esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 2 Materials and Methods  
The flowchart of the proposed method is shown in Figure 1. There are three main sub-processes: (a) superpixel segmentation and feature extraction, (b) selection of training samples, and (c) graph convolution networks construction, training, and prediction of unknown nodes.  
First, the PolSAR image was over-segmented using the method of SLIC to obtain the superpixels as the classification unit. The features of each superpixel, involving scattering features, texture features, and statistics features, were calculated. Their importance in our task was also discussed.  
Second, for unbiased selection of training samples (particularly to avoid the misclassification of the land with high water content), the t-SNE [32], a popular method for dimension reduction and visualization, was applied to roughly judge the numbers of the initial clustering center of superpixels. Then an unsupervised clustering algorithm was used for generating the coarse classification results to assist selecting samples by hand. Note that the processes of t-SNE and coarse classification are not indispensable in our methods and are just used for the extremely complex scenario where manual annotation is difficult.  
Third, an undirected graph was constructed according to the relationships between adjacency superpixels and the features of each superpixel. After converting the raster image into a graph, the GCN was trained with the training samples, used for prediction of the whole image, and evaluated based on the ground truth labels.
## 2 Materials and Methods
### Specim Preparation
Subsequently, the design files were sent to the respective open-source 3D printer, adhering to the printing guidelines published by each resin manufacturer. The printing process involved printing all layers with a thickness of 50 \(\upmu\)m, with each resin type placed in a separate resin tank within the 3D printer. After completion of the printing process, a five-minute cleaning procedure was carried out using a washing machine (Phrozen) and a 99.5% isopropyl alcohol solution to remove any unpolymerized residual resins. Following drying, the specimens were exposed to a UV light-curing unit (Phrozen) for 20 min to promote additional polymerization. The bonding surface of the specimens was standardized by polishing all milled and 3D-printed specimens using 1000- to 4000-grit silicon carbide papers (Metaserv 250 Grinder Polisher; Buehler, Waukegan Road Lake Bluff, IL, USA) at 350 rpm under running water. The final dimensions of the specimens were confirmed to be 10 \(\times\) 10 \(\times\) 2 mm\({}^{3}\) using a digital micrometer (Digital micrometer IP65; Mitutoyo MC, Tokyo, Japan) with a tolerance of \(\pm\)0.05 mm. Subsequently, all specimens were subjected to thermocycling (THE-1100\({}^{\mathrm{m}}\); SD Mechatronik, Feldkirchen-Westerham, Germany) in distilled water between 5 \({}^{\circ}\)C and 55 \({}^{\circ}\)C for 5000 cycles, with a dwell time of 30 s and a transfer time of 10 s, simulating a clinical service period of 6 months. Following thermocycling, the specimens were embedded in self-cured acrylic resin (Meliodent; Heraeus Kulzer GmbH, Hanau, Germany), leaving the polished surfaces exposed for surface treatments and bonding with metal orthodontic brackets.
## 4 Discussion
### Conventional Pyrolysis vs. Thermochemolysis  
When compared conventional pyrolysis (Figures 1-3 and Tables 1-3) and thermochemolysis (Figure 4 and Table 4), it becomes evident that a part of methylated compounds present in the thermochemolysis corresponded with their counterpart in conventional pyrolysis, namely fatty acids. The second group represented new compounds that were not detected without TMAH (e.g., aliphatic dicarboxylic acids) and a third group comprised compounds that were present in both thermal treatments (e.g., alkane and alkene doublets, alkylbenzenes, alkylthiophenes, etc.). The pyrolysis products comprised in the first and third groups have been widely discussed.  
In the second group are included short chain aliphatic dicarboxylic acid dimethyl esters. Interestingly, butenedioic and butanedioic dimethyl esters were the main thermochemolysis products from polymaleic acid, a polycarboxylic aliphatic polymer prepared by a pyridine-catalyzed homopolymerization of maleic anhydride [28; 32]. The polymer contains units of four carbon atoms in a chain highly substituted with carboxylic groups [90]. Conventional pyrolysis (500 \({}^{\circ}\)C) of polymaleic acid yielded different turandones, maleic anhydride, phthalic anhydride, and carboxybenzeneacetic acid indicating that pyrolysis of aliphatic polycarboxylic acids produced cyclization of the aliphatic polymer [32], which is avoided by the methylation. The diacid dimethyl esters probably reflect alkyl chains bonded to a macromolecular network by one or two ester linkages.  
Thermochemolysis of the melanins was characterized by the abundant presence of \(N\),\(N\)-dimethylbenzemamine and other benzenamine derivatives. [PERSON] et al. [91] found that some of the main products from thermochemolysis of 2-aminobenzic acid were benzenamine, \(N\)-methylbenzemamine, and \(N_{i}N\)-dimethylbenzemamine, denoting that de-carboxylation occurred in addition to methylation of the amino group. This fact suggests that the abundant benzenamine derivatives found in the thermochemolyate of the acid hydrolyzed melanin could derive from aminobenzic units. However, this assertion can be disregarded because in the thermochemolysis of the aliphatic polyesters cutin and suberin we identified up to C\({}_{30}\) fatty acids and aliphatic hydrocarbons, in addition to benzenamine derivatives (data not shown), in spite that no nitrogen compounds are present in these polyesters. These data indicate that benzenamine derivatives in the melanins are artifacts produced by rearrangement of the TMAH reagent.  
Surprisingly, most of the thermochemolysis papers neglected the artifacts introduced with the TMAH and only a few references can be found [33; 92]. Many artifacts contain up to four N atoms [93], and [PERSON] et al. [42] included triazines, pyrimidinediones, pyrolidinediones, and caffeine among the artifacts produced in pyrolysis from dissolved organic matter in freshwaters.
## 3 Safety Analysis and Environmental Modeling  
### Research Area  
The region defined in this article spans a latitude range of 67\({}^{\circ}\)\(\sim\)76\({}^{\circ}\) N and a longitude range of 88\({}^{\circ}\)\(\sim\)105\({}^{\circ}\) W. The starting point for the path planning is the Port of Houston (29\({}^{\circ}\)45\({}^{\prime}\)46\({}^{\prime\prime}\) N, 95\({}^{\circ}\)22\({}^{\prime}\)59\({}^{\prime\prime}\) W) and the endpoint is the Port of Cartagena (37\({}^{\circ}\)36\({}^{\prime}\)46\({}^{\prime\prime}\) N, 0\({}^{\circ}\)59\({}^{\prime}\) W), as shown in Figure 7.  
Sailing in densely islanded waters poses numerous challenges for USVs, primarily due to the widespread presence of shallow areas, the complex terrain, and the unpredictability of some of the obstacles. Firstly, islands are often surrounded by rocks or coral reefs that cause shallow depths. Combined with the effects of tides and currents, sedimentation can further reduce water depths. Such terrain conditions necessitate vessels to reduce speed or adjust course during navigation to avoid running aground. Additionally, the presence of islands and obstacles increases collision risks. It can influence surrounding sea currents, leading to the formation of local turbulence, increased flow velocity, or sudden changes in flow direction. These changes may generate underwater eddies and backflows, thereby increasing the complexity and risks of navigation. Therefore, when the USV navigates densely islanded waters, it is crucial to consider the specificity of the water area and poten  
Figure 6: GeoNetworking-based danger zone mapping for USVs.  
tial risks. This involves implementing appropriate navigation measures, such as setting minimum safe depths and increasing buffer zones around obstacles, before proceeding with path planning to complete missions successfully. Figure 8 depicts the USV information, while Table 1 details the ship parameters.
# Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather
## 2 Research Methodology
The calculation is as follows:  
\[S_{pa-n}=\mu\pm g\cdot\sigma,g=1,2,\ldots \tag{7}\]  
Select the corresponding point according to the average distance \(D_{avr-n}\) and the standard range \(S_{pa-n}\). That is, when the average distance \(q_{n}\) corresponding to the point \(D_{avr-n}\) is greater than the standard range \(S_{pa-n}\), the point is deleted; otherwise, the point is retained [24]. Radius filtering is used to further eliminate noise. Radius filtering refers to the point cloud model stipulating that each data point must have at least a certain number of nearest neighbors within a specified range. Assuming that in the point cloud model, data point \(q_{n}\) is the center, then the number of nearest neighbors within a sphere whose radius is \(r\) is m, and the minimum number of nearest neighbors is set to \(M\). According to m and \(M\), judge whether to keep the corresponding point \(q_{n}\). When \(m\) is greater than \(M\), keep the point; otherwise, remove the point [21]. The description is shown in Figure 1. When the radius (indicated by the green circle) specifies that there are at least three neighbors, only point b will be deleted. If it is specified that there must be at least five neighbors, both point a and point b will be deleted.
## 3 Results  
### Setup Specifications  
#### 3.1.1 Minimal Focus Diameter  
We measured the minimum achievable focus of our system using a single Fresnel zone plate of focal length \(f_{min}\), which calculates to 229 mm according to Equation (4). To achieve focus diameters of less then 10 \(\upmu\)m, we set the magnification \(m\) to \(1/6\). The resulting focus was observed by a camera through the microscope using a 20\(\times\) objective. The effective pixel pitch of the camera was calibrated using the MEA structure. A 2D Gaussian function was fitted to the recorded focus. The \(x\) and \(y\) cross-sections of the fit resulted in a mean focus diameter of about 8 \(\upmu\)m. Both of the components are displayed in Figure 5. Sidelobes of the focus are clearly visible and they indicate spherical aberrations that may be corrected in future iterations of out setup, e.g., by using phase conjugation. Over a field of view of (1.5 \(\times\) 1.5) mm\({}^{2}\), the spot diameter varies by a standard deviation of 5%.  
Figure 4: Fluorescence image of the electrode area of a multielectrode array with an adherent random network of human stem-cell-derived neurons. The inset in the top right-hand corner exemplarily shows a magnified electrode as well as the maximum and typical signal recording distance of a single electrode as indicated by the concentric white circles. A single electrode can therefore record extracellular electrical signals from more than one neuron. The bright area at the top of the picture is a folding region of detached neurons. Scale bar, 400 \(\upmu\)m.
## 1 Introduction  
At a fundamental level, [PERSON]'s properties of confusion and diffusion [1] often made for bench marking the security of a symmetric encryption. Confusion is defined as the complexity of the relationship between secret-key and cipher-text while diffusion is defined as the degree of influence of single input bit changed in the resulting ciphertext. To realize the confusion and diffusion of a symmetric encryption, several layers of substitution and permutation operation, called Substitution-Permutation Networks (SPNs), are applied. The substitution layer is a nonlinear operation used to improve the overall confusion of the encryption. On the contrary, the permutation layer is a linear operation that increases the measure of diffusion.  
In [PERSON] encryption, the substitution layer is typically referred to as Substitution box (S-box). An S-box plays a crucial role against various cryptanalysis. Therefore, an S-box needs to be cryptographically strong by at least having high nonlinearity, low differential uniformity, a high algebraic degree and complex algebraic expression to resist cryptanalysis, such as differential attack first introduced by [PERSON] and [PERSON] [2], linear attack described by [PERSON] [3], higher order differential attacks that were introduced by [PERSON] [4] and [PERSON] [5], interpolation attacks introduced by [PERSON] and [PERSON] [6]and, lastly, algebraic attack introduced by [PERSON] and [PERSON] [7]. As a result, research to improve the cryptographic properties of an S-box and its implementation efficiency can be found in the literature. To date, most published constructions of an S-box are based on the bijective functions of elements of four or eight bits.
# Reactive Polymer Composite Microparticles Based on Glycidyl Metacrylate and Magnetic Nanoparticles
## 1 Introduction
Precipitation, emulsion, and suspension polymerization methods are the most explored [39]. One of the main criteria for selecting the proper method is the size of the particles provided. Depending on the method used, the diameters of the polymer particles provided range from \(\sim\)50 nm to 2 mm. To encapsulate magnetic particles within polymeric materials, emulsion [40; 41], mini-emulsion [42; 43; 44], dispersion [45; 46], suspension [47; 48; 49], micro-suspension [50], precipitation [51], and in situ polymerization [52] and in situ coprecipitation [53] were previously used. Suspension polymerization and its modifications are among the most frequently used methods in the preparation of polymer magnetic microspheres. For example, [PERSON] et al. [48] used modified suspension polymerization to obtain porous magnetic microspheres from methyl methacrylate (MMA), divinylbenzene (DVB), and iron oxide coated with oleic acid (OA). Based on TEM and SEM analyses, most of the Fe\({}_{3}\)O\({}_{4}\)-OA particles were found to be within the copolymer network, and the composite particles obtained had a spherical shape and porous structure. [PERSON] et al. also used the modified suspension copolymerization method to produce magnetic polymer microspheres [54]. Styrene, divinylbenzene, and glycidyl methacrylate (GMA) were used in this case as comonomers with the magnetic fluid prepared by the precipitation method. The composite microspheres provided in this case showed a much smaller size (4-7 \(\upmu\)m) compared to traditional suspension polymerization methods and a narrower size distribution. The glycidyl groups introduced into the composite materials with GMA molecules allowed them to be used to bind proteins or enzymes. Since suspension polymerization, as a method of preparing epoxy functionalized terpolymers from mixtures of styrene, divinylbenzene, and glycidyl methacrylate, was also previously used in our laboratory to develop easily modifiable gel-type polymer microparticles [55; 56; 57], an idea to use magnetite nanoparticles to modify the morphology of the gel-type polymer beads was presented. It was expected that the addition of magnetite nanoparticles in the form of dispersion in toluene to the monomer composition diluted with an organic solvent will make it possible to obtain new modifiable composite materialsfor catalytic or sorption purposes. Unexpectedly, microparticles with unique multi-hollow or raspberry-like morphologies, characterized by very slight magnetic properties, were obtained when the polymerization conditions were optimized.
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 2 Related Work
### Graph-Based Approaches  
[PERSON] et al. [3] proposed a graph-based quality measure using spectral clustering. First, they compute a probabilistic divergence-based \(K\times K\) class similarity matrix \(S\), where \(K\) is the number of classes. Then, an adjacency matrix \(W\) is computed from the \(S\) matrix. The quality measure is defined as a cumulative sum of the eigenvalues gap which is called as cumulative spectral gradient (\(CSG\)), which represents the minimum cutting cost of the \(S\). The authors also used a convolutional neural network-based auto-encoder and t-SNE [12; 13] to find an embedding that can represent data points (images in their case) well. Although the method is designed for high-dimensional data, it requires to train a good embedding network to reach quality performance.  
[PERSON] and [PERSON] [14] proposed a quality measure based on a dissimilarity matrix of data points. Since calculating the dissimilarity matrix is a time-consuming process, the method is not adequate for large-scale high-dimensional data.
## 2 Materials and Methods  
### Plant Materials and Growing Conditions  
Twenty-six accessions of local Thai holy basil (O. _tenuiform_ L.) were utilised in this study, comprising both standard commercially available green (G) seeds from BENJAMITR ENTERPRISE (1991) Co., Ltd., Nonthaburi, Thailand and red (R) seeds from Chia Tai Co. Ltd., Bangkok, Thailand. These local Thai accessions were generously provided by the Tropical Vegetable Research Center (TVRC) at Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, Thailand. Seed germination followed the method described by [PERSON] et al. [38] with minor modifications. Seeds were sown on germination sponges (ESPEC Corp., Osaka, Japan) under green LED lighting, providing a photosynthetic photon flux density (PPFD) of 100 umol m\({}^{-2}\) s\({}^{-1}\), with a photoperiod of 16 h per day. After 14 d, seedlings with fully developed leaves were moved to a hydroponic system within a controlled environment at a plant factory utilizing artificial lighting (PFAL) for 16 d, as outlined by [PERSON] et al. [39], before being transplanted into a greenhouse.  
One-month-old plants, with seedlings having 2-3 true leaves and a shoot height of 6-10 cm, were transplanted into plastic pots (20 cm diameter) filled with commercial peat -mass substrate (Hortimed SIA, Riga, Latvia). Initially, each pot received 3 g of a 16-16-16 inorganic fertilizer (N P K; nitrogen, phosphorus from P\({}_{2}\)O\({}_{5}\), potassium from K\({}_{2}\)O). Soil moisture was maintained daily at 75-80% to support optimal plant growth. The plants were grown in a greenhouse at the Plant Phenomics Center, National Center for Genetic Engineering and Biotechnology (BIOTEC), under the National Science and Technology Development Agency (NSTDA) in Pathum Thani, Thailand. The greenhouse conditions included a 12-h photoperiod with 250 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) of light, temperatures of 28-32 \({}^{\circ}\)C, relative humidity of 75-90%, and CO\({}_{2}\) levels of 400-800 \(\upmu\)mol mol\({}^{-1}\) under natural sunlight.  
Holy basil plants were harvested three times at full bloom, with three harvests occurring: the first harvest occurred 42 d after transplanting, the second at 63 d, and the third at 84 d. Hyperspectral images of the plants were captured using a hyperspectral camera (Photon Systems Instruments, spol.s. r.o.; Drasov, Czech Republic) and analysed with PlantScreen(tm) data analyser software (version 3.1.7.21). After measuring the hyperspectral imaging, canopy leaves were harvested for analysis of flavonoid quantification.
# A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
## 5 Scenario Decomposition Methods
### Dual Decomposition (DD) Method  
The dual decomposition algorithm proposed by [PERSON] and [PERSON] [17] applies the Lagrangian relaxation to problem (2) and uses a _branch-and-bound_ procedure to restore the non-anticipativity conditions. The Lagrangian relaxation of the NACs results in the following dual function:  
\[D(\lambda)=\min_{x,y}\ \ \sum_{\omega\in\Omega}L_{\omega}(x_{\omega},y_{ \omega},\lambda_{\omega}) \tag{15a}\] \[\text{s.t.}\quad(x_{\omega},y_{\omega})\in G_{\omega}\quad\forall\omega\in\Omega \tag{15b}\]  
where  
\[L_{\omega}(x_{\omega},y_{\omega},\lambda_{\omega})=\tau_{\omega}(c^{T}x_{ \omega}+d_{\omega}^{T}y_{\omega})+\lambda_{\omega}^{T}H_{\omega}x_{\omega} \tag{16}\]  
In Equation (15a), vector \(\lambda\in\mathbb{R}^{n_{1}\times(|\Omega|-1)}\) represents the dual multipliers associated with the NACs (2b). \(\lambda_{\omega}\in\mathbb{R}^{n_{1}}\) represents the Lagrangian multipliers for the NACs associated with scenario \(\omega\), as given by Equation (3). Given the independence of the variables and constraints in each scenario, function \(D\) can be split into separate subproblems \(D_{\omega}(\lambda_{\omega})\):  
\[D(\lambda)=\sum_{\omega\in\Omega}D_{\omega}(\lambda_{\omega}) \tag{17a}\]\[D_{\omega}(\lambda_{\omega})=\{\underset{x_{\omega},y_{\omega}}{\text{min}}\;\;L_{ \omega}(x_{\omega},y_{\omega},\lambda_{\omega})\;\;\;\;\text{s.t.}\;\;\;(x_{ \omega},y_{\omega})\in G_{\omega}\} \tag{17b}\]  
According to the weak duality theorem, the relaxation (17) is always less than or equal to the optimal objective value of problem (2). The best lower bound of (PNAC) is computed by solving the following maximization problem, referred to as the Lagrangian dual problem:  
\[Z_{LD}=\underset{\lambda}{\text{max}}\,D(\lambda) \tag{18}\]  
The Lagrangian dual is a concave non-smooth program and can be solved by subgradient methods, cutting-plane methods, or column-generation methods. The details of these methods can be found in Guignard [47]. We illustrate the dual search approaches by describing the standard cutting-plane algorithm.
## 5 Application on the Lorenz-96 Model
In Figure 7, the first four statistical moments of \(\frac{dI}{dF}\) are computed and compared with the results from Monte-Carlo. The gPC is found to be in good agreement with the results from the Monte-Carlo simulation. Note that again the skewness and kurtosis are predicted to be close to the values of the Gaussian distribution. Figure 5: Comparison of sensitivities as computed by MSS and FD. The MSS was conducted for a segment of size \(\Delta T=0.7\), \(K=35\) total segments and \(N=40\). Figure 6: Convergence of the Multiple-Shooting Shadowing (MSS) algorithm for the Lorenz-96 system. The MSS was conducted on \(K=35\) time segments on a trajectory of total length \(T=50\), for \(N=40\) and \(F=8\). The PDF computed by the gPC coupled with the MSS can be compared with the one produced by the Monte-Carlo where the sensitivities are evaluated using FD. The PDF predicted by the gPC is computed as in Reference [14]. This is seen in Figure 8, where the PDF from the Monte-Carlo has been approximated by connecting the midpoints of the bins of its histogram. Notice that the two PDFs are in good agreement. \[J(F)=\frac{1}{NT}\sum_{i=1}^{N}\int_{0}^{T}X_{i}dt. \tag{32}\]0 Probability Density Function (PDF) comparison between Monte–Carlo ( \(N=10000\) samples) and gPC (\(c=4\)). Left: PDF of (32). Right: PDF of the sensitivity of Equation (32) w.r.t. \(F\). The PDF from the Monte–Carlo is approximated by evaluating the histogram and connecting the bin midpoints. Here, \(T=100\) and \(N=40\). \[J(F)=\frac{1}{NT}\sum_{i=1}^{N}\int_{0}^{T}X_{i}dt. \tag{32}\]1 UQ in the sensitivity of the QoI given by Equation (32) with respect to \(F\). Comparison of statistical moments, as computed by Monte–Carlo coupled with Finite-Differences and gPC (\(c=4\)) coupled with the MSS algorithm. Uncertainty is introduced through \(F=10+0.3\xi\) where \(\xi\sim\mathcal{N}(0,1)\). Here, \(N=40\) and \(T=100\).
# Laser Spot Centering Algorithm of Double-Area Shrinking Iteration Based on Baseline Method
## 3 Realization Principle of Proposed Approach  
An optical system that consists of a laser, laser attenuator, lens, and CCD camera was built to collect the laser spot image (Figure 1). The realization of spot center detection is shown in Figure 2. To eliminate the influence of noise on the laser spot, the method is divided into two steps. The first step is to calculate the background noise baseline using multi-frame background images acquired by the CCD, and the other locates the center of the laser spot after removing noise.