esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
## 4 Experimental Simulation
### Algorithm Testing and Analysis
In contrast, PSO experiences mean fluctuations due to particle interactions, SA maintains a high mean due to low search efficiency, HWSO suffers mean deviations from parameter conflicts caused by multi-algorithm coupling, and IWSO faces mean instability due to the \"black box\" nature of deep learning. FMWSO achieves high consistency in iterative results through transparent and collaborative strategies, demonstrating significantly superior stability compared to other algorithms. 3. Dispersion: Strategy complementarity suppresses fluctuations, ensuring strong consistency in output results. FMWSO effectively reduces dispersion through complementary strategies: the chaotic elite pool ensures a more uniform distribution of initial solutions, reducing random interference; the probabilistic acceptance mechanism of annealing avoids algorithm deviation from the optimal solution due to local oscillations; adaptive weights precisely control the search step size, maintaining convergence stability, resulting in extremely small standard deviations and negligible dispersion  
Figure 5: Variation curves of fitness values for four test functions. across multiple runs. In contrast, PSO experiences result fluctuations due to particle interactions, SA has high dispersion due to sensitivity to temperature parameters, HWSO has significant dispersion due to difficulties in coordinating multiple algorithm parameters, and IWSO amplifies dispersion due to data quality impacts. FMWSO builds a robust anti-interference barrier, outperforming other algorithms in all aspects of dispersion, ensuring ultra-stable output results.
# Parallel Computation of CRC-Code on an FPGA Platform for High Data Throughput
## 4 FPGA Implementation  
To keep the system flexible, the algorithm is divided into several sub-modules: the multiplier unit, the slicing unit, and the finite state machine (FSM) unit. The FSM is the core unit of the system, and it plays an important role in scheduling and synchronizing other units. The overall architecture of the system is shown in Figure 6. The flowchart of the FSM is shown in Figure 6.  
As shown in Figure 7, initially, the FSM is in the IDLE state waiting for a start signal to go from 0 to 1. When the FSM starts, in the first cycle, it asserts the LD signal to one to load the data into the input buffer and turn off the LD signal back to zero immediately in the next cycle. After the input message has been loaded into the buffer and hold valid during the process, the counter begins to count in the next cycle. The process iterates over \(K\) cycles where \(K\) is calculated as shown in the formula below:  
\[K\ =\ \frac{M}{S\times N}, \tag{7}\]  
with \(M\) is the data buffer length and \(S\) is the number of slicing-by-N units. After the process ends, each output from a Slicing-by-N computation unit is multiplied with its  
Figure 5: Hybrid system using both slicing-by-N and matrix transformation.  
Figure 6: Overall architecture of the parallel CRC computational system. For clarity, not all connections are shown. The counter signal and the ready signal are distributed to all slicing unit and register unit correspondingly.  
corresponding modular matrix. The multiplication is carried out in single clock since the modular matrix is precomputed and can be realized with only XOR operation. Finally, the ready is asserted to 1, and the partial CRC results are loaded into the registers. The final CRC of the whole input is obtained by XORing all partial CRC results as mentioned in the previous section.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Thermal Analysis
#### 3.7.3 Planetary Infrared Radiation  
All objects, including stars and planets, radiate energy to their surroundings. The heat radiated is described by the Stefan-Boltzmann law and depends on the temperature of the body (T) and its surface emissivity (\(\epsilon\)), which is the effectiveness of the surface material to emit energy as thermal radiation. For satellites orbiting the Earth, there are data related to the orbit's inclination from NASA's data library [22].  
The heat gained from a surface of the satellite (W) due to planetary radiation is as follows:  
\[Q_{planetary-IR}=aS_{planetary-IR}AF_{1-2}\]  
with \(S_{planetary-IR}\): the radiation flux of the planetary body (W m\({}^{-2}\)).
## 3 Methodology
### Accurate Transformation Estimation  
An accurate transformation estimation includes feature matching, coarse transformation estimation, and fine transformation estimation. By feature matching, we can find coarse correspondences between a pair of point clouds, and we can obtain a coarse transformation matrix between the pair of point clouds by coarse transformation estimation. After applying the coarse transformation matrix to the source point cloud which is a transformable point cloud in the pair of point clouds, we use a fine transformation estimation to estimate a fine matrix between the transformed source point cloud and the target point cloud, which is fixed point cloud in the original pair of point clouds. Through these three processes, the transformation between a pair of point clouds will become increasingly accurate.
# _Sci_
## 4 Discussion
### Advantages--Throughput, Power, Information Protection
Satellite-based QKD has the potential to help to establish a global-scale quantum network, owing to the negligible photon loss and decoherence experienced in empty space. Here we report the development and launch of a low-Earth-orbit satellite for implementing decoy-state QKD-a form of QKD that uses weak coherent pulses at high channel loss and is secure because photon-number-splitting eavesdropping can be detected. We achieve a kilohertz key rate from the satellite to the ground over a distance of up to 1200 kilometres. This key rate is around 20 orders of magnitudes greater than that expected using an optical fibre of the same length. The establishment of a reliable and efficient space-to-ground link for quantum-state transmission paves the way to global-scale quantum networks.\" [53; 97]
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Simulation Parameterization
Number of MEC servers (10-80)  
In small-scale scenarios (e.g., a small campus), only a small number of MEC servers may be needed, so the lower limit is set to 10. In a large-scale MEC deployment (e.g., an entire city), the number of servers may be larger, so the upper bound is set to 80 to accommodate larger-scale optimization problems. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Setting** & **Value Range** \\ \hline Number of users & 1000–6000 \\ \hline Number of base stations & 100–180 \\ \hline Base station network density & 0.02–0.1 \\ \hline Number of edge servers & 20–70 \\ \hline Number of iterations & 1000 \\ \hline Population size & 20–80 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation parameters. Number of iterations (1000)  
Multiple experiments have shown that QSA and other meta-heuristic algorithms usually stabilize after 500-1000 iterations, so set 1000 to ensure that all algorithms have sufficient search space to find a better solution. Population size (20-70)  
In meta-heuristic optimization algorithms, the population size affects the search capability and computational complexity of the algorithm. The range of 20-70 is chosen to strike a balance between search accuracy and computational time: smaller populations may lead to local optimization, while larger populations increase computational cost.
# Modeling of Historical Marine Casualty on S-100 Electronic Navigational Charts
## 3 Method: Data Modeling of Marine Casualty
### Portrayal Catalogue Development  
The PC defines the rules for visually displaying structured marine casualties on ENCs. It enables users to intuitively recognize and interpret incident information. Based on the feature and attribute type defined in the FC, the PC specifies how each feature should be portrayed--through symbols, colors, lines, and area styles--ensuring consistent visual representation across systems. These portrayal rules are structured to ensure consistent visualization across different systems. The PC was implemented in XML format, and is designed to operate in conjunction with the FC, allowing S-100-compliant systems to render marine casualty features in a standardized way. The XML-based PC document is publicly available in the GitHub repository referenced in the data availability section.  
Since all marine casualty features are represented as point geometries, corresponding symbology was developed to match the portrayal requirements for point-based features. A set of triangular warning symbols was created for this purpose, with each symbol incorporating an abbreviation to indicate the specific category of the incident. Figure 4 shows the complete set of these symbols, which provide a clear visual distinction between the different types of marine casualties on ENCs.
## 3 Results
### A Statistic Method to Evaluate the Threshold and Investigate the Threshold Conditions
As stated above, the peak-power intensity was approved to be a more suitable condition than the energy density for a Q-switched laser to determine the air breakdown threshold. For p-QS pulses, the \(\Delta\widetilde{P}\) of 0.048 is smaller than the \(\Delta\widetilde{E}\) of 0.059. Because there is no big difference in pulse shape, defining the threshold value based on the peak power is slightly better than energy. However, through the qualitative and quantitative results of  
Figure 3: (**a,d**) Relation of the success of air breakdown to the laser pulse energy and peak power with mode-locked Q-switched laser and pure Q-switched laser, respectively. (**b,c,e,f**) Histogram and fitting curve (red line) of the breakdown probability versus the pulse peak power and energy in (**a,d**), respectively. ML-QS pulses, we confirm that the peak-power intensity is the critical condition rather than the energy density. Furthermore, the statistic method can explicitly evaluate the threshold value and threshold width which is related to the peak power stability of the laser source and the breakdown environment. Reference [20] was chosen for comparison with previous literature works due to its similar breakdown conditions. The breakdown threshold energy was 13.4 mJ by use of the 1064-nm 6.5-ns p-QS pulses. With an estimated beam radius of 9 \(\upmu\)m, the \(ED_{th}\) would be 5.1 kJ/cm\({}^{2}\), close to the \(ED_{th}\) of 4.23 kJ/cm\({}^{2}\) for p-QS pulse in this work.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
###### Abstract  
This work provides a comprehensive review of the recent advancements in the toughening modification methods for epoxy resins. The study explores a variety of approaches, including the incorporation of liquid rubbers, core-shell rubber particles, thermoplastic resins, hyperbranched polymers, and the nanoparticle toughening method, each of which contributes to improving the mechanical properties and fracture toughness of epoxy resins. Special attention is given to the mechanisms underlying these toughening methods, such as reaction-induced phase separation, crack pinning, and energy dissipation through particle deformation. The paper also examines the synergistic effects achieved by combining different toughening agents, such as phenoxy thermoplastic rubber and core-shell rubber particles, which significantly enhance the critical fracture energy and impact strength of epoxy composites. Additionally, the challenges associated with each method, such as the potential reduction in mechanical properties and the influence of phase separation on material performance, are discussed. Through a detailed analysis of experimental studies, this paper highlights the effectiveness of various toughening strategies and suggests future research directions aimed at further optimizing epoxy resin toughening techniques for diverse industrial applications. Emerging computational modeling and machine learning applications in epoxy resin development are also systematically reviewed to highlight their potential in advancing predictive design frameworks.  
epoxy resin; toughness; modification +
Footnote †: journal:
# Conceptual Design of a Compact Divertor Heat Load Simulation Device: HIT-PSI
## 1 Introduction  
In tokamak operations, plasma-surface interaction (PSI) processes under extreme conditions have profound impacts on operational safety, performance metrics, and economical efficiency of fusion reactors, and have become one of the primary foci of fusion studies [1; 2; 3]. High heat and particle fluxes reaching plasma-facing components (PFCs) will cause erosion and tritium retention of materials [4]. Therefore, understanding and exploring the PSI mechanism is crucial to the realization of controlled nuclear fusion.  
However, divertors in future fusion reactors such as ITER and CFETR are subject to extreme long-pulse high heat/particle fluxes, far beyond the parameters of existing tokamaks [5; 6]. The performance of PFCs under such high-parameter plasma bombardments is thus an unknown territory. Various lab simulation devices for divertor plasmas have been developed for studying PSIs. Among them, linear plasma devices are the most widely adopted worldwide due to their economy, flexibility, and expandability, as well as their advantages as diagnostic test platforms. For example, a steady-state linear plasma device of PISCES-B is capable of testing radioactive and toxic materials such as Be [7; 8]; another divertor simulator facility, NAGDIS-II, is applied in high-density plasma detachment studies [9]; and a device at Sichuan University with similar functions, SCU-PSI, is applied in PSI investigations of liquid materials [10; 11]. The above-mentioned and many more established linear plasma devices have greatly advanced PSI research, along with diagnostic techniques, plasma sources, and plasma physics of low-temperature plasma [12; 13; 14; 15; 16; 17]. There arenevertheless only a few devices, including Magnum-PSI and MPEX, reported to be capable of simulating the divertor heat load in ITER-like steady-state operations. Magnum-PSI is an integrated linear plasma device with the most comprehensive performance in the world, where a steady-state particle flux of up to \(10^{25}\) m\({}^{2}\)s\({}^{-1}\) and a steady-state heat flux of up to 50 MW/m\({}^{2}\)[18, 19, 20] can be achieved. MPEX [21] is, on the other hand, designed for performing neutron-related materials testing experiments.  
To experimentally simulate the extreme divertor plasma environment with a heat flux exceeding 10 MW/m\({}^{2}\) and a particle flux over \(10^{24}\) m\({}^{-2}\)s\({}^{-1}\) in steady-state, a linear divertor plasma simulator for PSI, namely HIT-PSI, is designed and constructed at Harbin Institute of Technology. In this paper, the conceptual design of this low-cost and compact linear plasma device is reported, with numerical validation of its planned functions and performance prediction.  
The layout of the paper is as follows. After the Introduction, Section 2 goes into the details of the design and its scientific goals, as well as displaying the specifics of each component of the device. The expected parameters, functions, and performances of the device are predicted with numerical simulation in Section 3. The work ends with a summary in Section 4.
## 3 Results and Discussions  
In this section, we present the numerical simulation results for various scenarios based on the established simulation platform. It is worth mentioning that the characteristics of the optical channel in seawater are affected by many factors, mainly the seawater environment and the transceiver configurations and thus there exist many combination cases. To be more representative and not too complicated, we provide the simulation results under some typical configurations and our analysis focuses on spatial attenuation and temporal dispersion together with system BER performance with solar noises. Besides, although we mainly studied the LED-based diffusive cases, some laser-based results are also presented for comparison and improved analysis. Several important parameters for simulation are summarized in Table 3.  
Figure 4: The graphical user interface of the PTA-UOWC simulation platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline LED semi-angle at half power & \(\theta_{1/2}\) & (15\({}^{\circ}\), 20\({}^{\circ}\)) \\ Laser divergence angle & \(\theta_{div/2}\) & (0.75, 1) mrad \\ Link range & \(d\) & 1–35 m \\ Water refractive index & \(n\) & 1.33 \\ Total number of transmitted photons & N\({}_{T}\) & 10\({}^{8}\) \\ Aperture diameter & \(A_{d}\) & 5, 11, 20 cm \\ Depth & \(D\) & 10, 25, 100 m \\ Weight threshold & \(E_{th}\) & \(10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: UOWC system configuration.
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 5 Experiment and Results
### The Algorithm Runs in the \(Bn\) Group  
The \(BN\) group consists of algorithm runs that use d-separation as a CI test exclusively and no dataset was synthetically generated for them. There are 6 algorithm runs in total: each of the 2 algorithms has one run for each of the 3 Bayesian networks.  
Of interest are the following measures:  
* The total number of CI tests performed by the algorithms on each Bayesian network, considered a measure of the overall efficiency of the algorithms;
* The average size of the conditioning set used in the CI tests, considered a measure for the overall sample efficiency of the algorithms.  
Table 1 confirms what was already reported by the authors of IPC-MB [16], but the information presented in this table is far more accurate due to the exclusive usage of d-separation as a CI test instead of statistical tests. Indeed, IAMB performs far fewer CI tests than IPC-MB, but the tests it performs have much larger conditioning sets, unequivocally revealing that IAMB is sample inefficient. The values indicating the highest efficiency per column are shown bolded.  
It is worth emphasizing that these results are fully repeatable and deterministic, depending only on the structure of the Bayesian networks and on the design of the algorithms. There is no randomly synthesized dataset, no sampling process that could be biased and no scarcity of samples for specific combinations of variables. It can be considered that these runs to have been performed in ideal conditions, because the algorithms received perfect information about the CI relationships between variables.  
The \"Duration\" column in Table 1 shows that running MBD algorithms exclusively configured with d-separation consumes very little computing time, even with the simple and very inefficient method described by Algorithm 1. This is essential, because it means that such validation runs can be executed as software integration tests when developing MBD algorithms. The method described by [PERSON] and [PERSON] [19], mentioned in Section 3.1, would reduce the duration even further.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Network** & **Tests** & **Avgerage Cond. Set Size** & **Duration (s)** \\ \hline IAMB & CHILD & **148** & 9.66 & 0.001 \\ IAMB & ALARM & 319 & 11.41 & 0.005 \\ IAMB & MILDEW & 407 & 13.95 & 0.104 \\ IPC-MB & CHILD & 32,310 & 5.96 & 0.360 \\ IPC-MB & ALARM & 57,831 & **4.06** & 4.793 \\ IPC-MB & MILDEW & 313,479 & 5.60 & 87.281 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall d-separation test count and average conditioning set size.
# Exopolysaccharides from Cyanobacteria: Strategies for Bioprocess Development
## 2 EPS from Cyanobacteria
### Chemodiversity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strain** & **EPS Fraction** & **Compositional** & **Non-Carbohydrates** & **EPS Characteristic** & **Reference** \\ \hline _Aphanothescarum_ & CPS & Glc, Gal, Man, Xyl, Rha, Fuc, GalA, Glc & sulfate and carboxyl groups & anti-inflammatory; anti-alergic; adsorption of metal ions; liquid crystallization & [32] \\ \hline _Cyanothece_ PCC & RPS & \begin{tabular}{c} Man, Glc, uronic acids, Gal, Xyl, Rha, Fuc Ara \\ \end{tabular} & sulfate and acetate groups, peptides & \begin{tabular}{c} thermostatble, amorphous, pseudoplastic \\ behavior \\ \end{tabular} & [31] \\ \hline _Cyanothece_ sp. & 113 & CPS & D-Glc & nd\(\dagger\) & [33] \\ \hline _Cyanothece_ sp. & RPS & \begin{tabular}{c} 2-C-Me-Glc, \\ Ido-2-C-Carboxylic acid, 2-deoxy-Ido \\ \end{tabular} & calcium; sulfate groups & \begin{tabular}{c} gel formation; adsorption of metal \\ ions \\ \end{tabular} & [34] \\ \hline _Nostoc commune_ & RPS & \begin{tabular}{c} dXyl, dGlc, dGal, \\ dRib, Glc, Man \\ \end{tabular} & nd\(\dagger\) & [35] \\ \hline _Nostoc flagelliforme_ & CPS & \begin{tabular}{c} Glu, Gal, Xyl, Man, \\ GlcA \\ \end{tabular} & \begin{tabular}{c} anti-viral activity, \\ negative \\ antithrombin activity \\ \end{tabular} & [36] \\ \hline _Nostoc insulare_ & RPS & \begin{tabular}{c} Ara, 3-O-Methyl-Ara, \\ Glc, GlcA \\ \end{tabular} & Nd\(\dagger\) & [37] \\ \hline _Nostoc_ & CPS & \begin{tabular}{c} Man, Glc, Xyl, Gal, \\ GlcA \\ \end{tabular} & \begin{tabular}{c} immunological \\ activity \\ \end{tabular} & [38] \\ \hline _Spirulina_ & CPS & \
\begin{tabular}{c} Rha, Fru, Rib, Man, \\ Gal, Xyl, Glc, GlcA, \\ GalA \\ \end{tabular} &
\begin{tabular}{c} sulfate groups, \\ calcium \\ \end{tabular} & anti-viral activity & [39] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Isolated exopolysaccharides (EPS) from cyanobacteria.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 5 Performance Analysis  
We conduct a performance analysis of FBE variants in two settings: (i) all nodes in the network use the same variant, and (ii) all nodes use different (competing) variants.