esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Aiming at the problem of low prediction accuracy of concrete fatigue life under the condition of small datasets, this paper proposes a method based on the GRW-DBA data augmentation algorithm and ANN to build a prediction model in which the GRW-DBA algorithm is improved by grouping calculation and adding random weights based on the classic DBA algorithm. In this method, the ANN model is first trained based on the augmented data of the GRW-DBA algorithm, and the hyperparameters of the ANN model are optimized through experiments to improve the prediction accuracy. The data augmentation algorithm is compared with prediction models such as SVM, and finally, a graphical user interface is developed. The following conclusions can be obtained:  
1. The GRW-DBA data augmentation method proposed in this study can conveniently and effectively augment small datasets while reducing the impact of abnormal sequences on the results. Compared with GAN and classic DBA methods, the GRW-DBA can better improve the prediction accuracy of the ANN model.
2. The ANN fatigue life prediction model was trained based on the GRW-DBA augmented dataset, under the same conditions. Its prediction accuracy R\({}^{2}\) evaluation index increased by 24%, 10.4%, and 7.8% compared with LR, SVM, and AdaBoost. It also shows good generalization in datasets with different distributions.  
**Author Contributions:** Conceptualization, J.S. and Y.Z.; methodology, W.Z.; software, W.Z.; validation, J.S. and W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, J.S.; supervision, Y.Z.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China, Grant/ Award Numbers: 12162025, 11762015; Natural Science Foundation of Inner Mongolia, Grant/ Award Numbers: 2020 MS05031, 2021 LHMS05010; Basic Scientific Research Expenses Program of Universities directly under Inner Mongolia Autonomous Region, Grant/ Award Numbers: JY20220139.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** All the data used in this paper can be traced back to the cited references.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Nutritional, Anti-Nutritional and Health Beneficial Constituents of Lima Beans
### Dehulling  
The hull or seed coat of legumes has a bitter taste and is indigestible, and so legumes are sometimes dehulled of their seed coat to improve the taste. Dehulling is also used to reduce cooking time, as the seed coat impedes water uptake during cooking [101]. A comparison of the nutritional composition of undehulled and dehulled lima seed flours showed that carbohydrates, crude fiber, ash and protein contents were lower in the dehulled sample, while crude fat was higher in the undehulled sample [41]. The authors of this study concluded that the obtained values for fat were acceptable because lima beans have a low-fat content [41]. They also found that functional properties (oil absorption, foam and emulsion capacity) were higher in the dehulled lima bean, while bulk density and water absorption capacity were lower in the dehulled sample [41].  
According to another study, the protein content of the dehulled lima bean is lower than that of the undehulled sample. This could be attributed to the numerous distributions of protein within some legumes, as proteins in legumes can be spread across the endosperm, hulls, or bran [102]. A further study found that the fiber and fat content of undehulled lima bean samples were higher than those of a dehulled Christmas lima bean sample [102]. This indicates that dehulling lima bean seeds could reduce the fat content. The reduction in fat during dehulling can be linked to the leaching out of oil or fat molecules from the cotyledons [103].  
The ash and carbohydrate contents of undehulled lima beans appear to have no significant difference from dehulled lima beans [103]. In a study, dehulled samples had lower viscosity, swelling index, and oil and water absorption capacity than the undehulled samples. Low water absorption capacity could be due to the lower availability of polar amino acids [104]. The low oil absorption capacity may be attributed to the large proportion of polar (hydrophilic) amino acids on the protein molecule's surface [104; 105]. Dehulling increased the gelation capacity of dehulled samples in studies, and this may be linked to the higher protein content reported in dehulled samples as well as seed coat removal [103].
## 2 Method of Calculation
### Stress Field
#### 2.1.2 Category II Issues  
For the second type of problem, the stress at each point on the circular hole plate varies periodically with the angle. Therefore, the stress boundary condition for the second type of problem can be expressed as follows:  
\[\left\{\begin{array}{ll}\sigma_{\rho 2}=-\frac{(1-\lambda)}{2}q\cos 2\theta, &\tau_{\rho 02}=\frac{(1-\lambda)}{2}q\sin 2\theta&|\rho\rightarrow\infty\\ \sigma_{\rho 2}=-x\frac{(1-\lambda)}{2}q\cos 2\theta,&\tau_{\rho 02}=x\frac{(1- \lambda)}{2}q\sin 2\theta&|\rho=R\end{array}\right. \tag{4}\]  
The function of the stress potential of a circular orifice plate can be expressed in the following way:  
\[\phi=\cos 2\theta(A\rho^{4}+B\rho^{2}+C+\frac{D}{\rho^{2}}) \tag{5}\]  
where A-D are constants to be determined.  
A partial derivative of the stress function gives the stress component expressed in polar coordinates.  
\[\left\{\begin{array}{ll}\sigma_{\rho 2}=\cos 2\theta\Big{(}-2B-\frac{4C}{\rho^{2 }}-\frac{6D}{\rho^{2}}\Big{)}\\ \sigma_{\theta 2}=\cos 2\theta\Big{(}12A\rho^{2}+2B+\frac{6D}{\rho^{2}} \Big{)}\\ \tau_{\rho\theta 2}=\sin 2\theta(6A\rho^{2}+2B-\frac{2C}{\rho^{2}}-\frac{6D}{ \rho^{2}})\end{array}\right. \tag{6}\]  
An expression for the constant to be determined can be obtained by combining the boundary conditions described in Equation (4).  
\[\left\{\begin{array}{ll}A=0&B=\frac{(1-\lambda)}{4}q\\ C=-\frac{(1-\lambda)}{2}q(1-x)R^{2}&D=\frac{(1-\lambda)}{4}q(1-x)R^{2}\end{array}\right. \tag{7}\]  
A specific expression for the stress components is obtained by combining Equations (6) and (7).  
\[\left\{\begin{array}{ll}\sigma_{\rho 2}=-\frac{(1-\lambda)}{2}q\cos 2\theta \Big{[}1-4(1-x)\frac{R^{2}}{\rho^{2}}+3(1-x)\frac{R^{4}}{\rho^{4}}\Big{]}\\ \sigma_{\theta 2}=\frac{(1-\lambda)}{2}q\cos 2\theta\Big{[}1+3(1-x)\frac{R^{4}}{ \rho^{4}}\Big{]}\\ \tau_{\rho\theta 2}=-\frac{(1-\lambda)}{2}q\sin 2\theta\Big{[}1+2(1-x)\frac{R^{ 2}}{\rho^{2}}-3(1-x)\frac{R^{4}}{\rho^{4}}\Big{]}\end{array}\right. \tag{8}\]
# Privacy and Security in Digital Health Contact-Tracing: A Narrative Review
## 3 Results
### Technical Issues
#### 3.4.1 Accuracy  
The adoption of apps was influenced by factors such as the accuracy and reliability of the contact-tracing app. In the USA, a study revealed that a significant number of individuals indicated that their decision would be influenced more by the degree of accuracy than by the amount of privacy risk [126]. Among those who expressed that they did not intend to use the app, concerns were raised about a perceived lack of accuracy and the potential for false positives [54].
# Optimized Architecture for Efficient OFDMA Network Design
## 5 Simulation Results
### Robustness Study
#### 5.2.1 Conventional Scheduling Policies  
To verify the robustness of the analytical model for the ON data volume distribution, simulations were conducted for both exponential and truncated Pareto distributions (lower and upper bounds). The results are shown in Figure 11. Even for assumptions that deviate further from the analytical model, the obtained results remain close to those presented in the model.  
It is thus clear that a truncated Pareto distribution has little influence on the design parameters.  
Figure 12 shows the average instantaneous throughput of active users during an ON period \(X\) for different channel models with the three conventional scheduling algorithms. So far, we have always considered the memoryless channel model. Now, we will take into account two different channel models, where transitions between different MCS (Modulation and Coding Schemes) incorporate a memory process. The average channel model is a combination of good and bad channels (corresponding to stationary probabilities of the combined average given in Table 3). For the combined channel model, mobile stations (MS) are subjected to both good and bad channels in the simulations (corresponding to the stationary probabilities of good and bad channels provided in Table 3).  
From Figure 12, it can be deduced that even for a more complex wireless channel, the analytical model demonstrates robustness.  
Figure 11: The average instantaneous throughput for the throughput fairness algorithm and the different traffic distributions (\(N=10\), \(x_{\alpha n}=3\) Mbits, and \(t_{\mathit{off}}=3\) s).
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 5 Experimental Validations
### Practical Workspace Testing of the Mechanism  
The working space of the SMAM is the collection of all the spatial areas that the adjustable secondary mirror can reach, and the size of the working space is an essential  
Figure 17: Absolute positioning error of the attitude deflected around the x-direction.  
Figure 18: Repeat the positioning error of attitude angle at each position point.  
technical indicator of the SMAM. For analysis, the space of movement that can be reached by the center point of the secondary mirror chamber is evaluated. As the SMAM requires at least three DOFs to describe its attitude in any state, the working space of the SMAM can be divided into two parts: position space and attitude space. The position space is defined as the position that can be reached by the center point of the secondary mirror chamber throughout its range of travel. The attitude space is the maximum deflection attitude angle the secondary mirror chamber can reach in both the x- and y-directions once the chamber's center is known. According to the calibration requirements of the telescope's secondary mirror, the position and attitude of the secondary mirror must be corrected in several DOFs. Therefore, the position workspace and the SMAM's attitude workspace are measured separately.  
The operating range of the secondary mirror adjustment is measured by controlling the movement of the SMAM prototype and the movement of the adjustment mechanism to the boundary position of the working space. By making several attempts in each direction, it is possible to determine whether the SMAM can move into place or move further. The results for the actual working space for the translation of the SMAM along the z-direction and the deflection movement around the x- and y-directions are given in Table 2.
## 3 Proposed Architecture and Algorithm
### Wavelet Transform of Data Compression  
Signal processing has become an important topic in contemporary science and technology. The aims of signal processing are accurate analysis, diagnosis, compression coding and quantization, storage, and signal recovery [26]. Currently, the ideal tool for stationary signal analysis is still the Fourier transform [27]. However, in practical applications, the vast majority of signals are non-stationary, so Fourier analysis is not suitable. The ECG signal mentioned in this study is a typical non-stationary signal, and wavelet theory of multi-resolution analysis for ECG signal processing is a new idea. Compared with other time-frequency analysis, the advantages of wavelet theory are that it cannot only adapt to the time-frequency resolution characteristics of non-stationary signals, but also decompose signals on an orthogonal basis. It is also easy to describe the non-stationary signals' time-frequency characteristics with a small number of parameters. These all constitute excellent features for an extraction algorithm. Figure 5 is a compression flow diagram of the ECG signal.  
In the first compression, we use the wavelet function bior1.1. In the second compression, we use the wavelet function bior3.7. Therein, bior can also be expressed as bior Nr. Nd. Nr and Nd are related parameters for remodeling and decomposition filter length. The bior wavelet is a biorthogonal wavelet. _c1 (n)(nEZ)_ is the input of the filter, i.e., a watermark signal. After the conversion, the middle output is:  
\[\left[\right.c_{0}\left(k\right) = \sum_{n}\tilde{h}(2k-n)c_{1}(n)\left.\right] \tag{11}\] \[\left[\right.d_{0}\left(k\right) = \sum_{n}\tilde{g}(2k-n)c_{1}(n)\left.\right] \tag{12}\]  
Based on the output of the filter group:  
\[\left[\right.\tilde{c_{1}}\left(m\right) = \sum_{k}\left[\right.h\left(2k-m\right)c_{0}\left(k\right)+g(2k-m )d_{0}(k)\left.\right]\left.\right] \tag{13}\]  
Figure 4: Watermark extraction model.  
Figure 5: Data compression model.  
With merger and exchange, we can get the following formula:  
\[[\ \tilde{c}_{1}\left(m\right)=\sum_{n}\sum_{k}]h\left(2k-m\right)\tilde{h} \left(2k-n\right)+g\left(2k-m\right)\tilde{g}\left(2k-n\right)]c_{1}(n)\ ] (14)  
In order to fully reconstruct, even with the expression:  
\[[\ \ \tilde{c}_{1}\left(m\right)=c_{1}\left(m\right)\left(\forall m\in Z \right)\ ] \tag{15}\]  
we need the following equation to be established:  
\[[\ \sum_{k}\left[h\left(2k-m\right)\tilde{h}\left(2k-n\right)+g\left(2k-m \right)\tilde{g}\left(2k-n\right)\right]=\delta(m-n)\ ] (16)  
The wavelet method for compression plays a role in optimizing the waveform of the electrocardiogram. We choose to remove the high frequency components. The change in the generated waveform has no effect on the doctor's diagnosis. Instead, it is possible to make the waveform more easily identifiable. In this way, we reduce not only the transmission volume, but also the noise of the ECG.
## 2 Materials and Methods
### Energy Consumption Model  
An analytical model given in [51] is used to measure the total energy consumption in data fusion. This model includes details such as consumed energy by frame/packet retransmissions, acknowledgments, frame/packet length, and transceiver processing units. In addition, we include the energy consumption of microprocessor for data fusion as it is an important parameter in low power devices.  
The number of transmission attempts of a node can be given by a random variable \(X\varepsilon\{1,2,\ldots,N\}\) where N is the maximum number of transmissions including the first transmission. Similarly, the number of acknowledgments can be denoted with a random variable \(Y\varepsilon\{1,2,\ldots,N\}\). The transmitter receives 0 acknowledgment if all transmission or acknowledgment attempts fail. The total energy consumption of the transmitter to deliver a frame/packet is given by Equation (7) as  
\[E_{TX}=X(P_{c}+P_{t}/\eta)(L_{d}/R_{d})+Y(P_{t}L_{a}/R_{a}) \tag{7}\]  
\(P_{t},P_{r},P_{c}\), and \(\eta\) denote the transmit power, receiving power, circuit power, and efficiency of the power amplifier, respectively. The transmission rate of information and acknowledgment frames/packets are defined by \(R_{d}\) and \(R_{a}\). Lengths of information and acknowledgment frames/packets are given by \(L_{d}\) and \(L_{a}\) respectively. Similarly, we can compute the total energy using Equation (8) to receive an information frame/packet as  
\[E_{RX}=X(P_{r}L_{d}/R_{d})+Y(P_{c}+P_{t}/\eta)L_{a}/R_{a} \tag{8}\]  
The energy consumption for the information fusion is measured by Equation (9) as  
\[E_{A}=T_{A}*I_{CPU}*V_{CPU} \tag{9}\]  
where \(I_{CPU}\) and \(V_{CPU}\) denote the current and voltage consumption of the microprocessor respectively. Thus, the total energy consumption of a single link is given by Equation (10) as  
\[E_{link}=E_{TX}+E_{RX}+E_{A} \tag{10}\]  
As there are \(K-1\) links in a gathering process that consists of K nodes, the total energy consumption of the network can be given by Equation (11) as  
\[E_{Total}=\sum_{i=1}^{K-1}E_{link,i} \tag{11}\]  
In this paper, we use the network lifetime as the performance metric. It is defined as the maximum number of data collecting cycles in a network until all nodes in the network are alive.
## 3 GNSS Data and Results
### Zakynthos-Strofades Islands  
In the southern part of the Ionian Islands, GNSS data from three stations were processed (Table 4). For the sites of ZAKY and STRF, located in the city of Zakynthos and at the islet of Strofades, respectively, data were available prior to the strong 2018 earthquake (Mw6.7). Both stations exhibited a linear type of ground motion. ZAKY station revealed an ESE horizontal motion, with a negligible subsiding pattern, while STRF station showed an SE motion with lower amplitude (Figure 8). Baseline change calculation between these two sites for the pre-earthquake period showed a lengthening of the distance with an estimated velocity of V = 3.50 \(\pm\) 0.10 mm/year. ZAKY station's pre-seismic kinematic behavior indicated shortening relative to the GNSS station PYRG, located in the western Peloponnese (V = \(-\)5.12 \(\pm\) 0.09 mm/year). Likewise, the baseline distance between the STRF and PYRG stations decreased with a similar velocity, V = \(-\)4.52 \(\pm\) 0.08 mm/year. These observations revealed a compressional regime between the Peloponnese and the South Ionian Islands, while extension occurred between Zakynthos and Strofades.  
The 2018 earthquake, southwest of Zakynthos, caused strong ground displacement in both Zakynthos and Strofades. Regarding GNSS stations at the island of Cephalonia, significant ground displacement was recorded at the central ARGO and the southern SISS and SKAL stations (Appendix B), but not at the VLSM site. Moreover, static ground displacement was observed at stations located in the western (PYRG) and northwestern (PAT0) Peloponnese, even in the southwestern part of mainland Greece (KTCH).  
After the occurrence of the mainshock, a new continuous GNSS station was established in the island (station ZNTE). The two sites that operated in Zakynthos after the Mw6.7 event, together with the PYRG station, revealed an almost eight-month-long post-seismic relaxation period. During this period, the kinematic behavior of ZAKY and PYRG stations showed a significant alteration of motion with respect to the pre-seismic one (Figure 10). A distinctive alteration of the east motional component was observed at PYRG station, where the eastward pre-seismic pattern reversed to a westward post-seismic motion. The post-seismic relaxation period was estimated to last up to early July 2019. During the following period (from July 2019 to the end of 2021), both ZAKY and PYRG stations again presented a pre-seismic motion pattern, with small alterations in the horizontal components (Figure 10). Similarly, the ZNTE station indicated a velocity vector significantly different compared to that of the post-seismic period, with a pattern of motion consistent to that of ZAKY.
# Damage and Failure in a Statistical Crack Model
## 5 The Fracture Process in Graphs
### Sensitivity  
In Figure 6, the sensitivity of the model to three choices of process zones (PZ) is explored by plotting the evolution of damage in time. The three curves represent process zones that are 0%, 10% and 20% of the crack radius. The process zone determines when neighboring cracks may begin to interact. Despite choices that represent a significant fraction of the crack radius itself, the sensitivity is small. The damage at time of failure differs by much less than 1% and the residual stress shows even less sensitivity. The time to failure does show larger sensitivity, since the process zone effectively makes the cracks bigger.  
The sensitivity to ensemble size was tested by running the \(\widehat{SCM}\) at a strain rate of \(10^{5}\,\mathrm{s}^{-1}\) for ensembles of 100, 200, and 500 members. No sensitivity was seen at the level of 1%.  
Figure 5: The stress component \(\sigma_{yy}\) at time of failure is plotted vs. strain rate. The ensemble size is 100 and the process zone is 10% of the crack radius. That stress increases slightly with strain rate, but is essentially constant. The curve is captured by a quadratic trendline with \(R^{2}=0.999\).  
Figure 6: The ensemble-averaged damage is plotted as a function of time for an imposed strain rate \(\dot{\epsilon}=1\times 10^{5}\,\mathrm{s}^{-1}\). The three curves represent process zone sizes of 0%, 10% and 20% of the crack radius. The ensemble size is 100. A larger process zone implies slightly more damage at time of failure.
## 1 Introduction
### Contingencies
#### 1.2.1 Screening  
Power system contingency screening is a very important process in the field of power systems to ensure network reliability and stability. Contingency screening encompasses the  
Figure 2: Contingency selection methods.  
identification and evaluation of potential contingencies or disturbances that may disrupt the normal operation of the power system. Contingencies include equipment failures, sudden changes in demand, or unexpected events such as extreme weather.  
The most common selection method is to use the outcome of the first iteration of the fast decoupled power flow (FDPF) applied to each contingency. Tests and experience indicate that this is not a very reliable screening method. By using two iterations, a better result is usually obtained, although two iterations require double the computational resources and still do not provide useful/necessary information about the effect on reactive power sources. Another selection method (often used in the past) is the bounding method [15; 16; 17], which takes into consideration the localization of the fault. The effect of a line fault decreases rapidly with distance from the fault location. The electrical distance diminishes rapidly beyond a certain number of busbars/nodes surrounding the fault and transmission lines or nodes located beyond that certain number become of little significance from a contingency analysis point of view. For each contingency, the bounding method determines the bounding limit of nodes and computes the DC (or AC) power flow solution for the contingency in the bounded subnet/zone. However, this method is not widely used nowadays.  
Up to this stage, the DC power flow solution for the bounded network has been fully computed and then it was used for checking line flow violations and selecting contingencies accordingly. The bounding method used nowadays does not require any preliminary calculations, it is performed for each new case, and it provides maximum flexibility in selecting contingencies. This method has been extended to AC network analysis and it has a wider range of applications than its earlier version.