## 4 Discussion
### Localization Results of Anomalous Areas with 15.4% Target Moisture Content in Grain Piles  
This section evaluated the ability of the layout depicted in Figure 6e to localize anomalous regions with a moisture content of 15.4% within grain piles. Test results from four arbitrary positions within the higher reliable region based on the Blind Spot Test result, as illustrated in Figure 13, successfully located the anomalous regions. The maximum SMR  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Layout**} & \multirow{2}{*}{**Test Name**} & \multirow{2}{*}{**Position Number**} & \multicolumn{2}{c}{**Scattered Signal Number**} & \multicolumn{2}{c}{**Moisture Content**} & \multirow{2}{*}{\(D_{\text{max}}=D_{\text{pol}}\)?} & \multirow{2}{*}{\(\overline{SMR}\)} & \multirow{2}{*}{**Recall**} \\  & & & **Number** & & **Content** & & \\ \hline Single-layer & Feasibility Test & \#1 & 18 & 19.0\% & N & 6.60 & 27.08\% \\ \hline Double-layer & Feasibility Test & \#1 & 36 & 19.0\% & Y & 9.06 & 37\% \\ \hline Fan-shape & Feasibility Test & \#1 & \#2 & 108 & 19.0\% & Y & 23.36 & 66.67\% \\  & & \#1 & \#2 & & Y & 16.94 & 22.22\% \\ \hline \multirow{4}{*}{Cross-shape *} & Artifactual Regions Test & \#1 \#2 \#2 \#3 & & & Y & 10.27 & 29.17\% \\  & & \#2 & & 17.5\% & N & 8.96 & 33.33\% \\  & & \#3 & & & Y & 14.81 & 61.11\% \\ \cline{2-7}  & Blind Spot Test & \#1 \#2 \#3 * & & & Y & 6.29 & 0.00\% \\  & & \#2 & & & N & 6.18 & 25.00\% \\  & & \#3 * & & & Y & 15.38 & 66.67\% \\ \cline{2-7}  & & \#1 * & & & Y & 10.78 & 58.33\% \\  & Position Test * & \#2 * \#3 * & & 15.4\% & Y & 19.20 & 55.55\% \\  & & \#3 * & & & Y & 14.57 & 62.96\% \\  & & \#4 * & & & Y & 12.28 & 83.33\% \\ \hline \hline \end{tabular}  
* Effective layout and tests with high localization accuracy.  
\end{table}
Table 1: Experimental parameters table. The spatial resolution of the DMAS algorithm was 2 cm, corresponding to a 10% resolution of the length. \(\overline{SMR}\) represents the average SMR of voxels in the anomalous region. High recall values did not necessarily indicate that the localization result closely approximated the actual shape when using the wrong layout.  
Figure 12: The SMR values for the region \(p_{output}\) containing \(D_{\text{max}}\) and \(D_{\text{pol}}\) from Figure 11b. Red line: SMR value of \(D_{\text{max}}\) region. Black line: SMR value of \(D_{\text{pol}}\) region. In this case, \(D_{\text{max}}\) and \(D_{\text{pol}}\) were not identical, while the actual anomalous location was at \(D_{\text{pol}}\). Consequently, a method combining the \(D_{\text{max}}\) and \(D_{\text{pol}}\) regions was proposed to enhance localization accuracy. However, the recall (25.00%) was lower, leading to a decrease in the reliability of the localization results.  
values of the responses exceeded 20, aligning with the predicted SMR range from the previous section, which suggested that the SMR of anomalous regions should be distinguishable from the interference SMR of normal regions. The localization accuracy for these positions was calculated to be above 50%, with position 4 achieving a remarkable 88.33% accuracy. Figure 14 displays the localization result for the anomalous region at position 4.  
Finally, the device demonstrated the capability to localize anomalous regions in grain piles with 15.4% moisture content and dimensions of 7 cm \(\times\) 7 cm \(\times\) 7 cm, with the potential for localizing regions of even lower moisture content. As is evident from Figures 9 and 13, SMR results varied across different positions, with enhanced response when the target was penetrated by more electromagnetic fields. Additionally, SMR was influenced by factors such as the volume of the anomalous region and variations in moisture content. Consequently, evaluating detection capability requires extensive experimentation. Moreover, the choice of transmitters and receivers significantly impacts measurement detection capacity.