esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antibacterial and Antifungal Activities  
The antimicrobial properties of GA and PC against pathogenic bacteria and fungus have been reported [25; 59]. CuO NP treated CSC extracts of _G. sylvestre_ displayed strong antibacterial and antifungal activities compared to non-treated CSC extracts (Figure 6). CuO NP treated CSC extracts showed more distinct activities against Gram-positive than Gram-negative bacteria. Consistent with our results, the absence of the lipopolysaccharide containing outer membrane surrounding the cell wall of Gram-positive bacteria allows increased permeability of _Hypericum perforatum_ antimicrobial metabolites into the bacteria [60]. The obtained results support an earlier study of antimicrobial activity of _G. sylvestre_, which demonstrated a zone of inhibition in _B. subtilis_ and _S. aureus_ agar cultures, while the extracts had no activity against the gram-negative bacterium _E. coli_[61]. Furthermore, leaf extracts of _G. sylvestre_ displayed good antimicrobial activity [59]. AgNP-treated plants and CuO NP-elicited hairy root extracts were reported to have stronger antimicrobial activities in _Achillea millefolium_[62] and _Brassica rapa_ spp. _pekinensis_[29], in which NP elicitation enhanced the amount of bioactive compounds. Similar to the present observations, GA and PC reportedly displayed potent antimicrobial activities [25; 59]. Our data confirm that the CuO NP treated CSC extracts have potent antimicrobial activity against clinically important microorganisms due to the higher amounts of GA II and PC.  
Figure 6: Effect of CuO NPs on antimicrobial activity in CSC of _G. sylvestre_ using the disc diffusion method. Different letters indicate a significant difference at \(p\leq 0.05\).
## Appendix A Scattering Process of Acoustic Laser Mode  
For the acoustic laser mode, the incident wave can be neglected because the outgoing wave is strong and the corresponding scattering process is shown in Figure 1a. The distribution of the acoustic field in different regions is,  
\[\left\{\begin{aligned} & p_{1}=Ae^{jk_{x}x}e^{-jk_{y}y}& (x<0)\\ & p_{2}=\left(Be^{-jk_{x}x}+Ce^{jk_{x}x}\right)e^{-jk_{y}y}& (0<x<d)\\ & p_{3}=De^{-jk_{x}(x-d)}e^{-jk_{y}y}&(x>d)\end{aligned}\right.. \tag{10}\]  
And the relationship between velocity and acoustic pressure is given by \(v=-\frac{1}{\rho}\int\
abla pdt\). Assuming the \(e^{i\omega t}\) convention, the expressions of the acoustic velocity in each regions are give by  
\[\left\{\begin{aligned} &\overset{\rightharpoonup}{v_{1}}=[-\frac{k_{x}}{ \omega\rho_{0}}\overset{\rightharpoonup}{e_{x}}+\frac{k_{y}}{\omega\rho_{0}} \overset{\rightharpoonup}{e_{y}}]Ae^{jk_{x}x}e^{-jk_{y}y}&(x<0)\\ &\overset{\rightharpoonup}{v_{2}}=[\frac{k_{x}}{\omega\rho_{1}} \overset{\rightharpoonup}{e_{x}}+\frac{k_{y}}{\omega\rho_{1}}\overset{ \rightharpoonup}{e_{y}}]Be^{-jk_{x}x}e^{-jk_{y}y}+[\frac{-k_{x}}{\omega\rho_{1}} \overset{\rightharpoonup}{e_{x}}+\frac{k_{y}}{\omega\rho_{1}}\overset{ \rightharpoonup}{e_{y}}]Ce^{jk_{x}x}e^{-jk_{y}y}&(0<x<d)\\ &\overset{\rightharpoonup}{v_{3}}=[\frac{k_{x}}{\omega\rho_{0}} \overset{\rightharpoonup}{e_{x}}+\frac{k_{y}}{\omega\rho_{0}}\overset{ \rightharpoonup}{e_{y}}]De^{-jk_{x}(x-d)}e^{-jk_{y}y}&(x>d)\end{aligned}\right.. \tag{11}\]The acoustic pressure and normal velocity are continuous at the interface of two different media. So, according to the boundary conditions at \(x=0\) and \(x=d\), we can obtain the following equations,  
\[\left\{\begin{array}{l}A=B+C\\ A\frac{-k_{x}}{\rho_{0}}=B\frac{k_{x}^{\prime}}{\rho^{\prime}}-C\frac{k_{x}^{ \prime}}{\rho^{\prime}}\\ Be^{-jk_{x}^{\prime}d}+Ce^{jk_{x}^{\prime}d}=D\\ \frac{k_{x}^{\prime}}{\rho^{\prime}}Be^{-jk_{x}^{\prime}d}+\frac{-k_{x}^{ \prime}}{\rho^{\prime}}Ce^{jk_{x}^{\prime}d}=\frac{k_{x}}{\rho_{0}}D\\ \end{array}\right.. \tag{12}\]  
After further simplification and taking \(\eta=\frac{k_{x}}{\rho_{0}}/\frac{k_{x}^{\prime}}{\rho^{\prime}}=\frac{\rho^{ \prime}k_{x}}{\rho_{0}k_{x}}\), the dispersion relation for the laser mode can be obtained as  
\[\left(\frac{1-\eta}{1+\eta}\right)^{2}=\frac{e^{jk_{x}^{\prime}d}}{e^{-jk_{x}^ {\prime}d}}. \tag{13}\]
# A Guaranteed Approximation Algorithm for QoS Anypath Routing in WMNs
## 4 Simulation Experiment and Performance Evaluation  
In this section, we run HMAA to check if it can find a feasible solution in a reasonable time and evaluate its operational efficiency. According to the quality of the anypath routing in this experiment, the efficieney and performance of HMAA are evaluated. First of all, a large-scale random network (RNET) is produced by the Waxman model [20], which is used to perform the simulation experiment in this section. Secondly, in order to further verify the performance and efficiency of our proposed algorithm, the real WMNs network of wireless multimedia service (NTT) mentioned in the literature [16] is used for simulation calculation. All of the above simulations are run on a computer with 8 GB of RAM and a 1.66 GHz clock Core Duo CPU.
# VPP Participation in the FCR Cooperation Considering Opportunity Costs
## 3 Results
The average higher CBMP for the _04-08_ product suggests that the revenues in Portugal were marginally greater than those in Spain during this time of day, as depicted in Figure 11. Figure 12 indicates that the monthly average market share of the VPP in Portugal and Spain exhibited only slight differences, except the scale. The plot in Figure 13 shows the data aggregated per month regarding VPP non-participation in market sessions (due to opportunity costs being higher than VPP potential revenue), along with the energy market and FCR market prices. It can be concluded that energy prices increased more than the FCR market prices, but over time, there was no strong tendency of either more or less participation in the market due to higher opportunity costs. Figure 9: VPP monthly revenue in scenario 1. Used data from scenario 1. Figure 11: VPP revenues per product. Used data from scenario 1. Figure 12: VPP monthly market share. Used data from scenario 1. Figure 10: Distribution of VPP cleared volume per product. Used data from scenario 1. Figure 14 shows the upward and downward bid components organized from the highest to the lowest for all the available data (From Figure 7, note that the bid size was the minimum value between the upward and downward components). It was clear that increasing the upward bid component (by increasing \(VGS_{c}\)) would push the blue curve to higher values but would result in the same bid size because, according to Equation (4), the bid size was limited by the lowest value of the two curves. The bid size, which was limited by its upward capacity, was equal to \(PN_{c}\) for around 20% of the tenders.
# Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
## 2 Quantum Dots-Based Photocatalyst for Hydrogen Evolution
### Single-Semiconductor QDs as Photocatalyst
[PERSON] et al. [34] reported that the CuInS\({}_{2}\) QDs with Ru can be used as cocatalysts for photocatalytic H\({}_{2}\) generation, and their photocatalytic performance has been greatly improved, with an apparent quantum yield of 4.7% with Ru deposited on the surface of CuInS\({}_{2}\) QDs. [PERSON] et al. [79] reported a facile method for the controllable preparation of ZnCdS QDs. The as-obtained Zn\({}_{0.5}\)Cd\({}_{0.5}\)S QDs with suitable particle sizes, appropriate band matching, and abundant active sites facilitated the separation and transportation of charge carriers and exhibited significantly strong photocatalytic performance. Perovskite semiconductor QDs have attracted much attention due to their excellent light response and photoluminescence properties that are valuable for photocatalytic H\({}_{2}\) production. For instance, [PERSON] et al. [80] prepared Cu\({}_{\mathrm{x}}\)In\({}_{\mathrm{y}}\)S QDs via a hot-injection method, and the Cu\({}_{\mathrm{x}}\)In\({}_{\mathrm{y}}\)S QDs with optimized stoichiometric ratios of Cu and In achieved a maximum H\({}_{2}\) production rate of 256 mmol g\({}^{-1}\cdot\)h\({}^{-1}\). Another kind of perovskite semiconductor QDs is represented by CsPbBr\({}_{3}\) QDs. Notably, the photocatalytic H\({}_{2}\) production was significantly affected by the surface ligands of CsPbBr\({}_{3}\) QDs. For example, [PERSON] et al. [81] regulated the surface ligand density to optimize the photocatalytic performance. Under visible light irradiation, CsPbBr\({}_{3}\) QDs/Pt-TiO\({}_{2}\) with different thicknesses of the protective layer showed totally differently photocatalytic activities and selectivities. Meanwhile, [PERSON] et al. [33] reported that the monodispersed CsPbBr\({}_{3}\) QDs@PANI prepared by the photopolymerization method showed excellent properties in relation to photocatalytic water splitting performance, with the aid of the conductive polymer PANI as the encapsulating material.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 4 Results and Discussion
### Baseline Performance  
The ideal conditions for the baseline tests are:  
* No constant angle measurement errors.
* No random angle measurement errors.
* No tangential roll position errors.
* No radial roll position errors.  
The results are shown in Figure 10.  
The simplified geometric algorithm yields calculation errors despite the ideal conditions. This is due to the simplifying assumptions that were made for this algorithm: neglecting the strand curvature and the varying roll diameters. The points at which the roll diameters change are visible as steps in the diagram. In the horizontal part of the machine (beginning at roll 57), both assumptions are valid, which is why this region shows no additional calculation errors. Despite these systematic calculation errors, the maximum calculation error of about 0.02 mm is still small compared to the targeted accuracy, which proves the baseline functionality of the algorithm.  
The full geometric algorithm shows no calculation error, as expected, since it does not make any simplifying assumptions about the machine and roll geometry.  
Similar to the full geometric algorithm, the baseline performance of the optimization-based algorithm is nearly error free. However, due to the stochastic nature of this algorithm, some small deviations from the ideal solution are visible. In contrast to the deterministic  
Figure 10: Baseline performances of the algorithms. The y-axis represents the difference between the calculated and the actual radial component of the roll position error. Both the full geometric and optimization-based algorithm are effectively error-free. Even the simplified geometric algorithm only deviates about 0.02 mm from the target values. Since it is based on the assumption that each roll has the same diameter, changes in roll diameter cause jumps in this diagram, for example, at rolls 14/15 and 33/34.  
geometric algorithms, which always yield the same solution under the same conditions, the optimization-based algorithm shows stochastic noise in its output.
# Computational Study on Rogue Wave and Its Application to a Floating Body
## 3 Results and Discussion
### Response of Floating Body  
The effects of regular and rogue waves on a floating body, such as in onshore wave power generation, were investigated. The wave height used at the inlet boundary of the regular and rogue waves was 0.02 m. The Reynolds number based on water particle velocity (\(U_{H\text{max}}=0.0445\) m/s) and floating body length (2 m) was 88,900.  
The shape of the floating body was defined as a rectangular shape and was assumed to be a rigid body. The length, width, and height of the structure were set to 2 m, 0.28 m, and 0.15 m, respectively. The draft was set to 0.1 m. The center of the floating body was placed at the focal point of the rogue wave. Two degrees of freedom of heave (translational motion on the \(z\)-axis) and pitch (rotational motion around the \(y\)-axis) were allowed.  
The motion and forces acting on the floating body by the regular and rogue waves were compared.  
Figure 18 shows the heave and pitch motions of the floating body. Initially, the body was fixed and then allowed to move after the solutions were stabilized. Because the height of the rogue wave was higher than that of the regular wave, a higher heave motion was also predicted. The high slope and height of the rogue wave caused an increase in the pitch motion compared to the regular wave. From the results, when the floating body was exposed to the rogue wave, the motions became significantly larger than those of the regular wave.  
Figure 16: Z-velocity contours of water phase at \(y=0\) plane: (**a**) regular wave; (**b**) rogue wave.  
Figure 15: X-velocity contours of water phase at \(y=0\) plane: (**a**) regular wave; (**b**) rogue wave.  
Figure 19 shows the x-force and z-force acting on the floating body. The rogue wave increased the pressure and velocity of the water particles and increased the velocity at the free surface, which in turn greatly increased the force acting on the floating body. For floating structures that could be exposed to rogue waves, it is necessary to consider such an increased force.  
Figure 17: Velocity vectors on a free surface at \(y\) = 0 plane: (**a**) regular wave; (**b**) rogue wave.  
Figure 18: Motions of a floating body: (**a**) heave motion; (**b**) pitch motion.  
Figure 20 shows the free surface evolution around the floating body. The floating body motions were repeated according to the wave phase.
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 2 Structural Particularities of HEAs, Prediction and Estimation Concepts
### Prediction and Estimation Concepts
#### 2.3.2. CALPHAD Method
This revision highlights that the VEC range is generally influenced by factors such as temperature, concentration, and the specific constituent elements, provided no intermetallic or amorphous phases develop in the HEA. Furthermore, the VEC rule is found to be most applicable to HEAs primarily composed of 3d or 4d transition metals and is particularly reliable for equiatomic or semi-equiatomic HEAs. These constraints have impacted the accuracy of predicting the crystal structure types in HEAs [65]. To enhance the predictive power of the CALPHAD method, machine learning techniques are increasingly being integrated. These techniques can analyze vast amounts of data from thermodynamic databases and experimental results to identify patterns and improve the accuracy of phase predictions. Machine learning models can provide insights into the complex relationships between composition, processing conditions, and phase stability in HEAs. Despite its successes, the CALPHAD method faces several challenges in the context of HEAs [66]. The high configurational entropy and potential for numerous competing phases in HEAs can complicate the development of accurate thermodynamic models. Additionally, the presence of intermetallic compounds and amorphous phases can further challenge the predictions. Future research aims to refine thermodynamic databases and enhance the integration of CALPHAD with advanced computational techniques, such as density functional theory (DFT) and machine learning. These advancements will improve the reliability of phase diagrams and property predictions, thereby facilitating the design of novel HEAs with tailored properties for specific applications.
## 4 Improved Kernel Extreme Learning Machine
### KELM Optimized Using PSO  
When implementing the KELM learning algorithm, the parameters \(C\) and \(\sigma\) have significant impacts on the algorithm's performance. The PSO algorithm [25] is a random search and parallel optimization algorithm that has advantages such as simplicity, ease of implementation, and quick convergence. Therefore, in this paper, \(C\) and \(\sigma\) are optimized using the PSO algorithm to create a PSO-optimized KELM forecast model. When implementing the PSO-KELM method, the method for optimal parameter selection for KELM is an optimization of the penalty coefficients \(C\) and kernel function parameter \(\sigma\). The KELM classification accuracy is defined as \(acc\left(C,\sigma\right)\). In the KELM parameter optimization model, the maximum classification accuracy for the PSO fitness function is given by Equation (27). That is, a set of \(C\) and \(\sigma\) are identified within a given range to ensure the maximum classification accuracy for the KELM classifier.  
\[F=\text{max}acc\left(C,\sigma\right) \tag{28}\]  
The detailed modeling procedure of the PSO-KELM diagnosis model is as follows:  
1. A particle swarm is generated based on the number of groups. The position \(P(i).location=\left[C_{i}(t),\sigma_{i}(t)\right]^{T}\) and velocity \(P(i).velocity=\left[\Delta C_{i}(t),\Delta\sigma_{i}(t)\right]\) of each particle are randomly initialized.
2. \(F\) is calculated via Equation (28) as the individual fitness to determine the optimal position of individual particle \(P(i).best=\left[C_{i}^{best}(t),s_{i}^{best}(t)\right]^{T}\); the optimal position for each group is \(g(i).best=\left[C_{i}^{best}(t),\sigma_{i}^{best}(t)\right]^{T}\).
3. The particle velocity and position are updated using following equations: \[P(i).velocity(t+1)=\omega_{l}P(i).velocity(t)+\eta_{1}r_{1}[P(i). best(t)-P(i).location(t)]\] (29) \[+\eta_{2}r_{2}[g(i)best(t)-P(i).location(t)]\] \[P(i).location\quad(t+1)=P(i).location\quad(t)+P(i).velocity\quad(t+1)\] (30) \[\omega_{l}=\omega_{\text{max}}-iteration\times\frac{\omega_{ \text{max}}-\omega_{\text{min}}}{iteration_{\text{max}}}\] (31)
4. Steps (2) and (3) are repeated until the termination condition is satisfied. The optimal parameters \(C\) and \(\sigma\) are returned.
## 2 Materials and Methods
### Data Preprocessing  
First, the raw spectra were normalized using area normalization in order to mitigate the impacts of pulse energy fluctuations and matrix effects. Subsequently, discrete wavelet transform was employed for baseline correction and noise reduction, thereby weakening environmental noise and accentuating useful signals. Moreover, continuous wavelet transform was utilized for effective peak identification, acquiring the wavelength information of peaks for element recognition. Finally, based on the peak-seeking results, the central wavelengths of the test spectral peaks were compared with standard wavelengths from the Atomic Spectra Database (ASD) [33] of the National Institute of Standards and Technology (NIST) to determine the elemental attribution of the spectral lines.
## 2 Materials and Methods
### Methods
#### 2.2.6 Verification and Evaluation of Simulation Results  
We estimated ET from SEBAL model using MODIS and GF-4 at imaging time in Table 2 per minute by using the methods in Section 2.2. The following methods were used for verifying ET: (1) cross-validation ET obtained from different remote sensing data; (2) verify with ET data measured in field. In this paper, we first cross-verified ET of GF-4 at 11:36 (T\({}_{8}\)) using ET of MODIS at 11:35 (MT\({}_{0}\)). Then ET at 13:50 (T\({}_{5}^{\prime}\)) of GF-4 and 13:45 (MT\({}_{1}^{\prime}\)) of MODIS were cross-verified. Finally, we used the field measured ET data to verify them from the Flux sites in South Korea, which was close to the imaging time of remote sensing images. Since the measuring time interval at KoFlux sites was 30 min, we chose the measured ET at 11:30 and 14:00 to verify the simulated ET at 11:22 (T\({}_{4}^{\prime}\)) and 13:58 (T\({}_{7}^{\prime}\)) of GF-4 respectively. Then, the field measure ET at 12:00 (MT\({}_{0}^{\prime}\)) was used to verify the simulated ET of MODIS at 12:10 (MT\({}_{0}^{\prime}\)).  
In order to evaluate the difference between measured ET and remote sensing data, we use root mean square error (RMSE) and mean relative error (MRE) to analyze the error.  
\[\text{RMSE}=\sqrt{\frac{\sum\left(Y_{i}-Y_{i}^{\prime}\right)^{2}}{N}}, \tag{11}\]  
\[\text{MRE}=\sqrt{\frac{\sum\left[\left(Y_{i}-Y_{i}^{\prime}\right)/Y_{i}^{ \prime}\right]^{2}}{N}}, \tag{12}\]  
in Equations (11) and (12), \(Y_{i}\) is the measured value, \(Y_{i}^{\prime}\) is the simulated value obtained from the model, and n is the number of sample points [43; 44]. The smaller the values of RMSE and MRE, the higher the simulation accuracy of the model [43; 44].
###### Abstract  
A new scheme of radio-over-fiber (RoF) network based on spectral-amplitude-coding (SAC) optical code division multiple access (OCDMA) is herein proposed. Differential service is provided by a power control scheme that classifies users into several classes and assigns each of them with a specific power level. Additionally, the wavelength reuse technique is adapted to support bidirectional transmission and reduce base station (BS) cost. Both simulation and numerical results show that significantly differential quality-of-service (QoS) in bit-error rate (BER) is achieved in both downlink and uplink transmissions.  
radio-over-fiber (RoF); spectral amplitude coding (SAC); differential service; wavelength reuse +
Footnote †: journal: _photonics_
## 3 Results and Discussion
### Analysis of Relative Absorbance Ratios (t\({}_{0}\) and t\({}_{24}\) Cells)
Figure 6: Comparison of the absorbance ratios LS (**A**), PP1 (**B**), PP2 (**C**), PL1 (**D**) and PL2(**E**) with dose, for cells fixed immediately (**blue**) and 24 h after irradiation (**red**); the ratio variations (Mean \(\pm\) SD) are reported, as a value normalized with respect to the corresponding ratio value of the non-irradiated samples (0 Gy, t\({}_{0}\); 0 Gy, t\({}_{24}\)); blue and red asterisks indicate when a significant difference in respect to the control value for the t\({}_{0}\) and t\({}_{24}\) samples, respectively, occurred at \(p\leq 0.05\). In Figure 7A-C, ratio values for P/D1, P/D2, and DM are reported, respectively, for the different doses and times of fixation. In this case, the P/D1 and P/D2 ratios are related to the relative protein and DNA content, while the DM ratio indicates DNA modifications (see Table 1). The ratio P/D1 (Figure 7A), linked to the ratio between protein and DNA content of the cell, for the t\({}_{0}\) sample shows an increasing behavior with the dose, which becomes significant at 8 Gy (\(p\leq 0.05\)), and then a marked decrease (\(p\leq 0.05\)) for the value at 10 Gy in respect to the unirradiated sample. Similarly, the ratio P/D2 (Figure 7B), which is associated with similar biological changes, shows an increasing trend (\(p\leq 0.05\)) with the dose up to 8 Gy and then a decrease up to values lower than the control at 10 Gy (\(p\leq 0.05\)). The values for t\({}_{24}\) cells, instead, show a descending trend for doses higher than 4 Gy (\(p\leq 0.05\)) for the P/D1 ratio and an undefined trend with values significantly (\(p\leq 0.05\)) above (6 Gy) and below (4, 8 Gy) compared to the non-irradiated sample for P/D2. The ratio DM (Figure 7C), linked to DNA structure modifications, shows values significantly (\(p\leq 0.05\)) higher than the control at low doses (2-6 Gy) for the t\({}_{0}\) cells and at 6 and 10 Gy for the t\({}_{24}\) cells. The modifications evidenced by P/D1, P/D2, and DM are in agreement with the results obtained in other cell systems exposed to ionizing radiation that indicate the occurrence of damage in the primary, secondary, and tertiary structure of nucleic acid [54].