esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlations Between Phenolic Composition and Perceived Astringency of Wines
## 3 Results and Discussion
### Correlations between Astringent Attributes  
The relationships among the astringent attributes are shown in Figure 3. The correlation coefficients between the TPC and the PAC, the TPC and the PTC and the TPC and the TAC were 0.5832 (\(p<0.02\)), 0.7808 (\(p<0.0006\)) and 0.6241 (\(p<0.0129\)), respectively. The TPC showed moderate to strong positive correlations with the other attributes because it covered all phenolic compounds present in wines (i.e., phenolic acids, anthocyanins, hydrolysis tannins and monomeric, oligomeric and polymeric proanthocyanidins). Notably, the strong positive correlation between the TPC and the PTC was consistent with their strong correlations with perceived astringency. These results indicated that polymeric tannins were the primary attribute of perceived astringency in wines while the other phenolic forms, namely, anthocyanins, phenolic acids and monomeric proanthocyanidins, were a minor attribute.  
A strong positive correlation was observed between the PAC and the PTC (\(r=0.8674\), \(p<0.0003\)) due to their similar phenolic compositions (Figure 3). The most abundant phenolic compounds in both the PTC and the PAC were proanthocyanidins as mentioned earlier; however, they were associated with different astringent intensities upon consumption. Proanthocyanidins consist of flavan-3-ols in monomeric, oligomeric and polymeric forms depending on the degree of polymerization (DP). The most abundant flavan-3-ol subunits present in wines are (+)-catechin and (-)-epicatechin. However, they also exist in gallbladder form [25] while polymeric tannins contain oligomeric and high-molecular flavan-3-ols. Thus, the differences in astringent intensity between the PAC and the PTC may be derived from their differences in composition. This result ties in well with the results of previous studies. For example, [PERSON] et al. [29] evaluated the tannin-salivary protein complex and found no remarkable binding affinity between monomeric flavan-3-ols and salivary proteins but a gradual increase of binding affinity with the increase of DP of flavan-3-ols. Furthermore, [PERSON] et al. [30] observed an increasing trend in astringency intensity as the DP of flavan-3-ols increased from monomer to trimer. The PAC and the PTC showed negligible correlations with the TAC (\(r=-0.1273\) and 0.1186, respectively), as shown in Figure 3, but were not statistically significant.
## 4 Results and Discussion
### Drop Tests  
To verify the structural stability of the designed separation cage structure, drop resistance testing is conducted. The isolated separation cage assembly is mass-loaded to match the UAV's gross takeoff weight of 2.2 kg. Drop tests commence from a 1 m height with 0.2 m incremental elevation steps, performing ten trials per height level on concrete substrates. At the 10 m free-fall height, joint loosen occurs between carbon fiber rods and nylon connectors. Subsequent full-prototype drop testing replicates this protocol, incorporating both free-fall and motor-termination scenarios during hovering. The prototype exhibits no structural compromise or component loosening up to an 8 m impact height. These results demonstrate significant structural robustness in the fullerene-inspired separation cage design.
# Finite Element Analysis of a Novel Approach for Knee and Ankle Protection during Landing
###### Abstract  
There is a high risk of serious injury to the lower extremities during a human drop landing. Prophylactic knee and ankle braces are commonly used to reduce injury by restraining the motion of joints. However, braces that restrain joint range of motion (ROM) may have detrimental effects on the user's kinematical performance and joint function. The present study aimed to propose a novel set of double-joint braces and to evaluate its protective performance in terms of the ankle and knee. Accordingly, the finite element method was performed to investigate the biomechanical responses of the ankle and knee in braced and unbraced conditions. The results showed that the semi-rigid support at the ankle joint can share the high impact force that would otherwise be inflicted on one's lower extremity, thereby reducing the peak stress on the inferior articular surface of the tibia, menisci, and articular cartilages, as well as the horizontal force on the talus. Moreover, with knee bending, the elongated spring component at the knee joint can convert the impact kinetic energy into elastic potential energy of the spring; meanwhile, the retractive force generated by the spring also provides a more balanced interaction between the menisci and articular cartilages. This biomechanical analysis can accordingly provide inspiration for new approaches to place human lower extremities at lower risk during landings.  
landing; double-joint brace; finite element analysis; ankle; knee +
Footnote †: journal: Medical Engineering
## 2 DFIG-Based Wind Turbine System
### Modeling of the DFIG  
In the study, the convention of generators was adopted to specify the positive direction of stator voltage and current; i.e., the positive current generates negative magnetic flux. The motor convention specifies the positive direction of the rotor voltage and current; i.e., the positive current generates a positive magnetic flux. Conventionally, through Park transformation, the three-phase windings on the stator and rotor sides of the DFIG in the a-b-c coordinate system are equivalent to mutually perpendicular two-phase windings in the d-q synchronous rotating reference frame, which can simplify the mathematical model of DFIG. Additionally, the magnetic circuit is fixed and there is no coupling between the magnetic flux.  
In the d-q synchronous rotating reference frame, the voltage equation of the stator and rotor can be expressed as  
\[\left\{\begin{array}{l}u_{sd}=-R_{s}i_{sd}-\omega_{s}\psi_{sq}+D\psi_{sd}\\ u_{sq}=-R_{s}i_{sq}+\omega_{s}\psi_{sd}+D\psi_{sq}\\ u_{rd}=R_{r}i_{sd}-(\omega_{s}-\omega_{r})\psi_{rd}+D\psi_{rd}\\ u_{rq}=R_{r}i_{rq}+(\omega_{s}-\omega_{r})\psi_{rd}+D\psi_{rq}\\ \end{array}\right., \tag{1}\]  
The flux equation of stator and rotor is as follows:  
\[\left\{\begin{array}{l}\psi_{sd}=-L_{s}i_{sd}+L_{m}i_{rd}\\ \psi_{sq}=-L_{s}i_{sq}+L_{m}i_{rq}\\ \psi_{rd}=-L_{m}i_{sd}+L_{r}i_{rd}\\ \psi_{rq}=-L_{m}i_{sq}+L_{r}i_{rq}\\ \end{array}\right., \tag{2}\]  
Figure 1: The DFIG-based wind turbine system connected to the grid.  
where \(R_{s}\) and \(R_{r}\) are the resistance of stator and rotor winding, respectively; \(\omega_{s}\) and \(\omega_{r}\) are the synchronous speed and rotor speed; \(u_{sd}\), \(u_{sq}\), \(u_{rd}\), and \(u_{rq}\) are the d-axis and q-axis components of the stator voltage and rotor voltage; \(i_{sd}\), \(i_{sq}\), \(i_{rd}\), and \(i_{rq}\) are the d-axis and q-axis components of the stator voltage and rotor current; \(\psi_{sd}\), \(\psi_{sq}\), \(\psi_{rd}\), and \(\psi_{rq}\) are the d-axis and q-axis components of the stator magnetic flux and rotor magnetic flux; \(L_{s}\) and \(L_{r}\) are the self-inductance of the stator and rotor windings; and \(L_{m}\) is the mutual inductance between stator and rotor windings.  
The active and reactive output power of the DFIG stator can be calculated as  
\[\left\{\begin{array}{l}P_{s}=\frac{3}{2}(u_{sd}i_{sd}+u_{sq}i_{sq})\\ Q_{s}=\frac{3}{2}(u_{sq}i_{sd}-u_{sd}i_{sq})\end{array}\right., \tag{3}\]  
The active and reactive output power of the DFIG rotor can be expressed as  
\[\left\{\begin{array}{l}P_{r}=\frac{3}{2}(u_{rd}i_{rd}+u_{rq}i_{rq})\\ Q_{r}=\frac{3}{2}(u_{rq}i_{rd}-u_{rd}i_{rq})\end{array}\right., \tag{4}\]
## 3 Results and Discussion
### Analysis of Automatic Row-To-Row Field Trials  
A comparison of the results of automatic row alignment for the harvester at three different speeds is shown in Table 4. According to the analysis of the experimental data, with the increasing forward speed of the corn harvester [34; 38; 39], the deviation of the automatic alignment system adjustment trajectory and the corn reference rows showed a gradually increasing trend, and the corn harvester harvesting quality gradually decreased, which is mainly due to the increase in the forward speed of the harvester will lead to the decrease in the number of alignment adjustments at the same distance forward, which also leads to the increase in the alignment deviation, under the condition that the other conditions of the automatic alignment system remain unchanged. On the basis of the test data and the field water droplet trajectory, it can be seen that within the range of 0-5 km/h of the forward speed of the harvester, the average value of deviation of the automatic alignment system is 0.0633 m, the average value of standard deviation is 0.0668 m, the average value of percentage of deviation within \(\pm\)15 cm is 95.4%, and the deviation values are all within \(\pm\)30 cm, satisfying the requirements of corn harvester alignment operation. However, when the speed of the harvester exceeds 5 km/h, the deviation of automatic row alignment will increase, which is insufficient to fulfill the demand of automatic row alignment of corn, and lifting the speed restriction will be the focus of future research. [PERSON] et al. reported that the mean value of automatic row alignment deviation for the harvester in the range of forward speed from 0 to 5 km/h was 0.876 m, and the mean value of the percentage of deviation within \(\pm\)15 cm was 83.1% [13]. Our findings outperformed theirs by 12.3%, which supports the research of a corn harvester.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{NO.} & \multirow{3}{*}{
\begin{tabular}{c} **Speed of** \\ **Advance/(km h\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{3}{c}{**Deviation Values of Maize Plants from**} & \multicolumn{3}{c}{**Deviation (Absolute Value) of the Automatic Alignment Trajectory**} \\  & & \multicolumn{3}{c}{**Maze Benchmark Rows**} & & \multicolumn{3}{c}{**from the Fitted Corn Row**} & \\ \cline{3-8}  & & **Maximum** & **Minimum** & **Average** & **Average** & **Mead** & **The Percentage** & **The Percentage** & **of Deviation** & **of Deviation** \\  & & **Values/cm** & **Values/cm** & **Values/cm** & **Values/cm** & **tion/cm** & **within \(\pm\)15 cm/\%** & **within \(\pm\)30 cm/\%** \\ \hline \(1\) & \(2.20\) & \(9.40\) & \(-8.33\) & \(3.32\) & \(5.15\) & \(5.84\
## 4 Numerical Results and Discussion
### Case Study for Underground Engineering
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fracture** & **Water Pressure** & **Flow (m3/s)** & **Reynolds** & **Critical** \\
**Aperture (mm)** & **(MPa)** & **Number** & **Reynolds Number** \\ \hline  & 0.1 & 9.77 \(\times\) 10\({}^{-5}\) & 98 & \\
0.1 & 0.3 & 2.79 \(\times\) 10\({}^{-4}\) & 279 & \\  & 0.5 & 4.46 \(\times\) 10\({}^{-4}\) & 446 & \\  & 1.0 & 8.07 \(\times\) 10\({}^{-4}\) & 807 & \\ \hline  & 0.1 & 6.26 \(\times\) 10\({}^{-4}\) & 626 & \\
0.5 & 0.3 & 1.86 \(\times\) 10\({}^{-3}\) & 1863 & \\  & 0.5 & 3.10 \(\times\) 10\({}^{-3}\) & 3100 & \\  & 1.0 & 5.19 \(\times\) 10\({}^{-3}\) & 5191 & \\ \hline  & 0.1 & 2.39 \(\times\) 10\({}^{-3}\) & 2386 & \\
1.0 & 0.3 & 5.96 \(\times\) 10\({}^{-3}\) & 5960 & \\  & 0.5 & 8.95 \(\times\) 10\({}^{-3}\) & 8950 & \\  & 1.0 & 1.47 \(\times\) 10\({}^{-2}\) & 14,740 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameter values and calculation results of cases. shows that the smaller the hydraulic aperture, the higher the water pressure needed to make the flow regime change from linear to nonlinear. This elaborates that the larger the aperture is, the higher the flow velocity will be. The inertial force caused by high velocity plays a significant role in nonlinear flow, which results in an increased possibility that the nonlinear flow in a fractured rock mass will occur under lower water pressure. In contrast, when the aperture is small, the velocity is very low under the same water pressure conditions, so the inertial force can be neglected, and the flow obeys [PERSON]'s law. A comparison of the pore pressure in the fractures before and after excavation and support is shown in Figure 22. After tunnel excavation, the surroundings of the tunnel are permeable, and thus the water pressure around the tunnel is zero, corresponding to the \"\(p\) = 0\" stage in Figure 22. There is a gradient distribution of water pressure from the model boundary to the tunnel periphery. The \"T = 15 s\" stage in Figure 22 shows the water pressure distribution during the application of the lining. It indicates that the water pressure around the tunnel periphery increases from zero gradually during the process of the application of the lining. Along with the support being applied, the permeability of the fractured rock mass is relatively high, even if the aperture is 0.1 mm, and the large boundary water pressure makes the water pressure around the support increase quickly, resulting in a not very obvious difference as compared with the condition of the 1-mm aperture. After the lining is installed, the surrounding of the tunnel is impermeable, and the water pressure of boundary is equal to the tunnel periphery, finally corresponding to the \"Q = 0\" stage in Figure 22, and the distributions of the water pressures with apertures of 0.1 mm and 1 mm are consistent. Figure 21: Variation curve of Reynolds number and water pressure: (**a**) aperture is 1 mm, (**b**) aperture is 0.5 mm, (**c**) aperture is 0.1 mm.
# Spatio-Temporal Variability of Water Temperature of Arctic Rivers in Russia over the Past 60 Years
## 3 Results
### Intensity and Direction of Changes in Water Temperature
Despite this, the increase in water temperature during these years undoubtedly contributed to the long-term trend in increasing water temperature intensity. It is also important to note that, despite the long-term positive trends in water temperature in the rivers of the Russian Arctic, the direction and intensity of changes in water temperature can vary from decade to decade. This, in particular, forms a slight slope of the multi-year trend line. The water temperature trends in medium-sized rivers in the Russian Arctic for selected months are shown in Figure 8.  
Figure 8: Water temperature trends (Mann–Kendall, \(\alpha=0.05\)) 1961–2021 (solid lines), before and after (dashed lines) change point years (Buishand, \(\alpha=0.05\)) in May (**left column**) and July (**right column**)  
Figure 7: Decade average June stream water temperature [\({}^{\circ}\)C] in rivers with the most intense changes in water temperature in the north of West Siberia. of selected medium-sized rivers in the Russian Arctic: Pinega River (tributary of the lower Northern Dvina), Northern Sosva River (tributary of the Ob river below the confluence with the Irtysh River), Lower Tunguska river (tributary of the Yenisei), Adycha River (right tributary of the Yana River). For periods before and after change point years, there are red dashes for Pinega and Northern Sosva rivers and dark blue dashes for Lower Tunguska and Adycha rivers. At the same time, after change point years, significant trends are revealed at no more than 11% of hydrological stations, mostly east of the Yenisei River. In the period 1991-2021, statistically significant trends in mean monthly water temperature were detected at more hydrological stations than in the period from the change point year to 2021 (Figure 9). Statistically significant trends in water temperature over the past 30 years have been identified primarily in the Arctic part of Eastern Siberia (east of the basin of the Yenisei) and in the north of the EPR. Most of these trends in the average monthly water temperature occur in May, June, September, and October, i.e., in months when water temperature changes are at their maximum.
## 8 Discussion
### Challenges
#### 8.1.1 A New Internet Architecture  
The need to support nearly deterministic services, such as in high-precision manufacturing, while also ensuring very tight physical constraints such as latency and energy consumption, necessitates a new Internet architecture that brings together different resources, such as communication and computation, into a single framework.  
(1) a new data plane that can adapt to different operating modes dynamically and support holographic communications. (2) a new control plane that allows for the synchronisation of concurrent streams for holographic communications, as well as the use of preferred path routing protocols to provide almost deterministic networks for high-precision manufacturing. (3) a new management plane that incorporates self-configuration and self-optimization capabilities and takes advantage of machine learning's strong support.
## 4 Configuration of the Proposed Antenna  
Figure 8a-d illustrates the simulated geometry of the proposed antenna. It can be seen as two compact elements, each forming a triangular radiator structure. The antenna is fed by a narrow 50 \(\Omega\) CPW transmission line with a feedline length of \(l_{1}=8.86\) mm, width \(w_{4}=2.44\) mm, and two identical gaps of width \(s=1\) mm. It is followed by a T-junction divider which has two thin arms with a 0.42 width for each one, to correspond with the 100 \(\Omega\) line. The ground and the radiator planes are printed on a low-cost, 1.6 mm thick FR4 substrate (with a relative permittivity of 4.6 and loss tangent of 0.025). The metamaterial is applied in two locations: One is in the unit cells placed at the backside of the antenna array and the other is suspended within a 10 mm distance away from the antenna where the unit cells are used. Several aspects were considered in order to optimize and miniaturize the  
Figure 6: Metamaterial simulated result at y-axis: (**a**) permittivity; (**b**) permeability and (**c**) impedance.  
Figure 7: Metamaterial simulated refractive index results of the \(1\times 1\), \(1\times 2\) and \(2\times 2\) array structures: (**a**) x- axis; (**b**) y- axis.  
proposed antenna and the novel unit cell using an Agilent advanced design system (ADS) and computer simulation technology (CST), respectively. The final geometrical parameters are listed in Table 2.
## 3 Results
### Double Differences  
#### 3.2.1 Comparison between V8 and V6/V7  
The TMI/GMI DD distributions for channel 10.65 V for V6, V7, and V8 are shown in Figure 10. Consider first the V6 histogram, shown in red, where the major change was to remove the mean emissive antenna bias. This bias adjustment was based on an ad-hoc procedure involving several months of inter-satellite comparisons, but the MR physical temperature was incorrectly assumed to be constant. Thus, the orbital cycle of MR physical temperature produced a time-varying radiometric calibration error in the earth scene brightness temperature that resulted in the large histogram variance.  
Figure 10: Double Differences (DD) distribution of channel 10.65 V for 1B11 V6 (**red**), V7 (**blue**) and V8 (**green**).  
Figure 9: Single differences (SD) quarterly results for both orientations (yaw 0\({}^{\circ}\) in black and 180\({}^{\circ}\) in red) for 15 years (channel 10.65 V).  
Next, consider the V7 histogram, shown in blue in Figure 10. This version was an addition to version 6 that removed the effects of the orbital cycle of the MR physical temperature. Thus, the histogram mean was similar to V6 but the variance was significantly reduced. In addition, the basis of the emissive reflector correction were empirical comparisons with theoretical brightness temperatures provided by a RTM.  
Finally, V8 was developed based upon rigorous physical measurements and electromagnet theory, as described in the preceding section. This is a very important improvement over V7, because the long-time series of Tb observations have magnificent application in earth science for the study of global climate change. The credibility of the brightness temperature calibration is an important factor in the utility of the experimental data, and V8 is superior to all previous versions. Concerning the DD histograms, except for the difference in the mean value, V7 and V8 are closely resembling. This means that V7 provided an excellent Tb product, but it lacked credibility because it was based upon ad-hoc methods. Further, there is no significance to V8 bias being less than V7; rather it is only necessary that the mean value be accurately known. Similar results were also observed in all the other eight channels.
## 3 Results
### Grain Yield and Water Use Efficiency  
On average, grain yield and WUE with the LF treatment were highest in the three years, while CK produced the lowest yield and WUE (Figure 9). Generally, the yield increasing rate with fertilization and mulching treatments was greatest in the dry year (2016), moderate in the wet year (2017), and lowest in the normal year (2015). For example, LF, ZF, MF, and HF had increases by 130.4%, 127.8%, 115.8%, and 76.6% in yield compared to CK in the dry year; by 95.0%, 59.0%, 70.5%, and 51.4% in the wet year; and by 33.6%, 29.4%, 19.7%, and 14.0% in the normal year (\(p\) < 0.05). Grain yield with the LF treatment was significantly highest among the treatments in the wet year (\(p\) < 0.05). Moreover, LF showed the greatest increasing rate in WUE in the wet year. Compared with CK, the treatments with LF, ZF, MF, and HF produced significantly higher WUE. For example, LF, ZF, MF, and HF increased WUE by 96.6%, 99.2%, 89.4%, and 58.2% in the dry year; by 102.7%, 36.5%, 25.0%, and 30.0% in the wet year; and by 34.9%, 34.5%, 22.5%, and 11.3% in the normal year (\(p\) < 0.05).  
Figure 8: Dry matter accumulation at seeding, flowering, and maturity stages of tartary buckwheat treated with different fertilization application levels. In each growing season, mean values (n = 3) followed by different letters within a column are significantly different among treatments (\(p\) < 0.05). Bars are error bars of the means (n = 3).
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 5 The Energy Node Placement Algorithm
### Energy Models  
The total energy consumption, measured in \(mJ\), is calculated during the operation of the network, with the following equation:  
\[TotalEnergy=\sum_{i=1}^{n}energy_{i} \tag{5}\]  
To measure the energy consumption of the network, we calculate the energy (\(energy_{i}\)) consumed by each node \(i\), as shown in the equation below. The general energy model used for our nodes in the network is divided into two parts. The first one is for listening and the other for moving.  
\[energy_{i}=listening\_energy_{i}+moving\_energy_{i}, \tag{6}\]  
where \(listening\_energy_{i}\) is the energy computational usage and \(moving\_energy_{i}\) is the energy usage for moving. If node \(i\) is static, then \(moving\_energy_{i}=0\).  
Following [23], we compute \(listening\_energy_{i}\) as:  
\[\begin{split} listening\_energy_{i}&=(transmit\cdot 19.5\;\mathrm{mA}+ listen\cdot 21.8\;\mathrm{mA}+\\ CPU\cdot 1.8\;\mathrm{mA}+LPM\cdot 0.0545\;\mathrm{mA})\cdot 3\; \mathrm{V}/4096\cdot 8,\end{split} \tag{7}\]  
where \(transmit\) is the total time of the radio transmitting, \(listen\) is the total time of the radio listening, \(CPU\) is the total time of the CPU being active, and \(LPM\) is the total time of the CPU being in low power mode.  
In this work, we focus on the energy consumption of nodes used for moving. The mobile nodes need to use their energy as efficiently as possible in order not to waste too much energy while moving, and hence preserve more time for their operational time. We will present three different moving energy models from the literature, each being used by a different mobile robot. In the evaluation section (Section 6.3.3), we compare these different energy models in conjunction with our algorithm.