## 1 Introduction  
The management of onboard systems and health status monitoring of a spacecraft is a pivotal but extremely wide aspect of satellite design that is being reshaped as satellites evolve and autonomy increases. Onboard Fault Detection, Isolation, and Recovery (FDIR) provides supervision and control on the satellite behavior under unexpected situations and malfunctions. The main design philosophies for carrying the task of anomaly identification onboard spacecrafts, at the current time, are model-based and data-driven. Model-based techniques exploit an \"analytical redundancy\" of the onboard subsystems: by running an onboard model in parallel with real-time operations as they are performed, the spacecraft relies on a duplicated virtual version of part of itself. By checking simultaneously the outputs of real elements and their simulated version, residuals are periodically calculated, and it is possible to establish if any discrepancy from the expected scenario has occurred. Analytical redundancy adds a layer of cross-checks without the addition of hardware components to the system: the result of these checks shall be translated into a decision function to eventually proceed with the available recovery actions, if necessary [1]. In the context of CubeSats, [PERSON] et al. (2019) built an FDIR framework based on analytical redundancy and residual-based logic for fault isolation [2]. For similar live time-varying signal monitoring, in a more recent work [PERSON] et al. (2025) developed a model-based approach which utilizes input-compensated recursive least squares and disturbance observers, for a robust inverter fault diagnosis through parameters estimation [3]. Data-driven algorithms, on the other hand, can work with live acquired data, ranging from housekeeping parameters to raw sensor output, to notify the spacecraft and flight engineers when some anomalous behavior is detected [4]. By exploiting knowledge on process history, it is possible to notice novel or unusual behavior even without an explicit model of the system. Both of these philosophies come with potential weaknesses: one of the drawbacks with model-based techniques is the fact that they require sufficiently accurate dynamic models, which must be designed, optimized, and end up consuming a discrete amount of onboard computational resources. Data-driven approaches attempt to specifically overcome this issue while maintaining a high reliability of detection. However, it is clear that the focus must shift to the quality and relevance of training data, and the selection of statistical knowledge used to monitor the system must be extremely well targeted. Specifically, in the realm of fault detection, the adoption of data-driven methods is widespread and concerns many different fields: surveys such as [PERSON] et al. (2023) gathered useful and reliable diagnostic methods from recent years, in this specific case with a special focus on HVAC systems [5]. In the current study, attention is drawn on two very common (supervised) Machine Learning approaches for classification, with some meaningful research heritage that hints to potential cutting-edge space applications: Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs). In industrial mechanics, SMVs are already quite popular, with a few examples focusing on fault detection through feature extraction. For instance, [PERSON] et al. (2014) focused on bearing fault diagnosis [6], [PERSON] et al. (2017) confirmed the very same extracted features pool for sensor fault diagnosis through SVMs [7], while [PERSON] (2004) employed a further optimization of extracted features through a genetic algorithm [8]. In the space domain, examples are fewer, at least for what concerns fault detection: [PERSON], [PERSON] et al. (2012) employed principal component analysis and SVMs to classify output data from sensors and actuators, with promising results in all explored cases [9], while [PERSON] et al. (2021) focused on spacecraft leakage detection through SVMs, also applying a feature ranking algorithm [10].