esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 3 Data and Experiments
To mitigate the impacts from the phenological variation of rice paddy, the intensity mean values of the pre-event acquisitions were \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Acquisition Time** & **Polarization** & **Incidence Angle (\({}^{\circ}\))** & **Resolution (m)** & **Orbit** \\ \hline
01/07/17 & VV & 36.7 & 20 & Descending \\
07/07/17 & VV & 36.7 & 20 & Descending \\
13/07/17 & VV & 36.7 & 20 & Descending \\
19/07/17 & VV & 36.7 & 20 & Descending \\
25/07/17 & VV & 36.7 & 20 & Descending \\
31/07/17 & VV & 36.7 & 20 & Descending \\
06/08/17 & VV & 36.7 & 20 & Descending \\
12/08/17 & VV & 36.7 & 20 & Descending \\
18/08/17 & VV & 36.7 & 20 & Descending \\
24/08/17 & VV & 36.7 & 20 & Descending \\ \hline
30/08/17 & VV & 36.7 & 20 & Descending \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sentinel-1 intensity data used for the Houston case study (flood acquisition is marked in blue color). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acquisition Time** & \(B_{t}\) **(days)** & \(B_{p}\) **(m)** &
\begin{tabular}{c} **Window Size** \\ **(Range \(\times\) Azimuth)** \\ \end{tabular} \\ \hline
01/07 – 07/07 & 6 & 47 & \(28\times 7\) \\
07/07 – 13/07 & 6 & 31 & \(28\times 7\) \\
13/07 – 19/07 & 6 & 79 & \(28\times 7\) \\
19/07 – 25/07 & 6 & 45 & \(28\times 7\) \\
25/07 – 31/07 & 6 & 38 & \(28\times 7\) \\
31/07 – 06/08 & 6 & 38 & \(28\times 7\) \\
06/08 – 12/08 & 6 & 52 & \(28\times 7\) \\
12/08 – 18/08 & 6 & 58 & \(28\times 7\) \\
18/08 – 24/08 & 6 & 82 & \(28\times 7\) \\ \hline
24/08 – 30/08 & 6 & 55 & \(28\times 7\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sentinel-1 coherence data used for the Houston case study (flood acquisition is marked in blue color). only calculated by the acquisitions that dated in the same season of the co-event data. Therefore, only intensity data acquired on 29 August 2014 and 31 July 2015 were used for the average operation. Both 31 July 2015-11 September 2015 and 11 September 2015-23 October 2015 coherence pairs were co-event acquisitions and only the former was used. Besides, to mitigate the perturbation of \(B_{p}\), coherence of 2 January 2015-13 February 2015 was not used in the experiment. The validation dataset was virtually digitized on the basis of aerial photographs with a spatial resolution of 20 cm acquired on 11 September 2015 by the Geospatial Information Authority of Japan (GSI) [57].
# The Influence of Smartphone Games on Students' Self-Concept
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & \multicolumn{2}{c}{**Category**} & **Number of People** & **Percentage** & **Cumulative Percentage** \\ \hline \multirow{4}{*}{Player qualifications} & (1) & Less than half a year & 44 & 16.1 & 16.1 \\  & (2) & Half a year\({}^{-}\)1 year & 45 & 16.5 & 32.6 \\  & (3) & 1–2 years & 59 & 21.6 & 54.2 \\  & (4) & 2–3 years & 41 & 15.0 & 69.2 \\  & (5) & Over 3 years & 84 & 30.8 & 100.0 \\ \hline \multirow{4}{*}{Play days per week} & (1) & 0 days & 26 & 9.5 & 9.5 \\  & (2) & 1–2 days & 157 & 57.5 & 67.0 \\  & (3) & 3–4 days & 45 & 16.5 & 83.5 \\  & (4) & 5–6 days & 28 & 10.3 & 93.8 \\  & (5) & More than 7 days & 17 & 6.2 & 100.0 \\ \hline \multirow{4}{*}{Weekday play times} & (1) & Did not play & 108 & 39.6 & 39.6 \\  & (2) & Less than 1 h & 108 & 39.6 & 79.2 \\  & (3) & 1–2 h & 39 & 14.2 & 93.4 \\  & (4) & 2–3 h & 8 & 2.9 & 96.3 \\  & (5) & 3–4 h & 4 & 1.5 & 97.8 \\  & (6) & More than 4 h & 6 & 2.2 & 100.0 \\ \hline \multirow{4}{*}{Weekend play times} & (1) & Did not play & 24 & 8.8 & 8.8 \\  & (2) & Less than 1 h & 102 & 37.4 & 46.2 \\  & (3) & 1–2 h & 83 & 30.4 & 76.6 \\  & (4) & 2–3 h & 20 & 7.3 & 83.9 \\  & (5) & 3–4 h & 12 & 4.4 & 88.3 \\  & (6) & More than 4 h & 32 & 11.7 & 100.0 \\ \hline Can I play smartphone games at home? & (1) & Yes & 237 & 86.8 & 86.8 \\  & (2) & No & 36 & 13.2 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the analysis of the current situation of mobile games experience of middle-grade students in elementary and middle school. Can I play smartphone games at home: this research shows that those who can play smartphone games at home account for 86.8%; those who cannot play at home account for 13.2%. This shows that it is quite common to play smartphone games at home, but there are still 10% of school children who cannot play at home. Most of them have been exposed to smartphone games, but are not currently playing them.
# A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors
## 2 Methodology
### Formulations of Equation (8)  
If the input vector \(x\) is complex such that \(x\in\mathbb{C}^{N}\), we can split the real and imaginary components by \(x=x_{r}+jx_{i}\) where \(x_{r}=\mathrm{real}(x)\) and \(x_{i}=\mathrm{imag}(x)\). Since \(\tilde{X}=\mathrm{diag}\left(V^{\mathrm{T}}x\right)\), we can rewrite (8) by splitting the real and imaginary parts such that \(\tilde{X}_{r}=\mathrm{diag}\left(V^{\mathrm{T}}x_{r}\right)\) and \(\tilde{X}_{i}=\mathrm{diag}\left(V^{\mathrm{T}}x_{i}\right)\). It is clear that \(\tilde{X}=\tilde{X}_{r}+j\tilde{X}_{i}\). Now (8) becomes  
\[X =V\tilde{X}_{r}uz+jV\tilde{X}_{i}uz \tag{9}\] \[=V\tilde{X}_{r}uz+V\tilde{X}_{i}uz_{i} \tag{10}\]  
where \(z_{i}=jz=\left[\begin{array}{cc}j&-1\end{array}\right]^{\mathrm{T}}\). Alternatively, (9) can be implemented by modifying \(u\) and keeping \(z\) unchanged for the imaginary part. In this case (9) changes to  
\[X=V\tilde{X}_{r}uz+V\tilde{X}_{i}u_{i}z \tag{11}\]  
where \(u_{i}\) is given by (12). The first column of \(u_{i}\) contains \(m_{j}\) -1's and \(m_{-j}\) 1's and the second column contains \(m_{1}\) 1's and \(m_{-1}\) -1's. Thus, \(u_{i}\) can be formed by swapping the columns of \(u\) followed by negating the first column.  
\[u_{i}=\left[\begin{array}{ccccccccc}0&&\cdots&&&0&-1&\cdots&-1&1&\cdots&1\\ 1&\cdots&1&-1&\cdots&-1&0&&\cdots&&&0\end{array}\right]^{\mathrm{T}} \tag{12}\]  
In (10) and (11) both terms on the right-hand-side contain real and imaginary parts. This can be shown by factoring (10):  
\[X =V\big{(}\tilde{X}_{r}u+\tilde{X}_{i}u_{i}\big{)}z\] \[=VWz\]  
where the \(N\times 2\) matrix \(W=\tilde{X}_{r}u+\tilde{X}_{i}u_{i}\). The first column of \(W\) contributes to the real part of the DFT and its second column contributes to the imaginary part of DFT. Therefore, (8) and (9) can be manipulated such that the components of the input that lead to the real and imaginary parts of the DFT are grouped together before computing \(W\). Let \(\tilde{x}_{r}=V^{\mathrm{T}}x_{r}\), \(\tilde{x}_{i}=V^{\mathrm{T}}x_{i}\) and \(m^{\prime}=m_{1}+m_{-1}\). Create vectors \(y_{1}\) and \(y_{2}\) such that  
\[y_{1} =\big{[}\tilde{x}_{r}(0),\ldots,\tilde{x}_{r}(m^{\prime}-1), \tilde{x}_{i}(m^{\prime}),\ldots,\tilde{x}_{i}(N-1)\big{]}^{\mathrm{T}}\] \[y_{2} =\big{[}\tilde{x}_{i}(0),\ldots,\tilde{x}_{i}(m^{\prime}-1), \tilde{x}_{r}(m^{\prime}),\ldots,\tilde{x}_{r}(N-1)\big{]}^{\mathrm{T}}\]  
where the notation \(\tilde{x}_{\mathrm{s}}(k)\) represents the \(k^{\mathrm{th}}\) element of the vector \(\tilde{x}_{\mathrm{s}}\) and \(s=r,i\). Let \(Y^{\prime}=\big{[}\begin{array}{cc}Y_{1}&Y_{2}\end{array}\big{]}\) where \(Y_{1}=\mathrm{diag}(y_{1})\) and \(Y_{2}=\mathrm{diag}(y_{2})\). It is clear that \(Y^{\prime}\) is a \(N\times 2N\) matrix. Define a \(2N\times 2\) matrix \(u^{\prime}\) as given in (13). The first column of \(u^{\prime}\) is comprised of \(m_{1}\) 1's, \(\big{(}m_{-1}+m_{j}\big{)}\) -1's followed by \(m_{-j}\) 1's; and the second column consists of
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 2 Materials and Methods
### Laboratory-Simulated Canopy  
To explore the precise relationship between the area of laser point cloud-fitted leaves and the canopy leaf area density in an orchard, and to establish a mathematical model for calculating the canopy leaf area density, this research builds a laboratory-simulated test bench, with reference to the actual conditions of an orchard canopy. The test bench is shown in Figure 3. To facilitate the calculation of canopy volume, this research constructs the simulated canopy in a rectangular cuboid structure. The simulated canopy test bench is constructed using aluminum profiles, transparent lines, and artificial leaves. The aluminum profiles and transparent lines are used to fix the artificial leaves and effectively simulate the branch and trellis structure found in actual orchard canopy, while the artificial leaves are used to mimic real leaves. The specific dimensions of the simulated canopy are set as follows: canopy length is 1 m, canopy thickness is 1.3 m, and canopy height is 1 m. The artificial leaves are uniformly arranged in the detection area beneath the frame, with 3 to 13 layers placed along the direction of detection. The distance between each layer is 10 cm. By adjusting the number of layers and the quantity of artificial leaves, it is possible to simulate canopy conditions with varying canopy thicknesses and leaf area densities, thereby meeting the diverse requirements of the experiment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & **Factor 1** & **Factor 2** & **Factor 3** & **Canopy** & **Detection** & **Leaf Area** \\
**Number** & & & & **\(z_{1}\) (m)** & **\(z_{2}\) (m)** & **\(z_{3}\) (m\({}^{2}\)/m\({}^{3}\))** \\ \hline
1 & 1 & 1 & 1 & 0.58 & 1.63 & 4.  
The definition of leaf area density in this research refers to the literature [15]. The areas of the artificial leaves were measured by randomly selecting 10 leaf samples. The sampled leaves were scanned using a scanner. The image processing software (Image Pro Plus, Meyer instruments, Inc., Houston, TX, USA) is used to calculate the area of the simulated leaves. The average leaf area of the artificial leaves is 65.12 cm\({}^{2}\). The temperatures ranged from 20 \({}^{\circ}\)C to 23 \({}^{\circ}\)C.
## 2 General Context: Geocollaboration in the GeoWeb 2.0 Era
### The Wikification of Geographical Information
For instance, this tool provides the history of the advancement of a project by types of actors involved (citizens, land-planners or elected representatives). A WikiGIS thus makes it possible to support both integration and differentiation dynamics, which are core to Geocollaboration [20,21]. In other words, it offers a solution to integrate Geocollaboration to what [PERSON] [29] calls the differentiated consensus, that is to say, the building of a consensus, based on the identification and recognition of differences and divergences. The WikiGIS, as we define it in this paper, applies wiki management and integration strategies to geo-data-geometry (shape and location)-graphic attributes-descriptive attributes, and not solely to location-based texts, as in geoblogs for instance. Table 1 presents the characteristics of the WikiGIS compared to those of wiki technologies. More precisely, a WikiGIS is a \"geographic information system (GIS), created online (on the Internet) through collective interventions, which involves interactions between participants, followed by the combination and traceability of their contributions into coherent geospatial representations that are open to improvements\" [38]. Although geographic information practices and forms have come a long way, the quality and reliability of volunteered geographic information remain a key issue [39, 40, 41]. The limits and difficulties related to user-generated contents are of all kinds and not to be ignored (varying motivations of users, heterogeneous contents, legal issues, _etc._). Mapping applications 2.0 are still limited in terms of accuracy and data qualification. Therefore, it is necessary to develop mapping applications based on new approaches specific to the mobilization and representation of public knowledge, following the examples of Geocollaboration or wikification. The \"wiki\" approach for geographical information production opens new ways to qualify data (peer-review or successive iterations for instance). It also offers new opportunities, through the direct involvement of interested end-users, to produce redundancy that could improve geodata quality ([PERSON] [42]). Last but not least, this direct involvement of end users could probably contribute to improving the adequacy of geodata with users' needs (fitness for use) [40].
## 1 Introduction
This framework also offers higher accuracy than conventional machine learning models by dealing with data uncertainties and optimizing itself with JOPS. In terms of explainability, this framework outperforms machine learning models by providing domain knowledge-based explanation in human-understandable language. Moreover, we assess our proposed framework's balance between explainability and accuracy with the Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm [37]. To achieve our research objective, we focus on the following four research questions in this paper:  
* How to address the scarcity of supervised labeled training data of energy consumption? We employ SSL to overcome the scarcity of labeled energy data with weakly and strongly augmented pseudo-labeled data. * What is the benefit of predicting with BRBES? The benefit is prediction based on knowledge of the energy domain while dealing with data uncertainties. * Why and how to integrate self-training model with BRBES? For the SSL model, we employ self-training to obtain pseudo-labels of unlabeled energy data with the BRBES only. A mathematical model is proposed to combine self-training with the BRBES. * How to make the output of BRBES trustworthy to a human user? We make the predictive energy consumption output of the semi-supervised BRBES trustworthy by explaining it in nontechnical human language to a user through a user interface. This explanation is based on knowledge of the concerned domain. The remainder of the paper is structured as follows: Relevant works are presented in Section 2. We then demonstrate our proposed semi-supervised explainable BRBES framework in Section 3. In Section 4, we present our experimental results. These results are discussed in Section 5. We conclude the paper and present our future research direction in Section 6.
## 2 Materials and Methods
### In Situ Measurements
#### 2.2.4 SIF Retrievals from the Spectral Measurements  
The Fraunhofer Line Discrimination (FLD) principle makes it possible to extract the weak SIF signal from the vegetation-reflected radiance at the Fraunhofer lines or the atmospheric absorption bands [55; 12]. According to the accuracy assessment of the FLD-based SIF retrieval methods in [56; 57], the 3 FLD method [58] is most robust and can retrieve SIF with sufficient accuracy using spectral measurements by the QE pro spectrometer. Therefore, the 3 FLD method was used for canopy SIF retrieval in this study. In the 3 FLD method, the irradiance and radiance of a single reference channel used in the standard FLD method are replaced with the weighted averages for two channels at the left (for the shorter wavelength) and right (for the longer wavelength) shoulders of the absorption feature [58]. The weights of the two reference channels are defined as  
\[w_{left}=\frac{\lambda_{right}-\lambda_{in}}{\lambda_{right}-\lambda_{left}}, w_{right}=\frac{\lambda_{in}-\lambda_{left}}{\lambda_{right}-\lambda_{left}} \tag{1}\]  
in which \(\lambda\) is the wavelengths of the channels; and the subscripts _'in'_, _'left'_, and _'right'_ refer to the channels inside, at the left, and at the right shoulders of the absorption band, respectively. The SIF inside the absorption band can be calculated as Equation (2), in which \(I\) is the downwelling irradiance arriving at the top-of-canopy, and \(L\) is the total upwelling radiance at the TOC.  
\[SIF_{in}=\frac{(I_{left}w_{left}+I_{right}w_{right})L_{in}-I_{in}(L_{left} w_{left}+L_{right}w_{right})}{(I_{left}w_{left}+I_{right}w_{right})-I_{in}} \tag{2}\]  
To sum up, from ten field experiments, we can synchronously derive the vegetation parameters, meteorological variables, and GPP at half-hour intervals, as well as diurnal reflectance and SIF datasets. These high-accuracy experimental datasets are sufficiently reliable as the SCOPE model inputs or for the validation of SIF simulations.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 6 Structured Background Models
### Cluster or Mixture-based Models
#### 6.2.1 Gaussian-Mixture Model  
A more complex Gaussian-mixture model (GMM) can more closely describe the nonhomogeneous backgrounds (i.e., model the presence of different materials in the scene) [15; 35]. The basic idea of the GMM is as follows: the scene is divided into a set of mixtures, each of which follows a Gaussian distribution. Suppose that there are \(L\) Gaussian distributions on the scene (\(L\) mixtures), then the probability of the occurrence of each pixel can be shown as the weighted sum of the probabilities of those distributions:  
\[p_{GMM}(\underline{x}|\mathcal{B})=\sum_{i=1}^{L}w_{i}p_{\mathcal{N}}( \underline{x}|\underline{u}_{i},\underline{\underline{\Gamma}}_{i}) \tag{42}\]  
where \(\mathcal{B}=\left\{w_{i},\,\underline{u}_{i},\,\underline{\underline{\Gamma}} \right\}_{i=1}^{L}\).  
In the case of a multivariate Gaussian distribution, the unknown parameters \(\underline{u}_{i}\) and \(\underline{\underline{\Gamma}}_{i}\) are determined directly. In the GMM, the determination of \(w_{i},\,\,\,\,\underline{u}_{i}\), and \(\underline{\underline{\Gamma}}_{i}\) is done using algorithms such as expectation-maximization (EM) or some variants thereof, such as stochastic expectation-maximization (SEM) [118; 119] or classification expectation-maximization [119]. We consider anomalies to be those hyperspectral pixels that have a low probability of occurrence. Therefore, the detector statistic can be formulated as [82]:  
\[D_{GMM}(\underline{x}|\mathcal{B})=\sum_{i=1}^{L}w_{i}p_{\mathcal{N}}( \underline{x}|\underline{u}_{i},\underline{\underline{\Gamma}}_{i})\begin{array} []{c}H_{1}\\ <\\ >\\ H_{0}\end{array} \tag{43}\]  
It should be noticed that the inequality signs in the expression (43) are oppositely oriented concerning other detectors. The weights \(w_{i}\) are usually determined as the a-priori probabilities of the Gaussian distributions found in the background and can be determined, for example, by using SEM. Due to the introduction of the weighting coefficient \(w_{i}\) The GMM is less sensitive to overestimating the number of distributions found in the background than cluster or segmentation-based methods. That is because the mixtures with a small number of elements will also have a low weight value \(w_{i}\). GGMs are global detectors, and as they do not use a sliding window, they can detect targets of any size or shape. However, for the GMM to achieve successful results, the frequency of occurrence of targets needs to be low relative to the background (so that the targets do not create a separate class), and the anomaly spectra need to be significantly different from the background.
## 4 Discussion
### Interpretation of Image Similarity Evaluation Metrics  
To understand the improvement in the image quality between the motion-blurred CT and predicted images, we compared the SSIM and PSNR of motion-blurred CT and predicted images, respectively. The SSIM and PSNR differences between motion-blurred CT images and ground truth motion images are 0.41 \(\pm\) 0.02 and 21.6 \(\pm\) 0.9 dB, respectively. The SSIM and PSNR differences between the predicted motion images and ground truth motion images are 0.97 \(\pm\) 0.01 and 36.0 \(\pm\) 1.3 dB, respectively (Table 6). From the above-mentioned values, we can conclude that the image quality of predicted motion images is much higher than that of motion-blurred images. The improvement in image quality indicates that the trained DNN has learned motion information from the motion blur.  
Furthermore, by comparing the results of image similarity metrics in different phases, we found that the accuracy varied depending on the phases. The most significant difference among phases is velocity: aortic valves in the rapid opening phase move faster than those in other phases. Therefore, velocity may affect the accuracy. The opening distance profile, the swept area velocity profile, and its corresponding SSIM accuracy profile are shown as an example of predicted motion and ground truth in Figure 7. In the figure, we can find that the predicted swept area velocity profile can roughly fit the ground truth. Furthermore, by combining these images, we found that the SSIM accuracy tends to decrease as the swept area velocity increases.  
The reasons for these phenomena can be traced back to the CT imaging process. The higher the velocity is, the less significant the blur left by the motion image at that moment in the motion-blurred CT image. Motion-blurred CT images are reconstructed by projection data of different motion images from different projection angles at different times. The pixels in the motion-blurred CT image are related to the corresponding pixels in the motion images. Due to the motion of aortic valves, these pixels in the motion images switch between the material of blood and aortic valves, resulting in a blur in the motion-blurred CT images. To make a pixel more like the aortic valve, there must be multiple motion images in which the corresponding pixel is an aortic valve. When a motion image has high velocity, there are fewer shared pixels with adjacent motion images. Therefore, it is difficult to determine whether these pixels in motion-blurred CT images are aortic valves. Therefore, the blur left by the fast-moving motion image is insignificant.  
Since velocity influences the accuracy, we further explored the velocity limitation of the trained model. We quantitatively evaluated the relationship between the swept area velocity and SSIM (Table 11). We found that predicted images with the SSIM above 0.964 were very close to the ground truth images using empirical judgment. Therefore, our trained model can handle area swept velocity below 1400 mm\({}^{2}\)/s.