esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine
## 3 Materials and Methods
### Assessment Strategy for the Models  
Since we are working on a regression problem linked to archived historical data, we evaluated the studied models using the following metrics: the RMSE (root mean square error) [18] governed by Equation (3), the MSE (mean square error) [19] described by Equation (4), and R-squared (explained variance score) expressed by Equation (5).  
\[\textbf{RMSE=}\sqrt{\frac{1}{N}{\sum_{i=1}^{n}}{\left({{y_{
# A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System
## 3 A Hybrid State Estimator Based on SCADA and PMU Measurements
### Fast-State Tracking with PMUs Based on the Linear State Estimation  
PMU measurements with millisecond refresh frequency are used to fast track the system states during the SCADA data acquisition period. However, the limited number of PMUs cannot guarantee the observability, so other measurement information at the PMU data acquisition time should be included. In this paper, the estimation results from the previous estimation time (the branch currents) are combined with the PMU measurements (the bus voltage phasors and the branch current phasors) to guarantee the observability. The corresponding measurement weights of the PMU measurements and estimation results of the previous estimation time can be expressed in a matrix block as Equation (12):  
\[\mathbf{W}_{linear}=\sum\
olimits_{linear}{}^{-1}=\left[\begin{array}{cc} \sum\
olimits_{\varepsilon_{PMU}}\\ &\sum\
olimits_{\varepsilon_{PMU}}\\ \end{array}\right]^{-1} \tag{12}\]where the covariance matrix of the voltage phasors \(\sum_{\sigma_{PMU}}\) and the current phasors \(\sum_{i_{PMU}}\) in rectangular coordinate are obtained using Equation (6).  
The estimation results from the previous estimation time are used as the prior information whose covariance matrix \(\sum_{i_{SE}}\)[39] can be obtained using Equation (13):  
\[\sum{i_{SE}}=\left(\mathbf{H}^{\mathbf{T}}\overline{\mathbf{W}}\mathbf{H} \right)^{-1} \tag{13}\]  
The state variables are consistent with that of robust state estimation. Equation (5) can be used to obtain the measurement Jacobian matrix whose elements are constant. Therefore, the LSE can be utilized, and the characteristic of a short execution time can be used to track states with PMU measurements. The LSE results can be obtained using Equation (14):  
\[\mathbf{x}=\left(\mathbf{H}^{\mathbf{T}}\mathbf{W}_{linear}\mathbf{H}\right)^ {-1}\mathbf{H}^{\mathbf{T}}\mathbf{W}_{linear}\mathbf{z} \tag{14}\]  
The whole flowchart of a hybrid estimator based on SCADA and PMU hybrid measurements in the distribution system is shown in Figure 3. If the estimation time \(Tn\) is the SCADA sampling time, the robust estimation combining the SCADA measurements with PMU measurements is carried out. Also, the specific flowchart of the improved robust estimation is shown in the left part of Figure 3. Otherwise, the LSE combining the estimation results of the previous estimation time with PMU measurements is carried out.
###### Abstract  
High-dimensional systems are more secure than their lower-order counterparts. However, high security with these complex sets of equations and parameters reduces the transmission system's processing speed, necessitating the development of an algorithm that secures and makes the system lightweight, ensuring that the processing speed is not compromised. This study provides a digital image compression-encryption technique based on the idea of a novel quad key-secured 3D Gauss chaotic map with singular value decomposition (SVD) and hybrid chaos, which employs SVD to compress the digital image and a four-key-protected encryption via a novel 3D Gauss map, logistic map, Arnold map, or sine map. The algorithm has three benefits: First, the compression method enables the user to select the appropriate compression level based on the application using a unique number. Second, it features a confusion method in which the image's pixel coordinates are jumbled using four chaotic maps. The pixel position is randomized, resulting in a communication-safe cipher text image. Third, the four keys are produced using a novel 3D Gauss map, logistic map, Arnold map, or sine map, which are nonlinear and chaotic and, hence, very secure with greater key spaces (2\({}^{q88}\)). Moreover, the novel 3D Gauss map satisfies the Lyapunov exponent distribution, which characterizes any chaotic system. As a result, the technique is extremely safe while simultaneously conserving storage space. The experimental findings demonstrate that the method provides reliable reconstruction with a good PSNR on various singular values. Moreover, the applied attacks demonstrated in the result section prove that the proposed method can firmly withstand the urge of attacks.  
3D Gauss map; Arnold map; compression; encryption; logistic map; singular value decomposition; sine map +
Footnote †: journal: Applied
## 4 Discussion
### Why Do Changes in Groundwater Level Matter to Carbon Dynamics?  
The site has low topographic setting characterized by seasonally dynamic high water levels. The variance explained for the negative linear relationship between GWT and GPP (\(R^{2}\) = 0.19) and RE (\(R^{2}\) = 0.20) appeared to behave similarly. However, a higher RE level (max = 29.78 g C m\({}^{-2}\) d\({}^{-1}\)) than GPP (max = 27.45 g C m\({}^{-2}\) d\({}^{-1}\)) (\(p\) > 0.05) when the GWT levels were decreased (GWT = 0 and below) suggests that the change in hydrology is affecting respiratory processes more than photosynthesis.  
A previous study at our site reported that during non-flooded periods, 57% of the total ecosystem RE came from heterotrophic respiration, while 43% came from autotrophic respiration [55]. However, the reverse occurred during flooded periods. Autotrophic respiration contributed 69% of the total RE, while 31% came from heterotrophic respiration [55]. With this, we deduced that the greater sensitivity of ecosystem respiration to groundwater table drawdown was due to the higher sensitivity of soil respiration compared to plant respiration. Therefore, any reduction in water level at our site would positively stimulate soil respiration. More investigation is needed to better understand the hydrologic controls over RE in forested wetlands. Nevertheless, our results imply that increased hydrologic forcing and the changing climate could potentially shift the C balance trajectory of regional carbon fluxes and balance.
# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
## 5 Performance Evaluation
### Analysis of Variance (ANOVA) Test  
The statistical significance of the achieved experimental results is validated by applying the one-way ANOVA test [47]. It is used to find out whether there is any significance variation in the means of all groups or not. It includes a null hypothesis \(H_{0}\) and an alternate hypothesis \(H_{1}\) given as  
\[H_{0}:\mu_{1}=\mu_{2} \tag{30}\]  
\[H_{1}:\mu_{1}\
eq\mu_{2} \tag{31}\]  
where \(H_{0}\) states that there is no significant difference between the results of the groups and \(H_{1}\) states that there is significant difference between the results of the groups.  
Table 5 shows the results for each workflow. It can be seen that the difference between the groups is significant whereas the difference within the groups is trivial. It is clear that the proposed method is statistically significant from the comparative algorithm due to the greater F-statistic and lower \(p\)-value. The \(p\)-values in the tests are extremely small or close to zero, so they are not given here. Thus, the null hypothesis is rejected and the alternate hypothesis is accepted. Therefore, it is evident that the proposed HBMMO significantly outperforms the other state-of-the-art algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Workflow** & **Source of Variation** & **SS** & **df** & **MS** & **F** \\ \hline \multirow{3}{*}{Montage\_1000} & Between groups & \(5.20\times 10^{-7}\) & 2 & \(2.6\times 10^{-7}\) & \(9.3\times 10^{3}\) \\  & Within groups & \(7.50\times 10^{-10}\) & 27 & \(2.8\times 10^{-11}\) & \\  & Total & \(5.2\times 10^{-7}\) & 29 & & \\ \hline \multirow{3}{*}{CyberShake\_1000} & Between groups & \(2.9\times 10^{-12}\) & 2 & \(1.45\times 10^{-11}\) & \(2.3\times 10^{4}\) \\  & Within groups & \(1.7\times 10^{-14}\) & 27 & \(6.3\times 10^{-16}\) & \\  & Total & \(2.9\times 10^{-12}\) & 29 & & \\ \hline \multirow{3}{*}{Epigenomics\_997} & Between groups & \(1.3\times 10^{-12}\) & 2 & \(6.5\times 10^{-13}\) & \(1.5\times 10^{3}\) \\  & Within groups & \(1.2\times 10^{-14}\) & 27 & \(4.4\times 10^{-16}\) & \\  & Total & \(1.3\times 10^{-12}\) & 29 & & \\ \hline \multirow{3}{*}{LIGO\_1000} & Between groups & \(6.5\times 10^{-11}\) & 2 & \(3.2\times 10^{-11}\) & \(1.8\times 10^{3}\) \\  & Within groups & \(4.9\times 10^{-13}\) & 27 & \(1.8\times 10^{-14}\) & \\  & Total & \(6.5\times 10^{-11}\) & 29 & & \\ \hline \multirow{3}{*}{SIPHT\_1000} & Between groups & \(2.1\times 10^{-10}\) & 2 & \(1.05\times 10^{-10}\) & \(1.2\times 10^{3}\) \\  & Within groups & \(2.4\times 10^{-12}\) & 27 & \(8.9\times 10^{-14}\) & \\ \cline{1-1}  & Total & \(2.1\times 10^{-10}\) & 29 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: One-way ANOVA test result.  
Figure 9: Comparative analysis of makespan and cost of HBMMO versus MOHEFT on benchmark workflows.
## 3 Results and Discussion
### Numerical Simulation Issues: Simulation Results  
As a result of formal limitations, an analytical solution to nonlinear state equations is not generally possible, so system operation was analyzed using numerical methods. Conservative elements making up the system were replaced with associated models, which enabled transforming dynamic systems in corresponding DC systems for a single sampling step. Equations formulated using a modified balance nodal method, given in general form  
\[\mathbf{G}\mathbf{V}=\mathbf{I} \tag{1}\]  
where \(\mathbf{G}\in\mathbb{R}^{n\times n}\) is the system conductance matrix, \(\mathbf{V}\in\mathbb{R}^{n\times 1}\) is the sought nodal potential vector, and \(\mathbf{I}\in\mathbb{R}^{n\times 1}\) is the excitation (current) vector, were solved by combining the lower-upper decomposition (LU) method with the sparse matrix technique, which avoided the consideration of trivial cases [23].  
Selected results from the computer simulation are shown in Figures 6-8. Figures 6 and 7 show the amplitude-phase characteristics of the amplifier for the four configurations of the transistor pairs used: IRFP140/IRFP9140 and IRFP240/IRFP9240. These four transistor pairs were used to show that the DC level value at output does not depend on used semiconductor active elements. Figure 6 illustrates the results obtained from the simulation for the designed circuit with an OP1A preamplifier attached, while Figure 7 depicts it without the preamplifier. In both cases, the amplifier is designed to have a uniform signal gain in the frequency band in question (up to \(10^{4}\) Hz): approximately 28 dB for the system with the pre-processing stage attached (Figure 6a) and approximately 5 dB for the system without the OP1A amplifier (Figure 7a). The circuit inverts the phase of the input signal (Figures 6b and 7b) and has favorable phase characteristics (no undesirable shifts). Since the OP1A preamplifier does not cause any significant changes in the circuit's operation (apart from the gain value), it was excluded from further considerations. Figure 8 shows the results of the simulation for the DC component voltage \(U_{DC}\) on the system load for the tested pairs of transistors, with input capacitors C7 and C8 shorted to ground. The voltage \(U_{DC}\) measured at the output of the system was very low (on the order of tens of pV) and did not undergo rapid changes over time. The mean values of these voltages and their standard deviations for each transistor pair are shown in Table 2.  
Figure 6: Numerically simulated: (**a**) amplitude; and (**b**) phase characteristic of the LF amplifier (including the pre-processing stage).
## 3 Methodology
### Meta-Learners  
#### 3.2.1 Cascade Generalization  
Cascade generalization is a fast method that works at both the base and meta levels. A meta-level classifier trains a model directly from the projections of base-level classifiers in this stage to evaluate the eventual ensemble result [51]. The predictions of the base-level classifier (in this case, FPA) are used to expand the dimensionality of the input space in cascade generalization. This is achieved by attaching the output of FPA to each training sample as a new function. The corollary of this is that the initial input features are used by FPA and meta-level classifiers, with the meta-level classifiers having access to extra characteristics (predictions from the FPA classifier) [51]. The pseudocode for the Cas_FPA approach is shown in Algorithm 1.  
```
Input: The training set, \(X=\{x_{i},y_{i}\},\ i=1\ldots m,\ y_{i}\in Y,\ Y=\{c_{1},\ c_{2},\ \ldots,c_{k}\},\ c_{k}\), is the class label;  Baseline Learner: \(FPA\)
1. Choose a value for \(K\) that is a factor of \(m\); let \(F\) be randomly divided into \(K\) parts of the distinct subsets;  each subset must contain \(N=n/k\) number of features.  Select the corresponding columns of attributes in the subset, \(T_{ij,i}\) from the training dataset, \(X\); then, form a new matrix, \(X_{ij,i}\). Extract a bootstrap subset of objects \(\
icefrac{{3}}{{4}}\) of \(X\) to make a new training dataset, \(X^{\prime}_{ij}\)
3. Use Matrix \(X^{\prime}_{ij}\) as feature transform to produce the coefficient in the matrix, \(P_{ij,i}\), in which the \(j\)th column coefficient is the characteristic component, \(j\)th.  Construct a sparse rotation matrix, \(S_{i}\) using the coefficient obtained in the matrix, \(P_{ij,i}\). Output: classifier \(T_{i}\) of \(d_{ij}(X^{\prime}_{ij})\) to determine \(x\) belongs to the class \(y_{i}\). Then, calculate class confidence: \(\varepsilon_{j}(x)=\frac{1}{L}\sum\limits_{i=1}^{L}d_{ij}\big{(}X^{\prime}_{ ij}\big{)}\) Assign the category with the largest \(\varepsilon_{j}(x)\) value to \(x\).
```  
**Algorithm 1**Cascade Generalization based Forest Penalizing Attributes (Cas_FPA).
## 4 Comparative Analysis  
The methods mentioned above are among the most popular for removing EEG artifacts. Some of these artifact-reduction techniques limit eye movements and blinking during data collection or exclude artifact-contaminated trials from the analysis. An exhaustive comparison among the above-stated methods is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methodology**} & **Requirement of** & \multirow{2}{*}{**Automatic**} & \multirow{2}{*}{**Online**} & **Can Perform on** \\  & **Reference Channel** & & & **Single Channel** \\ \hline Wavelet Thresholding & No & Yes & No & Yes \\ \hline Wavelet-ICA & No & Yes & No & Yes \\ \hline ICA-wavelet & No & Yes & No & No \\ \hline EEMD-Wavelet & No & No & No & No \\ \hline EEMD-CCA-Wavelet & No & No & No & No \\ \hline CCR-Wavelet & No & Yes & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of wavelet-based EEG denoising techniques.  
The majority of EEG-based real applications necessitate real-time signal processing and are resistant to artifacts. This necessitated that the artifact removal methods used be both automatic and minimal in computational expense. The term \"automated process\" refers to a procedure that may automatically identify and eliminate artifact components without the need for human participation. The wavelet-thresholding-based technique is simple and efficient for handling all kind of artifacts in the EEG signals. However, [PERSON] et al. [24] showed that the reconstructed clean EEG signals using the universal threshold and statistical threshold are distinctly different from true clean EEG signals, even though the artifacts are removed. If we tune both the threshold calculation, it may reconstruct near the original shape of the EEG. However, tuning them may make the system unable to operate automatically. ICA is a widely used technique for the removal of artifacts from EEG signals. However, it should follow three assumptions: (i) the sources are statistically independent, (ii) each independent component has a non-gaussian distribution, and (iii) the mixing system is determined. Hence, it may not perform well for single-channel EEG signals or fewer channels. The wavelet transform is unable to detect artifacts that overlap with spectral features fully. The disadvantage of mode mixing is also present in EMD. As a result, finding a single solution that is both efficient and accurate enough to satisfy all of the prerequisites is extremely challenging.
###### Abstract  
Accurate prediction of airport delays under convective weather conditions is essential for effective traffic coordination and improving overall airport efficiency. Traditional methods mainly rely on numerical weather and traffic indicators, but they often fail to capture the spatial distribution of traffic flows within the terminal area. To address this limitation, we propose a novel image-based representation named Multi-Channel Traffic and Weather Scene Image (MTWSI), which maps both meteorological and traffic information onto a two-dimensional airspace grid, thereby preserving spatial relationships. Based on the MTWSI, we develop a delay prediction model named ADLCNN. This model first uses a convolutional neural network to extract deep spatial features from the scene images and then classifies each sample into a delay level. Using real operational data from Guangzhou Baiyun Airport, this paper shows that ADLCNN achieves significantly higher prediction accuracy compared to traditional machine learning methods. The results confirm that MTWSI provides a more accurate representation of real traffic conditions under convective weather.  
airport delay prediction; image recognition; convolutional neural network (CNN); convective weather; class imbalance +
Footnote †: journal: Applied Sciences
# Static Structures in Leaky Mode Waveguides +
## 3 Results  
The results of our analysis, run in Matlab for five negative orders, are shown in Figure 7 and Table 2. The no-overlap condition was met such that there was little or no overlap of view-zones (Figure 7a). The minimum no-overlap window was calculated to be 5.9 mm and the pulse timings to align these windows are given in Table 2. Figure 7b shows that the higher-order diffraction added 52.5\({}^{\circ}\) of view zone/field of view to the zero-order view zone/field of view of 4.5\({}^{\circ}\) to make a total of 57\({}^{\circ}\)--a more than ten-fold increase in the near-eye case. For \(K_{D}\approx 2\Delta\beta\), the angular duty cycle is 50% for one light traveling in only one direction (see discussion).  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Notes** \\ \hline \(\lambda_{air}\) & 633 nm & – \\ \(\theta_{0}\) & 70\({}^{\circ}\) & 10\({}^{\circ}\) bias \\ \(\theta_{1}\) & 77\({}^{\circ}\) & 10\({}^{\circ}\) bias \\ \(K_{D}\) & \(1.58\times 10^{6}\) & \(2\Delta\beta\) \\ \(K_{G}\) & \(2.1\times 10^{7}\) & \(\approx\beta_{0}\) \\ \(\Lambda_{D}\) & 3.99 \(\upmu\)m & Internal grating \\ \(\Lambda_{G}\) & 295 nm & Output grating \\ \(N\) & 5 & modes (neg.) \\ \(n_{ord}\) & 2.2864 & LiNbO\({}_{3}\) \\ \(d_{1}\) & 1 mm & chirp focus \\ \(d_{2}\) & 1 mm & to grating 1 \\ \(d_{3}\) & 5 mm & to grating 2 \\ \(d_{4}\) & 20 mm & to viewer \\ \hline \end{tabular}
\end{table}
Table 1: Near-eye parameters.  
Figure 7: Results of view angle and pulse delay calculations for the parameters in Table 1: (**a**) The five negative orders used show little or no overlap at the viewer. (**b**) The non-zero view angles aggregate to a total of 52.5\({}^{\circ}\). The relative pulse delay times are shown to all be under 10 \(\upmu\)s.
# Selection of a Photovoltaic Panel Cooling Technique Using Multi-Criteria Decision Analysis
## 2 Methods and Criteria
### Technology Feasibility  
The effectiveness of a cooling method on the PV panel is indicated by the temperature reduction attained and the efficiency enhancement achieved. Results from technology feasibility analysis efforts provide input to the implementation of the appropriate cooling method. The effect of the cooling technique on the panel temperature has been researched both numerically and experimentally by several researchers. Normally, the cooling effect is higher in the case of active methods than by passive methods. Active methods have higher convective heat removal rates due to the higher convective heat transfer coefficient achieved by the fans or pumps that drive the cooling fluid. Passive methods depend on natural convection heat transfer, which is much lower than forced convection. Thus, the electrical efficiency \(\eta_{el}\) is a function of cell temperature [25] (refer to Equation (1)). \[\eta_{el}=\eta_{ref}\times\left[1-\beta_{c}\times\left(T_{c}-T_{ref}\right)\right] \tag{1}\]  
In the above Equation (1), \(\eta_{ref}\) is the nominal efficiency of the panel at a reference temperature \(T_{ref}\) of 298 K, \(T_{c}\) is the cell temperature (K) and \(\beta_{c}\) is the cell temperature coefficient, which indicates a drop of efficiency with temperature. The temperature coefficient used in this calculation of panel efficiency is \(-0.4\%/^{\circ}\)C. Table 1 gives the technical performance of different cooling methods commonly employed along with the equivalent power gain for a solar irradiation of 1000 W/m\({}^{2}\). Active cooling systems require electricity that is taken from panel output [31]. The net quantity of power produced after considering efficiency enhancement due to cooling and power consumed to operate the cooling system components differs according to the type of method used. Air-cooled methods use fans and consume less energy compared to water-cooled methods that use pumps. The intensity of solar radiation, which depends on the location and time of the year, is another factor that determines power consumption by the auxiliary cooling equipment. The power consumed by active cooling methods reduces the net power output of the panel depending on the above factors. Power consumption (P\({}_{\text{C}}\)) depends (refer to Equation (2)) on pressure losses (\(\Delta\)H\({}_{\text{C}}\)) in the cooling conduits set up for the purpose, the density (\(\rho\)) of fluid used and its flow rate (Q). \[\text{P}_{\text{C}}=\rho\text{gQ}\Delta\text{H}_{\text{c}} \tag{2}\]  
The net power output of panel (P) with active cooling depends on the net power output after taking into account the power consumed by the cooling system, which is estimated using Equation (3). In Equation (3), \"\(\text{Tr}\)\" represents the intensity of solar radiation in W/m\({}^{2}\), \"A\" represents the area of PV panel in m\({}^{2}\) and \"\(\text{n}_{\text{el}}\)\" represents the electrical efficiency of the PV panel. \[\text{P}=\text{I}_{\text{T}}\text{A}\text{n}_{\text{el}}-\text{P}_{\text{C}} \tag{3}\]