techniques cannot solve optimally or are too complex to be implemented. In fact, [PERSON] et al. explained in [8] that the resource management problem in SatComs is, in most cases, nonlinear and nonconvex due to the logarithmic function as well as the nonlinear dependencies of the carrier/interference plus noise ratio (CINR) on problem formulation. In addition, typical RRM involving scheduling and/or carrier allocation translate into the use of binary or integer variables, which results in a combinatorial problem. For the latter, an optimal solution cannot be obtained using well-established optimization methods. One could attempt to solve the aforementioned problem by exhaustive search. Still, this strategy has a very high computational complexity, which is often beyond the capabilities of satellite ground segment processors in online operations. Currently, for the reasons mentioned above, SatComs still relies heavily on human expertise and manual operations. Satellite system control activity requires heavy human involvement, which generates high operational expenditure (OPEX) and implicit latency in human action that causes the degradation of the quality of service (QoS) [9; 10; 11; 12]. Furthermore, human-based decisions are typically far from the optimal ones, resulting in non-efficient system performance. In this context, Machine Learning (ML) has appeared as a promising alternative for dealing with computationally expensive optimization procedure. Lately, with the exponential increase in the amounts of data available, ML has become a fundamental technology in different areas of wireless communications [13; 14; 15]. In this context, ML has proven to be an interesting tool for accelerating complex optimization procedures for general wireless communications [16]. On the other hand, by focusing on ML applicability to SatComs, limited works such as [17; 18; 19] have resulted. Focusing on ML solutions in SatComs, we next present the most relevant works available in the literature. From a more general viewpoint, ESA opened the first AI-related call for SatComs in 2019, intending to investigate the applicability of AI techniques in the field of satellite communications. Two contracts were signed with a duration of 6 months each. During these six months, different potential use cases were shortlisted, and a preliminary evaluation of a small number of them was carried out to provide guidelines for future research. These two activities represent the most recent developments, and, although complete documentation is not available, we base the following analysis on the content included in both \"Final Reports\":  
* SATAI--Machine Learning and Artificial Intelligence for Satellite Communications [20]. Consortium: GMV, CTTC, Reply, Eutelsat;
* MLSAT--Machine Learning and Artificial Intelligence for Satellite Communication [21]. Consortium: Joanneum Research, Inmarsat, Gratz Technical University. Table 1 summarizes the use cases examined in SATAI and MLSAT. As seen in both projects, the resource management problem was identified as a potential use case. Continuing from a general approach, [PERSON] et al. introduced the application of Machine Learning (ML)-based procedures in real-world satellite communication operations [9]. The authors presented as a first approach some possible use cases of ML techniques for SatComs operations; for example, they proposed the use of an autoencoder for interference detection and the use of Recurrent Neural Networks (RNN) for traffic congestion prediction. The work in [9] was conducted during the ESA project, SATAI [20], for which the goal was more focused on investigating the applicability of ML concepts and techniques in the field of satellite communications rather than an in-depth analysis and study of ML techniques applicable to each selected use case. It is worth highlighting that a European funded project has recently kicked-off, for which its contribution can be considered a continuation of the SATAI project [22]. Along the same line, [PERSON] et al.