esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology
### Methodology
#### 2.3.3 Evaluation Metrics  
We use several widely adopted metrics [30; 31], such as the producer's accuracy (PA), the user's accuracy (UA), the overall accuracy (OA) and the Kappa coefficient (Kappa), to quantitively assess the accuracy of the classification in this paper, based on the resulting confusion matrix (Table 4)  
\[PA=\frac{P_{j,j}}{\sum_{i=1}^{n}P_{i,j}} \tag{6}\]  
\[UA=\frac{P_{i,i}}{\sum_{j=1}^{n}P_{i,j}} \tag{7}\]  
\[OA=\frac{\sum_{i=1}^{n}P_{i,j}}{T} \tag{8}\]  
\[Kappa=\frac{T\sum_{i=1}^{n}P_{i,i}-\sum_{i=1}^{n}(P_{i}\cdot\times P_{i})}{T^ {2}-\sum_{i=1}^{n}(P_{i}\times P_{i})} \tag{9}\]  
where \(j\) is the jth category of the predicted results, \(i\) is the ith category of the validated results, \(T\) is the overall number of samples, and \(P_{i}\) and \(P_{i}\) are the sum of the samples in the ith row and the ith column in the confusion matrix, respectively.
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
## 3 Numerical Simulation
### Research and Analysis of the Influence of Backfill Poisson's Ratio on Stress Distribution Patterns  
Figure 9 systematically reveals the influence law of Poisson's ratio on the stress distribution of the filling body. By combining numerical simulation with theoretical analysis, the stress response characteristics of the vertical centerline of the filling body, when the Poisson's ratio varies within the range of 0.2-0.4, were investigated. Studies show that when the Poisson's ratio increases from 0.2 to 0.4, the vertical stress of the filling body significantly decreases by 15%, while the horizontal stress increases by approximately 20%, accordingly, presenting an obvious stress redistribution phenomenon.  
This variation law can be reasonably explained from the basic theory of material mechanics: Poisson's ratio, as a key parameter characterizing the lateral deformation characteristics of materials, on increasing leads to an enhanced lateral expansion effect of the filling body under vertical loads, thereby causing a typ  
Figure 8: Comparative analysis of the influence of backfill elastic modulus on stress.  
Figure 9: Comparative analysis of the influence of backfill Poisson’s ratio on stress.  
horizontal stress increases and the vertical stress decreases. The research results not only verify the significant influence of the Poisson's ratio on the stress distribution of the filling body, but also emphasize that in the numerical simulation and theoretical analysis of filling engineering, the value of the Poisson's ratio parameter must be accurately considered. Only by reasonably determining the Poisson's ratio can the accuracy and reliability of the calculation results be ensured, providing a scientific basis for engineering design and stability assessment.
## 4 Application Example
### Comparison of Calculation Results  
In order to further compare the influence of considering the stiffness of the main tower, a deflection theoretical calculation program without considering the stiffness of the main tower is implemented. In Figure 7, based on the deflection theory, the deflection and internal force influence lines of the middle section of the left main span of the stiffened beam are also calculated without considering the stiffness of the main tower, considering the stiffness of the main tower and ANSYS finite element calculation. As can be seen from Figure 7, the influence lines of internal force and deformation of the three are very close. In Tables 1-3, the deflection, bending moment and shearing force of the middle section of the left main span of the stiffening beam are listed when the left main span is arranged with live load. It can be seen from Table 1 that the calculation results considering the longitudinal stiffness of the main tower are in good agreement with those obtained by nonlinear finite element method. The maximum error of deflection of the stiffening beam is within 8%. Even in the middle of the right span, the section deformation is only 1 mm error. As for the bending moment of the stiffening beam, as shown in Table 2, the calculation result considering the longitudinal stiffness of the main tower is highly consistent with that of the nonlinear finite element method, and the maximum error is within 12.5%. It can be seen that the accuracy of this calculation method completely meets the preliminary design and parameter analysis of the structure. Figure 7: Internal force and deformation influence line of middle section of left main span. (**a**) Deflection influence line. (**b**) Bending moment influence line. (**c**) Shear influence line. However, because the deflection theory regards the slings as vertically densely distributed along the whole bridge span and only provides the \"membrane\" of vertical resistance, without considering its elongation and inclination under live load, the calculated shear force error is large. The maximum error of shearing force has reached 36.55%. In addition, it should be pointed out that this is a method of approximate calculation. As far as the calculation results are concerned, there is a big difference in the deflection theory whether the longitudinal stiffness of the main tower is considered or not. The shape of the influence line of internal force and deformation is also very different. There is a big difference between positive and negative influences. Thus, the deflection theory without considering the longitudinal stiffness of the main tower cannot truly reflect the internal force and deformation characteristics of the structure. Therefore, the theoretical analysis of deflection of multi-tower self-anchored suspension bridge needs to consider the influence of the longitudinal stiffness of the main tower.
# Enhanced Flow Control for Low Latency in QUIC
## 4 Evaluation
### Four-Stream Connection with Large Buffer  
We evaluated the performance for a four-stream connection with a large buffer. The parameter settings are listed in Table 2. The initial maximum stream receive window was 8 KB and the upper bound of a stream was 1 MB. The received bytes over time for autotuning and fast autotuning allowance in Figure 10 show average throughputs of 4.8 and 5.4 MB per second, respectively. Compared to autotuning allowance, fast autotuning allowance increases throughput by 12.5% because it increases the maximum receive window significantly. Figure 11 depicts the maximum receive window for one stream with autotuning and fast autotuning. In autotuning, the maximum stream receive window is doubled from 8 KB to 512 KB for 2.5 s, and then maintained at 512 KB. However, in fast autotuning, the maximum stream receive window grows 16 times in 0.86 s, from 8 KB to 128 KB, and then four times in 1.03 s, from 128 KB to 512 KB, which is 1.5 s faster than autotuning.  
We also measured the transmission latency for each data size. Compared to autotuning, fast autotuning reduces transmission latency by around 29% on average (Figure 12).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of streams & 4 \\ Maximum connection receive window & 32 KB \\ Maximum stream receive window & 8 KB \\ Connection upper bound & 64 MB \\ Stream upper bound & 1 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter setting for a four-stream connection.  
Figure 8: Autotuning allowance in a two-stream connection with small buffer.  
Figure 9: Fast autotuning allowance in a two-stream connection with small buffer.  
The transmission latency is decreased by at least 30% when transmitting 1 MB to 5 MB data, the average data size communicated over the Internet.  
Figure 11: Maximum stream receive window.  
Figure 12: Transmission time by data size.  
Figure 10: Received bytes of a connection with large buffer.
## 4 Convergence
### Consistency and LTE  
An LTE is generally defined as the error committed in each time step, by replacing the differential equation by the difference equation. The approximating difference equation for CVM can be derived from Equation (6), after removing the accelerations and velocities, and is found to be:  
\[d_{i+1}-A_{1}d_{i}+A_{2}d_{i-1}-A_{3}d_{i-2}\ =\ 0, \tag{11}\]  
Consequently, after replacing Equation (1) by Equation (11), the LTE for CVM is:  
\[E\ =\ \frac{1}{\left(\Delta t\right)^{2}}[u(t+\Delta t)-A_{1}u(t)+A_{2}u(t- \Delta t)-A_{3}u(t-2\Delta t)], \tag{12}\]  
where \(u(t)\) is assumed to be frequently differentiable up to any desired order and, thus, the terms of \(u(t+\Delta t)\ =\ u(t_{i+1})\), \(u(t-\Delta t)\ =\ u(t_{i-1})\) and \(u(t-2\Delta t)\ =\ u(t_{i-2})\) can be expanded into finite Taylor series, at \(t\). As a result, after substituting \(A_{1}\), \(A_{2}\) and \(A_{3}\), as well as \(u(t+\Delta t)\), \(u(t-\Delta t)\) and \(u(t-2\Delta t)\) into Equation (12), the local truncation error for CVM is found to be:  
\[\begin{array}{ll}E&=\frac{1}{D}\Big{[}\Big{(}\gamma_{2}-\frac{1}{2}-\alpha \Big{)}\Big{]}\Big{[}\frac{1}{2}2\xi\Omega(\Delta t)\vec{u}_{i}+(\Delta t) \vec{u}_{i}+\frac{1}{2}(\Delta t)^{2}\vec{u}^{\ast}\Big{]}\\ &-\frac{1}{D}\frac{5}{2\Omega}2\xi\Omega_{0}(\Delta t)\vec{u}_{i}-\frac{1}{2} \frac{1}{D}\Big{(}\alpha+\frac{1}{3}\Big{)}(\Delta t)^{2}\vec{u}_{i}+O\Big{[} (\Delta t)^{3}\Big{]},\end{array} \tag{13}\]  
This equation shows that CVM has the second order accuracy if viscous damping is adopted, and the order of accuracy becomes one for zero viscous damping. Its consistency is verified for any values of the viscous damping ratio \(\xi\). CVM maintains second order accuracy, even for the value of \(\gamma_{2}-\alpha\ =\ \frac{1}{2}\).
## 1 Introduction
[21] evaluated friction coefficient  
Figure 2: Corrosion of bearing supports: (a) high-strength brass friction material rust; (b) corrosion of bearing supports due to rust. by vertical load and the repeated shear load with the double concave friction pendulum type seismic device to which a new friction material is applied. [PERSON] [22] studied that tensile specimens were produced for polyamide 12 materials, which are polymer materials, and polyamide 12 materials, which are reinforced with glass beads. The characteristic results according to the direction of specimen production and tensile test temperature were compared and analyzed. [PERSON] [23] introduced the prediction and analysis of the anisotropic behavior of materials according to various reinforcing agents of engineering plastic. In addition, engineering plastics are manufactured through injection models, and since the cage requires a complicated shape with high precision, the injection method for the bearing cage was analyzed as a finite element to identify factors affecting the surface, and various problems such as deformation, bending, torsion, etc. [24]. However, commonly used friction materials for bearing supports are insufficient to deal with friction material wear and damage caused by the cumulative travel distance and dynamic behavior of bearing supports depending on increased vehicle speed and traffic. Therefore, a demand of a new friction material with high compressive force, durability, and safety has increased. Ceramic can be applied as a substitute for the problems of the aforementioned materials. Ceramic has seen its application expanded to various fields. Notably, numerous studies tried to apply ceramic to bridge structures including railway bridges for its characteristics such as small deformation and semi-permanency. Accordingly, dedicated structural materials, elements, and construction methods are rapidly developing [25]. Improving the friction material is thus necessary. One promising improvement method is to use a ceramic material instead of PTFE as friction material in the bearing supports. The ceramic material develops high hardness, strength, and low friction coefficient by adjusting the surface illuminance. In addition, the ceramic hardly deforms even if it is used for a long time. However, there is no research case applying a friction material as ceramic for a bearing support yet. As a preliminary step prior to the implementation of actual friction test, this study applies for the very first time ceramic as friction material and evaluates the frictional behavior of the bearing support using finite element analysis (FEA). Comparing the two materials, that are steel and ceramic, in contact inside the loaded bearing support, steel will experience larger deformation than ceramic because ceramic materials have stronger hardness than steel. Therefore, various shapes of the ceramic friction material are analyzed to identify the shape minimizing the deformation of the two materials while providing stable frictional behavior.
## 4 Model Development
### Elapsed Time Threshold Determination  
As in Figures 1 and 2, the elapsed time threshold should vary with the time of day and day of the week. To distinguish interchange trips from metro to bus, a two-stage method is proposed to determine a dynamic elapsed time threshold between metro alighting and bus boarding with consideration of station layout, spatial restrictions, and time periods.
## 3 Research Progress on Adsorption and Separation of Hydrocarbon Molecules
### Aromatic Hydrocarbons
#### 3.2.3 Polymer Adsorption Technology  
Polymer adsorption technology has been widely used for the adsorptive separation of gaseous hydrocarbons for decades benefiting from the proper interaction between polymer and adsorbate [64; 65]. A high specific surface area is necessary for this type of material due to its dependence on capillarity. The adsorption occurs not only on the polymer surface, but also inside the polymer. Therefore, it is necessary to prevent polymer swelling by making the polymer into a three-dimensional network cross-linked structure [66]. [PERSON] [67] discussed the performance of \"Petrogel\" polyethylene-based hydrophobic adsorbent on adsorbing aromatic hydrocarbons in water. They found that materials with high density, high melting temperature, and high crystallinity tended to have lower adsorption capacity. Meanwhile, oil products with lower molecular weight such as toluene and diesel show high adsorption capacity compared to ANS crude oil with larger molecular weight. A-6-P with a more branched chain, low density, low melting temperature, and low crystallinity could adsorb more than 17 times its weight in toluene, 7.6 times in diesel oil, and 2.5 times in ANS crude oil. Non-crystalline x-D-DVB also shows higher toluene adsorption capacity compared to crystalline LLDPE. On this basis, [PERSON] [68] obtained \"i-Petrogel\" by mixing a semi-crystalline linear low-density polyethylene (LLDPE) thermoplastic and a thermally cross-linked polyethylene (1-decane) ene-co-divinylbenzene). This absorbent  
Figure 12: Desorption results of different aromatics by MTMS-aerogel [58] (Methanol:water = 70:30, room temperature, 307 h contact).  
shows high adsorption capacity (40 times its weight), high selectivity, and high adsorption rate for crude oil from low-temperature frozen soil.
## 1 Introduction
[16] built a refined mathematical model for a star-gearing reducer in consideration of different mesh stiffness and supporting stiffness for components and analyzed the load-sharing characteristics of the planet gear under the influence of interactions and various component errors. [PERSON] et al. [17] presented a nonlinear dynamic model of translation-torsion for a single-stage planetary gear train with three planet gears and analyzed the load-sharing characteristics of the planet gear under support stiffness, manufacture and assembly errors. [PERSON] et al. [18] studied the fatigue life of a sun gear in a wind turbine gearbox based on the fatigue-life prediction model of components under stochastic loading. [PERSON] et al. [19] established a pure-torsion coupling, nonlinear dynamic model of a Million Watt (MW)-level wind power generator, and researched the response of angular displacement, angular speed and gearing force of torsional vibration of the components in the transmission train system under random wind load. [PERSON] et al. [20] studied the effects of increasing torque and changing rotation direction of the planetary gearbox on the dynamic load-sharing among the planet gears. All these researches had important effects on the planetary gear transmission system but targeted other transmission types and focused on different fields, most of them are not involving the study of load-sharing characteristics of a multistage planetary gear transmission system under random wind load. In this study, a transmission scheme of a multistage gear train composed of a two-stage planetary gear train and a one-stage parallel-axis gear is the study objective, and the dynamic model is established on the basis of lumped parameter theory. By using a numerical method, the dynamic load-sharing coefficients of each gear pair in the same meshing period of the planetary gear transmission system are analyzed under the interaction of time-varying input speed and internal excitation.
## 2 Materials and Methods
### Nutrient Utilisation  
The ileal digesta samples underwent freeze-drying using a Christ Alpha 1-4 LD plus freeze dryer (Osterode am Harz, Germany). Both the dried ileal digesta samples and the feed samples were ground to a particle size of \(\leq\)0.5 mm using an ultra-centrifugal mill (Retsch ZM200, Fisher Scientific, Hampton, NH, USA). The concentration of titanium dioxide was determined in both the feed and ileal samples using the colorimetric method outlined by [PERSON] et al. (2018), measured on a UV spectrophotometer. The nitrogen concentration in both the digesta and feed samples was assessed using a nitrogen analyser (LECO Corporation, St Joseph, MI, USA) with EDTA as the calibration standard. The CP content of the diet was determined by multiplying the nitrogen value of the diet by 6.25. GE levels in the digesta and feed samples were determined using a Parr adiabatic oxygen bomb calorimeter (Parr Instrument Co., Moline, IL, USA) calibrated with benzoic acid as the standard. The DM content of the feed and freeze-dried ileal digesta was measured by oven drying the samples at 105 \({}^{\circ}\)C for 24 h, which were used to calculate digestibility of the nutrients on a DM basis. The equations below, described by [PERSON] et al. (2019), were used to calculate apparent ileal digestible energy (IDE), the coefficients of ileal digestible nitrogen (IDNC), and the coefficients of ileal digestible energy (IDEC).  
\[\text{IDE}=\text{GE}_{\text{diet}}-\left(\text{GE}_{\text{digesta}}\times \left(\frac{\text{Ti}_{\text{diet}}}{\text{Ti}_{\text{digesta}}}\right)\right)\]  
\[\text{IDNC}=1-\left(\frac{\text{Ti}_{\text{diet}}\times\text{N}_{\text{digesta }}}{\text{Ti}_{\text{digesta}}\times\text{N}_{\text{diet}}}\right)\]  
\[\text{IDEC}=1-\left(\frac{\text{Ti}_{\text{diet}}\times\text{GE}_{\text{digesta }}}{\text{Ti}_{\text{digesta}}\times\text{GE}_{\text{diet}}}\right)\]  
where \(\text{GE}_{\text{diet}}\) and \(\text{GE}_{\text{digesta}}\) represent the GE values of the diets and ileal digesta, respectively. \(\text{Ti}_{\text{diet}}\) and \(\text{Ti}_{\text{digesta}}\) represent titanium dioxide concentrations in the diet and ileal digesta, respectively. N indicates either feed or ileal digesta nitrogen content.  
In this study, we assumed that solely the PM feed was present in the ileal digesta of hens fed the AM/PM diet when the hens were sampled.
# SHPB Experiment and MPM Simulation on Dynamic Properties of Unsaturated Clay Under Impact Loading
## 2 SHPB Test on Unsaturated Clay  
### Specim Preparation  
In this study, the clay samples were collected from Guizhou Province, located in the southwest of China, using a borehole sampling method. After determining the basic physical parameters, the undisturbed soil was dried, crushed, and screened to prepare two sizes of cylindrical remolded specimens with a target water content of 40%. One set of specimens, measuring 39.1 mm in diameter and 80 mm in height, was used for triaxial shear tests to determine the static mechanical parameters. Table 1 presents the obtained physical and mechanical parameters of soil samples. Another set of cylindrical specimens, measuring 50 mm in diameter and 20 mm in thickness, was prepared for the SHPB test, following the method described by [PERSON] et al. [20].
## 4 Quantitative Analysis of the Internal Leakage in a Valve
### Quantification of Internal Leakage Rate Based on Wavelet-BP Neural Network
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Network Structure** & **MAE** & **RMSE** & **R** \\ \hline \(1,1,1\) & 0.461136 & 0.635044 & 0.989873 \\ \(1,1,2\) & 0.535604 & 0.642995 & 0.992614 \\ \(1,1,3\) & 0.530611 & 0.621348 & 0.992587 \\ \(1,1,4\) & 0.490249 & 0.611622 & 0.990989 \\ \(1,1,5\) & 0.572641 & 0.700308 & 0.986745 \\ \(1,1,6\) & 0.559857 & 0.699123 & 0.988644 \\ \(1,1,7\) & 0.785279 & 1.035246 & 0.964303 \\ \(1,1,8\) & 0.737673 & 0.888437 & 0.977561 \\ \(1,1,9\) & 0.738591 & 0.951983 & 0.971771 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Scores of model evaluation indexes with three hidden layers at different numbers of neurons. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Neural Network Structure** & **MAE** & **RMSE** & **R** \\ \hline \(1,1\) & 0.502898 & 0.621259 & 0.992559 \\ \(1,2\) & 0.60585 & 0.749687 & 0.989804 \\ \(1,3\) & 0.53966 & 0.663171 & 0.991061 \\ \(1,4\) & 0.579667 & 0.718581 & 0.988168 \\ \(1,5\) & 0.543189 & 0.662200 & 0.990392 \\ \(1,6\) & 0.747555 & 0.967281 & 0.973799 \\ \(1,7\) & 0.585091 & 0.741581 & 0.984737 \\ \(1,8\) & 0.711796 & 0.903873 & 0.974746 \\ \(1,9\) & 0.705377 & 0.929872 & 0.974641 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Scores of model evaluation indexes with double hidden layers at different numbers of neurons. the best, thus 0.992614. Comprehensively analyzed, when the third hidden layer of the model contains four neurons, the optimal structure of the model can be expressed as 3-1-1-1-4-1. In this structure, the scores of the three evaluation indexes are 0.490249, 0.611622 and 0.990989, respectively, which is weaker than the 3-1-1 structure and better than the 3-1-1-1 structure. Using the same method, the index scores of the model with four and five hidden layers were calculated in turn. The optimal structural models correspondingly were concluded. The optimal scores of each index of the models after the optimal structure of each hidden layer was obtained are shown in Figure 5.  
It can be seen from Figure 5 that as the number of hidden layers of the model increases, the scores of MAE and RMSE show an upward trend, while R shows a downward trend, indicating that the performance of the model is weakening. Therefore, when the model contains only one hidden layer, the structure of the model is optimal. It can be obtained that the optimal structure of the BP neural network model for quantifying the valve leakage rate is 3-1-1, as shown in Figure 6. In the optimal structure, the input layer contains three neurons, including only one hidden layer with only one neuron and the output layer contains one neuron. Figure 5: Evaluation indexes of models with different hidden layers: (**a**) MAE varies with the number of hidden layers; (**b**) RMSE varies with the number of hidden layers and (**c**) R varies with the number of hidden layers.