esa-sceva/satcom-chunk-collection huggingface dataset:
# Volatile, Microbial, and Sensory Profiles and Consumer Acceptance of Coffee Cascara Kombuchas
## 3 Results and Discussion
### Sensory Tests
#### 3.4.1 Rate All That Apply (RATA)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aroma and Flavor** & **Respective Volatile Compounds Identified** & **References** \\
**Attributesfrom RATA Test** & **in the Literature and in the Present Study** & **References** \\ \hline \multirow{4}{*}{Alcoholic} & Isoamyl alcohol, ethyl acetate, ethyl & \multirow{4}{*}{[97]} \\  & hexaanoate, ethyl decanoate, lanaloal oxide & \\ \hline \multirow{4}{*}{Apple} & Dodecanal, heptanal benzaldehyde, decanal, & \multirow{4}{*}{[98]} \\  & decanoic acid, ethyl 2-methylbutyrate, ethyl & \\  & butyrate, methyl salicate & \\ \hline \multirow{4}{*}{Berries} & Hexanal, decanal, benzaldehyde, octanoic & \multirow{4}{*}{[98]} \\  & acid, ethyl acetate, ethyl hexanoate, ethyl & \\  & perries & octanoate, lanaloal & \\ \hline \multirow{4}{*}{Citric} & Nonanal, octanal, dodecanal, limonene & \multirow{4}{*}{[30]} \\  & \(\alpha\)-terpineol, lanaloal & \\ \hline \multirow{4}{*}{Fermented} & Hexanal, benzaldehyde, nonanal, & \multirow{4}{*}{[101]} \\  & phenylacetaldehyde, ethanol, octanoic acid & \\ \hline \multirow{4}{*}{Flororal/flower} & Phenylethyl alcohol, \(\beta\)-damascenone, & \multirow{4}{*}{[102]} \\  & linalool, linalool oxide, cis/ & \\ \cline{1-1}  & Floral/flowery & trans-linalool oxide & \\ \hline \multirow{4}{*}{Herbal} & Hexanal, heptanal, nonanal, octanal, & \multirow{4}{*}{[103]} \\  & phenylacetaldehyde, phenylethyll & \\ \cline{1-1}  & Herbal & alcohol, linalool & \\ \hline \multirow{4}{*}{Hibiscus} & Dodecanal, nonanal, 2-ethylhexanol, acetic & \multirow{4}{*}{[104]} \\  & acid, linalool & \\ \hline \multirow{4}{*}{Honey} & Octanal, benzaldehyde, phenylacetaldehyde, & \multirow{4}{*}{[105,106]} \\  & phenylethyll alcohol, isovaleric acid, & \\ \cline{1-1}  & \(\beta\)-damascenone, cis/trans-linalool & \\ \cline{1-1}  & oxide, linalool, \(\alpha\)-terpineol & \\ \hline \end{tabular}
\end{table} 
Table 5: RATA attributes and the corresponding volatile compounds identified in the present study for coffee cascara kombuchas (see Table 3). Figure 6: Biplot of Principal Component Analysis (PCA) using as variables the volatile compounds identified as relevant for RATA attributes summarized in Table 5. CCB: coffee cascara from Brazil; CCN: coffee cascara from Nicaragua; Inf: infusion; K: kombucha; d0, d3, d6, and d9: days of fermentation.
## 2 Materials and Methods  
### Synthesis  
Ceria NPs were synthesized using a chemical precipitation technique for both relatively low-cost precursors and simplicity [15]. In detail, cerium chloride (Sigma Aldrich, St. Louis, MO, USA) of 0.5 g was added into 40 mL of distilled water. Then, the solution was stirred for two hours at a rotation rate of 500 rpm and a temperature of 50 \({}^{\circ}\)C for two hours at a stirring speed of 500 rpm. At the beginning of stirring, 1.6 mL of ammonium hydroxide (ammonia) was added as a catalyst. After two hours of warm stirring, the solution was stirred at the same rotation speed at room temperature overnight. This long stirring process helped to break most of formed ceria nanorods into NPs. Moreover, the first warming stage helped in converting cerium hydroxide into non-stoichiometric ceria NPs.  
The used plasmonic nanostructures were Au NPs, CdS/ZnS, and CdSe/ZnSQDs. Au NPs (Sigma Aldrich, St. Louis, MO, USA) were selected with a diameter of 20 nm to have plasmonic resonance wavelength overlapped with a fluorescence emissions wavelength of 530 nm. CdS/ZnS QDs (Sigma Aldrich, St. Louis, MO, USA) was selected to have a resonance overlap close to 430 nm, and CdSe/ZnS QDs (Sigma Aldrich, St. Louis, MO, USA) with a resonance close to 530 nm. We added all plasmonic nanoparticles with a weight concentration ratio of 1:3 compared to the solution of ceria nanoparticles. The different plasmonic nanostructures were added with two possibilities: in-situ which means to be added with the initial precursors stage; or post-treatment, which was to be stirred with ceria NPs after being synthesized.
## 3 Results
### Spatio-Temporal Variation Characteristics of Climatic Factors  
Figure 5 and Table 1 shows the trends of climate variables on the TP from 1970 to 2018. The \(T_{man}\) increased significantly (\(\alpha\) = 0.05) before and after 1997 at the rate of 0.125 and 0.373 \({}^{\circ}\)C/ decade, respectively. The upward trend was steeper during the latter period than during the former period. \(RS\) and \(RH\) increased by 0.019 (MJ\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\))/decade and 1.68%/ decade from 1970 to 1996 and decreased significantly at the rate of \(-\)0.163 (MJ\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\))/decade and \(-\)2.816%/ decade after 1997, respectively. The variation of \(U_{2}\) was opposite to the variation of \(RS\) and \(RH\). \(U_{2}\) decreased dramatically by 0.294 m/s/ decade during the former period but increased significantly by 0.158 m/s/ decade during the latter period.  
Figure 6 shows the spatial distribution of trends of four climate factors during 1970-1996 and 1997-2018. From 1970 to 1996, the increasing \(RS\) distributed partially in the southeast and west of the TP, and the decreasing \(RS\) majority distributed in the central area of the TP. From 1997 to 2018, the decreasing \(RS\) was found in most stations and the increasing \(RS\) distributed in some areas east and north-west of the TP. The increasing \(RH\) was found in most stations and the decreasing \(RH\) distributed in the north-western areas during 1970-1996, while the decreasing \(RH\) was found in most stations and the increasing \(RH\) distributed in some areas east of the TP during 1997-2018. \(T_{man}\) increased across the entire area before and after 1996. From 1970 to 1996, \(U_{2}\) showed a downward trend in most stations, while only a few stations in the eastern part of the plateau showed an increasing trend. However, from 1997 to 2018, \(U_{2}\) of most stations showed an upward trend, and only a few stations decreased.  
Figure 5: Annual variation of climatic factors on the TP during 1970–2018. (**a**) Solar radiation (\(RS\)); (**b**) relative humidity (\(RH\)); (**c**) air temperature (\(T_{man}\)); (**d**) wind speed (\(U_{2}\)).
## 2 Comprehensive Evaluation Method for AC/DC Hybrid Power Network Planning
### Evaluation Index System for AC/DC Hybrid Microgrid Planning
#### 2.1.3 The Power Quality Indicators  
The power quality indicators [21; 22] have mainly three aspects: voltage offset, frequency deviation, and harmonic distortion rate.  
Voltage offset is the difference between the actual voltage and the rated voltage of the network. Voltage offset is often expressed as a percentage. Voltage loss caused by reactive power load and line loss is the main cause of voltage offset. The calculation of voltage offset at each node of power supply system is shown in Equation (6).  
\[\Delta U=\frac{U_{re}-U_{N}}{U_{N}}\times 100\%. \tag{6}\]  
In Equation (6), \(\Delta U\) is the voltage offset. \(U_{re}\) is the actual voltage. \(U_{N}\) is the rated voltage of the system.  
Frequency deviation is the difference between the actual value and the nominal value of the system frequency under the normal operation conditions of the power system. According to the regulation, the allowable value of normal frequency deviation of power system is \(\pm 0.2\) Hz. When the system capacity is small, the deviation can be relaxed to \(\pm 0.5\) Hz.  
\[\Delta f=f_{1}-50. \tag{7}\]In Equation (7),\(f_{1}\) is the current frequency of the system and \(\Delta f\) is the frequency offset of the system.  
Ideally, the voltage waveform should be a periodic standard sine wave. However, there is much power supply equipment with non-linear impedance characteristics in power systems. These devices inject harmonic current into the public power grid or generate harmonic voltage in the public power grid, which is called harmonic source. Harmonic source makes the actual voltage waveform deviate from sinusoidal waveform, which is called voltage sinusoidal waveform distortion. Usually it is represented by harmonics. The degree of voltage waveform distortion is measured by voltage sinusoidal distortion rate, which is also called voltage harmonic distortion rate. \(T_{HDU}\) is commonly used to express the voltage harmonic distortion rate. The total distortion rate of voltage harmonics is calculated as shown in the equation.  
\[T_{HDU}=\frac{\sqrt{\sum\limits_{h=2}^{\infty}{\left({U_{h}}\right)^{2}}}}{{U_ {1}}}\times 100\%. \tag{8}\]  
In Equation (8), \(U_{h}\) is the \(h\)-th harmonic voltage and \(U_{1}\) is the fundamental voltage.
## 4 Test Setup, Instrumentation, and Crack Formation  
### Experimental Setup and Instrumentation  
The test setup is shown in Figure 5a. The wall is placed in the horizontal position on a steel table, between two stiff steel beams (HEB180), interconnected through four steel rods 27 mm in diameter (quality S275 steel). An actuator (1), fixed on a steel column (A), imposes the compressive stress of 0.20 MPa on the wall, held constant throughout testing. The compression load is uniformly distributed to the wall, thanks to the rigid steel beam between the wall and the actuator. After installation of the anchor and opening of the crack (for locations L\({}_{6}\) and L\({}_{\text{dT}}\), according to 4.2), a steel ring (Figure 5b) is positioned on the wall. The steel ring is designed with an internal diameter of 400 mm to allow for unconfined testing of the anchors. Actuator 2 (max. capacity equal to 100 kN) is fixed at the top of the ring in a perfectly vertical position. Its axis coincides with the longitudinal axis of the ring, allowing for the alignment of the ring with the axis of the anchor to be tested. The actuator is connected to the anchor by means of an adaptor device shown in Figure 5b. Displacements are imposed at a rate of 0.5 mm/min approximately. The displacement of the tested anchor is measured by two LVDTs (Figure 5b), of maximum measuring length equal to 50 mm (accuracy 0.05 mm), placed symmetrically to the anchor axis. The crack opening was measured using two LVDTs (Figure 5a) of measuring length 25 mm (accuracy 0.025 mm), attached to the wall surface symmetrically to the anchor axis and orthogonal to the crack plane.  
As the walls were constructed in a vertical position, an adequate procedure should be followed to ensure their safe transportation and rotation to a horizontal testing position, to avoid their premature uncontrollable cracking. To this purpose, each wallette, before movement to the testing position, was subjected to vertical compressive stress using threaded steel rods. Subsequently, using the bridge crane, the wallette was transported in a vertical position on the testing table and very slowly rotated. Once the wallette was safely positioned, the compressive stress was removed and the wallette was thoroughly inspected to confirm that it was free of cracks. This procedure proved to be efficient.  
Figure 4: (**a**) Loading pattern adopted for repeated tension tests. (**b**) Typical load vs. displacement curve obtained according to the load protocol of (**a**). Anchor in location L\({}_{\text{dT}}\), weak mortar.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 2 Potentials of VLC System
### Bandwidth resource  
RF, with a frequency band of 3 kHz to 300 GHz, is the most popular frequency band in the modern wireless communication systems. Each wireless communication system independently uses a frequency band to avoid interfere. Since the spectrum resources are limited, national regulation and authorization are usually essential. However, with the rapid growth of wireless communication applications and demand, this authorization and fixed distribution method results in an increasingly dwindling RF spectrum. Developing alternative technologies with larger bandwidth and spectral relief is urgent. Visible light (corresponding to a wavelength band of 380 nm to 780 nm) has 10,000 times a greater spectrum than radio waves [3]. In addition, VLC does not require spectrum regulation and authorization. Thus, in contrast to the RF communication, VLC has huge available and unregulated bandwidth resource for data transmission, making it a potential solution to overcoming the crowded RF spectrum in wireless communication.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 2 Study Area and Data  
### Study Area  
Figure 1 shows the study area. The background shows the land cover types from the GlobeLand30 datasets ([[http://www.globallandcover.com/](http://www.globallandcover.com/)]([http://www.globallandcover.com/](http://www.globallandcover.com/)) (accessed on 15 January 2022)) [47; 48]. The main dust source areas (i.e., the Taklamakan Desert and Gobi Desert) are located in northwestern and North China, respectively, which are dominated by bare land and shrubland. The Taklamakan Desert, located in the middle of the Tarim Basin in southern Xinjiang is the largest desert in China and the second largest mobile desert in the world. The Gobi Desert is located between China and Mongolia, bordered by the Taklamakan Desert to the west and surrounded by the Altai Mountains, the Mongolian steppe, the Hexi Corridor, and the Qinghai-Tibet Plateau, covering parts of northern and northwestern China and the southern part of Mongolia. The dust storms originated from the Taklamakan and Gobi Deserts not only caused severe dust problems in the northwest and north [33; 49], but also influenced regions far from dust sources in central China and eastern China due to the long-distance transport of dust plumes [50]. Most dust storms occur during spring due to the climatic and land surface conditions.
## 3 Load Forecasting Based on BiLSTM and the Leading Influencing Factors  
### BiLSTM-Based Load Forecasting Algorithm  
The modeling capability is enhanced in BiLSTM by running two independent LSTM networks in each of the two directions of the input sequence, which is able to simultaneously consider the trends and periodicities of different time scales of the load data series, such as short-term fluctuations and long-term trends of the loads, and thus better capture the diversity and complexity of the load data. The computational process is represented as [32]:  
\[\left\{\begin{array}{l}h_{ti}=LSTM(x_{t},h_{ti-1})\\ h_{to}=LSTM(x_{t},h_{to-1})\\ y_{t}=\omega_{t}h_{ti}+v_{t}h_{to}+b_{y}\end{array}\right. \tag{15}\]  
where the forward load feature vector \(h_{ti}\) characterizes the historical load pattern, which is obtained through time series feature extraction of historical load curves. The reverse load feature vector \(h_{to}\) characterizes the context-dependent features of load changes, which is generated by inverse analysis of the potential cyclic characteristics of the load. \(LSTM(\cdot)\) denotes the load feature extraction network. \(\omega_{t}\) and \(v_{t}\) denote the time series feature weight and context-dependent feature weight, which quantify the importance of the historical load evolution pattern and load fluctuation correlation features to the current forecast [33].  
The power feature weighting mechanism is adopted to the final feature fusion, which realizes the nonlinear coupling of historical load and contextual features of load variations through the feature weight matrix, and provides the fused feature vectors containing multiple power characteristics for the subsequent fully connected layer.  
The structure of the load forecasting model is shown in Figure 1, where the processed electricity load, temperature, humidity, wind speed and other influencing factors are used as feature inputs, and the BiLSTM model is used to learn the input features and output the load forecasting results.
## 3 Elastographic Methods
### Two-Dimensional Shear Wave Elastography (2D-SWE)
#### 3.3.6 Liver Fibrosis in Biliary Atresia (BA) Patients  
The availability of an effective non-invasive tool for monitoring liver fibrosis in children with BA is important, but evidence is limited. 2D-SWE is a more promising tool to assess liver fibrosis than APRI and fibrosis-4 (FIB-4) in children with BA after the Kasai procedure. The AUROCs of 2D-SWE, APRI and their combination were 0.79, 0.65 and 0.78 for significant fibrosis; 0.81, 0.64 and 0.76 for advanced fibrosis; and 0.82, 0.56 and 0.84 for cirrhosis, respectively [19].  
LSM was found to be higher in patients with BA as compared to controls: 12.6 kPa (10.6-18.8) versus 9.6 kPa (7.5-11.7) (\(p\) < 0.001), without any difference between gender (\(p\) = 0.071) [73]. The difference in LSMs between BA patients and controls also applied to the two age groups using the 60-day age cutoff (\(p\) < 0.001 below age cutoff and \(p\) = 0.002 above age cutoff). Using a cutoff value \(\geq\)10.2 kPa, the sensitivity, specificity, positive predictive value and negative predictive value for the diagnosis of BA were 81.4%, 66.7%, 76.0%, and 73.5%, respectively. Using the same cutoff value, the sensitivity of the test improved to 92.5% in the \(>\)60 days old age group (\(n\) = 60), whereas it decreased to 68.2% in younger babies (\(n\) = 53). In these patients, age, direct and indirect bilirubin levels significantly correlated with LSM (all \(p\) < 0.001), whereas both ALT and AST levels did not correlate (both \(p\) > 0.05) [73]. In 12 patients after the Kasai intervention (M:F = 3:9, mean age 9.3 \(\pm\) 4.4 years, age range 3-18 years old), without clinical evidence of acute illness including cholangitis, and no incidental mass or cystic lesion in the liver, the mean value from fifteen LSMs was 8.0 \(\pm\) 2.2 kPa.
## 2 Materials and Methods
### Finite Element Model  
As shown in Figure 2, the soil domain is set with the horizontal boundary width (horizontal distance from the wheel edge to the mesh boundary) as \(5\)\(D_{\mathrm{w}}\) and the vertical boundary height (vertical distance from the pile end to the mesh boundary) as \(2\)\(L\), which are large enough to ensure minimal boundary effects [30; 31; 32]. Three-dimensional 8-node linear brick elements (C3D8R) with reduced integration are adopted in the models. Relatively fine meshes are adopted around the foundation for a higher computational accuracy, and coarser mesh sizes are employed in the soil away from the foundation to optimizing the computer resources needed. Hinge supports are applied at the bottom of the model and roller conditions are set along the vertical sides of the soil domain. The vertical boundary in the front side of the model is fixed in the horizontal direction (y direction) and set free in the other two directions (x and z directions). In order to simplify the simulation, the analyses involve a \"wished in place\" pile installation before loading. Beam elements are used to model the tower structure with a linear elastic material property. A lumped mass is adopted to simulate the super-structure assembly, which applies the vertical pre-load by its self-weight [38; 39]. The corresponding load can be extracted from the loading point as shown in Figure 2. The resultant lateral load (\(H\)) is applied at a certain height (\(e\)) on the tower above the mudline, which leads to the combined lateral load (\(H\)) and the designed moment (\(M\) = \(H\times e\)) acting on the foundation. The height of the lateral load was determined according to the engineering practice [40]. The lateral load is applied in the form of continuous lateral displacement until the foundation reaches its ultimate bearing state, which is determined by the tangent intersection method [41; 42]: the load corresponding to the intersection point of the lateral load-displacement curve is regarded as the ultimate bearing capacity of the foundation.  
Figure 2: Geometry and mesh of the single pile-friction wheel composite foundation models.
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 2 Frame Rate Analysis for Video SAR  
Video SAR has been devised for onboard aircraft [6; 7; 9; 15], drone [14; 17] and satellites [12; 13; 16] platforms, and their operational modes are used for spot and tracking modes in circular [6; 7; 9; 14; 15; 17] and fly-by paths [12; 13; 16].  
For the generation of seamless SAR frames upon the streaming phase histories of the moving targets, the video SAR system needs to meet strict processing time constraints that are not plausible with the conventional SAR processors. These constraints become a critical performance measure that limits the frame rate of a video SAR system. The design of the video SAR system must take into account that the processing time varies according to the SAR operating mode, a function of antenna steering, the overlapping ratio of the aperture length, the bandwidth, PRF, and swath width. The difficulty is escalated when the time-varying properties of the incoming signals from the moving targets demand reduction of each frame's synthetic aperture length.  
A video SAR system with a circular path has the geometry shown in Figure 1. The azimuth step size \(\Delta\theta\) is calculated as follows.  
\[\Delta\theta=\frac{V_{s}}{R\cdot f_{PRF}}, \tag{1}\]  
where \(f_{PRF}\), \(V_{s}\), and \(R\) represent the pulse repetition frequency (PRF), platform velocity, and operating range, respectively.  
If a synthetic aperture for a single video SAR frame is constructed with \(N_{p}\) pulses, the synthetic aperture angle is given as follows.  
\[\theta_{a}=N_{p}\Delta\theta. \tag{2}\]  
Then, the corresponding synthetic aperture time is as follows.  
\[T_{a}=\frac{N_{p}}{f_{PRF}}. \tag{3}\]  
Video SAR frames can be constructed either in non-overlapped or in overlapped modes, as in Figure 2. The non-overlapped mode is a special case of the overlapped mode where the overlap ratio is zero. As in the previous works on video SAR frame rate analysis [12; 13; 14], the frame rate for the overlapped mode is as follows.  
\[\gamma = \frac{V_{s}}{R\Delta\theta(N_{p}-N_{ov})} \tag{4}\] \[= \frac{f_{PRF}}{N_{p}-N_{ov}}, \tag{5}\]  
where \(N_{ov}\) represents the number of overlapped pulses used for the generation of neighboring video SAR frames.  
Figure 1: Geometry for circular video SAR.  
The cross-range resolution is not affected by the overlap ratio nor the frame rate and is given as follows.  
\[\rho_{a}=\frac{\lambda}{2\theta_{a}}=\frac{\lambda R}{V_{s}}\cdot\frac{f_{PRF}}{N _{p}}, \tag{6}\]  
where \(\lambda\) represents the RF wavelength of the transmitted pulses.
## 3 Simulation Results and Discussion
### Optimization of XS Anode Parameters  
The relationship between the collected charge of the horizontal and vertical strips and the receiving area is shown in Figure 5. At different emission distances d, by changing the width and distance of the horizontal strips and the vertical strips, different proportions of receiving areas are obtained. Obviously, the charge ratio decreases with the increase in d, increases with the increase in the receiving area, and the rate increases. It can be seen that the singlelayer XS anode designed in this paper has a high charge collection efficiency. When the receiving area ratio s of the horizontal and vertical strips is 1, d is 2 mm and the charge ratio approaches 1. Furthermore, inaccuracies in charge collection can affect image distortion during imaging. That is, each of the horizontal and vertical electrodes receive half of the charge from mcp. This means more charge from the MCP charge footprint will be collected by the anode and less charge can be lost.