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.

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.