As the global climate changes, there is an increasing focus on the oceans and their protection and exploitation. However, the exploration of the oceans necessitates the construction of marine equipment, and the siting of such equipment has become a significant challenge. With the ongoing development of computers, machine learning using remote sensing data has proven to be an effective solution to this problem. This paper reviews the history of remote sensing technology, introduces the conditions required for site selection through measurement analysis, and uses cluster analysis methods to identify areas such as machine learning as a research hotspot for ocean remote sensing. The paper aims to integrate machine learning into ocean remote sensing. Through the review and discussion of this article, limitations and shortcomings of the current stage of ocean remote sensing are identified, and relevant development proposals are put forward.

Underwater remote sensing image enhancement is complicated by low illumination, color bias, and blurriness, affecting deep-sea monitoring and marine resource development. This study compares a multi-scale fusion-enhanced physical model and deep learning algorithms to optimize intelligent processing. The physical model, based on the Jaffe–McGlamery model, integrates multi-scale histogram equalization, wavelength compensation, and Laplacian sharpening, using cluster analysis to target enhancements. It performs well in shallow, stable waters (turbidity < 20 NTU, depth < 10 m, PSNR = 12.2) but struggles in complex environments (turbidity > 30 NTU). Deep learning models, including water-net, UWCNN, UWCycleGAN, and U-shape Transformer, excel in dynamic conditions, achieving UIQM = 0.24, though requiring GPU support for real-time use. Evaluated on the UIEB dataset (890 images), the physical model suits specific scenarios, while deep learning adapts better to variable underwater settings. These findings offer a theoretical and technical basis for underwater image enhancement and support sustainable marine resource use.

Towing cable arrays have made significant contributions across various fields, and their outspread process is crucial for realizing their functionalities. However, research on the dynamic characterization of the outspread process of towed cable arrays lacks systematic organization. This paper reviews, organizes, and analyzes the outspread process of towing cable arrays, drawing on relevant models, case studies, and structural features. It ingeniously applies concepts from parachute outspread to the analysis of towing-cable-array deployment. The study systematically examines the deployment of towing cable arrays under varying cable lengths, wave conditions, and the interactions between line arrays. The goal is to integrate existing research on the outspread of towing cable arrays, addressing the gaps in the description of this process and providing a comprehensive analysis of the outspread characteristics under different conditions. Additionally, this paper identifies current limitations in this area and provides insights for future developments. Furthermore, it explores the potential application of AI to address these challenges. The aim of this paper is to contribute meaningfully to this field.

Marine environmental monitoring and other missions demand long-term power supply for equipment. Addressing the shortcomings of existing power supply methods, this paper focuses on flexible triboelectric nanogenerators (FOCE-TENG), a novel marine current energy harvesting technology, and reviews its latest research progress. This technology efficiently captures low-velocity marine current energy and is suitable for scenarios where traditional technologies struggle to operate. Drawing on interdisciplinary theories, the paper elucidates key aspects including flow-induced vibration mechanisms, power generation performance regulation, and testing systems. It demonstrates that optimizing structural design and materials can enhance efficiency and stability. While challenges remain, such as adapting to extreme environments, future technological advancements hold promise for engineering applications, enabling sustainable power supply for underwater equipment.

Space tracking ships are critical assets in modern space exploration, overcoming the limitations of land-based tracking systems by providing real-time telemetry, trajectory monitoring, and communication support for satellites and spacecraft. However, the existing literature offers limited insights into the historical development, technological evolution, and collaborative research trends of these specialized vessels. This study employs a bibliometric analysis using CiteSpace (6.3.R1) to identify research trends, highlight key technological advancements, and assess international collaboration. By analyzing publications from major contributors, including the United States, the former Soviet Union, France, and China, the paper reveals technological innovations such as advancements in measurement systems, communication technologies, and the integration of artificial intelligence (AI) for enhanced tracking precision. Structural and operational analyses emphasize the importance of design factors, including ship dimensions and internal systems, for ensuring optimal performance in challenging maritime environments. The findings highlight evolving technological priorities and persistent gaps in international collaboration, suggesting opportunities for global partnerships to advance the field. This study bridges historical and technical gaps, providing valuable insights for enhancing the efficiency and strategic relevance of space tracking ships in future space exploration missions.