esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Learning Attitude Analysis  
In this analysis, an independent sample \(t\)-test was firstly used to investigate whether the students of the two groups had an equivalent learning attitude before participating in the experiment. As shown in Table 3, the mean value and standard deviation of the students in the control group were 3.58 and 0.81. Those values of the students in the experimental group were 4.18 and 0.55. Then, the homogeneity of variance was assessed by [PERSON]'s test, whose result showed that the within-group variances were considered equal (\(F=1.32\), \(p=0.264>0.05\)). The result of the independent sample \(t\)-test revealed no significant difference between the two groups with regard to the learning attitude before undertaking the experiment (\(t(20)=2.01\), \(p=0.058>0.05\)).  
To investigate the difference of the learning attitude depending on the two approaches between the two groups, a one-way ANCOVA was further conducted to neutralize the effect of the scores of learning attitude pre-questionnaire. The assumption of homogeneity of the regression slope (\(F=0.301\), \(p>0.05\)) was not violated, indicating that the ANCOVA was suitable to conduct. Table 4 shows a significant difference between the two groups in the learning attitude post-questionnaire (\(F\)(1,19) = 16.156, \(p=0.001<0.05\)). This means that the use of the board game-supported exercise approach in traditional prime factorization classes affected the students' learning attitude positively. To be more specific, students who engaged in the proposed prime factorization learning approach significantly surpassed those who engaged in the traditional prime factorization learning approach in terms of learning attitude.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group** & **N** & **M** & **SD** & **Adjusted M** & **Adjusted SD** & \(F\) & \(p\) \\ \hline Experimental Group & 11 & 94.09 & 6.64 & 92.55 & 5.81 & 4.647 & 0.044 \({}^{*}\) \\ Control Group & 11 & 73.18 & 29.85 & 74.72 & 5.81 & & \\ \hline \hline \end{tabular} \({}^{*}p<0.05\).  
\end{table}
Table 2: The ANCOVA result of the learning achievement for the two groups.
## 9 Artificial Neural Networks (ANNs) for Congestion Mitigation of WSNs
### DL for Congestion Mitigation of Wireless-Based IoT Networks
#### 9.1.1 Deep Neural Networks (DNNs)  
The practical implementation of ANNs, despite the potential answers offered by ANNs, encounters challenges related to the computing power required for training. These problems are partially solved by modern computer technology. The graphical processing units of modern graphics processing units (GPUs) speed up the training process by performing numerous weight calculations at once, which reduces the training time. In addition, fast GPUs open up the possibility of using ANNs with many hidden layers. DNNs are the classic example of DLs and have a large number of intermediate layers of neurons as well as more complicated connections between them. Although these applications are critically delayed, recent advances in DNNs enable their use in daily life. This is achieved by first training DNNs offline and then performing operations (optimization, tracking, etc.) online.  
Beam forming is one of the time-critical applications on the physical layer. The repetitive processes used here by conventional networks to introduce latency into the communication network will become obsolete in later communication networks. DNNs also face the biggest training challenge in a very long time. Quantum-based algorithms for DNN training could provide effective remedies. The architecture for training DDNs is still under development, although much has been achieved. The communication system, which is based on end-to-end communication, also uses auto encoders that employ DNNs [30]. The optimization of the peak-to-average power ratio (PAPR) and bit error rate (BER) of a DNN-based auto encoder in an OFDM system was presented in [118]. Researchers should pay close attention to the work in [119] to investigate the possibility of congestion mitigation in an IOE-based network. In addition, a localization-based architecture for symbol detection in MIMO systems was proposed in [120]. For M-MIMO systems, this approach uses fingerprinting and channel estimation. The aforementioned method is used in both physical layer resource allocation and congestion avoidance. Power optimization and control are two common DNN applications at the physical layer of communication networks [121]. To reduce congestion in wireless IoE networks, power optimization and control are essential. One of the most important solutions to reduce congestion in high-density wireless IoT networks could be the one proposed in [5].
# The Complex Relationship between Virulence and Antibiotic Resistance
### Antibiotic Inactivation  
A common mechanism of antibiotic resistance involves the enzymatic modification or destruction of the antibiotic [78; 79; 80]. Generally, four classes of antibiotic-modifying enzymes are known: hydrolases, group transferases, redox enzymes, and lysosomes [78]. Many of these are considered exotoxins and increase virulence while concurrently providing antibiotic resistance. Hydrolases, such as the host of amidases that cleave the \(\beta\)-lactam ring, cleave hydolytically susceptible chemical bonds such as esters and amides, which are often essential for antibiotic biological activity. This is a highly popular strategy to engender antibiotic resistance and increased virulence as these water-soluble enzymes can be easily excreted from bacteria as a prophylactic counter measure, intercepting antibiotic molecules prior to contact with the bacteria [78; 81]. The group transferases (e.g., acyltransferases [82], phosphotransferases [79], thioltransferases [83], nucleotidetransferases [84], ADP-ribosyltransferaseenses [85], glycosyltransferases [86]) represent a broad and well-characterized class of bacterial enzymes that impose covalent modifications on antibiotics to inhibit antibiotic target binding [87]. Aminoglycoscoside resistance is often conferred on the basis of transferase (acetyltransferases, nucleotidyltransferaseases (adenylyltransferases), and phosphotransferases) prompted antibiotic inactivation [88; 89].  
Redox enzymes do not represent a widely recognized strategy for antibiotic modifications; however, there are some noted exceptions, the most extensively studied being tetracycline resistance endowed by the TetX enzyme [90]. Finally, lysosomes cleave carbon-carbon, carbon-oxygen, carbon-nitrogen, and carbon-sulfur bonds by a mechanism independent of either hydrolytic or oxidative cleavage. An example is Vgb, responsible for streptogramin resistance [91; 92].
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes  
In this section, we first briefly review a general matrix-theoretic method for constructing QC-LDPC codes based on superposition. Then, we develop a class of RC-NB-LDPC codes based on masking and puncturing techniques.
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 4 System Architecture, Workflow, and Functional Design
### Workflow and Functional Design
#### 4.2.4 Working in a Real-Time Collaboration Session  
In a session with two or more programmers, each programmer edits the local source code in the same way as the traditional single-user programming. The client system automatically detects each fine-grained editing operation and immediately propagates it to all other active collaborators via the server. In the meantime, the client also maintains a live communication with the server for receiving real-time editing operations from other sites. Whenever a remote editing operation arrives, it will be replayed in the local source code copy. As a result, multiple programmers can concurrently edit any part of the shared source code, while changes are instantly visible and integrated at all sites.  
Precisely, there are two levels of real-time collaboration sessions. Firstly, the abovementioned session can be regarded as a _project-level session_. Secondly, within the project-level session, if any source code file is being viewed or edited by two or more programmers at the same time, then there is a corresponding _file-level session_ (which manages real-time editing operations on the contents of this particular file). Correspondingly, programmers may issue two types of editing operations: (i) the _project-level editing operation_, which creates, removes or renames a source code item (i.e. a file or directory within the source code tree) in the project; and (ii) the _file-level editing operation_, which edits the contents of a source code file. The timeliness of real-time operation propagation is designed as follows.  
Firstly, when a programmer issues a project-level editing operation, it will be immediately transmitted to other sites within the same project-level session. To deal with possible conflicts among concurrent project-level editing operations, certain mechanisms and techniques will be adopted and deployed (to be presented in Section 5.3.2).  
Secondly, when several programmers are viewing or editing the same file concurrently, operations performed by each programmer will immediately be transmitted to other sites within the same file-level session. However, only when this file is being saved by a programmer within the file-level session is the saved version transmitted to other programmers within the project-level session. The design rationale is that programmers outside the file-level session may not be interested in the latest content of a file's working copy before saving, and such propagation-on-saving feature also helps to improve system performance and reduce usage of communication bandwidth. To deal with possible conflicts among concurrent file-level editing operations and maintain the consistency of source code copies, certain mechanisms and techniques will be adopted and deployed (to be discussed in Section 5.3.1).
# The Analysis of WJ Distribution as an Extended Gaussian Function: Case Study
### Important Application of WJ Distribution to KWW Relaxation  
As an important example from the direct application of the WJ distribution [17], it is constructive to emphasize that the proposal is constructed on the basis of the WJ distribution to interpret the underlying mechanism of the famous KWW relaxation function [13; 22; 23]. Showing the ubiquitous irreversibility characteristics on the atomic, molecular, or electronic scale and the dynamic nature of irreversible processes, the KWW relaxation function or the stretched exponential relaxation function is a phenomenal observation in complex systems from the intricate behavior of liquids and glasses, the folding of proteins to the structure and dynamics of atomic and molecular clusters and well delineating the phenomena of important time-dependent dynamic processes [13; 22; 23]. The research has a long history of continuous interest, research inputs, and technological importance, in condensed matter physics in particular, as manifested by the fact that our research outcomes [17] have prompted wide attention from diverse fields [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34].
# Three-Dimensional Hyperbolic Mesh Generation Method Based on the Neural Network
## 3 Results
### Terrain Solids
Specifically, the maximum deviation angle for the neural network-generated mesh is less than 30 degrees, whereas it exceeds 50 degrees for the traditional method. Regarding equiangular skewness, the quality of the mesh generated by the neural network is consistently above 0.65, while the traditional method only achieves a minimum skewness value greater than 0.4. Additionally, in the best mesh quality intervals, such as the 0-5 degrees range for maximum deviation angle and the 0.95-1 range for equiangular skewness, the neural network-generated mesh contains fewer elements than the traditional method. Conversely, in the 5-10 degrees range for maximum deviation angle and the 0.9-0.95 range for equiangular skewness, the number of elements generated by the neural network far exceeds that of the traditional method, indicating that the overall mesh quality produced by the neural network is more uniform. Table 2 presents the specific maximum, minimum, and average values under different methods. It is evident that, in terms of the worst mesh quality, the neural network method outperforms the traditional method. However, since the number of poorly rated grids is relatively small in the overall mesh distribution, the difference in average mesh quality is minimal. Nonetheless, in practical computations, the success and accuracy of the numerical simulation depend on the worst mesh quality, thus making the neural network method superior to the traditional method.
## 2 Materials and Methods
### Statistical Analyses  
For all variables, descriptive statistics, including means, standard deviations (SDs), and 95% confidence intervals (95% CIs) were provided. Normality testing using the Shapiro-Wilk test was conducted before data analysis. A two-factor univariate general linear model was used to examine the mean differences in the anthropometric and performance parameters among the playing positions (Pivots, Wings, Backs, and Goalkeepers, among others) [32]. The differences between the means (position effect) were deemed meaningful if \(p<0.002\) and \(\eta_{\mathrm{p}}^{2}>0.20\)[33]. Following a Bonferroni correction and considering the number of parameters/tests, we adjusted the \(\alpha\) error level (0.05/23 = 0.002). Interpretation primarily relied on \(p\)-values, specifically for the dependent variable of playing position. This approach was adopted due to the limited number of instances (e.g., position-specific analysis) to avoid overestimating the mean differences.  
To ascertain a power of 80% for the current study (to detect a mean difference of 2.90 cm in CMJ height), a power calculation was conducted using a two-sided \(t\)-test with an alpha level of 0.05, assuming a pooled standard deviation of 2.50 cm [34]. This calculation, performed prior to recruitment using nQuery Advisor 4.0 (Statistical Solutions, Saugus, MA, USA), yielded a required sample size of n = 6 per group.  
Pearson's product-moment correlations were employed to assess the relationships between the anthropometric parameters and performance parameters across various categories (e.g., sprinting, jumping, throwing, strength, and anthropometry). The interpretation of coefficients (r) was as follows: \(<\)0.1 = trivial; 0.1-0.3 = small; 0.3-0.5 = moderate; 0.5-0.7 = large; 0.7-0.9 = very large; 0.9-1.0 = almost perfect [35]. Consequently, \(r^{2}>0.5\) (explained variance \(>\)25%) was considered meaningful and highlighted in bold. Given a sample size of n = 50, the critical value for the product-moment correlation, based on a two-sided \(t\)-test with \(\alpha\) = 5%, was determined to be r = 0.273 [36]. Statistical analyses were performed using SPSS version 28.0 for Windows (IBM, Armonk, NY, USA).
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 2 System Structure  
### Structure of the DC Multi-Microgrid System  
The MMGS discussed in this paper includes multiple relatively independent MGs in space. The DC multi-microgrid energy management system (MMGEMS) manages all energy transactions in MMGS. Each MG is integrated into the distribution network through power electronic devices and exchanges energy with the distribution network. Each MG contains RESs and EVs charging/discharging infrastructures (EVCDIs). There are two main types of MGs in the MMGS: MGs located in residential areas (RMG) and MGs located in office areas (OBMG). The structure of the MMGS is shown in Figure 1.  
The control of the system is mainly conducted by the collaboration of the MG energy management system (MEMS) and the EVs management system (EVMS). The MEMS is responsible for the energy dispatching of photovoltaics (PVs), wind turbines (WTs), and EVs in MGs, and the EVMS manages the charging and discharging behaviors of EVs. A DC multi-microgrid control system is shown in Figure 2.
# Integrated Photonics on Glass: A Review of the Ion-Exchange Technology Achivements
## 3 Telecom Devices
### Hybrid Devices  
Ion-exchanged waveguides being made inside the glass wafer, they leave its surface plane and available for the integration of other materials or technologies. The realization of deeply buried waveguides [81] and selectively buried waveguides [82] acting as optical via between two different layers increased furthermore the possibility of 3D integration. In order to overcome the quite weak chemical durability of an Yb-Er doped phosphate glass, [PERSON] et al. [83] wafer bonded it on a silicate glass substrate containing surface Tl ion-exchanged strips. The higher refractive index active glass was then thinned by an appropriate polishing process to become a single mode planar waveguide. At the place where the planar waveguide was in contact with the ion-exchanged strips, the variation of refractive index provided the lateral confinement creating hence a hybrid waveguide. A gain of 4.25 dB/cm has been measured with this device. This approach has been pursued by [PERSON] et al. [59] who realized a hybrid DFB laser combining a planar ion-exchanged waveguide made on IOG1 with a passive ion-exchanged channel waveguide realized on GO14. The Bragg grating was etched on the passive glass and encapsulated between the two wafers.  
Polymers have also been used to functionalize an ion-exchanged waveguide. As an example, a thin film of BDN-doped cellulose acetate deposited on the surface of ion-exchanged waveguide lasers allowed the realization of passively Q-switched lasers on  
Figure 9: Picture of a DFB laser realized by silver-sodium ion-exchange on P1 phosphate glass at the IMEP-LaHC (device similar to [77]).  
Nd-doped [84] and Yb doped [85] IOG1 substrates. A peak power of 1 kW for pulses of 1.3 ns and a repetition rate of 28 kHz has been reported by [PERSON] et al. [86] and used successfully to pump a photonic crystal fiber and generate a supercontinuum [87].  
Recently, a proof of concept of LiNbO\({}_{3}\) thin films hybridized on ion-exchanged waveguides have been reported [88]. The combination of these two well-known technological platforms for integrated photonics opens the route towards efficient low-loss non-linear integrated devices including electro-optic modulators.  
Hybrid integration of semiconductor devices on glass wafers containing ion-exchanged waveguides have been reported for the first time in 1987, by [PERSON] et al. [89] They bonded GaAs photodiodes on a metallic layer previously deposited and patterned on the glass wafer. Waveguides were done by a silver thin film dry process. Silicon [90] and germanium [91] photodetectors have been produced on potassium waveguides, while [PERSON] et al. proposed a lift-off approach to bound thin III-V semiconductor membranes on the surface of a glass wafer containing ion-exchanged waveguide and realize Metal-Semiconductor-Metal (MSM) photodetectors [92].
## 5 Experimental Results and Analysis
### Experimental Results and Discussion on Algorithms Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(6.39\times 10^{1}\) & \(7.36\times 10^{2}\) & \(1.81\times 10^{2}\) & \(2.42\times 10^{2}\) & \(1.82\times 10^{2}\) \\ MOPSO & \(1.01\times 10^{3}\) & \(8.85\times 10^{3}\) & \(2.95\times 10^{3}\) & \(3.37\times 10^{3}\) & \(2.04\times 10^{3}\) \\ MOAO & \(1.16\times 10^{3}\) & \(7.31\times 10^{3}\) & \(2.55\times 10^{3}\) & \(2.92\times 10^{3}\) & \(1.76\times 10^{3}\) \\ MOHBA & \(8.93\times 10^{2}\) & \(6.70\times 10^{3}\) & \(2.57\times 10^{3}\) & \(2.82\times 10^{3}\) & \(1.65\times 10^{3}\) \\ MOWOA & \(7.34\times 10^{2}\) & \(3.43\times 10^{3}\) & \(1.44\times 10^{3}\) & \(1.60\times 10^{3}\) & \(7.71\times 10^{2}\) \\ MOGWO & \(6.67\times 10^{2}\) & \(5.28\times 10^{3}\) & \(1.66\times 10^{3}\) & \(2.31\times 10^{3}\) & \(1.67\times 10^{3}\) \\ MOFECO & \(2.03\times 10^{2}\) & \(1.18\times 10^{3}\) & \(3.24\times 10^{2}\) & \(3.77\times 10^{2}\) & \(2.36\times 10^{2}\) \\ LSMOFECO & \(7.86\times 10^{1}\) & \(2.52\times 10^{2}\) & \(1.36\times 10^{2}\) & \(1.43\times 10^{2}\) & \(5.61\times 10^{1}\) \\ MOFECO-MS & \(3.40\times 10^{1}\) & \(1.07\times 10^{2}\) & \(6.69\times 10^{1}\) & \(7.10\times 10^{1}\) & \(2.07\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 33: Statistical results of SP indicator of the eight comparison algorithms for \(a280\_n279\). Figure 14: Plots of the non-dominated solutions in the objective space of \(a280\_n279\) obtained by nine algorithms in 15 independent runs (Pareto front obtained in each run are denoted by each color). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(1.93\times 10^{7}\) & \(3.83\times 10^{6}\) & \(9.63\times 10^{6}\) & \(9.92\times 10^{6}\) & \(3.44\times 10^{6}\) \\ MOPSO & \(1.87\times 10^{7}\) & \(1.02\times 10^{7}\) & \(1.44\times 10^{7}\) & \(1.42\times 10^{7}\) & \(2.52\times 10^{6}\) \\ MOAO & \(2.40\times 10^{7}\) & \(1.03\times 10^{7}\) & \(1.66\times 10^{7}\) & \(1.75\times 10^{7}\) & \(3.47\times 10^{6}\) \\ MOHBA & \(2.76\times 10^{7}\) & \(8.48\times 10^{6}\) & \(1.49\times 10^{7}\) & \(1.55\times 10^{7}\) & \(4.51\times 10^{6}\) \\ MOWOA & \(2.05\times 10^{7}\) & \(1.23\times 10^{7}\) & \(1.48\times 10^{7}\) & \(1.58\times 10^{7}\) & \(2.45\times 10^{6}\) \\ MOGWO & \(2.19\times 10^{7}\) & \(1.16\times 10^{7}\) & \(1.73\times 10^{7}\) & \(1.71\times 10^{7}\) & \(2.68\times 10^{6}\) \\ MOFECO & \(3.94\times 10^{7}\) & \(1.57\times 10^{7}\) & \(2.63\times 10^{7}\) & \(2.59\times 10^{7}\) & \(7.38\times 10^{6}\) \\ LSMOFECO & \(1.44\times 10^{7}\) & \(4.26\times 10^{6}\) & \(6.84\times 10^{6}\) & \(7.59\times 10^{6}\) & \(2.73\times 10^{6}\) \\ MOFECO-MS & \(1.06\times 10^{7}\) & \(4.61\times 10^{6}\) & \(5.97\times 10^{6}\) & \(6.25\times 10^{6}\) & \(1.47\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 34: Statistical results of PD indicator of the eight comparison algorithms for \(a280\_n279\).
## 2 Methods
### Scale Adaptive Wavelet Packet Transform
#### 2.3.2 Selection of Wavelet Base and Scale
If the correlation coefficient between each component and the original signal is less than the given threshold (usually with the threshold value of 0.3 in practice), then stop the decomposition, otherwise increase the scale, continue to decompose until the conditions are met.