esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Analysis and Simulations
### System Efficiency
#### 3.2.4 Discussion  
Numerically, for our simulations, the proposed CPV unit with the SOE and the Si low-cost cell (Figure 11c) provided an optical power ratio increase of about 17.12% compared to the conventional CPV unit without using the additional low-cost cell (Figure 11b), and about 10.26% compared to the conventional CPV unit without using both the SOE and the additional low-cost cell (Figure 11a). In other words, the optical power ratio of the proposed CPV unit is better than that of the other CPV units. By using both the SOE and the additional low-cost cell, the proposed CPV not only improves the irradiance uniformity but also increases the optical power ratio of the system.  
For the system conversion efficiency, the conversion efficiency ratio \(\tau\) depends on the type of MJ and additional low-cost solar cells as well as the optical system. The improvement factor decreases with an increasing ratio \(\tau\) because the electricity generated by the MJ solar cell is high compared to that generated by the Si solar cell. The ratio \(\rho\) depends on the atmospheric conditions, e.g., the annual mean value for a medium DNI region, such as Korea or Japan, is \(\rho\cong 0.4\), while that for a high DNI region, such as Phoenix (USA), is \(\rho\cong 0.2\)[28]. The improvement factor increases with an increasing ratio \(\rho\) because the amount of diffuse solar radiation collected by the additional solar cell increases compared to that for direct solar radiation concentrated on the MJ solar cell.  
Since the proposed CPV system uses an additional low-cost solar cell, the cost of the system is higher than that of conventional CPV systems. However, the proposed system uses low-cost optical components, including Fresnel and plano-concave lenses, and an additional low-cost cell; the cost of the system is thus not much higher than that of the conventional CPV systems. The use of the additional low-cost solar cell resulted in more electricity generated per dollar.
## 3 MT Method
### Three-Dimensional MT Inversion
The electromagnetic transfer function of the impedance tensor (Z), used in this study to characterize the electrical resistivity structure, is visualized as a quality in terms of apparent resistivity and phase. The impedance from 34 stations was used for inversion input. Logarithmic frequencies ranging from 320 Hz to 0.1 Hz were selected. The important parameters that were selected before starting the inversion were a mesh orientation, the starting model, and the smoothing parameters (covariance) because the modeling results were affected bythe choice of those parameters [56]. In this study, the inversion grid consists of 63,360 cells (48 \(\times\) 44 \(\times\) 30 cells), and the area of interest (AOI) consists of 38 \(\times\) 34 cells of 5 \(\times\) 5 km horizontal size, as is shown in Figure 5. The size of 5 \(\times\) 5 km for each cell was chosen based on the MT station spacing in a field that is approximately around 6-20 km so that each station can be represented by the model. If the cell size was reduced, the total number of cells would increase, and then the computation time would increase significantly considering the distance between stations, which is approximately above 6 km [57]. In addition, when the cell size is reduced to less than 5 km, the interpolation of the MT data inversion results for each station cannot be connected between one station and another because the MT data distance is above 6 km. The vertical cell increment is 500 m from the surface to 5 km of the model AOI, which consists of 10 cells. After 5 km, the range of vertical model cells grows logarithmically with an incremental factor of 1.2. The starting model we used as a prior model was homogenous with a 100 \(\Omega\)m value of the resistivity, which was selected based on the reference [51]. The total number of NLG iterations, which were used to reduce the nRMS value from 24.532 to 7.138, was 26 iterations. The inversion running time was four days. It was recorded that each iteration takes 3-4 h.
# Recent Advancement in Assessment and Control of Structures under Multi-Hazard
## 4 Multi-Hazard Assessment and Control of Bridges
### Multi-Hazard Assessment of Bridges
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of** & **Reference** & **Type of Bridge** & **Aims** & **Main Contribution/Conclusion** \\ \hline \multicolumn{5}{c}{Earthquake, Floods, and Ground-Failures} \\ \hline [PERSON] and [PERSON] [108] & RC bridges & Fragility assessment & A Bayesian Networks based & A Bayesian Networks based \\ \hline \multicolumn{5}{c}{Earthquake and Scour} \\ \hline [PERSON] et al. [109] & RC bridge & Estimation of & Risk-consistent load factors based on \\ \hline [PERSON] et al. [77] & Bridge with pile & Performance & Experimental evidence for effects of \\  & foundation & Assessment & scour depth on failure mechanisms of \\ \hline [PERSON] et al. [110] & RC bridges & Study of the effect of & Fragility surfaces and time-dependent \\  & & time-dependent scour & loss estimates with two case \\  & & hazard on seismic & study bridges \\ \hline \multicolumn{5}{c}{Investigation of} \\ [PERSON] et al. [111] & RC bridges & scour-load modification & Reliability-based load and \\  & & factors in seismic & resistance factors \\  & & assessment & \\ \hline [PERSON] [112] & Highway bridges & Risk and reliability & Framework for risk assessment and \\  & & assessment & uncertainty analysis \\ \hline [PERSON] and & & & An approach for retrofit optimization, \\ [PERSON] [113] & RC Bridge & Optimal retrofitting & case study results showing that \\  & & & column jacketing is effective \\ \hline [PERSON] and [PERSON] [114] & RC bridges & Lifecycle assessment & A framework for lifecycle assessment, \\  & & & case study highlighting the need for a \\  & & time-sensitive assessment & \\ \hline [PERSON] et al. [74] & High-rise pile cap & Performance & Seismic capacity of foundation is \\  & foundation & assessment & significantly affected by scour depth \\ \hline \multicolumn{5}{c}{Earthquake, Scour, Corrosion} \\ \hline [PERSON] et al. [115] & RC bridges & Sustainability & A framework for time-varying \\  & & assessment & sustainability, and an \\  & & & illustrative application \\ \hline [PERSON] et al. [116] & RC bridge & Performance & A framework for performance \\  & & assessment & assessment and life cycle cost analysis \\ \hline \multicolumn{5}{c}{Earthquake, Scour, Corrosion, and Traffic-Load} \\ \hline [PERSON] and & & & Framework for time-varying total risk \\  & & & and effect of structural redundancy \\ \hline \multicolumn{5}{c}{Earthquake, Scour, Corrosion, Wind, Traffic-Load and Liquefaction} \\ \hline [PERSON] et al. [118] & Highway bridges & Review & Summary of state-of-the-art in \\  & & & different aspects of resilience: loss \\  & & & assessment, recovery actions, \\  & & & and maintenance \\ \hline \multicolumn{5}{c}{Earthquakes, Corrosion, Surge, and Wave} \\ \hline [PERSON], and & & & Object oriented consequence-based \\  & & & framework for lifecycle risk \\  & & assessment and design & assessment considering \\  & & & structural deterioration \\ \hline \end{tabular}
\end{table} 
Table 1: Cont.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 8 Potential Applications in Other Fields
### Digital Copyright Protection  
In the realm of digital copyright protection, the proliferation of unauthorized copying and distribution poses significant challenges to content creators and rights holders. AE-PBFT can serve as a key component of a blockchain-based copyright management system by securely recording ownership, licensing agreements, and usage rights for digital assets such as music, videos, and software. The algorithm's trust acceleration mechanism enables rapid differentiation between legitimate and malicious users, ensuring that only authorized entities can participate in the consensus process. For example, if a malicious node attempts to falsify ownership records or distribute unauthorized copies, its behavior will be quickly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Malicious Nodes Ratio** & **PBFT** & **GPBFT** & **CBFT** & **AE-PBFT** \\ \hline
0 & 130.896491 & 32.104809 & 17.6887 & 0.3179906 \\
5 & 139.926127 & 34.705288 & 18.9092203 & 0.325614 \\
10 & 144.307101 & 36.438948 & 19.4929474 & 0.34115 \\
15 & 149.60185 & 38.8147 & 20.21641532 & 0.377724 \\
20 & 157.550822 & 40.9978296 & 21.2901193 & 0.396956 \\
25 & 164.154401 & 42.44254 & 22.1816298 & 0.416435 \\
30 & 172.214484 & 45.5567 & 23.2606405 & 0.460263 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Consensus latency under malicious nodes.  
identified and mitigated. The dynamic consensus group mechanism can also optimize the system's performance by adjusting to the network's trust landscape, ensuring scalability and reliability in environments with a high number of participants, such as global content distribution networks. By leveraging AE-PBFT, digital copyright protection systems can provide immutable, transparent, and efficient rights management, helping to safeguard intellectual property and incentivize creativity.
## 4 Discussion
The central carbon metabolism of heterotrophs plays an essential role in the conversion of biomass to cellular building [44]. The CCM in microorganisms provided energy in the form of ATP [45]. Glycolysis, is generally considered to be the breakdown of glucose to pyruvate in the CCM of most prokaryotes especially heterotrophs [46]. Glycolytic pathways can be classified into five categories: Embden-Meyerhof-Parnas (EMP)-derived pathways, Entner-Doudoroff (ED)-derived pathways, pentose phosphate (hexose monophosphate) pathways, methylglyoxal pathways, and phosphoketolase pathways [47]. The pentose phosphate pathway (PPP) is a route that can work in parallel to glycolysis in glucose degradation in most living cells [48]. Apart from glycolysis, the pentose phosphate pathway is a major route of intermediary carbohydrate metabolism in enteric bacteria and fulfills various roles in these organisms [49]. In addition to the breakdown of carbon sources such as glucose or gluconate, the pentose phosphate pathway is involved in the generation of reducing power (NADPH) for biosynthesis and the recruitment of essential metabolites for nucleic acids, amino acids, and vitamins [50]. This suggests that the carbohydrate metabolism of Foc TR4 was significantly affected. To verify the effect of _Streptomyces_ sp. Y1-14 extracts on Foc TR4 energy metabolism, activities of ATP, MDH, and SDH in the tricarboxylic acid (TCA) cycle were significantly inhibited. The respiratory pathways of glycolysis, the TCA cycle, and the mitochondrial electron transport chain are ubiquitous throughout nature [51]. They are essential for both energy provision in heterotrophic cells and a wide range of other physiological functions [52]. TCA has been known for decades as a hub for generating cellular energy and precursors for biosynthetic pathways [53]. The enzyme that catalyzed the dehydrogenation of succinic acid to fumaric acid is called SDH. SDH catalyzed the interconversion of a range of related sugar alcohols into their corresponding ketones [54]. It is one of the marker enzymes reflecting mitochondrial function, and its activity can generally be used as an indicator to evaluate the degree of tricarboxylic acid cycle operation. SDH has been identified as one of the most significant targets for fungicide discovery [55]. MDH is a widely distributed enzyme, and plays key roles in many important pathways [56]. MDH, with the help of a cofactor nicotinamide adenine dinucleotide (NAD\({}^{+}\)/NADH), catalyzed oxidation/reduction of malate/oxaloacetate, an important step in glycolysis. In our study, _Streptomyces_ sp. Y1-14 extracts disrupted Foc TR4 cell structure, leading to mitochondrial dissolution. Therefore, _Streptomyces_ sp. Y1-14 extracts affected mitochondrial structure of Foc TR4, resulting in the inhibition of energy metabolism.
# A Study on the Effect of O\({}_{2}\) Diffusion on the Retention Time of Inert Agents
## 4 Results
### Summary of the Experimental and Numerical Analysis Results
From this figure, it can be seen that the calculation of the retention time through the newly proposed model can express the actual measured retention time better than the existing models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Inert Agents** & **Interface Slope \(\alpha\) (kg/m\({}^{4}\))** & **Diffusion Velocity \(v_{d}\) (m/s)** & **Measured Retention Time \(t_{r}\) (s)** & **Calculated Retention Time \(t_{r}\) (s)** & **Difference (\%) Measured /Calculated /Calculated /Calculation \\ \hline IG-541 & 0.062 & \(-3.40\times 10^{-4}\) & 1164/901 & 1183/922 & \(-1.6\%\)/\(-2.3\%\) \\ IG-55 & 0.081 & \(-4.40\times 10^{-4}\) & 892/722 & 918/740 & \(-2.8\%\)/\(-2.4\%\) \\ IG-01 & 0.071 & \(-4.18\times 10^{-4}\) & 837/636 & 854/654 & \(-2.0\%\)/\(-2.75\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of experimental and numerical results. First, as _the sharp descending interface model_ does not consider species diffusion, the retention time is largely calculated. Therefore, it can be confirmed that this model shows optimistic results for the retention time. In addition, _the wide descending interface model_ quickly calculates the species diffusion. Therefore, the retention time was calculated to be faster than the actual measured data. That is, it is confirmed that the results are very conservative. In addition, _the sharp descending interface model_ and _the wide descending interface model_ do not reflect the unique species diffusion characteristics (the interface slope \(\alpha\) and the diffusion velocity \(v_{d}\)) of each inert agent, because the difference between IG-55 and IG-541 is not distinguished. On the other hand, it was confirmed that the newly proposed model reflects the fixed interface slope \(\alpha\) and the z-direction diffusion velocity \(v_{d}\) of each inert agent, thus representing the most accurate retention time. Therefore, when predicting the retention time of the inert agent, if the interface slope \(\alpha\) and diffusion velocity \(v_{d}\) proposed in this study can be reflected, a more accurate retention time can be predicted.
# Aerodynamic Sensitivity Analysis for a Wind Turbine Airfoil in an Air-Particle Two-Phase Flow
## 3 Validation of Numerical Method  
### One-Phase Air Flow Simulation for S809 Airfoil  
The present CFD method is validated by applying it to simulate the one-phase air flow over the S809 airfoil of the NREL Phase VI wind turbine blade. This airfoil has a thickness of 21%. Figure 1 displays the computational domain and structured C-grid with 147,552 cells, where \(c\) denotes the airfoil chord. The first grid node is located at the distance \(y+\leq 1\) from the wall with \(y+\) denoting the non-dimensional wall coordinate.  
Figure 2 shows the Reynolds (\(Re\)) number distributions of the Phase VI blade sections at different tip speed ratios defined as  
\[\lambda=\frac{\omega R}{V_{\infty}} \tag{6}\]  
where \(\lambda\) denotes the tip speed ratio, \(R\) and \(\omega\) are the rotor radius and angular velocity, respectively, and \(V_{\infty}\) is the wind speed.  
The one-phase air flows over the S809 airfoil are computed at four \(Re\) numbers of \(0.75\times 10^{6}\), \(1\times 10^{6}\), \(1.25\times 10^{6}\) and \(1.5\times 10^{6}\), which respectively correspond to the velocities: 10.955 m/s, 14.607 m/s, 18.259 m/s and 21.911 m/s. The velocity inlet and pressure outlet conditions are respectively implemented at the inlet and outlet boundaries shown in Figure 1a, and the no-slip condition is imposed on the solid wall. Figure 3 compares the computed lift (\(C_{L}\)) and drag coefficients (\(C_{dp}\) and \(C_{dw}\)) with the experimental data [23], where \(C_{dp}\) is the pressure drag coefficient and \(C_{dw}\) is the wake drag coefficient. In Figure 3, the computed and experimental lift coefficients are in good agreement. Due to the lack of friction drag in \(C_{dp}\), the computed drag coefficients are closer to \(C_{dw}\). In Ref. [18], the same numerical method was applied to the S809 airfoil, and a similar difference in drag between computation and experiment was obtained. In the rest of the paper, only the increments of lift and drag coefficients due to particles are discussed, but for the lift-to- drag ratio.  
Figure 1: Computational domain and structured grid of S809 airfoil: (**a**) Computational model; (**b**) structured C-grid.  
Figure 2: \(Re\) number distributions of Phase VI blade sections at different tip speed ratios and rated rotational speed of 72 rpm.  
Figure 3: Lift and drag coefficient comparisons for S809 airfoil in one-phase air flow: (**a**) \(C_{L}\) at \(Re=0.75\)\(\times\)\(10^{6}\); (**b**) \(C_{L}\) at \(Re=1\times 10^{6}\); (**c**) \(C_{L}\) at \(Re=1.25\times 10^{6}\); (**d**) \(C_{L}\) at \(Re=1.5\times 10^{6}\); (**e**) \(C_{D}\) at \(Re=0.75\times 10^{6}\); (**f**) \(C_{D}\) at \(Re=1\times 10^{6}\); (**g**) \(C_{D}\) at \(Re=1.25\times 10^{6}\); (**h**) \(C_{D}\) at \(Re=1.5\times 10^{6}\).  
The computed pressure coefficient (\(C_{p}\)) distributions are compared with the experimental data [23] in Figure 4 for several angles of attack at \(Re=1\times 10^{6}\). As can be seen, they are in excellent agreement, which means that the separation points can be well predicted.
## 4 Results and Discussion
### Performance of the SERS Substrates
Assuming that the density of the bulk R6G is 0.79 g\(\cdot\)cm\({}^{-3}\), \(N_{bulk}\) can be calculated as \(1.04\times 10^{-14}\) mol (\(N_{bulk}=0.79\) g\(\cdot\)cm\({}^{-3}\times\pi\times 1\)\(\upmu\)m\({}^{2}\times 4\)\(\upmu\)m/479.01 g\(\cdot\)mol\({}^{-1}=2.08\times 10^{-14}\) mol). For the vibration band at 1503 cm\({}^{-1}\), the \(I_{SERS}\) and \(I_{bulk}\) are 554082 and 791, respectively. Therefore, the EF was calculated to be \(1.12\times 10^{7}\), which is quite high compared to other studies [23; 24]. This result is assuming the SERS substrate is densely packed with R6G molecules after immersed in \(10^{-6}\) M R6G solution for 12 h. However, the R6G is likely to cover the substrate at a lower density. Therefore, the SERS enhancement factor calculated here is the lower limits. In order to calculate the reproducibility of the SERS active substrate, SERS spectra of R6G were obtained at 10 random locations of the SERS substrates with sputtering duration of 200 s. Figure 10a shows the measured spectra from 10 random positions. The SERS intensities of the Raman characteristic peak of the 1503 cm\({}^{-1}\) band from these locations are further displayed in Figure 10b. The relative standard deviation (RSD) of intensities of the 1503 cm\({}^{-1}\) Raman characteristic peak was calculated to be 7.28%, which is relatively low compared to other studies [25; 26; 27], indicating good uniformity and reproducibility of the fabricated SERS substrates. Figure 9: The SERS spectra of blank SERS substrate, bulk R6G, and various concentrations of R6G solution (\(1\times 10^{-6}\) M, \(1\times 10^{-7}\) M, \(1\times 10^{-8}\) M, and \(1\times 10^{-9}\) M, respectively) with our SERS substrates. The Raman band assignment of the main peaks of R6G is given in the figure. The developed SERS substrates were also tested with low concentration DEHP solution. Figure 11 shows measured SERS spectra for DEHP concentrations ranging from 1000 ppm to 2 ppm on SERS substrates. The Raman band assignment of the main peaks for DEHP are given in Figure 11. Raman peaks of DEHP at 1055 and 1433 cm\({}^{-1}\) are aromatic ring breathing and C=C stretch, respectively. The characteristic peaks of DEHP at 1055, 1433 cm\({}^{-1}\) are clearly observed from the spectrum. The SERS substrates were found to be able to detect a low concentration (2 ppm) DEHP methanol solution.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 3 Wi-Fi 6 Overview
### Additional Frequency Band (Wi-Fi 6E)  
The Wi-Fi 6 standard utilizes the 6 GHz frequency band in the range of 5.925-7.125 GHz for unlicensed use with seven 160 MHz channels. They are suitable for video streaming and virtual/augmented reality [25; 26]. This new capability is referred to as Wi-Fi 6E (Expended). It brings wider channels and thereby lower latency and higher throughput than Wi-Fi 6 in other bands. As this band is less occupied by other standards, there is mostly less noise than in other bands, which leads to better performance. There are no backward compatibility issues of Wi-Fi 6E with previous Wi-Fi standards as they do not use this frequency band.
## 1 Introduction  
The domestic meat industry in Kazakhstan has experienced significant growth in production volumes and diversification of product lines in recent years [1]. As a critical component of the national food system, the meat sector is responsible for supplying high-quality protein products that meet increasingly stringent safety and sensory requirements. One particularly promising area for innovation lies in the valorization of slaughter by-products, especially poultry bones, which constitute a substantial portion (20-25%) of the carcass after primary processing [2]. These include vertebrae and flat breast and back bones, as well as smaller fragments from drumsticks and thighs. The effective utilization of such by-products can significantly enhance the economic viability of meat-processing operations and contribute to a more circular economy in food production [3; 4]. Mechanically deboned meat (MDM), obtained after secondary pressing of residual carcass material, often contains bone particles up to 2-3 mm in size. These inclusions are organoleptically unacceptable in most processed meat products, such as sausages and deli meats, due to their gritty texture and negative impact on mouthfeel [5; 6].  
To address this challenge, there is a clear need for advanced processing equipment capable of reducing bone material to a fine paste with particle sizes below 0.1 mm, making it imperceptible during consumption [7]. Colloid mills, which are commonly used for the fine comminution of relatively soft materials such as nuts, plant tissues, and offal, offer a potential solution [8]. However, when applied to harder materials like bone tissue, these systems require significant design modifications. Specifically, specialized tooth geometries and enhanced wear resistance of cutting edges [9].  
Colloid mills utilize a high-speed rotor-stator mechanism to generate intense shear forces in a narrow gap, fragmenting raw material into fine particles. During milling, the rapidly rotating rotor and stationary stator act together to create a high shear field, enabling significant particle size reduction [10]. However, conventional rotor-stator designs often lack the ability to achieve uniform, ultra-fine grinding of tougher materials such as bone, and may leave unacceptable coarse fragments in the final paste. Addressing these limitations through improved tool geometry is essential for producing homogeneous meat-bone emulsions and enhancing the overall efficiency of meat by-product processing.  
This study focuses on the investigation and refinement of colloid mill design solutions aimed at the ultra-fine grinding of poultry bones. By modernizing the rotor-stator knife mechanism, the research seeks to improve the efficiency of bone processing and promote full valorization of raw materials. Ultimately, the proposed technological advancements are expected to facilitate the production of high-quality meat emulsions free from detectable bone inclusions, thus broadening the range and enhancing the sensory attributes of meat-based food products.