## 5 Discussion and Conclusions  
This study proposes a quantitative model for analyzing BTS. Leveraging circuit theory, an analytical model of mutual inductance between the OAU and the balise is established, culminating in a formula for calculating the transmission characteristics in water debris. Figure 14: The results of balise I/O transfer characteristics. Figure 13: Measurement layout for I/O transfer characteristics. The analysis examines various position changes of these factors within the main lobe zone. The calculated mutual inductance values and S21 parameters, simulation results, and experimental measurements exhibit generally consistent trends, with the maximum deviation between the calculated and measured S21 parameters not exceeding 0.49 dB, thus demonstrating good agreement. An electrical model is employed to predict the balise I/O characteristics for underwater debris. The uplink FSK signal attenuation in water debris is found to be approximately 0.62 dB greater than in free space, which aligns well with the experimental results. These findings validate the model's accuracy for quantitative analysis of balise transmission performance in water debris. The proposed electrical model facilitates the direct calculation of circuit conditioning parameters and their impact on the transmission characteristics of the BTS, thereby providing valuable insights for product development. The model is validated in a laboratory setting to better control the variables and minimize confounders, allowing for a more accurate study of the specific causal relationships. Utilizing this model, researchers can directly compute circuit conditioning parameters and assess how parameter matching influences the transmission characteristics of BTS. However, the effectiveness of the model in real-world conditions needs to be further verified. Additionally, the prediction model considering transmission loss under various conditions, including the presence of water debris, has significant implications for product development, such as optimizing the OAU transmission power and installation heights of the balise during upgrades. These measures enhance the system reliability and minimize failure risks. The predictions of the model can also aid operators in making informed decisions during adverse conditions, allowing them to take proactive measures to maintain communication integrity. The rapid growth of high-speed rail networks and BTS has heightened the importance of operational maintenance in product lifecycle management. This focus is essential for ensuring reliability and safety in complex transportation systems. Performance data analysis and electrical modeling have become valuable tools for identifying early failure modes in BTS, potentially preventing more serious issues. These analytical methods offer crucial insights for assessing the current state of BTS and predicting their lifespan, aiding in maintenance planning, performance optimization, and infrastructure longevity. However, while promising in theory, the practical effectiveness of these approaches requires further research and validation. Additional studies are needed to refine these techniques, establish their reliability across various conditions, and determine their long-term impact on system maintenance and performance. As the rail industry evolves, the development of predictive maintenance strategies will likely play an increasingly vital role in ensuring the safety, efficiency, and reliability of high-speed rail systems. Moreover, the installation of a balise continues to face various challenges. The effectiveness and reliability of the BTS are compromised when the balise is covered with substances such as metallic particles, coal dust, dirt, ice, snow, oils, or other debris. These foreign materials on the surface of the balise degrade its performance and functionality within the BTS.