esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hardware System Design
### Census Transform  
Census transform used in the design is adapted to exploit parallelization. Figure 13 shows a segment of an image where pixels are arranged in rows and columns (3 \(\times\) 5). In this case, window size for the census transform is set to: M \(\times\) M = 3 \(\times\) 3. The process starts by selecting center pixel \(P_{r}\) that is correlated with the surrounding pixels within the respective window. In the next iteration, window shifts to right and the next center pixel \(P_{ri+n}\) is selected and process goes on. These windows are represented by dotted lines and distinct colors. This process is completely pipelined in the proposed design, which allows iterations to run concurrently. Moreover, in hardware design, resource usage is optimized by using the arbitrary width data type. The corresponding number of bits is set to: \(M\times M-1\). The inter-iteration dependencies are resolved by using a buffer of depth \(M\times M-1\) to store the bit comparison results during the correlation process.  
Hamming distance is well established as a cost function for census transform. It is calculated for a target range and it results in dissimilarity between a pair of pixels. The pixel with the minimum Hamming distance corresponds to the best match for a given pixel \(P_{r}(x,y)\). For a \(M\times M\) window census transform, the number of bits \(n\) (bit length) of each transformed pixels is \(M\times M-1\). Sequential realization of Hamming distance implements long carry chains of the adders. The optimized hardware solution is a balanced tree implementation that uses adders with arbitrary number of bits for intermediate results, as shown in Figure 14. It consequently reduces the resource consumption. Total number of required adders are: \(n-1\). This figure shows the realization of a 5 \(\times\) 5 census transform window, so the bit length of pixels is 24, and the total number of adders required are 23. Maximum number of bits \(m\) for the adder is calculated by \(2^{m}\), which is equal to or greater than \(n\), so in this case \(m\) is 5. This implementation only requires two clock cycles to produce the results, which includes operations: comparing bits (XOR), shifting right, sum of the set  
Figure 12: Spatial parallelization during stereo-matching.  
Figure 13: Census transform with a reference pixel.  
bits and in the end a comparison to check if it is the minimum distance and calculating the corresponding disparity.
# Performance of Ore Sand as Aggregate for Interlocking Blocks
## 2 Materials and Methods  
### Production of Paves  
The relative amounts of components used by local companies were applied to the production of the traditional blocks. For the blocks constructed with OS, the proportion of aggregates was determined based on the ideal granulometry for block production. Pilot tests were performed in a block factory, where the ideal amount of aggregates to use in molding the blocks was determined. Based on these preliminary results, it was found that ore sand could replace 100% of the limestone sand and part of the natural sand (13.3%). The ordinary Portland cement (OPC) was the same for both blocks tested. This additive is a plasticizer. It was obtained in the regular market and used for all mixtures tested. This additive enables dry concrete to be produced with less water, improving the plasticity and lubricity of the compound. Table 1 shows the proportion of each material used for the production of interlocking blocks.  
Figure 1: X-ray-diffraction results for the ore-sand sample.  
Figure 2: Particle-size distributions of the aggregates.  
The blocks were produced in a factory specialized in manufacturing these components. The mixing of the raw materials is automated, and the compaction of the dry concrete is finished in a hydraulic vibro-press. The produced paves have a parallelepiped format with the dimensions 100 mm \(\times\) 200 mm \(\times\) 80 mm. After molding, the specimens were cured in a humid room (above 98% relative humidity). The compression test was performed after 15 and 28 days of curing, per the Brazilian standard [24]. For the strength test, standards require samples (blocks) to be saturated 24 h before the test, loading surfaces to be straight, auxiliary plates to be used, and loading to be carried out continuously at 550 kPa/s until complete rupture with equipment Class 1 (ISO 7500-1 [32]). Six replicate samples of each mixture were tested for conventional and OS block paves. Interlocked paving blocks subject to light vehicle traffic must have an estimated minimum typical compressive strength of 35 MPa [24].
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Robustness Against Common Attack Vectors
#### 5.3.5 Side-Channel Attacks  
Side-channel attacks exploit physical information leaked during computation (power consumption, timing, etc.). While our current evaluation does not directly address side-channel attacks, we acknowledge their importance, particularly for implementations on embedded devices. Mitigation strategies against side-channel attacks such as constant-time implementations and masking techniques are crucial for real-world deployments and are a subject of ongoing research in the PQC community. We recommend using side-channel-resistant implementations of the chosen PQC algorithms.
# Smart Clothing Framework for Health Monitoring Applications
## 4 Challenges and Future Development
### Social Acceptability
#### 4.3.4 Cost of Smart Fabric  
The high cost of smart fabric is an important factor to be considered, particularly for low-income people [154]. However, increasing the number of users through promoting the benefits of smart clothes will promote their mass production, which can bring down the cost to an affordable level for all classes of people.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### The Results of Information Communication Theory Analysis
#### 4.2.4 Effect of Clustering Coefficient  
To analyze the effect of clustering coefficient \(p\) on information propagation, we set the hypernetwork construction parameters as \(m_{0}=6\), \(m_{1}=3\), \(m_{2}=3\), \(m=3\), \(\beta=0.1\), \(\gamma=0.1\), and \(p=0,\,0.2,\,0.4,\,0.6,\,0.8,\,1\). The information propagation curves for different clustering coefficients are presented in Figure 8. As demonstrated, the information propagation rate slows down with increasing \(p\), but all the curves tend to reach the same steady state value. The larger the value of \(p\), the fewer direct connections between nodes and therefore, the slower the rate of information dissemination. However, when the steady state is reached, all curves converge to the same value, thus confirming the argument that \(p\) is independent of \(p\) made in Section 3.2. To enhance the scientific validity of the simulation, all subsequent experiments were set with \(p=0.6\) as the clustering coefficient.  
Figure 7: Simulation outcomes with various initial spreading seeds.  
4.2.5. Impact of Spreading Rate and Recovering Rate  
To assess the impact of spreading rate and recovering rate, the following parameters were set: \(m_{0}=6\), \(m_{1}=3\), \(m_{2}=3\), \(m=3\), and \(p=0.6\).  
(i) Effect of \(\beta\)  
By setting the spreading rate \(\beta\) to 0.05, 0.1, and 0.2, while keeping the recovering rate \(\gamma=0.1\) and the other parameters constant, we obtained the information diffusion graph presented in Figure 9. The dissemination rate depicts a person's capability to spread information. The propagation rate affects not only the speed of information dissemination but also the density of I-state nodes at the steady state. The spreading rate is proportional to the propagation speed as well as the steady-state value.  
Figure 8: Information dissemination curves with different clustering coefficients.  
Figure 9: Information diffusion curve with various spreading rates.  
2. Effect of \(\gamma\) We set \(\beta=0.1\) and \(\gamma=0.05\), \(0.10\), and \(0.20\), to obtain the information propagation curve depicted in Figure 10. The recovering rate reflects a person's ability to resist or forget information. As demonstrated in the figure, the three curves take almost the same amount of time to reach the maximum point, and the slope of the curves hardly varies when the recovering rate changes. This disparity indicates that the recovering rate has a small effect on the rate of information diffusion. Larger recovering rates result in lower densities of I-state nodes at the steady state.
## 4 PSO Based on the PSO-BP Model
### Results and Analysis  
#### 4.5.1 MSE Evaluation of the PSO-PB Model  
As the training cycles increased, the MSE of the training, validation, and test groups was used for performance evaluation, as shown in Figure 13. The optimal validation performance (MSE = 0.0052405) was achieved at the seventh training cycle, indicating successful training of the PSO-BP neural network. This network exhibits high fitting accuracy, fast convergence, and strong stability, meeting experimental requirements.  
Figure 12: Construction of the backpropagation (BP) neural network model.
## 5 Discussion
### Influence of Stone Content on the Key Shear Zone Thickness  
The key shear zone thickness initially increased and then decreased with the stone content increasing (Figure 15). The slope of the curves increased gradually, indicating that the increase in stone content enhanced the shear stiffness of the SRM to a certain extent. For the same block size, the interlocking effect of the block stone increased with stone content increasing. The \"gear effect\" of the block stone was gradually increased, and the key shear zone thickness increased. When the stone content was approximately 50%, the thickness of the key shear zone reached a maximum, and the \"gear effect\" was most prominent. At this time, the block stones could produce slippage, drag, flipping, and interlocking (Figure 16). When the stone content reached approximately 70%, the aerial structure of the block stones had formed in the sample. The fine-grained soil distributed among the stones was not dense. The stress state of a point contact was formed among the block stones. The relative motions of the block stones were characterized by the cutting of rock corners, stone crushing, or staggered redistribution. Therefore, the key shear zone thickness decreased accordingly.  
In the vicinity of the shear plane, two types of block stones arrangements occurred: One was a single row throughout the shear surface, and the other was a double row (Figure 17a,d). The upper parts of the block stones were dyed black. In the shear process, the block stones were compacted and rolled with the slippage of the shear surface. The stones marked with black pigment have clearly been reversed, as shown in Figure 14b,c. In contrast, it was difficult to flip the block stones arranged in two rows. Instead, the shearing was mainly manifested as horizontal compaction and climbing movement among the stones along the interface between the upper sample and lower sample (Figure 17e,f). The fracture surface of the SRM formed along the stones surrounding the shear plane, i.e., the block stones produced a dilatancy effect. Thus, under the conditions of high stone content and large particle size (Figure 15c), the large stones near the shear plane had a dilatancy effect with shear sliding, resulting in an increase of the effect range of the shear zone.  
Figure 14: Relationship between the key shear zone thickness and the levels of different factors.  
Figure 16: Schematic diagram of the movement state of block stones during shearing. (**a**) Before shearing; (**b**) after shearing.  
Figure 15: Effect of stone content on the key shear zone thickness. (**a**) Stone size \(L1\); (**b**) stone size \(L2\); (**c**) stone size \(L3\).
## 3 Results  
In this section, a comparison between experimental data and simulations obtained with the LILITA_N21 code is presented. For the purpose of comparison, the two calculations described in the following section were considered. The aim was to reproduce the observables of the 190 MeV \({}^{40}\)Ar + \({}^{27}\)Al reaction forming \({}^{67}\)Ga compound nuclei at an excitation energy of about 91 MeV and angular momentum up to 46 h. We considered this reaction because it represents a good test case for which high quality data that include proton and \(\alpha\)-particle energy spectra and angular distributions are available. The authors of [11] evidenced the limitations of the standard statistical model implemented in the single-step Monte Carlo code GANES in simultaneously reproducing all the observables. A satisfying reproduction of the full set of observables was not obtained by assuming reduced barriers for the charge particles and deformations much larger than those expected for nuclei at large angular momentum (i.e., in the extreme region [33]), Ref. [12].  
A peculiar parameter combination that reproduces only part of the reaction products cannot provide a good description of the CN; hence, we carried out a new analysis. The simulations here were performed with LILITA_N21, a multi-step Monte Carlo evaporative code into which the parameters shown in Table 1 were fed. As leading prescriptions in both calculations, a constant NLD \"a\" parameter (a \(=\) A/8 MeV\({}^{-1}\)) and an OMTC from the newest systematics for neutrons, protons, and \(\alpha\) particles [27; 28] were adopted. The difference among the two calculations is the model describing the nuclear shape. In the first, we considered emitting nuclei with a spherical shape (SS) and an r\({}_{0}\) = 1.2 fm, whereas for the second we used the Nuclear Stratosphere (NS) model according to the parameterization given in [13].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **NLD \"a\"** & **Nuclear Shape Parameters** & **TC** \\ \hline SS & A/8 & \(r_{0}\) = 1.2 fm & OM [27; 28] \\ NS & A/8 & \((h,b)\) = (0.079, 0.7) & OM [27; 28] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input parameters for the LILITA_N21 simulations.
# A Novel L-Shaped Metalens for Ultra-Wide Band (UWB) Antenna Gain Improvement
## 3 Design and Results
### AVA with Metalens Design
The simulated E-field distributions in the \(xy\)-plane of the AVA without a metalens and AVA L-shaped#1 at a frequency of \(f=14\) GHz are shown in Figure 10. As expected, the near-field of the antenna with a metalens exhibits wavefronts that are flatter than those of the AVA without a metalens, and the radiation in the end-fire direction is increased. Moreover, the L-shaped#1 metalens reduces the antenna back propagation. The simulated 3D view of the normalized radiation patterns at a frequency of \(f=14\) GHz for the AVA and AVA L-shaped#1 are compared in Figure 11. The lens improves the antenna's directivity and reduces the half-power bandwidth (HPBW). To conclude, the L-shaped#1 metalens is chosen as an interesting solution for prototype fabrication. To verify the effects of the metalens on the antenna performances, the full-wave simulation of the gain \(G\) as a function of the frequency for AVA L-shaped#1 (solid curve) and AVA with EHL, having a thickness of \(d_{EHL}=0.832\) mm and values of permittivity \(\varepsilon_{r}=4.98\) and permeability \(\mu_{r}=0.75\) (dashed curve) are compared in Figure 12. The dimensions in the \(x\)-direction and \(y\)-direction of the EHL are the same for the metalens L-shaped#1, and the thickness \(d_{EHL}\) corresponds to the sum of the substrate and double metal strips thicknesses. A very good agreement is evident.
## 3 Analysis of Damping Layer Parameters
### The Effect of the Damping Layer's Position  
We fix the thickness of the damping layer at \(h_{d}=0.0002\,m\), and consider four different scenarios of adhesive attachment positions:  
\[\begin{array}{llll}Case&1:&x_{d1}=0.14\,m,&x_{d4}=0.26\,m,\\ Case&2:&x_{d1}=0.15\,m,&x_{d4}=0.25\,m,\\ Case&3:&x_{d1}=0.16\,m,&x_{d4}=0.24\,m,\\ Case&4:&x_{d1}=0.18\,m,&x_{d4}=0.22\,m.\end{array} \tag{23}\]  
Other parameter values of the damping layers continue to utilize those listed in Table 1. Figure 4 illustrates the effect of various placements of damping layers on the modal loss factor. It can be observed from Figure 4 that although Case 1 has the longest adhesive length, the variation in the system's damping coefficient is not pronounced compared to other adhesive lengths. As the mode shape order increases, the impact of different adhesive lengths gradually converges. This suggests that the adhesive length has a minimal influence on the ABH effect in this case and can be disregarded.
## 2 Background and Key Features
### UAV-Assisted SAGINs
#### 2.1.2 Characteristics of UAV-Assisted SAGINs  
The hierarchical complexity of an SAGIN primarily stems from the integration of its various network layers. For example, the SN segment, particularly GEO, has a much lower data transmission rate compared to TNs. This is determined by the characteristics of network components; currently, satellite nodes have limited data bandwidth but can provide global network access. In addition to cross-layer performance gaps, differences within the same network layer also impact overall network performance. For instance, there are significant variations in propagation delay and data transmission rates among GEO, MEO, and LEO satellites. To address these differences, researchers are optimizing network structures and functions based on the characteristics of the nodes. As described in [16], we summarize the key characteristics of SAGINs and present them in Table 2.  
In addition, frequency band allocation is another critical issue in SAGINs and the frequency band allocation standards for wireless communications are set by the International Telecommunication Union (ITU) to promote the efficient global utilization of the spectrum and avoid signal interference. In SAGINs, the communication frequency bands for terrestrial services are widely distributed, while frequency band allocation for aerial services remains uncertain. Therefore, our focus is primarily on analyzing the frequency band information allocated for space-to-earth (StE) services, as shown in Figure 3. Moreover, the 3 GPP  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Component** & **Altitude (km)** & **Full Coverage Requirement** & **Transmission Delay** & **Propagation Loss** & **Data Rate** \\ \hline \multirow{3}{*}{Satellite} & GEO & 35,786 & 3 & \textgreater{}200 ms & High & Limited \\ \cline{2-7}  & MEO & 2000 to 35,786 & 6 to 12 & 100 to 150 ms & Medium & \\ \cline{2-7}  & LEO & 300 to 2000 & \textgreater{}100 & \textless{}20 ms & Low & \\ \hline \multirow{3}{*}{Aerial} & HAP & 10 to 50 & - & \multirow{3}{*}{Medium} & Low & \\ \cline{2-7}  & LAP & \(\leq\)10 & - & & Low & Up to 10 Gbps \\ \hline \multicolumn{2}{c}{Ground} & - & - & Lowest & Lowest & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of UAV-assisted SAGIN characteristics.  
is advancing the development of frequency band standards for 5G New Radio in NTNs, and more frequency bands applicable to SAGINs are expected to be further included.
## 4 Experiment Results and Discussion
### Vibration Control Experiments Based on Single Frequency Excitation
#### 4.2.2 **Case-2**: Diverse Frequencies and Diverse Amplitudes  
In Figure 12, the vibration control effects under diverse frequency and diverse amplitude excitations are shown. The three groups of experimental findings are presented in Table 4. According to the experimental results, the interference frequency is within 0.244\(-\)0.343 Hz and the interference amplitude is within 3.5-8.4\({}^{\circ}\). After the dual joint control, the vibration reduction effect can reach 15.23-18.63 dB.  
To summarize, in the case of single-frequency excitations (**case-1** and **-2**), the interference frequency was 0.23-0.343 Hz. Normally, with the external excitation frequency being the first natural frequency of the system, the vibration suppression effect is most obvious. However, herein the external excitation frequency was less when compared with the natural frequency of the system, when dual joints are loaded with DC-2 A, resulting in no obvious effect of the magnetic stiffness control of the system. Therefore, magnetic damping was the major contributor, while magnetic stiffness was the auxiliary. Furthermore, it was also experimentally verified that the dual joint actuation had good synchronization.  
Figure 11: Vibration control experiments based on the same frequency yet diverse amplitude excitations at 0.23 Hz. (**a**) interference amplitude of 11.71\({}^{\circ}\); (**b**) interference amplitude of 10.43\({}^{\circ}\); (**c**) interference amplitude of 9.23\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Major Frequency**} & \multicolumn{2}{c}{**Amplitude**} \\ \hline \multirow{4}{*}{**I**} & \multirow{4}{*}{0.244 Hz} & \multicolumn{2}{c}{Without control} & \multicolumn{2}{c}{With control-6 A} \\ \cline{3-5}  & & Time domain & \(-\)8.36–8.22\({}^{\circ}\) & \(-\)1.25–1.22\({}^{\circ}\) \\ \cline{3-5}  & & Frequency domain & 8.26\({}^{\circ}\) & 1.43\({}^{\circ}\) \\ \cline{3-5}  & & Control effect & 82.69\% (\(-\)15.23 dB) \\ \hline \multirow{4}{*}{**II**} & \multirow{4}{*}{0.282 Hz} & \multicolumn{2}{c}{Without control} & \multicolumn{2}{c}{With control-6 A} \\ \cline{3-5}  & & Time domain & \(-\)6.71–6.68\({}^{\circ}\) & \(-\)1.13–1.07\({}^{\circ}\) \\ \cline{3-5}  & & Frequency domain & 6.16\({}^{\circ}\) & 0.83\({}^{\circ}\) \\ \cline{3-5}  & & Control effect & 86.52\% (\(-\)17.41 dB) \\ \hline \multirow{4}{*}{**III**} & \multirow{4}{*}{0.343 Hz} & \multicolumn{2}{c}{Without control} & \multicolumn{2}{c}{With control-6 A} \\ \cline{3-5}  & & Time domain & \(-\)3.52–3.49\({}^{\circ}\) & \(-\)0.45–0.39\({}^{\circ}\) \\ \cline{3-5}  & & Frequency domain & 3.50\({}^{\circ}\) & 0.41\({}^{\circ}\) \\ \cline{3-5}  & & Control effect & 88.29\% (\(-\)18.63 dB) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vibration control effect based on diverse frequency and diverse amplitude excitations.  
Figure 12: Vibration control experiments based on diverse frequency and diverse amplitude excitations at (**a**) 0.244 Hz, (**b**) 0.282 Hz and (**c**) 0.343 Hz.
## 3 Airborne Segment
### Airborne Antennas Technologies
It is necessary to determine the best TX and RX beam pair for reliable communication. A beam alignment solution is represented by resorting to training and tracking scheme [174] by identifying the best beamforming array feeding among all beam direction combinations. However, if highly directive beams are adopted both at the TX and RX side, the wireless communication system will suffer a large beam setup time. To overcome this issue, the adaptive beamwidth approach has been proposed [175]. First, the TX and RX find their angular sectoral by using a wide beam. Then, the beam alignment management narrows down their beamwidth gradually to reach their maximum directivity. Other solutions are based on a combination of both mechanical adjustment for coarse alignment along with a fine beam tuning with electrical adjustment as proposed in [176]. In the mmWave and sub-terahertz domains, quasi-optical antenna solutions are also considered as a promising alternative to reduce the number of control nodes while keeping high gain figures [48]. In this respect, geodesic lenses have attracted some attention for their highly efficient fully-metallic design implementation [177]. Metamaterials are also considered to be a promising avenue to further enhance the performance of array designs, addressing their miniaturization and inter-element coupling mitigation [178].