esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Illustrative Example of Proposed Method
### Denoising Results  
Based on the optimal wavelet basis selection, threshold setting method and optimal decomposition layers selection mentioned above, wavelet denoising is carried out with the concrete strain data on 2 June 2020.  
Figure 7a shows the time series before and after denoising. It can be seen that the wavelet denoising can effectively remove the redundant information brought into the system by the external environmental noise, which helps reveal the true characteristics of the data sequence. Figure 7b zooms in to show a time section where the sequence trend changes from stable to declining. The denoised sequence manages to capture this drastic trend change and follows the original sequence closely. Figure 7c shows a relatively stable time section, where it can be seen that the denoising process significantly improved the smoothness of the sequence, and the small fluctuation of the original sequence is retained in the denoised sequence. Therefore, the proposed method achieves good denoising results under different fluctuation levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decomposition Layers** & **June** & **August** & **October** & **December** \\ \hline
1 layer & 0.5835 & 0.653 & 0.6664 & 0.6664 \\
2 layers & 0.2909 & 0.3429 & 0.3544 & 0.3544 \\
3 layers & 0.184 & 0.231 & 0.2386 & 0.2386 \\
4 layers & 0.152 & 0.1983 & 0.2056 & 0.2056 \\
5 layers & 0.1464 & 0.1945 & 0.1999 & 0.1999 \\
6 layers & 0.1505 & 0.2032 & 0.2043 & 0.2043 \\
7 layers & 0.1795 & 0.2549 & 0.2366 & 0.2366 \\
8 layers & 0.2063 & 0.287 & 0.2636 & 0.2636 \\
9 layers & 0.2626 & 0.3039 & 0.311 & 0.311 \\
10 layers & 0.4165 & 0.347 & 0.3336 & 0.3336 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comprehensive scores of wavelet denoising with different decomposition layers.  
Figure 7: Time series before and after denoising: (**a**) A global version; (**b**) A larger version at location 1; (**c**) A larger version at location 2.
# Enhancing Freeway Traffic Capacity: The Impact of Autonomous Vehicle Platooning Intensity
## 4 Case Study
### Simulation Validation  
In order to validate the simulation result, the traffic data generated by the simulation were compared to the data taken from previous research and the latest version of the Highway Capacity Manual as shown in Table 4.  
Most studies have implemented speed limit conditions, except for Ref. [36], which solely focuses on analyzing theoretical capacity based on headway without setting speed limits. Furthermore, this particular study solely conducted numerical analyses without incorporating any simulations. As for the scenarios, one lane is designated as a regular lane, while the other lane is exclusively dedicated to CAVs, which may result in significant differences in traffic under different MPRs. Consequently, when MPR is relatively large or small, the errors in the results are relatively significant.  
In the simulation scenario of Ref. [19], two out of four lanes are closed due to an incident at the downstream section, resulting in a large number of lane changes. Consequently, their flow calculation result is much smaller than our result.  
According to the HCM, at a speed limit of 80 km/h, 90 km/h, and 100 km/h, the average flow rate for a single lane is around 1994 veh/h, 2119 veh/h, and 2200 veh/h, respectively. The results align closely with our simulated values, with an error margin of less than 6%.  
In general, the scenarios in previous studies differ from each other and may not be directly comparable to our research. However, regardless of the involvement of CAVs, the disparity between our simulated values and the results obtained in other studies is minimal and falls within a reasonable range. This observation further substantiates the validity of our data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**References**} & **Speed Limit** & \multirow{2}{*}{**MPR**} & **Capacity in** & **Capacity in** \\  & **(km/h)** & & **Reference (veh/h)** & **This Study (veh/h)** \\ \hline
[14] & 45 & 0 & 2000 & 1847 \\ \hline
[19] & 104 & 0 & 1621 & 2125 \\ \hline
[9] & 96 & 0 & 2106 & 2100 \\ \hline
[44] & 108 & 0 & 2090 & 2140 \\
108 & 100 & 3200 & 3376 \\ \hline
[12] & 105 & 0 & 2018 & 2130 \\
105 & 105 & 60 & 2500 & 2548 \\
105 & 100 & 3970 & 3351 \\ \hline
[36] & / & 0 & 1805 & 2145 \\
100 & / & 67 & 2700 & 2660 \\
100 & / & 100 & 3600 & 3376 \\ \hline
[4] & 50 & 0 & 1620 & 1893 \\
105 & 50 & 50 & 1734 & 2102 \\
100 & 50 & 100 & 2044 & 2757 \\ \hline
[48] & 60 & 0 & 1746 & 1965 \\
100 & 80 & 0 & 1994 & 2059 \\
100 & 0 & 2200 & 2116 \\ \hline
[5] & 110 & 0 & 2133 & 2145 \\
100 & 20 & 2230 & 2232 \\
100 & 40 & 2366 & 2354 \\
100 & 60 & 2643 & 2584 \\
100 & 80 & 3170 & 2872 \\
100 & 100 & 3873 & 3376 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary and comparison with other research data.
# Determinants of Memory Encoding of Altruistic Messages: M-Delphi and F-DEMATEL Approach
## 2 Literature Review
### Factors Associated with Memory Encoding of Altruistic Messages
#### 2.5.2 Emotional Appeal  
According to dual-process theories, individuals' preferences and cognitive activity generally have a systematic, rational path and a peripheral, heuristic path [39]. Emotional information is processed without much cognitive effort and is processed via the peripheral path [41]. The link between emotion and memory encoding can be explained by [PERSON]'s somatic market hypothesis, which, along with bodily sensations, plays important roles in decision making and the strength and quality of memories [42]. Brain imaging studies have shown that emotional stimuli activate the amygdala, the brain part responsible for emotional memory [43]. In addition, [44] argues that the amygdala affects explicit memory by activating other brain regions involved in memory. Emotional appeal also has a positive association with the retainment of information because it increases the intrinsic value of the information to the receiver [44]. On the other hand, it induces a lasting altruistic concern in the receiver of information [45]. Thus, emotion has both neural and feeling connections to information storage. Therefore, this study proposes that the emotional appeal of altruistic content on television will lead to its viewers' greater encoding of the content. This factor is an environmental factor because the emotional load of a message depends on whether the framer designs it as such [46].
## 3 Visualization and Clustering
### Measures for Text Similarity Detection  
To evaluate the similarity between texts, it is necessary to use some mathematical expressions which can evaluate and give the answer to one single numeric value [25; 26]. The widest known and used texts similarity measures are cosine, dice, the extended Jaccard's, and the overlap coefficient:  
\[\cos(D_{1},D_{2})=\frac{D_{1}\times D_{2}}{\sqrt{|D_{1}|}\times\sqrt{|D_{2}|}} \tag{3}\]  
\[\text{dice}(D_{1},D_{2})=2\frac{D_{1}\times D_{2}}{|D_{1}|+|D_{2}|} \tag{4}\]  
\[\text{jaccard}(D_{1},D_{2})=\frac{D_{1}\times D_{2}}{|D_{1}|+|D_{2}|-D_{1} \times D_{2}} \tag{5}\]  
\[\text{overlap}(D_{1},D_{2})=\frac{D_{1}\times D_{2}}{\min(|D_{1}|,|D_{2}|)} \tag{6}\]  
Here \(|D_{N}|=\left(x_{N1}^{2}+x_{N2}^{2}+x_{N3}^{2}+\ldots+x_{Nm}^{2}\right)\), \(D_{1}\times D_{2}=x_{11}x_{21}+x_{12}x_{22}+\ldots+x_{1m}x_{2m}\). To show how these four measures are calculated, a simple example is presented. Let us say we have four texts \(D=\{D_{1},D_{2},D_{3},D_{4}\}\) with few words inside of them (Table 2).  
Let us say we do not use any filters, so the bag of words list contains all terms from texts follows: text, message, computer, science, data, mining, and, methods. According to the frequency of each term, the frequency matrix is obtained (Table 3).  
After a frequency matrix is obtained, we can calculate the similarity measures. The results of calculated measures as an example are given in Table 2, presented in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Text Inside** & **Texts** \\ \hline text message & \(D_{1}\) \\ computer science & \(D_{2}\) \\ data mining and text mining & \(D_{3}\) \\ methods of text data mining & \(D_{4}\) \\ \hline \end{tabular}
\end{table}
Table 2: Text dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline _text_ & _Message_ & _Computer_ & _Science_ & _Data_ & _Mining_ & _and_ & _Methods_ & _of_ & \\ \hline
1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & \(D_{1}\) \\
0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & \(D_{2}\) \\
1 & 0 & 0 & 0 & 1 & 2 & 1 & 0 & 0 & \(D_{3}\) \\
1 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 1 & \(D_{4}\) \\ \hline \end{tabular}
\end{table}
Table 3: Frequency matrix.  
As we can see, the results of cosine and dice measures are almost the same. The values of extended Jaccard's are lower compared to the others. The overlap measure shows the highest values, and as there is no difference between overlap (\(D_{1}\), \(D_{3}\)) and overlap (\(D_{1}\), \(D_{4}\)), it means that these texts are equal in the point of similarity. All measures can be used equally to find similarity between texts, so it is hard to say which one is the most accurate and the deep investigation has to be made.
# Recurrent Water Deficit and Epigenetic Memory in _Medicago sativa_ L. Varieties
## 3 Results  
### Shoot and Root Dry Weight  
The drought treatments had a significant effect (\(p<0.05\)) only on the dry weight of shoots and roots (Table 2). On the other hand, the varieties significantly affected (\(p<0.05\)) the dry weight of shoots and roots, as well as the shoot/root ratio (Table 2). Moreover, the interaction between the drought treatments and varieties was statistically significant for shoot and dry root weights (Table 2), indicating that the drought treatments' effect on the varieties was not consistent. The Levene test was not significant (\(p>0.05\)) in all cases indicating homogeneity of the variances. The eta-squared test was higher for the treatments (0.7) compared to the varieties (0.3). This means that the higher percentage of variance was explained by treatments compared to varieties.  
The drought treatments significantly reduced both the shoot and dry root weight, while they did not affect the shoot/root ratio. The plants (D1D2) that underwent both phases of drought stress had the lowest shoot and root weights (Table 2). The shoot dry weight was significantly reduced under drought treatments for both varieties (Figure 2). However, the dry shoot weight of Lamia did not significantly differ between CD2 and D1D2, while that of Chaironia was significantly reduced (Figure 2). On the other hand, Lamia had significantly lower dry root weight only under D1D2 drought treatment, while the dry root weight of Chaironia was gradually reduced from the control to CD2 and D1D2 treatments (Figure 3). The shoot dry weight of the studied varieties did not significantly differ under control and CD2, but under D1D2, Lamia had higher dry shoot weight compared to Chaironia (Figure 2). Regarding the dry root weight, the studied varieties did not significantly differ under the control, but Lamia had a higher dry root weight under both CD2 and D1D2 d  
\begin{table}
\begin{tabular}{c c c c} \hline
**Treatment** & **Shoot Dry Weight (g)** & **Root Dry Weight (g)** & **Ratio: Shoot/ Root** \\ \hline CC & 6.95 \(\pm\) 0.34a & 5.66 \(\pm\) 0.30a & 1.28 \(\pm\) 0.11a \\ CD2 & 4.04 \(\pm\) 0.31b & 3.53 \(\pm\) 0.27b & 1.25 \(\pm\) 0.11a \\ D1D2 & 2.57 \(\pm\) 0.35c & 1.79 \(\pm\) 0.30c & 1.56 \(\pm\) 0.12a \\ \hline
**Variety** & & & \\ \hline Lamia & 5.10 \(\pm\) 0.21a & 4.42 \(\pm\) 0.18a & 1.23 \(\pm\) 0.07b \\ Chaironia & 4.00 \(\pm\) 0.32b & 2.90 \(\pm\) 0.28b & 1.50 \(\pm\) 0.11a \\ \hline
**Source of variation** & & & \\ \hline Treatment (A) & \(p<0.05\) & \(p<0.05\) & Ns \\ Variety (B) & \(p<0.05\) & \(p<0.05\) & \(p<0.05\) \\ AXB (Interaction) & \(p<0.05\) & \(p<0.05\) & Ns \\ \hline \end{tabular}  
Lower case letters indicate significant differences (\(p<0.05\)); ns: non-significant difference.  
\end{table}
Table 2: Mean values +/\(-\) SE for each of the three different treatments (CC, CD2, and D1D2), as well as for each of the two varieties tested. Statistically significant differences between treatments (A), between varieties (B), and their interaction (AXB) are also presented.
# Leaf-Level Spectral Fluorescence Measurements: Comparing Methodologies for Broadleaves and Needles
## 4 Discussion
### Needle Arrangements and Morphology Effect
In the case of ChIF, accurate measurements are further complicated by the phenomena of re-absorption. Further work to investigate to what extent needle mats underestimate the red ChIF and whether new solutions would be possible to measure single needles without compromising the strength of the signal or the replicability of the measurement is required. Finally, it is also important to remember that needle mats--despite being relatively easy to measure - are not a realistic representation of the natural geometry of collections of needle leaves, commonly referred to as shoots. Hence, each scale of the ecosystem--from individual leaves and shoots to individual trees and plants to the forest canopies--has their own specific and interacting set of optical properties that combine to influence the observed remote sensing retrieval [9; 10]. Measurements and knowledge of each scale is, therefore, required to accurately infer physiology from coarse resolution remote measurements.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 3 Selected Case Studies and Numerical Results
### Network Reconfiguration and Distributed Generation
#### 3.2.1 Dynamic NR Modeling and Analysis  
When seeking to establish pre-emptive DG control, the research gap in NR modeling accuracy must first be considered. NR has usually been modeled employing the load to be restored or shed [61], rather than as a change in the network topology per se. This can compromise the estimation of the dynamic responses of DGs and loads to the SW operations of NR-aided load restoration, preventing pre-emptive DG control in a reconfigurable network. A frequency response rate (FRR) model has often been used to estimate frequency dips caused by sudden load pickups. However, the transient variations in bus voltages and line losses attributable to NR cannot be analyzed using FRR models. Figure 7 compares a new NR modeling method [63] to a conventional method [61]. To improve estimation accuracy, the new method incorporates SW operations directly, and the corresponding load variations indirectly, into changes in the node currents and voltages of a reconfigurable network. In the new method, step variations in the \(dq\)-axis node injection currents attributable to SW operations are identified and analyzed with respect to their effects on the dynamic responses of the low-voltage network. Using a defined test bed (Figure 6), Figure 8 shows the results of a case study, exploring the effectiveness of the new method in terms of improving the estimated accuracies of MG frequency and voltage variations attributable to NR. The new method has a great potential, for example, for application to the optimal MPC of DGs, which is difficult to achieve via either conventional modeling method or a trial-and-error approach with numerical simulation. The test grid in Figure 6 includes not only conventional synchronous machine-based DGs, but also inverter-interfaced VRE sources, and confirms the applicability of the NR modeling/analysis method to future VRE-dominated grids.
## 3 Results and Discussions
### Critical Dynamic Stress
#### 3.3.1 Calculation of CDS  
For each test series outlined in Table 1, the vertical cumulative strain differences \(\varepsilon_{1000}\)-\(\varepsilon_{100}\) were individually computed. Subsequently, the correlation between these strain differences and dynamic stresses, under varying temperature and moisture content conditions, was examined. The resulting fitted exponential curves for each test series are presented in Figures 15 and 16, respectively. Since the fitting exponential curves for the samples under the five conditions of T = \(-7\)\({}^{\circ}\)C, \(\omega\) = 14%; T = \(-9\)\({}^{\circ}\)C, \(\omega\) = 12.5%; T = \(-11\)\({}^{\circ}\)C, \(\omega\) = 14%; T = \(-13\)\({}^{\circ}\)C, \(\omega\) = 12.5%; and T = \(-3\)\({}^{\circ}\)C, \(\omega\) = 14% exhibit similar patterns, Figure 15  
Figure 14: Accumulated strain differentials at different moisture.  
Figure 13: Accumulated strain differentials at different temperatures.  
only presents the representative fitting exponential curves for the conditions of T = \(-7\)\({}^{\circ}\)C, \(\omega\) = 14% and T = \(-9\)\({}^{\circ}\)C, \(\omega\) = 12.5%. Similarly, Figure 16 only displays the fitting exponential curves for the conditions of T = \(-5\)\({}^{\circ}\)C, \(\omega\) = 11% and T = \(-5\)\({}^{\circ}\)C, \(\omega\) = 12.5%. The patterns for T = \(-5\)\({}^{\circ}\)C, \(\omega\) = 18.5%, and T = \(-5\)\({}^{\circ}\)C, \(\omega\) = 15.5% are similar to those previously shown and are therefore not presented again. Table 2 provides a concise summary of the fitted relationships derived from all the test series.  
As illustrated in Figures 15 and 16, the vertical cumulative strain disparity exhibits an exponential augmentation in response to the escalation of dynamic stress. A summary of the vertical cumulative strain difference, as it varies with dynamic stress changes, along with the fitting parameters and correlation coefficients for all test sequences, is presented in Table 2. An analysis reveals that the variations observed under different loading conditions display a similar trend, and the goodness of fit, as measured by the correlation coefficients, is satisfactory. Combined with the plastic limit criterion formula proposed by [PERSON] et al. (2014). The CDS \(\sigma_{\text{d}}\) can be determined for the plastic stability limit and plastic creep limit under varying stress levels, by employing the functions provided in Table 2 and performing calculations for specific \(\epsilon_{\text{sub}}\) values of 0.8% and 5.2%, respectively. Therefore, the effects of different temperatures and moisture contents on the CDS can be analyzed.  
Figure 16: Alteration in cumulative strain difference as a function of dynamic stress across varying moisture.  
Figure 15: Alteration in cumulative strain difference as a function of dynamic stress across varying temperatures.
## 4 Discussion
### Limitations
#### 4.7.2 Subjectivity of Interpretations of Study Methods and Findings  
The last two limitations have to do with the subjectivity of our interpretations of the studies' methods and findings. First, some of the papers' characteristics that we described were subjective. The \"Purpose\" characteristic (which coincidentally contains a value called \"Subjective\") is a prominent example of our opinions, and the \"Game Outcome\" characteristic is an example of other researchers' opinions. Second, since the studies we gathered were somewhat vulnerable to biases, especially selection bias and performance bias, our findings and interpretations may need to be taken with a grain of salt. Even our risk-of-bias analysis itself is based on subjective opinion; others may disagree with our ratings. Future reviews may seek to account for these limitations.
## 3 Experimental Setup
### Application of the Data Augmentation Tool  
The data augmentation tool developed in this research is also designed for ease of use and high practicality. It is employed during the data augmentation phase by data augmentation personnel. The original images and their corresponding annotation files are placed in separate folders, after which the system automatically generates augmented images and updated annotation files. Additionally, a prompt box assists reviewers by displaying annotation positions, facilitating the verification of annotation accuracy. Figure 6 presents examples of augmented data, where the red-framed sections indicate the automatically generated annotation boxes, enabling reviewers to inspect the correctness of annotations.  
As demonstrated in Figure 6, across a range of image types--including sewage outlets, sand yards, buildings, river and lake water pollution, and oil-based pollution--the data augmentation tool delivers favorable outcomes. It effectively enhances annotation efficiency and accuracy, underscoring the tool's practical value in processing real-world datasets.
# Strata Control by Roof Blasting for Bord and Pillar Mining Method in Mechanized Depillaring Panels
## 2 Empirical Approach to the Design of Underground Structures
### Width of Gallery  
Existing galleries of 3.8 m need to be widened for adaptability of the CM (Figure 4a) by cutting the sides of the coal pillar measuring 35.7 m \(\times\) 31.2 m (corner-to-corner). Considering the extent of spalling, frequency of slips/joints/discontinuities present in the panel, and available case studies of mechanized depillaring (MD) panels using CM Technology in different coalfields of India, it is proposed here to widen the 3.8 m width of the gallery to 5.5 m only. It is suggested to extract the sides of the pillars of 1.70 m in an L-shaped manner for efficient utilization of the CM in the panel. The extraction of the sides of the coal pillar will reduce its size from 35.7 m \(\times\) 31.2 m (corner-to-corner) to 34.0 m \(\times\) 29.5 m (corner-to-corner). Furthermore, it will also reduce the strength of the pillar. Considering the length of the machine to be 11.9 m, it will be difficult for the CM to turn around the junctions of the 5.5 m width of the gallery. A diagonal length of around 10.50 m is required around the junction for turning the entire length of the CM; therefore, it is also proposed here to reduce the corners of the pillar as shown in Figure 4b. Around 10.68 m of diagonal length will be available after the extraction of corners around the junction, which provides a sufficient turning radius for the movement of the CM. It has also been found that the sharp 90\({}^{\circ}\) corners become rounded ultimately due to induced stress-driven spalling. The suitability of the 5.5 m width of the gallery has also been simulated in numerical models and found to be appropriate, which is presented in the section on Numerical Modeling.  
\begin{table}
\begin{tabular}{c c c} \hline
**Classification of Roof** & **Cavability Index** & **Remarks** \\ \hline Easily cavable roof & 1 \(\leq\) 2000 & No perceptible weighting on the face \\ Moderately cavable roof & 2000 \textless{}1 \(\leq\) 5000 & Weighting but not dynamic in nature \\ Roof cavable with difficulty & 5000 \textless{}1 \(\leq\) 10,000 & Weighting may or may not be dynamic in nature \\ Cavable with substantial difficulty & 10,000 \textless{}1 \(\leq\) 14,000 & Weighting may or may not be dynamic in nature \\ Cavable with extreme difficulty & 1 \(\geq\) 14,000 & Weighting will be dynamic in nature \\ \hline \end{tabular}
\end{table}
Table 2: Caving index versus caving nature of strata [16].  
Figure 4: (a) Widening the 3.8 m width of the gallery to 5.5 m for the adaptability of the Continuous Miner machine and (b) Extraction of 1.45 m corners of pillars around the junction for the adaptability of the 11.9 m length of the cutting machine.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 5 Numerical Results  
In this section, we present numerical examples and results that demonstrate the quantification of the trustworthiness of the COP using our proposed model (Section 4.1). We show results for two cases: (i) COP trustworthiness for a high-trust information source that incorporates security primitives, and (ii) COP trustworthiness for a low-trust information source that does not incorporate security primitives. The trustworthiness of COP for other cases (i.e., between high-trust and low-trust information sources) can be determined following the same approach.