esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Experimental Setup
### MATLAB Simulation Setup
While the MATLAB Simscape's integrated battery models or circuit elements included in the Power Electronics toolbox present an attractive option of simplifying the schematics and reduced simulation parameters, in high frequency testing they display a less-transparent behavior. Moreover, since the toolbox's basic elements are modeled as current and voltage sources, they also restrict the parallel and series component's intuitive connections; therefore, the Simscape Foundation Library electrical component's alternative is used in this model. Each cell is generically represented by a DC power source limited by a serial resistor in series with a parallel RC element, as described in the classical Thevenin batterymodel [59]. The GFSK receiver is represented by a virtual spectrum analyzer, reading the output signal feed to a 50 \(\Omega\) resistor that mirrors the RF transceiver internal impedance. As the capacitive coupling is the straightforward interfacing method in PLC, the plus and minus lines were both connected to the Tx and Rx through capacitor sets of 1 \(\mu\)F, 1 nF and 1 pF, to determine the best high-frequency behavior for 868 MHz and 2.4 GHz.
# Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation
## 3 Adaptive Backstepping Controller Design
### Controller Design
\begin{split}\dot{e}_{i2}&=\dot{x}_{i2}-\dot{ \lambda}_{i2}-\dot{s}_{i}=b_{i}u_{i}+f_{i}(\mathrm{X})+w_{i}-\Delta u_{i}+p_{i2 }\lambda_{i2}-\dot{s}_{i}\\ &=b_{i}u_{i}+\xi_{i}^{*T}h_{i}+w_{i}-\Delta u_{i}+p_{i2}\lambda_{ i2}-\dot{s}_{i}\end{split}  \tag{24}\]  
The RBF neural network is used to approximate the unknown function \(f_{i}(X)\). \[f_{i}(X)=\xi_{i}^{*T}h_{i}(X)+\delta_{i} \tag{25}\]  
where \(\xi_{i}^{*}\) is the vector of ideal weights. \(h_{i}(X)\)is the Gaussian basis function. \(\delta_{i}\) is the minimum approximation error of the RBF neural network which used to approximate the unmodeled and uncertain part \(f_{i}(X)\). To balance the effects of approximation errors and external disturbances, an adaptive robust term is defined to compensate for the perturbations, and it can also compensate for the approximation error of RBF neural network. The external disturbances are time-dependent and the state of each control channel is bounded, so the total system perturbation \(w_{i}\) is also bounded. Suppose there exists an unknown constant \(w_{i}^{*}\) as the boundary of the perturbation, \(w_{i}\leq\left|w_{i}^{*}\right|\) is satisfied. The neural network minimum parameter learning method is used to improve the speed of adjusting parameters; let \(\Theta_{i}^{*}=\parallel\xi_{i}^{*}\parallel^{2}\). The Lyapunov function is designed as  
\[V_{i2}=V_{i1}+\frac{1}{2}e_{i2}^{2}+\frac{1}{2\beta_{i}}\tilde{\Theta}_{i}^{ 2}+\frac{1}{2\rho_{i}}\tilde{w}_{i}^{2} \tag{26}\]  
where \(\beta_{i},\rho_{i}\) are the two positive constants to be designed. \(\hat{\Theta}_{i}\) is the estimate of the neural network weights, and \(\hat{\Theta}_{i}=\Theta_{i}^{*}-\hat{\Theta}_{i}\) is the estimation error between the desired weights and the estimated weights. \(\tilde{w}_{i}\) is the estimate of the total perturbation of the system, and \(\tilde{w}_{i}=w_{i}^{*}-\tilde{w}_{i}\) is the estimation error between the actual perturbations and the estimated perturbations.
## 4 Results and Discussion
### Case 1: 250 W PEM Stack
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Operating Condition 3/5 Bar and T = 353.15 and Bound \#2 of Decision Variables**} \\ \hline
**PEMFC Parameter** & **HABC [28]** & **HADE [25]** & **MVO [27]** & **Proposed COA** \\ \hline \(\xi_{1}\) & \(-0.8540\) & \(-0.8532\) & \(-0.918163\) & \(-1.1449\) \\ \(\xi_{2}\) & \(2.8498\times 10^{-3}\) & \(2.810093\times 10^{-3}\) & \(3.212993\times 10^{-3}\) & \(3.0118\times 10^{-3}\) \\ \(\xi_{3}\) & \(8.3371\times 10^{-5}\) & \(8.092\times 10^{-5}\) & \(8.70306\times 10^{-5}\) & \(3.6000\times 10^{-5}\) \\ \(\xi_{4}\) & \(-1.2940\times 10^{-4}\) & \(-1.287\times 10^{-4}\) & \(-1.80253\times 10^{-4}\) & \(-1.4470\times 10^{-4}\) \\ \(\lambda\) & \(14.2873\) & \(14.0448\) & \(15.1921\) & \(24.0000\) \\ \(\mathrm{R_{c}}\) & \(1.0000\times 10^{-4}\) & \(1.0000\times 10^{-4}\) & \(4.22329\times 10^{-4}\) & \(1.0000\times 10^{-4}\) \\ \(b\) & \(3.40\times 10^{-2}\) & \(3.35374\times 10^{-2}\) & \(1.80021\times 10^{-2}\) & \(2.0856\times 10^{-2}\) \\ SSE & \(8.0047\) & \(7.9908\) & \(3.5846\) & \(0.6691\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal decision variables obtained with different method compared to COA for PEMFC at operating conditions #2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Operating Condition 1/1 Bar and T = 343.15 and Boun#1 of Decision Variables**} \\ \hline
**PEMFC Parameter** & **HGA Hybrid** & & & & & & **Particle** & \\
**Pearance Parameter** & **Genetic** & **HADE [25]** & **MVO [27]** & **SSA [46]** & **SCA [47]** & **MFO [48]** & **Swarm Optimization** & **Proposed COA** \\  & **Algorithm [18]** & & & & & & **(PSO [16])** & \\ \(\xi_{1}\) & \(-0.94495\) & \(-0.944\) & \(-0.9447\) & \(-0.94915082\) & \(-0.944\) & \(-0.952\) & \(-0.944\) & \(-0.94400281\) \\ \(\xi_{2}\) & \(3.018\times 10^{-3}\) & \(3.078\times 10^{-3}\) & \(3.049\times 10^{-3}\) & \(2.93\times 10^{-3}\) & \(2.96\times 10^{-3}\) & \(2.98\times 10^{-3}\) & \(2.96\times 10^{-3}\) & \(2.90\times 10^{-3}\) \\ \(\xi_{3}\) & \(7.4\times 10^{-5 Figure 4: Polarization (V-I) curve of PEM fuel cell at operating conditions #1 and # 2. Figure 5: Power (P-I) curve of PEM fuel cell at operating conditions #1 and #2. Figure 3: The convergence of COA curve of 250 W PEM fuel cell and polarization curve at operating conditions #1. At four working settings, two for examining the proposed model and the other ones for legalizing the model, a set of the polarization curves is built. Figure 6 clarifies the polarization curves (V-I curves) at (3/5 bar and 353.15 k/2.5/3 bar and 343.15 k/1.5/1.5 bar and 343.15 k/1/1 bar and 343.15 k). The results obtained show the effectiveness of the proposed algorithm in estimating an accurate model for the PEM fuel stack.
# CAVVPM: Challenge-Based Authentication and Verification of Vehicle Platooning at Motorway
## 7 Simulation Results and Real-World Limitations  
An open-source framework based on OMNet++ and SUMO, Vehicles in Network Simulation (VEINS), simulates networks of automobiles. Road traffic is modeled using SUMO, while OMNet++ simulates networks with the help of the physical layer modeling program MiXiM. The multi-channel operation, interference, and noise effects are all accounted for in VEINS' models of IEEE 802.11p, IEEE 1609.4 DSRC/WAVE network layers, and cellular networks such as LTE and 5G.  
Given how cheap it is to alter the network's topology and protocol, a computer simulation can be used to test out a wide variety of network topologies for vehicle platoons. As a bonus, the effectiveness of the defense mechanism and the platoon's reaction to an attack may be tested in various traffic scenarios. The vehicle platoon built on the upgraded protocol version can be tested for compatibility with older systems and validated after publishing the new version. While there are many advantages to employing computer simulation, the traffic and network in the model vehicles are inaccurate representations of the real world. Because of this discrepancy between the model and the real world, the predicted behavior of a vehicle platoon may be incorrect, leading to unintended con  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Methodology** & **Attack** & **Strength** & **Weakness** \\ \hline
[4] & Cryptography & \
\begin{tabular}{c} Eavesdropping \\ sybil \\ \end{tabular} &
\begin{tabular}{c} Lightweight hash-based \\ cryptography \\ \end{tabular} & Additional hardware \\ \hline
[8] & Cryptography &
\begin{tabular}{c} Eavesdropping \\ Sybil \\ \end{tabular} & Elliptic Curve cryptography & Additional hardware needed \\ \hline
[15] & Cryptography & Sybil, Jamming & session key-based certificate & Additional hardware \\ \hline
[19] & Cryptography &
\begin{tabular}{c} Jamming \\ Impersonation \\ Message altering \\ \end{tabular} & Certificate Revocation protocols & Additional hardware \\ \hline \hline \end{tabular}
\end{table}
Table 2: A Comparative Summary of Security Techniques.  
sequences in practice. Simulations platooning results and parameters are displayed in Figures 14 and 15.
## 3 DQN-Based Solution  
To address the problem defined by (2) a large number of variables must be considered, such as the propagation and interference conditions experienced by the UE in the links with the different cells/RATs, the existing load in each cell, and the QoS requirements, among others. Additionally, there is also a dependence of the behavior of the MAC layer in each cell/RAT that determines the amount of resources allocated to the UE. However, since the MC controller operates on top of the different RATs, in general does not have a precise model of how these resource allocation techniques work to determine the value of \(\gamma(\beta_{m,n})\) and its impact on the QoS metrics. Then, given the complexity and the multiplicity of inputs to the multi-connectivity problem, DRL techniques are considered as solid candidates for approaching it. Particularly, this paper relies on the DQN algorithm, which is a model-free and value-based DRL algorithm that considers discrete action spaces. This algorithm has been already successfully applied to other problems in the RAN, such as capacity sharing in [28], resource allocation in heterogeneous networks in [29], and spectrum sharing in 4G/5G networks in [30].  
Other more sophisticated techniques, such as Double DQN (DDQN) or Deep Deterministic Policy Gradients (DDPG), could be considered to overcome the overestimation of the Q values in DQN. A previous work of the authors in the area of capacity sharing [31], where DQN was compared against DDQN and DDPG, suggested retaining DQN for this work. While no significant differences in terms of performance would be expected, practicality considerations such as the speed of the training process, and the number of hyperparameters to configure, may favor DQN.  
In the proposed approach, learning is a dynamic process carried out by the DQN agent located at the MC controller, which makes decisions on the multi-connectivity configurations for the different UEs. The agent operates in discrete times with granularity equal to the time window duration \(\Delta T\). These discrete times are denoted as \(t\), \(t+1\),..., \(t+k\),... At time \(t\), the DQN selects an action _a(t)_ that contains the MC configuration to be applied for a given UE in the next time window. The action selection is based on the current state at time \(t\), denoted as _s(t)_, and on the decision-making policy available at this time. Then, as a result of applying the selected MC configuration, a reward signal \(r(t+1)\) is provided to the DQN agent at the end of the time window. This reward signal measures how good or bad was the last performed action _a(t)_ according to the considered optimization criteria and, in consequence, this obtained signal is used to improve the decision-making policy. The different components of this process are detailed in the following.
# Name-Based Security for Information-Centric Networking Architectures
## 5 Implementation and Evaluation
### Performance Evaluation of the Proposed Security Solutions  
In order to evaluate the storage and computation overhead of the proposed solutions, we implemented the IB-PRE scheme of [PERSON] [4] using the Charm cryptographic library [25]. In our measurements, we considered a setup that provides security equivalent to RSA with an 1024 bits key. The measurements related to the Encrypt, ReEncrypt, and Decrypt algorithms  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Operation** & **Time in Seconds** \\ \hline Check if DID exists & 0.0426 \\ Retrieve DID document & 0.030 \\ Create DID document & 2.59 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Execution time of chaincode operations measured in seconds. DID = Decentralized Identifier.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Operation** & **PC** & **RPi** & **Mobile Phone** \\ \hline Key generation & 13.6 & 126 & 32 \\ Signature creation & 13.2 & 126 & 18.9 \\ Signature verification & 24.1 & 231 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time of cryptographic operations measured in ms.  
concern the encryption of a 128-bit random number (i.e., the length of the symmetric encryption key used for encrypting files). Table 3 presents the computation overhead of the IB-PRE algorithms. Measurements were obtained in an Ubuntu 16.04 machine, running in a single core of an Intel i7-4440 3.1 GHz processor with 16 GB of RAM, using Charm v0.43. Table 4 presents the size of the various variables used by the IB-PRE algorithms.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Impact of Content Motion
#### 4.3.3 Impact on Cybersickness  
Question Q5 is to investigate the cybersickness for different contents. As shown in Figure 10, the scores of Q5 for Content #1 and Content #2 are higher than 3 MOS for both the device sets. Meanwhile, the scores for Content #3 are very low (i.e., approximately 2 MOS). Based on the results of the post-hoc test, it is shown that there are statistically significant differences between Content #1 and Content #3 (i.e., \(p<0.05\), r = 0.62) and between Content #2 and Content #3 (i.e., \(p<0.05\), r = 0.51). Meanwhile, no significant difference is found between Content #1 and Content #2 (i.e., \(p>0.05\)). This suggests that, when watching videos with fast camera motion such as Content #3, it is easy for the viewers to realize the differences between their movements and the displayed content,  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Aspect** & **Factor** & \(\chi^{2}\) & \(p\)**-Value** & \(\eta^{2}\) \\ \hline _Acceptability_ & & 0.81 & 0.668 & 0.001 \\ _Presence_ & & 23.83 & **\textless{0.0001}** & 0.022 \\ _Perceptual quality_ & & 13.18 & **0.001** & 0.012 \\ _Cybersickness_ & & 30.941 & **\textless{0.0001}** & **0.278** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical results about the effect of content motion on QoE aspects based on Kruskal-Wallis test. The bold numbers in column \(p\)-value represent to statistically significant effects of factors. The bold, italic, and underlined numbers in column \(\eta^{2}\) respectively correspond to “large”, “moderate”, and “small” effect sizes.  
and consequently causing more severe cybersickness. Hence, cybersickness is a serious problem for 360 video, especially for contents having fast camera motions.
## 1 Introduction
We confirm that the detection algorithm performed by the relay, proposed by us in [17], results in better overall performance of a two-way relaying system as compared with a typical successive interference cancellation (SIC) receiver applied in the relay. 2. We also show that the proposed detection algorithm performed in the relay jointly with channel code decoding of both end terminal signals performs better than the algorithm realizing joint detection of component signals performed based on the composite signal constellation that is achieved by summing the signals received from both end terminals in the multiple access phase. The performance of the proposed detection algorithm applied in the relay node of a two-way relaying system is measured by the estimation of the bit error rate (BER) in both terminals participating in the information exchange in two-way relaying. The aim of this paper is not to calculate outage probability for two-way relaying systems nor to calculate the capacity of two-way relaying channels. We focus on the detection algorithm performed by the relay in the multiple access phase of data exchange as it has a crucial meaning for the overall system performance. The rest of the paper is organized as follows. In Section 2, a two-way relaying system is recalled and the related works for such a system with channel coding are reviewed. Section 3 presents the physical layer network coding system models and explains two basic algorithms of signal detection in the relay. The first algorithm is based on symbol-by-symbol detection and creation of a broadcast relay signal, derived from composite received signals in the multiple access phase. The second algorithm is the one proposed by the authors of this paper, originally presented in [17] for the design of non-orthogonal multiple access (NOMA) receivers. We show that this algorithm can be also successfully applied in the relay of a two-way relaying system. Section 4 presents the simulation model used in the experiments and shows the simulation results. Higher layer issues are briefly discussed. Finally, in Section 5, conclusions are formulated.
# Internet of Drones Intrusion Detection Using Deep Learning
## 5 Simulation Results
### Performance Analysis
#### 5.2.3 LSTM_RNN Performance over NSL-KDD Dataset  
NSL-KDD was another dataset used to examine the performance of the proposed LSTM_RNN algorithm. Again, the algorithm was examined on binary, multiclass, and detailed attack classification. As shown in Figure 18, LSTM-RNN outperformed LR and KNN algorithms for all performance measures except the precision where LR precision increased by 1% over LSTM_RNN. The false positive rate of LR was much better than that of LSTM_RNN, in this case.  
LSTM_RNN in Figure 19 shows a better performance than LR and KNN algorithms with 88% accuracy, 80% precision, 98% recall, and 88% F-score. On the other hand, KNN showed good accuracy, Recall, and F-score but lower precision than LR, where the false positive rate of LR was lower than KNN by almost 1%.  
Figure 19: NSL-KDD Multiclass classification performance comparison.  
Figure 18: NSL-KDD Binary classification performance comparison.  
In detailed class classification, Figure 20, LSTM_RNN resulted in 99% accuracy over R2L classification and 97% accuracy classifying U2R, while it had the lowest performance in classifying the Normal attacks with 85%. On the other hand, KNN and LR had a close accuracy but were still lower than LSTM_RNN.
# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
## 1 Introduction  
Welschriesling is a white grape variety originating from the Danube Basin, namely in the Slavonia and Podunavlje sub-regions situated in northeastern continental Croatia. Grasevina (Croatian name for Welschriesling) is the most widely planted and economically important white variety in Croatia [1]. Welschriesling is also present in the regions of Eastern and Central Europe; however, its origin has not yet been fully proven. The latest DNA analysis showed that Welschriesling is probably the natural cross between the Italian variety Coccalona Nera and an unknown father variety [2].  
Although Welschriesling wine is an important part of the wine market in Europe, its aromatic profile has not been studied extensively. A semi-quantitative study was performed, in which 12 esters, 1 terpenoid, and 1 norisoprenoid \(\beta\)-damascenone were analyzed to assess the chemical aromatic profile of eight Welschriesling wines from Burgenland,Leithagebirge, Austria [3]. The outcome of the aromatic profile was apple-like aromas, which were further differentiated into green and mature apple profiles by the addition of exotic aromas and meadow flowers. It was also shown that soil, climate, geographical position, and vinicitation affect the aroma profile of Welschriesling wines. A study on the effects of controlled vinicitation on wine aroma profile has also been conducted [4]. However, both studies lack quantitative data on volatile compounds, and therefore, no concrete connection between volatile chemical composition and wine aroma perception could be deduced.  
In contrast, [PERSON] and [PERSON] quantified 28 aromatic compounds, including varietal thiols, esters, higher alcohols, and monoterpene alcohols in 8 commercial Welschriesling wines from Slovenia, and investigated the effects of commercial yeast starters and lactic acid bacteria on Welschriesling wines' aromatic profile. It was concluded that varietal thiols could impact the aromatic profile of Welschriesling wines, and that malolactic fermentation caused an increase in 3-mercaptoethanol-1-ol [5]. A complementary study suggested that the selection of grape clones of Grasevina and yeast strains can also modulate varietal thiol wine composition [6]. In addition, four new clones of Grasevina positioned in the Zagreb area were subjected to sensory evaluation and determination of volatile aromatic compounds [7].  
To the best of our knowledge, there are no published studies assessing volatile compounds in Grasevina wines originating from both the Slavonia and Podunavlje sub-regions (Croatia). Consequently, this study aimed to extend the knowledge about the volatile composition of Grasevina wine, particularly by focusing on terpenoids and norisoprenoids, which have not yet been analyzed in such wine. The second objective of this study was to establish the potential impact of volatile fingerprints between both sub-regions based on statistical analyses.
# Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
## 1 Introduction  
In general, the selection of the site of a new retail store is markedly different from the selection of a site for manufacturing facilities. The decision over the latter is primarily focused on cost minimization. Retail stores, however, generally seek to maximize revenues.  
A review of the limited literature on store site selection revealed that most germane studies use the following methods for site selection: analytic network process (ANP) [1, 2], analytic hierarchy process (AHP) [3, 4], quantitative methods, such as regression models [5], and weighted scores with the use of checklists [6]. This previous work mainly attempts to identify the influencing factors and obtain the weights of these influencing factors when establishing new stores. In addition, the results of these investigations provide limited assistance vis-a-vis the practical site selection problem. In recent years, use of artificial neural networks (ANNs) for site selection has been shown to be especially effective in various venues, such as the locations of wind farms [7], landfills [8], water waste [9], and CVSs [10]. Currently, the selection of a specific site is comprehensive in practice. The location selection techniques that use ANNs solve the problem for a region but not for a specific site. Therefore, the results obtained from an ANN are not comprehensive, and the reference value they provide is limited.  
Our study employed an ANN technique--a back-propagation neural network (BPN)--to generate suitable CVS site selection criteria from various inputs collected from a well-known CVS chain in Taiwan. To establish a set of robust site selection prediction models with the BPN, the Taguchi method (TM) [11] was adopted to identify the optimal parameters for performing the BPN.