esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description
### Static Topology  
We discuss the sensor deployment scheme based on a regular hexagonal topology, because the minimal overlap of coverage can be achieved by partitioning the target monitoring area into hexagonal grids, in which the sensors are centered, without overlap of physical area (see [2010 ]). As shown in Figure 11, a homogeneous WSN, made up by many identical wireless sensors and a base station, is used to monitor the target area. The base station is located in the center of the circular target area, and one or more sensors are deployed in the middle of each hexagonal grid around the base station. The Mica2 mote, the most commonly used sensor [2010 ], is used in this homogeneous WSN. Its parameters can be found in the CC1000 datasheet [2010 ]. The sensors have the same initial energy \(E_{0}\), while the initial energy of base station is unlimited. Each sensor has a sensing radius of \(R_{s}\) and a transmission radius of \(R_{t}\). Figure 11 depicts the geometric formulation of these regular hexagonal grids leads to multiple layers (the layer number increases from inside to outside), where the base station is located in Layer 0. The distance between the neighboring sensors is denoted \(d\), so the circumcircle radius of each regular hexagonal grid can be expressed as \(\frac{\sqrt{3}d}{3}\), and the circumcircle radius of a sensor in Layer \(i\) can be calculated as \(d_{c}(i)=id\).
## 3 Results and Discussion
### Remote Sensing Applications
#### 3.4.3 Health Monitoring and Disease Detection
[66] used a UAV with an RGB sensor to efficiently detect ochre spot disease in almond trees. They captured low-altitude images of leaves, processed them with MATLAB for contour identification and irrelevant object removal, and analyzed leaf color for symptoms. Ochre spot hue values ranged from 6 to 62, while healthy leaves fell between 64 and 128. The system provided disease progression percentage and GPS coordinates, triggering alerts when thresholds were exceeded. Finally, [PERSON] et al. [67] conducted a study in a rain-fed almond orchard in Portugal, using UAV data and ML models to identify aphids. Data processing included photogrammetry, canopy delineation, feature extraction, labeling, and ML model implementation. The results showed that SVM performed the best with 77% overall accuracy, followed by kNN (74%), XGBoost (71%), and RF (69%). Regarding Figure 11a, MAV was the most used platform in 50% of the studies, followed by UAV in 37% and satellite in 13%. Considering the diseases identified (Figure 11b), it was found that 41% of the studies focused on the disease Xylella fastidiosa, another 39% dealt with ochre spot, and only 20% addressed Verticillium dahlia (Figure 9). Xylella fastidiosa is a plant pathogenic bacterium that causes severe diseases in several crops. It blocks the movement of water and nutrients in the plant, eventually causing withering and death. Therefore, it is essential to adopt best management practices to control its spread, such as using tolerant or resistant cultivars, insect vector control, and early detection, which can be provided using RS platforms and sensors [68]. On the other hand, ochre spot disease causes yellow-red spots on the leaves, which can eventually lead to a high presence of ochre spots caused by the pathogen. The disease can have negative consequences for the yield and quality of almonds, and infected trees should be treated promptly to prevent the spread of the disease [66].
## 4 System Model and Design Goals  
### System Model  
Consider a setting in our system model with three entities: a function owner who wants to outsource the function's computation; two non-collusive cloud service providers \(CSP_{1}\) and \(CSP_{2}\) that collaborate to complete the computation; and a series of request users who want to obtain the computation results on their inputs. Figure 2 outlines a rough sketch of our system model. It consists of the following three phases: Preparation, Computation and Verification. The number before the text corresponds to Figure 2.  
* Preparation: 1. The function owner encrypts the function parameters and prepares the public verification data. Then, they upload the function's ciphertext to the cloud service provider \(CSP_{1}\). 2. A request user encrypts their input when they request \(CSP_{1}\) to perform the computation. 3. When \(CSP_{1}\) receives the request from the user, it asks the function owner for permission. 4. The function owner generates a temporary public/secret key pair and a re-encryption key when they permit the use of their function. 5. The function owner sends the temporary public key and the re-encryption key to \(CSP_{1}\) and sends the temporary secret key to \(CSP_{2}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data** & **Size** \\ \hline Matrix \(\mathbf{F}\) & \(m\times d\) \\ Verification data \(\mathbf{t}\) & \(1\times m\) \\ Verification data \(\mathbf{s}\) & \(1\times d\) \\ Vector \(\mathbf{x}\) & \(d\times 1\) \\ The final result \(\mathbf{y}\) & \(m\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data size.  
* Computation: (6) \(CSP_{1}\) re-encrypts the original function ciphertext with a mask. Then, the new ciphertext can be decrypted by \(CSP_{2}\) with the temporary secret key. \(CSP_{1}\) then collaborates with \(CSP_{2}\) to complete the computation. (7) \(CSP_{1}\) returns the result ciphertext to the user.
* Verification: (8) The public verification is performed using the public verification data, the input ciphertext and the result ciphertext. Simultaneously, the user decrypts the result ciphertext and verifies the result privately in a fast way with the public verification data and the plaintexts of their input and result.
## 2 Autoencoder for Normal Image Reconstruction
### Model Training for Normal Image Reconstruction  
In this study, first, we train Autoencoder with normal images to have a basic image-reconstruction capability. Instead of the MSE function, the SSIM (Structural Similarity Index Measure) function is used as the loss function for the model training. The reason is that the MSE loss function belongs to the global analysis, and the local difference information is not considered. In order to obtain the local difference between the reconstructed image and the input image, SSIM [28] is chosen as the loss function to train the model. Here, we apply the [PERSON] [29] algorithm to update the model parameters. SSIM is defined as follows:  
\[\text{SSIM}(X,Y)=l(X,Y)^{\alpha}c(X,Y)^{\beta}s(X,\ Y)^{\gamma} \tag{1}\]  
where \(l(X,\ Y)\), \(c(X,\ Y)\), \(s(X,\ Y)\) represent luminance, contrast, and structure similarity measures, respectively, and \(\alpha\), \(\beta\), and \(\gamma\) are the adjustment parameters. The three similarity measure functions are defined as follows:  
\[l(X,Y)=\frac{2\mu_{X}\mu_{Y}+C_{1}}{\mu_{X}^{2}+\mu_{Y}^{2}+C_{1}},c(X,Y)= \frac{2\sigma_{X}\sigma_{Y}+C_{2}}{\sigma_{X}^{2}+\sigma_{Y}^{2}+C_{2}},s(X,Y )=\frac{2\sigma_{XY}+C_{3}}{\sigma_{X}\sigma_{Y}+C_{3}}. \tag{2}\]  
where \(\mu_{X}\) and \(\mu_{Y}\) are the average values of the images \(X\) and \(Y\), \(\sigma_{X}\) and \(\sigma_{Y}\) are the standard deviations of the images \(X\) and \(Y\), and \(\sigma_{XY}\) is the cross covariance of the two images. \(C_{1}\), \(C_{2}\), and \(C_{3}\) are all constants, which are used to adjust the weight of the three items. To simplify Equation (2), the parameters are set as \(\alpha=\beta=\gamma=1\) and \(C_{3}=C_{2}/2\), and then the following simplified version can be obtained:  
\[L_{\text{SSIM}}=\text{SSIM}(X,Y)=\frac{(2\mu_{X}\mu_{Y}+C_{1})(2\sigma_{XY}+C_ {2})}{\big{(}\mu_{X}^{2}+\mu_{Y}^{2}+C_{1}\big{)}\big{(}\sigma_{X}^{2}+\sigma _{Y}^{2}+C_{2}\big{)}}. \tag{3}\]  
However, only using SSIM loss function to train the Autoencoder model without the Latent vector clustering can have a good reconstruction capability for normal images, but has a low repair capability for defective images.
### Key Contribution  
This study is designed to enhance the PQ in the distribution network. The highlights of this article are emphasized below:  
* Design of hybrid controller (NFIHC) involving both the properties of FL and ANN techniques for the WPGS and BS-integrated SHAPF (SH-WPBS).
* The prime aim of the proposed method is to minimize THD thereby improving PF and maintaining constant DC-Link voltage.
* The proposed STF is used to generate synchronization phases (SYP) for SHAPF instead of PLL. In addition, STF also does the work of LPFs and HPFs for splitting the FC of current.
* The results are obtained for variation in loads, distorted supply voltage, and wind uncertainties as case studies.
* Future performance analysis was carried out with PI, FL, and ANN controllers. To exhibit the superiority of the proposed technique, validation was done with those existing methods that are available in the literature.  
However, from the literature survey, it is noticed that the majority of research papers focused mainly on THD reduction and or DC-Link voltage regulation as the main objective with the conventional PLL and LPFs. In the proposed work, in addition to THD minimization with DC-Link voltage balancing, PF improvement was also considered an objective with STF instead of PLL and LPFs. Although quite a lot of techniques were recommended in the literature review, there is still a possibility of designing new controllers and techniques for efficiently eliminating PQ problems.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 2 Description of the Study Areas
### November 2017 Small Flash Flood Near Sevilla (Spain)  
This study area is located near the town of Arahal in southern Spain (Figure 1D). The area is characterized by smooth hills and valleys of minor streams; cultivated land and olive trees are the main land use of the area. In addition, the commercial area of Arahal town (20,000 inhabitants) was partly affected by floods. Intense storms that occurred on 29 November 2017 caused the rapid increase of some small streams. In this case, no river-gauge discharge data are available but, from ancillary data found on the web, it was possible to estimate that the flood peak occurred in the morning of 29 November 2017. The flood destroyed a segment of the Malaga-Sevilla railway, causing the derailing of a train and several injured [59]. The Copernicus EMS was not activated for this small and rapid flood. We used Sentinel-1, Sentinel-2 and Landsat-8 data jointly with the water-depth DEM to map the flooded areas. In this case, no official maps were available to validate our results.
# Multi-Field Models of Fiber Reinforced Concrete for Structural Applications
## 3 Models of Fiber Reinforcement  
Much effort has been devoted to modeling the strength and stiffness contributions of fibers to the behavior of hardened concrete. There has been interest in explicitly representing individual fibers, such that their local and collective effects can be simulated [21; 47; 48; 49; 50; 51; 52]. Such models permit the study of fiber distribution non-uniformity and its influence on the desired properties of the composite material, including cracking behavior during strain hardening [18; 53].  
A prime consideration is the balance between computational efficiency and accuracy in representing each fiber. On one end of the spectrum, fibers can be modeled using volume elements. This allows for accurate representation of the elasticity of the fiber and the possibility for directly modeling the fiber-matrix interface. Interface features (e.g., the lugs on ordinary steel reinforcing bars) can be explicitly represented [54; 55]. This approach is computationally expensive, however, which limits the number of fibers that can be considered.  
The number of computational degrees of freedom can be drastically reduced by modeling fibers as series of simple structural elements (e.g., line elements such as truss or frame elements). These line elements interact with the matrix via bond-link elements that account for relevant behaviors of the fiber-matrix interface [23; 56]. Going a step further, by constraining the actions of the fibers to the kinematics of a particle network representing the cement-based matrix, fiber additions do not inflate the number of computational degrees of freedom [21]. The research presented herein employs this semi-discrete approach, which enables the inclusion of larger numbers of fibers. To have benchmark results for comparison, volumetric representations of individual fibers are also considered. For both approaches, emphasis is placed on modeling the individual and collective effects of fibers on transport processes, which has received scarce attention within the cement-based composites community.  
The positioning of the fibers needs to be specified. Fiber locations can be based on sectional analyses [57, 58, 59] or 3D imaging [60, 61, 17, 62], which have the advantage of capturing directional and concentration biases caused by the casting process. Alternatively, as done herein, a procedure is used to sequentially place fibers in the computational domain without positional or directional bias. The spatial coordinates of the centroid of a trial fiber are assigned using a uniform random number generator. The orientation angles of the fiber are then determined using the algorithm of Knop [63]. To avoid fiber intersections, a minimum allowable distance is maintained between fibers [64]. Trial fibers that meet the minimum distance constraint are retained within the material volume. For the unit cell simulations of transport phenomena presented hereafter, periodic boundary conditions are implemented for fibers intersecting the domain boundaries.
## 3 Results and Discussion
### Stiffness Degradation  
Stiffness is an essential aspect of the seismic design of a load-bearing structure, and it degrades with the concrete cracks and the longitudinal rebar's yield. Secant stiffness was calculated using Equation (3), where \(K_{i}\) is the average secant stiffness at the ith loading cycle, \(+P_{i}\) and \(-P_{i}\) represent the positive and negative lateral load at the ith cycle, respectively, and \(+\Delta_{i}\) and \(-\Delta_{i}\) are the corresponding lateral displacement.  
\[K_{i}=\frac{\left|+P_{i}\left|+\right|-P_{i}\left|\right.\right.}{\left|+ \Delta_{i}\left|+\right|-\Delta_{i}\left|\right.\right.} \tag{3}\]  
Figure 11 shows the stiffness degradation curves for all the specimens. Here, secant stiffness quickly degrades at the initial stage as the concrete cracks and accumulates damage under repeated loading cycles. However, after reaching the yield point--with the development of plastic deformation--the stiffness degradation slows down and remains stable. Additionally, in Figure 11, the effect of material properties on the secant stiffness of the column is shown. It can be observed that the secant stiffness for BMRC1 before reaching the peak load was slightly higher than NEWRC1. However, they show a similar degradation process after reaching the peak load.  
Figure 11 further shows the effect of transverse reinforcement influence on the secant stiffness. From this, it can be seen that the initial stiffness of NEWRC2 and NEWRC3 is slightly higher than that of NEWRC1. However, there is no apparent difference in the degradation process as they follow a similar pattern. Meanwhile, Figure 11 also shows the effect of the axial load ratio on the secant stiffness of the columns. It can be observed that the secant stiffness of NEWRC4 is higher than that of NEWRC1.  
Moreover, the degradation process varies slightly here. Hence, it can be concluded that an increase in axial load ratio increases the secant stiffness and accelerates the stiffness degradation process. Figure 11 also depicts the secant stiffness between NEWRC1 and NEWRC5, where it can be seen that both specimens have a similar secant stiffness and follow a similar degradation pattern. Furthermore, Figure 11 shows the secant stiffness of the specimen NEWRCF. During the initial cycles, the rate of stiffness degradation is higher. However, after the specimen reaches its yield load, the stiffness degradation slows down gradually. In the later cycles, it remains stable, which shows that NEWRCF has good ductility.
## 5 Discussion
### ARTMO Toolboxes  
The retrieval of vegetation properties from at-sensor TOA radiance data was made possible thanks to the development of two new toolboxes integrated within the ARTMO framework: ALG and TOC2 TOA. These toolboxes allow to streamline RTM simulations and do the coupling between canopy simulations and atmosphere RTMs. ALG generates look-up tables based on a suite of atmospheric RTMs (6 SV, MODTRAN and Libratran) [19]. ARTMO already allowed to run vegetation RTMs in a forward and inverse direction at the leaf and canopy level. With the TOC2 TOA toolbox the coupling with atmospheric LUTs has been made possible. Yet, here only the first version of the TOC2 TOA toolbox has been presented, and new utilities and improvements are considered; e.g., (1) to take adjacency effects into account [17], (2) to couple surface with atmosphere for non-Lambertian surfaces [14], and (3) to add the possibility to couple atmospheric models with water RTMs.  
Furthermore, ARTMO incorporates several RTM post-processing toolboxes such as the retrieval toolboxes and the Emulation and GSA toolboxes. By combining both toolboxes, multiple TOA sensitivity studies or retrieval strategies can be developed and analyzed, e.g., for all kinds of atmospheric scenarios. All the presented toolboxes are freely downloadable at [[http://artmotoolbox.com](http://artmotoolbox.com)]([http://artmotoolbox.com](http://artmotoolbox.com)). They can facilitate the interested user to repeat the presented study or to conduct related at-sensor TOA radiance studies that involve the processing of RTM simulations, sensitivity, emulation or retrieval.
# A Convex Hull-Based Machine Learning Algorithm for Multipartite Entanglement Classification
## 2 The Construction of the Classifier
### Combining CHA and Machine Learning
#### 2.3.1 Data Preparation  
As for the data preparation, for any quantum state \(\rho\), the density operator acting on \(\mathcal{H}_{A}\otimes\mathcal{H}_{B}\otimes\mathcal{H}_{C}\) can be presented as a real vector in \(\mathcal{X}=\mathbb{R}^{d_{A}^{2}d_{B}^{2}d_{C}^{2}-1}\) due to the fact that \(\rho\) is Hermitian and of trace 1, where \(d_{i}\) is the dimension of \(\mathcal{H}_{i}\). Generalized Gell-Mann matrices [27] are a frequently used linear independent Hermitian orthonormal basis here.  
Let \(\{\ket{1},\,\ldots,\,\ket{n}\}\) be the computational basis of the n-dimensional Hilbert space and  
\[E_{j,k}=\ket{j}\bra{k} \tag{7}\]  
Thus, there are symmetrical matrices presented as follows:  
Figure 3: A plane Voronoi diagram schematic. \(q_{1}\), \(q_{2}\) and \(q_{3}\) are three arbitrary points in the given \(n\) points. Point \(O\) is the barycenter of \(\triangle q_{1}q_{2}q_{3}\). In a Voronoi diagram, there is a property where the vertex point is the barycenter of the triangle formed by the target points. Therefore, there will not be any other target points in \(\triangle q_{1}q_{2}q_{3}\). Point \(O\) is an arbitrary target point, so it must be outside of the triangle. In this case, \(\triangle q_{1}q_{2}q_{3}\) can be considered the minimum triangle.  
\[S_{j,k}=E_{j,k}+E_{k,j},\ 1\leq j\leq k\leq n \tag{8}\]  
There are also anti-symmetrical matrices, presented as  
\[A_{j,k}=-i\Big{(}E_{j,k}-E_{k,j}\Big{)},\ 1\leq j\leq k\leq n \tag{9}\]  
and diagonal matrices, presented as  
\[D_{l}=\sqrt{\frac{2}{l(l+1)}}\left(\sum_{j=1}^{l}E_{j,j}-lE_{l+1,l+1}\right), \ 1\leq l\leq n-1 \tag{10}\]  
Therefore, the generalized Gell-Mann matrices can be presented as follows:  
\[\{\lambda_{i}\}=\Big{\{}S_{j,k}\Big{\}}\cup\Big{\{}A_{j,k}\Big{\}}\cup\{D_{l}\} \tag{11}\]  
where \(tr(\lambda_{i})=0\) and \(tr\big{(}\lambda_{i}\lambda_{j}\big{)}=2\delta_{i,j}\) when \(i\
eq j\).  
Therefore, every \(\rho\) can be expanded into linear combinations as follows:  
\[\rho=\frac{1}{n}\left(\mathbb{I}+\sqrt{\frac{n(n+1)}{2}}x\cdot\vec{\lambda}\right) \tag{12}\]  
where \(x_{i}=\sqrt{\frac{n}{2(n-1)}}tr(\rho\lambda_{i})\).  
In supervised learning, the training set should have the following form:  
\[D_{train}=\{(x_{1},label_{1}),\ \ldots,\ (x_{m},label_{m})\} \tag{13}\]  
where \(m\) is the size of the training set, \(x_{i}\) is the state vector input and \(label_{i}\) is the corresponding tag such that \(label_{i}\in\{0,1\}\).
# Mobile Learning Technologies for Education: Benefits and Pending Issues
## 5 Results Analysis and Discussion  
Table 1 shows the total number of articles found according to the search criteria in the three databases chosen. The analysis of the content of each of the chosen articles yielded 10 generalizations about the different benefits of the use of mobile devices. In addition, 6 themes were found helping to have a much broader view of all the issues that must be faced for an adequate implementation of m-learning in today's education. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Articles** & **IEEE Xplore** & **Wos** & **Scopus** & **All** \\ \hline Total Articles (First step) & 33 & 21 & 22 & 76 \\ Selected Articles (Second step) & 27 & 15 & 17 & 59 \\ Relevant Articles (Third step) & 18 & 10 & 8 & 36 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of articles used in this study. The general themes associated with the benefits of m-learning are constructivist learning, student behavior, learning spaces, collaborative learning, informal and self-directed learning, teacher resources, technology and support, affordability and portability, availability and flexibility, and motivational learning. On the other hand, the issues inherent to the use of this technology are aligned with the content in mobile applications and the design of activities for m-learning, educators, technology, students, learning, and educational institutions. These generalizations are the results of the grouping of multiple benefits and characteristics mobile devices provide to students, educational institutions, and teachers. In the same way, the generalizations of the problems are based on multiple challenges, problems, issues, and weaknesses faced by the deployment and use of this technology. This summary of this analysis can be seen in Tables 2 and 3. \begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline \multicolumn{1}{c}{**Generalization**} & \multicolumn{1}{c}{**Issues of Mobile Learning**} \\ \hline Applications & Mobile content design, real learning experiences, pedagogical design \\ \hline Educators & Difficult understanding, difficulty of use, discomfort of use, culture of rejection of change, extra workload, updating of knowledge, extra effort \\ \hline Technological & Security and privacy, connectivity restrictions, equipment cost, technical deficiencies of the device, Internet access or mobile data, Internet access speed \\ \hline Students & Application usability, device accessibility, distraction/restrictive conditions, enjoyment, cost \\ \hline Educational Institutions & Technological infrastructure, strategies for implementation and deployment, prohibitions, digital security for students, cost \\ \hline Learning & Technological infrastructure, strategies for implementation and deployment, prohibitions, digital security for students, cost \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Generalization of the issues of using mobile learning in education.