Search Results (1,125)

The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R² of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions. Full article

Remote sensing of clouds and precipitation is undergoing a significant transition from descriptive observations toward process-oriented diagnoses. This transition has not progressed linearly, but has gradually taken place alongside the rapidly developing multi-source observation capabilities over recent decades. In terms of clouds and precipitation observational platforms, satellites provide continuous global-scale monitoring, airborne platforms complement high-resolution sampling of key processes, and ground-based observations offer long-term vertical structure evolution, which form an integrated space–air–ground observation system. In terms of clouds and precipitation retrieval algorithms, active–passive combination remote sensing significantly improves the ability to retrieve macro- and microphysical characteristics and structures, and machine learning methods further expand parameter estimation capabilities in complex scenarios. Nevertheless, key bottlenecks still persist in retrieval non-uniqueness, sensor trade-offs, cross-platform calibration, and validation over oceans, mountains, and polar regions. Based on the above background, this paper provides a systematic review of recent progress in clouds and precipitation physics remote sensing, focusing on the development of multi-platform collaborative observations, the evolution of microphysical parameter retrieval methods, and the improvements in remote sensing characterization of cloud and precipitation formation mechanisms. It further points out that future development will increasingly rely on improved uncertainty quantification, incorporation of physical constraints into retrieval frameworks, and the establishment of standardized multi-source datasets. Full article

The Christmas period reshapes urban cultural experience by temporarily suspending everyday routines and placing collective celebration at the centre of public space. During this time, festive illuminations transform cities, intensifying the symbolic role of light and reinforcing seasonal rituals of exceptionality. In many European cities, large-scale decorative lighting is used not only for aesthetic enhancement but also to create distinctive urban atmospheres. This study examines the effects of seasonal festive lighting on light pollution and perceptions of urban space, using Funchal (Madeira, Portugal) as a case study. Combining in situ photometric measurements, satellite-derived night-time data, and a questionnaire-based perception survey, this mixed-methods study compares periods when festive lighting was active and inactive. The results indicate that Christmas lighting was associated with higher levels of artificial illumination, shifts in the spatial distribution of artificial light at night (ALAN), and changes in night-sky brightness. These findings suggest that festive lighting shapes both the visual character of urban space and the seasonal dynamics of light pollution. Full article

Recent advances in numerical modelling, remote sensing, and artificial intelligence are bringing a transformation in our ability to assess sediment transport. Nevertheless, climate change is fundamentally altering sediment production, transport, and deposition through intensifying hydrological extremes, sea-level rise, changing storm regimes, cryosphere degradation, and increasing human modification of sediment pathways. These interacting drivers challenge conventional sediment transport assessment, which has largely evolved within separate fluvial, estuarine, coastal, and marine disciplines and often lacks an integrated perspective capable of representing source-to-sink sediment connectivity under non-stationary environmental conditions. Although significant advances have been made in process-based numerical modelling, Earth observation, and artificial intelligence (AI), these approaches are commonly reviewed independently, limiting their collective application to regional climate-responsive sediment assessment. This review examines state-of-the-art process-based numerical models, observational tools, and machine-learning approaches for sediment transport from source-to-sink. A transparent benchmarking scheme is used to compare leading modelling systems (e.g., AdH, SRH-2D, FLO-2D, HEC-RAS, TELEMAC, Delft3D, EFDC, SCHISM, XBeach, ROMS), highlighting differences in dimensionality, sediment-process representation, computational demands, and climate-scenario readiness. Remote sensing (optical, SAR, LiDAR, UAV) and AI/ML/DL methods (e.g., random forests) are reviewed as complementary tools that enhance model parametrization, improve validation, and address uncertainty in data-limited regions. A reproducible bibliometric synthesis based on Dimensions.ai records (2000–2026) reveals accelerating growth in sediment-transport research, with strong recent expansion in coastal, estuarine, and data-driven modelling applications. Major challenges include cohesive sediment physics, cross-environment coupling, limited long-term validation datasets, and the need for scalable workflows compatible with climate-model forcing. In this manuscript, we analyse and propose a future roadmap for near-term integration of satellite–field data streams, medium-term development of hybrid physics–AI models, and long-term coupling of sediment modules within Earth-system and regional climate frameworks. Collectively, this review provides a foundation for next-generation, climate-responsive sediment transport assessment supporting sustainable river basin and coastal management. Full article

Accurate information on atmospheric temperature profiles is crucial for improving numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which thereby fail to meet the high spatial resolution required for NWP. In this study, we present an integrated framework as an engineering refinement combining the variation method with an artificial neural network (Var-ANN) to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) aboard the polar-orbiting satellite FY-3C. The variation method is first applied to construct a spatially consistent reference field from available station observations, and this field is then used as the training target for a back-propagation neural network that learns the empirical relationship between satellite brightness temperatures and corrected atmospheric temperature. The calibrated temperature profiles were evaluated against independent radiosonde observations and further tested through assimilation into the Weather Research and Forecasting (WRF) model for precipitation simulation over the TP. Results indicate that the Var-ANN calibration reduces the root-mean-square error (RMSE) by approximately 60% and the mean bias from approximately −5 °C to −0.7 °C relative to radiosonde observations. In two WRF case studies, the calibrated profiles show potential for improving precipitation forecast skill, although the limited sample size precludes robust conclusions about operational forecast improvements. The Var-ANN framework provides a practical approach for enhancing the utility of FY-3C VASS temperature products for NWP applications over data-sparse complex terrain. Full article

Future low Earth orbit (LEO) communication constellations are evolving into integrated multi-mission infrastructure. Their signals of opportunity (SoP) are therefore becoming attractive for Doppler positioning. However, the conventional coverage- or rate-optimized configurations may not provide favorable Doppler geometry under realistic link-quality constraints. This paper considers this emerging requirement at the constellation-configuration design level and proposes a multi-layer Walker optimization framework for integrated communication and SoP Doppler positioning. A system-level positioning metric is developed to move beyond visibility and dilution-of-precision indicators. A link-quality-constrained multi-epoch Fisher information matrix (FIM) incorporates $C/N_0$-based link measurability and a general carrier-to-noise-density-dependent Doppler-noise formulation. In the reported simulations, $C/N_0$ controls observation admission, while all admitted Doppler observations use a fixed noise standard deviation of 0.5 m/s. An effective position-error bound is then obtained by marginalizing clock-drift and frequency-bias nuisance states. Based on a unified satellite–ground geometry, weighted service coverage, weighted best-link achievable rate, and the proposed positioning metric are jointly optimized using a constrained mixed-integer multi-objective artificial hummingbird algorithm (CMI-MOAHA). The FIM-based metric is consistent with the positioning root mean square error (RMSE) from a separately implemented nonlinear Doppler solver under matched observation and noise assumptions. With the total number of satellites fixed at 2000, the Pareto archive reveals clear trade-offs among coverage, best-link achievable rate, and positioning. When the positioning objective is included, the best obtained positioning metric decreases across all tested constellation sizes, with a maximum reduction of 77.1%. These results show that constellation-level joint optimization is warranted when LEO communication satellites also serve as SoP for Doppler positioning. Full article

Accurate discrimination between rice and artificial grassland remains challenging in regional agricultural monitoring because both herbaceous types share similar spectral signatures during vegetative growth, and existing land-cover products do not treat artificial grassland as a separate class. Using Jurong City, Jiangsu Province, as the study area, we propose a framework that optimizes red-edge vegetation index selection within crop-specific phenological windows to separate rice from grassland. Using Unmanned Aerial Vehicle (UAV) multispectral imagery and Sentinel-2 satellite data, we quantified spectral separability across eight phenological stages using Fisher ratios. We identified two optimal discrimination windows: early tillering (mid-June) and heading–flowering (early September). Within the heading–flowering window, a dual-index classification rule combining Normalized Difference Red-Edge Index (NDRE) and Green Normalized Difference Vegetation Index (GNDVI) was transferred from UAV to Sentinel-2 and used to produce a 10 m rice–grassland map for the entire city. Spatial agreement with two publicly available rice datasets reached 75.2% and 79.5% for rice pixels, reflecting differences in spatial resolution, reference year, and class definition rather than classification error. Independent field validation using 200 samples yielded an overall accuracy of 92.50% (F1-score = 0.93), confirming the effectiveness of the VI–window optimization strategy. The framework offers an interpretable, physiology-driven alternative for crop-type mapping that relies solely on widely available multispectral bands. Full article

Artificial light at night (ALAN) is an increasing component of urban environmental change, affecting vegetation dynamics and ecosystem functioning. Satellite nighttime light (NTL) data serve as proxies for urbanization and artificial illumination, aiding the analysis of vegetation responses to human pressures. However, NTL–vegetation relationships are often poorly synthesized, and ALAN is rarely included in frameworks linking urban vegetation, climate, and human drivers. Drawing on a 2014–2025 Scopus and Web of Science search, this review of 22 articles categorizes findings as (i) Lights Track Urbanization, (ii) Vegetation Modulates Light, and (iii) ALAN Shapes Ecology. Results show strong geographical concentration in China, followed by the United States, and high heterogeneity in sensors, metrics, and methods. Increasing nighttime radiance is consistently associated with vegetation decline and higher environmental pressure, while vegetation modulates light through canopy structure and phenology. ALAN effects on plant phenology are reported but vary relative to climatic drivers and are highly context-dependent. Despite these advances, the field remains methodologically inconsistent and geographically biased. This review highlights the need for harmonized multi-sensor frameworks that integrate radiance, vegetation, and climate data to improve assessments of urban environmental change and to support biodiversity conservation and light-sensitive urban planning, thereby preserving ecosystem service functions. Full article

The rapid evolution of connected and autonomous vehicles has significantly increased the importance of automotive telemetry as a fundamental component of intelligent transportation systems. Modern vehicles continuously generate large volumes of operational, environmental, and behavioral data that must be transmitted, processed, and analyzed in real time to support applications such as predictive maintenance, remote diagnostics, cooperative mobility, and autonomous driving. The growing demand for reliable and large-scale data exchange has highlighted the critical role of advanced communication infrastructures in connected mobility ecosystems. This review provides a comprehensive overview of automotive telemetry technologies with a particular focus on communication solutions enabling long-range data transmission. The study examines the architecture of automotive telemetry systems, connected vehicles, and the Internet of Vehicles, followed by an analysis of Vehicle-to-Everything (V2X) communication, fifth-generation (5G) mobile networks, satellite communication systems, and emerging hybrid 5G–satellite architectures. In addition, the review discusses Software Defined Vehicles, Digital Twin technologies, artificial intelligence integration, and cybersecurity challenges associated with highly connected transportation environments. The analysis demonstrates that no single communication technology can satisfy all requirements of future intelligent mobility systems. Instead, hybrid communication architectures combining terrestrial and non-terrestrial networks are expected to provide the reliability, coverage, scalability, and low-latency performance required for next-generation connected transportation. Finally, key research challenges and future development directions are identified, emphasizing the growing convergence of telemetry, communication networks, artificial intelligence, digital twins, and cybersecurity within future connected mobility ecosystems. Full article

This study presents a comprehensive global accuracy assessment of the medium-range (up to 15 days) forecast pressure-level data generated by the physics-based Integrated Forecast System (IFS) and the newly operational, data-driven Artificial Intelligence Forecasting System (AIFS) from the European Centre for Medium-Range Weather Forecasts (ECMWF) based on the radio wave signal delays during propagation through the troposphere. Troposphere signal path delays are calculated using the software package Ankara Ray-tracing Tools (ART). This newly developed troposphere ray-tracing software package integrates hydrostatic and wet refractivities along the ray path of a radio wave signal using an approximation of a two-dimensional piecewise-linear ray path. Along with the IFS and AIFS pressure-level data, the AIFS/IFS combination generated and appended in this study is used to compute 62 forecast runs for each of the January and August 2025 monthly periods. Each run includes 6-hourly forecast steps over 15 days and is initialized twice daily at 0 and 12 UT throughout January and August 2025. These forecasts cover 52 globally distributed Global Navigation Satellite Systems (GNSS) stations operated by the International GNSS Service (IGS). The forecast zenith delay accuracies were systematically evaluated using the root mean square (RMS) and bias error metrics with respect to the IGS troposphere product and the ECMWF Operational Analysis data as robust validation benchmarks. In addition to the Vienna Mapping Functions 3 (VMF3) troposphere delay product, the empirical troposphere delay models Global Pressure and Temperature 3 (GPT3) and the model utilized by satellite-based augmentation systems (SBAS, e.g., WAAS and EGNOS) GNSS receivers are incorporated into the assessments. Both IFS and AIFS models exhibit exceptional short-range capabilities, keeping global zenith total delay errors (RMS relative to IGS) below 2 cm up to a 2-day lead time. However, a critical performance crossover occurs between the 10-day and 11-day forecasting horizons, where the forecast accuracy of both IFS and AIFS declines below the threshold of the GPT3 model, whose zenith total delay RMS across all stations with respect to the IGS troposphere product is found to be about 4 cm. The findings of this study offer crucial insights for improving the accuracy of real-time satellite navigation, climate monitoring, and satellite-based high-precision positioning applications. Full article

Covert communication has emerged as a promising technique for protecting wireless transmissions by concealing the existence of legitimate communication from malicious wardens. However, achieving end-to-end covert communication in space–air–ground integrated networks (SAGINs) is challenging due to the coupled effects of satellite-to-ground relay access, ground multi-hop forwarding, and cooperative jamming. In this paper, we propose an incentive-aware end-to-end covert routing framework for SAGINs, where a low Earth orbit (LEO) satellite delivers information to a ground destination through a selected relay base station and a self-organizing ground route. We first establish a two-stage SAGIN model and characterize the satellite-to-ground covert capacity under satellite sidelobe interference, as well as the ground-route covert performance in the presence of multiple wardens and cooperative jammers. Since jammers are self-interested and incur power costs when generating artificial interference, we design an incentive mechanism to stimulate cooperative jamming for enhancing ground-route covertness. Specifically, the reward allocation and jamming-power response are jointly derived by considering both the route-dependent covertness gain and the power cost of jammers. Based on the resulting route-dependent utility, the ground routing problem is further transformed into a shortest-weighted path-finding problem. To improve the long-term stability of satellite-to-ground relay access, we model the repeated interaction between the LEO satellite transmitter and the satellite warden as a base-station selection process and develop a zero-determinant strategy to stabilize the long-term expected utility relation under different warden monitoring policies. Simulation results demonstrate that the proposed framework effectively balances satellite-to-ground covert capacity and ground-route utility, outperforms baseline relay selection schemes, and achieves stable long-term covert routing performance against uncertain warden behaviors. Full article

Background: Skeletal muscle (SM) plays a critical role in movement, metabolism, and organ protection, with its maintenance and regeneration relying on muscle satellite (stem) cells (MSCs). Prostaglandin E2 (PGE2) regulates MSCs, but PGE2 levels decline with aging due to increased catabolism by 15-hydroxyprostaglandin dehydrogenase (15PGDH), a negative regulator of muscle repair. Methods: This study aimed to employ artificial intelligence and machine learning (ML)-assisted, structure-based screening approaches to identify novel 15PGDH inhibitors. Supervised models (support vector machine, random forest, and XGBoost were trained on curated bioactivity data (IC₅₀ values) from the ChEMBL database and used to virtually screen the Maybridge compound library (~51,000 compounds). Results: The area under the curve (AUC) values of the developed models SVM, RF, and XGBoost were 0.96, 0.99, and 1.00, respectively. Promising inhibitors were further validated using structure-based virtual screening (docking), molecular dynamics simulations (200 ns), and MM-PBSA/GBSA analyses. The top five inhibitors (PD00616, HTS11491, HTS02629, AW00889, and HTS11190) were identified as active (ML analysis) and potential 15PGDH inhibitors based on their subsequent binding affinities, involvement of catalytic residues (Ser138, Tyr151, and Lys155), and complex stability. Additionally, these inhibitors were found to follow the drug-likeness criteria. Conclusions: These findings offer valuable insights for the development of novel therapeutics targeting 15PGDH to combat muscle degeneration and related pathologies, including aging and sarcopenia. Full article

Space-based solar (SBS) has the potential to provide spacecraft-to-spacecraft power transmission, powering space-based computers serving artificial intelligence, and contribute to addressing terrestrial solar resource intermittency. This paper identified and assessed system efficiencies, and the orbital analysis required for SBS power beaming to remote terrestrial areas and to other spacecraft from SBS systems on Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geosynchronous Orbit (GEO). Specific scenarios are presented to demonstrate simulation capabilities using Matlab/Simulink which provide orbit visualization, control of classical orbital elements (COEs) to determine power beaming overhead time to remote locations and other satellites, as well as eclipse cycles and solar capture forecasts. Other simulation results to support SBS operations include earth–space propagation and transmission losses for desired radiofrequency (RF) microwave power beaming wavelengths. We identified the SBS orbit requirements for continuous space-to-earth power beaming accounting for overhead time, earth coverage and RF spot size, solar capture, and power delivered at the receiver site. A total of 10,000 km MEO circular orbits with 55-degree inclination are potential candidates for SBS satellites, with overhead time for a single satellite forecasted at 28%, covering 30% of earth, while optimizing solar capture at 97%. Technology improvements can increase predicted power transmission efficiencies by 5–10% through RF beam and phase focusing. While other studies focus on a single orbital regime, this paper presents a novel comparative analysis, using an integrated model to quantitatively assess the tradeoffs in coverage and link performance between LEO, MEO, and inclined GEO architectures for SBS. Full article

Shipborne Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated navigation based on Factor Graph Optimization (FGO) is increasingly used for maritime positioning, but its performance can be limited in low-dynamic sailing scenarios. In conventional GNSS/INS FGO without direct heading measurements, the heading state is mainly propagated by inertial measurements. During prolonged straight-line or low-maneuver sailing, position-related constraints provide only weak heading correction, which may lead to cumulative heading errors. In addition, existing learning-based GNSS outage compensation methods often introduce pseudo-GNSS observations with fixed covariance, making it difficult to represent the direction-dependent and time-varying uncertainty of data-driven predictions. To address these issues, this paper proposes a heteroscedastic TCN-Assisted GRU Network (HTAGN)-enhanced FGO framework for shipborne GNSS/INS/gyrocompass integrated navigation. Within the unified framework, a gyrocompass heading factor is constructed to provide persistent GNSS-independent heading constraints, and an HTAGN is designed to generate pseudo-GNSS position increments during GNSS outages. The direction-dependent standard deviations predicted by HTAGN are further mapped to the covariance matrix of pseudo-GNSS position factors, enabling confidence-adaptive factor weighting in the optimization process. The framework was validated on a single real-world sea trial with artificially simulated GNSS outages. The gyrocompass heading factor reduced the heading Root Mean Square Error (RMSE) by 84.6% compared with conventional GNSS/INS FGO. During 90 s and 180 s outages, the proposed method reduced the horizontal positioning RMSE by 63.7% and 52.5%, respectively, relative to the fixed-covariance TAGN baseline, demonstrating improved heading estimation and short-term positioning continuity under the tested conditions. Full article

Due to the weak signal power and the publicly known structure of civil signals, Global Navigation Satellite Systems (GNSSs) face an increasingly severe threat from spoofing interference. Constrained by inherent limitations such as physical boundaries and the inability of self-validation, single-source detection methods struggle to effectively counter complex spoofing attacks. Consequently, the advantages of multi-source fusion detection have become increasingly prominent. By incorporating heterogeneous sensors such as Inertial Navigation System (INS), vision, and communication signals, multi-source fusion technology leverages independent reference sources for cross-validation, providing an effective approach to overcome the bottlenecks of single-source detection. This paper systematically reviews 67 representative publications in the field of multi-source fusion GNSS spoofing detection from 2021 to 2026. Existing methods are categorized into three main classes: filtering algorithm-based detection, multi-source data consistency-based detection, and Artificial Intelligence (AI)-based detection. The advantages and limitations of each category are systematically compared, and the current research status along with future development trends are summarized. Full article