esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis Conditions
#### 2.1.2 EPR Experiments  
X-band EPR spectra were recorded with a Bruker EMX Plus spectrometer equipped with a standard ER4102 ST Bruker cavity, either at room temperature or after heating. The instrument settings were the following: receiver gain 30 dB, modulation amplitude 1 G, and microwave power 0.02 mW. The tested samples were aqueous solutions of CuSO\({}_{4}\), Phen and Cu-Phen (called activators) with or without hydrogen peroxide, in the presence of DMPO as a spin-trap and DMSO or sodium formate as hydroxyl radical scavengers. After preparation, the solution was transferred to a bottom-sealed Pasteur pipette and immediately analyzed. For the trials requiring heating, a heat gun was used to heat the pipette for approximately 15 s, after which the pipette was placed in the EPR cavity. All spectra in Section 3 display the same intensity scale. Note that a first measurement was made immediately and was repeated after 5 and/or 10 min to verify that the signal was quite stable. As it was, the presented spectra were considered representative of the system during the first 10 min.  
Three complementary methods were used: simple spin-trapping (DMPO), spin-trapping after scavenging with DMSO (DMSO + DMPO), and after scavenging with HCOO\({}^{-}\) (HCOO\({}^{-}\) + DMPO).  
Depending on the method and experimental conditions used to measure the rate constants in the literature, k(DMPO/HO\({}^{\bullet}\)) ranges from 1.9 \(\times\) 10\({}^{9}\) to 4.3 \(\times\) 10\({}^{9}\) M\({}^{-1}\)s\({}^{-1}\)[13; 18; 22].  
The scavenging reactions of dimethyl sulfoxide and formate are the following:  
\[(\mathrm{CH_{3}})_{2}\mathrm{SO}\;+\;\mathrm{HO^{\bullet}} \rightarrow \mathrm{CH_{3}SO(OH)}\;+\;\mathrm{CH_{3}^{\bullet}}\xrightarrow{ \mathrm{DMPO}}\mathrm{DMPO}-\mathrm{CH_{3}^{\bullet}}(\mathrm{CH_{3}})_{2} \mathrm{SO}\;+\;\mathrm{HO^{\bullet}}\] \[\rightarrow \mathrm{CH_{3}SO(OH)}\;+\;\mathrm{CH_{3}^{\bullet}}\xrightarrow{ \mathrm{DMPO}}\mathrm{DMPO}-\mathrm{CH_{3}^{\bullet}}\]  
where k(DMSO/HO\({}^{\bullet}\)) = 4.2 \(\times\) 10\({}^{9}\) to 7.1 \(\times\) 10\({}^{9}\) M\({}^{-1}\)s\({}^{-1}\)[23; 24; 25].  
HCOO\({}^{-}\)\(+\;\mathrm{HO^{\bullet}}\)\(\rightarrow\)\(\mathrm{H_{2}O}\)\(+\;\mathrm{COCO^{\bullet-}}\xrightarrow{\mathrm{DMPO}}\mathrm{DMPO}-\mathrm{COO^{\bullet-}}\)  
where k(HCOO\({}^{-}\)/HO\({}^{\bullet}\)) = 2.5 \(\times\) 10\({}^{9}\) to 2.9 \(\times\) 10\({}^{9}\) M\({}^{-1}\)s\({}^{-1}\)[18; 26]. The rate constants of formic acid vary with pH: in neutral aqueous solution at room temperature, k(HCOOH/HO\({}^{\bullet}\)) = 2.45 \(\times\) 10\({}^{9}\) to 3.1 \(\times\) 10\({}^{9}\) M\({}^{-1}\)s\({}^{-1}\)[24]; at acidic pH, k(HCOOH/HO\({}^{\bullet}\)) = 5 \(\times\) 10\({}^{9}\) to 6.8 \(\times\) 10\({}^{9}\) M\({}^{-1}\)s\({}^{-1}\)[26].  
Some EPR experiments were also carried out at 100 K to study copper complexation. The instrument settings were the following: receiver gain 30 dB, modulation amplitude 4 G, attenuation 10 dB. In this case, the samples were introduced in EPR quartz cells of 4 mm outer diameter and 0.5 mm wall thickness.
# Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem
## 4 Genetic Programming Aided Optimization of Conceptual Aircraft Design  
Based on the multidisciplinary analysis described in Section 2, a preliminary aircraft design can be conducted using conventional methods. To overcome the low computational efficiency of traditional sequence approaches, multidisciplinary analysis and optimization (MAO) was used to apply the above theoretical principles in a multidisciplinary context across the disciplines of aerodynamics, structure, aircraft flight dynamics, etc. for efficient aircraft designs in this paper. However, the aircraft multidisciplinary design is actually a multi-parameter optimization problem, it is very difficult to predict and evaluate the influence of design variables on aircraft performance because there are many parameters. Based on this discussion, genetic programming, as a new artificial intelligence  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Variables** & **Lower Bound** & **Upper Bound** \\ \hline X1, Inboard taper ratio & 1.457 & 1.781 \\ X2, Outboard taper ratio & 3.658 & 4.471 \\ X3, Inboard aspect ratio & 0.937 & 1.146 \\ X4, Outboard aspect ratio & 2.509 & 3.067 \\ X5, Sweep angle (degree) & 24.651 & 30.130 \\ X6, Inboard dihedral angle (degree) & 4.346 & 5.312 \\ X7, Outboard dihedral angle (degree) & 0.645 & 0.789 \\ X8, Axis position (root) & 0.306 & 0.374 \\ X9, Axis percent (tip) & 0.338 & 0.413 \\ X10, Axis percent (kink) & 0.389 & 0.476 \\ X11, Vertical bending (a) & 0.600 & 2.000 \\ X12, Vertical bending (b) & 0.600 & 2.000 \\ X13, Vertical bending (c) & 0.600 & 2.000 \\ X14, Horizontal bending (a) & 0.600 & 2.000 \\ X15, Horizontal bending (b) & 0.600 & 2.000 \\ X16, Horizontal bending (c) & 0.600 & 2.000 \\ X17, Torsional (a) & 0.800 & 1.200 \\ X18, Torsional (b) & 0.800 & 1.200 \\ X19, Torsional (c) & 0.800 & 1.200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bounds of 19 design variables.  
Figure 1: Genetic algorithm flowchart.  
technique, is proposed to build mathematical expressions representing the relationship between the input parameters and output responses of the interests. Once this metamodeling process is completed, the optimal solution describing the aircraft configurations will be obtained with less computational cost by replacing the full model with a GP metamodel. The process of this metamodel-based MAO for the optimal design consisted of four steps and they were:  
1. Design of experiments
2. Metamodel building by genetic programming (GP)
3. Parameterized finite element model
4. Optimization formulation, design variables and constraints  
These four steps were integrated to perform the multi-parameter, multidisciplinary optimization of the conceptual aircraft design and explained in the following sub-sections.
## 3 Test Results and Discussion
### Structural Behaviour  
The results of the out-of-plane and axial deformation of the tested walls are summarised in Table 2 and compared against the limit values given in [18]. The fire resistance of the load-bearing fire wall was deemed to have failed if the limit deformations in the fire test were exceeded. The horizontal deflection, axial deformation, and the rates of change of these factors in each wall were far below the allowable values.
## 8 Seismic Response Analysis of the Curved Beam Bridge
### Separated Foundation-Bridge System Finite Element Model  
In order to consider the spatial variation effect of the ground motion, the foundation of the model was separated, and a separated foundation-bridge system model was established as shown in Figure 12. The curved beam bridge model and near-field soil were simulated by B31 beam element and solid element C3D8R in ABAQUS, respectively. For the pier, a uniaxial hysteresis model of concrete and steel materials was used based on implicit calculation [31]. The concrete constitutive was based on the Kent-Park model [32]. The ultimate stress was \(0.2 Kf_{c}^{\prime}\), and the corresponding ultimate strain was \(\varepsilon_{ctu}=0.004+0.9\rho_{s}\big{(}f_{yh}/300\big{)}\), \(K=1+\rho_{s}f_{yh}/f_{c}^{\prime}\); Here, \(\rho_{s}\) represents the volume-stirrup ratio; \(f_{yh}\), the yield strength of the stirrup; and \(f_{c}^{\prime}\), the axial compressive strength. For the steel, a double-fold line model was used. The platform was modeled by a rigid body. The mass of the platform is added to the gravity center of it. The foundation piles were assumed as elastic and were simulated using elastic beam elements. The connection unit was used to simulate the connection between the beam and lower structure; the connection unit was assigned a proper hysteresis relationship. The yield force of the movable support was \(F_{y}=\mu N\); where \(\mu\), the sliding friction coefficient, was 0.02. The vertical force \(N\) of the support was obtained by static analysis. The beam unit mesh of the area where the stress was concentrated near the beam and pier and the pier and platform was encrypted. The dimension of the separated foundation soil was \(10\times 10\times 60\) m, and that of the grid unit was \(1\times 1\times 1\) m. This is the same dimension as the one shown in Figure 7. The viscoelastic artificial boundary element was introduced at the same time [22]. The initial elastic modulus of the soil in the low strain state was \(E_{i}\)= \(2\rho_{i}V_{si}^{2}(1+\
u_{i})\), where \(\rho_{i}\) is the \(i\)th layer density; \(V_{Si}\) is the \(i\)th layer shear wave velocity; and Rayleigh damping (\(\alpha=0.3\), \(\beta=0.0005\)).
## 5 Results and Discussion
### Convergence Analysis  
The inertia weight is frequently introduced to improve the exploration and exploitation of metaheuristic algorithms. This is the motivation for the application of the IW-ABC here. The contribution of inertia weight can be observed using the convergence curve for a selected time instance.  
The convergence of the comfort index and energy consumption of the IW-ABC with exponential, linear increasing, and linear decreasing are compared with the original ABC in Figure 9. It could be observed (Figure 9a,e) that the original ABC suffers from fast convergence and imbalanced exploration and exploitation. The original ABC started to converge even before the 10 th iteration.  
The convergence of the linear increasing IW-ABC (Figure 9c,g) is delayed the most followed by linear decreasing (Figure 9b,f) and exponential (Figure 9d,h). This is desired as it balances between exploration during the first half of the iteration and exploitation in the later half.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**WBV**} & \multicolumn{2}{c}{**Sham**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{2-2} \cline{4-6}  & **Before** & **Post** & & & **\(p^{\star}\)** \\ \hline Plant right foot & 29.7 (\(\pm\)1.83) & 26.6 (\(\pm\)2.27) & 0.01 \({}^{\star}\) & 28.6 (\(\pm\)1.84) & 26.3 (\(\pm\)2.49) & 0.01 \({}^{\star}\) & 0.12 & 0.78 \\ Left foot plant & 26.9 (\(\pm\)2.33) & 26.7 (\(\pm\)1.89) & 0.84 & 28.7 (\(\pm\)2) & 26.3 (\(\pm\)2.21) & 0.05 & 0.18 & 0.67 \\ Back right foot & 29.6 (\(\pm\)1.64) & 29 (\(\pm\)1.7) & 0.33 & 30.2 (\(\pm\)1.93) & 29.2 (\(\pm\)1.23) & 0.07 & \(-\)0.16 & 0.77 \\ Back left foot & 27.9 (\(\pm\)1.85) & 29.3 (\(\pm\)1.06) & 0.06 & 30.2 (\(\pm\)1.93) & 29.7 (\(\pm\)1.25) & 0.30 & \(-\)0.32 & 0.45 \\ Anterior right leg & 30.1 (\(\pm\)1.59) & 30.1 (\(\pm\)1.59) & 0.52 & 30.3 (\(\pm\)1.06) & 30.3 (\(\pm\)1.06) & 0.48 & \(-\)0.19 & 0.74 \\ Anterior left leg & 30.1 (\(\pm\)1.52) & 30.1 (\(\pm\)1.52) & 0.21 & 30.4 (\(\pm\)1.26) & 30.4 (\(\pm\)1.26) & 0.36 & \(-\)0.24 & 0.64 \\ Posterior right leg & 30.4 (\(\pm\)0.97) & 30.3 (\(\pm\)1.06) & 0.80 & 30.4 (\(\pm\)9.7) & 30.4 (\(\pm\)0.84) & 1 & 0.12 & 0.82 \\ Posterior left leg & 30.6 (\(\pm\)0.97) & 30.3 (\(\pm\)1.34) & 0.54 & 30.5 (\(\pm\)1.08) & 30.6 (\(\pm\)0.84) & 0.76 & \(-\)0.32 & 0.56 \\ \hline \end{tabular} Data are expressed as mean (\(\pm\) standard deviation). Abbreviations: WBV, whole-body vibration; sham, dummy vibration. \(p^{\star}\), Wilcoxon; \(p\)-value, Mann-Whitney; \({}^{\star}\) statistically significant result.  
\end{table} 
Table 1: _Cont._There was a reduction in blood flow in the WBV group after the intervention, but there was only a statistically significant reductionin the left tibial artery, from 27.1 m/s (\(\pm\)25.36) to 20.5 m/s (\(\pm\)19.66), \(p\) = 0.01. In the sham group, there was only a reduction in flow velocity in the left dorsal artery and an increase in all other evaluated arteries. However, none of them had a statistically significant difference, just as there was no statistically significant difference between the groups in this variable. Alow effect size was also observed for this variable. The complete analysis of blood flow velocity is detailed in Table 3.
## 5 Conclusions  
To improve the accuracy associated with the quantitative analysis of surface elements using hyperspectral satellite images, we propose an HGF fusion method, which mainly integrates guided filtering and harmonic analysis. The proposed method was validated on two sets of hyperspectral satellite images, including GF-1 and GF-5, ZY1-02D and Sentinel-2B. The following conclusions are drawn: (1) The fused images were evaluated for soil, buildings, vegetation, and water bodies. The GS fusion techniques performed the best in the 390-730 nm interval and HGF performed the best for the 730-1400 nm and 1400-2260 nm intervals. (2) Different objects were targeted for studying surface elements, and the spectral ranges selected for their quantitative analysis were different. The HGF or GS method can be selected for performing a quantitative study on vegetation and water bodies, while the HGF method proves to be advantageous for a quantitative analysis of soil elements.
## 2 Theoretical and Computational Models
### Principle and Application of Antilons Digging Resistance Reduction
#### 2.3.1 Non-Smooth Surfaces and Vibration Resistance Reduction Mechanism
When the vibration source acts on the soil, the local area where the vibration source is in contact with the soil changes from a solid state to a fluidized state, reducing the cohesion \(c\) of the soil in the local area, or the cohesion \(c\) even disappears completely, and the local soil changes to a \"liquefied\" state. At this time, the shear line changes to OA, and the shear  
Figure 5: The relationship between Mohr–Coulomb failure criterion and soil vibration shear state. stress decreases from \(\tau_{0}\) to \(\tau_{1}\). Further, the vibration is able to change the soil particle size nested state and reduce the internal friction angle of the soil from \(\varphi_{0}\) to \(\varphi^{\prime}\), the shear line from OA to OB, and the shear stress from \(\tau_{1}\) to \(\tau_{2}\). The area enclosed by the static shear line and vibration shear line OB is the vibration shear zone, and the shear failure of the soil in this area generally has a resistance reduction effect. Although vibration is able to achieve drag reduction, the vibration source itself consumes extra energy, and the total energy consumed during soil shearing is usually higher than before vibration. The existing vibration subsoiling mechanism connects the rear end of the tractor power take-off shaft to the crank connecting rod, cam, crankshaft, and other structures [35], which produces a vibration effect. This way, the vibration itself produces great energy consumption and increases the total energy consumption while reducing resistance. Additionally, the vibration frequency is low, so the effect of resistance reduction is limited. This study took an electronic vibration motor as the vibration source, which not only produced relatively high vibration frequency but also avoided the rigid connection between the vibration block and soil-contacting components; it could also reduce the total energy consumption during vibration resistance reduction.
## Materials and Methods
### Landuse Information  
In order to obtain more finely detailed information regarding urban land-use, different land-use classes can be identified through combining multiple data sources such as aerial orthophotos, LiDAR data, and a publicly accessible database. This is the first configuration of the input land-use data for the TRENOE platform (Figure 5a), which is noted as \"detailed land-use\". In detailed land-use, the road surfaces are traced by using aerial orthophotos; as a result, the sidewalks can be identified automatically as the areas between buildings and roads. Then, inside every cadastral plot, the complement of buildings was considered. Orthophoto identification was applied in order to classify those parts into different classes such as bituminous surface, lawn, and vegetation. LiDAR data was used for recognizing low vegetation from trees. Moreover, an automatic image segmentation tool [22] was applied to aerial orthophotos for recognizing different types building roofs, such as flat, slopping, and tiled roofs. As for the overlapped areas within different data sources, a specific priority order of land-uses is defined by considering the hydrologic connectivity in urban landscapes, followed by roads, sidewalks, parking lots, various types of roofs, trees, grass, and others. Additionally, since the manholes have a key role in the coupling between the 2D surface and 1D network models, they are represented as a specific class of land-use in the TRENOE model.  
The second configuration of the input land-use data is simply extracted from the BD-TOPO(r) database, which is free access and much easier to achieve than the multiple data sources; this data is noted as \"coarse land-use\" (Figure 5b). The BD-TOPO(r) database only contains the information of buildings that are more than 20 m\({}^{2}\), and the urban public green spaces. The undefined areas that are considered impermeable zones include roads, sidewalks, and parking lots.
## 2 Materials and Methods
### LEACH (Low-Energy Adaptive Clustering Hierarchy)
#### 2.3.2 Clustering in LEACH  
Clustering means defining a data group by considering the characteristics of given data and finding a representative point that can be represented. In LEACH, the sensor detects the events and then sends the collected data to a faraway BS. Because the cost of information transmission is higher than the calculation, nodes are clustered into groups to save energy. It allows data to be communicated only to the CH, then the CH routes the aggregated data to the BS. The CH, which is periodically elected using a clustering algorithm, aggregates the data collected from cluster members and sends it back to the BS, which can be used by the end user. To transmit data over long distances in this way, only a few nodes are required, and most nodes need to complete short distance transmissions. So, more energy is saved, and the WSN life is extended. The main idea of a hierarchical routing protocol is to divide the entire network into two or more hierarchical levels, each of which performs different tasks [20]. In order to create these hierarchical levels, the clustering functions as a critical role in LEACH.
# Practical Performance Analyses of 5G Sharing Voice Solution
## 5 Performance Analysis
### SA Sharing VoNR Performance Analysis  
#### 5.4.1 Comparison of VoNR and EPS Fallback  
The comparison test based on Section 4.2.1 handover-based EPS fallback and Section 4.3 VoNR was carried out with the following results in Figure 17.  
Figure 16: Delay of EPS fallback based on handover/redirection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Solution**} & **Average Call** & \multirow{2}{*}{**Handover Delay**} & \multirow{2}{*}{**Refirection Delay**} & \multirow{2}{*}{**Success Rate**} \\  & **Setup Delay** & & & \\ \hline Handover & 3082 ms & 52 ms & - & 100\% \\ \hline Redirection & 3911 ms & - & 885 ms & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: EPS fallback call setup delay.  
The call setup delay of EPS Fallback-EPS Fallback call is about 1102 ms longer than that of the VoNR-VoNR call. The control plane delays are also generally longer compared to the VoNR-VoNR call. As can be seen from the setup flow in Figure 8, the VoNR voice setup process has no handover or redirection process compared to EPS fallback. After SIP invite, it enters the process of QCI1 bearer information distribution, and upon completion, it initiates SIP 180 ringing. Compared to EPS fallback, it eliminates TAU and other related delays. Therefore, the VoNR setup delay will be much shorter than that of EPS fallback.  
At the same time, the MOS of VoNR is better than EPS fallback. Therefore, from the perspective of time delay or user experience, VoNR is the more recommended voice solution.
# Transforming Monochromatic Images into 3D Holographic Stereograms Through Depth-Map Extraction
## 3 Proposed Method
### Acquisition of Perspective Images  
The generation of perspective images is essential for synthesizing holographic stereograms. These images are fundamental for achieving depth-dependent parallax effects, which are necessary for the accurate holographic visualization of a scene. In our approach, perspective images are generated from color-texture data, with the depth map providing the spatial information required to reconstruct multiple viewpoints. This process ensures that the holographic representation remains realistic by accurately portraying the relative positioning of the objects within the scene. Given that holograms are highly sensitive to the quality of input images, it is imperative to choose an appropriate method for generating the perspective images.  
Holographic stereograms are traditionally created using three main steps: acquisition of perspective images, transformation of these images into parallax-related data, and holo  
Figure 3: 3D reconstruction and perspective-image generation using colorized image and depth map in Adobe After Effects.  
gram encoding. A 3D scene was constructed in After Effects, and perspective projections were captured using an array of virtual cameras arranged linearly. Crucially, this array employs a recentering camera model: each virtual camera not only translates along the linear path but also rotates to maintain focus on a fixed point at the center of the intended hologram surface (as depicted in Figure 4). This minimizes the required camera field of view, potentially improving data quality. The perspective views are then exported directly as a JPEG sequence, where each image represents a distinct viewpoint and contains the required parallax information. This approach bypasses the traditional computationally intensive step of transforming perspective images into parallax-related data. Finally, this sequence of multiview images was used for hologram encoding.
###### Abstract  
Introduction: Pediatric ischemic stroke (IS) is a rare but severe neurological emergency, with an incidence of 2-13 per 100,000. Most cases occur in the prenatal period or early infancy. Integrating artificial intelligence (AI) into clinical practice may enhance the early recognition of stroke. This pilot **study aimed** to identify immunoenzymatic markers as early predictors of pediatric IS, supporting machine learning applications. **Materials and Methods:** A prospective study (2017-2019) in Moldova included 53 children with IS and 53 healthy controls. The serum levels of vascular endothelial growth factor (VEGF), ciliary neurotrophic factor (CNTF), the S100B protein, CD105 (endoglin), antiphospholipid antibodies (APAs), and interleukin-6 (IL-6) were measured using ELISA during the acute phase. **Results:** Endoglin levels were significantly lower in IS patients (2.06 \(\pm\) 0.012 ng/mL) vs. controls (2.51 \(\pm\) 0.071 ng/mL) (\(p\) \(<\) 0.001). S100B levels were elevated (0.524 \(\pm\) 0.0850 ng/mL vs. 0.120 \(\pm\) 0.0038 ng/mL, \(p\) \(<\) 0.01). VEGF levels were significantly increased (613.41 \(\pm\) 39.299 pg/mL vs. 185.50 \(\pm\) 12.039 pg/mL, \(p\) \(<\) 0.001), correlating with the infarct size and disease severity. CNTF levels were also higher (7.84 \(\pm\) 0.322 pg/mL vs. 5.29 \(\pm\) 0.067 pg/mL, \(p\) \(<\) 0.001). APA levels were elevated (1.37 \(\pm\) 0.046 U/mL vs. 0.92 \(\pm\) 0.021 U/mL, \(p\) \(<\) 0.001). IL-6 levels were 10 times higher in IS patients (22.02 \(\pm\) 2.143 pg/mL vs. 2.38 \(\pm\) 0.302 pg/mL, \(p\) \(<\) 0.001), correlating with the infarct size (\(p\) \(<\) 0.004) and neurological prognosis at six months (\(p\) \(<\) 0.01). **Conclusions:** IL-6, VEGF, CNTF, S100B, CD105, and APAs are key markers in pediatric IS, reflecting neuroinflammation, vascular disruption, and the long-term prognosis. Their integration into AI-driven diagnostic models may improve early stroke detection and pediatric monitoring.  
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