esa-sceva/satcom-chunk-collection huggingface dataset:
# Phenolic Compounds in Trees and Shrubs of Central Europe
## 1 Introduction
Typically, only one type of compound predominates in plants: alkaloids, phenolic compounds or terpenoids. For example, the main defence compounds in oak leaves are condensed tannins [18], while in coniferous trees these compounds are terpenoids [19]. Many species of trees and shrubs occurring in Europe are used in folk medicine and industry. Tree and shrub species are widespread in Europe and there are many that can be used in folk medicine as well as industry. Deciduous forests in the temperate zone are characterised by a relatively small number of tree species. The dominant trees are oak, beech and hornbeam. Elm, maple, linden and ash are of great importance. Forests in Northern Europe are dominated by two main species, Scots pine (_Pinus sylvestris_ L.) and Norway spruce (_Picea abies_ L. [PERSON]). In Poland, outside the mountainous areas, where species composition is observed, the share of spruce, fir and beech is higher in most of the country stands, while pine, as the dominant species, predominates. In addition, tree and shrub species that were not native but introduced species, which spread on a larger scale, e.g., bird cherry, were selected for the literature analysis. Enhanced biosynthesis of the above-mentioned bioactive compounds is an advantageous side effect of the action of stressors. Trees, in their anatomical parts most exposed to the action of pathogens, accumulate the greatest amounts of bioactive compounds, thanks to which shoots, fruits, leaves, needles and bark have become valuable sources of biologically active compounds. Secondary metabolites are most frequently synthesised via three metabolic pathways: terpenoid (mevalonate), phenolic (shikimate) and nitrogen metabolism (amino acids). For woody plants, the derivatives of the shikimate pathways are of greatest importance, e.g., phenolic compounds (phenols, alcohols and phenolic acids, phenylpropanoids, flavonoids, coumarins, tannins), hydroxamic acids and indole alkaloids [20; 21; 22; 23]. The aim of this study was to review the available literature in terms of the content of phenolic compounds in trees and shrubs grown in Europe. The literature research was supplemented with the results of our own research on the various anatomical parts of selected trees and shrubs growing in Poland.
## 2 Models
### Computational Domain and Boundary Conditions  
The entire geometric model was immersed into a 2.3 cm \(\times\) 17.8 cm \(\times\) 9.9 cm rectangular computational domain. The domain was discretized by a 64 \(\times\) 256 \(\times\) 92 Cartesian grid in the x, y, and z directions, respectively. A 0.8 kPa pressure drop was applied between the inlet and outlet to drive the flow. The total reflection boundary condition for acoustics was applied at the outlet. To eliminate the acoustic reflection at the inlet boundary, an anechoic zone was enforced in the LPCE solver to buffer the acoustics [34]. Vocal tract walls were treated as no-slip boundaries in the incompressible solver and hard wall boundary condition in the LPCE solver [35]. A traction boundary condition was applied at the fluid-structure interaction interface, while the lateral, anterior and posterior surfaces of the vocal folds were fixed in the solid solver. A penalty-method-based contact model was applied to prevent the penetration of the vocal folds. An artificial minimum gap of 0.2 mm was enforced between the vocal folds, which is necessary for the success of the flow solver. The vocal fold was discretized, using 28,997 tetrahedral elements. The time-steps of \(1.149\times 10^{-6}\) s and \(0.05\times 1.149\times 10^{-6}\) s were used in the incompressible and LPCE solvers, respectively, to satisfy their Courant-Friedrichs-Lewy condition of below 1. 256 processors were used to simulate \(4.6\times 10^{-2}\) s on the XSEDE COMET cluster (Intel Xeon E5-2680v3 processors) which resulted in the computational cost of 161,000 CPU hours.
## 2 Methods  
### FMCW Radar  
Radar is a sensor that transmits radio waves and receives the signals reflected from objects to detect their position and velocity. There are various types of transmission and reception signals and methods used in radar systems; however, in this study, a linearly modulated FMCW (Frequency Modulated Continuous Wave) radar signal, as shown in Figure 1, is used. This signal can be represented by (1). FMCW radar transmits a signal that consists of a single or multiple chirps during each scan. The frequency of the transmitted signal (TX) increases linearly and is radiated into space. The signal reflected by the target is received as the received signal (RX). The RX signal exhibits a delay \(T_{d}\), which can be observed in (2). In this context, \(t_{t}\), \(T_{d}\), \(T_{c}\), \(f_{c}\), \(S\) and \(BW\) represent the transmission time, time delay between transmission and reception, chirp duration, carrier frequency, signal slope, and bandwidth of the radar signal, respectively. In general radar systems, distance is calculated by measuring the time delay \(T_{d}\) of the reflected signal. However, in FMCW radar, the distance is calculated using the beat frequency \(f_{b}\), which is the frequency difference between the transmitted and received signals.  
\[s_{t}(t)=A_{t}\cdot\cos\left(2\pi\left(f_{c}+\frac{BW\cdot t_{t}}{2T_{c}} \right)t_{t}\right),\quad t_{t}<T_{c} \tag{1}\]\[s_{r}(t)=A_{r}\cdot\cos\biggl{(}2\pi\biggl{(}f_{c}+\frac{BW\cdot(t_{t}-T_{d})}{2T_{c }}\biggr{)}\cdot(t_{t}-T_{d})\biggr{)},\quad t_{t}<T_{c},\ T_{d}<T_{c} \tag{2}\]  
Figure 2 shows the functional block diagram of an FMCW radar. To calculate the beat frequency, the signal is first demodulated using a mixer, and then passed through a low-pass filter (LPF) to isolate the low-frequency component--the beat frequency--which is then digitized using an analog-to-digital converter (ADC). The demodulated radar signal is sampled at a rate of \(f_{s}\) over the chirp duration \(T_{c}\), resulting in discrete samples (\(n\)). Multiple chirps (\(m\)) are accumulated and considered as one scan (\(t\)). Using multiple chirps improves velocity resolution and reduces the impact of clutter. The accumulated data can be represented as (3), with parentheses, e.g., Equations (1) and (2). In this context, the beat frequency \(f_{b}\) is used to calculate distance, as shown in (4), and the phase difference \(\beta\) between consecutive chirps is used to estimate velocity, as shown in (5).  
\[x(n,m,t)=A(n,m,t)\cdot\cos(2\pi f_{b}n+\beta m+\phi) \tag{3}\]  
\[f_{b}=\frac{BW\cdot 2\tau}{c\cdot f_{s}} \tag{4}\]  
\[\beta=\frac{4\pi f_{c}T_{c}v}{c} \tag{5}\]
# Effect of Laser Surface Treatment on the Corrosion Resistance of Zircaloy-4 at High Temperature
## 3 Results  
### LST Layer Features  
Figure 2 presents the surface morphologies of the LST and the NLST Zircaloy-4 specimens. In Figure 2, it is clearly shown that the morphologies of the LST and the NLST specimens are different. Figure 2a clearly shows that round spots with diameters of \(\sim\)0.8 mm are distributed on the surface of the LST specimen, and the surface of this specimen is not flat. In contrast, the surface of the NLST specimen is flat, as shown in Figure 2b.  
Figure 3 shows the cross-sectional morphology of the LST Zircaloy-4 specimen and a magnified image of the cross-sectional morphology of the LST specimen. As can be seen in Figure 3a, the morphology of the LST layer with a thickness of \(\sim\)1.5 \(\upmu\)m is different with the matrix. It is not flat and forms \"cauliflower-like\" undulations, as shown in Figure 3b. The X-ray spectroscopy (EDS) spectrum of the black cross-sectional area indicated in Figure 3a is shown in Figure 3c: it is clearly shown that there is no O peak in this spectrum. This means that no Zr was oxidized during the whole LST process.  
Figure 1: A schematic diagram of the LST process for the Zircaloy-4 specimen.  
Figure 4 shows the XRD diffraction pattern of the LST Zircaloy-4 specimen. As can be seen in Figure 4, only the \(\alpha\)-Zr phase was identified in the LST Zircaloy-4 specimen. This indicates that LST cannot change the phase of the specimen. These peaks are consistent with those of the PDF97-065-3529 card for \(\alpha\)-Zr and have no shift. It means there is no residual stress distributed in the LST layer. In Figure 1, we can also see that there are no diffraction peaks of ZrO\({}_{2}\) and ZrH\({}_{x}\). It is shown that the specimen was not oxidized and absorbed H during the entire LST process.  
Figure 5 presents the TEM image of the cross-sectional morphology of the LST Zicaloy-4 specimen and the distributions of elements in the cross-sectional LST layer. It is clearly shown that there are Zr sub-grains formed in the LST layer. And from the Cr and Fe mapping images, we can see that no Fe-Cr precipitations are distributed in the upper part of the LST layer. This indicates that these Fe-Cr precipitations are dissolved into the matrix in the LST layer.  
Figure 3: The cross-sectional morphology of the LST Zircaloy-4 (**a**), magnified image of the cross-sectional morphology of the LST Zircaloy-4 (**b**), and EDS spectrum of the black cross-sectional area (**c**).  
Figure 2: The surface morphologies of the LST (**a**) and NLST (**b**) Zircaloy-4 specimens.
## 4 Discussion
The microstructure of the alnico sintered magnets was equiaxed with a grain size (30-300 \(\upmu\)m) that was very similar to both types of AM processed samples, without any porosity of significant size, but with some fine dispersed oxides from prior particle boundaries. In contrast, microstructural observations of Sm-Co and Nd-Fe-B sintered anisotropic magnets often reveal a much finer grain size (5-20 \(\upmu\)m). Thus, in spite of several factors that should reduce bend strength, e.g., coarser grain size and oxide particles, alnico still had a much higher bend strength than the ultra-fine grain size of the RE magnets, probably due to the intrinsic brittleness of the crystalline phases in these RE-based materials [37; 38; 39]. When compared to alnico magnets made by the more traditional fabrication method of sand casting or more specialized directional solidification, the sintered alnico also had 2 times greater bend strength [36]. The reason for this difference is likely due to typical micro-porosity and minor inclusion content from these large scale casting processes. Thus, it is expected that future mechanical property measurements of similar AM-built samples will be consistent with the sintered alnico properties and should have acceptable mechanical behavior for anticipated applications. Alnico is a surprising material for AM, not only that it can be built within an electron beam AM system, despite initial concerns about interaction with the magnetic field from the electron beam, but also that it is very buildable in a laser AM system through a wide range of processing parameters. Future work on AM of alnico will include transmission electron microscopy to confirm the Fe-Co rod nanostructure and the influence of chemistry change on the magnetic properties of the EBM/PBF builds. Additional compositions with higher Al and Cu content would compensate for volatilization and probably allow higher coercivity values to be achieved with EBM/PBF. Lastly, exploration of the possibility of better grain size control and orientation during processing by adjustment of the AM parameters and post-processing, as well as functionally graded permanent magnet materials should be performed. These accomplishments would greatly expand the use alnico permanent magnet materialsfor increased temperature and mechanically robust uses such as drive motors, electric generators for off-shore wind and other clean energy applications.
## 2 Methods
### Audio Signal Processing for the Detection of Breathing and the Extraction of Apnea/ Hypopnea Events
Thus, in normal cases, the minimum inhalation frame was expected to last approximately 1 s. To increase the precision in determining this threshold, we focused on extensive observation of the acquired audio signals and trial and error selections that led to the final value of 0.7 s.  
The output of the breathing detector consisted of zeros and ones to denote breathing or non-breathing respectively and was used to mask the audio signal in order to provide an output with the original audio signal in fragments of detected breathing and zero elsewhere (Figure 2a). For each detected breathing segment, the average root mean square (RMS) value was extracted and used as a representative metric of this segment (Figure 2a). Therefore, we ended up with a weighted breathing detector, with each detected frame represented by the mean RMS value of the audio signal within the frame (Figure 3c). We assumed that an apnea or hypopnea event presented a radically reduced breathing activity within the event. Thus, we selected all regions of the weighted detector in which the mean RMS value of the detected breathing frames within this region was reduced by at least 50% as compared to the RMS values of the previous and following detected breathing frames (Figure 2a). The selected episodes should last at least 10 s but not more than 150 s. The upper threshold in apnea/hypopnea event duration was estimated based on statistics of previously reported datasets [36] and the dataset used in this study, both  
Figure 3: Steps of the described methodology for extracting the apnea/hypopnea event detector based on the raw audio signal: (**a**) The audio signal, in this case, is recorded from the trachea of the patient. The annotated reference apneic event, from approx. the 22 nd s until the 51 st s of the represented signal excerpt is denoted by the two vertical dashed lines. The breathing activity is obvious before and after the event, while some low-level audio activity can also be observed during the event. (**b**) The breathing detector state, as described in Section 2.3, results in the detection of all breathing activity within the selected audio excerpt (1 denotes active, 0 denotes inactive). (**c**) The weighted detector, in which instead of 0 and 1 (corresponding to silence and breathing states), we have 0 for silence and non-zero values corresponding to the mean RMS of the audio signal within each detected breathing frame. The detected apneic event is also depicted. resulted in a maximum scored apnea duration slightly above 2 min. The threshold of 150 s was considered a safe selection to include all events of the maximum expected duration. We also selected a separating value of at least 8 s between the detected events. Indeed, statistically, we observed two complete respiratory cycles separating adjacent apneic episodes (corresponding to 6-10 s). The optimum value of 8 s was selected through a trial and error process. Thus, all events detected closer than this value to previously identified episodes were rejected as false detections.
# Micro-Actuators with Light and Magnetism Dual-Control for Maneuverable Movements
## 3 Results and Discussion
### Photothermal Property of C/Fe\({}_{3}\)O\({}_{4}\)/PDMS Film  
To quantitatively characterize the photothermal property of the C/Fe\({}_{3}\)O\({}_{4}\)/PDMS film, systematic investigations were conducted using a nanoscale photothermal tester (with a laser wavelength of 808 nm and cyclic stability of 2%). Figure 2 shows photothermal properties of the C/Fe\({}_{3}\)O\({}_{4}\)/PDMS film. The temperature of the water surface was 24.5 \({}^{\circ}\)C, and the highest temperature detected by infrared imaging was distributed in the center of the triangular-shaped boat, up to 60.5 \({}^{\circ}\)C. This measurement was performed at room temperature and the boat was irradiated by a laser (100 mW, 532 nm) for one second. Similarly, the content of Fe\({}_{3}\)O\({}_{4}\) and the temperature of the samples are positively correlated. As we suspected, a larger proportion of Fe\({}_{3}\)O\({}_{4}\) can increase the heat absorption effect of the sample, and a higher power laser can bring a higher temperature change to the sample. Under 500 mW cm\({}^{-2}\) laser irradiation, sample 3 warmed up at the fastest rate and reached the highest temperature at the end of the same one-minute test (Figure 2b). To investigate the heat absorption rates of the samples at different laser injection levels, the temperature variations in the three samples were tested under laser conditions of 250, 500, 750, and 1000 mW cm\({}^{-2}\), respectively, as shown in Figure 2c. The increase in laser power significantly improves the heat absorption rate of the sample. In both sets of tests, sample 3 showed better photothermal performance compared to the others. This indicates that sample 3 will be able to provide a higher temperature gradient at the two-phase interface under laser irradiation, creating a higher Marangoni force and the best driving effect of the actuator. Subsequently, to explore the maximum value of the elevated temperature of sample 3 under laser irradiation, it was tested with lasers from 0 to 5000 mW cm\({}^{-2}\), respectively, and the test results are shown in Figure 2d. The maximum temperature of the sample was obtained under 5000 mW cm\({}^{-2}\) laser conditions at a room temperature of 25 \({}^{\circ}\)C environment. The above tests show that the boat prepared by the composite film can have a good enough photothermal effect, which is a prerequisite for generating sufficient Marangoni force on the water surface.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Variable** & **Sample 1** & **Sample 2** & **Sample 3** \\ \hline Fe\({}_{3}\)O\({}_{4}\) (a) & 0.10 & 0.20 & 0.30 \\ SW [g] (b) & 2.00 & 2.00 & 2.00 \\ CA [\({}^{\circ}\)] (c) & 92.99 & 94.12 & 97.71 \\ TI [\({}^{\circ}\)C] (d) & 23.50 & 29.50 & 36.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic information and test data about the samples.
# RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks
###### Abstract  
Bluetooth Low Energy (BLE) is a wireless technology for low-power, low-cost and low-complexity short-range communications. On top of the BLE stack, the Bluetooth Mesh profile can be adopted to handle large networks with mesh topologies. BLE is a promising candidate for the implemention of Industrial Wireless Sensor Networks (IWSNs), thanks to its wide diffusion (e.g., on smartphones and tablets) and the lower cost of the devices compared to other wireless industrial communication technologies. However, neither the BLE nor the Bluetooth Mesh specifications can provide real-time messages with bounded delays. To overcome this limitation, this work proposes RESEMBLE, a real-time stack developed on top of BLE that is able to realize low-cost IWSNs over mesh topologies. RESEMBLE offers support to both real-time and non-real-time communications on the same network. Moreover, RESEMBLE provides clock synchronization, thus allowing for Time Division Multiple Access (TDMA) transmissions. The clock synchronization provided by RESEMBLE can be also exploited by the upper layers' industrial applications to implement time-coordinated actions.  
Bluetooth Low Energy; mesh networks; real-time networks +
Footnote †: journal: Medical  
this limitation and enable the BLE technology to be adopted in IWSNs, RESEMBLE introduces the ability to provide guaranteed bounded delays to real-time messages. Moreover, RESEMBLE offers connectivity to mobile nodes.  
RESEMBLE is a low-cost software solution that can be implemented on common mobile devices (e.g., smartphones and tablets) without hardware modifications.  
The rest of this paper is organized as follows. Section 2 presents and discusses related work. Section 3 provides an overview of the Bluetooth Mesh stack and discusses its limitations for real-time communications. Sections 4 and 5 describe RESEMBLE's design and implementation, respectively. Section 6 presents an experimental assessment of RESEMBLE on commercial-off-the-shelf (COTS) devices and discusses the synchronization accuracy and end-to-end delays that were obtained. Finally, Section 7 gives conclusions and hints for future works.
## 4 Conclusions  
We designed a bent monopole antenna with a good impedance matching characteristic by using an ML technique. We utilized the collected data, which is composed of antenna design parameters and the corresponding Cost for each sample antenna, to train the  
Figure 5: Impedance matching characteristics of the antennas with ranks 1 and 6 in Table 6.  
Figure 6: Possible equivalent circuits corresponding to two types of antennas in Table 6: (**a**) possible equivalent circuit of the antennas with rank 6; (**b**) possible equivalent circuit of the antennas with rank 1.  
DNN machine learning model. In the ML training, we checked the maturity of our ML model by evaluating MSE and R-squared values. After confirming that our ML model had an MSE of about 0.035, as well as an R-squared value above 0.99, we validated our ML model by comparing the actual AF and Cost values with the predicted ones. The resulting comparison showed a favorable agreement between both AFs and Costs due to the error being smaller than 1%. In addition, we compared the AFs and Costs of the antennas with a good matching performance with those calculated by NEC simulation. This comparison also showed high accuracy in the estimation of antenna performance. Finally, we interpreted the antennas with a good impedance matching characteristic in the perspective of a structure and a possible equivalent circuit. We found that the antennas with a good impedance matching performance are divided into two types of antennas with a top load and a middle load structure. In addition, we concluded that both types of antennas adequately utilize vertically and horizontally extended wire sections to achieve the good impedance matching characteristic. The accomplished works in this research provide us with the possibility to use the ML technique in the antenna design. From a practical point of view, we expect that it would improve our research to investigate various factors affecting antenna performance such as ground size and fabrication errors of bending angles and length of wire sections.  
Conceptualization, J.C. and Y.-H.K.; methodology, J.C., T.H.A.P. and Y.-H.K.; software, T.H.A.P.; validation, J.C., T.H.A.P. and Y.-H.K.; formal analysis, J.C., T.H.A.P. and Y.-H.K.; investigation, J.C. and T.H.A.P.; writing--original draft preparation, J.C. and T.H.A.P.; writing--review and editing, J.C. and Y.-H.K.; visualization, J.C. and T.H.A.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Korea National University of Transportation in 2021 and in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1 IIA3050649).  
Data Availability Statement: Data Sharing is not applicable.  
The authors declare no conflict of interest.