esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Assessment of Side-Wind Effects on a Bus in Urban Conditions
## 2 Materials and Methods
### Mesh  
Further steps of the modelling were taken with ANSYS Fluent 2022 R1 (ANSYS Inc., Canonsburg, PA, USA) software. During the discretisation, three types of cells--hexagonal, polyhedral, and prismatic elements--were used. The core domain was built up from hexagonal mesh, while on the surface of the bus and on that of the ground prismatic layers were used. For transitioning between the surfaces and the hexagonal mesh, the polyhedral mesh was generated. To establish an adequate mesh, the following quality indicators were used: growth rate 1.2; maximum skewness 0.8, and minimum orthogonal quality 0.1. For mesh sensitivity analysis the first layer height (\(y_{H}\)) and the maximum cell size (\(\Delta_{max}\)) were altered, as can be seen in Table 1. The almost linear increase in the cell count is attributed to the fact that by lowering the \(y_{H}\) only the cell count of the refinement zone was increased, while the rest of the domain was less affected by the refinement.  
In Figure 3, mesh2 can be seen. On the bus surface, ten pieces of prismatic layers were applied. For the ground, to ensure a smooth boundary layer three, levels of 20 mm high prismatic layers were applied. For the refinement zone behind the bus, a 100 mm large cell was used.  
The sum of force components in the cruising direction exerted on the bus was used as a control value, and in this way, the mesh independency could be analysed. During the simulation, the forces showed unsteadiness; thus, the results are presented in a box plot (see Figure 4).  
When the mean values of drag forces are compared, mesh2 showed a significant (4.08%) relative change compared with mesh3, while this tendency decreased (1.6%) with further refinement. One can see that the standard deviations of the drag forces decreased considerably (1.3% and 4.9%) with finer meshing. However, since the deviation was only 2.5% of the average value, it did not influence the final decision that mesh2 should be used in the further calculations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & \(y_{H}\) & \(\Delta_{max}\) & **Cell Count** \\ \hline Mesh1 & 0.25 mm & 160 mm & 13.6 \(\times\) 10\({}^{6}\) \\ Mesh2 & 0.5 mm & 320 mm & 6.5 \(\times\) 10\({}^{6}\) \\ Mesh3 & 1 mm & 640 mm & 4.4 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mesh properties.  
Figure 2: Sideview of the bus.
## 2 Materials and Methods
### Method
#### 2.3.6 Model Accuracy Evaluation Indicators  
The coefficient of determination (\(R^{2}\)), root mean squared error (RMSE), and normalized root mean squared error (nRMSE) were selected as the model accuracy evaluation indicators. The calculation methods are as shown in formulas (Equations (5)-(7)).  
\[R^{2}=\frac{(\sum_{i=1}^{n}y_{i}-\overline{y})^{2}}{(\sum_{i=1}^{n}x_{i}- \overline{y})^{2}} \tag{5}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1,j=1}^{n}\left(x_{i}-y_{i}\right)^{2}}{n}} \tag{6}\]  
\[\text{nRMSE}=\sqrt{\frac{\sum_{i=1,j=1}^{n}\left(x_{i}-y_{j}\right)^{2}}{n}} \bigg{/}\overline{y} \tag{7}\]  
where \(x_{i}\) is the measured value; \(y_{i}\) is the estimated value; \(\overline{y}\) is the mean value; and \(n\) is the number of samples.
# On-Board Wind Scatterometry
## 4 Experiment and Results
### Results and Analysis
Color indicates wind speed in m/s. observed from Figure 10 that (b) shows larger stdev. values than (a), indicating larger variance and more noise from the coarser GMF LUT. The better consistency observed in (a) also corresponded with the fact that, for ground wind inversion, interpolation was also employed to improve retrieval wind quality [39]. In Figure 11, it can be seen that the average differences in all wind speed bins for wind directions were close to 0\({}^{\circ}\). However, there was an increase at 18 m/s from 0.06\({}^{\circ}\) to 0.15\({}^{\circ}\) and at 26 m/s from \(-0.07^{\circ}\) to \(-0.16^{\circ}\). These two bins also formed the second largest and largest average differences in wind direction. The trend of larger stdev. values for (b) than (a) is easy to see in Figure 4:6 The stdev. values are almost doubled in (b) taking reference from (a). From Figures 5-11, it can be concluded that processing methods and LUTs proposed in this research for on-board processing can achieve good results for scatterometer wind compared with ground products. Better resolution LUTs for on-board processing are necessary for providing RT winds in good quality. To obtain a finer resolution of LUTs requires a longer processing time. The cost of calculation versus accuracy for on-board procedures is given in Figure 12, where the term standard represents full resolution LUTs applied in corresponding procedures. Simplified means that the half-number resampled LUTs, and more simplified indicates a quarter number of resampling in LUTs. The combination of different LUTs in pre-processing and inversion is expressed by different shapes. The solid curve is for wind speed, and the dashed curve represents wind direction. The RMSE value reached the minimum and time cost is the largest when using full resolution LUTs in both preprocessing and inversion. CFOSCAT takes up to 260 seconds (s) for obtaining this track of data. When excluding observation time, it generates L0 data at the speed of 1.3 GB/h [43]. According to processing procedures described in Section 2, it can be found that the L1 and L2 products takes 0.8 and 0.06 GB/h respectively. The observing duration for a track of data was 95 min; under this on-board processing environment, data processing would cost around 165 s for a track. On average, this constitutes about a maximum of 2.95 min for observing a WVC in CFOSCAT. To complete adjacent WVCs for this kind of WVC, 5.9 min are required. Time is mainly consumed in the scatterometer observing procedure. For ground products involving ground stations for receiving, products are generally distributed in unit tracks. The on-board wind products are distributed based on the unit of a WVC with a much shorter product distribution duration. It can be concluded that full resolution LUTs can be applied for on-board wind inversion, while a comparable accuracy with ground products can be obtained in an on-board environment for RT applications.
## 3 Analysis of Test Results
### Analysis of Thermal Conductivity Test Results  
The thermal conductivity test was conducted on PBT and PA6, according to the Al\({}_{2}\)O\({}_{3}\) content. Table 3 summarizes the results of the test, which were obtained from the average values of three specimens. When 60% alumina was added to PBT, the thermal conductivity was found to be 1.21 W/mK, which was four times as high as the thermal conductivity of the PBT polymer (0.29) [36]. For PA6, when the alumina content was 50%, the thermal conductivity was 0.425 W/mK, which was approximately 1.7 times higher compared to the case without alumina. The thermal conductivity was 1.13 W/mK for the alumina content of 60% and 1.163 W/mK for 70%. PA6 exhibited lower thermal conductivity than PBT. This appears to be because of the difference in thermal conductivity between the basic materials. The thermal conductivity of alumina is approximately 30 W/mK, but the mixtures of alumina and polymers exhibited low thermal conductivities because alumina was accumulated on the polymers and heat bridges could not be properly formed. Further, this is because the heat transfer is interrupted by a very small gap in the contact surface between the alumina and the polymer [37; 38].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters of** & **Tensile** & **Elongation (\%)** & **Flexural** & **Impact** \\
**Compounding Process** & **Strength (MPa)** & **Elongation (\%)** & **Strength (MPa)** & **Strength (kJ/m\({}^{2}\))** \\ \hline PBT (0) & 66.7 & 250 & 93.2 & 2.5 \\ PBT (60) & 14.2 & 0.58 & 41.6 & - \\ PA6 (0) & 72.2 & 216 & 95.6 & 2.9 \\ PA6 (50) & 65.9 & 1.8 & 114.8 & 3.6 \\ PA6 (60) & 60.5 & 1.3 & 100.4 & 3.3 \\ PA6 (70) & 61.8 & 1.0 & 95.6 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of PA6/PBT/Al\({}_{2}\)O\({}_{3}\) composites with different weight ratios.
## 3 Optimization Agent Model and Sensitivity Analysis  
### Optimization Agent Model  
In this study, the response surface method was used to establish an agent model to simplify the relationship between the key parameterization and objective function. The RSM established the regression response surface model between the objective function and design variables, according to a series of statistical and mathematical methods. The function expression between the objective function and the design variable is expressed as follows:  
\[y=w(a)+\varepsilon \tag{15}\]  
where \(w\) is the approximate function; and \(\varepsilon\) is the residual between the true value and the approximate value, which reflects the observation error or noise of the response.  
To reflect the nonlinear characteristics between the objective function and design variable, we set the approximate function as a quartic function polynomial. Considering the linear, square, and interaction terms, the quartic polynomial function used in this study can be expressed as follows:  
\[y=b_{0}+\sum_{i=1}^{N}b_{i}\times a_{i}+\sum_{i=1}^{N}b_{N+i}\times a_{i}^{2}+ \sum_{i=1}^{N}b_{2N+i}\times a_{i}^{3}+\sum_{i=1}^{N}b_{3N+i}\times a_{i}^{4}+ \sum_{i\
eq j}^{N}b_{ij}\times a_{i}\times a_{j}+\varepsilon \tag{16}\]  
where \(b_{i}\) is the linear effect of \(a_{i}\); \(b_{N+i}\) is the second-order response of \(a_{i}\); \(b_{2N+i}\) is the third-order response of \(a_{i}\); \(b_{3N+i}\) is the fourth-order response of \(a_{i}\); and \(b_{ij}\) is the interaction effect of \(a_{i}\) and \(a_{j}\).  
Since the stiffness flow regulation system involves different loads, the load factors must be considered synchronously when establishing the agent model. Therefore, the function of the optimized agent model is \(U=U(w,\eta)\), where \(\eta\) is the load coefficient, with a value of 30-100%.
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 5 Empirical Results
### Eavesdropping Probability with an Array Eavesdropper
#### 5.2.2 Eavesdropping Probability in AUSNs  
Figure 10 shows the results of eavesdropping probability in AUSNs with an array eavesdropper (\(M=10\)). Similarly, their transmission power can be calculated by Equation (15) and the reference transmission power of nodes is \(P_{t}^{i}=100\) dB. We can also see that the simulation results accurately match the analytical results. Besides, the eavesdropping probability increases with the increased node density \(\rho\) and decreases with the increased signal frequency \(f\). Moreover, the eavesdropping probability decreases with increased wind speed \(w\) or spreading factor \(k\). If we align Figures 9 and 10 together, we can find AUSNs have the lower eavesdropping probability than IUSNs, which implies that nodes have less chance of being eavesdropped upon in AUSNs than in IUSNs.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.11 Group ID  
This mechanism enables a receiver to determine whether the data payload is single- or multi-user. More specifically, the Group-ID field is utilized by a receiving node to decide if it is targeted in the followed multi-user (MU) MIMO transmission.
## 6 Discussion
### Separation Layer Analysis Based on the Micro-Strain of DOFS
As can be seen from the curve correspondence (Section III) in Figure 10, when the working face advanced past the LC1 borehole and No. 6 drainage hole by 64.3\(\sim\)101 m, the deformation inside the overlying strata moved upward to the depth of 390.4 m in LC1 borehole (according to 271.2 m above the roof of coal seam). However, the water level seemed unchanged. Accordingly, it can be deduced that the deformation was located above the water-conducting fracture zone. During this phase, the upward movement of deformation inside the overlying strata would not influence the further development of the water-conducting fracture zone. According to the general principle that there is no water level in the water-conducting fracture zone, the water level in the drainage hole should be the separation seeper in the Lo-ho Formation. According to the curve correspondence (Section IV) in Figure 10, when the working face advanced past the LC1 borehole and No. 6 drainage hole by 101 m, the deformation inside the overlying strata moved upward further to the depth of 351.3 m in the LC1 borehole (according to 310.3 m above the roof of coal seam). The water level dropped rapidly to 423 m in the No. 6 drainage hole. In this case, it can be deduced that the water level drop during this period should be due to the water accumulation in the separation layer with its development inside the Lo-ho Formation. Based on the corresponding variation characteristics of the internal deformation data and water level data, it can be inferred that the top boundary height of the water-conducting fracture zone is 186.1\(\sim\)207.9 m above the roof of the coal seam. According to the strain characteristics of DOFS in the LC1 borehole, the breakage occurred in the area near the depth of 390.4 m (the lower part of Lo-ho Formation, 271.2 m above the roof of the coal seam) and the depth of 351.3 m (the middle part of Lo-ho Formation, 310.3 m above the roof of coal seam). This was caused by the tensile strain at these two places exceeding the strain limit of DOFS. Since these two places are located above the top boundary of the water-conducting fracture zone, the increase in strain should be caused by the development of the separation layer, so it can be inferred that the separation layer appears at about 271.2 m and 310.3 m above the coal seam roof.
# Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential
## 3 Acceptable Clearance for Lane Changing
### Research on Acceptable Clearance of Lane Changing
#### 3.2.2 Acceptable Lane Changing Clearance between Vehicle \(M\) and Vehicle \(Tf\)  
There is a risk of collision between lane changing vehicle \(M\) and target lane following vehicle \(TF\) in the process of lane changing, so it is necessary to study their interaction state. As shown in Figure 7, in order to avoid collision accidents between vehicle \(M\) and vehicle \(TF\), the two vehicles should meet the conditions shown in the following,  
\[G_{a}^{M-TF}\geq S_{TF}-S_{M}+G_{\text{min}}+w\sin(\theta) \tag{12}\]  
where, \(G_{a}^{M-TF}\) represents the acceptable lane changing gap between vehicle \(M\) and vehicle \(TF\); \(S_{M}\) indicates the distance traveled by lane changing vehicle \(M\) after the end of lane changing; and \(S_{TF}\) indicates the distance traveled by the target lane following car \(TF\) after the end of the lane changing.  
Figure 6: Safe critical time of lane changing under different motion states.  
Based on the above lane changing scenario assumption, the speed of vehicle \(TF\) is larger than that of vehicle \(M\). In the early stage of lane changing, when the distance between the two vehicles is large enough, the vehicle \(TF\) can perform uniform or even accelerated motion. When the distance between the two vehicles is reduced, vehicle \(TF\), as the following vehicle after the lane changing of vehicle \(M\), belongs to the forced vehicle, and the vehicle will slow down under the influence of the repulsion force. It is assumed that the initial lane changing speed and acceleration of vehicle \(M\) are \(v_{M}\) and \(a_{M}\), respectively, and the initial speed and acceleration of vehicle \(TF\) are \(v_{TF}\) and \(a_{TF}\), respectively. Similarly, it is assumed that the lane changing is completed when the speed of the two vehicles is equal, and the distance difference does not change after that; setting \(v_{M}<v_{TF}\) can obtain the safe critical time of vehicle \(M\) and vehicle \(TF\) under different moving states as \(T=\frac{v_{TF}-v_{M}}{a_{M}-a_{TF}}\) and the distance difference between the two vehicles as \(\Delta S=\frac{(v_{TF}-v_{M})^{2}}{2(a_{M}-a_{TF})}\).  
In summary, the expression of acceptable lane changing clearance between vehicle \(M\) and vehicle \(TF\) can be obtained as  
\[\left\{\begin{aligned} G_{a}^{M-TF}&\geq\frac{(v_{TF} -v_{M})^{2}}{2(a_{M}-a_{TF})}+G_{\min}+w\sin(\theta)\\ G_{\min}&=[t_{0}-c_{\varphi}(v_{M}-v_{TF})-c_{a}(a_ {M}-a_{TF})]v_{TF}\end{aligned}\right. \tag{13}\]
# Concentrate Apple Juice Industry: Aroma and Pomace Valuation as Food Ingredients
## 3 Results and Discussion
### Volatile Composition of Industrial Apple Aroma
The prevalence of esters and alcohols is in accordance with the volatile composition of the industrial apple aroma stored at room temperature in comparison with the refrigerated conditions. There were only two compounds ((_E,Z_)-2,4-ethyl decadienoate and methyleugenol) that were not detected at room temperature, and the majority esters (20), followed by alcohols (7), 1 terpenoid, and 1 acid (Table 1). perature storage. However, these compounds were present in the sample stored at 4 \({}^{\circ}\)C with a GC peak area <0.05 \(\times\) 10\({}^{7}\). Otherwise, two other compounds, the esters methyl butanoate and butyl propanoate, were not detected in the sample stored at refrigerated conditions. The chromatographic area of the majority of the volatile compounds presented in the apple aroma stored at different temperatures were not significantly different, \(p\) < 0.05. Only four compounds had significantly higher areas at room temperature: 1-hexanol, butyl acetate, 2-methylbutyl acetate, and hexyl acetate. Therefore, these results highlight that temperature storage conditions seem not to affect the volatile profile of the industrial apple aroma. Thus, it is more economically favorable for storage at room temperature in closed containers. Overall, the major compounds present in apple aroma were hexyl acetate and hexanol, both with herbaceous and fruity descriptors, but with very different odor thresholds, 2 and 500 ppb, respectively (Table 1). In order to relate the volatile profile obtained with the concentration of each compound present, extractions of volatile compounds using SDE and quantification by GC-qMS using pentanoic acid as an internal standard were performed. A total of 17 volatile compounds were quantified (Table 2). The compound present in major concentration was hexanol (1426 mg/L), followed by butanol (549 mg/L), _trans_-2-hexenol (280 mg/L), and _trans_ 2-hexenal (112 mg/L), in accordance with the apple aroma volatile concentration obtained by several industrial samples from different origin countries [3]. Undesirable compounds such as 3-methyl-1-butanol and 3-methylbutyl acetate, associated with fermentative metabolites produced under industrial apple fruit juice processing conditions [3; 23], were not detected in the present study. In addition, furfural, a compound associated with thermal processing [24], was not present in the industrial apple aroma under study. The concentration of each volatile compound was divided by the corresponding odor threshold reported in the literature, giving the Aroma Index [25], or also called odor activity value [26]. As compounds present in concentrations higher than their odor threshold are considered as aroma-contributing substances, the compounds that exhibit an Aroma Index > 1 were considered to contribute individually to apple aroma [26]. An Aroma Index higher
## 4 Results and Discussions
### Results and Discussion of Friction Coefficient Test  
The friction coefficient test device, developed by the China University of Geosciences in Beijing, China, was adopted to conduct the friction coefficient test in triplicate on each tested soil after the mechanical mixing test. The angle of external friction and friction coefficient are shown in Figure 15. For soil group with \(w=25\%\), it can be seen that the friction angle and friction coefficient first increased to an apparent maximum as the _FIR_ increases from \(FIR=0\%\) (without foam) to \(FIR=10\%\). As the \(FIR\) continued to increase beyond \(10\%\) until \(FIR=30\%\), the friction angle and friction coefficient decreased. The trend is consistent with the trend of net motor power in Figure 13, which might have been caused by the increase of viscosity because of dissolution of foam. For the tested soils with \(w=40\%\) regardless of \(FIR\), the friction angle and friction coefficient were less than the tested soils with \(w=25\%\) and they  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \(w\) & \(C_{f}\) & \(FER\) & \(FIR\) & \multicolumn{3}{c}{**Slump (mm)**} \\ \cline{2-9}  & **(\%)** & **(\%)** & **(-)** & **(\%)** & **Test 1** & **Test 2** & **Test 3** & **Mean** \\ \hline Sample 1 & 25 & 4 & 7 & 0 & 0 & - & - & - \\ Sample 2 & 25 & 4 & 7 & 10 & - & 4 & 2 & - \\ Sample 3 & 25 & 4 & 7 & 20 & 7 & 5 & - & - \\ Sample 4 & 25 & 4 & 7 & 30 & - & - & 7 & - \\ Sample 5 & 40 & 4 & 7 & 0 & 160 & 154 & 149 & 154.3 \\ Sample 6 & 40 & 4 & 7 & 10 & 173 & 180 & 171 & 174.7 \\ Sample 7 & 40 & 4 & 7 & 20 & 179 & 183 & 175 & 179.0 \\ Sample 8 & 40 & 4 & 7 & 30 & 187 & 182 & 178 & 182.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of slump test of conditioned soil.  
decreased as \(FIR\) increased from 0% to 30%, as shown in Figure 15. This also indicates that the increase of water dramatically changes the characteristics of tested soils firstly, and then the foam reduces the viscosity and improve the flowability. The increase of flowability and decrease of viscosity are good for the discharging of the mixed soil in soil chamber through screw conveyor during EPBS operation in soft ground.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 7 Conclusions  
Based on the axial compression test results of DC joints, a validated finite element simulation has been conducted, and then parameter analysis was performed through a series of numerical simulations. Furthermore, based on the numerical results, a moment-rotation model suitable for DC joints is proposed. The results are as follows:  
1. The load-displacement curve and failure mode of DC joints obtained by numerical analysis are in good agreement with those of the experimental results. The finite element model can accurately simulate both the bearing capacity and flexural performance of DC joints under eccentric load.
2. Eccentricity significantly affects the flexural performance of DC joints. Flexural performance is reduced to 53% of the original, as eccentricity increases from 0 mm to 36.5 mm, so the effective control of eccentricity is beneficial in improving flexural performance.
3. The bearing capacity and flexural performance of DC joints decreases with eccentricity, steel wedge height and middle-rib plate height; they increase with steel wedge length, steel wedge width, channel steel thickness and steel tube wall thickness.
4. The calculation methods for both \(M_{\text{i}}\) and \(\theta_{0}\) in the moment-rotation model are deduced, and the calculation formulas of both \(n\) and \(\lambda\) in the moment-rotation model are fitted. The correctness of the calculation formulas of the four parameters is verified by comparison with the bending resistance index of the numerical model of DC joints.
5. The thickness of channel steel has a significant effect on the flexural performance of DC joints. Therefore, in practical projects, appropriate strengthening of channel steel should be considered to effectively improve the flexural performance of DC joints.  
Conceptualization, X.N.; Methodology, Z.X.; Software, P.L.; Resources, M.Z.; Data curation, X.L. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support provided by Natural Science Foundation of China under Grant Nos. 51978018, 51978019, 51538001 and 51627812.  
Not applicable.  
Data Availability StatementData are contained within the article. The data presented in this study can be requested from the authors.  
The authors declare no conflict of interest.