esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Importance of Results and Future Research Directions  
To the best of our knowledge, this study is the first wherein the effect of silicon anti-transpirant application on CO2 fluxes exchanged between the meadow and the atmosphere was evaluated. It proved that AT treatment has a positive effect on the growing season and annual cumulative NEE fluxes at the site with a higher WTD (Figure 13, Table 2), resulting in reduced net emissions (like in 2021) or increased net CO2 sequestration (like in 2022). Therefore, it can be concluded that the application of AT under suitable groundwater level conditions has the potential to be a tool to improve the carbon balance of grasslands and reduce the negative climatic impact of grasslands located on degraded peatlands, which are net emitters of CO2 [5]. On the other hand, the response of plants to the same treatment at the site with a lower WTD is not unequivocal, and this may lead to contradictory conclusions. Hence, it is necessary to continue this research on other grasslands under different  
Figure 16: Relationship between cumulative NEE fluxes in each cut and aboveground biomass yield for (**a**) HWL and LWL and (**b**) HWL_Si and LWL_Si plots. Vertical lines indicate the turning points when the meadow switched from being a net source to a net sink of CO2 for different yield levels.  
climatic conditions and management strategies to have a clearer picture of the impact of antitranspirants on CO\({}_{2}\) budgets.  
However, from the farmer's point of view, using AT with silicon in the meadow is disadvantageous, since this treatment contributed to a significant yield reduction. Therefore, it will not be easy to convince farmers that the application of silicon AT is beneficial for the climate, and that this outweighs the profits obtained from animal feed production. The results of this study show the potential for using antitranspirants; however, they are not fully beneficial. Therefore, it is desirable to also carry out further measurements in this respect on other ATs to determine their impacts on both CO\({}_{2}\) emissions and grassland yields. If positive results are obtained in both aspects, detailed economic analyses should be carried out in future studies to determine the costs incurred for applying AT to meadows and the profitability of this treatment.
## 3 Set Up Buildings' Settlement Prediction Model
### Determination of Datasets for Building Settlement Prediction
#### 3.1.2 Data Standardization and Anomaly Handling  
In order to reduce the influence of the magnitude on the settlement prediction results, the raw monitoring data were standardized, and the standardization formula was:  
\[x^{\prime}_{i}=\frac{(x_{i}-\overline{x})}{s} \tag{1}\]  
\(x^{\prime}_{i}\) is the standardized data, \(x_{i}\) is the original monitoring data, \(\overline{x}\) is the mean of the original monitoring data, and \(S\) is the sample standard deviation of the original monitoring data.  
The dataset obtained from the previous section is input into the network for training, the NAN missing values (abnormal data) appearing during the training process need to be deleted from the rows where they are located, and the deleted data rows need to be supplemented to obtain the complete dataset. The final dataset obtained is the settlement  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Layer** & **Soil Layer** & **Thickness/m** & **Unit** & **Cohesion/kPa** & **Internal** & **Void’s** & **Poisson’s** \\
**Number** & **Name** & & **Weight/kN-m\({}^{-3}\)** & **Angle/\({}^{\circ}\)** & **Ratio** & **Ratio** \\ \hline
1 & fill & 1.9 & 17.5 & 8 & 10 & 1.06 & 0.31 \\
2 & clay & 1.6 & 17.7 & 18 & 17 & 1.10 & 0.33 \\
3 & silty chalky & 4.2 & 17.1 & 13 & 12 & 1.27 & 0.34 \\
4 & silty clay & 11.3 & 16.5 & 11 & 13 & 1.46 & 0.36 \\
5 & clay & 3.5 & 17.2 & 16 & 15 & 1.21 & 0.34 \\  & clay chalk & & & & & & \\
6 & with chalky & 4.5 & 17.9 & 21 & 12 & 1.11 & 0.32 \\  & clay & & & & & & \\
7 & sandy chalk & 6.8 & 17.9 & 31 & 4 & 0.99 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Soil parameters.  
monitoring values for 100 days for each of the 14 monitoring points of buildings I, J, K, P, and Q. It is worth mentioning that the dataset used in this paper is relatively small and may not be suitable for overly complex studies.
## 5 Discussions and Conclusions
Therefore, similar to the real-time implementation of CPWC imaging, 2D-DMAS imaging can be also constructed by sequentially combining these magnitude-scaled low-quality images from distinct PW transmit angles and then by restoring the signal dimensionality of the high-quality image with a final \(p\)th power operation. In this way, 2D-DMAS beamforming can be efficiently implemented without the need formemory allocation for the huge echo matrix. Though Rx-DMAS may provide similar performances in lateral resolution, image CR, and ghost-artifact suppression to the proposed 2D-DMAS by selecting a \(p\) value higher than that of 2D-DMAS, the implementation of Rx-DMAS can be relatively complicated. Specifically, for Rx-DMAS, the echoes from all PW transmit angles have to be firstly synthesized before the DMAS processing in the receiving channels. Consequently, unlike the alternative form of 2D-DMAS in Equation (9), Rx-DMAS inevitably suffers from huge memory allocations for the echo matrix. Moreover, the axial resolution of 2D-DMAS is always higher than that of Rx-DMAS regardless of the \(p\) value of Rx-DMAS. This is because the spatial coherence of 2D-DMAS in the dimension of the PW transmit angle also helps to reduce the axial length of pulse responses by comparing the phase coherence among different PW transmit angles. Due to the aforementioned observations, the proposed 2D-DMAS appears to be a superior beamforming method to Rx-DMAS in terms of both implementation and overall performance in image quality. In future work, 2D-DMAS will be studied using the implementation in Equation (9) for its performance with a higher number of PW transmit angles.
## 5 Results and Analysis
### Roughness Loss  
The irregular variation on the surface of mines causes roughness, and such an uneven surface has a great influence on the propagated electromagnetic signal. The roughness of side walls causes signal scattering, which results in a deterioration in link quality. The expression of roughness loss for the mine gallery is given as [7; 37]:  
\[L_{R}=4.343\pi^{2}r^{2}\lambda\bigg{(}\frac{1}{2w^{4}}+\frac{1}{2h^{4}}\bigg{)}z \tag{41}\]  
where \(r\) is the Gaussian distributed root mean square roughness parameter. Figure 9 shows the roughness loss analysis under the operating frequency of 2.4 GHz and 5 GHz. The result clearly shows that roughness loss has a direct relationship with the link distance, and the response of roughness loss increases with the increase in link distance. Furthermore, it can be observed that the 5 GHz frequency is experiencing less roughness loss as compared to 2.4 GHz.
# Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets
## 3 Materials and Methods
### Observing Student Movement in LMS  
Moodle log files were used to track the movement of students in the e-course. Moodle is used for four primary purposes: (1) making course materials available for browsing when describing class contents, (2) performing a quiz during class, (3) referring to an external web page, and (4) submitting a report at the end of the term. To analyze learning history, it is desirable to clarify the purpose of collecting Moodle course logs [30]. Moodle provided an easy way to get student access logs. With just a few clicks in the Moodle administration page, Moodle would export the log files in.csv format. The logs contained:  
* Time
* User name
* Affected user
* Event context
* Component
* Event name
* Description
* Source
* IP  
Time, user name, event name, and context were essential for the purposes of the research. It was also necessary to remove teacher access from the log files, as they were also written to the log files, but were not needed to model the student behavior. The logs were organized as follows: The main unit was a student. The student contained a session number. Each session contained links to the Moodle logs that displayed which parts of Moodle the student had visited during that session. The sessions were obtained from the Moodle logs. To get a session, the data were organized by name and time. Based on the work [24], an upper time limit was created \(-\theta\). Exceeding this time limit meant a new session. If the time on the first page in one session was \(t_{0}\) and the user's current URL was \(t\), then this page was considered as one session if the inequality \(t-t_{0}\leq\theta\) was true. While for the next page, it was inequality \(t_{0}+\theta>t\).  
This was the first page of the next session. Normally, \(\theta\) was set to 30 min. The time of the following accesses were important. If the following access was created sooner than 45 min from the previous one, it was assumed to be a single session. If the time interval was greater than 45 min, it was a new session. An interval of 45 min was chosen because normally one lesson took 1.5 hours. In this case, if the student did not attend the course at least twice during one lesson, we could assume that they did not use the e-course during that lesson [31; 32; 33]. Based on these times, the sessions were removed that contained only one entry, as this was not a session but only one login per class.  
However, in addition to observing the sections visited, the focus was on the number of visits in the \"Book\", \"Lecture\" and \"Assignment\" sections. The log files showed that only these items were used by students to study. The aim was to find out how the number of study material visits would affect the final grade of students. First of all, attention was payed to visits to materials used to study the principles of operating systems as the exam itself was largely theoretical.
## 3 Results  
### Matching Accuracy  
Out of the 73 traffic signs used in the experiment, 27 (or 37%) signs did not pass the 67% comprehension test pass rate set by ISO 3864-1:2011 [24], as shown in Table 3. The 27 signs that failed can be broken down to 11 regulatory signs and 16 warnings signs. However, 24 out of the 27 signs that failed were signs displayed in symbols, which were often miscomprehended  
In terms of matching accuracy, four (4) regulatory signs obtained a 100% matching accuracy, namely minimum speed limit of 40, no waiting anytime, left lane must turn left, and no entry for cars. For warning signs, road narrows obtained the highest matching accuracy at 98%, followed by spillway and flooding area ahead, which both got a 95% matching accuracy (Table 4). On the other hand, traffic signs which obtained a matching accuracy of below 67% were considered unacceptable because of the low comprehension test result. Among the regulatory signs, the lowest matching accuracy was obtained by speed derestriction, as none of the participants matched  
Figure 4: Sample evaluation sheet for cognitive design features.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Traffic Sign Type**} & \multicolumn{5}{c}{**Above 67\%**} & \multicolumn{5}{c}{**Below 67\%**} \\ \cline{2-10}  & **Symbol** & **Text** & **Symbol + Text** & **Total** & **Symbol** & **Text** & **Symbol + Text** & **Total** \\ \hline Regulatory & 19 & 6 & 10 & 35 (76\%) & 8 & 0 & 3 & 11 (24\%) \\ Warning & 6 & 1 & 4 & 11 (41\%) & 16 & 0 & 0 & 16 (59\%) \\ Total & 25 & 7 & 14 & 46 (63\%) & 24 & 0 & 3 & 27 (37\%) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The evaluation result of the traffic signs based on the 67% passing rate set by ISO 3864-1:2011.  
The current study also investigated the effects of traffic sign type and display condition on the comprehension of drivers and nondrivers. In terms of traffic sign type, drivers had higher matching accuracies for both the regulatory and warning signs compared to nondrivers. For the regulatory signs, the matching accuracy of drivers was higher at 79.93% compared with nondrivers at 75.87%, which was significantly different at \(p\) = 0.033 (Table 5). On the other hand, warning signs tended to be more difficult to comprehend, as reflected by a lower matching accuracy of drivers and nondrivers at 56.05 and 48.77%, respectively.  
In terms of display condition, signs that were represented by symbols had the lowest matching accuracy, with drivers registering at 61.97%, which was significantly different to nondrivers at 55.03% (\(p\) = 0.004) (Table 6). Although there was no significant difference between drivers and nondrivers for both text and combination of text and symbol, matching accuracy was still the best with text only, at 92.86% for drivers and 91.90% for nondrivers. However, for both drivers and nondrivers, the matching accuracy for signs displayed in text and signs displayed in text and symbol were both significantly different from those signs displayed in symbols only.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 4 Simulation Setup and Result Analysis
### Simulation Results and Discussion
#### 4.3.4 Impact of Increasing Number of Considered Junctions Dynamically  
One important question that needs to be answered is that what is the optimal number of junctions that should be considered for achieving the best performance in terms of various parameters? For answering this question, we have compared the performance in terms of packet delivery ratio with respect to increasing the number of considered junctions. The simulation result shows that considering two junctions results in a better performance as compared to considering more than two junctions.  
Figure 8 shows that increasing the number of considered junctions from 1 to 2 results in an increase in packet delivery ratio. However, as we start increasing the number of junctions beyond 2, the performance of DMJSR starts to degrade in terms of packet delivery ratio. The major reason behind this observation lies in one of the very basic characteristics of VANETs, i.e., the network in VANETs is very dynamic and the network topology changes very rapidly. Because of this reason, considering more than 2 junctions while making a decision for packet forwarding degrades the performance instead of achieving an enhanced performance. Moreover, keeping all the information that lies between multiple junction such as density, direction of vehicles etc., in a very dynamic topology also leads to an overhead in terms of processing and storage. The simulation results revealed that the proposed protocol is capable of achieving better performance as compared to existing routing protocols. As a next step, performance evaluation using a more complex mobility model based on real traffic traces that fully capture the complexity involved in the movement of the nodes can be considered.  
Figure 8: Packet delivery ratio vs. increasing no of junctions dynamically at a time @ 350 vehicular nodes.  
Figure 7: Routing overhead vs. the number of nodes (@5 packets/second).
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
## 5 Discussion
The procedure followed to evaluate the results obtained consisted of transforming the RGB color values (Figure 11a), obtained by means of colorization and quaternionic processing techniques, to the Lab color space (Figure 11b). Next, the mean values and variances of the measurement areas of each defect are calculated for each of the Lab components (\(\mu_{L}\), \(\mu_{a}\), \(\mu_{b}\), \(\sigma_{L}\), \(\sigma_{a}\) and \(\sigma_{b}\)), and then the magnitude of the vector defined by  
Figure 11: Color spaces used in the study: (**a**) RGB space used to display color images resulting from the proposed processing, (**b**) Lab space used to evaluate the results obtained. these components is calculated through the expressions (25) and (26). Finally, the formulas of the SNR metrics are applied with the obtained values \(\mu_{q}\) and \(\sigma_{q}\). \[\mu_{q} =\sqrt{\mu_{L}^{2}+\mu_{a}^{2}+\mu_{b}^{2}} \tag{25}\] \[\sigma_{q} =\sqrt{\sigma_{L}^{2}+\sigma_{a}^{2}+\sigma_{b}^{2}} \tag{26}\]
# On Resolvability- and Domination-Related Parameters of Complete Multipartite Graphs
## 4 Domination-Related Parameters
### Locating-Dominating Number
Since (8) holds for \(r=k\), we obtain that:  
\[\gamma_{l-d}(K_{t_{1},t_{2},\ldots,t_{k}})=\sum\limits_{i=1}^{k}t_{i}-k. \tag{7}\]  
This implies that there exists a minimum locating-dominating set of cardinality \(\sum\limits_{i=1}^{k}t_{i}-k\) in \(K_{t_{1},t_{2},\ldots,t_{k}}\). Let \(S\) be the minimum locating-dominating set of cardinality \(\sum\limits_{i=1}^{k}t_{i}-k\). in \(K_{t_{1},t_{2},\ldots,t_{k}}\). Let \(U=\{x_{i}\in T_{i}:1\leq i\leq k\}\) and \(S=V(K_{t_{1},t_{2},\ldots,t_{k}})\setminus U\), then \(S\) comprises \(t_{i}-1\) elements from every \(T_{i}\) where \(1\leq i\leq r\). Note that \(S\) is a locating-dominating set of \(K_{t_{1},t_{2},\ldots,t_{k}}\), since \(S\bigcap N[x_{i}]\
eq S\bigcap N[x_{j}]\) for \(1\leq i<j\leq k\) and \(S\bigcap N[x_{i}]=S\setminus T_{i}\ 1\leq i\leq k\). Moreover,\(S\) is minimum as the inclusion of more than one vertices from any partite set \(T_{i}\) in \(S\) would cause a contradiction, as the two vertices from the same partite set would have the same intersections. Moreover, the minimality of \(S\) follows from (7), as \(S\) is a locating-dominating set. Now, we add \((k+1)^{\text{th}}\) partite set, say \(T_{k+1}\), to \(K_{t_{1},t_{2},\ldots,t_{k}}\) to obtain \(K_{t_{1},t_{2},\ldots,t_{k},t_{k+1}}\). For any \(x^{\prime}\in T_{k+1}\), let \(S^{\prime}=S\bigcup\{x^{\prime}\}\). Note that \(S^{\prime}\) is a locating-dominating set of \(K_{t_{1},t_{2},\ldots,t_{k+1}}\) since \(S^{\prime}\bigcap N[x^{\prime}]\
eq S^{\prime}\bigcap N[x_{i}]\) for \(1\leq i\leq k\) and \(S^{\prime}\bigcap N[x^{\prime}]=S^{\prime}\setminus T_{k+1}\). Moreover, \(S^{\prime}\) is minimum as \(S\) is minimum in \(K_{t_{1},t_{2},\ldots,t_{k}}\). Since \(|S|=\sum\limits_{i=1}^{k+1}t_{i}-(k+1)\), we obtain that:  
\[\gamma_{l-d}(K_{t_{1},t_{2},\ldots,t_{k+1}})=\sum\limits_{i=1}^{k+1}t_{i}-(k+1).\]  
By applying the induction hypothesis on \(r\), the proof is finished. The following example illustrates an application of Theorem 4. **Example 6**. : _Let \(K_{2,3,5}\) be the complete multipartite graph as depicted in Figure 3. Then, its seven black vertices form a minimum locating-dominating set. By Theorem 4.1, we obtain that \(\gamma_{l-d}(K_{2,3,5})=10-3=7\)._  
The next section studies an interaction between resolvability and domination of graphs.
## 2 Photon Crystal Design
In impedance representation [27], the condition for the  
Figure 1: The calculated dispersion of the 1D Photonic Crystal (PC)-based structure (shown out of the scale in the lower right corner), supporting long-range surface plasmon polaritons (LRSPP) propagation along silver/ZnS/air interface. Color scale presents a value of \(\log_{10}(\mathrm{I_{e}}/I_{0})\) where \(I_{e}\), \(I_{0}\) are respectively the intensities of light at the interface and at the entrance of the structure. In lower left corner, the magnified small part of the dispersion curve corresponding to the wavelength used is shown. See [27] for further details. surface wave propagation remains the same: \(Z_{left}+Z_{right}=0\), where \(Z_{left}\) is an impedance of the system SiO\({}_{2}\)/(HL)\({}^{19}\)/Al\({}_{2}\)O\({}_{3}\)/ZnS(1)/Ag calculated using recursion formulae of Ref. [27] together with the formula (13) describing impedance of the photonic crystal there (for current purposes we can consider this PC as an infinite one). However, now \(Z_{right}\) is _not an_ impedance of the infinite external media (air) equal to  
\[Z_{air}=\sqrt{1-\left(\rho/n_{air}\right)}/n_{air} \tag{1}\]  
but instead an impedance of the system ZnS(2)/air which is equal to  
\[Z_{right}=Z_{air}\frac{Z_{ZnS}-iZ_{air}\tan\alpha_{ZnS2}}{Z_{air}-iZ_{ZnS}\tan \alpha_{ZnS2}} \tag{2}\]  
where \(Z_{ZnS}=\sqrt{1-\left(\rho/n_{ZnS}\right)}/n_{ZnS}\), \(\alpha_{ZnS2}=\frac{2\pi}{\lambda}n_{ZnS}d_{ZnS2}\sqrt{1-\left(\rho/n_{ZnS} \right)^{2}}\), and \(\rho\) is a \"propagation parameter\", viz., an angular variable equal to \(\rho=n_{i}\sin\theta_{i}\). This variable, according to the Snell's law, remains the same for each layer having the refraction index \(n_{i}\) with \(\vartheta_{i}\) being an angle between the propagating light and the normal to all interfaces [27]. This change is not negligible and must be taken into account because already for 20 nm-thick ZnS layer with the refraction index \(n_{ZnS}=2.318\) and \(\rho=1.017\) we have comparable values of \(\alpha_{ZnS2}=0.41\), \(Z_{air}=0.1285i\) and \(Z_{ZnS}=0.39\). At the same time, for many practical purposes, the whole effect of this additional layer can be seen simply as a relatively slight modification of the refraction index of an infinite external medium. Comparing (1) and (2) we see that for \(\rho=1.017\) Equation (1) gives the same impedance as Equation (2) if one put \(n_{air}=n_{eff}=1.03\) into Equation (1). The results of our calculations are presented in Figure 1. It is easy to see that for He-Ne laser wavelength of 632.8 nm we do have a LRSPP wave characterized by the angular variable (propagation parameter) \(\rho\cong 1.02\) and possessing quite strong thirty-fold enhancement of the intensity of light.
## 3 Materials and Methods
### RSSI Measurements  
Figure 9 shows the measurement results of the value of RSSI as a function of distance for provider A (marked as a, b, and c) and provider B (marked as d and e). The measurements presented include a variation in speed, direction, and time. Following the same approach as for RSRP, each of these measurements has been spatially correlated with all others to check consistency for a single provider and prove sufficient differentiation between different providers. The result of this analysis is presented in Table 7. While repeated measurements under various conditions including speed, direction, and time of day indicate a spatial correlation \(0.8<sc<0.9\), a value of \(sc\leq 0.8\) is found correlating measurements for different providers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multirow{2}{*}{**Provider**} & \multirow{2}{*}{**Excellent**} & \multirow{2}{*}{**Good**} & \multirow{2}{*}{**Fair to**} & \multicolumn{2}{c}{**Risk of**} & \multirow{2}{*}{**Range**} \\  & & & & & **Paor to** & & **or No** \\  & & & & & **Signal** & & **Analysis** \\ \hline (a) & A & 520 m & 1164 m & 663 m & 654 m & 3001 m \\ (b) & A & 1014 m & 584 m & 1078 m & 361 m & 3001 m \\ (c) & A & 881 m & 500 m & 949 m & 671 m & 3001 m \\ (d) & B & 715 m & 735 m & 602 m & 949 m & 3001 m \\ (e) & B & 1176 m & 555 m & 439 m & 831 m & 3001 m \\ \hline \hline \end{tabular}
\end{table}
Table 6: Provider based assessment RSRP.  
Figure 8: Histogram of the confidence intervals for RSRP.  
The measurement shown in Figure 9 has been evaluated based on the criteria in Table 4 and the result is presented in Table 8. In contrast to the evaluation of RSRP, the RSSI indicates at least fair to poor coverage for the full range. Nevertheless, provider A delivers better performance than provider B in the overall assessment of RSSI.