esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Lens Design  
### Unit Cell Design  
The studied unit cells are composed of two metallic layers with a dielectric in between, illustrated in Figure 3a. A Jerusalem cross slot is placed in the top metallic layer, as illustrated in Figure 3b. The three studied structures have different bottom conductors. Two reference structures are analyzed, with a solid bottom conductor (Figure 3c) and with a mirror-symmetric Jerusalem cross slot in the bottom conductor (Figure 3d). The third structure has a glide-symmetric Jerusalem cross slot in the bottom conductor (Figure 3e). The metallic layers have a thickness of \(0.035\,\mathrm{mm}\).  
The three unit cells are modeled and simulated with the Eigenmode solver of CST. The normalized effective refractive index (with \(n_{0}=\sqrt{\epsilon_{\mathrm{r}}}\)) for a parametric sweep of the different structures is presented in Figure 3f-h. The parameters are (unless otherwise specified) \(L_{\mathrm{e}}=0.5\,\mathrm{mm}\), \(L_{\mathrm{t}}=2.9\,\mathrm{mm}\), \(w_{\mathrm{b}}=1.3\,\mathrm{mm}\), \(w_{\mathrm{c}}=0.5\,\mathrm{mm}\), \(p=7.2\,\mathrm{mm}\), \(h=0.8\,\mathrm{mm}\), and \(\epsilon_{r}=2.53\). From the parametric sweep, we observe that the glide-symmetric structure has the least dispersive response and provides a higher effective refractive index. Furthermore, in Figure 3h, the effective refractive index for waves propagating in two directions (\(0^{\circ}\) and \(45^{\circ}\) with respect to the \(x\)-axis) is presented. The response of the glide-symmetric structure is almost isotropic, which is not the case over a wider range of frequencies compared to the reference structures. A less dispersive behavior entails a broader bandwidth for the device. A relative measure of the dispersion can be obtained by comparing the change of the effective refractive index around a reference value at a given frequency. For instance, if we allow a maximum change of \(2\,\mathrm{\char 37}\) around the normalized effective refractive index value \(2\) at \(5\,\mathrm{GHz}\) the bandwidth is \(4.7\,\mathrm{\char 37}\), \(5.2\,\mathrm{\char 37}\) and \(19\,\mathrm{\char 37}\) for the conventional, mirror- and glide-symmetric structures. Hence, the \(2\,\mathrm{\char 37}\)-deviation bandwidth around an effective refractive index of \(2\) is almost four times larger in the glide-symmetric structure, compared to its non-glide counterparts. The acceptable deviation from the nominal value, and the bandwidth increase, depends on the intended application.  
Figure 2: Illustration of the effect of displacing the cross in the two PPW layers: (**a**) periodic structure with a top view of the unit cell as an inset, and (**b**) simulated dispersion diagram for different displacements.
## 4 Numerical Example
### Bridge System Reliability Estimation Obtained by Considering Multiple Failure Criteria
According to the comprehensive weight and system failure probability of each failure criterion of the case bridge, the system reliability obtained by considering multiple failure criteria was evaluated based on Equation (24), as follow:  
\[P_{f}=0.4588\times 1.099\times 10^{-4}+0.2287\times 1.049\times 10^{-5}+0.3125 \times 4.356\times 10^{-6}=5.418\times 10^{-5}.\]
## 2 Materials and Methods  
### Study Area  
The study was undertaken at the Carnarvon Agricultural Research Station in South Africa (31.0086\({}^{\circ}\)S, 21.8939\({}^{\circ}\)E; 1310 m above sea level). The station is situated within the Western Upper Karoo vegetation type [70] (Figure 1), which forms part of the Nama-Karoo biome that covers much of the western interior of South Africa (>400,000 km\({}^{2}\)) and extends into the south of Namibia [70]. The average rainfall received is 170 mm, though there is a gradient of increasing rainfall from the west (120 mm) to the east (220 mm) [71]. The Western Upper Karoo receives most of its rainfall during the autumn months, with the wettest month usually being March. The mainland use of the area is small stock farming [72].  
The vegetation layer of the Upper Karoo consists primarily of small-leaved dwarf shrubs and shrubby succulents, typically comprising of _Pentzia_ spp., _Ericcephalus_ spp. and _Ruschia intricata_. Grasses may be present soon after good summer rains and comprise _Aristida_ spp., _Emenapogon_ spp. and _Stipagrostis_ spp. [70; 73].  
This study forms part of a grazing trial established in 1988 by the Department of Agriculture to test the effects of stocking densities on vegetation conditions. Four stocking densities were set at a rate representing a high stocking density (4.0 hectares per small livestock unit (ha \(\mathrm{SSU}^{-1}\))), a recommended (5.5 ha \(\mathrm{SSU}^{-1}\)), a low (7.0 ha \(\mathrm{SSU}^{-1}\)) and a very low stocking density (8.0 ha \(\mathrm{SSU}^{-1}\)) [73] (Figure 2). The trial is managed on a three-camp rotational grazing management system, with twelve Afrino ewes per three-camp system. Additional information on the grazing trial and setup is available from [PERSON] and [PERSON] [73].  
Figure 1: Location of the Western Upper Karoo region within the Nama-Karoo biome. Insert map shows the location within South Africa. The Carnarvon research station is indicated in yellow; the Nama-Karoo biome is indicated in green and the Western Upper Karoo vegetation type is indicated in orange.  
Figure 2: The stocking density trial layout with 12 camps, soil survey points (black filled circles) and vegetation line transects (yellow lines). High stocking density = camps assigned the numbers A1, B1 and C1; recommended stocking density = A2, B2 and C2; low stocking density = A3, B3 and C3; and very low stocking density = A4, B4 and C4.
# Green Ultrasound-Assisted Extraction of Podophyllotoxin from _Juniperus scopulorum_ Needles
## 1 Introduction
Ultrasonic-assisted extraction appears to be the smartest choice, as this non-conventional technology requires less extraction solvent, a shorter extraction duration, and less thermal degradation while offering higher extraction yield than conventional approaches [40; 41]. Additionally, this extraction method is suitable with the use of a green solvent (e.g., ethanol, which also solubilizes phenolic compounds), enabling green extraction. Green extraction is often the preferred extraction approach since it is safer for the environment and human health. In the ultrasonic extraction approach, thermal, mechanical, and cavitation effects are used to extract the bioactive components from the cells. Indeed, above 20 kHz, ultrasonic  
Figure 1: HPLC chromatogram of _J. scopulorum_ extract and chemical structure of podophyllotoxin. radiation extraction of phenolic compounds from solid matrices using liquid solvents creates cavitation bubbles close to the plant cells, which collapse, rupturing the cell wall and cell membrane, and releasing the cell contents. Sonication time, temperature, solvent selection, wave frequency, and ultrasonic wave distribution affect the extraction. The probe and bath are the two main systems currently used, but the ultrasonic bath offers better reproducibility and repeatability and limits the problem of sample contamination and foam production [42; 43; 44; 45; 46; 47]. Therefore, we herein preferred to use an ultrasonic bath. The literature supports the value of ultrasound for podophyllotoxin extraction: ultrasonic pretreatment can help to extract podophyllotoxin from _Podophyllum peltatum_ rhizomes without podophyllotoxin degradation [48]. Next, [PERSON] et al. (2015) [49] developed the most efficient extraction protocol for secoisolaricinesinol (a precursor of podophyllotoxin) from flax seeds using ultrasound. However, to date, there is no protocol for extracting podophyllotoxin from _Juniperus_ needles using ultrasound. Therefore, it is interesting to develop this extraction approach. Considering all this data, green ultrasound-assisted extraction of podophyllotoxin from cultivated varieties of _Juniperus scopulorum_ will be developed in this work. First, single-factor experiments will identify the factors affecting the extraction. Then, a multifactorial approach will be developed to determine optimal extraction conditions. Finally, the optimized protocol will be applied to different _Juniperus scopulorum_ varieties to compare its efficiency with the reference protocol and measure the stability of the extracted podophyllotoxin. This study aims to determine the interest in _J. scopulorum_ as a new source of podophyllotoxin (for the pharmaceutical industry) and the credibility of ultrasonic-assisted extraction to achieve it.
## 4 Results and Discussion
### Experimental Environment  
The experimental environment is the basis of system testing. For the testing of a system, it is necessary to ensure that the environment is configured correctly. The correct experimental environment is a key step in the experiment. In this experiment, we used a deep learning server to configure the experimental environment. The production type of the central processing unit (CPU) was Intel (R) Core (TM) i7-9700, the production type of the GPU was NVIDIA GeForce GTX 745, and the memory size was 16 GB. In addition, Pytorch was used to build a network framework, Adam optimizer was used to iteratively train the same training set partitioning and data preprocessing methods, and Python was used as a programing environment. The experimental environment is shown in Table 1.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 2 Transparent Antennas: A Review
### Conductive Polymer  
Since the discovery of the conductive polymer polyacetylene in 1977 [21], the study of conductive polymers has been continuously performed. Conductive polymer has a structure in which the main chain is a repeating form of a double bond, which is a structure of an unsaturated hydrocarbon. It is also a form of a single bond, such as in a saturated hydrocarbon structure, and is referred to as a \(\pi\)-conjugated polymer. When the \(\pi\)-conjugated form is present in the polymer skeleton, the electron density is uniform due to doping. Also, electrical conduction occurs. Currently, the most actively studied conductive polymer is poly(3,4-ethylenedioxythiophene): PSS (polystyrene sulfonate), which is a thiophene-based polymer.  
In Reference [22], an optically transparent conductive polymer dipole antenna operating at 900 MHz is designed for radio frequency identification (RFID) applications, as illustrated in Figure 6a. An addition of 10% dimethyl sulfoxide (DMSO) is made to the conductive polymer, Clevios PH500 PEDOT-PSS (\(\sigma\) = 300 S/m), to increase the conductivity by \(5\times 10^{3}\) S/m. The modified conductive polymer (thickness: t = 250 nm) is fabricated on polyethylene terephthalate (PET) (with a thickness of 0.5 mm and a relative permittivity of 3.8) with screen printing and ink-jet printer methods. The optical transmittance of the antenna is between 40% and 70% at different optical wavelengths. The reference  
Figure 5: (**a**) Fabricated microstrip patch antenna and (**b**) simulated and measured reflection coefficients of the proposed antenna [20]. (With permission from IEEE).  
dipole copper antenna (\(\sigma=5.8\times 10^{7}\) S/m) features 2.5 dB and 8 dB higher gain than the screen-printed conductive polymer and ink-jet-printed conductive polymer antennas, respectively. The meandered dipole copper antenna has a 6 dB higher gain than the screen-printed conductive polymer meander antenna in Figure 6b.  
In Reference [23], a microstrip-fed proximity-coupled conductive polymer patch antenna resonating at 10 GHz is designed. A polyaniline (Pani) film featuring a conductivity, relative permittivity, and thickness of 6 \(\times\) 10\({}^{3}\) S/m, 6000, and 100 \(\upmu\)m is fabricated on alumina using a screen printing technique. The fabricated Pani antenna has a 2.08 dB lower gain than the reference copper antenna due to the many orders of magnitude difference in conductivity.  
Conducting polymer has been widely studied for use as a transparent electrode for 40 years. However, conducting polymer has a distinctive color and the disadvantage of lacking atmospheric stability. There is also a limit to the high sheet resistance that nano carbon can suffer. It should be noted that a hybrid process, in which two materials are mixed or multilayered, showed better performance than using one material. Typically, the AgNW/PEDOT:PSS-coated bar shows a sheet resistance of 25 \(\Omega\)/sq and an optical transmittance of 90% [24].
# Evaluation of the User Adaptation in a BCI Game Environment +
## 5 Results and Discussion
### Game Testing  
Moving on to online testing, players were introduced to the BCI game and Unity environment because they had zero experience in BCI experiments. With the aid of a researcher, subjects became familiar with the equipment and the whole procedure. After the training phase, they had to play level 1 of the game strictly 20 times in order to evaluate their performance. The game score was chosen as an evaluation metric, which increased proportionally to the distance covered and the obstacles that the player overcame. The 20 scores were split into 2 sets of 10; then, the average score of these 2 sets was calculated to examine how the user adapts to the game. To make the whole experiment more competitive and fun, subjects were informed that the 18 best players would advance to test level 2 of the game. Table 3 presents the level 1 game scores from the 37 subjects. The maximum score for level 1 is 100. The average overall score for all subjects is 52.70. The best performance is 66.85 by Sub17, while the worst one is 25 by Sub22. 18 subjects were able to finish level 1 at least 1 time; 5 of them managed to finish it twice, and only Sub17, the player with the best overall score, finished it 3 times. It is observed that 78% of the players (29 out of 37) improved their performance in the second set of 10 tries. This observation indicates that most of the players adapted to the BCI-controlled game and became better after they familiarized themselves with it. The main difficulty for some players was synchronizing the jump command because of the 1-second delay of the commands due to the 1-second time windows chosen for the classifier. Figure 6: Snapshot from BCI-controlled game. Table 4 presents the level 2 game scores from the 18 best subjects qualified from level 1. The maximum score for level 2 is 150. The average overall score for all subjects is 70.35. The highest score achieved in this level is 87.9 by Sub32. The worst score is 51.05 by Sub20. A total of 8 subjects managed to finish the level at least 1 time, 4 subjects finished it twice, and 1 subject finished it 3 times. The results show that 55.55% of the players (10 out of 18) adapted to this level's difficulty and improved their performance in the second set of tries. Level 2 is designed with increased difficulty compared to level 1 in order to test user adaptation in a more difficult BCI scenario. The results confirm that level 2 is indeed more challenging because the 18 players who advanced from level 1 have an average decrease of 13.33% in their level 2 scores. A paired samples t-test was conducted to determine the effect of adaptation between the 2 sets of 10 tries for each level. The results for level 1 indicate a significant difference between average score 1 (M = 48.66, SD = 11.04) and average score 2 ((M = 56.29, SD = 10.15), (t(37) = 0.583, \(p<0.01\))). The results for level 2 indicate a not significant difference between average score 1 (M = 68.85; SD = 12.11) and average score 2 ((M = 71.85, SD = 11.65), (t(18) = 0.265, \(p=0.287\))).
## 6 Numerical Example  
Consider the following CARAR system,  
\[A(z)y(t) = B(z)u(t)+\frac{1}{C(z)}v(t),\] \[A(z) = 1+a_{1}z^{-1}+a_{2}z^{-2}=1-1.60z^{-1}+0.66z^{-2},\] \[B(z) = b_{1}z^{-1}+b_{2}z^{-2}=0.64z^{-1}-0.34z^{-2},\] \[C(z) = 1+c_{1}z^{-1}+c_{2}z^{-2}=1-0.55z^{-1}+0.47z^{-2},\] \[\mathbf{\theta} = [a_{1},a_{2},b_{1},b_{2},c_{1},c_{2}]^{\mathrm{r}}=[-1.60,0.66,0. 64,-0.34,-0.55,0.47]^{\mathrm{r}}\]  
Here, the input \(\{u(t)\}\) is taken as an uncorrelated persistent excitation signal sequence with zero mean and unit variance, \(\{v(t)\}\) is a stochastic white noise sequence with zero mean and variances \(\sigma^{2}=0.10^{2}\) and is independent of the input \(\{u(t)\}\).  
Using the model equivalence-based recursive least squares (ME-RLS) algorithm and the recursive generalized least squares (RGLS) algorithm to estimate the parameters of this system, the parameter estimates and their errors are shown in Tables 1-3, and the estimation errors \(\delta\)_versus_ the data length \(t\) are shown in Figure 2, where \(\delta_{1}:=\|\hat{\mathbf{\vartheta}}(t)-\mathbf{\vartheta}\|/\|\mathbf{\vartheta}\|\) and \(\delta_{2}:=\|\hat{\mathbf{\theta}}(t)-\mathbf{\theta}\|/\|\mathbf{\theta}\|\) are the estimation errors of the ME-RLS algorithm and the RGLS algorithm, when \(\sigma^{2}=0.10^{2}\); the system noise-to-signal ratio is \(\delta_{\mathrm{ns}}=35.01\%\).  
From Tables 1-3 and Figure 2, we can obtain the following conclusions.
# Radio Channel Modelling for VHF System Operating in the Offshore Wind Farms Propagation Environment
## 3 Results and Discussion  
### The Measurement Results  
The measurements discussed in this article were carried out in 26-28 October 2021 at the Jasna wind farm located in the northeastern part of Poland, near the city of Malbork. The most important results obtained during the research that allowed us to characterize the radio channel in the presence of a wind farm are presented below.
# Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
## 5 Results
Then, the algorithm is trained with 750 images at different SNR levels (from 12 dB to 36 dB in steps of 6 dB) in the received signal, to test the functionality of the model at these noise levels. Once the functionality of the SVM model has been verified through the previous training, the images are processed with the model. For image processing, a database consisting of 10,000 images for each 4-bit data symbol combination (between 0000 and 1111) was used, resulting in a total of 160,000 processed images. The HOG features are extracted from each of these images to predict the combination of bits corresponding to each image, using the model. Finally, after each prediction, the acquired combination is compared with the original combination, and then the BER is calculated. Figure 4 shows the computed BER points as a function of signal-to-noise ratio (SNR) from 0 to 14 dB in steps of 1 dB for the two proposed SVM-ECOC models: the multicast one-vs.-one algorithm (Model 1) in the red curve and the Multicast one-vs.-all algorithm (Model 2) in the blue curve. Since the standard maximum BER for most optical systems is \(10^{-9}\)[51], and for applications in optical communications the maximum BER range is in the range \(10^{-9}\) to \(10^{-12}\)) [52], the adjustment curve for each model is also shown in Figure 4 in order to predict noise levels for these values. The BER curve for Model 1 reaches BER \(=10^{-9}\) for 12.8 dB of SNR (see green line in Figure 4), and BER \(=10^{-12}\) for 13.4 dB of SNR. For the case of Model 2 (blue curve in Figure 4), BER \(=10^{-9}\) for 10.2 dB of SNR (see green line in Figure 4), while for a BER \(=10^{-12}\) for 10.9 dB of SNR. Additionally, for comparison purposes, a BER estimation curve assuming a probability of error with a Gaussian random variable [53] is also shown in a black curve. It is observed that both SVM-ECOC models have better performance compared to the simplified Gaussian BER model in terms of noise levels, highlighting that Model 2 has a better probability of error compared to Model 1, with a difference of 2.65 dB of noise level for BER \(=10^{-9}\). Note that since the channel noises used in the simulation are AWGN and RIN, the bit errors generated in this case study are directly due to signal degradation by these types of noise. This fact is observed in the results of Figure 4 for each model, indicating that for an SNR level greater than 9 dB, the bit error probability is below 10% for Model 1 and below 0.0001% for Model 2.
## 3 Results and Analysis
### Sandstone Sample 1  
A cubic sandstone sample with dimensions of 14 inches is used for plasma stimulation. The sample has a vertical wellbore that is 10-inches in length and 2-inches in diameter. The sample outer boundaries are subjected to unconfined conditions and exposed to atmospheric pressure. The wire used for the plasma reaction is Aluminum (Al) of a 22-gauage size. Five inches of wire is used to connect the two electrodes immersed in the sample wellbore that is filled with water. The electrical energy used for the generation of the plasma reaction is 6 kJ.  
The sandstone sample was subjected to two identical plasma pressure pulses. The release of the first pulse was sufficient to develop one narrow fracture that extended along one side of the sample, as shown in Figure 4. However, by releasing the second pressure pulse inside the rock wellbore, the fracture generated by the first shot was further widened and a new wide fracture was created on the other side of the sample. The final shape of the fracture is a two-wing fracture that extends from one side of the sample to the other passing through the wellbore as shown in Figure 5 (the two sides of the sample are shown in the figure).  
Figure 4: Plasma-propagated fractures using first pressure pulse—unconfined sandstone sample.  
The experimental work is simulated numerically using HOSS(r). The pressure pulse generated from the plasma reaction is given in Figure 6. The pressure ramps exponentially to reach a peak value of 50 MPa and then declines exponentially to reach atmospheric pressure at 60 us. The pressure profile used in this study was developed based on experimental observations by [PERSON] et al. [30] and employed in a study by [PERSON] et al. [2]. A cubic rock model with a circular wellbore is generated and meshed using Cubit(r). The model's outer boundaries are kept free to move during the pulse application. The inner boundary of the rock model (wellbore) is subjected to the pressure profile. The sandstone properties are listed in Table 1.  
Figure 5: Plasma-propagated fractures after second shot—unconfined sandstone sample.  
Figure 6: Pressure pulse profile developed by plasma stimulation.  
The application of the first pressure pulse results in the fractures shown in Figure 7. In the figure, there are two graphs. The left graph shows the fractures only (separation between the finite elements). The blue color is for narrow fractures and the gray is for wide fractures. However, the graph to the right shows the whole model including the fractures. The intact rock is red, and the fractures are light gray. When a second pulse was applied to the same fractured sample, the old fractures widened and extended to the model boundaries, as given in Figure 8. The figures show the separations that represent the fractures between the model finite elements to the left, and the whole model to the right.