## 3 The Results of the Bibliometric Analysis
### Citation and Co-Citation Network Analysis
#### 3.3.2 Distribution and Co-Citation Analysis of Journal Sources  
The final phase of the bibliometric analysis involved an investigation of the sources of the documents contained within the dataset. The analysis is needed to identify which journals are most frequently read and productive in the field of new technology in USV and to provide the scholars with tips for contributing to their manuscripts. The 402 retrieved articles were published from 295 unique publication sources. The sources with at least eight published documents are presented in Table 5, while mapping is depicted in Figure 12.  
The preeminent sources in this context are primarily represented by two distinguished journals: _Ocean Engineering_ (OE) and _Journal of Marine Science and Engineering_ (JMSE). The total amount of papers published through the two journals, numbering 21 and 15, respectively, constituted nearly 11% of all articles within the data sample. It is noteworthy, however, that a considerable discrepancy existed in the quantity of documents between the fourth-ranked source and the subsequent source in the collation, as elucidated in Table 5. _Sensors_, being in the third position, exhibited one publication fewer than that of JMSE, but demonstrates the highest average citation in the list, followed by _Ocean Engineering_ with the second highest average citation (12.29). This is also consistent with the journal's impact factor ranking, where _Ocean Engineering_ and _Sensors_ have a relatively high impact factor among the top six most productive journals in the list. Additionally, there were also substantial groups of journals with either two or just one published paper, indicating that the papers in this field were not centralized but scattered in their quality, to some extent. The journal co-citation analysis is a valuable method for categorizing journals by subject and pinpointing the key journals within each category. This is particularly beneficial for researchers seeking to identify the most pertinent and influential journals related to their specific research area. Hence, the sources were also analyzed in terms of how often \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rank** & **Journal** & **TP** & **TC** & **AC** & **IF** \\ \hline
1 & _Ocean Engineering_ & 28 & 344 & 12.29 & 5.0 \\
2 & _Journal of Marine Science and Engineering_ & 16 & 89 & 5.56 & 2.9 \\
3 & _Sensors_ & 15 & 250 & 16.67 & 3.9 \\
4 & _IFAC-PapersOnLine_ & 14 & 80 & 5.71 & / \\
5 & _Applied Sciences-Basel_ & 9 & 100 & 11.11 & 2.7 \\
6 & _IEEE Access_ & 8 & 104 & 13.00 & 3.9 \\ \hline \hline \end{tabular}  
* Notes: TP = total publications; TC = times cited; AC = average number of citations per publication; IF = influence factor.  
\end{table} 
Table 5: The top six most productive journals on USV research. they were cited together, a concept known as co-citation [26]. Herein, the relationship between the two publications is determined by the number of documents that reference both of them [26]. This bibliometric network method was employed to visualize the interconnections among the sources. A minimum threshold of 60 citations per source was set, resulting in 28 sources meeting this criterion. The strength of these connections was determined by the number of citations. Through fractional counting and fractionalization, four clusters were identified as a means of normalizing the data in Figure 12. The detailed setting for the parameters used for the figure is provided in Appendix A Table A2. The co-citation analysis reveals that _Ocean Engineering_ and _IEEE Access_ were the most significant sources. Interestingly, the cluster associated with _IEEE Conference on Computer Vision and Pattern Recognition_ included several sources in the field of computing and robotics, such as _International Journal of Robotics Research_, _IEEE International Conference on Robotics_ and _IEEE Transactions on Intelligent Transportation Systems_.