esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Attitude and Perception Towards PA  
The high proportion of \"agree\" or \"strongly agree\" responses indicates that the perceived tangible advantages and the willingness to regularly practice exercise are widely recognized. From a psychological perspective, most respondents believed that PA had a positive impact on their psychological well-being. However, there is a significant minority who do not share this view or express a clear stance (responses of disagreement or neutrality). With regard to social aspects, the responses revealed a more heterogeneous picture: while many participants acknowledged their importance, the presence of neutral or disagreeing answers suggests that the relational component is not universally regarded as a key factor in exercise adherence. Finally, the analysis of barriers reveals that, while some individuals perceive no substantial obstacles, a considerable portion remain uncertain (neutral responses) or report actual difficulties in accessing PA. Overall, the participants exhibited a predominantly positive attitude towards PA, especially concerning physical benefits and the motivation to engage in it. The results of the questionnaire are shown in Table 2.  
The correlation analysis (Table 3) of the physical, psychological, motivational, and sociality domains showed positive and significant correlations between them. Psychological and motivational attitudes correlate with high level of significance (\(p<0.001\)) most strongly (\(\rho=0.775\)), followed by motivational-sociality (\(\rho=0.697\)), physical-motivational (\(\rho=0.691\)), and physical-psychological (\(\rho=0.674\)). In each case, the confidence interval sits well above zero, reflecting large effect sizes and tight estimates of the true association. Two further links showed medium-strength associations: motivational with barrier (\(\rho=0.320\)) and physical with sociality (\(\rho=0.495\)). Their intervals also exclude zero but are wider, signaling more moderate consistency. By contrast, none of the correlations between the barrier domain and physical (\(\rho=0.195\)), psychological (\(\rho=0.144\)), or sociality (\(\rho=0.255\)) attitudes reached significance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Domains** & & & & **Answers** & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Physical \\ domain \\ Psychological \\ domain \\ \end{tabular} } & **1—Totally** & **2—Disagree** & **3—Neither** & & & \\  & **Disagree** & & **Agree nor** & **4—Agree** & **5—Totally Agree** \\  & **n\({}^{\circ}\) (\%)** & **n\({}^{\circ}\) (\%)** & **n\({}^{\circ}\) (\%)** & **n\({}^{\circ}\) (\%)** & **n\({}^{\circ}\) (\%)** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Motivational \\ domain \\ \end{tabular} } & 0 (0.0) & 1 (1.9) & 4 (7.4) & 29 (53.7) & 20 (37) \\  & 0 (0.0) & 5 (9.3) & 5 (9.3) & 36 (66.7) & 8 (14.8) \\  & 0 (0.0) & 0 (0.0) & 12 (22.2) & 30 (55.6) & 12 (22.2) \\  & & & & & \\  & 0 (0.0) & 5 (9.3) & 13 (24.1) & 33 (61.1) & 3 (5.6) \\  & & & & & \\ Barrier domain & 0 (0.0) & 17 (31.5) & 19 (35.2) & 18 (33.3) & 0 (0.0) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the questionnaire by domain.
## 5 Discussion
### Research Question RQ1
However, it is not feasible to propose a specific study of each student for a personal allocation of time wherein different personal variables may also influence the result. In our opinion, it would be more sensible and less expensive to add the same amount of extra time for each group of students with different capabilities, considering the statistical comparison between students of each group and those without apparent limitations. We performed an analysis of the means of the control group (group 1) and groups 2, 3, and 4 to follow our objective of detecting the amount of extra time needed by students with different capabilities versus students without disability. The previous consideration, together with the comparative statistical analysis, provides an answer to RQ1: \"How much additional time do students with vision and motor problems need to interact with typical e-learning resources, such as questionnaires and tests, compared to those without impairment?\" The ratio of extra time interaction needed by blind students is 2.92; for the students with low vision, it is 1.88; and for the students with reduced motor skills, it is 1.58.
## Appendix A Unstructured Data Classification and Grading Framework
### Data Classification Based on Entity  
Entity-based unstructured data classification and grading builds upon the principles of structured data classification and grading, employing keywords and patterns to detect sensitive entities. While structured data utilizes a \"keyword+dictionary\" approach for identifying irregular entities, this method is not suitable for unstructured data. Therefore, we adopt the Named Entity Recognition (NER) model to handle irregular entities. The specific identification process is illustrated in Figure 19.  
Likewise, the selection of NER models can be tailored to specific scenarios. We provide an overview of various NER models along with their respective applicable scenarios, highlighting their individual strengths and limitations, as shown in Table 19.
## 2 Theory of Interferometric Spectral Imaging Based on Liquid Crystal Delayers
Using the effective interferogram, the recovered spectral information was obtained through the inverse Fourier transform, as shown in Figure 7.  
In summary, the maximum optical path difference for 1053 nm light was \(\Delta_{\mathrm{max}}=23\)\(\upmu\)m. The full width at half-height (FWHH) of the instrumental line shape can be used to determine the limiting resolution of the recovery spectrum. The tangent function used in this study is a rectangular function, which corresponds to \(\mathrm{FWHH}=1.207/2\Delta_{\mathrm{max}}\). The spectral resolution is calculated by \(\mathrm{d}\lambda=\lambda^{2}\times\mathrm{FWHH}\), where \(\mathrm{d}\lambda\) is the spectral resolution at \(\lambda\). The theoretical spectral resolution is 262 cm\({}^{-1}\), whereas the actual recovered spectral half-height width of 1053 nm light is 28.64 nm, as shown in Figure 6a. A thicker LC is required to obtain a larger optical path difference and further improve the spectral resolution. Figure 5: (**a**) Position of the measuring points for the LC. (**b**) Optical path difference and error bar at each position. Figure 6: Variation in interference light intensity with optical path difference. (**a**) The 1053 nm light and (**b**) the wide-spectrum halogen light.
## 1 Introduction
For biochar, ecotoxicological impacts and interactions with soil microbiota, such as negative effects on nitrogen-fixing Rhizobium and phosphorus-mobilizing mycorrhizal fungi, have been observed under arid conditions; however, these may be mitigated when combined with microbial inoculants [15; 16]. Applying organic amendments is a practical strategy to mitigate salinization, so these amendments boost bacterial diversity and promote the growth of beneficial, salt-tolerant microbes, thereby strengthening microbial interactions within the soil; the composition of microbial taxa changes in response to the presence of both easily degradable and resistant organic carbon in the amendments. Additionally, organic amendments enhance soil conditions by increasing nutrient availability, water retention, and aeration [17]. Although rich in nutrients, vermicompost may pose environmental risks due to nutrient leaching, unpleasant odors, insect proliferation, and GHG emissions during decomposition, especially when not properly managed [18]. Yellow hard corn is cultivated nearly year-round in Peru, particularly along the Peruvian coast. It is a short-cycle crop with a vegetative period ranging from 4.5 to 5.5 months, depending on the variety and sowing date. Popcorn varieties are characterized by small, round kernels with a soft, starchy core and a hard, glassy outer shell; when heated, the moisture within the starchy core expands and bursts through the hard shell, producing popcorn. These varieties account for less than 1% of global corn production [19]. The consumption of popcorn maize (_Zaa mays_ var. everta) in Peru has increased significantly, particularly in recent years, with an annual growth rate of 5527.6 tons (2016-2020). However, Peru imports popcorn maize from three main countries; in descending order of volume, these are Argentina, Brazil, and the United States [20]. Considering the growing salinization of coastal agricultural soils and the increasing interest in sustainable crop production, this study aimed to evaluate the effects of two organic amendments--biochar and vermicompost leachate--on soil quality, microbial community, and the growth and yield performance of popcorn maize. The objective was to assess their comparative performance under saline soil conditions in popcorn maize plants, observe if there is any alteration or disadvantage with their use, and contribute to the understanding of how organic inputs derived from agricultural residues influence crop productivity and soil health in salt-affected environments of the Peruvian coast.
# Analytical Electromechanical Modeling of Nanoscale Flexoelectric Energy Harvesting
## 4 Numerical Results and Discussion
### Tip Displacement FRFs  
The multi-modes expression for the tip displacement FRFs can be obtained as  
\[\frac{w(L,t)}{-\alpha^{2}W_{0}\varepsilon^{\mathrm{i}\omega t}}=\sum_{r=1}^{3} \left\{\left[\lambda_{r}-\frac{\sum_{r=1}^{3}\frac{\mathrm{j}\omega_{r}\kappa_{ r}}{\left(\frac{1}{\mathrm{k}}+\frac{\kappa_{33}\mathrm{H}}{\mathrm{h}}\mathrm{j} \omega\right)+\sum_{r=1}^{3}\frac{\mathrm{j}\omega_{r}\kappa_{r}^{2}}{\left( \omega_{r}^{2}-\omega^{2}\right)+2\mathrm{j}\omega_{r}\kappa_{r}}}K_{r}\right] \frac{\varnothing_{r}(L)}{\left(\omega_{r}^{2}-\omega^{2}\right)+2\mathrm{j} \omega_{r}\kappa_{r}\omega_{r}}\right\}. \tag{36}\]  
The tip displacement FRFs of the beam with the excitation frequency for the 3 \(\upmu\)m thickness beam with the tip mass \(M_{\mathrm{t}}\) are plotted in Figure 5. For the different load resistances, the values of the tip displacement FRFs were almost equal. In other words, the tip displacement FRFs were independent of the loading resistance. It is interesting to see that in Figure 5, there are two small anti-resonance frequencies between mode 2 and mode 3, which is different from the result of piezoelectric energy harvesting devices [42]. In the piezoelectric case without the tip mass, there was just one intense mechanical anti-resonance frequency between mode 2 and mode 3.  
Figure 6 plots the tip displacement FRFs of the 0.3 \(\upmu\)m thickness beam with the tip mass \(M_{\mathrm{t}}\) near the first mode. Comparing Figures 5 and 6, it is clear to observe that the tip displacement FRFs depend on the external loading resistance for the small scale flexoelectric energy harvesting. The significant resonance frequency shift from \(f_{1}^{\mathrm{sc}}\) to \(f_{1}^{\mathrm{cc}}\) could be observed with increasing load resistance. Apparently, at a small scale, the electromechanical coupling of the flexoelectric energy harvesting is enhanced, which was not the case for the piezoelectric energy harvesting. It is interesting to see that the peak amplitude of the tip displacement FRFs first decreases and then increases with  
Figure 4: Variation of the voltage output with load resistance under excitations at the short and open circuit resonance frequencies of the first vibration mode (\(h=0.3\)\(\upmu\)m).  
increasing load resistance. This was due to the power dissipation as a result of Joule heating in the electrical domain with the finite values of load resistance [42]. This phenomenon can also be found in the case of piezoelectric energy harvesting systems.
## 7 A case Study in Antarctica
### Analysis of Emission Reduction  
Analysis of cost and benefits may be susceptible to external factors, such as changing fuel purchase prices, the cost fluctuation of transporting fuel and etc. In Antarctica, the analysis of cost and benefits may encounter various complicated situations. The risk of oil spill in transport, atmospheric emissions, or hidden cost of maintenance requirements can rarely achieve comprehensive monetization. However, direct cost savings are in reduced use of fossil fuels. Based on the result of a one-year simulation operation of the standalone renewable energy system in Zhongshan Station, it is found that fuel was used to complete the power supply to the load only in August. The monthly fuel consumption of Zhongshan Station is shown in Table 12.  
The existing power supply system of the Antarctic Zhongshan Station has a fuel consumption of 241.33 t per year. The results of simulation operation of the standalone renewable energy system indicate that fuel consumption can be reduced to 2.08 t. The standalone renewable energy system has provided an average annual fuel saving of around 98.8%. Refer to the operation results of Australia's Mawson Station, which is similar in size to Zhongshan Station, Mawson Station used about 0.7 million liters of diesel fuel annually to provide power and heating. Through the introduction of renewable energy supply, the annual fuel saving of Mawson Station was around 32%, equivalent to a saving of 2918 t of carbon dioxide during the first six years of operation [10]. The estimated annual carbon dioxide emissions of existing power supply system of the Antarctic Zhongshan Station were 616.4 t and Zhongshan Station's estimated annual carbon dioxide emissions were 5.3 t in the year of simulation, which can reduced carbon emissions by 611.1 t in one year and 3666.6 t of carbon dioxide during the first six years of operation.  
The use of the standalone renewable energy system will improve the health of the local environment and reduce the cost of environmental governance.
# Using the Guided Fireworks Algorithm for Local Backlight Dimming
## 5 Conclusions  
In this paper, we have focused on improving displayed image quality without increasing the power consumption of the local dimming system. On the basis of our previous work [12], we used the SSIM index to replace the PSNR as our image quality evaluation method. Then, we designed and improved the GFWA local dimming algorithm, based on the FWA. In the GFWA, initial firework location is generated using the parameter-based algorithm, the guiding strategy is used to update bad locations in the population, and the step of generating Gaussian firework locations is also improved. Finally, we compared the GFWA with the LUT algorithm, the ISFLA and the original FWA. The experimental results show that the GFWA was able to produce higher quality images.  
There are two future research suggestions: (1) Both the PSNR and the SSIM index are full-reference image quality evaluation methods; when these methods are used, the reference images are required. Therefore, it would be meaningful if no-reference methods could be applied to local dimming. (2) As an EC algorithm, a certain number of iterations are required by the GFWA, but in practical applications, local dimming usually needs to be finished in a short time. The algorithm efficiency of the GFWA should therefore be further improved.  
Conceptualization, T.Z. and X.Z.; methodology, T.Z. and X.Z.; software, Y.W. and Q.Z.; validation, T.Z. and X.Z.; formal analysis, X.Z.; investigation, Y.W. and Q.Z.; resources, T.Z.; data curation, Y.W.; writing--original draft preparation, T.Z. and X.Z.; writing--review and editing, Y.W. and Q.Z.; visualization, X.Z.; supervision, T.Z.; project administration, T.Z.; funding acquisition, T.Z.  
This work was supported by the Research on HDR Backlight Liquid Crystal Processing Technology Based on Depth Neural Network under Contract HO2018085418.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **Indictor** & **GFWA** & **FWA** & **FWA** & **ISFLA** \\ \hline \multirow{2}{*}{1} & \(PC(\%)\) & 73.32 & 73.33 & 73.31 & 73.33 \\  & MSSIM & 0.8813 & 0.8151 & 0.8691 & 0.8592 \\ \hline \multirow{2}{*}{2} & \(PC(\%)\) & 66.22 & 62.22 & 62.22 & 62.22 \\  & MSSIM & 0.9442 & 0.9148 & 0.9418 & 0.9394 \\ \hline \multirow{2}{*}{3} & \(PC(\%)\) & 53.41 & 53.42 & 53.41 & 53.42 \\  & MSSIM & 0.8284 & 0.7865 & 0.8239 & 0.8230 \\ \hline \multirow{2}{*}{4} & \(PC(\%)\) & 35.20 & 38.11 & 35.07 & 35.05 \\  & MSSIM & 0.9021 & 0.8315 & 0.8995 & 0.8965 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The MSSIM index and \(PC\) values of the four algorithms. GFWA: Guided Fireworks Algorithm; LUT: Look-Up Table; FWA: Fireworks Algorithm; ISFLA: Improved Shuffled Frog Leaping Algorithm.
## 4 Design Considerations
### Design of \(C_{1}\)-\(C_{5}\)  
The values of \(C_{1}\)-\(C_{5}\) are figured out at rated condition. In this LED driver, the capacitors \(C_{1}\), \(C_{2}\), and \(C_{3}\) have the same operating behavior, namely, \(V_{C1}=V_{C2}=V_{C3}\), whereas the capacitors \(C_{4}\) and \(C_{5}\) have the same operating behavior, namely, \(V_{C4}=V_{C5}\). From state 2 with the switch \(Q_{1}\) being off and based on Kirchhoff's law (KVL), the following equation can be obtained:  
\[-V_{C4}-V_{o3}-V_{C2}+V_{C1}+V_{o1}=0 \tag{30}\]  
Hence, \(V_{C4}=0\) since \(V_{C1}=V_{C2}\) and \(V_{o1}=V_{o3}\). In the same way, \(V_{C5}=0\).  
From state 1 with the switch \(Q_{1}\) being on and based on KVL, the following equation can be obtained:  
\[V_{C1}-V_{o2}+V_{C4}=0. \tag{31}\]  
Hence, \(V_{C1}=V_{o2}\) since \(V_{C4}=0\). In the same way, \(V_{C2}=V_{o4}\) and \(V_{C3}=V_{o6}\).  
In addition, it is assumed that the voltage ripple for each capacitor is set at 1% of its DC voltage. Therefore,  
\[C_{1}=C_{2}=C_{3}\geq\frac{P_{o,rated}}{3\times V_{in,min}}\times\frac{D_{ max,rated}\times T_{s}}{V_{o1}\times 1\%} =\frac{29}{3\times 10.8}\times\frac{0.608\times 10\mu}{13.8\times 0.01}=39.1\mu \mathrm{F} \tag{32}\]  
Eventually, one 47 \(\mu\)F/25 V capacitor is for \(C_{1}\) and also for \(C_{2}\) and \(C_{3}\). As for \(C_{4}\) and \(C_{5}\), they have the same capacitances as that for \(C_{1}\) for design convenience.  
In the following, the component specifications are tabulated in Table 2.
## 3 Experimental Results and Discussion
GSLEO-AEFA achieves the best performance on F9-F12 and F14-F23 in the multi-modal benchmark functions (F8-F23). Each of the results obtained by GSLEO-AEFA is better than what the initial AEFA is able to achieve. Global search capacity and population diversity of GSLEO-AEFA in comparison to the initial AEFA are drastically improved. Additionally, the demonstration of multimodal functions with fixed dimension characteristics proves that the new mechanism introduced successfully achieves a good balance between global search and local exploitation. Tables 4 and 5 display the results of expanding the dimension from 50 and 100, respectively. The tables present the evidence that as the problem's dimension increases, it becomes less easy to find optimum solutions. Table 4 clearly shows that GSLEO-AEFA achieves the best performance in F1-F5, F7 and F9-F12 despite the growth in dimension. GSLEO-AEFA achieves the best results in thirteen benchmark function when dimension is 100 in Table 5. When the results from the tables are analyzed, it can be seen that GSLEO-AEFA is quite robust for handling complex problems. This indicates that population diversity is not a significant improvement over the original AEFA and other algorithms in F1-F7. This is expected due to the introduction of LEO, which improves GSLEO-AEFA ability to do local search. As a result, there is significant improvement in exploitation too.
# A Twitter-Based Comparative Analysis of Emotions and
## 3 Methodology  
To achieve the goal of this work, our suggested method for extracting and classifying societal emotions entails many interconnected components. The proposed method's workflow is depicted in Figure 1.  
As mentioned above, in this paper, we utilize a mixed-method approach, which we believe to be the most suitable technique. First, all tweets published in Arabic and Spanish during Argentina vs. Saudi Arabia World Cup 2022 football match were gathered. Next, they are analyzed quantitatively in terms of the number of tweets and their distribution between Arabs and Hispanics. The qualitative-content-analysis technique was then used to probe into the text to uncover cultural differences hidden within users' emotional and semantic traits.  
Within this methodology, our research adopted a novel approach. We compared two non-English-speaking communities from the developing world (Arab and Hispanic) based on their Twitter activity. We translated their tweets from their original languages into English, and then used NLP-based ML algorithms designed for English text to culturally assess these tweets and compare the sentiments and emotions of the two groups. Furthermore, in our approach, the problem of detecting users' physical locations from Twitter-based public information was solved by the language of the gathered tweets, which revealed whether the tweet's author was Arab or a Hispanic and, therefore, was used as a clue as to the author's home nation. However, since the sentimental and emotional contexts of tweets may change when they are translated from one language to another, we utilized the study's results as a guide to establish how effectively the translation preserved sentiments and emotions at a suitable level for cultural comparisons. Following translation, the tweets were emotionally rated using an English-based machine-learning model and presented in descriptive charts. Next, the collected emotions taken from the tweets were classified in terms of their arousal level, high vs. low, in accordance with the fundamental emotion distribution in the arousal-valence space [14; 15]. Next, we addressed how these data can be understood in terms of psychological researchers' proposed nation-based cultural dimensions. Subsequently, as mentioned above, the research results, when consistent with the literature about the cultural distinction between Arabs and Hispanics, were viewed as evidence. The results showed how effectively, to some extent, the machine translation preserved the context's sentimental and emotional clues. We provide more details in the following sections.  
It is important to highlight that the only way to obtain Arabic and Spanish tweets for a comparative emotional and sentimental analysis was to translate them into English. There is no method available to analyze emotions in native texts. Arabic text cannot be analyzed for  
Figure 1: Proposed method workflow.  
emotions by EmoRoberta, but Spanish text can. EmoRoberta could not have been used with native Spanish text and an English translation of Arabic text. This would have been unjust.