esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition to the conventional analysis, various methods for ERP and EEG analysis have been developed; fractional latency/amplitude measurement [21] aims to increase robustness of ERP measurement against fluctuating noise, and application of principal component analysis to ERP time series [22; 23] to disentangle overlapping components. Another type of approach tried to detect ERP responses sensitive to experimental manipulation without a priori specification of ERP components and latency range of interest. These include ERP spatio-temporal cluster-based permutation of scalp ERP field [24] and microstate segmentation analysis [25]. A recent surge of interest in machine learning led to the utilization of machine learning algorithms in ERP/EEG analysis. This line of research mainly focused on prediction of internal states [26; 27; 28; 29]. Besides a practical application, the machine learning approach had also given novel insights into the neurophysiological mechanism of perceptual and cognitive processing [30; 31]. The present study adopted a state-of-the-art multivariate analysis to evaluate conditional differences in ERP based on machine learning of single-trial data [32; 33]. In multivariate analysis of ERP, a classifier is trained to discriminate trials in one experimental condition from those in other experimental condition based on amplitude data from all the recorded channels at each time point. Multivariate analysis of ERP is largely data driven. Thus, in contrast to a conventional analysis of ERP peaks in which analyses are carried out for pre-chosen ERP components, multivariate analysis has the potential to find conditional differences that have hitherto been missed in the existing studies [33; 34]. Multivariate analysis had first gained popularity in fMRI studies on object recognition [35; 36]. The number of ERP studies adopting a multivariate approach is relatively small; however, several recent ERP studies have adopted this approach to further investigate the developmental course of face processing [37], face processing in pathological conditions [38], and the process of face memory formation [39].
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 6 Proposed Link Cost Estimation and Modified Route Selection Procedure
### Link Cost Estimation Procedure
As will become clear in the next section, if a route has a chance of being the best route, we would like to select it to send a batch of unicast packets on the route to improve our cost estimate \(\hat{c}_{z_{1},z_{2}}^{(URR)}(t)\). Thus, using the upper bound for \(N_{z_{1},z_{2}}^{(LS),rx}(t)\) prevents the exclusion of a route that would otherwise be selected if \(N_{z_{1},z_{2}}^{(LS),rx}(t)\) were to be known at node \(z_{1}\). It should be noted that, as mentioned in the previous paragraph, the estimate for \(N_{z_{1},z_{2}}^{(LS),rx}(t)\) becomes less and less relevant as \(N_{z_{1},z_{2}}^{(u),tx}(t)\) grows, which means that this estimate is relevant only while \(N_{z_{1},z_{2}}^{(u),tx}(t)\) is small. Since our proposed estimator \(\hat{p}_{z_{1},z_{2}}^{(URR)}(t)\) depends on the number \(N_{z_{1},z_{2}}^{(u),tx}(t)\) of unicast packets sent from \(z_{1}\) to \(z_{2}\), \(z_{1}\) needs to first select node \(z_{2}\) as the next hop towards the concentrator in order to start sending unicast packets to it. Furthermore, when node \(z_{1}\) has to select among multiple routes to the concentrator, it needs good estimates for the probability of successful transmission to each of the next hop candidates in order to build the various route costs. In other words, if node \(z_{1}\) has nodes \(z_{2}\) and \(z_{3}\) as potential candidates to reach the concentrator, \(z_{1}\) would have to send some packets through \(z_{2}\) and other packets through \(z_{3}\) in order to be able to obtain good estimates \(\hat{p}_{z_{1},z_{2}}^{(URR)}(t)\) and \(\hat{p}_{z_{1},z_{3}}^{(URR)}(t)\). For this, we propose the route selection procedure discussed next.
# Analysis of Potent Odour-Active Volatile Thioids in Foods and Beverages with a Focus on Wine
## 3 Thiol Isolation--Extraction and Derivatisation
### Derivatisation Approaches  
The adaptation of derivatisation for more selective, efficient, and simplified isolation procedures and/or stabilisation of thiols has been the major development in thiol isolation. These approaches are designed to improve the sensitivity of instrumental analyses, because, after derivatisation, volatile thiols are easier to be extracted, chromatographed, and detected. On one hand, derivatisation intends to block the sulfhydryl group (or mask a carbonyl group in the case of 4-MSP, for example) and the formed thiol derivatives are chemically stable for isolation, as well as thermally stable for GC analysis. On the other hand, introducing a substituent means that thiol derivatives exhibit greater hydrophobicity, less polarity, and/or stronger proton affinity, which leads to better liquid chromatography (LC) separation and signal enhancement for mass spectrometry (MS)-based detection [48]. When selecting suitable derivatisation reagents, the factors to consider include reaction specificity and efficiency, matrix compatibility, required sample manipulation, introduction of interferences, and whether it occurs before or after the extraction of analytes. Figure 2 shows common derivatisation reagents and related reaction conditions that are proposed for volatile thiols analysis in wine, foods, and other beverages, and categorised into those for GC analysis (Figure 2a), or LC analysis with conventional (Figure 2b) or stable isotope labelled (Figure 2c) derivatisation reagents.
# Determination of Hydrogen's Thermophysical Properties Using a Statistical Thermodynamic Method
## 2 Theoretical Model and Implementation
### Analytical Model for VCS-MLI Combined Structure  
#### 2.3.1 Physical Model for VCS-MLI  
To compare the statistical thermodynamic method with other methods for determining hydrogen properties, a multilayer insulation (MLI) structure combined with a VCS for liquid hydrogen storage is introduced in Figure 2. The MLI is separated into two parts by the VCS, the inner MLI between the liquid hydrogen tank and the VCS, and the outer MLI between the VCS and the environment. VCS is a metal shield with high thermal conductivity and temperature uniformity. A spiral tube twines around the shield, which serves as a flowing path for the hydrogen vapor vented from the tank [22,23]. As shown in Figure 2, there are three heat fluxes in the VCS-MLI: (1) the heat flux flows into the outer MLI from the warm boundary (\(q_{\rm Total}\)); (2) a part of the heat flux is offset by the VCS (\(q_{\rm VCS}\)); (3) the rest of the heat flux flows through the inner MLI and enters the liquid hydrogen storage tank (\(q_{\rm Tank}\)). The hydrogen vapor is heated when it flows through the VCS tube, and a considerable part of the heat flux from the outer MLI is absorbed by the hydrogen vapor. As a result, \(q_{\rm Tank}\) is much less than \(q_{\rm Total}\), and better thermal protection for the liquid hydrogen tank can be achieved.  
Variable density multilayer insulation (VDMLI) presents a better thermal insulation performance in comparison to the conventional MLI under the same total weight. The physical properties of the VDMLI are presented in Table 1. The configuration of the VDMLI is schemed in Figure 3. VDMLI is normally divided into three zones with the same thickness, the same spacer number, different layer densities and different radiation shield numbers. The numbers of radiation shields and spacers are denoted by \(m\) and \(n\), respectively. All configuration parameters of the low-density zone, medium-density zone and high-density zone are marked with subscripts 1, 2 and 3, respectively. The configuration parameters of the VDMLI are listed in Table 2, being exactly the same as those of 5#(VD) in [PERSON]'s study [24]. The VCS optimal position is at the midpoint of the VDMLI, as reported in [PERSON]'s study [22].  
Figure 2: Schematic of VCS-MLI combined structure.
## 3 Materials and Methods
### Test Routes  
The measurements were carried out on two different semi-trailer vehicle combinations and on 15 routes. The routes can be divided into:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **3—Axis Accelerometer and Gyroscope IMU** & **GNSS Dual-Band Sensor** \\ \hline \multirow{4}{*}{Sensor description} & STM LSM6 DSO (STMicroelectronics: Geneva, Switzerland) & GPS: L1 + L5 \\  & MEMS accelerometer +/\(-\) 8 g & Galileo: E1 + E5a \\  & MEMS gyroscope +/\(-\) 500 dps [X] & GLONASS: G1 \\  & MEMS gyroscope +/\(-\) 500 dps [X] & Beidou: B1 + B2a \\  & & Navio \\ Sampling frequency & 200 Hz & 1 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sensor parameters.  
Figure 3: Sensor positioning on the 16.5 m semi-trailer vehicle combination.  
* Routes from Zilina/Martin (Slovakia) towards Munich (Germany);
* Routes from Munich (Germany) towards Zilina/Martin (Slovakia).  
Table 2 shows the total distance travelled through the individual countries. From this, we have identified the total distance for highways and other roads. Highway feeder roads were considered as part of the highway, as well as entrances and exits from highway parking.  
Testing routes travelled with the indication of highways and other roads are in Figure 4.  
Highways and other roads account for 93.4% and 6.6% of the routes tested, respectively. The testing focused on the accelerations achieved by the semi-trailer combinations when driving on highways. Slovakia accounts for 29.2%, Austria 45.8%, and Germany 25.0% of the total distance travelled.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 5 Deformation Monitoring Visualization Platform
### Multi-Source Monitoring Module  
The multi-source monitoring module continuously accesses and iteratively updates real-time data from the database. Its functionalities include displaying GNSS data, presenting six meteorological elements, threshold-based warnings, data querying, and report downloading. Figure 13 shows the interface of this module.  
GNSS data are visualized with real-time timestamps on the X-axis and displacement changes on the Y-axis, displaying the morphology variables through curved line graphs (Figure 13a). The module supports querying historical data, calculating the mean and standard deviation of historical data, and setting the warning threshold as the mean \(\pm\) 2 times  
Figure 13: Screenshots of the multi-source monitoring module: (**a**) GNSS data, (**b**) temperature data, (**c**) humidity data, (**d**) rainfall data, (**e**) wind speed data, (**f**) wind direction data, (**g**) atmospheric pressure data, (**h**) PM2.5 data.  
the standard deviation. Data exceeding the threshold are recorded and alarm notifications are sent to the manager. It automatically generates timestamped GNSS coordinates when an alarm is triggered.  
Meteorological data, updated every 5 min, are displayed as line graphs. At the same time, rainfall greater than 30 mm/h automatically triggers a Level 3 alert and superimposes the alert area on the 3 DRS model. Managers can download daily and monthly reports (Figure 14) and combine platform GNSS data, meteorological data, 3 DRS models, and alarm information to build a unified, continuously updated lifeline big data repository.
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 4 Verification and Validation of the Numerical Model  
### Model Verification  
_Mesh Sensitivity Analysis_  
During the verification stage, it should be proven that the numerical solution provides a good approximation of the exact mathematical solution. Firstly, the accurate mesh size of the computation model was determined. Convergence analysis ensures a mesh size at which the outcomes are not affected [53]. The system response quantity (RSQ) used for the convergence study was the axial reaction force generated in the model during a displacement-controlled analysis. Given the model's elementary geometry composed of  
Figure 7: Geometrical imperfection in the computational model.  
four flat plates, h-type refinement was applied on the whole geometry. The criteria to consider appropriate mesh quality is considered with an error under 1%. This is set as a requirement to consider the numerical model and the computational result reliable and independent on the mesh quality and FE size. For one specific cross-section geometry it was reached by the fifth iteration, as shown in Figure 8. The ultimate resistance at each iteration alongside the meshing size is shown in Figure 8, where h is the depth of the analyzed cross-section. Error was calculated using Equation (17).  
\[\text{Error\_i}=\frac{\text{Fu}^{\text{i}}-\text{Fu}^{\text{i}-1}}{\text{Fu}^ {\text{i}}}\times 100 \tag{17}\]  
where  
\(\text{Fu}^{\text{i}}\) : Ultimate local buckling resistance at \"i\" iteration;  
\(\text{Fu}^{\text{i}-1}\) : Ultimate local buckling resistance at \"i \(1-1\)\" iteration.
## 1 Introduction  
In wireless transmissions, the absence of a physical boundary that limits the wave propagation makes transmitted information highly vulnerable to interception by potential eavesdroppers [1]. Traditionally, cryptographic techniques have been applied at the higher protocol layers to overcome this limitation; however, the exchange of the private key in key-based methods also requires a secure channel that may not be available [2]. Additionally, the relentless growth of computational power is making it possible to break ciphers that were considered virtually unbreakable in the past [3]. In this sense, an additional level of security will be achieved if encryption is also performed in the physical layer where the raw interchange of information takes place.  
Physical layer security (PLS) exploits intrinsic characteristics of the wireless channel to limit the amount of information that can be extracted at bit level by a potential eavesdropper [4]. A number of PLS schemes have been developed and published in the open literature [4; 5; 6]. Directional modulation (DM) is a keyless PLS transmitting technique that can be classified as an artificial noise (AN) approach [7]. DM is typically based on antenna arrays that generate multiple transmission radiation patterns. These radiation patterns are chosen to produce the desired constellation in the selected secure observation angle and, at the same time, a distorted or noisy version of the constellation in the other observation angles [8]. DM is termed static (SDM) when the distortion does not change with time and dynamic (DDM) when the distortion changes at the symbol rate [9].  
The generation of DM inherently entails an increase in the transmitted power with respect to a conventional modulation scheme that transmits the same desired constellation in every observation angle [9]. The ratio between the power transmitted in DM and a conventional modulation scheme defines a power efficiency (\(P_{EDM}\)) that allows for comparison between different DM implementations. For a conventional modulation,\(PE_{DM}=100\%\) and a decrease in this efficiency is typically related to an improvement in the transmission security [9].  
Bit error rate (BER), as a function of the observation angle, is another figure of merit that is typically used to evaluate the degree of security provided by DM [9]. Hence, a DM scheme is usually designed with two main goals: (1) to decrease BER in the side lobes of the conventional modulation radiation pattern and (2) to narrow the main lobe beam width where the signal is demodulated with a BER below a given threshold (information beam width). It is important to acknowledge that BER and \(PE_{DM}\) are not uniquely related, so two DM implementations with the same \(PE_{DM}\) can show a substantially different response in terms of BER.
## 3 Applications of Game Theory in WSN
### Game Theory for Wireless Communication in WSN
#### 3.2.3 Topology Optimization  
WSN has been used in many areas such as military sensing, environmental health monitoring, and home area security surveillance. Routing algorithm and topology control are extremely important in WSN to maintain a balance between throughput, delay and energy. Game theory has played an active role in this aspect. [PERSON] _et al_. [142, 143] introduced an incompletely cooperative game-theoretic heuristic-based constraint optimization framework into WSN to acquire a balance. [PERSON] and [PERSON] [3] considered a locally computable heuristic \"recommendation algorithm\" referring to as LocalHeur for convenience of incentive compatible topology control for selfish all-to-one routing which is modeled by a locally minimum cost forwarding game in the absence of complete global information for realistic scenarios. LocalHeur lies within the class of location based routing methods with particular similarity to the Nearest Forward Progress algorithm though from the perspective of game-theoretic reverse multicast routing. A game theoretic model presented in [37] yields decentralized optimization for joint topology control and power management. Global game equilibriums are iteratively reached by considering individual node degree, message delivery ratio and cost of increasing power. Researchers provide NEPow and BEPow schemes for implementations based on mathematical game analysis. [PERSON] and [PERSON] [144] designed a distributed, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol which based on the theoretical framework. The CC-MAC has two components: Event MAC (E-MAC) and Network MAC (N-MAC). As one of the most widely investigated topology control mechanisms of WSN, the clustering algorithm provides energy efficient communications by reducing transmission overhead and enhancing transmission reliability. [PERSON] _et al_. [126] developed a topology control algorithm for WSNs having a large scale structure and densely distributed nodes. The topology of the irrigation control sub-network is analyzed and a distributed two-tier routing (DTTR) protocol was proposed in literature [145]. The protocol includes intra-cluster and inter-cluster routing schemes. Game theory is introduced in the inter cluster multi-hop routing scheme. With the utilization of multistage finitely repeated games and the metric method based on link quality indication, the energy balance problem under multiple constraints is solved.
## 4 Shear Strength Prediction Using Neural Hybrids of SFLA-ANN and WDO-ANN
### Initializing the MLP for Representing the ANN  
Having a feed-forward hierarchical architecture, the multilayer perceptron (MLP) is the most commonly held notion of the ANNs [51; 52]. In this study, an MLP neural network was used as the basic predictive model. It performed the prediction task by applying some weights and biases to the input variables within three or more layers. This work was carried out in the computational neurons within each layer. Although the number of neurons in the input and output layers was equal to the number of variables, this could be varied for the middle-layer neurons (also called \"hidden neurons\"). It is also one of the most influential factors in the performance of the MLP. Based on a trial-and-error process, different numbers of hidden neurons were tested, and the results showed that five of them constructed the best-fitted network. Moreover, the same process was used to determine the best values of other ANN parameters including learning rate, momentum factor, and learning cycles. These parameters were set to 1.0, 0.5, and 1000, respectively. Note that, their activation function was \"tangent-sigmoid\" (i.e., Tansig). This function is expressed as follows:  
\[Tansig\ (x)=\ \frac{2}{1+\ e^{-2x}}-1. \tag{11}\]
## 5 Experimental Results
### Simple Architecture Testing  
First, the basic architecture (STP) was examined. The averaged results, obtained for various WCET benchmarks [32] during these experiments, are gathered in Table 1. A structure consisting of multiple processors, according to a scheme in which each core processes only one task, was implemented. The first part of Table 1 presents these results and the notation STP \(\times\)\(N\) refers to a structure consisting of \(N\) cores. Then, structures were modified (as described in Section 3.2), i.e., a clock-gating mechanism to block unused stages of the processors was added (the second part of the table--rows denoted by the symbols STP CG \(\times\)\(N\)). These results showed that the proposed clock-gating mechanism for idle core phases allowed us to achieve significant energy savings. The diagrams depicted in Figure 8 present the averaged results of the power savings achieved for the selected structures by clock gating, which are compared with the appropriate STP structures without clock-gating mechanisms for different frequencies. In turn, Figure 9 shows the percentage power savings, depending on the size of the structure (the number of cores). To make the results more objective, these graphs show the average power per task.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **(F**sys **[MHz])** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Total Power** \\ **(P\({}_{all}\) [mW])** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Cores’ Power** \\ **(P\({}_{CPU}\) [mW])** \\ \end{tabular} } & \multicolumn{4}{c}{**FPGA Utilization**} \\ \cline{5-8}  & & & & & & & \\ \hline STP \(\times\) 5 & 25 & 175 & 54 & 7053 & 5206 & 1170 & 505 \\ STP \(\times\) 5 & 50 & 22 & 108 & 7053 & 5206 & 1170 &  
It is worth noting that these savings were achieved with virtually the same hardware resources with only a slight increase in Slice Registers (less than 2%).
## 2 Modelling
### Positioning the Ellipsoid in 3D Space  
The ellipsoid with semiaxes \(b_{i}\), \(i=1,3\) is a set of points \(\mathbf{X}\), that satisfy the condition:  
\[(\mathbf{X}-\mathbf{X}_{0})^{T}\mathbf{Q}(\mathbf{X}-\mathbf{X}_{0})\leq 1, \tag{1}\]  
where \(\mathbf{X}_{0}\) is vector of ellipsoid center, \(\mathbf{Q}\succ 0\) is positively defined matrix.  
Any \(\mathbf{Q}\) can be converted to diagonal matrix \(\mathbf{A}\) as follows:  
\[\mathbf{Q}=\mathbf{R}^{T}(\phi,\theta,\Phi)\mathbf{A}\mathbf{R}(\phi,\theta, \Phi). \tag{2}\]  
Here \(\mathbf{A}\) is diagonal matrix with elements \(a_{ii}\) and \(\mathbf{R}(\phi,\theta,\Phi)\) is Euler's rotation matrix. The rotation matrix can be generally evaluated as a series of rotations:  
\[\mathbf{R}(\phi,\theta,\Phi)=\mathbf{R}_{z}(\phi)\mathbf{R}_{x}(\theta) \mathbf{R}_{z^{\prime}}(\Phi)=\]  
\[=\begin{bmatrix}cos\phi&sin\phi&0\\ -sin\phi&cos\phi&0\\ 0&0&1\end{bmatrix}\cdot\begin{bmatrix}1&0&0\\ 0&cos\theta&sin\theta\\ 0&-sin\theta&cos\theta\end{bmatrix}\cdot\begin{bmatrix}cos\Phi&sin\Phi&0\\ -sin\Phi&cos\Phi&0\\ 0&0&1\end{bmatrix}. \tag{3}\]  
Let \(b_{i}\) be the semiaxes of the ellipsoid then, elements of \(A\) may be computed as \(a_{ii}=1/b_{i}^{2}\). If \(b_{1}=b_{2}\) (for the ellipsoid of revolution), the rotation \(\mathbf{R}_{z^{\prime}}(\Phi)\) turns to unitary. So, the total number of the coordinates is 5: \(\mathbf{K}=(\mathbf{X}_{0},\phi,\theta)\). Using the described method, we can easily construct the ellipsoid of revolution with known orientation and position as a combination of the diagonal matrix \(\mathbf{A}\) and 5-component vector \(\mathbf{K}\).
# Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems
## 4 Evaluation  
For evaluation, we implement the proposed scheme upon a commercial public blockchain system. We choose the _Klaytn_ platform [10], although the Ethereum platform [27] is the most popular commercial blockchain system, because the Klaytn platform is much faster and cheaper than the Ethereum platform. Instead of the main network of the Klaytn platform, we use its non-commercial test network, called _Baobab_, where its operation costs are free.  
Figure 5: Flow chart of comparison procedure.  
Figure 6: Detailed structure of integrity preservation trace.