esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Estimation Method
#### 2.4.5. Calculation of Emission Reduction of GHG from Recycling of Renewable Resources  
Recycling of renewable resources can replace the production of related products, so as to save the energy consumption required for the production of raw materials. The calculation of GHG emissions is shown in Equation (21), in which \(R_{i}\) is the recycling amount of the \(i\)th category renewable resources in t, and \(F_{i}\) is the comprehensive equivalent coal consumption per unit output of the raw material that was replaced by the \(i\)th category renewable resources. As paper and plastics were the main components in recyclable resources, with the proportion of 20.83% and 20.74% in mixed waste, so the paper only considered \(F_{i}\) for these two categories. \(F_{i}\) for paper and plastics were 0.3332 and 0.5113 (unit: t standard coal/t recyclable resources). And the values were assessed from _China Statistical Yearbook 2018_[7]. \(EF_{c}\) is the GHG emission coefficient of standard coal, which was set to 0.69 in this paper, and it is close to the value of 0.68 used by the Institute of Energy Economics of Japan and 0.69 used by the Energy Information Administration of the Department of Energy of the United States. \(OF_{c}\) is the oxidation factor of standard coal, which was set to 0.93.  
\[E_{r}=\sum R_{i}*F_{i}*EF_{c}*OF_{c}*\frac{44}{12} \tag{21}\]
# Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data
## 3 Results and Discussion
### Water Area  
The optimal threshold for 2020 was selected as \(-16.87\) through the histogram, where the low peak corresponded to water pixels while the high peak corresponded to nonwater pixels (see Figure 3). Similarly, the optimal thresholds for each year from 2017 to 2019 were \(-14.88\), \(-16.93\) and \(-16.96\) respectively. Based on the auto-selected thresholds derived from the Otsu Method, the imagery was classified into water and nonwater regions. As a result, the surface water averages of Poyang lake from 2017 to 2020 were obtained and are presented in Table 4 and visualized from left to right in the Figure 4. The surface water area decreased by nearly 650 km\({}^{2}\) between 2017 and 2018, then increased by nearly 640 km\({}^{2}\) between 2018 and 2019 and finally decreased by nearly 856 km\({}^{2}\) between 2019 and 2020. This shows that surface water area of Poyang lake decreased with fluctuation, which is consistent with other research on variation of the surface water of Poyang lake during the time period of 1988-2016 [49].  
In addition to the significant interannual variation, our results also showed the spatial variation of surface water area. The dry or draw down areas mainly occurred in the center and the boundary of the lake at the same time. The water areas located to the north (connected to Yangtze River) and west (connected to Gan River) accounted for most of the variation, while the water areas located at the east and south maintained much less variation.  
The water area variations typically are closely associated to water level variations in Poyang lake basin [50]. Both water area and water level are dominant factors for wetlands in Poyang lake, and thus affect habitat distribution and accessibility. In this case, our classification results that show the spatiotemporal water area variations can provide robust linkages to habitat availability and suggest future research to further quantify this relationship.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Area (km\({}^{2}\))** \\ \hline
2017 & 1959.50 \\
2018 & 1308.67 \\
2019 & 1948.72 \\
2020 & 1092.82 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Area (km\({}^{2}\)) of Poyang lake from 2017 to 2020.  
Figure 2: Three Situations of Misclassification: (**a**) A pixel (116.4064 E, 28.8405 N) that was misclassified into nonwater. (**b**) Two pixels with equal dB values but different truth label \(y\) (116.2196 E, 28.9905 N) (116.2201 E, 28.9899 N). (**c**) A pixel (116.1584 E, 29.2136 N) that was misclassified into water.
## 3 Results and Discussion
### Validation of the NDL\({}_{\text{MB}}\) Based on Two Case Studies in 2016  
In order to validate the NDL\({}_{\text{MB}}\) chemical analyses of filter samples collected at three sites (Graz Don Bosco, DB; Graz Ost, GO; Gratwein, GW; compare Figure 1) were used to determine the crustal loads. This analysis was performed for three days (23.02.2016, 05.04.2016 and 06.04.2016) featuring desert dust intrusion in was thoroughly described earlier.  
For the strong transport event of desert dust in April, measured CLs match the calculated NDL\({}_{\text{MB}}\) very well, indicating that the NDL\({}_{\text{MB}}\) is mainly composed of mineral matter. CLs were found to account for more than 70% of the NDL\({}_{\text{MB}}\) with values ranging from 41.2 ug/m\({}^{3}\) at GO to 39.0 ug/m\({}^{3}\) at GW for the 5 th of April 2016, compared to the NDL\({}_{\text{MB}}\) of 50.6 ug/m\({}^{3}\) and from 31.5 ug/m\({}^{3}\) at GO to 25.3 ug/m\({}^{3}\) at GW for the 06 th of April 2016, compared to the NDL\({}_{\text{MB}}\) of 35.3 ug/m\({}^{3}\).  
For the dust event in February, measured CLs are more than 50% lower (17.7 ug/m\({}^{3}\) at DB, 16.5 ug/m\({}^{3}\) at GO and 10.9 ug/m\({}^{3}\) at GW) than the calculated NDL\({}_{\text{MB}}\) (37.1 ug/m\({}^{3}\)) pointing to additional contributions of other particulate matter sources. This result underlines the need of the threshold criteria. Using those the NDL\({}_{\text{MB}}\) is not allowed to be considered for a reduction, because the BGL is too low. Based on WRF-Chem model forecasts is can also be expected that the SD intrusion during this event was rather weak and other, maybe anthropogenic sources, dominate the NDL.
# Numerical Analysis of Human Cancer Therapy Using Microwave Ablation
## 1 Introduction
and [PERSON] et al. did not take into account any change in tissue properties such as tissue water vaporization, vapor condensation, and tissue shrinkage [21; 22]. [PERSON] et al. (2015) have presented a numerical evaluation of the heat transfer process in liver tumors according to temperature-dependent properties using a coaxial antenna with ten slots [25]. Their simulations showed that using a coaxial multi-slot antenna reduces over-treatment compared with a conventional coaxial antenna. In addition, they revealed that a coaxial antenna with multi-slots generates the smallest over-treatment region for spherical tumors. [PERSON] et al. (2015) performed a computer simulation in order to compare heating differences between cancer and normal breast tissue [26]. They demonstrated that the dielectric and thermal parameters relative to cancer and normal breast tissue are able to create special heating of tumors during microwave ablation. Although the available computational models provide a strategy for cancer treatment, they require further development. Numerical modeling remains primordial when it comes to carrying out a parametric study, evaluating the importance of the inherent phenomena, and leading to better optimization of the design of new devices. For example, clinical treatment with MWA requires the control of elevation of temperature in order to ensure the destruction of cancer cells without damaging healthy tissue. Hence,numerical simulation has been widely used in the investigation of MWA as an instructive tool guiding practical treatment. In the present work we have investigated the interaction between electromagnetic waves with tissue in order to treat liver cancer. A two-dimensional numerical simulation is performed using the finite element method. Maxwell's equations and the bio-heat equation are used to estimate the electric field and the heat distribution in the liver tissue. Heat generation by the antenna is used to destroy cancer cells but high temperature can damage healthy surrounding tissue. In addition, the influence of input microwave power, specific absorption rate (SAR), and temperature distribution on the liver tissue, and the fraction of necrotic tissue during the ablation thermal, are investigated.
## 2 Materials and Methods
### Process of Creating a Virtual Reality Game
#### 2.1.4 A Mathematical Tool for 3D Modelling and Animation  
Mathematics plays a major role in 3D modelling and animation in the creation of 3D games, and the mathematical apparatus used includes concepts and techniques from different areas of mathematics. Some key mathematical tools in 3D game modelling and animation are linear algebra, trigonometry, and geometry.  
Linear Algebra: Basic mathematical objects of linear algebra include vectors, matrices, and transformations, which describe spatial relationships in 3D space [39; 41].  
* Vectors represent positions, velocities, directions of motion, or forces in 3D space. They describe distances, speed, and direction of movement of characters or objects;
* Matrices are used to describe transformations such as rotation, scaling, and translation. In 3D animation, they allow the transformation of the coordinates of points and objects in different coordinate systems, which is essential for a realistic representation of movements and perspectives;
* Homogeneous coordinates allow the different transformations (such as rotation, scaling, and translation) to be combined into a single matrix, making calculations easier.  
Trigonometry is important for calculations related to angles and rotations, especially for motion and animation in 3D space. Basic trigonometric functions such as sine and cosine are used for the following [41]:  
* Rotations of objects or characters around a given axis;
* The trigonometric calculations control the cameras, for example, when they rotate around an object;
* Trigonometry helps describe motion along arcs and curves, such as for animating objects that move in a circular path.  
Geometry is essential in creating shapes, interactions, and collisions between objects in 3D games, such as the following [39; 43]:  
* Transformations: rotation, scaling, and translation are applied to geometric objects (models) through matrices;
* Curves and surfaces: splines and other curves (such as Bezier curves) are used to animate objects that move along a smooth path;
* Collision: the geometry is also key to detecting collisions between objects in space.
## 3 Classical NR Shell Theory for Moderately Thick, Linear Elastic, Shells
### Reduction from 3D to 2D Shell Elasticity by Means of the Principle of Virtual Power
#### 3.2.1 Effective Internal Stress Force and Stress Couple Resultants
In principle, [PERSON]'s generalized law must apply for a 3D linear isotropic elasticity body with [PERSON]'s shear modulus \(G(\tilde{\mathbf{\alpha}})\overset{\text{def}}{=}\frac{E(\tilde{\mathbf{\alpha}})}{2(1 +\
u(\tilde{\mathbf{\alpha}}))}>0\) being related to [PERSON]'s modulus \(E(\tilde{\mathbf{\alpha}})>0\) and [PERSON]'s ratio \(\
u(\tilde{\mathbf{\alpha}})>1/2\), in the local principal basis \((e_{1},e_{2},e_{3})\equiv(\mathbf{t}_{1},\mathbf{t}_{2},\mathbf{n})\). Nevertheless, in view of the shell thinness and owing to the linear framework, we adopted, as most classical shell models [74] (see also, e.g., the discussions in [76] (page 19)), the ensuing (seemingly ad hoc, but somehow common) approximate stress-strain constitutive relation:  
\[\sigma_{II}=2G\Big{(}\varepsilon_{II}^{s}+\tfrac{\
u}{1-\
u}| \epsilon_{II}|\varepsilon_{II}^{s}\Big{)}\,\ \text{for}\ I=1,2 \text{(plane stress)}\] \[\sigma_{12}=G(\varepsilon_{12}^{s}+\varepsilon_{21}^{s})\equiv 2G \varepsilon_{12}^{s}\equiv\sigma_{21} \text{(general case)} \tag{45}\] \[\sigma_{13}=\zeta G(\varepsilon_{13}^{s}+\varepsilon_{31}^{s}) \equiv 2\zeta G\varepsilon_{13}^{s}\equiv\sigma_{3I}\,\ \text{for}\ I=1,2 \text{(general case)}\]  
while letting \((G,E,\
u)\) take possibly values different from the 3D elasticity ones. Here, a \"_quasi-plane stress state_\" hypothesis is enforced only for the \"in-plane\" (tangent) normal stress components \(\sigma_{II}\) (in anticipation of static membrane problems), but not necessary for the surface \"in-plane\" (tangent) and \"out-plane\" (transverse) shear stress components \(\sigma_{12}=\sigma_{21}\) and \(\sigma_{3I}=\sigma_{3I}\), nor the _indeterminable_ \"out-plane\" normal stress one \(\sigma_{33}\). Note as well that these expressions assume \(\varepsilon_{33}^{s}=0\) (i.e., the shell is transversely undeformable), neglecting, therefore, Poisson's effects in the transverse direction. Moreover, in order to treat the moderately thick shell case, a classical \"_transverse shear correction factor_\" (or \"_shear reduced coefficient_\") \(\zeta(\tilde{\mathbf{\alpha}})>0\) (see the third line in Equation (45)) is introduced in the transverse shear stress-strain constitutive relations to make the theory (energetically or first vibrational mode) compatible with the complete 3D formulation, like for plates [86]. **Geometric in-plane extension-bending and in-plane twisting-shearing interaction effects.
## 2 Materials and Methods
### Detection of Cell Viability  
Hep G2 cells were chosen because of their functional relevance to fully differentiated primary hepatocytes, including the enzymatic activity of Phase I and Phase II ([PERSON] et al., 2017). Hep G2 cells in the logarithmic growth phase were collected into the cell culture medium (containing 10% bovine serum) after trypsin digestion, and the cell suspension was centrifuged (200\(\times\)\(g\), 5 min) to precipitate the cells. Cells were resuspended with a culture medium to obtain a suspension of individual cells. The concentration of cell suspension was adjusted via cell counting. Cell suspension (180 \(\upmu\)L) was added to each well of a 96-well plate, i.e., 1 \(\times\) 10\({}^{4}\) cells were inoculated, and then, 20 \(\upmu\)L volumes of different concentrations of AFB\({}_{1}\) working solution or degradation product samples were added. The plate was incubated in a thermostatic incubator containing 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. After 72 h, the medium was removed, and the wells were washed thrice with PBS. AFB\({}_{1}\) working solutions (200 \(\upmu\)L) containing 0.5 mg/mL MTT or degradation product samples were added to each well. After 72 h, the medium was removed, and each well was washed thrice with PBS. Furthermore, 200 \(\upmu\)L of medium containing 0.5 mg/mL MTT was added into each well, and the microplate was incubated in a carbon dioxide thermostat incubator (Shinetek, Beijing, China; WIGGENSWCI-180) under 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. After 4 h, the supernatant was discarded, and 200 \(\upmu\)L of DMSO was added to each well. A microporous plate thermostatic oscillator (HUXI, Shanghai, China) was used to gently shake the samples at 800 rpm for 10 min to fully dissolve the blue formazan crystals. The absorbance at 570 nm (OD\({}_{570\text{nm}}\)) of each well was measured using a microplate reader (TECAN, Mannedorf, Switzerland; SPARK 10M), and the cell morphology was observed using an inverted microscope (Leica, Wetzlar, Germany; DMIL LED). The absorbance values for each group were used to calculate the relative cell viability according to the following formula:  
Relative cell viability (%) = (OD\({}_{570\text{nm}}\) of experimental group \(-\) OD\({}_{570\text{nm}}\) of background)/ (OD\({}_{570\text{nm}}\) of control group \(-\) OD\({}_{570\text{nm}}\) of background) \(\times\) 100%.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Glacier Surface Velocity Maps
#### 3.1.2 Mont-Blanc Area in the French Alps  
The Mont-Blanc massif, located in the French Alps, includes some of the largest glaciers in the European Alps with the Mer de Glace ( 30 km\({}^{2}\)) and Argentiere (14 km\({}^{2}\)) flowing into the Chamonix valley, the Mage glacier flowing towards Italy and the Tre-La-Tete glacier at the southern tip of the massif. However, the fastest surface velocities are observed for smaller glaciers with steep surface slopes, such as the Bossons glacier or the Brenva glacier on the Italian side with velocities above 350 m/year (Figure 2A). We found that the surface speed of the Mer de Glace is on average 70 m/year in the lower part of the glacier tongue while exceeding 500 m/year in the ice fall below the equilibrium altitude (ELA) (Figure 2A), in agreement with previous measurements made with SPOT-5 images [(11)]. The Argentiere glacier also flows at about 70 m/year into the lower end of the glacier tongue (Figure 2A). We notice that the upper part of the glacier is almost constantly shaded with less surface features, resulting in fewer successful matching pairs and therefore higher uncertainty (Figure 2D). In general, it is noted that the areas with small standard deviations (1-2 m/year) are those with the gentlest slopes (i.e., Argentiere, Mer de Glace, tongue of the Mage glaciers), where the largest number of successful matching pairs are obtained (>300) (Figure S4B).
## 3 Experiments and Results
### Evaluation  
The learned policy was initially evaluated on the real robot with a 3D-printed version of the cuboid peg in the hole-insertion task with the true goal pose. During evaluation, observations and actions were recorded. Figure 7 shows the performance of the learned policy (sim2 real + retrain). The figure shows the relative position of the end effector with respect to the goal position, the contact force, and the actions taken by the policy for each Cartesian direction normalized to the range of \([-1,1]\), as described in Section 2.2.3. As shown in Figure 1, the insertion direction was aligned with the y axis of the robot's coordinate system. In Figure 7, we highlighted three phases of the task. Blue corresponds to the search phase in free space before contact with the surface, yellow is the search phase after initial contact with the environment, and green corresponds to the insertion phase. During the search phase, and particularly on the insertion direction (y axis), we could clearly observe that the learned policy properly reacted to contact with the environment by quickly adjusting the force control parameters. On top of that, during the insertion phase, the learned policy changed its strategy from just minimizing contact force to a mostly position-control strategy to complete insertion. This behavior is proper for this particular insertion task, as there is little resistance during the insertion phase, but it is not the desired behavior for other insertion tasks, as we discuss later in Section 3.4.2.1.  
Additionally, we compared the performance of the learned policy as a combination of sim2 real and refinement in the real robot versus just learning in the real robot or just directly transferring the learned policy from the simulation (sim2 real) without further training. We evaluated these policies in a 3D-printed version of the cuboid-peg-insertion task. Policies were tested 20 times with a random initial position assuming a perfect estimation of the goal position (true goal). Table 2 shows the results of the evaluation. The three policies had very high success rates, but the policy transfer from the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Condition**} & **Value Range** \\ \hline Initial position (relative to goal) & Position (mm) & \([-400,400]\) \\ \cline{2-3}  & Orientation (\({}^{\circ}\)) & \([-10,10]\) \\ \hline \multirow{2}{*}{Uncertainty error} & Position (mm) & \([-2,2]\) \\ \cline{2-3}  & Orientation (\({}^{\circ}\)) & \([-5,5]\) \\ \hline \multicolumn{2}{c}{Desire insertion force (N)} & \([0,10]\) \\ \hline \multicolumn{2}{c}{Stiffness} & \multirow{2}{*}{\([7.0\times 10^{-4},1.0\times 10^{-5}]\)} \\ \cline{2-3} \multicolumn{2}{c}{(in Gazebo: _surface/friction/ode/kp_)} & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Randomized training conditions.  
Figure 6: Simulation environment. Overlay of randomizable goal positions.  
simulation had difficulty with the real-world physics dynamics. As expected, the policy retrained from the simulation gave the best overall performance time.
## 3 Some Numerical Results and Discussions
### Transient Responses of Deflection and Stress  
The transient responses of \(w(a/2,b/2)\) (unit mm) are computed for \(a/h^{*}=10\) with fixed \(\omega_{11}=0.065469/\)s under \(c_{1}=0.925925/\)mm\({}^{2}\), \(R_{n}=1\), advanced nonlinear \(k_{\alpha}=-3.535402\), \(T=600\) K and \(\overline{T_{1}}=100\) K. Figure 6 shows the transient \(w(a/2,b/2)\) compared by using simply homogeneous equation and fully homogeneous equation with \(\gamma=785.3982/\)s, respectively, for \(t=0.001\) s-0.025 s. The \(w(a/2,b/2)\) amplitudes in the simply homogeneous equation are found in greater values and overestimated in the approach. The explanation provided more clearly for Figure 6. There are two equation types (simply homogeneous equation and fully homogeneous equation) used to calculate the values of \(\omega_{mn}\) for Equation (6). The simply homogeneous equation and fully homogeneous equation are listed in the Appendix A. Figure 7 also shows the transient \(w(a/2,b/2)\) compared with values of nonlinear varied \(k_{\alpha}\) (expression with containing the \(c_{1}\) factor) and linear varied \(k_{\alpha}\) (expression without containing the \(c_{1}\) factor) for the \(a/h^{*}=10\) case of \(\gamma=785.3982/\)s. The transient \(w(a/2,b/2)\) values in linear varied \(k_{\alpha}\) case are overestimated with respect to the nonlinear varied \(k_{\alpha}\) case.
## 2 Materials and Methods
### Plasmid Preparation and PCR Amplification of Repair Templates  
All plasmids were extracted using the ZymoPure Plasmid Maxiprep kit (Zymo Research, Irvine, CA, USA) as directed by the manufacturer. For all PCR analyses performed, TaqMan DNA Polymerase Master Mix RED (Ampliqon, Odense, Denmark) was used according to the manufacturer's instructions in an Applied Biosystems (Waltham, MA, USA) thermocycler. The plasmids pRDH180, pMI529 and pIBG-SSAD, were used to amplify the homology repair templates for _Trichoderma reseei eg2_ (_T.r.eg2_ GenBank:KX255673), _Talarmoyces emersonii_ (now called _Rasamsonia emersonii_) cbh1 (_T.e.cbh1_ GenBank:AAL89553), and _Aspergillus aculeatus BGL1_ (_A.a.bg1_ GenBank:D64088). Each of these plasmids was assigned specific primers for amplification of their target gene cassette (see Supplementary Table S1). The PCR reactions for the templates pRDH180 and pMI529 were conducted as follows: an initial denaturation step at 95 \({}^{\circ}\)C for 5 min, followed by 31 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 58 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 2 min 45 s; a final extension step of 7 min at 72 \({}^{\circ}\)C was allowed. For pIBG-SSAD as a template, the conditions were optimized and set as follows: an initial denaturation step at 94 \({}^{\circ}\)C for 5 min, followed by 31 cycles of denaturation at 95 \({}^{\circ}\)C for the 30 s, annealing at 55 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 3 min 10 s; a final extension step of 7 min at 72 \({}^{\circ}\)C was allowed. PCR products were resolved by gel electrophoresis to confirm the amplification. The PCR products were then purified using standard Phenol: Chloroform: Isoamyl alcohol (PCI) extraction, followed by quantification on a Nanodrop2000 Spectrophotometer (Thermo-Fischer Scientific) to determine the concentration of the purified PCR product to be used for transformation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strain** & **Abbreviation** & **Description** & **Reference** \\ \hline _S. cerevisiae_ MH1000 & MH1000 & Industrial yeast strain, diploid, no auxotrophy & [22] \\ _S. cerevisiae_ M1744 & M1744 & Haploid yeast strain with uracil auxotrophy (\(\Delta\)_ura_3) & [18] \\ _S. cerevisiae_ M1744 + & & _S. cerevisiae_ M1744 + & \\ pCas9 + pRS42-G\_ChX & M1744-Ch10-EG2 & the chromosome X & This study \\ + _T.r.eg2_ & & intergenic site using pCas9 NAT and & \\  & & pRS42H\_ChX & \\  & & _S. cerevisiae_ M1744 with & \\ _S. cerevisiae_ M1744 + & & & \\ pRSCG\_ChXI + _T.r.eg2_ & M1744-Ch11-EG2 & the chromosome XI & This study \\  & & & \\  & & & \\ \end{tabular}
\end{table} 
Table 2: Yeast strains used in this study.