esa-sceva/satcom-chunk-collection huggingface dataset:
# A Set-Theoretic Approach to Modeling Network Structure
## 3 Network Properties
### Network Centrality  
**Proposition 6**.: _If \(\mathcal{N}\) is not unbalanced, then the center \(C_{d}\) (in terms of distance) is an element of (or intersects with) the interior \(\mathcal{I}\) of \(\mathcal{N}\)._  
Proof.: If \(x\) and \(z\) are in separated \(\beta\)-sets, then \(\sigma(x,z)=\langle x=x_{k},x_{k-1},\ldots,x_{0}\rangle\cup\langle y_{1}, \ldots,y_{m}\rangle\cup\langle z_{0},\ldots,z_{n}\rangle\) where \(y_{1}=x_{0},y_{m}=z_{0}\) and \(y_{i}\in\mathcal{I}\). Since \(\mathcal{N}\) is not unbalanced, we may assume \(k\approx n\), so the center of \(\sigma(x,z)\) is one of the \(y_{1},\ldots,y_{m}\).  
If \(x\) and \(z\) are in connected \(\beta\)-sets and \(|\rho(x,z)|\geq 2\), then Proposition 5 establishes the existence of a shortest path through \(\mathcal{I}\) as well.  
If \(x,z\in\{x\}.\beta\), then no shortest path involves \(\mathcal{I}\); however, since \(\mathcal{N}\) is not unbalanced, these constitute a small number of cases and can be ignored.  
In Figure 2b, if \(y_{1}\) is in the center \(C\), then so are \(y_{0}\) and \(y_{2}\), implying \(C\cap I\
eq\emptyset\).  
Proposition 6 requires that \(\mathcal{N}\) not be too unbalanced. Figure 4 illustrates why. It is not hard to show that \(y_{5}\) is the center with maximum distance over all \(x\) being \(d(x,y_{5})=4\). Our rule of thumb is that a network is reasonably well-balanced if, given any \(x\in\{x_{0}\}.\beta\), then the probability that a randomly chosen \(y\) is also in \(\{x_{0}\}.\beta\) is small, that is \(pr(y\in\{x_{0}\}.\beta|x\in\{x_{0}\}.\beta)<\varepsilon\) where \(\varepsilon<0.20\).  
**Proposition 7**.: _If \(\mathcal{N}\) is not unbalanced, then any center \(C_{b}\) of \(\mathcal{N}\)(in terms of betweenness) is an element of \(\mathcal{I}\)._  
Proof.: This proof follows the line of Proposition 6, in which, unless \(x\) and \(z\) are in the same \(\beta\)-set, all shortest paths \(\sigma(x,z)\) either involve \(\mathcal{I}\) or have a path \(\rho^{\prime}(x,y,z)\) of the same length through \(\mathcal{I}\). Hence, a node \(y\) for which \(\sigma_{x,z}(y)\) is maximal will be an element of \(\mathcal{I}\).  
That \(\mathcal{I}\) contains the betweenness center is evident in the Karate network of Figure 1 and the small network of Figure 3.  
Figure 7 illustrates a somewhat different \"unbalanced\" network in which \(x\) and \(z\
ot\in\mathcal{I}\) are betweenness centers.  
One can calculate that \(nsp(x)=nsp(z)=6*6+4*6*6+4*6=204\) which are locally maximal.  
Calculating betweenness centers is computationally expensive, even with improved algorithms (e.g., [21]). Knowing that they must exist in the interior \(\mathcal{I}\) and restricting the calculation to just those nodes can greatly improve performance, especially when betweenness is employed in other procedures (e.g., [10]). Consequently, dwelling too much on unbalanced networks can be self defeating since the majority, and possibly almost all, networks are well-balanced.  
Figure 7: Another unbalanced network.
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
###### Abstract  
The characteristics and performance of satellite clocks are important to the positioning, navigation, and timing (PNT) services of Global Navigation Satellite System (GNSS) users. Although China's BeiDou-3 Navigation Satellite System (BDS-3) has been fully operational for more than one year, there is still a lack of comprehensive research on the onboard clocks of the entire BDS-3 constellation. In this study, the precise clock products of GeoForschungsZentrum (GFZ) from day-of-year (DOY) 1, 2021 to DOY 300, 2021 were used to analyze the characteristics and performance of BDS-3 onboard clocks from the following aspects: clock bias, frequency, drift rate, fitting residuals, periodicity, and frequency stability. Compared with BDS-2, the clock quality of BDS-3 satellites has been greatly improved, but there are still jumps in the clock offsets and frequency series of BDS-3 clocks. The drift rate of BDS-3 clocks varies within the range between \(-2\times 10^{-18}\) and \(2\times 10^{-18}\) s/s\({}^{2}\). The daily model fitting residuals of passive hydrogen masers (PHM) on BDS-3 medium Earth orbit (MEO), indeed geosynchronous orbit (GSO), and geostationary (GEO) satellites are 0.15, 0.28, and 0.46 ns, respectively. The overlapping Allan deviation (OADEV) of BDS-3 MEO clocks is \(4.0\,\times\,10^{-14}\) s/s at a time interval of 1000 s. The PHMs on BDS-3 MEO satellites exhibit fewer periodic signals than those of Rb clocks. In addition, the precise clock offsets of the BDS-3 PHMs carried on the MEO, IGSO, and GEO satellites show different periodicities, which are similar to those of the corresponding types of BDS-2 satellites.  
BeiDou Navigation Satellite System (BDS); satellite clock offset; clock stability; periodic characteristics 2022
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 4 Results
### The Sustainability of E-Health Solutions
#### 4.2.1 Sustainability Definitions  
Five studies defined sustainability, in which most of them were cited definitions. The scope of the definitions was centered around demand, continuity, and cost. Three studies [8; 9; 35] used the general sustainability definition that is provided by the United Nations (UN): \"meets the needs of the present without compromising the ability of future generations to meet their own needs\" [41]. Both [PERSON] et al. [7] and [PERSON] et al. [9] defined the sustainability of e-health solutions as the ability of the solution to continue a defined behavior and functioning into the future. Moreover, [PERSON] et al. [8] and [PERSON] et al. [35] referred to sustainability as continuous use, and addressed the three dimensions of sustainability and other important factors of the intended system. On the other hand, [PERSON] [31] associated sustainability of IT systems in healthcare with the cost needed for replacements over the years; \"a sustainable IT systems in healthcare should prevent the necessity of costly system replacements every five to ten years\" [31].
## 2 Materials and Methods
### Test Fuel
#### 2.2.3 Fuel Dynamic Viscosity Estimation  
To determine the equations that determine the behavior of viscosity with temperature, we use the equations presented by [PERSON] et al. [29] for diesel fuel (Equation (6)) and GtL (Equation (7)).  
\[\mu_{\text{{Diesel}}}(P_{0},T)=5.7694e^{-0.0169T} \tag{6}\]  
\[\mu_{\text{{GTL}}}(P_{0},T)=4.788e^{-0.0189T} \tag{7}\]  
In the case of HVO, an exponential expression (Equation (8)) obtained from the experimental data shown in Table 1 is used.  
\[\mu_{\text{{HVO}}}(P_{0},T)=4.1061e^{-0.018T} \tag{8}\]  
In these three expressions, the kinematic viscosity is given in cPo, and the fuel temperature is given in \({}^{\circ}\)C. To know the variation of viscosity with pressure, Equation (9), proposed by [PERSON], is used [55].  
\begin{table}
\begin{tabular}{l l l l} \hline
**Properties** & **Diesel** & **HVO** & **GtL** \\ \hline Cold filter plugging point (\({}^{\circ}\)C) b  & \(-19\) & \(-40\) & \(-45\) \\ Lubricity (WS1.4) (\(\mu\)m) b  & \(237\) & \(334\) & \(548\) \\ Cetane number b  & \(54.2\) & \(94.8\) & \(71\) \\ Flashpoint (\({}^{\circ}\)C) b  & \(61\) & \(70\) & \(63\) \\ Distillation (vol) b  & \(10\%\) (\({}^{\circ}\)C) & \(206.5\) & \(265.2\) & \(195\) \\
50\% (\({}^{\circ}\)C) & \(275.9\) & \(278.5\) & \(260\) \\
90\% (\({}^{\circ}\)C) & \(344.9\) & \(290.4\) & \(338\) \\ \hline \end{tabular} The prefixes “a\({}^{\text{a}}\)” and “b\({}^{\text{b}}\)” mean calculated and measured, respectively.  
\end{table}
Table 2: _Cont._\[\mu_{f}(P,T)=\mu_{f}(P_{0},T)exp\bigg{[}\frac{P}{10^{4}}\Big{(}7.9718+37.27967\mu_{ f}(P_{0},T)^{0.278}\Big{)}\bigg{]} \tag{9}\]  
In this work, kinematic viscosity (\(v_{f}\)) has been used instead of dynamic viscosity because the former indirectly includes the density value, which allows using one less parameter for the development of correlations.
# Seismic Design and Ductility Evaluation of Thin-Walled Stiffened Steel Square Box Columns
## 3 Finite Element Analysis
### Brief Description of the 2 SM Model  
The 2 SM model can more effectively capture the cyclic behavior of structural steel compared to existing material models, as it incorporates the yield plateau and explicitly accounts for its reduction and eventual disappearance under cyclic loading [6; 58]. A detailed description of the 2 SM and its application to simulating the cyclic behavior of structural steel is provided by [PERSON] et al. [58]. This model requires more constants than the multi-linear kinematic hardening (MKH) model, totaling 18 parameters: 5 material constants (\(\sigma_{y}\), \(E\), \(\
u\), \(\varepsilon_{st}p\), and \(E_{st}\)) and 13 model parameters [6; 58]. Here, \(\varepsilon_{st}p\) represents the plastic strain, and \(E_{st}\) denotes the initial hardening modulus at the end of the yield plateau. The 13 model parameters were calibrated using experimental data from a simple load test on a steel section [6; 58].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Column** & \(h\) **(mm)** & \(d\) **(mm)** & \(E\) **(MPa)** & \(\
u\) & \(\sigma_{y}\) **(MPa)** & \(R_{f}\) & \(\lambda\) & \(P/P_{y}\) \\ \hline TPRC9-9 & 2141 & 449 & 204,000 & 0.26 & 266.0 & 0.30 & 0.29 & 0.150 \\ B1 & 3403 & 900 & 206,000 & 0.30 & 378.6 & 0.56 & 0.26 & 0.122 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric and material parameters for the specimens.  
Figure 2: Finite Element Modeling (**a**) Column, (**b**) FE Meshing, (**c**) Stiffened Cross-section, and (**d**) Loading Path Protocol.  
The cyclic behavior of structural steel with a yield plateau influences the constitutive model based on the principles of time-independent plasticity theory. This model employs a Von Mises yield surface in conjunction with an associative flow rule [58]. The flow rule is described by the following equations:  
\[f=\left[\frac{3}{2}(\zeta-\eta):(\zeta-\eta)\right]^{\frac{1}{2}}-\kappa\leq 0 \tag{3}\]  
\[k=R+\rho \tag{4}\]  
\[de^{p}=\xi\frac{\partial f}{\partial\sigma}=\frac{3}{2}\xi\frac{\zeta-\eta}{K} \tag{5}\]  
\[dp=\left[\frac{2}{3}de^{p}:de^{p}\right]^{1/2}=\xi \tag{6}\]  
In these equations, bold letters represent second-order tensors, \(e^{p}\) represents the plastic strain tensor, \(\sigma\) represents the stress tensor, \(\xi\) is the stress deviator, \(\eta\) is the back stress, \(\rho\) is the initial size of the yield surface, \(k\) is the size of the yield surface, \(R\) is the isotropic hardening/softening variable, \(\xi\) is the plastic multiplier, \(p\) is the accumulated plastic strain, and the double dot represents the inner product [6; 58]. Although the stiffeners and diaphragms have different geometric properties, they share the same material properties as the column's component plate. The 2 SM model is also applied to these components since they are integral to the column and affect its buckling behavior.
## 5 Conclusions  
This study took the 2009 drought event in the Xilingol grassland as an example and utilized downscaled GOME-2 SIF satellite products to analyze the potential of SIF in assessing the changes in grassland GPP caused by drought. Negative anomalies of SPEL SM, and PPT indicated severe drought occurrence in the Xilingol grassland in 2009, and SIF was responsive to the spatiotemporal variations of drought indicators. These results demonstrated that downscaled SIF has the capability to track the temporal dynamics of drought, depict its spatial distribution, and monitor its occurrence. There was a significant correlation between SIF and GPP, with an R\({}^{2}\) of 0.95, while the correlation between NDVI and GPP was significantly lower, with an R\({}^{2}\) of 0.78, confirming that SIF can serve as an effective indicator of plant photosynthesis and accurately track the spatiotemporal changes in GPP. Under drought conditions, the relationship between SIF and GPP (R\({}^{2}\) = 0.90) was significantly higher than NDVI and GPP (R\({}^{2}\) = 0.62), demonstrating the superior ability of SIF to track and monitor GPP changes under drought conditions compared to NDVI. Drought reduces the ability of NDVI to monitor GPP but does not affect that of SIF to monitor GPP.  
This study highlights the strong capability of downscaled satellite SIF products in estimating grassland GPP changes caused by drought, providing a theoretical basis for quantifying GPP losses in grasslands due to drought in the future.  
Conceptualization, W.Z.; methodology, W.Z.; software, W.Z.; validation, W.Z. and Y.Z. ([PERSON]); formal analysis, W.Z.; investigation, W.Z., Y.Z. ([PERSON]) and S.L.; resources, L.L.; data curation, W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z.; visualization, W.Z.; supervision, Y.R. and L.L.; project administration, L.L.; and funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant No. 42101081) and the National Key R&D Program of China (Grant No. 2021 YFD1300503).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methods
### Atmospheric Infrared Sounder  
The Atmospheric Infrared Sounder (AIRS) is an instrument on the polar orbiting EOS Aqua satellite at an altitude of 705 km with equatorial cross times of 1:30 AM and 1:30 PM. It was launched on 4 May 2002, with an expected on-orbit lifetime of seven years and continues to provide global, daily observations with a native resolution of 14 \(\times\) 14 km. AIRS Level 2 product resolution is 3 \(\times\) 3 of the native resolution, or approximately 45 \(\times\) 45 km, and the CO\({}_{2}\) product is 2 \(\times\) 2 of that with a subsequent resolution of 90 \(\times\) 90 km. The AIRS Level 3 product is regridded to a spatial resolution of 2.5\({}^{\circ}\) longitude by 2\({}^{\circ}\) latitude, thus resulting in a mean concentration.  
Retrievals of CO\({}_{2}\) are from the 15-\(\mu\)m, thermal infrared band and selected cloud-cleared channels, where weighting function peaks occur between 500 hPa and 300 hPa [2]. The accuracy of CO\({}_{2}\) retrievals has been evaluated with information theory [38] and in comparisons with transport models, aircraft flasks, and Fourier Transform Infrared Spectrometer measurements, all resulting in an accuracy of 1-2 ppmv [1,39]. An additional 1-ppmv error in CO\({}_{2}\) retrievals can be caused by water vapor [40].  
In this study, AIRS mid-tropospheric CO\({}_{2}\) concentrations were compared with observed CO\({}_{2}\) at the surface. A time series of AIRS Level 3 daily observed CO\({}_{2}\) was obtained for 2007-2010 to correspond with EC tower data, 1260 days in total. The AIRS pixels that most closely cover the KFS and Konza towers were selected: 95\({}^{\circ}\)W, 40\({}^{\circ}\)N and 97.5\({}^{\circ}\)W, 40\({}^{\circ}\)N, respectively. Due to a high percentage of missing daily values (50.48\(\%\) for the AIRS pixel over KFS and 47.94\(\%\) for the AIRS pixel over Konza), the mean of the entire series for each pixel is used to fill in missing daily concentrations. The wavelet multi-resolution analysis does not allow for missing values, and filling the missing data with the mean of the entire time series results in a more predictable effect on longer timescales versus a less predictable effect on all timescales. However, since AIRS missing values occur on a sub-weekly timescale, it is expected that there will be some influence on the results at shorter timescales.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 7 Enhancement Methods to Optimize and Increase the Efficiency of FBEHs
### Nonlinear FBEHs
#### 7.1.1 Nonlinear VIV-Based PE-FBEHs
For misalignment correction, several beam substrates were examined, with thin airfoil profiles able to orient more quickly at greater misalignment angles. An extra inline mode was detected for the same frequency range in low-velocity flow, outperforming the standard rectangular beams with similar volumes in the airfoil-shaped harvester. In the absence of flow misalignment, the piezoelectric MFC produced an average RMS output voltage of 132 mV for transverse oscillations. Recently, [PERSON] et al. [158] investigated the nonlinear dynamic behavior of VIV-based PE-FEHs theoretically using a clamped-clamped beam; to create VIVs, two foam cylinders attached as a dumbbell were installed in the middle of the beam. The fluid interacted with the structure as it flowed over the cylinders, causing the system to oscillate and produce vortices behind the cylinders. The results show that middle layer stretching had a considerable impact on the lock-in domain and this effect was important while evaluating the output electric voltage. Likewise, [PERSON] et al. [159] investigated that the hardening stiffness effect can move frequency lock-in zones to higher wind speeds, hence increasing aerodynamic pressures and achieving wide wind speed bandwidths and large voltage outputs. The VIV energy harvester with nonlinear stiffness (Figure 12c) demonstrated a maximum voltage of 9.87 V with a frequency lock-in range of 2.27 to 5.36 m/s.
## 5 Conclusions  
In this paper, simulation data were employed to investigate the influence of different empirical parameters on far-red and red SIF retrieval. The fitting window, the polynomial order, and the number of feature vectors were the three main factors that influenced SIF retrieval in both far-red and red band. The retrieval accuracy was relatively higher for a wider fitting window, and an RMSE of lower than 0.7 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) was achieved with fitting windows wider than 735-758 nm and 682 nm-691 nm for far-red band and red band, respectively. Moreover, the retrieval accuracy was dependent on the polynomial order and the number of feature vectors. The RMSE decreased first and then increased with the increase in n\({}_{\text{p}}\) and n\({}_{\text{SV}}\) and can be lower than 0.7 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\) when n\({}_{\text{p}}\) is 2 or 3; such a result can also be obtained when n\({}_{\text{SV}}\) is 3-5 and 4-14 in the far-red and red band with an n\({}_{\text{p}}\) of 2, respectively. According to the specifications of SIFIS onboard TECIS-1, a fitting window of 735-758 nm, second-order polynomial, and four feature vectors were the optimized parameters for far-red SIF retrieval, with an RMSE of 0.63 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). For red SIF retrieval, using second-order polynomial and seven feature vectors in the fitting window of 682-697 nm was the optimal choice and resulted in an RMSE of 0.53 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\). The optimized parameters of the data-driven algorithm can guide the retrieval of satellite-based SIF and are valuable for generating an accurate SIF product of the TECIS-1 satellite after its launch.  
Conceptualization, C.Z., S.D., X.L., and L.L.; data curation, C.Z.; funding acquisition, L.L.; investigation, C.Z.; methodology, S.D.; project administration, L.L.; resources, X.L.; software, C.Z.; supervision, S.D. and L.L.; validation, X.L., Y.W., and Z.L.; visualization, Y.W. and Z.L.; writing--original draft, C.Z.; writing--review & editing, S.D. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2019-1), the National Key Research and Development Program of China, grant number 2017 YFA0603001, and the National Natural Science Foundation of China, grant number 41871239.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
It has been shown that it exhibits antioxidant activity by several antioxidant mechanisms including scavenging of reactive oxygen species (ROS), glutathione peroxidase activity, and metal-binding interactions [20]. There are several works regarding selenium tolerance of a diversity of wine-related yeasts as well as the effect on their antioxidant response mechanisms. Different concentrations of Se have been evaluated, and the data have shown that high doses of selenium might result in oxidative stress in yeasts, thereby increasing the process of lipid peroxidation. In addition, selenium might increase the activity of antioxidant enzymes, including glutathione peroxidase and glutathione reductase [21; 22; 23]. These results are considered as first indications of the possibility to use pre-enriched yeast cells as a selenium supplement in wine making. However, there is a lack of relevant studies focusing on the effect of selenium addition on wine chemical composition and brown color development. Browning development in white wines is of high nutritional and technological importance due to its influence on the sensory characters and antioxidant activity of the wines. For this reason, wineries should be equipped with a fast and simple method which could provide information on browning susceptibility of white wines and allow for suitable technological interventions. The investigation presented herein was undertaken in order to explore the possibility of further accelerating the duration of the browning test already used, after the addition of Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\) in white wines. Since both the presence of metal ions (such as Fe\({}^{2+}\)) and H\({}_{2}\)O\({}_{2}\) is known to increase browning rate [24; 25], it was anticipated that their simultaneous addition in white wines would further reduce the time needed for the completion of the browning test. There are few published works regarding the study of browning development in Greek wines [14; 26; 27] using the accelerated browning test, however, none of them were undertaken in the simultaneous presence of Fe\({}^{2+}\) and H\({}_{2}\)O\({}_{2}\). It was also of interest to monitor the polyphenic composition, since these compounds are mostly implicated in the oxidation phenomena, and to evaluate the effect of the addition of an antioxidant substance which could offer protection through various antioxidant mechanisms, such as Se. For this purpose, two temperatures were selected for maintaining the samples during the study, 35 \({}^{\circ}\)C and 50 \({}^{\circ}\)C, the first resembling transportation temperatures of wines during summer in Mediterranean countries such as Greece, while the second is commonly employed during accelerating browning studies [28; 29]. The outcomes of such a study would be of practical interest to the wine sector which is seeking rapid and economical methods which allow fast decision-making procedures in order to optimize the shelf life of their products.