esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods and Materials
### Suspension Designs  
Semi-active suspensions do not consume significant amount of energy and can provide a more energy efficient solution compared to fully active ones. At the same time, the reliability of passive suspensions can be maintained. Therefore, different control strategies have been developed exploiting the benefits of semi-active suspensions. In this work, the suspension system of the vehicle model operates as passive (PS) and semi-active. Several different control algorithms are used in the semi-active suspension case: SH-2 [30], ADD [31], SH-ADD-2 [32], PDD [33], SH-PDD [34] and GH-2 [35]. The operational conditions of the semi-active control algorithms are illustrated in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Masses [kg]**} & \multicolumn{3}{c}{**Springs [N/m]**} & \multicolumn{3}{c}{**Dampers [Nm/s]**} \\ \hline \(m_{s}\) & = & \(1301/4\) & \(K_{u}\) & = & \(25000\) & \(C_{u}\) & = & \(2500\) \\ \hline \(m_{u}\) & = & 43 & \(K_{T}\) & = & \(0.80*K_{t}\) & \(C_{T}\) & = & \(2508\) \\ \hline \(m_{t}\) & = & Equation (6) & \(K_{t}\) & = & \(367240\) & \(C_{t}\) & = & \(508\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle model parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Description** & **Control Law a** \\ \hline
1 & SH-2 & \(C_{u}=\begin{cases}C_{min}\quad,\quad if\;\dot{d}_{ST}\;\dot{z}_{s}<0\\ C_{max}\quad,\quad if\;d_{ST}\;\dot{z}_{s}>0\end{cases}\) \\ \hline
3 & ADD & \(C_{u}=\begin{cases}C_{min}\quad,\quad if\;d_{ST}\dot{z}_{s}\leq 0\\ C_{max}\quad,\quad if\;d_{ST}\dot{z}_{s}>0\end{cases}\) \\ \hline
4 & SH-ADD-2 b  \\ \hline
5 & GH-2 & \(C_{u}=\begin{cases}C_{min}\quad,\quad if\;d_{ST}\;\dot{z}_{u}>0\\ C_{max}\quad,\quad if\;d_{ST}\;\dot{z}_{u}<0\end{cases}\) \\ \hline
6 & PDD & \(C_{u}=\begin{cases}C_{min}\quad,\quad if\;\dot{K}_{u}d_{ST}+C_{min}d_{ST}^{2} \geq 0\\ C_{max}\quad,\quad if\;\dot{K}_{u}d_{ST}+C_{min}d_{ST}^{2}<0\end{cases}\) \\ \hline
7 & SH-PDD & \(C_{u}=\begin{cases}C_{min}\quad,\quad if\;\dot{z}_{s}^{2}-\dot{z}_{s}^{2}\geq 0 \\ C_{PDD}\quad,\quad otherwise\end{cases}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Semi-active control algorithms applied in the vehicle suspension systems (Figure 2).
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 1 Introduction
Specifically, the Taylor expansion terms of the ambiguity function (AF) of the JRC-CWG near the region of zero Doppler shift are derived, and this shows the nonzero-order Taylor expansion terms will impact the Doppler resilience of JRC-CWG. Hence, the coefficients of the low-order Taylor expansion terms are utilized in the formulated optimization problem to design the DR-JRC-CWG. To solve these optimization problems, an iterative algorithm is proposed based on the framework of the Fletcher-Reeves conjugate gradient (FR-CG) algorithm and the FFT algorithm. The contributions of our work are summarized as follows:  
1. A new co-use JRC waveform is devised by exploiting the philosophy of the complementary sequence and is called the JRC-CWG. 2. Compared with the JRC waveforms proposed in [31] and [13], the designed JRC-CWG has a much lower sidelobe level and better communications performance in terms of BER. 3. A new Doppler resilient waveform design method is proposed to design the DR-JRC-CWG that is not sensitive to Doppler shift caused by the relative radial velocity between the vehicle and target. 4. An algorithm based on the framework of the FR-CG algorithm and the FFT algorithm is proposed to design the DR-JRC-CWG. The rest of this paper is organized as follows. In Section 2, the model of JRC-CWG and the signal processing procedure at radar and communications receivers are introduced. In Section 3, the optimization problems for the JRC-CWG and DR-JRC-CWG design are developed and the optimal JRC-CWG and DR-JRC-CWG are devised. In Section 4, several simulation results are presented. Discussions are drawn in Section 5. Finally, a conclusion is made in Section 6. Notations: Non-bold letters and bold lower-case letters represent scalars and vectors, respectively. Bold capital letters represent matrices. The \(n\)-th element of vector \(\mathbf{s}\) is represented as \(s(n)\), and the \(k\)-th row and \(n\)-th column entry of matrix \(\mathbf{F}\) is represented as \(\mathbf{F}(k,n)\). \(\mathbf{0}_{K\times N}\) denotes the \(K\times N\) matrix of zeros. The \(N\)-order identity matrix is denoted by \(\mathbf{I}_{N}\). \((\cdot)^{*}\), \((\cdot)^{T}\), and \((\cdot)^{H}\) stand for conjugate, transpose, and conjugate transpose, respectively. \(\|\cdot\|_{F}\) denotes the Frobenius norm. \(\odot\) represents the Hadamard product operator. \(|\cdot|\) takes the absolute value of each element of a vector or matrix. \(\mathrm{Re}\{\cdot\}\) denotes the real part of a complex value. \(\mathbb{C}\) denotes the complex space.
## 2 Results
### Small-Signal Investigation for LNA Design
#### 2.1.2 Power Gain and Noise Figure  
Power gain and noise figure (NF), which are expressed in terms of maximum available gain (MAG) and minimum noise figure (NFmin), respectively, are the most important parameters when designing LNA. Here, in order to compare power gain among the configurations, MAG is used and given by the following formula [35]:  
\[\text{MAG}=\frac{S_{21}}{S_{12}}\left(K-\sqrt{K^{2}-1}\right) \tag{21}\]  
where \(K\) is Rollett stability factor and is expressed through the scattering parameters as:  
\[K=\frac{1-\left|S_{11}\right|^{2}-\left|S_{22}\right|^{2}+\left| \Delta\right|^{2}}{2\left|S_{12}S_{21}\right|} \tag{22}\] \[\Delta=S_{11}S_{22}-S_{12}S_{21} \tag{23}\]  
Figure 4: Power gain and noise figure characteristic of the proposed configuration.  
MAG in Equation (21) can be computed by using the expressions for \(S_{12}\) and \(S_{21}\) of the proposed configuration and using the general approximations as follows:  
\[\text{MAG}=\left(\frac{g_{\text{m1}}g_{\text{m2}}g_{\text{m3}}}{g_{\text{d1}}g_{ \text{d2}}g_{\text{d3}}}\right)\left(\frac{g_{\text{m1}}}{\omega C_{\text{gs1}} }\right)\left(K-\sqrt{K^{2}-1}\right) \tag{24}\]  
Equation (24) implies that the power gain of the proposed configuration is dominated by the first CS transistor due to the contribution of the term \(g_{\text{m1}}/\omega C_{\text{gs1}}\). This once again confirms the fact that the first gate bias voltage \(V_{\text{g1}}\) is the critical bias parameter for improvement of isolation, stability, and power gain when designing an LNA. For the noise figure analysis, it is well known that noise of a cascaded system is always dominated by the first-stage or the first HEMT of the proposed configuration. This means the first gate bias voltage \(V_{\text{g1}}\) is the main factor contributing to the noise improvement. This is illustrated in Figure 5. The figure clearly shows that when \(V_{\text{g1}}\) varies from \(-3.0\) V to \(-1.0\) V, MAG, as well as NFmin, can change significantly. This means \(V_{\text{g1}}\) can be also utilized to improve both the MAG and NF. Once again, it can be seen that \(V_{\text{g1}}\) should be biased at low values to improve both the MAG and NF. Moreover, it is very interesting to see in the figure that both MAG and NF can be simultaneously improved for the proposed configuration when making an adaptive control of the \(V_{\text{g1}}\).  
This is very important because as mentioned in the introductory section, MAG and NF are difficult to be improved simultaneously if using traditional design methods.
# The AI-Powered Evolution of Big Data
## 3 Related Work  
The related work is presented in Table 1, where it is compared to the proposed spectrum of Vs framework and Big D app (v1) in this study.  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Research Work** & **Year** & **Focus/Contribution** & **Comparison with the Proposed Study** & **Ref.** \\ \hline \multirow{3}{*}{Aminul} & \multirow{3}{*}{2024} & The impact of Big Data analytics on digital marketing strategies and customer engagement. & Both works explore the practical applications of Big Data. The spectrum of Vs goes beyond digital marketing. \\  & & The incorporation of AI and Big Data into virtual infrastructures, focusing on educational environments. & Both examine AI’s role in reshaping Big Data applications. The scope of the spectrum of Vs spans beyond education. \\  & & A comprehensive summary of 15 years of Big Data research, focusing on patterns, challenges, and prospects. & Both offer forward-looking perspectives on Big Data and its evolution, but this study introduces new dimensions to the framework; Big D focuses on enhancing real-time data analysis. \\  & & The evaluation of LLMs’ accuracy in medical settings, highlighting Big Data’s role in enhancing precision. & Both studies emphasize the role of Big Data in enhancing AI precision. The spectrum of Vs has applications beyond medical applications. \\  & & Innovative methods for encoding text using LLMs. & Both explore advanced AI techniques. The spectrum of Vs goes beyond text encoding. \\  & & Advanced AI models and large-scale remote sensing data are utilized to improve Earth observation. & Both studies apply AI to enhance data analysis. \\  & & The integration of ChatGPT with Big Data for improved text-to-speech processes. & The spectrum of Vs offers a broader framework beyond text-to-speech. \\  & & The influence of fast engineering on AI language models, focusing on extensive training datasets. & Both works highlight the importance of data quality in AI training. The spectrum of Vs incorporates it into its framework. \\  & & The impact of Big Data analytics and AI on green supply chain practices, emphasizing sustainability. & Both studies emphasize sustainability within Big Data and sustainability initiatives. \\  & & Correlation between Big Data and performance assessment research. & Both explore performance assessment within Big Data. Big D’s real-time processing enables continuous performance monitoring. \\  & & Both explore the application of advanced AI techniques. Big D applies these techniques practically in real-time Big Data analytics. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related work.  
As can be seen from Table 1, some of the most recent studies do emphasize AI's impact on Big Data analysis, but most of them have a narrower scope than the spectrum of Vs framework.
# NN-Based 8 FSK Demodulator for the Covert Channel
## 2 Model of the Cover Transmission
### The System Model for Cover Communication
The covert modulation is 4 PSK taken for the analysis result from comparable bit energy for the given BER (presented in Figure 2). Figure 5: EVM introduced by covert signal on the cover signal constellation. Figure 6: Comparison on BERs for 16 QAM and 16 QAM disturbed by covert signal in AWGN channel. Figure 7: Constellations and histograms for (**a**) cover (QAM); (**b**) cover + 4 PSK; (**c**) cover + 8 FSK for noiseless and gaussian noise. As shown in Figure 7, adding a hidden signal to the cover signal inevitably changes the properties of the second one. For the countermeasure for covert channels, steganalysis looks for these changes. When the warden (Figure 1) has perfect knowledge about the cover signal (plus channel gains, noise, and other disturbances), it is not possible to ensure covertness (Figure 7). Increased resistance 8 FSK to steganalysis comes from the fact that IQ histograms (estimation of the probability density functions) are more similar to the cover histograms than 4 PSK, especially in the case of the noisy channel (Figure 7b vs Figure 7c). Basically, coherent modulation (like N-order phase modulation) always provide a limited number of states on IQ constellation and, thus, bins on IQ histograms. In contrast, after observation of the N-FSK signal, the obtained histograms have many more bins, in a specified range. That is why FSK modulation is less prone to steganalysis. Graphical representation of the signal in histogram form is used to decide on the detection of hidden transmission. Numerical values of the similarity of the histograms can be calculated by Kolmogorov-Smirnov (KS) test [3; 19].
## 5 Performance Evaluation
### Simulation Scenario  
We implement a simulation scenario to evaluate the proposed \(Q\)-learning algorithm. An urban area of \(250\times 250\) m\({}^{2}\)--which is discretized by means of square-shaped grid--and \(n_{\rm t}=100\) total number of users (that are normally out-of-service from the terrestrial network) are considered. Consequently, the UAV can move in the discretized 3D space in terms of \(x\), \(y\), and \(z\) coordinates. Furthermore, due to the regulations, we impose a minimum and a maximum altitude of \(h_{\rm min}=30\) and \(h_{\rm max}=120\) m, respectively, and a certain number of NFZs, corresponding to specific not allowed grids. The users in the considered model are mobile and move at each time slot, and we consider a random walk model for the user mobility, meaning that the users move by a certain amount of distance from their original location in a random manner; such that the directions that they can move to have equal probability. The height from ground for all UEs is fixed to \(1.5\) m. Furthermore, we assume the directivity angle \(\theta=60^{\circ}\) and the carrier frequency \(f_{c}=1\) GHz. An initialization procedure is performed to explicate the initial conditions. In particular, the values for all the involved parameters are determined, considering arbitrary positions for the UAV BS and the users. An outage threshold is calculated considering a required minimum received power to establish and maintain with a certain QoS a connection between the UE and the UAV BS, called \(P_{\rm r_{min}}\). For a given transmitted power for the UAV BS, \(P_{\rm r}\)--lower than the maximum allowed value, \(P_{\rm r_{max}}\)--, and the above-mentioned \(P_{\rm r_{min}}\), the path loss experienced by the UE, \(L\), can be expressed by the following relation, \(L=P_{\rm t}-P_{\rm r_{min}}\). Considering the 2D position of the UAV BS (\(C_{\rm 2d}\)), the QoS constraint can be expressed in terms of \(L\) lower than \(L_{\rm max}\)[19], where \(L_{\rm max}\) is the path loss experienced by edge users. The footprint of the UAV BS, on the other hand, can be considered as a circle centered in the 2D position of the UAV BS (\(C_{\rm 2d}\)).
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 3 Thermoplastic Resin Toughening Method
### Advancements in Thermoplastic Resin Toughening
#### 3.4.3 Polyimide  
Polyimide (PI) is a high-performance polymer known for its exceptional thermal stability and mechanical properties, making it suitable for applications in aerospace, automotive manufacturing, and microelectronics [69; 70; 71]. Extensive research indicates that PI can be an effective additive for enhancing the mechanical and thermal properties of epoxy resins [72; 73; 74].  
[PERSON] et al. [75] synthesized a novel polyimide with trifluoromethyl groups (PIS) and used it to improve the toughness of epoxy resins. When the polyimide content was 7.5 wt%, the tensile strength reached a maximum value of 92.3 MPa, an increase of approximately 128% compared to the unmodified epoxy resin. The fracture toughness increased from 0.86 MPa\(\cdot\)m\({}^{1/2}\) to 1.56 MPa\(\cdot\)m\({}^{1/2}\), representing an 81.4% improvement.  
Polyether imide (PEI), a type of polyimide, exhibits similar excellent properties. [PERSON] et al. [11] investigated the use of low-content PEI in conjunction with amino-functionalized multi-walled carbon nanotubes (NH\({}_{2}\)-MWCNTs) to toughen epoxy resins. They found that, when the amount of NH\({}_{2}\)-MWCNTs was 0.4 parts and PEI was 5 parts, the KIC reached its maximum value, showing a 73.1% increase compared to pure epoxy resin, a 29.8% increase compared to PEI/EP binary composites, and a 45.1% increase compared to NH\({}_{2}\)-MWCNTs/EP binary composites. In this composition, the flexural strength of the composite also reached its peak at 145.7 MPa, which is 26.8% higher than that of PEI/EP composites and 41.4% higher than that of NH\({}_{2}\)-MWCNTs/EP composites. Additionally, during the reaction-induced phase separation process, the distribution of carbon nanotubes was observed to preferentially associate with PEI microspheres, which is likely related to the significant enhancement in toughness.
## 3 Nelder-Mead Algorithm for the Optimal Completion of Incomplete PCMs  
In this section, a Nelder-Mead algorithm [33] is implemented for the 'optimal completion' of an incomplete pairwise comparison matrix.  
Let \(\mathbf{x}=(x_{1},x_{2},\ldots,x_{k})\in\mathbb{R}^{k}_{+}\), and denote \(f(\mathbf{x})\coloneqq\lambda_{max}(\mathbf{A}(\mathbf{x}))\). The constrained eigenvalue minimization problem is defined by  
\[\begin{split}\min_{\mathbf{x}\in\mathbb{R}^{k}_{+}}& f(\mathbf{x})\\ \mathrm{s.t.}& l_{i}\leq x_{i}\leq u_{i},\ i=1,2, \ldots,k\end{split} \tag{4}\]  
where \(l_{i}\) and \(u_{i}\) are the lower and upper bounds for the variable \(x_{i}\), respectively. From now on, we shall consider a restriction \(1/9\leq l_{i},\ u_{i}\leq 9\) for all \(i=1,2,\ldots,k\).  
In general, a constrained minimization problem cannot be directly solved by the Nelder-Mead algorithm. However, the constrained problem can be transformed into an unconstrained problem by applying coordinate transformations and penalty functions. Then, the unconstrained minimization problem is solved using the Nelder-Mead algorithm or MATLAB's built-in function fminsearch[34; 35; 36]. Since our optimization problem (4) is constrained with scalar bounds, it is sufficient to use the coordinate transformation techniques [37] detailed in Section 3.1. It should be noted that our goal is to minimize the maximum eigenvalue (Perron eigenvalue) function, which, in general, cannot be expressed as an analytic function of the variables \(x_{1},x_{2},\ldots,x_{k}\).  
The procedure to specify the objective function (Perron eigenvalue), to minimize numerically, is given as follows:  
1. Fill in the missing positions with zeros in the incomplete PCM \(\mathbf{A}\);
2. Set initial value \(\mathbf{x}_{0}=(x_{1},x_{2},\ldots,x_{k})\), where \(k\) is the number of missing entries in the upper triangular matrix \(\mathbf{A}\);
3. Let \(\mathbf{t}_{0}=(t_{1},t_{2},\ldots,t_{k})\) such that \(t_{s}=\log(x_{s})\) for \(s=1,2,\ldots,k\);
4. Let \(i,j=1,2,\ldots,n\) and \(\mathbf{A}(i,j)\) be the \(i\)th row and \(j\)th column entry of \(\mathbf{A}\). For \(i<j\), put the exponential functions: \(e^{t_{s}}\) in place of \(\mathbf{A}(i,j)=0\), and \(e^{-t_{s}}\) in place of \(\mathbf{A}(j,i)=0\) for all \(s=1,2,\ldots,k\);
5. Calculate all eigenvalues of \(\mathbf{A}\);
6. Verify the Perron eigenvalue from step (5).  
Note that the initial value \(\mathbf{x}_{0}\) in step (2) can be replaced by the vertex \(\mathbf{x}_{m}=(x_{1},\ldots,x_{k})\), \(m=1,2,\ldots,k+1\), of a simplex in the Nelder-Mead algorithm. This is due to the fact that the exponential parameterization of \(\mathbf{x}_{m}\in\mathbb{R}^{k}_{+}\) is \(\mathbf{x}_{m}=(e^{t_{1}},e^{t_{2}},\ldots,e^{t_{k}})\), and hence \(\mathbf{t}_{m}=(\log(x_{1}),\ldots,\log(x_{k}))\). Thus, \(f(\mathbf{x}_{m})\) is obtained from step (6).
# Tunable Perforated Panel Sound Absorbers for Variable Acoustics Room Design
## 3 Methods  
### Impedance Tube Setup  
Once the tunable perforated panel solutions were fabricated, laboratory experiments were carried out using an impedance tube set up to determine their normal incidence sound absorption coefficient when mounted as a resonator system. The measurement procedure used implements the transfer function method which is well described in the standard ISO 10534-2 [19]. For this purpose, a custom-made circular cross-section stainless steel tube, 88 mm in diameter and 4 mm in thickness, was used together with two 4188 type % inch microphones (Bruel&Kjaer, Naerum, Denmark) and the OR34 Compact Analyzer acquisition system (OROS, Lieusaint, France), with the sound source being a CP-380M compression driver (Beyma, Valencia, Spain). The sound source was located at one end of the tube and the perforated panel solution under test at the other end, with a sample holder attached to the latter using adjustable screws and threaded bars together with an additional movable rigid piece to achieve a backing air cavity. A random excitation was provided to the driver from the analyzer and the pressure transfer function was measured using the two microphones mounted flush with the inner surface of the tube and spaced 80 mm (cut-off frequency around 2000 Hz). Given that a parametric study was to be performed as a function of the open area ratio (TS-A) and iris aperture (TS-B), pilot holes were drilled on the perimeter of each sample to guarantee a more precise placement on the sample holder of the tube (see Figure 1). It should be also mentioned that the samples were withdrawn  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solution** & **Panel Thickness (mm)** & **Perforation Radius (mm)** & **Open Area Ratio (\%)** \\ \hline TS-A & 4 & 1.5 & 0.6–5.6 \\ TS-B & 2 & - & 0.7–14.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical characteristics of the tunable perforated panel solutions.  
Figure 1: Proposed tunable perforated panel sound absorbers, images, and schematic concepts: (**a**) rotating perforated panels system (TS-A) and (**b**) iris-type aperture system (TS-B).  
from the sample holder every time a new configuration of either tunable solution A or B was to be tested, carefully checking that the holes were properly aligned and the aperture size was suitably fitted, respectively. As for the post-processing of the acquisition data, in-house MATLAB software v. 2021b was used to calculate the sound absorption coefficient from the measured pressure data using the corresponding expressions in [19].
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 4 Sun Track System Software
### Two-Axis Angle Control  
In solar elevation angle control, the reducer is used vertically (5:1) to rotate the stepping motor's downwards. This changes the \(y\)-axis height and the elevation angles \(\alpha\), because an increase in the \(y\)-axis results in a circular motion. This experiment thus uses a horizontal slide to mitigate this issue. The solution involves installing a mobile slide on the bottom of the elevator base, which changes its circumferential force and improves the base by changing its elevation angle \(\alpha\). The stepper motors are insufficient to meet the elevator's high torque requirement, which necessitates the use of a 9:1 gear.  
The angle is calculated using the following equation:  
\[\tan\alpha=\frac{H}{L} \tag{6}\]  
The installed solar panels are connected to the reducer's output side (30:1) and then directly to the stepper motor from the power side of the reducer. The reducer can accurately control the solar panels and guard them against wind and vibrations (Figure 10).  
In this study, the two-phase stepper motor controls the angle of excitation. The value is 0.9 degrees per step, plus gear (30:1); therefore, for every step, the stepper motor controls the angle of the solar panels by 0.03 degrees. The horizontal angle control of the system can be expressed using the following relation:  
\[\theta=\frac{P\times 0.9}{30} \tag{7}\]