## 3 Validation
### Identification  
In this section, we further evaluate the role of radiation emission components in the identification function. The proposed method was verified using different kinds of appliances, including laptops and digital cameras. Their brands and models are shown in Table 3. We tested 30 groups of background noise as control, and we tested 110 groups of Class 2-5 data and 220 groups of Class 6 test data as the validation data. In the following, about 80% of the test data for each appliance were used to establish the identification model. All of the test data were used to verify the effectiveness of the identification model. The details of the test data are shown in Table 4. The typical electromagnetic test spectrum data of these six classes of equipment are shown in Figure 12. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class and Status of EUTs** & **Test Data Volume** & **Modeling Data Volume** \\ \hline
1. Background & 30 & 24 \\ \hline
2. MacBook (Retina 12-inch, Early 2015) & 110 & 90 \\ \hline
3. HP Pavillion 15-p074 TX & 110 & 90 \\ \hline
4. ASUS-F250V & 110 & 90 \\ \hline
5. Digital camera (SONY Alpha 7 II) & 110 & 90 \\ \hline
6. ThinkPad E480 & 220 & 180 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: EUT identification test data set. Figure 11: Dissimilarity between the test data. According to the method proposed in Section 2, a sufficient group of spectrum data was randomly selected from all test data to establish the radiation emission model. After extracting the peak components, broadband components, and trend components from these data according to the different classes, Figure 13 shows the clustering results that were obtained of different kinds of equipment. Different kinds of equipment could be effectively differentiated based on the three kinds of radiation emission components. Figure 13a presents the classification based on the three kinds of components of the background, in which the three dissimilarities \(S_{1}\), \(S_{2}\), and \(S_{3}\) of the background noise were closer to the origin. Figure 13b presents the classification based on the three types of components of the laptops (HP Pavilion 15-p074 TX), in which the three dissimilarities obtained for the laptops (HP Pavilion 15-p074 TX) were closer to the origin. Another distinguished result based on the radiation emission components of MacBook and ASUS-FZ50V is shown in Figure 13c,d. The above two groups of results prove that the method based on radiation emission components has a good clustering property. These different kinds of spectrum data demonstrated good clustering characteristics, and the dissimilarity results of the equipment were gathered near the origin in the coordinate system, with its three kinds of radiation emission components as the reference. Therefore, the maximum value of the various dissimilarities in all the data used for modeling was taken as the identification threshold. Taking the peak components and broadband components in Figure 13a as examples, the clustering results can be acquired as shown in Figure  
Figure 14: The clustering results of the background noise and HP Pavilion 15-p074 TX based on the radiation emission components of the background. Figure 13: The test data showed good clustering characteristics based on three kinds of radiation emission components: (**a**) background and HP Pavilion 15-p074 TX spectrum test data were distinguished based on the radiation emission components of the background; (**b**) the background and HP Pavilion 15-p074 TX spectrum test data were distinguished based on the radiation emission components of the HP Pavilion 15-p074 TX. (**c**) MacBook and ASUS-FZ50V spectrum test data were distinguished based on the radiation emission components of the MacBook; (**d**) the MacBook and ASUS-FZ50V spectrum test data were distinguished based on the radiation emission components of the ASUS-FZ50V. All 690 groups of test data were used for model verification.