esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
The increasing number of vehicles on the road can be an excellent source for crowd-sourced applications, VIoT, and V2X services. In all of these applications, the authenticity of disseminated information among neighbor vehicles is much needed, as vehicular networks are highly affected by human factors and malicious attacks. Furthermore, previously proposed security solutions are mostly based on public key infrastructure. These traditional solutions much emphasis on security and privacy issues, but ignores the notion of uncertainty and trust management among vehicles in VIoT. Therefore, this paper proposes trust enhances routing TER by taking advantage of the TW algorithm for efficient route discovery. Also, trust assessment is efficiently achieved by the TW algorithm in terms of better execution time in a large vehicular network. A performance analysis is carried out to compare our proposed TER scheme with two other AODV and AASR routing schemes. Experimental results validate the accuracy of our proposed scheme in terms of better throughput, lower packet drop, and the end to end delay. In the future, we will enhance this research by considering more recent attack patterns e.g., Man in the middle attack, Masquerading attack, and so on, and try to establish an algorithm to tackle these attacks in vehicular networks.  
Conceptualization, M.S. and R.A.; Data curation, M.S.; Formal analysis, M.S., R.A. and M.K.; Investigation, M.S., R.A., M.K., S.A. and S.M.; Methodology, M.S., R.A., S.A., S.M., Y.B.Z. and H.Y.; Resources, M.K., S.A. and S.M.; Software, M.S., M.K., S.A. and S.M.; Supervision, Y.B.Z. and H.Y.; Validation, Y.B.Z. and H.Y.; Visualization, Y.B.Z. and H.Y.; Writing--original draft, M.S. and R.A.; Writing--review & editing, Y.B.Z. and H.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding  
This research was supported by the National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT; 2019R1A2C1083988) and by the Ministry of Science and ICT, Korea, under the Information Technology Research Center support program (IITP-2020-2016-0-00313) supervised by the Institute for Information & Communications Technology Planning & Evaluation. The authors declare no conflict of interest in the publication of this paper.
## 5 Ground-Based Calibrations
### Flux Accuracy Calibration
#### 5.2.2 Calibration Results  
According to the above calibration methods, the calibration results of different items can be obtained. The final calculation results of the flux accuracy errors of the high-energy particle detector are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Electronic** & **Sensor** & **Geometric** & **Flux Error** \\  & **Counting Error** & **Counting Error** & **Factor Error** & **Flux Error** \\ \hline Error & 0.95\% & 4.8\% & 3\% & 8.75\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Calibration results of particle flux.  
Figure 17: Influencing factors of flux calibration accuracy.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 6 Results and Discussion
### Intrinsic Parameter Estimation Results
#### 6.3.2 Distortion Parameter Estimation Results  
According to the previous calibration results, it can be seen that the overall variation trend of the camera's radial distortion parameter, \(k\), increases with an increase in focal length, but it is not strictly monotonic. We used the second-order polynomial and the third-order polynomial to establish the relationship between the distortion parameter, \(k\), and the zoom value in Section 4.1, and we compared their fitting degree with the ground truth, calibrated by [PERSON]'s method.  
The functional relationship of the quadratic polynomial is:  
\[k(z)=-0.003z^{2}+0.2016z+0.4819 \tag{26}\]  
The functional relationship of the cubic polynomial is:  
\[k(z)=0.0013z^{3}-0.0433z^{2}+0.6151z-0.4748 \tag{27}\]  
The fitting of the two functions and the ground truth is shown in Figure 8.  
Obviously, the quadratic polynomial has a better fit with the ground truth. We can obtain the well-fitted function of the radial distortion parameter using the zoom value by comparing it with differently ordered polynomials in the ground truth.
# Parametric Analysis of Tensegrity Plate-Like Structures: Part 1--Qualitative Analysis
## 1 Introduction
It should be noted that complete parametric analysis of tensegrity structures is a two-stage process. The first stage includes the identification of self-stress states and infinitesimal mechanisms (qualitative analysis), whereas the second stage focuses on the behaviour of tensegrities under external loads (quantitative analysis). In this paper, the qualitative analysis is conducted. This analysis is the basis for a quantitative analysis. Tensegrity plate-like structures built with modified Quartex modules are considered. Starting from a single-module structure, more complex cases are sequentially analysed. The different methods of plates support are considered. To carry out a qualitative assessment, the spectral analysis of the truss matrices (compatibility matrix and stiffness matrix with the effect of self-equilibrated forces) is used, including the singular value decomposition (SVD) of the compatibility matrix. The characteristic features of tensegrity structures are identified. On this basis, the plates are classified into one of the four groups defined in the paper. This classification is important due to different behaviours of the structure under external actions.
## 5 Experimental Validation
### Hit Rates Obtained by the Expert Systems for IMs Fault Diagnosis Generated by the Supra-System  
The hit rates obtained by the expert systems generated by the supra-system Table 5, are very high, giving a very efficient IM fault diagnostic system. It is relevant that, although SVM and ANN expert systems have different implementations, in both cases the hit rates are very similar. This similarity means that:  
* In both expert systems the maximum hits ratio has been reached (the optimal diagnostic system).
* None of these local diagnostic system shows over-training. Otherwise it would show a higher hit ratio much closer to 100% and hit ratios values would be more different between local diagnosis systems.  
HOTA implementing SFFT with SVM with Polynomial Kernel and HOTA implementing SFFT with ANN with 2 hidden layers lead to the same result. In both cases a global optimum diagnostic system is obtained, with the best hit ratio and with the lowest diagnosis time. Therefore the use of one or another for diagnosis would be only a user decision.  
Even so, the others diagnostic systems (optimal local but not optimal global) also show an excellent hit ratio with a low diagnostic time. All of them are very well optimized and could be used alike.  
\begin{table}
\begin{tabular}{l c c} \hline
**Classification Time (seconds)** & **HOTA + STFT** & **HOTA + SFFT** \\ \hline SVM with Polynomial Kernel & 36.64 & 5.60 \\ SVM with RBF Kernel & 36.64 & 5.60 \\ ANN with 1 hidden layer & 36.65 & 5.60 \\ ANN with 2 hidden layers & 36.65 & 5.60 \\ \hline \end{tabular}
\end{table}
Table 4: Time used by the diagnostic system generated by the supra-system to classify the IM condition (seconds).  
\begin{table}
\begin{tabular}{l c c} \hline
**Optimization Time (hours)** & **HOTA + STFT** & **HOTA + SFFT** \\ \hline SVM with Polynomial Kernel & 13.81 & 2.70 \\ SVM with RBF Kernel & 12.57 & 1.83 \\ ANN with 1 hidden layer & 13.58 & 3.03 \\ ANN with 2 hidden layers & 34.02 & 20.16 \\ \hline \end{tabular}
\end{table}
Table 3: Time needed by the supra-system to generate each local optimal diagnostic system (hours).  
\begin{table}
\begin{tabular}{l c c} \hline
**Hit Rate (\%)** & **HOTA + STFT** & **HOTA + SFFT** \\ \hline SVM with Polynomial Kernel & 98.62 & 98.89 \\ SVM with RBF Kernel & 97.24 & 97.38 \\ ANN with 1 hidden layer & 97.10 & 97.38 \\ ANN with 2 hidden layers & 98.89 & 98.89 \\ \hline \end{tabular}
\end{table}
Table 5: Hit rates obtained in each of the local optimal diagnostic systems generated by the supra-system.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 2 Study Site and Database
### Database  
#### 2.2.1 Satellite Data  
(a) Sentinel-1 radar data  
The first S-1A satellite was launched on 3 April 2014 and was followed by the S-1B Sentinel satellite on 25 April 2016. This dual-satellite constellation offers a six-day repeat frequency for all regions of the globe. The SAR payloads use a C-band frequency of 5.4 GHz and have the following standard operating modes: Strip Map (SM); Interferometric Wide Swath (IV); Extra Wide Swath (EW), and; WaVe (WV). In the present study, 10 IW S1 images were analyzed. They were characterized by a 10 \(\times\) 10 m spatial resolution, dual VV and VH polarization measurements, and an incidence angle of approximately 39-40\({}^{\circ}\) (Table 2). These were acquired between December 2015 and March 2017. All of the images were generated from the high-resolution, Level-1 Ground Range Detected (GRD) product. The data were preprocessed using the Sentinel-1 Toolbox, in four steps:  
* Thermal noise removal;
* Radiometric calibration;
* Terrain correction using Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) at 30 m;
* Filtering of speckle using a Lee filter.  
The calibration is designed to convert the digital values of the raw images into backscattering coefficients (\(\sigma^{0}\)).  
(b) Landsat optical data  
Seven Landsat-8 images downloaded from the USGS website ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))) were used to provide optical wavelength observations of the study area. The atmospheric correction of these images was performed using NASA's Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). This software uses the 6S (Second Simulation of a Satellite Signal in the Solar  
Figure 1: Location of the study site in Tunisia.  
Spectrum) radiative transfer model to estimate the surface reflectance. The NDVI vegetation index, expressed by NDVI = (RNIR \(-\) RRED)/(RNIR + RRED), where RNIR is the near-infrared (NIR) reflectance and RRED is the red reflectance, was calculated from the optical images. The data used in this analysis were derived from images recorded in the absence of clouds.
## 4 Performance Analysis
### Access Ratio
Here, the proposed technique has a smaller access proportion than that of the existing approaches. That means the proposed approach has a proportion of 0.1 then the existing approaches CDRAM (0.2), GFAVR (5), and vehicular TMS (2) in 10 RSUs. In this work, we consider 50 RSUs. In 50 th RSU, the proposed technique reaches a proportion of 0.5 than that of the existing techniques [CDRAM (3), GFAVR (10), Vehicular TMS (3.5)]. Finally, we prove the proposed approach is superior to the existing approaches. Figure 8: Network lifetime vs. number of nodes for the existing and proposed method. Figure 7: First node death vs. number of nodes for the existing and proposed method.
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 2 Forecasting Model and Missing Data Imputation Methodology
### Missing Data Imputation Methods
#### 2.2.2 Mi  
MI is a method wherein missing data are replaced with the most frequently-observed data. It is one of the simplest missing data imputation approaches, wherein the mode forms the criterion of the central trend. It includes the fundamental data distribution in the dataset. Thus, we can select the most frequent numerical quantity to fill in the missing data, which makes the method simple and easy to apply. However, this method is unsuitable when there is a complicated relation between the data in the dataset [11]. In addition, there is a specific pattern for daily solar irradiance data based on the position of the Sun in the sky, which cannot be restored by a simple method such as MI. Nevertheless, we select this method in order to show that when the missing data are processed in an undesirable way, the prediction deviates drastically.
# Deep Machine Learning of MobileNet, Efficient, and Inception Models
## 5 Discussion  
One of the goals of the research presented in this paper was to recognise the steps needed to create a model, based on the uploaded libraries, which included support for the Jupyter environment. Additionally, we were required to have the ability to input a model with.blob extension to an SMC memory card that could be inserted into an NPU module compatible with Siemens S7-1516 TF PLC. One of the first problems was the lack of file transfer possibilities between the Windows host system and Linux virtual system. This problem was solved by adding a shared folder from within the Oracle VM VirtualBox virtualisation software (Version 15.x) and then adding permissions for the Linux user to access and edit this folder.  
In the first study, a neural network model was built for colour recognition, and its tests results are shown in Table 2 and Figure 4. These showed that an accuracy of 98% could be achieved in as few as 15 epochs.  
Study two investigated how a model was taught to recognise images of red and blue containers. The research showed that training such a model is prone to random drops in accuracy despite a single peak. In contrast, models with 50 and 100 training images retained similar, steady results, achieving more than 95% accuracy in the final epochs of training. The best accuracy was achieved by the MobileNet model with a dataset of 1000 training images. This model had a high accuracy already in the initial training epochs and maintained this for most of the learning time. Table 3 and Figure 5 show that a satisfactory accuracy of 90% was already achieved with a model based on 100 photos at 10 to 15 epochs. It is worth emphasising that the number of images and their degree of sophistication are important. Additionally, with too few epochs, the model has low accuracy, while too many epochs extend learning time but may not improve accuracy sufficiently.  
The third study had its initial conditions based on the analysis of the results obtained in studies one and two. It was noted that with 100 images and 10 epochs, an accuracy of 80-90% could be obtained. On this basis, we decided to investigate a third training base with three CNN models. Due to the virtual machine PC parameters, model choices were limited to MobileNet, EfficientNetB0, and InceptionV3. The trained models were built using 50 images of a'recognised object' and 'non-recognised object' each at 10 epochs. Promising results, with an accuracy of 75%, were obtained for the InceptionV3 model, while the accuracies of the MobileNet and EfficientNetB0 models oscillated between 50 and 60%. In comparison, model training time, which is also a relevant factor for industrial process control, was analysed. It was observed that the best learning time was achieved by the MobileNet model, while EfficientNetB0 and InceptionV3 required around 80% more training time.
## 4 Case Study
### Statistical Analysis and Robustness Tests
Temporal stability: Using historical railway network data from 2018 and 2020, we verified that the core findings regarding disturbance impacts and recovery strategies remained consistent over time, with Spearman rank correlations of resilience measures between years ranging from 0.78 to 0.86 (all \(p<0.001\)). The statistical analyses and robustness tests strongly support our three primary hypotheses: (1) different disturbance types produce distinct patterns of impact on network resilience; (2) the three-dimensional resilience measurement framework effectively captures different aspects of network performance under stress; and (3) topology-based recovery strategies significantly outperform random recovery approaches.
## 3 Results and Discussion
### Analysis of Tar Absorption Process  
#### 3.2.1 Effect of Solvent Temperature  
Figures 7-9, show the effect of the temperature of soybean oil solvent on the removal efficiency of tar components at bed heights 0.5, 0.8, and 1.1 m, and solvent flow rate of 53 mL/min (above), 63 mL/min (middle), 73 mL/min (below). It is clear from the figures that the increase in solvent temperature has a significant effect on the removal efficiencies. The removal efficiency decreases with increasing the temperature from 30 to 50 \({}^{\circ}\)C. The principal reason for this effect is that by increasing the solvent temperature, the solubility of tar compounds decreased, hence increasing the equilibrium ratio (K-value) [19].  
On the other hand, increasing the solvent temperature leads to increasing the wettability of the solvent due to the decreased viscosity, as a result, the mass transfer and removal efficiency increase [19]. The effect of decreasing the solubility is more significant than increasing the wettability on decreasing the removal efficiency, so the removal efficiency is reduced by increasing the solvent temperature. The trend of this effect is similar for all the tar components, but the change rates (decreasing rates) of removal efficiency are different from component to component. The change rate for benzene is higher than toluene and ethylbenzene, for example at operating conditions of the volumetric flow rate of 53 mL/min and a bed height of 0.5 m. Here, the removal efficiency decreased for benzene by about 8% by increasing the temperature from 30 to 50 \({}^{\circ}\)C, i.e., the change rate of removal efficiency for benzene is 0.4%/\({}^{\circ}\)C. Whereas the change rate value is 0.14%/\({}^{\circ}\)C for toluene and 0.08%/\({}^{\circ}\)C for ethylbenzene. It is also observed that the effect of increasing the soybean oil solvent temperature on the change rates of removal efficiency is influenced by the increase of the solvent volumetric flow rate. Increasing the volumetric flow rate of the soybean oil leads to a change rate decrease of the removal efficiency. For benzene at bed height of 0.5 as an example, the change rate of the removal efficiency at volumetric flow rate of 53 mL/min is 0.4%/\({}^{\circ}\)C and decreases to value 0.33%/\({}^{\circ}\)C at volumetric flow rate of 63 mL/min and it continues decreasing to a value of 0.27%/\({}^{\circ}\)C at 73 mL/min.  
Furthermore, increasing the bed height influences the change rate of the removal efficiency. By increasing the temperature, the change rate of the removal efficiency decreases by increasing the bed height. This trend is shown in toluene and ethylbenzene for example at a volumetric flow rate of 53 mL/min. Here, the change rate of removal efficiency for toluene at a bed height of 0.5 m is 0.14%/\({}^{\circ}\)C, and it decreases to value 0.077%/\({}^{\circ}\)C at a bed height of 0.8 m, and it continues decreasing to a value 0.034%/\({}^{\circ}\)C at a bed height of 1.1 m.
## 4 Discussion
These limitations underscore the importance of further research that includes varied sample geometries and materials in order to improve the comprehensiveness and applicability of the findings. Additionally, the reconstruction method utilizing the EIDORS algorithm has proven effective, yet certain assumptions and parameters involved in the reconstruction process may introduce uncertainties. Further investigations into optimizing current patterns and refining reconstruction parameters are essential for advancing the precision and reliability of EIT imaging. The collective findings suggest that the empirical frequency range for copper lies between 15 and 38 kHz, while for acrylic resin, the range of 45-75 kHz is deemed empirical. These results provide parameters for selecting appropriate frequencies in EIT imaging scenarios involving different materials, enhancing the technique's precision and applicability across diverse environments.
## Evaluation and Results
### Grid Network Formation
#### 5.4.3 Results--MAC Layer Frame Delivery  
Figures 10-13 illustrate the number of MAC layer frames received by the sink during each simulation scenario. This is an important comparison between when MADCAL is in use and when not, as improved energy consumption would not be acceptable if detrimental to the network's ability to function in terms of delivering packets. In observing Figure 10 it can be seen that much like energy consumption, frame reception is easier to improve upon when the sink mobility is slower. What becomes clear from our studies is that improvements are difficult at sink mobility speeds of 20 mps and higher. But frame reception which is similar, even if slightly lower, could be seen as acceptable in the event that energy consumption is significantly improved.  
Figure 9: Average energy consumption (mWs), grid topology, significant nodes. Interference range 55.94 m.  
Figure 11 again shows the benefit of the sink moving more slowly, with frame reception the same or slightly worse for the faster speeds.  
In Figure 12, again the benefits are greater when the sink moves more slowly. However, as the interference range reduces, delivery to the sink node now becomes easier at faster speeds, with MADCAL showing a slight improvement. Therefore, despite the smaller interference range, the subsequent reduction in overlap of communication, both with and without MADCAL, enables more efficient frame reception. As such, MADCAL can be seen as negating sink speed to a certain degree when considering frame delivery to the MSN.  
Figure 11: Sink frame reception, grid topology. Interference range 69.13 m.  
Figure 10: Sink frame reception, grid topology. Interference range 77.52 m.  
In Figure 13 benefits once more are greater in lower speeds, but improvements can still be observed at faster speeds when MADCAL is in use. As in Figure 12, it can be observed that with MADCAL in use, frame delivery is now more consistent across all speeds.