esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study the influence of TiC content on the mechanical and electrical properties of Al\({}_{2}\)O\({}_{3}\)-TiC composites containing 30 and 40 vol.% TiC were investigated. The Vickers hardness and fracture toughness of the composites increased with the addition of TiC phase. The composite with 40 vol.% TiC showed the highest flexural strength (687 \(\pm\) 39 MPa), fracture toughness (7.8 \(\pm\) 0.4 MPa\(\cdot\)m\({}^{1/2}\)) and hardness (22.3 \(\pm\) 0.3 GPa) with a homogeneous distribution of the second phase within the ceramic matrix. Besides enhanced mechanical properties, it was found that ceramic composites with more than 30 vol.% TiC fabricated by the spark plasma sintering possess sufficient electrical conductivity for electrical discharge machining as well. Therefore, they do not limit the flexibility of the shape, and any intricate parts can be easily made with these composites which can be recommended for the production of cutting inserts in the tools for machining of superhard hardened steels, hard-to-machine materials, composites and other materials used in mechanical engineering.  
alumina; titanium carbide; composites; spark plasma sintering; microstructure; mechanical properties; electrical conductivity; EDM +
Footnote †: journal: Applied Physics Letters
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 2 EMERGE Onboard System Architecture
### EMERGE Navigation System Implementation
#### 2.1.4 Power Management System  
The Power Management System is the element that ensures that the system stays on and continues to operate regardless of the state of the vehicle. It consists of two elements: a HW and a SW element. The HW element is the Sixfab Power Management; UPS HAT [27], while the SW element is implemented using a dedicated API: Sixfab Power Python API [28]. In short, it is an uninterruptible power supply with a built-in 18650-Li-Ion battery, allowing it to automatically switch from one power supply to another without causing a reboot or system failure. The device can enter deep sleep mode in power-sensitive applications and save battery energy.
# Simulation and Evaluation of the Radiation Dose Deposited in Human Tissues by Atmospheric Neutrons
###### Abstract  
The evaluation of the radiation dose (RD) deposited by atmospheric neutrons in human tissues is of vital importance due to the potential damages that over exposure to this radiation may cause to human health. The goal of this work was to obtain the RD that atmospheric neutrons with energy from 1 to 1000 MeV deposit in tissues of the human body (blood, adipose, bone and brain) as a function of both altitude and latitude. With the help of the Geant4 software, we developed a numerical simulation that allowed us to reach our goal; atmospheric neutron fluxes were obtained from the Excel-Based Program for Calculating Atmospheric Cosmic-Ray Spectrum (EXPACS). We found that the RD deposited by atmospheric neutrons increases with the increase in altitude and latitude, e.g., for an altitude of high mountain (4 km), the RD is increased \(\sim\)19 times; while, for an altitude of commercial flights (10 km), the RD is increased \(\sim\)156 times; in both cases, regarding the RD at sea level. We also found that, in the range of energies from 1 to 100 MeV, the RD deposited in the bone tissue sample is considerably lower that the RD deposited in the blood, adipose and brain tissue samples. On the other hand, for the range of energies between 200 and 1000 MeV, the RD deposited in the bone tissue sample is considerably greater that the RD deposited in the blood, adipose and brain tissue samples.  
atmospheric neutrons; radiation dose; passengers and flight crew; cosmic rays +
Footnote †: journal: Applic. Sci.
###### Abstract  
This paper studies the safety control problem for rotating spacecraft proximity maneuver in presence of complex shaped obstacles. First, considering the attitude change of the target spacecraft, a dynamic model of close-range relative motion in a body-fixed coordinate system is derived using a novel approach. Then, the Gaussian mixture model (GMM) is utilized to reconstruct the complex shape of the spacecraft, and a novel GMM-based artificial potential function (APF) is proposed to represent the collision avoidance requirement. By combining GMM-based APF with fixed-time stability methodology, a fixed-time control (FTC) is designed for close-range proximity operation to a rotating spacecraft having a complex shape. The presented GMM-FTC scheme can guarantee the convergence of relative state errors, and ensure that no collision occurs. Finally, simulation results are provided to illustrate the feasibility of the proposed control approach.  
fixed-time control; Gaussian mixture model; rotating spacecraft; complex shape obstacle; collision avoidance +
Footnote †: journal: Applied Sciences
###### Abstract  
An equation of state (EOS) of CH\({}_{4}\)-N\({}_{2}\) fluid mixtures in terms of Helmholtz free energy has been developed by using four mixing parameters, which can reproduce the pressure-volume-temperature-composition (_PVTx_) and vapor-liquid equilibrium (VLE) properties of CH\({}_{4}\)-N\({}_{2}\) fluid mixtures. The average absolute deviation of all the _PVTx_ data available up to 673.15 K and 1380 bar from this EOS is 0.38%. Combining this EOS of CH\({}_{4}\)-N\({}_{2}\) fluid mixtures and the EOS of CH\({}_{4}\)-CO\({}_{2}\) and CO\({}_{2}\)-N\({}_{2}\) fluid mixtures in our previous work, an EOS of CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) fluid mixtures has been developed, which is named ZMS EOS. The ZMS EOS can calculate all thermodynamic properties of ternary CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) fluid mixtures and the average absolute deviation of the _PVTx_ data from the ZMS EOS is 0.40% for the CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) system. The ZMS EOS can be applied to calculate excess enthalpies of CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) fluid mixtures, predict the solubility of CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) fluid mixtures in brine and water, and quantitatively estimate the impact of the impurities (CH\({}_{4}\) and N\({}_{2}\)) on the CO\({}_{2}\) storage capacity in the CO\({}_{2}\) capture and storage (CCS) processes. The ZMS EOS can also be applied to calculate the isochrones of CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) system in the studies of fluid inclusions. All Fortran computer codes and Origin drawing projects in this paper can be obtained freely from the corresponding author.  
CO\({}_{2}\)-CH\({}_{4}\)-N\({}_{2}\) system; equation of state; CCS; excess enthalpies; fluid inclusion Article
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
###### Abstract  
Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment for patients. Current classification models were primarily developed by electrocardiogram (ECG) signal databases, which may be unsuitable for local patients. Therefore, this research collected ECG signals from 60 local Thai patients (age 52.53 \(\pm\) 23.92) to create a classification model. The coefficient of variance (CV), the median absolute deviation (MAD), and the root mean square of the successive differences (RMSSD) are ordinary feature variables of RR irregularities used by existing models. The square of average variation (SAV) is a newly proposed feature that extracts from the irregularity of RR intervals. All variables were found to be statistically different using ANOVA tests and [PERSON]'s method with a p-value less than 0.05. The methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were also tested and compared to find the best classification model. Finally, SAV showed the best performance using the ANFIS model with trapezoidal membership function, having the highest system accuracy (ACC) at 89.33%, sensitivity (SE), specificity (SP), and positive predictivity (PPR) for NSR at 100.00%, 94.00%, and 89.29%, PAF at 88.00%, 90.57%, and 81.48%, and AF at 80.00%, 96.00%, and 90.91%, respectively.  
Keywords: atrial fibrillation (AF); ANN (artificial neural network); ANFIS (adaptive neuro-fuzzy inference system); RR interval irregularity; SAV (square of average variation) +
Footnote †: journal: Applied Sciences
# The Impact of Grafted Larvae and Collection Day on Royal Jelly's Production and Quality
## 1 Introduction
Prolonged exposure of RJ within queen cells may lead to enzymatic degradation, oxidation, or alterations in moisture and pH levels, which in turn influence shelf-life and biological efficacy [17; 18]. Key compositional markers such as 10-HDA, total protein, and pH have been shown to vary depending on the collection day, with later harvests sometimes associated with lower concentrations of bioactive compounds [7]. Moreover, microbial activity or larval metabolism can further modulate RJ characteristics during extended secretion periods. Understanding the kinetics of these changes is essential to define standardized collection protocols that ensure the highest quality of RJ for both scientific study and commercial application. Despite the importance of larval age and harvest timing, existing studies tend to isolate one variable or are limited by small sample sizes and inconsistent methodologies. As a result, there remains a critical need for controlled, systematic research that evaluates the combined effects of grafted larval age and RJ collection day on product quality. The purpose of this study is to systematically evaluate the combined effects of grafted larval age, sex and timing of RJ harvest on the physicochemical characteristics and yield of the final product. To address the above objective, this study was designed to test specific hypotheses regarding the relationship between key biological and management variablesand the quantity and quality of RJ. The following hypotheses were formulated to guide experimental design and statistical analysis:  
**H1. **: _The physicochemical properties and yield of RJ are independent of the time elapsed between grafting and RJ collection (i.e., 24, 48, or 72 h)._
**H2. **: _The physicochemical properties and yield of RJ are independent of the age of the larvae used for grafting._
**H3. **: _The physicochemical properties and yield of RJ are independent of the sex of the larvae used for grafting (worker vs. drone)._  
Conducting a controlled experiment with standardized rearing practices, this study seeks to identify optimal RJ production parameters that maintain the nutritional and functional integrity of the product and improve the reproducibility of findings in the Mediterranean beekeeping context.
## 6 Comparative Analysis with National Standards
### Temperature Curvature Comparison  
The comparison of \(T_{v}\) derived from the various temperature gradient models with the maximum vertical linear negative temperature differential \(T_{v,\ max}\) obtained from measurements is illustrated in Figure 20. It can be observed that the temperature gradient proposed in this study exhibits a maximum negative temperature differential of \(-4.0\)\({}^{\circ}\)C, deviating by \(+0.7\)\({}^{\circ}\)C compared to \(T_{v,max}\). This finding aligns more consistently with the values specified in current standards. In JTGD60-2015, the specified \(T_{v}\) of \(-5.3\)\({}^{\circ}\)C exceeds the measured maximum negative temperature differential by \(+2.0\)\({}^{\circ}\)C. AASHTO and BS5400 prescribe smaller \(T_{v}\) values compared to \(T_{v,\ max}\), with differentials of \(-0.4\)\({}^{\circ}\)C and \(-1.7\)\({}^{\circ}\)C, respectively. \(T_{v}\) directly influences temperature-induced bending moments in bridges. While employing the temperature gradient model from JTGD60-2015 can encompass the effects of such moments, it often proves excessively conservative, resulting in less cost-effective bridge designs. Conversely, utilizing the temperature gradient models from AASHTO and BS5400 fails to adequately account for actual temperature differentials, leading to overly aggressive designs and an increased risk of cracking during operational phases.  
Figure 19: Comparison of residual temperatures of box girder.
## 3 System Model and Problem Formulation
### Np-Complete Proof  
Theorem 1: _The cloud-edge caching problem is an NP-complete problem._  
Proof of Theorem 1.: If the set cover (SC) problem for the NP-complete set can be reduced to a cache placement problem, then we can prove that the decision problem for cache placement is NP-complete. Firstly, we define two sets, the first being set \(G=\{g_{1},g_{2},\ldots,g_{l}\}\), and the second being \(H=\{h_{1},h_{2},\ldots,h_{l}\}\), representing the subsets of \(G\), where each element \(h_{j}\) has a weight. The objective of the SC problem is to select a subset from H such that the union of all elements in the subset equals the set \(G\), and the sum of the weights is either minimized or maximized.  
The steps to simplify the SC problem to the cache placement problem are as follows: Firstly, set the number of contents to 1. Let \(G=\{g_{1},g_{2},\ldots,g_{l}\}\) represent all BSs, and let \(H=\{h_{1},h_{2},\ldots,h_{l}\}\) represent the different ways to content caching on BSs. For instance, \(h_{1}=\{1,3,5\}\) denotes caching content on \(g_{1}\), \(g_{3}\), and \(g5\), while \(h_{2}=\{2,4,6\}\) denotes caching content on \(g_{2}\), \(g_{4}\), and \(g_{6}\). Then, let the cache value and link cost respectively denote the weight of each subset.  
Therefore, our problem can be formulated as an SC problem as follows: we have a set \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) for all edge servers, and each element in the set \(C=\{c_{1},c_{2},\ldots,c_{K}\}\) represents all possible caching decisions for a piece of content on all BSs. For each caching decision \(c_{k}\), there is a cache hit efficiency. The cache hit efficiency of caching decision \(c_{k}\) is the content caching revenue obtained with \(c_{k}\). The objective of our problem is to find a subset \(C^{\prime}=\{c_{1},c_{2},\ldots,c_{K}^{\prime}\}\), which is a subset of \(C\), and the union of all elements in \(C^{\prime}\) equals \(V\) such that the sum of all the content caching revenues is maximized.  
The set cover problem can be simplified to the content caching placement problem through the transformation described above. By integrating practical variables into the framework of the set cover problem, we identified that these practical variables correspond to the general variables in the set cover problem. In essence, our problem takes into account the same factors as the set cover problem and can be considered a variation of it. Consequently, the data caching problem is classified as an NP-complete problem.
## 2 Materials and Methods
### Validation of Numerical Model  
#### 2.2.1 Grid Study  
A three-dimensional schematic of the computational domain and grid used for single-phase NF flow are presented in Figure 2. A nonuniform structured grid is employed for the HE with elliptical corrugated tubes. The grid is refined near the heat transfer walls (\(y^{+}\leq 1\)) to capture the thickness of the viscous sublayer in the considered Re range. As shown in Figure 3, various grid resolutions are used to inspect the impact of grid sizes on the outcomes. Three grid resolutions of 3,243,983 (Grid I), 3,728,623 (Grid II) and 3,954,131 (Grid III)) are generated. By comparing the outcomes, it was decided that the grid resolution of 3,728,623 is sufficient to obtain reasonable outcomes with a maximum deviation of 0.01% for simulation time.
# Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps
###### Abstract  
A new approach for modeling low frequency noise is presented to enable the predictions of noise behavior from negative bias temperature instability (NBTI). The noise model is based on a capture-emission energy (CEE) map describing the probability density function of widely distributed defect capture-emission activation energies. To enlarge the capture-emission energy window and to perform the accurate estimation of the recoverable component of CEE, the Gaussian mixture model (GMM) is applied to the CEE map. This approach provides an efficient identification of noise sources and an in-depth noise analysis under both stationary and cyclo-stationary conditions.  
low frequency noise; negative bias temperature instability; capture-emission energy map; gaussian mixture model +
Footnote †: journal: Applied Sciences
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 8 Ultrasound
### Segmentation  
[PERSON] et al. [73] used a combination of random forest with GoogleNet for the segmentation of thyroid nodules in ultrasound videos. Their model comprised two main steps. The first was image preprocessing, where they normalized and removed the artifacts from images. The second was data augmentation to prevent the model from overfitting. The model could only classify thyroid nodules into malignant and benign classes. It depended on a doctor's expertise to mark the region of interest (ROI), as the system did not automatically detect it. [PERSON] et al. [74] collected and annotated a dataset for COVID-19 containing lung ultrasound videos from 35 patients. They proposed a model based on a CNN, spatial transformers network, and soft ordinal regression to help medical professionals estimate the severity of COVID-19 by assigning pathological scores to the ultrasound video frames. They also explored UNet for pixel-level segmentation of COVID-19 pathological artifacts.
# Application of Virtual Reality for Learning the Material Properties of Shape Memory Alloys
## 3 System Design and Operation
### SMA Applications  
The learning unit 'SMA Applications' introduces two examples of real-life applications, i.e., the heat-driven motor and the vascular stent. The user can observe how the SMA material changes its shape and crystal structure with temperatures to understand the working principles. In the first example, the user can turn on the power supply to see the motor driven by the SMA spring, which is heated by the electric power and shrink to generate the tensile force (Figure 13). After the rotation by a small degree, the heated spring is disconnected from the power supply such that it cools down and becomes loose. However, the succeeding SMA spring is connected with the power supply; it is heated and shrinks to generate the tensile force for the motor to continue rotation.  
Figure 12: Virtual experiment: designing a ship in a bottle using the SMA wire.  
Figure 13: The heat-driven motor using SMA springs.  
The second example shows the application of SMA in vascular stents for dilating the blood vessel. Before the vascular stent is implanted into the blood vessel, the blood is stopped by clamps to keep the vascular at a lower temperature. The vascular stent is stretched to become a very thin piece for inserting into the blood vessel. After that, the clamps are removed and the blood is allowed to flow through the blood vessel. The blood temperature will trigger the phase transition and enable the vascular stent to restore its original shape for dilating the blood vessel (Figure 14).
## 3 Methodology
### Creating the Discretization Mesh  
After defining and graphically representing the analysis domain, the discretization phase is carried out. For two-dimensional studies, the domain is often segmented using triangular or quadrilateral elements, whereas for three-dimensional configurations, tetrahedra, hexahedra, triangular prisms, quadrilateral pyramids, or irregular polyhedra are employed. The application of discretization with irregular polyhedra significantly reduces the number of cells required to achieve higher accuracy [54], although the volume of nodes and, consequently, the computational resource demand remains substantial. Therefore, for the present model, regular cubic hexahedra were chosen. Therefore, the discretization was performed using regular hexahedrons in the form of cubes.  
The solution process for the system of equations requires locating the constituent elements of the mesh, composed of cells, nodes, edges, and faces, organized within a connectivity matrix. In the case of structured meshes, characterized by a uniform distribution of nodes, this matrix can be dynamically generated, avoiding the need for separate storage. Conversely, unstructured meshes become essential for modeling complex geometries or integrating imported meshes, allowing adaptive refinements based on results obtained during simulation.  
Regardless of the discretization technique adopted, a system of linear equations is obtained for steady-state problems, while a set of differential equations requiring linearization processes is necessary for unsteady phenomena analysis [55]. The solution of these systems can be achieved through either direct or iterative solving methods.  
The mesh inside the DSF varied for each of the 5 cases, as presented in Table 1, and was made fine and structured near the wall to satisfy the turbulence model condition (Y\({}^{+}\) = 1). This mesh becomes coarser away from the wall in order to optimize the computational time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Crt. Nr** & **Channel Width (D)** & **Number of Cells** \\ \hline
1 & 4 & 519,246 \\
2 & 6 & 742,000 \\
3 & 8 & 1,038,492 \\
4 & 10 & 1,260,000 \\
5 & 12 & 1,483,560 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of cells for each of the 5 configurations.