## 7 Discussion  
This work introduces a novel methodology for respiration detection in real rubble environments, centered on the utilization of time-dependent transmission coefficients from UWB radar signals operating in the 150-650 MHz range. In particular, three well-established signal processing transformations--STFT, WT, and EMD--are integrated with deep learning frameworks to enhance classification performance. The proposed approach aims to accurately discriminate among three classes (empty/respiration/movement) while maintaining low computational overhead. Comparative analyses indicate that these signal processing methods significantly affect classification outcomes, underscoring the importance of carefully selecting data transformations in conjunction with deep neural architectures. Extensive evaluations have been carried out using TCN, LSTM, and CNN models across multiple frequency ranges and transformation techniques. The resulting performance metrics show that CNN architectures, especially those combined with STFT, yield high classification accuracy under various scenarios. Nonetheless, memory and computational demands become critical considerations when CNNs are applied in multi-class settings. In contrast, TCN and LSTM show variable behaviors: TCN's performance, particularly when paired with EMD, may decline at lower frequencies, possibly due to challenges in capturing the subtle time-frequency patterns. LSTM demonstrates the capability to achieve state-of-the-art accuracy at specific frequency bands but encounters difficulties in multi-class tasks, possibly due to inefficient handling of extended sequence dependencies. Another noteworthy outcome of the present study is the identification of misclassifications that occur with increasing task complexity, such as distinguishing among empty, respiration, and movement conditions. The lack of fine-grained error analysis impedes a deeper understanding of which specific class pairs are more prone to confusion. Analyzing such misclassification patterns--especially to ascertain the degree to which respiration signals might be mistaken for movement under certain conditions--would provide valuable insights for refining model architectures and data processing pipelines. Addressing these intricacies is vital to improving classification reliability and ensuring that the proposed framework generalizes well to a broad spectrum of real-world scenarios. In the context of this study, a single type of rubble environment was created under laboratory conditions, and multiple individuals were positioned at various angles and postures to generate distinct signal types (respiration and movement). These signals were then analyzed using LSTM, CNN, and TCN models to assess classification performance. While the findings demonstrate promising results in detecting different human activities within this controlled setup, real-world deployments pose further challenges. Future research endeavors will broaden the experimental scope to include diverse and realistic debris scenarios, which may introduce additional signal variations and complexities. This progression toward more varied settings aims to ensure that the proposed methodology evolves into a robust, efficient, and reliable system for detecting and classifying respiration-related signals across a wide range of practical--and potentially harsh--environments. Consequently, while the laboratory setup is realistic, the absence of field testing in actual post-disaster environments limits the generalizability of the findings. However, as part of our planned future work, we will conduct extensive real-world testing in controlled post-disaster environments with varied debris compositions, noise conditions, and environmental interferences. These experiments will validate the robustness of our approach and refine the model's adaptability to practical search and rescue scenarios, ensuring its effectiveness in real-world systems deployed in emergency situations. **Author Contributions:** Conceptualization, O.N., M.Z.E., A.S.T., S.A.C., B.E.