esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background  
### Thermal Inertia  
SWC represents the main soil parameter influencing the heat transfer processes involved in the soil. The thermal inertia approach is based on the solution of the first-order approximation of a one-dimensional (1-D) diffusion equation at the soil surface (\(z=0\)) by imposing the boundary and initial conditions. The method requires the evaluation of actual soil thermal inertia, \(P\left[\right]\text{m}^{-2}\text{K}^{-1}\text{s}^{-1/2}\)], and the corresponding values at dry, \(P_{dry}\), and wet, \(P_{wet}\), conditions. When soil bulk density \(\rho\left[\right]\text{kg}\text{m}^{-3}\) is known, \(P_{dry}\) and \(P_{wet}\) can be estimated by measuring the thermal conductivity, \(k\left[\right]\text{m}^{-1}\text{s}^{-1}\text{K}^{-1}\)], and the soil specific heat, \(c_{p}\left[\right]\text{kg}^{-1}\text{K}^{-1}\)]:  
\[P=k\sqrt{D}=\sqrt{\rho\,k\,c_{p}} \tag{1}\]  
where \(D\) (m\({}^{2}\) s\({}^{-1}\)) is the thermal diffusivity equal to \(D=k\,(\rho c_{p})^{-1}\). According to [PERSON] et al., 2009 [9], \(SWC_{Kp}\), can be estimated as:  
\[SWC_{Kp}=e\bigg{(}1-\frac{\ln Kp}{\epsilon}\bigg{)}^{-\frac{1}{\rho}} \tag{2}\]  
where \(e\left[\text{-}\right]\) is the soil porosity, \(\epsilon\) and \(\mu\) are two parameters controlling the function shape (dependent on the quartz content in soil) and \(Kp\) is the so called \"Kersten function\". Analogously to the normalized theory of the soil thermal conductivity (e.g., [27]), the Kersten function can be computed from thermal inertia according to [4]:  
\[Kp=\frac{P-P_{dry}}{P_{wet}-P_{dry}} \tag{3}\]  
The parameters \(\epsilon\) and \(\mu\) can be calibrated by means of a regression analysis between estimated and measured SWCs. Soil surface macroporosity could change due to rain falling causing mechanical disruption of aggregates and compaction [28]. Thus, inaccuracies in \(SWC_{Kp}\) retrievals might be related to the use of a constant value of \(e\) for scaling the \(Kp\).
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 4 Discussion  
AutoDock and Vina are efficient software alternatives for virtual screening, exhibiting on average similar performance when evaluating the ligand/decoy discriminating ability, across a large number of proteins. In spite of the similar average performance exhibited, both docking programs can present a marked difference when studying a particular protein target, or even when looking into proteins or enzymes from specific families, or for different types of ligands. Hence, for the common user wishing to embark in a virtual screening study, it is not easy to select _a priori_ the alternative that should be used.  
The goal of this study was to guide the selection of the docking software according to the type and characteristics of the target and its substrates. As demonstrated, the type of target, and specially the characteristics of the binding pocket could influence the outcome of the docking software. The results showed that AutoDock was clearly better in discriminating ligands and decoys in smaller targets, with more hydrophobic, poorly polar, and poorly charged pockets, while Vina tended to give better results for bigger targets with polar and charged binding pockets. According to the results presented, Vina provided better metrics for kinases, proteases, and cytochrome P450. On the other hand, ligand/decoy discrimination for GPCR, ion channels, and nuclear receptors was improved with AutoDock.  
For the substrates, however, this analysis across 22,432 active compounds and 1,380,513 decoy molecules showed that AutoDock and Vina exhibited comparable trends with the ligands size, charge, and the number of rotatable bonds. Bigger, more flexible, and more polar ligands were more difficult  
Figure 10: Influence of the number of rotational bonds of the active molecules in the ranking position for nuclear receptors and GPCRs.  
Figure 9: Relative distribution of active molecules present in the DUD–E database by the number of rotational bonds.  
to discriminate from decoys for both docking programs but the performance of Vina and AutoDock was quite similar.
## 2 Literature Review and Research Question Development  
### Studies on Carbon-Energy Correlations  
Climate change issues are triggering research interest in carbon-energy correlations in the past decade [9]. Carbon-energy correlations are critical for investors, energy-intensive industries, and policymakers. The literature has offered significant evidence of carbon-energy correlations. For example, [PERSON] [10] demonstrates that economic activities (proxied by industrial productions) and energy prices may drive carbon prices. [PERSON] and [PERSON] [11] show the impact of energy prices and macroeconomic risk factors (proxied by a stock market index, the commodity price, and US Treasury bill yield) on carbon. [PERSON] et al. [12] investigate the relationship between energy prices and carbon prices and indicate that one may use energy prices to predict carbon prices. [PERSON] and [PERSON] [13] examine the impact of the carbon price on economic outputs and suggest that the effect is more pronounced in energy industries than in other industries.  
In addition to testing the carbon-energy correlations based on the return spillover effect (i.e., the first moment), a few recent studies have turned their attention to the volatility spillover effect (i.e., the second moment). Notably, most of these studies employ the conventional GARCH and DCC models to examine this effect [14; 15; 16; 17; 18; 19] (some researchers develop other methodologies to address the volatility spillover effect. For instance, [PERSON] and [PERSON] [20] and [PERSON] et al. [9] follow [PERSON] and [PERSON] [21] to construct the spillover index for carbon-energy markets. [PERSON] et al. [22] employ the VAR model to test the effect.). While these prior studies have documented the volatility spillover effect in carbon-energy markets, we argue that the conventional GARCH and DCC models have certain shortcomings. First, the GARCH-based models fail to control for structural changes in volatility (i.e., volatility jumps) and, thus, encounter the high-persistence issue in volatility estimation and low accuracy in volatility prediction [23; 24]. Second, with regard to the DCC models, prior empirical studies invariably employ a two-step approach to estimate the model parameters. Specifically, in the first step, they estimate the univariate GARCH model for each asset (e.g., carbon and energy assets in this study). After calculating the residual of the univariate asset, they estimate the DCC models in the second step. This two-step approach implicitly assumes that volatility estimation is irrelevant to correlation estimation and, thus, cannot address their relationships [25]. Therefore, this study uses a regime-switching approach to address the volatility-correlation relationships and adopts a one-step estimation approach (see Section 3.1). Moreover, we use the proposed volatility-correlation relationships to develop a theoretical hypothesis regarding the intensity and direction of the volatility spillover effect in carbon-energy markets (see the subsequent subsection).
# Anti-Aging Performance and Action Mechanism of Asphalt Modified by Composite Modification
## 2 Materials and Methodology
### Materials
#### 2.1.2 Modifier  
Modified asphalt was prepared using two types of anti-aging additives: single modifiers and composite modifiers. Specifically, the single modifier is Layered Double Hydroxides (LDHs), while the composite modifier (ULDHs) is obtained by combining LDHs with a UV absorber (UV-928). The application dosage of the modifiers in the asphalt is approximately 3% by weight of the asphalt.
## 4 Discussion
### Effect of Data Collection Methods and Results Accuracy
#### 4.2.2 Effect of Results Accuracy  
The multi-perspective topography features were identified by combining the high-resolution DEM and the 3D model that restored the real topography. Visualization analysis, related topography feature analysis, and mathematical and statistical methods were combined for measurement and analysis. However, DEM with different resolutions may affect the analysis results. For environments with significant vertical differences, the possibility of gradual smoothing of topography feature parameter results increases with low DEM resolution [76].  
Compared with the widely used DEM (e.g., 30 m, 25 m, and 1.5 m resolution), the DEM with 0.2 m resolution in this study was able to retain more subtle changes in topographical features and to achieve high precision based on the assurance of data processing efficiency. The topographical features parameter extraction was more reliable, and the data results were more reliable. However, the use of a single resolution was relatively limited. In future research, a differentiated comparative analysis can be conducted on the accuracy of topographical features parameter extraction using different high-resolution DEMs, and the optimal resolution DEMs can be selected to conform to the needs of different studies in accordance with the meanings and demands of different topography feature parameters.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 4 Proposed Solution
### Next Hop Routing Protocol  
Routing is the main factor in a WSN, as it changes the energy consumption of the sensing nodes while communicating with the other devices inside the network. In this context, less efficient routing mechanisms increase the energy consumption. This results in a large number of expired nodes in the network. To achieve efficient energy consumption, an adaptive routing mechanism is considered to attain the advantage of multi-hop and dynamically changing routing patterns based on variable conditions. For efficient energy utilization at each node, a routing protocol is utilized for load balancing while gathering and transmitting data to the BS. Moreover, the proposed model considers the following characteristics; (i) Archive improved network model for data gathering by efficient data routing; (ii) Efficient location of next node with maximum residual energy; (iii) Energy-efficient routing algorithm; (iv) Efficient and changeable routing mechanism for both one hop and multi-hop. We consider an improved network establishment mechanism for efficient energy consumption in the heterogeneous network. In this context, it is a critical issue while formulating the network because it also considers more parameters along with energy levels. In heterogeneous networks, the presented mechanism shuffles the node position on the basis of multiple parameters for maximizing the network lifetime. In the proposed network development, we consider these parameters to construct a network model.
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 4 Experimental Results Analysis  
### Robustness Experiment  
Human daily activities are usually not fixed in a certain distance or area, so in this paper, we analyze the generalization ability and robustness of the system by setting different distances between the human body and the radar and different radar placement heights. Since we set the maximum detection range of the radar to 9 m, we fixed the radar height to 0.8 m to conduct experiments at 2 m, 4 m, 6 m, and 8 m from the radar, respectively. The results are shown in Figure 8. The accuracy is highest at 4 m from the radar, and the accuracy is also good at all the other distances. When the human body is too close to the radar, the millimeter wave emitted by the radar is not enough to cover the end of the human body as well as the changes in the limbs, so there is a decrease in accuracy when the human body is too close to the radar. Moreover, due to the severe attenuation of millimeter waves in the atmosphere, the accuracy rate declined when the human body was farther away from the radar. Therefore, we found that the recognition accuracy of the system is highest when the human body is 4 m away from the radar in this environment.  
Since the radar placement heights are different in different scenarios in daily life, we fixed the distance of the human body from the radar as 4 m. We experimented with four radar placement heights of 0.4 m, 0.8 m, 1.2 m, and 1.6 m, and the experimental results are shown in Figure 9, which shows that the accuracy rate is the highest in the radar placement height of 0.8 m. After experimental analysis, we learned that the radar placement was too low or too high, so it could not completely cover the ends of the human limbs, and when the radar placement was too low or too high, the millimeter-wave radar generated a point cloud in the xz-plane projection will produce deformation, which led to an obvious decrease in action recognition accuracy. Moreover, it can be seen through the experiment that the effect of the radar placement height on accuracy exceeds the effect of the distance between the human body and the radar.  
Figure 8: Confusion matrix of human body and radar at different distances: (**a**) 2 m from radar (**b**) 4 m from radar; (**c**) 6 m from radar; and (**d**) 8 m from radar.  
Figure 9: Confusion matrix for different radar placement heights. (**a**) Radar height: 0.4 m. (**b**) Radar height: 0.8 m. (**c**) Radar height: 1.2 m. (**d**) Radar height: 1.6 m.
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
## 2 Formulation of Wheel-Rail Contact Problem
### Contact Model  
Before we formulate the mechanical equation governing the strip deformation, let us introduce notation. We shall denote strip mass density by \(\rho\). Moreover, \(\lambda\) as well as \(\gamma\) are Lame coefficients of the strip material [32; 33]. The thermal expansion and the heat capacity coefficients are denoted by \(\alpha\) and \(c_{p}\), respectively. Moreover, by \(\text{div}\ \sigma(u)\), we denote the divergence operator \(\text{div}\ \sigma(u)\) of stress, i.e., it includes the derivatives of the stress function with respect to spatial variables. The stress \(\sigma\) satisfies conditions (3) and (4). For a given temperature \(\theta\), the displacement \(u\) and the stress \(\sigma\) satisfy the governing equation:  
\[\rho c_{p}\frac{\partial^{2}u}{\partial t^{2}}=\mathrm{div}\sigma-\alpha(3 \lambda+2\gamma)\
abla\theta, \tag{5}\]  
Equation (5) is considered in domain \(\Omega\) and in time interval \((0,T)\). The following boundary conditions are imposed on the displacement \(u\):  
\[u=0\ \ \mathrm{on}\ \ \Gamma_{C}\ \ \ \mathrm{and}\ \ \ \sigma\cdot n=F\ \ \mathrm{on}\ \ \Gamma_{C}. \tag{6}\]  
Normal traction vector \(F=(F_{1},F_{2})\) is a priori not known. This vector follows from conditions of nonpenetration and friction imposed on contact sub-boundary \(\Gamma_{C}\)[4, 6]. The normal outward vector to the boundary \(\Gamma\) is denoted by \(n=(n_{1},n_{2})\). The nonpenetration condition of the contacting surfaces along sub-boundary \(\Gamma_{C}\) takes the form:  
\[u_{2}+\bar{h}_{r}+w\leq 0,\ \ F_{2}\leq 0,\ \ (u_{2}+\bar{h}_{r}+w)F_{2}=0. \tag{7}\]  
In (7), \(\bar{h}_{r}\) and \(w\) denote, respectively, the gap between the wheel and rail as well as the wear depth function. From contact geometry, it follows that the gap function is equal to \(\bar{h}_{r}=h-h_{0}+\sqrt{r_{0}^{2}-(u_{1}+x_{1})^{2}}\). The wear depth function \(w\) is determined on the contact interface and obeys [PERSON]'s law (16). The Coulomb friction condition is given by [17]:  
\[|F_{1}|\leq\mu|F_{2}|,\ \ F_{1}du_{1}\leq 0,\ \ (|F_{1}|-\mu|F_{2}|)du_{1}=0, \tag{8}\]  
with \(\mu\) defined as a friction coefficient. The system of equalities (5) and (6) and inequalities (4), (7) and (8) governs the elasto-plastic contact problem in terms of the displacement and the generalized stress. In fact, this system is equivalent to the system of two coupled variational inequalities describing elasto-plastic material deformation and contact as well as the friction conditions on the boundary. The evolution of temperature \(\theta\) in (5) is governed by the heat conduction equation.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 3 RF Breakdown
### Material Studies  
One of the main results of the study of reproducibility and cavity geometry was the understanding that, for the same cavity shape, the high-gradient performance is determined by the properties of the bulk material. Therefore, studies of hard copper alloys, bimetallic cells, and cryogenic cavities followed. Note that usually the initial state of the surface has little effect on the final high-gradient performance, which, for example, contradicts our knowledge of the high-gradient operation of superconducting cavities, which typically operate at much lower surface fields.
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 2 Background and Threat Model
### Threat Model  
In the flow table saturation attack, the attacker aims to infer the configurations of victim switches and then saturate the flow table. This threat model assumes the SDN controller adopts OpenFlow protocol and implements reactive rule installation, which is widely used in most OpenFlow controllers [27].  
We assume the adversary has access to hosts or virtual machines in the SDN-based network and can send packets transmitting through the target switch. The attacker can send probing packets, monitor the responses to gather configuration information, and generate traffic accordingly. These assumptions are consistent with previous works on probing-based attacks in the SDN environment. We do not require the attacker to compromise other network entities such as switches, controllers, or any control application.
# High-Precision Manufacture and Alignment of Image Slicer Based on Thin Glass Bonding
## 4 Error Analysis
### Eccentricity Error
#### 4.1.1 Shape Error  
According to the fabrication process, the shape of the glass slices plays a role in the positioning of the eccentricity degree of the glass slices in the image slicer. Therefore, the shape error is essential to the analysis. Each glass slice is cuboid, and the shape errors are determined by the angle and length. An abridged general view of the slice parameters is given in Figure 8b. The length \(L\) determines the displacement in lateral X direction; therefore, the length error \(\Delta L_{1}\) must be precisely controlled. The errors \(\Delta H\) and \(\Delta W\) in the height H and width \(W\), respectively, can be removed through polishing the front and back surfaces in the slice sticking process. The errors \(\Delta A_{1}\), \(\Delta A_{2}\), \(\Delta A_{3}\), and \(\Delta A_{4}\) in the  
Figure 6: Error tree of image slicer fabrication. The errors produced in the fabricating process are classified into four kinds: length error, fixture error, stack error, and surface error.  
Figure 7: Three dimensions of the sphere to be out of position.  
angles \(A_{1}\), \(A_{2}\), \(A_{3}\), and \(A_{4}\) determine the cuboid shape which influences the error control in the stacking process. As shown in Figure 8c,d, different cuboid shapes are affected by the errors \(\Delta A_{1}\), \(\Delta A_{2}\), \(\Delta A_{3}\), and \(\Delta A_{4}\), which cause length measurement error and affect the eccentricity degree in the X direction. The relationship between the error factors and influence quantities is listed in Table 4.
## 3 The Proposed Optimization Procedure
### Problem Encoding  
For the proposed GARS procedure, a matrix-encoding scheme has been selected in order to simultaneously describe the sequence of groups and the sequence of jobs to be processed within each group. Making use of the notation introduced in the nomenclature Section, each chromosome is coded by an array of \(G+1\) row vectors. The first \(G\) vectors are the permutations \(\pi^{s}\) indicating the sequence  
Figure 1: Gantt chart for a FSDGS example with \(M=2\), \(G=2\), \(n_{1}=3\), \(n_{2}=2\).  
of jobs pertaining to each group \(g\). The last vector is the permutation \(\Omega\) representing the sequence of groups:  
\[\left[\begin{array}{c}\pi_{1}^{1},....,\pi_{n_{1}}^{1}\\...\\ \pi_{1}^{g},....,\pi_{n_{g}}^{g}\\...\\ \pi_{1}^{G},....,\pi_{n_{G}}^{G}\\ \hline\Omega_{1},.....,\Omega_{G}\end{array}\right] \tag{1}\]  
It is worth pointing out that the number of elements in each vector is generally unequal. Therefore, indicating with \(n_{\max}\) the maximum length of all vectors, _i.e._, \(n_{\max}=\max(G,\ \max\limits_{g\dash}^{G}\{n_{g}\})\), all rows having a number of elements lower than \(n_{\max}\) are completed with a string of zero digits. In such a way, a \((G+1)\times n_{\max}\) matrix is created, and the chromosome may be easily handled for executing all genetic operators. However, all the digits equal to zero do not take part either to the solution decoding or to the genetic evolutionary process.  
Each row of the partitioned matrix adopted in the proposed encoding scheme will be denoted as a _sub-chromosome_. Figure 2 depicts a feasible chromosome solution, named C, for a problem in which \(G=3\), \(n_{1}=2\), \(n_{2}=5\), \(n_{3}=4\). Sub-chromosomes from 1 to 3 hold the schedules of jobs within each group (_i.e._, schedule 2-1 for group 1, schedule 1-4-3-5-2 for group 2, schedule 2-1-4-3 for group 3. Sub-chromosome 4 fixes the sequence of groups \(\Omega\) = 3-2-1. Once the problem encoding is defined, the fitness function of each chromosome pertaining to the genetic population may be computed as the reciprocal of the total flow time, _i.e._, \(1/\sum\limits_{g=1}^{G}\sum\limits_{j=1}^{n_{g}}F_{gj}\).
# Prussian Blue: A Safe Pigment with Zeolitic-Like Activity
## 4 Adsorption Properties of PB
### Adsorption of Cations  
In addition to Cs\({}^{+}\) or Tl\({}^{+}\), PB also adsorbs other monovalent and divalent cations. Such cations can compete in a lesser extent with Cs\({}^{+}\) or Tl\({}^{+}\) by the exchange with hydrogen ions present into the crystal lattice of PB. The distribution coefficient of Cs on magnetic PB was decreased by the presence of other cations, where K\({}^{+}\) has the most detrimental effect. The similitude between the Stokes radii of both cations (Cs\({}^{+}\), 0.119 nm and K\({}^{+}\), 0.125 nm) or even the hydration radii (Cs\({}^{+}\), 0.325 nm and K\({}^{+}\), 0.330 nm) could explain such competition. At this point, it would be interesting to think of using PB as sorbent for K\({}^{+}\) in patients with hyperkalemia. The competition produced by Na\({}^{+}\) is much smaller. [PERSON] et al. developed an efficient hybrid capacitive deionization system for removal of NaCl from brackish water, in which PB embedded in a highly conductive reduced graphene oxide aerogel was used as a binder-free intercalation anode to remove Na\({}^{+}\) ions. The combination of redox-active PB and the three-dimensional porous graphene network yielded a high salt removal capacity of 130 mg/g at the current density of 100 mA/g [70].  
Although the size of divalent cations is quite different from that of Cs, in absence of Tl\({}^{+}\) or Cs\({}^{+}\), PB can adsorb divalent cations, such as Cu\({}^{2+}\), Co\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\)[71]. By using magnetic PB, [PERSON] et al. showed the high capacity of sorption: copper, 138 mg/g, cobalt, 111 mg/g, nickel, 155 mg/g, and Pb, 778 mg/g. It is important to remark the capacity of adsorption of PB by Cu\({}^{2+}\). This ion forms a high-affinity complex with the \(\beta\)-amyloid peptide, whose accumulation is observed in [PERSON]'s disease [72]. Furthermore, it could exist a relationship between the presence of copper ion and the formation of plaques of \(\beta\)-amyloid.