esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results & Discussion
### Factors Controlling Pore Cast Quality
#### 3.2.1 Vacuum Level  
To evaluate the efficiency of vacuum impregnation, we tested two scenarios: (i) residual pressure of 120 mbar, and (ii) residual pressure of 10 mbar. Figure 6 shows two micromodels impregnated according to the aforementioned scenarios. We observed two distinctive types of defects, Type 1 and Type 2, in both models. Green circles and ovals highlight Type 1 defects formed around trapped gas. These defects have round or elliptical shapes and appear mostly near the channel ends. The residual gas was snapped off and trapped in the channels. This interpretation is consistent with lower residual pressure experiments, where such defects were absent.  
Type 2 defects are highlighted yellow in Figure 6. These defects are smaller than Type 1, have corrugated boundaries and irregular shapes. Resin shrinkage causes type 2 defects, which are frequently referred to as \"volume decrease\". The two main causes of resin shrinkage are: (i) thermal expansion and contraction due to heating or cooling, and (ii) curing shrinkage due to polymer chain cross-linking after phase transition [53]. In this study, we were only concerned with the curing shrinkage since, in our experiments, the epoxy resin cross-linked at room temperature.  
Figure 7 shows the two subtypes of shrinkage defects: (i) most frequent type 2A: voids of 10-40 m in diameter, and (ii) rare type 2B: large cracks. Shrinkage defects are caused by tensile stresses that arise when the resin is cured inside a constrained volume to which it adheres [54; 55]. Evidently, from our experiments, the large cracks seem to appear near stress concentration points mostly.  
Shrinkage defects are more pronounced in Figure 6A, where the high vacuum level, 10 mbar, was applied. Reaching 10 mbar in our setup require  
Figure 6: Confocal images of the micromodels impregnated by resin under two vacuum levels: (**A**) 10 mbar, and (**B**) 120 mbar. Type 1 defects, trapped gas, are more abundant in model B due to the inefficient degassing and impregnation energy. Type 2 shrinkage defects are more abundant in model A, because the epoxy resin is more aggressively vaporized. Type 2A refers to void shrinkage defects, while type 2B refers to cracks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Defect Type** & **Description** \\ \hline Type 1 & Trapped gases \\ Type 2 & Shrinkage defects \\ Type 2A & Voids with corrugated boundaries and irregular shapes \\ Type 2B & Rough cracks \\ \hline \hline \end{tabular}
\end{table}
Table 3: Types of defects observed in resin-impregnated micromodels.  
than reaching 120 mbar. Long vacuum exposure results in excessive evaporation of the hardener despite the excess amount added. The hardener deficiency causes a lower resin conversion ratio, slows down gelation, and generates resin inhomogeneity and voids.
## 3 Model Formulation  
To propose a mathematical formulation for the model based on the problem statement of this study, the total cost function is expressed in Equation (1). The total cost function is the sum of the user, operator and social costs, which can be formulated as follows:  
\[C_{T}\ =\ C_{u}\ +\ C_{o}\ +\ C_{s} \tag{1}\]  
where \(C_{T}\), \(C_{u}\), \(C_{o}\) and \(C_{s}\) represent the total cost, user costs, operation costs and social costs, respectively. The well-structured cost classification for the proposed model is shown in Figure 1. Table 1 tabulates each cost mentioned more comprehensively later in this section. For nomenclature and convenience purposes, all variables and parameters used for the modified objective function are defined in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Total Cost (C\({}_{T}\))**} \\ \hline \multicolumn{3}{c}{**User Cost (\(C_{n}\))**\({}^{a}\)} & \multicolumn{3}{c}{**Operating Cost (\(C_{o}\))**\({}^{b}\)**} & \multicolumn{3}{c}{**Social Cost (\(C_{s}\))**\({}^{c}\)**} \\ \hline \multicolumn{3}{c}{**Feeder Bus and Train**} & \multicolumn{3}{c}{**Feeder Bus and Train**} & \multicolumn{3}{c}{**Feeder Bus**} \\ \hline Access & Waiting & User & Fixed & Operating & Maintenance & Personnel & Social \\ cost & cost & in-vehicle cost & cost & in-vehicle cost & cost & cost & cost \\ \hline \(C_{elF}+C_{elT}\) & \(C_{uF}+C_{uT}\) & \(C_{uF}+C_{uT}\) & \(C_{fF}+C_{fT}\) & \(C_{aF}+C_{uT}\) & \(C_{aF}+C_{uT}\) & \(C_{pF}+C_{pT}\) & \(C_{sF}\) \\ \hline \({}^{a}\) & \(C_{u}=C_{a}+C_{w}+C_{si}\) & & \({}^{b}\)\(C_{s}=C_{f}+C_{si}+C_{m}+C_{p}\) & & \({}^{c}\)\(C_{s}=C_{sF}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Illustration of the total cost with all terms in the proposed improved model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Unit** \\ \hline \(C_{T}\) & Total system cost & ($/h) \\ \(C_{TK}\) & Total cost function for route \(k\) & ($/h) \\ \(C_{u}\) & User cost & ($/h) \\ \(C_{o}\) & Operation cost & ($/h) \\ \(C_{e}\) & Access cost & ($/h) \\ \(C_{nr}\) & Waiting cost & ($/h) \\ \(C_{P}\) & Personnel cost & ($/h) \\ \(C_{ni}\) & User in-vehicle cost & ($/h) \\ \(C_{ei}\) & Operating in-vehicle cost & ($/h) \\ \(C_{niT}\) & Feeder running user cost & ($/h) \\ \(C_{niF}\) & Feeder dwell user cost & ($/h) \\ \(C_{niT}\) & Train running user cost & ($/h) \\ \(C_{dnT}\) & Train dwell user cost & ($/h) \\ \(C_{cnF}\) & Feeder running operating cost & ($/h) \\ \(C_{deF}\) & Feeder dwell operating cost & ($/h) \\ \(C_{enT}\) & Train running operating cost & ($/h) \\ \(C_{deM}\) & Train dwell operating cost & ($/h) \\ \(C_{f}\) & Fixed costs & ($/h) \\ \(C_{en}\) & Maintenance cost & ($/h) \\ \(C_{s}\) & Social cost & ($/h) \\ \(C_{oF}\) & Feeder bus operation cost & ($/h) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the parameters used in the proposed improved model. Figure 1: The cost structure of the proposed improved model.
## 2 Background
### Hybrid Automata  
Hybrid automata provide formal models for hybrid dynamical systems and can be observed as an extension of finite automata by adding continuous dynamics into each of its discrete states (also called modes). Each mode is associated with constraints, within which the continuous dynamics evolve. Edges between modes are annotated with guards that specify the conditions for the mode transition to be triggered; each edge is also associated with a reset map indicating how the continuous variables are being updated after the discrete transition. Hybrid automata are defined below [19].  
A hybrid automaton H is a collection:  
\[H\ =\ (Q,X,f,Init,Inv,E,G,R)\]  
where:  
\(Q=(q1,q2,\dots)\) is a finite set of discrete states;  
\(X\subseteq R^{n}\) represents the state space of the continuous state variables;  
\(f:QxX\to R^{n}\) assigns to each discrete state \(q\in Q\) an analytic vector field \(f(q,\cdot)\);  
\(Init\subseteq Q\ x\) is the set of initial states;  
\(Inv:Q\to 2^{x}\) assigns to each state \(q\in Q\) a set \(Inv(q)\subseteq X\) called the invariant set;  
\(E\subseteq QxQ\) is the set of discrete transitions;  
\(G:E\to 2^{x}\) assigns to each discrete transition \((q,q^{\prime})\in E\) a guard set \(G(q,q^{\prime})\subset X\);  
\(R:ExX\to 2^{x}\) is a reset map.
# Sliding Balance Control of a Point-Foot Biped Robot Based on a Dual-Objective Convergent Equation
## 2 Dual-Objective Convergence Equation for the Biped Robot
### Dual-Objective Convergence Equation  
To maintain a stable posture during sliding or standing, the derivative of the posture coordinate \(q_{p}=[\theta,\dot{q_{1}},q_{2}]^{T}\) should converge to zero. However, with one degree of under-actuation, the point-foot model, which has only two actuating joints at the hip and knee, is unable to drive these variables to zero directly. To control more variables with fewer DoFs, we design a virtual generalized velocity variable \(E=J_{E}(q_{p})\dot{q_{p}}\) as the posture stability criterion. We then use the variable to construct the dual-objective convergence equation:  
\[\dot{E}=J_{E}\ddot{q_{p}}+J_{E}\dot{q_{p}}=U(q_{p})\cdot P\big{(}q_{p},\dot{q_ {p}},\ddot{q_{p}}\big{)}, \tag{1}\]  
where \(J_{E}(q_{p})\in R^{1\times 3}\) is the matrix of the mapping from the derivative of the posture coordinate \(\dot{q_{p}}\) to the posture stability criterion \(E\), \(U(q_{p})\in R\) is a generalized coordinate that represents the equilibrium variable, and \(P\big{(}q_{p},\dot{q_{p}},\ddot{q_{p}}\big{)}\in R\) is an instantaneous constraint,  
Figure 2: Coordinates and structural configuration. \(X_{b}\) and \(Y_{b}\) are the x and y floating-base coordinates. \(\theta\) is the angle between the y-axis of the world frame and the torso. \(q_{1}\) and \(q_{2}\) are the joint coordinates of the hip and knee. \(m\), \(I\) and \(l\) are the mass, inertia and length of a rigid body.  
the value of which can be controlled directly using \(\tilde{q_{p}}\). We treat \(U\) as the controlled variable. If \(U\) can track \(E\) according to  
\[U\big{(}q_{p}\big{)}=U^{ref}=-\varepsilon E \tag{2}\]  
with  
\[P\big{(}q_{p},q_{p},\tilde{q_{p}}\big{)}>0, \tag{3}\]  
then the posture stability criterion \(E\) can converge to zero over time. Here, \(\varepsilon\in R\) is the dual-objective proportional constant and \(U^{ref}\) is regarded as a reference equilibrium target for \(U\big{(}q_{p}\big{)}\). Furthermore, in the case that \(E\equiv 0\) and \(P>0\), \(U\) can be quantificationally calculated using (1), we have  
\[U\big{(}q_{p}\big{)}\equiv 0. \tag{4}\]  
Therefore, the first derivative of \(U\) is written as  
\[\dot{U}=J_{U}\dot{q_{p}}=0, \tag{5}\]  
where \(J_{U}\big{(}q_{p}\big{)}\in R^{1\times 3}\) is the Jacobian matrix of \(U\big{(}q_{p}\big{)}\). Therefore, through the dual-objective convergence equation, the stabilization of \(E\) and \(\dot{U}\) can be realized simultaneously while controlling only one variable, \(U\). This approach can thus stabilize more variables than the number of DoFs of the robot. The method of formulating the dual-objective convergence equation and the QP optimization controller for tracking \(U^{ref}\) are discussed in later sections.
# Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm
## 2 Materials and Methods
### Optimization of Multilayer Material Structure  
The fitness function is formulated in Equation (8), a weighted sum of the absorber's total thickness and the maximum RC for a particular incidence angle, FR, and TE and TM polarizations. The thickness is attuned to provide the least RC and the fitness function is constructed to attain the minimum RC, where the weighted coefficients used to determine the total fitness value are \(\varphi_{1}\) and \(\varphi_{2}\), respectively [13]. [PERSON]'s recursive formula (Equation (1)) is used to calculate the RC of the MMA for both TE and TM polarizations. To obtain the required design, the aforementioned equation must be minimized.  
\[F=\varphi_{1}\times 20\text{log}_{10}\left(\text{max}(|R_{0,1}|)\right)+ \varphi_{2}\times\sum_{i=1}^{N}d_{i} \tag{8}\]  
The thicknesses of the layers and the frequency-dependent material properties are the parameters to be obtained for the optimum design with the least maximum RC. The number of variables is set at twice the number of layers. At the initializing stage, the thicknesses and materials of each layers are randomly assigned. The objective function for the initial materials and thickness values is calculated via Equation (8). The variables (materials of the layers and their thicknesses) are recalculated in the selecting (Equation (5)), searching (Equation (6)), and swooping (Equation (7)) stages based on the information that is now available from the previous iteration. Figure 4 shows the flow diagram of the BESOA.  
The materials listed in Table 1, which have been predefined and are the most often used materials in [10; 13; 18; 19], are chosen to create the ideal layer sequence and for consistency in comparisons.  
\begin{table}
\begin{tabular}{|c c c c|} \hline \multicolumn{3}{|c|}{Lossless Dielectric Materials (\(\mu^{\prime}=1,\ \mu^{\prime}=0\) )} \\ No: & & \(\epsilon^{\prime}\) \\ \hline
1 & & & 10 \\
2 & & & 50 \\ \hline \multicolumn{3}{|c|}{Lossy Magnetic Materials (\(\epsilon^{\prime}=15,\ \epsilon^{\prime}=0\) )} \\ \multicolumn{3}{|c|}{\(\mu=\mu^{\prime}-j\mu^{\prime\prime}\)} & \(\mu^{\prime}(f)=\frac{\mu^{\prime}(1\;\text{GHz})}{f^{2}}\) & \(\mu^{\prime\prime}(f)=\frac{\mu^{\prime\prime}(1\;\text{GHz})}{f^{3}}\) & \\ \hline No & \(\mu^{\prime}(1\,\text{GHz})\) & \(a\) & \(\mu^{\prime\prime}(1\,\text{GHz})\) & \(b\) \\ \hline
3 & 5 & 0.974 & 10 & 0.961 \\
4 & 3 & 1.00 & 15 & 0.957 \\
5 & 7 & 1.00 & 12 & 1.00 \\ \hline \end{tabular}
\end{table}
Table 1: Relative Permittivity and Permeability of the Predefined Materials (Reprinted with permission from ref. [7]). Copyright 1993 IEEE).  
Figure 3: Three Phases of Bald Eagle Hunting Behavior.
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 5 Performance Analysis and Simulation  
### Experimental Parameters  
This section builds a simulation environment based on the overlay network simulation framework OverSim [15]. OverSim is a P2P overlay network simulation module running on OMNeT++ [16].  
Figure 5: Markov Decision Construction (MDC) procedure.  
OMNeT++ is a discrete event driven simulator, which is widely used in the academic field. In order to simulate the application scene, the network topology is randomly created according to the typical Internet model InetUnderlay, and the network topology is composed of 10 backbone routers and 100 access routers. The number of mapping resolution servers is set in two cases. One is that the number of mapped parsing servers is less than 1000. In this case, the number of mapping resolution servers increases from 100 to 1000 with an interval of 100, which is mainly used to evaluate the performance of the mapping resolution system under the small regular mode network. Another case is that the number of mapping resolution servers is increasing from 1000 to 10,000 with an interval of 1000, which is mainly used to assess the performance of the mapping resolution system in a large network environment. Each access router has 100 terminals on average, so there are approximately 10,000 terminal nodes in the network. In every experiment, the number of mapping resolution servers is constant. This is because the mapping resolution system proposed in this paper is mainly used in an Internet backbone network. In a backbone network, the probability of node joining/leaving or failure in the mapping analysis system is very small. Therefore, the effect of node disturbance (churn) can be ignored in the evaluation process of the performance of the mapping resolution system.  
In each experiment, each mapping resolution server is registered with an average of 1000 mapping items, so when the number of mapping resolution servers is 1000, the overall mapping resolution system has approximately 1,000,000 mapping items on the whole in the process of experimental measurement, each terminal node runs a test program periodically sending mapping resolution requests with a request interval of 1 s. The request pattern follows the web page browsing model proposed in Reference [17]. The popularity of mapping resolution parses items follows a Zipf probability distribution similar to the popularity of web pages, uniformly distributed 160 bit length identifiers are used to map the parsing system. The activity threshold is set to (AD\({}_{\text{min}}\) + AD\({}_{\text{max}}\))/2, with AD\({}_{\text{min}}\) and AD\({}_{\text{max}}\) representing the minimum and maximum values of the mapping information activity in the local active mapping server, respectively. To reflect the general situation, the experiment was repeated 10 times and the average value of 10 times was taken as the final result. Furthermore, the time of each simulation was set to 1 h.
###### Abstract  
The ability of process-based biogeochemical models in estimating the gross primary productivity (GPP) of alpine vegetation is largely hampered by the poor representation of phenology and insufficient calibration of model parameters. The development of remote sensing technology and the eddy covariance (EC) technique has made it possible to overcome this dilemma. In this study, we have incorporated remotely sensed phenology into the Biome-BGC model and calibrated its parameters to improve the modeling of GPP of alpine grasslands on the Tibetan Plateau (TP). Specifically, we first used the remotely sensed phenology to modify the original meteorological-based phenology module in the Biome-BGC to better prescribe the phenological states within the model. Then, based on the GPP derived from EC measurements, we combined the global sensitivity analysis method and the simulated annealing optimization algorithm to effectively calibrate the echophysiological parameters of the Biome-BGC model. Finally, we simulated the GPP of alpine grasslands on the TP from 1982 to 2015 based on the Biome-BGC model after a phenology module modification and parameter calibration. The results indicate that the improved Biome-BGC model effectively overcomes the limitations of the original Biome-BGC model and is able to reproduce the seasonal dynamics and magnitude of GPP in alpine grasslands. Meanwhile, the simulated results also reveal that the GPP of alpine grasslands on the TP has increased significantly from 1982 to 2015 and shows a large spatial heterogeneity, with a mean of 289.8 gC/m2/yr or 305.8 TgC/yr. Our study demonstrates that the incorporation of remotely sensed phenology into the Biome-BGC model and the use of EC measurements to calibrate model parameters can effectively overcome the limitations of its application in alpine grassland ecosystems, which is important for detecting trends in vegetation productivity. This approach could also be upscaled to regional and global scales.  
alpine grassland; the Tibetan Plateau; Biome-BGC; GPP; remotely sensed phenology; parameter calibration 15 April 2019; Accepted: 28 May 2019; Published: 30 May 2019
# Biosorption of Heavy Metals by the Bacterial Exopolysaccharideaccharide FucoPol
## 1 Introduction
This study focused on evaluating the performance of FucoPol for the biosorption of different heavy metals (Pb\({}^{2+}\), Co\({}^{2+}\), Zn\({}^{2+}\) and Cu\({}^{2+}\)). Considering the results, metal toxicity and prevalence, Pb\({}^{2+}\) was selected for further tests. The effects of FucoPol dosage, initial metal concentration, pH and temperature were explored for Pb\({}^{2+}\) removal, envisaging the optimization of the biosorption process.
# HAZOP Analysis in Terms of Safety Operations Processes for Oil Production Units: A Case Study
## 2 Materials and Methods
### Production Unit Description
#### 2.1.1 Oil Treatment  
Crude oil from field production composed of gas, oil and water enters the plant to the first separation equipment, the free water knock-out drum (FWKO), where the first bulk water separation is performed. This FWKO vessel is a three-phase separator with enough capacity to separate most of the free water-fed along with the field inlet stream. The FWKO drum also has sufficient liquid holdup time to withstand any eventual slug produced in the feed pipelines. Water removal efficiency is assumed to bring 15% BS&W (Basic Sediment and Water) in the oily outlet stream for this separator. Separated water is conducted to the water treatment facilities to be treated. The separated gas is removed to be used as fuel gas, and excess gas is burnt in the burn pit.  
Oily stream from FWKO enters the Heater-Treater separator, which combines a three-phase separator with a heating media using a fuel gas fiertube to heat the inlet stream to the required temperature needed to achieve the desired separation between oil and emulsified water. The ideal temperature for the separation of the water-oil fluid is estimated to be in the range of 150-170 \({}^{\circ}\)F. It is assumed that the Heater-Treater's water removal efficiency produces 0.5% BS&W in the oily output stream.  
The oily stream goes from the Heater Treater to the Electrostatic-Desalter's final conditioning unit for final water and salt removal. This equipment uses an electric field to promote and speed up water coalescence, helping the final separation of the smallest water drops that previous equipment could not remove. The Electrostatic-Desalter water removal efficiency is assumed to produce 0.2% BS&W in the oil outlet.  
Fresh well water must be injected before the Electrostatic Desalter to achieve the final dilution required to remove the salt and produce treated oil with less than 15 pounds per thousand barrels (PTB). To economise dilution water requirements, the Electrostatic Desalter removed water outlet is recycled to the Heater-Treater. The same diluting effect occurs since the salt concentration in the Electrostatic-Desalter water outlet is below the salt concentration in the Heater-Treater water inlet. Figure 1 shows the Process Flow Diagram (FFD) of the Crude Oil treatment.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### NFT Manipulation Mapping
#### 5.5.4 Self-Trading
Applying graph theory in the analysis of NFT trading activities not only aids in detecting manipulative practices but also contributes to enhancing market transparency and integrity. By leveraging graph-based models to map out transactions and relationships, researchers and regulators can gain valuable insights into fraudulent behaviors and develop effective monitoring tools to safeguard the NFT market.