Search Results (2,516)

Space-to-ground collaborative remote sensing has become a promising mode for global-scale earth observation. However, the open satellite-ground communication link faces severe security threats, especially when unauthorized eavesdroppers deploy gradient or feature inversion attacks based on deep learning to reconstruct sensitive geographic targets. Traditional differential privacy methods apply uniform noise across the entire image space, which inevitably damages the geometric structure of the non-sensitive areas and the performance of downstream tasks. This paper proposes Fed-SMDP, an end-to-end Federated Spatially Modulated Differential Privacy framework for secure satellite-ground collaborative sensing. Specifically, an on-board self-supervised masked autoencoder is deployed to extract robust latent feature representations. To maintain geographical sovereignty, a lightweight spatial modulation mechanism is designed to identify sensitive regions dynamically, where non-uniform noise is injected directly into the corresponding neural activations. Therefore, the transmitted feature representations become visually ambiguous to potential eavesdroppers while preserving essential semantics for downstream applications. At the ground station, a weak-supervised alignment protocol is established to fine-tune task-specific heads using historical labeled datasets, periodically feedback the lightweight model parameter increments to the satellites, and complete the closed-loop federated evolution. The theoretical analysis establishes a rate-distortion-based lower bound on the expected reconstruction distortion of sensitive regions under the considered threat model. Extensive experiments on public datasets demonstrate that Fed-SMDP substantially degrades reconstruction quality under the evaluated generative inversion attacks while maintaining negligible accuracy degradation for global land-cover monitoring tasks. Full article

This work presents a compact, multi-mode reconfigurable filter fabricated in a 65-nm CMOS process for Ku-/Ka multiband applications. The filter employs lumped-element LC series and parallel resonant networks to realize two transmission zeros (TZs) and two poles, with the TZs intrinsically clamped between the poles. Coarse-tuning is achieved by controlling voltage-controlled switches, enabling wide-range relocation of the pole–zero frequencies, while voltage-controlled varactors provide continuous fine-tuning. This pole–zero tracking mechanism allows the filter to suppress high-frequency image signals in Ku-band satellite communication terminals and low-frequency image signals in Ka-band applications. Measurements demonstrate 15-dB stopband fractional bandwidths of 58% and 58.3% in the coarse-low and coarse-high modes, respectively, with maximum attenuations exceeding 33 and 40 dB. Fine-tuning shifts the stopband center frequencies continuously from 14.5–19.7 GHz and 24.1–26.9 GHz, respectively, in close agreement with electromagnetic simulations. The measurement results confirm its suitability for multi-band and tunable image-rejection applications. Full article

In this communication, a balanced Colpitts VCO with low phase noise and a wide tuning range is presented, which was implemented using a 2 μm GaAs BiHEMT process. To improve phase noise, a noise-shifting technique introduced by the cross-coupled pair was applied. A deep-learning-assisted genetic algorithm was employed to optimize the quality factor of an on-chip inductor, further improving the phase noise. Additionally, three-bit switches were utilized to expand the tuning range of this VCO. Finally, a VCO prototype was implemented and fabricated for verification. The measurement results demonstrate that the proposed VCO can achieve a wide tuning range of 33% (2.56–3.57 GHz) and an excellent phase noise of −136.25 dBc/Hz at a 1 MHz offset, validating the efficiency of the adopted circuit techniques and the machine learning-assisted optimization strategy for low-phase-noise design. Consequently, this VCO proves to be a highly competitive candidate for signal sources in S-band satellite communication systems. Full article

Low Earth orbit (LEO) satellite networks exhibit highly dynamic topologies, making it challenging to accurately identify critical inter-satellite links using conventional static graph methods. Existing approaches predominantly rely on delay or betweenness centrality as evaluation criteria and fail to account for temporal connectivity as a fundamental prerequisite for communication. This paper proposes the Evaluation of Temporal-Aware Path Connectivity (eTAPC), a method based on temporal connectivity for identifying critical links in LEO satellite networks. An event-driven slot partitioning strategy is adopted to construct a time-extended graph. Three complementary metrics are proposed, activity frequency, burstiness coefficient, and von Neumann entropy change, to characterize link importance from temporal coverage, fluctuation patterns, and topological bridging. These metrics are fused via the TOPSIS method with a predefined weight vector to produce a comprehensive ranking of link criticality. Experiments are conducted on real TLE data from both dense and sparse LEO constellations to validate the effectiveness of eTAPC. The results show that eTAPC outperforms existing methods on the dense Starlink constellation and achieves competitive performance on the sparse Iridium constellation. Overall, eTAPC provides a lightweight and effective solution for critical link identification in large LEO constellations, with low computational complexity. Full article

To address the urgent need for new satellite constellations for high-speed communication and high-precision orbit determination, this paper designs an intersatellite heterodyne coherent laser mixed-domain integrated communication and ranging system, achieving deep integration of communication and ranging functions. The system combines analog-domain linear equalization with a digital-domain feedback mechanism to establish an integrated processing strategy for clock and data recovery (CDR), along with high-precision ranging. A phase-augmented pseudo-noise ranging dual one-way ranging (PAPR-DOWR) scheme is implemented, which accurately accomplishes intersatellite distance resolution through the synergy between coarse measurements via frame-synchronization matched filtering and fine measurements via an optimized Gardner timing recovery algorithm.Thetest platform of the on-orbit experiment is a medium Earth orbit (MEO) satellite at an altitude of approximately 20,000 km, the two laser terminals under test are separated by approximately 50,000 km, and the Doppler frequency shift is within $\pm 5$ GHz. The experimental results show that at a symbol rate of 1.023 Gsps, the system achieves ranging standard deviations of 4.39 ps (approximately 1.3 mm, $1\sigma$) in a ground-based 4000 s long-term test and 5.69 ps (approximately 1.7 mm, $1\sigma$) in an on-orbit short-term dynamic test, fully demonstrating millimeter-level ranging performance under long-distance, low-SNR intersatellite conditions. Full article

The Long-Range Wide-Area Network (LoRaWAN) is one of the leading low-power wide-area network specifications owing to its capabilities for long-range communication and energy savings. For large-scale sensing applications by a large number of LoRa nodes, it is important to improve communication performance and energy saving. However, redundant sensing and transmissions consume node energy, while simultaneous transmissions, particularly from hidden nodes, cause packet collisions. Centralized optimization of these problems requires the collection of network-wide information and may impose substantial communication overhead due to its narrow communication bandwidth. In this paper, we propose a distributed method for jointly controlling sensing coverage, node energy consumption, and transmission timing using locally exchanged information. Our main idea is to learn from the swarm intelligence of organisms that perform efficient reproductive behavior. The proposed method extends a previously developed mathematical model that reproduced the chorus and satellite behavior observed in three Japanese tree frogs. Whereas the original model describes the satellite behavior of a frog relative to a nearby caller, the proposed method generalizes this interaction to multiple wireless nodes associated with the same sensing target. By embedding target-point and node-state information in transmitted packets, each node identifies the kth-ranked node associated with the target and autonomously determines whether to remain active or enter a low-power satellite state. This mechanism regulates the time- and target-averaged number of active sensing nodes toward k without collecting global node-distribution information. We further introduce an in-phase-flag mechanism that modifies node-specific phase interactions to suppress persistent packet collisions between hidden nodes located two hops apart. Simulation results show that the proposed method reduces transmission energy consumption by 65% for average 1-coverage and by 46% for average 2-coverage compared with the method without satellite-state control. In the collision evaluation, the two-hop packet collision rate was 9.36% without phase control and 4.49% with the basic phase-control mechanism. By additionally applying the in-phase-flag-based hidden-node collision-control mechanism, the two-hop collision rate was further reduced to 3.51%, while maintaining a low one-hop collision rate. These results demonstrate that the proposed extension of the frog-behavior model can jointly regulate sensing redundancy and suppress data collisions through distributed local interactions. Full article

Volcanic eruptions represent a significant natural forcing mechanism within the Earth’s climate system, capable of inducing profound atmospheric perturbations. Major explosive events inject vast quantities of volcanic ash and trace gases into the atmosphere, among which sulfur dioxide (${\mathrm{SO}}_2$) has the biggest influence on climate variability. Once injected into the atmosphere, the ${\mathrm{SO}}_2$ oxidizes to form sulphate aerosols which can produce negative radiative forcing and cool the Earth’s surface. At the same time, fine ash particles block sunlight and create a “dust veil” that further changes atmospheric temperatures. These complex cooling and warming effects highlight the need for accurate characterization of volcanic emissions. In this context, satellite remote sensing provides continuous global observations, making it an essential tool for monitoring volcanic emissions and assessing their long-term climatic impacts. This review provides a comprehensive synthesis of multi-sensor satellite observations to trace emissions from major volcanic eruptions. To highlight the potential of modern satellite observations to characterize volcanic emissions, we exploit data spanning the entire electromagnetic spectrum, from the ultraviolet (UV) to thermal infrared (TIR). We examine three key eruptions from the past decade representing this new era of spaceborne monitoring: Raikoke (Russia, 2019), Kīlauea (USA, 2025), and Hayli Gubbi (Ethiopia, 2025). These case studies demonstrate how contemporary satellite instruments deliver reliable data worldwide, which is crucial for both the scientific community and society in assessing the climatic impacts of volcanism. Full article

Physics-based fast radiative transfer models (RTMs), such as the Community Radiative Transfer Model (CRTM), face increasing computational demands from growing satellite data volumes and model complexity. We developed a physics-informed Liquid Neural Network emulator for CRTM (CRTM-LNN) that draws on continuous dynamical-system concepts while retaining physical interpretability. The framework combines an Ordinary Differential Equation (ODE)-inspired Liquid Neural Network for layer-by-layer optical-depth modeling with an analytic, differentiable radiative-transfer solver. This design preserves the explicit optical-depth-to-radiance pathway and incorporates physical constraints directly into the forward calculation. In the implementation evaluated in this study, the LNN uses discrete hidden-state updates on the fixed European Centre for Medium-Range Weather 91-layer (ECMWF91L) grid under clear-sky, absorption-only assumptions. Evaluation using Infrared Atmospheric Sounding Interferometer (IASI) observations and ECMWF91L forecast profiles shows that CRTM–LNN closely reproduces the reference CRTM simulations, with global, channel-mean brightness-temperature biases below 0.1 K in magnitude. Regional bias magnitudes nevertheless reach approximately 0.2–0.4 K for selected channels and latitude bands. Correlations with CRTM exceed 0.97 for cumulative optical depth, transmittance, and weighting functions in regimes with cumulative optical depth below five. It accelerates forward calculations by up to 18-fold and efficiently generates Jacobians through automatic differentiation. Compared with a conventional multilayer perceptron CRTM emulator, CRTM-LNN produces smoother and more physically consistent Jacobians, with improved vertical localization and fewer spurious oscillations. Absolute centroid-pressure errors for temperature Jacobians are reduced across all evaluated channels, ranging from 1.77 to 5.01 hPa for CRTM-LNN versus 5.88–8.17 hPa for CRTM–MLP, although the magnitude of improvement varies by channel. These results highlight the potential of the integrated CRTM–LNN framework, which combines ODE-driven neural emulation with physics-based radiative-transfer modeling, to provide a scalable foundation for atmospheric retrievals, satellite data assimilation, and near-real-time radiative-transfer applications. Current limitations and opportunities for extension to more complex radiative processes are also discussed. Full article

The regeneration of brownfield sites in Belgrade represents an important component of sustainable urban development. Over the past two decades, Belgrade has experienced extensive redevelopment of brownfield sites, although these transformations have largely taken place without a coherent strategic framework for integrating the provision of public amenities with the needs and priorities of local communities. This study evaluates the regeneration potential of 23 brownfield sites representing different activation statuses using a multi-criteria assessment framework based on the Analytic Hierarchy Process (AHP). As a case study, the proposed framework has practical applicability and can support decision-makers in the planning and activation of future brownfield sites by incorporating lessons learned from previous redevelopment processes. The ultimate contribution of this study therefore lies in bridging methodological assessment and practical decision-making, thereby supporting more sustainable, evidence-based, and context-sensitive brownfield regeneration. Thirteen criteria were grouped into three thematic categories: environmental safety; infrastructure development and spatial capacity; and quality of life and social well-being. The analysis integrated data from strategic planning documents, spatial datasets, satellite imagery, and field verification, while Geographic Information Systems (GIS) were used to process, spatially integrate, and visualize the results. The proposed framework integrates environmental, spatial, infrastructural, and social considerations to provide a systematic basis for assessing and prioritizing brownfield regeneration. The results indicate that brownfield activation in Belgrade has frequently occurred without sufficient alignment with a clearly defined urban development strategy. Brownfield sites have most commonly been converted into residential areas and shopping centers, while insufficient consideration has been given to the provision of green spaces, tourism-related uses, and potential environmental pressures. Sites located in the municipalities of Savski Venac and Rakovica demonstrate the greatest potential for the integration of green infrastructure. Based on the assessment results, three brownfield typologies were identified: sites suitable for ecological restoration and green corridor development; sites suitable for mixed-use regeneration incorporating public facilities; and sites requiring priority remediation before redevelopment. The findings provide practical guidance for environmental protection, urban safety, and multifunctional regeneration of post-industrial areas, while demonstrating the potential applicability of the proposed methodology to other urban areas facing similar brownfield regeneration challenges. Full article

With the increasing number of lunar missions, utilizing satellites in special orbits in the cislunar space as relay satellites to provide communication relay and navigation services for spacecraft near the Moon has become a hot topic. DRO (Distant Retrograde Orbit) and NRHO (Near-Rectilinear Halo Orbit) are widely used in lunar and deep space exploration missions, offering unique orbital advantages. With the development of a global constellation of LEO satellites, this paper utilizes intersatellite link measurements between LEO satellites and cislunar probes to determine orbital parameters. Under the adopted measurement-noise-only assumptions, Ka-band inter-satellite ranging yielded 3D position errors of 32.074 m for the DRO spacecraft and 20.665 m for the NRHO spacecraft. When laser ISL measurement accuracy reaches 10 times that of Ka-band measurements, simulations show positional accuracies of 2.206 m for NRHO satellite and 3.207 m for DRO satellite, respectively, representing an improvement of approximately 90%. The feasibility of orbit determination for cislunar spacecrafts based on intersatellite link measurements between LEO satellite constellation and cislunar spacecrafts has been verified through simulations. In the future, LEO satellite constellations could play a greater role in cislunar space exploration and even deep space exploration. Full article

This paper proposes a circularly polarized (CP) antenna array featuring high aperture efficiency, a low in-band radar cross section (RCS), and a low profile. First, two different patch antenna elements are designed, which exhibit desirable CP performance and a 180° reflection phase difference under x-polarized normal incidence within the same operation band. These elements are then arranged in a chessboard configuration to simultaneously achieve high aperture efficiency and in-band RCS reduction under x-polarized normal incidence. Subsequently, the parasitic units are uniformly embedded into the array elements, which are employed to realize reflection phase cancellation with the array elements under y-polarization normal incidence. The simulated results show that the proposed array achieves a peak monostatic RCS reduction of 12.8 dB, and the 6 dB RCS reduction bandwidth fully covers the antenna’s operating bandwidth under both x- and y-polarized normal incidences. Moreover, the gain of the antenna array reaches 18.2 dBic at 3 GHz with an aperture efficiency of 58% and a profile of 0.03λ. A good agreement is obtained between the simulated and measured results. This design successfully combines the advantages of high aperture efficiency, dual-polarized in-band RCS reduction, and a low profile, making it highly valuable for aerospace satellite communications and unmanned aerial vehicle data links. Full article

With the widespread application of Global Navigation Satellite Systems (GNSS) in critical industries such as transportation, aviation, maritime, communications, and finance, the increasing incidence of GNSS interference has become a global threat to the security of Positioning, Navigation, and Timing (PNT) services. Constrained by limited coverage and deployment density, traditional ground-based interference monitoring systems cannot provide continuous global-scale monitoring capability. Low Earth Orbit (LEO) satellite constellations offer the advantages of global coverage, wide-area monitoring capability, and continuous monitoring, providing a new solution for global GNSS interference monitoring. This paper provides a comprehensive review of the development history, key methods, system architectures, and future challenges of GNSS interference monitoring technology using LEO satellite constellations. First, the paper traces the evolution of space-based GNSS interference monitoring from scientific exploration and technical validation to commercial operation. Second, it presents interference detection technologies based on the observation-domain, frequency-domain, and correlation-domain methods, with increasing integration of artificial intelligence (AI)-based approaches. Furthermore, it analyzes interference source localization methods based on time-difference-of-arrival (TDOA), frequency-difference-of-arrival (FDOA), Doppler-based localization, GNSS radio occultation (GNSS-RO), and GNSS reflectometry (GNSS-R). Finally, the paper proposes a space-based GNSS interference monitoring architecture, comprising the space sensing layer, intelligent processing layer, and application service layer. It also analyzes key technical challenges and future development directions, providing support for the construction of an intelligent space-based monitoring system for global PNT security assurance. Full article

This study develops a techno-economic sizing and energy-management framework based on the Hippopotamus Optimization Algorithm (HOA) for hydrogen-integrated autonomous Hybrid Renewable Energy Systems (HRES) for rural electrification. A representative remote community in Tabuk, Saudi Arabia, is investigated using 11 years of NASA POWER satellite-derived meteorological data. The modelled community is constructed from a synthesised connected-load inventory representing approximately 600 households and 3000 residents; accordingly, the results represent a simulation-based planning case study rather than a validated design for a specific settlement. Seven configurations combining photovoltaic generation, wind turbines, battery storage, hydrogen production and storage, fuel cells, and diesel generation are evaluated considering Total Net Present Cost, CO₂ emissions, and Loss of Power Supply Probability (LPSP), with a Demand Response Management System (DRMS) incorporated into the framework. The three objectives are combined using a weighted-sum scalar formulation, complemented by a hard-constrained formulation for reliability. HOA is benchmarked against Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Walrus Optimizer (WO), and Osprey Optimization Algorithm (OOA) under a common budget of 10,000 objective-function evaluations per run and 10 independent runs. Under the constrained formulation, six of the seven configurations satisfy LPSP ≤ 5% within the investigated sizing bounds, with costs of energy (COE) ranging from $0.1046/kWh for PV/wind/battery to $0.1357/kWh for wind/battery/diesel; the fully renewable PV/wind/hydrogen configuration is feasible at $0.1195/kWh. Only the wind-free configuration fails to satisfy both imposed constraints because of the 30% diesel-energy limit rather than reliability. Evaluation over the eleven individual meteorological years shows that all designs violate the 5% reliability criterion in every year, reaching 2.0–2.9 times the design LPSP because hour-of-year averaging removes prolonged low-resource periods. Re-optimization against the worst observed year increases COE by 33–63% and storage capacity by factors of three to five, with hydrogen storage in the fully renewable configuration increasing from 10 to 75.4 kg. Sensitivity analysis identifies wind availability as the dominant economic parameter, with a 20% wind-speed reduction increasing COE by 36.1%. The DRMS reduces the peak-to-average ratio by 20.0% for the assumed evening-peaking profile, whereas no reduction is obtained for an afternoon-peaking profile consistent with measured Saudi residential demand. These findings demonstrate that meteorological and demand-profile representation materially affects autonomous HRES sizing and should be explicitly considered when interpreting techno-economic optimization results. Full article

Precise orbit information is generally unavailable to users of noncooperative low Earth orbit (LEO) communication satellites such as Iridium NEXT, making publicly available two-line element (TLE) data the primary source of orbital states. However, TLE-derived orbital-state errors degrade Doppler positioning performance. This paper proposes an adaptively weighted multi-station Doppler orbit determination method. Using TLE-derived states as the orbit prior, the method formulates an orbital-dynamics-constrained batch least-squares (BLS) model. A priori weights are constructed from satellite elevation angles, and multi-station, multi-epoch Doppler measurements are adaptively reweighted by minimizing the uncertainty of the estimated satellite velocity projected onto the line of sight (LOS) from the ground station network centroid to the satellite. A Levenberg–Marquardt (LM)-type damping mechanism with diagonal scaling constrains the state correction vector to improve iterative stability. Experiments using real measurements show that the estimated orbital states yield a user-station two-dimensional root-mean-square error (2D RMSE) of 41.30 m, which is 16.06% lower than that obtained with the a priori weighted solution, 24.64% lower than that obtained with the equally weighted solution, and 64.25% lower than that obtained with the TLE-derived orbit. These results indicate that the proposed method improves the utility of the estimated orbital states for positioning with Iridium NEXT signals of opportunity. Full article

In addition to its contractile role, skeletal muscle has important secretory functions and communicates with local and distant tissues through myokines, a heterogeneous group of cytokines, chemokines, growth factors, peptides, and extracellular-matrix-associated proteins released by muscle cells. Their production is regulated by contraction, mechanical loading, energy availability, hypoxia, inflammation, injury, and mitochondrial or metabolic stress. Through autocrine, paracrine, and endocrine mechanisms, myokines influence satellite-cell activity, myogenesis, protein turnover, muscle mass, metabolism, mitochondrial function, angiogenesis, immune-cell recruitment, extracellular-matrix remodeling, and inter-organ communication. This review provides a structured overview of current knowledge on the production, regulation, and biological actions of approximately 25 major myokines, with particular emphasis on the strength of evidence supporting their classification as bona fide muscle-derived factors. We distinguish local muscle signaling from changes in circulating concentrations of uncertain tissue origin and highlight the context-dependent nature of myokine actions. The same mediator may support adaptation and repair when transiently and locally produced but contribute to inflammation, metabolic dysfunction, fibrosis, or muscle wasting when signaling is excessive, prolonged, or disease-associated. Defining cellular sources, temporal regulation, receptor availability, and interactions within the muscle secretome will be essential for clarifying physiological relevance and translating myokine biology into biomarkers and therapeutic strategies. Full article