esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 2 Materials and Methods
### Data and Pre-Processing
#### 2.2.2 Spectral Data Acquisition and Pre-Processing  
The spectral data of the soil samples were measured using an AvaField portable spectrometer (Avantes, Inc., Apeldoorn, The Netherlands) with a band range of 340-2511 nm and a sampling interval of 0.6 nm. The soil samples were put in a dark room, and a 50 W halogen lamp with a 10\({}^{\circ}\) field of view was employed to simulate sunshine. The optical probe was located about 0.15 m above the soil sample [19]. A Savitzky-Golay filter (window size of 10) was used to smooth the spectral data to minimize noise [20; 21]. Differential transformation is a common analysis method for processing soil spectral data. It can smooth the signal intensity, reduce the effects of background noise, and improve spectral sensitivity [20; 22]. In this study, the spectral data of the dry soil were processed using the first derivative (FD) and second derivative (SD) (Figure 3).  
The HJ-1A HSI data were used to apply the CLQ estimation model to the regional scale in the Conghua district. The image was obtained on 30 October 2017 with a 100 m spatial resolution, and it contained 115 bands (459-956 nm). In this study, HJ-1A HSI was pre-processed (radiometric correction, atmospheric correction, geometric correction, and stripe noise reduction) in ENVI 5.3 software (Exelis Visual Information Solution, Inc., Boulder, CO, USA).  
Since mixed pixels existed in HJ-1A HSI data with 100 m spatial resolution, fully constrained least squares (FCLS) linear spectral mixture analysis was used to obtain pure pixels (Figure 4). The equations of this method are defined as [23]:  
\[f_{s}=\frac{\rho_{m}-\rho_{v}-\sigma}{\rho_{s}-\rho_{v}} \tag{1}\]  
where \(f_{s}\) is the abundances of the soil endmembers, \(\rho_{v}\) is the pure vegetation pixels reflectance values, \(\rho_{s}\) is the pure soil pixels reflectance values, \(\rho_{m}\) is the mixed pixels reflectance values, and \(\sigma\) is used to constrain mixed pixels.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 3 Polyaniline-Based Electrodes
### 3D Nanostructures  
Three-dimensional PANI nanostructures are most commonly obtained via the formation of hydrogels using crosslinking agents; this facilitates their application as diverse biosensor systems that are grounded in the working mechanisms governing the introduction of nanomaterials. Using amino trimethylene phosphonic acid as the crosslinking agent, [PERSON] et al. added silver nanoparticles functionalized by a luminol derivative, \(N\)-(aminobutyl)-\(N\)-(ethylsoluminol) (ABEI), onto the surface of 3D PANI nanostructures to generate electrochemiminescent sensors for xanthine detection (Figure 7a) [110]. [PERSON] et al. employed phytic acid as the crosslinking agent to facilitate the introduction of photoactive materials such as poly [4,8-_bis_[5-(2-ethylhexyl)]thiophen-2-yl]-benzo[1-2-b:4,5-b']dithiophene -2,6-diyl-alt-3-fluoro-2-[(2-ethylhexyl)]dbinom[3-4-b]thi-ophene-4,6-diyl] (PTB7-Th) to the electrode's surface for subsequent use as photoelectrochemical guanine sensors (Figure 7b) [111]. [PERSON] et al. also used 3D PANI derived from phytic acid as uric acid electrochemical biosensors [112]. Figure 6: **(a)** Patterned 2D PANI structures via inkjet printing and vapor deposition polymerization methods; these nanomolecules were useful for detecting neurotransmitters [105]. Copyright 2013, Elsevier. **(b)** NiO-coated PANI nanosheets via in situ polymerization to generate glucose biosensors [107]. Copyright 2020, Elsevier. **(c)** Flexible B-type natriuretic peptide biosensors based on electrodeposited 2D PANI nanostructures [109]. Copyright 2016, Springer. Additionally, research has been conducted on the production of unique 3D composite materials via a simultaneous synthetic route using PANI and other materials. [PERSON] et al. fabricated PANI-MoS2 nanofiber composites by taking advantage of the hydrothermal reaction between the PANI nanoneedle and the MoS2 precursor [113]. Here, the resulting nanocomposites were functionalized for chronic myelogenous leukemia (CML)-binding aptamers as biosensors for impedance spectroscopy. [PERSON] et al. made PANI-graphene composite microflowers via the in situ polymerization of aniline and graphene layers for use as cholesterol detecting biosensors [114]. The sensing performance of PANI-based biosensors are suggested in Table 2.  
Figure 7: (**a**) 3D PANI hydrogel-based electrochemiluminescence sensors for xanthine detection [110]. Copyright 2019, Elsevier. (**b**) 3D PANI hydrogel-based guanine sensors that feature photoelectrochemical signals [111]. Copyright 2020, Elsevier.
# Pattern Classification Based on RBF Networks with Self-Constructing Clustering and Hybrid Learning
## 4 Network Setup Phase
### Initialization of Basis Functions  
To determine the number of nodes and basis functions in the hidden layer, we find a group of clusters in the training set \(X\) through a clustering process. Firstly, we divide the training set \(X\) into \(m\) training subsets \(X_{1},X_{2},\cdots,X_{m}\) as follows:  
\[X_{h}=\left\{\mathbf{x}^{(i)}\Big{|}1\leq i\leq N\wedge y_{h}^{(i)}=1\right\} \tag{8}\]  
for \(1\leq h\leq m\). Then, each subset is fed to an iterative self-constructing clustering algorithm [53] and is grouped into \(K_{1},\cdots,K_{m}\) clusters, respectively. A cluster contains patterns which are similar to each other, and is characterized by its center and deviation.  
Let \(K=K_{1}+\cdots+K_{m}\). Therefore, we have \(K\) clusters in total and let them be denoted as \(C_{1},C_{2},\cdots,C_{K}\) with centers \(c_{k}=\left[c_{1,k}\cdots c_{n,k}\right]^{T}\) and deviations \(\mathrm{v}_{k}=\left[v_{1,k}\cdots v_{n,k}\right]^{T},1\leq k\leq K\). Then we set the number of hidden nodes, \(J\), to be equal to \(K\), i.e., \(J=K\). For hidden node \(j\), its associated basis function, \(B_{j}(\mathbf{x})\), is also determined, with its center and deviation initialized to be the center and deviation of cluster \(C_{j}\), i.e., we set  
\[B_{j}(\mathbf{x})=\prod_{i=1}^{n}\exp\left[-\left(\frac{x_{i}-c_{i,j}}{av_{i, j}}\right)^{2}\right] \tag{9}\]  
for \(1\leq j\leq J\), with \(c_{i,j}\) and \(v_{i,j}\) being the center and deviation, respectively, in the \(i\)th dimension of \(C_{j}\).
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 4 Traffic Sign Recognition Experiment
### Traffic Sign Recognition Experiment
#### 4.2.3 Statistics and Analysis of Experimental Results  
A total of 1000 test images are randomly selected respectively from six categories of traffic signs classified roughly in the GTSRB for classification and recognition to test the comprehensive recognition performance of the improved LeNet-5 network model for different types of traffic signs. Table 3 lists the classification and recognition test results of six categories of traffic signs.  
Figure 14: The recognition results of traffic sign test images in the network testing stage.  
As shown in Table 3, TP (True Positive) is the number of test images in which traffic signs are correctly recognized, and FN (False Negative) is the number of test images in which traffic signs are misrecognized and missed. In the traffic sign classification and recognition test experiments, the unique traffic signs perform best in the test results due to the advantages of fixed contours and distinctive features. The accuracy recognition rate reaches 100.00%, and the average processing time per frame is 4.7 ms. The derestriction traffic signs perform worst in the test results due to disadvantages of consistent contours and similar features. However, the accurate recognition rate also reaches 99.40%, and the average processing time per frame is 6.4 ms. In total, the average accurate recognition rate of six categories of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. On this basis, the improved LeNet-5 network model has excellent image recognition performance, and the proposed traffic sign recognition algorithm has good real-time performance and adaptability.  
By sorting and analyzing false or missed test images, the majority of these images are caused by extremely dark light, extremely low resolution, motion blur and excessive tilt. In the future, complex network models must be built aimed at these problems, and additional abundant datasets must be adopted to facilitate the accurate recognition of additional traffic signs with interference factors by CNN. In this manner, the inclusiveness and stability of traffic sign recognition algorithm are continuously improved.
# Fractal Aspects of Human S100 Protein Structures
## 3 Results  
The multiple sequence alignment of the investigated S100 proteins is shown in Figure 2 and the corresponding percent identity matrix is shown in Figure 3. Both figures reveal a low sequence identity of the investigated proteins. Figure 2: Multiple sequence alignments of S100 EF-hand calcium-binding proteins considered in the present study. Conserved residues are marked by \"\", residues with strongly similar physicochemical properties are marked by \"\" and residues with weakly similar properties are marked by \"\". Superimposition of the structural files for the investigated proteins reveals low structural similarity (Supplementary Table S2). As expected, the values of root mean square deviations (RMSD), obtained by superimposing the structures of every family of S100 proteins, are lower for crystallographic structures due to the reduced conformational flexibility in the crystal compared to the inherent flexibility existent in the solution state. The low sequence identity added to low structural similarity emphasizes that the fractal aspects were not assessed for similar structures. Moreover, the differences in the structures of the same protein in _apo-_ and loaded forms emphasize the conformational change undergone by the protein upon interaction with ions, substrate/ligand, or other proteins. Figure 4 shows the scatterplot of the double-log values for the number of amino acids of the protein versus the protein mean radius for the investigated proteins considering both solution (Figure 4a) and crystal (Figure 4b) structures. The linear fit of the data presented in Figure 4 leads to a mass fractal dimension D\({}_{\text{m}}\) = 1.540 \(\pm\) 0.07 (R\({}^{2}\) = 0.680) for solution structures and D\({}_{\text{m}}\) = 1.538 \(\pm\) 0.14 (R\({}^{2}\) = 0.628) for crystal structures, suggesting that they adopt extended global conformations. It is in good correlation with outcomes of the Protein Dipole Moments Server for the great majority of investigated structures (Table 1). The extended shape of S100 proteins, in conjunction with their calcium-binding ability, allows them to function as calcium sensors. Upon calcium binding, the proteins undergo conformational changes that expose previously hidden interaction surfaces, enabling them to regulate diverse biological processes such as cytoskeletal dynamics, cell migration, inflammation, and apoptosis. Figure 4: Determination of the mass fractal dimension for S100 proteins as the slope of the line when plotting the number of residues (N) versus the mean radius (R) of the proteins in a double logarithmical scale: (**a**) solution structures; (**b**) crystal structures. Figure 3: Percent identity matrix for the multiple sequence alignments of S100 EF-based calcium binding proteins considered in the present study. The values obtained for the surface fractal dimensions, the overall conformations, and the polar and apolar surface areas for the investigated proteins are presented in Table 1. The values of polar and apolar areas are computed using a sphere radius of 1.4 A.
## 2 Theoretical Foundations
### Comparative Analysis in Homogeneous Elastic Anisotropic Media
Figure 7a,b are the snapshots simulated inside the PML and M-PML at 0.1222 s, respectively; Figure 7d,e are simulated inside the PML and M-PML at 0.1889 s, respectively; and the wavefront calculated by the analytical group velocity in Figure 7c,f are consistent with the wavefront of snapshots at 0.1222 s and 0.1889 s, respectively. Figure 7g,h are recorded at 0.6 s, and significant reflections can be observed at the four corners in the snapshot of the PML; however, the M-PML does not. Figure 7i shows the energy decay curves of the wavefield calculated from medium 2. This numerical experiment was conducted to confirm that M-PML has superior absorptionperformance to PML in arbitrarily different anisotropic media. It can still be seen from the results that the wavefront in the wavefield snapshots Figure 7a,b coincides with the analytical wavefront [31] Figure 7c,f at the corresponding moment, verifying the rationality and applicability of the numerical results. Similar to the previous section, viscosity is still introduced in this medium, and its wavefield snapshots are displayed in Figure 8 to confirm that M-PML can still maintain good absorptive properties and stability in different viscous anisotropic media. Figure 5: Comparison between snapshots of the horizontal displacement in medium 1 at 0.1667 s, 0.2444 s, 0.7 s (**a**), (**d**), (**g**) calculated by M-PML and (**b**), (**e**), (**h**) PML. Wavefront calculated from analytical group velocity at (**c**) 0.1667 s and (**f**) 0.2444 s. (**i**) is the energy decay curve of PML and M-PML. Figure 6: Viscoelastic anisotropic wavefield of medium 1 calculated by M-PML at **(a)** 0.1667 s, **(b)** 0.2444 s, and **(c)** 0.7 s.  
Figure 7: Comparison between snapshots of the horizontal displacement in medium 2 at 0.1222 s, 0.1889 s, 0.6 s. **(a)**, **(d)**, **(g)** calculated by M-PML, and **(b)**, **(e)**, **(h)** PML. Wavefront calculated from analytical group velocity at **(c)** 0.1222 s and **(f)** 0.1889 s. **(i)** is the energy decay curve of PML and M-PML. Regarding absorption performance, observing the wavefield snapshots of PML and M-PML shows that both ABCs are stable in this homogenous anisotropic medium, so it is possible to compare their absorption performance visually. At 0.6 s, the effective wavefield wholly left the working area, and we can see that the boundary reflections of PML are strong at the four corners in Figure 7h, but those of M-PML are well attenuated. Further, in Figure 7i, it is illustrated that the energy decay curve of M-PML is lower than that of PML, which quantitatively displays the absorption advantage of M-PML over PML.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 5 Experiment and Results Analysis
### Classifier Selection  
Classifiers can be divided into discriminating classifiers and generating classifiers, and the discriminant classifiers are more flexible in the identification of features and the detection speed is faster. Discriminant methods classify samples based on probability density or discriminant functions, such as SVM. SVM is based on statistical learning theory and follows the principle of minimizing structural risks. It first trains samples to obtain a set of classification features, and then judges the actual input to minimize the error from the ideal classification result, which can be solved smoothly Regression problems, classification problems in pattern recognition, discriminant analysis, etc. It is widely used in subject areas such as prediction and comprehensive evaluation.  
This paper uses SVM as the classifier to detect steganography. The classifier will judge sample category based on the classification model. And the Lib-support-vector-machine(LIBSVM) software package developed by [PERSON] [22] from Taiwan University is used. The learning process and decision process of the classifier are shown in Figure 5.  
Figure 4: Experimental environment.  
Figure 5: Support vector machine detection process.  
Experiments were conducted on five categories of Chinese male voice (CM), Chinese women voice (CW), English male voice (EM), English women voice (EW), and hybrid voice (Hybrid). For each of the above data sets, 80% of the \"original carrier\" and the corresponding \"density carrier\" samples were selected for the training sets, and the remaining 20% of the samples were used as the test sets. To test the performance of the steganalysis algorithm, the specific process is shown in Figure 6[18].
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 4 Deployment of the IDS
### Performance  
For performance, it can be recognized that they majority of papers focus on the machine learning aspect for optimization. The authors of [55] propose that their model is much more effective than the previous state-of-the-art models, thanks to feature selection methods and feature dimensionality narrowed down to only 15 features. Compared to that, The authors of [56] defined new, novel traffic flow features for their ML model, which fit into fewer resources of IoT network platforms.  
Others, such as in Ref. [57], use specific tools, such as SPARK, a widely popular data engineering tool in Big Data contexts, for fast processing time and efficiency; utilized in the cloud layer, it improves ML training, and then the model is deployed at the edge. Ref. [58] states that better correctness and application performance brought on by deep learning's capacity for improved intellect can be used to spot new, novel strikes in IoT systems, which is vital in the context of securing lightweight IoT networks. According to the numbers, when the authors of [59] measured their ML model, after training and installation on the network's firewall, it reached a speed of classifying almost 3 million messages in 1 s.  
There have also been different approaches in terms of integrated deployment of IDSs, or using an additional system to append the existing network. Ref. [46] uses a designated Blockchain Server (BCS) bearing the responsibility of recording and validating transactions, while Ref. [60] choose to add a dedicated ML server, to combat these attacks on the network. Opposing that, SDNs often use their flexibility, such as [37], to integrate the IDS system into the already established network, where it is said that employing an SDN core enables real-time intrusion detection and mitigation. In [34], [PERSON] et al. adjusted the existing network by adding a sink module to the IoT controller, containing a logging module that logs all incoming packets in the forwarding layer. These logs are recorded in the controller's directory.
# A New Approach for Environmental Risk Assessments of Living Modified Organisms in South Korea
## 3 Results and Discussion  
### Domestic and International Regulatory Frameworks for LMOs  
Since 2008, South Korea has had a national management system for research on the testing, production, import, export, and use of genetically modified organisms (GMOs) based on the LMO law, an act under the biosafety protocol [8; 13]. The system designates the Ministry of Trade, Industry, and Energy as the national institution responsible for LMO safety management in South Korea, the Ministry of Foreign Affairs as the national liaison, and the relevant administrative agencies as accountable for each use. The Korean government is in charge of the risk review and assessment of all LMOs for import or export and the safe management of LMOs. In particular, the MOE, Ministry of Agriculture, Food, and Rural Affairs (MAFRA), Ministry of Oceans and Fisheries (MOF), and Ministry of Health and Welfare (MOHW) undergo consultation reviews for the environmental risk of LMOs that are released or are likely to be released into the environment [8; 9; 10; 11]. Furthermore, each relevant institution convenes a risk review expert committee to evaluate risks by examining the submitted documents. Since 2015, the NIE, an LMO risk review agency of the MOE, has also been responsible for the effects of LMOs on natural ecosystems. In addition, the NIE was designated as an institute of LMO risk assessment by the MOE in December 2018 and is equipped with isolation field facilities to conduct LMO risk assessment [10; 11]. The LMO risk review agency can request an LMO risk assessment from the institute of LMO risk assessment during the risk review process for a new LMO. The NIE has also been drawing up guidelines on risk review and assessment methods to establish a safety management system for LMOs for the purpose of environmental remediation and the general safe management of LMOs in natural ecosystems [10; 11]. The LMO safety management strategies of the MOE and NIE are shown in Figure 1.
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
## 3 Contact Fatigue State Identification  
### Introduction of Rolling Contact Fatigue Test Equipment  
According to \"Rolling Contact Fatigue Test Method for Metal Materials\" (YB/T 5345.2014) [30], the contact fatigue properties of materials are obtained by testing on a rolling contact fatigue testing machine. Our team successfully developed a rolling contact fatigue testing machine, as shown in Figure 6, which is mainly composed of the mechanical system, electrical system, vision system, measurement and control software system and lubrication system. It can monitor the running state of the specimen in real time and solved the problems of similar test technologies, such as discontinuous data collection, untraceable damage evolution and difficulty in accurately obtaining fatigue strength.  
During the test, various sensors were installed to realize the measurement and control of key parameters such as vibration, load, oil temperature, rotational speed and torque. At the same time, an image acquisition system with independent intellectual property rights has been developed, which realizes real-time acquisition and quantitative analysis of damage images.
## 2 Materials and Methods
### Adsorption Experiments  
The material (20 mg) was placed in an Erlenmeyer flask containing 50 mL of 90 mg\(\cdot\)L\({}^{-1}\) MO aqueous solution. The pH of the suspension was adjusted to 5. Adsorption experiments were performed in a temperature-controlled incubator. The temperature was maintained at 25 \({}^{\circ}\)C, and the adsorption process was performed under dark conditions throughout the whole experiment. For the preparation of all solutions, Milli-Q water was used. Adsorption of MO was quantified through the following process; aliquots of 1.5 mL were periodically taken, filtered using a 0.45 \(\upmu\)m PTFE Millipore disc, and measured using a Varian Cary 100 Scan spectrophotometer. Quantification of MO was based on comparison with standards. The adsorption capacity evaluation was performed using the following equation:  
\[\mathrm{q_{e}}=\frac{(\mathrm{C_{0}-C_{e}})\times V}{\mathrm{m}}, \tag{1}\]  
where \(\mathrm{q_{e}}\) (mg\(\cdot\)g\({}^{-1}\)) is the adsorbed amount at equilibrium, \(\mathrm{C_{0}}\) and \(\mathrm{C_{e}}\) (mg\(\cdot\)L\({}^{-1}\)) are the initial dye concentrations and at equilibrium, respectively, V is the volume of the mixture (mL), and m is the mass of the adsorbent (mg). The removal efficiency was calculated using the following equation:  
\[\mathrm{RE=}\frac{(\mathrm{C_{0}-C_{e}})\times 100}{\mathrm{C_{0}}}. \tag{2}\]