## 7 Ground-Segment Optimization Aspects
### Applicable Optimization Methods for Ground Segment
#### 7.2.2 Long Short-Term Memory (LSTM)
The targets in the context of future autonomous vehicles are stringent, as depicted in Table 4, and they can be reached with a combination of various complex optimization mechanisms, such as ML or PSO for the space segment, LSTM or NSGA-II for the ground segment, and a scenario-dependent optimization approach for the user segment. However, for the sake of higher control and reproduction of the results, it is our view that ML-based approaches are not the best-fit-to-problem in the context of LEO system design for autonomous vehicles, especially for safety-critical transportation applications, and therefore, methods such as PSO or NSGA-II are to be preferred. In addition to the segment-specific optimization tasks addressed here, we have mentioned various other issues, such as space debris, standardization constraints, and security targets, which can be further incorporated as optimization boundaries, but which are too broad to be inserted in a single optimization problem. While our paper has covered a broad area of LEO design aspects, issues such as physical-layer optimization (e.g., modulation, channel coding, and carrier frequency choice), medium access control (e.g., multi-access control scheme and joint multi-access-physical layer optimization), network routing aspects, or security aspects have not been addressed in detail and remain a matter of future investigation. Furthermore, as future directions, there is currently a dichotomy of approaches regarding communication, sensing, and, especially, positioning applications with LEO signals: on one hand, one could use any of the existing systems (e.g., such as those listed in Table 7) as signals of opportunity or as a basis for offering novel services to the end users; on another hand, one could design completely new systems, such as the on-going efforts towards a LEO-PNT concept. In the former case, the burden of the optimization problems will stay mainly within the receiver (thus at the user-segment part) because the infrastructure is assumed to be already deployed; in the later case, a three-segment optimization is needed in a joint or disjoint (segment-by-segment) manner. Interesting new research directions in this domain comprise the integration of LEO and MEO solutions, such as a GNSS receiver on-board LEO satellites to better support the PNT targets, integration of cellular (5G, Sixth-Generation of Cellular Networks (6G)) networks with LEO networks, the use of RL other ML approaches for increased security in the wireless communication and navigation links, beamforming-based positioning via ML, and LEO-based edge computing. Our future work will focus on designing novel LEO-PNT constellations by applying the identified segment-by-segment optimization methods in order to achieve improved accuracy for indoor and dense-urban positioning of devices with global coverage. Conceptualization, K.C., Z.S., J.P. and E.S.L. ; methodology, K.C., Z.S. and E.S.L. ; software, K.C., Z.S., R.M.F., J.P. and E.S.L. ; analysis, K.C., Z.S., R.M.F., J.P. and E.S.L. ; validation, K.C. and Z.S. ; writing--original draft preparation, K.C., Z.S., R.M.F. and E.S.L. ; writing--review and editing, K.C., Z.S., R.M.F., J.P. and E.S.L. ; visualization, K.C., Z.S. and R.M.F. ; supervision, J.P. and E.S.L. ; project administration, J.P. and E.S.L. ; funding acquisition, J.P. and E.S.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Jane and Aatos Erko Foundation (JAES) and by the Teknologiatelosius 100-year Foundation in the INCUBATE project. This work was also partly supported by the Academy of Finland, under the project ULTRA (328226, 328214). Not applicable. The authors declare no conflict of interest.