## 7 Ground-Segment Optimization Aspects
### Applicable Optimization Methods for Ground Segment
#### 7.2.2 Long Short-Term Memory (LSTM)
We have provided different illustrative examples for characterizing the user-segment performance, e.g., based on \(C/N_{0}\) or based on the average number of satellites in view by a terrestrial autonomous vehicle, and these can be used as a starting point for further exploration. In addition, the areas of applications from the user-segment point of view comprise communication, sensing, and navigation domains, with typically contradictory criteria to meet (e.g., wide receiver bandwidths and high carrier frequencies are highly suitable for high-throughput applications, while narrow bandwidths and smaller \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Convergence** & **Global** & **Large-Scale** \\  & **Speed** & **Convergence** & **Applicability** \\ \hline Traditional & & & \\ Methods & & & \\ \hline Search & & & \\ Methods & & & \\ \hline KF / UKF / EKF & & & \\ \hline NN and DNN & & & \\ \hline RL and MARL & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative Summary of Optimization Methods for LEO User-Segment Optimization. carrier frequencies are better for good link budgets and navigation applications in heavy urban or indoor scenarios). There are two general notes we would like to add on top of our recommendations in the previous paragraphs. The first is about the modeling part; as it should be apparent from explanations in Sections 6-8, underlying theoretical models are needed and included in most steps of all methods, at the very least in the evaluation metrics. Thus, it is a logical conclusion that there will be models that will influence the results in an equal or higher manner than the influence of choosing a particular optimization method in a per case basis. However, creating generic models is again a challenging issue, especially in problems with conflicting goals, such as most multi-objective multi-modal problems, as the modeling parameters are typically hard to tune simultaneously in an optimal direction. This is one reason why optimization methods are widely utilized while still depending on partial or simplified models of the problems they are optimizing. The second note is about ML-based methods, which include, for example, the NN and RL methods in Figure 3. It is important to remember that, despite their large-scale spread in the literature in all kinds of applications and problems, ML methods are still considered mostly 'black box' models, which means that, while we are able to reliably explain 'what' works in such models, we rarely have sufficient explanations to 'why' or 'how' they work, and the reproducibility of results cannot be automatically ensured. Due to this, they are very difficult to reproduce, which can lead to complications if applied in large-scale real-world applications and, in particular, for LEO system design for autonomous transportation applications. ## 10 Conclusions, Open Directions, and Future Studies

Our paper gave a broad overview of the optimization methods that can be used in the context of LEO system design for communication, navigation, and/or sensing applications within the future autonomous transportation applications. The optimization methods were presented both at a general level and segment-by-segment, following the three-segment architecture of most satellite systems (namely space, ground, and user segments). We have thoroughly identified the optimization metrics and constraints at each of the segments, and we compared various optimization methods in terms of their complexity, convergence characteristics, and feasibility. We have shown that the typical optimization issues in the LEO-system design context are complex, multi-objective, and multi-modal optimization problems, and therefore, they are to be tackled separately, segment-by-segment, with the target application criteria in mind.