esa-sceva/satcom-chunk-collection huggingface dataset:
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results
### Eight-Channel Data Simulations
XGBoost works much faster than the classical gradient boosting, however, it was still slightly outperformed by the proposed ensemble voting classifiers, except for the LDA-SVM-\(k\)NN-CNN. Table 3 represents the simulation results obtained from testing on five ALS patients' data. The overall performance of the classifiers decreased compared to the results of testing on the healthy subjects' data. Still, the proposed methods did work with ALS patients. This means that the classifiers are subject-independent even in terms of comparing healthy subjects with patients with a brain disorder. The baseline classifiers performed slightly better, reaching more than 85% F-score. The weighted voter classifier W-LDA-SVM-kNN outperformed gradient boosting and achieved the best performance metrics among the proposed classifiers in this case. So it can be assumed that the SVM classifier, which had the most value in the weighted voter, performed better on ALS eight-channel data than LDA and \(k\)NN. The simple ensemble averaging models LDA-SVM-\(k\)NN and LDA-\(k\)NN achieved about 84% accuracy, which is also a meaningful result, despite the fact that these models were slightly outperformed by the boosting algorithms. Again, LDA-SVM-kNN-CNN showed the worst result among the proposed models, meaning that the convolution of the eight-channel data was not a good choice. The proposed CNN architecture failed to extract the most essential features out of the EEG input data. Thus, it can be summarized that the CNN model is a poor choice for EEG time-series data classification in a subject-independent P300 speller. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Recall (\%)** & **F-Score (\%)** & **Time Elapsed (s)** \\ \hline Gradient Boosting & 98.21 & 80.45 & 81.91 & 16.25 \\ XGBoost & 99.90 & 97.01 & 97.89 & 4.88 \\ \hline LDA & 98.82 & 98.98 & 98.79 & 0.61 \\ \(k\)NN & 97.23 & 96.82 & 97.01 & 0.16 \\ SVM & 99.55 & 99.20 & 99.12 & 3.79 \\ CNN & 88.45 & 83.14 & 84.33 & 2686.98 \\ \hline LDA-kNN & 99.92 & 99.90 & 99.08 & 0.72 \\ LDA-SVM-\(k\)NN & 99.94 & 99.25 & 99.13 & 3.83 \\ LDA-SVM-\(k\)NN-CNN & 88.17 & 83.15 & 81.29 & 2687.57 \\ \hline W-LDA-SVM-\(k\)NN & 99.93 & 99.20 & 99.12 &
\begin{tabular}{c} general: 3.91 \\ weights search: 41.58 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results using 8-channel data of healthy subjects.
# Social-Aware Peer Selection for Device-to-Device Communications in Dense Small-Cell Networks
## 5 Simulations and Numerical Results
### Energy Efficiency  
Figure 10 demonstrates the performance of the proposed scheme in terms of \(\varepsilon\varepsilon\) computed by (18). Simulation experiments are performed for the macro- and small-cell with \(m\). The results show that the proposed scheme is energy efficient and suitable for small-cell D2D communication. The energy efficiency is increased with the increase of \(m\) in both the scenarios, as the number of UEs with social friendship is also increased. Moreover, our proposed scheme focuses on D2D peer selection in small-cell scenarios; therefore, it is clear from the results that the small-cell scenario has much better \(\varepsilon\varepsilon\) than the macro-cell scenario. The reason is that the UEs are much closer to each other in the small-cells than in macro-cells. Conversely, in the macro-cell, there are few UEs with social friendship in the proximity as the coverage area is wide. Therefore, higher transmit power is required for D2D communications, which reduces its \(\varepsilon\varepsilon\).  
Figure 10: D2D energy efficiency of small-cell and macro-cell scenarios vs. m.  
Figure 9: D2D throughput vs. \(m\).
## 4 Use Cases
### Outlier Identification  
MultiScaleAnalyzer provides a multi-resolution visualization approach that allows educators to thoroughly examine data and uncover subtle outliers or patterns. It is observed that many students exhibited long fixations in areas corresponding to higher positions on the y-axis, and the green color indicates the areas that had no task-related information (Figure 10). In the context of COMPS tasks, this region aligned with the upper section of the interface. Interestingly, this fixation pattern was not limited to a single student but was observed across multiple students. One notable case involved S10, who, after spending an extended period fixating on this region, unintentionally ended the task. Further investigation using the raw data view revealed that the area of focus was actually the menu button located in the upper-right corner of the interface. This button serves as a navigation tool to access the program's menu page. In the case of S10, curiosity led to the student clicking the menu button, which inadvertently terminated the task. This finding highlights a common phenomenon where students become distracted by navigational elements, especially when their attention is not fully engaged or when they have been working on a single task for a long period. Based on this observation, we recommend weakening the visual prominence of the menu button by using less noticeable colors, removing dynamic cursor changes when hovering over the button, and making it appear less interactive to reduce accidental interactions and mitigate distractions caused by non-essential interface elements.
## 5 Experimental Results  
### Heat-Dissipation Characteristics  
The temperature of the VCSEL of the fabricated AOC was measured by a thermocouple in a thermostatic chamber with the temperature was controlled so that the case temperature becomes constant at \(T_{\text{c}}\) = 70 \({}^{\circ}\)C. The measured VCSEL temperature under all-channels (4-ch) operation as shown in Figure 7. This result indicates that the heat-dissipation via structure in the multilayer substrate allows the optical device to operate stably even in a high-temperature environment.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 3 Experimental Setup and Data Collection
### Data Collection
The column labeled 'BAND' indicates the frequency bands used for telecommunications. For example, '2600 B?' refers to the uplink frequency range of 2500-2570 MHz and the downlink frequency range of 2620-2690 MHz. The 'BANDWIDTH' column represents the channel bandwidth, which defines the range of frequencies allowed to pass through the channel. Finally, 'LAT' and 'LON' refer to the latitude and longitude coordinates of the device location, respectively. Figure 3: Locations for data collection: (**a**) locations for Dataset1 and Dataset2; (**b**) location for Dataset3. Specifically, several important notes are outlined below. First, non-standalone 5G is primarily used in Australia at the time of writing, which means 4G base stations may also support 5G. To maintain consistency in our study, we have restricted the data collection to 4G, as the network configuration, such as the power management strategy and the advanced technology, might differ for non-standalone 5G and LTE. In the G-Mon Pro-captured data, the 'NR_STATE' field can be used to filter out any 5G NR data. Second, cell handovers may occur even when the mobile terminal remains stationary at the experimental location. The 'LOCLA_CID' field, as shown in Table 3, can be used to categorize data entries according to the connected cell.
# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 2 Datasets  
### Oco-2 Co\({}_{2}\) Data  
We use data from NASA's OCO-2 satellite [8]. OCO-2 was launched on 2 July 2014, and now leads the 705 km Afternoon Constellation (also known as the A-Train). The spacecraft carries and points a three-channel imaging grating spectrometer designed to collect high resolution, bore-sighted spectra of reflected sunlight. Spectra are collected in the molecular oxygen A-band at 0.765 microns and the CO\({}_{2}\) bands at 1.61 and 2.06 microns. The retrieved quantity is column-averaged dry air mole fraction of CO\({}_{2}\), XCO\({}_{2}\). The instrument collects science data while viewing the local nadir (nadir mode), near the \"glint spot,\" where solar radiation is reflected specularly from the surface (glint mode) or stationary surface targets (target mode). The study by [PERSON] et al. [8] provides more details on the OCO-2 measurement principle, instrument design, calibration approach and on-orbit performance.  
Validation and bias correction have been important elements of the data processing strategy for OCO-2 as the OCO-2 XCO\({}_{2}\) retrieval algorithm continues to evolve [22]. The study by [PERSON] et al. [18] provides a comprehensive validation of the Version 7 retrospective (V7r) XCO\({}_{2}\) product against the Total Carbon Column Observing Network (TCCON), and found that the absolute median difference is less than 0.4 ppm and the RMS difference less than 1.5 ppm. The bias appears to depend on latitude, surface properties, and scattering by aerosols [18]. Similar validation experiments on the V8r and V9r products were performed by [PERSON] et al. [22] and [PERSON] et al. [23].  
In this paper, we use the OCO-2 data version V9r, available from the MIRADOR platform at [[http://mirador.gsfc.nasa.gov](http://mirador.gsfc.nasa.gov)]([http://mirador.gsfc.nasa.gov](http://mirador.gsfc.nasa.gov)). We use the life files that include bias correction and data screening. We use data points from all three science viewing modes, where the quality flags are set to zero, indicating highest quality data.
## 3 Preliminaries
### Program Execution Monitoring Based on Basic Block  
Before integrating an instruction monitor into an SPU to monitor whether the CPU's executive instructions were modified or not, it is essential to preprocess instruction codes (InsCodes) for extracting the reference information of BB integrity monitoring. The offline preparation phase before the program execution contains three main stages: the partition of program basic blocks (BBs); the BB reference information extraction; and the integrity signature calculation of each BB.
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 2 Neural Network-Based DPD Architecture  
The proposed NN DPD system is cascaded with the RoF link so that the overall result of this system will be a linear system. The proposed method is shown in Figure 2. It must be noted that training an NN requires training data, whereas the ideal NN output is not known. Since the ideal RoF output is known, an RoF NN model is trained to emulate the RoF link. By doing this, we can backpropagate through the RoF NN model to update the parameters in the NN DPD.  
Consider that an RoF link with transfer function (discrete time domain) \(H(n)\) will have output signal \(y(n)\). Let us consider a complex baseband signal \(x(n)\) that must be sent through an RoF link. The goal of DPD is to calculate the inverse transfer function of the RoF expressed as \(\hat{I}^{-1}\), which will then have an output of \(\hat{x}(n)\). The simplified version can be expressed as:  
\[\hat{x}(n)=\hat{I}^{-1}(x(n)) \tag{1}\]  
while  
\[y(n)=Gx(n)=I(\hat{x}(n)) \tag{2}\]  
Figure 2: Block schematic of analog radio over fiber (A-RoF), digital radio over fiber (D-RoF) and \(\Sigma\Delta\)-RoF downlinks. Freq. Upconv.: Frequency Upconvter, BB: baseband, Tx: Transmitter, \(\Sigma\Delta\)MOD: Sigma-Delta Modulator, Data Rec.: Data Recovery block.  
This means that NN is used to find \(\hat{I}^{-1}\) for predistortion. Since a training input and output sequence are required for NN training, however, the ideal \(\hat{x}(n)\) is not known and, therefore, a direct training to establish an NN for DPD is unachievable. We must note that ILA can be used for this identification; however, we would like to evaluate the other option for performance measurements (see Figure 3).  
So, first of all, the second NN model is used to model the RoF link. Considering \(\hat{x}(n)\) and \(\frac{y(n)}{G}\) as input and output data for the generic RoF link, training is carried out with the regression-based NN for these data, causing the NN to learn and identify the approximated transfer function \(\hat{I}\). Once the RoF NN model is established, the weights of the NN RoF model are fixed and it is connected to NN DPD. Consequently, the actual input \(x(n)\) and output training data are used to determine the error via loss function. This is then backpropagated via \(I\) to train \(I^{-1}\).
## 5 Results and Discussion
### Timestamp Control of the CVROSS  
A while loop (a control flow statement that allows code to be executed repeatedly) can also be utilized in the servers. This allows the program to be run every second or even more frequently, also enabling data, information and reports to be saved every second or more frequently. Although this can provide users with regularly updated information, the server overloads easily, as large quantities of data, information and reports must be processed and saved. Thus, the stability of the system is negatively affected. For example, assuming a month has 30 days, there are 2,592,000 s in a month (for an Excel file generated programmatically utilizing a while loop). This means that there are 2,592,000 records per month, generating approximately 31,536,000 records per year. This could be problematic for big data analysis after a number of years, as more data are generated.  
As a result, a timed loop is suggested instead of a while loop. In this case, the timed loop is set with a five-minute delay. Thus, it can still update the real-time traffic information frequently for road users, facilitating the operations of the CVROSS and loading the server smoothly, to prevent overloading and to maintain stability, facilitating the process of big data analysis. In fact, the five-minute delay can be adjusted, based on the real needs of road users, hence providing them with a more user-friendly system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Area** & **UoM \({}^{\text{a}}\)** & **Before Using** & **After Using** & **\% of** \\  & & & **CVROSS** & **CVROSS** & **Improvement** \\ \hline \multicolumn{6}{c}{Perspectives from property management companies} \\ \hline
1 & Severity of traffic congestion & Scale (1–10) \({}^{\text{b}}\) & 8.5 & 5.0 & \(-\)41.2\% \\
2 & Severity of double parking & Scale (1–10) \({}^{\text{b}}\) & 9.7 & 6.5 & \(-\)33.0\% \\
3 & Labour force on controlling & people per shift & 10 & 6 & \(-\)40.0\% \\ \hline \multicolumn{6}{c}{Perspectives from drivers and truckers} \\ \hline
1 & Average fuel saving & Scale (1–10) \({}^{\text{b}}\) & 7.8 & 5.1 & \(-\)34.6\% \\
2 & Average time to locate suitable & min & 18.2 & 8.8 & \(-\)51.6\% \\
3 & Average driver satisfaction & Scale (1–10) \({}^{\text{b}}\) & 6.1 & 8.2 & \(+\)34.4\% \\ \hline \hline \end{tabular}  
* Notes: \({}^{\text{a}}\) UoM refers to unit of measurement, \({}^{\text{b}}\) Scale (1–10) refers to a Likert scale from 1 to 10, while 10 is the highest score in scale and 1 is the lowest score in scale.  
\end{table}
Table 2: Comparative analysis before and after using the CVROSS.
###### Abstract
This paper analyzes the Bayesian-structure learning method to estimate dynamic EEG functional connectivity in sensor spaces for a given cognitive task that avoids spurious pairwise correlations and ensures global functional connectivity. In addition, we aim to classify the functional-connectivity features using different machine learning models and compare the results with state-of-the-art methods. Furthermore, we have developed a graph convolutional network (GCN) model to classify the Bayesian functional-connectivity features. The proposed framework outperforms other classifiers and other combinations of functional-connectivity metrics and classifiers. Also, it outperforms state-of-the-art WM load classifiers using functional-connectivity metrics. Finally, we perform intrasubject statistical analysis of BSL connectivity metrics in alpha, beta, and theta frequency bands. This paper makes the following contributions:  
* We analyze a Bayesian approach to estimate EEG functional connectivity for mental arithmetic tasks. * We propose a novel GCN classifier to classify subject-specific Bayesian functional-connectivity features. * The two-sided \(t\)-test, Spearman correlation, and graph-theoretical analysis show that the proposed Bayesian-structure learning approach produces consistent results in alpha, beta, and theta bands. * The proposed framework outperforms state-of-the-art frameworks for EEG functional-connectivity-based classifications. The rest of the paper is organized as follows: Section 2 presents the EEG dataset for WM load classification. Next, the Bayesian algorithm is presented to estimate functional connectivity, and the GCN model is used to classify the functional-connectivity metrics. The results are presented in Section 3, and a discussion is presented in Section 4.
## 3 State-Of-The-Art
### Primary Research in Literature
#### 3.2.2 Cybersecurity  
Another important aspect extensively studied in vehicular communications is security, since all vehicles can be exposed to security breaches that can produce fatal consequences to the occupants [99; 100]. Several approaches in V2X communication are listed in [101] and [16], where the most used is the public key scheme [102; 103; 104; 105; 106] as it provides integrity and authentication through the IEEE 1609.2 standard [16]. In this field, other techniques such as the group signature [107] or the symmetric authentication schemes [108] also stand out but are less used as they are not part of the IEEE 1609.2 standard. However, the LTE authentication and key agreement protocol (LTE-AKA) is proposed to protect communications against possible attacks when 4G/LTE--or the coming 5G--is used as an access technology [11]. Beyond these studies, there are other novel works aimed at developing secure approaches applied to specific scenarios such as Tesla++, a protocol suitable for minimum data transmission and low energy consumption that prevents and protects against denial of service (DoS) attacks in VANETs. Despite of the deep understanding on security, there are aspects still unresolved. Among the most important for VANETs is to evaluate the reliability of the nodes that communicate through the network, to detect and revoke the trust on a rogue node, to guarantee the security and/or privacy--in terms of the vehicle traceability--as well as to detect malicious software [14].
## 2 Materials and Methods
### Orbit and Operation  
The PROBA3 orbit is suited to reasonably efficient observations, covering about 30% of the ecliptic strip during a one year elapse. The chosen orbit is highly elliptical, with 60,530 km apogee and 600 km perigee, and about 59 degree inclination, resulting in an orbital period of 19.7 h. Coronagraphic operation between the precisely aligned spacecraft will take place during the six hours around the apogee.  
The whole ecliptic strip may thus be covered in approximately three years. The longer mission lifetime allows for repeat observations, i.e., an increase in the integration time, thus improving the noise performance and/or the calibration accuracy. Further details on the formation flying satellite subsystems and operations are reported in the literature [26; 27; 28].  
The orbit altitude and/or ellipticity might be modified in order to increase the active observing time fraction above the nominal 6/19.6 h value. However, this trade-off has significant implications for the satellite attitude; it also affects telemetry aspects, changing  
Figure 4: GLADE focal plane **(left)**: a ring of 12 4\(k\times\) 4\(k\) CMOS detectors. Imaging performance (**right**): sections of the optical photon distribution (solid line) and of the effective detected photo-electrons (dashed line).  
the minimum distance (at the perigee) to accessible ground stations. Therefore, such aspects are left to subsequent mission definition study phases.  
The apparent displacement of the Sun on the ecliptic is about 0\({}^{\circ}\).986/day, i.e., \(\sim\)2\({}^{\prime\prime}\).5/min. The observation is performed in a step-and-stare mode: pointing on a star field for a while, then moving to keep the (occulted) Sun close to the optical axis. In a simple framework, we can move the pointing direction by 2\({}^{\prime}\) every 48 min, resetting the Sun position within the \(\pm\)1\({}^{\prime}\) range.  
During the six-hour eclipses, GLADE observes a sky region approximately 0\({}^{\circ}\).25 wide along the ecliptic over the \(\pm\)0\({}^{\circ}\).83 range covered by the focal plane (Figure 4, left). In practice, the exposure time per star changes depending on position within the field; the average values are considered in the following, leaving detailed analysis to subsequent analyses.
# Failure Prediction for the Tearing of a Pin-Loaded Dual Phase Steel (DP980) Adjusting Guide
## 3 Determination of Flow and Damage Properties
### Determination of Damage Parameters through Optimization  
The damage parameters (\(\varepsilon_{u}\), \(\varepsilon_{f}\), \(m\), and \(f\)) were determined by a metamodel-based optimization performed with LS-OPT. The initial values, together with the lower and upper boundaries for the damage parameters and the optimization flowchart, are given in Figure 8. The initial values for \(\varepsilon_{u}\) and \(\varepsilon_{f}\) were estimated from the experimental data (note that generally, the \(\varepsilon_{f}\) value for UT obtained through optimization differs from the one that would be achieved for the experimental data, because of different degrees of localness). The objective was to minimize, for all tensile tests (SHx, UT, and Nx), the total error between the numerical and experimental FD data, by applying the in-built curve matching algorithm. The numerical FD data were determined based on the same gauge length as that which was used in the tests (50 mm). First, a number of FE analyses were run, starting with the initial values and continuing with the sampled sets of damage parameters (LHC(Latin hypercube)and RBFNN(Radial Basis Function Neural Networks) sampling was used). The FD data were then extracted and compared with the experimental FD data, while a metamodel was being built. The total error (multi-objective) was computed, and the convergence (or not) was judged by comparing the error with the allowed error. If no convergence was reached, the next loop commenced by sampling and running further FE analyses. Once convergence was reached, further analyses were performed with mesh sizes of \(e\) = 0.8 and 4.0 mm, so as to mitigate mesh-dependence. The final damage parameters and the resulting TFD together with the regularization curve are shown Table 1 and Figure 9, respectively.  
To validate the GISSMO parameters, all of the tests were performed with the final parameters. As Figure 10 reveals, the numerical FD curves were found to follow the experimental ones very closely. Only N16 showed some deviation from the experimental data.  
Figure 7: UT specimen (\(e\) = 2.0 mm; left); true stress–strain data converted from UT FD data to \(F_{\max}\) and then linearly extrapolated, serves as the finite element (FE) input (right).  
Figure 8: Optimization procedure, including regularization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**DP980** & \(\eta\) & \(\epsilon_{u}\) & \(\epsilon_{f}\) & \(m\) & \(f\) \\ \hline SH45 & 0.06 & 0.219 & 0.257 & & \\ \hline SH30 & 0.11 & 0.040 & 0.156 & & \\ \hline UT & 0.36 & 0.022 & 0.590 & & \\ \hline N9 & 0.48 & 0.010 & 0.341 & & \\ \hline N16 & 0.53 & 0.010 & 0.316 & & \\ \hline N5 & 0.55 & 0.010 & 0.272 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Final damage parameters from optimization.  
Figure 9: TFD (left) and mesh dependence (right).