esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Existing MPR selection algorithms often do not sufficiently address the importance of trust metrics, potentially compromising network integrity. To summarize, this study presents several key contributions, including the following:  
* An improved MPR selection algorithm for MANETs based on multiple criteria: energy, mobility, and trust. * A multicriteria weighted function that reduces MPR changes, minimizes network overhead and improves energy efficiency. * Enhanced network performance, demonstrated through NS3 simulations using Random Waypoint and ManhattanGrid mobility models, showing better PDR, lower delays, and reduced packet loss. * Adaptability to high-mobility environments, ensuring reliable data relay even under frequent topological changes. * Trust metrics to strengthen network security, providing better protection against threats. * Comprehensive simulation results validating the effectiveness of the multicriteria-weighted MPR selection method. The rest of the paper is organized as follows. Section 2 delves into related works to contextualize this study within the existing body of knowledge. Section 3 presents a detailed explanation of the proposed multicriteria-weighted MPR selection methodology, breaking down the technical aspects of the algorithm and outlining the metrics used for evaluation. Extensive simulations and analyses are then presented in Section 4 to demonstrate the efficacy of the proposed algorithm, comparing it against traditional methods using various performance metrics. The paper concludes with a summary of findings and potential directions for future research in Section 5, suggesting ways this work could be extended or refined.
## 5 Finite Element Analysis and Shear Capacity Checking Calculation
### Finite Element Modelling
The composite beam's horizontal joint surface undergoes a process of artificial roughening during the construction phase, complemented with a sound stirrup configuration, allowing for a bond-slip-free horizontal composite surface. This surface would then necessitate the application of the \"Tie\" binding constraint. The interface relationship at the intersection of column joints and core areas is established by setting surface-to-surface contact. To designate contact attributes, one must separate the consideration of tangential behavior and normal behavior. For the tangential behavior, apply the \"penalty\" friction formula. Given that the surfaces of the upper and lower columns are irregular, the friction coefficient can be set at 1. Furthermore, normal behavior can be set to \"hard contact,\" implying unrestricted force transmission in the normal direction. As for the vertical overlapping surface at the beam and column intersection point, an approximate \"concrete softening\" method is employed. This signifies that while the compressive strength of the concrete remains the same at the overlapping surface, the tensile strength will decrease to roughly 65%-85% of the original value. Compared to the horizontal laminate surface, the vertical one tends to bear not only shear stress but also more significant tensile stress. When it comes to the vertical composite joints of new and old concrete, compared to cast-in-place joints, they exhibit weaker bonding performance alongside lower tensile strength. However, their compressive strength mostly remains the same. Using the \"concrete softening\" method thus seems more feasible. Use of the truss element for steel bars is common, and the standard mesh size stands at 50 mm. Figure 14 provides a representation of the joint models and mesh division. The beam-column connections' boundary conditions in the finite element simulation need to align with those established in the experiment.
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## Results
### Filter  
We compared three filters--two very similar Butterworth filters, the standard HOMER2 (BW#1), and a Butterworth filter of the same order designed in MATLAB (BW#2), as well as a zero-delay trapezoidal FFT filter (FFT). Figure 7 shows the correlation of BW#2 and FFT with the standard BW#1 (Figure 7a). Random data (MATLAB code \"rand\") was generated (Figure 5b) to visualize the power spectrum profile of the filters (Figure 7c-f) on data (the filter profile can also be viewed in MATLAB with \"freqz\"). The two Butterworth filters were, as expected, very similar (_r_ = 0.98 \(\pm\) 0.0) (see Figure 7c). Their profile showing the long edge in the higher frequencies can be seen in Figure 7d,e. The Zero-Delay FFT Filter shows the typical straight edges at the cut-off frequency (Figure 7c,f). The FFT filter was also highly correlated to BW#1 (_r_ = 0.90 \(\pm\) 0.0), but as expected, significantly less correlated (_p_ \(<\) 0.05) to BW#1 than BW#2 was to BW#1. In the following analysis, data was filtered with the standard BW#1 to avoid edge effects.  
Figure 6: Motion correction. Comparison between two Wavelet MC algorithms, MC#1 and MC#2 (after bandpass filter) for all participants shown as boxplots (**left**). In addition, an example time course for Subject 1 channel 12 is plotted after bandpass filter and PCA (**right**). Shaded sections depict finger tapping.
## 4 Discussion
### General Trends in the \(J\)-Couplings of Fluorobenzene  
With experimental data and calculations in hand, the signs and magnitudes of fluorobenzene \(J\)-couplings may now be assessed. In general, the magnitude of the \(J\)-couplings followed \(|^{3}J_{\rm orthd}|>|^{4}J_{\rm metal}|\)\(>|^{5}J_{\rm paraI}\) for H-H and H-F couplings, and \(|^{3}J_{\rm orthd}|>|^{5}J_{\rm paraI}>|^{4}J_{\rm metal}|\) for F-F couplings. As first identified by [PERSON] and [PERSON], the \(J\)-coupling magnitude is inherently tied to the bonding characteristics of the sigma and pi bonds, and their analysis correctly predicts the magnitudes of the H-H, H-F, and F-F couplings [68]. However, this analysis does not result in the correct assignments of the relative signs. In contrast, the JCS and _ab initio_ calculations can determine the correct relative \(J\)-coupling signs, as shown in Table 2. Of note are the \({}^{4}J_{\rm FF}\) and \({}^{5}J_{\rm HF}\) couplings, which showed a mixed behavior of both positive and negative values. For \({}^{5}J_{\rm HF}\), the change in magnitude and the eventual change in sign was due to the number of protons neighboring the F. As the number of protons increased, the \({}^{5}J_{\rm HF}\) coupling increased and eventually became positive proceeding from pentafluorobenzene (0 H neighboring, \({}^{5}J_{\rm HF}=-4.19\) Hz) to monofluorobenzene (2 H neighboring, \({}^{5}J_{\rm HF}=+0.50\) Hz). For \({}^{4}J_{\rm FF}\), as the total number of F atoms increased, the \(J\)-coupling became smaller and negative. Pentafluorobenzene is interesting because it has three different \({}^{4}J_{\rm FF}\) with some \(J\)-couplings positive while others are negative. Clearly, the nature and position of the H and F atoms in the ring dramatically influences the sigma and  
Figure 11: The 16 different possible sign permutations of the 5 \(J\)-couplings measured for the JCS of 1,4-difluorobenzene. The absolute signs are denoted between the \({}^{19}\)F (**left**) and \({}^{1}\)H spectra (**right**). The same spectra were obtained when there were degenerative, same relative sign combinations (e.g., ++++ is equivalent to \(-----\)). The signs from left to right correspond to absolute signs of \({}^{3}J_{\rm HF}\), \({}^{4}J_{\rm HF}\), \({}^{5}J_{\rm HF}\), \({}^{3}J_{\rm HH}\), and \({}^{4}J_{\rm HH}\), respectively.  
pi bonds, which alter the sign and magnitude of \({}^{4}\!J_{\rm FF}\). This is further supported by the values of \({}^{4}\!J_{\rm FF}\) for 1,3,5-trifluorobenzene (+5.8 Hz [69]), 1,2,3,4-tetrafluorobenzene (+2.56 Hz [70]), 1,3-difluorobenzene (+6.52 Hz), and pentafluorobenzene (+1.3 Hz).
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 2 Theoretical Background
### Modeling Equilibrium Traffic Flow Relationships
#### 2.1.1 Single-Regime Traffic Flow Models
Its disadvantage is that it is computationally complex (\(c_{1}\), \(c_{2}\),\(c_{3}\) are parameters that must be determined), and variable k is used as a dependent variable, which makes the model inconvenient to use [18]. [PERSON] [18] offered a simpler alternative to [PERSON]'s model. His own model reported comparable empirical accuracy to [PERSON]'s model. [PERSON] [4] proposed using the logistic function to describe the relationship \(\text{v}=\text{f}(\text{k})\). He assumed that even when traffic is very dense, vehicles move at a finite minimal speed \(\text{v}_{\text{min}}\). In the model, a, b are shape parameters. The outcome of modeling is a sigmoid curve v(k) with a very good match to empirical data [4]. On the downside, speed never reaches zero in the model and density never reaches a finite value, which is inconsistent with BC2. One of the recent models is the four-parameter v = f(k) model proposed by [PERSON] and [PERSON] [19] built based on a BPR speed-flow equation [20] and overcoming its limitation to represent only a free-flow traffic regime. The downside is that the model fails to satisfy BC2. Figure 1 graphically shows how the relationship \(\text{v}=\text{f}(\text{k})\) is represented by selected models. The evolution from the simplest linear form to the relationship being represented by an S-shaped curve can be seen from the figure. Figure 1: Representation of the speed–density relationship in selected traffic flow models. There is also a group of models (with \(v=f(q)\)) BPR function [20] as an example) that represent the speed-flow-density relationship for a free-flow regime only and do not consider traffic flow characteristics after the maximum flow and corresponding optimum density are reached. It is not a limitation if the purpose is to analyze traffic under free-flow conditions; however, to estimate values of traffic flow parameters at the maximum flow (\(q_{max},\ k_{opt},\ v_{opt}\)), some initial assumptions regarding the boundary of the free-flow regime need to be made. As a result of these reasons, these kinds of models are not included in Table 1.
## 3 Stochastic MPC Approach for Coordinated Scheduling Optimization of MES  
The studied MES operates in main grid connected mode. In the MES, two types of CHPs with complementary properties are considered, and the EVs are aggregated to be controlled as an electricity load or an electricity supplier. The electricity demands in the MES are satisfied by the main grid, CHPs and EVs; and the thermal demands are provided by two kinds of CHP units. The objective of coordinated scheduling optimization is to achieve the most economic operation while satisfying electricity and thermal power supply/demand balance, the operational constraints of CHP units and EVs.  
The stochastic MPC based scheduling optimization of MES, considering the coordination of electricity and heat supplies, is formulated into an MILP problem. The optimization process is composed of two steps. In the first step, the stochastic model of EVs availability is established by using scenario generation and reduction; and, in the second step, the MILP problem is solved, in which minimizing the operation cost is considered as the objective, while considering the supply and demand balance of electricity and heat power and other operational constraints. The schematic overview of the stochastic MPC approach for MES coordinated scheduling optimization is depicted in Figure 4.
# Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems
## 6 Experiment and Evaluation  
We conduct a series of experiments on two large-scale real-world QoS datasets, to evaluate the prediction accuracy of the proposed models, as well as compared to several well-known methods. We aim to address the following questions.  
1. How do the proposed models behave in different data sparsity cases?
2. How do the proposed models perform compared to other models?
3. How do the parameter \(\lambda\) and \(K\) impact the prediction accuracy?
# Exploratory Research on Satisfaction Degree in Distance Education
## 2 Related Work
They analyzed the relationship between university students' intention to use e-learning and selected constructs such as attitude, perceived usefulness, system accessibility, and so on, and they developed a general linear structural model of distance education acceptance of university students. At present, there are few studies on teachers' satisfaction with distance education, and most mainly focus on the study of the satisfaction with learning platforms. [PERSON]. [20] constructed a special technology acceptance model to predict the technology acceptance level for pre-service teachers. To sum up, research on satisfaction with distance education by teachers and students has the following problems: Existing literature usually selects a certain dimension to conduct a study of students' satisfaction with distance education and rarely integrates multiple dimensions. There is also a lack of research on teachers' satisfaction with distance learning. However, the teacher is a key part of the teaching process. If the teachers' satisfaction with distance learning is low, it inevitably affects the quality of student learning. Therefore, it is also necessary to study the teacher's satisfaction with distance education. Most studies focus on positive factors related to satisfaction and pay little attention to the negative factors that lead to a decrease in students' satisfaction, such as the lack of a learning atmosphere. The above study did not take into consideration the influence of different population factors on satisfaction, so this paper explores the importance of this factor from a different geographical distribution of the population. Therefore, this paper analyzes students' satisfaction with distance education from the aspects of online learning platforms, teaching resources, and teachers' satisfaction. In view of the teacher dimension, this paper investigates and analyzes the use of teaching resources, after-school guidance, and corrective assignments. At the learning platform, this paper investigates and analyzes the platform's sense of use and learning effect. Based on the results of the analysis, this paper concludes the key factors that affect students' satisfaction with distance education. Considering that distance learning can hardly integrate a learning environment the way face-to-face courses do, such as a unified classroom and learning environment, or unexpected network disconnection, possible factors may occur and negatively affect the satisfaction with distance education. This paper not only takes these negative factors into consideration but also studies why students from different groups and different regions show various satisfaction with distance education. At the same time, this paper provides suggestions for improving satisfaction of teachers and students with distance learning in emergencies.
## 8 Conclusions  
Because different train types, routes, and even different weather conditions affect the establishment of a train dynamic model, and because the relationship between train resistance and speed is highly nonlinear, it is not feasible to accurately obtain a dynamic model of train motion. Thus, RBFNN was used to approximate the additional resistance and unknown disturbance dynamics of train operation, and the adaptive mechanism of the Davis equation coefficient of basic resistance was introduced to enhance the robustness and adaptability of the control system. At the same time, aiming at precise control technology in the FAO, the RBFNN-POCRAC algorithm was proposed, and a simulation platform was used to verify the proposed control algorithm. When the FAO had parameter uncertainty, time-varying parameters, strong additional resistance, and external disturbance, the proposed control algorithm could achieve smooth, fast, and accurate tracking for the desired curve.  
Combined with the research content of this paper, future research work will mainly include two aspects:  
* Research on FAO control methods based on deep learning and knowledge automation. The existing control methods do not fully utilize the massive data generated by the train operation. However, the deep learning method can generate \"strategic\" data for the train operation control system. Combining deep learning and knowledge automation control methods, mining rules from the \"strategic\" data, and making accurate predictions will help real-time monitoring and automatic adjustment of the train operation status.
* Research on coordinated control methods for multi-train tracking operation. The purpose of multi-train coordinated control is to increase the traffic density by adjusting the minimum safe interval between the preceding train and the following train. FAO combined with multi-train cooperative control can effectively reduce the overall energy consumption and life-cycle cost of the system, which is the future development direction of urban rail transit.  
Conceptualization, J.Y. and Y.Z.; methodology, J.Y. and Y.Z.; software, J.Y. and Y.Z.; validation, J.Y., Y.Z. and Y.J.; formal analysis, J.Y. and Y.J.; investigation, J.Y. and Y.J.; resources, J.Y.; data curation, J.Y. and Y.J.; writing--original draft preparation, J.Y.; writing--reviewand editing, J.Y.; visualization, J.Y., Y.Z. and Y.J; supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSFC, grant number 61963023.  
Not applicable.  
The data are contained within the article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Sustainability Analysis Based on the LCA-Energy-Carbon Emission Approach in the Building System
## 6 Improvement Strategies
### Alternative Resource Utilization  
As the second factor for energy analysis and carbon emission evaluation in this paper, the influence of the building material phase cannot be ignored. According to the data in Appendix A, steel, cement and brick lead the role in the building material phase. Hence, the hypothesis was carried out according to their substitution. In this paper, we designed and implemented a hypothesis, as follows: how do the sustainability and carbon emissions of the entire building system change if the steel and cement materials are replaced with new renewable materials?  
Table 14 lists the calculation results and Figure 22 reveals the change after the assumption. From the point of view of renewable parameters (ESI), it has a noticeable improvement, from 0.849 to 1.487, with a 42.9% advancement.  
In addition, many researchers have investigated alternative materials for building sustainability enhancement and carbon reduction effects. For instance, for enhancing the performance of fibers reinforced cementitious composites, a steel-basal hybrid substitution has been considered [82]. Cement substitution has also been widely investigated and explored for cement production and cementitious composites [83,84].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Indicators** & **Previous Index** & **Improved Index** & **Unit** \\ \hline
1 & Renewable input (R\({}_{\text{s}}\)) & 9.38 \(\times\) 10\({}^{18}\) & 5.25 \(\times\) 10\({}^{19}\) & Sej \\ \hline
2 & Nonrenewable resource (N\({}_{\text{s}}\)) & 7.64 \(\times\) 10\({}^{20}\) & 8.75 \(\times\) 10\({}^{20}\) & Sej \\ \hline
3 & Energy feedback input (E\({}_{\text{f}}\)) & 1.14 \(\times\) 10\({}^{19}\) & 3.53 \(\times\) 10\({}^{19}\) & Sej \\ \hline
4 & Energy yield ratio (EYR) & 69.1 & 27.275 & - \\ \hline
5 & Environmental loading ratio (ELR) & 81.4 & 18.339 & - \\ \hline
6 & Energy sustainability indicator (ESI) & 0.849 & 1.487 & - \\ \hline \hline \end{tabular}
\end{table}
Table 14: Sustainable energy index change based on reuse material replacement.  
Figure 22: Index improvement range based on alternative material replacement.
## 3 Results and Discussion
### SEM-EDX Analysis  
To confirm the adsorption of the drug molecules on metal, SEM and EDX experiments were further carried out. The microscopic morphologies of the bronze surface before and after 48 h of immersion in the two corrosive electrolytes, in the absence and in the presence of the optimal concentrations of NAC, are presented in Figure 6, together with the corresponding EDX profile analyses.  
Figure 6: SEM micrographs (**a**–**e**) and EDX spectra (**a**–**e**) of the bronze surface before (**a**,**a**) and after 48 h of exposure to various electrolytes: 3.5% NaCl solution (**b**,**b**)?, 3.5% NaCl solution + 36 mM NAC (**c**,**c**); simulated acid rain (**d**,**d**); simulated acid rain + 6 mM NAC (**e**,**e**).  
Figure 6a, corresponding to the unexposed bronze, shows that the alloy was alpha-bronze, with local Sn enrichment; it also reveals some Pb globules, which appear as white spots in the BSE image. The optical micrographs included in Figure S1 from the Supplementary Materials also reveal the presence of Pb inclusions.  
After 48 h of immersion in the uninhibited corrosive electrolytes, the bronze showed significant surface damage, including visible pitting and corrosion products (Figure 6b,d). The high levels of oxygen, i.e., 36.70 at.%, and 29.64 at.%, identified on the bronze surface exposed to the saline and acid rain solutions confirmed that the corrosion products were mainly oxides (most likely Cu\({}_{2}\)O). As expected, the chloride (1.63 at.%) was also present on the sample exposed to the 3.5% NaCl solution (Figure 6b').  
The addition of the drug in the two electrolytes resulted in rather smooth and uniform surfaces, significantly preventing the occurrence of corrosion pits and reducing the metal roughness, most probably due to the adsorption of NAC on the bronze. As expected, the oxygen and chloride contents decreased markedly within the surface layer (Figure 6c',e') confirming that the NAC adsorption prevented corrosion product formation on the metal surface.  
SEM-EDX analysis aligned with the electrochemical findings, confirming the effectiveness of the _Fulinucil_ drug in mitigating the corrosion of bronze exposed to both 3.5% NaCl and simulated acid rain electrolytes.