esa-sceva/satcom-chunk-collection huggingface dataset:
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 2 Topologies of Electric Drives  
The most common inverter topology in electric drives is the classic three-phase two-level bridge (B6), connected to a three-phase machine (Figure 1) [17; 18]. The B6 converter consists of three transistor legs, each with two transistors, connected in series. The topology itself is quite simple, very well studied, uses only a few elements, and is cost-effective.  
However, with increased rated power, the B6 loses its dominant role. The main reasons for this are as follows: (i) intrinsic constraints of Si-based semiconductor switches (such as MOSFETs and IGBTs) limit the permissible DC link voltage (\(V_{\text{dc}}\)) and phase currents and (ii) the presence of a relatively large total harmonic distortion (THD) of the output voltages and winding \(dv/dt\) stress due to the use of only two voltage levels. Both deviations  
Figure 1: Schematic diagram of a three-phase two-level bridge (B6) connected to a three-phase electric machine.  
generate higher electromagnetic interference (EMI) and accelerate the deterioration of the machine (insulation and bearings wear), which is exacerbated when the DC link voltage increases or when wide bandgap (WBG) semiconductors are used instead of Si-based switches [19; 20].  
These drawbacks are solved by complex topologies that far outperform simple series and parallel connections of semiconductor switches. They also solve the problem of reliability and post-fault operation, which should be achieved even if one (or more) switches suffer a short or open circuit fault. This problem is not only relevant in high-power electric drives, but wherever safety aspects are crucial (automotive, aircraft, important grid-connected inverters). In the following chapters, these complex topologies are divided into series, parallel, and multiphase ones.
# Predicting the Risk of Chronic Kidney Disease (CKD) Using Machine Learning Algorithm
## 2 Materials and Methods
### Regression Methods  
#### 2.2.1 Random Forest  
Random forest (RF) is an ensemble learning method that constructs a multitude of decision trees at training time and outputting mean prediction from individual trees [15]. A basic structure of Random Forest is shown in Figure 1.  
Each sub-tree model does random sampling with replacement from training data and finally average results from all sub-models. Every sub-model runs in parallel without any dependency. In addition to constructing each tree using a different subset of the data, random forests differ in the way of how trees are constructed. In standard decision trees, each node is branched using the optimum decision for division among all variables, so as to minimize entropy due to the splitting of the data set represented by the parent node. In a random forest, split points of each node are randomly chosen from the best split point among a subset of predictors. Random forest thus avoids overfitting, which is common with a single deep decision tree.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ser** & **Variable** & **Type** & **Class** & **Description** \\ \hline
23 & BMI & Numerical & Predictor &
\begin{tabular}{c} calculated using height \\ and weight \\ \end{tabular} \\ \hline
24 & CREATININE & Numerical & Target\_1 & serum creatinine \\ \hline
25 & GFR & Numerical &
\begin{tabular}{c} calculated using Equation (1) \\ \end{tabular} \\ \hline
26 & Stage & Categorical &
\begin{tabular}{c} determined from GFR \\ \end{tabular} \\ \hline
27 & CKD & Binary & Target\_2 &
\begin{tabular}{c} GFR \textless{} stage 2 (class 1) or not \\ (class 0, normal) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage** & **GFR (mL/min)** & **Description** \\ \hline Stage 1 & 90 or higher & normal \\ \hline Stage 2 & 89 to 60 & mild loss \\ \hline Stage 3a & 59 to 45 & mild to moderate \\ \hline Stage 3b & 44 to 30 & moderate to severe \\ \hline Stage 4 & 29 to 15 & severe \\ \hline Stage 5 & less than 15 & failure \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stage of glomerular filtration rate (GFR) [1].
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 2 Requirements of Calibration Target for Radiometric Calibration  
Knowing how to select a calibration target is critical when evaluating the feasibility of a certain calibration target for bistatic SAR radiometric calibration. The calibration targets, including passive corner reflectors and active calibration targets, play an important role in SAR radiometric calibration. In the following, we discuss the basic requirements that should be considered when designing suitable calibration targets for radiometric calibration, including the beamwidth, the stability of the RCS, and the signal-to-clutter ratio (SCR). These requirements are derived from monostatic SAR radiometric calibration in principle; however, they are also prerequisites for determining the feasibility of bistatic SAR calibration targets.
## 5 Robustness Analysis
### Comparisons with Other Algorithms  
We compared the final trajectory error to evaluate the traditional dual-satellite navigation positioning algorithm and the existing advanced positioning algorithm. We chose the traditional dual-satellite positioning algorithm that requires the help of an altimeter (e.g., reference [12; 16; 39]), the traditional dual-satellite positioning algorithm that requires continuous observation (e.g., reference [9; 10; 40]), and other 3-satellite advanced positioning algorithms (reference [41; 42]) for comparison to verify the advantages of our algorithm. Without loss of generality, in our algorithm we only considered two algorithms based on 5 s switching and 60 s switching between adjacent orbits we transformed and unified the relevant data of these algorithms. Where, \"/\" means that the original article is not covered or provided, the collated statistical data are shown in Table 6.  
From Table 6, the comparison with traditional algorithms and some 3-satellite existing advanced algorithms shows:  
Our algorithm is approximately two or three orders of magnitude higher than traditional dual-satellite algorithms in terms of mean longitude performance. The standard deviation is also relatively stable, especially compared to algorithm [40; 41], in which the performance is two or three orders of magnitude higher. Both the mean of longitude and latitude performance are higher than the 3-satellite advanced algorithm [41] when we adopt the 5 s switching algorithm. In altitude performance, regardless of mean or standard deviation, the error of our algorithm is the smallest, indicating that our algorithm is more robust. Figure 13: Error statistics. From the above analysis, we can see that our algorithm has certain advantages in various indicators when compared with the traditional dual satellite positioning algorithm and some existing 3-satellite advanced positioning algorithms. This is due to some of the algorithms requiring the help of an altimeter to complete the positioning or requiring continuous observation to complete the navigation and positioning, while others require the help of many sensors and use three satellites. These types of algorithms inevitably increase the equipment cost, with volume and power consumption of the carrier increasing accordingly, and real-time performance cannot be guaranteed. Our algorithm does not need
# Numerical Analysis of the Forces on the Components of a Direct Diesel Engine
## 3 Kinetics and Dynamics of the Test Engine
### In-Cylinder Pressure and Friction Force Calculation
It reaches a very high value at the position near the top dead center. From there, we determined the force \(P_{T}=p_{c}A\) caused by the pressure applied to the top of the piston. In addition, the crankcase pressure generated by oil pressure on the underside is normally determined in the experiment. This force is very small compared to the total force exerted on the top of the piston. Consequently, it was ignored in this research. Furthermore, the friction force (\(F_{fr}\)) exerted on the piston needed to be determined in this calculated case. \(F_{fr}\) includes the piston-ring friction (\(F_{r}\)) and the friction from the piston surface and cylinder (\(\mu_{p}S\)). The total friction force can be calculated according to Equation (4) [15; 16]. \[F_{fr}=\mu_{p}S+F_{r}=\mu_{p}S+\mu_{r}2\pi\mathrm{D}_{3}rE\bigg{(}1-\frac{D_{3 }-d_{4}}{4r}\bigg{)}\sqrt{1-\frac{\left(D_{3}-d_{4}\right)^{2}}{16r^{2}}} \tag{4}\]  
where \(\mu_{r}\) is the friction coefficient of the contact surface between the ring and cylinder, \(D_{3}\) is the diameter of the cylinder, \(d_{4}\) is the diameter of the ram, \(r\) is the width of the cross-section of the cylinder element, \(E\) is the elastic coefficient of the spring materials, \(S\) is the force resulting from the contact between the piston and the cylinder, and \(\mu_{p}\) is the corresponding friction coefficient. The sliding friction is dramatically reduced by adding a lubricant between the contact surfaces. In addition, in the operation of an internal combustion engine, the piston ring operates from the hydrodynamic lubrication to the boundary lubrication depending on the crankshaft angle, viscosity, and load. This phenomenon can be described by [PERSON]'s equation [17]. In this research, the friction coefficient \(\mu_{r}\) was determined using [PERSON]'s equation as described in Equation (5):  
\[\mu_{r}=c\bigg{(}\frac{\eta v}{pw}\bigg{)}^{x} \tag{5}\]  
where \(c\) is constant, \(h\) is the dynamic viscosity, \(v\) is the piston speed, \(p\) is the pressure exerted on the piston ring, \(w\) is the width of the ring, and \(x\) is the exponent varying from 0.33 to 0.66. Therefore, we calculated the total friction force based on the number and parameter characteristics of the test engine. The inertia force caused by the movement of the piston and rod was also dramatically affected by the piston; it will be described in the following sections. Figure 3: The in-cylinder pressure profile using the experimental and numerical methods.
## 4 Case Study
### Conducting Research  
The research was conducted through five mutually complementary phases (Figure 1). This research begins by first defining the problem and research objectives. The problem itself arises from the set research objectives, which is that from all possible suppliers, it is necessary to choose the one that will contribute to the improvement of MAMEX's business and who will become a business partner that the company can rely on. Choosing the wrong supplier can lead to weaker business results because they cannot contribute to the same extent as another supplier could. Therefore, the next phase of this research is the identification and systematization of criteria. The DM was used in this phase. First, potential criteria that could be used were identified, and from that number of criteria, 15 were selected by elimination, systematization and further analysis and were reduced to 10 criteria that, in the opinion of the DM, were the most important for this selection. Next, the identification of suppliers was carried out with the DM and eight suppliers were selected who met the set conditions. The next phase follows the selection of criteria and potential suppliers, which is the creation of a questionnaire. This questionnaire was then forwarded to the DMs who filled it out and returned it. The data were then collected and prepared for analysis. During phase 5, intuitionistic fuzzy models were formed and prepared for determining the importance of the criteria and for evaluating and ranking suppliers. In this case, the SWARA and CORASO methods will be used.  
Figure 1: Research phases. Source: author composition.
# High-Speed Micro-Grooving of Metal by Angled Irradiation of Single-Mode CW Fiber Laser
## 3 Experimental Results and Discussion  
### Effects of Incident Angle and Scanning Speed  
The characteristics of the micro-grooves formed on the surface of mild steel (SS400) at various incident angles were examined. The mechanical slider setup was used for this experiment. Cross sections of the formed micro-grooves are shown in Figure 3, and the relationship between the incident angle, \(\theta_{\rm i}\), and the groove depth, \(D_{\rm g}\), is shown in Figure 4. The incident angle, \(\theta_{\rm i}\), was varied from 0 to 60\({}^{\circ}\). The scanning speed, \(V_{s}\), and irradiated laser power, \(P\), were fixed at 2 m/s and 200 W, respectively.  
As shown in Figure 3, deeper micro-grooves approximately 20 \(\upmu\)m in depth were formed by angled irradiation, whereas no noteworthy grooves were formed by perpendicular irradiation (\(\theta_{\rm i}=0^{\circ}\)). The depth of the micro-grooves increased with increasing incident angle despite the decreasing power density. Note that a micro-groove was formed not on the incident side but on the reflection side by angled laser irradiation. The cross-sectional profile was different from that obtained by pulsed laser processing reported in a previous study [17].  
The micro-groove shapes obtained at various scanning speeds were also examined using the beam scanning system. The scanning speed, \(V_{s}\), was varied from 1 to 6 m/s. The cross sections of the micro-grooves are shown in Figure 4. The incident angle, \(\theta_{\rm i}\), and laser power, \(P\), were fixed at \(\theta_{\rm i}=45^{\circ}\) and \(P=200\) W, respectively. Therefore, the irradiated power densities, \(P_{\rm d}\), on the surface of the workpiece were constant at 14.7 MW/cm\({}^{2}\) for all conditions. However, the energy density, \(E_{\rm d}\)  
Figure 4: Cross sections of micro-grooves obtained at various scanning speeds (\(P=200\) W, incident angle (\(\theta_{\rm i}\)) = 45\({}^{\circ}\), \(d_{\rm s}\) = 35 \(\upmu\)m, and power density (\(P_{\rm d}\)) = 14.7 MW/cm\({}^{2}\), mild steel).  
Figure 3: Cross sections of micro-grooves obtained at various incident angles (Power (\(P\)) = 200 W, scanning speed (\(V_{s}\)) = 2 m/s, and diameter in the processing direction (\(d_{\rm s}\)) = 18 \(\upmu\)m, mild steel).  
decreased with increasing scanning speed, \(V_{s}\), because of the decrease in the transit time, \(T_{t}\), as shown in Equation (3) and Figure 2. The transit time, \(T_{t}\), and energy density, \(E_{d}\), are also listed in Figure 4. A maximum groove depth of 30 \(\upmu\)m was obtained at a scanning speed of \(V_{s}\) = 2 m/s. The groove depth decreased at higher scanning speeds, as shown in Figure 4, because of insufficient energy density. Only shallow grooves were formed at a low scanning speed of \(V_{s}\) = 1 m/s. The behavior of the molten metal depends on the scanning speed, which affects formation of micro-grooves and upheaval. The groove formation mechanism will be discussed with the results of high-speed observation in the following sections.
## 2 Materials and Methods
### Analysis by GC-MS and HPLC-PDA/ESI-MS
#### 2.3.4 HPLC-PDA/ESI-MS Analysis  
HPLC-PDA/ESI-MS analyses were conducted on a Shimadzu liquid chromatography system comprising a CBM-20A controller, two LC-20 AD dual-plunger parallel-flow pumps, a DGU-20A5R degasser, a SIL-20 AC autosampler, and an LCMS-2020 MS system, through an ESI source (Shimadzu, Kyoto, Japan). Chromatographic separations were performed on 150 \(\times\) 4.6 mm; 2.7 \(\upmu\)m Ascents Express C18 column (Merck Life Science, Merck KGaA, Darmstadt, Germany). The mobile phase was composed of the following two solvents: water (solvent A) and acetonitrile (solvent B), both acidified with 0.1 \(v/v\) of formic acid. Flow rate was fixed at 1 mL min\({}^{-1}\) under the following gradient elution: 0-5 min, 0-2% B, 5-10 min, 2% B, 10-25 min, 2-12% B, 25-40 min, 12-60% B, 40-50 min, 60-70% B. MS analyses were carried out in negative and positive modes, and the conditions were as follows: scan range and scan speed were set at \(m/z\) 100-1200 and 5000 \(\upmu\)s, respectively, event time was 0.3 s, nebulizing gas (N\({}_{2}\)) flow rate was 1.5 L min\({}^{-1}\), drying gas (N\({}_{2}\)) flow rate was 15 L/min, interface temperature was 350 \({}^{\circ}\)C, heat block temperature was 300 \({}^{\circ}\)C, DL (desolvation line) temperature was 250 \({}^{\circ}\)C, DL voltage was 1 V, and the interface voltage was 4.5 kV. The volume of injection was 10 \(\upmu\)L.
## 4 Model Testing and Results Analysis
### Measurements of Effectiveness  
To evaluate the forecasting results, the following scores were adopted: The root-mean-square error (RMSE), the mean absolute error (MAE), the coefficient of determination (\(R^{2}\)), and the explained variance (EV).  
\[R^{2}=\frac{\sum_{i=1}^{n}[(y_{i}-g)\cdot(\hat{y}_{i}-g)]^{2}}{\sqrt{\sum_{i=1}^ {n}(y_{i}-g)^{2}}\cdot\sqrt{\sum_{i=1}^{n}(\hat{y}_{i}-g)^{2}}}, \tag{15}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}(y_{t}-\hat{y}_{t})^{2}}, \tag{16}\]  
\[\text{MAE}=\frac{\sum_{t=1}^{n}[y_{t}-\hat{y}_{t}]}{n}, \tag{17}\]  
\[\text{EV}=1-\frac{\text{Var}(\hat{y}-\mathbf{y})}{\text{Var}(\mathbf{y})}, \tag{18}\]  
where \(y_{t}\) are the actual values, \(\hat{y}_{t}\) are the corresponding forecasted values, and \(n\) is the number of measurements.
# Progressive Poling of Large Area, High r\({}_{33}\) Electro-Optic Polymer SEO100c
## 7 Conclusions  
Specific poling parameter behavior with respect to poling voltage, temperature, and long-term stability have been quantified. A non-linear relationship between the \(r_{33}\) and the strength of the poling voltage has been empirically measured and reported showing potential design and production pitfalls when assuming linear poling behavior. It has been shown that an \(r_{33}\) of 81 pm/V can be achieved without significant risk of dielectric breakdown in a SEO 100c waveguide device structure made from commercial polymers. For voltages in the linear poling region, the SEO100c polymer increases its \(r_{33}\) by 0.37 pm/V for every additional 1 V/\(\upmu\)m applied to the device during the poling process. Up until the \(r_{33}\) saturation that occurs at the poling temperature of 130 \({}^{\circ}\)C, increasing the temperature at which the SEO100c polymer is poled increases the \(r_{33}\) of the EO-polymer. Increasing both the temperature and poling voltage increase the measured \(r_{33}\) value of EO waveguide devices in a non-linear behavior. In practice, poling conditions must also be empirically validated as it was shown that measuring the air temperature does not accurately reflect the device temperature. Additionally, the stability of SEO100c \(r_{33}\) has been shown for a two-month period.  
Conceptualization, M.M., E.G. and C.M.; Data curation, M.M. and C.M.; Formal analysis, M.M. and E.G.; Funding acquisition, C.M.; Investigation, M.M. and E.G.; Methodology, M.M., E.G. and C.M.; Project administration, C.M.; Resources, M.M. and C.M.; Supervision, C.M.; Validation, M.M. and E.G.; Visualization, M.M.; Writing--original draft, M.
## 3 Results
### Mechanical Properties  
#### 3.4.1 Microhardness  
The microhardness of both as-built print sets (80 \(\upmu\)m and 140 \(\upmu\)m hatch distance) and that of as-cast sets were determined and compared. As illustrated by the bar chart in Figure 9, hatch distance variation did not significantly influence the microhardness of the as-built print sets as there was only a marginal difference in microhardness between the 80 \(\upmu\)m (247 HV\({}_{0.2}\)) and 140 \(\upmu\)m (244 HV\({}_{0.2}\)) hatch distance. However, the microhardness of both as-built print sets was far higher than the measured microhardness of as-cast (179 HV\({}_{0.2}\)). The higher microhardness recorded in the as-built print sets can be attributed to a combination of both the residual stresses build-up in the as-built condition and the finer microstructures of LPBF printed samples which enhance mechanical properties compared to conventional manufactured methods like casting [26; 39]. Post heat treatment (PHT) performed on the as-built samples at 1000 \({}^{\circ}\)C for one hour saw the microhardness significantly reduced to 221 HV\({}_{0.2}\) and 210 HV\({}_{0.2}\) for both as-built print sets with 80 \(\upmu\)m and 140 \(\upmu\)m hatch distance. The loss in microhardness may be associated with the relief of stresses and the coarsening of the microstructure through PHT. Even after PHT, the LPBF print set recorded higher microhardness than the as-cast material. Figure 9 and Table 3 show a comparison of the microhardness at different printing conditions.  
Figure 8: Inverse pole figures of as-built samples: (**a**) 80 \(\upmu\)m hatch distance; (**b**) 140 \(\upmu\)m hatch distance showing columnar orientation along the building direction.  
Figure 7: EDS mapping showing coarse NbC particles in an as-cast sample.
# Na-Doping Effects on Thermoelectric Properties of Cu\({}_{2-x}\)Se Nanoplates
## 4 Conclusions  
In conclusion, surfactant-free Cu\({}_{2-x}\)Se nanowires and nanoplates have been successfully synthesized by an aqueous approach, and a Na-doped Cu\({}_{2-x}\)Se nanoplate was prepared by heat treatment of Cu\({}_{2-x}\)Se nanoplates in a sodium hydroxide-ethylene glycol solution at 413 K for 24 h. The decreasing carrier concentrations and electrical conductivities upon Na-doping indicate that sodium cations act as an n-type dopant, which is a key for increasing Seebeck coefficient and decreasing electronic thermal conductivity. The \(ZT\) value of 0.24 was obtained at 673 K from Na-doped Cu\({}_{2-x}\)Se nanoplate, which is much higher than that of undoped Cu\({}_{2-x}\)Se nanoplate (\(ZT\) \(\sim\)0.09 at 673 K). This study provides a strategy for improvement of the thermoelectric performances.  
This research was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (NRF-2015R1A5A1036133).  
All authors participated in the research, data analysis and edition of the manuscript. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflicts of interest.