## 3 Results and Discussion
The data are pre-processed, which consists of synchronization, denoising, detrending, and normalization. The models are trained on datasets without damages. A set after augmentation consisting of 1200 samples was used for training, which was divided into a training part (80-960) and a validation part (20-240 samples). The model was tested using validation data. Detection of damages and their location is shown on the example of a real run with three longitudinal damages DL1, DL2, and DL3. Figure 13 shows synchronized indications of strain gauges as a function of time for four runs without damages and one with three longitudinal tape damages (DL1, DL2, DL3). Signal anomalies are most visible for strain gauge T2, although the remaining two also differ from the runs without damages. The described LSTM-AM model was used to reconstruct the signal. For each test sequence, the root mean squared error (RMSE) was calculated between the predicted and actual waveforms. The results are shown in the histogram (Figure 14). A maximum RMSE of 38.8 ADU was obtained. This is also the limit of the RMSE value above which the runs are characterized by an anomaly. In the next step, 50 samples of the test set were replaced with damaged ones. RMSE was calculated again. The histogram with the marked maximum RMSE value is shown in Figure 15.  
All samples with an RMSE value above 38.8 are considered anomalous. Their number is approximately equal to 50, which is the number of damaged samples. Then, the run was reconstructed by the LSTM network trained only on healthy samples. The reconstruction was performed using the LSTM network (Figure 16a) and using the LSTM-AM network (Figure 16b). Their waveforms were plotted on the original waveforms of strain gauge signals with damages. Figure 14: Histogram of RMSE between predicted and actual waveforms only for healthy samples. Figure 13: Time waveforms of strain gauge signals T1, T2, and T3 for four states without damages and one run with three longitudinal tape damages (DL1, DL2, DL3). To detect anomalies in the sequence, the RMSE was determined between the input sequence (original) and the reconstructed sequence. Identified anomalies are windows that contain data with values at least 10% above the maximum RMSE error value. Areas recognized by the algorithm as anomalies are marked in Figure 17.  
For some waveforms, areas incorrectly recognized as anomalies appear (Figure 17a--false alarm). The use of an attention mechanism allows to eliminate this phenomenon. The modification allows the model to better understand the context of the data, reduce noise, and thus allows the LSTM network to focus on the most important fragments of the input data, ignoring less important information. Thanks to this, the model can better identify real anomalies, and not normal deviations in the data. Another method to eliminate false alarms is to check whether they appear regularly in subsequent runs. However, some small single anomalies may not be detected by the algorithm. Next, for the LSTM and LSTM-AM models, the indicators used to evaluate the anomaly detection model were determined: accuracy, recall, precision, and F1-score (Table 2). All false positive (FP) cases were reviewed and it turned out that for the LSTM model, fragments such as in Figure 17a have an impact on this. The method to eliminate them is to check whether they appear regularly in subsequent runs. Another way is to reduce the detection sensitivity of the model by increasing the anomaly detection threshold (currently 110% RMSE max value). The LSTM-AM model is free of this defect. After reviewing all false negative (FN) cases, it turned out that these are most often cases of a single longitudinal damage of the DL1 type or other single damages, where the response of the strain gauge system does not significantly differ from the response for the