Search Results (828)

The 8.025–8.400 GHz band is one of the key X-band downlink ranges for modern Earth observation satellites, enabling high-rate transmission of imagery and sensor data for agriculture, environmental monitoring, greenhouse gas assessment, disaster response and security-related applications. The potential introduction of 5G networks into this band raises serious concerns about harmful interference to Earth observation ground stations cand, consequently, about the continuity and growth of the global Earth observation data chain. This paper investigates the feasibility of sharing this downlink band between Earth observation systems and 5G networks using a Monte Carlo simulation framework. The model includes a low-Earth-orbit Earth observation satellite with dynamically tracking ground stations and dense urban, suburban and rural deployments of 5G base stations and user devices, together with established radio-propagation and clutter models and representative protection objectives for satellite downlinks. The results suggest that, to keep interference at acceptable levels, ground stations would need to be located far from 5G deployments, which is difficult to achieve in practice and could seriously limit the future expansion of Earth observation infrastructure. Full article

With the rapid development of commercial aerospace, emerging applications such as satellite constellations, space-based communications, and orbital computing platforms have significantly increased the demand for efficient and reliable spacecraft power systems. Abundant exploitable energy exists in the space environment, including Air Mass Zero (AM0) solar radiation, spacecraft surface temperature gradients, ambient electromagnetic radiation, and radioisotope thermal energy, making multi-source energy harvesting a promising approach for improving satellite energy autonomy and system redundancy. This paper reviews the following four key space energy harvesting technologies: photovoltaic power generation, radio frequency (RF) energy harvesting, thermoelectric energy harvesting, and radioisotope thermoelectric generators (RTGs). The impacts of harsh space environmental factors on device performance and reliability are analyzed, and the applicability of different technologies in low Earth orbit (LEO), geostationary orbit (GEO), and deep-space missions is discussed. Furthermore, a multi-source self-powered satellite energy architecture integrating energy harvesting, energy storage, and power management is proposed. Finally, the major challenges and future development trends of satellite energy harvesting systems are summarized. Full article

Low-Earth-orbit (LEO) megaconstellations are transforming satellite communications from sparse, ground-controlled infrastructures into dense, dynamic, and increasingly autonomous space networks, while their global coverage capability is fundamentally enabled by large-scale symmetric orbital structures distributed across multiple planes and shells. As these systems expand to tens of thousands of satellites, maintaining such orbital symmetry under continuous perturbations, changing communication topologies, and varying onboard resources becomes a fundamental operational challenge. Future space systems must therefore manage, coordinate, and sustain large constellations for which their orbital configurations, communication topologies, and onboard resources vary continuously. Here, we review the management and configuration-maintenance problems of megaconstellations through a Starlink-informed perspective. We first summarize the multi-shell deployment architecture, satellite platform evolution, and dominant orbital perturbations that shape constellation behavior. We then examine hierarchical and cluster-based management strategies designed to reduce the burden on ground control and improve scalability. We further discuss in- and out-of-plane configuration maintenance. Finally, we identify open challenges in distributed autonomy, multi-shell coordination, dynamic topology management, and intelligent orbit control. This review highlights that the long-term viability of megaconstellations will depend not only on launch capacity and satellite manufacturing but also on scalable decision-making, autonomous coordination, and sustainable orbital operations. Full article

Using low-Earth orbit (LEO) mobile communication constellations to transmit remote sensing satellite data represents an emerging paradigm for overcoming the bottleneck in downloading massive amounts of Earth observation data. However, dense concurrent access across multiple satellites triggers intense resource competition, severe visible-window fragmentation, and strict resource-exclusivity constraints. To address the complex scheduling challenges caused by high laser link establishment overhead and the high-dynamic motion between remote sensing satellites and LEO communication nodes, this paper proposes an Adaptive Multi-Objective Reconstruction Evolutionary Algorithm (AMOREA). The algorithm incorporates a hybrid initialization strategy to improve the quality of the initial solution set and designs a mission-level topology reconstruction mechanism that uses four complementary decomposition operators and a multi-strategy reconstruction pool to achieve effective resource aggregation. Furthermore, an adaptive weight feedback mechanism is introduced to dynamically adjust search priorities and balance global exploration with local exploitation. Simulation results show that, under the simulation settings of this study, AMOREA reaches a 100.0% completion rate for urgent high-priority tasks and an overall average task completion rate of 89.2%. In terms of multi-objective optimization performance, AMOREA obtains the highest mean hypervolume (HV) value among the compared algorithms, improving the mean HV by approximately 19.1% over NSGA-II, 17.6% over MOEA/D, and 67.6% over the Greedy baseline. These results indicate that AMOREA can generate higher-quality Pareto solution sets and improve the efficiency of high-dynamic inter-satellite transmission scheduling under the tested simulation settings. Full article

High-precision onboard orbit propagation, often unavailable due to high demands of onboard resources, is crucial for autonomous optical link pointing and acquisition in large-scale low Earth orbit satellite constellations. For such large-scale constellations, an advantageous feature for efficient and accurate orbit propagation is the correlation or similarity in the orbit perturbations experienced by multiple satellites. Yet, this correlation has not been fully utilized in existing orbit propagation methods. In this work, we propose a cross-referential orbit propagation framework that leverages multiple historical reference arcs from other satellites within the same constellation to improve prediction accuracy and reliability. The framework achieves error reduction comparable to that of ensemble learning by aggregating the predictions from a single lightweight model under varying reference inputs, thereby preserving a simple and compact model architecture. To ensure the generalizability of this compact model, we further introduce a network architecture termed the Compressive Decoder for Orbit Propagation (CDOP). The CDOP predicts low-dimensional representations of the propagated orbits, from which the full time series are subsequently decoded. By incorporating modules from pre-trained compressive autoencoders, the CDOP mitigates overfitting while maintaining a low inference cost. The proposed method is validated on simulated Walker constellations with different geometries. The results demonstrate an average 24 h position error of approximately 200 m, with an inference cost 30 times lower than that of a reduced-dynamic numerical propagator. The framework is computationally lightweight, generalizes well across different initial conditions, and is well suited for onboard deployment in autonomous optical link acquisition. Full article

Atmosphere-breathing Hall thrusters (ABHTs) have emerged as a promising propulsion technology for very low Earth orbit (VLEO) satellites because they can utilize residual atmospheric particles as propellant, reducing the need for onboard propellant storage. In this paper, the feasibility of an ABHT system was investigated through a combined experimental and numerical approach. Experimental tests using the THT-VI Hall thruster demonstrated stable operation with air propellant and achieved specific impulses up to 2847 s under high-voltage conditions, indicating the potential for atmospheric drag compensation. To evaluate the intake performance, Direct Simulation Monte Carlo (DSMC) simulations were conducted at an altitude of 180 km to examine the effects of intake geometry, including the duct aspect ratio and intake-to-thruster area ratio. The results showed that the intake system can generate discharge chamber pressures of approximately 10⁻³–10⁻¹ Pa, which is sufficient for Hall thruster operation, but the maximum collected mass flow rate (0.298 mg/s) remained below the required 1.5 mg/s. Several modified intake configurations improved particle transport and reduced aerodynamic drag with the best design increasing mass flow rate by approximately 7.5 times compared with the baseline configuration. These findings indicate that the primary limitation of ABHT systems is the intake mass transport capability rather than the thruster performance itself. A further optimization of intake geometry and spacecraft integration is required to enable sustained VLEO operation. Full article

Online scheduling for low-Earth-orbit (LEO) remote-sensing constellations must coordinate short observation windows, sequence-dependent slews, finite onboard resources, intermittent downlink contacts, and rolling command commitment. We study an acquisition-driven downlink-constrained observation problem in which retained observations carry the primary mission value, while their generated data must remain storable until transmitted. This formulation targets observation-centric missions where downlink supports storage feasibility and future acquisition capability; missions with explicit delivery-value or latency requirements can include downlink completion and delivery delay directly in the objective. The central difficulty is suffix-dependent feasibility: a single locally attractive insertion can cascade into downstream infeasibility through altered slew margins, resource levels, and downlink opportunities. We propose Graph-Relational Insertion-Preference Multi-Agent Proximal Policy Optimization (GRIP-MAPPO), a feasibility-preserving learning-to-dispatch framework comprising a relation-aware satellite–task–ground encoder, orbital-group actors that output bounded insertion-preference vectors, and a deterministic scheduler that enforces hard constraints via insertion, repair, and rollback. Thus, GRIP-MAPPO learns dispatch preferences rather than direct task, satellite, or window assignments, while hard-constraint satisfaction remains in an auditable scheduler interface. Controlled benchmark simulations over 24–288 satellites and six operating regimes show that GRIP-MAPPO improves observation completion and transmitted-data fraction over greedy, heuristic, and graph-based online baselines in the simulated setting, while retaining sub-second replanning latency at reference scale. Full article

Multi-layer low Earth orbit constellations (ML-LEOs) have become a pivotal trend in the development of satellite network systems, where their layered orbital architecture improves system performance by strategically deploying satellites in distinct orbital layers. However, two critical issues remain open: how does the configuration of ML-LEO affect its performance, and how many layers are required to achieve optimal performance? This paper first investigates the impact of the number of layers L on the capacity of ML-LEOs. By analyzing the distribution of inter-layer inter-satellite links (ISLs) and the flow count on bottleneck links, we derive a closed-form mathematical expression for ML-LEO capacity under different values of L. In particular, we show that when each layer adopts an identical constellation topology and the number of satellites per orbit equals the number of orbits, the capacity of the ML-LEO is $\sqrt{L}$ times that of a single-layer low Earth orbit constellation (SL-LEO). Furthermore, we present the optimal parameter configuration for ML-LEOs: the number of orbits per layer should equal the number of satellites per orbit, the number of layers should be half the number of satellites per orbit, and the optimal number of inter-layer ISLs is twice the product of the number of orbits per layer and the number of layers. Finally, extensive simulations are carried out to thoroughly verify the accuracy of the analytical results. Our analysis reveals the performance benefits of multi-layer topology and establishes a theoretical framework for parameter optimization in ML-LEO. Full article

6G promises ultra-low latency, high data throughput, and seamless global connectivity. However, providing uninterrupted connectivity in remote and underserved regions remains a critical challenge for Terrestrial Networks (TNs), where the cost of deploying infrastructure is difficult to justify against sparse user density. Standardized under 3GPP Release 17, Non-Terrestrial Networks (NTNs) have emerged as a viable solution to close this digital divide. Among NTN platforms, High-Altitude Platform Stations (HAPS) occupy a strategic middle ground, as they deliver lower propagation delays than Low-Earth Orbit (LEO) satellites while achieving far broader coverage than TN-based Base Stations (BS). Despite these advantages, battery-powered Internet of Things (IoT) devices communicating via HAPS face a fundamental energy efficiency (EE) challenge: transmit power must be carefully managed to maximize data throughput while preserving battery life and minimizing packet queuing delays. To address this, we propose a Q-learning-based Reinforcement Learning (RL) framework. The RL agent observes the instantaneous battery level and queue state of the IoT device, and dynamically selects optimal power levels from a discrete action space across successive time slots. Unlike traditional heuristic algorithms, such as Round Robin (RR), Max Single-to-Noise Ratio (Max-SNR), and fixed-power allocation, which rely on static rules or greedy channel-based decisions, the proposed Q-learning agent learns adaptive, long-term optimal policies through direct interaction with the environment, without requiring explicit mathematical modeling of the channel or traffic dynamics. Extensive simulations demonstrate that the proposed framework achieves up to 40% higher average EE compared to all benchmark schemes, maintains consistently lower power consumption, and exhibits superior statistical reliability as evidenced by a right-shifted Cumulative Distribution Function (CDF) of EE. These results demonstrate Q-learning as a promising candidate for scalable, energy-aware power control of next-generation HAPS-assisted IoT deployments in 6G NTN ecosystems. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

This paper presents a system-on-chip field-programmable gate array (SoC FPGA) acceleration architecture for graph-neural-network- and deep-reinforcement-learning (GNN–DRL)-based routing inference in low-Earth-orbit (LEO) satellite networks. Because LEO satellites move at high orbital speeds, the network topology changes continuously, and routing decisions must track the current link state rather than rely only on static rules. GNN-based DRL routing can represent the graph structure of the network when selecting paths, but its message-passing and readout stages are computationally expensive for resource-constrained onboard platforms. To address this limitation, the trained GNN routing model is ported to an SoC FPGA and implemented with a collaborative processing-system (PS) and programmable-logic (PL) architecture. The PS handles candidate-path generation, environment setup, path selection, and network-state updates, whereas the PL executes the computationally dominant message-passing neural network (MPNN) and readout layers. Post-training INT8 quantization, nonlinear-function approximation, vector-level parallelization, and a parallel multiply–accumulate structure are applied to reduce memory pressure and execution time. Experiments on a ZCU104 board using a PYNQ-controlled PS–PL implementation and an NSFNET-based routing environment show that the proposed PS–PL structure reduces the evaluation time from 94.08 s to 12.63 s compared with the PS-only implementation while maintaining an evaluation score close to that of the original model. Full article

Microsatellites operate in highly dynamic thermal environments due to severe physical constraints, making temperature telemetry a critical onboard health indicator. Conventional threshold-based monitoring fails to distinguish normal operational mode transitions from genuine faults, causing excessive false alarms. To address this, we propose a two-stage framework integrating unsupervised thermal mode discovery with mode-specific deep learning prediction. Raw temperature telemetry is downsampled and segmented into orbital cycles. Unsupervised clustering identifies two nominal thermal regimes and four canonical fault-type libraries (step, spike, drift, and noise), each corresponding to distinct in-orbit failure mechanisms. For each nominal mode, a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) is trained on 7-day historical windows to forecast 3-day temperature evolution. Post-downlink, incoming cycle mode is inferred via nearest-neighbor DTW classification; anomalies are flagged when prediction residuals exceed mode-adaptive thresholds. Validation on Macau Science Satellite-1B (MSS-1B, COSPAR 2023-069-B, NORAD 56732) in-orbit telemetry from a 41° inclination low-Earth orbit—where solar illumination dominates external thermal loading and internal heat from the data-communication module and scientific payload constitutes the primary internal thermal source—shows the method reduces anomaly flags by 96.6% and improves prediction mean absolute error by 51.3% compared to a non-classified global baseline under nominal operating conditions, correctly detecting a known operational transient while suppressing spurious alarms. A synthetic fault injection experiment with four anomaly types and five baseline methods further confirms the framework’s detection capability, achieving an overall F1 score of 0.725 vs. 0.258 for the global baseline—a 2.8× improvement driven primarily by a 4× precision gain. Sensitivity analysis reveals that the two-stage advantage is most pronounced for low-magnitude and short-duration faults, where mode-specific context is essential. This work advances microsatellite autonomous health management by providing reliable anomaly detection with quantified fault detection performance. Full article

Satellites become critical to space exploration, global communication, Earth observation, and navigation. There is a growing need for satellite avionics that are highly integrated, reliable, and low-cost, which is essential for mass production and reliable on-orbit operation. This work demonstrates integrated satellite avionics with high reliability and low cost based on a monolithic programmable system-on-chip (SoPC) through highly synergistic hardware–software co-design, with successful on-orbit validation. The system highly integrates satellite management, attitude and orbit control, power management, telecontrol and telecommand (TC&TM), and data storage into a monolithic PolarFire^® SoC (System-on-Chip), and leverages an asymmetric multiprocessing (AMP) architecture. It achieves significant reductions in size, weight, power, and cost (SWaP-C) while ensuring comprehensive functionality and operational reliability. The Jilin-1 Gaofen-05A mission verified the proposed SoPC-based satellite avionics for low Earth orbit (LEO) commercial satellites. Long-term telemetry data confirms its stable operation, with a bus voltage ranging from 11.4 to 12.3 V, an average power consumption of 33.4 W, and a solar array output current of 6.2–6.5 A, all of which meet the design expectations. This work offers a feasible technical approach and engineering reference for commercial integrated satellite avionics featuring high reliability and cost efficiency. Full article

The integration of satellite and terrestrial networks within the same spectrum is a key enabler for extending mobile connectivity in future communication systems. In this context, the Direct Connectivity between Mobile Satellite Service and International Mobile Telecommunications user equipment (DC-MSS-IMT) paradigm, currently under study within the International Telecommunication Union foresees the use of terrestrial IMT frequency bands by satellite systems to directly serve conventional mobile devices. This paper presents an experimental study to assess the coexistence between a terrestrial 5G-NR receiver and a co-channel interfering signal representative of a Low Earth Orbit (LEO) satellite downlink. A controlled laboratory setup in a conducted configuration was implemented to ensure repeatability and accurate control of interference conditions. Measurements were performed over four carrier frequencies representative of IMT bands (763 MHz, 1482 MHz, 2150 MHz, and 2635 MHz), considering different traffic load conditions (100% and 50%) and Doppler shifts associated with satellite motion. The interference impact was evaluated in terms of receiver desensitization, defined as the increase in the total received power relative to the baseline noise level. The results show that a 1 dB desensitization threshold is consistently reached when the interfering signal power is approximately 5–6 dB below the receiver noise floor, corresponding to an interference-to-noise ratio (I/N) of about −6 dB. This behavior is observed across all tested frequency bands, traffic conditions, and Doppler scenarios, indicating limited sensitivity to frequency offsets within the considered range. The findings confirm the validity of commonly adopted coexistence criteria and provide experimentally derived reference values to support ongoing regulatory and technical studies on spectrum sharing between satellite and terrestrial IMT systems. Full article

Low Earth Orbit (LEO)-based positioning, navigation, and timing (PNT) systems have attracted growing interest owing to their strong transmission power and rapid Precise Point Positioning (PPP) convergence. A key challenge in realizing such systems is constructing a pseudo-noise (PN) code family large enough to accommodate hundreds of satellites while maintaining competitive correlation performance. In this study, we propose the Concatenated Weil (C.W.) family, an extended Weil construction that concatenates two Weil sequences whose prime periods sum to the target code length, motivated by the Goldbach conjecture. A two-stage search—auto-correlation screening followed by cross-correlation screening—identifies 608 candidate codes satisfying the correlation thresholds of established modernized Radio Navigation Satellite System (RNSS) families. A subsequent spectral refinement based on the minimum-to-average ratio of Power Spectral Density (PSD) removes codes with deep spectral nulls, yielding a final family of 588 balanced codes. Benchmarking against modernized RNSS PN families demonstrates that the proposed family achieves the largest family size while maintaining comparable correlation performance, thereby providing a viable PN solution for large-scale LEO-PNT constellations. Full article