esa-sceva/satcom-chunk-collection huggingface dataset:
# Adsorption of Hexavalent Chromium Ions Using Pine Sawdust Cellulose Fibres
## 3 Results and Discussion
### Adsorption Studies
#### 3.8.2 Effect of pH  
Figure 12 shows that both pine sawdust and chromium (VI) ions showed increased adsorption capacity as the pH increased. There was a rapid increase in adsorption capacity from pH 2 to 6, followed by a decline for cellulose fibres at pH 7. At the optimal pH of 6, cellulose fibres exhibited a chromium (VI) removal effectiveness of 68.3% and an adsorption capacity of 9.638 mg/g. The increased metal adsorption with a higher pH can be attributed to reduced competition between metal ions and hydrogen ions for available surface sites. At low pH levels, the presence of hydronium ions (H\({}^{+}\)) surrounding the adsorbent surface hindered the binding of metal ions to active sites. However, the consistent percentage removal of untreated sawdust at pH 6 and above was attributed to factors such as its affinity for chromium ions, its favourable surface charge, the increased activity of functional groups, and the pH-dependent solubility of chromium ions. This interference in metal sorption can be affected when the solution pH is supported by the adsorbent's pH\({}_{\text{2 pc}}\), as indicated in Section 3.6 [50]. The pine sawdust's pH\({}_{\text{2 pc}}\) value, determined to be 6.5 (Section 3.6), only slightly exceeds the results presented in Figure 8 by 0.5 [51].  
Figure 11: Effect of adsorbent dose on hexavalent chromium ions’ adsorption by untreated pine sawdust (UPSD) and cellulose fibres (CF) (pH of the solution of 6.5, agitation speed of 200 rpm, and adsorbate concentration of 100 mg/L).  
Figure 12: Effect of pH solution on hexavalent chromium ions’ adsorption by untreated pine sawdust (UPSD) and cellulose fibres (CF) (adsorbent dose of 2 g/L at agitation speed of 200 rpm and adsorbate concentration of 100 mg/L).
## 3 Results and Discussion
### Effect of Puree Addition on Gelation Temperature
#### 3.2.1. _Gelatine Solutions with Pumpkin Puree_
The different results of the modules in both works may be influenced by the use of gelatines of different origins, different cooling rates of the solutions, and different additives to the solutions--pumpkin puree increases the viscosity of the solution by 8.6 times compared to the pure solution, which may result in higher values modules G\({}^{\prime}\) and G\({}^{\prime\prime}\). [PERSON] et al. [34] studied the gelation temperature of gelatine solutions with and without the addition of milk proteins--why protein isolate (WPI), skimmed milk powder (SMP), and milk protein concentrate (MPC). The gelatine solutions had a concentration of 2.5 and 5%, and the milk protein gelatine solutions had a concentration of 2.5 and 5% gelatine and 4.5% WPI, SMP, or MPC. The gelation temperature was determined by cooling the solutions from 40 to 10 \({}^{\circ}\)C with a cooling rate of 1 K/min. They showed that the gelation temperature of the solutions increases with the increase in the concentration of the gelatine solution and that the addition of both MPC and SMP has a statistically significant effect on the gelation temperature of the tested solutions. The addition of WPI has no statistically significant effect on the gelation temperature. The gelatinization temperature of a 2.5% gelatine solution without the addition of milk proteins was approx. 16 \({}^{\circ}\)C, while the gelatine temperature of a 5% gelatine solution without the addition of milk proteins was approx. 21 \({}^{\circ}\)C (the exact date was not given). The gelatine temperature of the gelatine solution with the addition of MPC or SMP, both at the concentration of gelatine equal to 2.5% and 5%, was higher by approx. 1 \({}^{\circ}\)C. The different gelation temperatures compared to the literature data were explained by both the different origins of the gelatine and the different thermal histories of the gelatine solutions.
# Molecularly-Imprinted SERS: A Potential Method for Bioanalysis
## 2 Molecularly Imprinted Polymer (MIP)
### Component of MIP
#### 2.2.3 Crosslinkers  
The crosslinker grips the functional groups within the selective binding sites for template recognition by stabilizing the imprinted cavities. The kind and amount of crosslinker utilized also impact the shape and stability of the structure. A high crosslinker to functional monomer ratio results in more stiff materials with reduced swelling capacities since the structure cannot expand. Polymers created with low molecular weight crosslinkers have higher stiffness than those prepared with another one. It creates polymers with higher quality selectivity, affinity, and binding capacity. As a result, the kind and extent of crosslinker utilized in the imprinting process must be selected carefully [63; 64; 65; 66; 67; 68; 69].  
The ratio of crosslinkers utilized in relation to the total number of moles of functional monomers is relatively high. A mechanically strong polymer with a persistent porous structure and a large surface area was developed at this concentration. A low number of crosslinkers results in sticky polymers with restricted imprinting applications. For copolymerization, the reactivity of the functional monomers and the crosslinker must be matched. Figure 3 depicts a range of crosslinkers often employed in molecular imprinting.
## 2 Modeling a Fluid-Conveying Pipe with PVDF Piezoelectric Film Sensor  
Considering the circular cross-section of the pipe wall, it is advantageous to use PVDF piezoelectric film sensors to measure the stress and strain in curved surface because it is flexible and robust. In order to understand the physical principle among fluid, pipe, and PVDF film, we modeled this fluid-conveying pipe with PVDF film by commercial ANSYS software.  
Flows in pipes in real applications are normally turbulent. In practice, there are three main methods for the analysis of turbulent flows, namely, direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) models. Among them, the DNS has the highest accuracy and can be used to simulate turbulence of all scales, although the computation cost is high. Therefore, it is difficult to apply it to turbulence simulation. Although the RANS has the advantage of costing less computational resources, its accuracy is poor. The LES is a numerical simulation method between the RANS and DNS, which combines the advantages of both and is very suitable for turbulence simulation [15]. For the reasons above, we chose the LES to simulate the turbulent flow in the pipe.  
Fluid-structure interaction and electromechanical coupling are used in finite element method (FEM) simulation of a fluid-conveying pipe with PVDF piezoelectric thin film attached to the outer wall. The FEM is a numerical technique that provides approximate solutions of boundary value problems for partial differential equations.  
A two-way FSI analysis that coupled the Fluent solver with Transient Structural Solver in ANSYS Workbench was conducted in our study. In each time step, the force data obtained from the calculation of the fluid domain are transferred to the equation of motion of the pipe for calculation, and the displacement data obtained from the equation of motion of the pipe are transferred back to the equation of motion of the fluid for calculation.
# Damage Assessment of Shear Wall Structures in an Earthquake-Blast Disaster Chain
## 2 Methodology
### Constitutive Models  
Several concrete constitutive models are available within LS-DYNA, including the Holmquist-Johnson-Cook (HJC), the Continuous Surface Cap (CSC) model, the Karagozian and Case Concrete (KCC) model, and the Brittle Damage Concrete (BDC) model. In this manuscript, the HJC model [21] is used due to its ability to simulate the behavior of concrete at large strains and high strain rates. Existing studies [22; 23; 24] have proven that the HJC model can describe the damage and failure of concrete materials under intensive and impact loadings. The equivalent strength model treats damage, strain rate, and hydrostatic pressure as variables. The general overview of the HJC model is given as follows:  
\[\sigma^{*}=\Big{[}A(1-D)+BP^{*N}\Big{]}[1+C\ln(\varepsilon^{*})] \tag{3}\]  
where \(D\), \(P^{*N}\), and \(\hat{\varepsilon}^{*}\) are the damage parameter, the normalized pressure, and the dimensionless strain rate, respectively; \(A\), \(B\), and \(C\) are constants, where \(A\) is the normalized cohesive strength, \(B\) is the normalized pressure hardening coefficient, and \(C\) is the strain rate coefficient.  
The HJC model incrementally accumulates damage from both equivalent plastic and plastic, which means:  
\[D=\sum\frac{\Delta\varepsilon_{p}+\Delta\mu_{p}}{D_{1}(P^{*}+T^{*})^{D_{2}}} \tag{4}\]  
where \(\Delta\varepsilon_{p}\) and \(\Delta\mu_{p}\) are the equivalent plastic strain and plastic volumetric strain, respectively; both \(D_{1}\) and \(D_{2}\) are material damage constants; \(T^{*}\) is the normalized maximum tensile hydrostatic pressure.  
In this paper, gas explosions are converted into TNT explosions through the TNT equivalent method, as described in Section 2.2. The explosion of TNT is simulated by the \(*MAT\_HIGH\_EXPLOSIVE\_BURN\) material model and the equation of state \(*EOS\_JWL\) in LS-DYNA:  
\[p=A_{1}\bigg{(}1-\frac{\omega}{R_{1}V}\bigg{)}e^{-R_{1}V}+A_{2}\bigg{(}1- \frac{\omega}{R_{2}V}\bigg{)}e^{-R_{2}V}+\frac{\omega E}{V} \tag{5}\]  
where \(p\), \(V\), and \(E\) are the detonation pressure, the relative volume, and the initial unit volume energy, respectively; \(A_{1}\), \(A_{2}\), \(R_{1}\), \(R_{2}\), and \(\omega\) are the coefficients.  
Assuming that the air is a non-viscous ideal gas, it is described by the empty material model \(*MAT\_NULL\) and the equation of state \(*EOS\_LINEAR\_POLYNOMIAL\):  
\[P=C_{0}+C_{1}\mu+C_{2}\mu^{2}+C_{3}\mu^{3}+\Big{(}C_{4}+C_{5}\mu+C_{6}\mu^{2} \Big{)}E \tag{6}\]  
\[\mu=\frac{1}{V}-1 \tag{7}\]  
where \(P\), \(E\), and \(V\) are the pressure value, the initial specific internal energy, and the relative volume, respectively; and \(C_{0}\), \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(C_{5}\), and \(C_{6}\) are polynomial equation coefficients.
## 3 Electromagnetic Security of Selected HF-Band RFID Card Readers and Access Control Systems
### Reader Systems
#### 3.1.3 Galaxy A34 as an HF-Band RFID Reader
For example, in the case of an access control system, the estimated range of information penetration (i.e., the distance from which it is possible to read the data transmitted by the identifier) is about 2-3 m. For this system, it was possible to recreate information for the frequencies of 13.56 and 40.68 MHz. On the other frequencies, only queries sent by the system reader were received, and their estimated range should not exceed 10 m.  
The worst variant turned out to be the use of an NFC card reader/programmer to read the identifier. In this case, very clear revealing emission signals were observed up to the frequency of 691.56 MHz. These signals were characterized by a high level of field strength of up to 60 dBuV/m. In addition, the level of waveforms observed on the oscilloscope corresponding to the data sent from the identifier is comparable to the level of the signal corresponding to the queries. On the basis of these data, the range of the occurrence of revealing emissions was estimated at about 10-20 m (depending on the frequency) and the possibility of identifying the reader's signal at about 50 m, which is a great threat in relation to both the possibility of intercepting the transmission and tracking the card reader. As already mentioned in the summary, this research is the continuation of previous research studies on the electromagnetic security of RFID systems using low frequencies for communication. The continuation of the current research will be focused, on one hand, on another type of radio communication in UHF RFID systems and, on the other hand, on the electromagnetic security of RFID systems in terms of the use of the method of data retransmission from the identifier or system over much longer distances. This phenomenon can be particularly dangerous in cases of contactless payments, keyless entry and starting vehicles, and are already being exploited by criminal groups. Conceptualization, S.M. and A.F. ; methodology, S.M. and A.F. ; validation, S.M., A.F. and I.K. ; formal analysis, S.M. and I.K. ; investigation, S.M. and A.F. ; resources, I.K. and S.M. ; data curation, S.M. and A.F. ; writing--original draft preparation, S.M. ; writing--review and editing, A.F. ; visualization, S.M. ; supervision, I.K. ; project administration, S.M. ; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data is contained within the article. The authors declare no conflicts of interest.
# TRC-Based High-Precision Spot Position Detection in Inter-Satellite Laser Communication
## 4 TRC Based Photocurrent Amplitude Detection
### SNR Gain of TRC Algorithm  
Discretize the continuous signal \(x(t)\) and rewrite \(x(t)\) as:  
\[x(k)\ =\ s(k)+n(k)\ =\ A\cos(2\pi\cdot f\cdot kT_{s}+\varphi)+n(k)\ k\ =\ 0,\ 1,\ 2, \cdots,N-1 \tag{16}\]  
Then, the SNR of \(x(k)\) is:  
\[SNR_{x}\ =\ \frac{P_{s}}{P_{n}}\ =\ \frac{A^{2}}{2\sigma_{n}^{2}} \tag{17}\]  
The expression of \(F_{\rm ss}(k)\) is  
\[F_{\rm ss}(k)\ =\ \left\{\begin{array}{ll}\frac{A^{2}(N+k)}{2}\cos(2\pi\cdot f \cdot kT_{s})\ k\ =\ -N+1,\cdots,\ 0\\ \frac{A^{2}(N-k)}{2}\cos(2\pi\cdot f\cdot kT_{s})\ k\ =\ 1,\ 2,\cdots,\ N-1\end{array}\right. \tag{18}\]  
To suppress noise, we set \(F_{x}(0)\) to 0. Thus the noise and the effective signal both lose part of the energy, so the signal power \(F_{x}(k)\) component power \(P_{Fs}\) and noise power \(P_{Fn}\) are calculated as:  
\[P_{Fs}\ =\ \frac{1}{2N-1}\Bigg{(}\sum_{k\ =\ -N+1}^{-1}\left|F_{\rm ss}(k) \right|^{2}+\sum_{k\ =\ 1}^{N-1}\left|F_{\rm ss}(k)\right|^{2}\Bigg{)}\ =\ \frac{A^{4}N\cdot(N-3)}{24} \tag{19}\]  
\[P_{Fn}\ =\ \frac{1}{2N-1}\Bigg{(}\sum_{k\ =\ -N+1}^{-1}\left|F_{nn}(k) \right|^{2}+\sum_{k\ =\ 1}^{N-1}\left|F_{nn}(k)\right|^{2}\Bigg{)}\ =\ \frac{\sigma_{n}^{4}N}{2} \tag{20}\]  
So, the output SNR of TRC operation is:  
\[SNR_{F}\ =\ \frac{N-3}{3}SNR_{x}^{2} \tag{21}\]  
Then, the SNR gain is:  
\[G_{F}\ =\ \frac{SNR_{F}}{SNR_{x}}\ =\ \frac{N-3}{3}SNR_{x} \tag{22}\]
# Redesigning the Wheel for Systematic Travelling Salesmen
## 2 Methods
### Local Optimization Using \(k\)-Opt Permutations
#### 2.4.2 Optimization with 3-Opt Permutations  
Although the selection and application of 2-opt permutations are fairly straightforward, the selection of 3-opt permutations needs more thought. As the tour is now cut at three locations, two additional points, Q and its candidates Qc, can be used to define the objective of a permutation.  
\begin{table}
\begin{tabular}{|c|c|c c c|c|} \hline No. & \multicolumn{3}{c|}{Permutation} & Remark \\ \hline  & \multicolumn{3}{c|}{P} & \multicolumn{3}{c|}{Pc} \\  & 0 & **1** & 2 & 3 & **4** & **5** & 6 & original order \\ \hline
1 & 0 & **1** & **5** & 4 & 3 & 2 & 6 & cut after P \\
2 & 0 & 4 & 3 & **2** & **1** & **5** & 6 & cut before P \\ \hline \end{tabular}
\end{table}
Table 3: List of possible 2-opt operations based on a 7-points example.  
Because the tour is cut to the right of P, its current neighbour should also find a new matching partner from its candidates set. For this reason, the current right neighbour of P is called Q:  
\[\texttt{Q}:=\texttt{successorOf(P)}\;.\]  
This assignment has the great advantage of avoiding a second loop over all tour points, which reduces the complexity of searching for improving permutations.  
Table 4 shows the two variants how the candidates Pc and Qc can be located and it lists in part (a) the possible permutations that match the pairs (P,Pc) and (Q,Qc) when the cyclic order of the four points is PQ\(\rightarrow\)Pc\(\rightarrow\)Qc.  
The 10-point examples assign P, Q, Pc, and Qc to the points 1, 2, 5, and 8. After performing a permutation, points 1 and 5 as well as 2 and 8 have become neighbours.  
In the case in which Qc lies between Q and Pc with respect to the current cyclic order of points, a single 3-opt permutation can be applied (Table 4b). All permutations have in common that the removal of three edges creates six points that have to be reconnected to each other. For only four of them (P, Q, Pc, Qc), the new neighbour is preselected, whereas the remaining two points automatically become new neighbours. This ensures a certain degree of freedom in the sense that even those points can become neighbours that are not candidates of each other.  
The pseudocode of the optimization process is given in Listing 1.  
In total, three loops are required: one over all points in the tour specifying point P and two more (much shorter) loops over all candidates Pc and Qc. When looking for a suitable point Qc, it does not need to be checked against P because P is a neighbour of Q and the check in line 21 already verifies that Q and Qc are not neighbours. Once an improving permutation could be found, it is applied to the tour and the next point P is examined. This process continues until either no more improving permutations could be found or a maximum duration is reached.
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 6 Comparative Study  
Table 3 demonstrates a comparison of the proposed flexible MIMO array antenna with similar works currently in existence in the literature for the Sub-6 GHz band. The literature shows minimal recent works in the Sub-6 GHz band, where an antenna array is extended to a MIMO configuration. In terms of its small size, bandwidth, and flexibility, the suggested antenna outperforms those presented in the current literature, according to the comparative table given below. The proposed antenna also offers a dual-band response with better isolation, good MIMO characteristics, and a reduced SAR value that falls below the international values.
# Stochastic Models and Control of Anchoring Mechanisms for Grasping in Microgravity
## 3 Stochastic Models for Grasping
### Surface Properties
Considering the interval distance between adjacent asperities and the size of asperities, the process of the microspine traveling on the surface can be modeled as Poisson distribution:  
\[P_{travel}=\frac{\left(\lambda x\right)^{k}}{k!}e^{-\lambda x} \tag{2}\]  
where \(\lambda\) is the mathematical expectation of the number of asperities grasped by the microspine traveling on the surface, \(k\) is the number of asperities engaged with the microspine, and \(x\) is the distance the microspine travels on the surface to grasp an asperity. So far, the probability of the engagement with an asperity immediately and the probability of the engagement with an asperity after traveling in the non-ideal region have been derived. Then, the probability of the microspine grasping an asperity in the process of traveling on the surface is as follows:  
\[P(x)=P_{initial}+P_{travel}=\alpha+\frac{\left(\lambda x\right)^{k}}{k!}e^{- \lambda x} \tag{3}\]  
Figure 4 intuitively shows the influences of ground parameters \(\lambda\) and \(\alpha\) on grasping probability. Figure 4a shows that \(\lambda\) affects the probability that the microspine will engages once it touches the surface. Figure 4b shows that the starting points of the three curves are different, and the more asperities on the ground, the greater the grasping probability. The experiment was carried out on two samples with microspines in random positions. During the movement, the distance between the starting point and the first rough surface meshed with the microspore is measured by caliper. The microspine is pulled until it desorbs or reaches the limit tension 3 N. The experimental results are shown in Figure 5. Every circle in Figure 5 represents the results of 200 experiments. It can be seen from the figure that the adhesion probability of the microspine on brick is significantly higher than that on sandpaper. The experimental results on two materials are in good agreement with the prediction curve (red curve). It should be noted that the strength of the surface is not considered among the surface properties. After many experiments, we found that asperities of the surface are unlikely to be damaged by microspines. Figure 3: The discrete model of the non-ideal region.
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
## 2 Model and Method
### Numerical Method of Temperature Field of Bearing Chamber-Bearing System
The numerical value assigned to this boundary condition is set to the oil inlet temperature, which is 75 \({}^{\circ}\)C.  
Natural Convection Conditions  
The natural convection between the outer wall of the bearing chamber housing and the outside air is taken into account, with the convection heat transfer coefficient set at 25 W/m\({}^{2}\).K and the free stream temperature at 20 \({}^{\circ}\)C.  
Boundary Condition of Bearing Chamber Walls  
According to Section 2.5, convective heat transfer coefficients of chamber walls are obtained through oil-air two-phase flow numerical calculation using the FSI method, and are used as boundary conditions of bearing chamber walls in bearing chamber-bearing system thermal analysis. For the convection heat transfer temperature settings of each wall, the oil outlet temperatures of Bearings 1, 2, and 4 are taken as convection temperatures in the walls of Bearing Chambers 1, 3, and 5, respectively. For Bearing Chamber 2, the average oil outlet temperature of Bearing 1 and 2 is adopted. Similarly, for Bearing Chamber 4, the average oil outlet temperature of Bearing 3 and 4 is adopted, which is the same as the value of convection temperatures in Bearing Chamber 2. The same average oil outlet temperature is also applied to the oil outlet channel walls. The specific values are shown in Table 6.  
The distribution of oil and air within the Bearing Chambers 1, 2, and 5 is depicted in Figure 7. Owing to the rotational motion of the bearings, lubricating oil is ejected from the bearings and into the adjacent bearing chambers, where it impacts the chamber walls. This process leaves a minimal residue of oil on the main shaft. Observations from Figure 7a,b reveal that the volume fraction of the oil phase in both Chambers 1 and 2 is relatively low. Conversely, in Chamber 5, lubricating oil tends to accumulate at the base, struggling to be effectively expelled due to the chamber's distinctive design and the influence of gravity. Notably, the oil primarily exits Chamber 5 via the axial outlet circular tubes situated at the bottom, with a negligible amount of oil dispersed on the upper portion of the chamber [21].