esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 5 Framework Performance
### Solver Compared with Expert-Grade Results  
To evaluate the efficiency and accuracy of the proposed framework, a comparison was conducted against a manually prepared simulation, performed by an experienced CFD mechanical engineer. To ensure consistent and comparable results, the ONERA M6 wing geometry (Figure 12, Table 7) generated by the framework was also provided to the expert. The engineer manually meshed the geometry while preserving the features of the wing. The simulation parameters, including models, solution schemes, and boundary conditions, are appropriately configured to match the setup performed automatically by the framework, as detailed in Algorithm for Convergence Detection section. The validation focuses on key aerodynamic quantities, including the lift coefficient \(C_{L}\), drag coefficient \(C_{D}\), and residual convergence trends. The framework is assessed for its ability to replicate these results while reducing the time and effort required for pre-processing and simulation setup.  
The automated framework demonstrates high reliability, with the drag polar predictions deviating by less than 3% from the manually obtained results. This agreement highlights the framework's capability to deliver high-level accuracy for subsonic aerodynamic evaluations. As illustrated in Figure 16, the framework successfully captured the lift and drag characteristics across the drag polar of the ONERA M6 wing.  
One of the key strengths of the framework lies in its ability to significantly reduce the time required for geometry pre-processing, meshing, and simulation setup. The CFD engineer took approximately 40 min to manually generate the mesh and configure the solver for the ONERA M6 case. In contrast, the automated framework completed these tasks in under 5 min, achieving an 88% reduction in lead time. In this lead time, only the mesh generation and the solution setup are taken into consideration, since the solution execution time is the same for both cases (given that the grid resolution is similar).
###### Abstract  
With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite-terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite-terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.  
admission control; game theory; multi-domain network slicing; multi-sided ascending-price auction; satellite-terrestrial integrated network 2023  
10830  
11 August 2023
## 3 The Use of the Lozi Map in Electrical Devices, Memristors, and Computers
### Memristors
#### 3.3.2 3D Fractional Memristor-Based Lozi Map  
Another method to obtain a memristor-based Lozi map is to consider the fractional counterpart of this map (see Section 2.5). Khennaoui et al. [52] used the Caputo-like delta difference to obtain the fractional map corresponding to (19):  
\[\left\{\begin{array}{l}\Delta^{\alpha_{1}}x_{n}=1-a|x_{n}|+y_{n}-x_{n},\\ \Delta^{\alpha_{2}}y_{n}=bx_{n}+ky_{n}sin(z_{n})-y_{n},\\ \Delta^{\alpha_{3}}z_{n}=y_{n},\end{array}\right. \tag{20}\]  
in which \(\Delta^{\alpha_{i}}\)\((i=1,2,3)\) stands for the Grunwald-Letnikov difference operator.  
Obviously, when \(\alpha_{1}=\alpha_{2}=\alpha_{3}=1\), the fractional map (20) corresponds to the first-order map (19).  
This map exhibits hidden dynamic evolutions and multistability. The description of the fractional discrete memristor-based Lozi map was investigated according to its Lyapunov exponents, phase portraits, and bifurcation diagrams, demonstrating several kind of coexisting multiple hidden attractor behaviours that can be generated by the proposed model.  
Using the classical discretization scheme, the iterative equation of the fractional order memristor-based Lozi map with no equilibrium point (20) is provided by  
\[\left\{\begin{array}{l}x_{n+1}=1-a|x_{n}|+y_{n}+(\alpha_{1}-1)x_{n}-\sum_{p_{1} =1}^{n}\beta_{p_{1}}x_{n-p_{1}},\\ y_{n+1}=bx_{n}+ky_{n}sin(z_{n})+(\alpha_{2}-1)y_{n}-\sum_{p_{2}=1}^{n}\beta_{p_{ 2}}y_{n-p_{2}},\\ z_{n+1}=y_{n}+\alpha_{3}z_{n}-\sum_{p_{3}=1}^{n}\beta_{p_{3}}z_{n-p_{3}},\end{array}\right. \tag{21}\]  
where \(\beta_{p_{1}}\), \(\beta_{p_{2}}\), and \(\beta_{p_{3}}\) are the binomial coefficients:  
\[\beta_{p_{j}=0}=-\alpha_{1},\beta_{p_{j}}=\left(1-\frac{1+\alpha_{j}}{p+1} \right)\beta_{p_{j-1}},j=1,2,3. \tag{22}\]  
It can be shown that the value of \(\beta_{p}\) decreases when the number of iterations \(p\) increases, regardless of the value of the fractional order. Thus, the easiest way to numerically implement the iteration Formula (21) is to use a finite truncation of the memory length \(M\) to approximate the fractional discrete memristor map (20). The short memory model M is provided by  
\[\left\{\begin{array}{l}x_{n+1}=1-a|x_{n}|+y_{n}+(\alpha_{1}-1)x_{n}-\sum_{p_ {1}=1}^{M}\beta_{p_{1}}x_{n-p_{1}},\\ y_{n+1}=bx_{n}+ky_{n}sin(z_{n})+(\alpha_{2}-1)y_{n}-\sum_{p_{2}=1}^{M}\beta_{p_{ 2}}y_{n-p_{2}},\\ z_{n+1}=y_{n}+\alpha_{3}z_{n}-\sum_{p_{3}=1}^{M}\beta_{p_{3}}z_{n-p_{3}}.\end{array}\right. \tag{23}\]  
The authors used a hardware platform based on a microcontroller (Arduino Uno board) to realize such a fractional memristive Lozi map for which the initial conditions can be changed easily. They showed that this map exhibits the initial condition-dependent dynamical phenomena of multistability and confirmed this through numerical simulations.
## 3 2D-DOA Total Variation Regularization  
In this section, a novel 2D-DOA sparse estimation method with total variation regularization is present concretely. The definition of total variation matrix is given and the corresponding total variation regularization term is constructed, which is combined with the traditional LASSO to build the model of 2D-DOA sparse estimation. The proposed method (abbreviated 2 DTV-CES) can utilize the group's characteristics (block sparse) and spatial derivatives to promote the sparsity both on the spatial derivatives of the solution and on the solution itself.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Data Distribution  
Figure 12 exploits different possible data dispersal with the realization of the mapping algorithm illustrated in Section 3.2. This experiment was conducted in a network of 80 nodes in a 60 m \(\times\) 60 m (3600 m\({}^{2}\), total number of sectors is 16 (4 \(\times\) 4)) field with a 60 min simulation time. The data production rate per sector was five packets per second. Figure 12a shows uniform distribution, which means data are stored uniformly among all sectors of the network. Figure 12b shows a central distribution in which data are usually located among central sector of the network. Figure 12c,d represents the lower bound distribution and upper bound distribution, where data are stored among lower and upper sectors of the network, respectively.  
Figure 12: (**a**) Uniform distribution; (**b**) central distribution; (**c**) lower bound distribution; and (**d**) upper bound distribution.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 4 Experimental Results
### Baseline Performance Evaluation
#### 4.2.1 Impact of Adaptive Modulation  
We compared two scenarios: a fixed quantization step (\(\Delta~{}=6\)) and our proposed adaptive JND-based quantization. In both settings, a 128-bit watermark was embedded over 5 keyframes per bit. The results in Table 2 show that adaptive modulation significantly enhanced visual quality while also reducing BER.  
These results confirm that JND-modulated quantization better preserves both structural and perceptual quality, especially in HD videos.
## 2 Methodology  
### Proposed Framework for Fault Diagnosis  
Based on the theory of EWT and ResNet, this paper makes subtle improvements to EWT and proposes a multi-input ResNet model. Moreover, the improved EWT and multi-input ResNet are applied to analog circuit fault diagnosis. Figure 1 shows the framework of proposed method. The first step of the method is to take the square wave as the stimulus of the CUT and collect the response of the output of the circuit. Then, the improved EWT is employed to preprocess the collected data and divide the preprocessed data into a training set, validation set, and test set. The multi-input ResNet model is trained with the training set, and the optimal multi-input ResNet model corresponding to the highest verification accuracy is acquired with the validation set. Finally, the test set is input to the optimal multi-input ResNet model, and the result of fault diagnosis is obtained.  
Figure 1: Framework of the proposed method.
## 4 Results
### Spatial Analysis  
Based on the proposed methodology, the results of the number of extreme weather events, economic losses and fatalities for each of the countries are shown in Table 1. If we analyse the data by basin (Atlantic or Mediterranean), the results are shown in Table 2.  
As Table 2 shows, the distribution of extreme weather events by country is clearly unequal: Spain records 77.8% of the events, compared to 17.9% in mainland Portugal and 4.3% in the south of France. Table 2 shows a higher number of events in the Atlantic basin than in the Mediterranean one, despite the shorter coastline. The Mediterranean basin has a higher number of fatalities with 61.3% of the victims; this is in contrast to the number of injured persons, where the Atlantic basin has 67.8% of these.  
Figure 3 represents the number of events recorded in each of the regions of the study area. We can observe a greater concentration in the Spanish Mediterranean strip, particularly in the region of Andalusia (South Spain), where 28 extreme weather events have left significant after-effects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Country**} & **Length** & **Number** & \multicolumn{2}{c}{**Human Impact**} & \multirow{2}{*}{**Economic Losses**} \\  & **(km)** & **of EWE** & & **Fatalities** & & **Injuries** \\ \hline France & 864 & 4 & 50 & 79 & 3113.9 \\ Portugal & 848 & 17 & 7 & 14 & 8.38 \\ Spain & 6322 & 74 & 111 & 44 & 827.9 \\ \hline
**Total** & 8034 & 95 & 168 & 137 & 3950.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary by country of the number of extreme weather events recorded, fatalities, injuries and economic damage caused during the period analysed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Basin**} & **Length** & **Number** & \multicolumn{2}{c}{**Human Impact**} & \multirow{2}{*}{**Economic Losses**} \\  & **(km)** & **of EWE** & & **Fatalities** & & **Injuries** \\ \hline Atlantic & 3807 & 49 & 65 & 93 & 3043 \\ Mediterranean & 4227 & 46 & 103 & 44 & 907.2 \\ \hline
**Total** & 8034 & 95 & 168 & 137 & 3950.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary by basin (Atlantic and Mediterranean) of the number of extreme weather events recorded, fatalities, injuries and economic damage caused during the period analysed.  
Figure 2: Methodology used for the present work with the different stages.  
Below is an analysis by country, identifying the most catastrophic events in each country.
# Task Assignment of UAV Swarm Based on Wolf Pack Algorithm
## 4 Experiments of Task Assignment for UAV Swarm Using PSO-GA-DWPA
### Monte Carlo Simulation in Different Scenarios
The convergence performance and running time of different measures are shown in Figure 11. With the increase of dimension, PSO has become ineffective, (i) the optimal solution cannot be obtained; and (ii) the calculation speed is too slow, and the time required for 1000 iterations is nearly 2.5 times that of the other two algorithms. Running time for each iteration of the PSO-GA-DWPA is slightly longer than that of the WPA, but its convergence speed in the early stage is obviously faster, so its convergence time is shorter than that of WPA. The comparison of the three methods in 3 scenarios is shown in Table 2, which indicates visually that the overall performance of the PSO-GA-DWPA is better than that of the other two methods. According to Figures 9-11 and Table 2, compared with the PSO, which is recognized as fast and accurate, the advantage of the WPA and the proposed PSO-GA-WPA in the low-dimensional solution space (such as the 8-dimensional solution space in Scenario 1) is not obvious. The experiments in Scenario 1 prove the availability of the WPA and the PSO-GA-WPA for task assignment problem. The higher the dimension of the solution space is, the advantage of the proposed algorithm is more obvious. Compared with the WPA, the PSO-GA-WPA has the advantage of a faster convergence speed. Although their convergence costs are similar, the PSO-GA-WPA can obtain a convergent solution in a shorter time. Figure 11: Simulation results in scenario 3: (**a**) the mean cost of the Monte Carlo runs, and (**b**) the mean convergence time of the Monte Carlo runs.
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 4 Drugs Acting on Central Nervous System (CNS)  
Chiral drugs acting on the central nervous system (CNS) have been recently reviewed [79]. Ketamine, and particularly (\(S\))-ketamine, is used in clinics for premedication, sedation, induction, and maintenance of general anesthesia, which is then termed \"dissociative anesthesia\" [80]. The anesthetic potency of the (\(S\))-enantiomer is approximately three to four-fold higher than that of the (\(R\))-enantiomer. In 2019, the (\(S\))-isomer of ketamine (esketamine (**24**)) was approved for use in treatment-resistant depression in the United States (Spravato) and in Europe [81]. However, new preclinical findings suggest that (\(R\))-ketamine might produce better efficacy and tolerability relative to (\(S\))-ketamine [82]. Duloxetine (**25**) is a potent reuptake inhibitor of serotonin and nor-epinephrine and is used for the treatment of a major depressive disorder, diabetic neuropathic pain, stress urinary incontinence, generalized anxiety disorder, and fibromyalgia [83]. A rapid and low-cost liquid chromatography (LC) method for control of the enantiomeric purity of dloxetine has been recently reported [84]. Atomoxetine (**26**) is an inhibitor of the presynaptic norepinephrine transporter. It is a chiral drug used for the management of attention-deficit hyperactivity disorder (ADHD) in children and adolescents [85]. Recently, the combination of atom-oxetine and restoordine (Ato-Feso), a chiral anti-musccarinic drug, is under study as a possibility for the treatment of obstructive sleep apnea [86]. Finally, chirality has been recently considered an important factor for the development of new antiepileptic drugs [87]. Valnoctamide (**27**) is an anticonvulsant also bearing anxiolytic and antibipolar properties. It is an amide derivative of valpro acid that possesses stereo-selective pharmacokinetics both in animals and human [88, 89, 90].
## Appendix A Basic Equation Describing Large-Scale Atmospheric Dynamics  
Let us consider the following set of basic equations called the (hydrostatic) primitive equations in pressure coordinates describing the inviscid large-scale atmospheric motions (e.g., [21]):(a) The horizontal momentum equations  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac{\partial u}{ \partial y}+\omega\frac{\partial u}{\partial p}-fv+\frac{\partial\Phi}{\partial x}=0 \tag{10}\]  
\[\frac{\partial v}{\partial t}+u\frac{\partial v}{\partial x}+v\frac{\partial v }{\partial y}+\omega\frac{\partial v}{\partial p}+fu+\frac{\partial\Phi}{ \partial y}=0 \tag{11}\]  
(b) The hydrostatic equation  
\[\frac{\partial\Phi}{\partial p}=-\frac{RT}{p} \tag{12}\]  
(c) The continuity equation  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}+\frac{\partial \omega}{\partial p}=0 \tag{13}\]  
(d) The thermodynamic equation  
\[\frac{\partial T}{\partial t}+u\frac{\partial T}{\partial x}+v\frac{\partial T }{\partial y}-\frac{RT}{gp}(\Gamma_{d}-\Gamma)\omega=\frac{\dot{Q}}{c_{p}}=0 \tag{14}\]  
where \(\dot{Q}\) is the diabatic heating rate per unit mass.  
Equations (10)-(14) possess a steady-state solution  
\[\overline{u}=-f^{-1}\frac{\partial\Phi}{\partial y},\ \overline{v}=0,\ \overline{\omega}=0,\ \frac{\partial\overline{\Phi}}{\partial p}=-\frac{R \overline{T}}{p}, \tag{15}\]  
which describes the zonal flow induced by a meridional (along a longitude circle of the earth) temperature contrast defined by the temperature field \(\overline{T}=\overline{T}(y,p)\). A vertical wind shear in this zonal flow is defined by the thermal wind relation  
\[\frac{\partial\overline{u}}{\partial p}=\frac{R}{fp}\frac{\partial\overline{ T}}{\partial y} \tag{16}\]  
The boundary conditions will be considered below. The large-scale mid-latitude atmospheric motions are nearly geostrophic, which implies the balance between the Coriolis and horizontal pressure gradient forces. This results in the air moving along isobars, with the low pressure to the left in the northern hemisphere, and the low pressure to the right in the southern hemisphere. It follows from Equations (10) and (11) that the geostrophic wind components are given by  
\[u_{g}=-\frac{1}{f}\frac{\partial\Phi}{\partial y},\ v_{g}=\frac{1}{f}\frac{ \partial\Phi}{\partial x}. \tag{17}\]
## 1 Introduction
### Criteria for Porogen Selection  
The criteria for choosing porogenic solvents is often based on their respective molecular size [24], alkyl chain length [31], mixtures of lipophilic and hydrophilic porogens [32], and solubility parameter (SP) [33, 34, 35, 36, 37]. The SP was identified as a quantitative criterion for predicting the pore structure properties of polymers; however, the SP was not adequately addressed in other studies [12, 37]. There are three general types of SP values, _i.e._, a one-component SP is also referred to as the Hildebrand solubility parameter, a two-component solubility (physical-chemical) parameter, and the [PERSON]'s three-component solubility parameter [31, 32, 33, 34, 35, 36].  
The three approaches employing SP criteria have been applied in various studies. In view of its simplicity, the [PERSON] one-dimensional SP is used for solutions without molecular polarity or specific intermolecular interactions. The two-dimensional SP is preferred over the [PERSON]'s three-dimensional SP for solvents with lower molar volumes since they yield comparable results. [PERSON]'s three-dimensional SP is favored [33, 36] for solvents with greater molar volumes, such as phthalates, and provides reasonably accurate results.  
The tuning of PCR materials with the aforementioned porogens is determined by various factors [2, 4, 6, 7, 10, 15, 16, 17, 18, 19, 20, 21, 38], as follows: (_i_) the feed ratio of the porogen to monomer mixture (or porogen volume) yields optimal conditions when the ratio is maximized; (_ii_) porogens with variable SP; (_iii_) mixing multiple porogens, _i.e._, co-porogens; (_iv_) variable temperatures where higher temperatures produce smaller pores; (_v_) variable stirring speeds influence microemulsion formation and the particle size; (_vi_) crosslinking density affects the PSD; (_vii_) polymer composition where the relative monomer type and feed ratio affects the pore-size and PSD; and (_viii_) the nature of the polymerization technique. [PERSON] _et al._[39] investigated the influence of different parameters and found that the type of porogen and the relative volume have a large influence on the copolymer resin properties. Based on their findings, the goal of this review focuses on tunable porogens of variable types, both as pure solvents and their mixtures (co-porogens). In addition, the influence of porogen volume is examined while the other factors are treated as secondary in their importance.