esa-sceva/satcom-chunk-collection huggingface dataset:
## Mathematical Formulation  
### Analytical Analysis  
Complete general detailed analytical solution and creep analysis for the rotating thick cylinder subjected to internal and external pressures is derived and presented. The solution is based on the well-documented [PERSON]'s theoretical approach. In this analysis, a thick-walled axisymmetric cylinder with inner and outer radii of \(r_{in}\), and \(r_{out}\), under internal and external pressures \(P_{in}\) and \(P_{out}\) rotating about its own axis with constant angular velocity \(\omega\) considered. It is made of Al-SiC composite, and \(\rho\) is the material density. For the sake of analysis, in the current study, few assumptions have been made. These are:  
* Incompressible and isotropic cylinder material with uniform distribution of silicon carbide in aluminum matrix.
* Gradual increase in applied pressure but remains constant during loading history.
* Steady-state condition of stress at any point in the cylinder.
* Small elastic deformations can be neglected in comparison with creep deformations.  
However, a brief account of derivation and a summary of the main governing equations and formulations used in the study, together with the conditions applied, are given in the next sections.  
The equilibrium equation is given as  
\[\frac{\delta\sigma_{r}(r)}{\delta r}+\frac{\sigma_{r}(r)-\sigma_{t}(r)}{r}+ \rho\omega^{2}r=0 \tag{1}\]  
where \(\sigma_{r}(r)\) and \(\sigma_{t}(r)\) represent stresses in the respective directions.  
As the creep deformations do not undergo considerable volume changes, one may write.  
\[\dot{\epsilon}_{r}+\dot{\epsilon}_{t}+\dot{\epsilon}_{z}=0 \tag{2}\]  
where \(\dot{\epsilon}_{r},\dot{\epsilon}_{t}\) and \(\dot{\epsilon}_{z}\) are the strain rates in the radial, transverse, and \(z\)-axis, respectively.  
It is assumed that the condition of plain strain prevails as a cylinder is inhibited from contracting axially. Therefore,  
\[\dot{\epsilon}_{z}=0 \tag{3}\]  
Solving Equations (2) and (3) result in Equation (4)  
\[\dot{\epsilon}_{r}=-\dot{\epsilon}_{t} \tag{4}\]For cylinder material creep behavior, it is assumed to follow the Norton Equation, given as  
\[\dot{\epsilon}_{e}=B\sigma_{e}{}^{n} \tag{5}\]  
where, \(\sigma_{e}\) is effective stress, \(\dot{\epsilon}_{e}\) is effective strain rate subjected to multi-axial stress, \(B\) and \(n\) are creep constants of materials.  
The generalized constitutive creep equations for the isotropic composite are:  
\[\dot{\epsilon}_{r}=\frac{\dot{\epsilon}_{e}}{\sigma_{e}}\bigg{(}\sigma_{r}- \frac{1}{2}(\sigma_{t}+\sigma_{z})\bigg{)} \tag{6}\]  
\[\dot{\epsilon}_{t}=\frac{\dot{\epsilon}_{e}}{\sigma_{e}}\bigg{(}\sigma_{t}- \frac{1}{2}(\sigma_{r}+\sigma_{z})\bigg{)} \tag{7}\]  
\[\dot{\epsilon}_{z}=\frac{\dot{\epsilon}_{e}}{\sigma_{e}}\bigg{(}\sigma_{z}- \frac{1}{2}(\sigma_{r}+\sigma_{t})\bigg{)} \tag{8}\]
# Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm
## 3 Case Study  
This section presents the results of the case study for illustrating the applicability and performance of the hybrid NSGA-II algorithm. The results of the case study intend to provide an optimal resource scheduling solution considering a balance between the minimum operating costs and minimum CO\({}_{2}\) emissions. The case study is composed of a distribution grid with 37 buses that are connected to the upstream network by two 10 MVA power transformers [24]. This case study contains 1908 consumers: 1850 domestic houses, two industries, 50 commerce stores, and six service buildings. These consumers are aggregated by buses resulting in 22 aggregated loads. All consumers have two direct load control  
Figure 3: Membership function example presented in [22].  
DR programs: the maximum power reduction of DR_A and DR_B correspond to 3% and 2% of the power consumption in each period. The energy roadmap for 2050 proposed in [25], which takes into account the EU targets [26], was used to establish the scenario for the DG units' capacity. This resulted in a DG penetration composed of photovoltaic (PV) panels with a total installed power of 8 MW, three Combined Heat and Power (CHP) units with a total installed power of 1.5 MW, and four storage systems with the capability of storing 750 kWh. The penetration of EVs is around 50% and has been established based on the study developed in [27], resulting in 2000 EVs connected to this distribution network. Figure 4 shows the scheme of the 37-bus distribution network considering the DER's penetration, where the substation (bus 1) connects this network to the system. The substation is composed of two transformers, with a power capacity of 10 MVA each, in which the energy from the 10 external suppliers flows. The input data related to the distribution network and the distributed energy resources are attached to the paper, as Supplementary Materials.  
The hybrid NSGA-II was implemented on MATLAB software. In Appendix A, Table A1 shows the NSGA-II parameters used. For comparison purposes, another weighted sum method is used in this study and is designed by WMINLP (weighted sum mixed-integer nonlinear programming method). This is the most known classical method for handling multi-objective problems [18]. This method was implemented on GAMS software. Both optimization algorithms were executed on a computer with two processors Intel(r) Xeon(r) E5-2620 v2 210 GHz, each one with two cores, 16 GB of random-access-memory.
## 4 Experiment
### Experimental Results
#### 4.4.3 Comparison Experiment on the Effect of Meteorological Variables on the Prediction Results  
To validate how each meteorological variable affected the prediction results, several meteorological variables with fewer missing values, combined with the location (longitude and latitude), were used as the model input. There were six different input groups in total, which contained the location only, the location with VMAX, the location with MSLP, the location with MRD, the location with RADP, and the location with all meteorological variables. The experiment was implemented using the proposed AE-GRU model, and the results were predicted using the previous 72 h locations of the tropical cyclones and meteorological variables to forecast 72 h locations. The performance results, including the average APE, max APE, and min APE, are shown in Table 8, where the lowest values are emphasized in bold. When no meteorological variables were used as the input data, the  
Figure 8: Seventy-two hour forecast results. (**a**) Track prediction of Typhoon Tip. (**b**) Track prediction of Typhoon Meranti. (**c**) Track prediction of Typhoon Dujuan. (**d**) Track prediction of Typhoon Haiyan.  
average APE and min APE were the highest, which indicated that the forecast result was inferior to the use of meteorological variables. In addition, when all meteorological variables were used, the average APE, min APE, and max APE were the lowest. Moreover, when comparing the variables VMAX, MSLP, MRD, and RADP, the results demonstrated that using VMAX along with the location could achieve the best results, the average APE and min APE of which were the lowest. Overall, all meteorological variables could contribute to tropical cyclone track prediction, and the meteorological variable maximum sustained wind speed (VMAX) was the most effective.
## 3 Numerical Simulations
### Grid Strategy
The INLET boundary contains lines a, b, and c. The OUTLET boundary contains line d, and the WALL boundary has lines e, f, and g. To obtain more accurate results, more attention is needed for the grids near the WALL regions, and Figure 4b shows the close view of grids near the WALL region. Figure 5 shows the close view of grids near the blunt trailing edge and the sharp trailing edge. Three levels of grid refinement are adopted to ensure grid independency and in every refinement, the number of total grids is one times more. Taking the sharp trailing edge with the SST \(k\)-omega model as an example, the values of \(C_{d}\), \(C_{l}\), and error ratios are shown in Tables 1 and 2. As is depicted in Tables 1 and 2, the numerical calculation results of the 590,000 and 840,000 meshes are better than those of the 400,000 meshes. In the case of the 840,000 meshes, the results are better than the 580,000 meshes only in some cases, and the range is limited. Given comprehensive consideration of computing resources and computing time, the mesh with 580,000 was applied. The aspect ratio and the determinant are two critical indicators of the mesh evaluation parameters. In order to meet the conditions of \(y^{+}\) of 0.5 and \(y_{H}\) of 4.4 \(\times\) 10\({}^{-6}\) m, the maximum corresponding aspect ratio value would exceed 10,000 and floating-point overflow or calculation divergence may occur in the simulation process and cause the simulation to fail. In this paper, under the mesh condition with 580,000, the tests show that the maximum aspect ratio of the mesh should be within 5000, and the minimum value of the determinant should be greater than 0.8. For the blunt trailing edge, the maximum aspect ratio and the minimum value of the determinant at the far-field distance of 12 m, 16 m, and 20 m are (2200, 0.894), (2920, 0.89), and (3800, 0.863), respectively. For the sharp trailing edge, the maximum aspect ratio and the minimum value of the determinant are (1680, 0.839), (2280, 0.886), and (3640, 0.875) at the far-field distance of 12 m, 16 m, and 20 m, respectively. Figure 4: (a) Computational domain; (b) close view of grids near the WALL region. Figure 5: (a) Close view of grids near the blunt trailing edge; (b) close view of grids near the sharp trailing edge. Figure 3: The TVR plot with \(y^{+}\) value of 0.5.
## 2 Results and Discussion
### Changes in Polyphenols, Flavonoids and Polysaccharides during fermentation
* represents a significant difference between the laboratory and the factory at the same fermentation time point (\(p<0.05\)). oligosaccharides or monosaccharides, resulting in an increase or decrease in polysaccharide concentration during fermentation [27; 28]. The content of polysaccharide was found to be increased in the fermentation of carrot juice by _Lactobacillus gasseri_, which synthesized fructosyltransferase enzymes during fermentation that enabled the conversion of simple sugars, primarily into polysaccharides [29]. Meanwhile, _Lactobacillus plantarum_ reduced the content of polysaccharide during the fermentation process of five fruit juices [30]. The effects of different microorganisms to release cell wall polysaccharides showed significant differences under the same fermentation conditions [31]. The dominant microorganisms were influenced by fermentation conditions, which could result in changes in the enzyme system and matrix metabolism. [PERSON] et al. [19] reported that there were significant differences in the aroma, color and turbidity between laboratory- and factory-scale fermentation of _Cyclopia intermedia_. In this study, the different fermentation scales may have caused the change of fermentation density and temperature, which resulted in differences between the two fermentation scales. Furthermore, our results (unpublished) showed that the microbial profile of fermented noni juice differed significantly between the laboratory- and factory-scale. In short, our results show that the contents of phenolic, flavonoid and polysaccharide showed significant differences between the laboratory- and factory-scale fermentation processes, which may be due to the different microbial compositions caused by different fermentation scales.
## 4 Discussions
### Limitations and Future Directions  
At the level of experimental tools, this study uses a virtual reality modeling platform to model the experiment, but it has yet to fully exploit its advantage of realism. The experiment only takes spatial scale indicators as the object of study, and the constructed scene itself is relatively simple, with some differences from natural street scenes, which may only partially allow subjects to immerse themselves in the environment for evaluation.  
At the level of the research sample, the subjects selected for this survey were mainly a group of college students with a relatively similar age, which may indicate the uniformity of the education and other attributes of the subjects and may lead to the convergence of the results of this study in some aspects. Considering that the use of VR equipment is not yet widespread, 50.49% of people had experienced VR equipment before the experiment without considering factors such as habits and preferences, which may affect the experiment's results. In addition, considering the acceptance and comprehension issues, there are some difficulties in data collection for both younger and older subjects. Head-mounted VR devices may also make subjects feel emotions such as stress and anxiety, and in the future, more advanced devices are needed to mitigate such effects as much as possible.  
At the level of spatial perception evaluation, although the study covered all the indicators related to the spatial scale as much as possible and filtered them, adjectives such as beautiful, lively, engaging, etc., which are highly associated with the quality of street space, could not be used as evaluation indicators to measure the spatial scale. The study only focused on the factors that influence the spatial scale.  
Therefore, in future research on street space appropriateness, we can add as many research objects as possible to improve the experimental model, increase the fidelity of the model, and take into account the visual, acoustic, and thermal comfort environments, in order to record the state of the research objects from more dimensions and collect multimodal perceptual evaluation data to conduct interactive analyses and validate the accuracy of the experiments.
## Appendix A  
This Appendix gives the derivation of the clipping coefficient and the average optical power of the DCO-, ACO-, and U-OFDM signals.  
\[\begin{split}\alpha_{\text{D}}(b_{\text{DC}})&= \frac{1}{\sigma_{\text{x}}^{3}\sqrt{2\pi}}\int_{-\infty}^{\infty}x\psi_{ \text{D}}(x)\exp\left(\frac{-(x-b_{\text{DC}})^{2}}{2\sigma_{\text{x}}^{2}} \right)dx\\ &=\frac{1}{\sigma_{\text{x}}^{3}\sqrt{2\pi}}\left(\int_{0}^{P_{ \text{m}}}x^{2}\exp\left(\frac{-(x-b_{\text{DC}})^{2}}{2\sigma_{\text{x}}^{2} }\right)dx\right.\\ &\left.+P_{\text{m}}\int_{P_{\text{m}}}^{\infty}x\exp\left( \frac{-(x-b_{\text{DC}})^{2}}{2\sigma_{\text{x}}^{2}}\right)dx\right)\\ &=1-\frac{1}{2}\left(\text{erfc}\left(\frac{-P_{\text{m}}+b_{ \text{DC}}}{\sqrt{2\sigma_{\text{x}}^{2}}}\right)+\text{erfc}\left(\frac{b_{ \text{DC}}}{\sqrt{2\sigma_{\text{x}}^{2}}}\right)\right).\end{split} \tag{10}\]  
where:  
\[\psi_{\text{D}}(x)\!=\!\left\{\begin{array}{ll}0,&x<0\\ x,&0<x<P_{\text{m}}\\ P_{\text{m}},&x>P_{\text{m}},\end{array}\right. \tag{11}\]  
\[\begin{split}\alpha_{\text{U}}&=\frac{1}{\sigma_{\text{x}}^{3 }\sqrt{2\pi}}\int_{-\infty}^{\infty}x\psi_{\text{U}}(x)\exp\left(\frac{-x^{2} }{2\sigma_{\text{x}}^{2}}\right)dx\\ &=\frac{1}{\sigma_{\text{x}}^{3}\sqrt{2\pi}}\left(-P_{\text{m}} \int_{-\infty}^{-P_{\text{m}}}x\exp\left(\frac{-x^{2}}{2\sigma_{\text{x}}^{2} }\right)dx\right.\\ &+\int_{-P_{\text{m}}}^{P_{\text{m}}}x^{2}\exp\left(\frac{-x^{2} }{2\sigma_{\text{x}}^{2}}\right)dx\\ &\left.+P_{\text{m}}\int_{P_{\text{m}}}^{\infty}x\exp\left(\frac{-x^{2} }{2\sigma_{\text{x}}^{2}}\right)dx\right)\\ &=1-\text{erfc}\left(\frac{P_{\text{m}}}{\sqrt{2\sigma_{\text{x}}^{2} }}\right),\end{split} \tag{12}\]  
where:  
\[\psi_{\text{U}}(x)\!=\!\left\{\begin{array}{ll}-P_{\text{m}},&x<-P_{\text{ m}}\\ x,&-P_{\text{m}}<x<P_{\text{m}}\\ P_{\text{m}},&x>P_{\text{m}}.\end{array}\right. \tag{13}\]For DCO-OFDM, the average optical power can be calculated by:  
\[\begin{split}\bar{P}(b_{\text{DC}})&=\int_{0}^{P_{\text {m}}}xf_{\text{D}}(x,b_{\text{DC}})dx\\ &=\int_{0}^{P_{\text{m}}}x\frac{1}{\sqrt{2\pi\sigma_{x}^{2}}} \exp\left(\frac{-(x-b_{\text{DC}})^{2}}{2\sigma_{x}^{2}}\right)dx\\ &+P_{\text{m}}\frac{1}{2}\text{erfc}\left(\frac{P_{\text{m}}-b_{ \text{DC}}}{\sqrt{2\sigma_{x}^{2}}}\right)\\ &=\frac{b_{\text{DC}}}{2}\left(\text{erfc}\left(\frac{b_{\text{ DC}}}{\sqrt{2\sigma_{x}^{2}}}\right)-\text{erfc}\left(\frac{b_{\text{DC}}-P_{ \text{m}}}{\sqrt{2\sigma_{x}^{2}}}\right)\right)\\ &+\frac{\sigma_{x}}{\sqrt{2\pi}}\left(\exp\left(\frac{-b_{\text{ DC}}^{2}}{2\sigma_{x}^{2}}\right)-\exp\left(\frac{-(b_{\text{DC}}-P_{\text{m}})^{2}}{2 \sigma_{x}^{2}}\right)\right)\\ &+P_{\text{m}}\frac{1}{2}\text{erfc}\left(\frac{P_{\text{m}}-b_ {\text{DC}}}{\sqrt{2\sigma_{x}^{2}}}\right)\end{split} \tag{10}\]
## 6 Discussion  
1. Imbalanced classification can not be simply treated as a data redundancy problem. While our imbalance learning algorithm tries its utmost to achieve the main objective of imbalanced classification, _improving a classifier's recognition on minority class instances meanwhile keeping the accuracy of majority class from decreasing_, it does not need to remove any instances from training set as in training data reduction algorithms.
2. We have mentioned in the previous section that classic undersampling based ensemble approaches [56; 77], such as _UnderBagging_[38], samples instances randomly from majority classes to achieve a balance ratio. However, in imbalance learning, not only the imbalance ratio needs to be considered, but also the quality of the sampled instances. Our method focuses more on class decision boundary and difficult instances (lower margin instances) which are more informative for imbalance learning while safe samples (higher margin instances) give less contribution.
3. Most methods presented in the previous section such as [51; 57] deal with binary imbalanced problems. Due to the difficult extension of these methods, class decomposition, such as One-vs-One (OVO) [48] or One-vs-All (OVA) [49], is the way to extend these methods to multi-class classification. However, those class decomposition based schemes are not suitable when a large number of classes is considered. The novel proposed method trains each base classifier with the most important instances selected from each class; hence, this method has better generalization ability for addressing both binary and multi-class imbalance problems.
4. The change in ensemble diversity [78] depends on many factors, such as ensemble learning algorithm, size of training data set and training data complexity. Both the size and the distribution of the training set for constructing a base classifier are different in the margin ordering based bagging ensemble with respect to the original training set. Hence, our algorithm can result in increased diversity compared with the bagging built on original imbalanced data. Furthermore, under the condition of training base classifiers with a fixed amount of the training set, the employment of low margin instances can provide more diversity compared with random sampling involved in _UnderBagging_.
5. Our algorithm selects important instances from each class according to their margin values and does not produce additional instances in the training process. Therefore, our method avoids the potential noise effect induced by new interpolated samples (SMOTE) which is difficultly addressed in _SMOTEBagging_[8].
## 2 Materials and Methods
### Characteristics of the Biopolymer (Xanthan Gum)  
The xanthan gum (XG) is a commercial biopolymer produced by the Xanthomonas campestris bacterium and available in powdered form. It is a heteropolsaccharide, composed of glucose, mammose and glucuronic acid [47]; it is usually used as a thickening  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Organic Matter** & **Xanthan Gum** & **Curing Time** & **UCS Tests (N)** & **Oedometer** \\
**Content (\%)** & **Content (\%)** & **(Days)** & **Tests (N)** \\ \hline
1.5 & 0.0 1  & — & 3 & 2 \\  & 1.0 2  & 14 & 3 & 2 \\
2.4 & 0.0 1  & — & 3 & 2 \\  & 1.0 2  & 14 & 3 & 2 \\
4.1 & 0.0 1  & — & 3 & 2 \\  & 1.0 2  & 14 & 3 & 2 \\
5.5 & 0.0 1  & — & 3 & 2 \\  & 1.0 2  & 14 & 3 & 2 \\
7.7 & 0.0 1  & — & 3 & 2 \\  & 1.0 2  & 14 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing program. Number of UCS and oedometer tests carried out.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Property** & \multicolumn{5}{c}{**Organic Matter Content (\%)**} \\ \cline{2-6}  & **1.5** & **2.4** & **4.1** & **5.5** & **7.7 1**  \\ \hline Grain size distribution: & & & & & \\  & Clay (\%) & 23 & 23 & 23 & 23 & 23 \\  & Silt (\%) & 57 & 57 & 57 & 57 & 57 \\  & Sand (\%) & 20 & 20 & 20 & 20 & 20 \\ Specific gravity, G & – & – & – & – & 2.32 \\ Liquid limit, w\({}_{\text{L}}\) (\%) & 36.3 & 40.9 & 46.6 & 54.1 & 71.0 \\ Plastic limit, w\({}_{\text{P}}\) (\%) & 15.3 & 33.1 & 33.2 & 33.0 & 44.3 \\ Plasticity index, PI (\%) & 21.0 & 7.8 & 13.4 & 21.1 & 26.7 \\ Liquidity index & 1.09 & 1.09 & 1.09 & 1.09 & 1.09 \\ Soil classification, USCS 2  & CL & CL & OL & OH & OH \\ Chemical composition & & & & & \\ pH (BSI337-3) & – & – & – & 3.6 \\ SiO\({}_{\text{2}}\) (\%) & – & – & – & – & 62 \\ Al\({}_{\text{2}}\)O\({}_{\text{3}}\) (\%) & – & – & – & – & 16 \\ Fe\({}_{\text{2}}\)O\({}_{\text{3}}\) (\%) & – & – & – & – & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical and chemical properties of the soils tested.  
agent, since when in contact with water it generates viscous hydrogels which promotes an increase in the viscosity [22; 30]. XG is active under a wide range of temperature (10\({}^{\circ}\)-80\({}^{\circ}\)) and pH values (1-13), which promotes its use in agriculture, food and the pharmaceutical industry, cosmetics, drilling lubrication, industrial oils and enhanced oil recovery, among others [22; 47].
## 3 Effects of F-T Cycles on the Mechanical Properties and Microscopic Parameters
### Effect of F-T Cycles on Particle Arrangement
#### 3.3.2 Nightingale Rose Diagram of the Particle Distribution under F-T cycles  
To clarify the actual effect of the number of F-T on the particle arrangement, the Nightingale rose diagrams of S0 and S2 are drawn according to the distribution of particles in various zones, as Figures 11 and 12 show.  
Figure 11a displays that the particles of S0 showed a directional tendency after artificial compaction. With the increasing F-T cycles, the inter particle coupling force decreased due to particles' breaking and recombination, which caused the particle orientation tendency to weaken, especially after the fourth F-T cycle (Figure 11e). It shows that F-T cycles reduced the compactness of the soil and weakened the ability of soil to withstand loads, so the cohesion decreased.  
Figure 11: Nightingale rose chart of S0 after (**a**) 0, (**b**) 1, (**c**) 2, (**d**) 3, (**e**) 4, (**f**) 5, (**g**) 6, and (**h**) 8 F-T cycles.  
Figure 10: The curve of H\({}_{\text{m}}\) and F-T cycles.  
From Figure 12a, being unfrozen-unthaured, the grain orientation tendency of S2 was remarkable when subjected to manual compaction. The directional angle content of particles in the non-directional area increased with the number of F-T cycles, but the orientation trend remained significant after several F-T cycles (Figure 12c-f). It was evident that the addition of cement kept the soil at a high degree of compactness. Soils after F-T cycles still had certain bearing capacity for loads and maintained higher cohesion.