esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study proposes an alternative process for obtaining ZnO/ZnS:rGO heterostructures for use in DSSCs and as promising materials for potential applications in other photonic process, such as photocatalysis and photodetection. The compound was obtained through a microwave-assisted hydrothermal method, where the electromagnetic waves and temperature were crucial points for forming ZnO, ZnO/ZnS and reducing graphene oxide (GO). The XRD, Raman, FT-IR, and FESEM results presented the structural, morphological, and chemical structures, which suggest the conversion of ZnO to ZnS for samples with higher concentrations of reduced graphene oxide (rGO). Additionally, the optical properties were analyzed through photoluminescence and UV-Vis measurements. The electrical behavior of the photoelectrodes was investigated through J-V measurements in light and dark conditions. In addition, electrochemical impedance spectroscopy (EIS) was performed and Bode phase plots were created, analyzing the recombination processes and electron lifetime. The J-V results showed that for smaller amounts of rGO, the dye-sensitized solar cells (DSSC) efficiency improved compared to the ZnO/ZnS single structure. However, it was observed that with more significant amounts of rGO, the photocurrent value decreased due to the presence of charge-trapping centers. On the other hand, the best results were obtained for the ZnO/ZnS:1% rGO sample, which showed an increase of 14.2% in the DSSC efficiency compared to the pure ZnO/ZnS photoelectrode.  
annoheterostructure; ZnO/ZnS; microwave synthesis; graphene oxide; photovoltaic cell; DSSC +
Footnote †: journal: Applied sciences  
Article
## 3 Graphene and Graphene-Related Materials for Biological Applications
### Graphene and Related Materials as Drug Delivery Platforms
Molecular dynamics simulation confirmed the strong interaction between GO and drug as mentioned before due to both weak interactions and hydrogen bonds. Moving forward, graphene and its related materials could be used to drag and drop antibiotics and antimicrobial agents. [PERSON] et al. (2019) combined poly(vinyl alcohol), tragacanth gum, and GO composite nanofibre for tetracycline delivery, preserving the activity of the antibiotic and controlling the release in time. Authors tested the cytotoxicity against HUVECs as well as agar disk diffusion to determine the antibacterial activity. The cytotoxicity assay showed a high cell viability while antibacterial activity was interestingly high. The buccal administration of clotrimazole though a GO-based formulation was reported by [PERSON] et al. (2019). The authors reported an increased clotrimazole release from the GO films in vitro at up to pH 6.8. Furthermore, they reported an increased activity against _Candida albicans_. GO-based systems were also used for the delivery of ketamine ([PERSON] et al., 2017), ibuprofen ([PERSON] et al., 2017), and medicine for hormone therapy ([PERSON] et al., 2017). In all cases, the high control on drug release prevented the sudden increment of drugs, leading to a time-prolonged release.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 3 Results
### Model Usage Process  
This training model is designed to assess the quality of rural visual landscapes in practical settings. Consequently, we sought to establish a process for evaluating the emotional quality of rural visual landscapes using multi-source data (as illustrated in Figure 15). The process encompasses the following steps: First, we defined the experimental route and divided it into several segments. Next, we invited participants to engage in the study and sign a consent form. During the signal collection phase, we gathered both physiological and subjective data from participants while they viewed the images. After feature extraction, fusion, and dimensionality reduction, the processed data were input into the classification model. Based on the emotion score derived from the model, areas with a positive emotional value will be preserved, while spaces with a negative emotional value will undergo renovation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Emotional Category** & **Accuracy** & **Recall** & **Precision** \\ \hline Valence & 78.50\% & 70.23\% & 61.83\% \\ Arousal & 75.47\% & 67.94\% & 58.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of valency and arousal accuracy of binary, ternary, and quinary classification models of XGBoost model.  
Figure 14: Comparison of valence and arousal confusion matrix of XGBoost model binary, ternary, and quinary classification models. (**a**) binary; (**b**) ternary; (**c**) quinary.
## 3 Methods
### Data Augmentation: Generating Synthetic Data and Checking the Quality  
We developed deep generative models using the SDV package [36] in Python and generated synthetic data using the default parameters of the deep generative models (CTGAN, CopulaGAN, and TVAE), excluding the number of epochs. Furthermore, we set the number of epochs from 5 to 1000 in eight grades (5, 10, 30, 50, 100, 300, 500, and 1000) and generated synthetic data from two deep generative models (CTGAN, CopulaGAN, and TVAE) according to multiple numbers of real data from 100% to 3000%. As well as the number of those epochs, the other parameters set the default parameters. The default parameters are listed in Supplementary Table S1. We assessed the quality of the synthetic data from two perspectives: the reproducibility of each variable and the entire structure. Two measurements were obtained from the former perspective.  
First, the mean squared errors (MSEs) and correlation coefficients (CCs) of the real and generated synthetic data for each augmented sample in each epoch were calculated. These indices were calculated for the same variables in each dataset. Lower MSE values and higher CC values indicate a more satisfactory quality. The degree of constructive matching between real and generated synthetic data is the measurement in the latter view. This measurement calculates the total average of the squared error for each corresponding pair of elements of the CC matrices between the real and generated synthetic data. The small values of this measurement indicate that the synthetic data can be accurately generated by considering the relationships between the variables. We refer to this measurement as the structure matching value (SMV). The SMV is defined as follows:  
\[SMV=\frac{1}{N}\sum^{N}\left(r^{G}-r^{R}\right)^{2} \tag{6}\]  
where \(r^{G}\) is an element of the correlation coefficient matrix in the generated synthetic data, \(r^{R}\) is an element of the correlation coefficient matrix in the real data, and \(N\) denotes the number of variables. Consequently, if the MSE is small, the CC is high, and the SMV is low, the generated data can be sufficiently close to the real data.
# Parametric Simulations on Leakage and Performance of a Miniature Free-Piston Generator (MFPG)
## 5 Power Flow and Energy Distribution  
In order to predict the optimal energy conversion efficiency of the proposed MFPG, the optimum values listed in Table 2 are applied in the power and energy calculations of the MFPG. Figure 11 shows the transient powers of the major forces within an entire cycle. The positive power represents that the piston absorbs power from the external force, whereas the negative power indicates that the piston outputs power to the external force. The shadowed areas are the works performed by the corresponding forces. The simulation starts from the BDC. In the second half of the scavenging stroke, the voice coil motor drives the piston from BDC to exhaust port closing (EPC). The electromagnetic force transfers its power into the piston, so the power of electromagnetic force \(P_{ele}\) is positive during this period. At the same time, the in-cylinder gas power \(P_{p}\) and frictional power \(P_{f}\) are relatively minor due to the lower scavenging pressure and piston velocity. Thus, only a small portion of \(P_{ele}\) is used to balance the \(P_{p}\) and \(P_{f}\) and the major portion of the \(P_{ele}\) is converted to the kinetic energy of the piston, which is the inertial power of piston \(P_{a}\). In the compression stroke, from the exhaust port closing to top dead center, the VCM continues to accelerate the piston and outputs positive work. The positive \(P_{ele}\) is divided into three parts to compensate the piston inertia power \(P_{a}\), to overcome the friction power \(P_{f}\) and to compress the in-cylinder gas \(P_{p}\). In the later stage of the compression stroke, the in-cylinder gas pressure increases exponentially, so \(P_{ele}\) is not enough to maintain the acceleration of the piston. Thus, the piston decelerates and the inertia power \(P_{a}\), stored as kinetic energy of the piston, is released. After all of the \(P_{a}\) is converted into the compression power of the in-cylinder gas \(P_{p}\), the piston stops. After the onset of combustion, the gas pressure in the cylinder increases sharply and leads to a small spike between SOC and TDC on the \(P_{a}\) and \(P_{p}\) curves. Furthermore, the inertia power \(P_{a}\) balances within the range from BDC to TDC. That means that the areas on the two sides of the abscissa before TDC are equal. This indicates that \(P_{a}\) is only an intermediate state, used for the temporary storage of excessive power of \(P_{ele}\), while the ultimate states of the power conversion are between \(P_{ele}\) and \(P_{p}\).
## 3 Results and Discussion
### Comparison to Paper-Based Packaging  
In [4; 47], the authors determined the resonant frequency, transmissibility and damping ratio for honeycomb paperboard and X-PLY corrugated paperboard at different static loads, but without changes in temperature. The temperature during the tests was 21 \({}^{\circ}\)C for honeycomb and 23 \({}^{\circ}\)C for X-PLY. The studies showed that in general, the vibration transmissibility of X-PLY was low when the peak frequency was higher than 212 Hz. The value for honeycomb was 350 Hz.  
A similar study was performed in [51] for single-layer, two-layer, and three-layer corrugated paperboard pads. The critical frequencies found were 191 Hz for the single-layer pad, 208 Hz for the two-layer pad, and 185 Hz for the three-layer pad.  
At these frequency levels, PE25, PE30, and XPE attenuated the vibration intensity at all static loads and investigated temperature levels. Furthermore, when using honeycomb paperboard and X-PLY corrugated paperboard as cushioning in a package, it must be considered that the mechanical properties of these materials are highly influenced by changes in temperature and humidity.
# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 2 Related Work
### Graph-Convolution-Based Methods  
In recent years, significant strides have been made in the field of 3D human pose estimation through graph convolution, resulting in cutting-edge outcomes [26; 27; 28; 39]. [PERSON] et al. [27] introduced a semantic graph convolutional network featuring a stacked structure as a non-local module. This architectural choice facilitated the learning of weights between neighboring nodes, consequently enhancing the connections among 2D joints. Furthermore, [PERSON] and [PERSON] [26] proposed an innovative graph hourglass network model. This model employed a distinctive combination of pooling and anti-pooling layers to acquire intermediate features, which were subsequently fused with the SE block [40]. In another notable development, [PERSON] et al. [28] amalgamated the strengths of both Transformer and graph convolutions. Initially, they extracted global features using the conventional Transformer [41], excluding the MLP [42]. Subsequently, they harnessed the implicit higher-order connections between joints by employing Chebyshev graph convolution [43]. This integrated approach yielded state-of-the-art results on the Human3.6M dataset, surpassing other graph-convolution-based methods. However, it is essential to acknowledge that this approach, despite its advantages in terms of parameter count, utilizes a learnable adjacency matrix following the global feature extraction stage. This allows the network to learn without constraints, introducing an element of uncertainty. The learned adjacency matrix may potentially disrupt the kinematic structure of the human skeleton and influence the accuracy of the data refined by subsequent network layers.
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
## 6 Variable Sex Ratio Ecosystem Interaction Model (VSREI Model)
### Analysis of the Impact of Sex Changes on Ecosystem Stability  
We then conducted experimental simulations of lamprey populations with different sex ratios, applying the ecosystem stability model based on multilevel analysis developed in this chapter. The results from the experiments are displayed in Table 2.  
Based on the final rankings, we found that the sea lamprey population had the most favorable impact on ecosystem stability when the male ratio was about 0.60. When the male ratio was about 0.30, the sea lamprey population had the worst impact on ecosystem stability.  
In summary, changes in the sex ratio will affect the degree of community number fluctuation and community structural robustness index by influencing reproductive strategies and population number distribution in the ecosystem, which further impacts community resistance and resilience. Changes in the sex ratio of lamprey populations will directly affect the fluctuation of their population size, and may also affect the predation and prey relationship of lampreys, thereby affecting the balance of the whole food web. The stability of an ecosystem depends on the complex interactions between the species within it, and changes in the internal structure of any one population may cause a chain reaction that affects the health and functioning of the entire system. Briefly, adaptive changes in sex ratios may increase the resistance of ecosystems to environmental change, while at the same time affecting their resilience and internal stability, especially in the face of large-scale or long-term environmental perturbations.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Male Ratio** & **Stability** & **Resistance** & **Number** & **Variance** & **Total Score** & **Rank** \\ \hline
0.30 & 0.74 & 1.00 & 0.10 & 0.36 & 0.23 & 5 \\
0.40 & 0.47 & 0.10 & 0.59 & 0.08 & 0.40 & 4 \\
0.50 & 0.47 & 0.57 & 0.60 & 0.59 & 0.58 & 2 \\
0.60 & 0.10 & 1.00 & 1.00 & 0.10 & 0.62 & 1 \\
0.70 & 1.00 & 0.57 & 0.26 & 1.00 & 0.56 & 3 \\ \hline \end{tabular}
\end{table}
Table 2: The results of the AHP-based ecosystem stability model.  
Figure 15: Weight of each factor in the AHP-based model.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Design Optimization
#### 2.4.1 Design Variables
Thus, the design variables of the optimization problem under study are the resistors \(R_{i}\), the capacitances \(C_{i}\) and the inductances \(\widetilde{L}_{i}\) of the shunt branch in the \"current-flowing\" shunt circuit. The remaining inductances \(\widetilde{L}_{i}\) of the current-flowing branches are given by Equation (36).
## 1 Introduction
_Spirulina_ has been consumed by humans for thousands of years without side effects and has been selected as space food by the US National Aeronautics and Space Administration (NASA). Since _Spirulina_ has diverse active substances, such as C-phyocyanin, beta-carotene, chlorophyll, and functional fatty acids, it has been reported to have excellent anti-cancer, immunity enhancing, skin improving, and anti-inflammatory effects and to be useful in treating hypertension, diabetes, and diseases related to metabolism, such as liver dysfunctions [27; 28; 29]. In particular, _Spirulina_ contains at least 50-100% more beta-carotene and chlorophyll, which are highly antioxidative, than other natural products [27; 30]. Therefore, many study findings have been reported promising outcomes of _Spirulina_ on brain nerve cell protection from these strong antioxidants, and many positive effects of _Spirulina_ in cranial nerve protection are associated with the strong antioxidant activities of chlorophylls [31; 32]. Interestingly, _Spirulina_ is also known to contain at least 70-90% pure chlorophyll a, which has relatively higher activity than the other subtypes chlorophyll b and c which normally exist in a mixture in most plants. Therefore, studies using the antioxidant activity of _Spirulina_ are expected to have great potential [33; 34]. However, most of these studies have focused on the antioxidant activity and regulation of antioxidative signaling pathways in the body [32; 35; 36], while studies on enhancing memory by suppressing brain nerve cell inflammation via chlorophyll derived from _Spirulina_ are extremely rare. Although studies on brain cell anti-inflammation using chlorophylls from _Spirulina_ are promising, obtaining a high concentration of chlorophylls from _Spirulina_ using existing conventional extraction processes is difficult because chlorophylls are extremely vulnerable to heat and fat solubility [37; 38]. Low-temperature extraction is essential to obtain high-concentration chlorophyll that maintains its activity. However, it is difficult to obtain chlorophyll at a sufficient concentration by ordinary low-temperature extraction processes, due to very low extraction yields and longer process time [37; 38]. To overcome this difficulty, an ultrasonic low-temperature extraction process will be an excellent alternative to treat heat-sensitive _Spirulina_ extract. The ultrasonic process (UE) is a typical low-temperature extraction process that does not apply heat and can efficiently extract heat-sensitive active components through efficient destruction of the cell walls using air cavities generated by ultrasonic vibration even at room temperature or temperatures below 40 \({}^{\circ}\)C [39; 40]. Therefore, this study investigated the anti-inflammatory mechanism of brain nerve cells associated with the antioxidant activity of chlorophylls in the _Spirulina_ extract obtained by low-temperature ultrasonic treatment.
# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
###### Abstract  
Digital twin (DT) technology, which can provide larger and more accurate amounts of data, combined with the additional computtility brought by virtual environments, can support more complex connected industrial applications. Simultaneously, the development and maturity of 6G technology has driven the development of industrial manufacturing and greatly improved the operational efficiency of the industrial internet of things (IIoT). Nevertheless, massive data, heterogeneous IoT device attributes, and the deterministic and bounded latency for delay sensitive applications are major barriers to improving the quality of services (QoS) in the IIoT. In this article, we first construct a new DT-enabled network architecture and computation offloading delay model in the IIoT. Then, the computation offloading problem is formulated with the goal of minimizing the overall task completion delay and achieving resource allocation. Since the formulation is a joint optimization problem, we use deep reinforcement learning (DRL) to solve the original problem, which can be described by a Markov decision process (MDP). Numerical results show that our proposed scheme is able to improve the task success rate and reduce the task processing end-to-end delay compared to the benchmark schemes.  
6G industrial internet of things (IIoT); digital twin; edge computing; computation offloading; deep reinforcement learning (DRL) +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Numerical Simulation  
Figure 5 shows the temperature distribution of the consolidated adsorbent domain for a representative section at intervals of time during 430 s cycle time considering 30 s for both preheating and precooling phases. The section area has a 5 mm composite thickness. The average temperature of the selected section was 300.84 K at the start of the new cycle and after only 15 s it reached 328.31 K affected by the enhancement of the thermal conductivity of the composite adsorbent. The average temperature reached 359.87 K at the end of the desorption process after 215 s which was only 3.13 K less than the regeneration temperature of 363 K. At the end of the precooling process after 230 s, the average temperature reached 330.81 K, and it reduced to 302.01 K at the end of the adsorption process after 430 s. The arrows appearing in the figure represent the velocity magnitude and direction of the vapor  
Figure 6 shows how the spatial variation of the instantaneous uptake responds to the heat transfer enhancement achieved using graphene nanoplatelets in carbon-based composite adsorbent. For the selected section, the average uptake was 0.14264 kg\({}_{\alpha}\cdot\)kg\({}_{\text{ads}}^{-1}\) at the end of the precooling process of the previous cycle. During the preheating process, the amount released from the composite domain was used to build up the pressure in the bed vacuum space before opening the connecting valve with the condenser. Therefore, the change in the average uptake was hardly noticed due to the accumulation of the desorbed amount coming from the adsorbent near the tube side to the other side near to the vacuum space. After opening the connecting valve and the desorption process has taken place, the average amount of the uptake reduced gradually from 0.2044 kg\({}_{\alpha}\cdot\)kg\({}_{\text{ads}}^{-1}\)  
Figure 5: The temperature distribution during the whole cycle at different time steps for the consolidated form.  
at 15 s to 0.198, 0.176 and 0.158 kg\({}_{\text{a}}\cdot\)k\({}_{\text{ads}}^{-1}\) at 65, 115 and 165 s, respectively. Despite the spatial variation of the temperature being less noticed with the progress of time, a relatively high spatial variation of the uptake can be noticed in Figure 6. This can be attributed to the increased interparticle mass transfer resistance caused by the lower total porosity of the composite adsorbent estimated by 0.3483. As a result, the effective uptake, which represents the difference between the maximum and minimum uptake, was limited to only 0.062 kg\({}_{\text{a}}\cdot\)kg\({}_{\text{ads}}^{-1}\) in the complete cycle during the cyclic steady state at the given condition, as shown in Figure 7.  
Figure 6: The uptake distribution during the whole cycle at different time steps for the consolidated form.  
Figure 7: The average uptake of the two beds over a complete cycle.