esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 2 Method
### Calculation of the Surface Reflectivity  
The incidence angle is an important factor affecting the reflectivity of GNSS constellation; the incidence angle of CYGNSS ranges from 0 to 70\({}^{\circ}\). To improve data quality, the incidence angle greater than 60\({}^{\circ}\) is eliminated and the data in the direction of the lowest point are used for normalization. Based on the data of CYGNSS L1 from 1 January 2018 to 31 December 2019 in the Tibetan plateau, we calculated and converted all the values to  
Figure 3: (**a**) The snow depth map of Tibetan Plateau in summer (July 2018); (**b**) the snow depth map of Tibetan Plateau in winter (January 2019).  
the dB scale, according to Equations (1)-(3). In this paper, we assume that the energy of CYGNSS data is coherent, and incoherent scattering is not considered in the analysis. The effective sampling range of CYGNSS is between approximately 38\({}^{\circ}\)N to 38\({}^{\circ}\)S, while the latitude range of the Tibetan Plateau is about 26\({}^{\circ}\)N to 40\({}^{\circ}\)N, so some areas in the north lack data [29]. Figure 4 shows the surface reflectivity of CYGNSS specular points in the Tibetan Plateau on 1 March 2018. Comparing with the satellite image in Figure 1, we can see that the SR is higher in the area with water and snow. In order to exclude the influence of water bodies on the results, these data should be removed from the analysis.  
In order to be consistent with the time series analysis, the raw SR of the specular points was transformed into gridded data. Considering the study area and the spatial resolution of snow data (Section 2.1.3), a grid was generated with a resolution of 0.25 along the geodetic latitude and longitude. The data values for each grid cell were defined as the average value of the specular points falling into that grid.  
To test the reaction of SR to the changes in surface parameters on the Tibetan Plateau, the SR at the CYGNSS specular reflection points for January 2019 and July 2019 are given in Figure 5a,b. As seen in the figure, the reflectivity increases in the whole region in July compared to January. To exclude differences in CYGNSS observations due to different ground cover types, the variation in SR in response to surface snow depth is analyzed under the same ground cover types.  
Figure 4: CYGNSS specular point surface reflectivity in the Tibetan Plateau on 1 March 2018.  
Figure 5: CYGNSS-derived reflectivity at specular points on Tibetan Plateau in January (**a**) and July (**b**) of 2019.
# Multiplexing Perfect Optical Vortex for Holographic Data Storage
## 4 Discussion
### Effect of Axicon Periods on Reconstructed Data Quality and BER  
The impact of varying the axicon periods (\(d_{POV}\)) on the quality of the reconstructed data is illustrated in Figure 6; Figure 6a shows the POV of array the image with different axicon periods (\(d_{POV}\)). Figure 6b shows the BER with different axicon periods (\(d_{POV}\)). Figure 6c shows the minimum sampling distance (\(d_{min}\)) of OV and POV, respectively. As seen in Figure 6a, when the wavelength and the focal length of the lens are fixed, there exists an inverse relationship between the diameter of the POVs and the axicon period (\(d_{POV}\)). In other words, the storage density can be increased by increasing the axicon periods. However, increasing the axicon periods leads to an increase in crosstalk among POVs, resulting in a weakening of the properties of the POVs and, consequently, an increase in BER. As depicted in Figure 6b, the reconstructed data are found to be error-free when \(d_{POV}\) is set to 0.2 and 0.4. However, as \(d_{POV}\) increases, the BER of the reconstructed data exhibits an increasing trend. Therefore, in the experiment, the selection of axicon periods needs to strike a balance between storage density and BER.  
From the analysis of Figure 6c, it can be observed that the minimum sampling distance (\(d_{\text{min}}\)) of the optical vortex (OV) increases with the topological charge (\(\ell\)). In contrast, the minimum sampling distance of the perfect optical vortex (POV) remains independent of its topological charge. For topological charges lower than 8, OV exhibits a better minimum sampling distance compared to POV. However, when the topological charge exceeds 8, POV outperforms OV in terms of the minimum sampling distance, showcasing the advantages of POV. In simpler terms, when using larger topological charges, the advantage of the storage density offered by POV becomes more pronounced. This means that POV can achieve a higher storage density compared to OV when employing larger topological charges.  
Figure 6: The impact of varying the axicon periods (\(d_{POV}\)) on the quality of the reconstructed data. (**a**) The POV array of the image with different axicon periods (\(d_{POV}\)). (**b**) The BER with different axicon periods (\(d_{POV}\)). (**c**) The minimum sampling distance (\(d_{min}\)) of OV and POV, respectively.
## 2 Materials and Methods
### Remote Sensing Measurements
#### 2.3.1 Image Pre-Processing  
Hyperspectral Images  
A radiometric and spectral calibration of the Cubert hyperspectral camera was performed at DTU photonics lab. The spectral response of each pixel was obtained using a monochromator from 430-1030 nm at 1 nm intervals. Subsequently, a radiometric calibration was performed using an integrating sphere to obtain a calibration factor for each pixel in each specific band under nine different illumination levels. The camera integration time was set from 0.5 to 19.9 ms (0.1 ms intervals) at nine different illumination levels. In the growth chamber, we used a spectralon target to characterize the average incoming spectral radiation from the lamps and its fluctuation. On five dates, we acquired a hyperspectral image of the spectralon before measuring the crops. For the remaining dates, we used the average irradiance of the growth chamber. The variance of lamp irradiance was relatively low over time (coefficient of variation, CV \(\approx\) 0.3). Pixel reflectance was calculated by the ratio between pixel radiance and the average of spectralon pixels. We separated reflectance corresponding to vegetation cover pixels from background pixels [58], using a threshold NDVI \(\geq\) 0.6 and we calculated the average of top canopy pixels for each pot (see Figure 2).  
Thermal Images  
The thermal camera was previously calibrated with a Landcal P80P black body radiation source (Land Instruments, Leicester, United Kingdom), presenting an accuracy of 0.95 \({}^{\circ}\)C root mean square deviation (RMSD) inside the RERAF at differing ambient and target temperatures. For more details, refer to [PERSON] et al. 2016 and [PERSON] et al. (2018) [65; 66]. To extract leaf radiometric temperature (T\({}_{\rm L,Rad}\)  
Figure 2: Example of green area selected after pre-processing hyperspectral data using NDVI threshold \(\geq\) 0.6 and thermal data applying contour detection and histogram thresholding for soybean and maize plants (images taken the 28 April 2018 of dry WHC, replica 4).  
in each pot, we pre-selected vegetation cover pixels with an automatic procedure, using leaf contour detection for plant recognition, together with histogram thresholding (Figure 2). We then calculated the average temperature for vegetated pixels. The results of the algorithm were validated with visually photo interpreted leaf temperature from 15 thermal images. The images selected covered a full range of temperature variability. For further details, refer to [PERSON] et al. (2020) [67].
## 4 Discussion and Results
### Regional Distribution of Magnetic Susceptibility  
The expressions provided in Equation (4), and hence the resultant geothermal potential assessment, do not account for the influence of magnetic susceptibility, specifically, low- and high-frequency-dependent and mass-specific magnetic susceptibility, despite the relevance of these parameters in evaluating the region's RHP potential. To address this, we analyzed magnetic susceptibility from laboratory measurements of rock samples collected across the study area. As shown in Figure 4, the green, red, and purple regions represent areas with high magnetic susceptibility, while yellow and blue regions indicate lower values. Notably, zones with elevated magnetic susceptibility correspond to mineral-rich areas and locations where geothermal energy is actively utilized. This is likely due to the dissolution of minerals in the presence of hot water and their subsequent interactions under elevated temperatures. Elevated magnetic susceptibility values observed in areas of hydrothermal alteration suggest that geothermal processes enhance mineral transformations. These findings contribute to a more comprehensive understanding of the geothermal potential of the study area.
# Affine Formation Maneuver Control for Multi-Agent Based on Optimal Flight System
## 3 Agent Motion by Forces
### APF-Based Virtual Leader Forces
Therefore, \(U_{ij}(||\mathbf{p}_{ij}||)\) would obtain its boundary maximum value \(U_{0}\) when \(||\mathbf{p}_{ij}||\to 0\); minimum \(U_{d}\) when \((i,j)\) satisfies the desired position; that is, \(||\mathbf{p}_{ij}||=d\); and the constant \(U_{r}\) when \(||\mathbf{p}_{ij}||>d_{sen}\). Formulation (13) can describe both the fundamental forces of attraction and repulsion between agents, agents and virtual leaders, and agents and obstacles, \(a\), \(b\), and \(c\) are the positive constants. Figure 3: Illustration for passing through the rectangle obstacle by rotating and scaling the original formation. (**a**) Formation may collide the obstacles at preset path. (**b**) Passing through without collision. The leader is red, virtual leader red-dotted, followers blue, and obstacles are gray. Meanwhile, the obstacles could generate a repulsive force solely to prevent collisions and enable the formation to traverse smoothly among multiple obstacles. The obstacles then produce a repulsive force between the agents and polygon edges of obstacles (inversely related to the distance), as follows:  
\[U_{ob}=\sum_{k=1}^{m}\frac{b}{c(||\mathbf{p}_{io}||)^{2}},(b,c)_{ob} \tag{14}\]  
It is important to note that the system does not include a specific obstacle detection law. Instead, it assumes that information about obstacle position and shape is available, for example, through system perception and 3D modeling. However, it is observed that the value and direction can change suddenly to potential forces within the formation. As a result, some may find that their newly calculated trajectory could potentially lead to collisions with other agents or obstacles, when the formation is striving to reach an equilibrium point. Therefore, trajectory planning should be incorporated into the motion of the agents at each time step.
## 3 Results and Analysis
### Correlation of Raw and First-Order Derivative Spectral Indices with Winter Wheat NNI  
As depicted in the isopotential plot of the coefficient of determination (Figure 5a), the effectiveness of different spectral indices in determining the ideal range of band combinations for winter wheat NNI varied. The band combinations that exhibited correlations greater than 0.6 between the raw spectral normalized difference snow index (NDSI) and the normalized nitrogen index (NNI) of winter wheat canopy were primarily found in two sets of combinations. The first set consisted of bands in the range of 670-710 nm (transverse axis) and 490-650 nm (longitudinal axis). The second set consisted of bands in the range of 560-620 nm (transverse axis) and 530-570 nm (longitudinal axis). The most highly correlated band combinations are 682 nm and 578 nm, and the coefficient of determination for the normalized difference snow index (NDSI) using these bands (R\({}_{682}\), R\({}_{578}\)) is 0.77. Figure 5d shows that the first-order derivative spectral indices NDSI_FD at 800-830 nm (transverse axis) and 650-700 nm (longitudinal axis), as well as 650-800 nm (transverse axis) and 480-570 nm (longitudinal axis), have high coefficients of determination. After comparing them, it was found that the combination of the 830 nm and 678 nm bands had the highest coefficient of determination, with an R\({}^{2}\) value of 0.79. The most effective spectrum indices for determining the NNI of winter wheat are RSI (R\({}_{682}\), R\({}_{578}\)) and RSI (FD\({}_{666}\), FD\({}_{826}\)). Additionally, the most accurate difference spectral indices are DSI (R\({}_{826}\), R\({}_{830}\)) and DSI (FD\({}_{810}\), FD\({}_{690}\)).  
Figure 4: Correlation between spectral reflectance of UHD185 and resampled ASD in different periods. (a) Jointing period; (b) Blooming period.  
Figure 5: _Cont._
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 2 Fundamentals of the Classification Algorithms Applied
### Extreme Learning Machine  
Extreme learning machine (ELM) neural networks are known for their extremely fast learning speed, showing high levels of performance, compared to the ones found for multilayer perceptron network (MPL) and support vector machine (SVM) [16]. ELM has shown better performance than neural networks with backpropagation and other classification models, in terms of computational efficiency and proper generalization when applied to Landsat satellite images [17].  
The ELM algorithm was originally developed for single-hidden layer feedforward neural networks. One of its main features is its simple training since the weights and biases of the hidden layer are randomly created and the output weights can be determined by the resolution of an overdetermined linear system by the use of the Moore-Penrose generalized inverse [16]. The output expression of a single-hidden layer ELM network is as follows:\[f_{L}(x)=\sum_{i=1}^{L}\beta_{i}g(w_{1}*x_{1}+b_{1}),j=1,.....,N \tag{1}\]  
where:  
\(L\): the number of hidden neurons.  
\(N\): the number of training samples.  
\(\beta_{i}\): output layer's weight vector.  
\(w\): hidden layer's weight vector.  
\(g\): activation function.  
\(b\): biases parameters vector.  
\(x\): input vector.  
Equation (1) can be written in the matricial form:  
\[T=H\beta \tag{2}\]  
where \(H\) is the hidden layer output matrix and \(\beta\) is the weight vector of the output layer. Specifically, the H matrix has the following structure:  
\[H=\begin{bmatrix}g(w_{1}*x_{1}+b_{1})&..&g(w_{L}*x_{1}+b_{L})\\ :&..&:\\ g(w_{1}*x_{N}+b_{1})&..&g(w_{L}*x_{N}+b_{L})\end{bmatrix}_{N\;x\;L} \tag{3}\]  
where:  
\(m\): the number of outputs.  
\(H\): the hidden layer output matrix of the neural network.  
\(T\): the training data labels matrix.  
Unlike gradient descent-based neural networks, for ELM, weights of the hidden layer need not be fitted since they are randomly created. In order to train an ELM network, it is necessary to find a \(\hat{\beta}\) least-squares solution of the \(H\beta=T\) linear system, whose expression is as follows:  
\[\hat{\beta}=H^{\dagger}T \tag{4}\]  
where \(H^{\dagger}\) is the Moore-Penrose generalized inverse of matrix \(H\).  
Given a \(\aleph=\left\{(x_{i},t_{i})|x_{i}e\mathbb{R}^{n},t_{i}e\mathbb{R}^{L},i=1,..,N\right\}\) training set, \(g(x)\) activation function, and \(L\) number of hidden neurons, the ELM training algorithm has the following steps:  
* **Step 1:** assign arbitrary input weight \(w_{i}\) and bias \(bi,i=1,...,L\)..
* **Step 2:** calculate the hidden layer output matrix \(H\).
* **Step 3:** calculate the output weight \(\beta\) for \(\beta=H^{\dagger}T\).
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 1 Introduction
Alongside process optimization, the development of simple and rapid performance evaluation methods is also essential. To assess the CE of these MCPs, we proposed a new approach based on the detection efficiency of a photoelectron beam. While ionizing low-energy materials with high-energy ultraviolet light is a common method for generating free electrons [22, 23, 24], this approach offers limited control over beam energy and density. In our study, we employ the photoelectric effect by irradiating a zinc plate with UV light to produce a stable photoelectron beam. This configuration allows control of electron energy through applied electric fields. Then, it was detected using the tested MCP to produce a series of pulse signals, which correspond to the electrons that are \"captured\" into the channels. By comparing the pulse counts collected by different MCPs, the difference in CE between them can be obtained. Since this method has low hardware requirements and is easy to operate, it may be an alternative strategy for roughly evaluating the CE of MCPs.
## 3 Image Quality in Relation to Bit Depth of Wavelength-Multiplexed Holograms  
### Experimental Results  
The quality of the reconstructed image in relation to bit depth of wavelength-multiplexed holograms was investigated by using experimentally obtained holograms [40]. The constructed optical system, the method of generating phase shifts, phase shift \(\alpha\), and the specification of the lasers used are described in Reference [40]. Two lasers with oscillation wavelengths of 640 nm and 473 nm, respectively, were set to record five two-wavelength-multiplexed holograms. A monochrome complementary metal-oxide semiconductor (CMOS) image sensor was used to record the holograms. The sensor has 12 bits, 2592 \(\times\) 1944 pixels, and a pixel pitch of 2.2 \(\upmu\)m. Two overhead projector (OHP) transparency sheets were set as a color 3D object. The logo of the International Year of Light and the characters \"2015\" were drawn on the sheets, and blue- and red-color films were attached to the logo and characters, respectively. A red \"2015\" sheet and a blue logo one were set at different depths. Five wavelength-multiplexed holograms were obtained by utilizing 2\(\pi\) ambiguity of the phase, and a color 3D image was reconstructed with the algorithm described in Reference [40]. Holograms that have less than 8-bit resolution were generated from the recorded holograms numerically. Object images were reconstructed by using compressed holograms in which bit depth was changed from 1 to 7 bits. Then, the images obtained by holograms without compression were regarded as the true values, and the cross-correlations coefficient (CC) and root-mean-square error (RMSE) of the reconstructed images were calculated.  
Figure 1: Schematic of wavelength-selective phase-shifting DH (WSPS-DH).  
Color object images obtained from the compressed holograms are shown in Figure 2. The images were reconstructed by using two-wavelength-multiplexed holograms with resolution of more than 2 bits. As bit resolution was decreased, the reconstructed images degraded gradually. However, a clear color object image was reconstructed even when the number of bits was 4. Furthermore, a two-wavelength object image was reconstructed from holograms with 3-bit depth resolution. To investigate the quality of the reconstructed images quantitatively, CC and RMSE of the intensity images at respective wavelengths were calculated. Graphs of CC and RMSE are plotted in Figure 3. Maximum- and minimum-intensity values of the images were set as 255 and 0, respectively. A CC of nearly 0.8 and a RMSE of 1/10 maximum value were obtained when bit depth was 5. From the quantitative evaluations and Figure 2, it can be concluded that quite similar images are reconstructed even when the image sensor had a resolution of less than 8 bits.  
Figure 3: Quantitative evaluations of experimental results: (**a**) cross-correlations coefficient (CC) and (**b**) root-mean-square error (RMSE) of the reconstructed amplitude images.  
Figure 2: Experimental results: reconstructed images when bit depths of holograms are compressed to (**a**) 2, (**b**) 3, (**c**) 4, (**d**) 5, (**e**) 6, and (**f**) 7 bits.
# A Survey on Fault Diagnosis of Rolling Bearings
## 4 Rolling Bearing Fault Type Recognition  
Unlike the fault detection method, machine learning algorithms were used in the rolling bearing fault type recognition system to replace the manual observation of the fault-related spectral lines. These methods can achieve automatic recognition of different types of faults in rolling bearings.  
A summary of the commonly used methods of rolling bearing fault type recognition is shown in Figure 5. These traditional methods need multiple independent steps, such as feature extraction, feature transform or feature selection, and classifier selection and optimization, which often need to be manually set to achieve effective fault recognition performance. The results of the previous step often significantly affect the results of the latter step. Rich domain knowledge is required in the process of fault recognition. The deep-learning-based fault type recognition methods can automatically learn features from the original signals and train classifiers for effective fault recognition without human intervention. However, the deep architecture used in these methods needs rich expertise to design and a large number of samples to train.
# A Time-Frequency Image Quality Evaluation Method Based on Improved LIME
## 2 Materials and Methods
### Glcm  
Textural feature is a concept in computer science technology that is mainly used to describe the surface properties of images. GLCM (gray-level co-occurrence matrix) is a method proposed by [PERSON] et al. in 1973 to describe the texture features of gray images [21]. The texture is formed by the repeated occurrence of gray levels in spatial positions and is the expression of the relationship between pixels separated by a certain distance. GLCM can accurately express the texture features of the image through the statistical form of the joint gray distribution of two pixels in the image, which is defined as the probability of grayscale \(j\) from a pixel point \((x,y)\) with grayscale \(i\) and a point with distance \(d\) and direction \(\theta\):  
\[GLCM=\left[P\right]_{G\times G}, \tag{4}\]  
\[P(i,j|d,\theta)=\{(x,y)|h(x,y)=i,h(x+dx,y+dy)=j\} \tag{5}\]  
where \(d\) is the relative distance represented by the number of pixels, and \(dx\) and \(dy\) are the components of this distance in the \(x\) and \(y\) directions, respectively. \(\theta\) is the relative direction represented in angles, usually taking 0, 45, 90, and 135 in four two-way directions. \(x\) and \(y\) are the pixel coordinates in the image, and \(h\) is the gray value at that coordinates.  
The size of GLCM is determined by the maximum grayscale level \(G\) of the image. The default grayscale range of a grayscale image is 0-255, and its maximum grayscale level is 256. For an image with a size of \(N\times N\), the expected amount of computation when calculating the gray co-occurrence matrix is about \(N^{2}\times G^{2}\). For example, if the image size is \(128\times 128\), the basic calculation needs at least 10 billion times. To reduce the amount of computation, in practical application, it is generally necessary to reduce the gray level of  
Figure 1: The basic principle of LIME. The pink and blue areas are the two parts that need to be distinguished. \(f\) is the original model to be explained and \(g\) is the explainable model used to explain \(f\). \(x\) is the selected sample and \(Z\) is the area obtained by perturbing \(x\). In this area, model \(g\) has a similar decision-making ability to \(f\).  
the image through regularization while retaining the image texture characteristics as much as possible:  
\[F(x,y)=INT(f(x,y)\times G/G_{max})+1 \tag{6}\]  
where \(F\) is the regularized image, \(INT\) is the integer function, \(G_{max}\) is the maximum gray level of the original image, and \(G\) is the maximum gray level of the target, which is generally 8.