esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Effect of Rayleigh Number and Hartmann Number on Nanofluid Thermal Behavior  
The simulations were carried out under the following data:  
* The corner radius was \(R=0.25\)
* The solid volume fraction was \(\phi=0.05\)
* The Prandtl number was \(Pr=0.7\)
* The Rayleigh number was \(Ra=10^{3}\); \(10^{4}\); \(10^{5}\)
* The Hartmann number was \(Ha=0\); \(10\); \(30\); \(50\)
* The Eckert number was \(Ec=10^{-5}\)  
Figures 3 and 4 show the effect of Rayleigh number and the effect of Hartmann number on the isotherm contours and the surface temperature distribution, respectively. The Rayleigh number varies, from left to right (columns), as \(Ra=10^{3}\); \(10^{4}\); \(10^{5}\), while the Hartmann number varies, from up to down (lines), as \(Ha=0\); \(10\); \(30\); \(50\).  
Figure 2: Meshing of the nanofluid domain.  
Results show that the parallelism of isotherms to hot and cold walls was more pronounced at low Rayleigh number (\(Ra=10^{3}\)) and strongly degenerated as the Rayleigh number increased from \(Ra=10^{3}\) to \(Ra=10^{5}\), especially in the central zone of the nanofluid filled cavity, due to the enhancement of buoyancy.  
Thus, the heat transfer was dominated by convection instead of conduction when the Rayleigh number increased, as it was expected. Results show, also, that the parallelism of isotherms to hot and cold walls was enhanced when the Hartman number increased from \(Ha=0\) to \(Ha=50\), especially in the central zone of the nanofluid filled cavity, due to the enhancement of Lorentz force that weaken the flow within the cavity. Hence, the convection was retarded, and the heat transfer was dominated by conduction when Hartmann number increased, as it was predicted. It is to note, also, that the temperature homogeneity of the nanofluid was globally improved as the Rayleigh number increased and the Hartmann number decreased.  
Figure 3: Effect of Hartmann number and Rayleigh number on the isotherm contours.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 4 Proposed Solution  
This section contains the detail of the proposed scheme. It is a group-based location privacy scheme in the case of a vehicular network. There are some road network scenarios where vehicles have high speed and may have lower or higher traffic conditions. We proposed a new Dynamic Grouping and Virtual Pseudonym (DGVP) changing scheme for location privacy preservation. The proposed scheme is adaptive that considers the number of vehicles in transmission range. We consider also the velocity range, to make a group of vehicles to change the pseudonyms cooperatively. One of the vehicles is selected randomly as Group Head (GH) that will monitor all the vehicles' change of pseudonyms in the group. This kind of situation usually occurs on a main road where vehicles are moving at high speed. The group of vehicles is formed based on similar velocity range and same direction. The block diagram of the proposed scheme is shown in Figure 4. The GH collects information about transmission range vehicles and allows them to join the group. After that, the pseudonym change process is initiated. Pseudonyms of vehicles in the group are changed based on the number of neighboring vehicles. The neighbor threshold is used to apply the cooperative pseudonym update process or the virtual pseudonym update process. The proposed scheme's main components are vehicle grouping, pseudonym-changing protocol, and vehicle to LBS communication.
## 3 Numerical Simulation Method
### CFD Verification and Validation  
As a classical two-dimensional transonic flow simulation validation case, the RAE 2822 airfoil case is widely selected as a numerical simulation method validation case [63,64]. In this case, the Mach number of Case 9 in an original wind tunnel experiment [65] is 0.73, the angle of attack is 3.19\({}^{\circ}\), and the Reynolds number based on chord length (\(c\) = 0.61 m) is 6.5 \(\times\) 10\({}^{6}\). Here, the modified data [66] (Ma = 0.734, \(AOA\) = 2.79\({}^{\circ}\), \(Re\) = 6.5 \(\times\) 10\({}^{6}\)) are selected for verification and validation.  
In this paper, the C-type structure grid is chosen, and the far-field boundary is about 20 times the chord length. Five sets of grids with different cell numbers (3.5 \(\times\) 10\({}^{4}\), 7.0 \(\times\) 10\({}^{4}\), 14.0 \(\times\) 10\({}^{4}\), 28.0 \(\times\) 10\({}^{4}\), 56.0 \(\times\) 10\({}^{4}\)) are used for grid-independence research. Figure 2 shows the computational grid with a number of 14.0 \(\times\) 10\({}^{4}\) near the airfoil. The grids near airfoil wall, leading edge, and trailing edge are all encrypted. The first-layer grid near the wall grids is 3 \(\times\) 10\({}^{-6}\) m and the wall mesh has the y\({}^{+}\) < 1 to resolve the boundary layer problem.  
Table 2 shows the aerodynamic coefficient of different grids. The difference between the aerodynamic coefficient of the 14.0 \(\times\) 10\({}^{4}\) grid and the 28.0 \(\times\) 10\({}^{4}\) grid is less than 0.17%, which indicates that grid independence has been well achieved. It is verified that the numerical results obtained are independent of the density of the grid through the research of grid independence. Figure 3 shows the pressure coefficient \(Cp\) distributions on the airfoil surface resulting from numerical simulations and a wind tunnel experiment. The pressure coefficient distributions of the five grids are relatively close, but the shock waves captured by the 3.5 \(\times\) 10\({}^{4}\) and 7 \(\times\) 10\({}^{4}\) coarse grids are relatively inaccurate. Combining the results of \(Cp\) and aerodynamic coefficient, it is clear that the 14.0 \(\times\) 10\({}^{4}\), 28.0 \(\times\) 10\({}^{4}\), and 56.0 \(\times\) 10\({}^{4}\) grid cells could well reflect the flow state of the flow field, which confirms the accuracy of the numerical simulation method. On the premise of ensuring accuracy, the variable-camber and -chord-length airfoil will be investigated with 14.0 \(\times\) 10\({}^{4}\) grid cells to save computational resources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(C_{L}\) & \(C_{D}\) & \(C_{M}\) \\ \hline [PERSON] et al. [65], Exp. & 0.803 & 0.0168 & \(-\)0.099 \\
0.035M & 0.75551 & 0.017697 & \(-\)0.085625 \\
0.07M & 0.75672 & 0.017774 & \(-\)0.085838 \\
0.14M & 0.75744 & 0.017782 & \(-\)0.085960 \\
0.28M & 0.75743 & 0.017779 & \(-\)0.085974 \\
0.56M & 0.75748 & 0.017783 & \(-\)0.086020 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Aerodynamic coefficient for different grids.  
Figure 2: Computational grid near the airfoil.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 3 Proposed Algorithm
### Data Decryption Process  
Various steps are used to convert the ciphertext to plain text; firstly, the ciphertext is converted to ASCII, and then cipher XORation is implemented on the user-identified key, and binary values by converting the ASCII values to binary and decimal values are obtained. After obtaining the decimal values, the cipher matrix algorithm is implemented on the decimal values, and the values matrix text is obtained. Cipher matrix text is a decoded text. After that, the decimal values are obtained by implementing the Caesar cipher mechanism on the cipher matrix text. After obtaining the various decimal values, all decimal values are converted to eight-bit binary. Then, all the bits are reversed by first applying the bit-reversing mechanism, and then, salting is removed from the bit-reversing results, and plain text is obtained, as shown in Figure 9.  
In Algorithm 2, first, a ciphertext is taken, and each character of the ciphertext is converted to equivalent ASCII. After converting the ciphertext to ASCII, each ASCII value is converted to an 8-bit binary. After that, the binary of the ciphertext and key are XORed. After XORing the ciphertext and key, the binary values obtained are converted into a decimal by making eight-bit pairs, and the decimal values are converted into equivalent ASCII. After obtaining the various ASCII values, the cipher matrix algorithm is implemented, and the cipher matrix text is obtained. After obtaining the cipher matrix text, the Caesar cipher algorithm is implemented using the P = D \(-\) N formula. If the value of P is greater than 255, then mod 255 will be taken, and if the value of P is small, then the P values will be converted into decimals, and then, the eight-bit binary of each decimal will be obtained. After obtaining eight-bit binary values, salting is removed. Then, different decimal values are obtained by implementing a bit-reversing mechanism, and each decimal value is converted to its equivalent ASCII. Then, a plain text is obtained.  
Figure 9: Decryption Process.
## 2 Overview of the L-PBF Process
### Defects
#### 2.3.2 Porosity  
The porosity formation mechanism and its undesirable effects have been investigated frequently in the literature [55; 56; 57]. The presence of porosity affects the mechanical integrity, especially the fatigue life of L-PBF parts [58]. Figure 4 illustrates the effect of process parameters choice on the type of porosity formed in an aluminum alloy manufactured by L-PBF [59]. Generally, porosity can be classified according to its geometry, spherical (gas-induced) and non-spherical (process-induced). Gas entrapped in powder particles during the gas atomization process contributes to gas-induced pores [60]. These spherical porosities can also be attributed to the vaporization of low melting point elements during the L-PBF process [61]. Process-induced porosity formation mechanisms are mainly attributed to a lack of fusion and keyholing [62; 63]. The lack of fusion porosity is related to the insufficient overlap of successive meltpools [64; 65]. Therefore, powder remains un-melted, causing a reduction in the overall density of the part. In contrast, the keyholing phenomena are caused due to the significantly higher laser beam power that causes the metal evaporation and leads to the formation of plasma [66]. This causes the laser beam to penetrate the melted part deeply and forms a vapor cavity within the part [67]. The porosity formation can be monitored by in situ temperature measurements and a proper understanding of how thermal history affects the formation mechanism.
# Shape and Weighting Optimization of a Subarray for an mm-Wave Phased Array Antenna
## 2 Weighting Optimization for Individual Subarrays by Quadratic Programming
### Analysis of Subarray Weighting Optimization  
Figure 3 shows the distribution of the optimized weighting assigned to the first quadrant of the array antenna. The optimized weighting was derived using QP and was conducted via origin symmetry. Figure 4 shows the radiation pattern of the array antenna with optimized weighting applied. In addition, a single element pattern was applied with \(cos^{1.5}\theta\). When the beam is in the boresight direction, the HPBW and PSL values are \(3.6^{\circ}\) and \(-26.16\) dB at the azimuth cut and \(3.6^{\circ}\) and \(-27.36\) dB at the elevation cut, respectively.  
In Figures 5-7, the beam-scanning characteristics of the array antenna with TRMs in all 576 radiating elements and the array antenna with TRMs in only 96 subarrays are compared. Here, optimized weighting below a \(-26\) dB SL  
Figure 4: Radiation pattern in which optimized weighting is applied: (**a**) boresight direction in the 3-D plot, (**b**) boresight direction in the UV plot, (**c**) boresight direction at the azimuth cut, and (**d**) boresight direction at the elevation cut.  
Figure 3: Distribution of optimization weighting assigned to each subarray (1 st quadrant): (**a**) distribution of the optimized weighting of the subarray (3-D), and (**b**) distribution of the optimized weighting of the subarray (top view).  
antenna with the subarray, and \(-25\) dB Taylor weighting was applied to the array antenna without the subarray. The number of TRMs used in the proposed phased array antenna with a subarray was reduced by \(83.3\%\) compared to the phased array antenna in which TRMs were attached to all radiating elements. In a narrow-angle beam-scanning system, the SLLs and HPBWs in the two set of results above were similar. For the radiation patterns, a \(cos^{1.5}\theta\) single element pattern was applied. Table 1 shows that qualitative results, i.e., the difference between the above two results, can be confirmed.  
Figure 5: Radiation pattern about the boresight direction at 30 GHz (w/a subarray (96 TRMs) vs. w/o a subarray (576 TRMs)): (**a**) azimuth cut, and (**b**) elevation cut.  
Figure 6: Radiation pattern when scanning the azimuth at 30 GHz (w/a subarray (96 TRMs) vs. w/o a subarray (576 TRMs)): (**a**) \(1^{\circ}\) scanning, (**b**) \(3^{\circ}\) scanning and (**c**) \(5^{\circ}\) scanning.  
Figure 7: Radiation pattern when scanning the elevation at 30 GHz (w/a subarray (96 TRMs) vs. w/o a subarray (576 TRMs)): (**a**) \(1^{\circ}\) scanning, (**b**) \(3^{\circ}\) scanning and (**c**) \(5^{\circ}\) scanning.  
Calculation of the Array Pattern with Optimized Weighting Applied to Each Subarray Considering Mutual Coupling
## 2 Materials and Methods
### Class Definitions
#### 2.4.2 Open Water  
The incident radar pulse typically exhibits a single specular reflection on a smooth open water surface, resulting in little or no scattering of the signal back towards the SAR antenna. This in turn results in very low backscatter in both the co-polarised (HH) and cross-polarised (HV) channels, as shown above in Figure 2d. Wind and rain can create a rough surface with respect to the wavelength of the SAR, and therefore increase the backscatter from the water surface [47]. However, this increase is predominately within the HH polarisation and is normally small in comparison to the backscatter from the vegetation [47]. The normalised polarisation difference ratio is therefore a valuable indicator of open water as both bands show similarly low backscatter responses.
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 4 Transient Contents Caching in NDN
### Caching Policies in IoT and Sensor Networks
A different caching threshold is assumed that varies depending on the node's category: edge nodes have the smallest threshold in order to cache more data and reduce the retrieval delay. Vice versa, root nodes have the highest caching threshold and are supposed to cache only long-lasting contents. Moreover, the threshold is adapted according to the storage space and the number of incoming requests, as previously conceived in the popularity-based scheme in Reference [48]. The threshold reduces if the cache has free storage space and the incoming request rate decreases, while it increases in the opposite case. All in all, it can be observed that freshness and popularity are two crucial content parameters when dealing with caching transient contents. In addition, some attributes of the nodes, like storage space, topological position, and possible energy constraints, can be taken into account, as in traditional caching strategies for static Internet contents [49].
## 3 Experimental Scenario and Specifications
### NB-IoT Specifications  
NB-IoT, when operated at an altitude under \(600\,\mathrm{km}\) in a LEO constellation, is subject to Doppler shift, which is acknowledged but not further detailed here. To ensure robust performance in a satellite-based IoT-NTN environment, the frequency was set to \(1995\,\mathrm{MHz}\), with the single-carrier bandwidth limited to \(180\,\mathrm{kHz}\)[11]. These frequency and bandwidth selections are proposed based on conventional wisdom to be advantageous for the IoT-NTN environment.  
According to 3 GPP Release 13, the maximum bit rate (\(R_{b}\)) for single-tone NB-IoT transmission can reach \(16.9\,\mathrm{kbps}\); however, for this study, \(R_{b}\) was arbitrarily set to \(10\,\mathrm{kbps}\) based on IoT power consumption considerations and the resulting link margin requirements.  
The receiver sensitivity was calculated using a \(15\,\mathrm{kHz}\) sub-carrier spacing and the following equation [29]:  
\[\mathrm{RS}_{\mathrm{dBm}}=30+10\log_{10}(k_{B}T_{\mathrm{sys}}\cdot\mathrm{ BW}). \tag{6}\]  
Based on the NPUSCH format 2 settings from 3 GPP Release 16, with a sub-carrier spacing of \(15\,\mathrm{kHz}\) and a repetition factor of \(16\), the SNR was determined to be \(-3.9\,\mathrm{dB}\)[30], which requires an \(E_{b}/N_{0}\) of \(6.9\,\mathrm{dB}\). The transmission power was set to \(-7\,\mathrm{dB}\)W, balancing efficient power usage with reliable data transmission.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Quantum Deep Learning Techniques with Copyright Detection
#### 2.2.3 Recent Developments in Quantum-Enhanced Deep Learning  
Recent developments in quantum-enhanced deep learning have focused on creating hybrid models that combine classical neural networks with quantum circuits. Quantum convolutional neural networks (QCNNs) [46] have emerged as a promising approach, utilizing quantum layers and strongly entangled circuits to process both classical and quantum data [47]. Researchers have demonstrated potential advantages in certain computational tasks, such as image recognition and optimization problems, where quantum parallelism and interference effects could provide an edge over classical methods. However, challenges remain in scaling these models to larger problem sizes and demonstrating clear practical advantages over classical algorithms, mainly due to limitations in current quantum hardware.
# Production and Processing of a Spherical Polybutylene Terephthalate Powder for Laser Sintering
## 3 Results and Discussion
### Particle Morphology and Shape  
SEM images of the produced powder are shown in Figure 7a,b. The powder has a spherical morphology with smooth surfaces. The particles are solid and do not show any porosity on the surface. Despite improvements to the size distribution by classification, particles smaller than 10 \(\upmu\)m are still visible, often clustered together against larger particles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Powder** & **D10 [\(\upmu\)m]** & **D50 [\(\upmu\)m]** & **D90 [\(\upmu\)m]** & **Volume in 10–100 \(\upmu\)m Range [\%]** \\ \hline As-extruded (30 rpm, WSN) & 5.95 & 37.33 & 156.9 & 67.7 \\ After settling tank & 11.58 & 62.45 & 217.6 & 59.6 \\ After settling tank \& sieving & 8.87 & 35.0 & 111.0 & 78.71 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Particle size distributions of the powder after different classification process steps.  
Figure 6: (a) Schematic representation of the designed settling tank; (b) Particle size distribution in the effluent from the outlet of the settling tank.  
The particle shape was evaluated quantitatively with optical microscopy. Three parameters can be taken as useful shape descriptors, as elaborated in [21]. The elliptic smoothness \(\sigma_{\rm E}\) is defined as the ratio between the perimeter of the particle and that of its surrounding fitted ellipse. This gives an impression of the regularity of the particle surface. The shape is further defined by the particle solidity S, the ratio between the surface area of the particle and its surrounding convex hull. Finally, the aspect ratio A defines whether a particle is stretched or has a more isotropic shape.  
The shape descriptors found for the PBT powder and two reference materials are listed in Table 4. For the PBT powder, a \(\sigma_{\rm E}\) of 1.06 is determined. This corresponds well with successful current laser sintering materials such as Duraform(\(\delta\)PA) (3 DSystems, Rock Hill, SC, United States, 2018) and iCoPP (Asphia PP, Aspect Inc., Tokyo, Japan, 2018), a spherical polypropylene powder [31]. Similarly, the mean aspect ratio found for the PBT powder is 1.22, indicating only slight deviation from isotropic spherical particles. This value is similar to iCoPP, whereas Duraform(\(\delta\)PA typically has a higher aspect ratio. The solidity of the spherical materials is similar, the more irregularly shaped Duraform(\(\delta\)PA also has a lower solidity.  
These results show that the melt emulsification process is exceptionally suited to produce particles with sizes and shapes that resemble and even surpass commercially used laser sintering materials. As demonstrated in [15; 16; 17; 18; 19], the particle shape to a large extent determines the flowability and packing density of the powder, and in this way the density and mechanical properties of produced parts. The favorable shape of the current PBT powder permits the production of dense, mechanically stable parts.
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 4 Network Operation Phases  
In the context of mobile WSN operations, which encompasses deployment, maintenance, and retrieval, the proposed system adheres to a logical sequence depicted in Figure 1.  
In mobile WSNs, the deployment phase is carried out by a set of mobile nodes: vehicles, specialized workers, or mobile robots. The mobile nodes aim to reach maximum coverage by determining optimal positions according to different algorithms. Once the network is deployed, the network operates routing traffic and during this phase, the necessary adjustments must be performed to keep the network operational for as long as possible while maintaining the connected coverage. However, over time, the number of degraded nodes increases, which can lead to such a large number of inoperable nodes that the network is considered to be depleted and reach the end of its operational life.  
In this paper, we consider the mobile elements to be ground robots with embodied sensors. The movement of each robot is determined by the application of the concept of Social Potential Forces (SPFs) [44, 45]. The concept of SPFs comprises two distinct types of virtual forces: repulsion forces and attractive forces. The aforementioned forces are combined to form an emergent vector, which is then decomposed along the 2D or 3D axes in order to determine the motion and direction to be followed by the robot. Potential Forces algorithms are well known in the literature due to their low computational requirements and modest bandwidth demands.  
Figure 2 presents the operations performed during each phase for the two strategies that are developed in this article, SPF-only and Agents. The SPF-only strategy applies a different set of forces according to the network phase. The agents approach adds an intelligent layer when the network is in the maintenance phase, allowing to replace degrading robots and balancing traffic.  
Figure 1: General phases for MWSN.