esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cross-Layer, Anomaly-Based IDS for WSN and MANET
## 3 Mathematical Modeling of the CCIs
Figure 2: Data fitting for the CCIs of sensor 5: (**a**) Data fitting for three distributions, extreme value (EV), Gamma, and Nakagami in scenario NS1P3 for node under test (NUT) 13; (**b**) Data fitting for three distributions, EV, Gamma, and Nakagami in scenario NS1P3 for NUT 19. The distribution fitting shown in Figure 3 shows that EV distribution fits the CCIs better than the gamma and Nakagami distributions. Gamma and Nakagami distribution fitting are very close (Figures 2 and 3); this is expected considering the Nakagami distribution is a specific type of gamma distribution. The last set of distribution fitting results is presented in Table 4 and Figure 4, for scenario NS5P7, \(T_{r}\) = 25 s, and \(T_{s}\) = 5 s which has more fitting samples compared to the previous scenarios, in this case 2000/25 = 80 sample. Again, the EV distribution shows to be the best fit compared with gamma and Nakagami distributions. All the results presented in this section are based on experimental setup described in Section 5. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**NS5P7, \(T_{r}\) = 25 s, \(T_{s}\) = 5 s**} \\ \hline \multicolumn{2}{c}{NUT: 13} & \multicolumn{2}{c}{NUT: 19} \\ \hline \multicolumn{2}{c}{Log-likelihood for sensor 5} & \multicolumn{2}{c}{Log-likelihood for sensor 5} \\ \hline EV & 43.8455 & EV & 61.8714 \\ Gamma & 33.0146 & Gamma & 55.1129 \\ Nakagami & 34.326 & Nakagami & 56.2025 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The log-likelihood for three different density fitting in scenario NS5P7. Figure 3: Data fitting for the CCIs of sensor 5: (**a**) Data fitting for three distributions, EV, Gamma, and Nakagami in scenario NS1P3 for NUT 13; (**b**) Data fitting for three distributions, EV, Gamma, and Nakagami in scenario NS15P7 for NUT 19. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**NS15P7, \(T_{r}\) = 100 s, Ts = 10 s.**} \\ \hline \multicolumn{2}{c}{NUT: 13} & \multicolumn{2}{c}{NUT: 19} \\ \hline \multicolumn{2}{c}{Log-likelihood for sensor 5} & \multicolumn{2}{c}{Log-likelihood for sensor 5} \\ \hline EV & 14.4636 & EV & 32.4864 \\ Gamma & 13.1913 & Gamma & 31.0568 \\ Nakagami & 13.4487 & Nakagami & 31.2517 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The log-likelihood for three different density fitting in scenario NS15P7.
## 2 Methodology
### Partition Matching Using Shi_Tomasi and SIFT
#### 2.4.1 Feature Detection Using the Shi_Tomasi Algorithm  
The Shi_Tomasi corner detection algorithm is an improved version of the Harris corner detector. Considering a local window in the image, Harris corner points are detected based on the determination of the average changes in image intensity that result from shifting the local window by a small amount in various directions.  
Denoting the image intensity of the image as \(I\), the intensity difference (\(E\)) produced by a shift (\(u,v\)) of the local window is provided by Equation (3):  
\[E(u,v)=\sum\limits_{x,y}w(x,y)[I(x+u,y+v)-I(x,y)]^{2}, \tag{3}\]  
where \(w(x,y)\) is a window function at position (\(x,y\)).  
To search for the windows that produce a large \(E(u,v)\), \(I(x+u,y+v)\) can be expanded using the Taylor series:  
\[I(x+u,y+v)=I(x,y)+I_{x}u+I_{y}v+(u^{2},v^{2}), \tag{4}\]  
where \(I_{x}\) and \(I_{y}\) are image derivatives in the \(x\) and \(y\) directions, respectively. Combining Equations (3) and (4), we obtain Equation (5) as:  
\[\begin{array}{ll}E(u,v)&=\sum\limits_{(x,y)\in 0}\left[\begin{array}{cc}u&v \end{array}\right]\!w(x,y)\!\left[\begin{array}{cc}I_{x}^{2}&I_{x}I_{y}\\ I_{x}I_{y}&I_{y}^{2}\end{array}\right]\\ &\cong\left[\begin{array}{cc}u&v\end{array}\right]\!M\!\left[\begin{array}{ cc}u\\ v\end{array}\right]\end{array}, \tag{5}\]  
where \(M\) is defined using Equation (6),  
\[M=w(x,y)\!\left[\begin{array}{cc}I_{x}^{2}&I_{x}I_{y}\\ I_{x}I_{y}&I_{y}^{2}\end{array}\right]\!, \tag{6}\]  
Finally, a score \(R\) is calculated using Equation (7) and used to justify whether the window contains a corner:  
\[R=detM-k(traceM)^{2}, \tag{7}\]  
where \(detM=\lambda_{1}\lambda_{2}\); \(trace(M)=\lambda_{1}+\lambda_{2}\); and \(\lambda_{1}\) and \(\lambda_{2}\) are the eigenvalues of \(M\). \(K\) is an empirical value, and the general value of \(K\) is 0.04 [35]. If the value of \(R\) is greater than a threshold value (\(T\)), the window in which \(R\) is treated as a corner point is a good tracking point.  
Different from the Harris algorithm, the score \(R\) for the Shi_Tomasi algorithm is calculated using Equation (8).  
\[R=min(\lambda_{1},\lambda_{2}), \tag{8}\]  
If \(R\) is greater than \(T\), the point can be marked as a Shi_Tomasi corner. The schematic of the Shi_Tomasi algorithm is shown in Figure 4. In this work, the maximum number of corner points (\(N_{max}\)) for each image patch should be set in advance, usually to 1500.
# Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments
###### Abstract  
Autonomous driving technology in urban environments is a very important avenue of research. Notably, the question of how to plan safe lane-changing trajectories is a challenge in multi-vehicle traffic environments. In our research, three kinds of polynomial lane changing mathematical models were analyzed and compared. It was found that the fifth polynomial is the most suitable for lane changing trajectories; it is defined as a generalized lane-changing trajectory cluster, whereby the minimum lane change time is determined by the vehicle lateral stability threshold. Here, a collision avoidance algorithm is proposed to eliminate unsafe trajectories. Finally, the TOPSIS algorithm is used to solve the multi-objective optimization problem, and the optimal lane-changing expected trajectory is obtained from the safe trajectory cluster. The simulation results showed improvements in lane-changing efficiency of 6.67% and no collisions in the overtaking condition. In general, the proposed method of identifying the optimal lane changing trajectory can achieve safe, efficient and stable lane changing.  
autonomous vehicle; trajectory planning; collision avoidance algorithm; topsis algorithm; optimal lane change trajectory 2022
## 5 Conclusions  
The primary aim of this study is to conduct a thorough exploration of the thermal characteristics of power law liquid flow within a plus-shaped enclosure under conditions of natural convection. The analysis includes significant parameters and utilizes the finite element method (FEM) to investigate the heat transfer characteristics of the fluid. Results showed fluid behavior and the intricate interaction between important parameters like the Rayleigh number \((Ra)\), the power law index \((n)\), and the Prandtl number \((Pr)\), which affect velocity and temperature distribution. Different variables were considered for measuring kinetic energy and heat transfer rates. The results showed that both the kinetic energy and the heat transfer rate increased when the Rayleigh number increased. The Nusselt number is higher for the shear-thinning case \((n=0.7)\) compared to Newtonian \((n=1)\) and shear-thickening cases \((n=1.2)\). When the Rayleigh number \((Ra)\) increases, the heat transfer rate in the flow domain also increases due to the temperature difference. For a higher value of Darcy number, \((Da)\) and \((Ha)\) have a favorable impact on the fluid behavior within the cavity, leading to a greater intensity of convective fluid. The results of this investigation provide significant information for optimizing the design of a plus-shaped cavity for power law fluids, which is essential for effective heat transfer systems, contributing to the advancement of design strategies in this field.  
Conceptualization, I.S.C.; methodology, I.S.C.; software, I.S.C. and M.Y.; validation, I.S.C., J.L. and M.A.M.; formal analysis, I.S.C. and M.Y.; investigation, I.S.C. and Z.G.; writing--original draft preparation, I.S.C.; writing--review and editing, I.S.C., J.L. and Z.G.; supervision, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (grant no. 12272011) and also supported by the National Key R&D Program of China (grant no. 2022 YFB3806000).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 7: Variation of averaged Nusselt number for different values of inclined angle \(\gamma\) at specified parameters of \(Da=0.001,\ Pr=0.7,\ Ra=10^{4}\), and \(\gamma=0^{\circ}\).
# A Rectangular Toroidal Current-Based Approach for Lung Biopsy Needle Tracking
## 4 Discussion  
The diameter of the magnetic core used for the sensor probe in Figure 6d is 7.2 mm, which is relatively large compared to those typically used in classical electromagnetic tracking systems, resulting in decreased accuracy. The least squares method employed in this study is used to solve for the position. However, due to a pole problem during the solving process, the system may incorrectly identify the pole as the minimum value point, resulting in calculation errors. To address this, further modifications to the least squares method are proposed to minimize the error caused by this issue.  
In this study, several potential sources of error could impact the accuracy and performance of the electromagnetic tracking system. Environmental interference, such as external electromagnetic fields from nearby electronic devices or power lines, could distort the system's magnetic field and lead to incorrect position estimates. Additionally, sensor noise, originating from either the internal electronics of the sensors or external factors, can degrade the signal-to-noise ratio, reducing the precision of measurements. Calibration inaccuracies also represent a significant potential error source; misalignments or errors during the calibration process, such as incorrect alignment of the transmitter coils and sensors, can introduce considerable discrepancies between theoretical models and real-world measurements. Sensor positioning and alignment issues, even small deviations, can further affect the system's accuracy. Furthermore, variability in the electrical conductivity and magnetic permeability of biological tissues may influence the magnetic field distribution,  
Figure 14: Electromagnetic tracking experiment with equivalent human tissues: (**a**) Calibration without equivalent human tissues. (**b**) Tracking measurement with the placement of equivalent human tissues. (**c**) Error data plot from 500 trials.  
especially given that tissue properties can differ depending on composition or temperature. These factors must be carefully considered, and future research should focus on enhancing calibration methods, minimizing sensor noise, and improving system sensitivity to environmental variables in order to optimize tracking accuracy in practical applications.
# A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks
## 3 Problem Modeling
The computation delay for \(S_{v}\) processing \(\mathbb{B}_{vN_{a}}\) after aggregation is as follows:  
\[\tau_{ujv}^{c}=\sum_{f=1}^{N_{u}}\frac{B_{vl}\cdot\kappa}{f} \tag{15}\]  
The total computation delay for \(S_{v}\) after aggregation is as follows:  
\[\tau_{v}^{w}=\sum_{m=1}^{N_{v}^{\prime}}\frac{B_{vm}\cdot\kappa}{f} \tag{16}\]  
The maximum delay for all working satellites after aggregation is as follows:  
\[\tau^{w}=\max(\tau_{1}^{w},\ldots,\tau_{v}^{w},\ldots,\tau_{V}^{w}) \tag{17}\]  
The maximum delay without aggregation is as follows:  
\[\tau^{o}=\max(\tau_{1},\ldots,\tau_{i},\ldots,\tau_{M}) \tag{18}\]  
The delay increment due to aggregation is as follows:  
\[E_{T}=\sum_{u=1}^{U}\sum_{j=1}^{N_{u}}E_{T}^{ujv} \tag{11}\]0  
In order to comprehensively evaluate the impact of energy savings and delay increase, this paper defines Energy-Delay Ratio (EDR) as follows:  
\[E_{T}=\sum_{u=1}^{U}\sum_{j=1}^{N_{u}}E_{T}^{ujv} \tag{11}\]1  
where EDR represents the energy savings per unit delay increase (in J/s). The optimization objective of the business aggregation strategy is defined as follows:  
maximize EDR  
subject to:  
\[ \begin{array}{ll}\tau_{uj}+\tau_{ujv}+\tau_{ujv}^{c}<\tau_{uj}^{\max}&\text{(a )}\\ \sum\limits_{v=1}^{M}x_{ujv}=1&\text{(b)}\\ 1\leq U\leq M&\text{(c)}\\ U+V=M&\text{(d)}\end{array}  \tag{21}\]  
where constraint (a) ensures that the business processing time is less than the maximum tolerable delay for the business; constraint (b) ensures that each business \(\mathbb{B}_{uj}\) is assigned only once, with \(x_{ujv}\) taking a value of 0 or 1 to indicate whether the business is assigned to business satellite \(S_{v}\); constraint (c) defines the range of the number of working satellites; and constraint (d) ensures that the sum of working and sleeping satellites equals the total number of business satellites.
## 4 Example Experiments
### Comparison with the FCFS Rule
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Origin Number**} & \multicolumn{2}{c}{**GA\_GA**} & \multicolumn{2}{c}{**GA\_FCFS**} & \multicolumn{2}{c}{**[M]**} \\ \cline{2-6}  & **Upstream Station/Sequence** & **Downstream Station/Sequence** & **Upstream Station/Sequence** & **Downstream Station/Sequence** & **Upstream Station/Sequence** \\ \hline
1 & FY/2 & KEL/9 & NJ/2 & LS/4 & FY/1 & KEL/6 \\
2 & FY/1 & LS/4 & YT/1 & KEL/2 & FY/2 & LS/5 \\
3 & NJ/1 & KEL/3 & NJ/1 & AKS/1 & NJ/1 & KEL/3 \\
4 & YT/1 & KEL/5 & JC/2 & LS/7 & YT/1 & KEL/7 \\
5 & FY/3 & LS/6 & XA/2 & KS/2 & FY/5 & LS/8 \\
6 & XA/3 & KEL/7 & LZ/3 & KEL/3 & XA/2 & KEL/8 \\
7 & LZ/2 & KEL/2 & LZ/2 & KS/1 & LZ/2 & KEL/2 \\
8 & LZ/1 & KEL/1 & LZ/1 & LS/1 & LZ/1 & KEL/1 \\
9 & KM/3 & KEL/8 & KM/2 & LS/5 & KM/2 & KEL/5 \\
10 & FY/4 & LS/7 & JC/3 & KEL/7 & FY/3 & LS/6 \\
11 & KM/2 & LS/5 & JC/1 & KEL/4 & KM/3 & LS/4 \\
12 & KM/1 & KEL/4 & KM/1 & AKS/2 & KM/1 & KEL/4 \\
13 & HEB/2 & AKS/1 & HEB/1 & KEL/1 & HEB/1 & AKS/1 \\
14 & HEB/3 & KEL/6 & HEB/2 & KEL/6 & HEB/3 & KEL/9 \\
15 & FY/5 & LS/8 & NJ/3 & LS/6 & FY/4 & LS/7 \\
16 & HEB/1 & LS/2 & TL/1 & KS/3 & HEB/2 & LS/2 \\
17 & XA/2 & LS/3 & ZZ/3 & LS/8 & XA/3 & LS/3 \\
18 & XA/4 & KS/2 & ZZ/1 & LS/3 & XA/4 & KS/2 \\
19 & ZZ/1 & LS/1 & XA/1 & LS/2 & ZZ/1 & LS/1 \\
20 & XA/1 & KS/1 & ZZ/2 & KEL/5 & XA/1 & KS/1 \\ Makespan (h) & & 531.08 & & 540.50 & & 555.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: The road–rail multimodal transport schemes. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Downstream Station** & **Efficiency** \\ \hline KS & 5 \\ AKS & 2 \\ KEL & 15 \\ LS & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The refined transshipment efficiencies of downstream stations (unit/h). through equal distribution. For example, in the first example of [C1] (row 3 of Table 18), the total increase for the three upstream stations is 15 units/h, i.e., FY, KM, and TL increase 5 units/h, respectively (compared to the data in Table 14). GA_GA, IG+ECTT, and GA_FCFS were employed to calculate these examples, with the results detailed in columns 4 to 8 of Table 18.  
Figure 8: The sensitivity analysis results of equipment quantity. Figure 9: The average time consumed in each operation.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 8 Conclusions
Hazards Earth Syst. Sci._**2017**, _17_, 735-747. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Revisiting Flood Hazard Assessment Practices under a Hybrid Stochastic Simulation Framework. _Water_**2022**, _14_, 457. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] The Cordinet Project: Analysis of the Barriers Limiting a More Diffuse and Systematic Use of Earth Observation Copernicus-Based Solutions. In Proceedings of the IGARSS 2020--2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 26 September-2 October 2020; pp. 3166-3168. [CrossRef]
* ESA (2023) ESA. Sentinel-1B in-Flight Anomaly Summary Report. 2023. Available online: [[https://sentinel.esa.int/documents/247904/48193-94/Sentinel-1B+In-Flight+Anomaly+Summary+Report.pdf](https://sentinel.esa.int/documents/247904/48193-94/Sentinel-1B+In-Flight+Anomaly+Summary+Report.pdf)]([https://sentinel.esa.int/documents/247904/48193-94/Sentinel-1B+In-Flight+Anomaly+Summary+Report.pdf](https://sentinel.esa.int/documents/247904/48193-94/Sentinel-1B+In-Flight+Anomaly+Summary+Report.pdf)) (accessed on 2 April 2024). **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
# Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO
## 2 Methodology
### Estimation and Optimization of the EKF Rotor Angular Velocity and Rotor Position  
#### 2.2.1 EKF Design  
The Kalman filter (KF) is an optimal prediction estimation in terms of minimum variance. It is easy to design this filter in the time domain using the state space method and describe the dynamic characteristics of any complex multidimensional signal by using the state equation. In nonlinear systems, the EKF can be used to estimate unmeasurable states (such as rotational speed and rotor position) using a dynamic system model, measurable states (such as stator voltage and current), and system and measurement noise [34].  
Because the flywheel motor Formula (2) is nonlinear, the EKF algorithm must be used for Equation (2). The discrete form of this algorithm is as follows:  
\[\left\{\begin{array}{l}x(k+1)=f[x(k)]+B(k)u(k)+W(\mathrm{k})\\ y(k)=C(k)x(k)+V(k)\\ \cos(W)=E\{WW^{T}\}=Q\\ \cos(V)=E\{VV^{T}\}=R\end{array}\right. \tag{4}\]  
where \(V(k)\) and \(W(k)\) are the measurement and system noise, respectively; \(Q\) and \(R\) are the covariance matrices of \(V\) and \(W\), respectively; and \(E\{\}\) denotes the expected value.  
The state estimation of the EKF is divided into two phases, prediction and correction, and the algorithmic procedure is as follows:  
\[\left\{\begin{array}{l}\widetilde{x}(k+1)=T_{s}[B(k)u(k)+f(\hat{x}(k))]+ \hat{x}(k)\\ \widetilde{y}(k+1)=C\widetilde{x}(k+1)\\ \widetilde{p}(k+1)=T_{s}[\hat{p}(k)F^{T}(k)+F(k)\hat{p}(k)]+\hat{p}(k)+Q\\ K(k+1)=\widetilde{p}(k+1)C^{T}[R+C\widetilde{p}(k+1)C^{T}]^{-1}\\ \hat{x}(k+1)=K(k+1)[ywhere \(u(k)\) and \(y(k)\) are the input and output quantities predicted for the state vector, \(f[x(k),u(k)]\) is the motor model, \(T_{s}\) is the sampling period, \(p(k)\) is the error covariance matrix, \(K(k)\) is the gain matrix, \(k\) is the current moment, \(k+1\) is the next moment, and \"\(-\)\" and \"\(-\)\" represent the state predicted and estimated values, respectively.  
\[F(k)=\frac{\partial f(x)}{\partial x}\bigg{|}\begin{array}{c}x=\hat{x}(k)\\ \end{array}=\begin{bmatrix}-R/L_{s}&0&\varphi_{f}\sin\hat{\theta}_{e}(k)/L_{s }&\varphi_{f}\hat{\omega}_{e}(k)\cos\hat{\theta}_{e}(k)/L_{s}\\ 0&-R/L_{s}&-\varphi_{f}\cos\hat{\theta}_{e}(k)/L_{s}&\varphi_{f}\hat{\omega}_{ e}(k)\sin\hat{\theta}_{e}(k)/L_{s}\\ 0&0&0&0\\ 0&0&1&0\\ \end{bmatrix} \tag{6}\]  
EKF uses a recursive method; if the system is uniformly and completely randomly controllable and uniformly, completely randomly observable, then the EKF must be uniformly asymptotically stable. As the number of filtering steps increases, the effect of arbitrarily chosen initial values \(x_{0}\) and \(p_{0}\) on the filter values \(\hat{x}(k+1)\) and \(\hat{p}(k+1)\) decreases. In practice, although the EKF design is based on a deterministic mathematical model, the selection of \(Q\) and \(R\) greatly impacts the convergence and estimation accuracy of the EKF because the statistical properties and measurement noise are usually unknown. Therefore, the selection of \(Q\) and \(R\) is crucial.
## 2 State of the Art
### Skill Classification
#### 2.2.1 Skill Bases  
Public skill classifications and skill bases developed by experts are good starting points for the discussion of skill classification [8]. Well-known classification schemes include:  
* ESCO (European Skills, Comptences, qualifications, and Occupations; [[https://esco.ec.europa.eu/en/classification](https://esco.ec.europa.eu/en/classification)]([https://esco.ec.europa.eu/en/classification](https://esco.ec.europa.eu/en/classification)), accessed on 1 March 2024), a fine-grained classification scheme that covers 14,295 skills in all the languages of the EU. It is the main classification scheme used in the European labor market.
* O*NET ([[https://www.onetonline.org](https://www.onetonline.org)]([https://www.onetonline.org](https://www.onetonline.org)), accessed on 1 March 2024), a coarse-grained classification schema which covers over 900 skills and also includes generic skills and is mainly used in the United States.
* ROME ([[https://www.francetravail.fr/employeur/vos-recrutements/le-rome-et-les-fiches-metiers.html](https://www.francetravail.fr/employeur/vos-recrutements/le-rome-et-les-fiches-metiers.html)]([https://www.francetravail.fr/employeur/vos-recrutements/le-rome-et-les-fiches-metiers.html](https://www.francetravail.fr/employeur/vos-recrutements/le-rome-et-les-fiches-metiers.html)), accessed on 1 March 2024), a coarse-grained classification developed by France Travail. It is available in French and only covers French territories.
* ISCO-08 ([[https://www.ilo.org/public/english/bureau/stat/isco/index.htm](https://www.ilo.org/public/english/bureau/stat/isco/index.htm)]([https://www.ilo.org/public/english/bureau/stat/isco/index.htm](https://www.ilo.org/public/english/bureau/stat/isco/index.htm)), accessed on 1 March 2024) is the latest version (December 2007) of a job classification scheme which was adopted as early as 1957.  
These skill bases are built around ontologies and supported by organizations which are publicly funded by the US (e.g., O*NET), the European Union (e.g., ESCO), or national governments (e.g., ROME is supported by the French government).  
Skill identification technology [8] has been a foundational technology for building these widely used skill bases. Nevertheless, special use cases such as forecasting and predicting the impact of trends such as automation and offshoring may require custom classification systems, such as the one discussed in the remainder of this section.
## 4 Simulation and Experimental Verification  
With the VIENNA circuit verification platform, the proposed control strategy for NPV balance based on selection method of dynamic adjustment for small-voltage vector is compared with the traditional PI regulator-based ZSV-J through simulation and experimental verification, and the results are analyzed.  
The parameters of the VIENNA circuit platform: verification power: 5 kW; three-phase voltage is obtained from the power frequency isolation transformer with a ratio of 2:1, so the root-mean-square (RMS) value of \(e_{n}\): 110 Vac/50 Hz; DC voltage \(v_{\text{dc}}\) setting: 400 Vdc; filter inductor \(L\): 4 mH; DC voltage equalizing capacitors \(C_{1}\) and \(C_{2}\): 4700 \(\mu\)F.  
During the verification, the unbalanced NPV is achieved by the unequal resistance of the two loads above and below the neutral point \(N\) on the DC side. If the two loads are equal: \(R_{1}=R_{2}=16\)\(\Omega\); if not equal, \(R_{1}=20\)\(\Omega\) and \(R_{2}=12\)\(\Omega\).