esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results of Problem 2 and CNr Made Only of Capacitors  
The Pareto front of Problem 2 is shown in Figure 11a. As in the previous subsection, in this case, 36 of the optimized individuals satisfy the three conditions given in Section 3.1. Figure 11b shows that there is a group of individuals characterized by transferring to a load more or less the same \(\mathrm{P_{L}(0)}\), lower than 1 kW. Among them, the most suitable for the application described in the Introduction is denoted with the letter \"H\"; its transferred power in the aligned position is just enough to satisfy the specification of the prototypal WPTS; its \(\mathrm{xp_{max}}\) is rather long whilst its \(\mathrm{P_{L,P-u,max}}\) is acceptable. It could be compared with individual A considered in Section 3.2, but its efficiency in the aligned position is approximately 6% lower.  
Considering the power transfer capability, the individual denoted with \"I\" can be considered as one of the best because of its high \(\mathrm{P_{L}(0)}\) and low \(\mathrm{P_{L,P-u,max}}\), as shown in Figure 12. Individual I is comparable with individual F, whose characteristics have been described in the previous subsection, but offers a higher efficiency. The individual marked with \"J\" has a high \(\mathrm{xp_{max}}\) that reaches 175 mm. It is comparable with the individual B described in Section 3.2, but suffers from a lower efficiency.  
Figure 11: (**a**) Pareto front of Problem 2 and (**b**) Power vs. position fore results of Problem 2 and receiving side CN made only of capacitors. In panel (**a**), the green stars show the selected individuals in the analysis.  
Figure 12: Per unit power vs. position (Problem 2 and receiving side CN made only of capacitors).  
From the point of view of efficiency in the aligned position, individual K is the best: it is slightly overperformed only by individual A, but transfers more than twice its power. This result regarding efficiency can be explained considering that in the power transfer efficiency computation the losses of the CNs capacitors are neglected as they are considered much lower than the losses in the inductors. Consequently, the individuals H, I, J, and K, which do not have inductors on the receiving side, give higher efficiency than the individuals E, F, and G considered in Section 3.3. As shown in Figure 13, the efficiency profiles of I and J are practically equal. Individuals K and H give, respectively, the highest and the lowest efficiency in the aligned position and K exhibit a higher \(\mathrm{xp}_{\mathrm{max}}\) than H; however, when the coil misalignment exceeds 138 mm, the efficiency of H becomes the highest among the considered individuals, being 10% higher than that of K. All the analyzed individuals in terms of power profile are marked with green dots in Figure 11a. The power profile characteristics and the reactances relevant to individuals H, I, J, and K are listed in Tables 3 and 4.
# Long-Range Correlations and Natural Time Series Analyses from Acoustic Emission Signals
## 3 Applications
### Three-Point Bending Test of a Glass Fiber-Reinforced Polymeric Plate
#### 3.1.3 Discussion
A second approach to performing the NT analysis is to consider \(Q_{k}\) that equals the acoustic emission energy \(E_{AE,k}\), as suggested in RILEM [60] and shown in Figure 11 for the original dataset (5050 signals, 0.25 mV threshold). Since the methodology for calculating the NT parameters is the same as in the case of Figure 10, these new data are presented according to the same standards. Figure 11: Natural time analysis of the AE events during the three-point bending test on the GFRP plate considering \(Q_{k}=E_{AE,k}\). Figure 10: NT analysis of the AE time series for two threshold values \((A_{\text{max}}\)\({}^{1.5})_{Th}\): (**a**) 1.0 mV and (**b**) 2.0 mV. For ease of interpreting the results, the values for = 0.070 (dashed red), \(\langle D\rangle\) limit = 10\({}^{-2}\) (dashed green), entropy limit \(Su\approx\) 0.0966 (dashed blue).are also depicted as horizontal lines. In this case, we have four points of true coincidence. The first and second critical points precede the occurrence of large avalanches, that is, regions of considerable AE activity. The third point at 0.31 again indicates the approximation of a critical region (0.42, 0.52) of the normalized time, as already mentioned in the previous approach. Finally, the last point at 0.785 clearly represents the structure's entry into a critical pre-failure stage. Thus, the analysis of the natural time using the AE signal energy also presents as a valid amount of energy (\(Q\)) to find the critical stages of the structure that are monitored by AE in the NT domain. In Figure 11, the true coincidence points 2 and 3 were monitored to evaluate the behavior of the normalized power spectra \(\Pi(\phi)\) versus the natural frequency (\(\phi\)) relationship that was obtained by analyzing the series of signals, in comparison with the ideal estimate by Equation (7). For each point, we analyzed three normalized time instants prior to the critical time being reached, black circles and black stars, respectively. It can be seen in the results that are presented in Figure 12, that when approaching the point of true coincidence, the curves that are obtained from the series of AE signals, and those that were theoretically determined, approach from the bottom to the top. At the same time if we look at Figure 11, we notice that the value of \(\kappa_{1}\) also approaches to 0.07, so both order parameters for criticality converge.
## 3 Results
### Potato Starch  
In the case of potato starch (polyphenylol addition prior to starch gelatinization), the lowest values of RDS, TS, and eGI were obtained when epigallocatechin gallate (20 mg) was added to the starch (16.93 g\(\cdot\)100 g\({}^{-1}\) d.m., 17.78 g\(\cdot\)100 g\({}^{-1}\) d.m., 49.46, respectively, \(p\) < 0.05, Table 5). Epigallocatechin gallate (10 mg) exhibited the lowest value of SDI (41.41 g\(\cdot\)100 g\({}^{-1}\) d.m., \(p\) < 0.05, Table 5). The highest value of SDS was observed when quercetin (20 mg) was added to the starch (48.77 g\(\cdot\)100 g\({}^{-1}\) d.m., \(p\) < 0.05, Table 5, Figure 3a). The highest value of RS was observed with the addition of hesperidin (5 mg) (2.15 g\(\cdot\)100 g\({}^{-1}\) d.m., \(p\) < 0.05, Table 5).  
Considering the addition of polyphenol after potato starch gelatinization, epigallocatechin gallate (5 mg) contributed to the lowest value of TS, 10 mg--RS, and 20 mg--RDS and eGI (24.13 g\(\cdot\)100 g\({}^{-1}\) d.m., 5.46 g\(\cdot\)100 g\({}^{-1}\) d.m., 16.41 g\(\cdot\)100 g\({}^{-1}\) d.m., 52.65, respectively, \(p\) < 0.05, Table 6). In terms of SDS, the highest values were obtained when naringenin (10 mg) and kaempferol (10 mg) were added to the starch (66.70 g\(\cdot\)100 g\({}^{-1}\) d.m., 66.19 g\(\cdot\)100 g\({}^{-1}\) d.m., respectively, \(p\) < 0.05, Table 6, Figure 3b). The most efficient polyphenol in the case of SDI was naringenin (10 mg) (27.78 g\(\cdot\)100 g\({}^{-1}\) d.m., \(p\) < 0.05, Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RDS** & **SDS** & **RS** & **TS** & **SDI** & **eGI** \\ \hline \multicolumn{2}{c}{**Potato starch with**} & & & & & \\ \multicolumn{2}{c}{**the addition of**} & & & & & \\ \hline \multirow{4}{*}{(+)catechin} & 5 mg & 90.85 \(\pm\) 0.0 1 [FOOTNOTE:1]Footnote 1: [[https://github.com/pai/research](https://github.com/pai/research)]([https://github.com/pai/research](https://github.com/pai/research))  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **RDS** & **SDS** & **RS** & **TS** & **SDI** & **eGI** \\ \hline \multicolumn{2}{c}{**Potato starch with**} & & & & & & \\ \multicolumn{2}{c}{the addition of} & & & & & & \\ \hline \multirow{3}{*}{(+)catechin} & 5 mg & 96.27 \(\pm\) 0.3 \({}^{\rm\,r}\) & 2.51 \(\pm\) 0.0 \({}^{\rm\,b}\) & 0.82 \(\pm\) 0.0 \({}^{\rm\,b}\) & 99.60 \(\pm\) 0.3 \({}^{\rm\,tm}\) & 98.65 \(\pm\) 0.0 \({}^{\rm\,u}\) & 96.93 \(\pm\) 0.0 \({}^{\rm\,b}\) \\  & 10 mg & 39.18 \(\pm\) 0.0 \({}^{\rm\,s}\) & 56.08 \(\pm\) 0.0 \({}^{\rm\,n}\) & 0.0 \(\pm\) 0.0 \({}^{\rm\,a}\) & 99.86 \(\pm\) 0.0 \({}^{\rm\,m}\) & 39.23 \(\pm\) 0.0 \({}^{\rm\,d}\) & 87.39 \(\pm\) 0.0 \({}^{\rm\,hj}\) \\  & 20 mg & 57.85 \(\pm\) 0.2 \({}^{\rm\,m}\) & 38.57 \(\pm\) 0.1 \({}^{\rm\,h}\) & 0.0 \(\pm\) 0.0 \({}^{\rm\,a}\) & 96.42 \(\pm\) 0.3 \({}^{\rm\,g}\) & 60.00 \(\pm\) 0.0 \({}^{\rm\,l}\) & 89.56 \(\pm\) 0.0 \({}^{\rm\,l}\) \\ \hline \multirow{3}{*}{\(\begin{array}{c}\text{epigallocatechin}\\ \text{gallate}\end{array}\)} & 5 mg & 44.76 \(\pm\) 0.3 \({}^{\rm\,l}\) & 15.99 \(\pm\) 0.1 \({}^{\rm\,f}\) & 0.10 \(\pm\) 0.1 \({}^{\rm\,a}\) & **24.13 \(\pm\) 0.5 \({}^{\rm\,a}\)** & 69.89 \(\pm\) 0.2
Remote Sens._**2015**, \(7\), 135-152; doi:10.3390/rs70100135  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Article_  
The Performances of MODIS-GPP and -ET Products in China and Their Sensitivity to Input Data (FPAR/LAI)  
[PERSON] 1,2, [PERSON] 1,* and [PERSON] 1  
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;  
E-Mails: [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (J.L.)  
University of Chinese Academy of Sciences, Beijing 100049, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-10-648-365-25.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 8 October 2014 / Accepted: 15 December 2014 / Published: 24 December 2014_  
The aims are to validate and assess the performances of MODIS gross primary production (MODIS-GPP) and evapotranspiration (MODIS-ET) products in China's different land cover types and their sensitivity to remote sensing input data. In this study, MODIS-GPP and -ET are evaluated using flux derived/measured data from eight sites of ChinaFLUX. Results show that MODIS-GPP generally underestimates GPP (\(R^{2}\) is 0.58, bias is \(-\)6.7 gC/m\({}^{2}\)/8-day and RMSE is 19.4 gC/m\({}^{2}\)/8-day) at all sites and MODIS-ET overestimates ET (\(R^{2}\) is 0.36, bias is 6 mm/8-day and RMSE is 11 mm/8-day) when comparing with derived GPP and measured ET, respectively. For evergreen forests, MODIS-GPP gives a poor performance with \(R^{2}\) varying from 0.03 to 0.44; in contrast, MODIS-ET provides more reliable results. In croplands, MODIS-GPP can explain 80% of GPP variance, but it overestimates flux derived GPP in non-growing season and underestimates flux derived GPP in growing season; similar overestimations also presented in MODIS-ET. For grasslands and mixed forests, MODIS-GPP and -ET perform good estimating accuracy. By designing four experimental groups and taking GPP simulation as an example, we suggest that the maximum light use efficiency of croplands should be optimized, and the differences of meteorological data have little impact on GPP estimation, whereas remote sensing leaf area index/fraction of photo-synthetically active radiation (LAI/FPAR) can greatly affect GPP/ET estimations for all land cover types. Thus, accurate remote sensing parameters are important for achieving reliable estimations.  
**Keywords:** gross primary production (GPP); evapotranspiration (ET); MODIS; uncertainty; land cover types; ChinaFLUX
## 1 Introduction
The IEEE 802.15.6 group also released standards for BSNs in 2012 [31]. The MAC protocol adopts a combination of exclusive access period (EAP) and random access period (RAP) using slotted CSMA/CA and managed access period (MAP) which includes scheduled access, unscheduled bi-link access and improvised access. There are three modes for accessing: beacon mode with beacon period superframe boundaries, non-beacon mode with beacon period superframe boundaries and non-beacon mode without beacon period superframe boundaries. The IEEE 802.15.6 standard provides macroscopic and microscopic power management schemes named hibernation and sleep. To hibernate, the sensor nodes can sleep for a number of consecutive beacon periods by setting the wakeup period field in its last connection request frame. Within the beacon frame, a node should wake up to receive and transmit packets in its scheduled allocations. Outside the expected allocations, the node goes into sleep for energy conservation. The main limitation is that the standard does not consider the mobile scenarios. The fixed transmit powers cost energy waste when the sensors are near the coordinator. Another limitation is the ad hoc mode is not clearly defined. Based on the review and the nature of swallowable BSNs, this paper analyzes the performance of multi-hop communication within the human body and proposes a novel energy efficient solution for medium access control. Compared to other MAC schemes, this paper proposes an uplink, downlink asymmetric network topology. For uplink data transmission, a multi-hop transmission is adopted. In this way the sensor nodes could use less transmission power, therefore saving energy. For downlink data transmission, since the coordinator is outside the human body, normally without energy constraints compared with sensor nodes, single-hop communication is achieved, meaning that the coordinator directly sends data to sensor nodes. This way the sensor nodes do not need to consume extra energy for packet relay and bandwidth is also saved. Since this is a mobile scenario, the path-loss between sensor nodes is dynamic. This paper proposes the adaptive power control scheme trying to optimize the transmit power for energy saving. The main contributions of this paper are:  
1. Evaluation of the performance of multi-hop communication inside the human body in terms of energy consumption based on the IEEE 802.15.6 channel model. 2. Consideration of the mobile scenario of BSNs and proposal of an adaptive power control scheme. 3. Introduction of the dynamic topology of the network. Provision of the TDMA frame and schedules which is a centrally controlled TDMA-based MAC for energy saving. Control messages are transmitted in a single-hop way in both the uplink and downlink direction while the data messages in the uplink direction are transmitted in a multi-hop way. The rest of the paper is organized as follows: Section 2 discusses multi-hop communications through the human body. Section 3 introduces the details of our energy efficient MAC protocol design. A performance evaluation is given in Section 4. Section 5 concludes the paper.
## 4 Discussion
### LUCC and the NPP Response to LUCC  
In this study, we analyzed the LUCC in Inner Mongolia and its effect on the grassland NPP in 2000, 2010, and 2020. The grassland area increased by 2.66% from 2000 to 2010, and then decreased by 1.73% from 2010 to 2020. [PERSON] and [PERSON] (2020) found grassland of a low quality developed to a higher quality in Inner Mongolia from 1990 to 2015 based on their LUCC analysis result for dense grassland, moderate grassland, and sparse grassland. [PERSON] et al. (2018) quantitatively monitored the land use changes in Inner Mongolia and found the area of high- and moderate-coverage grassland decreased while the area of low-coverage grassland increased during 2000-2015. [PERSON] et al. (2020) also found that the area change trend of high- and low-coverage grassland is opposite during 2000-2012. In this study, we found the main outflow (59,141.81 km\({}^{2}\) in total) was grassland turned into cultivated land (26,130.44 km\({}^{2}\)), and the main inflow (63,870.50 km\({}^{2}\) in total) was from bare land to grassland (33,086.56 km\({}^{2}\)) over the 21 years. These above studies suggest that the combined effects of climate change and human activities caused the NPP frequent changes, so that both long and short time series of monitoring are required. Notably, it has been widely reported that the ecological environment of the Mu Us Desert in Ordos has been greatly improved because of a series of ecological restoration policies in the past decades ([PERSON] et al., 2017; [PERSON] et al., 2017), which is consistent with our results. Therefore, the impact of human activities on grasslands quality has both positive and negative effects.  
Although more land changed into grassland, the NPP of the increased grassland was lower than that of the lost grassland. In total, the NPP loss caused by LUCC was greater than the NPP growth caused by LUCC, at 17.63 and 16.38 Tg C, respectively. If the increase of grassland NPP caused by climatic factors is considered, the NPP loss caused by human activities (mainly reclaiming grassland) would be even greater. Although people have made great efforts to make the bare land green and turn it into grassland, the quality of these new grassland areas is on the low side. Thus, it is also important to protect the existing grassland from reclamation and desertification. Moreover, to explore the quality of grass, the higher time resolution and finer-grained vegetation types should be included in the future. All grassland ecosystem management projects should be evaluated by quality indices, such as NPP, and not just the area.
# Laboratory Evaluation of Rejuvenating Agent on Reclaimed SBS Modified Asphalt Pavement
## 3 Results and Analyses
### Freeze-Thaw Split Test Results  
The moisture susceptibility of recycled mixtures is evaluated by freeze-thaw split test, and the tensile strength ratio (TSR) is used as the evaluation index. The results are shown in Table 11, and * in the study represents the addition of rejuvenating agent.  
Table 11 shows that TSR of recycled mixtures decreases significantly with increasing of RSMAP contents. When the RSMAP content is 70%, the TSR is 66.9%, which could not meet China's specification. By adding rejuvenating agent, TSR can increase significantly. This indicates that RSMAP will decrease moisture susceptibility, especially with high RSMAP contents; adding rejuvenating agent can improve the moisture susceptibility of recycled modified mixtures including high RSMAP contents. However, the moisture susceptibility of recycled mixtures is not easy to recover to HMA level.
## Materials and Methods  
### Types and Models of Connection Components  
The Wave Glider consists of surface vessel, connection structure and submersible propeller. With the excitation of wave, the surface vessel moves in a heave direction. Then the wings rotate under the action of hydrodynamic force induced heave and the thrust generates and pushes the vessel forward.  
To study the effects of cable connection method on motion performance of Wave Glider, a dynamic model is established. In order to simplify the calculation, the two-dimensional small-amplitude wave is taken as the basis for dynamic simulation of Wave Glider, and the following assumptions are proposed:  
1. The Wave Glider does not change the forward direction during the movement and moves only in the two-dimensional plane;
2. The shape of the surface vessel has no effects on the motion performance of the Wave Glider;Only heave motion is considered of surface vessel and the heave motion is defined as follows:  
\[\eta=A\,cos(kx-\omega t);\]  
where \(\eta\) is the motion equation of the surface vessel in vertical direction; \(A\) is the wave amplitude; \(kx\) is the phase; \(\omega\) is the angular frequency
4. The hydrodynamic force of the surface vessel and the wings are given based on the Morison equation:  
\[f_{D}=\frac{1}{2}C_{D}\rho Au_{x}|u_{x}|\]  
where \(C_{D}\) is the drag coefficient, \(\rho\) is the density of the fluid, \(A\) is the projected area of a unit length of the surface vessel perpendicular to the flow direction, and \(u_{x}\) is the horizontal velocity.  
Four types of connection models of rigid rod, cable, multi-link and elastic rod are considered to analyze the propulsion performance of the Wave Glider, which are shown as Table 1.  
The rigid rod can be seen as a link connected the floating body and propeller; the cable is established with a finite number of small cylinders, which connected by spring and damping forces; the multi-link can be regarded as links connected by hinges; for the connection elastic rod, it can be looked on as several segments linked with massless elastic components.
# Dark Solitons and Grey Solitons in Waveguide Arrays with Long-Range Linear Coupling Effects
## 4 Grey Solitons  
We use the same method (ITP) to study the fundamental solution for grey solitons. Figure 3 shows the characteristics of grey solitons. Figure 3a,b show that the value of the imaginary part of \(\lambda\) (Im(\(\lambda\))) varies with \(P\) and \(d\). Grey solitons in this long-range linear coupling system have features similar to those of dark solitons: \(d\) increases as \(P\) increases (Figure 3a); when the effective length \(d\) of the coupling effect is fixed, Im(\(\lambda\)) increases with higher \(P\) (Figure 3a), and grey solitons become more unstable as \(P\) increases in the region of \(0.0001<d<5\) (Figure 3b). However, grey solitons also show some distinct features compared with dark solitons; for example, the instability of grey solitons is stronger than that of dark solitons. First, by comparing Figure 3a,b with Figure 1a,b, we can see that Im(\(\lambda\)) is larger for grey solitons than for dark solitons given the same \(P\) and \(d\). Second, the threshold in \(d\) where solitons transition from instability to stability lies at a higher value for grey solitons than for dark solitons. For example, as seen in Figure 1a, the threshold is \(d=2.2\) for \(P=1\), whereas in Figure 3a, the threshold is \(d=3\) for \(P=1\). Figure 3c,d show the amplitude and intensity, respectively, of the fundamental solution for grey solitons with \(P=1\) and \(d=3\). In Figure 3e, we present an example of the evolution of stable grey solitons with \(P=1\) and \(d=3\). In Figure 3f, we present an example of the evolution of unstable grey solitons with \(P=0.5\) and \(d=0.8\).  
We also plot the grey levels for grey solitons with different \(d\) and \(P\) values, as shown in Figure 4. The grey level is defined as follows:  
\[\begin{split} Greylevel=\frac{|max(u_{n})|^{2}}{|min(u_{n})|^{2}}. \end{split} \tag{7}\]  
The dotted lines represent unstable grey soliton solutions (with Im(\(\lambda\)) \(\
eq 0\)), and the solid lines represent stable solutions.  
Figure 3: _Cont._
## 4 Discussion
### Time Series Functionalization and Compression
Due to the seasonal segment time series fitting technique, the CCTM model can achieve higher accuracy in the seasonal time series pattern than that in the yearly time series pattern, but the compression in the seasonal time series pattern needs more storage space than the compression in the yearly time series pattern. The compression pattern should be decided by the trade-off between compression ratio and compression accuracy. Only when the yearly time series compression pattern can not meet the accuracy requirement, the seasonal time series compression pattern should be adopted.