esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Subject-specific boundary conditions, i.e., muscle forces and ground reaction force, determined by gait analysis and a subsequent musculoskeletal multibody simulation, were applied to the FE model. The individual bones were reconstructed from magnetic resonance imaging (MRI) data and ligaments, plantar fascia and muscle connectors were considered. For clinical practice, this would be too labour-intensive and time-consuming, and would not represent a reduction in workload. On the other hand, [PERSON] et al. [33] used simplified boundary conditions, but only analysed the plantar pressure distribution during the stance phase. In line with [PERSON] et al. [32], a very detailed foot model, i.e., individual bones, cartilage, ligaments, skin, and soft tissues, reconstructed from CT data, were considered. Contrarily, [PERSON] et al. [34] used a simplified foot model, i.e., bones of the foot, reconstructed from CT data, which were merged as one geometry. However, they also used simplified boundary conditions for stance and not for gait. These examples show the need for a simplified model with simplified structures and subject-specific boundary conditions of walking, which can be used for additive manufacturing in this field to reduce the amount of work and time required. Reviews have also highlighted the need for an established method for clinical application [22; 23]. In addition, there have been few studies that adjusted the stiffness of 3D-printed insoles using the FE method [32; 34]. Therefore, our present study aimed to develop an efficient FE model of the foot that enables the stiffness adjustment of an individual insole and its subsequent additive manufacturing. The stiffness adjustment should be based on the plantar pressure distribution during normal walking. For this purpose, ankle joint reaction forces and moments from a subject were integrated into the FE model based on a 3D gait analysis with subsequent musculoskeletal multibody modelling.
# Learning-Assisted Rain Attenuation Prediction Models
###### Abstract  
Rain attenuation becomes significant to degrade the earth-space or terrestrial radio link's signal-to-noise ratio (SNR). So, to maintain the desired SNR level, with the help of fade mitigation techniques (FMTs), it needs to control transmitted signals power considering the expected rainfall. However, since the rain event is a random phenomenon, the rain attenuation that may be experienced by a specific link is difficult to estimate. Many empirical, physical, and compound nature-based models exist in the literature to predict the expected rain attenuation. Furthermore, many optimizations and decision-making functions have become simpler since the development of the learning-assisted (LA) technique. In this work, the LA rain attenuation (LARA) model was classified based on input parameters. Besides, for comparative analysis, each of the supported frequency components of LARA models were tabulated, and an accurate contribution of each model was identified. In contrast to all the currently available LARA models, the accuracy and correlation of input-output parameters are presented. Additionally, it summarizes and discusses open research issues and challenges.  
Keywords:rain attenuation; artificial neural network; back-propagation algorithm; machine learning algorithm +
Footnote †: journal:  
system operate properly, accessing the possibility of terrestrial or inclined links can be improved with an efficient rain attenuation model. It is also possible to avoid overestimating the transmission system's necessary power by the estimation through the rain attenuation model.  
Besides, it is important to meet the spectrum management regulatory organization specification in each frequency range to comply with the permissible requirements for power transmission. In the case of these requirements not being met, the transmitted signal power can interfere with another neighboring frequency band. This intrusion can cause disruptions to neighboring telecommunications equipment.  
To the best of our knowledge, in the literature, there exists no survey paper on the learning-assisted rain attenuation model, and this paper presents a comparative analysis of all the available learning-assisted rain attenuation models, such as a classification based on the number of input features, earth-space or terrestrial application cases, advantages, disadvantages, and probable future development. Consequently, the main objective of this paper was to provide the reader with a survey on the existing LARA model, to show the available model's possible future improvement scope/scopes, and to focus on the future direction for improving the LARA model.
# Assessing of Losses and Dilution Impact on the Cost Chain: Case Study of Gold Ore Deposits
## 3 Results
According to the flow chart in Figure 3, transportation of the valuable component from the quarries to the intermediate stockpile is carried out using heavy trucks, then a blended rock mass with a standard (for the GPP) grade of gold is formed in the stockpile, averaged ore is rehandled by the loaders and transported to the GPP receiving hopper. The stages of transportation, blending and stacking use different types of equipment and vehicles. The cost of ore transportation is defined as a sum of tariffs across the stages of ore transition from the quarries to the intermediate stockpile and GPP. On a premise of increasing gold grade in the ore (due to changes in loss and dilution values), it is assumed that the number of blending, stacking, and partial rehandling operations will decrease. This implies reduction of operating costs due to changes in the workspace (Qij) at the stages of ore transportation, stacking and blending. Costs of stage 2--rehandling and formation of an intermediate stockpile--are not included in the transportation costs, a calculation unit of handling operations is 1 t of cargo or 1 hr of operations. Based on the assumption that it is possible to transport a part of the ore mass to the GPP excluding the blending stage, the economy of operating costs forms due to changes in the workspace at stages 2 and 3 (Figure 3)--ore blending and its transportation to the GPP. Figure 3: Costs chain of the production cycle. Simplified flow chart of ore transportation from the quarry to the intermediate stockpile for blending and to the GPP receiving hopper. Source: compiled by the authors. Estimation of economic efficiency is presented as an assessment of workspace susceptibility of the operating costs economy, caused by implementation of the project on orebody outlines adjustment. It is assumed that the workspace equals annual amount (6.0 million tons) of ore mass transportation to the GPP, estimation of economic efficiency is carried out for two types of equipment (CAT- R1600 H, CAT834), economy of operating costs is defined in the interval of workspace variation from 5% to 30%, Table 4.  
Analysis of project susceptibility indicators demonstrates that provided that amounts of extracted and transported ore fluctuate between 5% and 30%, project efficiency due to reduced annual workspace of ore blending and loading can vary in the interval 191.25-1147.5 $.
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 3 Results
### Thermal Viscosity
The results indicate that as the deflection angle \(\theta\) of the transducer gradually increases, the temperature range formed at the interface between the tissue and glass on the axial section also increases, with the maximum temperature during vertical incidence exceeding \(80\)\({}^{\circ}\)C presented in Figure 11. When the deflection angle of the transducer is \(15^{\circ}\), the temperature decreases by about \(6\)\({}^{\circ}\)C compared to vertical incidence. When the deflection angle is \(30\)\({}^{\circ}\)C, the temperature decreases by approximately \(10\)\({}^{\circ}\)C. Thus, with the continuous increase in the deflection angle, the temperature correspondingly decreases. This trend indicates that oblique incidence enhances the shear wave effect, significantly reducing the temperature in the tissue and expanding the temperature rise range on the surface of the tissue. As shown in Figure 10, at the same deflection angle, the temperature on the surface of the tissue is higher due to the influence of thermal viscosity. Compared to Figure 11, there is almost no change in the maximum temperature under vertical incidence as shown in Figure 12. When thermal viscosity is not considered, the maximum temperature decreases  
Figure 8: 3D oblique incidence acoustic, solid coupling model. as the deflection angle of the transducer increases. However, this linear change is no longer required, considering the thermal viscosity. Up to 3 s, the order of temperatures from high to low was 45\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), and 0\({}^{\circ}\). Between 3 to 3.5 s, the order shifted to 45\({}^{\circ}\), 15\({}^{\circ}\), 0\({}^{\circ}\), and 30\({}^{\circ}\). From 3.5 s to 5 s, the values from high to low were 45\({}^{\circ}\), 0\({}^{\circ}\), 15\({}^{\circ}\), and 30\({}^{\circ}\), respectively. The results indicate that the effect of the deflection angle on the temperature varies at different times and powers. When considering the thermal viscosity, 45\({}^{\circ}\) has the greatest impact on the temperature. Figure 11: Temperature rise curves corresponding to different incident angles when considering shear waves. Figure 9: Temperature distribution corresponding to different incident angles when considering shear waves (0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), respectively). Considering the thermal viscosity loss based on oblique incidence, the temperature distribution on the glass surface changes. Oblique incidence has an enhanced effect on the acoustic, thermal conversion of the thermal viscosity phenomenon.
# Maximizing the Capacity of Edge Networks with Multicasting
## 6 Optimal Number of Copies
### Optimal Number of Copies in the Last Hop Multicast  
For convenient calculation, the optimization objective for Equation (6) becomes  
\[min\left(\sum_{i=1}^{m}\frac{Q_{1}p_{i}}{C_{i}(m-s)}\right). \tag{9}\]  
_s.t._  
\[\left\{\begin{array}{l}C_{i}\leq\ n\\ \sum\limits_{i=1}^{m}C_{i}\leq\ ns\end{array}\right.\]  
**Theorem 3**.: _In the proposed multicast cooperation model, the optimal solution of Equation (9) is_  
\[C_{i}=\begin{cases}0&p_{i}\leq 0\\ \sqrt{\frac{Q_{1}p_{i}}{\beta(m-s)}}&0<p_{i}<\frac{n^{2}\beta(m-s)}{Q_{1}}.\\ n&p_{i}\geq\frac{n^{2}\beta(m-s)}{Q_{1}}\end{cases} \tag{10}\]  
The proof of Theorem 3 is placed in Appendix C.
# Wheat Bread with Dairy Products--Technology, Nutritional, and Sensory Properties
## 3 Results and Discussion
### Evaluation of Bread Properties
#### 3.2.3 Microstructure of Doughs and Breads  
Environment scanning electron microscopy was used to evaluate the microstructure of the doughs, obtained with different levels of yoghurt and curd cheese (30 g and 50 g), after 60 min of fermentation at 30 \({}^{\circ}\)C (Figure 5A1-A5). The microstructure of the breads crumb was also observed (Figure 5B1-B5). Figure 5A1-A5 show that dough is made up of the gluten network with small and large starch granules characteristic of the wheat dough.  
Comparing the Yg doughs, the dough with 30 g and 50 g (Figure 5A2,A3) displayed a remarkable gluten film, with defined glutenins strands when compared to control dough, which is more evident at 30 g of Yg addition. These results are in agreement with those obtained on the extension properties, supporting the improvement of the flexibility of the net responsible for the increase on the extensibility and deformation energy values (Figure 1B,C). With Cc additions, the gluten film became less notable, as shown in Figure 5 (\(A\)4--30 g and A5--50 g of Cc addition), revealing that denatured whey protein interfered with the development of the gluten network, reducing the extensibility of the net (Figure 1B) and thus affecting the gas retention. Therefore, with the structure morphology of these doughs, the reduction of the specific volume of the breads was expected, as observed (Figure 4B).  
Figure 5 (B1-B5) show the changes of crumb bread structure of the Yg bread (B2-B3) and Cc bread (B4-B5) compared to the control bread (B1). A more complex cell structure with higher number of gas cells between starch granules and denatured gluten in bread crumb with 30 g of Yg compared to the control is also noticed. This microstructural feature is associated with a lower staling rate, a low degree of firmness, and better bread volume [8]. For breads with 50 g of Yg addition, a more sheet-like structure was observed, probably due to the higher caseins interactions and most probably by the presence of exopolysaccharide that interact with the gluten network, leading to a more  
Figure 5: Scanning electron micrographs (400 \(\times\), scale bar = 200 um for dough; 150 \(\times\), scale bar = 500 \(\upmu\)m for bread crumb): A—fermmented dough (60 min/37 \({}^{\circ}\)C); **A1—**Control dough; **A2—**30 g Yg, A3—50 g Yg; A4—30 g Cc, A5—50 g Cc; B—breads: B1—control bread, B2—30 g Yg, B3—50 g Yg; B4—30 g Cc, B5—50 g Cc, at first day of storage time.  
homogeneous structure [21]. For the Cc addition, Figure 5B4,B5, a heterogeneous and disaggregated bread crumb is observed, which is a consequence of the interference of the denatured whey protein in the gluten network.  
The scanning electron microscopy images obtained for breads with Yg and Cc revealed that the interactions between dairy proteins with different nature and structures with gluten and starch matrix could partially explain and support the results obtained in dough extensibility and bread quality properties.
## 5 Results
### Ambient Vibration Results for 2022-2023
The division at 2.5 Hz aims to provide a more precise approach to analyzing the behavior of frequency dispersion as the system order increases. In Figure 11, the boxplots illustrate the variability within each order and allow for an evaluation of the potential impacts of dynamic modes that manifest below and above this established threshold. This separation is essential for interpreting how these modes may influence the overall structural behavior and for identifying possible correlations or interactions between the detected frequency groups. The graph presented in Figure 11 facilitates the analysis of the dispersion of identified frequencies, distinguishing two subsets based on the 2.5 Hz threshold. This separation underscores differences in the variability of frequencies across the different model orders. Frequencies exceeding 2.5 Hz exhibit greater dispersion compared to those below this threshold, suggesting the presence of dynamic modes or external factors that increase the complexity of the structural response in this range. Conversely, frequencies below 2.5 Hz display a more uniform dispersion, potentially indicating that the modes in this region are more stable or less affected by dynamic interactions. Figure 10: Frequency grouping and boundaries across model orders using k-means clustering. The numerous outliers observed in both subsets highlight the presence of frequencies that deviate significantly from the average behavior. For frequencies above 2.5 Hz, the outliers are more widely dispersed and appear across a greater number of orders, supporting the hypothesis that modes in this range are more sensitive to external influences or model complexity. In contrast, frequencies below 2.5 Hz show less dispersed outliers with greater uniformity, indicating a smaller deviation of modes from their central tendencies. As the model order increases, particularly for frequencies above 2.5 Hz, the boxplots reveal a trend toward greater dispersion. This pattern suggests that higher model orders capture additional modes or reflect more intricate structural responses due to increased model sophistication. Overall, this analysis elucidates the influence of dynamic modes across different frequency ranges and highlights how increasing the model order affects mode identification and frequency dispersion. These findings contribute to a deeper understanding of the structure's overall behavior and aid in identifying potential correlations or interactions among the detected frequency groups.
## 1 Introduction
### Injury Criterion  
In automotive crash safety assessment, injury criteria are quantitative metrics used to evaluate the likelihood of occupant injury under various impact conditions. Commonly used criteria include the Head Injury Criterion (HIC), which evaluates the risk of traumatic brain injury based on head acceleration; the Neck Injury Index (Nij), which combines axial force and bending moment at the neck; the Chest Deflection and Viscous Criterion (VC) for assessing thoracic injury; and femur loads and tibia indices for lower extremity evaluation. These criteria were originally developed for use in automotive crash testing to assess the risk of human injuries under high-acceleration, short-duration impacts. With the growing need to evaluate occupant responses in under-body blast (UBB) scenarios--especially due to landmine and IED threats--these criteria have been widely adopted in blast biomechanics research. HIC and Nij remain commonly used due to their simplicity and extensive validation history. NATO, for example, references HIC in its military vehicle occupant protection guidelines, including the HFM-090 [4] and the AEP-55 [5] for blast testing and evaluation. Moreover, finite element human body models, such as those used in studies funded by the NATO Research Task Groups, commonly utilize HIC and Nij as benchmark outputs to evaluate head and neck loading under under-body blast and overpressure scenarios [25]. While these criteria are not specifically designed for blast loading, their adoption reflects a practical need for standardized, quantitative injury measures in blast biomechanics.  
For under-body blast events, vertical acceleration of the head and neck axial compression are considered the principal contributors to injury. In consideration of space limitations, selected aspects of the vertical acceleration of the head and the axial compression force of the upper neck are considered in this work.
# A Novel L-Shaped Metalens for Ultra-Wide Band (UWB) Antenna Gain Improvement
## 2 Method and Theory  
The metalens is designed to improve a pristine AVA, consisting of two symmetrical, exponentially tapered patch flares printed on the opposite sides of the dielectric substrate. The equations reported in [5] have been used for the design of the exponentially tapered flares.  
Electromagnetic (EM) metamaterials are designed as periodic repetitions of elementary units with sizes significantly less than the wavelength of the propagating EM field. Therefore, the metalenses are inhomogeneous structures that can be globally considered as a homogeneous medium. Their EM properties can be described by the effective parameters, calculated considering the average of the local charge, current, and field distribution. Considering the state of the art, there are several approaches for modeling metamaterials [28; 29; 30]. The most largely employed is based on the evaluation of the volumetric effective electric permittivity and volumetric magnetic permeability [1; 2; 4; 8; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23]. This method requires the definition of an effective thickness \(d_{eff}\) related to volumetric effective parameters of the effective homogeneous layer modeling the metamaterial [28; 29; 30]. The metalens modeling has been performed by means of the SPRM approach, based on the assumption of layer homogenization. In this work: (i) the S-parameters have been numerically simulated with the commercial EM simulation software, CST Studio Suite(r); and (ii) the complex impedance \(Z_{eff}\), the complex refractive index \(N_{eff}\), the effective electric permittivity \(\varepsilon_{eff}\), and the effective magnetic permeability \(\mu_{eff}\) have been calculated from the simulated S-parameter, using the inversion algorithm based on the Kramers-Kronig relationship [28; 29]. The retrieved effective parameters have been used for the preliminary design. However, the coupling effects between the metalens and the antenna cannot be neglected. Therefore, the metalens integrated into the antenna substrate has been considered in the full-wave simulations performed for the optimization [29].
## 2 Modeling and Degradation Simulation
### Simulation Assumptions and Reproducibility  
To ensure reproducibility and transparency, the simulations were performed using a modular PV cell model based on bond graph methodology. The physical parameters used in the model--such as thermal capacitance, resistive losses, and current-voltage behavior--were selected based on standard datasheets for crystalline silicon PV cells and kept constant throughout all simulations. Each degradation mode was introduced by altering relevant parameters in the model, following established physical or empirical degradation mechanisms. For example, ageing effects on \(R_{p}\) and \(R_{s}\) were modeled according to [9], EVA discoloration was parameterized using the discoloration index defined in [10], and LID was modeled as an increase in photocurrent in line with [6]. Cracking-related leakage and current changes were modeled based on [12; 21; 22], while corrosion effects on series resistance followed the formulations in [11]. A theoretical outline for PID based on [7] is also included for future implementation.  
The simulation workflow involved varying environmental conditions, including multiple irradiance levels, ambient temperatures, and aging stages. Fault scenarios were independently simulated to isolate the behavior of each degradation mechanism. All simulations followed a uniform structure to generate time-series outputs (voltage, current, and temperature), which were post-processed to extract statistical and physically interpretable features.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Degradation** & **Parameter** & **Effect** & **Refs.** \\ \hline Ageing & \(R_{p}\downarrow,R_{s}\uparrow\) & Resistance shift & [9] \\ \hline EVA Discoloration & \(R_{p}\downarrow,R_{s}\uparrow\) & Transmittance loss & [10] \\ \hline LID & \(l_{cc}\uparrow\) & Saturation current rise & [6] \\ \hline Cracking & \(R_{p}\downarrow\) & Leakage current increase & [12; 21; 22] \\ \hline Corrosion & \(R_{s}\uparrow\) & Interconnection degradation & [11] \\ \hline PID & \(R_{p}\downarrow\), leakage \(\uparrow\) & Leakage current due to high-voltage-induced insulation breakdown & [7] \\ \hline \end{tabular}
\end{table}
Table 1: Summary of degradation effects.  
Figure 5: Power output of the PV system under different cell temperatures, illustrating the inverse relationship between temperature and efficiency at fixed irradiance.  
The neural network was trained on the resulting labeled dataset generated from these simulations. Although the precise implementation details (e.g., simulation step size, solver type, or software toolchain) are not the primary focus of this study, the overall methodology is modular and adaptable.