esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design Principle and Procedure
### Design Principle
\(\mathbf{P}_{objx}\) and \(\mathbf{P}_{objy}\) respectively denotes x- and y-polarized object waves, and \(\hat{x}\) and \(\hat{y}\) represent x- and y-polarized unit vectors, respectively. As for another essential parameter of the holographic metasurface, the reference wave \(\mathbf{H}_{ref}\), a surface wave with the cylindrical wavefront excited by the monopole, can be analytically expressed by the Hankel function [24] as follows:  
\[\mathbf{H}_{ref}=\mathbf{H}_{refx}+\mathbf{H}_{refy}=H_{0}^{(1)}(k_{g}r)\sin \varphi\hat{x}+H_{0}^{(1)}(k_{g}r)cos\varphi\hat{y} \tag{3}\]  
where \(k_{g}\) denotes the wave number in the dielectric, \(\varphi\) represents the azimuthal angle of any location on the metasurface, \(r\) is the radial distance from the origin. \(\mathbf{H}_{refx}\) and \(\mathbf{H}_{refy}\) are the x- and y-components of the reference magnetic field, respectively. After the object wave and the reference wave are determined, the phase holograms of two linearly polarized waves can be calculated based on the interference principle as follows:  
\[\left\{ \begin{array}{l}phase_{y-pol}=angle(M_{y})=angle(P_{objy}H_{refx}^{* })\\ \\ phase_{x-pol}=angle(M_{x})=angle(P_{objx}H_{refy}^{*})\end{array} \right., \tag{4}\]  
where \(phase_{y-pol}\) and \(phase_{x-pol}\) are the y- and x-polarized phase hologram distribution, respectively. \(M_{y}\) and \(M_{x}\) denote two linearly polarized interference results, i.e., y- and x-polarized holograms. It is noting that \(P_{objy}\), \(P_{objx}\), \(H_{refx}\) and \(H_{refy}\) are the corresponding scalar parameters. The * denotes the conjugation of complex parameters and \(angle(\cdot)\) denotes the operation of computing the complex phase angle. For determining the slot-type distribution of each linearly polarized hologram, a binary judging criterion was adopted. If the absolute value of its holographic phase (\(|phase(x,y)|\)) was smaller than the predefined threshold, a slot unit was set up. Similarly,if this condition was not satisfied, none of the slots were needed to be created in this unit. Following this guideline, two slot-type patterns generating x- and y-polarized quasi-non-diffractive vortex waves can be obtained, respectively. Due to the fact that a circularly polarized wave is composed of two orthogonal polarized waves with a phase difference of \(90^{\circ}\), the final slot-type pattern is achieved by superposing two linearly polarized slot-type patterns; four types of the slot are available: none, horizontal-, vertical- and cross-slot.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 4 Evaluation Methods  
Decolorization methods can be evaluated qualitatively and quantitatively.  
In [6], perceptual evaluation with 20328 observations was performed by 121 observers. Two types of experiments were conducted: preference and accuracy. In preference tasks, the observers were asked to choose the grayscale image they liked more from the presented pair. In accuracy experiments, observers had the task to decide which of the two grayscale images, presented alongside the original color image, more accurately reproduced the original. The results of the experiments, together with the dataset developed in [6] are the benchmark in decolorization. However, caution is needed during the interpretation of the user scores because human preference does not necessarily reflect the quality of the methods [13].  
Quantitative evaluation of decolorization methods is challenging because the preservation of contrast, color brightness, and avoiding conversion artifacts must be taken into account [13].
## 4 Numerical Experiment and Analysis
### Impact of the Relative Placement  
The number of measuring points was set as \(M=2\), and measurement error was set equal to \(\pm\) 0.1%, and the relative placement between two consecutive points was set as 4.3 mm, 8.6 mm, and 12.9 mm.  
The relative error is shown in Table 3. When the relative placement was 4.3 mm, the average relative error of \(\lambda\) was \(-\)0.43%; when relative placement was 8.6 mm, the average relative error of \(\lambda\) was \(-\)0.55%; and when the relative placement was 12.9 mm, the average relative error of \(\lambda\) was \(-\)0.31%. The results show that the inversion results are insensitive to the location interval.
# Independent System Calibration of Sentinel-1B
## 4 Geometric Calibration
### The Pixel Localization Accuracy in Azimuth  
To obtain the offset in-flight direction (azimuth shift), the orbit time of the minimum distance between the satellite and a deployed point target (Zero-Doppler plane) is compared with the radar time; the target is annotated in the corresponding SAR image. For the azimuth offset (in meters)  
Figure 13: Sentinel-1B amplitude and phase deviations w.r.t. the in-orbit reference for a total of 76 V-pol RFC measurements acquired between June and August 2016 during the CP. The two top plots show the deviations for the Tx-chain; the two bottom plots show the deviations for the Rx chain.  
the azimuth shift is multiplied by the ground velocity. As the annotated azimuth timestamps are related to the Zero-Doppler plane, an additional range-dependent offset is taken into account for deriving the azimuth offset. This bistatic correction considers the additional range shift of point target positions from the mid-range sample as described by [16] for StripMap mode; for the TOPS modes, the mid-range time of the related sub-swath is used.  
In Figure 14, the azimuth offset derived from each measurement is depicted over the acquisition period for all modes (blue: SM, red: IW, green: EW) using the precise orbit data. The azimuth offset varies between \(-6\) and \(+1\) m, whereby the mean value across all modes is \(-2.7\) m (Table 3). The three different StripMap beams (S1, S2, S5) show the same behavior and achieve a high pixel localization accuracy in azimuth direction which is expressed by a standard deviation of only \(0.2\) m. Within a sub-swath, the azimuth accuracy for the TOPS modes is comparable to StripMap; but small systematic offsets remain for different sub-swathes in the order of \(1\) m. Including all observed modes an uncertainty of \(1.0\) m remains that means the pixel localization accuracy in azimuth is in sub-pixel domain.
## 2 Performance Assessment  
In this section, the wavelength, pulse energy and receiving area are determined by taking into account the design specifications of Section 1. First, Section 2.1 studies the eye safety corresponding to several wavelengths typically used in lidar systems. As a result of this study, a shortlist of three possible wavelengths is obtained: 905 nm, 1064 nm and 1.5 \(\upmu\)m. In Section 2.2, it is established for each of these wavelengths the pulse energy and receiving area intervals required to detect a drift cloud located at 500 m. This analysis is carried out by signal-to-noise (SNR) simulation considering the atmospheric model defined in Section 2.2.1 and the SNR expression presented in Section 2.2.2. Available photodetectors in each of the wavelengths are assumed in the simulations. In Section 2.3, based on the pulse energy previously obtained, the required laser beam expansion for each wavelength is calculated to reach eye safety. A wavelength of 1.5 \(\upmu\)m is selected since the beam expansion is moderate, satisfying the design specification of compactness established in Section 1.
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 2 Processing Method for Airglow Images
### Image Enhancement  
The original airglow image is characterized by low brightness, and the grayscale value of the image is very low. At first, we need to improve the brightness of the original image according to certain rules. Here we use the airglow image at 19:39:20 UT on 2 September 2014, for image enhancement. Figure 2a shows the original airglow image, it is apparent that the original image has low brightness and dynamic range. The brightness of the original image was linearly increased to obtain the enhanced image, as shown in Figure 2b, and the plasma bubble can be observed in Figure 2b. The airglow imager has a large field of view, the optical data around the edge of the airglow image are mostly affected by the local light, and there is a lot of noise. Therefore, the middle part of the image was extracted according to the circular template, and the noise data at the edge of the circular image were removed. Then the smoothed circular airglow image can be obtained, as shown in Figure 2c.  
Figure 1: Processing steps for airglow images of plasma bubbles.  
Figure 2: Airglow image enhancement for 19:39:20 UT on 2 September 2014. (**a**) the original airglow image; (**b**) the enhanced airglow image; (**c**) the extracted circular airglow image; (**d**) the normalized airglow image.  
In order to highlight the features of plasma bubbles, it is necessary to remove the background noise from the airglow image. The airglow images for one consecutive hour were selected, there are 20 images in total, and the average image can be obtained by averaging these images. Then, we can take the average image as the background image, subtract the background image from each image, and obtain the processed images with the background noise removed. Finally, the images were normalized to obtain the final enhanced images [21], as shown in Figure 2d, so that the grayscale values of the enhanced image can be distributed in the range of 0-255, and the plasma bubble can be observed more clearly. After these processing steps, clear and high-contrast images of plasma bubbles can be obtained.
## 1 Introduction  
Greenhouse gases are major drivers of climate change [1] and are primarily produced by energy generation from fossil fuels [2]. Substantial research and political attention have been devoted to renewable energies in order to reduce the consumption of fossil fuels [3]. According to [PERSON] [4], renewable solar energy generation has continuously increased in the context of attempts to transition to a net-zero carbon economy, as shown in Figure 1. However, major challenges arise when a higher percentage of renewable energy is connected to the grid, due to its volatile nature [5]. If supply and demand are not of a similar magnitude, energy grids become unstable, potentially leading to blackouts [6]. Load balancing, ensuring that equal amounts of energy are generated and consumed, is one of the most important and difficult of these challenges [7]. This has conventionally been achieved by adjusting energy generation to demand patterns and scaling up power generation whenever necessary. Currently, the backup capacity for load balancing is mostly provided by fossil fuels, generation of which can be ramped up on demand [8].  
Renewable energy depends on environmental factors [10,11], and is, therefore, harder to match to demand patterns. This stipulates the need for appropriate energy management, including the organisation of generation, storage, and consumption. Understanding energy generation patterns plays a key part in developing effective management strategies. Therefore, the prediction of renewable power output is necessary to integrate more renewable energy into the grid and thus reduce the emission of greenhouse gases [12].  
In order to forecast the power output of any solar technology, the amount of available potential energy must be known. If prediction models are specific to one type of device, it is harder to adapt them to other use cases. The potential energy generated by many technologies, e.g., PV panels, depends on the amount of solar global horizontal irradiance received at a certain location. Global horizontal irradiance is the sum of direct and diffuse radiation on a horizontal plane and is also used to calculate the radiation on an inclined plane, such as a solar panel [13]. The prediction of solar radiation allows us to infer the power output of devices, such as photovoltaic cells or solar water heaters. Throughout this paper, global horizontal irradiance will also be referred to as simply irradiance or radiation.  
In recent years, solar prediction in particular has become more sophisticated. Much of this advancement is attributed to the development of machine learning (ML) algorithms [14]. There has been a tremendous increase in the use of ML for solar predictions in the last decade. It has been successfully employed and is extensively discussed in review papers by [PERSON] et al. [12] and [PERSON] et al. [14]. This paper builds on these insights and proposes a forecasting algorithm that predicts solar irradiance using ML algorithms and contextual optimisation.
# Computational Test for Conditional Independence
## 5 Discussion  
When testing CI with categorical variables, we can expect that CCI outperforms the continuous testing methods GCM and KCI since, in these cases, the fundamental assumptions of GCM and KCI are violated. Although the basic assumptions of multinomial regression are not violated when used to test CI, any parametric method is restrictive as the  
Figure 9: QQ plots: These plots visualize the distribution of \(p\)-values testing with the CCI test compared to the theoretical counterpart of a _uniform_(0,1) distribution. The different colors in each plot represent separate datasets within the simulation. (**a**) Multinomial Simulation, True Null Scenario: 500 \(p\)-values testing \(Y\perp X|Z_{1},Z_{2}\). (**b**) Multinomial Simulation, False Null Scenario: 500 \(p\)-values testing \(Y\perp X|Z_{2}\). (**c**) TrigData Simulation, True Null Scenario: 500 \(p\)-values testing \(Y\perp X|Z_{1},Z_{2}\). (**d**) TrigData Simulation, False Null Scenario: 500 \(p\)-values testing \(Y\perp X|Z_{2}\). (**e**) NonLinearData Simulation, True Null Scenario: 500 \(p\)-values testing \(Y\perp X|Z_{1},Z_{2}\). (**f**) NonLinearData Simulation, False Null Scenario: 500 \(p\)-values testing \(Y\perp X|Z_{2}\).  
functional form needs to be defined. As the simulation results show, the better performance of CCI is not due to superiority in rejecting false hypotheses but due to both better control of type I error and increasing power as the sample size increases.  
Given \(Y\perp\!\!\!\perp X|Z\), it should not matter if \(Y\) or \(X\) is the dependent variable in the classification algorithm in CCI. A criterion for choosing \(Y\) or \(X\) is if some variable in \(Z\) is highly correlated with either \(Y\) or \(X\). Then, choose the one with the highest correlation as the dependent variable to reduce the correlation among the features in the classification model. In practical settings, we recommend running a single case to see if there are any estimation problems by choosing \(Y\) or \(X\) as the dependent variable. Furthermore, to ensure optimal results, we recommend tuning the hyperparameters of the classification model using a separate dataset not involved in the final testing. This practice can potentially improve the performance and reliability of the test results. Given that CCI testing relies on ML techniques, familiarity with these methodologies is recommended for practitioners employing the CCI test.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
## Materials and Methods  
### Materials  
Styrene (St, 99%), methyl methacrylate (MMA, 98%), acrylamide (AM, 98%), potassium chloride (KCI, 99.5%), ethanol (99.7%), sodium dodecyl sulfate (SDS), 3-methacrylopropyltrimethoxysilane (KH570, 98%), ethylic acid (99.5%) and potassium persulfate (KPS, 99%) were obtained from Sinopharm Chemical Reagent Co., Ltd., Shanghai, China and used without further purification. KH570 was mainly used to improve the bonding properties of organic and inorganic materials. Nano-silica particles (99.8%, 7-42 nm) and octylphenol polyoxyethylene ether (OP-10, 99%) were purchased from Aladdin Reagent Co., Ltd., Shanghai, China and used as received. The TEM (JEM-2100 UHR, JEOL Ltd., Akishima, Tokyo, Japan) image and the particle size distribution (PSD) curve of nano-silica particles was shown in Figure 2. The particle size of nano-silica particles was mainly distributed in the range of 7-18 nm. Due to a few nanoparticles agglomerated, the larger particles were found in 32-42 nm. Polymeric alcohol (JHC) was provided by China Oilfield Services (Sanhe, China). Sodium bentonite was provided by Weifang Huawei Bentonite Group Co., Ltd., Weifang, China. Xanthan gum and low-viscosity polyanionic celluloses were obtained from Shida Chuangxin Technology Co., Ltd., Dongying, China. Furthermore, deionized water was used during the experiments. The shale samples used in this work were provided by Turpan Hami Basin, China and the X-ray diffraction analysis results were shown in Table 1.  
Figure 2: (**a**) TEM (transmission electron microscope) image and (**b**) PSD (particle size distribution) curve of nano-silica particles.
## 4 Conclusions  
This communication presents the modified PIFA antenna, which was designed to exclusively feature a UHF application with 55% of open space on a remote sensing nanosatellite's single face for solar irradiance penetration. The antenna was fabricated and integrated with a 1U nanosatellite structure. The antenna shows S11 \(<-15\) dB at 450 MHz with 67.45% total efficiency and 0.6 dB realized gain. This proposed antenna will facilitate the sensing nanosatellite's power scarcity.  
[PERSON] and [PERSON] conceptualized the proposed antenna, participated in critically revising the article for important intellectual contents, and supervised the whole study. [PERSON] and [PERSON] made significant contributions to this study regarding the conception, design, analysis and writing of the manuscript. M.C. provided measurement facilities and intellectual suggestions.  
This research was funded by Universiti Kebangsaan Malaysia research grant No. GUP-2017-095.  
This research is a collaboration work between Universiti Kebangsaan Malaysia and the Laboratory of Spacecraft Environment Interaction Engineering (LaSENIE), Kyushu Institute of Technology, Japan.  
The authors declare no conflict of interest.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 3 Results
### Temporal and Spatial Variations of Vegetation Characteristics
#### 3.1.2 LAI Change Trend  
In different seasons, the overall distribution of LAI was similar to NDVI, showing significantly higher values in northern Xinjiang compared to southern Xinjiang (Figure 4). The annual average LAI ranged from 0 to 2.52, with high-value areas (>1.0) primarily located in the western Tianshan Mountains and the northwestern part of the Altai Mountains. The highest LAI values during the growing and non-growing seasons reached 3.82 and 1.56, respectively. Due to the alternating distribution of mountains and basins, as well as the influence of climatic factors such as temperature and precipitation, vegetation predominantly grew in the more humid mountainous areas. As a result, some mountainous areas experienced active vegetation growth even during the non-growing season. In southern Xinjiang, the annual average LAI did not exceed 0.4. LAI values between 0 and 0.2 were mainly distributed in the Kunlun Mountains and Pamir Plateau, where alpine meadows and tundra dominated.  
The multi-year average LAI in Xinjiang was 0.34, ranging from 0.06 to 3.8. During the study period, the annual average LAI showed an increasing trend (_slope_ = 0.14, R\({}^{2}\) = 0.82), indicating healthy vegetation growth in Xinjiang. However, in the high LAI areas of western Tianshan and some scattered regions in northwestern Xinjiang, the annual LAI trend showed a decline (Figure 3d). Areas where the annual LAI trend value decreased by more than \(-\)1.0 were mainly grassland regions. Areas with an annual LAI trend value greater than 1.0 were mainly distributed in the Altai Mountains, parts of northern Tianshan, and the areas surrounding the Tarim Basin. Areas with increasing trend values were mainly grasslands and croplands. The overall trend distribution of LAI in Xinjiang was similar to NDVI, but there were slight variations in the Junggar Basin and its northwest region.  
Figure 3: The spatial distribution characteristics for (**a**) non-growing season, (**b**) growing season, and (**c**) annual value and changing trends (**d**) of NDVI.
# A Smart Framework for Managing Natural Disasters Based on the IoT and ML
###### Abstract  
Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage.