esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study: Application of MHAR-OLSR
### Path Calculation Step  
The OLSR principle requires nodes to renew their routing tables after each new Tc message is received. A routing table saves routes to all possible destinations.  
However, the multipath version does not always keep the routing table. When exchanging data packets, it applies the Dijkstra Multipath algorithm to fill a multipath routing table with the calculated paths that connect the source \"S\" to the specified destination \"D\".  
According to Figure 9, S has three neighbors in its communication range: F1, V1 and M2, thus three potential paths.  
* The first and shortest path calculated is: S \(\rightarrow\) V1 \(\rightarrow\) V4 \(\rightarrow\) D, since V4 is inside the range of V1 and D at the same time.
* The second established route is: S \(\rightarrow\) F1 \(\rightarrow\) F2 \(\rightarrow\) V3 \(\rightarrow\) D. F2 is connected to F1 via U2U, and to V3 using U2V. Then V3 is among D neighbors.
* The Last path is: S \(\rightarrow\) M2 \(\rightarrow\) M3 \(\rightarrow\) V2 \(\rightarrow\) D.  
In Figure 9, the source node (S) finds three paths to its destination (D) with disjoint links and nodes by applying the second case of the cost functions (Section 4.2.2, Equation (2)). Knowing that, initially, all costs for links and nodes are set to 1, thus the cost of each associated arc in these detected paths passes from 1 to 3 by using fp, and each cost of node takes value 2, by mean of fe.
# Coplanar Capillary Plasma Electrode Discharge Flat-Panel Lamps Based on Porous Anodic Alumina
## 2 Fabrication Process and Testing of Coplanar CPED Flat-Panel Lamps
### Results and Discussion
#### 2.4.1 Influence of Interval Distance of Electrodes on the Spark-Ignition Voltage  
The distance between the electrodes plays an important role in the DBD. When the interval distance is smaller, the electric field intensity will be stronger under the same discharge voltage. In addition, the free path of electrons is also reduced [9]. When a pulsed power supply is applied, the higher electromagnetic field generated by the rapid rise and fall of the pulse supply enhances the energy and free path of electrons, which improves the ion and ionization probabilities of the electron and gas molecules, respectively. Therefore, the interval distance must be optimized.  
Figure 7 shows the pressures and spark-ignition voltages for devices with CBD discharge interval distances of 0.5 mm and 1.0 mm. Evidently, when the interval distance is 0.5 mm, the ignition voltage of the device is lower than that of the 1.0 mm distance. The free path and energy of the electrons are increased by the electromagnetic field generated by the pulsed power supply, which leads to the free path of electrons being significantly shorter than the electrode interval distance [8].  
2.4.2. Influence of Pressure on Luminous Efficacy of Coplanar CPED Flat-Panel Lamps  
Luminous efficacy is an important parameter of CBD lamps [8]. In this study, the lumen efficacy was determined using Equation (1):  
\[\eta_{v}=\frac{\phi_{v}}{P}, \tag{1}\]  
where \(\eta_{v}\) and \(\phi_{v}\) represent the luminous efficacy and flux, respectively, and \(P\) represents power consumption. Figure 8 shows the relationship between the luminous efficacy and voltage of the coplanar CPED flat-panel lamps under different pressures. Evidently, the luminous efficacy of the lamps increases with the voltage, and the maximum luminous efficacy is 4.92 Im/W. The luminous efficacy of the lamps with a gas pressure of 50 Torr was lower than that with a gas pressure of 20 Torr.  
As the voltage increased, additional electric energy was converted into light energy, and the luminous efficacy of the lamps increased significantly. When the pressure was higher, the luminance of the lamps and luminous flux increased. However, at higher pressures, additional discharge channels in the lamps increased energy consumption,  
Figure 8: Luminous efficacy versus voltage at different pressures.  
Figure 7: Spark-ignition voltage versus pressure at different electrode gap distances.  
which significantly increased the electric power and decreased the luminous efficacy of the lamps.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 2 Fundamental Processes
### Reaction Pathways
#### 2.1.3 Cl-Containing Compound  
The Cl-containing compounds degraded in the SCWO process mainly included short-chain chloride and polychlorinated biphenyls (PCBs). The oxidation of short-chain chloride calls for chloroform as the dominant intermediate [30], while the degradation of PCBs had a more complex pathway. Since PCBs are barely soluble in water at normal temperature and pressure, organic solvents such as methanol and benzene are necessary to transport them into the SCWO reactor [46]. [PERSON] et al. [47] compared the difference of methanol and benzene as co-solvents on reaction products of 4-chlorobiphenyl in the SCWO process. Methanol could significantly enhance reaction rate compared to benzene. Additionally, relative to the fact that the number of intermediates including toxic dibenzofuran dramatically increased and significant oligomerization occurred when benzene as a co-solvent, methanol as a co-solvent could lead to reaction pathways with less toxic products. The competitive PCB reaction pathways in SCW are shown in Figure 3[48].  
Figure 2: The degradation path of N-containing compounds in SCWO (Adapted from [PERSON] et al.’s model [45]).
# Optimization-Based Approaches to Control of Probabilistic Boolean Networks
## 1 Introduction
As a result, a class of PBNs is restricted (see also Section 5 for further details). In our methods, transition probability matrices are not used. Moreover, since several solvers are available, optimization-based approaches are useful. To the best of our knowledge, such approaches have not been studied sufficiently, and their development in the future is necessary. First, we introduce an optimal control method using polynomial optimization. In this method, the expected value of the state in PBN is modeled by a polynomial system, which can be directly obtained by Boolean functions and information on probabilities. Then, the finite-time optimal control problem is reduced to a polynomial optimization problem. Using e.g., SparsePOP [45], which is available on MATLAB R2006b or later, the polynomial optimization problem can be easily described and can be solved. However, there is a weakness that the polynomial optimization problem for large-scale PBNs cannot be solved owing to memory (but, comparing this method with other methods, a relatively large-scale PBN can be handled). In addition, the computational complexity of the finite-time optimal control problem of a PBN is \(\Sigma_{2}^{p}\)-hard [17], which is harder than NP-hard (Non-deterministic Polynomial time hard). Next, we introduce an optimal control method using integer programming. Instead of the expected value of the cost function, we focus on upper and lower bounds of the cost function under a certain constraint on probabilities. Then, the optimal control problem is reduced to an integer linear programming problem. This review paper is organized as follows. In Section 2, PBNs are briefly explained. In Section 3, a control method using polynomial optimization is explained. In Section 4, a control method using integer programming is explained. In Section 5, discussion on numerical experiments is done. In Section 7, we conclude this review paper. _Notation:_ Let \(\mathcal{R}\) denote the set of real numbers. Let \(\{0,1\}^{n}\) denote the set of \(n\)-dimensional vectors, which consists of elements 0 and 1. For a matrix/vector \(X\), let \(X^{\top}\) denote the transpose of \(X\). Let \(I\) and 0 denote the identity matrix with an appropriate size and the zero matrix/vector/scalar, respectively. For an event \(A\), let \(P(A)\) denote the probability that \(A\) occurs. For two events \(A,B\), let \(P(A|B)\) denote the conditional probability of \(A\) given \(B\). For two events \(A,B\), let \(E[A|B]\) denote the conditional expected value of \(A\) given \(B\).
# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 4 Methodology Used in DRFTSA  
The Figure 2 represents the working of deep reinforcement learning for scheduling tasks in the cloud paradigm. It basically works based on agents and these agents will infer the knowledge by listening to consequences that occurred in the scheduling paradigm by giving rewards. The outcome of this model can be in the form of rewards which may be positive or negative and, if the reward is positive, the agent will update the schedule for the current state and update its state. Vice versa, if the outcome is negative, the agent will learn from the current schedule and it should not be generated for the current scenario next time. In this research, we used the DQN model which uses a Q-learning model [4] which does not need knowledge of previous history and it learns on its own by randomly spreading tasks based on two states available in the Q-learning table, i.e., state and action spaces. In this model, the Q-learning function automatically makes a decision by checking tuples available in the Q-learning table indicated as \(Q\big{(}sta^{T},act^{T}\big{)}\). This function updates for every iteration using Equation (11) below.  
\[Q\big{(}sta^{T},act^{T}\big{)}\leftarrow\ Q\big{(}sta^{T},act^{T}\big{)}+\mu* \bigg{[}RE^{T}+\mu*\max_{\mathsf{b}}Q\big{(}sta^{T+1},\ act\big{)}-Q\big{(}sta ^{T},act^{T}\big{)}\big{]} \tag{11}\]  
where \(\mu\) is learning rate of the agent, \(RE^{T}\) is the reward generated for an agent for a certain action that may be either positive or negative. The range of both the learning rate and reward generated for the agent always lies between 0 and 1. As discussed previously, in Q-learning there are different states which consist of certain actions that need to be carried out.
## 1 Introduction  
Graphene is a two-dimensional layer of carbon atoms in a honeycomb lattice. This unique atomic arrangement results in a linear energy-momentum dispersion of carriers in graphene and an ultrahigh carrier mobility exceeding 200,000 cm\({}^{2}\)/V at room temperature [1]. This ultrahigh mobility also implies a long propagation distance of graphene surface plasmons, which are quasiparticles arising from the quantized collective oscillations of the charged carriers on the graphene surface.  
These quasiparticles can be excited with the assistance of a grating structure, such as graphene nanoribbons [2; 3; 4], a dielectric grating [5; 6], or a metal grating [7; 8]. Surface plasmons can also be induced near the grain boundary [9], or generated through the interaction with metal particles [10] or sound waves [11]. Besides these momentum-transfer techniques, electron-energy-loss spectroscopy [12; 13] and near-field microscopy [14] are also used for the study of graphene surface plasmons. The excited surface plasmons have frequencies in the terahertz (THz) and far-infrared spectral regions. The wave number \(q\) of a graphene surface plasmon is proportional to the square of its frequency \(\omega\), _i.e._, \(q\propto\omega^{2}\), when \(q\) is much smaller than the Fermi wave number (\(q<<k_{\rm F}\)); correspondingly, \(q^{-1}\) is much smaller than the physical dimension of the graphene structure. This simple relation can be altered by the presence of other quasiparticles, such as surface phonons of a polar substrate [3; 12], or plasmons of a metal substrate or another adjacent graphene layer [7; 10; 15; 16; 17; 18; 19]. In this paper, we show that the coupling strength and the deviation from the \(q\propto\omega^{2}\) relation can be described by a simple analytical model, regardless of the type of quasiparticles being coupled.  
This paper is organized as follows. In Section 2, we discuss the primary scattering sources and express the scattering rate as a function of the carrier energy and the Fermi energy. Once the scattering rate is determined, the optical conductivity in the THz and far-infrared spectral regions can be obtained, which is discussed in Section 3. In Section 4, we use the derived optical conductivity along with the coupling model to show the plasmon dispersion of graphene on various substrates. The characteristics of the plasmon dispersion are discussed in terms of the distance between the substrate and the graphene layer, the substrate thickness, and other physical parameters of the system. The conclusion is presented in Section 5.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 4 Simulation Results and Analysis
### System Performance Comparison
Figure 3: Multi-user scheduling simulation results when \(M=2\) and \(N_{t}=256\). (**a**) BER of all the algorithms; (**b**) Spectral efficiency performance of all the algorithms. Figure 4a,b, respectively, show the BER and spectral efficiency performance comparison diagrams of the above-noted algorithms when 256 transmitting antennas are configured at the base station and four vehicle users are scheduled. We can see from Figure 4 that the capacity difference and BER difference between the proposed algorithm and the other three comparison algorithms are basically the same under the condition of low signal-to-noise ratio in which the number of vehicle user scheduling is four. With the increase in signal-to-noise ratio, the capacity obtained by the proposed algorithm is higher than that of the comparison algorithm, the BER decreases the fastest, and the overall performance of the proposed algorithm is still better than that of the comparison algorithm. This shows that the proposed algorithm can still maintain the optimal performance when the number of scheduling users increases. In addition, a comparative analysis of Figures 4b and 3b shows that the spectrum efficiency achieved by several algorithms in the scenario where the number of vehicle users is four is greater than that of the same algorithm in the scenario where the number of vehicle users is two. This is because with the increase in the number of scheduling users, the system capacity increases, and the transmitter can provide higher diversity gain, so the spectral efficiency performance increases. Figure 5 shows the comparison of BER and spectrum efficiency performance of the above algorithms when the number of scheduled users is two when 128 antennas are configured at the base station. Compared with Figure 3, the frequency efficiency of the system is also improved when the number of transmitting antennas is increased with the number of scheduling users fixed and the system resources limited. This is because increasing the number of antennas at the transmitting end can improve the antenna array gain and effectively improve the signal-to-interference plus noise ratio (SINR) at the receiving end of the system, to improve the spectrum efficiency of the system. However, with the increase in the number of transmitting antennas, the BER performance of the system decreases. The main reason is that when the number of transmitting antennas increases, the interference between channels also increases, which reduces the effective transmission signal of users. It can also be seen from Figure 5 that the performance of the algorithm proposed in this paper is the best regardless of BER or spectral efficiency. This is because the proposed algorithm not only considers the correlation between channels,  
Figure 4: Multi-user scheduling simulation results when \(M=4\) and \(N_{t}=256\). (**a**) BER of all the algorithms; (**b**) Spectral efficiency performance of all the algorithms.
## 6 Research Approach
### Technology Readiness Levels  
We used the NASA TRL framework [15] for assessing a set of pre-defined areas within CLC for C-ITSs: authentication, aggregated signatures, key agreement and signcryption. Each area is analyzed using the TRL, where each area consists of a set of articles found in the literature analysis phase. The TRL levels were transformed into the CLC in C-ITSs context using the translation provided in Table 1. There are nine levels in the framework, where TRL 1 is the lowest and TRL 9 is the highest. TRL 1 indicates that scientific research is just beginning and any results can be viewed for future research. TRL 2 corresponds to when the basic principles have been studied, and there is little or no proof-of-concepts  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**NASA TRL** & **CLC in C-ITS Translation: CLC TRL** \\ \hline TRL 1: basic principles & Only theoretical work is proposed, very few papers in the field. \\ TRL 2: technology concept & Theoretical work combined with comparative analysis with other technologies are given. \\ TRL 3: first experiments & There are several proof-of-concepts in at least a laboratory environment or simulations. \\ TRL 4: validation in lab & There are several proof-of-concepts using corresponding or similar C-ITS equipment, but not used in real infrastructure. \\ TRL 5: validation on site & Proof-of-concepts exists where the technology is tested in real C-ITS equipment in study- or pre-pilot sites. \\ TRL 6: component validation & Protocols are implemented and partially tested at real pilot C-ITS sites with real traffic. \\ TRL 7: system validation & Protocols are implemented and fully tested at real pilot C-ITS sites with real traffic. \\ TRL 8: tested and implemented & Protocols are implemented and fully tested at real pilot C-ITS sites with real traffic, and evidence for feasibility is presented. \\ TRL 9: proven in mission & Protocols are implemented and ready for scaled deployment in operating C-ITS sites. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Translation table of NASA TRL into corresponding CLC in C-ITS levels.  
for the technology at this level. TRL 3 corresponds to when the technology is actively researched; usually proof-of-concepts are constructed. The next level, TRL 4, is when the proof-of-concept is further advanced, and TRL 5 is a continuation of TRL 4 but with more extensive and more developed implementations, e.g., realistic simulations and tests are conducted. TRL 6 corresponds to a fully functional prototype of the technology. TRL 7 technology requires the prototype to be demonstrated in a space environment, i.e., in a realistic environment. TRL 8 means the technology is tested and ready for implementation, and TRL 9 corresponds to when the technology is \"flight proven\" during a successful application (mission as it is stated in the framework).
## 5 Computer Simulation
### Evaluation Metrics
If a node succeeded packet transmission, it attempts to transmit a subsequent buffered packet after the random back-off period. Uplink and downlink traffic is also fairly assigned to each channel in FIFO manner, i.e., originated packets are alternately assigned to each transceiver so as to equalize offered load on each channel. **(D)**: _DCH-Previous_--Here we also compare the performance of RR-IPT-based scheme previously proposed in [26] where downlink traffic is assigned to Ch.1 and is forwarded via RR-IPT. Ch.2 accommodates only uplink traffic with the conventional CSMA/CA. **(E)**: _SCH-Conventional_--Uplink and downlink traffic are forwarded using the conventional CSMA/CA. It is a single channel version of (C) DCH-Conventional. Above specifications are implemented on our original event-driven network simulator. Simulation proceeds per 1 \(\upmu\)s step, and detailed workflows, e.g., random back-off, packet transmission with acknowledge (ACK) response, collision detection and retransmission, are modeled. Although analytical models have been proposed in [52; 53], there are limitations on capable topologies: string or star types, the number of nodes, and the propagation ranges. As for the experiment, previous work validated the feasibility of IPT via prototype implementations [19; 20; 21]. This fact is applicable since our proposal separately treats each channel (transceiver).
## 2 Materials and Methods
### Calculation of Topographic Parameters  
Digital Elevation Model (DEM) data were utilized to evaluate the influence of topographic factors on soil Se concentrations. The DEM, obtained from the United States Geological Survey (USGS) at a spatial resolution of 30 m, was processed using ArcGIS to extract elevation, slope, and aspect. Notably, the DEM used to calculate the Stream Power Index (SPI) and Topographic Wetness Index (TWI) also had a 30 m resolution, which may not sufficiently capture microtopographic variability within small catchments. This limitation could introduce uncertainty in the estimation of hydrological indices and their association with soil Se concentrations.  
Topography plays a vital role in soil formation by regulating both surface and subsurface water flow patterns. These hydrological processes vary spatially and are influenced by topographic attributes such as upslope contributing area and slope gradient. As slope steepness increases, soil moisture content generally decreases due to reduced water retention capacity, and the upslope contributing area per unit contour length also tends to decline. To quantify the spatial distribution of soil moisture, [PERSON] and [PERSON] (2006), followed by [PERSON] et al. (2011), introduced the Topographic Wetness Index (TWI), which integrates the upslope contributing area per unit contour length and the local slope gradient:  
\[\text{TWI}=ln\big{[}\frac{\alpha}{tan\beta}\big{]}\]  
In this context, \(\alpha\) represents the area contributing per unit contour length, while \(\beta\) denotes the slope gradient at a specific location. Using a depression-free DEM, the flow direction is determined by identifying the steepest downslope path for each grid cell ([PERSON] et al., 2011). The contributing area is subsequently calculated based on the flow direction.  
The SPI serves as an indicator of water flow erosion potential, assuming that discharge is directly proportional to the contributing area. Higher SPI values indicate more concentrated runoff, which can lead to soil erosion ([PERSON] et al., 2011). [PERSON] et al. (2011) proposed the following formula:  
\[\text{SPI}=\alpha\text{-tan}\beta\]  
Here, \(\alpha\) denotes the upslope area contributing per unit contour length, and \(\beta\) denotes the slope gradient at a specific point.  
The precision of the extracted elevation, slope, aspect, TWI, and SPI is affected by the DEM resolution, with an error of 0.9 km\({}^{2}\) in this study.
## 4 Methods
### fPAR and LAI  
Recent studies have shown that both fPAR and LAI are strongly related to multispectral vegetation indices and can be determined from satellite derived NDVI data [14, 15, 43, 47]. Relationships between these both vegetation structure variables and NDVI have been found to be remarkably consistent over a range of non-woody vegetation types. In the present study, we estimated fPAR on pixel-by-pixel basis using a linear regression model between the ground-based fPAR values and corresponding NDVI values from SPOT VGT recorded on the sample plots at the time of the _in situ_ measurements (3 rd decade of June 2008):  
\[fPAR\ =1.25\ *\ NDVI\ -0.179 \tag{8}\]  
For calculation of LAI we employed an empirical model described by [41, 48]. This model was established using the data set of ground-based LAI from the study region with combination of Landsat ETM\(+\) and SPOT-VGT data. The formulation of the model is given by the following equation:  
\[LAI\ =0.4377\ *\ln(\ NDVI\ )+1.0205 \tag{9}\]  
Previous results have shown that both models were statistically significant at the level of \(p\)-value \(<\) 0.01 with values of R\({}^{2}\) = 0.59 and R\({}^{2}\) = 0.67, respectively [41, 48]. The models were considered to have sufficient prediction accuracy and were used for modelling 10-day time series of fPAR and LAI from SPOT VGT NDVI data. Thereafter, the derived fPAR data set was used for computing APAR as the product of PAR and fPAR, whereas the derived LAI data set was used for estimating leaf growth and maintenance respiration (see Equations 16 and 17).