esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Use Cases
### Dynamic Implementation of a Dashboard with Educative Purposes  
The last use case is focused on the possibility of integrating source code dynamically within an existing web application. A user interface has been designed to explore and visualize a dataset within the medical domain in the following example.  
Figure 8: Rendered Sankey visualization that formats data as flows.  
Figure 7: Formatting operation specification.  
The user interface allows users to explore the variables within the dataset and to craft personalized visualizations based on their variables' selection. Users can drag and drop the variables, select a visualization, and then, once confirmed, the front end computes the dashboard configuration and sends it to the dashboard generator service, which follows the same workflow as explained in the first example.  
The final source code returned from the service can be dynamically loaded through JavaScript's DOM manipulation functions. In this sense, the existing web application is benefited by not carrying out all the data visualization logic, only focussing on offering a usable didactic tool for the medical domain (Figure 9).
## 3 Results  
### ECAS Spray and Aerosol Treatment Reduced Total Bacterial Count on Egg Surface  
The mean bacterial load (log\({}_{10}\) CFU/egg) for untreated and after sanitising treatments are presented in Table 1 and Figure 1. Eggs treated with sprayed water (45 s) (2.4 \(\pm\) 0.1 log CFU/egg) showed no significant reduction (\(p\) = 0.3662) in total bacterial load when compared with the untreated control (2.2 \(\pm\) 0.2 log\({}_{10}\) CFU/egg; Table 1). Significant reduction in total bacterial load was observed when the eggs were treated with sprayed ECAS (45 s) (2.2 log reduction, \(p\) < 0.0001) and aerosolised ECAS (120 s) (1.4 log reduction, \(p\) = 0.01) compared to untreated control (Table 1). Aerosolised water (60 s and 120 s) and aerosolised ECAS (60 s) showed no significant reduction in total bacterial load when compared with sprayed water (45 s) or the untreated control (Table 1). In addition, the spray versus aerosol techniques were compared for their effectiveness in reducing the total bacterial load (Figure 1A). Sprayed ECAS (45 s) showed a significant reduction (\(p\) < 0.0001) in total bacterial load when compared with sprayed water (45 s) (Figure 1A). Aerosolised ECAS (120 s) also showed a significant reduction (\(p\) < 0.01) in total bacterial load when compared with aerosolised water (120 s); however, no significant reduction was shown for the 60 s treatment group (Figure 1A).  
The effectiveness of aerosolised ECAS was further assessed when the eggs were sanitised simultaneously (three eggs/tray) or individually (one egg/tray) for 60 s and 120 s, respectively. Aerosolised ECAS (120 s) significantly reduced the total bacteria load for both one egg/tray (\(p<0.0001\)) and three eggs/tray (\(p<0.01\); Figure 1B). The treatment of three eggs/tray with aerosolised ECAS (120 s) did not eliminate the total bacterial load completely; however, the level was not significantly different from the treatment of one egg/tray aerosolised ECAS (120 s) (Figure 1B).
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Mimo
#### 3.4.5 Conclusions  
In summary, the ML-based ICI and ISI cancellation methods for the MIMO-VLC system are expected to be promising due to the rapid development of computational power. ML methods such as DNNs can model the nonlinear phenomenon of VLC systems where the conventional linear methods become powerless. The future research trend for ML in high-speed MIMO-VLC systems is still mainly about spatial and temporal joint equalization, as well as compensating nonlinear effects.
# Experimental Study of See-Saw Mode Nano-Vibration on Orifice-Type Restrictors
## 2 Materials and Methods  
### Orifice-Type Flat Restrictor Vibration Model  
The orifice-type restrictor is currently the most widely used type. A flat circle restrictor with an orifice at its center is as shown in Figure 1a. When the air-supply of \(p_{s}\) flows into an orifice of diameter \(d\), a gas film is formed with a clearance of \(h\). Normally, \(h\) is under 20 \(\upmu\)m to avoid vortex-induced vibration. When analyzing the motion, the gas film between the slide plate (i.e., the restrictor) and the guide-way can be simplified as a spring system [8; 9], as shown in Figure 1b.  
Regarding the multi-orifice restrictor, each orifice corresponds to a mass-spring-damper system, and the motion equation applies to each orifice [10; 11]. Ignoring the drive chain, external load and overload torque [10], the orifice-type restrictor follows the motion of Equation (1). \(i=1\) implies that the one-orifice-type restrictor is considered:  
\[\left\{\begin{array}{cc}M\dot{z}+\sum\limits_{i=1}^{n}F_{i}=0&(F_{i}=K_{i}z+ C_{i}\dot{z})\\ J_{0}\ddot{\theta}+\sum\limits_{i=1}^{n}M_{i}=0&(M_{i}=F_{i}l_{i})\end{array}\right. \tag{1}\]  
where \(z\) is normal displacement, \(\theta\) is the rotation angle, \(M\) is the quality of the restrictor as a slide plate, \(J_{0}\) is the moment of inertia of slide plate, \(F_{i}\) is the force of the gas film in each orifice, \(K_{i}\) and \(C_{i}\) are the stiffness and damping of the gas, \(M_{i}\) is the torque generated by the gas film force in each orifice, and \(l_{i}\) is the force arm of each orifice to the mass plate's centroid point. When considering the static performance of the restrictors, Equation (1) is sufficient to describe the motion, even if the stiffness and damping of each are equal. However, for the dynamic vibration, \(K_{i}\) and \(C_{i}\) must be different from the gas fluctuation of pressure, viscosity, etc. In addition to the inhomogeneous production of restrictors and the FSI phenomenon, the vibration motion is a relatively complex mathematical equation; it inevitably encounters difficulties with integration and large calculations. CFD, therefore, provides the many conveniences of FEM [16; 24].
## 2 Materials and Methods
### Statistical Analysis  
All match-related performance indicators were exported to a Microsoft Office Excel (Microsoft Corporation, Washington, DC, USA) spreadsheet and then analyzed using Jamovi (version 2.3.18; Sydney, Australia). Data are presented as mean \(\pm\) standard deviation (SD). Density plots were calculated to visualize the data distribution. The differences between match performance variables with and without VAR were compared using the Independent Sample \(T\)-Test. Effect sizes (ESs) were interpreted according to [PERSON]'s d categorization as follows: small effect (d = 0.20), medium effect (d = 0.50), and large effect (d = 0.80) [26]. A generalized linear model was constructed for each variable, and standard error (SE), 95% confidence intervals (CIs), and \(p\)-values were calculated for each model. Differences between match performance indicators in the four seasons in which VAR was implemented were analyzed using the One-Way ANOVA test, and Tukey pairwise comparisons were used to determine which seasons the difference was between.
# Recognition of Rebar in Ground-Penetrating Radar Data for the Second Lining of a Tunnel
## 5 Discussion and Conclusions  
In this paper, the recognition of rebar was achieved, depending on the differences in the electromagnetic properties. The peak point of the rebar located in the A-scan was derived by analyzing the propagation process of electromagnetic waves within the concrete. The phase at this peak point was the inverse of the phase at the incident point. The amplitude of the peak point was smaller than that of the adjacent peak point in the same phase. The above features were used to reconstruct the hyperbolic shape of the rebar in the B-scan, and the discontinuity region was corrected. Finally, the rebar recognition in the second lining was achieved using the reconstructed rebar's hyperbolic features. The following is a description of the most key findings of this study:  
(1) The reflection coefficient was negative when electromagnetic waves propagated from low dielectric constant concrete to high dielectric constant rebar. According to this feature, it can be preliminarily judged that the rebar reflection point was located at the negative peak point in the A-scan.  
(2) The rebar reflection point was located at the negative peak point in the A-scan. This negative peak point amplitude was smaller than the adjacent negative peak point. According to these peak points containing the rebar feature, the hyperbolic shape of the rebar was reconstructed in the B-scan for recognition.  
(3) The recognition results from YK81+506 to YK81+542 in Husa Tunnel show that the rebar was located in the area from 5 ns to 7 ns in the second lining of the tunnel. The depth of burial of the rebar was approximately 0.142 m.  
Conceptualization, C.L. and Y.Z.; methodology, C.L., Y.Z. and X.Y. ([PERSON]); software, Y.Z.; validation, X.Y. ([PERSON]) and W.Z.; formal analysis, Y.Z.; investigation, X.Y. ([PERSON]); data curation, L.W., W.Z. and X.Y. ([PERSON]); writing--original draft preparation, Y.Z.; writing--review and editing, C.L.; project administration, C.L. and L.W.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 62263015).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 6 Conclusions  
MuLiWEA was developed to increase the potential of reliable lidar measurements of wind turbine wakes and is suitable to reconstruct two-dimensional wind fields based on measurements from multiple-lidar PPI scanning scenarios. Different ground-based dual-Doppler lidar PPI scenarios were simulated and applied on a LES wind field with a wind turbine wake. An evaluation on the mean averaged error of the absolute horizontal wind speed for the different simulated cases showed that the single elevation scan always provides more accurate results. This type of scan, which intersects the hub height at a given location, will provide measurement height offsets at other positions, but the distribution of the measurement points will be relatively homogeneous. Executing multiple PPI scans with varying elevation angles offers the possibility to decrease the altitude range and therefore the reconstructed wind field is more representative of the one at hub height. The downsides are that the measurements are spread more heterogeneously over the grid, and that full coverage of the area takes longer than with a single elevation scan, yielding less measurement points within fixed time frames. Especially in a highly turbulent wake this can induce errors.  
From the error analysis on the simulated dual-Doppler lidar scenarios, it was proven that, if PPI scans with low elevation angles are used, the vertical wind speed component has a negligible effect on the absolute error of the reconstructed wind field.  
The inclusion of the continuity equation in the MuLiWEA generally improves the accuracy of the reconstructed wind field. Especially in case the measurements from the two lidars are linearly aligned, the mean absolute error is decreased significantly.  
A dual-Doppler lidar measurement scenario in \"alpha ventus\" was considered to test a first real application of MuLiWEA. Unfortunately, there is no suitable reference to validate the reconstructed wind field, and some small angular sectors are blocked by the towers of the wind turbines. These measurements were filtered out and cause local data scarcity, increasing the uncertainty. Despite of these challenges, the application of MuLiWEA to dual-Doppler lidar PPI measurements of wind turbine wakes was successful and further development is promising.  
The presented work has been partly funded by the German Ministry of Economic Affairs and Energy in the scope of the GW Wakes project (Ref. No. 0325397A) associated with the research at alpha ventus (RAVE) initiative. Computing resources have been partly provided by the North-German Supercomputing Alliance (HLRN). [PERSON] executed the LES for us.  
[PERSON] conducted most of the research and the calculations and took the lead in the paper writing and editing process. [PERSON] implemented the lidar simulations and made a vital contribution to the research concept and writing and editing the paper. [PERSON] provided both conceptual ideas as well as thorough reviews during the research and paper writing stages.  
The authors declare no conflict of interest.
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 7 Results
### Results with the Black-Box Method by Means of Trigger Datasets  
This application has been used for both the cases described in Sections 5.1 and 5.2. In the following Sections 7.2.1 and 7.2.2, the results will be shown, delving into trigger sets used to embed the watermark on the two models introduced in Sections 5.1 and 5.2.  
The tables in this section report the accuracy measurements for various image classification models, specifically focusing on their performance with different types of watermark embedding. The resulting models are as follows:  
* MobilenetV1 is the original NN without the watermark;
* GANWM is the watermarked model embedded using GAN-generated images, as described in Section 6.2.4;
* PILWM is the watermarked model embedded using abstract images generated by the pillow library, as described in Section 6.2.5;
* ADIWM is the watermarked model embedded using pre-selected images, as described in Section 6.2.6.  
Each model was evaluated with the original dataset, and the trigger dataset was used to inject the watermark.  
For this NNW method, the models in which the watermark can be successfully detected are the ones with accuracy greater than 88%, considering the 12% threshold established in [36]. This threshold is critical for determining the effectiveness of the watermarking technique. Ref. [36] suggested that a watermark can be detected in a model with an accuracy above this threshold and that such a model can be considered robust and reliable.
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 3 Application of Graphene-Based Nanomaterials in Sensing
### Theophylline Sensor  
Theophylline (1,3-dimethylxanthine) has been used as a respiratory stimulator and bronchodilator for acute and chronic asthmatic treatments [63]. However, it causes adverse effects on the neurological system and other diseases. Hence, [PERSON] and co-workers [64] fabricated a GO-QDs-modified ssRNA aptamer for the sensing of theophylline (Figure 10). Initially, quantum dots (QDs) with modified ssRNA were prepared for the sensing of theophylline. The prepared **G8** had a low fluorescence intensity because of the presence of a noncovalent assembly between GO and ssRNA. Because of this ability, the fluorescence was quenched via the FRET process from QDs to GO. Under optimal conditions, **G8** was used for the detection of theophylline. Upon increasing the concentration of theophylline (0 to 300 nM), the maximum fluorescence intensity was achieved at 120 nM. The LOD of theophylline binding with **G8** was calculated to be 4 nM.
## 5 Results for RQ1
### ANOVA-Based Hypothesis Testing
#### 5.1.2 Organization Size  
As shown in Table A3, the contingency table for organization size shows unequal sample sizes. The number of smaller (i.e., \"<50\" and \"51-100\") and larger (i.e., \"1001-2000\", \"2001-5000\" and \">5000\") municipalities are relatively low, compared to the majority of medium-sized municipalities (i.e., \"101-200\", \"201-500\" and \"501-1000\").  
The ANOVA Welch's F test confirmed that at least one organization size differed from another for the green policymaking initiative scale (F = 368.392; df1 = 7; df2 = 362.026; \(p\) < 0.001). From the Games-Howell post-hoc testing (i.e., Table A4), it could be concluded that a significant difference exists between the distinct levels of organization size. Except for the \"<50 vs. 51-100\" comparison, we found significant differences for organization size (\(p\) < 0.001).
## 2 Materials and Methods
### Classification through Feature Extraction Technique
#### 2.4.2 Linear Discriminant Analysis  
Discriminant analysis is based on the principle that different classes generate data based on various Gaussian distributions (multi-dimensional and normal distributions). Being a supervised technique, it collects information from all the variables and plots a new margin so that the classification outcome is at its best. In LDA, the attributes are assumed to be a Gaussian mixture distribution with different means but with a common covariance matrix. To recall, this matrix contains the variance of the data along the diagonal and covariance along with the corresponding off-diagonal elements. The center of the distribution is determined by the mean, and the shape is determined by the covariance matrix. Once the distributions are fitted, the boundaries are estimated by determining the points around them where the probabilities are similar.  
Assuming there are \(C\) classes (all having a multivariate normal distribution), let \(\Sigma\) and \(\mu_{c}\) (\(c=1,\,2,\,\ldots,\,C\)) be the covariance matrix and the mean vector of the distribution of the samples in the \(c\) classes. Say, \(x_{i,c}\) is the \(i\)-th sample in class \(c\), the objective of LDA is to assign this observation to class \(\hat{c}\), minimizing the function \(h\) given by  
\[h=(x_{c,i}-\mu_{\hat{c}})^{T}\Sigma^{-1}(x_{c,i}-\mu_{\hat{c}}) \tag{6}\]  
The mathematics and computations behind the discriminant analysis and its regularized version can be further explored in [62] and [63]. These classifiers have also been extensively used for classification tasks in precision agriculture applications [56; 64; 65].
## 5 Case Study of Landslide Prone Regions
### United States of America
These surveys are created by trained geoprofessionals using a variety of rigorous investigative techniques, including LIDAR-based detection and deep investigations, as well as regional geologic mapping of major quaternary landslide deposits. NASA extracted a subset of landslides with specified dates from these state and local records and combined it with the GLC to generate a dataset of rainfall-triggered landslides with dates [135], e.g., SINMAP in conjunction with MaxEnt. This technique was put to the test on a seaside basin in Pacifica, California, with a very well-established landslide history that included three inventories of 154 scars on 1941 images, 142 scars in 1975, and 253 scars in 1983. Due to a lack of data on root cohesiveness, the results showed that SINMAP simply overstated vulnerability, as shown in Figure 20.
## 2 Electrohydrodynamic Model  
A self-consistent model based on the corona discharge mechanism has been proposed in [24; 25; 26] to describe the behavior of several key parameters of a specific EHD thruster. Figure 1 shows the geometry of the thruster and the RC coupling circuit, with a needle-type anode and a hollow cylindrical cathode with variable height and internal radius. The applied potential difference between the anode and the cathode varies in the range 0.9-40 kV, the anode grounded and the cathode potential running between those values. The gas pressure and temperature assumed were 10 Torr (1.3 kPa) and 300 K, respectively.  
To control and stabilize the corona discharge, an external RC series connects with a varying ballast resistor R = 500 - 2500 M\(\Omega\). This parameter is necessary to ensure the discharge does not reach the arcing region, while influencing the total current between electrodes and, consequently, the net thrust produced. A blocking capacitor of C = 1 pF avoids peaks between electrodes.  
The input voltage, \(V_{in}\), and the electrode voltage, \(V_{plasma}\), are related by the circuit equation:  
\[V_{plasma}=V_{in}-I_{P}R-RC\frac{dV_{plasma}}{dt} \tag{1}\]  
where \(I_{P}\) represents the current between the electrodes and includes the electron and ion current densities at the electrode walls.  
The power \(P\) dissipated to create and maintain the plasma is given by:  
\[P=V_{plasma}I_{P} \tag{2}\]  
The model used is well described in [PERSON] et al. (2014) but, in this paper, we make a synthesis of the model for the sake of completeness and clarify some physical aspects. Figure 2 is a schematic illustration of the pseudo-flow chart of the simulation, displaying the link between the several modules and entry parameters used in the EHD thruster. The commercial software COMSOL Multiphysics(r), version 5.3, based on finite element methods solved the equations included in the fluid and electrical modules ([PERSON], 2015). The simulation region consists of a 2D axisymmetrical cylindrical with a variable-element mesh refined at the surface of the electrodes.  
Figure 1: EHD thruster geometry: the needle-type anode on the left side and the cylindrical cathode on the right side, with typical dimensions and the RC coupling circuit.  
Figure 2: Pseudo-flow chart connecting all modules and entry parameters.  
The model consists of three main modules (see Figure 2):  
1. a heavy-species kinetic module including ground-states and ionized species;
2. a plasma module to obtain the self-consistent electric field and space-charge density, considering the interaction with the electrode walls (including secondary electron emission), using an external electric circuit to power the discharge;
3. a fluid module solving the Navier-Stokes equation under the assumption of laminar flow and with a force volume term due to electron-molecule and ion-molecule collisions acting on the neutral gas.
# Inhomogeneous Illumination Image Enhancement Under Extremely Low Visibility Condition
## 2 Materials and Methods
### Theory
#### 2.1.4 Contrast of the Enhanced Image  
The contrast of the object and the background is defined as the following:  
\[C=\frac{|i_{O}-i_{B}|}{i_{O}+i_{B}} \tag{7}\]  
where \(i_{O}\), \(i_{B}\) are the local average intensity of the object and the nearest local (\(10\times 10\) pixels) average intensity of the background, respectively. We denote the histogram of the inhomogeneous illuminated image as \(H^{\prime}(i)\). The corrected histogram is \(H(i)\). For \(\alpha=1\), we can write the contrast of the HE and HMHE-enhanced image as follows:  
\[C_{HE}=\frac{\Sigma_{k=0}^{i_{O}}H^{\prime}(k)-\Sigma_{k=0}^{i_{B}}H^{\prime}( k)}{\Sigma_{k=0}^{i_{O}}H^{\prime}(k)+\Sigma_{k=0}^{i_{B}}H^{\prime}(k)} \tag{8}\]  
\[C_{HMHE}=\frac{\Sigma_{k=0}^{i_{O}}H(k)-\Sigma_{k=0}^{i_{B}}H(k)}{\Sigma_{k=0} ^{i_{O}}H(k)+\Sigma_{k=0}^{i_{B}}H(k)-2H(i_{min})} \tag{9}\]  
It can be seen from Equations (8) and (9) that the contrast of the HMHE-enhanced image incorporates a negative term in the dominator; thus, compared to the HE-enhanced image, assuming identical histograms for both, the amplification of contrast is significant. Figure 1a-d illustrate the original, HE-enhanced, illumination-corrected, and HMHE-enhanced images and their histograms. Owing to the illumination, the histogram was inevitably widened, leading to a reduced enhancement space for the signal and an unwanted enhancement of the illumination, leading to poor image quality.  
In the example presented in Figure 1, the \(i_{O}\), \(i_{B}\) were obtained by averaging intensities within the largest square in the USAF target. The corresponding values for \(i_{O}\) and \(i_{B}\) in Figure 1a-d are 183, 187, 182, 202, 174, 176, 0, and 206, respectively, with the contrast values of 0.010, 0.052, 0.006, and 1, respectively. In this case, HMHE significantly improved the target contrast both numerically and visually (see the insets).