esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The measurements of the ablation results obtained for each of the cited conditions are reported in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**EXP**} & **Max.** & **Min.** & **Avg.** & **Max.** & **Min.** & **Avg.** & **Max.** & **Min.** & **Avg.** & **Avg.** & **Vol.** \\  & **Width** & **Width** & **Width** & **Height** & **Height** & **Height** & **Length** & **Length** & **Length** & **Length** & **Rad.** & **Vol.** \\ \hline
**Conditions** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(cm)** & **(mL)** \\ \hline \(1\) & 22 & 14 & 18 & 41 & 38 & 39 & 17 & 14 & 16 & 1.22 & 5.82 \\ \hline  & 13 & 10 & 11 & 46 & 41 & 44 & 22 & 10 & 16 & 1.19 & 4.18 \\ \hline Ave. & 17.3 & 12.0 & 14.7 & 43.7 & 39.4 & 41.6 & 19.6 & 12.2 & 15.9 & 1.2 & 5.0 \\ \hline Std. Dev. & 26.0\% & 15.2\% & 21.6\% & 5.8\% & 4.6\% & 5.2\% & 11.3\% & 17.8\% & 0.2\% & 1.3\% & 16.4\% \\ \hline Conditions & & & & & & & & & & & \\ \hline
2 & 20 & 15 & 17 & 36 & 24 & 30 & 19 & 13 & 16 & 1.06 & 4.37 \\ \hline  & 22 & 13 & 17 & 40 & 23 & 32 & 20 & 10 & 15 & 1.07 & 4.28 \\ \hline Ave. & 20.8 & 13.8 & 17.3 & 38.4 & 23.5 & 31.0 & 19.3 & 11.6 & 15.5 & 1.1 & 4.3 \\ \hline Std. Dev. & 3.8\% & 6.7\% & 0.4\% & 5.4\% & 1.5\% & 2.8\% & 2.6\% & 13.2\% & 3.3\% & 0.4\% & 0.9\% \\ \hline Conditions & & & & & & & & & & & \\ \hline
3 & 28 & 24 & 26 & 37 & 35 & 36 & 25 & 19 & 22 & 1.40 & 10.75 \\ \hline  & 20 & 14 & 17 & 39 & 24 & 31 & 22 & 19 & 20 & 1.15 & 5.78 \\ \hline  & 21 & 12 & 17 & 31 & 19 & 25 & 18 & 10 & 14 & 0.93 & 3.06 \\ \hline Ave. & 23.2 & 16.7 & 19.9 & 35.5 & 26.1 & 30.8 & 21.6 & 16.1 & 18.8 & 1.2 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation results—measurement of ablation zones along the x, y, and z axes for each set of conditions.  
Figure 2: Measurement of the ablation zone following MW delivery. Dimensions were measured along the x axis for the width, z axis for the height, and y axis for the length. Fibre optic probes and thermocouples were used to monitor the temperature during the ablation and were placed at 1 cm from the ablation probe.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 3 JPS Path Optimization Based on Angle-Propagation Theta* Algorithm
### Angle-Propagation Theta* Algorithm
#### 3.2.1 Definition of Angle-Propagation Theta* Algorithm Viewable Angle  
In the AP Theta* algorithm, any node \(x\) has two variables that represent the viewable angle, namely the upper angle region \(ub(x)\) and the lower angle region \(lb(x)\). The viewable angle of the node is denoted as \([lb(x),ub(x)]\). The meaning of \([lb(x),ub(x)]\) is the visible range angle of \(x\) from \(lb(x)\) to \(ub(x)\) from the parent node \(parent(x)\) of the current node \(x\), and the ray angle of \(parent(x)\) to reach \(x\) is 0.  
For the neighbor node \(x^{\prime}\) of the current node \(x\), if the angle between the ray arriving at \(x^{\prime}\) from \(parent(x)\) and the ray arriving at \(x\) from \(parent(x)\) is in the range \([lb(x),ub(x)]\), it means that \(x^{\prime}\) must be LOS reachable with \(parent(x)\), as shown in Figure 7.
# Development of Ultrasonic Pulsed Plasma Jet Source for Remote Surface Treatment
## 3 Results and Discussion
### Extension of Ultrasonic Pulsed Plasma Jet  
Figure 4 shows a schlieren image of a supersonic pulsed plasma jet for each gas species, and Table 1 shows the maximum values of the Mach number and gas velocity. In the schlieren image, the left side is the plasma jet side, and the right side is the atmospheric side. In the nitrogen, carbon dioxide, air, and argon plasmas, shock waves were generated 2 ms after the solenoid valve was opened, and diamond-shaped shock waves were observed after 3 ms. The bright and dark areas in the shock diamonds caused by the compressional and expansive waves suggest that the flow of the pulsed plasma jet is an underexpanded jet. The shock diamonds extended toward the atmosphere with a narrowing of the shock wave angle every 1 ms, reaching their maximum extension distance after 6 ms in argon plasma and after 7 ms in nitrogen, carbon dioxide, and air plasma. The maximum Mach number was calculated to be Mach 1.6 for argon plasma and Mach 1.7, for nitrogen, carbon dioxide, and air plasmas, with gas velocities of 501, 585, 452, and 575 m/s, respectively. In contrast, the helium plasma was confirmed to have a Mach number of 1 (1014 m/s) or higher by observing a shock wave near the plasma jet; however, it was difficult to calculate the detailed gas flow velocity because the schlieren image was unclear compared with the plasma of other gas species.  
Figure 4: _Cont._  
On the basis of Mach number measurements, the gas flow velocities of pulsed plasma jets of each gas species were compared with those of a conventional plasma system without pulsed operation, that is, a conventional plasma system. The gas flow velocity of the conventional system was 64 m/s, calculated from the gas flow rate (3 L/min) and plasma jet outlet  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plasma Gas** & \multirow{2}{*}{**Mach Number**} & **Sound Velocity** & **Gas Velocity** \\
**Type** & & **(25 °C)** & **[m/s]** \\ \hline Ar & 1.6 & 322 & 501 \\ N\({}_{2}\) & 1.7 & 352 & 585 \\ CO\({}_{2}\) & 1.7 & 271 & 452 \\ Air & 1.7 & 347 & 575 \\ He & \textgreater{}1 & 1014 & \textgreater{}1014 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mach number of plasma and gas flow velocities for each gas type.  
Figure 4: Schlieren image of pulsed plasma jet: (**a**) helium plasma; (**b**) argon plasma; (**c**) nitrogen plasma; (**d**) air plasma; (**e**) carbon dioxide plasma.  
area (0.79 mm\({}^{2}\)). Therefore, the pulsed plasma jets of each gas type evidently emit plasma with a velocity approximately 7-16 times higher than that of the conventional system.
## 2 Proposed Methodology
### New Signal Combination Model with Four Common Methods
#### 2.2.2 FBCSP [82; 83; 84] Using New Combinations of Signals
(5) Performing classification using classifiers: This step is the same as the fourth step in the previous section, i.e., Section 2.2.1. This model was applied to one public dataset (including left hand and right hand), which is described in the next section. Most of the steps in the two models are the same. The exception is that one phase of feature selection is added between CSP and the classifiers, which selects the best features by applying mutual information feature selection after collecting the extracted features of new combinations of signals. About 10 to 100 features are selected and sent to the classifiers for classification.
# Dynamics Analysis and Chaos Identification of Compound Pendulum Jaw Crusher with Joint Clearance
## 6 Conclusions  
Dynamics analysis and chaos identification of the compound pendulum jaw crusher with joint clearance is conducted in this paper. Because the existing research only focuses on the optimal design of the crusher structure and the existence of clearance is neglected, this study introduces chaos into the dynamic characteristic evaluation system and analyses the effect of clearance on the crusher under different driving speeds and clearance sizes. On the basis of the proposed theoretical investigations, the obtained major conclusions can be drawn:  
* Comparative simulations in MATLAB and Adams verify the correctness of the clearance mechanism modeling method and solution method, which provides a reference for the research on the dynamics of crushers with clearance.
* The dynamic simulation analysis verifies that the existence of clearance leads to significant oscillations in the vibration forces, which seriously affect the service life of the foundations. A larger driving speed and a smaller clearance size can be beneficial to reduce the shock and chaos phenomenon during the operation of the mechanism, reduce the influence of the clearance on the vibration force applied to the foundation, and improve the stability of the mechanism operation.  
Figure 16: Optimized dynamic characteristic images: (**a**) the axis trajectory of shaft; (**b**) the vibration force applied to the foundation; (**c**) the Poincare mapping.  
* Analysis of the combined effect of driving speed and clearance size shows that the optimal combination of 39 rad/s and 0.03 mm reduces the peak vibration force by 48.6% while meeting the crusher production capacity.  
Conceptualization, Y.C. and S.W.; methodology, S.W.; software, S.W.; validation, Y.C., S.W. and C.W.; formal analysis, S.W.; investigation, S.W.; resources, Y.C.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.W.; visualization, Y.C.; supervision, C.W.; project administration, C.W.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (12272217).  
Not applicable.  
The data are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 2 Numerical Experiment Design
### Numerical Modeling  
The numerical simulations were performed using COMSOL Multiphysics. The geometry was designed in two-dimension to reduce computational time. The whole detected region was eight meters in length and 4 m wide, including a 1.5 m layer of air thickness between the probe and soil. We also consider 2 m of the rocky layer under the thick layer of soil. The physical properties of the regular soil layer were defined as \(c=800\) m/s and \(\rho=2000\) kg/m\({}^{3}\), the rock layer was presumed to have \(c=2000\) m/s and \(\rho=3000\) kg/m\({}^{3}\). The room temperature speed of sound in the air was considered to be \(c=342\) m/s and density as \(\rho=1.225\) kg/m\({}^{3}\). The physical properties of air and rock layers were provided by the built-in materials library in COMSOL Multiphysics software. The parameters related to soil properties were used from the literature [18; 19]. The soft-soil was defined to exhibit a 5% reduction in the speed of sound and its density compared to regular soil. We also considered that the softer soil had a 5% decrease in the speed of sound and density from the soft soil. The time-dependent wave equation was simulated with a general pulse form with its pulse function expressed as \(\sin(\omega_{0}t)e^{-f_{0}(t-3T_{0})^{2}}\), where \(\omega_{0}\) was the angular frequency of the pulse at the operating frequency \(f_{0}\) (2000 Hz), and \(T_{0}=1/f_{0}\) is the time period. \(t\) was the time interval over which the event was simulated. The time window used for the estimation of the wave propagation was \(480T_{0}\).  
Each of the simulated model illustrated in Figure 2 shows the geometrical configuration used in this study, which were considered to be 2.05 m tall (in the vertical direction) and 1 m wide (in the horizontal direction). The top probe was 0.05 m-thick and 0.5 m-wide. The rest of the 2 m region was generally separated into three major zones. From top to bottom, the air ambient layer was 0.15 m thick. The major soil zone thickness was 1.6 m, which had a 0.25 m rock layer under it. For the cases of soft soil, softer soil, and air void existing in Figure 2B-D, the center of the anomalous regions located at the center of the soil layer. Figure 2B,C, show that the soft soil and softer soil regions were 0.8 m wide and 0.5 m thick. In Figure 2D, the air void was a circle with 0.05 m in diameter.  
Figure 2: Sound wave propagation through air, soil and rocky layers in four different cases. The red and blue color scale indicated the positive and negative amplitude of the sound wave pressure. Case **(A)**: healthy soil. Case **(B)**: healthy soil with 5% properties reduction for the soft soil region. Case **(C)**: healthy soil with a softer soil region with 5% lower values compared to the soft soil in **(B)**. **(D)**: healthy soil with a small internal air void. The size of the air void is smaller than the sound wavelength.
## 2 Materials and Methods
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)
Each IMF can represent a different frequency component of the original signal:  
* IMF 1 to IMF 4: These may be capturing high-frequency variations, which may be associated with rapid and noisy fluctuations in solar irradiance. * IMF 5 to IMF 8: These may be representing mid-frequency components, which could be related to more significant periodic changes throughout the day. * IMF 9 to IMF 14: These may be capturing low frequency variations, which correspond to longer trends and seasonal changes in solar irradiance. CEEMDAN was used in this research because it allowed the original signal (solar irradiance) to be decomposed into components that the model could quickly understand, facilitating the identification of specific patterns in the time series. This is useful for improving our forecasting model as it deals more effectively with rapid variations and long-term trends in the solar irradiance data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **Symbol** & **Value** \\ \hline trials & Number of realizations or iterations & \(N\), \(i\) & 100 \\ epsilon & Standard deviation of the added noise & \(\epsilon\) & 0.1 \\ noise\_scale & Scale of the added noise & \(\omega\) & 0.5 \\ num\_siftings & Number of siftings at each EMD stage & - & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters used in the implementation of CEEMDAN with their corresponding symbols and values. To enable real-time forecasting, CEEMDAN can be applied using a sliding window approach on recent historical data (e.g., one year) to ensure the inclusion of new information while maintaining computational efficiency, minimizing IMF variations, and preserving the method's robustness in high variability environments.
# Magnetic Nanomaterials for Magnetically-Aided Drug Delivery and Hyperthermia
## 10 Superparamagnetism vs. Ferromagnetism in Magnetic Nanoparticles  
The magnetic behavior and size dependence of magnetic nanoparticles is controlled by their domain structures [94; 95]. The critical size of single domains is governed by the shape of the particle, the strength of the crystal anisotropy, magnetic saturation, and the domain wall energy. As shown in Figure 8, superparamagnetic, single-domain and multi-domain nanoparticles can be discerned with increasing radius. The domain walls in magnetic particles have a major impact on the magnetization of the particles. Magnetic moments will scale with the volume of the nanoparticle. However, because of the presence of domain walls, this scaling is abruptly halted when multiple magnetic domains are formed in a single (nano) particle. The critical radius (r\({}_{\rm c}\)) represents the size where it is energetically favored for the magnetic nanoparticle to exist without a domain wall [96].  
\[r_{c}\sim f\left(\frac{\sigma}{M_{\rm s}^{2}}\right)\]  
where \(\sigma\) represents the magnetic moment per unit mass and \(M_{\rm s}^{2}\) is the saturation magnetization. In Figure 8, it is shown that the coercivity (=resistance of a magnetic material to changes in magnetization) is a function of nanoparticle diameter. In the multi-domain region, coercivity decreases with increasing particle diameter (due to subdivision into domains), while in the single-domain region it increases with the radius of the particle. The calculated critical radii for some common magnetic material are Fe\({}_{3}\)O\({}_{4}\) (4 nm), Co (8 nm), Fe (1 nm) and Ni (35 nm) [94]. The single-domain region contains subregions: \"the superparamagnetic region, where the coercivity is 0 due to the randomizing effects of the thermal energy, and the ferromagnetic region, where the coercivity increases dramatically with the particle diameter. In the absence of a magnetic field, the _net magnetic moment_ is 0 for superparamagnetic nanoparticles, while in an applied magnetic field there will be a _net alignment_ of the magnetic moments.\" [94].
## 3 Results and Discussion
### Antioxidant Properties
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameters** & \multicolumn{4}{c}{**JP**} \\ \cline{2-5}  & **JLcAp** & **JLhAp** & **JLpAp** & **JCON** \\ \hline
**Cell count (log\({}_{10}\) CFU/mL)**: & & & & \\ Before fermentation: LAB & 8.41 \(\pm\) 0.19 \({}^{\text{a,b}}\) & 8.83 \(\pm\) 0.14 \({}^{\text{a}}\) & 8.85 \(\pm\) 0.23 \({}^{\text{a}}\) & \textless{l} \({}^{\text{c}}\) \\ AAB: & 8.87 \(\pm\) 0.31 \({}^{\text{a}}\) & 8.87 \(\pm\) 0.31 \({}^{\text{a}}\) & 8.87 \(\pm\) 0.31 \({}^{\text{a}}\) & \textless{l} \({}^{\text{b}}\) \\ After fermentation: LAB & 11.10 \(\pm\) 0.11 \({}^{\text{b}}\) & 12.20 \(\pm\) 0.17 \({}^{\text{a}}\) & 11.39 \(\pm\) 0.42 \({}^{\text{b}}\) & \textless{l} \({}^{\text{c}}\) \\
**AAB** & 11.81 \(\pm\) 0.07 \({}^{\text{b}}\) & 12.07 \(\pm\) 0.15 \({}^{\text{a,b}}\) & 12.26 \(\pm\) 0.19 \({}^{\text{a}}\) & \textless{l} \({}^{\text{c}}\) \\
**Antioxidant properties**: & & & & \\ ABTS-RSA (mg AAE/100 g FW) & 33.41 \(\pm\) 0.13 \({}^{\text{c}}\) & 34.52 \(\pm\) 0.07 \({}^{\text{b}}\) & 34.61 \(\pm\) 0.04 \({}^{\text{b}}\) & 36.70 \(\pm\) 0.07 \({}^{\text{a}}\) \\ DPPH-RSA (mg AAE/100 g FW) & 34.70 \(\pm\) 0.04 \({}^{\text{d}}\) & 35.68 \(\pm\) 0.04 \({}^{\text{c}}\) & 35.96 \(\pm\) 0.04 \({}^{\text{b}}\) & 38.15 \(\pm\) 0.04 \({}^{\text{a}}\) \\
**Colorimetric**: & & & & \\ \(\Delta\)E & 4.99 \(\pm\) 2.41 \({}^{\text{b}}\) & 9.66 \(\pm\) 1.69 \({}^{\text{a}}\) & 3.07 \(\pm\) 0.62 \({}^{\text{b,c}}\) & - \\ \hline \hline \end{tabular} Data expressed as mean \(\pm\) standard deviation. Means in the same row with different superscript letters are significantly different (_p_ < 0.05). **Note:** CFU—colony forming unit; LAB—lactic acid bacteria; AAB—acetic acid bacteria; ABTS-RSA—2,2-azino-bis.3—ethylbenzothizable-6-sulfonic acid radical scavenging activities in milligram ascorbic acid equivalent per 100 g/resh weight; DPPH-RSA—2,diphenyl-1-1-picyclhydraz radical scavenging activities in milligram ascorbic acid equivalent per 100 g fresh weight; AE—the overall difference in color. JLcAp—_Lacticobacillus Lactiosei_ Lc 122—_Actobacter pasteuranus_ Ap-As.1.41 HuNiang 1.01 puree; JLhAp—_Lactobacillus heleticus_ Lh 43—_A. pasteuranus_ Ap-As.1.41 HuNiang 1.01 puree; JLpAp—_Lactplantobacillus plantarum—_A. pasteuranus_ Ap-As.1.41 HuNiang 1.01 puree; J(CON)—puree with no bacteria inoculants (unfermented).  
\end{table} 
Table 1: Viable cell counts, antioxidant properties, and overall color difference of bicultured Junzao jujube puree, JP. ing aligns with previous research on ginger slices and coffee leaves [25; 26]. Conversely, sinapic acid, ferulic acid, and gallic acid exhibited very negative relations, implying that increases in sinapic acid, ferulic acid, and gallic acid were associated with a significant reduction in antioxidant properties. This emphasizes the significant contribution of specific compounds to the radical scavenging ability in the bicultured jujube pures, highlighting that an increase in chlorogenic acid, rutin, and \(p\)-coumaric acid corresponds to heightened antioxidant properties (DPPH-RSA and ABTS-RSA).
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 6 Results and Discussion
### Analysis Phase 2--Accuracy with Pre-Processing  
In the second phase of the analysis, the classification accuracy is calculated on the feature datasets after pre-processing them using the feature selectors/extractors. The results of 'LB2', 'LB3' and 'LB4' are given in Figure 10. The results show that if PCA is applied then, for SVM, the accuracy improves for all three cases. This happens because the obtained traces are noisy, having redundant information. PCA extracts the important features/components so when SVM is applied on the reduced feature set then the accuracy is improved. The maximum accuracy attained is 87% for 'LB4'. However, Chi-square did not show any improvement in any of the LBs. It is worth noting that the accuracy of RF got worst after pre-processing with PCA, because RF works on the assumption that there is no dependence between features. PCA reduces the number of features and at the same time removes the col-linear features from the feature dataset. For MLP, accuracy increases after applying filters in case of LB4 but strange behavior is observed in case of LB3 and LB2, which requires further analysis.  
The authors in [37] have obtained 96% accuracy after applying SVM on 4-bit implementation of ECC leaked data. Our results of classification algorithms are obtained after applying SVM on 256-bit key (out of which first 31 bytes of the key are fixed random numbers). Our results show that, with PCA-SVM, accuracy of around 86% can be achieved to recover the least-significant nibble from a 256-bit key.  
Figure 9: Classification Accuracy without any pre-processing.
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 2 Data and Methodology
### Generation Mechanisms
#### 2.3.5 Spectral Clustering  
The Spectral algorithm is a modern clustering technique based on graph theory that constructs a graph by addressing each object as a node in given data set and weights the edges with pairwise similarities between them. This method is a powerful unsupervised learning algorithm based on the eigenvalue decomposition of this graph. It is very simple to implement by using standard linear algebra concepts and bioinformatics. In addition, this algorithm has a nonparametric model and the ability to capture a wider range of geometries, which makes it more flexible than other algorithms such as \(k\)-means. Despite their superiority, it is not widely used over classical algorithms such as hierarchical clustering and \(k\)-means due to their computational complexity for large-scale data sets [40].  
The spectral clustering process is conducted in three stages: constructing the (Laplacian) graph based on the affinity matrix of the data set, spectral representation, and clustering. For calculating the affinity matrix, several approaches such as \(\epsilon\)-neighborhood graph, \(\kappa\)-nearest neighbor graph, and fully connected graph are utilized [41]. A graph \"\(G\)\" commonly includes a set of \(V\) of vertices (nodes) together with a set \(E\) of edges (lines) and is shown \(G=(V,E)\). Given a group of n data objects \(X=x_{1},x_{2},\ldots,x_{n}\) in \(\mathbb{R}^{l}\) that we want to cluster them into c clusters:  
1. Compute affinity matrix \(A\in\mathbb{R}^{n\times n}\) defined using \(A=exp(-\left\|x_{i}-x_{j}\right\|^{2}/(2\sigma^{2}))\) if \(i\
eq j\) and \(A_{ij}=0\), where \(\sigma\) is scale parameter and the choosing value of \(\sigma\) is performed manually.
2. Construct degree matrix \((D)\) to be the diagonal matrix.
3. Calculate the normalized Laplacian matrix [42] defined using \(L=D^{-1/2}*A*D^{-1/2}\).
4. Find \(e_{1},e_{2},\ldots,e_{c}\), the \(k\) largest eigenvectors of Laplacian matrix \((L)\), and construct the matrix \(U=[x_{1},x_{2},\ldots,x_{c}]\in\mathbb{R}^{nc}\) by stacking the eigenvectors in columns.
5. Form the matrix \(Y\) from \(U\) by renormalizing each of \(U\)'s rows to have unit length \((Y_{ij}=U_{ij}/(\sum_{j}U_{ij}^{2})^{1/2})\).
6. Let each row of \(Y\) be points in \(\mathbb{R}^{C}\), cluster them into c clusters using \(k\)-means or any other algorithm.  
Finally, assign the original point \(x_{i}\) to cluster c if and only if the corresponding row \(i\) of the matrix \(Y\) was allocated to cluster \(c\)[43].