## 5 Conclusions
### Future Work  
Future work will expand data acquisition to better adapt the model to evolving railway scenarios. With the introduction of CR450 high-speed trains, we will document carriage structures, electromagnetic properties, and layout details. High-precision field strength measurements will be taken at key locations inside the train, covering terrains like tunnels and viaducts. Data will also be collected under varying operating conditions, including different speeds, operational hours, and base station distributions, to analyze their effects on signal propagation. This enhanced dataset will improve model training and ray-tracing accuracy and introduce interference factors to strengthen robustness. Additionally, train speed information will be integrated to refine the multipath model for better interference handling.  
Leveraging this new data, we will further optimize the multi-neural network integration model through cross-validation and grid search, refining network architecture, hyperparameters, and regularization techniques. We will explore the model structure for innovations such as attention mechanisms to enhance feature extraction. Additionally, we will integrate recurrent neural networks (RNNs) and their variants (LSTM, GRU) to improve the handling of dynamic signal propagation.  
Future research will focus on refining hyperparameter selection through automated optimization methods. This approach will ensure efficient and accurate identification of key parameters while avoiding local optima. The use of this approach will streamline model tuning and further enhance predictive performance.  
Transfer learning will help the model quickly adjust to new train types and materials, improving adaptability across different propagation conditions. By leveraging pre-trained knowledge, we aim to reduce data requirements and training time for new scenarios. Continuous monitoring of advancements in train technologies and materials will further support ongoing model optimization and expansion into diverse application contexts.  
**Author Contributions:** Conceptualization, H.Z. and Y.W.; data curation, Z.F. and Y.F; formal analysis, Z.F. and Y.S.; funding acquisition, Y.S.; investigation, Z.F. and G.Z.; methodology, Z.F., H.Z. and G.Z.; resources, G.Z. and H.Z.; software, Z.F. and Q.Y.; supervision, H.Z. and G.Z.; validation, Y.F., Y.W. and Q.Y.; visualization, Z.F. and Q.Y.; writing--original draft preparation, Z.F.; writing--review and editing, Y.W., Y.F. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Shanghai, Grant Number 22 ZR1422200; the National Key Research and Development Program of China, Grant Numbers 2021 YFB2900800, 2022 YFB2902005 and 111 Project (D20031); the Open Project of Shanghai Driverless Train Control System Engineering Technology Research Center, Grant Number SUTC-2024 KT-04.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** Author Ms. [PERSON] was employed by the company CASCO Signal Ltd., Shanghai 200072, China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.