esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Performance of Underlay Device-to-Device Communications
## 2 State-of-the-Art
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(\mathbb{E}\{.\},\Pr(.)\) & Expectation and probability operators \\ \(h_{i,j}\) & Channel coefficient between transmitter \(i\) and receiver \(j\) \\ \(\Omega_{i,j}\) & Large-scale path-loss between transmitter \(i\) and receiver \(j\) \\ \(K_{0},c_{0}\) & Path-loss constant, speed of light \\ \(v,f_{c},\beta\) & Wavelength, carrier frequency, path-loss exponent \\ \(d_{i,j}\) & Transmission distance from node \(i\) to node \(j\) \\ \(P_{\text{tot}}\) & Total transmit power of D2D networks \\ \(P_{\text{D}_{k}}\) & Transmit power of the \(k\)-th D2D transmitter under \(s\) scheme \\ \(P_{C}\) & Transmit power of the cellular user \\ \(I_{k}\) & Aggregate interference at the \(k\)-th D2D receiver from D2D networks \\ \(I_{b}\) & Aggregate interference at the BS from D2D networks \\ \(\alpha_{k}\) & Weighted coefficient of the \(k\)-th D2D transmitter under random scheme \\ \(N,O\) & Number of pair of D2D users, network area \\ \(x_{D_{k}},x_{C}\) & Transmitted signals of the \(k\)-th D2D transmitter and cellular user \\ \(y_{D_{k}},y_{C}\) & Received signals at the \(k\)-th D2D receiver and base station \\ \(m_{D_{k}}^{\prime},m_{D_{k}}^{\prime}\) & AWGN noise at the \(k\)-th D2D receiver and base station \\ \(\sigma_{k}^{\prime},\sigma_{D}^{\prime}\) & Noise variance at the \(k\)-th D2D receiver and base station \\ \(\text{N}\text{P}\text{W}\) & Noise figure, transmission bandwidth \\ \(\text{R},R_{C}\) & Targeted rate of D2D networks and cellular networks \\ \(\exp(.),\ln(.)\) & Exponential and logarithm functions \\ \(E_{1}(.)\) & Exponential Integral function \\ \(F_{X}(x)\) & Cumulative distribution function (CDF) of RV \(X\) \\ \(\overline{F}_{X}(x)\) & Complementary Cumulative distribution function (CCF) of RV \(X\) \\ \(M_{X}(x)\) & Moment generating function (MGF) of RV \(X\) \\ \(f_{X}(x)\) & Probability density function (PDF) of RV \(X\) \\ \(\text{OP}\) & Outage probability of the \(k\)-th D2D user under \(s\) allocation scheme \\ \(\overline{R}_{k}^{\prime}\) & Average rate of the \(k\)-th D2D user under \(s\) allocation scheme \\ \(\overline{R}_{k}^{\prime}\) & Average sum rate of the D2D networks under \(s\) allocation scheme \\ \(\text{Ao}\overline{F}_{k}^{\prime}\) & Amount of fading of the \(k\)-th D2D user under \(s\) allocation scheme \\ \(\text{Var}\{.\}\) & Variance operator \\ \(F_{\Delta}^{\prime}\) & Coverage probability of cellular user under \(s\) allocation scheme \\ BER & Bit error rate \\ BS & Base station \\ D2D & Device-to-device \\ FDMA & Frequency-division multiple access \\ LoRa & Long-range \\ MIMO & Multiple-input multiple-output \\ NOMA & Non-orthogonal multiple access \\ QoS & Quality-of-service \\ RVs & Random variables \\ SINR & Signal-to-interference-plus-noise ratio \\ TDMA & Time-division multiple access \\ e2e & end-to-end \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main notations and mathematical symbols.
## 3 Materials and Methods
### Soil Analysis and Pot Experiment  
During the 2023 cropping season, an experiment was conducted in the field at a private farm in New Beni-Suef City to evaluate the potential total or partial substitution of GW and Mg NP/GW by using nano-natural plants and its effect on the quality and quantity of geranium and mustard. Soil samples were collected from a clean area near Yousif Sea in Ethasia-Elmadina city at a depth of 5 to 30 cm. These soil samples were then sieved through a 2 mm mesh to remove large debris and pebbles. Subsequently, the obtained soil samples were air-dried for a week at a temperature ranging between 21 and 24 \({}^{\circ}\)C [86]. Before incorporating GW and Mg NP/GW powder nanocomposite into the sieved soil, the soil was thoroughly mixed by hand. The collected soil was then transferred into plastic pots, with one kilogram of mixed soil added to each pot along with 0.5, 1.0, and 5.0 g of Mg NP/GW and GW. Growth tests were conducted in three replicates, with two seedlings of _P. gravelens_ planted in each pot and five seeds of _S. alba_ germinated in each pot.  
To elaborate, separate handheld kitchen mixers were utilized to mechanically mix the powder nanoparticles for five minutes after their initial application to the soil at doses of 0.5, 1.0, and 5.0 g kg \({}^{-1}\) for Mg NP/GW and GW NPs. Soil without nanoparticles was employed as the control [87].  
Soil pH was measured in a 1:2.5 aqueous soil extract using a pH meter (model AD8000), while electrical conductivity was determined in a 1:5 aqueous soil extract using a conductivity meter (model AD8000). Organic carbon content, determined following the method described by [88], involving the oxidation of organic carbon with potassium dichromate, was employed to determine the amount of organic carbon (OC) present. The electrical conductivity (EC) of the soil was determined using a conductivity meter, with a soil/water extract prepared at a ratio of 1:2.5 (\(w/v\)). The pH readings of the soil were found to be 7.71, 7.65, and 7.74. The corresponding EC values were 202, 197.5, and 204 \(\upmu\)S, while the total dissolved solids (TDSs) were measured at 86.8, 84.7, and 87.6 ppm. The organic carbon (OC) content was determined to be 1.5%, 1.92%, and 1.98%. Each analytical determination was conducted in triplicate.
## 3 Results and Discussion
### Indirect FFF 3D Printing
As shown in Figure 7a,  
Figure 7: First trials for indirect FFF 3D printing: (**a**) open mold design, (**b**) cast part in rGFRPs (30% wt., 1 mm), and (**c**) in rCFs. (20% wt., 63 \(\upmu\)m). this mold comprises two halves, which were manufactured by following a similar approach used to fabricate the cut-off part described before. The mechanical interlocking between the rigid mold and the flexible cavity was developed by designing the specific shape of the modifier features in the CAD software, which was then imported into Prusslicer. Furthermore, the final design of the rigid parts was obtained by avoiding the use of any support material, trying to reduce the material waste, and the 3D printing timings for the production according to the principles of Design for Additive Manufacturing (DfAM) [48]. The counter holds were fixed on the rigid molds through six metric M5 screws by using the same design for the assembly of the final mold. The pouring channels were designed in the upper half of the mold by linking them to the flat surfaces on the back part of the component (Figure 8a). The counter molds and rigid molds are visible in Figure 8b, whereas Figure 8c shows the final molds with the flexible cavities after silicone curing. Although the silicone successfully reproduced the shape of the counter molds, some defects were generated by the presence of air bubbles in the mixture, (Figure 8c). The final parts from the mold are visible in Figure 9a. The part at the top has 30% wt. of shredder rGFRPs with a mean granulometry of 200 \(\upmu\)m, whereas the bottom one contains 30% wt. of shredded rCFs with the same mean granulometry. These two formulations were selected according to the previous tests, choosing the same percentage and particle size in both formulations. Especially in the rCFs batch (Figure 9b), no visible deformations were seen in both batches, as well as fewer air bubbles in the final parts, reducing the possible points of cracks in the event of an accident. Some post-processing is still required to remove the three pouring channels attached to the back part, and some defects of the silicone casting can be seen on the front part (Figure 9c). However, these results validate the use of this workflow linked to indirect FFF 3D printing to prototype and produce small batches of  
Figure 8: Design of the mold: (**a**) assembly design; (**b**) 3D printed parts in PETG (molds and counter molds for silicone); and (**c**) final version of the mold after silicone casting. customized components for technical sectors made of recycled GF or CF composites, i.e., automotive. The functional aim of the part, i.e., absorbing the forces from the battery floor pan of the car structure, can be achieved by this component manufactured with indirect FFF 3D printing, also considering the properties of similar rGFRPs [19] and the use of GFs and CFs for similar purposes in the automotive sector [28,29].
## 3 Comparison of Application Cases and Methods
### Troubleshooting of Variable Pressure States  
High-speed bearings and gear problems such as spalling, pitting, etc., are usually caused by insufficient local lubrication. Therefore, gear transmission and lubricant pressure are closely related.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optimization Algorithm** & **Parameter Is Action** \\ \hline CSA & The number of iterations is 200, and the initial c and g are random numbers from 1 to 5 in high \\ \hline Grid Search & Initially c and g are random numbers from 1 to 5 with an initial step size of 0.1 \\ \hline Genetic algorithm & The number of iterations is 200 and the c and \\  & & granges from 1 to 5 \\ \hline Particle swarm algorithm & The number of iterations is 200 and the c and \\  & & granges from 1 to 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter setting of each optimization algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization Algorithm** & **Computation Time/s** & **Test Set Correctness/\%** \\ \hline CSA & 3.87 & 100 \\ \hline Grid Search & 9.12 & 99.4 \\ \hline Genetic algorithm & 4.51 & 98.9 \\ \hline Particle swarm algorithm & 15.23 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison.  
Since the lubricant pressure is not always maintained at the rated pressure during the operation of a gearbox, to verify the effectiveness of the method in the fault diagnosis performance under variable pressure conditions, the method was developed and validated. The effectiveness of the method in fault diagnosis under variable pressure conditions is verified. We selected 20%, 40%, 60%, 80%, 100%, and 110% of the rated pressure of the gearbox transmission vibration data from 750 sets. Using the method proposed in this paper, gearbox transmission wear fault feature extraction and fault type identification were carried out. The diagnostic results are shown in Figure 6.  
As depicted in Figure 6, there is a marked increase in the correct diagnosis rate correlating with the rise in pressure, consistently peaking at 100%. In such scenarios, the vibration signals of the gearbox transmission are notably susceptible to various forms of interference, leading to the obscuring of essential fault-state information. However, a shift is observed with the augmentation of pressure. Distinctive vibration characteristics, emblematic of varied transmission states, are observed. During this phase, the pervasive influence of interference factors on the overall fault depiction is markedly subdued. This resilient performance amidst escalating pressures underscores the method's adeptness in meticulously extracting wear-related failure characteristics, attuned to the nuanced variations in lubricant pressure states of transmission. Such robust diagnostic precision reaffirms the method's validity, positioning it as a reliable asset in intricate fault detection and characterization.
## 2 State-of-the-Art and Related Topics  
Traffic information collection provides the data input for traffic prediction, and there are many applications that use traffic prediction results to improve traffic conditions. In this section, the state-of-the-art traffic information collection, traffic prediction, and urban traffic applications are reviewed.
## 2 Fracture Mechanics-Based Fatigue Assessment of Homogeneous Materials
### Determination of the Instant Rupture Section  
It can be seen from Equation (2) that, another key element needs to be known when performing fatigue assessment is the final crack size \(a_{c}\), which depends on the size of an instant rupture section. There are mainly two ways to determine the size of an instant rupture section. One can be concluded as the fracture toughness method, which believes that the instant rupture occurs when the stress intensity factor of a crack tip reaches the fracture toughness of the material (plane strain or plane stress fracture toughness) [27], namely,  
\[K_{max}\to K_{IC}(or\ K_{C}) \tag{5}\]  
where \(K_{max}\) represents the maximum stress intensity factor of a crack tip calculated using the upper limit of a fatigue stress, \(K_{IC}(or\ K_{C})\) is the plane strain fracture toughness (or plane stress fracture toughness) which can be obtained experimentally. However, the results in the literature indicate that the fracture toughness of a material obtained through experiments is affected by many factors, for example the size effect [28]. It is therefore a controversial matter whether the fracture toughness of a material can be used as a material constant.  
In addition, according to characteristics of the fatigue failure behavior, some scholars have proposed the concept of the fatigue fracture toughness [29; 30]. They believed that it is the fatigue fracture toughness rather than \(K_{IC}(or\ K_{C})\) that is the critical value of the \(K_{max}\), namely,  
\[K_{max}\to K_{fl} \tag{6}\]  
where \(K_{fl}\) represents the fatigue fracture toughness of a material which can either be determined experimentally or predicted according to a semi-empirical formula.  
In fact, the essential reason for the occurrence of an instant rupture is that the stress on the remaining load-bearing section of a test piece reaches a certain critical value. The method established based on this understanding is called the critical stress method [31]. Moreover, it is generally believed that during the last stress cycle, the stress on a remaining load-bearing section reaches the ultimate tensile strength of the material [24; 32], namely,  
\[\sigma_{re}\rightarrow\sigma_{u} \tag{7}\]  
where \(\sigma_{re}\) represents the mean stress on the remaining load-bearing section, \(\sigma_{u}\) is the ultimate tensile strength of a material. Since the tensile strength of a material is a recognized material constant and relatively easy to obtain, the critical stress method is selected to determine the instant rupture section in this work.
As a result, they considered the above frequency band to be induced by the friction between tribo-pair surfaces and it was extracted and reconstructed as the \"weak\" FIV by using HWPT. It can be seen that determining the frequency distribution of the \"weak\" FIV should be carried out with the help of friction coefficient and wear experiments, which is a challenging work in practical applications. Moreover, many repeated tentative calculations are required to determine extracted frequency and the number of IMFs, leading to inefficiency. Therefore, it is necessary to propose a more operable and efficient method for detecting the frequency range of the \"weak\" FIV. According to the driving mechanism of the \"weak\" FIV, when the elastically deformed asperities return to the stable position and the damaged asperities break off suddenly in the friction process, the waves originate at the contact interface, vibration is excited, and part of the energy is radiated in the form of sound energy, that is, FIV and FIS occur [20]. Thus, the \"weak\" FIV components in vibration and sound are correlated, which makes it possible to identify \"weak\" FIV by cross-correlation analysis (CCA) without the help of the friction coefficient. Of course, the discussion of detecting \"weak\" FIV signals by CCA is in no sense new, for example, see [21,22]. However, most of the present work focused on the CCA of the vibration signals in the tangential and normal directions rather than the vibration and sound signals. In addition, most studies used the cross-correlation coefficient to finish the goal, which required the assumption of the frequency distribution of \"weak\" FIV based on experience and then confirming the hypothetical results according to the cross-correlation coefficient, still requiring many repeated tentative calculations. Therefore, the present work strove to implement a more efficient method of using the CCA of vibration and sound signals to detect the \"weak\" FIV. This study conducted a varying wear state experiment with a standard commercial friction-wear tester under oil-lubricated conditions to confirm no \"strong\" FIV emissions, such as squeal and squeak. First, the vibration-sound signals were collected simultaneously. Then, the mathematical principle of CCA and HWPT was briefly elaborated. Then, a detecting method for \"weak\" FIV based on the mean value of the frequency amplitude of the CCA function between vibration and sound signals was proposed. It was possible to determine the frequency range of a \"weak\" FIV directly through this method without assuming many possibilities, thereby avoiding repeated tentative calculations. Finally, the conclusions were presented.
###### Abstract  
The spectral response of the periodic antenna structure placed in a dielectric homogeneous medium depends on the antenna geometry, the parameters of the medium, the angle of incidence, polarization, and the geometry of the excitation field. Increasing the number of antenna structure parameters can be achieved by introducing a multilayer dielectric medium with a certain number of metallized periodic surfaces located on flat boundaries between the dielectric layers. There are two complementary approaches to the analysis of such structures. In the first, the composite antenna system is analysed by constructing supermodes of the entire structure. In the second, the system is considered as a cascade assembly of flat discrete elements, i.e., the boundaries between two dielectrics, periodic metallized planes, and dielectric layers. The latter approach leads to the definition of the scattering, transmission, or impedance matrix of the entire structure by cascading the corresponding matrices associated with the individual discrete elements of the antenna structure. It is particularly useful in modelling dielectric multilayer antenna walls, where the stored data on one planar antenna element can be used many times in the analysis of various antenna systems with modified parameters of other discrete structure elements. Microstrip antennas combine field and peripheral problems and require the use of analytical methods of a high degree of complexity. Therefore, at present, there are no standard methods that can be used in engineering practice. This work is a step towards filling these gaps.  
keywords: harmonics Floquet; periodic antenna multilayer dielectric Article Multilayer Dielectric Periodic Antenna Structure in a Cascade View
# An Effective Simulation Scheme for Predicting the Aerodynamic Heat of a Scramjet-Propelled Vehicle
## 1 Introduction
The external flow field model for simulation is primarily based on blunt bodies, and the initial conditions of freestream are usually selected from a certain stage in the flight path, such as the hypersonic stage. However for the scramjet-propelled vehicle with a wedge section as its characteristic component, research on the aerodynamics heat prediction in the flight path is relatively sparse. In this study, a 2D external flow field model was built based on a wedge section, and an aerothermal prediction was performed for its flight path is performed. This approach can further enrich and improve aerothermal research into hypersonic vehicles. This manuscript is organized as follows. In the second section, we briefly overview the flight path of a scramjet-propelled vehicle and the numerical procedures applied in the CFD. Next, according to the Mach number, the flight trajectory of the scramjet-propelled vehicle is divided into different stages, and the determination of the appropriate grid strategy and numerical method through validation tests is presented in the third section. Based on the simulation parameters confirmed in these sections, an effective simulation scheme for the scramjet-propelled vehicle is then proposed, and the prediction of the aerodynamic heat during the flight is discussed in in the fourth section. The last section presents our conclusions.
## 2 Methods
### DL Algorithms
#### 2.2.3 Lstm  
LSTM is a variation of the RNN, which has the advantages of memorability, shared parameters, and Turing completeness [46]. It overcomes the vanishing and exploding gradient problem of the RNN and is suitable for the processing of time sequences. The structure of LSTM model is shown in Figure 2c. Based on RNN, LSTM adds mechanisms, such as memory cell, input gate, forget gate, and output gate, to control the transmission of information at different times, thus greatly improving the ability of the model to learn long-term dependencies. The input gate determines how much new information will be recorded, and the forget gate decides how much information will be forgotten, and the output gate determines the output using the information and cell state from the previous two gates. The working mechanism of each gate and information flow can be expressed using the following equations [10; 47]:  
\[\overset{\rightarrow}{i}_{t} =\sigma(\textbf{W}_{\textbf{i}}\cdot[\overset{\rightarrow}{h}_{t -1},\overset{\rightarrow}{x}_{t}]+\overset{\rightarrow}{b}_{i}), \tag{7}\] \[\overset{\rightarrow}{f}_{t} =\sigma(\textbf{W}_{\textbf{f}}\cdot[\overset{\rightarrow}{h}_{t -1},\overset{\rightarrow}{x}_{t}]+\overset{\rightarrow}{b}_{f}),\] (8) \[\overset{\rightarrow}{o}_{t} =\sigma(\textbf{W}_{\textbf{o}}\cdot[\overset{\rightarrow}{h}_{t -1},\overset{\rightarrow}{x}_{t}]+\overset{\rightarrow}{b}_{o}) \tag{9}\]  
The mathematical symbols of the above formula are defined as follows: \(\overset{\rightarrow}{i}_{t}\), \(\overset{\rightarrow}{f}_{t}\), and \(\overset{\rightarrow}{o}_{t}\) are the vector values of the input gate, forget gate, and output gate of a node in the LSTM neural network at time \(t\), respectively; \(\overset{\rightarrow}{b}_{i}\), \(\overset{\rightarrow}{b}_{f}\), and \(\overset{\rightarrow}{b}_{o}\) are the offset items corresponding to each structure; \(\textbf{W}_{\textbf{i}}\), \(\textbf{W}_{\textbf{f}}\), and \(\textbf{W}_{\textbf{o}}\) are the weights applied to the concatenation of the new input \(\overset{\rightarrow}{x}_{t}\) and output \(\overset{\rightarrow}{h}_{t-1}\) from the previous cell; \(\overset{\rightarrow}{x}_{t}\) is the input at time \(t\); \(\overset{\rightarrow}{h}_{t-1}\) is the output at time \(t-1\), which represents the hidden state of LSTM, and can be expressed as \(\overset{\rightarrow}{h}_{t-1}=\overset{\rightarrow}{o}_{t-1}\odot\tanh( \overset{\rightarrow}{c}_{t-1})\). \(\overset{\rightarrow}{c}_{t-1}\) is the vector value of the memory cell at \(t-1\), and tanh is the hyperbolic tangent function, which can map real numbers to \((-1,1)\); \(\sigma\) is the sigmoid activation function, which can map real numbers to \((0,1)\).