esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Verification of Test Methods and Certification Criteria  
Compared to the standardization process that usually takes several years from the development of an initial draft to the confirmation of the final draft, the six-month period, set by the law of the compliance certification, can be seen as a relative advantage to enterprises. However, it is a big burden for regulators on the other side. Because of other important processes such as product testing, the time available to develop new test methods and criteria is only two-three months. Although the development of certification test methods and criteria is limited solely to the applied product, it is never easy to complete the process (e.g., draft, discuss, revise, and finalize) in such a short time. Moreover, the process for verification that usually requires a multiple-staged test or experiment should also be followed to confirm the suitability of the test methods and thresholds developed [21]. Hence, a more efficient way for the whole process of the compliance certification has been constantly explored.  
In light of this, the present study focuses on the verification process and how to improve it. As explained earlier, an accredited testing for certification should be able to guarantee that a product can operate normally, even under much worse conditions than the actual use environment [7]. However, newly-developed products such as NCPs have never been used in real life by individuals ororganizations, since the development of those products is based on new technological initiatives or attempts. This indicates that the situations, conditions, or environments in which the new products will be used should be analyzed in advance, and that the implications from the analysis should be reflected in the verification process, to make sure that the test methods and certification criteria are valid enough to confirm there will be no quality and safety issues after sales.  
With these issues in mind, this study proposes a new verification method that uses living labs as a way to consider the primary elements of real-life product use environments, such as the characteristics of users and their activities, other technological devices or systems that the product works with, and the natural or artificial environments (e.g., temperature, buildings, and social events). Below, the concept of living labs is first explained, followed by the framework of using living labs for the verification purposes.
# Blockchain-Based Model for Incentivized Cyber Threat Intelligence Sharing
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Incentive** & **Blockchain** & \begin{tabular}{c} **Enhanced CTI** \\ **Feature** \\ \end{tabular} & **User Roles** & **Storage** \\ \hline [PERSON] et al. [11] & reputation & Ethereum & trust & not defined & on-chain \\ \hline [PERSON] et al. [12] & tokens & public Ethereum & motivation & \begin{tabular}{c} CTI data provider, CTI \\ data consumer, investor, \\ owner, and donor \\ \end{tabular} & on-chain \\ \hline [PERSON] and [PERSON] [13] & reputation & public Ethereum & trust & \begin{tabular}{c} contributor, consumer, \\ and feed \\ \end{tabular} & on-chain \\ \hline [PERSON] et al. [14] & reputation & \begin{tabular}{c} Hyperledger \\ Fabric \\ \end{tabular} & trust & not defined & on-chain \\ \hline [PERSON] et al. [15] & tokens & \begin{tabular}{c} public EOS \\ blockchain \\ \end{tabular} & motivation & \begin{tabular}{c} CTI data provider, CTI \\ data consumer, and \\ verifier \\ \end{tabular} & off-chain \\ \hline [PERSON] and [PERSON] [16] & tokens & \begin{tabular}{c} Hyperledger \\ Fabric \\ \end{tabular} & \begin{tabular}{c} quantity and \\ quality, anonymity \\ \end{tabular} & not defined & on-chain \\ \hline Chatziamanetoglou and Rantos [17; 18] & reputation & permissionless & trust & \begin{tabular}{c} CTI feed, validator, and \\ consumer \\ \end{tabular} & on-chain \\ \hline [PERSON] et al. [19] & fees & \begin{tabular}{c} Hyperledger \\ Fabric \\ \end{tabular} & motivation & \begin{tabular}{c} CTI consumer, CTI \\ contributor, authority, \\ insurer, industry CERTs, \\ CTI verifier, analytics \\ \end{tabular} & off-chain \\ \hline [PERSON] et al. [21] & reputation & \
\begin{tabular}{c} consortium \\ blockchain \\ \end{tabular} & trust &
\begin{tabular}{c} leader, candidate, follower, \\ and supervisor \\ \end{tabular} & on-chain \\ \hline [PERSON] et al. [23] & reputation & public Ethereum & trust & not defined & on-chain \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the reviewed works introducing incentives. Sensitive CTI data can only be shared on private blockchains [14; 16; 19; 21]. 3. CTI data can be large in volume; therefore, they must be stored off-chain [15; 19]. 4. The sharing of sensitive CTI data must be in accordance with the Traffic Light Protocol, which defines four levels for sharing CTI data [19; 20]. These conclusions will guide the construction of the proposed model.
## 4 Simulation Results
### Verification and Validation  
The verification and validation is one of the common critical issues in agent-based simulation, and it is indeed a difficulty in this area. This is because such computational systems or simulations are difficult to verify in terms of checking program-bugs and their outcomes are also difficult to validate even when there are no program-bugs [31].  
We have adopted a bottom-up approach to model the search and rescue (SAR). Different from physical phenomena, we cannot experience landslides repeatedly to collect data that show some relationship between the parameters of rescue operations and the rescue efficiency [23]. Thus we had interviewed some first responders and experts who specialize in on-site search and rescue for advice before we built the conceptual model. We built the model step by step, and at each step, we will check the logic and internal relationships to guarantee its reasonability. Therefore, we can ensure that the assumptions, logic, and casual relationships in the conceptual model are reasonable, and the model is an adequate conceptualization of the real world [32].  
It is important to validate whether or not the results from the virtual experiments match the results from the real world. However, we can hardly find a complete record of a landslide. Thus we will validate it mainly through experts' knowledge and experience. We have conducted a large number of repetitions of the simulation, evaluated the simulation through robustness analysis and sensitivity  
Figure 7: The average output results of 200 runs at every step in scenario “Normal”.  
analysis, and the simulation results could match the results from experts' experience. Therefore, this model is verified and valid to a certain degree.  
Moreover, we have calculated the relative error \(\delta\) of three indicators in three scenarios, shown in Table 5. Relative error measures the relative difference between an individual value and the true value (or average value).  
Table 5 shows the mean relative error and the interval in which 90% of the sample's relative error falls. For instance, the mean relative error of \(p_{rs}\) under cooperative rescue plan in scenario \"Fatal\" is 7.5%, and 90% of the relative error of \(p_{rs}\) falls in interval [0.7%, 16.2%]. As can be seen from Table 5, the mean relative error of three indicators in three scenarios ranges from 3 to 10%. The results show that the relative error in our simulation is maintained at a low level, which means our simulation is quite stable. We can be sure that our simulation results have reflected system behavior and not simulation errors.
# DNS over HTTPS Tunneling Detection System Based on Selected Features via Ant Colony Optimization
## 1 Introduction
However, dimensionality reduction is crucial because of the high dimensionality as well as the other complex aspects of traffic data in DoH traffic. Consequently, selecting the most important features leads to a reduced computational load and processing time, making analysis more efficient and aiming to balance security and privacy [13]. This study aims to improve the analysis and classification of DoH traffic by applying an ACO for dimensionality reduction by reducing the number of essential features. ACO optimizes feature selection by choosing the most efficient features to increase model efficiency by deploying machine learning models such as XGBoost, K-nearest neighbors (KNN), random forest (RF), and Convolutional Neural Networks (CNNs) for binary class and multiclass classification via the CIRA-CIC-DoHBrw-2020 dataset. The contributions of this study are as follows:  
* The ACO algorithm is used to select the most effective features, which greatly reduces the dimensions of the CIRA-CIC-DoHBrw-2020 dataset while preserving the underlying information. * Combining ACO-based feature selection with XgBoost, KNN, RF, and CNNs classifiers improves classification performance, outperforming existing methods in accuracy, precision, recall, and F measure. * This study confirms the robustness and generalizability of the proposed feature selection and classification approach by maintaining consistent performance on live network traffic structured to match the CIRA-CIC-DoHBrw-2020 dataset across both binary and multiclass classification tasks. * The proposed method decreases computational execution time and complexity compared to existing feature selection approaches, making it convenient for real-time DoH tunneling detection. * The proposed work is applied to both binary (DoH-Normal, DoH-Malicious) and multiclass (Non-DoH, DoH-Normal, DoH-Malicious) classification scenarios, which demonstrates its efficiency in selecting the best features for various classification tasks. The remainder of this study is arranged as follows: The second section is a literature review that reviews the relevant research, especially those that focus on the use of meta-heuristic optimization algorithms using benchmark datasets in the IDS field, including the CIRA-CIC-DoHBrw-2020 dataset. The third section provides a detailed explanation of the proposed method structure, including ACO as a mechanism to extract the most effective features and a brief overview of the ML algorithms used. Furthermore, a mechanism is used for tuning parameters and data processing methods. Finally, the fourth section presents and discusses the obtained results, including the features selected for both binary and multiclass scenarios and the measurements utilized to evaluate the model.
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 2 System Model
As shown in (1), the target echo in the direct channel and all the auxiliary channels are added in the target echo \(r(t)\) in the 2-D radar. One target will appear \(M+I\) times in the radar echo, resulting in \(M+I\) measurements under the assumption that the detection probability is 1. By designing a specific coding sequence for each RIS, which can be identified in the radar echo, the target measurements in different channels can be identified. Therefore, for each target to be localized, its measurements in \(M+1\) channels can be obtained, and they are combined to realize the three-dimensional target localization.
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 1 Introduction  
Both AI and immersive technologies have undergone blistering growth over the last few decades in diverse sectors, including retail. The application of AI-powered recommendation systems integrated into VR environments could be developed as a way for consumers to turn online shopping into an engaging and immersive experience. This fundamental change is due to increased consumer expectations for richer and more customized shopping experiences [1].  
In fact, such AI-powered recommendation systems have been proven in recent studies to increase user satisfaction with very relevant product suggestions based on actual user behavior and preferences. Most of these run with two-dimensional interfaces, such as websites or mobile applications, and they lack the volumetric and interactive experience given by VR. AI used with VR does not just make the user interaction more engaging but also lends time-bound and voice-based product recommendations with the help of in-game NPCs [2].  
Indeed, the effectiveness of recommendation systems powered by AI has been validated for the traditional e-commerce setting, yet their application in a natural way within immersive VR still remains relatively unexplored. There is a wide gap in how these systems could be optimized in order to better the user's shopping experience within a VR setting,providing real-time, on-the-fly, individualized recommendations using NPCs. This study tries to fill in the gap by developing and testing a new model named 'Shopping Assistance Automatic Suggestion' (SAAS) for a virtual reality setting.  
This paper identifies the differences between how well the SAAS model can execute these compared to other AI models when delivering personalized shopping recommendations. It further explores user satisfaction and engagement by using NPCs in the VR setting. This study hopes to establish, first, how well the SAAS model in a VR environment works and, secondly, how NPC integration advances the interactive experience even further, achieving an e-commerce level that can be paralleled with the truly next-level AI-based personalization that matches the immersive capabilities of VR.  
This thus provides the basis of the main research inquiries: How does this SAAS model perform compared to other AI models in a VR setting? How does NPC integration affect user satisfaction and engagement? How do AI and VR combine to make an improved shopping experience for the customer?
# Special Issue on Applications of Artificial Neural Networks for Energy Systems
###### Abstract  
In the 21 st century, newer and cleaner technologies like renewable energy technologies, fuel cells, and smart grids are promising to transform the way we generate and distribute electric energy.  
Long-term and ambitious commitments and stable regulatory policies have established renewable energy as a major option in the energy sector in almost all parts of the world.  
For example, in 2018, within a climate and energy framework that applies until 2030, the EU decided to develop a clean, cheap, and reliable energy system. To this end, greenhouse gas emissions must decrease by at least 40% compared to 1990 levels, and renewable energy sources like wind and solar must provide 32% of our energy. The global goal for renewable energy translates into a 57% share of renewable energy in the European energy sector by 2030, which implies that wind and solar will reshape the European energy system. Such a target for greenhouse gases will impact drastically the presence of thermoelectric power plants, as coal demand will be cut by two thirds, and oil and gas demand will be cut by a quarter.  
However, seamless integration of renewables with the grids poses significant challenges due to their intermittent generation, which is intensified by the uncertainties and fluctuations existing on the demand side.  
A solution is increasing energy storage and standby power generation. Alternatively, improving the predictability of wind and solar energy and demand can replace such expensive hardware with advanced control and optimization systems. In line with the increasing complexity of modern energy systems, where data are massive and inherently noisy, applications of neural-networks in solving energy problems will assume a crucial role. On the other hand, artificial neural-networks have useful specific features, such as the ability to learn the key information patterns within a multi-dimensional information domain, being fault-tolerant, robust, and possessing noise-immunity [1].
# Applied Engineering Using Schumann Resonance for Earthquakes Monitoring
## Materials and Methods
### Seismic Phenomena and ELF Waves
These changes are due to displacement of the inner boundary of the ionosphere in a few kilometers when seismic activity occurs [48]. Other publications have shown the fact of the signals derived from this phenomenon in a period in which large earthquakes occurred, such as the case developed and understood in Taiwan between 1999 and 2004. It is noted that earthquakes produce a variation of about 1 Hz in the localization of the peak frequency. Another anomaly that is detected in this type of work is an atypical amplitude increase of all its modes, highlighting the variation in the fourth mode of resonance, as observed in large earthquakes M > 6 [49]. Other publications indicate that the monitoring this signal allows the checking of the existence of depressions in the frequency of the fourth mode resonance occurring between two and six days before one of the study earthquakes [50]. In most of the works, variations or anomalies in these signals can be observed with days or even weeks in advance before the occurrence of any earthquake. For example, variations have been detected three weeks before the earthquake that happened on the 11 of March 2011, with an 8.9 magnitude in front of the Japanese coast [51]. Signals anomalies were detected for few days, as well ahead before the Sichuan earthquake that took place with a magnitude of 8.0 [52], and the Kobe earthquake of 6.0 magnitude in 2013 [53]. Therefore, the study and analysis of these signals ELF may be a method for detecting seismic events. The presence of ELF stations or observatories in regions with seismic activity or nearby is useful, so that detection is more effective, despite the resonance phenomenon being a global one.
## 2 Experimental Methods and Apparatus (Experimental Apparatus)  
### Density Matching Approach in High-Pressure Gas Flow Standard System of KRISS  
The density-matching approach for the Coriolis mass flowmeters for use during hydrogen gas refueling was utilized with an alternative fluid in the high-pressure gas flow standard system of KRISS [11]. The conditions of the pressure and temperature of compressed air, 40 bar and 20 \({}^{\circ}\)C, were selected so that test could be carried out at a density of around 46 kg/m\({}^{3}\), equivalent to hydrogen gas at 700 bar and \(-\)40 \({}^{\circ}\)C. As shown in Figure 3, the high-pressure gas flow standard system at KRISS consists of two compressors, storage tanks with a capacity of 52.4 m\({}^{3}\), a temperature loop of 19.7 m\({}^{3}\), and control valves. This flow standard system is a blow-down type that generates a steady air flow in the pressure range of 1 to 5 MPa (10 to 50 bar). The maximum flowrate was 10,000 m\({}^{3}\)/h at standard conditions (101.325 kPa and 293.15 K). The extended uncertainty of this standard system was 0.18% at the 95% confidence level. A sonic nozzle (ISO 9300) [12] was used as a reference meter; it was calibrated by a gravimetric standard with a fast-acting diverter and a weighing tank.  
Figure 3: High-Pressure Gas Flow Standard System of KRISS.  
As shown in Figure 4, The Coriolis mass flowmeter for the density-matching approach experiment was installed at the position of the test section between the second pressure control valves and the sonic nozzles. Sonic nozzles with throat diameters of 1.4 mm, 2.0 mm, 2.9 mm, and 3.6 mm were used to evaluate the accuracy of the measurement performance of the Coriolis mass flowmeter in the range of flowrates from 1.0 kg/min to 4.5 kg/min. The limits of maximum flowrate in the SAE J2601 protocol for light-duty vehicles is 3.6 kg/min [4]. Four sonic nozzles in the high-pressure gas flow standard system can cover up to the maximum flowrate specified by the SAE J2601. The inlet pressure condition of the Coriolis mass flowmeter was controlled by the first and second pressure control valves in the high-pressure gas flow standard system of KRISS at 40 bar, which is a density condition identical to that during hydrogen gas refueling into vehicles at a hydrogen station at 700 bar. The operating temperature condition for the ambient temperature (around 20 \({}^{\circ}\)C) in the density-matching approach was controlled and stabilized by the temperature control loop with a capacity of 19.7 m\({}^{3}\), located upstream of the Coriolis mass flowmeter and sonic nozzles.
###### Abstract  
In recent years there has been an increasing need to improve satellite attitude control performance in terms of agility and attitude accuracy in large-angle attitude maneuvers. To achieve such control performance, single-gimbal control moment gyros (SGCMGs) should be mounted as modern-type actuators. Conventionally, based on the torque command calculated by the attitude control system of the satellite, SGCMGs were controlled by solving inverse kinematics through a pseudo inverse matrix steering law. However, in such a control system structure, it may be difficult to obtain the desired torque required by the attitude control system because of the singularity problem of SGCMGs. Furthermore, with respect to implementation, since the condition number of the Jacobian matrix of SGCMG becomes extremely large in the singularity, the numerical calculation error of the pseudo inverse matrix increases greatly. Therefore, we propose an overall control system that can solve above-described problems and the state-dependent Riccati equation (SDRE) control system that integrates the satellite and SGCMG system. The proposed optimal control system, which does not solve the pseudo inverse matrix, can realize gimbal angle guidance by gimbal angle feedback and singularity avoidance/escape using the biased weighting matrix. In the numerical simulation, the usefulness of proposed system is shown in comparison with the conventional system.  
satellite; attitude control; state-dependent Riccati equation control; single-gimbal control moment gyro; biased weighting matrix +
Footnote †: journal: Applied Sciences