esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Instructions on Data Set Creation
#### 4.2.2 MDC System Overview  
Intelligent monitoring plays an important role in the intelligent automation of manufacturing systems, and advanced data collection technology has been widely used to promote real-time data collection [44]. The Manufacturing Data Collection & Status Management (MDC) system is a software and hardware solution for real-time acquisition, charting, and reporting of detailed manufacturing processes and data on the shop floor. MDC uses a variety of flexible methods to obtain real-time data from the production site (including equipment, people, production tasks, etc.), store it in databases such as Access, SQL, and Oracle, and build on the lean manufacturing management philosophy. Combined with nearly 100 kinds of special calculation, analysis, and statistical methods of the system, it directly reflects the production status of the workshop in the form of a variety of reports and charts and helps the production department of the enterprise to make scientific and effective decisions. Figure 9 is the MDC system diagram.  
The MDC system has the following characteristics when processing data:  
(1) Real-time data acquisition: The MDC system can collect all kinds of data in the production process in real time, including equipment operation data, sensor data, etc. Compared with traditional data acquisition methods, the MDC system has the characteristics of automation and real time, which can greatly improve the efficiency and accuracy of data acquisition.  
(2) Data consistency: By automating data collection and processing, the MDC system ensures data consistency and accuracy, avoiding errors and inconsistencies caused by manual operations and ensuring data reliability and availability.  
(3) Historical data recording: The MDC system can record and store historical data to form a complete historical data record to ensure the integrity and quantity of data, which is important for fault analysis and the fault prediction model.  
Using the MDC system to obtain data ensures data availability and quantity requirements are met.
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 5 Conclusions  
In this article a new bio-inspired RSA based MPPT algorithm is introduced to maximize the power extracted from a grid connected PV system under various weather conditions. The RSA algorithm was first tested under uniform varying irradiance condition with gradual and rapid change. After that, Performance of the proposed technique was analyzed under three different scenarios of PSC. The obtained results were evaluated and compared with prominent MPPT techniques in regard to many criteria. The results examination and evaluation show that RSA algorithm outperformed the other simulated techniques in terms of power efficiency, settling time and oscillation in the transient state. Furthermore, in varying irradiance condition RSA method shows a high tracking efficiency with an average of 99.90% through the different irradiation levels, smallest steady state oscillation of less than 20 watts and shorter settling time. Moreover, an average efficiency of 99.91% was reported under PSC. However, the proposed RSA MPPT algorithm still has some limitations including steady state oscillations especially in case of partial shading conditions, and difficulties in parameters initialization. The remarkable results from the novel RSA-MPPT based technique prove its efficacy and superiority in enhancing the optimal power of the PV system, especially when the efficiency is the main criteria.  
Conceptualization, N.D.; Methodology, N.D.; software, N.D. and A.A.; validation, N.D. and K.Y.; formal analysis, N.D., K.Y. and N.R.; investigation, N.D., A.A., F.H., K.Y., M.M. and N.R.; Resources, K.Y. and M.M.; Data curation, N.D. and N.R.; writing--original draft preparation, N.D.; writing--review and editing, N.D., A.A., F.H., K.Y., M.M. and N.R.; visualization, F.H.; supervision, A.A. and F.H.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding authors.  
The authors declare no conflict of interest.
# Optimization of Glass Edge Sealing Process Using Microwaves for Fabrication of Vacuum Glazing
###### Abstract  
Among the various methods used for glass edge sealing, this study uses microwaves to seal glass edges. Through basic experiments, the main process conditions for edge sealing of glass were derived, and the experimental plan and analysis were carried out using the Box-Behnken method of response-surface analysis based on 3 factors and 3 levels. The step height which influences sealing was set as a response variable. If the step height becomes too large, the glass can be damaged, and if the step height is too small, the edge sealing will not be performed. Accordingly, process optimization that edge sealing is possible while minimizing the step height was carried out. A predictable regression equation was derived for the step height of edge sealing and the main-effect analysis was performed for the step height. Using the response-optimization tool, we derived the optimum process condition that minimized the step height of the edge sealing and verified that it matched the error value of 4.1% compared with the target value of the step height measurement result confirmed through the verification experiment.  
microwaves; glass edge sealing; DOE; process variable optimization Article
# FEM Analysis and Sensor-Based Measurement Scheme of Current Distribution for Grounding Electrode
## 5 Conclusions  
1. According to the special geological conditions of frozen soil and considering the difference in soil resistivity at different depths, this paper established a three-layer soil model and a mixed-layer soil model, using the finite element Galerkin method to study the dispersion characteristics of different grounding electrodes. The results show that the best to worst order of the dispersion characteristics of the three grounding electrodes is as follows: ring-shaped, cross-shaped, and linear. Furthermore, under the same conditions, the current and voltage in the soil around the ring-shaped grounding electrode are 35-40% lower than those of the linear grounding electrode.
2. Based on the simulation results, we built a grounding electrode simulation experiment platform in the laboratory. Then, we proposed a measurement scheme based on a \"drainage wire\" with a Hall sensor and designed and manufactured a grounding electrode current measurement device. The weak DC in soil around the grounding electrode can be measured in the laboratory, and the error can be compared with simulated data. It is found that the error is always maintained at about 10%, which confirms the feasibility of the scheme and the reliability of the measuring device.  
Figure 16: The standard error of measurement under the mixed-layer soil model. (**a**) The standard error of the cross-shaped grounding electrode; (**b**) the standard error of the ring-shaped grounding electrode.  
Figure 15: The standard error of measurement under the three-layer soil model. (**a**) The standard error of the cross-shaped grounding electrode; (**b**) the standard error of the ring-shaped grounding electrode.  
**Author Contributions:** [PERSON] performed the experiments and wrote the paper; [PERSON] contributed analysis tools; [PERSON] conceived and designed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] provided the experimental environment and the soil resistivity data of Inner Mongolia. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by STATE GRID EAST INNER MONGOLIA ELECTRIC POWER MAINTENANCE COMPANY, grant number NO.SGTYHT/17-JS-199.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
## 1 Introduction
The deep learning model adopted is enhanced ResNeXt50 (Residual Neural Network), which is an improved version derived from the integration of ResNet and Inception Networks. It combines the repetition strategy with the split-transform-merge strategy, effectively addressing the issue of gradient vanishing or exploding during the training process, increasing the network's width, and enhancing the model's performance. By adding the CBAM (convolutional block attention module) to ResNeXt, we have increased the model's focus on important features. This, combined with the ArcLoss function, enables the model to learn more discriminative features and enhance its generalization capability. Addressing the challenge of achieving a high recognition rate for gearbox faults under complex and variable loading conditions, this study proposes an acoustic intelligent diagnosis model incorporating the attention mechanism with ResNeXt and the ArcLoss function, known as CBAM-ResNeXt50-ArcLoss. Utilizing STFT (short-time Fourier transform) to extract the time-frequency matrix of acoustic signals, and after reducing its dimensionality through Mel filters, the resulting spectrogram is used as the input for the model. Using a deep learning model, classification and detection are performed to determine the fault type in wind turbine gearboxes. This article consists of four parts: audio signal preprocessing, establishing a CBAM-Resnext50-ArcLoss deep learning classification model, experiment and training results, and conclusions.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BLER & BLock Error Rate \\ DVB-S2 & Digital Video Broadcasting Second Generation Satellite \\ DVB-S2X & Digital Video Broadcasting Second Generation Extension \\ FSO & Free Space Optical \\ GEO & Geostationary Earth Orbit \\ HTS & High Throughput Satellites \\ IM/DD & Intensity Modulation / Decision Detection \\ MCS & Modulation and Coding Scheme \\ NOMA & Non Orthogonal Multiple Access \\ NPBCH & Narrowband Physical Broadcast Channel \\ NPDCCH & Narrowband Physical Downlink Control Channel \\ NPDSCH & Narrowband Physical Downlink Shared Channel \\ NPSS & Narrowband Primary Synchronization Signal \\ NSSS & Narrowband Secondary Synchronization Signal \\ NB-IoT & Narrow-Band Internet of Things \\ OFDM & Orthogonal Frequency Division Multiplexing \\ OTA & Over-The-Air \\ PAM & Pulse Amplitude Modulation \\ QPSK & Quadrature Pulse Shift Keying \\ THR & Throughput \\ \end{tabular}
## 4 Influence of Liquid-Filling Ratio on Hydrodynamic Ram Effect
### Perforation and Stress on Front and Back Walls  
After the debris penetrated the satellite tanks with liquid-filling ratios of 70%, 80%, 90%, and 100%, the diameters of the front and back perforations, the bulge heights on the front and back walls were recorded and are summarized in Table 6. For a satellite tank with the same filling ratio, the diameter of the front perforation was larger than that of the back perforation, and the bulge height on the front wall was smaller than that on the back wall. For satellite tanks with different liquid-filling ratios, with the reduction in the liquid-filling ratios, the diameters of the front and back perforations decreased and the bulge heights on the front and back walls diminished, indicating that the damage caused by the hydrodynamic ram effect weakened.  
The stress-time curves of element H (Figure 8a) on the front wall with liquid-filling ratios of 70%, 80%, 90%, and 100% are shown in Figure 11. The stress-time curves of element I (Figure 8b) on the back wall with liquid-filling ratios of 70%, 80%, 90%, and 100% are shown in Figure 12. When the debris impacted the front wall, the stress on the front wall rapidly reached the maximum under the action of the shock wave. Subsequently, stress oscillation occurred due to the extrusion of liquid hydrazine on the front wall. When the shock wave arrived at the back wall, the stress on the back wall reached the first peak, then high pressure hydrazine in front of the debris exerted a pressure load on the back wall,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Deformation (cm)** & **100\%** & **90\%** & **80\%** & **70\%** \\ \hline Diameter of front perforation & 10.23 & 9.46 & 9.20 & 9.08 \\ Diameter of back perforation & 6.90 & 6.24 & 5.51 & 5.05 \\ Bulge height on front wall & 1.12 & 0.99 & 0.79 & 0.71 \\ Bulge height on back wall & 1.58 & 1.45 & 1.33 & 1.29 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Perforation diameters and bulge heights at different liquid-filling ratios.  
Figure 10: Simulation models of the satellite tanks with liquid-filling ratios of 70%, 80%, and 90%.  
resulting in multiple stress peaks. At the moment of debris penetration, the stress on the back wall reached the maximum.  
As can be seen from Figures 11 and 12, at the same time, the smaller the filling ratio, the smaller the stress on the front and back walls. This is because nitrogen acts as a buffer layer between the liquid hydrazine and the tank wall. As the liquid-filling ratio decreases, the gas volume increases, and the compressible space of the liquid hydrazine enlarges. The shock wave compresses the liquid hydrazine, then the liquid hydrazine compresses the nitrogen. The energy of the liquid hydrazine squeezing tank wall is dispersed, and the pressure load exerted on tank wall is reduced. In a word, the nitrogen in satellite tank diminishes the stress on the wall by reducing the pressure load exerted by liquid hydrazine, thus alleviating the damage caused by the hydrodynamic ram effect.
## 3 Data Source and Analysis
### The Improved AHP is Used to Solve the Expert Weight
#### 3.1.2 Constructing Judgment Matrix  
Questionnaires are sent to 80 experts, including the project safety director, project manager, the person responsible for safety management, company manager and safety supervisor. All have more than 10 years of engineering practice experience and certain achievements in the field of safety management. Out of the 50 questionnaire results recovered, 37 qualified questionnaires were selected, thereby reaching the number of experts required by the AHP. The judgment matrix was constructed according to the 37 expert questionnaires, where each expert has the same weight.  
The judgment matrix of the target layer and the criterion layer is constructed as follows:  
\[A=\begin{bmatrix}1.000&1.169&1.313\\ 0.855&1.000&1.169\\ 0.762&0.855&1.000\\ \end{bmatrix}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Criterion Layer** & **Factor** \\ \hline \multirow{3}{*}{Human factor} & Workmate impact, skills level, age of workers, security attitude, work experience, workers’ fatigue, safety compliance, workers’ character; workers’ emotions \\ \cline{2-3}  & Group cohesion, safety communication, management level, safety education, security culture, safety regulation, safety supervision; safety input \\ \cline{2-3} Environmental factor & Safety climate, construction conditions; protection measures \\ \hline \hline \end{tabular}
\end{table}
Table 6: A hierarchical structure table.  
The judgment matrix of human factors at the criterion layer and its index layer is constructed.  
\[B_{1}=\begin{bmatrix}1.000&1.636&1.666&1.268&1.356&1.720&1.557&1.499&1.927&2.360\\ 0.611&1.000&1.636&1.666&1.268&1.356&1.720&1.557&1.449&1.927\\ 0.600&0.611&1.000&1.636&1.666&1.268&1.356&1.720&1.557&1.449\\ 0.788&0.600&0.611&1.000&1.636&1.666&1.268&1.356&1.720&1.557\\ 0.737&0.788&0.600&0.611&1.000&1.636&1.666&1.268&1.356&1.720\\ 0.581&0.737&0.788&0.600&0.611&1.000&1.636&1.666&1.268&1.356\\ 0.642&0.581&0.737&0.788&0.600&0.611&1.000&1.636&1.666&1.268\\ 0.667&0.642&0.581&0.737&0.788&0.600&0.611&1.000&1.636&1.666\\ 0.519&0.667&0.642&0.581&0.737&0.788&0.600&0.611&1.000&1.636\\ 0.424&0.519&0.667&0.642&0.581&0.737&0.788&0.600&0.611&1.000\\ \end{bmatrix}\]  
A judgment matrix of management factors at the criterion level and its index level is constructed.  
\[B_{2}=\begin{bmatrix}1.000&1.655&0.909&0.922&0.919&0.986&0.939&0.859&0.873\\ 0.604&1.000&1.655&0.909&0.922&0.919&0.986&0.939&0.859\\ 1.100&0.604&1.000&1.655&0.909&0.922&0.919&0.986&0.939\\ 1.085&1.100&0.604&1.000&1.655&0.909&0.922&0.919\\ 1.014&1.088&1.085&1.100&0.604&1.000&1.655&0.909&0.922\\ 1.065&1.014&1.088&1.085&1.100&0.604&1.000&1.655&0.909\\ 1.164&1.065&1.014&1.088&1.085&1.100&0.604&1.000&1.655&0.909\\ 1.145&1.164&1.065&1.014&1.088&1.085&1.100&0.604&1.000\\ \end{bmatrix}\]  
The judgment matrix of environmental factors at the criterion level and its index level is constructed.  
\[B_{3}=\begin{bmatrix}1.000&1.147&1.260\\ 0.872&1.000&1.147\\ 0.793&0.872&1.000\\ \end{bmatrix}\]
# A Risk Assessment for Ozone Regulation Based on Statistical Rollback
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Reg.** & **Prop.** & **Prop. w/ BG** & **Quadratic** & **Weibull** & **Stat.W** & **Stat.LN** \\ \hline \multirow{4}{*}{CDL} & level 75 & 1.96 & 1.20 & 0.98 & –0.52 & 1.02 & 0.99 \\  & level 75 & 1.96 & 1.20 & 0.98 & –0.52 & 1.02 & 0.99 \\  & level 70 & 2.75 & 1.66 & 1.38 & 0.44 & 1.42 & 1.39 \\  & (1.47, 4.02) & (0.87, 2.46) & (0.74, 2.03) & (–0.36, 1.26) & (0.78, 2.09) & (0.75, 2.05) \\  & level 60 & 4.62 & 2.76 & 2.40 & 2.67 & 2.56 & 2.49 \\  & (2.64, 6.69) & (1.58, 4.06) & (1.36, 3.52) & (1.38, 3.99) & (1.52, 3.70) & (1.45, 3.62) \\ \hline \multirow{4}{*}{UDL} & level 75 & 1.96 & 1.19 & 0.98 & –0.51 & 1.01 & 0.99 \\  & (0.93, 2.91) & (0.52, 1.79) & (0.45, 1.46) & (–1.36, 0.36) & (0.49, 1.49) & (0.50, 1.48) \\  & level 70 & 2.78 & 1.69 & 1.40 & 0.45 & 1.44 & 1.41 \\  & (1.50, 3.96) & (0.88, 2.42) & (0.74, 1.99) & (–0.42, 1.23) & (0.79, 2.08) & (0.74, 2.03) \\  & level 60 & 4.81 & 2.88 & 2.50 & 2.79 & 2.62 & 2.58 \\  & (2.66, 7.05) & (1.55, 4.26) & (1.36, 3.70) & (1.41, 4.31) & (1.49, 3.84) & (1.44, 3.79) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Posterior means and 95% confidence intervals of total mortality reduction per 1000 deaths (1998–2000): city-specific rollback. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Covariate** & **Reg.** & **Prop.** & **Prop. w/ BG** & **Quadratic** & **Weibull** & **Stat.W** & **Stat.LN** \\ \hline Daily & level 75 & 1.96 & 1.20 & 0.98 & –0.52 & 1.02 & 0.99 \\ Ave & & (1.06, 2.92) & (0.61, 1.79) & (0.51, 1.46) & (–1.33, 0.25) & (0.54, 1.51) & (0.50, 1.48) \\  & level 60 & 4.62 & 2.76 & 2.40 & 2.67 & 2.56 & 2.49 \\  & & (2.64, 6.69) & (1.58, 4.06) & (1.36, 3.52) & (1.38, 3.99) & (1.52, 3.70) & (1.45, 3.62) \\ \hline Daily & level 75 & 2.32 & 1.91 & 1.66 & 2.48 & 2.12 & 2.09 \\ Max & & (1.54, 3.14) & (1.26, 2.59) & (1.09, 2.27) & (1.62, 3.40) & (1.59, 2.79) & (1.55, 2.73) \\  & level 60 & 6.19 & 5.10 & 4.25 & 5.13 & 4.87 & 4.59 \\  & & (4.11, 8.31) & (3.33, 6.87) & (2.79, 5.75) & (3.41, 6.91) & (3.38, 6.35) & (3.09, 6.06) \\ \hline Daily & level 75 & 2.10 & 1.61 & 1.36 & 1.90 & 1.56 & 1.52 \\
8 h Max & & (1.26, 2.92) & (0.96, 2.23) & (0.79, 1.94) & (1.13, 2.67) & (0.96, 2.15) & (0.94, 2.12) \\  & level 60 & 5.53 & 4.23 & 3.43 & 4.34 & 3.67 & 3.55 \\  & & (3.37, 7.50) & (2.52, 5.76) & (1.99, 4.69) & (2.52, 5.91) & (2.26, 4.94) & (2.14, 4.81) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Posterior means and 95% confidence intervals of total mortality reduction per 1000 deaths (1998–2000): rollback and CDL model.
## 4 Simulations and Results
### Security Features of Proposed System
#### 4.6.3 Data Confidentiality  
All the communications between vehicles are encrypted using symmetric key encryption, which makes it difficult for the malicious node to tamper the communication data. Only an authorized user has access to the encrypted data. By using an encryption mechanism, malicious activities are prevented.
## 1 Introduction
The distance from a certain reference point or a form has been measured [25; 29; 30], and 3D analysis has been conducted by overlapping a CAD reference model onto a CAD test model [5; 24; 25; 26; 28]. This 3D analysis relies on an alignment process whereby the CRM is overlapped using software. In general, the overlap is investigated through best fit alignment [31; 32]. It is important to determine whether the prosthesis fit is within a clinically allowable range. More important than this, however, is the trueness of the manufactured prosthesis. \"Trueness\" is the similarity between the prosthesis designed first in CAD (the CAD reference model) and the ultimately manufactured prosthesis (CAD test model) [28; 33; 34; 35]. A trueness evaluation is important because an inaccurate inner surface on the prosthesis is one of several factors (dental preparation, preparation's scan) that affect the marginal and internal fit. An inaccurate inner surface on the inner prosthesis requires the dentist to postprocess the crown with a dental handpiece. This operation can increase the risk of fatigue crack initiation, leading to a shorter crown service life. Furthermore, a prosthesis with an excellent degree of trueness can reduce chairside time [13; 36]. Many previous studies have evaluated the trueness of the prosthesis [13; 35; 36; 37; 38]. The benefit of evaluating the trueness according to the region is to assess which region exhibits poor trueness, which can be used as reference data for the marginal and internal fit of the prosthesis. However, there have been no studies that have evaluated the trueness of the inner surface of the crowns and how that varies with the type of lithium disilicate block. In the present study, therefore, we began with a 3D comparison of the trueness of crowns produced using three types of lithium disilicate block. Then, we undertook a 3D comparison of the trueness according to the position of the crown. To this end, a null hypothesis was set as follows: There is no difference in the trueness of the crowns produced, regardless of the type of lithium disilicate block used, and there is no difference in the trueness according to the positions of the inner surfaces of the crowns.
# Smart Cities in Russia: Current Situation and Insights for Future Development
## 5 Russian Cities in Terms of the Proposed Decomposition
### Smart Transport  
There were successful experiments in the dynamic regulation of traffic lights. For example, in Voronezh, 140 traffic lights decreased the time to pass the crossroads by 18% [32]. Such experiments are going to continue in the future.  
Public transport systems in Russia are actively developing. Most of the major cities already have electronic payment and tracking systems.  
There are attempts to make public transport green. For example, as of 2020, 475 electric buses are operating in Moscow. Although the use of electric buses is an arguable solution, the trend for green public transport exists.
## 2 The Principle and Components of CPFTA
### Simulated and Measured Results  
The simulated and measured results of GCPFTA are shown in Figure 13. The GCPFTA obtains S11 bandwidth at range of 8.5-10.6 GHz and the 3-dB realized gain bandwidth of 8.8-10.3 GHz. Comparatively, the 3-dB AR bandwidth is narrow at range of 9.0-9.6 GHz, which is basically in accordance with the circularly polarized narrow bandwidth of transmitarray shown in Figure 3c. Ultimately, the narrow 3-dB AR bandwidth limits the working bandwidth of GCPFTA.  
Figure 12: (**a**) The fabricated GCPFTA. (**b**) The measurement of GCPFTA in anechoic chamber. (**c**) The integrated reflectarray and planar feed antenna with total size of 238 mm \(\times\) 238 mm \(\times\) 25 mm. (**d**) The E-shaped patches on the bottom layer of transmitarray with size of 240 mm \(\times\) 240 mm. (**e**) The anisotropic patches on the top layer of transmitarray with size of 240 mm \(\times\) 240 mm.  
Figure 13: Simulated and measured results of GCPFTA (**a**) S11, realized gain and (**b**) AR.