esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Segmentation of Nanostructured Materials for Gas Sensing
### Titanium Oxide (TiO\({}_{2}\))
For example, Pt-modified TiO\({}_{2}\) sensors exhibit a strong response to hydrogen sulfide (H\({}_{2}\)S), where Pt acts as a catalyst, promoting H\({}_{2}\)S oxidation on the TiO\({}_{2}\) surface and enhancing electron transfer, resulting in a pronounced change in conductivity. This catalytic effect not only improves sensitivity, but also accelerates the recovery process, making the sensor suitable for environments with fluctuating gas concentrations [57] (Figure 6). UV activation is another essential aspect of TiO\({}_{2}\)-based sensors, as it significantly boosts sensor performance by generating electron-hole pairs that participate in surface reactions. Upon UV illumination, TiO\({}_{2}\) produces reactive oxygen species (ROS), such as superoxide ions (O\({}_{2}\)\({}^{-}\)) and hydroxyl radicals (\(\bullet\)OH), which interact readily with gas molecules. For example, in the presence of VOCs, these ROS facilitate the oxidation of gas molecules, enhancing the depletion or accumulation of charge carriers in the TiO\({}_{2}\) surface layer, which translates to a detectable change in resistance. The efficiency of UV activation makes TiO\({}_{2}\)-based sensors highly effective in low-temperature environments, extending their applicability to settings where thermal activation may not be feasible [58]. Moreover, composite materials incorporating TiO\({}_{2}\) with other oxides, such as SnO\({}_{2}\), ZnO, or WO\({}_{3}\), have shown enhanced performance due to synergistic effects. For instance, TiO\({}_{2}\)-SnO\({}_{2}\) composites leverage the high surface area and fast electron mobility of SnO\({}_{2}\) while maintaining TiO\({}_{2}\)'s stability and photoactivation capabilities. These composites have demonstrated excellent response to NOx and H\({}_{2}\)S gases, as the heterojunctions formed at the interface improve charge separation and enhance the overall sensing response [59; 60; 61]. TiO\({}_{2}\)-nanostructure-based gas sensors demonstrate a remarkable sensitivity to acetone, up to 1.88 at a detection limit of 5 ppm at room temperature and 180.08 at a detection limit of 200 ppm at an optimal operating temperature of 65 \({}^{\circ}\)C. Meanwhile, the device exhibits long-term stability, good cycle-to-cycle repeatability, and selectivity to trace acetone at room temperature. Moreover, benefiting from the 3D penetrating porous structure, the average response time of the device is only about 1.8 s [62; 63]. In summary, the intrinsic properties of TiO\({}_{2}\), combined with advances in nanostructuring, doping, and composite formation, make it a versatile and effective material for gas sensing. The continued development of TiO\({}_{2}\)-based sensors is expected to yield devices with even higher sensitivity, faster response and recovery times, and broader selectivity, suitable for applications across environmental monitoring, industrial safety, and healthcare.
## 3 Lens Construction
### [PERSON] et al. [75] first proposed constructing a two-dimensional gradient photonic crystal by embedding an array of DE tubes in an air background and adjusting the radius of the DE tubes by applying different voltages to change the filling factor of the two-dimensional gradient photonic crystal, which in turn adjusts the photon dispersion relation and the effective refractive index and achieves the focal length and field of view adjustment. With the development of technology, the theory gradually developed into hyperlens technology [76]. [PERSON] et al. [77] developed a hyperlens based on DE actuation, as shown in Figure 12, which can simultaneously adjust the focal length, aberration, and image shift by DEA actuation and has a magnification ratio of 207%, which demonstrates an excellent potential for microscopy and compact optical systems.  
The emergence of dielectric elastomer-driven metalenses significantly reduces lens thickness and paves the way for innovative high-performance DETLs. However, compared to other DETL structures, its preparation process is more complex and demands more precise and coordinated control of focal length, aberration, and image quality offset.
## 2 Materials and Methods
### Finite Element Analysis
#### 2.2.2 Failure Criteria  
In order to facilitate the comparison of the calculation results of the hybrid joint and the adhesive joint, the same bilinear constitutive relation as in [30] is used to simulate the failure process of the adhesive layer. For damage to the bolt, the three-dimensional Hashin failure criterion is used, and it can be expressed as follows:  
(1) Fiber tensile failure (\(\sigma_{11}>0\)) (2) \[\left(\frac{\sigma_{11}}{X_{T}}\right)^{2}+\left(\frac{\tau_{11}}{X_{T}}\right) ^{2}+\left(\frac{\tau_{13}}{S_{13}}\right)^{2}\geq 1\] (1) (3) Fiber compressive failure (\(\sigma_{11}<0\)) (4) \[\left(\frac{\sigma_{11}}{X_{C}}\right)^{2}\geq 1\] (2) (5) Matrix tensile failure (\(\sigma_{22}>0\)) (6) \[\left(\frac{\sigma_{22}}{Y_{C}}\right)^{2}+\left(\frac{\tau_{12}}{S_{12}} \right)^{2}+\left(\frac{\tau_{23}}{S_{23}}\right)^{2}\geq 1\] (3) (7) Matrix compressive failure (\(\sigma_{22}<0\)) (8) Matrix/fiber shear failure (\(\sigma_{11}<0\)) \[\left(\frac{\sigma_{11}}{X_{C}}\right)^{2}+\left(\frac{\tau_{12}}{S_{12}} \right)^{2}+\left(\frac{\tau_{13}}{S_{13}}\right)^{2}\geq 1\] (5) (9) Tensile delamination failure (\(\sigma_{33}>0\)) \[\left(\frac{\sigma_{33}}{Z_{T}}\right)^{2}+\left(\frac{\tau_{13}}{S_{13}} \right)^{2}+\left(\frac{\tau_{23}}{S_{23}}\right)^{2}\geq 1\] (6)  
Figure 5: (**a**) Parts and constraints; (**b**) mesh generation and boundary conditions; (**c**) master and slave surfaces in each contact pair.  
10 Compressive delamination failure (\(\sigma_{33}<0\)) \[\left(\frac{\sigma_{33}}{Z_{C}}\right)^{2}+\left(\frac{\tau_{13}}{S_{13}}\right)^ {2}+\left(\frac{\tau_{23}}{S_{23}}\right)^{2}\geq 1\] (7) where \(\sigma_{11}\), \(\sigma_{22}\), \(\sigma_{33}\), \(\tau_{12}\), \(\tau_{13}\), and \(\tau_{23}\) are the respective normal stress and shear stress in the three directions of the element. \(X_{T}\), \(X_{C}\), \(Y_{T}\), \(Y_{C}\), \(Z_{T}\), and \(Z_{C}\) are the strength parameters of the element, as shown in Table 1. The element stiffness degradation model of Camanho [34] was adopted for the damage to the bolt element, as shown in Table 3.  
The calculation process of progressive damage of composites is demonstrated in Figure 6.
# Gravity Field Recovery Using High-Precision, High-Low Inter-Satellite Links
## 4 Results
### Gravity Field Retrieval Performance Due to Instrument Errors
The close to radial observation geometry of MOBILE was comparable to satellite laser ranging (SLR) observations, showing superior performance in observing the very long wavelength gravity field variations, in particular the zonal SH coefficient of degree 2, which physically represented the Earth's dynamic oblateness [46]. \begin{table}
\begin{tabular}{c c c} \hline
**Errors Included** & **RMS Low–Low** & **RMS MOBILE** \\
**in Simulation** & **Pair (cm EWH)** & **(cm EWH)** \\ \hline Instrument errors up to SH degree 100 & & \\ Orbit error (GNSS) & 1.58 & 0.13 \\ Accelerometer + star camera & 4.18 & 0.51 \\ Laser interferometer & 4.12 & 14.80 \\ \hline Temporal aliasing errors up to SH degree 50 & & \\ AOHIS aliasing + instruments & 28.27 & 15.76 \\ Ocean tide aliasing + instruments & 5.36 & 3.84 \\ HIS aliasing + instruments & 5.13 & 2.12 \\ HIS aliasing + instruments (ext. processing) & - & 1.29 \\ \hline Averaged AOHIS signal up to SH degree 50 & & 4.06 \\ Averaged AOHIS signal up to SH degree 100 & & 4.63 \\ Averaged HIS signal up to SH degree 50 & & 3.35 \\ Averaged HIS signal up to SH degree 100 & & 3.96 \\ \hline \end{tabular}
\end{table} 
Table 3: Global RMS values in cm EWH for the low–low pair and MOBILE constellation considering different isolated error sources. RMS values are computed up to SH degree 100 for instrument errors, and up to SH degree 50 for temporal aliasing errors. Additionally, the global RMS value of the monthly averaged AOHIS and HIS signal is given (computed up to SH degree 50 and 100). In order to make the effect of the different error spectra of both satellite concepts even more visible, spatial covariance functions were computed for a position at the equator and at 45\({}^{\circ}\) latitude (see Figure 7). They describe the correlation of the computation point with its neighborhood in the normal equation system due to the used stochastic model and the observation geometry. The spatial characteristics and the pattern of the covariances provide information about the spatial behavior of the retrieved signals. In our case, the figures show the typical stripes for the low-low tracking concept caused by the north-south observation direction that are known from the GRACE temporal gravity models, while the MOBILE concept exhibited an isotropic error structure at both latitudes. Figure 6: Formal error triangle plots in \(\log_{10}\) up to SH degree and order 100 (**a**,**c**), and up to SH degree and order 10 (**b**,**d**), for the low–low pair formation (**a**,**b**), and the MOBILE constellation (**c**,**d**).
## 6 Numerical Methods and Algorithms
### Models at the Meso Scale
#### 6.2.3 The Cellular Automata Model Algorithms  
Let \(\mathcal{A}\) be Cellular Automata defined by  
1. A \(N\)-dimensional grid \(\mathcal{G}\) of \(N_{Cells}=\prod_{m=1}^{N}N_{Cells}^{n}\) cells \(\mathcal{C}=\{\mathfrak{e}^{n}\}_{n=1,\ldots,N_{Cells}}\) where, for each of them, the set of neighborhood \(\mathcal{N}^{n}\) is defined;
2. A set \(\mathcal{I}\) of \(N_{Ingredients}\) ingredients, i.e., \(\mathcal{I}=\left\{i^{l}\right\}_{l=1,\ldots,N_{Ingredients}}\);
3. A set \(\mathcal{R}\) of \(N_{rules}\) rules, i.e., \(\mathcal{R}=\{\mathfrak{e}^{n}(\mathcal{I})\}_{n=1,\ldots,N_{rules}}\).  
Given the number \(M\) of the iteration defining the length of each run and the number \(N_{run}\) defining the number of the runs to be executed, the \(M\times N_{run}\) states \(\big{\{}\mathcal{G}_{i,j}\big{\}}_{i=1,\ldots,Mj=1,\ldots,N_{run}}\) of asynchronous simulation based on the automata \(\mathcal{A}\) can be computed by Algorithm 9.  
```
1:procedureAsynchronous Cellular Automata(\(\mathcal{A}\), \(\mathcal{G}_{0}\), \(N\), \(M\))
2:Input:\(\mathcal{A}\), \(\mathcal{G}_{0}\), \(N\), \(M\)
3:Output:\(\big{\{}\mathcal{G}_{i,j}\big{\}}_{i=1,\ldots,Mj=1,\ldots,N_{run}}\)
4:for\(j=1\) to \(N_{run}\)do
5:Generate an initial condition state \(\mathcal{G}_{0,j}\) for the grid \(\mathcal{G}\);
6:for\(i=1\) to \(M\)do
7:for\(n=1\) to \(N_{Cells}\)do
8:if (cell \(\mathfrak{c}_{i-1,j}^{n}\) of the grid \(\mathcal{G}_{i-1,j}\) is occupaied by some ingredient \(i^{l}\in\mathcal{I}\)) then
9:for\(m=1\) to \(N_{rules}\)do
10: Apply rule \(\mathfrak{v}^{m}(\mathcal{I})\) to \(\mathfrak{c}_{i-1,j}^{n}\) to define \(\mathfrak{c}_{i,j}^{n}\), and its neighborhood \(\mathcal{N}_{i,j}^{n}\), on grid \(\mathcal{G}_{i,j}\)
11:endfor
12:endif
13:endfor
14:endfor
15:endfor
16:endprocedure
```  
**Algorithm 9** The asynchronous cellular automata algorithm.
## 3 Application of the Proposed Methodology  
### Heterogeneity in Car-Following Behavior Analysis  
In this section, we try to understand the heterogeneity of drivers' car-following behavior through the distribution of some variables with different car-following combinations and different traffic flows.  
As shown in Figure 4, the left picture shows the overall distribution, and the right shows the distribution under different car-following combinations. It can be found that the speed of the Car-Truck, Truck-Truck, and Truck-Car combinations is concentrated around 25 m/s, while the speed of the Car-Car combination is more widely distributed. From the perspective of distribution, the second peak of the overall distribution is mainly caused by Car-Car combinations, and other types of car-following combinations are mainly concentrated on the first peak.  
The distribution of the time headway is shown in Figure 5. It can be found that the tail of the overall distribution is mainly occupied by the other three combinations. The THW of the Car-Car combination is mainly distributed between 1-3 s. THW is an indicator used to measure the driver's perception of risk, which represents how long it will take for the following vehicle to reach the heading vehicle at the current speed. It illustrated that  
Figure 4: The distribution of the following vehicle velocity.  
the other three types have a stronger sense of risk and tend to maintain lower speeds and higher THW to guarantee safety.  
With different traffic flows, the distribution of the following vehicle velocity is shown in Figure 6. The mixture distribution of velocity almost divided the three normal distributions with different traffic flows.  
It is not difficult to find that, after distinguishing car-following combinations and traffic flows, the heterogeneity of car-following behavior has been preliminarily explained. The driver's behavioral decision (referring to the velocity of ego vehicles in the car-following scenario) is affected by the type of heading vehicles and ego vehicles, as well as the traffic density of the road where they are driving.  
In Sections 3.3 and 3.4, we will encode these heterogeneity factors into car-following models to obtain better car-following prediction performance and further quantify their impact on car-following behavior through the model results.  
Figure 5: The distribution of the following vehicle’s time headway.  
Figure 6: The distribution of the following vehicle’s velocity.
## 4 The GA-TNE+DRO Algorithm
```
0:\(f(\mathbf{X})\): the objective function \(n\): the number of variables in \(\mathbf{X}\) \(dom(\mathbf{X})\): domains of the \(n\) variables \(N\): the size of initial population \(P_{c}\): the probability of crossover operation \(P_{m}\): the probability of mutation operation \(P_{r}\): the probability of recombination operation \(G\): max number of iterations \(\varepsilon\): accuracy threshold Output: The value of \(\mathbf{X}\) that optimizes \(f(\mathbf{X})\) Method:
1. POP = a population of \(N\) randomly generated individuals
2. For each \(p\) in POP
3. Calculate the fitness value of \(p\) using _fdecode(\(p\))_
4. \(\mathbf{X}\) = the best individual in POP
5. Old\(\mathbf{X}\) = any individual in POP that is not \(\mathbf{X}\)
6. While termination condition is not satisfied do
7. Old\(\mathbf{X}\) = \(\mathbf{X}\)
8. NewPOP = [\(\mathbf{X}\)]
9. NEU = {N/2 neutral individuals in POP}
10. DEL = {N/2 deleterious individuals in POP}
11. For each individual \(p\) in NEU
12. Apply crossover operations to \(p\) according to \(P_{c}\) and add results to NewPOP
13. For each individual \(p\) in DEL
14. Apply mutation operations to \(p\) according to \(P_{m}\) and add results to NewPOP
15. For each pair of individual \(p\) and \(p^{\prime}\) in POP
16. Apply the recombination operation according to \(P_{r}\) and add results to NewPOP
17. For each \(p\) in NewPOP
18. Calculate the fitness value of \(p\) using _fdecode(\(p\))_
19. \(\mathbf{X}\) = the best individual in NewPOP
20. If \(\mathbf{X}\) is not better than Old\(\mathbf{X}\) then
21. Generate randomly new individuals and pick up the best one into NewPOP
22. POP = NewPOP
23. End while
24. Return decode (X)
```  
**Algorithm 1** GA-TNE+DRO
## 2 System Model
Let \(SNR_{k,n}^{Direct\_link}\) be the signal-to-noise ratio for user \(k\) via Direct Link, and \(SNR_{k,n}^{Relay\_link}\) be the signal-to-noise ratio for user \(k\) via Relay Link, then, the \(SNR_{k,n}\) is given as  
\[SNR_{k,n}=\max(\mathbb{E}\{SNR_{k,n}^{Direct\_link}\},\mathbb{E}\{SNR_{k,n}^{ Relay\_link}\}), \tag{3}\]  
\[SNR_{k,n}^{Direct\_link}=\frac{p_{k,n}^{BS}\delta_{k,n}^{Direct\_link}}{N_{0} \frac{B}{N}}, \tag{4}\]  
\[SNR_{k,n}^{\text{Relay\_link}}=\min(SNR_{k,n}^{buchaul\_link},SNR_{k,n}^{access\_ link}), \tag{5}\]  
where, \(SNR_{k,n}^{buchaul\_link/access\_link}\) is  
\[SNR_{k,n}^{buchaul\_link/access\_link}=\frac{p_{k,n}^{BS/RN}\delta_{k,n}^{ buchaul\_link/access\_link}}{N_{0}\frac{B}{N}} \tag{6}\]  
The Energy Efficiency EE in terms of \(bits/s/Watts\) can be expressed as  
\[EE=\frac{\sum\limits_{k=1}^{K}\sum\limits_{n=1}^{N}\mu_{k,n}\text{log}_{2}(1 +SNR_{k,n})}{\sum\limits_{k=1}^{K}\sum\limits_{n=1}^{N}p_{k,n}}. \tag{7}\]  
Figure 1: Topology. The EE optimization problem for the above scenario can be written as  
\[ \begin{split}\text{maximize}& EE\\ \text{subject to}&\sum_{k=1}^{K}\sum_{n=1}^{N}p_{k,n} \leq P_{total}\\ & p_{k,n}\geq 0,\forall\;k,n\\ &\mu_{k,n}=\{0,1\},\forall\;k,n\\ &\sum_{k=1}^{K}\mu_{k,n}=1,\forall\;n\\ & R_{1}:R_{2}:........R_{K}=\alpha_{1}:\alpha_{2}:........\alpha_{K} \end{split}  \tag{8}\]  
where \(\alpha_{k}\) is the proportional rate constraint [22]. We assume that channel state information (CSI) of all the users is known to the BS. Also, it is assumed that the RB allocation decision and assignment is done in less than channel coherence time so that CSI information can be used. This further puts constraints on the RB allocation algorithm complexity. The two-hop transmission to the RN users will be carried out in two TTI's. In the first TTI, the BS will only send data to the RN users that are in close proximity of RN or have better RN-UE channel conditions than the direct link BS-UE. In the second TTI RN-UE data will be sent. BS will choose the path to the user (direct or via RN) with best channel coefficient in each TTI. The centralized scheduling minimizes the possibility of interference for In-band type of RNs. Frequency division duplexing ensures that the RN may handle backhaul data simultaneously with the access link data so that from the second TTI onwards backhaul BS-RN transmission is carried out simultaneously with the access link RN-UE transmission. The LTE-A downlink is an OFDM based system which supports M-ary quadrature amplitude modulation (MQAM). We can use Equation (2) to calculate the throughput of user \(k\) on RB \(n\) for both direct and relay-link paths. The two paths provide channel diversity to increase the users and system level throughput. We use MT and SAMM criteria for RB allocation with equal power allocation to all RBs or BOPA as explained below.
## 3 Analysis and Design
### Semi-Physical Simulation Test for the Interferometry of Inter-Satellite Ranging
There are two main types of noise: one is noise with a frequency range above 0.5 Hz, caused by the shake of buildings, trees, and human activities; the other type is noise with a frequency range of 0.05-0.5 Hz caused by surface waves and body waves in the crust, which are originated from collisions on the coast by sea waves. For the simulation of the micro-vibration environment of a spacecraft, a commercial precision active vibration isolation platform is used for effective isolation of ground vibration noise. The precision active vibration isolation platform needs to be placed horizontally on the desktop. From the analysis above, it can be seen that the spacecraft simulation platform must have the ability for both micro-vibration and high-resolution attitude adjustment; therefore, the spacecraft simulation platform is made up of the precision active vibration isolation platform and the piezo-driven hexapod described in Section 3.1, with the latter placed on the former. Based \begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Parameters** & **Technical Specifications** \\ \hline Optical interference platform & Degree of spot coincidence for the interfering beams & \(<\)100 μm \\  & Included angle of the interfering beams & \(<\)100 μm \\ \hline  & Effective aperture & 100 mm \\  & Optical efficiency & 0.853 \\  & Magnification & 80\(\times\) \\ Telescope & Wavefront quality & \(\lambda/20\) rms \\  & Optical path stability & 50 μm\(/\surd\)Hz \\  & Stray light & \(1\times 10^{-6}\) \\ \hline  & Wavelength & 1064 nm \\ Laser & Frequency stability & 30 Hz\(/\surd\)Hz \\  & Power & 20 mW \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Main parameters of the optical interference platform, telescope, and laser. on these analyses, the TS150 [52], whose isolation frequency can be as low as 0.7 Hz, is selected. The technical solution to the semi-physical simulation test for the interferometry of inter-satellite ranging is shown in Figure 10. Two test mass simulators, which are based on active suspension vibration isolation technology, are used to simulate the two freely suspended test masses in adjacent spacecrafts. The spacecraft simulation platforms, with the ability for both micro-vibration and high-resolution attitude adjustments, are used to simulate the spacecraft. The dual-spacecraft simulation platforms are placed between the two test mass simulators. Ranging noise of better than 100 pm/Hz can be expected for the interferometry of inter-satellite ranging, which can be tested by measuring the system noise.
## 2 Research Context
### Product Architecture  
Product architecture is defined as a grouping of functions and interfaces that connect functional, physical, and operational domains [22]. Product architecture identifies the link between a product's function and its physical components that fulfill this function [5]. The literature presents three types of product architectures: integral, modular, and open architecture.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Production Strategy** & **Mass** & **Mass** & **Mass** \\
**Objectives** & **Production** & **Customization** & **Personalization** \\ \hline \multirow{3}{*}{Mass Production Cost} & \multirow{3}{*}{Present (Lowest)} & Present & Present \\  & & (Lower than MPE but higher & (Low Compared to Custom \\  & & than MP) & Products but higher than MC) \\ \hline \multirow{3}{*}{Product Variety} & Low Product Variety (Minimal to no choices) & \multirow{3}{*}{Customized Products (Many configured choices)} & Individualized \\  & & & Products \\ \cline{1-1}  & & (Many configured choices) & (Unique product, \\ \cline{1-1}  & & & market of one) \\ \hline \multirow{3}{*}{Product Performance} & Present & Absent & Absent \\  & & (No trade-off) & (Some trade-off) \\ \hline Design Flexibility and Agility & Absent & Present & Present \\ \hline Design Updatability & Absent & Present & Present \\ \hline Manufacturing Flexibility/Agility & Absent & Present & Present \\ \hline Client Involvement & Absent & Present & Present \\ \hline Lifecycle Updatability & Absent & Absent & Present \\ \hline Sustainable Products & Absent & Absent & Present \\ \hline \hline \end{tabular}
\end{table}
Table 1: Production strategies objectives.
## 2 Materials and Methods
### Apparatus and Methods  
All amperometric measurements were performed in a three-electrodes system, consisting of CPE/MWCNTs/Tyrosinase/Nafion(r) as a working electrode, Ag/AgCl/3.0 mol\(\cdot\)L\({}^{-1}\) KCl (Metrohm, Czech Republic) as a reference electrode and platinum wire as a counter electrode connected to potentiostat EmStat (Ivium Technologies B.V., Eindhoven, The Netherlands). Then, 0.01 mol\(\cdot\)L\({}^{-1}\) PB (pH7.0) was chosen as supporting electrolyte and every measurement was repeated minimally five times. Batch injection analysis (BIA) with amperometric detection was carried out in a voltammetric glass cell under constant stirring speed 400 rpm and at constant working potential of \(-\)0.25 V. For analysis, usually 1.0 mL of berries extract was injected into the 9.0 mL of the above-mentioned electrolyte. After that, three additions of standard Trolox solution (0.5 mL 0.01 mol\(\cdot\)L\({}^{-1}\)) were successively added.  
[PERSON] ([PERSON], Schwabach, Germany) and Helios Delta UV-VIS spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) have been used for other experiments. Images of electrode surface structure were obtained by scanning electron microscope (SEM) JEOL JSM7500F (Tokyo, Japan). The DPPH assay was selected as a reference method. The procedure consisted of several steps: (i) 100 \(\upmu\)L of ethanolic extract of individual berries was added into 5.0 mL of 25 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) DPPH methanolic solution and kept for reaction in darkness for 10 min; (ii) the colour of DPPH radicalsolution changed from violet to yellow, causing a decrease in absorbance, which was measured at \(\lambda\) = 517 nm.