## 4 Results II--Review of Research Clusters
### Cluster 4: Operation  
The proper operation of fishing vessels implies navigating efficiently and safely. However, accidents or safety issues during fishing operations can result in harm to personnel or damage to vessels due to collisions. Potential causes of accidents include fishermen's misjudgments and internal system errors, making it crucial not only to prevent such accidents but also detect them in advance. To ensure the safety of fishermen during navigation, numerous studies have focused on monitoring the activities of both fishermen and their vessels. These studies help detect illegal fishing activities or support effective search and rescue (SAR) operations during accidents. For example, [PERSON] et al. (2019) [27] analyzed the drift characteristics of fishing vessels in Chinese coastal waters under different wind conditions and developed a model to support SAR operations. The study used the AP98 leeway model and a drift dynamics model to compare and analyze two approaches, predicting drift trajectories and search areas during marine accidents. The AP98 model estimates leeway coefficients based on experimental data to accurately predict the drift speed and direction of vessels under various wind conditions, utilizing Lagrangian particle tracking and the Monte Carlo method for trajectory simulation. The results showed that the AP98 model provided higher accuracy compared to the drift dynamics model, especially when the Probability of Positive Crosswind (POPC) was considered. Although the AP98 model is effective for predicting drift in various conditions, it needs to incorporate external factors such as currents to improve accuracy. Similarly, [PERSON] et al. (2023) [39] compared the accuracy of Global navigation satellite system (GNSS) data stored in V-pass and Automatic identification system (AIS) terminals on fishing boats against Differential Global Positioning System (DGPS)-corrected data from Electronic Chart Display and Information System (ECDIS) to assess the reliability of these systems for emergency response and accident investigation. The study revealed that V-pass terminals, which transmit data every 30 s, exhibited positional errors of up to 41 m in certain scenarios, especially during sharp turns. This finding was visually and quantitatively verified using a Ship Collision Reproduction System, highlighting discrepancies between navigation devices and emphasizing the need for improved positional accuracy in SAR operations. Likewise, [PERSON] et al. (2023) [44] investigated the reliability of V-pass data for the timely rescue of fishing vessels, identifying transmission delays of up to 7 min, which resulted in critical data gaps during emergencies. To mitigate these issues, the study suggested refining V-pass transmission protocols and adopting a shorter data-saving interval to ensure precise tracking during SAR missions. Regarding the monitoring of vessels engaging in risky behavior, [PERSON] et al. (2022) [35] utilized AIS data to analyze vessel movements and identify abnormal behaviors, such as illegal fishing, cross-border activities, and unexpected route changes. The study employed an Automatic Tracking System (ATS) to periodically collect AIS data and detect deviations from normal operational patterns. Identified anomalies were integrated into international maritime monitoring systems, like Global Fishing Watch (GFW), to accurately locate illegal vessels and support rapid responses from marine protection agencies. In a similar context, [PERSON] and [PERSON] (2023) [47] proposed a system to monitor fishing vessel activities in the Indian region using MIMO (Multiple Input Multiple Output) technology and database management, enabling real-time monitoring and rapid alerts. However, the study pointed out that intentional communication disruptions on vessels pose a challenge, necessitating the development of redundant communication pathways to ensure continuous monitoring.