esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Variable Temperature
#### 3.2.2 Carbonation Depth  
The relationship between the carbonation depth of GC0 and GC0.05 with temperature is shown in Figure 7.  
Figure 5: SEM images of plain concrete in different temperatures: (**a**) 29, (**b**) 37, and (**c**) 45 \({}^{\circ}\)C.  
Figure 6: SEM images of graphene concrete in different temperatures: (**a**) 29, (**b**) 37, and (**c**) 45 \({}^{\circ}\)C.  
Under the condition of climate change, the temperature increases with time. It can be seen that the carbonation depth of the two kinds of concrete increases gradually. This may be because the increase in temperature will accelerate the diffusion speed of CO\({}_{2}\) and the movement speed of ions, and enhance the chemical reaction. Hence, it will accelerate the carbonation reaction. It is worth noting that the reaction rate first ascends and then grows slowly with the increase in temperature. At the initial stage of the test (the temperature is between 25 and 29 \({}^{\circ}\)C), the carbonation depth of graphene concrete is lower than that of ordinary concrete. The incorporation of graphene accelerates the hydration reaction of cement. Meanwhile, graphene nanoparticles effectively fill the spaces among pores and make the concrete denser than ordinary concrete. This slows down the carbonation reaction of concrete. However, when the temperature reaches 33 \({}^{\circ}\)C, there is little difference between the carbonation depth of the two kinds of concrete. When the temperature is higher than 33 \({}^{\circ}\)C (carbonation time is 21 days), Figure 7 shows that the carbonation depth of graphene concrete is deeper than that of ordinary concrete, which indicates that graphene will promote the carbonation reaction of concrete during this period, probably because graphene has excellent thermal conductivity. Moreover, the large specific surface area of GNP results in a larger contact area with concrete materials, which is conducive to the heat conduction of concrete and accelerates the carbonation reaction of graphene concrete.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 3 Background on Algebraic Level Sets
### Boolean Operations by R-Functions  
As observed in Figure 3, the direct implicitization extends the parametric curve beyond its end points, and yields an invalid distance measure in the extended region. Therefore, it is desirable to trim the curve \(\mathbf{C}(X(u),Y(u),W(u))\) within its parameter range \(u\in[a,b]\). In related prior work, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) constructed an approximate distance from a line segment as:  
\[g=\sqrt{\Gamma^{2}+\frac{(|\phi|-\phi)^{2}}{4}} \tag{9}\]  
with \(\Gamma\) being the distance in the normal direction from a piecewise linear approximation of the curve, and \(\phi\) being distance to the region formed by a circle circumscribing the line that is positive inside and negative outside. This form yields a smooth distance function across the boundary \(\phi=0\). [PERSON] et al. (2019) extended the above idea by carrying out Boolean operations on fields obtained on (individual curve segments of) an arbitrarily shaped parametric curve and an enclosing convex region using R-functions (Figure 4). The R-functions ([PERSON], 2018; [PERSON] and [PERSON], 2018) enable a smooth and purely algebraic Boolean operation, and result in a continuous distance measure. Two specific R-functions used in this study are:  
1. R-conjunction, equivalent to Boolean intersection: \[g_{1}\wedge g_{2}=g_{1}+g_{2}-\sqrt{g_{1}^{2}+g_{2}^{2}}\] (10)
2. R-disjunction, equivalent to Boolean union: \[g_{1}\lor g_{2}=g_{1}+g_{2}+\sqrt{g_{1}^{2}+g_{2}^{2}}\] (11)  
A well known property of Bezier and NURBS geometry is the convex hull property, which assures that the curve/surface is contained within its convex hull constructed using the control points. [PERSON] et al. (2019) used the convex hull property of Bezier and NURBS curves to provide a natural convex region bounded by control points for curve trimming. Assume that the \(i\)th hyper-plane of the convex hull is expressed as:  
\[h_{i}(\mathbf{x})=\mathbf{n}_{i}\cdot\mathbf{x}+b_{i}=0 \tag{12}\]  
where \(\mathbf{x}\in\mathbb{R}^{n}\) is a spatial point, \(\mathbf{n}_{i}\in\mathbb{R}^{n}\) is inner normal, and \(b_{i}\in\mathbb{R}\) is offset. Thus, the exact distance from any point \(\mathbf{x}\) to the hyper-plane \(h_{i}(\mathbf{x})=0\) is \(h_{i}(\mathbf{x})\). The function \(\phi\) can be obtained by applying R-conjunction operation of Equation (10) to all \(h_{i}(\mathbf{x}),i=1,2,...,n\). An example of a cubic Bezier curve is shown in Figure 5.  
Figure 4: A convex region \(\phi\geq 0\) is used to trim the implicitized curve \(\Gamma(\mathbf{x})=0\) constructed from a parametric curve \(\mathbf{C}(u)\).
year [2]. Recently, [PERSON] et al. [3] estimated that photoproduction of CO\({}_{2}\) from Chromophoric Dissolved Organic Matter (CDOM) has already increased by \(\sim\)15% in Arctic waters due to an increase in ultraviolet radiation and the decrease in sea ice associated with global warming. Positive feedbacks such as this have potentially serious consequences for humans and ecosystems alike, and emphasize the urgency to develop robust, global algorithms for retrieving oceanic carbon products remotely and synoptically. CDOM (refer to Table 1 for terms and abbreviations) is used to describe an often difficult to define fraction of the DOC pool (see Section 1.4) which has historically been called gilvin, _gelbstoff_, or simply \"yellow substance\". As its name suggests, the presence of CDOM imparts color to the water column through absorption of light by various chromophores, thereby providing an effective means of detecting CDOM remotely from ocean color reflection. Found in all natural waters and generally in highest concentration near shore, CDOM results from the breakdown products of plants and other organic matter into humic materials, and plays a significant role in aquatic photochemistry, photobiology, and as a tracer of the origins of oceanic water masses, e.g., [4; 5]. DOC and CDOM can be terrigenous or autochthonous (i.e., deriving from in situ primary and bacterial production in river to ocean waters), with the DOC variously composed of high molecular weight (HMW) humic substances (which tend to be more labile) and low molecular weight (LMW) humics (such as fulvic acids), depending on its origin, labile fraction, age, and whether it has transitioned from fresh waters to marine [6; 7; 8; 9; 10; 11; 12]. Most estuarine and nearshore CDOM is terrigenous, and as it mixes in rivers on its transit to marine waters, the amount of HMW material declines from flocculation, photo-oxidation and microbial decomposition leaving marine waters dominated by LMW CDOM (e.g., [6]), a condition imparting a characteristic spectral shape to inherent light absorption by CDOM (\(a_{g}(\lambda)\), where \(\lambda\) is wavelength) [7]. Inherent optical properties (IOPs) of the water column, such as the absorption and backscattering coefficients, depend on the composition and concentration of the dissolved and suspended material present, as well as the size and structure of the particles, and water itself. CDOM concentration--for which \(a_{g}(\lambda)\) is the common proxy following [PERSON]'s law--varies widely in the ocean, tending to be highest near river outflows, but may also be high in upwelling regions and other regions of autochthonous, plankton-based production through exudation, excretion, and microbial breakdown of detritus [8]. It is degraded over time both by microbial activity, photooxidation, and other abiotic processes, ultimately resulting in remineralization of the carbon, and release from the ocean as CO and CO\({}_{2}\). In the case of the CDOM fraction of DOC, degradation over time scales of days to millennia can significantly change the magnitude and spectral characteristics of \(a_{g}(\lambda)\).
## 4 Results and Discussion
### GC (Gas Cycle) and CC (Combined Cycle) Results
As a result, both monthly average energy and exergy efficiencies were low in comparison to Figures 10 and 11 particularly in the warm month in which the solar radiation was relatively high. Figure 10: Monthly average of exergy efficiency of four types of GT, GT + HRSG, GT + HSR, and GT + HRSG + HSR. Figure 9: Monthly average of energy efficiency of four types of GT, GT + HRSG, GT + HSR, and GT + HRSG + HSR. The annual average energy and exergy efficiencies of four types of GT, GT + HRSG, GT + HSR, and GT + HRSG + HSR are shown in Figure 12. The annual average energy efficiency for standalone GT was 28.4%, which could be significantly increased by up to 48.5% by adding HRSG to the GT cycle, which is system (a). Although adding the HSR cycle to the GT could increase the cycle up to 44%, this increment was 9% lower than the energy enhancement achieved by adding the HRSG unit to the GT. This outcome suggests that between adding HRSG and HSR, the annual average energy efficiency of using HRSG was higher. Additionally, for system (b) or GT + HRSG + HSR, the annual average energy efficiency was significantly high about 76.5% revealing the high energy performance of this system. The annual average exergy efficiency for GT type was 29.2% increased up to 49.8 for the GT + HRSG or system (a). For the GT + HSR, the annual average exergy efficiency was slightly lower about 45.2%. Additionally, finally, for system (b) or the GT + HRSG + HSR, the parameter reached 78.5%. Figure 11: Monthly average of energy and exergy efficiencies of the GT + HRSG + HSR system considering solar radiation. Figure 12: The annual average of energy efficiency for four types. The NPV economic factor for all four types was demonstrated in Figure 13. The NPV for the GT standalone was 1.92 \(\times 10^{8}\) US$, which experienced a significant increase of up to 2.83 \(\times 10^{8}\) US$ by adding the HRSG unit presented as system (a). On the other hand, for the GT + HSR type, the NPV factor reduced down to 1.44 \(\times 10^{8}\) USFigure 9.0. Therefore, regarding the economic performance of these types of configurations, the most and the least beneficial system were respectively GT + HSRG and the GT + HSR types. Figure 14 reports the SPP and PP economic factors for the mentioned four types. The SPP factor of the GT system was 2.79 years, which adding HRSG unit increased this factor to about 58% up to 4.41 years. Additionally, for the GT + HSR type, the SPP factor rose  
The annual average exergy destruction rate of four types of GT, GT + HRSG, GT + HSR, and GT + HRSG + HSR are shown in Figure 9.1 By adding each cycle to the gas cycle, the exergy destruction rate was increased. Since, each cycle had a component and each component had a value of the exergy destruction rate during operation time. Figure 9.2 Simple payback period (SPP) for four types. Figure 9.3 Internal rate of return (IRR) for four types. Figure 17 shows the levelized cost of electricity (LCOE) of four types of GT, GT + HRSG, GT + HSR, and GT + HRSG + HSR. The LCOE was increased by adding each individual cycle.
# Regional Seismicity of the Northeastern Tibetan Plateau Revealed by Crustal Magnetic Anomalies
## 7 Curie Point Depths
### Calculation of the Curie Point Depth  
A magnetic anomaly reflects a combination of effects from shallow and deep crustal magnetic sources [35]. However, the CPD reflects deep magnetic sources. The presence of shallow magnetized bodies tends to reduce the accuracy of the CPD. Therefore, the magnetic anomalies shown in Figure 5 were subjected to low-pass filtering to remove the effects of shallow magnetic sources. Low-pass-filtered magnetic anomalies in the study area (i.e., at a cut-off frequency of 0.0621 km\({}^{-1}\)) were obtained from the wavenumber of the shallowest crossing and dipping segments of the power spectra (Figure 9) using the method of [PERSON] and [PERSON] (2013) [36]. Deep magnetized sources are enhanced in the low-pass-filtered magnetic anomaly map of the study region (Figure 10).  
In our inversion process, the value of the 3D fractal exponent \(\beta\) was 3.0. The magnetic anomaly map in Figure 8 was first divided into a grid of 15 \(\times\) 8 blocks, each of which had a grid size of 280 \(\times\) 280 km. To increase the data coverage, all the blocks overlapped with adjoining blocks by 50%. The radially averaged \(\ln[A_{\Delta T}(|k|)^{1/2}]\) and \(\ln([A_{\Delta T}(|k|)^{1/2}]/|k|)\) spectra were then calculated for each block. Finally, the \(\ln[A_{\Delta T}(|k|)^{1/2}]\) and \(\ln([A_{\Delta T}(|k|)^{1/2}]/|k|)\) spectra in the frequency range of 0.0503-0.1017 and 0.0053-0.0471 km\({}^{-1}\) were selected to fit \(Z_{t}\) and \(Z_{0}\), respectively. The CPDs were obtained with Equation (7). Figure 11 shows an example of the fitting of the power spectrum to a block, in which the centroid depth \(Z_{0}\), the depth with respect to the top of \(Z_{t}\), and CPD \(Z_{\rm b}\) were 17.91 \(\pm\) 0.98, 7.59 \(\pm\) 0.01, and 28.23 \(\pm\) 2.00 km, respectively.  
Figure 10: Low-pass-filtered magnetic anomalies in the NETP and adjacent areas. For black lines, please see Figure 1. The deep magnetized sources appear to be enhanced in the filtered data.  
Figure 9: Power spectrum depth estimate of the magnetic anomalies shown in Figure 4. The arrow shows the cut-off frequency of k = 0.0621 km\({}^{-1}\) for the low-pass filtering.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 2 Data
### NWP Forecast Product  
The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The entire globe is covered by the GFS at a base horizontal resolution of 18 miles (28 kilometers) between grid points. The wet refractivity estimated by Equation (1) from GFS forecast product is taken as initial values in tomography.
## 2 Materials and Methods
### Methods
#### 2.3.4. Geographically Weighted Regression (GWR) and Multiscale GWR (MGWR)
Unlike the GWR model, the MGWR model allows for different degrees of spatial smoothing for each variable separately, which lowers estimation bias and produces a more realistic and valuable model of the spatial process [61]. Thus, the heterogeneity of bandwidth in the MGWR model is what distinguishes it from the GWR model, and this improvement can be achieved by redefining GWR as a generalized additive model (GAM) [66]. The optimal bandwidth was determined using Gauss functions and the Akaike information criterion (AIC) to examine the fit of the OLS, GWR, and MGWR models. In general, if the difference in AIC values between the two models is greater than 3, there is a significant difference between them. The model with a lower AIC value is considered to have a better fit. In addition, the Moran's \(I\) statistic is usually used to check spatial autocorrelation in the error terms of the model. The residuals of the model are randomly distributed when the Moran's \(I\) value is close to 0, which indicates a good fit. On the other hand, the model shows a poor fit. The study area was divided into regular grid cells measuring 5 \(\times\) 5 km. The dependent variable was the difference in land NPP from 2001 to 2019, and the independent variables were seven influencing factors (temperature, precipitation, elevation, slope, forest cover, population count, and human modification). All variables needed to be standardized using outlier procedures before being fitted to the model. Furthermore, the influence factor corresponding to the maximum absolute value of the regression coefficients was identified as the dominant component in land NPP changes. In this study, the MGWR model was implemented using MGWR 2.2 software developed by the Spatial Analysis Research Center of Arizona State University (SPARC), and data pre-processing and map production were carried out by ArcGIS 10.3 software.
## 5 Time-Resolved Coherent Excitation and Decay of Strongly Correlated Autoionizing States
It was demonstrated in [16] that when the field is weak, the numerical results from applying this formula are identical to those from the nonperturbative SSEA calculation ([16], Figure 2). The formula can be used for appropriate predictions in many-electron atoms, provided its parameters are known from computation or experiment. For example, its application shows that Auger states of atoms in the middle of the periodic table, whose width is sufficiently large, about 2 eV or more, are suitable candidates for observing the formation of their stationary profiles with attosecond duration. The predictions for _Al_ have not yet been tested experimentally. However, those for helium were indeed subjected to a novel pump-probe experiment with a time delay of sub-femtosecond precision [7; 8]. The findings and analysis of [7; 8] confirmed the theory of [16], although the experiment did not cover the full duration of the generation of the complete resonance profile of the \((2s2p)\)\({}^{1}P^{o}\) state on the energy axis, which was calculated to be about 180 femtoseconds (i.e., about 10 lifetimes) [16]. Given the continuing experimental progress on refined measurements on hypershort time scales, we expect that novel experiments such as those of [7; 8] (see also [53]) can be developed further, in order to allow the study of even more intriguing time-resolved processes involving multiply excited or inner-hole-excited states inside the continuous spectrum. Such states are normally unstable (autoionizing). However, due to symmetry restrictions, it is also possible for a few of them to belong to the nonrelativistic discrete spectrum in spite of their very high excitation. For example, as a prototypical case we have treated aspects of the time-dependent dynamics of the preparation of the bound _triply excited_ state of a negative ion, \(He^{-}2p^{3}\)\({}^{4}S^{o}\), which is 59.33 eV above the helium ground state, usingtwo pulses with time delay [54]. The wavelengths of the two pulses resonantly connecting the states are in the infrared (10,080 A) and soft x-ray (323 A) regions. The ladder of excitation is [54],  
\[He^{-}\ 1s2s2p\ ^{4}P^{0}\stackrel{{ 1008\ nm\ pulsc}}{{\rightarrow}} \left|{}^{4}P(\varepsilon)>\stackrel{{ 32.3\ nm\ pulse}}{{\rightarrow}}2p^{3}\ ^{4}S^{o}\ (\ B),\right.\]  
where \(1s2s2p\ ^{4}P^{0}\) is metastable and \(\left|{}^{4}P(\varepsilon)>\) is a shape resonance labeled by the zero-order \(\left(1s2p^{2}\right)\ ^{4}P\).
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 4 Experiments
### Datasets and Implementation
#### 4.1.2 Implementation Details  
In all experiments, the batch size was fixed at 32, using the SGD optimizer with momentum of 0.9 and weight decay of \(5\times 10^{-4}\) for different episodes. We used BN and ReLU in all hidden layers and linear activation in the output layers, and initialized parameters by Kaiming initialization [20]. To eliminate random factors, the random seed is manually fixed when training the model to ensure that the initialized parameters of the model are consistent across different runs so as to avoid compromised validity and rationality of the comparative experiments. We use warmup [23] to update the learning rates, and the initial learning rates of TL-Branch, Value-Branch, and RL-Branch are set to 0.001, 0.001, and 0.01, respectively. The buffer size of replay memory is set to \(1\times 10^{5}\) and  
\begin{table}
\begin{tabular}{p{42.7 pt} p{284.5 pt}} \hline
1. & **Input**: \\
2. & Frequency range \(\Delta f\) and time range \(\Delta t\) \\
3. & Number of tasks \(N\) \\
4. & Area ratio \(\gamma\) and aspect ratio \(\rho\) \\
5. & **Initialize set**\(\_\)_list \(=[[\Delta f,\Delta t]]\) \\
6. & **For**\(n\)**\(\mathbf{in}\)\(N-1\)**do**: \\
7. & Calculate max product \(m=f_{m}*t_{m}\) in _task\_list_ \\
8. & Randomly select \(tl\) in _task\_list_, satisfying \(f_{fl}*t_{tl}>2m/\gamma\) \\
9. & Delete \(tl\) from _task\_list_ \\
10. & Randomly select slicing dimension, satisfying \(1/\rho<f_{tl}/t_{tl}<\rho\) \\
11. & Slice \(tl=[tl_{1},tl_{2}]\) to subtasks \\
12. & Add \(tl_{1},tl_{2}\) in _task\_list_ \\
13. & **End For** \\
14. & **Return**\(\_\)_task\_list \\ \hline \end{tabular}
\end{table}
Table 4: Zero-waste satellite task list dataset generation.  
the hyperparameters of replay memory are set as follows: \(\alpha=0.3\), \(\beta=0.5\), \(\eta=5\times 10^{-6}\), and \(\varepsilon=1\times 10^{-7}\), respectively. Gradient clipping is set to 20 and the discount factor reward \(\gamma\) to 0.95. The target network solves the overestimation problem in the training phase with a target update interval of 100. The nonnegative multiplier \(\varepsilon\) is empirically set to 0.1.
## 4 Results and Discussion
### Numerical Model  
The established numerical model revealed multiple stages in the bending behaviour of the TRC sandwich beams, based on the stress and strain development in the different materials of the sandwich beam. Four stages were distinguished, and indicated with I, II, III, and IV in Figure 7a. The first stage showed linear elastic behaviour of the beam. At a load of 5 kN (start of stage II), the matrix cracking stress of 2.28 MPa was reached at the surface of the tensile face, in the area with the constant moment (see Figure 8), which physically corresponds to the initiation of the first crack. The first crack initiation and the development of multiple cracks in the tensile face of the beam are specified for the second stage in Figure 7. Once the matrix cracking stress reaches through the complete thickness of the face (at a load of 10 kN in Figure 7, and illustrated in Figure 9), a clear reduction in stiffness was noticed, leading to the start of the third stage. Starting from a load of 25.5 kN, the core no longer deformed linearly and elastically but plasticly (see Figure 10), which led to another reduction in stiffness and the start of the fourth stage. The part of the plastic shear strain and total shear strain strain are presented in Figures 10 and 11, respectively. The maximum displacement in Figure 7 was a result of the applied maximum displacement of 100 mm during the analysis. Failure of the TRC sandwich beam was obtained when the ultimate shear stress (0.24 MPa) of the core was reached, which happened at a displacement of 91 mm as illustrated in Figure 12.  
Figure 7: (**a**) force-displacement curve of the four-point bending tests on sandwich beams with TRC faces and the numerical prediction, and (**b**) failure in the shear of the core of a representative sandwich beam.  
Figure 11: Total shear strain at a load of 25.5 kN.  
Figure 8: Start of the second stage, where the matrix cracking stress is reached at a vertical load of 5 kN. (**a**) Vertical displacement (U, in mm) and (**b**) horizontal stress (S, in MPa) in the tensile TRC face.  
Figure 10: (**a**) Vertical displacement (U, in mm) at the start of the fourth stage (25.5 kN), and (**b**) plastic shear strain (PE [-]) of the core at a load of 25.5 kN.  
Figure 9: Start of the third stage at a load of 10 kN. (**a**) Vertical displacement (U, mm) in the middle of the beam; (**b**) the matrix cracking stress (S, in MPa) reaches through the entire cross-section of the tensile face in the constant moment area.