esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Experiments
#### Influence of the Initial Dye Concentration  
The extent of removal of dyes from an aqueous mixture depends heavily on the dye concentration. The influence of the initial concentration of dye ions in a series of 10-70 mg L\({}^{-1}\) on adsorption was examined, and the results are shown in Figure 9. By using MWCNTs, the maximum uptake was found at the concentration of 10 mg L\({}^{-1}\) (91%) for IV2R dye. After this, the dye removal rate decreased with the rising concentration of IV2R dye until the end of the experiments, at the same contact time and adsorption temperature. However, the results showed that the amount of dye adsorbed per unit mass of adsorbent increased from 1.2 to 5.5 mg g\({}^{-1}\) when the concentration of dye increased from 10 to 70 mg L\({}^{-1}\). The increase in the adsorption capacity is possibly due to greater interaction among the dye and adsorbent, in addition to an increase in the driving force of the concentration gradient with the increase in the initial concentration [17]. Moreover, the higher quantity of dye removal at higher concentrations is probably because of increased diffusion and decreased resistance to dye uptake [56]. Furthermore, the rapid initial stage of dye removal with this adsorbent is ascribed to the availability of active sites on the adsorbent and to the gradual occupancy of these sites; the sorption converts to be less efficient. Generally, increasing the dye concentration leads to a decrease in the percentage of removal of dye, which may be because of the saturation of sorption sites on the surface of the adsorbent [1,39]. Moreover, the major quantity of dye adsorbed at a high dye concentration could be related to two main effects, namely, the high probability of collision between dye ions with the adsorbent's surface and the high rate of dye ion diffusion onto the adsorbent's surface [57].  
Figure 8: Influence of agitation time on the removal of IV2R.
## 3 System Implementation and Test
### DATA Transmission and Repeater  
To manage the wide area of grazing, the system needs repeaters shown in Figure 5. The grazing has no power line, so wired electrical devices cannot be used. The repeater gets energy from solar panels and transmits the data from SNR to the main system receiver [24], and transfers the call signal from the main system to each sensor which can be recognized by the target ID. In that case, the wireless signals get errors for several reasons such as target movements, weather and obstacles. So considering these reasons, the data transmitter needs a special platform.
## 3 Experiment and Analyses
### Analyses of Static Experiments
#### 3.1.4 Results of the Static Experiments after the Ambiguities are Fixed  
Figure 8 shows the results of static experiments after the ambiguities are fixed correctly. The reason why the zero baseline experiment is performed is that our pseudolite system should be proved to be intact and the data quality is good without considering the interference from the external environment. The short baseline experiment is used to verify the feasibility of the dynamic positioning initialization.  
According to Figure 8, it can be found that the precision of the positioning can reach the millimeter level. This illustrates the feasibility of our algorithm. The ambiguities can be fixed to achieve high precision positioning.  
Figure 8: Results of static experiments after the ambiguities are fixed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Search Step of 1 cm** & \\ \hline
**X Coordinate** & **Y Coordinate** & **AFV** \\ \hline
0.63 & \(-\)0.42 & 0.9641 \\
0.23 & \(-\)0.87 & 0.9614 \\
0.23 & \(-\)0.86 & 0.9607 \\
0.63 & \(-\)0.43 & 0.9602 \\
0.62 & \(-\)0.42 & 0.9555 \\
0.22 & \(-\)0.86 & 0.9508 \\ \hline \multicolumn{3}{c}{**Search Step of 2 cm**} \\ \hline
**X Coordinate** & **Y Coordinate** & **AFV** \\ \hline
0.62 & \(-\)0.42 & 0.9555 \\
0.22 & \(-\)0.86 & 0.9508 \\ \hline \multicolumn{3}{c}{**Search Step of 4 cm**} \\ \hline
**X Coordinate** & **Y Coordinate** & **AFV** \\ \hline
0.64 & \(-\)0.42 & 0.9219 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of 1 m search scope under different step lengths (short baseline).
# Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks
## System Model
### SS-RSRP Model
\right) \tag{3}\]  
where \(n\)th block \(\mathbf{H}_{k}(n)\) represents the OFDM channel gain at the \(n\)th subcarrier and can be written as  
\[\mathbf{H}_{k}(n)=\sum_{l=0}^{L-1}\mathbf{h}_{k}(l)\exp\biggl{(}-j2\pi\frac{nk}{n_{c}} \biggr{)} \tag{4}\]  
The received frequency-domain signal \(z_{n,k}\) at the DFT output can be expressed as  
\[z_{n,k}=\mathbf{H}(n)x_{n,k}+w_{n,k} \tag{5}\]  
In 5G NR the Synchronization Signal Block SSB is used for signal measurement, therefore \(N\) special sub-carriers dedicated to SSB measurement are deployed. The SS-RSRP signal is defined as average of channel power during one measurement period of (SSB). Hence  
\[\text{SS}_{\text{RSRP}}=\frac{1}{N}\sum_{k=0}^{N-1}\|\mathbf{H}_{k}\|^{2}\approx \frac{n_{c}LP_{h}^{2}}{N} \tag{6}\]  
The frequency domain channel \(\mathbf{H}_{n,k}\) is a diagonal, with the following expression  
\[\|\mathbf{H}_{k}\|^{2} =\left( \begin{array}{ccc}\|\mathbf{H}_{k}(0)\|^{2}&&0\\ &\ddots&\\ 0&&\|\mathbf{H}_{k}(n_{c})\|^{2}\end{array} \right)\] \[=\mathbf{H}_{k}\mathbf{H}_{k}^{H}=n_{c}\|\mathbf{H}(n)\|^{2}\forall\;1\leq n \leq n_{c}\]  
whereas  
\[\|\mathbf{H}_{k}(n)\|^{2} =\|\sum_{l=0}^{L-1}\mathbf{h}_{k}(l)\exp\biggl{(}-j2\pi\frac{n_{k}}{n _{c}}\biggr{)}\|^{2} \tag{7}\] \[\leq\sum_{l=0}^{L-1}\|\mathbf{h}_{k}(l)\exp\biggl{(}-j2\pi\frac{n_{k} }{n_{c}}\biggr{)}\|^{2}\] (8) \[\leq\sum_{l=0}^{L-1}\|\mathbf{h}_{k}(l)\|^{2}=L.P_{h}^{2} \tag{9}\]  
where \(P_{h}=E\{h_{k}^{p}(l)\left[h_{k-k^{\prime}}^{m}(l^{\prime})\right]^{*}\}\) denotes the mean power to each channel coefficient in time domain. On the other side, the mathematical expression of RSSI at the receiver could be interpreted as:  
\[\text{RSSI}=\left|\sum_{n=1}^{N}\sqrt{(H_{k}(n))}\right|^{2}\approx LP_{h}^{2} \tag{10}\]  
Henceforth, the SS-RSRP in dBm could be re-written as  
\[\text{SS}_{\text{RSRP}}[\text{dBm}]=\text{RSSI}[\text{dBm}]+10\log 10(n_{c})-10 \log(N) \tag{11}\]Whereas the RSSI values are fully known at the receiver side, hence we can estimate the SS-RSRP values as follows:  
\[\widehat{\text{SS}_{\text{RSRP}}^{\text{-}}[\text{dBm}]} =\text{RSSI}[\text{dBm}]+10\log 10(\frac{n_{c}}{N}) \tag{12}\] \[=P_{E}[\text{dBm}]+G_{R_{x}}[\text{dBm}] \tag{13}\]  
When combining (2) and (12), we can derive the expression of the angle \(\hat{\theta}_{i}\) and the frontal distance with the MC as  
\[\hat{\theta}_{i} =\sqrt{(\frac{70\pi}{180})^{2}(\widehat{\text{SS}_{\text{RSRP}} ^{i}-P_{E}})} \tag{14}\] \[\hat{d}_{i} =d_{0}10^{-\frac{\text{RSSI}^{i}-\text{RSSI}^{i}+\text{d}_{i}+ \mathbbm{z}_{i}}{\text{dBm}}} \tag{15}\]  
Finally, when we refer to Figure 1 the distance between two COs in 5G cell will be estimated as  
\[\|\mathbf{H}_{k}(n)\|^{2} =\|\sum_{l=0}^{L-1}\mathbf{h}_{k}(l)\exp\biggl{(}-j2\pi\frac{n_{k}}{n _{c}}\biggr{)}\|^{2} \tag{7}\]0
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Data Processing
#### 2.3.4 SAM Scale Data Preprocessing  
The SAM questionnaire responses were classified at three granularity levels--binary, ternary, and five-category. In the binary schema, valence and arousal are dichotomized into two poles (\"positive\"/\"negative\" or \"high arousal\"/\"low arousal\"); for example, a participant's sense of pleasure toward a rural architectural scene is tagged \"1\", whereas perceptions of monotony are tagged \"-1\", facilitating rapid screening of landscapes in need of renovation. The ternary schema introduces a neutral category \"0\" to capture ambivalent states; for instance, when participants neither favor nor dislike a given rural road scene, it is assigned \"0\", supporting refined assessments that distinguish landscapes requiring optimization from those warranting preservation. The five-category schema further subdivides emotional intensity into \"\(-\)2, \(-\)1, 0, 1, 2\" (ranging from \"extremely negative\" to \"extremely positive\"), such as rating a sea of flowers as \"2\" and a dilipidated farmhouse as \"\(-\)2\". Although theoretically more granular, this approach may suffer from sample-imbalance, degrading model performance. Accordingly, for the binary model, samples with valence/arousal \"0\" were excluded; values \"\(-\)2\" and \"\(-\)1\" were consolidated as negative/low \"\(-\)1\" and \"1\" and \"2\" as positive/high \"1\". In the ternary model, \"0\" samples are retained as neutral for both valence and arousal.  
After data processing, four incomplete entries were removed, resulting in a final valid dataset of 34 participants. This dataset comprises eye movement data (12,648 entries), EEG data (3264 entries), and beauty evaluation data (3264 entries), yielding a total of 19,176 data points.
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 5 Numerical Results and Analysis
### Experimental Implementations  
The related critical simulation parameters are set as follows: (1) all the RIS channels undergo Rician fading with Rician factor 3; (2) all other channels experience Rayleigh fading; (3) the path loss exponent is 2.5 for all the channels; (4) the ambient signal frequency is 2.4 GHz with transmit power 20 dBm; (5) the initial phase shift of the RIS and the initial received combined vector are set randomly and then are optimized gradually with TD3; (6) the critic and actor networks are fully connected deep neural networks (DNNs), which have one input layer, one output layer and two hidden layers; (7) the size of the actor and critic nets are (\(2K\), \(2N+2K\), \(4N+4K\), \(2N+2K\)) and (\(2N+4K\), \(4N+8K\), \(2N+4K\), 1), respectively. The DNN parameters are shown in Table 1.  
The simulated performance is achieved by performing the average of 5000 independent channels. All the training is executed by Windows 10 and CPU with 11 th Gen Intel(R) Core(TM) i7-1165G7 @ 2.80 GHz.
## 2 Vibration Mechanism of Planetary Gearbox
### Meshing Stiffness of the Fault External Gear Pair  
Based on the normal meshing stiffness variation, the meshing stiffness variation in the broken teeth of the sun gear is further investigated. Figure 4 is a simplified schematic diagram of the fractured tooth of the sun gear, \(ds\) is the size of the broken tooth of the sun gear, the cross-section of the broken tooth intersects the tooth profile at point \(D\), and point \(K\) is the intersection of the tooth profile and the tooth apex circle. The meshing force in the case of a broken tooth is shown in Figure 5. In Figure 5, the gear pair enters meshing from point \(A\). Point \(C\) is an arbitrary meshing point on the tooth profile, and point \(O\) is the rotation center of the sun gear. Theoretically, the meshing point of the external gear pair enters the mesh from point \(A\) and exits from point \(K\). However, due to the presence of the fault, the meshing process ends early at point \(D\), resulting in the stiffness of the gear pair being \(0\) in the \(DK\) range.  
Based on the broken tooth mesh force distribution variation, the \(k_{b}\), \(k_{s}\), \(k_{a}\), and \(k_{h}\) are first calculated using Equations (13)-(16) and substituted into Equation (12) to calculate the mesh stiffness for different degrees of broken teeth and plot the variation curve (as shown in Figure 6).  
\[k_{b,\;fault}=\left\{\begin{array}{l}k_{b,\;normal},\alpha\leqslant\alpha _{d}\\ 0,\alpha>\alpha_{d}\end{array}\right. \tag{13}\]  
\[k_{s,\;fault}=\left\{\begin{array}{l}k_{s,\;normal},\alpha\leqslant\alpha _{d}\\ 0,\alpha>\alpha_{d}\end{array}\right. \tag{14}\]  
Figure 4: Schematic diagram of the sun gear with a broken tooth: (**a**) three-dimensional diagram; (**b**) two-dimensional diagram.  
Figure 3: Meshing stiffness of the healthy planetary gear: (**a**) \(R_{f}>R_{b}\); (**b**) \(R_{f}<R_{b}\).  
\[k_{a,\,fault}=\left\{\begin{array}{l}k_{a,\,normal},\alpha\leqslant\alpha_{d}\\ 0,\alpha>\alpha_{d}\end{array}\right. \tag{15}\]  
\[k_{h,\,fault}=\left\{\begin{array}{l}k_{h,\,normal},\alpha\leqslant\alpha_{d} \\ 0,\alpha>\alpha_{d}\end{array}\right. \tag{16}\]  
Figure 5: The meshing force on the broken sun gear tooth.  
Figure 6: Stiffness curve of external gear pair under the broken tooth condition of sun gear: (a) ds = 1 mm(1/4); (b) ds = 2 mm(2/4); (c) ds = 3 mm(3/4); (d) ds = 4 mm(completely broken teeth).  
The comparative analysis of Figure 6 shows that when the sun gear suffers a broken tooth fault, the meshing stiffness decreases in the range of broken teeth. With the increase in the fault size, the fall time of the gear pair is significantly earlier, and the meshing process is terminated earlier. It is also found that when the fault size is less than \(\frac{1}{2}\) of the tooth height (ds \(\leq\) 2 mm), the gear pair has a single tooth meshing zone. The variation law of this meshing stiffness with the fault degree provides theoretical support for constructing signal models characterizing different fault degrees.
## Theoretical Framework
### Dynamic Response
Assuming a free vibration system without damping, the equation of motion is as follows:  
\[\mathbf{M}\ddot{\boldsymbol{d}}+\mathbf{K}\boldsymbol{d}=\boldsymbol{0}. \tag{47}\]  
Substituting \(\boldsymbol{d}=\boldsymbol{\Phi}\boldsymbol{q}(\boldsymbol{t})=\boldsymbol{ \Phi}\sin(\boldsymbol{\omega}\boldsymbol{t})\) in Equation (47), the following expression is an eigenvalue problem:  
\[\mathbf{K}\boldsymbol{\Phi}=\mathbf{M}\boldsymbol{\Phi}\boldsymbol{\omega}^{ 2}, \tag{48}\]  
where \(\boldsymbol{\Phi}\) is modal matrix that represents the deflected shape for each vibration modes; \(\boldsymbol{q}\) is a vector for each instant time and represents the modal coordinates; and \(\boldsymbol{\omega}\) is a diagonal matrix with the angular frequency of vibration. Figure 4: (**a**) Prismatic buildings with rectangular cross section; (**b**) multi-degree-of-freedom structure; (**c**) story drift. Rewriting the equation of motion to the modal form and multiplying all expressions by \(\mathbf{\Phi}^{\mathrm{T}}\):  
\[\overline{\mathbf{M}}\tilde{\mathbf{q}}+\overline{\mathbf{C}}\tilde{\mathbf{q}}+ \overline{\mathbf{K}}\mathbf{q}=\overline{\mathbf{F}}, \tag{49}\]  
where \(\overline{\mathbf{M}}=\mathbf{\Phi}^{\mathrm{T}}\mathbf{M}\mathbf{\Phi}\), \(\overline{\mathbf{C}}=\mathbf{\Phi}^{\mathrm{T}}\mathbf{C}\mathbf{\Phi}\), \(\overline{\mathbf{K}}=\mathbf{\Phi}^{\mathrm{T}}\mathbf{K}\mathbf{\Phi}\) and \(\overline{\mathbf{F}}=\mathbf{\Phi}^{\mathrm{T}}\mathbf{F}\) are modal transformation matrices corresponding to the mass, damping, stiffness, and forces matrices, respectively. The procedure of the Newmark method for MDOF structures is shown in Table 2, see [PERSON] [30], where \(i\) indicates the time step. The factors \(\beta\) and \(\gamma\) are constant values taking \(\beta=1/4\) and \(\gamma=1/2\) for the case of average acceleration and \(\beta=1/6\) y \(\gamma=1/2\) for the linear acceleration.
## 10 Experimental Analysis
### Statistical Comparisons on CEC2014 Problems
#### 10.1.2 Analyzing the Effectiveness of the Proposed Hybridization
\begin{table}
\begin{tabular}{c l c c c c c c c} \hline \hline \(D\) & **Algorithms** & \(R^{+}\) & \(R^{-}\) & \(p\)**Value** & \(+\) & \(\approx\) & \(-\) & **Dec.** \\ \hline \multirow{4}{*}{10} & ICDEBOA vs. BOA & 432 & 33 & \(5.97\times 10^{-6}\) & 28 & 2 & 0 & \(+\) \\  & ICDEBOA vs. SHADE & 465 & 0 & \(1.86\times 10^{-9}\) & 30 & 0 & 0 & \(+\) \\  & ICDEBOA vs. HPSOBOA & 412 & 53 & \(7.91\times 10^{-5}\) & 21 & 5 & 4 & \(+\) \\  & ICDEBOA vs. jDE & 465 & 0 & \(1.86\times 10^{-9}\) & 29 & 0 & 1 & \(+\) \\ \hline \multirow{4}{*}{30} & ICDEBOA vs. BOA & 398 & 67 & \(3.45\times 10^{-4}\) & 24 & 6 & 0 & \(+\) \\  & ICDEBOA vs. SHADE & 465 & 0 & \(1.86\times 10^{-9}\) & 30 & 0 & 0 & \(+\) \\  & ICDEBOA vs. HPSOBOA & 292 & 173 & \(2.29\times 10^{-1}\) & 17 & 6 & 7 & \(\approx\) \\  & ICDEBOA vs. jDE & 443 & 22 & \(9.98\times 10^{-7}\) & 28 & 2 & 0 & \(+\) \\ \hline \multirow{4}{*}{50} & ICDEBOA vs. BOA & 398 & 67 & \(3.45\times 10^{-4}\) & 24 & 6 & 0 & \(+\) \\  & ICDEBOA vs. SHADE & 465 & 0 & \(1.86\times 10^{-9}\) & 30 & 0 & 0 & \(+\) \\  & ICDEBOA vs. HPSOBOA & 334 & 131 & \(3.64\times 10^{-2}\) & 17 & 5 & 8 & \(\approx\) \\  & ICDEBOA vs. jDE & 443 & 22 & \(9.98\times 10^{-7}\) & 28 & 2 & 0 & \(+\) \\ \hline \multirow{4}{*}{100} & ICDEBOA vs. BOA & 398 & 67 & \(3.45\times 10^{-4}\) & 28 & 2 & 0 & \(+\) \\  & ICDEBOA vs. SHADE & 465 & 0 & \(1.86\times 10^{-9}\) & 30 & 0 & 0 & \(+\) \\ \cline{1-1}  & ICDEBOA vs. HPSOBOA & 320.5 & 144.5 & \(8.70\times 10^{-2}\) & 22 & 4 & 4 & \(+\) \\ \cline{1-1}  & ICDEBOA vs. JDE & 434 & 31 & \(4.42\times 10^{-6}\) & 29 & 0 & 1 & \(+\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of performing Wilcoxon test on CEC2014 objective functions for all considered methods. The results in Table 4, show that the obtained Friedman \(p\) values are less than significance \(\alpha\) in each considered case. The results demonstrate that the proposed ICDEBOA algorithm has the best performance among all the algorithms taken for considerations. The HPSOBOA algorithm shows the second-best performance. According to statistical analysis, hybridising DE and BOA algorithms improves optimization performance while overcoming their drawbacks. The expanded mutation operators, which incorporate butterfly algorithms equations for position updating, and the inclusion of a chaotic map to the sensory fragrance greatly improve the ICDEBOA method, proving the usefulness of the introduced changes.
## 5 Conclusions  
In this paper, we have shown how networking IoT sensors and streaming data together with a spatial model can provide valid maps of comfort indices in a historical garden. These technologies can provide a valid support to planning visits, planning modifications of the seating arrangements in the garden and other aspects related to planning and decision support.  
Adding an accurate 3D point cloud model from surveying with geomatics tools, i.e., drones and laser scanners, allows to map the direct-beam vs. diffuse solar radiation in the urban garden, and thus provide a real-time map of comfort indices. This map can be used to understand what areas are ideal for people to visit, in particular for people with limitations related to health or other factors.  
Future developments are listed below:  
* vectorize the R code to provide better performance for PET calculation. PET calculation iterates to find the optimal energy balance, but this requires an average of 20 iterations for each process;
* use PostgreSQL libraries optimized for big data, such as TimescaleDB. Up to now, we have data from three months and in a single historical garden, but to foresee storage of data from more IoT devices to replicate this process in other gardens, it must be noted that better solutions based on sharding and distributed data have to be implemented;
* continue ongoing collaboration with research teams that study human well-being, which will foster new applications across disciplines.  
**Supplementary Materials:** The following supporting information can be downloaded. S1: video of hourly shadow mapping--[[https://user-images.githubusercontent.com/1391292/181439231-4d9c09](https://user-images.githubusercontent.com/1391292/181439231-4d9c09) ff-c552-499a-8d95-eea988079e55.mp4]([https://user-images.githubusercontent.com/1391292/181439231-4d9c09](https://user-images.githubusercontent.com/1391292/181439231-4d9c09) ff-c552-499a-8d95-eea988079e55.mp4) (accessed on 20 September 2022). S2: video of hourly comfort index--[[https://github.com/fpirotti/rPET/raw/master/man/figures/gif_filePMV.gif](https://github.com/fpirotti/rPET/raw/master/man/figures/gif_filePMV.gif)]([https://github.com/fpirotti/rPET/raw/master/man/figures/gif_filePMV.gif](https://github.com/fpirotti/rPET/raw/master/man/figures/gif_filePMV.gif)) (accessed on 20 September 2022).  
**Author Contributions:** Conceptualization, R.C. and F.P.; methodology, R.C. and F.P.; software, F.P. and M.P.; M.D. supported data processing and revision of article. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the VARCHTIES project, Grant Agreement number: 869505--VARCHITIES--H2020-SC5-2018-2019-2020/H2020-SC5-2019-2.  
**Data Availability Statement:** All methods are fully replicable with the R package code available in [[https://github.com/fpirotti/rPET](https://github.com/fpirotti/rPET)]([https://github.com/fpirotti/rPET](https://github.com/fpirotti/rPET)) (accessed on 20 September 2022) by running \"prepareData.R\". Users can replace the \"dtm\" and \"pointcloud\" objects with their own.
## 4 Simulation Results and Experimental Results  
### Simulation Results  
To validate the effectiveness and superiority of the automatic demagnetization fault diagnosis method for DDPMSMs proposed in this paper, test data covering the different demagnetization faults and considering different operating conditions of the machine are required. For this purpose, the employed DDPMSM is modeled by FEM, which can collect data under healthy operating conditions and different operating conditions, and with different demagnetization fault modes with different operating conditions and different demagnetization fault parameters, since generally, those data are not available  
Figure 12: The training results of the three PNNs. (**a**) The first PNN. (**b**) The second PNN. (**c**) The third PNN.  
Figure 13: The testing results of the three PNNs. (**a**) The first PNN. (**b**) The second PNN. (**c**) The third PNN.  
for the real machine. Two healthy conditions and sixteen faulty modes are adopted (it is worth mentioning that the number, location, and severity of demagnetized PMs, and load and speed during testing are different from those for the training process). The healthy conditions are composed of different loads and different speeds (25% rated speed and 75% rated load, and 75% rated speed and 25% rated load). The faulty conditions are composed of two demagnetization faulty modes (PM1 demagnetization; PM2, PM43, and PM44 demagnetization), two demagnetization severities (5% and 15%), two load conditions (25% rated load and 75% rated load), and two speed conditions (50% rated speed and rated speed). The details of fault conditions and test results are listed in Table 14.  
The simulation results in Table 14 show that the developed method can accurately determine the position of demagnetized permanent magnets under various operating conditions and different demagnetization fault parameters, indicating the high accuracy and strong robustness of the proposed demagnetization fault automatic diagnosis method based on three PNNs and toroidal-yoke-search coils.  
It also can be seen from Table 14 that when the demagnetization severity is greater than 10%, the demagnetization fault can be detected and demagnetized permanent magnets can be located. The results indicate that the proposed demagnetization fault automatic diagnosis system can accurately diagnose demagnetization faults at the early stage. The results indicate that the method has high sensitivity.