esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Slope Stability Analysis and Discussion
### Displacement Analysis  
By analyzing the slope displacements under different wetting-drying cycles, the landslide trend and extent of the slope can be derived, and the slope displacements after different rainfall cycles are shown in Figure 5. In Figure 5, the red area represents the maximum value of slope displacement and the blue area represents the minimum value.  
Figure 4: Slope pore water pressure distribution of slope detection line under wetting\(-\)drying cycles.  
Analyzing the effect of wetting-drying cycles on slope displacement from the displacement point of view, the peak slope displacement increases with the increase in the number of wetting-drying cycles. According to the displacement change, the slope displacement under different numbers of wetting-drying cycles can be divided into three stages. The first stage is when there is no rainfall, and the maximum value of slope surface displacement is located in the third to fifth steps. The range of internal slope displacement distribution is also large, but the peak displacement is low, at only 12 mm, and the risk of a landslide is low. As the rainfall proceeds, according to the pore water pressure distribution in the previous section, it is known that the slope surface will be infiltrated under the high matrix suction gradient, the infiltration of rainfall will saturate part of the rock body inside the slope, and the effective stress of the rock body will decrease with the buoyancy of the infiltrated rainwater. Then, the slope displacement enters the second stage. The second stage includes 1-3 wetting-drying cycles, and the slope displacement in this stage has a significant increase compared with the previous stage's displacement; the displacement peak is also shifted from the interior of the slope body to the single-step slope. However, the range of slope displacement is reduced with the wetting-drying cycles, and the slope changes from overall displacement to shallow surface displacement. In the third stage, there are four to five wetting-drying cycles. At this time, the deformation range of the slope is completely concentrated in the single-step slope, the peak displacement of the slope is the largest, and shallow landslides are likely to occur under multiple wetting-drying cycles.  
Figure 5: Slope displacement under wetting–drying cycles. (**a**). Slope displacement under 0 wetting–drying cycle. (**b**). Slope displacement under 1 wetting–drying cycle. (**c**). Slope displacement under 2 wetting–drying cycles. (**d**). Slope displacement under 3 wetting–drying cycles. (**e**). Slope displacement under 4 wetting–drying cycles. (**f**). Slope displacement under 5 wetting–drying cycles.
## 5 Challenges  
At present, despite the plentiful results obtained in the field of high-temperature shotcrete research, most of the research is still in the field of tunnel engineering, and there are few studies in other underground engineering fields (such as mine engineering). Moreover, the environments of mine engineering and tunnel engineering are not the same. The mining depth of mine engineering is far greater than that of tunnel engineering. The mining environment is more severe, and the high-temperature environment is more complicated. Therefore, the research on sprayed concrete in the field of underground engineering still faces great challenges, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Challenges** & **Current Technologies** & **Future Challenges** \\ \hline \multirow{6}{*}{High temperature detection technology research} & \multirow{6}{*}{Thermocouple temperature measurement, infrared temperature measurement, and other technologies. However, the temperature can change in the hot and humid environment of the roadway, and a complete test system has not been formed.} & Develop a new temperature measurement method, establish a temperature measurement system, predict and forecast the high temperature area of the roadway, ensure that the construction environment is in a balanced state, and reinforce the area where the roadway sprayed concrete may be damaged in advance. & Research and development of shotcreting equipment and performance testing system in high temperature. Accurately simulate high-temperature environment to improve the authenticity of research results. Provide reliable results for improving the development of infrastructure under high temperature. Study the performance change trend of high-temperature roadway shortage with the thickness of shotcrete layer. Get the optimal spray layer thickness under high temperature. Study the adhesion between spray layers. Research and develop high-efficiency, environmentally friendly, and low-cost adhesives Study the mechanical properties of shotcrete in high-temperature acid and alkaline environments. Investigate the non-linear relationship between the compatibility of the sprayed concrete and the cemented surface of the high-water-spray area and the interface’s mechanical properties. Study the microscopic changes of shotcrete under high temperature. Establish a damage model of shotcrete under high temperature. Study the impact properties of shotcrete layers under high temperature. Establish a damage prediction model for shotcrete layer structure under high temperature, using acoustic emission technology to predict the development trend of shotcrete deterioration in advance. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Challenges of shotcrete performance under high temperature.
## 5 Experimental Hardware Realization  
The proposed method was implemented on a hardware platform. The platform is depicted in Figure 15. The platform comprised a motor drive control module, a PMSM motor with the parameters listed in Table 1, a generator, and an electronic load module.  
The proposed control algorithm was implemented on a TI motor-drive control consisting of TI DSP LAUNCHXL-F28069M and TI BOOSTXL-DRV8301 motor drivers. This DSP had a 90-MHz CPU, 256 kB of flash memory, 96 kB of RAM, 16 enhanced pulse-width modulation (e-PWM) channels, and 16 analog-to-digital converter (ADC) channels. Additionally, the DSP supports various serial communication protocols, including SCI, SPI, and I2C. It enables real-time command and response data transmissions to and from hardware via a Simulink host file.  
In the hardware implementation, the speed control and current loop had sampling frequencies of 1 kHz and 10 kHz, respectively. The inverter's PWM switching frequency was set at 10 kHz.  
Figure 14: Speed and current simulation responses for external load variations (0.1 to 0.2 of rated torque) at 0.45 p.u-speed.
## 2 Materials and Methods
### Feature Selection Phase
#### 2.1.1 Support Vector Machine Recursive Feature Elimination  
[PERSON] et al. [31; 32] first suggested the support vector machine recursive feature elimination (SVM-RFE) approach for extracting features while identifying cancer cells [33; 34]. SVM-RFE is a sequential backward selection algorithm that is based on the SVM maximum interval principle. Consequently, the SVM-RFE ranking criteria are closely related to the SVM [35].  
Give a training sample set \(\{(x_{i},y_{i})\}_{i=1}^{N},x_{i}\in R^{D},y_{i}\in\{+1,-1\}\), where \(y_{i}\) is the category label of \(x_{i}\), \(N\) is the number of training samples, and D is the feature dimension of the samples. Furthermore, the SVM seeks the optimal classification plane \(\omega x+b=0\), where \(\omega\) is the weight vector of the optimal hyperplane and \(b\) is the threshold, so that the optimal classification plane not only separates the two classes of samples without error, but also maximizes the classification interval between the two classes.  
In order to calculate the weight vector and threshold, the SVM needs to solve the following optimization problems:  
\[\min\frac{1}{2}\|\omega\|^{2}+C\sum_{i=1}^{N}\zeta_{i} \tag{1}\]  
and  
\[y_{i}(\omega\cdot x_{i}+b)\geq 1+\zeta_{i};i=1,2,\cdots,N\]  
\[\zeta_{i}\geq 0;i=1,2,\cdots,N\]  
where \(C>0\) is the penalty parameter and \(\zeta_{i}\) is the relaxation variable. The role of parameter \(C\) is to adjust the level of penalty for sample misclassification and to achieve a trade-off between the percentage of sample misclassification and the algorithm's complexity.  
By introducing Lagrange multipliers, the optimization problem of SVM can be transformed into the following pairwise programming problems:  
\[\min\frac{1}{2}\sum_{i=1}^{N}\sum_{i=1}^{N}\alpha_{i}\alpha_{i}y_{i}y_{i}y_{j }\big{(}x_{i}\cdot x_{j}\big{)}-\sum_{i=1}^{N}\alpha_{i} \tag{2}\]  
and  
\[\sum_{i=1}^{N}y_{i}\alpha_{i}=0;0\leq\alpha_{i}\leq C,i=1,2,\cdots,N\]  
where \(\alpha_{i}\) is the Lagrange multiplier.  
The relationship between the weight vector and the solution of the pairwise optimization (2) is:  
\[\omega=\sum_{i=1}^{N}\alpha_{i}y_{i}x_{i} \tag{3}\]  
In SVM-RFE, the ranking criterion score of the ith feature is defined as:  
\[c_{i}=\omega_{i}^{2} \tag{4}\]  
where \(\omega_{i}\) is the vector of weights of the optimal hyperplane.  
Each round of recursive feature elimination needs training the SVM in order to obtain the scoring criterion. The feature with the lowest score is eliminated from the initial feature set in order to generate a new one, as it has the least impact on classification performance. The next iteration updates the feature set used to train the SVM. Repeat this procedure until all features have been removed, and then arrange the features in descending order of removal. The later eliminated elements are more important.
## 3 Results and Discussion
UNVI is therefore expected to contain all-band information and is less sensor-dependent. It is shown that UNVI derived from MODIS and OLI exhibits a sensor-consistent sensitivity and shows a relatively strong sensitivity to canopy water content (CWC). The sensor independency of the predecessor of UNVI, i.e., VIUPD (VI based on UPDM), has been demonstrated in a previous study [33]. Recent studies have demonstrated the advantage of VIUPD with respect to NDVI in agricultural drought monitoring, which may benefit from its sensitivity to CWC [34; 35]. These characteristics may make UNVI perform better than traditional VIs in sophisticated land surface models that require all-band information and multi-sensor data as input. Unsurprisingly, MTCI exhibits strongest sensitivity to Cab. Cab is important for quantifying leaf photosynthesis and physiological status. It alters the shape of the red edge and can be typically estimated from red edge-based VIs. The strong sensitivity of MTCI to Cab indicates great potential of Sentinel-derived red-edge VIs for Cab estimation. However, in RS cases, the additional sensitivity of these VIs to other influential parameters (e.g., leaf structure parameter, N, and LAI) should be appropriately modeled [5]. Figure 3 shows the sensitivity of MODIS-derived PRI and OLI-derived TCTG and scatter plots between predicted and true values for the two VIs. We use MODIS band 11 (531 nm) and band 4 (green, 555 nm) to derive PRI. Correspondingly, Car and Cab, which affect leaf reflectance at 531 nm and 555 nm a lot, respectively, are two of the most influential parameters for PRI. Leaf Car is important for modeling leaf environmental stress. On the basis of the strong sensitivity of PRI to Car, the potential of PRI for modelling non-photochemical quenching (NPQ) and light-use efficiency (LUE) has been widely discussed [36; 37]. When applied at seasonal or regional to global scales, the sensitivity of PRI to Cab should also be modeled. For OLI-derived TCTG, the most influential parameter is LAI, which is similar to most other VIs. Compared to UNVI, which also takes all-band information, TCTG shows a stronger sensitivity to NIR-sensitive parameters such as Cdm, LIA, and SZA, while exhibiting a weaker sensitivity to CWC. Therefore, TCTG might be more appropriate to indicate biomass or greenness rather than overall healthiness of canopy. It is also interpreted that both PRI and TCTG are well predicted by RF learning, with a high coefficient of determination and very low bias.
# Geo-Proxy-Based Site Classification for Regional Zonation of Seismic Site Effects in South Korea
## 5 Conclusions
The seismic zonation map of site effect based on the empirical geo-proxy-based site classification provides a decision making tool to understand the site-specific amplified ground-motion characteristics considering topographic effects and geotechnical uncertainty in multi-scale target areas. In particular, the complementary seismic hazard map, which has possible applications for ShakeMap, can be developed for the Korean Peninsula including regions lacking in borehole datasets. Additionally, topographic gradients can be easily converted to site amplification factors for estimating ground motions in direct conjunction with standard ground-motion prediction equations. Finally, a dual solution framework for site response parameters and geo-proxy-based site classification was used to identify the local or regional-scale site effects considering the geotechnical and geographical influences on site effects. The correlations and site classification criteria according to geological and land coverage maps of the broad areas in South Korea should be analyzed in future research to consider the geotechnical characteristics of area far from Seoul and Incheon. Furthermore, high-precision seismic hazard spatial zonation of subdivided and various small areas in metropolitan regions in South Korea should be performed in the future. In addition, various geographic coverage maps, such as the geological map, need to be developed for the site-specific site classification criteria by considering geological features in South Korea. The authors wish to express their gratitude for the support from the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources (KIGAM). [PERSON] designed and applied the geo-proxy-based site classification framework and wrote the paper. [PERSON] supervised the research activities and provided guidance and suggestions for the proposed framework and the paper. [PERSON] collected the geotechnical database in the Seoul and Incheon areas and supported the GIS work. The authors declare no conflict of interest.
# Preparation and Properties of Waterborne Polyurethane and SBS Composite-Modified Emulsified Asphalt
## 4 Conclusions  
This study explores the feasibility of further modifying SBS-modified emulsified asphalt with cationic waterborne polyurethane. The following conclusions are drawn:  
1. Physical Mixing: The preparation method of mechanical stirring after mixing did not result in any chemical changes between cationic waterborne polyurethane and SBS-modified emulsified asphalt, indicating that the process involves only physical mixing.
2. Enhanced Shear Modulus and Rutting Coefficient: Experimental results show that the addition of cationic waterborne polyurethane improves the complex shear modulus and rutting coefficient of SBS-modified emulsified asphalt. However, MSCR tests reveal that the addition of cationic waterborne polyurethane may reduce the elastic recovery performance of modified asphalt, thereby weakening its resistance to rutting. Among the samples, the modified asphalt with a PU+ content of 6% exhibited good high-temperature shear resistance and elastic recovery performance, demonstrating the best anti-rutting performance.
3. Improved Fatigue Performance: LAS test results indicate that the addition of cationic waterborne polyurethane can enhance the fatigue performance of modified asphalt.
4. Cohesive Energy and Adhesion: Surface free energy tests show that the addition of cationic waterborne polyurethane improves the cohesive energy and adhesion performance of modified asphalt. However, it also somewhat weakens the water damage resistance of the modified emulsified asphalt.
5. Modification Potential and Future Research: In summary, cationic waterborne polyurethane shows good modification potential for SBS-modified emulsified asphalt.  
Conceptualization, R.C.; formal analysis, W.X.; methodology, W.X.; investigation, R.C. and Y.C.; writing--original draft preparation, R.C. and Y.C.; writing--review and editing, R.C. and Y.C.; supervision, W.X.; project administration, W.X.; funding acquisition, W.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Science and Technology Project of the Jiangxi Provincial Transportation Department (2023H0026).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## 2 Sources and Impacts of Training Data Error  
In the following two sections we describe the common causes of TD error and explore its potential impacts. To describe these causes, we divide the sources of TD error into two general classes: (1) errors stemming from the design of the training sample, including some aspects of sample and response design that are shared with standards for the collection of map reference data (see 1.2.1 above), and (2) errors made during the collection of the training sample, including additional elements of response design such as the process of digitizing and labeling points or polygons when interpreting imagery or when collecting field measurements. In addressing the impacts of error, we provide a summary of potential problems, and then two concrete case examples for illustrative purposes.
## 3 Results and Discussion
### Interfacial Tension Measurements (IFT)
#### 3.3.1 Effect of Concentration on IFT  
The IFT with varying surfactant concentrations is depicted in Figure 3. DCB and DSB can reach ultra-low interface tension with crude oil in the measured concentration range and had better adaptability with Xinjiang crude oil, 3.9 \(\times\) 10\({}^{-3}\) mN/m and 5.5 \(\times\) 10\({}^{-3}\) mN/m, respectively. With the same length of carbon chain, the carboxyl group is more hydrophilic than the hydroxypropyl sulfonic group and exhibits lower IFT at the same solution concentration. Meanwhile, IFT first decreased and then increased with time. When the adsorption and desorption of surfactants at the oil-water interface reached a balance, the interfacial tension approached a certain equilibrium value, which is the dynamic interfacial tension (DIFT) behavior [26; 27]. The reason is that there is a dynamic  
Figure 2: Variation of the surface tension with the surfactant concentration for DCB and DSB at 25 \({}^{\circ}\)C.  
balance between the adsorption and desorption of surfactant molecules at the oil-water interface, both of which occurred simultaneously. Initially, the adsorption of surfactant molecules at the interface was higher than desorption, and the DIFT decreased as the molecules moved from the inside of the solution to the interface. Then, with the passage of time, the molecules reached adsorption saturation at the interface, the adsorption rate was gradually lower than the desorption rate, and the DIFT increased. Finally, when adsorption and desorption reached equilibrium, DIFT was stable [28].
# Data Element Sharing in Convergence Media Ecology Based on Evolutionary Game
## 4 Results and Discussion
### Evolutionary Stable Strategy
The punishment for the negative sharing of the business organization node and the punishment for no regulation of the regulator node plays an important role in whether the nodes choose to positively share or participate in regulation, and when the punishment is large enough, it will urge business organization nodes and regulator nodes to choose to positively share or participate in regulation. Inference 2: Under condition c, \(H+E_{11}+I_{1}+F_{1}+G_{12}>G_{11}+E_{12}\), \(H+E_{2}+I_{2}\)\(>G_{2}\), \(\mathrm{R}*(\alpha+\beta)+\mathrm{F}_{3}<G_{3}\), the replication dynamic system has stable points, E7(1,1,0). Under condition d, \(H+E_{11}+I_{1}+F_{1}+G_{12}>G_{11}+E_{12}\), \(H+E_{2}+I_{2}\)\(>G_{2}\), \(\mathrm{R}*(\alpha+\beta)+\mathrm{F}_{3}>G_{3}\), the replication dynamic system has stable points, E8(1,1,1). Proof: According to the Lyapunov discriminant method, condition c and condition d, the eigenvalues \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\) of the Jacobian matrix corresponding to the replicated dynamic system at the equilibrium points E7(1,1,0), E8(1,1,1) are all less than 0, so the equilibrium points E7(1,1,0), E8(1,1,1) are the stable points of the system. It follows from Inference 2 that the collaborative revenue of participation of the two parties and the revenue in sharing or regulation play an important role in promoting the participation or regulation. When the benefits of participation are large enough, the system nodes choose to actively participate in the sharing of data elements or regulation; the incentive revenue of nodes positively sharing or participating in sharing and the additional rate of revenue after the success of regulation play a role in promoting the participation of nodes in sharing or regulation, and when the participation revenue or the system incentive revenue is large enough, the system nodes will choose to positively participate in the data element sharing or regulation.
## 4 Results  
### Data Transfer Utilizing the POC Platform  
The features that were deemed necessary for the exchange of DCCs were implemented using five application programming interfaces (APIs):  
1. A signing API for creating digital seals for DCCs;
2. An import API for uploading DCCs to the DCC Platform;
3. A sharing API for granting access rights to DCCs;
4. An export API for retrieving DCCs from the DCC Platform; and
5. A validation API for validating the digital seals of received DCCs.  
The data exchange process based on the APIs is presented in Figure 6.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multirow{3}{*}{Universal features} & User management based on organizational user credentials \\  & Uploading and downloading of DCCs \\  & A list view of DCCs created by or shared with the user \\  & An easy-to-use user interface (UI) to enable manual use of the \\  & other features \\ \hline \multirow{3}{*}{Features necessary for a calibration provider} & DCC creation using an input form type of a tool, for cases \\  & where a more advanced DCC creation is not available \\ \cline{1-1}  & DCC signing \\ \cline{1-1}  & DCC sharing \\ \hline \multirow{3}{*}{Features necessary for a calibration customer} & A notification when a DCC is received \\ \cline{1-1}  & A human-readable format for viewing DCCs \\ \cline{1-1}  & Signature validation \\ \hline \hline \end{tabular}
\end{table}
Table 2: The necessary features for the platform based on the defined aims and requirements.  
To keep the platform simple, easy to use and deploy, and cost-efficient, existing and commonly used technologies were intended to be utilized. Consequently, the file management and storage of DCCs on the platform was implemented based on Nextcloud ([[https://nextcloud.com/about/](https://nextcloud.com/about/)]([https://nextcloud.com/about/](https://nextcloud.com/about/)), accessed 6 June 2022) and the supporting features such as the user interface (UI) and access management were developed to enable better compatibility for the requirements of the use cases.  
Respectively, the securing of the confidentiality of the data exchange on the platform through user management and authorization was implemented using Azure Active Directory (AD) ([[https://azure.microsoft.com/en-us/services/active-directory/](https://azure.microsoft.com/en-us/services/active-directory/)]([https://azure.microsoft.com/en-us/services/active-directory/](https://azure.microsoft.com/en-us/services/active-directory/)), accessed 6 June 2022), which is a widely utilized user identity management system also used by the participating organizations.