esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 2 Materials and Methods
### The Role of the \(\epsilon\) Parameter  
Thanks to its simplicity, the number of parameters is relatively small compared to other metaheuristics. One critical parameter is the step size (\(\epsilon\)), which has a massive impact on the results of the algorithm. The disadvantages of using overly small \(\epsilon\) values are as follows:  
* Slower convergence: The search process becomes overly cautious, taking very small steps. This can make convergence to an optimal solution much slower, increasing computational time and making it practically impossible to find the global optimum.
* Becoming stuck in local optima: Small steps may prevent the algorithm from escaping local optima. This can be extremely problematic in the case of noisy data, where the perturbation of data leads to a lot of locally optimal locations.
* Reduced exploration: The algorithm may not adequately explore the solution space, missing out on potentially better solutions that lie further away. On the other hand, excessively large \(\epsilon\) values also have several disadvantages as follows:
* Reduced precision: The most easily imaginable effect is that by using an excessively large step size, the algorithm simply steps over (or stops before) the ideal point and fails to approach it sufficiently. A special variant of this case is when the HC method oscillates around the optimal location.
* Reduced exploitation: In complex search spaces with many peaks and valleys, large steps might miss small, promising regions of improvement. The algorithm may be able to explore the whole search space, but it is not applicable for exploiting the possibilities of potential areas to reveal the highest quality results.  
As is visible, using the right \(\epsilon\) value is crucial in balancing exploration and exploitation in the HC algorithm. Moreover, this value has an effect on the convergence speed and the resistance against noise.
## 3 Results and Discussion
### Sand Characterization  
The adsorption process that occurs during the EOR surfactant flooding depends on the type of surfactant used and on the morphological characteristics and mineralogical composition of the rock [48; 49]. The surface charge on the porous medium must be considered to understand the adsorption mechanism involved in the transport test [50; 51; 52]. It is known that silica normally adsorbs cationic surfactants, because, at neutral pH, silica surface presents a weak negative charge, affecting the efficiency of a surfactant flooding [53]. Considering these factors, a semi-quantitative phase analysis of the XRD spectrum was carried out to determine the mineralogical composition of the sand. For that, the diffraction pattern was analyzed with the software _Match!_ (Crystal Impact GbR, Bonn, Germany) using IUCr/COD/AMCSD as the reference database. The composition of the sand was 71.9% of quartz and 28.1% of berlinite (AlPO4). Berlinite is isostructural with \(\alpha\)-quartz and presents a negatively charged surface at neutral pH [54; 55; 56].  
TGA measurements did not identified any organic matter or other volatile compound loss during the sample heating which ensures that the preliminary sand treatment was efficient.
## 4 Discussion  
This study recommends the use of excess electricity for hydrogen production. On the basis of the data available in the literature, the possible proportions of individual gases in the mixture were determined. According to the research described by [PERSON] et al. [16], 10% hydrogen content was assumed to be used in the gas mixture. On the other hand, 8% hydrogen content was adopted for the gas injection into the network based on the data provided by [PERSON] et al. [18] on the explosion safety of measuring devices  
Figure 9: Change in gas volume as a function of hydrogen mass content.  
intended for use in explosive atmospheres. These parameters should be further analyzed for specific engines, notably those used in agriculture.  
This technology primarily assumes the use of excess amounts of energy generated at off-peak electricity hours at night, overproduced or coming from renewable energy sources. By using this source to produce hydrogen, it is possible to improve the environmental impact and energy balance of the process.  
In addition, by introducing hydrogen into the fermenter during the methane fermentation process, an increased methane yield is expected as a result of combining hydrogen with carbon. The possibility of using more biomass carbon to produce methane would also have a positive environmental impact.  
In view of the aforementioned difference in energy density, an increase in the mass proportion of hydrogen results in a significant increase in the volume of gas mixture. The gas mixture containing 40% of hydrogen has almost four times the volume of biogas alone. Therefore, difficulties arise in storing this type of gas mixture, since the storage system needs to be equipped with fittings comprising compressors and tanks designed storage under increased pressure. However, both methane and hydrogen must have a gaseous form in the mixture. This makes the gas mixture homogeneous and does not pose a challenge to the plants applied. For example, in automotive CNG systems, methane is stored at 210-250 bar. Consequently, a tank with a water volume of 42 L and a gas volume of 8.4 m\({}^{3}\) can store 190 MJ (52.78 kWh) of energy for a mixture containing 10% hydrogen. On the basis of the calculations of the density of the gas mixture and the fuel demand of the tractor engine, it can be calculated that the available amount of gas in such a tank would allow the tractor engine to produce 41.26 kWh.  
Due to the low energy density of hydrogen, the mixture of hydrogen and biomethane needs to be compressed to higher pressures to compensate for the lower energy in the tanks than with biomethane alone. The authors claim that both gases must be in a gaseous state to ensure that the mixture is as homogeneous as possible. However, it is necessary to bear in mind that the benefit of using the mixture to power internal combustion engines is not only the use of excess electricity to produce hydrogen, but also the improvement of combustion parameters and reduction of emissions when burning this type of mixture [16].
## 3 Results and Discussion
### PAH Sources in the Adriatic Sea
The contribution of Source 2 to the total PAH concentration was highest at each station in Sibenik Bay, especially at station SB5, where 94.57% of the PAH sediment concentration came from Source 2. High contributions from Source 2 were also observed in October at stations SB2 (91.07%) and SB4 (68.71%). These results can be explained by the influence of coke-burning in the ferroalloy factory that operated on the shores of the bay from 1880  
Figure 7: Factor (source) contributions (%) in the total PAH concentrations in the sediment of the investigated stations. * Refers to the sediments obtained in October of 2013.  
to 1990. The high concentrations of PAH in the surface sediment are probably due to the diffusion of PAH from the deeper sediment layers that are oversaturated with PAH. The highest contributions from Source 3 were detected at station SB1 in February, where model results indicate that 58.72% of PAH originated from Source 3. Station SB1 is located near the tourist marina where most of the vessel maintenance is performed during the winter, suggesting that the high Source 3 contribution detected at this station in February is caused by accidental leaks of petroleum derivatives during vessel maintenance. An elevated contribution from Source 4 was also detected at this station, confirming the assumption that this station is greatly affected by vessel maintenance activities.
## 5 Analysis of Selected Design Variants of the Considered Structure
### Solution II: Cross-Bracing for Columns  
Solution II introduces X-shaped steel bracing as a reinforcement measure, as depicted in Figure 25(b). Using ST12 material, this scheme aims to resist deformation and stress under combined seismic and snow loadings by providing additional lateral support.  
The simulation results are shown in Table 11, leading to the following conclusions:  
1. Deformation Analysis: Across all wall thicknesses, the deformation of the side walls in the Y direction shows little variation, indicating relative rigidity in this direction. In the Z direction, the deformation in the 2.0 mm- and 1.5 mm-thickness side walls is slightly less than that of the 2.5 mm thickness, although the differences are minimal. This suggests that the reinforcement measures are also effective in resisting deformation in the Z direction.
2. Stress Analysis: For the 2.5 mm and 2.0 mm wall thicknesses, the maximum stress is primarily concentrated in the Z and XZ directions, indicating that external loads have the greatest impact in these directions. For the 1.5 mm-thickness side walls, the stress values are relatively lower in all directions, particularly in the Z direction, where the stress significantly reduces to 2.8339 MPa. This demonstrates that the X-shaped steel supports are more effective at dispersing stress in thinner wall panels.  
From the simulation results after adjusting the structure, the stress contour plot for the 2.5 mm-wall-thickness solution is shown in Figure 26, indicating a significant improvement in the previously observed deformation concentration on the side wall and the stress concentrations at the brace connections. The reinforcement measures of the X-shaped steel supports in Solution II have a significant effect in reducing stress and deformation in the Z direction; for side wall panels with a wall thickness of 1.5 mm, the improvement effect is most obvious.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Thicknesses** & **Response** & **X** & **Y** & **Z** & **XZ** & **ZY** \\ \hline \multirow{2}{*}{2.5 mm} & Von Mises Stress (MPa) & 0.38186 & 0.41728 & 3.7005 & 3.7065 & 3.7005 \\  & Total Deformation (mm) & 0.052959 & 0.082141 & 1.1546 & 1.1558 & 1.1546 \\  & Von Mises Stress (MPa) & 0.30365 & 0.41812 & 3.923 & 3.9295 & 3.923 \\  & Total Deformation (mm) & 0.053293 & 0.082145 & 1.1496 & 1.1508 & 1.1496 \\  & Von Mises Stress (MPa) & 0.21748 & 0.41858 & 2.8339 & 2.8385 & 2.8339 \\  & Total Deformation (mm) & 0.055705 & 0.080392 & 1.1499 & 1.1512 & 1.1499 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Combination simulation results of three thicknesses under Solution II.  
Figure 26: Solutions I-V simulation results.
## 4 Conclusions  
Mg nanocomposites with Si\({}_{3}\)N\({}_{4}\) as reinforcement were successfully fabricated using the ultrasonic-assisted stir casting method. The effect of Si\({}_{3}\)N\({}_{4}\) nanoparticles on the Mg matrix's ignition temperature and compression properties was investigated. From this study, the following conclusions are drawn:  
1. The ultrasonic vibration during the fabrication process resulted in a uniform distribution of Si\({}_{3}\)N\({}_{4}\) nanoparticles in the Mg matrix. The distribution is more homogeneous in the 0.5 and 1 vol% Mg/Si\({}_{3}\)N\({}_{4}\) nanocomposites compared to the 1.5 vol% Si\({}_{3}\)N\({}_{4}\) nanoparticles, as the latter had some clustered nanoparticles when observed under SEM;
2. Similar to the EDS analysis, the XRD results revealed the presence of Mg and Si\({}_{3}\)N\({}_{4}\). Moreover, the XRD analysis showed no evidence of interfacial reaction products;
3. The porosity of the samples increased with the increasing vol% of the Si\({}_{3}\)N\({}_{4}\) nanoparticles, with Mg-1.5 Si\({}_{3}\)N\({}_{4}\) showing a higher porosity (1.3%) compared to the remaining samples;
4. The ignition temperature of the nanocomposites is enhanced by increasing the amount of the Si\({}_{3}\)N\({}_{4}\) nanoparticles by forming a dense and more stable oxide layer on the surface. The nanocomposite with 1.5 vol% Si\({}_{3}\)N\({}_{4}\) nanoparticles showed the highest ignition temperature of 614 \({}^{\circ}\)C, -6% more than pure Mg;
5. Adding Si\({}_{3}\)N\({}_{4}\) nanoparticles gradually improved the compressive yield strength and ultimate strength, with 1.5 vol% Si\({}_{3}\)N\({}_{4}\) nanocomposites having a maximum yield strength of 118 MPa and an ultimate strength of 323 MPa. The failure strain improved with the addition of Si\({}_{3}\)N\({}_{4}\) nanoparticles, with 1 vol% Si\({}_{3}\)N\({}_{4}\) nanocomposites displaying a maximum failure strain of 21.2%, -30% greater than pure Mg.  
Conceptualization, M.B.P. and R.N.R.; methodology, M.B.P., S.I. and M.G.; investigation, M.B.P.; data analysis, R.N.R., S.I. and M.B.P.; writing--original draft preparation, M.B.P. and S.I.; writing--review and editing, R.N.R. and M.G.; funding acquisition, R.N.R. and S.I. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the scheme for promotion of academic and research collaboration (SPARC), India, grant number P1393.  
Not applicable.  
The authors declare no conflict of interest.
# On the Communication Performance of Airborne Distributed Coherent Radar
## 3 Results and Discussion
It can be seen from the subgraphs in Figure 3 that the analytical expressions we proposed are in good agreement with the results of Monte Carlo numerical simulation for any part of theoretical derivation in Section 2.2. Figure 3a-d shows the PDF curves in the process of deriving the PDF of \(\sqrt{\gamma_{c}}\). It can be seen that the analysis and simulation in Figure 3a,b are almost identical, but from Figure 3c, there are some slight deviations between the two. This is because (30) approximates the weighted sum of multiple generalized \(K\) distributions to a unified generalized \(K\) distribution. This error is also transferred to Figure 3d. However, on the whole, the error introduced by this approximation is acceptable. Figure 3e-g show the PDF curves in the process of deriving the PDF of \(\sqrt{\gamma_{b}}\). Comparing Figure 3e,f, it can be seen that the approximation of weighted summation of multiple generalized \(K\) distributions does not introduce too much error in this case. In order to clearly show the influence of each parameter on OP, we show the theoretical value of OP and the results of Monte Carlo experiment in Figure 4. It should be noted that in Figure 4, only one parameter is changed for each figure, while other parameters remain the same as the default parameters. Figure 4 shows the comparison between the theoretical OP and the OP obtained by Monte Carlo simulation when the six propagation-related parameters change. On the  
Figure 4: Numerical simulation of OP. The parameters changed in each subfigure are (**a**) \(L_{1}\), (**b**) \(L_{2}\), (**c**) \(m\), (**d**) \(\Omega\), (**e**) \(\alpha\), (**f**) and \(K\), respectively. whole, the theoretical OP derived in Section 2.2 is well in agreement with the simulation results. This proves that our proposed statistical distribution is accurate. Analysis one by one: Figure 4a shows the change of OP with the distance \(L_{1}\) of communication link 1. We note that when \(L_{1}<50\ m\), the OP does not approach 0 because the overall OP of the system is limited by \(L_{2}\). This trend of OP is also applicable to Figure 4b, but in order to maintain the same range of distance (i.e., the range of the \(x\)-axis), this trend is not obvious in Figure 4b. Figure 4c,d shows the change of OP with Nakagami fading parameters. For the Nakagami fading model shown in (10), when \(m\) or \(\Omega\) becomes larger, it means that the fading is reduced, so OP gradually decreases. Figure 4e shows the change of OP with propagation loss coefficient. In general, \(2\leq\alpha\leq 6\), it can be seen that OP is very sensitive to \(\alpha\) compared with other parameters. On the contrary, OP is not very sensitive to the parameters of Rice fading as shown in Figure 4f. However, since the larger \(K\) means lesser Rician fading, OP shows a slow downward trend here.
## 3 Experiment and Discussion
### Comparative Experiments
Through the results of Canny edge detection, we observe that the high-resolution images generated by DSpix2 pix exhibit fewer edges, which does not indicate information loss but rather signifies that noise in the images has been effectively suppressed while retaining the original structure and details. The reduction in edges typically suggests that the images are smoother, cleaner, and less affected by noise, presenting a more natural visual effect. In contrast, an increase in edges may reflect higher levels of noise contamination, leading to excessive sharpening or distortion of the image. For example, the pix2 pix method extracts more edges, which results in greater noise contamination and image distortion. The superior performance of DSpix2 pix in terms of PSNR, SSIM, and LPIPS further confirms the effectiveness of this method in enhancing image quality. Specifically, DSpix2 pix not only effectively removes unnecessary noise but also generates clearer and more realistic images while preserving structural information, demonstrating a significant performance advantage. In addition, we calculated the standard deviation of the gray-level histogram for the super-resolution results of the 4\(\times\) super-resolution task, including two zoomed-in regions from Figure 6b. The results are presented in Table 4. Analysis of the data in Table 4 shows that, except for CycleGAN, the gray-level histogram standard deviation of the images generated by DSpix2 pix is the smallest, indicating that DSpix2 pix produces smoother images with less noise influence. Although CycleGAN performs well in terms of the standard deviation metric, its over-smoothing of the generated images affects the preservation of land cover information, making it difficult to maintain the content of the image. Therefore, considering the overall performance, DSpix2 pix undoubtedly exhibits superior results. Figure 7: Original image and edge detection image.
## 3 Theory  
Explosive properties, standoff distances and the environment of the explosion are important in blast load design. The most commonly used correlation, which exemplifies the time-dependent behaviour of the explosion, is the equation developed by [PERSON] (Equation (1)) [18].  
\[P_{s}(t)=P_{o}+P_{so}(1-t/t_{o})\exp(-bt/t_{o}) \tag{1}\]  
Here, \(P_{o}\) is the ambient air pressure, \(P_{so}\) is the peak incident overpressure, \(t_{o}\) is the total duration of blast wave and \(b\) is the parameter controlling the rate of wave amplitude decay coefficient.  
A number of full-scale tests are desired while experimentally studying (or for other purposes) the effect of explosions on structures. However, these full-scale tests may be risky and costly. [PERSON] (1915) and [PERSON] (1926) developed the Hopkinson-Cranz Scaling Law (Cube-Root Scaling Law) to expand the applicability of experiments conducted at different scales [18; 23]. There is a linear relation between explosive weight and standoff distance according to [PERSON]'s law (Equation (2)).  
\[Z=R/\sqrt[3]{W_{e}} \tag{2}\]  
Here, \(R\) is the standoff distance [m] from the blast source to the blasting point, and \(W_{e}\) is the equivalent explosive weights [kg]. The variety of explosives has led to a universal acceptance for the explosive parameters required in calculations. TNT, whose detonation property is similar to many explosives, was chosen in the generalization. The equivalent weight of TNT that relates the weight of the explosive to the equivalent weight of TNT will be calculated according to Equation (3) using the ratio of heat generated during blasting [23].  
\[W_{e}=W_{\text{exp}}H_{\text{exp}}^{d}/H_{TNT}^{d} \tag{3}\]  
Here, \(W_{e}\) is the equivalent weight [kg] of TNT, \(W_{\text{exp}}\) is the weight [kg] of the actual explosive, \(H_{\text{exp}}^{d}\) is the actual explosive heat [MJ/kg], and \(H_{TNT}^{d}\) is the heat of explosion of TNT [MJ/kg]. [PERSON], who expressed the scaled distance in kPa in peak incident overpressure calculations, developed the formula in Equation (4) [33].  
\[P_{\text{SO}}=1772/Z^{3}-114/Z^{2}+108/Z \tag{4}\]  
The explosion occurs as an instant impact load. Blast load profile (Figure 1) is defined to be the time-dependent function of the explosion. The time-varying blast load will be impacted on an existing base-isolated structure at building storey levels in the content of numerical work using SAP2000 software [34], in which analyses are based on the finite element method.  
Figure 1: Blast load profile.  
The design of seismic base isolators, according to TBEC 2018, is calculated based on the maximum displacement demand, target damping, and target period values. Base isolators reduce relative storey drifts by extending the structure's period. As a result, the isolator stiffness is determined according to a longer target period value than a fixed-base structure. The isolators are sized by controlling unit deformation limits. The algorithm used in this study for isolator design is presented in the following Figure 2.  
Figure 2: Isolator design algorithm.
# High-Aspect-Ratio Silicon Metasurfaces: Design, Fabrication, and Characterization. _Applied_
## 5 Conclusions  
In this paper, we propose a fabrication technology with an optimized Bosch process to realize high-performance silicon meta-atoms in a THz regime with different functionalities in transmission geometry. We first designed the high-performance meta-atom consisting of high-aspect-ratio (AR) micro-pillars and systematically demonstrated the influence of fabrication quality on the final performance of the designed meta-device serving as a THz meta-half-wave-plate. After demonstrating the importance of finding a proper Bosch process with balanced working phases, we successfully fabricated a meta-device consisting of high-AR meta-atoms, 400 \(\upmu\)m in etch depth and a high aspect ratio of 20:1, by employing the optimized Bosch process with a gradient parameter recipe. At last, we conducted experimental characterization of the polarization-control performance for the designed meta-device. Our findings not only provide a design concept to realize ultra-compact THz meta-devices with high-performance transmission geometry, but also improve the Bosch process to fabricated high-AR micro-structures, paving the avenue for future THz meta-devices for applications in bio-sensing, tele-communication and so on.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13179607/s1](https://www.mdpi.com/article/10.3390/app13179607/s1)]([https://www.mdpi.com/article/10.3390/app13179607/s1](https://www.mdpi.com/article/10.3390/app13179607/s1)), Figure S1: Influence of parameters on the fabrication quality of silicon metasurfaces; Figure S2: Optimized broadband meta-half-wave plate.  
Fabrication, simulation and experimental characterization, Y.Y.; experimental characterization, Z.W.; fabrication, Z.Z. and Y.H.; writing--review and editing, Y.Y., S.S., Q.H. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the following projects: National Key Research and Development Program of China (No. 2022 YFA1400200), National Natural Science Foundation of China (No. 11734007, No. 12221004, No. 62192771), Natural Science Foundation of Shanghai (No. 20 JC1414601), and China Postdoctoral Science Foundation 2021 TQ0077.  
Not applicable.  
The authors declare no conflict of interest.