Search Results (1,038)

The range-dependence on polarization distortion of spaceborne polarimetric synthetic aperture radar (SAR) affects the accuracy of wide-swath polarization applications, such as environmental monitoring, sea ice classification and ocean wave inversion. Traditional calibration methods, assessing the distortion mainly based on ground experiments, suffer from tedious active calibrator deployment work, which are time-consuming and cost-intensive. This paper proposes a novel polarimetric assessment and calibration method for the quad-polarized wide-swath ScanSAR imaging mode. Firstly, by using distributed target data that satisfy the system reciprocity requirement, we assess the polarization distortion matrices for a single beam in the mode. Secondly, we transfer the matrix results from one beam to another by analyzing data from the overlapping region between beams. Thirdly, we calibrate the quad-polarized data and achieve an overall assessment and calibration results. Compared to traditional calibration methods, the presented method focuses on using cross-beam (overlapping area) data to reduce the dependence on active calibrators and avoid conducting calibration work beam-by-beam. The assessment and calibration experiment is conducted on Gaofen-3 quad-polarized ScanSAR experiment mode data. The calibrated images and polarization decomposition results are compared with those from well-calibrated quad-polarized Stripmap mode data located in the same region. The results of the comparison revealed the effectiveness and accuracy of the proposed method. Full article

Frequency-Modulated Continuous-Wave (FMCW) Laser Detection and Ranging (LiDAR) systems are widely used due to their high accuracy and resolution. Nevertheless, conventional distance extraction methods often lack robustness in noisy and complex environments. To address this limitation, we propose a deep learning-based signal extraction framework that integrates a Dual Convolutional Neural Network (DCNN) with a Transformer model. The DCNN extracts multi-scale spatial features through multi-layer and pointwise convolutions, while the Transformer employs a self-attention mechanism to capture global temporal dependencies of the beat-frequency signals. The proposed DCNN–Transformer network is evaluated through beat-frequency signal inversion experiments across distances ranging from 3 m to 40 m. The experimental results show that the method achieves a mean absolute error (MAE) of 4.1 mm and a root-mean-square error (RMSE) of 3.08 mm. These results demonstrate that the proposed approach provides stable and accurate predictions, with strong generalization ability and robustness for FMCW LiDAR systems. Full article

Background/Objectives: Race-based cardiac screening criteria in sports cardiology, including the “Black athlete’s heart” concept, assume biological distinctions that may not reflect physiological reality. This systematic review evaluates whether geographic ancestry provides more clinically relevant predictors of cardiac adaptation than racial categorization. Methods: PubMed was searched (January 2005–July 2025) for studies examining cardiac adaptations in athletes by ethnicity. Data extraction captured demographics, geographic origin, cardiac assessments, and outcomes. Narrative synthesis was employed due to methodological heterogeneity. Results: Forty-seven studies (n = 66,130) revealed substantial within-race heterogeneity. The “Black athlete repolarization variant” prevalence ranged from 1.8% (Brazilian) to 30% (Ghanaian) Black athletes. Left ventricular wall thickness >12 mm (normal <11 mm) occurred in 7.1% of Black versus 0.4% of White athletes, yet varied significantly within Black populations—10.8 ± 1.2 mm in Sub-Saharan versus 9.4 ± 1.1 mm in African-American athletes (p < 0.001). Relative wall thickness ≥0.44 (normal ≤0.42) was presented in 43% of West/Middle African, 23% of East African, and 7% of White athletes. T-wave inversion showed four-fold variation within Black populations (3.6–8.5% West African versus 0.5–2.0% African-American/Caribbean). Current International Criteria demonstrated inequitable specificity: 3.3% false-positive rate in Black versus 1.4% in White athletes. Conclusions: Geographic ancestry explains more cardiac variation than racial categories, supporting contemporary understanding of race as a sociopolitical construct. The persistent diagnostic disparities in ECG screening specificity highlight the need for reform. Transitioning toward protocols incorporating continental origin, anthropometric factors, and social determinants of health—while eliminating terminology like “Black athlete’s heart”—represents an important step toward achieving equity in cardiovascular care for diverse athletic populations. Full article

Accurately estimating the time of concentration (Tc) is critical for hydrological modelling, flood forecasting, and hydraulic infrastructure design. However, conventional methods often overlook the combined effects of rainfall intensity and antecedent soil moisture, thereby limiting their applicability under changing climates. This study presents a novel approach that integrates data-driven techniques with remote sensing data to improve Tc estimation. This method was successfully applied in the Kalu River Basin, Sri Lanka, demonstrating its performance in a tropical catchment. While an overall inverse relationship between rainfall intensity and Tc was observed, deviations in several events underscored the influence of initial soil moisture conditions on catchment response times. To address this, a modified kinematic wave-based equation incorporating both rainfall intensity and soil moisture was developed and calibrated, achieving high predictive accuracy (calibration: R² = 0.97, RMSE = 1.1 h; validation: R² = 0.96, RMSE = 0.01 h). A hydrological model was developed to assess the impacts of Tc uncertainties on design hydrographs. Results revealed that underestimating Tc led to substantially shorter lag times and significantly increased peak flows, highlighting the sensitivity of flood simulations to Tc variability. This study highlights the need for improved $T_C$ estimation and presents a robust, transferable methodology for enhancing hydrological predictions and climate-resilient infrastructure planning. Full article

We study unconventional multipole moments arising from noncollinear magnetic structures within an augmented framework encompassing electric, magnetic, magnetic toroidal, and electric toroidal multipoles. Employing a tight-binding model for an s-p hybridized orbital system, we analyze two spiral magnetic textures and classify the resulting multipoles according to magnetic point group symmetry. Different spiral wave types, such as cycloidal and proper-screw forms, activate distinct multipole components, with odd-parity multipoles emerging from local s-p parity mixing induced by magnetically driven inversion-symmetry breaking. Calculated multipole structure factors reveal finite-q peaks originating from higher-order magnetic-dipole-scattering processes and their characteristic couplings between Fourier components of the magnetic dipole texture. Our results demonstrate that magnetic ordering can generate parity-mixed states without intrinsic structural inversion asymmetry, offering new pathways to realize cross-correlation phenomena in functional magnetic materials. Full article

Introduction: Twelve-lead electrocardiography (ECG) remains an essential diagnostic tool for patients presenting with chest pain. Timely recognition of specific electrocardiographic patterns is critical for guiding reperfusion strategies and predicting adverse outcomes. Among these, Wellens’ pattern is a high-risk marker of critical left anterior descending (LAD) artery stenosis and an impending anterior myocardial infarction. Although typically described in clinically stable patients without heart failure, its occurrence in the setting of acute decompensation is rare. Case Report: We report the case of a 66-year-old male with hypertension, obesity, and active smoking who presented with exertional chest pain, dyspnea, and signs of acute heart failure. Initial ECG revealed biphasic T waves in V2–V4, consistent with type A Wellens’ pattern. Laboratory evaluation demonstrated elevated troponin I, while point-of-care ultrasound (POCUS) identified systolic and diastolic dysfunction, lateral wall hypokinesia, pericardial effusion, and cardiogenic pulmonary edema. The patient received acute management with antiplatelet therapy, statins, diuretics, and anticoagulation, followed by referral for coronary angiography. This revealed critical stenosis (>90%) of the proximal LAD, successfully treated with percutaneous coronary intervention and drug-eluting stent implantation. The in-hospital course was uneventful, and guideline-directed medical therapy was optimized at discharge, including dual antiplatelet therapy, beta-blocker, renin–angiotensin system inhibitor, and SGLT2 inhibitor. Conclusions: This case highlights the need for early recognition of Wellens’ pattern, even in atypical contexts such as acute heart failure. Integrating ECG interpretation with bedside POCUS facilitated diagnostic accuracy and guided an early invasive strategy, preventing extensive myocardial infarction. In resource-limited settings, strengthening frontline diagnostic capabilities and referral networks is crucial to improving patient outcomes. Full article

Concealed smartphone use while driving (CSUWD), a prevalent and covert form of distracted driving, poses significant threats to road safety. However, the psychological determinants underlying this illegal behavior remain underexplored. A two-wave longitudinal study based on the expanding theory of planned behavior (TPB) investigates the intention and prospective behavior of CSUWD in China. In the first wave, 256 respondents assessed the standard TPB constructs, alongside extended constructs of descriptive norms, moral norms, and perceived risks. Subsequently, 156 participants reported their actual behavior in the second wave. Hierarchical multiple regression results revealed that the traditional TPB variables accounted for 57.1% of intention variance and 45.2% of behavior variance, while extended variables contributed an additional 11.7% to intention variance. All variables, except perceived crash risk, emerged as significant determinants of intention. Notably, the perceived risk of being caught and fined inversely correlated with intention, suggesting a potential disinhibition effect. Both perceived behavioral control and intention were significant determinants of subsequent behavior. The findings underscore the validity of TPB in predicting CSUWD, informing the design of non-legal interventions (e.g., public education advertisement, road awareness campaigns, and technological interventions) to mitigate CSUWD-related distracted driving and promote sustainable transportation systems. Full article

Wave-induced silty seabed liquefaction is one of the key threats to offshore infrastructure stability. The excess pore pressure (EPP) response is the key parameter for judging seabed liquefaction. Many studies have examined the EPP response to surface waves in initially well-consolidated seabed; few works have explored initially less-consolidated seabed, which is widely distributed in estuaries due to the massive river sediment discharge and, thereafter, rapid accumulation. Here, we investigate the EPP response of silty seabed with various initial consolidation degrees using wave flume experiments. We found that (1) in initially liquefied seabed, the EPP magnitude monotonically increases with wavelength, while in initially consolidated seabed, there is a maximal response wavelength which is inversely related to consolidation degree. (2) Furthermore, we found two opposite EPP responses to cyclic surface wave loading under varying seabed conditions in initially liquefied and consolidated seabeds. That is, under the same waves, the EPP magnitude is inversely related to the consolidation degree in initially liquefied seabed, while the EPP magnitude is positively related to the consolidation degree in initially consolidated seabed. In other words, the influence of initial seabed consolidation degree on EPP magnitude behaves like a “√” shaped curve. Our findings provide some implications for further understandings of wave-induced silty seabed liquefaction. Full article

We design a one-dimensional magnetoelastic phononic crystal slab composed of the smart magnetostrictive material Terfenol-D and pure tungsten. Band inversion and topological phase transitions are achieved by modifying the geometric parameters of the non-magnetic medium within the unit cell. The emergence of topological interface states within overlapping bandgaps, exhibiting distinct topological properties, along with their robustness against interfacial structural defects, is confirmed. The coupling effects between adjacent topological interface states in a sandwich-like supercell configuration are investigated, and their tunability under external magnetic fields is demonstrated. A Su-Schrieffer-Heeger (SSH) phononic crystal slab system under gradient magnetic fields is proposed. Critically, and in stark contrast to previous static or structurally graded designs, we achieve reconfigurable rainbow trapping of topological interface states solely by reprogramming the gradient magnetic field, leaving the physical structure entirely unchanged. This highly localized, compact, and broadband-tunable topological rainbow trapping system design holds significant promise for applications in elastic energy harvesting, wave filtering, and multi-frequency signal processing. Full article

The extraction of ocean wave wavelengths from optical imagery via Fast Fourier Transform (FFT) exhibits significant potential for Wave-Derived Bathymetry (WDB). However, in practical applications, this method frequently produces anomalously large wavelength estimates. To date, there has been insufficient exploration into the mechanisms underlying image spectral leakage to low wavenumbers and its suppression strategies. This study investigates three plausible mechanisms contributing to spectral leakage in optical images and proposes a subimage-based preprocessing framework: prior to executing two-dimensional FFT, the remote sensing subimages employed for wavelength inversion undergo three sequential steps: (1) truncation of distorted pixel values using a Gaussian mixture model; (2) application of a polynomial detrending surface; (3) incorporation of a two-dimensional Hann window. Subsequently, the dominant wavenumber peak is localized in the power spectrum and converted to wavelength values. Water depth is then inverted using the linear dispersion equation, combined with wave periods derived from ERA5. Taking 2 m-resolution WorldView-2 imagery of Sanya Bay, China as a case study, 1024 m subimages are utilized, with validation conducted against chart-sounding data. Results demonstrate that the proportion of subimages with anomalous wavelengths is reduced from 18.9% to 3.3% (in contrast to 14.0%, 7.8%, and 16.6% when the three preprocessing steps are applied individually). Within the 0–20 m depth range, the water depth retrieval accuracy achieves a Mean Absolute Error (MAE) of 1.79 m; for the 20–40 m range, the MAE is 6.38 m. A sensitivity analysis of subimage sizes (512/1024/2048 m) reveals that the 1024 m subimage offers an optimal balance between accuracy and coverage. However, residual anomalous wavelengths persist in near-shore subimages, and errors still increase with increasing water depth. This method is both concise and effective, rendering it suitable for application in shallow-water WDB scenarios. Full article

Assessing the driver’s state in real time is a critical challenge in modern vehicle safety systems, as human factors account for the vast majority of traffic accidents. Heart rate (HR) is a key physiological indicator of the driver’s condition, yet contactless measurements in dynamic in-vehicle environments remain difficult due to motion artifacts, vibrations, and varying operational conditions. This paper presents a novel two-stage method for HR estimation using a commercial 60 GHz frequency-modulated continuous wave (FMCW) radar sensor, specifically designed and validated for in-vehicle applications. In the first stage, coarse HR estimation is performed using the discrete wavelet transform (DWT) and autoregressive (AR) spectral analysis. The second stage refines the estimate using an inverse application of the relevance vector machine (RVM) approach, leveraging a narrowed frequency window derived from Stage 1. Final HR estimates are stabilized through sequential Kalman filtering (SKF) across time segments. The system was implemented using an Infineon BGT60TR13C radar module installed in the sun visor of a passenger vehicle. Extensive data collection was conducted during real-world driving across diverse traffic scenarios. The results demonstrate robust HR estimations with an accuracy comparable to that of commercial wearable devices, validated against a Polar H10 chest strap. This method offers several advantages over prior work, including short measurement windows (5 s), operation under varying lighting and clothing conditions, and validation in realistic driving environments. In this sense, the method contributes to the field of biomimetics by transferring the biological principles of continuous vital sign perception to technical sensorics in the automotive domain. Future work will explore the fusion of sensors with visual methods and potential extension to heart rate variability (HRV) estimations to enhance driver monitoring systems (DMSs) further. Full article

Due to the difficulty of directly measuring external loads on jacket platform structures and the challenges in accurately expressing them through analytical formulas, this study proposes a load inversion method based on local strain measurement data to obtain the time–history curves of structural loads. The method establishes a mapping relationship between unknown loads and measured strains based on the quasi-static superposition principle. An Improved Sine Cosine Algorithm, combined with an Opposition-Based Learning, is introduced to optimize the placement of strain sensors. The unknown loads are solved using a least squares approach integrated with Tikhonov regularization. The method was validated through indoor loading experiments under eight conditions, where the inverted load time–history curves accurately reflected the periodic characteristics of the applied loads, achieving a maximum Mean Absolute Relative Error (MARE) of 6.91%, demonstrating high stability and accuracy. The further application of the method to an in-service jacket platform in a marine environment yielded inverted wind and wave loads with a maximum MARE of 11.63% compared to loads calculated from measured wind and wave data, validating the method’s practical applicability and robustness. This approach offers a more accurate load basis for the safety assessment and residual life prediction of jacket platform structures. Full article

Synthetic Aperture Radar (SAR) is one of the few instruments capable of providing high-resolution global two-dimensional (2D) measurements of ocean waves. Since 2014 and then 2016, the Sentinel-1A/B satellites, whenever operating in a specific wave mode (WV), have been providing ocean swell spectrum data as Level-2 (L2) OCeaN products (OCN), derived through a quasi-linear inversion process. This WV acquires small SAR images of 20 × 20 km footprints alternating between two sub-beams, WV1 and WV2, with incidence angles of approximately 23° and 36°, respectively, to capture ocean surface dynamics. The SAR imaging process is influenced by various modulations, including hydrodynamic, tilt, and velocity bunching. While hydrodynamic and tilt modulations can be approximated as linear processes, velocity bunching introduces significant distortion due to the satellite’s relative motion with respect to the ocean surface and leads to constructive but also destructive effects on the wave imaging process. Due to the associated azimuth cut-off, the quasi-linear inversion primarily detects ocean swells with, on average, wavelengths longer than 200 m in the SAR azimuth direction, limiting the resolution of smaller-scale wave features in azimuth but reaching 10 m resolution along range. The 2D spectral partitioning technique used in the Sentinel-1 WV OCN product separates different swell systems, known as partitions, based on their frequency, directional, and spectral characteristics. The accuracy of these partitions can be affected by several factors, including non-linear effects, large-scale surface features, and the relative direction of the swell peak to the satellite’s flight path. To address these challenges, this study proposes a novel quality control framework using a machine learning (ML) approach to develop a quality flag (QF) parameter associated with each swell partition provided in the OCN products. By pairing collocated data from Sentinel-1 (S1) and WaveWatch III (WW3) partitions, the QF parameter assigns each SAR-derived swell partition one of five quality levels: “very good,” “good,” “medium,” “low,” or “poor”. This ML-based method enhances the accuracy of wave partitions, especially in cases where non-linear effects or large-scale oceanic features distort the data. The proposed algorithm provides a robust tool for filtering out problematic partitions, improving the overall quality of ocean wave measurements obtained from SAR. Moreover, the variability in the accuracy of swell partitions, depending on the swell direction relative to the satellite’s flight heading, is effectively addressed, enabling more reliable data for oceanographic studies. This work contributes to a better understanding of ocean swell dynamics derived from SAR observations and supports the numerical swell modeling community by aiding in the refinement of models and their integration into operational systems, thereby advancing both theoretical and practical aspects of ocean wave forecasting. Full article

In this paper, we develop the explicit finite difference method (FDM) to solve an ill-posed Cauchy problem for the 3D acoustic wave equation in a time domain with the data on a part of the boundary given (continuation problem) in a cube. FDM is one of the numerical methods used to compute the solutions of hyperbolic partial differential equations (PDEs) by discretizing the given domain into a finite number of regions and a consequent reduction in given PDEs into a system of linear algebraic equations (SLAE). We present a theory, and through Matlab Version: 9.14.0.2286388 (R2023a), we find an efficient solution of a dense system of equations by implementing the numerical solution of this approach using several iterative techniques. We extend the formulation of the Jacobi, Gauss–Seidel, and successive over-relaxation (SOR) iterative methods in solving the linear system for computational efficiency and for the properties of the convergence of the proposed method. Numerical experiments are conducted, and we compare the analytical solution and numerical solution for different time phenomena. Full article

Classical active thermographic testing of industrial goods has mostly been limited to generating 2D defect maps. While for surface or near-surface defect detection, this is a desired result, for deeply buried defects, a 3D reconstruction of the defect geometry is coveted. This general trend can also be well observed in widely used NDT methods (radiography, ultrasonic testing), where the progression from 2D to 3D reconstruction methods has already made profound progress (CT, UT phased array transducers). Achieving a fully 3D defect reconstruction in active thermographic testing suffers from the diffusive nature of thermal processes. One possible solution to deal with thermal diffusion is the application of the virtual-wave concept, which, by solving an inverse problem, allows the diffusiveness to be extracted from the thermographic data in the post-processing stage. What is left follows propagating-wave physics, enabling the usage of well-known algorithms from ultrasonic testing. In this work, we present our progress in the 3D reconstruction of deeply buried defects using spatially structured laser heating in conjunction with applying the well-known total focusing method (TFM) in the virtual-wave domain. Full article