esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Specifications of Geographically Weighted Multivariate Poisson Inverse Gaussian Regression  
The MPIGR model is a model that was expanded from the PIGR model, which is a GLMs model with a logarithmic link function. Let (\(Y_{1}\), \(Y_{2}\),..., \(Y_{m}\)) \(\sim\) MPIG (\(\mu_{1}\), \(\mu_{2}\),..., \(\mu_{m}\)), and the joint probability mass function is as follows:  
\[P(y_{1},y_{2},\ldots,y_{m})=\left(2z\pi^{-1}\right)^{\frac{1}{2}}e^{\frac{1}{2} }K_{s}(z)(z\tau)^{\frac{-\mu}{\sum\limits_{j=1}^{m}y_{j}}}\prod\limits_{j=1}^{ m}\frac{\left(\mu_{j}\right)^{y_{j}}}{y_{j}!} \tag{1}\]  
with \(y_{j}\) = 0, 1, 2,... and \(\tau\) is the dispersion parameter \((\tau>0)\), \(s=\sum\limits_{j=1}^{m}y_{j}-\frac{1}{2}\), \(z=\frac{1}{\tau}\sqrt{1+2\tau\sum\limits_{j=1}^{m}\mu_{j}}\), and \(K_{s}(z)=K_{\sum\limits_{j=1}^{m}y_{j}-\frac{1}{2}}\frac{1}{\tau}\sqrt{1+2\tau \sum\limits_{j=1}^{m}\mu_{j}}\) is the third modification of the Bessel function [8]. The properties of the PIGD can be found in [29,30]. The MPIGR model can be described as  
\[E\big{(}Y_{j}\big{)}=\mu_{j}=q_{j}\exp\Big{(}\mathbf{x}^{T}\mathbf{\beta}_{j} \Big{)} \tag{2}\]where \(j=1,2,\ldots,m\), \(\mu_{j}\) is the predicted mean of the \(j\)th response variable, \(q_{j}\) is the exposure for the \(j\)th response variable, and \(\tau\) is the overdispersion parameter. \(\beta_{j}\) is the regression parameter that corresponds to the predictor variable \(k\), for \(k=1,2,\ldots,p\)[10,18]. The GWMPIGR model is based on MPIGR model when it incorporates spatial or location-based aspects into the model. The GWMPIGR model is a point-based spatial model where the regression parameters depend on the geographic location. Therefore, each location has different functions and parameters. The GWMPIGR model for the \(j\)th response variable can be written as follows:  
\[\mu_{j}(u_{i},v_{i})=q_{j}e^{\pi^{T}\beta_{j}(u_{i},v_{i})} \tag{3}\]  
where \((u_{i},v_{i})\) refer to the longitude and latitude coordinates of the \(i\)th location, \(\beta_{j}(u_{i},v_{i})=\left[ \begin{array}{cc}\beta_{j0}(u_{i},v_{i})&\beta_ {j1}(u_{i},v_{i})&...&\beta_{jp}(u_{i},v_{i})\end{array} \right]^{T}\) is a regression parameter vector of the \(j\)th response variable for location \(i\) with the following dimension \((p+1)\times 1\). For the GWMPIGR model, parameter estimation is performed using the maximum likelihood estimation (MLE) method and the Berndt-Hall-Hall-Hausman (BHHH) iteration method. At each observation location (point), parameter estimation is carried out using a spatial weighted matrix (**W**). The elements of matrix **W** show how much influence the observations at a particular location have on the surrounding observations (\(w_{ii^{*}}\)).
## 2 Materials and Methods
### Cell Cycle Analysis by Flow Cytometry  
HL-60 cells were synchronized in the G\({}_{0}\)/G\({}_{1}\) phase for the cell cycle analysis by serum starvation for 24 h. Then, cells (1.5 mL) were seeded in 12-well plates at a concentration of 1 \(\times\) 10\({}^{6}\) viable cells/mL along with 1.5 mL treatment and incubated at a temperature of 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). On Days 1, 3, and 5 of the experiment, cells were harvested then washed with PBS and fixed in 70% ethanol at 4 \({}^{\circ}\)C for 30 min. For DNA staining, samples were washed twice in PBS and centrifuged at 805\(\times\)\(g\) for 5 min. To the pellet, 200 \(\mu\)L of Ribonuclease A in PBS at a concentration of 100 \(\mu\)g/mL and 200 \(\mu\)L of a 50 \(\mu\)g/mL propidium iodide solution were added. Samples were then incubated at a temperature of 37 \({}^{\circ}\)C and a 5% CO\({}_{2}\) concentration for an hour. Samples were read using the FACS Calibur(tm) and CellQuest(tm) Pro software using the FL2 channel. For each sample, 1 \(\times\) 10\({}^{4}\) events were acquired. For gating, debris was excluded and the percentage of cells in the Sub G\({}_{0}\)/G\({}_{1}\), G\({}_{0}\)/G\({}_{1}\), S, and G\({}_{2}\)/M phases was determined. Analysis was carried out on four samples per condition.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 3 Reinforcement Learning-Based Routing
### Clustering
#### 3.1.2 Clustering-Kmean  
K mean clustering is an unsupervised machine learning algorithm to cluster nodes in the network. From a network perspective, it is a centralized scheme and requires distance information of all the nodes. This algorithm comprises of two key steps. In the first step, K centres are placed randomly in the given geographic area and all nodes must associate themselves with the closest centre. In the next step, the mean of each centre with the nodes is calculated and these new means then become new centres. These same steps arerepeated until the criterion function (\(C_{r}\)) becomes minimum. We can find this function by the formula:  
\[C_{r}=\sum_{i=1}^{I}\sum_{d_{i}}|x_{d_{i}}-x_{i}|. \tag{14}\]  
Here \((x_{d_{i}}-x_{i})\) gives the average distance of nodes with point \(x_{i}\) in cluster \(i\). In the end, the node which is closest to the respective centre will then become a CH and all other nodes will become member nodes of the cluster. Performance of K mean clustering mostly depends on the ideal K value. An elbow [20] algorithm is used to identify the optimum value of K.  
The above discussed scheme can be modified by incorporating the GW nodes. The chances of GW nodes in this clustering are very rare and the new scheme is called the clustering-K mean-GW. These schemes have a low maintenance cost although a central authority is needed in this case. The comparison of all these schemes are shown in Table 2. All the schemes which are discussed in Table 2 are modified by us.
# Blockchain for Future Wireless Networks: A Decade Survey
## 2 Background
### Security in Wireless Networks (From 4G to 6G)
#### 2.2.2 Security in 5G  
The 5G wireless network is the next-generation mobile communication system initialized first by NASA in April 2008. After ample R&D conducted by numerous countries, it was commercially launched in December 2019 by the American telephone and telegraph company (AT&T) [42]. Nowadays, 5G has become truly ubiquitous in the field of mobile communication systems. It unlocks features such as service optimization, effective decision-making, and better end-user experience [34]. 5G speeds range from 50 Mbps to up to 1 Gbps with the spectrum of sub 6 GHz (5G macro optimized). In addition, it can achieve the speed of 4 Gbit/s in MIMO with the mmWave bands and carrier aggregation. Network latency would be around 1 to 2 ms, which is 50 times lower than 4G.  
The previous generation (2G, 3G, and 4G) of WN are compatible with each other, which means 3G is evolved by incorporating the technologies of 2G; similarly, 4G is evolved using 3G technologies. However, the 5G technology has adopted new protocols and standards to leverage promising use cases, such as enhanced mobile broadband (eMBB), URLLC, and massive machine-type communications (mMTC). However, the 5G network uses conventional security protocols, such as secure hypertext transfer protocol (HTTPS) and secure socket layer (SSL); this raises anxiety, as attackers are well versed in internet technologies. Therefore, they can search for different vulnerabilities residing in the security protocols and the applications used by the 5G end users from the databases, such as common vulnerabilities and exposures (CVE). For example, most of the web application uses JavaScript object notation (JSON) to represent the structured data; the attackers can maneuver the JSON object to proliferate the DoS attack and gain control over the network devices. Furthermore, the technologies used in the 5G network are harder to manage; hence, there is a wide possibility that the services are misconfigured. Consequently, the attackers can target the incorrect configuration of such services or equipment [43].  
To overcome the aforementioned security issues, [PERSON] et al. [44] discussed various technological aspects and services for 5G security, such as authentication, integrity, availability, confidentiality, and non-repudiation. [PERSON] et al. in [45] studied the security aspects of 5G WN. They discussed the potential attacks and the security service requirements needed for the new uses cases of 5G WN. Their prime focus is to blend the new security features in the different technologies of 5G WN, such as heterogeneous networks, software-defined networks, IoT, and MIMO. Further, it is necessary to verify the routing information between various nodes of the WN. The attackers can easily intercept the data traffic and manipulate the WN application if the route is not secure. Therefore, adopting a secure route optimization protocol by formulating secure authentication, key exchange, and privacy protection can significantly improve WN security.
# The Interaction between the LEO Satellite Constellation and the Space Debris Environment
## 2 The Starlink Constellation  
According to data published by SpaceX and the Space-track website [11], the Starlink constellation plans to deploy a total of 42,000 satellites in LEO by four phases, as shown in Table 1. Each satellite will have a mass of about 260 kg, the design life is 5 years, and the orbit altitude is adjusted by electric propulsion. At present, the project has completed the construction of the first phase of the orbital layer, and the number of satellites in orbit has reached 1628.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **Orbital Altitude (km)** & **Inclination (\({}^{\circ}\))** & **Number of Satellites** \\ \hline i & 550 & 53.0 & 1584 \\ ii & 540 & 53.2 & 1584 \\  & 570 & 70.0 & 720 \\  & 560 & 97.6 & 348 \\  & 560 & 97.6 & 172 \\ iii & 340 & 42–53 & 7518 \\ iv & 328–580 & 30–87.7 & 30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Starlink constellation plan.  
Starlink satellite is released at 300 km circular orbit, and climb to the mission orbit at 550 km altitude in three months, then active mission for 5 years. The satellite is equipped with an active collision-avoidance system to reduce the impact of satellite collision. At the end of the mission, the satellite took the initiative to break out of orbit and re-enter the atmosphere to be destroyed. According to the first test satellites launched in May 2019, 95% of the parts of the satellite during the reentry phase can be burned quickly and do not endanger the operation of other spacecraft.  
The mission of the Starlink satellite is split into three phases:  
1. Release at 53\({}^{\circ}\) inclination at 300 km, and climb to the mission orbit of 550 km in 2-3 months.
2. Running normally on the mission orbit for 5 years.
3. Decelerating by an electric engine to re-enter the atmosphere, burn-up and be destroyed.  
The Starlink satellite is characterized by a flat plate design with a size of 2.8 m \(\times\) 1.4 m \(\times\) 0.15 m, with one solar panel with the length of 9 m [12]. The maximum cross-section of the satellite is calculated to be 25.2 m\({}^{2}\).
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 2 Literature Review
### PV Array to Inverter Sizing Strategies
#### 2.2.1 Manufacturers' Recommendations Based on PV Guidelines
However, as larger inverters cost more per watt, the optimal ratio must not be larger than 20% of the power rating of the PV array. The highest factor \"over-dimensioning\" of a Solar-Max inverter might be up to 15%, which could lead the PV-rated power to design with 15% more than the chosen AC power capacity of the inverter, according to two university-industry collaboration studies conducted by Danfoss PV Inverters A/S with ISE Germany, Fraunhofer, and Sputnik Engineering. However, the authors recommended that the inverter capacity and PV array power must be rated at 1.0:1.0 ratio as an ideal case. In the second study, [PERSON] tested the two types of PV panel technologies to match the inverter Danfoss products with the PV array-rated power in sites around central Europe. The suggested ratio ranged from 1.06 to 1.11 for the Thin-Film PV plant [54]. According to ABB Solar [55], the inverter might be sized between the PV array power and active power of the inverter ratings (0.80 to 0.90). The recommended size ratio considered all power losses that would affect the inverter's power generation and conversion efficiency when it was in use. A summary of the PV-to-inverter ratio considered in previous studies according to software packages, books' syllabi, and guidelines is listed in Table 2, while a chronological summary of the main related PV-to-inverter sizing ratio approaches is shown in Figure 3.
## 2 Research Process
### Simulation Section  
#### 2.2.1 Governing Equations  
The weakly constrained structures used in this study were carried out in small-size pipes, ignoring the effect of gas gravity on the explosion. Compared to the DNS model, the LES model only needs to resolve larger-scale eddies, which is less expensive to solve. Also, compared to the RANS model, LES can capture the transient properties and large-scale structure of turbulence, although it approximates the effects of small-scale eddies through a subgrid model. The LES model was chosen for simulation calculations due to its higher accuracy in predicting oil and gas explosion overpressure, flame propagation velocity, and changes in flame morphology, capturing microscopic changes between flame and turbulence, and exhibiting a more refined structure of the flow field [25]. The solution equations used are given below:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Angle (\({}^{\circ}\))** & **Length (mm)** & **Breadth (mm)** & **Thickness (mm)** \\ \hline
30 & 50 & & \\
45 & 35 & & \\
60 & 28 & & \\
90 & 25 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant dimensional data for obstacles.  
Figure 3: Arrangement of obstacles in the pipeline.  
Mass conservation equations:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(}\rho u_{j} \big{)}=0 \tag{4}\]  
Momentum equation:  
\[\frac{\partial(\rho u_{i})}{\partial t}+\frac{\partial\big{(}\rho u_{i}u_{j} \big{)}}{\partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\Bigg{[}\mu\Bigg{(}\frac{\partial u_{j}}{\partial x_{i}}+\frac{ \partial u_{i}}{\partial x_{j}}\Bigg{)}-\frac{2}{3}\frac{\partial}{\partial x _{i}}\Bigg{[}\mu\Bigg{(}\frac{\partial u_{j}}{\partial x_{j}}\Bigg{)}\Bigg{]} \tag{5}\]  
Energy conservation equation:  
\[\frac{\partial}{\partial t}(\rho E)+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{j}E\big{)}=-\frac{\partial pu_{i}}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\Bigg{[}\mu u_{i}\Bigg{(}\frac{\partial u_{j}}{\partial x_{i} }+\frac{\partial u_{i}}{\partial x_{j}}\Bigg{)}\Bigg{]}-\frac{2}{3}\frac{ \partial}{\partial x_{i}}\Bigg{[}\mu u_{i}\Bigg{(}\frac{\partial u_{j}}{ \partial x_{j}}\Bigg{)}\Bigg{]}+\frac{\partial}{\partial x_{j}}\Bigg{[}k \Bigg{(}\frac{\partial T}{\partial x_{j}}\Bigg{)}\Bigg{]}+\omega Q_{s} \tag{6}\]  
\[\tau_{ij}\text{ is the subgrid scale stress (SGS):}\]  
\[\tau_{ij}=\rho\overline{u_{i}u_{j}}-\rho\overline{u_{i}u_{j}} \tag{7}\]  
\(\rho\) is the density, kg/m\({}^{3}\). t is the time, s. \(u_{i}u_{j}\) are the velocity components in the i, j direction, m/s. P is the pressure, Pa. \(\mu\) is the turbulent viscosity, m/s\({}^{2}\). E is the energy, J. \(x_{i},x_{j}\) are the displacements in the i, j direction, mm. k is the coefficient of thermal conductivity, W/ (m-K). T is the temperature, K. \(\omega\) is the chemical reaction rate, mol/(L-S). \(Q_{s}\) is the heat, J.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 5 Spacecraft-Relative Navigation Model
### Gyro Measurement Model  
The classical model of the angular rate of the gyro output [41] is expressed by the following:  
\[\left\{\begin{array}{l}\tilde{\mathbf{\omega}}=\mathbf{\omega}+\mathbf{\beta}+\mathbf{\eta}_{v} \\ \tilde{\mathbf{\beta}}=\mathbf{\eta}_{u}\\ \end{array}\right. \tag{64}\]  
where \(\tilde{\mathbf{\omega}}\) is the actual angular rate, and \(\mathbf{\omega}\) is the true inertial angular rate. The variable \(\mathbf{\beta}\) is the gyro drift, and \(\mathbf{\eta}_{v}\) and \(\mathbf{\eta}_{u}\) are Gaussian white-noise, which satisfy  
\[\left\{\begin{array}{l}E\big{\{}\mathbf{\eta}_{v}(t)\mathbf{\eta}_{v}^{\mathcal{T}} (\tau)\big{\}}=\sigma_{v}^{2}\delta(t-\tau)\mathbf{I}_{3\times 3}\\ E\big{\{}\mathbf{\eta}_{u}(t)\mathbf{\eta}_{u}^{\mathcal{T}}(\tau)\big{\}}=\sigma_{u}^ {2}\delta(t-\tau)\mathbf{I}_{3\times 3}\\ \end{array}\right. \tag{65}\]  
where \(\sigma_{v}\) and \(\sigma_{u}\) indicate the angle random walk and angular rate random walk, respectively.  
The discrete recursive form of Equation (64) is defined as follows:  
\[\left\{\begin{array}{l}\tilde{\mathbf{\omega}}_{k+1}=\mathbf{\omega}_{k+1}+\frac{1} {2}(\beta_{k+1}+\beta_{k})+\Big{(}\frac{\sigma_{v}^{2}}{\Delta t}+\frac{1}{12 }\sigma_{u}^{2}\Delta t\Big{)}^{1/2}\mathbf{N}_{v}\\ \mathbf{\beta}_{k+1}=\mathbf{\beta}_{k}+\sigma_{u}\Delta t^{1/2}\mathbf{N}_{u}\\ \end{array}\right. \tag{66}\]  
where \(\Delta t\) is the discrete sampling period. The variables \(\mathbf{N}_{v}\) and \(\mathbf{N}_{u}\) are the zero-mean Gaussian white-noise.
## 4 Numerical Example  
The described method has been applied to structures made of one-dimensional beams. The beams are plane and are subjected to flexural loads. Shear effects are negligible so that only bending moment has been considered. The compatibility condition has reduced to the simple limitation of the bending moment, namely,  
\[M_{y}^{-}<M<M_{y}^{+} \tag{20}\]  
In Equation (20), \(M_{y}^{-}\) [\(M_{y}^{+}\)] represents the negative (positive) limit bending moment of the beam cross-section, respectively.  
The results have been compared with the experimental ones presented in [15]. The experimental set up has been briefly recalled in the following. The tested structure was an aluminum beam, over three supports. The supports were equally spaced; the beam was loaded by one-point forces applied at the middle of each span. The forces, F\({}_{1}\) and F\({}_{2}\) (see Figure 2), have varied accordingly with the load program whose diagram has been drawn in Figure 3. In Figure 4, a flow-chart explains the numerical procedure used in the paper.  
Figure 2: Structural scheme, loading position, and constraints.  
The structural scheme of the specimen is depicted in Figure 4, where the half span length was \(l=400\) mm. The mechanical constitutive constants of the specimen were reported in Table 1, where it is illustrated only the average of the parameters.  
The cross-section of the beam was box-shaped, as reported in [15] and in Table 1 too, where the meaningful parameters have been highlighted.  
The experimental report has furnished the relevant parameters of the material constituting the beam, and the limit bending moment has been calculated from small specimens and one span bending tests previously developed. It has to be considered that some buckling of the cross-section of the beam occurs, but the effects have been neglected in the present work [22; 23].  
The upper bound of the load programs, \(F_{max}\), has been progressively increased from the theoretical predicted elastic limit to ratcheting. During the test, the displacement was recorded at any load step. It was possible, consequently, to record the loads for the displacement stabilized after a few cycles  
Figure 4: Flow chart of the procedure.  
Figure 3: Loading Domain.  
and ascribe it to be less than the shakedown limit. When the displacement average was increased in time indefinitely, the load bound was comprised between the shakedown and the collapse limits; however, the test was concluded when no meaningful reduction of displacement increase occurred up to 10 cycles. Finally, when the load equated to the collapse limit, the structure failed suddenly at first load application.
# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 4 Experimental Results
### Model Comparison Experiment
#### 4.2.2 Model Comparison Result
With its top mAP@50:95 of 88.7% and a minimal weight size of 6.1 MB, this model achieves an optimal balance between computational efficiency and detection performance. YOLOv10n excels with 588 frames per second (FPS) and a total latency of just 1.7 milliseconds (ms). Its post-processing phase is highly efficient, taking only 0.2 ms, far surpassing similar models. Conversely, YOLOv9m shows the lowest performance at 192 FPS due to an inference latency of 3.9 ms, making it more suitable for high-precision tasks. For YOLOv11-OBB, post-processing time increases to 2.4 ms due to the complexity of rotated bounding box detection, resulting in a reduced FPS of 222. The YOLOv11-OBB model demonstrates exceptional performance across multiple key metrics, including Accuracy, Recall, mAP@50, mAP@75, and mAP@50:95, while maintaining a favorable balance between model size and computational complexity. As illustrated in Figure 10, the training process exhibits dynamic trends in precision, recall, mAP@75, and mAP@50:95. The model exhibits exceptional target detection performance on the test set, achieving nearly perfect recognition for both target types. For the base class, only 3 out of 550 test samples were missed, yielding a missed detection rate of 0.545% (3/550). For the pointer class, all 569 test samples were correctly detected, resulting in a missed detection rate of 0%. These results demonstrate the model's absolute robustness in recognizing pointer targets and maintain the detection error for the base category strictly below 1%.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 4 Experimental Validation
### Fault Signature Construction  
The LBP operator performs a texture analysis on the grayscale map obtained from the conversion of the vibration signals. The LBP operator compares the difference in grayscale values between each pixel in the image and its surrounding pixels. Binary patterns representing local texture information are generated. These binary patterns are further converted into LBP feature maps, as shown in Figure 6. The LBP feature maps reveal the micro-texture properties in the original vibration signal.  
In order to simplify the complexity of the input features and reduce the difficulty of training, we used the uniform mode to compile all the local descriptors into a global LBP histogram. This is used as a feature vector to capture the texture distribution across the image. These feature vectors act as unique fault signatures and provide inputs to the subsequent deep learning models. This helps to classify and identify different fault patterns. Uniform patterns are considered more representative and discriminative in texture analysis. By focusing on these uniform patterns, the generated global histogram not only emphasizes the micro-texture structures of the image but also maintains the relative stability of these  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Type** & **Fault Location** & \
\begin{tabular}{c} **Fault Diameter** \\ **(Inches)** \\ \end{tabular} &
\begin{tabular}{c} **Fault Depth** \\ **(Inches)** \\ \end{tabular} \\ \hline Inner raceway fault & Inner raceway & 0.007 & 0.011 \\ Outer raceway fault & Outer raceway & 0.007 & 0.011 \\ (ORF) & Ball fault(BF) & Ball & 0.007 & 0.011 \\ Normal & Nil & Nil & Nil \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of fault types.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Order**} \\ \cline{2-6}
**FCF (Hz)** & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** \\ \hline \(f_{OR}\) & 107.36 & 214.72 & 322.08 & 429.44 & 536.80 \\ \(f_{IR}\) & 162.19 & 324.38 & 486.57 & 648.76 & 810.95 \\ \(f_{bull}\) & 141.17 & 282.34 & 423.51 & 564.68 & 705.85 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Theoretical fault characteristic frequency of CWRU datasets.  
structures across different rotational speeds. This enables the model to more effectively identify specific texture patterns associated with faults, thereby significantly improving the accuracy and efficiency of fault diagnosis.