esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, the fatigue-life assessment method of corroded weathering steel is extended to the multi-scale refined finite-element model, and the remaining fatigue life of the fatigue detail of the corroded weathering-steel anchor box is predicted. The main conclusions of this paper are as follows:  
1. The key fatigue detail of a typical weathering-steel anchor box of a cable-stayed bridge is identified by using the nominal stress method and the finite element analysis; a multi-scale refined finite-element model for the weathering-steel anchor box is established; and the fatigue-life assessment method of corroded weathering steel is extended to the multi-scale model to predict the remaining fatigue life of the corroded fatigue detail;
2. The remaining fatigue life of the key fatigue detail is calculated when the initial crack size is 1 mm, 2 mm, and 3 mm, with results of 20.3%, 12.7%, and 11.5% of the fatigue life when the initial crack size is 0 mm, respectively. The corrosion pits are equivalent to the introduction of the initial cracks, and even if the initial crack size is small, the fatigue life of the structure is greatly reduced;
3. The fatigue life of the key fatigue detail of the weathering-steel anchor box calculated by numerical simulation is more than 100 billion times greater, which far exceeds the millions of times that are of concern in engineering practice. The weathering-steel anchor box has excellent fatigue resistance;
4. A fatigue-life assessment method for corroded weathering-steel structures based on the finite element method and fracture mechanics is proposed, which makes up for the lack of a fatigue-life assessment method for corroded weathering-steel bridges which are in service;
5. Since the investigated bridge in this paper has been in service for a short period of time, it lacks data on corrosion pits, and the shape and size of the initial cracks are all assumed, for scientific research purposes, to demonstrate the feasibility of the proposed method. The status of the investigated bridge will be monitored and the proposed method will be improved accordingly in future studies.  
Figure 12: Relationship between initial crack length \(a_{0}\) and fatigue life \(N\).  
**Author Contributions:** Conceptualization, H.S. and J.D.; methodology, H.S.; software, H.S.; investigation, J.W.; writing--original draft preparation, H.S.; writing--review and editing, J.W.; supervision, J.D.; project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Jiangsu Provincial Transportation Engineering Construction Bureau: ZG-KT-5-1-0; Transportation Technology Project of Department of Transport of Shaanxi Province: 20-10K; Major Science and Technology Research Program of China National Railway Corporation Limited: K2019G004.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Most previous studies on this topic use the TEC anomalous variations as earthquake precursors [21; 22; 23]. Only a small fraction of the studies on ionospheric perturbations related to earthquakes use ionospheric scintillation to do their correlation [24]. These studies take GPS data from ground stations [25], or ground-based ionosondes [26] to measure the \(S_{4}\) index and correlate it to earthquakes in the region. The novelty of this study is the use of the GNSS-R technique to obtain global oceanic maps of ionospheric scintillation and correlate them to earthquake precursors, allowing studying a large number of earthquakes globally distributed and making use of statistical tools such as the confusion matrixes and ROC. Section 2 describes the data used, and the methods applied during this study; it is divided into four subsections. Section 2.1 describes how ionospheric scintillation is derived from GNSS-R CYGNSS measurements, Section 2.2 explains how changes in the scintillation index are calculated, Section 2.3 introduces the earthquake database used, and Section 2.4 describes how the statistical analysis is performed by means of the confusion matrix method. Section 3 presents the results of this study, and it is divided into two parts: the first shows the results for a particular case study in July 2019, and the second presents the statistical results of the complete analysis over a period of 6 months from March 2019 to August 2019. Finally, a discussion on the results is performed in Section 4, and the main conclusions are presented in Section 5.
## 4 Experiment and Result Analysis
### Unsupervised Feature Selection  
#### 4.1.1 Evaluation Metrics  
We compared the clustering performance of our UFS-ID method with that of competing approaches on five incomplete data sets. Similarly to previous works, we evaluated the performance of UFS methods using two extensively employed evaluation metrics: clustering accuracy (ACC) and normalized mutual information (NMI). ACC denotes the percentage of samples that are correctly classified, and it can be computed as follows:  
\[\text{ACC}=\frac{n_{c}}{n}, \tag{38}\]  
where \(n\) is the number of instances and \(n_{c}\) denotes the number of correctly clustered instances. Hence, a larger ACC indicates better performance.  
NMI expresses the correlation between the predicted labels and the real labels, that is,  
\[\text{NMI}=\frac{\sum\limits_{k_{1}=1}^{c}{\sum\limits_{k_{2}=1}^{c}{n_{\text {C}_{k_{1}}}}^{c}{c}_{k_{2}}\log(\frac{n_{\text{C}_{k_{1}}}^{c}{c}_{k_{2}}}{n_{ k_{1}}n_{k_{2}}})}}{\sqrt{\sum\limits_{k_{1}=1}^{c}{n_{1}}{\log(\frac{n_{k_{1}}}{n})}} \sqrt{\sum\limits_{k_{2}=1}^{c}{n_{2}}\log(\frac{n_{k_{2}}}{n})}}, \tag{39}\]  
where \(n_{k_{1}}\) is the number of instances in the cluster \(C_{k_{1}}\), \(n_{k_{2}}\) is the number of instances in the cluster \(C_{k_{2}}\), and \(n_{\text{C}_{k_{1}}\cap C_{k_{2}}}\) denotes the number of instances in the \(C_{k_{1}}\cap C_{k_{2}}\) set.  
All experiments were carried out on a PC installed with Matlab2019a, Intel Core i7-8750H CPU, and 16-GB RAM. We adopted the tenfold cross-validation scheme, and the number of clusters in the k-means clustering was set to the absolute number of classes in the data set.  
The parameters used in the comparison schemes were consistent with the corresponding literature and the regularization parameters \(\gamma\), \(\beta\), and \(\lambda\) were all tuned in the grid \(10^{-3},10^{-2},10^{-1},1,10,10^{2},10^{3}\) in our experiments.  
Moreover, in our experiments, paired-sample \(t\)-tests (at the 95% significance level) between our UFS-ID method and competing UFS methods were adopted in terms of ACC and NMI. Specifically, the symbols \"*\" and \"**\" denote that our UFS-ID method had statistically significant differences with \(p<0.05\) and \(p<0.001\), respectively, in the paired-sample \(t\)-tests at the 95% significance level compared with the other competing UFS methods.
## 2 Results and Discussion
### Susceptibility to Metabolic Transformation
#### 2.5.2 Phase II Metabolism with Rat Liver Microsomes  
The compounds **8m**, **8i** and **8j** were also studied in respect to their phase II metabolisms. The incubation with rat liver microsomes was performed in the presence of glucuronyltransferase (UGT) and the cofactor of this enzyme family, UDPGA. As a result, we did not observe any glucuronidation product of **8m**, **8i** and **8j** after 60 min of incubation of these compounds with rat liver microsomes in the presence of UDPGA. The ESI-MS analysis also did not indicate any characteristic mass value increase for glucuronidation product equal to m/z + 176 Da. Therefore, the results demonstrated that none of the studied compounds were the substrate for UGT in RLM.  
Finally, the three studied compounds **8m**, **8i** and **8j** were incubated with rat liver microsomes (RLMs) together with two activating cofactors, NADPH and UDPGA, to stimulate both phase I and phase II metabolism. However, only the products of phase I (m/z + 16, m/z \(-\) 14 and m/z \(-\) 28) were found in the chromatograms with ESI-MS detection. The results indicate that the phase I metabolites did not undergo transformation in the following phase II metabolism in the presence of UGTs.  
In conclusion, we proposed similar metabolic pathways of pyrazolo[4,3-e][1,2,4]triazine sulfonamides for compounds **8m**, **8i** and **8j**, which are presented in Figure 6. The studied compounds were sensitive to metabolic transformations with phase I enzymes, which led to oxidized metabolites as hydroxylation and dealkylation products. Phase II transformations were not demonstrated either directly by UGT or upon activation with phase I enzymes. Therefore, the best known detoxification pathway, UGT-mediated glucuronidation, was not observed in the case of the studied compounds. It cannot be excluded that the proposed phase I metabolites were responsible for the deactivation.
# Accuracy Characterization of a MEMS Accelerometer for Vibration Monitoring in a Rotating Framework
## 1 Introduction
The influence of angular velocity was also studied by repeating all the measurements with angular velocities between 0 and 300 rpm. The wireless system power consumption was always lower than 6 W during the whole measurement campaign. An active vibration damping system in rotating blades might benefit from the proposed wireless system, since it would allow not only for the real-time and remote monitoring of vibration, but also optimize the effectiveness of the damping system according to the resonance excited by the external load. The paper is organized as follows: the wireless vibration monitoring system and the measurement setup of the reference accelerometer are described in Section 2 alongside the adopted measurement procedure. Experimental results are provided and discussed in Section 3.
# Wireless Power Transfer System Based on Strapping Resonators
## 3 Simulation and Experiment Results  
The performance of the proposed WPT system based on SR was numerically simulated and experimentally validated. Figure 6 illustrates the magnetic field distribution in the system. When the system is operating at the \(\pi\) mode, the resonators are primarily coupled by magnetic field. The working state of the system operating at the \(\pi+1\) mode is shown in Figure 6b. As shown in Figure 6b, part of the energy can also be coupled from the source resonator to the load resonator by magnetic field.  
A simulation model for WPT is established as shown in Figure 7a. Referring to Figure 1a, the experimental setup is shown in Figure 7b. Two resonators are connected to a vector network analyzer (VNA) by coaxial lines. In order to get more convincing simulation results, both test environment and package structure of the SR shown in Figure 7b are taken into account in the model. In terms of the \(S\) parameters, the efficiency of the system can be calculated by,  
\[\eta=\frac{\left|S_{21}\right|^{2}}{1-\left|S_{11}\right|^{2}} \tag{6}\]  
where \(S_{21}\) is the transmission coefficient and \(S_{11}\) is the reflection coefficient. Figure 7c,d illustrate the WPT efficiency in different operation modes. The measured results closely match the simulated results. When the system operates in the \(\pi\) mode, the efficiency is over 80%, while in the \(\pi+1\) mode, the efficiency of the system can still reach 70%.  
Figure 6: Coupling between two resonators: (**a**) Magnetic coupling for the \(\pi\) mode, (**b**) magnetic coupling for the \(\pi+1\) mode.  
From simulation and experiment results, both of \(\pi\) and \(\pi+1\) modes can be used for WPT with high efficiency. However, as shown Figure 6, it is obvious that the system stability of the \(\pi\) mode is better than \(\pi+1\) mode due to stronger coupling in the \(\pi\) mode resonant magnetic field. As shown in Figure 2b, the magnetic field of the \(\pi\) mode is axially distributed throughout the resonator. When the system operates at the \(\pi\) mode, the coupling is weakly affected by the distance within a certain range. Therefore, the system stability is stronger.
## 4 Performance Analysis of Basic SC Unit
### Hardware Resources of Basic SC Unit  
The significant advantage of SC is its relatively low consumption of hardware resources. This study conducted experiments to assess the hardware resource overhead of different SC units based on a Field-Programmable Gate Array (FPGA) platform.  
Table 3 illustrates the resource overhead of the inverters and multipliers based on SC and binary computing. The hardware resource of stochastic inverters is approximately one-tenth that of binary inverters, and the stochastic multipliers consume approximately 1% compared to their binary implementations. Furthermore, the resource consumption of stochastic complex multipliers is significantly less than that under binary computing. Consequently, this study adopted SC for the correlation calculation in the MSD algorithm.  
Table 4 illustrates the hardware resource overhead of the proposed adder, in comparison with the stochastic adders in References [36; 37; 38; 39; 40] and binary adders. In comparison to both the stochastic non-scaled adder [38; 40] and the binary adder, the proposed adder  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Basic Unit**} & \multicolumn{3}{c}{**Inverter**} & \multicolumn{3}{c}{**Real Multiplier**} & \multicolumn{3}{c}{**Complex Multiplier**} \\ \cline{2-10}  & **SC** & **8-Bit** & **9-Bit** & **SC** & **8-Bit** & **9-Bit** & **SC** & **8-Bit** & **9-Bit** \\ \hline LUT & 1 & 6 & 7 & 1 & 72 & 90 & 2 & 297 & 366 \\ \hline FF & 1 & 8 & 9 & 1 & 66 & 117 & 2 & 252 & 431 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardware resource overhead of inverters and multipliers.  
Figure 13: The RMSE of the proposed adder under different configurations.  
demonstrated a slightly lower resource overhead. However, when compared to the scaled adder presented in the literature [36; 37; 39], the proposed adder achieved non-scaled operations at the cost of a marginally increased resource overhead.  
Table 5 illustrates the hardware resource overhead of both stochastic adders and binary adders under different input numbers. The table reveals that the resource consumption of the stochastic adders does not significantly increase with the number of input ports. Additionally, stochastic adders demonstrate a lower resource consumption compared to binary adders, indicating their advantages for hardware implementation.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 5 Simulations  
In this section, we conducted extensive simulations to evaluate the accuracy and the effectiveness of our proposed analytical model on the link connectivity and the probability of coverage in UCANs. In particular, we describe the simulation method in Section 5.1. We then present the simulation results on the link connectivity and the probability of coverage in Sections 5.2 and 5.3, respectively. Finally, we discuss the implications of our results and point out the future directions in Section 5.4.
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 2 Related Works and Our Approaches
### Generative Adversarial Network for Data Augmentations
**  
To solve this issue, we present a novel approach to generate new constellation data using a GAN [27] based on actually measured constellation data and we demonstrate the effectiveness of the generated constellation data in optical filter shift detection. Furthermore, the effectiveness of the fault prediction method in ROADMs will be examined by combining the data augmentation method and BER monitoring. This article extends our previous work [21].
## 6 Uses of UAVs in Agriculture Advancement
### Health Assessment of the Crops  
Scanning agriculture crops using infrared and visible (IR) light sensor technology mounted on the UAVs can recognize which farming plants may be infested via fungicide or bacteria. Detecting any such issues in advance helps prevent the disease from spreading to other crops or plant parts [160].
## 2 Analytical Strategies for Authentication and Quality Control
### Spectroscopic Methods
#### 2.1.1 Infrared Spectroscopy
In the discriminating part of the study, 59 samples of pure Gr and 17 fruit and cereal distillates were analyzed. The NIR and MIR spectra were processed, separately, by PLS-DA [23; 24] and simultaneously by three different data fusion tools, namely multi-block partial least squares (MB-PLS-LDA) [25; 26], sequential and orthogonalized covariance selection (SO-CovSel-LDA) [27] and sequential and orthogonalized partial least squares (SO-PLS-LDA) [28]. The best strategy was the data fusion carried out with SO-PLS-LDA and SO-CovSel-LDA, which provided the best results with classification rates around 77% for Gr and 60% for the other spirits. In the second part of the work, a group of 36 different Gr spirits, intentionally adulterated with vodka, was analyzed by NIR and MIR spectroscopies. The resulting NIR and MIR spectra were processed individually by PLS-DA and simultaneously by multi-block analysis. All the obtained models showed very good prediction capabilities (up to 100% classification rate).
# Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
## 4 Annealing Methods and the QUBO Model
### Quantum Annealing  
Quantum annealing (QA) is a heuristic method for solving combinatorial optimization problems, similar to simulated annealing [27]. Quantum annealing is a derivative of adiabatic quantum optimization [4], which is based on the time-dependent Schrodinger equation  
\[i\hbar\frac{\mathrm{d}^{2}\psi(t)}{dt}=H(t)\Psi(t) \tag{11}\]  
where \(\psi(t)\) denotes the quantum mechanical wave function of an underlying physical system and \(H(t)\) is the time-dependent Hamiltonian that drives the dynamics [4]. A generic form of this Hamiltonian is  
\[H(t)=A(t)H_{0}+B(t)H_{1} \tag{12}\]  
with \(t\in[0,T]\) and \(T\) being the final evolution time. The schedules \(A(t),B(t)\) are monotonic and satisfy \(A(0)=1,B(0)=0\) and \(A(T)=0,B(T)=1\). Therefore, the quantum state \(\psi(0)\) evolves under an interpolation from \(H_{0}\) to \(H_{1}\) in order to prepare the final state \(\psi(T)\). Assuming the initial state is an eigenstate of \(H_{0}\), then the adiabatic theorem promises that the quantum state will remain an instantaneous eigenstate of \(H(t)\) provided the dynamics evolve sufficiently slow. The latter condition may be enforced by choice of the annealing time \(T\) or the schedules. Consequently, we may select the final Hamiltonian \(H_{1}\) to represent a computational problem in which the eigenstates encode a well-defined solution [4].
## 3 Results
### Basic Model Training
#### 3.2.2 Time-Step Selection  
In time series forecasting, the time step length for each sample input into the model, that is, how many past timestamps there are whose data are used, is crucial for the model to unearth patterns in the time series. The data from office group one and residential group four are easily predictable and can more clearly demonstrate the impact of the time step length on the model prediction error. Therefore, these two groups' data were selected as benchmarks for conducting time step length experiments. Experiments were carried out with time steps of 72 h, 48 h, 24 h, 12 h, 6 h, 3 h, and 1 h, with specific experimental results presented in Table 4.  
It is evident that for both office group one and residential group four, the worst predictions occurred with a time step length of 1 h, while the best predictions occurred with a time step length of 48 h. Moreover, as the time step length increased, the error values  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Time Step** & **RMSE** & **MAE** \\ \hline \multirow{6}{*}{Office Group 1} & 1 h & 114.624 & 35.112 \\  & 3 h & 114.158 & 35.021 \\  & 6 h & 106.223 & 26.599 \\  & 12 h & 26.273 & 16.013 \\  & 24 h & 13.287 & 6.982 \\  & 48 h & **6.142** & **4.105** \\  & 72 h & 17.809 & 12.120 \\ \hline \multirow{6}{*}{Residential Group 4} & 1 h & 49.946 & 35.363 \\  & 3 h & 44.805 & 30.101 \\ \cline{1-1}  & 6 h & 37.849 & 27.433 \\ \cline{1-1}  & 12 h & 33.277 & 26.455 \\ \cline{1-1}  & 24 h & 27.232 & 22.149 \\ \cline{1-1}  & 48 h & **24.697** & **20.195** \\ \cline{1-1}  & 72 h & 35.663 & 29.567 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of selecting the time step.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Division Ratio** & **RMSE** & **MAE** \\ \hline \multirow{3}{*}{Office Group 1} & 8:1:1 & 22.108 & 8.532 \\  & 7:1.5:1.5 & 13.380 & 8.572 \\  & 6:2:2 & **13.287** & **6.982** \\ \hline \multirow{3}{*}{Residential Group 4} & 8:1:1 & 34.381 & 25.224 \\  & 7:1.5:1.5 & 29.730 & 23.174 \\ \cline{1-1}  & 6:2:2 & **27.232** & **22.149** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of the dataset division ratio.  
of RMSE and MAE progressively decreased, reaching their optimum at 48 h. When the time step length was set to 72 h, the model's prediction results began to decline from their optimum, indicating that although increasing the model's input time step length can reduce the prediction error, too long a time step length can also increase the error. The optimal time step length for this model is 48 h, meaning that when the past two days' data are used as a single input sample for the model, the predictive performance is optimal, effectively utilizing the data's correlations and time dependencies.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Current Amplitude Optimization  
To optimize the current amplitude of the antenna elements in an LAA, the following assumptions are made for the other two parameters. The phase is assumed to be constant, i.e., \(\phi_{n}\)= 0, and the array elements are placed at equal distances from each other with \(x_{n}=n\times(\lambda/2)\), where \(n=-\)N to N. Using these two conditions, the array factor used for current amplitude optimization is provided in Equation (5). The magnitude of the excitation current amplitude lies between 0 and 1. Five designs of LAA are optimized using MAOA along with other intelligent optimization algorithms for comparison purposes. In designs 6 and 7, the current amplitude of the array elements is considered for optimization, respectively, for reducing PSLL and CSLL minimization, while side lobe average power minimization without any condition on FNBW is discussed in design 8. Side lobe suppression with null positioning is discussed in design 9, whereas design 10 illustrates the side lobe average power minimization with a fixed FNBW constraint.