## 7 Datasets
### Astronomy  
In the field of astronomy, datasets typically include various observational and simulation data, mainly used to study phenomena such as the universe, galaxies, stars, and planets [106; 107]. The Ionosphere dataset [82] comes from the UCI Machine Learning Repository and is used for a binary classification task, with the goal of determining whether there are electrons present in the ionosphere based on received signals. The dataset includes individual feature information as well as ionosphere thresholds. It contains 351 samples and 33 features, where each sample represents a measurement of a radar echo signal. Various classification algorithms can be used for model training and validation when working with the Ionosphere dataset. The dataset has a high feature dimension, which may lead to the \"curse of dimensionality\" during model training. Additionally, handling noise and outliers in the dataset is another important challenge. The AERO [14] model can distinguish normal temporal patterns from potential anomalies in astronomical observations, effectively differentiate concurrent noise, and reduce the number of false alarms. SWAN-SF (Space Weather \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Datasets/Benchmark** & **R/S** & **Samples** & **Entities** & **Dims** & **Rate** & **Domain** \\ \hline MSL [25] & Real & 132,046 & 27 & 55 & 10.48 & Aerospace \\ NASA Shuttle Valve Data [81] & Real & 49,097 & 1 & 9 & 7.0 & Aerospace \\ IOnsphere [82] & Real & 351 & & 33 & 36.0 & Astronomy \\ SWAN-SF [83] & Real & 355,330 & 5 & 51 & 23.8 & Astronomy \\ SMAP [25] & Real & 562,800 & 55 & 25 & 12.83 & Environmental science \\ OPPORTUNITY [82] & Real & 36,224 & 24 & 133 & 3.4 & Environmental science \\ GECC [84] & Real & 138,521 & 1 & 9 & 1.25 & Internet of things (IoT) \\ CICIDS [85] & Real & 170,231 & & 72 & 1.28 & Internet of things (IoT) \\ Kitsune [82] & Real & 3,018,973 & 9 & 115 & 17.0 & Cybersecurity \\ Http (KDDCUP99) [86] & Real & 567,479 & & 3 & 0.4 & Cybersecurity \\ Smtp (KDDCUP99) [86] & Real & 95,156 & & 3 & 0.03 & Cybersecurity \\ NAB-realAdeChrange [87] & Real & 9616 & 3 & 2 & & Business and finance \\ Creditcard [88] & Real & 284,807 & 1 & 29 & 0.17 & Business and finance \\ Genesis [89] & Real & 16,220 & 1 & 18 & 0.3 & Industrial control systems \\ GHL [90] & Synth & 200,001 & 48 & 22 & 0.4 & Industrial control systems \\ SWAT [91; 92] & Real & 946,719 & 1 & 51 & 11.98 & Industrial control systems \\ WADI [93] & Real & 957,372 & 1 & 123 & 5.99 & Industrial control systems \\ trimSyn [38] & Synth & 17,680 & 1 & 35 & 2.34 & Industrial control systems \\ MSDS [94] & Real & 292,860 & 1 & 10 & 5.37 & Industrial control systems \\ Arrhythmia [95] & Real & 452 & 1 & 274 & 15.0 & Healthcare \\ MBA [96] & Real & 200,000 & 8 & 2 & 0.14 & Healthcare \\ Thyroid [97] & Real & 3772 & & 6 & 2.5 & Healthcare \\ SVDB [98] & Real & 230,400 & 78 & 2 & 13.6 & Healthcare \\ Daphnet [82; 99] & Real & 32,594 & 35 & 3 & 13.2 & Healthcare \\ Callt2 [82; 100] & Real & 10,080 & 2 & 2 & 4.1 & Infrastructure \\ Metro [82] & Real & 48,204 & 1 & 5 & 0.1 & Infrastructure \\ NYC [101] & Real & 17,520 & & 3 & 0.57 & Infrastructure \\ Occupancy [82] & Real & 6208 & 2 & 8 & 28.7 & Infrastructure \\ Exathion [102] & Real & 47,530 & 39 & 45 & 18.3 & Server monitoring \\ MBD [103] & Real & 8640 & 5 & 26 & & Server monitoring \\ MMS [103] & Real & 4370 & 50 & 7 & & Server monitoring \\ PSM [104] & Real & 132,480 & 1 & 25 & 27.76 & Server monitoring \\ SMD [105] & Real & 1,416,825 & 28 & 38 & 4.16 & Server monitoring \\ \hline \end{tabular}
\end{table} 
Table 2: Public dataset/benchmark used mostly for MTSAD. R/S: The source of the data, whether it is real world data or synthetic data. Samples: Individual data points or observations within a dataset. Entities: Independent observation unit or object, such as sensors, machines, devices, etc. Dims: The number of features or variables in the data. Rate: The proportion of anomalous samples within the dataset.