esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design
### Two-Element MIMO Antenna Design
#### 2.2.3 Analysis of MIMO Antenna with Defective Ground Structure  
Defective ground construction is added to improve the isolation between the antennas. The DGS is utilized within the ground plane, which consists of a single vertical rectangular slot of 1 mm width, as depicted in Figure 9. The reflection coefficient parameters are plotted in Figure 10a. The near-field coupling effects between MIMO antennas are mitigated by the inclusion of a slot, as evidenced by the transmission coefficient curves depicted in Figure 10b. It is noteworthy that there is an enhancement in the maximum level of isolation, increasing from \(-40\) dB to \(-52.72\) dB within the antenna's components. Consequently, deployment of the slot in ground plane resulted in an approximate 12 dB increase in isolation. Summarized results of MIMO and MIMO with DGS are presented in Table 6. Figure 11 illustrates the radiation efficiency of the proposed multiple input multiple output (MIMO) antenna. The first band, ranging from 8.2 to 10.4 GHz, exhibits a maximum radiation efficiency of 72%. In the second band, spanning from 16.1 to 16.5 GHz, the highest radiation efficiency reaches 55%. Lastly, the third band, covering the frequency range of 18.1 to 28 GHz, has a maximum radiation efficiency of 73%.  
The effects of current distribution at 9.7 GHz, 18.8 GHz, and 21.4 GHz are shown in Figure 12. One port of the MIMO antenna must be excited while the other port is terminated so that the distribution of surface currents can be studied. Hence, port one is stimulated while port two of the proposed antenna is terminated. The observation may be made that in Figure 9, there is a prominent distribution of high current within the initial radiating patch via port 1. Conversely, the surface current near port 2 experiences a decrease. The observed phenomenon definitely signifies a decrease in the level of mutual coupling between two antennas.  
Figure 8: Orientation analysis, scattering parameters (in dB): (**a**) S11; (**b**) S22; (**c**) S12 and S21.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Bandwidth (MHz)** & **f\({}_{\text{r}}\) (GHz)** &
\begin{tabular}{c} **S12 (dB)** \\ **(Max. Isolation)** \\ \end{tabular} \\ \hline Orientation I & 3100, 600, 10,100 & 9.7, 16.3, 18.8, 21.4 & \(-40\) \\ \hline Orientation II & 3000, 600, 9900 & 10.3, 16.4, 18.8, 21.5 & \(-38.74\) \\ \hline Orientation III & 2700, 9800 & 8.2, 19.2 & \(-37.21\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: MIMO orientation analysis.  
Figure 10: MIMO with DGS scattering parameters (dB): (**a**)S11 and S22; (**b**)S12 and S21.  
Figure 9: MIMO with DGS: (**a**) Frontal view; (**b**) Lateral view.  
Figure 11: Simulated radiation efficiency of the proposed MIMO antenna.  
Figure 12: Distribution of surface current by exciting port 1 and discontinuing port 2 at **(a)** 9.7 GHz (**b**) 18.8 GHz, and **(c)** 21.4 GHz.
## 2 Methods
### Solved Equations  
This study employs the Petrov-Galerkin method for numerical stability and accuracy in solving the Navier-Stokes equations, which govern fluid flow and are fundamental to the computational fluid dynamics (CFD) analysis of blood flow in the aorta. This method optimizes the weighting functions in the finite element method to reduce numerical oscillations and improve solution accuracy, particularly in advective transport problems [8].  
\[\text{a}(\varepsilon_{\text{n}},\text{w}_{\text{m}})=\text{a}(\text{v},\text{ w}_{\text{m}})-\text{a}(\text{v}_{\text{n}},\text{w}_{\text{n}})=\text{f}(\text{w}_{ \text{m}})-\text{f}(\text{w}_{\text{m}})=0\text{ for all w}_{\text{m}}\text{ in W}_{\text{m}} \tag{1}\]  
For turbulence modeling, this study utilizes the k-epsilon (k-\(\varepsilon\)) model, a standard two-equation model for simulating turbulence kinetic energy (k) and its dissipation rate (\(\varepsilon\)). This model is chosen for its effectiveness in high Reynolds number flows typical of arterial blood flow. The k-epsilon model is calibrated as follows to accurately capture the flow dynamics and turbulence characteristics in the aortic environment, especially in regions with fatty streaks:  
\[\text{K}=\frac{1}{2}\Big{[}(\text{IU})^{2}+(\text{IV})^{2}+(\text{IW})^{2} \Big{]} \tag{2}\]Turbulence Intensity: 0.05, indicating moderate turbulence at the inlet, suitable for arterial flow.  
Turb/Laminar Ratio: Set to 0.3, to balance turbulent and laminar viscosity effects, acknowledging the predominance of laminar in the flow.  
Wall Function Parameters: Including surface roughness set to model the interaction of flow with the arterial wall and fatty streaks. Specific parameters like Wall Parameter (7.76), Van Driest Damping Constant, and others are adjusted according to standard practices for k-\(\varepsilon\) model application in cardiovascular flow [9].  
Incorporating the Energy Equation into our turbulence modeling framework, we have applied the Boussinesq approximation to estimate eddy viscosity. This approximation allows us to assume that the density variations are negligible, except where they appear in buoyancy forces. By solving the Energy Equation:  
\[\rho\mathrm{C_{p}}\left(\frac{\partial T}{\partial t}+\mathrm{u}\frac{ \partial T}{\partial\mathrm{x}}+\mathrm{v}\frac{\partial T}{\partial\mathrm{y }}+\mathrm{w}\frac{\partial T}{\partial\mathrm{z}}\right)=\
abla\cdot\left( \mathrm{k}\
abla T\right)-\rho\mathrm{C_{p}}\left(\mathrm{u^{\prime}}\!\cdot \!\
abla\overline{T}\right)+\mathrm{q_{v}}, \tag{3}\]  
We derive the temperature distribution within the flow field, which is critical for accurate computation of the eddy viscosity coefficient via the turbulent kinetic energy and its dissipation rate.  
\[\mu_{t}=\rho\mathrm{C_{\mu}}\frac{\mathrm{k}^{2}}{\varepsilon}\ \left(\mathrm{ Eddy Viscosity}\right) \tag{4}\]  
The obtained eddy viscosity, along with the turbulence kinetic energy and its dissipation rate, directly informs our k-epsilon model, ensuring that the model reflects not only the mechanical turbulence but also the thermal energy transport effects within the aortic flow.
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 4 Hydrogen Utilization
### Hydrogen as the End Product  
Arguably the best industry to sell green hydrogen is the already existing industry for hydrogen, predominantly used in refineries and for ammonia production. The estimated demand in 2018 was above 80 million tons [27]. Since this industry is already in place, the source of hydrogen can simply be gradually replaced by green hydrogen, especially as the LCOH of green hydrogen continues to decrease.  
While ammonia can be the end product, it can also act as a carrier of hydrogen in order to facilitate transport with an equivalent hydrogen density of 122.4 kg/m\({}^{3}\) at a temperature of around 25 \({}^{\circ}\)C and a pressure of 10 to 20 bar. When compared to liquid hydrogen, the density is roughly 75% higher, and it can be transported at ambient temperatures and low pressures. The main disadvantage is the increased cost and complexity of producing the ammonia and afterwards decomposing it to recoup the hydrogen [20].
## 4 Simulation Results
### Transitions in Grid-Connected Operation Mode  
Figure 8 illustrates the time interval A corresponding to the grid-connected operation of the UFCS when supplying three EVs. Figure 8 displays the occupied charging points  
Figure 7: Steady-state simulation of the UFCS for one-day operation providing service to 180 EVs with two highlighted time-interval A and B: (**a**) power flow in the main bus among HESS, SST, and the ChPs; (**b**) sequence of operation modes of the UFCS; (**c**) state of charge of the battery bank.  
that are delivering energy. The sequence is as follows: while ChP2 is occupied, a new vehicle arrives at ChP1, and, shortly after, another one occupies ChP3. The power demand of these points is shown in Figure 8b. Initially, when only ChP2 is absorbing power, the remaining available power from the SST is used to recharge the HESS. It can be observed that the power demanded by the ChPs does not exceed the grid limit of 600 kW until the arrival of the second EV, which results in a direct charging operation. Hence, the HESS does not inject power until the arrival of a third EV increases the demand. At that moment, the HESS contributes to the charging process and the system transitions to the complementary charging mode [14]. Once the EV at ChP2 is charged, the demand falls below the SST's maximum power and the system returns to direct charging mode, as depicted in Figure 8c.  
Figure 8: _Cont._  
Figures 9-11 depict the results of the dynamic simulation using the parameter values in Table 3 for the circuit components of the HESS.  
Figure 9a shows the inductor currents \(i_{b}\) and \(i_{s}\) in the BESS and SESS modules, respectively, while Figure 9b depicts the current flow in the main bus. The HESS is not delivering power to the bus at the beginning, and current \(I_{2}\) demanded by the EV of ChP2 (see Figure 11) is supplied by the SST, as the system is in the charging mode. The power flows to the BESS as in Figure 7b with the smooth start-up dynamics predicted in Section 3.  
Figure 10 illustrates the bus voltage, which is regulated to 1500 V with the contributions of both the SST and battery voltage \(v_{b}\), which is slowly increasing during the charging mode and slowly decreasing during the power injection. The power distribution strategy can be observed at the arrival of the last vehicle in second 112 when the demand exceeds the limit imposed on the SST. The interaction of the HESS modules decoupling the demand of the ChPs towards each ESD can be appreciated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(C_{sc}\) & 29 F \\ \hline \(C_{s}\) & 20 \(\mu\)F \\ \hline \(R_{s}\) & 46 \(\  
Finally, Figure 11 depicts the current injected into each ChP. This graph can be compared with Figure 7, which shows which ChP is actually active supplying an EV.  
Figure 10: Bus voltage and ESD voltages during interval A.  
Figure 9: PSIM simulation during interval A showing the current flow: (**a**) in the main bus; (**b**) in the HESS.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 2 Materials and Methods
### Determination of the Antioxidant Activity
#### 2.8.2 ABTS Radical Scavenging Capacity  
The 2,2'-Azinobis-(3-ethylbenzthiaazoline-6-sulphonate) (ABTS) assay was conducted following the modified method [23]. Briefly, ABTS radical stock solution was prepared by mixing 7.4 mM ABTS (Sigma-Aldrich, St. Louis, MO, USA) with 2.6 mM potassium persulfate (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China) both in purified water, and incubated in darkness at 25 \({}^{\circ}\)C for 12 h. The solution was diluted with 80% (_v/v_) aqueous methanol to achieve an absorbance of 1.10 \(\pm\) 0.02 at 734 nm. Subsequently, 15 \(\upmu\)L sample extract (Section 2.1) was mixed with 285 \(\upmu\)L diluted ABTS solution, incubated in darkness at 25 \({}^{\circ}\)C for 2 h, and absorbance measured at 734 nm. A blank was prepared identically using purified water instead of the sample. ABTS radical scavenging activity (%) was calculated using the equation:  
\[\text{ABTS radical scavenging activity (\%)}=[1-(\text{A}_{\text{sample}}/\text{A}_{\text{blank}})] \times 100 \tag{4}\]
## 1 Introduction
A related study [28] also demonstrated the high accuracy of these methods. However, in practical engineering applications, they often lack operability and are less friendly to workers. Non-destructive estimation methods, such as in the field of pressure vessels, have been a research hotspot due to their advantages of simple operation, widespread use, and low cost. As one of the non-destructive methods, the non-destructive approach of [PERSON]'s column analysis is now extended to spherical shells, which are subjected to uniform external pressure [32]. However, there are few non-destructive methods for estimating the buckling of pressure spherical shells of this type. The paper [33] established a non-destructive estimation method for a spherical shell under external pressure that can predict its critical load. It is based on an exact first-order solution of the critical stress and requires two assumptions: one is that the thickness of the shell does not change; and the second is that the temperature does not affect the critical buckling of the shell. In any case, when the shell is in a corrosive environment, the thickness of the shell varies and the effect of temperature on both the stress and the corrosion rate is unavoidable. No non-destructive method has been found as of yet to predict the critical loads (stresses) of the shell under this condition, including the service life. Therefore, in the industry, people still hope to obtain a new lossless method. This method can predict the critical load (or stress) and critical thickness of the shell under external pressure from corrosion on the basis of a higher-order (second-order) exact solution. At the same time, we are also oriented to predict its useful (remaining) life. For this reason, in this paper, we will analytically extend the Southwell process method for the non-destructive prediction of critical loads, critical stresses, and service life of hemispherical shells subjected to uniform external pressure considering corrosion and ambient temperature through rigorous mathematical derivation.
## 2 Materials and Methods  
### Description of AA6082  
The typical chemical composition and its permissible variations of the aluminum alloy AA6082 can be summarized as follows (in wt.%): Fe \(\rightarrow\) 0.0-0.5, Si \(\rightarrow\) 0.7-1.3, Mn \(\rightarrow\) 0.4-1.0, Mg \(\rightarrow\) 0.6-1.2, Cu \(\rightarrow\) 0.0-0.1, Zn \(\rightarrow\) 0.0-0.2, Cr \(\rightarrow\) 0.0-0.25, Ti \(\rightarrow\) 0.0-0.1 and others \(\rightarrow\) 0.0-0.15. During the solidification process, intermetallic compounds with various bases such as Al-Fe, Al-Fe-Si, Al-Fe-Mn-Si, and Mg-Si can form through a sequence of eutectic reactions and complex ternary and quaternary eutectic reactions. An extensive literature review revealed that, besides Mg\({}_{2}\)Si, there are also other intermetallic phases, i.e., \(\alpha\)-Al(FeMn)Si, \(\beta\)-Al\({}_{5}\)FeSi as well as Al\({}_{9}\)Mn\({}_{3}\)Si, \(\alpha\)-Al\({}_{12}\)Fe\({}_{5}\)Si, \(\alpha\)-Al\({}_{15}\)(FeMn)\({}_{3}\)Si, and \(\pi\)-Al\({}_{8}\)Si\({}_{6}\)Mg\({}_{3}\)Fe were found in matrix [31; 32]. Properties referring to intermetallic phases (i.e., amount, size, shape, type, distribution, etc.) are related to chemical composition as well as to particular values of process parameters in each phase of the AA production (i.e., casting, solidification, homogenization, deformation, cooling, aging, etc.) [33; 34] and influence the obtained mechanical properties (strength, ductility, hardness, etc.). In Figure 2, the microstructure of the deformed material is depicted. The samples were prepared by grinding with SiC 220# paper for 2 min, then by polishing using Lango 9 mm for 5 min, Mol 3 mm for 8 min, and Chem (OPS) for 2 min; finally, Barker's etchant was used. The microstructures reveal a band of intermetallic phases aligned in the extrusion direction whereas wavelength-Dispersive X-Ray Spectroscopy (WDS) analysis reveals that these particles are primarily Mg-Si and Al-Fe-Si-Mn based. Furthermore, from comparing both figures it is visible that the microstructure obtained on the edge (Figure 2a) of the extruded round profile does not differ significantly from the microstructure obtained in the center (Figure 2b) of the round profile. This indicates a similar microstructure across the entire cross-section of the extruded round profile.  
Figure 1: General flowchart of the automated explanation of material phenomena using ANNs.
# Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
## 4 Experiment Setup
### Parameter Tuning  
The parameters of the machine learning algorithms are shown in Table 2. The employed activation function of ANN and DNN was ReLU; the output function was Softmax; the optimization function was Adam.  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline \multirow{2}{*}{**Family**} & \multirow{2}{*}{**Rank**} & \multirow{2}{*}{**\# of Apps**} & \multicolumn{2}{c}{**\# of Permissions**} \\ \cline{3-5}  & & & **Built-In** & **Custom** \\ \hline Fakeinstaller & 1 & 920 & 42 & 1 \\ DroidKungFu & 2 & 660 & 59 & 5 \\ Plankton & 3 & 621 & 64 & 6 \\ Opfake & 4 & 601 & 35 & 2 \\ GinMaster & 5 & 336 & 43 & 5 \\ BaseBRIDGE & 6 & 327 & 36 & 1 \\ Iconosys & 7 & 152 & 21 & 0 \\ Kmin & 8 & 145 & 24 & 2 \\ FakeDoc & 9 & 132 & 36 & 4 \\ Adrd & 10 & 91 & 50 & 5 \\ Geinimi & 11 & 89 & 29 & 5 \\ DroidDream & 12 & 81 & 50 & 2 \\ MobileTx & 13 & 69 & 8 & 0 \\ ExplitLinuxLotoor & 14 & 66 & 48 & 4 \\ Glodream & 14 & 66 & 29 & 6 \\ FakeRun & 16 & 61 & 17 & 4 \\ Gappusin & 17 & 58 & 31 & 1 \\ Sendpay & 17 & 58 & 14 & 0 \\ Imlog & 19 & 43 & 8 & 0 \\ SMSreg & 20 & 39 & 25 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of samples and permissions of the top 20 families.
## 3 Results and Discussions
### Secondary Electron Energy Spectra Characterization  
Figure 6 shows all the normalized SEES curves. Here, the normalization process can be briefly illustrated as follows. First, calculate the sum of the data on the vertical axis of original SEES curve to acquire the total number of the ejected electrons since the vertical axis refers to the relative quantity of the ejected electrons. Second, utilize the ejected electron quantity corresponding to each energy being divided by the sum of ejected electrons so as to acquire the ejected electron proportion. Third, multiply the calculated proportion by the corresponding SEY value (\(E_{\rm p}\) equals to 1000 eV) and then obtain the normalized SEES curve. From Figure 6, we see that every SEES curve contains two peaks, in which the left low-energy peak is induced by the true secondary electrons, and the right high-energy peak is induced by the backscattered electrons. By comparing the SEES curves in Figure 6, three conclusions can be drawn. First, the integral area of low-energy range (less than 50 eV) is much larger than that of high-energy range, which indicates that the true secondary electrons account for a large proportion in all the ejected electrons since the integral area of SEES curve directly represents the SEE level. Second, a large integral area for true secondary electrons also reveals that the high SEY property of MgO is benefitted by a great many excited inner secondary electrons after the primary electrons' penetration; on the contrary, the backscattering process contributes little to the ejected electron yield. Third, Figure 6 shows that the integral area of low-energy peak augments with the increase of MgO coating thickness, indicating that the sample with large thickness of MgO coating has a higher true SEE ability, which is consistent with the SEY curves shown in Figure 4. It should be noted that the initial electron energy set in this part of the energy spectrum test experiment is 1000 eV, and the peak position of the backscattered electron peak obtained is 993 eV, which may be caused by the error of the electron gun itself.
## 10 Numerical Experiments
In Figures 16 and 17, we show the ratios \(\delta U_{ij}^{lin}/|\delta U_{ij}|,\;\;i=1,2,\ldots,m\); \(j=1,2,\ldots,n\) and \(\delta V_{ij}^{lin}/|\delta V_{ij}|,\;i=1,2,\ldots,n\); \(j=1,2,\ldots,n\), respectively, along with the corresponding mean values (averages) \(E(\delta U_{ij}^{lin}/|\delta U_{ij}|)\) and \(E(\delta V_{ij}^{lin}/|\delta V_{ij}|)\) of the ratio for \(\delta A=10^{-8}\times A_{0}=4.4679\times 10^{-7}\). The overestimates are mainly due to overestimating the perturbation parameters \(x_{(1)},y\) and \(x_{(2)}\) in the Equations (36)-(38). In these equations, each entry \(\delta A_{ij}\) is replaced by the quantity \(\|\delta A\|_{2}\), leading to an overestimate approximately proportional to \(m\) in case of random perturbations. To illustrate this point, in Figures 18 and 19 we show the entries of the matrices \(|\delta U_{1}|\) and \(|\delta V|\), respectively, along with the corresponding componentwise estimates \(\delta U^{lin}\) and \(\delta V^{lin}\) for \(\delta A=10^{-8}\times A_{0}=4.4679\times 10^{-7}\), where the asymptotic estimates are computed for the exact (non approximated) values of the perturbation parameters \(x_{k}=u_{i}\delta u_{j}\) and \(y_{\ell}=v_{i}\delta v_{j}\), obtained by using the exact \(\delta A\). Obviously, the estimates are very close to the exact values, which confirms their good quality. The small differences between the actual quantities and their estimates are caused by taking the absolute values of \(U,V\) and \(\delta W_{U},\delta W_{V}\) in finding the asymptotic bounds \(\delta U^{lin}=|U|\delta W_{U}^{lin}\) and \(\delta V^{lin}=|V|\delta W_{V}^{lin}\). We note that the overestimates are inevitable if we want to obtain guaranteed asymptotic perturbation bounds of the SVD elements, and they can be significantly reduced only if we use probability perturbation bounds [28]. \[M_{1} = I_{m}-2 ee^{T}/m,\;\;M_{2}=I_{m}-2 ff^{T}/m,\]7 Values of \(|\delta U_{ij}|\) and the corresponding linear estimates for the exact values of \(x_{k}\). The perturbation bounds derived were also tested with higher-order examples, including a \(500\times 300\) example. The examples presented in this section confirm that the new componentwise perturbation bounds of the singular value decomposition can be used efficiently in the analysis of high-order problems and may compare favorably with the known bounds for some particular cases, for instance in the sensitivity analysis of the singular subspaces. The componentwise bounds of the perturbations in the orthogonal matrices \(U\) and \(V\) in the last example clearly show that for some specific entries the perturbation magnitudes differ \(10^{5}\) times and the implementation of normwise perturbation bounds for such examples is not informative. It should also be emphasized that the asymptotic estimates, although not global, ensure valid perturbation bounds for sufficiently large perturbations of the given matrix.
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 4 Practical Application and Result Analysis  
### Data Preparation  
Two exploration wells drilled in a block of the Ordos Basin in China are investigated in this study. The Ordos Basin has the character of a stable depression tectonic basin, with the metamorphic crystalline rock series of Archaea and Middle Proterozoic as the rigid sedimentary basement, overlying the platform type sedimentary caps of Middle and Upper Proterozoic, Early Paleozoic, Late Paleozoic, and Mesozoic. The Lower Paleozoic is mainly composed of Marine carbonate deposits. The Upper Paleozoic is transformed from coastal marsh and bay lagoon facies to fluvial and lacustrine facies, mainly composed of terrigenous clastic deposits, and only carbonate interbeds developed in the early stage. The Mesozoic is a single fluvial and lacustrine terrigenous detrital deposition, especially characterized by the large thickness and wide distribution of lacustrine deltas in the Triassic Yanchang Formation, which is one of the most important hydrocarbon enrichment horizons in Ordos Basin.  
To keep the information of the wells private, we call the two selected wells Well M and Well N, separately. Moreover, the intervals of interest for Well M and Well N are both located in the mainly oil-bearing formation (Upper Triassic Yanchang) in the studied block. The log graphs of Well M and Well N are shown in Figures 4 and 5, separately. The included logging sequence is as follows: depth, caliper (CAL), sonic log (DT), neutron porosity (NPHI), density log (RHOB), gamma ray (GR), shear sonic log (DTS), and true electrical resistivity (RT).  
Figure 4: Well logging graph of Well M.  
Figure 5: Well logging graph of Well N.  
In Table 1, the statistical characteristics of Well M and Well N are displayed. There are 1600 and 960 data points in Well M and Well N, separately.