esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Instrument Design  
We developed a leaf-level measurement system capable of concurrently measuring gas exchange, reflected and transmitted radiance, actively induced chlorophyll fluorescence, and the spectra of passively induced chlorophyll fluorescence from both sides of a leaf (Figure 1a). The system integrates a Multiphase Flash(tm) Fluorometer from the LI-6800 portable photosynthesis system (LI-COR Inc., Lincoln, NE, USA) with two HR2000+ high-sensitivity spectrometers (Ocean Optics Inc., Dunedin, FL, USA) and two QE Pro high-sensitivity spectrometers (Ocean Optics Inc., Dunedin, FL, USA). The HR2000+ spectrometers cover a broad wavelength range of 296-1203 nm, providing an optical resolution of 5.316 nm and a spectral sampling interval of 0.443 nm. In contrast, the QE Pro spectrometers are optimized for a narrower range of 634-863 nm, offering higher optical resolution at 5.266 nm and a finer spectral sampling interval of 0.219 nm.  
The bottom plate of the Multiphase Flash(tm) Fluorometer was replaced with a custom-designed metal plate containing three airtight slots: one for securing the temperature sensor and two for holding bifurcated optical fibers of identical design (Figure 1b). One fiber was positioned vertically upward with a 25\({}^{\circ}\) field of view to collect downward emissions from the leaf. Its bifurcated ends were connected to an HR2000+ spectrometer for measuring transmitted radiation (\(Rad_{\text{DOWN}}\), mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)), and a QE Pro spectrometer for detecting downward chlorophyll fluorescence (ChlF\({}_{\text{DOWN}}\), mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)). The second fiber was connected to a 30\({}^{\circ}\) field-of-view side-view lens oriented at a 45\({}^{\circ}\) zenith angle to observe upward spectral emissions from the leaf. This setup allowed the HR2000+ spectrometer to collect reflected radiation (\(Rad_{\text{UP}}\), mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)) and the QE Pro spectrometer to capture upward chlorophyll fluorescence from theaxial side of the leaf (ChlF\({}_{\text{UP}}\), mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)). Additionally, the distance between the side-view lens and the leaf was carefully adjusted to ensure alignment of the observation areas for both the upward- and downward-facing fibers (Figure 1b).  
Notably, to reduce the impact of excitation light emitted by the Multiphase Flash(tm) Fluorometer (blue light centered at 475 nm and red light centered at 625 nm) on chlorophyll fluorescence spectra, long-pass filters (670 nm) were inserted at the ports of each QE Pro spectrometer (Figure 1a). This configuration allows for the measurement of ChlF\({}_{\text{DOWN}}\) and ChlF\({}_{\text{UP}}\) within the wavelength range of 670-800 nm.
# CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC
## 2 Related Works
### Code-Expanded Random Access Procedure  
To utilize the preamble extension in an mMTC environment, various studies related to CeRA have been conducted [9; 10; 25; 26]. In [10], the RA procedure and frame structure of CeRA were designed. The authors proposed an adaptive CeRA method to reduce the number of phantom codewords and improve the utilization of preamble codewords by adaptively allocating them according to the user device density. The authors of [25] allowed devices to transmit preamble codewords that remove the similarity between codewords through \(q\)-ary maximum average distance code to solve the problem of CeRA's low preamble codeword utilization. The author of [25] proposed a codeword similarity removal scheme based on \(q\)-ary maximum average distance code to solve the low-preamble codeword utilization problem of CeRA. In [9], codeword ambiguity and PUSCH resource allocation probabilities were analyzed. To improve the RA success probability, a balanced preamble codeword set allocation scheme with low ambiguity and high PUSCH resource allocation probability was proposed. The authors of [26] conducted a study to increase the success rate of grant-free RA through successive preamble transmissions. The author proposed a massive MIMO-based user equipment detection algorithm to improve the RA success rate based on preamble codeword transmissions.  
Previous studies have analyzed the preamble codeword utilization in the preamble transmission process. Most studies do not consider the utilization of PUSCH resources, and it is possible that the RA success rate will decrease owing to a shortage of PUSCH resources. In the case of studies considering PUSCH, it is difficult to allocate an appropriate preamble codeword set because the preamble codeword increases exponentially in units of the codeword length. To improve the overall RA success rate, it is necessary to consider the efficiency of PUSCH resource allocation and preamble codeword set allocation.
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Sample Preparation
Pressurized closed-vessel MW systems are preferred to open-focused systems that work under atmospheric pressure because of their reduced risk of sample contamination and loss of volatile elements. Employing tetrafluorometoxil polymer digestion vessels (TFM/PTFE) can accommodate higher temperatures (350 \({}^{\circ}\)C) and pressures (up to 160 bar), which results in more complete destruction of organic matrix constituents using only HNO\({}_{3}\) as the digestion medium [48]. Although the use of MW-assisted mineralization processes allows shorter digestion times than wet ashing techniques, the long cooling times required before opening the vessels remain as the main limitation. Ultrasound (US)-assisted digestions are reported to be a promising alternative to MW-assisted techniques in the search for faster sample preparation procedures [49]. Other advantages of US-assisted digestion methods include improved safety since the method involves neither high pressure nor high temperature, lower reagent consumption, reduced acid waste generation, and lesser risk of contamination due to the lower number of analytical steps involved. In a study by [PERSON] et al., the procedural detection limits for rare earth elements in the case of the developed US-assisted digestion procedure were found to be five-fold better than those obtained with the MW-assisted digestion method [50]. When no standard method exists for digestion conditions of a certain type of sample matrix, modification of regulatory or standard methods may become necessary. The accuracy of an optimized procedure can be evaluated by the analysis of standards. Several individual digestion methods have already been validated in the field of food analysis, but only a little comparative information is available to help the analyst select the most appropriate method for a specific group of elements [51]. The choice of digestion method has a major influence on the precision and accuracy of a measurement with atomic spectroscopy techniques for a given element.
# Double-Constrained Consensus Clustering with Application to Online Anti-Counterfeiting
## 5 Experimenting with Counterfeit Web Shops
### Experiment Design and Preparation
#### 5.2.3 Clustering Features and Distance Matrices  
Various clustering features have been proposed for this or related tasks, usually associated with the structure ([25; 28; 29; 51]), content ([26; 27; 30; 31]), and visual appearance of malicious websites ([30; 32]), or with information about their registration or network infrastructure ([25; 30; 31]). We followed two main criteria to select the features for the experiments: that they were representative of main feature categories, and that they were present and easily acquirable for almost all web shops in CAP. For instance, we did not use any feature related to website registration because the WHOIS service available to us covered only a small fraction of the sample. Also, unlike [25], we did not include network features such as name servers or autonomous system numbers because we found that they were very weak clustering signals, whereas the IP address can be regarded as a very strong signal and used to acquire constraints, as discussed in Section 5.2.5. The selected raw features, along with the actual clustering features extracted from them and the similarity function associated with each, are described below.  
As a structural feature, we relied on the DOM tree associated with each of the web pages. Following [51], we encoded the structure of each DOM tree in CAP as a bit string through SimHash fingerprinting, and then computed the pairwise similarities using the Hamming distance. As a visual feature, we chose the website header of the web shops. We created five visual clustering features from the homepage screenshot, corresponding to website headers with a variable number of rows (from 30 to 150, with a step of 30). The similarity matrix for a selected header was found by first extracting, for each website in CAP, an image tensor containing the HSV value of the pixels in the region of interest associated with that header, and by successively computing the Chi-squared distance between the HSV histograms of any pair of elements in CAP. We finally used two novel clustering features related to the specific content items of web shops, namely the privacy policy and shipping policy. The rationale was that policies between affiliated fake web shops can be reused to reduce the effort involved in website authorship, often with only minor adjustments, e.g., the name of the site owner. We did not use other possible, and probably relevant, policies (e.g., 'payment methods' and'returns and refunds') because these pieces of information were not provided in the majority of the elements in CAP. For both features, we extracted the textual content of the policies from each website in CAP and then measured the pairwise similarity based on the number of shared sentences.  
Figure 5: An example of two fake web shops grouped in the same cluster.
# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 4 Performance Evaluations
### Training Procedure
We also leveraged pre-trained weights for transfer learning, as summarized in Table 9.  
The pre-trained weights were selected based on their relevance to video recognition tasks. The 3D CNN model was trained from scratch due to the lack of suitable pre-trained weights, whereas the R(2+1)D, VST, and MViT models utilized pre-trained weights provided by Torchvision from large-scale datasets such as KINETICS400 and IMAGENET22K. These pre-trained weights significantly enhance feature extraction capabilities by enabling the models to leverage knowledge from large-scale video action recognition datasets, leading to improved generalization and faster convergence during training. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyperparameters** & **3D CNN** & **R(2+1)D** & **VST** & **MViT** \\ \hline Loss Function & \multicolumn{4}{c}{LabelSmoothingCrossEntropy} \\ \hline Learning Rate & \multicolumn{4}{c}{\(1\times 10^{-3}\)} \\ \hline Optimizer & \multicolumn{4}{c}{SGD} \\ \hline Epochs & \multicolumn{4}{c}{200} \\ \hline Batch Size & 4 & 4 & 4 & 2 \\ \hline Input Size (pixels) & \(112\times 112\) & \(112\times 112\) & \(224\times 224\) & \(224\times 224\) \\ \hline Frames per Clip & 16 & 30 & 30 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Hyperparameter configuration for training sign language recognition models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Pre-Trained Weights** \\ \hline
3D CNN & None \\ \hline R(2+1)D & R2 Plus1D\_18\_Weights.KINETICS400\_V1 \\ \hline Video Swin Transformer (VST) & Swin3D\_B\_Weights.KINETICS400\_IMAGENET22K\_V1 \\ \hline Multiscale Vision Transformers (MViT) & MViT\_V2\_S\_Weights.KINETICS400\_V1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Pre-trained weights used for training. The loss function used was LabelSmoothingCrossEntropy, which helps improve generalization by preventing the model from becoming overly confident in its predictions. The learning rate was set to \(1\times 10^{-3}\), a commonly used value that balances convergence speed and stability during training. Stochastic Gradient Descent (SGD) was chosen as the optimizer due to its robustness and effectiveness in optimizing deep learning models. The number of epochs was set to 200, ensuring sufficient training time for convergence while avoiding excessive overfitting.
## 2 Methods
The basic theory is as follows:  
Given the known fluctuating power spectrum \(S(n)\) and simulation time T, the Fourier amplitude spectrum \(F(n)\) can be calculated from the following formula because of the ergodicity of the process of fluctuating wind speed from Equation (3) as:  
\[F(n)=\sqrt{T\cdot S(n)}. \tag{3}\]  
After inverse discrete Fourier transform of complex sequence \(F(n)\), the real part represents the fluctuating wind in Equation (4) as:  
\[v(t)=real[IDFT(\sqrt{T\cdot S(n)})]. \tag{4}\]  
Considering that the wind turbine blade is a flexible structure with a large slenderness ratio, the spatial correlation of each simulation node in the wind field should be taken into account while generating fluctuating wind speed. The expression of the correlation function is shown in Equation (5) [41]:  
\[coh(i,k)=e^{(-\frac{k_{i}-2k_{i}}{L_{z}})}. \tag{5}\]  
The aerodynamic and dynamic characteristics of the wind turbine are analyzed using the commercial software GH-Bladed [42]. The unsteady blade element momentum theory is used in the aerodynamic calculation, adding the dynamic inflow and stall models for the unsteady aerodynamic effects. The expression of structural response is based on the multibody dynamics. The floquet analysis method is used to determine the modal characteristics of the periodic systems. Different coordinate systems are established for the main components of wind turbines, so that the modal characteristics of each component can be calculated independently. The motion equation is established at the period of response analysis. The differential equation is time-advanced by using fourth-order Runge-Kutta numerical integration method with variable step size. Finally, the coupling of aerodynamics and structure is obtained by solving the equation and getting the response of each mode. It should be noted that, although there is a module of wind modeling in GH-Bladed, the software can only generate gusts and turbulent winds under normal conditions, but not inflows with typhoon structure characteristics. Therefore, the typhoon generation codes are developed in this study and are incorporated into the GH-Bladed to obtain the wind turbine structural dynamic response in typhoons.
## 5 Conclusions  
In this paper, we conducted a series of direct shear tests on artificial plaster joints, and the influences of normal stiffness and shear rate on peak and residual shear stress and AE characteristics were analyzed and discussed. From the test results, the main conclusions can be drawn.  
1. With the increase of normal stiffness, the peak shear stress of the joint decreases slightly, but the final shear stress increases linearly. With the increase of normal stiffness, the joint dilatancy decreases significantly, and normal stress increases significantly. The normal stiffness has little influence on the joint surface resistance index.
2. With the increase of normal stiffness, both the failure area and the failure roughness weight of the joint surface show a linear increase trend, which indicates that under the condition of high normal stress, more failure occurs to the roughness of the joint surface, which is the main reason for the significant increase of final shear stress.
3. With the increase in shear rate, both the peak shear stress and the final shear stress decrease, and both have a nonlinear relationship with shear rate. When the shear rate is 0.5-5 mm/min, the decline rate of the curve is much higher than that of the curve when the shear rate is 5-20 mm/min. The shear rate has little effect on joint dilatancy, and with the increase of shear rate, the joint dilatancy decreases slightly.
4. When the shear rate is between 0.5 mm/min and 5 mm/min, the joint failure area and the weight of the damaged, rough body have little difference, but when the shear rate is between 5 mm/min and 20 mm/min, the joint failure area and the weight of the damaged, rough body increase obviously.
5. In the shearing process, AE signals are mainly generated in the post-peak stage. Generally speaking, the cumulative number of AE impacts and cumulative energy rise with the increase of normal stiffness, which demonstrates that the greater the normal stiffness is, the more serious the failure of the rough surface of the joint.
6. When the shear rate is low (0.5-5 mm/min), the accumulated AE energy increases from 239,235 to 362,421 with the increase of shear rate; when the shear rate is high (5-20 mm/min), the accumulated AE energy decreases from 362,421 to 241,423 with the increase of shear rate.  
Conceptualization, formal analysis, methodology, writing and editing, YJ. and X.L.; data curation, writing and editing, C.W. and H.L.; software, S.Z.; review and editing, G.W. and P.W. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (No. 52104093, 52209141) and Shandong Provincial Natural Science Foundation China (No. ZR2021 QE069, ZR2020 QE120).  
Not applicable.  
Data Availability Statement: Data associated with this research are available and can be obtained by contacting the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## Appendix A  
// SPDX-License-Identifier: MIT pragma solidity '0.8.26;  
import \"@openzeppelin/contracts/token/ERC721/ERC721.sol\"; import \"@openzeppelin/contracts/utils/Counters.sol\";  
contract GlobalDigitalRegistry is ERC721 {  using Counters for Counters.Counter; Counters.Counter private _tokenIds;  
struct Item {  string identifier;  string metadata; }  
mapping(uint256 => Item) private _items;  mapping(string => uint256) private _identifierToTokenId; constructor() ERC721(\"GlobalDigitalRegistry\", \"GDR\") {}  
function registerItem(string memory _identifier, string memory _metadata) public returns (uint256) {  require(_identifierToTokenId[_identifier] == 0, \"Item already registered\");  _tokenIds.increment();  uint256 newTokenId = _tokenIds.current();  _mint(msg.sender, newTokenId);  _items[newTokenId] = Item( _identifier, _metadata);  _identifierToTokenId[_identifier] = newTokenId;  return newTokenId; }  
function transferOwnership(uint256 _tokenId, address _newOwner) public {  require(ownerOf(_tokenId) == msg.sender, \"Not the owner\");  _transfer(msg.sender, _newOwner, _tokenId); }  
function updateMetadata(uint256 _tokenId, string memory _newMetadata) public {  require(ownerOf(_tokenId) == msg.sender, \"Not the owner\");  _items[_tokenId].metadata = _newMetadata; }  
function verifyItem(string memory _identifier) public view returns (bool) {  return _identifierToTokenId[_identifier]!= 0; }  
function getItemDetails(uint256 _tokenId) public view returns (string memory, string memory, address) {  require(_identifierToTokenId[_items[_tokenId].identifier]!= 0, \"Item does not exist\");  Item memory item = _items[_tokenId];  return (item.identifier, item.metadata, ownerOf(_tokenId));function batchRegister(string[] memory_identifiers, string[] memory_metadataList) public { require(_identifiers.length ==_metadataList.length, \"Arrays must have the same length\"); for (uint i = 0; i <_identifiers.length; i++) { registerItem(_identifiers[i],_metadataList[i]); } }
## 2 Materials and Methods
### Spectroscopic Determination  
After diluting the extracted DOM samples to 10 mg C L\({}^{-1}\), a portion was used to measure the absorbance of DOM at 254 nm and 260 nm (UV254 and UV260) using a UV-Vis spectrophotometer (Shimadzu UV360, Shimadzu, Kyoto, Japan). The absorbance coefficients at 254 nm and 260 nm (a254, a260) were calculated according to Equation (1), and SUVA254 and SUVA260 (L mg\({}^{-1}\) m\({}^{-1}\)) were calculated according to Equation (2) [31].  
\[\mathrm{a}_{254/260}=UV_{254}/UV_{260}\times 2.303\times 100 \tag{1}\]  
\[SUVA_{254/260}=\frac{a_{254}/a_{260}}{DOM} \tag{2}\]  
Another portion was subjected to 3D fluorescence scanning (Hitachi F-7000, Kyoto, Japan), with excitation wavelength scanning ranges of 200-500 nm and emission wavelength scanning ranges of 200-550 nm, both at intervals of 5 nm. The scanning speed was 2400 nm/min, and the excitation/emission slit widths were 5 nm each. During sample scanning, deionized water was also scanned to subtract its spectral values during analysis, thus eliminating Raman and Rayleigh scattering effects [32; 33].  
After removing the Raman and Rayleigh scattering from the scanned spectra, the fluorescence indices were calculated by integrating the areas of DOM spectra at E(x) = 254 nm, E(m) = 435-480 nm, E(x) = 254 nm, E(m) = 300-345 nm, and the humification index (HIX) was calculated using Equation (3). The fluorescence index (FI) was calculated using the fluorescence intensity at E(x) = 370 nm, E(m) = 470/520 nm (Equation (4)); and the biological index (BIX) was determined using the fluorescence intensity at E(x) = 310 nm, E(m) = 380/430 nm (Equation (5)) [34].  
\[HIX=\frac{\sum E_{(m)}435-480\,\mathrm{nm}}{\sum E_{(m)}300-345\,\mathrm{nm}},\ E(x)=254\,\mathrm{nm} \tag{3}\]\[BIX=\frac{E(m)=380\,\mathrm{nm}}{E(m)=430\,\mathrm{nm}},\ E(x)=310\,\mathrm{nm} \tag{4}\]  
\[FI=\frac{E(m)=470\,\mathrm{nm}}{E(m)=520\,\mathrm{nm}},\ E(x)=370\,\mathrm{nm} \tag{5}\]
## 3 Results  
### Sensor Systems and Data Collection  
The sensors were integrated into a data acquisition system (Figure 4) based on the IoT microcontroller ESP32 WROOM 32 (Espressif Systems, Shenzhen, China), which recorded measurements every minute and transmitted the data via Wi-Fi to Telegram cloud storage. This technology enabled continuous monitoring and precise control of the drying conditions, optimizing both energy efficiency and product quality. A digital PID controller, implemented as a subroutine within the microcontroller program, regulated the hot air temperature.  
Figure 3: Integration of the Telegram application with the laboratory drying chamber. (1—Laptop, 2—Telegram application bot, 3—Laboratory-scale experimental drying chamber, 4—Layer with the bread slices, 5—IR lamp, 6—Air outlet, 7—Air inlet).  
The digital thermometer DS18B20 was used to monitor the hot air temperature. Relay 1 controlled the heater, while Relay 2 managed the fan, which operated in a simple on/off mode. To maintain a stable hot air temperature, the heater's solid-state relay functioned in a quasi-PWM mode with a one-second control period, which was sufficient due to the high thermal inertia of the heating element.