esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
## 4 Shock and Vibration Tests
### Base Excitation
#### 4.1.2 Dual Frequency Base Excitation  
The cantilever beam is excited by a shaker with two different frequencies--46 Hz and 50 Hz--simultaneously. Figure 10 plots the vibrational response of the cantilever beam subjected to base excitation with dual frequencies. The chirp effect can be observed in the dynamic response since the dual frequencies are close to each other. A large difference is observed in the transient period due to a small strain. The sensitivity of the FBG sensor is 5 \(\mu\varepsilon\) better that of the strain gauge with a resolution of 10 \(\mu\varepsilon\). Thus, the dynamic strain measured by the proposed FBG sensor is more accurate than the strain gauge. In the steady oscillation with large strain, the dynamic response measured by the FBG sensor is in good agreement with the strain gauge. The average difference between the FBG and strain gauge is 10.6%.
on miners' productivity and safety [2; 3; 4]. Until now, due to the extreme physical conditions, such as the presence of rock, water, and dust, as well as restricted access points, the underground mining environment can pose considerable difficulties for ensuring reliable connectivity [5]. Another important aspect is the dynamic nature of underground mines as it is a vast network of tunnels, shafts, and chambers that can stretch for miles. These tunnels and chambers are often deep in the earth, making it challenging to establish a stable connection with the surface. As a result, a reliable, resilient, and effective communication system is necessary for continuous connectivity in an underground mining environment [6].  
Underground mine communication systems can be categorized in a variety of ways, but they are primarily divided into three categories [7; 8]: (1) Communication through the Earth (C-ITE), (2) Communication through the Wire (C-TTW), and (3) Communication through the Air (C-TTA). In addition, any combination or all three of these methods can be used, which is commonly referred to as a hybrid communication system. The most common communication systems used in underground mines are two-way radio, leaky feeder, tag-based systems, and Wi-Fi, but fiber optics are also used in some large mines, each with its own set of advantages and disadvantages [9].  
Even with the advancements in recent technologies in communication systems, several research studies have reported weak signals and dead spots (areas with no communication coverage) in underground mining communications [10; 11]. A range of factors contribute to the occurrence of weak signals and dead zones in underground mining sites. These factors include the following: distance traveled (particularly through solid rock); interference from other electronic devices and systems operating in the same frequency range; physical obstructions such as rocks, walls, pillars, and machinery; environmental conditions like atmospheric changes, seismic activity, and geological formations; attenuation; reflection; scattering; and many more [12; 13]. Weak signals and dead spots in underground mining communication might have serious consequences. These can lead to delayed emergency responses, decreased productivity, and increased safety hazards. In critical situations, such as when a worker is unable to communicate with colleagues or the surface, rescue attempts may be delayed, putting lives at risk.
# Galerkin's Spectral Method in the Analysis of Antenna Wall Operation
## 4 Dissipation Radiation Field Analysis of Antenna Array of Limited Dimensions
### Solution to Distraction Problem
\begin{bmatrix}\underline{\mathrm{S}}_{11}&\underline{\mathrm{S}}_{12}\\ \underline{\mathrm{S}}_{21}&\underline{\mathrm{S}}_{22}\end{bmatrix} \ \begin{bmatrix}\underline{V}^{i-}\\ \underline{V}^{i+}\end{bmatrix}  \tag{200}\]  
finally illustrating the complete solution of the scattering problem analyzed. 1. Solving M \(\times\) N systems of algebraic equations with a window function in the form (171 \(\div\) 172) separately for each cell (m,n) of the antenna with metallization;
2. Solving one global system of equations (190) \(\div\) (191) with a window function (190), (191), including determining the current distributions on all cells (_m,n_) with metallization simultaneously;
3. Performing the iterative procedure (197) \(\div\) (198) in order to verify the accuracy of the obtained solution. It should be emphasized that the proposed procedure allows for determining the current distribution separately in each antenna cell, considering the following:  
* The influence of the remaining cells by using the method of moments separately for each cell;
* The finite dimensions of the antenna array and thus considering the edge (diffraction) effects by introducing a window function into the strict spectral representation of the scattered field. However, the applied spectral Galerkin method enabled the transformation of this representation into a matrix system of equations. This is a proposal for an accurate and effective solution to the problem of reconnaissance on a periodic antenna array of limited size. In the available literature, there is no publication about the efficiency of the methods used. According to the author's knowledge, this is an original solution that has not been published anywhere. To verify the developed procedure, numerical calculations were carried out, preceded by tests checking the effectiveness and accuracy of the individual stages. Then, calculations were made for the structure shown in Figure 3. The results obtained, compared with the measurements, are shown in Figure 4, and they encourage further work in this direction, despite the very long duration of the calculations. The designed and tested antenna is a multilayer structure. The power supply system based on Wilkinson dividers was placed on a dielectric with a dielectric constant of \(\epsilon_{\mathrm{r}}\) = 4.6 and lossiness \(\mathrm{tg}\delta\) = \(10^{-3}\). On the other side of the dielectric, we have the entire metalized surface, the surface of which acts as a screen. The radiating system was made on a dielectric with \(\epsilon_{\mathrm{r}}\) = 2.1. The thickness of both dielectrics is 1.5 mm. Such a selection of the dielectric results from the need to ensure a good fit for the surrounding space. The antenna is designed for a frequency of 3 GHz.
# Performance Investigation of Coated Carbide Tools in
## 3 Results  
### Machine Learning Models  
The integration of ML models in milling operations is revolutionizing the manufacturing industry, offering data-driven approaches to extend tool life, optimize performance,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{f_{z}=0.1}\) [mm/rev] & \(\mathbf{f_{z}=0.3}\) & \(\mathbf{f_{z}=0.5}\) & \(\mathbf{f_{z}=1}\) \\ \hline \(\mathrm{a_{e}}\) [mm] & \multicolumn{4}{c}{V = 100 [m/min]} \\ \hline \(\mathrm{a_{e}}=1\) & 31.08 [min] & 32.25 & 28.88 & 25.72 \\ \(\mathrm{a_{e}}=3\) & 32.59 & 34.56 & 30.41 & 27.02 \\ \(\mathrm{a_{e}}=13\) & 38.71 & 40.88 & 36.37 & 31.64 \\ \(\mathrm{a_{e}}=20\) & 43.49 & 45.3 & 41.64 & 35.85 \\ \hline \multicolumn{4}{c}{V = 200} \\ \hline \(\mathrm{a_{e}}=1\) & 11.44 & 12.56 & 10.18 & 9.22 \\ \(\mathrm{a_{e}}=3\) & 12.15 & 13.42 & 10.85 & 9.98 \\ \(\mathrm{a_{e}}=13\) & 17.32 & 18.78 & 15.51 & 13.92 \\ \(\mathrm{a_{e}}=20\) & 23.09 & 24.56 & 21.75 and reduce operational costs. By leveraging historical and real-time data collected from milling operations, ML algorithms were trained to identify patterns and relationships between the input parameters and the tool's wear. ML techniques are particularly useful in creating regression models, which can predict the lifespan of the employed cutting insert as a continuous output, enabling manufacturers to optimize tool usage and reduce downtime. In particular, three learning-based models were developed within the present study that are capable of estimating the performance of the machining operation considering the values of crucial manufacturing parameters like feed rate (f\({}_{\text{z}}\)), velocity (V\({}_{\text{c}}\)), and radial depth (a\({}_{\text{e}}\)). Taking into account the generated dataset presented in Table 2, various ML algorithms were employed to build regression models, namely SVR, KNN, and more advanced methods like ANN networks. These models are trained on the labeled dataset, where the target variable is the tool's life and the input features are the milling conditions.
# Contact Analysis and Friction Prediction of Non-Gaussian Random Surfaces
## 5 Conclusions  
Throughout this paper, a numerical method is prepared to produce non-Gaussian surface with the specified amplitude parameters and ACF. A deterministic contact model is used to investigate the contact behaviors of non-Gaussian surfaces with different skewness, kurtosis, roughness amplitude, and applied load. A friction coefficient can be derived by a proposed friction model. The main conclusions are drawn as follows:  
1. Rough surfaces with positive skewness, because of more sharp peaks and less valleys, present larger contact pressure as well as a smaller contact area than the negatively skewed surfaces. With the increasing of roughness amplitude, the contact pressure increases, while the contact area decreases, both for the surface with positive skewness and negative skewness. In addition, the trends of friction coefficients with increasing roughness amplitude are similar for negatively skewed surfaces and positively skewed surfaces.
2. As the kurtosis increases, the contact area decreases while the contact pressure increases. Nevertheless, when the kurtosis becomes comparatively large, the variations of contact area and pressure become slight. The influence of kurtosis on friction coefficient is very slight, especially for \(K_{u}\geq 5\).
3. The contact area increases as applied load increases. However, contact pressure first presents a clear increasing trend and then gradually declines because of more asperities coming into contact. Similar to the trend of contact pressure, friction coefficient first increases due to the increase in contact area and pressure, finally becoming constant.  
Figure 14: Effect of load on friction coefficient, plastic ratio, and contact area ratio.  
Figure 15: Variations of average contact pressure and Herzian width with applied load.  
**Author Contributions:** Conceptualization, J.R. and H.Y.; methodology, H.Y.; software, J.R.; validation, J.R. and H.Y.; formal analysis, J.R.; investigation, J.R.; resources, H.Y.; data curation, H.Y.; writing--original draft preparation, J.R.; writing--review and editing, J.R.; supervision, H.Y.; project administration, H.Y.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Foundation of China, grant number No. 51775093.  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
## 2 Design and Simulation
### High-Compression-Ratio CNT Electron Gun Based on Dual-Gridded System
In the cathode region the magnetic field is approximately 0.01 T. At the beam output port, the magnetic field is 0.95 T. Figure 4c,d shows the beam spot cross-section at the cathode surface and anode output port plane of the electron gun. The electron beam cross-section is compressed from the cold cathode emission area of 1.25\(\pi\) mm\({}^{2}\) into a beam spot approximately 0.032\(\pi\) mm\({}^{2}\) (beam spot radius = 0.18 mm). At the gun exit port, the electron beam cross-sectional area is compressed approximately 39 times, with the beam current density correspondingly enhanced by a comparable amount. Figure 4: Beam trajectories within the dual-gridded, CNT-based electron gun. (**a**) Simulated beam trajectories. (**b**) Expanded view of the dual-gridded system; there is no electron interception. (**c**) CNT cold cathode emission area. (**d**) Electron beam spot size in the anode plane. (**e**) Magnetic field intensity profile along the tube central axis. Figure 3: Fitting and experimental emission current density as a function of applied electric field. Insets from Ref. [26]: (**a**) top view scanning electron microscope (SEM) image of the as-grown honeycomb structure CNT mesh. (**b**) Cross sectional SEM image of the CNT array showing uniform, high density, and vertically aligned CNTs. Compared with existing CNT-based cathode electron guns, this kind of dual-gridded electron gun has several advantages. Firstly, the fabrication process of the CNT cathode array is simple and easy to implement. The CNTs film uniformly cover the whole plane substrate surface with high areal density. Then, the dual-gridded system is placed onto the cathode, thereby enabling the FSG to be attached to the cathode surface, and the mesh-holes of the FSG are employed to shape the CNT cathode array. Secondly, an electron beam transparency of 100% can be achieved theoretically, due to the coaxial components of the dual-gridded field emission system. Our findings suggest that the dual-gridded electron gun can successfully avoid grid interception and arcing, and hence offers extended operation life times. As shown in Figure 4a, the electron beam is firstly compressed by the focusing electrode electric field and then is accelerated by the anode prior to entering the beam tunnel. Here, the electron beam undergoes a second compression along the radial direction with the magnetic field gradually increasing with length along the tunnel. The electron beam then passes through the beam tunnel and moves into an interaction cavity.
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 4 Experiments and Results
### Ablation Studies
#### 4.5.1 The Effectiveness of Proposed Blocks  
Table 4 provides a comparison of performance across model variations. Model 1 serves as the baseline model: Swin UNETR. In Model 2, we integrate the SMA blocks, resulting in a Dice score improvement of 1.25% over Model 1. Subsequently, Model 3 incorporates our NiN blocks, yielding a Dice score of 81.86%, which marks a 0.90% improvement relative to Model 1. Finally, the LG UNETR model, which combined both SMA and NiN blocks, achieves the highest performance, with a Dice score of 82.51%. Moreover, our model maintains the lowest FLOPs among these variants, highlighting the efficiency of our proposed configurations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Params (M) (\(\downarrow\))**} & \multirow{2}{*}{**GFLOPs (\(\downarrow\))**} & \multicolumn{4}{c}{**Dice (\%) (\(\uparrow\))**} & \multirow{2}{*}{\(HD_{95}\) (mm) (\(\downarrow\))**} \\ \cline{4-5}  & & & **ET** & **TC** & **WT** & **Avg.** \\ \hline MedNeXt [47] & **61.7** & **1079.9** & 84.80 & 86.25 & 90.84 & 87.30 & 5.11 \\ UNETR [46] & 173.6 & 2095.4 & 84.91 & 86.86 & 90.79 & 87.52 & 5.59 \\ UNETR++ [48] & 280.7 & 1912.6 & 85.58 & 86.85 & 91.91 & 88.12 & 5.08 \\ Swin UNETR [21] & 97.1 & 1234.5 & 85.77 & 87.95 & 92.10 & 88.61 & 5.06 \\
3D UX-NET [53] & 82.9 & 2362.4 & 86.21 & **88.79** & 92.51 & **89.17** & 4.90 \\ LG UNETR (ours) & 83.9 & 1150.9 & **86.41** & 88.28 & **92.69** & 89.12 & **4.81** \\ \hline \hline \end{tabular}
\begin{tabular}{c} ET = Blue labels, TC = Blue labels + Red Labels, WT = Blue labels + Red Labels + Green Labels. \\ \end{tabular}
\end{table}
Table 3: Comparison with state-of-the-art methods on the BraTS2023-GLI dataset. The best results are in **bold** and the second best underlined. The up arrow indicates that the higher the better and the down arrow indicates that the lower the better.
## 2 Materials and Methods
### Haralick Texture Features  
The Haralick texture features are angular second moment, contrast, correlation, sum of squares, inverse difference moment, sum average, sum variance, sum entropy, entropy, difference variance, difference entropy, and information measures of correlation [3]. Other measures characterize the complexity and nature of tone transitions in each channel of the image. The usual practice is to use the first 13 Haralick features with a co-occurrence matrix [4], but in this work, we extracted the 13 Haralick features directly from the images because they provide spatial information from different regions within each image.  
The Haralick features used to extract the texture features were Equations (1)-(13), and Equations (14)-(21) explain the notation employed.  
Angular second moment:  
\[f_{1}=\sum_{i}\sum_{j}p(i,j)^{2}. \tag{1}\]  
Contrast:  
\[f_{2}=\sum_{n=0}^{N-1}n^{2}\sum_{i=1}^{N}\sum_{j=1}^{N}p(i,j), \tag{2}\]  
where \(\left|i-j\right|=n\).  
Correlation:  
\[f_{3}=\frac{\sum_{i}\sum_{j}(ij)p(i,j)-\mu_{x}\mu_{y}}{\sigma_{x}\sigma_{y}}, \tag{3}\]  
where \(\mu_{x}\), \(\mu_{y}\), \(\sigma_{x}\), and \(\sigma_{y}\) are the means and standard deviations of \(p_{x}\) and \(p_{y}\).  
Sum of squares:  
\[f_{4}=\sum_{i}\sum_{j}(i-\mu)^{2}p(i,j). \tag{4}\]  
Inverse difference moment:  
\[f_{5}=\sum_{i}\sum_{j}\frac{p(i,j)}{1+\left(i-j\right)^{2}}. \tag{5}\]  
Sum average:  
\[f_{6}=\sum_{i=2}^{2\text{N}}ip_{x+y}(i). \tag{6}\]  
Sum variance:  
\[f_{7}=\sum_{i=2}^{2\text{N}}(i-f_{8})^{2}p_{x+y}(i). \tag{7}\]  
Sum entropy:  
\[f_{8}=-\sum_{i=2}^{2\text{N}}p_{x+y}(i)\text{ log}\Big{(}p_{x+y}(i)\Big{)}. \tag{8}\]  
Entropy:  
\[f_{9}=-\sum_{i}\sum_{j}p(i,j)\text{ log}\left(p(i,j)\right). \tag{9}\]  
Difference variance:  
\[f_{10}=Var\Big{(}p_{x-y}\Big{)}. \tag{10}\]  
Difference entropy:  
\[f_{11}=-\sum_{i=0}^{N-1}p_{x-y}(i)\text{ log}\Big{(}p_{x-y}(i)\Big{)}. \tag{11}\]Information measures of correlation:  
\[f_{12}=\frac{f_{9}-HXY1}{max(HX,HY)}, \tag{12}\]  
\[f_{13}=1-\exp(-2(HXY2-f_{9}))^{\frac{1}{2}}, \tag{13}\]  
where \(HX\) and \(HY\) are the entropies of \(p_{x}\) and \(p_{y}\), and  
\[HXY=-\sum_{i}\sum_{j}p(i,j)\,\log(p(i,j)), \tag{14}\]  
\[HXY1=-\sum_{i}\sum_{j}p(i,j)\,\log\Bigl{(}p_{x}(i)p_{y}(j)\Bigr{)},\text{ and} \tag{15}\]  
\[HXY2=-\sum_{i}\sum_{j}p_{x}(i)p_{y}(j)\,\log\Bigl{(}p_{x}(i)p_{y}(j)\Bigr{)}. \tag{16}\]  
In Equations (1)-(13), to calculate the features, the following notation was used:  
\[p(i,j), \tag{17}\]  
which is the (\(i\),\(j\))th pixel in a gray sub-image matrix.  
\[p_{x}(i), \tag{18}\]  
which is the \(i\)th entry in the sub-image matrix, obtained by summing the rows of \(p\).  
\[p_{y}(j), \tag{19}\]  
which is the \(j\)th entry in the sub-image matrix, obtained by summing the columns of \(p\).  
\[P_{x+y}(\text{k})=\sum_{i=1}^{n}\sum_{j=1}^{n}p(i,j), \tag{20}\]  
where \(i+j=\text{k}\) and \(\text{k}=2,3,\ldots,2\text{N}\).  
\[P_{x-y}(\text{k})=\sum_{i=1}^{n}\sum_{j=1}^{n}p(i,j), \tag{21}\]  
where \(|i-\bar{j}|=\text{k}\) and \(\text{k}=0,1,\ldots,\text{N}-1\).
## 2 Security and Privacy Challenges in IoE
### Next-Generation Attack Vectors in IoE  
Today, the IoE has formed a large-scale ecosystem where devices, systems, and users are interconnected. However, this interconnectivity has also made IoE extremely vulner  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Security Model** & **Advantages** & **Disadvantages** & **Recommended Use Cases** \\ \hline Centralized Security & Easy management, Low computational cost, Simple integration & Risk of SPOF, the Central server becomes an attack target & Small-scale IoE systems, Low-threat environments \\ \hline Decentralized Security (Blockchain, Edge Security, SSI) & Distributed security, High attack resilience, Better scalability & High computational cost, Management complexity & Large-scale IoE networks, Critical infrastructures \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of centralized and decentralized security models.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Security Layer** & **Key Technologies** & **Security Features** & **Challenges and Limitations** \\ \hline Data Security and Privacy & Federated Learning [23; 25; 47], Homomorphic Encryption [46], ZKP [48; 49; 50] & Privacy-Preserving AI, Secure Data Sharing & Computational Overhead, Model Poisoning Attacks \\ \hline Network and Communication & Blockchain [8; 9; 10; 11] SMPC [46], Quantum Key Distribution (QKD) [18; 51] & Data Integrity, Tamper-Proof Transactions & Latency, Scalability Issues \\ \hline AI-Driven Security & Edge AI [25; 44], Anomaly Detection [26], Defense [29; 30] & Real-Time Threat Detection, Localized Privacy & Model Training Complexity, Limited Computational Power \\ \hline Quantum-Resilient Cryptography & Lattice-Based [52; 53], Hash-Based [54; 55], and Multivariate Polynomial Cryptography [56] & Future-Proof Security, Resistance to Quantum Attacks & Large Key Sizes, High Processing Overhead \\ \hline \hline \end{tabular}
\end{table}
Table 2: IoE security and privacy classification framework.  
able to next-generation attack vectors. In this environment, where traditional security approaches are no longer sufficient, various risk factors have emerged, as illustrated in Figure 2, including adversarial AI attacks, model poisoning, quantum computing-based threats, and edge/cloud security vulnerabilities.  
These attacks target AI models, blockchain-based security systems, cloud infrastructures, IoE networks, and edge devices, posing a significant threat to the holistic security structure of IoE. In particular, adversarial AI attacks and model poisoning directly target AI models, while quantum attacks threaten blockchain security and network communication protocols by introducing vulnerabilities that could compromise encryption and consensus mechanisms. Table 3 summarizes the prominent threat types along with their targeted components, potential consequences, and recommended solutions.
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 5 Conclusions  
This paper presented a two-step fault diagnosis method based on WPT and CNN. In the first step, the WPT is used to obtain 1-D time-frequency coefficients from vibration signals, which are converted into the 2-D gray images through a designed data-to-image conversion method. In this second step, a CNN model with three convolutional layers is designed to automatically learn representative fault features from the gray images, and the classification of these high-level abstraction representative features is achieved by using a logistic regression layer. A real-time rolling bearing fault dataset is applied to evaluate the diagnosis performance of the proposed method. From the test and comparison results, the proposed fault diagnosis method presents a more superior fault diagnosis capability than other machine learning-based methods.  
Methodology, G.L. and J.W.; algorithm design, G.L., C.D., and J.W.; validation, J.W., X.X., and G.L.; formal analysis, G.L., C.D., and Z.C.; writing--original draft preparation, G.L. and C.D.; writing--review and editing, J.W., C.D., G.L., and Z.C.; visualization, J.W., G.L., and C.D.; project administration, J.W. and Z.C.; funding acquisition, J.W., C.D., and Z.C. All authors have read and agreed to the published version of the manuscript.  
This work is funded in part by the National Key Research and Development Program of China under the Grant No. 2018 YFB1702300, in part by the National Natural Science Foundation of China under the Grant No. 51875225, and in part by the Key Research and Development Program of Guangdong Province under the Grant No. 2019B090916001.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Class Label**} \\ \cline{2-7}  & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline Proposed method & 100 & 92.78 & 99.72 & 99.44 & 98.89 & 98.06 \\ Complex Tree & 100 & 90.0 & 94.72 & 97.22 & 93.33 & 95.83 \\ KNN & 100 & 91.39 & 98.06 & 95.00 & 99.44 & 94.44 \\ SVM & 100 & 98.33 & 92.11 & 97.78 & 91.67 & 95.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of diagnosis accuracy of different models (%).  
Figure 6: The fault diagnosis confusion matrix.