## 2 Materials and Methods  
### Data Generation with Ground Penetrating Radar  
The proposed system consists of two data-generating devices: a Geophysical Survey Systems (GSSI) SIR-30 GPR system (Geophysical Survey Systems Inc., Nashua, NH, USA) and an Intel RealSense T265 tracking camera (Intel, Santa Clara, CA, USA). These two data streams are merged together to create both traditional B-Scans as well as F-Scans. In a traditional B-scan, individual traces are stacked along the \(x\)-axis creating a cross-sectional image with an \(x\)-axis corresponding to the scan's length and \(y\)-axis corresponding to depth. Conversely, an F-scan depicts a subsurface cross-section that curves across the x-y plane  
\begin{table}
\begin{tabular}{c c} \hline
**Studies** & **Focus** \\ \hline [PERSON] et al. [16] & RealSense Camera for Localization \\ [PERSON] et al. [18] & RealSense Camera for SLAM \\ [PERSON] and [PERSON] [19] & Wireless Networks \\ [PERSON] et al. [20] and [PERSON] et al. [21] & 4G Network \\ [PERSON] et al. [22] & 5G Network \\ [PERSON] [24] & 4G and 5G Network Comparison \\ [PERSON] et al. [25], [PERSON] et al. [26], and Purat & HaLow Network and applications \\ et al. [28] & \\ [PERSON] et al. [27] & HaLow and LoRa Comparison \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
with the \(z\)-axis representing depth. An F-scan is unique in that the original radar data are collected in conjunction with positional data, which is then used to project the initial flat scan into 3D space. GPR DZT data files are passed through a Panasonic Toughpad (Panasonic, Kadoma, Japan) to a Raspberry Pi 4 Model B and packaged together with pose data from RealSense. The total size of these packages is dependent on the survey they represent and is typically less than one megabyte in size. A TCP connection is then made to the edge processing unit through a 4G or 5G link to Verizon Wireless (Figure 1). After receiving the data, it is unpacked and processed using custom geometry creation algorithms to generate both an F and B scan image, which are returned along the same link.  
Position data are collected from RealSense every 6 ms over the course of the scan and are used to associate each trace to an approximate X-Y coordinate. This creates a relatively dense dataset, which must be reduced to create parsable 3D geometry. The reduction is carried out by stepping through the series of points sequentially and merging vertices within the module's stated accuracy, approximately 1%. This creates a relatively smooth path while maintaining the features of the survey line. Following the reduction, the points are loaded into Blender and extruded in the Z direction to create a surface. The generation of these models can be seen below in Figure 2. A B-Scan can then be applied to that surface as a texture map, allowing the B-Scan to conform to the surface geometry and create the desired F-Scan. These digital objects are finally transferred to a Unity application run on an AR headset (Microsoft HoloLens 2), allowing for an AR view of the subsurface features with the ability to interact with the 3D object for thorough inspection of the underground infrastructure in the environment.  
Figure 1: Schematic of the process to create F-Scans and wireless edge processing.  
Figure 2: Generation of an F-Scan in Blender: (**a**) merging of vertices to create a smoother path; (**b**) 3D model in open space.