# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 3 Dataset Description
### Description of UXO and Non-UXO Objects  
The dataset includes a diverse set of objects categorized into two classes: UXO (Unexploded Ordnance, positive class) and non-UXO (negative class). UXO (positive class) This class consists of cylindrical projectiles representative of typical underwater Unexploded Ordnance. The cylinders have diameters ranging from 60 mm to 200 mm and projectile lengths between 240 mm and 650 mm. These dimensions correspond to commonly encountered UXO types such as artillery shells and mortar rounds. The magnetic signatures of these objects are influenced by their ferromagnetic material properties, shape, and orientation relative to the Earth's magnetic field, resulting in complex and distinctive magnetic anomalies. \begin{table}
\begin{tabular}{l l c c c c c c} \hline
**UXO** & **Scanning** & \multirow{2}{*}{**Orientation**} & \multirow{2}{*}{\(R^{2}\)**} & **RMSE** & **MAE** & **MAX-MIN** & **ME** & **PSNR** \\
**Orientation** & & & & & & & \\ \hline N\(-\)S & N\(-\)S & 0.984 & 1.37 \(\times 10^{-8}\) & \(1.04\times 10^{-8}\) & \(4.15\times 10^{-7}\) & \(1.25\times 10^{-8}\) & 29.62 \\ \hline E\(-\)W & N\(-\)S & 0.950 & \(1.79\times 10^{-9}\) & \(1.49\times 10^{-9}\) & \(2.85\times 10^{-8}\) & \(1.60\times 10^{-8}\) & 26.06 \\ \hline NE\(-\)SW & N\(-\)S & 0.985 & \(8.50\times 10^{-9}\) & \(7.14\times 10^{-9}\) & \(2.61\times 10^{-7}\) & \(8.04\times 10^{-9}\) & 29.74 \\ \hline Perpendicularly & N\(-\)S & 0.984 & \(2.08\times 10^{-8}\) & \(1.64\times 10^{-8}\) & \(5.44\times 10^{-7}\) & \(1.94\times 10^{-8}\) & 28.35 \\ \hline E\(-\)W & E\(-\)W & 0.968 & \(1.57\times 10^{-8}\) & \(1.46\times 10^{-8}\) & \(3.22\times 10^{-7}\) & \(1.55\times 10^{-8}\) & 26.23 \\ \hline N\(-\)S & E\(-\)W & 0.872 & \(1.24\times 10^{-8}\) & \(1.15\times 10^{-8}\) & \(1.08\times 10^{-7}\) & \(4.85\times 10^{-9}\) & 18.79 \\ \hline NE\(-\)SW & E\(-\)W & 0.968 & \(1.22\times 10^{-8}\) & \(1.09\times 10^{-8}\) & \(2.46\times 10^{-7}\) & \(1.18\times 10^{-8}\) & 26.11 \\ \hline Perpendicularly & E\(-\)W & 0.993 & \(1.38\times 10^{-8}\) & \(8.90\times 10^{-9}\) & \(5.14\times 10^{-7}\) & \(1.17\times 10^{-8}\) & 31.43 \\ \hline \end{tabular}
\end{table} 
Table 1: Results comparing the physical system with the digital twin (\(R^{2}\)—Coefficient of determination, RMSE—Root Mean Square Error; MAE—Mean Absolute Error; MAX-MIN—range of values; ME—Mean Error; PSNR—Peak Signal-to-Noise Ratio). (Source: [3]). Non-UXO (negative class) The negative class comprises objects that may produce magnetic signals but are not considered hazardous UXO. This class includes the following:  
* Too-small projectiles: Cylindrical objects with diameters ranging from 30 mm to 50 mm and lengths between 100 mm and 240 mm, representing metallic debris or small fragments unlikely to pose a threat. * Too-large projectiles or pipes: Cylindrical objects with diameters between 240 mm and 400 mm and lengths from 750 mm to 1200 mm, which are too large to be classified as UXO and typically represent pipes or large metallic structures. * Low-height cylinders: Objects simulating manhole covers or similar structures, characterized by diameters ranging from 110 mm to 400 mm and a thickness of approximately 20 mm. * Rectangular prisms: Objects with lengths between 400 mm and 1000 mm, widths from 50 mm to 200 mm, and thicknesses ranging from 20 mm to 500 mm, representing various metallic clutter shapes. * Rectangular cubes: Larger block-shaped objects with lengths between 900 mm and 2000 mm, widths from 50 mm to 200 mm, and thicknesses between 50 mm and 200 mm, simulating bulky debris or structural elements. This diverse representation of non-UXO objects ensures that the dataset challenges classification algorithms to distinguish hazardous ordnance from a wide variety of non-dangerous metallic clutter commonly found in underwater environments. Using described generation method, 41,880 records were generated. Among them, 11970 samples-that is, 28%-belong to the UXO class, and 29,910 samples 72% to the negative class of objects--non-UXO.