esa-sceva/satcom-chunk-collection huggingface dataset:
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 3 Method
### Implementation Details  
We downsampled the high-resolution (HR) images by double-cubic interpolation [51] and set the downsampling factor r = 4 to generate the low-resolution (LR) images required in the training phase. Each input batch contains four images, where the LR images have a size of 120 \(\times\) 120 pixels and the corresponding HR images have a size of 480 \(\times\) 480 pixels. During the training process, the weight hyperparameters \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) were set for the different loss functions, which were 0.1, 0.001, 1, and 0.001, respectively. The Adam optimizer [52] was chosen for the model optimization, with parameters set to \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999, and \(\varepsilon=1\times 10^{-8}\). For both the generator and the discriminator, we set the learning rate to be \(\varepsilon=1\times 10^{-4}\), and halved it at 40 k, 60 k, and 80 k iterations during training. At the beginning of training, we perform a warm-up of 20 k iterations, applyingonly the reconstruction loss Lrec and the gradient reconstruction loss Lg.rec. After that, all the loss functions are combined and applied to the training of the model, with a total of 120 k iterations in the whole process. Our model is implemented based on the PyTorch v2.0.1 framework and trained using a single NVIDIA A100 GPU (NVIDIA, Santa Clara, CA, USA) to ensure computational efficiency and model performance.
# Scalable Fabrication of Si-Graphene Composite as Anode for Li-ion Batteries
## 4 Discussions  
With the rapid development of advanced technology, there is huge and increasing needs for Li ion batteries with high performance. Compared to other carbon nanomaterials such as carbon nanotubes or nanofibers [40,41], graphene nanosheet and its derivatives have attracted more attention in the fabrication of Si-based anodes mainly due to the 2D layered structure that can better tolerate the volume expansion of Si nanoparticles [42]. However, the reported approaches of fabricating Si/graphene electrode require either multistep synthesis or complex surface modification, despite the enhanced capacity per  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**EIS Parameter** & **R\({}_{0}\) (\(\Omega\))** & **R\({}_{1}\) (\(\Omega\))** & **R\({}_{2}\) (\(\Omega\))** \\ \hline Si & 23.4 & 355 & 206 \\ Si-graphene & 12.9 & 224 & 183 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EIS parameters of the Li/Si and Li/Si-graphene coin cells.  
Figure 6: (**a**) Full-scale and (**b**) magnified EIS spectra of Li/Si and Li/Si-graphene coin cells.  
formance that has been achieved [30; 31; 34]. The commercialization of Si-based anode materials requires cost-effective and scalable synthesis. Table 2 summarizes the synthesis techniques/conditions and capacity performance of various Si/graphene anodes from literature published in the recent years. Compared to other publications, the Si-graphene anode reported in this work did not show significant improvement in capacity performance. However, the synthesis techniques and conditions in this work are much easier than other reported methods. The Si nanoparticles are electrostatically attached to graphene nanosheets by utilizing SDBS surfactant. This facile and cost-effective method of uniformly dispersing nanoparticles, prompted from the authors' previous research in the field of nanofluids, can potentially lead to advanced commercialization of anode materials for LIBs. Despite the unsatisfying results, the authors believe that the fabrication approach reported in this work is worth exploring in the future. The capacity performance could be boosted in further research by improving the quality of the graphene, reducing the amount of the surfactant, or optimizing the synthesis procedure.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 1 Introduction
Finally, based on the thruster redundancy, a nonlinear model-based numerical control prediction method is proposed to evaluate whether the remaining fault-free thruster can track the attitude control response performance under thruster valve failure, and conducted for longitudinal attitude control as an example.
# Plant-Origin Additives from _Boswellia_ Species in Emulgel Formulation for Radiotherapy Skin Care
## 2 Materials and Methods
### Characteristics of Extracts and Essential Oil from Boswellia Species
#### 2.2.2 Antioxidant Activity by 1,1-Diphenyl-2-Picrylhydrazyl (DPPH) Assay  
The free radical scavenging activity of plant-origin additives from _Boswellia_ was determined using the DPPH method. Three cm\({}^{3}\) of methanolic DPPH solution (2.8 mg/50 cm\({}^{3}\)) was added to 0.4 cm\({}^{3}\) of analyzed extract/essential oil or water (in the case of a blank sample) and were stored in dark place. After 30 min, the absorbance at a wavelength of \(\lambda\) = 517 nm was measured on the NANOCOLOR UV-Vis spectrophotometer (Macherey-Nagel, GmbH & Co, KG, Duren, Germany). Absorbance measurements were repeated three times. The inhibition percentage was calculated using Formula (1):  
\[\%Inhibition=\frac{A_{0}-A_{sample}}{A_{0}}\cdot 100\%, \tag{1}\]  
where:  
\(A_{0}\)--absorbance of blank sample;  
\(A_{sample}\)--average absorbance of samples.
# Mussel Inspired Polydopamine as Silica Fibers Coating for Solid-Phase Microextraction
## 3 Results
### Soot Samples Analysis  
The analysis of selected PAHs was then successfully performed in soot samples with the PDA fiber using the optimized conditions, and the results compared with those obtained using the commercial PA fiber. Table 2 reports the validation parameters obtained with the  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Parameter**} & **Biphenyl** & \multicolumn{3}{c}{**Benzo(a)pyrene**} \\ \cline{2-5}  & **PDA** & **PA** & **PDA** & **PA** \\ \hline Linear range & 0.010–1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL & 0.003–3 \(\mathrm{\SIUnitSymbolMicro g}\)/mL & 0.050–1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL & 0.100–3 \(\mathrm{\SIUnitSymbolMicro g}\)/mL \\ \hline Equation & y = 129,661x + 3338 & y = 895,434x + 2931 & y = 370,910x + 15,390 & y = 200,304x + 19,203 \\ \hline R\({}^{2}\) & 0.9993 & 0.9995 & 0.9996 & 0.9994 \\ \hline LOD & 0.007 & 0.001 & 0.016 & 0.030 \\ \hline LOQ & 0.023 & 0.003 & 0.054 & 0.100 \\ \hline Within-day & 5.5\% & 6.0\% & 6.9\% & 7.1\% \\ \hline Between-day & 10.2\% & 9.3\% & 10.0\% & 10.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the analytical performances obtained with PDA and PA fibers.  
Figure 6: Comparison of the extraction efficiencies of the PDA fiber with those of four commercial SPME fibers, i.e., polydimethylsiloxane (PDMS), polydimethylsiloxane/divinylbenzene (PDMS/DVB), polyacrylate (PA), and polyethylene glycol (PEG).  
two fibers using PAHs-free soot samples prepared as described in the experimental section. Figure 7 reports the LC-UV-DAD \"max plot\" chromatograms, generated by plotting the maximum spectral absorbance measured at each time point, of the same soot sample extracted with the PA and PDA fibers, respectively. As observable, the detection of various target analytes from soot samples was possible using both the extraction devices, which showed slightly different extraction yields. Then, the quantification of PAHs found in soot using the PDA fiber was accomplished and the relevant results reported in Table 3, demonstrating the potential of the fiber developed in the present work.  
Figure 7: LC-UV-DAD chromatograms (max plots) of the same soot samples extracted with the PA and PDA fibers, respectively. The numbers refer to the analytes detected at \(\hat{\alpha}\)max for each peak and are: (1) naphthalene (275 nm); (2) biphenyl (247 nm); (3) acenapthtene (226 nm); (4) anthracene (251 nm); (5) pyrene (239 nm); (6) chrysene (267 nm); (7) benzo(ghi)perylene (298 nm).
## 3 Results
### Batchelor Vortex Fitting  
To gain more information about the vortex center, the velocity fields are examined by plotting two lines: isocurves of the spanwise velocity subtracted by the upward velocity set to zero, and vice versa. This method provides a robust approach to locating the vortex center, avoiding the influence of the zero spanwise (w) or upward (v) velocity components near walls, as can be seen in Figure 3. Figure 4 presents an example of the velocity profiles along the horizontal and vertical lines through the vortex center, where the negative of the vertical velocity is plotted. This approach allows the two profiles to overlap, providing a clearer view of any asymmetry. Significant asymmetry would result in a large difference between the velocity profiles. A notable observation is the tendency of the W velocity profile to approach zero as it reaches the wall. These plots also illustrate the impact of low spanwise periodicity, which often causes the V velocity to not return to zero due to the influence of the adjacent vortex pair.  
As discussed in Section 2.3, a Batchelor vortex is fitted to the average velocity profiles to compare the real vortices generated by the MVGs with an idealized vortex. Figure 5  
Figure 4: Horizontal and vertical velocity profiles through the vortex center.  
Figure 3: Vector plot showcasing the vortex contour intersection for finding vortex centers.  
illustrates this comparison, where the red line depicts the idealized vortex's velocity profile around the vortex core and the blue line shows the average velocity of the actual vortex. This fitting process enables the estimation of important parameters, specifically the circulation (\(\Gamma\)) and vortex radius (\(R_{c}\)).
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.10 Node Capture Attack  
Even if it is possible to assume that the adversary has the node's secret \(x_{j}\) value and retrieve \(r_{g}\) and \(e_{i},h(r_{u}\parallel T_{1}),h(V_{i}\parallel EID_{i})\), this adversary cannot re-calculate session key \(SK\) unless they can effectively solve the difficulty of large number factorization in RSA [18] in obtaining another important value \(r_{s}\).
## 5 Optimization Algorithm  
### Particle Swarm Optimization  
In order to solve optimization problems of the distribution network, the PSO algorithm has been used in this research. PSO is an optimization method that uses the general idea of a flock of birds that can find the path to food by coordination. This is modeled by the particle swarm, which has a search position and velocity information in multidimensional space. Each step of the PSO algorithm is as follows:  
**Step 1:**: Generate an initial searching point for each swarm.
**Step 2:**: Evaluate the objective function using each swarm's searching point.
**Step 3:**: Finish searching if stopping conditions are satisfied. If not, go to Step 4.
**Step 4:**: Search the next point considering the best of the current swarm's searching point and every swarm's best searching point. Go to Step 2.  
The searching algorithm communicates the best positions' information to all swarms, and each continues updating its own positions and velocities until searching is finished. The updating of velocity and search position is shown by following equation:  
\[\begin{array}{rl}V_{k+1}(i)=&w\cdot V_{k}(i)+c_{1}\cdot rand_{1}\cdot(pbest (i)-S_{k}(i))\\ &+c_{2}\cdot rand_{1}\cdot(gbest-S_{k}(i))\end{array} \tag{24}\]  
\[\begin{array}{rl}S_{k+1}(i)&=&S_{k}(i)+V_{k+1}(i)\end{array} \tag{25}\]  
where,  
\(\begin{array}{rl}V_{k+1}(i)\text{:}&i\text{-th particle velocity in the $(k+1)$-th search}\\ rand_{1}\text{:}&\text{uniform random numbers from 0--1}\\ S_{k+1}\text{:}&\text{search position of the $i$-th particle in the $k$-th search}\\ w\text{:}&\text{weighting of inertia}\\ c_{1}\text{:}&\text{weighting for the best position of the self-particle}\\ c_{2}\text{:}&\text{weighting for the best position of the particle swarm}\\ pbest\text{:}&\text{best position of the self-particle}\\ gbest\text{:}&\text{best position of the particle swarm.}\end{array}\)  
The OLTC integer variables are rounded in order to apply the PSO algorithm to MINLP.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 3 5G Services and Technical Requirements of Other Groups  
In this section, we examine the diverse 5G services, categorization of services and their technical requirements defined by other groups, such as ITU-R, 3 GPP services and markets technology enablers (SMARTER) and NGMN. In addition to the Introduction, more details and the relation between groups are discussed. As shown in Figure 1, ITU-R classifies 5G services as three categories, such as eMBB, mMTC and URLLC. Furthermore, NGMN proposes 14 service categories and 24 use-cases, and 3 GPP suggests five service categories and 97 use-cases for 5G mobile services. Figure 1 shows the relationship between service categories proposed in ITU-R [1], NGMN [10] and 3 GPP [11]. This relationship is made between service categories with commonalities to core technical requirements. In most eMBB service scenarios in ITU-R, peak data rate, user experienced data rate, spectrum efficiency, mobility, network energy efficiency and area traffic efficiency all have high importance, as shown in Table 1[1]. Furthermore, low latency and mobility are most important in some URLLC service scenarios. In mMTC service scenarios, the tremendous number of devices that occasionally transmit their packets should be reliably and seamlessly supported in 5G networks. As mentioned before, NGMN has 14 service categories, and their technical requirements (user experienced data rate, end-to-end latency, mobility, device autonomy, connection density, traffic density) corresponding to each category are specified in Table 2[10; 12]. For each category, the denoted requirement values are representatives of the extreme use cases in the category. Therefore, If the requirements of each category are satisfied, those of all the use cases in each category can be met. Recently, 3 GPP service and System Aspects 1 (SA1) completed its study item of feasibility on new services and markets technology enablers (SMARTER). 3 GPP SMARTER has developed a number of use cases covering five service categories such as eMBB, critical communications (CriC), massive IoT (mIoT), network operation (NEO) and eV2X [11]. Furthermore, 3 GPP SMARTER identifies the high-level potential requirements (data rate, latency, reliability, communication efficiency, traffic density, connection density, mobility, position accuracy) for each service categories. Here, the communication efficiency includes spectrum efficiency, energy efficiency and network resource efficiency. Table 3
# Effect of Metallic Waste Addition on the Physical and Mechanical Properties of Cement-Based Mortars
## 2 Materials and Methods
### Manufacturing of the Test Specimens  
Table 1 shows the proportions in weight and volume of the raw materials used. A water/cement ratio by weight of 0.5 was set for all the mortar mixtures with the aim of obtaining a normal consistency according to EN 196-3:2005+A1:2009 [27]. Nine different types of cement mortars with the same water/cement ratio but with four different metallic waste amounts from 4% to 16% by cement weight were manufactured. Raw materials were mixed in a standard laboratory planetary mixer, adding the materials in the following order: first the cement and fine sand, second the water with the additive, and finally the metallic waste, when applicable. The mixing and manufacturing methodologies of the cement-mortar specimens were as follows:  
* First, cement and sand were mixed under dry conditions for 60 s at low speed. At the same time, water and additive were manually mixed in a separate bowl. Then, water with additive was added to the laboratory mixer slowly for 60 s, and then quickly for 60 s.
* After that, the mix was left for 120 s, and then the process was finished with 60 s of fast mixing. In cases where metallic wastes was added to the mortar, they were gradually incorporated while mixing at low speed in order to avoid clusters. Then, the reinforced mortar mixture was mixed at low speed for 120 s.
* After the mixing process, the cement-mortar mixtures with and without metallic waste were poured into the prismatic metallic moulds (40 \(\times\) 40 \(\times\) 160 mm) from one end of the specimen to the other end using a wide scoop. The specimens were slightly vibrated for 5 s to minimise the presence of air bubbles inside them.
* Following the casting and compaction procedures, the prismatic moulds were covered with acetate sheets and placed in the laboratory at ambient conditions for 24 h to set prior to demoulding.
* After demoulding, the specimens were cured in a water tank with a standard controlled atmosphere at a temperature of 22 \(\pm\) 5 \({}^{\circ}\)C and 97 \(\pm\) 2% humidity for 7 and 28 days.  
Figure 1: Optical images of recycled metallic waste: (**a**) steel wool fibres; and (**b**) steel shavings.  
Finally, a total of 216 prismatic test specimens were manufactured, see Figure 2a. A total of 192 specimens contained fibres and shavings, while 24 reference specimens did not incorporate any metallic waste.
## 4 Evaluation
### Sensitivity to Parameter Values  
To assess the sensitivity of SymCHMerge to changes of model parameters, we analysed its performance by varying the inhibition and hallucination parameters \(\tau_{I}\) and \(\tau_{H}\), which affect inference.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Piece** & \(n_{P}\) & \(n_{Q}\) & \(P_{est}\) & \(R_{est}\) & \(F_{test}\) & \(P_{occ(c=0.75)}\) & \(R_{occ(c=0.75)}\) & \(F_{1 occ(c=0.75)}\) \\ \hline
**bach** & 3 & 2 & 100.00 & 66.67 & 80.00 & 100.00 & 45.65 & 62.68 \\
**beet** & 7 & 7 & 65.81 & 60.02 & 62.78 & 80.71 & 80.71 & 80.71 \\
**chop** & 4 & 5 & 47.95 & 49.81 & 48.86 & 62.36 & 51.96 & 56.69 \\
**gbs** & 8 & 3 & 78.16 & 35.49 & 48.81 & 100.00 & 100.00 & 100.00 \\
**mzrt** & 9 & 8 & 47.88 & 41.39 & 44.40 & 100.00 & 100.00 & 100.00 \\ \hline
**Average** & 6.2 & 5 & 67.96 & 50.67 & 56.97 & 88.61 & 75.66 & 80.02 \\ \hline
**Piece** & \(P_{3}\) & \(R_{3}\) & \(TLF_{1}\) & \(P_{occ(c=0.5)}\) & \(R_{occ(c=0.5)}\) & \(F_{1 occ(c=0.5)}\) & \(P\) & \(R\) & \(F_{1}\) \\ \hline
**bach** & 62.96 & 41.97 & 50.37 & 100.00 & 45.65 & 62.68 & 100.00 & 66.67 & 80.00 \\
**beet** & 77.38 & 64.95 & 70.62 & 79.24 & 72.44 & 75.69 & 0.00 & 0.00 & 0.00 \\
**chop** & 46.96 & 39.92 & 43.15 & 57.00 & 46.29 & 51.09 & 0.00 & 0.00 & 0.00 \\
**gbs** & 81.82 & 34.33 & 48.37 & 100.00 & 100.00 & 100.00 & 66.67 & 25.00 & 36.36 \\
**mzrt** & 57.21 & 47.54 & 51.93 & 79.92 & 79.92 & 79.92 & 12.50 & 11.11 & 11.77 \\ \hline
**Average** & 65.27 & 45.74 & 52.89 & 83.23 & 68.86 & 73.88 & 35.83 & 20.56 & 25.63 \\ \hline \end{tabular}
\end{table}
Table 3: A detailed list of JKU Patterns Development Dataset results for the SymCHMerge approach. The \(n_{P}\) and \(n_{Q}\) columns represent the number of annotated patterns and the number of discovered patterns respectively. Song names are shortened, using a four letter abbreviation of the composer’s name.  
We observed the behaviour of occurrence and establishment measures in order to estimate the balance between the two. Due to the large number of possible parameter combinations, we evaluated how changes in one parameter (set for all layers) affect performance when all other parameters are fixed.
## 3 Synthesis of Transition-Metal-Sulfide-Based Nanomaterials
### MoS\({}_{2}\)
The authors concluded that the mechanism for the photo-enhancement was from the photo-oxidation of water at both the anode and cathode surfaces. [PERSON] et al. performed Co doping of MoS\({}_{2}\) nanoparticles, which were applied to the electrocatalytic reduction of CO\({}_{2}\) to form methanol [152]. The authors synthesized a material with an average size of 30 nm, which gave a 35 mmol/L of methanol after 350 min of reaction. In addition, the over-potential for the CO\({}_{2}\) reduction was observed to be \(-0.64\) V. [PERSON] et al. synthesized Cu on graphitic g-C\({}_{3}\)N\({}_{4}\)/MoS\({}_{2}\) composites for the reduction of CO\({}_{2}\) to alcohols [153]. The Cu-g-C\({}_{3}\)N\({}_{4}\)/MoS\({}_{2}\) composite showed faradaic efficiencies of 19.7 and 4.8% for the formation of methanol and ethanol, respectively. The faradaic efficiency was better than that observed for Cu-gC\({}_{3}\)N\({}_{4}\) and CuMoS\({}_{2}\) alone. The material also showed good stability to hold a constant current density over 30 h of reaction. [PERSON] et al. investigated the effect of the edge structure of MoS\({}_{2}\) in electrocatalytic reduction of CO\({}_{2}\)[154]. The authors used an ionic liquid and showed that a 5% dopant of NbS\({}_{2}\) showed a 10\(\times\) enhancement of the reduction of CO\({}_{2}\) over pristine MoS\({}_{2}\). The results showed the over-potential working range was between 0.05 and 0.150 V in 1-ethyl-3-methylimidazolium tetrafluoroborate as the ionic liquid with CO as the product of the reaction. [PERSON] et al. investigated a CuO-ZnO-MoS\({}_{2}\) composites material for CO\({}_{2}\) reduction to make alcohols [155]. The MoS\({}_{2}\) was used as a support for the CuO-ZnO materials and provided a synergistic effect on the electrocatalytic properties of the CuO-ZnO system. The composite showed excellent performance in the reduction of CO\({}_{2}\) into methanol, which showed approximately 25% conversion, with a current density of 17.3 mA/cm\({}^{2}\) at a potential of \(-\)1.3 V (compared to Ag/AgCl). [PERSON] et al. studied Cu nanoparticles inter-dispersed with MoS\({}_{2}\) nanoflowers for the electrocatalytic reduction of CO\({}_{2}\)[156]. In that study a microwave-assisted synthesis was used to produce the nanoparticles, which had an average size between 5 and 20 nm [156]. The composite material was successful in producing CO, CH\({}_{4}\), and C\({}_{2}\)H\({}_{4}\). The MoS\({}_{2}\) nanoflowers in the absence of copper nanoparticles were still active but at a strongly reduced rate of approximately 1/7 time. The results discussed above show great progress for the application of MoS\({}_{2}\) and MoS\({}_{2}\)-based composite nanomaterials in CO\({}_{2}\) reduction reactions. However, the applications that show the most promise of providing valued-added materials appear for the most part to utilize MoS\({}_{2}\) as a base support for a secondary material. In the following section, we review existing modeling (simulation) results reported on the use of MoS\({}_{2}\) and MoS\({}_{2}\)-based composite nanomaterials for CO\({}_{2}\)RR
## 3 Results and Discussion
### Analysis Using Field-Emission Scanning Electron Microscopy (FESEM)  
The structural homogeneity and morphology of the prepared nanomaterials were examined using FESEM. Furthermore, the FESEM images in (Figure 12a) The quasi-flower morphology of the (CNCs-PEG/NaOH) was observed with good structural homogeneity. To determine the size of the nanoparticles, Image J software was used to analyze more than 50 randomly selected particles from the FESEM micrographs shown in (Figure 12b). The micrographs revealed that the CNCs-PEG/NaOH assembled with an average diameter of 27.29 \(\pm\) 5.84 nm.  
Figure 11: \({}^{13}\)C-NMR spectra of (**a**) PEG, (**b**) CNCs, (**c**) (CNCs-PEG/NaOH) solution, \({}^{*}\) New peaks at (52–89 ppm), belong to PEG chain.