esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study aims to address the issues associated with enhancing magnesium-lithium separation and reducing lithium loss through variable-temperature treatment. The microscopic structural changes, magnesium-lithium separation efficiency, and lithium loss mechanisms in high-magnesium-lithium-ratio brine solutions at different temperatures during the variable-temperature process were investigated. The following conclusions were drawn:  
Through the analysis of FTIR spectra and radial distribution functions of different brine solutions, it was found that temperature can change the structure of MgSO\({}_{4}\) solutes and the water structure of Li\({}_{2}\)SO\({}_{4}\) in the liquid phase. The arrangement of SO\({}_{4}\)\({}^{2-}\) and H\({}_{2}\)O in the second hydration layer of MgSO\({}_{4}\) in the brine solution is changed with temperature. The hydrogen bonding between SO\({}_{4}\)\({}^{2-}\) and H\({}_{2}\)O in the Li\({}_{2}\)SO\({}_{4}\) solution is broken, resulting in changes in the surrounding water molecule structure. This change enhances the structural differences in the magnesium-lithium solution in the brine, thereby promoting magnesium-lithium separation. In a multi-ion system, the concentration of Cl\({}^{-}\) in the brine determines the changes in water structure. A lower Cl\({}^{-}\) concentration and increasing temperature result in greater changes in the water microstructure in the brine, leading to more pronounced differences in the magnesium-lithium structure and enhancing magnesium-lithium separation. In high-magnesium-lithium-ratio brine, by controlling the temperature and supersaturation state, lithium loss can be controlled within 5%. Therefore, the regulation of temperature and the supersaturation state aids in the separation of high-magnesium-lithium-ratio brine. During the cooling process, the magnesium-lithium separation is enhanced, and the cooling promotes changes in the water structure in the liquid phase, resulting in an increase in the number of hydration layers around Li\({}^{+}\)-SO\({}_{4}\)  
Figure 9: XRD results of solid-phase crystallization by cooling brine with different magnesium–lithium mass ratios.  
clusters and the formation of Li\({}_{2}\)SO\({}_{4}\) crystals. Thus, lithium is trapped in the MgSO\({}_{4}\) crystals in the form of Li\({}_{2}\)SO\({}_{4}\) crystals.  
Conceptualization, D.C. and H.C.; software, D.C.; investigation, D.A. and X.C.; data curation, D.C.; writing--original draft preparation, D.C. and D.A.; writing--review and editing, H.C. and X.C. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Research Program of Qinghai Province (2023-ZJ-920M) and the Natural Science Foundation of China (U20A20149).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 3 Simulation Results
### Simulations  
This section describes simulations that were performed to reconfigure a PV array with dual-adaptive parts in the TCT structure with a developed GA-based control algorithm.  
Simulations were performed with two different partial-shade scenarios on a three- and four-row array. Partial shading was achieved by assigning random irradiance values to the panels on the array. There were three PV panels in each row of the fixed part of the array, and the temperature of the panels was determined as 25\({}^{\circ}\). As explained before, since it is difficult to measure the irradiance values of the panels, the short circuit currents of each  
Figure 6: The impact of population diversity and mutation rate on convergence time.  
Figure 7: The impact of population diversity and mutation rate on best fitness.  
row of the fixed part and the adaptive panels were measured and given to the algorithm. The irradiance values (\(G\times 100\) W/m\({}^{2}\)) used in the simulation and the short circuit currents (Isc) corresponding to these values are given in Table 10.  
The P-V curves of the series obtained from the simulations are given in Figure 8. In these curves, the state of each adaptive panel connection to the opposite side of the row of the fixed part is shown as \"initial\", and after the reconfiguration process, the state is shown as \"after reconfig\". As can be seen from the results, the performance of the PV array increased significantly after reconfiguration. MPP increase rates in the given scenarios are given as 8.7%, 12.3%, 23.9%, and 48.2%, respectively. As can be seen, since there will be more combinations in the four-row PV array, the proportional increase in MPP is higher. However, these increases may vary depending on the current penumbra values of the array. The complexity brought by the increase in the number of combinations can be achieved within a feasible conversion time with the developed GA optimization, as given in Table 4.  
Figure 8: P-V characteristics of the PV array: (**a**) scenario 1: three-row PV array; (**b**) scenario 2: three-row PV array; (**c**) scenario 3: four-row PV array; (**d**) scenario 4: four-row PV array.
## 5 Results and Discussion
### Coverage and Connectivity
[PERSON], [PERSON], and [PERSON] [45] proposed a deployment scheme that analyzes the event detection probabilities for different detection ranges, considering the relationship between the sensing range and the communication range and associating the event detection probability with specific parameters of the communications system. In contrast, our approach separates these two parameters. The overlap value is a user requirement, and the sensing range is not dependent on the parameters associated with the communication system. [PERSON] et al. [46] employed an annealing method to optimize the degree of coverage. It is not possible to make a comparison on equal terms because our results show the number of nodes after optimization. Considering that the simulation uses a 200 m long tunnel and that the small mine that we reconstructed has a 265 m main tunnel, we observe that for k = 3, approximately 65 nodes are deployed. In contrast, our proposal deploys 55 nodes with k = 1. It can be inferred that there is a 54.2% probability of reaching k = 2 and an 8.6% probability of reaching k = 3 without optimizing the degree of coverage.
## 2 Materials and Methods
### Display of the Data and Statistical Analysis  
The statistical analysis was performed using GraphPad PRISM (GraphPad Software, San Diego, CA, USA). All experiments were conducted with eight replicates, each from a different donor. The ROUT method was used to identify any outliers. The analysis of the released proteins led to non-detectable amounts of protein for some donors for varying proteins. Therefore, and through the ROUT method, the number of replicates was reduced to n \(\geq\) 7 for osteoclasts and n \(\geq\) 6 for the PBMCs. The normal distribution was verified with the Shapiro-Wilk test. The two-way ANOVA with Tukey was used to compare the results of the electric stimulations among each other and with the control, as well as the different time points. \(p\)-values smaller than 0.05 were included as significant. The data were shown in boxplots and heat maps. Boxplots represented the median, the 25% and 75% quartile, and whiskers for indicating minimum and maximum.
## 2 Network Slicing Concepts
### Soft Network Slicing  
Soft slicing corresponds to a lower level of isolation between the services a network is transporting. Soft slicing implies sharing the physical infrastructure but creates logical segmentations between the customers. According to this definition, soft slicing is not a new concept from the IP/MPLS networks perspective [4; 14; 15].  
Traditional L3 VPNs are samples of soft-slicing implementations in an MPLS network, because a VPN can be thought of as a series of tunnels connecting customer sites, each site can potentially have different QoS treatment, and all traffic to and from each site is internal to the customer. In the VPN service, the provider ensures that each customer's traffic is logically discriminated over shared physical infrastructure based on routing policies configuration across the network.  
In that sense, Network slicing at the MPLS level can be implemented using:  
* **Virtual Routing and Forwarding** (VRF), that enables multiple routing environments over a shared MPLS transport network.
* **Virtual Interfaces** (VSI), that enables multiple switching environments over the same shared infrastructure.  
Each physical router is able to host multiple VRF(s) and multiple VSI(s) (along with their attached logical interfaces), effectively slicing it into multiple routing and switching environments that can be assigned to different tenants/customers/services.
## 2 Data and Methods
### Contribution of Each Layer to \(r_{rs}(0^{-},\lambda)\)  
In accordance with radiative transfer theory, Chla(z) profiles changed the AOPs(\(\lambda\), z) of the underwater light field by modifying the IOP(\(\lambda\), z) profiles. The relationship between the IOP(\(\lambda\), z) of each layer and \(r_{rs}\) immediately below the water surface (\(r_{rs}(\lambda,0^{-})\)) can be constructed through the \(K_{x}(\lambda,z)\) profiles based on the average weighting approach [8], and this relationship can then be used to calculate the contribution of each layer to the \(r_{rs}(\lambda,0^{-})\).  
The contribution of each layer between z\({}_{1}\) m and z\({}_{2}\) m to a remotely sensed signal can be derived using Equation (8) [10]:  
\[Fr(z_{1},z_{2})=\int_{z_{1}}^{z_{2}}IOP^{\prime}(z)\frac{d}{dz}[\exp\{-\int_{0 }^{z}[g_{x}(z^{\prime})]dz^{\prime}\}]dz/\int_{0}^{\infty}IOP^{\prime}(z) \frac{d}{dz}[\exp\{-\int_{0}^{z}[g_{x}(z^{\prime})]dz^{\prime}\}]dz, \tag{8}\]  
\[IOP^{\prime}(z)=\frac{b_{b}(z)}{a(z)+b_{b}(z)}, \tag{9}\]  
where \(Fr(z_{1},z_{2})\) is the fraction of \(r_{rs}(\lambda,0^{-})\) at the layer between z\({}_{1}\) m and z\({}_{2}\) m, \(g_{x}(z)\) is the average weighting function. Three weighting average functions, namely, \(g_{GC}\)[8], \(g_{Z}\)[10], and \(g_{S}\)[9], are as follows:  
\[g_{GC}(\lambda,z)=exp(-2\int_{0}^{z_{0}}K_{d}(\lambda,z^{\prime})dz^{\prime}), \tag{10}\]  
Figure 1: Bio-optical properties of six representative examples: (**a**) Chla(z), (**b**) a(550), (**c**) \(b_{b}\)(550), and (**d**) \(b_{b}\)/(\(a+b_{b}\))(550). Different lines represent different vertical structure parameters (C\({}_{0}\), h, \(\sigma\)) of Chla(z) profiles: L1 (5, 16, 0.6), L2 (10, 16, 0.6), L3 (10, 21, 0.6), L4 (5, 16, 0.2), L5 (10, 16, 0.2), L6 (10, 21, 0.2).  
where \(z_{90}\) is the depth at which 90% of the surface \(E_{d}(\lambda)\) has been attenuated.  
\[g_{Z}(\lambda,z)=2K_{d}(\lambda,z)\exp(-2\int\limits_{0}^{z}K_{d}(\lambda,z^{ \prime})dz^{\prime}), \tag{11}\]  
where the depth dependence of \(g_{Z}\) is a function of the derivative of the round-trip attenuation of the downwelling and backscattering light. [PERSON] and [PERSON] (2016) stated that \(2K_{d}(z)\) should be the sum of \(K_{d}(z)\) and \(K_{Liu}(z)\), hence \(K_{d}+K_{Liu}\) is also used to compare their performance with \(2K_{d}\) in \(g_{GC}\) and \(g_{Z}\).  
\[\begin{array}{l}g_{S}(\lambda,z)=\int_{0}^{z}b_{bd}(\lambda,z)\exp[-[K_{d}( \lambda,z)+K_{u}(\lambda,z)]]dz^{\prime}\\ \approx\int_{0}^{z}K_{d}(\lambda,z)\exp[-3K_{d}(\lambda,z)]dz^{\prime}\end{array}, \tag{12}\]  
where \(b_{bd}\) is the diffuse backscattering coefficient, which is difficult to obtain. Thus, \(g_{S}\) is written as a function of \(K_{d}\) in the extreme case of \(b_{b}(z)\propto K_{d}(z)\)([PERSON], 1997).  
To evaluate the performance of the model, the coefficient of correlations (\(R^{2}\)) and the average absolute percentage difference (APD, %) were calculated to identify the difference between the simulated data (Xi) and the modeled data (Yi).  
\[\mathrm{APD=}\frac{1}{n}\sum_{i=1}^{n}\frac{|Y_{i}-X_{i}|}{X_{i}}\times 100\% \tag{13}\]
## 2 Materials and Methods  
### Study Area  
This study was conducted at a sub-continental scale between 10\({}^{\circ}\)S to 26\({}^{\circ}\)S and 113\({}^{\circ}\)E to 138\({}^{\circ}\)E, which encompassed northern and central Australia by a relatively constant decrease in rainfall with distance inland (Figure 1). This region, particularly for northern Australia, has a classic monsoon climate pattern, which receives more than 80% of annual precipitation during November to April [42]. From northern mesic tropics to the xeric central Australia, mean annual rainfall ranges from 1700 mm to approximately 300 mm (Bureau of Meteorology, Available online: [[https://www.bom.gov.au](https://www.bom.gov.au)]([https://www.bom.gov.au](https://www.bom.gov.au)) (accessed on 20 January 2020)), in line with the aridity index (AI) decreasing from 0.8 to 0.1 (Figure 0(b)). Correspondingly, the vegetation follows a wet-dry gradient that shifts from _Eucalyptus_ dominated forests, open forests, and woodlands in the coastal northern areas to _Acacia_-dominated open woodlands, scattered shrubs, and hummock grassland into the vast inland [42] (Figure 0(a)).  
To assess satellite observation with ground-based evidence, we selected five representative flux tower sites across the extensive study area: Howard Springs (AU-How), Dry River (AU-Dry), Sturt Plains (AU-Stp), Alice Spring Mulga (AU-ASM), and Ti Tree East (AU-TTE) [43]. The detailed description of five sites is shown in Table 1 (Available online: www.ozflux.org.au (accessed on 15 July 2019)). Apart from two northern sites (AU-How and AU-Dry), the remaining sites in the interior of Australia experienced extreme drought in 2018-2019.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Abbreviation** & **Longitude (\({}^{\circ}\)E)** & **Latitude (\({}^{\circ}\)S)** & **Vegetation Type** & **Data Coverage** \\ \hline Howard Springs & AU-How & 131.15 & 12.495 & Eucalypt woodlands & 2001–2019 \\ Dry River & AU-Dry & 132.371 & 15.259 & Open forest savanna & 2008–2019 \\ Sturt Plains & AU-Stp & 133.3502 & 17.1507 & Tussock grasslands & 2008–2018 \\ Alice Spring Mulga & AU-ASM & 133.2493 & 22.2828 & _Acacia_ woodlands & 2010–2019 \\ Ti Tree East & AU-TTE & 133.64 & 22.287 & _Crymbia_ savanna & 2012–2019 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of five eddy covariance flux tower sites.
## 2 Theoretical Background
### Active Power Definition  
The standard IEEE std. 1459-2010 [27] defines the active power (\(P\)) as the average value of the instantaneous power during a time interval \(\tau\), as shown in (1):  
\[P=\frac{1}{\tau}\int_{0}^{\tau}p(t)dt, \tag{1}\]  
where \(p(t)=v(t)*i(t)\) is the instantaneous power. Equation (1) can be discretized resulting in (2):  
\[P=\frac{1}{\tau}\sum\
olimits_{k=1}^{N}v(k)i(k), \tag{2}\]  
where \(N\) is the number of samples comprising the time interval \(\tau\) ; and \(v(k)\) and \(i(k)\) are the \(k\) -th element of the voltage and current signals, respectively.
## 4 Discussion  
### Ground Phenology Reference and Agro-IBIS  
Our choice of ground phenology reference was based on the model performance in simulating the evolution of LAI. This method allowed us to choose the ground phenology reference quantitatively. Due to data limitation (data missing for some required variables), the model was driven with gridded climate data rather than site-specific measurements. We therefore expect some uncertainties associated with the comparison of LAI at different spatial scales (AmeriFlux site _vs._ simulation in a 5-min grid cell). In addition, the differences in MPE resulting from different combinations of BBRK and LCOLOR are relatively small (Table 2). This is due to the small difference between phenological levels, which were derived from interpolation. On average, it takes 4.7 days for the buds to break from 10%-90%. Therefore, in some years, the adjacent two phenological levels (e.g., BBRK20 and BBRK30) could be the same. As it takes longer for the leaves to change color (e.g., 9.5 days on average from 10%-50% coloring), the difference resulting from different LCOLOR levels is larger (Table 2). Although uncertainties remain in the chosen ground phenology reference, they were confined to a small range based on our choice of metrics. The uncertainties in the offset are larger than those in the onset due to the larger variability in the offset across species and individual trees.  
Agro-IBIS captured the magnitude of productivity variables when compared with observations, although uncertainties exist because of model limitations. For example, the slower increase in LAI in spring compared with the observations (Figure 1) results from a small underestimation in net primary productivity (NPP), a component of simulated LAI. The faster decrease in simulated LAI in autumn might be because of the relatively simple scheme used in Agro-IBIS. Once the offset is triggered, the LAI decreases linearly to a minimum over a 30-day period. However, as the canopy photosynthesis is scaled using LAI in the model, the faster decrease in LAI can partly correct the errors caused by not taking into account the effect of leaf age and coloring.  
The discrepancies between the simulated carbon cycle components and the flux measurements can be explained by the following possible reasons: (1) the grid cell was simulated as temperate deciduous forest so that only one PFT existed [34]; (2) the footprints of other PFTs such as evergreen trees and understory shrubs were not simulated although they are likely small; (3) the meteorological data used to drive the model represents the average condition of a grid cell, which could be slightly different from the real condition at the site; and (4) there might also be uncertainties in the flux measurements and the post-processing such as gap-filling [48].
## 3 Results and Discussion  
### Detection Limits and Sample Blank Values  
The detection limit of our CV-AFS was 0.1 pg. The average sample blank values \(\pm\) SDs for the Hg\({}^{0}\)aq and Hg\({}^{2+}\)aq were 20 \(\pm\) 10 pg L\({}^{-1}\) and 100 \(\pm\) 50 pg L\({}^{-1}\) for the ultrapure water (\(n=33\)) and 31 \(\pm\) 9 pg L\({}^{-1}\) and 109 \(\pm\) 46 pg L\({}^{-1}\) for the seawater (\(n=4\)), respectively. The blank values for the two different types of water were comparable and significantly lower than the concentrations of mercury found in the uptake solutions (presented and discussed below) but substantially higher than the amount of background mercury in the purging gas. This may imply that their source or sources were the same and possibly that the thoroughly cleaned impingers and bubblers may have still been contaminated. Based on the SDs, the detection limits for Hg\({}^{0}\)aq and Hg\({}^{2+}\)aq in a solution were approximately 30 pg L\({}^{-1}\) and 150 pg L\({}^{-1}\) or 9 pg and 44 pg, respectively.
## 3 Results
### Surface Characterization through AFM  
Figure 15a shows a 3D map of the topography obtained with AFM on a 100 \(\upmu\)m\({}^{2}\) surface of Obourg Chalk. In addition to the surface roughness, AFM is also able to provide a measurement of the adhesion force F\({}_{\text{adh}}\) on the sample surface. Figure 15b shows a color-coded map for the adhesion force on a 100 \(\upmu\)m\({}^{2}\) surface in Obourg Chalk. The sampling per scanning line was either 256 or 512 points, providing maps with either 65k or 262k points. As the rock was filled with resin in the pores, we assumed that the resin (less resistant to polishing) would be found in the topography troughs, whereas the rock grains would be mostly found in the reliefs. We then selected the forces by correlating the topography with the adhesion maps in order to investigate the rock part only.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Average Advancing Contact Angle (\({}^{\circ}\))**} & \multicolumn{3}{c}{**Roughness Ra (nm)**} \\ \cline{2-7}  & **DW** & **DIO** & **Form** & **DMSO** & **Glyc** & **AFM** & **Confocal** \\ \hline Obourg + Resin & 89.4 \(\pm\) 4.7 & 74.2 \(\pm\) 4.9 & 77.6 \(\pm\) 3.5 & 53.5 \(\pm\) 4.3 & 77.8 \(\pm\) 3.6 & 72.5 \(\pm\) 19.5 & 893 \(\pm\) 40 \\ \hline Ciply + Resin & 86.0 \(\pm\) 4.3 & 72.4 \(\pm\) 4.0 & 70.5 \(\pm\) 5.3 & 56.3 \(\pm\) 3.0 & 75.8 \(\pm\) 4.1 & 58.8 \(\pm\) 21.9 & 511 \(\pm\) 480 \\ \hline Calcite crystal & 77.8 \(\pm\) 4.9 & 71.3 \(\pm\) 3.5 & 73.8 \(\pm\) 7.1 & – & – & – & 2766 \(\pm\) 919 \\ \hline Resin & 80.7 \(\pm\) 4.2 & 75.2 \(\pm\) 5.0 & 69.5 \(\pm\) 4.4 & 55.1 \(\pm\) 4.8 & 70.3 \(\pm\) 4.8 & – & – \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average advancing contact angle of different model fluids on the different studied surfaces (see caption of Table 2 for fluid nomenclature) and surface roughness measured by AFM and laser confocal microscopy. For calcite crystal only three fluids were tested.  
Figure 14: (**a**) Application of the Owens–Wendt method to get the two components of surface energy from the linear regression. (**b**) Contact angle measurements vs. ionic strength for NaCl and MgCl\({}_{2}\) brines in contact with a single calcite crystal.  
For Oboury Chalk, an average adhesion force of \(\mathrm{F_{adh}}^{\mathrm{(dry)}}\) = 2.14 nN and \(\mathrm{F_{adh}}^{\mathrm{(wet)}}\) = 6.94 nN was found respectively, in dry and in the presence of distilled water. For Ciply Chalk, the adhesion forces were \(\mathrm{F_{adh}}^{\mathrm{(dry)}}\) = 2.02 nN and \(\mathrm{F_{adh}}^{\mathrm{(wet)}}\) = 5.91 nN. The resulting estimation of the surface energy will be discussed in Section 4.2.
# Online EEG Seizure Detection and Localization
## 2 Materials and Methods
### Correlation Network
The CN is generated for all 2-second sub-epochs of the event. The connection ratio of CN in the high gamma band in brain regions (R-F (right-frontal), L-F (left-frontal), occipital, central, R-T (right-temporal), L-T (left-temporal), parietal) is calculated for each sub-epoch (Figure 4b). The first sub-epoch with a region connectivity more than 40% is defined as the start of the seizure. If none of the sub-epochs pass this criterion, the first sub-epoch (which is the first 2 s of the epoch containing the beginning of the seizure) is assumed to contain the seizure origin. As mentioned before, seizures are assumed to result in increased similarity between neighboring electrodes. Therefore, to locate the seizure origin, two CN parameters of the chosen sub-epoch, the connection ratio (indication of the malfunction of neuronal inhibition  
Figure 4: Correlation within the network (CN) example at the beginning of a seizure; (**a**) CN contains connection, marked target of the seizure origin, and colored-background by the normalized power of the channels; (**b**) average number of connections in each region (right, left, right-frontal (R-F), left-frontal (L-F), occipital, central, right-temporal (R-T), left-temporal (L-T), parietal, general); and (**c**) strength of connection in each region. and excitation activity) and strength of connections (an indication of the hypersynchronization of neighboring neuronal regions) are considered simultaneously. This is done by computing the product of these values. The region with the maximum product of these two parameters is picked as the origin of the seizure. For instance, in Figure 4, the seizure is located in the L-T lobe. Also, a finer localization is marked on the CN schematic calculated by the three electrode coordinates of the two strongest connections in the defined seizure origin region. The centroid of the three electrodes is defined as the seizure origin and visualized by filled circles. The marked origin of the seizure in Figure 4a is located in the L-T region, closer to the electrodes with higher relative power. As mentioned in Section 2.3, the background color of the CN, as well as the DN network, represents the relative power of channels.