## 3 Validation
### Classification  
Two laptops (HP T0Z10 PA and HP Pavilion 15-p074 TX) were used as EUTs to validate the effectiveness of the algorithm in classifying radiated emission data, as mentioned in this paper. Ten groups of electromagnetic emissions of the above two EUTs were tested. In addition, four groups of background noise were also tested, as shown in Table 1. The numbers were named as follows. Groups 1 to 4 corresponded to background noise, Groups 5 to 14 corresponded to the radiation emission test results of the HP T0Z10 PA, and Groups 15 to 24 corresponded to the radiation emission test results of the HP Pavilion 15-p074 TX. The results of these spectrum tests are shown in Figure 6.  
During classification, all groups of test data were taken as both standard groups and verified groups. The dissimilarity between each group was calculated to replace the dissimilarity between these groups with a specific group. However, this process should not be construed as a means of obtaining general conclusions; rather, it helped us to observe that the test data between different pieces of equipment have strong convergence under the processing of the proposed algorithm. First, the peak components _Fre_ from the radiation emission spectrum data were extracted, and a group of data from each of the three types of data was selected as examples (Groups 2, 11, 20). The parts of the peak component data used are listed in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Vector Length of _Fre_** & **Typical Characteristic Frequency Point (MHz)** \\ \hline
2 & 19 & 32.04, 49.38, 49.55, \(\ldots\) 71.99, 95.96, \(\ldots\), 200.00 \\ \hline
11 & 31 & 36.63, 51.93, 52.27, \(\ldots\), 71.99, 95.96, \(\ldots\), 100.04, \(\ldots\), 200.00 \\ \hline
20 & 13 & 36.29, 50.23, 52.44, \(\ldots\), 71.99, \(\ldots\), 140.50, \(\ldots\), 199.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The peak components of the test spectrum of two EUTs and background. Figure 6: The electromagnetic test spectrum of two EUTs and background noise. The black line is the background noise, the red line is the test result of the HP Pavilion 15-p074 TX, and the blue line is the spectrum of the HP T0Z10 PA. Using Equation (1), the dissimilarity values based on the peak components (\(S_{1}\)) between 24 groups are shown in Figure 7. Since the spectra of the same pieces of equipment were similar, the dissimilarity based on peak components was close to 0 for the same class of test results and close to 1 for different classes of test results. Therefore, observing \(S_{1}\), Groups 1-4 and Groups 5-14 showed good clustering characteristics. However, for the more complex radiated emission spectra, as in Groups 15-24, the dissimilarity values based on their peak components were all close to 1. Thus, the classification method based only on peak components may lead to misjudgment, which also confirms the necessity of introducing other kinds of dissimilarity criteria. After the peak components are extracted, all the peak components are removed from the spectrum to reform the radiation emission spectrum, as shown in Figure 8. Based on these spectra without peak components, the broadband components were picked out as described below. Figure 8: The radiation emission spectra of 24 groups of spectrums without peak components. Figure 7: Dissimilarity values between the test data valued on the basis of peak components (\(S_{1}\)). Referring to the features of three different kinds of spectra, 3-5 broadband components were extracted separately; these are shown in the yellow rectangle in Figure 9.  
Using Equations (5)-(16), the dissimilarity between all spectra based on broadband components (\(S_{2}\)) and trend components (\(S_{3}\)) can be calculated. As shown in Figures 7 and 10, the three components mentioned in this paper have good clustering characteristics when distinguishing test data from different equipment. In order to obtain better classification results, the overall similarity parameters needed to be obtained.