## 1 Introduction  
Terrestrial, maritime, and aerospace transportation solutions are increasingly relying on automated tasks and on energy-saving enhancements, such as route optimization, edge and cloud processing via machine learning algorithms, as well as the convergence of communication, positioning, and sensing tasks at the receiver side or in the edge/cloud. The networks needed to support the wireless tasks in future transportation solutions are no longer limited to terrestrial networks, but they are being expanded with satellite-communication networks, such as those based on LEO satellites. Such expansion is needed in order to increase the coverage areas and the end-user ubiquitous access to wireless services and to provide equal accessibility worldwide. LEO orbits have altitudes ranging between about 200 km to 2000 km above the Earth's surface (below the Van-Allen radiation belts), which makes LEO satellites cheaper to build and launch in comparison with satellites launched to MEO and GEO orbits. The lower costs of building and launching LEO satellites (compared to MEO and GEO ones) have also enabled better commercial viability of autonomous-transportation services. As a result, there is a significant effort worldwide to build new LEO-based systems for a variety of broadband and narrowbandcommunications (e.g., Iridium, Oneweb, Starlink, Kuiper), Internet of Things (IoT) solutions (e.g., Hiber, Astrocast, Athena, Myriota) Earth Observation (e.g., Iceye, RapidEye, Capella Space), autonomous transportation (e.g., Pulsar, GeeSpace), and, possibly, new Position, Navigation, and Timing (PNT) systems. Integrative solutions of edge/cloud solutions with LEO have already been proposed, e.g., in [1]. Many LEO communication mega-constellations are already deployed in the sky, such as SpaceX Starlink, OneWeb, Amazon Kuiper, accompanied by smaller-sized more specialized constellations, for example, for IoT applications, such as Myriota, Hiber, Inmarsat, and others. In addition, the emerging concept of Low Earth Orbit-based Positioning, Navigation, and Timing (LEO-PNT) [2; 3; 4; 5] is receiving more attention in the research world by focusing on alternative satellite-based navigation methods via LEO satellites. Based on payload applications, LEO constellations can be broadly distributed into three categories: (i) remote sensing (which includes Earth Observation); (ii) wideband and narrowband communications; and (iii) navigation--with the latter having extensive use in transportation and logistics. In terms of orbital altitudes, LEO orbits present practical advantages over MEO-based solutions in terms of lower-latency communications, shorter positioning time, possible higher positioning accuracy, higher image resolution and lower launching, building, and maintenance costs than MEO and GEO satellites [6]. A novel challenge brought in by the combination of intelligent transportation solutions with LEO-based wireless links is the high-speed relative motion between the LEO satellites and the vehicle of interest, which can be in the order of thousands of meters per second. The design of future LEO systems should be able to take into account not only the target application scenario (such as intelligent transportation via high-speed trains or Unmanned Autonomous Vehicle (UAV)) but also the multi-dimensional services to be offered by future LEO systems in terms of communication, positioning, and sensing targets. Optimization methods to ensure such co-design are tremendously important and have not yet been addressed in the context of designing LEO-system parameters to the best of the Authors' knowledge. Two LEO systems are currently being built with the specific target of future mobility and unmanned vehicles, namely GeeSpace from the Geely Technology Group in China and Pulsar from Xona Space Systems in the United States. Currently, there is very little public information about the design parameters of these two systems. We will include a discussion about the known design parameters of these systems later in our paper.