## 4 Experimental Results and Discussion
### Comparative Analysis of Model Performance with Different Recursive Layers  
Based on the aforementioned analysis, the DFFormer method demonstrates a superior performance compared to other methods. To further enhance the model's predictive capabilities, we focused our attention on the recursive layer's design. To assess its contribution to the DFFormer method, we conducted a series of experiments designed to analyze the recursive layer's impact on the model's overall predictive performance. By comparing models both with and without recursive layers, as well as those incorporating various types thereof (MLP, RNN, GRU, and LSTM), we extensively explored the performance of these structures in managing time-series data. In the experiments, we constructed various model configurations to evaluate the effects of different recursive layers. Initially, the baseline model was modified by removing the recursive layer, establishing a comparison benchmark. Subsequently, we integrated MLP, RNN, GRU, and LSTM as distinct recursive layers within the model to process time-series data. We introduced these layers aiming to enhance the model's capacity to capture temporal dependencies, thereby improving predictive performance. The experimental results are shown in Figures 13 and 14 and Table 4.  
The experimental results indicate that the selection of different recursive layers within the model significantly influences predictive performance. Specifically, when utilizing LSTM as the recursive layer, the model achieved the best performance across all evaluation metrics, highlighting its strengths in managing time-series data with long-term dependencies. In contrast, the performance of MLP exhibited a relatively weaker performance, suggesting its limited capacity to capture complex temporal dependencies. Additionally, the GRU demonstrated a commendable performance, effectively balancing model accuracy and computational efficiency in certain scenarios. Meanwhile, the RNN, despite its potential to enhance model performance, was marginally less effective. Through these experiments, the significance of the recursive layer in the new method becomes evident. The LSTM's primary advantage is its robust capacity to capture temporal dependencies, whereas the GRU maintains an effective equilibrium between performance and computational efficiency. Consequently, these results furnish compelling evidence for selecting the most appropriate recursive structure. The effectiveness of DFFormer depends not only on prediction accuracy but also on computational efficiency, particularly with different recurrent layer configurations, such as LSTM, GRU, MLP, and RNN. Table 5 shows a comparison of computational resource consumption for DFFormer with different recurrent layers, including training  
Figure 14: The evaluation indicators of different recursive layers in the climbing phase. Figure 13: The evaluation indicators of different recursive layers in the diving phase. time, inference time per prediction, and memory usage. All metrics were measured on the same hardware setup (NVIDIA RTX 3090 GPU, 32 GB RAM). As shown in Table 5, the MLP configuration achieves the shortest training and inference times with the least memory usage, making it suitable for resource-constrained environments. However, the LSTM recurrent layer, while requiring more computational resources, provides a stronger sequence modeling capability, which enhances predictive accuracy. These comparisons indicate that DFFormer can effectively balance computational load and prediction accuracy, allowing users to choose the optimal configuration based on specific application requirements. While DFFormer achieved high predictive accuracy and stability during trials in the South China Sea, its potential generalizability to other UG types and different marine environments warrants further discussion. Notably, the fundamental sensor configurations and operational mechanisms of various UGs are often quite similar.