Search Results (217)

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

Background: Metabolite identification and annotation remain major bottlenecks in untargeted metabolomics because mass spectral features often lack sufficient specificity. High-confidence annotation requires experimental validation using authentic standards analyzed under matched chromatographic and ionization conditions, providing greater reliability than in silico predictions or database matching alone. This study aimed to develop a practical and scalable workflow for constructing a high-quality mass spectral library using a commercially available analytical standards kit. Methods: A total of 603 metabolites from the MSMLS kit were organized into 42 mixtures, each containing approximately 15 compounds. Mixture design was based on molecular mass and distribution coefficient values, specifically logD at pH 3.1, with a minimum logD spacing of 0.15 to improve chromatographic separation and reduce co-elution. This strategy was used to minimize the total number of injections while maintaining spectral quality. The resulting spectra were evaluated against online spectral resources and in silico fragmentation predictions. A preliminary proof-of-concept analysis was also performed using human serum samples. Results: Using this workflow, 471 metabolites, corresponding to approximately 78% of the standards, were successfully detected and incorporated into the spectral library. Comparison with online resources and in silico fragmentation predictions demonstrated improved spectral quality and reliability. The proof-of-concept serum analysis enabled identification of endogenous metabolites using the constructed library. In addition, the robustness and applicability of the workflow were further supported by a method validation study using metabolites derived from this library. Conclusions: This workflow provides a scalable strategy for constructing mass spectral libraries that balances spectral quality with analytical throughput. By using rational mixture design and authentic standards analyzed under matched experimental conditions, the approach enables substantial metabolite coverage while maintaining data reliability and minimizing experimental effort. Full article

Patent infringement detection requires coordinated interpretation of technical claims, legal standards, and contextual evidence. This study proposes a semantic AI framework for patent infringement detection grounded in the DIKWP network and artificial consciousness theory. The DIKWP network organizes the analytical modules as interacting semantic spaces rather than as a strictly layered pipeline. This design supports iterative semantic interpretation, knowledge integration, and purpose-oriented reasoning. The framework integrates document ingestion, semantic information extraction, ontology-based knowledge representation, rule-guided inference, and decision support. The system processes patent claims, product descriptions, and prior-art documents with patent-oriented NLP. Named entity recognition and subject–action–object parsing convert unstructured text into structured semantic representations. Legal and technical ontologies support claim-element interpretation. Knowledge graphs, semantic pattern matching, and inference rules then align claim elements with product features and identify potential infringement risks. A prototype implementation demonstrates end-to-end processing from raw text to infringement-oriented assessment. The evaluation was conducted in two layers. First, a controlled synthetic patent–product corpus was used to isolate claim-element reasoning, rule-guided inference, and purpose-conditioned operating modes. Second, a real-world pilot corpus was constructed from publicly available patent claims and real product technical descriptions, including manufacturer manuals, technical datasheets, official product webpages, installation guides, and technical brochures. The controlled-corpus results show that the DIKWP network improves over keyword-matching and ontology-only baselines by integrating semantic coverage, claim-level legal reasoning, and explainable output. The real-world pilot provides a preliminary external-validity check of whether the framework can preserve element-level reasoning under realistic drafting styles, domain terminology, incomplete product evidence, and borderline claim-to-product correspondences. These findings provide preliminary evidence of feasibility and analytical value, rather than a final benchmark of litigation-level performance. Full article

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article

The widespread availability of image editing tools and generative AI has made image forgery more accessible and deceptive, demanding more advanced localization techniques. Existing CNN-based methods are limited by local receptive fields, struggling with long-range dependencies, while Transformers suffer from the quadratic complexity of self-attention, hindering practical deployment. Moreover, effectively utilizing multi-scale features remains challenging. To address these challenges, we propose a Multi-scale Wavelet-enhanced U-Mamba network (MWEU-Mamba). The proposed framework employs a Mamba-based state space model as the backbone to achieve global contextual modeling with linear complexity. A wavelet enhancement module is introduced to integrate spatial–frequency representations, improving sensitivity to subtle manipulation traces across scales, while a channel attention mechanism further amplifies forgery-relevant feature responses. Extensive experiments on six public benchmark datasets (e.g., CASIA and Coverage) demonstrate that the proposed method achieves state-of-the-art performance on multiple datasets in terms of pixel-level F1-score. 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

Aiming at the inherent bottlenecks of traditional smoke detection technologies in high and large-volume building scenarios, this paper conducts research on an early fire smoke detection method for high and large-volume spaces based on Light Detection and Ranging (LiDAR). A special experimental platform was independently designed to obtain the physical characteristics of smoke particles from standard smoldering fires. Combined with the optical scattering and reflection interaction mechanism between laser and particulate matter, the theoretical feasibility of LiDAR for smoke detection was systematically verified. Smoke irradiation experiments were carried out in the full detection distance, and the LiDAR point cloud characterization characteristics of smoldering smoke were clarified. A special smoke detection algorithm based on point cloud features was designed, a LiDAR smoke detection system was built, and multi-condition comparative experiments with traditional photoelectric smoke detection methods were carried out in a full-scale laboratory. The experimental results show that the LiDAR-based smoke detection method proposed in this paper has significant advantages over traditional detection methods in terms of alarm response speed, detection coverage, and height adaptability. This research provides a brand-new technical path and reference for the theoretical research and engineering application of early fire warning technology for high and large-volume buildings. Full article

A dedicated data acquisition system has been developed and commissioned for the tender-energy spectroscopy beamline BL16U1 at the Shanghai Synchrotron Radiation Facility. The system implements a distributed architecture integrating EPICS-based hardware control with the Bluesky experiment orchestration environment, supporting multiple X-ray absorption spectroscopy modes including transmission, total electron yield, total fluorescence yield, and partial fluorescence yield detection. A key technical feature is the hardware-level synchronization between a multi-channel silicon drift detector and a multichannel scaler, enabling precise timing for fluorescence-XAS measurements. A unified graphical interface based on Control System Studio provides streamlined experiment control and real-time data visualization. System validation using standard reference samples demonstrates successful acquisition of high-quality Cl K-edge XANES spectra in fluorescence mode, high signal-to-noise Co K-edge EXAFS data in transmission mode with extended k-space coverage up to 16 Å⁻¹, and high-sensitivity Ti K-edge fluorescence XAFS on dilute (1–3%) TiO₂ polymorphs. These results confirm the system’s capability for reliable, high-precision spectroscopy across the tender-energy range (2–16 keV), supporting both trace-element analysis and detailed local-structure determination. The fully integrated system is now operational at the beamline, providing a robust platform for advanced X-ray absorption studies in environmental, catalytic, and materials science. Full article

Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. Full article

To address the core challenges in air-to-ground target re-identification (ReID), including network focus on invalid background information, poor adaptability to nonlinear feature distribution, and insufficient cross-domain generalization, this paper proposes a novel air-to-ground ReID framework based on Neighborhood Feature Centralization Attention (NFCA). On the basis of Coordinate Attention, the framework introduces a parameter-free Neighborhood Feature Centralization mechanism to build a lightweight attention module, which enhances cross-feature semantic interaction and suppresses background noise while retaining precise position encoding. It achieves end-to-end direct optimization of sample pair similarity through binary cross-entropy loss, eliminating the proxy task bias of traditional classification loss and adapting to the nonlinear structure of feature space. A multi-source data-driven training strategy is constructed by fusing ReID datasets and general classification datasets, which expands the coverage of feature space and narrows the distribution gap between training data and real air-to-ground scenarios without additional manual annotation. Experiments show that the proposed method achieves leading mAP values on the self-developed UAV air-to-ground dataset JC-1, the public person ReID dataset Market-1501, and the public vehicle ReID dataset VehicleID. Sufficient statistical validation, ablation experiments and cross-domain tests verify the advancement, reliability and generalization of the proposed method in complex air-to-ground scenarios. Full article

Fuzzing is an essential element of software supply chain security governance. Despite its importance, the widespread adoption of library fuzzing is limited by the significant costs associated with constructing fuzz drivers. Without a clear entry point, the reachable path space of the target library is determined by the interplay of API call sequences, parameter dependencies, and state constraints. As a result, fuzz drivers must achieve not only successful builds but also provide sufficient semantic context to enable exploration of deeper state machine interactions, thereby avoiding premature stagnation at superficial validation logic. To systematically assess advancements in automated fuzz driver generation, this paper develops a taxonomy organized around the primary data sources used to derive driver-generation constraints, categorizing existing approaches into four technological trajectories: Usage Artifact Mining, Source Code Constraint Inference, Binary Semantics Recovery, and Heterogeneous Data Fusion. Large language models are increasingly integrated into these workflows as generators and as components for constraint alignment and repair. To address inconsistencies in experimental methodologies, this paper introduces a bounded comparability-oriented evaluation perspective focused on three dimensions: validity, reachability-related evidence, and reproducibility and cost. Together with a disclosure and reporting protocol for metric comparability, this perspective clarifies the information needed for cross-study comparison and examines the unique features and inherent limitations of each technical trajectory. Based on these findings, three key directions for future research are identified: facilitating structural evolution in response to coverage plateaus to address deep logic unreachability; coordinating dynamic closed-loop orchestration that utilizes on-demand heterogeneous data retrieval to resolve context challenges; and developing language-agnostic driver representations with pluggable adaptation mechanisms to improve cross-ecosystem portability and scalability. Full article

Low-orbit thermal infrared bidirectional whisk-broom imaging offers wide-swath coverage and high spatial resolution for monitoring moving targets such as aircraft, but large scan angles and terrain undulation cause non-rigid geometric distortion and radiometric inconsistency between forward and backward scans. These effects generate strong clutter in difference images and degrade small and weak target detection. To address this problem, we propose an infrared small target detection method that fuses accurate registration and weighted difference. First, we propose a hybrid multi-scale registration algorithm that achieves coarse affine registration through sparse feature–point matching and then iteratively corrects nonlinear deformations by integrating a global grayscale-driven force with a local sparse-feature-guided force, yielding a registration error of 0.3281 pixels. On this basis, a multi-scale weighted convolutional morphological difference algorithm is proposed. A novel dual-structure hollow top-hat transform is constructed to accurately estimate the background, and a multi-directional convolution mechanism is introduced to effectively suppress anisotropic edge clutter and enhance target saliency. Experiments on SDGSAT-1 thermal infrared bidirectional whisk-broom data show an SCRG of 18.27, and a detection rate of 91.2% when the false alarm rate is below 0.15%. The method outperforms representative competing algorithms and provides a useful reference for space-based aerial moving target detection. Full article

With the explosive growth of the Internet of Things (IoT) and cloud-computing traffic, Intrusion Detection Systems (IDSs) have become a cornerstone of network security. However, modern traffic data often exhibits extreme class imbalance and long-tailed distributions, leading to persistently high miss rates for minority attack categories in Machine Learning (ML)-based IDSs. Conventional oversampling may introduce decision noise, whereas standard Generative Adversarial Networks (GANs) can suffer from training instability and mode collapse when modeling high-dimensional tabular traffic features. To address these challenges, we propose a high-fidelity traffic augmentation framework based on an Adaptive Hybrid-loss Conditional GAN (AH-CGAN). Specifically, AH-CGAN introduces an iteration-dependent adaptive gradient penalty (AGP) schedule to enforce the Lipschitz continuity constraint more effectively during training and incorporates a feature-matching objective to align intermediate critic representations between real and synthetic traffic. Experiments on the CIC-IDS2017 benchmark show that AH-CGAN generates distribution-consistent synthetic samples and that augmentation improves downstream detection across multiple classifiers. In particular, the weighted F1-score of Logistic Regression increases from 0.8237 to 0.8697 (Δ = +0.0460, i.e., +4.6%). Overall, the proposed approach enhances minority coverage in the feature space and can improve class separability, providing a practical solution for long-tailed IDS. Full article

Distributed optical fiber vibration sensing (DVS) has shown strong potential in perimeter security, pipeline leakage monitoring, transportation safety, and structural health diagnostics owing to its high sensitivity, long-range coverage, and immunity to electromagnetic interference. However, severe cross-scene distribution mismatch is often encountered in real-world deployments: indoor, outdoor, and pipeline environments exhibit markedly different noise patterns and time–frequency characteristics, thereby degrading the generalization ability of models trained in a single scene. To address this challenge, we propose a Progressive Multi-Strategy Domain Alignment (PMSDA) framework for label-disjoint cross-scene vibration recognition. PMSDA uses a compact expansion–compression encoder together with complementary alignment mechanisms—maximum mean discrepancy (MMD), correlation alignment (CORAL), and adversarial domain discrimination—to learn a scene-robust latent space from a labeled indoor source and two unlabeled target domains (outdoor and pipeline) within a single alternating-training model. Because the fine-grained source and target label spaces are disjoint, PMSDA is formulated as a representation-transfer framework rather than a standard label-shared unsupervised domain adaptation method; target-domain recognition is therefore performed through domain-specific prototype clustering in the aligned latent space. On three representative scenes with nine event classes in total, PMSDA achieved 89.5% accuracy, 86.7% macro-F1, and 0.93 AUC for Indoor→Outdoor, and 85.8%, 84.7%, and 0.87, respectively, for Indoor→Pipeline, outperforming traditional feature+SVM/RF pipelines, CNN/ResNet baselines, and representation-transfer baselines adapted from DANN/CDAN/SHOT under the same evaluation protocol. These results indicate that PMSDA is a promising and effective framework for offline cross-scene DVS evaluation under disjoint target event sets. Full article

In emergency communication networks, service demands and user mobility change dynamically. Low service rates and limited coverage are significant challenges that hinder the effectiveness of emergency services. Due to the flexibility, low deployment cost, and adjustable coverage range of unmanned aerial vehicles (UAVs), UAV-assisted emergency communication networks can serve as a viable method to address these challenges. Given the strong coupling between UAV trajectory optimization and resource allocation, joint optimization is crucial to meet dynamic service demands and user mobility. In this paper, we establish a user mobility model based on the Maxwell–Boltzmann distribution and a service model based on the Poisson process. We formulate an optimization problem to maximize the data transmission rate of emergency services. To address the challenges of high-dimensional continuous action spaces, we propose a shared feature extraction-enhanced PPO (SPOR) algorithm for joint trajectory optimization and resource allocation. Simulation results show that the proposed SPOR algorithm significantly outperforms benchmark methods. Specifically, it achieves at least a 20% improvement in data transmission rate, a 28% improvement in emergency communication service ratio, and a 12% reduction in average service distance. Full article