esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary Theory and Related Work
### Koopman Theory
\begin{pmatrix}f(\mathbf{X}_{0})&f\circ\mathbf{T}(\mathbf{ X}_{0})&f\circ\mathbf{T}^{2}(\mathbf{X}_{0})&\cdots&f\circ\mathbf{T}^{n}( \mathbf{X}_{0})\\ f\circ\mathbf{T}(\mathbf{X}_{0})&f\circ\mathbf{T}^{2}(\mathbf{X}_{0})&f \circ\mathbf{T}^{3}(\mathbf{X}_{0})&\cdots&f\circ\mathbf{T}^{n+1}(\mathbf{X }_{0})\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ f\circ\mathbf{T}^{m-1}(\mathbf{X}_{0})&f\circ\mathbf{T}^{m}(\mathbf{X}_{0})& f\circ\mathbf{T}^{m+1}(\mathbf{X}_{0})&\cdots&f\circ\mathbf{T}^{m+n-1}(\mathbf{X}_{0}) \end{pmatrix} , \tag{11}\]where each column and row can be viewed as trajectories (flows) sampled for observable function \(\mathcal{K}^{-1}f\) based on the definition of the Koopman operator, then \(\mathbf{\tilde{H}}\) can be rewritten as:  
\[\mathbf{\tilde{H}}=\left(\widetilde{f}_{m},\mathcal{K}\widetilde{f}_{m},\dots, \mathcal{K}^{n-1}\widetilde{f}_{m}\right), \tag{12}\]  
which can also be considered as a Krylov sequence of observable functions. Therefore, the expression can also be rewritten as:  
\[\mathbf{F}_{n}=\Big{[}f,\mathcal{K}f,\dots,\mathcal{K}^{n-1}f\Big{]}. \tag{13}\]  
Most current Hankel-DMD analysis articles use a simplifying assumption that there exists a finite-dimensional subspace in \(\mathbf{H}\) that remains invariant under the action of the Koopman operator and includes the observable function \(f\) of interest. This paper continues with this assumption, meaning that matrix (3) can be expressed as:  
\[\mathbf{H}= \begin{pmatrix}x(t_{1})&\mathcal{K}x(t_{1})&\cdots&\mathcal{K}^{n }x(t_{1})\\ \mathcal{K}x(t_{1})&\mathcal{K}^{2}x(t_{1})&\cdots&\mathcal{K}^{n+1}x(t_{1}) \\ \vdots&\vdots&\ddots&\vdots\\ \mathcal{K}^{m-1}x(t_{1})&\mathcal{K}^{m}x(t_{1})&\cdots&\mathcal{K}^{m+n-1}x (t_{1})\end{pmatrix} . \tag{14}\]  
To obtain a finite-dimensional approximation of the Koopman operator matrix form for the system (1), we apply SVD to the matrix (3).
## 1 Introduction
Users firstly make the network selection between GEO satellite and LEO satellites using utility functions which consider the communication delays and load status of satellites, then choose an LEO satellite using the Importance-TOPSIS if users select to access the LEO satellite network in step one. In addition, each user does not need to rely on other users' information when making decisions. The simulation results demonstrate the performance of the strategy in system throughput, number of handovers and the forced termination probability. The main contributions of this paper are as follows:  
1. A GEO/LEO heterogeneous satellite network is established to ensure that users can access GEO satellite when the size of LEO constellation is too small to cover the Earth seamlessly. Users can access a GEO satellite or an LEO satellite, and make decisions independently. 2. The utility functions are designed to reflect the demands of services with different delay tolerance for each satellite. The communication delays and load status of each satellite are considered to ascertain the urgency and satisfaction of each service accessing each satellite, so that the load balancing can be improved. The utility functions we design can not only better show the user's demands for a certain attribute, but also drive a normalized value. 3. The Importance-TOPSIS scheme is proposed. The importance of each attribute to the target is calculated by using the continuous replacement method proposed in this paper, which is used as the weight of each attribute to improve the TOPSIS scheme. The rest of this paper is organized as follows: Section 2 describes the related works about heterogeneous satellite network and handover strategies. In Section 3, we present the system model of GEO/LEO heterogeneous satellite networks and the service model. We present the two-step access and handover strategy based on utility function and the Importance-TOPSIS algorithm in Section 4. Simulation results and analysis is given in Section 5. Finally, the conclusions of this paper are drawn in Section 6.
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 3 Sparrow Search Algorithm Optimizes Variational Mode Decomposition
### SSA Optimizes the Parameters of the VMD
#### 3.3.2 Ssa-Vmd  
The detailed steps of the SSA-optimized VMD algorithm proposed in this paper are as follows:  
Step 1--Input the original power load sequence \(f(t)\);  
Step 2--Set the sparrow population size to \(np\), the maximum number of iterations to \(iter_{\max}\), the number of searchers to \(d_{Num}\) and the number of vigilantes to \(g_{Num}\);  
Step 3--Initialize the population. Set the optimality dimension as 2, which shows that each individual in the sparrow population contains a number of IMF \(K\) and a penalty factor  
Figure 2: The flow chart of SSA.  
\(\alpha\). Different individuals contain different combinations \([K,\alpha]\). The goal of the optimality search is to obtain the optimal combination of parameters \([K_{0},\alpha_{0}]\);  
Step 4--According to the fitness function, according to Equations (8) and (9), update the positions of searchers and followers; select a random portion of sparrows and update the positions according to Equation (10). The global optimal individual and the optimal fitness under the current population are retained. The fitness is \(Loss\) and is calculated as in Equation (13). At the end of the iteration, the fitness values are calculated for the individuals in different positions.  
Step 5--Determine whether the preset maximum number of iterations \(iter_{\max}\) is reached--if not, repeat Step 4. If it is satisfied, the global optimal individual and the optimal fitness are output. At this point, the global optimal individual is the optimal decomposition parameter of VMD decomposition, and the optimal fitness is the minimum decomposition loss.
## 1 Introduction
The effect of the hybrid synergistic interaction of NPs of magnetite covered by polyvinylpyrrolidone (PVP)-coated iron oxide and oil-degrading bacterial cells was established [65]. At relatively high concentrations of crude oil (375 mg/L), due to rapid saturation only with magnetic NPs (MNPs), approximately 70% of alkane molecules with a carbon chain length of C\({}_{9}\)-C\({}_{22}\) and 65% of alkanes with a chain length of C\({}_{23}\)-C\({}_{26}\) can be removed in 1 h. Microbial bioremediation only after 24-48 h provided removal of 80-90% of oil, whereas a combination of MNPs with BCs such as _Halomonas_ sp., _V. gazogenes_ or _M. hydrocarbonelasticus_ cells provided 100% oil destruction for the same period of time. A similar effect was obtained during the degradation of naphthalene [63]. The successful decomposition of naphthalene was demonstrated with a combination of _S. aesturai_ 357 bacteria and functionalized MNPs such as FeNP-NDI-DA, obtained on the basis of Fe\({}_{3}\)O\({}_{4}\) and the diimide-dopamine ligands. MNPs in an environment with naphthalene formed magnetic complex naphthalene@FeNP-NDI-DA during 0.5 h of stirring. MNPs with different compositions and unique structural, chemical and functional characteristics were recommended for use in the demulsification and pretreatment of oil-water emulsions. It has been shown that different methods can be used to obtain MNPs: the hydrothermal method, co-precipitation, sol-gel synthesis, and synthesis with the use of redox reactions or microemulsification [89]. The most highly effective demulsifiers are obtained by modifying and functionalizing the MNPs surface, while it is important that the MNPs surfaces contain suitable polymers or surfactants [89]. Sponges modified with MNPs can repel water and selectively absorb organic solvents as well as various lubricating oils with high adsorption capacity from water. Two-stage fabrication of Fe\({}_{3}\)O\({}_{4}\) polyurethane with polystyrene resulted in the production of a superhydrophobic magnetic sponge of PU@Fe\({}_{3}\)O\({}_{4}\)@PS. To obtain this hydrophobic, magnetic nanomaterial, photopolymerization was carried out under ultrasonic dip coating. A magnetic sponge was used for the effective remediation of oil and various organic solvents from water [90]. A wide range of different magnetic composite amphiphilic, hydrophobic and hydrophilic exterior/hydrophobic interior nanomaterials for demulsification and adsorption with the possibility of their reuse is known today: Fe\({}_{3}\)O\({}_{4}\)@oleic acid@graphene oxide, Fe\({}_{3}\)O\({}_{4}\)/ethyl cellulose, PU@Fe\({}_{3}\)O\({}_{4}\)@PS, PVP-coated iron oxide and polystyrene/Fe\({}_{3}\)O\({}_{4}\)/graphene aerogel [65; 91; 92; 93; 94]. It is obvious that further increasing the diversity among such developments will contribute to improving their characteristics and increasing their attractiveness for practical use.
## 2 Materials and Methods
### Synthesis of Copper-Silica Catalysts  
#### 2.3.1 Synthesis of Copper Nanoparticles (CuNPs)  
The greener synthesis route of CuNPs was inspired in a previous protocol reported by [PERSON] et al. (2015) that combined the selection of copper chloride (CuCl\({}_{2}\cdot\)2H\({}_{2}\)O) as metal precursor (25 mL, 0.2 M) and ascorbic acid (25 mL, 1 M) as both reductant and stabilizing agent. The CuNP sample is referred to as CuAsc. Figure 1 shows the synthesis scheme and the suspension color evolution. The synthesis was carried out under refluxing conditions at 80 \({}^{\circ}\)C during 20 h of reaction employing water as solvent. The solution pH was not adjusted, reaching a value near to 3.  
The second strategy to synthesize Cu colloidal NPs involved the use of a solvothermal synthesis approach previously proposed by [PERSON] and co-workers [PERSON] and co-workers (2016). This second protocol was conducted in diethylene glycol (DEG) as solvent, using cupric sulphate (CuSO\({}_{4}\cdot\)5H\({}_{2}\)O) as metallic precursor, sodium hypophosphite (NaH\({}_{2}\)PO\({}_{2}\)) as a reductant agent, and polyvinylpyrrolidone (PVP, K-30) together with cetyltrimethylammonium bromide (CTAB) as stabilizing agents. Figure 2 summarizes the different synthesis steps. The CuNPs obtained is referred to as CuCTAB. Three solutions were prepared: 25 mL of a solution of PVP/DEG (4% w/v) and CTAB/DEG (0.1 M), 25 mL of a solution (0.6 M) of NaH\({}_{2}\)PO\({}_{2}\), and 50 mL of an aqueous solution of CuSO\({}_{4}\cdot\)5H\({}_{2}\)O (0.2 M). Firstly, the solutions containing the reducing and stabilizing agents were mixed at 100 \({}^{\circ}\)C. Secondly, the solution with the metallic precursor was added at 140 \({}^{\circ}\)C (reaction temperature) and magneti  
Figure 1: Scheme of the environmentally friendly protocol established to obtain CuNPs using water as solvent and ascorbic acid as mild reducing agent.
###### Abstract  
This study aims to determine the effect of the treatment of banana stem fibers (BSF) with grade three liquid smoke on changes in the micromechanical properties of the BSF, single fiber tensile strength, morphology, crystal properties, and functional groups. This study used four variations of the specimen model, namely, fiber without treatment and immersion in liquid smoke for 1, 2, and 3 h. BSF with treatment was dried in an oven at 40 \({}^{\circ}\)C for 30 min. Several tests were carried out, including the tensile test for single fiber capacity of 50N standard ASTM 3379-02, Scanning Electron Microscope (SEM), X-Ray Diffraction (XRD), and Fourier Transform Infra-Red (FTIR) observation. The results showed that the highest increase in fiber strength occurred in P2J, which was 43.78%, with crystal intensity of 34.97%, compared to TP fiber. Treatment of fiber with liquid smoke can form a strong C-C elemental bond caused by the H2O degradation process in BSF so that the carbon atom (C) becomes solid; under conditions of excessive H2O degradation, the fiber strength will become brittle, however, liquid smoke can increase the fiber tensile strength. The morphology of the fiber changed where the untreated fiber was covered in lignin, while the treated fiber had a rectangular pattern of elongated lines, was porous, and the lignin was eroded. The fiber crystallization index increased due to changes in fiber structure, where the highest peak of TP BSF occurred at point two, while the highest peaks in BSF P1J, P2J, and P3J occurred respectively at points two and three. These results prove that the innovation of BSF treatment with liquid smoke can change the morphology, crystalline, and functional aspects of BSF, so that it becomes the choice of composite reinforcement material in the future, an option that is lightweight and environmentally friendly.  
liquid smoke; banana stem fiber; tensile strength; morphology; crystalline properties; functional groups; light weight; environmentally friendly +
Footnote †: journal: Applied system
# Correction Strategy of Online Midcourse Guidance for High-Speed Gliding Target Interceptor
## 3 Design of Online Dynamic Correction Strategy for Interceptor
### Design of Online Correction Strategy for Single PIP  
For a single PIP, according to the interceptor position vector \(r_{I}\), flight velocity vector \(\mathbf{V}_{I}\), and remaining flight time \(t_{f}\), the required velocity and engine thrust direction could be solved. By determining the interceptor's engine switch logic in the midcourse guidance  
Figure 6: The IIA of interceptor.  
stage, the interceptor engine can be timely switched on for online correction. An online correction algorithm for the interceptor in the midcourse guidance is designed as shown in Figure 7.  
The specific description is as follows:  
Step 1: After the target is detected by the early warning detection system, the defense system calculates the PIP (PIP in Figure 1) and launches the interceptor;  
Step 2: According to the position \((x,y,z)\) and velocity \(\left(V_{x},V_{y},V_{z}\right)\) in the East-North-Up coordinate system of the interceptor, through coordinate system transformation, the velocity vector \(\mathbf{V}_{I}\) and position vector \(\mathbf{r}_{I}\) of interceptor under the geocentric coordinate system are obtained, the remaining flight time \(t_{f}\) given by the interceptor mission and the position vector \(\mathbf{r}_{I}\) of the PIP, the required velocity \(\mathbf{V}_{r}\) could be solved by the method in Section 2.3. Then, the velocity gain \(\mathbf{V}_{g}\) could be obtained, where  
\[\mathbf{V}_{g}=\mathbf{V}_{r}-\mathbf{V}_{I} \tag{30}\]  
Step 3: To save fuel, there is no need for the continuous start-up of the engine, a certain start-up threshold should be set, and \(\varepsilon\) is the engine start-up threshold in this paper. If \(\left|\mathbf{V}_{g}\right|>\varepsilon\), start the engine, and the starting direction is  
\[\mathbf{I}_{g}=\frac{\mathbf{V}_{g}}{\left|\mathbf{V}_{g}\right|} \tag{31}\]  
the time \(t_{on}\) of the engine starting is recorded, and the position and velocity information after one step of maneuver is solved integrally. Continue with step 2. To avoid system instability caused by a too short engine start-up time, set the shortest engine start-up time  
Figure 7: Design of midcourse guidance correction algorithm based on velocity gain guidance.  
\(t_{min}\), if \(\left|V_{g}\right|<\varepsilon\) and \(t_{on}\geq t_{min}\), shut the engine down and clear recorded starting instant \(t_{on}\); if \(t_{on}<t_{min}\), then, keep the engine on.  
Step 4: Continue the above process until \(t_{f}=0\).
# A Variable-Order Dynamic Constitutive Model for Clay Based on the Fractional Calculus
## 6 Model Validation and Sensitivity Analysis
### Model Validation  
The efficacy of the dynamic constitutive model is dependent on its ability to adequately fit experimental data; the predictions of the model for the accumulative strain of Zhan Jiang clay and Tian Jin soft clay are shown in Figures 12 and 13, respectively.  
Figure 12: (**a**) Predication of dynamic constitutive model in Zhan Jiang clay; (**b**) Comparison between experimental data and fitting results (\(\sigma\_3\) = 100 kPa, \(\sigma\_1/\sigma\_3\) = 0.5).  
Figure 13: (**a**) Predication of dynamic constitutive model in Tian Jin soft clay; (**b**) Comparison between experimental data and fitting results (\(\sigma\_3\) = 100 kPa, \(\sigma\_1/\sigma\_3\) = 0.48).  
Since the model agrees well with experimental data for both Zhan Jiang clay and Tian Jin soft clay, given space limitations, the applicability and parameter sensitivity of the model are described based on the test results for Zhan Jiang clay only.  
It can be discerned from Figure 12a and Table 2 that the model agrees well with the experimental data, which indicates that the model describes well the steady, critical and failure curves of clay in the dynamic triaxial test. However, it can also be seen that the experimental curves are not always identical with any of the three different types of accumulative strain-time curve. Some experimental curves are normally composed of one to three classical accumulative strain-time curves, so it is potentially inaccurate to say that an accumulative strain-time curve is expressed by only one function expression. In order to obtain an accurate assessment of strain characteristics, our focus of attention must shift from amplitude of stress load on the specimen to determine time points \(t_{1}\) and \(t_{2}\) as accurately as we can. The prediction of the model largely depends on \(t_{1}\) and \(t_{2}\); for accumulative strain-time curves, the method endows the model with more applicability. In addition, the new constitutive model with damage variables can accurately describe the strain-time characteristics of clay under cyclic load, as shown in Figure 12b, especially in the accelerating stage. Furthermore, as can be understood from Table 2, under the same confining pressure, the orders \(\alpha\) and viscosity coefficients \(\eta\) in the initial stage are greater than those in the steady stage, which indicates that the viscous resistance of the soil is reduced. This is because the successive cyclic loading causes the soil to become dense, the soil particles are arranged in a more orderly manner after mutual dislocation and adjustment, and the overall mechanical properties of the soil tend to be ideal when it is solid. Finally, it can be seen from Table 2 that all correlation coefficients have values of more than 0.90, which indicates the accuracy and applicability of the constitutive model, to some extent, at least.
## 3 Experimental Setup
### Relay Operations  
In this paper, we experiment on different relay strategy, forward power, and locations to discuss the influence on secrecy capacity. In general, the main task of a relay is to receive the signal from the source, decode and forward them to the destination. After received desired signal, it saves the complex signal for further processing and finally transmit processed signal in a certain power.
## 4 System Model Based on Power Splitting and NOMA  
The proposed system model based on PS and NOMA protocol is shown in Figure 4. In this PS relaying scheme, power constrained (\(IoT_{R}\)) node first harvests the energy from the source node signal using \(\varepsilon P_{s}\) where \(P_{s}\) is the power of the source transmit signal. \(IoT_{R}\) uses remaining power \((1-\varepsilon)P_{s}\) for information processing.
## 3 Results and Analysis  
### Disc Cutter Wear Analysis  
#### 3.1.1 Morphological Characteristics of Tool Wear  
After the experiment, a comparison of the wear morphology characteristics of the disc cutter is shown in Table 3. The cutting edge morphologies are shown in Table 3a,b. Table 3a shows the cutting edge morphology before cutting, and the cutting edge was sharp. When the longitudinal high-frequency vibration of the cutting edge was cutting the honeycomb core material, the cutting edge of the disc cutter was continuously worn to make the cutting edge gradually blunt, as shown in Table 3b. Table 3c,d show the morphologies of the flank face: there was no wear on the flank face before cutting, as shown in Table 3c. The wear on the flank face after cutting was mainly concentrated near the cutting edge, as shown in Table 3d. The reason for this is that the positive clearance angle of the disc cutter makes the part away from the cutting edge have little contact with the honeycomb core processing surface during the cutting process, and thus no wear is generated. However, the flank face of the disc cutter near the cutting edge is in constant high-frequency contact and friction with the aramid fiber and phenolic resin layer in the honeycomb material and interacts with the honeycomb core material to produce wear and breakage, thus producing flank wear.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 6 Conclusions  
The accuracy limits of collaborative global referencing of digital maps with standard GNSS were explored through simulation and real data. The asymptotic average of position errors due to thermal noise, satellite orbit and clock errors, and tropospheric modeling errors were assumed to be negligible. It has been shown that the position error due to inaccurate ionospheric modeling may lead to persistent dm-level biases in the horizontal position if the corrections are sourced from the IGS GIM, but other recent models such as the Fast PPP IONEX GIM perform better in this regard. Multipath errors persist with multiple mapping sessions through the same urban corridor and may not be zero mean. With adequate multipath exclusion, persistent multipath biases may be reduced below 50 cm on average. In conclusion, sub-50-cm accurate digital mapping has been shown to be feasible in the horizontal plane after multiple mapping sessions with code-phase-based GNSS, but larger biases persist in the vertical direction. A globally-referenced electro-optical SLAM pipeline, termed GEOSLAM, has been detailed and demonstrated to achieve sub-50-cm horizontal localization accuracy in a moderate urban environment by incorporating code-phase-based GNSS position estimates in the visual SLAM framework and jointly optimizing maps merged across time-separated sessions.  
Conceptualization, L.N. and T.E.H.; Data curation, L.N., J.M.W., M.J.M. and D.M.L.; Funding acquisition, T.E.H.; Methodology, L.N.; Project administration, T.E.H.; Software, L.N., J.M.W. and M.J.M.; Supervision, T.E.H.; Writing--original draft, L.N.; Writing--review & editing, L.N. and T.E.H.  
This work has been supported by the Data-supported Transportation Operations and Planning Center (DSTOP), a Tier 1 USDOT University Transportation Center, and by funding from Toyota, Honda, and Qualcomm through the University of Texas Situation-Aware Vehicular Engineering Systems (SAVES) Center ([[http://utsaves.org/](http://utsaves.org/)]([http://utsaves.org/](http://utsaves.org/))), an initiative of the Wireless Networking and Communications Group.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.