esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The PERCs were fabricated on mono-crystalline p-type commercial-grade CZ silicon wafers with a thickness of 230 \(\pm\) 20 \(\upmu\)m and a resistivity of 0.5 to 3 \(\Omega\)-cm. The original lifetime of a bare wafer is under 5 \(\upmu\)s. Initially all wafers were cleaned using a standard Radio Corporation of America (New York City, NY, USA) cleaning process and were textured using 5% KOH solution. A rear-side polishing process with the same alkaline solution was applied to three of the samples for 10, 20, and 30 min after the first etching. The p-n junction and antireflection coatings (ARC) were made by thermal diffusion and plasma-enhanced chemical vapor deposition (PECVD). The ARC is a 75 nm-thick Si\({}_{3}\)N\({}_{4}\) film that helps trap more incident light. A 25 nm Al\({}_{2}\)O\({}_{3}\) layer and a 135 nm silicon nitride (SiN\({}_{x}\)) layer were then deposited by self-developed spatial atomic layer deposition (ALD) at room temperature and by PECVD at 120 \({}^{\circ}\)C on rear-side wafers, respectively. The post-annealing at 450 \({}^{\circ}\)C was performed on all samples. The rear openings were created by a 532 nm laser after the post-annealing of the stack Al\({}_{2}\)O\({}_{3}\)/SiN\({}_{x}\) films. Then the front Ag grid screen-printing and Al local contacts to p-type Si were formed to complete the PERCs [7]. Finally, all samples were co-fired in a belt furnace for front contacts and local Al back surface field [8].  
The surface morphologies were characterized using a field emission scanning electron microscope (FE-SEM). The orientation of the Al\({}_{2}\)O\({}_{3}\) films was confirmed by X-ray diffraction (XRD) using 45 kV-40 mA CuK\(\alpha\) radiation. The effective minority carrier lifetime and implied \(V_{\mathrm{oc}}\) were measured by the quasi-steady-state photoconductivity method (WCT-120) using lifetime samples with only Al\({}_{2}\)O\({}_{3}\)/SiN\({}_{x}\) stack film deposition on both sides (without emitter layers). The stack films were annealed at 450 \({}^{\circ}\)C for 20 min. On the solar cell samples, quantum efficiency measurements were also performed to obtain the internal quantum efficiency (IQE) and rear-side reflectance. We measured the current density-voltage (J-V) characteristics of the cells using a solar simulator under AM1.5G (100 mW/cm\({}^{2}\)) conditions.
# Effect of Segregation on Deformation Behaviour of Nanoscale CoCrCuFeNi High-Entropy Alloy
## 3 Results and Discussion  
### Shear Deformation  
The material can reveal different deformation behavior when subjected to shear deformation depending on the presence of segregations that can lead to localized stress concentrations and deformation heterogeneities. Figure 2a shows the stress-strain curves obtained for the considered bicrystals after subjected to the shear deformation at 300 K.  
Figure 1: (**a**) The computational cell used for the simulations. (**b**) The bicrystal with the GB segregation of copper (colored in purple) and a crack in the middle of the GB.  
It is revealed that the segregation of copper and chromium at the GB had a noticeable strengthening effect on the nanoscale CoCrCuFeNi HEA. The shear yield strength increased by 2 and 1.5 times, respectively, compared to the segregation-free bicrystal. The segregations of nickel, cobalt, and iron increased the strength insignificantly. The drop in the shear stress with further shear loading was associated with the plastic flow and structure relaxation through the GB migration (Figure 2b). Unlike the bicrystal without the GB segregation, for the other bicrystals, the middle GB with segregation remained stable, while the second GB, without segregation, moved towards the middle one. In Figure 2a, the sharp decrease in shear stress down to negative values at 17% shear stain and its subsequent linear growth were due to the GBs approaching each other, forming a single crystal (Figure 2b). The subsequent linear growth of the shear stress is explained by the strengthening of the single crystal as a result of elastic deformation.
## 4 Methodology
### Spectral Indices
#### 4.4.1 Normalized Difference Vegetation Index (NDVI)  
In NDVI, healthy vegetation absorbs more red light in the visible area, while unhealthy vegetation has higher reflectance than healthy vegetation. NDVI values range between \(-1\) and \(1\). NDVI is also used for drought monitoring worldwide and has fairly widespread use. NDVI is a good indicator for differentiating vegetation from artificial areas. It was used in both the five-band and seven-band datasets in the study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectral Index** & **Formula** & **Authors** \\ \hline NDVI & \(\frac{NIR_{88}-R_{84}}{NIR_{88}+R_{84}}\) & [PERSON], [PERSON], [PERSON], and [PERSON] (1974) [41] \\ \hline DVI & \(NIR_{88}-R_{84}\) & [PERSON] and [PERSON] (1992) [42] \\ \hline SR & \(\frac{NIR_{88}}{R_{84}}\) & [PERSON] and [PERSON] (1972) [43] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multispectral indices used in this study.  
Figure 9: Schematic explanation of majority voting within segmentation.
# The Effect of Processing Methods on Phytochemical Composition in Bergamot Juice
## 5 Conclusions  
BJ can be a good dietary source of nutrients like L-ascorbic acid and phytochemicals with antioxidant and health properties. However, nutritional content of bergamot juice varied as consequence of different processing techniques. Our results indicated that the juice extraction processes employed could influence the chemical composition and functional properties of BJ. The industrial processed juice, obtained by conditions (Ep-3), was markedly different from the fresh squeezed juice in the same proximate constituents as pectic substances and phytochemicals content.  
Results from this study suggest that extracting juice under harsh conditions (Ep-3) increased the amount of phytochemical content and total antioxidant activity than FMC and hand squeezing juicing process.  
Several limitations of the present study must be considered. This research looked at the effect of juice processing on the phytochemical content of the resulting juice. Further studies are necessary to understand the influence of the processing conditions (thermally or non-thermally) on increased shelf life of BJ.  
Despite these beneficial effects, the unprocessed fresh bergamot juice showed a bitter taste due to limonoids and flavanone-7-O-neohesperidosides, hence a sensory evaluation should be addressed in order to evaluate consumer acceptance of processed BJ.  
As the health benefits from the phytonutrients was better understood by addressing their bioavailability, future efforts will therefore aim to compare the different types of juices considering the bioavailability of phytonutrients in clinical trials.  
Author Contributions: Conceptualization, D.C and B.L.; formal analysis, D.C., F.M.V.; investigation, D.C. and F.M.V. writing-original draft, D.C. and B.L.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
In order to solve the problem of self-energy supply of vehicle-mounted micro-sensors, bridge detection and some other low-power electronic devices in their working state, a vortex-induced flutter composite nonlinear piezoelectric energy harvester (VFPEH) with symmetrical airfoils on both sides of a cylindrical bluff body is designed. The VFPEH consists of a cantilever beam, a cylindrical bluff body connected to the free end of the cantilever beam, and two airfoil components symmetrically fixed at both ends of the shaft, which enables coupling between vortex-induced vibration and flutter. The airfoil symmetrically arranged on both sides of the cylindrical bluff body induces the cantilever beam to produce bending and torsional composite vibrations at high wind velocities, realizing energy harvest in the two degrees of freedom motion direction, which can effectively improve the output power of the energy harvester. Based on a wind tunnel experimental platform, the effect of key parameters matching impedance and the diameter of the cylindrical bluff body on the output performance of the VFPEH is investigated, together with the output performance of the classical vortex-induced energy harvester (VEH), the flutter energy harvester (FEH) and the VFPEH. The experimental results show that for the VFPEH under a combination of vortex-induced vibrations and flutter vibrations has a better output performance than the VEH and the FEH when using the same size. The coupling of vortex-induced vibration and flutter can reduce the start-up wind velocity of the VFPEH and expand the wind velocity range of the high output power of the VFPEH. The VFPEH has a better output performance at the cylindrical bluff body diameter of 30 mm and a load resistance of 10 k\(\Omega\). When the wind velocity range is 2 m/s-15 m/s, the maximum output power of the VFPEH is 6.47 mW, which is 129.4 times and 24.9 times of the maximum output power of the VEH (0.05 mW) and FEH (0.26 mW), respectively.  
wind-induced vibration; vortex-induced vibration; flutter; piezoelectric energy harvester; composite energy harvester +
Footnote †: journal: Applied Sciences
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
###### Abstract  
The growing density of space objects in low Earth orbit (LEO), driven by the deployment of large satellite constellations, has elevated the risk of orbital collisions and the need for high-precision conjunction analysis. Traditional methods based on Two-Line Element (TLE) data suffer from limited accuracy and insufficient uncertainty modeling. This study proposes a hybrid collision assessment framework that combines Monte Carlo simulation, spline-based refinement of the time of closest approach (TCA), and a multi-stage deterministic refinement process. The methodology begins with probabilistic sampling of TLE uncertainties, followed by a coarse search for TCA using the SGP4 propagator. A cubic spline interpolation then enhances temporal resolution, and a hierarchical multi-stage refinement computes the final TCA and minimum distance with sub-second and sub-kilometer accuracy. The framework was validated using real-world TLE data from over 2600 debris objects and active satellites. Results demonstrated a reduction in average TCA error to 0.081 s and distance estimation error to 0.688 km. The approach is computationally efficient, with average processing times below one minute per conjunction event using standard hardware. Its compatibility with operational space situational awareness (SSA) systems and scalability for high-volume screening make it suitable for integration into real-time space traffic management workflows.  
collision avoidance; space debris; orbital safety; time of closest approach; region of interest; Monte Carlo
## 1 Introduction
In view of the low efficiency of single UGV task execution and blind exploration of UGVS random selection of actions under Q-learning algorithm in complex environments, resulting in low learning efficiency, slow convergence speed in complex environments, this paper proposes an improved Q-learning algorithm to solve the path planning problem of multi-UGV based on the idea of Q-learning algorithm and distributed collaboration strategy between multiple agents. The algorithm expects to obtain better path planning results than the traditional Q-learning algorithm, which can enable multiple UGVs to find their respective paths in the same iteration, reduce the time of path planning, and obtain a shorter final path. The specific improvement of the Q-learning algorithm in this paper is to change the update method of the Q function, use a more aggressive learning rate to accelerate the convergence speed of the algorithm, and select actions more effectively by improving the \(\varepsilon-greedy\) strategy. The main contributions of this paper are as follows:  
1. Aiming at the problem of path planning of multiple UGVs in the complex environment of smart parks, a grid environment model is established, and an anti-collision coordination mechanism between multiple UGVs is proposed. 2. Aiming at the problem that the Q-learning algorithm converges slowly in the complex environment of multi-agent systems, the Q estimate of the next state and the Q value of the current state is weighted to change the update mode of the Q function, so as to overcome the problem of slow convergence speed of the Q-learning algorithm. By using a more aggressive learning rate and a \(\varepsilon-greedy\) strategy with improved action selection, the relationship between exploration and utilization of the Q-learning algorithm is balanced, the convergence speed of the algorithm is accelerated, and an improved reinforcement learning algorithm optimized-weighted-speedy Q-learning (OWS Q-learning) algorithm is proposed. 3. The proposed algorithm is compared with the SARSA algorithm, Q-learning algorithm, and speedy Q-learning algorithm, the comprehensive performances of stability and robustness of the proposed algorithm are explained from the evaluation indexes such as shortest path steps, path length, calculation time, and reward function convergence. The rest of this article is organized as follows. In Section 2, the literature related to multi-agent autonomous path planning is briefly summarized. In Section 3, a simulation environment model is established, and the collision avoidance cooperation mechanism in multi-UGV path planning and evaluation indexes of the algorithm are introduced. InSection 4, the theory of reinforcement learning and the proposed improved algorithm are introduced. In Section 5, the experimental scenario and experimental parameters are set, the simulation experiments are carried out, and the experimental results are compared and analyzed. In Section 6, the full text is summarized and an outlook for future work is provided.
## 2 Related Research  
A number of papers have addressed the problem of comparatively evaluating image descriptors for colour texture analysis (e.g., [10; 11; 12; 13; 14]), though none of these considers CNN-based features. Yet, results from recent studies seem to suggest that CNN-based descriptors can be effective for texture classification as well, in most cases outperforming hand-designed descriptors.  
[PERSON] et al. [15; 16] compared a number of hand-designed image descriptors including LMfilters, MR8 filters, LBP and SIFT against a set of CNN-based features in texture and material recognition and concluded that in most cases, the latter group outperformed the former. Notably, their findings are mainly based on the results obtained on the Describable Texture Dataset (DTD; more on this in Section 3), which to a great extent, is composed of very irregular and non-stationary textures acquired \"in the wild\"; by contrast, their results look rather saturated and levelled in other datasets (i.e., Columbia-Utrecht Reflectance and Texture Database (CUReT), UMDand UIUC). [PERSON] et al. [17] investigated colour texture classification under variable illumination conditions. Their study included a large number of hand-designed texture descriptors (e.g., Gabor filters, wavelet and LBP), descriptors for object recognition (dense SIFT) and CNN-based features. Experimenting on a new dataset of colour texture images (RawFooT, also included in the present study), they concluded that features based on CNN gave significantly better results than the other methods. [PERSON] et al. [1] evaluated a large selection of LBP variants and CNN-based features for texture classification tasks. In their experiments, CNN-based features outperformed LBP variants in six datasets out of eleven, but in this case, all the LBP variants considered were grey-scale descriptors, whereas CNN by default operates on colour images. The presence/absence of colour information may account--at least in part--for the different performance. Recently, [PERSON] [18] comparatively evaluated a number of hand-crafted descriptors and CNN-based features over five datasets of colour images and found that, on average, the latter group outperformed the former.  
In summary, there is mounting evidence that off-the-shelf CNN-based features can be suitable for texture classification tasks and may in certain cases outperform (therefore, potentially replace) traditional, hand-designed descriptors. Interestingly, both [17; 1] seem to suggest that CNN-based methods tend to perform better than hand-designed descriptors in the presence of complex textures and intra-class variations, though none of the references investigated this point further. There remains a need to clarify under what circumstances CNN-based features can replace traditional, hand-crafted descriptors and what are the pros and cons of the two strategies.
## 5 Classification of EV Charging Infrastructures
### Microgrid-Based EV Charging Infrastructure  
The infrastructure for charging EVs on microgrids creates a link between EVs, renewable energy sources, and the main power grid. The system is an intelligent and decentralized grid. The infrastructure is specifically engineered to charge EVs rapidly, economically, and efficiently by utilizing renewable energy sources like solar and wind power. There are numerous advantages associated with this technology, including but not limited to the mitigation of carbon emissions, the reduction of electricity expenses, the provision of a dependable power supply in the event of a large-scale grid failure, and the seamless integration of EVs into the energy infrastructure. The infrastructure not only aims to optimize the utilization of renewable energy sources but also serves to safeguard against fluctuations in energy demand and mitigate the risk of power grid overload by effectively managing electricity consumption [81; 82]. This solution offers a secure method for charging EVs in regions with limited access to public charging infrastructure.  
The main components of a microgrid-based EV charging infrastructure are as follows:  
* Microgrid Controller: The microgrid controller assumes a critical function in the regulation of power distribution between the microgrid and the electric vehicle (EV) charging station. The system incorporates algorithms that are responsible for regulating voltage and current, while also optimizing power flow.
* EV Chargers: The microgrid-connected charging stations are designed to facilitate the charging of EVs. The package contains essential electrical components, including cables, connectors, and power converters.  
Figure 18: (**a**) DC- and (**b**) AC-microgrid-based EV charging infrastructure.  
* Battery Storage: The battery storage device serves the purpose of temporarily storing the energy produced by the microgrid. The necessary components such as batteries and controllers are provided.
* Renewable Energy: Several renewable power sources have the capability to provide electricity to the microgrid.
* Power Management System: By leveraging this advanced technology, the microgrid has the capability to autonomously regulate its power consumption. The integrated algorithms enable the management of power flow to both EV chargers and the battery storage system.
* Communication System: The communication system of the microgrid facilitates connectivity with various devices, including EV charging stations and energy storage units. The inclusion of protocols for the transmission and management of data is a fundamental aspect.  
There are two types of microgrid-based EV charging systems: AC microgrid and DC microgrid. Its future classification is based on grid availability. Figure 19 shows the division of microgrids into various categories.
## 3 Results and Discussion
### OPLS-DA Analysis  
To better analyze the differences in the effects of tannase, tyrosinase, and laccase on Rock Tea extract, the OPLS-DA method was utilized in this study. Based on the contents of GA, EGCG, ECG, EGC, C, theaflavins, and thearubigins in the extract, the difference in the contribution rate of the three enzyme treatments to the extract components was analyzed. To verify the accuracy of the established OPLS-DA model, 200 permutation tests were performed for internal validation (Figure 11a). The permutation test results showed that R\({}^{2}\) = (0.0, 0.173) and Q\({}^{2}\) = (0.0, \(-\)1), where R\({}^{2}\) < 0.4 and Q\({}^{2}\) < 0.05. The slope was positive, and the regression line of Q\({}^{2}\) intersected the vertical axis below the origin, indicating no overfitting of the model, making the results reliable [42]. The model scores chart, shown in Figure 11b, had values of R\({}^{2}\)x = 0.999, R\({}^{2}\)y = 0.989, and Q\({}^{2}\) = 0.977, indicating a good fit to the model [1]. In the evaluation group, significant separation was observed between the different groups, indicating significant changes between the enzyme-treated samples and the control group. The changes caused by tyrosinase and laccase were similar; however, a considerable difference between tannase and the other two enzymes was observed. In the OPLS-DA loading diagram, tannase was the furthest away from and in the negative direction of EGCG and ECG, indicating that tannase effectively reduced the levels of these two gallated catechins. Moreover, the theaflavin and thearubigin contents were more closely related to those of tyrosinase and laccase (Figure 11c), likely because these two polyphenol oxidases significantly influence the formation of theaflavins and thearubigins.  
Figure 10: Content of theaflavins and thearubigins in the tea extract. The error bar represents the standard error of the mean, and by the LSD test, the average of different lowercase letters indicates a significant difference (\(p\) < 0.05).