Search Results (4,578)

Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity threats make these systems vulnerable to false data injection attacks. Most existing detection approaches focus only on temporal dependencies in time-frequency features and lack diagnostic mechanisms for identifying malicious UAVs, which limits their ability to effectively detect and mitigate such attacks. To address this issue, this paper proposes an intelligent collaborative localization framework that safeguards localization integrity by identifying and correcting false ranging information from malicious UAVs. The framework captures spatio-temporal correlations in multidimensional ranging sequences through a graph attention network (GAT) coupled with a time-attention-based variational autoencoder (VAE) to detect anomalies through anomalous distribution patterns. Malicious UAVs are further diagnosed through an anomaly scoring mechanism based on statistical analysis and reconstruction errors, while detected anomalies are corrected via a K-nearest neighbor-based (KNN) algorithm to enhance localization performance. Simulation results show that the proposed model improves localization accuracy by 25.9%, demonstrating the effectiveness of spatial–temporal feature extraction in securing collaborative localization. Full article

Land surface temperature (LST) is essential for studying land–atmosphere energy exchange, the impact of climate change, and its influence on crop yields and hydrology. Although satellite remote sensing provides large-scale LST data, existing spatiotemporal fusion methods face challenges. Traditional algorithms have difficulty with heterogeneous surfaces, and deep-learning models often produce blurred details and inaccurate temperatures, which limits their use in high-precision applications. This study addresses these issues by developing a Deep-Learning Spatial and Temporal Fusion Model (DLSTFM) for Landsat-8 and MODIS LST imagery in Griffith, Australia. DLSTFM employs a dual-branch structure: one branch is dedicated to dual-temporal fusion, and the other branch is dedicated to multi-source feature fusion. Key innovations include the Spatial Adaptive Feature Modulation (SAFM) module, which performs adaptive multi-scale feature fusion, and the Temperature Adaptive Correction Module (TCM), which makes pixel-wise adjustments using reference data. Experiments demonstrate that DLSTFM significantly outperforms traditional methods and existing deep-learning fusion methods. DLSTFM achieves clearer surface features and a mean absolute temperature error of approximately 2.1 K. The model also demonstrated excellent generalization performance in another test area (Ardiethan) without retraining, showcasing its substantial practical value for high-accuracy LST fusion. Full article

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

Surface deformation induced by the extraction of natural resources constitutes a non-stationary spatiotemporal process. Modeling surface deformation time series obtained through Interferometric Synthetic Aperture Radar (InSAR) technology using deep learning methods is crucial for disaster prevention and mitigation. However, the complexity of model hyperparameter configuration and the lack of interpretability in the resulting predictions constrain its engineering applications. To enhance the reliability of model outputs and their decision-making value for engineering applications, this study presents a workflow that combines a Tree-structured Parzen Estimator (TPE)-based Bayesian optimization approach with ensemble inference. Using the Rhineland coalfield in Germany as a case study, we systematically evaluated six deep learning architectures in conjunction with various spatiotemporal coding strategies. Pairwise comparisons were conducted using a Welch t-test to evaluate the performance differences across each architecture under two parameter-tuning approaches. The Benjamini–Hochberg method was applied to control the false discovery rate (FDR) at 0.05 for multiple comparisons. The results indicate that TPE-optimized models demonstrate significantly improved performance compared to their manually tuned counterparts, with the ResNet+Transformer architecture yielding the most favorable outcomes. A comprehensive analysis of the spatial residuals further revealed that TPE optimization not only enhances average accuracy, but also mitigates the model’s prediction bias in fault zones and mineralize areas by improving the spatial distribution structure of errors. Based on this optimal architecture, we combined the ten highest-performing models from the optimization stage to generate a quantile-based susceptibility map, using the ensemble median as the central predictor. Uncertainty was quantified from three complementary perspectives: ensemble spread, class ambiguity, and classification confidence. Our analysis revealed spatial collinearity between physical uncertainty and absolute residuals. This suggests that uncertainty is more closely related to the physical complexity of geological discontinuities and human-disturbed zones, rather than statistical noise. In the analysis of super-threshold probability, the threshold sensitivity exhibited by the mining area reflects the widespread yet moderate impact of mining activities. By contrast, the fault zone continues to exhibit distinct high-probability zones, even under extreme thresholds. It suggests that fault-controlled deformation is more physically intense and poses a greater risk of disaster than mining activities. Finally, we propose an engineering decision strategy that combines uncertainty and residual spatial patterns. This approach transforms statistical diagnostics into actionable, tiered control measures, thereby increasing the practical value of susceptibility mapping in the planning of natural resource extraction. Full article

Indoor WiFi fingerprint localization often suffers from abnormal fluctuations in received signal strength indicator (RSSI) measurements and from the sensitivity of least-squares support vector machine (LSSVM) hyperparameters to local optima. To address these issues, this paper presents an improved end-to-end localization method that integrates fingerprint reconstruction with adaptive model optimization. First, a knowledge-enhanced anomaly detection and spatial fingerprint repair (KADSFR) model is used to enhance fingerprint database consistency by combining robust Mahalanobis distance, median absolute deviation, and local outlier factor for anomaly detection, followed by weighted k-nearest neighbors interpolation based on composite signal–physical distances. Then, an adaptive particle swarm optimization (APSO) scheme with stagnation detection and spatial exclusion mechanisms is employed to tune the LSSVM regularization coefficient and RBF kernel width under five-fold cross-validation. Experiments show that KADSFR improves fingerprint quality by approximately 10 percent, and the proposed method achieves an average error of 0.74 m, outperforming KNN, WKNN, LSSVM, and APSO-LSSVM by 63.5 percent, 62.8 percent, 34.5 percent, and 16.9 percent, respectively. Sensitivity analysis further confirms strong robustness and stability. Full article

This study investigates the integration of 3D laser scanning technology in the retrofitting of Ballast Water Treatment Systems (BWTS) on existing commercial vessels, addressing the global challenge of invasive aquatic species. The methodology combines a bibliometric analysis of keywords—indicating recent trends and knowledge gaps, a feasibility study, and detailed engineering design with on-site supervision. A case study is presented on a crude oil tanker, employing a multi-station 3D scanning strategy across the engine and pump rooms—performed using 63 and 45 scan positions, respectively. These data were processed with removal filters and integrated into specialized CAD software for detailed piping design. The implementation of high-fidelity point clouds served as the digital foundation for modeling the vessel’s existing piping infrastructure and retrofitting with the installation of an electrolysis-based BWTS. Results confirm that 3D scanning enables precise spatial analysis, minimizes retrofitting errors, reduces installation time, and ensures regulatory compliance with the IMO Ballast Water Management Convention. By digitally capturing complex onboard environments, the approach enhances accuracy, safety, and cost-effectiveness in maritime engineering projects. This work underscores the transition toward point cloud-based digital twins as a standard for sustainable and efficient ship conversions in the global shipping industry. Full article

To address the challenge of bearing load monitoring in elastically supported marine shafting systems, a multi-data fusion-based prediction model is constructed. In view of the small-sample nature of measured bearing load data, transfer learning is adopted to migrate the physical relationships embedded in finite element simulations to the measurement domain. A limited number of actual samples are used to correct the simulation data, forming a high-fidelity hybrid training set. The system—supported by air-spring isolators mounted on the raft—is divided into multiple sub-regions according to their spatial layout, establishing local mappings from air-spring pressure variations to bearing load increments to reduce model complexity. On this basis, a Stacking ensemble learning framework is further incorporated into the prediction model to integrate multi-source information such as air-spring pressure and raft strain, thereby enriching the model’s information acquisition and improving prediction accuracy. Experimental results show that the proposed transfer learning-based multi-sub-region bearing load prediction model performs significantly better than the full-parameter input model. Furthermore, the strain-enhanced Stacking-based multi-data fusion bearing load prediction model improves the characterization of shafting features and reduces the maximum prediction error. The proposed multi-data fusion modeling strategy offers a viable approach for condition monitoring and intelligent maintenance of marine shafting systems. Full article

Traditional fusion methods for integrating multi-source gravity data rely on predefined mathematical models that inadequately capture complex nonlinear relationships, particularly at wavelengths shorter than 10 km. We developed a convolutional neural network incorporating differential marine geodetic data (DMGD-CNN) to enhance marine gravity anomaly recovery from HY-2A satellite altimetry. The DMGD-CNN framework encodes spatial gradient information by computing differences between target points and their surrounding neighborhoods, enabling the model to explicitly capture local gravity field variations. This approach transforms absolute parameter values into spatial gradient representations, functioning as a spatial high-pass filter that enhances local gradient information critical for short-wavelength gravity signal recovery while reducing the influence of long-wavelength components. Through systematic ablation studies with eight parameter configurations, we demonstrate that incorporating first- and second-order seabed topography derivatives significantly enhances model performance, reducing the root mean square error (RMSE) from 2.26 mGal to 0.93 mGal, with further reduction to 0.85 mGal achieved by the differential learning strategy. Comprehensive benchmarking against international gravity models (SIO V32.1, DTU17, and SDUST2022) demonstrates that DMGD-CNN achieves 2–10% accuracy improvement over direct CNN predictions in complex topographic regions. Power spectral density analysis reveals enhanced predictive capabilities at wavelengths below 10 km for the direct CNN approach, with DMGD-CNN achieving further precision enhancement at wavelengths below 5 km. Cross-validation with independent shipborne surveys confirms the method’s robustness, showing 47–63% RMSE reduction in shallow water regions (<2000 m depth) compared to HY-2A altimeter-derived results. These findings demonstrate that deep learning with differential marine geodetic features substantially improves marine gravity field modeling accuracy, particularly for capturing fine-scale gravitational features in challenging environments. Full article

Aiming at the problem that the current traditional apple yield estimation methods rely on manual investigation and do not make full use of multi-source information, this paper proposes an apple orchard yield prediction framework combining multispectral remote sensing features and ground features. The framework is oriented to the demand of yield prediction at different scales. It can not only realize the prediction of apple yield at the district and county scales, but also modify the prediction results of small-scale orchards based on the acquisition of orchard features. The framework consists of three parts, namely, apple orchard planting area extraction, district and county large-scale yield prediction and small-scale orchard yield prediction correction. (1) During apple orchard planting area extraction, the samples of some apple planting areas in the study area were obtained through field investigation, and the orchard and non-orchard areas were classified and discriminated, providing a spatial basis for the collection of subsequent yield prediction-related data. (2) In the large-scale yield prediction of districts and counties, based on the obtained orchard-planting areas, the corresponding multispectral remote sensing features and environmental features were obtained using Google Earth engine platform. In order to avoid the noise interference caused by local pixel differences, the obtained data were median synthesized, and the feature set was constructed by combining the yield and other information. On this basis, the feature set was divided and sent to Apple Orchard Yield Prediction Network (APYieldNet) for training and testing, and the district and county large-scale yield prediction model was obtained. (3) During the part of small-scale orchard yield prediction correction, the optimal model for large-scale yield prediction at the district and county levels is utilized to forecast the yield of the entire planting area and the internal local sampling areas of the small-scale orchard. Within the local sampling areas, the number of fruits is identified through the YOLO-A model, and the actual yield is estimated based on the empirical single fruit weight as a ground feature, which is used to calculate the correction factor. Finally, the proportional correction method is employed to correct the error in the prediction results of the entire small-scale orchard area, thus obtaining a more accurate yield prediction for the small-scale orchard. The experiment showed that (1) the yield prediction model APYieldNet (MAE = 152.68 kg/mu, RMSE = 203.92 kg/mu) proposed in this paper achieved better results than other methods; (2) the proposed YOLO-A model achieves superior detection performance for apple fruits and flowers in complex orchard environments compared to existing methods; (3) in this paper, through the method of proportional correction, the prediction results of APYieldNet for small-scale orchard are closer to the real yield. Full article

Accurate global mapping of lunar iron oxide (FeO) abundance is essential for understanding the Moon’s geological evolution and for supporting future in situ resource utilization (ISRU). While hyperspectral data from the Moon Mineralogy Mapper (M3) provide a unique combination of high spectral dimensionality, hectometre-scale spatial resolution, and near-global coverage, existing FeO retrieval approaches struggle to fully exploit the high dimensionality, nonlinear spectral variability, and planetary-scale volume of the Global Mode dataset. To address these limitations, we present an integrated machine learning pipeline for estimating lunar FeO abundance from M3 hyperspectral observations. Unlike traditional methods based on raw reflectance or empirical spectral indices, the proposed framework combines Discrete Wavelet Transform (DWT), deep autoencoder-based feature compression, and ensemble regression to achieve robust and scalable FeO prediction. M3 spectra (83 bands, 475–3000 nm) are transformed using a Daubechies-4 (db4) DWT to extract 42 representative coefficients per pixel, capturing the dominant spectral information while filtering high-frequency noise. These features are further compressed into a six-dimensional latent space via a deep autoencoder and used as input to a Random Forest regressor, which outperforms kernel-based and linear Support Vector Regression (SVR) as well as Lasso regression in predictive accuracy and stability. The proposed model achieves an average prediction error of 1.204 wt.% FeO and demonstrates consistent performance across diverse lunar geological units. Applied to 806 orbital tracks (approximately $3.5\times {10}^9$ pixels), covering more than 95% of the lunar surface, the pipeline produces a global FeO abundance map at 150 m per pixel resolution. These results demonstrate the potential of integrating multiscale wavelet representations with nonlinear feature learning to enable large-scale, geochemically constrained planetary mineral mapping. Full article

Population activity drives urban development, and high-spatiotemporal-resolution population distribution provides critical insights for refined urban management and social services. However, mixed population activity patterns and spatial heterogeneity make simultaneous high-temporal- and -spatial-resolution estimation difficult. Therefore, we propose the High-Spatiotemporal-Resolution Population Distribution Estimation Based on the Strong and Weak Perception of Population Activity Patterns (SWPP-HSTPE) method to estimate hourly population distribution at the building scale. During the weak-perception period, we construct a Modified Dual-Environment Feature Fusion model using building features within small-scale grids to estimate stable nighttime populations. During the strong-perception period, we incorporate activity characteristics of weakly perceived activity populations (minors and older people). Then, the Self-Organizing Map algorithm and spatial environment function purity are used to decompose mixed patterns of strongly perceived activity populations (young and middle-aged) and to extract fundamental patterns, combined with building types, for population calculation. Results demonstrated that the SWPP-HSTPE method achieved high-spatiotemporal-resolution population distribution estimation. During the weak-perception period, the estimated population correlated strongly with actual household counts (r = 0.72) and outperformed WorldPop and GHS-POP by 0.157 and 0.133, respectively. During the strong-perception period, the SWPP-HSTPE model achieves a correlation with hourly population estimates that is approximately 4% higher than that of the baseline model, while reducing estimation errors by nearly 2%. By jointly accounting for temporal dynamics and population activity patterns, this study provides valuable data support and methodological insights for fine-grained urban management. Full article

Time delays are intrinsic to mitotic regulation, particularly within the spindle assembly checkpoint (SAC) and the spindle position checkpoint (SPOC). These delays emerge from multi-step protein activation, molecular transport, force-dependent conformational transitions, and spatial redistribution of regulatory complexes. They span seconds to minutes and strongly influence checkpoint activation, maintenance, and silencing. Increasing evidence shows that such delayed processes shape mitotic timing, checkpoint robustness, and cell-fate decisions. While classical ordinary differential equation (ODE) models assume instantaneous biochemical responses, delay differential equations (DDEs) provide a natural framework for representing these finite timescales by explicitly incorporating system history. Recent DDE-based studies have revealed how delayed signaling contributes to bistability, oscillatory responses, prolonged mitotic arrest, and variability in checkpoint outputs. This review summarizes the biological origins of delays in SAC and SPOC, including Mad2 activation, MCC assembly and turnover, APC/C reactivation, tension maturation at kinetochores, and Bfa1–Bub2 regulation of Tem1. The article further discusses how mechanistic models with explicit delays improve our understanding of SAC–SPOC ordering, error-correction dynamics, and mitotic exit control. Finally, open challenges and future directions are outlined for integrative delay-aware modeling that unifies biochemical, mechanical, and spatial processes to better explain checkpoint function and chromosomal stability. Full article

In the context of mineral resource exploration, spatial prediction must cope with heterogeneous, non-normal data distributions and limited sampling. While conventional geostatistics and standard machine learning provide baseline estimates, they often suffer from excessive smoothing or fail to capture continuous spatial dependencies. This study proposes a geospatially aware Wasserstein conditional Generative Adversarial Network (GSA-cGAN) to complement existing workflows for multivariate mineral interpolation. The framework augments a baseline cGAN with WGAN-GP for stable adversarial training, CoordConv to encode absolute spatial coordinates and Self-Attention to capture long-range spatial dependencies. Eight model configurations were trained on 272 samples from a mineralized zone in the Taebaek Mountains, Korea, and strictly benchmarked against Ordinary/Universal Kriging and multivariate machine learning baselines (Random Forest, XGBoost). Under the adopted experimental design, the full GSA-cGAN achieved the lowest test root mean squared error and highest coefficient of determination, demonstrating a significant performance improvement over the baselines. Furthermore, distribution analysis confirmed that the model effectively overcomes the smoothing limitations of regression-based methods, generating high-resolution 10 m × 10 m maps that preserve statistical variance, hotspot anomalies, and complex spatial patterns. The results indicate that deep generative models can serve as practical decision-support tools for identifying drilling targets and prioritizing follow-up exploration in geologically complex settings. Full article

This study proposes a lightweight framework for transferring pretrained land cover classification architectures without additional training. The system utilizes French IGN imagery and Korean UAV and aerial imagery. It employs FLAIR U-Net models with ResNet34 and MiTB5 backbones, along with the AI-HUB U-Net. The implementation consists of four sequential stages. First, we perform class mapping between heterogeneous schemes and unify coordinate systems. Second, a quadratic polynomial regression equation is constructed. This formula uses multispectral band statistics as hyperparameters and class-wise IoU as the dependent variable. Third, optimal parameters are identified using the stationary point condition of Response Surface Methodology (RSM). Fourth, the final land cover map is generated by fusing class-wise optimal results at the pixel level. Experimental results show that optimization is typically completed within 60 inferences. This procedure achieves IoU improvements of up to 67.86 percentage points compared to the baseline. For automated application, these optimized values from a source domain are successfully transferred to target areas. This includes transfers between high-altitude mountainous and low-lying coastal territories via proportional mapping. This capability demonstrates cross-regional and cross-platform generalization between ResNet34 and MiTB5. Statistical validation confirmed that the performance surface followed a systematic quadratic response. Adjusted R² values ranged from 0.706 to 0.999, with all p-values below 0.001. Consequently, the performance function is universally applicable across diverse geographic zones, spectral distributions, spatial resolutions, sensors, neural networks, and land cover classes. This approach achieves more than a 4000-fold reduction in computational resources compared to full model training, using only 32 to 150 tiles. Furthermore, the proposed technique demonstrates 10–74× superior resource efficiency (resource consumption per unit error reduction) over prior transfer learning schemes. Finally, this study presents a practical solution for inference and performance optimization of land cover semantic segmentation on standard commodity CPUs, while maintaining equivalent or superior IoU. Full article

Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm. The detection module uses YOLOv8 as the baseline model, incorporating Swin Transformer to enhance global feature modeling capabilities in complex scenes. CBAM and CA attention are embedded into the Neck and backbone, respectively: CBAM enables dual-dimensional channel-spatial weighting, while CA precisely captures object location features by encoding coordinate information. The Neck layer incorporates GSConv convolutional modules to reduce computational load while expanding feature receptive fields. The loss function is replaced with Focal-EIoU to address sample imbalance issues and precisely optimize bounding box regression. For tracking, to enhance long-term tracking stability, ReID feature distances are incorporated during the BoT-SORT data association phase. This integrates behavioral category information from YOLO-SGCF, enabling the identification and tracking of abnormal pedestrian behaviors in complex environments. Evaluations on our self-built dataset (covering four abnormal behaviors: Climb, Fall, Fight, Phone) show mAP@50%, precision, and recall reaching 92.2%, 90.75%, and 86.57% respectively—improvements of 3.4%, 4.4%, and 6% over the original model—while maintaining an inference speed of 328.49 FPS. Additionally, generalization testing on the UCSD Ped1 dataset (covering six abnormal behaviors: Biker, Skater, Car, Wheelchair, Lawn, Runner) yielded an mAP score of 92.7%, representing a 1.5% improvement over the original model and outperforming existing mainstream models. Furthermore, the tracking algorithm achieved an MOTA of 90.8% and an MOTP of 92.6%, with a 47.6% reduction in IDS, demonstrating superior tracking performance compared to existing mainstream algorithms. Full article