esa-sceva/satcom-chunk-collection huggingface dataset:
# XR Technology Deployment in Value Creation
## 2 Theoretical Foundations and Background
### Existing Research on XR Technology Deployment in Value Creation
#### 2.3.3 Implementation Reports  
The second research stream focuses on the detailed description of implementation projects. The research field suggests concrete initiatives where XR technologies are implemented. Same as in the research reviews, the implementation reports show different focuses in terms of technology comprehensiveness as well as use case comprehensiveness in the context of value creation. While the reviews showed a more comprehensive perspective, the implementation reports suggest concrete technology system setups [5; 23] or dedicated approaches for operating XR technologies. Also, the comprehensiveness of the use case is characterized by a concrete activity in the value creation, such as development [59], manufacturing [23], sales [58], or the organization of the activity performance [62]. Table 2 summarizes selected implementation reports. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & \begin{tabular}{c} **Technology** \\ **Comprehensiveness** \\ \end{tabular} & \
\begin{tabular}{c} **Use Case** \\ **Comprehensiveness** \\ \end{tabular} &
\begin{tabular}{c} **Summary** \\ \end{tabular} \\ \hline
[5] & CAVE VR & Sales-oriented customer analysis & VR-based study to analyze customer behavior \\ \hline
[66] & VR configuration & Design review process & System suggestion for deploying VR in design \\ \hline
[59] & VR model creation & Plant design review & Rule-based system to automatically transfer CAD \\  & & data to VR for design reviews \\ \hline
[23] & Web VR & Manufacturing task planning & Web VR-based tool for executing planning of task \\  & & execution in a dynamic context \\ \hline
[13] & AR content creation & Employee guidance in manufacturing & Methodology for converting manuals to AR content \\  & & in production environment \\ \hline
[67] & AR application & Maintenance task performance & Concept for collaborative assistance in maintenance \\  & & processes through AR \\ \hline
[62] & VR engine enhancement & Generic collaboration scenario & Method for enabling synchronization in a segregated \\  & & immersive collaboration environment \\ \hline
[61] & VR setup & Generic learning scenario & Setup for VR-based e-learning with multiple users \\ \hline
[58] & XR & Point-of-sales modelling & Framework for enhancing customer touchpoints \\  & & through VR and AR \\ \hline
[21] & Adaptive AR system & Assistance in maintenance task execution & System for AR-assisted support in executing \\  & & execution & maintenance tasks with adaption to \\ \hline
[68] & VR system & Reviewing product design & VR-based system for collaborative product \\  & & design review \\ \hline
[69] & AR paradigms & Customer engagement & Guidelines for AR deployment with the focus on customer engagement \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Implementation reports on XR Technologies in value creation.
## 2 Preliminaries
### RSA Cryptosystem  
In the realm of public key cryptography, the RSA cryptosystem [18], founded on the intricacies of the large number factorization problem, is elucidated below. To aid comprehension, an example involving a message sender denoted as \(\mathbf{Sed}\) who transmits a message \(\mathbf{m}\) to a message receiver \(\mathbf{Rev}\) is presented.  
Initiation: The message receiver \(\mathbf{Rev}\) selects two substantial prime numbers \(p\) and \(q\). Subsequently, \(\mathbf{Rev}\) computes \(n=p\cdot q\) and Euler's totient function of \(n\), which is denoted as \(\varphi(n)=(p-1)\cdot(q-1)\). Next, \(\mathbf{Rev}\) chooses an integer \(e\) that satisfies \(\gcd(e,\varphi(n))=1\). The receiver then computes \(d\equiv e^{-1}(\text{mod}\varphi(n))\). The outcome is that \(\mathbf{Rev}\) publicizes the public key \((e,n)\) while keeping the private key \(d\) confidential.  
Encryption: The message sender \(\mathbf{Sed}\) takes the message \(m\) and performs an encryption operation \(c=m^{e}\bmod n\) using \(\mathbf{Rev}\)'s public key \(e\). Consequently, \(\mathbf{Sed}\) transmits the resultant cipher \(c\) to \(\mathbf{Rev}\).  
Decryption: Upon the receipt of the cipher \(c\), message receiver \(\mathbf{Rev}\) employs private key \(d\) to decipher the message. This is accomplished via the computation \(m=c^{d}\bmod n\).  
In Section 3 (i.e., step L2 in the user login phase and step V10 in the authentication and key agreement phase), we provide the detailed method of using RSA to securely transmit secret values.
## 5 Conclusions  
In this paper, we investigated a counterpart estimation technique for establishing an aviation communication network. Considering that communication stations for the networks are airborne stations located in mobile and maneuvering environments, the estimation of fast antenna locations with fast beam-steering performance is required. Rapid beam-steering performance can be achieved by utilizing an active phased array antenna system. Among the various communication counterpart location estimation techniques available, the monopulse technique can produce the fastest estimation locations. However, due to the nonlinearity of the monopulse ratio curve, the estimation range is limited, and the estimation accuracy is low. To solve these problems, in this paper, we present a technique for improving the estimation accuracy while extending the estimation range by correcting the sum and difference patterns based on the beamforming technique using an active phased array antenna. To verify the performance of the proposed technique, we presented an antenna system configuration suitable for the construction of an airborne communication network. Also, based on the presented antenna system, the performance of the proposed technique was verified, and we  
Figure 19: Simulation results for antenna system: (**a**) counterpart location; (**b**) estimation error.  
Figure 18: Simulation results: (**a**) RSSI via synthesized pattern; (**b**) estimation results.  
found that the error of the counterpart location estimation average error was 0.021\({}^{\circ}\). We also believe that the technique proposed in this paper can be used to improve the performance of a tracking system via the monopulse technique.  
Conceptualization, S.P.; methodology, J.J.; validation, J.J. and B.S.; formal analysis, S.P. and J.J.; investigation, B.S.; data curation, B.S.; writing--original draft preparation, J.J. and B.S.; writing--review and editing, J.J.; supervision, S.P.; project administration, S.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the research fund of Hanbat National University in 2023 and this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (NRF-2021R1A2C2011560).  
Data are contained within the article.  
The authors declare no conflict of interest.
# A Fast Ray-tracing Method for Locating Mining-induced Seismicity by Considering Underground Voids
## 3 Synthetic Tests
### Comparison and Analysis
In this example, the number of nodes in the proposed method is 634, and the number of nodes in FMM and SPR is 1 million. We greatly reduce the number of nodes. The proposed method takes 2 s, FMM takes 4 s, and SPR takes 34 s. In summary, the accuracy and efficiency of the proposed method are higher than those of classical FMM and SPR. How is the accuracy of the proposed method affected? Let us look at another example. Figure 12 is a void model with a length of 50 m, a width of 30 m, and a height of 40 m. The source and receiver are arranged on the surface, and the coordinates are shown in Table 2. The propagation velocity of the wave is 5000 m/s. We set the step size s to four different values of 1, 5, 10, and 20 to arrange the second category of nodes. The results of ray tracing using these step lengths s are shown in Table 2. Since the void model is a regular cuboid, we calculate the theoretical arrival time from the source to the receiver based on the cuboid expansion principle, and the results obtained are shown in Table 2.  
Figure 11: The relative error of the arrival time of the three methods compared to the analysis solution. = 1 m, the error of arrival time is between 0.0006 and 0.0056 ms. It also can be seen from Figure 13 that as the step size s decreases, the arrival time error is smaller. Moreover, when s is less than 5, the change of arrival time error is no longer obvious. The step length s = 5 m, the error of the maximum arrival time is about 0.1 ms, and the corresponding distance error is 0.5 m, which fully meets the needs of microseismic location in underground mines. In short, the accuracy of our proposed method is only related to the step size s.  
In conclusion, our proposed method is superior to the classic FMM and SPR in terms of accuracy and efficiency and is suitable for microseismic location in complex and multi-voids in underground mines. However, our proposed method also has certain limitations. It cannot be applied to diversified velocity models. Table 3 lists the comparison of the proposed method with FMM and SPR.
## 3 Results and Discussion
### Untmodified Silica Nanofluids
SEM imaging was performed to better visualize the adsorption/adhesion of the NP on the rock surface, which causes the surface modification. Figure 6a shows the image of a slice of berea core. It may be noted that the slice of the berea core was cleaved along the injection plane approximately at the center of the core. The integrated energy-dispersive X-ray spectroscopy (EDX) analyzer was used to identify the minerals. As shown, the core was mainly composed of well-defined quartz with some feldspar and the core has pores of several microns in diameter. Another cylindrical slice of berea, which was vacuum saturated with 1 g/L silica NP in DIW, was examined using SEM (Figure 6b). The adsorbed/adhered NP were clearly shown on the mineral's surface. Figure 6c is a magnified view where the adsorbed/adhered silica NP is present. The adhered NP were in successive layers. This might be due to the drying effect. In addition, it was observed that most of the NP adsorption/adhesion was on quartz mineral. This is an interesting observation and static adsorption test and quantitative analysis based on the theory of surface forces were performed to further test this observation. Thus, it may be concluded that the NP adhere/adsorb on the minerals and this perhaps causes in situ surface modification. This modified surface is more effective at capturing fines, which can cause injectivity improvement. To test this, the theory of surface forces was utilized to quantify the interaction between the fines and the mineral before and after the application of NP. This addressed in a latter section. Figure 5: Pressure drop as function of injection rates (Experiment 3).
## 3 Classifier Design
### Deep Neural Network  
We designed the binary classifier and the multiclass classifier using deep-neural-network (DNN). Figure 2a shows the architecture of the binary classifier and Figure 2b shows the architecture of the multiclass classifier. The DNN based binary classifier has one input layer, six hidden layers and one output layer. The input layer has 512 nodes which corresponds to \(N\), the hidden layer has 32 nodes in each layer and the output layer has one node. The Exponential Linear Unit (ELU) is used as the activation function, which has been reported to have an excellent performance [21]. The activation function for the output layer used the sigmoid function. Likewise, The DNN based multiclass classifier has one input layer, seven hidden layers and one output layer. The input layer has 512 nodes which corresponds to \(N\), the hidden layer has 32 nodes in each layer and the output layer has eight nodes. The activation function for the output layer used the SoftMax. We use Batch Normalization (BN) for performance improvement, which helps train the optimal scale (mean and variance) for each layer's input data. Generally, it is known that BN in DNN can produce a large enhancement in the result [22].
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 2 Methodological Strategy  
The present review is based on an exhaustive search that was performed to obtain information about using EEG signals to integrate a BCI and applying diverse technologies to implement RR. Searching scientific results was done using various motor engines, such as Google Scholar, Web of Science, and Scopus. The keywords used for the search and their combinations are listed below.  
* Virtual rehabilitation.
* Remote rehabilitation.
* EEG BCI.
* Remote rehabilitation BCI.
* Machine learning BCI.  
Two types of searches were done to filter the information. The first search was limited by a period that begins in 2018 and ends in the present year, 2022. This enabled the discovery of recent papers relevant to the general context of this study. A second filter allowed us to retrieve documents from before 2018. The CiteScore of Web of Science and the journal impact factor of the papers were used to obtain remarkable information.  
The exclusion criteria removed studies of EMG signal classification BCI and other kinds of BCI that did not employ EEG signals as their main control input, and studies on BCI that are not related to RR or rehabilitation.  
The PRISMA diagram on the searching criterion is portrayed in Figure 2[7]. This figure explains the studies included in the present paper.  
Figure 3 represents the number of publications under the BCI topic over the last 20 years, which could bring information about the growth of the number of publications that alludes to this topic.  
Figure 3: BCI tendency of number of publication by year.  
Figure 2: PRISMA diagram of the present work.  
Figure 4 reflects the number of publications under \"Virtual Rehabilitation and Assistive devices\" in the period of 2011 to 2022.  
Figure 5 presents the literate map done with Litmaps application [9]. The articles here are presented as a node map, with 165 nodes, where a large number of citations represents a greater node size. The node connections represent the concept or area of the different used studies.
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 3 Phase Modulation in Graphene Metasurfaces
### Wavefront Shaping in Reflection
Taking advantage of the highly-confined surface plasmon in graphene, a dual-band focusing lens is proposed by stacking two layers of graphene nanoribbons above the gold reflector with negligible crosstalk [64]. The wavefront can be independently engineered in two distinct frequencies with each layer being responsible for one frequency.
## 4 Validation of the Proposed Disturbance Torque Model
### Experimental Results
There are errors between the peak frequencies and the theoretical frequencies of 1.19 Hz and 53.33 Hz, which were caused by the frequency adjustment error of the SAS and the test system error. Figure 10: The frequency-domain curve of the disturbed torque. Figure 9: The time-domain curves of disturbed forces/torques. The focus here is on the frequency analysis of the disturbance torque to get the peaks and the amplitudes; therefore, the frequency domain curves of the test and simulation results are analyzed in the following study. Figure 11 shows a spectrum curve comparison of the disturbance torques corresponding to states 1-3. The figure shows that the increase of the fundamental frequency of the SAS does not change the frequency composition of the disturbance torque when the moment of inertia is constant. Meanwhile, the disturbance torque amplitudes at the fundamental frequency increase with the increase of the fundamental frequency. The reason for this is that the fundamental frequency of the load is closer to the driving frequency of the motor, so it has a stronger coupling relationship with the SADA. Figure 11 shows that the range of the amplitudes at the driving frequency and its harmonics is no greater than 0.00166 N-m, which indicates that the change of fundamental frequency has little effect on the driving frequency and its harmonic disturbance torque component when the moment of inertia of the load remains unchanged. Figure 12 shows the spectrum curve comparison of the disturbance torques corresponding to tests 1, 4, and 5. As shown in the figure, when the fundamental frequency of the load is constant, the amplitude of each frequency component increases with the increase of the moment of inertia of the solar array. The reason for this is that the increase of load inertia leads to the increase of the driving torque, the fluctuation of the driving torque increases correspondingly, and the load vibration increases. Figure 11: The frequency-domain curve of the disturbance torque in tests 1–3. Figure 12: The frequency-domain curve of disturbance torque in tests 1, 4, and 5.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 5 Proof of Correctness  
The correctness and accuracy of the mutual authentication phase of the proposed ECSSP-SC can be confirmed by analyzing the keys \(K\) and \(K^{\prime}\) created by the \(USR\) and the \(SVR\), respectively. If the key \(K\) of the \(USR\) and key \(K^{\prime}\) of the \(SVR\) is same, then the proposed protocol correctly performs the mutual authentication process.  
* The key \(K\) generated by the \(USR\) is given by: \[\begin{array}{l}K=\left(V\oplus A_{RI}\oplus R_{U}\right)=vP_{S}\oplus A_{ RU}\oplus R_{U}\\ =\left(v\ I_{S}G\oplus K_{RS}\right.^{\prime}\oplus R_{U}\oplus R_{U}\right) \\ =\left(v\ I_{S}G\oplus K_{RS}\right.^{\prime})=\left(v\ I_{S}G\oplus v_{R} \oplus I_{R}\right)\end{array}\] (36) since \(P_{S}=I_{S}G\), \(A_{RU}=K_{RS}\)' \(\oplus\)\(I_{U}\oplus R_{U}\), and \(K_{RS}\)' \(=\)\((v_{R}\oplus I_{R})\).
* The key \(K^{\prime}\) generated by the \(SVR\) is given by: \[\begin{array}{l}K^{\prime}=\left(I_{S}\varpi T\oplus K_{RS}\oplus I_{S} \right)=\left(I_{S}\overset{\varpi}{\tau}FG\oplus v_{R}\oplus I_{S}\oplus I_{ R}\oplus I_{S}\right)\\ =\left(v\ I_{S}G\oplus v_{R}\oplus I_{R}\right)\\ =K\text{ which is the key generated by }USR\end{array}\] (37) since \(K_{RS}=\left(v_{R}\oplus I_{S}\oplus I_{R}\right)\). Therefore, the correctness of the ECSSP-SC is verified.
## 2 AI Techniques in Communication Networks
### Natural Language Processing (NLP) in Network Security and Automation
#### 2.3.1 Automated Intrusion Detection  
Intrusion Detection Systems (IDSs) powered by advanced NLP models [73], particularly Transformer-based architectures [74], can analyze vast amounts of network data, such as logs, system messages, and security alerts, to identify anomalous patterns that may signify an ongoing or potential security breach. These systems can achieve up to 98% accuracy [75], enabling highly effective detection and preventing intrusions that could otherwise go unnoticed [76]. The integration of NLP with traditional IDS methods allows for a more comprehensive approach to security, as it enhances the system's ability to understand the context of various network events [77].  
One of the most significant advantages of using NLP in IDS is its ability to process natural language logs, which are often less structured than traditional machine-generated data. For example, error messages, debug logs, and textual descriptions from security analysts can contain valuable insights that may not be easily captured by traditional anomaly detection algorithms. By analyzing this textual data, NLP models can detect subtle patterns and correlations that indicate malicious activity, such as insider threats or Advanced Persistent Threats (APTs), which are often harder to detect with conventional rule-based systems [73].  
Moreover, NLP-powered IDS systems can enhance the detection of sophisticated attack techniques, such as those involving obfuscated code or social engineering tactics, where the malicious behavior is disguised within normal network traffic [78]. These systems can examine historical logs, correlate events across different network layers, and analyze the sequence of actions leading to a potential breach. NLP models can also assist in identifying zero-day attacks by recognizing anomalous patterns that deviate from normal network behavior, even if those patterns have never been seen before [79].  
The real-time nature of NLP-based IDS ensures that potential threats are flagged immediately, allowing security teams to respond swiftly and effectively [80]. Additionally, the increased accuracy of these systems reduces the number of false positives, ensuring that security teams are not overwhelmed with irrelevant alerts [76]. This leads to more efficient security operations, improved response times, and a more proactive approach to network defense.  
In summary, NLP-enhanced intrusion detection systems offer a powerful solution for identifying and mitigating security risks in modern communication networks. By processing large volumes of unstructured data and identifying hidden threats, NLP modelscan significantly improve the accuracy and efficiency of network security measures, making them indispensable tools in the fight against cybercrime.
## 3 Results and Discussion
### MEG/EEG-Based Hyperscanning Research
#### 3.1.2 Decision-Making Task  
The decision-making task application makes up 27% (16/60) of the research done with the MEG/EEG neuro-imaging methodology [3; 6; 16; 29; 36; 42; 92; 93; 100; 101]. The goal of these studies was to see the neural-synchrony between participants as they either collaborated and made decisions (trust/cooperative) or made secret decisions (deception/competitive). These experiments most frequently used the prisoner's dilemma game and/or a card game to test participants ability to decide under different conditions. Four studies looked at decision-making between pilots and co-pilots in a flight simulator [6; 92; 16; 98]. One study used a version of the ultimatum game to study decision-making [42], while one other study used a cooperation/competitive game [99].  
_Setting._ Most of the studies (75%, 12/16) were done in the lab, while the rest were done in a natural setting. For example, [PERSON] et al. [95] used the Prisoner's Dilemma Game, which has the players choose to either cooperate or defect, to understand the cerebral processes generating and generated by social cooperation or competition. [PERSON] et al. [6] used the pilot flight simulator to investigate brain-to-brain synchrony of cooperative behavior in ecological settings.  
_Orientation._ Half of the studies (9/18) were done with participants facing each other. For example, [PERSON] et al. [94] was the first study to use EEG hyperscanning and PDC for brain network. This study investigated the links between prefrontal areas of the different subjects while they played a card game. Two research papers in this application performed two different experiments with different participant orientations and a different number of participants. The first study [100] focused on understanding the cerebral processes generated by social cooperation or competition by doing the following two different experiments: Prisoner's Dilemma Game (pairs seated side-by-side) and a Card game (more than two people seated face-to-face). The second study [3] expanded on the previous study. To account for the different experiments, we counted those two studies twice in the correct category.  
_Group Size._ More than half of the studies (81%, 13/16) looked at the neural responses in pairs compared to more than two participants (19%, 3/16). For example, [PERSON] et al. [96] focused the concurrent multiple-brains activity depending on cooperation or competition activities during a card game. [PERSON] et al. [101] investigated simultaneous multiple-brains activities for cooperation between individuals in the card game domain.