Search Results (285)

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

Ensuring the reliability and robustness of spacecraft systems remains a key challenge, particularly given the limited feasibility of continuous real-time monitoring during on-orbit operations. In the domain of Fault Detection, Isolation, and Recovery (FDIR), no universal strategy has yet emerged. Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. A high-fidelity functional engineering simulator is employed to generate realistic datasets from typical onboard signals, including sensor and actuator outputs. Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. Comparative analysis across multiple failure conditions highlights the strengths and limitations of the proposed strategies. Results indicate that Machine Learning techniques are best applied not as replacements for classical methods, but as complementary tools that enhance robustness through higher-level self-diagnostic capabilities. This synergy enables more autonomous and reliable fault management in spacecraft systems. Full article

The southwestern mountainous region of China (SMRC), characterized by complex geological environments, experiences frequent landslide disasters that pose significant threats to local residents. This study focuses on the Qijiang District of Chongqing, where we conduct a systematic evaluation of wavelength and observation geometry effects on InSAR-based landslide monitoring. Utilizing multi-sensor SAR imagery (Sentinel-1 C-band, ALOS-2 L-band, and LUTAN-1 L-band) acquired between 2018 and 2025, we integrate time-series InSAR analysis with geological records, high-resolution topographic data, and field investigation findings to assess representative landslide-susceptible zones in the Qijiang District. The results indicate the following: (1) L-band SAR data demonstrates superior monitoring precision compared to C-band SAR data in the SMRC; (2) the combined use of LUTAN-1 ascending/descending orbits significantly improved spatial accuracy and detection completeness in complex landscapes; (3) multi-source data fusion effectively mitigated limitations of single SAR systems, enhancing identification of small- to medium-scale landslides. This study provides critical technical support for multi-source landslide monitoring and early warning systems in Southwest China while demonstrating the applicability of China’s SAR satellites for geohazard applications. Full article

This study examines the impact of geomagnetic disturbances quantified by the Kp and Dst indices on the accuracy of single-frequency GPS positioning across mid-latitudes and the equatorial zone, with a focus on temporal and spatial positioning errors variability. GNSS data from a globally distributed network of 14 IGS stations were analyzed for September 2017, featuring significant geomagnetic activity. The selection of stations encompassed equatorial and mid-latitude regions (approximately ±45°), strategically aligned with the distribution of the Dst index during geomagnetic storms. Satellite navigation data were processed using RTKLIB software in standalone mode with standardized atmospheric and orbital corrections. The GPS was chosen over GLONASS following preliminary testing, which revealed a higher sensitivity of GPS positional accuracy to variations in geomagnetic indices such as Kp and Dst, despite generally lower total error magnitudes. The ECEF coordinate system calculates the total GPS error as the vector sum of deviations in the X, Y, and Z axes. Statistical evaluation was performed using One-Way Repeated Measures ANOVA to determine whether positional error variances across geomagnetic activity phases were significant. The results of the variance analysis confirm that the variation in the total GPS positioning error is non-random and can be attributed to the influence of geomagnetic storms. However, regression analysis reveals that the impact of geomagnetic storms (quantified by Kp and Dst) displays spatiotemporal variability, with no consistent correlation to GPS positioning error dynamics. The findings, as well as the developed methodology, have qualitative implications for GNSS-dependent operations in sensitive sectors such as navigation, timing services, and geospatial monitoring. Full article

The BeiDou-3 Navigation Satellite System (BDS-3) precise point positioning (PPP) service through the B2b signal (PPP-B2b) leverages precise correction data disseminated by satellites to eliminate or mitigate key error sources, including satellite orbit errors, clock biases, and ionospheric delays, thereby enabling high-precision timing and positioning. This paper investigates the disparities in time comparison and positioning capabilities associated with the PPP-B2b signals transmitted by the BDS-3 Geostationary Earth Orbit (GEO) satellites (C59 and C61). Three stations in the Asia–Pacific region were selected to establish two time comparison links. The study evaluated the time transfer accuracy of PPP-B2b signals by analyzing orbit and clock corrections from BDS-3 GEO satellites C59 and C61. Using multi-GNSS final products (GBM post-ephemeris) as a reference, the performance of PPP-B2b-based time comparison was assessed. The results indicate that while both satellites achieve comparable time transfer accuracy, C59 demonstrates superior stability and availability compared to C61. Additionally, five stations from the International GNSS Service (IGS) and the International GNSS Monitoring and Assessment System (iGMAS) were selected to assess the positioning accuracy of PPP-B2b corrections transmitted by BDS-3 GEO satellites C59 and C61. Using IGS/iGMAS weekly solution positioning results as a reference, the analysis demonstrates that PPP-B2b enables centimeter-level static positioning and decimeter-level simulated kinematic positioning. Furthermore, C59 achieves higher positioning accuracy than C61. Full article

Silicon Photomultipliers (SiPMs) are optical sensors widely used in space applications due to their high photon detection efficiency, low power consumption, and robustness. However, in Low Earth Orbit (LEO), their performance degrades over time due to prolonged exposure to ionizing radiation, primarily from trapped protons and electrons. The dominant radiation-induced effect in SiPMs is an increase in dark current, which can compromise detector sensitivity. This study investigates the potential of thermal annealing as a mitigation strategy for radiation damage in SiPMs. We designed and tested PCB-integrated heaters to selectively heat irradiated SiPMs and induce recovery processes. A PID-controlled system was developed to stabilize the temperature at 100 °C, and a remotely controlled experimental setup was implemented to operate under irradiation conditions. Two SiPMs were simultaneously irradiated with 9 MeV protons at the EDRA facility, reaching a 1 MeV neutron equivalent cumulative fluence of (9.5 ± 0.2) × 10⁸ cm⁻². One sensor underwent thermal annealing between irradiation cycles, while the other served as a control. Throughout the experiment, dark current was continuously monitored using a source measure unit, and I–V curves were recorded before and after irradiation. A recovery of more than 39% was achieved after only 5 min of thermal cycling at 100 °C, supporting this recovery approach as a low-complexity strategy to mitigate radiation-induced damage in space-based SiPM applications and increase device lifetime in harsh environments. Full article

The need to use environmentally friendly and cost-effective methods to remove heavy metals from wastewater is a permanent concern worldwide. Eggshells have been indicated as a worthy biosorbent for the adsorption of heavy metals due to their bioavailability and composition. In the present study, the absorption capacity of untreated chicken (CEs) and quail (QEs) eggshells for the removal of Pb and Zn ions from aqueous solutions was evaluated at room temperature and 40 °C, using four types of agitation systems: classical and orbital agitation and ultrasonic and microwave-assisted activation. The monitoring of aqueous solutions was performed by electrochemical and spectro-analytical (AAS) procedures before and after the adsorption process. FTIR and RAMAN spectroscopy, SEM-EDAX microanalysis, and X-ray diffraction were used to investigate the characteristics of eggshell samples post-exposure to Pb²⁺ or Zn²⁺. For any type of agitation and temperature, the CEs were able to induce more than 65% removal efficiency for lead and over 80% in the case of zinc. Concerning the Zn removal efficiency of QEs, notable results were recorded when microwaves were applied (>90%) and at 40 °C for orbital shaking and ultrasound (>80%). The results of the present study may offer new and valuable information for the optimal removal of Pb²⁺ and Zn²⁺ using eggshells, thus contributing to the sustainable management of waste through the recycling of this type of biomaterial. Full article

This study analyzes the potential of optical and radar satellite data to monitor faba bean (Vicia faba L.) phenology over six years (2016–2021) in southwestern France. Using Sentinel-1, Sentinel-2, and Landsat-8 data, temporal variations in NDVI and radar backscatter coefficients (γ⁰_VV, γ⁰_VH, and γ⁰_VH/VV) are examined to assess crop growth, detect anomalies, and evaluate the impact of climatic conditions and sowing strategies. The results show that NDVI and the radar ratio (γ⁰_VH/VV) were suited to monitor faba bean phenology, with distinct growth phases observed annually. NDVI provides a clear seasonal pattern but is affected by cloud cover, while radar backscatter offers continuous monitoring, making their combination highly beneficial. The signal γ⁰_VH/VV exhibits well-marked correlations with NDVI (r = 0.81) and LAI (r = 0.83), particularly in orbit 30, which provides greater sensitivity to vegetation changes. The analysis of individual fields (inter-field approach) reveals variations in sowing strategies, with both autumn and spring plantings detected. Fields sown in autumn show early NDVI (and γ⁰_VH/VV) increases, while spring-sown fields display delayed growth patterns. This study also highlights the impact of climatic factors, such as precipitation and temperature, on inter-annual variability. Moreover, faba beans used as an intercropping species exhibit a shorter and more intense growth cycle, with a rapid NDVI (and γ⁰_VH/VV) increase and an earlier end of the vegetative cycle compared to standard rotations. Double logistic modeling successfully reconstructs temporal trends, achieving high accuracy (r > 0.95 and rRMSE < 9% for γ⁰_VH/VV signals and r > 0.89 and rRMSE < 15% for NDVI). These double logistic functions are capable of reproducing the differences in phenological development observed between fields and years, providing a reference set of functions that can be used to monitor the phenological development of faba beans in real time. Future applications could extend this methodology to other crops and explore alternative radar systems for improved monitoring (such as TerraSAR-X, Cosmos-SkyMed, ALOS-2/PALSAR, NISAR, ROSE-L…). Full article

Artificial intelligence is comprehensively transforming the food safety governance system by integrating modern technologies and building intelligent control systems that provide rapid solutions for the entire food supply chain from farm to fork. This article systematically reviews the core applications of AI in the orbit of food safety. First, in the production and quality control of primary food sources, the integration of spectral data with AI efficiently identifies pest and disease, food spoilage, and pesticide and veterinary drug residues. Secondly, during food processing, sensors combined with machine learning algorithms are utilized to ensure regulatory compliance and monitor production parameters. AI also works together with blockchain to build an immutable and end-point traceability system. Furthermore, multi-source data fusion can provide personalized nutrition and dietary recommendations. The integration of AI technologies with traditional food detection methods has significantly improved the accuracy and sensitivity of food analytical methods. Finally, in the future, to address the increasing food safety issues, Food Industry 4.0 will expand the application of AI with lightweight edge computing, multi-modal large models, and global data sharing to create a more intelligent, adaptive and flexible food safety system. Full article

In this study, we analysed remote sensing data collected during the Beirut port explosion on 4 August 2020 at 15.08 UT. For this purpose, we selected three Low-Earth-Orbit (LEO) satellite missions that passed near the Beirut port explosion site immediately after the event. The satellites involved were Swarm-B, the Defence Meteorological Satellite Program (DMSP-F17), and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2). This study focused on identifying the possible ionospheric signatures of explosion in both hemispheres. The conjugate hemispheric points were traced using the International Geomagnetic Reference Field (IGRF) model. We found that the satellite data revealed disturbances not only over the explosion site in the Northern Hemisphere, but also in its corresponding conjugate region in the Southern Hemisphere. Ionospheric electron density disturbances were observed poleward in the conjugate hemispheres along the paths of the Swarm and DMSP satellites, whereas the magnetic field data from Swarm-B showed both equatorward and poleward disturbances. Additionally, the ionospheric disturbances detected by Swarm-B (18:52 UT) and DMSP-F17 (16:30 UT) at the same location suggested travelling ionospheric disturbance (TID) oscillations with identical spatial patterns for both satellites, whereas the disturbances observed by COSMIC-2 south of the explosion site (10°N) indicated the radial propagation of TIDs. COSMIC-2 not only recorded equatorward topside (>550 km) ionospheric electron density disturbances, but also in the conjugate hemispheres, which aligns with the time frame reported in previous studies. These ionospheric features observed by multiple LEO satellites indicate that the detected signatures originated from the event, highlighting the importance of integrating space missions for monitoring and gaining deeper insight into space hazards. The absence of equatorward ionospheric disturbances at the altitudes of DMSP-F17 and Swarm-B warrant further investigation. Full article

This paper explores the usage of LEO-PNT (Positioning, Navigation, and Timing) for providing navigation integrity to GNSS (Global Navigation Satellite System) constellations. LEO mega-constellations, which are positioned between GNSSs and users, offer closer-to-the user geometry, improving performance, reducing the time to alarm (TTA) and enabling integrity monitoring without complex ground segments of any sort. The aim is to use future LEO mega-constellations as integrity monitors for a forthcoming European Global Navigation Satellite System (EGNSS) specifically focused on automotive users, which has minimal onboard satellite capabilities and no ground involvement. This plan builds on earlier studies, anticipating the performance of the upcoming LEO-PNT In-Orbit Demonstration. Full article

Sea surface height (SSH) serves as a fundamental geophysical parameter in oceanographic research. In 2023, China successfully launched the world’s first spaceborne interferometric GNSS-R (iGNSS-R) altimeter, which features dual-frequency multi-beam scanning, interferometric processing, and compatibility with three major satellite navigation systems: the BeiDou Navigation Satellite System (BDS), the Global Positioning System (GPS), and the Galileo Satellite Navigation System (GAL). This launch marked the first in-orbit validation of the iGNSS-R altimetry technology. This study provides a detailed overview of the iGNSS-R payload design and analyzes its dual-frequency delay mapping (DM) measurements. We developed a refined DM waveform-matching algorithm that precisely extracts the propagation delays between reflected and direct GNSS signals, enabling the retrieval of global sea surface height (SSH) through the interferometric altimetry model. For validation, we employed an inter-satellite crossover approach using Jason-3 and Sentinel-6 radar altimetry as references, achieving an unprecedented SSH accuracy of 17.2 cm at a 40 km resolution. This represents a breakthrough improvement over previous GNSS-R altimetry efforts. The successful demonstration of iGNSS-R technology opens up new possibilities for cost-effective, wide-swath sea level monitoring. It showcases the potential of GNSS-R technology to complement existing ocean observation systems and enhance our understanding of global sea surface dynamics. Full article

The last decade has seen a surge in the development and deployment of low Earth orbit (LEO) constellations primarily serving broadband communication applications. These developments have also influenced the interest providing positioning, navigation, and timing (PNT) services from LEO. Potential services include new ranging signals from LEO, augmentation of global navigation satellite systems (GNSS), and monitoring of GNSS. The latter promises an advantage over existing ground-based monitoring due to the reception of observables with reduced atmospheric error contributions and the potential for lower costs. In this paper, we investigate the influence of LEO constellation design on the line-of-sight visibility conditions for GNSS monitoring. We simulate a series of Walker constellations in LEO with a varying number of total satellites, orbital planes, and orbital heights. From the simulated data, we gather statistics on the number of visible GNSS and LEO satellites, durations of visibility periods, and the quality of this visibility quantified by the dilution of precision (DOP) metric. Our findings indicate that increasing the total number of LEO satellites results in diminishing returns. We find that constellations with relatively few total satellites equally yield an adequate monitoring capability. We also identify orbital geometric constraints resulting in suboptimal performance and discuss optimization strategies. Full article

The B[e] phenomenon is observed in a wide range of stars at various evolutionary stages. Its nature remains uncertain. The B[e] phenomenon is defined as the simultaneous presence of low-excitation forbidden line emission and strong infrared excess in the spectra of early-type stars. Here, we present new spectral observations of a representative of this class: the star CI Cam. A monitoring campaign was carried out for the He II 4686 Å emission line, which serves as an indicator of binarity in this system. The aim was to detect variations in this line not only due to orbital motion but also those associated with the pulsations of the system’s primary component, the B[e] star. Two maxima in the equivalent width were detected over the pulsation period, during which the equivalent width increased by a factor of three. We refine the classification of CI Cam, assigning it to the FS CMa group of B[e] stars by all criteria, and we refer the secondary component of the system to a group of recently discovered “stripped” stars. Full article