esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Evaluation of Streamflow Simulations  
As mentioned in Section 4.1, both IMERG and 3B42V7 products contained non-negligible errors. Their biases must be removed before they can be employed for streamflow simulations to effectively reduce the uncertainties of hydrological models arising from precipitation inputs. By applying the bias-correction method described in Section 3.2, both IMERG and 3B42V7 daily precipitation products on a 0.25\({}^{\circ}\) resolution were corrected over the entire basin. Consequently, five precipitation data sets were constructed, namely, the gauge-based precipitation data, the original and corrected 3B42V7 data sets, and the original and corrected IMERG data sets. Subsequently, the XAJ model was driven by these five data sets to perform daily streamflow simulations from 1 April 2014 to 31 December 2015. The simulated streamflow processes using the five different precipitation inputs were compared with the observed streamflow to evaluate the feasibility of satellite precipitation products in streamflow simulations.  
Figure 6: Scatter plots of light rain events from 3B42V7 and IMERG precipitation products against gauge-based precipitation data at the five weather stations in the Chindwin River basin (1 April 2014-31 December 2015).
# Consolidating ICESat-2 Ocean Wave Characteristics with CryoSat-2 during the CRYO2 ICE Campaign
## 4 Results
### Orbit Pairing Segmentation  
To be able to compare ICESat-2 derived data with CryoSat-2 measurements at different resolutions, the data have been binned in 0.2\({}^{\circ}\) segments that are approximately 22.2 km long in the northern direction. Then, the SWH observations are averaged, and the location is set as the mean latitude and longitude of the bin. Some specific tracks with large stretches of data available can be seen in Figure 5. In Figure 5 (top), there are stretches with CryoSat-2 SWH data that are missing from all ICESat-2 models due to clouds, and in Figure 5 (bottom), there are gaps for both satellites due to islands. As CryoSat-2 has been used as the baseline, only tracks with available CryoSat-2 SWH data have been used. By binning all of the models using the same interval, the spatial difference has been taken into account. The 5 th, median, and 95 th percentiles as well as the maximum heights of the observed waves can be seen in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **CryoSat-2** & **ATL12** & **Histogram Model** & **Standard Deviation Model** \\ \hline
5\% & 1.01 m & 0.89 m & 1.07 m & 1.11 m \\
50\% & 1.96 m & 1.76 m & 2.06 m & 2.07 m \\
95\% & 4.96 m & 4.32 m & 4.70 m & 4.82 m \\ Max. & 10.68 m & 10.98 m & 10.19 m & 12.17 m \\ \hline \end{tabular}
\end{table}
Table 2: Distribution of SWH observations for each model.
# Scenario-Based Configuration Refinement for High-Load Cellular Networks: An Operator View
## 5 High-Speed Scenario
### Idle Detection  
According to the field data, we observe that UEs are preferred to be out of the private networks in the idle state. Around 80% of UEs out of the private networks are failures to reselect networks. After analyzing network logs, we realize that the signal quality at the cell edge is higher than that in the cells by 10 dB, at least when reselecting cells. The delay caused by reselecting cells is significantly obvious. We adjust some parameters for network configuration as reported in Table 14, which results in the number of UEs being out of private networks in the idle state decreases by around 50%.
# Use of Dairy and Plant-Derived Lactobacillus as Starters for Cherry Juice Fermentation
## 3 Results
### Principal Component Analyses: Overview on Volatiles, Phenolic Compounds, Sugars and Organic Acids  
The main organic acids (lactic, malic, tartaric and citric) and sugars (tructose, glucose and sucrose) (Table S1) were identified and semi-quantified in fermented and unfermented cherry juice. Among volatile components, alcohols, acids, ketones, esters, terpenes and norisoprenoids were detected and semi-quantified (57 compounds) (Tables S2 and S3A,B). Also, several phenolic acids and flavonoids (Tables S4 and S5) were identified (34 compounds) and quantified (30 compounds). PCA was performed considering the semi-quantified organic acids, sugars and volatiles and the quantified polyphenols, to highlight differences among the analyzed samples. Considering a total of 94 variables, the total variance explained by PCA reached 59%, with component 1 and component 2 describing 35% and 24%, respectively. A defined clustering among fermented and unfermented (control) samples was observed (Figure 1A,B). Unfermented samples were characterized based on those variables showing a negative value on component 1 and a positive value on component 2, such as sucrose, fructose and caffe acid, at their highest concentrations. On the other hand, fermented juices were all grouped together, even if some differences could be observed. Indeed, after storage, the juices inoculated with 4186, C1, 285 were found to be correlated with slightly positive variables for both components (volatile compounds as propyl acetate) and were clustered separately from the main group. The most different sample was the juice containing _L. plantarum_ 1 LE1, which was characterized by more positive variables, especially volatile compounds (such as 4-hydroxybutanoic acid, 1-heptanol and trans-geraniol) (Figure 1A,B).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Species** & **Strain** & **Origin** & **\(\Delta\)(T\({}_{48\text{h}}\)–T\({}_{0}\))** & **\(\Delta\)(T\({}_{14\text{d}}\)–T\({}_{0}\))** \\ \hline  & POM1 & Plant & 1.82 \(\pm\) 0.05 & 1.69 \(\pm\) 0.03 \\ _L. plantarum_ & C1 & Plant & 1.22 \(\pm\) 0.10 & 1.11 \(\pm\) 0.04 \\  & 1 LE1 & Plant & 1.32 \(\pm\) 0.07 & 1.39 \(\pm\) 0.17 \\  & 285 & Diary & 1.44 \(\pm\) 0.07 & 1.46 \(\pm\) 0.03 \\ \hline _L. rhamosus_ & 2360 & Diary & 1.02 \(\pm\) 0.10 & 0.97 \(\pm\) 0.03 \\ \hline _L. paracasei_ & 4186 & Diary & 0.96 \(\pm\) 0.13 & 0.80 \(\pm\) 0.14 \\ \hline \end{tabular}
\end{table}
Table 2: Lactic acid bacteria growth. Difference in Log CFU/mL detected between cells number after 48 h of incubation and the initial value, \(\Delta\)(T\({}_{48\text{h}}\)–T\({}_{0}\)), and between cells number after storage (14 days) and the initial value, \(\Delta\)(T\({}_{14\text{d}}\)–T\({}_{0}\)). The isolation origin (plant/dairy products) is specified. Only the strains able to grow are reported.
## 3 Communication at Ground Level: Radio Channel Modeling
### Packet Reception Ratio  
The probability of successfully receiving a packet over a wireless link depends on the packet length and the probability of successfully receiving each bit of the packet. The number of bits transmitted at the physical layer depends on the encoding scheme. The probability of successfully receiving a bit, or its complementary, the bit error rate (BER), depends on the used modulation and on the signal-to-noise ratio (SNR) at the receiver side.  
For a received power \(P_{r}(d)\) at distance \(d\), the SNR \(\gamma_{d}\) is given by Equation (3):  
\[\gamma_{d}=\frac{P_{r}(d)}{P_{t}} \tag{3}\]  
Figure 1: Theoretical received signal strength over a WSN link: \(\alpha=3.56\), \(\sigma=6.06\), \(P_{t}=0\) dBm, \(d_{0}=1\) m.  
where \(P_{n}\) is the noise floor (the total sum of noise in area and frequency range). In interference-free environments, \(P_{n}\) is given only by the thermal noise and it is constant. Nevertheless, in most environments, \(P_{n}\) varies with time, either because of interference or because of changes in temperature. Similar to the work in [32], we consider this realistic hypothesis (variable \(P_{n}\)) and model \(P_{n}\) as a Gaussian random process of mean 0 and standard deviation \(X_{\gamma}\).  
For a quadrature phase-shift keying (QPSK) modulation, such as the one used by platforms used in our experiments, described in Section 4, the BER at distance \(d\), \(P_{e}^{d}\) is given by Equation (4) [17]:  
\[P_{e}^{d}=\frac{1}{2}erfc\left(\sqrt{\gamma_{d}\frac{B_{N}}{R}}\right) \tag{4}\]  
where \(R\) is the bit data rate, \(B_{N}\) is the bandwidth of the additive white Gaussian noise, and \(erfc(x)\) is the complementary error function, defined as \(ercf(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}\!\!e^{-t^{2}}\,\mathrm{d}t\).  
Then, the probability \(p_{d}\) of successfully receiving a packet of size \(f\) bits at distance \(d\) is given in Equation (5):  
\[p_{d}\simeq(1-P_{e}^{d})^{f} \tag{5}\]  
For several encoding schemes and modulations, the BER and the PRR are given in [17].  
Figure 1(a) shows the theoretical PRR values for links of different distances. For the log-normal shadowing model, we used the same parameters values used for Figure 1. We considered a QPSK modulation, NRZ encoding and a packet sized of 100 Bytes. For each distance, we generated many PRR values. As in [16; 17], we distinguish three different areas in this figure: a _connected area_, in which links are almost perfect (\(PRR\approx 1\)); a _transitional area_, in which the links are unstable; and a _disconnected area_, in which we have very poor or no links. Figure 1(b) shows the cumulative distribution function (CDF) of link PRR for several distances. It highlights the distances of 3 m, 10 m and 27 m, located in connected, transitional and not connected areas, respectively. The results presented in Figure 1(b) show that most of the links in the network are expected to be either of poor quality (PRR less than 10%) or of good quality (PRR superior to 90%), with very few links in the transitional category.
## 3 Damper-Cable Interaction Model under Wind Action
### Modelling the Wind Action  
Subsequently, the effect of the wind-induced forces was simulated numerically. The wind simulation was carried out under the assumption that the cable is a cylinder immersed in a turbulent flow [2]. Hence, the wind flow is composed of three components: (i) a mean wind velocity, \(\mathcal{U}\) [m/s]; (ii) a fluctuating longitudinal velocity, \(u(t)\) [m/s]; and (iii) a fluctuating transversal velocity, \(v(t)\) [m/s].  
The wind forces can be decomposed into a mean and a fluctuating component assuming the following hypothesis: (i) a quasi-steady behavior of the wind-induced forces; and (ii) small components of the turbulence with respect to the mean wind velocity, \(\mathcal{U}\)[51]. The expression of these two components can be expressed as follows (assuming a linearized approximation [52]):  
\[F_{D}(t)=F_{D}+f_{Du}(t)+f_{Dv}(t) \tag{11}\]  
\[F_{L}(t)=F_{L}+f_{Lu}(t)+f_{Lv}(t) \tag{12}\]  
where \(F_{D}(t)\) is the drag force [N]; \(F_{L}(t)\) is the lift force [N]; \(F_{D}\) is the mean wind drag force; \(F_{L}\) is the mean wind lift force; \  
where \(\rho\) is the density of the air [kg/m\({}^{3}\)]; \(D\) is the outer diameter of the cable [m]; \(C_{D}\) is the drag coefficient [\(-\)]; and \(C_{L}\) the lift coefficient [\(-\)]. The coefficients \(C_{D}^{\prime}\) and \(C_{L}^{\prime}\) are the derivative of \(C_{D}\) and \(C_{L}\), respectively, with respect to the angle \(\alpha\) neighboring \(\beta\) (Figure 3). As the section of the cable is assumed to be circular in this study, these derivatives are therefore null because of the symmetry, and hence these two coefficients can be neglected.  
Finally, in order to determine the wind forces it is necessary to generate simulations of wind velocities. For this purpose, the wave superposition spectral-based method was considered [8]. This method allows the numerical determination of a series of wind velocities via the superposition of trigonometric functions. On the one hand, the amplitude of these functions is obtained in terms of a coherence function, which considers the spatial variability of the wind velocity, and the power spectral density function of the turbulent wind velocity. On the other hand, the phase of the trigonometric functions is generated randomly. The coherence function is defined using the relationship proposes by [PERSON] [53]. The power spectral density function proposed by the European guidelines [54] was considered herein.
## 3 Discussion
This is an interesting finding supported by previous data, since evidence of the overlapping among ABA, ethylene, and sugar signaling in the modulation of the redox status of the cells have been reported in Arabidopsis [55; 56; 57]. Moreover, ABA treatments induced the expression of _DHAR1_ and _DHAR2_ in pepper leaves [58] and the promoter of Arabidopsis _APX2_ gene contains ABA response elements [59]. Additionally, ABA treatment increased AsA content in raspberries [60] and in strawberry also stimulated the expression of genes involved in its biosynthesis [61]. The presence of ABA-responsible elements in the promotor region of many genes related to AsA metabolism in Valencia Late orange suggest a potential influence of this hormone in the regulation of AsA content. This suggestion is not unexpected, since it is well documented that ABA concentration increased in the flavedo of orange fruit during color break [62], and it is likely that this may participate in the mechanisms controlling AsA accumulation in this tissue. Ascorbic acid is known to modulate plant growth processes including flowering [6; 63; 64]. Hence, the fact that APETALA2 (AP2) binding motifs are found in genes involved in AsA metabolism could indicate a plausible link for this relationship (Tables S3 and S4), especially taking into account that AP2 is involved in a wide variety of developmental processes [5]. In tomato, NAC TFs were co-expressed with AsA metabolic genes and in our analysis the binding motifs to ATAF-2, which is a NAC domain containing protein, were found in _GalUR5_ and _MIOX_[65]. Even the presence of these regulatory signatures in the promotor of genes related to AsA metabolism does not allow us to yet explain their transcriptional pattern of expression in the different organs, and tissues of Valencia Late oranges are illustrative of the myriad of endogenous factors and environmental signals that may regulate AsA content in a tissue-specific manner.
# Investigation on Aerodynamic Noise Characteristics of Coaxial Rotor in Hover
## 3 Results and Discussion
### Noise Radiation Characteristics  
Figure 12 shows the thickness noise SPL contour map. It can be seen that the thickness noise of a single rotor is distributed in concentric circles on the hemisphere surface. The noise of the coaxial rotor shows a periodic distribution with the azimuth angle. Moreover, around the blade-meeting azimuths, the noise is enhanced, due to the source superposition of the two rotors. The minimum SPL is located immediately below the rotor hub center, which is similar to the single rotor.  
Figure 11: Frequency spectrum of loading noise at different observation points. (**a**) Single rotor; (**b**) Coaxial rotor.  
The comparison of loading noise SPL distributions between the coaxial rotor and the single rotor is shown in Figure 13. The SPL of loading noise increases significantly near the azimuths where the twin rotors meet each other. This is consistent with the fluctuation characteristics of the rotor load in hover. The intensity of loading noise caused by the upper rotor is obviously stronger than the lower rotor. It should be noted that the strongest loading noise of the single rotor occurs at an approximately \(-30^{\circ}\) latitude angle. For the coaxial rotor, the noise intensity increases with the approach to the bottom of the sound radiation hemisphere, which reaches the maximum at the bottom of the sphere (\(-90^{\circ}\) latitude angle). It should be noted that, for the single rotor, the noise of the point immediately below the rotor center is the lowest one, while for the coaxial rotor, it is the highest location. This is mainly determined by the unsteady interaction loads of the coaxial rotor.  
Figure 12: Thickness noise SPL contour maps. (**a**) Single rotor; (**b**) Coaxial rotor.  
Figure 13: Loading noise SPL contour maps. (**a**) Single rotor; (**b**) Coaxial rotor; (**c**) Upper rotor; (**d**) Lower rotor.  
The total noise SPL results of the coaxial and single rotors are given in Figure 14. Combined with the distribution of loading noise in Figure 13, it can be seen that the contribution of thickness noise mainly distributes near the rotor disk plane. In addition, the loading noise is dominant on the sound radiation hemisphere deviating from the rotor disk plane. Moreover, the dominant effect of the coaxial rotor loading noise on the total noise is more obvious. This is consistent with the previous analysis of the noise radiation characteristics of the observation points at different locations.
## Appendix A Equations and the FEM Numerical Method
### FEM Numerical Method  
The main principle of FEM is to solve the problem on a discrete domain represented by a mesh [22; 34; 35]. The unknown physics quantities (here, the electromagnetic field \(\mathbf{E}\) and the temperature \(T\)) are computed at the nodes of the mesh through a variational method. The use of an improved 3D method and an iterative remeshing process [18; 19; 20], ensures the control of the error on the numerical solution. The computed solution accuracy is directly related to the quality of the mesh [36; 37]. Such a quality of the mesh is automatically improved by adapting the size of the mesh elements to the solution of the physics through adaptive loops of the remeshing process [19; 38]. Such mesh adaptions are the only requirements to ensure the convergence of the numerical solution to a stable solution [18; 19]: the approximations of the electromagnetic field \(\mathbf{E}\) and the temperature \(T\) (Equation (2)) are computed, at each step of the adaption process, on the basis of the interpolation polynomials between nodes. The difference between the computed solution at the nodes and the maximum deviation between the solution associated with the mesh is controlled by the interpolation error. The interpolation error is based on an estimation of the discrete Hessian of the solution) [39; 40]. The adaptive remeshing is obtained from the OPTIFORM software (generator of isotropic/anisotropic remeshing) [18; 19] and, during the iterative process, the domain is entirely remeshed and a new mesh is produced. In the present case, the volume of the computational domain \(\Omega\) is 100 \(\mu\)m\({}^{3}\). The remeshing of the domain is only governed by the minimum and maximum element sizes, set to \(h_{min}=0.1\) nm, \(h_{max}=500\) nm, and with a maximum tolerance \(\delta=0.2\%\) for the relative error of the computed unknowns.
# Plant Foliar Geometry as a Biomimetic Template for Antenna Design
## 4 Discussion  
By exploring a wide set of geometries derived from 100 species, we were able to distinguish features that provided a flowchart to select geometries apt to satisfy technological applications. In particular, the subset of seven species offers a template to deepen our comprehension of the design requirement involved in biomimetic antenna design. This work, to our knowledge, is a comprehensive effort to study plant biomimetic antenna designs, and the simulations performed in CST stimulate further research, given the excellent properties that certain leaf architectures provide in comparison to the conventional geometries currently employed.  
For example, Table 9 illustrates a comparative analysis of the performance of each plant leaf design. This comparative analysis is achieved in terms of the leaf geometry, design impact and operation frequencies below 10 GHz. Although more than one geometry could contribute to the same design impact (or similar performance characteristics), several interesting design characteristics can be considered as a result of this study: the obovate geometry presented the best behavior for high frequencies, the toothed case provided more frequency bands below 10 GHz, the circular base geometry (the widest plant leaf) generated the best radiation characteristics and the pinnately lobed case provided the widest frequency band.  
Some limitations of this work and potential future work can be extended from the current study as follows:  
* Each leaf architecture (in this work) considers vein characteristics of the first order. Therefore, more vein characteristics (of higher order) can be considered in order to obtain better performance features.
* This study only considers the response of the leaf geometry found in nature. It could be interesting to try to generate a geometry that considers the optimization of the design parameters in order to improve performance. Furthermore, some of the interesting characteristics found in this study could be used (or mixed) to try to create new design geometries or super-geometries. Therefore, we consider that future work searching the optimization of biomimetic designs based on plant leaf architectures will further support the development of specific applications in antenna design.
## 3 Results and Discussion
### Effect of Contact Time on MB Adsorption  
Figure 11 shows the effect of the contact time. The adsorption of the MB molecule was not as fast in general terms for all the adsorbents compared to previous works [41; 53; 54; 55].  
The performance based on the contact time was described for all the adsorbents: at 1 min, the initial concentration of methylene blue decreased around 16.03%, the value continued to increase, and at 40 min, a remove percentage of 52.85% was observed; at 120  
\begin{table}
\begin{tabular}{c c} \hline \hline
**adsorbent** & \(\mathbf{h}\) \\  & **(mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1}\))** \\ \hline AC & 0.012 \\ \hline AC-21 & 0.033 \\ \hline AC-HAp & 0.022 \\ \hline AC-1h & 0.038 \\ \hline AC-1h21 & 0.083 \\ \hline AC-1 hHAp & 0.024 \\ \hline AC-2h & 0.018 \\ \hline AC-2h21 & 0.216 \\ \hline AC-2 hHAp & 0.046 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of kinetic models.  
Figure 11: Effect of contact time as a function of the adsorbed concentration and removal rate of each material studied at experimental conditions of: 1 ppm, pH 7, and an initial dose of 60 mg. (**a**) Samples without oxidation, (**b**) samples with 1 h oxidation, (**c**) samples with 2 h oxidation.  
and 220 min, there was an average value for the removal of 82.21 and 98.31%. For this reason, it is possible to attribute that the minimum contact time for the adsorbents to remove 95.61% of the MB was 180 min. The removal rate was compared with the commercial adsorbent (AC), 90.7%, and modified HAp with the highest amount on the surface (AC-2 hHAp), 99.4%. The results demonstrated that the modification with hydroxyapatite improves the removal rate at low concentrations of MB.