Search Results (25,667)

Amphibious assault vehicles serve as core equipment for coastal defense and amphibious operations worldwide, with irreplaceable strategic value. Featuring outstanding comprehensive performance, modern amphibious assault vehicles can maintain stable navigation under Sea States 3–4 and adapt to complex nearshore hydrological environments, emerging as the primary platform for mechanized landing operations of the Marine Corps. Cluster navigation is an inevitable tactical form in the operational application of amphibious assault vehicles. When multiple vehicles sail in formation, the wave-making and water pressure effects induced by individual vehicles generate prominent wave interference drag within the formation, which significantly impacts the overall navigation efficiency and stability. Based on the nearshore combat background of amphibious landing, this paper investigates different formation layouts of amphibious assault vehicle clusters to determine the optimal configuration for group navigation. First, a numerical simulation and a physical experiment are combined; a certain type of amphibious assault vehicle is taken as the prototype for 3D geometric modeling via SOLIDWORKS. Then, adopting the CFD numerical simulation method, with navigation speed and optimal inter-vehicle spacing fixed, variables including formation layout and number of vehicles are controlled to simulate the flow field characteristics and total resistance of different cluster formations in calm water. Meanwhile, 3D printing technology is applied to manufacture scaled-down models for towing tank tests. The experimental results are in good agreement with numerical simulations, revealing the fundamental hydrodynamic laws of formation navigation. Under optimal inter-vehicle spacing, the longitudinal tandem formation achieves the best drag-reduction effect, while the double-column staggered formation (diamond/V formation) can effectively suppress wave interference drag and improve the overall hydrodynamic performance and tactical coordination. The research provides a solid theoretical basis and data support for optimizing formation sailing strategies, enhancing cluster navigation stability and safety, and improving maritime maneuver efficiency. It is also of universal reference value for the tactical deployment of amphibious combat equipment globally. Full article

Automated license plate readers (ALPRs) are often evaluated as discrete police tools, although their public power arises from cross-vendor socio-technical infrastructure. This article examines roadside and mobile sensors, vehicle-attribute classification, cloud archives, commercial databases, real-time crime center integration, interagency access, automated alerts, and police action. Flock Safety supplies the principal documentary case because unusually extensive public records permit system-level tracing; Axon/Fusus, Motorola Vigilant/VehicleManager, and federal access to commercial ALPR data establish the wider vendor-independent boundary. A structured documentary analysis of 59 sources triangulates official records, peer-reviewed research, vendor materials used only for stated functions, and record-based investigations. It integrates boundary critique, control-structure mapping, feedback analysis, constitutional doctrine, a STRIDE-informed threat model, and empirical research on policing effectiveness and surveillance effects through 3 August 2026. The analysis identifies four conditional mechanisms: infrastructure aggregation, authority diffusion, asymmetric feedback, and rights invisibility. The article reformulates the Rights Control Deficit (RCD) as a non-arithmetic profile relation between operational demands and effective governance capacity and applies it to three documented configurations and a clearly labeled normative benchmark. Seven falsifiable propositions specify variables, indicators, suitable methods, and disconfirming conditions for later empirical study. A rights-preserving hybrid-intelligence architecture combines bounded automation with judicial authorization, short retention, sensitive-location protections, immutable audit, availability safeguards, independent review, contestability, sanctions, and credible termination authority. The evidence identifies capabilities, activated pathways, and conditional risks; it does not estimate population prevalence or a universal ALPR-specific causal effect. Meaningful human oversight is an institutional control property, not merely an officer’s presence at an interface. Full article

To meet the growing demand for reliable space–air–sea-integrated communications and underwater information backhaul, this paper proposes and analyzes a three-hop heterogeneous space–air–sea communication system consisting of a satellite, a high-altitude platform (HAP), a sea-surface buoy, and an autonomous underwater vehicle (AUV). Specifically, the satellite-to-HAP link employs free-space optical (FSO) transmission, the HAP-to-sea-surface buoy link adopts mixed FSO/radio-frequency (RF) transmission, and the sea-surface buoy-to-AUV link utilizes underwater wireless optical communication (UWOC). To enhance the reliability of the HAP-to-sea-surface buoy link in complex atmospheric and maritime environments, a threshold-based adaptive combining scheme for mixed FSO/RF transmission is designed. Meanwhile, nonzero-boresight pointing error models are incorporated into the FSO and UWOC links to characterize practical link misalignment. Based on the proposed system model, analytical expressions for the end-to-end bit error rate (BER) are derived and validated through Monte Carlo simulations. The numerical results show that the proposed adaptive combining scheme achieves better BER performance than conventional dual-hop and hard-switching schemes. In addition, the effects of pointing errors, underwater turbulence, underwater transmission distance, shadowed fading, detection techniques, and modulation schemes on the system BER performance are further investigated. This work provides theoretical guidance for reliable cross-domain heterogeneous transmission in future space–air–sea integrated communication systems. Full article

Electric Vehicle Charging Systems (EVCSs) have become a critical component of the global transition toward sustainable and intelligent transportation. However, their tight integration with heterogeneous cyber–physical, vehicular, and cloud-based infrastructures exposes them to an expanding attack surface, including data poisoning, malware injection, denial-of-service, and man-in-the-middle (MITM) attacks. Existing security solutions largely rely on isolated detection mechanisms and lack a unified semantic representation of EVCS assets, attack propagation paths, and mitigation dependencies, limiting their effectiveness in complex and evolving threat scenarios. To address these challenges, this paper proposes EVCS-SecOnt, an ontology-driven cybersecurity framework for modeling, reasoning, and mitigating security threats in EVCS infrastructures. The proposed ontology formalizes relationships across four core modules, namely Attack Surface, Attack Classification, Protection Mechanisms, and Risk and Mitigation, enabling holistic threat representation and TARA-based risk assessment. EVCS-SecOnt incorporates standard semantic namespaces (em:, seas:, uiote:, sch:, and time:) to ensure interoperability and is instantiated using the CICEVSE2024 dataset to support observation-level security reasoning. A unified SPARQL-based analytical workflow is employed to perform global ontology validation, attack–risk–severity correlation, mitigation prioritization, and observation-level inference using statistical feature vectors. Experimental results demonstrate that the ontology captures multiple attack classes, risk levels, severity categories, and mitigation strategies, enabling automated identification of critical attack scenarios and context-aware defense recommendations. The validation demonstrates logical consistency, semantic traceability, and query-based coverage of the ontology across attack classes, risk levels, severity categories, and mitigation strategies. EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence. The proposed framework supports adaptive protection, risk-aware decision-making, and ontology-driven security analytics, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures. Full article

Time delays are inherently present in active control systems as a consequence of sensor acquisition, communication lags, and actuator dynamics, and their impact on system behavior cannot be overlooked. This paper examines the effect of delayed displacement and speed feedback gains on the nonlinear lateral and vertical vibrational behavior of a MAGLEV vehicle subjected to aerodynamic and centrifugal forces. A delayed nonlinear dynamic model incorporating a fractional-order PD controller under aerodynamic excitation is first established for the MAGLEV system. Subsequently, the method of multiple scales is employed to derive the frequency response relationships, while the corresponding steady-state solutions are analyzed to determine system stability. The investigation further explores how the delays alter the nonlinear dynamic response. It also considers the impact of changing the value of the fractional-order parameter $\alpha$ on the vehicle’s dynamics. The results showed that increasing controller delays reduces the stability region, with displacement-feedback delays having a more pronounced effect than speed-feedback delays, while fractional-order derivatives ($0<\alpha <1$) further degrade stability; consequently, the integer-order case ($\alpha =1$) is recommended to achieve lower vibration levels and improved dynamic stability. The outcomes of this work provide valuable understanding of vibration phenomena encountered in MAGLEV systems and contribute to the development of improved control and optimization strategies for safer, smoother, and more reliable vehicle performance. Full article

This paper presents the development of a multi-domain simulation for the Porsche Taycan’s all-wheel-drive (AWD) electric propulsion system to investigate the impact of the rear drive unit’s two-speed transmission on performance and drive quality during maximum acceleration. This study was undertaken independent of the vehicle and propulsion system OEM. A lumped-parameter model of the front and rear electric drive units (EDU) and the high-voltage battery was developed and calibrated against the published data for key benchmarks, including 0–100 kph acceleration times and peak longitudinal acceleration. The mechanical shifting mechanism was reverse-engineered to simulate high-performance shift trajectories. To manage the transition, a clutch control scheme integrates a reduced-order clutch-to-clutch model featuring a feedforward (FF) torque estimator and a closed-loop feedback (FB) controller to achieve target input shaft speeds and shift durations. The study concludes with a comprehensive analysis of the propulsion system’s behavior at a battery state of charge of 96% and 25% and three electric motor speeds at which the upshift is commanded. The simulation results demonstrate that executing an early upshift at 10,700 rpm with 96% of SOC yields a 0.100-s inertia phase shift time, restricts the clutch thermal dissipation to 21 kJ, and achieves an 8-s velocity of 203.4 kph, outperforming the upshift at 15,300 rpm (0.210 s, 34 kJ, and 202.8 kph). Furthermore, the transient regenerative braking on the rear axle during the inertia phase reduces the peak current draw from 675 A to 87 A, recovering the DC bus voltage to enable cross-axle torque boosting on the front axle. Full article

Highly automated vehicles often rely on takeover requests (TORs) that lack contextual transparency, treat drivers as passive fallbacks, and lead to poor situational awareness. To address this challenge, this study presents the Empowering Language Interaction (ELI) framework, a conversational interface powered by a large language model that supports bidirectional negotiation and collaborative human–AI teamwork. Using the CARLA driving simulator, 28 participants compared ELI with a conventional TOR baseline in both urban and peri-urban driving scenarios. The study employed a multidimensional evaluation approach, integrating telemetry data on driving performance with continuous monitoring of physiological indicators (electrocardiogram and electrodermal activity) and subjective questionnaires to assess driver trust and engagement. Results indicated that ELI sustained continuous driver engagement and improved the subjective comprehension of the vehicle’s state. Physiologically, the conversational interface maintained active cognitive load, preventing the abrupt autonomic spikes characteristic of traditional takeover requests. Furthermore, ELI outperformed the TOR baseline in safety metrics by reducing out-of-lane events and maintaining greater safety margins. Conversational interaction has shown potential to transform drivers from passive supervisors into active teammates, improving joint decision-making without inducing over-reliance on the automated system. Full article

FINGERTRAP is a network encryption and authentication protocol that extends the X3DH
and Double Ratchet frameworks with three novel mechanisms inspired by the Chinese
finger trap (zh˘ı w˘ang): a friction ratchet that exponentially increases computational cost
for each failed authentication attempt; a recursive annihilation protocol that irreversibly
destroys all cryptographic state after a configurable failure threshold; and a committhen-
challenge handshake that requires a counterintuitive “inward” action for legitimate
authentication. A bidirectional weave hash extends the Double Ratchet’s transcript binding
to cover every message in both directions. Together, these mechanisms provide permessage
forward secrecy, post-compromise security (self-healing), clock-free operation,
and a self-destruct capability. The individual ingredients-client puzzles, key erasure, and
ratcheting-each build on established lines of work; their combination into a single stateful
protocol, in which failed authentication attempts cryptographically tighten the session
state and ultimately destroy it, is not to our knowledge offered by deployed transport
protocols such as TLS 1.3, Signal, or WireGuard. The design targets deployments in which
interception or capture of a device implies endpoint compromise, such as Unmanned Aerial
Vehicle (UAV) telemetry links and body-worn sensors, where denial of exploitation requires
guaranteed loss of past and future session material. We describe the full protocol, provide
game-based security arguments under an explicit adversarial model, give analytic cost
estimates for the friction mechanism, analyse the denial-of-service surface and a two-layer
mitigation strategy, and specify a post-quantum extension using hybrid X25519/ML-KEM-
768 ratcheting. Full article

For malicious distributed unmanned aerial vehicle (UAV) swarms operating in civilian airspace, this paper proposes a novel dual-channel induced countermeasure strategy (DCICS) with prescribed countermeasure flight trajectories, in which the designable countermeasure signals are injected into the sensor–controller (SC) channel and the controller–actuator (CA) channel of partial or all UAVs, simultaneously. Firstly, we establish a dual-channel induced countermeasure protocol, in which the sensor-induced countermeasure signal is modeled as a continuous function of swarm dynamics, whereas the actuator-induced countermeasure signal is generated through a countermeasure signal generation exosystem (CSGES). Then, we derive a closed-form expression of the countermeasure flight trajectory, explicitly characterizing the effect of the countermeasure signals on the swarm motion. Furthermore, a necessary and sufficient condition for the feasibility of the DCICS is derived. By introducing adjustable performance factors, we construct a robust $H_{\infty }$ framework and provide the design criteria for the dual-channel induced countermeasure signals accordingly. The proposed strategy can drive the formation of the distributed UAV swarm along a prescribed countermeasure flight trajectory while maintaining the original formation structure. Finally, we present numerical simulation examples to validate the effectiveness of the theoretical results. Full article

With the growing use of digital twins and 3D city models, 3D point cloud data have become increasingly important. Such data, however, may contain privacy-sensitive objects, including people and vehicles, which poses challenges for public release and secondary use. This study proposes a sequential 2D–3D recognition framework for extracting privacy-sensitive objects by integrating 2D image recognition and 3D point cloud recognition. The proposed framework first detects candidate regions in images and associates them with the corresponding 3D point cloud through multi-view projection, after which 3D semantic segmentation is applied only to the candidate point cloud. By restricting 3D recognition to candidate regions, the proposed method suppresses background-point contamination while reducing unnecessary 3D processing. We evaluate the method using SfM-derived 3D point clouds containing people and vehicles. The results show that the proposed method achieves higher F-scores than the selected direct 2D-projection and 3D-only baselines, reflecting a better balance between precision and recall. These findings suggest that sequentially combining 2D image recognition with 3D point cloud recognition provides an effective approach for privacy-sensitive object extraction and supports the privacy-preserving publication and secondary use of digital twins and 3D city models. Full article

To address the broadband noise distribution, complex excitation sources, and insufficient low-frequency attenuation of conventional porous acoustic packages in commercial vehicle cabs, this study proposes a PU–six-cavity Helmholtz metamaterial composite acoustic package for broadband noise reduction. The proposed structure consists of a 30 mm PU porous layer for mid-to-high-frequency dissipation and a 30 mm six-cavity Helmholtz metamaterial layer for low-frequency absorption, forming a 60 mm composite acoustic package. A full-vehicle acoustic model of a commercial vehicle cab was established in VA One to identify the A-weighted sound pressure level (SPL) spectrum at the driver position. The results show that the PU porous acoustic package improves the mid- and high-frequency noise response, whereas pronounced peaks remain in the low- and low-to-mid-frequency ranges. To enhance these bands, the six sub-cavities of the Helmholtz metamaterial were tuned to 200, 250, 315, 400, 500, and 630 Hz through spatial partitioning and cavity grouping. COMSOL (version 6.1) simulations and particle velocity distributions confirmed the multi-peak absorption mechanism and the selective excitation of the corresponding sub-cavities. The composite structure was further validated through impedance-tube measurements, full-vehicle acoustic simulations, and in-vehicle tests. The VA One simulation shows that the total A-weighted SPL at the driver position decreases from 68.25 dB for the PU porous package to 66.01 dB after introducing the six-cavity Helmholtz metamaterial, corresponding to an additional reduction of 2.24 dB. A preliminary in-vehicle test under a stationary idling condition shows that the total A-weighted SPL near the driver’s ear decreases from 55.83 dB(A) to 54.56 dB(A). These results demonstrate that the proposed PU–six-cavity Helmholtz metamaterial composite acoustic package combines broadband porous dissipation with low-frequency resonant absorption, providing a feasible solution for broadband noise control in commercial vehicle cabs. Full article

This paper presents RICO-3D (Roadway Infrastructure in Context), a new large-scale Mobile Laser Scanning (MLS) dataset for semantic segmentation of French urban roadways, together with GA-Attention, a geometry-aware attention U-Net designed for this task. RICO-3D was acquired with a Leica Pegasus TRK300 mobile mapping system across Marseille, Rennes, and Opoul-Périllos (France), and provides per-point geometry, RGB, intensity, GPS time, scan angle rank, and semantic labels for 6 classes: vegetation, road, pole, building, cable, and vehicle. The dataset contains 780,981,961 labeled points and captures realistic MLS challenges, including severe class imbalance, sparse thin structures, occlusions, and varying seasonal and weather conditions. GA-Attention combines enriched geometric descriptors, attentive local aggregation, saliency-guided downsampling, attention-gated skip fusion, and curriculum-based training within a point-based encoder-decoder framework. On RICO-3D, the proposed method achieves 83.36% overall accuracy and the best IoU for road (91.35%), pole (49.91%), and cable (56.08%), with an inference time of 8.17 s. On Toronto-3D, it reaches 82.18% overall accuracy and 56.50% mIoU. These results show the relevance of RICO-3D for infrastructure-oriented MLS segmentation and the effectiveness of GA-Attention for thin and under-represented roadway infrastructure classes. To support reproducible research, the RICO-3D dataset, source code, trained models, and evaluation scripts will be publicly available once the Orange’s legal and data-governance validation process has been completed. Full article

With the increasing integration of distributed photovoltaics, energy storage systems, electric vehicle chargers, and intelligent terminals, accurate identification of heterogeneous edge-end devices in distribution networks has become challenging due to their diverse operating characteristics and similar current signatures. This paper proposes an identification method based on current time–frequency features and Random Forest. Equivalent grid-connected current models are developed for five types of edge-end devices, considering different capacity levels, operating states, ripple characteristics, and transient behaviors. A 13-dimensional feature set is extracted from time-domain, frequency-domain, and time–frequency characteristics, covering 11 device subclasses. Feature analysis is conducted to evaluate the separability of the extracted features, and Random Forest is employed for multi-class device identification. The results show that the proposed method achieves an overall accuracy above 98% on independent test samples and 96.91% in the IEEE 33-bus validation case, demonstrating its effectiveness for distribution-network edge-end device identification. Full article

In the capacitated arc routing problem (CARP), a fleet of capacitated vehicles based at a depot must cover the streets of a network where the demand is located at the lowest possible total cost. Waste collection, street sweeping, winter gritting, and mail delivery are among its best-known applications. This work introduces the CARP with vehicle dependence (CARP-VD), an extension in which the cost of servicing an edge, and that of traversing it without service, are specific to each vehicle type and formulates it as a mixed-integer linear program. A two-stage matheuristic is proposed: the first stage distributes the required edges among the vehicles without exceeding their capacities, and the second builds the route of each vehicle. A bound is derived that limits the optimality loss of this decomposition by its own deadheading cost. Both approaches are evaluated on 47 benchmark instances adapted from the literature under a common one-hour budget, and their robustness is assessed over six scenarios that vary the parameters of the adaptation. The matheuristic returns good-quality solutions in a fraction of the time on the smaller instances, and on those in which almost every edge requires service it improves the best solutions found by a commercial solver applied to the complete model by up to 44%. Full article

Off-highway vehicle electrification requires traction motors combining high torque density with reliable performance across demanding duty cycles, yet finite-element-based optimization remains computationally demanding for broad design-space exploration. This study addresses the gap with a sensitivity-analysis-based, surrogate-assisted multi-objective optimization framework for a 12-pole/72-slot, 120 kW Interior Permanent-Magnet Synchronous Motor (IPMSM) for a compact wheel-loader drivetrain, coupling Ansys Motor-CAD with Ansys OptiSLang. A Latin Hypercube sensitivity study of thirteen geometric parameters identifies the dominant design drivers, and an evolutionary algorithm operating on the validated surrogate produces a Pareto-optimal set, from which the final design is selected using the CRITIC–TOPSIS method applied to finite-element-validated feasible designs. Relative to the baseline, the validated performance shows a 7.4% increase in continuous torque, a 4.0% increase in peak torque, a 4.0% increase in efficiency, and a 53.5% reduction in torque ripple, with mass essentially unchanged, while also revealing that surrogate predictions were markedly optimistic relative to the finite-element results. These findings demonstrate an efficient, reliable route to high-performance IPMSM design for off-highway applications. Full article