esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Traditional Digital Image Processing Techniques  
### Color Feature-Based  
The target recognition method is based on color feature segments and recognizes the target fruit in images with background objects such as leaves by predicting the information of pixel points and combining the color space, such as RGB HSI and HSV. The target recognition method based on color features can be used when the target fruit has distinct colors and color features that differ significantly from those of the background objects. For example, [PERSON] et al. [11] used color features as recognition features to distinguish pineapples from the background, and the correct recognition rate of the target fruit was 90% during sunny weather and 60% when cloudy. To improve the accuracy of fruit recognition segmentation, [PERSON] et al. [12] performed segmentation recognition of Fuji apples by using color difference and optimal thresholding. The chromatic aberration measure in this approach is to enhance the image so that the optimal threshold depends on the maximum grayscale variance of the chromatic aberration. The experiment showed that the overall recognition success rate was higher than 80%, but the recognition based on color features failed to eliminate the influence of changing lighting conditions, and the recognition segmentation algorithm in this paper had an error rate of 18% under backlight conditions. Therefore, when a picking robot uses color feature-based recognition segmentation of target fruits, its recognition accuracy and efficiency will be affected by the type and ripeness of fruits, the complexity of the background, and different lighting conditions.  
To further recognize and segment the target fruits with different maturity levels, [PERSON] et al. [13] used RGB and HSI color spaces on Fuji apples (Figure 3). The difference between the different channels of RGB in the captured image is an important basis for the initial segmentation of the apples. In addition, in order to reduce the influence of light conditions, the threshold of the saturation channel in the HSI image was used to segment the red apples. The system was evaluated using regression coefficients of 0.8 for automatic recognition counts of unripe and ripe apples and 0.85 for manual counts of  
Figure 2: Species and quantity of fruit involved in the citation.  
apples, but the accuracy of the system was impacted by the occlusion of the target fruit by background objects.
## 2 Study Area, Dataset, and Methodology
### Meteorological and Human Activity Data Sources and Preprocessing
#### 2.3.1 Temperature and Precipitation Data  
Meteorological data were obtained from the ERA5-Land dataset, covering the period from 2000 to 2020, with a spatial resolution of 0.1\({}^{\circ}\). Monthly temperature and precipitation data were used to calculate annual mean temperature and total precipitation, aligning with the interannual variability of NDVI [39]. These meteorological datasets were spatially matched with NDVI data using grid interpolation methods to ensure spatial consistency across all datasets.
## 3 Numerical Applications and Discussion  
In this section, a preliminary validation of the proposed methodology is attempted. Comparisons against literature data on stiffened panels with circular delaminations have been used to prove the effectiveness of the proposed approach if compared to standard algorithms implemented in commercial finite element method (FEM) codes. The proposed fast delamination growth initiation procedure has been adopted here to investigate the damage behavior of stiffened CFRP panels subjected to compressive loads characterized by delaminations of different sizes and depths. Two test cases taken from literature [38; 40] have been considered. The results obtained with the mode I _Fast_ and mixed mode I and II _Fast_ approaches have been compared each other and to standard non-linear VCCT results to verify the accuracy of the proposed methodology.
## 4 Challenges
### CPS Challenges  
As a complex system that requires the coordination of network, computing, and control, CPS has strict performance requirements in different application scenarios. Generally speaking, CPS applications require very low latency, high reliability, and large scalability. Taking the smart transportation system as an example, suppose we are designing applications related to autonomous driving. Vehicles need to be able to quickly collect pertinent information. This information includes data collected by sensors, transmitted through the Internet of Vehicles, and so on. Then, the vehicle needs to process this data quickly, extract usable information, and control the vehicle according to the determined situation at the time. Since vehicle driving is closely related to personal safety, there is no doubt that the reliability of autonomous driving is our primary consideration. The issues that we need to consider include network failures, computing hardware failures, and many more.  
Moreover, because the number of vehicles in each scenario will be different, we need to ensure the scalability of the autonomous driving application that we design to handle the problems caused by the large number of vehicles, such as network congestion and high control algorithm complexity. From this example, we can conclude that the performance requirements of CPS are very strict. The overall performance of CPS is the result of multiple subsystem (network, computing, and control) interactions and is synthesized within the CPS. This interaction means that any slight change in the performance of any one subsystem can have a compounding effect on the performance of other subsystems, thereby causing a complex impact on the performance of the entire CPS system. Thus, when building a DT for CPS, it is challenging to not only meet all these strict performance requirements but also to reflect the interactions between the various subsystems in a timely manner.
###### Abstract  
Current commercial software tools implement turbulence models on computational fluid dynamics (CFD) techniques and combine them with fluid-structural interaction (FSI) techniques. There are currently a great variety of turbulence methods that are worth investigating through a comparative study in order to delineate their behavior on scaffolds used in tissue engineering and bone regeneration. Additive manufacturing (AM) offers the opportunity to obtain three-dimensional printed scaffolds (3D scaffolds) that are designed respecting morphologies and that are typically used for the fused deposition model (FDM). These are typically made using biocompatible and biodegradable materials, such as polyetherimide (PEI), ULTEM 1010 biocompatible and polylactic acid (PLA). Starting from our own geometric model, simulations were carried out applying a series of turbulence models which have been proposed due to a variety of properties, such as permeability, speed regime, pressures, depressions and stiffness, that in turn are subject to boundary conditions based on a blood torrent. The obtained results revealed that the detached eddy simulation (DES) model shows better performance for the use of 3D scaffolds in its normal operating regime. Finally, although the results do not present relevant differences between the two materials used in the comparison, the prototypes simulated in PEI ULTEM 1010 do not allow their manufacture in FDM for the required pore size. The printed 3D scaffolds of PLA reveal an elastic behavior and a rigidity that are similar to other prototypes of ceramic composition. Prototypes made of PLA reveal unpredictable variability in pore and layer size which are very similar to cell growth itself and difficult to keep constant.  
3D scaffold; CFD-structural; fluid-structural interaction; tissue engineering; bone regeneration; 3D printing materials +
Footnote †: journal: Applied Physics Letters  
Article  
A Comparative Study of Turbulence Methods Applied to the Design of a 3D-Printed Scaffold and the Selection of the Appropriate Numerical Scheme to Simulate the Scaffold for Tissue Engineering  
[PERSON], [PERSON], [PERSON].  
A Comparative Study of Turbulence Methods Applied to the Design of a 3D-Printed Scaffold and the Selection of the Appropriate Numerical Scheme to Simulate the Scaffold for the required pore size. The printed 3D scaffolds of PLA reveal an elastic behavior and a rigidity that are similar to other prototypes of ceramic composition. Prototypes made of PLA reveal unpredictable variability in pore and layer size which are very similar to cell growth itself and difficult to keep constant.
## 3 Numerical Examples  
Numerical models are established to verify the reliability of the 3D improved XFEM. Different materials may be found in one element under improved XFEM. This characteristic makes it possible to omit the step of repartition of mesh while the bubble rises and deforms.  
According to [11; 12; 13; 14; 15], under the action of a uniform electric field at MV/m level, bubbles are gradually stretched and lengthened as they rise, from spheres to ellipsoids. To verify the accuracy of improved XFEM, numerical examples are designed imitating the experiments in the literature.  
In Sections 3.1 and 3.2, the potential distribution in liquid nitrogen is analyzed without considering the bubble movement and deformation. In these two parts, bubbles are considered to be fixed. Only the electric field at a certain moment is analyzed. Referring to [11; 14], the radius of bubbles is around 1.5 mm. The center position and radius of multiple bubbles are generated randomly in the solution domain.  
In Section 3.3, the motion of a bubble under an electric field in liquid nitrogen is calculated. The movement and deformation process are calculated through the commercial software COMSOL Multiphysics 5.1 provided by COMSOL AB from Stockholm, Sweden. The position of the gas-liquid interface is applied to the XFEM calculation as a known condition.
## Abbreviations  
The following abbreviations and symbols are used in this manuscript:  
\begin{tabular}{l l} AF & Activity Factor \\ AMI & Arbitrary Mesh Interface \\ BEMT & Blade Element Momentum Theory \\ BET & Blade Element Theory \\ BLDC & Brushless Direct Current \\ CIRA & Italian Aerospace Research Centre \\ CFD & Computational Fluid Dynamics \\ CST & Class-Shape Transformation \\ DMSOD & Deep Mean Subset of Data \\ DOE & Design of Experiments \\ EPS & Electric Propulsion System \\ FR & Frozen Rotor \\ GT & Gamma Transition \\ HAA & High Altitude Airship \\ HALE & High Altitude Long Endurance \\ HAPS & High Altitude Pseudo Satellite \\ MFNN & Multi-Fidelity Neural Network \\ MIGA & Multi-Island Genetic Algorithm \\ MIL & Minimum Induced Loss \\ PCE & Polynomial Chaos Expansion \\ PDF & Probability Density Function \\ PMSM & Permanent Magnet Synchronous Motors \\ PSO & Particle Swarm Optimization \\ RANS & Reynolds-Averaged Navier-Stokes \\ RFC & Regenerative Fuel Cells \\ RPAS & Remotely Piloted Aircraft Systems \\ UAV & Unmanned Aerial Vehicle \\ UQ & Uncertainty quantification \\ VFEIM & Variable Fidelity Expected Improvement Matrix \\ Nomenclature \\ \(a\) & axial induction factor \\ \(a_{i}\) & Induced angle \\ \(a^{\prime}\) & Tangential induction factor \\ \(c\) & Local blade chord \\ \(C_{a}\) & Axial force coefficient \\ \(C_{d}\) & 2D drag coefficient \\ \(C_{D}\) & Drag coefficient \\ \(C_{D,p}\) & Volumetric drag coefficient \\ \(C_{l}\) & 2D lift coefficient \\ \end{tabular}
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 2 Materials and Methods  
This section presents a structured hybrid framework for estimating the time of closest approach (TCA) and collision probability between space objects using TLE data. The methodology comprises four main stages: (1) Monte Carlo-based probabilistic screening, (2) spline-based deterministic refinement of the TCA, (3) multi-stage temporal resolution enhancement, and (4) collision probability estimation.
## Appendix B Profile Likelihood and Likelihood Ratio Profile  
Profile Likelihood (PF) refers to the possibility of expressing how \(\mathcal{L}\) (or \(\ell\)) depends on the parameters [61; 63; 64; 80; 81]. PF is formulated by constraining one (or more) parameter/s and calculating the new maximum likelihood. In other words, LR describes the trend of the constrained maximum likelihood in a certain range of the constrained parameter. However, as \(\mathcal{L}\) cannot always be easily computed, it is typical to describe PF using \(\ell\) rather than \(\mathcal{L}\). If \(\gamma_{1}\) is constrained to be equal to \(\overline{\gamma_{1}}\), the constrained maximum log-likelihood for \(\overline{\gamma_{1}}\) can be defined as:  
\[\ell_{\overline{\gamma}_{1}}=\max_{\gamma_{2},\ldots,\gamma_{p}}\ell_{(\gamma _{1}=\overline{\gamma}_{1},\gamma_{2},\ldots,\gamma_{p})} \tag{11}\]  
An example of PF is shown in Figure 11, where the PF has been calculated for several values of \(s_{\log{(\sigma_{\gamma_{0}})}}\).  
The usage of PF allows for the description of the confidence interval of the parameter in its natural range [61; 63; 64] via the usage of the Likelihood Ratio (LR). It is possible to demonstrate that the ratio between \(\hat{\ell}\) and \(\ell_{\overline{\gamma}_{1}}\) can be related to a \(\chi^{2}\) distribution with one degree of freedom [82]:  
\[-2\ln R_{(\gamma_{1})}=-2\ln\biggl{(}\frac{\mathcal{L}_{\overline{\gamma}_{1} }}{\hat{\mathcal{L}}}\biggr{)}=2(\hat{\ell}-\ell_{\overline{\gamma}_{1}})\sim \chi^{2}_{(1)} \tag{12}\]  
According to Equation (12), it is possible to express the confidence interval of the parameter \(\gamma_{1}\) with a \(1-\alpha\) confidence level (i.e., \(\widetilde{\gamma}_{1}\)) by finding the value of \(\overline{\gamma_{1}}\) that satisfies Equation (13) [61; 63; 64].  
\[\ell_{\overline{\gamma}_{1}}=\hat{\ell}-\frac{1}{2}\chi^{2}_{(1-\alpha,1)} \tag{13}\]  
Figure 11 also shows the confidence interval \(\widetilde{s}_{\log{(\sigma_{\gamma_{0}})}}\) evaluated according to Equation (13).  
It is worth mentioning another utility of the LR framework. Indeed, most minimization tools such as (e.g., MatLab's _fminsearch_) require an estimation of the solution as an input parameter. By calculating the LP, several guess solutions are provided, thus allowing one to properly investigate, whereas the solver has calculated a proper solution.
###### Abstract  
Magnetic-liquid double suspension bearing (MLDSB) is a new type of suspension bearing with electromagnetic suspension as the main part and hydrostatic supporting as the auxiliary part. It can greatly improve the bearing capacity and stiffness of rotor bearing system and is suitable for medium speed, heavy load, and frequent starting occasions. The electromagnetic system adopts PD control, and hydrostatic system adopts constant pressure supply model to adjust and control the rotor's displacement in real time. Once bearing electromagnetic system fails, the \"dropping-collision\" phenomenon of the rotor will be triggered in operation process, leading to cracking and shedding of magnetic sleeve plating and magnetic pole plating. Then the operational reliability and stability of MLDSB will be greatly reduced. So in this paper, Firstly, the drop impact-rubbing equation of the single DOF bearing system under four failure models (upper unit failure, lower unit failure, bilateral failure, and power amplifier failure) is established. Secondly, the paper simulates influence laws of different structure and operation parameters (plating/liquid film thickness and oil pocket pressure, bias current) on falling rotor impact-rubbings behavior. The results show that: (1) the degree of \"dropping-collision\" of the rotor under the four failure models is successively as follows: power amplifier failure > upper unit failure > bilateral failure > lower unit failure. (2) Due to the impact-rubbing damping effect of hydraulic oil, it plays a certain inhibitory and buffering role on the phenomenon of \"drop-collision\". The degree and probability of \"dropping-collision\" of rotor can be effectively reduced by increasing the pressure of oil chamber appropriately. (3) The rotor drop impact-rubbing behavior obtained from the test is basically consistent with the theoretical simulation, and the correctness of the theoretical simulation can be effectively verified. The research provides a theoretical basis for fault prevention and diagnosis of MLDSB.  
magnetic-liquid double suspension bearing; single DOF supporting system; dropping impact-rubbings; electromagnetic failure model; structural parameters; operation parameters +
Footnote †: journal: Applied Sciences
## 5 Experimental Scenarios and Results
### ADR-Det Stage 1 Detection Validation  
Using the toll post images of two toll post stations, the authors selected photos in the 6 h ranging from 10:00:00 a.m. up until 15:59:59 p.m. in March 2021. This evaluation step examines the number of photos exiting ADR-Det algorithm stage 1 (recognized as containing large vehicles by the algorithm). The results are shown in Table 2.
## 3 Results and Discussion
### Fracture Behavior
With stress increase, the rapid growth of the AE energy and accumulative events can be observed as the result of the propagation, coalescence and penetration crack processes. At the stress peak, AE energy reaches its highest value and afterward gradually reduces due to the multiple crack penetration processes, and the final failure of the material occurs. Sample No. 5 (Figure 8a) shows a similar trend as described above: a gradual step-up trend with activity absence at the beginning stage and its increasing with the load increasing. The cumulative AE energy curve of other samples has a greater slope meaning in faster AE energy increase that reflects the more serious damage degree in the material. AE activity in samples No. 9 and No. 12 starts to generate earlier and shows an obvious high intensity at the beginning of loading compared to the rest of the samples (Figure 8c,e). Sample No. 11 (Figure 8d), similar to No. 5, has a silent stage at the initial loading, but in the middle, it has a great emission event corresponding to the main fracture occurrence. The initial AE energy release \(\gamma\) in all samples varies insignificantly: \(\gamma\) does not exceed 1% for all samples. Thus, this parameter cannot be used for damage \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Total AE Hits**} & \multirow{2}{*}{**Total AE Energy**} & \multirow{2}{*}{**Initial AE Energy Release Rate \(\gamma\)**} \\  & & & **(0–200)** \\ \cline{2-3}  & **[hit]** & **[V\({}^{2}\)]** & **[\%]** \\ \hline No. 5 & 33,133 & 13,319.6 & 0.18 \\ No. 8 & 65,340 & 28,661.8 & 0.06 \\ No. 9 & 119,056 & 24,480.3 & 0.45 \\ No. 11 & 56,582 & 21,516.5 & 0.31 \\ No. 12 & 74,888 & 15,215.5 & 0.43 \\ \hline Average & 69,800 & 20,638.8 & 0.29 \\ Max & 119,056 & 28,661.8 & 0.45 \\ Min & 33,133 & 13,319.6 & 0.06 \\ SD & 28,253 & 5707.5 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: AE test results. degree investigation in our case. Consequently, these concrete samples demand a more complex assessment of fracture behavior regarding strain and displacement evolution.
# Toughening Behavior of Carbon/Epoxy Laminates Interleaved by PSF/PVDF Composite Nanofibers
## 2 Materials and Methods
### Electrospinning  
The \"lab unit\" electrospinning machine by Spinbow company (Bologna, Italy) was used for producing 30 m thick nanofibrous mats. The polymeric solutions of PSF and PVDF were made as follows: (1) PSF solution was prepared by dissolving 23 g of polymer in 90 mL of DMAc and 10 mL of acetone. (2) The second solution was produced by dissolving 15% (_w/v_) PVDF powder in a 30:70 (_v/v_) of Dimethyl sulfoxide (DMSO) and Acetone. The solutions were poured into two separate syringes and then transferred to the electrospinning machine. The electrospinning parameters are presented in Table 2.  
A continuous electrospinning process was conducted for producing pure PSF and PVDF nanofibrous mat, but as the electrospinning machine was not equipped with two separate high-voltage sources and syringe pumps, and due to different feed rates for the two polymers, the process was discontinuous for the mixed (PVDF/PSF) nanofibrous mat: PSF and PVDF nanofibers were electrospun for 1 and 2 min, respectively, until the desired thickness was obtained. SEM pictures of PVDF and PSF nanofibers are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PSF (Udel® 3500)** & **PVDF (Solef® 6008)** \\ \hline Forms & Pellets & Powder \\ Density (g/cm\({}^{3}\)) & 1.24 & 1.75–1.8 \\ Water absorption (24 h, 23 \({}^{\circ}\)C) & 0.3 & \textless{}0.04\% \\ Melt temperature (\({}^{\circ}\)C) & 316–371 & 170–175 \\ Glass transition temperature (\({}^{\circ}\)C) & 50 & \textless{}40\) \\ Tensile modulus (GPa) & 2.48 & 1.8–2.5 \\ Tensile strength (MPa) & 70.3 & 30–50 \\ Tensile elongation (\%) & 50 to 100 & 20–300\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Polysulfone (PSF) and polyvinylidene fluoride (PVDF) properties (source: datasheet provided by Solvay website).  
Figure 2: Produced nanofibers: (**a**) PVDF and (**b**) PSF.
# Predictive Current Control for Switched Reluctance Motor Based on Local Linear Phase Voltage Model
## 3 Experimental Verification
### Experimental Results under Heavy Load  
In order to evaluate the heavy load performance at different speeds, experimental waveforms for both control strategies under a 5 Nm load at speeds of 100, 400 and 700 rpm are presented in Figures 9-11, respectively. The quantitative comparison is shown in Figure 12, where the current and torque RMSEs of the two strategies are given under a 5 Nm load at speeds of 100, 250, 400, 550 and 700 rpm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Value** & **Parameter Name** & **Value** \\ \hline Rated power & 1 kW & Number of phases & 3 \\ Rated voltage & 72 V & Number of stator poles & 12 \\ Rated speed & 4300 rpm & Number of rotor poles & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the experimental switched reluctance motor.  
Figure 8: Block diagram of the current control strategies. (**a**) Proposed predictive current control (PCC). (**b**) Hysteresis current control (HCC).  
It can be seen that over the tested speed range, the current- and torque-tracking errors of the proposed control scheme are significantly lower than those of HCC under heavy load. The current RMSE using the proposed PCC is 44.4% lower on average than that using HCC, and the torque RMSE using the proposed PCC is 48.6% lower on average. Moreover, the proposed control method has the advantage of maintaining a fixed switching frequency without increasing the phase current sampling rate. It can also be observed that under the proposed PCC, the peak inverse current ripple occurs immediately after the phase conduction. This is due to the application of the maximum average voltage (80% of the DC-link voltage in this case) in stage II. However, the peak inverse ripple occurs somewhere near the unaligned rotor position, where the inductance profiles are nearly flat, and consequently, the torque production ability is weak. Therefore, this inverse peak will not cause a large torque ripple, as can be seen from the torque-related waveforms in Figures 9-11.