esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.3.2 Correlation Analysis  
The correlation analysis of single influence factor between ET based on pixel scale and driving factors (P, T, SR, SH, and U) was carried out [44; 45]. The calculation formula of correlation coefficient is as follows:  
\[\text{R}_{\text{xy}}=\frac{\sum_{\text{i}=1}^{\text{n}}[(\text{x}_{\text{i}}- \overline{\text{x}})(\text{y}_{\text{i}}-\overline{\text{y}})]}{\sqrt{\sum_{ \text{i}}^{\text{n}}(\text{x}_{\text{i}}-\overline{\text{x}})^{2}}\cdot\sqrt{ \sum_{\text{i}=1}^{\text{n}}(\text{y}_{\text{i}}-\overline{\text{y}})^{2}}} \tag{4}\]  
where \(\text{R}_{\text{xy}}\) represents the correlation coefficient of variables x and y, x\({}_{\text{i}}\) represents the ET value/mm of the i\(-\)th year, y\({}_{\text{i}}\) represents the value of driving factors in year i, and \(\overline{\text{x}}\) and \(\overline{\text{y}}\) represent the average values of ET and drivers in n years, respectively. The value of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Level of** & Extremely significant & Significant & Non-significant & Significant & Extremely significant \\
**variation** & negative trend & negative trend & trend & positive trend & positive trend \\ \hline
**Case** & \(\beta<0\) and \(\alpha\leq 0.01\) & \(\beta<0\) and \(0.01<\alpha\leq 0.05\) & \(\alpha>0.05\) & \(\beta>0\) and \(0.01<\alpha\leq 0.05\) & \(\beta>0\) and \(\alpha\leq 0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Level of variation trend.  
correlation coefficient \(\mathrm{R_{xy}}\) is between [\(-\)1, 1], and \(\mathrm{R_{xy}}=0\) indicates that the two variables are not correlated. The closer the \(\mathrm{R_{xy}}\) value is to 1, the stronger the positive correlation is; the further it is from 1, the stronger the negative correlation is.  
The significance of the correlation coefficients between each driver and ET was tested on the pixel scale by consulting the table of critical values of correlation coefficients.
## 4 Results and Discussion
### Driving Stability Analysis on Single-Factor
#### 4.1.2 Effects of Various Driving Conditions on SA  
Figure 9 shows the influence of different factors on SA. The SA is always smaller than the threshold value during the whole process. It is concluded that the variation of SA generally consists of four stages: rapid increase, slow increase, fluctuating increase, and the stable stage. An initial rapid increase in SA happens from 0 to 2 s, mainly due to centrifugal force during curve driving, while the slow increase in SA before the first wave crest of curve is caused by continuous application of lateral wind. The fluctuation of the SA from 15 s to 25 s is influenced by a compound effect of the lane change, across rutting, and lateral wind action. A more significant fluctuation effect on SA is shown in Figure 9b, in which the rutting is extremely severe (WHR = 5), with the largest simultaneous wave crest.  
Figure 9: _Cont._  
As presented in Figure 9a-g, with the increase of vehicle speed, crosswind angle, and crosswind speed, a sharp decrease of the radius of the circular curve, superelevation, and pavement friction coefficient, the SA gradually increases in the negative direction. Particularly, when driving on the pavement, when the friction coefficient decreases to 0.18 (in snowy weather), the absolute value of SA increases to 2.6, and shortly afterward, it increases in the opposite direction. The vehicle appears to swing back and forth with a pretty high degree of risk, even though the SA is still smaller than the threshold value.
## 4 The WBANs Communication Stack
### Mobility and Radio Channel Modeling for WBANs
#### 4.1.1 Proposed IEEE 802.15.6 Channel Models
\begin{table}
\begin{tabular}{c c c c} \hline
**Scenario** & **Description** & **Frequency Band** & **Channel Model** \\ \hline S1 & Implant to Implant & 402–405 MHz & CM1 \\ S2 & Implant to Body Surface & 402–405 MHz & CM2 \\ S3 & Implant to External & 402–405 MHz & CM2 \\ S4 & Body Surface to Body Surface (LOS) & (13.5, 50, 400, 600, 900) MHz & \\  & & (2.4, 3.1–10.6) GHz & CM3 \\ S5 & Body Surface to Body Surface (NLOS) & (13.5, 50, 400, 600, 900) MHz & CM3 \\  & & (2.4, 3.1–10.6) GHz & CM4 \\ S6 & Body Surface to External (LOS) & (13.5, 50, 400, 600, 900) MHz & CM4 \\  & & (2.4, 3.1–10.6) GHz & CM4 \\ S7 & Body Surface to External (NLOS) & (13.5, 50, 400, 600, 900) MHz & CM4 \\  & & (2.4, 3.1–10.6) GHz & CM4 \\ \hline \end{tabular}
\end{table} 
Table 4: Scenarios and description of the IEEE 802.15.6 channel models [31]. \begin{table}
\begin{tabular}{c c c} \hline  & **Hospital Room** & **Anechoic Chamber** \\ \hline Path-Loss Model and Parameters & \(PL(d)=a.\,log_{10}\left(d\right)+b+N\) \\ a & 6.6 & 29.3 \\ b & 36.1 & 16.8 \\ \(\sigma_{N}\) & 3.80 & 6.89 \\ \hline \end{tabular}
\end{table} 
Table 5: Proposed Channel Model for NB [34]. The resulting path loss is modeled as a combination of exponential decaying function of the distance and average indoor attenuation, which is expressed by the following formula:  
\[\textit{PL(d)}[\text{dB}]=-10\;log_{10}\;\left(P_{0}\;e^{-md}\;+\;P_{I}\;\right)+ \sigma_{p}n_{p} \tag{2}\]  
where \(P_{0}\) is the average loss close to the antenna and will depend on the type of antenna, \(m\) represents the average decay rate in dB/cm for the surface wave travelling around the perimeter of the body, \(P_{1}\) is the average attenuation of components in an indoor environment radiated away from the body and reflected back towards the receiving antenna, \(\sigma_{p}\) is the log-normal variance in dB around the average representing the variations measured at different body and room locations. This parameter depends on variations in the body curvature and antenna radiation properties at different body locations. The small-scale fading is modeled by a Ricean distribution with K factor that decreases as the path loss increases. The delay spread is normally distributed. Table 6 summarizes the model and corresponding parameters. It should be noted that the above channel models are not realistic since they do not take into consideration important effects such as body mobility and channel temporal as well as spatial characteristics. As the IEEE 802.15.6 channel models were finalized in the year 2009, since then there have been many updates and progress for more realistic channel modeling. The next section highlights the latest research works on WBAN mobility and channel modeling.
###### Abstract  
Oxidative stress has major implications for health and disease. At the same time, the term collectively describes the reactions to different types of reactive oxygen species (ROS) and oxidants, including hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and hypochlorous acid (HOCI). However, how both compare in terms of cytotoxicity and mechanism of action is less known. Using two leukemia cell lines, Jurkat and THP-1, as model systems at similar cell concentrations, we found an 8-fold greater sensitivity of the former over the latter for H\({}_{2}\)O\({}_{2}\) exposure. Unexpected, this was not the case with HOCI exposure. Jurkat cells were 2-fold more resistant to HOCI-induced cytotoxicity than THP-1 cells. In each cell type, the relatively more toxic oxidant also induced activation of caspases 3 and 7 at earlier time points, as time-lapse fluorescence microscopy revealed. The effects observed did not markedly correlate with changes in intracellular GSH and GSSG levels. In addition, siRNA-mediated knockdown of the Nrf2 target HMOX-1 encoding for HO-1 protein and the growth and survival factor IL-8 revealed Jurkat cells to become more sensitive to HOCI, while HO-1 and IL-8 siRNA-mediated knockdown in THP-1 cells produced greater sensitivity towards H\({}_{2}\)O\({}_{2}\). siRNA-mediated knockdown of catalase increased oxidant sensitivity only negligibly. Collectively, the data suggest striking HOCI-resistance of Jurkat and H\({}_{2}\)O\({}_{2}\) resistance of THP-1 cells, showing similar protective roles of HO-1 and IL-8, while caspase activation kinetics differ.  
apoptosis; Jurkat; heme oxygenase 1; oxidative stress; THP-1 +
Footnote †: journal: Applied Sciences
## 4 Experiment and Discussion
### Worldwide Availability Coverage Test
#### 4.1.2 Performance Outputs of ARAIM with Constellation Dynamic Selection  
The performance outputs of ARAIM with constellation dynamic selection are analyzed and evaluated further. In particular, only the scheme that using two constellations with the minimum VDOP (corresponding to the fifth group in the above section) is considered. The outputs for LPV-200, number of fault modes and the worldwide distribution of constellation dynamic selection are shown and talked in this section.  
Figure 5 shows the four indicators for LPV-200. The ARAIM is not considered available once any of the four indicators are not met the requirements of LPV-200. The figure shows the biggest values of 99.5% time at the test points. The plots are discretized to six color bins. Two colors of red represent values beyond the threshold and the other four colors of green represent values below the threshold. Regarding the VPL, most areas of the world are below 25 m and equatorial and mid-latitude regions are even below 20 m, which is far below the requirement of VAL = 35 m. The same phenomenon can be found in HPL, EMT, and vertical accuracy. The two constellations selected keeps the performance of ARAIM.  
Figure 6 shows the number of fault modes between ARAIM with constellation dynamic selection and ARAIM with four constellations. Please note that the scales of two figures are not the same. In Figure 6a, ARAIM only needs to monitor fewer than 30 fault modes in most parts of the world. On the other side, the number of fault modes that need to be monitored is larger than 800 using ARAIM with four constellations. It also verified the analysis about the exponential increase in fault subsets in above sections.  
Last is the distribution of constellation dynamic selection in the whole world. Figure 7 gives the percentages of constellation combination selected in one sidereal day and Figure 8 shows selection results in one random epoch, where six colors represent six constellation combination. The selection is fairly average, no matter from the total number or worldwide distribution. There are, indeed, performance differences of four GNSS constellations in different regions, but this difference is definitely small and relatively random.
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 4 Autonomous Orbit Determination Simulation
### Autonomous Orbit Determination Results

Table 9: Initial simulation settings for Experiment 3.  
satellites continue to improve. In summary, considering the errors in the dynamic model, lunar laser ranging technology can improve the robustness of satellite orbit determination, ensuring the convergence speed and accuracy.
## 4 Biological Applications
### Diagnostic Applications  
The use of nanoparticles in diagnostic applications can be classified into two categories: sensing and imaging. Typically, plasmonic nanoparticles are employed as plasmonic biosensors for the detection of specific biomolecules. For example, metal nanoclusters of gold, silver, and copper have been well established as imaging agents in both in vitro and in vivo systems [155]. Magnetic nanoparticles as labels for biomolecule detection provide the advantage of low background signal for most biological samples. In addition to their unique magnetic separation properties used for sensing, magnetic nanoparticles can also act as contrast agents in imaging applications used for diagnosis.  
Figure 10: Schematic representations of (**a**) electrostatic stabilization and (**b**) steric stabilization in metallic nanoparticles.
## 2 FSO Communication System
### Transmitter Side  
The Tx is responsible for generating and encoding the optical signal that carries the data. It typically comprises a light source, such as a laser diode (LD) or Light-Emitting Diode (LED), which converts electrical data signals into optical signals. The Tx also includes optics to focus and direct the light beam towards the Rx. The FSO Tx operates by encoding and modulating data, amplifying the modulated signal if necessary, and then precisely directing the laser beam through beam-forming optics for transmission across the optical channel.  
Figure 5 provides a depiction of a typical FSO Tx. This system comprises several essential components: an optical source, a modulator (in case of external modulation), an optical amplifier (used as needed), and beam-forming optics. Initially, the data bits generated from the information source undergo channel coding, an optional step performed before the modulation process. This coding step enhances the reliability and performance of the communication by adding redundancy to the data. Following this, the encoded data are modulated onto an optical carrier generated by the optical source. This modulated signal is a laser beam that carries the information through the optical channel. To ensure the signal's strength and maintain the quality of transmission over long distances or through adverse conditions, the modulated laser beam may be amplified using an optical amplifier. This amplification process boosts the optical intensity of the beam, making it more robust against attenuation and interference. Once the laser beam is sufficiently amplified, it is then passed through the beam-forming optics. These optics play a critical role in collecting the amplified light and refocusing it into a precise and well-collimated beam. This process ensures that the light beam maintains its integrity and alignment as it travels through free space to reach the Rx [1; 3; 18].  
Figure 4: A typical block diagram of an FSO system.
# Nonparametric Probability Density Function Estimation Using the Pade Approximation
## 5 Quantitative and Comparative Assessment
### Example 1: Gaussian Mixture  
For the first experiment, we chose a Gaussian mixture distribution model. This is because many real-world problems are based on Gaussian distributions or their combinations so as to simulate real-world problems. The multi-modal distribution was constructed by summing three Gaussian distributions:  
\[f(x)=\frac{1}{0.75}\left(\frac{1}{2}N(20,10)+N(40,20)+\frac{1}{4}N(70,10)\right) \tag{36}\]  
The value of 0.75 ensured that the properties of a probability distribution were preserved, with its integral being equal to unity. In this equation, \(N(\mu,\sigma)\) represents a Gaussian distribution with mean \(\mu\) and standard deviation \(\sigma\), as defined in Equation (37). The coefficients before the Gaussian distributions were chosen arbitrarily.  
The results of the experiments are displayed in Figure 6.  
\[N(\mu,\sigma)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(x-\mu)^{2}}{2\mu^{2}}}. \tag{37}\]  
Tables 1-3 detail the outcomes for varying sample sizes. We observed that, for small and medium and large sample sizes, our method outperformed the strategies proposed in prior studies. As the sample size increased, the GMM technique surpassed our method in performance. The GMM's reliance on the underlying distribution has been noted for its effectiveness in estimating Gaussian density functions. However, when handling datasets where the histogram shape diverges significantly from a Gaussian distribution, its accuracy tends to decline. This makes parameter estimation challenging for methods like the GMM. Our method excels in such scenarios, as evidenced by Examples 2, 3, and 4 below. Notably, it produced results without visible distortions at the beginning of the graph, highlighting its robustness under these conditions.  
Figure 6: Visual assessment of different density estimation methods. The true Gaussian mixture PDF (black), KDE (blue), Adaptive KDE (red), GMM approximation (green), QHBS (pink), One-point approximation (orange), and our method (cyan). The results for different sample sizes are shown from left to right: 50 samples (**a**), 100 samples (**b**), and 300 samples (**c**).
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 4 FESS Applications
### Spacecraft  
Flywheels find applications in space vehicles where the primary source of energy is the sun, and where the energy needs to be stored for the periods when the satellite is in darkness [7; 8]. FES for the international space station (ISS) was discussed in 1961 and was first proposed in the 1970s [7]. For the past decade, the NASA Glenn Research Centre (GRC) has been interested in developing flywheels for space vehicles. Initially, designs used battery storage, but now, FES are being considered in combination with or to replace batteries [7; 8]. The combined functionality of batteries and flywheels will improve the efficiency, and reduce the spacecraft mass and cost [7]. The proposed flywheel system for NASA has a composite rotor and magnetic bearings, capable of storing an excess of 15 MJ and peak power of 4.1 kW, with a net efficiency of 93.7%. Based on the estimates by NASA, replacing space station batteries with flywheels will result in more than US$200 million savings [7; 8]. It has been reported that a flywheel system would be significantly smaller and offer a better weight reduction than the use of NiH\({}_{2}\) battery devices for use on EOS-AMI-type spacecraft. It has been shown that the flywheel offers a 35% reduction in mass, 55% reduction in volume, and a 6.7% area reduction for solar array [86]. FESS is the only storage system that can accomplish dual functions, by providing satellites with renewable energy storage in conjunction with attitude control [24; 25].
## 2 Materials and Methods
### Image Acquisition  
Multispectral and RGB images were acquired on 31 January 2023. Surveys were performed using an Unmanned Aerial Vehicle (UAV), DJI Phantom 4 Multispectral RTK model, equipped with a high spatial resolution camera that shoots simultaneously six images, one in RGB composition and five monochrome spectral images: blue (B), green (G), red (R), red edge (RE), and near-infrared (NIR) bands, respectively, generating images at 2 MP resolution.  
The flight characteristics were determined according to technical recommendations while respecting the regulations of ANAC (National Civil Aviation Agency), the regulatory body responsible for flight regulations in Brazil. The flight was performed at a uniform speed of 2 m/s and a height of 40 m. An automatic surveying mission was set up to ensure 75% overlap and sidelap. The photogrammetric process, including image alignment, stacking, and radiometric correction, was carried out using Agisoft Metashape software (Version 2.1), along with a reflectance calibration panel.  
Figure 2: Climate information of the study area. Period from 31 December 2022 to 1 February 2023. Parnamirim, Pernambuco State, Brazil. PET: Potential Crop Evapotranspiration; CET: Crop Evapotranspiration.
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 4 Experiments
### Experimental Results
Figure 6: TeaFarm dataset classification results using KNN classifier: (**a**) ASPS\_MN; (**b**) S\({}^{\textbf{t}}\)P; (**c**) GRSC; (**d**) RLFFC; (**e**) SEASP; (**f**) CALM; (**g**) Color labels. Figure 8: Salinas dataset classification results using KNN classifier: (**a**) ASPS_MN; (**b**) S\({}^{4}\)P; (**c**) GRSC; (**d**) RLFFC; (**e**) SEASP; (**f**) CALM; (**g**) Color labels. Figure 7: TeaFarm dataset classification results using SVM classifier: (**a**) ASPS_MN; (**b**) S\({}^{4}\)P; (**c**) GRSC; (**d**) RLFFC; (**e**) SEASP; (**f**) CALM; (**g**) Color labels. Figure 10: Indian Pines dataset classification results using KNN classifier: (**a**) ASPS_MN; (**b**) S\({}^{4}\)P; (**c**) GRSC; (**d**) RLFFC; (**e**) SEASP; (**f**) CALM; (**g**) Color labels. Figure 9: Salinas dataset classification results using SVM classifier: (**a**) ASPS_MN; (**b**) S\({}^{4}\)P; (**c**) GRSC; (**d**) RLFFC; (**e**) SEASP; (**f**) CALM; (**g**) Color labels. As is well known, raw hyperspectral images often contain noise and a large amount of redundant information, which can significantly negatively impact classification accuracy [38]. Therefore, it is not easy to consistently achieve optimal performance for HSI classification using a complete set of bands. On the contrary, selecting the appropriate number of bands can improve classification performance to a certain extent. The optimal number of bands for 3 HSI datasets is obtained with the OA curves, which is shown in Figures 12-14. The OA curves describe the OA values with different numbers of selected bands. Figures 12-14 show that the OA values increase obviously when the number of selected bands increases from 5 to 10 for all six band selection methods. The CALM method stays stable, and its OA values are better than those of the other methods. Figure 11: Indian Pines datasets classification results using SVM classifier: (**a**) ASPS_MN; (**b**) S\({}^{4}\)P; (**c**) GRSC; (**d**) RLFFC; (**e**) SEASP; (**f**) CALM; (**g**) Color labels. Figure 12: OA of two different classifiers with varied numbers of selected bands on the TeaFarm dataset. (**a**) OA obtained by KNN; (**b**) OA obtained by SVM.