esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antimicrobial Activity
#### 2.5.2 Antibacterial and Antifungal Susceptibility Test  
The agar well diffusion technique as described by [PERSON] et al. [2] was modified and used to test for the antibacterial and antifungal activity. Briefly, 100 \(\upmu\)L of 0.5 McFarland solutions of bacterial or fungal strain cultures in 0.85% sterile distilled water (SDW) was placed over the surface of an agar plate and spread with a sterile inoculation loop. In each agar plate, three wells were cut with a cooled, flamed cork borer of 5 mm diameter, while a sterile needle was used to remove the agar plugs. A 50 \(\upmu\)L of the amoxicillin (0.0125 mg/mL) or nystatin (0.03 mg/mL) were added to the first well for antibacterial and antifungal susceptibility test respectively to serve as a positive control. In the second well, 50 \(\upmu\)L of the corresponding extract solvent were added while in the third, 50 \(\upmu\)L of the acetone extract/aqueous extract/essential oil (50 mg/mL) were added. The essential oil had been prepared by dissolving in 10% DMSO with Tween 80 (0.5% \(v/v\) for easy diffusion) and sterilised by filtration through a 0.45 \(\upmu\)m membrane filter [2]. Preliminary studies confirmed that the carrier vehicle did not inhibit the growth of bacteria. Each test was done in triplicate. The culture plates were incubated at 37 \({}^{\circ}\)C, and the results were examined after 24 h and 72 h for antibacterial and antifungal susceptibility test, respectively. The clear zone around each of the wells was measured in mm, showing the plant fractions' activity against the tested organisms.
# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 1 Introduction
The use of coastal surface-based observations and TCA allow for the validation of GNSS-R wind estimates on a scale that is not contained to a particular model or technology. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Satellite** & **Provider** & **Years** & **Sampling/Sat** & **No. of Sats** & **Orbit** & **Antenna** & **Reference** \\ \hline TDS-1 & SSTL & 2014–2019 & 4 & 1 & Polar & 13.3 & [2] \\ SMAP-R & NASA & 2015 - & 0.25 & 1 & SSO * & 36 & [15] \\ CYGNSS & NASA & 2016 - & 4 & 8 & \(+/-35\) & 14.5 & [16] \\ LEMUR-2 & Spire & 2019 - & 7 & 5+ & SSO & 9.6 & [11; 17] \\ BuFeng & CASC & 2019 - & 2 & 2 & \(+/-45\) & 14 & [4] \\ FSSCat & ESA & 2020–2021 & 2 & 1 & SSO & 12.2 & [16] \\ FY-3X & CMA & 2021 - & 1 & 3+ & SSO & 15 & [5] \\ Triton & TASA & 2023 - & 8 & 1 & SSO & 14.5 & [6] \\ MuSat & Muon & 2024 - &? & 3+ & SSO & 14 & [12] \\ HydroGNSS & ESA & Expected 2025 & 2 & 2+ & SSO & 14 & [7] \\ \hline \hline \end{tabular}  
* * SSO = sun-synchronous orbit.  
\end{table} 
Table 1: GNSS-R space-borne projects, current and future. This study examines CYGNSS validation for global high wind speed events. Data from weather stations used at the Australian Bureau of Meteorology and TCA are explored to compare CYGNSS ocean wind-speed estimates. Section 2 provides information about the datasets used and the methods involved in the analysis. Section 3 shows the results of the comparison to weather stations and TCA, which are then discussed in Section 4. Section 5 contains the conclusions.
# Study on Influence of Multi-Parameter Variation of Bladed Disk System on Vibration Characteristics
###### Abstract  
As the main components of the rotor system of aero-engines and other rotating machinery equipment, the bladed disk system has high requirements on its structure design, safety and stability. Taking the rotor disk system of aero-engines as the research object, modal calculation of the rotor disk system was based on the group theory algorithm, and using the fine sand movement on the experimental disk to express the disk vibration shape. The experimental vibration mode is used to compare with the finite element calculation results to verify the reliability of the finite element analysis. Study on the effect of dissonance parameter changes on the bladed disk system vibration characteristics concluded that the vibration mode trends of the blisk system and the disc are, basically, consistent. As the mass of the blade increases, the modal frequencies of the entire blisk system gradually decrease, and the amplitude slightly increases. When the mass increases at different parts of the blade, the effect on the modal frequencies of the bladed disk system are not obvious. When the bladed disk system vibrates at low frequency, the disc will not vibrate and each blade will vibrate irregularly. The bladed disk should be avoided to work in this working area for a long time, so as not to cause fatigue damage or even fracture of some blades.  
bladed disk system; mistuning; vibration characteristics; group theory; finite element analysis (FEA) +
Footnote †: journal: Applied Sciences
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
###### Abstract  
The presence of ice on transmission lines threatens their integrity. The thickness of ice is crucial with respect to the autonomous operation of de-icing robots. Therefore, we propose a Cross-Guide-UNet (CG-UNet) model for detecting the thickness of ice on ice-covered transmission lines. The CG-UNet model uses an encoder-decoder architecture and incorporates a cross-guide module (CGM) at the crosslayer connection of the model. Initially, we redesign the attention calculation method to suit the shape characteristics of transmission lines. Channel and spatial attention are computed for the encoder and decoder features, respectively. Subsequently, the attention weight is cross-applied to the encoder and decoder features, allowing for comprehensive utilization of feature information to guide the generation of ice-covered edges. Additionally, we introduce a joint loss function comprising cross-entropy loss and boundary loss for model training, enhancing the distinction of easily confused edge pixels. The CG-UNet model effectively detects ice-covered edges through experimental analysis, achieving optimal dataset scale (ODS) and optimal image scale (OIS) scores of 0.934 and 0.938, respectively. The thickness detection error is constrained within 7.2%. These results confirm that the proposed algorithm can provide a control basis for the high-precision operation of de-icing robots.  
transmission lines; edge detection; ice thickness; cross-guide Article
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Processing
#### 2.4.5 Geometry Definition for Sampling  
Polygon geometries were drawn on the images to identify areas of water, land, weed and channel (containing both water and weed). These areas are easily identifiable from red-green-blue images, so the polygons were drawn by visual inspection of the images. In each case, stratified sampling algorithms in GEE were used to randomly sample pixels within the geometries, with approximately 1000 pixels being taken from each geometry. Given the large number of samples, and the random distribution of the samples within the geometries, the sampled reflectance characteristics should be a good representation of the actual reflectance characteristics of each of the classes. These samples were then used for classifier training and validation. Separate geometries were used for training and validation to ensure fair assessment of classification accuracy.  
The samples were also used to assess the distribution of vegetation index values within each class. Statistics of the sampled pixels were computed. To assess the separability of the water, land and weed classes based on vegetation indices, the normalized mean difference was used [9]:  
\[NMD=\frac{2(\mu_{1}-\mu_{2})}{\sigma_{1}+\sigma_{2}}, \tag{1}\]  
where \(\mu_{n}\) denotes the mean of the vegetation index within the \(n\)-th class, and \(\sigma_{n}\) the standard deviation. This provides a measure of how different the means of the indices of pairs classes are with respect to their variance.  
\begin{table}
\begin{tabular}{l l l} \hline \hline NDVI & Normalized Difference Vegetation Index & \((NIR-R)/(NIR+R)\) \\ VARI & Visible Atmospherically Resistant Index & \((G-R)/(G+R-B)\) \\ NDWI & Normalized Difference Water Index & \((G-NIR)/(G+NIR)\) \\ NDAVI & Normalized Difference Aquatic Vegetation Index & \((NIR-B)/(NIR+B)\) \\ WAVI & Water Adjusted Vegetation Index & \((1+L)(NIR-B)/(NIR+B+L)\) \\ SAVI & Soil Adjusted Vegetation Index & \((1+L)(NIR-R)/(NIR+R+L)\) \\ EVI & Enhanced Vegetation Index & \(2.5(NIR-R)/(NIR+6R-7.5B+1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vegetation index definitions, where \(B\), \(R\), \(G\) and \(NIR\) are blue, red, green and near-infrared reflectance respectively, and \(L=0.5\).
# Convective-Microwave-IR Hybrid Drying of Kaolin Clay--Kinetics of Process
## 3 Results and Discussion
### Calculation of the Drying Constant k  
All experimentally obtained drying curves were approximated using Equation (6). In this way, the values of the drying constant k were determined. The results of the calculations are presented in Table 3.  
A comparison of CV and CV + MW processes indicates that the use of microwaves as an additional heat source resulted in a very large increase in drying efficiency. The drying constant more than doubled. Shortening the time of microwave application (CV + MW\({}_{60}\) and CV + MW\({}_{\rm per}\) processes) resulted in a slight decrease in the drying constant. The use of infrared radiation as an additional heat source (CV + IR\({}_{80}\) process) resulted in an increase in drying efficiency of the same order as the use of microwaves. The highest increase in drying efficiency was obtained for processes where both additional heat sources were used simultaneously (CV + (MW \(-\) IR)\({}_{40}\) and CV + (MW \(-\) IR)\({}_{\rm per}\)).  
The experimental results obtained in this work coincide partially with the results obtained by [PERSON] and [PERSON] [13] through numerical simulations. The numerical simulations indicate a greater impact of infrared radiation than microwaves. These differences between the experimental and numerical results result from different sample geometries and different power values of the heat sources used.
## 3 Results and Discussion
### PE Quantum Yields at Different Photon Energies during Wavelength Scan for Scratching Environments
 } & \multirow{4}{*}{
\begin{tabular}{c} **Activation** \\ **Energies for** \\ **Intersity of O\({}^{2-}\)** \\ **Intersity of O\({}^{2-The activation energies for the \(Y\) values at each wavelength, \(\Delta E_{Y\lambda}\), were obtained from the Arrhenius plot of the \(Y\) values against the reciprocal temperature in Kelvin, 1/\(T\). Figure 15 shows the values of \(\Delta E_{Y}\), where \(\Delta E_{Y}\) represents \(\Delta E_{Y200}\), \(\Delta E_{Y210}\), \(\Delta E_{Y220}\), and \(\Delta E_{Y230}\) for all the environments. The \(\Delta E_{Y}\) values are the mean values of the scan data of two wavelengths. For each environment, as the photon energy was increased, the values of \(\Delta E_{Y\lambda}\) almost linearly increased and, finally, \(\Delta E_{Y200}\) became close to \(\Delta E_{\alpha A}\) of the environment. Therefore, it seems that \(\Delta E_{\alpha A}\) may be the energy needed to increase the escape depth for the hot electrons. The change in \(\Delta E_{Y\lambda}\) values with the environments was nearly similar to that of \(\Delta E_{\alpha A}\). In addition, it was found that the difference between the \(\Delta E_{Y\lambda}\) values at 200 nm  
Figure 14: PE quantum yield, \(Y\), as a function of the photon energy at wavelengths of 200 nm (photon energy: 6.199 eV), 210 nm (5.904 eV), 220 nm (5.636 eV), and 230 nm (5.391 eV) during the 300- to 200-nm wavelength scan at 25, 200, and 339 \({}^{\circ}\)C for different environments: (1) air, (2) benzene, (3) cyclohexane, (4) water, (5) methanol, (6) ethanol, and (7) acetone. The \(Y\) values are the mean values of the two wavelength scan data. and 230 nm, (\(\Delta E_{Y200}-\Delta E_{Y230}\)), considerably depended on the environments. The values of (\(\Delta E_{Y200}\) - \(\Delta E_{Y230}\)) increased in the following order: 0.025 eV (cyclohexane) \(<\) 0.032 eV (benzene) = 0.032 eV (water) \(<\) 0.038 (ethanol) \(\approx\) 0.039 eV (acetone) = 0.039 eV (methanol) \(<\) 0.043 eV (air), but the mechanism remains unclear. Furthermore, regarding the dependence of the activation energy on the incident light wavelength, we previously reported that the activation energy, \(\Delta E_{a}\), at 200, 210, 220, and 230 nm for rolled iron surfaces cleaned only in acetone decreased as follows: 0.112 eV (200 nm), 0.100 eV (210 nm), 0.073 eV (220 nm), and 0.040 eV (230 nm) [13]. In this case, the value of (\(\Delta E_{Y200}-\Delta E_{Y230}\)) was 0.072 eV, which was remarkably greater than that in the present scratched iron surfaces.
# Angle-Resolved Fluorescence of a Dye Coupled to a Plasmonic Nanohole Array
## 4 Discussion
Furthermore, the pumping wavelength is set within the spectral region below the band gap around 680, exciting selectively the plasmonic resonances lying down on the front surface, as reported in [35,36]. These modes are characterized by a higher efficiency with respect to resonance centered around 780 nm (at 0\({}^{\circ}\)) thanks to a narrower bandwidth and a longer decay time [26]. In fact, the fluorescence signal is systematically and significantly larger when pumping from the front side. A contribution to the enhancement can also be identified in emission, in this case for wavelengths above the band gap, but it is much less effective. Figure 6: Panel (**a**): map of the fluorescence spectral ratios at the different collection angles with respect to the one at normal incidence (collection angle 0\({}^{\circ}\)) for a pumping angle of 20\({}^{\circ}\). Panel (**b**): second derivative of the T spectra (TE-component) in the fluorescence range detection. The synergic combination of all the listed contributions to both pumping and collection, leads to an enhancement in the fluorescence signal. Figure 8 reports a comparison of the fluorescence signals for each of the four measurement configurations. The enhancement was evaluated as the ratio of the fluorescence spectra collected on the PEF platform with respect to the analogous measurement performed on the ATTO/700 directly drop-casted on a bare SiO\({}_{2}\) substrate with PEL layers and no plasmonic metasurface underneath. Figure 8: Fluorescence spectra measured on the PEF platform and on the reference glass slide. The signal is multiplied by a factor of 20 for the different measurement configurations. Pump and collection from the front (**a**) and back sides (**c**), respectively. (**b**) Pump from the front side and collection from the back side. (**d**) Pump from the back side and collection from the front side. Figure 7: Angular dependence of the absorbance evaluated at the pumping wavelength of 632 nm. It can be clearly seen how the enhancement in the fluorescence signal for all the measurements performed with the support of the PEF platform is constantly relevant. Consistent with our findings, the highest enhancements are visible when pumping from the front, as visible in Figure 8 panels (a) and (b). In fact, the fluorescence signal collected from the PEF platform is enhanced roughly 40 and 60 times with respect to the bare glass reference, considering the collection from the same side (the front) and the back side, respectively. Remarkably, the fluorescence signal is also enhanced when pumping from the back side and collecting from the back or the front sides, as shown in Figure 8 panels (c) and (d), respectively, but less efficiently, roughly about 20 times with respect to the reference.
## 3 Results and Discussion
### Quantitative Analysis and Normalized bOCT Contrast
#### 3.2.3 Comparison of Age Groups  
Figure 10 shows the average speckle contrast given as a function of the three conditions for the shallow ROI1 for the two different age groups, with 20s on the left and 30s on the right, respectively. The average biospeckle contrast was calculated from the shallow ROI1 for all male and female participants separately, irrespective of age. * denotes the statistical significance of the change between the time before and five minutes after heating for all of the participants. On the other hand, for the subjects aged 30 and above, the biospeckle contrast was significantly different between the before heating and soon after heating conditions as well as between the before heating and 5 min after heating conditions. Depending on the age group, there was a difference in the response of the skin structures in terms of returning to the preheated state. In the younger group, there was a significant difference between before heating and soon after heating, but not between before heating and after 5 min of heating. In the older group, there was a significant difference between the before heating and soon after heating conditions, as well as between before heating and after 5 min of rest after heating. This implies that the changes within the skin structure may take longer to return with age, indicating the loss of elasticity of the blood vessels.
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 5 Performance Analysis
### Theoretical Analysis
FDW has the lowest noise power, which makes the system much more stable than previous systems. At \(10\) km distance of optical fiber, an optical signal-to-noise ratio (OSNR) at \(-20\) dBm Psr for the FDW system is \(29.98\) dB (OSNR (dB) = Signal Power \(-\) Noise Power). Table 8 shows a comparison of various types of OCDMA code performance. FDW has the largest OSNR value compared with other codes. Higher OSNR means better signal quality with less noise interference. This is important to ensure the signal can travel a long distance with low degradation. The noise power for Hadamard and MFH is much higher than FDW and MDW code, which causes the OSNR to drop at \(-15\) dBm and above. The DW family code has proved its superior performance by suppressing the shot noise. The signal power can be increased by increasing the amount of weight of OCDMA codes. \[\int_{0}^{\infty}G(v)dV=\int_{0}^{\infty}\Bigg{[}\frac{P_{sr}}{\Delta V}\sum_{ k=1}^{K}d_{K}\sum_{i=1}^{L}C_{K}(i)C_{l}(i)\bigg{\{}u\bigg{[}\frac{\Delta V}{N} \bigg{]}\bigg{\}}\bigg{]}dV \tag{17}\]1 Performance of OCDMA code BER versus received power, Psr (dBm). \begin{table}
\begin{tabular}{c c c c c} \hline
**OCDMA Code** & **No of Users** & **No of Weight** & **Code Length, N** & **OSNR (dB)** \\ \hline Hadamard & 9 & 8 & 16 & 26.21 \\ MFH & 9 & 6 & 31 & 24.6 \\ MDW & 9 & 4 & 27 & 23.33 \\ EDW & 9 & 3 & 18 & 24.21 \\ FDW & 9 & 3 & 12 & 29.89 \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of OCDMA code based on code length and OSNR. Figure 2: Performance of OCDMA code BER versus the number of users (K).
## 3 PiFM Measurement for a Single-Molecule  
We demonstrate PiFM measurements of a single molecule using a simplified model to understand the microscopic interaction between the molecule and the localized plasmon. We consider a PiFM system and six-membered ring molecule by DDA with a discretized unit cell size of 1 A, as shown in Figure 1a,b. In the present study, we put stress on discussing the relationship between the geometrical structure of a molecule and the PiFM image having spatial structures inside the single molecule. That is why we use the simplified model of a molecule. The results obtained by using the orbital functions of a more realistic model will be published elsewhere. The tip is assumed to be a 60 nm diameter gold sphere with several protrusions, forming picocavity, on the apex, which is known in the field of tip-enhanced Raman spectroscopy [14; 22]. The substrate is assumed to be a gold thin film with a thickness of 9 nm. The susceptibility of the metallic substrate is given by the Drude model with the parameter of Au, and each of the six elements of the molecule are determined by the Lorentz model as follows:  
\[\chi_{\text{np}}(\omega)=\frac{1}{4\pi}\Bigg{[}\epsilon_{\text{np}}-\epsilon_{ 0}-\frac{|\mu|^{2}/V_{\text{np}}}{\hbar\omega_{\text{np}}-\hbar\omega-i\gamma_{ \text{np}}}\Bigg{]}, \tag{5}\]  
where \(\epsilon_{\text{np}}\), \(\mu\), \(V_{\text{np}}\), \(\hbar\omega_{\text{np}}\), and \(\gamma_{\text{np}}\) are the background dielectric constant, dipole moment, volume, resonant energy, and damping constant of the elements of the six-membered ring molecule, respectively. For simplicity, we considered that the polarization is restricted in the molecular plane and the susceptibility is local. We assume elements with resonant energies of \(\hbar\omega_{\text{np}}=2.0\), and the dipole moment of 1 Debye, which is similar to the value for porphyrin-based dye molecules [18; 27; 28], with parameters \(\epsilon_{\text{np}}=1.5\), \(\gamma_{\text{np}}=5\) meV, and \(V_{\text{np}}=1\) A\({}^{3}\), which is the same as the unit cell \(V\). We assume a gap of 3 A between the molecule and substrate, considering the experimental situation. A few atomic layers of a dielectric, such as NaCl, are laid down as spacers to avoid hybridization between the molecular orbitals of the sample and the metal substrate in the measurement of molecules, such as STM [11; 12].  
The six dipoles of the molecule can take on various spatial structures, each of which has a specific resonance state. Figure 1c,d show the absorption cross-section spectrum in a vacuum and the polarization structures of the molecule for each resonant energy, respectively. These peaks correspond to optical allowed transitions under the long-wavelength approximation.
## 3 Results and Discussion
### Tensile Properties
The _UTS_ of AAHDPE30 is significantly higher than the _UTS_ of HDPE30 (Figure 7). Furthermore, the _\(\varepsilon_{UTS}\)_ of AAHDPE30 is three times higher than the one measured for HDPE30 (Table 6). Such \begin{table}
\begin{tabular}{c c c c c} \hline  & **HDPE00** & **HDPE30** & **AAHDPE00** & **AAHDPE30** \\ \hline
**UTS (MPa)** & 26.75 \(\pm\) 0.7 & 25.56 \(\pm\) 2.1 & 25.37 \(\pm\) 1.3 & 30.01 \(\pm\) 1.07 \\ _\(\varepsilon_{\text{UTS}}\) (\%)_ & 11.15 \(\pm\) 1.43 & 1.03 \(\pm\) 0.13 & 5.81 \(\pm\) 1.1 & 3.13 \(\pm\) 0.49 \\ _E\({}_{S}\)_ (GPa)** & 1.44 \(\pm\) 0.05 & 3.41 \(\pm\) 0.13 & 1.33 \(\pm\) 0.15 & 2.64 \(\pm\) 0.09 \\ \hline \end{tabular}
\end{table} 
Table 6: Average and standard deviation of the ultimate tensile strength (UTS), strain at UTS (\(\epsilon_{\text{UTS}}\)) and secant modulus (E\({}_{S}\)) values for the HDPE00, HDPE30, AAHDPE00 and AAHDPE30. Figure 7: Strain–stress curves for HDPE00, HDPE30, AAHDPE00 and AAHDPE30. The average ultimate tensile stress and its corresponding strain are plotted for all the compounds. behavior can be attributed to the higher adhesion of the AAHDPE than the HDPE to the zirconia surface, which seems to have a big effect in the high strain region. A high adhesion results in a strong polymer-filler interface and thus in a more effective transfer of load from the polymer matrix to the solid particles [21; 22; 23; 27; 74]. Moreover, the high dispersion of the powder in AAHDPE30 compared to HDPE30 (Figure 4) contributes to difference in the mechanical properties. In polymer composites, defects such as agglomerates or cavities result in a concentration of stresses around those points and eventually the failure of the material in those areas [16; 22; 23; 27]. Thus, it can be concluded that the acrylic acid-grafting of the HDPE results in composites with a higher strength and flexibility by the combination of a strong polymer-filler interface and the reduction in the filler agglomerates.
# Multiple Instances QoS Routing in RPL: Application to Smart Grids +
## 4 Experiment Setup  
In this section, we detail our network setup and provide a quick overview about the wireless sensor testbed used to validate our proposition.
site of Lille. These nodes are distributed inside a 200 m\({}^{2}\) room and on the different corridors of the Inria building, enabling a large-scale multi-hop topology (Figure 5).