esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Current Trends in Teaching and Learning Evaluation Methods  
Before the COVID-19 pandemic, traditional evaluation methods, which involved in-person assessments, were predominantly followed. These included written exams, quizzes, presentations, and practical demonstrations [1]. These activities were carried out in physical classrooms, enabling educators to observe directly and receive immediate feedback. Standardised tests were also a common tool for assessing student performance at various educational levels. This teaching type was carried out both in Science, Technology, Engineering, and Mathematics (STEM) and non-STEM courses. During COVID-19, a rapid shift to remote learning environments was needed, leading to significant changes in learning and evaluation methods [1]. Schools and universities had to adopt digital tools and rethink their assessment and teaching strategies to adapt to remote education [2].  
The rapid evolution towards online learning, accelerated by the COVID-19 pandemic, has underscored the pivotal role of Learning Management Systems (LMSs) in modern education [3]. LMS platforms are robust frameworks for delivering course content, facilitating student engagement and, especially, conducting assessments remotely [4; 5].  
Among the innumerable LMS platforms available, Moodle (v.4.4) stands out as it is well-known for its versatility and extensive tools for online assessments [5; 6]. Furthermore, Moodle provides educators with a diverse array of question types, ranging from fundamental Multiple-Choice and True/False formats to more sophisticated structures like Cloze and Numerical questions [7]. These question types ensure educators can vary learning styles and assessment methods according to their needs [8]. The number of users of the most popular LMS platforms worldwide is shown in [9], with Moodle standing out significantly above the rest.
## 3 Results and Discussion
### Effect of Process Parameters
#### 3.1.2 Effect of Solvent Volume/Sample Ratio  
The influence of solvent volume/sample ratio on the extraction of quercetin and rutin was evaluated. As shown in Figure 4, the extraction yields rapidly increased with the solvent volume/sample ratio increase from 20 to 40 mL/g. In general, more solvent volumes can effectively dissolve more target components, leading to an enhancement of extraction efficiency. However, it was found that there were no significant changes on the extraction yields when the ratios further increased from 40 to 70 mL/g. One of the possible reasons is that ultrasound can promote the establishment of the dissolution equilibrium of the target compounds between the extraction solvent and the cell walls of plant samples, so it is very difficult to recover [37; 38]. This result demonstrated that a ratio of 40 mL/g is enough for extraction and is determined as the optimum ratio. A similar result was reported in the earlier literature [10].
## 2 Dark Current Noise Correction Method Based on Dark Pixels
### Dark Current Noise Correction Model
#### 2.4.4 Correction Method  
At every temperature the blackbody's target imaging data \(F\) frames were acquired to verify the correction effect; these data should be replaced by the imaging data of the corresponding scene in practical applications. To accomplish this, \(g_{\text{RNN\_dark}}(k)\) was input into the model; the obtained output value, \(g_{\text{RNN\_img}}(i,j,k)\), reflects the dark current response of the imaging pixel in a specific moment. Finally, target data were corrected as follows:  
\[g^{{}^{\prime}}_{\text{ppixel}}(i,j,k)=g_{\text{pixel}}(i,j,k)-\left[g_{\text {RNN\_img}}(i,j,k)-\overline{g}_{\text{RNN\_img}}(i,j)\right]-\overline{g}_ {\text{RNN\_dark}} \tag{8}\]  
where \(g^{{}^{\prime}}_{pixel}(i,j,k)\) is the imaging response after correction using this method; \(\overline{g}_{\text{RNN\_img}}(i,j)\) is the mean value of \(g_{\text{RNN\_img}}(i,j,k)\) at F frames; and \(\overline{g}_{\text{RNN\_dark}}\) is the mean value of \(g_{\text{RNN\_dark}}(k)\) at F frames. The significance of the correction parameter is as follows. \(\left[g_{\text{RNN\_img}}(i,j,k)-\overline{g}_{\text{RNN\_img}}(i,j)\right]\) represents the increase in the response value of the imaging pixel in the \(k\)th frame due to dark current fluctuations; \(\overline{g}_{\text{RNN\_dark}}\) represents the focal plane's overall dark level response value in the cycle of acquiring target imaging data. Data unaffected by the dark current can be restored after correction using Equation (8). It should be noted that except for \(g_{\text{RNN\_img}}(i,j,k)\), the other correction values did not affect temporal noise.  
Figure 6: Model of feature data built using RNN.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 5 Parameters Analysis on Deformation
### Waist Beam Size and Strength  
The impacts of the size and material strength of the waist beam on the horizontal deformation of the pile at x = 70 m are presented in Figure 11(a) and Figure 11(b), respectively. It can be observed that as the size and strength of the waist beam increase, the horizontal deformation of the pile gradually decreases. This is attributed to the increase in overall stiffness of the structure. However, since the waist beam has a limited effect on the overall deformation of the structure, the horizontal deformation of the pile does not change significantly with the increase in size and strength. The findings of this analysis can serve as a useful reference for the design of parameters and selection of materials for similar engineering projects.
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 4 Initiation of the Satellite Tank under Debris Impact
### Verification of the Simulation Model
The aluminum projectile and steel vessel are modelled using the constitutive equation MAT_JOHNSON_COOK and the state equation EOS_GRUNESEN. The Johnson-Cook model express the flow stress as follows:  
\[\sigma_{y}=(A+B(\overline{\epsilon}^{p})^{n}(1+C\ln\epsilon^{*})\Big{[}1-(T^{ *})^{m}\Big{]}, \tag{47}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & \(\rho\) (**kg/m\({}^{3}\)**) & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(\gamma_{0}\) & \(C_{4}\) & \(C_{5}\) \\ \hline Nitrogen & 1.25 & — & — & — & — & 0.4 & 0.4 \\ Hydrazine & 1008 & 1.979 & 0 & 0 & 0.11 & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Material parameters of nitrogen and liquid hydrazine. Figure 5: Attenuation curves of the shock wave pressure along the impact line at different mesh sizes. where \(A\) is the quasi-static yield stress, \(B\) is the strain hardening modulus, \(C\), \(n\), and \(m\) are the material constants. The equations are widely used for metallic materials as numerous efforts have been exerted to determine these parameters. The material properties and parameters used for aluminum and steel [23] are summarized in Table 5.  
Figure 6a presents the shock wave pressure and duration in liquid hydrazine calculated by Zeus, and Figure 6b shows the pressure nephogram of the shock wave obtained by ANSYS/LS-DYNA software at 0.10 \(\upmu\)s, 0.25 \(\upmu\)s, 0.32 \(\upmu\)s, 0.50 \(\upmu\)s, 0.70 \(\upmu\)s, and 0.88 \(\upmu\)s. \(t=0\) indicates the moment when the projectile contacts with liquid hydrazine, and \(t=0.88\)\(\upmu\)s indicates the moment when the projectile completely enters liquid hydrazine. As seen from Figure 6b, a hemispherical shock wave is formed when the projectile impacts liquid hydrazine, which is consistent with Figure 6a. As the projectile penetrates deeper, the hemispherical shock wave expands. As shown in Figure 6b, element A is located at the impact point in liquid hydrazine. Figure 7 exhibits the pressure-time curve of element A. The peak pressure is 80.42 kbar and the duration is 0.88 \(\upmu\)s. Meanwhile, the shock wave pressure and duration obtained by Zeus calculations were 83 kbar and 0.8 \(\upmu\)s, respectively [8]. The relative errors of the shock wave pressure and duration between the ANSYS/LS-DYNA simulation and Zeus calculation are 3.1% and 9.1%, thereby verifying the effectiveness of the simulation model. Figure 6: (a) Zeus calculation of the shock wave pressure and duration in liquid hydrazine [8]; (b) The pressure nephogram of the shock wave in liquid hydrazine obtained by ANSYS/LS-DYNA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & \(\rho\) **(kg/m\({}^{3}\))** & \(A\) **(MPa)** & \(B\) **(MPa)** & \(n\) & \(C\) & \(m\) \\ \hline Aluminum & 2700 & 200 & 144 & 0.62 & 0.01 & 1.00 \\ Steel & 7830 & 496 & 434 & 0.307 & 0.008 & 0.804 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Material parameters of aluminum and steel.
# An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
## 3 Methodology
### Data Set Construction  
The dataset has been constructed from historical data that was provided by the company, and from the customer satisfaction rate that was measured through Twitter mining [44]. We collected a sample of 100,000 customers' data from the Saudi telecom Company. From this figure, 27,000 were churners while 73,000 were non-churners. These historical data of customers were collected randomly within a time window of six months, from January 2017 to June 2017.  
Earlier studies differed in setting the time window for churning analysis and prediction. For instance, reference [28] proved that a customer mood in Twitter could be a predictor for churning three months later. In addition, reference [48] collected the three-month call data of customers from a Jordanian telecommunication company. Their results agreed with those found by [82] that two to three months is a sufficient time window to prepare a strategy for retaining customers and preventing churning.  
On the contrary, reference [34] stated that the four months is needed to predict a customer churning based on his/her dissatisfaction. However, reference [3] increased this to a five-month collection of tweets as dataset to predict their customer growth model. Other studies set even six months as the time window for churn prediction [6,8,33,36]. [PERSON] and [PERSON] [67] found that a customer should be with a company six months or longer to have an accurate prediction model.  
Thus, our selected time window is adequate conform to even the strictest previous studies. We take [67] suggestions into account, as we agree that a customer could become resentful but may take a longer period to carry out the churning action. Thus, we can consider that, as our dataset is from January 2017 to June 2017, the churning can only be estimated between July and December 2017 (Figure 4).
###### Abstract
proposed using the genetic algorithm to plan the path for a mobile robot equipped with the UVC lamp [20], although they focused more on robot trajectory planning for disinfection. [PERSON] and [PERSON] proposed an approach that considered the shapes of the objects in the environment, although they were limited to those with flat and unobstructed surfaces [21]. Based on the discussions above, we thus propose a novel approach for disinfection so that a more thorough and even disinfection can be achieved for all kinds of objects in a 3D environment, along with an assessment of the UVC irradiation intensity for validation of homogenous disinfection. With the introduction of the proposed autonomous system, it is expected that the risk of infection should be alleviated, which will be evaluated by the proposed quantitative assessment method. For demonstration, this system was applied to a series of experiments by navigating the robot through task execution in a smart ward, which is a hospital-like setting designed specifically for evaluating intelligent medical systems developed in our university. Questionnaire based on the system usability scale were also administered to obtain subjects' feedback [22]. Compared with previous approaches, we consider that the proposed system has the following merits:  
* Novel algorithms for automatic fever detection and medicine-taking recognition that ensure both human safety and comfort, as well as environmental disinfection that covers blind spots well, were developed. * A quantitative assessment method for evaluating the reduction in infection risk through the use of robots based on the duration and proximity of contact with confirmed cases was developed. * Via integration with multi-sensors and a collision-free path planning strategy, an autonomous system that was capable of navigating the mobile robot manipulator among static obstacles and moving people was developed and applied for a field study in a hospital-like environment. The rest of this paper is organized as follows: Section 2 describes the proposed system, including the modules for pose and action recognition, control strategy, environmental disinfection, and infection risk assessment. Section 3 presents the experimental results conducted in our university smart ward, along with the results of infection risk assessment and the analysis of questionnaire surveys from the subjects. Finally, concluding remarks are given in Section 4.
## 4 Results
### Quantitative Evaluation of the SSDA Smoke Detections  
#### 4.2.1 Comparison of AOD Values between Smoke and Non-smoke Pixels  
Figure 9 shows the median AOD values at 0.55 \(\upmu\)m in smoke and non-smoke pixels from the SSDA detection, TROPOMI smoke, and VIIRS ADP. The SSDA detection indicates higher AOD values in smoke pixels than in non-smoke pixels (Figure 9a). In the regions of North America (a) and Siberia (f) where large and dense smoke plumes were mainly emitted from forest fires during August, the AOD values were 1.42 (a) and 1.35 (f) in SSDA detected smoke pixels while they were only 0.24 (a) and 0.13 (f) in the background non-smoke pixels. In Indonesia (e) where the smoke plumes were generally associated with smoldering forest or peatland fires and tended to produce large amounts of coarse smoke particulate matter ([PERSON] et al., 2017), the SSDA smoke AOD value (1.73) was also much larger than the non-smoke AOD value (0.33). In contrast, in the regions of South America (c), Africa (d), and Australia (g) where thin smoke plumes were mainly released from small flaming savanna or grassland fires during August, the difference of AOD values (c: 0.55, d: 0.50, and g: 0.52) between smoke and non-smoke pixels were much smaller.  
Figure 8: Comparison of (**b**) the SSDA smoke with the smoke detections using algorithms developed by (**d**) [PERSON] et al. (2007) and (**e**) [PERSON] et al. (2007) from MODIS TOA reflectance over the southeastern North America (\(-\)93.96\({}^{\circ}\) E–91.31\({}^{\circ}\) E and 34.24\({}^{\circ}\) N–36.93\({}^{\circ}\) N) on 19 March 2019. (**a**) is the true-color image obtained from S-NPP VIIRS overpassing at 18:50 (UTC) while (**c**) is the true-color image obtained from Aqua MODIS overpassing at 18:44 (UTC). N is the number of detected smoke pixels of each algorithm.  
For the TROPOMI smoke detections, the AOD differences were also highest in regions of North America (a) and Siberia (f) which were similar to SSDA smoke detection results. However, in regions of South America (c) and Indonesia (e), the AOD difference values from TROPOMI smoke were negative with a lower AOD value in smoke than non-smoke pixel. The AOD in smoke pixel were zero in these two regions (c & e) because no smoke was detected in some evaluation polygons due to the high omission error of TROPOMI smoke in these two regions (Figure 9b). As for the VIIRS ADP product in Figure 9c, the AOD difference between smoke and non-smoke pixels was much larger than that of SSDA detections, which implies that VIIRS ADP primarily detects dense smoke at the center of smoke plumes with high AOD values, therefore missing the majority of pixels at plume edges or from thin plumes with low AOD values.
# Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem
## 4 Overview of Application of ABRKGA to LCPP
### An Eulerian Heuristic to Generate Improved Individuals for LCPP
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{2}{c}{**Best**} & \multicolumn{2}{c}{**Worst**} & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Std. Deviation**} & \multicolumn{2}{c}{**Variation**} \\  & **Fitness** & **Exec.** & **Fitness** & **Exec.** & **Fitness** & **Exec.** & **Fitness** & **Exec.** & **Fitness** & **Exec.** \\ \hline shapes4\_[0.997] & 454.80 & 464.50 & 458.54 & 484.06 & 456.44 & 465.41 & 1.13 & 11.18 & 0.25 & 2.40 \\ shapes4\_[0.998] & 454.91 & 291.62 & 458.95 & 287.36 & 456.82 & 289.34 & 1.29 & 4.15 & 0.28 & 1.43 \\ shapes4\_[0.999] & 456.35 & 235.71 & 461.07 & 237.20 & 458.01 & 236.44 & 1.52 & 4.79 & 0.33 & 2.03 \\ spfc\_[0.997] & 149.98 & 28.08 & 151.45 & 28.40 & 150.70 & 28.55 & 0.46 & 0.46 & 0.30 & 1.63 \\ spfc\_[0.998] & 150.15 & 28.53 & 151.54 & 28.58 & 150.70 & 28.56 & 0.38 & 0.23 & 0.25 & 0.80 \\ spfc\_[0.999] & 149.94 & 28.08 & 152.21 & 28.04 & 150.61 & 28.08 & 0.68 & 0.50 & 0.45 & 1.77 \\ trousers\_[0.997] & 298.75 & 718.47 & 302.70 & 745.63 & 300.84 & 734.46 & 1.57 & 21.92 & 0.52 & 2.99 \\ trousers\_[0.998] & 302.52 & 511.29 & 305.81 & 526.19 & 303.92 & 520.26 & 1.09 & 11.52 & 0.36 & 2.21 \\ trousers\_[0.999] & 301.90 & 543.22 & 305.16 & 511.10 & 303.36 & 528.66 & 1.03 & 22.34 & 0.34 & 4.23 \\ \hline Exec. time (avg) & & 100.47 & & 101.47 & & 101.04 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(e\)-ABRKGA applied to the separated set of instances (S). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**GAP**} & \multicolumn{3}{c}{**GAP**} \\
**Instances** & \multicolumn{3}{c}{**Connected (C)**} & \multicolumn{3}{c}{**Separated (S)**} \\  & **Fitness (\%)** & **Time (s)** & **Time (\%)** & **Fitness (\%)** & **Time (s)** & **Time (\%)** & **Time (\%)** \\ \hline albano\_[0.997] & 6.70\% & 125.05 & \(-\)152.10\% & 0.10\% & 48.41 & \(-\)64.06\% \\ albano\_[0.998] & 6.29\% & 119.54 & \(-\)143.63\% & 0.41\% & 47.01 & \(-\)61.67\% \\ albano\_[0.999] & 6.65\% & 115.73 & \(-\)143.25\% & 0.17\% & 45.40 & \(-\)62.25\% \\ blazi\_[0.997] & 1.34\% & 18.95 & \(-\)60.28\% & 0.40\% & 3.93 & \(-\)12.67\% \\ blazi\_[0.998] & 1.37\% & 18.68 & \(-\)59.48\% & 0.30\% & 5.16 & \(-\)16.94\% \\ blazi\_[1.999] & 1.34\% & 17.01 & \(-\)54.83\% & 0.29\% & 5.11 & \(-\)17.20\% \\ blazi\_[0.997] & 1.33\% & 179.77 & \(-\)153.98\% & 0.84\% & 38.56 & \(-\)32.82\% \\ blazi\_[0.998] & 1.68\% & 131.89 & \(-\)144.56\% & 0.67\% & 28.14 & \(-\)32.49\% \\ blazi\_[0.999] & 1.54\% & 132.69 & \(-\)136.05\% & 0.25\% & 31.02 & \(-\)32.92\% \\ blazi\_[0.997] & 0.64\% & 204.89 & \(-\)51.14\% & 0.82\% & 335.83 & \(-\)59.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: GAP comparison ABRKGA x \(e\)-ABRKGA to connected (C) and separated (S) instances.
# Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem
## 4 Overview of Application of ABRKGA to LCPP
### Initialization and Basic Concepts
Note that the complete cutting time (\(CCT\)) is incremented by \(Chebyshev_{dist}(edge(i))/V_{c}\). The next element, \(individual[i+1]=5\) and \(individual[n+i+1]=1\), needs to perform the air movement only to then cut edge 5 in the direction (\(1\to 5\)). Thus, add to \(CCT\)\(Chebyshev_{dist}(airEdge(i.v,(i+1).u))/V_{m}+Chebyshev_{dist}(edge(i+1))/V_{c}\). The time considered to cut in \(i\) and \(i+1\) is computed by this function \(CT(edge(i),edge(i+1))\):  
\[CT((u,v),(z,w))= \begin{cases}Dist(u,v)/V_{c}+Dist(v,z)/V_{c},&\text{if }v=z.\\ \text{ }Dist(u,v)/V_{c}+Dist(v,z)/V_{m}+Dist(z,w)/V_{c},&\text{otherwise.}\end{cases}  \tag{3}\]  
Figure 4: Input layout and solution constructed, for example illustrated in Figure 3.  
Figure 3: Encoder and decoder for an individual representation. In other words, the faster the cut is made, the higher the quality of the individual. Therefore, Equation (4) denotes the fitness function for an individual \(i\), Equation (5) denotes the best individual \(i\) of a population \(P\), and Equation (6) the quality of a population \(k\). These concepts are expressed more formally as:  
\[fitness(ind_{i}[e_{1},e_{2},...,e_{n},...])=(\sum_{e=1}^{n-1}CT(edge(e),edge(e +1)))+t_{offset} \tag{4}\]  
\[best_{ik}=ind_{ik}\in P_{k}\iff\{1,2,...,p\}:fitness(ind_{ik})\geq fitness( ind_{ij}) \tag{5}\]  
\[PQ_{k}=\sum_{i=1}^{popsize(K)}fitness(ind_{i})/popsize(K) \tag{6}\]  
The quality of the solutions generated and inserted into the population, either initially or during the execution of the evolutionary algorithm, has a positive impact when associated with some knowledge about the problem at hand. In light of this, we apply a strategy to transform the input graph (computational representation of the layout) into another graph containing at least one Eulerian path. Thus, this heuristic guides the construction of the initial population. Section 4.3 describes the heuristic in the context of solutions for the LCPP.
###### Abstract  
Channel modeling is crucial to the development and evaluation of modern wireless communication systems including satellite communication system, since there might be critical safety-of-life applications. Also, the channel model is of great importance to the performance evaluation of mobile communication systems. In recent years, encouraged by the widely application of unmanned aerial vehicles, the research on channel modeling for aerial and aeronautical communications attract lots of interests. In the published articles, stationary and non-stationary channel models have been developed for air-to-ground communications based on regular shaped geometry-based stochastic model (RS-GBSM). The modeling of air-to-air or satellite-to-aircraft (S2A) communication is still quite simple or completely lacking. For obtaining more precise model of S2A channel, this paper presents an analytical non-stationary S2A channel mode based on RS-GBSM with considerations on line-of-sight path, specular reflection path, and ground scattering path. Analytical expressions of the channel impulse responses, the transfer functions, the auto-correlation functions, and the Doppler power spectrum density based on 3-path model are derived and simulated. Also, the distributions of the path antenn, the path delay, and the normalized Doppler shift based on uniform distribution of the scatterers are derived, simulated and fitted.  
analytical channel model; non-stationary channel model; RS-GBSM; ground scattering; satellite-to-aircraft communication; mobile communications Article