esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Result
### Demonstration of Real-Time Video Transmission  
A real-time video streaming considering the case of streaming a black-box video via V2V-VLC is demonstrated. Here, the blue beam-steering channel is used. As shown in Figure 10, two PCs work as a streaming host and a client. The host PC streams a real-time video through the Ethernet. In this demonstration, user datagram protocol (UDP) is applied, and through the Tx driver unit, the signal is conditioned and applied to the LD transmitter. Following the beam-steering and tracking mechanism introduced in Section 2.4, the data are transmitted to the FC-coupled license plate. The received signal is converted back to the Ethernet signal through the gain adjusting unit, subsequently being played at the client PC. To test the robustness of the proposed system, a demonstration is conducted with a beam-steering angle of 25 degrees, also under an ambient light level of 2000 lux. Even with these conditions, as shown in the figure, real-time black-box video streaming is successfully demonstrated.  
Figure 9: BER performance under ambient light for (**a**) headlamp at 100 lux and (**b**) measured SNR, and (**c**) BER performance under ambient light for blue LD beam at 0.08 W/m2 and (**d**) measured SNR.
## 3 Results and Discussion
### Tree-Based Algorithms: Random Forest and Gradient-Boosted Tree Models
#### 3.3.2 Gradient-Boosted Tree-Based Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Measures** & **Mean of Test Set** & **With Validation (Mean of 10 Models)** \\ \hline \(p=0.75\) & \(R^{2}\) & 0.8195 & 0.8772 \\ \(a=0\) & MAE & 2.9042 & 2.4375 \\ \(\lambda=0\) & MAPE & 16.1032 & 13.6244 \\  & RMSE & 3.7821 & 3.1402 \\ \hline \(p=0.75\) & \(R^{2}\) & 0.8196 & 0.8773 \\ \(a=0\) & MAE & 2.9202 & 2.4371 \\ \(\lambda=0.001\) & MAPE & 16.0921 & 13.6238 \\  & RMSE & 3.7812 & 3.1396 \\ \hline \(p=0.9\) & \(R^{2}\) & 0.8172 & 0.8761 \\ \(\lambda=0\) & MAE & 2.9261 & 2.4408 \\ \(\lambda=0\) & MAPE & 16.2213 & 13.6526 \\  & RMSE & 3.806 & 3.1545 \\ \hline \(p=0.9\) & \(R^{2}\) & 0.8175 & 0.8761 \\ \(a=0\) & MAE & 2.9256 & 2.4416 \\ \(\lambda=0.001\) & MAPE & 16.2244 & 13.6555 \\  & RMSE & 3.8027 & 3.1549 \\ \hline \(p=1.0\) & \(R^{2}\) & 0.8172 & 0.8731 \\ \(a=0\) & MAE & 2.9356 & 2.4693 \\ \(\lambda=0\) & MAPE & 16.274 & 13.8164 \\  & RMSE & 3.8051 & 3.1926 \\ \hline \(p=1.0\) & \(R^{2}\) & 0.8167 & 0.8730 \\ \(a=0\) & MAE & 2.9393 & 2.4698 \\ \(\lambda=0.001\) & MAPE & 16.3016 & 13.8291 \\  & RMSE & 3.8104 & 3.1936 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results for XGBoost models; learning rate \(=0.05\), number of estimators \(=130\), maximum depth \(=3\), \(p=\) proportion of samples for training each base learner, minimum sum of instance weight needed in a child node \(=1\). Figure 18: Results for XGBoost models; 10 models randomly generated with a 75:25 ratio by means of simple random sampling, maximum depth \(=3\), learning rate \(=0.05\), minimum sum of instance weight needed in a child node \(=1\), \(p=\) proportion of samples for training each base estimator. (**a**) RMSE, \(p=0.75\); (**b**) MAE, \(p=0.75\); (**c**) RMSE, \(p=1\); (**d**) MAE, \(p=1\).
## 4 Results and Discussion  
In this section, the focus is to discuss the numerical results and their contributions to produce a better understanding of the investigated physics. Firstly, Section 4.1 presents the convergence of Equation (11) applied for the advection problem of a pair of vortex blobs of equal strength \(\Gamma_{\mathrm{v}}\); a simple motion is simulated, in which time elapses over a succession of elementary but finite steps \(\Delta\mathrm{t}\). The convergence of the diffusion problem is presented through the application of Equation (12) to solve the diffusion of a vortex sheet.  
Secondly, Section 4.2 investigates the trajectory of the centroid of the primary vortical structure from the right, as sketched in Figure 1. The trajectories are influenced by interaction with secondary vortical structures generated from the ground plane.  
The sensitivity of the new technique is captured, and the results are interpreted by varying the Reynolds number value (i.e., \(\mathrm{Re}=7650\) and \(75{,}000\)), the relative roughness height (i.e., \(\varepsilon/\Delta\mathrm{s}=0.0\), \(0.001\) and \(0.007\)), and the crosswind velocity (i.e., \(\mathrm{U}_{\infty}=0.0\), \(0.02\) and \(0.04\)).
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 3 Proposed Methods
### The Method Processing Flow
After the multiple spoofing sources are identified, the method described in Section 3.2 can be applied to each spoofing source to achieve efficient localization of multiple spoofing sources.
## 3 Results and Discussion
### Calibration Model Using the Entire Range of Wavelengths  
Compared with all the pre-treatments, R\({}_{\text{c}}\)\({}^{2}\) (0.7157) after normalization + 1\({}^{\text{st}}\) derivation treatment was higher than that of raw data (R\({}_{\text{c}}\)\({}^{2}\) = 0.2602) (Table 3), indicating model improvement. It is recognized that normalization can improve spectral characteristics, leading to spectra with consistent areas under the curve that make it easier to compare features within the same plot [44]. When dealing with hyperspectral spectra data, normalization is more effective than the conventional spectrophotometer method in achieving a higher signal-to-noise ratio. The application of first and second derivations can also remove background noise and baseline drift, as well as enhance the clarity of subtle spectral features. It has been reported that SNV is intended to eliminate the variability in reflectance spectra caused by light scattering [45]. Likewise, multiplicative scatter correction is to offset the additive and multiplicative effects [46]. The lower sample amounts may explain the low R\({}^{2}\) values.  
The calibration model established using the full spectra allows for the detection and correction of any instrument drift or non-linearity that may be present in the data. By using the full spectra, all spectral features are included in the calibration, which can improve the accuracy of the model. Furthermore, using the full spectra helps ensure that all relevant information is captured and accounted for in the calibration process. This is especially important in applications where subtle differences between samples can have significant implications. Finally, using the full spectra can help reduce the risk of overfitting, where the calibration model is overly complex and does not generalize well to new data. Although there are several advantages to developing a calibration model using full spectra, the full model is too long (there are 143 spectra and so there will be 143 variables for full spectra) and may not be of practical use for industrial applications. Important wavelength selection is thus meaningful because if the precision of these models based on the selected important wavelengths is comparable to that of the models that utilize full wavelengths, it can simplify the complex full model and offer valuable insights for using the multi-spectral imaging  
Figure 3: Mean reflectance spectral of samples with treatments 10 and 19 (as the representatives).  
system to monitor the moisture properties of sausage cores with varied modified casings in real-time.
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 5 Performance Evaluation  
The results calculated by the TACRP-based traffic management are described in this section. To analyze the betterment of the TACRP routing protocol the results are compared with earlier works, such as the Efficient Cluster Head Selection (ECHS) scheme [14] and Novel Routing and Hybrid based Clustering Scheme (NRHCS) [13]. The TACRP routing protocol is examined based on performance using four parameters, such as energy efficiency, network throughput, end-to-end delay, packet delivery ratio, packet loss, and overhead. The mathematical expressions for the calculation of these parameters are shown below.  
The packet delivery ratio is defined as the ratio of the quantity of packets received by the destination to the total amount of sent packets from the source.  
\[PDR=\left[\frac{\sum\text{ {received packets}}}{\sum\text{ {sent packets}}}\right]\times 100 \tag{8}\]  
Throughput is defined as the ratio of the number of received messages from the destination at each instant of time.  
\[THR=\left[\frac{\sum\text{ {received packets}}}{\sum\text{ {end to end delay}}}\right]\times 1000 \tag{9}\]  
End to end delay is defined as the delay time that is produced during the process of communication among the vehicles for the entire transmission.  
\[End\text{ to end delay}=\left[\frac{\sum\text{ {end to end delay}}}{\sum\text{ {sent packets}}}\right] \tag{10}\]  
Figure 3: Inter CH Communication.  
Packet loss ratio is defined as the amount of packets lost during the process of data transmission between the source and the destination.  
\[Loss=\left[\frac{\sum\textit{ lost packets}}{\sum\textit{ sent packets}}\right]\times 100 \tag{11}\]  
Communication overhead is the summation of overall forwarded messages in terms of transmission rate, direct broadcast, indirect broadcast, and shared messages:  
\[Overhead=\left[\frac{\sum\textit{ forwarded packets}}{\sum\textit{ sent packets}}\right]\times 100 \tag{12}\]  
Energy efficiency is termed the residual energy of the network and is the calculation of the remaining energy at the end of the data transmission between the vehicles for the entire network:  
\[Energy\;Efficiency=\left(\sum initial\;energy\right)-\left(\sum consumed\;energy\right) \tag{13}\]
# A Survey of CNN-Based Network Intrusion Detection
## 3 Background of Study
### IDS Datasets
#### 3.4.4 CIC-IDS2017  
On the other hand, the Canadian Institute for Cyber Security (CIC) assembled the CIC-IDS2017 dataset in 2017, aided by the B-Profile system [107; 108]. Favorable and common attacks are contained in the dataset, which reflects true real-world data (PCAPs). The results relating to network traffic were analyzed using a CICFlowMeter, with flows being labeled according to their timestamps, destination ports, sources, destination IPs, protocols, and attacks (CSV files). The abstract behaviors of 25 users are contained in the CIC-IDS2017 benchmark dataset according to SSH, HTTP, FTP, and HTTPS. The data were collected between 9 a.m. on 3 rd July 2017 and 5 p.m. on 7 th July 2017; thus, the timeframe spanned five days. Normal activity was only reported on Monday, during which only benign traffic was circulated. However, several attempted attacks were carried out on other days, including brute force SSH, brute force FTP, Heartbleed, DoS, web attack, Botnet, infiltration, and DDoS. Of the 2,830,473 network traffic samples contained in the dataset, there are 1 benign and 14 attack categories. Benign traffic constitutes 80.30% of the data, while attack traffic constitutes 19.70%. The most common attacks are included (i.e., DoS, DDoS, Botnet, PortScan, and web attacks). The dataset extracted 84 features  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Attack Name** & **Training Set** & **Test Set** \\ \hline Normal & — & 97,278 & 60,593 \\  & DoS & 391,458 & 223,298 \\ Attack & Probe & 4107 & 2377 \\  & R2L & 1126 & 5993 \\ Total & — & 494,021 & 292,300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: KDD Cup99 dataset details.(Data from [PERSON] [19], Discrete Dynamics in Nature and Society, published by Hindawi, 2020).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**File** & **Description** & **Num. of Samples** & **Num. of Normal** \\ \hline Train+ & Full training set & 125,973 & 67,343 \\ Train20 & 20\% of the training set & 251,92 & 13,499 \\ Test+ & Full testing set & 22,544 & 9711 \\ Test\(-\) & A subset of training set & 11,850 & 2152 \\ \hline \hline \end{tabular}
\end{table}
Table 3: NSL-KDD training and testing dataset files [102].  
from the network traffic, with the final column being the multiclass category. Moreover, in comparison to publicly accessible datasets from 1998 to 2016, this dataset meets eleven performance assessment criteria.
## 3 Results and Discussion  
### Reference Data  
Figures 1 and 2 illustrate some examples of XRD spectra for binary samples of MA-CaS and CaS-Al2O3, prepared for phase identification and measured with an XRD instrument. Figure 1 represents XRD spectra for selected samples for CaS-MA, where (_i_), (_ii_), and (_iii_) correspond to the 20%, 60%, and 80% phase fractions of CaS in the sample. Similarly, Figure 2 also shows Al2O3-CaS, where (_a_), (_b_), and (_c_) correspond to the 20%, 60%, and 80% phase fractions of CaS in the sample. To further examine and verify the samples' component, XRF measurements were made for elemental compositional analysis.  
These two analytical techniques were considered in this study as complementary methods, where XRD provided information concerning individual phase weight percentage in the sample, and XRF for estimating the elemental composition of the prepared samples [28]. Tables 1 and 2 illustrate some examples of binary samples (MA-CaS and CaS-Al2O3) used to establish a relationship between the initial sample composition and composition by XRD and XRF analysis. The XRD and XRF data were obtained from the samples where the phases estimated based on elemental analysis from XRF were compared with the phase weight percentages for the samples' XRD composition. Table 1 shows the values for the initial sample composition, XRD, and XRF analysis for MA-CaS, and Table 2 represents a binary sample for Al2O3-CaS, which served as a comparison for initial sample composition and XRD and XRF results.  
Figure 1: XRD spectra for the binary sample of MgO-Al\({}_{2}\)O\({}_{3}\) (MA)–CaS: (_i_) 20% of CaS, (_ii_) 60% of CaS, and (_iii_) 80% of the CaS phase component.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Initial Phase**} & \multicolumn{4}{c}{**XRD Phase**} & \multicolumn{4}{c}{**XRF Calculated Phase Composition (wt\%) and Elemental Data**} \\ \multicolumn{2}{c}{**Composition (wt\%)**} & \multicolumn{4}{c}{**Composition (wt\%)**} & \multicolumn{4}{c}{**XRF Calculated Phase Composition (wt\%) and Elemental Data**} \\ \hline MgO-Al\({}_{2}\)O\({}_{3}\) & CaS & MgO-Al\({}_{2}\)O\({}_{3}\) & CaS & MgO-Al\({}_{2}\)O\({}_{3}\) & CaS & Ca & S & Al & Mg \\
90 & 10 & 91 & 9 & 87 & 13 & 7.27 & 7.03 & 33.27 & 12.68 \\
80 & 20 & 84 & 16 & 76 & 24 & 13.29 & 12.63 & 27.77 & 11.22 \\
30 & 70 & 34 & 66 & 31 & 69 & 43.78 & 35.02 & 7.97 & 3.70 \\
20 & 80 & 25 & 75 & 21 & 79 & 38.48 & 30.86 & 11.63 & 5.17 \\
10 & 90 & 12 & 88 & 7 & 93 & 51.94 & 41.55 & 2.31 & 1.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial sample composition, XRD and XRF analyses for binary calcium sulphide-aluminate spinel phases of MgO-Al\({}_{2}\)O3 (MA)–CaS.  
Figure 2: XRD spectra for the binary sample of CaS-Al\({}_{2}\)O\({}_{3}\): (_i_) 20% of CaS, (_ii_) 60% of CaS, and (_iii_) 80% of the CaS phase component.
# Preprocessing for Unintended Conducted Emissions Classification with ResNet
## 2 Materials and Methods
### UCE Preprocessing Techniques
#### 2.3.5 FFT Window Type  
Nonuniform windows are used to compensate for the facts that \(N\) is not infinite and the window is not an integral number of cycles of each of the frequency components in a given segment of data. The question of which window type to apply can be determined a priori if both spectral characteristics of the noise and features used for classification are known. However, this is always difficult or impossible to know in NILM-obtained UCE data collections. In many applications, a Hanning window or other similar window can be used to strike a balance in the spectral resolution and spectral leakage trade space. The authors have introduced a method of determining important features of the signal and noise by sweeping the window type and looking at the classification success. Additional background information to help in understanding the interpretation of the results of this work can be found in [17].  
In this application, the authors control the noise generated and can partially anticipate the effect that it might have on the classification. Therefore, because the window type is swept across a continuum of characteristics and noise levels and can monitor the classification accuracy for each trial, the authors can better hypothesize what features of the data set (of a subset of features) are most important. In addition, this allows the authors to hypothesize concerning the effectiveness of the system, given the addition of other types of noise to the data set being studied.  
The five window types used in this comparison are rectangular/uniform and Kaiser \(\beta=4,8,12,16\). The FFT of each of the window functions is given in Figure 2.  
Because multiplication in the time domain is equivalent to convolution in the frequency domain, the spectral domain plots of the windows provide a view of what is happening to the signal in the frequency domain as it is windowed. Windows with more spectrally narrow main lobes will have better spectral resolution and those with the lowest sidebands will have the best spectral leakage suppression. These key characteristics of the five window types are described in Table 2.  
As the main lobe of the window spectrum is widened, the sidelobe attenuation increases and leakage factor decreases. As the mainlobe width is narrowed, the window achieves a higher level of spectral resolution. This is the main basic trade-off in window selection. The goal in the authors' selection of these window types was to relatively evenly space the windows in the trade space and observe the differential in classification performance, which would make it easier to draw inferences regarding features used by the ResNet.
# Improving Singing Voice Separation Using Curriculum Learning on Recurrent Neural Networks
## 1 Introduction  
Single-channel singing voice separation aims to separate instrument sounds and vocal sounds from a given music data recorded by a single microphone. This problem has been considered as a difficult separation task in comparison with multi-channel signal separation that handles data recorded by two or more microphones. In recent years, deep neural network (DNN)-based modelling approaches such as convolutional neural network (CNN) [1; 2; 3], recurrent neural network (RNN) [4; 5; 6], and U-Net [7; 8; 9; 10] have been adopted to overcome this difficulty. Although the conventional DNN-based approaches have reported improvement of separation performance, most of them have difficulties in obtaining a reliable convergence and they requires tremendous learning time. To overcome these limitations, we propose a new single-channel singing voice separation approach based on curriculum learning [11].  
Curriculum learning is a type of learning method, in which learning is started with only easy examples and then task difficulty is gradually increased. Thus, it is capable of learning a model by gradually adjusting the difficulty of training data according to learning stages. Several successful applications of the curriculum learning include image classification [12], object detection [13; 14],and optical flow estimation [15]. The difficulty level of the training data can be determined either by humans [14; 15] or automatically [16]. The curriculum learning used in the proposed method is implemented by adjusting the weight of the loss function in RNN according to the relative dominance of one source to the other. Giving different weights according to the dominance allows reducing learning time, as the dominant data tend to converge rapidly to a vicinity of the dominant region. In addition, it can make sophisticated learning, as higher weights given to less dominant data lead to fine-tuning of the models. This paper is organized as follows--in Section 2, the conventional DNN-based singing voice separation approaches are addressed. Section 3 explains the proposed curriculum learning-based approach. In Section 4, several experimental results are described. And Section 5 concludes this paper.
# Density and Viscosity in Biodiesel + Diesel Mixtures from Recycled Feedbacks
## 3 Results and Discussions
### Viscosity
This parameter presented negative values throughout the composition range; therefore, the repulsion forces between molecules were stronger than attraction ones, caused by the smaller molecules  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(T\) (K) & **293.15** & **303.15** & **313.15** & **323.15** & **333.15** & **343.15** \\ \hline \(x_{1}\) & \multicolumn{6}{c}{\(\eta\times 10^{3}\) (Pa-s)} \\  & \multicolumn{6}{c}{**waste cooking oil methyl ester biodiesel (1) + diesel (2)**} \\ \hline
0.0000 & 2.74 & 2.24 & 1.79 & 1.51 & 1.31 & 1.12 \\
0.1006 & 3.18 & 2.53 & 2.02 & 1.72 & 1.47 & 1.27 \\
0.2003 & 3.56 & 2.81 & 2.23 & 1.86 & 1.60 & 1.38 \\
0.2998 & 3.92 & 3.11 & 2.45 & 2.03 & 1.74 & 1.47 \\
0.4996 & 4.72 & 3.75 & 2.97 & 2.42 & 2.04 & 1.71 \\
0.7007 & 6.36 & 4.69 & 3.69 & 3.06 & 2.54 & 2.13 \\
0.7998 & 7.27 & 5.20 & 4.10 & 3.35 & 2.77 & 2.32 \\
0.8990 & 8.21 & 5.74 & 4.52 & 3.66 & 3.02 & 2.52 \\
1.0000 & 9.25 & 6.29 & 4.93 & 3.98 & 3.27 & 2.74 \\ \hline \(x_{1}\) & \multicolumn{6}{c}{**waste beef fallow butyl ester biodiesel (1) + diesel (2)**} \\ \hline
0.0000 & 2.74 & 2.24 & 1.79 & 1.51 & 1.31 & 1.12 \\
0.1001 & 3.14 & 2.54 & 2.03 & 1.70 & 1.46 & 1.26 \\
0.2003 & 3.67 & 2.91 & 2.30 & 1.93 & 1.65 & 1.41  
were inserted on the structure of the bigger molecules in the absence of interactions and the steric hindrances [57; 70; 71]. The viscosity deviation increased with respect to temperature rising and tended to zero due to a better interaction between the components because of the molecular motion generated by the solution heating [52]. The minimum value was reached at about equimolar composition in both systems, being the system that contained waste cooking oil biodiesel, the one with the lowest value (highest negative). The greater dispersion forces observed for waste cooking oil biodiesel + diesel in contrast with the waste beef tallow biodiesel + diesel system could be associated with (1) the greater number of saturated esters and (2) the lower alkyl carbon chain esters in the waste cooking oil biodiesel that increased its polarity. This behavior could be indirectly confirmed by the relation found in the binary mixtures hexadecane + fatty acid methyl ester [54], alkyl propanoate + alkane [72] and hexadecane + fatty acid ethyl ester [73]; the ester polarity decreased as its carbon chain was larger, allowing a better molecular arrangement.