## 5 Validation of the Proposed Method
### Datasets  
The experimental datasets include five publicly available datasets and one real engineering dataset from the Payload of China's aerospace satellite. Table 3 gives a brief summary of the six datasets. Shuttle dataset: This dataset was utilized to delineate the position of radiators on NASA space shuttles, primarily for classification purposes. Initially comprising 58,000 samples, 80% were in the first category. The version used in this study is post-processed data, where samples belonging to the first category are considered normal, while other categories are considered anomalous. In the experiments, the test set consisted of 1778 samples evenly distributed between normal and anomalous data. KDD-CUP99 HTTP dataset: This dataset was collected over a period of 9 weeks from a simulated Air Force network and includes network connection and system audit data, which are commonly used to validate the performance of intrusion detection algorithms. To meet unsupervised or semi-supervised requirements, this study ultimately adopted a simplified version of KDD99, known as KDD-CUP99 HTTP. This subset used only HTTP traffic data from the original dataset, which consists of 620,000 samples, including 1053 anomalous samples, accounting for 0.17%. In the experiment, 80,000 normal samples were used for training, and 2100 samples were used for testing. Satellite Dataset: This dataset was generated by the Australian Centre for Remote Sensing from NASA data. This dataset contains 36 parameters and serves as a multi-class classification dataset. Throughout the experiment, all normal data were considered the positive class, while all anomalous data were considered the negative class. A selection of 4100 normal samples was used for training, and 1000 samples were reserved for testing, of which 925 were normal and 75 were anomalous. SMAP dataset: This dataset was obtained from a NASA spacecraft and contains real-world data. It consists of 55 telemetry channels, 429,735 telemetry values, and 69 anomalous sequences. Anomalies within the dataset are divided into two categories: point anomalies and contextual anomalies. For the experiment, 70,000 normal samples from channels A1 to A9 were selected for training, and 7000 samples were selected for testing, with 10% of the testing samples being anomalous. MSL dataset: This dataset is a real-world dataset derived from NASA's spacecraft via the Mars Science Laboratory (MSL). It comprises anomalous data stemming from Incident, Surprise, and Anomaly (ISA) reports from a spacecraft monitoring system. The data set consists of 27 telemetry channels, 66,709 telemetry values, and 36 anomalous sequences. To perform the experiments, 15,000 normal samples were selected for training, and 1500 samples were selected for testing, with 10% of the testing samples being anomalous. Payload Dataset: This dataset consists of operation data collected from the Payload of China's aerospace satellite. It includes variables such as current, voltage, temperature, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Train Set Samples** & **Test Set Samples** & **Dimension** \\ \hline Shuttle & 44,686 & 1778 & 9 \\ KDD-CUP99 HTTP & 80,000 & 2100 & 27 \\ Satellite & 4100 & 1000 & 36 \\ SMAP & 70,000 & 7000 & 26 \\ MSL & 15,000 & 1500 & 56 \\ Payload Dataset & 80,000 & 16,662 & 65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the six experimental datasets. and command parameters, totaling 66 dimensions. To represent a typical anomaly of a complex mode type during its operational phase, a subset of 96,662 samples was extracted following preprocessing steps, including noise reduction. Upon analyzing the data samples, a change in current was observed during abnormal operational stages, although it remained within the valid threshold values. Figure 2 shows that under normal conditions, the payload operates with a current of approximately 1.44 A within the South Atlantic Anomaly (SAA) and around 1.33 A outside the SAA.