esa-sceva/satcom-chunk-collection huggingface dataset:
# The Electronic Switch of Windings of a Standard BLDC Motor
###### Abstract  
Nowadays, one of the most promising areas in the motor industry is the production of BLDC motors, which are used in a wide range of applications and products--from power tools to electrical vehicles. The windings of BLDC motors are connected according to the Delta or Star circuits. However, such circuits (Delta or Star) do not allow dynamically changing the parameters of the BLDC motor during operation and thus reducing the overall motor efficiency. This paper deals with and proposes an electronic switch of the BLDC motor windings, which brings an advantage in having a capability of dynamically switching the windings during operation and thus increasing the efficiency of the BLDC motor performance.  
brushless DC; BLDC; motor; multistator; windings; electronic switch +
Footnote †: journal: Applied Sciences  
signal, switches the winding circuits to obtain maximum efficiency, torque or speed [16; 17; 18; 19]. When working with multi-pole and high-speed motors (at high PWM frequencies), the imperfections of the electromechanical system should be taken into account [20].  
As internal power switching elements, there are utilized pairs of unipolar FET transistors connected in anti-series to eliminate the influence of internal protective diodes. This solution makes it possible to rebuild the production of BLDC motors to a completely new product line with minimal costs leading to BLDC motors capable of wide operational ranges and still being more economical, reliable, and efficient without failures.  
To test and demonstrate the benefits of the proposed BLDC motor solution with the electronic winding switch, the BLDC motor with 16 magnets in the rotor and a stator winding circuit ABCABCABC was chosen. All windings have the same direction, the end of the first winding is connected to the beginning of the following one, etc. Each phase of this BLDC motor has four poles connected in series. With such a connection, it is possible obtain a connection diagram of the Star type, as shown in Figure 2. A standard 12-pole BLDC motor can have up to six different dynamic circuits of the winding configuration if the Delta and Star scheme are considered. An electronic switch can be used to commutate the windings during motor operation [21], which is taken in the proposed approach as an advantage.  
Four-phase windings can be switched according to three different schemes (the total relative phase resistance is indicated next to Figure 3).  
By changing the switching circuit of the windings, different energy characteristics is obtained due to the changes in the inductance and current strength. The relationship of these values is given by the formula:  
\[W=\frac{L*I^{2}}{2}\]  
where \(W\) represents a magnetic field energy, \(L\) stands for the total inductance of the phase windings, and \(I\) is an operating current of the phase windings.  
Figure 1: The BLDC motor animation with 12 poles, 16 rotor magnets.
# Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
## 3 Geoportal Implementation Framework
### Geospatial Datasets and Development Indicators  
Agenda 2063 has 20 strategic goals that crosscut the seven aspirations described earlier. For demonstration purposes, we selected a few economic and health development indicators to showcase the functionality of the geoportal. Development indicators used in the development of the geoportal include GNI per capita growth (annual %), mortality rate for children under five years per 1000 live births, access to electricity (% of population), unemployment with intermediate education, improved water sources, tax revenue (% of GDP), prevalence of HIV, total (% of population), manufacturing, value added (% of GDP), incidences of tuberculosis (per 1000 people) and incidences of malaria. Our aim was to use public domain datasets. We integrated a number of geospatial datasets to supplement visual analysis and these include vector and raster data layers. The vector and raster layers include base layers of member states, transport networks, geology, population, industry, information and communication networks, human settlements, disaster, sustainable land and water management, energy, health services, education, and satellite imagery.
## 4 Results
### Effect of Cavitation  
To study the impact of the cavitation on the erosion characteristics, the conditions (pressure in the blade passage should drop below the saturation pressure of the water) under which cavitation may occur were found. It was noticed that cavitation might be introduced in the blade passage at a higher flow rate, i.e., \(\frac{Q_{\eta}}{Q_{\eta(max)}}=2.7\). Here, \(Q_{\eta}\) is the minimum required value of flow rate to trigger the cavitation, while \(Q_{\eta(max)}\) is the flow rate that corresponds to the maximum efficiency of the pump. A higher flow rate resulted in greater velocity values in the blade passage. The pressure became lower than the saturation pressure of the liquid in the region around the entry section of the blades. Contours of the water vapor volume fraction and mass fraction are displayed in Figure 21a. A study bubble pattern could be noticed on the blade's suction side triggered from the blade's leading edge. Some shaky bubble fields could also be observed on the pressure side near the blade's trailing edge. Velocity vectors in the blade channels with no cavitation and with cavitation are shown in Figure 21b,c. Figure 21b indicates that the velocity vectors were aligned with the blade passage, and no recirculation region could be observed for the case with no cavitation. At the same time, in Figure 21c, a huge recirculation region can be spotted in the suction which was triggered by the bubble structure initiating from the leading edge of the blade. There was also a minor separation region on the pressure side of the blade that was an outcome of small-scale cavitation taking place on the pressure side. Owing to these dead zones in the passage developed by the bubble structures, the development of local velocities in the passage and at the trailing edge grew substantially.  
Figure 21: (**a**) Volume concentration of water vapor contours in the blade passage (**b**) Velocity vectors in the blade channels with no cavitation, (**c**) Velocity vectors in the blade channels with cavitation while volume concentration of the particles is 5%.  
Figure 22 shows the erosion rate density distribution for different concentrations of particles under the cavitation conditions. Comparison of the results in Figures 11 and 22 indicated that cavitation shifts the location of the peaks towards the exit of the pump, and the magnitude of the location of the erosion rate intensity increased noticeably.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were conducted using SPSS v26 (Inc. IBM., version 29.0.2.0, Chicago, IL, USA). Data are expressed as the mean \(\pm\) standard deviation (SD) and 95% confidence interval (CI). Reliability was not assessed, given prior studies reporting adequate-to-excellent test reliability [47; 48], with intraclass correlation coefficients (ICCs) ranging from 0.78 to 0.99 for heel raise performance [49] and 0.82 to 0.99 for triceps surae EMG during dynamic tasks [50]. The Shapiro-Wilk test confirmed normality (\(p\) > 0.05). For the repeatedmeasures ANOVAs, the assumption of sphericity was assessed. Given that all within-subject factors (ROM and type) had only two levels, the assumption of sphericity was automatically met and could not be violated ([PERSON]'s test: df = 0, \(\varepsilon\) = 1.000). Therefore, no corrections to the degrees of freedom (such as Greenhouse-Geisser or Huynh-Feldt) were required for the within-subject effects tests. A 2-way repeated measures ANOVA with muscle as the within-participants factor was applied to compare maximum LG and MG activation across exercises. Pairwise _t_-tests examined differences in MG:LG ratios by ROM and exercise type. For all pairwise comparisons, Bonferroni correction was applied to adjust for multiple comparisons and control the Family-Wise Error Rate. To assess the magnitude of the significant pair differences, effect sizes (ESs) were computed using the package \"effectsize\". Effect sizes were calculated for all key comparisons to support interpretations of practical relevance. Partial eta-squared (\(\eta^{2}\)) values are reported for ANOVA-based effects, and Hedges' g was used for pairwise comparisons. The effect size magnitude was interpreted following [PERSON]'s guidelines as small (0.2-0.5), moderate (0.5-0.8), or large (>0.8). [51]. Significance was set at \(p\leq 0.05\).
## 3 Results and Discussion
### Expected Ecosystem Performance  
The EEP model had an R\({}^{2}\) of 0.53 (\(p\) \(<\) 0.0001) from 15,049 spatially and temporally stratified locations across the study area. Extrapolating weather in Alaska is problematic because of the low density of weather stations especially at high elevations, which also contribute to EEP model outliers. The EEP model was also impacted by burn perimeter boundary errors. A 10-fold cross validation of EEP demonstrated equivalent prediction accuracy for unseen data to training data and demonstrated the robustness of the EEP model to the wide range of environmental conditions in the study area through time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Metric** & **2000** & **2001** & **2002** & **2003** & **2004** & **2005** & **2006** & **2007** & **2008** & **Average** \\ \hline EPA & \(-\)33.3 & \(-\)34.6 & \(-\)46.5 & \(-\)42.0 & \(-\)49.0 & \(-\)70.4 & \(-\)14.4 & 31.3 & 0.3 & \(-\)28.7 \\ EPA SE 1  & 0.64 & 0.55 & 0.59 & 0.60 & 0.66 & 0.76 & 0.69 & 0.69 & 0.65 & 0.64 \\ Burned (\%) 2  & 0.8 & 0.3 & 1.2 & 0.6 & 5.2 & 3.1 & 0.2 & 0.2 & 0.1 & 1.3 \\ Insect Damage (\%) 3  & 0.8 & 1.9 & 1.0 & 1.2 & 2.1 & 2.9 & 2.1 & 2.9 & 1.2 & 1.8 \\ Total Disturbance(\%) 4  & 1.6 & 2.2 & 2.2 & 1.8 & 7.3 & 6.0 & 2.3 & 3.1 & 1.3 & 3.1 \\ \hline EPA Class & & & & & & & & & & & \\ \hline Severe Under 3  & 25.0 & 21.1 & 26.7 & 24.7 & 28.4 & 35.8 & 20.2 & 11.4 & 15.8 & 18.3 \\ Under 3 [FOOTNOTE:3]Footnote 3: footnotemark:The EEP time series was grouped into 22 natural classes with similar time series trends with an unsupervised cluster analysis [94]. A review of the temporal signatures of these 22 classes showed that some of the classes were characterized by a significant drop in EEP in 2004, a record fire year in interior Alaska (Table 1). The clusters with this significant drop of EEP in 2004 were combined into a drought group and those without the 2004 EEP drop were put in a non-drought group. These two groups were overlaid with the 2004 fire perimeters and the percentage of area burned in 2004 for each group was calculated. The drought group had 24% of its area burned and was concentrated on the north and central to eastern parts of the Yukon River Basin. The non-drought group had 5% of its area burned and was concentrated on the southern part of the area with northward extents in western and a small part of central Yukon River Basin. The higher probability of 2004 fires in areas with a significant drop in 2004 EEP supports the EEP models ability to track moisture stress in this boreal system and demonstrates drought and fire associations in the Yukon Basin.
## 2 Related Work and Discussion
### Work Related to Skyline Query  
The skyline query problems are related to the maximum vector problems [22; 23]. [PERSON] et al. [24] first addressed the skyline operator. They introduced three algorithms: Block Nested Loops (BNL), Divide and Conquer (D&C), and B-tree-based schemes. For BNL, every item is contrasted with each other item in the database, and the item is accounted for if no other item dominates. [PERSON] et al. [25] addressed a variant of BNL by introducing an algorithm named Sort-Filter-Skyline (SFS) as an improved version of BNL. This algorithm requires a pre-sorting data set. [PERSON] et al. [26] proposed \"Bitmap\" and \"Index\", two progressive processing algorithms. In the Bitmap algorithm, every dimensional value of a point is represented by a few bits. It can be determined whether a given data point is in the skyline by applying a bit-wise AND operation on these vectors. In the Index algorithm, the entire data set is organized into some lists, and points are assigned to a list only if the value in the dimension is the best among all the dimensions. [PERSON] et al. [27] addressed the k-dominant skyline in high-dimensional space. They proposed efficient algorithms to solve the k-dominant skyline problem. [PERSON] et al. [28] addressed the problem of skyline queries in web information systems. They vertically partitioned the data set and retrieved the skyline in a distributed environment.
## 3 Analysis of Monitoring Results
### Jacking Parameter Analysis  
Figure 3 shows the fluctuations in the jacking force, jacking speed, and face pressure of the pipe jacking machine with increasing number of jacking rings. When the pipe jacking machine is jacking in the reinforcement area of the starting well, the values of the three parameters clearly increase. When the pipe jacking machine moves out of the reinforcement area, the pressure of the working face begins to fluctuate within the range of 100-140 kPa, and the jacking speed is stable at 55 mm/min, while the jacking force continues to grow and then fluctuates around 4700 kN. When the pipe jacking machine is jacked into rings 32-45, the jacking force and pressure of the tunnel face fluctuate violently, and there is a trend of first increasing and then decreasing, which will lead to the trend of the ground settlement first increasing and then decreasing. When the pipe jacking machine is jacked into rings 45-50, the jacking force and the pressure of the tunnel face increase, and the ground settlement also increases. The description of the above two stages is similar to the change trend of B3 and B1 in Figure 2b, confirming that the soil layer may lead to the jacking parameters being too large when the pipe jacking machine is jacking to the corresponding position, and further illustrating the necessity of research on the response of the jacking parameters to the stratum.  
Figure 3: Jacking parameter fluctuation diagram.  
Figure 2: Surface subsidence monitoring curves: (**a**) lateral settlement; (**b**) longitudinal settlement.
## 4 Discussion
### Water Quality Indices  
Other studies indicate an ambiguous influence of hydropower plants on the shaping of the values of water quality indicators. In some, there was an improvement downstream of hydropower plants (Malaysia; [PERSON] et al., 2016 [92]), and in others, there was a deterioration of water quality (Brazil; [PERSON] et al., 2021 [93]). Other studies do not provide a clear answer regarding the impact of hydropower plants on water quality (China; [PERSON] et al., 2019 [60], Poland; [PERSON] et al., 2021 [94], and [PERSON] and [PERSON] 2021 [62]). The differences in the values of the indices fluctuated in the cited studies ranging from \(-\)69.09% to \(+\)24.26% downstream of hydropower plants compared with the points upstream of the hydropower plants. A detailed summary of the described results is presented in Table 16.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Percentage Difference in the Values of Water Quality Indices Downstream and Upstream of Hydropower Plants**} \\ \hline \multirow{2}{*}{**WQIs**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**[PERSON]**} & \multicolumn{1}{c}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**[PERSON]**} & \multirow{2}{*}{**This**} \\  & & & & & & & \\  & & \(\pm\)0.2016 [92] & et al., 2021 [93] & et al., 2021 [94] & et al., 2021 [94] & and [PERSON], 2021 [62] & & **Research** \\ \hline ATI & 14.25\% & \(-\)9.13\% & \(-\)1.48\% & \(-\)2.22\% & \(-\)0.49\% & n/a & n/a \\ \hline OWQI & 24.26\% & \(-\)39.10\% & 5.15\% & \(-\)69.09\% & 2.40\% & \(-\)5.65\% & 2.18\% \\ \hline OIP & 1.62\% & \(-\)60.35\% & n/a & \(-\)0.63\% & 0.44\% & 0.61\% & \(-\)21.75\% \\ \hline DWQI & 2.55\% & \(-\)29.41\% & n/a & 0.74\% & 2.07\% & 1.64\% & \(-\)1.04\% \\ \hline CPCB WQI & 4.93\% & \(-\)37.38\% & n/a & \(-\)3.13\% & 1.72\% & 8.59\% & \(-\)3.69\% \\ \hline UWQI & n/a & n/a & n/a & 5.40\% & 6.21\% & 1.35\% & 2.96\% \\ \hline Median & 4.93\% & \(-\)37.38\% & 1.83\% & \(-\)1.42\% & 1.90\% & 1.35\% & \(-\)1.04\% \\ \hline Average & 9.52\% & \(-\)35.07\% & 1.83\% & \(-\)11.49\% & 2.06\% & 1.31\% & \(-\)4.27\% \\ \hline \multicolumn{6}{c}{**Information about hydropower plants**} \\ \hline
**Installed capacity** & 2400 MW & 399 MW & 900 MW & 0.045 MW & 0.07 MW & 1.0 MW & 4.83 MW \\ \hline
**Damming height** & 205 m & 208 m & 105 m & 2.5 m & 2.2 m & 5.2 m \\ \hline
**Type of hydropower** & \multicolumn{3}{c}{impoantment (storage)} & \multicolumn{3}{c}{run-of-river} \\ \hline
**River** & Balui & Jequitinhonha & Lancang & Widawa & Bystrzyca & \multicolumn{1}{c}{Odra} \\ \hline
**Country** & Malaysia & Brazil & China & \multicolumn{1}{c}{Poland} \\ \multicolumn{6}{c}{n/a—data not available.} \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison of water quality indices (WQIs) results within selected hydropower plants.
# Study on Ring Deformation and Contact Characteristics of Thin-Walled Bearing for RV Reducer
## 3 Results and Discussion
### The Effect of the Outer Ring Thickness
\begin{aligned} Q_{i}=\bigg{(}\frac{1}{2}\sum \limits_{k=1}^{Z}Q_{ik}^{3}\bigg{)}^{1/3}\\ Q_{o}=\bigg{(}\frac{1}{2}\sum\limits_{k=1}^{Z}Q_{ok}^{10/3}\bigg{)}^{0. 3}\end{aligned} \right. \tag{37}\]  
where \(Q_{ik}\) and \(Q_{ok}\) are the contact loads of the ball-inner raceway and ball-outer raceway, respectively, calculated by the mathematical model of the bearing. It is evident that the fatigue life remains unaffected by variations in the thickness of the rigid ring. When considering ring deformation, the fatigue life decreases as the ring thickness increases. With the increase in ring thickness, the ring deformation gradually decreases. Consequently, the optimization effect of ring deformation on the load distribution gradually diminishes, resulting in a decrease in the number of loaded balls and an increase in the maximum ball-contact load under flexible ring conditions. According to Equations (30)-(32), the fatigue life decreases and becomes closer to the fatigue life of bearings with rigid rings. Due to the inability to fully simulate rigid constraints, it is difficult to achieve the same bearing life as the rigid ring model, even with larger ring thicknesses. Figure 21 illustrates the axial ultimate carrying capacity of the thin-walled bearings under the influences of radial force and outer ring thickness. It is apparent that the carrying capacity decreases as the radial force increases. The increase in radial force promotes the movement of the contact ellipse toward the raceway rib, making it easier for the bearing to experience elliptical truncation under high radial force conditions. In addition, the thickness of the rigid ring does not affect the carrying capacity. However, the overall carrying capacity of the bearing decreases compared to that of the rigid condition after considering the ring deformation, and the carrying capacity decreases as the ring thickness decreases. From the above discussion, it is evident that while reducing the thickness of  
Figure 20: Influence of the outer ring thickness on contact loads and fatigue life: (**a**) contact loads; (**b**) fatigue life. the ring can improve the optimization effect of the ring deformation on the contact load, it concurrently diminishes the carrying capacity. These results are difficult to obtain from the rigid ring model.
# Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability
## 2 Reliability Assessment Approach  
This part discusses the generally known facts regarding the reliability assessment for the sake of a complete problem overview. One of the main targets of distribution system operators is to supply end-users' demand with acceptable continuation and power quality. However, it is sometimes possible to interrupt large parts of the load point's electrical demand due to small-scale faults in any equipment such as lines, cables, transformers, breakers, switches, busbars, and relays.  
Therefore, the measurements of outage rates and power interruption duration should be taken into account in this sophisticated architecture to determine reliability performance standards [24].  
Load point indices and system reliability indices are frequently used parameters [25] in the literature that are essential in planning, management, and operation stages from the master controller's viewpoint. The fundamental below-mentioned criteria to calculate reliability performance of distribution feeder at the load point are as follows [26]:  
* Load point failure rate (\(\lambda\))
* The average outage time (\(r\))
* Average annual unavailability or outage (\(U\))  
The basic reliability parameters of a distribution system are described in Equations (1)-(3) in which \(i\) shows the index of the components that are assumed to be connected in series between the source and the load point, in terms of failure.  
\[\lambda_{s}=\sum_{i}\lambda_{i} \tag{1}\]  
\[U_{s}\cong\sum_{i}\lambda_{i}r_{i} \tag{2}\]  
\[r_{s}=\frac{U_{s}}{\lambda_{s}}\ \cong\ \frac{\sum_{i}\lambda_{i}r_{i}}{\sum_{i} \lambda_{i}} \tag{3}\]  
It is to be highlighted that load point indices have importance for individual end-users' perspective but are not sufficient for evaluating the whole system performance. Therefore, system reliability indices are additionally used in this study by taking into account the number and the load of end-users connected at each load point in the system [27; 28]. The most commonly used criteria for measuring distribution system reliability performances are categorized into two parts as follows: