Xiulin Qiu

Accurate prediction of plasma composition during the phase transitions of ablation products remains challenging under thermodynamic nonequilibrium conditions. To address this issue, a plasma composition calculation method based on an electron–heavy-particle–cluster three-temperature assumption is proposed in this work. In the proposed method, condensed-phase clusters formed through the aggregation of gaseous atoms and molecules are treated as independent species, while individual temperatures are assigned to electrons, heavy particles, and clusters to investigate the effects of thermal nonequilibrium on plasma composition. A total Gibbs energy function is then formulated for the three-temperature plasma system and transformed into an objective function compatible with metaheuristic optimization. The proposed method is assessed through comparisons with previously reported gas-phase composition and equilibrium phase-transition results and subsequently applied to investigate the phase transition of Cu-containing ablation species in an air–Cu plasma system. The results reveal that thermodynamic nonequilibrium between heavy particles and clusters can lead to a pronounced difference between the deposition and melting temperatures of ablation products, which significantly modifies the plasma composition across phase transitions. These findings highlight the importance of accounting for heavy-particle–cluster nonequilibrium in plasma composition calculations involving ablation products. Further independent validation of the proposed method under three-temperature nonequilibrium conditions is still required.

Cluster maintenance in a flying ad hoc network (FANET) becomes difficult to interpret when cluster-head selection, membership authority, and short-term adaptation change together. We separate these responsibilities through a cluster-head-selection policy, HELLO/CHA/CMR/CHR membership procedures, and an interchangeable controller that selects only the bounded cluster-head announcement (CHA) forwarding-hop value. The protocol is implemented in NS-3.48; deterministic cluster-head assignments are frozen in the formal comparison to isolate the controller effect. A validation-selected Fixed h=1 controller and a frozen Multi-threshold controller are compared under RandomWaypoint and Gauss--Markov mobility. The prospectively specified and SHA-256-hash-frozen design comprises 160 runs and 80 scenario--seed pairs. Across the two mobility models, only 4 of 16 scenario--primary-metric effect directions agreed. The S2 PDR difference, for example, was 0.0246 under Random Way point (95% bootstrap CI: 0.0049--0.0475) and -0.0232 under Gauss--Markov (-0.0663--0.0217). Adaptive CHA-hop effects are therefore conditional on scenario and mobility dynamics. Within this experimental scope, the paired workflow supports Fixed h=1 as a defensible conservative default while retaining the architecture for evidence-based controller selection.

Ship detection in complex maritime and remote sensing scenes is of great importance for Earth observation, maritime surveillance, and intelligent ocean monitoring. However, existing methods often struggle with severe background interference, adverse weather conditions, and insufficient structural feature representation of ship targets. To address these challenges, we propose VSTF-Net, a Vision-Semantic and Target-aware Fusion Framework for Ship Detection in Complex Maritime Sensing Scenarios. Specifically, to enhance high-level semantic representation under complex maritime environments, a Visual-Semantic Environment Modulation (VSEM) module is designed to introduce semantic priors extracted from a CLIP-pretrained visual encoder for adaptive feature modulation. To capture the elongated structural and directional characteristics commonly exhibited by ship targets in maritime and remote sensing scenes, a Multi-branch Asymmetric Dilated (MAD) module is developed to strengthen structural and directional feature modeling through asymmetric and dilated convolutions. Furthermore, to suppress high-frequency background interference in complex water environments, a Frequency-Adaptive Denoising (FAD) module is integrated to adaptively recalibrate frequency-domain features and improve the discriminability between ship targets and surrounding backgrounds. Experimental results show that the proposed method achieves 89.2% mAP@0.5 and 56.2% mAP@0.5:0.95 on the enhanced SeaShips dataset, with improvements of 1.9% and 2.4% over the baseline model, respectively. Further experiments on the HRSC2016 and MEIWVD datasets demonstrate the effectiveness and robustness of the proposed method across both remote sensing and maritime scenes.

Ship semantic segmentation based on unmanned aerial vehicle (UAV) imagery has important application value in maritime scenarios such as marine surveillance, port management, and maritime safety. However, UAV images often contain large scale variations of ships, a high proportion of small targets, and complex background interference, including sea surface reflections, waves, and clouds. These factors make accurate segmentation and boundary localization difficult. To address these issues, this paper proposes a UAV-based ship semantic segmentation network, termed MPFT-UNet. The network introduces a Multi-scale Dynamic Sparse Cross-gating (MDSC) module to improve the representation of small targets. A Boundary Supervision Refinement (BSR) module is used to enhance boundary delineation. In addition, a Transformer-based Feature Fusion (FFT) module is applied at the bottleneck layer to strengthen global semantic representation. Experimental results show that MPFT-UNet achieves better performance than existing methods across multiple evaluation metrics. The model obtains an IoU of 0.8365, Dice coefficient of 0.9028, Recall of 0.8881, and AP of 0.95731. These results indicate stable segmentation performance under complex maritime conditions. Compared with the baseline U-Net model, the IoU is improved by approximately 5.1%.

By incorporating opportunistic coding, network throughput is enhanced, resulting in improved overall performance. However, applying this paradigm to Flying Ad-hoc Networks (FANETS) faces significant challenges due to the highly dynamic topology caused by the high-velocity mobility of UAVs, alongside the NP-hard complexity of identifying optimal coding opportunities in rapidly evolving aerial network architectures. To address these challenges, this paper proposes a novel coding-aware routing protocol based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). We formulate the routing problem as a multi-agent continuous decision-making process, employing the MADDPG algorithm to optimize routing policies in real-time through decentralized execution and centralized training. To maximize network utility, we design a comprehensive reward function that integrates coding benefits, throughput, energy distribution, and end-to-end delay, ensuring a balance between throughput maximization and the energy sustainability of individual UAV nodes. Simulation results demonstrate that the proposed protocol significantly outperforms state-of-the-art coding-aware routing protocols in terms of throughput, Packet Delivery Ratio (PDR), and transmission delay, exhibiting superior robustness in highly dynamic FANET scenarios. Notably, at a network density of 20 UAVs, MARL-CAR outperforms COPE, DCAR, TSCAR, and RLCAR in terms of coding ratio by 32.23%, 18.93%, 20.35%, and 5.5%, respectively. This research provides a scalable and intelligent networking solution for the next generation of autonomous UAV swarms and collaborative aerial missions.

The effective management of smart cities relies on real-time data from urban environments, where Unmanned Aerial Vehicles (UAVs) are critical sensing platforms. However, deploying high-performance detection models on resource-constrained UAVs presents a major challenge, particularly for identifying small, dense targets like pedestrians and vehicles from high altitudes. This study aims to develop a lightweight yet accurate detection algorithm to bridge this gap. We propose YOLOv10-DSNet, an improved architecture based on YOLOv10. The model integrates three key innovations: a parallel dual attention mechanism (CBAM-P) to enhance focus on small-target features; a novel lightweight feature extraction module (C2f-LW) to reduce model complexity; and an additional 160 × 160 detection layer to improve sensitivity to fine-grained details. Experimental results demonstrate that YOLOv10-DSNet significantly outperforms the baseline, increasing mAP50-95 by 4.1% while concurrently decreasing computational costs by 1.6 G FLOPs and model size by 0.7 M parameters. The proposed model provides a practical and powerful solution that balances high accuracy with efficiency, advancing the capability of UAVs for critical smart city applications such as real-time traffic monitoring and public safety surveillance.

Future flying ad-hoc networks (FANETs) need to address issues related to delay and channel interference while ensuring high data transmission accuracy. In this paper, we propose a proximal policy optimization (PPO)-based adaptive adjustment algorithm for sliding window-based network coding to mitigate channel contention and signal degradation caused by increased traffic load. We model the adaptive adjustment of the sliding window size as a Markov decision process, considering effective rate, node congestion, and total delay in the reward function. To further improve the algorithm’s performance, we enhance PPO with long short-term memory (LSTM) networks to process time-series data. Experimental results demonstrate that our method reduces delay, improves packet delivery rate and throughput compared with traditional sliding window network coding algorithms.