esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
###### Abstract  
Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure.  
acoustic emission; bearing fault diagnosis; deep neural network; Grad-CAM Article  
Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 4 Description of the Physical System
### Turbulence Model  
During the last few years, computational fluid dynamics has gained considerable recognition from many sectors (industrial, academic, among others), thanks to its ability to emulate the behavior of countless fluids that are present in real life using numeric simulations. The precision of the results in CFD is conditioned to the modeling of the case study and the choice of the ideal turbulence model to solve it.  
The selection of the turbulence model is subject to the characteristics of the flow. In the case of the cyclone separators, the internal flow is complex since it is highly turbulent and anisotropic. Flow in the cyclones can be modeled by using commercial software, such as ANSYS Fluent [37], which has several turbulence models that go from the two equation models (k-\(\varepsilon\) standard and RNG) to the seven equations Reynolds Stress Model (RSM), and the large-scale model (LES). However, it should be considered that not all of these models properly solve the flow in cyclone separators. For example, equation models (k-\(\varepsilon\) standard and RNG) that are based on isotropic eddy viscosity are not suitable for cyclonic flows. In addition, researchers evaluated the performance of these turbulence models the moment they simulate the flow inside the cyclone. Results showed that the k-\(\varepsilon\) standard and RNG models cannot predict the complex periodic and tridimensional characteristics intrinsic to the internal flow in the cyclone separators [11]. Furthermore, the literature highlights numerous papers that have obtained good results when using the RSM turbulence model [10; 14; 16; 20; 38; 39] and the LES model [40; 41; 42; 43].  
The RSM model with wall functions will be used for this study because it is able to predict the flow turbulence, as well as simulate factors such as the flow pattern, axial velocity, tangential velocity, pressure drop, and separation efficiency of the cyclone separator [19].  
The transport equation for RSM is given as:  
\[u_{k}\frac{\partial}{\partial x_{i}}\Big{(}\overline{u^{\prime}_{i}u^{\prime }_{j}}\Big{)}=D_{ij}+P_{ij}p+\theta_{ij}-\varepsilon_{ij} \tag{3}\]  
The diffusive transport term \(D_{ij}\) is given as:  
\[D_{ij}=-\frac{\partial}{\partial x_{K}}\left(\frac{D_{t}}{\sigma_{k}}\frac{ \partial\overline{u^{\prime}_{i}u^{\prime}_{j}}}{\partial x_{K}}\right) \tag{4}\]  
The stress generation term \(P_{ij}\) is given as:  
\[P_{ij}=-\rho\Big{(}\overline{u^{\prime}_{i}u^{\prime}_{k}}\frac{\partial u_{j }}{\partial x_{k}}+\overline{u^{\prime}_{j}u^{\prime}_{k}}\frac{\partial u_{i }}{\partial x_{K}}\Big{)} \tag{5}\]  
The pressure strain correlation term \(\theta_{ij}\) is given as:  
\[\theta_{ij}=P\overline{\left(\frac{\partial u^{\prime}_{i}}{\partial x_{j}}+ \frac{\partial u^{\prime}_{j}}{\partial x_{i}}\right)} \tag{6}\]The dissipation term \(\varepsilon_{ij}\) is given as:  
\[\varepsilon_{ij}=2v\frac{\overline{\partial u^{\prime}_{i}}}{\partial x_{k}} \frac{\overline{\partial u^{\prime}_{j}}}{\partial x_{k}} \tag{7}\]
# Structural Modal Time Domain Identification Method Based on the Bayesian Uncertain Quantification
## 3 Bayesian Modal Identification Based on Modal State Space Prediction Models  
### Time Domain Bayesian Theoretical Framework  
The first step in embedding the theoretical prediction model in Section 2 into the Bayesian identification model is to define the prediction error \(\boldsymbol{\epsilon}_{k}\) such that \(\mathbf{y}_{k}\big{(}\overline{\theta}\big{)}+\boldsymbol{\epsilon}_{k}\) is equal to the measurement response \(\{\boldsymbol{\hat{y}}_{k}\in R^{n}:k=1,\ldots,N_{s}\}\) of the corresponding measured DOF in the actual structure, i.e.,  
\[\boldsymbol{\hat{y}}_{k}=\mathbf{y}_{k}\big{(}\overline{\theta}\big{)}+ \boldsymbol{\epsilon}_{k} \tag{16}\]  
Prediction error describes the difference between the theoretical response and the actual measured response, and this paper considers only the uncertainties caused by measurement noise and modeling errors. Assuming equal variances and stochastic independence for the prediction errors of different measurements of DOFs, \(\boldsymbol{\epsilon}_{k}\) is modeled as the zero mean, which is uncorrelated Gaussian white noise [27]:  
\[\boldsymbol{\epsilon}_{k}\sim N\Big{(}0,\sigma_{\epsilon}^{2}\mathbf{I}_{n} \Big{)} \tag{17}\]  
where \(\sigma_{\epsilon}^{2}\) is the variance and \(\mathbf{I}_{n}\) is the identity matrix of \(n\times n\). \(\theta=\{\overline{\theta},\sigma_{\epsilon}^{2}\}\) is defined, given the probability model and modeling assumptions. Let \(\mathcal{D}\) be the set of measured response data, according to [PERSON]'s theorem, and one obtains the following:  
\[p(\theta|\mathcal{D})=\frac{p(\theta)p(\mathcal{D}|\theta)}{P(\mathcal{D})} \tag{18}\]  
where \(P(\mathcal{D})\) contains information about the measured response data and is modeled as a normalization constant. In Bayesian inference, \(p(\mathcal{D}|\theta)\) depends entirely on the measured data given parameters \(\theta\). It varies faster due to the impact of the data \(\mathcal{D}\) compared to \(p(\theta)\). Therefore, \(p(\mathcal{D}|\theta)\) is the dominant factor for the Bayesian inference. It is usually assumed that \(p(\theta)\) follows a uniform distribution [20; 24; 27], and Equation (18) can be simplified as follows:  
\[p(\theta|\mathcal{D})=\kappa_{0}p(\mathcal{D}|\theta) \tag{19}\]  
where \(\kappa_{0}\) is a constant containing \(P(\mathcal{D})\) and \(p(\theta)\).
## 4 Numerical Results and Discussion
### Analysis of Numerical Simulation Results of Pilot Tunnel Excavation
#### 4.2.1 Deformation Analysis of Surface and Surrounding Rock
Therefore, the abscissa 0-140 m indicated the excavation of the middle pilot tunnel, and 140-180 m represented the excavation time of the remaining side pilot tunnel. Y = 0 m, 10 m... 140 m indicated the distance between the surface settlement monitoring point above the central axis of the middle pilot tunnel and the tunnel portal. As the excavation of the pilot tunnel continued, the surface settlements at various locations continued to increase. When the excavation surface advanced to about 20 m in front of the monitoring point, the settlement increased sharply, indicating that the rock and soil within 20 m behind the excavation surface was greatly disturbed by the excavation. Comparing the final settlement values of various  
Figure 8: Horizontal deformation of surface and surrounding rock after pilot tunnel excavation (unit: m): (a) Y = 20 m; (b) Y = 40 m; (c) Y = 60 m; (d) Y = 80 m; and (e) Y = 100 m.  
locations, it can be found that the surface settlement value of the portal section was small, and the surface settlement value of the section away from the portal was large. Since the surface monitoring points corresponding to Y = 90 m and Y = 100 m were located near the fault fracture zone, the strength of the surrounding rock was low, so their final settlement was greater than the surrounding surface settlement.
# Single-Pixel Color Imaging Method with a Compressive Sensing Measurement Matrix
## 2 Introduction to CS Theory  
The core of CS theory is completing the compression during data sampling. In mathematics, CS theory uses a specific matrix to project the sparse or compressible signals into low-dimension space for compression. When restoring, CS theory uses the sparsity of the original signal as a priori condition to restore the original signal based on linear or nonlinear restoration model algorithm.  
Original signal \(X\) is used as \(N\) dimension, that is, \(X\in RN\), and \(Y\) refers to the measurement signal in \(M\) dimension, that is \(Y\in RM\). \(Y\) is a linear projection of \(X\), and \(M\leq N\). The relationship between \(X\) and \(Y\) can be expressed as Formula (1):  
\[Y\ =\ \Phi X, \tag{1}\]  
where \(\Phi\) is the measurement matrix of \(M\times N\). Formula (2) is an underdetermined issue, and a direct solution will result in infinitely many groups of solution.  
If signal \(X\) is sparse in a conversion \(\Psi\) domain, then,  
\[X=\Psi\alpha,Y\ =\ \Phi\Psi\alpha, \tag{2}\]  
where \(\Psi\) is the corresponding conversion domain matrix, and \(\alpha\) is the linear indication of original signal \(X\) in \(\Psi\) domain, and is sparse. The restoration of signal \(X\) can be accomplished by solving norm \(l_{0}\).  
Measurement matrix \(A\) is defined, as indicated in Formula (3).  
\[A=\Phi\Psi, \tag{3}\]  
[PERSON] and [PERSON] [24] proved that the projection measurement matrix must meet the restricted isometry property (RIP) for the precise reconstruction of sparse signal.  
Assume \(A\) is a matrix with \(m\times n\) as the dimension. If for all \(x\in\sum_{k}\),  
\[(1-\delta_{k})\|x\|_{2}^{2}\leq\|Ax\|_{2}^{2}\leq(1+\delta_{k})\|x\|_{2}^{2} \tag{4}\]  
Then, matrix \(A\) meets the RIP of \(k\) order.
## 4 Results
### Characteristics of Sea Surface Temperature Errors
#### 4.4.3 Latitudinal Distribution of SST Errors  
Figure 9 displays SST residuals (satellite-derived SST minus buoy SST) as a function of latitude. The mean and standard deviation of SST residuals for each 10-degree latitude bin are also illustrated as error bars. Overall, the MCSST residuals have a certain dependence on latitude (Figure 8a,b), but the NLSST residuals in Figure 9c,d do not have any dependency on latitude, especially during the nighttime. MCSST retrievals have a slightly negative bias in the tropical region, and the bias changes to positive toward higher latitudes in both the Southern and Northern Hemispheres. As can be inferred from Figure 9a, MCSSTs are higher than in situ SSTs in the Southern Hemisphere compared to the Northern Hemisphere. This might be associated with a high density of the matchups at mid-latitude regions in the Northern Hemisphere. It may be concluded that the results in Figure 9 did not reveal any characteristic distribution of the errors with regard to the latitude. This suggests that the derived coefficients can be applied to the estimation of both MCSST and NLSST without serious latitudinal dependence.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **B-1** & **B-2** & **B-7** & **B-30** & **1-2** & **1-7** & **1-30** & **2-7** & **2-30** & **7-30** \\ \hline
**G1—G2** & 0.032 * & 0.128 & 0.999 & 0.999 & 0.999 & 0.051 & 0.999 & 0.001 ** & 0.999 & 0.138 \\
**G1—G3** & 0.999 & 0.999 & 0.999 & 0.999 & 0.988 & 0.999 & 0.109 & 0.999 & 0.999 \\
**G1—G4** & 0.097 & 0.426 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.781 & 0.999 & 0.999 \\
**G1—G5** & 0.061 ** & 0.426 & 0.999 & 0.999 & 0.999 & 0.391 & 0.999 & 0.052 & 0.999 & 0.288 \\
**G1–G6** & 0.001 ** & 0.001 ** & 0.999 & \textless{}0.001 ** & 0.999 & 0.999 & 0.060 & 0.013 * & 0.999 & \textless{}0.001 ** \\
**G2–G3** & 0.357 & 0.387 & 0.999 & 0.933 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\
**G2–G4** & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\
**G2–G5** & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\
**G2–G6** & 0.999 & 0.961 & 0.559 & 0.017 * & 0.999 & 0.999 & 0.026 * & 0.999 & 0.387 & 0.693 \\
**G3–G4** & 0.891 & 0.999 & 0.807 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\
**G3–G5** & 0.609 & 0.999 & 0.999 & 0.541 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\
**G3–G6** & 0.009 ** & 0.002 ** & 0.078 & \textless{}0.001 ** & 0.554 & 0.999 & 0.003 ** & 0.999 & 0.256 & 0.012 * \\
**G4–G5** & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\
**G4–G6** & 0.999 & 0.319 & 0.999 & 0.013 * & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\
**G5–G6** & 0.999 & 0.319 & 0.999 & 0.034 * & 0.999 & 0.999 & 0.040 * & 0.999 & 0.782 & 0.011 * \\ \hline \end{tabular} Post hoc evaluations with Bonferroni correction. * \(p<0.05\), ** \(p<0.01\).  
\end{table} 
Table 4: Pairwise comparison of changes over time between groups. Figure 1: Box plot representation of mercury release over time, with all groups evaluated collectively.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 5 Experimental Verification of Adsorption Properties of GO
### Results and Discussions
#### 5.2.2 Effect of Adsorption Conditions on Adsorption  
Under the conditions of initial concentration \(c_{0}\) = 10 mg/L, adsorption temperature T = 30 \({}^{\circ}\)C and adsorption time t = 3 h, the effect of pH on the adsorption of Co\({}^{2+}\) by GO is shown in Figure 10. In the acidic range, the adsorption capacity also increases gradually with the increase in pH. When pH = 6, the adsorption capacity of Co\({}^{2+}\) by GO is 8.04875 mg/g, which is consistent with the Gaussian simulation results.
# MBHAN: Motif-Based Heterogeneous Graph Attention Network
## 5 Experiments
### Analysis of the Hyperparameters  
The parameter sensitivity was analyzed by using Micro-F1 metrics for MBHAN's classification performance in relation to the Author type nodes in the DBLP_four_area dataset. The analyzed parameters included the embedding dimensions of the node-level attention mechanism output, the motif subgraph-level attention vector dimensions, \(q\), and the multi-head attention parameter, \(K\).  
**Node-level attention mechanism output dimensions:** As the final node embedding of MBHAN was a concatenation of the outputs from the motif subgraphs' attention process, its size was impacted by the number of motif subgraphs that could be subdivided by the specific heterogeneous graphs. The results are shown in Figure 5a, where it can be seen  
Figure 4: Average rank results of Nemenyi test.  
that its performance initially improved as the number of embedding dimensions increased, but then began to slowly decrease. This is due to the fact that the final representation of the nodes requires a suitable dimension for encoding. Furthermore, when the output dimensions of the node-level attention process became too large and multiple motif subgraphs with various kinds of representations had to be stacked, this introduced redundancy into the node embedding. Note, however, that the dimensions of the node-level attention output did not cause significant performance fluctuations in the downstream tasks because MBHAN not only learned the attention weights at the node level and motif subgraph level, but also the differentiation between different types of heterogeneous graph node. Thus, the concatenation strategy for the final embedding of the nodes could assign useful knowledge to the representation feature space.  
**The motif subgraph-level attention vector, \(q\):** The results for how the motif subgraph-level attention mechanism was affected by the dimensionality of vector \(q\) are shown in Figure 4(b). Here, it can be seen that MBHAN's performance increased in line with the dimensions of \(q\) and reached its best performance at 128. After that, its performance started to decline because of overfitting of the training process caused by the large number of dimensions.  
**The multi-head attention parameter, \(K\):** The influence of the multi-head attention parameter, \(K\), on MBHAN's performance is shown in Figure 4(c). When K = 1, MBHAN did not adopt the multi-head attention strategy. Its performance improved slightly as the number of heads increased. This is because the multi-head attention strategy essentially involves integrating several independent attention coefficient computations. This strategy not only characterizes the nodes from multiple perspectives, but also prevents overfitting and makes the training process more stable.
# Management Platforms and Protocols for Internet of Things: A Survey
## 5 IoT Network Management Platforms
### OpenNMS  
OpenNMS (Network Management System) open-source platform [34] is used to the management and monitoring of business networks. Developed under the FCAPS (Fault, Configuration, Accounting, Performance, Security) network management model, it is distributed under the GPL license.  
OpenNMS is written in Java, in addition to using database PostgreSQL or RRDTool, specifically JRobin (Java port for RRDTool), and supports Red Hat, Debian, Fedora, Mandriva, SuSE, Solaris, Mac OS X and Microsoft Windows.  
The architecture presented in Figure 13 has the features to determine the availability and latency of services, storage and collecting of data, event management (such as SNMP traps), alarms and notifications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ad-Hoc Commands** & **Description** \\ \hline getAvailableDrivers & Returns the list of drivers available on the repository. \\ \hline getConfiguration & This operation is used to get the list of the current \\ \hline \multirow{3}{*}{setConfiguration} & values in the parameters that can be configured in the component. \\  & Updates the component configuration and setting the \\ \cline{1-1}  & values passed as a parameter. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration Management Ad-Hoc commands.  
It uses two flows for data collection in Round Robin Database (RRD). The first is through so-called monitors that connect to a network resource and perform a test to verify if it responds correctly. If this does not happen an event is generated. The second flow is through the use of so-called collectors, which can be collected by SNMP (native protocol), NETCONF, Java Management Extensions (JMX), and HTTP.  
The generated events are of two types; those generated internally by OpenNMS and those generated externally by SNMP traps, which are characterized according to their description and gravity [59].  
_Pros: It supports many Operation Systems and open source._  
_Cons: No basic security and support SNMP native._
## 1 Introduction  
To enhance the reliability and maintenance efficiency of three-phase induction motors, which are widely used in modern industrial environments, various fault diagnosis techniques have been actively researched. These techniques can be broadly categorized into model-based approaches grounded in signal processing and data-driven approaches utilizing artificial intelligence (AI).  
Among model-based methods, representative techniques include fast Fourier transform (FFT) for frequency domain analysis, D-Q transformation for converting time-series data into a fixed reference frame, and [PERSON]'s vector analysis [1, 2, 3]. In particular, the D-Q transformation using [PERSON]'s vector allows three-phase current data to be represented on two axes (Id and Iq). Among these, the Id-axis current reflects the motor's magnetic flux component and offers advantages such as simplified analysis, elimination of phase imbalance, and reduced sensitivity to noise [2, 3]. Consequently, many studies have suggestedthat using only the Id-axis signal obtained through D-Q transformation enables a simple yet reliable fault diagnosis, in contrast to directly analyzing the original three-phase signals.  
Recently, data-driven fault diagnosis methods using convolutional neural networks (CNNs) have gained considerable attention. As CNNs demonstrate excellent performance when processing visually structured data, various studies have transformed three-phase current signals into image formats suitable for CNN input. Typical approaches involve direct RGB mapping of the three-phase currents or applying transformations such as the discrete wavelet transform (DWT) and short-time Fourier transform (STFT) to create RGB image representations, which are then used for fault classification via CNN models [4; 5; 6].  
These RGB-based methods have been reported to provide a significantly better diagnostic performance than grayscale or 1D time-series inputs [7; 8]. However, most prior studies utilized all three-phase current signals, which inherently increases the computational complexity due to inter-phase variations and noise.  
To address this limitation, the present study draws inspiration from the model-based diagnosis literature, which highlights the benefits of using the Id current component. The aim is to experimentally verify whether a comparable diagnostic performance can be achieved in CNN-based fault detection using only the Id current instead of all three-phase signals. Specifically, this study substitutes the conventional CNN input image formats, such as three-phase RGB mapping, DWT, and STFT, with corresponding formats constructed solely from the Id current and performs a comparative evaluation.
## 2 Background and Related Research
### Deep Learning Model
#### 2.1.2 1D Convolutional Neural Networks  
Early 2D CNNs have been widely used in image processing, but they have only recently been used in text classification tasks and have outperformed sequence-based approaches [12]. The CNN constructs a feature map through a chain of convolutions and pooling using a convolutional layer, and a subsampling layer (or maximum pooling layer). With the 1D CNN, the convolution layer uses a 1D cross-correlation operation that involves a sliding convolution window with variable-size kernels over the input text starting from left to right [13]. It uses a max-overtime pooling layer that consists of a 1D global maximum pooling layer, which reduces the number of features needed to encode the text. Figure 2 shows the basic architecture of the 1D CNN [14].
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 3 Blockchain vs. Federated Learning
### Reasons Why Blockchain Enables Federated Learning
#### 3.3.1 Information Sharing  
In blockchain-based federated learning, the consensus and incentive mechanism of the blockchain can use smart contracts and consensus algorithm technology to issue reward resources according to signed smart contracts after the federated learning model training is completed, and write the reward resources into the blockchain, effectively promoting the sharing and interaction of information in FL. For example, [PERSON] et al. [52] proposed the use of smart contracts to realize the management of the entire federated reinforcement learning and realize the sharing and collaborative training of intelligent driving models among operators.  
More importantly, P2P uses network edge resources such as storage and computing power to make the blockchain distributed, and the blockchain can further provide a shared storage system for federated learning. Therefore, BFL can store participant information in a distributed manner and realize distributed management of FL. For example, [PERSON] et al. [24] proposed an asynchronous federated learning scheme using blockchain technology, and further improved the efficiency of federated learning information transfer by using DRL to select optimized participating nodes. [PERSON] and [PERSON] [53] discussed the distributed storage of medical information obtained from multiple medical service providers by relying on blockchain technology in a peer-to-peer overlay network.