esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[26] employed random forest approach to predict the compressive strength of high-performance concrete. Similarly, [PERSON] et al. [39] used evolved random forest algorithm on 138 data samples to predict the compressive strength of rubberized concrete which was collected from published literature. This advanced-based approach gave better performance with a strong coefficient correlation of R\({}^{2}\) = 0.96. ANN and other models have been adopted for predicting the mechanical strength parameters of high-performance concrete and recycled aggregate concrete [40; 41; 42; 43; 44]. [PERSON] et al. [45] studied the influence of silica and fly ash on the compressive strength \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Data Points** & **Algorithm** & **References** \\ \hline Compressive strength, Slump test & 187 & ANN & [7] \\ Elastic modulus & 159 & ANN & [8] \\ Elastic modulus & 159 & FUZZY & [9] \\ Elastic modulus & 159 & SVM & [10] \\ Elastic modulus & 159 & ANFIS and nonlinear & [11] \\ Compressive strength & 20 & ANN & [12] \\ Compressive strength & 324 & ELM & [13] \\ Compressive strength & 357 & GEP & [14] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithm used in prediction properties of high strength concrete. of concrete. A comprehensive experimental was carried out to analyze the impact of varying w/c ratios and varying percentages of silica and fly ash on the performance of concrete. In addition, ANN was adopted to depict the effect on the strength parameters of concrete [45]. [PERSON] et al. [44] used a GEP-based machine learning algorithm to predict the compressive arch action of a reinforced concrete structure. The author found that GEP is an effective tool for prediction performance. This paper aimed at evaluating the performance of compressive strength of a high strength concrete (HSC) using ensemble random forest (RF) and gene expression programming (GEP). The data points used to model were attained from published articles and are listed in Table S1. Anaconda spyder python-based programming [46] and GENEXprotocol software [47] are used for prediction of the compressive strength of HSC. The parameters used in model contain cement, water, coarse aggregate to fine aggregate ratio, superplasticizer as input, and compressive strength as output for model development. Hex contour graphs are made to show the relationship of the input and output parameters. Sensitivity analysis (SA) and permutation feature importance (PFI) that address the relative importance of each variable on the desired output parameters are conducted. Moreover, the model evaluation is also carried out by using statistical measures.
## 4 Simulation Study  
In this section, several simulations under different scenarios have been studied to verify the effectiveness of the proposed method, in which the maximum matching and the identification algorithm are programmed through the M-function. The first case is used to show the identification results using the proposed controllability index without any fault or attack. The second and the third cases show the performance of the test system under targeted attacks and random attacks, respectively. In the first three cases, the IEEE 118 bus system as the test system is shown in Figure 3. Then, the proposed method is applied to a larger IEEE 300 bus system to further verify its effectiveness. In the last case, the comparison between the proposed method and some conventional method will be studied. In addition, all the simulation studies are under steady-state conditions.  
In practice, there are many types of problems that can lead the power systems to be in fault (e.g., under-voltage, overload, _etc._). However, in the theory framework of a complex network, when a fault happens to a node, it can be equivalently seen as an invalid node, meaning the node is disconnected from the network [3]. Therefore, in this paper, the nodes will be disconnected from the network if they are in any kind of fault. In addition, all faults in this paper can be considered as a result of the targeted attacks and random attacks, which are two typical types of attack in power systems. The targeted attacks mean that the few critical nodes with higher controllability index values are attacked to failure. On the contrary, random attacks mean that any of the less important nodes with low controllability index values are attacked to failure. During the simulations, a node will be in fault and disconnected from the network if it is subjected to targeted attacks or random attacks.
## 3 The Proposed Optimization Hybrid Algorithm
### OPF Solutions IEEE 30-Bus Network [56]
#### 4.1.1 Case 1: Minimization of Fuel Cost  
In Case 1, the fuel cost of all generating units is minimized as in Equation (46):  
\[\begin{split}& J\,=\,\sum\limits_{i=1}^{NG}\left(\alpha_{i}+b_{i}P_{Gi}+c_{ i}P_{Gi}^{2}\right)\,+\,\lambda_{P}\,(\,P_{G1}-P_{G1}^{\text{lim}}\,)^{2}\,+\, \lambda_{V}\,\sum\limits_{i=1}^{NL}\,(\,VL_{i}-VL_{i}^{\text{lim}}\,)^{2}\\ &\,+\,\lambda_{Q}\sum\limits_{i=1}^{NG}\left(Q_{Gi}-Q_{Gi}^{ \text{lim}}\,\right)^{2}\,+\,\lambda_{S}\sum\limits_{i=1}^{NTL}\,(\,S_{i}-S_{ i}^{\text{lim}}\,)^{2}\end{split} \tag{46}\]  
Simulation results, shown in Table 3, illustrate that the fuel cost when applying the TLTFWO is 800.4780 (USD/h), which is less compared with those of the results reported in the literature and novel optimization approaches listed in Table 4, such as tabu search (TS) [57], artificial bee colony (ABC) [58], hybrid shuffle frog leaping algorithm (SFLA) and simulated annealing (SFLA-SA) [59], differential evolution (DE) [60], adaptive group search optimization (AGSO) [61], MSA [56], GWO [62], evolutionary programming (EP) [63], modified Gaussian bare-bones imperialist competitive algorithm (MG-BICA) [64], Aquila optimizer (AO) [65], hybrid particle swarm optimization (PSO) and GSA (gravitational search algorithm) (PSOGSA) [66], hybrid of imperialist competitive algorithm (ICA) and TLBO (teaching-learning-based optimization) (MICA-TLA) [67], adaptive real coded biogeography-based optimization (ARCBBO) [68], a modified honey bee mating optimization (MHBMO) [9], manta ray foraging optimization (MRFO) [69], flower pollination algorithm (FPA) [56], stud krill herd algorithm (SKH) [70], an improved EP (IEP) [71], hybrid firefly algorithm (FA) and JAYA (HFAJAYA) [72], JAYA [73], firefly algorithm (FA) [72], moth-flame optimization (MFO) [56], hybrid phasor PSO (PPSO) and GSA (PPSOGSA) [55], hybrid modified PSO (MPSO) and SFLA (MPSO-SFLA) [11], teaching-learning-based optimization (TLBO), and TFWO. Figure 5 illustrates the convergence of the objective function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & **Fuel cost (USD/h)** & **Emission (t/h)** & **Power Losses (MW)** & **V.D. (p.u.)** \\ \hline TS [57] & 802.29 & - & - & - \\ ABC [58] & 800.660 & 0.365141 & 9.0328 & 0.9209 \\ SFLA-SA [59] & 801.79 & - & - & - \\ DE [60] & 802.39 & - & 9.466 & - \\ AGSO [61] & 801.75 & 0.3703 & - & - \\ MSA [56] & 800.5099 & 0.36645 & 9.0345 & 0.90357 \\ GWO [62] & 801.41 & - & 9.30 & - \\ EP [63] & 803.57 & - & - & - \\ MGBICA [64] & 801.1409 & 0.3296 & - & - \\ AO [65] & 801.83 & - & - & - \\ PSOGSA [66] & 800.49859 & - & 9.0339 & 0.12674 \\ MICA-TLA [67] & 801.0488 & - & 9.1895 & - \\ ARCBBO [68] & 800.5159 & 0.3663 & 9.0255 & 0.8867 \\ MHBMO [9] & 801.985 & - & 9.49 & - \\ MRFO [69] & 800.7680 & - & 9.1150 & - \\ FPA [56] & 802.7983 & 0.35959 & 9.5406 & 0.36788 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal results of the current research in Case 1.  
Figure 5: Convergence trends for Case 1.
# Immersive Virtual-Reality System for Aircraft Maintenance Education: A Case Study
## 2 Literature Review
The students acknowledged that the system was beneficial to their learning, which proved that the developed system is highly effective for training 148 students to improve aircraft maintenance skills. Despite the fact that the innovative characteristics of these types of systems make them very attractive, it is relevant to consider the aspects of motivation and adherence so that their acceptance is adequate. Gamification has been widely used in VR simulations to make them more attractive and encourage their adherence by incorporating elements such as points, badges, leaderboards, and rewards. [PERSON] et al. [18] stated in their work that using augmented reality and gamification in education can yield several benefits for students and assist educators. In work presented by [PERSON] et al. [19], gamification was applied to train chemistry students in complex content, demonstrating its acceptance and the desire of students to integrate it into regular classes. Similar results were obtained in [20], providing teachers and students with techniques to learn how to use search algorithms. [PERSON] et al. [21] applied these techniques in the training of architecture students, again with results that show that motivation and adherence improve with them. From a perspective less focused on the student and more on the professional world, [PERSON] et al. [22] compared a gamified virtual environment with the real one. The environment consisted of a manual work station with assembly parts, an Allen key, and a monitor showing a picture of the final assembly. The training presented to two groups was compared, and it was shown that VR reduced error rates and the need for support. The same results were obtained by [PERSON] et al. [23], who presented a simulation-based training system to support the training in x-ray imaging of operating room personnel in a virtual environment. As a way of structuring the use of gamification, Octalysis [10], the framework mentioned above, has been used in different works. In [24], [PERSON] et al. used the framework to classify 12 stress management mHealth apps. [PERSON] et al. [25] relied on it and the k-means clustering approach to explore which drives most influence customer behavior for the Customer Lifetime Value indicator. In learning, [PERSON] et al. [26] used Octalysis to design and build a React Native learning app and measure the level of intention to use the behavior. By taking all these reviews into account, this work presents a method based on the learning-by-doing methodology addressed to the representation of gamified VR environments related to aircraft maintenance. It represents a virtual environment that motivates students to take action in the VR scenarios to investigate and test the different actions to be performed in this field. All the requirements and the user design environment are analyzed in the following sections.
## 5 Computations for Model Example with Non-Linear Dynamics
### Nash Equilibrium  
For the non-cooperative case, we define the Hamiltonian function as \[H_{i}(\psi_{i},u)=\psi_{i}\left(-\sum_{j=1}^{n}u_{j}^{2}\right)+(c_{i}\sqrt{u_{i}}). \tag{43}\]  
The optimal controls \(u^{*}\) are computed from the first-order optimality conditions \(\frac{\partial H_{i}}{\partial u_{i}}=0\) as  
\[u_{i}^{*}=\left(\frac{c_{i}}{4\psi_{i}}\right)^{\frac{2}{3}},\quad i=\overline {1,n}. \tag{44}\]  
Note that the adjoint variable is constant according to optimality condition \(\frac{\partial H_{i}}{\partial x}=0\), \(\psi_{i}=\psi_{0i}=const\) and, furthermore, has to satisfy \(\psi_{i}>0\). Thus, we substitute this into (44) and then substitute the expression of optimal control into system (4).  
Solving this system, we come to the condition  
\[\sum_{i=1}^{3}\left(\frac{c_{i}}{4\psi_{0i}}\right)^{\frac{4}{3}}=\frac{x_{0}- x_{f}}{T-t_{0}}. \tag{45}\]  
Let us assume that the coefficients are equal to each other, i.e., \(c_{i}=c_{j}=c\). Then, the players are symmetrical, which means that their optimal controls should be the same, i.e., \(u_{i}=u_{j}\). Consider the first equation of the system for one player and solve it with respect to the adjoint variable:  
\[\dot{x}=-nu_{1}=-n\bigg{(}\frac{c}{4\psi_{01}}\bigg{)}^{\frac{4}{3}}. \tag{46}\]  
Finally, we obtain the solution \(x^{*}(t)\) and the optimal control of the first player:  
\[x^{*}(t)=\bigg{(}\frac{x_{f}-x_{0}}{T-t_{0}}\bigg{)}(t-t_{0})+x_{0}, \tag{47}\]  
\[u_{1}^{*}=\sqrt{\frac{(x_{0}-x_{f})}{n(T-t_{0})}}. \tag{48}\]  
The same can be done for other players.  
Next, we check the control constraints according to the conditions of the problem, i.e., \(u_{i}\in[0,u_{max}]\). The controls are admissible if the following condition is satisfied:  
\[x_{f}\geq x_{0}-nu_{max}^{2}(T-t_{0}).\]  
Find the payoffs of each \(i\) player  
\[K_{i}(x_{0},x_{f},T-t_{0},u^{*})=c\sqrt[4]{\frac{(x_{0}-x_{f})(T-t_{0})}{n}}, \quad i=\overline{1,n}. \tag{49}\]  
As a result, substituting the optimal control into the objective function, we obtain  
\[K(x_{0},x_{f},T-t_{0},u^{*})=c\sqrt[4]{n^{3}(X_{0}-X_{f})(T-t_{0})}. \tag{50}\]  
Similarly to the calculations from Section 5.2, we can consider two cases of uncertainty in a non-cooperative scenario separately for each player. As a result, the values of the information coincide with the expressions (39) and (42).
## 4 Impacts of HTST and UHT on Food Quality
### Antioxidant Activity  
Antioxidant activity (AA) is closely related to the contents of bioactive components found in food, such as phenolics, as well as their bioaccessibility and bioavailability. The literature points to numerous methods for determining total antioxidant activity in foods based on single-electron transfer reaction or SET assays or based on a hydrogen atom transfer reaction or HAT assays [79].  
Heating alters the content and profile of bioactive compounds found in plant raw materials and, consequently, as well as antioxidant activities. A study by [PERSON] et al. [39] conducted on a papaya beverage showed that the AA measured by FRAP and DPPH radical methods decreased significantly immediately after preservation (110 \({}^{\circ}\)C/8.6 s), while there was no change in carotenoids content and a decrease in total phenols content, which are strong antioxidants. Decreases in antioxidant activities were shown by [PERSON] et al. [38] (8.2% for DPPH radical and 6.8% for FRAP) and [PERSON] et al. [37] (10.2% for DPPH radical and 6.0% for FRAP), with a significant decrease in TPC, and by [PERSON] et al. [53] in red grapefruit juice (1.4% for DPPH radical and 8.5% for FRAP), as well as other researchers [17; 42]. On the other hand, [PERSON] et al. [27] observed no significant change in AA using the DPPH radical method but noted a significant increase using the FRAP method immediately after preservation. In the second case, the phenomena was associated with an increase in TPC and the tendency of phenolics to undergo polymerization reactions. The opposite relationship was shown by [PERSON] et al. [44]. [PERSON] et al. [35] indicated neither an increase nor a decrease in AA after preservation of purple sweet potato nectar by the UHT method, which was associated with no change in phenolics content, while over 12 weeks of storage, AA decreased by more than 20%. [PERSON] et al. [54] observed no significant change in AA despite significant decreases in TPC and ascorbic acid content. A decrease in AA during storage was also demonstrated by [PERSON] et al. [44].  
HTST under conditions of 72 \({}^{\circ}\)C/15 s resulted in a significant (77.5%) increase in antioxidant activity immediately after treatment. Over the course of preservation, the AA in the preserved sample increased from 1554.64 (0 day) to 2489.16 \(\upmu\)mol TE/L (28 day). However, from 14 to 28 days of preservation, the AA in heated peach juice was lower than in a nonprocessed one [31]. [PERSON] and [PERSON] [26] came to similar conclusions, as follows: in strawberry juice immediately after the process, AA increased by 27% and was significantly higher by the 7 th day of storage compared to fresh juice. In contrast, the AA values for ultrasound or high-pressure homogenized juices were significantly higher throughout the working principles of these processes [26; 31]. On the other hand, the AA was not affected by thermal processing [30; 57]. In contrast, some researchers point to a significant reduction in AA due to decreases in antioxidants, such as vitamin C and carotenoids, in sea buckthorn juice as a result of HTST [56].
## 1 Introduction
Therefore, as the emissions standard became more stringent recently, such as the replacement of the New European Drive Cycle (NEDC) with the World-Harmonised Light-duty Transient Cycle (WLTC) for type approval in 2017, including more rigorous cold start testing for light duty vehicles [25], significant attention is now being given to the diesel engine cold start problem. Consequently, a number of recent studies can be found on diesel engine cold start, such as [1; 2; 16; 17; 26; 27; 28; 29; 30; 31], focusing on engine performance and emissions during cold start. However, none of the cold start investigations in the literature included the intermediate stages of the engine warm up comprehensively, which is critical due to the influence of sub-optimal oil temperature. Moreover, the cold start investigations found in the literature have been conducted on standard drive cycles such as the NEDC, WLTC, or FTP and for a fundamental investigation such as this, owing to the continuously transient engine speed/load trace, standard cycles such as the WLTC or the FTP were not deemed suitable [25; 32]. Comparatively, the cycle implemented in this study involved engine stop/start, with abrupt load changes, from high to low and vice versa with short quasi-steady state periods, which ensured that the engine warm up behaviour could be comprehensively studied during the different warm up stages, which has not been attempted in any previous study. Therefore, owing to the importance of a comprehensive study on engine cold start operation and the need to gain more insight into the different stages of engine warm up, this study has been designed to investigate the cold, intermediate, and hot start behaviour, as an engine goes through different stages during warm up, denoted as: Stage 1--coolant and oil \(<\) 70 \({}^{\circ}\)C (cold start); Stage 2--70 \({}^{\circ}\)C \(<\) coolant \(<\) 90 \({}^{\circ}\)C and oil \(<\) 70 \({}^{\circ}\)C (intermediate start--I); Stage 3--coolant \(>\) 90 \({}^{\circ}\)C and 70 \({}^{\circ}\)C \(<\) oil \(<\) 90 \({}^{\circ}\)C (intermediate start--II); Stage 4--coolant and oil \(>\) 90 \({}^{\circ}\)C (completely warmed up or hot start). This study investigates the engine performance and emissions of a six cylinder, turbocharged, diesel engine, over a custom designed cold and intermediate start drive cycle. The current study offers a fundamental understanding of the engine's performance and emissions over a rough transient behaviour during the different stages of warm up, when started from cold, intermediate, and hot conditions.
# Measurement of Heat Flow Transmitted through a Stacked-Screen Regenerator of Thermoacoustic Engine
## 1 Introduction  
A random stack of mesh screens is used as a regenerator of many Stirling engines, including thermoacoustic Stirling engines [1], because it achieves a high surface-area-to-volume ratio and good thermal contact with the working gas. Wide varieties of mesh numbers and wire diameters allow for flexible choices, but choosing an appropriate mesh screen is often difficult. This difficulty is attributable to tortuous flow channels inherent to the stacked-screen regenerator, which poses an obstacle from a theoretical perspective. Many experimental studies [2; 3; 4; 5; 6; 7] have addressed this issue in oscillatory flow with frequencies below several tens of Hertz, aiming at gaining fundamental knowledge related to the mechanical Stirling engine. In the frequency range of thermoacoustic engines, however, experimental studies remain insufficient because they usually operate at several tens to hundreds of Hertz [1; 8; 9; 10; 11; 12; 13], even up to 23 kHz [14].  
Our previous studies have measured the flow resistance and the acoustic power production in the regenerator [15; 16] in oscillatory flow of pressurized Ar gas with a maximum frequency of 100 Hz. The results obtained with small amplitude oscillations show good agreement with predictions obtained using thermoacoustic theory [17; 18; 19] when the regenerator is assumed as a bundle of cylindrical tubes having effective radius \(r_{0}=\sqrt{d_{\mathrm{h}}d_{\mathrm{w}}}/2\) proposed by [PERSON] et al. [20], where \(d_{\mathrm{h}}\) and \(d_{\mathrm{w}}\), respectively, denote the hydraulic diameter and the mesh wire diameter. Recently, [PERSON] et al. [21], based on results of experiments conducted at different frequencies and a fixed oscillatory velocity amplitude (=2 m/s) in air at atmospheric pressure, have also reported the applicability of \(r_{0}\) for predicting the axial heat flow transported by the oscillatory gas in the regenerator. Further applicability of \(r_{0}\) should be examined with different gases while varying the velocity amplitude.  
[PERSON] and [PERSON] [22] proposed a set of equations of momentum, energy, and mass conservation for a stacked-screen regenerator by adopting the steady-flow data of Kays and London plots [23]. The equations are incorporated into the thermoacoustic calculation program--Design Environment for Low-amplitude Thermoacoustic Energy Conversion (DeltaEC) [24]. Although DeltaEC is now regarded as a standard design tool in the field of thermoacoustic engines, direct experimental verification is necessary to ascertain the heat transfer coefficient estimated from steady flow data.  
This study was undertaken to test the empirical equations proposed for the axial heat transport experimentally using gas oscillations in the stacked-screen regenerator, when pressurized Ar gas is used as the working gas. Results show that the thermoacoustic theory provides a good starting point for elucidating heat transport in stacked screen regenerators, but further improvements are expected to be necessary for a more quantitative estimation.
###### Abstract
Hence, a 6G-based unconventional communication environment will generate several fundamental security threats related to Confidentiality, Integrity and Availability (CIA), known as the CIA triad [17]. Furthermore, IP-based network architecture will also entail specific conventional vulnerabilities. These findings show the requirement of a state-of-the-art security architecture for authentication in 6G networks [18]. The concept of 3D handover will allow devices to remain holistically connected; they will formulate a notion of social nodes in the 6G network [19]. The clusters will be easily trackable and vulnerable in various terms such as impersonation, eavesdropping, Distributed Denial of Service (DDoS), Man-in-the-middle (MitM), repudiation and replay attacks. Communication networks that ensure the least latency and seamless bandwidth are prone to becoming unmanageable once integrated with secure and privacy-preserving architectures. Thus, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Project** & **Scope** \\ \hline
2019 & 6G Flagship & Formed and finished an academic and industrial consortium aiming at developing key enabling technologies for 6G [3] \\ \hline
2021 & Hexa-X & 6G European flagship initiative of future [4] \\ \hline
2021 & RISE-6G & RISE-6G project aims at investigating innovative solutions [5] \\ \hline
2021 & 6G Sentinel Lighthouse & Targeting improvements to device antennas and front-end modules [6] \\ \hline
2021 & 6G UT (University of Texas) & New sensing methods, wireless-specific machine learning algorithms and networking innovations [7] \\ \hline
2020 & 6G Brazil project & Allowing the construction of a nation-wise view for the future mobile network [8] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Prominent Research Projects on 6G communication. formulation of global level security ensured QoS architecture is substantially critical in achieving a systematic emergence of 6G communication.