esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Interlayer Interactions  
In condensed matter physics, most materials are ruled by the Schrodinger equation, this usually being sufficient to describe their electronic properties. Single layer graphene (1 LG) is an exception and its charge carriers mimic relativistic particles and are more easily and naturally described by the Dirac equation [31]. The electronic structure of 1 LG has a linear dispersion around the K point of the Brillouin zone and it is a zero gap semiconductor (see Figure 1).  
The atomic orbitals in graphene are in a \(sp^{2}\) hybridization, in which the carbon atoms are bounded covalently to each other forming a \(120^{\circ}\) angle. There are three in-plane \(\sigma\) orbitals and one out-of-plane \(\pi\) orbital. The electronic structure of 1 LG can be described by tight-binding calculations considering in-plane interactions with just first neighbors (\(\gamma_{0}\)) [32]. 1 LG is a zero gap semiconductor, where the valence \(\pi\) and conduction \(\pi\)* bands touch each other at the K point, and this is where the Fermi level is located. Close to the K point of the Brillouin zone, the electronic dispersion of 1 LG can be described as a linear dispersion with massless Dirac fermions and is given by [32]:  
\[E(k)=\hbar v_{\mathrm{F}}k \tag{1}\]  
Figure 1: The electronic structure of 1 LG, which is a linear dispersion around the K point of the Brillouin zone. Note that the valence and the conduction bands touch each other, which makes 1 LG a zero gap semiconductor.  
where \(v_{\rm F}=\sqrt{3}\gamma_{0}a/2\hbar\) is the Fermi velocity of the electrons near the Dirac point and is close to \(1\times 10^{6}\) m/s, and \(\gamma_{0}=3.0\)  eV [5].  
Since 1 LG has two carbon atoms per unit cell, its vibrational structure is composed of six phonon branches, three of them optical (one in-plane longitudinal mode-LO, one in-plane transverse mode-TO and one out-of-plane transverse mode-ZO) and three of them acoustic (one in-plane longitudinal mode-LA, one-in plane transverse mode-TA and one out-of-plane transverse mode-ZA). The LO and TO modes are degenerated at the \(\Gamma\) point and give rise to the famous G band and the TO mode at the K point gives rise to the G\({}^{\prime}\) band. Figure 2a shows the phonon dispersion for 1 LG [33,34] and Figure 3 shows the Raman spectra for both (a) \({}^{12}\)C and (b) \({}^{13}\)C 1 LG.  
Figure 3: Raman spectra showing the G band and the G\({}^{\prime}\) band of \({}^{12}\)C 1 LG and \({}^{13}\)C 1 LG (black), \({}^{12/12}\)C turbostratic 2 LG (dotted blue), \({}^{12/12}\)C AB-stacked 2 LG (solid blue), \({}^{12/13}\)C turbostratic 2 LG (dotted red) and \({}^{12/13}\)C AB-stacked 2 LG (solid red) [11].  
Figure 2: (**a**) The phonon dispersion for 1 LG and (**b**) 2 LG. LO stands for in-plane optical longitudinal mode, TO stand for in-plane transverse optical mode, ZO stands for out-of-plane transverse mode, LA stands for in-plane longitudinal acoustic mode, TA stands for transverse acoustic mode and ZA stands for out-of-plane transverse. In (**b**), ZO\({}^{\prime}\) is layer breathing mode (LBM), in which the layers of the 2 LG vibrates up and down out of phase [33].
# Meander Structure Analysis Techniques Using Artificial Neural Networks
## 5 Conclusions  
The scientific novelty of this article is the collected dataset of different sets of geometrical dimensions, top view images, and associated electrodynamical \(t_{d}\), \(S_{21}\) and \(S_{11}\) characteristics of meander structures, as well as three artificial neural network models (FFNN, TDNN, and CNN) applied for the prediction of the characteristics of an MS.  
The obtained results confirmed that the FFNN, TDNN and CNN artificial neural network models are suitable for predicting the electrodynamical parameters of meander structures. The calculated MAEs with SD margins are sufficient for the analysis of MSs.  
The usage of ANNs has allowed the real time analysis of a meander structure since the prediction time is reduced to a margin of 0.4 s, compared with 120 min of calculations using traditional numerical methods.  
The selection of parameter variation ranges for the training dataset is extremely important for determining the prediction limits of artificial neural networks. In our particular case, the meander strip width and the width of the gap between adjacent conductors varied. Therefore, the selected artificial neural networks could only predict within the limits of the chosen parameters. Other geometric dimensions were held constant but were added to the network training for future plans. The cross-sectional dimensions of the MS and the properties of the materials used for the design of the MS were not included in this study.  
Future plans include expanding the training dataset by gathering all important geometric parameters for the MS while also incorporating the cross-sectional geometrical parameters of the MS and the properties of the used materials.  
Conceptualization, D.B.-P. and A.K. ([PERSON]); methodology, A.K. ([PERSON]); software, V.A. and A.K. ([PERSON]); validation, D.B.-P. and V.U.; formal analysis, V.A. and A.S.; investigation, A.K. ([PERSON]); data curation, D.B.-P., A.K. ([PERSON]), and A.K. ([PERSON]); writing--original draft preparation, V.A. and A.K. ([PERSON]); writing--review and editing, A.S.; visualization, D.B.-P.; supervision, A.K. ([PERSON]); project administration, V.U.; funding acquisition, V.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author.  
The authors declare no conflicts of interest.
# A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
## 2 Methodology  
### Principle of the SVM Algorithm  
SVM is a class of supervised machine learning algorithms that excels at creating optimal planes for regression and prediction in high-dimensional spaces [13]. SVM has gained considerable interest and shown remarkable performance in different fields owing to its robust generalization ability, resilience, and the wide array of kernel functions it accommodates. Particularly effective in high-dimensional data scenarios, SVM offers faster and more stable prediction speeds than neural networks, which are commonly employed in small-sample classification. Moreover, SVM places lesser demands on computer resources and does not necessitate extensive training samples to attain satisfactory outcomes.  
SVM to create the optimal plane can be expressed as follows:  
As shown in Figure 1, the SVM model generates a hyperplane to separate data points with the objective of balancing the preservation of a wide margin and the minimization of errors. The width of this margin is controlled by the hyperparameter C. When C is larger, as depicted in the left graph, the error rate decreases while the margin narrows, ultimately diminishing the model's ability to generalize to new data. On the other hand, a smaller value of C, as seen in the right graph, yields a wider margin, enhancing the model's capability to generalize to unseen data [14].  
The choice of the kernel function of the SVM algorithm depends on the characteristics and nonlinear relationship of the problem. The commonly used kernel functions are as follows:  
\[\text{K}(\text{x},\text{y})=\text{x}\times\text{y} \tag{1}\]  
\[K(x,y)=(x\times y+c)^{d} \tag{2}\]  
\[K(x,y)=e^{(-\frac{\|x-y\|^{2}}{2\sigma^{2}})} \tag{3}\]  
\[K(x,y)=\tanh(\alpha x\times y+\beta) \tag{4}\]  
Equation (1) represents a linear kernel function, Equation (2) represents a polynomial kernel function, Equation (3) represents a Gaussian radial basis kernel function, and Equation (4) represents a Sigmoid kernel function. In the aforementioned equations, (\(x\)) and (\(y\)) denote input vectors; \(\alpha\) and \(\beta\) are adjustable parameters, which can control the shape of the hyperplane and affect the performance of SVM. \(c\) represents the constant term, which determines the offset of the kernel function; \(d\) is the degree of polynomial,  
Figure 1: SVM generates the optimal hyperplane.  
which determines the high-dimensional space complexity of the polynomial kernel function after mapping. \(\sigma\) is a constant; it determines the shape of the Gaussian kernel function in high-dimensional space and affects the smoothness of the kernel function and the selection of the decision boundary [14].  
The Gaussian kernel function is chosen for modeling in this study for its effectiveness with both large and small sample sizes, as well as its ability to efficiently reduce noise in daily data.
# Microwave Heat Treatment Induced Changes in Forage Hay Digestibility and Cell Microstructure
## 2 Materials and Methods
### Sample Preparation and Microwave Treatment  
The five forage treatments consisted of lucerne (Medicago sativa), canola (Brassica napus), pasture (perennial ryegrass; Lolium perenne. L + white clover; Trifolium repens), oat (Avena sativa), and wheat (Triticum aestivum) hay. Wheat and canola hay (loose, 18-20 cm) were collected from the Dookie Campus sheep farm at The University of Melbourne, northern Victoria, Australia. Lucerne and pasture have bale (50 cm \(\times\) 30 cm) were purchased from a farm supplies store in Shepperton, northern Victoria, Australia. Out hay (loose, 25-50 cm) was collected from a sheep farm near Dookie Campus, northern Victoria, Australia. All the selected hay samples were previously sundried (DM ranges from 89%-91%); as a result, further drying was not required. Each of the hay was sub-sampled from the different point of the bag or bale of the hay to make a composite bulk sample for MW treatment and analysis.  
Each treatment consisted of three replications (50 gm/replication) and was subjected to MW treatment. According to the designed MW treatments, hay samples were treated for 0 (control), 20, 40, 60, or 80 s (s), which is equivalent to 0, 440, 880, 1320, and 1760 kJ kg\({}^{-1}\) of MW energy, respectively, using a bench-scale MW oven (EMS8586V, Sanyo, Tokyo, Japan). The MW oven had a cavity dimension of 370 mm \(\times\) 380 mm \(\times\) 210 mm, an operational frequency of 2.45 GHz, and a rated power level of 1.1 kW. Based on the pre-experimental test, the maximum MW treatment time (80 s) in this study was selected as it was the closest time to start burning the hay. In order to achieve a constant treatment pattern in all the treated samples, the turntable of the MW oven was marked with the container dimension before treatment to minimize the MW treatment inconsistency. All the treated samples were cooled at room temperature for 3 h (h) before grinding them through a 1 mm sieve in a benchtop ultra-centrifugal benchtop hammer mill (RETSCH, ZM 200, Germany).
## 5 Conclusions  
This article provides a way to solve the problem of reducing the early-age strength of internal curing of mortar as well as providing technical reference for the applications of SAP in the projects. Based on the results of this study, the following conclusions can be drawn:  
1. By adding two types of alkanolamines, we effectively inhibited the early-age compressive strength of mortars without affecting the crack resistance of SAP itself.
2. Cement pastes with different alkanolamines mixed had higher major products (AFt) than the reference group mortar. The diffraction peaks of the main constituent minerals (C\({}_{3}\)S, C\({}_{2}\)S) were significantly lower than those of the reference group. It can also be concluded that the alkanolamines promoted cement hydration and sped up water hydration, although their influences and mechanisms are different.
3. Take the effect of composite early-strength internal curing materials on the early mechanical properties of mortar and drying shrinkage into comprehensive consideration, the early performance of the mortar with alkanolamine-A and alkanolamine-B added was the best. Its 3- and 7-day strengths reached 23.4 and 35.7 MPa, respectively. The pore size of 10-50 nm of the cement paste reached 70.06%. The average pore size was 15.17 nm, which had a 27.8% decrease compared with that of the reference group.  
Conceptualization, D.W.; data curation, T.G. and D.W.; writing--original draft preparation, D.W. and T.G.; writing--review and editing, D.W., R.L., T.G., D.J. and F.L.; supervision, R.L.; project administration, R.L.; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the funding provided by the National Key Research and Development Program of China (project 2019 YFC1906202), Guangxi Key Research and Development Plan (Guike AB19259008, Guike AB20297014).  
Not applicable.  
The authors declare no conflict of interest.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
## 3 Coupling Mechanism Design and Array Switching Control Strategy  
### Coupling Mechanism Design  
#### 3.1.1 The Basic Unit Shape Design of Transmitting Array Coil Group  
The planar coil structure can generally be divided into a circular spiral structure and square spiral structure, and the magnetic field distribution of the two is shown in Figure 3. It can be seen from the diagram that the magnetic field intensity of the circular coil is close to that of the square coil, but the magnetic field distribution of the square coil is more uniform.  
Figure 1: Image of proposed WPT system for AUVs.  
Figure 2: Simple circuit configuration of WPT system.  
The two structures are extended into an array form, as shown in Figure 4. It can be seen from the diagram that there is a large gap between the circular coil arrays, and the splicing between the coils is poor; on the contrary, the square coil array can be closely arranged, the coil gap is small, and it has stronger splicing. In summary, this paper selects the square coil as the basic unit shape of the transmitting array coil group. According to the charging area and the size of the underwater electromechanical equipment, the side length of the transmitting coil \(L_{1}\) is selected as 200 mm.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 18 Dispatching of Mobile Sensor Nodes in a WSN to Sense a Region of Interest
### Optimization Problem Formulation  
The details of the optimization problem are as follows:  
* Decision variables: The sensor transmission powers and transmission durations, for all sensors in the nodes are the decision variables. These are all continuous variables.
* Objective function: is the mean distortion of the system.This is given as an undetermined function of the power vectors of the sensor variables. That is \(\underset{\mathbf{P},\mathbf{T}}{minD}\left(\mathbf{P}\right)\). The relation between the objective function, the transmission powers and transmission durations in the system was not provided in [14].
* Constraints: The following are the constraint sets of the problem, 1. A linear constraint in the transmission time duration variables to guarantee that sensed data arrive at the fusion center within the desired delay limits, that is \(\sum_{k=1}T_{tk}-T_{T}\leq 0\) 2. A bilinear quadratic constraint in the transmission powers and delays that guarantees that the aggregate transmission energy does not exceed an allowable limit. That is \(\sum_{k=1}^{K}E_{k}-E_{T}\leq 0\) where \(E_{k}\) was given by Equation (36) 3. Non-negative constraints on the transmission power values.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 5 Simulations Results  
### Approach I-A Simulation  
The first simulation demonstrates the design of the radar OLFM waveform parameters to minimize the interference in Equation (29) between the coexisting systems using waveforms with the same duration (see Approach I-A in Section 4.2.1). As discussed in Section 4.2, the target's range resolution in Equation (28) can be improved by selecting the FM rate of the radar OLFM waveform equal to \(-b_{M}\). In this simulation, \(M=20\) and \(N_{\mathrm{c}}=3\), \(T_{\mathrm{c}}=T_{\mathrm{r}}=4\ \mathrm{\mu s}\), \(B=10\) MHz. The OLFM waveform FM rates selected for the three users are \(b_{18}\), \(b_{19}\), and \(b_{20}\), following the notation in Equation (25) for minimizing MAI. The SINR at the radar receiver is \(-18\) dB. As can be seen in Figure 8, when the radar OLFM waveform FM rate is selected to be \(-b_{20}=-b_{M}\), the MSE for range estimation is lower than when the FM rate is selected to be \(-b_{1}\).  
Figure 7: Block diagram overview of coexistence waveform design Approach II: Pareto optimization is used to jointly optimize the OLFM waveform parameters of both systems; the designed parameters are relayed to both the radar and communications receivers.  
The remaining simulations, which were used to demonstrate Approach I-B and Approach II, used the following common parameters. An MU AWGN communications channel was used with \(N_{\mathrm{c}}=4\) active communications users, each employing a 16-PSK modulation. The required gross bit rate is \(R=1\) Mbps, resulting in a symbol duration \(T_{\mathrm{c}}=4\)\(\upmu\)s. The allocated bandwidth available to the radar and the communications systems is \(B=10\) MHz. The pulse-Doppler radar uses \(K=50\) pulses per CPI, and it is operating at a constant 10 kHz pulse repetition frequency (PRF).
## 4 Discussion
### Overall Discussion
[80] followed a similar strategy; they also considered the wind direction and the satellite overpassed time to calculate the footprint of the eddy covariance tower for modeling evapotranspiration in a Mediterranean olive orchard. The validation of the Sentinel-2 models for the year 2020 showed that both models showed very similar R\({}^{2}\) and RMSE values. This fact is due to the model having only been validated until mid-July 2020, and the inclusion of the meteorological parameters would have more weight during all the summer and winter periods.
# Edge-Event-Triggered Synchronization for Multi-Agent Systems with Nonlinear Controller Outputs
## 2 Preliminaries and Problem Formulation  
### Preliminaries on Graph Theory  
For an undirected graph \(\mathcal{G}\) consisting of \(m\) edges and \(N\) nodes, it has the form \(\mathcal{G}=(\mathcal{V},\mathcal{E})\) with node set \(\mathcal{V}=\{v_{1},v_{2},\cdots,v_{n}\}\) and edge set \(\mathcal{E}=\{(v_{i},v_{j}):v_{i},v_{j}\in\mathcal{V}\}\). The set of neighbors of node \(i\) is denoted by \(N_{i}=\{v_{j}\in\mathcal{V}:(v_{j},v_{i})\in\mathcal{E}\}\), where \(|N_{i}|\) represents the cardinality of \(N_{i}\), i.e., \(2m=|N_{1}|+|N_{2}|+\cdots+|N_{N}|\). Then, we introduce several important matrices in the graph theory. The matrix which relates the nodes to the edges is called incidence matrix, denoted by \(H=\{h_{ki}\}\in\mathbb{R}^{m\times N}\)[47]. By choosing arbitrary orientation for all edges in the undirected graph \(\mathcal{G}\), the entries of its incidence matrix are defined as \(h_{ki}=1\) if the \(k\)th edge sinks at node \(i\), \(h_{ki}=-1\) if the \(k\)th edge leaves node \(i\), or \(h_{ki}=0\) otherwise. Therefore, the Laplacian matrix is represented as \(L=H^{T}WH\) and the edge Laplacian matrix is \(L_{E}=HH^{T}\), where \(W=\text{diag}(w_{1},w_{2},\cdots,w_{m})\) and each \(w_{p}>0\), \(p=1,2,\cdots,m\), is the weight of \(p\)th edge. Moreover, the matrices \(L_{E}\) and \(H^{T}H\) share the common nonzero eigenvalues.  
For the edge \((v_{i},v_{j})\in\mathcal{E}\) with the orientation from agents \(v_{j}\) to \(v_{i}\), let \(y_{p}(t)=y_{(i,j)}(t)=x_{i}(t)-x_{j}(t)\) be the corresponding edge state.
## 3 Derivation of the Algorithm
Taking the derivative of Equation (4) yields the following:  
\[u_{ij}\ \geq\ 0\quad\quad\text{for all }i\in\{1,\ldots,k\}\text{ and for all }j\in\{1,\ldots,n\} \tag{6}\]4  
This cubic equation, which can be solved by [PERSON]'s formula, should have (up to) three real solutions. Because the coefficient of \(x^{3}\) is negative, this implies that the coefficient of \(x^{4}\) in Equation (4) is negative, meaning that Equation (4) goes to \(-\infty\) for \(x\to\pm\infty\). Therefore, the three real solutions of the derivative should correspond to two local maxima and one local minimum between the two local maxima. This local minimum is the solution we are looking for. The global minimum of Equation (14) is at \(\pm\infty\), i.e., moving the cluster centre to infinity. This would, however, imply that the membership degrees to the cluster become negative according to Equation (13). Algorithm 1 describes the basic clustering algorithm, which only works if \(\alpha\) is chosen sufficiently large. The problem of \(\alpha\) being too small will be discussed in the next section. ```
1:procedureoneCluster(\(x,\delta,\alpha\))\(\triangleright\) Input \(x\in\mathbb{R}^{n}\) values to be clustered
2:\(\triangleright\)\(\delta>0\) noise distance, \(\alpha>0\) (see Equation (4))
3:\((y_{1},y_{2},y_{3})\leftarrow\) Cardano (Equation (15))\(\triangleright\) Roots of Equation (15), \(y_{1}<y_{2}<y_{3}\)
4:\(\triangleright\)\(y_{2}\) is the cluster centre. 5:for\(i\in\{1,\dots,n\}\)do\(\triangleright\) Compute membership degrees according to
6:\(u_{j}\leftarrow\frac{1}{2}+\frac{\delta-(x_{j}-y_{2})^{2}}{2\alpha}\)\(\triangleright\) Equation (13)
7:endfor
8:return\(y_{2},u_{1},\dots,u_{n}\)\(\triangleright\) Cluster centres and membership degrees
9:endprocedure
```  
**Algorithm 1** Basic clustering algorithm
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Distribution Network (DN)  
The DN in this paper is a distribution network with renewable energy sources. It can exchange power with the MGs. The mathematical model is shown in Equations (14)-(18):  
\[\begin{array}{l}\sum\limits_{i\in\Gamma}P_{G,i}^{t}-\sum\limits_{i\in\Omega}P_ {L,i}^{t}-\sum\limits_{i\in\Phi}P_{MG,i}^{t}-\\ U_{i}^{t}\sum\limits_{i=1}^{N_{\text{max}}-1}U_{j}^{t}(G_{ij}\cos\theta_{ij}^{ t}+B_{ij}\sin\theta_{ij}^{t})=0\\ \sum\limits_{i\in\Gamma}Q_{G,i}^{t}-\sum\limits_{i\in\Omega}Q_{L,i}^{t}-\sum \limits_{i\in\Phi}Q_{MG,i}^{t}-\\ U_{i}^{t}\sum\limits_{i=1}^{N_{\text{max}}-1}U_{j}^{t}(G_{ij}\sin\theta_{ij}^ {t}-B_{ij}\cos\theta_{ij}^{t})=0\\ \\ N_{operation}=\sum\limits_{t=2}^{\Gamma}\sum\limits_{i=1}^{N_{\text{br}}} \abs{s_{i,t}-s_{i,t-1}}\\ N_{operation}\leq N_{\text{max}}\\ U_{i}^{\text{min}}\leq U_{i}^{t}\leq U_{i}^{\text{max}}\\ S_{brk}\leq S_{br}^{\text{max}}\\ \end{array} \tag{14}\]  
where \(P_{G,i}^{t}\), \(P_{L,i}^{t}\), \(P_{MG,i}^{t}\) are the active power of power sources, loads and the active power injected into MGs at bus \(i\) in DN at time slot \(t\), respectively. \(\Gamma\), \(\Omega\) are the sets of nodes connected to the power sources and loads, respectively. \(\Phi\) is the set of nodes connected to the MGs. \(U_{i}^{t}\), \(U_{i}^{t}\), \(U_{\text{min}}\), \(U_{\text{max}}\) are the voltage of node i and node j at time slot \(t\), the lower and upper boundaries of voltage, respectively. \(N_{bus}\) number of electric buses. \(G_{ij}\), \(B_{ij}\) is conductance and susceptance of the branch between the node \(i\) and node \(j\) at time slot \(t\), respectively. \(\theta^{t}_{ij}\) is the phase angle difference between the ith and jth node at time slot \(t\). \(Q_{G,i}^{t}\), \(Q_{L,i}^{t}\), \(Q_{MG,i}^{t}\) are reactive power of power sources, loads and active power injected into MGs at bus \(i\) in the DN at time slot \(t\), respectively. \(N_{operation}\) is the number of remote-controlled switches actions in a day. \(s_{i,t}\) is the state of the ith switch at time slot \(t\). \(N_{br}\) is the number of remote-controlled switches in the DN. \(N_{\text{max}}\) is the upper limit of the switches' actions. \(S_{brk}\), \(S_{br}\)max represent the apparent power of branch \(k\).