esa-sceva/satcom-chunk-collection huggingface dataset:
# DC Voltage Induces Quadratic Optical Nonlinearity in Ion-Exchanged Glasses at Room Temperature
## 2 Materials and Methods  
The specimens were \(1\) mm thick slides of soda--time glass, the composition of which is presented in Table 1.  
The ion exchanges were carried out in a pure KNO\({}_{3}\) melt at 365 \({}^{\circ}\)C or in a 95 NaNO\({}_{3}\)/5 AgNO\({}_{3}\) (in wt.%) melt at 325 \({}^{\circ}\)C for the enrichment of the subsurface regions of the specimens with K\({}^{+}\) or Ag\({}^{+}\) ions, respectively. Note that, despite the glass being multicomponent, the domestic ions that preferably participate in the IE process are sodium ones, since the mobilities and diffusion coefficients of all the others are significantly lower than those of Na\({}^{+}\) and the invasive K\({}^{+}\)/Ag\({}^{+}\) ions from the melt [27; 28; 29; 30], and the concentration of potassium in the used glass is low and can be neglected. We varied the duration of IE, \(t_{\text{IE}}\), from 15 min up to 8 h, and the specimens were fully immersed in the melt; therefore, the same IE process occurred on both sides of the glass plates.  
In the charging experiments, we used an Sh0105 source (NAUEL, Schelkovo, Russia) to apply DC voltage \(U\) (we used 1300 V) to the ion-exchanged glasses using pressed stainless-steel electrodes about 1 cm\({}^{2}\) in size. Also, we used transparent ITO electrodes, which allowed us to simultaneously measure the electric current through the specimens and the impact of the charging on their nonlinear optical properties. Note that since the experiments were carried out at room temperature, at which glass conductivity is extremely low, the electric currents were as low as fractions of nA and were measured using a picoammeter A2-4 (MNIPI, Minsk, Belarus).  
To measure the signal of the second optical harmonic generated by a specimen, we used the classical Maker fringe (MF) technique [31]. A detailed description of the setup can be found elsewhere [32]. Essentially, a specimen was fixed in a rotating holder, and the second harmonic output was measured as a function of the angle of incidence of the fundamental beam of a 1.06 \(\upmu\)m Q-switched laser generating 6 ns pulses (Litron, Rugby, UK) onto the specimen's surface. In dynamic experiments using transparent ITO electrodes, the angle of incidence was fixed (corresponding to the maximum SH output), and the SH signal was measured as a function of time after the DC voltage was turned on and off.
## 5 Discussion
### Limitations  
The tested subject in this study is a certain type of navigation receiver, and it is uncertain whether for other types of navigation receiver the proposed sensitivity criterion can be immediately applied. However, the sensitivity criterion based on the sensitive phenomenon level could be taken as a reference for the definition of the sensitivity criterion on other types of navigation receivers. Compared with the direct use of C/N0 as a sensitivity criterion in the previous literature, the subjective sensitivity criterion proposed in this study can be used to accurately assess whether the navigation receiver is effectively interfered. In the experiment, the measured sensitivity data is relatively stable, but the C/N0 should be required to recover to the initial positioning state before you enter into the next accurate sensitivity measurement.  
The purpose of this study is to measure the critical interference threshold of a navigation receiver under the interference of single-frequency continuous wave electromagnetic radiation. The critical sensitivity will be used in the prediction model of electromagnetic radiation interference. Therefore, in this study, only single-frequency continuous wave is used to carry out the irradiation experiment on the tested navigation receiver. However, it is still uncertain whether the effect phenomenon is consistent with the results in this study when the tested equipment is interfered with various other types of electromagnetic radiation interference or the injection method is adopted in the experiment. We have emphasized that the tested equipment is more sensitive for single-frequency continuous wave in before section. Hence, even if the tested result in other electromagnetic environments is not completely consistent with our study, the critical threshold may be greater than our experiment result for each frequency point.
# Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform
## 3 Proposed Watermarking Scheme
### Watermark Extraction Process  
A watermark, \(W\), that has been inserted into a watermarked image, \(Aw\), which subsequently has been subjected to image manipulation attacks, can be extracted by the procedure described here. The extracted watermark (\(W^{\prime}\)) may be distorted or of the same quality as when inserted:  
* Step 1: The pixels of the distorted watermarked image '\(Aw\)' are redistributed, as described in Section 3.1. The image is divided into \(4\times 4\) blocks, and the blocks with the watermark information are selected to directly compute the invariant maximum singular value in the spatial domain instead of the SVD transform by Equation (4).
* Step 2: With the help of the singular values obtained in Step 1 and quantization parameter 'Q', the encrypted watermark can be extracted by the following Equation (21). \[W^{\prime}=mod\left(ceil\left(\frac{\sigma_{\text{max}}^{\prime}}{Q}\right),2\right)\] (21)
* Step 3: The decryption process is applied with the proper key to obtain the embedded watermark \(W^{\prime}\) that was extracted in Step 2.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Cost  
As previously mentioned, wearable sensors are cheaper than external sensors. However, this does not mean that cost is not an issue with wearable sensors. Cost can increase for multiple reasons, such as the number of sensors used, type, brand, and computer equipment and software needed to process the data. Researchers and clinicians with low resources may not be able to afford costly wearable sensor systems. That is why some researchers tend to evaluate the subjects using smart devices, such as smartphones, since nowadays millions of people already owns one (Table 3). Others prefer to build their own device using their own specifications to reduce the cost [47].
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 3 System Design  
In order to give flexibility and enhance coverage expansion to the existing terrestrial communication network, we suggest FRL-based AAN, composed of LEO satellites and UAVs, which could be adaptable to various changing communication network environments. In this section, we first explain the overall design and operations in the proposed system. After that, we give a detailed explanation about the proposed system, including the learning method used in the proposed system.
## 5 Conclusions  
In this paper, we considered the application of using a solar-powered UAV for securing the communication with a ground node in the presence of eavesdroppers in urban environments. A path planning problem was formulated with the objective of jointly maximizing the residual energy of the solar-powered UAV at the end of the mission, maximizing the time period in which the UAV can securely communicate with the intended node and minimizing the time to reach the destination. A special attention was paid to the impact of the buildings in the urban environments, which may block the transmitted signals and also create some shadow region where the UAV cannot harvest energy. An RRT-based path planning scheme was presented. The effectiveness of the proposed scheme was tested via computer simulations. The comparison with a general selection of the path to minimize the energy consumption showed that the path generated by the proposed scheme enables the UAV to securely transmit data to the intended node in a larger time window. One of our future efforts is to explore a UAV team for the purpose of securing the communication, where one of the UAVs transmits data and others jam the eavesdroppers. Another research direction is to extend the current work to dynamic environments with either the mobile intended node or moving obstacles in the environment. Then, we will focus on the re-planning of the UAV path.  
**Author Contributions:** Formal analysis, H.H.; funding acquisition, A.V.S.; investigation, H.H.; methodology, H.H.; project administration, A.V.S.; supervision, A.V.S.; writing--original draft, H.H.; writing--review & editing, A.V.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Australian Research Council. In addition, this work received funding from the Australian Government, via grant AUSMURIB000001 associated with ONR MURI grant N00014-19-1-2571.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: 3D (**a**) and 2D (**b**) views of the UAV trajectory by the proposed method in another urban environment.  
Figure 9: Simulation results of when the UAV can securely transmit data (**a**) and the residual energy (**b**) in case 2.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 3 Results and Discussions
### PL Analysis  
The crystalline quality and energy bands were examined using photoluminescence (PL) spectroscopy. Figure 6 shows the PL spectra emissions of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) at room temperature at an excitation wavelength of 360 nm. Figure 6 reveals that all NPs showed the emission peaks at 433 nm, 456.13 nm, 504.37 nm, and 525.12 nm due to defect energy levels from oxygen vacancies and tin interstitials in the band gap of SnO\({}_{2}\) NPs [22; 31; 48]. The results show that the emission peak intensity of SnO\({}_{2}\) NPs decreased with increasing Zn doping. This effect indicates that the valance band electron recombination rate (electron holes) was reduced owing to oxygen vacancies [49; 50; 51]. PL analysis suggests that Zn-doped SnO\({}_{2}\) NPs (1% and 5%) can be used in catalytic and biomedicine applications.  
Figure 5: UV-vis spectra of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) **(A)**. Band gap evaluation from the plots of \((\alpha\)hv)^{2}\) versus photon energy (h\(\upsilon\)) of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) **(B)**.  
Figure 6: PL emission spectra of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%).
## 1 Introduction
### Motivation and Contribution  
For extremely large data, the current automated feature estimation algorithms are not able to capture sufficient valuable information about the feature dynamics [34]. The research aims of this study are to implement and explore the efficacy of the heart rate and respiratory rate signal-based (time-series) features extraction algorithm for accurate stress classification, using a stress-predict dataset [35]. The study also determines the best (well-distinguishable) time-series feature from respiratory and heart rate signals for accurate stress monitoring. The algorithm calculates several time-series features using the tsfresh library and then performs anomaly detection (leading to feature reduction) using principal component analysis (PCA) and correlation co-efficient analysis (Pearson, Spearman, and Kendall) to shortlist the most discriminative features.  
For validation of the proposed method, a combination of different extracted features is fed into supervised linear, ensemble, and clustering classifiers. The proposed method of time-series features estimation/extraction and feature selection, due to its fast computation and selection of well-distinguishable stress features, can potentially be deployed on photoplethysmography (PPG) sensor-based watches and can detect the anomalies (stress) in real-time.  
The rest of the paper is organised as follows. Section 2 discusses the stress-predict dataset and methods implemented for time-series features extraction and selection; Section 3 reports the detailed analysis and results of supervised machine learning classification and provides discussion around the results; Section 4 concludes the paper and provides future direction towards the development of the reliable stress-monitoring device.
## 2 Materials and Methods
### Method Validation  
The newly developed method was validated by examining the selectivity, the lower limit of detection (LLOD), the lower limit of quantification (LLOQ), linearity, recovery, precision, and stability according to the FDA guidelines [43]. The blank matrices, the matrices spiked with ZEN and ZEN-14G (20 \(\upmu\)g/L), and the real collected samples after administration of ZEN-14G by gavage were investigated to illustrate the selectivity of the developed method. Standard solutions with concentrations of 0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100, and 200 \(\upmu\)g/L for ZEN and ZEN-14G were prepared in the blank matrices of the plasma, liver, kidney, stomach, small intestine, large intestine, ovary, urine and acetonitrile-water containing 5 mmol/L ammonium acetate (30/70, \(v/v\)). The responses of MS (peak areas) to the concentrations of the analytes were plotted to construct the calibration curves (1/x weighted). The ratio of the slope of the matrix-matched calibration curve to that of pure solvent was calculated to illustrate the matrix effect (signal suppression/enhancement, SSE). The sensitivity was expressed as the LLOD and LLOQ. The LLOQ was defined as the lowest concentration of the calibration curve that acquired a signal-to-noise ratio (S/N) value of 10, with the precision within 20%, and an accuracy of 80-120%. The LLOD was obtained by dividing the LLOQ by 3.  
Samples of blank plasma, urine, and various tissues spiked with ZEN and ZEN-14G at the LLOQ, and low (5 \(\upmu\)g/L), intermediate (20 \(\upmu\)g/L), and high (50 \(\upmu\)g/L) levels were used for the recovery, precision, and stability tests (\(n\) = 5). Equation (1) was used to calculate the recovery through a comparison of the detected concentrations with the ideal (spiked) levels of each analyte [44]. The relative standard deviation (RSD) measured within one day and on five consecutive days represented the intra-day and inter-day precision, respectively. Plasma, urine, and various tissue samples containing ZEN and ZEN-14G at the LLOQ and low, intermediate, and high concentrations were determined to assess the short-term (room temperature, 8 h), long-term (\(-\)20 \({}^{\circ}\)C, 20 days), and freeze-thaw (three cycles) stability.  
\[\text{Recovery}=\text{Concentration measured}/\text{Concentration spiked}\times 100\% \tag{1}\]
## 3 Correlation Measurement
Cross-correlation measures the correlation signal between the test pulse \(I(t)\) and a gate pulse \(I_{\mathrm{g}}(t)\) which is different from \(I(t)\), expressed as  
\[\mathrm{C}^{(2)}(\tau)=\int\!I(t)I_{\mathrm{g}}(t-\tau)\mathrm{d}t, \tag{6}\]can break the symmetry of the correlation signal. If the gate signal is the harmonic of the test pulse, the cross-correlation is called high-order autocorrelation. For example, third-order autocorrelation [6; 10] uses frequency-doubling pulse as the gate pulse, and the signal is written as \(\mathrm{A}^{(3)}(\tau)=\int\!I(t)l^{2}(t-\tau)\mathrm{d}t\). Scanning cross-correlation, benefiting by the multi-shot measurement, easily realizes high contrast [26; 27]. However, the typically reported dynamic range of traditional single-shot cross-correlation is \(10^{6}\)-\(10^{7}\)[28; 29; 30]. In 2014, [PERSON] firstly realized a dynamic range higher than \(10^{10}\) in a 50-ps time window in a single-shot cross-correlator based on time-to-space encoding and fiber-array-based detection. Most recently, a dynamic range of \(\sim\)\(10^{11}\) within a time window of 65 ps was obtained by using a single-shot four-order autocorrelator, as shown in Figure 1. The gate pulse, generated in a frequency-degenerate four-wave mixing process [31], has the same frequency as the test pulse, which can avoid group-velocity mismatching and realize time resolution up to \(\sim\)160 fs.
## 5 Results Discussion
### Analysis of PSO Algorithm Optimization Results
The impact of fractional order, film thickness, and film length on the reflection matrix element \(\mid\,R_{01}\,\mid\,\) is shown in the 3D graph in Figure 15. It can be seen that the surfaces in the three figures all exhibit periodic fluctuations with frequency changes. In Figure 15a, the range of fractional-order values within the range of [0.6, 1] will cause significant fluctuations in \(\mid\,R_{01}\,\mid\,\). When the order \(\alpha\) is 0.8, \(\mid\,R_{01}\,\mid\,\) is at its minimum and the frequency is around 8000 Hz. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Thickness of** & \(\alpha\) &
\begin{tabular}{c} **Length of** \\ **Film** \\ \end{tabular} & \multicolumn{2}{c}{**ML Parameters**} & \multicolumn{2}{c}{**Fitness Value**} \\ \hline \(h_{0}\) & & & \(\xi\) & \(\gamma\) & \(a\) & \\ \hline \multirow{6}{*}{0.01 mm} & & 0.8 L & 0.5886 & 0.6468 & \(2.7745\times 10^{-5}\) & 0.4845 \\  & 0.2 & 0.5 L & 0.6556 & 0.6830 & \(1.1889\times 10^{-4}\) & 0.9638 \\  & & 0.2 L & 0.6562 & 0.6827 & \(7.7302\times 10^{-6}\) & 0.11 \\ \cline{2-6}  & & 0.8 L & 0.5694 & 0.4231 & 0.0012 & 0.0086 \\  & 0.5 L & 0.6512 & 0.4369 & \(6.2344\times 10^{-4}\) & 0.4313 \\  & & 0.2 L & 0.6529 & 0.4184 & \(5.2108\times 10^{-5}\) & 0.9066 \\ \hline \multirow{6}{*}{0.7 mm} & & 0.8 L & 0.7765 & 0.4625 & 0.0003 & 0.0605 \\  & 0.2 & 0.5 L & 0.7765 & 0.4625 & 0.0003 & 0.0640 \\  & & 0.2 L & 0.7462 & 0.3927 & 0.0001 & 0.9494 \\ \cline{2-6}  & & 0.8 L & 0.8564 & 0.4014 & 0.0012 & 0.9752 \\ \cline{1-1}  & 0.8 & 0.5 L & 0.8759 & 0.3678 & 0.0006 & 0.9944 \\ \cline{1-1}  & & 0.2 L & 0.8658 & 0.4034 & 0.0009 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The shape parameters and fitness values obtained through the PSO algorithm optimization. The range of \(h_{0}\) values in Figure 15b is [\(10^{-5}\), \(10^{-3}\)], and there is a significant change in \(\mid\!R_{01}\!\mid\) within the frequency range of [7500 Hz, 8000 Hz] and \(h_{0}\leq 0.0003\) mm. \(\mid\!R_{01}\!\mid\) has the smallest value when \(h_{0}\) is 0.0001 mm. The range of \(x_{0}\) values in Figure 15c is [0.001, 0.049]. When \(x_{0}\) falls within the range of [0.001, 0.014] and frequency is within the range of [5300 Hz, 9691 Hz], \(\mid\!R_{01}\!\mid\) can achieve multiple small values, with the minimum value occurring at \(x_{0}=0.2\) L. The result is consistent with the optimization results.
## 2 Wellbore Attitude Measurement Method and Error Analysis  
### Wellbore Attitude Calculating Method  
#### 2.1.1 Accelerometer-Based Horizontal Attitude Calculation  
Before describing the attitude measurement method, the relevant reference coordinate frames are defined in this paper. As shown in Figure 1, the main four coordinate frames are utilized to describe the wellbore attitude. We define \(I\) to denote the Earth-centered inertial frame (\(I\) frame), and \(E\) denotes the Earth-centered Earth-fixed frame (\(E\) frame). \(G\) denotes the geographic coordinate frame (\(G\) frame), and \(D\) denotes the drilling tool coordinate frame (\(D\) frame).  
The wellbore attitudes are usually described in terms of inclination angle, hole direction angle and tool face angle, which correspond to the pitch, azimuth and roll angles in the inertial navigation system, respectively. To regulate the formulas and parameters, we adopt the description in inertial navigation to uniformly denote the pitch, azimuth and roll angles as the symbol \(inc\), \(az\) and \(tf\). The wellbore attitudes are the angular relationship between the \(D\) frame and the \(G\) frame. The Euler rotation matrix of the three attitude angles is represented by \(\mathbf{C}_{G}^{D}\). The elements of the matrix are shown in Equation (1).  
\[\mathbf{C}_{G}^{D}=\begin{bmatrix}cos\ az\ costf-sin\ az\ sin\ inc\ sin\ tf&sin\ az\ costf+cos\ az\ sin\ inc\ sin\ tf&-cos\ inc\ sin\ tf\\ -sin\ az\ cos\ inc&cos\ az\ cos\ inc&sin\ inc\\ cos\ az\ sin\ tf+sin\ az\ sin\ inc\ cos\ tf&sin\ az\ sin\ tr-cos\ az\ sin\ inc\ cos\ tf&cos\ inc\ cos\ tf\end{bmatrix} \tag{1}\]  
The three-axis accelerometers are the basic devices for measuring horizontal angles. The angle between the component of the gravity field from the axial accelerometer and the gravity field indicates the inclination angle of the instrument. The radial accelerometers can be used to calculate the tool face angle. The vector of the gravity field in the \(G\) frame is denoted as \(\mathbf{f}^{G}=\left[0\quad 0\quad-g\right]^{T}\), and the output of the tri-axial accelerometers in the  
Figure 1: The spatial relationships of the coordinate frame.  
frame is \(\mathbf{a}^{D}=\left[a_{x}\quad a_{y}\quad a_{z}\right]^{T}\). Therefore, the tri-axial accelerometers can be rewritten as Equation (2) combined with \(\mathbf{C}_{G}^{D}\).  
\[\left[\begin{array}{c}a_{x}\\ a_{y}\\ a_{z}\end{array}\right]=\mathbf{C}_{G}^{D}\left[\begin{array}{c}0\\ 0\\ -g\end{array}\right]=\left[\begin{array}{c}g\ cos\ inc\ sin\ tf\\ -g\ sin\ inc\\ -g\ cos\ inc\ cos\ tf\end{array}\right] \tag{2}\]  
Thus, the expression for pitch and roll angle are given as Equation (3).  
\[\left\{\begin{array}{c}inc=arctan(-a_{y}/\sqrt{a_{x}^{2}+a_{z}^{2}})\\ tf=arctan(-a_{x}/a_{z})\end{array}\right. \tag{3}\]
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 2 Probabilistic Forecasting Models
### Artificial Neural Network-Based Probabilistic Forecasting
#### 2.3.1 Examples of Probabilistic Forecasting Models Using Feedforward ANNs  
Mixture density networks (MDN) and softmax regression networks (SRN) are two of the main feedforward ANN-based models aiming to obtain the distribution of uncertain parameters [23]. Regarding MDN, as a parametric model, the associated probability density is obtained from a linear combination of kernel functions [24]:  
\[F(y)=\sum_{i}a_{i}(x)K_{i}(y|x) \tag{14}\]  
where \(x\) represents the input vector of the forecasting model, \(y\) is the output vector, \(K_{i}(y|x)\) is the kernel function selected for the model, and \(a_{i}(x)\) represents the mixing coefficients that control the inputs. In MDN models, the output neurons are the parameters of the distribution functions as well as the mixing coefficients. For example, the outputs can be the parameters of Gaussian distribution functions, including the mean values and the standard deviations as well as the mixing coefficients.  
In comparison, in an SRN model, which is a non-parametric probabilistic forecasting model, each output of the neuron associates a probability fraction with a value of \(y\). In this way, there should be an interval for possible values of \(y\). Hence, the output of an SRN represents the probability related to each member within the interval. Figure 3 compares the architectures of MDN with SRN, two ANN-based approaches utilized for probabilistic forecasts.  
Figure 3: Examples of the network architectures related to two ANN-based probabilistic forecasting models.  
Figure 2: Examples of the network architectures of RNN and feedforward NN.