Search Results (1,588)

Using high-resolution remote sensing imagery to obtain the wind turbine height is a fast and effective method for monitoring the status of wind turbines after natural disasters such as earthquakes, landslides, and typhoons. A height estimation method tailored for wind turbines is proposed using high-resolution satellite images. First, deep learning techniques are employed to identify wind turbines and extract their shadow information from GaoFen-2 (GF-2) satellite imagery. Specifically, YOLOv5-CBAM and MSASDNet are used for target recognition and shadow extraction, achieving an identification accuracy of 96% and a shadow extraction accuracy of 82.53%. Next, the line-by-line scanning method is applied to remove blade shadow from the whole wind turbine shadow. By calculating the number of pixels occupied by the shadow length of the wind turbine after removing the blade shadow and multiplying by the image resolution, the wind turbine shadow length is obtained. Finally, a spatial geometry model involving the satellite angles, solar angles, and wind turbine shadow length is constructed to retrieve the wind turbine height. An experiment was conducted using GF-2 satellite remote sensing data from a wind farm in Huailai County of China. The actual heights of wind turbines in the estimation area were measured by the field experiment, and the average absolute error was verified to be 2.2m, demonstrating the effectiveness of the proposed method. The experimental results show that this method can detect the post-disaster status of wind turbines. Full article

Accurate spatial information on individual tree locations is essential for precision forestry, the integration of field and remote sensing data, and tree-level forest analyses. This study compared the positional accuracy and tree identification performance of four tree-mapping approaches: legacy paper maps, a pseudolite-based field positioning system (TerraHärp), drone-based laser scanning, and mobile laser scanning (MLS). The analysis was conducted in five long-term experimental forest sites in Lithuania, comprising pine- and spruce-dominated stands with varying stand densities. Tree locations derived from legacy maps and the TerraHärp system were compared to assess systematic and random positional discrepancies. TerraHärp-derived tree positions were subsequently used as a reference to evaluate the laser scanning-based methods. Positional accuracy was assessed using Hotelling’s T² test, root-mean-square error, and the National Standard for Spatial Data Accuracy (NSSDA), while spatial autocorrelation of deviations was examined using Moran’s I. The results indicated that discrepancies between TerraHärp and legacy maps were dominated by systematic horizontal shifts in the historical maps, whereas random positional variability was relatively small and consistent across stand types. Drone-based laser scanning showed a strong dependence of tree identification accuracy on stand density and mean tree diameter. Overall, CHM-based segmentation yielded more accurate tree identification than 3D point cloud segmentation, with mean F1-scores of 0.78 and 0.72, respectively. Positional accuracy varied by method, with the largest errors from CHM apexes and highest 3D point cloud points (mean NSSDA ≈ 1.8–2.0 m), improved accuracy using the lowest 3D cluster points (1.45–1.72 m), and the highest accuracy achieved using mobile laser scanning (mean NSSDA 0.76–0.90 m; >95% of trees within 1 m). These results demonstrate that pseudolite-based field mapping provides a reliable reference for high-precision tree location and for integrating field and laser scanning data in managed conifer stands. Full article

Accurate field-scale meteorological information is required for precision agriculture, but operational numerical weather prediction products remain spatially coarse and cannot resolve local microclimate variability. This study proposes a data fusion superresolution workflow that combines global GFS predictors (0.25°), regional station observations from Southern Moravia (Czech Republic), and static physiographic descriptors (elevation and terrain gradients) to predict the 2 m air temperature 24 h ahead and to generate spatially continuous high-resolution temperature fields. Several model families (LightGBM, TabPFN, Transformer, and Bayesian neural fields) are evaluated under spatiotemporal splits designed to test generalization to unseen time periods and unseen stations; spatial mapping is implemented via a KNN interpolation layer in the physiographic feature space. All learned configurations reduce the mean absolute error relative to raw GFS across splits. In the most operationally relevant regime (unseen stations and unseen future period), TabPFN-KNN achieves the lowest MAE (1.26 °C), corresponding to an ≈24% reduction versus GFS (1.66 °C). The results support the feasibility of an operational, sensor-infrastructure-compatible pipeline for high-resolution temperature superresolution in agricultural landscapes. Full article

Magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) offers the flexibility to position sensors closer to the scalp, which improves the signal-to-noise ratio compared to conventional superconducting quantum interference device (SQUID) systems. However, the spatial resolution of OPM-MEG critically depends on sensor placement, especially when the number of sensors is limited. In this study, we present a methodology for optimizing OPM-MEG sensor layouts using each subject’s anatomical information derived from individual magnetic resonance imaging (MRI). We generated realistic forward models from reconstructed head surfaces and simulated magnetic fields produced by equivalent current dipoles (ECDs). We compared multiple simulation strategies, including ECDs randomly distributed across the cortical surface and ECDs constrained to regions of interest. For each simulated magnetic field map (MFM) database, we applied the sequential selection algorithm (SSA) to identify sensor positions that maximized information capture. Unlike previous approaches relying on large measurement databases, this simulation-driven strategy eliminates the need for extensive pre-existing recordings. We benchmarked the performance of the personalized layouts using OPM-MEG datasets of auditory evoked fields (AEFs) derived from real whole-head SQUID-MEG measurements. Our results show that simulation-based SSA optimization improves the coverage of cortical regions of interest, reduces the number of sensors required for accurate source reconstruction, and yields sensor configurations that perform comparably to layouts optimized using measured data. To evaluate the quality of estimated MFMs, we applied metrics such as the correlation coefficient (CC), root-mean-square error, and relative error. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95) capture most of the information contained in full-head MFMs. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles and found that localization errors were < 5 mm. The results further indicate that SSA performance is insensitive to individualized head geometry, supporting the feasibility of using representative anatomical models and highlighting the potential of this approach for clinical OPM-MEG applications. Full article

Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing. Full article

Focusing on the construction of a 58-m-diameter double-layer steel space frame dome at the Han Culture Museum Assembly Hall, this study addresses scheme selection and safety control challenges in staggered jacking of large-span spatial structures. A three-dimensional finite element model in MIDAS Gen simulated the three-stage jacking process to compare three temporary support layouts. Numerical evaluation metrics included maximum vertical displacements, peak internal forces, the proportion of members undergoing stress state transitions, and spatio-temporal evolution of stress concentrations. Scheme B demonstrated superior performance, reducing peak vertical displacement by 44% under critical conditions, lowering peak stresses, and enabling more uniform internal force redistribution—effectively mitigating tension–compression cycling and buckling risks. Crucially, only nodal displacements and support elevations were monitored in situ using a 3D system based on magnetic prisms and total stations; no strain or force measurements were conducted due to practical constraints during construction. Monitoring data show good agreement with simulated displacements and support elevations under Scheme B, validating the model’s deformation response. However, localized deviations—including a 29 mm deflection discrepancy and elevation errors up to 28 mm—reveal the influence of uneven boundary conditions, with potential implications for long-term structural behavior. The findings confirm that numerical predictions of deformation are reliable, while internal forces remain unvalidated by field data. The integrated approach of “scheme comparison–construction simulation–full-process displacement monitoring” proves effective for safety control and decision-making in complex jacking operations, offering a transferable framework for similar large-span double-layer space frame projects. Full article

This study addresses a critical knowledge gap in quantifying strategic mineral resources within hyper-arid, tectonically complex terrains by establishing a recursive framework that reconciles deterministic resource estimation with the nonlinear dynamics of tectonically mediated metamorphic systems. Using Libya’s Wadi El Shati as a case study, legacy lithological misclassifications are rectified through the fusion of Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, and Digital Elevation Model analytics within a unified geospatial workflow. The methodology synergizes atmospherically corrected optical data, processed via supervised Maximum Likelihood Classification, with calibrated radar-derived structural lineaments. Classified marble-bearing zones within the Al Mahruqah Formation are integrated with DEM data and field-validated thickness measurements using Triangulated Irregular Network models to resolve surface–subsurface dependencies and compute volumes. The results demonstrate a 91% lithological classification accuracy, rectifying a 22% error in legacy maps. Structural analysis of 1213 lineaments confirms a dominant NE–SW extensional regime (σ₃) that facilitated fluid conduits. The quantified marble-bearing horizon spans ~334 km² with a volume of 6.0 km³ (±9%). Spatial analysis reveals a causal link between high-grade marble clusters, basaltic intrusions, and NE–SW fault systems, refining models of contact metamorphism in rift-related settings. Full article

To address the problems existing in nuclear reactor accident emergency training, a design scheme and system prototype of radiation detection training equipment for penetrating radiation fields in enclosed spaces, based on inertial sensors and wireless Bluetooth communication is proposed. First, the penetrating radiation field is modeled. On this basis, a calculation model of the neutron/γ dose equivalent rate is established. This model is based on the motion path of simulated radiation detection equipment. Second, the MPU6050 inertial sensor is designed and developed. It monitors the three-axis acceleration and three-axis angular acceleration values in real time. This enables the indoor positioning function of the simulated detection training equipment. The Digital Motion Processor (DMP) filtering algorithm is used to process the measured data. This improves the detection accuracy. Finally, a Bluetooth communication module is designed and developed. It transmits the detected position data to the main control computer in real time. The main control computer performs calculation and analysis to obtain the radiation intensity value. This value is sent to the Arduino controller. The Arduino controller controls the display of the value on the liquid crystal screen. Experimental verification is carried out. Experimental verification indicates that the maximum error of the system’s three-dimensional spatial positioning is 0.08 m, the mean relative error of the radiation dose rate simulation is 4.81%, and the maximum relative error is 7.8%. The system relatively accurately achieves radiation dose simulation and radiation source localization according to different working modes, providing a high cost-effectiveness training method for radiation detection training with high safety and good economy. Full article

Cost efficient, spatially resolved water quality monitoring is essential for managing pollution and protecting aquatic ecosystems. This study presents a low-cost (approximately USD 200), open-source, unmanned aerial vehicle (UAV)-deployable in situ sensor node for real-time assessment of surface-water conditions. The system integrates sensors for pH, turbidity, temperature, and total dissolved solids (TDSs), with onboard data logging and real-time clock (RTC) synchronization. Bench validation of the sensor package yielded mean absolute percentage errors of 1.34% for pH, 5.23% for TDS, and 0.81% for temperature, and the device operated continuously for 42 h. Field deployment demonstrated its ability to resolve spatial gradients, with observed ranges in the tested water body of pH 6.0–6.7, turbidity 11–18 NTU, TDS 44–51 ppm, and temperature 22.8–24.6 °C. Ordinary Kriging was used to interpolate measurements and generate continuous spatial maps. The open-source, UAV-deployable design provides an accessible platform for community-scale and research-oriented water quality mapping. Full article

Remote towers are increasingly deployed at small-to-medium airports globally for cost efficiency, yet safety optimization for large airport remote apron control remains underexplored. This study proposes a human error identification framework for air traffic controllers (ATCOs) in large airport remote apron operations. Using hierarchical task analysis (HTA), a cognitive-behavioral model, and the technique for retrospective analysis of cognitive errors (TRACEr), we analyzed error probability and severity through field research. Key findings reveal critical divergences. Memory functions showed the highest error probability, while perception errors caused the most severe outcomes. Working memory errors were most prevalent, but visual detection errors were most severe. Attention deficits were most frequent, while spatial confusion and information integration failures exceeded severity thresholds. Personal factors dominated performance-shaping factors, with low vigilance and equipment unavailability as primary high-risk conditions. This research provides an error identification checklist and analysis methodology to enhance human performance and aviation safety in remote apron control. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article

Hydrological modelling in lowland catchments is often constrained by flat terrain and sparce meteorological station networks, which limits the accuracy of spatial interpolation of precipitation and temperature. In these conditions, the nearest available station may be located tens of kilometres away, making interpolated meteorological inputs highly uncertain and prone to systematic bias. This study aims to improve interpolated meteorological data for hydrological applications by developing and evaluating a practical bias correction approach suitable for low-relief regions with insufficient station density. Long-term temperatures and precipitation records from 18 meteorological stations in Lithuania (1961–2020) were used as reference data. Meteorological fields were reconstructed using Ordinary Kriging and Spline interpolation and evaluated against observations at monthly and daily time scales using correlation (r), Root Mean Square Error (RMSE), Percent Bias (PBIAS), Nash–Sutcliffe Efficiency (NSE), and Probability of Detection (POD) for precipitation. Bias correction was applied to interpolated datasets using inverse distance weighting (IDW) based on one to four neighbouring stations, reflecting typical distances of 50–70 km between observation sites. The results show that while the interpolation method strongly influences precipitation accuracy, bias correction substantially reduces systematic errors without altering temporal structure. The most robust improvements were obtained using two to three neighbouring stations and an IDW power parameter of one, particularly under flat terrain conditions. When applied as input to the HBV rainfall–runoff model for three representative lowland catchments, bias-corrected interpolated meteorological data consistently improved runoff simulations, bringing model performance closer to that achieved using historical station observations. The findings demonstrate that targeted bias correction is an effective and computationally simple strategy for improving interpolated meteorological data in data-sparse lowland regions. The proposed approach provides practical guidance for hydrological modelling where dense observation networks are unavailable and reliance on interpolation is unavoidable. Full article

Accurately quantifying the amount of corn straw returned to the field is crucial for evaluating conservation tillage measures and phaeozem protection. This study proposes a framework for quantitatively estimating the amount of corn straw returned to the field based on UAV multispectral imaging, integrating a standardized spectral correction strategy, a novel straw index (SI), and an improved deep learning model (convolutional neural network-straw, CNN-Straw). By combining multispectral images acquired by UAVs with ground-measured straw weight data, regression datasets covering autumn and spring conditions were constructed. The proposed straw index aims to enhance the spectral differences between non-photosynthetic straw residues and living vegetation. Furthermore, the CNN-Straw model, combining frequency domain convolution and local spatial attention mechanisms, has an improved ability to represent the complex texture of straw features. Experimental results show that CNN-Straw outperforms traditional machine learning models, including random forest (RF), support vector regression (SVR), and XGBoost, achieving a high coefficient of determination (R²) of 0.82 on different seasonal datasets and effectively reducing the root mean square error (RMSE) and mean absolute error (MAE). Cross-seasonal experiments further demonstrate the stable performance of the framework under different environmental conditions. The proposed method provides an efficient and scalable solution for the quantitative assessment of straw return to the field, supporting precision agricultural management and phaeozem conservation practices. Full article

The proliferation of massive antenna arrays and the consequent intensification of near-field effects with 6G necessitate addressing critical security challenges in near-field communication environments. This paper presents a novel artificial noise-aided spatial and directional modulation (SDMN-AN) framework, specifically tailored for secure near-field communications. The proposed system integrates legitimate receiver indices, modulation symbols, and artificial noise (AN) confined to the null space of legitimate channels, thereby enhancing both spectral efficiency and communication security. Two precoding strategies—maximum-ratio transmission (MRT) and zero-forcing (ZF)—are investigated, offering trade-offs between hardware complexity and detection overhead. Analytical derivations of bit error rate (BER) bounds, corroborated by simulation results, underscore the superiority of the SDMN-AN framework in mitigating eavesdropping threats while significantly improving spectral efficiency, positioning it as a compelling solution for next-generation secure wireless networks. Full article

This paper presents an Implicit Neural Representation method for Event-Based Imaging Velocimetry (INR-VG) to reconstruct dense velocity fields from sparse event streams. The core idea is to learn a mapping (multilayer perceptron) from spatial coordinates to flow velocities, $\mathit{v}\left(\mathit{x}\right)=f(\mathit{x};\theta )$, which thereby enables dense velocity measurements at any desired spatial resolution. The neural network is optimized through test-time optimization by minimizing the alignment error between warped voxel grids of events. Extensive evaluations on synthetic datasets and real-world flows demonstrate that INR-VG achieves high accuracy (errors as low as $0.05 \mathrm{px}/\mathrm{ms}$) and maintains robustness in challenging conditions where existing methods typically fail, including low event rates and large displacements, significantly outperforming optical-flow-based baselines. To the best of our knowledge, this work represents a successful application of implicit neural representations to event-based imaging velocimetry (EBIV), establishing a new paradigm for dense and robust event-based flow measurement. The implementation and experimental details are publicly available to support reproducibility and future research. Full article