esa-sceva/satcom-chunk-collection huggingface dataset:
# Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression
## 4 Results and Discussion
### Discussion
Firstly, the reduction in data volume alleviates bandwidth constraints in video transmission, which optimizes network resource utilization. Secondly, the improved video quality contributes to a superior user experience, particularly in real-time applications such as live streaming and  
Figure 6: Comparison of experimental results for VVC Class B–D datasets on PSNR and MS-SSIM. teleconferencing. Furthermore, the techniques developed are versatile and applicable across various video content types and domains. MDER can be integrated as a plug-and-play module into other neural encoders, serving as an independent preprocessing step before the training of encoding networks. MDER is utilized to provide input video data with reduced high-frequency noise, which helps to save encoding bitrates and enhances encoding performance. This integration is achieved without necessitating changes to the underlying end-to-end codec architecture, which facilitates its incorporation into existing systems. Additionally, expanding the application of this approach to handle diverse video content types and varying streaming conditions could provide valuable insights into its practical applicability and effectiveness. Future research could explore additional preprocessing methods combined with neural video encoders to further improve coding efficiency.
# Metabolic Syndrome and Its Components among Taxi Drivers in the City of Tshwane, South Africa
## 3 Results
### Association of MetS Components and Selected Variables by Age  
Taxi drivers were divided into two age groups; \(<\)50 years (young drivers, n = 271) and \(\geq\)50 years (older drivers; n = 91). Using a Chi-square test, age was associated with hypertension (\(p\) = 0.002), diabetes (\(p\) = 0.020), marital status (\(p\) = 0.001), education (\(p\) = 0.001), duration in the taxi industry and was high among older taxi drivers (44%; \(p\) = 0.005), and alcohol use (\(p\) = 0.001) (Table 2).  
Taxi drivers were divided into two groups based on the duration of service in the taxi industry: \(<\)10 years (n = 174) and \(\geq\)10 years (n = 188). Using a Chi-square test, duration in the taxi industry was associated with age (\(p\) = 0.001), hypertension (\(p\) = 0.001), marital status (\(p\) = 0.001), and education (\(p\) = 0.001) (Table 3). Further analysis (result not shown in tables), marital status was significantly associated with diabetes (\(p\) = 0.002) and hypertension  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & \multicolumn{3}{c}{**Age Categories**} \\ \hline  & **\textless{}50 Years** & \(\geq\)**50 Years** & \(p\)**-Value** \\ \hline Hypertension & & & \\ No & 185 (68) & 46 (51) & 0.002 \({}^{\star}\) \\ Yes & 86 (32) & 45 (49) & \\ \hline Diabetes & & & \\ No & 163 (60) & 42 (46) & 0.020 \({}^{\star}\) \\ Yes & 108 (40) & 49 (54) & \\ \hline BMI (kg/m\({}^{2}\)) & & & \\ Normal & 82 (30) & 22 (24) & 0.178 \\ Overweight & 101 (37) & 28 (31) & \\ Obesity & 82 (30) & 39 (43) & \\ Underweight & 6 (2) & 2 (2) & \\ \hline Exercise regularly & & & \\ No & 196 (72) & 61 (67) & 0.336 \\ Yes & 75 (28) & 30 (33) & \\ \hline Marital status & & & \\ Cohabiting & 29 (11) & 1 (1) & 0.001 \({}^{\star}\) \\ Single & 133 (49) & 11 (12) & \\ Ever married & 109 (40) & 79 (87) & \\ \hline Education status & & & \\ No formal schooling & 2 (1) & 4 (4) & 0.001 \({}^{\star}\) \\ Primary school & 10 (4) & 21 (23) & \\ High school & 226 (83) & 61 (67) & \\ Post high school & 33 (12) & 5 (6) & \\ \hline Duration in taxi & & & \\ industry & & & \\ \(<\)10 years & 153 (56) & 21 (23) & 0.001 \({}^{\star}\) \\ \(\geq\)10 years & 118 (44) & 70 (71) & \\ \hline Sleeping hours at & & & \\ most/day & & & \\ \(<\)5 h & 37 (14) & 83 (91) & 0.224 \\ \(\geq\)5 h & 234 (86) & 8 (9) & \\ \hline Alcohol use & & & \\ No & 93 (34) & 57 (63) & 0.001 \({}^{\star}\) \\ Yes & 178 (66) & 34 (37) & \\ \hline Smoking & & & \\ No & 185 (68) & 67 (74) & 0.336 \\ Yes & 86 (32) & 24 (26) & \\ \hline \hline \end{tabular} \({}^{\star}\) significant at 5% level.  
\end{table}
Table 2: Comparison of age and selected variables among taxi drivers.  
(\(p\) = 0.025). Additionally, marital status was associated with age (\(p\) = 0.001), being a family provider (\(p\) = 0.001), and alcohol use (\(p\) = 0.002).
## 6 Conclusions  
This paper proposes an innovative approach to obtaining the real-time global stress of marine structures with a limited number of stress sensors using AIGC. By dividing the spatial structure into correlated subsets of finite elements, feature elements are selected as stress measurement points. Individually, these feature elements exhibit low correlation, but, when combined, they effectively capture the overall state of the structure. Subsequently, an ANN is employed to establish the relationship between the feature elements and their corresponding subsets, enabling the reconstruction of the global stress of the structure.  
A neural network is established based on FE simulation data using 20 feature elements as input variables to predict the remaining 30,858 element data. Uncertainty analysis demonstrates that 95% of the elements have solution errors within 9% [29]. The accuracy of the proposed method is validated through the physical structural model test. The fine-tuned ANN consistently generates stress for both 10\({}^{\circ}\) and 13.3\({}^{\circ}\) shear force angles, with deviations below 10% and an average deviation of less than 4%.  
These results highlight the feasibility and strong generalization performance of the method in enhancing traditional SHM systems. While this study provides insights into real-time stress field solutions in marine structures, further research is needed. Future directions include investigating the impact of different correlation coefficients on the description of complex structures and optimizing the neural network for improved performance. Additionally, it is crucial to identify a suitable evaluation method to ensure the reliability of the data generated by AIGC in engineering applications.  
Methodology, T.Z.; Investigation, G.C.; Data curation, Z.J.; Writing--review & editing, S.O.; Supervision, J.H. and E.O.; Project administration, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China, grant number 2021 YFC2802300.  
Not applicable  
The authors declare no conflict of interest
## 2 Materials and Methods
### Data
#### 2.2.3 Climate Data  
Climate factors significantly influence vegetation activities. To analyze the correlation between vegetation growth (EVI and GPP) and the drought condition (SPEI), we also included other potential dominant climate factors, specifically long-term temperature (TMP) and shortwave radiation (RAD), in our study. Since the SPEI dataset used the CRU climate dataset as an input, we chose the CRU 0.5 degrees monthly dataset (version TS 4.03, [[https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/](https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/)]([https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/](https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.03/)), accessed on 21 September 2019) as the temperature data source. Radiation data were not included in the CRU database, so instead, we downloaded the monthly downward solar radiation from the state-of-the-art NCEP/NCAR Reanalysis II dataset ([[ftp://ftp2.psl.noaa.gov/Projects/Datasets/ncep](ftp://ftp2.psl.noaa.gov/Projects/Datasets/ncep)]([ftp://ftp2.psl.noaa.gov/Projects/Datasets/ncep](ftp://ftp2.psl.noaa.gov/Projects/Datasets/ncep)). reanalysis2/gaussian_grid, accessed on 26 July 2021). These RAD data were originally binned into the Global T62 Gaussian grid, which equated to approximately 1.85 longitude degrees, and it covered the period from 1 January 1979 to 31 July 2021. Similar to the SPEI data, we selected the CRU-TMP data and the NCEP/NCAR-RAD data during 2001-2018 and resampled the data into a 500-m spatial resolution for our analysis.
## 1 Introduction  
Transverse maxillary deficiency currently affects 8-23% of adults and adolescents [1]. One of the most widely used orthodontic treatments today in patients with transverse maxillary defects is the maxillary skeletal expander (MSE) [2]. In clinical practice, micro-implants were first introduced in the palatal region 25 years ago. The possibility of using these micro-implants at the level of the palatal region is favored by the gingival mucosa at this level having favorable elasticity [3]. With the use of micro-implants associated with maxillary expanders, the incidence of post-interventional complications, such as tooth-borne forces leading to limited skeletal movement and the potential for undesirable tooth movement, root resorption, and lack of firm anchorage to retain sutural long-term expansion, has been reduced [4; 5]. The reason is that the micro-implants play the role of the anchoring system for the maxillary expander [6; 7]. Transverse jaw deficiency is a common condition in young adults. Untreated or treated incorrectly, it can affect the health of these patients over time. In this sense, modifications of the occlusal plane, damage to periodontal structures, gingival retraction, change in tongue position, asymmetry of the facial planes, and, in severe cases, even sleep apnea syndrome may occur in these patients. Given these possible co-morbidities, orthodontic treatment is particularly important in these cases and is required in clinical practice [8; 9].  
To obtain skeletal changes with micro-implant assisted rapid palatal expanders (MARPEs), the applied force should be enough to overcome areas of resistance located in the midface region such as the mid-palatal suture (the first that needs to be disrupted), pterygoid junctions, piriform aperture pillars, and zygomatic buttresses [10]. For this reason, using a MARPE device, force is applied directly into the center of resistance of the maxilla using micro-implants and not the tooth as in classical tooth palatal expanders. As a result of this applied force, buccal tipping of the tooth is prevented, and a more parallel suture opening is promoted [11].  
Cortico-puncture (micro-perforation) was introduced in clinical practice as a surgical procedure to shorten orthodontic treatment time. It removes the cortical bone that resists orthodontic force in the jaw and keeps the blood circulation and continuity of bone tissues to reduce the risk of necrosis and facilitate tooth movement [11].  
In the present study, we evaluated the treatment outcomes of adult patients with maxillary compression who underwent orthodontic cortico-puncture CP surgery in association with MARPE.
## 5 Case Analysis and Results
### The Compensation Target Is a Three-Phase Current Priority Balance  
Due to the asymmetry of the three-phase load, the three-phase current is unbalanced. In order to balance the three-phase current, the negative-sequence reactive power compensation weight coefficient is \(w_{n}=1\), and the zero-sequence reactive power compensation weight coefficient is \(w_{z}=1\). At the same time, considering the limitation of the capacity of the compensation device, in order to minimize the reactive power after compensation, \(w_{r}\) needs to be 0.8, and the compensation reference current is the sum of \(w_{i}\), \(w_{n}\), and \(w_{z}\). The waveforms of the three-phase compensation reference currents \(i^{i}_{Acc^{\prime}}i^{*}_{Bcc^{\prime}}\) and \(i^{*}_{CCc}\) are shown in Figure 7. The waveforms of the three-phase output currents \(i^{{}^{\prime}}{{}_{A}}\), \(i^{{}^{\prime}}{{}_{B}}\), and \(i^{{}^{\prime}}{{}_{C}}\) on the power supply side after compensation and the phase \(A\) voltage \(u_{A}\) are shown in Figure 8. The imbalance phenomenon has thus disappeared. However, a phase difference still exists between the voltage and the current, and the inductive reactive component still exists. The sum of squares of the amplitudes of all reactive current components after compensation is 509 A\({}^{2}\), which is 96.26% lower than that before compensation.  
Figure 4: Reactive power compensation reference—current waveform.  
Figure 5: Voltage and current waveform before compensation.  
Figure 6: Voltage and current waveform after reactive compensation.  
Figure 7: Three-phase reference compensation current.
## 2 Materials and Methods
### Data Preprocessing  
Data preprocessing is the stage in which spectral data collected prior to analysis is prepared. This process enhances the effectiveness of data analysis and the accuracy of model training. During this stage, various data cleaning, filtering, and normalization operations were performed.  
Figure 4 illustrates the normalized spectral data of milk samples with different starch concentrations. In this figure, the \"Case 0\" label represents raw milk (unadulterated), while the \"Case 1\", \"Case 2\", \"Case 3\", and \"Case 4\" labels represent milk samples containing 0.01%, 0.02%, 0.04%, and 0.08% starch by volume, respectively. Each \"case\" label is designed to demonstrate how the spectral data of starch-adulterated milk samples changes compared to raw milk. It can be observed from this graph that there are noticeable changes in the spectral data as the concentration of starch increases. These changes are crucial for the MSANN model to accurately classify adulterated milk samples.
## 5 Implementation and Evaluation
### Complexity Analysis for the Lifetime of WSNs in CH_Rotations and WC + DT Approaches
#### 5.2.2 Experimental Metrics and Results
The reason for the increase in this variance was due to the increase in the rate of packets received by the CH nodes, which in turn led to an increase in the rate of inspection and verification messages. This in turn led to an increase in the rate of power  
Figure 8: Analysis of the effect of WC+DT technique on WSN lifetime. Figure 7: Simulation WSN nodes. consumption within the CH nodes, and thus the result was a decrease in the network lifetime. The results show that the WC+DT detection algorithm decreased the network lifetime by 2%, 4%, 6%, and 6% compared to the WC+DT-free scenario for WSN node initial power of 0.5, 0.75, 1, and 1.25 J, respectively.
## 4 Electrochemical Analysis
### Rate Performance  
To investigate the rate performance of this composite electrode, CR2032 coin-type cells were cycled at different current densities. The composite fiber electrodes were cycled ten times at current densities of 50, 100, 200, 400, 500 and then again at 50 mA g\({}^{-1}\). This was done to evaluate the cycling performance at higher current densities, as well as to observe if the capacity would be recovered from being cycled at high to low current density. Figure 12 shows the rate performance of the ternary \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C electrode. Again, the addition of the stable TiO\({}_{2}\) can improve the electrochemical performance of the ternary electrode by allowing cycling at increased current densities. Maintaining a specific capacity of more than 200 mAh g\({}^{-1}\) at a current density of 500 mA g\({}^{-1}\), this electrode showed excellent cycling stability at elevated current densities. Interestingly, when the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C composite anode was cycled back at low current density (50 mA g\({}^{-1}\)), it was able to recover to a specific capacity of 549 mAh g\({}^{-1}\), indicating good reversibility. The dispersion of TiO\({}_{2}\) and \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles in the carbon matrix helped reduce the severe volume expansion of the iron oxide, resulting in a more stable anode material with exceptional lithium storage abilities.  
Figure 12: Rate performance for the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C electrode performed at current densities of 50, 100, 200, 400, and 500 mA g\({}^{-1}\). Dashed (charge) and solid (discharge).
## 2 Related Techniques
### Strategies for Surrogate Model
#### 2.2.2 Gaussian Process  
The Gaussian process (GP) [27; 28], alternatively referred to as the Kriging model, is represented by the following expression:  
\[Y(x)=f(x)+z(x) \tag{4}\]  
where \(Y(x)\) is the function that is under consideration for solving. \(f(x)\) is a set of selected basis functions. \(z(x)\) denotes a stochastic process characterized by a mean of 0, variance \(\sigma^{2}\), and covariance that is not zero. The covariance matrix is \(\mathrm{cov}(Y,Y)=\sigma^{2}\varphi\), which is used to measure the correlation between two or more sets of random variables.  
\[\mathrm{cov}(X,Y)=E[(X-\mu_{X})(Y-\mu_{Y})]=E[XY]-\mu_{X}\mu_{Y} \tag{5}\]Covariance fundamentally quantifies the interrelation between multiple sets of random variables. GP models are esteemed for their proficiency in offering predictions of elusive functions, coupled with assessments of the affiliated prediction uncertainties. Such attributes have catalyzed their widespread integration into stochastic algorithmic evolutionary approaches. Nonetheless, GP requires elements like the covariance matrix, which, in high-dimensional contexts, presents complexities and might culminate in less-than-optimal outcomes.
# Establishing a Model for the User Acceptance of Cybersecurity Training
## 4 Results  
This section presents the results of the literature review aimed at identifying the factors influencing users' acceptance of cybersecurity training. First, an overview of the TAM and its various extensions is provided to establish a basis for identifying all possible factors influencing technology acceptance. Subsequently, the study conducted a systematic literature review in order to examine how these factors have been discussed in cybersecurity training studies, both directly and indirectly. As part of the review, a number of studies were analysed to determine whether TAM-related factors had been empirically examined in the context of cybersecurity training. The direct factors are those explicitly identified in the studies, while the indirect factors are those discussed in broader terms, but which are relevant to cybersecurity training. In addition, the review identified gaps in the literature where certain TAM factors have not been examined for cybersecurity training. New factors unique to this context were also explored, providing a more comprehensive understanding of what influences cybersecurity training acceptance. These findings provide insight into the development of more effective cybersecurity training programmes that increase user compliance and enhance the overall organisational security culture. Figure 2: PRISMA flow diagram outlining the searching and screening process. The table below lists the publications that were included and the factors they discussed. Some included publications provide empirical data that show that one or more factors impact the acceptance of cybersecurity training. Other publications present findings that are indirectly related to the acceptance of cybersecurity training. That includes, for instance, the perception of cybersecurity training or willingness to adopt cybersecurity measures at large rather than training specifically. This is reflected in Table 2.  
Several studies have examined the factors that directly influence users' acceptance of cybersecurity training. Ref. [11] suggest that combining different methods of text-based, game-based, and video-based delivery for awareness training is superior to an individual delivery method. This finding indicates that integrating diverse, engaging learning formats is effective. Also, participants in the same study [11] preferred simpler text and video formats due to their lower complexity even when game-based methods were used. Ref. [13] found that users frequently seek advice from trusted colleagues or those who frequently assist them with computer issues, which illustrates the importance of social presence and trust. The study by [16] identified attitude, apathy, and social trust as significant barriers to user participation in cybersecurity training, and found that using lengthy or complex language in materials may result in user fatigue, preventing some individuals from
# Synthesis and Porous Structure Characteristics of Allyl Methacrylate/Divinylbenzene Polymers
## 3 Results and Discussion
### Effects of Various Solvents on the Porous Structure of AllMeth/DVB
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Solvent**} & \multicolumn{2}{c}{**Hildebrand**} & \multirow{2}{*}{**Resin**} & \multirow{2}{*}{**Pore Volume**} & \multicolumn{3}{c}{**Specific**} & \multirow{2}{*}{**Micropores**} \\  & **Solubility** & & & & **Surface Area** & & **Median Pore** \\  & **Parameter \&** & & **[cm\({}^{3}\)/g]** & & **Size [nm]** & **(Total) BET** & **Area [m\({}^{2}\)/g]** & **Width [Å]** \\  & **[MPa\({}^{1/2}\)]** & & & & & & \\ \hline \multirow{3}{*}{Toluene} & \multirow{3}{*}{18.3} & 1 & 0.71 & 6.0 & 474 & 147 & 8.4 \\  & & 4 & 0.58 & 6.6 & 353 & 123 & 8.5 \\  & & 5 & 0.17 & 5.4 & 126 & 59 & 9.2 \\ \hline \multirow{3}{*}{Acetone} & \multirow{3}{*}{19.7} & 1 & 0.72 & 6.1 & 472 & 137 & 8.4 \\  & & 4 & 0.59 & 6.5 & 365 & 123 & 8.4 \\  & & 5 & 0.22 & 5.3 & 170 & 78 & 9.3 \\ \hline \multirow{3}{*}{Methanol} & \multirow{3}{*}{29.7} & 1 & 0.69 & 5.8 & 476 & 131 & 8.5 \\  & & 4 & 0.57 & 6.4 & 356 & 109 & 8.6 \\  & & 5 & 0.16 & 5.1 & 121 & 51 & 9.1 \\ \hline \multirow{3}{*}{Water} & \multirow{3}{*}{48.0} & 1 & 0.72 & 6.0 & 483 & 111 & 8.5 \\  & & 4 & 0.59 & 6.2 & 382 & 107 & 8.5 \\ \cline{1-1}  & & 5 & 0.35 & 5.3 & 262 & 79 & 8.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characterization of the porous structure after swelling in various solvents and subsequent drying: toluene, acetone, methanol, and water for samples 1 (50% DVB), 4 (20% DVB), and 5 (15% DVB).