Search Results (1,764)

The electrocatalytic nitrate reduction reaction (NO₃RR) enables sustainable ammonia synthesis from nitrate waste, yet its complex mechanism and severe competition from the hydrogen evolution reaction (HER) demand precise control over interfacial electronic structures. This review provides a mechanistic overview of interfacial electron engineering for NO₃RR via charge transfer, d-band center modulation, and d-p orbital coupling. We propose a reverse-engineering framework that starts from the three kinetic bottlenecks of NO₃RR (nitrate activation, *H supply, and intermediate poisoning) and back-extracts the required electronic effects (charge transfer, d-band shift, and d-p orbital coupling). From this perspective, we cover the construction of built-in electric fields (BIEFs) in heterojunctions, engineering atomic-scale active sites (e.g., single-atom and dual-atom catalysts), and exploiting hydrogen spillover and reverse spillover for cross-spatial proton delivery. Given that rational interfaces dynamically evolve under operating conditions, we highlight that in situ/operando characterization captures the dynamic restructuring of valence states, coordination environments, and morphologies, establishing clear structure–electron–activity relationships. Finally, we discuss key challenges and outline future directions, including machine learning-accelerated screening, dynamic interface regulation, and synergistic integration of multiple electronic effects. This review offers a comprehensive framework for interfacial electron engineering, guiding rational design of next-generation NO₃RR electrocatalysts. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

Post-disaster scenarios such as tsunamis require rapid terrain assessment that cannot wait for the next satellite synthetic aperture radar (SAR) revisit, yet a readily deployable system remains lacking. We present an off-the-shelf K-band drone-borne dual circular polarimetric (DCP) SAR and a processing pipeline for on-demand terrain classification. Compared with fully polarimetric (FP) SAR, DCP requires only a single transmit polarization and two receive channels, providing a wider swath than FP for the same acquisition, while still separating odd-bounce and even-bounce scattering mechanisms, which dual linear polarimetric modes with the same channel count provide with greater ambiguity due to their sensitivity to target orientation angle. To compensate for platform motion, we implemented RTK global navigation satellite system (GNSS) guided time-domain backprojection (TDBP) with phase gradient autofocus (PGA), yielding an 11.98 dB improvement in peak amplitude. We then applied single-target wire calibration to correct a measured 8.91 dB inter-channel complex gain difference between co-polarization and cross-polarization. As a result, H/α decomposition of the calibrated DCP data classifies canonical reflectors, artificial structures, gravel roads, vegetation, and a pond surface. These field experiments extend compact polarimetric H/α decomposition to drone-borne SAR data for terrain discrimination, establishing a practical pathway toward rapid post-disaster terrain assessment. Full article

The accumulation of training and competition loads represents a critical determinant of musculoskeletal injury risk in professional team sports, yet contemporary monitoring systems remain limited by their reliance on single-domain temporal analysis that overlooks the multi-scale rhythmic patterns inherent in athletic workload signals. This study introduces FDTM (frequency-aware dual-stream temporal model), a deep learning framework that jointly encodes time-domain dependencies and frequency-domain spectral signatures from digital athlete monitoring streams to predict individual injury risk over a forward-looking seven-game horizon. The framework integrates a stacked bidirectional long short-term memory branch augmented with temporal self-attention pooling, a spectral encoding branch employing discrete Fourier transform decomposition across high-frequency (weekly), mid-frequency (bi-weekly), and low-frequency (seasonal) bands, and a cross-modal gated attention fusion module that adaptively balances temporal and spectral representations conditioned on player context. We evaluate FDTM on three heterogeneous public sports datasets spanning basketball (NBA game-log corpus 2013–2023), Australian rules football (AFL Player Workload Dataset), and soccer (SoccerMon open monitoring corpus), comprising 612 athletes and 247,830 player-game observations across ten competitive seasons. FDTM achieves AUC-ROC values of 0.858, 0.833, and 0.821 on the three datasets respectively, outperforming the strongest deep-learning baseline (FEDformer) by 2.0 to 3.3 percentage points and the strongest non-spectral baseline (TCN) by 3.2 to 4.5 percentage points while maintaining a Brier score below 0.04. Ablation studies confirm that the spectral branch contributes 5.1 percent to overall discriminative performance. SHAP attribution analyses identify high-frequency weekly components as the dominant injury-relevant signal, followed by low-frequency seasonal trends and the cumulative acute-to-chronic workload temporal feature, with gating-weight visualizations revealing dynamic modality contributions consistent with established sports science theory. Direct spectral analysis of the raw workload signal confirms that injury-preceding windows exhibit significantly elevated weekly-band power across all three datasets (Mann–Whitney U test, p < 1 × 10⁻⁷), and the architectural advantage is shown to be robust across 30 independent training seeds. These findings suggest that frequency-aware modeling may serve as a transferable methodology for sports engineering applications in injury prevention, return-to-play planning, and individualized rehabilitation, pending further external validation in female athletes and additional team sports. Full article

The calibration accuracy of key weather radar antenna parameters, including beam pointing, beamwidth, and antenna gain, directly affects quantitative precipitation estimation (QPE) and multi-radar network products. Conventional calibration approaches such as external field beacons and far-field tests are often constrained by site conditions and high implementation costs, making them difficult to apply routinely in operational radar networks. To address this limitation, this study proposes a robust solar calibration method for key antenna parameters of weather radars based on a dedicated Volume Coverage Pattern for Sun calibration, hereafter referred to as VCPSun. The proposed method uses a high-density solar scanning strategy with midpoint time alignment and feed-forward control of solar apparent motion. Combined with solar sample identification, propagation path correction, two-dimensional Gaussian surface fitting, and deconvolution of solar-source broadening and scan-smearing effects, the method enables reliability retrieval of beam pointing, beamwidth, and antenna gain. A high-frequency intensive observing experiment was conducted using a China New Generation Weather Radar, model SA-D (CINRAD/SA-D), deployed at the Changsha Meteorological Radar Calibration Center, with independent far-field test results used for validation. The results show that the retention rate of quality-controlled solar samples reached 85.7%, supporting stable reconstruction of the main-lobe power pattern. The retrieved mean beam pointing biases for both polarizations were within ±0.05°. After correction, the relative differences in beamwidth with respect to far-field measurements were respectively 3.26% and 1.52% for the H-polarization azimuth and elevation directions and 2.09% and 1.84% for the V-polarization azimuth and elevation directions, with the overall mean relative difference being less than 3.5%. The antenna gain differences relative to the independent far-field reference values were within 0.2 dB, at −0.062 dB for H-polarization and −0.144 dB for V-polarization. Comparative analysis with historical one-dimensional SunCheck records and an ablation test of the beamwidth correction chain further demonstrate that high-density two-dimensional sampling and physical deconvolution corrections improve the robustness and quantitative accuracy of the solar-based retrieval. These results demonstrate the feasibility of reliable in situ calibration of key antenna parameters for operational weather radars. The proposed method provides a potential technical pathway for in situ quantitative assessment of antenna performance in S-band CINRAD/SA-D radars, although further validation using additional radars and longer observation periods is required prior to network-wide application. Full article

Relative radiometric normalization (RRN) is fundamental to multi-temporal remote sensing analysis; however, conventional techniques often struggle with nonlinear distortions, outlier contamination, and heterogeneous land-cover conditions. To address these challenges, we propose a diffusion-based probabilistic framework that models radiometric inconsistency as a combination of deterministic residuals and stochastic perturbations. In this framework, the forward process injects structured noise and stochastic perturbations, while the reverse process restores radiometric consistency through a dual-objective variational formulation. At the core of this framework is a spatial–spectral attention residual network (SSARN), which integrates residual learning with dual attention mechanisms to capture cross-band dependencies and multi-scale spatial context. A preprocessing stage guided by the structural similarity index (SSIM) further enhances robustness by automatically selecting stable pseudo-invariant regions for model training. Comprehensive experiments on multi-temporal Sentinel-2 datasets demonstrate that the proposed method consistently outperforms existing approaches, achieving higher accuracy and enhanced spectral fidelity. Moreover, the framework ensures greater consistency of the normalized difference vegetation index (NDVI) and preserves fine-grained textural details, underscoring its potential as a scalable and resilient solution for large-scale RRN in remote sensing applications. Full article

Simultaneous transmit-and-receive (STAR) antennas are important components of in-band full-duplex wireless front ends. However, transmit-to-receive leakage through the antenna limits the achievable antenna-domain isolation. This paper presents a dual-circularly polarized patch antenna incorporating a decoupling feeding network (DFN) to suppress the residual coupling between the transmit and receive ports. A stacked patch radiator with cross-aperture coupling generates right-hand and left-hand circularly polarized radiation from two separate ports. The DFN introduces an additional coupling path whose magnitude and electrical phase are adjusted to produce destructive interference near the target frequency. The fabricated prototype exhibits overlapping −10 dB impedance and 3 dB axial ratio bandwidths from 4.1 to 4.4 GHz, a minimum measured S₂₁ of −50 dB, isolation higher than 30 dB from 4.25 to 4.28 GHz, and a peak realized gain of 7 dBic. The measured high-isolation range has an absolute bandwidth of 30 MHz and a fractional bandwidth of approximately 0.70% around 4.265 GHz. Therefore, the proposed DFN provides narrowband antenna-domain isolation enhancement around the designed frequency without requiring additional patterned decoupling elements on the radiating aperture. The proposed antenna is primarily intended for fixed-frequency or narrow-channel STAR front ends rather than broadband high-isolation operation. Full article

Low-slow-small (LSS) target recognition using multi-dimensional radar remains challenging due to weak signatures, similar kinematics, and overlapping short-term Doppler patterns. Digital-array radar provides continuous, complementary Doppler-spectrum and kinematic measurements; however, their heterogeneity in dimension, distribution, and physical meaning often makes direct fusion under-exploit discriminative complementarity and inadequately model temporal track evolution. To address this, we propose a Doppler-Kinematic Spatio-Temporal Graph Learning framework named Dual-Stream Spatio-Temporal Cross-Attention Graph Convolutional Network (DS-STCAGCN) for LSS target recognition using multi-dimensional radar observations. The method separately encodes Doppler-spectrum and kinematic features to preserve their modality-specific characteristics, fuses them through bidirectional cross-attention, captures long-range temporal dependencies via self-attention, and aggregates local frame-to-frame correlations through graph convolution on a time-ordered observation graph. On the public L-band digital-array dataset LSS-DAUR-1.0, DS-STCAGCN achieves 99.73% mean accuracy and maintains 98.64% at 5 dB signal-to-noise ratio (SNR). On the passive-radar dataset LSS-PR-1.0, it reaches 99.86% mean accuracy, demonstrating strong cross-modal generalization. This work provides an effective spatio-temporal modelling framework for multi-dimensional radar sensing and robust LSS target recognition. Full article

To address the limitations of traditional day-ahead bidding strategies in handling the time-varying uncertainty of photovoltaic output, and considering that single-point forecasts are insufficient for reliable risk-based decision-making, this paper proposes a day-ahead bidding strategy for PV power plants based on dynamic error-band optimization. First, a dynamic uncertainty quantification method based on dual-model prediction discrepancy is proposed. It couples two complementary forecasting mechanisms—Long Short-Term Memory, and Seasonal Autoregressive Integrated Moving Average—and utilizes the Dynamic Time Warping algorithm to extract their discrepancy as a dynamic input for subsequent risk assessment and decision-making. Secondly, based on this uncertainty indicator, a probabilistic mapping model is constructed to link prediction uncertainty to the risk of power violation, translating the abstract prediction discrepancy into a concrete economic risk probability. Finally, considering the trade-off between economic benefits and security, a dynamic error-band optimization mechanism is introduced to adaptively determine the bidding margin at different time periods. Case results for a 20 MW PV plant show that the dynamic strategy reduces the number of violation events to zero in the tested daily bidding case, compared with four violations under a fixed 5% error band and one violation under a fixed 10% error band. The corresponding economic revenue increases by 5.3% and 11.2% relative to the fixed 5% and fixed 10% strategies, respectively. Full article

Translating Synthetic aperture radar (SAR) images into optical images is intrinsically ill-posed because microwave backscatter and optical reflectance describe different physical properties of the observed scene. Although frequency-domain modeling has been introduced into diffusion-based translation, existing methods mainly rely on independent weighting of individual Fourier coefficients and provide limited modeling of interactions among neighboring frequencies and feature channels. To address this limitation, we propose dualCDM, a conditional diffusion model that jointly exploits spatial- and frequency-domain representations. In the diffusion backbone, a spatial-frequency hybrid residual block (SFHRB) combines a spatial convolution branch with complex-valued convolution in the Fourier domain. The complex convolution aggregates neighboring Fourier coefficients across all input feature channels, enabling local cross-frequency and cross-channel modeling, while its response is modulated by the diffusion timestep. In the SAR conditional encoder, an adaptive frequency-domain feature recalibration block (AFFRB) predicts input-dependent real-valued gains from magnitude and trigonometric phase representations of intermediate GRD features. These gains adaptively recalibrate the complex frequency responses without introducing an additional phase shift, while the residual connection preserves the original conditional information. A dual-domain objective further constrains both the predicted diffusion noise and the one-step optical reconstruction in the spatial and frequency domains. We also construct the S1S2 dataset using 16-bit Sentinel-2 reflectance data, retaining the original 0–10,000 value range and including the near-infrared band. Experiments on SEN1-2 and S1S2 show that dualCDM improves radiometric accuracy, spectral consistency, and structural preservation over six representative methods. Paired statistical tests further confirm significant improvements over the strongest competing method across all six evaluation metrics on both datasets. Full article

Achieving scalable monitoring of Alternate Wetting and Drying (AWD) for methane mitigation in rice cultivation depends on establishing field benchmarks for drainage behavior and demonstrating that satellite observations can reliably detect corresponding changes in water status. We analyzed about two million high-frequency in situ water-level observations from hundreds of sensors deployed in rice fields across the Philippines and Japan to quantify drainage duration from near-surface conditions to 15 cm below the soil surface and to test the sensitivity of open-access PALSAR-2 dual-polarization L-band SAR to vertical water-level variations. Across 564 drainage events, the median drainage duration was 19.0 h, and only 0.9% of events exceeded 240 h, indicating that drainage happens generally within a day. Seasonal differences were evident in Pangasinan, while small Chiba and Cagayan samples suggested exploratory longer-duration patterns; multiple drainage events occurred in 48.0% of Philippine dry-season fields but only 21.6% of wet-season fields. PALSAR-2 data showed a statistical significance in detecting inundation at Mid crop growth stage with cross-polarization band, but the significant overlap induces challenges in operational applications. These results provide empirical benchmarks for AWD-related drainage dynamics while showing that dual-polarization PALSAR-2 alone is unlikely to support robust field-scale monitoring of rice-field water status. Full article

Evidence suggests that protracted conflicts persist because several forms of socio-political organization run simultaneously on the same population, resources, and territory. Reading Service’s typology of bands, tribes, chiefdoms, and states not as evolutionary stages but as coexisting and superposed social organizations, we model conflict as a reaction network where each social form is a self-maintaining set of stocks—a chemical organization—and conflicts arise where competing productive logics between organizations generate stocks with negative connotation, such as grievances and displacement. Taking the Lake Chad Basin as inspiration, we build a ladder of progressively richer models arriving a mixed chiefdom–state configuration compatible with current views on the conflict. As the model complexifies, kinetic approaches become uninformative; we therefore develop complementary stoichiometric methods that are parameter-free and thus are far easier to measure and compute. These diagnostics reveal a structural bias toward conflict: transitions into conflict regimes are systematically richer than transitions out. We show how a dual chiefdom–state form acts as a conflict attractor within a closed conflict–peace loop that transits among documented different forms of organization. Conflict management then becomes the identification of the mechanisms that redirect rather than change the state of a self-sustaining organization—here, elite-surplus redistribution—and of the timescales at which such redirection is observable, turning intervention design into a structural rather than a parameter-tuning problem. Full article

Accurate pixel-level segmentation of weld defects in radiographic images is essential for automated non-destructive testing (NDT) and quantitative weld-quality assessment. However, this task remains challenging because weld defects often exhibit severe foreground–background imbalance, ambiguous boundaries, weak grayscale contrast, and defect-like weld background structures, which can lead to boundary over-expansion and false-positive predictions. To address these issues, this paper proposes a boundary-guided feature modulation framework with false-positive suppression for weld radiographic defect segmentation. The method constructs boundary bands from training annotations and uses them only as label-derived training-time regularizers for attention-driven feature modulation and region-aware optimization; during validation and testing, no ground-truth mask, boundary band, or predicted boundary map is provided to the model. Multi-scale feature fusion is used to recover weak defect responses, boundary-guided dual attention enhances boundary-sensitive feature representation, and a false-positive suppression loss penalizes foreground leakage above a tolerated confidence margin in stable non-boundary background regions. Experiments on a real-world pipeline weld radiographic dataset containing 10,079 images show that the proposed method achieves a Dice score of $0.810\pm 0.003$, a Precision of $0.809\pm 0.004$, and a Surface Dice at 3 pixels of $0.394\pm 0.008$, outperforming representative CNN-based and Transformer-based segmentation baselines. Ablation studies, qualitative visualization, and distance-based false-positive analysis further demonstrate that the proposed framework improves contour reliability and reduces background false positives. Full article

Digital watermarking secures electroencephalogram (EEG) data in distributed Brain–Computer Interface (BCI) environments. However, existing single-domain deep learning schemes struggle to maintain robustness against clinical signal processing attacks due to EEG’s joint time–frequency nature. We introduce the Dual-Stream Frequency–Time Attention Network (DS-FTAN), utilizing an adaptive Spectral Gating Mechanism to embed information within robust, high-energy EEG spectral regions. A robustness simulation layer—encompassing resampling, spectral dropout, and band-pass filtering—is incorporated during training. Validations confirm DS-FTAN balances imperceptibility (PSNR > 36 dB) with reliable recovery. Specifically, it achieves >99.99% accuracy under no-attack conditions and maintains 86.52–98.77% accuracy across complex attacks (e.g., 50% cropping, band-pass filtering). This significantly outperforms time-domain baselines. Furthermore, DS-FTAN exhibits excellent zero-shot cross-channel generalization. It preserves diagnostic integrity, causing merely a 0.42% accuracy drop in downstream EEGNet intention recognition. Ultimately, this framework provides a reliable solution for privacy-preserving EEG data sharing. Full article

This paper presents a low-power 30/60 GHz dual-band CMOS voltage-controlled oscillator (VCO) for 5G applications. The design employs an LC-VCO core that simultaneously generates differential fundamental-frequency outputs and a single-ended second-harmonic output. To improve second-harmonic spectral purity, a second-harmonic quarter-wavelength (λ/4) transmission line is inserted in the V_DD bias path of the VCO core. A body-to-ground-with-resistor (B-to-GND-with-R_B) NMOS varactor configuration is adopted to provide a wide, monotonic tuning range while suppressing substrate leakage and noise coupling. In addition, a fundamental-frequency λ/4 transmission line is introduced in the control-voltage bias path to improve AC grounding of the differential varactor center node. The VCO consumes 2.19 mW and achieves a tuning range of 24.59–30.5 GHz (21.5%). At 27.48 GHz, it exhibits a phase noise of −117.69 dBc/Hz at a 10 MHz offset, corresponding to a figure of merit (FoM) of 189.72 dBc/Hz. The second-harmonic output covers 49.18–61 GHz, with the same fractional tuning range. The VCO core occupies a compact chip area of only 0.021 mm². Full article