esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Recurrence Quantification Analysis
### Recurrence Plot  
The Recurrence Plot (RP) illustrates recurrences contained in a signal. The RP is based on the recurrence matrix \(\mathbf{R}_{i,j}^{(e,m)}\)[30, 36]:  
\[\mathbf{R}_{i,j}^{(e,m)}=\Theta\Big{\{}\epsilon-\left\|\mathbf{x}_{i}- \mathbf{x}_{j}\right\|_{2}\Big{\}} \tag{15}\]  
where \(i,j\in\{1,\cdots,K\}\) and \(K=N-(m-1)\tau\) denotes the number of reconstructed state vectors \(\mathbf{x}_{i};\epsilon\) is the recurrence threshold, \(\Theta(.)\) represents Heavisides' step function, and \(\left\|.\right\|_{2}\) is the \(L_{2}\) or Frobenius norm. \(d_{ij}=\left\|\mathbf{x}_{i}-\mathbf{x}_{j}\right\|_{2}\) are the coefficients of the distance matrix \(M\). According to Equation (15), the states \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) are recurring states if \(\left\|\mathbf{x}_{i}-\mathbf{x}_{j}\right\|<\epsilon\). In the RP, a recurrence is represented by a black dot. If the parameters \(m\), \(\tau\), and \(\epsilon\) are optimal,the RP presents some intrinsic patterns of the system. For example, Figures 3-5 represent, respectively, the RP of 16-QAM signal, GMSK signal, and a random zero mean White Gaussian Noise (WGN) with a variance \(\sigma_{b}^{2}\). We can notice that the patterns in the RP of the 16-QAM signal are different from those of the GMSK signal, whereas the RP of the WGN has no particular pattern. Heuristically, we can set the recurrence threshold as \(\varepsilon=0.5\sigma_{y}\) where \(\sigma_{y}^{2}\) denotes the variance of the observed signal \(y(n)\); the embedding parameters are established as \(m=16\) and \(\tau=6\).  
As the visual analysis of RP is not objective, [PERSON] and [PERSON] introduced a procedure to quantify RP structures [45; 46]. In the literature, one has five classical tools to perform the RQA. Some RQA tools are based on the recurrence density, while others use the line structures of the RP [36]. The classical RQA tools are developed and extended in [30; 36]. The main challenge with classical or extended RQA tools is the choice of an optimal recurrence threshold \(\varepsilon\) of Equation (15). A small value of \(\varepsilon\) does not reveal noticeable occurrences, while a large value of \(\varepsilon\) may lead to the appearance of neighbors for most of the existing points and cause false occurrences [30; 47]. The choice of \(\varepsilon\) is a delicate issue affecting the reliability of the spectrum sensing based on classical or extended RQA tools [20]. To overcome this issue, we propose in the following sections a new algorithm called the Recurrence-Analysis-based Detector (RAD), which only exploits the distance matrix. This distance matrix does not depend on the recurrence threshold \(\varepsilon\) and nor does the proposed detection model RAD.
# Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis
## 4 SFT Dynamic Frequency Response Analysis
However, the tensile stress of the anchor cable in the prototype SFT was 4175 MPa when the seismic wave acceleration strength was 0.05\(\times\)\(g\), which far exceeds the ultimate tensile strength of the steel strand. Therefore, the calculation shows that the cable tension component of this SFT was the most affected parameter under seismic action. Thus, in real-life applications of this SFT, the tensile strength of the cable would need to be appropriately increased to ensure that the cable had a sufficient level of safety redundancy. Additionally, based on the tensile strength performance of the steel strands during seismic activity, it was found that the seismic warning threshold for the SFT was 0.03\(\times\)\(g\). Therefore, in real-life applications of this SFT, an earthquake early warning alarm would be needed when the motion acceleration reached this level, allowing the operating company to have ample time to implement earthquake response measures. The horizontal acceleration of the tube body significantly increased as the seismic wave strength increased, and the peak acceleration of the prototype SFT tube body was much larger than the peak acceleration of the corresponding seismic wave, resulting in an amplification effect. This might have been due to the interaction between the tube body and the anchor cable vibration under seismic action, causing an increase in the acceleration response of the tube body. Additionally, the interaction between a seabed-anchored cable and the seabed itself under the action of seismic waves would further increase the acceleration response of the tube body of an SFT. Therefore, the seismic design of an SFT should include measures to suppress this interaction, such as dampers applied to the anchor cables.
## 4 Numerical Modelling and Analysis
### Selection of Material Constitutive Models  
For finite element modeling, the choice of material constitutive models significantly impacts numerical analysis results; thus, selecting appropriate constitutive relationships is crucial for establishing finite element models.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 4 ITGA Design
### Termination Condition  
Set the maximum number of iterations to Generations. When the algorithm reaches the specified number of Generations, the optimal chromosome solution from the latest population is selected as the final ground station resource allocation result.
## 6 Conclusions  
A novel two-stage CPR scheme for C-mQAM constellations based on the n-PSK partitioning algorithm is presented. The low-performance ring selection process in the single-stage n-PSK algorithm is alleviated by using optimal decision boundaries in the second stage of the algorithm. Simulation results on the performance of the proposed CPR algorithm show a noticeable improvement in phase noise tolerance compared to the single-stage n-PSK partitioning algorithm. The phase noise tolerance of the proposed algorithm is also compared with the BPSC-mQAM algorithm showing a superior performance. Furthermore, the computational complexity of the algorithm is studied in detail and compared to the BPS algorithm in C-mQAM and Sq-QAM constellations in order to assess the computational complexity reduction factors. Utilization of sub-optimal boundaries in the \(DC\) module is also proposed to further reduce the computational complexity of the algorithm while achieving similar \(\Delta\
u T_{s}\) tolerance. Computational complexity reduction factors of at least \(3.8\mid 3.3\) in the form of _multipliers \(\mid\) adders_ relative to BPSSq-16 QAM are reported which can reach values of \(24.5\mid 30.1\) for the best-case implementation of the \(DC\) module. Compared to the BPSC-16 QAM, computational complexity reduction group factors of \(24.5\mid 30.1\) are achieved.  
**Acknowledgments: This work was supported by EU projects ICONE, gr. # 608099, and GRIFON, gr. # 324391, and the Center for Silicon Photonics for Optical Communications (SPOC).**  
**Author Contributions: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] proposed the concept, [PERSON], [PERSON], and [PERSON] initiated the study. [PERSON] and [PERSON] carried out the simulations. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conducted the experimental analysis. [PERSON], [PERSON], [PERSON], and. [PERSON] guided the studies. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
## 2 Materials and Methods
### Water Quality Sampling  
In situ measurements were performed and water samples were collected from each study site (K1-K10 and S) on a monthly basis during six summer months, both before and after restoration. A total of 120 samples were collected from the Kwacza River and 12 samples were collected from the Shupia River. Physicochemical parameters, including water temperature, electrolytic conductivity (EC), pH, dissolved oxygen (DO, mg\(\cdot\)L\({}^{-1}\)), oxygen saturation (DO, %), chlorophyll-a (Chl-a), total dissolved solids (TDS), and salinity, were measured in situ with a calibrated YSI 6600 multiparametric probe (Ohio, USA). Ion concentrations in water samples were assessed in a laboratory where cations (Ca\({}^{2+}\), Na\({}^{+}\), K\({}^{+}\), and Mg\({}^{2+}\)-NH\({}_{4}^{+}\)-N) and anions (NO\({}_{3}\)\({}^{-}\)-N, NO\({}_{2}\)\({}^{-}\)-N, Cl\({}^{-}\)) were analyzed with the use of the 881 Compact IC Pro ion chromatograph (Metrohm, Herisau, Switzerland). Before analysis, the samples were passed through 0.2-\(\upmu\)m sterile filters, and examined on Metrosep C4 250/4.0 and Metrosep A Supp 5 250/4.0 columns with Metrosep C4 Guard/4.0 and Metrosep A Supp 4/5 Guard 4.0 pre-columns, respectively. Total phosphorus (TP) and orthophosphates (PO\({}_{4}\)\({}^{3-}\)-P) were measured with ascorbic acid and ammonium molybdenum. The unfiltered fraction of P was determined by acid digestion, followed by peroxididisulphate oxidation and the addition of ammonium chloride [42]. Chemical oxygen demand (COD\({}_{\rm Cr}\)) was measured with potassium dichromate. All chemical parameters were assessed with the use of standard analytical methods [43]. Annual loading, expressed as the product of average flow (Q, m\(\cdot\)s\({}^{-3}\)) and the concentration of an individual element in water (mg\(\cdot\)L\({}^{-1}\)), was calculated in tonnes\(\cdot\)year\({}^{-1}\), while its load outflowing from a unit catchment area was determined in kg\(\cdot\)year\({}^{-1}\)\(\cdot\)km\({}^{-2}\).
## 3 Results and Discussion
### Hydration Behaviour  
The comparison of the TG-curves for Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\) and Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\) is presented in Figure 4. The experimental data are shown as the temperature dependence of degrees of hydration \(x\)(H\({}_{2}\)O), where degrees of hydration is a number of water moles per formula unit. The weight gain occurred over a wide temperature range for both compounds: 150-950 \({}^{\circ}\)C for Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\), and 200-900 \({}^{\circ}\)C for Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\). For Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\), the main weight decrease occurs in the temperature range of 150-400 \({}^{\circ}\)C, and less significant weight change takes place at the temperature range of 400-950 \({}^{\circ}\)C. For Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\), the main mass change occurs in the temperature range of 200-500 \({}^{\circ}\)C, with less significant weight changes taking place at the temperature range of 500-900 \({}^{\circ}\)C. At temperatures higher than 950 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, mass stabilization is observed. It should be emphasized that the presence of OH\({}^{-}\)-groups in the crystal lattice up to fairly high temperatures (900 \({}^{\circ}\)C) is a common feature for hexagonal perovskites [55].  
Figure 3: IR spectra for the hydrated samples of Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\)[56] and Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\).  
The presence of different stages on the TG-curves correlates with the existence of the energetically nonequivalent OH\({}^{-}\)-groups in the hydrated samples that was shown in the IR analysis. This nonequivalence is caused by different crystallographic locations of OH\({}^{-}\)-groups.  
As can be seen from Figure 4, the sample Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\) exhibits higher water uptake values than Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\). Mass stabilization for Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\) starts at lower temperatures in than for Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\), however, the dehydration process starts at higher temperatures. This difference can be explained the by structural differences. Both intergrowth structures contain two Ba\({}_{2}\)InAlO\({}_{5}\)-blocks, but differ in other intergrowth blocks--the perovskite BaZrO\({}_{3}\) block for Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\) and the Ba\({}_{3}\)In\({}_{4}\)O\({}_{9}\) block for Ba\({}_{7}\)In\({}_{6}\)Al\({}_{2}\)O\({}_{19}\). It is assumed that the presence of the perovskite block (BaZrO\({}_{3}\)) in Ba\({}_{5}\)In\({}_{2}\)Al\({}_{2}\)ZrO\({}_{13}\) creates an additional opportunity for hydration.
## 4 Experimental Validation and Analysis
### Point Cloud Resampling Method  
The validity of the method is verified using the public dataset from Stanford University [28]. The Stanford Bunny is a 3D test model widely used in the field of computer graphics. The model contains data on 69,451 triangles that were determined by a 3D scanner to form a ceramic rabbit statue. First, the original point cloud data are transformed and the transformed point cloud data are resampled, and then the effectiveness of the proposed method is verified by comparing the alignment accuracy of the resampled data with the original point cloud (Figure 2).  
The point cloud resampling results are shown in Figure 3. From the figure, the other sampling methods cannot achieve the balance between point cloud feature retention and point cloud uniformity, which can easily lead to missing point cloud features (e.g., points at the rabbit's foot profile). In addition, the proposed method has adjustable hyperparameters that can be easily defined in conjunction with point cloud data processing requirements.  
Figure 2: Three-dimensional spatial transformation process of point cloud data by rotation matrix R and translation matrix T.  
To compare alignment errors, the point cloud is rotated and translated in the space. The results of point cloud alignment using the Iterative Closest Point (ICP) algorithm are shown in Figure 4. There are obvious alignment deviations in other methods, while the proposed method is basically deviation-free. The misalignment angles and translations between the aligned point cloud and the original point cloud are evaluated, and the results are shown in Table 1. The \(\Delta\theta_{\mathrm{x}}\), \(\Delta\theta_{\mathrm{y}}\), and \(\Delta\theta_{\mathrm{z}}\) are the misalignment angles of the \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively. The \(\Delta\mathrm{T}_{\mathrm{x}}\), \(\Delta\mathrm{T}_{\mathrm{y}}\), and \(\Delta\mathrm{T}_{\mathrm{z}}\) are the misalignment translations of the \(x\)-axis, \(y\)-axis, and \(z\)-axis, respectively. As can be seen from the table, the proposed method outperforms the other methods in almost all alignment errors. Among other methods, the nonuniform method has the lowest error. Compared with the nonuniform method, the proposed method reduces the maximum misalignment angle deviation from 0.0086 rad to 0.0041 rad in absolute value and reduces the maximum misalignment translation deviation from 3.12 mm to 0.65 mm.
## 4 Conclusions  
This paper presented a novel approach for integrated EMS and speed control using A-ECMS considering look-ahead data. A data-driven approach using ANN was developed to predict the optimal EF for ECMS. A unique combination of a DP-based optimal control solution for speed profiling and ECMS with an optimal EF predictor was shown to be capable of online implementation on vehicle hardware. It was shown that a simple rule-based approach to handling the traffic light signal phase and timing avoids unnecessary stops at red lights and consequently reduces fuel consumption. A receding horizon optimal control framework was used to adapt to disturbances along the route and predict the future speed and SOC trajectories. A data-driven EF adaptation approach was shown to be beneficial, since the neural network is able to learn the nonlinear relationship between the input features extracted from optimal future trajectories and the corresponding optimal EF. This further allows ECMS to compute the optimal torque split ratio for each future horizon as opposed to simply reacting to the information available at each instant. A statistical analysis showed that DP-ECMS always performs equal to or better than the baseline, and an average 10% FE improvement was achieved while maintaining an approximately equal travel time. The steps towards implementing the proposed methods on vehicles should include various levels of x-in-the-loop testing, 1/O considerations like CAN delay, errors and synchronization, and optimization of the code for the target hardware.  
Conceptualization, W.P., P.T., S.M.-M. and G.R.; methodology, W.P., P.T., S.M.-M. and G.R.; software, W.P.; validation, W.P., P.T., S.M.-M. and G.R.; formal analysis, W.P.; investigation, W.P.; resources, P.T., S.M.-M. and G.R.; data curation, W.P.; writing--original draft preparation, W.P.; writing--review and editing, P.T., S.M.-M. and G.R.; visualization, W.P.; supervision, P.T., S.M.-M. and G.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: Fuel economy percent gain of DP-ECMS over baseline.
## 5 Conclusions  
In this paper, a novel technique for milling chatter mitigation is demonstrated using an active control strategy. In the first phase, the mathematical modeling of the process is carried out. The DSMC is combined with type-2 fuzzy logic for an effective control mechanism. Numerical analysis validated that the innovative controller is able to mitigate vibration significantly. Using the Lyapunov analysis technique, a theorem is laid down to prove that the system states of the DSMC-T2 fuzzy controller are bounded. An efficient approach of the type-2 fuzzy system is implemented to handle the nonlinearities in a suitable manner. The results from numerical analysis establish that the most superior controller is DSMC-T2 fuzzy. As the literature review suggests, SMC in combination with fuzzy is utilized for vibration control, but this combination along with stability analysis is not used for the milling process. In this paper, the stability of the controller is given due importance. The stability of the controller is validated using Lyapunov analysis. Moreover, the concept of implementing type-2 fuzzy logic for nonlinearity compensation in the milling process is done for the first time. Moreover, the research is made cost effective by placing the AVD in an inclined position. The higher computational cost is the primary concern associated with the toolbox of type-2 fuzzy logic and there is a need to deal with this situation with a suitable methodology. The techniques of minimizing the computational cost will be investigated in future work. Moreover, in future, an experimental setup will be developed to validate the theoretical concept. There is also the requirement of designing a torsional actuator for the control of chatter in the theta direction (torsional vibration).  
Conceptualization, S.P.; Methodology, S.P. and R.M.-M.; Software, S.P.; Validation, R.M.-M. and S.P.; Investigation, S.P.; Writing--original draft preparation, S.P.; Writing--review and editing, S.P. and R.M.-M.; Supervision, R.M.-M.; Project administration, R.M.-M.; Funding acquisition, R.M.-M. These authors contributed equally to this work.  
This research was funded by Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, NL, Mexico.  
The authors declare no conflict of interest.  
Figure 16: Control signal of DSMC with type-2 fuzzy logic.  
Figure 15: Cutting force along \(y\)-direction.
## 2 Multi-Fluid Model and Numerical Method
### Numerical Setup  
The schematic of the computational domain used was shown in Figure 1, using axisymmetric simulations. As the size of the nozzle was small, we simplified the fluid field to the maximum diameter of 160 mm, maximum height of 80 mm, and the stand-off distance is 6 mm, which can fully reflect the pressure oscillation and cavitation characteristics at the outlet of the waterjet nozzle. The boundary conditions were shown in Figure 1, the inlet and outlet boundaries were defined as pressure, the adjacent side of the wall was defined as fluid, and the fluid properties were shown in Table 1. We employed the HyperMesh commercialis software [32] to acquire the structured grid of the simulation domain, which was shown in Figure 2. The grid was refined at the outlet of the nozzle, the number of the grids elements was 14,465,000. During the simulation the time step was defined as \(5\times 10^{-6}\) s. The computer we used was with two Intel(R) CPU E5-2683 v3, which had a main frequency of 2 GHz, and the number processors was defined as 28. Symmetric boundary conditions were used, half of the computational domain was considered for the numerical simulation. The SIMPLE algorithm for unsteady simulations was employed to solve the coupling between pressure and velocity [33]. At the nozzle exit, the fluid oscillated violently, so that the transient term was discretized using a first-order upwind method in the governing equations to stabilize the numerical simulation. All the governing equations could be found in the software users' manual ANSYS/Fluent (2017).
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 3 Design Architecture
### VDCLS Controller Module  
The capacity of the Checker Module buffer is limited; moreover, the slave core cannot overtake the master core in the program execution. As the result, the program execution freedom by master and slave cores has to be constrained. The VDCLS Controller Module analyzes the current delay between two cores and injects stall cycles to the master core, when the slave core approaches the buffer limits, or to the slave core, to keep to slave core behind the master core (Figure 5). The necessity of injecting stall cycles is the main drawback of the proposed solution. However, the results shown in the later sections indicate that the impact of these additional cycles is negligible. The size of the Checker Module buffer constraints the stall injection thresholds. A bigger module reduces the need for stall cycles that impacts the processor performance.  
Figure 4: Block diagram of the VDCLS Checker Module.