Search Results (7,397)

Positioning, navigation, and timing (PNT) services require stable time transfer, but satellite-based PNT signals are vulnerable to interference, attenuation, and limited availability in constrained environments. Very-low-frequency (VLF) signals propagate over long distances in the Earth–ionosphere waveguide and can serve as a terrestrial complement to satellite-based timing systems. Their timing performance, however, is affected by propagation-delay variation, especially the diurnal component associated with changes in the effective ionospheric reflection height. This study presents a propagation-delay correction method for VLF timing signals based on a phase-variation model. The total delay error is separated into primary path delay, secondary propagation delay, and residual random error. The primary delay is calculated from the transmitter–receiver path, while the periodic secondary delay is corrected using the predicted phase variation. Historical Alpha observations recorded at Chongqing and Guilin were used to evaluate the correction performance. The results show that the corrected standard deviation is reduced to 2.0054–2.2500 μs for the Chongqing paths and 2.7987–4.4792 μs for the Guilin paths. The corrected root mean square error (RMSE) ranges from 2.1316 μs to 4.5641 μs across the six Alpha propagation paths. These results indicate that the proposed method can suppress the main diurnal propagation-delay component in the selected historical Alpha datasets, although further validation with contemporary and multi-season VLF observations is still needed. Full article

The ionosphere is the ionized region of the atmosphere, extending roughly from 60 km to 1000 km in altitude. During flares, the near-Earth space is subjected to high-energy X-ray and EUV (extreme ultraviolet radiation) radiation, which also impacts the ionosphere. The changes in the ionospheric parameters measured by ionosondes, namely the f_min (minimum frequency) and foF2 (F2-layer ordinary-mode critical frequency) values, were examined during solar flares that occurred in geomagnetically quiet conditions (Dst (Disturbance Storm Time index) > −40 nT, Kp (planetary K-index) < 4). The necessary data were obtained by manually evaluating ionograms recorded by the Czech DPS4D ionosonde at Pruhonice (PQ052). The degree of variation was compared to quiet reference days, allowing for the determination of the deviations in the required values (df_min, dfoF2). The time series of the deviations were investigated. Furthermore, the relationship between the deviations and a “geoeffectiveness” parameter of the solar flare was also examined. The X-ray flux, the solar zenith angle of the station at the time of the event, and the position of the flare on the solar disk were also taken into account for the determination of the “geoeffectiveness” parameter. A positive correlation was observed between df_min and the geoeffectiveness parameter of the flare, which was more significant than the correlation between the dfoF2 and the geoeffectiveness parameter. Full article

Orbit determination in Earth–Moon space is challenged by dynamic-model mismatch and unstable GNSS observation conditions, especially under weak and intermittent signals. To address this issue, this paper proposes a GNSS single-point positioning (SPP)-aided orbit determination method based on adaptive sliding-window filtering. A tightly coupled framework is constructed by integrating orbital dynamics propagation with SPP pseudo-range observations, allowing propagation errors to be corrected in real time through measurement updates. To enhance adaptability under time-varying observation conditions, a dynamic sliding-window strategy is introduced, in which the observation-noise covariance is adjusted according to carrier-to-noise ratio ($C/N_0$) variations. Simulations for three representative Earth–Moon trajectories, including a near-rectilinear halo orbit (NRHO), a distant retrograde orbit (DRO), and a Halo orbit, show that the proposed method significantly outperforms the conventional tightly coupled solution. The three-dimensional RMS position error is reduced from 6.65 m to 1.27 m for NRHO, from 6.57 m to 1.27 m for DRO, and from 5.91 m to 1.44 m for Halo, corresponding to improvements of 80.9%, 80.4%, and 75.4%, respectively. Under a simulated 200-epoch GNSS interruption in the Halo case, the method also improves outage robustness and post-recovery performance, reducing the three-dimensional RMS error by 23.2% in the interruption-centered interval and by 26.1% over the full arc. Full article

Rare-earth-doped microsphere cavities have attracted significant interest for applications in miniaturized photonic devices due to their unique optical properties. In this work, Yb³⁺/Er³⁺ co-doped microsphere cavities were fabricated via a melting method, which enables uniform interior doping at high and tunable rare-earth concentrations through a simpler and more cost-effective process compared with existing coating and fiber-etching approaches. Whispering gallery modes (WGMs) enhanced upconversion luminescence, which was observed using tapered fiber coupling, producing a vivid green fluorescence ring near the equatorial region of the microsphere. The luminescence characteristics of the microsphere cavity were investigated by measuring the fluorescence spectra under varying excitation powers. The results indicated that the fluorescence emission follows a two-photon absorption process, consistent with the upconversion emission mechanism of Er³⁺. A finite difference time domain (FDTD) model was employed to simulate the optical field distribution within the microsphere cavity. At a microsphere diameter of 90 μm and a coupling gap of 0 μm, both the 980 nm pump light and the emitted light were effectively confined near the equatorial region of the microsphere, forming WGM confinement patterns. These findings are expected to advance the application of rare-earth-doped microsphere cavities in fields such as biosensing, bioimaging, optical communications, and upconversion microlasers. Full article

Remote sensing image classification is one of the fundamental tasks in the field of remote sensing and plays a critical role in Earth observation applications. However, the inherent multi-scale characteristics of this task pose significant challenges to scene classification. To address these issues, we propose a novel framework that integrates the Contrastive Language–Image Pre-training (CLIP) model, multi-scale data, and efficient inference strategy. The proposed framework transfers general-purpose features learnt from natural images to remote sensing image classification. Specifically, this framework leverages the rich feature representations learnt by the CLIP model in the contrastive learning procedure and adopts it as the backbone network of the model to extract fine-grained and multi-scale features for remote sensing images. That is, the model can learn local fine-grained details but also encode global contextual information useful for the classification of visually similar scene categories. Afterwards, AdapterFormer module is inserted into the few selected layers of CLIP model, which can effectively enhance model performance and have low computational overhead. This helps efficient knowledge sharing and introduces new features at the model level. Furthermore, to alleviate possible performance deterioration brought about by multi-scale feature variation, a multi-scale training set is constructed at data level, providing complementary multi-scale information. Through the synergy of all these strategies above, the proposed method greatly improves the classification performance of multi-scale remote sensing images. Extensive experiments on the MEET dataset (it includes 80 fine categories and more than 800,000 samples) show that the proposed method greatly improves the performance. Compared with general-purpose classification networks and remote sensing-related models, the proposed method always gets state-of-the-art results. Full article

The Polarization and Directionality of the Earth’s Reflectances (POLDER)-3/GRASP (Generalized Retrieval of Aerosol and Surface Properties) aerosol products have been widely used in studies on radiative balance and climate change. However, the stability and consistency of the products have yet to be comprehensively evaluated, despite their critical importance for long-term studies. POLDER-3/GRASP products mainly consist of three variants: High-Precision (HP), Components, and Models. This study aims to evaluate the accuracy, stability, and consistency of these aerosol products at global and regional scales, and to provide usage recommendations. Compared with AERONET observations, the Components product shows the best performance for both aerosol optical depth (AOD) and Ångström Exponent (AE) retrievals, with Root Mean Square Error (RMSE) of 0.114 for AOD and 0.319 for AE. The Models AOD and HP AE also demonstrate relatively high validation accuracy, with RMSE of 0.138 for Models AOD and 0.366 for HP AE. Regionally, Components AOD and AE outperform those from the HP and Models products in 8 out of 10 regions. Stability evaluation shows that the stability metrics of the three AOD products range from 0.034 to 0.036 per decade, and none of them meet the Global Climate Observing System (GCOS) stability requirement (i.e., 0.02 per decade), which indicates that caution should be exercised when using POLDER-3/GRASP products for long-term analysis. In terms of consistency, Components AOD and Models AOD exhibit high agreement, while HP AOD is systematically higher than them. The AE retrieved by the three products shows considerable discrepancies, highlighting uncertainties in AE and spectral-AOD retrievals and pointing toward directions for future algorithmic improvements. In summary, considering global and regional accuracy, stability, and consistency, the Components AOD and AE products are generally recommended for use. For different regions, users can choose the appropriate product based on detailed validation and intercomparison results. Full article

Remote sensing scene classification (RSSC) faces severe challenges from data scarcity and complex background clutter. To overcome these limitations, this paper draws inspiration from few-shot fine-grained image classification (FSFGIC) to filter noise and capture subtle details. However, existing methods often process global context and local features separately, which limits their ability to suppress background noise in complex scenes. Consequently, the Guided Relational Cross-Attention Dual-branch Network (GRCD-Net) is proposed. Its core Guided Relational Cross-Attention (GRC) block leverages global semantics to filter local background noise prior to bidirectional feature interaction. Additionally, Iterative Global Relation (IGR) and Patch-level Dual-Metric (PDM) modules are integrated to robustly refine global relations and capture local similarities. Extensive experiments demonstrate that GRCD-Net consistently outperforms baselines by 2–4% on standard FSFGIC benchmarks. Notably, on the challenging NWPU-RESISC45 RSSC dataset, it achieves an 81.39% one-shot accuracy and exceeds current state-of-the-art methods by 7.55%, validating its efficacy for complex Earth observation. Full article

Subduction zones are crucial for regulating volatile exchange between the Earth’s surface and interior. Specifically, volatile migration in the mantle wedge controls arc magma genesis and outfluxes. However, the poorly constrained capacity of the forearc mantle wedge to retain volatiles limits our ability to quantify global volatile cycling. This study focuses on serpentinites from the Heimulin area and investigates volatile behavior during shallow forearc serpentinization and subsequent recrystallization within the forearc mantle wedge. This is achieved through analyses of carbon and sulfur contents and isotopic compositions, combined with thermodynamic modeling. The carbon content and isotopic composition of the two sample types, which represent different degrees of serpentinization, show no significant difference. However, carbon enrichment and magnesite formation were observed in serpentinites containing ribbon-textured lizardite. Sulfur systematics suggest that slab-derived dehydrating fluids can introduce sulfur into the mantle wedge, where it can be effectively retained in serpentinite systems as pyrite under low water–rock ratios. These findings imply that forearc serpentinites may play a role in volatile transport and serve as reservoirs for carbon and sulfur, which may have implications for understanding volatile cycling in subduction zones. Full article

Spaceflight associated neuro-ocular syndrome (SANS) is a significant ophthalmic complication observed in astronauts during and after long-duration missions, characterized by optic disc edema, globe flattening, choroidal folds, and hyperopic shifts. Unlike papilledema in terrestrial idiopathic intracranial hypertension, optic disc edema in SANS is often asymmetric. The mechanisms underlying this asymmetry remain poorly understood. In this narrative review, we synthesize and critically interpret existing clinical observations, anatomical studies, neuroimaging findings, and experimental evidence, and propose that uneven ocular venous congestion, arising from microgravity-induced cephalad fluid shifts, pre-existing transverse sinus asymmetry, and orbital venous overload, leads to asymmetric optic disc edema by differentially disrupting anterograde ocular glymphatic transport between the eyes. This mechanistic framework highlights the interplay between venous hemodynamics and ocular glymphatic flow as a key factor in SANS pathophysiology. Targeted in-flight monitoring and ground-based analog studies will be essential to rigorously test this hypothesis. To this end, we outline a feasible experimental approach that prospectively integrates preflight cerebral magnetic resonance venography, providing data on transverse sinus dominance, with serial in-flight ophthalmic imaging on the International Space Station. This combined strategy could directly determine whether dural venous sinus anatomy predisposes to uneven ocular venous congestion and asymmetric optic disc edema in microgravity. Insights gained from this work may guide the development of effective countermeasures against SANS and broaden our understanding of ocular fluid dynamics under conditions of altered venous physiology on Earth. Full article

Small satellites provide cost-effective platforms for environmental monitoring. Open-source commercial off-the-shelf (COTS) hyperspectral payloads, such as those launched with HYPSO-1 and -2, have a ground sampling distance (GSD) of 100 m. However, detecting smaller features, such as water quality in lakes, requires a GSD below 10 m and a high signal-to-noise ratio. Terrestrial COTS Schmidt–Cassegrain telescopes lack launch-load stiffness and in-orbit refocus capability. This study presents a deployable modified COTS (MCOTS) Schmidt–Cassegrain telescope that uses the original optical COTS components, a 3D-printed high-performance polymer (HPP) structure, and a dual-lead-screw deployment and focusing mechanism. The telescope has a stowed length of 280 mm and deploys to an additional 110 mm, making integration into a 16U platform with a payload length of 290 mm feasible. The modified structure is evaluated using shock and sine-sweep vibration testing, with collimation and focus verified before and after testing. Collimation remained concentric within measurement uncertainty. Complementary random-vibration finite-element simulations predicted a $3\sigma$ von Mises stress of 26.5 MPa, yielding a safety factor of 2.8. The results demonstrate a feasible pathway for adapting COTS telescopes toward space-grade COTS (SCOTS) payloads, bridging the gap between rapid production, cost efficiency, and performance for small Earth observation missions. Full article

Nearshore mariculture is a major contributor to the supply of “blue food”; however, its rapid expansion in bay systems has intensified sea-space competition and environmental pressures, underscoring the need for accurate and long-term monitoring. This study used multitemporal Sentinel-2 imagery processed using Google Earth Engine (GEE) to develop an automated identification framework for raft and cage aquaculture along the coast of Fujian, China, from 2017 to 2024. Three widely used classifiers—U-Net, DeepLabV3+, and random forest (RF)—were comparatively evaluated. Of these methods, U-Net had the most stable overall performance under optically complex nearshore conditions and was, therefore, used for province-scale mapping. Based on the U-Net-derived maps, the spatiotemporal evolution of mariculture was quantified. The results showed that mariculture in Fujian exhibited a persistent bay-oriented, dual-core clustering pattern, with major hotspots concentrated in Ningde and Zhangzhou. In the 2024 winter–summer comparison, raft aquaculture displayed a clear seasonal contrast, characterized by expansion in winter and contraction in summer, whereas cage aquaculture showed relatively smaller seasonal variation. Interannually, the mariculture system shifted from a mixed cage–raft configuration toward the dominance of raft aquaculture, accompanied by a spatial redistribution of mapped aquaculture density from inner nearshore waters toward bay mouths and more open waters. Overall, in this study, we demonstrate the potential of deep learning-enabled Sentinel-2 remote sensing for monitoring nearshore mariculture structures and provide mode-specific observational evidence for marine spatial planning, environmental risk management, and sustainable mariculture development in nearshore waters and semi-enclosed bay systems. Full article

Based on the E5018 zirconia–alkali low hydrogen iron powder electrode as the base composition, this paper incorporates nano-rare earth CeO₂ powder into the coating. Using the orthogonal experimental method, a total of nine groups of experiments were established, each with four factors and three levels. These factors include (A) nano-rare earth CeO₂ powder at levels of 1.1%, 1.3%, and 1.6%; (B) iron powder at levels of 30%, 35%, and 40%; (C) fluorite at levels of 6%, 8%, and 10%; and (D) zirconium quartz at levels of 5%, 7%, and 9%. The arc combustion stability of the welding rod is determined by an arc analyzer, and the formation and slag removal of the weld seam are evaluated by wide slope welding. Welding a spatter is evaluated by an observation method. The range and variance of the orthogonal experiment results were calculated, and the process performance was studied and analyzed. The results indicate that samples No. 1, 4, 5, 7, and 9 demonstrate superior performance in the welding process, specifically in terms of arc stability, weld formation, slag detachment, and spatter. The addition of nano-rare earth CeO₂ powder has the most significant impact on weld formation, while iron powder, fluorite, and zirconium quartz have notable effects on arc stability, spatter, and slag detachment, respectively. The optimal combination of these four factors at three levels for optimal welding process performance is A2B1C2D3, with the recommended amounts being 1.3% of nano-CeO₂ powder, 30% of the iron powders, 8% of fluorite, and 9% of zirconium quartz. Full article

Remote sensing imagery is often degraded by cloud cover, causing severe information loss and hindering downstream Earth observation tasks. Although recent deep learning methods, including CNN- and Transformer-based models, have achieved promising progress in cloud removal, they mainly operate in the spatial domain and largely overlook the frequency-domain discrepancies introduced by clouds of different types and densities. This limitation restricts their ability to generalize across diverse cloud corruption scenarios. To address this issue, we propose a Frequency Interaction Cloud Removal Network (FI-CRNet), which introduces a novel Frequency-Aware Modulation (FAM) mechanism for high-fidelity cloud-free image reconstruction. The FAM module consists of two components. First, the Frequency Decomposition (FD) module explicitly separates input features into low-frequency cloud-affected components and high-frequency detail-rich components through spectral analysis, while aligning them with decoder features via cross-attention. Second, the Cross-Frequency Interaction (CFI) module adaptively integrates these components through a dual-gate weighting mechanism, including spatial and channel gates, to suppress cloud interference while enhancing structural and textural details. By jointly modeling frequency-domain cues and spatial features, FI-CRNet enables robust and adaptive reconstruction under diverse cloud conditions. Extensive experiments show that our method outperforms state-of-the-art techniques across diverse cloud scenarios. Full article

Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide insight into their emotional well-being in chronic disease management. Currently, the process of assessing each partner’s emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, none of these works have used data collected from couples’ interactions in daily life. In this work, we collected 85 h (1021 5-min samples) of real-world multimodal smartwatch sensor data (speech, heart rate, accelerometer, and gyroscope) and self-reported emotion data (n = 612) from 26 partners (13 couples) managing diabetes mellitus type 2 in daily life. We extracted physiological, movement, acoustic, and linguistic features, and trained machine learning models (support vector machine and random forest) to recognize each partner’s self-reported emotions (valence and arousal). Our results from the best models—balanced accuracies of 63.8% and 78.1% for arousal and valence respectively—are better than the results from (1) chance, (2) prior work that also used data from German-speaking, Swiss-based couples, and (3) partners’ perceptions of each other’s emotions. This work contributes toward building automated emotion recognition systems that would eventually enable partners to monitor their emotions in daily life and enable the delivery of interventions to improve their emotional well-being. Full article

Large portions of rural population in South Africa lack access to basic water and sanitation. This advocates for urgent interventions in support of water supply. This study assessed the performance of solar-powered groundwater pumping systems established at nine pilot sites in rural areas of Greater Giyani Municipality (Limpopo, South Africa). Performance assessment indicators, namely weather, groundwater abstraction, power supply, water supply, water quality, number of beneficiaries and farm productivity, were monitored (2023–2024). Increased groundwater abstraction reduced groundwater levels by 0.4–11 m, depending on the monitored borehole. This was replenished by above-average rainfall in 2023 (≈650 mm). Power supply and pump discharge rates were stable with generally low fluctuations at recommended pumping rates (0.5–2.0 L s⁻¹). Groundwater quality was generally fit to marginal for irrigation and drinking. High levels of NO₃⁻ and total organic carbon, especially in the proximity of villages, mandated the installation of mini water treatment plants for drinking water. The implementation of solar-powered groundwater pumping schemes was generally successful, with more than 5000 villagers benefiting directly from the interventions, whilst smallholder farms turned into commercial and financially viable enterprises. Long-term monitoring of bio-physical and socio-economic drivers is essential to ensure long-term sustainability of the solar-powered groundwater pumping systems. Full article