Search Results (30,850)

Orbital maneuver detection is a core component of space situational awareness. The multi-scale characteristics of satellite orbital behavior and sample imbalance issues lead to challenges in existing methods, including insufficient feature adaptation and limited detection accuracy. This paper proposes an Adaptive Spiking Gating Multi-Scale Liquid State Machine (ASG-MSLSM) for orbital maneuver detection based on variations in satellite orbital parameters. The method integrates multi-scale reservoir pools with different scale-dependent decay factors and Leaky Integrate-and-Fire (LIF) neurons to enhance multi-scale temporal feature extraction capability. A spiking gating network is designed to adaptively learn fusion weights for multi-scale features, replacing traditional fixed equal-weight fusion strategies. During training, weighted binary cross-entropy loss is employed to address class imbalance. Experimental results based on real satellite data demonstrate that the proposed method significantly outperforms baseline models in maneuver detection metrics, achieving higher recall, improving feature separability, and reducing both missed detections and false alarms. These results indicate that the proposed method provides a robust solution for orbital maneuver detection. Full article

This research focuses on the design and simulation of a V-band single-chip transmit-and-receive front-end integrating an LNA, PA and switching functions for ISL terminals. Two technologies are compared: a 60 nm GaN/Si HEMT from MESC and a 100 nm GaAs HEMT from UMS. In Tx mode, the proposed design targets a saturated output power of at least 20 dBm and a power-added efficiency of no less than 5%. In Rx mode, the goal is 4 dB noise figure. In both cases, the small signal gain must exceed 20 dB across the 59–71 GHz band. Full article

In the field of drone navigation systems, a high-precision positioning solution can be provided by an integrated strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS). But when satellite signals are interfered with or blocked by tall buildings, the errors of SINS will disperse rapidly due to the complex air and mechanical vibrations, leading to a serious degradation of navigation accuracy. To enhance the positioning performance in this situation, this paper proposes a hybrid information fusion method based on attention-augmented temporal convolutional network (TCN) for drone SINS/GNSS navigation system. A feature integration and prediction model is constructed to provide a pseudo-positioning reference for the integrated navigation filter during GNSS-denied periods, in which TCN is used to establish a predictive positioning error correction model based on inertial measurements and SINS data, while a self-attention model is incorporated to extract complex global drone motion features. The performance of the proposed method has been experimentally verified using Global Positioning System (GPS) and SINS data collected from real drone flight test. Comparison results among the proposed model, SINS with TCN, SINS with convergent Kalman filter (KF) prediction section and SINS-only indicate that the proposed method can effectively improve the drone positioning accuracy in specific GNSS-denied environments. Full article

Quicker identifying abilities are required due to the rising frequency and severity of wildfires. Although polar-orbiting satellites with medium and high resolution can accurately identify wildfires, the majority of available fire detection images originate from such platforms. However, their low temporal revisit rates restrict the potential for early warning. Geostationary satellites provide minute-level, continuous monitoring that corresponds with the quick onset of wildfires; however, their dependence on conventional threshold methods and coarse spatial resolution result in notable detection errors. This study developed an integrated deep learning framework for accurate wildfire detection in low-resolution geostationary imagery in order to get over these restrictions. A novel dynamic index, the Dynamic Normalized Burn Ratio—Thermal (DNBRT), was proposed to characterize wildfire progression by integrating instantaneous thermal anomalies with dynamic vegetation signals. Based on this, a Fire Spatiotemporal Network (FST-Net) was designed, with an efficient residual backbone, a Convolutional Block Attention Module (CBAM) for feature refinement, and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal evolution. Trained and evaluated on an FY-4B-based fire/non-fire dataset, the proposed framework demonstrated superior performance. FST-Net outperformed benchmark models, improving accuracy and recall by averages of 10.30% and 9.32% respectively while achieving faster inference speed. An ablation experiment confirmed the critical role of fusing thermal and vegetation features in DNBRT, with 92.7% accuracy and 94.9% recall. Compared to the FY-4B fire product, the proposed framework enables earlier detection, maintains more complete tracking of fire progression, and exhibits greater robustness under complex burning conditions while achieving sub-hectare (0.36 ha) detection sensitivity at the 2 km resolution. By synergizing a discriminative dynamic index with an efficient spatiotemporal architecture, this work provides an effective solution for operational, real-time monitoring of small and early-stage wildfires from geostationary satellites. Full article

We present and discuss time-lapse gravity variations recorded by a large-scale absolute gravity network operating in Central Italy. The network comprises four stations distributed across the Lazio, Umbria, and Abruzzo regions, areas affected by the significant seismic activity of 2009 and 2016–2017. From 2018 to 2023, six campaigns were carefully conducted using an FG5 absolute gravimeter. We detected significant gravity decreases around 2020 reaching between −15 and −20 μGal in three sites and approximately −37 μGal at the fourth. The Sentinel-1 time series of permanent scatterers (PS) allowed us to exclude significant contribution from vertical deformations to the observed gravity changes. We analyzed both ground-based data (rainfall gauges and well water levels) and satellite-based observations (the Gravity Recovery and Climate Experiment-Follow-On, GRACE-FO, mission) together with the Global Land Data Assimilation System (GLDAS) and precipitation models. The results reveal a significant decrease in the regional groundwater content from 2018 to the end of 2020, which coincides temporally with the observed gravity decrease. We show that the absolute gravity variation trends observed at all stations are consistent with regional-scale hydrological processes, pointing to a significant decrease in terrestrial water storage (TWS) during the same time interval. At L’Aquila (AQUI), the gravity anomaly is larger than expected from regional hydrological products alone, suggesting an additional local component possibly related to the hydrogeological response of the fractured karst system undergoing significant post-seismic activity. Full article

To support intelligent maritime applications, space–air–ground–sea integrated networks (SAGSINs) have been introduced in maritime communications to provide wide coverage and reliable network services. In unmanned aerial vehicle (UAV)-assisted SAGSIN architectures, UAVs can flexibly extend coverage and provide on-demand communication and computing support. However, due to the high mobility of low Earth orbit (LEO) satellites and the limited endurance of UAVs, single-platform deployment strategies struggle to provide both flexibility and scalability in maritime communication networks. To mitigate the service instability caused by satellite orbital dynamics and limited UAV endurance, we propose a Hybrid Multi-Agent Proximal Policy Optimization (H-MAPPO)-based joint satellite–UAV deployment scheme for UAV-assisted SAGSIN systems. The proposed method optimizes joint UAV positioning and resource allocation to enhance communication coverage while reducing overall operational cost. By incorporating satellite orbital dynamics and UAV mobility into a multi-agent reinforcement learning (MARL) framework, adaptive resource scheduling can be achieved under time-varying maritime demands. Simulation results show that the proposed H-MAPPO algorithm achieves superior convergence performance, higher user coverage, and lower total system cost compared with learning-based, random, and heuristic methods while maintaining stable and robust performance under varying user densities and network scales. Full article

This paper provides a comprehensive overview of the progress in fiber-optic gyroscope technology, covering 260 key studies of the last ten years. A critical comparative analysis of fiber-optic gyroscope with alternative inertial sensors (Micro-Electro-Mechanical Systems, Hemispherical Resonator Gyroscope, Ring Laser Gyroscope) has been carried out. Confirming the unique advantages of fiber-optic gyroscope for autonomous navigation. Fundamental limitations of accuracy are considered in detail: temperature drifts, polarization noise, and Rayleigh backscattering. Modern hardware methods for suppressing these errors, including the use of photonic crystal and hollow fibers (Air-Core/Hollow-Core), are also considered in this work. The central place in the review is occupied by the analysis of the technological paradigm shift from bulky discrete circuits to hybrid integrated photonics (Indium Phosphide, Silicon Nitride, Lithium Niobate) and hybrid architectures to reduce weight and size characteristics. The role of artificial intelligence (Deep Learning, Long Short-Term Memory) methods in nonlinear drift compensation and calibration is discussed. The usage of the Brillouin effect and optomechanics promising areas are outlined, necessary to create a new generation of navigation systems operating in the absence of Global Navigation Satellite Systems signals. Full article

Digital elevation models (DEMs) are key fundamental inputs in hydrological modelling, yet the influence of spatial resolution on basin delineation and process representation remains insufficiently understood, particularly in small catchments. This study investigates the influence of DEM resolution on topographic characterization and hydrological response in a small agroforestry basin in central Portugal. Three DEMs with resolutions of 5 m, 10 m, and 30 m were generated from contour data and satellite sources and processed using the TOPAZ-based TopAGNPS delineation framework. The sensitivity of basin structure to delineation parameters—critical source area (CSA) and minimum source channel length (MSCL)—was assessed, and the resulting configurations were used as inputs to the AnnAGNPS model. Results show that DEM resolution strongly influences the representation of hydrological cells and stream reaches. Increasing resolution from 30 m to 5 m leads to a nearly doubling of average cell slope and increases reach slope by more than four times, with corresponding changes in drainage network density and connectivity. Log-linear relationships were identified between slope and contributing area, as well as between slope and reach length, consistent with established geomorphic scaling laws. Hydrological simulations further indicate that resolution-dependent delineation significantly influences runoff, erosion, and peak discharge estimates, with finer resolutions increasing sensitivity to parametrization. Among land-cover scenarios, desertified conditions generate substantially higher runoff and peak flows compared to naturalized and forested conditions. Overall, the findings demonstrate that DEM resolution, together with preprocessing and delineation choices, exerts a critical control on hydrological model outputs. These effects are particularly pronounced in low-relief, human-influenced catchments, where coarse-resolution DEMs may lead to systematic underestimation of hydrological responses. The study highlights the need for resolution-aware modelling strategies and careful parametrization to improve the reliability and transferability of hydrological simulations. Full article

Dry soil spectral reflectance provides a stable baseline for characterizing soil optical properties and supporting the retrieval of soil attributes from remote sensing. However, despite the large number of studies on soil spectral reflectance, most existing research primarily focuses on empirical relationships between spectra and soil properties. The representation and prediction of dry soil reflectance as a baseline condition, particularly under the influence of environmental factors, remain insufficiently explored, and the generalizability of existing models still needs improvement. Therefore, this study collects 700 dry soil samples with laboratory-measured spectral reflectance from Northeast China and quantitatively analyzes the contribution of environmental covariates (soil properties, parent material, and geographical location) using the SHAP method. Then, an environmental and edaphic-factor-driven smooth dry soil reflectance model (EEDSR) model covering 400–2500 nm is developed based on gradient boosting regression (GBR), and its accuracy is evaluated using global ISRIC soil datasets. Our results indicate the following: (1) the reflectance of dry soil is closely related to the soil properties in the VIS to SWIR range. The reflectance of dry soil of 400–2500 nm is positively correlated with clay percentage, longitude, and parent material but negatively correlated with latitude, sand percentage and silt percentage. And its correlation with other variables (such as soil organic matter, pH, and EC) varies with wavelength. (2) The EEDSR model exhibited high predictive accuracy across the 400–2500 nm spectral range (R² = 0.93, RMSE = 0.018). Additionally, incorporating parent material (PM) and geographical factors into the predictor set enhanced the accuracy of dry soil reflectance prediction by 13.4%. (3) The spatial consistency between the predicted soil reflectance in Northeast China and the satellite observations indicates that the EEDSR model has good performance in predicting soil reflectance, as the bias of reflectance gradually increasing from west to east is consistent with the precipitation distribution in Northeast China. (4) The generalization ability of the EEDSR model was confirmed by global ISRIC datasets (R = 0.94), outperforming the deep learning-based Soil Optical Generative Model (SOGM) (R = 0.27). Overall, this study presents an efficient and interpretable framework for modeling dry soil spectral reflectance, providing a robust reference for soil reflectance prediction and remote sensing-based soil property retrieval. Full article

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FengYun-4B weather satellite provides critical upwelling atmospheric infrared radiance. To address the limitations of the previous sounder (FY-4A/GIIRS) in terms of spatial resolution and spectral coverage, FY-4B/GIIRS has increased the spatial resolution to 12 km and added more spectral channels in the long-wave band to enhance the observation details and information content of weather systems. To evaluate its baseline performance, a comprehensive preflight test campaign—encompassing spectral and radiometric assessments—was conducted in a thermal vacuum (TVAC) chamber. Spectral characterization via laser measurements confirmed the instrument spectral response function (ISRF) is highly consistent with the theoretical cardinal sine function (sinc). Gas-cell tests demonstrated that, after correcting for off-axis effect, the spectral calibration errors are on average less than 5 ppm, validated against Line-By-Line Radiative Transfer Model (LBLRTM) simulations. The radiometric calibration employed temperature-variable blackbodies for noise performance and radiometric accuracy assessments. The radiometric sensitivity, characterized by Noise Equivalent differential Radiance (NEdR), is less than 0.5 and 0.1 mW/(m²·sr·cm⁻¹) in the long-wave infrared (LWIR) and mid-wave infrared (MWIR) bands, respectively. To address the LWIR detector nonlinearity, an iterative polynomial fitting algorithm based on spectral responsivity invariance was implemented. This correction reduces the radiometric deviation from >1.0 K to ~0.2 K, meeting the 0.7 K accuracy requirement across a 180–315 K dynamic range. Conversely, the MWIR band exhibits high linearity but is limited by noise when observing low-temperature scenarios and can only meet the 0.7 K requirement within the range of 250 to 315 K. Full article

Accurate quantification of atmospheric column-averaged dry-air CO₂ mole fractions (XCO₂) is pivotal for quantifying carbon sources and supporting China’s dual carbon goals. However, existing satellite observations are limited by spatiotemporal gaps due to orbital constraints and atmospheric conditions. To bridge these gaps, we utilized a deep learning framework featuring a dual self-attention mechanism, Air-Transformer, to capture complex long-range spatiotemporal dependencies and non-linear interactions among variables. Utilizing OCO-2 retrievals and multi-source data, this approach generated a spatiotemporally consistent, daily 0.1° XCO₂ dataset over China during 2015–2020. Cross-validation demonstrates superior accuracy (R² = 0.98), with robust performance confirmed by spatial and temporal validation and ground-based TCCON benchmarks. The estimated full-coverage outputs reveal a national mean annual increase of 2.68 ppm, characterized by a distinct east-high/west-low pattern. Interpretable analysis based on Shapley Additive Explanations (SHAP) elucidates the non-linear interactions between XCO₂ and environmental drivers and exhibits significant regional heterogeneity. This spatiotemporally consistent and interpretable XCO₂ dataset offers vital data support for regional carbon monitoring and differentiated policy-making. Full article

Remote sensing is widely used in archaeological prospection to detect surface anomalies (crop marks) indicating buried remains, typically through recognition of visual patterns in high- or very high-resolution imagery acquired by means of satellite, airborne, or drone sensors. In contrast, spectroscopic approaches focusing on variations in spectral signatures still remain rarely applied in archaeological research. This study proposes a technological barrier-free method addressed to archaeologists which is based on pixel-level analysis of the Reflectance Values (RV) and spectral shape variations in the visible, near-infrared and short-wave infrared (VIS-NIR-SWIR) range derived from Sentinel-2 imagery. Spectral signatures are extracted through sampling polygons designed to account for the spatial resolution of the different Sentinel-2 bands and their spatial relationship with the location and size of the archaeological features. The RV method is tested on two Roman archaeological contexts: the ancient city of Telesia Vetere (San Salvatore Telesino, Benevento) and a Roman villa at Podere Colle Agnano (Labro, Rieti) using the full Sentinel-2 archive since 2017. While Telesia has previously been investigated through aerial photo interpretation and archaeological fieldwork, the Roman villa at Labro is documented here for the first time. Results show consistent seasonal repeated spectral separability between areas corresponding to known buried archaeological features and surrounding areas. Similar anomalies were also detected in areas without previously documented remains, thus suggesting the possible presence of buried structures and highlighting the predictive potential of the RV method. Owing to its easiness to use beyond image processing specialism and reliance on open-access data, the method can support archaeological decision-making and guide further investigation with higher-resolution remote sensing data or targeted field surveys, particularly in the framework of preventive archaeology. Full article

In Chuckwagon racing, teams of four Thoroughbred horses pull wagons at high speeds. Movement symmetry is a key locomotion metric linked to force production, racing direction, and lameness. Racehorse symmetry in trot during on-track warmups and cooldowns was assessed. Over 10 days, 60 horses (average 8 per day) were fitted with Global Navigation Satellite Systems combined with Inertial Measurement Unit (GNSS-IMU) sensors. Weight-bearing asymmetry was quantified using the minimum difference (MnD) in vertical trunk displacement between diagonal limb pairs, and push-off asymmetry was quantified using the upwards difference (UpD). Absolute (mm) and normalized (% ROM) asymmetries were compared between warmups and cooldowns using linear mixed models. Mean MnD was similar between warmup (6.2 mm; 17.6%) and cooldown (6.4 mm, 19.7%). Mean UpD increased from warmup (11.3 mm, 31.7%) to cooldown (12.8 mm, 38.0%), with UpD% significantly higher in cooldown (p = 0.046). No other differences were significant (all p ≥ 0.202). One horse sustained a catastrophic musculoskeletal (MSK) injury. This horse’s UpD ranged from 3.3–29.7 mm (11.4–69.3%) during warmups and 24.3–25.5 mm (47.8–76.4%) during cooldowns. Push-off asymmetry may increase after Chuckwagon racing. The injured horse showed high asymmetries, but high values also occurred in uninjured horses. Further work needs to establish normal asymmetry ranges in Chuckwagon racing and identify patterns associated with MSK injuries. Full article

Relation Extraction is crucial for knowledge graph construction, but extracting complex relations in specialized domains like Satellite Navigation Countermeasures (SNCM) remains challenging due to long semantic spans and high relational density. While Large Language Models (LLMs) possess strong semantic understanding, they often suffer from severe recall deficiency and hallucinations in high-density multi-entity contexts. Conversely, traditional small models generate excessive redundant noise. To address these limitations, this paper proposes an evaluation-model-guided relation extraction method (E-guidedRE). This framework employs a two-stage collaborative mechanism. First, a lightweight evaluation model utilizing a GlobalPointer network with Rotary Position Embedding (RoPE) and a sparse multi-label loss function acts as a structural filter to generate high-coverage candidate entity pairs. Second, these candidates guide the frozen LLM to perform deep semantic discrimination and retrospective denoising. Furthermore, we construct a dedicated SNCM dataset to fill the vertical domain data void. Extensive experiments across five heterogeneous datasets, including general, biomedical, financial, and our self-built SNCM corpus, demonstrate that E-guidedRE exhibits remarkable robustness. In ablation studies on the SNCM dataset, our method improved the F1-score from 36.54% to 54.93% compared to standalone LLM extraction, boosting recall from 27.81% to 47.13%. The proposed paradigm effectively mitigates the LLM’s attention divergence in complex contexts, dynamically balancing precision and recall, and offers a highly reliable technical pathway for knowledge extraction in specialized vertical domains. Full article

This paper presents the Performance Prediction Tool developed within the SATERA project. The tool evaluates the performance of a space-based composite ADS-B and multilateration system for independent aircraft localization. It uses receivers deployed onboard a constellation of LEO satellites. Multilateration can be evaluated using time-based measurements, as well as additional measurements such as, frequency and angle of arrival of the received signals. The tool is based on the evaluation of the Cramér–Rao lower bound and it is implemented in MATLAB with a user-friendly graphical interface. The tool allows the user to define the satellite constellation, link budget, measurement types and errors, and to simulate the system performance over an aircraft trajectory or an area. Moreover, the outputs include DOP, number of visible satellites and system availability, which can be visualized and exported for further analysis. Full article