The dynamic positioning of the mass-switched unmanned vessel is remotely controlled via networks from the land-based control station. However, the modal and location information transmitted over networks is susceptible to tampering by deception attacks. Existing attack detection methods have significant limitations in accuracy, as they cannot effectively distinguish between modal tampering caused by deception attacks and modal switching resulting from mass variation. To address this issue, this paper proposes a bumpless detection approach for deception attack based on dynamic residual. By suppressing amplitude bumps caused by switching and data updates, it effectively distinguishes genuine attack behaviors. Simultaneously, the dynamic residual not only more accurately reflects the actual observed state but also effectively suppresses the fluctuating disturbances introduced by static residuals. Finally, this paper designs a resilient learning observer. This observer's resilient storage mechanism and resilient coefficient can proactively discard historical data before the current switching instant and incorporate attack detection results as the basis for data selection, while also enabling dynamic adjustment. The effectiveness of the proposed bumpless deception attack detection method is validated through simulation experiments
Current research on dynamic spectrum access (DSA) has approached the limits of performance and resource utilization at the individual or single-organization level. Specifically, acquiring comprehensive data under strict privacy constraints is challenging, while incorporating external data often incurs prohibitive computing costs. This paper proposes a new swarm learning (SL) architecture for DSA in edge computing, which improves spectrum sensing accuracy and resource utilization efficiency by aggregating group data under strict privacy protection, facilitating deterministic spectrum fair scheduling to suppress access conflicts and ensure the orderly access of massive IoT devices in highly dynamic environments. To address challenges such as community instability induced by edge device mobility, aggressive spectrum contention, communication overhead surges, and interference of similar models in dense areas on global aggregation, a dynamic tree-hierarchical graph architecture is designed. This architecture can adapt to the heterogeneous characteristics of different geographic communities and solve model failure problems caused by edge device mobility without increasing the model parameters or computing power burden of individual devices. Furthermore, to ensure long-term efficiency, we propose a cross-cycle adjustment mechanism based on model groups under the new architecture, enabling SL communities to dynamically select the most suitable models over long cycles, thereby continuously improving overall system efficiency.
The energy consumption from vessel waiting and handling at ports needs to be addressed, giving rise to policies such as Shore Power Connection (SPC) and Just In Time (JIT). However, converting these policies into operational decision metrics is challenging. This study fills that gap by embedding key indicators, such as Berth Occupancy Rate (BOR), Shore Power Utilization Ratio (SPUR), Quay Crane Utilization Ratio (QCUR), and Average Vessel Completion Rate (AVCR), into a novel optimization model for the integrated Berth Allocation and Quay Crane Scheduling Problem (BAQCSP). The model incorporates a dynamic prediction mechanism to estimate quay crane handling times, accounting for real-world variables such as tidal conditions and container stowage positions. Experimental results demonstrate that the proposed model demonstrates superior performance in resource utilization compared to sequential scheduling, single-objective optimization, and fixed quay crane handling time strategies. Additional ablation studies quantify the individual impact of each sustainability indicator on scheduling outcomes. The findings provide port authorities with actionable evidence to operationalize sustainability goals into enforceable strategies.
Thermoplastic polyurethane (TPU)-coated woven fabrics exhibit anisotropy and tensile–shear coupling associated with evolving yarn geometry under off-axis loading. Conventional macroscopic models often represent these changes implicitly, whereas detailed mesoscopic models require substantial computational effort. This study develops a low-order natural-grid constitutive framework to predict the geometry-dependent in-plane response. Its central contribution is the unified treatment of the inter-yarn angle α and cell orientation angle β as distinct kinematic variables updated from the deformation gradient. The angle α and yarn stretches jointly determine the local network geometry and equivalent stiffness, while β governs the local-to-global stiffness transformation. Multi-orientation tensile tests over 50–300 N evaluate predictions at global, regional, and central-cell scales. Mean absolute relative errors in global displacement are 4.33 %, 5.30 %, 6.00 %, 4.34 %, and 9.27 % for the 0°, 15°, 30°, 45°, and 90° specimens, respectively; regional and central-cell mean errors range from 5.25 % to 5.59 %. The framework provides a compact, physically interpretable representation of the measured in-plane response within the investigated loading range.
