esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation
### The Computational Mesh & Geometry  
To generate the 3-D meshes based on the advancing-front method (AFM) meshing algorithm, tetrahedral meshes were used for the discretization in 3-D geometry models. The mesh in the quasi-coaxial TEM model consists of approximately \(8.7\times 10^{5}\) elements and the mesh in the circular TE\({}_{11}\) model consists of approximately \(7.5\times 10^{5}\) domain elements. The meshes used in these models were set finer (0.1-1.2 mm) near the quartz window surface than those in the rest of the discharge chamber (10 mm in maximum).
## 3 Results
### Penetration by Paraffin Oil Mist  
The mean paraffin oil mist aerosol penetration results for the tested nonwoven composites containing zeolite and silica materials are given in Table 5, with relative changes shown in Figure 11. Deterioration of protective and functional properties of the composite materials in relation to the reference sample is marked with negative values, while the improvement of protective and functional with positive values. Based on the statistical analysis performed, no significant differences were found, although absorbent materials with different textural parameters were used.  
The lowest paraffin oil mist penetration values were found for the composite with zeolite Na-A and unmodified and modified silica MCM-41 and MCM-41 HDTMA, at 0.69, 1.30, and  
Figure 11: Relative change in paraffin oil mist aerosol penetration for nonwoven zeolite and silica composite.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Variant** & \
\begin{tabular}{c} **Mean Paraffin Oil Mist** \\ **Aerosol Penetration, \%** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Median** & **MAX \%** & **MIN, \%** \\ \hline
1 & PPQ + MCM-41 & 1.30* & 0.27 & 1.20 & 1.80 & 0.98 \\
2 & PPQ + Na-A & 0.69* & 0.36 & 0.80 & 1.20 & 0.99 \\
3 & PPQ + Na-P1 & 2.19* & 0.58 & 2.20 & 3.10 & 1.10 \\
4 & PPQ + 34.4 & 3.18* & 0.19 & 3.20 & 3.40 & 2.90 \\
5 & PPQ + SM & 2.73* & 0.91 & 2.75 & 4.00 & 1.50 \\
6 & PPQ + MCM-41 HDTMA & 0.51* & 0.04 & 0.49 & 0.58 & 0.48 \\
7 & PPQ + Na-A HDTMA & 14.25* & 0.83 & 14.50 & 15.00 & 13.00 \\
8 & PPQ + Na-P1 HDTMA & 2.12* & 0.91 & 1.70 & 3.90 & 1.50 \\
9 & Pure PPference sample & 4.13* & 0.42 & 4.15 & 4.70 & 3.60 \\ \hline \hline \end{tabular}  
*a,b  
\end{table}
Table 5: Mean paraffin oil mist aerosol penetration for nonwoven composites containing zeolite and silica materials (descriptive statistics).  
0.51%, respectively, with the corresponding relative changes amounting to 83.27, 68.63, and 87.61%, respectively (as compared to the reference sample without zeolite or silica materials). The introduction of mineral additives in the structure of elementary fibers in the melt-blown process improved filtration efficiency as compared to the reference nonwoven in three cases, that is, zeolite Na-A as well as unmodified and modified silica materials MCM-41 and MCM-41 HDTMA.  
The highest paraffin oil mist penetration (14.25%) was found for the nonwoven composite containing the modified zeolite Na-A HDTMA, with the relative change being 244.76%. Similarly, as in the case of the NaCl aerosol, this is attributable to the fact that the applied zeolite contains chemical elements that may neutralize the electrostatic charges induced during the technological process.
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 2 Materials and Methods
### Preliminaries of Adding Doubling (AD) Algorithm
#### 2.1.5 Constructing the Entire Layer  
Once the reflectance and transmittance matrices of the initial thin layer are obtained, we can proceed to construct the entire layer. However, even before performing any calculations for the initial layer matrices, we must select an appropriate value for the initial layer optical depth. In the literature, two criteria have been proposed for this selection: \(\mathrm{d}\tau<0.01\) and \(\mathrm{d}\tau<v_{c-}\). For example, when using criterion \(\mathrm{d}\tau<0.01\), we can calculate number of doubling steps required:  
\[n_{d}=\mathrm{floor}\left(\log\left(\frac{d}{0.01|v_{1}|}\right)/\log(2)+1\right) \tag{18}\]  
The initial optical depth is then calculated as \(\mathrm{d}\tau=\tau/(2)^{n}_{d}\). with \(n_{d}\) defined by Equation (18). To obtain the reflectance and transmittance matrices for the entire layer, the following iterative procedure is performed:  
\[\begin{split}\mathbf{G}_{k}=\left(\mathbf{I}-\mathbf{R}_{k}{}^{ 2}\right)^{-1}\\ \mathbf{R}_{k+1}=\mathbf{T}_{k}\mathbf{G}_{k}\mathbf{R}_{k} \mathbf{T}_{k}+\mathbf{R}_{k}\\ \mathbf{T}_{k+1}=\mathbf{T}_{k}\mathbf{G}_{k}\mathbf{T}_{k}\end{split} \tag{19}\]  
where \(k\) is the number of steps from 0 to \(n_{d}-1\). The matrices calculated under Equation (17) obtain an index of 0. After that, with each doubling of the layer, the index increases by 1.
# Does Intensive Land Use Contribute to Energy Efficiency?--Evidence Based on a Spatial Durbin Model
## 2 Literature Review and Theoretical Hypotheses
### Intensive Land Use  
The concept of intensive use of land was first proposed by the classical economist [PERSON] in the theory of land rent, and it mainly refers to the increase in capital, labor and other factors input on the land to obtain higher output [12]. However, the input and output of capital and labor in the land is not an eternal, single positive correlation. After the input exceeds a certain range, the output will decrease as the input increases. Therefore, the increase in intensive land use is limited; when the continuous investment of capital and labor in land reaches the point of diminishing economic returns, that is, when the marginal revenue equals the marginal output, the operator will not add additional input. This critical point is the intensive boundary of land use. Land use that reaches the intensive boundary is called intensive land use. Conversely, land use that does not reach the intensive boundary is called extensive use.  
Under these constraints, by changing the investment intensity and utilization intensity, the utilization efficiency can be maximized [13]. It emphasizes the sustainable development of intensive land use under the constraints of resource environmental protection. Technological progress and green policy system support are generally considered to be important ways to improve energy efficiency. This is because green emission reduction policies adjust industrial and energy structures to eliminate outdated production capacity and jointly achieve the goal of reducing carbon emissions [14]; technological progress can improve energy efficiency, save energy and increase the development and utilization of clean energy. It is worth noting that the intensive use of land at the urban level can help increase density, cause creative destruction, environmental regulation, and ultimately increase energy efficiency [15].
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 2 Results
### TPS Classification and Motif/Domain Annotation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subfamily**} & \multirow{2}{*}{**Species**} & \multirow{2}{*}{**Release Version \({}^{\star}\)**} & \multirow{2}{*}{**The Prefix of Gene**} & \multirow{2}{*}{\begin{tabular}{c} **Toral** \\ **Symph** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Numbers** \\ **Genes** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **of TPSs** \\ **of TPSs** \\ **of TPSs** \\ **of TPSs** \\ **of TPSs** \\ **with Both** \\ **Domains** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Percent of** \\ **TPSs with** \\ **TPSs with** \\ **Downs TPSs** \\ **Know TPSs** \\ \end{tabular} } \\ \hline \multirow{4}{*}{_Prunoidae_} & _Prunus persica_ & GDR, v2.0 & Pr.pLo & 26,873 & 45 & 38 & 84.44\% & 38 (both domains) [2] \\ \cline{2-8}  & _Prunus mira_ & GDR, v1.0 & Pr.mir & 26,958 & 10 & 9 & 90.00\% & \\ \cline{2-8}  & _Prunus mume_ & NCBI, v1.0 & Pr.mum & 28,638 & 30 & 27 & 90.00\% & \\ \hline \multirow{4}{*}{_Maloidae_} & _Pyrus betulifolia_ & GDR, v1.0 & Py.bet & 59,552 & 48 & 37 & 77.08\% & \\ \cline{2-8}  & _Malus x domestica_ & GDR, HFTH1 v1.0 & Ma.dom & 44,677 & 56 & 36 & 64.29\% & 55 (all) [4] \\ \cline{2-8}  & _Malus haccata_ & CNGB, v1.0 & Ma.bac & 45,931 & 48 & 33 & 68.75\% & \\ \hline \multirow{2}{*}{_Rosoidae_} & _Fragaria vesca_ & GDR, v4.0 & Fr.ves & 35,914 & 65 & 43 & 66.15\% & \\ \cline{2-8}  & _Roso chinensis_ & GDR, v1.0 & Ro.chi & 39,669 & 76 & 57 & 75.00\% & \\ \hline \hline \end{tabular}  
* \({}^{\star}\) GDR, Genome Database for Rosaceae; CNGB, China National GeneBank DataBase; NCBI, National Center for Biotechnology Information. Species used for the phylogenetic tree are highlighted in bold.  
\end{table} 
Table 1: Summary of genome information and TPSs of sequenced Rosaceae species used in this study. Figure 1: Phylogenetic relationship and distribution of motif/domain of TPSs in three Rosaceae species (_P. persica_, _M. domestica_, _F. vesca_). The phylogenetic tree is shown on the left panel, while conserved motifs, conserved domains, and Pfam domains are shown on the right three panels. The phylogenetic tree from full-length amino acid sequences was constructed using the MEGA with maximum likelihood (ML) method. The conserved motifs were assessed using the online MEME software. The conserved domain was annotated based on the conserved domain database in NCBI, whereas the gene structure and domains were annotated by using the PfamScan tool. The conserved motifs and domains are shaded in different colors. The root nodes of TPS-a, TPS-g, TPS-b, TPS-c, TPS-e, and TPS-f clades are indicated by blue, green, yellow-green, red, benzo, and purple, respectively.
# Novel Processing Methods of Low-Clinker Multi-Component Cementitious Materials--A Review
## 3 Magnetic Field Treatment (MF-T) Methodology
### MF-T of Cementitious Materials/Concrete and SCMs
The influence of M-Water as mixing water on the fc, workability, and required cement content was studied by [PERSON] et al. [97]. Water treated with a 1.3 T MF-T intensity showed the greatest increase in concrete fc. [PERSON] and [PERSON] [98] conducted experimental studies on static MF-T on prepared mix concrete (Figure 8), showing slump increases ranging from 7% to 26%, with the best slump increase for fresh concrete corresponding to 400 mT of MF-T (26% higher). M-Water mixed concrete showed 30% and 16% increased compressive and splitting strengths, respectively, with enhancement directly influenced by MF-T length. The penetration of M-Water into cement particles during hydration improved activity and resulted in more crystalline hydration products, denser microstructure, and improved concrete performance, including shrinkage cracking resistance. [PERSON] and [PERSON] [91]  
Figure 8: Water-reduction potential due to static MF-T on fresh concrete (open access status) [98]. Figure 7: System for M-Water (with the permission of and recreated using data published in [77; 87; 91]). observed that M-Water in S-CC reduced the use of superplasticizer by up to 34.1% and improved compressive, bending, and tensile strengths by up to 34.1%, 52.4%, and 74.2%, respectively (Figure 9). The strength development of concrete under various curing conditions was compared by [PERSON] et al. [99] using M-Water and untreated water. M-Water resulted in concrete with a 10% higher fc than regular concrete after 28 days. [PERSON] et al. [100] studied the early-age shrinkage cracking resistance of M-Water mixed concrete, finding optimal performance at 260 mT MF-T, 280 mm MF-T length, 0.8 m s\({}^{-1}\) water flow, and a 72.2% reduction in total cracking area per unit specimen area.
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 2 Extra-Terrestrialization: Make Life Multi-Planetary
### Foundation on Another Celestial Body
Moreover, due to the major motivations of conducting scientific research, resource mining, and ISRU [42], the Earth's Moon can be another crewed base although it is much smaller than a planet and has only 16.5% gravity as Earth and no atmosphere. The Moon has water ice, oxygen (in iron-rich lunar minerals and glasses) [43], and abundant solar power and mineral resources such as iron, aluminum, magnesium, silicon, calcium, titanium, and rare-earth elements that are used for manufacturing electric vehicles, wind turbines, electronic devices, and clean energy technologies. More importantly, the Moon is estimated to contain more than 1 million tons of helium-3 (\({}^{3}\)He) at the surface [44], which can be potentially harvested for nuclear fusion. Furthermore, building a Moon base requires reliable systems that can operate remotely and accumulate considerable experience launching trips to Mars [45] and even further celestial bodies. As illustrated in Figure 8, a large number of (wirelessly) connected spaceships enabled with AI and advanced telecommunication and computing capabilities can mitigate the cross-planets/moons missions' challenges and enable seamlessly cooperative transportation. Figure 8: _Cont._
## 3 Results  
### Phenolic Acids and Their Analogs Exhibit Robust Antioxidant Activity  
Three antioxidant assays, FRAP, ORAC, and ABTS were employed to measure the antioxidant potential of the compounds of the five series as well as the standards (see the Supplementary Data). The selection of the potent compound(s) from each series was based on the cumulative antioxidant activity based on the three assays. Supplementary Information has been added indicating the detailed antioxidant values of all compounds in the three assays (Figure S1A-F and Table S1A-F). Herein, we report the summary of the antioxidant activity of selected efficacious compounds. Among analogs of series 1, ester **12**, the phenyl acetic acid analog of CAPE, exhibited the strongest antioxidant activity among the tested compounds (\(p<0.01\)) in vitro (Figure 1A). It exhibited FRAP, ORAC, and ABTS values of 374 \(\pm\) 9.2, 421 \(\pm\) 11.3, and 523 \(\pm\) 0.4 \(\upmu\)mol TE/L (Figure S1A and Table S1A). Ester **16**, the CAPE's analog, exhibited the strongest antioxidant activity (Figure 1B) with FRAP, ORAC, and ABTS values of 315 \(\pm\) 6.5, 336 \(\pm\) 2.1, and 523 \(\pm\) 1.3 \(\upmu\)mol TE/L, respectively, among the related analogs of series 2 (\(p<0.05\)) in vitro (Figure S1B and Table S1B. Further, in series 3 and 4, esters **26** and **35** exhibited the strong ability to quench free radicals (\(p<0.05\)) in vitro (Figure 1C,D). Bearing 3-phenylpropargyl (**26**) and 3-phenylallyl (**35**) moieties, esters **26** and **35** are less flexible compared to CAPE or ester **1**. Their antioxidant capacity as indicated by the FRAP, ORAC, and ABTS was 288 \(\pm\) 2.7, 226 \(\pm\) 8.4, 486 \(\pm\) 4.4 and 303 \(\pm\) 6.6, 459 \(\pm\) 15.3, and 510 \(\pm\) 18.1 \(\upmu\)mol TE/L, respectively (Figure S1C and Table S1C). Finally, in series 5, 3,4-dihydroxychakcone **42** and its related phenethyl analog **44** exhibited a stronger activity in comparison to the other analogs (\(p<0.05\)) in vitro (Figure 1E). Their antioxidant capacity as indicated by the FRAP, ORAC, and ABTS was 222 \(\pm\) 13.8, 444 \(\pm\) 1.1 and 511 \(\pm\) 1.0 and 231 \(\pm\) 3.5, 438 \(\pm\) 2.8, 517 \(\pm\) 6.4 \(\upmu\)mol TE/L, respectively (Figure S1E and Table S1E). The modification of the caffe acid moiety as well as the replacement of the ester functional group by an ether did not have a significant effect on the antioxidant activity. None of the compounds in series 2 and 6 exhibited consistent and strong activity in the three assessment assays conducted in vitro (Figure 1B,F; Table S1B,F). The extensive antioxidant analysis using multiple assays revealed robust antioxidant activities of phenolic acid derivatives in vitro (Figure 1A-F, Table S1A-F).  
Figure 1: Antioxidant activity of Caffe acid and CAFE analogs (100 uM) as measured using ABTS, FRAP, and ORAC antioxidant assays. Selective compounds from each series of analogs (**A–F**) with significant antioxidant activities were selected for further studies following statistical analysis: \({}^{*}p<0.05\), \({}^{**}p<0.001\), \({}^{***}p<0.0001\), \({}^{**}\)n: non-significant.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 3 Path Segmentation-Based Hybrid Caching
### Caching Idea Based on Path Segmentation
The cache_pressure and migrate_node_address fields: These two fields are used to record the minimum cache pressure value and the corresponding node address in the non-edge area on each data return path, respectively. The calculation of cache pressure value and the migration node's selection in the non-edge area will be discussed later. Figure 2: Path segmentation. The popularity field: This field is used by both migrated and normal data packets. In this paper, we present the concept of caching value. Each cache node calculates the cache value for the incoming content and updates the cache value for the hit content; caching decisions on incoming content and the replacement and migration of cached content are determined by cache value. The popularity of the content is a major factor in determining the size of the cache value. We choose the number of times the content is requested in the cache node as the content's popularity. The _popularity_ field is used to record the popularity of the content delivered by the packet at the source node of the packet.
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 2 Materials and Methods
### Methods
#### 2.3.3 Land Use Pattern Simulation Using Patch-Generating Land Use Simulation Model (PLUS)  
The patch-generating land use simulation (PLUS) model is a multi-type LULC simulation model developed by [PERSON] et al. [35]; it integrates the influences of anthropogenic and natural factors. This model proposes a framework for rule mining based on Land Expansion Analysis Strategy (LEAS). On this basis, a Cellular Automata (CA) model based on multi-type Random Patch Seeds (CARS) was proposed to better simulate the fine-scale patch growth of various LULC types. The working principle of the model is shown in Figure 2. LEAS is used to obtain the land expansion map in this period by superimposing the LULC maps for two periods. Then, the land expansion map and selected driving factors (Figure 3) are sampled, and the sampled data are used to develop dual-state Random Forest Models. The relationship between the growth of each LULC type and a variety of driving factors is discussed, and a map of the growth probabilities of each land use type and the contribution rate of each driving factor to land expansion is generated. CARS is a preset scenario-driven LULC simulation model that simulates LULC from the macroscale (target LULC amount) to the microscale (each grid LULC type) using a series of competitive mechanisms.  
Figure 2: Framework of the patch-generating land use simulation model and the modeling workflow.  
The model simulates LULC changes based on the driving factors behind the dynamics of land use transition, taking into account the background conditions of climate during land use simulation, the interaction and competition between different land use types during simulation training, and other related factors (nature reserves, cities, transportation routes, etc.) on land use simulation. This model can better simulate the LULC pattern with scattered patches. Moreover, research has shown that the PLUS model can simulate LULC and landscape patterns well, and that the simulation accuracy is better than that of models such as the Future Land Use Simulation (FLUS) model, the Conversion of Land Use and its Effects (CLUE) model, and Artificial Neural Networks-Cellular Automata (ANN-CA) [35; 36].
## 3 Enhanced SoC-Balancing Strategy with Saturation
### Expressions of CDRs  
Above all, we assume that \(SoC_{10}>SoC_{20}>SoC_{30}\). Moreover, some intermediate variables are needed, and they are defined as follows:  
\[\left\{\begin{array}{l}CDR_{11}=SoC_{1}^{n}/\left(SoC_{1}^{n}+SoC_{2}^{n}+SoC_{ 3}^{n}\right)\\ CDR_{12}=SoC_{1}^{n}/\left(SoC_{1}^{n}+SoC_{2}^{n}+SoC_{3}^{n}\right)\\ CDR_{13}=SoC_{1}^{n}/\left(SoC_{1}^{n}+SoC_{2}^{n}+SoC_{3}^{n}\right)\\ CDR_{22}=(1-i_{\text{sat}}/i_{\text{sum}})\times SoC_{2}^{n}/\left(SoC_{2}^{n }+SoC_{3}^{n}\right)\\ CDR_{23}=(1-i_{\text{sat}}/i_{\text{sum}})\times SoC_{2}^{n}/\left(SoC_{2}^{n }+SoC_{3}^{n}\right)\\ CDR_{31}=(1-i_{\text{sat}}/i_{\text{sum}})\times SoC_{1}^{n}/\left(SoC_{1}^{ n}+SoC_{2}^{n}\right)\\ CDR_{32}=(1-i_{\text{sat}}/i_{\text{sum}})\times SoC_{2}^{n}/\left(SoC_{1}^{ n}+SoC_{2}^{n}\right)\end{array}\right. \tag{7}\]  
Figure 6a gives the flow path used to assign CDRs in series applications, and it is performed once in each control period. Detailed expressions of CDRs are listed in Table 3. Here, \(g\) and \(h\) are the unit amounts at the upper and lower saturations. The main workflow is summarized in three steps. Firstly, the judging conditions are used to confirm the values of \(g\) and \(h\). Secondly, the current distributions of all the units are kept within an allowable range again. Finally, the CDRs are assigned according to the specific case. The assigning task in parallel applications is shown in Figure 6b. Compared with that in series applications, it is regarded as a simplified version, setting \(i_{\text{sat}}=0\) and \(h=0\). In fact, its expressions of CDRs correspond to the first three cases in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Case** & \(g\) & \(h\) & \(CDR_{1}\) & \(CDR_{2}\) & \(CDR_{3}\) \\ \hline
1 & 0 & 0 & \(CDR_{11}\) & \(CDR_{12}\) & \(CDR_{13}\) \\
2 & 1 & 0 & \(i_{\text{sat}}/i_{\text{sum}}\) & \(CDR_{22}\) & \(CDR_{23}\) \\
3 & 2 & 0 & \(i_{\text{sat}}/i_{\text{sum}}\) & \(i_{\text{sat}}/i_{\text{sum}}\) & \(1-2i_{\text{sat}}/i_{\text{sum}}\) \\
4 & 0 & 1 & \(CDR_{31}\) & \(CDR_{32}\) & \(i_{\text{sat}}/i_{\text{sum}}\) \\
5 & 0 & 2 & \(1-2i_{\text{sat}}/i_{\text{sum}}\) & \(i_{\text{sat}}/i_{\text{sum}}\) & \(i_{\text{sat}}/i_{\text{sum}}\) \\
6 & 1 & 1 & \(i_{\text{sat}}/i_{\text{sum}}\) & \(1-(i_{\text{sat}}+i_{\text{sat}})/i_{\text{sum}}\) & \(i_{\text{sat}}/i_{\text{sum}}\) \\ \end{tabular}
\end{table}
Table 3: Expressions of CDRs with saturation.  
Figure 6: Flowchart of CDR assignment: (**a**) series application; (**b**) parallel application.