esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Categories**} & \multicolumn{2}{c}{**Tool Types**} \\ \hline \hline \multirow{7}{*}{\begin{tabular}{c} One-way \\ (monologic) tools \\ \end{tabular} } & \multirow{7}{*}{\
\begin{tabular}{c} Presenting and \\ disseminating \\ information \\ \end{tabular} } & \multirow{7}{*}{
\begin{tabular}{c} Text and graphic \\ \end{tabular} } & \multicolumn{2}{c}{Publications: studies, thematic reports, books, articles, periodicals (journals)} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Information brochure (NGO presentation)} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Institutional yearbooks (annual reports)} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Publication of an events calendar} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Online pressroom (press releases)} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Blogs not allowing users to comment} \\ \cline{3-4}  & & & \multicolumn{2}{c}{News published on a website} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Photographs and images} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Multimedia files embedded in a website (audio files, videos)} \\ \cline{3-4}  & & & \multicolumn{2}{c}{External links to other websites or institutions} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Interactive resources (graphics and infographics)} \\ \cline{3-4}  & & & \multicolumn{2}{c}{Participatory resources (“follow” on social media buttons/tools)} \\ \hline \hline \end{tabular} Source: authors’ own compilation based on the work of [PERSON] et al. [28].  
\end{table} 
Table 1: Types of one-way (monologic) tools used on NGO websites. were assigned to each of the established categories based on their interaction potential. A score of 1 (very low interactivity) was assigned to \"connect\", 2 (low interactivity) was assigned to \"share\", 3 (medium interactivity) was assigned to \"review and comment\", 4 (high interactivity) was assigned to \"participate\", and 5 (very high interactivity) was assigned to \"collaborate\". Subsequently, the weighted sum of each resource on each NGO's website was averaged, allowing for the determination of the degree of interactivity for each. This degree was measured on a scale of 0 to 3 points, where a score of 0 to 1 is considered \"low or poor interactivity\", that of 1.1 to 2 is understood to be \"medium interactivity\", and that of 2.1 to 3 is considered \"high or significant interactivity\". To validate the design of the analysis templates, a preliminary exploratory study of ten NGO websites was conducted. This initial evaluation allowed for the consideration of the appropriateness of the methodology, making modifications and adjustments to elements that could hinder the achievement of the proposed objectives.
## 4 Proposal of Tower-Base Damage Index Ratio  
The nonlinear dynamic analysis using STERA_3D was conducted for all 35 frames with three input earthquake ground motions to calculate the damage index (\(DI\)) of each story. The damage index of each story is calculated using Equation (5). The following \(DI_{\text{ratio}}\) is proposed to examine the distribution of the \(DI\) along the height of the frame.  
\[DI_{ratio}=\frac{\text{max}(DI_{lower})}{\text{max}(DI_{base})} \tag{12}\]  
where the \(DI_{lower}\) and \(DI_{base}\) are taken as the average for the ten input ground motions. If the value of \(DI_{ratio}\) is larger than one, the damage will be concentrated at the tower structure. Conversely, if the value is less than one, the base structure will suffer more damage than the tower.  
Figure 17: Time history acceleration waves of input earthquake motions.  
The mathematical model is developed for predicting \(DI_{ratio}\) from irregularity indices \(\varphi_{s}\) and \(\varphi_{b}\) using the nonlinear regression analysis so that the damage distribution is easily evaluated without conducting the nonlinear dynamic analysis. The relation between the calculated \(DI_{ratio}\) of all the frames and irregularity indices \(\varphi_{s}\) and \(\varphi_{b}\) are shown in Figure 18 for the stepped model and Figure 19 for the towered model type, respectively.  
Values of \(\varphi_{s}\) and \(\varphi_{b}\) equal to 1 correspond to a structure with a uniform configuration. As values of \(\varphi_{s}\) and \(\varphi_{b}\) increase, the degree of the setback in the frame structure becomes larger. A large value of \(DI_{ratio}\) indicates a relatively large damage concentration in the tower. The nonlinear regression analysis is performed to derive the quadratic polynomial equation for estimating the damage index ratios from irregularity indices,\(\varphi_{s}\) and \(\varphi_{b}\). The obtained equations are shown in Equation (13) for stepped type and Equation (14) for towered type. The red lines in Figures 18 and 19 indicate the prediction of the damage index ratio using Equations (13) and (14). The values of R-squared of red lines, which indicate the goodness-of-fit measure for the regression model, vary between 0.865 and 0.999.  
\[DI_{ratio}=98.19-124.29\varphi_{s}-105.06\varphi_{b}+142.75\varphi_{s}\varphi_{ b}+38.34\varphi_{s}{}^{2}+25.47\varphi_{b}{}^{2}-37.09\varphi_{s}\varphi_{b}{}^{2}-45.44 \varphi_{s}{}^{2}\varphi_{b}+13.22\varphi_{s}{}^{2}\varphi_{b}{}^{2} \tag{13}\]  
\[DI_{ratio}=-3.09+16.89\varphi_{s}+17.22\varphi_{b}-35.99\varphi_{s}\varphi_{b}- 12.88\varphi_{s}{}^{2}-12.53\varphi_{b}{}^{2}+17.34\varphi_{s}\varphi_{b}{}^{2 }+19.59\varphi_{s}{}^{2}\varphi_{b}-6.22\varphi_{s}{}^{2}\varphi_{b}{}^{2} \tag{14}\]  
Figure 19: \(DI_{ratio}\) with different \(\varphi_{s}\) and \(\varphi_{b}\) for towered model.
# Laboratory and Field Experiments on the Effect of Vinyl Acetate Polymer-Reinforced Soil
## 1 Introduction  
With the development of the social economy, human engineering activities are having a greater and greater impact on the ecological environment, which leads to a series of ecological environmental problems, such as water and soil loss, landslides, dust emission, and so on [1; 2; 3]. According to some investigations, the average loss of soil in the United States is 8.7 \(\times\) 106 kg/km\({}^{2}\) every year [4]. Due to large-scale engineering construction, many bare soil slopes have been formed. These slopes are prone to slope erosion, soil erosion, and other disasters. Therefore, adopting various physical and chemical methods to prevent soil erosion and other geological and environmental problems has always been a hot research topic globally.  
Soil stabilizer, as a new environmentally friendly soil improvement material, was advanced in developed countries such as the United States and Japan in 1970s. According to the main components, soil stabilizers can be divided into five major categories: lime cement soil stabilizer [5; 6; 7; 8], slag silicate soil stabilizer [9; 10; 11; 12], ionic soil stabilizer [13; 14; 15; 16], bio-enzyme soil stabilizer [17; 18] and polymer soil stabilizer [19; 20; 21; 22; 23]. Polymer soil stabilizer has become a hot spot of research in soil stabilization materials, because of advantages that include less incorporation, stable curing effect and ecological environmental protection. [PERSON] et al. [24] used self-developed hydrophilic polymers to strengthen the soil. The polymer can significantly increase the compressive strength of soil. [PERSON] et al. [25] developed an electrolyte polymer that can effectively solve the problem of radioactive soil particlediffusion. [PERSON] et al. [26] found that a high molecular polymer can reduce the thickness of the hydrated film around the soil particles, which makes the connection of soil particles more compact.  
In the present study, polymer soil stabilizer solution is synthesized from vinyl acetate polymer as a chemical treatment material of soil. The water properties test, including water-retaining property test and rainfall erosion test, mechanical properties test, including unconfined compression test and direct shear test, durability test and seed growth test were carried out in the laboratory. Also, the effect of vinyl acetate polymer was verified by field tests. These results could be applied as a reference for vinyl acetate polymer-improved soil engineering, especially for the surface protection of slopes.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL
### End-to-End Delay Optimization
#### 3.3.3 Multi-Path Traffic Distribution  
According to the above model, we propose a multi-path traffic distribution algorithm based on RPL to reduce the waiting time. This algorithm makes use of greedy thoughts. A heuristic scheme is used to determine the weight of each associated parent node. Specifically, the divided nodes distribute the traffic into N parts. Each part assigns to its parent node which can minimize the maximum delay among all of the bottleneck nodes.  
Algorithm 3 provides the formal description, the main idea is to test all the parent nodes by greedy iteration, and find out the approximate optimal flow distribution scheme. Take Figure 3 as an example. Node A first allocates traffic evenly for each parent node, and then tests the smallest of the maximum waiting times in the bottleneck node by gradually adjusting the weight of the parent node. If the maximum waiting time can be reduced, it saves the current node and reinitializes traffic distribution so that other parent node can be tested. Finally, it assigns a step length to the most appropriate parent node and restarts the iteration, and finds a reasonable flow distribution.  
The selection of the step size determines the optimal result of the flow distribution and the energy consumption of the node. Using a small step length can be able to obtain less waiting time for the bottleneck nodes, while strengthening the complexity of the calculation. Conversely, the traffic distribution scheme is not reasonable enough. That is, we should set a proper step length with regard to the real situation and request.  
Figure 6: Uneven cache usage.
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 4 Comparison and Results
### The New Proposed Method
#### 4.3.3 Influence of the Span in the Moving Average Method
The detailed SNR results calculated by Equation (8) and comparison by different signal processing methods mentioned in Section 4 are shown in Table 1.  
Obviously, although the conventional signal processing methods such as the LP-WA, median filtering, and wiener filtering method could denoise the original steel wire rope signals to some extent, the SNR are the highest when they were processed by the proposed algorithm through the successive techniques such as the HHT, lowpass filtering, and moving average, which also demonstrated the validity and feasibility of the proposed weak signal processing methods for steel wire rope.
# Immunohistochemical Expression of ABCB5 as a Potential Prognostic Factor in Uveal Melanoma
## 3 Results
### Clinico-Pathological Characteristic of Uveal Melanomas
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Sex** & **Age (Yrs)** & **Location** & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Largest** \\ **Diameter (mm)** \\ \end{tabular} & **Cell Type** & \begin{tabular}{c} **Pathological** \\ **T Stage** \\ \end{tabular} & \begin{tabular}{c} **DFS** \\ **(Months)** \\ \end{tabular} & \
\begin{tabular}{c} **Follow-up** \\ **(Months)** \\ \end{tabular} &
\begin{tabular}{c} **ABCB5** \\ **IS** \\ \end{tabular} & \\ \hline F & 58 & Ch & 6.04 & 17.8 & mixed & pT2a & 63 & 64 (t) & 1 & 4 & 0 & L \\ M & 69 & Ch & 7.21 & 15.8 & mixed & pT2a & 54 & 81 (t) & 0 & 0 & 0 & L \\ F & 75 & Ch/CB & 15.5 & 15.3 & mixed & pT30 & 44 & 62 (t) & 2 & 2 & 6 & H \\ F & 50 & Ch & 7.36 & 15.6 & epith & pT2a & 41 & 81 & 3 & 3 & 9 & H \\ M & 62 & Ch & 13.68 & 16 & mixed & pT3a & 38 & 51 (t) & 1 & 2 & 2 & L \\ F & 51 & Ch/CB & 11.4 & 18.5 & mixed & pT3b & 38 & 61 & 1 & 1 & 1 & L \\ M & 71 & Ch & 13.14 & 17.1 & epith & pT3a & 33 & 34 (t) & 2 & 3 & 2 & L \\ M & 76 & Ch/CB & 11.6 & 6.5 & mixed & pT1a & 31 & 39 & 2 & 3 & 6 & H \\ M & 72 & Ch & 10.3 & 15.4 & mixed & pT3b & 27 & 35 (t) & 1 & 2 & 2 & L \\ F & 85 & Ch/CB & 7.3 & 14.7 & spindle & pT2d (EE) & 26 & 49 (t) & 1 & 2 & 2 & L \\ M & 73 & Ch & 5.73 & 11.7 & epith & pT2a & 26 & 42 (t) & 3 & 3 & 9 & H \\ F & 51 & Ch & 9.42 & 19 & mixed & pT3a & 25 & 39 & 3 & 3 & 9 & H \\ F & 74 & Ch & 5.7 & 12.1 & spindle & pT2a & 24 & 37 (t) & 3 & 3 & 9 & H \\ F & 67 & Ch & 3.49 & 20 & mixed & pT4a & 24 & 31 (t) & 1 & 2 & 2 & L \\ M & 74 & Ch & 11.35 & 10.5 & epith & pT3a & 19 & 47 & 3 & 3 & 9 & H \\ M & 82 & Ch & 9.7 & 11 & epith & pT2a & 19 & 42 & 3 & 3 & 9 & H \\ F & 72 & Ch & 6.7 & 15.2 & epith & pT2a & 14 & 28 (t) & 2 & 3 & 12 & H \\ M & 76 & Ch & 13.7 & 17.1 & mixed & pT2a & 14 & 70 & 3 & 3 & 9 & H \\ M & 79 & Ch & 13.91 & 16.1 & epith & pT3b & 13 & 38 & 1 & 2 & L \\ F & 66 & Ch/CB & 8.95 & 12.5 & mixed & pT2b & 12 & 37 (t) & 3 & 4 & 12 & H \\ F & 60 & Ch & 8.25 & 16.5 & epith & pT2a & 11 & 37 (t) & 3 & 3 & 9 & H \\ F & 57 & Ch/CB & 13.6 & 19 & epith & pT2b & 6 & 55 & 2 & 2 & 12 & H \\ M & 72 & Ch/CB & 13.3 & 15.4 & mixed & pT3b & 0 & 51 & 3 & 4 & 12 & H \\ \hline \hline \end{tabular} Abbreviations: DFS, disease free survival; ARCRS, ATP-binding cassette sub-family B member % Ch, choroid; CB, ciliary body; epith, epitheloid; FE, extrasolarlexets; (t) death.  
\end{table} 
Table 2: Demographics, tumor parameters, disease free time, follow-up and ABCB5 expression in primary uveal melanoma with metastasis (\(n=23\)).
## 6 Conclusions  
1. A new analysis method useful for the evaluation of the seismic behavior of underground structures in loess area was proposed based on the DBM and it is named the modified DBM. The DBM was modified according to the results obtained from a shaking-table test and numerical simulations. The results show that the modified displacement-based method improves the accuracy of structural axial force output and compensates for the defects of the DBM. The modified DBM is applicable to any typology of underground structure and it can be applied to the seismic design of underground structure.
2. The modified DBM was compared with the most commonly used methods of analysis (FBM, DESANM, DBM, and FDTHM). The results were validated considering using data from a shaking table test based on the Feitian Road underground subway station in Xi'an. The comparison of output results shows that the modified DBM offers good accuracy, a simple model, rapid modeling, and easy convergence.
3. Based on the finite element analysis method, a practical table of horizontal relative displacements of strata in loess areas, and the comprehensive recommended equation  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Observation Point** & **Modified DBM/kN** & **DBM/kN** & **FDTHA/kN** & **Error in Modified DBM** & **Error in DBM** \\ \hline \multirow{4}{*}{Central column} & a & \(-1172.70\) & \(-510.95\) & \(-1031.70\) & \(13.70\%\) & \(-50.47\%\) \\  & b & \(-1184.20\) & \(-522.45\) & \(-1154.50\) & \(2.60\%\) & \(-54.75\%\) \\  & c & \(-1399.20\) & \(-712.10\) & \(-1421.90\) & \(-1.60\%\) & \(-49.92\%\) \\  & d & \(-1411.90\) & \(-724.80\) & \(-1558.60\) & \(-9.40\%\) & \(-53.50\%\) \\ \hline \multirow{4}{*}{Flank} & e & \(-509.40\) & \(-388.26\) & \(-506.90\) & \(0.50\%\) & \(-23.41\%\) \\  & f & \(-671.30\) & \(-739.00\) & \(-794.80\) & \(-15.50\%\) & \(-7.02\%\) \\  & g & \(-823.80\) & \(-330.18\) & \(-854.30\) & \(-3.60\%\) & \(-61.35\%\) \\  & h & \(-1307.50\) & \(-188.70\) & \(-1395.00\) & \(-6.30\%\) & \(-86.47\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of axial force predicted by the modified DBM.  
of foundation reaction spring stiffness, were provided. The practical table and the comprehensive recommended equation provide a method of estimation of those seismic calculation parameters required by designers of underground subway stations in loess areas.
4. Due to the special properties of loess, the modified DBM is suitable for the seismic response analysis of underground structures in loess area. However, whether it is applicable to other soils remains to be studied in future research.  
Conceptualization, R.Z. and D.Q.; methodology, D.Y.; Project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Chongqing Postdoctoral Special Fund (020919014) and The Natural Science Foundation of Chongqing, China (cstc2020 jcyj-bshX0118) and National Natural Science Foundation of China (U20A20314).  
Not applicable.  
Data Availability Statement: All data are available from the author.  
The authors declare no conflict of interest.
# Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis
## Materials and Methods  
### DFA Method  
Long-range correlation has been proven to exist in various systems from different research fields, including physics [25; 26], engineering [27], economy [28], medicine [29], etc. The long-range correlated data possess unique statistical characteristics compared to short-range correlated and uncorrelated data. One of the main features of these data is an autocorrelation function that decays hyperbolically with time. The slow decay of the autocorrelation function shows a significant correlation between the data samples as the lag increases.  
Several methods and techniques have been derived to quantify the level of long-range memory in stationary and non-stationary time series. One of the robust methods for non-stationary time series is the DFA method. In [30], the method was first introduced to study the existence of long-range correlations in DNA nucleotides. DFA evaluates the strength of long-range correlations by calculating the scaling exponent (\(\alpha\)). The steps to compute the scaling exponent (\(\alpha\)) of a time series, \(x(t)\), with \(N\) samples are summarized below:  
1. Subtract the mean of the time series and compute the integrated time series, \(y(t)\), as shown in Equation (1). \[y(t)=\sum_{k=1}^{t}(x(k)-\overline{x})\] (1)
2. Divide the integrated series into equally spaced boxes, \(n\). Inside each box, detrend the series segment by subtracting it from its best linear fit, \(y_{n}(t)\). Repeat this step for different box sizes (\(n\)).
3. Compute the fluctuation function (\(F(n)\)), as shown in Equation (2), by finding the RMS value of the detrended time series: \[F(n)=\sqrt{\frac{1}{N}\sum_{k=1}^{N}(y(k)-y_{n})^{2}}\] (2)
4. Plot the fluctuation function (\(F(n)\)) versus the box size (n) on log-log scale. The scaling exponent (\(\alpha\)) represents the slope of the best linear fit.  
All the steps of the DFA method are shown in Figure 3. The DFA is applied on a simulated series with a scaling exponent equal to 0.6, as shown in Figure 3a. The integrated and detrended time series at a box size of 100 samples are shown in Figure 3b,c. In Figure 3d, the estimated scaling exponent represents the slope of the best linear fit of the fluctuation function versus the box size on a log-log scale. The estimated scaling exponent (\(\alpha=0.59\)) is within the accepted margin of error.  
The scaling exponent (\(\alpha\)) provides a quantitative measure of the degree of long-range correlation in the time series. The long-range correlated time series has a scaling exponent between 0.5 and 1. The increases in these series are likely to be followed by further increases and the decreases are likely to be followed by further decreases. For \(0<\alpha<0.5\), the time series is called anticorrelated with a decrease expected to follow the increases, and vice versa. The uncorrelated time series has a scaling exponent of 0.5.
# Geotechnical Evaluation of Diesel Contaminated Clayey Soil
## 3 Results and Discussion
### Effect of Diesel Contamination on the Soil's Mineralogy  
Figure 6 shows the X-ray diffractograms of both the natural and the contaminated soil. Both soil samples showed the presence of montmorillonite clay mineral [PDF 00-013-0135: montmorillonite-15A: Ca\({}_{0.2}\)(Al,Mg)\({}_{2}\)Si\({}_{4}\)O\({}_{10}\)(OH)\({}_{2}\)-4H\({}_{2}\)O] and anorthite [PDF 00-018-1202: anorthite, sodian, intermediate: (Ca,Na)(Si,Al)\({}_{4}\)O\({}_{8}\)]. Montmorillonite belongs to the smectite group and is formed by two Si-based tetrahedral sheets. Anorthite is a plagioclase feldspar mineral that occurs in some igneous and metamorphic rocks.  
The presence of quartz, cristobalite, and tridymite was also observed. Silica or silicon dioxide (SiO\({}_{2}\)) can exhibit both crystalline (quartz, tridymite, and cristobalite) and amorphous forms. Cristobalite and tridymite are silica mineral quartz polymorphs that exhibit the same chemical formula but have different crystal structures.  
In most cases, cristobalite is the first crystallization product of the amorphous SiO\({}_{2}\) at high temperatures. In tridymite, the silica tetrahedra are packed in a two-layer structure, while in cristobalite, the silica tetrahedra are packed in a three-layer structure [43]. Cristobalite and tridymite have a more open structure than quartz, which allows the inclusion of other elements into its crystal structure [43].  
A change was observed in the reflective angle of the montmorillonite in diesel-contaminated soil (2\(\theta=5.70^{\circ}\)) in comparison with natural soil (2\(\theta=5.88^{\circ}\)). This shift to the left of montmorillonite peak caused an increase in the lattice spacing of the montmorillonite mineral [44]. It implies that montmorillonite may undergo a swelling effect of the internal layer, from d\({}_{001}\) = 15.018 A (for natural soil) to d\({}_{001}\) = 15.492 A (for diesel-contaminated soil), due to its diesel contamination.  
Soil contamination also decreased the peak area of the montmorillonite minerals, which was due to a reduction in the column length of the reflective minerals. This indicated a decrease in the montmorillonite minerals' concentration [44].  
Although the peaks of quartz, cristobalite, and tridymite minerals did not show a shift in their reflective angle, their intensities were reduced after soil contamination. This was due to a coating effect on the mineral surface, caused by the peripheral adsorption of diesel on these minerals [44]. Such an effect resulted in an apparent decrease in the concentration of mineral crystals, since they were not able to reflect the incident X-rays as the natural soil could.
###### Abstract  
Global Positioning System (GPS) kinematic precise point positioning (KPPP) is an effective approach for estimating the Earth's tidal deformation. The accuracy of KPPP is usually evaluated by comparing results with tidal models. However, because of the uncertainties of the tidal models, the accuracy of KPPP-estimated tidal displacement is difficult to accurately determine. In this paper, systematic vector differences between GPS estimates and tidal models were estimated by least squares methods in complex domain to analyze the uncertainties of tidal models and determine the accuracy of KPPP-estimated tidal displacements. Through the use of GPS data for 12 GPS reference stations in Hong Kong from 2008 to 2017, vertical ocean tide loading displacements (after removing the body tide effect) for eight semidiurnal and diurnal tidal constituents were obtained by GPS KPPP. By an in-depth analysis of the systematic and residual differences between the GPS estimates and nine tidal models, we demonstrate that the uncertainty of the tidal displacement determined by GPS KPPP for the M2, N2, O1, and Q1 tidal constituents is 0.2 mm, and for the S2 constituent it is 0.5 mm, while the accuracy of the GPS-estimated K1, P1, and K2 tidal constituents is weak because these three tidal constituents are affected by significant common-mode errors. These results suggest that GPS KPPP can be used to precisely constrain the Earth's vertical tidal displacement in the M2, N2, O1, and Q1 tidal frequencies.  
KPPP; tidal displacement; accuracy +
Footnote †: journal: Sensors