This paper addresses the challenge of energy-constrained maritime monitoring networks by proposing an unmanned aerial vehicle (UAV)-enabled integrated sensing, communication, powering and backhaul transmission scheme with a tailored time-division duplex frame structure. Within each time slot, the UAV sequentially implements sensing, wireless charging and uplink receiving with buoys, and lastly forwards part of collected data to the central ship via backhaul links. Considering the tight coupling among these functions, we jointly optimize time allocation, UAV trajectory, UAV-buoy association, and power scheduling to maximize the performance of data collection, with the practical consideration of sea clutter effects during UAV sensing. A novel optimization framework combining alternating optimization, quadratic transform and augmented first-order Taylor approximation is developed, which demonstrates good convergence behavior and robustness. Simulation results show that under sensing quality-of-service constraint, buoys are able to achieve an average data rate over 22 bps/Hz using around 2 mW harvested power per active time slot, validating the scheme’s effectiveness for open-sea monitoring. Additionally, it is found that under the influence of sea clutters, the optimal UAV trajectory always keeps a certain distance with buoys to strike a balance between sensing and other multi-functional transmissions.

In the era of prevalent large language models (LLMs), efficient LLM inference systems deployed on the neural processing units (NPUs) have gained widespread adoption. During NPUbased LLM inference, both user privacy inputs and proprietary model parameters require stringent protection. While traditional trusted execution environments (TEEs) can be applied to NPU inference processes, we identify that they introduce challenging security-related overheads, including communication for security metadata management and secure startup costs. This paper proposes SecNPU, a CPU-decoupled and LLM-inference-optimized NPU TEE. SecNPU effectively eliminates communication overhead caused by coupled security metadata and leverages the characteristics of LLM inference to conceal security initialization latency. Experimental evaluations demonstrate that our design achieves$1.51 \times$overall secure inference speedup and$1.61 \times$secure boot performance improvement, requiring merely 1.63 % additional area and 6.6 % more power.

Multi-link operation (MLO) is one of the key features of 802.11be, which can achieve higher throughput, and lower latency than single-link operation in a heavily loaded network. For a nonsimultaneous transmit and receive (NSTR) multi-link device (MLD), synchronous transmissions across multiple links are required to avoid the in-device coexistence (IDC) interference. Compared to legacy access mechanisms of single-link, the modeling and performance theoretical analysis of synchronous multi-link access mechanisms face some new challenges. The two-dimensional Markov performance analysis model for single-link cannot be applied to multi-link scenarios. In this paper, we present an analytical model to compute the multi-link synchronous access probability and the saturation throughput in the assumption of ideal channel conditions. The proposed Markov model combines the distributed coordination function (DCF) mechanism with the process of multi-link synchronous access based on the threshold of waiting time. Different from other proposed MLO models, we consider the stochastic state of channel during the synchronous access backoff procedures of each link, and define the concept of waiting time threshold. Comparison with simulation results show that the proposed model is accurate in predicting the multi-link synchronous access probability, as well as system saturation throughput.

Code clone detection technology aims to automatically detect code similarity and help developers identify and reduce code duplication.While code syntax analysis-based methods are commonly used for clone detection, they may not capture semantic information due to bypassing the analysis of code text.To address this issue, this paper proposes a new method called visualization representation learning for code clone detection (VRL4CCD).This method converts source code fragments into grayscale images to preserve textual information and then utilizes VGG16 and a self-attention mechanism to extract features related to code semantic similarity.A siamese neural network is used to learn the similarity pattern between code features.Experimental results on the Big Clone Bench and Google Code Jam datasets demonstrate that VRL4CCD outperforms current clone detection methods regarding precision, recall, and F1-score, indicating the effectiveness of code visualization technology in clone detection tasks.