esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Overview of Machine Learning Methods
### Machine Learning Classifiers
#### 5.1.3 Support Vector Machine (SVM)  
The Support Vector Machine (SVM) is advantageous in scenarios involving noise or outliers, where perfect data separation using a plane may not be feasible. Additionally, the SVM is suitable when the data can only be separated by employing a high-order polynomial. Figure 22 provides visualizations of SVM techniques for graphs with higher dimensions. The SVM utilizes a method called the kernel trick, which maps the original data into a higher-dimensional space where it becomes linearly separable. In this higher-dimensional space, SVM can effectively find a hyperplane that separates the data [11].
# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 5 Results and Performance Evaluation
### Implications and Limitations  
The results demonstrate the effectiveness of thermal imaging combined with deep learning for oil classification. However, it is important to emphasise that the health-related interpretations of thermal profiles remain hypothetical. Further validation through biochemical assays or clinical testing is necessary to confirm any physiological relevance. Future work should explore multi-modal approaches combining IR thermography with spectroscopic or chromatographic methods to deepen the biochemical correlation.
## 3 Results
### Preparation of Test Data
#### 3.1.1 Preparation of the Teaching Set for the CNN YOLO Model  
In the subsequent phase, the automated preparation of the YOLO model training dataset was initiated and based on clipped orthophoto fragments, with dimensions of \(2048\times\ 2048\) pixels (equivalent to \(102.4\times 102.4\) m). These fragments were then subjected to annotation. The clipped fragments were created with an overlap of 200 pixels (10 m). This raster geometric resolution and overlap value were selected following an analysis of the physical size of the buildings and their architectural differentiation, as well as the orthophoto resolution. This approach ensured that each building with the assumed buffer fit in its entirety on the image clipped for training. The scaling process was conducted using bilinear interpolation, which guaranteed seamless transitions between pixel values and minimized the occurrence of scaling artifacts. A graphical representation of the aforementioned assumptions is provided in Figure 3. In accordance with the aforementioned methodology, 325 images were created. For each generated image, the requisite annotation text files were prepared in accordance with the specifications set out by YOLO. The format of the YOLO text file is as follows: <class_id> <x_center> <y_center> <width> <height>. The initial element is the object class (integer), followed by the coordinates of the centroid of the object, and then the dimensions of the object bounding box. The coordinates and dimensions are expressed in values from 0 to 1, defined within the local raster coordinate system, with the top left corner assigned the value (0,0).  
Figure 2: Repository of certain roof type classes: axonometric view (**a**) and top view (**b**) with geometrical parameters involving examples from orthophoto (**c**) (source: author’s own elaboration).  
Furthermore, chromatic normalization of the images was conducted to mitigate the influence of variable lighting and atmospheric conditions (present during image acquisition) on the quality of the input data. This normalization entailed the standardization of brightness and contrast values, which was pivotal for the stability of the ML models' execution and is typically employed in image data preprocessing.
# Plume Divergence and Discharge Oscillations of an Accessible Low-Power Hall Effect Thruster
## 2 Materials and Methods  
The study presented in this manuscript was a sweep study wherein a single thruster parameter was adjusted while holding all other control variables constant and observing the effects. Notably, changes in the the thruster discharge current, high-speed behavior, and plume measurements were recorded at every operational data point. This study was performed over two vacuum chamber evacuations. Under the first evacuation, three sweep studies were performed:  
* Discharge voltage was swept from 170 V to 210 V while holding magnet current to 200 mA, anode mass flow rate, \(\dot{m}_{a}\), to 1.27 mg/s, and using a cathode flow fraction (CFF) of 20.7%
* Discharge voltage was swept from 170 V to 220 V while holding magnet current to 200 mA, \(\dot{m}_{a}=1.08\) mg/s, and using a cathode flow fraction of 20.7%
* Magnet current was swept from 200 mA to 230 mA while holding discharge voltage to 200 V, \(\dot{m}_{a}=1.27\) mg/s, and using a cathode flow fraction of 20.7%
* CFF is the ratio of the cathode mass flow rate to the anode mass flow rate, \(\dot{m}_{c}/\dot{m}_{a}\).  
Under the second evacuation, the CFF was swept from 1.5% to 30.8% while holding discharge voltage to 170 V, magnet current to 200 mA, and xenon anode flow to 1.27 mg/s. The 170 V discharge voltage on the second evacuation was selected to support a concurrent study. A complete summary of the parameterization study and the resulting power levels are shown in Table 1. Parameter limits were selected to keep the total discharge power below 300 W or to keep the thruster from extinguishing, whichever came first. The remainder of this section will describe the experimental facility, test equipment, and data acquisition system used in this study.
## 5 Experimental Validation
### Experimental Results of Related Algorithms
#### 5.2.1 Oscillation Type Data Generation  
For oscillation type data, the four comparison algorithms use SGD as the optimizer. As shown in Figure 7, compared with DCGAN, Bilstm-CNN GAN and TimeGAN, which have better effects in image generation and time-series data generation, the algorithm in this paper is more similar to the original data in terms of the change trend and jump position of the generated data in terms of the oscillation type characteristics of the negative sample of the industrial control underlying business data.
## 2 Design and Analysis of the SUWB-BPF
Now, the configuration in Figure 2(a),b is replaced with the stubs proposed in [21], as shown in Figure 2(c),d. Initially, conventional stubs, as shown in Figure 2(c), were used at the center of the UTL. Due to this arrangement, a wideband filtering response with poor sharpness and one TP (transmission pole) at 12 GHz was observed, as shown in Figure 4. The corresponding transmission matrix of the conventional stub in Figure 2(c) is given below [21]:  
\[[A]= \begin{pmatrix}1&0\\ j\frac{1}{Z_{s}}\tan\theta&1\end{pmatrix}  \tag{11}\]  
In order to achieve a higher degree of freedom in the design, the conventional stub is replaced with a new folded shunt stub with pads, as shown in Figure 2(d), and two more of the same stubs are placed symmetrically on the opposite side of the UTL at a distance equal to \(\lambda/2\), and thus, a modified topology is obtained, as shown in Figure 2, respectively. The corresponding transmission matrix of the proposed stub in Figure 2(d) is given below:  
\[[A]= \begin{pmatrix}1&0\\ -j\frac{1}{Z_{s}}\cot\theta&1\end{pmatrix}  \tag{12}\]Due to this arrangement of the stubs, the performance of the filter is greatly improved, as shown in Figure 5, in terms of its wide flat passband, excellent IL, and good sharp rejection level with two TZs at the lower and upper stopband frequency, and five TPs appear within the passband at different frequencies, respectively. The reflection zeros will appear at \((2n-1)f_{\omega}\) while the transmission zeros appear at \(2 nf_{\omega}\).
# Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data
## 3 AF Arrhythmia Detection Methods
### Application of Univariate Data Analysis on RR Time Series
Afterwards, a fractal analysis based on Fourier transform was used to generate a general trend that helps slope study of a log-log plot. This procedure permits classifying high variability periods into physiological (SR) or pathological (AF) rhythms. Results, obtained on local datasets, showed that specificity has reached 99.9% in the SR group, sensitivity has reached 99.2% in the chronic AF group; and in the paroxysmal AF group, sensitivity and specificity have reached 96.1% and 92.6%, respectively. However, short AF episodes may go undetected since at least 64 consecutive beats are needed to compute the features. Supraventricular premature beats, supraventricular tachycardia, and other kinds of arrhythmias may be confused with AF detection. [PERSON] et al. have exploited a subcutaneous insertable cardiac monitor device to detect AF [51]. The device is optimized for the subcutaneous R-wave sensing [52], and classification of all cardiac rhythms is based on the captured RR intervals. Two-minute segments' analysis via Lorenz plot of the RR interval differences was used for HRV evaluation. Tests have been done on an internal dataset containing 2982 h of valid recordings. The detection of AF episodes reached 90.6% and 98.1% sensitivities on average, for AF episodes \(\geq\)2 min and \(\geq\)4 min, respectively. However, episodes shorter than 2 min are unlikely to be covered by these approach capabilities.
## 3 Results and Discussion  
### Near-Surface Burst Experiments  
A total of 45 effective data resulted from the near-surface burst experiments, as shown in Figure 4. The coefficient of determination for regression analysis of scaled distance and peak overpressure (kPa) was 0.92, which indicates a negative relationship between the two variables. In order to determine the degree of dispersion of each blast in the experiment, the mean, standard deviation and coefficient of variance of Z = 2.15 to 3.26 m/kg\({}^{1/3}\) are shown in Table 2.  
The coefficient of variance is between 1.76-14.8%, indicating that there is no significant dispersion in the data of this experiment. To determine the regression analysis of near-surface burst experiments, the power regression model with a maximum coefficient of determination (R-squared value of 0.92) was the best-fitting regression formula that can be used as an empirical equation for near-surface bursts at the portal of tunnel. The power formula of near-surface burst pressure, P\({}_{\text{so}}\), is given as follows:  
\[\text{P}_{\text{SO}}=629.06\text{Z}^{-2.02} \tag{6}\]
## 2 Principle  
Three blocks are comprised in our OAM-DM link including LG mode generation, modulation, and multiplexing. Firstly, the orthogonal property of LG mode and OFDM modulation method are described theoretically. Figure 1 illustrates the transmitter structure of the hybrid OAM-DM-WDM-OFDM-PON system, where the OAM multiplexing is achieved by LG modes with different radial values. In fact, OAM and OFDM have been jointly used. [PERSON] et al. proposed the 2D-FFT-based OAM-OFDM framework for broadband wireless communication [21]. As shown in Figure 1, the modulated two LG modes are multiplexed at the transmitter terminal, together with its 2D and 3D mode diagram.
## 4 Case Study
### Experiment  
In this subsection, the performance of the proposed SAC-based path planning is tested using two real robot manipulators. For the moving obstacle in the experiment, it is assumed that there is one moving obstacle that moves back and forth along a horizontal line segment. It is also assumed that the obstacle moves with a random speed belonging to a small interval. Figure 14 shows the experimental setup.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Value** & **Notation** & **Unit** \\ \hline Policy network size & \(12\times 800\times 500\times 400\times 400\times 300\times 6\) & \(\theta\) & Node \\ Soft Q network size & \(18\times 800\times 500\times 400\times 400\times 300\times 1\) & \(\phi_{1,2}\) & Node \\ Soft value network size & \(12\times 800\times 500\times 400\times 400\times 300\times 1\) & \(\psi\). \(\vec{\psi}\) & Node \\ LSTM network size & \([3,5]\times\text{LSTM}(800)\times\text{LSTM}(400)\times\) & & Node \\  & \(\text{LSTM}(200)\times 100\times 50\times 9\) & & Node \\ Learning rate & 0.0001 & & \\ Replay memory size & \(10^{6}\) & \(\mathcal{D}\) & Buffer \\ Episode maximum step & 50 & \(T\) & Step \\ Soft value target copy rate & 0.005 & \(\tau\) & \\ Mini batch size & 512 & \(m\) & Batch \\ Environment noise deviation & 0.002 & \(e_{e}\) & \\ Action step size & 0.3813 & \(\alpha\) & \\ Goal boundary & 0.2 & \(\eta\) & \\ Dicount factor & 0.99 & \(\gamma\) & \\ Entropy temperature parameter & 0.2 & \(\beta\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tuning parameters for the designed SAC with HER.  
Figure 13: LSTM prediction results in simulation and experiment. (**a**) Simulation environment. (**b**) Experiment.  
In applying the proposed SAC-based path planning to the real robot, the training procedure is exactly the same as the simulation-based case study if the moving obstacles behavior can be generated artificially and the LSTM is trained using the artificially generated data. If it is not the case, the time series data for the moving obstacles can be measured independently from the real moving obstacle as explained before. Figure 15 shows the training results of the LSTM. In light of the convergence of the loss function, it is confirmed that the LSTM is successfully trained.  
In the case of training the SAC-based path planning agent, the entire procedure is exactly the same as in the simulation-based case study. Figures 11 and 12 show the success ratio and reward history, respectively. The graph shows faster learning ability and convergence than the results in Figures 7 and 8 because we learned in an obstacle environment that moves only on the \(y\)-axis according to the experiment. From the success ratio and reward, it can be seen that the SAC-based path planning agent's learning ends successfully.  
With the trained SAC-based path planning agent, the experiment for the testing in Figure 4 is carried out with arbitrarily chosen starting and ending points in the workspace.  
Figure 14: The experimental setup using two open manipulators and obstacles.  
Figure 15: Loss function of experiment LSTM training.
# Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks
## 3 Results and Discussion
### Compression and Reconstruction
In the following experiments, we compare the performance of our algorithm with other compression algorithms. Performing CS algorithm on 40,000 data points for node 7. Since the length of stream data for CS algorithm cannot be too long, we divide these points into eight segments with segment length of 5000. We average the results of all segments as the final result of CS algorithm, and set the \(CR\) at 10. The results are shown in Table 1. At the same time, we test the performance of our algorithm on different data sets. The results are shown in Table 2.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method
### Notations on 3D Line Representation and Triangulation in Plucker Coordinates  
In this paper, we use the derivation of a Plucker line representation based on two given 3D points \(\mathbf{X}_{0}\) and \(\mathbf{Y}_{0}\) described in [42]:  
\[\mathbf{L}(\mathbf{X}_{0},\mathbf{Y}_{0})=\left(\frac{\mathbf{Y}_{0}-\mathbf{ X}_{0}}{\mathbf{X}_{0}\times\mathbf{Y}_{0}}\right)=\left(\frac{\mathbf{L}_{h}}{ \mathbf{L}_{o}}\right). \tag{1}\]  
The first part \(\mathbf{L}_{h}\) is homogeneous and indicates the direction of the line. The second part \(\mathbf{L}_{o}\) is the normal of the plane defined by \(\mathbf{X}_{0}\), \(\mathbf{Y}_{0}\) and the origin, we can conclude that:  
\[\mathbf{L}_{h}^{T}\mathbf{L}_{o}=\mathbf{L}_{o}^{T}\mathbf{L}_{h}=0. \tag{2}\]  
A Plucker line is represented by two three-vectors: \(\mathbf{L}=(\mathbf{m};\mathbf{d})\), while \(\mathbf{d}\) denotes the direction and \(\mathbf{m}\) denotes the momentum, corresponding to \(\mathbf{L}_{h}\) and \(\mathbf{L}_{o}\), respectively.  
As derived in [19], the projection constraint equation of lines in Plucker coordinates is:  
\[\left(\left[\mathbf{n}_{c}^{i}\right]_{\times}\mathbf{R}_{wc}^{i}\quad-( \mathbf{n}_{c}^{i})^{\mathrm{T}}\mathbf{t}_{wc}^{i}\mathbf{R}_{wc}^{i}\right) \begin{pmatrix}\mathbf{m}\\ \mathbf{d}\end{pmatrix}=\mathbf{0}_{3\times 1}, \tag{3}\]  
where \([\cdot]_{\times}\) denotes the skew-symmetric matrix of a three-dimensional vector, \(\mathbf{n}_{c}^{i}\) denotes the normal vectors of projection plane in the \(i\)-th camera coordinate system, \(\mathbf{R}_{wc}^{i}\) and \(\mathbf{t}_{wc}^{i}\) denote the rotation and translation from the \(i\)-th camera coordinate system to the world coordinate system, respectively.
# Similarity-Based Malware Classification Using Graph Neural Networks
## 3 Proposed Problem-Solving Mechanism
### Graph Neural Networks (GNN)  
This section introduces the general core concepts on which the GNN model is based [33]. Modern GNN models are based on these core concepts and have various characteristics. Let \(G=(V,E)\) denote a graph with node feature vectors \(X_{v}\) for \(v\in V\). \(h_{r}\in\{G,V\}\) denotes a vector that represents a node or entire graph. GNNs learn a representation vector \(h_{r}\) from the graph structure, and node features \(X_{v}\). \(h_{v}^{(k)}\) is a representation vector \(v\) in the k-th layer; \(N(v)\) are neighboring nodes of \(v\), and \(u\) are neighboring nodes \(\in N(v)\).  
Figure 5 presents the learning strategy of the GNN, which incorporates the aggregate, combine, and readout functions. First, the aggregate and combine functions are used to update the representation vector of the target node by aggregating the representations of its neighbors and combining them with the vector. The updating of the node features reflects graph structures and the relationship between nodes. Similarly, as in the convolution of the CNN, values of neighboring pixels are summed to update a particular pixel. In the graph classification task, the model uses the feature of nodes in the graph to generate a vector that represents the entire graph's feature. Hence, GNNs apply a readout function that outputs a representation of the whole graph. The readout function aggregates node features from the final layer and is like the flattening and pooling of the CNN. Various GNN models differ primarily in their aggregation schemes. GNNs aggregate the features of collected nodes. GNNs also update a node feature by combining the feature that aggregates from its neighbors with its feature. Accordingly, after the aggregation of \(k\), the representation of each node captures the graph structure and features from its neighbors, and this representation can be used in downstream tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Family** & **Number of Samples** \\ \hline
1 & Heodo & 775 \\
2 & Dridex & 749 \\
3 & Quakbot & 542 \\
4 & TrickBot & 654 \\
5 & AveMariaRAT & 286 \\
6 & RemcosRAT & 330 \\
7 & MassLogger & 226 \\
8 & Gozi & 785 \\
9 & CobaltStrike & 225 \\
10 & IcedID & 675 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset distribution.  
This study uses PyTorch Geometric [34], a geometric deep learning extension library for PyTorch, to construct training models. PyG has implemented many state-of-the-art methods for quickly building GNNs. In the proposed models, first, the GraphSAGE model [35] is used to aggregate and combine the neighborhood features, and then the Set2 Set model [36] is used to readout the graph into a fixed size embedding vector.
## 1 Introduction
### Security Evaluation  
We evaluate the side-channel security ensured by the proposed method with those ensured by other methods. To measure the amount of information leakages in side-channel signals, we acquired power traces of the proposed, existing, and unprotected cases. It is found that the signal-to-noiseratios (SNRs) and peaks of correlation power analysis (CPA) are reduced by up to approximately 2697% and 318% compared with the weak implementation against side-channel attacks, respectively. Through experiments, we determined the side-channel vulnerabilities of the address bits of LUTs. By not applying PB methods to address bits, information leakages that are similar to the unprotected case occur.
## 3 Pseudolite-Based Positioning System for Legacy GNSS Receivers
### Pseudolite-Based Positioning Algorithm for Legacy GNSS Receivers  
In this section, the proposed user algorithm will be described with detailed formulas. Figure 7 illustrates the \(j-th\) pseudolite and user terminal.  
Figure 5: (**a**) A pseudolite-based navigation system with identical travel time for both signals (2-D case); (**b**) Position result as given by the GPS receiver in the user terminal (2-D case).  
Figure 6: (**a**) A pseudolite-based navigation system with different travel times for each signal (2-D case); (**b**) Position result given by a GPS receiver in the user terminal (2-D case).
## 3 Results and Discussion
It was noted that extremely low values of \(\kappa_{\rm lat}+\kappa_{\rm bp}\) were obtained in all the samples. There was little contribution of \(\kappa_{\rm bp}\) to \(\kappa\) at low temperatures due to the predominant acoustic phonon scattering, whereas the bipolar contribution (\(\kappa_{\rm bp}\)) was slightly increased with increasing temperature from 500 K. Considering the small contribution of bipolar thermal conduction to \(\kappa\), especially at low temperatures, and the high \(\sigma\) over 5000 S cm\({}^{-1}\) at 300 K, which triggered the transport of heat through the majority electron carriers (\(\kappa_{\rm ele}\)), the \(\kappa_{\rm lat}\) values (\(<\)2.5 W m\({}^{-1}\) K\({}^{-1}\)) at 300 K were much lower than those of (Hf,Zr,Ti)NiSn systems (e.g., \(\kappa_{\rm lat}\) value of  
Figure 4: Temperature dependence of (**a**) total thermal conductivity, (**b**) electronic thermal conductivity, (**c**) lattice and bipolar thermal conductivity, and (**d**) dimensionless figure of merit for SPSed bulks of Hf\({}_{0.35}\)Zr\({}_{0.35}\)Ti\({}_{0.3}\)NiSn\({}_{1-x}\)Sb\({}_{x}\) (\(x\) = 0, 0.01, 0.02, 0.03). Inset of (**b**) is the temperature-dependent Lorenz number. polycrystalline Sb-doped ZrNiSn is \(>\)6 W m\({}^{-1}\) K\({}^{-1}\) at 300 K [21; 22]). This result suggested that grain boundary phonon scattering effectively suppressed the lattice thermal conduction besides the phonon scattering by point defects (substituted Zr and Ti at the Hf-site). The \(zT\) values for SPSed bulks of Hf\({}_{0.35}\)Zr\({}_{0.35}\)Ti\({}_{0.3}\)NiSn\({}_{1-x}\)Sb\({}_{x}\) (\(x\) = 0.01, 0.02, 0.03) were increased with temperature and the maximum \(zT\) of 0.5 at 800 K was achieved in Hf\({}_{0.35}\)Zr\({}_{0.35}\)Ti\({}_{0.3}\)NiSn\({}_{0.99}\)Sb\({}_{0.01}\) (Figure 4d). The obtained \(zT\) in the present study was moderate owing to low power factor and high \(\kappa_{\rm ele}\), however, this approach of generating sub-micron grains to activate boundary phonon scattering highlighted the possibility to enhance the \(zT\) of half-Heusler-based thermoelectric materials by a combination of compositional tuning strategies for high power factors.