esa-sceva/satcom-chunk-collection huggingface dataset:
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 3 System Model
### Space-Time Topology Model  
To characterize TSIN resources and capture the dynamic topology evolution, according to the link information divided by discrete time intervals, a space-time topology graph is proposed to represent the relationship of link resources in the time and space domain. Figure 1: The transmission architecture of TSIN. In particular, the path plan time is divided into consecutive time intervals indexed by \(t\in\{1,\ldots,D\}\). During each time interval \(\Delta\tau\), the network topology of TSIN is considered as stable or quasi-static since the span of the time interval is short. Therefore, a static graph can be adopted to describe the interactions of nodes in each time interval. We defined this static graph as a snapshot of the topological evolution. Through a sequence of static graphs, the time-varying topology can be presented as a space-time topology graph as follows: the space-time topology graph \(G(t)=(V,E(t))\) is composed of \(D\) layers, corresponding to the network topology of \(D\) time intervals shown in Figure 2. The vertices of \(G\) corresponding to the set of UTs, LEO satellites, and TSEs for each time interval. The vertices set is denoted as \(V=V_{UT}\cup V_{s}\cup V_{g}\), where \(V_{UT}=\{UT_{i}^{l}|UT_{i}\in UT,1\leq t\leq D\}\), \(V_{s}=\{s_{i}^{l}|s_{i}\in S,1\leq t\leq D\}\), \(V_{g}=\{g_{i}^{l}|g_{i}\in G,1\leq t\leq D\}\). The key notations are summarized in Table 2.  
Since the graph representing the connectivity between these nodes varies with time, \(E(t)\) is a time varying set of links between these nodes, which consists of a spatial link and a temporal link. For the sake of simplification of the model, the link between UT and TSE includes the potential terrestrial paths between TSEs. Wherein, the set of spatial links is denoted as \(E_{l}(t)=E_{gu}(t)\cup E_{sg}(t)\cup E_{ss}(t)\), and the links in \(E_{gu}(t)\) are shown as black lines in Figure 2, which represent the communication opportunities for UTs to TSE in each time interval, i.e., \(E_{gu}(t)=\{(UT_{i}^{l},g_{i}^{l})|1\leq t\leq D,g_{i}\) is the access TSE of \(UT_{i}\) in the \(t\)-th time interval). Similarly, the links in \(E_{ss}(t)\) and \(E_{sg}(t)\) represent the communication opportunities for satellite to satellite and satellite to TSE in each time interval, where \(E_{ss}(t)=\{(s_{i}^{l},s_{i}^{l})|1\leq t\leq D,s_{i}^{l}\) is the neighbor satellite of \(s_{i}^{l}\) in the \(t\)-th time interval and \(E_{sg}(t)=\{(s_{i}^{l},s_{i}^{l})|1\leq t\leq D,g_{i}\) is in the coverage of \(s_{i}\) in the \(t\)-th time interval]. Since nodes \(v_{i}^{l},t=1,\ldots,D\) in \(G(t)\) correspond to the single node \(v_{i}\) in the network, \(v_{i}^{l}\) and \(v_{i}^{l+1}\) can be connected by a temporal link \(v_{i}^{t}\to v_{i}^{l+1}\) (horizontal links in Figure 2), which represents node \(v_{i}\) storing messages in the \(t\)-th time interval, i.e., traversing a temporal link denotes \"carrying\" a message by a satellite. Intuitively, the data transmission on
## 3 Results
### Homology Modeling and Molecular Docking of Pm TPSs
Each of the above amino acids formed one hydrogen bond with the diphosphate group of GPP. Figure 4: (**a**) Predicted three-dimensional structure and (**b**) amino acid sequence alignment of the six terpenoid synthases (TPS) from _P. massoniana_. The protein resolution method is X-ray. (**a**) “N” represents the N-terminal domain on the left side of the dash line, while “C” represents the C-terminal domain. The \(\alpha\)-helix segments are blue and ordered in white in alphabetical order, \(\beta\)-sheet/turns are in green, and random coil/loops are in white; (**b**) shows amino acid sequence alignment, \(\alpha\)-helical segments are marked and underlined red (\({}^{*}\) indicate fully aligned amino acids). The homology modeling templates of the six terpene synthases (Pm TPS (+)-\(\alpha\)-pinene, Pm TPS (\(-\))-\(\alpha\)-pinene, Pm TPS (\(-\))-\(\beta\)-pinene, Pm TPS limonee, Pm TPS soleninee, Pm TPS longifolene) are respectively \(\alpha\)-bisabolene synthase (PDB no. 3 ae.1.A), taxadiene synthase (PDB no. 3p5r.1.A), \(\alpha\)-bisabolene synthase, \(\alpha\)-bisabolene synthase, taxadiene synthase, and taxadiene synthase. The consistency between the modeling templates and Pm TPSs was greater than 30%. The side chain of two closely related amino acids, Asp 321 and Asp 325 in helix D, formed a hydrogen bond with the diphosphate group of GPP in the substrate-binding pocket of Pm TPS solienene. For Pm TPS longifolene, four residues in the binding pocket formed hydrogen bonds with FPP: Asp 331 in helix D, the amino acid Ser 433 near the end of helix G1, and Arg 473 and lysine (Lys) 484 in helix H2. The amino acid Arg 473, Asp 331, Ser 433, and Lys 484 formed three, two, one, and one hydrogen bonds with the diphosphate group of FPP, respectively.
## 2 Method  
The adsorption of three kinds of SF\({}_{6}\) decomposition products (SO\({}_{2}\), SOF\({}_{2}\) and SO\({}_{2}\)F\({}_{2}\)) on the surface of Pt-graphene was all carried out in Dmol\({}^{3}\) package [24; 25]. The generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof function (PBE) was used in considering of the exchange-correlation functional [26]. The basis set chosen for this study was the double numerical plus polarization (DNP) and considering that Pt is a heavy element, the DFT semi-core pseudopotential (DSSP) was selected. To make a good description of long range vdW force, the DFT-D method was chosen force [27]. The cutoff radius for all atom in all structures was set as 5 A. All the geometric optimizations were set of 1 \(\times\) 10\({}^{-5}\) Ha energy convergence, 0.002 Ha/A force convergence, and 0.005 A displacement convergence between the two iteration steps. The k-point sample of Monkhorst-Pack grid was set as 6 \(\times\) 6 \(\times\) 1 when carrying out geometric optimization as well as the calculations of electronic properties [28]. The Gaussian smearing was 0.005 Ha.  
A 6 \(\times\) 6 \(\times\) 1 graphene supercell including 72 C atoms was first built with height of 15 A vacuum region. To build the structure of Pt doped graphene, one C atom was substituted by one Pt atom and then the geometric optimization using the convergence criteria above. Finally, the structure of one Pt-doped graphene was obtained. For gas adsorption on the surface, one or two gas molecules were placed above the Pt atom and then the final structures were also obtained by fully geometric optimization.  
The adsorption energy of one or two gas molecules on the surface of Pt-graphene is:  
\[\Delta E_{ads}=E_{(molecule-Pt-graphene)}-E_{(Pt-graphene)}-E_{(molecule)} \tag{1}\]  
where \(E_{molecule-Pt-graphene}\), \(E_{Pt-graphene}\) and \(E_{molecule}\) are the calculated total energy of gas molecules/Pt-graphene adsorption structure, Pt-graphene surface before adsorption and gas molecules before adsorption. The charge transfer was based on Mulliken method [29]. The electron transfer is calculated as:  
\[Q_{T}=Q_{1}-Q_{0} \tag{2}\]  
where \(Q_{1}\) and \(Q_{0}\) represent the carried charge of adsorbed gas molecules and isolated gas molecules. The carried charge of isolated gas molecule is zero and when \(Q_{T}\) is negative, gas molecule obtains electrons and when \(Q_{T}\) is positive, gas molecule acts as electron donor.  
The energy gap of the structure reflects the difficulty level of electron transition from the highest occupied molecular orbital (HOMO) to the lowest unoccupied molecular orbital (LUMO). The smaller value indicates the easier process of electron transferring from the valence band to the conduction band resulting in large conductance. The value of energy gap \(E_{g}\) is defined as:  
\[E_{g}=\abs{E_{HOMO}-E_{LUMO}} \tag{3}\]  
We also calculated the density of states of adsorption structures in this study.
## 1 Introduction
Particularly, the SN has some computational capabilities and can assist the BS to process the important tasks to be transmitted in advance, as in [15]. In addition, the SN enables transmitting data to the BS or the upper SN directly for extremely important data. 2. We then propose a game-based, energy-efficient underwater communication mechanism based on the presented HUWST framework. We integrate the economic game theory in our mechanism to trade off variations in communication energy consumption due to sensors in different water depth layers. 3. In addition, we formulate the optimal mechanism as a nonlinear integer programming (NIP) problem, while satisfying the data volume constraint of the upper SN. We skillfully transform such a sophisticated NIP problem into a linear programming problem by means of a binary variable relaxation and decompose the problem. An energy-efficient distributed data transmission mode decision algorithm (E-DDTMD) based on the alternating direction method of multipliers (ADMM) is further proposed to tackle this complicated NIP problem. The rest of this paper is organized as follows. In Section 2, we present related works and elaborate on the contributions of existing work and analyze their weaknesses. In Section 3, we have further statements with explanations about the applicability and practicality of our proposal. In Section 4, we describe the network model and propose the novel hierarchical underwater wireless sensor transmission (HUWST) framework. In Section 5, we formulate the computation and communication overhead of SN. In particular, based on economic game theory, we propose a payment mechanism. We then propose a game-based, energy-efficient underwater communication mechanism in the presented HUWST. In Section 6, a new E-DDTMD is thus further proposed to tackle this sophisticated NIP problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters in Section 7.
# Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM-FNN
## 3 Field Data Processing
### Time Series Data Processing  
As shown in the Figure 5, before the time series transform processing for drilling data, the drilling data shall be sampled every 1.0 m. The series data containing different characteristic parameters and ROP values, and the data matrix shape is [n, m], where n is the number of samples and m is the characteristic number of each sample. The time series data needs to combine the data within a time interval to form a sequential data set. The depth was taken as the timing scale to divide the time series. Since the drilling rate before the t time step also belonged to known data, the drilling rate before the t time step was taken as the new input feature when constructing the time series data, and the input of the time series data included as all sampling points 10 m before the t time [(\(x^{t-10}\), \(y^{t-10}\)), (\(x^{t-9}\), \(y^{t-9}\)), (\(x^{t-8}\), \(y^{t-8}\)),..., (\(x^{t-3}\), \(y^{t-3}\)), (\(x^{t-2}\), \(y^{t-2}\)), (\(x^{t-1}\), \(y^{t-1}\)), (\(x^{t}\), \(y^{t}\))].  
The output time \(t+1\) is the drilling rate of one meter below the sampling point (\(y^{t+1}\)). The time steps of the input parameters have an important influence on the models. In order to ensure the precision of the ROP prediction models, the time steps of the drilling data should be greater than 1 m. For the input parameters, the longer the time steps are, the more information is usually contained. However, with the increase of time steps length, the complexity of the model increases and the calculation speed decreases. In order to ensure the timeliness of the model, the time steps of input parameters should not be too large.  
Figure 5: Schematic diagram of time series data construction.
## 3 Lattice Boltzmann Method and Comprehensive Incremental Benefit Model  
### Lattice Boltzmann Method  
The enthalpy-based multiple-relaxation-time lattice Boltzmann method derived by [PERSON] was used to simulate the solid-liquid phase change and the conjugate heat transfer in the building's roof [22]. The evolution equation for the distribution function is given as:  
\[g_{i}(\mathbf{x}+\mathbf{e}_{i}\Delta t,t+\Delta t)=g_{i}(\mathbf{x},t)-\mathbf{M}^{-1}\mathbf{S}[ \mathbf{m}(\mathbf{x},t)-\mathbf{m}^{eq}(\mathbf{x},t)] \tag{11}\]  
where the distribution function for the momentum space is given by:  
\[\mathbf{m}=Mg_{i}=(m_{0},\ m_{1},\ \ldots,\ m_{8})^{T},\ \mathbf{m}^{eq}=Mg_{i}^{eq}= \Bigl{(}m_{0}^{eq},\ m_{1}^{eq},\ \ldots,\ m_{8}^{eq}\Bigr{)}^{T}. \tag{12}\]  
The matrix for transforming the distribution function between momentum space and velocity space is given by:  
\[\mathbf{M}=\left\{\begin{array}{ccccccccc}1&1&1&1&1&1&1&1&1\\ -4&-1&-1&-1&-1&2&2&2&2\\ 4&-2&-2&-2&-2&1&1&1&1\\ 0&1&0&-1&0&1&-1&-1&1\\ 0&-2&0&2&0&1&-1&-1&1\\ 0&0&1&0&-1&1&1&-1&-1\\ 0&0&-2&0&2&1&1&-1&-1\\ 0&1&-1&1&-1&0&0&0&0\\ 0&0&0&0&0&1&-1&1&-1\end{array}\right\}. \tag{13}\]  
The equilibrium distribution function \(\mathbf{m}^{eq}\) in momentum space is given by:  
\[\mathbf{m}^{eq}=(H,\ -4H+2c_{p,ref}T,\ 4H-3c_{p,ref}T,0,\ 0,\ 0,\ 0,\ 0,\ 0)^{T}. \tag{14}\]  
In the current work, the specific heat of PCM or SIM was chosen to be the reference specific heat \(c_{p,ref}\). The relaxation time matrix, \(\mathbf{S}\), in momentum space is given as:  
\[\mathbf{S}=diag\bigl{(}s_{0},\ s_{e},\ s_{e},\ s_{i},\ s_{q},\ s_{j},s_{q},\ s_{e}, \ s_{e}\bigr{)}. \tag{15}\]  
To reduce the numerical diffusion, [PERSON] pointed out that the following relation should be satisfied [22]:\[\left(\frac{1}{s_{e}}-\frac{1}{2}\right)\left(\frac{1}{s_{j}}-\frac{1}{2}\right)= \frac{1}{4}. \tag{16}\]  
When the collision step was completed in the momentum space, the post-collision distribution function in velocity space could be then calculated using an inverse transformation:  
\[g_{i}(\mathbf{x},t+\Delta t)=\mathbf{M}^{-1}\mathbf{m}(\mathbf{x},t+\Delta t). \tag{17}\]  
Then, the streaming process is completed as:  
\[g_{i}(\mathbf{x}+\mathbf{e}_{i}\Delta t,t+\Delta t)=g_{i}(\mathbf{x},t+\Delta t). \tag{18}\]  
The thermal boundary condition scheme, derived by [PERSON] and [PERSON], was used in this paper [20], and the enthalpy, \(H\), was computed as:  
\[H=\sum_{i=0}^{8}g_{i}. \tag{19}\]  
The corresponding temperature, \(T\), is given as:  
\[T=\begin{cases}&T_{m}-\frac{H_{s}-H}{\varepsilon_{p}}\quad H\leq H_{s}\\ &T_{m}\quad H_{s}<H<H_{l}\\ &T_{m}+\frac{H-H_{l}}{\varepsilon_{p}}\quad H\geq H_{l}\end{cases} \tag{20}\]  
where \(T_{m}\) is the PCM melting temperature, \(H_{s}\) and \(H_{l}\) are the enthalpies of the solid and liquid state PCM respectively. The liquid fraction \(f_{l}\) of PCM is computed by:  
\[f_{l}=\begin{cases}&0\quad H\leq H_{s}\\ &\frac{H-H_{s}}{H_{l}-H_{s}}\\ &1\quad H\geq H_{l}\end{cases}\quad H_{s}<H<H_{l}. \tag{21}\]
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 5 Solution Method
### Proposed Algorithm  
In this study, the PSO technique is applied to solve the optimization problem in Equation (17). The aim is to determine the feasible control actions to the participating control devices in power grids. The search procedures for the proposed PSO-based control algorithm is described below:  
Step 1: Input branch parameters and bus data.  
Step 2: Input pilot-bus measured voltages.  
Step 3: If the measured voltages are not within the specified limits, the proposed PSO-based control algorithm will be activated automatically.  
Step 4: Form the matrices \(J_{1}\), \(J_{2}\), and \(J_{p}\).  
Step 5: PSO Initialization.  
\begin{tabular}{l l} (1) & Set iteration counter \(t=0\) and maximum iteration \(t_{\text{max}}\). \\ (2) & Set population size. \\ (3) & Set learning coefficients \(c_{1}\) and \(c_{2}\), and \(\psi\) in Equation (21). \\ (4) & Initialize \(x_{i}^{0}\) and \(v_{i}^{0}\) according to the constraints of the control variables. \\ (5) & Initialize _pbest_ and _gbest_. \\ \end{tabular}  
Step 6: Update iteration counter \(t\).  
Step 7: For each \(x_{i}^{t}\), evaluate the objective function in Equation (17), i.e., measure the fitness value for each particle (_pbest_) and store the particle with the best fitness (_gbest_) value.  
Step 8: Update \(v_{i}^{t}\) using Equation (20).  
Step 9: Update \(x_{i}^{t}\) using Equation (19).  
Step 10: If \(t\) reaches \(t_{\max}\), then go to Step 11. Otherwise, go back to Step 6.  
Step 11: Print out the latest _gbest_ which is the optimal control strategy.
## 6 Conclusions  
In this paper, we have highlighted an area of a problem that has long been neglected--the packet scheduling scheme with network coding enabled. The problem is tackled first in a simplified and static version. With our scheduling method, intermediate nodes can clear packet queues faster, and the scalability of the algorithm is also tested. The findings are then applied to the dynamic form of the scheduling problem, where we consider multiple queuing systems. Though it is hard to analyze the system, we proposed a heuristic scheduling method addressing the challenges posed by a dynamic system. The solution to this dynamic problem is then incorporated into a routing protocol. Simulation results for this routing protocol reveal that with proper scheduling, we can have better control over the fairness of the network. When fairness is not a concern, one can easily adapt our protocol to put more emphasis on those higher coding gain pairs to improve throughput.
###### Abstract  
Underwater wireless sensor networks have a wide range of application prospects in important fields such as ocean exploration and underwater environment monitoring. However, the influence of complex underwater environments makes underwater wireless sensor networks subject to many limitations, such as resource limitation, channel openness, malicious attacks, and other problems. To address the above issues, we propose a routing scheme for underwater wireless networks based on a trust model and Void-Avoided algorithm. The proposed scheme establishes a trust model, evaluates the behavior of underwater nodes through direct trust, indirect trust, and environmental trust, and finds malicious nodes while taking into account evaluation of the channel, which provides support for the next data transmission event. The proposed scheme prioritizes the total cabling distance and introduces a two-hop availability checking model for data transmission, checking the nodes for voids and avoiding the void areas, to find the transmission path with the lowest energy consumption and lowest latency as much as possible. In this study, simulation experiments were conducted on the proposed scheme, and the results showed that the target scheme can effectively detect malicious nodes through anomalous behaviors and outperforms existing work in terms of malicious node detection rate, energy consumption, and end-to-end latency, and network performance.  
trust model; secure transmission; fuzzy logic; underwater wireless sensor networks +
Footnote †: journal: Sensors  
0000-0001-5589-780X]C.[PERSON][PERSON], [PERSON], [PERSON]
# Ten Years of Experience with Scientific TerraSAR-X Data Utilization
## 3 Results
### Analysis of the Scientific Publications based on TerraSAR-X Data
Not only longer-term but also short-term events like natural disasters are part of scientific investigations. 122 papers used TerraSAR-X data for hazard mapping and vulnerability analyses.
# Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks
## 3 Arc Modeling
### Model Verification  
In this subsection, we discuss the simulation results to verify the success of arc modeling. To show the effectiveness of the proposed arc model, simulations were performed in a single-phase distribution feeder with conditions similar to those of the experiment using Matlab/Simulink. The description for the simulation is shown in Figure 1. To implement the fault, two switches were used. Switch 1 was opened for the open phase  
Figure 6: Proposed arc model (proposed Z–V curve).  
Figure 7: Comparison of simulated and experimental arc characteristics. (**a**) Voltage-current curves for both simulated and experimental arc models. (**b**) Impedance-voltage curves for both simulated and experimental arc models.  
fault when the simulation started. Switch 2 was closed to implement the fault with arc at the arc fault time (0.037 [s]). The sampling frequency and control frequency (e.g., control input generation period at each sample) were 50 [kHz]. The arc equations were solved using the Runge-Kutta 4 th order solver. The supplying phase voltage and fundamental frequency were set to 20 kV and 60 Hz, respectively, which were identical to those in the experiment.  
The [PERSON]'s arc model [20] was used as a comparison method as follows:  
\[\frac{dg}{dt}=\frac{1}{\tau}(\frac{|i|}{V_{arc}}-g) \tag{3}\]  
where \(\tau=Ae^{Bg}\). In addition, \(g\) is the time varying arc conductance, \(|i|\) is the absolute value of the arc current, \(V_{arc}\) is a constant arc voltage, \(\tau\) is the arc time constant. To implement [PERSON]'s arc model, the parameters, \(A\), \(B\), and \(V_{arc}\), are set to \(6.6\times 10^{-5}\), 41.97, and 4520, respectively.  
Figure 8 shows the phase voltage and phase current by using the proposed and [PERSON]'s arc model in the simulations. The phase voltages are the same in both method because phase voltages are injected from utility. Before the ignition, the phase impedance should be almost infinite in the remaining load path because there is no arc model path. Therefore, the phase current became almost zero when the phase was opened. Then, fault currents were generated after the arc fault time (0.037 [s]) in both methods. In Figure 8c, we observe that the proposed arc model had the shoulder phenomenon with nonlinearity around zero current at every half cycle because of the arc generated by (1) and (2). This nonlinearity is generated by time-varying impedances where the arc impedance is not constant. There is a buildup phenomenon with arc fault characteristics. However, because this phenomenon is not well observed due to the fast time constant in the arc experiment, it was not considered in the arc modeling procedure. Based on these results, the arc model appeared to have been accurately represented.  
Figure 8: _Cont._