esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
## 4 Result Analysis
### Direct Validation  
Scatterplots of the FCover products versus the mean values of the high-resolution FCover maps are shown in Figure 7. The majority of points in Figure 7b are above the 1:1 line, which indicates that GEOV1 FCover values are higher than the mean values of the high-resolution FCover maps, especially for high FCover values. These overestimation of high FCover values should be partly explained by a slightly too large scaling factor which was applied to scale CYCLOPES FCover values [7]. Compared with GEOV1 FCover values, those of TRAGL are distributed more closely around the 1:1 line against the mean values of the high-resolution FCover maps (Figure 7a), showing that TRAGL FCover product achieves better agreement across the FCover range than the GEOV1 FCover products.  
It is apparent that the TRAGL FCover product provides slightly better accuracy against the mean values of the high-resolution FCover maps (RMSE = 0.0865, and bias = 0.0171) compared with the GEOV1 FCover product (RMSE = 0.1541, and bias = 0.0754). The correlation between the TRAGL FCover values and the mean values of the high-resolution FCover maps (R\({}^{2}\) = 0.8848) is also superior to the correlations of the GEOV1 FCover values (R\({}^{2}\) = 0.7621) with the mean values of the high-resolution FCover maps.
# Time-Domain Aeroelasticity Analysis by a Tightly Coupled Fluid-Structure Interaction Methodology
## 6 Results and Discussion
### Flutter Boundary Analysis of AGARD 445.6 Wing
The flutter frequency ratios predicted in the paper are closer to the experiment compared with those of [20; 21]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mach Number**} & **Computational Result** & \multirow{2}{*}{**Experimental Data**} & \multirow{2}{*}{**Relative Error**} \\ \hline
0.499 & 0.4456 & 0.4459 & 0.07\% \\
0.678 & 0.4220 & 0.4174 & 1.10\% \\
0.901 & 0.3441 & 0.3700 & 7.00\% \\
0.960 & 0.2918 & 0.3076 & 5.14\% \\
1.072 & 0.4015 & 0.3201 & 25.43\% \\
1.141 & 0.5378 & 0.4031 & 33.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of predicted flutter speed index with experimental data. Figure 10: Flutter speed for the AGARD 445.6 wing. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mach Number**} & **Computational Result** & \multirow{2}{*}{**Experimental Data**} & \multirow{2}{*}{**Relative Error**} \\ \hline
0.499 & 0.5469 & 0.5353 & 2.49\% \\
0.678 & 0.4886 & 0.4722 & 3.47\% \\
0.901 & 0.4174 & 0.4224 & 1.18\% \\
0.960 & 0.3699 & 0.3648 & 1.40\% \\
1.072 & 0.3803 & 0.3623 & 4.97\% \\
1.141 & 0.4154 & 0.4592 & 9.5 The results show that the tightly coupled preconditioned N-S equations with CSD methodology provides reasonable flutter speed indexes in the subsonic and the transonic regions, for the supersonic cases there is some improvement compared with other simulations. Note that the methodology predicts the flutter frequency ratios well even in the supersonic region, while other computational methods cannot still provide satisfying results in the supersonic cases. Generally speaking, the flutter speed values are higher in the computational solutions than the experimental ones in the supersonic regime. Some researchers attribute this phenomenon to the inaccuracy of the experimental data [22; 23]. It was believed that it was difficult to model correctly the physics processes occurring in the experiment at supersonic case for only minimal information on the experimental flow field was available. The differences between the predicted and measure results have also been noted in the previous studies, which is commonly believed that there exist measurement errors in the experiment of supersonic regime.
## 2 Materials and Methods
### Experimental Procedure  
We use Figure 2 to explain the experimental prototype of the aBCI. The participant sits comfortably ahead of the aBCI system before the experimental procedure. The first preparation step before the test was to explain the test scheme, audio stories, and how to mentally repeat the keyword in the target story to the participant. Then, the experimenter attached electrodes to the participant's scalp, helped the user to put on the headphones, and checked the headphone volume. After completing all the preparations, the experimental procedure of 20 trials (10 test runs, there are 2 trials in each test) began immediately, up to 30 mins. That is, each test run includes two trial stages. Each participant must perform the test run ten times. They must make one choice on each trial (two selections on a test run). Figure 6 shows the flowchart of the test procedure.  
Figure 5: The positions of the electrodes based on the International 10–20 Location System. The green circles, A1 and A2, represent the reference electrodes. The ground electrode is the blue circle, FP2.  
Before each trial, the experimenter specified one audio story as the target stimulus. There is an additional instruction: a mindfulness induction, played before each trial stage to help the participant focus on the target sound stimuli. During each trial, the participant must pay attention to the target story and mentally repeat the keyword in the story. The system then obtained the ERPs of both options from EEG equipment. Next, our aBCI system determined the sound-evoked potentials of every ERP component for both options. The choice having the highest potential should be the one the participant was paying attention to during the operation. Then, the system outputted the results of each trial with R or L. If the user focuses on the audio story played through the right earphone, the code is R; otherwise, the code is L. The system will judge it as correct when the output result comes together with the target specified by the experimenter. For example, the experimenter asks the participant to listen to the audio story of the right channel (R), and if the output is R, it is correct. Therefore, after each test run (two trial stages), the user can choose which one he prefers from four options (LL, LR, RL, RR).  
Figure 6: The flowchart of the test procedure. The audio stories in blue squares were played in Chinese by the system during the operation.
## 4 Analysis
### Societal Risk Assessment and Uncertainty Analysis  
To assess the risk tolerability of various design solutions, F-N curves are constructed to enable comparison of SR for each case study. Figure 16 shows the F-N curves with and without HOEs for Case #1 to #4. ABCB tolerability curves are represented graphically by red dotted and blue dot-dash diagonal lines and are similar to British Standards Institution Published Documents (BSI) PD\(-\)7974\(-\)7:2019 [37] tolerability limits which are represented by red and yellow diagonal lines. However, the rate of change in allowable frequency is much faster (steeper slope) than BSI. The ABCB slope of \(-\)1.5 indicates a higher risk aversion than BSI's neutral risk aversion slope of \(-\)1 [37]. The area between the tolerability curves defines the region where a design is considered to be safe, or as low as reasonably practicable (ALARP). The upper and lower bound uncertainties in SR are presented as 95% and 5% Confidence Intervals are represented by black dash-dot and grey dash-dot lines, respectively. The uncertainty analysis generates an area plot for a certain level of confidence in the F-N curve with upper (95%) and lower (5%) bounds of Societal Risk instead of only one mean F-N curve. The methodology to generate these uncertainty bounds is based on [PERSON] et al. [38] described in Appendix D.  
Figure 15: ERL Cumulative distribution plots—HOE Case #1 to #4.  
The inclusion of HOEs in the analysis results in variations to SR values as shown in the F-N plots in Figure 17 for Case #1 to #4. The following observations can be made from Figure 16. For all cases where HOEs are considered, SRs are higher than the corresponding case with no HOEs. All cases are below the upper tolerability bounds indicating acceptable SRs, however, when HOEs are considered, Case #2 marginally exceeds BSI tolerance but meets ABCB acceptable limits. Case #4 exceeds both BSI and ABCB tolerability limits. When confidence-interval uncertainty bounds are considered, Case #2 marginally meets ABCB upper tolerability limits while Case #4 clearly exceeds the tolerability limits. To lower the curve such that it falls in the ALARP region, either additional fire safety measures can be installed, or systems reliability can be improved. Among the four cases, Case #4 results in maximum SR. This is followed by Case #2, Case #1 and Case #3 in decreasing order. For case #3 and #4, the F-N curves are shifted to the right resulting in higher consequences even though frequencies are within similar range as the other two cases. The CI-95% uncertainty bounds indicate that Case #2 & #4 exceed the BSI upper tolerance limit but only Case #4 exceeds the ABCB upper tolerance limit. Thus, when uncertainty ranges are considered, tolerability thresholds can be exceeded in some cases (Case #2 & #4) when mean values do not.  
Figure 16: F-N curves for Case #1 to #4—Performance, HOE.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 2 Theoretical Model of VBB Seismometer
### Noise Source Models of Seismometer
#### 2.2.1 Brownian Noise  
Brownian noise results from the constant motion of air molecules that collide with the suspension pendulum. The thermal motion of the air molecules can be modeled as a force exerted on the pendulum's mass via the damping components. This mechanical thermal noise adds to the input of the suspension pendulum, and its acceleration PSD is denoted as \(S_{n_{m}}(f)\). The PSD of mechanical noise in terms of acceleration is expressed as [28]:  
\[S_{n_{m}}(f)=16\frac{\tau kT\zeta f_{0}}{M}. \tag{19}\]  
Here, \(S_{n_{m}}(f)\) represents the PSD of Brownian noise in acceleration, \(k\) is the Boltzmann constant (\(1.38\times 10^{-23}\) J/K), \(T\) is the ambient temperature (293 K), \(\zeta\) is the damping ratio of the spring-mass system, \(M\) is the mass of the pendulum, and \(f_{0}\) is the natural frequency of the spring-mass system.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Link Quality Prediction Model Development
#### 2.3.2 Link Quality Data Acquisition  
One set of four nodes was placed as receivers at 5 m intervals from 0 m to 50 m horizontally, and the height of the receivers from the ground was 0.3 m, 0.7 m, 1.1 m, and 1.5 m, respectively. One set of four nodes was placed at the 0 m position horizontally to serve as transmitters, with respective heights of 0.3 m, 0.7 m, 1.1 m, and 1.5 m above the ground. RSSI measurements were performed by using the CC2530, a low-power wireless communication chip developed by Texas Instruments (Dallas, TX, USA), which used the ZigBee wireless communication protocol based on the IEEE 802.15.4 standard. The RSSI value for each node was recorded 20 times, and the average value was used to calculate the path loss according to Equations (1) and (2). The main hardware parameters of the experiment were set as shown in Table 1.
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments
### Effect of Different Settings of the Network Components
#### 4.5.3 Deformable Convolution in Different Encoder Layers  
The deformable convolution (DConv) adds 2D offsets, learned from the preceding feature maps via additional convolutional layers, to the regular grid sampling locations in the standard convolution, enabling free-form deformation of the sampling area. This module can readily replace their plain counterparts in any of the CNN layers. In order to explore the best optimization scheme for the encoder network while taking into account the operational efficiency, deformable convolutions are used to replace the standard ones in different layers of the encoder network, while the decoder network structure is kept unchanged. We evaluate the segmentation results with three metrics, including F1 score, FPS, and FLOPs, and the results are shown in Figure 13. It can be concluded from the figure that the introduction of deformable convolution has improved the segmentation performance of the network. However, as the number of network layers to which deformable convolution is applied continues to increase, the segmentation performance does not improve significantly. On the contrary, there is a significant decrease in FPS. This is because the feature maps in shallow layers of the network are large in size and it takes lots of time to generate the offset fields, leading to a decrease in the prediction efficiency. Considering both performance and efficiency, we apply deformable convolution to the fourth and fifth layers of the encoder network.
## 2 Related Theory
### Collection and Arrangement of Coordinates for Improvement of Systems' Accuracy  
We addressed the problem of misinterpreting an intact part for a defect when the sound pressure changes due to the misalignment of the two sensors, which is caused by the deformation of the mechanism. Consequently, an algorithm was developed that is capable of measuring the degree of deformation by a stepwise procedure that involves using a 3D laser measuring instrument, calculating the correction values based on the measurements, and compensating for the axis misalignment. This algorithm first follows a motion along the bogie axis (_yy_-axis) after setting the coordinate system, as shown in Figure 5; it also measures the coordinate values of the \(x\)-, \(y\)-, and \(z\)-coordinates at the robot control point position to correct the coordinate misalignment of coordinates due to the deformation of the mechanism. In this instance, it is assumed that the control of the traveling multi-joint robot is accurate and that deformation occurs only in the bogie that moves the robot.  
To confirm the roll, pitch, and yaw using the measured spatial coordinates, the misalignment of the robot bogies in 3D space is calculated on all _yy_-axes using Equations (3)-(5), respectively.  
\[\mathrm{Roll(deg)}=\sin^{-1}\left(\frac{z}{\sqrt{\left(x+x_{0}\right)^{2}+z^{ 2}}}\right) \tag{3}\]  
\[\mathrm{Pitch(deg)}=\sin^{-1}\left(\frac{z}{\sqrt{\left(y_{0}+y\right)^{2}+z^{ 2}}}\right) \tag{4}\]  
\[\mathrm{Yaw(deg)}=\sin^{-1}\left(\frac{y}{\sqrt{\left(x+x_{0}\right)^{2}+y^{ 2}}}\right) \tag{5}\]  
As the actual multi-joint robot is controlled using the \(x\)-, \(y\)-, and \(z\)-coordinate values, the calculated roll, pitch, and yaw values are applied to the rotation matrices of Equations (6)-(8), respectively, to calculate the misaligned coordinate values on all the _yy_-axes.  
Figure 5: Robot coordinate setting for collecting coordinate values.  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\\ z^{\prime}\\ 1\end{array}\right]=\left[\begin{array}{cccc}\cos yaw&0&\sin yaw&0\\ 0&1&0&0\\ -\sin yaw&0&\cos yaw&0\\ 0&0&0&1\end{array}\right]\left[\begin{array}{c}x\\ y\\ z\\ 1\end{array}\right] \tag{6}\]  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\\ z^{\prime}\\ 1\end{array}\right]=\left[\begin{array}{cccc}1&0&0&0\\ 0&\cos pitch&-\sin pitch&0\\ 0&\sin pitch&\cos pitch&0\\ 0&0&0&1\end{array}\right]\left[\begin{array}{c}x\\ y\\ z\\ 1\end{array}\right] \tag{7}\]  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\\ z^{\prime}\\ 1\end{array}\right]=\left[\begin{array}{cccc}\cos roll&-\sin roll&0&0\\ \sin roll&\cos roll&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\left[\begin{array}{c}x\\ y\\ z\\ 1\end{array}\right] \tag{8}\]  
Using the obtained coordinate values, the misaligned coordinate values can be accurately restored even when the position changes due to movement of the robot.
# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 3 Power Distribution Network Reconfiguration Mathematical Formulation
### Objectives Function  
The goal of the distribution network reconfiguration problem is to find the optimal configuration. In the present study, the reconfiguration problem is solved taking into consideration active power loss and voltage offset of the distribution network. The objectives of the optimization problem are detailed as follows.  
* Active Power Losses Criterion  
In the process of transmitting electricity to users, there will inevitably be losses due to factors such as transformers, lines, and the environment. Among them, the network losses of the distribution network account for a large proportion of the losses in the entire transmission system [33,34]. If the loss is too large, not only will the voltage, quality, and stability of the electricity obtained by the user be poor, but it will also bring economic losses to the power supply company. Therefore, in the reconstruction process, the optimization objective is to minimize active power losses, and its mathematical formulation is shown in Equation (11):  
\[F_{1}=\min\sum_{ij=1}^{L}k_{ij}R_{ij}\frac{P_{ij}^{2}+Q_{ij}^{2}}{U_{ij}^{2}} \tag{11}\]  
where \(L\) is the set of all branches in the distribution network, \(U_{ij}\) is the initial voltage of the branch, \(R_{ij}\) is the resistance value of branch \(ij\), \(K_{ij}\) is the opening and closing state variable of the switch on the branch, where one represents the line is closed and zero represents the line is open. \(P_{ij}\) and \(Q_{ij}\) represent the active and reactive power of the incoming branch \(ij\), respectively.
* Voltage Offset Criterion  
Voltage offset is a very important indicator for the stable operation of distribution networks. When the load of the distribution network changes, if the voltage offset is too large, it will cause voltage instability in the system, affecting the lifespan of power equipment and the normal operation of the power system [35,36]. Therefore, with the optimization goal of minimizing voltage offset, the voltage of each node in the distribution network can be as close to the rated voltage as possible, thereby improving the stability of the distribution network. The mathematical formulation of this problem can be represented as:  
\[F_{2}=\min\sum_{i=1}^{n}\frac{|U_{i}-U_{N}|}{U_{N}} \tag{12}\]  
where \(i\) is the node number, \(n\) is the number of distribution network nodes, \(U_{i}\) represents the voltage of node \(i\), and \(U_{N}\) represents the rated voltage of the system.  
The selected multi-objective formulation considers two critical aspects of distribution network performance: active power loss and voltage deviation. Minimizing power losses enhances energy efficiency, while reducing voltage deviation improves power quality and system stability. These two indicators are widely used in existing literature [37, 38, 39] and are particularly suited for evaluating reconfiguration performance under varying DG and load conditions.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### Implementation  
The ViTGNN architecture is a hybrid model that combines Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Vision Transformers (ViTs) to make use of their strengths in image analysis. The architecture captures both local and global features as well as structured relational information within an image. CNNs are good at extracting low-level and mid-level visual features such as edges, textures, and shapes through localized reception, but CNNs struggle to model long-range dependencies and relationships [45; 46]. GNNs introduce a graph-based representation that can model spatial relationships or other structured connections between image regions, addressing the limitation that CNN feature maps alone do not explicitly encode higher-level topological relations [10; 46]. Although ViT, on the other hand, uses self-attention to capture global context and long-range interaction across an entire image, pure ViT models can miss local details without a large training dataset [11; 12; 45].  
The integration of these three components, called ViTGNN, as shown in Figure 2, is specifically designed to improve the detection of SARS-CoV-2 in CT scans, which aims to explore CNNs for hierarchical local feature extraction, GNNs for spatially structured feature refinement, and ViTs for global feature interaction and classification. This synergy addresses the shortcomings of using each network type in isolation, and information passed through these stages can achieve a more effective representation and detection of COVID-19 and non-COVID-19 cases. The following sections detail each component and its integration with ViTGNN.
## 4 Discussion  
The three tests carried out allows us to make the following remarks.  
The reliability of the method, if complete information on the materials and on the structural scheme is available, has been demonstrated with the first test. The displacements obtained under static loading conditions, approximated using a cubic polynomial, differ by approximately 2.5% from those obtained from the FEM analysis and by about 5% from the results provided by independent precise measurements.  
The applicability of the method to structures more complex and with less information was verified with the second test, carried out on a bridge characterized by relaxation and creep phenomena, in normal traffic conditions. The difference between the results obtained with the method described and those of a FEM analysis is about 16%. Compared to the outcomes of the aforementioned measurements carried out by the technicians of ANAS, the difference decreases to about 10%. In any case, the results obtained are acceptable for an estimate of the behavior of the structures.  
As regards the third test, the comparison with the results obtained with other techniques and instrumentations (total station and inclination sensors) confirmed the validity and precision of the methodology.  
In all the tests, the use of interpolating lines allowed to get higher precision with respect to the unmodeled observations. The working hypotheses, therefore, proved to be reliable.  
A peculiarity of the technique described is the possibility of obtaining the 3D model of the monitored structure and the dynamic linear survey with the same instrument and reference system (Figure 18).  
Figure 22: Horizontal displacements for a point of the pylon 85 m above the TLS during the test, correlated to the position of the mobile loads. The trend-line is a 10 sample moving average. In the thumbnails, GPS time and position of mobile loads (local coordinates) are reported.  
Possible fields of application of the methodology presented are both road and railway infrastructures, tall structures and buildings. The method proves particularly valid for constructions characterized by remarkable spans and/or great heights.  
A limit to its usability is given by the need to have a point to position the TLS, with a clear view of the whole or at least of most of the structure to be monitored.  
The integration with different techniques opens up the prospect of interesting uses. In the immediate future, activities are planned to exploit the high acquisition rate of TLS, in order to determine the natural frequencies of structures. To this end, acquisitions will be performed simultaneously using TLS and Ground Based Radar Interferometry.  
Conceptualization, S.A.; methodology, S.A.; software S.A.; validation, S.A. and R.Z.; structural investigation, R.Z.; resources, R.Z.; data curation, S.A.; writing--original draft preparation, S.A.; writing--review and editing, S.A. and R.Z.; visualization, S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.