esa-sceva/satcom-chunk-collection huggingface dataset:
# Incoherent Digital Holography: A Review
## 3 Principle of FINCH
### Point Spread Function  
In this subsection, the PSF of FINCH is analyzed. We assume that the object is a point source and is located on the front focal plane of the lens and that \(d=z_{3}/2\). After the calculation of the diffraction integral, the intensity distribution, which refers to the PSF, is reconstructed. According to the theory of Fourier optics, the 2D PSF in the configuration is calculated and is given by the following [37]:  
\[I_{0}Jinc\bigg{(}\frac{2\pi R_{H}}{\lambda(z_{3}-d)}\sqrt{\left(x-M_{T}x_{1} \right)^{2}+\left(y-M_{T}y_{1}\right)^{2}}\bigg{)} \tag{12}\]  
where \(Jinc(x)=J_{1}(x)/x\), \(R_{H}\) is the radius of the recorded interference fringe pattern, and \(M_{T}\) is the total in-plane magnification of the lens and the SLM. \(M_{T}\) has been derived and is given by the following [36]:  
\[M_{T}=\frac{z_{3}f(f-z_{1})}{(f-z_{1})[f(f-z_{1})+z_{1}z_{2}]}. \tag{13}\]  
The width of the PSF, \(w_{PSF}\), is defined as the width of the main lobe of \(J_{1}(x)\) and is  
\[w_{PSF}=\frac{1.22\lambda d}{R} \tag{14}\]  
where \(R\) is the radius of the smallest aperture in the system. In Figure 4, for example, if the radii of the SLM and image sensor are larger than that of the lens that the light is passing through, the smallest aperture \(R\) is just the radius of the lens. Here, \(w_{PSF}\) is the same as the PSF of a coherent imaging system, but \(M_{T}\) gives a specific property to FINCH, and recording with the interference of incoherent object waves gives a specific effective PSF for FINCH. \(M_{T}\) is not \(d/f\) but \(z_{3}/f\) when \(z_{1}=0\), while the PSF is the same as that of a conventional incoherent imaging system. This is called a violation of the Lagrange invariant, which is seen for a self-reference interferometer using two object waves to obtain an incoherent hologram [68]. Therefore, the magnification of FINCH is higher than that of a conventional imaging system, while it keeps the same functional form of the PSF. If there are two object points on the object plane at a distance of \(x_{0}\), the two points are apart at a distance of \(x_{0}M_{T}\) on the reconstructed image plane with the same PSF. By shrinking the distance of the two points \(x_{0}M_{T}/2\), the magnification becomes the same as that in a conventional incoherent imaging system, and, therefore, the width of the PSF is half of that of a coherent system. As a result, the effective PSF becomes narrower by a factor of \(z_{3}/d=2\), which has been derived [37]:  
\[I_{0}Jinc\bigg{(}\frac{4\pi R}{\lambda d}\sqrt{\left(x-M_{T}x_{1}/2\right)^{2 }+\left(y-M_{T}y_{1}/2\right)^{2}}\bigg{)} \tag{15}\]  
and the effective width of the PSF \(w^{\prime}_{PSF}\) is as follows:  
\[w^{\prime}_{PSF}=\frac{0.61\lambda d}{R}. \tag{16}\]  
Therefore, FINCH is regarded as a super-resolution 3D imaging technique.
# Optimized Multiport DC/DC Converter for Vehicle Drivetrains: Topology and Design Optimization
## 2 Analysis of Non-Isolated Interleaved DC-DC Converter
### Input Current Ripple  
In the IBC, the phase interleaving technique enables one to decrease the input current ripple by shifting each interleaved phase by \(360^{\circ}/N_{ph}\) such that the current is cancelled out, as shown in Figure 4a. More phases are added in the interleaved converter; the \(\Delta I_{in}\) peak is further reduced for each additional phase added. However, even though the amplitude of the ripples is reduced, the frequency of the ripples increases with increase in the number of phases.  
The input current ripple cancellation effect of an interleaved converter in the Continuous Conduction Mode (CCM) has been analyzed and quantified in [17; 18; 19]. However, their derived equations are complicated to use in formulating the optimization problem. For the sake of convenience in the optimization process, we rewrite the function of input current ripple in terms of the duty ratio.  
According to [17; 18; 19], the function of input current ripple \(\Delta I_{in}\) with regard to the duty ratio \(D\) can be recognized as a parabolic equation, \(\Delta I_{in}=aD^{2}+bD+c\), as shown in Figure 4b. As can be seen, if the IBC has \(N_{ph}\) phases, the peak of current ripple occurs separately in \(N_{ph}\) regions of duty ratio. Each region is associated with an integer number \(k\in[0,N_{ph}-1]\).  
The vertex of the parabola and the points where \(\Delta I_{in}\) is zero (dashed red circles in Figure 4b) are considered to determine the coefficients a, b and c. It is noted that the peak of the inductor current ripple \(\Delta\hat{I}_{L}\) in one single phase is calculated as Equation (1), therefore, the peak of the input current ripple \(\Delta\hat{I}_{in}\) becomes Equation (2):  
\[\Delta\hat{I}_{L}=\frac{V_{o}}{4f_{sw}L} \tag{1}\]  
\[\Delta\hat{I}_{in}=\frac{\Delta\hat{I}_{L}}{N_{ph}} \tag{2}\]As a result, the input current ripple analytical equation can be derived by the following system in Equation (3):  
\[\left\{\begin{array}{c}a\Bigg{(}\dfrac{k}{N_{ph}}\Bigg{)}^{2}+b\dfrac{k}{N_{ph}}+ c=0\\ a\Bigg{(}\dfrac{k+1}{N_{ph}}\Bigg{)}^{2}+b\dfrac{k+1}{N_{ph}}+c=0\\ a\Bigg{(}\dfrac{2k+1}{2N_{ph}}\Bigg{)}^{2}+b\dfrac{2k+1}{2N_{ph}}+c=\dfrac{V_{o} }{4L_{fsw}N_{ph}}\end{array}\right. \tag{3}\]  
Figure 4: (**a**) Reduction of input current ripple through phase interleaving. (**b**) Normalized peak-to-peak input current ripple as a function of the duty ratio.  
By solving the system, the analytical expression of \(\Delta I_{in}\) is derived as Equations (4) and (5):  
\[\Delta I_{in}=\frac{V_{o}\left(D-\frac{k}{N_{ph}}\right)}{Lf_{sw}}\left[1-N_{ph} \left(D-\frac{k}{N_{ph}}\right)\right] \tag{4}\]  
\[1-\frac{V_{max}}{V_{o}}<D<1-\frac{V_{min}}{V_{o}}k\ \in\ [0,\ N_{ph}-1] \tag{5}\]
# Point Cloud Registration Algorithm Based on Adaptive Neighborhood Eigenvalue Loading Ratio
## 3 Experiment
### Results
#### 3.3.3 Point Cloud Registration Algorithm Based on Feature Point Similarity Formula
The ColorICP algorithm: Compared with the ICP algorithm, the inclusion of color features in the ColorICP algorithm resulted in major improvements in some datasets, particularly the \"tree stool\" point cloud scene, where the RMSE was markedly reduced. However, the ColorICP algorithm tended to be more time-consuming than the ICP algorithm. 3. The FPFH+ICP algorithm: This algorithm employs a coarse plus fine registration scheme, which allowed it to outperform both the ICP and ColorICP algorithms in most datasets. It demonstrated higher accuracy and speed, with much lower RMSE values, greater registration accuracy, and reduced time consumption. 4. The algorithm in this study: The algorithm proposed in this study exhibited the lowest RMSE values in all datasets, with clear advantages in terms of time consumption. Specifically, in the complex indoor scenes of the \"chamber\" and \"office\", the RMSE was considerably lower than that of the other algorithms, and the processing time was greatly reduced, showing a high level of accuracy and efficiency.
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## Materials  
### C-ITS Services for Public Transport  
Among current C-ITS features and upcoming C-ITS services in the near future, the most useful services within the field of public transportation are \"Traffic Signal Priority\" (TSP) and \"Green Light Optimized Speed Advisory\" (GLOSA). A comprehensive literature review of the international research conducted on the C-ITS service GLOSA can be found in [7]. At this point in time, the research clearly indicates that there are no imminent plans for a roll-out, despite the fact that the service has already been scientifically examined in a wide variety of studies and evaluated accordingly in interaction with adaptive controls [8; 9; 10; 11]. These insights bring forth the novelty of this paper.  
Traffic Signal Priority is a system that allows public transport, emergency vehicles, and other authorized vehicles to receive prioritized treatment at traffic signals. In the future, several priority levels will be implemented when using C-ITS. When an authorized vehicle approaches a traffic light, the TSP system detects its presence and adjusts the signal timing to provide a green light for the authorized vehicle, or it reduces the waiting time, allowing the vehicle to pass through the intersection faster and more safely. TSP can also help improve the efficiency and reliability of public transport services, reduce response times for emergency vehicles, and enhance overall traffic flow in urban areas [12].  
The TSP system conclusively raises the efficiency of traffic lights under certain traffic conditions in which the optimization of green light phases is possible, thus resulting in an undersaturated traffic system. In circumstances in which a traffic light cannot be optimized freely, the Green Light Optimized Speed Advisory provides another possible option. This system makes use of vehicle-to-infrastructure (V2I) communication in order to provide drivers with speed recommendations that are optimized to help them pass through a series of traffic lights without stopping. GLOSA also utilizes real-time data on the timing of traffic lights combined with information on the speed and location of individual vehicles in order to calculate the optimal speed advisory for each vehicle approaching their next traffic light. In general, GLOSA aims to reduce fuel consumption and emissions while improving traffic flow and safety by providing drivers with the previously described data and information. The remnant availability of the saved travel time for public transport can subsequently be used for passenger stops and passenger changeovers [13; 14]. GLOSA can generally be recommended from an economic point of view under certain constraints, e.g., the complexity of the junction itself [15].  
The European project C-ROADS deploys different C-ITS services all over the European continent and is responsible for the harmonization of these services [16; 17].
## 2 Materials and Methods
### Characterization  
The as-synthesized samples were characterized by various techniques. The crystallinity of the samples was analyzed by X-ray diffraction (XRD). The diffractograms were produced using Cu K\(\alpha\) radiation (\(\lambda\) = 0.15418 nm) in a Siemens D500 X-ray powder diffractometer (Karlsruhe, Germany). The diffraction was examined in the range of 10\({}^{\circ}\)-80\({}^{\circ}\). Anatase and rutile percentages of TiO\({}_{2}\) were measured by the Spurr equation [15].  
\[\mathrm{F_{R}}=\frac{1}{1+0.8[\mathrm{I_{A}(101)}/\mathrm{I_{R}(110)}]} \tag{2}\]  
where F\({}_{R}\) is the quantity of rutile phase; I\({}_{A}\)(101) and I\({}_{R}\)(110) are the intensity of anatase peak and rutile peak respectively. The crystallinity of the samples was calculated using the Scherrer equation [55].  
\[\Phi=\frac{\mathrm{K}\lambda}{\beta\cos\theta} \tag{3}\]  
where \(\Phi\) is the crystallite size; K is the shape factor with a value close to unity; \(\lambda\) is the wavelength of X-rays; \(\beta\) is the full width half-maximum (FWHM) of the main intensity peak; and \(\theta\) is the Bragg angle.  
The phase transformation was also analyzed using Raman spectroscopy (Horiba Jobin Yvan LabRAM HR 800, Villeneuve d'Ascq, France) with a grating of 300 gr/mm. A 660 nm solid state diode laser standard bandwidth version with double edge filter upgrade was the laser used. The acquisition time was 3 s.  
The bonding interactions and oxidation state of elements were studied with the help of X-ray photoelectron spectroscopy. XPS analyses were performed on a ThermoFisher Scientific Instruments (East Grinstead, UK) K-Alpha+ spectrometer. XPS spectra were acquired using a monochromated Al K\(\alpha\) X-ray source (hn = 1486.6 eV). An X-ray spot of \(\sim\)400 \(\upmu\)m radius was employed. Survey spectra were acquired employing a Pass Energy of 200 eV. High resolution, core level spectra for all elements were acquired with a Pass Energy of 50 eV. All spectra were charge referenced against the C1s peak at 285 eV to correct for charging effects during acquisition. Quantitative surface chemical analyses were calculated from the high resolution, core level spectra following the removal of a non-linear (Shirley) background. The manufacturers' Avantage software was used which incorporates the appropriate sensitivity factors and corrects for the electron energy analyzer transmission function.  
The optical properties of the as prepared samples were measured using a Perkin-Elmer UV-Vis Spectrophotometer (Beaconsfield, Buckinghamshire, UK) Barium sulphate (BaSO\({}_{4}\)) was used as a reference, and slit width was set to 1 nm. Additionally, band gap was calculated from the UV DRS spectrum by using Kubelka-Munk function F(R) [56].  
\[\mathrm{F(R)}=\frac{(1-\mathrm{R})^{2}}{2\mathrm{R}} \tag{4}\]  
where R is the absolute reflectance of the sample.  
The photocatalytic experiments were performed inside a weathering and corrosion photo-reactor (Q-sun Xe-1-S) from Q-Labs (Saarbrucken, Germany).
## 3 A New Hybrid Technique for Finding DMU Targets in Non-Radial FHD Models
### Extended RBA  
In this section, extended RBA (the second part of proposed hybrid technique) to find DMU targets in non-radial FDH models of the interactive algorithm is described. As described in Section 3.1, finding DMU targets of three non-radial FDH models of the interactive algorithm (models 12-14) are required. All mentioned models are mixed 0-1 LP and finding DMU targets by solving them is difficult. RBA can find DMU targets of radial FDH models without solving them. Only non-radial model 12 has a lot of similarity to radial FDH models (models 7 and 8). Therefore, finding DMU targets of non-radial model 12 may be possible using RBA with a little modification. However, RBA cannot apply to find DMU targets of non-radial FDH models 13 and 14, because these models have some complicated constraints. In this section, extended RBA is proposed to find DMU targets of non-radial FDH models without solving any mathematical programming models.  
Define \(\text{x}_{\rm io}=\text{x}_{\rm ij},\text{i}=1,\ldots,\text{m}\), and \(\text{y}_{\rm no}=\text{y}_{\rm ij},\text{r}=1,\ldots,\text{s}\) for DMU\({}_{\rm o}\), o = 1,..., n, for j = 1... n. The extended RBA then find DMU targets of non-radial FDH models of the interactive algorithm (models 12-14) through two target finding stages. In the first target finding stage, DMUs of feasible region are found. In this regard, DMUs that have one, two or three conditions are considered as DMUs of feasible region. The number of required conditions for this stage depends on the constraints of the model. The first condition of extended RBA is similar to RBA and can be applied for non-radial FDH models 12-14. The second and third conditions are applied for non-radial model 14. In the second step, the optimum objective function value is calculated and DMU targets are found. Extended RBA are described in the following subsections.
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 4 Compute and Simulation Results
### Spectral Efficiency Calculation
The results show that within the working SNR range of the fixed service, the shortfall from Shannon for the fixed service in the En-LDM system is lower than that of the traditional LDM system under the AWGN or Rayleigh channel. In other words, the En-LDM system has better spectrum efficiency than the traditional LDM system. Specifically, the spectral efficiency for the mobile service with En-LDM system is 0.20/0.22 bit/s/Hz higher than the traditional LDM system under the AWGN/Rayleigh channels when the SNR is 32 dB. The above conclusion can also be obtained in Figure 5. The modulation constellation used by the bits extracted by LDPC puncturing is QPSK, which accounts for 1/8 of the CL spectrum resources. Another observation can be extracted from Figures 4 and 5; increasing the occupied bandwidth ratio helps the fixed service obtain better spectrum efficiency, which is entirely due to the fact that each layer of services occupies the same time-frequency resources and the increase in the number of LDPC-based punctures can improve the performance of the fixed service. Figure 4: The shortfall from Shannon for the fixed service of the LDM and En-LDM systems under the AWGN and Rayleigh channels, where the parameters of the LDM and En-LDM systems were set to \(\Delta=10\) dB, \(B_{f}/B_{t}=1/4\), the CL constellation was set to QPSK, and the EL constellation was set to NU\(-\)64 QAM.
# Deep Eutectic Solvents as Extraction Media for
## 2 Factors Affecting the Extraction of Flavonoids Using DES Separation Techniques
### Viscosity  
The usually high viscosity of DES or NaDES is the major drawback that can restrict their use as extraction solvents, as it hampers penetration of the solvent in the extraction matrix. Increasing the temperature of the extraction process can lead to a decrease in viscosity, however, this option is not always the ideal choice, as it is energy consuming and some heat-sensitive phytochemicals may not tolerate the elevated temperature.  
A simple way to overcome this problem is the addition of a co-solvent in the extraction medium. In most cases, this co-solvent is water, which maintains the green character of the process; however, organic solvents such as methanol have also been used. In this way, the viscosity is lowered and the extraction is facilitated [35; 36]. In the recent work of [PERSON] et al., water was used as a co-solvent for the extraction of phenolic compounds from brown propolis using ChCl/tartaric acid NaDES, with methanol used as the co-solvent for the extraction of carotenoids from apricot pulp and shrimp head by-products using the same NaDES [37].  
The concentration of water in the DES-water or NaDES-water mixture affects the efficiency of the extraction, as has been shown by [PERSON] et al. [38], who showed that a mixture of the ChCl/1,4-butanediol NaDES with 35% water is the optimum medium for the extraction of myricetin and amentofluavone from _Chamaecyparis obtusa_. As another indicative example, the work of [PERSON] et al. [39] indicates that very efficient extraction of ruin from the flower buds of _Sophora japonica_ can be successful using the DES ChCl/triethyleneglycol containing 20% water. The researchers studied the viscosities of 20 DES and concluded that the viscosity increases significantly when more hydrogen bonds are possible among the DES constituents.  
The amount of added water in a DES is a factor that should be carefully monitored when DES or NaDES are applied as extraction solvents. [PERSON] et al. [40] showed that the viscosity of DES is affected by the water content, and that if more than 50% water is present the hydrogen bond framework of the DES components is destroyed.  
Another approach to overcome the problem of high viscosity is to take advantage of the enormous number of possible combinations of natural compounds that can produce NaDES, in order to design solvents of low viscosity. The latest research from [PERSON] et al. [41; 42; 43] introduced a new concept--the design of less-polar NaDES with lower viscosity, formed by mixing fatty acids of different alkyl chain lengths or by combining menthol with various organic acids.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 5 Conclusions  
The prediction of compaction parameters of soil based on machine algorithms is a promising option; however, this method requires knowledge of the characteristic parameters of the soil. In this paper, OMC and MDD prediction models of machine learning algorithms such as SVM, ANN, RF, and XGBoost are developed and compared based on collected soil sample data. Some beneficial conclusions can be drawn.  
1. When dealing with large and complex data, SVM based on Gaussian kernel function can predict OMC and MDD better than SVM based on linear kernel function.
2. In ANN modeling, the model performance varies greatly for different neurons. When the hidden layer is certain, the number of neurons has a great impact on predicting OMC and MDD.
3. Comparing the four machine learning algorithms, XGBoost is the best model for predicting OMC and MDD, while RF has good performance in predicting OMC. This shows that the ensemble algorithm has better model performance and generalization ability than the other algorithms.
4. Regarding outcomes, ANN is less successful in predicting both OMC and MDD. This suggests that ANN requires large high-quality databases paired with optimized algorithms to achieve higher model performance.
5. Finally, the feature importance output was analyzed by RF and XGBoost. The results show that PL and LL are the two features that affect the performance of the model the most, which is in line with reality.  
According to this research, it is proved that machine learning, especially ensemble learning, can be applied for the compaction parameter prediction of soil-filling materials. With this method, the amount of laboratory work can be significantly reduced and the efficiency optimization significantly improved, which is meaningful for soil resource utilization in engineering construction.  
Figure 14: The actual and predicted OMC results for the test set in XGBoost.  
Figure 15: The actual and predicted MDD results for the test set in XGBoost.  
**Author Contributions:** Conceptualization, B.L. and Y.W.; methodology, Z.Y. and K.N.; software, Z.Y. and K.N.; validation, Z.Y.; data curation, K.N.; writing--original draft preparation, B.L. and Y.W.; writing--review and editing, B.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the IOT Technology Application Transportation Industry R & D Center (Hangzhou) (No.2023-02)) and Suzhou Science and Technology Plan Project (No. 2022 SS57).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Author [PERSON] was employed by the company ZhongYifeng Construction Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest..