esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Measurement of OH- Groups  
As shown in Figure 2, fused silica samples, after different processes, show strong absorption peaks at 3660 cm\({}^{-1}\). When the silica surface is removed by IBS at 600 nm and 1000 nm, respectively, the hydroxyl absorption peak intensity decreases to a certain extent compared with that of the HF-etched surface. After a removal depth of 1000 nm, the sample was immersed in deionized water for 30 min, then dried with high-pressure nitrogen gas and immediately measured by infrared spectroscopy. Figure 2 shows that the intensity of the hydroxyl absorption peak is significantly enhanced after water immersion, even stronger than that of the HF-etched surface.  
The hydroxyl content is calculated as follows [16]:  
\[C=96.5\times(1/d)\lg(I_{0}/I) \tag{1}\]  
where \(C\) is the hydroxyl content of the sample, ppm (\(10^{-6}\)). \(d\) is the sample thickness, cm. \(I_{0}\) is the distance from the baseline to the zero line at wavelength 2.73 \(\upmu\)m, mm. \(I\) is the distance from the absorption peak to the zero line at wavelength 2.73 \(\upmu\)m, mm. Based on the infrared spectral transmittance curve shown in Figure 2, the hydroxyl content is 93.2 ppm with a removal depth of 1000 nm and 110.0 ppm after being cleaned by deionized water, in which the hydroxyl content increased by 18%. It should be noted that the calculation of hydroxyl content is greatly affected by the thickness of the sample, and the transmittance of the hydroxyl peak increases as the thickness increases. Therefore, the change rate in hydroxyl content here is only used as a reference to characterize the degree of surface hydroxylation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Surface Treatment Process** & \begin{tabular}{c} **LIDT of Non-Megasonic** \\ **Cleaning (J/cm\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **LIDT of Megasonic Cleaning** \\ **(J/cm\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Change** \\ **Amplitude** \\ \end{tabular} \\ \hline HF etching 120 min & 8.11 \(\pm\) 0.32 & & \\ \hline IBS removal 900 nm & 10.38 \(\pm\) 0.28 & 8.17 \(\pm\) 0.31 & \(-\)21\% \\ \hline IBS removal 1500 nm & 8.25 \(\pm\) 0.34 & 6.36 \(\pm\) 0.23 & \(-\)23\% \\ \hline IBS removal 2000 nm & 8.27 \(\pm\) 0.24 & 7.08 \(\pm\) 0.19 & \(-\)14.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of LIDT tests.
## 3 Results
### Photocatalytic Activity of the Catalyst  
To research the photocatalyst efficiency under each source of light, the process of photolysis must be considered. In order to remove the impact of photolysis, a set of experiments were carried out without the catalyst ZBO in a 100 mL solution of CFX with a concentration of 10 mg/L at pH = 6 in each light irradiation UV, LED and solar light. Only a small percentage of CFX degradation, not exceedingly even 5% has been recorded by photolysis in all sources of light. So, it can be concluded that the photolysis impact could be ignored.  
After neglecting the effect of photolysis, we performed the same set of experiments in the same conditions but in the presence of ZBO photocatalyst. Before exposing those experiments to the sources of irradiation, all sets have been mixed in the dark for 120 min to established the adsorption-desorption equilibrium. Figure 9 demonstrated the difference in degradation under different light sources. As can be shown, while the catalyst radiated by solar light, the CFX degradation rate reached 89% in just 30 min, while that for other sources was lower. ZBO has more efficient degradation in the solar light because the solar light contained all visible, UV and other light irradiation. It has the largest light  
Figure 7: The capacitance-potential characteristic of ZnBi\({}_{2}\)O\({}_{4}\) in NaOH (0.1 M) solution.  
spectrum. Moreover, the intensity plays a role in this part as the solar light has great intensity (98 mW/cm\({}^{-2}\)). The UV irradiated gives a good degradation performance within the time, giving an 88.1% degradation in 2 h. This difference can be due to the color and the gap energy of the catalyst (2.8 eV), which gives a higher light absorption in the range of 200-600 nm, which is very close to the UV region. For the sunlight, it has the greatest light spectrum also containing the UV region causing a rapid degradation for CFX. On the other hand, the LED containing only visible light for that only small degradation was observed with it. This result was confirmed the finding of the optical and dielectric characterization as demonstrated previously in the optical study. ZBO has shown more efficiency in the degradation compared to our previous research about the degradation of CFX using ZnO [62] which gives a 60% degradation rate in 3 h. That can be explained by the ZBO bandgap (2.8 eV) being smaller than ZnO. Increasing the photocatalytic efficiency of semiconductors by lowering the bandgap and facilitating the separation of photo-generated electrons and holes is a difficult task.  
Figure 8: The EIS (Nyquist) diagram of ZnBi\({}_{2}\)O\({}_{4}\) in NaOH solution, inset: equivalent electrical circuit.  
Figure 9: The photocatalytic activity of ZnBi\({}_{2}\)O\({}_{4}\) under UV, sunlight and LED lamp: CCFX = 10 ppm, pH \(\sim\)6, T = 25 \({}^{\circ}\)C and catalyst dose = 1 g/L.
## 2 Array Factor Formulation  
The conformal microstrip array antenna consists of \(M\times N\) identical radiating elements, and the spacing between adjacent elements is \(dx\) and \(dy\) along the \(x\) and \(y\) direction, respectively, with main beam along the \(z\)-axis. The design for \(4\times 4\) array is illustrated in Figure 1. The inter-element space between radiating patches is fixed at \(dx=dy=0.5\lambda\) and all the four prototypes optimized for 28 GHz.  
The position (spatial coordinates) and orientation (azimuthal and elevation angles) of each antenna element array is determined in CST. Based on the location and normalized angle of the radiating elements of \(M\times N\) conformal array imported from CST into the algorithm, Equation (1) is used to calculate the array factor \(AF(\theta,\varphi)\) for both cylindrical and spherical arrays.  
\[AF(\theta,\varphi)=\sum_{m=0}^{M-1}\sum_{n=0}^{N-1}w_{m,n}e^{j[\psi_{x}+\psi_{ y}]} \tag{1}\]  
For cylindrical arrays, the expressions for \(\psi_{x}\) and \(\psi_{y}\) are given by \(\psi_{x}=kd_{x}\sin\theta+\beta_{x}\) and \(\psi_{y}=kd_{y}\sin\theta+\beta_{y}\), while in spherical arrays, the values of \(\psi_{x}\) and \(\psi_{y}\) are given by \(\psi_{x}=kd_{x}sin\theta cos\varphi+\beta_{x}\) and \(\psi_{y}=kd_{y}sin\theta sin\varphi+\beta_{y}\). The variables \(m\) and \(n\) denote the positions of array elements in \(M\times N\) array along the \(x\) axis and \(y\) axis, respectively. The vector \(w_{m,n}=I_{(m,n)}e^{j\Delta\varphi}\) represents the complex weighting function (Amplitude and Phase) required to steer the \(mxn^{\text{th}}\) element position within the array. \(\lambda\) represents the  
Figure 1: Design of \(M\times N\) Conformal Array on flexible ground surface.  
wavelength, and '\(\theta\)' and '\(\varphi\)' correspond to the elevation steering angle and azimuth steering angle, respectively. The variable '\(k\)' represents the propagation constant, while '\(\beta_{x}\)' and '\(\beta_{y}\)' denote the progressive phase shifts associated with each radiating element. These phase shifts can be represented as the matrix to be utilized within the algorithm to control the Array Factor as follows:  
\[\beta_{m,n}=\begin{bmatrix}\beta_{1,1}&\cdots&\beta_{1,n}\\ \vdots&\ddots&\vdots\\ \beta_{m,1}&\cdots&\beta_{m,n}\end{bmatrix} \tag{2}\]  
The Array Pattern, denoted as \(AP(\theta,\varphi)\), is determined by taking the Kronecker product (kron product) of \(AF(\theta,\varphi)\) and a matrix \(K\). This matrix \(K\) is compiled by concatenating the individual element pattern vectors. Mathematically, it is given by the following:  
\[AP(\theta,\varphi)=AF(\theta,\varphi)\otimes K \tag{3}\]  
Equation (3) represents the array pattern for \(M\times N\) conformal array antenna with uniform spacing. The resulting radiation pattern of the antenna can be obtained by calculating the magnitude of the array factor and plotting it as a function of the elevation and azimuth angles. In the analysis of the algorithm, the azimuthal angle '\(\varphi\)' is assumed constant, and the results are obtained for only varying the elevation angle '\(\theta\)'.
# A Compact Sequentially Rotated Circularly Polarized Dielectric Resonator Antenna Array
## 4 Measurement Results and Discussions  
### Single Element Parametric Study  
The single RDR coupled to an ES shown in Figure 7 is firstly prototyped and numerically simulated to clarify the relationship between the proposed antenna parameters to achieve optimum performance. Figure 8 depicts the parametric study carried out to optimize the ES radii when coupled to the RDR. Figure 8a shows that if the radius \(a\) varies from 5.8 to 5.2 mm, the resonance is shifted to the left as the radius \(a\) increases. Figure 8b shows the resonance variation according to changes in the radius \(b\). It is also observed that the resonance moves toward the left as \(b\) increases. To fine tune the resonance at 5.8 GHz, the radii dimensions for \(a\) and \(b\) were selected to be 5.5 and 2.5 mm, respectively. Figure 8c shows the resonance behavior once the length and width of the RDR is varied between 8.5 to 9.5 mm if the height is kept constant at 6 mm. The optimum resonance occurs if the length and width are 9 mm with \(-\)22.9 dB at 5.81 GHz.  
Figure 8: _Cont_.  
Figure 7: The modeled single resonating element: (a) Front plane; (b) Bottom plane.  
Figure 9 depicts the AR variation at the main beam angle of 0\({}^{\circ}\) with respect to the ES radii \(a\) and \(b\) alteration while keeping the dimensions of the RDR unchanged. From Figure 8a, it can be observed that if the radius \(a\) increases, the 3 dB AR bandwidth moves toward higher frequency. Also, Figure 8b shows that the 3 dB AR bandwidth shifts toward the left as the radius \(b\) decreases. The optimum polarization purity occurs when radii \(a\) and \(b\) equal 5.5 and 2.5 mm respectively.
## 4 Model Analysis
### System Reliability Analysis  
The reliability of an unrepairable system is quantified through probability-based measurements. By setting all rates of repair to zero in Equation (56), the inverse Laplace transform gives the probability of the system functioning without any interventions or repairs.  
Case I: one can obtain an expression for the reliability of the system subject to the failure rates values outlined in Section 4.1. Proceeding in the same fashion as for the availability and considering the same cases, in essence, the exploration is centered on an alternative perspective regarding the reliability of the system.  
\[R(t)=-0.420601e^{-0.22t}+0.75e^{-0.1t}+0.428571e^{-0.08t}+0.036e^{ -0.07t}\] \[+0.2025e^{-0.06t}+0.00352941e^{-0.05t} \tag{61}\]  
Case II: Once the reduction approach is implemented, it is anticipated that the failure rates of the system's units will decrease by a factor of \(\rho\), in (0,1). This is confirmed by substituting the parameter values from Section 4.1 into Equation (56) and performing the inverse Laplace transform.  
\[R(t)=-0.398789e^{-0.132t}+0.75e^{-0.06t}+0.428571e^{-0.048t}+0.02 16e^{-0.042t}\] \[+0.1965e^{-0.036t}+0.00211765e^{-0.03t} \tag{62}\]  
The values of reliability, represented by \(R(t)\), may vary when using Equations (61) and (62), as evidenced in Table 3 and the corresponding Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Time** & **Case 1** & **Case 2** \\ \hline
0 & 1 & 1 \\
1 & 0.96434 & 0.992194 \\
2 & 0.922462 & 0.984121 \\
3 & 0.876713 & 0.975801 \\
4 & 0.828879 & 0.967258 \\
5 & 0.780305 & 0.958509 \\
6 & 0.731992 & 0.949574 \\
7 & 0.684668 & 0.940472 \\
8 & 0.63885 & 0.931218 \\
9 & 0.594894 & 0.92183 \\
10 & 0.553027 & 0.912322 \\ \hline \end{tabular}
\end{table}
Table 3: Variation of reliability with respect to time in various cases.  
Figure 3: Availability analysis of two cases.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 3 Locking Detection, Assessment, and Resolution
### The Idea and Algorithm of a Posteriori Phenomenon Detection  
The idea of a posteriori detection of the numerical locking was originally proposed by [PERSON] [38]. Here, we develop and verify this idea. It consists in solution of two local problems for each element of the thick- or thin-walled part of the structure. The solutions to these problems are obtained from the equilibrated residual method. These two problems differ with the longitudinal order of approximation \(p\). In the first problem, it is equal to its current value from the global problem under consideration. In the second local problem, its value corresponds to the problem potentially free of locking. For two mentioned solutions, the strain energy norms are calculated and compared. This corresponds to sensitivity analysis where the sensitivity of the solutions of the local problems to the change of the longitudinal order of approximation is assessed. Once the locking is detected, one may modify the mesh by increasing the current order of the longitudinal approximation to its value corresponding to the problems free of locking. The similar approach is used also for determination of the optimized value of the longitudinal approximation order which corresponds to the minimum value sufficient for removal of the phenomenon.
# Chitosan Films with Caffeine and Propolis as Promising and Ecofriendly Packaging Materials
## 3 Results and Discussion
### Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)  
In order to evaluate the interaction among chitosan and used natural additives--caffeine and propolis extract--an FTIR infrared study was carried out, and the outcomes in the form of spectra are illustrated in Figure 4.  
The spectra of all the films exhibited a pattern akin to that of chitosan films devoid of additives, with the majority of peaks displaying chitosan characteristics. However, there were variations in transmittance intensities at specific peaks. The peaks in the range of 3500-3000 cm\({}^{-1}\) and at 2880 cm\({}^{-1}\) are related to the -OH bonds and aliphatic C-H stretching vibration [62; 72]. The peaks at 1650, 1557 and 1371 cm\({}^{-1}\) can be assigned to C-O stretching in amide I, N-H bending in amide II and C-N stretching in amide III [9; 62]. The peak at 1025 cm\({}^{-1}\) is attributed to C-O stretching in the alcohol group [72].  
In the spectra of films containing caffeine, distinctive bands corresponding to the caffeine molecule were observed, specifically at 1700 and 1650 cm\({}^{-1}\), which can be attributed to the stretching vibrations of C=O bonds within the acetamide groups of amide I, as well as at 1550 and 765 cm\({}^{-1}\), indicative of the stretching vibrations of N-H and/or C-N groups of amide II [73; 74; 75]. On the other hand, in the spectra of films containing both caffeine and propolis extracts, distinctive bands originating from propolis, primarily associated with phenolic compounds, can be discerned. The bands at 2924 and 2850 cm\({}^{-1}\) are ascribed to C-H stretching vibrations and confirm the presence of long-chain alkyl compounds in the bee glue extract. The peak at 1732 cm\({}^{-1}\) corresponds to C-O stretching vibration. The stretching and bending bands at 1634 (C=O), 1515 and 1451(C=C) cm\({}^{-1}\) may be assigned mainly to aromatic rings of phenolic compounds. Moreover, the wide band around 3420 cm\({}^{-1}\) corresponding to the stretching vibrations of the O-H band also confirms the presence of phenolic compounds from propolisis [62; 76; 77]. Alterations, such as shifting and broadening of certain bonds observed in the FTIR spectra, may be attributed to the presence of intermolecular interactions between the -OH and -NH2 groups within chitosan and the phenolic compounds found in propolisis. This aligns with information from the existing literature [9; 62; 72].  
Figure 4: ATR-FTIR spectra of chitosan-based films.
# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 4 Experimental Results and Analysis
### The New Feature Index in the Data Set of the University of Pavia  
In addition to comparing new features learned by the data-driven method with traditional indexes NDVI and NDWI, a new type of ground feature index can also be learned automatically. In the Pavia University data set, there is a category of features called metal. There is no traditional index that can extract this type of feature. Using the method proposed in this article, we can automatically learn the interactive features of metal. When the regularization coefficient is 1, the coefficient results are shown in Table 16.  
The wavelength of band22 is 535 nm, which represents the G band, indicating that the metals in the data set are more sensitive to the G band. Among interactive features like BAND22, BAND23, and BAND29, BAND22 performs best, and BAND22 can be expressed as:  
\[\text{BAND22}=\frac{\text{band22}-\text{band52}}{\text{band22}+\text{band52}}= \frac{G-R}{G+R} \tag{14}\]  
The wavelength of band52 is 685 nm, which represents the R band. The metal is extracted by BAND22, and the binarization result is shown in Figure 10  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **22** & **BAND22** & **BAND23** & **BAND29** \\ \hline Coefficient & 4.51 & 1.82 & 0.84 & 1.06 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Interaction characteristic coefficients of metal plates.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Pavia Center**} & \multicolumn{2}{c}{**Washington DC**} & \multicolumn{2}{c}{**Pavia University**} \\ \cline{2-5}  & **NDVI** & **Ours** & **NDVI** & **Ours** & **NDVI** & **Ours** \\ \hline
3 & & 0.939 & & 0.582 & & 0.791 \\
5 & & 0.951 & & 0.59 & & 0.803 \\
7 & 0.935 & 0.942 & 0.551 & 0.611 & 0.768 & 0.882 \\
9 & & **0.945** & & **0.605** & & **0.895** \\
11 & & 0.942 & & 0.596 & & 0.837 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The overall accuracy of vegetation extraction for the different number of bands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Pavia Center**} & \multicolumn{2}{c}{**Washington DC**} & \multicolumn{2}{c}{**Pavia University**} \\ \cline{2-5}  & **NDVI** & **Ours** & **NDVI** & **Ours** & **NDVI** & **Ours** \\ \hline
3 & & 0.939 & & 0.582 & & 0.791 \\
5 & & 0.951 & & 0.59 & & 0.803 \\
7 & 0.935 & 0.942 & 0.551 & 0.611 & 0.768 & 0.882 \\
9 & & **0.945** & & **0.605** & & **0.895** \\
11 & & 0.942 & & 0.596 & & 0.837 \\ \hline \hline \end{tabular}
\end{table}
Table 15: The overall accuracy of water body extraction for the different number of bands.  
BAND22 has an outstanding effect on the extraction of metal, and quantitative results are also given in Table 17.  
The interactive feature BAND22 can be used as a new ground feature index for extracting metals.
## 2 Materials and Methods
### Detection and Quantification of Norovirus
#### 2.3.2 Nucleic Acid Extraction  
Viral RNA was extracted from 500 \(\upmu\)L of the shellfish supernatants using the EGENEPUP\({}^{\otimes}\) platform and the NucliSens magnetic extraction reagents (bioMerieux, Marcy I'Etoile, France) following the manufacturer's instructions. RNA was eluted into 100 \(\upmu\)L of elution buffer and stored at \(-\)80 \({}^{\circ}\)C until testing.  
Norovirus Quantification  
For NoV GGI and GGI, real-time reverse transcription-PCR amplification was performed using a C1000 Thermal Cycler (Biorad Laboratories, Hercules, CA, USA) and the RNA Ultrasense one-step kit qRT-PCR (Invitrogen, Life Technologies, Waltham, MA, USA). For both NoV genogroups, 5 \(\upmu\)L of sample was added to 20 \(\upmu\)L of mixes containing 1 X of Ultrasense reaction mix, 500 nM of the forward primer and 900 nM of the reverse primer, 250 nM of the probe, 1.25 \(\upmu\)L of RNA Ultrasense enzyme mix, and ultrapure water by vol ume. The primers and probes used were in accordance with ISO 15216-1:2017/A1:2021 [39]. Reverse transcription was performed at 55 \({}^{\circ}\)C for 60 min, followed by preheating to 95 \({}^{\circ}\)C for 5 min, and 45 cycles of PCR (denaturation at 95 \({}^{\circ}\)C for 15 s, annealing at 60 \({}^{\circ}\)C for 60 s, and extending at 65 \({}^{\circ}\)C for 60 s). Negative and positive controls were included in each plate to monitor the correctness of the assay. Plasmid RNA of the various targets was used as a positive control to verify the amplification efficiency, and the RNA of MV after virus extraction was used as a control of the extraction efficiency. Standard curves for NoV quantification were created using the tenfold serial dilution of a dsDNA standard for NoV GI and NoV GII. Consequently, the number of NoV RNA copies in each sample was determined by comparing the Ct value to the standard curves. The final concentration was then calculated based on the volume of the analyzed extract, and expressed as genomic copies per gram (g.c./g). All samples were evaluated for extraction efficiency by comparing the Ct value of MV obtained in spiked samples with a standard curve derived from the extraction of 10 \(\upmu\)L of MV. Samples with extraction efficiency greater than 1% were deemed acceptable. The presence of RT-qPCR inhibitors was evaluated by adding 1 \(\upmu\)L of external control (EC) RNA for each genogroup. The Ct value obtained in samples spiked with the EC RNA was then compared to that of water spiked with the EC RNA. Inhibition values below 75% were considered valid. Curves with amplification efficiencies of \(\sim\)90% to 110% were used for calculations. The limit of quantification (LOQ) for NoV GI and NoV GII assays was 140 g.c./g and 130 g.c./g, respectively. The limit of detection of 50% (LOD\({}_{50}\)) was 7.8 g.c./g for NoV GI and 12.4 g.c./g for NoV GII.
## 2 Design of the Pattern Reconfigurable Antenna  
### Antenna Design  
A pattern reconfigurable is designed and its structure is illustrated in Figure 1, the close-ups shown in Figure 1b are the RF MEMS switch and its DC actuating circuit. The geometry configurations of Figure 1a are shown in Table 1. The antenna consists of one rectangle driven patch radiator, four rectangle parasitic patches, two assistant patches, and two RF MEMS switches. The RF MEMS switch was terminated by a \(\lambda_{g}/4\) sector open stub and a high resistivity bias line. Parastic patch 1 and 2 are used for extending the operating bandwidth. The proposed pattern reconfigurable antenna patches and RF MEMS switches were all fabricated on a high resistivity silicon substrate with a thickness of 400 \(\upmu\)m and a dielectric constant of 11.9.  
Figure 1: Proposed pattern reconfigurable antenna. (**a**) The configuration of proposed antenna. (**b**) The 3D view of the proposed antenna.  
By changing the mode of the RF MEMS switches, the antenna is capable of switching among four operating modes (00, 01, 10, 11; the \"0\" represents the up state, and the \"1\" stands for the down state) and achieving three pattern reconfigurable states in the _yoz_ plane (\(\phi\) = 90\({}^{\circ}\)) accordingly, because two of the operating modes are at the same pattern reconfigurable state. According to the measurement results of the fabricated pattern reconfigurable antenna, the three reconfigurable radiating patterns at operating frequency 35 GHz were obtained, i.e., left (approximately \(-20^{\circ}\)), middle (approximately 0\({}^{\circ}\)), and right (approximately +20\({}^{\circ}\)), respectively. The detailed results of the reconfigurable radiating pattern main lobe direction and its associated RF MEMS switches operating modes are shown in Table 2.