esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### SMCI Model Adaptations  
Once a reliable model was developed using data from members of the typically developing population (DEAP dataset), it could also be applied to data collected from a population of persons with SMCIs. Four of the participants with SMCIs were able to use an alternative communication device to provide a self-reported emotion word to express how they were feeling after each image was shown. This provided the ground-truth data for the model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**\# of Categories**} & \multicolumn{5}{c}{**Scores**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Pleasure**} & \multicolumn{2}{c}{**Arousal**} & \multicolumn{2}{c}{**Dominance**} \\ \cline{2-7}  & **Model** & **Acc.** & **Model** & **Acc.** & **Model** & **Acc.** \\ \hline
**2** & SVM-rbf & 56\% & Random Forest & 60\% & SVM-rbf & 61\% \\
**3** & SVM-rbf & 47\% & SVM-rbf & 49\% & SVM-rbf & 54\% \\
**9** & SVM-rbf & 20\% & SVM-rbf & 21\% & SVM-rbf & 24\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of complexity (categories) of all three signals when fused (PPG, GSR, and RESP).  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Score** & & **Pleasure** & **Arousal** & **Dominance** & \
\begin{tabular}{c} **PAD** \\ **Average** \\ \end{tabular} \\ \hline \multirow{2}{*}{RESP} & Model & SVM-poly & SVM-linear & Random Forest & \\ \cline{2-5}  & Accuracy & 20\% & 21\% & 23\% & 21\% \\ \hline \multirow{2}{*}{PPG, GSR, and RESP} & Model & SVM-rbf & SVM-poly & SVM-rbf & \\ \cline{2-5}  & Accuracy & 19\% & 21\% & 22\% & 21\% \\ \hline \multirow{2}{*}{PPG and GSR} & Model & SVM-rbf & SVM-rbf & SVM-rbf & \\ \cline{2-5}  & Accuracy & 20\% & 22\% & 26\% & 23\% \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Dimension \\ Average \\ \end{tabular} } & \multirow{2}{*}{Accuracy} & \multirow{2}{*}{20\%} & \multirow{2}{*}{21\%} & \multirow{2}{*}{24\%} & \multirow{2}{*}{22\%} \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._The same experimental protocol used for the DEAP dataset could not be replicated with participants who had SMCIs. Changes in the data collection were required and the duration of the trials was reduced. As a result, the DEAP model had to be modified to account for these differences (Figure 2). The following changes to the DEAP model were implemented (and are further discussed): the removal of the RESP signal, the cleaning of the BDAT data was required using Neurokit (version 2), modifying the duration of the data samples (the BDAT data was only collected for 6 s, while the DEAP data was collected for 60 s), and the use of level 5 wavelets instead of the level 8 wavelets. Instead of looking at the various model complexities with different categories, the nine categories were evaluated and the emotion word from the detected physiological signals was compared to the DEAP equivalent.
# Electrospinning of Essential Oils
## 4 Conclusions  
This review discusses the research conducted so far on the encapsulation of therapeutic essential oils in polymeric electrospun fibres to create functional membranes for biomedical and food packaging applications. The studies in the literature show that the bioactivity of the essential oils is maintained after they are mixed with different polymeric systems and processed by electrospinning. The EOs still possess antimicrobial, anti-inflammatory and antioxidant activity when released from the electrospun mats.  
When compared with polymer films, electrospun membranes are characterised by a higher specific surface area (high surface-to-volume ratio of the nanofibres) and microscale porosity (interstitial space between nanofibres). This allows control over the amount and release profile of EOs contained in the electrospun fibres, and therefore limits the cytotoxic effects that some components of EOs have on human cells. One aspect to consider, however, is the possible alteration of the EO chemical composition during fibre formation because of evaporation. For example, chemical analyses have recorded the disappearance of some characteristic bands of peppermint EO when encapsulated in PCL fibres [49], but not in gelatin [50] and cellulose acetate [44] fibres. Similarly, the main components of Manuka EO are retained in electrospun PLA fibres, but this is not the case for tea tree EO in PLA fibres, possibly due to the different evaporation rates of the constituents of the two oils [68].  
Future research could investigate the long-term effect of EOs on the physicochemical properties of the polymeric fibres. EOs can work as plasticisers for some polymers, for example PLA, and induce a concentration-dependent reduction of the glass transition temperature of the composite fibres. However, it has not been elucidated yet if EOs can affect the aging of the electrospun mats and induce changes in the fibre's morphology, mechanical stability, and degradation rate, over time. This could impact on the performances of electrospun fibres containing EOs that are used for biomedical devices and food packaging systems.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Methods
#### 2.2.3 Classification  
For classification purposes, we involved and compared two classifiers: the support vector machine and random forest.  
SVM [28] is a well-recognized tool that is efficient in regression and classification, especially in two-class tasks. It aims to separate samples from different classes in the feature space with a surface that maximizes the margin between them using optimization methods. We used the SVM classifier with a linear kernel [29] due to its superior performance compared to other kernels under consideration, including Gaussian and RBF. As a result, the tool provides probabilities of a given ICH subtype's presence in the image. Hemorrhage is confirmed if the probability exceeds 50%.  
Figure 4: Illustration of the double-branch convolutional neural network (CNN) based on the ResNet-50 architecture (**a**). The feature extraction CNN is covered by a gray background. Dashed frames surround the full ResNet-50 structures. Rightmost blocks refer to the classification stage. Convolutional and identity blocks from ResNet-50 are presented in detail in (**b**,**c**), respectivelyRF [30] is an ensemble classifier that parallelly uses multiple models of decision trees (DTs). RF takes the decision of the majority of the trained DTs as its final choice. Single trees are trained in parallel using various subsets of a given dataset (bootstrapping) followed by an aggregation, together known as bagging. The training data are divided into bootstrap sample data and out-of-bag (OOB) data that allow RF for cross-validation. RF is considered more complex in interpretation than DTs, yet more convenient in respect of hyperparameter adjustment. We set the maximum number of splits to \(n-1\), where n is the number of observations in the training sample with the number of trees equal to 200.
## 4 Conclusions  
In this study, CSM is added as an admixture to the 3 DPM. From the experimental results, the following conclusions can be drawn:  
Figure 9: Scanning electron microscopy (SEM) image of the sample.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multirow{2}{*}{**Porosity (\%)**} & \multicolumn{4}{c}{**Pore Size Situation (\%)**} \\ \cline{3-6}  & & **0–10 nm** & **10–50 nm** & **50–1000 nm** & **>1000 nm** \\ \hline YN0.37 & 16.9841 & 6.38\% & 65.86\% & 20.02\% & 7.74\% \\ YN0.38 & 15.5971 & 5.52\% & 62.87\% & 17.79\% & 13.82\% \\ YN0.39 & 17.438 & 8.07\% & 62.81\% & 16.41\% & 12.71\% \\ YN0.40 & 19.1634 & 6.15\% & 66.09\% & 18.88\% & 8.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pore size distribution.  
(1) The CSM can increase the material's yield stress without affecting the material's apparent viscosity. The appropriate amount of CSM added is beneficial for improving the printability of the slurry.  
(2) Increasing the w/c ratio appropriately can promote the hydration of GBFS by CSM, increase the total heat release, and reduce total porosity. However, with the increase in w/c ratio, although the total porosity will increase, the pH of the pore solution decreases, leading to reduced hydrolysis and hydration of GBFS, resulting in more calcium vanadium stone formation and optimizing pore size distribution, thereby alleviating the decrease in mechanical properties.  
(3) The addition of CSM can alleviate the problem of increased w/c ratio caused by the addition of RCA, providing a solution for the application of RCA from construction waste in 3 DPM. It provides a new idea for the recycling of waste SM.  
Conceptualization, S.H.; methodology, Y.W.; validation, Y.W.; formal analysis, Y.W. and Y.Z.; investigation, Y.W., S.J. and Y.Z.; resources, S.J. and Y.Z.; data curation, Y.W., S.J. and Y.Z.; writing--original draft preparation, Y.W.; writing--review and editing, S.H. and H.Y.; supervision, S.H. and H.Y.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
This work was supported by funds from Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and Social Development of Science and Technology Department of Jiangsu Province--Major Science and Technology Demonstration Project (BE2022608).  
The authors declare no conflict of interest.
# Use of a Weighted ICP Algorithm to Precisely Determine USV Movement Parameters
## 2 Evaluation of the Accuracy of Determining the Coordinates of a Position Using LIDAR
Then, using this, to determine the mean square error \(\sigma_{\beta}{}^{2}\) of the determination of the position coordinates, but in the specified direction \(\beta\):\[ \begin{array}{ll}\sigma_{\beta}{}^{2}=\mathbf{S}_{\beta}\cdot\mathbf{cov}(x,\ y) \cdot\mathbf{S}_{\beta}^{T}&=\sin\beta\Big{(}\sin\beta\Big{(}\beta\Big{(}\beta \Big{(}r^{2}\cdot\alpha_{\alpha}{}^{2}\cdot\sin^{2}\alpha_{a}+\sigma_{r}{}^{2} \cdot\cos\alpha_{a}\Big{)}-\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad- \cos\alpha_{a}\cdot\cos\beta\cdot\sin\alpha_{a}\Big{(}\sigma_{r}{}^{2}-r^{2} \cdot\sigma_{\alpha}{}^{2}\Big{)}\Big{)}+\\ &+\sin\beta\Big{(}\sin\beta\Big{(}r^{2}\cdot\sigma_{\alpha}{}^{2}\cdot\cos^{2} \alpha_{a}+\sigma_{r}{}^{2}\cdot\sin^{2}\alpha_{a}\Big{)}-\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad-\cos \alpha_{a}\cdot\sin\alpha_{a}\cdot\sin\beta\Big{(}\sigma_{r}{}^{2}-r^{2} \cdot\sigma_{\alpha}{}^{2}\Big{)}\Big{)},\end{array}  \tag{9}\]  
where \(\mathbf{S}_{\beta}=\Big{[}\ \cos\beta\ \ \ \sin\beta\ \ \Big{]}\).
## 2 The Front-End of the Italian BIRALET Radar
### The Radio Frequency Transmitter  
In particular, TRF is a fully steerable parabolic antenna with a 7 m diameter dish (see Figure 1a), with a primary focus configuration, located in the Italian Joint Test Range in the region \"Salto di Quirra\" (Lat. 39.6050\({}^{\circ}\) N-Log. 9.4396\({}^{\circ}\) E, Cagliari, Sardinia, Italy) [25]. The antenna reflector is manufactured to operate in a large frequency range up to 10 GHz with a high antenna efficiency (greater than 50%). And in spite of the currently operative frequency is 410-415 MHz, this aspect is very attractive for future system upgrades to high frequencies observations. Regarding the microwave components chain, the TRF system is made up of a set of seven power amplifiers able to transmit a root-mean-square (RMS) power between 1 kW and 10 kW [23]. The TRF amplification block is designed to work in the bandwidth of 400-420 MHz and filtered using a tunable central frequency (i.e., 410 MHz) with bandwidth of 5 MHz [23]. At this frequency, the system has a half power beam width (HPBW) of about 7 degrees and an antenna gain of about 27 dBi.  
An important feature of the system is represented by its mechanical pointing speed, equal to 3 degrees per second in both azimuth and elevation directions, that permits to move fast between one observation target and another and, consequently, guaranteeing high performances in tracking mode.  
\begin{table}
\begin{tabular}{c c c} \hline
**Antenna Name** & **Radio Frequency Transmitter (TRF)** & **Sardinia Radio Telescope (SRT)** \\ \hline Frequency range & 410–415 MHz & 399–419 MHz \\ Antenna gain @410 MHz & 27.3 dBi & 46.6 dBi \\ Half Power Beam Width (HPBW) & 7.3 deg & 0.8 deg \\ Azimuth speed & 3 deg/s & 0.85 deg/s \\ Elevation speed & 3 deg/s & 0.5 deg/s \\ Polarization & Circular & Circular and linear \\ Side-lobes & \(<\)\(-\)20 dB & \(<\)\(-\)20 dB \\ Noise Temperature & N/A & 20 K \\ \hline \end{tabular}
\end{table}
Table 1: Main features of the Bi-Static Radar for LEO Tracking (BIRALET) radar.  
Figure 1: (**a**) The Radio Frequency Transmitter (TRF) antenna; (**b**) the block diagram of the transmitting chain.  
A simplified block diagram of the transmitting chain, which includes a central control unit (CCU), an automatic gain control (AGC), a radio frequency splitter, a phase adjust block and seven amplifier blocks, is given in Figure 1b. In detail, the CCU, which takes in an input signal, is linked to the AGC for the system gain adjustment. The AGC then sends the signal to the radio frequency splitter that creates seven signals, adjusted in phase, for all power amplifier. After the amplification block, the signals are combined, filtered and sent to the antenna [23].
## 1 Introduction
In this study, we tested which training dataset is more effective when applying deep learning technology to the Korea Multi-Purpose Satellite (KOMPSAT), specifically by using TOA reflectance and derived indices during model training, compared to using the default DN values. As mentioned, the KARI distributed a training dataset of cloud masks as DN images with corresponding label files. Therefore, we newly calculated the TOA reflectance from KOMPSAT-3 and the 3A DN training dataset by using the solar geometry and ESUN of the selected images. In addition, we also tested the performance of a deep learning algorithm changing the composite of channels for the DNs and TOA reflectance because the combination of satellite channels affects the accuracy of the cloud mask results [26].
# Compact MIMO System Performances in Metallic Enclosures
## 3 Measurement Process and Loading of Enclosure  
We used transmitter and receiver baseband models with the QPSK modulation for over-the-air transmission. After the Tx and Rx USRPs were initialized and configured in the MIMO mode, the Tx and Rx QPSK baseband models were also configured to operate in MIMO communications. The baseband transmitter generated two-column QPSK modulated waveforms that were passed to X310 USRP for the over-the-air transmission in the metal enclosure. Two-column QPSK modulated waveforms represent each channel of the USRP. Digital samples from the host PC to USRP for upconversion and transmission were transferred using a 1 Gig Ethernet cable. Another X310 USRP captured the received signals, which were digitally down-converted and passed to the host PC for baseband processing and two-channel BER and EVM measurements. Measurements were conditioned only to proceed when there were no underruns or overruns, respectively. Underruns and overruns affect the reliability of KPI measurements, particularly two-channel BER measurements. Underruns and overruns depend on the base sampling rates of the USRP, waveform design, speed of the cable connecting USRP, host PC, simulation time, and data-logging techniques in baseband software. X310 USRP can offer higher sampling rates to stream the data to and from the host PC, but in most of the cases, sample rates are limited by the speed of the Ether  
Figure 2: The \(2\times 2\) MIMO BER and EVM measurement setup inside the cavity (**a**) MIMO antennas and an inside view of the cavity, (**b**) front view of the antennas, (**c**) side view of the antennas, (**d**) top view of the antennas.  
net cable or the general purpose processors, and are not configured to handle the maximum sampling rates of the USRPs. The Simulink Tx and Rx baseband models were configured to stop in case any single underrun and/or overrun event occurred. The USRP Tx gain was configured in Simulink and programmatically controlled so that two-channel near-field MIMO BER and EVM measurements were performed without lifting the metal lid off and creating any possibility of changing the EM environment and propagation conditions. The propagation environment could be changed after the BER and EVM measurements are performed. The flowchart of the measurement process is shown in Figure 3, which shows the critical design and measurement steps.  
Figure 3: Flowchart of MIMO BER and EVM measurements.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### Meta-Heuristic Algorithms
#### 3.4.3 Improved Particle Swarm Optimization  
Authors in [32] improved the standard PSO to enhance the optimality and speed of convergence. Improvements include (1) improved initialization stage of swarm particles using chaos-based particle initialization as opposed to the conventional random initialization. This step helps to provide diverse swarm particles and thus speeds up the convergence. To accomplish this, the simplest logistic map for the IoD formation initialization is applied as presented in [32].  
\[X_{n+1}=\mu X_{n}(1-X_{n}), \tag{18}\]  
where \(X_{n}\) represents the \(n^{th}\) chaotic variable, and \(\mu\) is a bifurcation coefficient. Other improvements include (2) an improved update strategy by replacing inactive particles,  
Figure 2: Graphical representation of PSO evolution.  
where particles lose their ability to search locally or globally, which may cause premature convergence, and (3) adaptive mutation of parameters such that their values change with time. In specific, the inertia parameter \(\omega\) and a newly introduced parameter, denoted by \(\epsilon\), are computed as follows:  
\[\omega(t)=\omega_{min}+\frac{MaxIt-t}{MaxIt}*(\omega_{max}-\omega_{min}), \tag{19}\]  
where \(MaxIt\) is the maximum simulation time, \(t\) is the current simulation time, and (\(\omega_{min},\omega_{max}\)) are minimum and maximum values of inertia, respectively.  
\[\epsilon=\epsilon_{max}-\frac{(\epsilon_{max}-\epsilon_{min})t}{MaxIt} \tag{20}\]  
where \(\epsilon_{max}\) and \(\epsilon_{min}\) are constants, where \(\epsilon_{max}>\epsilon_{min}\).  
\[x(t+1)=x(t)+\epsilon\
u(t+1) \tag{21}\]  
IPSO for the IoD path planning is explained in Algorithm 2.  
```
begin  Limit: Threshold of trail  Max_Iteration: maximum iteration  INITIALIZATION:,  initialize c1,c2  initialize position and velocity with logistic map by Equation (18) begin  iter=0  Calculate fitness value and obtain gBest and pBest whileIter<Max_Iterationdo  Update \(\omega\) by Equation (19)  Update \(\epsilon\) by Equation (20) for\(i\gets 1\)to PopSizedo  Update the velocity of particle using Equation (16)  Update the position of particle using Equation (21)  Calculate fitness of generated particle; ifparticle is inactivethen  Increment its trail else  Clear its trail  Update gBest and pBest  end if  end for for\(i=1:\)to PopSizedo iftrail(\(i\))>limitthen  Replace inactive particle by new fresh one.  Clear its trail  end if  end for  Increment Iter  end for  Repeat until ideal best is obtained or run out of time  end for  Return Optimal Paths for IoD  end for
```  
**Algorithm 2**Improved PSO algorithm
# Fake News Detection on Social Networks: A Survey
## 4 Datasets  
For fake news detection, useful features are extracted from social media datasets, and an effective detection model is established to detect fake news in the future. Supervised learning methods are widely used in the field of fake news detection. Although these methods have shown promising results, reliable annotated datasets are needed to train the detection model [70]. Therefore, establishing a large-scale dataset with multidimensional information is very important.  
[PERSON] and [PERSON] [71] were the first to publish a dataset in the field of fake news, but only 221 statements were issued. In recent years, several fake news datasets have been proposed, which involve politics, security, health, and satire. The relatively new and representative datasets are classified based on the data sources of the news, as shown in Table 4. The CHECKKED [72] and Weibo21 [73] datasets were collected from Weibo, COVID-19 [74] and FibVID [75] were collected from Twitter, and BuzzFeed [76] and Fakeddit [77] were collected from Facebook and Reddit, respectively. Datasets such as IFND [78], FakeNewsNet [79], FA-KES [80], ISOT [81], GermanFakeNC [82], BanFakeNews [83] and LIAR [84] are sourced from news websites.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Dataset Name** & **Feature Types** & **Modality** & \begin{tabular}{c} **Annotation Methods for** \\ **True News** \\ \end{tabular} & \begin{tabular}{c} **Annotation Methods for** \\ **Fake News** \\ \end{tabular} & \begin{tabular}{c} **News** \\ **Domain** \\ \end{tabular} & **Language** & **Time** \\ \hline \multirow{4}{*}{Weibo} & \multirow{4}{*}{CHECKED} & \begin{tabular}{c} Textual, \\ visual, \\ temporal, \\ network \\ \end{tabular} & Text, images, & \begin{tabular}{c} Weibo accounts \\ video \\ \end{tabular} & \begin{tabular}{c} Weibo accounts \\ (People’s Daily) \\ \end{tabular} & \begin{tabular}{c} Community \\ Management \\ Center \\ \end{tabular} & COVID-19 & Chinese & 2019–2020 \\  & & & & & & Weibo & & & \\  & & & & & & Community & & \\  & & & & & NewsVerify & & & & \\ \hline \multirow{4}{*}{Twitter} & \multirow{4}{*}{COVID-19} & \multirow{4}{*}{Text} & \multirow{4}{*}{Text} & \multirow{4}{*}{Text} & \
\begin{tabular}{c} The official \\ government \\ accounts \\ \end{tabular} & \multirow{4}{*}{Text} & \multirow{4}{*}{Text} &
\begin{tabular}{c} Fact-checking \\ websites \\ \end{tabular} & \multirow{4}{*}{COVID-19} & \multirow{4}{*}{English} & \multirow{4}{4}{*}{-} \\  & & & & & & \\ \cline{1-1} \cline{6-10}  & & & &  
Table 4 shows the characteristics of the datasets, including the source, dataset name, feature types, modality, news domain, language, annotation methods for true news, annotation methods for fake news, and time. The main differences between these datasets lie in the data features they contain, the granularity of classification, and the language of the news. The CHECKED and Weibo21 are Chinese datasets; IFND, GermanFakeNC, and BanFakeNews are Indian, German, and Bangladesh datasets, respectively; and the rest are English datasets.
# 3D Point Cloud Generation Based on Multi-Sensor Fusion
## 4 Results
### Filtered Smooth Trajectory Verification  
In the experiment, firstly, the trajectory was calculated before and after the correction of the IMU attitude angle, and the odometer speed was compared with the trajectory obtained by integrated navigation. Figure 21 shows the error comparison of the trajectories before and after the filter correction and the integrated navigation solution. Before the filter correction, the trajectory deviation gradually increased with an increase in distance. After the filter correction, the error level was greatly improved. In Figure 21, \"a simple IMU position calculation deviations\", the track coordinate was obtained by using only the original data of the IMU and odometer.  
Figure 19: Overall distribution of the target.  
Figure 20: Target photo (**left**) and Point cloud extraction target (**right**).  
As shown in Table 4, the trajectory deviation before filter correction gradually increased with the distance. After filtering and correction, the maximum lateral deviation was 0.0482 m, the average deviation was 0.0239 m, the vertical maximum deviation was 0.0405 m, and the average deviation was 0.0138 m.