esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### In Vitro Assessment of Antioxidant Activity of GCPs  
The antioxidant activity of GCPs with different molecular weights and concentrations was determined experimentally, as shown in Figure 1. \"VC\" refers to Vitamin C, used as a positive control in the antioxidant assays.  
The analysis revealed that the purified GCPs exhibited considerable antioxidant activity in vitro. The antioxidant activity of GCPs increased with concentration. The effect of molecular weight on its antioxidant activity was unstable, but in most cases, GCPs with lower molecular weight had stronger antioxidant capacity.  
GCPs with molecular weight <3 kDa were further purified by G-15 dextran gel chromatography to obtain GCP-I, GCP-II, and GCP-III. As shown in Figure 2, after in vitro antioxidant activity assays of these three fractions, it was found that GCP-II exhibited the highest antioxidant activity in vitro. Its ABTS radical scavenging activity and ferrous ion chelating ability were relatively good, with maximum values reaching 78.22% and 72.91%, respectively. In comparison, its DPPH radical scavenging activity was weaker, and its total reducing power was only 30.08%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Content (g/100 g)** \\ \hline Gly & 19.632 \\ Phe * & 8.584 \\ Lys * & 7.157 \\ Arg & 5.370 \\ Leu * & 4.454 \\ Glu & 4.429 \\ Tyr & 4.298 \\ Ala * & 2.965 \\ Asp & 2.936 \\ Ser & 2.084 \\ Ile * & 2.012 \\ Thr & 1.815 \\ Val * & 1.533 \\ Pro * & 1.078 \\ His & 0.391 \\ Met * & 0.344 \\ Cys & 0.010 \\ \hline \hline \end{tabular} Note. * indicates essential amino acids for humans, and * indicates hydrophobic amino acids.  
\end{table}
Table 3: Amino acid composition of peptides derived from grass carp swim bladder.  
Figure 1: ABTS\({}^{+}\) scavenging activity (**a**), DPPH scavenging activity (**b**), OH\({}^{-}\)- scavenging activity (**c**) of GCPs, \(\rm{O_{2}}^{-}\)- scavenging activity (**d**), total reducing capacity (**e**), Fe\({}^{2+}\)- chelating activity (**f**) with different molecular weight and concentration of GCPs.  
Figure 2: In vitro antioxidant activity of GCP-I, GCP-II, and GCP-III at same concentration.
## 5 Conclusions  
This paper proposes virtual admittance feedforward compensation (VAFC) and reference current phase correction to adjust the input admittance of the PFC converter to pure conductance. By thoroughly modeling the current loop and calculating input admittance of the PFC converter, it was found that the current distortion is caused by the limited bandwidth and input filter capacitor. With the proposed VAFC and input current phase correction, the input admittance is tuned as pure conductance, which improves PF and reduces THD. The VAFC is realized based on a phase lock loop, which avoids the interference induced by input voltage sampling noise. A SiC-based totem-pole PFC converter is built to verify the effectiveness of the proposed method. The proposed controller can generate a virtual admittance of 0-3.2 mS, which eliminates the imaginary part of the input admittance under various load power. Thus, the proposed control can achieve a pure conductance input characteristic. Under the proposed control, the phase of the input current is near 0, which improves the PF and THD of the converter.  
Figure 21: PF and THD curves of the prototype with respect to power rates under different control (a) PF; (b) THD.  
Figure 22: Power efficiency of the prototype.  
**Author Contributions:** Conceptualization, F.Z.; Data curation, H.H.; Formal analysis, D.Z.; Investigation, H.H.; Methodology, H.H.; Project administration, A.Z.; Resources, A.Z.; Software, H.H.; Supervision, X.Z.; Validation, J.Y. and M.W.; Visualization, H.H.; Writing--original draft, H.H.; Writing--review and editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Ministry of Industry and Information Technology of the People's Republic of China; Science and Technology Project of State Grid Corporation of China Headquarters (5700-202258309A-2-0-QZ) Research on low-propagation-delay and high-stability digital gate driver chip technology for high-voltage and high-power silicon carbide (SiC) power device.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** This work was supported by Huazhong University of Science and Technology and Beijing Institute of Spacecraft System Engineering.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results of QoE\({}_{i}\) Modeling and Discussion
### Predictive Models of \(QoE_{i}\)
#### 5.5.2 Boosted Decision Tree Model  
By implementing the modified code [(60)] in the MATLAB software package, according to the supervised learning paradigm, a machine learning model based on boosted decision trees was developed. The test results show that the MSE of the created model prediction  
Figure 4: Correlogram of research variables.  
is 0.388, which is almost twice the error compared to the linear model. In addition, based on the results shown in Figure 5, which were obtained by executing the code [60], it is concluded that the user experience expressed by the characteristics of each of the four traffic classes (X\({}_{8}\)) and the user experience expressed by the levels for the four traffic classes (X\({}_{9}\)), represent the variables with the greatest influence (predictor importance) on the prediction of \(QoE_{i}\). In last place in terms of influence is the variable representing the age (X\({}_{1}\)) of the respondent.  
If a decision tree is created with 15 inputs, which represent indicators of satisfaction and dissatisfaction with the quality of user experience, the \(QoE_{i}\) prediction error is then MSE = 0.16. As a result of training and testing the model, by executing the code [60], the MATLAB program package generates a graphic representation of the importance of the influence of each of the mentioned inputs on the prediction of \(QoE_{i}\) (Figure 6a). By creating a decision tree with inputs that are exclusively satisfaction indicators, Figure 6b shows as a result the ranking of the importance of the influence on the prediction of \(QoE_{i}\) for each of them. It can be concluded that the _rating of the service impact on the user's charisma_ has the greatest influence (D\({}_{7}\)). The prediction error of the quality of user experience in this case has a value of MSE = 0.45. Nevertheless, the smallest prediction error is achieved if only five dissatisfaction indicators denoted by C are considered as inputs (MSE = 0.09). This result is in accordance with the presented mathematical model for estimating \(QoE_{i}\) (expression (5)) in which higher-weighting coefficients according to absolute value (0.2) are associated with indicators of dissatisfaction. Figure 6c graphically shows the importance of the influence of indicator C on the prediction of the target variable, from which it can be concluded that the rating of the connection of services with prospective anxiety (C\({}_{4}\)) is at the top of the ranking by the value of prediction importance. So, taking into account the MSE values, it is clear that the prediction using the boosted decision tree model shows significantly more accurate results compared to the linear model.  
Figure 5: The importance of the influence of certain independent variables on the prediction of \(QoE_{i}\).
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 3 Background
### Call Redirection Detection  
Vishing applications include Call Redirection Attacks to alter the outgoing call on the victim's phone using setResultData() or CallRedirectionService. These applications also implement Display Overlay Attacks that obscure changes to the outgoing number, making it challenging for victims to detect Call Redirection. To detect Call Redirection Attacks, we retrieve and compare both the original outgoing number and the redirecting outgoing number using Android APIs. Additionally, we obtain the number displayed on the call standby screen and compare it with the actual outgoing number for double verification. This approach reduces the false positives, thereby protecting users from both inadvertent and malicious call alterations.
## 4 RNN Structures Used for Traffic Prediction  
### RNNs  
RNNs are a class of iterative learning machines that process sequence data by cyclically reusing the same weights. This cyclical structure enables them to maintain a memory of previous data points using this accumulated information to process current inputs, thereby capturing temporal dependencies. Specifically, an RNN applies a transfer function to update its internal state at each time step. The general formula for this update is as follows:  
\[\mathbf{h_{t}}=f(W_{x}(\mathbf{x_{t}}+\mathbf{b_{x}})+W_{h}(\mathbf{h_{t-1}}+\mathbf{b_{h}})) \tag{2}\]  
Here, \(\mathbf{x_{t}}\) is identified as the input vector at time step \(t\) and \(\mathbf{h_{t-1}}\) is the previous hidden state. \(W_{x}\) is the weight matrix for the connections between the input at the current time step and the current hidden state. \(W_{h}\) is the weight matrix for the connections between the previous hidden state and the current hidden state. \(\mathbf{b_{x}}\) and \(\mathbf{b_{h}}\) represent bias vectors. The function \(f(\cdot)\) acts as the activation function.  
The hidden state \(\mathbf{h_{t}}\) at each time step \(t\) is updated using a combination of the current input \(\mathbf{x_{t}}\) and the previous hidden state \(\mathbf{h_{t-1}}\). Weight matrix \(W_{x}\) facilitates the incorporation of new information and weight matrix \(W_{h}\) helps in transferring past learned information. \(\mathbf{b_{x}}\) and \(\mathbf{b_{h}}\) adjust the inputs and the state transition. The function \(f(\cdot)\), typically a non-linear activation function, is applied to introduce non-linearity into the system, enabling the network to capture complex patterns in sequential data.
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 2 Theoretical Conceptualization
### Variational AutoEncoder  
A Variational AutoEncoder extends a standard AutoEncoder by employing a regularized latent space based on principles of probabilistic Machine Learning. Instead of encoding an input \(\mathbf{x}\) as a single point, the encoder \(q_{\phi}(f(\mathbf{x})|\mathbf{x})\) receives the input as a distribution characterized by its mean (\(\mu_{x}\)) and standard deviation (\(\sigma_{\mathbf{x}}\)), from which a sample is drawn, \(f(\mathbf{x})\), and subsequently decoded. The decoder \(p_{b}(\mathbf{x}|f(\mathbf{x}))\) receives the latent variables \(f(\mathbf{x})\) and the probabilistic density function provides the matrices containing the mean and covariance of the model \(g(f(\mathbf{x}))\). The weights \(\phi\) need to be learned by the ANN so that  
Figure 1: Example of an SAE network structure ([PERSON] et al. (2021) [13]).  
it can configure a loss function and generate both the latent space and the output. As shown in Equation (4), the loss function combines a reconstruction term, \(L(\mathbf{x},g(f(\mathbf{x})))\), and a Kullback-Leibler (KL) divergence term \(L_{KL}\). This regularization component organizes the latent space by aligning it with a standard normal distribution. Similar to the SAE, in this work, the VAE models also incorporate MSE as their loss function for comparative reasons. Figure 2 shows the structure of a general VAE network.  
\[L_{VAE}=L(\mathbf{x},g(f(\mathbf{x})))+L_{KL} \tag{4}\]
## 3 Problem Formulation
### Objective Function  
In the proposed multi-objective problem, three objectives are considered as the MG operator's profit (\(F_{1}\)) maximization, and minimization of the total emission (\(F_{2}\)) as well as the cost corresponding to frequency deviation of the MG (\(F_{3}\)). The total MG operator's profit is formulated as:  
\[\begin{array}{l}F_{1}\ \ =\sum\limits_{t=1}^{T}\sum\limits_{j=1}^{N_{j}}C_{j,t} \cdot D_{j,t}\\ \ -\sum\limits_{t=1}^{T}\sum\limits_{i=1}^{N_{G}}[(A_{i}\cdot u_{i,t}+B_{i} \cdot P_{i,t})+SUC_{i}\cdot y_{i,t}+SDC_{i}\cdot z_{t,t}]\\ \ -\sum\limits_{t=1}^{T}\sum\limits_{w=1}^{N_{W}}C_{w,t}\cdot P_{w,t}-\sum \limits_{t=1}^{T}\sum\limits_{p=1}^{N_{p}}C_{p,t}\cdot P_{p,t}\\ \ +\sum\limits_{t=1}^{T}\sum\limits_{k}^{N_{ch}}EB_{t}\cdot P_{k,t}^{ch}-\sum \limits_{t=1}^{T}\sum\limits_{k}^{N_{ch}}ESP_{t}\cdot P_{k,t}^{dis}\end{array}. \tag{6}\]  
The first line of Label (6) represents the MG operator's revenue from selling energy to the consumers. The second line stands for the fuel cost of generation units and the start-up/shut-down costs. The third line denotes the costs associated with energy provided from the wind turbine (WT) and photovoltaic (PV) units. Here, it is assumed that the MG operator is not the owner of the renewable resources and is only responsible for the scheduling of the renewable units in the MG, so he should pay for energy provided by WT and PV. Finally, the last line expresses the costs associated with charge and discharge of EVs.  
Figure 3: Flowchart of delivered power of electric vehicle (EV) \(k\).  
The second objective is to minimize the MG pollutants' emissions generated by DG units that consist of CO\({}_{2}\), NO\({}_{x}\) and SO\({}_{2}\):  
\[F_{2}=\sum_{t=1}^{T}\left[\sum_{i=1}^{N_{G}}\left(Em_{t,i}^{CO_{2}}+Em_{t,i}^{ NO_{x}}+Em_{t,i}^{SO_{2}}\right).\right. \tag{7}\]  
The cost corresponding to MG frequency deviation should be minimized during scheduling horizon, which can be considered as a quadratic function of SMI as shown in Figure 4.  
The piecewise linear model for one period is represented as the following:  
\[f_{3}(t)=\sum_{m=1}^{NS(i)}v_{m,t}\cdot SMI_{t}\cdot u_{t}\quad\forall t, \tag{8}\]  
\[F_{3}=\sum_{t=1}^{T}f_{3}(t), \tag{9}\]  
where \(v_{m,t}\) is the slope of segment \(m\) in linearized total penalty cost and \(NS(i)\) is the number of segments. In addition, \(u_{t}\) is a binary variable, equal to 0 if SMI has its expected limitation value (system stability is not at risk); otherwise, it is 1.
## 2 Materials and Methods
### Speckle Whitening of Complex SAR Data
#### 2.1.3 Point Targets  
The success of the whitening process outlined in Section 2.1.2 is diminished if speckle cannot be assumed to be fully developed. This happens wherever single-pixel reflectors or point targets are present in the scene. Point targets should be detected, removed, and substituted with patches of synthetic complex speckle, whose variance is equal to the average variance of its neighbors before the deconvolution is carried out [6]. For single-polarization images, point targets may be detected through percentile thresholding of the amplitude or intensity of the SLC image [34]. For polarimetric data, advantage may be taken of the capability of PolSAR to characterize the nature of scattering mechanisms [24].  
The Fourier-domain whitening procedure [6] requires the preliminary suppression of point targets. In fact, the deconvolution process diminishes the benefits of the tapering window: the impulsive autocorrelation function is restored over distributed targets soft targets). However, on hard targets, if they are not detected, removed, replaced, and reinserted after deconvolution, the inverse filter will generate artifacts similar to the PSF of the SAR system (see Figure 3c), originating in azimuth due to the increase in the antenna sidelobes [39]. Processing point targets before the spectral whitening operation is crucial.  
Figure 3: Effects of SAR processor: (**a**) spatially correlated speckle originating from frequency windowing; (**b**) correlated speckle whitened using the inverse filter in Figure 2b; (**c**) example of a point target focused without a tapering window (negative grayscale).  
Figure 2: Power spectra: (**a**) periodogram of SLC data correlated in slant-range direction; (**b**) frequency response of inverse filter; (**c**) periodogram of SLC data in (**a**) after whitening with the filter in (**b**).  
Besides a simple yet effective thresholding, a more sophisticated detector could be used. Contextual information on targets may be exploited after detecting the \"brightest\" pixels because if the target is more than one pixel wide, [PERSON] effects do not occur after inverse filtering. In fact, if bright spots wider than one pixel are erroneously detected, removed, and replaced before whitening and reinserted after whitening, pixels surrounding a point target exhibit high heterogeneity, which makes their speckle less developed [40; 41]; thus, their spatial decorrelation is unnecessary and can be skipped.  
It is noteworthy that the whitening procedure changes the relative calibration of the SLC image by a scale factor equal to the power gain of the inverse filter. The calibration is restored before point targets are inserted back into the whitened image [6].
## 5 Discussion  
This study proposed the use of the NCCS for BESS- and PMSG-type wind turbines to improve the frequency nadir while the system frequency drops during fast frequency response, and it was verified using a small-scale grid. In the proposed strategy, the RoCoF deadband was adapted, and coordinated control between the wind turbine based on the PMSG and the BESS was performed to prevent the second frequency drop and to improve the frequency nadir. This approach has the advantage of improving the frequency nadir because it enables the output of the BESS near the frequency nadir through the RoCoF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Without** & **FR with Only** & **FFR with BESS** & \multirow{2}{*}{**FFR with NCCS**} \\  & **Control** & **BESS** & **and PMSG** & \\ \hline Frequency nadir (Hz) & 58.4703 & 58.7687 & 59.0283 & 59.0735 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of frequency nadir results.  
Figure 11: Simulation results: (**a**) system frequency; (**b**) output power of the PMSG type wind turbine; (**c**) output power of synchronous generator #1; (**d**) output power of the BESS.  
Figure 12: Simulation results of the PMSG type wind turbine: (**a**) electrical torque and torque command; (**b**) rotor speed.  
The conventional fast frequency control was not efficient in improving the frequency nadir because it focused on supporting the frequency response as quickly as possible. Simulation results showed that the proposed control strategy can utilize the same resources as in conventional fast frequency control to improve the frequency nadir and prevent the second frequency dip from being lower than the first frequency drop.  
Conceptualization, software, and writing (original draft preparation), H.K.; methodology and validation, H.K. and [PERSON]. ([PERSON]); writing (review and editing), [PERSON] ([PERSON]) and [PERSON] ([PERSON]); supervision, G.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institution of Energy Technology Evaluation and Planning (KETEP) under grant (20183010025440).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Application of the Model for Spectral Irradiance Estimation
We also evaluated the empirical coverage achieved by the predicted uncertainty on the independent testing set, assessing the percentage of ground-truth values that actually fall within the model's prediction interval for each wavelength bin. The results are showcased in Figure 7:0  
The majority of the wavelength bins, particularly in the 450 nm-750 nm region, fall within or acceptably close to the expected 80% coverage level. This indicates that the prediction interval generated from split conformal prediction provides a valid and reliable uncertainty quantification for these regions. However, the shorter wavelength bins (approximately 360 nm-420 nm) exhibit notable over-coverage, with the prediction interval capturing the true values nearly 100% of the time. This can indicate that the prediction interval is excessively wide for these wavelength regions. These shorter wavelengths tend to have weaker irradiance in many terrestrial environments, especially under atmospheric  
Figure 7:1 Relative MSE values across the 421 spectral bins evaluated on the test set after incorporating conformal prediction. Figure 7:2 MSE values on each of the 421 spectral bins evaluated on the training and testing sets after incorporating conformal prediction. attenuation. As a result, these wavelength bins generally contain irradiance values smaller in magnitude compared to the rest of the spectrum. Therefore the prediction interval generated from split conformal prediction for the model can be disproportionately large relative to the signal strength in these bins. This does not reflect the model's predictive accuracy (which is measured by metrics such as \(R^{2}\) and MSE), and solely pertains to the practical usefulness of the predicted uncertainty in these spectral regions. Specifically, overly wide intervals reduce the informativeness in the uncertainty quantification. Figure 7:3 Model-estimated spectra at selected time points, shown with 80% confidence intervals and compared against ground-truth spectra. Figure 7:4 Empirical coverage.
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 2 Materials and Methods
### Methods of Fabrication of Cast Foils and Characterization
#### 2.2.4 Mechanical Characterization  
PMMA foils of 100 mm in length and about 3 mm wide were chosen for the tensile test using a thermomechanical tester (Walter + Bay) with a strain-controlled mode at room temperature. The sample stretching in the tester was controlled by engineering strain. The span length was kept fixed at 80 mm. Thermomechanical analyzer (TMA) test was performed on the PMMA foils in flat punch bending mode using the constant load of 50 mN. The sample was tested in thermal cycles from room temperature to \(-100\)\({}^{\circ}\)C. Tensile experiments that failed in the clamping position are excluded from the data. The sample configure followed the standard from the in-house built Mitter machine [22].  
Dynamical mechanical characterization was performed on the commercial PMMA foil and prepared foils in tensile mode. Thermodynamic analysis was conducted using DMA analysis (DMA 850 tester from TA Instruments). The fixed frequency was 1 Hz, and the temperature range was from 30 to 160 \({}^{\circ}\)C. The loss and storage modulus and tan(delta)--internal friction was evaluated in the temperature range. In this instrument, a force was applied to a sample in sinusoidal form, and the amplitude and phase of the resultant displacement were measured [23; 24; 25]. By measuring both the amplitude of the deformation at the peak of the sine wave and the lag between the stress and strain sine waves, quantities such as the modulus, viscosity, and damping can be calculated in the viscoelastic material such as PMMA. The stored energy as storage modulus, dissipation energy as loss modulus, and damping behavior of PMMA foils were measured as the function of temperature.  
The shape memory effect (SME) was characterized by combining deformation, heating, cooling, and heating cycles for PMMA commercial and cast foils. The SME behavior of the foils was carried out by manual heating and cooling cycles.