Search Results (1,654)

Reliable gridded precipitation products (GPPs) are essential for effective hydrological simulations, particularly in mountainous regions with limited ground-based observations. This study evaluates the performance of two widely used GPPs, CHIRPS and ERA5, in estimating precipitation and supporting runoff generation using the Soil and Water Assessment Tool (SWAT) across three headwater catchments (Sill, Drava and Isel) in the Austrian Alps from 1991 to 2018. The region’s complex topography and climatic variability present a rigorous test for GPP application. The evaluation methods combined point-to-point comparisons with gauge observations and assessments of generated runoff and runoff trends at annual, seasonal and monthly scales. CHIRPS showed a lower precipitation error (RMAE = 25%) and generated more consistent runoff results (RMAE = 12%), particularly in smaller catchments, whereas ERA5 showed higher spatial consistency but higher overall precipitation bias (RMAE = 37%). Although both datasets successfully reproduced the seasonal runoff regime, CHIRPS outperformed ERA5 in trend detection and monthly runoff estimates. Both GPPs systematically overestimate annual and seasonal precipitation amounts, especially at lower elevations and during the cold season. The results highlight the critical influence of GPP spatial resolution and its alignment with catchment morphology on model performance. While both products are viable alternatives to observed precipitation, CHIRPS is recommended for hydrological modelling in smaller, topographically complex alpine catchments due to its higher spatial resolution. Despite its higher precipitation bias, ERA5’s superior correlation with observations suggests strong potential for improved model performance if bias correction techniques are applied. The findings emphasize the importance of selecting GPPs based on the scale and geomorphological and climatic conditions of the study area. Full article

To overcome the limitations of existing maritime radar identification analysis methods, which are only applicable to sea-skimming aircraft and fail to quantitatively calculate the probability of radar correctly identifying the target under electromagnetic influence from marine geographical features (MGFs), an advanced method is proposed for calculating the radar identification probability in marine areas using 3D MGF models. The method first established the radar identification criteria in 3D space, considering radar line of sight (LOS), radar target adhesion (RTA), and radar resolutions in range, azimuth angle, and elevation angle. It then comprehensively analyzed errors from both the aircraft and MGFs. Finally, the probability of a target at a specific marine location being correctly identified by radar was calculated using the Monte Carlo method. Theoretical derivations and simulation results demonstrated that: (1) Unlike existing methods limited to sea-skimming aircraft, the proposed method is applicable to aircraft at any altitude, better aligning with current aircraft performance and requirements; (2) While existing methods provide only a binary result of “identified” or “unidentified,” the proposed method offers a probability value. For the same marine location point T_a, the proposed method yields radar identification probabilities of 0.0877 for sea-skimming aircraft and 0.5887 for high-altitude aircraft, providing more precise and intuitive decision-making support for mission planners. Full article

Due to the high-precision angle measurement performance, the monopulse technique plays a key role in fields such as remote sensing and space surveillance. The accuracy of monopulse angle measurement depends on the received amplitude and phase information, which is sensitive to the polarization component. Previous research has demonstrated that the performance of monopulse radar equipped with a parabolic antenna suffers from the cross-polarization component. However, it is not clear whether phased arrays (PAs) with higher degrees of freedom will also be affected by the cross-polarization component, and the parameter tolerance for performance degradation remains uncertain. In this paper, we establish a mathematical model of monopulse angle measurement in PA radar, which provides a comprehensive consideration of the cross-polarization component. Then, the received amplitude and phase patterns of PA radar are analyzed, and the theoretical angle errors caused by the cross-polarization jamming are derived. The experiments are conducted based on the measured amplitude-phase patterns of both co-polarization and cross-polarization. Experimental results are consistent with the theoretical analysis: the angle errors caused by cross-polarization jamming can reach half of the beamwidth in both azimuth and elevation dimensions, provided that the power of the cross-polarization and co-polarization components at the receiver is equal. Full article

This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article

The Global Precipitation Measurement (GPM) mission’s Dual-Frequency Precipitation Radar (DPR) serves as a critical benchmark for calibrating satellite-based precipitation products, with its retrieval quality directly governing the accuracy of global precipitation estimates. While the updated version 07 (DPR-V07) algorithm introduces substantial refinements over its predecessor (DPR-V06), systematic evaluations of its operational advancements in precipitation monitoring remain limited. This study utilizes ground-based rain gauge data from Mainland China (2015–2018) to assess improvements of DPR-V07 over its predecessor’s (DPR-V06) effects. The results indicate that DPR-V07 reduces the high-altitude precipitation underestimation by 5% (vs. V06) in the west (W) and corrects the elevation-linked overestimation via an improved terrain sensitivity. The seasonal analysis demonstrates winter-specific advancements of DPR-V07, with a 3–8% reduction in the miss bias contributing to a lower total bias. However, the algorithm updates yield unintended trade-offs: the High-Sensitivity Scan (HS) mode exhibits significant detection performance degradation, particularly in east (E) and midwest (M) regions, with Critical Success Index (CSI) values decreasing by approximately 0.12 compared to DPR-V06. Furthermore, summer error components show a minimal improvement, suggesting unresolved challenges in warm-season retrieval physics. This study establishes a systematic framework for evaluating precipitation retrieval advancements, providing critical insights for future satellite algorithm development and operational applications in hydrometeorological monitoring. Full article

This paper proposes a systematic ground experimental method to address the insufficient initial pointing accuracy of optical terminals in free space optical communication (FSO). By utilizing a multi-coordinate system transformation model combined with geodetic coordinates obtained from a Global Navigation Satellite System (GNSS), the elevation and azimuth angles of the optical terminal are calculated to achieve initial pointing. High-precision horizontal installation and true north direction calibration are accomplished using a GNSS dual-antenna system and a digital inclinometer to suppress mechanical installation errors. Furthermore, an iterative stellar calibration method is proposed, leveraging ephemeris to precompute stellar positions and optimize correction values through multiple observations, significantly improving pointing accuracy. In a 104.68 km span experiment conducted in the Qinghai Lake area, the azimuth and elevation angle errors of the optical terminal were reduced to −0.0293° and −0.0068°, respectively, with the uncertainty region narrowed to 0.0586°. These results validate the effectiveness of the proposed method in high-precision rapid link establishment, providing technical support for the engineering application of satellite-to-ground laser communication. Full article

The full-waveform spaceborne laser altimeter improves footprint geolocation accuracy through waveform matching, providing critical data for on-orbit calibration. However, in areas with significant topographic variations or complex surface characteristics, traditional waveform matching methods based on the Pearson correlation coefficient (PCC-Match) are susceptible to errors from laser ranging inaccuracies and discrepancies in surface structures, resulting in reduced footprint geolocation stability. This study proposes a terrain-constrained cross-correlation matching (TC-Match) method. By integrating the terrain characteristics of the laser footprint area with spaceborne altimetry data, a sliding “time-shift” constraint range is constructed. Within this constraint range, an optimal matching search based on waveform structural characteristics is conducted to enhance the robustness and accuracy of footprint geolocation. Using GaoFen-7 (GF-7) satellite laser footprint data, experiments were conducted in regions of Utah and Arizona, USA, for validation. The results show that TC-Match outperforms PCC-Match regarding footprint geolocation accuracy, stability, elevation correction, and systematic bias correction. This study demonstrates that TC-Match significantly improves the geolocation quality of spaceborne laser altimeters under complex terrain conditions, offering good practical engineering adaptability. It provides an effective technical pathway for subsequent on-orbit calibration and precision model optimization of spaceborne laser data. Full article

The solid solution cooling heat treatment of powder, high-temperature alloys is a crucial part of the process for ensuring the strength of materials during the forging processing. The influence of the γ′ phase and other microstructures in high-temperature alloy forgings on their macroscopic mechanical properties has been confirmed in numerous studies. Among them, the performance of the γ′ phase during the solid solution cooling process varies significantly depending on the cooling rate. This study uses the FGH99 nickel-based high-temperature alloy as the research material. It examines the precipitation and microstructure evolution law of the material under different cooling rates and its impact on the macroscopic mechanical properties of the material. Additionally, a prediction model of the organizational properties based on the cooling rate is constructed. The research findings indicate that there is a distinct positive correlation between the yield strength of the material and the cooling rate. As the cooling rate increases, the yield strength rises from 910.8 MPa to 1025.4 MPa, showing an increase of 12.6%. Moreover, an increase in the cooling rate has an evident promoting effect on the refinement of the precipitation phase. When the cooling rate is elevated from 50 °C/min to 250 °C/min, the average size of the γ′ phase decreases from 106 nm to 82.1 nm, and its morphology transforms from an irregular state to a spherical shape. For the microstructure of the material, such as the size of the precipitated phase and dislocation density, the maximum prediction error of the heat treatment organization performance prediction model established in this study is 2.97%. Moreover, the prediction error of the yield strength is 1.76%. Full article

The unique vegetation in riparian zones is fundamental for various ecological and socio-economic functions in these transitional areas. Sustainable management requires detailed spatial information about the occurring flora. Here, we present a Deep Learning (DL)-based approach for processing multimodal high-resolution remote sensing data (aerial RGB and near-infrared (NIR) images and elevation maps) to generate a classification map of the tidal Elbe and a section of the Rhine River (Germany). The ground truth was based on existing mappings of vegetation and biotope types. The results showed that (I) despite a large class imbalance, for the tidal Elbe, a high mean Intersection over Union (IoU) of about 78% was reached. (II) At the Rhine River, a lower mean IoU was reached due to the limited amount of training data and labelling errors. Applying transfer learning methods and labelling error correction increased the mean IoU to about 60%. (III) Early fusion of the modalities was beneficial. (IV) The performance benefits from using elevation maps and the NIR channel in addition to RGB images. (V) Model uncertainty was successfully calibrated by using temperature scaling. The generalization ability of the trained model can be improved by adding more data from future aerial surveys. Full article

The measurement of atmospheric moisture content is essential for the monitoring of severe weather events and hydrological studies. This paper proposes a multivariate linear regression correction model that integrates elevation data with Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) to refine the water vapor content based on FY-4A satellite remote sensing data, thereby improving its accuracy. Taking Hong Kong as an experimental area, we investigated the correlation between GNSS PWV and FY-4A PWV, confirming the feasibility of utilizing GNSS PWV to calibrate FY-4A PWV. Subsequently, by examining the differences between the two PWV values, we found that the elevation of the stations affects the consistency of PWV measurement. Based on this finding, the elevation data are introduced to construct a multivariate linear regression correction model with a first-order polynomial. To evaluate the performance of the proposed model, a comparison with other correction models is made, including second-order polynomials and power functions. The results indicate that the elevation-integrated water vapor correction model improves the root mean square error (RMSE) by 27.4% and the MAE by 26.7%, and reduces the bias from 0.592 to nearly 0. Its accuracy surpasses that of second-order polynomial and power function models, demonstrating a considerable improvement in the precision of FY-4A. Full article

Background/Objectives: Unilateral hearing loss (UHL), particularly single-sided deafness (SSD), disrupts spatial hearing in children, leading to academic and social challenges. This study aimed to (1) compare azimuthal sound-localization accuracy and compensatory strategies between children with single-sided deafness (SSD) and their normal-hearing (NH) peers within a virtual reality environment, and (2) investigate sound-localization performance across various azimuths by contrasting left-SSD (L-SSD) and right-SSD (R-SSD) groups. Methods: A cohort of 44 participants (20 NH, 24 SSD) performed sound localization tasks in a 3D virtual environment. Unsigned azimuth error (UAE), unsigned elevation error (UEE), and head movement distance were analyzed across six azimuthal angles (−75° to 75°) at 0°elevation. Non-parametric statistics (Mann–Whitney U tests, Holm–Bonferroni correction) compared performance between NH and SSD groups and within SSD subgroups (L-SSD vs. R-SSD). Results: The SSD group exhibited significantly higher UAE (mean: 22.4° vs. 3.69°, p < 0.0001), UEE (mean: 5.95° vs. 3.77°, p < 0.0001) and head movement distance (mean: 0.35° vs. 0.12°, p < 0.0001) compared with NH peers, indicating persistent localization deficits and compensatory effort. Within the SSD group, elevation performance was superior to azimuthal accuracy (mean UEE: 3.77° vs. mean UAE: 22.4°). Participants with R-SSD exhibited greater azimuthal errors at rightward angles (45°and 75°) and at −15°, as well as increased elevation errors at 75°. Hemifield-specific advantages were strongest at extreme lateral angles (75°). Conclusions: Children with SSD rely on insufficient compensatory head movements to resolve monaural spatial ambiguity in order to localize sounds. Localization deficits and the effort associated with localization task call for action in addressing these issues in dynamic environments such as the classroom. L-SSD subjects outperformed R-SSD peers, highlighting hemispheric specialization in spatial hearing and the need to study its neural basis to develop targeted rehabilitation and classroom support. The hemifield advantages described in this study call for further data collection and research on the topic. Full article

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article

Background: We assessed the relationship between uterine artery (UtA) indices and the occurrence of obstetrical complications in twin pregnancies. Methods: It was a longitudinal, prospective observation of the UtA indices and obstetric outcomes in twin pregnancies between 11 weeks of gestation and delivery. We used a logistic regression model with reliable estimators of standard errors considering the longitudinal structure. In 150 patients with twin pregnancies, 1086 ultrasound examinations were performed. The analysis incorporated nomograms for singletons and dichorionic (DC) twins. Results: In twin pregnancies, we observed a positive relationship between UtA indices and obstetrical complications (OR = 1.32, p = 0.043 for standardized PI and OR = 1.38, p = 0.018 for standardized RI). The risk increased with increasing UtA indices. There was a significant positive relationship between the UtA indices and analyzed pathologies in DC twins. We observed that both DC twins’ UtA indices below the 5th percentile were associated with favorable outcomes, while those above the 95th percentile were associated with adverse outcomes. According to the singleton nomograms, only the UtA PI above the 95th percentile showed significance. In MC twins, only significantly elevated UtA indices above the upper limit of nomogram were associated with adverse outcomes. Conclusions: The UtA nomogram for singleton and DC twins may be used in the prediction of twin pregnancy outcome, but DC nomograms are more accurate. The mechanism of obstetric complications in MC twins differs, and it requires further research. However, it seems that DC twin nomograms can be used in MC twins, but they will be less effective. Full article

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article

For automotive GFRP structural components, beyond structural design, the warpage, residual stress/strain, and fiber orientation inevitably induced during the injection molding process significantly compromise their service performance. These factors also diminish the reliability of performance assessments. Thus, it is imperative to develop a process–structure co-optimization approach for GFRP components. In this paper, the performance of a front-end module is evaluated through topological structure design, injection molding process optimization, and simulation with mapped injection molding history, followed by experimental validation and analysis. Under ±1000 N loading, the initial design shows excessive displacement at the latch mounting points (2.254 mm vs. <2.0 mm limit), which is reduced to 1.609 mm after topology optimization. By employing a sequential valve control system, the controls of the melt line and fiber orientation are is superior to thatose of conventional gating systems. The optimal process parameter combination is determined through orthogonal experiments, reducing the warpage to 1.498 mm with a 41.5% reduction compared to the average warpage of the orthogonal tests. The simulation results incorporating injection molding data mapping (fiber orientation, residual stress–strain) show closer agreement with experimental measurements. When the measured displacement exceeded 0.65 mm, the average relative error $E_r$, range $R$, and variance $s^2$ between the experimental results and mapped simulations were 11.78%, 14%, and 0.002462, respectively, validating the engineering applicability of this method. The methodology and workflow can provide methodological support for the design and performance assessment of GFRP automotive body structures, which enhances structural rigidity, improves control over injection molding process defects, and elevates the reliability of performance evaluation. Full article