esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Amino Acid Composition of Hydrolysates
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Amino Acids** & **KHN18 pro** & **MBF19 H** & **KHM19 H** & **MBMay19H** & **Soy Protein** & \begin{tabular}{c} **Whey** \\ **Protein** \\ **Hydrolysate** \\ \end{tabular} & \begin{tabular}{c} **Icelandic** \\ **Salloop** \\ **Hydrolysate** \\ \end{tabular} & \begin{tabular}{c} **Atlantic** \\ **Salmon** \\ **Hydrolysate** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Essential \\ amino acids \\ \end{tabular} & Methionine & 0.6607 & 0.1135 & 0.1799 & 0.3687 & 0.3 & 1.8 & 0.457 & 1.3 \\  & Threonine & 1.2815 & 0.2494 & 0.2855 & 0.2355 & 2.3 & 5.4 & 0.856 & 2.9 \\  & Lysine & 1.9237 & 0.2726 & 0.7896 & 0.4005 & 3.4 & 7.1 & 3.778 & 0 \\  & Valine & 1.1967 & 0.0063 & 0.4275 & 0.7775 & 2.2 & 3.5 & 1.681 & 2.5 \\  & Leucine & 1.6869 & 0.2679 & 0.2968 & 0.8958 & 5 & 8.6 & 0 & 2.4 \\  & Isoucine & 1.1182 & 0.1956 & 0.2297 & 0.6471 & 1.9 & 3.8 & 0 & 1.2 \\  & tryptophan & 0 & 0 & 0.0989 & 0 & 0 & 0.812 & 0 \\  & phenylalanine & 1.0326 & 0.2275 & 0.347 & 0.565 & 3.2 & 2.5 & 2.811 & 3.1 \\  & histidine & 1.4382 & 0.3174 & 0.5225 & 0.5066 & 1.5 & 1.4 & 0.229 & 16.4 \\  & arginine & 1.5961 & 0.1854 & 0.2495 & 0.1888 & 4.8 & 1.7 & 4.611 & 1.3 \\ \hline
\begin{tabular}{c} Non-essential \\ amino acids \\ \end{tabular} & glutamate/glutamine & 3.6814 & 0.5535 & 1.5894 & 1.9325 & 12.4 & 15.5 & 2.678 & 3.1 \\  & proline & 0.9729 & 0.1508 & 0.2229 & 0.5169 & 3.3 & 4.8 & 0.741 & 0 \\  & serine & 0.8765 & 0.2015 & 0.2646 & 0.2063 & 3.4 & 4 & 0.941 & 4.8 \\  & glycine & 2.0381 & 0.2704 & 0.5315 & 1.5489 & 2.7 & 1.5 & 1.6 & 13.7 \\  & cysteine & 0.8134 & 0.0385 & 0.643 & 0.5137 & 0.2 & 0.8 & 0 & 0 \\  & aspartate/asparagine & 2.4696 & 0.6104 & 0.7903 & 0.66 & 0 & 0 & 4.116 & 0.4 \\  & alanine & 1.5091 & 0.2281 & 0.7916 & 0.8975 & 2.8 & 4.2 & 2.03 & 24.7 \\  & tyrosine & 0 & 0.0124 & 0.0078 & 0.021 & 2.2 & 2.4 & 1.909 & 1.9 \\  & GABA & 0.0065 & 0.0243 & 0.0507 & 0 & 0 & 0 & 0 & 0 \\  & Taurine & 1.8639 & 2.3182 & 2.5995 & 2.3964 & 0 & 0 & 0 & 0 \\  & Total & 26.166 & 6.2437 & 10.7793 & 13.3776 & 0 & 0 & 0 & 0 \\  & TCAA & 11.9346 & 1.8356 & 3.328 & 4.6844 & 24.6 & 35.8 & 15.235 & 31.1 \\  & TNEAA & 12.361 & 2.0656 & 4.8411 & 6.2968 & 27 & 33.2 & 14.015 & 48.6 \\ \hline \multicolumn{2}{c}{Ratio of ENE} & 0.965044 & 0.8886522 & 0.6874707 & 0.743933427 & 0.911111 & 1.078313 & 1.08704959 & 0.639917695 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total amino acid composition (%, \(\eta\)%) of selected mussel by-product Protame® hydrolysates compared to other marine and non-marine protein hydrolysates.
## 3 Proposed DNN Based UC in NOMA
### Working Principle of DNN Based UC  
Figure 2 depicts the architecture of the proposed DNN-UC. It is a fully connected feedforward neural network, i.e., each of the nodes is linked to all the nodes in the adjacent layer and there is no connection between the nodes in the same layer, which consists of an input layer with \(I\) nodes, \(J\) hidden layers, and an output layer with \(V\) nodes. The number of  
nodes in \(q\)th hidden layer is denoted by \(N_{q}\). On the other hand, the input and output vectors of DNN-UC are represented by \(x=[x_{1}\ x_{2}\ \dots\ x_{l}]^{T}\) and \(z=[z_{1}\ z_{2}\ \dots\ z_{V}]^{T}\), respectively. More explicitly, \(x\) contains the users' channel gains and initial transmit powers, while z corresponds to the clustering information. Hence, for a K-user NOMA system, there are \(2K\) input nodes and \(K\) output nodes, i.e., \(I=2K\) and \(V=K\). Mathematically, the \(i\)-th input of DNN-UC \(x_{i}\) can be formulated as  
\[x_{i}\ =\ \left\{\begin{array}{c}\mathcal{S}_{(\frac{i\pm 1}{2}),m^{\prime} }\ \text{if}\ \text{$i$ mod $2\ \
eq 0$}\\ \ \ \ \ \ \ \ \
## 2 Results and Discussion  
CO\({}_{2}\) is a well-mixed gas in the atmosphere; its spatial and temporal variations reflect a combination of anthropogenic emissions, exchange with the biosphere, atmospheric transport, and boundary layer dynamics. On the basis of atmospheric mixing patterns alone, CO\({}_{2}\) is expected to build up during the nighttime hours due to atmospheric stratification and decrease in the morning with the break-up of the nocturnal boundary layer [7, 37]. However, the mixing ratio also reflects biogenic uptake, ecosystem respiration, and anthropogenic emissions including human respiration, each of which has different diurnal, seasonal, and spatial patterns. The biogenic signal tends to draw down daytime CO\({}_{2}\) during the summer growing season when photosynthesis is active, resulting in lower overall CO\({}_{2}\) mixing ratios. CO\({}_{2}\) mixing ratios are higher during the winter months when respiration dominates in the biosphere and heating-related emissions are highest.  
These diurnal and seasonal trends in CO\({}_{2}\) mixing ratios were evident at each of our measurement sites, which spanned a gradient of urbanization intensities. All three sites had a larger seasonal amplitude in CO\({}_{2}\) than the global background measurements from Mauna Loa, HI and the measurements from Niwot Ridge [38], Colorado, which is a site at a similar latitude to our study area and within the free troposphere. These differences highlight broad scale patterns such as the increasing strength of seasonality with distance from the equator and the influence of local to regional uptake and release processes. In all cases, seasonal maxima and minima occurred during winter and summer months, respectively (Figure 1(a)). Total anthropogenic emissions estimates [39] also have a strong seasonal signal due to residential heating demand, and were roughly 4 and 24 times higher in Boston than similar estimates in Worcester and Harvard Forest, respectively (Figure 1(b)).  
Figure 1: (**a**) Time series of daily median CO\({}_{2}\) mixing ratios and (**b**) 2002 daily total Vulcan emissions estimates for all sectors. Vulcan emissions are drawn from the nine 10 km \(\times\)10 km grid cells surrounding each tower site.
## 2 Materials and Methods  
### Model Development  
A validated cervical spine (C2-C7) FE model was developed using computed tomography (CT) images of a healthy adult subject [9]. Use of the CT scans was approved by the Institutional Review Board of the authors' affiliated institutions and informed consent was obtained from the patient and documented in the electronic medical record. Three-dimensional reconstruction of the CT scans was performed in MIMICS v15.0 (Materialise, Leuven, Belgium) and mesh construction was performed in IA-FEMSH (Iowa, IA, USA) with hexahedral elements. The meshed structures of the vertebrae and intervertebral discs was then assembled in ABAQUS software (Dassault Systemes, Simulia Inc., Providence, RI, USA). Ligaments including the anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), interspinous ligament (ISL), supraspinous ligament (SSL), capsular ligament (CL), and ligamentum flavour (LF) were incorporated onto the FE model via truss elements. The cortical bone was modeled as a shell with a 0.5 mm shell surrounding the inner cancellous bone. The intervertebral discs composed of the annulus fibrosis, which consisted of ground substances and contained embedded fibers oriented at \(\pm\)25\({}^{\circ}\)[10], and the nucleus pulposus. Facet joints were modelled using surface-surface sliding contacts and [PERSON]'s joints present in the lower intervertebral discs were modelled using GAPUNI elements [11]. The FE model material properties are summarized in Table 1[12; 13; 14].
## 2 Precise Calculation of the Aluminum Extraction Flow Rate
### Program Analysis
#### 2.3.4 Flowchart and Pseudocode  
Figure 2 shows the flowchart of the UKF, and the program pseudocode is shown in Algorithm 1.  
Figure 2: Flowchart of an unscented Kalman Filter algorithm.  
```
Initialize the parameters: numSteps = 200 stateDim = 2 observationDim = 1 Initialize the matrices: G = [1, 1/8; 0, 1] H = [1, 0] processNoiseCov = 0.6 * eye(stateDim) observationNoiseCov = 1 Set the initial state and covariance: initialState = [0; 10] initialCovariance = 1e6 * eye(stateDim) Generate observations: v = whiteNoise(1, numSteps) x(;, 1) = initialState + [1; 0] * v(1) for i from 1 to numSteps-1: if i < 50: x[; i+1] = G * x[; i] else: x[; i+1] = G * x[; i] x[2, i+1] = 15 y = x(1,.) + v Initialize the UKF parameters: alpha = 0.9 beta = 1e7 kappa = 0 lambda = alpha^2 * (stateDim + kappa) - stateDim gamma = sqrt(stateDim + lambda) Initialize the estimated state and covariance: estimatedState = zeros(stateDim, numSteps) P = initialCovariance estimatedState(;, 1) = initialState UKF loop: for t from 2 to numSteps: Compute sigma points Predict sigma points Calculate predicted mean and covariance Compute observation sigma points Calculate predicted observation mean and covariance Compute cross covariance Calculate Kalman gain Update state and covariance Compute the velocity change: deltaEstimatedState = 8 * diff(estimatedState(1,.)) deltaEstimatedState1 = zeros(1, numSteps) for i from 1 to numSteps-1: deltaEstimatedState1(i+1) = deltaEstimatedState(i)
```  
**Algorithm 1**Pseudocode for the UKF Algorithm  
To highlight the advantages of the UKF, its results are compared with those of the KF. The pseudocode for the KF is shown in Algorithm 2.  
In a comparison of the aluminum's weight, both the KF and UKF provided estimates close to the true weight. This is because weight changes are relatively smoother compared to speed changes, and the impact of noise is less significant. For the UKF, the final estimated weight was 342.24572 kg, while the KF's final estimate was 342.10987 kg. Compared to the  
Figure 4: Comparison of aluminum discharge weights.  
Figure 3: Comparison of aluminum discharge speeds.  
actual weight of 341.875 kg, the relative errors were 0.108% for the UKF and 0.0686% for the KF. The difference was minimal, and both filtering methods provided fairly accurate weight estimates for most time steps.  
Meanwhile, the computation time for the KF in MATLAB R2021b was significantly higher than that of the UKF. The UKF achieved a computation time of approximately 0.2 s, while the KF required a computation time of approximately 0.4 s. This difference likely arised from the UKF's higher initial accuracy, enabling it to quickly converge, produce reliable results and thus reduce the overall computation time.  
Overall, the UKF demonstrated superior adaptability, greater estimation accuracy, and faster computation in scenarios involving abrupt speed changes. By leveraging sigma points, the UKF effectively captured nonlinear system dynamics, making it more suitable for estimating aluminum extraction speeds in nonlinear systems than the KF.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 6 Decomposition Methods
### Dual Decomposition  
In dual decomposition, the partial Lagrangian is first obtained:  
\[\begin{split}\mathcal{L}\left(\mathbf{x}_{1},\mathbf{x}_{2}, \lambda\right)&=f_{1}\left(\mathbf{x}_{1}\right)+f_{2}\left( \mathbf{x}_{2}\right)+\lambda^{T}\left(\mathbf{g}_{1}\left(\mathbf{x}_{1} \right)+\mathbf{g}_{2}\left(\mathbf{x}_{2}\right)\right)\\ &=\left(f_{1}\left(\mathbf{x}_{1}\right)+\lambda^{T}\mathbf{g}_{1 }\left(\mathbf{x}_{1}\right)\right)+\left(f_{2}\left(\mathbf{x}_{2}\right)+ \lambda^{T}\mathbf{g}_{2}\left(\mathbf{x}_{2}\right)\right)\end{split} \tag{16}\]  
which is separable in \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\). Given the dual variable vector \(\lambda\), the _master problem_ then becomes:  
\[\underset{\lambda}{\text{\emph{max}}}\;\;\Phi\left(\lambda\right)=\Phi_{1} \left(\lambda\right)+\Phi_{2}\left(\lambda\right) \tag{17}\]  
To find \(\Phi_{1}\left(\lambda\right)\), we solve:  
\[\underset{\mathbf{x}_{1}}{\text{\emph{min}}}\;\;\left(f_{1}\left(\mathbf{x}_{ 1}\right)+\lambda^{T}\mathbf{g}_{1}\left(\mathbf{x}_{1}\right)\right)\] (18a) s.t. \[\mathbf{x}_{1}\in\mathcal{X}_{1}\] (18b) and to find \[\Phi_{2}\left(\lambda\right)\], we solve \[\underset{\mathbf{x}_{2}}{\text{\emph{min}}}\;\;\left(f_{2}\left(\mathbf{x}_{ 2}\right)+\lambda^{T}\mathbf{g}_{2}\left(\mathbf{x}_{2}\right)\right)\] (19a) s.t. \[\mathbf{x}_{2}\in\mathcal{X}_{2} \tag{19b}\]A subgradient for \(-\Phi_{1}\) at \(\lambda\) is \(\mathbf{g}_{1}\left(\tilde{\mathbf{x}}_{1}\right)\) is any feasible solution to Equation (18). To find a subgradient of **Phi**, both subproblems need to be solved to get their optimal solutions \(\mathbf{x}_{1}^{*}\) and \(\mathbf{x}_{2}^{*}\). A subgradient of \(-\textbf{Phi}\) is \(\mathbf{g}_{1}\left(\mathbf{x}_{1}^{*}\right)+\mathbf{g}_{2}\left(\mathbf{x }_{2}^{*}\right)\). The master problem updates the dual variable vector \(\lambda\) based on this subgradient, in a _projected subgradient method_[37]. The framework of the dual decomposition is as follows:  
**while**_Stopping criteria not satisfied_**do**  
solve subproblems (12) and (13) (possibly in parelle) to obtain \(\mathbf{x}_{1}^{*}\), \(\mathbf{x}_{2}^{*}\);  
Update the dual prices:  
\[\lambda\leftarrow\lambda+\alpha_{k}\left(\mathbf{g}_{1}\left(\mathbf{x}_{1}^ {*}\right)+\mathbf{g}_{2}\left(\mathbf{x}_{2}^{*}\right)\right)_{+} \tag{20}\]  
**end**  
Each iteration gives a lower bound on the optimal solution objective value. The iterates are not necessarily feasible, however, feasible solutions can be obtained using certain techniques, whose objective function value becomes an upper bound on the optimal solution.
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 3 Proposed Random Vibration--Stress Method
### Weibull Statistics Analysis  
The two-parameter Weibull distribution is used to analyze statistical fatigue behaviors [10]. It lets us perform accurate fatigue failure analysis [19]. The probability density function \(f(t)\) and cumulative distribution function \(F(t)\) are described by Equations (9) and (10), respectively [10].  
\[f(t)=\frac{\beta}{\eta}\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta-1}exp\Bigg{\{}- \bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\Bigg{\}} \tag{9}\]  
\[F(t)=1-exp\Bigg{\{}-\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\Bigg{\}} \tag{10}\]  
where \(\beta\) is the shape parameter, \(\eta\) is the scale parameter, and \(t\) is a random variable value that represents the fatigue life. The corresponding reliability function \(R(t)\)[10] is given as  
\[R(t)=exp\Bigg{\{}-\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\Bigg{\}} \tag{11}\]  
From [20], the Weibull stress \(\beta_{s}\) and \(\eta_{s}\) parameters are determined as  
\[\beta_{s}=\frac{-4\mu_{y}}{0.995*\ln\Big{(}\frac{\sigma_{1}}{\sigma_{2}}\Big{)}} \tag{12}\]  
\[\eta_{s}=exp(\mu_{x}) \tag{13}\]  
where \(\mu_{y}\) represents the mean of the \(Y\) vector, determined by the median rank approach (See Section 3[20]). Additionally, \(\mu_{x}\)[20] represents the log mean of the failure time data, which here is determined directly from the addressed maximum \(\sigma_{1}\) and minimum \(\sigma_{2}\) stresses values of Section 3.1 step 5. Thus \(\mu_{x}\) is determined as  
\[\mu_{x}=\ln(\sigma_{1}\sigma_{2})^{\frac{1}{2}} \tag{14}\]  
Here, we note that the efficiency of the Weibull parameters \(\beta_{s}\) and \(\eta_{s}\) only depends on the efficiency on which the \(\sigma_{1}\) and \(\sigma_{2}\) values are determined in Section 3.1 step 5. In this paper, they are obtained from an electrodynamic shaker acceleration response once the mechanical component installed in its application system is submitted to a random  
Figure 1: Diagram overview of the vibration stress calculation.  
vibration profile. Once the \(\beta_{s}\) and \(\eta_{s}\) values are known, the Weibull stress random analysis is performed as shown in the next section.
## 3 Calculation of the Outage Rate  
The outage rate of the substation in the case of a lightning hit to a phase, \(R_{ph}\) (outages/year), is given as [22]:  
\[R_{ph}=R_{p,ph}\cdot\left[P(I_{cr,SF})-P(I_{m,SF})\right] \tag{1}\]  
where:  
* \(R_{p,ph}\), the shielding penetration rate of the connected line within the limit distance,
* \(P(I_{cr,SF})\), the probability that corresponds to the current that causes a line insulation flashover at negative polarity,
* \(P(I_{m,SF})\), the lightning current probability that corresponds to the maximum shielding current.  
The shielding penetration rate (shielding failures/year) is calculated according to procedures presented in [18; 23; 24]. The currents \(I_{cr,SF}\) and \(I_{m,SF}\) that correspond to the above probabilities can be extracted from the lightning stroke current probability distribution, concerning the shielding failure range, according to [19; 23; 25; 26]. The probability \(P(I)\) of lightning crest current being greater than \(I\) is computed as following [23; 26]:  
\[P(I)=\frac{1}{1+\left(\frac{I}{3I}\right)^{2.6}} \tag{2}\]  
The limit distance is calculated according to the procedure presented in Annex F of [22], depending on the lowest considered overvoltage amplitude, the longest travel time between any point in the substation to be protected and the closest arrester, the lightning impulse protective level of the arrester and corona damping effects.  
When a lightning hits the overhead ground wire of the connected high voltage line, the outage rate of the substation, \(R_{gw}\) (outages/year), is given as [24]:  
\[R_{gw}=0.6\cdot R_{p,gw}\cdot P(I_{cr,BF}) \tag{3}\]  
where  
* \(R_{p,gw}\), the rate of lightning strokes to the overhead ground wires of the connected transmission line within the limit distance (strikes/year) [22], and
* \(P(I_{cr,BF})\), the probability corresponding to the current striking to the overhead ground wires resulting in the overvoltages that exceed the insulation withstand capability of the system [22; 25; 26].  
Considering the above equations, the total substation outage rate, \(R\) (outages/year) is given as:  
\[R=R_{ph}+R_{gw} \tag{4}\]
## 2 Materials and Methods
Optical density (OD) was read on a microplate reader at 550 nm, (FLUOstar Omega, BMG Labtech). Cell viability for each treatment was calculated as the ratio of the mean optical density of replicated wells relative to that of the control. Nanoparticle preparation for cell culture: 2 mg/mL stock solution of hydronium jarosite was prepared using deionized water. The NPs were sonicated in (Qsonica, LLC, Newtown, CT, USA. Misonixsonicators, XL-200 Series) for approximately 5 min. Nanoparticles were freshly prepared prior to each experiment. Microbial assay: The agar well diffusion assay was used to investigate the antibacterial activity of the hydronium jarosite NPs. Colonies of each selected bacterial strain from a fresh 24 h stock plate were inoculated (by streaking) on Muller Hinton (MH) agar plates. The plates were dried at room temperature under sterile conditions followed by boring of 9 mm diameter wells in them. Different concentrations of hydronium jarosite NPs (1000 \(\upmu\)g/mL, 500 \(\upmu\)g/mL, and 250 \(\upmu\)g/mL) and standard drug ciprofloxacin were added in the wells as positive control and distilled water as negative control. Plates were incubated at 37\({}^{\circ}\)C overnight for 24 h. After incubation, the antibacterial activities of the nanoparticles were assessed by the Zone of Inhibition (ZI) around the wells as the microbe growth inhibition. Bacterium strains were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). Micro-plate dilution assay: This assay was used to determine the minimal inhibitory concentrations (MIC) of hydronium jarosite NPs. Briefly, serial dilutions of the hydronium jarosite NPs were added to Mueller Hinton (MH) broth, followed by the addition of overnight incubated bacterial suspensions (adjusted to 0.5 McFarland standard), and incubated at 37 \({}^{\circ}\)C for 24 h. The MIC was recorded as the lowest concentration of the hydronium jarosite NPs that inhibit bacterial growth. Ciprofloxacin was used as a positive control.
## 2 Related Theory and Work
### Related Work  
Bearing fault detection and diagnosis through intelligent methods have become an important part of production engineering. Different ML- and DL-based supervised and unsupervised algorithms have been applied for fault detection and diagnosis. Deep neural structures such as auto-encoders [32], deep belief networks [33], generative adversarial networks [34], recurrent neural networks [35], reinforcement neural networks [36], and CNNs have generated highly satisfying results in this field. Moreover, the fusion method of ML- and DL-based and signal processing approaches is also practiced. Different graph modeling-based approaches are also frequently proposed and implemented. Some worthy mentions are [7; 8; 9; 20; 37; 38; 39; 40; 41].  
Regarding the CNN architectures, both 1-D CNNs and 2-D CNNs have been employed to detect and classify bearing faults. [PERSON] proposed a 1-D CNN model in [42], which provided 97.1% accuracy. An intelligent rotating machinery fault diagnosis system based on DL using a data augmentation technique is proposed in [43]. The authors used two data augmentation methods and five data augmentation techniques, and the best testing accuracy obtained on the CWRU dataset was 99.91%. Similarly, a CNN-based approach with multiple sensor fusion is proposed in [44]. The average accuracy with two sensors was 99.41%, whereas that with only one sensor was 98.35%. In ref. [45], 2-D representation of 1-D signals is used to analyze bearing faults employing a 2-D CNN and the authors also compared their model with a 1-D CNN, where they showed that the 2-D CNN was more efficient than the 1-D CNN. In ref. [46], vibration signals, which are split into segments with the same length, are used directly as input data for the deep structure of the CNN. The amplitude of each sample in the vibration signal is normalized into the range (\(-\)1, 1), which becomes the intensity of the corresponding pixel in the corresponding image. The accuracy, with 10 and 20 filters in the first and second layers, was found to be 96.75%. In ref. [47], a deep distance metric learning method is presented in which a deep CNN is used as the leading architecture. A representation clustering algorithm is proposed to decrease the distance of intra-class variations and maximize the length of inter-class differences simultaneously. A domain adaptation method is adopted to reduce the maximum mean discrepancy between training and testing data, and 99.34% accuracy was stated when the sample length was 8192. In ref. [5], [PERSON] et al. proposed a model that detects bearing failures using the continuous wavelet transform and classifies them using a switchable normalization-based convolutional neural network (SN-CNN). The stated testing accuracy was between 99.44% and 100% for different batch sizes and datasets.