esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The MKMC-CSVSF method is presented for the state estimation with system uncertainties and heavy-tailed non-Gaussian measurement noises are proposed in this paper. The integration of the CKF and SVSF is achieved by the relationship of a constant and time-varying smooth boundary layer. Additionally, the utilization of the MKMC is introduced to improve the robustness to multivariate heavy-tailed non-Gaussian measurement noises. Furthermore, modifications to the covariance matrices in the CKF and SVSF are implemented via a fixed-point algorithm. A fuzzy rule is devised for optimal selection of kernel bandwidths. Experimental results demonstrate that the MKMC-CSVSF outperforms other methods when dealing with systems containing uncertainties and multivariate non-Gaussian noises, thereby producing notable enhancements in accuracy and robustness. Moreover, it is advisable to apply this approach in fields such as aerospace, mechanical systems, and aircraft, where heavy-tailed non-Gaussian noises are frequently encountered, to achieve accurate estimation.  
Conceptualization, H.N.; Formal analysis, S.L.; Funding acquisition, H.N.; Methodology, H.N.; Project administration, S.L.; Validation, H.N.; Writing--original draft, H.N.; Writing--review and editing, H.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Shannxi Province (No. 2023-JC-QN-0751).  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.
## 3 Results and Discussions
### Material Removal Rate  
Denoting the relative displacement of the workpiece with respect to the tool bit as \(L_{k}\), the material removal rate (MRR) after cutting time \(T_{k}\) can be expressed as follows:  
\[\text{MRR}_{k}=\frac{\pi D^{2}}{4}\frac{L_{k}}{T_{k}} \tag{1}\]  
where \(D\) is the tool diameter.  
Figure 6: Photographs taken at cutter edges after producing 5 holes in CD (**a**,**c**,**e**) and UAD (**b**,**d**,**f**); an axial force of 6 kgf (**a**,**b**), 9 kgf (**c**,**d**), or 12 kgf (**e**,**f**) was applied.  
The average MRR of each test was calculated using Equation (1), where \(L_{k}\) was substituted by the maximum hole depth obtained and \(T_{k}\) was the corresponding time taken. It should be noted that \(L_{k}/T_{k}\) is actually the feed rate obtained. Ratios between the MRRs of UAD and CD tests at the same axial force and hole number were also found. Figure 7a shows the MRR (values on the left axis) and feed rate (values on the right axis) obtained in all tests. The MRR ratios of UAD compared with those of CD tests are shown in Figure 7b.  
As indicated in Figure 7a, at the same level of axial force, the MRR of UAD was significantly higher than that of CD. Under the same drilling conditions, MRRs of later holes were usually smaller than those of the earlier ones as a typical effect of tool wear. It was also noted that the MRR of UAD at 9 kgf axial force was always smaller than that at 6 kgf or 12 kgf axial force. In contrast to CD, higher axial force did not provide a higher MRR and thus there would exist a proper force providing the highest MRR.  
In Figure 7b, when applying 12 kgf axial force, the highest MRR ratio of 3.5 appeared at hole 5, meaning that the MRR of UAD was as high as 3.5 times the MRR of CD. In addition, both MRR ratios at 12 kgf and 9 kgf of axial force were all increased for later holes. In these cases, the cutting ability of the tool would decrease faster in CD, causing the MRR in CD to gradually drop down as shown in Figure 7a. However, the MRR ratio in the case of 6 kgf axial force fluctuated in the range of 2.2 to 3, distinctive from that of either 12 kgf or 9 kgf.
## 5 Discussion  
We have previously discussed the effect of processing parameters on porosity, the pore size distribution and the fraction of each defect type. In general, the aim of such a parameter study is usually to minimize porosity and pore sizes (especially maximum pore size) and to avoid cracks, which can easily expand when the material is stressed. We have shown that not all these aims could be achieved at the same time. We have selected five process conditions, which are favorable regarding one or more defects.  
Table 5 summarizes the descriptions of two different parameters. While the parameters (950 mm/s, 285 W) feature the lowest porosity of 0.37%, the parameters (1400 mm/s, 315 W) lead to a lightly higher porosity of 0.43%. However, this condition leads to a smaller pore median \(d_{50,2}\) (61 vs. 92 um), a smaller maximum pore size (116 vs. 126 um) and with a lower area fraction of cracks, which may be a critical issue for mechanically loaded applications. The perimeter accuracy is lower which may be further optimized by specific contour parameters.  
Figure 6: Shape and contour accuracy determined from the ratio of the actual sample perimeter and its theoretical perimeter \(P_{C}\)/\(P_{th}\). Low values stand for a smooth sample surface.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Scan** & **Laser** & **Porosity** & \(d_{50,2}\) & \(\sigma\) & \(d_{max}\) & \(\varepsilon_{cracks}\) & **PA** \\
**Speed** & **Power** & **\%** & **\#m** & **-** & **\#m** & **ppm** & **-** \\
**mm/s** & **W** & & & & & & \\ \hline
950 & 285 & **0.37** & 92 & 1.18 & 126 & 57 & **2.46** \\ \hline
1400 & 315 & 0.43 & **61** & 1.59 & **116** & **29** & 3.15 \\ \hline \end{tabular}
\end{table}
Table 5: Selection of process conditions, optimal values are marked bold.  
The decision as to which of these conditions is used to build final parts depends greatly on the application. For very high cycle fatigue applications, a strict minimization of cracks and maximum pore diameter is crucial. For applications with minor stressing, the weighting of objectives may be different. In our opinion, it is important to be aware of these different targets quantitatively instead of focusing on the porosity level.  
Such detailed data can now be acquired in much shorter time. Previous analysis methods took around 10 min for each sample, including determination of pore size distributions. Our integrated analysis solution now takes approximately 10 s per sample, resulting in a time decrease with a factor of 50-60.
# Comprehensive Evaluation of a Sparse Dataset, Assessment and Selection of Competing Models
## 5 Results
### Comparison Between Hurdle Models, Truncated Count Component, with Different Distributions  
Based on the discussion in the paragraph above, hurdle models were identified as the best-fit model across the included competing models. However, this model is a two-component model with a truncated count component. The truncated count component, which is used for positive count, can take different distributions. This section considers only Poisson and negative binomial distributions as these two distributions are more likely to be able to fit non-zero portions of this model. It is clear that Poisson is not suitable for our dataset, but for comparison purposes and to visualize how they fit the observations, these two models are compared to see how they fit the observations. The rootogram was chosen for comparison across these models to make sure the right distribution is chosen for a final model.  
The rootograms allow us to visualize if the model is over- or under-fitting for different values of EPDO as response. For rootograms, only the values less than EPDO = 50 were considered to improve readability. These values accounted for more than 98% of all the included observations.  
As discussed in the method section, the first line observed in these figures is related to the height of observations with zero value (EPDO = 0). As can be seen from rootograms, Figure 2a,b, the hurdle components of the models that model zero counts fit zero values quite well. It is expected for these models to fit zero count perfectly by design. However, when EPDO is equal to 2 or 3, while a dramatic under-fitting can be observed for Poisson distribution, the negative binomial layer could fit those values quite well.  
In other words, for the Poisson model, the substantial amount of over-dispersion in the data could not be accounted for by this model. It should be noted that the EPDO values equal to 2 or 3 account for 50% of all the EPDO > 0. From EPDO = 6 onward to EPDO = 14, the Poisson model basically does not predict any count because it is over fitting, while the negative binomial distribution, for the most part, fits the data fine. In summary, as can be seen from Figure 2, the negative binomial model fits the model quite well while the Poisson model suffers from over-dispersion/under-dispersion issues.
## 3 Simulation Results and Discussions
### CMOR Wavelet Parameters for Water Molecule Vibration  
In our previous study, the FT of the atomic trajectories indicated that the water molecule vibration includes three frequencies, namely, O-H bending vibration (1560.6 cm\({}^{-1}\)), O-H symmetric stretching vibration (3711.1 cm\({}^{-1}\)), and O-H asymmetric stretching vibration (3819.3 cm\({}^{-1}\)) [34]. To select the appropriate \(f_{b}\) and \(f_{c}\), the three vibrational frequencies of the water molecule here are represented as cosine signals, which are \(u_{1}(t)=\cos(2\pi 1560.6\%c_{c}t)\), \(u_{2}(t)=\cos(2\pi 3711.1v_{c}t)\), and \(u_{3}(t)=\cos(2\pi 3819.3v_{c}t)\) in order. The \(v_{c}\) represents the speed of light in cm/s. The signal duration is \(T_{0}=10^{-10}\) s and the sampling frequency \(f_{s}=5\times 10^{14}\) Hz.  
For O-H bending vibration, it is clear that \(f_{k}=1560.6\) cm\({}^{-1}\), \(f_{n}=3711.1\) cm\({}^{-1}\), and \(\delta f=2150.5\) cm\({}^{-1}\). Let \(\delta f^{\prime}=100.0\) cm\({}^{-1}\); then, we can get \(3.75\times 10^{-12}\) s \(<10^{-10}\) s by plugging \(\delta f^{\prime}\) and \(T_{0}\) into Equation (16). This indicates that it is unnecessary to extend the signal duration. By plugging the values of \(f_{k}\) and \(\delta f^{\prime}\) into Equation (17), we can get \(f_{b}=6.0\) and \(f_{c}=3.0\). Consequently, the CMOR wavelet transform of O-H bending vibration is shown in Figure 2.  
It can be seen from Figure 2a that the target frequency \(f_{k}\) always has the maximum intensity over the signal duration \(T_{0}\). From Figure 2b, the intensity of 3711.1 cm\({}^{-1}\) and 3819.3 cm\({}^{-1}\) are both close to zero. The edge effect of 1560.6 cm\({}^{-1}\) is almost negligible. From Figure 2c, we can find that \(\Delta f<\delta f\). These results indicate that \(f_{b}=6.0\) and \(f_{c}=3.0\) given by Equation (17) are the optimal wavelet parameters for the target frequency \(f_{k}\). Similarly, the optimal CMOR wavelet parameters for the symmetric and asymmetric stretching vibration of O-H were also derived from Equation (17) and listed in Table 1. As shown in Table 1, the CMOR wavelet parameters of \(f_{b}=20.0\) and \(f_{c}=6.0\) for symmetric stretching vibration are obtained when the artificial \(\delta f=70.0\) cm\({}^{-1}\) is set. Obviously, we can find that \(\Delta f=74\) cm\({}^{-1}\) and \(\delta t=1.024\)\(\times\)\(10^{-12}\) s are smaller than \(\delta f=108.2\)cm\({}^{-1}\) and \(0.2T_{0}\), respectively. Similar results can be obtained from the CMOR parameters \(f_{b}=24\) and \(f_{c}=7.0\) of asymmetric stretching vibration. These results show that the CMOR wavelet parameters of symmetric and asymmetric stretching vibration not only satisfy the requirement of frequency resolution but also satisfy the limit of edge effect.
## 3 Solution Methodology  
### Perturbation/Series Solution  
Equation (20) is a nonlinear PDE and its closed form solution is not possible. Therefore, it can be solved analytically by perturbation technique about \(W_{e}\) parameter, by expanding \(\Psi\), \(\Theta\), \(dp/dx\) and \(f\) in the following forms [25]:  
\[\Psi=\Psi_{0}+W_{e}^{2}\Psi_{1}+O\big{(}W_{e}^{2}\big{)}^{2}, \tag{34}\]\[\Theta=\Theta_{0}+W_{e}{}^{2}\Theta_{1}+O\Big{(}W_{e}{}^{2}\Big{)}^{2}, \tag{35}\] \[\frac{dp}{dx}=\frac{dp_{0}}{dx}+\frac{W_{e}{}^{2}dp_{1}}{dx}+O\Big{(} W_{e}{}^{2}\Big{)}^{2},\] (36) \[f=f_{0}+W_{e}{}^{2}f_{1}+\Big{(}W_{e}{}^{2}\Big{)}^{2}. \tag{37}\]
## 6 Simulation Results and Discussion
### Scenario (1)
#### 6.1.3 IEEE 85-Bus Test System  
The 85-bus test case employed a radial distribution system consisting of a main feeder, four sub-feeders (laterals), and 13 sub-laterals. The data for the system were obtained from [27] and can be found in the Appendix C. The total load of the system was 2574.3 kW and 2622.6 kVAr.  
For the 85-bus radial distribution system case, as detailed in Table 5, installing three DSTATCOMs at buses 8, 34, and 67 resulted in the most cost-effective outcome, realizing an annual profit of USD 47,212.50. This optimal configuration substantially reduced the total losses to 190.51 kW, marking a decline of 39.75% compared to the base case. As illustrated in Figure 12, the voltage profile improved significantly, along with the economic advantages and loss reductions, with the minimum voltage level elevated to 0.95304 p.u., while maintaining the maximum voltage level of 1.0 p.u. at the source bus. The convergence behavior of the I-GWO algorithm for the three test systems is depicted in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Outputs**} & \multirow{2}{*}{**Base Case**} & \multicolumn{3}{c}{**Number of DSTATCOMs**} \\ \cline{3-5}  & & **Single** & **Two** & **Three** \\ \hline \multirow{3}{*}{Optimal size (kVAr) and location} & & & 556.34 (17) & 820.24 (9) \\  & & 1850 (61) & 1474.55 (61) & 500.03 (17) \\ \cline{1-1} \cline{2-5}  & & & 1351.73 (61) \\ \cline{1-1} \cline{2-5}  & & 224.99 & 158.49 & 153.73 & 154.12 \\ \cline{1-1} \cline{2-5}  & & 29.56 & 31.67 & 31.50 \\ \cline{1-1} \cline{2-5}  & & 0.90919 & 0.93665 & 0.95144 & 0.95219 \\ \cline{1-1} \cline{2-5}  & & 0.68331 & 0.76968 & 0.81947 & 0.82205 \\ \cline{1-1} \cline{2-5}  & & 118,254.41 & 83,301.84 & 80,798.93 & 81,006.31 \\ \cline{1-1} \cline{2-5}  & & 9812.33 & 10,771.76 & 14,172.17 \\ \cline{1-1} \cline{2-5}  & & 25,140.24 & 26,683.72 & 23,075.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for the IEEE 69-bus system.  
Figure 11: Effect on voltage profile for the 69-bus system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Outputs**} & \multirow{2}{*}{**Base Case**} & \multicolumn{3}{c}{**Number of DSTATCOMs**} \\ \cline{3-5}  & & **Single** & **Two** & **Three** \\ \hline \multirow{3}{*}{Optimal size (kVAr) and location} & & & 2150.00 (9) & 1929.15 (8) \\  & & 2150 (32) & 1286.23 (32) & 896.70 (34) \\ \cline{1-1} \cline{2-5}  & & 229.11 & 200.08 & 190.51 \\ \cline{1-1} \cline{2-5}  & & 32.12 & 36.72 & 39.75 \\ \cline{1-1} \cline{2-5}  & & 0.87129 & 0.93176 & 0.95219 & 0.95304 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for the IEEE 85-bus system.
## 2 Materials and Methods
### ZnO-NPs Characterization
#### 2.3.3 Transmission Electron Microscopy (TEM)  
TEM was utilized as a characterization technique to ascertain the size and crystalline properties of the different ZS-ZnO-NPs. To prepare nanoparticles for TEM measurements, the ZS-ZnO-NPs were first dispersed in ethanol dispersant solvent, sonicated for 30 min to ensure the proper distribution of the nanoparticles, and then a small drop of the dispersion (\(\approx\)2 \(\upmu\)L) was placed onto a carbon-coated copper grid (CCCu grid) and left to dry completely. The CCCu grid was loaded onto a Talos S200 microscope (FEI, Hillsboro, OR, USA) and examined at an acceleration voltage of 200 kV under the electron beam to obtain high-resolution images of the nanoparticles and analyze their crystal structure. The elemental information labeling and the FFT function (Fast Fourier Transform) of ZS-ZnO-NPs were accomplished using ImageJ-J, a widely used open-source image processing software in scientific research [43; 44]. This software provided valuable insights into the structure and content of the image.
# Forced Circulation of Nitrogen Gas for Accelerated and Eco-Friendly Cooling of Metallic Parts
## 4 Conclusions and Future Work  
An innovative nitrogen cooling furnace system was developed in this study for achieving a high-quality cooling process. In this process, the dry nitrogen in the cooling chamber exchanged heat with the workpiece it contacted, resulting in an accelerated cool-down. The heated nitrogen gas was forcibly circulated and transported to the chiller in a closed loop, where the heat was reduced, and the cooled gas was circulated again. The implementation tests using tubular THA confirmed that nitrogen gas cooling is an eco-friendly cooling medium, which can produce cleaner products. In addition, it was observed that this cooling system provided more uniform cooling and less distortion than water cooling. The cooling rate of nitrogen gas cooling is lower than that of water cooling; however, it was significantly enhanced by controlling the circulation speed and pressure. Future work concerns system upgrades to increase the cooling capacity of the nitrogen cooling system. Obviously, the optimal combination of gas pressure with circulation speed will be investigated since they have an important influence on the cooling capacity.  
Formal analysis: J.H.K.; investigation: Z.S.P.; methodology: J.K.; software: Y.Y.W.; supervision: Y.H.M.; validation: H.L.  
This research was funded by National Research Foundation of Korea, grant number 12R1A5A1048294.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Nitrogen Cooling (NC)** & **Water Cooling (WC)** & **Remarks** \\ \hline Equipment cost & Intermediate & Low & NC: needs vacuum chamber \\ Floor space & Low & Intermediate & WC: needs water reservoir \\ Operation cycle & Fast & Slow & - \\ Installation cost & Low & Very High & WC: water-proof design \\ Rearrangement cost & Low & Very High & WC: needs pit reconstruction \\ Eco friendly & High & Low & WC: contaminated sludge \\ Operating cost & Low & Low & \\ Maintenance cost & Very Low & Intermediate & WC: plumbing, leakage, water pumping \\ Post machining & Low & High & WC: low circularity \\ Yield ratio & High & Low & WC: large machining loss \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of costs for both nitrogen and water cooling.
## 5 Development and Utilization of the CCM (Color-Coded Map)  
### Development Method of the CCM  
The equation for water content found from hyperspectral information, developed through machine learning in Section 4, was applied to the orthophoto image shown in Sec  
Figure 14: Comparison of measured and predicted water content.  
Because the developed CCM includes coordinates, it can be used by overlapping with programs that can check existing aerial satellite images, such as Q-GIS 3.34.3 and Google Earth Pro 7.3.3. Figure 17 shows an example of the use of CCM colors for specific purposes in Google Earth. Based on the Google Earth linkage of the basic orthophoto in Figure 17a, the water content can be expressed in units of 2.5% increases in water content, shown in Figure 17b. In addition, it is possible to indicate high and low areas simply based on the optimal water content of the ground, as shown in Figure 17c, and the area of dust scattering generated at the construction site can be displayed, as shown in Figure 17d.  
To confirm the accuracy of CCM data, the actual function ratio was measured for 10 spots. The results are shown in Table 3. At this time, the coefficient of determination (R\({}^{2}\)) was 0.872, which was similar to the coefficient of determination of 0.888 in the prediction equation of water content through MLR. Through this, the reliability of the CCM can be confirmed.
# Role of Ion-Exchange Resins in Hydrogen Reactions
## 3 Ion-Exchange Resins Application to Hydrogenation Reactions
### Hydrogenation of Nitrates
The simultaneous catalytic reduction of nitrate ions and reductive dehalogenation of organochlorinated pollutants from water was studied by [PERSON] and associates, who prepared bimetallic Pd-Cu catalysts over Purolite(r) A520E, which is a strong base anion exchange resin of the macroreticular type, presenting quaternary ammonium functional groups on an ST-DVB polymer matrix [101]. Four catalysts were prepared and compared: (i) a monometallic Pd (2.01 wt.%) over A520E was prepared by ion exchange; (ii) a bimetallic Pd-Cu (1.97 wt.% and 0.45 wt.%, respectively) catalyst sample was prepared by simultaneous ion-exchange of both metal salts with the resin; (iii) a second bimetallic Pd-Cu (1.98 wt.%, 0.48 wt.%) catalyst was prepared by consecutive stages of ion-exchange of each metal salt with the resin, with Pd being exchanged in the first place; and (iv), finally, a third bimetallic Pd-Cu (2.01 wt.%, 0.49 wt.%) catalyst was prepared by Pd ion-exchange, followed by Cu deposition by controlled surface reaction. The latter catalyst sample was able to achieve selective nitrate reduction, with a 95% conversion and 92% selectivity towards N\({}_{2}\), with almost quantitative hydrodecllorination of 4-chlorophenol [101].
## 4 Discussion
In our study, 13 patients with 26 screws (23.2%) in the ASP group and 3 patients with 6 screws in the non-ASP group (9.7%) showed signs of bilateral facet joint violation, accounting for a significant difference between the groups (\(p<0.5\)). This indicated that bilateral facet joint violation plays a role in the onset of ASP. [PERSON] et al. reported that when the cephalad pedicle screws were at the L5 (L5-S1 fusion), facet joint violations were significantly more likely to occur as compared with any other level [24]. In our study, since the L3-4 facet joint is the most superior facet (L4-5 and L4-5-S1 fusion), there was no difference between 44 of 98 screws (44.9%) in the ASP group and 17 of 54 screws (31.5%) in the non-ASP group. However, since the L4-5 facet joint is the most superior facet joint (L5-S1 fusion), 9 of 14 screws (64.3%) in the ASP group and 2 of 8 screws (25%) in the non-ASP group showed facet joint violation. A study on a higher number of patients with superior segment facet joint violations will be necessary to evaluate the significance of these findings. The most important finding of the current study is that the ASP group showed higher superior facet joint violation rates than the non-ASP group: 31 patients (55.4%) had 53 screws (47.3%) which violated the superior facet joint in the ASP group and 13 patients (41.9%) had 19 screws (30.6%) which violated the superior facet joint in the non-ASP group (\(p<0.05\)). Additionally, the bilateral facet joint violation rate was higher in the ASP group than in the non-ASP group: 13 patients (23.2%) had 26 screws (23.2%) which violated the superior facet joint in the ASP group, and 3 patients (9.7%) had 6 screws (9.7%) which violated the superior facet joint in the non-ASP group (\(p<0.05\)). In addition, facet joint involvement was scored according to its severity; the ASP group had an average score of 1.4, whereas the non-ASP group had a score of 0.8 (\(p<0.05\)). These results point to superior facet joint violation caused by transpecicular screws as an important factor in the development of ASP in this study. However, according to result of this study, ASP also occurred in 25 patients without facet joint violation. This is probably due to the presence of other important factors that cause ASP in addition to facet joint violation, and it is necessary to identify other factors that affect ASP thorough further research. There are some limitations of our study. First, the number of patients in this study is small. If the follow-up duration was longer, it seems that clearer results could be obtained. Second, as mentioned above, in addition to facet violation, there are many other important factors that affect ASP including length of fusion, type of instrumentation, sagittal parameters, etc. How these factors actually affect ASP needs to be clarified through future study. Third, as with any retrospective study, selection biases may be present.