esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamical Manipulation of Surface Plasmon Polaritons
## 2 Directional Excitation of Surface Plasmon Polaritons (SPP)
### Directional SPP Propagation Based on Nonlinear Optical Materials  
Nonlinear optical materials whose optical property strongly dependents on the pump light play vital roles in manipulating photons. Combing SPP and nonlinear optical materials can enrich the functionalities of SPP devices. Figure 2b schematically shows the structure of the all-optical plasmonic switch, an asymmetric T-shape slit covered with nonlinear polymer film [91]. SPP generated by the slit propagate along the bottom of the grooves which can be regarded as Fabry-Perot (FP) resonators. Without the pump beam, by carefully designing the length of FP resonators (\(w_{\text{G1}}=280\) nm, \(w_{\text{G2}}=600\) nm), the SPP excited by 790 nm incident light can interfere constructively in one direction and interfere destructively in the other direction. When the slits are illuminated by the 532 nm pump beam, the refractive index of the polymer film is changed and the effective length of the FP resonator is changed as well. Thus, the propagation direction of SPP changes from left to right, which can be seen from the SPP distribution in Figure 2b.  
Figure 2: (**a**) Controlling the propagation direction of SPP by changing the topological charge of the incident vortex beam [82]; (**b**) Coating the slits with nonlinear polymer film, an all-optical directional SPP coupler can be achieved [91]. Reproduced with permissions from: [82], [PERSON], 2018, [91], Springer Nature, 2013.
## 3 Results and Discussion
### Conventional Extraction  
#### 3.1.1 Chlorophyll a, Chlorophyll b, and Carotenoids  
Figure 3 shows the chlorophyll a, chlorophyll b, and carotenoid content of the extracts obtained after conventional extraction with respect to the extraction time and solvent used. After performing a 2-factor analysis of variance (ANOVA) (time and solvent), it was observed that both extraction time and solvent had a significant effect (\(p<0.05\)) on the extraction of chlorophyll a, chlorophyll b, and carotenoids, observing a higher extraction of all these compounds with the longer extraction times, and in general, observing higher values of chlorophyll a, chlorophyll b, and carotenoids when an EtOH/H\({}_{2}\)O mixture was used compared to that seen with the DMSO/H\({}_{2}\)O mixture. Thus, at 180 min, the highest values of chlorophyll a (0.57 \(\pm\) 0.01 mg/g DM), chlorophyll b (0.55 \(\pm\) 0.01 mg/g DM), and carotenoids (0.50 \(\pm\) 0.01 mg/g DM) were obtained after using the EtOH/H\({}_{2}\)O mixture, representing an increase of 34%, 54%, and 84.2%, respectively, compared to those obtained with the DMSO/H\({}_{2}\)O solvent at equivalent extraction times.  
Figure 3: Chlorophyll a, chlorophyll b, and total carotenoids. Conventional extraction (5–180 min) using two binary mixtures (EOH or DMSO in 50% deionised water). Different lower-case letters in the same parameter and solvent indicate statistical differences as a function of extraction time. Different capital letters in the same parameter and time indicate statistical differences as a function of solvent.  
However, it should be noted that this was not verified for all the compounds evaluated. For example, when using the solvent EtOH/H\({}_{2}\)O, no significant differences were found in the chlorophyll a content after 5 and 15 min of extraction. Moreover, no statistically significant differences in chlorophyll b content were observed after 15 and 30 min of extraction with the solvent DMSO/H\({}_{2}\)O. When analysing the statistical results between solvents, no differences were found in chlorophyll a extraction after 5 and 15 min of extraction.  
These results are in agreement with those obtained by other authors, who observed a higher extraction of chlorophyll a, chlorophyll b, and carotenoids when the EtOH/H\({}_{2}\)O mixture was used [13]. They attributed this effect to a change in the polarity of the medium, which made it easier for the compounds to cross the lipid membrane of the cell, favouring the extraction of the compounds studied.
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
When the emitted light is detected by a photodetector, the optical variations in the signal at multiple near-infrared wavelengths can be used to infer the concentrations of oxygenated haemoglobin (HbO2) and deoxygenated haemoglobin (HHb), thus determining changes in cerebral oxygenation. More details on the technical aspects and applications of cerebral NIRS can be found in a recently published review by [PERSON] and [PERSON] [9]. The raw optical signals measured by NIRS can be further exploited by analysing the cardio-synchronous pulsations (Photoplethysmograms (PPGs)) caused by blood volume changes in light absorption during each cardiac cycle [14; 15]. The hypothesis underlying the utilisation of the PPG in this application is based on the fact that increased pressure surrounding the cerebral arteries on the cortex surface will affect the shape of these arteries; consequently, their squeezed blood volume could yield to changes in the morphology of the detected optical signal. Therefore, this PPG signal will contain features that can be extracted using signal processing techniques, and which can be related to physiological effects of increased intracranial pressure such as increased cerebrovascular vascular resistance and decreased cerebrovascular compliance. This paper describes the in vitro evaluation of a novel non-invasive intracranial pressure (nICP) monitor which utilises the technique of PPG to measure the ICP by analysing the features of the acquired optical signal. The nICP device was evaluated in an in vitro model which simulated the haemodynamics of the cerebral circulation. The signals from the nICP monitor were acquired in different monitoring protocols in which the pressure was incrementally changed and the relevant features from the PPGs were analysed to estimate the ICP non-invasively.
## 4 Tests and Results  
Several tests were performed with the IEEE-39 RTS system for validating the proposed model. In the specialized literature, this test case does not present renewable generation; however, in [33] it is proposed to include six NCRES generators located at nodes 3, 5, 7, 16, 21, and 23. A laptop with an Intel (R) core (TM) i5-4200U @ 1.6 GHz 2.3 GHz processor, 6.00 GB of RAM, and a 64-bit operating system was used in all tests.  
Although the system chosen to demonstrate the applicability of the proposed approach is relatively small, the scalability of the problem is straightforward. This is due to the fact that the model was implemented in GAMS (general algebraic modeling system) software. On the other hand, to reduce the computation time in real applications, several strategies can be explored such as parallelization or the use of computation equipment with higher performance.
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **With Noise** & \begin{tabular}{c} **Without Noise** \\ **Noise** \\ \end{tabular} & **With Noise** & \begin{tabular}{c} **Without Noise** \\ **Noise** \\ \end{tabular} & **With Noise** & \begin{tabular}{c} **Without Noise** \\ **Noise** \\ \end{tabular} & **With Noise** & \
\begin{tabular}{c} **Without Noise** \\ **Noise** \\ \end{tabular} \\ \hline
\begin{tabular}{c} ORR- \\ RVFLN \\ \end{tabular} & 0 ohm & \multicolumn{4}{c}{30 ohm} & \multicolumn{4}{c}{50 ohm} & \multicolumn{4}{c}{100 ohm} \\ \hline
0–50 m & 55 & 53 & 58 & 56 & 61 & 58 & 63 & 59 \\ \hline \
\begin{tabular}{c} \(>\)400 m \\ \end{tabular} & 0 & 0 & 2 & 3 & 4 & 6 & 8 & 9 \\ \hline
\begin{tabular}{c} Cauchy-M- \\ RVFLN \\ \end{tabular} & 0 ohm & \multicolumn{4}{c}{30 ohm} & \multicolumn{4}{c}{50 ohm} & \multicolumn{4}{c}{100 ohm} \\ \hline
0–50 m & 57 & 55 & 60 & 58 & 63 & 59 & 64 & 60 \\ \hline
\begin{tabular}{c} \(>\)400 m \\ \end{tabular} & 2 & 2 & 4 & 5 & 6 & 8 & 9 & 10 \\ \hline RVFLN & 0 ohm & \multicolumn{4}{c}{30 ohm} & \multicolumn{4}{c}{50 ohm} & \multicolumn{4}{c}{100 ohm} \\ \hline
0–50 m & 58 & 56 & 61 & 59 & 64 & 61 & 66 & 62 \\ \hline
\begin{tabular}{c} \(>\)400 m \\ \end{tabular} & 3 & 3 & 5 & 6 & 6 & 8 & 10 & 11 \\ \hline CNN & 0 ohm & \multicolumn{4}{c}{30 ohm} & \multicolumn{4}{c}{50 ohm} & \multicolumn{4}{c}{100 ohm} \\ \hline
0–50 m & 61 & 59 & 64 & 62 & 67 & 64 & 69 & 65 \\ \hline
\begin{tabular}{c} \(>\)400 m \\ \end{tabular} & 4 & 4 & 6 & 7 & 8 & 9 & 11 & 12 \\ \hline LSTM & 0 ohm & \multicolumn{4}{c}{30 ohm} & \multicolumn{4}{c}{50 ohm} & \multicolumn{4}{c}{100 ohm} \\ \hline
0–50 m & 62 & 60 & 65 & 63 & 68 & 64 & 69 & 65 \\ \hline
400 m & 5 & 5 & 7 & 8 & 9 & 10 & 12 & 13 \\ \hline SVM & 0 ohm & \multicolumn{4}{c}{30 ohm} & \multicolumn{4}{c}{50 ohm} & \multicolumn{4}{c}{100 ohm} \\ \hline
0–50 m & 63 & 60 & 65 & 63 & 68 & 65 & 70 & 66 \\ \hline
\begin{tabular}{c} \(>\)400 m \\ \end{tabular} & 6 & 6 & 8 & 9 & 10 & 11 & 13 & 14 \\ \hline ELM & 0 ohm & \multicolumn{4}{c}{30 ohm} & \multicolumn{4}{c}{50 ohm} & \multicolumn{4}{c}{100 ohm} \\ \hline
0–50 m & 64 & 62 & 67 & 65 & 70 & 66 & 71 & 67 \\ \hline
\begin{tabular}{c} \(>\)400 m \\ \end{tabular} & 8 & 8 & 10 & 11 & 12 & 14 & 16 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of fault location performance of different methods for a single 2 ph.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
### VANETs Characteristics
#### 1.2.2 Storage and Computing Capabilities  
The information interchange within VANETs depends on users linked at a particular time. Therefore, network bandwidth and adequate processing power are required to store, process and communicate important messages.
## 3 Results and Discussion
### Facemask Prototype: Assembling of the Different Layers and Their Evaluation
The thermal images were taken during inhalation and exhalation, as demonstrated in Figure 6.  
As can be seen by Figure 6, the facial temperature changed according to the type of facemask used, as well as to each condition (inhalation and exhalation). During inhalation, lower temperatures were observed using Cotton + 2\(\times\) PCL/MgO NPs electrospun membrane + Cotton, since more green and yellow areas were visible. During exhalation, higher temperatures were detected (indicated by red zones), these being the temperature values recorded in the nose, mouth and cheek (Table 5). During exhalation, facemasks composed of only cotton showed homogeneous red zones over the whole face, exhibiting temperatures between 34.4-35.6 \({}^{\circ}\)C, which may induce slight sensations of thermal discomfort [57]. On the other hand, the incorporation of one electrospun membrane promoted a decrease in temperature in the nose and cheek,  
Figure 6: An example of facial temperature distribution for each condition. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Facial Temperature (\({}^{\circ}\)C)**} \\ \cline{2-4}  & **Nose** & **Mouth** & **Cheek** \\ \hline Cotton + Cotton & 35.0 & 35.6 & 34.4 \\ \hline Cotton + PCL/MgO NPs electrospun membrane + Cotton & 34.5 & 36.0 & 32.7 \\ \hline Cotton + 2\(\times\) PCL/MgO NPs electrospun membrane + Cotton & 34.1 & 33.5 & 30.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Facial temperatures recorded in three different locations (nose, mouth and cheek) during exhalation using different facemask prototypes. showing more yellow areas. Interestingly, the introduction of two electrospun membranes resulted in a reduction in temperature in the three locations (nose, mouth and check). These results demonstrated that the incorporation of electrospun membranes can improve the thermal comfort when compared to cotton-based facemasks.
## 3 Results and Discussion
### Stage III
#### 3.3.6 Particle Size and Particles Morphology  
The quadratic model allowed the significant prediction (_p_-value \(<\) 0.05) of \(D_{50}\) of model MD powders. A very well-fitting model to the data verified experimentally was noted, which was confirmed by the high value of the determination coefficients (R\({}^{2}\) = 0.9610) and the insignificant result of the lack of fit test (_p_-value \(>\) 0.05) (Table A2). Based on the estimated parameter values (Equation (9)) and response surface plots (Figure 13), it can be concluded that increasing IAT, IAH, and FC had a positive effect on \(D_{50}\). The effect of FC was larger than IAT and IAH; however, as ANOVA showed, all of the three terms had a significant influence on \(D_{50}\) (both linear and quadratic effects) (_p_-value \(<\) 0.05).  
Figure 12: Flowability (HR) response surface plots for paired factors: (**A**) inlet air temperature (IAT) and inlet air humidity (IAH); (**B**) inlet air temperature and feed solution concentration (FC); (**C**) feed solution concentration (FC) and inlet air humidity (IAH), when the third factor was adjusted to its midpoint.  
[PERSON] et al. (2024) also found that grapeseed oil microcapsules on maltodextrin base, obtained by spray-drying at a higher IAT (180 \({}^{\circ}\)C), had a higher proportion of large-sized particles (average microcapsule diameter was 18.31 \(\upmu\)m), compared to powders obtained at 140 \({}^{\circ}\)C (average microcapsule diameter was 8 \(\upmu\)m) [39]. This may be due to the fact that at 180 \({}^{\circ}\)C the rate of evaporation of surface water of microcapsules was higher than the rate of water migration from the interior to the surface. According to the drying kinetics theory, unbalanced water migration leads to the expansion of microcapsules. [PERSON] and [PERSON] (2007) and [PERSON] et al. (2012), during spray drying the tomato pulp and mannitol, respectively, also observed that at higher FC, the particle size of powder particles was larger [31; 40]. This phenomenon was explained by higher viscosity, which increases the droplet size of solutions at higher concentrations.  
\[D_{50}=14.07+0.6292\cdot IAT+0.6014\cdot IAH+1.22\cdot FC-0.2167\cdot IAT \cdot IAH-0.2202\cdot IAT\cdot FC-0.1139\cdot IAH\cdot FC+1.53\cdot IAT^{2} \tag{9}\]  
The particles were characterized by typical morphology for maltodextrin-based spray-dried powders (Figure 14). The particles were spherical with characteristic cavities. The maltodextrin-based powders obtained by [PERSON] et al. (2020) had similar appearances [33]. Similarly to [PERSON] et al. (2020), no effect of IAH or IAT on the morphology of the obtained particles was found. The maltodextrin concentration also did not affect the particle morphology [33].
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 3 Models excluding Blockchain Technology
### NS Model: SataaS Channel without Blockchain  
In this section, we explore the optimal decisions of the SO in the SataaS channel. The market demand derivation for the SataaS channel is similar to that in the sell channel. By taking Equation (2) to be zero, we derive the marginal customers who are indifferent in relation to subscribing to SataaS and not subscribing to SataaS. However, in contrast to the NR model, the marginal cost of SataaS can be ignored. Nevertheless, the upfront investment in the satellite constellation is significant, and we set it as a fixed cost \(C\). Thus, the SataaS channel demand and the SO's payoff function can be measured as follows:  
\[\begin{split} D_{s}^{N}&=1-p_{s}-s\\ \pi_{s}^{N}&=p_{s}\times(1-p_{s}-s)-C\end{split} \tag{4}\]  
After solving the equation, the optimal price in the SataaS channel is \({p_{s}}^{*}=\frac{1-s}{2}\), and the profit is \({\pi_{s}^{N}}^{*}=\frac{(1-s)^{2}}{4}\).  
It can be observed that the optimal price is decreasing in \(s\) (\(0<s<1\)). The profit of the SO first increases and then decreases as \(s\) increases. In other words, the price of SataaS decreases as the degree of loss of consumer utility due to the data risk increases. The variation in profit depends on the trade-off between the price and market demand, which both change with \(s\). This is also intuitive.
## 2 Harvesting System
### Model and Test Results Comparison and Proposed System Verification
Figure 19: LISpice model vs. measured values comparison with a 27 \(\Omega\) resistive load (impedance matching condition). (**a**) Simulated vs. measured voltage. (**b**) Simulated vs. measured power. (**c**) Simulated vs. measured power frequency. one of the values (voltage, power, and frequency) and not affecting the final results of the modelled equivalent circuit. As a conclusion, if the harvester is installed inside a windy pipe, the absolute kinetic energy is zero, and the harvested power will be only the potential energy, as Equation (8) states. According to [PERSON]'s principle and considering that the air is incompressible, wind velocities must be the same in both sides of the pipe, \(\mathrm{v}_{1}=\mathrm{v}_{2}\). In addition, due to the theorem of the energy principle, \(P_{1}\) must be greater than \(P_{2}\), \(P_{1}>P_{2}\), and consequently \(\Delta P=P_{1}-P_{2}\), and the harvested energy is given by Equation (19). \[E_{harvested}=E_{potential1}-E_{potential2} \tag{19}\]  
where \(E_{potential1}\) and \(E_{potential2}\) are the potential energy of the wind before and after the harvester, and \(E_{harvested}\) is the energy harvested by the harvester. The following hydraulic Equation (20) gives potential wind power:  
\[P_{wind}=\gamma QH \tag{20}\]  
where \(\gamma\) is the specific weight of the air, \(Q\) is the flow quantity across the pipe (A\(\cdot\)v), and \(H\) is the dynamic pressure difference between both sides. Electric power is achieved from wind potential power including mechanical power loses and it is calculated as in Equation (21):  
\[P_{electric}=\Delta P_{wind}-P_{mechan\_losses} \tag{21}\]  
where \(\Delta P_{wind}\) is the power of the wind, \(P_{mechan\_losses}\) is the power losses of the harvester from the mechanical parts, and \(P_{electric}\) is the generated electric power by the harvester. So, the harvested energy corresponds to the potential energy of wind, not the kinetic energy. In addition, mechanical efficiency is achieved by subtracting air power with electrical power. Moreover, [PERSON]'s numbers are not applicable in this case as in the case of wind turbines. This law is only for turbines that harvest energy from wind velocity, i.e., from kinetic energy. For the describedreasons, i.e., this system harvests energy from available pressure and not from wind velocity; thus, the mini wind harvester is not a mini-turbine but a mini-reactor. Furthermore, the correct or appropriate environment to work for this harvester is inside a pipe.
## 4 Discussion
Their low prediction accuracy should be indeed caused simply by their inappropriateness for Slovak companies in the automotive industry. On the other hand, some models (Tomczak, Taffler-Tisshaw) were selected as suitable for predicting Slovak automotive companies, even though they were created for different industries. This highlights the importance of verification of the prediction performance of the already existing models. Next, one of the limitations of this study is the relatively small sample size, particularly concerning the number of companies classified as financially unhealthy. However, it is important to note that the primary objective of this study was not to develop new prediction models but to evaluate and re-estimate the coefficients of existing ones. Therefore, the validity of the models and their predictive power were not influenced by the sample size in this study. Our focus was on observing how the models' performance changed during the pandemic period rather than drawing definitive conclusions about the financial health of the Slovak automotive industry. Future research could benefit from expanding the sample size or including multiple industry sectors to enhance the robustness and generalizability of the results. Last, we highlighted the necessity of models' adjustments under the changed economic conditions. However, it must be noted that these adjustments should be performed only ex post, after collecting enough data on the crisis period, which decreases the usage of re-estimated models in practice. Nevertheless, by understanding how model performance is affected during unprecedented events, practitioners and policymakers can be better prepared to interpret predictions with caution and consider adaptive strategies for future crises. This highlights the importance of similar studies. Future research could explore several directions. First, expanding the scope of analysis to include additional industries and countries would provide a broader understanding of how economic disruptions impact financial distress prediction models. Second, adding more post-pandemic years to the analysis could offer deeper insights into the long-term effects of economic shocks on financial prediction models, helping to determine whether the observed coefficient changes persist or diminish over time. This extended timeline would enhance the robustness of the findings and provide further validation of the adjusted models' utility in post-pandemic environments. However, this would be possible only in case of the availability of the necessary data. Next, it should be beneficial to reevaluate the variables or potentially even the modelling approach for such unprecedented periods as the COVID-19 crisis. In this sense, including the exploration of non-linear models or interaction terms in the models that might better capture the complex financial behavioursobserved during the pandemic. However, this approach would lead to constructing a completely new model instead of re-estimating the existing ones.