esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
The observed _F-statistics_ did not exceed the critical value, _Fc_, and the _p_-values were substantially higher than the significance, \(\alpha\), threshold. This suggests that the variability observed in the DoB for each soil type can be attributed to random variation rather than to inherent differences among the CDFs. Consequently, it can be inferred that the CDFs within each soil type are similar, and any apparent differences are not statistically meaningful. Since these CDFs represented different normal and lognormal distribution combinations of \(\rho,C,R\), it can be deduced that the choice of a distribution does not significantly affect the computed DoB. Figure 4: CDFs of DoB in clayey sand using: (**a**) Restrict Normal Distribution to COV Values. Lower than 30%, (**b**) Restrict Lognormal Distribution to COV Values Lower than 30%, (**c**) Unrestricted Lognormal Distribution, and (**d**) Use of Mixed Normal and Lognormal Distributions. The effect of three sampling techniques (PRS, LHS, and GP_RSM) is presented in Table 4 and Figure 7. The DoB in sand ranged between 0.09 and 0.56 m, while DoBs in clayey sand ranged between 0.21 and 1.14 m, and for clay the DoB ranged from 0.56 to 2.14 m for the same ballistic conditions. The mean values for the DoB in each soil are stated in Table 4.  
Figure 5: CDFs of DoB in clay using: (**a**) Restrict Normal Distribution to COV Values Lower than 30%, (**b**) Restrict Lognormal Distribution to COV Values Lower than 30%, (**c**) Unrestricted Lognormal Distribution, and (**d**) Use of Mixed Normal and Lognormal Distributions. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**F-Statistic**} & \multicolumn{1}{c}{**Fc**} & \multicolumn{1}{c}{**P**} \\ \hline
**Sand** & 0.73 & 1.96 & 0.67 \\ \hline
**Clayey Sand** & 0.19 & 1.96 & 0.99 \\ \hline
**Clay** & 0.42 & 1.96 & 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: One-way ANOVA results. Figure 6: Comparison of CDFs of DoB in (**a**) sand, (**b**) clayey sand, and (**c**) clay using a variety of distributions. Figure 7: CDFs of DoB using different sampling techniques for (**a**) sand, (**b**) clayey sand, and (**c**) clay. The values shown in Table 4 demonstrate the significance of considering uncertainty in the penetration problem, which can range from 19% to 37%. The analysis of the studied soils showed that the COV for the final DoB in sandy soils is the highest, followed by clayey sandy soils, and finally clayey with the lowest COV for the DoB. Run times for various soils and sampling methods are summarized in Table 4, where data were normalized by the effort to sample using PRS. It was noticed that every method consumed nearly the same execution time whatever the soil type was. It was also observed that GP_RSM was consistently the fastest sampling technique, using approximately one-third of the effort consumed by the other two sampling techniques, including the time required to generate the initial samples, while PRS and LHS consumed approximately similar, but longer, run times.
## 3 Experimental Results and Discussion
### Basic Physical Parameters
#### 3.1.4 Comparison between Ordinary Concrete and Basalt Fiber-Reinforced Concrete  
As shown in Tables 3 and 4, the density of basalt fiber-reinforced concrete is higher than ordinary concrete. In addition, as can be seen in Figure 8, although the overall mass loss rate declined, the mass loss rate of basalt fiber-reinforced concrete was lower at every temperature compared with ordinary concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **25 \({}^{\circ}\)C** & **200 \({}^{\circ}\)C** & **400 \({}^{\circ}\)C** & **600 \({}^{\circ}\)C** \\ \hline Ordinary concrete wave velocity & 2243 m/s & 2196 m/s & 2013 m/s & 1997 m/s \\ Basalt fiber-reinforced concrete wave velocity & 2365 m/s & 2226 m/s & 2078 m/s & 2047 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 5: The wave velocity of concretes after heating.  
Figure 6: Ordinary concrete.  
Figure 7: Basalt fiber-reinforced concrete.  
Figure 9 shows, at the same temperature, that the wave velocity of basalt fiber-reinforced concrete is higher than ordinary concrete. Here, the compactness of basalt fiber-reinforced concrete is better than ordinary concrete and the effect of the high temperature on the basalt fiber-reinforced concrete is more slight than ordinary concrete.  
Figures 6 and 7 show that, compared with ordinary concrete, the change of color in basalt fiber-reinforced concrete performed a little hysteresis after heating. In addition, during heating, the number of cracks of basalt fiber-reinforced concrete increased more than ordinary concrete.  
In this research, ordinary concrete (PC) and basalt fiber-reinforced concrete (fiber volume parameter is 0.2%), with a diameter of \(\mathcal{O}\)75 mm and height 100 mm, were heated to 200, 400, 600, and 800 \({}^{\circ}\)C, respectively, to carry out a static mechanical contrast experiment and compare it with the static compressive strength of concrete at room temperature, and carry out a comparative study on its broken form. This paper will analyze and discuss the strength variation trend of basalt fiber in concrete under the action of high temperatures, and briefly describes the strengthening mechanism of basalt fiber on concrete strength. At the same time, two cooling methods were adopted to analyze the influence of different cooling methods on the concrete strength under high-temperature conditions.
## 3 Attempts at Model Interpretation
### DNN Model Interpretation  
The XAI community does of course acknowledge that the behavior of dNNs (how they create an output from a given input) can not be causally explained. It recognizes that the ideal 'comprehensive explanation [which] would extract the whole causal chain from input to output' is'so far not available'. Rather, they seek'reduced forms of explanation' such as a 'collection of scores indicating the importance of each input pixel/feature for the prediction' [20], so that explanation is in effect replaced by interpretation. Other XAI authors define an 'explanation model as any interpretable approximation of the original model' [21], which is to say that explanation and interpretation are used as synonyms. Consequently, [PERSON] asks the community to'stop explaining black-box machine learning models for high stakes decisions and use interpretable models instead' [7].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Type** & **Approach** & **Section/Equation** & **Significance** \\ \hline Classical model & Z score & Equation (3) & Positive Z scores indicate that the independent variables contribute to the outcome. \\  & Gini importance & Section 3.1 & Rough understanding of a variable’s contribution to the model’s prediction output \\ dNN local & approximation function & Equation (4) & lower interrater reliability than Z-score \\ dNN & basis-changing & Equation (5) & Feature visualization \\ global & inverse functional & & \\  & heatmap clustering & Section 3.2.2 & Improved feature visualization \\  & concept-labelling & Equation (6) & Hermeneutic interpretability \\  & activation atlases & Section 3.2.2 & High-quality intuitive interpretation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview: Types of interpretations of models.  
There are two approaches to creating an interpretation of this type, involving local and global models, respectively [22]. We review what we believe are the most important examples of these approaches in order to identify the limitations of dNN interpretation in general, without however claiming completeness.
## 3 Methodology  
The technical roadmap of this study is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Number of Pixels** & **Observations** & **Mean** & **Standard Deviations** & **Minimum** & **Maximum** \\ \hline GPP2010 & 13,555,431(4523, 2997) & 736 & 0.013162 & 0.018770 & 0.00000 & 0.101799 \\ \hline GPP2015 & 13,555,431(4523, 2997) & 720 & 0.012964 & 0.018558 & 0.000000 & 0.101599 \\ \hline GPP2020 & 13,555,431(4523, 2997) & 736 & 0.012224 & 0.017662 & 0.000000 & 0.103699 \\ \hline LAI2010 & 8,175,867(4417, 1851) & 736 & 0.686858 & 1.284290 & 0.000000 & 7.000000 \\ \hline LAI2015 & 8,175,867(4417, 1851) & 736 & 0.752034 & 1.449256 & 0.000000 & 7.000000 \\ \hline LAI2020 & 8,175,867(4417, 1851) & 736 & 0.721788 & 1.423109 & 0.000000 & 7.000000 \\ \hline LST2010 & 1,968,544(2168, 908) & 736 & 45.612089 & 9.433674 & 0.000000 & 69.000000 \\ \hline LST2015 & 1,968,544(2168, 908) & 736 & 45.612089 & 9.433674 & 0.000000 & 69.000000 \\ \hline LST2020 & 1,968,544(2168, 908) & 736 & 45.606350 & 9.438040 & 0.000000 & 69.000000 \\ \hline NDVI2010 & 3,390,738(2262, 1499) & 368 & 0.271135 & 0.240782 & \(-\)0.183699 & 0.998699 \\ \hline NDVI2015 & 3,390,738(2262, 1499) & 368 & 0.275488 & 0.247503 & \(-\)0.163000 & 0.996899 \\ \hline NDVI2020 & 3,390,738(2262, 1499) & 368 & 0.269964 & 0.242245 & \(-\)0.188199 & 0.999399 \\ \hline WET2010 & 8,774,109(4577, 1917) & 736 & 0.079296 & 0.196396 & \(-\)0.607913 & 0.889902 \\ \hline WET2015 & 8,774,109(4577, 1917) & 736 & 0.004665 & 0.235381 & \(-\)0.997439 & 0.912903 \\ \hline WET2020 & 8,774,109(4577, 1917) & 736 & 0.042862 & 0.197213 & \(-\)0.863188 & 0.945436 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical table of indicators.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Life Expectancy (Years)** \\ \cline{2-4}
**Cities or Autonomous Prefectures along the Belt and Road** & **2010** & **2015** & **2020** \\ \hline Xi’an City & 75.86 & 76.27 & 80.86 \\ \hline Baoji City & 72.12 & 75.00 & 77.88 \\ \hline Xianyang City & 72.00 & 74.50 & 77.00 \\ \hline Lanzhou City & 72.54 & 73.50 & 74.50 \\ \hline Jiayuguan City & 77.79 & 77.99 & 78.19 \\ \hline Baiyin City & 72.84 & 74.00 & 75.16 \\ \hline Wuwei City & 71.34 & 73.50 & 75.66 \\ \hline Zhangye City & 70.41 & 72.30 & 74.20 \\ \hline Pingliang City & 72.23 & 73.25 & 74.00 \\ \hline Jiuquan City & 72.81 & 74.80 & 75.60 \\ \hline Haibei Tibetan Autonomous Prefecture & 70.41 & 71.33 & 72.24 \\ \hline Urumqi City & 73.70 & 75.80 & 76.40 \\ \hline Turpan City & 71.40 & 73.60 & 74.35 \\ \hline Hami City & 74.00 & 75.00 & 76.00 \\ \hline Changji Hui Autonomous Prefecture & 73.25 & 75.49 & 76.16 \\ \hline Guyuan City & 70.45 & 71.85 & 76.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: LE of cities or states along the Belt and Road.
## 3 Results and Discussion
### Effect of Grip Force  
Figure 12 shows the effect of different grip forces on the vibration transmissibility of the wrist part. As can be seen from Figure 12, the effect of grip force on the vibration transmissibility of the wrist part under six different test conditions is similar to that of the thrust force. The resonance peaks value and peaks frequency of the vibration transmissi  
bility of the wrist part increase with the increase of the grip force, but this changing trend is related to the size of the thrust force; the frequencies of the resonance peaks and peaks value increase significantly with the increase of the grip force (as shown in Figure 12a,d, while at the thrust force increases from 20 N to 50 N and 80 N. The trend of the resonance peak increases with the increase of the grip force and the frequency of the peak tends to be constant (as shown in Figure 12b-f).  
The results of this study are similar to those of study [15] and study [16]; Ref. [15] shows that below 15 Hz, impedance does not change with grip force and the difference appears in the resonance frequency, and [16] shows that within the frequency 5-15 Hz, grip force has almost no effect on vibration transmissibility of the wrist in the \(z\)-axis directions, and above 15 Hz, vibration amplitude and resonance frequency increase with grip force increase. As seen from the six plots in Figure 12, the vibration transmissibility under grip force 30 N and 60 N is basically equal in the low frequency band for the same arm posture and thrust force, which is consistent with the results of the literature [15; 16], but there is some difference in the frequency range where they equal, and the frequency band where the vibration transmissibility of grip force 30 N and 60 N are equal in this study varies from 5-10 Hz to 5-20 Hz. This should be related to the arm posture and the size of the thrust force. In addition, the difference between the vibration transmissibility under grip force 0 N (i.e., with only thrust force) in the low frequency band and that of grip force 30 N and 60 N is significantly larger than that between grip force 30 N and 60 N.
## 3 Results and Discussion  
### Taguchi Analysis Results  
The Taguchi OA process optimization analysis revealed several important findings. The main effect plots for the S/N ratio are shown in Figures 1 and 2. It is clear from the main effect plots that the dimensional accuracy in the horizontal direction is mostly influenced by the nozzle temperature, whereas the accuracy in the vertical direction depends on the layer thickness. The ANOVA analysis results in Tables 6 and 7 show that with a high confidence level of \(p\) = 0.022, the dimensional accuracy in the horizontal direction is influenced by printing temperature, and 110 \({}^{\circ}\)C is the optimal value. As for the influence level of the rest of the factors, the high values of the \(p\) number suggest their small effect on the output.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **I** & **II** & **III** & **S/N Ratio for Height** & **S/N Ratio for Width** \\ \hline
1 & 105 & 0.2 & 40 & 17.07 & 22.69 \\
2 & 105 & 0.25 & 50 & 11.05 & 15.22 \\
3 & 105 & 0.3 & 60 & 16.28 & 15.05 \\
4 & 110 & 0.2 & 50 & 14.73 & 31.48 \\
5 & 110 & 0.25 & 60 & 13.7 & 35.56 \\
6 & 110 & 0.3 & 40 & 13.15 & 32.64 \\
7 & 115 & 0.2 & 60 & 19.17 & 18.41 \\
8 & 115 & 0.25 & 40 & 13.28 & 12.64 \\
9 & 115 & 0.3 & 50 & 20 & 9.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculated S/N ratio values.
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
## 5 Parametric Study
### Flexure in Girders Due to Live Load
#### 5.1.2 Effect of Deck Slab Thickness  
The influence of slab thickness on girder splayedness is examined in this study by using a 33.3% lower and 33.3% higher value of the concrete deck slab thickness compared with reference bridge B1. Bridges B6 and B7 are identical to bridge B1, except for their slab thickness being 150 mm and 300 mm, respectively. The finite element models for the considered bridges showed that for the interior girders, increasing the 225 mm slab thickness by 33.3% leads to a 5.2% decrease in the flexure GDF value, while decreasing it by the same percentage results in a 6.7% increase in the GDF value. This is because increasing the slab thickness results in a stiffer member, which transfers the truck(s)' load more uniformly across the supporting interior girders and leads to less differential deflection between the girders. This results in more load sharing among the girders, decreasing GDF in bridge B7. In contrast, the thinner slab in bridge B6 results in less load sharing, which leads to a higher GDF. However, the exterior girders did not act in a similar way to the interior girders, where increasing or decreasing the slab thickness by 33.3% compared to the reference bridge resulted a change in GDF of only about 1%. The reason the GDF of the exterior girders was not significantly affected by the slab thickness can be explained by the fact that the load on these elements is mainly due to a single truck placed near the overhang or in the vicinity of the girder region. This mechanism is equivalent to a statically determinate element (in this case, a cantilevered overhang) where slab thickness does not contribute to the load effect. Figure 16 shows the finite element and equivalent AASHTO GDF values versus the slab thickness for critical interior and exterior girders in the considered bridges. The equivalent AASHTO GDF approach showed the same trend for the interior girders as the finite element results but exhibited a different tendency for the exterior girders. This is because the GDF is based on the AASHTO LRFD formulas, where the exterior GDF is a product of the interior girder GDF and a factor related only to the overhang distance, resulting in a constant value for all the considered bridges. Comparing the outcome of the refined method with that of the hand calculations showed that the equivalent AASHTO GDF for both the interior and exterior girders yielded slightly conservative results compared to the finite element values in most cases. As the slab thickness increased, the difference between the finite element and equivalent AASHTO GDF factors diminished.
## 4 Experimental Evaluation
### Speech Recognition Evaluation  
The performance metric used to measure the accuracy of the transcriptions is the Word Error Rate (WER) defined in Equation (6).  
\[WER=\frac{S+I+D}{N}, \tag{6}\]  
where \(S\) stands for substitution, i.e., a word is replaced by another, \(I\) are the insertions of words, i.e., a word that was not pronounced was added to the transcription, \(D\) is a deletion, i.e., a word was left out of the transcription, and \(N\) is the number of words in the reference label. After evaluating the speech recognition task, we observed that transcriptions with a WER of less than 10% (0.1) had a few misspellings and the text was still understandable. Based on this observation we decided that a WER value less than 0.1 may be considered as a correct pronunciation, otherwise it is incorrect. The results of this task are presented in Section 5.
# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 2 Related Works
### Emergency Refuge System  
According to the Regulations for Construction of Emergency Refuge System in Metal and Non-metallic Underground Mines, the emergency refuge system refers to the emergency escape routes, emergency refuge facilities, and equipment and measures to provide protection for the underground personnel to avoid danger in the event of an accident in the underground mine [20]. In order to improve the emergency response capacity of mines and safeguard the safety of underground workers, metal and non-metal underground mines should build a perfect emergency refuge system. Emergency refuge facilities (ERAs) are spaces that can be used to keep people safe in the event of a fire, collapse, or other disaster and include permanent refuge chambers and a mobile refuge chamber. A number of studies have been carried out on the temperature, humidity, and air quality of underground refuges, as well as their protection capabilities [21; 22; 23]. In addition, many researchers have investigated the location of underground refuge chambers. For example, [PERSON] et al. used a tree network-based algorithm to select the optimal location for underground refuge chambers [24]. [PERSON] et al. used Fluent to establish the diffusion of CO gas when a fire broke out in a coal mine belt roadway and established a speed model for the escape of people in the roadway, thereby determining the layout position of the escape cabin in the roadway [25].  
The self-rescuer is an isolated oxygen generator carried by personnel entering the well to prevent poisoning and suffocation [26]. Mines should be equipped with self-rescuer's with a rated protection time of not less than 30 min for downhole personnel, and 10 per cent of the total number of people entering the well should be equipped with spare self-rescuer's. Underground personnel should be proficient in wearing the self-rescuer, when disaster occurs, they can quickly wear the self-rescuer to escape.
## 3 Results and Discussion
### Influence of Compressed Air Supply Direction on BGD Power Characteristics
Due to the higher pressure in the chamber at the horizontal inlet, a greater dilution of the BGD is provided, which, at the given parameters of the system, increases the attraction force by 1.1 N. In order to conduct a more detailed analysis, the dependence of the lifting force of the BGD on the gap between the BGD and the OM is derived (Figure 8). To obtain the simulation results, changes were made in the height of the gap (\(l_{c}\)) with a discreteness of 0.05 mm and in the zone \(l_{c}\) = 0.2... 0.3 mm with discreteness 0.025. It can be seen from Figure 8 that the tendency continues to be that the lifting force at a vertical inlet is less than at a horizontal inlet. Furthermore, as the gap between the BGD and the OM increases, the decrease in force at a vertical inlet is clear and can reach up to 13%. This is because the compressed air flow through the annular nozzle of the BGD increases as the gap increases. To ensure a greater lifting force of the BGD, it can be concluded that it is advisable to use a horizontal inlet. In addition, with a horizontal inlet providing a greater lifting force at a greater gap between the BGD and the OM, it is possible to grip objects of manipulation from a greater distance. This characteristic is very significant since Bernoulli's gripping devices are characterized by a very narrow range of  
Figure 8: Influence of the gap between the BGD and the OM on the minimum lifting force at a pressure of \(p\) = 300 kPa. Figure 7: Pressure distribution on the plane of symmetry of a BGD: (**a**) inlet vertical; (**b**) inlet horizontal. gripping distance (0.2 to 2 mm), depending on the weight of the OM. For further studies of the effect of compressed air supply parameters on the power characteristics of a BGD, a horizontal inlet scheme will be used.
## 4 Results Analysis
### Pile Deformation Analysis  
Figure 10 shows the horizontal displacement cloud diagram of the group piles after the construction is completed. The group piles on the right side of pipe jacking I are relatively far from the excavation position when construction is complete, with a relatively small deformation value and a maximum deformation of 3.5 mm compared to the group piles on the left, which are closer to the excavation position and have a maximum value of 7.0 mm for horizontal displacement. The final horizontal displacements of the pile foundations on both sides are essentially the same after pipe jacking II construction is complete. The maximum horizontal displacements of the pile foundations on the right side are 10.9 mm, and those on the left side are 10.7 mm, showing that pipe jacking II has a greater impact on the pile foundations during construction. The lateral displacement of the pile induced by  
Figure 8: Lateral surface settlement when pipe is completed (Y = 40 m).  
Figure 9: Longitudinal surface settlement when pipe jacking is completed.  
pipe jacking II is greater because the soil stiffness surrounding the pile degrades as a result of the tunnel stress release brought on by pipe jacking I's excavation.  
According to Figure 11, the horizontal displacement values along the depth direction (Z-axis's negative direction) of the third pile in the left and right group piles are extracted, respectively. When pipe jacking I is finished being built, the maximum value of the pile foundation displacement on the left side is located above the pipe jacking's range of vertical diameter, while the maximum value of the pile foundation displacement on the right side is situated within that range. Because the deformation of the pile foundation is mainly caused by the deformation of the surrounding soil layer due to the construction of the pipe jacking, the deformation of the soil body near the side wall of the pipe section is the largest. And once pipe jacking II is built, the maximum value of horizontal displacement of the pile foundation on both sides will be situated inside the pipe jacking tunnel's range at a depth of around 17 m. Due to the fact that 28 m is the interface between the upper and lower soil layers, and the lower soil layer is more robust and difficult to disrupt, the horizontal displacement suddenly decreases at this level.