There are thousands of satellites in orbit around the Earth and in deep space, each performing different tasks crucial to sectors such as research, communication, Earth observation, and space science. Since the space environment differs significantly from Earth’s, the robustness and resilience of these spacecraft must be guaranteed. One of the main issues they face is radiation, which can compromise their lifespan or effectiveness. To prevent these problems, space applications use different systems designed to detect anomalies. Machine learning and artificial intelligence systems have been adopted to exploit their efficiency and decision-making capabilities. More specifically, modern anomaly detection techniques leverage neural networks to capture temporal dependencies and handle high-dimensional data effectively.