## 1 Introduction
Many of the strategies employ sophisticated reduction methods to diminish the size of the input vector, but a lot of these methods do not involve MIL testing, applying classification offline, or do not match up different SVM architectures for comparison in bulk. Support Vector Machines are relatively easy to intuitively comprehend, and can achieve high efficiencies, thanks to the use of kernels [11]. Support vectors are intended to be used as binary classifiers, but through structured architectures, they have become extremely widespread also for multi-class categorization [12]. Artificial Neural Networks are slightly less obvious to fully understand theoretically, and their hyper-parameters suffer from a lack of physically meaningful interpretation [13]; despite this, their use is following a rapid increase in popularity due to high performance for what concerns pattern recognition and classification, especially when exploiting a feature extraction mechanism to select their inputs, much like SVMs. [PERSON] et al. (1991) showed 10-class fault classification on a realistic continuous stirred tank reactor system through ANNs [14], [PERSON] and [PERSON] (2001) focused once again on fault diagnostic for rolling element bearings with only five extracted features [15], while [PERSON] et al. (2023) used networks to apply both detection and diagnosis on high-speed train air brake pipes [16]. The variety of layer structures and node types is incredibly wide, and therefore, the topic is still under close study and within a heavy experimenting phase [17]: space applications are many and diversified, and extremely high accuracy in classification problems can be achieved with a multitude of layer setups. [PERSON] et al. (2009) developed a dynamic Neural Network Fault Detection and Isolation (FDI) framework for pulsed plasma thrusters, with hints to an integrated scheme for combined high and low level FDI [18], [PERSON] et al. (2018) used different Neural Network architectures to perform automatic feature extraction, anomaly detection, and telemetry prediction [19], and [PERSON] et al. approached voltage anomalies detection through a deep belief network architecture [20]. Once again, many tests are run offline, with no consequential MIL testing: tests in space rarely concern a satellite's ADCS and only concern a few network architectures, without a critical comparison with other kinds of data-driven methods, such as threshold-based, that could be considered a useful signal monitoring benchmark algorithm. Fault detection and diagnosis through Neural Networks and SVMs is an incredibly wide topic, which is being tackled by dozens of different directions at once, by exploiting the state of the art on both these models. Despite the proven efficacy of Deep Learning solutions for systems' health monitoring and fault detection, FDIR in space applications still misses a shared standard or universal strategy. In a loosely structured field such as this, especially on the pivotal Attitude Determination and Control Subsystem, Machine Learning is a better choice for what concerns computational resources, fast training, heritage, and most importantly, when the aim is to build a basic but wide comparison of a large range of architectures: for this reason, a rigorous but gradual approach is adopted. The aim of this research is, starting from a realistic framework in a functional simulator, reproducing an Earth-orbiting satellite's Attitude Determination and Control System (ADCS), to focus on the analysis of signals onboard the spacecraft and the diagnosis process of faults: the task is to examine performances of a collection of basic detection algorithms employing Machine Learning, in comparison to the architecture of a simple threshold-based data-driven fault detector, to find the most appropriate shape for an enhanced FDIR architecture.