esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Durability Testing
#### 2.5.2 Chloride Penetration Profile  
For chloride ingress, nine cylindrical specimens (dimensions: diameter \(\mathcal{O}\)100 mm \(\times\) height 50 mm) were prepared for each concrete mixture, using the same concrete as that used for the construction of the different walls. The specimens were demolded 24 h after casting and cured for 28 days in a controlled atmosphere (temperature 20 \(\pm\) 2 \({}^{\circ}\)C and relative humidity over 95%). At 28 days, six specimens of each concrete mixture were cracked using a controlled splitting test, targeting a crack width of 250 \(\upmu\)m. Three test conditions were studied:  
* Uncracked specimens, constantly submerged in a NaCl solution (35 g/L);
* Cracked specimens, constantly submerged in a NaCl solution (35 g/L);
* Cracked specimens subjected to wet/dry cycles (6 h wet/6 h dry) in a NaCl solution (35 g/L).  
The selection of the different test conditions was made to simulate different exposure conditions, in addition to the realistic case of the walls. Constant submersion was chosen to simulate underwater structures, and the wet/dry cycles to simulate structures in coastal areas subjected to the variations in the tide (partly submerged and partly exposed to air).  
Before being submersed in the chloride solution, the sides and bottom of the samples were coated with a double layer of epoxy resin (Episol Designtop from Resiplast, Wommelgem, Belgium) to allow the chloride to penetrate only through the top surface (unidirectionally) of the cylinders. The two coating layers were applied 24 h apart, after which the samples were placed in a closed container with the NaCl solution. After a 19-week immersion period, the samples were removed from the solution and evaluated. The chloride profile was determined using a colorimetric test with a spray of silver nitrate solution (AgNO\({}_{3}\), 0.1 mol/L). All samples were cracked, the solution was sprayed onto the surface, and then the samples were protected from light for a period of three hours. The depth of chloride penetration was measured subsequently. For samples cracked before submersion in the sodium chloride solution, the new crack was made perpendicular to the first, so that the chloride profile could be visualized in the direction perpendicular to the original crack.
###### Abstract  
Building roofs are sources of unwanted heat for buildings situated in zones with a warm climate. Thus, reflective coatings have emerged as an alternative to reject a fraction of the solar energy received by roofs. In this research, the thermal behavior of concrete slab roofs with traditional and solar reflective coatings was simulated using a computational tool. The studied slab configurations belong to two groups, non-insulated and insulated roofs. In the second group, the thermal insulation thickness complies with the value recommended by a national building energy standard. Weather data from four cities in Mexico with a warm climate were used as boundary conditions for the exterior surface of the roofs. The computational tool consisted of a numerical model based on the finite volume method, which was validated with experimental data. A series of comparative simulations was developed, taking a gray roof as the control case. The results demonstrated that white roofs without insulation had an exterior surface temperature between 11 and 16 \({}^{\circ}\)C lower than the gray roof without insulation. Thus, the daily heat gain of these white roofs was reduced by a factor ranging between 41 and 54%. On the other hand, white roofs with insulation reduced the exterior surface temperature between 17 and 21 \({}^{\circ}\)C compared to the gray roof with insulation. This temperature reduction caused insulated white roofs to have a daily heat gain between 37 and 56% smaller than the control case. Another contribution of this research is the assessment of two retrofitting techniques when they are applied at once. In other words, a comparison between a non-insulated gray roof and an insulated white roof revealed that the latter roof had a daily heat gain up to 6.4-times smaller than the first.  
solar reflective coatings; heat transfer; daily heat gains; cool roofs +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: [PERSON] Influence of Traditional and Solar Reflective Coatings on the Heat Transfer of Building Roofs in Mexico.
# Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions
## 4 Results
The aORs show greater probabilities of being cybervictims in students who do not profess any religion in the provinces of Jaen (aOR 1.6457; 95% CI 0.7-3.6), Seville (aOR 2.0588; 95% CI 0.1-23.6), and Huelva (aOR 1.1428; 95% CI 0.8-2.6). Highest odds among Muslim students in the provinces of Granada (aOR 1.5918; 95% CI 1.0-4.0), Malaga (aOR 1.2121; 95% CI 0.9-1. 9), Cordoba (aOR 2.5762; 95% CI 1.2-14.1), Cadiz (aOR 0.23611; 95% CI 0.2-0.3), Melilla (aOR 1.1666; 95% CI 0.9-3.3), and Ceuta (aOR 4.3333; 95% CI 0.9-18.3). In Almeria, the students of Buddhist religion present the highest rate of probability of being a cybervictim (aOR 1.6500; 95% CI 0.4-1.2). As for the probabilities of being cyberbullies, the aOR are presented at higher rates for the students of the Jewish religion in the province of Malaga (aOR 0.18885; 95% CI 0.2-0.3), Jaen (aOR 1.3963; 95% CI 1.1-3.0), Cordoba (aOR 3.5213; 95% CI 1.4-18.2), and Melilla (aOR 2.2812; 95% CI 1.0-5.7) and in students who do not profess any religion in the provinces of Granada (aOR 1.3618; 95% CI 0.9-3.6), Almeria (aOR 1.0526; 95% CI 0.2-0.3), Seville (aOR 1.3214; 95% CI 0.5-4.3), Huelva (aOR 1.5213; 95% CI 0.6-3.2), and Ceuta (aOR 2.2361; 95% CI 0.9-14.3). In Cadiz, the probability of being a cyberbully is highest among students of the Taoist religion (aOR 3.2857; 95% CI 1.5-24.8). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cybervictims** & **Cyberbullies** \\ \hline
**Ethnicities** & **aOR (95\% CI)** & **aOR (95\% CI)** \\ \hline Gypsy & 1.532874 (0.7–3.3) & 2.116573 (1.0–4.6) \\ Celtic & 1.208824 (0.2–8.6) & 0.0283916 (0.3–0.4) \\ Armenian & 1.210682 (0.4–4.2) & 0.7433155 (0.2–3.5) \\ Mongolian & 1.106063 (0.7–1.7) & 1.009394 (0.6–1.6) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of the Binary Logistic Regression showing the association between ethnicities and the profile of cybervictims and cyberbullies. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cybervictims** & **Cyberbullies** \\ \hline
**Race** & **aOR (95\% CI)** & **aOR (95\% CI)** \\ \hline African & 2.054859 (1.0–4.0) & 0.6846154 (0.3–1.6) \\ Asian & 3.273453 (0.9–12.4) & 4.043243 (0.9–18.2) \\ Indigenous & 1.331949 (0.9–1.9) & 1.093528 (0.7–1.6) \\ White & 0.5846154 (0.4–0.8) & 0.7903896 (0.6–1.1) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of the Binary Logistic Regression showing the association between races and the profile of cybervictims and cyberbullies.
# Post-Digital Learning for Rural Development: A Case Study of _Open Biotec_ MOOCs in Colombia
## 2 Materials and Methods
### Pilot Test of Training Route in Biotechnology: Open Biotec
#### 2.3.3 Evaluation Instruments for the Learning Process and Statistical Analysis  
The performance of the students in evaluative exercises was monitored before and after participating in the central training activity of the Agricultural Residues MOOC. The evaluation instruments to assess the learning outcomes were analyzed by inferential statistical methods using a diagnostic test at the beginning of the MOOC and a knowledge test at the end. The diagnostic and knowledge tests were composed of 10 multiple choice questions, the number of options per question was 4, and the quantitative grade comprised a range between 0 to 10.  
The internal consistency of diagnostic and knowledge tests were assessed using the Cronbach's alpha coefficient with five experts. The board of experts was composed by a doctor in bioprocess from Federal University of Rio de Janeiro (expert 1), a doctor in engineering from Universidad Pontificia Bolivariana (expert 2), a master in education from Universidad de Antioquia (expert 3), a doctor in communication sciences from Universidad Complutense de Madrid (expert 4), a master in biotechnology from Universidad Pontificia Bolivariana (expert 5). A Cronbach's alpha value above 0.5 indicates an acceptable level of reliability [57].  
The experts assessed the tests using the following five criteria: Number of questions in tests (C1); Clarity in the formulation of questions (C2); Coherence between the questions and the course objectives (C3); Level of relevance of the questions in the tests (C4); Overall degree of difficulty for the tests (C5). Each criterion was evaluated using a Likert scale from 0 to 5.  
The data were processed with the statistical software Statgraphics Centurion XVIII, to obtain the percentile distribution. To evaluate the statistical difference in student performance in both the diagnostic and knowledge test at the end of the MOOC it was performed the analysis of means and medians. A \(t\)-test was performed to compare the means and establish the respective confidence intervals and the distribution by estimating standardized bias and kurtosis. One-way ANOVA with Tukey multiple comparison test was used to evaluate the differences of the student grades between the diagnostic and knowledge tests.  
For the comparison of medians, the Mann-Whitney W test was used to evaluate if there was a statistically significant difference between the results of the diagnostic and the knowledge test. The effect size was estimated as the absolute difference between mean of the grades for each test, divided by the standard deviation of either group.
Moreover, the evaluation metrics of LoRa such as data rate, receiver sensitivity, and link budget are calculated, and concluded that the optimal parameters should be embedded in the coordinator node. The main contributions of this study are classified as below:  
* A hybrid architecture is proposed with a customized hardware based on Zigbee, LoRa, and Wi-Fi to monitor the multiple health parameters and environmental parameters of construction sites;* The evaluation metrics of LoRa network such as Link budget, data rate and receiver sensitivity are evaluated;
* A real-time hardware setup is implemented and the sensor data logged on cloud customized hardware and gateway. The organization of the paper is as follows: Section 2 provides the previous research carried out in the field of construction. Section 3 covers the proposed architecture. Section 4 covers the hardware implementation, such as customized sensor nodes and the gateway of the proposed architecture. Performance analysis of LoRa is covered in Section 5. Real-time implementation is discussed in Section 6 and the article concludes in the final section.
## 3 Problem Definitions for the Point-to-Point Network and Dynamic Scheduling  
### Problem Definition for Point-to-Point Network  
The VRP can be seen as an mTSP in which there are one or more depots with vehicles, such that each customer is visited by exactly one vehicle, forming a hub-and-spoke network. The problem addressed in this paper is a multidepot, multivehicle point-to-point network problem for the vertiport-to-vertiport operation of UAMs. In UAM operations, there may be numerous vertiports and several UAMs at each vertiport, depending on the capacity. Each vehicle at each depot needs to travel to another depot while minimising the cost function. The depot locations of the other vehicles are the customer locations for the vehicles that are at other depot locations, forming a point-to-point network in which each vehicle travels to another depot location exactly once and then returns to its original depot location. An example of our proposed solution is shown in a step-by-step manner in Figure 2.  
Figure 2a shows a network of possible routes. Figure 2b shows that vertiport V1 has a vehicle (k1) that travels from V1 to other locations and then returns to its place of origin. Figure 2c shows vertiport V1 with two vehicles, k1 and k2; each vehicle selects a different optimum route and returns to its original location. Figure 2e shows multiple vehicles at multiple depots, which travel to all destinations by selecting different optimum routes and then return to their origin locations. Our proposed solution to the problem is shown in Figure 2f, where each depot has multiple vehicles that travel to all the other depot locations without the need for additional depots for the vehicles.
# Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport
## 5 Discussion  
In this work, energy harvesting and a dynamic power management strategy are adapted to extend the battery life of an AloT-based wearable, automated cardiac arrhythmia detection system. Table 4 compares this work with recent similar systems. In [30, 41], wearable systems for ECG signal monitoring are developed, but no algorithm for diagnosis is proposed. Other works [26, 27, 29, 42] develop algorithms for heart rate detection only. In contrast, our system analyzes ECG signals and detects real-time arrhythmias with high precision using ultra-low-power devices. Experimental results demonstrate a battery charge that lasts for approximately 12 consecutive workouts of 45 min, without the need to manually recharge or replace it, meaning an attractive feature that other studies in [26, 38, 39] have not incorporated. A physiological adaptation of athletes to exercises was also shown in training tests showing non-continuous abnormal ECG signals. This study could also serve to determine the physiological adaptation to the training, improving the performance of athletes. Another possible application is the prediction of cardiac pathologies in athletes, decreasing the risk of sudden death. Further studies could also be directed to a reliability analysis of the system performance considering the fatigue life of the system components.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Epoch**} & \multirow{2}{*}{**Processing Time (seg)**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} \\ \cline{3-6}  & & **Error** & **Precision (\%)** & **Error** & **Precision (\%)** \\ \hline
1 & 2138 & 0.6779 & 56.21 & 0.6511 & 61.11 \\
2 & 2172 & 0.6132 & 66.60 & 0.5660 & 70.94 \\
3 & 2661 & 0.4885 & 78.33 & 0.3891 & 85.48 \\
4 & 1888 & 0.3523 & 85.79 & 0.2682 & 91.77 \\
5 & 2123 & 0.2461 & 90.69 & 0.1779 & 94.15 \\
6 & 2392 & 0.1773 & 93.84 & 0.1425 & 95.08 \\
7 & 1756 & 0.1295 & 95.98 & 0.1869 & 92.57 \\
8 & 1927 & 0.1032 & 96.87 & 0.0865 & 97.49 \\
9 & 1879 & 0.0823 & 97.66 & 0.0911 & 97.18 \\
10 & 1881 & 0.0684 & 98.15 & 0.0570 & 98.60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CNN precision evaluation results at each epoch.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Age**} & \multirow{2}{*}{**Years**\({}^{a}\) & **Arrhythmia** & \multicolumn{2}{c}{**Results**} \\ \cline{3-6}  & & & **(\(n\)-Sample per Training)** & **Accuracy** & **Training Section** \\ \hline
1 & 22 & 2 & (5), (-), (-) & 1.0 & Medium \\
2 & 23 & 1 & (-), (-), (-) & - & - \\
3 & 20 & 2 & (55), (-), (-) & 0.949 & High \\
4 & 21 & 2 & (-), (-), (-) & - & - \\
5 & 22 & 3 & (43, 63), (-) & 0.998, 0.993 & High, Medium \\
6 & 23 & 1 & (14), (15), (-) & 0.780, 0.916 & Medium \\
7 & 22 & 2 & (-), (-), (-) & - & - \\ \hline \hline \end{tabular} \({}^{a}\) Years refers to the number of years in a high-performance training program.  
\end{table}
Table 3: ECG Cardiac arrhythmia detection results.
## 4 Forest Fire Emissions Estimation in China
### Forest Biomass Simulation Based on BEPS Model and Satellite data  
In the CFFEES model, it is important to have an accurate estimation of forest biomass (or fuel loading). [PERSON] (1984) started to estimate biomass of a tropical forests based on the forest volumes [80]. Recently, many bio-ecological models using remote sensing data as input have been used to estimate the net primary productivity (NPP) and biomass [81-85]. [PERSON] _et al._ developed a boreal ecosystems productivity simulator (BEPS) model based on the Forest BioGeochemical Cycles (FOREST-BGC) model for quantifying the biophysical processes governing ecosystems productivity [81]. The BEPS model has been used to accurately estimate forest NPP in two forest sites of Northeast China and one forest site in Western China when using the remotely sensed data of high spatial resolution as input. The results show that the observed NPP agrees well with the modeled NPP from BEPS [86,87]. Others examples in China also support that the estimated forest NPP are highly related to field data in the study area by using BEPS model [88-91].
## 1 Introduction
All factors illustrated in Figure 1 play a role in determining GPP, \(\mathrm{F_{760}}\), and their relationship. However, the strength of these influences, and whether leaf nutrient content and canopy structure influence the GPP-\(\mathrm{F_{760}}\) relationship directly (through \(\mathrm{LUE_{P}}\), \(\mathrm{LUE_{f}}\) and Fesc) or occur indirectly (mediated by APAR or by a third variable), is not clear. In this study, we aimed to fill the gap in understanding on how nutrients and canopy structure control \(\mathrm{LUE_{P}}\), \(\mathrm{LUE_{f}}\) and Fesc, and we investigated the mechanisms that drive GPP and \(\mathrm{F_{760}}\) in a nutrient manipulation experiment. We asked the following questions:  
How do the treatments (N, NP, and P) influence \(\mathrm{LUE_{P}}\), \(\mathrm{LUE_{f}}\), and Fesc?What are the drivers of the light use efficiency equations terms (LUE\({}_{\text{P}}\), LUE\({}_{\text{f}}\), Fesc) that relate GPP and F\({}_{760}\)? What are the direct and indirect effects of nutrients (in particular N%) and canopy structure on GPP and F\({}_{760}\)? To answer these questions, we used GPP, F\({}_{760}\), and additional data on vegetation properties from a nutrient manipulation experiment in Mediterranean grassland with addition of N, P and N and P together (NP). The aim of the fertilization was to induce a changed in both plant nutrient content and structural traits (through changes in LAD mediated by plant community and LAI) within the ecosystem.
# Towards Incidence Management in 5G Based on Situal Awareness
## 5 Analysis and Decision-Making
### Analysis
#### 5.1.1 Detection  
The detection module is the connection between monitoring/aggregation tasks and the functions for understanding the information. Its inputs are aggregated high-level metrics built from correlated data and reports of situations directly issued by the sensors. After processing this information, the detection module builds the primitive situations (events or risks) to be analyzed. The rules to infer situations from the perceived data are provided by security operators and determined after the risks/events identification. Note that the same combination of metrics could trigger different situations; because of this, the proper management of the detection component implies adaptation of expert systems, especially those based on rules.
###### Abstract
Intelligent caching, intelligent forwarding, and computational offloading are some of the future directions in edge intelligence. ICN [7] has emerged as a paradigm promising to cope with the limitations of the current host-based network architecture by providing mobility support, efficient content distribution, scalability and security based on content names regardless of the location. The existing host-based network architecture faces significant challenges to meet demands in terms of scalability, mobility, security, and resource utilization [8]. Numerous investigations have confirmed the feasibility of applying ICN in different domains of IoV applications [9]. [PERSON] et al. [10] demonstrated video streaming over ICN/NDN on a global NDN (named data networking) test bed. Other studies [11; 12] have investigated the potential of ICN using NDN with IEEE802.15.4 for constrained IoT devices to implement a real-world smart agriculture scenario. Our paper aims to provide a comprehensive survey of the development and state-of-the-art of the convergence of edge-computing intelligence and ICN for realizing IIoV (Intelligent Internet of Vehicles) solutions. In the past few years, some survey articles have been presented in the context of ICN for IoT summarizing the applicability and potential of information centric networks for the Internet of Things [13; 14; 15; 16; 17]. [PERSON] et al. [18] carried out a brief survey on named data networking in vehicular ad hoc networks, while [PERSON] et al. [19] analyzed IoV and artificial intelligence for information-centric networks [20]. Our survey differs from previous studies in that it examines intelligence computing, edge caching, and networking in the context of ICN-based IoV. Figure 1 shows the converged architecture for IIoV. To the best of our knowledge, this is the only comprehensive study that discusses AI based solutions for the convergence of ICN-based IoV with edge computing as well as their potential benefits. In this regard, we summarize the major contributions of the paper as follows:  
* Presenting a comprehensive review regarding the utilization of edge intelligence and ICN for IoV solutions, addressing existing challenges. * Performing an in-depth review of the leading enablers of IoV, including wireless communications, mobile edge/fog computing, machine learning/AI, intelligent computing, and information-centric networking. * Providing a detailed ICN architecture suitable to meet the requirements of IoV networks, such as naming, caching, mobility, and security. * Highlighting challenges and issues faced by ICN-based IoV, indicating future research directions which may illuminate efforts to develop new intelligent IoV applications. The rest of the paper is organized as follows: In Section 2, a review of IoV, ICN, EC and AI is presented. Section 3 introduces a general model for ICN and edge computing
## 3 Computational Approach
### Weight Calculation
#### 3.1.4 Specific Example  
Table 1 displays the tagging information for different resources (R1-R5) by three users (U1-U3). Each cell under a resource column for a user row represents a tagging activity by that user for that resource. Every tagging action comprises one or several tags, with the date of the action specified in parentheses.  
1. Tag-Based Weight  
In the case of U1 for resource R3, we can see that the user used Tag1, Tag4, and Tag5. If U1 has used Tag1 three times, Tag2 one time, Tag3 one time, Tag4 two times, and Tag5 one time across all their activities, then the tag-based weight for resource R3 can be calculated as a proportional representation of these counts. For example, the weight for Tag1 can be \(3/(3+1+1+2+1)=0.375\); for Tag4 it can be \(1/(3+1+1+2+1)=0.25\); and for Tag5 it can be \(2/(3+1+1+2+1)=0.125\). Thus, \(W_{tag}\)(U1, \(R_{3}\)) = 0.375 + 0.25 + 0.125 = 0.75. Table 2 shows an example of tag-based weight.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **R1** & **R2** & **R3** & **R4** & **R5** \\ \hline \multirow{3}{*}{U1} & Tag1, Tag2 & & Tag 1, Tag 3, & Tag1, Tag4, Tag5 & \\  & (May 15) & Tag4 & & (May 11) & \\  & & (May 15) & & Tag4, Tag6 & \\ \multirow{3}{*}{U3} & Tag1, Tag7 & & Tag 8 & Tag 4 & Tag1, Tag 7 \\  & (May 15) & & (May 11) & (May 6) & (May 6) \\ \hline \end{tabular}
\end{table}
Table 1: User-resource tagging information.