esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The symmetrical fissures located within the surrounding rock of the roadway (borehole) in tunnel engineering activities can easily induce damage and instability of the surrounding rock. Therefore, studying the impact of perforated symmetrical fissures on the mechanical properties of rock with a hole has significant practical significance. Based on indoor experimental results, conventional triaxial compression simulations were performed on symmetrical fissure-hole sandstone using PFC2D. The impact of the dip angle and length of symmetric fissures on the mechanical properties of the hole-containing sandstone was analyzed. Furthermore, the relationship between crack propagation and the macroscopic mechanical properties of the specimen was discussed. The results show that: (1) The deterioration effect of symmetric fissures on hole-containing sandstone can be controlled by increasing the fissure dip angle, suppressing the stress drop phenomenon. However, increasing the fissure length exacerbates the deterioration effect. (2) The effect of symmetrical fissure dip angle on the displacement field near the hole decreases with increasing dip angle while increasing fissure length exacerbates the effect of fissures on the displacement field. (3) As the angle between the fissure and the vertical principal stress increases, the degree of tensile failure weakens while the degree of shear failure increases. (4) During the crack development phase, the extension of the stress concentration zone drives rapid crack growth. It exhibits a stress drop in the macroscopic mechanical properties, followed by the evolution of the stress field with loading, allowing rapid expansion of the microcracks and eventually leading to rock destabilization damage.  
symmetric fissures; hole; mechanical properties; particle flow simulation +
Footnote †: journal: Applied sciences
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Key Encapsulation Mechanism
#### 3.2.4 The Reference Implementation
Regarding the decapsulation mechanism for SIKE, as described by Algorithm 3, it basically computes the shared string \(\texttt{j}\) from \(c_{0}\) and the secret key \(\texttt{sk3}\). It then recomputes h, then m and then r\({}^{\prime}\), which is used to compute \(c_{0}^{\prime}\). At this point, it compares \(c_{0}^{\prime}\) with the given \(c_{0}\). If they are equal, then the real shared key \(\texttt{K}\) is computed and returned, otherwise a fake shared key is computed and returned. ```
functionDecaps(\((\texttt{s},\texttt{sk3},\texttt{pk3}),(c_{0},c_{1}),\texttt{Params })\)  j \(\leftarrow\texttt{isoex}_{3}(c_{0},\texttt{sk3},\texttt{Params.e}_{3})\); h \(\leftarrow\texttt{SHAKE}_{\texttt{256}}(\texttt{j},\texttt{Params.n})\); m\({}^{\prime}\leftarrow\texttt{h}\oplus\texttt{c1}\); r\({}^{\prime}\leftarrow\texttt{SHAKE}_{\texttt{256}}(\texttt{m}^{\prime}\mid\mid\texttt{pk}_{3}, \texttt{Params.e}_{2})\); c\({}_{0}^{\prime}\leftarrow\texttt{isogen}_{2}(\texttt{r}^{\prime}, \texttt{Params})\); ifc\({}_{0}^{\prime}=c_{0}\)then K \(\leftarrow\texttt{SHAKE}_{\texttt{256}}(\texttt{m}^{\prime}\mid\mid(c_{0},c_{1}), \texttt{Params.k})\); else K \(\leftarrow\texttt{SHAKE}_{\texttt{256}}(\texttt{s}\mid\mid(c_{0},c_{1}), \texttt{Params.k})\); endif returnK; endfunction
```  
**Algorithm 3** Decapsulation algorithm for SIKE. SIFKE Parameters  
Table 1 shows relevant entries from each SIKE parameter for our key-recovery attack. For a comprehensive list of all entries defined for each SIKE parameter, such as \(P_{2}\), \(Q_{2}\),\(R_{2}\), \(P_{3}\), \(Q_{3}\) and \(R_{3}\), we refer the reader to [1,9].
## 4 Simulation and Physical Experiments
### Reliability Simulation
#### 4.3.2 Continuous Motion Trajectory Simulation  
Continuous motion trajectory is set to test the robustness of PSO and SA-PSO. As shown in Table 6, the complex motion of SGP is composed of six sets of postures and each set is a single degree of the motion. Two seconds are cut out and the sampling rate is 1 kHz. Then two thousand posture points are solved into two thousand length vectors by inverse kinematic function, then the vector series are passed to the algorithms. Figure 6 shows the six degrees motion and the moving trajectory of six outriggers.  
\begin{table}
\begin{tabular}{c c c} \hline
**Posture** & **Dimension** & **Motion Trajectory** \\ \hline _X-trans_ & \(Q_{1}(t)\) & \(0.15\sin(2\pi\mathrm{t})\) \\ _Y-trans_ & \(Q_{2}(t)\) & \(0.12\sin(2\pi\mathrm{t})\) \\ _Z-trans_ & \(Q_{3}(t)\) & \(0.1t\) \\ _X-rot_ & \(Q_{4}(t)\) & \(0.10\sin(\pi\mathrm{t})\) \\ _Y-rot_ & \(Q_{5}(t)\) & \(0.20\sin(\pi\mathrm{t})\) \\ _Z-rot_ & \(Q_{6}(t)\) & \(0.05\sin(\pi\mathrm{t})\) \\ \hline \end{tabular}
\end{table}
Table 6: The six-dimensional trajectory.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**SA–PSO**} \\ \cline{2-4}  & \(\mathbf{k_{ev}}\) & **Success Rate** & \(\mathbf{k_{ev}}\) & **Success Rate** \\ \hline
1 & 146.36 & 94.54\% & 116.07 & 99.92\% \\
2 & 140.81 & 100\% & 113.51 & 100\% \\
3 & 144.56 & 95.91\% & 113.51 & 99.96\% \\
4 & 140.53 & 100\% & 112.61 & 100\% \\
5 & 142.83 & 96.23\% & 114.88 & 99.97\% \\
6 & 142.10 & 95.47\% & 114.00 & 99.99\% \\
7 & 144.54 & 98.49\% & 115.11 & 99.93\% \\
8 & 140.55 & 100\% & 112.45 & 100\% \\
9 & 147.17 & 94.28\% & 115.00 & 99.96\% \\
10 & 148.02 & 93.89\% & 114.12 & 100\% \\ \hline \end{tabular}
\end{table}
Table 5: The average number of iterations and success rate.
## 4 Simulation and Analysis
### Siemens Simcenter Flomaster Simulation Environment
When the battery SoC is 90%, a similar situation is realized, and the battery is prevented from creating a security weakness by charging to full capacity. Considering all the different SoC values, the battery consumed 20.43% less energy in the HESS. The battery utilization rate also changed with the change in outdoor temperature,  
Figure 21: Battery SoC replacement in NEDC driving cycle in Simcenter Fomaster program. Figure 22: Battery variation at different SoC values. and the battery utilization rate decreased by 21.3% in the tests performed at six different outdoor temperatures. When the outdoor temperature was 50 \({}^{\circ}\)C, the battery SoC increased as the system reduced its battery usage. Because of the reduced battery utilization, the heat generated by the battery pack is reduced, and the energy consumption for cooling is reduced. Figure 24 shows the heat energy generated by the battery pack during the driving cycle at 70% battery, a 50% SC SoC, and a 20 \({}^{\circ}\)C outdoor temperature. Figure 23: Battery SoC obtained at different temperatures. Figure 24: Heat generation of the battery in the NEDC driving cycle (kW). The HESS produced 46.84% less heat than the standard battery system during the NEDC driving cycle. Thus, the battery's health was preserved by preventing the increase in internal resistance that would occur in the battery because of battery heating. Thus, the HESS contributed positively to both the vehicle range and battery life. During the driving cycle, the range of the EV without regenerative braking with battery storage was 218 km, 235 km with only regenerative braking, and 243 km with a combination of regenerative braking and hybrid storage. Thus, both the range of the EV has increased and the battery life has been positively contributed to due to less battery use.
###### Abstract  
According to [PERSON]'s theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO\({}_{2}\) fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows [PERSON]'s theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999-2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R\({}^{2}\) values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84-0.61, RRMSE (%) reduced from 13.1-8.9, MBE (t/ha) decreased from 0.05-0.03 and the index of model performance (\(E_{1}\)) increased from 0.08-0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world.  
dry matter productivity; yield estimate; SPOT VEGETATION; water stress; maize; common wheat 201681703390/rs030170
# An Analysis of the Supply of Open Government Data
## 3 Material and Methods
### Empirical Analysis
To estimate our model of regression analysis, we developed a sample with country cross-section data. Our variable for open government data is the global open government data index (defined as \(Od_{i}\)), published by the Open Knowledge Foundation (OKF), which provides cross-country differences on the supply of open government data. To estimate the model in Equation (2), we used a cross-section analysis with data on the GODI for the year 2016. A well-identified regression analysis needs to consider the possibility of endogeneity, which might bias the estimates of the model. Endogeneity might arise when changes in the independent variables \(X\) might be correlated with changes in the dependent variable \(Y\), and changes in \(Y\) might also lead to changes in the \(X\) variables. In this case, the marginal effects in the regression model in (2) would not be properly identified. A standard way to solve this issue is to use the independent variables \(X\), but lagged for one period (for technical analysis on this issue, see [64]). Hence, to avoid the possibility of endogeneity in our estimates, we used data for the year 2015 for our control variables; that is, we used the lagged observation for the control variables, such as the size of the country, the quality of democracy, political participation, civil liberties, transparency, the size of the population, the sociodemographic characteristics in a country, and efficiency of the government. In this case, changes in \(X\) could be correlated with changes in the dependent variable \(Y\), but not the opposite case. In summary, our assessment criteria for our empirical analysis was constituted as follows. First, we used theoretical analysis from the literature on economics on the main determinants of public goods and services provided by governments to identify control variables of the regression analysis (as a way to determine the structure of the \(X\) variables in the regression analysis, seen in Section 2.1). The theoretical analysis provided a rationale for a probabilistic link between the independent variable (GODI) and the explanatory variables of the model in Equation (2). Second, we used standard techniques of regression analysis to determine the best way to obtain unbiased and efficient estimators of the marginal effects of the independent variables over the dependent variable GODI. Third, we conducted a robustness test of our estimates and hypothesis testing by estimating several models (see Models I, II, III, IV and V in Table 1 in the following section) to test whether our results were sensitive to specific forms of linear regression analysis.
## 3 Methodology  
The assessment of ELM triggering by pellets is a typical problem where it is not possible to apply supervised methods. From now on, the pellet monitor signal is indicated as x(t), while the ELM signal (Be II or UV diamond) is y(t). Both pellets and ELMs are characterized on their respective signals by rapid pulses. Such features usually appear as rapid increases of the signals followed by slower decays to their base value of the type shown in Figure 4a. The purpose of the method is to determine whether the pulse x(t) at the time instance \(t_{i}\) has triggered the pulse y(t) at the time \(t_{i}+\Delta t_{trig}\), with the \"triggering time\" \(\Delta t_{trig}>0\). This goal is achieved by three main steps:  
1. ELMs' and pellets' peaks detection (Section 3.1).
2. Spectrogram cross-correlation (SPCC) (Section 3.2).
3. Clustering of the SPCC signals (Section 3.3).
## 2 KGs Extraction
### Advanced Entity and Relation Extraction
#### 2.2.1 Graph-Based Methods (GNN and Attention)  
Graph-based extraction methods organize the analysis results (entities and relations) from text into a graph structure and use Graph Neural Networks (GNN) and attention mechanisms to infer and validate information. For example, one approach achieved high precision by combining NER with a deep learning transformer to verify the entities and relations extracted at the sentence level [27].  
In the biological domain, studies have combined BERT-based NER with topological clustering to learn the gene interaction structures within protein networks. In addition, research has been conducted on extracting drug-drug interactions by integrating heterogeneous knowledge graphs [28] and methods that extract relationships by merging web tables [29]. Other approaches include graph-based methods using multimodal learning [30], research on expanding KGs from large heterogeneous data sources [31], and comprehensive information extraction pipelines, where BERT-based NER plays a core role [32].  
Studies have reported the use of multi-task graph convolution networks (MGCN) to extract entities and relations. For example, by integrating dependency trees into a GCN, structure information can be captured while simultaneously learning node and edge features, effectively handling overlapping or complex relation labels [33]. Furthermore, various methods have been employed, such as combining BERT-based NER with translation-based frameworks like TransE for accurate extraction from domain datasets [34] and constructing KGs using multimodal educational data [35].  
Other approaches include using BiLSTM-CRF models to extract named entities and adding entity type information through unsupervised learning to reduce false positives [36] and employing TrellisNet-CRF to complement the accuracy of BERT-based NER [37]. Strategies that integrate graph structure learning, attention-based BERT NER, and automated feature generation (combining deep learning with graph learning) further enhance KG extraction performance [38]. Finally, a strategy was developed that uses BERT-based NER to automatically extract triple structures from geological reports and link them to a predefined ontology [39].
## 2 Nonlinear Wiener Process with Covariates
### Deducing Relation of Parameters in the ADT for Nonlinear Wiener Model
The relation between accelerated stress variables and parameters can be described by engineering background-based acceleration models, including Arrhenius models, Eyring models, inverse Power models whose expressions and acceleration factors are shown in Table 1.  
In the above table, there are three models, \(\varsigma(S_{k}/b)=\exp(-b/S_{k})\), \(\varsigma(S_{k}/b)=\frac{1}{S_{k}\exp(-b/S_{k})}\), and \(\varsigma(S_{k}/b)=\exp(-b\ln(S_{k}))\) to describe Arrhenius models, Eyring models, and Inverse  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accelerated Models** & **Drift Parameter** & **Diffusion Parameter** & **Acceleration Factor** \\ \hline Arrhenius models & \(\lambda_{k}=\eta\exp(-b/S_{k})\) & \(\left(\sigma_{B}^{2}\right)_{k}=\kappa\exp(-b/S_{k})\) & \(A_{k,h}=\exp\Bigl{(}-b\left(\frac{1}{S_{k}}-\frac{1}{S_{k}}\right)\Bigr{)}\) \\ \hline Eyring models & \(\lambda_{k}=\frac{\eta}{S_{1}\exp(-b/S_{k})}\) & \(\left(\sigma_{B}^{2}\right)_{k}=\frac{\kappa}{S_{k}\exp(-b/S_{k})}\) & \(A_{k,h}=\frac{S_{k}}{S_{k}\exp(-b\left(1/S_{k}-1/S_{k}\right))}\) \\ \hline Inverse Power models & \(\lambda_{k}=\eta\exp(-b\ln(S_{k}))\) & \(\left(\sigma_{B}^{2}\right)_{power models, respectively. The accelerated models of drift parameter \(\lambda\), the diffusion parameter \(\sigma_{B}^{2}\), and the acceleration factor \(A_{k,h}\) can be uniformly written as  
\[ \begin{array}{c}\lambda_{k}=\eta\varsigma_{k}\\ \left(\sigma_{B}^{2}\right)_{k}=\kappa\varsigma_{k}\\ A_{k,h}=\frac{\varsigma_{k}}{\varsigma_{h}}\end{array}  \tag{13}\]  
where \(\varsigma_{k}\) is the simplified form of \(\varsigma(S_{k}/b)\).
## 1 Introduction  
The rapid development of the Internet and digital technologies has significantly transformed the landscape of education, making e-learning platforms widely accessible. This accessibility has enabled the collection of vast amounts of student learning data. And, the data have gone beyond simple statistics such as login frequency, exam accuracy, and course completion rates. They now extend to complex spatiotemporal data like mouse movement and eye-tracking data, which contain rich details about student learning process. These types of data can reflect students' attention distribution and reveal how students perceive, process, and apply information, aiding educators in understanding students' cognitive process and problem-solving difficulties [1].  
However, despite the potential of spatiotemporal learning data, much of the existing research focuses on theoretical analyses or experimental studies. These efforts often result in static conclusions that remain confined to academic publications and fail to transition into practical tools or platforms accessible to educators or decision-makers. While there is growing interest in leveraging visualization techniques to make sense of learning data, most of them emphasize the presentation of simple data patterns or the direct output of analysis results, rather than supporting the dynamic, multidimensional exploration of complex spatiotemporal learning datasets.  
Therefore, we propose \"MultiScaleAnalyzer\", a novel visualization platform designed to bridge the gap between complex spatiotemporal datasets and actionable educational insights. This paper first details the design and development of MultiScaleAnalyzer, with a focus on its framework and functionalities. It then evaluates the platform's potential and practical applications through a qualitative analysis involving five educators, whose findings are synthesized into illustrative use cases. Finally, the discussion outlines the platform's strengths and limitations and proposes potential directions for future research and improvements. By systematically addressing these aspects, we aim to answer the following research questions:  
1. How can the visualization framework of MultiScaleAnalyzer support educators in presenting and analyzing complex spatiotemporal learning data?
2. What learning behaviors or patterns can MultiScaleAnalyzer help educators to identify?
3. What is the role of different types of spatiotemporal learning data (i.e., mouse movements and eye-tracking data) in understanding students' cognition and behavioral processes in educational contexts?
# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 7 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AmpN**} & \multicolumn{3}{c}{**SubjFeatN**} & \multicolumn{3}{c}{**PersFreqN**} \\ \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Acc**} & **High CL** & **Low CL** & & **High CL** & **Low CL** & & **High CL** & **Low CL** \\  & & **F1-Score** & **F1-Score** & **Acc** & **F1-Score** & **F1-Score** & **Acc** & **F1-Score** & **F1-Score** \\ \hline SVM Linear & 66\% & 0.65 & 0.67 & 67\% & 0.68 & 0.67 & 76\% & 0.77 & 0.76 \\ SVM Cubic & 74\% & 0.7 Comparing the datasets, the best performance was observed on the CLAS dataset. In this case, the proposed PersFreqN allowed reaching an accuracy of 81% adopting the SVM classifier with the polynomial cubic kernel. This result significantly outperformed the accuracy of 74% reached using AmpN and the 73% obtained using SubjFeatN. For the CLAWDAS datasets, the highest accuracies achieved were 79% for CLAWDAS Young and 80% for CLAWDAS Elderly, both obtained with the proposed PersFreqN and SVM with a linear kernel. These values show a significant improvement with respect to the other normalization strategies AmpN and SubjFeatN, which were always lower than 66%. Another consideration regards the classifier that allowed reaching the best results. In general, from the three Tables 4-6, it emerged that the highest accuracy values were usually achieved by the SVM classifiers with the linear or cubic kernel, whereas the lowest ones were generally obtained by the CART classifier. Finally, a last consideration should be made on the SubjFeatN normalization strategy. In general, this normalization produced a performance higher than that of AmpN, even if it appeared less effective compared to the proposed PersFreqN strategy. These results confirmed that a normalization strategy that takes into account not only amplitude normalization, but also subject's characteristics should be adopted to remove inter-subject variability. As a final remark, all the adopted classification settings were able to classify with comparable performance both classes, as indicated by the values of the single-class F1-score in all the tables. However, the introduction of the proposed PersFreqN seemed to produce even more balanced classification results.
## 3 Methodology
### Computational Analysis  
#### 3.2.1 Control Equation for CFD  
In this study, the control equation for analyzing CSS internal flow is as follows. First, a viscous state, incompressible, and turbulent flow field is assumed. Then, a computational analysis is performed, assuming a steady state. The governing equations are the continuity equation, momentum equation, and turbulence transport equation. In order to computationally analyze the governing equations, algebraic equations are discretized. The most widely used k-\(\varepsilon\) model among turbulence models is shown in Equations (1)-(3) below. In the equation, \(C_{1}\) and \(C_{2}\) are empirical constants, and A is the turbulence component.  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial}{\partial x_{i}}(\partial u _{i}k)=\frac{\partial}{\partial x_{i}}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}} \bigg{)}\frac{\partial k}{\partial x_{i}}+G_{k}-\rho\varepsilon \tag{1}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial}{\partial x_{i}} (\partial u_{i}\varepsilon)=\frac{\partial}{\partial x_{i}}\bigg{(}\mu+\frac{ \mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{\partial x_ {i}}+C_{1}\frac{\varepsilon}{k}G_{k}+C_{2}\rho\frac{\varepsilon^{2}}{k} \tag{2}\]  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\varepsilon} \tag{3}\]  
In general, in a turbulent flow field, changes in physical quantities near the wall are large. Therefore, a dense grid system is needed to improve the accuracy of the analysis. CSS is very large in size and has a complex internal structure. Therefore, in this paper, the wall function is applied to simulate the flow around the wall surface. The CSS grid is a tetrahedron, and the total number of grids is about 8,000,000 cells.
## 4 Experiments and Results
### Ablation Study
#### 4.5.3 The Ablation Studies for the OHEM Strategy  
To improve the convergence performance of our method, we incorporated the OHEM training strategy into our method. Table 3 shows the results, indicating that the mean intersection over union (mIoU) score of our method increased by 0.4%, demonstrating its effectiveness.