esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Face-to-Face P2P (Peer-to-Peer) Real-Time Token Payment Blockchain System
## 6 Conclusions  
This paper has proposed a new server-based real-time P2P token payment blockchain system designed to address the problems of non-face-to-face transactions in the financial sector, especially in the situation where sophisticated voice phishing is spreading and reliance on open banking systems is increasing. This solution overcomes the limitations of private key management difficulties and gas fee management while utilizing the advantages of blockchain technology.  
The server-based key management module effectively minimizes the risk of loss by securely storing private keys in keystore files and databases. The server-based token management module simplifies token generation and management, ensuring smooth token transactions through automatic gas charging. The transaction verification module, which utilizes transaction IDs without exposing personal information, guarantees transaction integrity and non-repudiation. In addition, advanced security measures such as external access IP blocking and DDoS protection are introduced to keep user data safe.  
It also aims to provide convenient, secure and accessible online payment solutions to the general public by implementing identity verification using smartphones and platform-agnostic web applications. This comprehensive approach has the potential to innovate non-face-to-face transactions in the financial sector by providing user-friendly and secure alternatives to traditional methods. As the digital environment continues to evolve, the non-contact P2P real-time tokenized payment system can become an important tool for protecting users from financial fraud and creating a more inclusive and flexible financial ecosystem.  
Through this research, it was demonstrated that blockchain token payments can be utilized as an anti-voice phishing technology by blocking the possibility of exposure to crime through  
Figure 18: TC3 Active number of threads vs. response time: (**a**) without DDoS protection (**b**) with DDoS protection.  
transparent transactions where all details can be made public and both the payer and the payee can be verified. Third-party authentication for non-repudiation is also possible. Furthermore, it is expected to create business opportunities for innovative finance, such as IoT automatic regular payments, small loan services and 24-h uninterrupted payment services. The feasibility and efficiency of the proposed system were confirmed through experimental results and it is expected to have a significant impact on the financial industry.  
Conceptualization, C.-S.J.; Methodology, C.-S.J.; Software, H.-J.K. and S.-S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request form the corresponding author. The data are not publicly available due to research contract.  
The authors declare no conflict of interest.
# Information and Communication Technology Solutions for the Circular Economy
## 2 Information and Communication Technology Solutions for the Circular Economy
### Categorization Method  
All of the solutions described in Section 2 are categorized in Table 1. We used a two-fold classification, focusing on both the technological categorization of each solution as well as the 4R concept that each solution is most relevant to. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**General Categorization**} & \multirow{2}{*}{**Subcategorization**} & \multirow{2}{*}{**Specific Technology**} & \multicolumn{4}{c}{**Circular Economy Concepts**} \\ \cline{5-6}  & & & **Reduce** & **Reuse** & **Reycle/** & **All Four** \\  & & & & **Restore** & **Concepts** \\ \hline \multirow{8}{*}{**Communications**} & Adaptive links & [14] & & & \\  & Cache-Based Transmission & [15] & & & \\  & Communication Infrastructure & 5G & [12, 13] & & \\  & Cognitive Radio & [17] & & & \\  & M2M Communications & [12, 13, 19] & & & \\  & P2P Communications & [18] & & & \\  & Routing Protocols & [19] & & & \\  & Wireless Transmission & [20] & & & \\ \hline \multirow{8}{*}{**Computing Technologies**} & Cloud Computing & [22, 38, 70] & & [24] \\  & & Cloud Manufacturing & [13] & & \\  & & Industrial Cloud Streaming & [25] & & \\  & Distributed Computing & [19] & & & \\  & Edge Computing & [27] & & & \\  & Thin Client Computers & & & [28, 29] & \\ \hline \multirow{8}{*}{**CPS**} & Core CPS Technology & [32] & [31] & & \\  & Additive Manufacturing & & [33] & & \\  & Digital Twin & & & [35] & \\  & Robotics & [36] & & [37] & \\  & UAVs & [38] & & & \\ \hline \multirow{8}{*}{**Data Analysis and AI Algorithms**} & Big Data Analytics & [81] & [55] & & [40] \\  & Case-Based Reasoning & & [49, 56] & & \\  & Data and Model Integration & & [49, 56] & & \\  & Data Visualization & VR & & [35] & \\  & & AR & [41, 42] & & [91] \\  & Dynamic Game Theory & & [51] & & \\  & Dynamic Programming & [52] & & & \\  & Evaluation Models & & & & [50] \\  & Fuzzy Logic & & [45, 46] & & [50] \\  & Heuristic Algorithms & & & & [50] \\  & Machine Learning & & [48, 49] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categorization of the ICT Solutions for the Circular Economy (CE).
## 3 Results and Discussion
### Establishment of DBA-LSTM Model
#### 3.2.1 Training Samples  
The orthogonal test is an effective way to construct DBA-ML samples. It starts with the determination of the test factors and their levels as well as the selection of a reasonable orthogonal table for the table header design based on the factors and levels. Considering  
Figure 7: Monitoring data.  
that the number of target parameters is six and the interaction between the internal friction angle and cohesion is not considered, we planned to choose an orthogonal table with eight factors and eight levels. According to the field investigation report and some bibliographic data [28; 44], we determined the range of values of the target parameters, and the values of other physical and mechanical parameters are listed in Table 2, where the levels of the target parameters are determined according to the equal interval principle.  
According to the orthogonal table \(L_{49}(7^{8})\), a total of 49 FLAC3D forward calculations needed to be arranged, and the Y-directional displacement and vertical displacement of the nodes GD01-GD07 were used as test indicators for the result output (the X-directional displacement was basically neglected, so it is not involved in the analysis, and only the Y-directional and Z-directional displacements were output), and we obtained 49 sets of samples (Table 3). Only some of the arrays are shown in the table, and the input layer information is only populated with the displacement data of node GD03, and data for the other six nodes are not shown. Test samples (Table 4) were also constructed using the orthogonal tests.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Group & \(p/\{L_{49}\}\) & \(X/\{M_{P}\}\) & \(G/\{M_{P}\}\) & \(G/\{M_{P}\}\) & \(G/\{M_{P}\}\) & \(G/\{M_{P}\}\) & \(G/\{M_{P}\}\) & \(G/\{M_{P}\}\) \\ \hline Sub clay1–3 & 2200 & 20–40 & 13–22 & 10–14.5 & 10–16 & 400 & 50 \\ Gravel soil & 2200 & 60 & 36 & 16 & 18 & 1000 & 50 \\ \hline Bedrock & 2900 & 1300 & 800 & 400,000 & 48 & - & - \\ \hline \end{tabular} Note: \(M\) is the Biot modulus, and \(n\) is the porosity; the other parameters involved in the fluid calculation are Brio coefficient = 1, soil permeability coefficient = \(10^{-13}\), and water density = 1000 kg/m\({}^{3}\).  
\end{table}
Table 2: Physical and mechanical parameters and target parameters of the landslide.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Num. & & & Output layer & & & Input layer \\ \hline
1 & 10 & 10 & 10 & 10 & 10 & 10 & \(-\)95.4 & \(-\)57.8 \\ \hline
2 & 10 & 11 & 10.75 & 11 & 10.75 & 11 & \(-\)84.2 & \(-\)51.8 \\ \hline
3 & 10 & 12 & 11.5 & 12 & 11.5 & 12 & \(-\)76.9 & \(-
## 2 Materials and Methods
### Molecular Docking  
The protein structural file of ERK2 in complex with caffeic acid (PDB ID: 4N0S was obtained from the RCSB Protein Data Bank [36]. Protein Preparation Wizard workflow was used to prepare the protein structure [37]. In the preprocessing stage, bond orders were assigned, missing hydrogen atoms were added, missing side chains and loops were filled in using prime [38; 39; 40]. Water molecules, beyond 5 A from the ligand and that are not forming less than three hydrogen bonds to non-water resides, were deleted. In the final stage of protein preparation, hydrogen bonds were assigned after sampling water molecules, and the protein structure was subjected to restrained minimization allowing heavy atoms to converge to RMSD of 0.3 A. A receptor grid was then generated by identifying the ligand coordinates (\(-15.26\), 12.73, 40.72) to define the docking site. In order to add some flexibility to the docking process, the potential for nonpolar parts of the receptor was softened by scaling the van der Waals radii of receptor atoms that have a partial charge cutoff of 0.25, with a scaling factor of 0.8. All hydroxyl and thiol groups of the amino acids in the receptor grid were allowed to rotate during the docking process. Ligand structures were sketched in maestro using OPLS3 forcefield and prepared with LigPrep [41; 42]. Possible ionization and tautomerization states were generated at pH 7.4. The lowest energy conformer was kept for each ligand. Molecular docking was performed using a Glide standard precision approach [43; 44; 45]. CA was docked to validate the docking protocol, and the crystallized binding mode was retrieved from the best pose. Five poses per ligand were written in the pose viewer file. The docking results were then refined by running Prime MM-GBSA calculation [38; 39]. Amino acid residues within 10 A of the ligand are allowed to be flexible during the optimization step.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 4 Modeling of Uncertainty  
The ellipsoidal state estimation technique discussed in this paper relies on suitable uncertainty models for both bounded process and measurement noise. This section givesan application-driven description on how suitable bounds can be derived for real-life system models. To perform the parameterization of the uncertainty models, we rely on the identification experiments and measured data summarized in Section 2.
# Potassium Recovery from Potassium Solution and Seawater Using Different Adsorbents
## 2 Materials and Methods
### Sorption Experiments with KCl Solutions  
#### 2.2.1 Batch Sorption Experiments with KCl Solution  
Batch sorption tests were conducted at room temperature (21-23 \({}^{\circ}\)C) to investigate the K sorption characteristics of the prepared adsorbents. The K stock solution (1000 mg L\({}^{-1}\)) was prepared from KCl (Yakuri Pure Chemicals Co. Ltd., Kyoto, Japan, Guaranteed Reagent), and the solution pH was adjusted to pH 4.57 \(\pm\) 0.03 and pH 6.99 \(\pm\) 0.04 with a pH 4 buffer solution (CH\({}_{3}\)COOH-CH\({}_{3}\)COONa) and a pH 7 buffer solution (NaH\({}_{2}\)PO\({}_{4}\)-Na\({}_{2}\)HPO\({}_{4}\)), respectively. The K stock solution was diluted to prepare solutions with different initial K concentrations (0-200 mg L\({}^{-1}\) for tests at pH 4 and 0-150 mg L\({}^{-1}\) for tests at pH 7). The prepared adsorbents (0.1 g) were placed in a 100 mL polypropylene bottle and the K solutions of different initial concentrations (100 mL) were added. The bottles were then shaken at 100 rpm on a shaker (SH30L, FinePCR, Seoul, Korea). Duplicate samples were used for each condition. After 48 h, the samples were filtered using 0.45 um syringe filters. The K concentrations of the filtrates were analyzed by using ICP-OES (iCAP 7400 Duo, Thermo Fisher Scientific, Waltham, MA, USA).  
With the zeolite-based adsorbents, the K sorption kinetics were studied at the average pH of 4.4. The natural zeolite or ammonium acetate-treated zeolite (0.1 g) was placed in a 100 mL polypropylene bottle and the K solution (100 mL, initial concentration = 170 \(\pm\) 0.084 mg L\({}^{-1}\)) was added. The bottles were then shaken at 100 rpm in a shaking incubator (SH30L, FinePCR, Seoul, Korea). During the 48 h sorption tests, samples were taken at 0, 2, 4, 6, 8, 10, 24, and 48 h. Duplicate samples were used for each condition.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)
#### Clinical Applications of TD-FS  
Many studies have been done toward clinical applications of TD-FS, but only a few studies are reviewed in this article. For evaluating excised tumor specimens, [PERSON] et al. induced tissue autofluorescence with a pulsed (1.2 ns) nitrogen laser at 337 nm and measured its decay profiles in a wavelength range from 370 nm to 500 nm using a fast oscilloscope and a photomultiplier [199]. Obtaining data at two times in the decay profiles (times at the intensity of \(1/e\) and 10% of the maximum intensity) at six wavelengths, a linear discrimination analysis enabled discrimination of meningioma tissue from normal tissue with a sensitivity larger than 89% and specificity of 100%. The feasibility of using TD-FS for evaluating tumor tissue was discussed.  
[PERSON] et al. tried a method to measure the inflow and washout of an optical contrast agent (ICG) for the purpose of providing information on the blood supply to the brain by TR measurements of fluorescence at the head surface [200]. In order to provide information for interpretation of the measured fluorescence signals, they performed MC simulations and in vivo TR measurements to find the effects of the SD distances, positions of the SDs, and doses of injected ICG. Furthermore, [PERSON] et al. made MC simulations of light propagation of the excitation and emission light in a two-layered medium mimicking intra- and extracerebral tissue compartments after injecting ICG [201]. They found that the knowledge of the absorption properties of the medium is essential for interpretation of the TR fluorescence signals measured at the head surface.
# The Legal Framework for Space Debris Remediation as a Tool for Sustainability in Outer Space
## 3 The Definition and Scope of Space Debris Remediation
### Further Issues to Be Addressed by the Law  
Apart from the challenges in the legal dimension of SDR, other aspects will also be defining the practicability of remediation. They include a solution to overcome the financial hurdle for SDR as well as finding an effective approach to consider the strategic, security and military aspects of on-orbit servicing and active debris removal. Thus, a suitable form of international cooperation and organizational framework will be required.  
The findings of the International Congress on Space Debris Remediation included following practical conditions as necessary [44]:  
* a \"cost effective\" technique;
* a proper legal and policy framework to protect the parties involved and to deal with \"alternative use\" concerns;
* available and willing targets for removal or customer for servicing;
* funding: for the time being, for establishing, testing and developing technologies and SDR techniques; in the future--for carrying out such operations;
* accurate tracking and necessary assistance during operations;
* capability to locate, approach, connect deorbit/servicing device, control orientation and to move the target object to desired destination;
* safety of the public on the ground, at sea, travelling by air and in space.  
As far as the prospects for developing economic incentives for debris removal are concerned, a solution of creating a global fund in which all operators should contribute on an equitable basis has been proposed. This approach has become a standard in other activities taking place in global commons--e.g., in the telecommunications sector using fibre optics in the high seas. The main benefit of such an approach is the distribution of financial load between multiple entities. The justification for such an approach is that outer space activities are, ex definitone, ultra-hazardous, and very costly so that a distribution of risk is more practicable and effective than imposing costs only on single entities.  
Also, these challenges, although they might not be directly originating from the law, could be facilitated through adequate regulation.
# Real-Time Statistical Measurement of Wideband Signals Based on Software Defined Radio Technology
## 3 Materials and Methods
### Parameterization and Test System Implementation
For bandwidths of 160 MHz, an additional RF daughterboard is required, implicitly increasing the price of the measurement system. The bandwidth upgrade has an additional cost compared to the basic version, SDR plus 40 MHz receiver board (price 11,000 Euro). If the basic bandwidth of the USRP platform is to be extended from 40 MHz real-time to 160 MHz, the price increases by about 2000 Euro. For two 80 MHz TwinRX channels (two highly synchronized receivers), the price of the basic version increases by about 4600 Euro. The other important parameter, resolution, affects the sensitivity (the level of the received signal) and the dynamic range (the range of amplitudes that can be measured) of the SDR receiver. To illustrate the importance of the converter resolution in terms of bits, if it has one more bit, the dynamic range will improve by 6.02 dB (20 lg2). Given a sinusoidal signal with a certain amount of quantization noise, the maximum signal-to-noise ratio (\(SNR\)) of the converter is defined by this relationship [39]:  
\[SNR=(1.76+6.02N)[\mathrm{dB}] \tag{3}\]  
where, \(N\) = number of bits of the analog-to-digital converter. Since this measured signal-to-noise ratio is always lower than the theoretical one, the effective bit number \(ENOB\) is used:  
\[ENOB=\frac{SNR_{dB}-1.76}{6.02}[\mathrm{dB}] \tag{4}\]  
where \(SNR\) is the measured signal-to-noise ratio. Another important aspect is that the measurement system needs high-performance hardware in the processing part, especially for large bandwidths. For SDR boards that allow these bandwidths, no packet loss is detected because the USRP platforms use different types of FPGAs, ADCs and DACs with high dynamic range and the ability to transfer measurements at high transfer rates based on 10 Gigabit Ethernet or PCIe Express connections. The problem arises in the acquisition and data processing, and for this, the designer who wants to implement such a system must consider a PC or server with powerful processing capabilities according to the complexity of his application. The SDR-based measurement system has additional features and limitations, which are described below:
## 4 Dataset Description
### Scenarios and Environments
#### 4.1.3 Dynamic Scenario in Urban Area (S3)  
The third scenario is a dynamic pedestrian acquisition (45 min), performed in a light urban area. Similarly to S2, the S3 scenario starts and ends with a short static acquisition for the usual landmark. The southern part has good open-sky conditions, contrary to the northern part, which has tall buildings around it. Strong noise can be seen in the trajectory due to a tall building with an outside ceiling, which as expected leads to noisier measurements. Finally, the part located on the road shows a small underground tunnel with possible GNSS cutoffs. The data from all the devices were acquired at the same time; all devices were placed in separate side pockets of the backpack. This allows comparison of the devices over the exact same-scenario conditions. Figures and statistics of the S3 survey are provided in Appendix D.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline  & **Motion** & **Environment** & **Duration** & **Device Carrying Mode** \\ \hline S1 & Static & Open-sky & 45 min & Placed on tripod \\ S2 & Pedestrian & Urban canyoning & 10 min & In right-hand, texting * \\ S3 & Pedestrian & Suburban area & 30 min & In backpack, pocket * \\ S4 & Pedestrian & Forest \& lake & 30 min & In backpack, pocket * \\ \hline \hline \end{tabular}  
* Smartwatch placed on left wrist, texting mode.  
\end{table}
Table 2: Summary of the scenarios.
## 5 Conclusions  
The accurate prediction of RBPs based on sequences has been a challenging problem. In this paper, we have proposed a deep model (i.e., CnnRBP) based on short sequence motifs to improve the performance of RBP prediction. Cross-validation and independent validation tests on the datasets of three species demonstrated the effectiveness of our method and its superiority over existing shallow and deep learning based methods. The short motifs are easily computed and discriminative, as shown by experimental results. The LightGBM algorithm was utilized to select important features and the convolution neural network found relations among features, which contributes to performance improvements for our RBP prediction method. Our method can help to quickly find candidate RBPs for further verification through biological experiments.  
In summary, experimental results on the benchmark datasets show that our method achieved a fairly competitive prediction performance. Compared with other deep models, the features used by our method were computationally efficient. In addition, species-specific predictions can capture unique features of different species. However, our method still has some disadvantages. When the protein is too short, the statistical features may be too sparse to make correct decisions. The CNN captures local features but cannot find out the context information. The contributions are summarized below: We proposed a novel species-specific RBP predictor. We also verified that the discriminant features for  
Figure 8: Performance on cross-species prediction: (**a**) MCC; (**b**) AUC.  
RBP prediction differed between species. In addition, we introduced LightGBM to select optimal features.  
Although deep learning has been applied to improve the performance of species-specific RBP prediction in this paper, there is still room for further improvement. In some species, such as E.coli and Salmonella, the number of RBP samples is relatively small, which limits the advantages of deep learning. Next, we will further explore transfer learning to improve the performance for species with small RBP samples by transferring from the models trained on the datasets of species with large RBP samples.  
Conceptualization, Z.-S.W. and J.R.; data curation, J.R.; formal analysis, Z.-S.W.; funding acquisition, Y.-J.L.; investigation, Z.-S.W.; methodology, Z.-S.W. and J.R.; project administration, Z.-S.W.; software, J.R.; supervision, Z.-S.W.; validation, Z.-S.W.; visualization, Z.-S.W. and J.R.; writing--original draft, Z.-S.W.; writing--review and editing, Z.-S.W. and Y.-J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 62076116), the Natural Science Foundation of Fujian (No. 2022)01913) and the Education and Research Project for Young and Middle-aged Teachers of the Education Department of Fujian Province (No. JAT190362).  
Not applicable.  
The datasets used are available publicly.  
The authors declare no conflict of interest.