esa-sceva/satcom-chunk-collection huggingface dataset:
# Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking
## 2 Materials and Methods
### Participants  
We recruited Amazon Mechanical Turk (MTurk) workers located in the United States with high reputations (i.e., \(\geq\)500 approved HITs with \(\geq\)95% approval rating). We used these criteria because Amazon MTurk workers with high reputations are likelier to produce higher-quality data than those with low reputations [38]. Our target sample size was 500 participants based both on prior studies as well as recommendations for correlations [39] and standard multiple regression [40]. A total of 776 Amazon MTurk workers enrolled in our study, and 280 did not complete the entire study because they did not pass an embedded attention check block. Of the 496 Amazon MTurk workers who completed the entire study, we excluded participants from data analysis if they did not pass one or both attention check items that we disguised within two separate questionnaire items (\(n=21\)), completed the entire study in less than 10 minutes (\(n=28\)), or indicated that they answered randomly at some point during any of the study (\(n=74\)). Thus, 373 participants (age \(M=37.40\) years, \(SD=9.94\) years; 38% women; 16% Hispanic, Latino, or Spanish origin; 79% White) were included in data analysis. All participants were compensated a flat rate of $5.43.
# ChatGPT and Open-AI Models: A Preliminary Review
## 2 ChatGPT Training Process
### Pre-Processing of Text Data  
The pre-processing of text data constitutes a critical aspect of the ChatGPT training process as it plays a significant role in determining the quality and suitability of the input data for the model [27]. To this end, the pre-processing stage of text data for ChatGPT involves a sequence of procedures comprising tokenization, subword encoding, and data cleaning.  
* Tokenization is a fundamental step in natural language processing that involves segmenting text into discrete units of meaning, known as tokens [27]. The purpose of tokenization is to facilitate the subsequent processing of text by the model. In the case of ChatGPT, tokenization is performed using a pre-trained tokenizer designed explicitly for natural language processing tasks. This tokenizer converts the input text into a sequence of tokens, where each token represents a specific word or subword unit. The resulting token sequence is then used as input for the model in further processing.
* Subword encoding is a widely used technique in natural language processing to handle rare or out-of-vocabulary words in the input text. It involves breaking down the input text into smaller units or subwords, which the model can then process. Subword encoding has been shown to improve the performance of language models on various natural language processing tasks. In the case of ChatGPT, subword encoding is performed using a pre-trained subword encoder, such as the Byte Pair Encoding (BPE) algorithm, specifically designed for natural language processing tasks [27; 28].
* Data cleaning is a crucial step in pre-processing text data as it aims to eliminate irrelevant or noisy information from the input text, ultimately improving the quality and suitability of the input data for the model [29]. It involves a series of steps, such as removing punctuation, numbers, and special characters and correcting spelling and grammatical errors, among others. Data cleaning transforms the input text into a more coherent and standardized form, thereby enhancing the model's ability to capture meaningful patterns in the data.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Comparative Methods
#### 5.3.5 Comparative Analysis of Daily Demand Forecasting Orders Based on Delay  
The UDAF model's effectiveness in demand forecasting is evaluated by comparing it with other methodologies using MAE, MSE, RMSE, and RMSLE, in line with the delay of 25.  
According to Figure 8a, the UDAF model surpasses NARX in demand forecasting, with an MAE of 2.94.  
Figure 8: Comparative analysis of daily demand forecasting orders based on delay: (**a**) MAE, (**b**) MSE, (**c**) RMSE, (**d**) RMSSE, and (**e**) R-squared.  
Furthermore, Figure 8b demonstrates the UDAF model's proficiency in demand forecasting through MSE, outperforming NARX by 0.88% with a 7.85 MSE for a delay of 25.  
Regarding demand forecasting, Figure 8c showcases the superior performance of the UDAF model compared to NARX. It achieves a 0.36% improvement with an RMSE of 2.80 for a delay of 25, surpassing previous techniques.  
Similarly, in demand forecasting, as shown in Figure 8d, the UDAF model exhibits superior performance compared to NARX, achieving a 3.70% improvement with an RMSSE of 0.026 for a delay of 25, surpassing previous techniques.  
Furthermore, Figure 8e demonstrates the UDAF model's proficiency in demand forecasting through R-squared, outperforming NARX by 3.33% with a 0.90 MSE for a delay of 25.
# Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage
## 1 Introduction
In this paper, we show the methodology developed in the scope of the SILKNOW project [8], for the spatiotemporal representation of silk heritage objects and their relationships, using a combination of map-based tools that retrieve information from a knowledge graph. In this way, the datasets that are visualized come from an ontology that it is used to harness the semantic information within the database of the project. This ontology has a multilingual web access and semantically enriches the digital data contained in the database. The database upon which this ontology is built collects data--mainly images and textual information--from a series of museum catalogue records and other small institutions related to silk heritage. In addition, another ontology is used to define how data should be visualized, allowing our visualization tool to work with different datasets or even with different source-data ontologies designed for other CH applications or even for non-CH related problems. Our methodology includes a strategy to map objects with multiple locations and time spans, a quite common case in the CH domain. The proper visualization of these large datasets, including the relationships among objects, helps appreciating the rich silk-related cultural heritage and allows discovering connections between silk textiles and places, by means of a spatio-temporal understanding of the historical evolution of silk textile design and techniques, for instance. The rest of the paper is structured as follows. Section 2 reviews related work about data visualization, focusing on spatio-temporal information and ontologies. Section 3 describes the technical details of the STMaps visualization tool. Section 5 shows the results of using the SILKNOW implementation of STMaps in the CH domain. Finally, Section 6 deals with the conclusions and outlines future works on this matter.
## 4 Case Study
### Risk Assessment
Under the minor corrosion level, the cumulative risk ratio remains close to 1.10 in all locations, indicating a minimal long-term impact on seismic vulnerability. When considering moderate corrosion, the cumulative risk ratio increases more noticeably, reaching 1.59 in Bik'at HaYarden and rising to 2.01 in Jerusalem, reflecting a significant escalation in total risk over time. The most severe impact is observed under intensive corrosion conditions, with the cumulative risk ratio escalating to 6.06 in Jerusalem, and nearly tripling the baseline risk in both Bik'at HaYarden and Haifa, where the ratios reach 2.91 and 3.26, respectively. This analysis highlights the substantial amplification of seismic  
Figure 11: Risk ratios vs. time by corrosion level for different locations: this plot illustrates the risk ratios, defined as the ratio of the annual seismic risk for corroded structures relative to the baseline (non-corroded) risk, across a 120-year period for various corrosion levels and locations. risk due to progressive corrosion, particularly in regions with higher baseline seismic risks and under higher corrosion levels. Notably, the variability of risk increases significantly as the level of corrosion intensifies, underscoring the heightened uncertainty and potential for severe outcomes under higher corrosion levels. This analysis highlights the substantial amplification of seismic risk due to progressive corrosion, particularly in regions with higher baseline seismic risks and under higher corrosion levels.
## 4 Results
### Experienced System Characteristics and Automated Driving Style Preference  
When reporting the preferred driving style, 26 participants selected ADS as their exclusive or one of several preferred driving styles for conditionally automated driving (Selected ADS), whereas 16 participants selected among the options personal, demonstrated, or other driving style (Unselected ADS). The mean rating of Anthropomorphism, Likeability, Intelligence, Safety, and Overall Driving Strategy within the group Selected ADS is above four and, therefore, considered high on all scales (see Table 2). Similar results can be seen for the group Unselected ADS. Participants of the group Selected ADS perceived the experienced automated driving style as significantly more anthropomorphic and safer than participants of the group Unselected ADS. No correlation was found between the rated anthropomorphism and the estimated percentage of being driven by the automated system (\(r=-0.024,\,p=0.879\)). Furthermore, the overall driving strategy is rated significantly better and perceived as more comprehensible in group Selected ADS. No difference was found for Likeability and Intelligence (see Table 2). \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Selected ADS** & **Unselected ADS** & & \\
**Scales** & **M (SD)** & **M (SD)** & **\(t\)** & **\(p\)** \\ \hline Anthropomorphism & 4.35 (0.40) & 3.80 (0.74) & 2.71 & 0.013 \({}^{*}\) \\ Likability & 4.44 (0.46) & 4.20 (0.61) & 1.34 & 0.191 \\ Intelligence & 4.33 (0.46) & 4.09 (0.56) & 1.46 & 0.156 \\ Safety & 4.56 (0.46) & 4.25 (0.45) & 2.19 & 0.036 \({}^{*}\) \\ Overall Driving Strategy & & & & \\ Assessment & 4.54 (0.51) & 3.94 (0.86) & 2.55 & 0.018 \({}^{*}\) \\ Comprehensibility & 4.31 (0.47) & 4.00 (0.37) & 2.37 & 0.023 \({}^{*}\) \\ \hline \hline \end{tabular} Note. \({}^{*}\)\(p<0.05\).  
\end{table} 
Table 2: Comparison of perceived system characteristics between group Selected ADS and group Unselected ADS. Figure 4: Effects of experienced automated driving style (ADS) relative to participants’ personal driving style (PDS) and time of survey before and after the AV drive on trust (confidence intervals: 95%). For further analysis of the overall driving strategy, the rated driving situations are taken into account. Based on the participants' verbal feedback about the reason for their rating, 99 different categories were derived that describe the respective driving situations. Out of 43 rated driving situation categories that have been mentioned at least ten times, 7 have been identified showing a significant difference in comfort and/or safety ratings between the groups Selected ADS and Unselected ADS (see Table 3). The meaning of each category is explained in Table 4. Interestingly, the majority of participants did not know about the correct rules for when it is allowed to overtake on the right side. A common assumption was that thicker lane markings would always allow overtaking on the right side without any further conditions.
## 2 The Bus Synchronization Problem
### Overall Description of the Problem Model
For example, in the case study solved in this article, i.e., the bus network of Montevideo, Uruguay, this assumption holds: in the time period 12:00-14:00 on working days, the time between consecutive buses is almost constant for all lines (the standard deviation of their values is between 0.80% and 1.37%). However, trips of line \(i\) and line \(j\) are relevant for defining the number of synchronizations and the number of synchronized passengers on each trip. In the case with non-uniform departing times, the trip number \(r\) is relevant to determine synchronizations. The time between consecutive buses is given by \(X_{r}^{i}-X_{r-1}^{i}\) for trip \(r\) of line \(i\), and the problem formulation must be solved considering the number of trips that are synchronized. When computing the objective function in this model, the demand is split uniformly among the \(f_{j}\) trips of line \(j\). This assumption is also realistic  
Figure 1: The considered model for the BSP with expanded transfer zones [blue circle]. when passengers' demand has slight variations between consecutive trips. For modeling purposes, we are simply assuming that the planning period is determined because of its regularity. Whenever buses' interarrival times are uniformly distributed along a planning period, a fairly precise reference weight (i.e., average number of passengers) for each transfer zone is the ratio of the demand of transfers for the considered lines \(i\) and \(j\) over the number of trips of line \(i\) in the planning period \(T\). However, due to the fact that the passengers transferring onto a bus previously alighted from another, they arrive in bursts. There are more accurate formulations for weights in transfer zones [13]. Defining and studying an accurate model to handle variable demands are proposed as future work.
# _electronics_
## 4 Coordinated Distribution of Coded Packages  
To control the distribution of coded packages more precisely, as shown in Figure 4, this paper adds a rank field in the interest package to mark the number of coded blocks required by the user. When the user receives the required number of coded packages, they can decode and restore them to obtain the original data packages. In the process of distributing the coded package, the cluster head node checks the rank field of the interest package and the connection status of each satellite node to control it.  
If the required coded package is found on the satellite in the cluster and is connected to the user, the cluster head node controls the unified distribution of the coded package of each satellite node. If there is a required coded package on the satellite node but it is not connected to the user, the cluster head node controls each satellite node to forward the coded package to the access satellite node closest to the user and delivers it to the user.  
If the cluster head node does not find the required content or the number of coded packages sent by the cluster head node is less than the value of the rank field, i.e., the coded packages in the cluster do not meet the needs of the user. Then, the cluster head node forwards the interest package to the GEO satellite and modifies the value of the rank field, and the GEO satellite retrieves the cache entry table of each cluster except this cluster. If the GEO satellite finds the required content, it forwards the interest package to the corresponding cluster head node; the cluster head node searches the cache table to find the corresponding satellite node, returns all the cached coded packages of the node to the satellite closest to the satellite cluster that issued the original interest package, and then sends them to the user. If the GEO satellite does not retrieve the content requested by the interest package, we forward the request to the ground gateway to obtain the required content from the content server. The distribution process of the coded package is shown in Figure 11.
# Dynamics of Mechanisms with Superior Couplings
## 4 Discussion
The efficiency of a toothed transmission is calculated, taking into account the fact that at a certain moment of gearing several pairs of teeth are in contact (in gearing), and not just one. The starting model was chosen as having four pairs of teeth engaged simultaneously. The first pair of teeth in the gear has the point of contact \(i\), defined by the radius \(r_{i1}\), and the pressure angle \(\alpha_{i1}\); the torque forces acting at this point are: the driving force \(F_{mi}\), perpendicular to the position vector \(r_{i1}\) in \(i\) and the force transmitted from the driving wheel 1 to the driven wheel 2 through point \(i\), \(F_{ii}\), parallel to the gear line and having the direction from the wheel 1 to the wheel 2, the transmitted force being practically the projection of the driving force on the gear segment; the defined speeds are similar to the forces (considering the original, precise, described kinematics); the same parameters will be defined for the other three simultaneous contact points, \(j\), \(k\), \(l\) (Figure 18) [1,58]. We do not intend in this paper to present the whole theory developed even if it is very interesting but only the final relations obtained (58)-(60). Figure 18: Distribution of forces and speeds in a cylindrical gear when there are several pairs of teeth engaged simultaneously. \[\eta_{m}=\frac{z_{1}^{2}\cdot\cos^{2}\beta}{z_{1}^{2}\cdot(\{\textsl{g}^{2}\alpha_{ t}+\cos^{2}\beta\}+\frac{2}{3}\cdot\pi^{2}\cdot\cos^{4}\beta\cdot(\epsilon-1)\cdot(2 \cdot\epsilon-1)\pm 2\cdot\pi\cdot tg\alpha_{t}\cdot z_{1}\cdot\cos^{2}\beta\cdot( \epsilon-1)} \tag{58}\] \[\epsilon^{a.c.}=\frac{1+\textsl{g}^{2}\beta}{2\cdot\pi}\cdot \left\{\sqrt{\left[(z_{1}+2\cdot\cos\beta)\cdot tg\alpha_{t}\right]^{2}+4\cdot \cos^{3}\beta\cdot(z_{1}+\cos\beta)}+\right.\] (59) \[+\sqrt{\left[(z_{2}+2\cdot\cos\beta)\cdot tg\alpha_{t}\right]^{2}+4 \cdot\cos^{3}\beta\cdot(z_{2}+\cos\beta)}-(z_{1}+z_{2})\cdot tg\alpha_{t}\}\] \[\epsilon^{a.i.}=\frac{1+\textsl{g}^{2}\beta}{2\cdot\pi}\cdot \left\{\sqrt{\left[(z_{e}+2\cdot\cos\beta)\cdot tg\alpha_{t}\right]^{2}+4\cdot \cos^{3}\beta\cdot(z_{e}+\cos\beta)}-\right.\] (60) \[\left.-\sqrt{\left[(z_{i}-2\cdot\cos\beta)\cdot tg\alpha_{t}\right] ^{2}-4\cdot\cos^{3}\beta\cdot(z_{i}-\cos\beta)}-(z_{e}-z_{i})\cdot tg\alpha_{t}\right\}\]  
The mechanical efficiency of a general gear will be obtained with the help of Equation (58), in which the degree of coverage, determined in advance with one of the Relation (59) or (60), will be introduced, depending on whether the gear is external or internal. In Equation (58), the plus sign is taken as long as the driving wheel has external teeth and the minus sign will be given only when the driving wheel is a ring (crown) and has internal teeth.
# COTS Battery Charge Equalizer for Small Satellite Applications
## 5 Results  
The following section presents the results of the tests that were carried out in this work. As mentioned before, different scenarios were used for the tests. These tests were conducted in a static scenario, in which the battery was not being charged or discharged. Figure 9 shows the results of all the tests performed. The first column is associated with the DSSCE and the second column with the SFCBE. Each row is associated with a specific scenario. The first row corresponds to the first scenario, and the last row corresponds to the fourth scenario.  
In order to provide a more straightforward basis for comparison, the balancing speeds from the graphs in Figure 9 are collected in Table 9. This table contains the time required for each topology to balance up to a certain voltage difference between the most-charged and the least-charged cell.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **DSSCE** & & & **SFCBE** & \\ \hline
**Gap** & **100 mV** & **50 mV** & **20 mV** & **100 mV** & **50 mV** & **20 mV** \\ \hline Scenario 1 [h] & 5.617 & 10.420 & 18.177 & **3.855** & **4.794** & **5.561** \\ Scenario 2 [h] & **1.926** & **3.320** & 5.671 & 3.952 & 5.081 & **5.656** \\ Scenario 3 [h] & **1.917** & **4.316** & **7.928** & 4.331 & 5.365 & 11.653 \\ Scenario 4 [h] & **2.702** & **5.291** & 10.782 & 4.368 & 5.416 & **6.047** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Time to reach each level of equalization in hours.  
Figure 8: Representation of all the cell voltages in each scenario.  
As previously demonstrated, the DSSCE exhibits a more rapid balancing rate during the initial stages, where the voltage differences between the cells are more pronounced. However, as the voltages begin to equalize, the SFCBE maintains a more consistent balancing rate through the process. Furthermore, in the initial scenario where the conditions are less favorable for the DSSCE, the SFCBE demonstrates superior performance.  
Figure 9: Balancing results. The first column is associated with the DSSCE and the second one with the SFCBE. The first to fourth rows show the results for the first to fourth scenarios, respectively.  
On the other hand, it is worth noting in Figure 9 that the final voltage of the cells after equalization is always higher with the DSSCE than with the SFCBE. This means that the efficiency of the DSSCE is higher, and the losses associated with the equalization method are lower. For a better understanding, in Table 10 the battery voltage is shown after the equalization for different cell gaps.
# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
## 2 Observations and Data Processing
### Influencing Factor  
Due to the constant movement of GPS satellites and LEO satellite receivers, changes in satellite parameters can affect the observed values. Figure 4 shows the trend of satellite elevation angle and SNR on 1 April 2017. The binomial function can fit it well with the following formula:  
\[SNR*=0.0048\theta_{ele}^{2}-0.59\theta_{ele}+21.096 \tag{3}\]  
Therefore, we can reduce the influence of satellite elevation angle on SNR according to the relationship between the SNR and the satellite elevation angle.
## 3 Results and Discussion
### Fresh Performance
#### 3.1.5 Statistical Validation  
To conclude the evaluation of the experimental results related to the fresh behavior of SCC containing RCWTB, two statistical analyses were performed. On the one hand, the significance of the factor \"RCWTB content\" was analyzed for all the properties by means of a one-way analysis of variance (ANOVA) at a significance level of 5%. On the other hand, both linear (Pearson) and monotonic (Spearman) correlations between the RCWTB content and all the fresh properties were determined. All these analyses are common in concrete science [74,75]. The results obtained are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Parameter** & \(\mathbf{p}\)**-Value** & **Pearson’s Correlation** & **Spearman’s Correlation** \\ \hline \multirow{2}{*}{Slump-flow test} & Slump flow & 0.0010 & \(-\)0.98 & \(-\)0.95 \\  & \(t_{500}\) time & 0.0005 & +0.92 & +0.96 \\ \multirow{2}{*}{3-bar L-box test} & Blocking ratio & 0.0001 & \(-\)1.00 & \(-\)0.98 \\  & Slump flow & 0.0009 & \(-\)0.98 & \(-\)0.96 \\ \multirow{2}{*}{16-bar J-ring test} & \(t_{500}\) time & 0.0005 & +0.91 & +0.93 \\  & Blocking ratio & 0.0603 & \(-\)0.46 & \(-\)0.48 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(p\)-Values for the one-way ANOVA and correlations of the experimental fresh results.  
Figure 11: Relationships (linear simple regression) between the results of the 16-bar J-ring test: (a) slump flow and \(t_{500}\) time; (b) slump flow and blocking ratio; (c) \(t_{500}\) time and blocking ratio.  
Figure 12: Inter-relationships (linear simple regression) between the results of the different fresh SCC tests: (a) slump flow; (b) \(t_{500}\) time (c) blocking ratio.  
The RCWTB content significantly affected all the fresh-SCC properties, as \(p\)-values markedly lower than 0.05 were obtained [74]. The only exception was the blocking ratio of the 16-bar J-ring test, as the content of this waste did not significantly affect it. The correlations were consistent with the findings of the one-way ANOVA, the RCWTB content having a close relationship with all properties since the absolute values of the correlations were greater than 0.90. The exception once again was the blocking ratio obtained in the 16-bar J-ring test. The correlations also showed that all properties decreased with the RCWTB amount (negative sign), except for the \(t_{500}\) time, which increased (positive sign).