esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Inference-Time Re-Parameterization of MRAB
#### 3.2.3 Loss Function  
To recover high-frequency details and colors, we design two new loss functions, namely the Laplace pyramid loss and the color attenuation loss. During training, the total loss function is  
\[L_{\Theta}=L_{1}+\alpha_{1}L_{\text{Laplace}}+\alpha_{2}L_{\text{CA}}, \tag{8}\]  
The Laplace pyramid is commonly used to extract high-frequency features from an image. To calculate the Laplace pyramid loss, the recovered clean image \(RRA(I_{\text{hazy}})\) and its ground-truth \(I_{\text{gt}}\) undergo the same process to build two 3-layer Laplace pyramids. Let \(G_{1}\) denote \(RRA(I_{\text{hazy}})\) or \(I_{\text{gt}}\). \(G_{1}\) is downsampled 3 times successively by Gaussian filtering to generate \(G_{k}\), \(k=2,3,4\). The Laplace pyramid is constructed as \(LP_{k}=G_{k}-u(G_{k+1})\), \(k=1,2,3\), where \(u\) is the bilinear interpolation upsampling operation. Finally, the proposed Laplace pyramid loss is calculated as  
\[L_{\text{Laplace}}=\frac{1}{N}\sum_{k=1}^{3}\sum_{i=1}^{N}\Bigl{\|}LP_{k}(RRA( I_{\text{hazy}}^{i}))-LP_{k}(I_{\text{gt}}^{i})\Bigr{\|}_{2}^{2}. \tag{9}\]  
Color Attenuation Loss  
The saturation and brightness of an image are influenced by nonhomogeneous haze. The saturation of the hazy area usually is decreased because the color fades under haze, while the brightness of the hazy area is usually increased. A color attenuation prior was presented in [11] where the haze concentration \(P(x)\) at position \(x\) can be calculated by the following formula:  
\[P(x)=\|S(I(x))-V(I(x))\|_{1}, \tag{10}\]  
where \(S(I(x))\) is the saturation at position \(x\) of image \(I\), and \(V(I(x))\) is the brightness at position \(x\) of image \(I\). Inspired by this prior, we designed a color attenuation loss \(L_{\text{CA}}\) to regulate the RRA-Net to recover the saturation and brightness. \(L_{\text{CA}}\) is calculated as:  
\[L_{\text{CA}}=\alpha\frac{1}{N}\sum_{i=1}^{N}\Bigl{\|}S(RRA\left(I_{\text{hazy }}^{i}\ )\right)-S(I_{gt}^{i})\Bigr{\|}_{2}^{2}+\beta\frac{1}{N}\sum_{i=1}^{N}\Bigl{\|} V(RRA\left(I_{\text{hazy}}^{i}\ )\right)-V(I_{gt}^{i})\Bigr{\|}_{2}^{2}, \tag{11}\]  
where \(\alpha\) and \(\beta\) are the balancing coefficients. Empirically, the values of \(\alpha\) and \(\beta\) are set to 1 and 0.5, respectively.
## 3 Results
### Stability Analysis of Multi-Temporal Calibration Results
#### 3.1.2 Ohs-F CMOS2 Calibration Results and Analysis  
Figure 10 shows the comprehensive gains of the CMOS2 sensor in 2020 and 2021. In 2020, the consistency between the comprehensive gains on different dates was not very high. The RB of the comprehensive gains for the bands at the wavelength range of 550 nm to 880 nm was basically approximately 15%, while it was up to more than 25% for the bands with central wavelengths outside this wavelength range, indicating that the radiometric stability of the CMOS2 sensor in each band in 2020 was not very good on the whole. In 2021, the consistency between the comprehensive gains on different dates was significantly better than that in 2020. Except for the four bands of 441 nm, 910 nm, 926 nm, and 940 nm, the RB of the comprehensive gains of other bands on different dates was less than 10%, indicating that the radiometric performance of the CMOS2 sensor in 2021 was very stable.  
Figure 9: Calibration results of OHS-F CMOS1. (**a**) Description of comprehensive gains in 2020; (**b**) description of averaged value and SD of comprehensive gains in 2020; (**c**) description of comprehensive gains in 2021; (**d**) description of averaged value and SD of comprehensive gains in 2021.
## 2 Methodology
### Revision of Rating Scores and Weights
#### 2.2.3 Monte Carlo Method  
The essential idea of the MC method is the use of randomness to obtain numerical results of problems that might be deterministic in principle [37]. It is often used for the optimization of mathematical problems [38] and is most useful when it is difficult or impossible to use other approaches. Generally, the MC method follows a pattern: (1) define a domain of possible inputs, (2) randomly generate inputs from a probability distribution over the domain, (3) perform a deterministic input computation, and (4) aggregate the results.  
In this study, one million random weight vectors were produced and each component of every vector was uniformly distributed across the range of 0-5. One million Pearson's \(\rho\) correlation coefficients were calculated; then, the maximum coefficient was selected, and the corresponding weight vector was regarded as the solution.
# A Maximum-Entropy Fuzzy Clustering Approach for Cancer Detection When Data Are Uncertain
## 4 Simulation Study
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{_Centers-Radii_} & \multicolumn{2}{c}{_Centers_} & \multicolumn{2}{c}{_Radii_} \\  & **Fuzzy Points** & **Fdist** & **w** & **Fdist** & **w** & **Fdist** & **w** \\ \hline \multicolumn{8}{c}{**Entropy-based fuzzy clustering for interval-valued data (EFC-ID)**} \\ \hline  & 0\% & 0.000 & 0.490 & 0.000 & 0.200 & 0.023 & 0.500 \\ \multirow{2}{*}{\(p=0.10\)} & 1\% & 0.000 & 0.500 & 0.158 & 0.254 & 0.140 & 0.500 \\  & 5\% & 1.578 & 0.500 & 1.581 & 0.380 & 0.665 & 0.500 \\  & 10\% & 2.236 & 0.500 & 2.236 & 0.445 & 1.664 & 0.500 \\ \multirow{2}{*}{\(p=0.20\)} & 0\% & 0.000 & 0.490 & 0.000 & 0.200 & 0.522 & 0.492 \\  & 1\% & 0.014 & 0.500 & 0.043 & 0.254 & 0.513 & 0.493 \\  & 5\% & 1.480 & 0.500 & 1.544 & 0.381 & 0.580 & 0.496 \\  & 10\% & 2.231 & 0.500 & 2.232 & 0.445 & 0.902 & 0.500 \\ \hline \multirow{2}{*}{\(p=0.40\)} & 0\% & 0.000 & 0.490 & 0.000 & 0.200 & 7.062 & 0.338 \\  & 1\% & 0.006 & 0.500 & 0.017 & 0.254 & 7.028 & 0.340 \\ \multirow{2}{*}{\(p=0.40\)} & 5\% & 0.996 & 0.500 & 1.205 & 0.388 & 6.887 & 0.350 \\  & 10\% & 2.092 & 0.500 & 2.097 & 0.452 & 6.707 & 0.364 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Clustering models performance with well-separated clusters (0% of fuzzy points) and not well-separated clusters (1%, 5%, 10% of fuzzy points). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Fuzzy Points** & \multicolumn{2}{c}{_Centers-Radii_} & \multicolumn{2}{c}{_Centers_} & \multicolumn{2}{c}{_Radii_} \\  & **Fuzzy Points** & **Fdist** & **w** & **Fdist** & **w** & **Fdist** & **w** \\ \hline \multicolumn{8}{c}{**Fuzzy relational clustering (FRC)**} \\ \hline  & 0\% & 5.584 & - & 5.670 & - & 5.257 & - \\ \multirow{2}{*}{\(m=1.10\)} & 1\% & 5.815 & - & 5.630 & - & 5.315 & - \\  & 5\% & 5.773 & - & 5.606 & - & 5.275 & - \\  & 10\% & 5.769 & - & 5.480 & - & 5.955 & - \\ \hline \multirow{2}{*}{\(m=1.50\)} & 0\% & 0.125 & - & 3.428 & - & 4.433 & - \\  & 1\% & 0.496 & - & 2.565 & - & 4.232 & - \\  & 5\% & 1.554 & - & 2.765 & - & 4.112 & - \\  & 10\% & 2.428 & - & 2.548 & - & 4.233 & - \\ \hline \multirow{2}{*}{\(m=2.00\)} & 0\% & 0.796 & - & 1.673 & - & 8.342 & - \\  & 1\% & 0.827 & - & 1.842 & - & 8.661 & - \\  & 5\% & 1.234 & - & 1.662 & - & 7.844 & - \\  & 10\% & 2.139 & - & 1.992 & - & 7.645 & - \\ \hline \multicolumn{8}{c}{**Hierarchical clustering (HC)**} \\ \hline  & 0\% & 0.000 & - & 1.400 & - & 2.144 & - \\ Single linkage & 1\% & 0.707 & - & 3.659 & - & 2.558 & - \\  & 5\% & 1.581 & - & 8.483 & - & 2.799 & - \\  & 10\% & 2.283 & - & 9.105 & - & 2.721 & - \\ \hline \multirow{2}{*}{Complete linkage} & 0\% & 0.000 & - & 0.009 & - & 2.144 & - \\  & 1\% & 0.707 & - & 0.723 & - & 2.558 & - \\ \multirow{2}{*}{\(p=0.20\)} & 5\% & 1.581 & - & 1.613 & - & 2.799 & - \\  & 10\% & 2.236 & - & 2.286 & - & 2.721 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 3 Proposed Work
### Noise Reduction  
Noise reduction is among the most critical phases in the overall network flow. This is a technique for reducing noise from an image, which can affect both the visual qualities and the efficiency of future processing operations. There are various objects and signals, such as traffic indicators and directions in traffic streams, on the roadside, and in traffic lanes in this scenario (according to the simulation). Nevertheless, since these are presumably not related to vehicle and object detection and comparing, they shall harm matching findings and the effectiveness of following parking space calculations. Moreover, even identical vehicles, such as two individual cars in this condition, shall be of a similar design but varying in color. Image quality may be assessed using the signal-to-noise ratio (\(SNR\)). An imaging system's sensitivity is commonly measured in terms of the \(SNR\). When the signal is an optical intensity, it is defined as the ratio of the average signal value \(\mu_{sig}\) to the standard deviation of the signal \(\sigma_{sig}\), or as the square of this value when the signal and noise are seen as amplitudes.  
\[SNR=\frac{\mu_{sig}}{\sigma_{sig}} \tag{1}\]  
It may guarantee that irrelevant items, signs, and indications are deleted from images before they are subsequently processed. It also eliminates color classification issues. Before noise mitigation, the image created from vision acquisitions was loaded with obstructions such as a highway sign, a traffic cone, and yellow circle marks. It was tough to distinguish and identify cars and goods as a result of all of this. Noise minimization was attained utilizing an \"image mask\" to prevent unrelated areas of the images, \"color plane extraction\" to transform the color picture to a binary picture in only black and white, and \"basic morphology\" to enhance the structure of binary objects in the picture, and also to modify the illumination. Following noise minimization, the indicator, traffic cone, and yellow circle marks are eliminated and only the individual vehicle stayed on the display with its structure displayed in white.
## 3 Results
### Radiometric Consistency
#### 3.2.2 Comparison with Sentinel-2 Imagery  
As described in Section 2.5, the reliability of Sequoia maps was assessed with respect to Sentinel-2 data to evaluate the feasibility of data integration. First, an upscaling of maps derived from Sequoia imagery was required, then correlation analysis was computed (N = 265 samples). Results for  
Figure 11: Spatial distribution of differences in the overlapping area for Ig/Ir scenario, with respect to block 4: green (**a**), NIR (**b**), NDVI (**c**).  
Figure 10: Spatial distribution of differences in the overlapping area for Ig/Ir scenario, with respect to block 3: green (**a**), NIR (**b**), NDVI (**c**).  
the correlation analysis are reported in Figure 12 and map statistics are summarized in Table 6. For the sake of brevity, only results for NDVI are shown. As a matter of fact, other studies are present in the literature focusing on the comparison of NDVI only [51; 52]. RMSE values reported in Table 6 were calculated by considering the NDVI map from S2 imagery as a reference.  
The correlation with NDVI map from S2 imagery shows a good correspondence: coefficients of determination are 0.5197 for 3 Ig/Ir, 0.5249 for 4 Ig/Ir, and 0.4840 for Ig/Ir. The NDVI map with the highest correspondence against S2 imagery is the one derived from 4 Ig/Ir data, with Pearson's correlation coefficient \(\varrho\) and RMSE equal to 0.7245 and 0.0984, respectively. Nevertheless, the regression lines show a slight overestimate of Sequoia data compared to S2; NDVI maps from Sequoia imagery report higher values with respect to the S2 map (as also summarized by higher values for max. and mean in Table 6) and cover wider ranges (lower values for min. and higher values for STD in Table 6).
## 3 Calculation Results and Analysis
### The Influence of Nonlocal Parameter on Frequencies  
Figures 5-7 show the influence of the nonlocal parameter on the natural frequencies during the rotation of the nano- annular plates. \(\overline{\Omega}\)= 5, \(s=0.1\), \(\Delta T=0\) K. It can be seen from the figure that the increase in the nonlocal parameter reduces the equivalent stiffness of the nano-annular plates and decreases the first three order natural frequencies. Compared with the second and third order natural frequencies, the fundamental frequencies change less obviously. Comparing the law of the influence of nonlocal parameter on frequencies with the numerical results of literature [29], it can be found that the law of the influence of the nonlocal parameter on frequencies is not affected by the state of motion and rotational velocity and is consistent with the law of change without rotation. In the figures, the natural frequencies under the two boundary conditions are compared. When the values of the dimensionless rotational velocity, gradient index, and other parameters are constant, it can be found that the natural frequencies of the nano-annular plates under the C-C boundary condition are higher than the natural frequencies of nano-annular plates under the C-F boundary. When the inner and outer rings of the annular plates are clamped, the mechanical properties are enhanced, and there is good stability during the working process.  
Figure 3: The relationship between dimensionless natural frequencies and the rotational velocity under two different boundary conditions. (**a**) [PERSON] plate; (**b**) [PERSON] plate; (**c**) [PERSON] plate; (**d**) [PERSON] plate; (**e**) [PERSON] plate; (**f**) [PERSON] plate.  
Figure 4: The relationship between dimensionless natural frequencies and the functionally gradient index under two different boundary conditions. (**a**) [PERSON] plate; (**b**) [PERSON] plate; (**c**) [PERSON] plate; (**d**) [PERSON] plate; (**e**) [PERSON] plate; (**f**) [PERSON] plate.  
Figure 5: The relationship between dimensionless natural frequencies and the nonlocal parameter under two different boundary conditions (Kirchhoff plate). (**a**) The first-order frequencies; (**b**) The second-order frequencies; (**c**) The third-order frequencies.  
Figure 6: The relationship between dimensionless natural frequencies and the nonlocal parameter under two different boundary conditions (Mindlin plate). (**a**) The first-order frequencies; (**b**) The second-order frequencies; (**c**) The third-order frequencies.
# Bell-Clauser-Horne-Shimony-Holt Behavior Under Quantum Loss and Decoherence
## 3 Qubit Quantum Channels  
Assume now that the two quantum bits do not arrive with [PERSON] and [PERSON] in the maximally entangled state, \(\varrho^{(|\Phi^{(+)}\rangle)}\), but they undergo some unwanted change on their way. For instance, optical qubits are subjected to loss or decoherence when traveling through an optical fiber. In quantum mechanics, such loss is modeled by completely positive trace preserving (CPTP) maps, i.e., quantum channels. After a brief summary of the description of the CPTP maps we will be using, we turn our attention to our main point: their effect on the behavior of the CHSH experiment.  
We use the Choi-Jamiolkowski representation of CPTP maps of quantum channels to describe (quantum) loss and imperfections. A detailed introduction can be found, e.g., in [27]. Here, we just recapitulate the way of calculating the output density matrix from the input. Let \(\mathcal{H}_{\text{in}}\) and \(\mathcal{H}_{\text{out}}\) be the complex, finite-dimensional Hilbert spaces of the input and the output of the quantum channel; these are two dimensional in the case of qubits. Let us fix a basis in both spaces. A CPTP map \(\mathcal{E}\) can be described by its Choi matrix: a Hermitian positive semidefinite matrix, \(X_{\mathcal{E}}\), representing a map \(\mathcal{H}_{\text{in}}\otimes\mathcal{H}_{\text{out}}\mapsto\mathcal{H}_{ \text{in}}\otimes\mathcal{H}_{\text{out}}\) on the product of the two fixed bases. Then, the output of the quantum channel when its input is \(\varrho\) is calculated as  
\[\mathcal{E}(\varrho)=\text{Tr}_{\text{in}}\Big{(}\big{(}\varrho^{\top}\otimes \mathbb{I}\big{)}X_{\mathcal{E}}\Big{)}. \tag{18}\]  
Any positive semidefinite Hermitian matrix with the property  
\[\text{Tr}_{\text{out}}X_{\mathcal{E}}=\mathbb{I}_{\text{in}} \tag{19}\]represents a completely positive map; thus, our analysis could be carried out for generic channels using a parametrization of these. This would, however, require to use a large number of parameters; hence, we will consider two particular types of qubit channels (as our investigation will be restricted to pairs of quantum bits). Each have a parameter \(p\in[0,1]\); for \(p=0\), they coincide with the identity channel, whereas for \(p=1\), they change state the most. The considered channel types are presented here.
# The Presence of Marine Filamentous Fungi on a Copper-Based Antifouling Paint
## 3 Results and Discussion
### Species of Fungi Isolated from Different Substrata  
In total, six fungal isolates were obtained from the antifouling paint and four were isolated from unprotected substrata (Table 1). Based on the phylogenetic analysis, the majority of isolates belonged to _Aspergillus_ and _Cladosporium_ genera (Supplement Figures S1-S4). The genera _Aspergillus_ and _Cladosporium_ are commonly found in the marine environment [39, 40, 41]. Additionally, _Aspergillus_ and _Cladosporium_ are associated with marine sponges [42,43]. There is limited information about marine derived filamentous fungi in Oman, but we have been able to isolate _Aspergillus terreus_ from mangrove areas [44]. _Cladosporium onanense_ found in this study (Table 1) was previously isolated from living leaves of _Zygophyllum coccineum_ in Oman [45]. The presence of _C. Omanense_ on all investigated substrates could be due to several reasons. It could suggest that this species is very common in Omani waters and can colonize protected and unprotected substrata. Alternatively, it could be due to contamination of our culture by spores of this fungus. This is highly unlikely, as there were no fungi recovered from the control plates with autoclaved seawater.  
Filamentous fungi belonging to the genera _Parengyodontium_ were isolated from biofouled ropes only (Table 1). _Parengyodontium album_ is an environmental saprobic mold and an opportunistic pathogen [46]. This species has been observed on buildings composed of limestone and plaster [47]. Additionally, _P. album_ was found in sediments of polar-boreal White Sea [48]. The presence of this fungus in Oman waters suggests that this species can be found in tropical waters as well.  
The genera _Aspergillus_, _Cladosporium_ and _Alternaria_ were found on the copper-based antifouling paint (Table 1). Moreover, _A. tubingensis_, _A. terreus_, _A. niger_ and _C. haltoloreans_ were found only on the antifouling paint. _Alternaria_ isolates were obtained exclusively from the paint. Previously, the fungi _Alternaria_ were isolated from soft corals [49], sponges [50] and algae [51]. While 185 RNA of fungi belonging to the class Agaricomycetes was detected on an antifouling paint using Illumina amplicon sequencing [52], fungal isolates were obtained from antifouling paints for the first time in this study. Previously, only bacteria and diatoms were detected in biofilms on antifouling paints [3,21].
## 3 Simulation, Results, and Discussion
### Flat Structure  
Figure 4a illustrates the symmetrical two-port rectangular microstrip-fed patch metamaterial-based flat microwave sensor presented in [41]. A CSRR cell is etched on the ground plane, with a centered hole crossing the patch and substrate, so as to place the test tube of liquid samples normally relative to the surface of the sensor. First, we validated the first basic sensor structure (Figure 4a) by comparing CST simulation results with experiments published in [41], where only experimental results were reported, without simulation validation. The second structure, where the test tube placement was changed to be parallel to the lower side of the sensor from the CSRR side (Figure 4b), was fabricated as illustrated in Figure 5. The simulations showed good agreement with the experiments (Figure 6).  
Figure 4: (**a**) Sensor structure with a normal glass tube [41]; (**b**) flat sensor with a parallel glass tube.  
Figure 3: CST simulations of the flat sensor: (**a**) \(\mathbf{S_{21}}\) parameter and (**b**) Q factor of mixtures with different water–ethanol concentrations for the proposed sensor, with test glass radius r = 3.75 mm.  
The glass capillary tube was easily produced using a Creality Ender-3 Pro 3D printer using a 0.2 mm nozzle and PLA filament. This was used in the case of the flat structure measurements. This kind of capillary tube is commonly used in clinical and laboratory settings; it is 75 mm long, with an outer radius of 0.75 mm, an inner radius of 0.5 mm, and relative permittivity of 5.5. After the confirmation of the microwave sensing application's potential through the numerical computations, the microwave sensor model was fabricated on 0.75 mm thick Roger RO3035 substrate with Eleven Lab PCB prototyping system, as shown in Figure 5.  
This new tube placement brought a significant benefit in terms of field interaction with the liquid being tested, significantly enhancing the sensor sensitivity.  
Figure 6 illustrates the simulated S21 parameter compared to the experimental data reported in [41]. Validation tests on the design show that the simulation results and measurement data are in good agreement for the three considered cases (water concentration 0%, 100%, and air (Figures 6a and 7b)). It should be noted that the higher the concentration of water in the mixture, the lower the frequency of the peak. On the other hand, the resonant frequency and quality factor patterns are nearly overlapping, with a shift of less than 5 MHz. Figures 6b and 7b illustrate the parallel glass tube design simulations of water-ethanol mixture, with an outer radius of r = 0.75 mm. As a result, the sensitivity and quality factor values are doubled compared to the normal test tube case. Note that the normal test tube sensor sensitivity factor was 38 MHz [41], while it was 89 MHz for the parallel case, and the quality factors were 47% and 73%, respectively (Figures 6b and 7b).  
Figure 5: Fabricated sensor prototype: (**a**) top view; (**b**) bottom view; (**c**) flat sensor with embedded tube.
## 3 Results and Discussion
### Energy Utilization in The Convective Solar Dryer
#### 3.5.2 Specific Energy Consumption (SEC) in Forced Convection Solar Drying  
The evolution of the specific energy consumption (SEC) at different air-drying temperatures in the forced convection solar drying is presented in Figure 10.  
The SEC values (energy necessary per unit mass) varied between 21.40 and 49.91 MWh/kg at drying air temperatures of 60, 70, and 80 \({}^{\circ}\)C. The specific energy utilization decreased when the air-drying temperature increased. The decrease in temperature resulted in the increased demand for dryer energy, which is due to the prolonged drying time needed at lower temperatures. Furthermore, we observed an increase in the total energy consumption (Figure 10) at lower drying air temperatures. Same observations were reported in rosemary leaves by [PERSON] et al., (2021) on rosemary leaves [62], by [PERSON] and colleagues on carob pulp [70], and on apple slices by [PERSON] (2016) [49].