Search Results (16,367)

This perspective consolidates recent advances that position plasma and metamaterials as a unified metasystem for next-generation space micropropulsion systems. The need for new approaches arises because advanced small form-factor satellites and propulsion systems face physical and technological limits of conventional methods, including restricted scalability, limited electromagnetic control, and insufficient efficiency for complex and long-duration missions. These causes motivate the implementation of concepts based on metamaterials, plasma-based subsystems used in unconventional ways, and engineered ionized media. The paper integrates three complementary domains: plasma-enabled fabrication of complex metamaterials, engineered plasma–metamaterial interaction for controlled electromagnetic environments, and structured plasmas functioning as metamaterials with tunable effective properties. Emerging examples include metasurface-assisted waveguides for compact plasma sources, plasma-induced transparency platforms, machine learning-optimized metamaterial absorbers, and inverse-designed plasma metamaterials. Advances in plasma-based additive manufacturing and hierarchical material synthesis further expand the design space for multifunctional architectures, enabling adaptive, efficient and miniaturized propulsion concepts for CubeSat-class and small-satellite systems. Full article

Phased arrays are key for next-generation mobile communication, satellite communication, and radar systems. Radiation patterns are essential performance metrics for evaluating antennas, but conventional mechanically scanned measurements become inefficient when repeated for every beam of the phased array. Existing active-element-pattern-based methods reduce this burden by grouping elements with similar coupling environments, yet their grouping and representative-element selection are mainly based on physical intuition, and on–off-mode-based measurements may differ from the actual all-on operating state. This paper proposes a data-driven adaptive grouping method based on K-means unsupervised learning. Element positions and broadside phase information construct the feature matrix, and a composite evaluation function considering group size and electric-field variation subdivides high-dynamic regions. Representative element patterns are acquired in the all-on mode, with sparse angular sampling to further reduce measurement time. Simulation results of an $8\times 8$ dipole array show that the proposed method reconstructs multi-beam array patterns using 16 representative elements, fewer than the 25 representative elements required by a $5\times 5$ grouping strategy, with the same accuracy in the main lobe and improved accuracy in sidelobe nulls. The effectiveness of the proposed grouping method has also been demonstrated for the measured $4\times 4$ mmWave phased array. Full article

Spotted seals (Phoca largha) exhibit an unusual ecological feature within the population of Peter the Great Bay (PGB). Following the breeding and molting seasons, one group performs long-distance foraging migrations, whereas another group remains in the bay year-round. To examine how space use differs between these two groups, nine satellite tags were deployed on juvenile spotted seals within PGB between 2017 and 2022: four seals were tagged at the rookery located on the Rimsky-Korsakov Archipelago (RKA), whereas the remaining five were tagged at the haul-out on the Verkhovskogo Islands (VI). Spotted seals tagged at RKA undertook long summer migrations toward the northeast of the East Sea/Sea of Japan and subsequently to the south of the Okhotsk Sea. Four spotted seals tagged at VI remained in PGB throughout the warm season. Seals were allocated to two clusters based on median speed during transit travel. The slower-moving group consisted of three spotted seals that remained in PGB; these seals also made fewer transit passages and used smaller areas of water. Full article

Soil water erosion is one of the most serious environmental problems worldwide, with major consequences for agricultural output, food security, and terrestrial ecosystems, particularly in the Mediterranean basin. This study compares modelled soil-loss conditions across Andalusia, Spain (87,268 km²), between the 2018 and 2025 reference years using a cloud-based implementation of the Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE). The framework couples daily precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) at its native information scale of approximately 5.5 km, 10 m satellite imagery, and a machine learning-derived, year-specific soil-erodibility update on a common 30 m output grid. The regional mean annual soil loss in 2025 (45.86 t ha⁻¹ yr⁻¹) was 24.98% higher than in 2018 (36.70 t ha⁻¹ yr⁻¹). The 2025 reference year also showed a 12.28% higher R-factor and a 7.83% higher C-factor. The area under Severe erosion (>50 t ha⁻¹ yr⁻¹) increased from 16,330 to 20,636 km² (+26.37%). Exact signed Shapley attribution on the common erodible support assigned +7.43, +3.52, +0.23, and −2.70 t ha⁻¹ yr⁻¹ to R, C, P, and K, respectively. These results describe a marked contrast between two modelled reference years without establishing a continuous trend or causal change, and demonstrate a transparent framework for regional erosion screening to support sustainable land-use planning and soil-conservation strategies. Full article

After major disasters, news media selectively construct the public record of what has been lost. This study examines that pattern by comparing an independently derived satellite-based inventory of damaged cultural heritage in Hatay, Türkiye, with a corpus of 79 Turkish-language online news reports published between February 2023 and February 2024. Under the primary retrieval protocol, only 16 of the 86 ARIA-export assets appeared directly in the coverage; with six further off-export or proximity-based cases, 22 reported assets entered the news in total. A targeted asset-name search then located 15 further reports, containing 17 additional direct references to damaged assets. Even on the most inclusive reading, in which every one of these references is treated as a previously uncounted and non-overlapping asset, at least 53 of the 86 assets (61.6%) remained outside the news record. The analysis also covers attribute salience, temporal patterns, and outlet concentration in the coverage. Drawing on first- and second-level agenda-setting theory, and treating gatekeeping and media-system conditions as contextual mechanisms, it defines visibility deficit as the measurable gap between independently documented damage and the damage that enters a defined news corpus. This gap need not imply deliberate suppression. It may follow from editorial priorities, limited access to sites, the availability of visual material, or the limited capacity of newsrooms to work with geospatial data. By attending to what stays outside the news agenda rather than only to differences in salience among covered items, the study identifies a structural boundary of agenda setting and extends agenda-setting research into measurable non-coverage after disaster. Full article

The advancement of precision agriculture and smart tillage relies on high-precision, real-time perception of unmanned ground vehicle (UGV) positions. In large-scale farmland operations, conventional Global Navigation Satellite System (GNSS)-based positioning may suffer from short-term signal loss, degrading accuracy. Multi-unmanned aerial vehicle (UAV)-assisted vehicle localization based on direction-of-arrival (DOA) estimation can provide critical positioning compensation for UGV, where compressed sensing (CS)-based DOA-assisted localization algorithms are commonly employed. However, existing schemes neither account for the bias induced by the local positional oscillation of UAVs, nor address the limited accuracy and real-time performance of CS-based DOA estimation, restricting their agricultural deployment. To this end, this paper first develops an assisted-localization architecture that explicitly incorporates the local positional offsets of multiple UAVs, together with a corresponding array signal reception model. To overcome the accuracy–efficiency trade-off of conventional CS-DOA methods, an adaptive local overcomplete dictionary (LOD) is then constructed to robustly refine the angular resolution around the region of interest. With the number of sources K assumed to be known and fixed, a particle swarm optimization (PSO)-based local refinement algorithm is further introduced to adaptively optimize the DOA estimates within the constructed local dictionary, thereby improving estimation robustness under low-SNR and coherent-source conditions. Consequently, the proposed method improves robustness while maintaining favorable localization accuracy and computational efficiency in the simulated scenarios. Simulation results show that it substantially reduces localization error compared with state-of-the-art algorithms, suggesting its potential as a localization-assistance approach for UGV navigation in sustainable tillage. Full article

We analyzed AIRS CH₄ volume mixing ratio (VMR) Standard L3 v6/v7 IR-Only Daily products and ground-based measurements from 16 stations of the Network for the Detection of Atmospheric Composition Change (NDACC) at 24 pressure levels from 1000 to 1 mbar. We assessed the dependence of maximum AIRS sensitivity on latitude. At high latitudes, the zone of maximum sensitivity is closer to the surface, at 700–500 mbar; in mid-latitudes, it is 500–250 mbar; and in tropical and subtropical regions, good initial agreement between satellite and ground-based data is observed at 400–200 mbar for both AIRS product versions. At the vast majority of pressure levels and all comparison sites, a unidirectional negative drift in the difference between satellite and ground-based measurements (i.e., discrepancy drift) was observed. Drift coefficients were calculated for each statistically supported pressure level. Two regions of maximum drift were identified: one in the lower atmosphere (925–850 mbar) and another near 50 mbar. The smallest drift was observed at 400–200 mbar. As the main result of the study, we developed and applied correction factors for all 23 AIRS v6 and v7 levels. Using these coefficients led to much better agreement between long-term methane trends from ground-based and satellite measurements and to higher correlation coefficients across all comparison sites. Full article

Coastal humid-heat and photochemical-oxidant episodes are commonly studied through concentration estimation, leaving it unclear whether satellite observations improve warning decisions under explicit false-alarm constraints. This study introduces CoAST-EWS Japan, a six-station, validation-locked hindcast benchmark across Osaka Bay and Tokyo Bay. Models were developed using June–July 2023 data, calibrated and thresholded on August 2023 predictions, and retrospectively evaluated on June–August 2025 station-hour observations. The strong non-satellite route combines recent ground history, ERA5 meteorology, CAMS composition, and static station geometry. Adding previous-day MODIS thermal context to an otherwise identical XGBoost route increased average precision from 0.3153 to 0.3429, reduced the Brier score from 0.05032 to 0.04874, and improved recall/F1 under a validation-locked budget of 0.5 false alarms per station-day (FPDs) from 0.1864/0.2511 to 0.2402/0.3042. Japan-local calendar-day intervals supported the improvements in Brier score, recall, and F1. In a dimension-matched comparison using the same 18 MODIS variables, previous-day context increased average precision over the same-day route by 0.0378 (95% CI: 0.0144–0.0603), demonstrating that the timing advantage was not attributable to a larger satellite feature set. The MODIS increment was strongest during high-heat issue times and in Osaka Bay, and its ranking value was reproduced by a 36 h Temporal FLOW model. Matched spatial controls identified distance-based coastal context as the most stable 24 h graph component, while wind-aligned information operated as a complementary route. These results establish latency-aware MODIS thermal context as a measurable decision input for neighborhood-scale coastal compound warning. Strict station-level localization, cross-bay transfer, and forecast-consistent deployment define the next validation frontier. Full article

Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which was further used to illustrate its potential for monitoring the temporal dynamics of different forest habitat types. Very high-resolution multispectral data from the WorldView-2/3 satellites were used, complemented by multispectral and LiDAR data acquired by an unmanned aerial vehicle (UAV). Four target forest vegetation types were mapped within a six-class classification scheme that also included “Other vegetation” and “Soil/Others” as non-target/background classes. The performance of ten supervised classification algorithms was evaluated, including Minimum Distance, Mahalanobis Distance, Parallelepiped, Spectral Angle Mapper, Maximum Likelihood, Naïve Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine, and the transformer-based deep learning model SegFormer. The results indicate that Random Forest achieved the highest overall accuracy, while Support Vector Machine and SegFormer also showed competitive performance, particularly when spectral information was integrated with vegetation indices and topographic variables. The study provides practical evidence on the selection of input data and classifiers for detailed forest vegetation mapping in a large and topographically complex island. Full article

Satellite precipitation products like GPM IMERG are crucial for hydrological modeling and disaster prevention; yet, their reliability in complex mountainous monsoon regions remains challenging. While the latest IMERG V07 introduces key upgrades, including a Climatological Calibration Algorithm (CCA), its cross-scale error propagation mechanisms and performance heterogeneity in complex underlying surfaces are poorly understood. This study evaluates the daily and monthly performance of IMERG V07 and V06 (Early, Late, and Final Runs) from 2014 to 2020 against 91 rain gauges in Guangxi, China—a typical mountainous monsoon region. The evaluation employs multiple statistical metrics and a multi-dimensional stratification approach based on elevation, precipitation intensity, and seasonality to quantify error propagation and climate-topography coupling effects. The results reveal that V07, particularly the Late Run, enhances daily precipitation detection capabilities, it significantly increases the proportion of systematic positive bias from 62.3 to 64.8% (V06) to 67.2–68.9% (V07). Consequently, upon temporal aggregation to the monthly scale, this systematic overestimation is severely amplified, leading to degraded performance, with the Final Run suffering the most substantial accuracy loss. Furthermore, retrieval accuracy is heavily constrained by surface heterogeneity, with systematic overestimation surging in areas where relatively dry (mean annual precipitation < 1300 mm) and complex terrain (elevation 100–500 m) coincide. The introduced CCA effectively improved dry season estimations but failed during wet season by introducing substantial positive biases. Ultimately, while V07 better captures short-term precipitation dynamics, its structural systematic biases compromise long-term cumulative reliability, highlighting the necessity for physics-based bias correction in hydrological applications and dynamic calibration in future algorithm upgrades. Full article

In April 2024, the Hellenic Cadastre became one of Europe’s first land registries to use a generative AI model (a large language model served through Azure OpenAI) for the legal review of property deeds. Unlike similar European initiatives using classical NLP, Greece applied state-of-the-art generative AI to a massive legacy issue: 390 historical mortgage registries holding an estimated 600 million to one billion paper pages. By April 2026, the system had processed 310,000 acts, reducing the average per-act review time from about thirty minutes to under ten; a very large per-act cost reduction is also reported by the implementation partner, which we treat as a vendor-stated figure. Additionally, the cadastre’s geodetic infrastructure found a second use following the 2023 Tempi rail disaster. In 2026, the Hellenic Positioning System (HEPOS), a 98-station GNSS reference network, began providing corrections for Greece’s real-time train tracking platform. While satellite-based train positioning is not novel in Europe, where consortia such as CLUG have run a decade of research and pilots, this marks its operational deployment in Greece. The Greek case is unique institutionally rather than technically: it repurposed a national CORS network for a citizen-facing train tracking platform as a short-term crisis response, alongside an incomplete ETCS rollout. This paper documents both deployments, measures their impact, maps them onto the nine FELA pathways, and identifies transferable practices. Greece is not presented as a technological frontier, but as an example of how a country can put existing geospatial infrastructure and AI to rapid use in delivering build-back-better policies for the public, in line with the UN 2030 Agenda. Full article

Wide- and narrow-band composite-axis servo systems in inter-satellite laser communication face critical challenges in balancing high-precision dynamic tracking and strong robust anti-disturbance performance under the coupling effect of high-speed inter-satellite relative motion and multiple strong disturbances. To address this issue, this paper proposes a composite control method integrating adaptive non-singular terminal sliding-mode control and a nonlinear extended state observer. First, a full-link dynamic model covering electromechanical coupling and inter-axis disturbance transmission is constructed to accurately quantify the disturbance characteristics of coarse- and fine-tracking loops. Second, a third-order nonlinear extended state observer is designed to realize real-time high-precision estimation and feedforward compensation of lumped disturbances. On this basis, a self-consistent adaptive non-singular terminal sliding-mode control law is formulated. Under the explicitly stated observer-residual and reaching-phase assumptions, the ideal continuous model provides finite-time convergence of the sliding variable and tracking error. Finally, a wide- and narrow-band cooperative strategy based on error frequency division is introduced to achieve complementary performance between large-stroke coarse tracking and ultra-high-precision fine tracking. Numerical simulations yield a steady-state tracking-error point estimate of 0.30 μrad and a 20 dB disturbance-suppression bandwidth of 1200 Hz. In the semi-physical dynamic-tracking test, the proposed controller limits the peak error to 1.2 μrad; the instrument-only expanded uncertainty of the detector output is estimated as 0.12 μrad (coverage factor k = 2). At the reported evaluation points, the proposed method outperforms PID, conventional sliding-mode control, and linear active-disturbance-rejection control. Deterministic robustness simulations also show smaller tracking errors and shorter recovery times under parameter perturbation, actuator saturation, and temporary link occlusion. No Monte Carlo loss-of-lock probability is claimed. Full article

Accurate precipitation data are critical for hydrological and climatic studies in Guangdong Province, where complex terrain and frequent extreme rainfall pose substantial challenges. However, the performance of gridded precipitation products is still not well understood. This study evaluates nine products, including gauge-based (CHM_PRE, CN05.1, GMCP, NOAA CPC), satellite-based (IMERG-E, IMERG-F, TMPA RT, TMPA 3B42), and ERA5 reanalysis against NCDC observations from 2001 to 2019 using metrics including trend significance, correlation (R), root mean square error (RMSE), categorical statistics (POD, FAR, ETS), and relative bias across rainfall intensities. The results indicate that, based on validation against NCDC observations, CHM_PRE performs the best across all temporal scales, capturing significant increasing trends (p < 0.05) and achieving the highest consistency with observations at the annual (R = 0.99), monthly (R = 0.99), and daily (R = 0.89) scales. Using CHM_PRE as the reference, CN05.1 shows the highest spatial consistency with it, especially for extreme events. NOAA CPC exhibits the best performance in monthly event detection (ETS = 0.42; BIAS ≈ 1). Satellite products show acceptable performance at the monthly scale but exhibit intensity-dependent biases and high daily variability, with pronounced “light rain overestimation and heavy rain underestimation.” ERA5 shows limitations, particularly in its severe underestimation of extreme precipitation. CHM_PRE is thus identified as the most suitable dataset for Guangdong based on its agreement with NCDC observations. With CHM_PRE as the reference, CN05.1 provides a reliable alternative for spatial analyses; NOAA CPC performs the best in monthly event detection. Satellite products suit monthly use but require daily-scale caution; ERA5 shows a relatively poor performance. Full article

Earth Observation (EO) analysis increasingly relies on large and heterogeneous satellite datasets, yet developing EO workflows often requires specialized expertise in data selection, geospatial programming, and cloud-based processing. Recent advances in Large Language Models (LLMs) offer new opportunities for natural-language interaction with EO systems, although challenges related to transparency, reproducibility, and domain-specific reasoning remain. This study presents SeaScope, an explainable AI framework that integrates LLMs, Retrieval-Augmented Generation (RAG), scientific knowledge retrieval, and Google Earth Engine (GEE) to transform natural-language requests into transparent and executable EO workflows. The framework combines knowledge retrieval, code generation, cloud execution, provenance tracking, and interactive visualization within a unified environment. A pilot implementation is demonstrated through maritime and coastal monitoring applications, including oil spill detection, vessel monitoring, water quality assessment, floating debris detection, and air quality analysis. Multiple state-of-the-art LLMs are evaluated under both RAG and non-RAG configurations using representative EO case studies. The results indicate substantial differences among model families and show that retrieval augmentation can significantly improve workflow generation quality and reliability for capable models, while providing more limited benefits for smaller models. The proposed framework demonstrates the potential of explainable AI agents to support transparent, reproducible, and scalable EO analysis. Full article

Shoreline determination is fundamental to coastal research, spatial planning, and legal boundary delineation but remains challenging in low-relief coastal areas where small sea-level variations can produce substantial horizontal shoreline displacements. This study compares shoreline determination methods based on tide gauge (TG) observations, LiDAR data, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical satellite imagery using the low-relief coast of Pärnu Bay, Estonia, as a case study. The comparison was based on shorelines derived from Sentinel-1 and Sentinel-2 imagery acquired on selected common acquisition dates within the 2015–2025 study period, rather than on a temporally continuous annual dataset, and compared with temporally matched LiDAR-derived shorelines extracted from a Digital Terrain Model (DTM) generated from a 2021 LiDAR survey. The LiDAR-derived shorelines were extracted using the mean sea level (MSL) observed at the Pärnu and Häädemeeste TGs at the satellite overpass time, while the satellite-derived shorelines were additionally validated against RTK GNSS measurements. The results demonstrate that the evaluated methods produce substantially different shoreline positions. Sentinel-2-derived shorelines generally corresponded more closely to the temporally matched LiDAR-derived shorelines than Sentinel-1-derived shorelines and most accurately represented the instantaneous land–water boundary during field validation. These findings demonstrate that different shoreline determination methods represent different shoreline definitions. Consequently, shoreline datasets should be interpreted according to their intended purpose rather than treated as directly interchangeable. Full article