esa-sceva/satcom-chunk-collection huggingface dataset:
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 2 Working Principle and Characteristics  
### Working Principle  
DEAs consist of a thin polymer film that is coated with compliant electrodes on both sides. When voltage difference is applied to the actuator, an electrostatic pressure is induced between the two electrodes, which then causes compression in its thickness direction. As dielectric elastomers are essentially incompressible, the compression causes an in-plane expansion, as shown in Figure 1.  
DEA is essentially recognized as a compliant variable capacitor which converts the electrical energy to mechanical energy. The capacitance \(C\) of the DEA can be expressed as  
\[C=\frac{\varepsilon_{0}\varepsilon A}{z} \tag{1}\]  
where \(\varepsilon\) is the relative permittivity, \(\varepsilon_{0}\) is the permittivity of free space, \(A\) is the area of the compliant electrode, and \(z\) is the thickness of the DE material. The stored electrical energy \(U\) on the DEA with charge \(Q\) is written as  
\[U=\frac{0.5Q^{2}}{C}=\frac{0.5 zQ^{2}}{\varepsilon_{0}\varepsilon A} \tag{2}\]  
Figure 1: The dielectric elastomer (DE) operation principle by electrostatic forces.  
As noted above, DE material is essentially incompressible, which leads to the constant volume equation.  
\[Az=Volume=constant \tag{3}\]  
Then, the electrostatic pressure \(p\), known as Maxwell stress, which acts across the two electrodes, can be derived.  
\[p=\varepsilon_{0}\varepsilon E^{2}=\varepsilon_{0}\varepsilon\Big{(}\frac{V}{ z}\Big{)}^{2} \tag{4}\]  
where \(E\) is the electric field and \(V\) is the applied voltage. In the electrostatic pressure equation, DE material is assumed to have uniform thickness and uniform electrical charges on the surface. Using free boundary conditions and a linear elasticity assumption for small strains (<10%), the thickness strain \(s_{z}\) is given by [1].  
\[s_{z}=-\frac{p}{Y}=-\frac{\varepsilon_{0}\varepsilon E^{2}}{Y}=-\frac{ \varepsilon_{0}\varepsilon}{Y}\Big{(}\frac{V}{z}\Big{)}^{2} \tag{5}\]  
where \(Y\) is the elastic modulus. Since the polymers are assumed to be incompressible, a contraction in thickness results in a planar area expansion. As shown in the equation, the thickness strain is related to two mechanical parameters of the DE material: the elastic modulus \(Y\), and relative permittivity \(\varepsilon\). However, as these two parameters are functions of temperature, actuation frequency, and stress state, these variable terms should be dealt with to derive the dynamic behavior of the DEAs [2]. Furthermore, this model assumes that the material is linearly elastic, which is not suitable for most polymers. As polymers have a nonlinear and viscoelastic behavior, the viscoelastic model of a polymer should be considered to derive a more accurate equation. This is discussed in detail in Section 3.  
Generally, DEAs are reported to have a large strain (>100%), high efficiency, high energy density (>3.4 MJ-3), and a fast response (in the order of milliseconds). The performance of DEAs is mainly determined by two factors, namely DE materials and compliant electrodes.
## Results
### Aroma Series  
The wine aroma wheel can be used to classify wine aroma into several aroma series [47]. To link the chemical composition to the sensory profile, the odor descriptors of the volatile compounds in the wine samples were grouped into several aromatic series, using volatile compounds with an OAV \(\geq\)0.1. The aromatic series used to define the aroma of white, rose, and red wines were floral, fruity, fatty, pungent, spicy, vegetative, and earthy. Due to the difficulty in identifying their odor sensations, several volatile compounds were assigned to one or more aromatic series [55; 62; 88]. Figure 2a-c shows the total intensity of each aromatic series used to characterize the aroma of white, rose, and red wines (except for the earthy, pungent, and spicy aromatic series), calculated as the sum of each volatile compound assigned to each series with OAV \(\geq\)0.1. As can be seen, fruity and floral aromas were the dominant aroma series of the white, rose, and red wines, carried mainly by some esters (such as isoamyl acetate, ethyl hexanoate, ethyl octanoate, ethyl decanoate, hexyl acetate, and phenethyl acetate) and terpen (i.e., \(\beta\)-damascenone, linalool, citronellol, and geranyl acetone) (Supplementary Table S1). Although all the wine samples tested had a similar order of aroma series, the content of each aroma series differed (Figure 1a-c), suggesting that dealcoholization played a significant role in altering the aromatic quality of the wines.  
Both the RO and VD dealcoholization methods affected all aroma series of all wines, with VD dealcoholization showing the greatest reduction in all aroma series than RO. The reduction of the most dominant aroma series (fruity) in all RO dealcoholized wines (white, rose, and red) varied from 55% to 67%, while the floral aroma series decreased by 4% to 62%, the fatty aroma series by 67% to 89% and the vegetative aroma series by 60% to 67%. VD Dealcoholization, on the other hand, resulted in a 94% to 99% decrease in the fruity aroma series, 94% to 97% in the floral aroma series, 83% to 100% in the fatty aroma series, and 100% in the vegetative aroma series for all wines. The complete loss (no odor value) of the vegetative (in all wines) and fatty (in red wine) aroma series as a result of dealcoholization by VD indicates that these types of aromas cannot be perceived by humans. Although the fatty and vegetative aroma series were the lesser aroma series in all original, RO, and VD wine samples, they could still enhance wine aroma quality because these aromas can be mediated by some alcohols (1-hexanol and 1-octanol), fatty acids (including decanoic acid, hexanoic acid, octanoic acid) and esters (hexyl acetate). However, high levels of these aromas can negatively affect the wine aroma quality [63; 64; 65]. Between RO and VD, RO had the least effect on all aroma series, except for the fatty aroma series of rose wine, where both RO and VD resulted in similar levels of redaction (89%) compared to the original rose wine.
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 5 Discussion and Conclusions
### Convergence of Technologies as Reflected in Thematic Evolution  
The bibliometric evidence reveals a clear convergence of technologies such as mobile payment systems, blockchain, artificial intelligence (AI), and cybersecurity within the e-payment research landscape. Rather than developing in isolation, these technologies frequently co-occur in keyword networks and thematic clusters (Figures 6 and 7), illustrating a co-evolutionary trajectory.  
This convergence is particularly evident in the rise of themes like \"trust,\" \"security,\" \"blockchain,\" and \"mobile payment,\" which have transitioned from peripheral to central positions in the thematic map over time. These now-core \"motor themes\"--including cryptography, trust, and e-business--demonstrate not only technical sophistication but also a deep embedding within broader social and behavioral contexts. This shift reflects a transition from technology-push models to sociotechnical integration, consistent with contemporary theories of innovation convergence.  
The longitudinal evolution of research topics--from foundational keywords such as \"EDI\" and \"language\" in the 1990s to more recent terms like \"Fintech,\" \"P2P systems,\" and \"COVID-19\" in the 2020s (Figure 5a,b)--further highlights the transformation of e-payment from a simple transaction mechanism to a complex digital infrastructure encompassing security, user behavior, and business innovation.  
The integration of blockchain, AI, NFC, and mobile technologies in modern e-payment systems signals a paradigm shift in the fintech landscape and offers fertile ground for theoretical advancement. This study contributes to the theoretical foundations of digital finance and innovation by showing how technological convergence can resolve long-standing challenges in e-payments--particularly in security, privacy, and operational efficiency.  
By combining decentralized ledgers, intelligent fraud detection algorithms, and seamless contactless interfaces, convergent technologies not only enhance technical functionality but also improve user experience. Furthermore, the research addresses critical theoretical dimensions of interoperability--an essential condition for achieving universal access to digital financial services--and considers the tension between regulatory compliance and innovation, especially in contexts demanding consumer protection.  
This study advances interdisciplinary understanding by integrating perspectives from information systems, cybersecurity, and behavioral finance, offering a robust theoretical lens for exploring how converging technologies shape the future of financial services. In doing so, it also opens new avenues for investigating broader societal impacts--such as the digital divide and financial inclusion--in an increasingly interconnected world.  
Ultimately, this research enriches existing knowledge and sets the stage for future theoretical inquiries into the dynamic interplay between technological convergence and societal transformation in the digital economy.
# Wideband UHF Antenna for Partial Discharge Detection
## 3 Wideband Ultra-High Frequency (UHF) Antenna  
The bandwidth of the basic loop antenna is extremely narrow because of its resonant structure, as mentioned in the previous section. In this work, in order to enhance the bandwidth of the antenna, the rectangle #1 loop antenna was modified.  
To increase the bandwidth, first, we designed the antenna (modified antenna 1 in Figure 6b) on the FR4 (a relative permittivity of 4.4, a thickness of 1.6 mm, and a loss tangent of 0.02) and changed the position of the feed line. It is worth noting that the employment of FR4 could lead to size reduction and the variation of the feeding point could lead to a change in the return loss. In specific, the antenna could have two resonance points at 0.7 GHz and 1.29 GHz, as shown in Figure 7a. However, th antenna matching performance was still not good, especially at the center frequency. Therefore, the antenna was further modified by adding two stubs (modified antenna 2 in Figure 6c). This antenna could increase the effective electrical length and improve the antenna impedance matching from 0.76 to 1.2 GHz. However, S\({}_{11}\) was still greater than \(-\)10 dB in the range of 0.93-1.10 GHz. In order to tackle this issue, the strip width of the antenna was increased to further improve the antenna impedance matching. The final design of the proposed antenna was performed by using HFSS optimization. The S\({}_{11}\) parameters and the smith charts of the step-by-step design antennas are shown in Figure 7. As shown in the figures, S\({}_{11}\) parameters got lower and loci were located further into the center of the smith chart as the antenna design evolved. Note that the final antenna could have S\({}_{11}\) \(<\)\(-\)10 dB from 0.71 to 1.5 GHz.  
Figure 6: The geometry of loop antennas: (**a**) basic loop antenna; (**b**) modified antenna 1; (**c**) modified antenna 2; and (**d**) final antenna.  
Figure 7: Antenna matching performance: (**a**) S\({}_{11}\) parameters. (**b**) Smith chart.
# Effect of Dynamic Loading Conditions on Maximizing Energy Dissipation of Metallic Dampers
## 4 Dynamic Optimization
### Effect of the Excitation Frequencies to the Dynamic Optimization  
The change in the trend of these optimization variable values was affected not only by the scale of peak acceleration, but also by the frequency of the applied sine excitation. Figure 15 illustrates the optimized variables when the dynamic optimization is performed by increasing the frequency of applied excitation from 0.25 Hz to 1.5 Hz. Here, the values in Figure 15b are the same as those in Table 3 with the frequency of 0.5 Hz. Through the results with respect to the acceleration frequency, the peak acceleration value can be compared at the point where the trend of optimized geometry changes as the frequency increases.  
In the previous optimization results at 1 Hz, the trend changed between 0.125 g and 0.15 g, which are the peak values of applied acceleration (Figure 15b). However, at a lower frequency (0.25 Hz), the trend changed at a smaller peak acceleration (Figure 15a). In contrast, when the frequency was higher (1.0 Hz), the peak acceleration value that changes the trend became larger (Figure 15c). Further, when the applied frequency exceeded 1.5 Hz, only one trend of the optimized shapes was derived in all cases between the given peak acceleration value (0.1-0.25 g), which was the newly derived optimal shape suitable for the small earthquake (Figure 15d).  
Therefore, it can be concluded that the novel shape derived through dynamic optimization will dissipate energy more effectively in a relatively moderate earthquake. Here, a moderate earthquake can be defined as an earthquake with low seismic energy, which means an earthquake with a smaller peak ground acceleration and higher frequencies. In order to examine the behavioral characteristics of the derived damper shapes more thoroughly, virtual excitations with various frequencies and accelerations similar to an actual earthquake were introduced in the next section, by further exploring the simple sine waves applied as the conditions of the dynamic optimization.
## 4 Materials and Methods
### Real-Time qPCR Analyses  
The leaves of MeJA(ALA)-treated and control _S. moellendorffii_ plants were used to extract the total RNA. Then, 2 ug RNA was reverse transcribed for analyses of gene expression. Quantitative reverse transcription PCR (qRT-PCR) was performed in a 20\(\upmu\)L reaction volume using SYBR Green dye. The expression level of _S. moellendorffii glyceraldehyde-3-phosphate dehydrogenase_ (_GAPDH_) gene was used to normalize the expression of _SmMTPSLs_. The PCR program was 95 \({}^{\circ}\)C (3 min), followed by 45 cycles at 95 \({}^{\circ}\)C (10 s) and 60 \({}^{\circ}\)C (30 s). Specific primer pairs were designed with Primer 6.0 software. All primers used are listed in Table S3. The relative expression level of _SmTPSLs_ genes was calculated using the 2\({}^{-\Delta\Delta\mathrm{CT}}\) method. Three biological replicates were analyzed.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method
### Analysis Method of Error Distribution Patterns in MLS Point Clouds
#### 3.4.4 Measurement Process  
In the measurement process, ICP registration, C2M distance measurement, and Gaussian distribution fitting were conducted. For ICP registration, the compared MLS point clouds were translated and aligned with reference planes. After ICP registration, the C2M distance measurement algorithm was employed to measure the distance from each point to the reference planes.  
The C2M distance represents the shortest distance from a point to the nearest reference mesh (plane). In this study, the C2M distance is applied after ICP registration to quantify how MLS point clouds deviate from the reference planes, which represent the actual surfaces of the Columns. The C2M distance is obtained by calculating the perpendicular distance from a point to a plane, using the plane's normal vector to establish the directionality of the distance. The C2M distance can be mathematically represented as follows:  
\[d=n\cdot(p-p_{0}) \tag{2}\]  
where \(d\) is the distance; \(n\) is the normal vector of the plane; \(p\) is a point in the point cloud; and \(p_{0}\) is a point on the plane. The sign of d indicates whether a point is inside (negative) or outside (positive) relative to the plane. C2M distance measurement is performed for each reference plane and its corresponding segment of the compared MLS point clouds. This process prevents inaccurate C2M distance measurements caused by incorrect normal vector (\(n\)) calculations near the edges of the Columns. By analyzing the measured C2M distances, the error distribution pattern in MLS point clouds can be identified.  
Additionally, the proposed analysis method fits a Gaussian distribution to the measured C2M distance. The Gaussian distribution is mathematically defined as follows:  
\[f(x)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(x-p)^{2}}{2\sigma^{2}}} \tag{3}\]  
where \(x\) represents the C2M distance values, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. The mean value (\(\mu\)) represents the systematic bias in MLS point clouds, while the standard deviation (\(\sigma\)) quantifies the variability of the errors. By analyzing the Gaussian distribution of the C2M distances for each plane of the Columns, the error distribution pattern in MLS point clouds can be comprehensively analyzed. The purpose of applying Gaussian distribution fitting is to mathematically represent the error distribution patterns in the MLS point clouds. In this study, the derived error distribution patterns are utilized in a simplified form for testing purposes. However, in future research, these patterns areexpected to be used for dimensional correction in the measurement of building components, where a more precise numerical representation will be required. Accordingly, Gaussian fitting is conducted in this study to examine whether the derived distribution functions adequately represent the full set of C2M distances measured on each plane.
## 3 Results
### Mathematical Model Development
#### 3.3.1 Sensitivity Analysis  
The results of the sensitivity analysis (summarised for the top 5 parameters with higher sensitivity in Table 5), carried out to assess the effect of the different parameters on the concentration of TPH in the solid and liquid phases, are presented in Table S4 and corroborate the results described in the previous section. The two parameters from hydrocarbon adsorption isotherm (\(K_{\text{f,TPH}}\) and \(n_{\text{TPH}}\)) are among the most sensitive parameters, demonstrating the importance of adsorption/desorption for the dynamics of TPH in the soil column, both for \(C_{\text{TPH}}\) and for \(S_{\text{TPH}}\). In addition, the maximum TPH biodegradation rate under methanogenic conditions (\(\mu_{\text{max,met,20}}\)) is the most sensitive parameter for adsorbed TPH (\(S_{\text{TPH}}\), Table 5). As such, STPH is highly dependent of TPH biodegradation coupled to the activity of methanogenic microorganisms. This is possibly related to the fact that, as hydrocarbons are consumed and converted into methane, their concentration in the liquid phase decreases, which facilitates the subsequent desorption and increases their availability to biodegradation. The death rate for anaerobic microorganisms is also a very sensitive parameter, which is most probably linked with the high sensitivity of maximum TPH biodegradation rate under methanogenic conditions. Soil porosity (\(\theta\)) and soil density (\(\rho\)) are also very sensitive parameters, showing the importance of both soil physical properties, and of the physicochemical processes.
## 2 Materials and Methods
### Cell Wall Sugar Composition Analysis  
#### 2.5.1 Cell Wall Preparation  
Leaf samples of 3-week-old wild type and _OsVTC1-1_ RNAi lines, approximately 25 mg, were collected. Leaf samples were snap-frozen in liquid nitrogen and then ground to a fine powder using a Qiagen TissueLyser, as described previously. Plant cell wall preparation was performed using alcohol-insoluble residue (AIR) preparation method according to [PERSON] et al. [36]. Following this step, ice-cold 80% ethanol (\(v/v\)) was added to samples. Then, centrifugation at 12,000\(\times\)\(g\) for 2 min was used to collect insoluble material. The supernatant was discarded and washed three times with 80% ethanol (\(v/v\)). Oligosaccharides associated with glycoprotein and polysaccharides in samples were hydrolyzed with 2 M trifluoroacetic acid (TFA) at 110 \({}^{\circ}\)C for 1 h. Non-hydrolyzed component was eliminated by centrifugation at 12,000\(\times\)\(g\) for 2 min. The supernatant was evaporated to remove TFA. After hydrolysis, dried samples were added with myo-inositol (5 \(\upmu\)M final concentration) and derivatized using methoxyamine hydrochloride dissolved in pyridine, followed by N-Methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) (Sigma-Aldrich, St. Louis, MO, USA).  
A 25 \(\upmu\)M standard mix was prepared to contain D-glucose, D-galactose, D-mannose, D-xylose, D-arabinose, L-tucose, L-rhannose, D-galacturonic acid, D-glucuron acid, ribitol and Myo-inositol. This was diluted by half 6 times to a final concentration of 0.39 \(\upmu\)M, to produce a calibration curve.
# The Quirky Rot Fungi: Underexploited Potential for Soil Remediation and Rehabilitation
## 1 Soil Ecology, Health, and Erosion Control
### Pesticides
In a 12-day experiment, [PERSON] et al. [30] confirmed that _Phanerchaete chrysosporium_ could degrade the organochlorine pesticide pentachlorophenol and its metabolites by approximately 80%. In the same line of evidence, [PERSON] et al. [31], using the same white-rot fungi species, have reported degradation rates between 73% and 98% for the neonicotinoid insecticide imidalcoprid (for concentrations ranging from 10 to 30 mg/L), after only three days of incubation. In another study, _P. chrysosporium_ was shown to significantly degrade several PAHs (acenaphthene, anthracene, fluoranthene, naphthalene, phenanthrene, and pyrene) in seven days in the presence of an inducer of manganese peroxidase [32]. For pyrene, the degradation rate reached 100% under all tested conditions of agitation and inducer concentrations [32]. Most of these studies were carried out under laboratory-controlled conditions, supplying conditions for optimized degradation. However, the effectiveness of the rot-fungi-mediated remediation of pesticide-contaminated soils may be influenced by environmental factors such as water availability, temperature, pH, substrate condition, and availability. Therefore, suitable conditions for species thriving and/or compound degradation may not always be desirable. For example, [PERSON] et al. [32] showed that, unlike pyrene, phenanthrene degradation is concentration- and agitation-dependent. Despite this, the non-specificity of white-rot fungi for substrates and their high adaptability and resilience under stressful conditions foresee their successful application in realistic environmental scenarios. A few studies conducted in this sense have shown the potential of white-rot fungi to degrade crude oil contaminants in the soil of Nigeria (an oil-producing country often confronted with spillage situations). [PERSON] and [PERSON] et al. [33] were able to reduce the total petroleum hydrocarbons in crude oil-contaminated soils by 20% and 40%, respectively, after 3 and 6 months of incubation with _Lentinus subnudus_, while [PERSON] et al. [34] reduced the total PAH concentration in oil-based drill cuttings by 90% within 56 days. In situ research regarding this issue is scarce and lacks further confirmation; this is herein flagged as a knowledge gap ready to be filled for other types of organic compounds. However, white-rot fungi have strong potential for the in situ remediation of pesticide-contaminated soils (Figure 2), particularly under challenging environmental conditions, making them a promising tool for ecological restoration.