esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Class Data Descriptors for Mapping
## 2 Materials and Methods
### Shadenet Structures  
Plastic sheets are used as materials to build the shadenet structure which can be regarded as a micro-scale planting environment and are relatively common [56], having unique characteristics [57], i.e., optical transparency, shade percentages, gas-tightness, and high-reflectivity. Agriculturalists have long known the importance of the planting environment for crop growth, always by manipulating the growing environment to provide a more conducive environment for crop growth. As for _Panax notoginseng_, sunlight is often modified by shading to provide the more optimal growing environments so as to enhance their production. Therefore, the production of _Panax notoginseng_ takes place within an enclosed growing structure called a shade house. The shade house (see Figure 3) provides the protection and maintains the optimal growing conditions throughout the development of _Panax notoginseng_. The shade house of _Panax notoginseng_ is a framed structure often covered with the black plastic nets which are made of the polyethylene thread with different shade percentages. It provides a partially-controlled atmosphere and environment by reducing the light intensity and effective heat during daytime to _Panax notoginseng_ grown under the very large plastic sheets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cloud (\%)** & **Number** & **Percentages** \\ \hline
0–10 & 3 & 3.41 \\
10–20 & 7 & 7.95 \\
20–40 & 13 & 14.77 \\
40–60 & 17 & 19.32 \\
60–80 & 23 & 26.14 \\
80–100 & 25 & 28.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cloud cover statistics of a 16-day revisited Landsat-8 OLI scene (path/row 128/044) until May 2017.  
Figure 2: Study area in Wenshan City, Wenshan Prefecture, Yunnan Province, China.  
Additionally, attributed to the standardized cultivation technique of GAP, the shade house provides a distinct spatial texture to interpret the spatial distribution of _Panax notoginseng_ fields in reference to Google Earth historical images based on a calendar, i.e., there are 20 days when the satellite images were captured from 6 March 2013, to 12 December 2015, which is associated with the different sampling sites (see Figure 4), i.e., there are a total of 123 sites in Wenshan City. Polypropylene fabric shade is the dominant shade for field-cultivated _Panax notoginseng_ in Wenshan City. Its black color maintains the proper shade and also forms the distinct texture of the shade net in satellite images, which can be visually interpreted and makes it easier to collect training samples using region of interest (ROI) tools.  
Figure 3: Shade house. Sub-figures are the snapshots and photos in the different phases of the construction of a shade house of _Panax notoginseng_ including the materials for building, i.e., sticks (\(5\,\mathrm{cm}\times 2\,\mathrm{m}\)), upright rods, and black plastic net over the poles; viewpoints for the observation, i.e., parallel to bed, perpendicular to bed, at close range, over a long distance, and from a satellite image.
# Biosensors-Based In Vivo Quantification of 2-Oxoglutate in Cyanobacteria and Proteobacteria
## Results
### Evaluation of Single-Protein Based FRET Sensors for 2-OG Measurements
Hence, the F-mT_PII is suitable to measure levels of 2-OG ranging from 10 uM to 1 mM whereas F-mT_NtcA is suitable to measure 2-OG levels from 0.1 mM to 10 mM. The two biosensors thus offer the possibility to measure 2-OG levels in different hosts. Indeed, the internal pool of 2-OG varies from one microorganism to the other. The F-mT_NtcA is therefore adapted to bacteria with high 2-OG  
Figure 2: _In vitro_ features of the 2-OG FRET biosensors. (**A**,**B**) Relative FRET ratio of F-mT_PII (**A**) and F-mT_NtcA (**B**) measured in vitro as a function of 2-OG concentration. Three different concentrations of proteins were tested (0.25 uM, green line; 0.5 uM, blue line; and 1 uM, red line); (**C**,**D**) 2-OG titration curve of F-mT_PII (**C**) and F-mT_NtcA (**D**) as a function of pH values (from 6.4 to 8.3) with 2-OG concentrations from 10\({}^{\circ}\) M to 10\({}^{\circ}\)2 M (0.5 uM protein at 30 \({}^{\circ}\)C); (**E**,**F**) 2-OG titration curve of F_PII-mT (**E**) and F_NtcA-mT (**F**) as a function of temperature (from 25 \({}^{\circ}\)C to 37 \({}^{\circ}\)C) with 2-OG concentration from 10\({}^{\circ}\) M to 10\({}^{\circ}\)2 M (1 uM protein at pH 7.4); (**G**) FRET ratio change (%) of F_NtcA-mT (red) and F_PII-mT (blue) obtained after the addition of 1 mM of different metabolites. All results were obtained from three independent measurements. internal concentrations such as _E. coli_ (~0.5 mM [45]) whereas F-mT_PII may be more suitable for bacteria with lower 2-OG internal concentrations such as _Anabaena_ (~0.1 mM [3]). Both biosensors tolerate a wide range of temperature (25-37 \({}^{\circ}\)C) (Figure 2E,F) and pH values (6.4-8.7) with little changes in sensitivity especially for F-mT_NtcA (Figure 2C,D). They show a high specificity toward 2-OG excepted F-mT_PII, which also responded to 2-MPA, a non-metabolizable analogue of 2-OG (Figure 2G) [46]. We then tested how fast the two purified biosensors respond to 2-OG changes. For this, we performed a time-lapse recording of the FRET ratio upon addition of 2-OG. Figure 3A,B show that F-mT_PII and F-mT_NtcA responded within a relatively short time. The measured half-times (time for reaching half of the FRET plateau) in presence of 1 mM of 2-OG were 44 s and 76 s for F-mT_PII and F-mT_NtcA, respectively. In addition, we observed that stable FRET ratios were maintained for long periods after the equilibrium. Overall, these in vitro results demonstrate that the two biosensors can respond rapidly to 2-OG changes.
## 3 Design
### System Technical Requirements
#### 3.1.2 Scientific Requirements  
The focal length f of the optical system is closely related to the maximum image height d on the image plane and the field of view \(\omega\) of the system. When \(f>h\), \(\omega\approx h/f\). As shown in Figure 4b, according to the determination of the parameters such as the location L of the MEPI and the size D of the measured target, the focal length and the field of view need to meet the following relationship:  
\[f=\frac{d\cdot L}{D} \tag{8}\]  
According to the main applications of the MEPI, the scientific requirements of the system can be clarified.  
1. The field of view (FOV) of the instrument should cover the entire Earth at the Earth-Moon system's Lagrange point L1;
2. The instrument measures the polarization of the whole Earth. Instantaneous imaging is applied to avoid losing climate change information during operation [14];
3. The resolution is 10 km, which can distinguish between cloudy and clear sky scenes. It can be clearly imaged in nine spectral bands;
4. The continental map is derived from the integrated signal of the disc, and the salient features such as vegetation, aerosol, and surface can be identified.  
The MEPI will achieve its scientific objectives by recording and analyzing the reflected sunlight of the Earth and the Moon. The target technical requirements of the MEPI are shown in Table 3. When the index is set, a certain safety margin is set aside to ensure that the MEPI can function stably and efficiently in the face of a complex environment and potential changes.  
Figure 4: (a) Detector pixel coating method; (b) Space observation diagram.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Payload Summary
#### 2.2.2 Earth-Observation Payload (EOP)  
The EOP is a normalized difference vegetation index (NDVI) imager. The payload consists of two cameras (Figure 5) based on the design of the European Student Earth Orbiter's (ESEO) secondary camera [35]. Each camera was modified with a simplified, more cost-effective baffle design [36] and a minor electronics redesign for CubeSat space protocol (CSP) compatibility. Each camera sensor has a different COTS band-pass filter: 660 (30) nm and 857 (30) nm. The spectral bands have been selected to be similar to Sentinel-2 bands 4 and 8a: 665 (30) nm and 865 (20) nm, respectively [37]. This will facilitate potential comparative analysis; however, we expect some discrepancies caused by mismatched COTS filters. The diagonal field of view is 9.56\({}^{\circ}\), and from an altitude of 500 km, the ground resolution is 22 m per pixel.  
Basic image compression and storage are performed onboard the cameras. Additionally, raw images are read from the image sensor, bit-packed from 16-bit to 12-bit pixel values, and stored onboard the cameras. The cameras communicate with the rest of the spacecraft via a modified CSP communication protocol [18]. The EOP firmware is primarily based on the firmware developed for the ESEO cameras. The cameras run on FreeRTOS, and microSD cards are mounted as FatFS volumes, while both ferroelectric random-access memory (FRAM) and synchronous dynamic random-access memory (SDRAM) use a minimal custom file system developed for the ESTCube-1 [38]. Several modules, such as firmware updates, command scheduler, exception handling, error logging, and several low-level drivers, are based on the ESTCube-1 legacy with minor improvements.  
Figure 5: A render of the Earth Observation Payload camera modules.
# Usefulness of Rowanberry for Improving the Nutritional Value of Buckwheat Flour Extrudates
## 1 Introduction
In general, the production of extruded snacks with plant ingredients rich in bioactive compounds is a new field that has not been fully explored, and the available information is very limited. In previous studies, gluten-free (purple potato, yellow pea, rice, and oat flours) [17; 18; 19] extruded samples were enriched with antioxidant-rich fruits such as banana, apple, strawberry and tangerine [20], rosehip [21], and chokeberry [22]. However, no study on the enrichment of corn- and buckwheat-based extruded products with rownberry fruit was found in the literature. The addition of rownberry, thanks to its many bioactive compounds, increases the nutritional value and stability of food products. It also makes the colour of the final product more attractive [23]. _Sorbus aucuparia_, commonly known as rownberry, is a popular ornamental plant widely cultivated in gardens and parks in Europe and North America. Rowanberry fruits are spherical and red. Due to the content of parasorbic acid, they cannot be eaten raw. They have a bitter taste and can cause problems in the digestive system [24]. Rowanberry fruits contain organic acids (malic, tartaric, and parasorbic acids), carotenoids, tannins, carbohydrates, vitamin C (0.2%), and phenolic compounds (quercetin, isoquercitrin, rutin, catechin, etc.) [25;26]. Rowanberry extracts are characterised by a high concentration of phenolic components, namely hydroxycinnamic acids, including caffeoylquinic acid and its derivatives (chlorogenic acid, neo- and crypto-chlorogenic acids), and flavonols, including quercetin and kaempfer conjugates. In addition, p-coumaric and ferulic acids (hydroxycinnamic acids), p-hydroxybenzoic and gallic acids (hydroxybenzoic acids), and catechin, proanthocyanin, and anthocyanin (flavonoids) have also been detected. A review of the available literature showed that 56-80% of the phenolic acids present in rownanberry extract are composed of caffeoylquinic acid, with more than 50% of flavonoid glycosides being composed of quercetin 3-O-(6''-malonyl)-glucoside [25; 26]. Furthermore, the rowanberry extract is rich in carotenoids including zeaxanthin, \(\beta\)-cryptoxanthin, all-trans-\(\beta\)-carotene, and organic acids, including malic acid [25]. The rowanberry extracts reportedly have different biological effects such as antioxidants, anti-inflammatory, anti-atherosclerotic, anti-alcohol, and vasodilating effects. Therefore, the extracts are used to treat intestinal obstruction, chronic diarrhoea, and various liver and gallbladder diseases [27]. In addition, due to the content of carotenoids, the addition of rowanberry fruit can make the colour of food products (extrudates, etc.) more attractive. The present study aimed to determine the effect of rowanberry addition and corn-buckwheat ratios on the colour, nutritional value, expansion properties, phenolic content, flavonoid content, antioxidant activity, and textural characteristics of extruded flat cereal crips.
## 1 Introduction
To apply MAS to such configurations, a set of auxiliary sources (ASs) is positioned on a fictitious surface, which is generally conformal to the physical boundary, except in the vicinity of the tips. In the areas surrounding the wedges, the ASs are densely packed, simultaneously approaching the tips, to account for the edge effects, as suggested in [19]. Similar strategies were employed in the case of a scattering problem associated with coated perfectly electric conducting (PEC) surfaces including wedges [20], where the surface was modeled via the standard impedance boundary condition (SIBC) [21]. While this deformation of the auxiliary surface has proven efficient for straight wedges, especially when the latter form right angles, no evidence is known from the literature about its applicability to arbitrarily shaped wedges. The aim of this paper is to investigate whether MAS accuracy improves through this deformation, when the wedge is shaped as an intersection of circular arcs with non-coincident centers. Moreover, as an improvement over the heuristic approaches in [19; 20], explicit algorithms should be developed for the definition of the deformed auxiliary surface. The scatterer is thus defined as an infinite cylinder, either conducting or dielectric, with an eye-shaped cross-section. The auxiliary surface is generally retained as conformal to the scatterer's boundary, except in the neighborhood of the wedge tips, where various deformation schemes are employed and accuracy comparisons are drawn. The format of this paper, which is an extension of [22], including additional data on perfectly conducting case, lossless, and lossy dielectrics, is as follows: Section 2 briefly presents the mathematical formulation of MAS for 2D scatterers, illuminated by a transverse magnetic (TM) polarized plane wave. Furthermore, in Section 3 various algorithms are proposed for the deformation of the auxiliary surfaces close to the wedge tips. Section 4 includes a variety of data for the eye-shaped scatterer and checks the satisfaction of the boundary condition. Finally, the method is summarized and discussed, whereas useful conclusions are drawn. A + _jot_ time variation convention is assumed and suppressed throughout the paper.
## 3 Results
### Characterization of the Microstructure after RTA
SAED analysis was performed before and after RTA and both perpendicular and longitudinal sections were analyzed. As expected before RTA, ring patterns (Figure 7a,b) were obtained, which are characteristic of polycrystalline structures. Patterns corresponding to Al fcc and Ni fcc were obtained in both areas, at a distance of 3 \(\upmu\)m (perpendicular section) and 1 \(\upmu\)m (longitudinal section) from the silicon needle tip. After RTA (Figure 7c,d), the Bragg reflex of Al (111) disappears in both areas and new diffraction rings appear. These new patterns correspond to AlNi (B2) and AlNi3 (L12). During RTA, the Al concentration will decrease in the interdiffusion region because the diffusion of Ni in Al occurs preferentially, since the necessary activation energy for diffusion of Al in Ni is about two times the value for the diffusion of Ni in Al [32]. In both areas, closer to the needles and closer to the surface, the presence of both phases AlNi and AlN3 is observed. These results confirm the presence of both phases in areas close to the silicon needles and in areas close to the surface.
## 2 Materials and Methods
### Glutathione (GSH) Quantification  
Glutathione (GSH) content was quantified according to the spectrophotometric technique established by [PERSON] et al. [39], utilizing the reaction with 5,5'-DTNB. Biomolecule extract (0.48 mL), 0.067 mol dm\({}^{-3}\) Na\({}_{2}\)HPO\({}_{4}\) (2.2 mL), and 1 mM DTNB dye (0.32 mL) were placed in a test tube and mixed. The absorbance of the samples was read at 412 nm in a UV-Vis spectrophotometer. Values were reported as mg L\({}^{-1}\).
# Shock Wave Formation and Cloaking in Hyperelastic Rods
## 4 Numerical Analyses
### Harmonic Excitations  
To achieve harmonic force excitation and large strain in a range of frequencies \(10^{-1}\leq f\leq 10^{2}\) Hz and with force loading amplitudes varying in a range \(50\leq p_{0}\leq~{}400\) KN. The typical plots for strain varied with time are shown in Figure 4 for \(p_{0}=50\) KN.  
The analogous plots are shown in Figure 5 for \(p_{0}=400\) KN; this loading amplitude produces an oscillating strain with much larger amplitude, see Figure 5a.  
The plots for energy variations with time are shown in Figure 6. In these plots, the corresponding energy values are defined according to Equations (3g) and (3i).  
Comparing these results reveals the following:  
(A) Appearance of the substantial asymmetry in positive and negative strain pulses at the left end at large \(p_{0}\) (Figure 5a) and almost symmetric pulses at small \(p_{0}\) (Figure 4a); the explanation follows from the dependence of \(E\) on \(\varepsilon\) (Figure 2a) revealing that at small strain the function \(E(\varepsilon)\) is almost symmetric, while at large \(\varepsilon\) the corresponding tangent elastic modulus values become \(E(-0.5)\approx 1.8\) MPa and \(E(+0.5)\approx 1.51\) MPa.  
Figure 3: 1D rod of length \(L\) with points of observation marked as \(x_{0},\dots,x_{3}\).  
Figure 2: (a) Tangent elastic modulus variation vs. strain. (b) Rod velocity variation vs. strain.  
Figure 4: Nominal strain variation vs. time at points of observation: (**a**) \(x_{0}\); (**b**) \(x_{1}\); (**c**) \(x_{2}\); (**d**) \(x_{3}\); at \(p_{0}\) = 50 KN and \(f\) = 1 Hz.  
(B) When the distance from the left end increases, the strain magnitudes in both cases begins to decrease with forming shock wave fronts. In Figures 4b-d and 5b-d, these shock wave fronts represent straight vertical lines between pulses of opposite signs. Another observation concerns the existence of multiple shock wave fronts; these fronts actually fill the whole region from the left end to the farthest point. A detailed analysis of the appearing shock wave fronts can be traced in Figure 7, where, according to Figure 2b, the faster moving parts associated with \(|\epsilon|<0.1\) start to overtake the slower moving parts related to a large negative \(-0.2<\epsilon<-0.1\).  
(C) In both cases \(p_{0}=50\) KN and \(p_{0}=400\) KN, strain energy starts to decrease in expense of an increase of thermal energy. At the same time, kinetic energy stabilizes, as plot 6b shows. Finally, the plots in Figure 6b reveal a substantial discrepancy between strain and kinetic energy, which appears when shock wave fronts begin to propagate and, hence, thermal energy is generated. Such a discrepancy is also observed at propagation of the dispersive surface acoustic waves, and acoustic waves in dissipative media.  
The dimensionless distance (\(l*\)) from the left end of the rod that appeared in Figure 7, is defined as \(l*=l/L\).
# Estimation of Live Load Distribution Factor for a PSC I Grider Bridge in an Ambient Vibration Test
## 2 Estimation of Live Load Distribution Factor Using Empirical Mode Decomposition  
### Empirical Mode Decomposition  
For the displacement response of a bridge attributed to vehicle loads, the low-frequency response is overlapped with the high-frequency component. The low-frequency response is the static component that represents the displacement caused by vehicle loads. The dis placement response of the dynamic component mostly corresponds to the high-frequency response that occurs due to the interaction between the bridge and vehicles. Consequently, the response of the static component must be extracted from the measured response to estimate the LLDF of the bridge.  
EMD is a mode decomposition method for the dynamic response, and gradually decomposes the high-frequency component initially through the procedure shown in Figure 1[43]. If an average curve is acquired from the envelope curves obtained using the maximum and minimum values of the displacement response as shown in Equation (1) and the average curve is removed from the measured displacement response, the displacement response of the high-frequency component with the low-frequency component reduced can be obtained as shown in Equation (2). Here, \(u(x,t)\) is the displacement response measured at measurement position \(x\) and time \(t\), \(i\) is the number of the displacement response subjected to mode decomposition, and \(k\) is the number of repetitions.  
\[u_{i1}(x,t)=\frac{u_{max}(x,t)+u_{min}(x,t)}{2} \tag{1}\]  
\[u(x,t)-u_{i1}(x,t)=h_{i1}(x,t) \tag{2}\]  
The average curve was removed from the measured data to reduce the low-frequency component. Repeating Equations (1) and (2) leaves only the high-frequency component with the low-frequency component reduced. The energy difference tracking method (EDTM) was used for these repetitions [44; 45]. EDTM assumes that the sum of the energy of the decomposed displacement response and the remaining energy is equal to the total energy, \(E_{t}\). The displacement response is decomposed by repeating the processes of Equations (1) and (2) until the difference between Equation (3) and the total energy converges to the minimum value.  
\[E_{t}=\int_{-\infty}^{\infty}{u_{i}}^{2}(x,t)dt+\int_{-\infty}^{\infty}{[u(x, t)-u_{i}(x,t)]}^{2}dt \tag{3}\]  
\[u_{i}=h_{ik} \tag{4}\]  
Data obtained by performing the processes of Equations (1)-(3) are defined as the intrinsic mode function (IMF), which can be expressed as Equation (4). The second IMF is obtained by removing IMF from the measured displacement response and repeating the processes of Equations (1)-(3). If this process is repeated, Equation (5) can be obtained.  
Figure 1: Procedure for extracting IMF using EMD.  
Here, \(u_{i}(x,t)\) is the \(i\)-th IMF of the decomposed signal and \(r_{n}(x,t)\) corresponds to the residual response after removing IMF.  
\[u(x,t)=\sum u_{i}(x,t)+r_{n}(x,t) \tag{5}\]