esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### GHG Emitted from Waste Incineration  
According to the relevant waste incineration standards in China, the moisture content of the waste entering the furnace should not be higher than 50%, and the low heating value (wet base) should not be lower than 5000 kJ. The heating value of primary waste in China is relatively high and generally meets the heating value standard. Because of the high proportion of kitchen waste in China as well as its moisture content, the moisture content of the primary waste is slightly higher than 50%. Before the waste is put into the incinerator, it must be stored and dried to reduce the moisture content. The GHG emissions from incineration of waste include two parts: the emission of GHG and the emission reduction effect of electricity generation. Figure 8 shows the GHG emissions per unit under the five scenarios. The GHG emissions and electricity generation under Scenario 2, 3 were 0.5181 t CO2e/t MSW and 0.4868 MWh/t MSW, higher than Scenario 1, because only part of the kitchen waste was separated, which reduced the water content per unit waste and increased the fossil carbon content. Under Scenario 4 and Scenario 5, after sorting out recyclables such as plastics with high heating value and fossil carbon content, the GHG emissions and electricity generation per unit waste from incineration decreased, but the net emissions were still lower than those under Scenario 1, with 0.1802 _t CO2e/t MSW_. Compared with landfill, incineration was an effective way to reduce GHG emissions. In recent years, the proportion of waste incineration in Beijing has gradually increased, and the waste disposal method has gradually become optimized.  
The research shows that the recycling of recyclables had a great impact on the GHG emissions from incineration [11]. Paper and plastic account for a large proportion of the recycled materials. With the characteristics of high fossil carbon content and high heating value, the separation rate of plastic will directly affect the GHG emissions from incineration. And because of the very low fossil carbon content in paper, the impact on the GHG emissions from incineration is very small. In this paper, based on the separation rate of 50% of plastic, a sensitivity analysis was carried out by increasing or decreasing this rate by 10% and 20%. The results are shown in Figure 9. When the separation rate of plastic was increased from 50% to 60%, the net GHG emissions from incineration of per unit waste were reduced from 0.0623 _tCO2e_ to 0.0295 _tCO2e_ (a decrease of 52.64%), which means the separation rate of plastic is a highly sensitive factor to the GHG emissions from incineration of waste. Therefore, as a key category affecting the GHG emissions from MSW incineration, the government should consider collecting plastic separately to improve the separation rate.  
Figure 8: GHG emissions from MSW incineration under five scenarios.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 6 Conclusions  
This study proposes an IoT-based sensing model and its overall technical architecture by using IoT, computers, and communication technologies for the tensioning management of prestressed concrete beams. Three aspects are described: the sensing, transport, and application layers. The data collection and preprocessing of the sensing layer, the data-pushing mechanism of the transport layer, and the specific work division and the work flow of the application service layer are designed. App terminals and web pages are used as display media. The application system based on the technical framework in this study is applied to the Hubei section of Zhengzhou-Wanzhou high-speed railway for verification. The feedback shows four points. First, job notifications are pushed actively, job site construction is monitored remotely, and management methods and levels are improved. Second, automated data collection reduces human interventions and the risk of false data. Third, intelligent analysis and statistics provide data support for subsequent process upgrades and construction progress control decisions. Fourth, intelligent data processing and synthesis reduces complicated data entry work and improves work efficiency.  
This research can be expanded in three aspects considering the application scenario of precast beam fields. First, the encapsulated structure of data can be optimized, and the amount of data transport can be reduced. Second, an in-depth study of the relationship between the tensioning process curve and the conclusions can be conducted. Tensioning equipment can be regulated in advance, and the qualified rate of tensioning results can be improved through analysis. Third, an app based on the ISO system can be developed. Otherwise, the intelligence of the system for decision making, perception, and equipment fault diagnosis, etc. also needs to be furthered.  
Design and implementation, X.C.; Analysis and testing, G.Y.; All authors contributed to editing and reviewing the manuscript.  
This research was partially supported by National Natural Science Foundation of China under Grant nos. 61863005 and 91746116 and 61562011, the Science and Technology Foundation of Guizhou Province under grant PTRC[2018]5702, LH[2016]7381, and LH[2016]7433, Foundation of Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, and the Student innovation and entrepreneurship training program under grant 201610660006.  
The authors declare no conflict of interest.
## 5 Results of the WDM and OAM Channel Allocation Methods
### Algorithms for Allocation Methods Used in the WDM Operating Technology
International Telecommunication Union (ITU): Geneva, Switzerland, 2012. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Efficiency Tests of DBA Algorithms in XG-PON. _Electronics_**2019**, \(8\), 762. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Real-Time Demonstration of 20\(\times\)25 Gb/s WDM-PON for 5G Fronthaul with Embedded OAM and Type-B Protection. In Proceedings of the 24 th OptoElectronics and Communications Conference (OECC) and International Conference on Photonics in Switching and Computing (PSC), Fukuoka, Japan, 1-3 July 2019. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Passive Optical Networks Progress: A Tutorial. _Electronics_**2020**, \(9\), 1081. [CrossRef]
* ITU-T G.893.3 (40-Gigabit-Capable Passive Optical Networks (NG-PON2): Transmission Convergence Layer Specification. International Telecommunication Union (ITU): Geneva, Switzerland, 2015. * [PERSON] et al. (2023) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] MS3F: Maximizing Bandwidth and Data Transmission Performance in SUCCESS-HPOON Architecture for WDM-PON Using AI Techniques. In Proceedings of the 2 nd International Conference on Advances in Computational Intelligence and Communication (ICACIC), Puducherry, India, 1-6 December 2023. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Dynamic Computing Power Wavelength Allocation Based on Deep Q-Learning for WDM-PON. In Proceedings of the IEEE Opto-Electronics and Communications Conference (OECC), Melbourne, Australia, 1-3 June 2024. [CrossRef]
* [PERSON] and [PERSON] (2024) [PERSON]; [PERSON] Optimization of WDM-PON and TDM-PON Optical Networks for High Rate Information Communication. In Proceedings of the 11 th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 1-6 March 2024. [CrossRef]
* [PERSON] and [PERSON] (2024) [PERSON]; [PERSON]; [PERSON] Laboratory-Based Investigation of the Performance Efficiencies of a TDM-PON and a WDM-PON. In Proceedings of the International Workshop on Fiber Optics in Access Networks (FOAN), Athens, Greece, 29-30 October 2024; pp. 34-37. [CrossRef]

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# A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
## 5 Conclusions  
This paper presents a two-stage fault diagnosis framework for planetary gearboxes, including a U-net based T-F spectrum analytical method in the first stage and a decision tree-based health state classification method in the second stage. Both the specific indications of fault feature regions and the white-box property of the decision tree model can improve confidence in the fault diagnosis conclusion for operators, which is lacking in conventional deep learning models. Using the real data collected from wind turbines, we prove that the U-net model is better than the traditional image processing methods in the T-F spectrum analytical task of planetary gearboxes with a F1-score of 0.942. With a decision tree classifier, the final accuracy of planetary gearbox fault diagnosis reaches 97.4%. Further experiments show that the proposed framework presents high robustness against variable operating environment. Moreover, it is worth noting that our method provides a flexible paradigm for solving similar problems with high openness.  
In future work, the authors plan to improve the proposed method from two aspects. First, knowledge generation based on interpretable machine learning is a promising direction for improvement. Second, the authors will follow up on advanced semantic segmentation methods to enhance the robustness of time-frequency analysis.  
Figure 10: The segmentation performance metrics and fault classification accuracy of the trained model on the robustness test dataset: (**a**) NS; (**b**) RP; (**c**) MA; (**d**) CO.  
**Author Contributions:** Conceptualization, P.Z. and C.C.; methodology, P.Z. and C.C.; software, P.Z.; validation, P.Z.; formal analysis, P.Z.; investigation, P.Z.; resources, P.Z.; data curation, P.Z.; writing--original draft preparation, P.Z.; writing--review and editing, P.Z.; visualization, P.Z.; supervision, C.C.; project administration, C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the National Natural Science Foundation of China (No. 51675350) and the Natural Science Foundation of Liaoning Province (No. 2019 MS245).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available online at [[https://ieee-dataport.org/documents/vibration-time-frequency-images-planetary-gearboxes](https://ieee-dataport.org/documents/vibration-time-frequency-images-planetary-gearboxes)]([https://ieee-dataport.org/documents/vibration-time-frequency-images-planetary-gearboxes](https://ieee-dataport.org/documents/vibration-time-frequency-images-planetary-gearboxes)) (accessed on 2 May 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Performance of the Inversion Model with the Validation Dataset  
Although the robustness results of the VIs of OSAVI, WDRVI and mSR for interpreting the variability in FPAR were given in the previous section, the performance of the optimal estimation model was unstable when applied to different datasets. Therefore, in order to address this issue, hybrid inversion models based on the ANN and SVR were established to explore the optimal estimation of FPAR. The inversion performances of these models are displayed in Table 5. Consistent results were achieved showing that OSAVI had better predictive capability than the other VIs for all three inversion methods, with an outstanding R\({}^{2}\) and minimum RMSE. Among the three inversion methods, the ANN algorithm worked  
Figure 7: Quantitative variability of three VIs and FPAR in response to the variations in PROSAIL model parameters: (**a**) ALA; (**b**) Cab; (**c**) SZA; (**d**) posil.  
best, surpassing both the curve fitting and SVR methods. Overall, the ANN-OSAVI hybrid inversion model based on PROSAIL model data presented the optimal capability for estimating FPAR, with an R\({}^{2}\) of 0.822 and RMSE of 0.055 for general dataset 1 and an R\({}^{2}\) of 0.775 and RMSE of 0.059 for specific dataset 2.
## 3 Results and Discussion  
### Results of 3D CFD Modeling of Liquid-Gas Ejector  
#### 3.1.1 Validation and the Effect of Different Parameters on Gas Entrainment Rate  
The experimental and predicted values of gas entrainment rate as a function of water flow rate and separator pressure in the liquid-gas ejector with 6 mm nozzle diameter are shown in Figure 4a,b. These figures evidently show that the experimental values attained  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Position** & **Type of Boundary Condition** \\ \hline Walls & Adiabatic and non-slip \\ \hline Inlet liquid & velocity inlet + mass fractions of components (30\% amine solution) \\ \hline Inlet gas & Pressure inlet + mass fractions of components (19.1\% CO2 and 80.9\% air) \\ \hline Outlet flow & Pressure outlet \\ \hline Symmetry & Axis \\ \hline \hline \end{tabular}
\end{table}
Table 4: The boundary conditions of the simulated 2D ejector.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fluid** & **Density (kg/m\({}^{3}\))** & **Viscosity (kg/m-s)** & **Surface Tension (N/m)** \\ \hline Water & 997 & 0.00091 & - \\ \hline Air & 1.168 & \(1.86\times 10^{-5}\) & - \\ \hline Water–air & - & - & 0.072 \\ \hline CO2 & 1.784 & \(1.919\times 10^{-5}\) & \\ \hline MEA & 999.83 & 0.018 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fluid properties of the designed 3D liquid–gas ejector.  
for gas entrainment [23] are consistent with the predicted ones. Furthermore, Figure 4a demonstrates that the gas entrainment rate increases with liquid flow rate. This increment can be justified by the fact that an increment in water flow rate leads to an increase in driving force. This ejector's pressure drop is the primary driving force for gas entrainment that is a pressure difference between the beginning and end of the nozzle (\(\Delta p\)). The greater the pressure difference, the more liquid can be entrained into the ejector. Moreover, the momentum transfers from water to air, and consequently, air entrainment rate increases. At high liquid flow rates, the driving force remains approximately constant; therefore, gas entrainment rate does not change. Figure 4b shows the relationship between separator pressure and gas entrainment at a constant liquid flow rate of 2.85 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s. The findings reveal that the gas entrainment rate reduces with increasing separator pressure when the liquid flow rate remains constant. If the separator pressure increases, the entrained gas bubbles have more difficulty moving downward in the ejector, resulting in lower entrainment.  
Figure 4: (**a**) Effect of motive liquid flow rate on the air entrainment (nozzle diameter = 6 mm) and pressure difference between the beginning and the end of the nozzle. (**b**) Effect of separator pressure on gas entrainment at constant liquid flow rate of 2.85 \(\times\) 10\({}^{-4}\) m\({}^{3}\)/s for the air–water system (nozzle diameter = 6 mm).
###### Abstract  
The Mobile Ad Hoc Network (MANET) is a network that does not have a fixed infrastructure. Migratory routes and related hosts that are connected via wireless networks self-configure it. Routers and hosts are free to wander, and nodes can change the topology fast and unexpectedly. In emergencies, such as natural/human disasters, armed conflicts, and emergencies, the lowest configuration will ensure ad hoc network applicability. Due to the rapidly rising cellular service requirements and deployment demands, mobile ad-hoc networks have been established in numerous places in recent decades. These applications include topics such as environmental surveillance and others. The underlying routing protocol in a given context has a significant impact on the ad hoc network deployment power. To satisfy the needs of the service level and efficiently meet the deployment requirements, developing a practical and secure MANET routing protocol is a critical task. However, owing to the intrinsic characteristics of ad hoc networks, such as frequent topology changes, open wireless media and limited resources, developing a safe routing protocol is difficult. Therefore, it is vital to develop stable and dependable routing protocols for MANET to provide a better packet delivery relationship, fewer delays, and lower overheads. Because the stability of nodes along this trail is variable, the route discovered cannot be trusted. This paper proposes an efficient Neighbour Feedback-based Trusted Multi Authenticated Node (NFbTMAN) Routing Model. The proposed model is compared to traditional models, and the findings reveal that the proposed model is superior in terms of data security.  
mobile ad hoc network; MANET; authenticated routing mode; neighbour feedback +
Footnote †: journal: Notic  
0
Footnote 0: _Sustainability_
## 2 Materials and Methods
### Data Processing  
Configured, AWR1642 streams the processed data via USB interface to the controlling PC. Decoded information is a frame filled by targets' parameters. A single frame is a \(3\times N\) dimensional matrix, where \(N\) stands for the number of detected targets. Three columns correspond to the respective parameters: Doppler velocity (m/s), \(X\) and \(Y\) position (m). For machine learning, those values are considered intermediate parameters, i.e., one is not concerned about the error of each individual value as long as their cumulative effect yields the recognition of the correct gesture. Each gesture consists of M frames, creating a matrix of \(3\times N\times M\) size. The examples of the matrix representation of the detected targets with their respective pixel vs. frame image are shown in Figure 4. The program automatically transfers a gesture matrix into CSV file, which contains a single gesture sample per file. Each file stores 20-40 kB of data. It allows training directly on the original digitized radar-based data, without generating a spectrogram-like image and adopting the techniques used for visual image recognition. Overall, the above features make the training process more computationally efficient. The database with all gestures is freely available at IEEE Data Port [23].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Gesture Type** & **Person1** & **Person2** & **Person3** & **Person4** & **Sum** \\ \hline Arm to left & 100 & 100 & 100 & 100 & 400 \\ Arm to right & 100 & 100 & 100 & 100 & 400 \\ Closing fist horizontally & 100 & 100 & 100 & 100 & 400 \\ Close fist perpendicularly & 150 & 50 & 100 & 100 & 400 \\ Hand away & 200 & 100 & 100 & 0 & 400 \\ Hand closer & 100 & 100 & 100 & 100 & 400 \\ Hand down & 100 & 100 & 100 & 100 & 400 \\ Hand up & 100 & 100 & 100 & 100 & 400 \\ Hand rotating palm down & 300 & 0 & 100 & 0 & 400 \\ Hand rotating palm up & 300 & 0 & 100 & 0 & 400 \\ Hand to left & 100 & 100 & 100 & 0 & 300 \\ Hand to right & 100 & 100 & 100 & 0 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of samples per person.  
Figure 4: Exemplary gesture samples recorded by the radar with detected targets along \(X\) and \(Y\) position for each frame.
## 2 Materials and Methods
### Test Methods  
To calculate the FRF experimentally, the impact energy and response energy must be measured. The experimental equipment was constructed as shown in Figure 2: (a) the impact hammer (PCB 086C03) was used to measure the impact energy of the specimen, (b) the accelerometer (PCB 208C05) was used to measure the response according to impact, and (c) the signal conditioner (PCB 482C16) and (d) DAQ (NI PXIe 6366) were used for data collection by the experimental equipment. The measurement program used an (e) NI Labview signal express to store the data at a sampling rate of 500 ks/s, and because the stored data were values in the time domain, they were converted to the frequency domain using the FFT using the MATLAB signal process toolbox. Next, the FRF was calculated using Equation (1).  
The FRF graph according to the specimen sample is shown in Figure 3. Figure 3a is the FRF result of the normal specimens for cristobalite and alumina materials. In the graph, C1 is the result for cristobalite and A1 for alumina. Depending on the material of the porcelain insulator, the shift of the natural mode frequency was confirmed, and the alumina material had a higher natural mode than the cristobalite material. The same materials of porcelain insulators have similar FRF results, but there may be differences due to the years of use and exposure conditions. Figure 3b shows the FRF results of the normal and cracked and broken specimens. In the graph, D1 is the result for a porcelain cracked specimen and D2 for a broken disc specimen. According to the type of breakdown of the porcelain insulator, it has been confirmed that the natural mode is completely different and may vary depending on the degree of breakage. Figure 3c shows the FRF results for normal and internal defect specimens. In the graph, D3 and D4 indicate the FRF results for the specimen with internal defects. For the internal  
Figure 2: The frequency response function (FRF) test method.  
defect, a new natural mode was created at low frequencies, and some natural modes were switched to low frequencies. Additionally, porcelain insulators are used externally, so even though they are normal insulators, they show various differences in the waveform of FRF. Determination of normality and defects can be difficult because new peaks may be generated around the main peak, or other features may occur that shift some eigenmode frequencies. Through the basic FRF analysis, the characteristics of the materials and defects were identified, and based on this, the obtained data were used as basic data for feature extraction for machine learning applications.  
Figure 3: FRF results and images of materials and defective specimens. (**a**) FRF results and images of normal specimens, (**b**) FRF results and images of specimens with porcelain defects, and (**c**) FRF results and images of specimens with internal defects.