Low Earth orbit (LEO) satellite systems are becoming an increasingly important component of global communications infrastructure, providing broadband access, enterprise connectivity, and direct-to-device services in competition with terrestrial networks. At the same time, orbital space is a congestible shared resource: satellite deployments increase conjunction risk and debris, imposing external costs on other operators. This paper analyses how these features interact by modelling LEO satellite broadband as a capacity-constrained oligopoly operating under an orbital congestion externality. We develop a two-stage model in which satellite operators first choose constellation size and research and development (R&D) investment, and subsequently compete in quantities subject to binding capacity constraints. Orbital congestion damages depend on aggregate satellite deployment, while operators are privately exposed to only a fraction of the resulting congestion risk. Three results emerge. First, oligopolistic competition and incomplete congestion internalisation generate distinct distortions: output and innovation are inefficiently low due to market power, while satellite deployment is excessive when firms do not face the full marginal social cost of congestion. Second, R&D interacts non-trivially with congestion risk through its effect on throughput per satellite, creating substitution between “more satellites” and “smarter satellites.” Third, regulatory instruments such as Pigouvian satellite charges or tradable conjunction-risk permits can correct deployment incentives but do not eliminate distortions arising from imperfect competition. These results highlight that congestion pricing and competition policy operate as complementary instruments in the governance of emerging satellite broadband markets, with implications for spectrum policy, launch regulation, and the management of shared orbital resources. • LEO satellite broadband is modelled as a capacity-constrained oligopoly. • Orbital congestion creates external costs that operators only partially internalise. • Market power can lead to excessive satellite deployment despite output constraints. • R&D incentives may amplify congestion and overinvestment in constellations. • Second-best regulation complicates Pigouvian taxes and tradable permits.

The integration of Low Earth Orbit (LEO) satellites into Position, Navigation, and Timing (PNT) frameworks offers an opportunity to enhance and complement Global Navigation Satellite Systems (GNSSs). In particular, this study focuses on employing Doppler-based positioning techniques. An innovative reduced-parameter Doppler positioning algorithm is proposed, and it is compared to the conventional linearized least-squares approach. The findings show that the proposed algorithm leads to better Dilution of Precision (DOP) and improved accuracy. Both algorithms have been tested using simulated data within a hardware-in-the-loop framework, incorporating measurements from both GNSS and LEO satellites. The study highlights a key limitation of GNSS Doppler positioning when used alone: Medium Earth Orbit (MEO) satellites, which are typical of GNSS, move relatively slowly, thereby affecting the Doppler effect and consequently reducing positioning performance. The introduction of faster-moving satellites such as those in LEO leads to enhanced Doppler-based positioning accuracy. The results underscore the substantial impact of LEO satellites in augmenting GNSS capabilities, with error reduced to approximately ten meters. This integration significantly boosts positioning performance, offering users a more reliable and precise navigation solution.

Low Earth orbit (LEO) satellite systems provide ubiquitous global connectivity for massive grant-free random access Internet of Things (IoT) applications. Full frequency reuse (FFR) improves spectrum efficiency in spectrum sharing scenarios but introduces severe adjacent beam and cross-system co-channel interference. Meanwhile, the high mobility of LEO satellites hinders accurate instantaneous channel state information (iCSI) acquisition, and random direction-of-arrival (DOA) estimation errors cause statistical CSI (sCSI) mismatch, which degrades beamforming performance and makes it difficult to balance transmission robustness, user fairness, and onboard computational complexity. To address these issues, we propose a low-complexity Hybrid Optimized Robust Beamforming (HORBA) algorithm. We first construct a robust joint optimization model to characterize the coupling effects of DOA errors, outdated CSI, and multi-dimensional interference, with constraints on per-user minimum SINR and cross-system interference temperature. Then, based on the block coordinate descent framework, we decouple the original non-convex problem into two convex subproblems, which are solved via generalized eigenvalue decomposition and first-order Taylor expansion, combined with an adaptive sampling mechanism that balances accuracy and complexity. Simulation results verify that our algorithm outperforms typical benchmarks in sum rate and robustness, maintains low onboard processing complexity, and effectively alleviates edge user rate polarization.

Wildfires are becoming increasingly frequent, with potentially devastating consequences, including loss of life, infrastructure destruction, and severe environmental damage. Low-Earth-orbit satellites equipped with onboard sensors can capture critical information related to active wildfires and enable near-real-time detection through machine learning algorithms applied to the acquired data. We propose a framework that automates the complete wildfire detection and satellite scheduling pipeline, entitled the WildFire-applicable Intelligent and Responsive Ensemble for Detection and Scheduling (WildFIRE-DS). This paper develops an algorithm to realize the vision of the WildFIRE-DS as a proof of concept, integrating three key components: wildfire detection in satellite imagery, statistical updating that incorporates data from repeated flyovers, and multisatellite scheduling optimization. The algorithm enables wildfire detection using convolutional neural networks with sensor fusion techniques, incorporates subsequent flyover information via Bayesian statistics, and schedules a constellation of satellites using the state-of-the-art Reconfigurable Earth Observation Satellite Scheduling Problem. Simulated experiments conducted using real-world wildfire locations and the orbits of operational Earth observation satellites demonstrate that this autonomous detection and scheduling approach effectively enhances wildfire monitoring capabilities.

The growth in the number of satellites in Low Earth Orbit, coupled with the possibility of their catastrophic disruption, may lead to more orbital debris, which in turn has increased the risk of damage to spacecraft arising from impacts by small pieces of debris. There is thus an urgent need to monitor the small particle population in Low Earth Orbit, using a new generation of dust detectors. Various designs are in preparation, and several use the principle of observing particles via their impact penetration of thin films. Previously, most laboratory studies of penetration of thin films have used spherical impactors for ease. However, these are not representative of the shapes of orbital debris. Accordingly, here, impacts are reported at 5 km s -1 , by various shaped projectiles (sizes typically 0.5 – 2 mm) on thin (12.5 μm thick) Kapton films. The shapes used were spheres, rods, cubes and platelets, and represent a selection of the shapes present in the orbital debris population that arises from catastrophic disruption of spacecraft. The size and shape of the holes in the Kapton arising from the impacts, are shown to reflect the size and cross-sectional area of an impactor as it passes through the film; even the presence of angular corners in the impactors can be seen in the holes. However, due to the variable aspect of an individual impactor presented to the film during an impact, identification of the exact 3-dimensional shape cannot be obtained from the 2-dimensional hole. Nevertheless, with minor exceptions it is possible to separate more spherical (i.e., natural dust) impactors from the other shapes (i.e. variously shaped anthropogenic debris). • Cosmic dust impact detectors which use thin films are sensitive to impactor shape • It is shown that the angles at corners of impactors are preserved in the hole shape • In general, the impact hole shape in a thin film retains the impactor aspect at impact • But since the aspect at impact is unknown, the full 3D impactor shape cannot be found

Satellite Communication (SatCom) offers internet connectivity where traditional infrastructures are too expensive to deploy. When using satellites in a geostationary orbit, the distance from Earth forces a round-trip time of at least 550 ms. Coupled with the constrained capacity of the physical link, this challenges the traditional internet access quality we are used to. In this paper, we present a complete passive characterization of the traffic carried by an operational SatCom provider. With this unique vantage point, we observe the performance of the SatCom technology, as well as the usage habits of subscribers in different countries in Europe and Africa. We highlight the implications of such technology on Internet usage and functioning, and we pinpoint technical challenges due to the CDN and DNS resolution issues, while discussing possible optimizations that the ISP could implement to improve the service offered to SatCom subscribers. We complete the characterization of the adoption and performance of newer protocols with a focus on IPv6 and QUIC.

Thermal constraint assessment method for mission verification of sun-synchronous orbit satellites

Hybrid integration of Low Earth Orbit (LEO) satellite systems with terrestrial networks is a critical facilitator for beyond-5G connectivity, especially in situations where terrestrial coverage is constrained by cost or feasibility. At Ka-band frequencies, link performance is extremely susceptible to propagation impairments, including rain attenuation and atmospheric absorption, as well as terrestrial interference resulting from coexistence with ground-based networks. This study offers a simulation-driven, system-level analysis of hybrid LEO-terrestrial downlink performance. A Monte Carlo framework integrates established propagation models, free-space path loss, ITU-R P.676 atmospheric absorption, ITU-R P.838 rain attenuation, and Rician small-scale fading with a distance-dependent, spatially distributed terrestrial interference formulation. The emphasis is on identifying performance regimes and reliability limitations resulting from the cumulative effects of environmental and interference factors, rather than suggesting new channel models. The study measures received power, SNR, SINR, outage probability, bit error rate, and spectral efficiency at different satellite altitudes, carrier frequencies, rainfall intensities, and levels of interference. The results show that the satellite signal weakens with increasing slant distance, causing a transition from noise-limited to interference-limited operation, and demonstrate how rain attenuation degrades Ka-band link reliability. This work further illustrates that sustaining link reliability under adverse conditions requires adaptive adjustment of performance thresholds. The simulated SNR and capacity values for clear-sky conditions are verified against published Ka-band link budget benchmarks and ITU technical data. The results offer system-level perspectives for the design and planning of hybrid satellite-terrestrial communication systems.

Robust state-domain quality monitoring of precise satellite orbit and clock corrections considering reference receiver faults

Low Earth Orbit (LEO) satellites are characterized by high received signal power and high signal propagation rate, and their pseudorange accuracy far exceeds traditional navigation constellations. However, the current LEO satellite's beam coverage is small, resulting in a single UAV only able to receive 1-2 satellite beams, rendering positioning impossible. To address this issue, this paper proposes a cooperative positioning method for UAV clusters. This method uses the ranging information between UAV (Unmanned Aerial Vehicle) clusters for pseudorange shifting. Considering the asynchronous information from UAV's LEO satellite navigation, inertial navigation, and ADS-B navigation, an information geometric probability model is constructed to unify the navigation information parameter format. Combined with factor graph theory, a collaborative positioning fusion framework is built to achieve rapid positioning of UAV clusters. Positioning tests are performed using China's test satellites, and compared with existing cooperative positioning methods. The results show that the method proposed in this paper has improved positioning accuracy and ability to suppress drastic changes.

HighlightsWhat are the main findings?Integration of Galileo observation significantly improves PPP-B2b performance, increasing satellite availability and reducing DOP values.GPS/BDS-3/Galileo PPP shortens convergence time by approximately 13–17% horizontally and 18–20% vertically compared with the GPS/BDS-3 solution based on the PPP-B2b service and broadcast ephemeris (68%).What are the implications of the main findings?Galileo effectively enhances satellite geometry and serves as a robust complement to the regional PPP-B2b service.The integration improves positioning robustness and availability, especially in challenging environments with limited satellite visibility.The BeiDou-3 (BDS-3) Precise Point Positioning service (PPP-B2b) can realize decimeter-level positioning by broadcasting satellite orbit, clock offset, and code bias corrections via GEO satellites, enabling PPP without reliance on ground communication networks. However, the current PPP-B2b service only provides corrections for BDS-3 and GPS satellites, which limits the number of available satellites and may affect positioning performance in challenging environments. To further enhance the positioning performance, we propose to incorporate Galileo observation into the PPP-B2b positioning. A PPP model integrating PPP-B2b service and broadcast ephemeris was established. First, the accuracy of the Galileo broadcast ephemeris was evaluated using precise orbit and clock products as references. The results show that the mean signal-in-space range error (SISRE) standard deviation of Galileo broadcast ephemeris is 0.30, which is only a little worse than that of GPS from PPP-B2b service. Then, the positioning experiments were conducted under different elevation cutoff angles. The experiments were conducted using data from 94 reference stations in China over a 7-day period. The results demonstrate that the inclusion of Galileo satellites significantly increases the number of visible satellites and improves satellite geometry. Compared with the BDS-3/GPS dual-system PPP solution, the BDS-3/GPS/Galileo triple-system PPP solution reduces the horizontal convergence time by approximately 13.70–16.67% and the vertical convergence time by about 18.75–20.00% under cutoff angles from 7° to 30° based on the 68th percentile statistics. The 95th percentile results further confirm the advantage of the triple-system solution under a more stringent statistical criterion. Where convergence is achieved, the triple-system solution reduces the horizontal convergence time by approximately 6.0–7.3% and the vertical convergence time by about 15.3–26.0%. Moreover, the triple-system solution exhibits a smaller re-convergence jump under abnormal observation conditions. In addition, under high elevation cutoff conditions, the introduction of Galileo satellites effectively improves PPP availability, thereby enhancing the continuity and robustness of PPP. These results indicate that incorporating Galileo observation within the PPP-B2b framework can effectively improve PPP performance and provide a simple and practical approach for high-precision real-time positioning.

Multi-temporal orbital satellite imagery is an alternative for measuring behavioral patterns or trends in different physiognomies through vegetation indices (VIs) and Spectral Linear Mixture Models (SLMMs). In this study, time series of Landsat 7/8/9 and Sentinel-2 have been used to classify a considerable quantity of areas spread over the São Paulo state from 2021 to 2024. Because the large amount of samples considered in our analysis, self-organizing maps (SOMs) have been applied as a convenient method to group similar satellite image time series samples with respect to a certain vegetation index or green vegetation fraction (VEG). Since every dataset area belongs to different types of physiognomies, each cluster has been labeled according to the plurality technique. Additionally, we obtained the mean spectral behavior of the VIs and VEG in the 2021–2024 seasonal cycle of all samples. The results showed similar variations from the rainy to the dry season for most of the physiognomies. On the other hand, this research indicates that the proposed method for classification the Brazilian areas spread over the São Paulo state is consistently good, obtaining the best performance (quantization error) associated with Normalized Difference Vegetation Index (NDVI) time series samples.

The Internet of Things (IoT) supported by satellites is becoming indispensable for remote sensing in the forthcoming sixth-generation (6G) communication network. However, it is tempting and easy for unauthorized users to exploit the Direct Sequence Spread Spectrum (DSSS) technique to quietly complete their own transmissions due to the open nature of propagation and the publicly available satellite orbits and frequencies. Therefore, it is necessary to conduct a blind estimation of the DSSS signal to take a more proactive approach to protect satellites against illegal use. However, the modulation information is unknown, and the spreading code structure varies, making the blind estimation of spreading codes a significant challenge. Additionally, under data modulation, the dimensionality of the received spread spectrum sequence increases, greatly raising the complexity of spreading code estimation. Against this background, we propose a hybrid learning-based blind estimation algorithm for spreading codes, which combines K-means clustering and Convolutional Neural Networks (CNN). This algorithm achieves low-complexity blind estimation of spreading codes with unknown modulation information and low signal-to-noise ratio. Specifically, the K-means clustering algorithm uncouples the data modulation from the spreading code, reducing the dimensionality of the estimation process. On this basis, the CNN-based parallel convolution architecture is employed to achieve low-complexity and accurate estimation of the spreading code. Simulation results demonstrate that our proposed algorithm outperforms existing algorithms in both computational complexity and estimation performance.

Modern networks of Low Earth Orbit (LEO) satellites promise to offer low-latency connectivity between any pair of devices on Earth. However, as the length of inter-satellite links constantly changes, the lowest-delay paths between satellites change over time even if the network topology does not. In this paper, we characterize the lowest-delay paths in LEO satellite networks whose topologies are lattice graphs. We show how the shape of these paths can be determined by knowing the satellites’ orbits, their movement direction and the topological positions of source and destination satellites. Our characterization has the potential to inspire several practical applications, including fast, resource-efficient routing algorithms and protocols.

ABSTRACT Satellite communication fulfills the demand of global connectivity for next‐generation networks. Free space optical (FSO) communication connects these high‐bandwidth global links for seamless transmission. High altitude platform station (HAPS) improves the outage probability by relaying the signal when transmission to the long distances in one hop is not reliable. In this work, we consider adaptive beam divergence (ABD) technique for a low earth orbit (LEO) satellite, which transmits the FSO signal to the HAPS, where the divergence angle of the signal is selected based on the altitude of the HAPS. We considered the effects of threshold signal‐to‐noise ratio (SNR), receiver aperture diameter, jitter standard deviation, optical‐to‐electrical conversion efficiency and the horizontal distance between satellite and HAPS on the link outage probability. The results show that by using an appropriate beam divergence angle according to the height of the HAPS, the performance of the system improves. Additionally, it is observed that for a fixed outage probability, ABD achieves communication for longer transmission range with the HAPS located at an altitude, which is lower in comparison with the altitudes at lower and upper fixed divergence angles by 1.610 and 2.169 km, respectively.

Orbit maintenance strategy integrated with power management for ultra low Earth orbit satellites

FPGA-based doppler frequency offset simulation method for low earth orbit satellite communication systems

Abstract. Geostationary satellites allow continuous sub-hourly monitoring of the Earth including land surfaces and aerosols, which can now benefit from the advanced measuring performance of the new Meteosat Third Generation-Imager and its Flexible Combined Imager on board (FCI). In this study, we aim to improve our understanding of the impact of the Earth's sphericity on geostationary observations. Although sphericity effects in satellite data have been studied for many years, the curvature of our planet is still not accounted for in many operational radiative transfer-based retrieval algorithms due to the required increase in processing time, and therefore a plane-parallel atmosphere-surface system is assumed instead. While the limitations of this approximation have been widely assessed in the case of low Earth orbit satellites, they must be reevaluated with regard to geostationary satellites, which have a broader range of observing and illumination geometries. Furthermore, we currently lack precise benchmarking of the errors caused by neglecting the Earth's sphericity in the case of land surface and aerosol applications, which show significant differences with respect to the commonly considered ocean color applications. For example, surface/aerosol algorithms use instrument channels in the red and near-infrared spectral ranges where there is a growing impact of molecular absorption compared to the ocean color-sensitive blue channels where Rayleigh scattering predominates. In this context, we perform quantitative analyses of the impact of ignoring the Earth's curvature on FCI-like top-of-atmosphere reflectance calculations using the accurate Monte Carlo radiative transfer code SMART-G. Results enable quantification of important biases introduced by the plane-parallel assumption, with a strong dependency on the sun-satellite acquisition geometry and, to a lesser extent, the measurement wavelength. Significant dependencies on surface and aerosol properties are also identified, but only under extreme solar and viewing conditions. We also find that 36 % of FCI observations are significantly affected by sphericity effects, in particular in the channels centered at short visible wavelengths (i.e., 444 and 510 nm for FCI). Based on these results, this study makes recommendations, that can be found in the Discussion section of this paper, on the development of methods to account for sphericity effects in the inversion of geostationary data so that one can correct the outcome of plane-parallel radiative transfer codes for near-real-time operational applications.

The operation of low-earth orbit satellites, Antarctic research stations, and certain extreme cold environments demands energy storage devices (ESDs) capable of functioning at -100°C or lower. Conventional electrolytes are limited by sluggish ion transport and unstable electrode-electrolyte interphases at low temperatures, severely degrading the performance of electrochemical ESDs under extreme cold. Herein, we report a weak-aggregation (AGG-w) electrolyte reconciling bulk-phase ion transport with interfacial kinetics at ultra-low temperatures (ULT). This is achieved through the strategic incorporation of unilaterally fluorinated motif as strong electron-withdrawing group, which enhances steric hindrance and reconfigure molecular dipole to reinforce dipole-dipole interaction with the solvents anchored in the primary solvation shell. Such restructuring enables unprecedented solvent-anion cooperativity by weakening Li+-dipole interaction and promoting greater anion participation, thereby accelerating desolvation kinetics, reducing interfacial resistance, and simultaneously preserving low viscosity and high ionic conductivity at ULT. Notably, 1100 F real pouch cells with AGG-w electrolyte maintain 97.9% capacity retention after 7 months of continuous operation at -40°C and demonstrate emerging discharge capability at -100°C, a milestone never previously reported. This work underscores weak-interaction engineering as a critical paradigm for electrolyte design and establishes a generalizable strategy for high-performance electrochemistry in extreme conditions.

Low-Earth orbit satellites are gradually becoming the core infrastructure of integrated aerospace communication networks, with their significant advantages of high communication rates, small transmission delay, and wide coverage. Interference with military communications in response to their security and protection needs is a current research challenge. Consequently, this paper introduces an interference technique optimized for low-Earth orbit satellite signals using a multimodal learning transformer model (OI-MLT). The proposed method incorporates symmetry-aware design by exploiting the inherent time–frequency structural characteristics of LEO satellite signals and the spatially distributed topology of interference sources. An optimized model for distributed interference sources is developed, and multimodal information of spectra and numerical values is processed in parallel through the self-attention mechanism. This approach effectively addresses the problem of dynamic matching between the interference signal and target signal in high-speed LEO scenarios, as well as high-precision interference synchronization under time-varying channels. Experimental results demonstrate that this technique enhances the precision of frequency tracking, reduces the time required for synchronization establishment, and improves the interference success rate by 27.52% on average compared with existing methods.