esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Variational autoencoder and generative adversarial networks are the latest models that perform superior to traditional models in generating synthetic data. The synthetic data generated through deep learning models are processed through classification, clustering, and regression algorithms to validate their correlation with the original data and evaluate how they impact the performance and accuracy of the models when compared to using original data. Often, original data has missing values that, when imputed, play an important role in enhancing the performance of any model. Prediction of missing values in original data is one of the most important tasks and the initial task for generating synthetic data. In this paper, we propose multiple imputation ensemble to predict the missing values in the original data and then apply generative adversarial networks to generate synthetic fermentation data for Shindari from the original tabular data. The main contribution of this paper is summarized below:  
1. In the preprocessing stage, we proposed a multiple imputation model which is a combination of multivariate imputation by chained equations (MICE) and random forest imputation model (RFI) to impute the original missing data. 2. Multiple imputation was then combined with the bagging and stacking ensemble approach to predict and complete the tabular fermentation data. 3. We proposed tabular GAN with skip connection and adapted WGAN-GP architecture to generate synthetic tabular data. We proposed TGAN-skip-WGAN-GP (Tabular GAN-skip with Wasserstein GAN with gradient penalty), which could enhance the performance by reducing convergence time and eliminating the mode collapse problem. 4. In the evaluation stage, we visually and quantitatively produced the performance of the proposed imputation and GAN model. 5. The proposed approach can be utilized for generating synthetic data for any kind of fermentation process that involves tabular data. We present the outcome of our proposed approach in the Results section. Imputation with ensemble was evaluated mean dissimilarity, regressor accuracy, and Kappa error plots. GAN was evaluated through correlation coefficient, mean absolute error, root mean square error, percent root mean square difference, Frechet Distance, and mirror column association.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 3 Experimental Results
### Number of Kernel Parameters Comparison among Different Steganography Networks
#### 3.2.1 Capacity of Information Hiding  
The capacity of information hiding is the basic index to measure the steganography algorithm. If the embedding capacity is too small, more covers are needed to realize the transmission of the same amount of information, which will greatly limit the practicability of the steganography algorithms.  
BPP, defined in Equation (7), is the bits of hidden messages per pixel, where \(L\) is the length of the hidden information. \(H\) and \(W\) represent the height and width of the cover video frames, respectively. Since our stego frames and the cover frames have the same size, which is 256 \(\times\) 256, the capacity of the proposed method is 1 BPP. In the second row of Table 2, the BPP of proposed PyraGAN_F32_2, PyraGAN_F16_3, and SteganoGAN [22] is the same, which is two hundred times that of HiDDeN [21] owing to the hidden messages reshaped to \(H\)\(\times\)\(W\), which is the size of the cover frames. Such an information embedding capacity is at a high level in most steganography algorithms.  
\[BPP=L/(H\times W) \tag{7}\]
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### The Mechanical Behavior of Stabilized and Unstabilized Soil
#### 3.3.2 The Impact of Curing Time on the UCS  
During the curing process, water reacts with the clay and WMP particles to form a solid matrix through the hydration reaction. This reaction continues over time, causing the sample material to increase in strength. The more time the hydration reaction proceeds, the greater the UCS [58]. A curing period of 28 days is generally considered standard for determining the UCS of stabilized soils [59; 60]. However, the specific curing time required for optimum strength depends on a number of variables, and there is a maximum gain in strength after a certain point [61; 62]. The strength of clay soils can be enhanced by mixing in marble powder. How much strength increases depends on how much marble powder is added and how long the combination cures. This study cured the treated and untreated samples for 0, 3, 7, 14, and 28 days.  
When WMP is added to clayey soils, microscopic particles cluster together to form a crystalline structure that fills the gaps between the sample material particles. This solid structure improves soil strength and reduces soil plasticity, which improves workability and gradation [63].  
Additionally, the increased soil matrix compactness resulting from a reduction in porosity can contribute to the overall improvement of the soil's properties [64; 65]. Furthermore, at the end of the process, chemical compounds resulting from inter-reactions between marble dust, soil alumina, and soil silica had increased the strength of the soil. Over time, as the curing process takes place, these reaction products solidify and become more integrated into the soil matrix. This solidification further improves the soil's stability and resistance to erosion [39]. In addition, this soil stabilization method has the added benefit of preventing soil liquefaction during seismic events. Soil liquefaction is a phenomenon in which saturated soil loses its strength and behaves like a liquid during an earthquake or other seismic activity. By increasing the soil's strength and stability, using marble dust, soil alumina, and soil silica can help reduce the risk of soil liquefaction in areas prone to seismic events [66; 67; 68]. Recycling marble powder for soil stabilization is an environmentally friendly way to increase soil strength and stability while reducing waste.
# C-RAN Traffic Aggregation on Latency-Controlled Ethernet Links
## 2 Related Works
However, if size-based encapsulation is utilized, the time to perform encapsulation varies as a function of the CPRI link bit rate, thus causing encapsulation delay and jitter. In [24], the authors provide an analysis of the impact of different parameters (e.g., BH packet size) on latency and latency variation of a converged FH and BH Ethernet-based network. This analysis is conducted on a real field trial and a discussion of the challenges when dealing with design of ultra low latency traffic aggregators is also reported. The capability of the integrated hybrid approach to support the high data rate required by CPRI have been demonstrated in recent experiments [25]. Mapping and decision-making on the placement of Ethernet encapsulated CPRI traffic in integrated hybrid networks is the step beyond the state of the art that this paper addresses. Some evaluations of the additional BH throughput achieved by the integrated hybrid approach aggregating FH and BH according to extension of the IHON principle were presented in [26]. Limitations of throughput for high capacity CPRI option were outlined. In this paper, the term aggregation is used to express the combination of BH and FH traffic into the same optical link. This paper intends to introduce the new multiplexing concept taking place on the Ethernet interface and discusses the benefits that can be achieved in the presence of synchronous traffic that needs to be transferred in a time transparent way. Further extension of the mechanism based on BH traffic segmentation is here proposed to achieve better throughput for BH traffic.
# Dynamic Stability Analysis of Isolated Power System
## 7 Analysis of the System's Emergency Modes  
The capability of the isolated system to withstand disconnection of the unit operating in pump mode and the HVDC BtB converter's disconnection during emergency cases is analyzed in this section. The impact of the HVDC BtB converter's frequency control function during disconnection of the unit operating in pump mode is considered.  
Emergency Operating Mode of Isolated EPS When Disconnection of Unit Operating in Pump Regime Is Necessary  
In the numerical simulation, the disconnection of the unit operating in a pump mode is analyzed. In order to minimize the impact on the isolated EPS, the system demand must be reduced to 110 MW before disconnecting the pump from the mains. The simulation was performed for two cases: 1. The frequency control function in the HVDC BtB converter was activated; 2. The frequency control function in the HVDC BtB converter was deactivated.  
The HVDC BtB converter frequency control parameters were as follows: the gain \(K\) was 250 MW\(/\)Hz and corresponded to 4% droop; active power ramp speed was 100 MW\(/\)s; dead-band was 10 mHz. The simulation results are shown in Figure 26a,b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Insensitivity Range of HVDC BtB Converter, mHz** & **Frequency, Hz** \\ \hline
0 & 50.00 \\
50 & 50.05 \\
100 & 50.11 \\
150 & 50.16 \\
200 & 50.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Frequency increase in the isolated power system.  
Figure 25: Voltage fluctuations on the 330 kV side.  
A significantly greater, instantaneous frequency increase is seen when the frequency control function in the HVDC BtB converter is switched off; in this case, the frequency instantaneously increases to 51.4 Hz. When the frequency control mode is activated in the HVDC BtB converter and the frequency control capabilities are assumed to be \(\pm\)100 MW, the frequency increase stops at the limit of 50.82 Hz [15; 21; 22; 32]. In both cases, the isolated EPS does not reach the maximum permissible frequency limit of 51.5 Hz [16; 17; 18; 19; 24].
## 5 Performance Evaluation
### Performance Metrics  
We compare DRLSA-IA with the other two algorithms in the same simulation environment. Following the past works, the throughput, packet delivery ratio, and fairness index are used to evaluate the performance of algorithms.  
* We use the number of successfully received messages over the period \(T\) to calculate the average throughput.
* As shown in Equation (13), the Packet Delivery Ratio (\(PDR\)) is calculated by dividing the number of messages created by all nodes (\(M_{c}\)) by the number of messages that were successfully sent (\(M_{s}\)). \[PDR=\frac{M_{s}}{M_{c}}.\] (13)
* The Fairness Index (\(FI\)) is used to evaluate the fairness of the node access channel. It is an important metric to measure the performance of the underwater MAC protocol. As shown in Equation (14), we use the Jain's Fairness Index. \[FI=\frac{(\sum_{i=1}^{N}x_{i})^{2}}{(N\times\sum_{i=1}^{N}x_{i}^{2})},\] (14) where \(N\) denotes the total number of nodes. The throughput of node \(i\) is represented by \(x_{i}\). The fairness index has a value between 0 and 1. Network fairness rises when the fairness index draws closer to 1. Network fairness decreases as the fairness index gets closer to 0. When the fairness index is equal to 1, accessing the channel is entirely fair.
## 2 Test Methods for ME
### Material Extrusion  
The AM technique material extrusion (also, fused deposition modeling--FDM, trademark of Stratasys--or fused filament fabrication--FFF, non-commercial name) is characterized by an extrusion of a thermoplastic material (see Figure 1). For this purpose, a polymeric stock material (filament) is directed via conveyor unit into an extruder, plasticized, and applied to the previous layer in accordance with the cross section to be generated [6]. As a result of thermal fusion, the applied layer bonds with the surrounding layers and solidifies shortly after. The part is thus built up layer by layer.  
Figure 1a shows the ME principle schematically. It can be seen that several materials are, in principle, processable within one layer. The range of materials available in this respect involves commodity, engineering, and high performance plastics [6]. However, combining material A and B commonly depends on their respective glass transition temperature (T\({}_{\text{g}}\)) (temperature range in which a plastic is subject to the greatest change in its deformability) or heat distortion temperature (HDT) (softening temperature under a specific load). Only materials with similar transition temperatures allow a combination in the form of a multi-material ME part. As a major gap in these temperatures results in warping (deformation induced by residual thermal stresses) of the material with the higher value, a combination of, for instance, commodity plastics (e.g., PLA, T\({}_{\text{g}}\) = 55-60 \({}^{\circ}\)C [9] or ABS, T\({}_{\text{g}}\) = 105 \({}^{\circ}\)C [10]) and high performance plastics (e.g., PEI (polyetherimide), trade name ULTEM [11], T\({}_{\text{g}}\) = 186 \({}^{\circ}\)C) is commonly not possible.  
An interface zone in which two different plastic materials are successfully combined, on the other hand, shows a distinct overlap region (see. Figure 1b). The development of this region is primarily characterized by three successive steps [12]: first, the surfaces of material A and B come into contact; second, a neck (interface zone) grows between these two strands driven by surface tension; third, a molecular diffusion and randomization of the different polymer chains within this neck take place. Further coupling mechanisms relevant for the interface zone are adsorption, polarity, and mechanical interlocking [13]. Adsorption is considered as a pre-condition for initial bonding in the material transition, followed by electrical polarity, such as dipoles, that are present in oxides, hydroxides, hydroxyl groups, and so on in the interface layers. These polarities initiate interactions that cause an increase of adhesion between two corresponding layers [14]. Mechanical interlocking, however, is based on undercuts that generate a coupling in the material transition.  
In general, the mechanical properties of multi-material ME parts are correlated to the bonding quality in the interface zone. This is primarily affected by the characteristics of the distinct overlap region in the material transition caused by neck growth and intermolecular diffusion [12].
###### Abstract  
This study investigated whether young adults' social media use and subjective well-being (SWB) changed during the COVID-19 pandemic. It examined the possible relationships between social media use, SWB, and personality traits. It included generalized trust, self-consciousness, friendship, and desire for self-presentation and admiration, in relation to different patterns of social media use and genders. Data were collected from university students in Japan from 2021 to 2023 and were analyzed based on different social media use patterns. The conceptual model was based on the cognitive bias and social network mediation models. Data were analyzed using ANOVA and regression analyses. The findings revealed that, over time, young adults' anxiety toward COVID-19 decreased, while their SWB improved and their social support increased. Depression tendencies showed a negative association, whereas social support was positively related to improvement of SWB for all three patterns of social media use. Furthermore, online communication skills had a positive relationship with improvements in students' SWB in Patterns 1 (LINE + Twitter + Instagram) and 2 (LINE + Twitter + Instagram + TikTok). The self-indeterminate factor had a positive relationship with students' SWB for all patterns in 2022 and 2023, and the praise acquisition factor had a positive relationship with improvements in students' SWB in Patterns 1 and 2. These results suggest that young adults maintained their mental health through different social media usage patterns, considering their personality traits and social situations associated with COVID-19. Particularly, receiving social support, decreasing people's depression tendencies, and displaying different aspects of the \"self\" online can improve SWB. This study elucidates the mental health situations of university students in Japan and will help public health authorities develop new support programs that help digital natives improve their mental health in the context of social environmental changes.  
COVID-19 pandemic; personality traits; social media use patterns; subjective well-being (SWB); university students in Japan; comparative changes +
Footnote †: journal: _Future Internet_
## 3 Results
### Test Quality Parameters of the Prediction Methods
#### 3.2.2 Logistic Regression  
In addition to the TRS, a (binary) logistic regression also offers a chance to create a talent prognosis for the test participants. The 5-fold cross-validation was performed five times, and the results were averaged. The _omnibus test_ of the model's averaged coefficients showed a significant result (chi-square(11) = 33.67, \(p<0.001\)). The model quality was determined based on [PERSON]'s \(R\)-square, which had a value of 0.476. The Area Under ROC Curve was 0.810 (standard error = 0.054; 95% confidence interval: min = 0.705, max = 0.915). Overall, however, in most analyses, none of the regression coefficients showed a significant result, so this form of analysis should be interpreted with caution.  
Approximately 6 of the 16 athletes who were ultimately competitively ranked were correctly identified by their test values (Table 5). The sensitivity was therefore 37.5%. It should be noted, however, that only about twelve children in total received a top-level prognosis in this form of analysis. The specificity was 96%. With the exception of six participants, all low performers could be correctly classified. The overall accuracy of the analysis method was 90.4%. The summed percentage result for the sensitivity and specificity of the logistic regression was 133.5%, which is 33.5% better than that of a random sample. If a random prediction were considered with the same selection rate, the sensitivity would be 7.2%. Thus, it would be 26.3% less than the sensitivity value of 33.5% achieved via the logistic regression analysis. The same applies to a random specificity. This would amount to 92.6%, as compared with the 96% achieved here, and the overall result in terms of correctly identified test participants would only be 84.4%. Among participants predicted to be future top performers via the logistic regression, 50% of such prognoses were correct (positive predictive value). This means that every second child classified as a top performer actually made it into the top group.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Self-Organizing Control Using Artificial Potential Functions
#### 2.2.2 Design of Artificial Potential Functions for Single Satellite Control
\(\mathbf{K}=k_{a}\). \(a_{t}\) is the designed value of the orbital semi-major axis. Semi-major axis control will be implemented when the continuous coverage constraint has been satisfied. \[\phi_{i,a}=\left\{ \begin{array}{cc}k_{a}(a_{i}-a_{t})^{2},&\mathrm{if}\quad \widetilde{d}_{ij}\geq d_{\mathrm{mint}}\cap\widetilde{d}_{ij}\leq d_{\mathrm{ mart}}\cap\delta\Omega\geq\delta\Omega_{\mathrm{min}}\cap\delta\Omega\leq\delta \Omega_{\mathrm{max}}\\ 0,&\mathrm{if}\quad\widetilde{d}_{ij}<d_{\mathrm{mint}}\cup\widetilde{d}_{ij}> d_{\mathrm{mart}}\cup\delta\Omega<\delta\Omega_{\mathrm{min}}\cup\delta\Omega>\delta \Omega_{\mathrm{max}}\end{array} \right. \tag{39}\]  
The velocity increment of \(\phi_{i,a}\) in the geocentric inertial frame is  
\[\Delta\mathbf{V}_{i,a}=-\frac{\partial\mathbf{X}_{i}^{\mathrm{T}}}{\partial \mathbf{V}_{i}}\frac{\partial\phi_{i,a}}{\partial\mathbf{X}_{i}}=-\left[ \begin{array}{cc}\frac{\partial\phi_{i,a}}{\partial a_{i}}\frac{\partial a _{i}}{\partial X}\\ \frac{\partial\phi_{i,a}}{\partial a_{i}}\frac{\partial a_{i}}{\partial Y}\\ \frac{\partial\phi_{i,a}}{\partial a_{i}}\frac{\partial a_{i}}{\partial Z} \end{array} \right] \tag{40}\]  
The relationship between \(a_{i}\) and \(\mathbf{V}_{i}\) is given as  
\[a_{i}=-\mu\bigg{[}\left(\dot{X}^{2}+\dot{Y}^{2}+\dot{Z}^{2}\right)-\frac{2\mu }{\sqrt{X^{2}+Y^{2}+\dot{Z}^{2}}}\bigg{]}^{-1} \tag{41}\]  
To sum up, the control potential functions for satellite \(i\) are given as follows. \[\phi_{i}=\left\{ \begin{array}{cc}k_{i\Omega}(\sin i_{i}\sin\Omega_{i}-\sin i _{t}\sin\Omega_{t})^{2}+k_{i\Omega}(\sin i_{i}\cos\Omega_{i}-\sin i_{t}\cos \Omega_{t})^{2},\\ \mathrm{if}\quad\delta\Omega>\delta\Omega_{\mathrm{max}}\cup\delta\Omega< \delta\Omega_{\mathrm{min}}\\ k_{bound}\left(\widetilde{d}_{ij}-d_{\mathrm{mint}}\right)^{2},&\mathrm{if} \quad\widetilde{d}_{ij}<d_{\mathrm{mint}}\\ k_{bound}\left(\widetilde{d}_{ij}-d_{\mathrm{mart}}\right)^{2},&\mathrm{if} \quad\widetilde{d}_{ij}>d_{\mathrm{mart}}\\ k_{a}(a_{i}-a_{t})^{2},&\mathrm{if}\quad\delta\Omega\geq\delta\Omega_{\mathrm{ min}}\cap\delta\Omega\leq\delta\Omega_{\mathrm{max}}\cap\widetilde{d}_{ij}\geq d_{ \mathrm{mint}}\cap\widetilde{d}_{ij}\leq d_{\mathrm{mart}}\end{array} \right. \tag{42}\]
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 2 Corrosion Factors and Mechanisms in Low Orbit Environment
### Simulated Corrosion Experiments  
In-flight experiments on space materials are very expensive and require a long time for preparation and analysis. In this light, simulations and theoretical investigation are very important for designing new materials for space applications. Many researchers studied \(AO\)-induced degradation of various materials in simulated space environments. [PERSON] et al. conducted decay tests on Poly(p-phenylene benzobisoxazole) (PBO) fibres which are used in tether cables and composite reinforcements. Morphological studies indicated severe roughening and erosion of the fibres and mechanical studies revealed a significant decrease in tensile strength retention ratios [37]. [PERSON] et al. reported the bending strength loss and surface resistivity changes on zirconium carbide coated carbon composites [35]. Cyanate-based shape memory polymers were shown to undergo an accelerated aging, by peeling off the material's outer layer after reacting with \(AO\). However, shape memory behaviour, thermal stability and mechanical properties were not significantly affected after the experiment. Interestingly, the reactions with \(AO\) resulted in the formation of new carbonyl and hydroxyl bonds within the polymer, with new peaks detected in the FTIR spectra [36]. A similar study was conducted by [PERSON] et al. by introducing carbon fibre into the cyanate ester for extra durability [38]. [PERSON] et al. studied the stability of TiO\({}_{2}\)- and SnO\({}_{2}\)-based coating materials against \(AO\) erosion [39]. Polytetrafluoroethylene (PTFE), another polymeric material often used in spacecraft, showed mass loss, changes in chemical properties and optical properties due to \(AO\) reactions. However, thermal properties were unaffected according to the TGA studies.  
An example of the efficient application of simulation-based research is illustrated in Figure 5. [PERSON] et al. conducted experimental studies and DFT simulations on reduced graphene oxide (RGO)-enhanced polypropylene composites. Theoretical studies indicated that defective graphene shows lower binding energy with \(AO\) than pure graphene. It was experimentally investigated and 0.5 wt% of RGO loading found ideal for enhanced mechanical properties [40].  
Figure 5: Proposed mechanism by which alkylated-reduced graphene oxide additive affords protection against \(AO\) damage to a polymer matrix. The uniform dispersion of ultra-high modulus RGO in the polymer matrix can effectively mitigate the impact of high-energy \(AO\), which effectively protects the polymer matrix from direct impact (route 1). Reprinted with permission from [PERSON] et al., 2015 [40] © 2015 Elsevier.  
Simulation and theoretical studies are very important in the design of space materials, and novel approaches including those based on artificial intelligence (AI) and machine learning (ML) need to be implemented to support fast progress in this field.