esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### uCLUST
#### Illustrative Example  
Images belonging to batch A are trained using the ELM neural network using which model A is created. Similarly, images in batch B are trained to create model B. The mean of the images in a specific class is computed and stored. In _ELM++_, these models are combined. The mean of each test image is computed and matched against every class mean. The test image is classified using the model with which the class mean matches. If the mean of the test image matches with two models, then the image is tested using those two models, and the results are combined.  
Figure 8 shows the individual model created by ELM for each batch. After learning and creating the models for two batches (batch A, batch B), _ELM++_ creates two objects which contains three attributes 'class, mean and model' values. When a test image arrives, its intensity mean is calculated and is checked with the values available in stored objects. As in Figure 9, consider if the mean of the test image \(t_{1}\) is 0.53209, _ELM++_ checks the attribute value (mean) of the objects and based on the threshold value (0.01), it chooses a model whose difference in intensity is \(\pm\) threshold from the image intensity. So, for test image \(t_{1}\), it classifies the image with _model M2_ since the class intensity mean of the model is 0.54271, which lies between the range (0.53209 \(\pm\) threshold). Similarly for test image \(t_{2}\), if the  
intensity mean is 0.44372 and threshold = 0.01, ELM++ classifies the image using both models \(M1\) and \(M2\) since they have a class intensity mean of 0.45627 and 0.46211 respectively, which lies within the range (0.44372 \(\pm\) threshold). The classified results of \(t_{2}\) by both the models \(M1\) and \(M\) are combined using Algorithm 6.
```
Input: Classified output for test image got from model \(M_{i}\) and \(M_{i-1}\) Output: Concatenated classified output for test image 1. Convert the classified output \(classify_{i}\) and \(classify_{i-1}\) to their equivalent decimal form \(decimal_{i}\) and \(decimal_{i-1}\), that represents the class to which the test image belongs.
2.if\((decimal_{i}=expected\_class)and(decimal_{i-1}=expected\_class)\)then \(\text{classify}_{result}\leftarrow\text{classify}_{i-1}\) elseif\((decimal_{i}=expected\_class)\)then \(classify_{result}\gets classify_{i}\) elseif\((decimal_{i-1}=expected\_class)\)then \(classify_{result}\gets classify_{i-1}\) else \(\mid\) append either \(classify_{i-1}\) or \(classify_{i}\) to \(classify_{result}\)  end if  end if  end while
```  
**Algorithm 6**_ELM++: Combining the classified results.  
Figure 8: Models created for each train set - **(a)** creates a model M1 for batch A and **(b)** creates a model M2 for batch B.  
Figure 9: _ELM++ to classify test images.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Inverters as VRE Interfaces with Grids
#### 2.2.4 Short-Circuit Currents  
Short-circuit current magnitudes over time are used to estimate grid impedance and hence, network strength [41]. The replacement of synchronous machines (traditional generators) by the power electronics converters of VRE sources significantly reduces the magnitudes of short-circuit currents, triggering unexpected unstable interactions between the operation and control of nearby VRE sources and other inverter-interfaced devices on the supply, storage, and demand sides. Moreover, individual VRE inverters exhibit different responses to grid-side faults. This renders it complicated in practice to calculate the short-circuit impedances of individual VRE inverters, particularly when unbalanced faults are in play, and hence, establish the appropriate schemes of protection coordination and fault-ride-through (FRT) operation of VRE-dominated grids [47]. Most existing positive-sequence simulators lack the detail required for accurate short-circuit analysis of wind turbines and solar PV generators. The simulators simply assume that VRE inverters operate only in a grid-following mode, rather than in a grid-forming or grid-supporting mode, underlining the concerns on the performance of VRE inverter controllers during faults and the effects thereof on voltage stability and controllability in a VRE-dominated grid. To address these issues, EMT-level simulators with high fidelity of models and controllers are essential to improve analysis of short-circuit currents and establish new protection and FRT schemes with high performance.
## 2 The South Atlantic Anomaly  
The South Atlantic Anomaly (SAA) is one of the most well-known features of the Earth's magnetic field. It emerges as a consequence of the tilt (\(\sim\)10\({}^{\circ}\)) between the magnetic dipole axis of the Earth and its rotational axis and of the offset (\(\sim\)500 km) between the dipole and the Earth centers. It can be considered as the response of an inverse flux path at the core-mantle boundary of the radial component of the geomagnetic field--located approximately under the South Atlantic Ocean, which generates the hemisphere asymmetry of the Earth's magnetic field [14]; this region is characterized by an extremely low intensity of the geomagnetic field, and its behavior suggests that this asymmetry could be connected to the general decrease of the dipolar field and to the significant increase of the non-dipolar field in the Southern Atlantic region [15; 16]. The extent area of the SAA at the surface of the Earth has been continuously growing since instrumental intensity measurements were made available. Several studies relate this as an indicator of a possible upcoming geomagnetic transition (excursion or reversal). It is generally accepted that such transitions are anticipated by flux patches of reversed polarity, slowly appearing at low- or mid-latitude, which migrate towards the pole [17; 18].  
The spatial and temporal evolution of the geomagnetic field has been monitored since 1832, when [PERSON] performed the first intensity measurements in this region [19]. It has been shown that the magnetic dipole strength has been continuously decreasing [20], and data from the Swarm mission [21] revealed that two different patches are present over South America and near the coast of Africa, the latter growing at a rate of \(-2.54\times 10^{5}\) nT per century [22]. A correct modelization of the SAA is of capital importance due to the high impact it has on human health and on instrumental efficiency [23]. Furthermore, recent studies indicate that the extent of the anomaly follows a log-periodic acceleration, resembling the behavior of a system that moves toward a critical transition [24].
# A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
## 4 Case Study
### Performance Validation
U-net, as a deep learning method, can automatically capture multi-scale features without manual tuning of model parameters. A trained model, in addition, can process T-F samples quickly, which meets the real-time requirements in fault analysis. In conclusion, as the first stage of the proposed framework, the U-net method is competent for the T-F analysis task among planetary gearbox fault diagnosis. Figure 6: The results of identifying fault feature regions using U-net. White represents the characteristic area, and black represents the background. Besides analyzing the T-F information, the ultimate goal of presenting a high-accuracy analytical method is to realize the fast classification of planetary gearbox faults. The labeled fault feature regions are used in the second stage of the proposed framework. This paper uses three geometric moments to extract valuable information from segmented images, including image mass \(M_{00}\) and moment of inertia in two directions (\(M_{20}\), \(M_{02}\)). In our conception, the former is defined to distinguish between characteristic patches and characteristic bands, while the latter two judge the relative position in the T-F spectrum. The significantly compressed feature space makes it possible to use a simple classifier. In this experiment, we choose the decision tree algorithm to classify the four health states of planetary gearboxes quickly. The simplicity of the model, convenience of training, and high interpretability of the model are several factors that we focus on. Obviously, the decision tree algorithm is an excellent choice to reveal the faulty information in T-F images. Figure 8 shows the classification confusion matrix of the decision tree algorithm on the test dataset. Overall, the average accuracy of the diagnosis reaches 97.4%. The limited amount of real data and small feature space have no adverse impact on the diagnosis results. Specifically, meshing misalignment fault (MA) and pitting-misalignment concurrent fault (CO) are easy to confuse because they both have the characteristics of high energy of double meshing frequency bins. Ring gear tooth pitting (RP) fault is a health condition with the lowest diagnosis accuracy. Referring to the observation in the previous subsection, it is more polluted by noise. Figure 9 shows the trained decision tree in the experiment. In summary, the decision tree algorithm combined with the shape feature extraction method can effectively extract the status features in T-F images and accurately classify the health states of planetary gearboxes.
## 2 Materials and Methods
### Analysis Method  
The framework of the proposed method is shown in Figure 1. A total of 47 GRF data (29 PD patients with different disease grades and 18 normal subjects) were used. First, the GRF data were preprocessed, including categorizing subjects according to the gait cycle and calculating the gait characteristics of each gait cycle during walking, then a time series of the gait characteristics was obtained. During the gait-cycle division, the period should be as short as possible on the premise of guaranteeing the complete representation of the gait-cycle information for both the left and right feet. Therefore, we choose two gait cycles as the period and divided them into sections, so that the information in the original signal was completely retained.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group** & **Number** & **Male** & **Female** & **Age** & **HY = 2** & **HY = 2.5** & **HY = 3** \\ \hline PD & 29 & 20 & 9 & \(71\pm 8\) & 15 & 8 & 6 \\ Co & 18 & 10 & 8 & \(72\pm 6\) & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subject information.  
Regarding the extraction of gait characteristics on the GRF, we referred to the method on the previous study [43]. In this way, potential characteristics were selected that affected the severity classification, including the coordinates of the center of pressure (CoP), stride time, gait phase, and sample entropy. The random forest method was used to evaluate the importance of these characteristics/variables, and the most significant ones were selected for further analysis. After obtaining the time-series data with a great influence on the difference, the time-delay embedding theorem was used to reconstruct the phase space, and the data were mapped to the phase space to obtain the data point cloud. The topology features of the obtained phase-space-data point cloud were extracted and the persistent entropy was calculated. The Borderline-SMOTE algorithm was used to enhance the data in the training dataset, and the balanced sample data was used as the input to train using SVM to realize the grade recognition of PD.
## 5 Experimental Design and Result Analysis
### Experiment in Field
\begin{table}
\begin{tabular}{c|c c c c c c c c c} \hline \multicolumn{2}{c|}{**Noise /Method /SNR(DB)**} & **10** & **5** & **2.5** & **0** & \(\mathbf{-2.5}\) & \(\mathbf{-5}\) & \(\mathbf{-7.5}\) & \(\mathbf{-10}\) & \(\mathbf{-15}\) \\ \hline \multirow{3}{*}{NOISEX-92 white noise} & MFCC & 100\% & 100\% & / & 97.5 \% & / & 90\% & / & 50\% & 50\% \\  & original PNCC & 100\% & 100\% & / & 100\% & / & 100\% & / & 100\% & 100\% \\  & improved PNCC & 100\% & 100\% & / & 100\% & / & 100\% & / & 100\% & 99.5\% \\ \hline \multirow{3}{*}{NOISEX-92 factory noise} & MFCC & 100\% & 100\% & / & 50 \% & / & 50\% & 50\% & / \\  & original PNCC & 100\% & 100\% & / & 98.5 \% & / & 91\% & 50.5\% & 50\% & / \\ \hline \multirow{3}{*}{Storm reverberation} & MFCC & 100\% & 100\% & / & 85 \% & 50.5\% & 50\% & / & 50\% & / \\  & original PNCC & 100\% & 100\% & / & 82\% & 64\% & 50\% & / & 50\% & / \\  & PNCC + PCA & 100\% & 99.5 \% & / & 95.9\% & 73\% & 57.5\% & / & 50\% & / \\ \hline \multirow{3}{*}{Heavy rain reverberation} & MFCC & 100\% & 100\% & 98.5 \% & 50\% & / & 47.5\% & / & 50\% & / \\  & original PNCC & 100\% & 100\% & 90.5\% & / & 78\% & / & 62\% & / \\  & PNCC + PCA & 100\% & 100\% & 99\% & / & 88\% & / & 66.5\% & / \\ \hline \multirow{3}{*}{Thunder reverberation} & MFCC & 100\% & 99\% & / & 97.5 \% & 50\% & 51.5\% & / & 50\% & / \\  & original PNCC & 99.5 \% & 100\% & / & 97\% & 78\% & 62\% & / & 53\% & / \\  & PNCC + PCA & 100\% & 99.5\% & / & 97\% & 95\% & 82\% & / & 50.5\% & / \\ \hline \multirow{3}{*}{Stream reverberation} & MFCC & 100\% & 100\% & / & 100 \% & / & 98.5\% & / & 50\% & 50\% \\  & original PNCC & 100\% & 100\% & / & 100\% & / & 100\% & / & 99\% & 100\% \\  & PNCC + PCA & 100\% & 100\% & / & 100\% & / & 100\% & / & 99\% & 100\% \\ \hline \multirow{3}{*}{NOISEX-92 pink noise} & MFCC & 100\% & 100\% & / & 52.5 \% & / & 50\% & / & 50\% & 50\% \\  & original PNCC & 100\% & 100\% & / & 100\% & / & 100\% & / & 100\% & 50\% \\  & PNCC + PCA & 100\% & 100\% & / & 100\% & / & 100\% & / & 100\% & 50\% \\ \hline \multirow{3}{*}{Train reverberation} & MFCC & 100\% & 97.5\% & / & 60 \% & 50\% & 50\% & / & 50\% & / \\  & original PNCC & 97.5\% & 97.5\% & / & 82\% & 50\% & 50\% & / & 50\% & / \\  & PNCC + PCA & 99.5\% & 97\% & / & 91\% & 52.5\% & 50\% & / & 50\% & / \\ \hline \multirow{3}{*}{TDAAS} & MFCC & 100\% & 99.5625\% & / & 74.0625\% & / & 60.9375\% & / & 50\% & / \\  & original PNCC & 99.625\% & 99.6875\% & / & 93.6875\% & / & 78.25\% & / & 70.5\% & / \\ \cline{1-1}  & PNCC + PCA & 99.9375\% & 99.5\% & / & 97.625\% & / & 83.1875\% & / & 70.75\% & / \\ \hline \end{tabular}
\end{table} 
Table 1: Detection accuracy of acoustic signal (DAAS) under noise reverberation
# Dynamics of Polar Resonances and Their Effects on Kozai-Lidov Mechanism
## 2 Semi-Analytical Model
### Secular Dynamics inside MMRs  
The semi-analytical method is a practical approach to study secular dynamics. For a particular MMR (except the exterior \(1/n\) resonance), the critical angle \(\sigma\) is always around \(0^{\circ}\) or \(180^{\circ}\). Following our previous work on the Kozai mechanism inside retrograde co-orbital resonance, we can numerically average the disturbing term around the librate center of resonant angle \(\sigma_{0}\). To make the computation easier, we can simply set the semi-major axis \(a=a_{0}\)[13]. The secular dynamics in the \(e-\omega\) space can be obtained.  
Similarly, we can discuss the effect of the resonant amplitude. If we focus on the exact resonance, then the resonant angle \(\sigma=\sigma_{0}\) is always held, and we can calculate the value of the disturbing term by single numerical averaging:  
\[\overline{\mathcal{H}}_{1}=-\frac{\mu}{2\pi k}\int_{0}^{2\pi k}\big{(}\tfrac{1 }{|r-r^{\prime}|}-\tfrac{rr^{\prime}}{|r^{\prime}|^{3}}\big{)}\bigg{|}_{a=a_{ 0},\sigma=\sigma_{0}}\mathrm{d}\lambda^{\prime}, \tag{5}\]  
where \(\mu\) is the gravitational parameter of the planet. Considering the effect of libration amplitude of resonant angle, we can force the \(\sigma\) to vary along a sinusoidal curve with a given amplitude \(\sigma_{\mathrm{amp}}\). This sinusoidal approximation of the libration of the resonant argument is also applied to study the resonant Kozai-Lidov dynamics [13; 49; 51; 64; 86]. Then, we can calculate the Hamiltonian by double numerical averaging as follows:  
\[\overline{\mathcal{H}}_{1}=-\frac{\mu}{4\pi^{2}k}\oint_{\sigma}\int_{0}^{2\pi k }\big{(}\tfrac{1}{|r-r^{\prime}|}-\tfrac{rr^{\prime}}{|r^{\prime}|^{3}}\big{)} \bigg{|}_{a=a_{0}}\mathrm{d}\lambda^{\prime}\mathrm{d}\sigma. \tag{6}\]  
This way, we can evaluate the Hamiltonian and plot its level curves on the \(e-\omega\) space, which is critical in secular dynamics research. It should be noted that we do not restrict the inclination \(i=90^{\circ}\), so the semi-analytical model introduced here can also be used to study the general inclined problems.
## 1 Introduction  
In implant dentistry, passive fitting of an implant-supported fixed prosthesis is essential to ensure correct and successful oral rehabilitation, especially in cases of immediate placement and implant loading [1, 2, 3]. There are several clinical and laboratory variables that affect the accuracy of an implant cast [3], but the most significant factor is the impression procedure. [PERSON] et al. [4] reported that 50% of errors in terms of accuracy are because of the impression technique performed by the clinicians, while the remaining 50% are related to inaccurate laboratory procedures. The development of CAD/CAM systemshas resulted in new and more accurate methods that have replacedconventional techniques, particularly in implant prosthetic dentistry. In the digital workflow for full-arch fixed screw-retained restoration, final restorations can be fabricated by correlation techniques [5, 6, 7, 8, 9]. Several studies have recommended the advantage of digital impression methods as compared with conventional methods [4; 10; 11; 12], however, there are relatively few studies reporting on the precision of final prostheses fabricated by digital workflow in dedentulous patients.  
The marginal fit of a final prosthesis is one of the most important factors for quality assessment of successful prosthetic treatment [11; 12; 13; 14; 15]. Numerous studies have evaluated the value of passive fit using multiple methods to demonstrate the importance of this point [16; 17; 18]. A misfit at the implant-abutment junction results in complications, including screw loosening/fracture, ceramic veneer fracture/wear/chipping, and crestal bone loss [2; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. Hence, attempts should be made to ensure that an accurate master cast is produced to generate an accurately fitting implant-supported fixed final prosthesis.  
The role of an implant and final prosthesis interconnection on the accuracy of implant casts generated with provisional prosthesis scanning technique for dedentulous jaws has still not been fully investigated. Thus, additional studies are necessary to evaluate the accuracy of this method as compared with those of conventional techniques.  
In this study, we aimed to compare the accuracy of full-arch prosthesis frameworks fabricated with the provisional prosthesis scanning versus a conventional impression technique, in completely dedentulous cases. The null hypothesis was that the provisional prosthesis scanning technique would provide more accurate frameworks than the conventional impression technique.
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
## 1 Introduction
In addition, it was found that the acceleration effect of the mountain is related not only to the roughness of the mountain surface, but also to the roughness near the ground in the incoming direction [26,27]. At present, most studies pay more attention to the acceleration effect of the average wind speed caused by the terrain and less attention to the torsional effect (change in wind direction angle), but the torsional effect is also of great significance to the wind load of the structure [28, 29, 30, 31]. The average wind speed at each position of multiple continuous hills is lower than that of a single hill, and the acceleration effect of continuous hills is less than that of a single hill [32,33]. For the real terrain environment, this difference is often greater, so further research is needed on the basis of simple mountain research. [PERSON] and [PERSON] [34, 35, 36] used the LES method and [PERSON] used the modified RANS model to simulate the real terrain with good accuracy. [PERSON] and [PERSON] [37] studied the effects of four different Reynolds numbers on the results of the wind tunnel topographic test by means of a wind tunnel test. [PERSON] [38] found that the average wind speed, wind shear and turbulence level are very sensitive to the terrain. A small modification of the terrain will lead to changes in the results, and the characteristics of the wind field are affected by the incoming wind direction. [PERSON] and [PERSON] [39] used the combination of numerical simulation and a wind tunnel test to simulate the wind field within 1000 km\({}^{2}\) by the CFDmethod, and selected the three most unfavorable wind directions from 12 directions. Taking the obtained results as the entrance conditions of the wind tunnel test, they studied the wind field characteristics of a mountain range in Columbia, Canada. The terrain studied in this paper is an island in Guangdong, China, which is rich in wind energy resources. Many wind farms have been built on the island, with many still being planned. As we all know, the location of a wind farm is directly related to the power generation of the wind farm. The influence of artificial cliffs at the edge of the model does not need to be considered due to the seawater around the island, which greatly improves the accuracy of the wind tunnel test and numerical simulation [40; 41; 42; 43]. The island belongs to class A landform, but the topographic characteristics of different areas on the island are also different, so it is slightly inappropriate to summarize it with the same type. Therefore, numerical simulation and a wind tunnel test are used to study the wind characteristics of the typical location of the island. The characteristics of the wind field are analyzed and discussed, which may provide a reference for the siting of wind farms and wind field research.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
###### Abstract  
Structural health monitoring with wireless sensor networks has been increasingly popular in recent years because of the convenience. In this paper, a real-time monitoring system for cracks on the surface of reactor containment buildings is presented. Customized wireless sensor networks platforms are designed and implemented with sensors especially for crack monitoring, which include crackmeters and temperature detectors. Software protocols like route discovery, time synchronization and data transfer are developed to satisfy the requirements of the monitoring system and stay simple at the same time. Simulation tests have been made to evaluate the performance of the system before full scale deployment. The real-life deployment of the crack monitoring system is carried out on the surface of reactor containment building in Daya Bay Nuclear Power Station during the in-service pressure test with 30 wireless sensor nodes.  
structural health monitoring; wireless sensor networks; containment crack monitoring; real-life deployment +
Footnote †: journal: Sensors