esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Position Error and PDOP of a Single Satellite  
As previously noted, the observation point is capable of simultaneously detecting 18 satellites. Nonetheless, for the purposes of this analysis, it is posited that only one of these 18 satellites is observable at any given moment. Consequently, the four satellites exhibiting the highest elevation angles are chosen for positioning. These satellites, in descending order of elevation, are identified as S2, S16, S8, and S10. Figure 9 illustrates the positioning errors associated with these four satellites over the duration of the observation period. The data indicate that satellite S2 incurs the least positioning error, whereas satellite S10 experiences the greatest error. This finding aligns with the conclusions drawn in the third section of this study. It is thus recommended that, when selecting a satellite for positioning, preference be given to those with higher elevation angles.  
Figure 8: Satellite observable time.  
Figure 6: The satellite ground trace.  
Figure 7: The observable time of 200 satellites in one day.  
Figure 10 presents the PDOP for a single satellite, which quantifies the variability in the satellite's spatial orientation. A higher degree of variability corresponds to a lower PDOP value, signifying improved positioning precision. In Figure 10, it is evident that the PDOP values for satellites S2 and S16 are the lowest, suggesting that these satellites exhibit the most substantial positional fluctuations within the observable timeframe. As depicted in Figure 9, the positioning precision of satellites S2 and S16 surpasses that of satellites S8 and S10. Consequently, when observing a single satellite, it is advisable to select one that demonstrates significant positional changes during the observation period to achieve more accurate positioning.
# An Experimental Study on the Sealing Mechanism of a Karst Pipeline by Dynamic Water Grouting
## 3 Dynamic Water Sealing Test Study
### Results and Discussion  
Plugging Pressure Analysis  
The magnitude of the slurry plugging pressure under dynamic water conditions responds to the effect of anti-seepage plugging. An intuitive analytical method was used to analyze the test data, revealing the primary and secondary relationships between the factors and the slurry plugging pressure. The analyzing steps are as follows:  
1. Calculate the sum of the test results under each factor condition, defined as \(k_{i}\);
2. Take the average value of the above \(K_{i}\), defined as \(k_{i}\);
3. Calculate the extreme difference R using the difference between \(\max\,\overline{k}_{i}\) and \(\min\,\overline{k}_{i}\) and determine the degree of influence of each factor on the test index by the R-value.  
The experimental results are visually analyzed in the table, as shown in Table 7. Based on the above experimental data, it can be seen that the primary and secondary relationships of the factors affecting the effect of grouting sealing are plugging length > Fe\({}_{3}\)O\({}_{4}\) power ratio > water-cement ratio. Each of the factors analyzed in Figure 12 has an obvious correlation with the water plugging pressure. The specific trends are as follows.  
1. With the increase in plugging length, the average pressure increased by 51.2% when the plugging length increased from 100 mm to 200 mm. When the plugging length increases from 200 mm to 300 mm, the average pressure increases by 29.7%. There is a significant positive correlation between plugging length and plugging pressure.
2. As the water-cement ratio increases, the plugging pressure decreases. As the water-cement ratio increases, the solid phase concentration decreases, so the slurry's plastic viscosity and ultimate shear decrease, significantly reducing the contact force between the slurry and the pipeline, making the sealing effect of the slurry worse.
3. The plugging pressure also increases with the increase in the amount of Fe\({}_{3}\)O\({}_{4}\) powder tragedy. When Fe\({}_{3}\)O\({}_{4}\) powder tragedy increases, the plastic viscosity of the magnetic slurry reduces, and under the action of the external magnetic field, the slurry's original loss of rheology viscosity will instantly increase, and the contact stress between the particles are used to enhance the angle of friction, thus making the magnetic slurry plugging shear capacity increase.  
Figure 12: Influence of various factors on water plugging pressure.
# Cellulose Recovery from Agri-Food Residues by Effective Cavitational Treatments
## 3 Biomass Treatments for Cellulose Recovery
### Cavitational Treatments in Comparison  
The effect of acoustic and hydrodynamic cavitation on efficiency of lignocellulosic biomass pretreatment was previously reported in Sections 2.1 and 2.2. The overall benefits are related to chemical and physical effects of cavitation, which contribute to oxidative degradation and removal of lignin from the biomass in short and mild process conditions. The cavitational-pretreated biomass is more susceptible to cellulases action, which can be associated not only to chemical action of cavitation but also to physical effects as high speed microjets and shockwaves generation during the violent collapse of cavities, which increase the specific surface area, total pore and micropore volumes in biomass, favouring the enzymatic conversion of cellulose and hemicellulose in fermentable sugars. The comparison between the US and HC was summarized in Table 4.  
For the sake of comparison, an overview of the results reviewed in this paper was reported in Table 5.  
Figure 16: HC devices exploited for semi-continuous pretreatment of sugarcane bagasse. Reprinted from _Bioresource Technology_, 2019, 290, 121,777 with permission from Elsevier. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cavitational** & \multicolumn{2}{c}{**Possible**} & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Remarks**} \\
**Pretreatment** & \multicolumn{2}{c}{**Biomass**} & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Remarks**} \\
**for** & \multicolumn{2}{c}{**Alterations**} & & & \\
**Cellulose Recovery** & & & & & \\ \hline \hline \multirow{6}{*}{Acoustic cavitation [3; 6]} & \multirow{6}{*}{-} & \multirow{6}{*}{Biomass deconstruction} & - & Low frequencies & \\  & & & - & (20–100 kHz) & \\  & - & & High-energy & & \\  & - & Decreases the polymerization & & microenvironment & - & Improves mass transfer \\  & & degrees of cellulose & - & Possible combination & - & low energy requirements \\  & & & with other chemical & & \\  & & - & pretreatment & & \\ \hline \hline \multirow{6}{*}{Hydrodynamic cavitation [3; 112]} & \multirow{6}{*}{-} & Enables the oxidative & - & Continuous flow process & - & Reactor geometry is \\  & & degradation and removal of & - & Short residence time & crucial to achieve good \\  & & lignin from & - & Mild temperature and & cavitation number \\  & - & biomass & pressure conditions & - & Physical properties as \\  & - & Decreases the polymerization & - & Possible combination & velocity, pressure, vapor \\  & & degrees of cellulose & & with other chemical & fraction and turbulence \\  & - & Increases the specific surface & & pretreatment & through the cavitation \\  & - & area, total pore and micropore & - & Scalable at industrial & device must be carefully \\  & & volumes in biomass & level & & considered \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cavitational methods for cellulose recovery from lignocellulosic feedstocks.
## 3 Experimental Results and Discussion
### Waveguide Characterization and Power Handling
The measurements here reported show how, in the absence of a guiding structure, the coupling efficiency rapidly decreases as the input power exceeds the value of about 1 W, correspondingly to an optical energy density of 1.3 \(\times\) 10\({}^{5}\) W/cm\({}^{2}\). Conversely, using a channel-shaped waveguide, up to 3.6 W of IR power (corresponding to 10\({}^{7}\) W/cm\({}^{2}\) intensity) have been coupled to the \begin{table}
\begin{tabular}{c c c c} \hline \(r_{0y}\) & \(r_{0z}\) & \(n_{pump}\) & \(\Delta n\) \\ \hline \((8.3\pm 0.2)\) μm & \((2.95\pm 0.27)\) μm & 2.13 & \((4\pm 1)\times 10^{-3}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Measured waveguide parameters. \begin{table}
\begin{tabular}{c c c c} \hline \(dn/dT\) & \(\alpha\) & \(k\) & \(r_{33}\) \\ \hline \(3\times 10^{-5}\) K\({}^{-1}\) & \(10^{-4}\) cm\({}^{-1}\) & \(5\) Wm\({}^{-1}\)K\({}^{-1}\) & \(30\) pm/V \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristic parameters of LN crystal [52]. guided mode, with a constant coupling efficiency of about 57.3%. This result demonstrates the channel waveguide to be a fundamental tool in high-power frequency down-conversion experiments.
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 4 Load Forecasting Methodology
### Feature Selection
#### 4.3.2 Maximal Information Coefficient  
The Maximal Information Coefficient [45] was first proposed in 2011; it can not only measure whether there is a linear relationship between two variables, but it can also observe whether there is a non-linear relationship between the variables, such as sinusoidal and periodicity. MIC also has better robustness for samples containing noise. MIC values range from 0 to 1, with larger MIC values indicating a higher degree of association between the two variables. MIC is developed upon Mutual Information (MI), and the MI between variables can be expressed as in Equation (24):  
\[I(x;y)=\int p(x,y)\log_{2}\frac{p(x,y)}{p(x)p(y)}dxdy \tag{24}\]  
where \(p(x,y)\) represents the joint probability density of \(X,Y\); \(p(x)\) and \(p(y)\) represent the edge probability density of \(X\) and \(Y\), respectively.  
A grid division is performed on the scatter plot of variables \(X,Y\), and the mutual information between the grids is calculated to obtain the maximum MI value, which is normalized to obtain the final MIC value. The calculation is shown in Equation (25),  
\[MIC(x;y)=\max_{a\,b<B(n)}\frac{I(x;y)}{\log_{2}\min(a,b)} \tag{25}\]  
where \(a\) and \(b\) are the numbers of grids divided in the direction of \(X,Y\), respectively. \(n\) is the amount of data, which represents the number of historical load nodes in this paper. \(B(n)\) is a function on \(n\), and this paper set \(B(n)=n^{0.6}\).
## 3 Results and Discussion  
In this study, two distinct nanocomposite membranes based on P(_bis_MEP) and BC were produced via in-situ free radical polymerization of the respective phosphate ester bearing monomer, i.e., _bis_MEP, inside the BC three-dimensional network under eco-friendly reaction conditions, namely innocuous solvent (water) and low reaction temperature. The selection of this mono-acidic phosphate monomer was based on: (i) the lower acidity of the phosphoric acid groups when compared e.g., to sulfonic acid groups, which expectedly translates into membranes with higher thermal-oxidative stability [21; 29]; (ii) the bifunctionality of the monomer that precludes the use of a cross-linker to efficiently retain the polyelectrolyte inside the BC network; and (iii) its demonstrated potential for application as polymeric cation-exchange monoliths for capillary liquid chromatography [30] and cation exchange membranes for blocking complexing ions in presence of non-complexing ions [31].  
Overall, the nanocomposite membranes P(_bis_MEP)/BC_1 and P(_bis_MEP)/BC_2 were very homogeneous with no noticeable irregularities on both surfaces of the membranes as illustrated in Figure 1, which is indicative of a good dispersion of P(_bis_MEP) inside the BC network. Furthermore, the thickness of the membranes increased from 42 \(\pm\) 11 \(\upmu\)m for pure BC to 64 \(\pm\) 11 \(\upmu\)m for P(_bis_MEP)/BC_1 and 132 \(\pm\) 12 \(\upmu\)m for P(_bis_MEP)/BC_2 (Table 1).
# Characteristics of Plane Gate Vibration and Holding Force in Closing Process by Experiments
## 3 Results and Discussion
### Characteristics of Vibration
#### 3.1.3 Comparison of Vibration in Two Operation Modes  
The above describes the characteristics of gate vibration in fixed gate opening and closing process, respectively. This section mainly compares gate vibration for two operation modes. When gate is in fixed gate opening, the acceleration in the three directions decreases gradually with gate opening decrease. The lateral and horizontal vibrations are larger than the vertical vibration. However, in closing process, acceleration increases gradually with gate opening decrease, which is contrary to  
Figure 5: Characteristics of the vertical vibration in closing process: (**a**) amplitude of acceleration; and (**b**) dominant frequency of acceleration.  
Figure 6: Characteristics of acceleration amplitude in closing process: (**a**) lateral direction; and (**b**) horizontal direction.  
the fixed gate opening. Besides, vertical acceleration is the largest, followed by the lateral direction and horizontal direction is the smallest. From the perspective of the frequency, vibration frequency is mainly related to the gate opening and is independent of the upstream water depth in fixed gate opening. Frequency in each direction is markedly different. Nevertheless, in closing process, upstream water depth and gate opening both impact the vibration frequency. As the upstream water depth increases and gate opening decreases, vertical vibration frequency reduces gradually. The above differences are mainly caused by two aspects: the gate motion state and unsteady flow. Firstly, the gate motion state influences the boundary conditions and nature frequency of simplified system. Secondly, unsteady flow arising from gate-closing process significantly changes the hydrodynamic loads acting on the gate body. These two effects motivate a strong fluid-solid coupling in closing process, and it is markedly different from that of fixed gate opening.
# Nanotechnology Applications in Ground Heat Exchanger Pipes: A Review
## 2 Literature Review
### Nanomaterials
#### 2.2.1 Theoretical and Numerical Simulations
However, the thermal conductivities of polymer CNT nanocomposites are relatively low compared to expectations for the intrinsic thermal conductivity of CNTs. Another work that addresses the improvement of thermal properties in piles is found in [45]. This paper found that the values for the heat rejected for U- and double-shaped pipes configurations were 53.81 W/m and 68.71 W/m, respectively, in an energy pile system of air conditioning for buildings such as offices and houses. The main objective was to reduce the cost of GHE. The average COP for heating was quite high, 3.9, and the seasonal primary energy reduction rate compared to a typical air conditioning system reached 23.2%. The authors of [46] used single and double U-tubes for application with \(DN32\) HDPE pipes in boreholes and foundation piles. With the same flow rate with an inlet temperature of 35 \({}^{\circ}\)C for the heat reject mode and 3 \({}^{\circ}\)C for heat extract mode, the heat transfer values of the double U-pipes were about 50% and 45%, respectively, greater than those of a single U-pipe. The summary of results reported by the previous works in theoretical and numerical simulations of nanomaterials is given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Nanomaterials** & **Applications** & **Results and Remarks** & **Model or Method** \\ \hline \multirow{4}{*}{[PERSON] et al. [37]} & \multirow{4}{*}{\(CNTs\), \(LTEG\), Ag, Al, and Cu} & Thermal conductive polymer composites applied to HDPE pipes to reduce the borehole GHE & It is recommended to add \(LTEG\) composite as a suitable filter to the HDPE polymer. With a 10 wt\% of \(LTEG\), the length of the GHE was reduced to near 10\% of the borehole pipe & Thermal Network Model for U-Tube GHE [47] \\ \hline \multirow{4}{*}{[PERSON] et al. [4]} & \multirow{4}{*}{TE} & \multirow{4}{*}{\ \begin{tabular}{c} Different GHE configurations: single \\ U-pipe, double U-pipe \\ and coaxial, each with \\ standard HDPE pipes and \\ thermally enhanced (TE) \\ pipes \\ \end{tabular}  } & A performance comparison of different \(\begin{array}{c}\\ \text{GHE configurations. GLHEPro used to }\\ \text{calculate different parameters. The use }\\ \text{of TE pipes instead of HDPE pipes}\\ \text{allowed a reduction in the Bore }\\ \text{Therrally Resistance (BTR) of between }\\ \text{22.3 and 24.4%, a reduction in the total }\\ \text{GHE length of between }9.0 and 14.8\%\) and a reduction in construction costs of \( \begin{array}{c}\\ \text{between 3.3 and 8.6\%}\\ \end{array} \) \\ \hline \multirow{4}{*}{[PERSON] et al. [38]} & \multirow{4}{*}{ \begin{tabular}{c} HDPE-aluminum \\ wires \\ \end{tabular}  } & HDPE pipes with aluminum wires used for ground source applications & Improvement of thermal conductivity depending on the number of aluminum threads used in HDPE. Increasing the number of wires increases the outer surface temperature, which can save heat exchanger piping length & Finite volume method \\ \cline{1-1} \cline{3-3} \cline
## 5 Feature Fusion Based on Skewness Factor
### Comparison with SVM  
In this section, the result of neural network is compared with SVM, as shown in Table 16. The main indicators for the verification are the classification accuracy and the training time.  
1. For the classification ability, the degenerative model built by neural network or SVM has the same effect. Even in the case of 75 training samples, the classification accuracy of the degraded model built by SVM is also in the oscillation range of neural network.
2. The training time of the degenerative model built by neural network is actually longer than the model by SVM. It can be seen that SVM is the better model than neural network for the 6205 bearing.
3. Although the performance of neural network is weaker than that of SVM, it should be noted that for neural network, with an increase in the number of training samples and the inclusion of fusion features, the improvement of training speed is obvious. In contrast, for SVM, with an increase in the number of training samples, although the computational speed of each iteration increases, the total training time is not significantly reduced. The training time ratio of SVM and neural network has increased from 11.57% in 50 training samples to 16.41% in 150 training samples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of New Features** & **Minimum Number of Iterations** \\ \hline
2 & 293 \\
3 & 247 \\
4 & Overfitting \\
5 & Overfitting \\ \hline \hline \end{tabular}
\end{table}
Table 15: Test results of new features with 150 samples.
## 2 Materials and Methods  
### Stacking Ensemble Learning  
Ensemble learning is a technique that involves the combination of multiple models to make predictions, aiming to achieve more accurate and robust results compared to those of a single classification model. Ensemble learning typically falls into three categories: boosting, bagging, and stacking. In boosting and bagging, the same base classifier is utilized, making them homogeneous ensemble methods. In contrast, stacking [22] employs various types of base learners for integration, earning it the designation of a heterogeneous integration method. In order to integrate multiple classification models, a stacking ensemble model first divides the original dataset into several sub-datasets. Then, each classification model is considered as a base model, and their prediction results for the data are output as meta-features of the data. Finally, the meta-model is trained using the previously extracted meta-features to make the final predictions. To avoid overfitting, we used k-fold cross-validation to partition the dataset before training multiple heterogeneous models. Our approach is rooted in our decision not to assign explicit weight coefficients to individual base models. Instead, we harness the predictions generated by these base models. This approach empowers the meta-model to correct discrepancies in the predictions made by the base models, effectively reducing the risk of overfitting. Therefore, our model is a \"two-layer\" ensemble model, rather than a \"weighted\" model. The following are the steps to establish a \"two-layer\" stacking ensemble model:  
1. The dataset \(S=[(x_{i},y_{i}),i=1,...,N]\) is divided into a training set \(S_{train}=[(x_{tr},y_{tr}),\)\(tr=1,...,N_{train}]\) and a test set \(S_{test}=[(x_{te},y_{te}),te=1,...,N_{test}]\). In these sets, \(x_{i}\) represents the \(i\)-th sample in the dataset, and \(y_{i}\) corresponds to the class of the \(i\)-th sample. For the training set, \(x_{tr}\) represents the \(tr\)-th sample, and \(y_{tr}\) is the class associated with the \(tr\)-th sample. In the test set, \(x_{te}\) represents the \(te\)-th sample, and \(y_{te}\) is the class associated with the \(te\)-th sample.
2. \(k\) base classifiers are selected, and the training set \(S_{train}\) is divided into \(k\) equal size subsets \(S_{1},S_{2},...,S_{k}\). Define \(S_{-k}\) as a test set; then, \(S_{sk}=S-S_{k}\). All \(k\) base models are trained using the training set \(S_{train}\), and prediction probabilities \(z_{k,p}\) are obtained using the test set. A new training set is obtained by combining the predicted probabilities of each base model on the test set during cross-validation. At the same time, the predicted probabilities of each base model on the total test set are averaged, and a new test set is obtained after concatenation.
3. The second level meta-model is trained with the newly generated training set. By training the meta-model, the prediction deviation of the base model can be corrected and the prediction accuracy can be improved.
## 3 Results
### Molecular Dynamics Simulation  
In order to obtain a better insight as well as to assess the stability of the binding mode of molecules, we conducted 10 ns long molecular dynamic simulations for all the nine proteins-ligand complexes selected after the visual inspection step. The poses selected from the Geom X docking experiment were used as the starting poses for simulation studies. Keeping in consideration the large size of FAS-I, simulation was done for the enoyl reductase domain (amino acid residue 394-1107). The NADPH binding site of the ER domain is far away from the interaction sites between the ER domain and other neighbouring domains; it is thus unlikely that the neighbouring domains contribute to the interaction between the ER domain and the proposed inhibitors. The structure of the ER domain is close to the one in the FAS-I complex, as indicated by the small RMSD in all simulations. In the next step, the resultant trajectory was analyzed for assessing the stability of the predicted protein-ligand complexes. The r.m.s.d of the ligand was calculated using g_rms command in GROMACS. The r.m.s.d for ligands was plotted for all complexes from the end of equilibration phase and was found to be low, indicating the stability of ligand poses. The average r.m.s.d of ligand atoms was calculated to 0.24, 0.18, 0.12, 0.15, 0.21, 0.11, 0.16, 0.20, and 0.21 nm for BTB14516, BTB14738, CD01000, HTS07760, HTS09453, RH00608, RJF01717, SEW02765, and SPB02705, respectively (Figure 5). The FMN molecule was found to be very stable with average r.m.s.d calculated in the range of 0.05-0.11 nm (Figure S2). The average r.m.s.d for backbone atoms of complexes was calculated in the range of 0.19-0.26 nm (Figure S2). Overall, the protein-ligand complexes were found to be significantly stable. Subsequently, the last five ns MD trajectory was used for performing MM/PBSA-based binding free energy calculations and is discussed in the next section.  
Figure 4: The 3D interaction plots for selected protein-ligand complexes (**a**) BTB14738, (**b**) BTB14516, (**c**) CD01000, (**d**) SEW02765, (**e**) HTS 07760, (**f**) HTS09453, (**g**) RH00608, (**h**) RJF01717, (**i**) SPB02705.  
Figure 5: The r.m.s.d plots for ligand are given for (**A**) BTB14516, HTS07760, RJF01717, BTB14738 (**B**) CD01000, HTS09453, RH00608, SPB02705, and SEW02765.