esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In the present work, a two-dimensional elasticity model for in-plane free vibration of FG-GPLRC circular arch is developed. GPL nanofillers are dispersed into the matrix uniformly and non-uniformly, and the effective material properties of the GPL-reinforced nanocomposites are evaluated via the modified Halpin-Tsai formula and rule of mixtures. The state-space procedure is employed to derive the governing equation for free vibration, and semi-analytical numerical solutions are obtained with the help of DQM for various end conditions. An exact solution for a simply-supported circular arch is also given as a reference. The employment of approximate laminate model makes it possible to deal with non-uniformly dispersion of GPLs through the thickness of the arch. The validity and efficiency of the current methodology is clarified by considering several numerical examples.  
Comprehensive parameter studies are presented in both graphical and tabular forms to examine the fundamental frequency of FG-GPLRC circular arch. The GPL distribution pattern, weight fraction, as well as geometry and size are disussed in detail. This reveals that the natural frequency of the GPL-reinforced nanocomposite arch can significantly increase even by adding a quite small content of GPLs into polymer matrix. The pattern FG-X works much more powerfully in the increase of natural frequency among all GPL distribution patterns. That means more GPL reinforcements dispersed near the top and bottom surfaces of the arch can make the best use of the advanced properties of GPLs to increase the stiffness hence the frequencies of the arch. It is also found that larger and thinner GPLs exhibit better reinforcing effects.  
It should be mentioned that the present method has a perfect performance even for thick arch. The arch with arbitrary end conditions can be dealt using the present method, that can provide a benchmark for clarifying various equivalent single layer theories or numerical methods.  
[PERSON] and [PERSON] conceived and designed the research. [PERSON] conducted the theoretical derivation and numerical examples. [PERSON] and [PERSON] wrote and/or edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
The present work is fully supported by the National Natural Science Foundation of China (Nos. 11402310 and 11902086). The authors are grateful for the supports.  
The authors declare no conflict of interest.  
Figure 10: The fundamental frequency of S-S FG-GPLRC circular arch against thickness-to-radius ratio \(\lambda\) (\(\theta_{0}=\pi/3\)).
# Multi-Layer Problems and Solutions in VANETs: A Review
## 2 Multi-Layer Challenges in VANETs
### Multi-Layer Challenges  
#### 2.1.1 PHY Layer  
The Physical Layer of the DSRC consists of the 802.11p OFDM which works in the range of 5.9 GHz band (5.885-5.905) with a 10 MHz wide channel in the WAVE. The basic data rate is almost 3 Mbps and the default data rate is 6 Mbps. The Physical Layer is a thoroughly researched area in VANETs. From transmission power control to using multiple (or single) antennas and from channel estimation to channel selection. There are many aspects of the physical layer that contribute to the limitations imposed on the scalability of the network. The Multi-path environment makes the communication very challenging due to the multi-path delay spread and mobility. The delay spread causes frequency selective fading and mobility cause the time selective fading [1]. Non Line of Sight (NLOS) conditions result in a large delay spread due to scattering and highways have an increased Doppler spread. This leads to two different challenges: the channel estimation error and the lack of time-interleaving. The Physical layer challenges in VANETs include:  
1. Single and Dual Radio: The coexistence between single and dual radio is still unclear. While dual radio has some obvious advantages, the addition of a second radio in the presence of single radios does not improve the performance of safety communication under the default scheme [2].
2. Propagation Model: VANETs operate in three different kinds of environment: rural, highway, and city. The free space model which is usually used for the highway is not very accurate as the signal goes through reflections due to surrounding. In cities there are many obstacles which cause shadowing and multipath fading; therefore, free space cannot be used to model the cities. For the rural environment there are factors like trees and hills which can cause lot of reflections. So the propagation model should consider these environments.
3. Channel Selection: An analytical and simulation study is needed for the Channel Selection at the Physical layer. A game theoretic approach to selecting the best channel and data rate is mentioned in [6].
4. Channel Estimation: In order to get an accurate Channel State Information (CSI) we need advance channel estimation techniques in VANETs.
5. Diversity Techniques: Using different diversity techniques, the effects of fading and interference can be minimized.  
Apart from that many transmission parameters influence the communication, e.g., message frequency, size, transmission power, modulation schemes, and coding rate. Transmission Power and Channel Load Assessment plays a crucial role [7]. The effect of fading can be reduced by using diversity techniques. Using multiple antennas, we can improve the bit error rate (BER).
## 6 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S. No.** & **Classifier** & **Accuracy (\%)** & **Recall (\%)** & **Precision (\%)** & **F1-Score (\%)** & **ROC-AUC (\%)** \\ \hline
1 & RF & 99.41 & 100 & 98.89 & 99.40 & 99.97 \\ \hline
2 & DT & 99.60 & 99.23 & 100 & 99.61 & 99.62 \\ \hline
3 & GB & 99.41 & 99.50 & 99.45 & 99.53 & 100 \\ \hline
4 & KNN & 86.06 & 86.19 & 86.25 & 86.04 & 94.86 \\ \hline
5 & AB & 90.93 & 89.99 & 92.24 & 91.05 & 97.57 \\ \hline
6 & LR & 84.59 & 89.98 & 82.31 & 85.59 & 91.88 \\ \hline
7 & NN & 95.03 & 95.64 & 94.76 & 95.19 & 98.54 \\ \hline
8 & QDA & 94.16 & 94.49 & 94.27 & 94.33 & 98.74 \\ \hline
9 & Nu SVC & 94.83 & 95.45 & 94.60 & 94.97 & 98.54 \\ \hline
10 & LDA & 93.56 & 93.54 & 93.93 & 93.71 & 98.60 \\ \hline
11 & NB & 82.15 & 85.94 & 80.63 & 83.12 & 90.76 \\ \hline
12 & SVM & 92.19 & 93.36 & 91.68 & 92.49 & 97.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of ML classifier using 5-fold cross-validation. Figure 15: Performance of ML classifier using StakingCV Classifier. Figure 16: The confusion matrix of the predicted results. Figure 17: Combine plots for ROC-AUC. Figure 21: Stratified (K = 5)-Fold Cross-validation Results for KNN (ROC-AUC). Figure 20: Stratified (K = 5)-Fold Cross-validation Results for Gradient boosting Classifier (ROC-AUC). Figure 23: Stratified (K = 5)-Fold Cross-validation Results for Logistic Regression (ROC-AUC). Figure 22: Stratified (K = 5)-Fold Cross-validation Results for Ada Boost Classifier (ROC-AUC). Figure 24: Stratified (K = 5)-Fold Cross-validation Results for Artificial neural network (ROC-AUC). Figure 25: Stratified (K = 5)-Fold Cross-validation Results for Quadratic Discriminant Analysis (ROC-AUC). Figure 15:0 Stratified (K = 5)-Fold Cross-validation Results for Linear Discriminant Analysis (ROC-AUC). Figure 15:1 Stratified (K = 5)-Fold Cross-validation Results for NuSVC (ROC-AUC). Figure 15:2 Stratified (K = 5)-Fold Cross-validation Results for Naive Bayes (ROC-AUC). Figure 15:3 Stratified (K = 5)-Fold Cross-validation Results for Support Vector Machine (ROC-AUC). We also compared our strategy to a number of other researchers' previously published methodologies. [PERSON] et al. [41], for example, used a combination of the PCA feature extraction technique and DNN to achieve a high accuracy classification, but the recall was only 97%. The findings of the specific comparison are shown in Figure 15:4
## Results
### Inter-Correlations among SOCD, SR, Q10 and TT
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **SOCD** & **SR** & **Q\({}_{10}\)** & **TT** \\ \hline \multirow{4}{*}{Forest types} & RF & \(-0.186\) & \(0.029\) & \(0.334\) & \(-0.534\) \\  & AF & \(0.280\) & \(0.093\) & \(-0.335\) & \(0.172\) \\  & LF & \(0.828\)** & \(0.471\) & \(-0.722\)** & \(0.866\)** \\  & PF & \(-0.608\) & \(-0.426\) & \(0.617\) & \(-0.418\) \\  & EF & \(-0.519\) & \(-0.238\) & \(0.335\) & \(-0.472\) \\ \hline \multirow{4}{*}{Compositional changes} & Betulaceae & \(-0.328\) & \(0.189\) & \(0.571\) & \(-0.504\) \\  & Aceraceae & \(0.379\) & \(0.210\) & \(-0.338\) & \(0.347\) \\  & Rosaceae & \(-0.193\) & \(-0.450\) & \(0.203\) & \(-0.120\) \\  & Pinaceae & \(0.367\) & \(0.055\) & \(-0.468\) & \(0.645\)* \\  & Salicaceae & \(-0.569\) & \(-0.226\) & \(0.353\) & \(-0.481\) \\  & Ulmaceae & \(0.358\) & \(0.601\) & \(-0.449\) & \(-0.047\) \\ \hline \multirow{4}{*}{Tree species} & Other SPP. & \(0.598\) & \(0.732\) & \(-0.440\) & \(0.159\) \\  & Conifer & \(0.348\) & \(0.024\) & \(-0.459\) & \(0.659\)** \\  & Broadleaf & \(-0.348\) & \(-0.024\) & \(0.459\) & \(-0.659\)** \\ Soil properties & BD & \(0.090\) & \(0.149\) & \(0.133\) & \(0.052\) \\  & pH & \(-0.084\) & \(0.166\) & \(0.122\) & \(-0.402\) \\  & EC & \(0.930\)** & \(0.813\)** & \(-0.903\)** & \(0.637\)* \\ \hline \multirow{4}{*}{Tree size properties} & DBH & \(0.224\) & \(0.493\) & \(-0.051\) & \(0.088\) \\  & TH & \(0.499\) & \(0.739\)* & \(-0.331\) & \(0.188\) \\ \cline{1-1}  & HUB & \(0.223\) & \(0.121\) & \(-0.185\) & \(0.407\) \\ \cline{1-1}  & CS & \(0.161\) & \(0.216\) & \(0.186\) & \(0.076\) \\ \hline \hline \end{tabular}  
* indicates highly significant correlation and
* indicates significant correlation. SOCD, soil organic carbon density; SR, soil basal respiration rate; and TT, turnover time. Forest types are: RF, roadside forest; AF, affiliated forest; LF, landscape and relaxation forest; EF, ecological and public welfare forest; and PF, production and management forest. Other abbreviations are: BD, soil bulk density; EC, soil electrical conductivity; DBH, tree diameter at breast height; TH, tree height; HUB, tree height under branch; and CS, tree crown size.  
\end{table} 
Table 3: Pearson correlations of SOCD, SR, Q\({}_{10}\) and TT with forest types, tree species, soil properties and tree sizes, for Changchun city of China.
# The Impact of Sulforapahane on Sex-Specific Conditions and Hormone Balance: A Comprehensive Review
## 3 Sulforaphane  
Plants from sixteen different families of higher plants produce and contain compounds called glucosinolates. The Brassica family, which contains over 350 genera and 3000 species, most of them edible, is by far the most thoroughly researched of these families. Brassica vegetables include broccoli, cauliflower, arugula, Brussels sprouts, cabbage, and many others familiar to those who frequent the produce aisles of supermarkets [23]. The broccoli plant--whether it be the ungermented seed, broccoli sprouts, or any byproduct, including the broccoli crowns you can buy in a supermarket--will contain glucoraphanin, the precursor to sulforaphane [5]. Glucosinolates such as glucoraphanin are \"activated\" or converted to isothiances such as sulforaphane by an enzyme called myrosinase, which is present in that same plant tissue (e.g., seed, sprout, broccoli head, or microgreen) and/or in bacteria that all humans possess in their gastrointestinal tracts (the gut) [24]. Scientists refer to this population of bacteria resident within as the microbiome. In raw broccoli the active myrosinase enzyme is present and facilitates this conversion of glucoraphanin to sulforaphane. Cooking, however, will denature the enzyme, rendering it inactive. If this happens, the conversion to sulforaphane is accomplished entirely by the existing myrosinase from gut bacteria [5; 25; 26].  
In this paper, we focus on sulforaphane (SF), the bioactive compound derived from this process, and its significant implications for health and disease.  
Sulforaphane is the most potent naturally occurring inducer of the body's cytoprotective response. Its modes of action include antioxidant, anti-inflammatory, antibiotic, cancer preventive, and many more [1]. From the perspective of enhancing human healthspan and reducing the burden of chronic disease, it is one of the most important phytochemicals or phytonutrients--defined as non-nutritive compounds made by plants in small quantities--that we know of [27]. Glucoraphanin is present at much higher levels in broccoli seeds and sprouts than in broccoli plants (or in the heads that are commonly eaten). Sulforaphane itself is not present in any appreciable amounts in any parts of fresh, healthy broccoli plants [5]. Broccoli seeds or sprouts can be used to produce glucoraphanin-rich supplements as sources of sulforaphane. All cruciferous vegetables (also known as. cole crops or brasscas) contain glucosinolates, compounds similar to glucoraphanin, many of which also have beneficial properties [24]. However, only broccoli is rich in glucoraphanin, and it is thus the primary source of sulforaphane in diets and for the production of supplements.
## Transcriptome Analysis Reveals Putative Genes Involved in
glutinosa_ is characterized by the presence of catalpol, the most extensively investigated active ingredient in this species that has been shown to possess important pharmaceutical activities [2; 3; 4; 5; 6]. The  
Figure 1: Overview of iridoid biosynthesis in plants. CPR: cytochrome P450 reductase; DMAPP: dimethylly diphosphate; DXP: 1-deoxy-d-xylulose 5-phosphate; DXS: 1-deoxy-d-xylulose-5-phosphate synthase; DXR: 1-deoxy-d-xylulose-5-phosphate reductoisomerase; FPP-farnesyl diphosphate; FPPS-farnesyl diphosphate synthase; GPP: geranyl diphosphate; GPPS: geranyl diphosphate synthase; GES: geraniol synthase; GGPP: geranylgeranyl diphosphate synthase; G10H: geraniol 10-hydroxyylase; 10 HGO: 10-hydroxygeraniol oxidoreductase; HMBPP: 1-Hydroxy-2-methyl-2-butenyl-4 diphosphate; HMGR: 3-hydroxy-3-methylglutaryl-CoA reductase; IPP: ispentenyl diphosphate; IDI: IPP isomerase; LAMT:adenosyll-L-methionine: loganic acid methyl transferase; MEP: 2-C-methyl-d-erythntol 4-phosphate; MVA: mevalonic acid; SLS: secologanin synthase; STR: strictosidine synthase; Route I: the route leading to the biosynthesis of secologanin, Route II: the route leading to the biosynthesis of catalpol. catalpol biosynthetic route was established by [PERSON] in 1994 through feeding experiments, which starts from 8-epi-iridodial, via 8-epi-deoxyloganic acid, bartisoside, and aucubin to catalpol (Figure 1) [11], namely, the iridoid biosynthetic route II termed by [PERSON] [12]. The upper steps of catalpol biosynthetic pathway have not been established experimentally, but it is suggested that catalpol and other iridoids generated through route II are probably derived from geraniol [8]. In this study, in order to identify genes involved in iridoid biosynthesis, a cDNA library generated from the tuberous root of _R. glutinosa_, which accumulates a high amount of catalpol, was sequenced using the 454 GS FLX Titanium platform and 58,822 unique assembled sequences were obtained. Bioinformatic analysis indicated that all candidate genes involved in terpenoid backbone biosynthesis were within the 454 assembly. Furthermore, we also discovered several genes that encode putative enzymes catalyzing the early enzyme steps of the secoiridoid pathway, including geraniol synthase (GES), geraniol 10-hydroxylase (G10H), cytochrome P450 reductase (CPR) and 10-hydroxygeraniol oxidoreductase (10 HGO), which are also very likely to be involved in the early steps leading to route II iridoids. The genes described in this study constitute an important resource for future iridoid biosynthesis studies and increase the practical potential of molecular engineering of the iridoid pathway in this plant. In this study, the \"iridoid pathway\" is referred to as the catalpol biosynthetic pathway.
## 4 Simulation  
### Simulation Setup  
We conducted a computer simulation to compare and verify the proposed controller with the previous controller. The locomotion of the snake robot is implemented as a combination of sinusoidal waves of different phases. Consequently, the snake robot's head also swings in a sinusoidal wave while it is being driven. Therefore, it needs to evaluate the sinusoidal signal tracking performance of the controller. Next, our proposed controller used a neural network. As mentioned above, neural networks are vulnerable to sudden signal changes. Therefore, we configured disturbance and control inputs with large signal changes to test whether the controller has satisfactory control performance [52].  
Figure 5 is the continuous control input. This control input is similar to snake robot's head movement. Figure 6 is a control input with rapid signal change. Although this control input is not similar to the movement of the snake robot's head, it is used to test the control performance by implementing a situation that is a disadvantage of the proposed controller using a neural network.  
Figure 7 is position disturbance. The disturbance resembles the snake robot's head movement. Moreover, we combine sinusoidal signal and high-frequency noise that for unknown reasons such as topographical conditions.  
Figure 5: Continuous control input.  
Figure 6: Mixed control input.  
The snake robot's head system parameter is \(m_{1}=0.1\) kg, \(m_{2}=0.2\) kg, \(l_{1,2}=0.07\) m, initial angle \(\theta_{1}=1.5\) rad, \(\theta_{2}=1.8\) rad, \(\dot{\theta}_{1,2}=0\) rad/s, and \(\dot{\theta}_{1,2}=0\) rad/s\({}^{2}\). The control gain parameter is \(\alpha=10\), \(\lambda=1.67\), \(k=8\), \(c=0.5\times[-1,-0.5,\ 0,\ 0.5,\ 1]\), \(b=15\), \(\varpi=3.5\), \(\gamma=80\), \(\sigma=0.15\), \(\beta=0.2\), \(C=21\), \(\zeta=0.001\), and \(N=0.1\). Assuming no offline training is performed, all network initial weights are set to 0.1. All control gain parameters can be obtained through simulation iterations. The simulation sampling time is 1 ms. For comparison of the control performance, the control gain of the previous robust term is used as 20 to achieve similar performance to the proposed controller in all situations. While in simulation, nonsingular terminal sliding mode control (NTSMC), and nonsingular terminal sliding mode control with RBF neural network (NTSMC-NN), and previous robust term (RT-NTSMC-NN) are used.
## 4 Experimental Analysis  
### Data Introduction  
The experimental data in this paper were obtained from the PHM Challenge [32] organized by the Institute of Electrical and Electronics Engineers in 2012, and the data were obtained from the PRONOSTIA experimental bench, as shown in Figure 6. The experimental bench consists of three main parts: the rotation part, the degradation generation part and the sensor part. The rotating part consists of an asynchronous motor with a gearbox and two shafts to provide the working environment for the test bearing. The degradation generating part can apply radial load to the testing bearing to simulate the actual working operation with load and accelerate the degradation, which can complete the whole process of bearing operation to failure in a short time. The sensor part consists of three sensors, two of which are vibration sensors positioned 90\({}^{\circ}\) apart, measuring the magnitude of horizontal and radial vibrations, respectively, and the other is a temperature sensor, measuring the temperature of the bearing during operation.  
The PHM2012 challenge data give the full cycle life data of the bearings from operation to failure for three operating conditions. The three operating conditions are 1800 rpm and 4000 N; 1650 rpm and 4200 N; and 1500 rpm and 5000 N. There are 7, 7, 3 test bearings for each of the three operating conditions. The PRONOSTIA test bench has a sampling interval of 10 s, a sampling duration of 0.1 s, and a sampling frequency of 25.6 kHz, indicating that every 10 s, the sensor can collect 2560 data points.  
In order to verify the effectiveness of the MSCAE proposed in this paper, the data set needs to be divided into training data and testing data. The division method in this paper is shown in Table 1. As can be seen from Table 1, all bearings under three operating conditions are used in this paper to validate the proposed method. The training set contains 5, 6, 2 bearings, and the testing set contains 2, 1, 1 bearings.
## 3 Simulation Approach  
Two sets of two-dimensional axisymmetric computational simulations were performed using Ansys Autodyn 19.0. The first set, referred to as velocity simulations, followed the approach in reference [17], with the front clamp, back clamp, and HPB modelled as fixed boundaries. The witness plates were modelled using two rectangular parts and their rear faces were fixed in the x-direction. A 3.2 mm line detonation was used to simulate the 6.4 mm detonator used in the validation blast tests. The detonation simultaneously initiated across its line in the simulation. A schematic, shown in Figure 4a, defines the positioning of the parts, the dimensions of the model, and the boundary conditions for the velocity simulation approach. The bearing diameter was varied in the 3-10 mm range.  
The second set of simulations, referred to as ball bearing simulations, included a much finer bearing mesh size and a damage model for the bearing material. A principal stress failure model was used to capture the internal spalling mechanism; a schematic of this approach is shown in Figure 4b. It employed a 75 mm by 50 mm air mesh with a ball bearing modelled with a very fine mesh of 0.02 mm across the ball diameter. The very  
Figure 4: Schematic showing the geometry and boundary conditions used in (**a**) the velocity simulations and (**b**) the ball bearing simulations with an inset to represent the approximate locations of the four gauges used in the hydrostatic pressure histories.  
fine mesh meant that the ball bearing simulations were very computationally expensive, therefore they were run for a very short time to capture the failure of the bearings and did not include the modelling of the clamps, aluminium plates, or the tube sides (which only became important in later stages of the flight).  
In both sets of simulations, a 0.2 mm mesh side length was chosen for the air and explosive parts. This allowed for a minimum of 45 elements (18 mm diameter charge) across the charge radius.