## 5 Conclusions  
This paper, based on DMAS beamforming techniques and the grain pile anomalous moisture region localization algorithm, successfully located a 7 cm \(\times\) 7 cm \(\times\) 7 cm region  
Figure 14: Example of anomaly area localization results at the fourth position in Figure 13.  
Figure 13: The four test results of SMR response and localization accuracy for anomaly areas using the layout of Figure 6e. with 15.4% moisture content within an experimental cubic bin containing 10.5% dry long-grained rice. The average localization accuracy recall exceeded 50%. Through testing various layouts of electromagnetic wave transmitters and receivers, an effective configuration and detectable space were identified. Detection capabilities varied across different positions, with stronger detection observed when the target was penetrated by more electromagnetic fields. To achieve enhanced spatial resolution and reduce artifacts, at least three rows of electromagnetic wave transmitters or receivers were arranged along each axis. Extensive experimental validation revealed that after preliminary localization using the grain pile anomalous moisture region localization algorithm, the reliability of unknown sample localization results could be effectively assessed by the SMR value and comparing \(D_{\text{max}}\) and \(D_{vol}\). When \(D_{\text{max}}=D_{vol}\) with higher SMR, reliability was high, with the anomalous region exhibiting improved recall. Conversely, when \(D_{\text{max}}\
eq D_{vol}\), reliability decreased, typically accompanied by lower SMR values and reduced recall. While the developed method demonstrated rapid detection and accurate localization, several challenges remained for practical application. Future research directions include adaptation for larger grain storage facilities; improvement in detection speed; and reduction of external interference. For application in large-scale grain storage facilities, specialized hardware is required to meet practical performance needs in terms of increased penetration depth and resolution. Furthermore, due to large volume constraints in such facilities, the detection resolution requirements are reduced to 1 m\({}^{3}\). The method's sensitivity to interference was attributed to the small difference in moisture content between anomalous and normal regions, resulting in similar calculated responses. While increasing sampling data could enhance the response, this approach would increase both cost and computation time. Moreover, moisture content evaluation remained a significant challenge. The relationship between moisture content, size, location of anomalous regions, and response intensity exhibited complex non-linear characteristics. Conceptualization, X.D. and Z.H. ; methodology, X.D. ; software, X.D. ; validation, X.Y. and J.Z. ; formal analysis, X.D. ; investigation, X.D. ; resources, X.D. ; data curation, X.D. ; writing--original draft preparation, X.D. ; writing--review and editing, X.D. ; visualization, X.D. ; supervision, J.Z. ; project administration, Z.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. This work was technically supported by Shanghai Jiao Tong University. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. This appendix explains the specific algorithms in Figure 2.  
The set of points within the grain pile region \(P=\{p(1)p(2),\ldots,\ p(n)\}\). The corresponding response values \(r(i)\) of \(p(i)\) have been calculated according to Equations (1) and (2).