esa-sceva/satcom-chunk-collection huggingface dataset:
# Ibuprofen Removal by Aluminum-Modified Activated Carbon (AC@Al) Derived from Coconut Shells
## 3 Results and Discussion
### Effect of pH  
The influence of pH was examined in the pH range of 2.0-9.0 \(\pm\) 0.1 with a static dosage of 0.8 g/L applied to 100 mg/L of IBU at T = 298 K for 24 h. The pH was checked and adjusted before the adsorbent's addition by adding appropriate low concentrations of NaOH or HCl. As shown in Figure 2, in acidic conditions, the removal of Ibuprofen was enhanced. Specifically, the highest removal rate was achieved at pH = 2.0 \(\pm\) 0.1 (98.8%). Further increases in pH gradually reduced the absorbent capacity. As the pH values increased, the total surface charge of AC@Al became negative. Therefore, the overall negative value increased with further increases in pH, preventing negatively charged species from binding to the surface of the material due to the increasing repulsive forces between both the negatively charged IBU molecules and the surface [17].  
The point of zero charge (pH\({}_{\text{pzc}}\)) of AC@Al, which is the point at which a surface charge becomes neutral, was found by measuring it within a pH range of 2.0-10.0 \(\pm\) 0.1. This was done by plotting \(\Delta\)pH vs. pH\({}_{\text{initial}}\) using the pH drift method [29] (Figure 3) for pH\({}_{\text{pzc}}\) calculation. The pH\({}_{\text{pzc}}\) of AC@Al was determined in a pH range of 2.0-10.0 \(\pm\) 0.1. For a pH lower than the point of zero charge (pH\({}_{\text{pzc}}\) = 5.72) the surface of an adsorbent is positively charged due to protonation and the electrostatic repulsion between negatively charged Ibuprofen, while for higher values, there is a dissociation of the functional groups that creates a negative charge. As the pH increases, the effectiveness of adsorption decreases because of the repulsion occurring between the negative charge of the AC@Al surface and the Ibuprofen [15]. Ibuprofen is considered a weak acid, and its removal was advantageous at pH 2.0 \(\pm\) 0.1 and unfavorable at pH \(>\) 7.0 [28]. IBU has a pKa = 5.2, and at the optimum pH of 2.0, which is <pKa, it occurs in the neutral form and is adsorbed via non-electrostatic interactions with the settling surface. With increases in pH, the relative rate of IBU removal decreased with the deprotonation of surface-active agents. Similar results have been reported in the literature regarding the optimum pH of 2.0 [30].  
Figure 2: Effect of pH on the adsorption of IBU onto AC@Al; IBU \(C_{0}\) = 100 mg/L, dose = 0.8 g/L, pH 2.0–9.0 \(\pm\) 0.1, \(T\) = 298 K, \(t\) = 24 h.
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Sub-Component** & **Attribute** & **K1** & **K1A1** & **K2** \\ \hline \multirow{8}{*}{Turret assembly} & & Type & Rifted gun & Smoothbore \\ \cline{2-5}  & & Calliber (length) & 105 mm (52) & 120 mm (44) & 120 mm (55) \\ \cline{2-5}  & & Rounds & \(\mathcal{A}\) & 32 & 40 \\ \cline{2-5}  & & Ammo Loading & Manual loader & Auto loader \\ \cline{2-5}  & & Firing range & 2500 m (8202 ft) & 3500 m (11,482 ft) & 3000 m (98425 ft) \\ \cline{2-5}  & & Ammunition & APAM, HESH, APFSDS, & APFSDS-T, HEAT-MP \\ \cline{2-5}  & & Type & HEAT-MP, HEAT-PS & \\ \cline{2-5}  & & Gun and turret driving & Electric and Hydraulic & Electric \\ \cline{2-5}  & & Gun stabilization & 2 xis stabilization & \\ \cline{2-5}  & & Ballistic computing & Digital & \\ \cline{2-5}  & system (FCS) & Gunner’s primary sight & Day and night, Optics and thermal, Zis stabilization, Laser range finder & \\ \cline{2-5}  & & Commander’s panoramic sight & 360-degree view, Day, Optics, & \\  & & Commander’s panoramic sight & 2 xis stabilization, Laser range finder & 360-degree view, Day and night, Optics and thermal, 2 xis stabilization, Laser range finder & \\ \hline \multirow{2}{*}{Frame /Body} & & Crews & 4 (Commander, Gunner, Loader, Driver) & 3 (except Loader) \\ \cline{2-5}  & & Size & L: 9.67 m (31.7 ft) * W: 3.59 m & L: 9.71 m (31.8 ft) * W: 3.59 m & L: 10.8 m (35.4 ft) \\  & & Size & (11.8 ft) * H: 2.25 m (7.4 ft) & (11.8 ft) * H: 2.25 m (7.4 ft) & * H: 2.4 m (7.9 ft) \\ \hline \multirow{8}{*}{Powerpack} & & \multirow{3}{*}{Speed} & Road: 65 km/h & Road: 60 km/h & Road: 70 km/h \\  & & (40.4 m), & (73.2 m) in/h & (43.5 m) in/h \\ \cline{1-1}  & & Cross country: & Cross country: & Cross country: & Cross country: \\ \cline{1-1}  & & 40 km/h (24.9 m) & 40 km/h (24.9 m) & 50 km/h (31.1 m) \\ \cline{1-1} \cline{2-5}  & & Operational range & 550 km (341.8 m) & 500 km (310.7 m) & 450 km (279.6 m) \\ \cline{1-1} \cline{2-5}  & & HP & 1200 & 1500 \\ \cline{1-1} \cline{2-5}  & & HP/ion & 23.5 & 22.0 & 27.3 \\ \cline{1-1} \cline{2-5}  & & Gradeability & 60\({}^{\circ}\) (mas) & \\ \cline{1-1} \cline{2-5}  & & Gear shifting & 4-speed forward, & 6-speed forward, & \\ \cline{1-1} \cline{2-5}  & & & 2-speed reverse & 3-speed reverse \\ \hline \multirow{2}{*}{Suspension} & \multirow{2}{*}{Type} & \multirow{2}{*}{Steel torsion bars - Hydropneumatic suspension system} & Semi-active, in-arm \\  & & & & suspension unit \\ \cline{1-1} \cline{2-5}  & & Attitude control & Pitch & Pitch, Roll, Height \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specifications of K-series main battle tanks (MBT).
# Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
## 4 Unsupervised Feature Selection for Streaming Outlier Detection
### Operation Principle  
The operation principle of UFSSOD is shown in the block diagram of Figure 1. Data instance (data point) \(\mathbf{x}_{t}\) with dimension \(d\) of the data stream \(\{\mathbf{x}_{t}\in\mathbb{R}^{n_{t}\times d},t=1,2,...\}\) streams into the module UFSSOD at each time step \(t\) composed of the global feature space \(\mathcal{F}=\{f_{1},f_{2},...,f_{d}\}\). Thus, it is capable of computing a ranked list of features contributing the most for the purpose of OD leading to a subset \(\mathcal{F}_{S}=\{f_{i1},f_{i2},...,f_{i\gamma}\}\) of the top-\(\gamma\)-features for each time step \(t\) where a higher value of \(f_{i}\) indicates a highly contributing feature for the purpose of OD. With respect to unsupervised feature selection on streaming data for the purpose of outlier detection, UFSSOD's main objective is to reduce the set of features in \(\mathbf{x}_{t}\) obtaining \(\mathbf{x}_{t}^{*}\) according to Equation (2) by using the subset \(\mathcal{F}_{S}\subset\mathcal{F}\). Besides, if desired, UFSSOD is able to produce possible outlier candidates by providing scaled outlier scores \(\hat{y}_{0}\) for each \(\mathbf{x}_{t}\) without increasing the overall complexity in big \(\mathcal{O}\) notation referring to [52]. Algorithm 1 provides a high-level overview of UFSSOD's operation principle consisting of the scoring and clustering functionality for feature scores, denoted as _ufsod_scoring_ and _ufsod_clustering_. The internal working of both key modules is described in detail in Section 4.3.  
\[\mathbf{x}_{t}^{*}=\mathbf{x}_{t}\cap\mathcal{F}_{S} \tag{2}\]
## 3 Numerical Simulation and Analysis
Scintillation indices and average received power were computed at 20 equidistant points spanning 0-15 km (750 m intervals), as shown in Figure 3.2  
Within the 0-15 km range, OPB exhibits notable scintillation suppression efficacy and enhanced average received power. For uplink satellite-to-ground optical communications, where atmospheric turbulence predominantly occurs at altitudes of 15-20 km, the  
Figure 3.3 OPB and GB propagation under atmospheric turbulence (\(D\)/\(r_{0}\) = 5) (**a**) Statistics diagram of SI (**b**) Statistics diagram of ARP. Figure 3.4 Statistics diagram of SI for OPB under atmospheric turbulence (\(D\)/\(r_{0}\) = 5). transmission medium transitions to a vacuum beyond this turbulent layer. Consequently, OPB has considerable application potential in space-ground links thanks to its simplified modulation architecture and superior turbulence suppression capability.
# Investigation of Guidewire Deformation in Blood Vessels Based on an SQP Algorithm
## 3 Results
### Deformation by ABAQUS Simulation  
ABAQUS is widely considered to be a powerful finite element software with a library of various types of material models. Here, ABAQUS is used to simulate the deformation of the guidewire for the two-point contact situation seen in Figure 4. The simulation parameters of the guidewire in ABAQUS are shown in Table 3.  
The simulation results from ABAQUS when \(\theta_{B}\) takes different values  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Cross Section** & **Length (mm)** & **Elastic Modulus (GPa)** \\ \hline beam & Circle, d = 0.78 mm & 100 & 3.56 \\ \hline
**Poisson’s ratio** & **Constraint point A** & **Constraint point B** & **Mesh part** \\ \hline
0.3 & coordinate: (0,0) & coordinate: (60,40) & \\  & deflection angle: 0 & deflection angle: \(\theta_{B}\) & 100 elements \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input data for the ABAQUS simulation of the two-point contact guidewire problem.  
Figure 6: Optimized guidewire shapes for different contact conditions. (**a**) The equation for an oblique vessel wall is \(y=1.5x-30\); (**b**) The equation for a vertical vessel wall is \(x=45\).  
Then, we compare the results of the ABAQUS simulation with the results of the SQP algorithm to obtain Figure 8.  
If (\(\rm{h_{i}}\), \(\rm{q_{i}}\)) represents the coordinates of each point on the deformation curve obtained by the SQP algorithm, and (\(\rm{F_{i}}\), \(\rm{Q_{i}}\)) represents the coordinates of the corresponding point obtained by the ABAQUS simulation. Hence, the plane distance between the two results can be expressed by Equation (25), which is the error between the results of SQP algorithm and the result of ABAQUS simulation.  
\[\rm{z_{i}}=\sqrt{\left(\rm{h_{i}}-\rm{H_{i}}\right)^{2}+\left(\rm{q_{i}}-\rm{ Q_{i}}\right)^{2}} \tag{25}\]  
In Equation (25), the smaller the \(\rm{z_{i}}\), the closer the deformation result calculated by the SQP algorithm and the result of ABAQUS simulation are, that is to say, the better the simulation accuracy of the solution proposed in this paper is. Figure 9a shows the calculation error of each point of the guidewire at different \(\theta_{B}\) if there are only two touch points. We can calculate that the average position  
Figure 8: Comparison of the optimized deformed shape of the two point-contact guidewire computed via sequential quadratic programming (SQP) optimization or by ABAQUS.  
error of all the guidewires is 0.2556 mm. We know that the average diameter of blood vessels is about 5 mm in interventional procedures. Therefore, the calculation accuracy (\(\rho\)) of the deformation simulation based on the SQP algorithm can be given by Equation (26), and we can better understand that from Figure 9b. The simulation accuracy can be used in an interventional training system to accurately reflect the deformation process of a guidewire in a blood vessel, and meet the requirements of the system for authentic simulation.  
\[\rho=\frac{Z_{\text{avg}}}{d_{\text{vessel}}}\times 100\%=\frac{0.2556}{5} \times 100\%=5.11\%. \tag{26}\]
## Materials and Methods
### General Procedure for Enzymatic Production of Flavor Esters  
The enzymatic production of flavor esters via direct esterification of octanoic acid with several short-chain alcohols (methanol, propanol, fused oil, isoamyl alcohol, hexanol and benzyl alcohol) was conducted in closed glass flasks with a capacity of 100 mL (ratio height to diameter of \(\approx\)2) containing 6 g of reaction mixture composed of stoichiometric acid:alcohol molar ratio (1:1). The reactions were performed using a fixed biocatalyst concentration of 3.7 mg of protein per gram of reaction mixture (0.6 mL of soluble or 0.63 g immobilized lipase that corresponded to 20 mg of protein--3.33 mg protein per gram of reaction mixture). The reaction systems were immersed in a temperature-controlled thermostatic water-bath at 40 \(\pm\) 1 \({}^{\circ}\)C under continuous stirring (240 rpm). Samples from the reaction mixtures (50 \(\mu\)L) were periodically withdrawn, diluted in 10 mL of hydrous ethanol solution (92.5% m\(\cdot\)m\({}^{-1}\)) and titrated with a NaOH solution (40 mmol\(\cdot\)L\({}^{-1}\)) using phenolphthalein as indicator. The acid conversion percentage (Y--%) was determined as shown in Equation (18) [61; 70]. Esters production were performed with two replications and experimental data are represented as mean \(\pm\) deviation. Blank assays were conducted with PSty-DVB beads and no acid consumption was detected under such experimental conditions, thus confirming that all consumed octanoic acid in the reaction is converted into esters.  
\[\rm Y=\left(\frac{A_{0}-A_{t}}{A_{0}}\right)\times 100 \tag{18}\]  
where: \(\rm A_{0}\) and \(\rm A_{t}\) are the initial and residual octanoic acid concentration after a given reaction time t (mol\(\cdot\)L\({}^{-1}\)), respectively.  
Productivity (P--\(\mu\)mol\(\cdot\)min\({}^{-1}\cdot\)mg\({}_{\rm prot}\)\({}^{-1}\)) was determined as follows (Equation (19)) [70]:  
\[\rm P=\frac{A_{0}\times Y\times V}{t\times m_{\rm prot.}} \tag{19}\]where: A\({}_{0}\) is the initial octanoic acid concentration (\(\mu\)mol\(\cdot\)L\({}^{-1}\)), Y is acid conversion percentage (%), V is the reaction volume (\(\approx\)7.5 \(\times\) 10\({}^{-3}\) L for all reaction mixtures), t is reaction time (min), and m\({}_{\text{prot}}\) is the protein amount in the reaction mixture (mg).
## 1 Introduction
Only two ACSs are utilized on the transmitter to achieve the CC/CV mode. This study's main contributions are as follows: (1) Using the same drive signal to control the ACSs, the control strategy is relatively simple. (2) Compared with the conventional DS-LCC topologies hybrid switching method, only two ACSs need to be added on the secondary side, implying that no additional compensation devices are needed. Thus, the proposed system avoids unnecessary increases in the secondary side's volume and weight. (3) Secondary-side control is adopted, and wireless communication links for CC/CV charging control are not required, which ensures the proposed IPT system's robustness and reliability. (4) A systematic analysis of the proposed IPT systems was conducted. Finally, a 1.4 kW experimental prototype was established to validate it. Measurement results indicated that the system's maximum efficiency in the CC and CV modes was about 92.2% and 92.3%, respectively. The remainder of this paper is organized as follows: Section 2 presents a theoretical analysis of the proposed IPT systems. In Section 3, a hybrid compensation IPT system that can realize CC/CV outputs is proposed. Section 4 describes the experimental procedure used to validate the proposed system. We present our conclusions in Section 5.
# AIoT Monitoring Technology for Optimal Fill Dam Installation and Operation
## 5 Conclusions  
In this study, the convenience and safety management of dam monitoring were improved using the AIoT technology for fill dams, where damage is frequently caused by heavy rainfall and monitoring is performed using conventional safety management technologies, which have several challenges.  
Therefore, we developed an AIoT monitoring system with the following functions: an autonomous operation function that detects abnormal conditions and autonomously adjusts the measurement frequency; a malfunction prevention function that utilizes a statistical analysis-based data filtering technique; an optimal communication environment construction technology that automatically searches for channels and determines the optimal frequency in the frequency range; a machine learning prediction function; and a data authentication and security function. The autonomous operation of the load sensor and pore pressure gauge has been tested. Through this function, continuous monitoring (once per hour) transitions to precision measurement (once per minute) due to the occurrence of abnormal conditions. The data filtering system confirmed the generation of cumulative distribution and normal distribution data based on measurement values. Also, a filtering reliability of 90% or higher is drawn. Establishing an optimal communication environment and enhancing data authentication functions are worked seamlessly to the Korean testing without any error. However, this is configured with sensors suitable for the Korean environment. Subsequently, this system is scheduled to be tested in Vietnam.  
The performance of the developed AIoT monitoring system was verified by applying it to a test bed in Korea. The normal operation of each function was confirmed, and the reliability of the data was improved by removing the error values using a data filtering technique.  
In the future, additional data will be obtained, and predictions will be performed using machine learning with the test bed in Korea. In addition, a system suitable for Vietnam will be constructed by developing a measurement data correction technology based on the on-site temperature. This system will be applied to a site in Vietnam to test its application to diverse sites.  
[PERSON]-[PERSON] prepared the manuscript. [PERSON] and [PERSON] performed system development and performance verification. [PERSON]-[PERSON] researched the existing literature and analyzed the related data. [PERSON]-[PERSON] conceptualized the research and performed the final verification. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2023 International Matching Joint Research Project \"AIoT monitoring optimization considering the characteristics of the fill dam structure installation, operation, and analysis technology research (20230417-001)\" funded by the Korea Institute of Civil Engineering and Building Technology.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Figure 15: Test bed operation plan in Vietnam.
## 3 Model Validation and Analysis
### Validation and Analysis of Model Reliability  
The stress-strain curves of the plutons were obtained by carrying out triaxial compressive tests at four peri-compressive pressures (0, 1, 3, 7 MPa) on rock samples collected from a coal mine well in Niaoshan, Heilongjiang, China. The obtained model parameters and mechanical parameters of the plutons were substituted into strain-softening models to draw model curves of the rock at four peri-pressures. A comparative analysis of the two classes of curves (Figure 8) is performed separately to verify the reliability of the strain-softening model after reaching the peaks of the equivalently jointed plutons.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Confining Pressure \(\sigma_{3}\)/MPa**} & **0** & **1** & **3** & **7** \\ \hline \(m_{i}\) & 10.00 & 9.67 & 9.08 & 8.02 \\ \(m_{b}\) & 2.40 & 2.31 & 2.17 & 1.92 \\ \hline \end{tabular}
\end{table}
Table 2: Rock mass material parameters \(m_{i}\) and \(m_{b}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multicolumn{2}{c}{**Confining Pressure \(\sigma_{3}\)/MPa**} & **0** & **1** & **3** & **7** \\ \hline \(m_{i}\) & 10.00 & 9.67 & 9.08 & 8.02 \\ \(m_{b}\) & 2.40 & 2.31 & 2.17 & 1.92 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of the strain-softening model for jointed rock mass.  
According to the comparative analysis results shown in Figure 8, the model curves of the peak and residual stresses of the pluton body change linearly with the surrounding pressure, which is consistent with rock mechanics theory. During the linear elastic deformation stage before reaching the peak stress, the pluton model curves at different ambient pressures largely coincide with the experimental curves and all agree to a higher extent. This shows that the strain-softening model better describes the mechanical behavior during the linear elastic phase, which conforms to the actual stress-strain variation law of plutons. The model results also generally agree with the test curve variation law during the strain-softening phase after the peak has been reached.  
The tests show that the equivalent jointed pluton strain-softening model well characterizes the post-peak nonmechanical properties of plutons. The major source of difficulty in controlling deep rock lies in the contradiction between the uncoordinated nature of the established support structures and the strain-softening properties of plutons, the latter of which are poorly understood. The softening model is, therefore, applied in engineering design to further verify the reliability of the strain-softening model for equivalent jointed plutons by field application, and a reasonable scheme for rock support in a deep well is accordingly established.
# Influence of the Slope and Gate Offset on Movement Variability and Performance in Slalom Skiing
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Statistical Analysis  
The analysis encompassed a total of 77 runs for a sample of five skiers. In this sense, the runs from each skier were considered as repeated measures. To assess the influence of different slopes and slalom gate offsets on MV and total descent time, a one-way repeated measures analysis of variance (ANOVA) was implemented. If a significant result was obtained, post hoc comparisons were performed and _p_-values were adjusted considering Tuckey correction for multiplicity of contrasts. To show the differences more visually, bar plots depicting the mean SampEn for each SL course and the distribution of skiers were used. Additionally, the total descent times for each SL were also plotted. Additionally, a two-way ANOVA was employed to determine the significance of the explanatory variables, specifically the steepness of the slope and gate offset. Partial eta squared was used to determine the effect size as small (n\({}^{2}\)P = 0.01), medium (n\({}^{2}\)P = 0.06), and large (n\({}^{2}\)P = 0.14) [53]. To assess the relationship between MV and performance, a Pearson's linear correlation coefficient was used to calculate the correlation between SampEn and total descent time. A scatter plot was created with each point representing the average of each skier's runs for each SL course. All database management tasks and statistical analyses were performed using R v4.0.4 software [54]. The significance level for all statistical tests was set at 5% (_p_ < 0.05) and 10% (_p_ < 0.1) [55].