Modular space systems are an innovative and future-oriented concept, offering the flexibility to adapt to diverse mission requirements. In our previous paper, we introduced the Twins4Space project, a modular and flexible architecture utilizing a SpaceWire network to connect distributed nodes. In this paper, we focus on the empirical performance evaluation of the system, presenting detailed measurements of its communication layer. Key performance indicators such as SpaceWire Time-Code latency and network data rates are assessed, which are essential for maintaining predictable operational conditions within decentralized nodes. Our findings demonstrate that Twins4Space can effectively meet the rigorous demands of future space applications, providing robust communication and swift reconfiguration capabilities. Furthermore, areas with optimization potential are identified, offering opportunities for further performance improvements. This validation underscores the practical potential of the Twins4Space approach in enhancing the efficiency and reliability of space mission infrastructure.

not-yet-known not-yet-known not-yet-known unknown This article digs into the current significance of Low Earth Orbit (LEO) Satellite Networks in both academia and industry, starting with an examination of the various uses envisioned for Low Earth Orbit Satellites (LEOS) and its clear advantages over 5G technology, LEOS offer global internet access, low latency, and scalability, surpassing 5G’s coverage. The applications of LEO satellites include global connectivity, remote sensing, and aerospace advancements, revolutionizing communication and space exploration. Notably, These networks, which include SpaceX, OneWeb, LeoSat, Telesat, and Amazon Kuiper, have been in development since 2018. The satellites operate as routing nodes connected via ISLs, and terminals connect dynamically to one or more satellites in these networks, which also have onboard processing capabilities. The dynamic changes in the network architecture present particular challenges for service planning and traffic routing. To determine an optimal path for transmitting data from a single source terminal to a single destination terminal within the dynamic and complex network architecture of LEO satellites is known as unicast routing. We are interested in unicast routing because it addresses the specific needs of individual communication, optimizing network efficiency, minimizing latency, and enhancing security, all of which are critical factors in the success of these networks. There are changes in routing information that might be frequent and have a latency of 100 ms due to the continual movement of satellites and the Earth. Routing traffic from a source terminal to a destination terminal connected to several satellites is a challenge. In this paper, we will present different approaches from literature to model the satellite constellation and solve the routing problem (with or without inter-satellite links and w/wo dynamic routing). This paper also introduces the simulators to model satellite constellation such as Starlink constellation.

In order to efficiently transmit spatial data with diversified service types, based on the virtual channel multiplexing technology of Advanced Orbiting Systems ( AOS ), this paper proposes an Intelligent Frame Algorithm in Advanced Orbiting Systems( AIFG ) for differentiated QoS requirements. To expedite the transmission of delay-sensitive services, the optimal time threshold for framing such services is modeled as a Markov Decision Process. The algorithm utilizes Proximal Policy Optimization (PPO), achieving an optimal solution based on performance metrics such as frame multiplexing efficiency, average framing time, and average packet delay. To improve the multiplexing efficiency for non-delay-sensitive services, this paper requires that these services are framed only after the frame is fully filled. Simulation results show that, compared to traditional frame generation algorithms, the AIFG algorithm reduces the average queuing delay of services by at least 33%, increases throughput by at least 37%, and improves multiplexing efficiency by at least 52%. AIFG algorithm takes into account the different transmission requirements of real-time services and non-real-time services, and enhances the network quality of service (QoS) in the AOS system.

As launch and manufacturing costs have become affordable, the industrial and academic interest in Low-Earth Orbit (LEO) satellites has increased in recent years. With this interest, the concept of Low-Earth Orbit-based Positioning, Navigation and Timing (LEO-PNT) has also gained popularity as a complementary and/or standalone system in addition to the already existing Global Navigation Satellite Systems (GNSS). This article examines the LEO constellation optimization from the perspective of a LEO-PNT design, identifies and discusses the state-of-art of the LEO satellite constellation optimization approaches, introduces relevant performance and feasibility related metrics and parameters, and addresses key concepts and underlying trade-offs that must be considered for any LEO-PNT system design. In addition, a case study for a LEO-PNT constellation optimization is presented, where we showcase the discussed trade-offs. We present optimization results obtained with the adaptive weighting algorithm “ADaW” applied to the Pareto-optimization algorithm “NSGA-III”. A detailed performance analysis is done for six relevant scenarios with varying receiver location properties, namely by considering indoor/outdoor, rural/urban and line of sight/non-line of sight cases.

Free Space Optical Communication has emerged as a promising technology for high-speed and secure data transmission between ground stations on Earth and orbiting satellites. However, this communication technology suffers from signal attenuation due to atmospheric turbulence and beam alignment precision. Low Earth Orbit satellites play a pivotal role in optical communication due to their low altitude over the Earth surface, which mitigates the atmospheric precipitation effects. This paper introduces a novel data path control law for satellite optical communication exploiting Artificial Intelligence-based predictive weather forecasting and a node selection mechanism based on Reinforcement Learning. Extensive simulations on three case studies demonstrate that the proposed control technique achieves remarkable gains in terms of link availability with respect to other state-of-the-art solutions.

To meet the increased bandwidth demand from remote locations, new satellites are being launched to allow networks to operate at higher frequencies (i.e., Ka-band – 27.0GHz - 40.0 GHz and V-band 40.0GHz – 75GHz). At these frequencies high availability is challenged by rain attenuation. Consequently, satellite network operators are offering hybrid solutions where, during a rain outage, traffic is moved to a lower frequency secondary heterogeneous satellite network not affected by rain.   This paper evaluates a mobile and network centric handover and admission strategy using a bespoke simulation model of a rain affected star topology hybrid satellite network with primary network operating on Ka band and secondary satellite network operating at a lower frequency.    Results show that a network centric prioritised queue-based admission strategy provides a reduction in handover failure probability of about 5% without extra capacity being made available for the handover of traffic to the secondary network. When queue-based admission is used in combination with adding extra capacity in the secondary satellite network a reduction in handover failure probability of nearly 30% was achieved.   However, when more capacity is made available in the secondary network, the advantages of the network centric queue-based admission strategy slowly disappear with added capacity. This means that operators have a clear choice based on whether they believe that their secondary network has sufficient spare capacity, or whether in fact this is unlikely and therefore the extra cost of a queue-based admission strategies should be weighed against loss of revenue due to lost connections.