esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Comparison of Grid and Coordinate Methods  
The selection of wind farm design methods (including selection of grid densities) only affects the means of representation of wind turbine positions. For instance, the wind turbine positions are continuously depicted by Cartesian coordinates in the coordinate method. For the \(i\)-th wind turbine in the wind farm, its position is represented by \((x_{i}\), \(y_{i})\). In comparison, the representation of the wind turbine positions is discrete and determined by the grid divisions/densities for the grid method. For the \(i\)-th wind turbine in the wind farm, its row and column indices are \(m_{i}\) and \(n_{i}\), respectively. When choosing the 20 \(\times\) 30 grid, the row index value is between 1 and 20, while the column index value is between 1 and 30. There are 600 available places for locating wind turbines and the minimal interval of available wind turbine positions is 200 m in both the X and Y directions. When choosing the 40 \(\times\) 60 grid, the row index value is between 1 and 40, while the column index value is between 1 and 60. In total, there are 2400 available places for locating wind turbines and the minimal interval is 100 m. When the grid cells are further refined, there will be more available places and the less minimal interval. Based on the row and column indices for the grid method, the \(x\) and \(y\) coordinates of wind turbines are calculated as the product of the indices and the interval.  
Figure 3: Schematic of the boundary handling technique for the optimization using the coordinate method.  
Though the coordinate method has the prominent advantage in the representation of wind turbine positions from concept, the wind turbine number is fixed in one optimization running, and hence it requires repetitive running to obtain the optimal number of wind turbines. Instead, the grid method can simultaneously optimize the wind turbine positions and the number of wind turbines in one optimization running, and hence it is more advantageous in this regard. Note that, except for the difference of representation of wind turbine positions, the subsequent steps of evaluating the wind farm power output (or objective function) in Section 3.3 are the same for the different wind farm design methods.
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
###### Abstract  
In this paper, a two-dimensional numerical model for wave-seabed-pipeline interaction is developed to examine the wave-induced uplift force onto pipelines buried in sloping seabeds. The Reynolds-averaged Navier stokes equation and the poro-elastic equation are used to simulate the wave motion and seabed response, respectively. Meanwhile, the pipeline is considered to be elastic. Firstly, three laboratory experiments are taken to verify the effectiveness of the numerical model. Then, the effects of pipeline characteristics, soil properties and wave parameters on the nonlinear wave-induced uplift force onto a pipeline buried in a sloping seabed are analyzed. Finally, an empirical formula for predicting the nonlinear wave-induced uplift force onto buried pipelines under different slope angles is proposed. It can be found that the slope angle can greatly affect the nonlinear wave-caused pore pressure response, as well as the uplift force onto the pipeline. Moreover, the simple method for predicting the uplift force proposed in this paper can facilitate engineering applications.  
sloping seabed; nonlinear wave; buried pipeline; pore pressure; uplift force +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Negative reconciliation value
#### 2.3.3 Hot Spot Analysis  
Hot spot analysis calculates the Getis-Ord \(\rm G_{i}\)* statistic for each feature in the dataset [67,68]. By analyzing the resulting z-scores and p-values, we can know the location of high or low value features clustering in space. z is the local sum of a feature and its neighbors compared to the sum of all features to produce a statistically significant value. The calculation method is as follows:  
\[\rm G_{i}^{*}=\frac{\sum\limits_{j=1}^{n}\omega_{ij}x_{j}-\frac{\sum\limits_{ j=1}^{n}x_{j}}{n}\cdot\sum\limits_{j=1}^{n}\omega_{ij}}{\sqrt{\frac{\sum \limits_{j=1}^{n}x_{j}^{2}}{n}-\left(\frac{\sum\limits_{j=1}^{n}x_{j}}{n}\right) ^{2}}}\cdot\sqrt{\frac{\left|\frac{n}{\sum\limits_{j=1}^{n}\omega_{ij}-\left( \sum\limits_{j=1}^{n}\omega_{ij}\right)^{2}}{n-1}}} \tag{24}\]  
where, the \(\rm G_{i}\)* statistic is the score of z; \(x_{j}\) is the attribute value of feature \(j\); \(\omega_{ij}\) is the spatial weight between features \(i\) and \(j\); \(n\) is the total number of elements.  
For the positive z-score with significant statistical significance, the higher the z-score, the closer the high value (hot spot) clustering. For statistically significantly negative z-scores, the lower the z-scores, the closer the low (cold spot) clustering. In this study, the hot spot analysis tool of ArcGis 10.4 was used to quantitatively evaluate and map the spatial variability of net ESV in a PFE on a pixel-by-pixel basis. The z-score of the Getis-Ord \(\rm G_{i}\)* statistics of each pixel was divided into seven classes, of which three hot spots indicated high-value net ESV clustering and were statistically significant, three cold spots corresponded to low-value net ESV clustering and were statistically significant, and one class was non-significant (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Spot** & **Z-Score** & **Confidence (\%)** \\ \hline Hot spot & \(>\)2.58 & 99 \\ Hot spot & \(2.58\geq z>1.96\) & 95 \\ Hot spot & \(1.96\geq z>1.65\) & 90 \\ Not Significant spot & \(1.65\geq z\geq-1.65\) & / \\ Cold spot & \(-1.96\leq z<-1.65\) & 90 \\ Cold spot & \(-2.58\leq z<-1.96\) & 95 \\ Cold spot & z \(<-2.58\) & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The z-score classification of Getis-Ord \(\rm G_{i}\)* statistics.
# Simulation-Based Model-Updating Method for Linear Dynamic Structural Systems
## 3 Methodology
The Sum Squared Error (SSE) method, which works by default in the software, is chosen as the objective function of the system. The design variables are bounded to a range close to the values obtained by the classical method. Thus, the intention is to ensure that the optimization process remains aligned with the theoretical values without deviation. As depicted in Figure 8, the displacement values of all floors measured from the free vibration test data of the structural system constitute the experimental dataset for the simulation model. The parameters to be estimated are chosen as the stiffness and damping coefficients. The software tries to minimize the difference between the numerical model and experimental results, aiming to determine the most suitable coefficients while attaining minimal error. Accordingly, the corrected stiffness and damping matrices of the building model calculated with the simulation-based model update technique are shown in the equations below. \[[K_{s}]= \begin{bmatrix}176632&-88316&0&0&0\\ -88316&176632&-88316&0&0\\ 0&-88316&176,632&-88316&0\\ 0&0&-88316&176632&-88316\\ 0&0&0&-88316&88316\end{bmatrix} \text{N/m}\]  
Figure 8: The Software interface for model updating. \[[C_{s}]= \begin{bmatrix}152.752&-76.376&0&0&0\\ -76.376&152.752&-76.376&0&0\\ 0&-76.376&152.752&-76.376&0\\ 0&0&-76.376&152.752&-76.376\\ 0&0&0&-76.376&76.376\end{bmatrix} \text{Ns/m}\]
## 3 Results and Discussion
### Thermodynamic Studies  
Different thermodynamic parameters Gibbs free energy change (\(\Delta G\)), enthalpy change (\(\Delta H\)), and entropy change (\(\Delta S\)) for the adsorption of P onto Al-Ti bimetal oxide composite were calculated using the following equations:  
\[\Delta G=-RT\ln Kc \tag{19}\]  
where \(R\) is the gas constant (8.314 J/mol K). \(K_{C}\) is the equilibrium constant and defined in Equation (20):  
\[K_{C}=\frac{C_{A}}{C_{S}} \tag{20}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Adsorbent** & **pH** & **Temp (K)** & **Dose (g/L)** & \(q_{m}\) **(mg/g)** & **References** \\ \hline Fe-Ti bimetal oxide & 4.5 & 293 & 0.2 & 32.95 & [31] \\ Fe-Al hydroxide & 6.0 & 298 & 1.0 & 51.80 & [33] \\ Mg/Al LDHS & 6.0 & 298 & 0.6 & 54.90 & [34] \\ Amorphous ZrO\({}_{2}\) & 6.2 & 298 & 0.1 & 99.00 & [57] \\ Modified La\({}_{2}\)O\({}_{3}\) & 5.6 & 298 & 0.5 & 58.70 & [58] \\ Ai-Ti bimetal oxide & 6.8 & 293 & 0.2 & 68.20 & This study \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of phosphate adsorption capacity among different adsorbents.  
where \(C_{A}\) and \(C_{S}\) are the equilibrium concentrations of P on Al-Ti oxide adsorbent (mg/L) and in solution (mg/L), respectively [41]. Standard enthalpy (\(\Delta H\)) and entropy (\(\Delta S\)) of adsorption can be estimated from van't Hoff equation (Equation (21)):  
\[\ln K_{C}=\frac{-\Delta H}{RT}+\frac{\Delta S}{R} \tag{21}\]  
The plot of ln\(K_{\rm C}\) versus \(\frac{1}{T}\) is given in Figure 9.  
From Table 4, the positive value of \(\Delta H\) confirms that P adsorption on Al-Ti oxide is an endothermic reaction due to increased adsorption from successive increases in temperature [59]. The value of \(\Delta G\) is more negative with increased temperature, showing that spontaneous reaction is enhanced by temperature. The \(\Delta\)G values varied in range from 6.01 to \(-\)11.17 kJ/mol, revealing that physisorption contributes to the adsorption mechanism (physical range for \(\Delta G^{0}\) is \(-\)20! \(\Delta G^{0}\)! 0 kJ/mol and chemical range is \(-\)80! \(\Delta G^{0}\)! \(\leftarrow\) -400 kJ/mol) [60; 61]. The value of enthalpy (\(\Delta H\)) shows physical adsorption (less than 40 kJ/mol for physisorption and 80-450 kJ/mol for chemisorption) [62; 63]. The positive \(\Delta S\) value elaborates increased randomness at the solid/solution interface during P adsorption on Al-Ti hydroxide.  
Figure 9: Plot of ln \(K_{\rm C}\) versus 1/T for phosphorus sorption onto Al-Ti bimetal oxide.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Concentration** **(mg/L)**} & \multicolumn{2}{c}{\(-\Delta G\) **(kJ/mol)**} & \multicolumn{2}{c}{\(\Delta H\)} & \(\Delta S\) \\ \cline{2-5}  & **293 K** & **303 K** & **313 K** & **(kJ/mol)** & **(J/mol K)** \\ \hline
2.5 & 8.11 & 9.25 & 11.17 & 36.62 & 152.25 \\ \hline
15.0 & 6.01 & 7.67 & 9.03 & 38.36 & 151.58 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermodynamic parameters of P adsorption on Al-Ti oxide.
## 3 Numerical Simulation Analysis  
### Numerical Model  
In order to conduct further parametric analysis, a numerical simulation model for the PC shear wall is developed using ABAQUS software.  
Due to the fact that the solid element is easy to set up and understand, this paper uses a solid element instead of a shell element, despite the fact that a shell element has a better computational efficiency than a solid element [47]. Specifically, concrete, steel, and bolt are simulated using a three-dimensional (3D) stress solid element (C3D8R). In addition, rebars are simulated using a two-dimensional (2D) truss element (T3D2). In terms of a constitutive model, the concrete damage plasticity model provided by ABAQUS is used as a stress-strain model to define concrete. Furthermore, the tension and the compression stress-strain curves are determined according to the Code for Design of Concrete Structures [48]. Additionally, the damage parameters are calculated according to the formulas proposed by [PERSON] et al [49]. In addition, the bilinear kinematic hardening model is used as a stress-strain model to simulate steel, rebars, and bolt, which includes an elastic stage and a strengthening stage. Furthermore, the modulus magnitude of the strengthening stage is 1% of that of the elastic stage.  
In the assembling zone, the contacts in the model are mainly between the upper and lower parts of a pair of connectors, and the connectors and bolts. In this paper, normal contact is defined as \"hard\" contact, while tangential contact is \"penalty\" contact, whose friction coefficient is 0.3. Embedded region constraint is used for rebars and concrete, whereas \"tie\" constraint is used between rebars and the connector, as well as concrete and the connector.  
Figure 13: Equivalent viscous damping coefficient curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & \(\delta\)**y/mm** & \(\delta\)**u/mm** & \(\mu\) \\ \hline \multirow{2}{*}{RC} & 5.7 & 27.1 & 4.7 \\  & \(-\)5.3 & \(-\)29.0 & 5.4 \\ \hline \multirow{2}{*}{PC} & 5.0 & 34.7 & 6.9 \\  & \(-\)4.7 & \(-\)34.7 & 7.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Displacement ductility coefficient.  
To simulate loading, a fixed constraint is used for the bottom of the foundation beam (see Figure 14). The axial load is applied as a uniform load on the top of the loaded beam (Figure 14). The cyclic horizontal load is applied to the side of the loaded beam according to the loading procedure. Three steps are required to set the bolt load [50; 51]. In the first step, the temporary boundary conditions of bolts are defined, and \"Apply Force\" of 10 N is applied to bolts. In the second step, temporary boundary conditions are removed and \"Apply Force\" is modified to 30 kN. In the third step, the bolt loads are defined as \"Fix at Current Length\".
## 5 Numerical Results
### A PEC Cube with Four Monopole Antennas  
The wideband EM scattering from 15 GHz to 30 GHz of a PEC cube (\(4\lambda_{h}\times 4\lambda_{h}\times 4\lambda_{h}\)) with four monopole antennas shown in Figure 1 is analyzed. \(\Delta f=1\) GHz is selected as the frequency interval. The cube with four monopole antennas is discretized with 23,044 triangular patches resulting into 34,566 RVG basis functions.  
Figures 2 and 3 show the bistatic RCS of the PEC cube with four monopole antennas obtained by direct RFGG\(-\)FG\(-\)FFT, the proposed method, FGG\(-\)FG\(-\)FFT. It can be also noted that direct RFGG-FG-FFT, the proposed method and FGG-FG-FFT gave identical numerical results. Hence, it indicates that the results of the proposed method, the direct RFGG-FG-FFT and FGG-FG-FFT are almost indistinguishable. In Table 1, the time costs of near field calculation at 24 GHz are given by direct RFGG-FG-FFT and the proposed method. The proposed method took 1307 s CPU time to obtain the results of the cube with four monopole antennas in the frequency domain, whereas the direct RFGG-FG-FFT took 2723 s. This comparison clearly shows that the proposed method can obtain the RCS simultaneous versus frequency more quickly.  
Figure 1: A PEC Cube with four monopole antennas.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 6 Performance Evaluation and Discussions  
To ensure the robustness of our findings and account for the inherent variability associated with random node placement within the OMNeT++ environment, a multi-run simulation approach was employed. Each simulation scenario was executed 10 times with a different random seed, introducing variation in the node positions to the random distribution scenario and network behavior. This approach allows us to capture a broader range of possible outcomes and generate more reliable average results, calculated with a 99% confidence level.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 5 Performance Analysis  
In this section, we give the performance analysis of the DTEM algorithm and system stability. Specifically, the maximum storage of buffer, minimum capacity of the battery, and the gap between the optimality of DTEM and optimal system utility are derived, respectively.
# Multi-Objective Optimization for Plug-In 4 WD Hybrid Electric Vehicle Powertrain
###### Abstract  
This paper focuses on the parameter optimization for the CVT (a continuously variable transmission) based plug-in 4 WD (4-wheel drive) hybrid electric vehicle powertrain. First, the plug-in 4 WD hybrid electric vehicle (plug-in 4 WD HEV)'s energy management strategy based on the CD (charge depleting) and CS (charge sustain) mode is developed. Then, the multi-objective optimization's mathematical model, which aims at minimizing the electric energy consumption under the CD stage, the fuel consumption under the CS stage and the acceleration time from 0-120 km/h, is established. Finally, the multi-objective parameter optimization problem is solved using an evolutionary based non-dominated sorting genetic algorithms-II (NSGA-II) approach. Some of the results are compared with the original scheme and the classical weight approach. Compared with the original scheme, the best compromise solution (i.e., electric energy consumption, fuel consumption and acceleration time) obtained using the NSGA-II approach are reduced by 1.21%, 6.18% and 5.49%, respectively. Compared with the weight approach, the Pareto optimal solutions obtained using NSGA-II approach are better distributed over the entire Pareto optimal front, as well as the best compromise solution is also better.  
Keywords:plug-in 4 WD hybrid electric vehicle; powertrain; electric energy consumption; fuel consumption; acceleration time; multi-objective optimization +
Footnote †: journal: Applied
## 3 Results and Discussion
### XRD and FT-IR Analysis
The vibrations of the bonds attributed to the vitreous phase of the FA-1 of origin, when activated, transform and vibrations at lower wavenumbers are observed, which is characteristic of the formation of the gel product of the polymerization of the activated material. It can be observed that the green band cedes almost all of its color towards the darker blue; however, sets G1 and samples G2-7 and G2-14 continue showing small green areas, indicating the continued vibration of the amorphous material, which suggests that it is still present in some geopolymers (see Figures 6-8). The change of color or the change of phase (the amorphous phase of FA-1 to the semicrystalline phase of geopolymers) is derived from the precipitation of Si and Al reactive bonds in the original FA with NaOH. The FA contains a higher percentage of Si\({}^{4+}\) than Al\({}^{3+}\) and, together with the reactivity of the same and the increase in the curing time, Si\({}^{4+}\) bonds are gradually integrated into the environment of the aluminosilicate gel, so Si\({}^{4+}\) begin to occupy the spaces of Al\({}^{3+}\)[46]. This causes displacements to higher wavenumbers, due to the higher Si-O bond strength (80 kcal/mol) with respect to the Al-O bond strength (60 kcal/mol) [47], moving from 1002.5 cm\({}^{-1}\) at the lowest end (G1-7) to 1015.26 cm\({}^{-1}\) at the highest end (G3-28). With this information (1026 cm\({}^{-1}\)-1002 cm\({}^{-1}\) = 100%), Figure 9 attempts to relate the highest wavenumber with the geopolymerization of the specimens. It can be seen that the set G3 has the highest displacements, with percentages greater than 60% in relation to the extremes. In this list, G2-14 and G2-28 are integrated with 60% and 66%, respectively. Samples that were obtained through FA without milling or sifting show percentages below 30%. As previously mentioned, the specific surface area of the FA plays a more important role in relation to the curing time, since after 14 days sets G-2 and G-3 show a high or acceptable geopolymerization. The band that appears in a violet color in FA-1 disappears for the samples of the geopolymers. This behavior indicates that the Si-O and Al-O vibrations become part of the color blue or the crystalline phase, represented by the color red. The vibrations of the bonds found in the phases of the mullite or quartz for the FA are identified in the red band. However, this band widens considerably in the geopolymers, which is caused by the formation of new zeolitic phases. Figure 9: Geopolymerization by displacements (in %) of the FT-IR band (1200–850 cm), considering that the G3-28 sample has 100% and the G1-7 sample has 0% of geopolymerization.
## 2 Results and Discussion
### XRD Analysis  
The XRD diffractograms are reported in Figure 3. In both the FSP-TiO\({}_{2}\) and FSP-TiO\({}_{2}\)/WO\({}_{3}\) 60/40 samples, one can observe the main reflections at 25.3\({}^{\circ}\) (20) and 27.5\({}^{\circ}\) (20) due to the anatase (JCPDS 21-1272--anatase) and rutile (JCPDS 21-1276--rutile) phases, respectively. The FSP-WO\({}_{3}\) diffractogram showed correspondence with the monoclinic WO\({}_{3}\) across all ranges of measurement. These results are in accordance with the fact that  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **BET SSA** \\ **(m\({}^{2}\)/g)** \\ \end{tabular} & \begin{tabular}{c} **Total Pore** \\ **Volume (cm\({}^{3}\)/g)** \\ \end{tabular} & \begin{tabular}{c} **BJH ads. Pore** \\ **Width (nm)** \\ \end{tabular} & \begin{tabular}{c} **Crystallite** \\ **Size (nm)** \\ \end{tabular} & \
\begin{tabular}{c} **Phase \%** \\ \end{tabular} &
\begin{tabular}{c} **BG (eV)** \\ \end{tabular} \\ \hline P25 & 52.7 & 0.225 & 18.7 & 15(A); 26(R) & 78(A); 22(R) & 3.22 \\ FSP-TiO\({}_{2}\) & 29.7 & 0.109 & 14.5 & 20(A); 24(R) & 48(A); 52(R) & 3.10 \\ FSP-WO\({}_{3}\) & 3.4 & 0.015 & 35.0 & 42 & & 2.73 \\ TiO\({}_{2}\)/WO\({}_{3}\) 60/40 wt & 29.2 & 0.077 & 9.8 & 22(A); 16(R) & 45(A); 65(R) & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characterization data for tested catalysts.  
Figure 2: SEM images of (**a,b**) WO\({}_{3}\), (**c,d**) WO\({}_{3}\)/TiO\({}_{2}\) and (**e,f**) TiO\({}_{2}\). Magnification: (**left**) \(\times\)5000, (**right**) \(\times\)10,000.  
this phase is the most stable at room temperature. Furthermore, authors have reported that WO\({}_{3}\) is generally unable to retain alternative phases such as \(\beta\)-WO\({}_{3}\) (orthorhombic) and \(\alpha\)-WO\({}_{3}\) (tetragonal) that are more stable at high temperatures [27]. The main reflection of the WO\({}_{3}\) phase was still visible at 23\({}^{\circ}\) (20) in the FSP-TiO\({}_{2}\)/WO\({}_{3}\) 60/40, though much less crystalline and possibly dispersed into the titania phase, whose crystallinity was also decreased with respect to the bare titania sample. The crystallite sizes of the different catalysts are reported in Table 1, together with the calculated anatase/rutile compositions of the titania-containing compounds. The TiO\({}_{2}\) prepared through FSP showed a significant difference with commercial P25 regarding the anatase/rutile composition. The FSP sample showed an anatase/rutile ratio of 48/52, compared to the 78/22 ratio of the commercial P25 sample. As reported by some authors, a higher rutile content can improve charge separation due to the formation of a homojunction with anatase [28].