esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### EEG Processing  
In the following, all the presented results will be restricted to a region of interest of electrodes surrounding the motor cortex (see illustration in Figure 1). For our analysis, due to our concerns about the muscle activity contaminating the EEG signal in peripheric electrodes, we decided to discard all the electrodes that were farther from the two channels closest to this area (C3 and C4). Thus, the following electrodes were systematically discarded for the analysis (Fp1, Fp2, AF8, F8, FT8, T8, TP8, P8, PO8, O2, Oz, O1, PO7, P7, TP7, FT7, F7, AF7, FT9, PT9, PO9), as seen in Figure 1.  
The EEG signals associated with the 64 channels were artifact-corrected through the Independent Components Analysis and Multiple Artifact Rejection Algorithm [25]. The rejection of the anomalous channels was performed through the Automatic Channel Rejection using the Probability Measure with a default coefficient 5 of the EEGLab tool  
Figure 1: Schematic representation of 64-channel electroencephalography montage. The inner circle represents the region of interest in our measurements. Yellow electrodes are those corresponding to the left hemisphere (dominant side) and orange electrodes correspond with the right non-dominant hemisphere. Gray central electrodes have been chosen as reference in asymmetry measurements. Blue electrodes have been systematically eliminated from the analysis.  
box [24]. Next, the discarded channels are reconstructed by means of the tool Interpolate Electrodes, using the spherical method [26]. It is worth noting that signals have not been re-referenced to a common average reference (CAR) to preserve the signal integrity since some small signal features could be hidden or lost, and even to spread isolated signal artifacts giving rise to non-realistic estimations of the entropy values [27].  
In order to minimize the influence of spatial filtering procedures on entropy estimations, artifact-cleaned raw signals in the range of 1-30 Hz (delta to beta band) have been analyzed. EEG time-series were cropped to a total length of 30 s, in such a way that those properties depending on the series length can be directly compared.
###### Abstract
A comparison of three Runs of IMERG over the Sichuan basin of China for 2016-2018 revealed that all three Runs perform better during summer precipitation than autumn precipitation; however, IMERG-E underestimated wet precipitation substantially [30]. A comprehensive analysis of versions 5 and 6 products of the IMERG suite over Iran for June 2014 to June 2018 against 76 rain gauge observations showed an improvement in V6 than V5, especially for near-real-time products [31]. The IMERG-E product unexpectedly showed a higher correlation with rain gauge observations than IMERG-F over the arid regions of the United Arab Emirates for the period of 2015-2017 [32]. Better performance of IMERG-E than IMERG-L and IMERG-F was also reported for a tropical storm \"Imelda\" over the southeast coastal regions of Texas in the US when compared with Stage-IV radar precipitation estimates [33]. In addition, it was observed that IMERG-F V5 was better than V6 over the global mountainous regions in the estimation of light and heavy precipitation because V6 utilizes total column water vapor to derive a motion vector [34]. Although the IMERG-F V6 product was shown to be one of the best multi-satellite precipitation products over the Hindu Kush mountains of Pakistan, it has large uncertainty in the detection of light and moderate precipitation events [35]. However, there are limited studies to comprehensively evaluate the IMERG precipitation products over India owing to distinct topographical characteristics (e.g., Figure 1a). IMERG-F showed notable improvement over TMPA-3B42 in systematic error at basin scale over India across all precipitation intensities [36]. In addition, IMERG-F showed better performance than TMPA-3B42 at a sub-daily scale across India [37]. However, similar to other satellite precipitation products, IMERG has a rather larger bias over the orographic regions of the Western Ghats and foothils of the Himalayas [38]. But, all these studies over India utilized an earlier version of IMERG products (e.g., V4 or V5) for a limited period (e.g., one monsoon season or few specific rain events). IMERG products were upgraded to V06B in 2020, and IMERG-F was retrospectively processed for TRMM-era as well. As IMERG V06B supersedes all prior IMERG versions [39], it becomes imperative to assess the accuracy of the recent version of IMERG products for the Indian monsoon precipitation. Hence, the objectives of this study are as follows:  
1. To quantify error characteristics of V06B near-real-time (IMERG-E and IMERG-L) and research (IMERG-F) products;
2. To assess the changes in error characteristics of the IMERG-F product from V05B to V06B;
3. To assess the consistency of error characteristics of IMERG-F V06B for pre-GPM and GPM periods. This study is carried out over India at different spatial scales such as at the grid scale, sub-regional scale, and country scale for the southwest monsoon season spanning from June to September.
## 5 Experimental Setup
Two in-house developed applications based on Java, one for the transmitter and the other for the receiver, have been used in order to configure easily the parameters shown in Table 4, as well as to calculate the PER by reading the sequence number of the received packets. The measured values of PER are shown in Figure 18. As can be seen, the values are, for every case, lower than 5%, so the number of lost packets is very low. It is worth noting that PER depends strongly (but not exclusively) on the received power level. Taking into account that the measured PER results have been obtained with the transmitted power level set to the lowest value, ACK option disabled and a quite high transmission rate (as for a real ZigBee applications lower rates are usually needed), it can be concluded that no channel quality problems in terms of packet losses would be within this kind of scenarios. This is consistent with previous power level estimations obtained in simulation as well as by direct measurement, where in all cases the values are above the threshold values for conventional QPSK modulation schemes. In this way, probability of error is low, leading to a small amount of lost packets. Furthermore, PER could be improved increasing transmitted power level, activating ACK options or reducing transmission rate. \begin{table}
\begin{tabular}{c c} \hline
**Transmitted Power** & **10 dBm (Maximum Default Value)** \\ \hline Transmission rate & 57,600 bps \\ Frequency & 2.41 (ZigBee Channel 12) \\ Transmitted packet & 100,000 \\ \hline \end{tabular}
\end{table} 
Table 5: Configuration of the parameters of the XBee Pro wireless devices to measure the ZigBee radio link quality.
# Computer-Generated Holography Methods for Data Page Reconstruction Using Phase-Only Medium
## 6 Conclusions  
In this work, a problem of complex data page image reconstruction in holographic memory was investigated. Phase Fourier computer-generated holograms of data pages were synthesized using bipolar intensity and double-phase coding methods. The quality of reconstructed images was analyzed in numerical and optical experiments.  
The bipolar intensity method implemented with a \(2\pi\) phase modulation depth appeared most suitable for data page reconstruction. It allows the formation of data page images represented by multilevel complex modulation (for example, quadrature modulation) with better quality. The reconstructed data pages for the double-phase coding method have high phase noise. However, in numerical experiments, the amplitude noise of double-phase holograms was lower than in the case of bipolar intensity holograms. Therefore double-phase holograms are promising for creating data pages in the form of multilevel amplitude modulation types. Selection of data page size and proper modulation format appeared to be the major challenge in designing of optoelectronic information processing and storage system providing a suitable reconstruction of embedded information on one hand and high throughput of a system on another.  
In the experiment, the reconstruction accuracy for a ternary modulation was higher in the case of the bipolar intensity method. One of the possible reasons for the low accuracy of the double-phase coding method is the high sensitivity of such holograms to the spatial phase error. A highly linear representation of phase modulation within the modulation depth of \(2\pi\) radians is required to obtain high-quality images in the reconstructed light field.  
The obtained results can be used in optoelectronic data storage and processing systems, which use spatial phase light modulation to form and manipulate information signals.  
Conceptualization, E.Y.Z. and R.S.S.; methodology, E.Y.Z. and T.Z.M.; software, T.Z.M.; validation, T.Z.M. and E.Y.Z.; formal analysis, P.A.C.; investigation, T.Z.M., P.A.C., and E.Y.Z.; resources, R.S.S. and N.N.E.; data curation, T.Z.M.; writing--original draft preparation, T.Z.M. and P.A.C.; writing--review and editing, T.Z.M. and P.A.C.; visualization, T.Z.M.; supervision, N.N.E.; project administration, R.S.S.; funding acquisition, P.A.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation (RSF), Grant No. 22-79-10340.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Synthesis Method** & \(M_{1}\) & \(M_{0}\) & \(\sigma_{1}\) & \(\sigma_{0}\) & **SNR** & **BER** \\ \hline Double-phase coding & 13.25 & 0 & 1.14 & 4.75 & 2.24 & 7.4 \(\times 10^{-4}\) \\ Bipolar intensity & 15.05 & 0 & 0.23 & 3.81 & 3.73 & 6.6 \(\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Obtained results for data page image analysis in case of taking into account data page specificities.
# Cross-Coupled Contouring Control of Multi-DOF Robotic Manipulator
## 4 Simulation Verification and Discussion
### Discussions  
In all the simulation studies, the same control gains were used for contour tracking of both linear and nonlinear contours; good tracking performances were obtained for PD and CC-PD control, and better contour tracking performances were obtained by CC-PD control. Such results demonstrated the effectiveness of the CC-PD control and the simplicity of choosing control gains in the implementation. In addition, simulation results also proved that large control gains will reduce the contour errors that are coincident with the theorem proposed in this paper. Of course, in order to obtain good tracking performances for different contours, tuning the control gains properly will be a good practice.  
Figure 8: Contour tracking performance improvement with the increase of CC control gains.  
Figure 9: Contour errors under high control gains for circular contour tracking.  
It should be mentioned that the same PD control gains were used for both PD and CC-PD control in the simulations, which is a simplification process for the purpose of comparison study. In order to pursuit a comprehensive and fair comparison between PD control and CC-PD control, it is necessary to use some artificial intelligent algorithms such as GA, PSO, and DE [26; 27] to optimize the control gains based on contour tracking performances and the bounded control torques, and that will be a desired future research work.  
In this paper, only the linear and circular contours were tracked under the PD and CC-PD control laws. Theoretically, if the contour error estimation methods in [28; 29] are used to replace Equation (4), the proposed CC-PD control can be applied to free-path contour tracking problems, and that will be a future research work. Additionally, this proposed control method can be applied in position domain [30; 31] for contour tracking performance improvement.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology
### Gamma Test (GT)  
Hydrological processes are highly complex, dynamic, and non-linear. To find the best-input combination, researchers need to perform a trial-and-error approach, which is laborious and cumbersome. To overcome this problem, a novel mathematical tool, the Gamma test (GT), which is a non-parametric test, is adopted for the assessment of the best-input variables, which are proficient enough to build a reliable and smooth model. The GT is derived by [PERSON] et al. [59] to carry out a nonlinear analysis for the estimation of the variance of noise in model output, called best mean square error (MSE) or Gamma statistic (\(\Gamma\)), which is attained by a smooth and noiseless model, by considering all the input data. Further explanations regarding GT can be obtained from [PERSON] et al. [60], [PERSON] [61] and [PERSON] et al. [62].  
A primary dataset [(\(x_{i}\),\(y_{i}\)), \(1\leq I\leq M\)] is applied to construct an algorithm that is proficient to understand the link amid the input x and output y. The algorithm is developed based on the logic that \(y\) is a function of \(x\). Further, it is disintegrated into smooth and noisy parts. If \(f\) is a smooth function and r is the noise part that cannot be considered near any smooth data model, then the relationship is given as [59].  
\[y=f(x)+r \tag{8}\]  
By taking the mean of noise \"\(r\)\" is zero, a constant bias can be employed into the unknown function \(f\), which will be our anticipated gamma statistics.  
Further, outcomes within GT can be normalized by taking a scaled variant noise approximation, generally ranges from 0 to 1, named Vratio, and is described as  
\[V_{ratio}=\frac{r}{\sigma^{2}(y)} \tag{9}\]  
where \(\sigma^{2}(y)\) denotes the variance of output (\(y\)). The lesser the value of \(V_{ratio}\), the higher the foreseeability of the given output.  
The Gamma test could be applied to all combinations of inputs to find the best combination that produces the minimum absolute Gamma Value. If there are \(m\) scalar inputs, then there are \(2^{m}-1\) possible combinations, but this can produce numerous unrealistic input combinations. Thus, the best input combination was identified by employing various model identification approaches in the WinGamma software. In this study, the Hill-Climbing model identification technique is applied to find the best input combinations for the daily SSL prediction based on a minimum value of gamma (G) and Vratio.
## 3 Experiments
### Monitoring Technique  
The AE monitoring technique consists of four parts: the sensors, the pre-amplifiers, the data acquisition device, and the data analysis system. Figure 5 shows the principle of the AE monitoring technique [28,29]. The elastic waves emitted from the AE sources eventually transmitted from inside to the surface of the material, causing surface displacements that can be detected with AE sensors. The elastic waves are translated into electrical signals through the AE sensors. Then, they are acquired by the data acquisition device after being processed by the pre-amplifiers. Finally, the signal is analyzed by the data analysis system [30, 31, 32, 33, 34].
# Improvement of Learning Stability of Generative Adversarial Network Using Variational Learning
## 4 Experimental Results and Discussions  
### Dataset and Model Configuration  
To evaluate the performance of the proposed method, we conducted experiments with the MNIST [32] and the CelebA datasets [33] that are mainly used to compare the performances of models in the field of computer vision. The MNIST dataset consists of 60,000 handwritten images using the Arabic numerals from 0 to 9. The CelebA dataset contains facial images of celebrities and consists of a total of 202,599 images. All the data samples of the studied datasets were normalized to have zero means and unit standard deviations based on the mean and standard deviation of the training set. Facial images of the CelebA dataset were resized to 64 x 64 after the facial area was aligned based on the eye coordinates, which were identified manually. All of the data samples included in each dataset were subdivided based on the ratio of 4:1:1, and the data segments were used as the training, validation, and test sets, respectively.  
We designed the network for the experiments based on the structure of DCGAN [6] that is used in many studies on the MNIST and CelebA datasets. The latent variable was set to have dimensions of two and 64 in the cases of the MNIST and the CelebA datasets, respectively. The weights of each model were initialized using the Xavier method [40]. The adaptive momentum optimizer (ADAM) [41] was used to optimize the weight of the network in our experiments (other methods for weight initialization and optimization can be also applied to the proposed method). The hyperparameter of ADAM, \(\beta\), was set to (0.5, 0.999). We used 0.0002 as the learning rate of the generator and encoder, and 0.002 as the learning rate of the discriminator.
## 2 Background and Related Work
### Related Work
#### 2.3.1 SAST Tools Comparisons Studies  
In the work of [PERSON] and [PERSON] [28], nine SAST tools for C language comparison is accomplished to rank them according a set of selected metrics using SAMATE tests suites for C language. This comparison includes several leaders' commercial tools. It is very useful to the practitioners to select the best tool to analyze the source code security.  
The work of [PERSON] et al. [9] present an approach to design benchmarks for evaluating SATS tools having into account distinct levels of criticality. The results of selected SAST tools show that the metrics could be improved to balance the rates of the true positives (TP) and false positives (FP). However, this approach could improve their representative with respect to security vulnerabilities coverage for other types, besides SQLI and XSS.  
The work of [PERSON] et al. [6] is based on a previously published data set that resulted from the use of five different SAST tools to find SQL injections (SQLI) and cross-site scripting (XSS) vulnerabilities in 132 WordPress Content Management System (CMS) plugins. This work could be improved using a benchmark with more vulnerability categories and including leader commercial tools.  
Finally, a paper [8] can be examined that presents a benchmarking approach [55] to study the performance of seven static tools (five commercial tools) with a new methodology proposal. The benchmark is representative and it is designed for the vulnerability categories included in the known standard OWASP Top Ten project for SAST tools evaluations.
# China's Water Intensity Factor Decomposition and Water Usage Decoupling Analysis
###### Abstract  
As the most populous country in the world, China has a great shortage pressure of water resources. With the acceleration of urbanization, China's water usage in different sectors will change significantly in next few years. In order to investigate the main reasons behind water usage change in China, the Logarithmic Mean Divisia Index (LMDI) model was adopted in this paper from 2000 to 2020 with provincial data. Three effects, including that of technology, industrial structure, and regional scale, were analyzed. In addition, the decoupling effect between water usage and economic growth was also considered. The results show that: (1) from 2000 to 2020, the technological effect, industrial structure effect, and regional scale are \(-\)376.54, \(-\)89.85 and 20.66, respectively; (2) the technical effect and industrial structure effect have the greatest impact on primary industry, followed by secondary industry; (3) the technical effect is greater than the industrial structure effect in most provinces; and (4) the decoupling state gradually changes from weak decoupling to strong decoupling. In the future, the key policy recommendations for water saving are the following: (1) technological innovation has the most efficient effect on the reduction of water usage in China, and (2) the optimization of industrial structure can be helpful in water-saving in the future.  
water intensity; LMDI model; Tapio model; technical effect; industrial structure effect; regional scale effect +
Footnote †: journal: Applied sciences
## 3 Results
### Non-Optimized SBHs  
Figure 4 shows the absorption coefficient spectra (calculated employing LNSE-FEM model) of non-optimized SBHs with linear and quadratic profiles, both with \(n=1\,\mathrm{cm}^{-1}\) (\(N=25\) ribs), \(h_{\mathrm{r}}\ =\ 1\,\mathrm{mm}\), and \(A_{\mathrm{min}}=0.25\,\mathrm{mm}\).  
Figure 4: Absorption coefficient spectra of non-optimized SBHs with coarse internal structure.  
Figure 3: Shape function \(G(X)\) introduced by employing three control points interconnected using cubic splines.  
It can be observed that the absorbing performance of these SBHs is really poor, namely \(\langle\mathcal{A}\rangle\ =\ 0.153\) for the linear profile, and \(\langle\mathcal{A}\rangle\ =\ 0.408\) for the quadratic profile. These structures could be easy to manufacture; however, their application potential is rather limited. As it has recently been pointed out by [PERSON] et al. [27], this is caused by insufficient losses in the structure.
# An Interleaved DC/DC Converter with Soft-switching Characteristic and high Step-up Ratio
## 2 Proposed Circuit Configuration and Operation Analysis  
### Proposed Circuit Configuration  
Figure 1 shows the proposed interleaved dc/dc converter. It is mainly composed of two boost converters where the coupled inductor (\(T_{1}\)) served as the energy-stored inductor. The coupled inductor is made up of a magnetic core and two windings with the same number of turns. Two MOSFETs (\(S_{1}\), \(S_{2}\)) play the role of active switches. Two diodes (\(D_{1}\), \(D_{2}\)) are used to freewheel the inductor current while the other two diodes (\(D_{S1}\), \(D_{S2}\)) respectively in parallel with \(S_{1}\) and \(S_{2}\) are the intrinsic diodes of the MOSFETs. The gate-source voltages (\(v_{GSI}\), \(v_{GSI}\)) are used to alternatively turn the active switches on and off at a high switching frequency \(f_{\mathrm{s}}\). The waveforms of \(v_{GS1}\) and \(v_{G52}\) are shown in Figure 2. They are complementary rectangular waveforms with a short overlap time between them.  
Ignoring the winding resistance and the core loss, the equivalent circuit of the coupled inductor is shown in Figure 3. \(T_{M}\) is a transformer with a turn ratio equal to 1, and \(L_{M}\) is its mutual inductance. \(L_{1}\) and \(L_{2}\) represent the leakage inductances in the primary winding and the secondary ones, respectively. Based on the equivalent circuit, the currents flowing into \(L_{1}\) and \(L_{2}\), denoted by \(i_{L1}\) and \(i_{L2}\) can be expressed as  
\[i_{L1}=i_{LM}+i_{T1}, \tag{1}\]  
\[i_{L2}=i_{T2}=-i_{T1}, \tag{2}\]  
where \(i_{T1}\), \(i_{T2}\), and \(i_{LM}\) are the primary current, the secondary current and the magnetic exciting current of the transformer, respectively. From Figure 1, the input current denoted by \(i_{in}\) would be equal the sum of \(i_{L1}\) and \(i_{L2}\). Based on Equations (1) and (2), \(i_{in}\) is equal to the magnetic exciting current.  
\[i_{in}=i_{L1}+i_{L2}=i_{LM}, \tag{3}\]  
The voltages across both windings of the transformer are equal and can be expressed as:  
\[v_{T}=\frac{L_{M}di_{LM}}{dt}, \tag{4}\]  
Figure 1: Proposed interleaved dc-to-dc converter.  
Figure 3: Equivalent circuit for the coupled inductor.  
Figure 2: Gate-source voltages \(v_{GS1}\) and \(v_{GS2}\).