esa-sceva/satcom-chunk-collection huggingface dataset:
## Results
### Canopy and Satellite Scales: Results for Broadleaf Deciduous Temperate Trees at JP-TKY
#### 3.3.2 Relationship of VIs and \(\text{P}_{\text{max\_capacity}}\)2000  
The relationships between the five candidate VIs (CI\({}_{\text{green}}\), EVI, NDVI, GNDVI, and SR) and \(\text{P}_{\text{max\_capacity}}\)2000 at the canopy scale are shown in Figure 7. All five indices exhibited strong correlations  
Figure 6: Light-response curve of low-stress GPP (GPP\({}_{\text{capacity}}\)) and PAR in broadleaf deciduous temperate trees (JP-TKY) in **(a)** 2003 and **(b)** 2004. Lines represent the least-squares fitting curve for each 16-day period. Dots in different colors indicate half-hourly data (no average) of each 16-day period of GPP\({}_{\text{capacity}}\).  
with \(\rm P_{max\_capacity2000}\), with \(\rm R^{2}\) values of 0.78, 0.62, 0.64, 0.63, and 0.69, respectively (all \(\rm P<0.001\)). EVI, NDVI, GNDVI, and SR became clearly saturated when \(\rm P_{max\_capacity2000}\) was more than about 0.4 mg\(\cdot\)CO\({}_{2}\)\(\rm m^{-2}\cdot\)s\({}^{-1}\), whereas Cl\({}_{\rm green}\) did not saturate. The strongest linear correlation occurred for Cl\({}_{\rm green}\). We obtained the following empirical equation for the relationship between Cl\({}_{\rm green}\) and \(\rm P_{max\_capacity2000}\) (mg\(\cdot\)CO\({}_{2}\)\(\rm m^{-2}\cdot\)s\({}^{-1}\)): \(\rm P_{max\_capacity2000}\)\(=0.13\times\rm CI_{green}-0.13\).  
Figure 8 presents the relationships between the five candidate VIs (CI\({}_{\rm green}\), EVI, NDVI, GNDVI, and SR) and \(\rm P_{max\_capacity2000}\) at the satellite scale. EVI, NDVI, GNDVI, and SR exhibited strong correlations with \(\rm P_{max\_capacity2000}\), with \(\rm R^{2}\) values of 0.63 (\(\rm P<0.05\)), 0.64 (\(\rm P<0.05\)), 0.69 (\(\rm P<0.01\)), and 0.59 (\(\rm P<0.05\)), respectively. However, the strongest linear correlation occurred for Cl\({}_{\rm green}\) (\(\rm R^{2}=0.72\), \(\rm P<0.01\)). Thus, compared to EVI, NDVI, GNDVI, and SR, Cl\({}_{\rm green}\) was the best VI for estimating  
Figure 7: The best fit regression of the linear relationships between various VIs calculated from daily HSSR datasets and \(\rm P_{max\_capacity2000}\) of broadleaf deciduous temperate trees at JP-TKY 2004 at the canopy scale: (**a**) Cl\({}_{\rm green}\), (**b**) EVI, (**c**) NDVI, (**d**) GNDVI, and (**e**) SR.  
P\({}_{\text{max\_capacity}}\)2000. We obtained the following empirical equation for the relationship between CI\({}_{\text{green}}\) and P\({}_{\text{max\_capacity}}\)2000 (mg\(\cdot\)CO\({}_{2}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)): P\({}_{\text{max\_capacity}}\)2000 = 0.15 \(\times\) CI\({}_{\text{green}}\) - 0.37.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Double-Balanced Down-Conversion Mixer Noise Calculation and Simulation
#### 4.2.4 Total Mixer Output Noise  
From Equations (12), (20), and (24), the total noise at mixer output can be written as follows:  
\[\overline{V_{n0,total}^{2}}=\overline{V_{n,R_{L}}^{2}}+2\Big{(}\overline{I_{n0,s }^{2}}\Big{)}~{}R_{L}^{\ 2}+\overline{V_{n,\rho\_RF}^{2}} \tag{25}\]  
\[=8 KTR_{L}+\Big{(}\frac{I_{\RF}}{\pi V_{LO}}\Big{)}4 KT(2r_{b2}g_{m2}+1){R_{L}}^ {2} \tag{26}\]  
The simplified expression for signal-balanced mixer noise down-converted to IF is given by the following:  
\[\overline{V_{n0,total}^{2}}=4 KTR_{L}^{\ 2}\bigg{[}\frac{2}{R_{L}}+(2r_{b2}g_{m2}+1) \bigg{(}\frac{I_{\RF}}{\pi V_{LO}}\bigg{)}+{g_{m}}^{2}\bigg{(}r_{b1}+\frac{1} {2g_{m1}}\bigg{)}\bigg{]} \tag{27}\]
## 4 Deep Learning Methods for HDR Reconstruction
### Transformers for HDR Reconstruction
#### 4.3.3 Convolution-Based Methods  
Some methods seek to refine features exclusively with the Vision Transformer architecture, i.e., they do not employ alignment blocks or spatial or temporal attention blocks for input features. In contrast, the methods described below simply extract image features and concatenate them to carry out all relevant processing in the Vision Transformer blocks, requiring the architecture to handle in some way the importance of the central frame relative to over- and underexposed frames, as shown in Figure 9c. One example is the architecture proposed by [PERSON] et al. [27], which extracts features from each input frame through convolutional blocks. These features serve as input to the Transformer blocks, being concatenated beforehand. The architecture performs the self-attention typical of Vision Transformers but also the cross-attention between the central frame and the other exposure frames to handle issues like blur, noise, and misalignment. In the final stage, refined features are added to the central frame features, and a final convolution reconstructs the HDR image. [PERSON] et al. [162] introduced a multi-scale architecture, reducing by a factor of two until reaching eightfold reduction and then upsampling back to the original scale. A convolution block extracts features from the initial frames at each scale, refining them with a vision transformer specialized in multi-exposure feature fusion, including additional convolutions after each transformer block. [PERSON] et al. [118], using a slightly different approach, propose a self-supervised network for HDR reconstruction called SSHDR. Similar to others, the model extracts features from each input frame using a convolutional layer. The features are then refined through a SwinTransformers [163] architecture with a multi-scale structure. A final convolution merges features into an HDR image. Furthermore, the self-supervised approach enables training with LDR images, both with or without HDR references. Table 5 summarizes all the cited architectures, highlighting the strengths and limitations grouped under the three main methods. Table 6 shows the results of common HDR metrics for Transformer-based architectures evaluated on the [PERSON] et al. [30] dataset. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Architecture** & **PSNR-\(\mathbf{\upmu}\)** & **SSIM-\(\mathbf{\upmu}\)** & **HDR-VDP2** \\ \hline CA-ViT [17] & 44.32 & 0.9916 & 66.03 \\ \hline SCTNet [27] & 44.49 & 0.9924 & 66.65 \\ \hline [PERSON] et al. [157]1 & 40.10 & 0.9619 & 75.57 \\ \hline HDT-HDR [161] & 44.36 & 66.08 & — \\ \hline HFT-HDR [156] & 44.45 & 0.988 & — \\ \hline PASTA [50] & 44.53 & 0.9918 & 65.92 \\ [PERSON]hline HyHDRNet [159] & 44.64 & 0.9915 & 66.05 \\ \hline SV-HDR [162]1 & 37.30 & 0.9826 & — \\ \hline SSHDR [118] & 41.97 & [PERSON] & 67.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of metrics for HDR reconstruction models based on Transformers using the Kalantari dataset.
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 3 The Proposed GGS-MAF Algorithms
### Mixed Error Criterion Algorithm  
As we know, the disturbance in the error signal with a shape of an outlier probably affects the performance of the LMSs such as the impulsive noise. To improve the LMS's performance, two estimation errors called two-order logarithm error and \(p\)-order sign errors are constructed and used to realize the MEC algorithm. Within the AF theory, we can write the modified CF for the MEC algorithm as  
\[J_{\text{MEC}}(n)=\frac{\alpha}{2}\log\left[1+\frac{e^{2}(n)}{2\tau^{2}} \right]+\frac{1-\alpha}{p}|e(n)|^{p} \tag{7}\]  
where the parameter \(\alpha\in[0,1]\) controls the mixed freedom between the two different errors. Taking the gradient on \(J_{\text{MEC}}(n)\) by \(\mathbf{\hat{h}}(\mathbf{n})\), we have  
\[\
abla_{\mathbf{h}}J_{\text{MEC}}(n)=-\alpha\frac{e(n)}{e^{2}(n)+2\tau^{2}} \mathbf{x}(n)-(1-\alpha)|e(n)|^{p-1}\text{sgn}(e(n))\mathbf{x}(n) \tag{8}\]  
Then, the MEC's update equation obtained from the SGM is  
\[\mathbf{\hat{h}}(n+1)=\mathbf{\hat{h}}(n)+\mu_{\text{MEC}}\left[\frac{\alpha e (n)}{e^{2}(n)+2\tau^{2}}+(1-\alpha)|e(n)|^{p-1}\text{sgn}(e(n))\right]\mathbf{ x}(n) \tag{9}\]  
with a step-size of \(\mu_{\text{MEC}}\). \(\tau>0\) is used for preventing the denominator from zero. It is worth noting that the MEC algorithm behaves like the squared error algorithms for \(\alpha=1\), while it works toward \(p\)-order sign error algorithm when \(\alpha\) is close to 0.
# Monitoring of Liquid Viscosity for Viscous Dampers through a Wireless Impedance Measurement System
## 1 Introduction
[PERSON] et al used the series/parallel multi-sensing technique to simultaneously detect the looseness of multiple bolts [37]. [PERSON] et al used the piezoceramic smart washer to monitoring bolt pre-load [38; 39]. [PERSON] et al used electromechanical impedance to monitoring corrosion damage [40; 41; 42]. [PERSON] et al used electromechanical impedance to monitor the grout compactness in a concrete filled fiber reinforced polymer tube [43]. Based on the phase shift, [PERSON] et al. developed a non-destructive evaluation device to monitor the fluid viscosity [44]. [PERSON] at al. used the wave propagation method to monitor the viscosity of fluids [45]. While the above methods have unique benefits for the precise and accurate monitoring of damage, all require wired connections, which can impede practical implementation. A wireless, piezo-based impedance measurement system would be much more practical. Furthermore, although the above methods can distinguish changes in liquid viscosity, implementation can be highly complex, and require the use costly, high-end data acquisition equipment. Requirements are compounded when more than one sensor is needed, which limits the practical application of the piezoelectric impedance method. To overcome this drawback, this paper proposes a viscosity monitoring method for viscous damper liquids based on an innovative wireless impedance measurement system that is designed for practical use. In the following experiment, a damper cylinder specimen is designed, and a sensor is embedded inside the specimen. A total of ten different viscosities of damping fluid were tested. A wireless impedance measurement system is used to capture changes in the resonance frequency of the damping fluid over different viscosities. Finally, based on the RMSD method, a normalized viscosity index (VI) is proposed in this paper to evaluate the viscosity variation of the damping fluid.
## 3 Numerical Models and Parameter Selection
### Material Parameters of ALE
#### 3.3.2 Water and Air Material  
The empty matter material model MAT-NULL represents water and air and describes their state changes by the joint equations state Gruneisen and Liner, respectively [37]. The main parameters of water are the density of 1000 kg/m\({}^{3}\), software parameters \(GAMAO\) = 0.5, \(A_{w}\) = 1.393, \(C\) = 1480, \(S_{1}\) = 2.56, \(S_{2}\) = \(-\)1.99, and \(S_{3}\) = 0.23. Air parameters are the density of 1.29 kg/m\({}^{3}\), the initial internal energy is 2.5 \(\times\) 10\({}^{-4}\) Gpa, and constants \(C_{4}\) and \(C_{5}\) are 0.4.
# Silk Protein-Based Membrane for Guided Bone Regeneration
## 4 Silk Mat as GBR Membrane
### Animal Experiment and Clinical Trials  
Cellular experiments have indicated that the silk mat from the middle layer of the cocoon has an advantage to the others, and this hypothesis has been studied in an animal model. The silk mat from the middle layer was compared to collagen and e-PTFE membranes in a rat calvarial defect model [22]. The silk mat showed bone regeneration similar to collagen and superior to e-PTFE membranes [22]. When the whole layer of cocoon was used for the GBR membrane, its bone formation ability was inferior to the silk mat from the middle layer [21]. If silkworm cocoons are separated into four silk mats of even thickness, the middle two groups showed better bone formation than the innermost and outermost groups [55].  
Based on cellular and animal model results, the KFDA approved clinical trials for silk mat as a GBR technique (SPENSER-TS101, approved on 27 November 2015, Figure 10). These clinical trials were also approved by the institutional review board of Hallym Medical Center (2016-S016). A total of 25 patients having bilaterally impacted third molars were enrolled. Patients received e-PTFE membranes and silk mat membranes after tooth removal, and the application side was selected randomly. A single patient received both type of membranes, the comparison between membrane groups was done by paired samples t-test. When bone regeneration was evaluated at 6 months after the operation, there was no significant difference between the groups (Figure 10; \(p\) > 0.05). In addition, both groups showed significant bone gain when compared immediately after the extraction and 6 months after the extraction (_p_ < 0.05). The amount of bone gain was approximately 4 mm. When the bone defect after the removal of a deeply impacted mandibular third molar was allowed to heal naturally, the amount of bone gain was approximately 2 mm. The differences between application groups were also statistically significant (_p_ < 0.05). There were no serious complications in the silk mat application. When the silk mat was exposed to an oral cavity by wound dehiscence, it could be contaminated by foods and cause inflammation, similar to other types of membranes. However, wound dehiscence was surgical-technique sensitive and not caused by the material.
# Rheological Study of an Extruded Fish Diet with the Addition of Hydrolyzed Protein Flour
## 3 Results
### Effect of Humidity  
Figure 3 shows the effect of moisture content variation on the shear stress and apparent viscosity in the treatments studied with a protein flour content of 35%. It is observed that the increase in moisture content caused a decrease in both shear stress and apparent viscosity.  
According to Table 7, when the moisture content decreases from 24% to 20%, the consistency index (\(K_{\text{lp}}\)) increases from values between 159 and 2299 Pa\(\cdot\)s to values between 333 and 6362 Pa\(\cdot\)s, indicating that the moisture affects the apparent viscosity of the treatments. Similar results were found where researchers studied the effect of the extrusion parameters on the rheological behavior of corn using an in-line slit die rheometer, reporting that the viscosity, specific mechanical energy, torque, and pressure in the head decreased with increasing humidity of the matrices, consistent with an increase in the molecular mobility of the treatments [10; 13]. On the other hand, it is expected that at lower moisture contents, both starch molecules and proteins suffer more damage, destructuring the molecules causing Maillard reactions, minimizing this effect at moisture contents higher than 25% [27].  
The gelatinization of starch during extrusion occurs at a humidity lower than 30%, lower than other ways of processing food [35]. The limitation of the water proportion between 20% and 24% in the extrusion-cooking process, conditions in which the present test was developed, affected the macromolecules present in the processed mixture, possibly obtaining starch molecules in different gelatinization stages, such as gelatinized but not swollen granules, swollen and gelatinized granules, and even wholly damaged granules. Therefore, the interaction between moisture content and temperature affects molecular transformation at different levels [36]. An increase in the moisture content and the extrusion temperature promotes molecular mobility, with the corresponding decrease in viscosity, as shown in Figure 3. On the other hand, the temperature increases the system's energy, the water acts as a solvent, and the plasticizer makes it easy for macromolecules to unfold [11; 17; 25; 36].  
Other studies investigated the structural and rheological changes of textured mung bean protein induced by feed moisture during extrusion. They found that extrusion at 49.3% feed moisture favorably produced partially denatured textured mung bean protein (TMBP), the formation of small aggregates, better solubility, and digestibility with a strong gel formation behavior, while the moisture content of 30.0-60.0% resulted in a complete denaturation of the protein and undesirable formation of large aggregates and weak gels [37]. Furthermore, both amylose and amylopectin could significantly reduce pressure and torque during the high humidity extrusion process, further decreasing the extrudates' color difference (\(\Delta\)E), hardness, elasticity, and chewiness [38].
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
## 4 Laser Wavelengths Versus FSO Links
Since some attenuation processes strongly depend on the wavelength (Rayleigh scattering from air molecules), the proposed choice of wavelength is highly important. It is known that the attenuation is much greater for shorter wavelengths in the case of scattering. In fact, it varies inversely to the fourth power of the wavelength [81]. Fog causes the Mie scattering, larger particles of snow cause the geometric scattering (having similar effects to the Rayleigh scattering), while rainfall contributes to nonselective scattering [82]. Other phenomena that may affect transmission efficiency are smoke [83], sandstorms [84], and clouds [85]. In addition to physical assessments, other media should also be considered when selecting the type of laser [86]. For a wavelength range of 780-880 nm, cost-effective and reliable lasers with very good parameters as well as detectors, which are used in consumer devices, are easily available. Silicon pin and avalanche photodiodes, LEDs, semiconductor laser diodes, and Vertical-Cavity Surface-Emitting Lasers (VCSEL) are used to work at these wavelengths [87]. Within a wavelength range of 1520-1600 nm, both sources and detectors of optical radiation are also readily available. In this band, semiconductor lasers made of Indium Gallium Arsenide (InGaAs) are used. Although 1550-nm lasers were developed for fiber optic connectivity, they have also found application in FSO links. For the detection of optical  
Figure 6: Atmospheric transmission spectrum up to 14 \(\upmu\)m showing the wavelength regions where particular molecules absorb the electromagnetic wave. radiation in this wavelength range, PIN (P-type, Intrinsic, N-type semiconductor) and Avalanche Photodiode (APD) photodiodes are made of InGaAs are mainly used [88]. Currently, research is being conducted on the use of Quantum Cascade Lasers (QCLs) [89; 90] in optical communication systems in the range of 10-12 \(\upmu\)m [91]. Currently, there are no commercially available links operating at this wavelength range.
# Mathematics-Driven Analysis of Offshore Green Hydrogen Stations
## 3 Results
The average photovoltaic panel surface area required to produce \(1\ \frac{\text{mol}_{\text{H}_{2}}}{\text{s}}\) and \(1\ \frac{\text{kg}_{\text{H}_{2}}}{\text{s}}\) is represented in Figure 8, expressed in a day-year format. As observed, the estimated average photovoltaic panel surface area decreases when the levels of received solar radiation density (Figure 8a) and electrolyzer efficiencies are higher and vice versa. The graphs exhibit vertical symmetry centered at the points of the minimum surface area, which coincide with the points where \(\alpha_{solar\_elevation}\) reaches its maximum value, and, consequently, so does the received solar radiation density. According to the relationship \(R_{ALK\_DEM}\), the estimation  
Figure 6: Hydrogen production as a function of the electrolyzer type (a) \(\frac{\text{mol}_{\text{H}_{2}}}{\text{s}\times\text{m}^{2}}\) and (b) \(\frac{\text{kg}_{\text{H}_{2}}}{\text{s}\times\text{m}^{2}}\). for the year 2025 (Figure 8b) suggests that the ALK electrolyzer will be more efficient than the PEM electrolyzer. As a result, this improvement will lead to lower energy consumption and a smaller required panel surface area for the same hydrogen production capacity. In the case under analysis, this reduction in photovoltaic surface area corresponds to a 4.03% decrease. These differences will gradually diminish as PEM technology improves its efficiency relative to ALK. Regardless of the scenario, the continuous improvement of electrolyzer technology is vital, as increased efficiency directly contributes to lower energy consumption and a corresponding reduction in the required photovoltaic surface area. Figure 9a presents the aggregation of the panel surface areas obtained in Figure 8a,b for both types of electrolyzers. This facilitates the comparison process by allowing an analysis of the required surface area values for each of the represented variables, considering their orders of magnitude. On the other hand, Figure 9b shows that the production of \(1\ \frac{\text{kg}_{12}}{\text{s}}\) requires a larger panel surface area (due to higher energy consumption) compared to the case of \(1\ \frac{\text{mol}_{12}}{\text{s}}\). In the case under analysis, this ratio is equal to 500.
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 2 Related Work
### Self-Calibration  
Self-calibration, which is also called autocalibration, is a method for calibrating cameras. This method is often applied when there is no possibility of performing pattern-based calibration. [PERSON] et al. described performing self-calibration on a stereo vision system of a lunar rover [29]. [PERSON] et al. presented the self-calibration of stereo cameras placed on a satellite [30].  
There is a similarity between self-calibration and bundle adjustment; both of these methods identify keypoints in images and, on this basis, estimate camera parameters. [PERSON] et al. proposed a self-calibration method for stereo cameras based on SIFT [31]. [PERSON], [PERSON], and [PERSON] analyzed the usage of various kinds of descriptors for the purpose of autocalibration [32]. [PERSON] and [PERSON] presented an in-depth description of self-calibration methods for multi-view geometry [22].  
There are also calibration methods designed for Simultaneous Localization and Mapping (SLAM) [33]. The main feature of SLAM is that it applies to robots or moving vehicles in some areas. In this process, a large number of images can be captured by cameras placed on a moving device. These are images of different scenes taken from different points of view, and these data are used to perform self-calibration on cameras used with SLAM.