esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Prioritization of the Floating Photovoltaic System Potential for Reservoirs in Korea
## 2 Study Area and Data
### Solar Irradiance Map based on Shadow Analysis  
As the study area has mountainous terrain, calculating shadowing is an essential process. Whether the sky is covered by terrain or not is determined using the DEM for each position in the Sunmap grid. This sky coverage map is referred to as Viewmap and is used to calculate the shadowed irradiance or sunshine duration. Details of the process are described in [PERSON] et al. [17]. The annual sunshine duration is calculated based on Sunmap and Viewmap.  
The annual sunshine duration is calculated considering the effect of shadows, but the difference in irradiance at different times is not considered. For example, even though the sunshine duration in the morning or evening is the same as the sunshine duration at noon, the irradiance in the morning or evening is smaller. To solve this problem, this study used an hourly weight. Figure 5 shows the hourly distribution of irradiance, which is the hourly irradiance divided by the total irradiance, for each city in the TMY dataset. This shows that the hourly distribution for all cities is similar. Therefore, this study assumes that the pattern of hourly distribution of irradiance is the same for every location. Based on this assumption, the sunshine duration for each hour is calibrated as the shadowless ratio by considering the weighting according to the hourly distribution of irradiance.  
Equation (2) shows the equation for the hourly weight calculation, and Equation (3) shows the equation for irradiance considering shadows,  
\[W_{h}=\frac{S_{h}}{S_{maxh}}\times\frac{I_{th}}{\sum_{i=1}^{24}I_{ti}} \tag{2}\]  
\[I_{sp}=\left(\sum_{h=1}^{24}W_{h}\right)I_{p} \tag{3}\]  
where \(h\) is the time index \(W_{h}\) is the weight at each time \(h\), \(I_{th}\) is annual irradiance of the TMY dataset at time \(h\), \(S_{h}\) is sunshine duration at time \(h\), \(S_{maxh}\) is maximum sunshine duration at time \(h\), and \(I_{sp}\) is irradiance considering shadows of the unknown point. The ratio of sunshine duration and the hourly distribution of irradiance of the TMY dataset is calculated in Equation (2). Mean values for the entire TMY dataset are used in the calculation. The weight at each time is the multiplication of these two ratios. The summation of all hourly weights is used as the total weight, equal to \(1\) when there are no shadows and equal to \(0\) when that position is fully shadowed.  
The irradiance map assuming shadows (Figure 6c) is generated by the multiplication of the interpolated irradiance map (Figure 6a) and total weight map (Figure 6b). This map can represent both the influence of weather and terrain. The spatial resolution of the map is \(90\) m, which is equal to that of the DEM.  
Figure 5: Graph of the ratio of hourly irradiance of each city in TMY dataset.
## 3 Application of Uncertainty Quantification Methods and Results
### Application Outline
#### 3.1.3 Quantitative Precipitation Estimation  
For the QPE model, the Radar-Automatic Weather Station (AWS) rain rate (RAR) calculation system (hereafter the RAR system), which was developed by the KMA in 2006, was used in this study. The RAR system is operated on site based on the 11 single-polarization radars and 642 AWSs (321 gauges used for calibration and 321 gauges used for validation) located on the Korean Peninsula. In real time, the RAR system generates various merged weather radar precipitation fields for the Korean Peninsula through the following steps: (1) producing the radar reflectivity field of each single radar; (2) calculating AWS precipitation; (3) deriving the Z-R relationship using the window probability matching method (WPMM) [32] or Marshall-Palmer (M-P) relation [33]; (4) estimating precipitation for each single radar; and (5) merging the radar precipitation field for the entire region.  
The RAR system implements WPMM [32] using reflectivity and AWS precipitation from the previous hour to estimate radar-based precipitation amounts at each radar site. It then merges the estimated precipitation fields of the radar sites to produce composite precipitation fields for the Korean Peninsula. Quality-controlled reflectivity, with non-meteorological echoes removed, on three-by-three pixels are averaged around each specific AWS location. The WPMM reproduces the probability density functions of ground precipitations from each specific AWS and averaged radar reflectivity, and then determines parameters of the Z-R relationship using these probability density functions to generate radar-based precipitation estimates. These processes are repeated for the 11 single-polarization radars. Thus, various merged radar-precipitation fields for the entire country can be produced using radar-precipitation estimates from each radar. However, appropriate merging methods including maximum value, average value, minimum value, and distance weighting schemes must be advanced because the scan ranges of the radar sites overlap.
## 2 Related Work  
The existing research on the topology optimization of satellite-UAV-integrated networks mainly focuses on two categories. One category improves the topology structure of networks through optimization using distributed minimum spanning tree algorithms. The other category involves heuristic randomized search algorithms, such as the algorithm proposed in this work. Heuristic randomized search methods aim to randomly explore the solution space in search of the global optimal solution for the objective function.  
There are also numerous other precise or approximate algorithms within this research category. [PERSON] et al. (2013) proposed a simulated annealing algorithm that considers multiple time slices as states under multiple constraints. Additionally, greedy algorithms ([PERSON] et al., 2016), other simulated annealing algorithms ([PERSON] et al., 2016), scheduling algorithms ([PERSON] et al., 2016), load balancing algorithms ([PERSON] et al., 2016), and immune algorithms ([PERSON] et al., 2016) based on single time slices were proposed one after another.  
In simulated annealing-based solving algorithms, there are multiple options for generating neighborhood solutions, including the spanning tree random selection method, the random link switching method ([PERSON] et al., 2016), and the mixed maximum flow.  
It is also worth mentioning that on the basis of the time slice model of satellite networks, there are usually little changes in the visibility matrix between adjacent time slices. There is an adaptive method ([PERSON] et al., 2016) for the simulated annealing algorithm, which utilizes the solution in the last time slice to reduce redundant computing and iterations.  
Most of the existing research is less concerned with the initial solution of the optimization algorithm and excessively focused on the efficiency of the iterations. Sometimes, a smartly constructed initial solution will largely determine the number and efficiency of the following iterations. The algorithm proposed is not only an excellent initial solution generation method but also a recursive partition method. At the same time, this algorithm also retains and integrates some optimizations from the above-related research and further optimizes the indexes on this foundation.
## 2 Materials and Methods
### GC-MS On-Fabric Delivery Assessment  
The _On-Fabric delivery_ evaluation utilized the P&G standard method TMD01389. Precisely Miele w1714 washing machines for fabric treatment were employed. Each machine was loaded with 3 kg of fabric, consisting of \"terry towel cotton fabric tracers\" and a mixed variety of fabrics. The Terry towel cotton weighed approximately 870 g. The mixed load comprised roughly 1065 g of knitted cotton fabric and 1065 g of polyester-cotton fabrics, maintaining a 50/50 ratio between cotton and polyester. This fabric load also included twenty terry towel tracers.  
Before the washing procedure, the machine was thoroughly cleaned. Four ethanol wipes were used: one for the first half of the inox drum, another for the second half, a third for the washing machine's rubber seal, and the fourth for the detergent drawer. The washing machine door was left open for at least a minute. A cleansing cycle was then run at 95 \({}^{\circ}\)C. Before the test treatment, the fabric load underwent two pre-conditioning cycles at 95 \({}^{\circ}\)C using the short cotton cycle, each time with 79 g of unscented IEC A Base detergent from WFK Testgewebe GmbH (Bruggen, Germany). Two more 95 \({}^{\circ}\)C washes followed this without any detergent.  
For the actual test treatment, the fabric was washed on a 30 \({}^{\circ}\)C short cotton cycle at a spin speed of 1200 rpm. We used 79 g of the IEC A Base detergent, introduced at the beginning of the cycle in the designated dispenser. Additionally, 40 mL of the LFE fabric treatment composition, as detailed in Table 3, was added to the correct dispenser.  
After the washing cycle, the fabric tracers were taken out and line-dried overnight for approximately 12 h in an enclosed space. Two fabric samples, each 4 \(\times\) 4 cm in size, were cut from two of the terry towel cotton tracers and placed into 25 mL headspace vials. The headspace above these samples was then analyzed using the SPME headspace GC/MS method, as outlined in the _Encapsulation Efficiency_ Determination section. The perfume concentration in the headspace is presented in nmol/L.
## 2 Materials and Methods
### FDTD EM Simulation: \(B_{1}^{+}\) Mapping  
To verify PFT \({}^{13}\)C birdcage RF coil performance, B\({}_{1}^{+}\) fields were simulated using the \({}^{1}\)H body RF coil for transmission and reception for three cases as follows:  
1. Body coil with a uniform fat phantom;
2. Body coil with a uniform fat phantom with 16-leg \({}^{13}\)C PFT low-pass birdcage RF coil containing CPSCs;
3. Body coil with a uniform fat phantom with 16-leg \({}^{13}\)C low-pass birdcage RF coil without CPSCs.  
For the FDTD EM simulation of case 2, the CPSCs were modeled as open circuits. The input power was normalized to 1 W. B\({}_{1}^{+}\) uniformity maps were calculated for cases 1 to 3 in the ROI using percent image uniformity (PIU), which can be calculated as Equation (5) [32]. All FDTD EM simulations are performed using Sim4 Life (ZMT, Zurich MedTech AG, Zurich, Switzerland). The expression is as follows:  
\[\text{PIU }[\%]= \left(1-\frac{\text{S}_{\text{max}}-\text{S}_{\text{min}}}{\text{S}_ {\text{max}}+\text{S}_{\text{min}}}\right)\times 100 \tag{5}\]  
where the S\({}_{\text{max}}\) and S\({}_{\text{min}}\) are the maximum and minimum intensity values.  
A 16-leg high-pass birdcage RF coil was designed for \({}^{1}\)H signal transmission and reception based on the following specifications, which is identical with the body RF coil in a commercial 3.0 T MRI system: resonant frequency = 127.74 MHz; diameter = 600 mm; length and width of legs = 540 mm and 30 mm, respectively; width of end-rings = 30 mm; diameter and length of RF shield = 650 mm and 1000 mm, respectively. A 19-pF capacitor was attached to each end-ring capacitor position of the body coil. The diameter and height of the uniform fat phantom corresponded to 250 mm and 300 mm, respectively, and their electric conductivity and relative permittivity corresponded to 0.0697 S/m and 12.37, respectively. The 16-leg low-pass PFT \({}^{13}\)C birdcage RF coil was designed identical to the actual structure, whereas the design of the 16-leg low-pass \({}^{13}\)C birdcage RF coil did not include any CPSCs. The 3D B\({}_{1}^{+}\) maps calculated using Sim4 Life software were resampled to a mat file (grid resolution: 256 \(\times\) 256 \(\times\) 256) and exported to MATLAB (R2017a, The MathWorks Inc., Natick, USA) for post-processing. The B\({}_{1}^{+}\) map uniformity and field intensity were calculated by selecting a 20 cm region-of-interest (ROI) at the center of the phantom for each simulation case.
# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 3 Results and Discussion
### Exploratory Classification Analysis  
To accelerate computational process and make the results more distinct, 500 wheat seeds were randomly selected from each variety, which was repeated five times to perform PCA for exploring the distinguishability of five varieties of wheat seeds. The same outcomes occurred, as described below. In general, the contribution rate of the first three PCs were 90.092%, 9.405%, and 0.334%, respectively. That means that they can reflect spectral variation above 99%. Hence, the first three PCs were selected for PCA analysis. Figure 3a displays the three-dimensional scattering plot. No significant boundary was observed among five varieties of wheat seeds from the figure. Even some crossovers existed between varieties, such as 1 and 3, 4, and 5. This was consistent with the spectral curve analysis. Different from the research [37; 38], PCA did not give us a dramatically distinguishable basis for wheat varieties. That is to say, PCA does not work well for all grain classifications in terms of exploratory analysis. This was understandable, since PCA aims to maximize the variance of variables rather than to maximize the discriminability of different varieties of samples after projection.  
Thus, another exploratory approach, LDA, was introduced for wheat seeds' identification. Unlike PCA, the purpose of LDA is to find a projection space where the samples from different classes can be separated as far as possible. LDA was implemented in same condition as PCA. Its three major components were selected as well and its result was shown in Figure 3b. It could be clearly observed that, generally, samples from same varieties were clustered and seeds from different kinds were divided. When compared with the displayed results of PCA, LDA presented obvious boundaries among different wheat varieties. Therefore, LDA can be also served as a potential tool for exploratory  
Figure 3: Exploratory classification maps of five different varieties of wheat seeds: (**a**) Three-dimensional scattering plot performed by principal component analysis (PCA); (**b**) Three-dimensional scattering plot performed by linear discrimination analysis (LDA).; Note: 1, 2, 3, 4, 5 refer to Annong1124, Longpingmia6, Shannong102, Weilong169, Zhenmia9, respectively.  
analysis of discriminability between the wheat samples of different classes. This exploratory analysis provided us a powerful distinguishable basis quantitatively.
# Green Approaches to Carbon Nanostructure-Based Biomaterials
## 2 Green Routes to Prepare Carbon Nanomaterials for Biomedical Applications
### Carbon Nano-Onions (CNOs)  
CNOs are multi-layered fullerenes that have also attracted attention for potential biomedical uses that range from bioimaging and sensing [6; 91] to drug delivery [92], also thanks to a good biocompatibility profile, as shown in vertebrate models [93; 94]. Furthermore, for certain applications, they can surpass other carbon nanostructures, as shown for instance by their promising performance as terahertz contrast agents for breast cancer imaging [95].  
Their chemical structure similarity to fullerenes poses analogous challenges for their synthesis as described in the previous section. Nevertheless, in this case, there are efforts towards the development of green routes for their production and modification. Tomatoes have been thermally decomposed in alkaline conditions to this end, and lycopene was hypothesized as a possible carbon source [52]. Citric acid was also successfully used as a starting material in a hydrothermal route to CNOs (Figure 2) [53].  
By contrast, CNO functionalization typically requires organic solvents and harsh conditions at least during the initial steps, for instance, to oxidize defective sites into carboxylic groups for further derivatization [96]. Then, other functionalities can be appended  
Figure 2: Hydrothermal treatment of citric acid (**a**) yields comprise nano-onions (CNOs), as shown by TEM (**b**). Adapted with permission from [53], Copyright © 2020 Wiley-VCH GmbH.  
under mild conditions, for instance, as shown for chitosan and poly(vinyl alcohol) to yield composite biomaterials for tissue engineering [97], or for gelatin to yield hydrogels for drug delivery [98], or for protein fibrils to improve CNO biocompatibility [99]. Alternatively, pristine CNOs can be non-covalently functionalized with water-soluble species in aqueous environments, as shown for hyaluronic acid-phospholipid conjugates that allowed the selective targeting of cancer cells for drug delivery [100].
## 2 Materials and Methods  
### Instruments and Solutions  
2,2-Diphenyl-1-picrylhydrazyl (DPPH) and the dialysis tubes Pure-A-Lyzer (1 kDa MWCO) were purchased from Sigma-Aldrich. Sodium hydroxide and absolute ethanol were from VWR, potassium dihydrogen phosphate was from Merck, boric acid was from Probus and 10\(\times\) Dulbecco's Phosphate Buffered Saline (10\(\times\) DPBS) was from Thermo Fisher Scientific. Cell proliferation was evaluated with a Kit Cell Titer 96 Non-Radioactive Cell Proliferation Assay from Promega. Glutathione (GSH) and citric acid (CA) were obtained from Sigma-Aldrich and Acros Organics, respectively.  
Absorption measurements of the aqueous suspensions of carbon dots were taken in a Cary 60 UV-Vis spectrophotometer from Agilent Technologies (Palo Alto, CA, USA). Excitation and emission luminescence spectra were obtained with a Varian Cary Eclipse (Agilent Technologies), whereas the quantum yields were determined using an FS5 spectrofluorimeter from Edinburgh Instruments.  
FTIR spectra were taken in a Varian 670-IR (Agilent Technologies) and the size distribution histogram was established through high-resolution transmission electron microscope (HRTEM) images taken with a MET JEOL-JEM 2100 F and ImageJ free software, analyzing 100 nanoparticles per image.
## 3 Proposed Method
### Physical Distance Calculation  
To calculate the actual size, the spatial information corresponding to each detected measurement point should be provided. In order to accomplish this process, as shown in Figure 1c, we mapped a two-dimensional image and a depth map taken from the same viewpoint and angle. The depth map contained data recorded by the LiDAR sensor(ToF sensor with 10 \(\upmu\)m pitch px, Sony Group Corp.), which recorded point-by-point distance information between the real space and the device. We filed clothes images, depth maps, and point clouds simultaneously using iPad Pro(A2228, Apple Inc., assembled in China) device equipped with a camera and LiDAR sensor. The camera and LiDAR sensor simultaneously shoot at the same region and viewpoint; we could map the image and the depth map without errors.  
As a final step, the target size is calculated using the distance between two detected points and the angle between them, as illustrated in Figure 1d. Each point in the LiDAR sensor is projected at equal angular intervals. Therefore, the angle between two points is equal to the unit angle of points multiplied by the point interval between the two points. In this case, \(d_{\overline{ab}}\) in Equation (3) is the straight line distance between the two points. It is the same as the manual size measurement method with a tape measure.  
\[\begin{split} d_{\overline{ab}}&=\sqrt{(d_{b}\cdot \sin\theta_{ab})^{2}+(d_{a}-d_{b}\cdot\cos\theta_{ab})^{2}}\\ d_{\overline{ab}}&=Distance\ between\ point_{a}\ and\ point_{b}\\ d_{a}&=Distance\ of\ point_{a}\ from\ a\ LiDAR\ sensor\\ \theta_{ab}&=Angle\ between\ point_{a}\ and\ point_{b}\\ \end{split} \tag{3}\]
## 1 Introduction
However, traditional ML algorithms typically require extensive expert knowledge for manual hyperparameter tuning, and they often demand high computational resources and time, especially when dealing with large datasets or complex parameter sets. To overcome these limitations, Automated Machine Learning (AutoML) has emerged as a promising solution. AutoML frameworks automate the processes of neural architecture search (NAS), hyperparameter optimization, and model selection [12]. AutoKeras, in particular, is an open-source AutoML system built on top of Keras and TensorFlow, which enables the automatic construction and training of deep learning models with minimal human input [13]. AutoKeras has been highlighted for its advantages in small-sample modeling, efficient hyperparameter tuning, and architecture optimization, making it particularly suitable for engineering applications where traditional modeling approaches may be challenging [14]. Furthermore, AutoML's ability to handle multi-output regression allows for different loss weights and evaluation metrics to be assigned to each output node. This feature is particularly useful in inverse problems, such as mesoscopic parameter calibration, where multiple parameters need to be inverted simultaneously. In this study, AutoML was introduced into the inverse modeling of the mesoscopic parameters in PFC3D simulations. Based on 361 uniaxial compression simulations with orthogonally designed particle flow models, a multi-objective AutoML model was developed to capture the nonlinear mapping from particle-scale mesoscopic parameters to the stress-strain responses of geomaterials. The automated model search and tuning capabilities of AutoML provide an efficient, accurate, and user-independent solution for mesoscopic parameter calibration in PFC3D modeling. The proposed framework provided an intelligent and automated alternative to traditional ANN-based methods, significantly improving the efficiency and accuracy of mesoscopic parameter inversion in geotechnical simulations.
## 3 Results and Discussion  
### Effective Properties of the Membrane Layers  
The total ratio of the overall membrane porosity to the overall membrane tortuosity was found to be \(\frac{\varepsilon_{tot}}{R_{tot}}\) = 0.23, which translates to a total effective diffusivity of 2.25 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s for creatinine. The calculated value is close to the effective transport value for similar, hollow fiber membranes commonly contained in hemodialysis, such as the Synta160(r) (Baxter Healthcare, Deerfield, IL, USA) and the HF80S(r) (Fresenius Medical Care, Bad Homburg, Germany), with effective diffusivity of creatinine equal to 1.97 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s and 1.85 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s, respectively [27].  
On the grounds that HSA cannot transport through the membrane, the pore radius should be equal to or smaller than the HSA radius. In the literature, it was found that albumin effective radius is 3.4 nm [28]; however, albumin does not have a spherical shape. The minimum radius of this protein, \(R_{\text{min}}\) is calculated by [29]:  
\[R_{\text{min}}=0.066(MW)^{0.33}\,nm, \tag{32}\]  
where \(MW_{D}\) is the molecular weight in Daltons. Thus, the value of 2.6 nm is set as the characteristic capillary radius for the skin layer. The characteristic capillary radius for the second layer is set to 7.2 \(\upmu\)m, which is the average macrovoid size, sampled from the SEM image [30]. Considering the above formulation, the effective layer transport properties are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Thickness** & \(\mathbf{K}_{eff,i}\) & \(\frac{D_{eff,i}}{D_{\text{bulk},i}}\) \\ \hline Inner Layer & 21 \(\upmu\)m & \(1.6\times 10^{-19}\) m\({}^{2}\) & 0.19 \\ Outer Layer & 47 \(\upmu\)m & \(1.6\times 10^{-12}\) m\({}^{2}\) & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effective transport properties of the membrane layers.