esa-sceva/satcom-chunk-collection huggingface dataset:
# Sintering of Two Viscoelastic Particles: A Computational Approach
## 3 Governing Equations  
### Balance Equations and Constitutive Models  
The flow behavior of the sintering process of polymer particles as introduced in the previous section, assuming an isothermal flow, can be described using the momentum and mass balance. We assume the fluid to be incompressible, leading to the following set of equations:  
\[\rho\frac{\mathrm{D}\boldsymbol{u}}{\mathrm{D}t}=\
abla\cdot \boldsymbol{\sigma}+\rho ge_{\mathrm{g}} \mathrm{in}\;\;\Omega, \tag{1}\] \[\
abla\cdot\boldsymbol{u}=0 \mathrm{in}\;\;\Omega, \tag{2}\]  
where \(\mathrm{D}(\,)/\mathrm{D}t\) denotes the material derivative, \(\rho\) is the fluid density, \(\boldsymbol{u}\) the fluid velocity, \(\boldsymbol{\sigma}\) the Cauchy stress tensor and \(g\) and \(\boldsymbol{e}_{\mathrm{g}}\) the magnitude and direction of gravity, respectively. For the Newtonian constitutive equation, the Cauchy stress tensor is:  
\[\boldsymbol{\sigma}=-p\boldsymbol{I}+\mu\left(\
abla\boldsymbol{u}+(\
abla \boldsymbol{u})^{\mathrm{T}}\right). \tag{3}\]  
Herein, \(p\) is the pressure and \(\mu\) the viscosity. For viscoelastic fluids, the Cauchy stress tensor can be written as:  
\[\boldsymbol{\sigma}=-p\boldsymbol{I}+\eta_{\mathrm{s}}\left(\
abla\boldsymbol{u }+(\
abla\boldsymbol{u})^{\mathrm{T}}\right)+\boldsymbol{\tau}, \tag{4}\]  
with the viscoelastic extra-stress tensor \(\boldsymbol{\tau}\) written in the conformation tensor form:  
\[\boldsymbol{\tau}=G(\boldsymbol{c}-\boldsymbol{I}), \tag{5}\]  
where \(\eta_{\mathrm{s}}\) is the solvent viscosity and \(G\) the modulus. For the Giesekus constitutive equation, the evolution of the conformation tensor \(\boldsymbol{c}\) is described by:  
\[\lambda\overset{\triangledown}{\boldsymbol{c}}+\boldsymbol{c}-\boldsymbol{I}+ \alpha(\boldsymbol{c}-\boldsymbol{I})^{2}=\boldsymbol{0}. \tag{6}\]  
Herein, \(\overset{\triangledown}{(}\,)=\mathrm{D}(\,)/\mathrm{D}t-(\
abla\boldsymbol{u })^{\mathrm{T}}\cdot(\,)-(\,)\cdot\
abla\boldsymbol{u}\) denotes the upper-convected derivative, \(\lambda\) the relaxation time and \(\alpha\) the material parameter defining the amount of anisotropy. For the XPP model [21], the evolution of the conformation tensor \(\boldsymbol{c}\) is given by:  
\[\overset{\triangledown}{\boldsymbol{c}}+2\frac{\exp[\
u\sqrt{\mathrm{tr}( \boldsymbol{c})/3}-1]}{\lambda_{\mathrm{s}}}\left(1-\frac{3}{\mathrm{tr}( \boldsymbol{c})}\right)\boldsymbol{c}+\frac{1}{\lambda}\left(\frac{3 \boldsymbol{c}}{\mathrm{tr}(\boldsymbol{c})}-\boldsymbol{I}\right)= \boldsymbol{0}, \tag{7}\]  
where \(\
u=2/q\) with \(q\) the number of arms at the end of the backbone, \(\lambda_{\mathrm{s}}\) the relaxation time for the stretch and \(\lambda\) the relaxation time of the backbone orientation.  
The conformation tensor \(\boldsymbol{c}\) is, besides the velocity \(\boldsymbol{u}\) and pressure \(p\), the third unknown to be solved as a function of position and time.
## 3 Results  
### Coherence Maps  
In the Corrientes site, co-located SAOCOM-1 and Sentinel-1 interferometric pairs were formed and processed with the Free Open-Source Software GMTSAR. Range spectral filtering has been applied to SAOCOM-1 images to mitigate the baseline spatial decorrelation effect. The time series of SAOCOM-1 consisted of 12 scenes with 66 interferometric pairs, with a minimum temporal baseline of 8 days and a maximum temporal span of 128 days, at a nominal incidence angle of 32\({}^{\circ}\). On the other hand, the Sentinel-1 time series comprised 10 images and 45 interferograms, with a minimum temporal baseline of 12 days and a maximum of 108 days, at a nominal incidence angle of 40\({}^{\circ}\). A similar processing was carried out in the Neuquen site, but this section will focus on the first study site.  
The visual comparison of the obtained VV InSAR coherence maps \(|\gamma_{VV}|\) can be observed in Figure 4, for two different temporal baselines, to observe the effect of the temporal baseline at the minimum time span for each times series, 16 days in the case of SAOCOM-1A and 12 days in the case of Sentinel-1, and then taking a longer one that is coincident in both cases, being 48 days.  
Figure 4: Comparison of the \(|\gamma_{VV}|\) for SAOCOM-1 (red) and Sentinel-1 (green). (**A**) SAOCOM-1 16-day (September) interferogram and a Sentinel-1 12-day (September). (**B**) 48-day interferogram for both systems (September–October).  
To see the detailed effect of the temporal decorrelation at the two frequencies, for the previous temporal series of SAR data for SAOCOM-1 and Sentinel-1, the complete InSAR coherence matrices were calculated for the forested area indicated as region C on Figure 1(a). Figure 1(a),b show the values of \(|\gamma_{VV}|\) for the pixels of this region. For comparison purposes, the complete InSAR coherence matrices for bare soil pixels, region B on Figure 1(b), were also derived. As expected, on bare or short vegetation-covered surfaces volumetric decorrelation, in general, tends to be lower than in vegetated pixels, see Figure 1(a),b. A comparison of the four orbital InSAR coherence matrices shows that InSAR coherence is lower for vegetated areas, if compared with bare areas, independently of the working frequency, but that L-band coherences are larger than C-band coherences, independently of the observed type of target. This has a relation with the different wavelengths at which each instrument operates, but there is also an important influence of the perpendicular baseline, which is further discussed in Section 4.1.
# A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests
## Appendix A Environmental Impacts  
The only foreseen potential environmental impact of this system is the acoustic effects on marine life. A typical multibeam echosounder on a surface ship designed for deep water produces a 240 dBuPA@1m sound pressure level (SPL) and has a wide beam of around 140\({}^{\circ}\). Given the expense of ship operations, the sonar is run continuously. Our current rigid sonar array is intentionally power limited such that it does not require permitting to use. The acoustic projectors are standard commercial fathometers modified to play alternative waveforms at reduced maximum output power. They also have a very narrow, 22\({}^{\circ}\), downward-pointing beam. Using 160 dB SPL criteria for Level B harassment, a marine mammal within 10,000 m of the ship would be behaviorally harassed. However, with our current array, a marine mammal would need to be within 8 m and 11\({}^{\circ}\) off-vertical from our transducer to be behaviorally harassed. An animal must be underneath the array and within 8 m to be affected by our testing. Overall, the environmental impact of the prototype sparse-aperture array is less than that of other vessel traffic employing a low-frequency fathometer.  
For a future operational system using ASVs, the beam width of the transmitters and power level will be increased. The power level will still be less than that of a surface ship multibeam. To mitigate the increase in SPL and beam width, we intend to develop an automated detector of marine mammal sounds and pause testing when marine mammals are suspected to be in the vicinity of the array. The testing pause is much less impactful for an autonomous system than for a large capital ship. Figure 11 compares the rough areas underneath a conventional multibeam, our existing array, and a future potential sparse-aperture operational system in which a marine mammal may be harassed.
## 2 Preliminaries  
### Multi-objective Optimization Problems  
The definition of the MOPs is as follows [1; 2; 3]:  
\[\begin{array}{l}\underset{\mathbf{x}\in\Omega}{\text{min}}\,\mathbf{F}( \mathbf{x})=[f_{1}(\mathbf{x}),\,f_{2}(\mathbf{x}),\,\ldots,\,f_{m}(\mathbf{x })]^{T},\\ \text{subject to }\mathbf{x}_{i}\in(\mathbf{x}_{i}^{low},\mathbf{x}_{i}^{high}),\,\,i=1,2,\ldots,n,\end{array} \tag{1}\]  
where \(\mathbf{x}\) is an \(n\)-dimensional decision vector in the decision space \(\Omega\); \(F\left(\mathbf{x}\right)=[f_{1}(\mathbf{x}),f_{2}(\mathbf{x}),\,\ldots,\,f_{m }(\mathbf{x})]^{T}\) is an objective vector containing \(m\) objectives in the objective space. \(\mathbf{x}_{i}^{low}\) and \(\mathbf{x}_{i}^{high}\) are the boundaries of the \(i\)-th decision vector, respectively.  
The following are the basic concepts of MOPs:  
**Definition 1**.: _(Pareto Dominance): For the vector \(\mathbf{u}\)= \([u_{1},\,u_{2},\,\ldots,\,u_{m}]^{T}\) and \(\mathbf{v}\)= \([v_{1},v_{2},\,\ldots,\,v_{m}]^{T}\), if \(\forall j\in\{1,2,\ldots,m\}\), \(\mathbf{u}_{j}\leq v_{j}\), and \(\exists\,s\in\{1,2,\ldots,m\}\), \(\mathbf{u}_{s}<v_{s}\), then the vector \(\mathbf{u}\) dominates another vector \(\mathbf{v}\), expressed as \(\mathbf{u}\prec\mathbf{v}\)._  
**Definition 2**.: _(Pareto Optimal Set): For a solution \(\mathbf{x}^{*}\in\Omega\), if there is no other solution \(\mathbf{x}\in\Omega\) which satisfied \(F(\mathbf{x})\prec\mathbf{F}(\mathbf{x}*)\), \(\mathbf{x}^{*}\) is called the Pareto optimal solution. All Pareto optimal solutions constitute PS, denoted by \(\mathbf{X}^{*}\)._  
**Definition 3**.: _(Pareto Front): All objective vectors corresponding to PS form Pareto front (PF), denoted as \(\text{PF}=\{\mathbf{F}(\mathbf{x}^{*})|\mathbf{x}^{*}\in\mathbf{X}^{*}\}\)_
# Non-Centrifugal Sugar (NCS) and Health: A Review on Functional Components and Health Benefits
## 6 Biological Properties of NCS
### Non-Centrifugal Sugar and Metabolic Syndrome
#### 6.3.2 Antidiabetic Effect
However, glucose level and weight gain were the only parameters that have been measured as an indicator of the beneficial effect on the diabetic subject. One possible mechanism behind the effectiveness of NCS to control blood glucose depends on its capability to hinder \(\alpha\)-glucosidase functions. \(\alpha\)-amylase is the enzyme that hydrolyses complex starch to oligosaccharides, while alpha-glucosidase is responsible for the hydrolysis of different forms of oligosaccharides into monosaccharides as the final products. It is noteworthy that suppression of such enzymes could reduce postprandial blood glucose levels [65]. Accordingly, the antidiabetic capacity of a different variety of unrefined brown sugar was examined using in vitro alpha-amylase and alpha-glucosidase enzyme retardation assays [46]. Different varieties of BS showed \(\alpha\)-Glucosidase inhibitory properties in the range of 25% to 50%, which was correlated with high phenolic content and radical scavenging capacity [46]. Thus, it was suggested that total phenolic content might be linked to the capability of cane sugar to hinder this enzyme. Furthermore, a significant effect of the sugar color was noticed on the anti-diabetes and antioxidant effect, especially among sugar cane derivatives [46]. Additionally, NCS minerals content, particularly selenium or chromium, could represent a relevant component in regulating glucose and insulin levels because they were reported to improve the diabetic condition [13; 23]. Chromium is an essential element that enhances insulin activity. Previous reports showed that patients with T2 DM have lower blood chromium levels than those devoid of the disease [66]. The mechanism of action in which chromium was able to control or prevent diabetes includes improving insulin attachment to cells, insulin receptor number, and activation of insulin receptor kinase, thus reducing insulin resistance [67]. On the other hand, selenium is an insulin mimic [22], where it was recorded to exhibit several insulin-like actions both in vivo and in vitro. These insulin-like actions promote glucose digestion and control metabolism (glycolysis, gluconeogenesis, fatty acid synthesis) and the pentose phosphate pathway [22; 68]. Additional studies in animal and human trials are required to determine the biological role of NCS in diabetes management and its complication, and to elucidate metabolic pathways.
## 3 Results and Discussion
### Plume Height Analysis  
#### 3.4.1 Buoyancy Flux  
Before presenting the experimental results, preliminary calculations were made to estimate the height of the thermal emission plume in the Martian atmosphere. To calculate this, it is first necessary to determine the buoyancy flux, \(F_{B}\), and then the height, \(h_{B}\).  
\[F_{b}=\frac{g\cdot Q_{E}}{\pi\cdot\rho_{a}\cdot c_{p}\cdot T_{a}} \tag{7}\]  
\[h_{B}=\left(\frac{3}{2\beta^{2}}\right)^{1/3}\frac{F_{B}^{1/3}x^{2/3}}{u} \tag{8}\]  
where  
* \(g\): gravitational acceleration = 3.72 (m/s\({}^{2}\));
* \(\rho_{a}\): atmospheric density = 0.02 (kg/m\({}^{3}\));
* \(c_{p}\): specific heat at constant pressure = 7.4 (J/(kg\(\cdot\) K));
* \(\beta\): thermal expansion coefficient \(\sim\) 0.6 (1/K);
* \(Q\): heat emitted by the MMRTG \(\sim\) 2 (W/m\({}^{2}\));
* \(T_{a}\): ambient temperature (K);
* \(x\): downstreams coordinate x (m);
* \(u\): wind speed (m/s).  
As can be observed in Table 6, the height of the thermal plume decreases as wind speed increases. Therefore, it will be particularly interesting to analyze lower wind speeds, as they are more likely to generate a significant influence on the mast. This is due to the fact that at lower wind speeds, the plume remains more concentrated and rises higher, increasing the potential for thermal interaction with the MEDA station's sensors.
# A Memetic Algorithm for an External Depot Production Routing Problem
## 3 Memetic Algorithm  
### Encoding and Evaluating the Solution  
#### 3.1.1 Encoding  
We use the tuple (\(P\), \(Y\), \(X\), \(R\)) to describe a complete solution to the problem. In this tuple, \(P\) is a list of the quantity produced in each period \(t\) of the planning horizon. Each quantity produced \(P_{t}\) or \(P[t]\) is an integer between 0 and the maximum production capacity of the plant (C). \(Y\) is a list of binary numbers over each period \(t\) of the planning horizon. \(Y_{t}=1\) means that there is production at the plant on date \(t\) and 0 otherwise. \(X\) is a \((n+1)\times l\) matrix in which \(X_{it}\) represents the amount of product sent to the depot (\(i=0\)) or to a customer (\(i\in N_{c}\). \(X_{it}\) is an integer between 0 and the maximum storage capacity \(L_{i}\) of the node \(i\in N_{dc}\) and can be made up of several demands for the customer \(i\). \(R\) is a list of integers with \(R_{p}\in\{-1\}\cup N\). It is a successive list of trips delimited by the symbol (0) when the trips belong to the same day or the symbol (\(-1\)) when the trips belong to different days. Thus 0 and \(-1\) also refer to the depot in the modeling of the solution. As the symbol (\(-1\)) is also the day delimiter, a succession of this symbol indicates a period without a vehicle tour.  
The knowledge of \(R\), \(X\) and \(I_{0}(i\in N_{c})\) allows to compute \(I_{it}(i\in N_{c})\) for all \(t\in T\) and to verify the constraints related to the storage capacity of customers \(L_{i}(t\in T)\), the vehicle limit load (\(Q\)) as well as the limitation of the number of vehicles in the fleet (m). With the knowledge of \(Y\), \(X\), and \(I_{00}\), it is also possible to compute \(P_{t}(t\in T)\), \(I_{0t}(\forall t\in T)\) and check the constraints on the storage capacity of the depot (\(L_{0}\)). The dataset of Table 1 allows to build an example of a complete solution for \(n=10\), \(l=3\), \(m=2\), \(C=304\) and \(Q=198\). This solution can be described by Table 2. In Table 2, \(R\) is a succession of trips that begin at the depot (0 or \(-1\)) and end at the depot over a planning horizon of 3 days (or three periods) delimited by the symbol \(-1\). \(X_{it}\) is the amount sent to each customer or depot \(i\) at the period \(t\). The amount distributed to customers in the first period is \(25+18+33=76\). This quantity means that the demand of the first period is met based on the initial stocks of the depot (\(I_{00}=76\)). No quantity is sent to the plant since it has no storage capacity. \(Y_{1}=1\) means that there is production in the first period of \(T\). This automatically leads to a replenishment of the depot (0 or \(-1\)) with a quantity \(P_{1}=137\) units of products. \(P\) and \(Y\) can therefore be represented by Y [0,0,1] and P [137,0,0]. Finally, with the knowledge of R, X, Y, and P it is possible to evaluate a solution.  
Figure 1: Collection and distribution network in an EDPRP.
# A Topological Sensitive Node Importance Evaluation Method in Aerospace Information Networks
## 3 Node Importance Evaluation Model
### Node Importance Transfer Probability Matrix
On the contrary, if the node degree is larger, when one of the nodes fails, due to the existence of other adjacent nodes, its importance will not change too much. Because node \(i\) has different important contributions to nodes on the same layer, degree centrality can directly reflect the influence of nodes in the network. In addition, the dependence between nodes is inversely proportional to the shortest distance between nodes. Therefore, we propose the following probability calculation formula of node importance. **Definition 3**. : _Node importance transfer probability. It reflects the contribution probability of node \(i\) to node \(j\) when node \(i\) assigns its initial importance to all nodes in the second-order domain. Its calculation formula is:_  
\[p(i,j)=\frac{DD(i,j)/DC(j)}{\sum\limits_{j\in\tau}\left(DD(i,j)/DC(j)\right)} \tag{4}\]  
\[DD(i,j)=\left\{ \begin{array}{ll}\frac{1}{d_{ij}}&,j\in\tau\\ 0&,other\end{array} \right. \tag{5}\]  
_where \(\tau\) is the set of adjacent nodes within the second layer and \(d_{ij}\) is the shortest distance between two nodes._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Node** & **Node 1** & **Node 2** & **Node 3** & **Node4** \\ \hline Original & 0.140 & 0.128 & 0.357 & 0.697 \\ After removing & / & 0.063 & 0.355 & 0.345 \\ node 1 & / & 0.065 & 0.002 & 0.180 \\ difference & 0.238 & 0.223 & 0.238 & / \\ difference & 0.098 & 0.095 & 0.118 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The effect of deleting node 1 and node 4 on node 2. Since the efficiency reflects the average difficulty from one node to other nodes in the network and is a measurement of the importance of nodes in the global scope, we initialize the importance of node \(i\) with the efficiency \(C_{i}\) of node \(i\), and then obtain the node importance transfer correlation matrix:  
\[H= \begin{bmatrix}0&p_{12}\cdot C_{1}&\cdots&p_{1n}\cdot C_{1}\\ p_{21}\cdot C_{2}&0&\cdots&p_{2n}\cdot C_{2}\\ \vdots&\vdots&&\vdots\\ p_{n1}\cdot C_{n}&p_{n2}\cdot C_{n}&\cdots&0\end{bmatrix}  \tag{6}\]  
In Equation (6), \(H_{ij}\) represents the importance contribution of node \(i\) to node \(j\). Considering the global network efficiency and local transfer probability of nodes, the importance \(I_{j}\) of node \(j\) can be defined as:  
\[I_{j}=C_{j}\times\left(\sum_{i=1,i\
eq j}^{n}P_{ij}\cdot C_{i}\right) \tag{7}\]  
It can be seen that the importance of a node is related to the efficiency of the node itself, its degree centrality, and the distance between the nodes. It integrates the global and local information of the node, which can improve the evaluation accuracy. When the topology structure changes, the distance between the nodes will change accordingly and the evaluation method can effectively and accurately estimate the value of node importance, which meets the needs of the satellite network node importance evaluation.
## 4 Results and Discussions
### Effect of Different Injection Pressure on CO\({}_{2}\)-Ecbm
#### 4.2.2 Optimal Injection Pressure Selection  
Figure 13(a),b illustrate the distribution of cumulative production and point B's permeability change during CO\({}_{2}\) injection exploitation under the pressure of 1 MPa to 6 MPa. The permeability near the injection well is lower than the initial permeability, and the permeability decreases with the increase of injection pressure at the same production time. This is because the increase of gas injection pressure will lead to the increase of reservoir adsorption amount, which will lead to the decrease of permeability. The decrease of permeability will limit the flow capacity of CO\({}_{2}\) and weaken the injection capacity. In this study, point B near the injection well is taken to analyze the change of reservoir permeability near the well when the injection pressure changes. It can be seen that when the injection pressure increases, the permeability of a point near the injection well decreases rapidly. When the injection pressure is 6 MPa, the reservoir permeability decreases by 0.6 times of the initial permeability. At the same time, as the injection pressure increases, the pressure differential in the reservoir increases, prompting faster gas seepage and diffusion, and CH\({}_{4}\) production will increase. When the injection pressure is 6 MPa, the CH\({}_{4}\) production increases to 3.79 \(\times\) 10\({}^{5}\) m\({}^{3}\), which is 75% higher than that when the injection pressure is 1 MPa.  
In summary, increasing gas injection pressure can promote CH\({}_{4}\) production and CO\({}_{2}\) storage, but the increase of gas injection pressure will also lead to rapid attenuation of  
Figure 13: 300 days, 500 days, 1000 days, 1500 days Reservoir pressure cloud diagram (**a**) The injection pressure is 2 MPa (**b**) The injection pressure is 4 MPa (**c**) The injection pressure is 6 MPa.  
near-well reservoir permeability, resulting in the weakening of injection capacity. We analyzed the change rate of CH4 production. The peak value of the change rate of CH4 production was 0.153 at 3 MPa, indicating that the injection pressure continued to increase after the displacement pressure reached 3 MPa, and the recovery degree did not change much. Consequently, we can take 3 MPa as the optimal injection pressure in this case.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 4 Discussion  
In the above experiment, we evaluated performance of RSIMG with remote sensing images from different sources, including 14,187,101 image metadata and 2000 image files. The experimental results show the high and stable performance of RSIMG in large amounts of remote sensing images spatial-temporal retrieval and binary files read.