Search Results (667)

Vehicular ad hoc networks (VANETs) enable direct wireless communication between moving vehicles for safety and cooperative driving. Routing in VANETs is challenging due to high mobility, frequent topology changes, and variable node density. The Greedy Perimeter Stateless Routing (GPSR) protocol maintains only a one-hop neighbor position table through periodic beacon exchanges, making it highly scalable. Each node forwards packets to the neighbor geographically closest to the destination. However, this distance-only criterion leads to a low packet delivery ratio (PDR). Existing improvements, such as Weight-Based Path-Aware GPSR (W-PAGPSR) combining distance progress, velocity direction, neighbor density, and link duration, incorporate multiple factors but complicate parameter tuning and lack a unified neighbor quality metric. This paper proposes Directional Propagation Capacity Index–GPSR (DPCI-GPSR), integrating neighbor information into a single directional metric capturing propagation capacity. Two enhancements are introduced: (1) an eight-direction DPCI computing a composite propagation capacity index per sector, exchanged via Hello packets, and (2) a trapezoidal link quality function treating 30–200 m as optimal while penalizing edge-zone neighbors. Implemented in NS-3 with SUMO-generated mobility, results across four node densities (30–120 vehicles), five concurrent sender–receiver pairs, and 15 random seeds show DPCI-GPSR achieves 63.08–98.39% PDR, outperforming both W-PAGPSR (52.38–80.14%) and standard GPSR (50.23–66.31%). Full article

Accurate Remaining Useful Life (RUL) prediction is pivotal for the intelligent operation and maintenance of high-precision equipment. However, existing deep learning-based prognostic methods predominantly focus on point estimations and often overlook the non-Markovian characteristics and stochastic uncertainties inherent in complex mechanical degradation. To bridge this gap, this study proposes a novel uncertainty-aware hybrid prognostic framework by synergizing TCN–Transformer architectures with fractional Brownian motion (FBM). Specifically, a TCN–Transformer hybrid network is developed to adaptively learn a multi-scale drift function, effectively capturing both localized causal features and global long-range temporal dependencies. Concurrently, the FBM component is employed to model the diffusion process, explicitly accounting for the long-range dependence and inherent stochasticity of degradation. By leveraging the first hitting time (FHT) principle, an approximate analytical expression for the RUL probability density function (PDF) is derived based on an established approximation treatment for FBM-driven degradation processes, enabling robust uncertainty quantification. Experimental results on both the XJTU-SY bearing dataset and the servo tool holder power head system dataset demonstrate that the proposed method achieves superior predictive accuracy and reliable uncertainty quantification, thereby providing effective support for condition-based maintenance and intelligent decision-making. Full article

Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide on-demand coverage; however, ship mobility, heterogeneous emergency priorities, and UAV endurance limitations make the joint optimization of user association and multi-UAV deployment a challenging mixed-integer, long-horizon decision problem. This paper considers a multi-UAV maritime emergency communication system where ships are categorized into multiple priority classes and served links must satisfy a minimum signal-to-noise ratio (SNR) constraint. We formulate a long-term system-utility maximization problem that jointly determines (i) per-slot association between UAVs and ships under capacity, priority, and SNR constraints, and (ii) dynamic UAV deployment under mobility, geofencing, and battery constraints. To obtain tractable and high-quality solutions, we decompose the problem into two coupled subproblems. For user association, we propose a Priority-Aware Branch-and-Cut (PA-BAC) algorithm that integrates linear programming relaxation, cutting-plane tightening, and priority-guided branching, with a priority-greedy feasible initialization to accelerate incumbent improvement. For dynamic deployment, we develop an Enhanced Multi-Agent Proximal Policy Optimization (E-MAPPO) method featuring a global value network, entropy regularization, and sequential actor updates to enhance learning stability and exploration. Importantly, the PA-BAC association is embedded into the learning loop to provide reliable, constraint-satisfying per-slot rewards and reduce the burden of end-to-end learning over hybrid-action spaces. Simulation results demonstrate that PA-BAC consistently improves normalized priority-weighted throughput over heuristic association baselines. Moreover, by mathematically enforcing priority and QoS feasibility at every slot and delegating only continuous mobility to MARL, the integrated E-MAPPO-PA-BAC framework achieves higher long-term system utility, improved energy efficiency, and strong robustness across varying ship densities—properties that are vital for time-sensitive maritime emergency communications. Additional runtime, sensitivity, and AIS-driven trace evaluations further verify the computational practicality of PA-BAC and the applicability of the proposed framework under realistic ship mobility patterns. Full article

Remote sensing imagery is often degraded by cloud cover, causing severe information loss and hindering downstream Earth observation tasks. Although recent deep learning methods, including CNN- and Transformer-based models, have achieved promising progress in cloud removal, they mainly operate in the spatial domain and largely overlook the frequency-domain discrepancies introduced by clouds of different types and densities. This limitation restricts their ability to generalize across diverse cloud corruption scenarios. To address this issue, we propose a Frequency Interaction Cloud Removal Network (FI-CRNet), which introduces a novel Frequency-Aware Modulation (FAM) mechanism for high-fidelity cloud-free image reconstruction. The FAM module consists of two components. First, the Frequency Decomposition (FD) module explicitly separates input features into low-frequency cloud-affected components and high-frequency detail-rich components through spectral analysis, while aligning them with decoder features via cross-attention. Second, the Cross-Frequency Interaction (CFI) module adaptively integrates these components through a dual-gate weighting mechanism, including spatial and channel gates, to suppress cloud interference while enhancing structural and textural details. By jointly modeling frequency-domain cues and spatial features, FI-CRNet enables robust and adaptive reconstruction under diverse cloud conditions. Extensive experiments show that our method outperforms state-of-the-art techniques across diverse cloud scenarios. Full article

High-rise LiDAR scanning produces vertically sparse point clouds where upper-layer defects are hardest to detect due to inverse-square ranging law (1/r²) density gradients, noise contamination, and complex geometries. This paper presents PC-TowerNet, a physics-aware AI pipeline that achieves state-of-the-art reconstruction through sequential modules: (1) 50D geometric feature classification outperforming CloudCompare SOR (100% accuracy vs. 91.3% retention); (2) Physics-Constrained VAE (PC-VAE) recovering 28.7 ± 2.1% upper density vs. 8.3 ± 1.7% standard VAE; (3) multi-modal PointNet++/GNN/Transformer fusion; and (4) Bayesian uncertainty maps (ECE = 0.042 ± 0.008). Synthetic tower evaluation (10 × 5 seeds) demonstrates 48.9% surface smoothness improvement and 38.2% volume error reduction over tuned RANSAC baselines, with clear paths to real-data validation. Full article

Remote sensing (RS) imagery often suffers from non-uniform atmospheric scattering, resulting in severe contrast degradation, detail blurring, and spectral distortion. While recent advanced State Space Models (SSMs) offer efficient long-range modeling, they frequently struggle with spectral–spatial coupling interference and lack explicit physical constraints, leading to over-smoothed textures and color biases in high-reflectance regions. In this paper, we propose PhysWave-SSN, a Physics-Inspired Frequency-Decoupled Network specifically designed for high-fidelity RS image dehazing. The architecture employs a task-adaptive frequency-specific screening strategy to effectively isolate structural details from atmospheric interference. Specifically, we first introduce a Frequency-Aware Selection Gate (FASG) that unifies adaptive channel screening with physical transmission estimation, enabling precise recalibration of frequency components. To bridge the gap between physical scattering principles and state space representation learning, we develop a Physics-Informed SSM (PI-SSM), where the discretization step size of Mamba is dynamically modulated by the estimated haze density. This mechanism allows the model to adaptively adjust its spatial receptive field according to local degradation levels, enhancing physical interpretability. Furthermore, a Luminance-Adaptive Fusion Module (LAFM) is presented to protect high-reflectance land covers and maintain spectral consistency. Extensive experiments on multiple RS datasets demonstrate that PhysWave-SSN achieves superior performance, notably attaining a maximum PSNR gain of 2.49 dB while ensuring high structural and spectral fidelity. Full article

Linear assets (LAs), such as conveyor systems, road networks, pipelines, and power transmission lines, are typically maintained through localized, segment-based interventions. While such approaches effectively address spatially heterogeneous degradation, they often neglect the system-level consequences of repeated local actions. In particular, improvements in segment condition may be accompanied by increased structural complexity, leading to reduced reliability and higher lifecycle costs. This paper proposes a unified engineering framework that integrates segment-level condition assessment with system-level structural effects. The framework is based on a dual representation of asset condition, distinguishing between material state (MS) and structural state (SS), which correspond to material aging (MA) and structural aging (SA), respectively. A key contribution is the introduction of the fragmentation penalty (FP), capturing the negative impact of increasing segmentation and interface density on system performance. The framework incorporates multi-threshold decision logic, enabling differentiation between operational, refurbishment, and replacement regimes, and interprets maintenance actions as transformations affecting both condition and structure. A formal model is developed to represent the asset as a dynamic system of segments and interfaces. It provides a basis for future empirical calibration and structure-aware optimization. Although the model is developed using conveyor belt loops as a reference case, its broader relevance is discussed for other classes of linear assets with repeated local intervention and evolving structural heterogeneity. A simple worked example is included to demonstrate the operational meaning of the proposed fragmentation-aware perspective. The results show that maintenance decisions may change when structural side effects are considered together with local condition improvement, and they provide a basis for future empirical calibration and structure-aware optimization of maintenance strategies. Full article

Tantalum pentoxide (Ta₂O₅) has emerged as a versatile material at the intersection of optical coatings and integrated photonics because it combines a high refractive index, a wide bandgap, low optical loss, and compatibility with multiple thin-film deposition routes. Over the past decade, the literature has expanded from conventional dielectric coating studies to low-loss waveguides, micro-ring resonators, wavelength conversion, and broadband supercontinuum generation, while more recent work has increasingly emphasized defect engineering, nonlinear absorption, and laser damage reliability under strong optical fields. The objective of this review is to establish a process–structure–composition–property–function–reliability framework for understanding Ta₂O₅ and non-stoichiometric Ta₂O_5−x optical coatings in high-power photonics. Unlike previous reviews that mainly emphasized dielectric properties, deposition methods, or general thin-film applications, this review highlights how deposition-induced composition changes, oxygen vacancy-related defects, nonlinear optical response, and laser damage reliability jointly determine the operational limits of tantalum oxide photonic materials. Particular attention is given to ion-assisted and ion gun-assisted processes, which have repeatedly been associated with higher film density, smoother morphology, reduced oxygen vacancy-related loss, and more stable high-field behavior. By linking coating-level process control to device-level functions such as four-wave mixing, self-phase modulation, wavelength conversion, and supercontinuum generation, this review highlights how thin-film engineering governs both optical performance and operational limits. It also identifies several persistent gaps, including the need for standardized reporting of nonlinear absorption, unified damage metrics across film and device geometries, and stronger correlations among microstructure, composition, defects, and long-term optical stability. Overall, this review provides a composition-aware and coating-informed framework for interpreting Ta₂O₅ photonics and a practical roadmap for developing durable high-power photonic components. Full article

Dynamic multi-criteria evaluation plays a central role in assessing complex systems based on panel data. However, existing objective weighting methods often suffer from sensitivity to extreme observations and may produce unstable or even negative weights, limiting their reliability in practice. To address these issues, this study proposes an improved dynamic weighting approach by integrating sampling-based resampling with a density-weighted aggregation (DWA) scheme. The proposed method extends the traditional Vertical–Horizontal Scatter Degree (VHSD) framework by stabilizing weight estimation across repeated samples and aggregating weights in a distribution-aware manner. This design effectively reduces the influence of extreme observations and ensures non-negative and consistent weighting results. An empirical analysis based on panel data is conducted to evaluate the performance of the proposed method. The results show that, compared with the classical VHSD, the proposed approach consistently eliminates negative weights, achieves higher stability across different sampling schemes, and demonstrates improved robustness under data perturbations. In addition, the method exhibits greater sensitivity to structural variations in the data while maintaining overall consistency in evaluation outcomes. Overall, the proposed framework provides a transparent and reproducible approach for composite indicator construction and dynamic multi-criteria evaluation, and is particularly suitable for applications involving complex and heterogeneous datasets. Full article

Astronomical positioning of space targets is an important task in space situational awareness. In both ground-based and space-based optical observation scenarios, accurate positioning relies on the reliable matching of numerous stars in observational images. However, dense star scenes increase the ambiguity of local patterns and the computational burden of candidate retrieval. Building on established geometric voting and catalog-indexing strategies, this paper develops a two-stage star map matching method that specifically combines adaptive magnitude stratification with seed-guided residual-star diffusion for large-field dense star scenes. In the first stage, an adaptive magnitude-stratified bright-star subset is selected according to field density, and angular-distance voting is used to obtain reliable seed correspondences. In the second stage, residual-star candidates are retrieved from seed-centered dual-feature sub-libraries indexed by angular distance and magnitude difference, and are then refined through single-seed local diffusion and multi-seed global fusion. Experimental results from both simulated and real observational data demonstrate that the proposed method achieves a high matching success rate with low computational cost and performs effectively in large-field, dense star scenes. The proposed method provides a practical matching solution for astronomical positioning in dense star scenes. Full article

Sustainable water allocation, drought mitigation, and operational planning require reliable forecasting models that account for hydroclimatic variability while respecting physical constraints. This study proposes Chd-Gamma, a chained correlated Gaussian Process (GP) framework for multi-output hydrological forecasting. The proposed model extends chained GPs beyond independent or single-output settings by embedding their latent likelihood-parameter functions in a Linear Model of Coregionalization. Chd-Gamma also enhances conventional multi-output GP hydrological forecasting by replacing Gaussian likelihood assumptions with a Gamma likelihood, thereby enforcing non-negativity and representing skewed and heteroscedastic storage distributions. The proposed model was contrasted with the well-known Long Short-Term Memory (LSTM) network, the multi-output Linear Model of Coregionalization GP (LMC), and the chained correlated GP with Gaussian likelihood (Chd-Normal) for forecasting the daily useful storage volumes from 23 Colombian reservoirs recorded from 2010 to 2022 across multiple prediction horizons. The results over a two-year testing period show that Chd-Gamma provides the strongest overall performance across the four metrics considered. Chd-Gamma reduced the mean squared error by 80% with respect to LSTM and 20% relative to Chd-Normal. In terms of probabilistic performance, the average Negative Log Predictive Density (NLPD) improved by up to 21%. Compared to LMC, with narrow prediction intervals but low coverage, and Chd-Normal, also narrow but overcovering, Chd-Gamma achieves near-nominal coverage of 0.992 with a moderate increase in interval width, pointed towards the best calibration–sharpness trade-off. These findings demonstrate that Chd-Gamma improves accuracy and uncertainty representation while maintaining physically consistent forecasts, making it suitable for risk-aware reservoir-operation support. Full article

Scale variation remains a fundamental challenge for intelligent surveillance sensors in crowd-counting and localization tasks. While recent selective inheritance methods have shown promise through multi-resolution feature fusion, they typically rely on conventional CNN backbones with limited representation capacity. Foundation models such as DINOv3 offer powerful self-supervised representations, yet directly applying frozen DINOv3 features to selective scale-aware counting is non-trivial: these features are semantically strong but not directly aligned with density estimation or scale-conditioned inheritance requirements. In this paper, we propose D3-CalibCount, a trainable-parameter-efficient framework for adapting frozen DINOv3 representations to selective scale-aware crowd counting and localization. We introduce a lightweight Scale Harmonization Adapter (SHA) that performs resolution-conditioned feature calibration, transforming generic DINOv3 representations into scale-selective counting features suitable for progressive inheritance across resolution levels. Extensive experiments on three widely used benchmarks show consistent improvements over selective inheritance baselines, especially under severe scale variation. From a deployment perspective, the method reduces the trainable portion of the network, while the frozen DINOv3-L backbone still introduces a higher inference cost than lighter CNN baselines. The target application scenario is camera-based intelligent surveillance sensing, where crowd density estimation is performed from visual sensor inputs at GPU-backed monitoring nodes. These results suggest that lightweight adaptation of frozen foundation features is a practical direction for crowd counting and other dense prediction tasks. Full article

To address the challenge of apple picking-point localization under severe occlusion and complex backgrounds in dwarf rootstock high-density orchards, this paper proposes a target-region-segmentation-based 3D localization method in which the fruit sphere center is defined as the picking point. A lightweight occlusion-aware semantic segmentation network, OA-LiteSegNet, is developed to generate accurate foreground masks for partially visible apples. Guided by the segmentation result, a local target-region point cloud is reconstructed from the depth map and further processed by robust sphere fitting to estimate the apple sphere center and fruit diameter. Experimental results show that OA-LiteSegNet achieves 90.35% mIoU and 76.27% BF1 with only 5.92 M parameters and 11.30 GFLOPs, while reaching an inference speed of 128.40 FPS, achieving a favorable accuracy–efficiency trade-off among the compared mainstream models. The proposed 3D localization method achieves an average sphere-center error of 2.88 mm, which is 68.4% lower than that of the conventional bounding-box-center back-projection method (9.11 mm), and maintains stable performance across different occlusion types. These results demonstrate the potential of the proposed method for real-time and accurate apple localization under controlled laboratory occlusion scenarios, while further field validation in real orchards remains necessary. Full article

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news regions, to be detected and targeted where strategic shifts are most likely to occur. Importantly, fact-checking is modeled as an external intervention that may induce a probabilistic lasting correction on agents’ latent opinions after removal, capturing more realistic post-intervention behavior. Unlike static strategies that assume fixed fact-checker distributions, the proposed approach continuously reallocates interventions toward structurally critical regions, while adaptively adjusting resource intensity based on misinformation prevalence. Extensive simulations on small-world, scale-free, and random networks show that the adaptive model consistently outperforms static baselines, reducing the final fake-news prevalence by over 90%, accelerating suppression, and improving overall system efficiency. Statistical tests confirm the significance of these improvements ($p<0.001$), while sensitivity analyses demonstrate robustness across parameter settings and intervention assumptions. Full article

In smart substations, long-term operation and environmental disturbances accelerate equipment aging, often leading to abnormal operating states and frequent alarm events. These alarms provide important early indications of potential equipment faults. Situational awareness technologies offer effective means for real-time monitoring and early warning in substations. Meanwhile, Structural Causal Models (SCMs) can uncover underlying causal relationships in operational data, improving prediction stability and interpretability compared with conventional correlation-based methods. This study proposes a novel situational awareness framework for smart substations that integrates deep learning-based causal inference with expert domain knowledge. By guiding the model with the causal diagram derived from substation alarm data as a strong prior, our method learns causal relationships that are statistically significant. Compared with traditional correlation-based statistical approaches, causal inference enables the explicit modeling and adjustment of potential confounding effects under given assumptions, leading to more reliable relationship estimation and a more interpretable model structure. Finally, a case study using real substation data shows improved predictive performance of the proposed method relative to conventional correlation analysis. Full article