esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experimental Analyses of Hybrid Piston Rods Used in Hydraulic Cylinders under Axial Load
## 2 Materials and Methods
### Test Samples  
To determine the buckling strength of the rod, 16 specimens were prepared. They were categorized as Solid type hybrid rod and hollow type hybrid rod depending on the percentage of CFRP material combined with steel to form a single rod. For each category, there were 2 specimens for testing. The length of each rod was 650 mm and outer diameter was 30 mm. For hollow rod, the inner diameter was fixed at 10 mm. AISI 1045 steel and unidirectional CFRP were used for the analysis. Rust or primer on the steel surface was removed by sandblasting, and the surface was cleaned with Ace-Tone. To improve the adhesion between steel and CFRP, the surface of the bars was knurled. A special toughened epoxy urethane adhesive (DP6310 NS) was used between steel and CFRP to ensure sufficient strength. This adhesive is readily available at our local market and also showed better strength compared to other adhesives available in the market. These specimens are loaded axially in the machine using wedge grips. The upper end of the specimen is loaded along the wedge and load is applied at this end. A specially adapted fixture has been used to fix the specimen at the lower end. A guard is provided to protect the tester from accidents during the test. The buckling load on the specimen is significantly affected by the axial stress distribution that develops during the test. All discrepancies are carefully removed before the test begins. Mechanical properties of steel and CFRP are depicted in below Tables 2 and 3, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Steel** & **Elastic Modulus E\({}_{\text{s}}\)** & **Yield Strength Y\({}_{\text{s}}\)** & **Poisson’s Ratio** \\  & **[MPa]** & **[MPa]** & **[g/cm\({}^{3}\)]** \\ \hline AISI 1045 & 206,000 & 530 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of steel rod.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Continuous** & **Tensile Modulus E\({}_{\text{f}}\)** & **Tensile Strength s\({}_{\text{f}}\)** & **Density r\({}_{\text{f}}\)** \\
**Reinforcement** & **[MPa]** & **[MPa]** & **[g/cm\({}^{3}\)]** \\ \hline Intermediate & & & \\ \hline Modulus (IM) & 300,000 & 5500 & 1.80 \\ \hline Carbon Fiber & & & \\ \hline
**Matrix** & **Tensile Modulus E\({}_{\text{m}}\)** & **Tensile Strength s\({}_{\text{m}}\)** & **Density r\({}_{\text{m}}\)** \\  & **[MPa]** & **[MPa]** & **[g/cm\({}^{3}\)]** \\ \hline Epoxy & 3800 & 100 & 1.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of CFRP.  
Figure 9: Numerical model built using ABAQUS.
## 3 Study Methods and Evaluation Process
### Determination of Index Weight  
Different indexes have different influences on the evaluation results, that is, they are endowed with different weights in the calculation process. Accurate determination of the importance of each factor is an important premise for the evaluation results to conform to the reality. In previous studies, the analytic hierarchy process (AHP) and expert scoring method were mostly adopted. This method based on the decision makers' subjective information to determine the weight of each factor relies too much on the level and ability of the decision makers, and the weight is not necessarily reasonable. So, the evaluation results may appear to have large differences. In this paper, the method of combining rough set and conditional entropy is adopted, which can fully reflect the objectivity of the data without providing any prior information outside the sample data set. Thus, the weight determination is more reasonable, and the evaluation results are more in line with the reality.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## Vehicle Model and Its Coupling with Road and Environment
### Wheel Rolling Resistance and Aerodynamic Drag of the Vehicle  
The rolling resistance is manifested in the resultant ground reaction force that is shifted forward with respect to the tire contact area, as discussed in Section 1. In practice the rolling resistance force is assumed as acting on the wheel center of gravity along the road, while effects occurring on different types of road surface are described by a rolling resistance coefficient, denoted here as \(f_{\text{roll}}\). The rolling resistance was assumed as constant and independent on vehicle speed. Value of the coefficient \(f_{\text{roll}}\) = 0.008 was set according to [2] assuming a rigid or only slightly deformable surface, e.g., concrete test road. Consequently, the roll resistance force can be calculated as:  
\[F_{\text{roll}}=-\text{sign}(v_{u,\text{friction}})\cdot f_{\text{roll}}\cdot F _{n}, \tag{14}\]  
which depends on direction of wheel motion along the averaged surface described by \(\text{sign}(v_{u,\text{friction}})\). Components \(x\) and \(z\) of \(F_{\text{roll}}\) can be obtained, similar to the friction force \(F_{r}\), based on average inclination angle \(\gamma\): \(F_{\text{roll},x}=\cos(\gamma)\cdot F_{\text{roll}}\), \(F_{\text{roll},z}=\sin(\gamma)\cdot F_{\text{roll}}\).  
Air flow over a moving vehicle counteracts its motion. An analysis of an actual effect of air flow is complex; thus, semi-empirical models are used, as presented in [47]. Here, the following relationship was applied in order to take into account aerodynamic drag acting on the vehicle model:  
\[F_{\text{drag}}=-0.5\cdot\rho_{\text{air}}\cdot c_{\text{air}}\cdot A_{\text {air}}\cdot v_{s,\text{drag}}^{2}, \tag{15}\]  
where \(v_{s,\text{drag}}^{2}=\dot{x}_{s}^{2}+\dot{z}_{s}^{2}\) denotes squared absolute value of the body velocity. The velocity included in Equation (15) is dependent on the body motion, since it was assumed that body motion has a decisive influence on aerodynamic drag of the vehicle. Further, \(x\) and \(z\) components of drag force \(F_{\text{drag}}\) are defined as \(F_{\text{drag},x}=\cos(\delta)\cdot F_{\text{drag}}\), \(F_{\text{drag},z}=\sin(\delta)\cdot F_{\text{drag}}\). Angle \(\delta=\text{atan2}(\dot{z}_{s},\dot{x}_{s})\) describes the orientation of the body velocity.  
The evaluation of aerodynamic drag of the considered model requires an aerodynamic drag coefficient, denoted as \(c_{\text{air}}\), and the frontal cross-sectional area of the vehicle \(A_{\text{air}}\). The values of these parameters, i.e., \(c_{\text{air}}=0.72\), \(A_{\text{air}}=1.75\) m\({}^{2}\), were taken from a study that is presented in [48] and related to a similar all-terrain vehicle Polaris Sportsman 500. The value of air density was assumed to be \(\rho_{\text{air}}=1.223\) kgm\({}^{-3}\).
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 3 Range Extension of the Indoor Standard Device for Large-Scale Length Measurement
### Analysis of Range-Extension Measurements  
The data obtained after the range extension within a range of 52 m are presented in Table 1. The nominal values are measured by a laser interferometer (model: HP 5519B, factory number: US52140378) fixed in the laboratory. The laser interferometer shows that the stability of its laser frequency is 2.5 \(\times\) 10\({}^{-10}\) at 1000 s during the 10,000 s time interval after frequency stabilization. Its average wavelength in the vacuum is \(\lambda\) = 0.632991374 \(\upmu\)m, and the uncertainty of the average wavelength in the vacuum is \(U\) = 2 \(\times\) 10\({}^{-8}\) (\(k\) = 2). The laser interferometer for stationary use is featured by stable data and superior performance. The measured values are measured by an interferometer (model: XL-80, number: 747X57), and the stability of its laser frequency is 2.8 \(\times\) 10\({}^{-9}\) at 1000 s. The measured data of these  
Figure 5: A diagram of the simulated optical path for the range-extension measurement of a laser interferometer.  
Figure 6: A diagram of the optical path in a laser interferometer’s range-extension experiment.  
two laser interferometers are both from the secondary standard device with a 633 nm wavelength. The reflectors of the two laser interferometers are more than 1 m away from the laser interferometer. Therefore, the maximum allowable error at a nominal 1 m position is 0.5 \(\upmu\)m, the maximum allowable error at a nominal 2 m position is 1.5 \(\upmu\)m, and the maximum allowable error at a nominal 3 m position is 2.0 \(\upmu\)m, and so on. As shown in the table, it is evident that the maximum measurement error was \(-\)13.02 \(\upmu\)m, occurring at a distance of 102 m. At specific distances (of four meters, six meters, and eight meters), the data slightly deviated from the specified limits. Overall, the maximum indication error upon the range extension satisfied the standards for calibrating the laser interferometer as it fell within the required indication error range of \(\pm\)0.5 \(\upmu\)m/m. This experimental measurement requires repeated debugging of the optical path. Due to the large amount of measured data, only one measurement result can be presented in this paper. After several experiments, it was found that the proposed measuring method has strong repeatability.  
The measurement error in the range extension at a measuring range of 52 m is presented in Figure 7, which visualizes measurements. The measurement error resulted in a sawtooth pattern and oscillated as the measuring length increased.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Nominal** & **Measured** & **Measurement** & **Nominal** & **Measured** & **Measurement** \\
**Value (mm)** & **Value (mm)** & **Error (\(\upmu\)m)** & **Value (mm)** & **Value (mm)** & **Error (\(\upmu\)m)** \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Measurements upon the range extension at a measuring range of 52 m.
## 4 Results
### Results CWRU Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Experiment Type** & **Motor Speed** & **Number of Sequences** & **Training Data (\%)** & \multicolumn{6}{c}{**Accuracy**} \\ \hline  & & & & **K-NN** & **RF** & **SVM** & **WDCNN** & **A-WMCNN** & **Ada-WMCNN** \\ \hline
**Drive End** & 1797/1750 & 1.889 & 80 & 85.71 & 97.35 & 97.62 & 99.38 & **100** & **100** & **100** \\  & & & 40 & 82.98 & 94.27 & 91.62 & 95.62 & 97.27 & 97.34 & **97.74** \\  & & & 20 & 73.88 & **93.65** & 87.37 & 56.53 & 76.79 & 93.20 & 93.44 \\ \cline{2-10}  & 1797/1750 & 2.892 & 80 & 63.56 & 81.87 & 81.69 & 87.89 & **89.86** & 89.46 & 89.59 \\  & 1772 & & 40 & 57.89 & 80.13 & 77.82 & 80.35 & 70.13 & **85.31** & 84.93 \\  & & & 20 & 52.33 & 77.18 & 69.88 & 66.20 & 77.39 & **77.56** & 65.22 \\ \cline{2-10}  & 1797/1750 & 2.894 & 80 & 74.44 & 94.13 & 93.61 & 99.69 & 99.97 & 99.80 & **100** \\  & 1730 & & 40 & 67.47 & 89.81 & 87.16 & 91.13 & 96.07 & 96.29 & **96.79** \\  & & & 20 & 58.38 & 86.57 & 78.76 & 68.10 & 87.46 & 87.41 & **90.32** \\ \cline{2-10}  & 1797/1750 & 3.897 & 80 & 85.95 & 81.28 & 81.15 & 91.99 & 92.68 & **93.31** & 91.67 \\  & 1730/1772 & & 40 & 53.95 & 78.58 & 75.07 & 81.51 & 88.44 & **89.58** & 86.55 \\  & & & 20 & 47.47 & **74.15** & 66.04 & 39.47 & 42.04 & 65.63 & 71.58 \\ \hline
**Fan End** & 1797/1750 & 1.710 & 80 & 83.33 & 90.64 & 87.43 & 99.43 & **99.94** & 99.83 & **99.94** \\  & & & 40 & 76.02 & 87.91 & 83.43 & 97.69 & 99.52 & 99.19 & **99.87** \\  & & & 20 & 69.74 & 84.21 & 77.63 & 40.55 & 60.98 & **90.56** & 72.66 \\ \cline{2-10}  & 1797/1750 & 2.593 & 80 & 61.85 & 73.60 & 71.10 & 85.27 & 89.92 & 89.28 & **90.15** \\  & 1772 & & 40 & 55.27 & 70.05 & 64.91 & 77.89 & 84.16 & **86.61** & 85.57 \\  & & & 20 & 49.69 & 66.22 & 99.18 & 53.43 & 68.03 & **77.20** & 55.85 \\ \cline{2-10}  & 1797/1750 & 2.595 & 80 & 69.36 & 83.82 & 80.73 & 99.39 & 99.85 & 99.92 & **99.96** \\  & 1730 & & 40 & 56.07 & 82.66 & 74.63 & 94.40 & 98.34 & 98.07 & **99.49** \\  & & & 20 & 54.87 & 79.82 & 64.98 & 59.07 & 86.88 & 91.67 & **92.46** \\ \cline{2-10}  & 1797/1750 & 3.478 & 80 & 55.32 & 73.85 & 69.97 & 91.75 & 92.85 & 92.15 & **92.94** \\ \cline{2-10}  & 1730/1772 & & 40 & 47.29 & 69.81 & 64.54 & 82.96 & 88.86 & **89.87** & **90.76** \\  & & & 20 & 40.71 & 66.12 & 52.71 & 38.04 & 54.08 & 55.64 & **68.37** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test accuracy scores (%) averaged across the 5-fold cross validation for each of the models with their respective train split on the CWRU Bearing dataset. to the sparsity of the amount of sequences available. Especially with just 20% train data, the number of samples per class is in the most complex case lower than 20 samples resulting in major performance drops, sometimes even below 50% accuracy. This phenomenon can be better observed in Figure 4 where the results of the CNNs are averaged across all the CWRU bearing experiments for their given train split.
###### Abstract
It has been acknowledged that achieving a precise model for the detailed near-field dynamics of liquid hydrogen release in CFD is impractical. Furthermore, the implementation of such a model cannot guarantee accuracy due to the highly complex nature of the phenomena, and there are too few proper experiments with low uncertainties available for validation [20]. Therefore, at this stage, it seems more appropriate to employ a simplified approach that can simulate the characteristics of liquid hydrogen release, rather than rigorously considering two-phase behaviors when analyzing the release of liquid hydrogen. The pseudo-source model and the liquid pool model are one of the straightforward models for liquid fuel evaporation and dispersion. These models have been commonly used in the computations of liquid hydrogen leak and dispersion [17; 19; 20; 22]. However, there has been no specific evaluation conducted to date on the application of these two models to computational analysis of practical problems, particularly regarding the conditions under which each model demonstrates superior predictive performance. Therefore, in this study, we conducted a computational analysis of the dispersion following a liquid hydrogen leak from a storage tank to assess the predictive performance of the relatively straightforward evaporation-dispersion model. We utilized well-established models, including the pseudo-source model and the liquid pool model. Additionally, we explored a hybrid model that combines both approaches. The predictive performance of each model for hydrogen concentration and temperature was assessed by comparing the results with data from previous experiments [17]. Our findings suggest a dispersion model suitable for computational analysis from a practical standpoint among the three examined models, based on leaked mass flow rate conditions. Additionally, we examined the effects of explosions on humans by utilizing an overpressure-impulse diagram when the released liquid hydrogen was ignited.
# Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV
## 2 Overall UAV Design
### Wing Design  
#### 2.4.1 Wing Shape and Function  
First, in the wing shape selection, the preliminary selection between CLARK Y and NACA 4412, two kinds of planoconvex wings, Re = 250,000 under the CLARK Y and NACA 4412 lift and drag characteristics, as shown in Figure 2. The black line represents the characteristic curve of the NACA 4412 wing. The blue line represents the characteristic curve of the CLARK Y airfoil. Combined with the lift-to-drag ratio characteristic curve, as shown in Figure 3. The black lines also represent the lift-to-drag characteristic curve of the NACA 4412 airfoil. The blue line represents the lift to drag characteristic curve of the CLARK Y airfoil. It can be seen that for the NACA 4412 in the same headway angle under the lift, the coefficient is higher, and the lift-to-drag ratio characteristics are also better. Combined with NACA 6409 and NACA 6412, although the lift coefficient is higher, the critical stall angle is too small; the drag and fabrication are more difficult, and the extreme load capacity is not needed for the ground. Moreover, because of the choice of the trapezoidal wing, the effective angle of the wing profile increases toward the wingtip, and the separation occurs first in the wingtip, which reduces the aileron rudder maneuvering. Thus, the critical stall angle is preferred. Therefore, the airfoil with a larger stall critical angle is preferred, and the planoconvex NACA 4412 airfoil is selected.  
The shape of the wing designed in this study is [9] shown in Figure 4, which has the following main features:  
1. A trapezoidal planar wing is used; this shape is simple and practical, suitable for low- and medium-speed flights, and easy to manufacture and install;  
Figure 3: Lift-to-drag ratio characteristic curves of two airfoils.  
Figure 2: Lift and drag characteristic curves of two airfoils.  
2. NACA 4412 is chosen as the wing model, which has a high lift coefficient and low drag coefficient and is suitable for low- and medium-speed flights, as well as under takeoff and landing and stall conditions;
3. The wing sweep angle is designed as 3\({}^{\circ}\); this camber can reduce the induced drag of the wing and improve the lateral stability and maneuverability of the aircraft.
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
## 3 Results
### Historical Trends
The same number of significant trends is found for both parametric and non-parametric approaches, with the exception of the loss of 95% significance of the one observed trend when using the Mann--Kendall approach. If real, this single trend would imply a very small (0.02 NDVI / year) progressive increase of forest vegetation cover in the eastern Jabal Al Qamar. However, we do not ascribe any physical meaning to this trend, because the small amplitude of the trend pushes the detection limit of the NDVI metric beyond our confidence. Furthermore, on average, an examination of 20 time series will yield one with a significant trend at 95% confidence based on random chance alone. The lack of any apparent trend in the daily MODIS NDVI contradicts the conclusion of a recently published study [44] which infers widespread land degradation throughout Jabal Dhofar from an analysis of 16-day MODIS NDVI composite imagery. We attribute this disparity, at least in part, to the difference between daily and composite MODIS data, as discussed below. Figure 8: Interannual trends in vegetation cover. Panel A shows forests and Panel B shows grasslands. The Julian Day with the fewest missed or cloudy (NDVI \(<\) 0.1) observations was chosen for three 30-day post-monsoon time windows. Anniversary time series for spatial averages of each region are plotted, along with best fit linear trends. P values for both linear regression and 2-sided non-parametric Mann-Kendall (MKp) shown. Only one trend is significant to 95% confidence, but the slope is small (0.02), the number of years in the time series is modest, some early years of the time series appear affected by cloud contamination, and no trend is apparent in that EM either earlier or later in the decade. We do not ascribe physical significance to this trend without further evidence.
## 4 Results and Analysis
### Analysis of the Spatial Correlation between Habitat Quality and Geological Hazard Susceptibility
In the west, although these regions have comparable geological settings, those with a high habitat quality are smaller and predominantly located at interfaces between farmland and grassland or near construction sites or bodies of water adjacent to farmlands and experience more human activity. The \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Area (km2)** & \
\begin{tabular}{c} **Total Number of Disaster** \\ **Points (Count)** \\ \end{tabular} &
\begin{tabular}{c} **Disaster Point** \\ **Density (Count/km2)** \\ \end{tabular} \\ \hline Low & 151.08 & 3 & 0.0199 \\ Medium & 745.24 & 24 & 0.0322 \\ High & 195.68 & 18 & 0.0920 \\ Extremely High & 1556.00 & 88 & 0.0566 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Different habitat quality grades and geological disaster number and density. Figure 7: LISA clustering diagram. terrain here is steeper and higher, making transportation challenging. Local construction practices, such as slope undercutting for roads and building directly into slopes, have modified the natural slope configurations, decreasing their stability. Additionally, under conditions of heavy rainfall or other extreme weather, the likelihood of landslides increases significantly. Human activities, including overgrazing and uncontrolled deforestation, have degraded the surface environment, diminishing vegetation cover and the ground's ability to stabilize sand and soil, thereby enhancing the risk of soil erosion. While various factors such as topography, climate, vegetation, geological structure, and human interventions impact susceptibility to geological disasters, it is apparent in areas of generally a high habitat quality that despite a weak negative correlation between habitat quality and disaster susceptibility, the trend of a poorer habitat quality correlating with higher disaster risks persists.
# Virtual Scene Construction for Seismic Damage of Building Ceilings and Furniture
## 3 Technical Method
### Damage Prediction for Suspended Ceilings and Moveable Furniture  
The fragility curves in FEMA P-58 are critical for the prediction of seismic damage to nonstructural components. However, the corresponding PGs for components need to be determined before the fragility curves can be used, because these curves are classified based on the PGs in FEMA P-58.  
The corresponding PG for each component can be determined according to the detailed classification criteria in FEMA P-58. These criteria involve the geometry, material, construction, and damage mechanisms of the components. The geometric and material properties can be directly obtained from the BIM, while information for the construction and damage mechanism generally need to be added manually. Therefore, a solution combining automatic and manual procedures [35] is used to determine the corresponding PGs for components.  
The determination process is divided into four levels (Levels 1-4): building, story, component category, and PG. In FEMA P-58, the seismic damage is predicted story-by-story; therefore, the PG determination process is also conducted story-by-story to be consistent with FEMA P-58. A BIM (Level 1) is first split into stories (Level 2) according to the elevations of the stories. The categories of nonstructural components in each story (Level 3) are then identified in the BIM. Finally, the PGs (Level 4) are identified manually according to the attribution of component categories in the BIM and the necessary information regarding the construction and damage mechanisms. This determination process can be implemented using the application programming interface (API) of Revit [35].  
Typical fragility data (e.g., identity documents (IDs) of PGs, fragility curves, required EDPs, etc.) of a suspended ceiling and movable furniture obtained using the above procedure are listed in Table 1. From Table 1, it can be observed that the seismic DSs of both suspended ceilings and movable furniture are controlled by the PFAs. Suspended ceilings have three DSs (See Figure 2), while movable furniture has only a single DS. In FEMA P-58, each DS of suspended ceilings has a clear description, e.g., the ratio of the area of falling ceiling tiles. However, the DS for movable furniture has no clear description to determine whether the furniture has been overturned or moved. Therefore, the DSs of suspended ceilings are used to determine the fallen area in the virtual scene, but the DS of the movable furniture will not be used. The movement of the furniture will be simulated directly using the data from the seismic responses and a physics engine, which is clarified in Section 3.3.  
It should be noted that FEMA P-58 includes the fragility data of a wide range of nonstructural components (e.g., partitions and stairs), thus the above proposed solution can also predict seismic damage of other components and is not limited to the suspended ceilings and movable furniture mentioned in this study.