esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of the Spatial Variability of the VIUPDD and NDVI Derived VCIs
As shown in Figure 9, the performance of VIUPD-derived VCI was greatly correlated to the mean temperature of the growing season. When correlated to _in situ_ drought indices of PDSI, Z-index, 3-, 6-, 9- and 12-month of SPI, the r-values of VIUPD-derived VCI had a good relation with temperature. Especially for PDSI, when VIUPD-derived VCI correlated to it, the coefficient of determination (\(R^{2}\)) of r-value and temperature was as high as 0.7616. It indicates that when correlated to PDSI, 76.16% of the performances in the CDs were influenced by temperature. When VIUPD-derived VCI correlated to Z-index, 3-, 6-, 9-, 12-month SPI, the coefficients of determination are also high (0.6569, 0.5566, 0.6756, 0.7577 and 0.7127 for Z-index, 3-, 6-, 9-, 12-month SPI respectively). The \(R^{2}\) for longer term SPIs (6-, 9-, 12-month SPI) was higher than shorter term SPIs (1- and 3-month SPI). In the CDs with low mean temperature, the performances of VIUPD-derived VCI were not considerable, especially the CDs with mean temperature lower than 30 \({}^{\circ}\)C. In addition, from Figures 7 and 8a, the CDs with worse performance of VIUPD-derived VCI are CDs with lower mean temperature than other regions. As mentioned above, at least in this study, the VIUPD-derived VCI may not be used in the cold regions, especially the regions with mean temperature of the growing season less than 30 \({}^{\circ}\)C.  
Figure 9: The correlations of r-values of VIUPD-derived VCI with _in situ_ drought indices and mean temperature of the growing season from 2002 to 2011.
## 2 Methods
### Ground Data Processing  
#### 2.3.1 SPAD Calibration  
Several equations describing the statistical relationship between the SPAD readings and CC are available in the literature. We used the homographic equation of [PERSON] (1997) for _R. mangle_ to  
Figure 2: Schematic diagram of the procedures in relating CC to VIs. In this paper the leaf level refers to the CC and hyperspectral response measured at individual leaves. ESU level refers to CC and hyperspectral response averaged at Elementary Sampling Unit level (a plot of 30 m by 30 m to represent the spatial resolution of Landsat 8). Chl maps were computed using the Landsat 8 VI, which had the best performance in terms of correlation with Chl at ESU level. convert the SPAD readings to CC. Table S1 in the Supplementary Information shows the published conversion equations including polynomial, exponential, linear, and homographic. The relationship depends on leaf structure factors such as leaf thickness, leaf mass per area (LMA) [28], and the proportion of vascular tissue [59], characteristics known to vary among species. Equations reported by [PERSON] _et al._[28], [PERSON] _et al._[59], and [PERSON] _et al._[60] have general applicability as they are estimated from different species covering a range of leaf characteristics. For the particular case of mangroves, the only equation reported in the literature is that of [PERSON] [30], established for the red mangrove (_R. mangle_). This equation has a close agreement with equations estimated from different species covering a range of leaf characteristics (Figure S1, Supplementary Information) and, therefore, was deemed to be suitable for the purposes of the study. Once SPAD readings were converted to CC, each individual leaf CC was compared with its corresponding spectral response to establish the CC and spectral characteristics at the leaf level.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 1 Introduction
### Taxonomy and Requirements  
Let us summarize the primary requirements and design considerations of wireless communication technologies that can potentially be applied in WBAN systems before ending this section. These requirements can be categorized into four main subjects: low power consumption, transmission reliability and latency, data rates, security and privacy.
# Review of Advanced Implementation of Doppler Backscattering Method in Globus-M
## 3 Application Results
### Geodesic Acoustic Mode
The zero phase delay between the GAM oscillations in the two simultaneously detected DBS signals was discovered by coherent spectrum analysis of the velocity oscillations, which is presented in Figure 3. The observation is consistent with an m = 0 mode structure of the GAM E \(\times\) B flow as predicted by theory in [41]. Figure 2: Radial profiles of: (**a**) GAM amplitude and frequency; (**b**) electron temperature [39]. Cross-bicoherence analysis of perpendicular velocity and amplitude of the complex DBS signal was successfully used to demonstrate the interaction between the GAM oscillations and plasma turbulence [32; 44; 45]. The results of this analysis indicate that a nonlinear interaction takes place that manifests itself in the appearance of the amplitude oscillation at the GAMs frequency \(f_{\text{GAM}}\). This can be caused by the influence of turbulent Reynolds stress on zonal flows or GAMs [41].
## 5 Sensitivity and Comparison Analysis
### Comparison with Other MCDM Method  
For validation, in this section, the ranking results are determined based on the Final Utility matrix by TOPSIS and EDAS methods. Figure 8 illustrates the ranking results of the proposed method with loss attenuation coefficient equal to 60, TOPSIS, and the Evaluation Based on Distance from Average Solution (EDAS) method. The comparison shows that there is no significant difference in ranking results with the same weights of criteria. Accordingly, the Philippines has the greatest fluctuations in ranking. However, the proposal method allows adjusting the psychological behavior coefficients to better suit the decision maker.  
Figure 6: Indicator weighting scenarios.  
Figure 7: Sensitivity analysis results of loss attenuation coefficient.
## 5 Numerical Results  
In this section, we provide a detailed analysis of the DRL-JCBRTP framework's performance through simulations and comparative assessments. We begin by outlining the simulation environment along with the training, testing, and deployment phases in Section 5.1. Section 5.2 covers the parameter settings for our reward components, which are critical for optimizing performance. In Section 5.3, we examine the impact of different NN architectures on beaconing performance. Finally, Section 5.4 presents a comparison of the DRL-JCBRTP scheme with other beaconing schemes to highlight its effectiveness in various vehicular scenarios.
# Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
## 3 Synthetic Seismic Data Processing Results  
We first used synthetic seismic data to test the denoising performance of the proposed method. For the original simulated data, we added different levels of noise to the data to test the DWT-Resnet18 performance, forming SNRs from low to high of \(-7.512\) dB, \(-5.467\) dB, \(-2.756\) dB, \(4.074\) dB, and \(18.074\) dB, respectively. The original data consisted of 8100 seismic traces, with 512 sample points per seismic trace and a 0.001 s sampling period. Therefore, for the obtained noisy data and original simulated data, the corresponding first 80% of the data were extracted, respectively, with the mirror flip and up-down flip for data enhancement, are made into the training data, and the remaining 20% of the data 10% as the validation set 10% as the test set. Five commonly used wavelet bases haar, db, sym, coif, bior, rbio, were selected to verify the noise-reducing performance of DWT-Resnet with different wavelet bases and different numbers of decomposition levels. The related outcomes of the trials were listed in Table 1. The red-marked data in the table represent the best denoising outcomes, while the blue-marked data represent the second-best denoising results. As can be seen in Table 1, except for the rbio wavelet, the best denoising results were achieved in decomposing levels 2 and 3 when processing seismic data with different SNRs. The rbio wavelet offers the best denoising impact of all wavelets when processing seismic data with an SNR of 18,074 dB, but only when the wavelet's decomposition level is 5. There is a non-significant difference between the best denoising result and the second-best denoising result in the denoised seismic data, with a difference of 0.116 dB for decomposition level 3 and 0.108 dB for decomposition level 4. Overall, the optimal denoising results differ from the suboptimal denoising results when dealing with high SNR seismic data, while the optimal denoising results are similar to the suboptimal denoising results when dealing with low SNR seismic data. Therefore, considering this situation, it can be said that 2- and 3-level decomposition denoising can be preferred when different wavelet basis functions are used to denoise this synthetic seismic data. Figure 2 displays the ideal denoising results of each wavelet basis with the input of different SNRs seismic data obtained using DWT-Resnet18 denoising with multiple wavelet bases for different SNRs seismic data, providing deeper insight into the outcomes obtained by each wavelet. It can be seen that the denoising effects of different wavelet bases vary widely when processing high SNR seismic data, but the denoising effect of haar wavelet and bior wavelet is especially different from the other three. As the SNR decreases, the denoising results of haar wavelet and other wavelets gradually decrease from 1.312 dB to 0.595 dB, while the denoising results of other wavelets tend to overlap, i.e., the denoising effects of different wavelets will tend to be the same as the noise is further enhanced.
## 2 Methods
### PV End of Life Management
#### 2.2.2 Cost of Transportation  
In analyzing the cost of transportation in EoL management of PV waste, we used a bottom-up approach to estimate each cost parameter. The cost data for transportation were provided by the logistic and transport industry in the US. The cost of transportation with trucks is USD 1.47 per km for distances longer than 1500 km and USD 2.55 per km for distances shorter than 1500 km in the US. The cost of fuel is also incorporated into the cost analysis as the fuel surcharge, which fluctuates based on the cost of fuel at the time. We use a typical 35% to accommodate the fuel surcharge because it is based on the average diesel fuel cost in the US over the past several months [43].  
To assess the transportation of PV wastes, we calculate how many PV panels a truck can carry considering the weight and the specifications of each PV technology. Note that the environmental impacts from transportation are not affected by these specifications, because the transportation associated impacts are calculated in the LCA databases per metric ton*km which affects the impacts  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **c-Si** & **CdTe** & **CIGS** \\ \hline \multirow{6}{*}{**Disposal**} & c-Si: & CdTe: & CIGS: \\  & • 581 g sludge, containing & • 620 g wiring plastic & • 620 g wiring plastic & • 620 g wiring plastic \\  & metallic residues-disposal & for increiteration & • 620 for increiteration & • 130 g contaminated glass, \\  & • 135 g wiring plastic & • 130 g continued glass, & disposed in landfill & • 130 g continued glass, \\  & • 162 g contaminated glass, & & & \\  & • 3543 g liquid waste, & & & \\  & • & disposed in landfill & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
directly. However, the financial cost of transportation is calculated per km of a defined type and size of the vehicle. Therefore, additional specifications of PV technologies need to be defined.  
Table 2 summarizes the output power and the physical specifications of the PV technologies assessed in this study. The output power information was used to estimate the numbers of PV panels needed for different sizes of PV farms. We made our baseline analysis for a 1 MW solar farm and calculated how the transportation cost varies for a 50 MW and 100 MW solar farm. The surface area and weight of the panel were used to estimate the number of trucks is needed for the transportation of PV panels. In light of this information, we calculated the transportation-associated cost of 1 m\({}^{2}\) PV waste for each technology type.
# External Field Response and Applications of Metal Coated Hemispherical Janus Particles
## 2 Experiment
### Sample Preparation
#### 2.1.2 Metal Coating on the Particles and Preparation of Suspensions of the Particles  
For the reflectance measurement of a single Janus particles, silver of 100 nm thickness was vapor-deposited on the equatorial plane of hemispherical particles that were arranged on a cover glass at a pressure of 4.0 \(\times\) 10\({}^{-3}\) Pa and a deposition rate of 0.3 nm/s.  
For the experiments of electric field application to a water suspension of particles, a 100 nm thick silver film was vacuum-deposited on the equatorial plane of the particles arranged on a glass slide at a pressure of 5.4 \(\times\) 10\({}^{-3}\) Pa and deposition rate of 0.3 nm/s. The film was sonicated in aqueous solution of 5% Pluronic F-127 (Anatrace, Maumee, OH, USA) for particles to disperse. The particles were centrifuged at 8000 rpm for 5 min, and the supernatant of the Pluronic F-127 solution was replaced with water. This operation is effective to prevent particles from sticking together [30]. The suspension of particles was sandwiched between Indium Tin Oxide (ITO) electrodes with a 0.1 mm spacer made of polypropylene.  
For the experiments of magnetic field application, a 50 nm thick nickel film was vacuum that was deposited on the equatorial planes of hemispherical particles at 2.0 \(\times\) 10\({}^{-3}\) Pa and 0.3 nm/s. Care was required because nickel was readily to peel off the glass substrate. They were dispersed in water  
Figure 1: (**a**) The metal coated hemispherical Janus particle. (**b**) The scanning electron microscope (SEM) image of sulfurized silver on the equatorial plane of the hemispherical polymethylmethacrylate (PMMA) hemispherical particles. Silver is deposited only on the equatorial plane.  
by sonication, dropped into a 6 mm diameter well surrounded by a 0.1 mm spacer on a glass slide, and were covered with a cover glass. Then, a ferrite magnet was brought closer from the side and the particles were observed with a microscope. The water suspension of particles was also placed in a 10 mm optical-path-length glass cell for the transmittance measurement.
# Conductivity Enhancement of Graphene and Graphene Derivatives by Silver Nanoparticles
## 4 Conclusions  
Ag nanoparticles were deposited by a simple reduction of Ag\({}^{+}\) cations on two different graphene substrates: a FLG nanosheet, which was more aromatic and hydrophobic, and a functionalized reduced graphene oxide, \(f\)-rGO, that was rather hydrophilic, less aromatic, and mostly single-layered. The as-formed \(f\)-rGO/AgNP and FLG/AgNP hybrids showed enhanced electrical conductivity after their deposition on paper, which was linearly increased with the Ag percentage without the assistance of an annealing process. The homogenous dispersion of Ag nanoparticles on the hydrophilic \(f\)-rGO substrate creates conductive interconnections between the graphene nanosheets, improving the electrical conductivity of the hybrids. In contrast, on the hydrophobic FLG substrate, the AgNPs are organized easier in aggregates, and above an Ag percentage close to 30%, the conductivity increased radically due to the formation of highly conductive networks of AgNP rows. However, the low stability of the FLG/AgNPs hybrids in water as well as the fact that only highly doped AgNPs hybrids showed significantly higher conductivity are serious obstacles for the use of FLG/AgNPs hybrids in conductive inks. On the other hand, a representative \(f\)-rGO/AgNP hybrid was used for the preparation of a water-based conductive gravure ink, which showed very good printability on a commercial photo paper.  
Figure 9: Gravure-printed items on a high-gloss photo paper using the \(f\)-rGO/AgNP\({}_{2}\) ink depict (**a**) four zones of different thicknesses of the ink layer with the R\({}_{8}\) values in k\(\Omega\) sq\({}^{-1}\). (**b**) Stripes of various thicknesses and widths. The patterns with red colors were derived using a common commercial printing ink for comparison. (**c**) R\({}_{8}\) measurements of the printed zones of image (**a**).  
**Author Contributions:** Conceptualization, A.K., V.G. and V.B.; methodology, A.K., V.B. and K.V.; writing--original draft preparation, V.B. and V.G.; writing--review and editing, V.B. and V.G.; supervision, V.B. and V.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financed by the European Union and Greek national funds through the Operational Program for Competitiveness, Entrepreneurship, and Innovation under the call RESEARCH--CREATE--INNOVATE (project code: T1 EDK-02093).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The authors gratefully acknowledge [PERSON] and the Laboratory of Electron Microscopy and Microanalysis, School of Natural Sciences, University of Patras, for the TEM measurements.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Starch Films  
Starch has been vastly studied because of its abundance, biodegradability, low cost, and edible nature. Starch is a polysaccharide that consists of amylose and amylopectin, of which amylose is responsible for the starch's film-forming abilities. The crystalline portion consists of amylopectin's short-branched chains, whereas the amorphous regions are made of amylose and amylopectin branching points. Starch granules are insoluble in cold water; thus, when they are heated in water, their crystalline structure becomes disrupted and causes water molecules to interact with the hydroxyl groups of amylose and amylopectin, which results in partial solubilization. To obtain a homogeneous film-forming solution, gelatinization is required [44]. High tensile strength values and low elongation percentages result in the starch films having poor mechanical properties. Also, starch films are brittle due to the presence of the amorphous regions created by amylose. However, this problem can be solved by adding a plasticizer, which results in improved flexibility and extensibility [46].
# Frequency-Separated Attention Network for Image Super-Resolution
## 4 Experiments
### Ablation Study
#### 4.2.1 The Effectiveness of Res ASPP Feature Extraction  
We utilized the Res ASPP Module to enhance the field of view for improved feature extraction. To verify the effectiveness of the Res ASPP Module, we conducted the following experiments: (1) removing the Res ASPP Module from the FEM; (2) retaining the Res ASPP Module within the FEM. As shown in Figure 6, we visualized the average feature maps after feature extraction from both experiments. The outputs of feature extraction were as follows: for experiment (1), the result of the first convolution \(g_{conv}\) in the Feature Extraction Module, and for experiment (2), the results of the first convolution \(g_{conv}\) and the output of Res ASPP Module-2 \(g_{K_{2}}\). From the feature maps, it is evident that the contours of the feature maps without the Res ASPP Module are discontinuous and blurred. This could be due to misinterpreting adjacent similar features as dissimilar or vice versa within a limited field of view. Based on the test results on the benchmark dataset outlined in Table 1, the outcomes without the Res ASPP Module consistently underperform compared to those with it, thereby validating the effectiveness of the Res ASPP Module. Moreover, as evident from Figure 7a, the utilization of the Res ASPP Module accelerates the convergence of the network during training by enhancing feature extraction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Deactivate Res ASPP Module** & **Activate Res ASPP Module** \\  & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline Set5 & 38.19/0.9610 & **38.20**/**0.9612** \\ Set14 & 33.79/0.9190 & **33.88**/0.9200 \\ BSD100 & 32.29/0.9009 & **32.33**/0.9016 \\ Urban100 & 32.69/0.9337 & **32.83**/0.9351 \\ Manga109 & 39.03/0.9778 & **39.11**/0.9781 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results on the standard dataset without and with the use of the Res ASPP Module.  
Figure 6: To explore the impact of the Res ASPP Module, it was removed from the FEM, followed by retraining. A comparison was made between the feature extraction results of the network with and without the Res ASPP Module. (**a**) illustrates the feature map of the output \(g_{conv}\) (in Equation (1)) without utilizing the Res ASPP Module. (**b**) and (**c**) depict the feature maps of \(g_{conv}\) (in Equations (1)) and \(g_{K_{2}}\) (in Equation (1)), respectively, with the utilization of the Res ASPP Module within FEM. The features regarding the wings and head are slightly blurrier in (**a**) compared to (**b**) and (**c**).
## 2 Methodology
### Performance Measures
#### 2.4.2 Correlation Coefficient (CC)  
The correlation coefficient quantitatively evaluates the similarity between the intensities of two CTs, \(A\) and \(B\). Unlike the mean squared error, the values range from 0 to 1 and this allows us to analyze the value of similarity between the original scans and the estimated scans. The correlation coefficient can be expressed as:  
\[S^{corr}(A,B)=\frac{\sum(A-\overline{A})\cdot(B-\overline{B})}{\sqrt{\left( \sum(A-\overline{A})^{2}\right)\cdot\left(\sum(B-\overline{B})^{2}\right)}} \tag{11}\]  
where \(\overline{A}\) and \(\overline{B}\) correspond to the mean gray-level intensity in images \(A\) and \(B\), respectively. A slightly less strict assumption would be that there exists a linear relationship between the intensity values in the registration. In this case, the optimal similarity measure is the correlation coefficient.