esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Another study [17] showed that an optimized LED spectrum illumination based on human perception of color differences significantly improved the visibility of tissue texture compared to illuminants such as CIE D65. In order to provide an approximate representation of the available range of white LED sources, the CIE proposed a series of 9 specific LED illuminants [2]: LEDS B1 to B5 illuminants (typical phosphor-converted blue LEDs with different CCTs); LED BH1 illuminant (of mixing phosphor-converted blue and red LEDs); LED RGB1 illuminant (mixture of red, green, and blue LEDs); and LEDs V1 and V2 (traditional phosphor-converted violet LEDs with modified CCTs) [2]. Currently, B1 to B5 LEDs are the most frequently used. The SPDs of the 9 LED illuminants proposed were obtained from experimental measurements of a total of 1298 commercial white LED conventional sources [18; 19]. The effect that these nine CIE white illuminants have on visual appearance has been evaluated for cosmetic applications [20] and museum lighting [21]. In dentistry, a recent study [22] evaluated the color inconstancy of natural teeth measured under white light-emitting diode illuminants. The authors concluded that the CIE-proposed LED illuminants produced perceptible and no acceptable color and whiteness differences on the evaluated teeth when compared with CIE standard illuminants D65 and A. Nevertheless, to the best of our knowledge, there are no studies that have evaluated color inconstancy for dental ceramics when illuminated with the newly proposed CIE LED illuminants relative to their color as illuminated with the CIE-recommended standard illuminant. Therefore, the main purpose of this study was to evaluate the color changes of dental ceramics based on CIE LED illuminants relative to the CIE D65 standard illuminant recommended for dentistry, considering color difference thresholds in dental ceramics.
## 3 Methodology
### Feature Selection
#### 3.2.2 Mutual Information-Based Feature Selection  
Mutual information (MI), also known as transinformation, measures the reduction in uncertainty about one random variable (X) when given knowledge of another random variable (Y) [34]. It is a non-negative value that indicates the strength of the dependency between X and Y. Higher MI values mean stronger dependency. In other words, knowing one variable provides more information about the other if their MI value is higher. MI is a filter-based method.  
MI is particularly useful for univariate feature selection, a method for identifying the most important features in a dataset. By calculating the MI between each feature and the target variable, it is possible to select the features that provide the most information about the target variable and potentially enhance the performance of machine learning models. Mutual information-based feature selection can capture any kind of statistical dependency, including linear, non-linear, and even monotonic relationships.  
The mutual_info_classif function from scikit-learn was utilized to apply MI. This function relies on nonparametric methods based on entropy estimation from k-nearest neighbor distances as described by [35].  
Figure 4 shows the mutual information values for the top 100 EEG-based features with the highest mutual information scores relative to the target variable (depressive disorder). These features are ranked by their MI values, indicating a strong relationship with the target variable.
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results
### GF3-02 SAR Wind Speed from HH Polarization
#### 3.2.1 Compared with NDBC Wind Speed  
Figure 3b shows the comparison between wind speed retrieval results from GF3-02 SAR HH polarization data and the matched NDBC wind speed. The RMSE of GF3-02 SAR wind speed retrieval results are 1.87 m/s, 1.39 m/s, 1.72 m/s, 1.81 m/s, and 1.45 m/s, respectively. Based on this, the GF3-02 SAR wind speed retrieval results using the CMOD5.N+PR_Zhang model are optimal with an RMSE of 1.39 m/s. Moreover, the RMSE of CMODH_HH is close to the CMOD5.N+PR_Zhang model.
## 2 Data Collection and Quality Evaluation
### Data Quality of Multi-GNSS Observations  
Figure 3 shows the magnitude of the onboard code multipath errors with respect to elevation. In Figure 3, each value represents the RMS of the data falling within the range of 3\({}^{\circ}\). For GPS satellites, the multipath error decreases as the elevation increases. The average multipath error of GPS L1 and L2 was 0.26 m and 0.20 m, respectively. The data characteristics of BDS2 were different from that of GPS. When the elevation angle was greater than 45\({}^{\circ}\), the average multipath error of B1/B3 decreased as the elevation increased. MEOs B1 and B3 were 0.39 m and 0.15 m, respectively, IGSOs B1 and B3 were 0.21 m and 0.13 m, respectively, and GEOs B1 and B3 were 0.22 m and 0.14 m, respectively.  
High-precision carrier-phase observation is essential for GNSS-based orbit solutions, as the accuracy of carrier phase measurements is at the millimeter-level. The fitting residuals of the L1-L2 combinations are often used to evaluate the noise level of the carrier phase [24]. The average value of the daily RMS of GPS, BDS2 MEOs, IGSOs and GEOs was 4.7 mm, 3.8 mm, 4.0 mm, and 2.6 mm, respectively (see Table 1). The quality of the BDS2 observations was comparable to that of GPS. This provided a good condition for orbit determination using GPS and BDS2 observations.  
Figure 1: The number of observations for Global Positioning System (GPS) satellites.  
Figure 2: The number of observations for BDS2 satellites.
###### Abstract  
As renewable energy (RE) penetration has a continuously increasing trend, the protection of RE integrated power systems is a critical issue. Recently, power networks developed for grid integration of solar energy (SE) have been designed with the help of multi-tapped lines to integrate small- and medium-sized SE plants and simultaneously supplying power to the loads. These tapped lines create protection challenges. This paper introduces an algorithm for the recognition of faults in the grid to which a solar photovoltaic (PV) system is integrated. A fault index (FI) was introduced to identify faults. This FI was calculated by multiplying the Wigner distribution (WD) index and Alienation (ALN) index. The WD-index was based on the energy density of the current signal evaluated using Wigner distribution function. The ALN-index was evaluated using sample-based alienation coefficients of the current signal. The performance of the algorithm was validated for various scenarios with different fault types at various locations, different fault incident angles, fault impedances, sampling frequencies, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, different types of transformer windings and the presence of noise. Two phase faults with and without the involvement of ground were differentiated using the ground fault index based on the zero sequence current. This study was performed on the IEEE-13 nodes test network to which a solar PV plant with a capacity of 1 MW was integrated. The performance of the algorithm was also tested on the western part of utility grid in the Rajasthan State in India where solar PV energy integration is high. The performance of the algorithm was effectively established by comparing it with the discrete Wavelet transform (DWT), Wavelet packet transform (WPT) and Stockwell transform-based methods.  
alienation coefficient; protection; power system fault; power system network; solar energy; wigner distribution function 2020
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 2 Data and Methods
### The Data Presentation
The occurrence probability of different intensities of scintillation is calculated and used to analyze the latitude and height distribution of scintillation characteristics.
# Electrically Equivalent Head Tissue Materials for Electroencephalogram Study on Head Surrogates
## 4 Results
### EEG and Signal-to-Noise (SNR) Ratio Calculation  
The signal-to-noise ratio measures the ratio of the supplied signal and the noises received by the electrodes along with the supplied signal. The higher the signal-to-noise ratio, the more the electrodes receive the noise-free signal. The signal-to-noise ratio was the  
Figure 6: Boxplot showing the variation in head tissue conductivity in different studies reported in [24]. It highlights the interquartile range, median, and extremes (minimum and maximum) of different head tissue layers that were manufactured. The recommended conductivity from study [23] is the black dot and the achieved conductivity [our study] from the sample for each layer is the red dot.  
difference between the peak-to-peak amplitude between the source and the scalp potential. Here, the scalp potential refers to the data that were received by the EEG electrode. For practical purposes, noise can be added depending on the experimental requirement. Two types of SNR experiments were performed. In type A (\(t_{A}\)), the amplitude of an artificially supplied sine wave was kept constant at 10 mV and the frequency varied from 10 to 1000 Hz. In type B (\(t_{B}\)), the frequency of the sine wave was maintained at 10 Hz and the amplitude was varied. The amplitude of signals received by scalp or wet EEG is around 100 \(\mu\)V [48]. However, the generated signal is supposed to be higher than the signal received by the scalp EEG. Electrocorticography (ECoG) which is referred to as subdural EEG places the electrodes directly on the surface of the brain [48]. As we placed our signal generator on the grey matter of the brain, the amplitude generated by the generator should be equivalent to the signal received by the ECoG. The signal amplitude SNR comparison between the EEG and ECoG [49] shows that ECoG has a 20-100 times higher SNR than the EEG. So, the amplitude range considered for the experiment was 500-20,000 \(\mu\)V or 0.5-20 mV.  
In Figure 7a, the results of SNR for type \(t_{A}\) are shown. There is a trend of increasing SNR between 0 and 100 Hz and 500 and 1000 Hz. The SNR drops between 100 and 500 Hz. All the SNR ratios except for 500 Hz show a positive value. This means that the signal received by the electrode is higher than the noise it received along with the signal. The highest SNR of 7.2616 dB for the \(t_{A}\) experiments was found for 100 Hz. The lowest SNR of \(-1.71\) dB was found for 500 Hz. In Figure 7b, the results for type \(t_{B}\) are shown. A general increase in SNR with an increase in amplitude is observed with the exception of 10 mV. The SNR for the 10 mV amplitude still provides a positive SNR, which proves that the signal received is higher than the noise received with the signal. The lowest SNR of \(-9.9\) dB is for the 0.1 mV amplitude, which is a very low amplitude. For this type of low-amplitude signal, different techniques can be applied to improve the SNR of received EEG signals [50].
## 2 Materials and Methods
### GC/MS Analysis  
The isolation and detection of the aromatic compounds of the raising were executed in a GC-7890 attached with a MS-5975 and fitted out with a 60 m \(\times\) 0.25 mm (HP-INNOWax) capillary column with a film thickness of 0.25 \(\upmu\)m (J&W Scientific, Folsom, CA, USA). The condition of the GC/MS temperature was similar to our previous study [2; 5]. The carrier gas \"helium\" was adjusted at a flow rate (1 mL per min) with the splitless GC inlet mode for SPME extract injection. The oven temperature was adjusted, as mentioned in our previous studies [2; 5; 13].  
Mass spectra were achieved in an electron impact manner with an ionization energy and source temperature of 70 eV and 230 \({}^{\circ}\)C, respectively. The mass range (20-450 \(m/z\)) was used as full-scan mode, and then the process proceeded with an auto-tuning condition for the selective ion mode. The retention indices (RI) were obtained by the retention time (RT) of C6-C24 n-alkane in similar chromatographic circumstances. Identification was based on the RI of available standards and matching with mass spectra in the NIST-08 library. Wherein the unavailability of reference standards, a tentative determination was performed by comparing mass-spectra with NIST-08 library and retention indices present in the aforementioned literature.
## 3 Results and Discussion
### Sorbent Characterization
#### 3.3.2 Scanning Electron Microscopy (SEM)  
The SEM micrographs obtained from the fresh calcium-based sorbents are shown in Figures 4 and 5. The SEM images of the samples that exhibited higher reactivity, i.e., Ca6 Mg2 Ce1 and Ca4 Mg2 Ce1 (see Figure 4), showed significant differences in surface topography compared to those of the poor CO\({}_{2}\) uptake group, i.e., Ca11 Mg2 Ce1, CaO and Ca1 Mg2 Ce1 (shown in Figure 5). The Ca6 Mg2 Ce1 and Ca4 Mg2 Ce1 samples had more porous surfaces and grains of different sizes compared to the group including Ca11 Mg2 Ce1, CaO and Ca1 Mg2 Ce1.  
The energy-dispersive X-ray (EDX) image, shown in Figure 6, depicts a dark and smooth surface with fewer pores, indicating a calcium-rich area. In contrast, the lighter area with small particle size is the magnesium- and cerium-rich area.  
It may be concluded thatorbents with a higher number of small grains and porous surfaces display an initial high CO\({}_{2}\) uptake. MgO and CeO\({}_{2}\), as inert elements dispersed among CaO particles, separate the CaO particles and provide micro-voids in the sorbent for the CO\({}_{2}\) pathway. Additionally, MgO and CeO\({}_{2}\), as inert oxides dispersed between the CaO particles, prevent agglomeration and size growth during multiple carbonation/decarbonation cycles, as previously reported by [PERSON] et al. (2023) [21]. Therefore, it was concluded that the CaO content and the inert percentage in the CaO-based sorbent should be optimized to achieve substantial CO\({}_{2}\) uptake capacity and reasonable stability during the carbonation/decarbonation cycles.
# Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder
## 4 Results and Discussion
### Performance Analysis of Feature Extraction Methods  
The SNR of some bands in the hyperspectral data was relatively low because of the influence of noise. At the same time, a linear correlation or redundant information between the data in different bands may exist. Modeling with the whole band may lower the prediction accuracy of the model. Therefore, the method of extracting feature wavelengths can eliminate some of the band information and improve the prediction performance of the model [35]. The training process of the SAE was the process of extracting features from the raw data. The output of the hidden layer also completed the function of dimension reduction on the basis of feature extraction. PLSR was used as the prediction model in this study. Three feature wavelength extraction methods, namely, SPA, CARS, and genetic algorithm, were selected and integrated with PLSR. The three integrated models were compared with the SAE-PLSR model. Figure 8 shows the prediction results of the different models. Table 3 shows the results of comparison of the different models.  
Table 3 shows that the CARS method had poor performance on the green plum SSC prediction. The prediction accuracy of the CARS method was lower than that of the simple PLSR model. Compared with that of the PLSR model, the prediction set of the CARS method had a 5.7% decrease in \(R_{P}\) and a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(\mathbf{R_{C}}\) & **RMSEC** & \(\mathbf{R_{P}}\) & **RMSEP** \\ \hline SPA–PLSR & 0.925 & 0.675 & 0.918 & 0.716 \\ CARS–PLSR & 0.869 & 0.870 & 0.844 & 0.969 \\ GA–PLSR & 0.955 & 0.526 & 0.906 & 0.762 \\
**SAE–PLSR** & **0.957** & **0.542** & **0.938** & **0.654** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the prediction performance of the SAE–PLSR and the traditional integrated models.  
Figure 8: Comparison of the prediction results of the SAE–PLSR and the traditional integrated models.  
20.1% increase in RMSEP. The SPA and the GA methods had good performance on reducing the data dimension and improving prediction performance. The \(R_{P}\) for the prediction set of the SPA and the GA methods increased by 2.6% and 1.2%, respectively, and the RMSEP for the prediction set of the SPA and the GA methods decreased by 11.3% and 5.6%, respectively. Compared with the previous two methods, the SAE-PLSR model still had better prediction performance. For feature wavelength extraction, the prediction set of the SAE-PLSR model had a 2.2% increase in \(R_{P}\) and an 8.7% decrease in RMSEP compared with that of the SPA-PLSR. The green plum SSC prediction verified the SAE-PLSR model.
## 2 Research and Design of Slope Displacement Monitoring System
### Design of Slope Displacement Monitoring System  
In this paper, the designed slope displacement detection system is composed of a remote video monitoring system plus a video processing centre. According to the requirements of the slope displacement detection system, the framework of the systems' function is analysed and designed starting from the topmost layer. The system structure framework diagram is shown in Figure 2.  
Real-time video playback module: This module is the main component of this system. Its main functional modules are as follows:  
Real-time video playback: This component's primary responsibility is to render real-time video footage of the current slope. It serves as the initial source of images for subsequent slope displacement measurements. The system can simultaneously play multiple video frames of the target slope, enhancing the comprehensiveness of monitoring.  
This function involves capturing immediate images from the video stream, which are then employed for slope displacement measurements. To assess slope movement accurately, image processing is a prerequisite. The images used for this purpose are directly derived from the real-time video stream.  
This component is tasked with recording and saving the video content of the slope. The stored footage facilitates the reconstruction of the slope's changing conditions at a later stage, which, in turn, aids in analysing the root causes of slope-related disasters.  
Slope Displacement Detection Module. This module is the core module of this system, including the Target Object Body Recognition Module, Displacement Measurement Module, and Alarm Module.  
Target Object Recognition Module: This module is responsible for automatically recognising the manually set target object, calculating the coordinates of its centre point, and saving the result into the database, which is the most direct criterion for judging whether the slope has been displaced or not.  
Displacement Calculation Module: This module processes images acquired from the \"Real-Time Video Screenshot Module.\" This includes identifying key detection points, calculating slope displacement in image coordinates, converting between two-dimensional and three-dimensional coordinates, and ultimately determining displacement in geodetic coordinates.  
Alarm Module: In the event of a natural disaster affecting the slope, the Alarm Module comes into play. It is responsible for issuing timely alerts to facilitate prompt response efforts at the slope site. The triggering of alarms is based on the calculation results provided by the Displacement Calculation Module, ensuring timely assistance and rescue operations.  
Figure 2: Schematic diagram of the structural framework.  
Login Module, Non-Real Time Video Playback Module, Management Module  
These three modules are responsible for user login, the restoration of slope site conditions, and setting relevant parameters, which will not be repeated here.
## 3 Problem Statement and Assumptions for the Localization Scheme
### PRP-SNR Relation  
The probability of a successful reception of a packet is the probability of successfully receiving all its bits, assuming that there is no error correction. It is well known that the probability of bit error depends on the encoding and modulation of the radio signal, thus, the packet reception probability will also depend on them. For example, for QPSK and BPSK modulation schemes, the packet reception probability in presence of additive white Gaussian noise and in absence of interferers is given by [24]:  
\[PRP=(1-BER)^{\textit{sl}}=\left(1-Q\left(\sqrt{2\frac{E_{b}}{N_{0}}}\right) \right)^{\textit{sl}}=\left(1-Q\left(\sqrt{2 snr\cdot\frac{W}{v_{b}}}\right) \right)^{\textit{sl}} \tag{1}\]  
where \(l\) is the length of the packet (in bytes), \(BER\) is the bit error rate, \(E_{b}/N_{0}\) is the signal to noise ratio per bit, \(snr\) is the signal to noise ratio (in natural units), \(W\) is the channel bandwidth, \(v_{b}\) is the bit rate and the function \(Q(x)\) is the tail probability of the standard normal distribution.  
More complete models have been proposed [25] to take into account complex phenomena such as asymmetry, interference, different quality of receivers and transmitters, correlations between reception rate of links, _etc._ However, this simpler model has been selected for this work because it enables a closed-form mathematical formulation and also fits quite well to experimental data (see Section 5.1).
## 6 Simulation Results and Discussion
### Network Performance Parameters: Discussions
#### 6.2.3 Residual Energy of the Network  
The residual energy of the network by all the three schemes being analysed is shown in Figure 9. The EE of Co-CESat is improved by using incremental cooperation in InCo-CESat and ElnCo-CESat. It is already shown in Figure 2 that two-relay incremental cooperation is more energy efficient than three-relay incremental cooperation. Figure 9 supports the results in Figure 3. In order to reduce PER and to achieve a higher diversity order, three cooperative links are used in ElnCo-CESat, which consumes more transmission and reception energy than InCo-CESat with two cooperative links. However, ElnCo-CESat is still more energy efficient than Co-CESat. Therefore, ElnCo-CESat and Co-CESat have increased throughput at the cost of increased energy consumption. In Co-CESat,  
Figure 8: Number of packets dropped.  
Figure 7: Number of packets received successfully at the sink.  
cooperative nodes always forward the data and consume energy, even when it is not needed. Therefore, it is seen from Figure 9 that at any instant of time, InCo-CESat has the highest residual energy, which leads to the highest network lifetime. Incremental relaying saves the channel resources; however, extra energy is consumed in redundant transmissions.
## 3 Results and Discussion
### Characterization
#### 3.1.1 Point of Zero Charge Determination  
PZC measurements for the Al-, Cu-, and Fe-TMCs were compared with NaAlg and Chi to reveal the effect of different metal centers on the surface chemistry of the materials. The PZC results were obtained by taking the x-intercept of the change in pH vs. the initial pH plot (see Table 1), where the PZC of chitosan was higher than the TMCs or NaAlg.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Materials** & **PZC** \\ \hline Al-TMC & 4.7 \\ Cu-TMC & 5.6 \\ Fe-TMC & 5.1 \\ NaAlg & 6.6 \\ \({}^{\mathrm{a}}\) Chi & 8.4 \\ \hline \hline \end{tabular} Note. \({}^{\mathrm{a}}\) The PZC was obtained from Ref. [17].  
\end{table}
Table 1: Point-of-zero-charge (PZC) results for TMCs and precursor materials.  
The incorporation of a metal center into the biopolymer framework resulted in a decrease in the point-of-zero charge (PZC), with the Al-TMC being the lowest, followed by Fe-TMC, and finally Cu-TMC with the highest value. The decreasing order of PZC values with Lewis acid strength suggests a direct relationship between the metal speciation and the surface charge of TMCs [9; 16]. With Al being the stronger Lewis acid, the PZC of the Al-TMC was shown to be the lowest. Conversely, Cu(II) is the weakest Lewis acid among the three systems, which can account for Cu-TMC having the highest PZC or lowest surface charge. Combined with the isotherm profiles of the TMC materials, a strong correlation between the surface charge of the materials and adsorption capacity may be established.