent of the validation/testing subsets to ensure that the obtained results are not biased due to over-training issues. If the database size is not large enough, cross-validation techniques must be applied.
* Regarding the feature extraction module, we recommend the use of frequency domain-based features, since these have provided good results in the systems presented in the literature review, and they can actually integrate all the meaningful behavior of the analyzed signals.
* The acquired signals must be properly normalized (either at the signal or feature levels) to deal with the signal degradation due to the distance to the sensing equipment.
* Regarding the pattern classification algorithm, it is not possible to propose any of the alternatives as being superior to the others. The choice is affected by multiple factors: database size (discriminative models typically need larger datasets than generative ones), signal variability and number of classes (for more complex signals and more classes, more complex models are needed, thus demanding larger datasets), signal properties (those generated by linear processes may, in general, be handled by less complex models), etc. Therefore, the best approach would be to select different pattern classification techniques, and make a thorough evaluation of their performance.  
* The evaluation metrics must be precisely described, so that there is no doubt about how these are being calculated.
* The evaluation process should provide details on the statistical significance of the results to properly assess their impact. As stated above, this also requires precise information of the experimental procedure (training/validation/testing subset partition, recording durations, etc.).