esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the Transition Zone in the Cu/Zn1.5 Mg Joint  
The analysis focused on the transition zone at the boundary of the Cu/Zn1.5 Mg joint, where a significant interaction took place between the molten solder and the copper substrate, leading to the creation of a broad transition zone consisting of two novel Cu-Zn phases. The bonding process is primarily influenced by the presence of zinc from the solder. The copper solubility range in the zinc solder is minimally 35 \(\upmu\)m. EDX analysis was utilized to examine the chemical composition of the distinct phases newly formed in the transition zone. The measurement results are presented in Figure 18 and Table 7 below.  
Measurement points for EDX analysis were chosen at four locations, ranging from Spectrum 1 to Spectrum 4.  
In measurement point Spectrum 1, planar analysis was carried out in the one-phase zone, where the intermediary phase \(\gamma\) (electron compound type Cu\({}_{5}\)Zn\({}_{8}\)) was found in agreement with the binary diagram Cu-Zn [22]. It concerned a cubic lattice with 52 atoms.  
\begin{table}
\begin{tabular}{c c c} \hline
**Spectrum** & **Zn [at. \%]** & **Mg [at. \%]** \\ \hline Spectrum 1 & 65.6 & 34.4 \\ Spectrum 2 & 83.9 & 16.1 \\ Spectrum 3 & 84.1 & 15.9 \\ Spectrum 4 & 83.5 & 16.5 \\ \hline \end{tabular}
\end{table}
Table 7: Results of point EDX analysis of Cu/Zn1.5 Mg joint.  
Figure 18: EDX point analysis of boundary in Cu/Zn1.5 Mg joint.  
The measurements in Spectrums 2 to 4 revealed a band zone containing about 84 at. % Zn and 16 at. % Cu. According to the binary diagram [22], it concerns a zone of \(\epsilon\) phase (CuZn\({}_{4}\) or Cu\({}_{0.2}\)Zn\({}_{0.8}\), respectively).  
In measurement point Spectrum 1, a zone of pure copper occurs-a composite matrix with Si admixture.  
The map of Cu and Zn elements (Figure 19b,c) in the boundary of the Cu/Zn1.5 Mg joint proves the presence of the \(\gamma\)Cu (Cu\({}_{5}\)Zn\({}_{8}\)) phase and the band zone of the \(\epsilon\) (CuZn\({}_{4}\)) phase.
## 4 Proposed Epileptic Seizures Detection Model
### Classification Techniques
#### 4.4.3 Random Forest (RF)  
Random Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees. This technique mitigates the overfitting problem associated with single decision trees by averaging multiple trees, thus improving generalization. RF's robustness and high accuracy make it a popular choice for EEG classification tasks. Its ability to handle large datasets with higher dimensionality, along with its built-in feature importance estimation, helps in identifying the most relevant features for seizure detection. The ensemble nature of RF ensures stable and reliable predictions, enhancing the overall performance of the model.
## 5 Non-Iterative BER-Driven DDM Synthesis
### General Procedure  
We start by selecting the modulation scheme, the antenna array (\(\mathbf{AEP}(\phi)\)), the secure observation angle \(\phi_{0}\) and BER(\(\phi_{0}\)). We apply (2) to derive \(\mathbf{B}_{m\_conv}\) and, thereafter, (1) and (10) to evaluate \(rp_{\_conv}(\phi)\). Taking into consideration that, at \(\phi_{0}\), zero-mean  
Figure 5: Array of four omnidirectional antennas along the x-axis, spacing \(\lambda/2\). QPSK modulation, \(\phi_{0}=90^{\circ}\), SNR(\(\phi_{0})=\infty\), \(PE_{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{  
AWGN is the only noise contribution, we derive \(SNR_{av}(\phi_{0})\) using BER(\(\phi_{0}\)), the modulation scheme, and [36], subsequently, we apply (12) to obtain \(N_{AWGN}\).  
Secondly, for \(K\) observation angles \(\phi_{k}\), we define restrictions BER(\(\phi_{k}\)). Assuming that the total noise distribution at \(\phi_{k}\) follows a zero mean Gaussian distribution, \(SNR_{av}(\phi_{k})\) is calculated using BER(\(\phi_{k}\)), the modulation scheme and [36]. Then, we use (12) to obtain \(rp_{av\_W}(\phi_{k})\).  
Next, we compute column vectors \(\mathbf{C}_{k}\) with the antenna weights that generate an interference radiation pattern with the main beam pointing to \(\phi_{k}\)  
\[\mathbf{C}_{k}=\mathbf{AEP}(\phi_{k})^{*}/\|\mathbf{AEP}(\phi_{k})\|\quad k\in \{1\dots K\}. \tag{15}\]  
In general, the interference radiation patters generated with \(\mathbf{C}_{k}\) will be also present in the secure observation angle \(\phi_{0}\). Therefore, these antenna weights need to be modified to steer a null of the interference radiation pattern along \(\phi_{0}\). According to the power pattern projection method in [23], the antenna weights \(\mathbf{A}_{k}\) that produce the desired radiation patterns can be obtained from \(\mathbf{C}_{k}\) as  
\[\mathbf{A}_{k}=\big{(}\mathbf{I}_{\mathbf{N}}-(\mathbf{B}_{m\_conv})^{-1} \mathbf{B}_{m\_conv}\big{)}\mathbf{C}_{k}\quad k\in\{1\dots K\}, \tag{16}\]  
where \(\mathbf{I}_{\mathbf{N}}\) is the N-by-N identity matrix and \({}^{-1}\) is the Moore-Penrose pseudoinverse.  
Finally, the weights that produce DDM are obtained as  
\[\mathbf{B}_{mp\_DDM}=\mathbf{B}_{m\_conv}+\sum_{k=1}^{K}G_{k}e^{i\phi_{pk}} \mathbf{A}(\phi_{k})\quad m\in\{1\dots M\},\quad p\in\{1\dots P\}, \tag{17}\]  
where \(\psi_{pk}\) is a random phase with uniform distribution updated for every \(p\) and \(k\). \(G_{k}\) is a constant to be computed in order to satisfy  
\[rp_{av\_W}(\phi_{k})=\|\ \mathbf{AEP}(\phi_{k})^{T}\cdot G_{k}\mathbf{A}_{k} \|^{2}\quad k\in\{1\dots K\}. \tag{18}\]  
It must be noted that, after (17), the injected artificial orthogonal noise follows a zero-mean non-Gaussian distribution as in Figure 4(b).
# Energy-Saving Breakthrough in the Point-to-Point Control of a Flexible Manipulator
## 3 Relationship between Initial Deflection and Drive Energy  
Figure 4 depicts the impact of initial deflection on both the displacement (\(w\)) and the drive torque (\(\tau\)) of the manipulator when it follows cycloidal motion (Equation (4)) under specific driving conditions (\(T_{E}\) = 0.6 s and \(\theta_{E}\) = \(\pi/6\) rad). In the figure, the dotted, thin, and thick lines represent the results for initial deflections \(w(l,\,0)\) of 5, 0, and \(-5\) cm, respectively. As depicted in Figure 4c,d, the maximum amplitude is smaller with a negative initial deflection (thick line) compared to scenarios with no initial deflection (thin line) and a positive initial deflection (dotted line). Correspondingly, the driving torque is also reduced. The driving energies, calculated using Equation (6), are listed in Table 1:  
\[E=\int_{0}^{\theta_{E}}\lvert\tau\rvert\,d\theta\,. \tag{6}\]  
Consistent with the observations from Figure 4, a negative initial deflection results in the lowest driving energy. Thus, it is inferred that driving energy can be minimized by inducing a substantial negative deflection in the flexible manipulator immediately after initiating the drive.
# Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
## 3 Proposed Algorithm for Content Protection in JPEG Images
### Generating the Visually Transformed JPEG Images
#### 3.1.4 Permutation of the Selected Blocks  
A random sequence R1 of 1 s and \(-1\) s is generated with a PRG, which takes a secret key as the seed. Then the signs of AC coefficients in the selected regions are flipped by multiplying the sequence of AC coefficients with R1, while the selected blocks are further scrambled with another random sequence, R2, generated with the same secret key. Specifically, the DC coefficients are kept unmoved and the lengths of the two random sequences depend on the number of selected blocks. Suppose there are \(n\) blocks in the selected region, the length of R1 should be \(n\times 63\) and R2 contains all integers between 1 and \(n\). Then the JPEG image with the permuted blocks is generated.
## 4 Measurement Results
connected to coplanar lines, and they are put under the ground plane, so the construction requires an insulating layer between DC wires and the ground plane. For this design, the insulator is SiO\({}_{2}\), while DC wires are made of gold. Totally, there are three technological stages. The first one is for golden DC connection lines under cells ground planes. The second one creates insulation for these lines, providing no connection between DC wires and ground planes. At the third stage, we make ground planes with double-slot design and coplanar lines. Major DC connection wires are also created at this stage. A quarter of the rounded array used for calculations is shown in Figure 6.  
The diameter of this rounded array should not exceed 4.5 mm. It is because of a predefined configuration of a back-to-back feedhorn, which will be used with this system. The optimal antenna cells number to fill this feedhorn configuration is 112. The feedhorn is located on the opposite side of the substrate. In the numerical modeling, the feedhorn is simulated by a waveguide port as a source of radiation. Here we work only with the mode E\({}_{01}\). Calculation of such quite a large array will require a lot of computational resources, so we use symmetry planes to decrease the calculation time. For the mode E\({}_{01}\) it is electric (E\({}_{t}\) = 0) symmetry plane at YZ plane and magnetic (H\({}_{t}\) = 0) symmetry plane at XZ plane. Due to this, we have to perform calculations for 28 antenna cells and the mesh cells number is about 3.5 million only. Without symmetry planes, these numbers would have increased by a factor of four. Figure 7 shows the accepted power versus frequency curves for all CEBs of the array at 220 and 240 GHz frequency channels, respectively. The bandwidth for the 220 GHz channel at half of the accepted power is about 18 GHz or 8.2%, from 210 to 228 GHz. It is quite close to meet the requirements for LSPE. The deviation from the required bandwidth  
Figure 6: A quarter of the antenna cells array, used for numerical modelling with symmetry planes. Blue circle shows the 4.5 mm waveguide feedhorn opening. is mostly at the lower frequencies, so to decrease the bandwidth an additional external filter can be used. The 240 GHz channel bandwidth is very close to meet LSPE project requirements. At half of the accepted power, the bandwidth is about 14 GHz or 5.8%, from 232.5 to 246.5 GHz. The absorption efficiency of the array has reached 81% and 77% for 220 and 240 GHz channels, respectively. The work on optimization of frequency response for 220 and 240 GHz channels is being continued.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## 3 Results and Discussion
### PAPR Analysis  
The second analysis involved the calculation of PAPR GFDM before and after the predistorter was given. PAPR occurs because in a multicarrier system it consists of a number of subcarriers modulated independently. Linearity requirements can be met by moving the PA far below the saturation point, but this causes poor power efficiency at the mobile transmitter. PA will be more power efficient if it operates close to the saturation region. Similarly, if the PA gets closer to the saturation region, the amount of nonlinear distortion is greater, which will reduce the system performance. Consequently, the nonlinear distortion produces harmonic and intermodulation signals outside the desired frequency band, which is disturbing for other users, especially in the adjacent frequency band. Therefore, interference must be reduced for both systems to operate satisfactorily.  
The PAPR value is greater than 5 dB because GFDM, like other multicarrier systems, has a high PAPR value. The simulation results show the PAPR GFDM before and after the predistorter was given, as shown in Figure 10. For the complementary cumulative distributive function (CCDF) curve presented, the x-axis is the PAPR (dB), while the y-axis is the probability (X \(\leq\) x). By applying the predistorter, the PAPR of GFDM dropped from 6.8 to 5.2 with a probability of \(10^{-2}\). The visible performance is very good, which should be around the value of 5 dB for it to be applied in an environment with thousands of users.  
Figure 11 is the result of CCDF display on a GFDM system with IBO variations. From the graph, if the IBO value gets bigger, the CCDF from the GFDM PAPR will decreases. Nonlinear distortion gets smaller if the IBO value gets higher. As a result, the HPA  
Figure 10: Comparison of PAPR GFDM with and without predistorter (IBO = 3 dB).
# Development and Characterization of High-Fiber, Gluten-Free Pasta for Celiac Disease Patients
## 3 Results and Discussion
### Chemical Composition of the Pasta Treatments
#### 3.1.5 Lipid Content  
The results of the lipid content percent are shown in Table 7. The percentages of lipid content of the control sample and pasta treatments were 9.32 for the control group, 14.29 for T1, 14.0 for T2, 15.33 for T3, 16.64 for T4, 19.87 for T5, and 21.99 for T6, respectively. The results of the lipid contents indicate that there was a significant difference between the control and different gluten-free pasta treatments. It is shown that the lipid content of the gluten-free pasta formulations was higher than the control because of the incorporation of flaxseed and lupin flour into the samples, since the average fat content is 30-40% in flaxseed [24] and 15% in lupin [30]. The control sample showed the least lipid content since wheat flour contains about 2.10% fat. The percentage of lipid content in gluten-free pasta treatments varied significantly (\(p<0.05\)) from each other except for T1 and T2.
## 3 Extending the Telco USN-Platform towards the Semantic Sensor Web  
The World Wide Web Consortium's (W3C) Semantic Sensor Network Incubator Group (SSN-XG) ([[http://www.w3.org/2005/Incubator/ssn/](http://www.w3.org/2005/Incubator/ssn/)]([http://www.w3.org/2005/Incubator/ssn/](http://www.w3.org/2005/Incubator/ssn/))) has observed that OGC-SWE specifications provide \"limited interoperability and data exchange based on XML and standardized tags. However, they do not provide semantic interoperability and do not provide a basis for reasoning that can ease development of advanced applications\" [13]. To overcome this limitation, and in the same vein as the approach proposed by the W3C's SSN-XG, we integrated several W3C semantic technologies into our USN-Platform.  
The SSN-XG has focused its activities on both the development of ontologies to describe sensors (for example, mapping OGC standards to OWL) and the extension of XML-based sensor and observation markup languages to support semantic annotations (facilitating the integration with linked open data applications relying on semantic web technologies like RDF and SPARQL). To progress towards these Semantic Sensor Web benefits, the base USN-Platform was extended to include two major developments:  
1. New capabilities to manage semantically annotated observations coming from smart devices (mobile phones, connected vehicles, home appliances, interactive urban infrastructures, _etc._).
2. Extension of the Observation Storage in the USN-Enabler so that applications can make use of functionalities offered by Semantic Web repositories: semantic queries, intelligent search, inference, reasoning, _etc._
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 3 Results  
### Comparison of Evolutionary Algorithms  
The optimal configurations for 2D-LFC or 3D-LFC composed of \(n(n=6,7,8,\ldots\ldots 12)\) satellites are obtained separately based on the two different evolutionary algorithms, and the corresponding fitness values are compared in Figure 3. The higher the fitness value, the better the GNSS RO performance of the LEO constellation. As mentioned in Section 2.4, the same fitness function and number of iterations are applied in the two algorithms, which ensures the fairness of the comparison between them. In Figure 3, the comparison between the blue columns and the red columns shows that for the 2D-LFC pattern, the PSO algorithm always performs better than the GA over a varying number of LEO satellites between 6 and 12. The comparison between the green columns and the yellow columns shows that for the 3D-LFC pattern, a PSO algorithm also generally performs better than a GA except when \(n=11\) and \(n=12\). So, for the LEO constellations composed of \(n(n=6,7,8,\ldots\ldots 12)\) satellites, the final optimal 2D-LFCs and 3D-LFCs are generally (although not necessarily) the solutions of a PSO algorithm.
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 4 Experimentation and Results
### Comparison of Index Construction and Data Query Efficiency  
To compare our proposed SbMBR tree with quadtree and brute R-tree, we performed about 150,000 random queries with an allowable error of 0.1% to 0.5% on data of different densities and recorded the creating time and query time for different methods. The temporal information is analyzed in Figure 7, where Figure 7a represents the performance of each method in sparse data and Figure 7b represents the performance of each method in dense data.  
Figure 5: \(eps\) = 0.0026, compression effect in different methods.  
Figure 6: \(eps\) = 0.0005, compression effect in different methods.  
Figure 7: \(Cont.\)In the Tables 3 and 4, relative time can be defined as follows:  
\[RelativeTime=\frac{t}{t_{0}} \tag{12}\]  
where \(t\) means the absolute time of different methods and \(t_{0}\) means the fiducial time; in this case, the time scale of the quadtree is the fiducial time.  
A comprehensive analysis of Figure 7 and Tables 3 and 4 show that the brute R-tree always leads to more time spent than the SbMBR tree and quadtree in construction, and even more so in the case of dense data. In addition, although the quadtree shows a better construction speed, its query speed is slow and will be a serious shortcoming in large-scale queries, and the SbMBR tree seems to outperform the other methods in general.  
From analyzing the reason, we can see that the bottleneck of the brute R-tree is that it needs to traverse all the neighboring information to calculate and update the MBR, which will lead to an order of magnitude increase in time complexity compared with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Relative Time of** & **Relative Time of** & **Relative Total Time** \\  & **Creating Tree** & **Multiple Querying** & \\ \hline quadtree & 100.0\% & 100.0\% & 100.0\% \\ \hline SbMBR tree & 496.1\% & 73.0\% & 73.2\% \\ \hline brute R-tree & 136,196.9\% & 70.5\% & 156.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative time of different methods under the same dense data.  
Figure 7: Comparison of different methods under the same data. (**a**) Time comparison of different methods under the same sparse data. (**b**) Time comparison of different methods under the same dense data.  
other methods. Each internal node has exactly four children in a quadtree, so a quadtree should not spend time considering the surrounding data, but rather should keep recursion down. So, quadtree has the best construction consumption. However, although brute R-tree spends so much time in construction, the detailed nodes' information makes it query recursive depth lower and more efficient to query. On the contrary, the simple splitting of the quadtree makes the overall depth higher, and the query must recurse more layers.
# A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
## 6 Conclusions  
The article provided an overview of the current status of advanced technologies for IRS-assisted UAV communications, which is important to realize NTNs for future wireless communications systems. We first identified several scenarios for which IRSs can be incorporated into UAV communications systems. The high degree of freedom in the system configuration makes the IRS-assisted UAV communications systems a promising solution to enable advanced services in future wireless systems. We then investigated recent advances in IRS-assisted UAV communications in terms of the design goal, such as spectral efficiency, energy efficiency, and security. Through the investigation, we confirmed the benefits and performance improvement resulting from the combination of the UAV and IRS, as compared to the advantages that each of the IRS and UAV can bring. It was also shown that optimization of various parameters, such as UAV altitude and speed, IRS location, IRS phase shift, and beamforming weights, is essential to maximize the gain. We also discussed optimization approaches adopted in the previous works, particularly focusing on ML algorithms. Because a wide variety of variables are usually involved in IRS-assisted UAV communications systems, it is important to optimize them to improve the system performance. As methodologies to solve the problems, traditional optimization and ML were introduced together. The ML algorithm, which was judged to be more suitable for solving complex problems, was especially elaborated. Although the previous studies showed great potential of IRS-assisted UAV communications, there are still many technical issues and research challenges. We highlighted channel estimation, IRS phase shift, reflection efficiency, UAV energy consumption, security vulnerabilities, and environmental factors as future research challenges. In particular, to realize performance gain with joint designs of UAVs and IRSs, it is of paramount importance to improve the accuracy of channel estimation, while minimizing training overhead and power consumption at the same time. The channel estimation should also be designed to be robust to external factors, such as UAV mobility and jittering.  
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (Ministry of Science and ICT (MSIT)) under Grant 2019R1A2C1084834 and Grant 2017R1A5A1015596.  
Not applicable.  
The authors declare no conflict of interest.
# Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction
###### Abstract  
A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. \(L_{\text{eq}}\) A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.  
Keywords:road traffic noise; honking; machine learning; prediction; modelling +
Footnote †: journal: Medical Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India