# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 5 Discussion
* On-board processing limitations: Ideally, anomalies should be detected and processed onboard the spacecraft, but this means the model needs to run on the spacecraft computer system, and this brings issues with regard to computational complexity and possible issues with retraining. * Downlinking issue: Mission details, including telemetry data, are periodically transmitted by spacecraft to ground operators on Earth for analysis. Downlinking is the process of transmitting data from the spacecraft to Earth [69]. The link budget, or the amount of data that can be downlinked, is a constraint on every space mission. Even though the link budget is usually developed for every satellite mission during the planning stage, there are several factors that can affect the link and cause signal attenuation [70]. Therefore, it is not possible to downlink all data, and they may be lost, which consequently affects the anomaly detection performance if such anomaly detection is not done onboard. A promising avenue for future research would be the application of deep generative models such as GANs to generate realistic anomalies from normal data, in particular to augment ground-truth datasets to include difficult and unexpected anomalies. This could offer a better solution for the limited labeled anomaly issue in datasets. There are a few other works which use self-supervised learning [71], contrastive learning [72], and transfer learning [22], which were recently used to detect anomaly telemetry data. These methods can capture the complex correlations between the various dimensions and generate accurate anomaly detection results. Exploring such algorithms may be an interesting direction for future research. Since it would make it possible to react to failures and hazards faster, real-time anomaly detection is of extremely high importance to the community. An efficient suggestion for real-time anomaly detection is the exploration of real-world streaming algorithms such as Hierarchical Temporal Memory (HTM) [68]. Another potential research direction for spacecraft anomaly detection is onboard anomaly detection, where anomalies are identified in spacecraft systems. Given the limited resources available in spacecraft devices, studies involving onboard anomaly detection are limited in the existing literature [73; 74; 75]. Due to their parallelization capacity and limited parameter requirements, DL models, especially CNNs [76], have demonstrated computational efficiency. This benefit makes CNNs a promising direction in anomaly detection for spacecraft onboard applications. Despite their prominent performance in the spacecraft anomaly detection field, ML/DL models face challenges in explainability [10]. Providing a clear explainability analysis is required to understand why a specific data instance has been identified as anomaly, how ML/DL algorithms make decisions, and what factors contributed to that detection [77]. In this context, explainable ML/DL is a very valuable tool to enhance spacecraft safety and avoid system failures, and it presents an important future research direction. Training ML/DL models on real spacecraft telemetry data is often a time-consuming and resource-intensive task. Transfer learning, which takes advantage of the knowledge learned from another satellite model, may help to bypass this problem [22]. This method overcomes the need to retrain a model for every new setup. It makes the detection of anomalies with little or even no labeled data or limited computational resources feasible [78; 79]. Due to the large volume and complexity of the data generated by spacecraft, big data processing engines such as Apache Spark [80; 81] and Apache Flink [82] are key frameworks for evaluating and real-time monitoring spacecraft data. Spacecraft datasets can be efficiently stored, processed, and analyzed thanks to these engines, which would present an important research direction in the future, especially for real-time processing applications. Finally, digital twins of spacecraft are useful tools for telemetry anomaly detection [83].