esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
At present, the joint surface roughness characterization formula considers only the morphology contribution of the waviness and unevenness, and not their full mechanical contribution. In this study, the standard JRC profiles were taken as the initial morphology and were decomposed into waviness and unevenness by the method combining the ensem \begin{table}
\begin{tabular}{c c} \hline \hline
**Formula** & **Ref.** \\ \hline \(JRC=1979R_{d}^{0.92}\epsilon^{1.46}\) & [49] \\ \hline \(JRC=15179W_{d}^{0.79}(D-1)^{1.46}\) & [50] \\ \hline \(JRC=12.862R+10.316 arctan(100R_{a})-4.017\) & [51] \\ \(JRC=2.719R+0.585 arctan(100R_{a})^{2}+6.169\) & \\ \(JRC=10.544R+0.327 arctan(100R_{a})^{2}+10.377\) & \\ \hline \(JRC=41.14\log(Z_{2})+4.93A_{1}^{1.53}+26.72\) & [52] \\ \(JRC=33.86\log(Z_{2})+3.19A_{2}^{2.02}+28.92\) & \\ \(JRC=26.31\log(Z_{2})+2.20A_{2}^{2.02}+27.73\) & \\ \hline \(JRC=69.23\sqrt{Z_{2}}-28.17h_{A}^{-0.03}\) & [53] \\ \(JRC=64.75\sqrt{Z_{2}}-20.45h_{A}^{-0.11}\) & \\ \(JRC=58.92\sqrt{Z_{2}}-15.74h_{A}^{-0.06}\) & \\ \(JRC=57.62\sqrt{Z_{2}}-13.73h_{A}^{0.08}\) & \\ \hline \(JRC=75.11Z_{1}^{1 st}+3.332^{2 nd}-2.79\) & [54] \\ \(JRC=91.735F^{1 st}-57.135F^{2 nd}+3.89\) & \\ \(JRC=472.16(R_{P}-1)^{1 st}-20.42(R_{P}-1)^{2 nd}+3.32\) & \\ \hline \(JRC=A\times\sum\left(S_{up}^{2 nd}\right)^{a}+B\times\sum\left(S_{up}^{1 st} \right)^{b}+C\) & [55] \\ \(JRC=A\times\sum\left(V_{up}^{2 nd}\right)^{a}+B\times\sum\left(V_{up}^{1 st} \right)^{b}+C\) & \\ \hline \(JRC=1.2\theta_{2d}+867.5h_{1}^{2}-2.9\) & [9] \\ \(\theta_{2d}=[\theta_{\text{max}}^{a}/(C+1)]_{2d}\) & [9] \\ \(h_{1}=h/L\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The characterization formula by two statistical parameters. ble empirical mode decomposition (EEMD) and the criterion of critical decomposition level. Three types of joint specimens with different asperity orders were prepared with the 3D carving method. Shear tests were carried out on 39 groups of red sandstone joint specimens with seven roughness degrees (5\({}^{\#}\)-10\({}^{\#}\) standard JRC profiles and flat joint planes) and two asperity orders (one including only waviness and one including waviness and unevenness) under three normal stresses. The mechanical contribution of each asperity order was analyzed, and the relationship between the mechanical contribution of each asperity order and its statistical parameter was explored. Finally, the characterization formula of joint roughness that considers the mechanical contribution of each asperity was established.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 5 Design
### Network Coding
#### 5.3.3 Decoding Process  
The network server generates a list of \(submatrix\) that are used to store the incoming coded packet \(P_{c}\) from the gateway. As shown in Algorithm 1, the network server checks partial packets, a list of the end-devices packets used to form the coded packet. If any partial packets of \(P_{c}\) are a subset of that in a \(submatrix\) that already created, we append \(P_{c}\) to it. Otherwise, we create a new \(submatrix\) containing \(P_{c}\).  
```
1:procedure\(submatrix\) = SLICING(\(P_{c}\),\(submatrix\))
2:if\(submatrix\) is undefined or \(P_{c}\) is independent to \(submatrix\)then
3: New \(submatrix\) \(\leftarrow\) new List(\(P_{c}\))
4:else
5:\(submatrix\).append(\(P_{c}\))
6:endif
7:endprocedure
```  
**Phase 1**--Slicing procedure  
```
1:procedure\(packets\) = Decoding(\(submatrix\))
2:\(sparsematrix\) \(\leftarrow\) decompress header of each \(P_{c}\) in \(submatrix\)
3:if\(sparsematrix\) is full rank then
4: Decode \(sparsematrix\) by Equation (6)
5: Remove \(submatrix\)
6:else
7: Request additional \(P_{c}\)
8:endif
9:endprocedure
```  
**Algorithm 1** Decoding procedure  
The decoding process is started when there is no incoming coded packet during a timeout. For each \(submatrix\), we create a \(sparsematrix\) by decompressing header of each coded packets in the \(submatrix\). If the \(sparsematrix\) is full rank, the network server decodes it using Equation (6). Otherwise, the network server requests additional coded packets that contain the partial packets from all relays that generate these coded packets.  
For example, the network server receive two headers of coded packets \(h=[1||001||\theta||01||010||\theta]\), \(h=[0||001||\theta||1||010||\theta]\) from relay 1 and one header \(h=[0||010||\theta||1||011||\theta]\) from relay 2 (see Figure 5). Originally, each sender only combines two packets, namely packets A and B in relay 1 and packets B and C in relay 2. During the slicing process, the network server merge these coded packets because they have similar partial packet B. Hence, the \(submatrix\) contains partial packets A, B, and C. After timeout, the network server starts decompressing the header to generate an \(E_{3*3}\) sparse matrix:  
\[E_{3*3}=\left|\begin{matrix}4&14&0\\ 14&4&0\\ 0&4&14\\ \end{matrix}\right|\]  
Because the generated matrix is full rank or linearly independent, the network server decodes it as per Equation (6).
# Carbon from Bagasse Activated with Water Vapor and Its Adsorption Performance for Methylene Blue
###### Abstract  
This research work reports on the potential of bagasse, a solid waste from sugar factories, to produce activated-carbon (AC) as an adsorbent. The activation was conducted under 500, 600, and 700 \({}^{\circ}\)C using steam as the activation agent to produce AC500, AC600, and AC700, respectively. The prepared-materials were characterized to understand their elemental content, surface morphology, thermal properties, functional groups identification, surface area, and pore size. AC700 provided the highest surface area of 592.36 m\({}^{2}\)/g and indicated the contribution of mesopores distributes along 1.5-8.0 nm of pore size. Therefore, an adsorption test was conducted with AC700 as adsorbent. The results show that methylene blue (MB) adsorption reached equilibrium after 30 min of adsorption time. The adsorption isotherm applied to a monolayer Langmuir isotherm was fitted by linearization, resulting in a constant R\({}^{2}\) of 0.999. The MB adsorption to AC700 favorably occurred, as proven by the Freundlich parameter 1/n of 0.881, which is less than 1. The Dubinin-Radushkevich isotherm confirmed that the adsorption proceeded through physical interaction with adsorption energy of 3.536 kJ/mol.  
activated-carbon; water vapor activation; bagasse; adsorption
## 4 Results and Discussion
### ORC Performances
#### 4.2.2 ORC Literature Comparison
The significant increase in efficiency values for the ORC distribution between 0.8 and 1 comes from the level of electrical power production at their expander. Indeed, these ORCs are mostly producing \"high\" power close to 10 kW, which leads to a size factor on the global performances of the installations.
# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
## 5 Discussion and Open Issues
### Research Limitations  
In terms of the limitations of our research, we agree that further optimization of the classification-model parameters is needed to improve accuracy. Additionally, our study focused on a limited dataset, and more industrial data are needed to validate the model's performance in real-world applications.  
As for future research directions, we plan to explore more advanced optimization techniques to further improve the accuracy of the classification model. We also plan to collect more diverse and comprehensive industrial data to better validate the model's performance in various industrial applications. In addition, we plan to investigate the potential of incorporating other datasets to further enhance the model's accuracy. We  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Operation Conditions** & \multicolumn{3}{c}{**Medium–High Speed (1540 r/min)**} & \multicolumn{3}{c}{**High Speed (1788 r/min)**} \\ \hline
**Model** & **SVM** & **KNN** & **RF** & **GBDT** & **SVM** & **KNN** & **RF** & **GBDT** \\ \hline VMD energy entropy & 98.40\% & 98.53\% & 98.53\% & 97.44\% & 98.53\% & 98.53\% & 98.17\% & 97.44\% \\ SVD energy entropy & 98.17\% & 95.60\% & 95.24\% & 94.14\% & 94.46\% & 97.42\% & 94.46\% & 94.10\% \\ VMD fuzzy entropy & 94.56\% & 98.40\% & 98.53\% & 98.17\% & 96.70\% & 98.90\% & 91.58\% & 95.97\% \\ SVD fuzzy entropy & 97.07\% & 98.53\% & 95.97\% & 96.34\% & 96.31\% & 97.42\% & 95.94\% & 94.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of GDS 3000 experimental platform.  
Figure 14: GDS3000 experimental platform.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Applicable Data** & **Classification Problem** & **Training Time** & **Storage Space** \\ \hline SVM & Low-dimensional & Linear classification & Long & Low \\ KNN & Low-dimensional & Nonlinear classification & Short & High \\ RF & High-dimensional & Nonlinear classification & Long & Low \\ GBDT & High-dimensional & Nonlinear classification & Long & Low \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of classification models.  
believe that these future research directions will contribute to the development of more robust and accurate models for industrial applications.
## 5 Conclusions  
In the present study, the crack propagation of the axial-flow mine ventilation fan blade subjected to operational loads is analyzed based on the co-simulation of FRANC3D and ABAQUS. Using experimental and analytical results, the co-simulation approach is validated. The center double slant crack specimen with a hole specimen is used to prove the accuracy of the method for mixed-mode stress intensity factor calculation. The polymethyl methacrylate (PMMA) beam specimen is used to demonstrate the method's precision for crack growth path prediction.  
The results confirmed that the proposed co-simulation approach is a promising method for crack growth path prediction. Based on the overall validation, the axial fan blade crack propagation path under aerodynamic, centrifugal, and combined loads was examined for different crack steps. The results clearly demonstrated that the crack growth length of the crack tip center (L2 = 17.84 mm) was maximum under centrifugal loading. However, the crack growth length of the two endpoints of the crack tip was maximum under the aerodynamic load (L1 = 73.75 mm). Moreover, compared to the crack center, the crack endpoint's growth rate was substantially higher. Under the three types of loads, the crack center growth rate was 2.94, 4.49, and 4.39 times less than the crack endpoint growth rate. The results pinpoint that the combined centrifugal and aerodynamic loads tended to close the crack, which prevented and limited the crack from growing.  
This study can provide a resource for assessing blade crack propagation paths under real conditions. However, the boundary conditions defined in this study did not consider extreme conditions such as stall conditions and fan instabilities, which may affect the blade  
Figure 14: Comparison of the total crack length for the different loads.  
crack growth. In further work, the presented approach will confront extreme conditions to better meet industrial requirements.  
M.B.H.: methodology, investigation, formal analysis, validation, writing--original draft, writing--review and editing. S.B.-E.: validation, investigation, writing--review and editing. H.M.: funding acquisition, resources, supervision, writing--review and editing, validation. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), grant number [2020-560991].  
Data Availability Statement: Data is unavailable due to privacy restrictions.  
This work was supported by Hyperflo, 100 Jacques-bibeau, Rouyn-Noranda, QC, J9Y 0A3.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Bearing Fault Recognition Based on SSL-VCST Method
### Experiment 2: Variable Speed Conditions of Rotating Machinery  
Experimental study of rotating machinery under variable speed conditions is conducted using the bearing datasets (SQV datasets) [39] publicly available from Xi'an Jiaotong University. The test rig is shown in Figure 9. The experimental data involve seven states, including normal state, inner ring failure, and outer ring failure, with three different damage levels, respectively. Each experiment process gradually accelerates from the stationary state to 3000 rpm and then gradually decelerates to 0 rpm. The sampling frequency is 25.6 KHz. Based on the SQV datasets, an unbalanced experiment is designed, and sample sizes are listed in Table 5. The proportion for the three damage levels under each fault state is consistent with that under constant speed conditions.  
Due to the time-varying nature of fault characteristic frequencies under variable speed conditions, the texture of CWT time-frequency images is prone to being indistinct, which limits the classification performance. Therefore, STFT time-frequency images are used as inputs instead. The sample supplementation and fault diagnosis operations are the same as those in Experiment 1. According to the comparison results shown in Figure 10, it can be seen that the accuracy of the SSL-VCST model is still the highest. It is verified that the proposed methods can still effectively solve the problems of sample imbalance and limited label samples under variable speed conditions of rotating machinery.
# Contact Force Surrogate Model and Its Application in Pantograph-Catenary Parameter Optimization
## 2 Surrogate Model Training by Feedforward Neural Network  
### Design of Data Sets  
The input parameters consisted of the speed (\(v\)), nine pantograph parameters, and five catenary parameters, i.e., \(X=\{m_{3},m_{2},m_{1},k_{3},k_{2},k_{1},c_{3},c_{2},c_{1},T_{c},T_{m},A_{c},A _{m},L,v\}^{T}\). The output parameter was the SDCF at a low-pass filtering frequency of 20 Hz or 40 Hz. The value of each input parameter \(X_{r}(r=1,2,\ldots,15)\) was determined according to engineering practice (Table 1). Among them, \(m_{i}\), \(k_{i}\), and \(c_{i}\) are the equivalent mass, stiffness, and damp, respectively, and \(i=1,2,3\) represent the mass point of the frame rotation, upper frame vertical vibration, and panhead vertical vibration; \(T_{c}\) and \(T_{m}\) are the tension of contact wire and messenger wire, \(A_{\rm c}\) and \(A_{\rm m}\) are the cross-section area of contact wire and messenger wire, and \(L\) is the span length.  
The steps for data set design are as follows: First, draw 12,000 samples by using the Latin hypercube sampling method [20]. Secondly, the time domain curves of contact force for the above 12,000 samples were obtained using the pantograph-catenary finite element method [21], and the lifting force exerted on \(m_{1}\) was adjusted during calculation to ensure the deviation between the average contact force and the upper limit [6] was lower than 0.5 N. Based on those time domain curves after low-pass filtering at 20 Hz or 40 Hz, the theoretical values of the SDCF could be calculated, and databases of the SDCFs at the two low-pass filtering frequencies were constructed. Finally, the training set and the validation set were divided from the data set based on the hold-out method, and the test set was composed of the remaining examples. The ratio of the sample size of the three sets was 8:1:1. When dividing the training set and validation set, stratified sampling was used to maintain the consistency of the data distribution and avoid introducing additional bias and affecting the generalization ability of the surrogate model due to the data dividing.
# Design of WPT System Based on Interleaved Boost Converter
## 3 Efficiency Improvement Strategy Analysis
The abscissa of the region is greater than 1 in this paper, that is, \(P_{\text{loss--pri}}>P_{\text{loss--sec}}\). When the multiple of the primary side self-inductance increases a \(\geq\) 2, the ordinates are all greater than 0, which also proves that increasing the primary side DC bus voltage can reduce the loss on the coil and effectively improve the efficiency of the system. It can be seen from Figure 3a,b that when the relative voltage gain is different, the trend of the curve is also different. In order to ensure that the ordinate of the image is greater than 0 under the condition that a takes different values, that is, the efficiency is improved, \(G_{0}\) The value of is generally around 2. In this paper, the input impedance angle of the system is analyzed to make it inductive, so as to create a condition for the realization of ZVS. The equivalent circuit of the proposed WPT system is shown in Figure 4.  
In the resonant state, the equivalent impedance of the resonant elements are \(X_{1}{}^{*}\) and \(X_{2}{}^{*}\)  
\[ \begin{cases}X_{1}^{*}=\omega L_{\text{P}}=\frac{1}{\omega C_{1}^{*}}\\ X_{2}^{*}=\omega L_{\text{S}}=\frac{1}{\omega C_{2}^{*}}\end{cases}  \tag{13}\]  
Figure 3: Relationship between loss reduction and \(P_{\text{loss--pri}}\) and \(P_{\text{loss--sec}}\) under different relative voltage gain \(G_{0}\). (**a**) \(G_{0}=2\); (**b**) \(G_{0}=3\). \(\alpha\) and \(\beta\) are the disturbance coefficient. Equation (14) shows the relationship between the equivalent impedance and the disturbance coefficient, where \(C_{1}\) and \(C_{2}\) are the actual values, and \(X_{1}\) and \(X_{2}\) are the actual impedance. \[ \begin{cases}X_{1}=\frac{1}{\omega\text{C}_{1}}=\alpha X_{1}^{*}\\ X_{2}=\frac{1}{\omega\text{C}_{2}}=\beta X_{2}^{*}\end{cases}  \tag{14}\]  
Then, the expression of input impedance \(Z_{\text{in}}\) can be obtained. \[Z_{\text{in}}=\frac{\omega^{2}M^{2}R_{\text{E}}+\text{j}\left[-\omega^{2}M^{2} X_{2}(1-\beta)+X_{1}(1-\alpha)R_{\text{E}}{}^{2}+X_{1}X_{2}{}^{2}(1-\alpha)(1- \beta)^{2}\right]}{R_{\text{E}}{}^{2}+X_{2}{}^{2}(1-\beta)^{2}} \tag{15}\]  
According to Equation (15), input impedance angle \(\theta_{\text{in}}\) can be described as:  
\[\theta_{\text{in}}=\arctan\left(\frac{X_{1}(1-\alpha)R_{\text{E}}{}^{2}+X_{2 }(1-\beta)\big{[}X_{1}X_{2}(1-\alpha)(1-\beta)-\omega^{2}M^{2}\big{]}}{\omega^ {2}M^{2}R_{\text{E}}}\right) \tag{16}\]  
According to the above formula, the input impedance angle \(\theta_{\text{in}}\) can be turned into inductive to realize ZVS. Figure 5 shows the block diagram of the proposed closed-loop control system, where \(G_{\text{c}}(s)\), \(G_{\text{M}}(s)\), \(G_{\text{P}}(s)\), \(G_{\text{delay}}(s)\), and \(H(s)\) are the transfer functions of the compensator, PWM modulator, WPT system, wireless communication delay, and voltage sampling, respectively.
## 4 Results and Discussion
### Performance Analysis
#### 4.5.2 Analysis in Terms of Different Evaluation Metrics
Even if the model shows a significant FPR, the real-world implementation of such a system in different use cases may need higher FPR thresholds, particularly in sensitive environments like healthcare systems, financial institutions, or critical infrastructure where false alarms could result in major resource wastage or operational disruptions. Through the combination of an IFSA with a deep AE for approximating the Q-function and optimizing policy selection, a more sophisticated method to manage FPR is obtained. The likelihood of misclassifying normal traffic as attacks is minimized by using the deep AE to support the model in learning typical traffic patterns and differentiating them from anomalies. Furthermore, the decision thresholds can be altered using the IFSA in accordance with the specific needs of the deployment environment. Owing to this flexibility, practitioners can establish more stringent thresholds if needed since the model can be customized to adjust its sensitivity to false positives. The comparison of the kappa score of the proposed and existing works is presented in Figure 12. The Kappa score is reflected as a useful metric to evaluate the agreement between the actual and predicted classifications while comparing the performance of the proposed EDRL model and existing solutions for classifying attacks and normal instances in IDS. Better agreement between actual and predicted classifications is designated by a higher kappa score in the EDRL model, which recommends improved detection abilities for both attack and normal instances. However, a lower Kappa value resembles the shortcomings of the proposed EDRL model in contrast to existing methods. In the graphical depiction, it is perceived that the proposed EDRL model is in contrast to existing methods. In the  
Figure 10: Accuracy comparison of the proposed and existing works. graphical depiction, it is perceived that the proposed EDRL model has achieved a maximum kappa value of 99.2%, which is higher than the existing methods. The MCC derived from the proposed EDRL and existing methods for attack detection are shown in Figure 13. Compared to the current methods, the proposed EDRL achieved higher MCC values of 99.21%. The EDRL has successfully predicted the class labels based on the input parametric values, demonstrating its superiority over current methods. MCC values of 93.21%, 94.57%, 95.12% and 96.43% have been achieved using the current methods for RF, CNN, GRU and DQN. The performance comparison of the proposed approach in terms of different metrics is presented in Table 4. From the performance outcomes, it can be justified that the proposed approach is comparatively better than the other methods. The results are taken for the classification performed by the proposed EDRL approach. The overall simulation outcomes proved that the proposed model is more stable and optimal than the other existing frameworks. Figure 7:0 FPR comparison of the proposed and existing works. Figure 7:1 Kappa score comparison of the proposed and existing works.
## 4 Conclusions  
In this article, a new SSODTL-CC model was developed to properly identify the crop type in HSIs. To do so, the proposed SSODTL-CC model performed feature extraction using MobileNet with an Adam optimizer. In addition, the BiLSTM model received feature vectors and performed crop type classification. To enhance the classifier efficiency of the BiLSTM model, the SSO algorithm was employed for hyperparameter optimization. To demonstrate the better performance of the SSODTL-CC model, a wide-ranging experimental analysis was performed on two benchmark datasets, namely dataset-1 (WHU-Hi-LongKou) and dataset-2 (WHU-Hi-HanChuan). The comparative analysis pointed out the better outcomes of the SSODTL-CC model over the recent approaches, with a maximum of 99.23% and 97.15% on test datasets 1 and 2, respectively. Therefore, the SSODTL-CC model can be utilized for effective crop type classification on HSIs. In the future, the classification performance of the SSODTL-CC model can be enhanced by the design of hybrid DL models.  
Conceptualization, M.A.H.; Data curation, F.A.; Formal analysis, F.A. and J.S.A.; Investigation, J.S.A.; Methodology, M.A.H.; Project administration, H.M.; Resources, H.M.; Software, N.M.S.; Supervision, N.M.S.; Validation, R.M.; Visualization, R.M.; Writing--original draft, M.A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by King Khalid University, grant number RGP 2/46/43, Princess Nourah bint Abdulrahman University, grant number PNURSP2022R77 and Umm al-Qura University, grant number 22 UQU4340237 DSR24.  
Not applicable.  
Data Availability StatementData sharing not applicable to this article as no datasets were generated during the current study.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number (46/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R77), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The  
Figure 7: Comparative analysis of the SSODTL-CC technique under dataset-2.  
authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: 22 UQU4340237 DSR19.