## 5 Discussion  
The framework presented successfully integrates transport microsimulation (VISIM) with physics-based modeling (Flamant-Boussinesq) to generate synthetic strain-rate datasets simulating DAS measurements under diverse traffic conditions. The results demonstrate the potential of using these synthetic datasets to train machine learning models (U-Nets) for tasks like spatial resolution enhancement, directional filtering, and virtual sensor relocation (layout simulation). The validation on synthetic data (Section 4.1) showed that the U-Net models could learn the target transformations with high fidelity when the task complexity was lower (e.g., WB filtering with original layout). Increased complexity, particularly involving simulating significant changes in cable geometry (EB and MID layouts), led to higher quantitative errors, suggesting limitations in the model's ability to perfectly extrapolate signals under large spatial transformations based solely on the provided examples. Nevertheless, visually, the intended effects were achieved in the synthetic domain. The application to real DAS data (Section 4.2) provided critical insights into the methodology's practical applicability and current limitations. The successful enhancement of faint bicycle signals, especially in the challenging Strip A region, highlights a key benefit: using synthetic data to train models can improve the detection of difficult-to-observe events in real measurements. This \"transposition of intended benefits\" from synthetic training to real-world application is a promising outcome. However, the significant amplification of background noise and the presence of filtering artifacts clearly expose the domain gap between the current synthetic data generation process and real-world DAS signals. The primary limitation appears to be the simplistic noise model (additive white noise). Real DAS noise is far more complex, potentially exhibiting frequency dependence, spatial correlations, and influences from environmental factors (temperature, terrain characteristics) and infrastructure vibrations not related to traffic. Future work must incorporate more sophisticated, data-driven noise models derived from spectral analysis of real background DAS measurements (periods without traffic) to improve the realism of the synthetic training data and reduce noise amplification upon real-world application. Data standardization methods also warrant further investigation. While mean/variance scaling was used for synthetic data, the high dynamic range of DAS signals (heavy vs. light vehicles) might benefit from robust scaling methods (e.g., based on median and interquartile range), as was used for the real data input. Consistent and appropriate scaling across training and application is crucial. Another key limitation is the accuracy of the input models--both the transport model and the physical model. Discrepancies between the modeled and actual fiber-optic cable path (including exact 3D position, potential slack in manholes causing channel misalignments between features like Strip A and Strip B), variations in burial depth and coupling efficiency, and inaccuracies in the assumed geophysical parameters (\(E\), \(\
u\)) all contribute to mismatches between synthetic and real signals. Addressing this requires careful calibration. Incorporating ground truth data from controlled vehicle passes (with precise GPS tracking) could help calibrate the VISSIM model, geophysical parameters, and channel-to-location mapping. The assumption of uniform fiber sensitivity and coupling inherent in the basic Flamant-Boussinesq model is another simplification. The real cable layout exhibits variations in response to the terrain and pavement characteristics along its length. While explicitly modeling this is complex, acknowledging it as a limitation is important. Future physical models could attempt to incorporate spatially varying coupling coefficients if calibration data allow. Despite these limitations, the framework offers significant advantages.