esa-sceva/satcom-chunk-collection huggingface dataset:
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant
### Cloud-Based Implementation
#### 4.3.1 Amazon Web Services  
For big data management, a general AWS architecture is presented in Figure 6. The edge nodes transmit the sensed data to the AWS IoT core at user-specific intervals or upon any initiated event. The messages from individual edge nodes are first authenticated at the AWS IoT Core. Depending upon the message's type, location, and time, an appropriate predefined rule (in Rule Engine) can be implemented. The Rule Engine is responsible for routing the incoming message/data to the desired back-end application in the cloud. The AWS Kinesis Firehose encrypts the data and loads them to the Kinesis Analytics, where the data are further processed for anomaly detection, as shown in Figure 6. The data from the Kinesis analytics are used to predict system behavior and overall performance. Different scenarios can be initiated depending on the analytical results of incoming data/messages.  
codes in the DynamoDB tables and an SNS notification can be triggered with the translated trouble code.  
The system can interact with the outside world securely and efficiently through the Amazon API Gateway, which hosts RESTful APIs and deploys the Amazon Cognitive user pool. The Amazon Cognitive service authenticates users to make a request via the API Gateway. In turn, a Lambda function can be invoked to perform the desired task on the data stored in the DynamoDB tables. The RESTful APIs allow the existing applications and other third-party applications to interact or access the data in DynamoDB securely. These data can be used by the user to build detailed monitoring reports, graphs, and other performance evaluation charts.  
The performance of solar plants depends on various meteorological and electrical parameters. Any fault in the system may lead to under-performance or system failure. Therefore, a refined and sophisticated algorithm needs to be in place for fault diagnostics and predictive maintenance. In this context, several artificial intelligence (AI)-based supervised learning algorithms have been proposed in the existing literature. The ANN technique is widely used for fault identification and classification [24; 25; 55]. In [38], the Artificial Neural Network (ANN)-based solar monitoring algorithm has been presented to predict the output power of heterogeneous solar plants. The proposed algorithm is trained on the solar panel's power, temperature, voltage, and solar irradiance. A solar panel is marked as faulty if the predicted power output differs by more than 10% from the actual solar panel's output power.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 4 Centralized Protocols  
In this section, we briefly review the architectural design and characteristics of all current centralized MAC protocols that operate in the mmWave systems. Tables 2 and 3 summarize the centralized protocols in terms of applications and comparative analysis, respectively.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 2 Background
### Applicability of TCP in the IoT
#### 2.2.4 [PERSON]'s Algorithm  
[PERSON]'s algorithm [18], shown in Algorithm 1, limits the amount of data in flight to a single full-sized segment (=MSS) unless another full-sized segment is available for transmission.  
```
1:procedureTransmit(\(D\))
2:ifwindowsize\(>=\) MSS and \(|D|>=\) MSS then
3:\(\texttt{Send}(D)\)
4:else
5:ifdata in flight then
6:\(\texttt{Queue}(D)\)
7:else
8:\(\texttt{Send}(D)\)
```  
**Algorithm 1** [PERSON]'s AlgorithmConstrained devices typically use statically-allocated packet buffers. If the MSS value corresponds to the internal packet buffer size, [PERSON]'s algorithm ensures that the buffers are utilized at maximum efficiency. Without Nagle, many small segments could occupy buffer spaces foreseen for larger TCP segments, thereby wasting RAM.
# ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security
## 7 Conclusions  
We propose a prototype of a distributed learning system, ZenoPS, that integrates message compression, infrequent synchronization, both asynchronous and synchronous training, and score-based validation. The proposed system provides communication  
Figure 6: ZenoPS in the asynchronous mode with multiple validators. In each experiment, we launched 2 servers, 2 validators, and 8 workers. (**a**) No attacks. (**b**) Random attack rescaled by 8.  
Figure 7: Sensitivity to \(\rho\). \(\rho\) varies, with fixed \(\epsilon=0\) and \(\gamma=0.6\). ZenoPS in the synchronous mode with multiple validators. In each experiment, we launched 2 servers, 2 validators, and 16 workers. Random attack is rescaled to 1.  
reduction, asynchronous training, Byzantine tolerance, and local differential privacy, with theoretical guarantees, and was evaluated on an open benchmark.  
This work also raises some open problems to be solved in future work. One limitation of ZenoPS is the relatively high computation overhead of the score-based validation algorithm, which gives improved protection from attacks at the cost of consuming more computation resources. How the score-based validation can be more efficient remains to be explored. The learning system could also be made to automatically and adaptively choose the compression ratio and validation methods for different training tasks, to make the system more user-friendly in practice. For theoretical analysis, it will be interesting to establish theories for optimal values of hyperparameters such as \(\rho\) in score-based validation, and to study how hyperparameters such as the learning rate \(\eta\) affect the optimal value of \(\rho\).  
Conceptualization, C.X., O.K., and I.G.; methodology, C.X., O.K., and I.G.; software, C.X.; resources, O.K. and I.G.; writing--original draft preparation, C.X.; writing--review and editing, O.K. and I.G.; visualization, C.X.; supervision, O.K. and I.G.; project administration, O.K. and I.G.; funding acquisition, O.K. and I.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by J.P. Morgan 2020 AI Research Ph.D. Fellowship Awards 098804, a 2021 Sloan Fellowship, and a gift from Microsoft. This work was also funded in part by NSF 2046795, 1909577, 1934986, and NIFA award 2020-67021-32799.  
This research uses the following publicly archived dataset: the CIFAR-10 [58] dataset, available on the website [[https://www.cs.toronto.edu/kriz/cifar.html](https://www.cs.toronto.edu/kriz/cifar.html)]([https://www.cs.toronto.edu/kriz/cifar.html](https://www.cs.toronto.edu/kriz/cifar.html)) (accessed on 15 February 2021).  
We would like to thank Intel and Microsoft Azure for granting the computation resources to support this research.  
The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
## 4 Discussion and Conclusions  
Non-destructive testing is one of the important procedures for ensuring the quality and service life of the material. The advantage of this investigation is that the properties of the material, whether functional or characteristic, remain unaffected after the investigation process. Electromagnetic methods are often used in the investigation of conductive materials. Using these methods, it is possible to quantify and qualify cracks in the material, as well as the selected properties of such materials. By examining the cracks and their selected parameters, the reason for their occurrence can also be determined. Then it is possible to prevent their spread or prevent their occurrence. The eddy current method is very widespread and often used. The area of interest was mainly an innovative approach to the eddy current method: frequency sweep. Its use for non-destructive testing is currently not sufficiently explored by scientific research institutions. Based on previous experiments and simulations we published earlier [16; 17], a probe with a ferromagnetic core was chosen for the experiments, which provided the best responses to the change of artificial crack dimensions. Subsequently, the work was devoted to the optimization of the test area. The SFECT method uses a static probe, so it was necessary to choose the optimal area of interest. Based on previous experiments [16; 17], a measuring network composed of eleven points was chosen. After optimizing the area, the method was applied to artificially made corrosion defects similar to the real cracks in the biomaterial.  
In total, seven material specimens with the presence of corrosion cracking were successfully inspected. Multi-point inspection was applied, where the specific measuring points were precisely defined. The results of the experiments confirmed the assumed hypotheses. The SFECT method, as a method originally intended for the field of the strategic industry, is equally suitable for use in the interdisciplinary field of biomedicine as a method of non-destructive investigation of biomaterials. The great potential of this approach lies in  
Figure 12: Map of the maximum value of the results, Specimen 1 (**a**), Specimen 2 (**b**), Specimen 3 (**c**), Specimen 4 (**d**), Specimen 5 (**e**), Specimen 6 (**f**), Specimen 7 (**g**), corrosion crack.  
the fact that the measuring probe can detect the presence of corrosion cracks, even when it is not fixed directly in place over the presumed defect.  
Conceptualization, M.S., D.G.; methodology, M.S., F.V.; software, F.V.; validation, M.S. and D.G.; formal analysis, M.S.; investigation, F.V.; resources F.V. and L.J.; data curation, M.S.; Writing--original draft preparation, M.S. and D.G.; writing--review and editing, M.S. and D.G.; visualization, F.V. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Loaded Tooth Contact Analysis of Spiral Bevel Gear Pairs
### Loaded Tooth Contact Analysis Considering Assembly Error and Tooth Tip Chipping  
In the absence of load, there is local point contact. The contact points can form a line known as the initial contact trajectory during a mesh cycle. To simplify calculations, this trajectory is represented by dispersed points which are called initial contact points. When under load, SBGs transition to elliptical contact. To simplify calculations, we utilize a line which is long enough but cannot exceed the tooth profile along the long axis of the elliptical contact region instead of elliptical contact surfaces. Additionally, the line is represented by dispersed points, referred to as potential contact points, for further simplification.  
The proposed method can accurately incorporate and take into consideration the actual shapes of the tooth tip chipping. Initially, it needs to extract the shape (see Figure 5a) of tooth tip chipping from the gear affected by the chipping fault. When tooth tip chipping occurs, there is a reduction in the clearances between the pinion and gear within the chipping area (see Figure 5b). For simulating the occurrence of tooth tip chipping, this paper adjusts the clearances (see Figure 5c) between the potential contact points within the chipping area to represent the chip. The increased values in clearance reflect the depth of tooth tip chipping, whereas the extent of the chipped area is presented by the potential contact points for which clearances are altered. Subsequently, the equation governing compatibility is solved anew using the updated clearances, enabling the analysis of the mesh characteristics of the gear under tooth tip chipping fault.  
The method presented in this paper is outlined in a simplified flowchart depicted in Figure 6. Firstly, based on the gear and machine tool parameters, the assembly errors are incorporated into the TCA to acquire the data for LTCA and to calculate the unloaded transmission error. Subsequently, LTCA is executed. Prior to assessing the compatibility, tooth tip chipping is indicated by noting the changes in clearance within the tooth tip chipping area. Following this, the TVMS can be computed using the compatibility equation. Finally, contact stress is determined using the _Hertz_ contact formula, leveraging the curvature and normal force at potential contact points obtained from LTCA.  
Figure 5: Contact area and chipping area on the rotating projection of the pinion tooth surface, the actual shape of tooth tip chipping and the clearances change: (**a**) actual chip; (**b**) contain chipping and \(\Delta\)AG = 0.5 mm (**c**) clearances change due to the chipping.  
Figure 6: Flow chart of LTCA considering assembly error and tooth tip chipping.
## 3 Results and Discussion
### Scanning Electron Microscope  
In order to further verify the composition change in doped CSM samples, this study performed SEM tests on 28d samples varying in water-to-cement ratios. In the electron microscope images used in this paper, the working distance and voltage of the electron microscope are 10 mm and 10 kV, respectively. At the same time, considering that when the w/c ratio changes little, the microstructure of the printing material will not change obviously, so this part only compares and discusses the samples with a w/c ratio of 0.37 and 0.40. From the comparison in Figure 9,b, it can be seen that compared with the calcium hydroxide precipitation of the YN0.37 sample, the calcium hydroxide precipitation of the YN0.40 sample is more, and this also shows that when the w/c ratio increases from 0.37 to 0.40, the hydration degree of the GBFS decreases, the consumption of calcium hydroxide decreases, and there is more calcium hydroxide precipitation in the long-term sample. In addition, compared with the pores of sample YN0.37, it can also be found that there is more ettringite in the pores of sample YN0.40, which also verifies that although an increase in the w/c ratio leads to the decrease in hydration of GBFS in YN0.40, less Ca\({}^{2+}\) and SO\({}_{4}\)\({}^{2-}\) are absorbed into the gel structure, and more ettringite is formed and filled into the pores of the printing material, which makes up for an increase in porosity caused by an increase in the w/c ratio. The compressive strength of the printing material is improved.
# Identification and Optimization Study of Cavitation in High Power Torque Converter
## 5 Validation of Cavitation Optimization for High Power Torque Converter  
The stator for Scheme 2 was 3D printed using the results of the first optimization calculations to verify their accuracy. Figure 14 compares the original stator with the new design for Scheme 2.  
The design of the stator blades was optimized and tested with the original torque converter's impellers, the turbine, and the pump wheel, while the stator was updated to the Scheme 2 design stator. The torque converter was tested at a constant speed to obtain test results for the new torque converter assembled with the original scheme torque converter and the Scheme 2 stator, ensuring that the test input conditions (system pressure, flow rate, etc.) were consistent with the original scheme test conditions. The obtained results are shown in Figure 15.  
Figure 14: Comparison of stator samples from the two schemes.  
Figure 13: Comparison of torque converter simulation performance before and after optimization.  
As shown in Figure 15, the new hydraulic torque converter scheme exhibits a slight decrease in the overall \(TR\) when compared to the original scheme, with a maximum difference rate of 2.31%. Additionally, the \(\eta_{max}\) generally decreases, with a maximum decrease of 0.9 percentage points. However, this change maintains the consistency of the torque converter's \(TR\) and \(\eta_{max}\).  
Figure 16 shows that the \(M_{bg}\) at 1700 r/min pump input speed measured by the original scheme and Scheme 2 are almost identical with a small error in the speed ratio with an interval of 0.4-0.9. However, in the low speed ratio region of 0.0-0.4, the \(M_{bg}\) of the hydrodynamic torque converter is significantly improved. The \(M_{bg0}\) is improved from the original 735.6 N\(\cdot\)m to 946.8 N\(\cdot\)m, resulting in a 28.7% improvement rate.  
The change curve of \(\Delta L\) in Figure 16 shows that cavitation occurs in the original scheme when the \(SR\) is below 0.5 and exacerbates as the \(SR\) decreases; the flow difference, \(\Delta L\), is \(-2.1\) L/min in stall conditions. The \(\Delta L\) of Scheme 2 has been significantly improved and only slight cavitation occurs when the speed ratio is \(\leq\)0.15. The \(M_{bg}\) is not affected sig  
Figure 16: Comparison of \(M_{bg}\) and \(\Delta L\) between simulation and test of the optimized scheme.  
Figure 15: Comparison of \(\eta\) and \(TR\) between simulation and test of optimized scheme 2 (S2).  
nificantly compared to the simulation data. These improvements greatly reduce cavitation in the hydrodynamic torque converter.
# Special Issue on \"Mapping and Monitoring of Geohazards\"
## 3 Future Developments for the Mapping and Monitoring of Geohazards  
The papers published in the MDPI Special Issue on \"Mapping and Monitoring of Geohazards\" reveal the latest trends in characterising and enhancing our understanding of the risks posed by geohazards and landslides in particular. With a wide array of spaceborne, airborne, and ground-basedsensors available, encompassing different spatial, temporal and radiometric resolutions, and characteristics of the phenomena, we are in the 'Golden Age' of EO. As a consequence, EO is now, more than ever, routinely playing a crucial role in the mapping and monitoring of natural hazards.  
With regards to the spaceborne domain, two programmes in particular have been largely responsible for driving the uptake of EO data for mapping and monitoring geohazards. These are the Landsat constellation from the United States Geological Survey and the Sentinel missions from the European Space Agency. In large part, this is due to the increased data accessibility, with the provision of free satellite imagery since 2008 [17] and 2014 [18], respectively.  
Along with new and more readily accessible EO data, the papers published in the Special Issue clearly demonstrate that computer science techniques, such as artificial intelligence and cloud computing platforms, are being progressively developed or adapted in geohazards research. Such techniques are being employed in order to tackle the issues related to downloading, managing, analysing, and interpreting big volumes of data [19].  
The increased provision of data and associated processing techniques has clearly had a significant impact within the scientific community, which is reflected by the rapid growth of the number of papers in journals, books, and series on the use of EO for geohazards studies (Figure 1). Out of the 7196 publications between 1976 and 2019, around half of them were produced during the 'Sentinel Era', namely during or after 2014.  
The next challenge we face is translating what are largely scientific outputs into products and information that the civil society can more routinely use in the disaster management cycle and in policy formation. It is then when the direct impact of the innovative and ground-breaking work of our scientific community in helping to mitigate the human and socio-economic impact of geohazards will be fully realised.  
Figure 1: Number of earth observation (EO)-related geohazards publications per year for papers in journals, books, and series. The search was performed through the Web of Science portal based on the following keywords (in quotes) and Boolean Operators (in uppercase): “Earth Observation” OR “Remote Sensing” AND “Geohazards”.  
**Acknowledgments:** We are greatly indebted to all authors, reviewers and the editorial team of _Applied Sciences_, without whose support, time and expertise this Special Issue would not have been possible. We extend our thanks to all who have been involved in the compilation of this Special Issue at any stage.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
## 5 Case Simulation
### Case Simulation Without DR and Uncertainty of Wind and Solar Power
The comparison of converge curves is shown in Figure 9.  
It can be seen that the IDBO significantly outperforms the GWO and DBO. Specifically, the initial value of IDBO is about 18.6% better than GWO and 5.5% better than DBO, enabling the algorithm to converge to the optimal value more quickly in fewer iterations. Figure 8: MG output at the lowest total cost without considering DR and uncertainty of wind and solar power. Non-dominated sorting allows for clearer evaluation and ranking of individuals within the population, which can improve convergence speed. As a result, IDBO reached the optimal range in fewer than 100 iterations. Additionally, the elite reverse learning strategy enhances convergence accuracy of IDBO in later iterations and improves the quality of the final value, which is about 56.6% better than GWO and 37.9% better than DBO. IDBO not only demonstrated a faster convergence speed, but also achieved higher solution accuracy and stronger local optima escape capability. These results fully demonstrate the significant advantages of the IDBO algorithm in terms of convergence, accuracy, and robustness. A comprehensive evaluation framework was established to assess the performance of IDBO, DBO, and GWO, incorporating convergence half-life, relative convergence speed, and stability as key metrics, in addition to initial and final values. Convergence half-life is the number of iterations required to reach half of the final value  
The IDBO demonstrates excellent performance in both initial and final values, achieving lower initial and final values, indicating its outstanding optimization capability. Additionally, IDBO also performs better in terms of convergence speed and stability compared to DBO and GWO. The GWO performs worse in this case, showing good stability but obtaining high initial and final values, along with slower convergence speed. Additionally, the running time of IDBO is slightly worse than DBO, which is a negative impact resulting from the increased algorithmic complexity. However, considering the overall performance, IDBO still maintains a significant competitive edge. The performance indicators after 100 runs also show that IDBO has lower initial and final values. Although the GWO is slightly advantageous in terms of overall stability, IDBO still demonstrates better performance in this case.