esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunable Plasmonic Talbot Effect Based on Graphene Monolayer
## 2 Model of the Plasmonic Talbot Effect and Calculation Methods
The effective wavelength \(\lambda_{n}\) can also be defined by Equation (8)  
\[n_{eff}=\frac{\beta}{k_{0}} \tag{7}\]  
\[\lambda_{n}=\frac{\lambda_{0}}{n_{eff}} \tag{8}\]  
where \(\lambda_{0}\) is the wavelength of the plasmon in vacuum. In this paper, all the effective wavelengths in graphene \(\lambda_{n}\) are smaller than period \(D\), which fully conforms to the conditions of scalar diffraction theory [31]. Consequently, the Talbot distance \(Z_{T}\) is given by  
\[Z_{T}=\frac{D^{2}}{\lambda}=\frac{D^{2}}{\lambda_{n}} \tag{9}\]On the other hand, finite element method-based commercially available software COMSOL multiple physics is employed to numerically explore the plasmonic Talbot effect. Figure 1b describes the calculation window of the proposed structure, where the plasmon incident at \(z=0\) and propagate along the \(Z\) direction. In order to simulate the periodic grating structure in X direction and reduce the computation, the Floquet periodic boundary conditions are utilized on the top and bottom of the graphene. The scattering boundary condition is set on the right side of the calculation window.
## 3 MPTCP Congestion Control Background  
The MPTCP protocol contains a scheduler to select the traffic path for each packet and a congestion control algorithm to estimate the amount of data to be sent over each path in order to avoid congestion. The default scheduler of the Linux kernel implementation splits the traffic on each path based on the round-trip time (RTT) experienced over that path. In this section, we provide a basic background of the congestion control algorithms available as a Linux kernel implementation, namely LIA, OLIA, Cubic, BALIA, and wVegas.
## 1 Introduction
Differently from other surveys already published in the literature, in this paper: (i) we narrow the attention to short-range V2V-based applications, (ii) we discuss the design choices and factors that determine the development of different wireless enabling technologies, (iii) we discuss potential economic motivations to install wireless technologies on board and (iv) we provide an overview of how international organizations and countries are moving to provide suitable business models and potential mandatory rules to push connectivity on board and thus efficiently support manufacturers to develop applications that employ V2V communications. Besides touching these main three points, we also provide a description of the main enabling technologies, from IEEE 802.11p and its related standards, to Cellular-V2X (C-V2X) and other complementary technologies, such as VLC and mmWave, opening to future vehicular networks. The paper is organized as follows: in Section 2, the main related works are examined, highlighting the main differences with our proposal; in Section 3, the economic pushes to vehicular communications are discussed; then, the main requirements and applications are highlighted. In Section 4, wireless enabling technologies are detailed with a glance at 5G and hybrid systems, in Section 5, mandatory rules at international levels are presented, and, in Section 6, example results of performance comparison are provided. Finally, in Section 7, our conclusions are drawn while opening up to future perspectives.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 6 Conclusions  
In this study, a novel routing system called the Compacted Area with Effective Links (CAEL) is proposed. This routing protocol first compacts the communication area and then determines the most reliable route inside the compacted region based on the RF. RF takes into account RET and HC to identify the most reliable routing path with the fewest hops. The value of RF is used to determine the most reliable path for data transfer; a higher value of RF implies a more reliable method of data transmission. Upon further research and the analysis of the simulation results, it was discovered that DyTE and RGoV impose a large amount of routing strain on the network as a result of the blind flooding of RREQ packets that occurs during the route discovery stage. This flooding process increases the number of times the RREQ packet is re-transmitted throughout the network, resulting in increased network congestion and significant network performance degradation. According to the reported literature, this re-transmission of RREQ packets is known as a \"Broadcast Storm\". To re-establish the broken path, it was necessary to undertake route maintenance or route rediscovery method which resulted in higher network traffic and a detrimental impact on network performance.  
We developed a routing method that is based on the dependability factor, which may select a highly stable route, hence boosting the longevity and performance, especially for high-density networks. In the future, we aimed to boost throughput and PDR while significantly reducing the latency time. This may be accomplished by tweaking the parameters of RET as well as by adding conditions to handle those nodes that reside near the border of the trilateral region to avoid link disconnections.  
Conceptualization, A.K.K. and S.M.K.; methodology, A.K.K. and S.M.K.; validation, A.K.K. and S.H.; formal analysis, A.K.K., Waseemullah, and U.F.; investigation, A.K.K. and S.M.K.; writing--original draft preparation, A.K.K. and S.H.; writing--review and editing, A.K.K., W. and U.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Safety Control for Satellite Formation Flying via High-Order Control Barrier Functions
## 2 Problem Formulation and Preliminaries
### Control Barrier Function
Constructing an appropriate control barrier function and defining the safe set for System (4), one should design a controller, which can enable the states of System (4) to satisfy the safe set all the time.
# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 10 Conclusions, Open Challenges, and Future Work
### Conclusions  
This work presented the integration of machine learning-based attack detection exercises into SPIDER, an innovative Cyber Range as a Service (CRaaS) for 5G networks. Two recently appeared attacks, cryptomining attack and DNS over HTTP (DoH) flooding, were selected as support use cases for the proposed exercises. For didactic purposes, poisoning techniques were also introduced in the DoH flooding exercise. The proposed exercises use realistic 5G network traffic that is generated in a new environment based on a fully virtualized 5G network. We can conclude in this respect that in addition to the usefulness of experimenting with the detection of two recently appeared attacks, the proposed exercises help to exemplify how other attacks and their counterparts, the ML-based attack detectors, can be integrated as cyber range exercises.  
From an industrial perspective, the proposed Smart Traffic Analyzer (STA) using container technology and standard interfaces will facilitate the design and deployment of new exercises based on the emulation of other attacks.  
Finally, the application of the recently discovered Generative Adversarial Networks (GANs) to obtain on-demand synthetic flow-based network traffic allows replication of high fidelity network attacks and normal traffic and, therefore, the synthetic data can be seamlessly integrated into SPIDER exercises to be used instead of real traffic. This solution allows for the generation of unlimited amounts of synthetic data for a concrete type of exercise and thus prevents trainees from always learning an exercise with the same set of real data. In addition, this GAN-based solution can foster the interconnection of federations of cyber ranges as it allows importing and exporting network traffic data from one cyber range to another without incurring any privacy violation as the shared data are only synthetic.
# Compact Design of Annular-Microstrip-Fed mmW Antenna Arrays
###### Abstract  
In this paper, a series of four novel microstrip antenna array designs based on different annular-microstrip feeding lines at 60-GHz millimeter wave (mmW) band are proposed, aiming at the potential usage of the mmW coverage antenna with multi-directional property. As the feeding network, the annular contour microstrip lines are employed to connect the patch units so as to form a more compact array. Our first design is to use an outer contour annular microstrip line to connect four-direction linear arrays composed of 1 \(\times\) 3 rectangular patches, thus the gain of 8.4 dBi and bandwidth of over 300 MHz are obtained. Our second design is to apply the two-direction pitchfork-shaped array each made up of two same linear arrays as the above, therefore the gain of 9.65 dBi and bandwidth of around 250 MHz are achieved. Our third design is to employ dual (inner and outer contour) annular-microstrip feeding lines to interconnect the above four-direction linear arrays, while our fourth design is to bring bridged annular-microstrip feeding lines, both of which can realize the goal of multi-directional radiation characteristic and higher gain of over 10 dBi.  
microstrip antenna; array; mmW; feeding line; multi-direction Article Compact Design of Annular-Microstrip-Fed mmW Antenna Arrays [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] 2  
1 Hubei Key Laboratory of Roadway Bridge and Structure Engineering, Wuhan University of Technology, Wuhan 430070, China; [EMAIL_ADDRESS]  
2
# An Efficient VQ Codebook Search Algorithm Applied to AMR-WB Speech Coding
## 2 ISF Coefficients Quantization in AMR-WB
### Quantization of ISF Coefficients  
Before a quantization process, a mean-removed and first order MA filtering are performed on the ISF coefficients to obtain a residual ISF vector [1], that is:  
\[\mathbf{r}(n)=\mathbf{z}(n)-\mathbf{p}(n) \tag{6}\]  
where \(\mathbf{z}(n)\) and \(\mathbf{p}(n)\) respectively denote the mean-removed ISF vector and the predicted ISF vector at frame \(n\) by a first order MA prediction, defined as:  
\[\mathbf{p}(n)=\frac{1}{3}\mathbf{\hat{r}}(n-1) \tag{7}\]  
where \(\mathbf{\hat{r}}(n-1)\) is the quantized residual vector at the previous frame.  
Subsequently, S-MSVQ is performed on \(\mathbf{r}(n)\). As presented in Tables 1 and 2, S-MSVQ is categorized into two types in terms of the bit rate of the coding modes. In Stage 1, \(\mathbf{r}(n)\) is split into two subvectors, namely, a 9-dimensional subvector \(\mathbf{r_{1}}(n)\) associated with codebook CB1 and a 7-dimensional subvector \(\mathbf{r_{2}}(n)\) associated with codebook CB2, for VQ encoding. As a preliminary step of Stage 2, the quantization error vectors are split into three subvectors for the 6.60 kbps mode or five for the modes with bitrates between 8.85 and 23.85 kbps, symbolized as \(\mathbf{r}_{i}^{(2)}=\mathbf{r}_{i}-\mathbf{\hat{r}}_{i},i=1,2\), respectively. For instance, \(\mathbf{r}^{(2)}{}_{1,1-3}\) in Table 1 represents the subvector split from the 1 st to the 3 rd components of \(\mathbf{r_{1}}\), and then VQ encoding is performed thereon over codebook CB11 in Stage 2. Likewise, \(\mathbf{r}^{(2)}{}_{2,4-7}\) stands for the subvector split from the 4 th to the 7 th components of \(\mathbf{r_{2}}\), after which VQ encoding is performed over codebook CB22 in Stage 2. Finally, a squared error ISF distortion measure, that is, Euclidean distance, is used in all quantization processes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Structure of S-MSVQ**} \\ \hline Stage 1 & \multicolumn{2}{c}{CB1: \(\mathbf{r_{1}}\) (1–9 order of \(\mathbf{r}\)) (8 bits)} & \multicolumn{2}{c}{CB2: \(\mathbf{r_{2}}\) (10–16 order of \(\mathbf{r}\)) (8 bits)} \\ Stage 2 & CB11: \(\mathbf{r^{(2)}}{}_{1,1-3}\) (6 bits) & CB12: \(\mathbf{r^{(2)}}{}_{1,4-6}\) (7 bits) & CB13: \(\mathbf{r^{(2)}}{}_{1,7-9}\) (7 bits) & CB21: \(\mathbf{r^{(2)}}{}_{2,1-3}\) (5 bits) & CB22: \(\mathbf{r^{(2)}}{}_{2,4-7}\) (5 bits) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of S-MSVQ in AMR-WB in the 8.85–23.85 kbps coding modes.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 3 Results and Discussion
### Performance of Step 3  
Step 3 was one of the main contributions of this study. Current metaheuristics studies mainly concentrate on developing a hybrid model with ANN or primary neural networks. Therefore, this study initiated the hybrid models for the deep learning algorithm, mainly the LSTM. Three metaheuristic frameworks, the bat algorithm, firefly algorithm and Jaya algorithm, were selected for this study. The bat algorithm and firefly algorithm belonged to swarm intelligence algorithms. They required trials on nature-based characteristics to find the optimum yield. Jaya algorithm, on the other hand, was designed based on searching for the best solutions. The effort to introduce numerous hybrid optimization algorithms was intended to further enhance the model performance results from steps 1 and 2.  
Tables 3-5 represent each of the selected optimization algorithms. From the three tables, it was determined that all the hybrid models produced better results. However, the best model identified was the bat-LSTM hybrid algorithm where the \(\delta Q\) model yielded \(R\).train 0.9757, \(R\).test 0.9046, MSE.train 4.7187, MSE.test 19.8966, MAE.train 0.4672 m\({}^{3}\)/s, MAE.test 0.8565 m\({}^{3}\)/s, NSE 0.9514 and RMSE 2.1723 m\({}^{3}\)/s.  
The results also proved that the choice of metaheuristic algorithms did not significantly impact the performance. The performance inclination is still the same as the LSTM-only model in step 2, where 30 min ahead of forecasting yielded the best results. As the time of  
Figure 9: Graphs of optimization-LSTM hybrid models.  
forecasting increased, the results deteriorated accordingly. \(\delta Q\) models consistently yielded the best results by keeping the error values to a minimum.  
This process was then followed by the plotting of a peak-to-peak flow graph between the observed and the forecast values. Figure 9 indicated that the best graph with the highest accuracy was the \(\delta Q\) models.  
A further experiment was performed to seek high flow performance for each hybrid model in terms of MAPE, MAE and maximum error. This study concentrates on the flows equal to or greater than 150 m\({}^{3}\)/s, which was the high flow indicator to initiate modes 3 and 4 in the SMART control center's standard operating procedure. The results are tabulated in Table 6. From the results, it could be seen that \(\delta Q\) models again outperformed the rest with the smallest error values, where the bat and Jaya algorithms yielded the best with MAPE 6.33%, MAE 12.2865 m\({}^{3}\)/s and maximum error 97.70% for bat-LSTM algorithm while MAPE 6.22%, MAE 12.6687 m\({}^{3}\)/s and maximum error 97.70% for Jaya-LSTM algorithm. The maximum error values could be ignored in this case as they could not represent the overall performance of the models.
## 3 Results and Discussion
### Concentration Profile  
Figures 15-19 represent the variations of \(M,\ Nb,\ Nt,\ N,\ \text{and}\ Le\) on the concentration profile. The magnetic field strength plays a favorable role for the concentration profile (see Figure 15). As discussed earlier in the case of velocity profile, the magnet field produces the Lorentz force, thus creating resistance, which causes improvement in the thermal and concentration boundary layer thicknesses. Figure 16 exhibits the Brownian motion effect on the concentration profile. It is noticed that the concentration outline decreases with the growing magnitude of the Brownian motion impact. The boundary layer thickness is moderated by the enhancement in Brownian movement, which is the reason for the reduction of concentration profile. The thermophoretic effect boosts the concentration field (see Figure 17). This is due to the fact that the molecular kinetic energy of the fluid particles is enhanced by enhancing the thermophoretic impact. The variation in radiation impact causes the reduction in the concentration field (see Figure 18). The effect of Lewis number is demonstrated in Figure 19. It is noticeable that the concentration profile falls with the improvement of the Lewis number. The boundary layer viscosity decreases when the values of Lewis number \(Le\) are improved.  
Figure 19: Variations in concentration profile for several value of \(Le\).  
Figure 17: Variations in concentration profile for several values of \(Nt\).  
Figure 18: Variations in concentration profile for several values of \(N\).
## 6 Results and Discussion
The impedance modulus (Figure 8a) reaches the value of approximately 6 \(\times\) 10\({}^{10}\)\(\Omega\cdot\)cm\({}^{2}\) at the lowest frequency; this value remains practically constant until the end of the observation period. The shape of the curves demonstrates a capacitive behaviour of the sample, exhibiting a slope \(-\)1 straight line in the entire frequency-range studied. As for the CCrR sample, the data demonstrates the good quality of the organic coating used, which absorbs a very small quantity of the electrolyte during the immersion in the test solution, proved by the almost unnoticeable variation of the modulus of the impedance in the range 10\({}^{-1}\)-10\({}^{4}\) Hz. These observations are confirmed by analysing the phase-angle data (Figure 8b), which remain constant throughout the frequency range and for the entire sample observation-period, at a value between 76 and 86 degrees. Figure 6: (**a**) Impedance modulus \(|\)Z1\(;\) (**b**) phase angle plots of bare sample immersed in an aerated 3.5 wt.% NaCl solution for 7 days. Figure 7: (**a**) Impedance modulus \(|\)Z1\(;\) (**b**) phase angle plots of CCrR sample immersed in an aerated 3.5 wt.% NaCl solution for 7 days. It is interesting to discuss the experimental results by comparing the data obtained by studying the CCrR and the CNR samples. As can be seen from Figures 7 and 8, the samples highlighted a significant difference in their electrochemical response at low frequencies. It is appropriate to describe the physical meaning of such differences by looking at the phase-angle plot of the samples which is known to be more \"sensitive\" to some kinds of the sample's characteristics variations than the modulus of impedance. As said before, the phase-angle variations exhibited by the CCrR sample during the sample test period are due to the electrolyte penetration through the coating, the subsequent corrosion of the substrate and the protective action played by the conversion coating (the self-healing properties of CrIII). The CNR sample, instead, showed a practically constant phase-angle in the same observation period. As the two samples were \"fabricated\" in such a way as to minimise (if not eliminate) the possibility of differences between the samples caused by the deposition and cure of the organic coating, it is possible, therefore, to charge the observed difference in the behaviour of the two samples exclusively to the different characteristics existing between the two conversion-coatings used. The experimental results highlighted the fact that the Zr/Ti-based one seems to be \"very fast\" in blocking the deleterious effect of the electrolyte penetration. This effect could be addressed by the presence of the thin organic-layer in the conversion coating and the faster-reaction kinetics at the interface performed by the conversion layer. This hypothesis should be verified by more in-depth analysis, which is beyond the aim of this paper.
# Reliability by Using Weibull Distribution Based on Vibration Fatigue Damage
## 5 Discussion
Therefore, among other applications, the proposed model can be used in the automotive and telecommunications industries, where mechanical supports are used to hold electrical and electronic devices that are subjected to environmental vibration due to their field application.
# Chaotic Java Approaches to Solving Electromagnetic Optimization Benchmark Problems
## 6 Conclusions and Future Scope  
This paper presented novel Jaya approaches based on chaotic sequences applied to electromagnetic optimization. In order to evaluate the new CJ algorithms' performance, we applied them to two real-world engineering problems, namely [PERSON]'s solenoid and the brushless DC motor.  
Based on the results in Tables 3-6, some CJ approaches offered good performance in terms of both efficiency and solution quality when compared with the other optimization approaches presented in the literature.  
The programming environment for numerical computations in the current work was Matlab version 2020a. The operating system was Windows, running in an Intel Core i7-5820 processor (3.30 MHz) that had 128 GB of random-access memory. The mean computational times considering all Jaya approaches at each run were 0.3 s and 0.2 s for the [PERSON]'s solenoid and brushless DC motor cases, respectively.  
In our future work, we are planning to study the proposed CJ approaches and recently proposed optimizers [26; 27; 28; 29; 30] for additional optimization problems in telecommunications.  
**Author Contributions:** Conceptualization, L.d.S.C., V.C.M., S.K.G., A.D.B. and N.V.K.; formal analysis, L.d.S.C., V.C.M., S.K.G., A.D.B. and N.V.K.; software, L.d.S.C. and V.C.M.; supervision, L.d.S.C., V.C.M., S.K.G., A.D.B. and N.V.K.; validation, L.d.S.C., V.C.M., S.K.G., K.K., A.D.B. and N.V.K.; visualization, L.d.S.C., V.C.M., S.K.G., A.D.B., K.K. and N.V.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to thank the National Council of Scientific and Technologic Development of Brazil--CNPq (Grants number: 307958/2019-1-PQ, 307966/2019-4-PQ, 404659/2016-0-Univ, and 405101/2016-3-Univ) and PRONEX 'Fundacao Araucaria' 042/18 for their financial support of this work.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The authors wish to thank the editor and anonymous referees for their constructive comments and recommendations, which have significantly improved the presentation of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimizer** & \(\eta\) & **NE * \\ \hline Sequential quadratic programming (SQP) [24] & **95.32** & 90 \\ Genetic algorithm (GA) [24] & 95.31 & 3380 \\ GA and SQP [24] & 95.31 & 1644 \\ Ant colony optimization (ACO) [25] & **95.32** & 1200 \\ Particle swarm optimization (PSO) [25] & 94.98 & 1600 \\ CJ (3), (5), CJ (6), CJ (8), CJ (9), CJ (10) & **95.32** & 900 \\ \hline \hline \end{tabular}  
* _NE_: number of evaluations of the objective function.  
\end{table}
Table 6: Results of the optimizers for the brushless DC motor. Data in bold font indicates the larger values.
## 1 Introduction  
Most industrial processes are modeled as nonlinear MIMO systems, which include high nonlinear couplings between subsystems and are subjected to both internal uncertainties and exogenous disturbances. Therefore, it is necessary to design a controller that is relatively simple to be implemented. The parameters of the required controller should be easy to be tuned to accommodate the operating conditions. Initial presentations of the active disturbance rejection control (ADRC) were based on a nonlinear version of the extended state observer (ESO) [1] and were subsequently streamlined and parameterized in Reference [2].