## 4 Experiments
### Experimental Setup  
The telemetry data are made into 16 data documents according to different channels. The first column of each data document is the sequence to be fitted and reconstructed, and the data of the remaining 15 channels follow. The Sinc-LSTM network is used to fit and reconstruct the first column of data in the data document. The network setup is shown in Figure 2. The main structure consists of one SincNet network and three LSTM networks. First, the data of the large-scale Mars orbiter MRO is input into the network for pre-training. The SincNet filter layer contains 96 Sinc filters and 288 LSTM units are configured for each of the three LSTM networks. Two fully connected layers were then set up. The three-layer LSTM network uses the hyperbolic tangent function as the activation function. Dropout is set in the network for regularization. Dropout is a regularization technique to avoid overfitting. It eliminates the joint adaptability of each unit in the neural network by randomly setting some units to 0, so as to enhance the generalization ability of the model. The Dropout ratio of the model was set to 0.3. The mean square error (MSE) was used as the loss function. The model parameters of Sinc-LSTM network are configured as shown in Table 1. The same hyperparameter settings were used for all groups of experiments. LSTM prediction requires setting the input sequence length \(p\) and output sequence length \(l\). After adjustment, \(l=10\) and \(p=180\) were set. The initial learning rate was set to 0.001. The LSTM three-layer model has a shallow capacity, but it can provide sufficient model depth for each telemetry channel to be modeled separately. For each telemetry channel, the prediction reconstruction model is trained separately, and the segmented data are input into different batch networks according to the working condition category for training. Due to the poor parallelism of LSTM model training and the need to train 16 different models, the training time will be long. The two-stage transfer learning greatly reduces the time required for model convergence. Early stops are also set to reduce the overall training time. After testing, the model of each channel was set for training for 60 epochs, and the loss value was stopped in advance when it did not decline for 8 epochs. Assuming that the point judged to be anomalous by the threshold is \(t_{a}\), the anomaly sequence corresponding to this anomaly is selected as \([t_{a}-300,t_{a}+200]\). Selecting an anomaly sequence of a certain length can help the model perceive the sequence characteristics before the anomaly is generated and the impact on the telemetry data after the anomaly. \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Model Parameters**} \\ \hline sinc function filters & 96 \\ LSTM layers & 1 \\ LSTM units in each layer & 288 \\ dropout & 0.3 \\
1 & 10 \\ P & 180 \\ optimizer & Adam \\ input dimentions & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters configure of Sinc-LSTM Network. There is also a need for a balance between computational accuracy and computational cost. When two anomaly sequences produce some overlap, they are determined to belong to the same anomaly event and are combined into a single anomaly sequence. The following criteria are used to determine the results of anomaly sequences. * True positive (TP): For the \(i^{th}\) detected anomaly sequence \(\mathbf{S}_{a}^{d(i)}\), if any point \(s_{a}^{d(i)}\) of it falls in a true anomaly sequence \(\mathbf{S}_{a}^{t(j)}\), then the detected sequence is True positive. * False negative (FN): For the true anomaly sequences \(\mathbf{S}_{a}^{t(j)}\), if no point in the detected anomaly sequence falls within it, it is determined to be False negative. * False positive (FP): For the detected anomaly sequences \(\mathbf{S}_{a}^{d(i)}\), if there is no overlap with any true anomaly sequence, this detected anomaly sequence is False positive.