esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discrimination of the White and Red Musts and the Corresponding Wines
Strong negative correlations were observed between abscisic acid and kaempfer, isorhamethein and chrysin, and between quercetin and pinochemprin (Table S5), indicating the same origin sources for the correlated phenolic compounds. In addition to white varieties, strong positive correlations were observed for (+)-catechin and (-)-epicatechin with caffeic acid, t-resveratrol, quercetin, kaempfer, and isorhamethein (Table S6). Figure 4: Discrimination of white musts and wines based on individual phenolic compounds: (**a**) Score plot of the first two discriminant functions showing separation between different white musts and wines (SBm/v—Sauwignon Blanc must/wine, Rim/w—Riesling Italian must/wine, Mom/w—Muscat Ottonel must/wine, FRm/w—Feteasca Regala must/wine) and (**b**) correlation between the individual phenolic compounds (3,4-dihydroxybenzoic acid (3,4-DHBA), 4-hydroxybenzoic acid (4-HBA), gallic acid (GA), syringic acid (SyA), caffeic acid (CafA), p-coumaric acid (p-Coum), ferulic acid (FA), cinnamic acid (CinA), chlorogenic acid (ChIA), abscisic acid (AbsA), ellagic acid (EIA), (+)-catechin (Cat), (–)-epicatechin (Ecat), quercetin (Qu), kaempfer (Kae), isorhamethein (iRh), apigenin (Apig), pinochemprin (Pin), chrysin (Chry), galangin (Ga), t-resveratrol (Res)) and the factors. This study provides significant data addressing the evolution of phenolic compounds from musts to wines of different white and red varieties. In general, the fermentation processes of white and red musts lead to an enrichment of phenolic compounds in the wines compared with the musts. Thus, the increase in the content of gallic, syringic, and p-coumaric acids in wines compared with musts were much more obvious in the case of red varieties. Contrary, the amounts of some phenolic compounds (caffeic and t-cinnamic acids, (+)-catechin, (-)-epicatechin, quercetin, and t-resveratrol) decreased after the fermentation process, being quantified in lower amounts in wines compared with musts.
## 2 Project Overview
### Design of Test Piles  
In accordance with the design specifications, two engineering piles, designated as TP1 and TP2, were selected as test piles at Pier 85# of the site. The pertinent parameters of these two test piles are detailed in Table 2. Both piles were constructed using the rotary excavation technique, and the elastic modulus of the concrete for the test piles was 31.5 GPa. Figure 1 depicts the soil layer distribution within the pile lengths at the test location, along with the arrangement of the testing apparatus. During pile construction, measures were taken to enhance the mud casing on the pile sides and eliminate debris from the pile tips. Furthermore, to bolster the load-bearing capacity of individual piles while minimizing settlement, both test piles underwent reinforcement through the application of combined tip-and-side post-grouting methodologies. The test piles were plugged 12 h after concrete placement, and the combined post-grouting process was initiated subsequent to the completion of the pile bearing capacity test before grouting. To guarantee the quality and uniformity of the grouting, three grouting pipes were arranged for pile end grouting, and two pipes were placed for the pile side grouting. Additionally, grouting reinforcement was performed at the load box following the completion of the testing. The pressurized grout was composed of P.O.42.5 ordinary silicate cement mixed with a water-cement ratio of 0.55. The specific grouting parameters are detailed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Layer No.** & **Soil Name** & \begin{tabular}{c} **Basic Permissible Value** \\ **of Load Capacity (kPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Standard Value of Side** \\ **Resistance (kPa)** \\ \end{tabular} &
\begin{tabular}{c} **Grading of Geotechnical** \\ **Construction Work** \\ \end{tabular} \\ \hline
1-1 & Miscellaneous fill & & & I \\
2-1 & Silty clay & 120 & 40 & II \\
2-2 & Silty clay & 160 & 55 & II \\
2-3 & Silty clay & 150 & 50 & II \\
2-4 & Silty clay & 160 & 50 & II \\
2-5 & Silty clay & 200 & 65 & II \\
3-1 & Clay & 180 & 60 & II \\
3-2 & Clay & 120 & 35 & II \\
3-3 & Clay & 180 & 60 & II \\
3-4 & Clay & 180 & 55 & II \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of each soil layer on the test site.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pile No.** & \begin{tabular}{c} **Diameter** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Length** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Bearing** \\ **Layer** \\ \end{tabular} & \begin{tabular}{c} **Grouting** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Grouting Pressure (MPa)** \\ **Pile End** \\ \end{tabular} & \
\begin{tabular}{c} **Water-Cement** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Quantity of** \\ **Cement (103 kg)** \\ \end{tabular} \\ \hline TP1 & 1.5 & 56.5 & Clay & Combined & 2.7 & 3.8 & 0.55 & 6.69 \\ TP2 & 1.5 & 56.5 & Clay & Combined & 2.5 & 3.3 & 0.55 & 6.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of test piles and grouting parameters.
# An Enhanced Crowned Porcupine Optimization Algorithm Based on Multiple Improvement Strategies
## 5 Engineering Application  
To verify the feasibility and effectiveness of ICPO in practical engineering design applications, this paper tests ICPO on the Pressure Vessel Design (PVD) [26] optimization problem. The results are compared with PSO, WOA, Walrus Optimization Algorithm (WO), and CPO to evaluate ICPO's performance when addressing real-world problems.  
The objective of the PVD problem is to minimize the total cost \(f(x)\) of the pressure vessel while meeting production requirements. The four design variables are: Internal radius \(R(x_{1})\), Container length \(L(x_{2})\) (excluding the head), Shell thickness \(T_{s}(x_{3})\), Head thickness \(T_{h}(x_{4})\). Here, \(R\) and \(L\) are continuous variables, while \(T_{s}\) and \(T_{h}\) are discrete variables that must be integer multiples of 0.625. The specific mathematical model is formulated through Equations (19) to (24).  
Objective function:  
\[\text{min}f(x)=0.6224x_{1}x_{3}x_{4}+1.7781x_{2}x_{3}^{2}+3.1661x_{1}^{2}x_{4}+ 19.84x_{1}^{2}x_{3} \tag{19}\]  
Constraints:  
\[g_{1}(x)=-x_{1}+0.0193x_{3}\leq 0 \tag{20}\]  
\[g_{2}(x)=-x_{2}+0.00954x_{3}\leq 0 \tag{21}\]  
\[g_{3}(x)=-\pi x_{3}^{2}x_{4}-\frac{4}{3}\pi x_{3}^{3}+1296000\leq 0 \tag{22}\]  
\[g_{4}(x)=x_{4}-240\leq 0 \tag{23}\]  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Function**} & \multicolumn{2}{l}{**DBO-ICPO**} & \multicolumn{2}{l}{**PSO-ICPO**} & \multicolumn{2}{l}{**WOA-ICPO**} & \multicolumn{2}{l}{**CPO-ICPO**} \\ \cline{2-9}  & \(\mathbf{p}\)**-Value** & \(\mathbf{H}\  
Boundary constraints:  
\[0\leq x_{1}\leq 99,\ 0\leq x_{2}\leq 99,\ 10\leq x_{3}\leq 200,\ 10\leq x_{4}\leq 200 \tag{24}\]  
From Table 5, ICPO produces the most optimized solution, achieving the lowest total cost for the pressure vessel design problem when compared to the other four algorithms. This result underscores ICPO's superior effectiveness in addressing complex engineering design challenges.
# RCS and Aero Surfaces Control Allocation Research on RLV's Re-Entry Phase
## 2 Mathematical Model  
### Nonlinear Model of RCS and AS  
As known, the control output generated by RCS is obtained from the reaction force of the thrusters' fuel consumption, and the force arm is from the reaction force point to the mass center of RLV. Assuming that the number of RCS's thrusters is \(N\), the mathematical model of RCS in the body coordinate is:  
\[\mathbf{F}_{RCS}=\sum_{i=1}^{N}\Bigl{(}v_{i}\cdot F_{R_{i}}\Bigr{)}=\sum_{i=1} ^{N}\Biggl{(}v_{i}\cdot\left[\begin{array}{c}0\\ F_{i}\cos\varepsilon_{i}\\ F_{i}\sin\varepsilon_{i}\end{array}\right]\Biggr{)}\quad\mathbf{M}_{RCS}=\sum _{i=1}^{N}\Bigl{(}v_{i}\cdot F_{R_{i}}\times b_{i}\Bigr{)} \tag{1}\]  
where the firing command \(v_{i}=\{0,1\}(i=1,2,\cdots,N)\) means the \(i^{th}\) thruster of RCS only has 'on' and 'off' two conditions; \(F_{i}\) is the constant force generated by the \(i^{th}\) thruster; \(\varepsilon_{i}\) is the deflection angle of the \(i^{th}\) thruster; \(\mathbf{b}_{i}\) is the force arm vector of the \(i^{th}\) thruster in the body coordinate. Based on Equation (1), the output of RCS is discrete and non-smooth, and not affected by the external factors. Since RCS consumes the fuel, which is limited and hard to refill while working, it makes RCS suitable for any flight phase, but in the short term.  
In this paper, aero surfaces of RLV are the wing-body combination, which includes the elevator, body flap, rudder, and aileron. Normally, the body flap and elevator can be used for the pitch control; the aileron and rudder are corresponding to the roll and yaw control, respectively. Therefore, the allocation problem for the aero surfaces is between the body flap and the elevator. Additionally, the corresponding expressions in the body coordinate are shown as follows:  
\[\mathbf{R}=qS\left[\begin{array}{c}C_{x0}+C_{x}^{\delta_{du}}\delta_{du}\\ C_{y0}+C_{y}^{a}\alpha+C_{y}^{\delta_{dr}}\delta_{dbf}+C_{y}^{\delta_{dr}} \delta_{de}\\ C_{z0}+C_{z}^{\delta_{z}}\beta+C_{y}^{\delta_{dr}}\delta_{dr}\end{array} \right],\quad\mathbf{M}_{R}=qS\left[\begin{array}{c}m_{x}\\ m_{y}\\ m_{z}\end{array}\right] \tag{2}\]  
where the dynamic pressure is \(q=\rho V^{2}/2\), wherein \(\rho\) is the air density, \(V\) is the velocity; \(S\) is the reference area, \(l\) is the reference length; \(\alpha\) is the attack angle, \(\beta\) is the sideslip angle; \(\delta_{da},\delta_{dr},\delta_{de},\delta_{dbf}\) are the deflection angle of the aileron, rudder, elevator, and body flap, respectively; \(C_{x}^{\delta_{du}},C_{y}^{\delta_{dr}},C_{y}^{\delta_{de}},C_{y}^{\delta_{dr}}\) are the corresponding aerodynamic coefficients; \(C_{x0},C_{y0},C_{z0}\) are the zero-lift drag, lift, and lateral force coefficients; \(m_{x},m_{y},m_{z}\) are the aerodynamic moment coefficients [16]. Based on Equation (2), different from RCS, the aerodynamic moments are continuous and closely related to the current dynamic pressure, which means despite other aerodynamic factors, the bigger the dynamic pressure, the bigger the aerodynamic output. Thus, it leads to the fact that AS is only applicable in the atmosphere.
## 5 MSC-EV and Wound Healing
### Inflammation  
In this phase, the wound bed is cleaned of cells and bacteria, by neutrophiles. Also, macrophages phagocytize tissue debris and produce some cytokines and growth factors,enhancing the inflammatory response. In fact, macrophage polarization takes place. They change their inflammatory M1 into an anti-inflammatory M2 phenotype. In that case, MSC-EV might have an important role in promoting immunomodulatory effects, producing suitable wound-healing environments [39; 51] (Figure 2a).  
Figure 2: Role of extracellular vesicles during wound healing. EV may carry miRNA and proteins that can modulate a variety of physiological processes in different skin wound-healing phases. (**a**) In the inflammation phase, MSC-EV promote anti-inflammatory processes, suppressing apoptosis, inducing  
macrophage phenotype change from pro-inflammatory (M1) to anti-inflammatory (M2) and decreasing reactive oxygen species (ROS) synthesis. (**b**) In the proliferation phase, they contribute to neovascularization and re-epithelialization, stimulating fibroblast migration and proliferation into wounds. (**c**) In the remodeling phase, EV increase collagen I production and promote scarless wound healing. In brackets are shown bibliographic references related to functions of these molecules in different wound healing phases.  
Furthermore, it has been found that transmission of some miRNA could produce macrophage polarization. Among others, miR-34a-5p, miR-124-3p, miR-146a-5p and miR-223 [56; 57]. MSC-EV miR-181c overexpression suppressed the TLR signaling pathway and alleviated inflammation in burned rats [58]. Proteomic analyses of MSC-EV have found that EV may contain chemoattractant proteins such as chemokine CXCL8, CXCL16, DEFA1, HERC5, IFITM2 and XCL2. They might enhance the immune response, protecting against infectious diseases [59].  
Additionally, MSC-EV contain prostaglandin E2, which regulate the migration of neutrophils and macrophages polarization. MSC-EV also carry IL-6, IL-10, TGF-\(\upbeta\) and VEGF, that act as chemotactic and anti-inflammatory factors [42]. Due to their action on dendritic cells, MSC-EV might shift the production of inflammatory T-cells towards FOXP3+ regulatory ones, reduce the secretion of inflammatory cytokines like IL-6, and expression of chemokine receptor CCR7. Furthermore, an increase in the secretion of anti-inflammatory TGF-\(\upbeta\) has been observed [60]. In another study, the HaCaT cell line, after hydrogen peroxide exposure treatment with MSC-EV, reduced expression of apoptotic and inflammatory proteins (caspase-3 and IL-6) and increased expression of Bcl-2 and IL-10 (anti-apoptotic and anti-inflammatory ones). Also, an increase of miRNA-19b levels was found in these cells. This miRNA binds to inflammatory factors, resulting in the activation of the TGF-\(\upbeta\) pathway, which inhibits inflammation [61]. Another study demonstrated that EV could modulate endothelial precursor cells. inhibiting ROS and inflammatory cytokine expression [62]. These data showed that MSC-derived EV had a high immunomodulatory capacity, through multiple mechanisms.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 2 Materials and Methods
### Batch Experiment  
The composite material was added to the methylene blue solution with the dosage of 1 g/L, and the solution was adjusted to pH 7 using 0.1 M HCl and NaOH. Then, the flasks were placed in a thermostatic oscillator set at 160 rpm and 25 \({}^{\circ}\)C until equilibrium concentration between the adsorbent and solution was attained. Then, the removal effect of methylene blue was studied by sampling the residual methylene blue concentration in the solution using a UV-Vis spectrophotometer (Thermo Fisher UV 2350 spectrophotometer, Xi'an, China) at a maximum wavelength of 665 nm.  
With the removal effect of methylene blue as a criterion, a series of batch experiments were carried out to study the effects of impregnation time, impregnation ratio, dosage, contact time, and solution concentration on the removal of methylene blue. Among them, the optimal impregnation time and impregnation ratio were selected, and the materials were named AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\), respectively. Then, AC-CuO, AC-ZnO, and AC-TiO\({}_{2}\) were used to further study the effect of dosage (0.5-8.5 g/L), contact time (5-180 min), and solution concentration (1.0-9.0 mg/L) on the removal process of methylene blue.  
The methylene blue removal amount (\(Q_{e}\)) and removal effect (\(\eta\)) of the composite material can be calculated according to Formulas (1) and (2) [24].  
\[Q_{e}=\frac{(C_{0}-C_{e})\,V}{m} \tag{1}\]  
\[\eta=\frac{(C_{0}-C_{e})\,V}{C_{0}}100 \tag{2}\]  
where \(C_{0}\) and \(C_{e}\) are the initial and e-time concentrations of methylene blue, mg/L, respectively; \(V\) is the solution volume, L; \(m\) is the weight of the composite material, g; and \(\eta\) is the adsorption percentage, %.  
Unless otherwise specified, other factors were maintained as follows: initial concentration = 5 mg/L, impregnation ratio (AC-CuO) = 0.2:1, impregnation ratio (AC-ZnO) = 0.05:1, impregnation ratio (AC-TiO\({}_{2}\)) = 0.3:1, impregnation time (AC-CuO) = 70 min, impregnation time (AC-ZnO) = 50 min, impregnation time (AC-TiO\({}_{2}\)) = 50 min, dosage = 1 g/L, contact time = 60 min, vibration rate = 160 rpm, and temperature = 25 \(\pm\) 0.5 \({}^{\circ}\)C.
## 2 Materials and Methods
### Spatial Frequency Analysis  
The goal of this section is to write the cable equation, including the activating function, using Fourier analysis and express the mathematical functions as sums of sines and cosines of different spatial frequencies. We then show that terms with high spatial frequency--meaning those terms that vary over short distances--are suppressed (that is, filtered out) of the solution for the transmembrane potential.  
Our model is based on the cable equation, which describes an axon as a long cable with intracellular resistance, and membrane conductance and capacitance. We include the activating function, \(S=-\lambda^{2}\frac{\partial E_{x}}{\partial x}\), in the cable equation [6]:  
\[V-\lambda^{2}\,\frac{\partial^{2}V}{\partial x^{2}}+\tau\frac{\partial V}{ \partial t}=S, \tag{1}\]  
where \(\lambda\) is the axon length constant, and \(\tau\) is its time constant. Typically, \(\lambda\) is on the order of 0.1 cm, and \(\tau\) is about 1 millisecond. We assume that the system is in a steady state (this restrictive assumption is relaxed in the next section); therefore, the time derivative is zero. As the first step in Fourier analysis, we examine the behavior of an activating function in the form of a sine wave with a single spatial frequency \(k\),  
\[S=S_{0}\text{sin}(kx). \tag{2}\]  
Although a sinusoidal source is not characteristic of magnetic stimulation, any spatial distribution of \(dE_{x}/dx\) can be expressed through Fourier analysis as a sum of sines and cosines; therefore, considering a sinusoidal source is useful.  
To determine the transmembrane potential \(V\), we assume a solution of the cable equation, Equation (1), of the form  
\[V=V_{0}\text{sin}(kx). \tag{3}\]  
We plug (2) and (3) into (1) and find  
\[V_{0}\text{sin}(kx)+\lambda^{2}k^{2}V_{0}\text{sin}(kx)=S_{0}\text{sin}(kx), \tag{4}\]  
so  
\[V_{0}=\frac{S_{0}}{1+k^{2}\lambda^{2}}. \tag{5}\]  
The induced voltage depends on the membrane length constant and the spatial frequency. For \(k\lambda<<1\) (the activating function \(S\) is nearly uniform over distances that are greater than the length constant)  
\[V_{0}=S_{0}. \tag{6}\]  
For \(k\lambda>>1\) (the activating function changes over distances that are small compared to the length constant)  
\[V_{0}=\frac{S_{0}}{k^{2}\lambda^{2}}. \tag{7}\]
# Mechanical Analysis of Ground Sleeve
## 5 Mechanical Analysis of Grouted Sleeve Lapping Connector
### Strain Analysis of Reinforcement Bars  
The load-strain curves of the bars outside the sleeve (i.e., SG7 and SG8), the color of which is translucent, are shown in Figure 19. The load-strain curve of the control bar (i.e., the bare bar in tension), is also presented using a black curve with dark spots as a reference. The strain was tensile at all times and increased with the load during the elastic range in the control bar, whereas the strain gauges in the bars outside the sleeve indicated a compressive or tensile strain before the bar started yielding. This result was obtained because the gauges were located in the compressive or tensile region of the bended bar outside the sleeve.  
The load-strain curves of the bars in the middle section of the sleeve (i.e., SG5 and SG6) are shown in Figure 20. For specimen 100-3, the development of the load-strain curve of SG6 differed considerably from that of SG5. As the sleeve deflected significantly, and the segment of the bar where SG6 was located bended, the tensile value of SG6 was larger than the strain value of the control bar under the same load.  
For specimens with lap lengths larger than 100 mm, the strain values of SG5 and SG6 were near each other under the same load, and these values were both smaller than the strain value of the control bar. This was because a considerable portion of the pulling force was borne by the bond force between the bar and grout from the loaded end to the middle section, and the tensile forces at the bar where SG5 and SG6 were located were smaller than the pulling force.  
Typical load-strain curves of SG5 and SG6 are shown in Figure 20f. The slope of the curves clearly decreased as the load increased, which was different from the curve of the control bar for which the slope remained unchanged as the load increased during the elastic stage. As the load increased, the bond failure between the bar and grout developed from the loaded end to the free end, and the rate of the internal force of the bar at the middle section accelerated. Thus, the rate of the strain of SG5 and SG6 increased and the slope of the curves decreased.  
Figure 19: Load-strain relationship for bars outside the sleeve in a grouted sleeve lapping connector.
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 3 Results and Discussion
### Model Analysis of Elastic Modulus of Rock
#### 3.3.2 Modeling the Elastic Modulus of Rock under the Influence of Confining Pressure
The fitting results for granite and shale are particularly accurate, whereas the fitting for red sandstone and gray sandstone is slightly less precise. This discrepancy is mainly due to the higher degree of fracture development and porosity in red sandstone and gray sandstone, which can cause abrupt changes in the tangent elastic modulus under high temperature and pressure conditions. Consequently, the model finds it challenging to accurately fit these abrupt trends. Future research could focus on investigating the specific characteristics of these rock types further. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **R\({}^{2}\)** \\ \hline G & \(-1.5519\times 10^{-5}\) & 0.00319 & \(-0.01056\) & 23.53821 & 11.65526 & 0.98819 \\ RS & \(3.6669\times 10^{-6}\) & \(-0.00245\) & 0.26988 & 2.36925 & 8.99786 & 0.81258 \\ GS & \(1.7180\times 10^{-4}\) & \(-0.02891\) & 1.35263 & 35.58206 & 15.28693 & 0.71489 \\ SH & \(-1.5150\times 10^{-6}\) & \(-6.6366\times 10^{-5}\) & 0.11000 & 6.38303 & 20.27230 & 0.95229 \\ \hline \end{tabular}
\end{table} 
Table 2: Fitting results of tangent elastic modulus under thermo-mechanical coupling. Figure 14: Fitting results of tangent elastic modulus in relation to temperature and confining pressure for rocks (The red dots in the graph indicate actual test data): (**a**) granite; (**b**) red sandstone; (**c**) gray sandstone; (**d**) shale.