# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 4 Datasets and Performance Evaluation Indices
### Datasets
Proportionally, 59% of anomalous data correspond to point anomalies and 41% to contextual abnormalities. * JAXA datasets: Some approaches in the literature were designed for different JAXA missions. For example, the authors in [21; 47; 50] have studied the efficiency of DR methods on the EPS of different JAXA satellites such as the Mission Demonstration Satellite 1 (MDS-1) and Data Relay Transponder Satellite (DRTS). In [39], a clustering approach was implemented on a telemetry data acquired by Small Demonstration Satellite 4 (SDS-4) using onboard software by the JAXA satellite. * Other telemetry datasets: Other spacecraft telemetry datasets such as KOMPSAT-2 [62], the Power System of a Rocket (PSR) [60], and Tianping-2B [45] have been used to study telemetry anomaly detection approaches. For example, KOMPSAT-2 was used to assess the performance of combining the LSTM and MPPCA algorithms in detecting anomalous events. A more advanced LSTM variant was applied to Tianping-2B, where the authors developed an anomaly detection method based on GANs. The work proposed in [60] presents the performance of clustering method to a power system of a rocket satellite. * Anonymized spacecraft in-orbit datasets: For confidentiality or security purposes, many approaches chose to do not publish the real parameters names, timestamps, source or any other details about the spacecraft datasets used [24; 28; 44; 45]. Indications of telemetry anomalies for many spacecraft, including the SMAP and MSL, can be found within \"Incident/Surprise/Anomaly\" (ISA) reports. ISA reports provide valuable information about the incidents and anomalies which appear in specific telemetry channels. We summarize the characteristics of various spacecraft telemetry datasets in Table 4. Several articles do not list certain dataset parameters such as the number of telemetry values, the number of channels, and the number of anomalies. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Source** & **Type and Format** & **Nbr of** & **Nbr of** & **Nbr of** & **No of** & **Anomalies** & **Approach(es)** \\  & & & & Synthetic datasets & & \\ \hline Unknown & 5635 & — & — & 224 & Clustering [45] \\ \hline \multicolumn{6}{c}{Simulated datasets} \\ \hline  & & & & 4 parameters : & & \\ Solid state power & & — & + Temperatures & & \\ amplifier & — & — & + Current81 & — & TCN, LSTM [54] \\  & & & + Currents2 & & \\ \hline Control subsystem & & & & & \\ data of an actual & 25,000 & — & 12 & — & SVM, NN [37] \\ in-orbit satellite & & & & & \\ \hline \multicolumn{6}{c}{Real datasets} \\ \hline ESA Sentinel-1 & Multi-variate time series & 146,887 & 10 & — & 7 & PCA, KNN, OCSVM, LSTM [36] \\ \hline _ESA OPS-SAT_[65] & Univariate time series & 303,493 & 9 & - 3 parameters of magnetometer - 6 parameters of photo diode & 445 & SVM, KNN [26]; KNN, PCA, OCSVM [40] \\ \hline ESA XMM-Newton & Multi-variate time series & — & — & 2000 & — & OOL [9] \\ \hline \multicolumn{6}{c}{LSTM [20]; LSTM + transfer learning [22]; Attention TCN [7]; ResNet, CNN [10]; ELMs [27]; SVM [38]; Transformers [29, 31, 32]; GCN [30]; GRU + VAE, LSTM + GAN [60]; CNN-DTAN [55]} \\ \multicolumn{6}{c}{LSTM+multi-scale strategy [58]} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of spacecraft telemetry datasets gleaned from research articles presenting spacecraft anomaly detection techniques. The datasets in italics are publicly available.