esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Hyperspectral Image Analysis
#### 3.1.2 Further Processing of Bad Pixels and Separability Computation of Contamination Levels  
The mosaic image containing the eight clean images was further resized to remove the blank edges around the kernels. Then, PCA was applied on the mosaic. The first four PCs had a total variance percentage of 99.90%, while PC\({}_{5}\) to PC\({}_{8}\) had a total variance percentage of 0.08%. However, as shown in Figure 3, loading line plot of PC\({}_{5}\) to PC\({}_{8}\) had several sharp peaks. Corresponding wavelengths at the locations of the sharp peaks were 1016, 1067, 1100, 1111, 1117, 1229, and 1263 nm. Correspondingly, images at these monochromatic wavelengths were found to have several bad pixel lines, which images at other wavelengths did not have. For an example, images at 1111 and 1240 nm were shown in Figure 4. Therefore, the sharp peaks in the loading line plot of PC\({}_{5}\) to PC\({}_{8}\) were produced due to noise induced by bad pixel lines. Thus, before subsequent analysis, it is necessary to cancel the data at these wavelengths.  
Figure 4: Images at (**a**) 1111 and (**b**) 1240 nm.  
Figure 3: Loading line plot of PC\({}_{5}\)–PC\({}_{8}\) of the mosaic image.  
Figure 2: (**a**) PCA scatter plot of PC\({}_{3}\) vs. PC\({}_{4}\) (0.92% and 0.2%) of the masked image of D\({}_{7}\); (**b**) corresponding score image where the green class represented the Petri dishes; and (**c**) a clean image.  
In our experiment, only a few kernels of D\({}_{6}\) and D\({}_{7}\) had obvious hyphae on the surface, and almost all of these kernels showed symptomatic appearance of fungal infection originally in the germ region, which is consistent with life experience. Pseudo-color images composited by 1095, 1291, 1543 nm of kernels with symptomatic appearance of fungal infection (have obvious hyphae on kernel surface) and asymptomatic appearance were shown in Figure 5. It was proposed that the germ region data might be better than the whole kernel data for further classification of contamination levels and feature extraction of fungi infection.  
Separability computation in ENVI software was applied to investigate classification effects of whole kernel data and germ region data. The results for 28 (\(C_{8}^{2}\)) pairs of comparison among eight treatments were plotted as distribution histograms (Figure 6). Compared to the spectral data based on whole kernel data (Figure 6a), the separation values calculated based on germ region data (Figure 6b) were more concentrated at the value of 2. It indicated that data of germ regions were more effective for classification, which might be due to the fact that a greater proportion of fungi start growing from and gather more in germ regions than in whole kernels. Therefore, spectral data of germ regions of all kernel samples were firstly extracted and used for later spectral analysis.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 4 Right-of-Way Trade Strategy
### Privacy-Preserving Static Game-Based Optimal Bids Problem
#### 4.4.2 Optimal Bids with Incomplete Information  
The above result is for two convoys with complete information. Usually, however, convoys need privacy protection. To facilitate the solution, four assumptions are made for the incomplete information game problem.  
* Assumption 1: Suppose that \(\alpha_{A}\) and \(\alpha_{B}\) are independently and identically distributed according to a uniform distribution in the range of \([0,1]\).
* Assumption 2: Convoy A knows the values of \(\alpha_{A}\), \(\alpha_{B}\), \(\Delta t_{AB}\), \(\Delta t_{BA}\). Whereas convoy B knows the values of \(\alpha_{B}\), \(\Delta t_{AB}\), \(\Delta t_{BA}\), and that \(\alpha_{A}\sim U[0,\ 1]\).
* Assumption 3: Because convoy A has complete information, the optimal bid of A is \[S_{A}^{*}=\frac{\alpha_{A}\Delta t_{AB}+\alpha_{B}\Delta t_{BA}}{2}.\] (27)
* Assumption 4: Because B knows that A has complete information, A will bid \(S_{A}^{*}\).  
According to Equations (15) and (19), we derive the expected profit for B, which is a function of \(S_{B}\)  
\[\mathrm{E}[G_{B}]=\begin{cases}0,&\text{if }S_{B}<\frac{\alpha_{B}\Delta t_{ AB}}{2}\\ \frac{1}{\Delta t_{AB}}\left[-\frac{3}{2}S_{B}{}^{2}+\frac{5}{2}S_{B}\Delta t _{BA}\alpha_{B}-\frac{7}{8}\Delta t_{BA}{}^{2}\alpha_{B}{}^{2}\right],&\text{ if }\frac{\alpha_{B}\Delta t_{AB}}{2}\leq S_{B}<\frac{\Delta t_{ AB}+\alpha_{B}\Delta t_{BA}}{2}\,.\\ -\frac{1}{2}S_{B}+\frac{3}{4}\alpha_{B}\Delta t_{BA}-\frac{1}{8}\Delta t_{AB},& \text{if }S_{B}\geq\frac{\Delta t_{AB}+\alpha_{B}\Delta t_{BA}}{2}\end{cases}. \tag{28}\]  
Because each function of the three segments and their connecting points are continuous, the function \(\mathrm{E}[G_{B}]\) is continuous. We calculate the maximum value of each segment and make a comparison and analysis.  
To summarize, in the static game where A has complete information and B has incomplete information, the optimal bids of A and B are the following, respectively.  
\[\begin{cases}S_{A}^{*}=\frac{\alpha_{A}\Delta t_{AB}+\alpha_{B}\Delta t_{BA}}{ 2}\\ S_{B}^{*}=\min\left\{\frac{5}{8}\alpha_{B}\Delta t_{BA},\frac{\Delta t_{AB}+ \alpha_{B}\Delta t_{BA}}{2}\right\}\end{cases} \tag{29}\]  
The corresponding maximum expected profit for B is  
\[\mathrm{E}[G_{B}]_{\max}=\begin{cases}\frac{\Delta t_{BA}{}^{2}\alpha_{B}{}^ {2}}{6\Delta t_{AB}},&\text{if }\Delta t_{BA}\alpha_{B}\leq\frac{3}{2}\Delta t_{ AB}\\ -\frac{8}{8}\Delta t_{AB}+\frac{1}{2}\Delta t_{BA}\alpha_{B},&\text{if }\Delta t_{ BA}\alpha_{B}>\frac{3}{2}\Delta t_{AB}\end{cases}. \tag{30}\]
## 3 Design Solution for FBG Inclinometer for Cantilever-Beam-Type Hydraulic Support
### Mechanical Analysis of FBG Inclinometers for Cantilever-Beam-Type Hydraulic Supports
The stretching side becomes increasingly taut, with the tensile stress showing a linear change continuously; on the other hand, the compression side is also more and more compressed, eventually saturating the pre-tensioning force applied in advance during installation. This causes the structure to reach a bending state, presenting a non-linear change. Consequently, when the inclination angle is greater than 5 degrees, the aforementioned theoretical formula becomes invalid, and a new correction formula is required for replacement. Due to the series grating string, when the cantilever beam is tilted, one side is in the tensile state with linear change, and the other side is in the compression state with linear and then nonlinear change; thus, the grating with linear change can be used to calculate the tilt angle, and the calibration experiments are carried out to find out the calibration function relationship between the change value of the wavelength of the grating with linear change and the tilt angle:  
Figure 5:2  
where \(\theta\) is the tilt angle measured by the hydraulic support FBG inclinometer, \({}^{\circ}\);  
\(\lambda\) is the wavelength value of the grating in the stretching state, nm;  
\(\lambda_{0}\) is the initial wavelength value of the grating in the stretching state, nm;  
\(K_{1}\) is the tilt angle coefficient; the value is constant, nm/\({}^{\circ}\). In view of the harsh working environment in underground coal mines, the cantilever-beam-type hydraulic support FBG inclinometer employs a novel design strategy. In this design, the bare grating is not directly subjected to heavy loads. This approach effectively prevents the suspension-type inclinometer from suffering irrecoverable creep deformation caused by the long-term suspension of heavy loads, thereby enhancing the measurement accuracy. Additionally, by arranging series gratings symmetrically on the cantilever beam, the requirements for angle measurement are met, and the sensitivity is effectively improved. The incorporation of a zero-knob (limit knob) design allows for zero-setting when the inclinometer is not in operation or during transportation. This not only simplifies maintenance but also extends the service life of the inclinometer [37; 38; 39].
## 2 FE Guided-VMD
### Fitness Function  
The mean value of FE emphasizes the contribution of each mode component; the resulting maximum mean value has properties of maximizing fault information in the time and frequency domain. Therefore, the novel proposed FE is chosen as the basis of the fitness function, which can be expressed as:  
\[k_{\text{optimal}}=\underset{k\in\{2,\ldots,8\}}{\text{argmax}}\Big{\{}\sum_{ k}[1/FE(u_{k})]/k\Big{\}} \tag{16}\]  
It is not difficult to see from the above formula that when FE is smaller, the fault information contained in the decomposition mode is more concentrated, thus effectively avoiding the feature loss caused by the dispersion of fault information in multiple frequency bands. The standard deviation reflects the dispersion degree between individuals in the data set. The larger the standard deviation, the greater the difference between individuals, that is, the more prominent the information characteristics of each mode component and the better the decomposition effect of VMD. Therefore, we employ the standard deviation of FE to find the optimal balancing parameter, as shown in the following formula:  
\[\alpha_{\text{optimal}}=\underset{\alpha\in[100\quad 4000]}{\text{argmax}} \sqrt{\sum_{k}\left[FE(u_{k})-mean(FE(u_{k}))\right]^{2}/k} \tag{17}\]  
Furthermore, if the balance parameter is too large, the signal may lose the edge feature or produce pulse omission; if the balance parameter is too small, the noise will be too large. Using standard deviation as a measure can better juggle the impulse characteristics. After achieving the optimal decomposition, considering both FE and kurtosis would provide a more reliable mode selection process. The best mode component can then be found by:  
\[u_{best}=\left\{\,k|\text{max}[K_{a}(u_{k})/(FE(u_{k})-1)]\right\} \tag{18}\]  
where \(K_{a}\) is the kurtosis value, and \(u_{best}\) is the selected best mode. It can be seen that the mode component selected by Equation (18) contains the signal property closest to the impulse generated by the bearing fault. Therefore, this selection method can reduce the interference of noise and aliasing signals.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 5 Challenges in Designing a Flexible Antenna
### Substrate Selection  
The design of a flexible antenna for different applications has a patch that is embedded on a flexible substrate. The choice of proper conductive and substrate materials is the main consideration in the design of any flexible antenna. Substrates with low dielectric constants, such as fabrics, would reduce the dielectric loss and the surface waves are negligible, thereby improving the bandwidth. However, fabrics are filled with air gaps and are highly prone to absorb moisture which degrades the antenna performance. Moreover, the substrate having a relatively low dielectric constant increases the patch size. For size reduction of such an antenna, techniques, such as shorting pins, slot-loading, etc., can be implemented. The choice of proper substrate materials that exhibit good electrical and mechanical properties and which offers a good antenna performance is thus a primary parameter that has been considered while designing any flexible antenna.
## 1 Introduction
Currently, much research is being carried out using GPS technology, because the impact of the ionosphere on GPS signals provides the information necessary to understand how the ionosphere varies temporally and spatially, and how this affects navigation and navigation. satellite communication systems [18]. This study investigates the noteworthy influence of solar flares on TEC at high frequencies, with a focus on their subsequent implications for space weather. Solar flares, powerful bursts of high-energy radiation emitted by the Sun, have been recognized as influential space weather events that disrupt the ionospheres electron density and cause variations in TEC. This research methodically analyzes data from multiple Global Navigation Satellite System (GNSS) stations during various solar flare events. By applying wavelet analysis and careful consideration of sampling intervals, this study establishes the optimal conditions for detecting and characterizing ionospheric disturbances induced by solar flares. This research not only highlights the complex interplay between solar flares and the ionosphere but also establishes a robust methodology for identifying and analyzing these disturbances, contributing to our understanding of space weather impact on Earth's technological infrastructure.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 3 Proposed Association Utility  
In addition to the standard SINR utility for UE association, we define a new utility that takes into account also the security and the power consumption.
# Monitoring Rainfed Alfafa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
## 1 Introduction
The normalized difference vegetation index (NDVI) has been widely used to identify and monitor areas covered with vegetation [24; 25]. This index exploits the fact that green, healthy vegetation displays contrast-reflecting behavior between the red and near-infrared spectral bands, providing a good indication of the vegetation \"greenness\" [26]. Different authors have shown the correlation of NDVI with FVC [27; 28] and with leaf area index (LAI) [16; 22; 29; 30; 31; 32]. Although the phenological development, yield, LAI, and height of alfalfa have also been monitored using remote sensing techniques [3; 33; 34; 35; 36; 37; 38], there is a lack of information on the FVC assessment of alfalfa using satellite images; more specifically, there is a lack of this information on semiarid agrosystems. Soil background conditions exert considerable influence on partial canopy spectra when using NDVI [39], and to minimize this factor, other VIs, such as soil adjusted VI (SAVI) and perpendicular VI (PVI), have been proposed [39; 40]. The green normalized difference VI (GNDVI) presents band variations to NDVI and uses green reflectance instead of red reflectance, reporting high efficiency in crop monitoring [41]. The normalized difference water index (NDWI) and the specific leaf area VI (SLAVI), which use SWIR regions and are directly related to leaf water content, are highly recommended for semiarid areas [42; 43]. Recently, concerns have increasingly been raised regarding the red-edge region, which was introduced to increase the sensitivity of biomass estimation, leading to the development of several VIs: Sentinel-2 LAIgreen (SeLI) [44], NDVI705 [45], MERIS terrestrial chlorophyll index (MTCI) [46], and the modified chlorophyll absorption in ratio index (MCARI) [47]. For VIs that use the red-edge region, there has been reported a significant improvement in indices using the saturated bands in the red [44]. Recently, a new kernel-based NDVI (kNDVI) has been shown to improve accuracy in monitoring key vegetation parameters such as LAI or gross primary productivity [48]. The aim of this study was to assess Sentinel-2 images for their suitability in the monitoring of the FVC of rainfed alfalfa in semiarid areas. Together with some cereal crops, rainfed alfalfa participates in the rotation system used for cattle grazing in this type of environment. The natural reserve of Bardenas Reales, Spain, is a typical semiarid environment used for cattle grazing, and it was selected as the study area. The relationships established between FVC field measurements and several vegetation indices derived from Sentinel-2 data were used to produce time series over the cultivation period in twenty-eight experimental rainfed alfalfa fields throughout the study area.
# Influence of Hot Carrier and Thermal Components on Photovoltage Formation across the p-n Junction
## 2 Experimental  
The object of our investigation, GaAs p-n junction, was fabricated from 5 \(\upmu\)m-thick p-type layer (hole density \(5\times 10^{17}\) cm\({}^{-3}\)) liquid phase epitaxy-grown on n-type substrate with electron density \(3\times 10^{17}\) cm\({}^{-3}\). Traditional photolithography technique and thermal evaporation of the Au-Ge-Ni alloy  
Figure 1: (**a**) Formation of generation-induced photovoltage (blue arrows) and hot carrier photovoltage (red arrows) across the p-n junction; stepped shape of the red arrows indicates gradual thermalization of a hot carrier. 1—process of electron-hole pair generation, 2—free electron heating and 3—generation of hole and hot electron pair; (**b**) schematic of the sample and photovoltage measurement circuit.  
were applied to form the \(2.5\times 2.5\) mm\({}^{2}\) sample with ohmic contacts. The contacts were built at the edge of the sample to prevent them from a direct laser beam and thus to avoid photosignal formation in the vicinity of the contacts (Figure 1b).  
A laser is a useful tool to investigate the hot carrier phenomena for its unique features of monochromatic and wide power range emission and for the possibility of generating short light pulses. In the experiment, Nd:YAG laser with wavelength 1.06 \(\upmu\)m, pulse duration 25 ns, repetition rate 50 Hz and maximum pulse intensity 1.2 MW/cm\({}^{2}\) was used. The measurement scheme is also presented in Figure 1b. Temporal behavior of the photovoltage and laser pulse shapes was recorded by digital storage oscilloscope Agilent Technologies DSO6102A, and the laser pulse shape was registered by high speed optical signal reference detector 11 HSP-FS1 (Standa Ltd., Vilnius, Lithuania). The calculations were performed by means of multi-paradigm numerical computing environment MATLAB (R2019b, The MathWorks, Natick, MA, USA, 2019).
## 4 Phase Statistics and Pixel Selection Candidates
As expected from the study carried out in the previous section, the PS approach provides a greater number of selected pixels (roughly 10 percent more), but including a significant amount of unreliable ones selected over vegetated areas, which are characterized by a high phase dispersion index; see Figures 10 and 11. As seen, the reduced number of SAR images available yields to a poor \(D_{A}\) estimation. At this time, it may be thought that this large number of unreliable pixels identified could be reduced restricting the \(D_{A}\) threshold to lower values, which is only partially true. Figure 11d corresponds to the selection of pixel candidates for the TerraSAR-X dataset employing a more restricted \(D_{A}\) threshold (\(0.15\), which corresponds to \(10^{\text{o}}\) of phase standard deviation). Indeed, almost all of the outliers have disappeared, but the density over the persistent scatterers available has been also drastically reduced. The road and the man-made structures clearly identified in Figure 11a,b are now diluted and hardly detected. The study carried out in this section evidences once more that a minimum number of \(20\) images is required in order to produce a reliable \(D_{A}\) estimation. Contrarily, the TSC approach performs well, even when a small dataset of SAR images is available. Figure 9: Relationship between TSC and phase standard deviation considering (**a**) \(10\) and (**b**) \(30\)SAR images; (**c**) phase standard deviation as a function of coherence for different numbers of multi-look (ML) factors; comparison of the amplitude dispersion index \(D_{A}\) and the phase standard deviation (in degrees) as a function of noise computed by numerical simulation considering (**d**) \(10\) and (**e**) \(30\)SAR images available. Figure 11: Pixel candidates selected using (**a**) the TSC with a threshold of \(0.82\); (**b**) the coherence stability with a threshold of \(0.65\); and the \(D_{A}\) with a threshold (**c**) of \(0.25\) and (**d**) of \(0.15\) for the TerraSAR-X dataset. Figure 10: Pixel candidates selected using (**a**) the TSC with a threshold of \(0.82\); (**b**) the coherence stability with a threshold of \(0.65\); and (**c**) the \(D_{A}\) with a threshold of \(0.25\) for the GB-SAR dataset.
## 2 Statistical Methodology
### CORE-Clustering  
CORE-clustering consists of estimating an optimal set of CORE-clusters, so that the representative variables they contain explain as much information as possible in the observed complex system. We use the following definition:  
**Definition 2**.: _CORE-clustering with parameters \(\xi\) and \(\tau\) consists of finding \(\widehat{U}\) variable subsets \(\widehat{\mathbf{S}}=\left\{\widehat{S^{u}}\right\}_{u\in\{1,\ldots,\widehat{ U}\}}\), where \(\widehat{U}\) is not fixed, by optimizing:_  
\[\left(\widehat{\mathbf{S}},\widehat{U}\right)=\operatorname*{arg\,max}_{( \mathbf{S},U)}\sum_{u=1}^{U}\mathbf{c}(S^{u})\,, \tag{4}\]  
_under the two constraints:_  
1. _All_ \(S^{u}\) _are connected variable sets having a size higher than_ \(\tau\) _and a coherence_ \(\mathbf{c}(S^{u}_{\xi,\tau})>\xi\)_. They therefore correspond to CORE-clusters and can be denoted_ \(S^{u}_{\xi,\tau}\)_._
2. _There is no overlap between the clusters, i.e.,_ \(S^{u_{1}}\cap S^{u_{2}}=\emptyset\) _for all_ \((u_{1},u_{2})\in\{1,\ldots,U\}^{2}\)_._  
It may first seem that \(\widehat{U}\) should be as high as possible, so that the union of all \(S^{u}_{\xi,\tau}\) contains all the variables of \(X\). Each CORE-cluster \(S^{u}_{\xi,\tau}\) must however have a coherence higher than the threshold \(\xi\). As illustrated in Appendix A, the variables of \(X\) which are not coherent with at least \(\tau\) other variables should ideally not be contained in any CORE-cluster, as they would make their coherence drop. The CORE-clusters in \(S\) should then only contain pertinent variables so that the optimal value for \(U\) is implicitly defined during the CORE-clustering procedure.  
It is also important to remark that the potential number of subsets of \(X\) to find good CORE-cluster candidates is huge, even for moderate values of \(p\). Moreover, computing Equation (4) is particularly demanding. The two optimization algorithms of Section 3 are then aggregative and divisive algorithms designed to optimize Equation (4) without explicitly computing it.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 3 Experiments and Results
### Accuracy Analysis of Matching between ZY-1-02C Images and ETM+ Images  
The second experiment used Datasets 2 and 3 to conduct a case study on AMSIM. Matching Level 1 images and the DOM was difficult, because of large topographic variations. The method for solving the problems of the initial value prediction model and gross error elimination in matching was applied. The case study on ZY-1-02C and DOM of ETM+ was a method to improve the direct georeferencing accuracy of the ZY-1-02C images. After processing, the RPCs of these ZY-1-02C images were redefined via matching points.