esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bayesian Techniques--MCMC and Particle Filter
### Particle Filter--SIR  
The SIR particle filter depends on both the state evolution model and the measurement model, being used as a tool for estimating state variables. Filter initialization consistsof generating samples from a given set of the probability distribution that represents the uncertainty associated with the initial condition with the number of \(N_{part}\), samples, such that this information is equivalent to the number of particles. The particles can be represented vectorially by Equation (14) [42; 43; 44]:  
\[\mathbf{X}_{\mathbf{k}}^{i}=\left[\begin{array}{c}\mathbf{x}_{1}\\ \mathbf{x}_{2}\\ \vdots\\ \mathbf{x}_{Q}\end{array}\right]^{i}_{\mathbf{k}}\text{ for }\left\{i=1,\ldots,N_{part};k=t_{0},t_{1}, \ldots,t_{film}\right\} \tag{14}\]  
where \(Q\) is the total number of nodes in the mesh. Thus, the SIR filter algorithm is started by generating some particles, \(N_{part}\), so that, Equation (15):  
\[\mathbf{X}_{\mathbf{k}}^{i}\qquad\left\{i=1,\ldots,N_{part};k=t_{0}\right\} \tag{15}\]  
It should be noted that vector \(\mathbf{X}\) represents the state variables, which are cell, uranium, Fe\({}^{2+}\), and Fe\({}^{3+}\) concentrations. Then, the weights related to each particle are calculated, as shown in Equation (16):  
\[\mathbf{W}_{\mathbf{k}}^{i}\qquad\left\{i=1,\ldots,N_{part};k=t_{0},t_{1},\ldots,t_{ film}\right\} \tag{16}\]  
The algorithm for the SIR particle filter is presented below [42; 43; 44]:  
1. Start 1.1 Take a set of particles of initial distributional \(\pi(\mathbf{X_{0}})\) and obtain \(\{(\mathbf{X_{0}^{i}},\mathbf{W_{0}^{i}});\mathbf{i}=1,\ldots N_{part}\}\) 1.2 Do \(k=1\)
2. Evaluation of the weights: advance the states in time \(k-1\) for time \(k\) using the state evolution model \(\mathbf{X_{\mathbf{k}}}=f_{k}(\mathbf{X_{\mathbf{k}-1}},\mathbf{v_{\mathbf{k}}})\), where \(\mathbf{f}\) is in general a non-linear function that depends on \(\mathbf{X}\) and has uncertainties represented by \(\mathbf{v}\). 2.1 Calculate the new weights \(\mathbf{W}_{k}^{i}\) 2.2 Normalize the weights \(\hat{\mathbf{W}_{\mathbf{k}}^{i}}=\frac{\mathbf{W}_{k}^{i}}{N_{part}}\) 3. Resampling 3.1 Construct the sum of the cumulative weights, being computed by \(c_{i}=c_{i-1}+w_{k}^{i}\) for \(i=2,\ldots N_{part}\) with \(c_{1}=0\) 3.2 Take \(i=1\) and generate \(u_{1}\) from a uniform distribution \(U\big{[}0,N^{-1}\big{]}\) 3.3 For \(j=1,\ldots N_{part}\) do \(u_{j}=u_{1}+N^{-1}(j-1)\)  While \(u_{j}>c_{i}\) do \(i=i+1\)  Designate the particles \(\mathbf{X_{\mathbf{k}}^{i}}=\mathbf{X_{\mathbf{k}}^{i}}\)  Assign the weights to \(\mathbf{W_{\mathbf{k}}^{i}}=N^{-1}\) 4. Calculation of the current state estimate \(\pi(\mathbf{X_{\mathbf{k}}}|\mathbf{Z_{\mathbf{k}}})\approx\sum\limits_{i=1}^{N_{part}}\mathbf{X_{ \mathbf{k}}^{i}}\hat{\mathbf{W_{\mathbf{k}}^{i}}}\) 5. Do \(k=k+1\), if \(k=t_{end}+1\), then stop. 6. With the new particles, return to step two.
## 5 Results
### Optimization Results Verification
#### 5.3.2 Applications and Verification in Robotic Grinding of Aerospace Test Workpiece  
The optimization of parameters determines the quality and consistency of the processed workpiece surface. As can be seen from the test workpiece surface in Figure 11, when the optimal robotic grinding parameters are not used, the grinding marks are prominent, and over- and under-grinding occur. In contrast, after adopting the optimal robotic grinding parameters, the surface quality improves significantly, which is reflected in the improvement of surface roughness. Therefore, robotic grinding parameter modeling and optimization are highly effective and practical for the grinding of aerospace workpieces based on small-sample data.
# Wild _Saccharomyces_ Produced Differential Aromas of Fermented Sauvignon Blanc Must
## 2 Materials and Methods
### Quantitative Analysis of Fermented SB  
The following volatile compounds were purchased from Sigma-Aldrich (Saint Luis, EUA): ethyl acetate (141-78-6), ethyl butanoate (105-54-4), ethyl pentanoate (539-82-2), ethyl hexanoate (123-66-0), ethyl heptanoate (106-30-9), ethyl octanoate (106-32-1), ethyl nonanoate (123-29-5), ethyl ecanoate (110-38-3), ethyl undecanoate (627-90-7), ethyl dodecanoate (106-33-2), diethyl succinate (123,25-1), ethyl lactate (97-64-3), ethyl cinnamate (103-36-6), ethyl anthranulate (87-25-2), ethyl isobutanoate (97-62-1), ethyl 3-hydroxybutanoate (5405-41-4), ethyl isovalerate (108-64-5), ethyl 2-methylbutanoate (7452-79-1), phenylethyl acetate (103-45-70), hexyl acetate (142-92-7), \(S\)-furfuryl thioacetate (13678-68-7), furfuryl acetate (623-17-6), isobutyl acetate (110-19-0), isoamyl acetate (123-92-2), 3-methyl-1-butanol (123-51-3), methanol (67-56-1), 1-butanol (71-36-3), 2-butanol (78-92-2), 1-propanol (71-23-8), 2-phenyllethanol (60-12-8), 1-hexanol (111-27-3), furfuryl alcohol (98-00-0), propanoic acid (79-09-4), butanoic acid (107-92-6), valeric acid (109-52-4), hexanoic acid (142-62-1), heptanoic acid (111-14-8), octanoic acid (124-07-2), pelargonic acid (112-05-0), decanoic acid (334-48-5), undecanoic acid (112-37-8), 10-undecenoic acid (12-38-9), isobutyric acid (79-31-2), isovaleric acid (503-74-2), \(\alpha\)-pinene (7785-70-8), \(\beta\)-pinene (19902-08-0), geraniol (106-24-1), \(\alpha\)-terpineol (985-55), limonene (5989-27-5), citronellal (2385-77-5), cedrene (469-61-4), \(\gamma\)-nonalactone (104-61-0), \(\beta\)-damascenone (23696-85-7), \(\alpha\)-ionone (127-41-3), \(\beta\)-ionone (14901-07-6), and 4-methyl-2-pentanol (108-11-2).  
Volatiles were quantified as described recently by [PERSON] et al. [17] using a Thermo Scientific Trace 1310 (Waltham, MA, USA) gas chromatograph equipped with a flame ionization detector (GC-FID) and ChromQuest software (Waltham, MA, USA). The contribution of a chemical compound to the overall aroma of fermented SB was quantified through the olfactory activity value (OAV). The OAV is an indicator of the importance of a specific compound in the aroma of the sample and was calculated as the ratio of the concentration of the individual compound relative to the threshold of perception described in the literature. Only those compounds that reached OAV >1 were considered important for the overall aroma of SB. The clustering of OAV for each strain was represented by a heat map as described by [18].
# Stability Optimization of Explicit Runge-Kutta Methods with Higher-Order Derivatives
## 1 Introduction
[41] proposed implicit-explicit TDRKMs for the solution of stiff reaction-diffusion PDEs. In [42], the new approach to the discretization of PDEs, based on TDRKMs, is presented. This approach differs from the classical MOL, where the PDE is first discretized in space and then an ODE solver is applied to solve the semi-discretized system. [PERSON] and [PERSON], in [28], derived three-derivative RKMs (ThRKMs). In [43], stability-preserving ThRKMs are proposed. The presented study is devoted to the optimization of explicit RKMs with higher-order derivatives. The applied method is based on an idea described in [16] and is realized by the numerical maximization of the integral characteristics of the stability domains, such as the area and length of the stability interval. These characteristics are considered as functions of the free parameters. The optimal values of parameters are computed for RKMs with higher-order derivatives presented in [28; 31; 33]. The cases of fourth, fifth, and sixth order methods are considered. Obtained values are used in numerical examples, which deal with the construction of the high-order schemes applied in MOLs. The problems for linear and nonlinear PDEs are considered. In most cases, it is demonstrated that stability intervals on the Courant-Friedrichs-Lewy (CFL) numbers for optimal schemes have a larger length than for schemes based on parameter values from [16; 28; 31; 33]. The paper has the following structure. In Section 2, explicit RKMs with higher-order derivatives, proposed in [28; 31; 33], are described. Expressions for the stability polynomials are presented in Section 3. In Section 4, the numerical optimization of the considered methods is realized. The results of the solution are discussed, and the obtained stability domains are compared with the domains for methods presented in previous works. In Section 5, numerical examples are presented. Some concluding remarks are made in Section 6.
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
## 3 Results
### Characteristics of Spatial and Temporal Changes in SPEI in Central Asia  
In this research, the space distribution of the average annual SPEI of vegetation in Central Asia from 2000 to 2018 was analyzed using the annual SPEI (SPEI-12) as the average annual water balance condition, as shown in Figure 5. The space distribution of SPEI in Central Asia from 2000 to 2018 showed strong geographic variability, and the degree of rapidity was characterized by a decreasing distribution from southwest to north and a wetter distribution on both sides of the mountain range. The average annual SPEI of the vegetation areas in Central Asia ranged from \(-\)1.36 to 0.38, with an average of \(-\)0.25, and 70.80% of the areas had an average annual SPEI < 0, indicating drought conditions. The wetter areas were mainly located in Tajikistan, Kyrgyzstan, and northeast Kazakhstan, and the southwest of Kazakhstan was more arid. More severe drought conditions were found in Turkmenistan, Uzbekistan, and the eastern part of Xinjiang, China, where there was less rainfall, low vegetation cover, weak ecosystem resilience, and vulnerability to drought.  
Figure 4: Change in the average annual GPP of vegetation over time in Central Asia from 2000 to 2018.  
Figure 5: Spatial distribution of annual mean SPEI of vegetation in Central Asia from 2000 to 2018.  
Temporally, using the mean SPEI (SPEI-12) data of Central Asia to represent the drought situation of each year, the interannual change characteristics of the SPEI in Central Asia from 2000 to 2018 were obtained, and a one-dimensional linear regression model was established, as shown in Figure 6. Based on the interannual trend of SPEI, drought in Central Asia fluctuated but was more stable, with an overall decreasing trend and a decrease rate of 0.02 y\({}^{-1}\). The analysis revealed that the corresponding SPEI value (\(-\)1.43) in 2008 was low, indicating that the Central Asia region experienced one moderate drought in the past 19 years. The wet year in 2003 corresponded to an SPEI value of 1.04, which was relatively high. The change in drought conditions in Central Asia from 2000 to 2018 was mainly divided into two periods: in the first period, the drought conditions from 2000 to 2008 changed from semi-arid and semi-wet in 2000 to moderate drought in 2008; in the second period, the drought conditions gradually changed from moderate drought in 2008 to semi-arid and semi-wet in 2018.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using SPSS version 26.0 (SPSS Inc., Chicago, IL, USA). The normality of data distribution was tested with the Shapiro-Wilk test and histogram analysis. Data are expressed as mean \(\pm\) SD or median (interquartile range); mean differences are expressed with their two-sided 95% confidence interval (CI). The percentage of change in each outcome from baseline to post-session was calculated as follows: ((post-session value \(-\) baseline value)/baseline value) \(\times\) 100. Between sessions differences at baseline and in the change from baseline to the end of the study were tested with paired Student's \(t\) tests. Paired Student's \(t\) tests were also performed for within-session comparisons from baseline to the end of the exercise session. Between-group (sport modalities: soccer versus basketball) differences at baseline and in the change from baseline to the end of the study were tested with independent \(t\)-tests. The effect size was calculated and an effect size of 0.2, 0.5, and 0.8, respectively, interpreted as small, medium, and large. Paired Student's \(t\) tests were also conducted to compare baseline from first and second sessions (irrespective of the exercise type that followed) to determine whether there were within-session learning effects. The intraclass correlation coefficient (ICC) was calculated to determine the reliability of the baseline assessments from the first and second sessions (irrespective of the exercise type that followed). The level of significance was set at \(\alpha\) < 0.05.
## 3 EEG Essentials: Unveiling Standard Protocols for Advanced Clinical Practice
### Sampling Rate  
The importance of the choice of sampling rate in the recording and analysis of EEG signals is examined, with a particular focus on studies concerning AD. The studies analyzed in this review generally adopt a sampling frequency of 256 Hz. This choice is closely linked to the need to accurately capture the relevant frequency bands of the EEG, which include the \(\delta\), \(\theta\), \(\alpha\), \(\beta\), and \(\gamma\) bands, already discussed in the introduction.  
A sampling rate of 256 Hz is adequate to detect the specific characteristics of brain electrical activity associated with AD, including slowing brain rhythms, and allows for the phenomenon of aliasing to be avoided. This phenomenon, which can introduce distortions into the data when high-frequency components are not sampled correctly, is mitigated through the use of low-pass filters with a cut-off frequency around 128 Hz, compliant with the principles of the Nyquist theorem.  
The adoption of a sampling rate of 256 Hz offers a good compromise between temporal resolution and efficient data management, ensuring that the frequency bands critical for the diagnosis of AD are adequately represented. This frequency helps maintain convenient data management, reducing storage requirements and facilitating analysis without compromising the quality of EEG recordings.  
For studies requiring more detailed analysis of the microstructures of brain activity, such as short-duration events or rapid oscillations, a higher sampling rate could be considered. However, for standard analysis of AD-related neurophysiological alterations, 256 Hz is considered ideal to ensure accurate representation of relevant frequency bands and practical data management.
## 3 Results and Discussion
### Smoke Particle Size Distribution  
Table 1 presents the comparison of the _MMAD_ and _TPMC_ value results, measured from a normal and modified eight-stage Andersen air sampler system. For the modified system, the Andersen impactors and tubing were insulated with glass fiber to prevent condensation, but for the normal system, these were not insulated. The results showed the _MMAD_ value results measured from the modified system were less than the normal system by about 10-15%, but _TPMC_ was not significantly different. For this reason, the modified system was improved to prevent its condensation.  
Figure 4 shows the effect of the wood combustion period and moisture content on the smoke particle distribution measurement results using an eight-state Andersen sampler from five samples with moisture content varying from 31.5% to 89.7% d.b. The results showed that the increase in moisture content level had a strong influence on increasing smoke particle size distribution. The smoke particle size distribution indicated a single-mode behavior, suggesting that most of the particles were associated with small particles. This agreed with the results reported by [1; 2; 16; 18; 26].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Andersen Air Sampler System** & **Moisture Content (\% d.b.)** & **Burning Period (min)** & _MMAD_ (Micron) & _TPMC (mg/m\({}^{3}\))** \\ \hline \multirow{4}{*}{Normal} & \multirow{2}{*}{89.70\%} & 15 & 1.37 & 79.28 \\ \cline{3-5}  & & 30 & 1.29 & 62.59 \\ \cline{3-5}  & & 45 & 0.91 & 49.14 \\ \hline \multirow{4}{*}{Modified} & \multirow{2}{*}{89.70\%} & 15 & 1.25 & 77.42 \\ \cline{3-5}  & & 30 & 1.16 & 61.51 \\ \cline{3-5}  & & 45 & 0.82 & 48.23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison of _MMAD_ and _TPMC_ value results measured from a normal and modified 8-stage Andersen air sampler system.  
Figure 3: The temperature at 500 mm above the base of the fire for the different para rubber wood moisture contents.  
The mass median aerodynamic diameter (_MMAD_) values were found to be 0.62 to 1.27 microns throughout different wood burning periods and with different moisture contents, as shown in Figure 4. The highest _MMAD_ values were found to be 0.97 to 1.25 microns at the initial combustion period, having the highest para rubber wood moisture content. As shown in Figure 4, at 89.7% d.b., the _MMAD_ values decreased rapidly at 15 to 45 min after the wood's initial combustion. This result illustrated that the vapor from wood combustion caused the size of smoke particles to be larger than normal from addition and impaction. After 90 min, the _MMAD_ values reduced to 0.79 to 0.91 microns. In the final period (after 150 min after initial combustion), _MMAD_ values reduced to 0.64 to 0.67 microns. The results showed that the time increase had a high influence on decreasing the _MMAD_ values because the degree of incomplete burning is decreased after the evaporation of the water in every type of wood [27; 28; 29; 30; 31], including para rubber wood in this experiment.
## 4 Results
### Results Paderborn Dataset  
As can be seen in Table 6, the results on the Paderborn dataset show different characteristics compared to the results on the CWRU dataset. One of the most prominent observations is the consistency in the results across multiple classification tasks with different train splits, which can also be observed when averaging the accuracy scores of the CNNs across all experiments (see Figure 5). This particular behavior is ought to be caused by three factors: first, the amount of data available is much higher than for the CWRU experiments, even with lower train splits; second, there are fewer classes to choose from, indicating that the classification task at hand is slightly easier; third, the data consist of three signals instead of two representing motor currents and vibrations.  
Figure 4: Averaged accuracy scores (%) across all CWRU experiments with their respective train split (%).  
Figure 5: Averaged accuracy scores (%) across all Paderborn experiments with their respective train split (%).  
Another remarkable observation is the much higher performance of the traditional ML approaches compared to the results in the CWRU experiments. It seems that the extracted features are particularly well-suited for motor current signals when detecting bearing faults, especially in the case of the Random Forest algorithm. However, the results clearly reveal that in this case the _A-WCNN_ model is the favorite over the other models, showing the highest accuracy performance in every single experiment, followed by the _Ada-WMCNN_ that runs closely behind. This result suggests that having an overarching first wide-kernel layer with many filters performs slightly better compared to the multichannel separation in the _Ada-WMCNN_ model, while completely separating the signals and feed them as univariate time series results in lower performance (results from the _A-WMCNN_ model). A reason for this might lie in the nature of the data where motor current signals tend to be more correlated than separately installed vibration sensors as used in the CWRU bearing experiments.
## 2 Numerical Models for Concrete Cracking in Statics and Dynamics
### Strain Rate Effect Related to the Uniaxial Tensile Behavior  
Previous works on the rate effect related to the uniaxial tensile strength of concretes detailed the physical mechanisms involved in this mechanical observation [2; 3]. It was demonstrated that this rate effect is, at the material level, linked to the water within the nanopores of the concrete hydrates. The physical mechanism proposed is similar to the well known (in physics) Stefan Effect. It involves the water viscosity [2; 3] in this way: when a thin film of a viscous liquid is placed between two flat plates, the liquid exerts a restoring force on the plates proportional to the displacement rate of the plates (Equation (8)).  
\[F=\frac{3\eta V^{2}}{2\pi h^{5}}\frac{dh}{dt} \tag{8}\]  
where \(\eta\) is the viscosity of the liquid, \(h\) is the initial distance between the plates, \(\frac{dh}{dt}\) is the displacement rate, and \(V\) is the volume of liquid.  
Concrete Youngs' modulus, which is highly linked to the presence of aggregates, is not very affected by Stefan Effect, as aggregates are not highly influenced by viscous mechanisms [12]. Experimental data [13] appear to show that strength increase does not depend on the water-to-cement (w/c) ratio (Figure 2), that is, on the concrete mix design. This is due to the fact that all concretes have the same quantity of water within their nanopores (hydrates porosity).  
Figure 1: General aspects of the 3D probabilistic semi-explicit cracking model of concrete.  
Figure 2 shows that the tensile strength increase follows Equation (9), which is graphically depicted in Figure 3.  
\[f_{t,dyn}=f_{t,stat}+2.8+0.3\ln(\sigma) \tag{9}\]  
In the past, tests on a large (3.50 m \(\times\) 1.10 m \(\times\) 0.30 m) Double Cantilever Beam (DCB) specimen have been performed [14; 15; 16]. Due to the difficulty in performing dynamical tests on this type of specimen, an assumption has been made, which is presented in Equation (10)  
\[G_{c,dyn}=G_{c,stat}\left(\frac{f_{t,dyn}}{f_{t,stat}}\right) \tag{10}\]  
It is important to point out that Equation (10) is physically based, because the cracking process inside a specimen in uniaxial tension is the same inside the process zone at the macroscopic crack tip during its propagation. Therefore, the rate effect should be equivalent. Equation (10) leads the stress-strain curve to display the behavior depicted in Figure 4 (for the sake of simplicity, it is considered to be a linear curve, but the same would apply for a general stress-strain curve), indicating that the material becomes more brittle as the loading rate increases.  
Figure 3: Tensile strength increasing as a function of the logarithm of the tensile stress rate.  
Figure 2: Tensile strength as a function of the loading rate logarithm for w/c = 0.3, 0.5 and 0.7 [13].
###### Abstract
Through the Business Applications and Space Solutions (BASS) program ([[https://business.esa.int/](https://business.esa.int/)]([https://business.esa.int/](https://business.esa.int/))) (accessed on 22 December 2021), dedicated to the development and validation of new Applications and Services in the downstream sector, the European Space Agency (ESA) has been active for several years in the forefront of promoting the utilization of space technologies, like Global Navigation Satellite System (GNSS), Satellite Communications (SAT-COM) or Earth Observation, and know-how to support the improvement and development of solutions in various domains, including the Health sector. The UV-SAN Project has been developed with the support of the ESA (contract reference 4000131345/20/NL/AF) in the frame of a dedicated Call for Proposals named \"Space in response to COVID-19 outbreak\" ([[https://business.esa.int/space-for-covid19](https://business.esa.int/space-for-covid19)]([https://business.esa.int/space-for-covid19](https://business.esa.int/space-for-covid19))) (accessed on 20 March 2021). To determine the effectiveness of the UV-SAN system, the sanitation procedure has been firstly verified on SARS-CoV-2 by testing the effect of UVC irradiation on the physiological decay of infectivity, under laboratory controlled conditions in an experimental model of disinfection on different surfaces, and secondly validated in operative environment (ambulance) by measuring its capability to reduce and inactivate multi drug resistant (MDR) bacteria.