esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Life Analysis of Cylindrical Roller Bearings Considering Elastohydrodynamic Lubrications
## 5 Bearing Fatigue Life Test
### Fatigue Life Test Results  
Fatigue life tests were conducted on a total of eight sets of N324 bearings under the light-load conditions specified in Table 3 and the heavy-load conditions specified in Table 4. The resultant data from these completed tests are presented in Tables 8 and 9.  
Photographs of the failed components of the specimen after the fatigue life test are presented in Figure 15.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Condition of Bearing Components** & **Judgment** & **Time/r** \\ \hline No. 1 & Spalling phenomenon occurred on the roller & Invalid & \(1.401\times 10^{8}\) \\ No. 2 & No component failure occurred & Valid & \(1.525\times 10^{8}\) \\ No. 3 & No component failure occurred & Valid & \(1.573\times 10^{8}\) \\ No. 4 & Slippage of the bearing inner ring was observed with no & Invalid & \(1.387\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Data under light-load operating conditions.  
Figure 14: The CGSM-220 medium-sized conventional life-testing machine.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Condition of Bearing Components** & **Judgment** & **Time/r** \\ \hline No. 5 & Spalling occurred on the bearing inner ring & Invalid & \(1.578\times 10^{7}\) \\ No. 6 & Slight indentation was observed on the bearing outer ring & Invalid & \(1.492\times 10^{7}\) \\ No. 7 & No component failure occurred & Valid & \(1.781\times 10^{7}\) \\ No. 8 & No component failure occurred & Valid & \(2.223\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Data under severe-load operating conditions.  
The experimental results shown in Tables 8 and 9 as well as Figure 15 reveal that a flaking phenomenon occurred on the rollers of bearing No. 1; bearing No. 4 failed due to inner ring slippage during operation; flaking occurred on the inner ring of bearing No. 5; and slight indentation microcracks appeared on the outer ring of bearing No. 6.  
From these observations, it can be concluded that after completion of the tests, most failed bearings exhibited fatigue flaking and microcracking phenomena, indicating that the primary failure mode of the bearings was fatigue failure.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA  
### Overview of the Proposed Platform Architecture  
The proposed ViMindXAI platform introduces a next-generation hybrid architecture for VBDA as illustrated in Figure 3. The name ViMindXAI reflects the platform's core design: \"Vi\" for video-centric data, \"Mind\" for semantic analysis and adaptive intelligence, \"X\" for extended scalability and innovation, and \"AI\" for the end-to-end use of artificial intelligence, from edge inference to federated learning and explainable reasoning. It seamlessly integrates edge and cloud computing to meet critical requirements in scalability, low-latency inference, and privacy-conscious processing. Unlike conventional centralized or cloud-only approaches, ViMindXAI enables intelligent workload orchestration, executing real-time analytics at the edge, utilizing the cloud for large-scale storage and deep model training, and deploying on-premise infrastructure for security-critical and context-sensitive operations.  
ViMindXAI employs a unified Hybrid AI architecture that combines Edge AI, FL, and adaptive video storage to support essential VBDA tasks such as semantic understanding, anomaly detection, and privacy-aware surveillance. The platform is organized into four layers: the Data Acquisition Layer (DAL), the Data Infrastructure Layer (DIL), the Data and Application Layer (DaAL), and the User Experience Layer (UEL). Each layer incorporates mature and widely adopted technologies that align with prevailing trends in the VBDA ecosystem. Notably, foundational components such as Apache Kafka, Spark, YOLO, LSTM, Elasticsearch, and FedAvg, which appear in more than 50% of surveyed platforms, constitute the technological core of the ViMindXAI architecture, as illustrated in Figure 2. This strategic alignment ensures compatibility with modern deployment environments, reinforcing the platform's scalability, resilience, and applicability across diverse domains, including smart cities, healthcare, and content moderation.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 4 Results
### Meissner Effect  
To effectively elucidate how superconducting washer loops interact with magnetic fields, it is first necessary to explore the Meissner effect. This work could model the behavior of superconductors in the presence of external magnetic fields.  
The x-axis and y-axis in each plot represent the spatial dimensions of the washer loops in micrometers. These axes define the plane in which the washer loop lies and, consequently, where the magnetic field distribution is being observed.  
Figure 7 is the results of the Meissner effect simulations, the analysis of magnetic field distribution within washers of varying geometrical shapes reveals a consistently uniform low magnetic field area across all three configurations, characterized by the prevalence of deep blue hues in the visualizations. This uniformity suggests an effective repulsion of magnetic fields within the interiors of washers of all shapes under the predefined conditions of film thickness and penetration depth. The alignment of the orange curves with the baseline in subsequent graphs further substantiates this effective exclusion of magnetic fields. Upon examining the symmetry of magnetic field distribution across different washer geometries, it becomes evident that circular washers exhibit a higher degree of symmetry compared to square washers, while octagonal washers demonstrate an intermediate level of symmetry. Quantitative simulations of magnetic fields at the boundaries of these washers reveal values of 0.23 mT for square washers, 0.20 mT for circular washers, and 0.18 mT for octagonal washers. This underscores the superior efficacy of octagonal washers in minimizing the boundary effects, aligning with the stringent design standards crucial for optimal SQUID functionality.  
Furthermore, considering the manufacturing complexity and uniformity aspects, the octagonal washer loop presents a more practical manufacturing solution than the circular counterpart. The octagonal geometry strikes a balance between minimizing undesirable boundary effects and maintaining symmetry, making it the optimal configuration. It offers  
Figure 6: Models of washers in three different shapes: (**a**) square washer model, (**b**) circular washer model, and (**c**) octagonal washer model, where the octagonal washer is constructed by selecting eight points at equal angular intervals on a circle.  
a simplified manufacturing approach without compromising device functionality, thus addressing the practical implications of washer shape on SQUID device design.
# Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models
## 2 Methodology
### Models Employed
#### 2.1.1 U-Net  
U-Net is a popular deep learning network used for medical image segmentation, as mentioned in [21]. U-Net is recognized for its encoder-decoder architecture, which is crucial for accurate segmentation. The U-Net structure consists of two key elements: the contraction path and the expansion path, which resembles a 'U' shape. In the contraction path, convolution operations are applied, followed by specific maximum pooling operations. On the other hand, the expansion path involves transposed convolution operations. This U-Net architecture includes two \(3\times 3\) convolutions, the activation of the Rectified Linear Unit (ReLU), and \(2\times 2\) maximum pooling with a stride of two for down sampling. In the up sampling path, they use \(2\times 2\) transposed convolution operations to reduce the feature channels. An important feature of the U-Net is the inclusion of convolution path skip connections, which allow the transfer of features from the contraction path to the expansion path. This helps recover spatial features lost during down sampling operations, as explained in [35]. In total, the U-Net network consists of 23 convolutional layers as in [21].
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 4 Analysis and Discussion
### Analysis A  
#### 4.1.1 Normalization of the Data  
Normalization is a technique often applied as a part of data preparation for machine learning. The goal of normalization is to ensure that every data point has the same scale so that each factor is rated as equally important. Hence, it is only required when features have different ranges. In this study, normalization was used to compare the contribution of each identified influence factor towards the risk of subsidence and was performed by scaling the values of each factor from 0 to 10 points.
## 2 Literature Survey
Therefore, the main objective of this article was to design the simplest, via-less, multi-section impedance transformer-less, microstrip line to rectangular waveguide transition for Ka-band operations and interconnects for high-frequency flexible organic substrate screen printing. Another objective of this work was to design and develop an E-plane probe transverse transition with high transmission (i.e., low IL) and low reflections (i.e., RL \(<-10\) dB) over a wide fractional bandwidth (FBW). This paper presents the design and development of an E-plane, broadside inserted 90\({}^{\circ}\) K-/Ka-/Q-/U-band RWG-to-MSL transition. The proposed transition eliminates the need for complex multi-section microstrip transformers for impedance matching. This makes the design simpler than the existing one, as field matching and impedance matching, i.e., low reflections, are achieved just by varying the fishtail structure probe width and depth (a novel technique for impedance matching), slot height, and back-short distance. These parameter variation effects are discussed throughout the paper. The proposed transition also shows a wide bandwidth for a return loss lower than \(-10\) dB.
## 2 Methods
### Different Interpolation Methods for GIM Products  
It is necessary to interpolate the ionospheric grid point from GIM products to obtain the observation station VTEC before performing the ionosphere-constrained modeling. In this study, a comparative analysis of four interpolation methods is performed. These methods include nearest-neighbor extrapolation [36], bilinear interpolation [18], Junkins weighted interpolation, and bicubic interpolation. The principles of the Junkins weighted and bicubic interpolation methods are described below.
## 3 Distributed Jamming System Model for LEO Constellation Downlink
### Jamming Evaluation Metrics  
#### 3.1.1 Total Jamming Power  
In the downlink, satellite beams and ground user terminals often adopt a staring mode. Ground user terminals typically have multiple antenna transceiver capabilities. The main lobe and side lobes of the receiving antenna at the user terminal are susceptible to jamming signals from other systems and can encompass or accommodate the main characteristics of all potential jamming scenarios.  
In practical scenarios, due to the large number of low-Earth-orbit satellite constellations, most jamming manifests as overlapping jamming from multiple jammers affecting the same ground user terminal, known as aggregate jamming. Therefore, the aggregate jamming power received at the ground user terminal is the sum of the jamming power caused by distributed jammers within the communication elevation angle range. This can be expressed as follows:  
\[\left\{\begin{aligned} & P_{total}=\sum\limits_{i}P_{ri}\\ & P_{ri}=P_{t}+G_{t}-L+G_{r}\\ & G_{t/r}=10 lg\left[4.5\times\left(\frac{D}{\Lambda}\right)^{2} \right]\\ & L=92.4+20 lg(f)+20 lg(d_{i})\end{aligned}\right. \tag{1}\]  
where \(P_{t}\) represents the transmit power of the jamming signal, \(G_{t}\) represents the gain of the jamming signal transmit antenna, \(L\) represents the free-space loss, and \(G_{r}\) represents the gain of the receiving antenna in the jammed system. Additionally, \(D\) is the size of the antenna, \(\lambda\) is the wavelength at the center frequency, \(f\) is the signal frequency, and \(d_{i}\) is the distance between the ground user and the \(i\)th jammer.
## 2 Integrated Modeling  
Considering a pointing scene, the pointing error is described as the difference between the actual orientation and the desired orientation of the spacecraft's LOS with respect to the target plane [31], as shown in Figure 1. It is described by two translations (\(e_{x}\), \(e_{y}\)) and one rotation (\(\psi\)) on the target plane or by three rotations (\(\theta\), \(\phi\), \(\psi\)) about the body axes of the pointing system.  
The disturbance force and torque of the RWA can result in the image motion of the sensitive optical payload. The image motion is decomposed into a sum of displacement, smear, and jitter [32; 33]. The image motion \(\mathbf{p}(t)\) can be modeled by:  
\[\mathbf{p}(t)=\left[\begin{array}{c}x(t)\\ y(t)\end{array}\right]. \tag{1}\]  
In this paper, we take an optical remote-sensing satellite as the research object. The payload of the satellite is a high-resolution optical remote sensing camera with a resolution greater than 1.1 m. The satellite is in a 535 km sun-synchronous orbit and weighs 65 kg. Three RWAs were used by the satellite to change its attitude. Figure 2 depicts a 3D model of the satellite, and Table 1 lists its parameters.  
Figure 1: Schematic diagram of pointing error.  
The integrated model of the satellite consists of three parts, ignoring the effect of AOCS: disturbance model, structural dynamic model, and optical model. The three parts are connected to each other through input and output interfaces. The output of the disturbance model is \(\mathbf{F}\), which acts on the structural dynamic model of the satellite and causes the camera's optics to shift slightly. This displacement \(\boldsymbol{\eta}\) is used as the input of the optical model to obtain the final image motion \(\boldsymbol{p}\). The block diagram of the integrated model is shown in Figure 3.
## 3 Results and Discussion
### Source Identification of Heavy Metals
#### 3.3.2 Principal Component Analysis  
In order to explore the relationship between the contents of heavy metals in road dust and the sources of pollutants in Yangxin County, principal component analysis (PCA) was carried out by SPSS. KMO (Kaiser-Meyer-Olkin) test and Bartlett sphericity test were used to test the heavy metal content (KMO = 0.708, Bartlett = 0.000). After rotating by the Kaiser normalization maximum variance method, three principal components with eigenvalues greater than 1 were extracted, and they accounted for 77.478% of the total variance (Table 4), which can better represent the information contained in the data. Cd (0.707), Cu (0.866), Pb (0.909), and Zn (0.802) were strong positive loadings in the PC1, and the variance contribution rate was 34.658% (Table 5). Cd and Cu were the most commonly used materials in cables and electrical and electronic components, which can be used to form many kinds of alloys and coatings [56; 57]. Pb and Zn were mainly affected by traffic activities. Tire wear and corrosion may cause substances containing Pb or Zn to accumulate in road dust [58; 59]. Co (0.747), Mn (0.803), and V (0.937) were higher in the PC2, and the variance contribution rate was 24.843%. Co, Mn, and V had low mean contents and were close to their background values. They were less affected by human activities. It was inferred that these elements were from the natural environment. Therefore, PC2 might be the natural source. Cr (0.883) and Ni (0.635) were higher in the PC3, and the variance contribution rate was 17.977%. Cr and Ni were the main elements added to stainless steel [60; 61]. They were also used in electroplating, metallurgy, and other industrial activities. Therefore, PC3 might be the industrial source.
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 5 Results and Discussion
### Scenario 2: DL Known, DI Unknown  
The JDA-kernel was further examined via identification of DI labels for target domain ( \(\mathcal{Y}_{t}\ \in\{0,\ 1,\ 2,\dots,10\}\)) at select locations when DL was assumed known. Figure 6 demonstrates the algorithm performance for target models \(M_{1},\ M_{2},M_{3},\ M_{5}\) at representative DL 18 (see Figure 1b). As illustrated in the figure, JDA-kernel performance increases with increasing damage intensity in each model. In addition, false positives occur at intensities with magnitudes close to expected intensities for all presented target models, indicating promising performance of the JDA-kernel method. The JDA-kernel DI estimation  
Figure 5: Confusion matrix for model \(M_{1}\) as target at DIs (**a**) 40%, (**b**) 60%, (**c**) 80%, and (**d**) 100%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DI (\%)** & **Accuracy, Test Data (\%)** & **Accuracy, Entire Data (\%)** \\ \hline
40 & 72 & 92 \\
60 & 85 & 98 \\
80 & 90 & 100 \\
100 & 94 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Coupled POD JDA–kernel Accuracy, Target: \(M_{1}\).  
performance at other DLs was investigated and similar patterns were observed. Table 4 presents JDA-kernel accuracies associated with results observed in Figure 6. As shown, the JDA-kernel algorithm could adequately identify DI at DL 18 for the studied models.  
\(M_{1}\) was then assigned as the target domain and influence of the known DL on the JDA-kernel performance investigated, with results shown in Figure 7. Four representative DLs were selected, with DLs 13 and 18 located beneath the loaded track and DLs 3 and 8 not beneath the loaded track. It is observed that, for the representative DLs shown in the figure, accuracy again improves at higher Dis. Similar results patterns were observed in other locations. Table 5 summarizes these results. Improved accuracy was observed in DL 13 and 18 when compared against DL 3 and 8. POM variations were more significant in DLs 13 and 18 due to their locations relative to applied loads.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Model** & **Accuracy, Test Data (\%)** & **Accuracy, Entire Data (\%)** \\ \hline \(M_{1}\) & 85 & 89 \\ \(M_{2}\) & 92 & 84 \\ \(M_{3}\) & 90 & 93 \\ \(M_{5}\) & 90 & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy results for coupled POD JDA–kernel, DL 18.  
Figure 6: Confusion matrix at DL 18 (see Figure 0(b)): (**a**) \(M_{1}\), (**b**) \(M_{2}\), (**c**) \(M_{3}\), and (**d**) \(M_{5}\).
## 3 Method to Obtain the Rolling Information
### Model Verification Based on 6 DoF Trajectory
(b) shows that phase of output changes from \(0^{\circ}\) to \(360^{\circ}\), which means the phase of \(B_{Z}\) gets locked stably, and (d) describes the error of phase tracking, which proves an excellent tracking performance of system designed: phase error between input and output gets steady within 3 seconds and keeps within 3\({}^{\circ}\), Average error is \(-0.0228^{\circ}\) and standard deviation (SD) is 0.7868\({}^{\circ}\), which indicates a stable phase tracking result. Figure 13 shows the tracking results of speed which verify a desirable speed tacking performance of designed system. Figure 13a is the contrast between real one and tracking system and b indicates that error of speed tracking becomes steady after 10 seconds: average error is \(-0.034\) Hz and SD is 0.067 Hz. Thus, the way how to select optimal parameters of designed 2-order FLL-assisted 3-order PLL has been clearly explained through a 6-DoF trajectory model and the results of simulation show the designed system presents an excellent tracking performance in both phase (error less than 3\({}^{\circ}\)) and speed (error less than 0.1 Hz). Figure 11: Amplitude of \(B_{Z}\) changing in flight: (**a**) Description of the amplitude of \(B_{Z}\). (**b**) Description of part of the amplitude of \(B_{Z}\) (within 0.03 second). \begin{table}
\begin{tabular}{c c c c} \hline
**Type of Projectile** & **Rotational Speed** & **Parameters of 3-Order PLL** & **Parameters of 2-Order FLL** \\ \hline \multirow{3}{*}{155 mm Howitzer} & \multirow{3}{*}{300–134 Hz} & \(B_{\rm PLL}=0.65\) & \(B_{\rm PLL}=0.7\) \\  & & \(T_{\rm roll}=40\) ms & \(\xi=0.707\) \\ \cline{1-1}  & & \(T_{\rm roll}=40\) ms & \(T_{\rm roll}=40\) ms \\ \hline \end{tabular}
\end{table} 
Table 3: Optimized parameters.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 4 Detecting Radiation Frequency  
When building a covert channel over the EM radiation of Ethernet cables, it is necessary for the attacker to be aware of the frequency of the EM radiation. The attacker would be able to receive and demodulate data only if this information-leaking frequency is known. Therefore, the first step of developing an EM-based covert channel is to identify the information-leaking EM radiation frequency of the target Ethernet cable using the most reliable approach. Different categories of Ethernet cables have different emission frequencies. The following experiment was conducted to identify the radiation frequency of a cable when two dissimilar network packet patterns are delivered through it. Those two network packet patterns can later be used to modulate a digital 1 and 0 on the physical layer of the covert channel.