## 4 Results
### Application to Real DAS Data  
To evaluate real-world applicability, the U-Net models, trained *exclusively* on the synthetic datasets, were applied to actual DAS measurements from the NA-150 test site. These real measurements (Figure 7b) contain signals from mixed traffic (including faint bicycle signals), real-world noise, and the true effects of the cable layout (Strips A/B). * **Observed Outcomes:*
* The models successfully processed the real data, applying the learned transformations (see Figures 7 and 8). * _Feature Enhancement_: All models produced outputs with visually enhanced spatial resolution compared to the input. Notably, the MID model output (Figure 7h) significantly improved the visibility of the weak signals from a group of west-bound bicycles traveling alongside heavier eastbound traffic. This enhancement is particularly striking in the challenging Strip A region (zoomed comparison in Figure 8), where the original signal (Figure 8a) is noisy and the bicycles are barely discernible, but they become much clearer in the processed output (Figure 8b). * _Directional Filtering_: The WB model (Figure 7d) demonstrated reasonable suppression of strong eastbound vehicle signals while enhancing westbound signals (including the bicycles). The EB model (Figure 7f) isolated eastbound traffic, though visual inspection suggests the filtering might be less clean than the WB result, with some potential residual WB signal. * _Layout Simulation Effects_: The outputs reflected the target layout characteristics learned from synthetic data. The EB model output (Figure 7f) shows reduced signal degradation in Strips A and B compared to the input (Figure 7b), consistent with simulating the straight EB shoulder layout. The MID model output (Figure 7h) appears to balance the amplitudes between the closer WB vehicles and the farther EB vehicles, as expected from simulating a mid-road position. * **Quantitative Comparison Limitations:*
* Objective quantitative evaluation (MSE, PSNR, etc.) against a ground truth is not feasible for the real data application, as no \"ideal\" high-resolution, perfectly filtered real measurement exists. Performance assessment relies primarily on qualitative visual inspection and consistency checks against known events (e.g., the presence and direction of the bicycles and heavy vehicles). * **Performance Gap and Limitations (Synthetic vs. Real):** Applying the synthetic-trained models to real data revealed key differences and limitations compared to the performance on synthetic validation data:  
Figure 7: Application to real DAS measurements. Column 1 (**a,c,e,g**) shows cable distance to road centerline (same as Figure 6). Column 2 (**b,d,f,h**) shows real DAS data (waterfall plots). (**b**) Input real DAS signal (960 ch), showing WB bicycles (faint, slow traces), heavier EB vehicles (stronger, faster traces), and effects of Strips A and B. (**d, f, h**) Processed outputs (1920 ch) from models trained on synthetic data: (**d**) WB-filtered result, enhancing bicycles; (**f**) EB-filtered result (simulating EB layout); (**h**) all-traffic result (simulating MID layout), notably enhancing bicycle visibility. Overall noise level and clarity are compared in (**d,f,h**) vs. (**b**). _Noise Amplification:_ A major observed difference was the significant **amplification of background noise** in all processed real data outputs (clearly visible in Figures 7d,f,h and 8b compared to Figures 7b and 8a). While target features like bicycles were enhanced, the overall SNR appeared degraded. This strongly indicates that the simple additive white noise model used during synthetic training (std = 0.005) failed to capture the complex structure and characteristics of real-world DAS noise. * _Artifacts and Imperfect Filtering:_ Some low-level artifacts and residual signals from the suppressed direction were visually apparent in the filtered outputs (e.g., faint diagonal traces potentially corresponding to strong EB vehicles in the WB-filtered output, Figure 7d).