esa-sceva/satcom-chunk-collection huggingface dataset:
# Influences of High-Speed Train Speed on Tunnel Aerodynamic Pressures
## 2 Numerical Simulation  
### Governing Equations  
The flow field, produced by an HSR train travelling through the tunnel, is three-dimensional, viscous, compressible, and turbulent [20; 21; 22; 23]. Computational fluid dynamics is commonly used to solve the problems involving fluid flows. The key governing equations in fluid dynamics are continuity equations, momentum equations, and energy equations.  
Continuity equations:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})=0 \tag{1}\]  
where \(\rho\) and \(t\) are air density and time, respectively. \(u_{i}\) is air speed of \(x_{i}\)-axis. The subscript \(i\) denotes the \(i\)th axis of the Cartesian coordinate.  
Momentum equations:  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial \tau_{ij}}{\partial x_{j}}+\rho g_{i} \tag{2}\]  
where \(p\) is air static pressure. \(g_{i}\) is gravitational acceleration of \(x_{i}\)-axis. \(\tau_{ij}\) is the stress tensor, and can be expressed as follows [24]:  
\[\tau_{ij}=\left[\mu\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\right)\right]-\frac{2}{3}\mu\frac{\partial u_{k}}{ \partial x_{k}}\delta_{ij} \tag{3}\]  
where \(\mu\) is molecular viscosity. \(\delta_{ij}\) is the kronecker symbol. The definitions of the subscripts \(j\) and \(k\) are similar to \(i\).  
Energy equations:  
\[\frac{\partial(\rho T)}{\partial t}+\
abla\cdot(\rho uT)=\
abla\cdot\left(\frac{k}{ c_{p}}\
abla(T)\right)+S_{T} \tag{4}\]  
where \(T\) is temperature. \(k\) and \(c_{p}\) are thermo-conductivity and specific heat capacity of fluid, respectively. \(S_{T}\) is a viscous dissipative term [25].  
The airflows around the train inside the tunnel are considered compressible, so the state equation of ideal gas is satisfied. That is:  
\[p=\rho RT \tag{5}\]  
where \(R\) is gas constant.
## 3 Results
### Data Analyses  
All results demonstrate extremely high effect sizes, reflecting significant differences between pre- and post-intervention outcomes.  
* PAIN: Effect sizes such as 10.425 for [PERSON] [26] and 9.615 for [PERSON] [15] indicate a massive impact of the intervention on pain reduction. Even the lowest values (e.g., 4.152 for [PERSON] [20]) are far above the thresholds for large effects (0.8).
* ODI: Values of 5.205 for [PERSON] [11] and 2.413 for [PERSON] [13] confirm a substantial improvement in physical functioning, as measured by the Oswestry Disability Index (ODI).
* CRP: Effect sizes ranging from 1.149 to 3.505 (for [PERSON], [PERSON], and [PERSON]) demonstrate significant reductions in inflammatory markers, underscoring the intervention's effectiveness in controlling inflammation.  
Most results show a power of 1.000, indicating virtually zero probability of a Type II error (failing to detect a true effect). The only exception is [PERSON] [13] (CRP), with a power of 0.977, which is still highly acceptable. The pooled standard deviations, ranging from 0.728 ([PERSON] [15]) to 11.344 ([PERSON] [11], ODI), reflect variability within the groups. Lower SD values (e.g., PAIN for Duan) indicate more precise data. These results highlight sig  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline [PERSON] [11] & ODI (pre-op vs post-op) & 11.344 & 5.205 & 1.000 \\ [PERSON] [13] & ODI (pre-op vs. post-op) & 5.179 & 2.413 & 1.000 \\ [PERSON] [11] & PAIN (pre-op vs. post-op) & 1.003 & 6.603 & 1.000 \\ [PERSON] [13] & PAIN (pre-op vs. post-op) & 1.061 & 6.411 & 1.000 \\ [PERSON] [15] & PAIN (pre-op vs. post-op) & 0.728 & 9.615 & 1.000 \\ [PERSON] [20] & PAIN (pre-op vs. post-op) & 1.204 & 4.152 & 1.000 \\ [PERSON] [24] & PAIN (pre-op vs. post-op) & 1.000 & 5.790 & 1.000 \\ [PERSON] [26] & PAIN (pre-op vs. post-op) & 0.823 & 10.425 & 1.000 \\ [PERSON] [11] & CRP (pre-op vs. post-op) & 3.930 & 1.460 & 1.000 \\ [PERSON] [13] & CRP (pre-op vs. post-op) & 6.005 & 1.149 & 0.977 \\ [PERSON] [20] & CRP (pre-op vs. post-op) & 2.405 & 3.505 & 1.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of results for ODI, PAIN, and CRP (pre-op and post-op).  
nificant clinical benefits in reducing pain, improving physical functioning, and reducing inflammatory markers. Such large effect sizes indicate the high effectiveness of the methods used, while the high power of the study confirms the reliability of these findings.
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 4 Experimental Evaluations
### Impact on Various Parameter Settings  
There were three parameters in the methodology that we focused on: amount of recent data (\(r\)), similar data (\(s\)) and adjacent devices (\(n\)) were detected in the forecast. In the following, we divide these parameter experiments into two sub-sections. In Section 4.2.1, the experiment is used to determine temporal parameters \(r\) and \(s\), and Section 4.2.2 shows an experiment for spatial parameters.
# Development of Folded Expanded Metal Mesh with Sound Absorption Performance
## 4 Field Validation of FEMM
### Acoustic Index  
Room acoustical parameters are commonly used to evaluate or predict the acoustical performance in rooms. For instance, the distribution of sound energy in a space can be obtained by measuring the sound pressure level (SPL). Furthermore, the comfort and quality of user experience can be obtained through reverberation time (RT) and speech clarity (C\({}_{50}\)).  
RT, the time for sound energy to fade away or decay in a closed space, is defined as the time it takes for sound to decay by 60 dB and was also written as T\({}_{60}\). However, accurately measuring T\({}_{60}\) is difficult. Therefore, it is often common to measure T\({}_{20}\) and T\({}_{30}\) and then multiply these by 3 and 2, respectively, to obtain the overall T\({}_{60}\). Speech clarity (C\({}_{50}\)) is the ratio of early-to-late arriving sound energy ratio. When C\({}_{50}>0\), early sound energy dominates the sound field and satisfies basic speech intelligibility. In general, C\({}_{50}\) has a high relation with RT, where the lower the RT, the better the C\({}_{50}\). In this paper, these acoustical parameters are introduced and given by Equations (2)-(5).  
\[SPL=10\log{(\frac{p}{p_{0}})}^{2} \tag{2}\]  
\[p_{0}=2\times 10^{-5} \tag{3}\]  
where \(SPL\) is the sound pressure level (dB); \(p\) the instantaneous sound pressure of the impulse response measured at the measurement point (Pa); and \(p_{0}\) is the basic sound pressure (Pa).  
\[T=\frac{0.161V}{S\overline{\pi}} \tag{4}\]  
where \(T\) is the reverberation time (s); \(V\) is the volume of the room (m\({}^{3}\)); \(S\) is the total surface area of the room; and \(\overline{\pi}\) is the average sound-absorption coefficient of materials in the room.  
\[C_{50}=10\lg{\frac{\int_{0}^{50}p^{2}(t)d_{t}}{\int_{50}^{50}p^{2}(t)d_{t}}}\ dB \tag{5}\]  
where \(C_{50}\) is the early-to-late index (dB); and \(p(t)\) is the instantaneous sound pressure of the impulse response measured at the measurement point (Pa).
# Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes
## 2 Materials and Methods
### Single-Train Single-Interval Energy-Consumption Model  
To reduce the complexity of the model, only the traction force, basic resistance, additional resistance (including ramp resistance and curve resistance), and braking force are considered in this paper. According to the Train Traction Calculation Regulations [12], trains of different models have different traction and braking forces, and the standard traction and braking forces are obtained by fitting the data of a certain model train as follows [13]:  
\[F_{t}=\left\{\begin{array}{l}203\ 0\leq v\leq 51.5\ \mathrm{km/h}\\ -0.002032v^{3}+0.4928v^{2}-42.13v+1314\ 51.5\leq v\leq 80\ \mathrm{km/h}\end{array}\right. \tag{1}\]  
\[F_{b}=\left\{\begin{array}{l}166\ 0\leq v\leq 77\ \mathrm{km/h}\\ 0.1343v^{2}-25.07v+1300\ 77<v\leq 80\ \mathrm{km/h}\end{array}\right. \tag{2}\]  
where \(F_{t}\) is the traction force (kN), \(F_{b}\) is the braking force (kN), and \(v\) is the real-time speed of the train (km/h).  
The formula for calculating the resistance of the train during operation is as follows:  
\[F_{r}=F_{bas}+F_{add} \tag{3}\]  
\[F_{bas}=\frac{w_{0}\times M\times g}{1000} \tag{4}\]  
\[w_{0}=a+bv+cv^{2} \tag{5}\]  
\[F_{add}=\frac{M\times g\times(F_{ram}+F_{cur}+F_{tun})}{1000} \tag{6}\]  
\[F_{ram}=i=\frac{h}{l}\times 100\% \tag{7}\]  
\[F_{cur}=600/R \tag{8}\]  
\[F_{tun}=0.00013L_{tun} \tag{9}\]  
where \(F_{r}\), \(F_{bas}\), \(F_{add}\) are the total resistance, basic resistance, and additional resistance of the train operation, respectively; \(w_{0}\) is the basic resistance per unit mass of train (N/kN); \(M\) is the total mass of train (kg), \(g=9.8\ \mathrm{m/s^{2}}\). \(a\), \(b\), \(c\) are the constants related to the structure determined by the test; \(F_{ram}\), \(F_{cur}\), \(F_{tun}\) are the grade resistance, curve resistance, and tunnel resistance of train operation, respectively; \(i\), \(h\), \(l\), \(R\), \(L_{tun}\) are the unit grade resistance, elevation difference, distance, curve radius, and tunnel length, respectively.  
The differential equation model for single-train operation with the energy-consumption model was obtained by combining the above analyses as follows:  
\[\frac{dv}{dt}=\frac{xF_{t}-yF_{b}-F_{r}}{M} \tag{10}\]  
\[\frac{ds}{dt}=v \tag{11}\]  
\[E_{s}=\frac{\alpha}{\prod\eta}\frac{dvds}{dtdt}\times Mdt \tag{12}\]Equation (10) indicates the acceleration of train operation, \(x\), \(y\) is a 0-1 variable that varies according to the operating state, traction state \(x=1,y=0\); inert state \(x=0,y=0\); braking state \(x=0,y=1\). Equation (11) indicates the speed of train operation. Equation (12) indicates the energy consumption of a single train in a single zone, \(\alpha\), \(\prod\eta\) are the percentage of traction requested by the automatic train operation (ATO) system and the product of energy transfer efficiency of each motor of the train, respectively.
## 2 Log Likelihood Spectral Distance Measure
The challenge with the log likelihood ratio has always been that the interpretation of the value of \(d\) when it is less than the perceptually significant threshold and greater than 0.3 is not evident. In the range 0.3 \(<d\) < 0.9, the log likelihood was simply interpreted to be an indicator that the spectral match was less than perfect, but _perhaps_ acceptable perceptually. It appears that the lack of clarity as to the perceptual meaning of the possible values taken on by \(d\) caused it to lose favor as a useful indicator of performance in various applications. In the sections that follow, we introduce two new additional expressions for \(d\) that allow us to better interpret the log likelihood ratio when it is less than the perceptually significant threshold of 0.9. These new interpretations admit a new modeling analysis that we show relates directly to speech codec design.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 5 Methods for Improving Photocatalytic Efficiency
### Semiconductor Compound
Experimental results showed that CdS and Pt have a synergistic effect in which CdS can attract holes to form an active center and Pt can promote photogenerated electron transfer. Therefore, TiO\({}_{2}\) co-doped with CdS and Pt has a significant improvement in photon capture rate and carrier separation efficiency. [PERSON] et al. [274] used a hydrothermal method to prepare a multilayer semiconductor composite TiO\({}_{2}\) nanotube array. Under the irradiation of solar light, the composite TiO\({}_{2}\) nanotube can completely degrade p-nitropenol and rhodamine B in 20 min and 80 min, respectively.
## 3 Triangular Impulse Response Function (TIRF)  
The sloshing impact load in the membrane-type LNG tank due to the motion of the ship should be a typical irregular impact load with various rise times/impact peak loads/duration times. The corresponding dynamic structural responses are also complicated. Moreover, a time-consuming process is required to deal with the enormous amount of pressure data from the model test and/or simulation results depending on the size of the LNG cargo tank and design environmental conditions. Therefore, the linear superposition approach could be applied to evaluate the dynamic structural assessment for computational efficiency [8; 9; 14].  
In a linear and time-invariant system, its response \(y(t)\) for any input \(x(t)\) can be expressed by an impulse response function (IRF) based on a convolution equation as follows:  
\[y(t)=x(t)\times h(t)=\int_{-\infty}^{+\infty}x(t)h(t-\tau)d\tau \tag{7}\]  
where \(h(t)\) is the impulse response of the system at the origin in time. When the convolution equation in Equation (7) is applied to the LNG cargo containment system, \(y(t)\) is the response of the box, i.e., the stress component, force, moment, and displacement at critical locations; \(x(t)\) represents the sloshing load acting on the box; and \(h(t)\) is the dynamic response calculated at the critical location when a unit impulse is applied to the box.  
[PERSON] and [PERSON] and [PERSON] et al. [8; 14] applied a discrete form of an IRF with a triangular impulse response to calculate the efficiency. The TIRF can be defined as the time history of the structural response to a symmetric triangular impulse with a short duration, from which triangular impulses with durations and rise times that are integer multiples of the short duration can be generated. Equation (8) is the discrete form with the TIRF of Equation (7).  
\[y(t)=\sum_{i=1}^{n}x_{i}\cdot H_{i}(t) \tag{8}\]  
\[S_{i}(t)=\left[\begin{array}{ll}\frac{1}{\Delta t}(t-t_{i-1})&for\ \ t_{i-1}\leq t<t_{i}\\ 1-\frac{1}{\Delta t}(t-t_{i})&for\ t_{i}\leq t<t_{i+1}\\ 0&elsewhere\end{array}\right. \tag{9}\]  
where \(x_{i}\) is the sloshing load at \(t=t_{i}\cdot H_{i}(t)\) is the TIRF for the unit triangular impulse \(S_{i}(t)\) in Equation (9) and Figure 1. \(\Delta t\) is the size of the time increment. The calculation process for Equation (8) is described in Figure 2.  
Figure 1: Unit triangular impulse.
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
## 3 Methodology
### LAPGWO Algorithm
#### 3.1.3 Improvement and Optimization Based on the Artificial Potential Field Method  
By incorporating logistic chaotic mapping, the improved GWO algorithm can evenly distribute the population on the initial map, reducing the likelihood of missing the global optimal solution. The new position update also avoids premature convergence, enhancing the algorithm's global exploration ability in the initial stage. However, when faced with large-scale scenarios, problems such as long calculation time and slow convergence still exist.  
To address this, this paper aims to further improve the convergence speed of the algorithm based on the introduction of the logistic chaotic mapping to adapt to the path planning of multiple mobile robots in large-scale scenarios. The formulas for the gravitational field of the new potential field and the environmental repulsive field are as follows:  
\[F_{att-global}=\zeta\cdot\frac{X_{p}-X_{i}}{||X_{p}-X_{i}||^{2}} \tag{12}\]  
\[F_{rep-global}=\sum_{j=1}^{m}\rho(t)\cdot(\frac{1}{||X_{i}-O_{j}||}-\frac{1}{ d_{o}})\cdot\frac{X_{i}-O_{j}}{||X_{i}-O_{j}||^{3}} \tag{13}\]  
Among them, \(F_{att-global}\) is the global attractive field, \(F_{rep-global}\) is the international environmental repulsive field, \(\zeta\) is the gravitational gain coefficient, \(X_{p}\) is the position of the target point, \(X_{i}\) is the current position of the grey wolf, \(\rho(t)\) is the dynamic repulsive gain coefficient, \(d_{o}\) is the maximum effective radius of the obstacle repulsive force, and \(d_{o}\) is the position of the \(j\)th obstacle.  
The resultant force \(F_{APF-global}\) of the potential field is as follows:  
\[F_{APF-global}=F_{\textit{{{rrep}}}}+F_{\textit{{att}}} \tag{14}\]  
Substitute formula \(\backslash(\kappa\backslash)\) into formula \(\backslash(\kappa\backslash)\) to obtain the final optimized formula, as follows:  
\[X_{(i+1)}=\frac{X_{1}(i)+X_{2}(i)+X_{3}(i)}{3}+\eta\cdot X_{c}^{(i)}+\lambda \cdot F_{APF-global} \tag{15}\]  
Among them, \(\lambda\) is the adaptive potential field weight, which decays exponentially with the number of iterations. In the initial stage, it enhances the global search ability, and in the later stage, it focuses on the local search.
## 2 Materials and Methods
### Cyclic/Fatigue Fracture Tests  
The 3 PB tests were also carried out to determine the basic fatigue characteristics of the investigated composite with an alkali-activated slag matrix. The central edge notch with a nominal depth of about 1/10 of the specimen's height was made by a diamond blade saw. The span length was set to 120 mm. The specimens were tested at different ages of their hardening, namely between the age of 140 and 160 days. The cyclic fracture tests were conducted using the servo-hydraulic testing machine Zwick/Roell Amsler HC25 with the load range of 0\(-\)25 kN (Figure 6a). An arrangement of a 3 PB cyclic fracture test configuration is shown in Figure 6b. The loading process was governed by a force; the force amplitude was controlled. The stress ratio \(R=F_{\text{Fmin}}/F_{\text{Fmax}}=0.1\), where \(F_{\text{Fmin}}\) and \(F_{\text{Fmax}}\) refer to the minimum and maximum load of a sinusoidal wave in each cycle. The load frequency was set to 10 Hz. The number of cycles before failure was recorded for each specimen. The specimens were loaded in the range of high-cycle fatigue from \(10^{3}\) to \(10^{6}\) number of cycles. Therefore, the upper limit to the number of cycles \(N\) to be applied was selected as 2 million cycles. The test was finished when the failure of the specimen occurred or the upper limit of loading cycles was reached, whichever occurred first.  
The results of the cyclic fracture test are presented in the form of an empirically derived \(S_{F}-N\) curve, which is known as the Wohler curve [4]:  
\[S_{F}=a\cdot N^{b} \tag{22}\]  
where \(S_{F}\) is the maximum stress in 3 PB, \(N\) is the number of cycles, and \(a\) and \(b\) are material constants. The maximum stress \(S_{F}\) is calculated as follows:  
Figure 6: The testing machine used for cyclic/fatigue fracture test (**a**); the arrangement of test (**b**).  
\[S_{F}=\frac{3F_{\rm{Fmax}}S}{2B(W-a_{0})^{2}}. \tag{23}\]  
The parameters of the \(S_{\rm{F}}\)-N curve are determined only for test specimens that were broken during the cyclic test, and the specimens that withstand 2 million cycles are not taken into consideration. The fatigue limit was determined as the highest stress level at which three test specimens withstand 2 million cycles.
# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 2 Methods
### Improvement Method  
#### 2.3.1 Overall Framework  
Given the small scale of FOD targets, the utilization of visible light imagery for detection introduces difficulties in discriminating the target from background clutter. For example, it is difficult to distinguish between metallic (nuts, screws) foreign objects and cement concrete backgrounds with similar color characteristics. Particularly in nighttime or dim conditions, the efficacy of visible light imaging is severely compromised. Numerous established and widely utilized object detection algorithms are not directly transferrable for the task of identifying foreign objects on airport runways. Therefore, we introduce infrared images to compensate for the shortcomings of visible light images. However, the fusion between infrared and visible light images can be challenging and requires careful design to effectively enhance the feature information of targets. On the contrary, such approaches may introduce interference that obscures small targets, precluding the network from sufficiently focusing on subtle features. The proposed methodology is illustrated in Figure 3, which outlines the overall framework. Firstly, image fusion of infrared and visible modalities is not implemented during preliminary data processing. Instead, both  
Figure 2: Structural diagram of benchmark network.  
infrared and visible images are input simultaneously in the network's input layer. This enables the preservation of more low-level small target information at the input level. Then, we introduce our multiple attention module (represented by MAM in the diagram), which is designed for small targets in the benchmark network and uses multiple attention mechanisms to enhance the extraction of small target feature information. Finally, the bounding box regression loss function is designed as CIOU loss to improve the positioning accuracy of small targets.
## 4 Results and Discussion
### Accuracy Assessment  
The algorithm for individual tree matching (Section 3.6.3) achieved an overall accuracy of 89.1% and a [PERSON]'s kappa of 0.78 using the synthetic validation datasets (Table 4). The number of matching trees tended to be slightly underestimated, which resulted in a reduced producer's accuracy.  
The proposed co-registration method achieved an overall accuracy of 82.7% and a [PERSON]'s kappa of 0.70 using the synthetic validation datasets (Table 5).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Attribute**} & \multicolumn{3}{c}{**Overall**} & \multicolumn{3}{c}{**On Plot Level**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Characteristic Values**} & \multicolumn{1}{c}{**Two Sample**} & \multirow{2}{*}{**F-Test**} & \multirow{2}{*}{**Pearson’s**} & \multirow{2}{*}{**Paired**} \\  & **(Minimum, Mean, Maximum)** & & & & & **Wilcoxon** \\  & & & & & **Wilcoxon** \\  & & & & ** coefficient** & **Test** \\ \hline \hline \multirow{2}{*}{\begin{tabular}{l} Amount of \\ Surveyed Trees \\ \end{tabular} } & \(0_{min}=2\); \(\overline{\mathcal{O}}=7.6\); \(O_{max}=16\) & \(\overline{S}=\overline{\mathcal{O}}\) & \(\sigma_{S}=\sigma_{O}\) & 0.98 & 0.69 & \(\overline{S-O}>0\) \\  & \(S_{min}=2\); \(\overline{S}=7.6\); \(S_{max}=22\) & (\(p=0.66\)) & (\(p=0.47\)) & & & (\(p=0.00\)) \\ \hline \hline \multirow{2}{*}{\
\begin{tabular}{l} Amount of \\ Detected Trees \\ \end{tabular} } & \(0_{min}=13\); \(\overline{\mathcal{O}}=46.9\); \(O_{max}=83\) & \(\overline{S}=\overline{\mathcal{O}}\) & \(\sigma_{S}>\sigma_{O}\) & 0.64 & 15.00 & \(\overline{S-O}=0\) \\  & \(S_{min}=9\); \(\overline{S}=46.9\); \(S_{max}=104\) & (\(p=0.50\)) & (\(p=0.00\)) & & & (\(p=0.74\)) \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{l} Amount of Detected \\ Trees per Survey Tree \\ \end{tabular} } & \(0_{min}=1.6\); \(\overline{\mathcal{O}}=7.9\); \(O_{max}=32\) & \(\overline{S}=\overline{\mathcal{O}}\) & \(\sigma_{S}=\sigma_{O}\) & 0.83 & 3.30 & \(\overline{S-O}=0\) \\  & \(S_{min}=1.1\); \(\overline{S}=7.6\); \(S_{max}=49.5\) & (\(p=0.69\)) & (\(p=0.91\)) & & & (\(p=