esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data
### GPP Products by Satellite Remote Sensing Observations  
Five global GPP products derived from satellite remote sensing observations, i.e., MOD17 GPP [20], revised EC-LUE GPP [68], OCO-2-based SIF product (GOSIF) GPP [10], GPP based on near-infrared reflectance of vegetation (NIRv GPP) [7], and GPP from a coupled diagnostic biophysical model -- Penman-Monteith-Leuning (PML-v2) model (called PML-V2 GPP) [69], were used for cross comparison with our model results. All these five GPP products provided long-term time series of GPP in global scale and were widely used by researchers. Both the MOD17 GPP and the revised EC-LUE GPP products were produced using LUE-based models, and it would be very interesting to compare these two LUE-based GPP datasets with our EF-LUE model result. Recent studies found that SIF and NIRv performed well in tracking seasonal changes in GPP and were regarded as a proxy for GPP [5, 9]. Additionally, the PML-V2 is a water-carbon coupled canopy conductance model that is different in model mechanism from either LUE-based or spectral-index-based methods.  
Table 4 gives the summary of the five GPP data products used in this study for cross comparison, and details of each GPP dataset and website for download can be found in the corresponding publications. We used the GPP data from the overlapped years of the five GPP products, i.e., 2002 and 2018, for cross-comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Dataset** & **Resolution** & **Reference** \\ \hline Air temperature (K) & ERA5 & \begin{tabular}{c} 0.25\({}^{\circ}\times\) 0.25\({}^{\circ}\) \\ 1 h \\ \end{tabular} & \multirow{4}{*}{[60]} \\ Dew point temperature & & 0.25\({}^{\circ}\times\) 0.25\({}^{\circ}\) \\ (K) & ERA5 & \begin{tabular}{c} 1 h \\ \end{tabular} & \\ Surface solar radiation & ERA5 & \begin{tabular}{c} 0.25\({}^{\circ}\times\) 0.25\({}^{\circ}\) \\ 1 h \\ \end{tabular} & \\ downwards (Jm\({}^{-2}\)) & & 1 h & \\ \hline Landcover map & ESA CCI & \begin{tabular}{c} 300 m \\ 1 year \\ \end{tabular} & [62] \\ \hline FAPAR & GGLS-GEOV2 & \begin{tabular}{c} 1 km \\ 10 days \\ \end{tabular} & [63] \\ \hline ET, Rn, G & ETMonitor & \
\begin{tabular}{c} 1 km \\ 1 day \\ \end{tabular} & [32, 33, 64] \\ \hline Climate classification & Köppen-Geiger &
\begin{tabular}{c} 1 km \\ One map based on \\ data from 1980 to \\ 2016 \\ \end{tabular} & [56] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Input datasets used to drive the EF-LUE model for global GPP estimate in this study.
###### Abstract  
The aim of this work was to determine the sorption capacity of the cationic dyes Basic Red 46 (BR46) and Basic Violet 10 (BV10) on the prepared sorbents: Beech bark (BBe) and birch bark (BBi). Two fractions of bark were used in the research: fine (2-3 mm) and coarse (4-5 mm). The carried out tests made it possible to determine the influence of the pH value on the sorption efficiency, the sorption equilibrium time and the maximum sorption capacity of the two tested sorbents. The Langmuir model and the Freundlich model were used to describe the obtained experimental data. Beech and birch barks are effective sorbents for cationic dyes; however, the efficiency of dye sorption on both bark sorbents depends on the type of cationic dye. According to the obtained data, beech and birch bark sorbents showed higher sorption efficiency for Basic Red 46 than for Basic Violet 10. The pH correction was a necessary condition for sorption, and the sorption pH value for the cationic dyes Basic Red 46 and Basic Violet 10 was be determined individually for each dye. The most favourable pH value for the sorption of the BR46 dye on the beach and birch bark sorbents was pH = 6, while for the dye BV10, it was pH = 3. The sorption equilibrium time for Basic Red 46 was 300 min and for Basic Violet 10-240 min. The fine fraction of beech bark had the highest sorption capacity for both BR46 (128.45 mg/g dry matter) and BV10 (18.07 mg/g dry matter).  
beech bark; birch bark; Basic Red 46 (BR46); Basic Violet 10 (BV10)
# Shear Performance for Prestressed Concrete Hollow Core Slabs
## 4 Experimental Results and Discussion  
### Shear Force-Deflection Relationship  
The shear force-deflection relationships of HCS are shown in Figures 7 and 8. Shear force is expressed using the load measured from the hydraulic load cell of the universal testing machine. Deflection is derived from the displacement measured using the LVDT installed at the bottom middle of the loading points of the specimens. Regardless of cross-sectional height or presence of topping concrete, the behavioral characteristics observed in all specimens were linear up to the formation of diagonal cracks. Even after diagonal cracks occurred, the specimens exhibited linear behavior with increasing load. Due to the influence of web-shear cracks, stress decreased after reaching peak stress. Figure 7: Shear force-deflection relationships of S series: (a) S265; (b) S320; (c) S400. As shown in Figure 7 and Table 3, S265 of S series experienced diagonal cracks on average at 202.2 kN, and deflection on average at 1.58 mm. The peak shear force of S265 averaged 308. 1 kN, and the corresponding deflection was 6.74 mm. S320 and S400 experienced diagonal cracks on average at 322.9 kN and 231.1 kN, and deflection at 1.88 mm and 1.59 mm, respectively. The peak shear forces  
Figure 8: Shear force-deflection relationships of CS series: (**a**) CS265; (**b**) CS320; (**c**) CS400. of S320 and S400 were 434.1 kN and 403.4 kN, respectively, with corresponding deflection values of 12.25 mm and 11.16 mm. As shown in Figure 8 and Table 3, CS265 of CS series experienced flexural cracks on average at 356.8 kN, and deflection on average at 1.50 mm. The peak shear force of CS265 averaged 481.8 kN, and the corresponding deflection was 7.88 mm. CS320 and CS400 experienced diagonal cracks on average at 491.2 kN and 488.7 kN, and deflection at 2.17 mm and 2.15 mm, respectively. The peak shear forces of CS320 and CS400 were 697.9 kN and 715.9 kN, respectively, with corresponding deflection values of 17.28 mm and 13.72 mm. As shown in Table 3, compared to S265, S320 had average shear force increases of 58.9% and 40.9% during crack formation and peak stress with increasing effective depth, and average deflection increases of 19.0% and 81.8% respectively. Similarly, CS320 and CS265 had average increases in shear force of 37.7% and 44.9%, and average increases in deflection of 44.7% and 119.3%, respectively. In general, HCS with topping concrete had an average increase in shear force of 52.1-111.5% during crack formation and 56.4-77.5% during peak stress with decreasing shear span-to-depth ratio. With the exception of the comparison between S265 and CF265 during crack formation, the deflection at crack formation and peak stress increased by an average of 15.4-35.2% and 16.9-41.1%, respectively. These results indicate that an increase in effective depth significantly increases shear resistance in the case of the CS series, even for non-overlapping experimental sections.
## 3 Results
### SHE Inhibits NO Production and iNOS and COX-2 Expression in RAW 264.7 Cells by LPS Treatment
We examined whether SHE had an antioxidant capacity by using DCFH-DA as a ROS sensor. Fluorescence image analysis and quantification of ROS showed that SHE decreased on LPS-induced ROS production in RAW 264.7 cells (Figure 4A,B). Moreover, SHE significantly scavenged DPPH and ABTS radicals (Figure 4C,D). HO-1 plays a critical role in maintaining intracellular redox homeostasis [27]. Thus, we investigated the effect of SHE on the NRF2/HO-1 pathway in RAW 264.7 cells. SHE increased HO-1 induction regardless of whether LPS treatment was administered (Figure 5A). Nuclear translocation and accumulation of NRF2 has a relationship with HO-1 expression [28]. Dose-dependently, SHE promoted NRF2 translocation to nuclear in RAW 264.7 cells (Figure 5B). Figure 3: Effects of _Soroseris hirsuta_ extract (SHE) on lipopolysaccharideaccharide (LPS)-induced p65 nuclear translocation in RAW 264.7 cells. (**A**,**B**) The p65 nuclear translocation induced by LPS was inhibited by SHE. (**A**) Western blotting, and the value obtained by quantifying the intensity of p65 in the nucleus of the Western blotting result, using image J. (**B**) Immunofluorescence measurement of p65 was performed as reported in the Materials and Methods, and cells were treated with SHE 1 h before exposure to LPS for 30 min. Data represent the mean \(\pm\) SD of three independent experiments. \({}^{\#}\)\(p<0.05\) between the control and LPS-exposed cells (no SHE); \({}^{\star\bullet}\)\(p<0.01\) and \({}^{\star\bullet\bullet}\)\(p<0.001\) between the control and LPS-exposed cells with SHE. N, nucleus; C, cytosol. Figure 4: Radical scavenger effects and antioxidant of _Soroseris hirsuta_ extract (SHE) after lipopolysaccharide-chamide (LPS) treatment in RAW 264.7 cells. (**A**) Reactive oxygen species (ROS) were induced in RAW 264.7 cells due to LPS treatment (1 \(\upmu\)g/mL) for 24 h. The reduction of ROS production due to the SHE pretreatment was carried out by measuring the amount of fluorescence expression using 2′,′′-dichlor fluorescein diacetate (DCFH-DA) analysis. (**B**) The measurement of ROS inhibition was performed using a fluorescence microscope to verify the fluorescence emitted by DCFH-DA and was performed in the same experimental method as (**A**). (**C,D**) SHE scavenging activity of DPPH and ABTS radicals. A series of processes was carried out as described in the Materials and Methods section. # \(p\) < 0.001, *** \(p\) < 0.01 and *** \(p\) < 0.001. Figure 5: Effects of _Soroseris hirsuta_ extract (SHE) on the nuclear factor erythroid 2-related factor 2 (NRF2) and heme oxygenase-1 (HO-1) pathway in RAW 264.7 cells. (**A**) SHE increased HO-1 dose-dependently with or without LPS. Before the cells were treated for 6 h with LPS, SHE was treated for 1 h. (**B**) Western blotting of nuclear and cytoplasmic fractions. Fractionation was performed as described in Materials and Methods section. LPS was treated for 4 h, and SHE was treated 1 h earlier. * \(p\) < 0.05 and *** \(p\) < 0.001. The data represent the mean \(\pm\) SD of three separate experiments.
# Non-Probabilistic Reliability Analysis of Slopes Based on Fuzzy Set Theory
## 3 Non-Probabilistic Reliability Analysis Method Based on the Ellipsoidal Model  
The non-probabilistic reliability analysis method is a structural stability assessment method that uses a convex model [16] to describe parameter uncertainty, which requires only the value bounds of uncertain variables without their specific distribution characteristics. Currently, the commonly used convex models are the interval model and the ellipsoidal model [18; 19; 20]. Compared with the interval model, the ellipsoidal model provides a more compact uncertainty region, has the ability to consider the correlation between parameters when reflecting the unknown and bounded nature of uncertain parameters, and provides better results in line with the actual geotechnical engineering situation. In this paper, the ellipsoidal model non-probabilistic reliability analysis method was adopted for the example analysis.
## 3 Results and Discussion
### Hardness Analysis
This indicates the Weibull model comprises three components, as illustrated in Figure 15 and listed in Table 9.  
Figure 14: The hazard and probability density of failures functions drawn based on the parameters from the linear fit parameters from Table 7 for the experimental hardness for ZrO\({}_{2}\)–5 wt.% CeO\({}_{2}\) sintered sample; the solid lines represent the Weibull model. The black points on the graph illustrate only the experimental data, the \(\sigma\) values corresponding to the experimental and hazard function (**top**) or probability density of failures (**bottom**) values are calculated based on the Weibull model. Figure 15: Three phases in the experimental hardness for ZrO\({}_{2}\)–5 wt.% CeO\({}_{2}\) sintered sample. The black triangles in the graph represent high content of m-phase, the red points represent mixture of m-phase and t-phase, and the blue squares represent high content of t-phase. The solid lines represent linear fit and dashed line the total Weibull model. Figure 15 shows that all three lines have a positive slope, but only the blue points at the top match the whole dataset slope (dashed line). Thus, the Weibull model describes in fact three different distributions. The three lower points delineate phase I, the subsequent seven points delineate phase II, and the final five points delineate phase III. The Weibull modulus is different in each phase, Table 9. In the lower range, m is almost 18.44, in the middle, it does not exceed 9.23, and in the upper range, it reaches 10.72. Although the differences in the m values may seem large, a comparison of the differences between the slope of the total Weibull model and each linear fit (phase I, II, or III) converted to degrees (inclination angles in degrees), \(\Delta\)m, shows that they are small. The percentages of the phases are listed in the table. The characteristic hardnesses estimated based on these data are equal to 20.703, 21.762, and 21.367 GPa, respectively. Thus, the three lower points (370, 390, and 400; depicted in black) reveal a negligible influence of the m-phase, which has lower symmetry. The seven points in the middle of the graph (depicted in red) reveal a mixture of the m-phase and t-phase, and the top five (476, 480, 484, 490, and 494; depicted in blue) reveal a negligible influence of the t-phase. The difference between the hardness in the phases I, II, and III is negligible. Thus, the hardness is very stable. The Weibull modulus, when greater than 4, is indicative of the inherent property limitations of materials, e.g., ceramic brittleness. However, it also is indicative of severe problems in the manufacturing process, or of minor variability in the manufacturing process or in the material itself. The results obtained indicate a high homogeneity of the material tested, not only as regards strength but also hardness. Thus, the high values of the Weibull modulus suggest that the nanopowder studied is high quality and reliable in terms of hardness and strength.
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
###### Abstract
This is expected to reduce the cost of image reconstruction calculations, improve image accuracy, and reduce artifacts in reconstructed images, especially in imaging systems. The phase calibration was performed because of the large phase contribution to the image reconstruction in the image reconstruction method used in this study. After calibrating the reference port using the ECal module, the reflection coefficients were analyzed in the time domain to calculate the electrical length difference in each port, and phase calibration was performed. Direct coupling of the Tx and Rx antennas and reflections at the antenna array were removed during reconstruction. Using the phase-calibrated system, image reconstruction experiments were performed using the simple back-projection method of waves.
# Super-Resolution Remote Imaging Using Time Encoded Remote Apertures
###### Abstract  
Imaging of scenes using light or other wave phenomena is subject to the diffraction limit. The spatial profile of a wave propagating between a scene and the imaging system is distorted by diffraction resulting in a loss of resolution that is proportional with traveled distance. We show here that it is possible to reconstruct sparse scenes from the temporal profile of the wave-front using only one spatial pixel or a spatial average. The temporal profile of the wave is not affected by diffraction yielding an imaging method that can in theory achieve wavelength scale resolution independent of distance from the scene.  
SPAD; time-of-flight; multi-bounce; scattered light; super-resolution +
Footnote †: journal: Applied Sciences
# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
## 2 Characteristics of SDN Architectures:
To this end, many research contribution has conducted on mobility management of SDN controller based Wireless Cellular Network [18]. The exiting distributed SDN-based WSNs architectures perform better than complete centralized SDN-based architectures. However, these distributed architectures have limitations as clustering decisions still solely reside at deployed multiple SDN controllers. In this paper, we proposed the SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol to extend the distributed property of clustering formulation by engaging the network nodes of Internet of Things(IoT). The major contributions of this paper are the following:  
* The proposed SADFIR routing protocol computes the network topology with multiple SDN-controllers at control layer and computes the residual resources to save the computational cost of IoT devices. * At the initial stage of proposed SADFIR, the SDN controllers select the set of most eligible IoT nodes to be selected as Cluster-Heads (CHs) and then multicast the notification to eligible nodes to further proceed the distributed cluster formulation. * At the final stage of cluster formulation, the proposed SADFIR performs distributed selection of final CHs at infrastructure layer of deployed IoT nodes. This extendedly distributed property of SADFIR routing protocol provides the novelty to our proposed routing protocol. * The proposed SADFIR routing protocol manages the heterogeneity-aware distributed cluster formation of heterogeneous network. Furthermore, the application-awareness property of the proposed model enables the IoT nodes to transmit the sensed information of multiple applications. The rest of the paper is organized as follows: Section 2 explains the literature review and provide a comparative view of the state-of-the-art approaches. In Section 3, we precisely explain the heterogeneous network model of the proposed SADFIR. In Section 4, we propose the SADFIR routing protocol and explains all the phases in detail. We define the simulation parameters and evaluate the proposed SADFIR in Section 5. We draw the conclusion of this paper in Section 6.
## 5 Results and Discussion
### Control Synchronization of Changing Velocity  
Figure 16a shows the velocities of three motors, and Figure 16b shows the phase differences of motors. The velocities of three motors reached the stable state value quickly after the power supply. Because the master motor is controlled at the beginning of the power supply, the velocity controller suffers from the interference of the self-adjusting of the vibration system moment by moment. Comparing the performance between the velocity controller and phase controller in Figure 16, it can be seen that the stability of the controller also depends on the accuracy of feedback data and control period according to the analysis from Section 3. After several seconds, the velocity controller overcomes the interference and begins to adjust the synchronous velocity to the target value. Due to the limit of the calculating velocity period being longer than calculating phase period, the synchronous velocity changes relatively slowly. However, it does not affect engineering applications because it does not take a very long time to stabilize. In the second stage, the fluctuation of velocity is greater than that in the first stage because of the robustness of the phase controller. Once the synchronous motion of the zero phase difference is achieved by the phase controller, the interference of the velocity controller becomes less obvious even if the velocity is changing such as the third stage.  
From Figure 16c,d, the motion pattern of two positions is similar to the cases of control synchronization in Figures 11 and 15. Because the synchronous velocity in the first stage is less than that in the second stage, the acceleration amplitudes in the first stage are also less than those in the second stage. In the same way, the acceleration amplitudes in the second stage are also less than those in the third stage.  
In this experiment, when two types of controller simultaneously start working, the fluctuations of the data curves of velocity and the phase difference are more obvious than other cases with only controlling slave motors. Because the interference comes from the  
Figure 16: Comparing with the different velocities of the master motor: (**a**) the velocities of three motors; (**b**) the phase differences; (**c**) Mass center trajectory; (**d**) Edge position trajectory.  
vibration system itself and its changes are consistent with the input of the controller, the effect of the interference is also obvious at the beginning of each state, which reflects the control complexity of the vibration system.  
By analyzing three group experiments, we may conclude that the same phase motion of three ERs in the vibration system can be achieved by control synchronization of two slave motors, but the synchronization velocity is not guaranteed because of the self-adjustment of the vibration system. Only the master motor using velocity control and slave motors using phase control can achieve the ideal synchronous motion of three ERs in the vibration system.