esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Economy-Quality Bi-Objective Optimization Model  
### Problem Description  
* Model Structure  
As depicted in Figure 1, a ME-VPP generally consists of CCHPs, hydro units, wind turbines, photovoltaic panels, storage devices, electric appliances, and cooling/heat facilities. The dispatch of a ME-VPP refers to coordinative operation of these producers and consumers in three forms of energy: electricity, cooling, and heat. In such an interconnected multi-energy carrier framework, CCHPs are particularly significant due to the establishment of coupling among different energy carriers [25].  
EC and PQ are two critical indexes evaluating the performance of ME-VPP. An ideal dispatch of ME-VPP is to supply the best PQ by spending the lowest EC. However, better quality generally causes higher cost in practice and vice versa. Considering such an inherent conflicting nature, we employ the framework of multi-objective optimization [9] for problem formulation. A bi-objective dispatch model is established, in which EC and PQ are two independent objectives to be optimized. The Pareto frontier illustrates all noninferior solutions to the dispatch problem.  
* Variable Definition  
In an ME-VPP, three forms of energy are illustrated by three power buses and each bus is associated with a power balance. As shown in Figure 1, the entity number, electrical energy productions, thermal energy productions, and loads are denoted by \(N\), \(P\), \(Q\), and \(L\), respectively. The subscripts specify the entities with which the variables are associated, e.g., 'h' indicates hydropower units and 'w' indicates wind turbine. Variables in Figure 1 can be categorized into two groups: predictable and controllable. The wind power output, solar power output, electrical load, and cooling/heat load are of uncertainty but can be predicted by historical data analysis. The electric/thermal output of CCHP, charge/discharge flow of storage, and exchange volume with main grid are all controllable. In the following subsections, the predictable ones are treated as uncertain external parameters of the bi-optimization model, and the controllable ones are set as the decision variables. The purpose of optimization is to accurately predict external parameters and then to efficiently derive decision variables.  
Figure 1: Block diagram of multi-energy virtual power plant (ME-VPP).
## 3 Results and Discussion  
### Evaluation of Different Extraction Techniques on the Yield and Purity of Obtained Extracts  
In the first part of this study (Figure S1, Step 1), the efficiency of different solvent systems, namely distilled water and phosphate buffers at different pH, to extract PBP was evaluated with the results summarised in Figure 1. The highest yield was achievedusing 0.1 M sodium phosphate buffer at pH 7, followed by distilled water, in which a green taint in the extract was observed. Similar observations were reported previously by [PERSON] et al., with the authors reporting that the buffered system probably minimises chlorophyll extraction [34].  
The pH is crucial since it affects the PC solubility because of the way the ionic strength can influence the protein structure. Reports indicate that the isoelectric point of PC is around 4.1-6.4, depending on the source and species of biomass [35]. In another study, [PERSON] et al. evaluated the influence of sodium phosphate buffers of different pH ranges from 5 to 8 on phycocyanin extraction [36]. The authors reported that the maximum concentration of PC after two hours of solid-liquid extraction was obtained at pH 7, although after 50 min of extraction, the PC yield was higher at pH 8 than at pH 7, and the PC content decreased during the prolonged extraction process, possibly due to protein denaturation [36]. The higher solubility could also explain the higher extraction yield at pH 7, as it was reported that at pH 7, about 82% of PC is in trimeric form, which is highly soluble [37]. Considering this, 0.1 M sodium phosphate buffer at pH 7 was used for further extractions.
# EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model
## 2 Research Methods
### Machine Learning Models
#### 2.3.5 Rabbit Optimization Algorithm  
The Rabbit Optimization Algorithm (ROA) [33] is an emerging population-based optimization algorithm inspired by the foraging behavior, reproductive patterns, and social characteristics of rabbits. During foraging, rabbits adopt movement strategies characterized by uncertainty and directionality, which are used to simulate the search process within the solution space. The social characteristics of rabbit groups, such as information sharing and learning, are incorporated to enhance solution quality, while their high reproductive rates generate new solutions, strengthening the algorithm's exploration capabilities. These behaviors are translated into the search, update, and optimization mechanisms of the algorithm.  
ROA achieves a balance between global search and local exploitation with a simple structure. It is parameter-efficient, easy to implement, and highly adaptable, making it suitable for applications in engineering optimization, parameter tuning, path planning, and image processing. For instance, in machine learning, ROA can be used to optimize model parameters, while in energy management, it can address power dispatch optimization.  
As a novel intelligent optimization method, ROA demonstrates outstanding performance and efficiency, particularly for solving high-dimensional, complex nonlinear optimization problems. It provides a new tool for the field of intelligent optimization.  
In CatBoost hyperparameter optimization, the Rabbit Optimization Algorithm (ROA) employs a random perturbation + gradual convergence search mechanism, achieving faster convergence speed and lower computational cost compared to the traditional Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Unlike GA, which relies on crossover and mutation with high computational complexity, ROA adopts an adaptive search strategy, performing extensive global exploration in the initial phase and gradually narrowing the search space in the later stages to avoid local optima. Compared to PSO, which is significantly influenced by inertia weight, ROA dynamically adjusts perturbations to ensure efficient optimization, enabling CatBoost hyperparameters to quickly adapt to different datasets and improving prediction accuracy while significantly reducing computational time, making it a highly efficient, stable, and precise hyperparameter optimization method.
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 4 Prototype System  
The objective of this work was to build a final prototype of an anti-UAV system, which should be portable and easily manageable by an operator.  
The design of the implemented prototype is shown in Figure 6. The main hardware supports the implementation of three distinct modules, which were introduced in Figure 1. The processor module, which is the brain of the entire system being responsible for all interconnecting sensors/actuators, is implemented using a raspberry pi 3 board. A microcontroller which is part of two different modules, the sensor module and the security module, is implemented using an Arduino uno (AtMega329p microcontroller). It has the functionality of collecting and processing all the information coming from the sensors. The Tx module is responsible for transmitting jamming and spoofing signals and is implemented on a software defined radio platform, namely, the BladeRF x40.  
To create and transmit the jamming signals, the GNU radio toolbox was used [20], whereas the spoofing signals are generated resorting to the simulator GPS-SDR-SIM available on GitHub [21].
# The Application of Computed Tomography to Study the Soil Porosity of Mountain Red Earth
## 3 Results and Discussion
### Fractal Characteristics of Pores  
Key indicators such as pore area, proportion, number, and roundness provide a quantitative framework for analyzing the pore structure of mountainous red soil. The spatial and planimetric variations show significant heterogeneity and complexity, which can be effectively described using fractal analysis. By applying the box-counting method and linear regression between lnr and lnNr, the fractal box dimension (D) is calculated, reflecting the distribution's complexity. A higher box dimension indicates more complexity, while a lower dimension suggests simplicity. The study, using ImageJ software, confirms the self-similarity in pore distribution with a high determination coefficient (R\({}^{2}\) > 0.99), supporting the use of fractal theory to describe the soil's pore characteristics, as shown in Figure 11.  
Figure 11: _Cont._  
Through computational analyses, we can discern the variation trends of box dimensions with respect to depth as exhibited in Figure 12. The results underscore the efficacy of the box dimension in encapsulating the individual conditions and the evolutionary trajectories of each soil slice. The soil column's upper segment exhibits a relatively larger box dimension, peaking at a value of 1.7980 at the surface level. With increased depth, the box dimension follows a linear declining trend, reaching its nadir at a value of 0.9878 at the base.  
Quantitative pore analysis shows that surface soil, affected by plant roots and fauna, has a higher pore quantity, area, and ratio, with most pores being small. The complexity of the surface soil's pore structure, driven by these small pores, leads to a fractal pattern. As depth increases, pore formation factors weaken, reducing pore quantity and complexity, which lowers the fractal box dimension. While the box dimension effectively describes pore characteristics, pore roundness remains consistent with depth and is not a reliable indicator of pore complexity.
# Cooperative Power-Domain NOMA Systems: An Overview
## 4 Open Issues and Challenges
### Imperfect CSI  
The case where the BS has perfect knowledge of the CSI has been the focus of majority of research on NOMA systems. However, it can be difficult to achieve the perfect CSI in reality. Several authors working on NOMA systems have addressed a significant amount of work with optimal CSI for resource allocation and identification of multiple users [180; 181; 182; 183]. The evaluation of NOMA networks with imperfect CSI is particularly essential because the quality of CSI has a significant impact on the encoding and decoding of NOMA. To deal with the model with imperfect CSI, ideal joint precoders are required to eliminate the interfering signals from the BS. Given the preceding explanation, it is an open problem that must be handled in order to decrease the impact of errors.  
In terms of CSI, there are two types: instantaneous CSI and statistical CSI. The present channel conditions are known in instantaneous CSI (or short-term CSI), which can be compared to the impulse response of a digital filter. This allows the sent signal to be adapted to the impulse response, allowing the received signal to be optimised for spatial multiplexing or low bit error rates. The authors of [182; 183; 184] analyzed instantaneous signal strength with imperfect CSI in NOMA systems. Statistical CSI (or long-term CSI) denotes that the channel's statistical characterization is known. The type of fading distribution, the average channel gain, the line-of-sight component, and the spatial correlation can all be included in this description. This information, similar to instantaneous CSI, may be utilised to optimise transmission. The authors of [185; 186; 187] analysed statistical CSI-based NOMA systems.
###### Abstract  
Maneuverability is one of the submarine's most important features, and is closely related to hydrodynamic coefficients. The submarine standard motion equation is the most commonly used hydrodynamic mathematical model, estimating more than 100 hydrodynamic coefficients. This paper applies the Bayesian ridge regression model to identify a submarine's hydrodynamic coefficients. Specifically, the proposed approach combines the URANS equation with a six-degree-of-freedom motion model and a body force propeller model, and uses overset grid technology to simulate the underwater six-degree-of-freedom navigation motion of the SUBOFF submarine. The submarine's required hydrodynamic coefficients are obtained by collecting relevant velocity and angular velocity data and applying the Bayesian ridge regression model for data identification. Meanwhile, CFD simulation of the restraint model test for the SUBOFF submarine is conducted to obtain the related hydrodynamic coefficients. Through comparative experiments, we validate that the proposed Bayesian ridge regression model identification method is effective and reliable; most of the errors in the hydrodynamic coefficients were within 10%, with a maximum error of \(-43.26\%\), providing more comprehensive and timely hydrodynamic coefficients than traditional CFD restraint model tests. Furthermore, the hydrodynamic coefficient identification results were used to invert the submarine's spatial motion, and we demonstrated that the resulting trajectory, velocity, and angular velocity curves all fit well.  
hydrodynamic coefficients; parameter identification; Bayesian ridge regression model; spatial motion; restraint model test +
Footnote †: journal: Applied Sciences
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 2 Hydrogel Materials
### Natural Hydrogels
#### 2.2.2. Agarose  
Agarose is a natural polysaccharide derived from marine algae and is composed of alternating D-galactose and 3,6-anhydro-L-galactopyranose units. It has an uncharged molecular structure, which renders agarose to exhibit similar properties to the extracellular matrix, making it an excellent material for biomedical applications [82,83]. Agarose is particularly valued for its thermo-reversible behavior, which allows it to transition between sol and gel states.  
The synthesis of agarose hydrogels typically involves a heat-assisted sol-gel transition. At ambient conditions, agarose exists in an entangled state, tightly held together via hydrogen bonds, which appears as a solid powder and is hard to dissolve in aqueous solutions. When dispersed in a solution and heated to 80-90 \({}^{\circ}\)C, these molecular entanglements uncoil, resulting in a fully dissolved sol. Upon cooling, the agarose molecules re-associate to form helices, which bundle into a macroscale network stabilized by hydrogen bonding. This network creates the gel, and since hydrogen bonding is a non-covalent intermolecular interaction, this structure is thermo-reversible and can be returned to the sol state when reheated above its melting temperature [83, 84, 85, 86]. Also, the mechanical stiffness or elasticity of the resulting agarose gel can be controlled by adjusting thermal profiles [82].  
Agarose hydrogels are widely used in cell cultures and in vivo implantations largely owing to their excellent biocompatibility and ease of handling. Agarose also exhibits similar characteristics to the extracellular matrix, which supports stable cell adhesion and proliferation, while allowing facile control over gel stiffness, making it one of the most practically suitable soft materials for tissue engineering [83, 87, 88, 89]. Additionally, agarose hydrogels are commonly utilized in electrophoresis and biomolecular separation for proteins and nucleic acids.  
Major challenges of agarose hydrogels in biomedical applications are insufficient mechanical stability, intrinsic hydrophobicity, and ironically, its thermo-reversible nature. Agarose is relatively hydrophobic compared to other polysaccharides, which makes chemical modification necessary to promote directed intermolecular interactions [84, 88, 89, 90, 91]. Although the reversible phase transition between the sol state and gel state is straightforward, the mobility of the agarose molecules and association dynamics between bundled agarose chains, or between water molecules and agarose chains, directly affects this phase transition. The accumulative and combined effects of those lead to thermal hysteresis of native agarose gel, which is often observed as shifts in gelation temperature over repetition [92].
## 4 Results and Discussion
### Data Processing
The waveform and frequency spectrum of the vibration signal before and after reconstruction are shown in Figure 6 (with a sampling frequency of 2000 Hz). In Figure 6, the changes in the time and frequency domains of the original signal before and after EEMD and reconstruction are shown. The time-domain signals display the changes in the signal over time, while the frequency-domain signals reveal the frequency components of the signal. By comparing the signals before and after reconstruction, we can observe the following points:  
1. Time-domain signal: The reconstructed signal is smoother in the time domain and significantly reduces noise components. This indicates that the EEMD and reconstruction process effectively removes noise interference from the original signal, making the main features of the signal more prominent. 2. Frequency-domain signal: In the frequency domain, the reconstructed signal has clearer frequency components. By comparison, we can find that the key frequency components in the reconstructed signal are consistent with the original signal, but the noise frequency components are effectively suppressed. This further validates the effectiveness of the EEMD and reconstruction method in signal processing. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**IMF** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline r & 0.8788 & 0.3753 & 0.2869 & 0.2532 & 0.1486 & 0.0170 & 0.0145 & 0.0078 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation coefficients between IMF components and original signal. Figure 6: Time-domain waveforms and spectra before and after reconstruction. (**a**) Time-domain signal before reconstruction; (**b**) Time-domain signal after reconstruction; (**c**) Spectral signal before reconstruction; (**d**) Spectral signal after reconstruction. In summary, the results in Figure 6 demonstrate the significant effect of the EEMD and reconstruction method in improving signal quality, providing a more accurate and reliable signal foundation for subsequent diagnostic analysis.
# Mobility-Multihoming Duality
## 4 ILNP Implementation in Linux
### Load Sharing across Interfaces (Paths)  
Load sharing is an application of host multihoming where the capacity of more than one interface available to the host is aggregated, and the traffic is shared amongst those interfaces.  
For our experiments, Deficit Round-Robin (DRR) scheduling was used [51] as a general mechanism for distributing packets across interfaces. However, the load-sharing scheme can be changed to whatever better suits a particular application.  
The state management mechanism for each L64 entry holds the DRR score derived from the bytes sent from each packet's length. Each time an additional interface is added, the DRR score for each L64 entry in the ILCC is reset. For both the source L64 and the destination L64, the L64/interface with the lowest score at any given time is selected for transmission. As each L64 value is selected for outgoing packets, their respective DRR scores are increased by the size of the packet transmitted, whilst the scores on each of the other L64 values are decreased by the same amount.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### Blockchain-Based Data Integrity and Traceability Solutions  
Blockchain-based data storage systems offer robust mechanisms for ensuring data integrity and traceability [26; 28; 30]. These systems guarantee data accuracy and immutability, while also enabling transparent tracking of access and usage records [28; 31; 33]. However, the applicability of these mechanisms varies depending on the chosen data storage method [32; 34]. On-chain approaches provide full traceability and verifiable authenticity, but they come with high transaction costs [6; 36]. In contrast, off-chain methods offer lower costs and greater efficiency, but they may be less secure in terms of data integrity and accessibility [35; 165]. Hybrid systems combine the strengths of both on-chain and off-chain solutions, delivering a balanced approach in terms of cost efficiency and security [37; 47]. Table 9 summarizes the advantages and disadvantages of these storage mechanisms by evaluating data storage methods, data integrity mechanisms, and traceability levels across various industry sectors.
## 3 Results and Discussion
### Crack Growth Trend Simulation  
#### 3.2.1 Test Specim  
ABAQUS software with XFEM functions was employed to reveal the crack initiation position and crack growth trend in the test specimen. The results are shown in Figure 14. Figure 14 describes the location of the fatigue crack initiation, and the crack growth patterns at different step times are displayed in Figure 14.  
Figure 14: Crack propagation path of the test specimen: (**a**) Location of fatigue crack initiation; (**b**) Crack growth under different analysis stages.  
Figure 13: Stress distribution of inner web.  
At Step \(-1.9\), a crack emerges in the girder's inner web at its junction with the extra stiffener, which is of relatively small size and stays within the stiffener plate thickness. As the analysis time increases, two other small cracks appear on both sides of the stiffener, and eventually, multiple small cracks merge into a large continuous one. At Step \(-4.0\), the crack propagates quickly in the web toward the other web stiffener at a slight inclination.  
We speculate that the crack that appeared on the girder's inner web at the end of the extra stiffener is caused by severe stress concentration from the abrupt geometric change and poor welding process. Compared to the fatigue test results shown in Figure 12, the crack location and general growth pattern at the inner web stiffener are close to each other, demonstrating the effectiveness and advantage of XFEM in projecting the crack propagation path.
# Study on Seismic Attenuation Based on Wave-Induced Pore Fluid Dissolution and Its Application
## 3 Synthetic Data Test
### Rock Physics Modeling and Velocity Dispersion Analyzing
Velocity Variation from Logging Data to Seismic Data Based on Rock Physics Model and Backus Averaging Method  
In this section, logging data is used for demonstration of velocity difference between upscaling by the pore fluid dissolution model and the Backus averaging method. Firstly, the Backus averaging method is applied, and the velocity variation is shown in Figure 5. Using different sizes of windows, the velocity results from the Backus averaging method show different properties. As the size of the window becomes larger, the velocity curve shows larger divergence from the real data. However, the velocity from all these three cases varies in the same range as that of the real data. Figure 4: Comparison of 1/Q curves from the pore fluid dissolution model and the SLS model. The red solid curve is the pore fluid dissolution model, and the dash blue curve is the SLS mode. (**a**) Porosity 0.04 and gas saturation 0.25%, (**b**) Porosity 0.06 and gas saturation 0.25%, (**c**) Porosity 0.08 and gas saturation 0.25%, (**d**) Porosity 0.08 and gas saturation 0.15%, (**e**) Porosity 0.08 and gas saturation 0.2%, (**f**) Porosity 0.08 and gas saturation 0.25%. Then, the SLS model and the constant Q model are both applied to the real data for velocity correction. The results are shown in Figure 6. Compared with that of the Backus averaging method, the velocity upscaled by the SLS model and constant Q model contains obvious variation from read data. Both the models result in smaller velocity values than the real data. The SLS model always yields smaller velocity than the constant Q model in these cases.