## 2 Related Works  
The work related to LEO system design and optimization for autonomous transportation applications is typically focused on only one of these two domains: a LEO focus only or a focus on the intelligent/autonomous transportation side only. Moreover, the papers with LEO tend to only focus on one LEO segment at a time, among the three architectural segments (space, ground, and user), which are described in more detail in Section 3. Few papers are also addressing the LEO and autonomous transportation aspects. For example, the authors in [7] focused on LEO networks for communication between autonomous vehicles, with a testbed example based on a UAV. No optimization aspects were addressed and the speed of the UAV used in the testing was not mentioned. The two LEO commercial systems targeting the automotive industry, namely Pulsar of Xona Space [8] and GeeSpace of Geely Technology Group [9], have very little public-domain information regarding the design parameters or the adopted optimization steps. It is known that both Pulsar and GeeSpace systems aim at offering centimeter-level positioning to end users and acting as enhancers of Global Navigation Satellite Systems (GNSS)-based positioning technology, but the exact design parameters and mechanisms for achieving these targets are not yet available in the open literature. The authors in [10] focused on ground-segment optimization of large LEO constellations. The optimization metric was the overall system capacity and the optimization output was the number of ground stations. Monte-Carlo (MC) optimization was employed. Similarly in [11], the authors presented their review of the literature surrounding marine systems and unmanned vehicles, with numerous real-life and academic examples of state-of-art systems, which included high-speed scenarios that utilized LEO satellite systems, such as Iridium. Particularly, the work in [11] considered LEO networks in terms of remote-control applications. However, the focus mostly stayed on the transportation domain; the satellite constellations themselves were only briefly mentioned and optimization aspects were not discussed for any scenario. The work in [12] addressed the problem of navigation services via MEO and LEO satellites for autonomous vehicle applications, which have stringent positioning requirements of decimeter-level accuracy. Several possible LEO advantages in terms of complementary positioning methods to MEO GNSS were listed, such as stronger received signals, better resilience to interference, and fast LEO -satellite speeds, enabling carrier phase differential precise positioning. No optimization method was discussed in [12], but several optimization metrics were presented, such as Carrier-to-Noise Ratio (C/N\({}_{0}\)), jamming mitigation ability, and material-penetration ability (e.g., LEO signal penetration through brick or concrete walls). In [13], a Genetic Algorithm (GA)-based optimization, with Geometric Dilution of Precision (GDOP) and the number of satellites as optimization metrics, was employed for space-segment optimization of a LEO-based navigation system relying on a Walker constellation. It was found that good LEO coverage for navigation purposes can be reached with constellations between 180 and 264 and satellites placed at orbital altitudes between 900 and 1500 km. The work in [14] explored possible Dense Small Satellite Network (DSSN) applications on LEO networks, focusing mostly on the DSSN in terms of its architecture, requirements, and performance. The work did a good job in determining parameters that needed to be optimized for DSSN networks and determined the boundaries the LEO networks were subject to, but it did not address the actual process of optimization. However, some optimization methods for resource management were mentioned but not presented in detail. In [15], the authors proposed a Software-Defined Networking (SDN)-enabled LEO constellation satellite network and formulated an optimization problem for SDN controller placement and assignment.