esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Digital Twin Model in RSCAD
### Aggregated OWF
#### 2.1.2 High Pass Filter (HPF) with Series Reactor  
The switching operation of PE converters leads to generation of harmonics. Thereby, to mitigate these harmonics, filters are provided at the output of the GSC. There are various types of filters available for this application. The common ones are the LCL filter, L filter and high pass filter [19]. In RSCAD, there is a HPF block readily available in the small time step library, as shown in Figure 2[16]. After the HPF parameters are calculated and set, then it would only be required to tune the inductance of the series reactor to limit the flow of current and thereby stabilizing the voltage. Hence, it was decided to go with HPF.  
Figure 1: Single line diagram of the 66 kV High Voltage Alternating Current (HVAC) offshore test network in RSCAD.  
Figure 2: High Pass Filter in RSCAD.  
The base impedance on the low voltage side of transformer is calculated in ohms as in Equation (1).  
\[Z_{\text{{{\it baseLV}}}}=\frac{LV^{2}}{BaseMVA}=\frac{(2 kV)^{2}}{6 MVA}=0.6667\Omega. \tag{1}\]  
The HPF should have a high impedance value at the nominal frequency (50 Hz) so that it works as an open circuit. Therefore, a higher value of 10 times base impedance is chosen. The impedance of the capacitor at 50 Hz is computed by the following equation.  
\[Z_{c}=10\times Z_{baseLV}=10\times 0.6667\Omega=6.667\Omega. \tag{2}\]  
Hence, the value of capacitance at 50 Hz in Farad is calculated by using Equation (3),  
\[C=\frac{1}{2\pi f\times Z_{c}}=\frac{1}{2\pi\times 50\text{ Hz}\times 6.667\Omega}=4.77462\times 10^{-4}F. \tag{3}\]  
The next step is to calculate the inductance 'L'. The inductance must be selected so that during the switching or modulating frequency (950 Hz in this case), the impedance of the inductor and capacitor cancel each other. The switching and higher frequencies can be passed to the ground and thereby purely sinusoidal signals will be transferred to the PCC. The impedance of the inductor, resistor and capacitor must be equal at the switching frequency in order to pass the high frequencies to the ground. Therefore, to calculate inductance, it is considered as a series LC circuit. The resonant frequency of the LC circuit is given by Equation (4).  
\[\omega=\frac{1}{\sqrt{LC}}. \tag{4}\]  
Hence, the inductance value can be calculated as per the following equation.  
\[L=\frac{1}{\omega^{2}C}=\frac{1}{(2\pi\times 950\text{ Hz})^{2}\times 4.77462\times 10^{-4}F}=5.8783\times 10^{-5}H. \tag{5}\]  
Upon deriving the inductance, the resistance is selected to match the impedance of the parallel inductor at the modulating frequency.  
\[R=\omega L=(2\pi\times 950\text{ Hz})\times 5.8783\times 10^{-5}H=0.3508\Omega. \tag{6}\]  
The series reactor is used for limiting the current to the AC network. The impedance value of the reactor is selected based on sensitivity analysis. A value of 0.9 mH is chosen to achieve a voltage of 1 p.u. at the PCC.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 2 Background
### Handover Decision  
The decision for handover relies on numerous parameters, including signal strength, network congestion, mobile speed, and the type of data being communicated (Figure 4). In traditional algorithms, handover is carried out in line with fixed thresholds of signal quality [30]. However, modern systems apply machine learning and fuzzy logic approaches to predict handover situations more accurately [31; 32; 33]. Handover optimization reduces latency and the number of dropped connections, and enhances the overall network performance. Intelligent handover management systems handle huge real-time data to adjust dynamically to the prevailing network environment.
## 3 Results and Discussion
### Characterization of Fiber-Cement Nanohybrids
#### 3.2.2 Thermal Gravimetric Analyses  
For these series of fiber cement pastes, the 1% nC2, nC3 and nC4 content were analyzed. It should be noted that, again, the amount of decomposing modifier and/or clay were deducted from the mass loss occurring at 400-500 \({}^{\circ}\)C temperature range and at 600-800 \({}^{\circ}\)C temperature range, respectively, as discussed in literature [19; 46]. No consumption of Ca(OH)\({}_{2}\) seemed to have taken place (Figure 8A). In addition, it can be claimed that no carbonation occurred, since the lines representing the nanomodified pastes are below that of the reference paste (Figure 8B).  
Further analysis was carried out with respect to the temperature range within which ettringite and C-S-H decompose. As shown in Figure 9, nC2 seemed to have generated greater quantities of ettringite (100-125 \({}^{\circ}\)C) and C-S-H, with nC3 following, given the time. In full agreement with the flexural strength results, nC3 showed the greatest production of ettringite and C-S-H at day 90. Lastly, nC4 did not show much difference compared to the reference paste, either in terms of TGA or in terms of flexural performance.  
Figure 8: Effect of nMT type on (**A**) Ca(OH)\({}_{2}\) and (**B**) CaCO\({}_{3}\) content of fiber–cement nanohybrids based on F.PC60 LS40 PVA3 SP2.
## 4 Proposed Model
### Observation from Rating Distribution
To obtain a relatively generic result, we assumed that the cumulative distribution of a specific user's normal distribution is equal to that of the system's normal distribution, and we made the following hypothesis according to different rating ranges and distributions. **Hypothesis 1 (H1). **_In a RecSys, the cumulative distributions of \(\varphi_{u}(x)\) and \(\varphi(x)\) constrained to related rating ranges remain the same, meaning that the coverage area of the local normal distribution \(\varphi_{u}(x)\) between \([\mu_{u}-\frac{Len_{u}}{2},\mu_{u}+\frac{Len_{u}}{2}]\) is equal to the coverage of the global distribution \(\varphi(y)\) between \([\mu-\frac{Len_{sys}}{2},\mu+\frac{Len_{sys}}{2}]\); that is:_  
\[\Phi_{u}\bigg{(}x\in\bigg{[}\mu_{u}-\frac{Len_{u}}{2},\mu_{u}+\frac{Len_{u}}{2 }\bigg{]}\bigg{)}=\Phi\bigg{(}y\in[\mu-\frac{Len_{sys}}{2},\mu+\frac{Len_{sys} }{2}]\bigg{)}=\mathrm{K}, \tag{6}\]  
_where \(K\) is a constant and less than 1.0._  
Based on this hypothesis, we focused on the standard deviations and personalized rating ranges and propose the following. **Proposition 1. **_The proportion of \(\Big{(}\frac{\varphi_{u}}{o}\Big{)}\) is equal to \(\Big{(}\frac{Len_{u}}{Len_{sys}}\Big{)}\) that is \(\Big{(}\frac{\varphi_{u}}{o}\Big{)}=\Big{(}\frac{Len_{u}}{Len_{sys}}\Big{)}\)._  
**Proof. ** The cumulative distribution of \(\varphi_{u}(x)\) is:  
\[\Phi_{u}(x)=\int_{\mu_{u}-\frac{Len_{u}}{2u}}^{\mu_{u}+\frac{Len_{u}}{2}}\ \varphi_{u}(x)dx=\int_{\mu_{u}-\frac{Len_{u}}{2}}^{\mu_{u}+\frac{Len_{u}}{2}} \ \frac{1}{\sqrt{2\pi}\sigma_{u}}e^{-\frac{(x-\mu_{u})^{2}}{2\mu^{2}}}dx.\]  
Let \(\Big{(}\frac{x-\mu_{u}}{\sigma_{u}}\Big{)}=t\), then:  
\[\Phi_{u}(x)=\int_{-\frac{Len_{sys}}{2u}}^{\mu+\frac{Len_{sys}}{2}}\ \frac{1}{\sqrt{2\pi}}e^{-\frac{t^{2}}{2}}dt=\frac{2}{\sqrt{2\pi}}\int_{0}^{ \frac{Len_{sys}}{2\sigma_{u}}}e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}}dt.\]  
The cumulative distribution of \(\varphi(y)\) is:  
\[\Phi(y)=\int_{\mu-\frac{Len_{sys}}{2}}^{\mu+\frac{Len_{sys}}{2}}\ \varphi(y)dy=\int_{\mu-\frac{Len_{sys}}{2}}^{\mu+\frac{Len_{sys}}{2}}\ \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}}dy.\]  
Let \(\Big{(}\frac{y-\mu}{o}\Big{)}=t\), then:  
\[\Phi(y)=\int_{-\frac{Len_{sys}}{2o}}^{\frac{Len_{sys}}{2o}}\frac{1}{\sqrt{2 \pi}}e^{-\frac{t^{2}}{2}}dt=\frac{2}{\sqrt{2\pi}}\int_{0}^{\frac{Len_{sys}}{2 o}}e^{-\frac{t^{2}}{2}}dt.\]  
According to the hypothesis, \(\Phi(y)=\Phi_{u}(x)=\mathrm{K}\), then:  
Figure 3.0  
Thus, the proportion of \(\Big{(}\frac{Len_{sys}}{2o}\Big{)}\) is equal to the proportion of \(\Big{(}\frac{Len_{sys}}{2o}\Big{)}\); that is, \(\Big{(}\frac{Len_{sys}}{2o}\Big{)}=\Big{(}\frac{Len_{u}}{2o_{u}}\Big{)}\). Thus, we obtained the following relation:  
Figure 3.1
# Modeling the Directional Clumping Index of Crop and Forest
###### Abstract  
The Clumping Index (\(\Omega\)) was introduced to quantify the spatial distribution pattern of vegetation elements. It is crucial to improve the estimation accuracy of vital vegetation parameters, such as Leaf Area Index (LAI) and Gross Primary Production (GPP). Meanwhile, the parameterization of \(\Omega\) is challenging partly due to the varying observations of canopy gaps from different view angles. Many previous studies have shown the increase of \(\Omega\) with view zenith angle through samples of gap size distribution from in situ measurements. In contrast, remote sensing retrieval algorithms only assign a constant value for each biome type to roughly correct the clumping effect as a compromise between the accuracy and efficiency. In this paper, analytical models are proposed that estimate the directional clumping index (\(\Omega(\theta)\)) of crop and forest at canopy level. The angular variation trend and magnitude of crop \(\Omega(\theta)\) was analyzed within row structure where vegetation elements are randomly spaced along rows. The forest model predicts \(\Omega(\theta)\) with tree density, distribution pattern, crown shape, trunk size, and leaf area and angle distribution function. The models take into account the main directional characteristics of clumping index using easy-to-measure parameters. Test cases showed that \(\Omega(\theta)\) magnitude variation for black spruce forest was 102.3% of the hemispherical average clumping index (\(\tilde{\Omega}\)), whereas the Larch forest had 48.7% variation, and row crop variation reached 32.4%. This study provided tools to assess \(\Omega(\theta)\) of discontinuous canopies.  
gap fraction; vegetation; zenith angle; directional variation +
Footnote †: journal: Remote Sensing  
10.3390/rs10101576
## 4 Tt-Vfd-Sim
### TT-VFDL-SIM for Classification
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **FM** \\ **Structure** \\ \end{tabular} } & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**TT-VFDL-SIM \({}^{*}\)**} & \multirow{2}{*}{**DT-VFDL-AE**} & \multirow{2}{*}{**P-VFDL-SL**} & \multirow{2}{*}{**P-VFDL-SL**} & \multirow{2}{*}{**CINTR**} \\ \cline{3-3} \cline{6-9}  & & & **(RD)** & & & & \\ \hline \multirow{8}{*}{MFCMLFM} & \multirow{2}{*}{AIRQUAL} & MSE & **0.00372** & 0.00382 & 0.00427 & 0.00424 & 0.00429 & 0.00422 \\ \cline{2-9}  & & \(R^{2}\) & 0.75 & 0.74 & 0.71 & 0.71 & 0.71 & 0.71 \\ \cline{2-9}  & \multirow{2}{*}{HOMENV} & MSE & 0.00129 & 0.00104 & **0.00229** & 0.00259 & **0.00223** & 0.00240 \\ \cline{2-9}  & & \(R^{2}\) & 0.37 & 0.49 & \(-\)**0.12** & \(-\)0.27 & \(-\)**0.10** & \(-\)0.18 \\ \cline{2-9}  & \multirow{2}{*}{GASVOLT} & MSE & 0.00373 & 0.00328 & 0.00783 & 0.00491 & **0.00419** & 0.00459 \\ \cline{2-9}  & & \(R^{2}\) & 0.98 & 0.98 & 0.95 & 0.97 & **0.97** & 0.97 \\ \hline \multirow{8}{*}{MLFM} & \multirow{2}{*}{AIRQUAL} & MSE & 0.00385 & 0.00357 & **0.00409** & **0.00423** & **0.00418** & 0.00441 \\ \cline{2-9}  & & \(R^{2}\) & 0.74 & 0.76 & 0.72 & **0.71** & **0.72** & 0.70 \\ \cline{2-9}  & \multirow{2}{*}{HOMENV} & MSE & 0.00106 & 0.00100 & **0.00225** & 0.00288 & **0.00227** & 0.00281 \\ \cline{2-9}  & & \(R^{2}\) & 0.48 & 0.51 & \(-\)**0.10** & \(-\)0.41 & \(-\)**0.12** & \(-\)0.38 \\ \cline{2-9}  & \multirow{2}{*}{GASVOLT} & MSE & 0.00395 & 0.00398 & 0.00789 & 0.00489 & **0.00432** & 0.00479 \\ \cline{2-9}  & & \(R^{2}\) & 0.98 & 0.98 & 0.95 & 0.97 & **0.98** & 0.97 \\ \hline \multirow{8}{*}{SLFM} & \multirow{2}{*}{AIRQUAL} & MSE & 0.00375 & 0.00353 & 0.00446 & 0.00445 & 0.00451 & 0.00442 \\ \cline{2-9}  & & \(R^{2}\) & 0.75 & 0.76 & 0.70 & 0.70 & 0.69 & 0.70 \\ \cline{2-9}  & \multirow{2}{*}{HOMENV} & MSE & 0.00182 & 0.00119 & **0.00216** & **0.00227** & **0.00220** & 0.00235 \\ \cline{2-9}  & & \(R^{2}\) & 0.11 & 0.42 & \(-\)**0.06** & \(-\)**0.12** & \(-\)**0.08** & \(-\)0.15 \\ \cline{2-9}  & \multirow{2}{*}{GASVOLT} & MSE & 0.00428 & 0.00353 & 0.01496 & **0.00447** & **0.00451** & 0.00473 \\ \cline{2-9}  & & \(R^{2}\) & 0.98 & 0.98 & 0.91 & **0.97** & **0.97** & 0.97 \\ \hline \hline \end{tabular}

* \({}^{*}\)The proposed method.

\end{table} 
Table 8: Best performance of the proposed method and other comparison methods for the regression task.
# Motion Deblurring for Single-Pixel Spatial Frequency Domain Imaging
## 3 Results
### Experimental Validations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Speed (mm/s)**} & \multicolumn{2}{c}{**RMSE (\%)**} & \multicolumn{2}{c}{**SSIM**} \\ \cline{2-5}  & **Conventional** & **Proposed** & **Conventional** & **Proposed** \\ \hline \(v\) = 0.5 & 0.19 & 0.12 & 0.9960 & 0.9984 \\ \(v\) = 1.0 & 0.27 & 0.13 & 0.9926 & 0.9982 \\ \(v\) = 1.5 & 0.31 & 0.14 & 0.9902 & 0.9979 \\ \(v\) = 2.0 & 0.34 & 0.15 & 0.9878 & 0.9975 \\ \(v\) = 2.5 & 0.38 & 0.15 & 0.9846 & 0.9975 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: SSIM and RMSE of the experimental results. Figure 5: Experimental results of moving phantom. Absorption and scattering property maps of (**a**) the static phantom and (**b**) the phantom at \(v\) = 2 mm/s obtained using the conventional single-pixel SFDI (top) and the proposed method (bottom), respectively. (**c**) Absorption maps of the phantom at different speeds. The dashed circles and white arrow indicate the true location and moving direction of the target, respectively.
## 4 Anomalies and Sources  
The performance of a fleet of AGVs on a factory floor is strongly linked to the behavior of other components of the factory, such as machines that request transport tasks. Hence, anomalies in the behavior of the fleet can not only have fleet-internal sources but can also arise from external sources. This leads to a great variety of anomalies and their sources in the use-case. Table 1 gives examples of anomalies that are relevant for the fleet of AGVs. The sources are categorized into three types: defects in the hardware or software of the AGVs or external components, changes in processes and cyber-attacks, as introduced in Section 2.1. Additionally, the table shows indicators that support the identification of anomalies.  
Different anomalies can cause the same effect but can result from different sources. In general, it is not sufficient to just focus on mitigating the effect of an anomaly without searching for the source of it. In this section, different sources for anomalies with the same effect are sketched. In a fleet of AGVs on a factory floor, it is noticed that one of the AGVs is not acting as expected but is only moving slowly or comes to a complete stop. Looking into the monitoring data of this AGV shows that it is still communicating with the fleet, as it is still operational. In particular, the AGV still participates in bidding processes for new transport jobs. All of the other AGVs are acting normally. As a consequence, the malfunctioning AGV is collecting pending transport jobs in its local queue over the time without being able to fulfill them. In the worst case, this forces the production to stop, since machines can run out of material. Other negative consequences of the anomaly depend on its source, e.g., a corrupted AGV could also send inadequate bids to other AGVs. In the following, three different sources for the effect of the non-moving AGV are presented.
## 3 Results and Discussion
### Flood Depth Estimation with FwDET  
Starting from the flooded area obtained from satellite observations and the 2 m-resolution DEM, it was also possible to estimate the water depth using the The Floodwater  
Figure 11: Map of flooded areas generated by AUTOWADE using the CSG image acquired on 30 October 2021 at 04:30 UTC.  
Figure 10: Panel (**A**): RGB false color image produced using the S1 data acquired on 19 October 2021 (red) and 31 October 2021 (green/blue). Panel (**B**): Flooded areas map generated by AUTOWADE using the S1 images shown in Panel (**A**). Cyan: floodwater; blue; permanent water.  
Depth Estimation Tool (FwDET). Figure 12 in the right panel shows the results obtained from the FwDET application fed by the AUTOWADE flooded areas.  
Thus, from this chain in near real time, we obtained two different water depth maps, one coming from the TELEMAC-2D model driven by weather observation and another one coming from the FwDET tool fed by both Sentinel-1 and CosmoSkyMed flooded areas retrieved with AUTOWADE (Figure 12). The comparison between the water depth produced by TELEMAC-2D (Figure 12, left panel) and FwDET (Figure 12, right panel) shows differences in the flooded area. This comparison emphasizes the benefits and limitations of individual approaches, even if the overall degree of agreement is noticeable; especially for fast floods, the timing of the satellite passage is not synchronous with the flooding peak, and this can lead to an underestimation of flooded area detection. The hydraulics modeling is affected by the correct estimation of the boundary condition of the flood (peak value of discharge estimated by hydrological modeling) and by the accuracy of the DEM and its ability to include small embankments.  
Thus, the best map is obtained by the merging of these two water depth maps in a unique one by taking for each pixel the maximum value between the two maps. Figure 13 shows the result obtained combining the two different water depth sources. This map obtained in near real time can help to evaluate the most affected areas and estimate damages in a very short time after the events hit the area.  
Figure 12: Water depths maps for the Catanian floodplain (Sicily) from TELEMAC-2D (left panel) and FwDET tool (right panel).  
Figure 13: Water depth map obtained for the Catanian floodplain (Sicily) from the merging of TELEMAC-2D and FwDET tool results.
## 2 Hybrid Edge-Cloud Approach
### Motivation for the Edge-Cloud Approach
#### 2.1.3 IEC 61850-90-5 Evaluation--Results  
No packet loss or bandwidth reduction was observed using either the TCP or the UDP protocol during the emulated scenario. However, it is worth noting that not all possible interference sources were emulated in the experiment. Therefore, the packet loss, which  
Figure 1: IEC 61850-90-5 evaluation testbed.  
seemingly presented no problem in the emulation environment, can still become a severe issue for a UDP connection in a real environment since this protocol does not authenticate received packets.  
The results for the one-way delay of the use case with the UDP protocol are presented in Figure 2. The solid blue line represents one-way delays for each second of the scenario, whereas the dashed red line represents the threshold for time-critical applications. The slowly increasing one-way delay is the consequence of the non-synchronised clocks in the PMU emulator and the testbed PC. The latter has a much less precise clock requiring a periodic re-synchronization with the PMU emulator indicated in Figure 2 in the period between 340 s and 380 s.  
In Figure 3, the results for the TCP connection type in respect to the one-way delay are presented. It is clear that the delay in this case is well above the required threshold. The reason for the much higher delays compared to the UDP connection type lies in the authentication of the received packets. In contrast to the UDP connection type, TCP packets need to be acknowledged by the TCP/IP network layer before they are forwarded to higher processing layers. Although data packets were sent only one way, the underlying TCP/IP layer sent acknowledgement packets in both ways, thus adding to the latency.
## 4 Phase Shift Optimization
### Delay Spread Minimization in Multi-RIS Systems  
The delay spread is mitigated from the perspective of designing the phase shift set of the RISs in (33). However, we can also minimize \(T(t)\) further through the deployment of the multiple RISs. Consider the definition of delay spread in (18). \(T(t)\) can be minimized if the distance of cascaded link is minimized. In order to prevent mutual interference from the RISs themselves, the distance between any two RISs cannot be too small. Therefore, the cascaded link distance of the multiple RISs is often longer than that of the single-RIS case. This implies that a system with \(K\) RISs often has a higher delay spread than the case with a single RIS near the BS.  
Furthermore, when an extra RIS is added to the multi-RIS system, we have to design a deployment strategy so that the new coming RIS does not increase the delay spread. The following proposition will provide the deployment guidelines.  
Proposition 1: _The delay spread of the system with \(K\) RISs depends only on the RIS that is the farthest from the BS._  
Proof of Proposition 1.: See Appendix A.  
Remark 4: _For the proposed system, let \(d\) denotes the distance between the BS and the farthest RIS; then, the system delay spread does not change when an extra RIS is added to the system if the distance between the new RIS and the BS is smaller or equal to \(d\). In other words, the delay spread \(T(t)\) can be mitigated by minimizing \(d^{i_{m}i_{n}}_{\text{BS}}\). Another way to mitigate delay spread is minimize \(d^{\text{V}}_{i_{m}i_{n}}(t)\). However, putting the RIS on the road side is more convenient, compared to deploying it on the vehicle._
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 5 System Testing
### Comparison of CNN-Running Results on Microcontroller and PC  
The network model was built on the PC side; the specific parameters of the CNN model were obtained by using 4000 sets of data as the training set; and the recognition accuracy of each discharge type was obtained after training, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Discharge (pC)**} & \multirow{2}{*}{\begin{tabular}{c} **Plate Discharge** \\ **Mode** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ball-Plate** \\ **Discharge** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Tip Discharge** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Accuracy Rate** \\ \end{tabular} } \\  & & & & \\ \hline
10 & 99\% & 98\% & 94\% & 97\% \\
50 & 98\% & 95\% & 86\% & 93\% \\
500 & 92\% & 84\% & 80\% & 82.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results under different noise environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Discharge (pC)**} & \multirow{2}{*}{\begin{tabular}{c} **Plate Discharge** \\ **Mode** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ball-Plate** \\ **Discharge** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Tip Discharge** \\ **Model** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Accuracy Rate** \\ \end{tabular} } \\  & & & & \\ \hline
6 KV & 98\% & 95\% & 89\% & 94\% \\
7 KV & 99\% & 97\% & 94\% & 96.7\% \\
8 KV & 99\% & 99\% & 97\% & 98.3\% \\
9 KV & 99\% & 99\% & 95\% & 98.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results at different voltages.  
The models were trained and imported into the STM32H743 microcontroller, and the discharge types were identified using the system built in the paper. The test conditions were kept the same as those when the training data were collected: the thickness of the insulation layer used was 4.5 mm and the discharge voltage was 8 kV. The recognition accuracies of the three discharge models were obtained through 100 actual tests, as shown in Table 6.  
According to the test results, it was concluded that the transplanted CNN model still had a high recognition accuracy and that the system could achieve the purpose of recognizing discharge types with an average recognition accuracy of 98.33%.