esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Soil Damping on the Soil-Pile-Structure Interaction Analyses in Cohesionless Soils
## 2 The Method
### Soil Models
[11], [PERSON] et al. [33], and [PERSON] [34]. In this study, effective stress-dependent curves of Darendeli [14] were employed to better consider the variation with depth (Figure 2a). The shear modulus reduction equation is given in Equation (5). The reference strain in the equation depends on the effective confining stress as in Equation (6), according to [PERSON] [14]. The hysteretic damping command in FLAC\({}^{\mathrm{3D}}\)[32] was utilized for the soil domain in the model. The reference strain values were assigned to each zone by considering the initial effective stress (total stress approach). However, the maximum shear strain was on the order of 0.1-0.3% in the study performed by [PERSON] [14], which could not involve the large strain (>1%) behavior. In contrast, the study of [PERSON] and [PERSON] [16] showed that the \(G\)/\(G_{max}\) value for modulus degradation curves at 3% shear strain varies between 0.03 and 0.05. Therefore, a cut-off for \(G\)/\(G_{max}\) is required to accurately represent the large strain behavior in the modulus degradation. Since the experimental study presented by [PERSON] (2001) does not include the large strain data (\(\geq\)1%), 0.05 was assumed for minimum \(G\)/\(G_{max}\) considering the [PERSON] and [PERSON] [16] curves. The minimum cut-off value of 0.05 was applied for the modulus reduction ratio to prevent further increase in damping beyond a certain shear strain. The damping ratio variation with shear strain curves in the hyperbolic model is shown in Figure 2b. \[\frac{G}{G_{max}}=\frac{1}{1+\left(\frac{\gamma}{\gamma_{\pi\prime}}\right)^{0.919}} \tag{5}\]  
\[\gamma_{r}=0.0352\left[\frac{\sigma^{\prime}_{m}}{p_{a}}\right]^{0.3483} \tag{6}\]  
It should be noted that the damping values in Figure 2b are calculated using Equation (1), which obeys [PERSON]'s [35] criterion for unloading/reloading. However, the MC model approach yields different damping ratio values than the hyperbolic model. Equations (1) and (2) can be used to compare the damping ratios at various cyclic strains for a given confining stress. Figure 3 shows the damping ratio variation with shear strain for hyperbolic and MC models for the given confining stress levels. According to the figure, the damping at relatively higher strains is significantly greater in the MC model, especially at the lower confining stresses. Consequently, a perfectly plastic MC model could cause the damping to be considerably higher than the hyperbolic model.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 2 Project Overview
### Numerical Models and Simulation Scheme
#### 3.2.5 Analysis of the Factors Influencing the Loose Zone in the Surrounding Rock Mass  
Three geometric quantities were selected as evaluation indexes reflecting the scope of the closed annular loose zone, namely \(L\) (the span of the loose zone at the tunnel vault), \(\mathbf{H}_{1}\) (the height of the loose zone at the tunnel vault), and \(\mathbf{S}\) (the area of the loose zone). The geometric quantity diagram of the loose zone is shown in Figure 10. The data were processed for the 16 groups of working conditions, and the test number of the closed annular loose zone and the evaluation indexes of the loose zone scope were then summarized. The evaluation indexes of the loose area are shown in Table 5.  
Figure 10: Loose zone of surrounding rock mass.  
Figure 9: Search path and loose zone boundary.  
The CCR model quantitatively evaluates the factors influencing the loose zone in the surrounding rock mass using a fuzzy restriction on the weight [35]. This model can realize the evaluation and prediction of multi-index input and multi-index output problems with the help of the linear programming method. The model overcomes the shortcomings of the traditional CCR model, which discards influencing factors (the weight value is 0) when solving for optimal efficiency. The maximum fuzzy membership function constrains the upper and lower limits of the weight values to obtain the model's optimal weight solution. The CCR model with a fuzzy restriction on the weight can be expressed as:  
\[\left\{\begin{array}{l}\max\,\mu\\ \text{s.t.}\sum\limits_{i=1}^{s}u_{r}y_{rj}\,-\sum\limits_{i=1}^{m}u_{r}x_{ij} \,\leq 0(j=1,2,\ldots,n)\\ \mu\cdot u_{l}^{U}\leq u_{r}\leq(1-\mu)\cdot u_{l}^{U}(r=1,2,\ldots,s)\\ \mu\cdot v_{i}^{U}\leq v_{i}\leq(1-\mu)\cdot v_{i}^{U}(i=1,2,\ldots,m)\end{array}\right. \tag{6}\]  
where \(\mu\) is the maximum fuzzy membership function; \(y_{rj}\) is the output value of the \(r\)-th term; \(x_{ij}\) is the input value of the \(i\)-th term; \(\mu_{r}\) is the weight value of the \(r\)-th output; \(v_{i}\) is the weight value of the \(i\)-th input; \(u_{r}^{U}\) and \(v_{l}^{U}\) are the upper limit values of the output weight and input weight, respectively.  
When analyzing the factors influencing the loose pressure in the surrounding rock mass of a super-span tunnel, the input factors are \(H\), \(B\), \(\lambda\), \(K\), and GSI. The values of each impact factor are shown in Table 2. The output factors are \(L\), \(H_{1}\), and \(S\), and their geometric statistics are shown in Table 6.  
The weight values of the influencing factors are summarized in Table 1. It can be concluded that the order of the weight values of the influencing factors in the loose zone of a super-large section tunnel is \(B>\text{GSI}>K>\lambda>H\), which indicates that two factors (\(B\) for the tunnel span and GSI for surrounding rock quality) have a high degree of influence on the surrounding rock loose zone, while the other factors have less of an impact.
## 4 Conclusions  
In this research, epoxy composites filled with multi-walled carbon nanotubes and expanded graphite with different loadings were prepared. The thermal oxidative degradation of the prepared samples at heating in an oxidizing environment was analyzed using thermogravimetric analysis. The parameters of the thermal-oxidative degradation of the filled samples were compared to those of the unfilled sample. The hardness of the samples was measured using the Shore D hardness test. The ignition temperature and time-to-ignition of the pure epoxy resin, epoxy/MWCNTs and epoxy/EG composites were determined. It was found that there was a rise in temperature corresponding to a 5%  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Ignition Temperature (\({}^{\circ}\)C)** & **Time-to-Ignition (s)** \\ \hline E0 & 308 & 437 \\ E/0.01 MWCNT & 318 & 346 \\ E/0.05 MWCNT & 328 & 356 \\ E/0.1 MWCNT & 316 & 381 \\ E/0.5 MWCNT & 315 & 446 \\ E/0.5 EG & 319 & 371 \\ E/1.0 EG & 325 & 334 \\ E/1.5 EG & 326 & 367 \\ E/2.0 EG & 326 & 348 \\ E/2.5 EG & 321 & 346 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ignition temperature and time-to-ignition.  
weight loss during heating for both epoxy/MWCNT and epoxy/EG composites. The Shore D hardness of epoxy/MWCNTs composites increased with the content of up to 0.1 wt.% and decreased with further concentration growth. The addition of MWCNTs and EG led to an increase in the ignition temperature, but the time-to-ignition decreased, which is probably due to the growth of sample thermal conductivity. It has been shown that MWCNTs improve the thermal behavior of epoxy resin in a low-temperature region (below \(\sim\)300 \({}^{\circ}\)C), whereas EG shows almost the same thermal behavior above 300 \({}^{\circ}\)C.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/19/6928/s1](http://www.mdpi.com/2076-3417/10/19/6928/s1)]([http://www.mdpi.com/2076-3417/10/19/6928/s1](http://www.mdpi.com/2076-3417/10/19/6928/s1)). Figure S1: C1s XPS spectrum of EG sample. Figure S2: DSC curve of the oxidation of EG sample (air, 10 K/min, Al\({}_{2}\)O\({}_{3}\)). Figure S3: Results on ASTM E698 analysis of E0 sample. Figure S4: Results on ASTM E698 analysis of E/0.5 MWCNT sample. Figure S5: Results on ASTM E698 analysis of E/0.5 EG sample.  
Methodology, A.G.B.; Writing--Original draft preparation, Supervision, O.B.N.; Resources, E.A.M.; Formal analysis, M.V.P.; Investigation, I.S.B. All authors participated in the discussions of the results. All authors have read and agreed to the published version of the manuscript.  
The work was funded by the State Task of Ministry of Science and Higher Education of Russia (project no. FSUN-2020-0008).  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Average Surface Roughness and Surface Quality  
Figure 10 details Ra results achieved by both systems. Surface roughness generally ranged between 0.51 and 0.56 \(\upmu\)m, and 0.57 and 0.71 \(\upmu\)m for Cut-list and conventional systems, respectively. Values of Ra are relatively lower with the use of Cut-list for all conditions investigated. The new system improved the penetration of the impinged fluid for accessing the machining zone efficiently and forming the boundary of the oil film between workpiece and cutter, thereby reducing friction and improving the quality of the machined surface. Additionally, the new Cut-list and the conventional system showed similar trends in decreasing surface roughness with the increased cutting speed regardless of feed rate. This is due to the increased cutting fluid flow rate (see Table 3) associated with increased cutting speed which was found in agreement with research by [PERSON] et al. [64]. SEM analysis showed that surfaces machined using the conventional supply systems had chips re-deposited/adhered onto the machined surfaces, as detailed in Figure 11. Feed marks were apparent on all machined surfaces regardless of the fluid supply system. No major damage such as cracks or material tearing were observed, especially when the Cut-list system was used. This is also evident with the relatively low surface roughness values. However, larger smearing was apparent when the conventional system was employed, which is likely the result of the inability of the fluid to reach machining zone, particularly at a higher cutting speed. The adhered chips observed onto the machined surfaces produced by the conventional supply system were probably the result of trapped material between tool flute, which was subsequently pressure welded to the machined surfaces due to the insufficient cutting. The findings are in agreement  
Figure 9: Effect of cutting fluid supply systems on burr height at various working conditions.  
with the results by [PERSON] et al. (2017) who reported similar observations when drilling titanium/ carbon fibre reinforced plastics (CFRP)/aluminium stacks.  
Figure 11: SEM images of surface topography for Ti-6 Al-4V machined parts under conventional supply system at cutting speed of 200 m/min and feed rate of 0.1 and 0.15 mm/rev respectively (**a**,**b**), and Cut-list at speed of 200 m/min and feed rate of 0.1 and 0.15 mm/rev respectively (**c**,**d**).  
Figure 10: Average surface roughness results versus cutting conditions for the two systems.
## 6 Performance Evaluations
### Parameters Setup  
The parameters used throughout the simulations are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Routing Protocol** & **Routing Category** & **Harvested Energy Attribute** & **Network Attribute** & **Is Perpetual Network Operation Achieved** \\ \hline
[26] & Flat & Supplement & Single-hop & Not achieved \\ DEHAR & Flat & Only source & Multi-hop & Achieved \\ ENR & Flat & Only source & Single-hop & Achieved \\
[29] & Clustering & Supplement & Single-hop & Not achieved \\ AEHAC & Clustering & Only source & Single-hop & Not achieved \\ EP-LEACH & Clustering & Only source & Single-hop & Not achieved \\ ENC & Clustering & Only source & Single-hop & Achieved \\ MENC & Clustering & Only source & Multi-hop & Achieved \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison among energy harvesting aware routing protocols.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Packet size \(q\) (bits) & 1000 \\ \(E_{disc}\) (n/bit) & 50 \\ \(E_{Tx}\) (n/bit) & 50 \\ \(E_{Rx}\) (n/bit) & 50 \\ \(\varepsilon_{fs}\) (p/bit/m\({}^{2}\)) & 10 \\ \(E_{DA}\) (n/bit/message) & 5 \\ Node density \(p\) (node/100m\({}^{2}\)) & 1 \\ Compression ratio \(a\) & 0.2 \\ Energy harvesting rate \(P_{e}\) (J/h) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Since all sensor nodes in the first unit act as CHs and transmit data to BS directly in MENC, let sensor nodes in the first unit in LEACH, AEHAC, EP-LEACH, ENC and P-29 adopt the same method. Besides, the probability \(P_{C}\) of a node (not in the first unit) to be selected as a CH in LEACH is computed by  
\[P_{C}\!\!=\!\!\frac{1}{n-5/m}\sum\limits_{i=2}^{m}c_{i} \tag{44}\]  
where \(m\) is the optimal number of units in MENC; \(S\) is the area of sensor field; \(c_{i}\) is the number of clusters in unit \(i\) and \(n\) is the total number of sensor nodes in the network.  
Equation (44) indicates that the probability of a node to be selected as a CH in LEACH is equal to the average probability of a node to be CH in MENC.  
The probability of a node to be a Center Node (CN) in ENC is also computed by Equation (44), to keep consistency of the number of clusters between MENC and ENC. In addition, the initial probability of a node to be the CH in AEHAC is calculated by Equation (44) in the following simulations. Moreover, we set the number of clusters \(N_{C}\) predefined in P-29 to be the total number of clusters in MENC.
## 2 Materials and Methods
### Poisson EWMA Control Chart  
[PERSON] et al. [9] extended the EWMA control limits to PEWMA where observations are Poisson count, then the basic EWMA recursion remains unchanged given in Equations (1) and (2). Nevertheless, there are some issues that need to be addressed regarding the presence of the Poisson distribution. The random variable \(X\), which represents the number of events occurring in a fixed interval, follows a Poisson distribution with a parameter \(\alpha>0\), and its probability mass function is given in Equation (9).  
\[f(x)=\frac{\alpha^{x}e^{-\alpha}}{x!}\quad x=0,1,2,\ldots \tag{9}\]  
where \(\alpha\) is the mean number of events. it is known that the standard deviation of Poisson distribution is \(\sigma=\sqrt{\alpha}\), hence the control limits of the PEWMA chart are written in Equations (10) and (11).  
\[UCL=\alpha+A_{u}\sqrt{\frac{\lambda\alpha}{(2-\lambda)}[1-(1-\lambda)^{2i}]} \tag{10}\]  
\[LCL=\alpha-A_{L}\sqrt{\frac{\lambda\alpha}{(2-\lambda)}[1-(1-\lambda)^{2i}]} \tag{11}\]  
where \(L\sigma\) is represented by \(A\) and in symmetric cases \(A_{U}=A_{L}=A\). The center line of the PEWMA chart is \(\alpha\). The chart gives an out-of-control signal when the point is not between the interval of \(UCL\) and \(LCL\). The variance of the PEWMA statistic \(z_{i}\) is given in Equation (12).  
\[var(z_{i})\approx\lambda\alpha/(2-\lambda). \tag{12}\]To demonstrate the PEWMA control chart, Figure 1 displays a plot of 100 randomly generated observations (\(n=100\)) from a Poisson distribution with a mean of 5. The original data \(x_{i}\) is represented by the cyclic symbol, while the EWMA data \(z_{i}\) is shown with a solid line. The initial value, \(z_{0}\), is set to 5, and \(\lambda\) is chosen to be 0.2 for the calculations.
## 4 Experiments
### Path Search Module Training  
In the experiment, to increase the training speed of the path search module, we pre-trained the path search module and used a bandwidth-based breadth-first search (BFS) algorithm to improve the search speed [33]. This method can provide more stable training samples and controllable time costs. Compared with direct joint training, the pre-trained path search module can reduce the probability of search failure to less than 5%.
## 2 System Design and Its Components
### Applied Computing Resources
Jupyter is a web platform for writing and executing code in various programming languages, including Python. ODC supports access and processing of time-series data through the Python API. Therefore, internal users can search for and process the desired data by receiving the assigned machine. Finally, the Synchronous service connects to SaaS as a publicly configured service and synchronizes data on S3 through AWS-CLI. All virtual machines can be expanded in the future after optimization. In particular, the Jupyter service provides individual virtual machines for users to utilize immediately. AWS has configured ODC Core and Explorer as virtual machine services and PostgreSQL as a shared resource, following the same approach as for a private cloud. S3 is used as the data-sharing resource. ODC is satellite image processing software designed for cloud services and supports accessing and processing time-series data via S3. The top-level directory name in the S3 bucket is set to 'KOMPSAT-public', and the data shared in the private cloud is synchronized within the bucket.
## 2 Results
### Commercially Available Formulations Screening  
Figure 2 shows that the highest yields of vinyl laurate conversion into SL were obtained by Lipase B from _Candida antarctica_ (CA) formulations such as Novozym 435\({}^{\text{\textregistered}}\), Lipozyme 435\({}^{\text{\textregistered}}\) and Lipozyme CALB L (liquid formulation). Regardless of the applied form, all of them seem to reach statistically equivalent results of ~20% under non-optimized  
\begin{table}
\begin{tabular}{c c} \hline \hline Retention time (SL) * & 3.55–3.59 min \\ Peak width ** & 0.060–0.091 min \\ Resolution\({}_{\text{orbitol--SL}}\) (\(n=3\)) & 17.5 \\ Resolution\({}_{\text{SL--lauric acid}}\) (\(n=3\)) & 7.6 \\ Baseline noise (\(n=3\)) & 0.22 \(\pm\) 0.07 mV \\ Limit of detection (signal/noise = 3) & \textless{}0.04 g/L \\ Limit of quantification (signal/noise = 10) & 0.04 g/L \\ \hline \hline
**1 st Range of Linear Calibration** & \\ Correlation coefficient (R\({}^{2}\), \(n=3\)) & 0.9967 \\ Equation of linear calibration & y = 0.001x + 0.988 \\ Linear range of calibration & 0.75–15 g/L \\ \hline \hline
**2 nd Range of Linear Calibration** & \\ Correlation coefficient (R\({}^{2}\), \(n=3\)) & 0.9993 \\ Equation of linear calibration & y = 0.002x \(-\) 21.639 \\ Linear range of calibration & 20–30 g/L \\ \hline \hline \end{tabular}  
* Inter-day variance of retention time measured at 3 different days. ** Concentration 0.75–30 g/L.  
\end{table}
Table 1: Chromatographic and analytical characteristics of SL analysis using HPLC-ESLD.  
reaction conditions. However, it is important to note that liquid formulations potentially contain more catalytic material than the immobilized ones, showing the limitations of our comparison.  
As we compare these lipase formulations on the basis of applied concentrations (20 g/L), the quantity of actual enzyme present in the reaction most likely differs; moreover, very little data on the matter are made available by the producers. However, the quantity of enzyme is directly linked to the amount of formulation used; thus, when comparing immobilized enzymes, this variable is intrinsically highlighted. As a result, we are, among other factors, comparing qualitatively the immobilization efficiency of the various formulations tested. In this aim and in our system, either using the food-grade formulation (Lipozyme 435(r)) or the technical grade one (Novozym 435(r)) did not lead to significant differences in titer after 48 h of reaction (Table 2). Interestingly the Cross-Linked Enzyme Aggregate (CLEA) from CA seemed to reach statistically similar levels of performance. Therefore, in the following sections, we chose to report the influence of several factors affecting our system. For this purpose, we used Novozym 435(r) as the archetype of the commercially available lipase formulation for SE synthesis. As a first step, we decided to investigate the evolution of SL titer over time.
## 5 Experimental Results
### Comparison with a Deep Learning Approach  
We conducted a comparison with two deep learning approaches to further assess the power of the handcrafted features, as described in Section 4. Hence, we performed two experiments: the first tests the AlexNet and the ResNet50 as a whole classification approach, whereas the second tests the two deep networks as feature extractors feeding the same four classifiers used herein-before. All of the results are summarised in Tables 7 and 8 and, in general, they show that the most performing network is the ResNet50.  
In the first experiment, this deep network achieves an AUC equal to 76.49% and an accuracy equal to 71.11%. Furthermore, the performance differences between the ResNet50 and the AlexNet are statistically significant according to the Wilcoxon signed rank test (\(p=0.0396\)).  
In the second experiment, when the networks are used as feature extractors, we find that the automatic features they compute combined with the Adaboost classifier provide accuracy and AUC equal to 73.33% and 73.42%, respectively. Furthermore, using this learner, the performance differences using the features computed by the AlexNet and by ResNet50 are statistically significant with \(p<0.1\) according to the Wilcoxon signed rank test. We do not find statistical differences in the pairwise comparison between the automatic features while using the other three classifiers.  
As it is evident, these values were lower than those achieved by the proposed approach (accuracy = 83.51% and AUC = 82.78%), showing the successful discriminative power of hand-crafted features. We also assess this difference using again the [PERSON] signed rank test, comparing our proposal (AdaBoost+CTV in Table 3) with the best one provided by deep learning approach (ResNet50 features in Table 7). The results of such two approaches are statistically different with \(p<0.05\). A possible reason for this finding could be the size of our dataset: indeed, this could hinder the power of deep approaches, leading to the superiority of handcrafted features with respect to measures automatically learned.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Adaboost**} & \multicolumn{3}{c}{**Decision Tree**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**XGBoost**} \\ \cline{2-13}  & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **PTV** \\ \hline Accuracy & 63.16 & 67.37 & 60.00 & 63.16 & 66.32 & 63.16 & 73.68 & **77.89** & 73.68 & 46.32 & 46.32 & 46.32 \\ \hline AUC & 62.88 & 67.42 & 59.94 & 63.13 & 66.13 & 62.88 & 72.84 & **76.92** & 72.68 & 50.00 & 50.00 & 50.00 \\ \hline Precision & 65.38 & 70.83 & 63.27 & 67.39 & 68.63 & 65.38 & 71.67 & **74.19** & 70.97 & 0 & 0 & 0 \\ \hline Recall & 66.67 & 66.67 & 60.78 & 60.78 & 68.63 & 66.67 & 84.31 & **90.20** & 86.27 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance with the signature presented by [PERSON] et al. (2019) combined with the classification algorithms used here. The largest performance is highlighted in bold.