## 3 Experimental Design and Case Study
### Machine Learning Approach (U-Net)  
We employed a U-Net convolutional neural network architecture (Appendix B), known for its effectiveness in image segmentation and processing tasks involving spatial context, which is relevant to DAS data (waterfall plots). The U-Net consists of an encoder path (down-sampling) to capture context and a symmetric decoder path (upsampling) to enable precise localization, with skip connections linking corresponding encoder/decoder levels.  
Figure 4: Section of the Geographical location of the road NA-150 (red) layout and approximate fiber-optic cable location at westbound shoulder (cyan).  
Our implementation used three encoder/decoder blocks with filter numbers [128, 256, 512] and a bridge with 1024 filters. The input to the network was a segment of synthetic DAS data (representing the \"measured\" signal), and the output was the corresponding \"target\" synthetic signal (e.g., higher resolution, filtered, or alternative layout). To achieve resolution enhancement, the target label datasets were generated at double the spatial resolution of the input feature datasets.  
The input data were structured as strips covering the full fiber length (960 channels, gauge length \(L=1.034\) m) over a temporal window of 6.4 s (64 timesamples at 10 Hz). The target labels had 1920 channels (\(L/2=0.517\) m).  
Training used the combined synthetic datasets from all three traffic densities. The data were split into 75 % training and 25 % validation. The training parameters were as follows: Adam optimizer, MSE loss, learning rate 0.001, batch size 8, and max 100 epochs with early stopping (patience 6).  
**Experiments Conducted:** Separate U-Net models were trained for three distinct tasks, using specifically prepared synthetic feature/label pairs:  
1. **Westbound (WB) Filtering and Resolution Enhancement:**  
* _Feature Input:_ Synthetic signal (960 ch) from all traffic, using the actual WB shoulder layout (incl. Strips A/B), with added noise (std = 0.005).
* _Label Output:_ Synthetic signal (1920 ch) containing \"only\" WB traffic, using the actual WB shoulder layout, with no added noise.
2. **Eastbound (EB) Filtering, Resolution Enhancement and Alternative Layout:**  
* _Feature Input:_ Same as the WB experiment.  
Figure 5: VISSIM Micro-Transport model of the NA-150 road section. Road links are shown with lane markings. The blue line represents the codified fiber-optic cable path in the westbound shoulder, including depth and diversions at (A) the abandoned service station area and (B) the bridge.  
* _Label Output_: Synthetic signal (1920 ch) containing \"only\" EB traffic, simulating a hypothetical straight cable layout on the \"eastbound\" shoulder (no Strips A/B), with no added noise.
* **Middle-of-lane (MID) Layout Simulation and Resolution Enhancement:*
* _Feature Input_: Same as the WB experiment.
* _Label Output_: Synthetic signal (1920 ch) containing \"all\" traffic (WB+EB), simulating a hypothetical straight cable layout placed in the \"middle\" of the roadway (between lanes), with no added noise.  
These experiments test the model's ability to learn complex transformations involving spatial upscaling, directional filtering, and geometric compensation based purely on synthetic examples.