esa-sceva/satcom-chunk-collection huggingface dataset:
# Roadway Backfill Mining with Super-High-Water Material to Protect Surface Buildings: A Case Study
## 5 Conclusions  
As the exhaustion of coal resources in coalmines continues, \"under-three\" mining has long exhibited crucial issues that need to be further and continuously addressed, especially under surface buildings. Based on the KS theory, the design and implementation of roadway backfill mining with a super-high-water material was carried out under surface buildings. The conclusions are as follows:  
1. According to the control principle and theoretical calculation, an engineering design was established, including the width of coal pillars in the working face, layout and supporting parameters of the roadway, and the filling rate.
2. According to the measured data of the underground roadway, the combination of roof bolts and cable bolts can effectively suppress roadway deformation, and ensure safe production at the working face. Meanwhile, the deformation decreases with the distance from the opening in the same roadway.
3. By using the roadway backfill mining method with a super-high-water material, the ground surface subsidence has been effectively reduced, and surface buildings and the ecological environment well protected. Meanwhile, the recovery ratio of coal resources has also been improved. Moreover, this approach provides a reference for the extraction of coal resources under similar conditions and is vital for the sustainable development of the mining industry and economic growth.  
Conceptualization, E.B., W.G. and Y.T.; data curation, E.B. and Y.T.; formal analysis, E.B. and M.G.; funding acquisition, W.G. and Y.T.; investigation, G.H., M.G. and Z.M.; methodology, W.G. and Y.T.; project administration, W.G.; supervision, W.G.; writing--original draft, E.B. and G.H.; writing--review & editing, W.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (51774111), Key Project of the National Natural Science Foundation of China (U1810203), Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining (GJNY-18-76.13) and Innovation and Outstanding Talent Project of Henan Province Science and Technology (184200510003).  
The authors declare no conflict of interest.
# Current Context and Research Trends in Linear DC-DC Converters
## 2 Switch Mode Power Supplies (SMPS)
### Issues with SMPS
#### 2.1.2 Capacitors  
Capacitors are commonly used in SMPS for two purposes: (1) to stabilise the control loop, and (2) to filter out high-frequency noise. In the practical sense, every capacitor has an equivalent series resistance (ESR) which cannot be neglected and also contributes to ohmic losses.
## 4 Experimental Validation
### Response of the Resonator  
The ratio of acceleration and input force, namely the structural inertance, is shown in Figure 12 for values of initial horizontal displacement \(\delta\) increasing from 1.5 to 5 mm. These values are within the validity range of (1) since the maximum value of \(\delta\) as 5 mm corresponds to \(0.035L\). The values of axial forces required by the analytical model were calculated using the theoretical curved beam model (1), supplied with the real values of measured axial displacements.  
Experimental responses in Figure 12 are plotted together with the corresponding coherence function to attest to the quality of the measurement. The trend associated with a decrease in frequency as increasing force is applied is confirmed experimentally for values of initial displacement \(\delta\) from 1.5 to 5 mm. The analytical results from the dynamic bending model and spring-mass model are also shown in Figure 13 and they predict the same operating frequencies. Good qualitative agreement is obtained between experimental and theoretical responses, mostly in terms of frequency prediction. The structural response shows the frequency shift due to the softening effect as a consequence of increasing initial load, from 30 Hz down to 20 Hz, as well as the marked gradual decrease in amplitudes.  
For large values of force, the structural integrity of the 3D-printed curved beams is uncertain and questionable, as they become inelastic and brittle. Other discrepancies may be caused by fabrication tolerances and slight misalignment in the manufacturing of the experimental setup.  
Figure 11: Picture of experimental setup with components of the resonator, aluminum rigid elements, 3D-printed soft components, and instrumentation; accelerometer on mass and impact hammer.
## 2 Fundamental Loss Model of PMSMs
### Fundamental Loss Model  
From fundamental copper loss (Equation (8)) and fundamental iron loss (Equation (12)), the fundamental loss of the PMSM can be derived as  
\[P_{loss\_f}=P_{Cu\_f}+P_{Fe\_f}=f(\omega_{r},i_{d},i_{q}) \tag{17}\]  
Equation (13) shows that the fundamental loss of the PMSM is only a function of the rotational speed \(\omega_{r}\), the \(d\)-axis current \(i_{d}\) and the \(q\)-axis current \(i_{q}\). This proposed fundamental loss calculation method is verified by finite element analysis (FEA) based on a 5 kW PMSM. The FEA model is shown in Figure 3.  
Based on the FEA, the analytical results of the motor fundamental loss compared with the simulation results of the FEA at different speeds and torques are illustrated in Figure 4. Additionally, the difference of the motor loss calculated by the FEA method and the proposed analytical method is shown in Figure 5.  
Figure 4 shows that the fundamental loss of the PMSM in the proposed analytical method could match well with the simulation results of the FEA, and Figure 5 shows that the loss calculation error at the rated operation point where the rated speed is 2000 rpm and the rated torque is 19.1 N\(\cdot\)m stays within 4.2%. The proposed loss calculation method can be used in the motor loss optimization.  
Figure 3: Simulation model of the FEA.
## 2 Numerical Analysis Model
### Grid Size and Boundary Conditions  
For CFD simulation of a full-scale WTG in ANSYS CFX software, grids were defined according to the rotating frame (rotor blade) and the stationary frame (nacelle, tower) as shown in Figure 2.  
Considering the relative motions between the rotating frame and the stationary frame, a sliding mesh method was applied to the present analysis [23]. As shown in Figure 2b, the velocity distribution of the sliding mesh boundary region was smoothly formed by the sliding mesh technique. Structured H-type grids were adopted in the rotating frame in order to evaluate the aerodynamic performance of the wind turbine with high accuracy.  
\begin{table}
\begin{tabular}{c c} \hline Number of blades & 3 \\ Rotor diameter & 93 m \\ Rotational speed & Variable \\ Rotational direction & Clockwise \\ Target wind class & Class IIA \\ Target Max. Cp & Min. 0.49 \\ \hline \end{tabular}
\end{table}
Table 1: Design specifications of the rotor blade.  
\begin{table}
\begin{tabular}{c c} \hline \hline Nacelle length & 10.6 m \\ Nacelle height & 3.67 m \\ Nacelle width & 4.10 m \\ Nacelle drag coefficient & 1.20 \\ Hub height & 80.0 m \\ Outside diameter on each height & 3.32–4.20 m \\ Tower drag coefficient & 0.60 \\ \hline \end{tabular}
\end{table}
Table 2: Design specifications of nacelle and tower.  
Figure 2: Grids by sliding mesh method and velocity distribution. (**a**) Overall view, (**b**) enlarged view on the boundary region.  
The computational grids of a blade passage in the rotating frame consist of 115 cells in the streamwise direction, 140 cells in the spanwise direction, and 148 cells in the tangential direction. However, the stationary frame area excluding the rotor blade was too large to apply the structure grids. Due to this reason, the computational grids of the stationary frame are composed of 3.04 \(\times\) 10\({}^{6}\) un-structured tetrahedral cells, and the total number of whole computational grid systems is approximately 1.028 \(\times\) 10\({}^{7}\) cells [24].  
Figure 3 shows the whole computational domain. The distance from the center of the rotors to the inlet boundary is four times longer than the rotor radius. This distance is enough to avoid interaction between the pressure field around the rotor blade and the inlet boundary condition with a wind speed in the direction perpendicular to the rotating plane, that is, the yaw angle of 0\({}^{\circ}\). The uniform wind speed has a wind shear profile, \(V_{shear}\), as below.  
\[V_{shear}(H)=V(H_{htub})\bigg{(}\frac{H}{H_{htub}}\bigg{)}^{0.2} \tag{1}\]  
where \(V(H_{htub})\) is the wind speed at hub height and \(H\) is the height from ground.  
The distance from the center of the rotors to the outlet boundary is ten times longer than the rotor radius to capture the complicated and vortical flow phenomena of wake precisely. The outlet boundary condition was adopted as atmospheric pressure. Free-slip and no-slip boundary condition was applied to the ceiling surface and the wall surface of the rotor, nacelle, tower, and ground, respectively.
## 2 Materials and Methods
### Model Development  
The pre-processed data from each pre-processing method were used to develop a partial least squares discriminant analysis (PLS-DA) multivariate model to classify the 3 categories of beef samples. PLS-DA is a powerful multivariate data analysis tool that is modified from a partial least square regression (PLS-R) and was used for spectral and hyperspectral imaging in earlier studies [27; 28]. The relationship between the data matrix and the response matrix is revealed by this method. Initially, a transformation of the data matrix generates a set of intermediate linear latent variables, which is used for regression with the response matrix. The final regression/beta coefficients can be applied to predict a new or unknown samples data matrix. PLS-DA is generally expressed as:  
\[\mathrm{Y}=\mathrm{X}\times\mathrm{b}+\mathrm{E}, \tag{2}\]  
where \(\mathrm{X}=\mathrm{n}\times\mathrm{p}\) that holds the spectral values of each class, \(\mathrm{b}\) is the regression coefficient, and \(\mathrm{E}\) is the error term. To develop the model, the spectral data of all groups were arranged in a matrix \(\mathrm{X}\) and the calculated values were entered into the \(\mathrm{Y}\) matrix as:  
\[\mathrm{Y}=\left\{\begin{array}{c}1=\mathrm{sample\ belongs\ to\ AK}\\ 2=\mathrm{sample\ belongs\ to\ USDA\ prime}\\ 3=\mathrm{sample\ belongs\ to\ USDA\ choice}\end{array}\right.\]  
To develop the linear relationship between the predictors and response variables, both \(\mathrm{X}\) and \(\mathrm{Y}\) values were changed by latent variables (LVs):  
\[\mathrm{X}=\mathrm{TP}^{\mathrm{T}}+\mathrm{E}_{\mathrm{X}} \tag{3}\]  
\[\mathrm{Y}=\mathrm{UQ}^{\mathrm{T}}+\mathrm{E}_{\mathrm{Y}} \tag{4}\]  
Here, \(\mathrm{P}\) and \(\mathrm{Q}\) represent the loading matrix, and \(\mathrm{T}\) and \(\mathrm{U}\) represent the score matrix. \(\mathrm{E}_{\mathrm{X}}\) and \(\mathrm{E}_{\mathrm{Y}}\) are the residual matrices of \(\mathrm{X}\) and \(\mathrm{Y}\), respectively.  
Due to the effectiveness of producing a robust result for multivariate data analysis, the PLS method is the most commonly applied technique in chemometrics [29].
# Mineral Fibres and Asbestos Bodies in Human Lung Tissue: A Case Study
## 3 Results and Discussion
### New Insights in the Mechanism of Formation of ABs
(**a**) non-coated fibre; (**b**) first stage of fibre coating, AB appears like a smooth coverage; (**c**) AB evolves developing a segmented structure; (**d**) mature AB with a pearl structure, where the individual segments are well separated from each other. The ends of the fibre are larger than the other segments and sub-rounded; (**e**) some fibres may show multiple coatings. or only minor release of Fe outside the structure to act as an iron pool for the formation of ABs. As proven by the FTT patterns, assuming that abiotic processes are negligible, the Fe detected by EDX analyses of ABs is associated with ferroproteins, such as ferritin [64]. Therefore, the open question is why and how macrophages use ferritin to coat the asbestos fibres. Some speculation can be done considering the polyvalent nature of ferritin. Ferritin is a nanobox protein designed to contain and maintain Fe in solution [65]. In addition, as suggested by [PERSON] and [PERSON] [66], this protein plays an important role in the protection of cells against oxidative damage. Ferritin inhibits oxidant-mediated cytolysis [67] and reduces lipid peroxidation induced by Fenton-type reactions [68]. The proliferation of ferroproteins (e.g., ferritin) in the lungs is regulated by the siderophages (hemosiderin-containing macrophages) [69; 70; 71]. Cells exposed to pro-oxidant xenobiotic substances, such as asbestos fibres, induce recruitment of monocytes (e.g., macrophages) and cytoprotective agents (e.g., ferritin) as defence mechanisms (see above). Consequently, it is possible that when a macrophage interacts with a fibre, its affinity with the ferritin (recruited by antioxidant response) prompts a combined action to build the ABs. Other mechanisms may be active in the ferritin adsorption onto the fibre surface (e.g., electrostatic interaction, van der Waals dispersion, solvation) [64]. In this regard, [PERSON] et al. [72] suggested that positively charged functional groups of ferritin (e.g., ammonium group) can eventually bond with a silanol group located at the fibre surface. In this case, ferroproteins cluster on a portion of fibre with higher concentrations of silanol, leading to the formation of an AB [72]. However, the interaction of ferritin with asbestos fibres is more complex and evolves with time [73; 40; 64]. A long-term stay of ABs on the fibre surface causes profound alterations of the ferritin. In fact, ferritin of mature ABs shows high degrees of alteration, which can eventually lead to exposure of its ferrihydrite core and a potential release of Fe [73]. In support of this hypothesis, we observed goethite, identified by the FTT patterns (Figure 11), which documents alteration of ferrihydrite while ABs remain in the lung tissue. Uncontrolled release of Fe from ABs into the extracellular space may eventually mediate Fenton-like reactions and generate reactive oxygen species (ROS), thereby increasing oxidative stress.
## 4 Development of the Profit Rate of an ESS-Connected PV Analysis Method
### Economic Evaluation on a System Only with ESS  
The reviewed cases 1 to 7 were assumed to have both a PV system and an ESS. Among those seven cases, three were randomly chosen and they were subjected to an economic evaluation with a system configuration where only the ESS is installed. Table 15 shows the conditions of the three cases and the result of the evaluation done to those cases are shown in Table 16.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**VVCapacity [kW]**} & \multicolumn{4}{c}{**Insolation [h]**} & \multicolumn{4}{c}{**ROI [year]**} \\ \cline{3-7} \multicolumn{2}{c}{} & **3** & **4** & **5** & **6** \\ \hline
200 & 10.45 & 10.09 & 11.16 & 13.24 \\
250 & 8.62 & 8.81 & 9.19 & 9.59 \\
300 & 9.72 & 9.8 & 10.39 & 10.76 \\
350 & 9.32 & 9.92 & 10.44 & 10.77 \\
400 & 9.46 & 10.26 & 10.24 & 10.56 \\
500 & 10.56 & 10.62 & 10.9 & 11.19 \\
800 & 12.77 & 14.28 & 12.8 & 13.04 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Price Trend Expected of Rechargeable Batteries.  
Figure 6: ROI Analysis (unit: year).  
As a result of the simulation in each case, the ROI time of the ESS-only case was reduced by 28.6% on average, when compared to the ROI of the PV-ESS case. This shows that the cost of the PV system is a large fraction of the entire system cost. Rather than charging during the daytime and discharging during the nighttime, charging with nighttime power is more favorable in achieving economic feasibility. In case 2, the cost of installing a PV system is around $300,000 that is about 42.1% of the total construction cost. When a PV system is not installed in the building, the cost of installing facilities greatly decreases. However, the total cost of construction decreases even more, so the economic feasibility is ultimately improved. This result is because of the current electric fee system that makes it possible to use nighttime power at a very low price. However, at some point in the future, using an ESS-connected PV system that can generate and consume energy by itself would be more economically feasible since the government is trying to strengthen the policies regarding the electric fee so that the price of nighttime power would increase.
## 2 Results and Discussion
### Predicted vs. Actual  
The predicted vs. actual graph shows the overall group of data verification that obtained from the experimental works or simulation works. It shows the effect of the model and compares it against the null model. Furthermore, this graph also determines the percentage or number of outlier data that is not reliable for the overall study. Figures 9 and 10 show the predicted vs. actual graph for single story and double story eco-efficiency index of fly ash and blast furnace slag reinforced concrete residential house.  
Figure 9: Predicted vs. actual for single story housing for two types of concrete: (**a**) fly ash, (**b**) blast furnace slag.  
In addition to that, all cases found that all the points were close to the fitted line with narrow confidence bands. In other words, the values were statistically significant. All the data for all four types of residential houses were found in this study in a range of predicted lines that produce low outliers' percentage which is less than 10%. It is essential to determine the outliers, to ensure the data found in this study can develop a satisfactory correlation and reliability. On the predicted versus actual analysis of eco-efficiency index for reinforced concrete residential houses, it was found that the actual values from the simulation work was found to be accurate and produced a good fit, because it close to the fitted line with narrow confidence bands of the relationship. It was found to be a good fit for all four cases of the eco-efficiency design. Points on the left or right of the plot, furthest from the mean, have the most leverage, and effectively try to pull the fitted line toward the point. Points that are vertically distant from the line represent possible outliers. Both types of point can adversely affect the fit. Therefore, all of the cases produce the best fit between predicted and actual, with fewer than 10 percent outliers and between the acceptable range.
# Crop Land Change Detection with MC&N-PSPNet
## 2 Materials and Methods  
The network architecture in this paper primarily consists of two main components. Firstly, the replacement of the backbone feature extraction network. The lightweight MobileNetV2 backbone feature extraction network is utilized instead of the ResNet50 backbone feature extraction network to reduce model parameter count and enhance network training efficiency and accuracy. Secondly, after feature fusion, a lightweight and efficient attention mechanism (Normalization-Based Attention Module, NAM) is introduced to improve model accuracy and robustness without increasing model complexity. The workflow of the proposed agricultural area change detection method is illustrated in Figure 1.
## 3 Results and Discussion
### Morphology and Elemental Composition
#### 3.1.2 C/HEO Composite NFs  
C/CrMnFeCoNi-HEO composite NFs consist of carbon NFs with embedded oxide NPs (Figure 1c). The diameters of these NFs are distributed over a slightly wider range  
Figure 1: Main results of the morphological and compositional analyses. (**a**–**d**) Representative SEM images of (**a**) sHEO NPs, (**b**) sHEO NFs, (**c**) C/sHEO composite NFs, and (**d**) C/rHEO composite NFs. (**e**) Diameter distribution of electrospun NFs. (**f**–**i**) Elemental composition of (**f**) sHEO NPs, (**g**) sHEO NFs, (**h**) C/sHEO NFs, and (**i**) C/rHEO NFs. (**j**,**k**) TEM images of (**j**) sHEO NPs and (**k**) sHEO NFs. (**l**) Size and size range of HEO NPs in the samples. The left arrow is located in correspondance of the grey bar and indicates that the y-axis for bars is on the left (particle size range); the right arrow is located in correspondance of the blue symbol and indicates that the y-axis for coloured symbols is on the right (average particles size).  
compared with those of CrMnFeCoNi-HEO NFs (Figure 1e); in addition, on average, composite NFs are thicker than pure HEO NFs (0.79 vs. 0.66 \(\upmu\)m). As previously shown, both in the case of common LEOs [39; 40] and HEOs with different composition [31], the host C matrix limits the development of the oxide grains and prevents their agglomeration. As a result, the CrMnFeCoNi-HEO NPs incorporated in the C NFs have sizes distributed in a narrower range (40-180 nm) and, on average, are smaller compared with those forming pure HEO NFs (110 vs. 200 nm). The results of the compositional analysis by SEM/EDX (Figure 1h) provide evidence that the C-embedded HEO NPs contain a nearly equimolar combination of Cr, Mn, Fe, Co, and Ni.  
In the C/MgCoNiCuZn-HEO NFs (Figure 1d), the HEO partly protrudes out of the C NFs, as previously observed in the case of C/Ge/GeO\({}_{2}\) electrospun NFs [40]. This is likely the effect of the outward diffusion of the five metals occurring during the carbonization at 700 and 900 \({}^{\circ}\)C [40]. Out of the fiber body, the MgCoNiCuZn clusters coalesce, giving rise to particles with a broad size distribution (120-470 nm) and, on average, larger size (250 nm) compared with C-embedded CrMnFeCoNi-HEO NPs. C/MgCoNiCuZn-HEO NFs have diameters distributed over a wider range with respect to both C/CrMnFeCoNi-HEO NFs (Figure 1e) and MgCoNiCuZn-HEO NFs, the subject of a previous study [29]. Analogously, the diameter distribution is centered at a higher value (1.23 \(\upmu\)m) compared with both MgCoNiCuZn-HEO NFs (0.72 \(\upmu\)m [29]) and C/CrMnFeCoNi-HEO NFs (0.79 \(\upmu\)m).
# Modulation of Host Lipid Pathways by Pathogenic Intracellular Bacteria
## 1 Introduction
Specifically, intracellular bacteria can be separated into those that persist in a vacuolar compartment inside the host cell and those that survive and replicate within the host cell cytosol. Together, the differences among the intracellular bacteria indicate a requirement of host cell lipids that varies among and within vacuolar and cytosolic groups. This review addresses current knowledge on the alteration and use of host lipids during human pathogenic intracellular bacterial infection. The research discussed highlights differences in vacuolar and cytosolic bacteria's use of host lipids for pathogen membrane structure, host LD alterations during infection, and lipid utilization for energy production.