Search Results (2,115)

The accurate simulation of sub-GeV particle detectors is essential for interpreting experimental data and optimizing detector design. This work identifies and addresses several critical aspects in modeling such detectors, taking as a case study the High-Energy Particle Detector (HEPD-02), a space-borne instrument developed within the CSES-02 mission to measure electrons in the ∼3–100 MeV range, protons and light nuclei in the ∼30–200 MeV/n. The HEPD-02 instrument consists of a silicon tracker, plastic and LYSO scintillator calorimeters, and anticoincidence systems, making it a representative example of a complex low-energy particle detector operating in Low Earth Orbit. Key challenges arise from replicating intricate detector geometries derived from CAD models, selecting appropriate hadronic physics lists for low-energy interactions, and accurately describing the detector response—particularly quenching effects in scintillators and digitization in solid-state tracking planes. Particular attention is given to three critical aspects: the precise CAD-level geometry implementation, the impact of hadronic physics models on the detector response, and the parameterization of scintillation quenching. In this study, we present original solutions to these challenges and provide data–MC comparisons using data from HEPD-02 beam tests. Full article

As space systems evolve towards cis-lunar missions and beyond, the demand for precise yet low-size, -weight, and -power (SWaP) clocks and synchronization methods becomes increasingly critical. We introduce a novel clock synchronization approach based on the Kuramoto oscillator model that facilitates the creation of an ensemble timescale for satellite constellations. Unlike traditional ensembling algorithms, the proposed Kuramoto method leverages nearest-neighbor interactions to achieve collective synchronization. This method simplifies the communication architecture and data-sharing requirements, making it well suited for dynamically connected networks such as proliferated low Earth orbit (pLEO) and lunar or Martian constellations, where intersatellite links may frequently change. Through simulations incorporating realistic noise models for small-scale atomic clocks, we demonstrate that the Kuramoto ensemble can yield an improvement in stability on the order of 1/√N, while mitigating the impact of constellation fragmentation and defragmentation. The results indicate that the Kuramoto oscillator-based algorithm can potentially deliver performance comparable to established techniques like Equal Weights Frequency Averaging (EWFA), yet with enhanced scalability and resource efficiency critical for future spaceborne PNT and communication systems. Full article

Sea ice concentration (SIC) is crucial to the global climate. In this study, a new single-channel SIC retrieval algorithm utilizing spaceborne L-band brightness temperature (TB) measurements is developed based on a microwave radiative transfer model. Additionally, its four uncertainties are quantified and constrained: (1) variations in seawater reference TB under warm water conditions, (2) variations in sea ice reference TB under extremely low-temperature conditions, (3) the freeze–thaw dynamics of sea ice captured by Diurnal Amplitude Variation (DAV) signals, and (4) Land mask imperfections. It is found that DAV has the most pronounced effect: eliminating its influence reduces RMSE from 10.51% to 8.43%, increases R from 0.92 to 0.94, and minimizes Bias from -0.68 to 0.13. Suppressing all four uncertainties lowers RMSE to 7.42% (a 3% improvement). Furthermore, the algorithm exhibits robust agreement with the seasonal variability of SSM/I SIC, with R mostly exceeding 0.9, RMSE mostly below 10%, and Biases mostly within 5% throughout the year. Compared to ship-based and SAR SIC data, the new L-band algorithm’s Bias and RMSE are only 2% and 2% (ship-based)/2% and 1% (SAR) higher, respectively, than those of the SSM/I product. Future algorithms can integrate the DAV signal more effectively to better understand sea ice freeze–thaw processes and ice-atmosphere interactions. Full article

Synthetic aperture radar (SAR), with its unique imaging principle and technical characteristics, has significant advantages in surface observation and thus has been widely applied in tasks such as object detection and target classification. However, limited by the lack of labeled SAR image datasets, the accuracy and generalization ability of the existing models in practical applications still need to be improved. In order to solve this problem, this paper proposes a spaceborne SAR image simulation technology and innovatively introduces the concept of bounce number map (BNM), establishing a high-resolution, parameterized simulated data support system for target recognition and classification tasks. In addition, an electromagnetic scattering characteristic-enhanced dual-branch network with simulated image guidance for SAR ship classification (SeDSG) was designed in this paper. It adopts a multi-source data utilization strategy, taking SAR images as the main branch input to capture the global features of real scenes, and using simulated data as the auxiliary branch input to excavate the electromagnetic scattering characteristics and detailed structural features. Through feature fusion, the advantages of the two branches are integrated to improve the adaptability and stability of the model to complex scenes. Experimental results show that the classification accuracy of the proposed network is improved on the OpenSARShip and FUSAR-Ship datasets. Meanwhile, the transfer learning classification results based on the SRSDD dataset verify the enhanced generalization and adaptive capabilities of the network, providing a new approach for data classification tasks with an insufficient number of samples. Full article

Regarding the functions of Cave 285 at the Mogao Caves 莫高窟 during the Western Wei period, scholars have generally considered it a meditation cave. The main chamber has four small chambers each on the southern and northern walls, believed to serve as meditation spaces. However, a close examination of the architectural features of these eight small chambers reveals that they may have had another purpose, fundamentally different from meditation. Close visual analysis shows that the lintels of each small chamber are adorned with honeysuckle patterns, between which stand two birds forming paired bird images, with considerable variation in the types of birds. The lintel imagery of the eight small chambers in Cave 285 differs from the honeysuckle and lotus-rebirth themes commonly emphasized in the lintel designs of the main niches of contemporaneous caves that highlight the significance of the Pure Land of the Buddha. It also does not align with the flame-pattern-dominated designs seen in other niches on various faces of the central pillar during this period. This indicates a difference in symbolic meaning. At the same time, the paired birds or individual birds appear in depictions of the Pure Land on the truncated-pyramidal ceilings of caves from the same period, alongside images of honeysuckle, lotus-born beings, celestial beings, winged deities, jewels, and animals. Similarly, paired birds (such as parrots, vermilion birds, phoenixes, and bluebirds) found on the walls, heavenly gates, and screens of the Wei and Jin dynasty tombs in Dunhuang symbolize the deceased’s ascension to immortality. The frequent appearance of paired birds on lintels, doors, door frames, and walls outside the doors of tombs from the medieval period signifies the deceased’s ascension to immortality. Considering the funerary nature of the eight small chambers in Cave 285 and the symbolic meaning and development trajectory of paired birds in tombs and caves during the medieval period, the eight pairs of birds on the lintels of these small chambers were meant to aid the deceased’s soul in its ascension to immortality and rebirth in the Pure Land. Full article

Global water environment monitoring urgently requires remote sensing data with high temporal resolution and wide spatial coverage. However, current space-borne ocean color spectrometers still face a significant trade-off among spatial resolution, swath width, and system compactness, which limits the large-scale deployment of satellite constellations. To address this challenge, this study developed a lightweight high-resolution spectral imager (LHRSI) with a total mass of less than 25 kg and power consumption below 80 W. The visible (VIS) camera adopts an interleaved dual-field-of-view and detectors splicing fusion design, while the shortwave infrared (SWIR) camera employs a transmission-type focal plane with staggered detector arrays. Through the field-of-view (FOV) optical design, the instrument achieves swath widths of 207.33 km for the VIS bands and 187.8 km for the SWIR bands at an orbital altitude of 500 km, while maintaining spatial resolutions of 12 m and 24 m, respectively. On-orbit imaging results demonstrate that the spectrometer achieves excellent performance in both spatial resolution and swath width. In addition, preliminary analysis using index-based indicators illustrates LHRSI’s potential for observing chlorophyll-related features in water bodies. This research not only provides a high-performance, miniaturized spectrometer solution but also lays an engineering foundation for developing low-cost, high-revisit global ocean and water environment monitoring constellations. Full article

The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds. Full article

Background: Mosquito-borne viral diseases are a growing global health threat, and artificial intelligence (AI) and machine learning (ML) are increasingly proposed as forecasting tools to support early-warning and response. However, the available evidence is fragmented across pathogens, settings and modelling approaches. This review provides, to the best of our knowledge, the first comprehensive comparative assessment of AI/ML models forecasting mosquito-borne viral diseases in human populations, jointly synthesising predictive performance across model families and appraising both methodological quality and operational readiness. Methods: Following PRISMA 2020, we searched PubMed, Embase and Scopus up to August 2025. We included studies applying AI/ML or statistical models to predict arboviral incidence, outbreaks or temporal trends and reporting at least one quantitative performance metric. Given the substantial heterogeneity in outcomes, predictors and time–space scales, we conducted a descriptive synthesis. Risk of bias and applicability were evaluated using PROBAST. Results: Ninety-eight studies met the inclusion criteria, of which 91 focused on dengue. The forecasts spanned national to city-level settings and annual-to-weekly resolutions. Across classification tasks, tree-ensemble models showed the most consistent performance, with accuracies typically above 0.85, while classical ML and deep-learning models showed wider variability. For regression tasks, errors increased with temporal horizon and spatial aggregation: short-term, fine-scale forecasts (e.g., weekly city level) often achieved low absolute errors, whereas long-horizon national models frequently exhibited very large errors and unstable performance. PROBAST assessment indicated that most studies (63/98) were at high risk of bias, with only 24 judged at low risk and limited external validation. Conclusions: AI/ML models, especially tree-ensemble approaches, show strong potential for short-term, fine-scale forecasting, but their reliability drops substantially at broader spatial and temporal scales. Most remain research-stage, with limited external validation and minimal operational deployment. This review clarifies current capabilities and highlights three priorities for real-world use: standardised reporting, rigorous external validation, and context-specific calibration. Full article

Thermal control of spaceborne micro-hyperspectral imagers (MHIs) operating in inclined low-Earth orbits (LEOs) presents significant challenges due to the complex and dynamically varying external heat flux, which lacks a stable heat dissipation surface. This study proposes a thermal radiation analysis method capable of rapidly deriving accurate numerical solutions for the thermal radiation characteristics of spacecraft in such orbits. A dedicated thermal control system (TCS) was designed, featuring a radiator oriented towards the +z_s plane, which was identified as having stable and low incident heat flux across extreme solar–orbit angle conditions. The system employs efficient thermal pathways, including thermal pads and a flexible graphite thermal ribbon, to transfer heat waste from the imaging module to the radiator, supplemented by electric heaters and multilayer insulation for temperature stability. Steady-state thermal analysis demonstrated excellent temperature uniformity, with gradients below 0.017 °C on critical optics. Subsequent thermo-optical performance analysis revealed that the modulation transfer function (MTF) degradation was maintained below 2% compared to the ideal system. The results confirm the feasibility and effectiveness of the proposed thermal design and analysis methodology in maintaining the stringent thermo-optical performance required for MHIs on inclined-LEO platforms. Full article

To meet the real-time, multi-task processing demands of spaceborne synthetic aperture radar (SAR) systems under limited onboard resources, this paper presents a configurable field-programmable gate array (FPGA) architecture that supports both water body and oil spill detection. First, an efficient computing engine partitioning method at coarse and fine granularities is proposed. The operations of the water body and oil spill detection algorithms are clustered and analyzed at two levels, and both general-purpose and specialized computing engines are designed to minimize resource usage. Second, a high-reuse storage optimization strategy is introduced. Based on the data buffering cycle, a shared on-chip memory is designed to minimize storage resource consumption. Building upon these foundations, a software and hardware co-programmable efficient processing system is developed, successfully mapping both detection algorithms onto the FPGA. Finally, the effectiveness of the proposed architecture is confirmed through experimentation, and processing performance is analyzed. Processing times for a 16K × 16K water body scene and a 16K × 16K oil spill scene are 15 s and 13 s, respectively, at a clock frequency of 100 MHz, meeting the real-time multi-task processing requirements of on-board operations. Full article

Ship targets are usually high-value targets, and synthetic aperture radar (SAR) moving ship indication is of great importance in maritime traffic monitoring. However, due to the motion of the ocean, maritime ships may have rotational motion in addition to the conventional translational motion. The rotational motion, including the yaw, pitch, and roll, will cause the signal characteristics of the ship to become very complex, which increases the difficulty of designing moving target indication methods. This paper studies the effect of each rotation motion on the ship’s signal characteristics in image domain for spaceborne multichannel SAR. Firstly, the range equation of an arbitrary scatterer on the ship with both rotational and translational motions is developed. Then, the influences of each rotation motion on the coefficients of the range equation and the scatterer’s along-track interferometric (ATI) phase are revealed. Finally, numerical experiments are conducted to investigate the effect of each rotation motion on the scatterer’s azimuth position shift, azimuth defocusing, azimuth sidelobe symmetry, and ATI phase, which are important parameters for moving target indication. Full article

The Space Solar Power Station/Satellite (SSPS) is a large-scale space-borne facility intended for the direct collection and conversion of solar energy in the extra-stratospheric region. The optimization of its light collection and conversion (LCC) structures, analysis of dynamic characteristics, and design of attitude control systems represent core technical bottlenecks impeding the advancement of SSPS. To address these issues, this study investigates a novel conceptual line-focusing SSPS. Firstly, a multi-objective collaborative optimization model is developed to optimize the structural parameters of the concentrator and photovoltaic (PV) array. Subsequently, based on the optimized parameters, a coupled multi-body dynamic model is formulated, incorporating gravity-gradient torque and other space-borne disturbance factors. Finally, a distributed Proportional–Integral–Derivative (PID) controller is proposed to achieve three-axis attitude stabilization of the SSPS. Simulation results demonstrate that the light collection efficiency achieves 81.9% with a power density of 4792.24 W/m²; concurrently, a balance between the geometric parameters of the LCC system and the aforementioned key performance indicators is attained, and the proposed controller possesses favorable anti-disturbance performance. Full article

Approaches, methods, and corresponding ground-based and air/space-borne instrumentation currently utilized for detecting, studying, and monitoring fogs (including in situ and remote sensing techniques) are summarized. Special attention is paid to the existing and some emerging methods enabling reliable assessments and quantification of basic fog parameters, such as visibility, liquid water content, droplet number/volume concentration, effective radius, and size distribution. Along with purely natural fogs and those resulting directly or indirectly from industrial, combustive, or other human activities (smog, chemical fogs), entirely artificially created fogs are also subject to consideration in this study. Systems and apparatuses for the generation and control of artificial fogs are presented and discussed in terms of operational principles, design, and applicability. Methods and devices for fog water collection/harvesting are presented in view of their importance for solving the lack of water problem in dry and desert regions. Some other actual and potential applications of natural and artificial fogs are summarized and discussed related to air freshening or cleaning from chemicals and radioactive aerosols, fire extinguishing, nebulized therapies in medicine, spray coating of tablets or material surfaces, aeroponic agriculture, dust-proof coatings, etc. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

Eutrophication has emerged as a critical threat to water quality degradation and ecosystem health on a global scale, calling for prompt management actions. Remote sensing enables the monitoring of eutrophication by detected changes in ocean color caused by fluctuations in chlorophyll a (chl a). Although chl a is a crucial indicator of phytoplankton biomass and nutrient overloading, it reflects the outcome of eutrophication rather than its cause. Nutrients, the primary “drivers” of eutrophication, are essential indicators for predicting the potential phytoplankton growth in water bodies, allowing adoption of effective preventive measures. Long-term monitoring of nutrients combined with multiple water quality indicators using remotely sensed data could lead to a more precise assessment of the trophic state. Retrieving non-optically active constituents, such as nutrients and DO, remains challenging due to their weak optical characteristics and low signal-to-noise ratios. This work is an attempt to review the current progress in the retrieval of un-ionized ammonia (NH₃), ammonium (NH₄⁺), ammoniacal nitrogen (AN), nitrite (NO₂⁻), nitrate (NO₃⁻), dissolved inorganic nitrogen (DIN), phosphate (PO₄³⁻), dissolved inorganic phosphorus (DIP), silicate (SiO₂) and dissolved oxygen (DO) using remotely sensed data. Most studies refer to Case II highly nutrient-enriched water bodies. The commonly used spaceborne and airborne sensors, along with the selected spectral bands and band indices, per study area, are presented. There are two main model categories for predicting nutrient and DO concentration: empirical and artificial intelligence (AI). Comparative studies conducted in the same study area have shown that ML and NNs achieve higher prediction accuracy than empirical models under the same sample size. ML models often outperform NNs when training data are limited, as they are less prone to overfitting under small-sample conditions. The incorporation of a wider range of conditions (e.g., different trophic state, seasonality) into model training needs to be tested for model transferability. Full article