esa-sceva/satcom-chunk-collection huggingface dataset:
# Locating Multiple Sources of Contagion in Complex Networks under the SIR Model
## 2 Model and Method
### Potential Concentration Label Definition  
In the early period of severe contagion propagation, disease outbreaks through a crowd quickly. It comes to the situation that the nodes around sources are more likely to be the infected nodes, that is, the sources are surrounded by many infected nodes. Only by depending on the infection states can we locate the sources in a complex network accurately.  
Inspired by Figure 1a, which shows the concentration of a pollutant, it is clear that the sources are more likely to be the node set \(\{d,k\}\), whose concentration is the highest (10). In fact, to get the state of each node is not easy, for example, some sensors do not have the capacity to measure concentrations, and can only judge whether the concentration surpasses a threshold value or not, and even then we may lose the concentration information. Therefore, the information we can obtain is incomplete, just like in Figure 1b. We can see two concentration states easily, 0 or 1 (1 denotes concentration over 8, 0 denotes concentration under 8) in a network, where an error occurred with node \(c\). It seems we have no ability to identify the sources according to these concentrations, which is similar to the infection situation of contagion.  
Therefore, a new index needs to be proposed so as to distinguish between the sources and other nodes for incomplete pollutant diffusion and contagion propagation. We think the node with more infected neighbors, including the first order neighbor, the second order neighbor and so forth, is closer to the sources. Based on the above analysis, we propose a new concept, namely a potential concentration label, denoted by \(\mathcal{L}\). The potential concentration label is determined by its initial label  
Figure 1: A snapshot of the pollutant diffusion process. The letter denotes the node in network and the digital represents the concentration label of each node. The propagation sources are \(S^{\star}=\{d,k\}\). (**a**) pollutant concentration of network diagram. (**b**) incomplete pollutant concentration of network diagram, which is similar to the contagion situation of the network.  
and the labels of neighbor nodes. The experiments demonstrate that it is a good index for locating multiple sources of contagion in complex networks under the SIR model.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 3 Overview of Wound Pathophysiology and Wound Healing Process
### Wound Dressings  
The conventional healing process includes the covering of the wound with traditional dressings such as gauze [47], plasters [48] and cotton [49]--products that protect the wound area from external factors and absorb excess exudate. One of the most important factors that negatively affects the healing process is the disruption of acute and chronic wounds during the detachment of traditional dressings. Separating the traditional dressing from the wound may result in damage to the tissue and even bleeding. It may also cause serious complications, including fatal infections in time. Traditional wound dressings are only recommended for clean and dry wounds. In essence, they function by absorbing the exudate from the wound and maintaining its dryness. Unfortunately, traditional wound dressings are insufficient compared to modern wound dressings, which are competent in maintaining the moist environment the wound requires [8]. In contrast to traditional ones, modern wound dressings provide appropriate humidity and temperature for the wound site, as well as reducing the bacterial load of the wound with antibacterial materials or antibiotics. Due to their similarity to the ECM structure, they facilitate cell adhesion as well. In addition, active substances such as nutritional supplements, growth hormones, antibacterial agents, and other drugs may be loaded into the matrix of wound dressing to ensure the release of active components to the wound area. Unlike traditional dressings, modern ones do not cause any damage to the area or bleeding during dressing removal.  
The functional effectiveness of a wound dressing is directly correlated with its chemical and physical properties, as well as therapeutic ingredients and production method. Appropriate dressings should be chosen considering the corresponding wound type and requirements for healing such as drug administration, cells, nutrient addition, and antibacterial elements [50]. In the next section, types of 3D-bioprinted dressings utilized in wound healing, specifically hydrogel-based dressings, will be discussed.
## 3 Presentation of the Proposed Model
### Sub-Modules of the Presented Model
#### 3.3.3 ANN Target Selection Module  
Weight coefficients (\(w\)) are based on the calculation of the average categories (\(k\)), returned as results from the different product classification (\(n\)) methods (4):  
\[w\ =\ \frac{\overline{k}}{\sum_{i=1}^{n}\overline{k}} \tag{4}\]  
For the selected case study, the module has obtained the following weighting factors (5):  
\[w\ =\ \begin{cases}0.2490&for\ COI\ Index\\ 0.2628&for\ ABC\ analysis\ (weight)\\ 0.2589&for\ ABC\ analysis\ (volume)\\ 0.2292&for\ Method\ of\ relationships\ search\\ \end{cases} \tag{5}\]  
The final category (\(k_{main}\)) employed in the learning process of ANN is described in expression (6):  
\[k_{main}\ =\ 0.2490\cdot\mathit{kat}_{COI}+0.2628\cdot\mathit{kat}_{ABC}+0.2589 \cdot\mathit{kat}_{ABC2}+0.2292\cdot\mathit{kat}_{AP} \tag{6}\]
# On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks
## 6 Conclusions  
In this paper, we studied the operation of a sensor network based on WET, in a scenario where a WMCD was employed to charge the sensor nodes wirelessly inside the network. We analyzed the energy consumption of the entire system and pointed out that the traveling distance of WMCD is the main factor influencing total energy consumption. Based on energy consumption analysis and periodic strategy, we proposed a new new strategy called ERMS-PSO to decrease total energy consumption, travel distance and the charging number of cycles while maintaining the network operational with low complexity. In this new strategy, we introduced two sets of energies called emin (our minimum energy) and ethresh (our threshold energy), where, when the first node reaches our emin, it will inform the base station; then, the base station will calculate all nodes that fall under ethresh and send a WMCD to change them in one cycle. Our emin must be greater than the Emin (general minimum energy in the node) and our ethresh must be greater than emin but less than the general Emax. In contrast to previous schemes in which the WMCD visited and charged all nodes in each cycle, the WMCD in ERMS-PSO only needs to visit a subset of nodes by accounting for the difference in energy consumption rates at each node. As a result, the WMCD's travel distance and total energy consumption can be significantly reduced. In ERMS-PSO, we first devised a system for combining each node's charging period, the visiting set and the traveling trail during every cycle by first formulating a practical optimization problem with a flow rate to determine the energy consumption rate. Following this, we showed how ERMS-PSO can keep the network running. Based  
Figure 6: WMCD travel distance.  
Figure 7: Ratio of vacation time.  
on simulations, ERMS-PSO can significantly reduce the total energy consumption while maintaining vacation time ratio performance. For future work, the ERMS-PSO strategy can be tested using a variety of new heuristic algorithms to determine which one performs the best and thus extends the network life time.  
Formal analysis, A.U.; Funding acquisition, K.X.; Investigation, S.M.; Methodology, S.M.; Project administration, S.M.; Supervision, K.X.; Writing--original draft, S.M.; Writing--review & editing, A.U. and E.O.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. U181322, No. 42075129), Hebei Province Natural Science Foundation (No. E2021202179) and Key Research and Development Project from Hebei Province (No. 19210404D, No. 20351802D, No. 21351803D).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Kubernetes Capabilities for Industrial Edge Cloud
### Infrastructure Accelerators  
Infrastructure accelerators are hardware devices that provide specialized functions either to guarantee the quality-of-service measures or offload some work from the Central Processing Unit (CPU); examples are Field Programmable Gate Arrays (FPGAs), Graphics processing units (GPUs), or (Smart) network interface cards (NICs). Industrial applications with real-time and latency-sensitive requirements make such accelerators more important in edge computing environments. To use them in a virtualized edge cloud, mechanisms are needed to bypass the system software and the virtualization platform for directly exposing the hardware to the applications.  
Kubernetes can handle such hardware devices with a device plugin framework [30]; however, it is in the beta stage and showing no activity in the project since 2018. Its operation is to advertise system hardware resources towards pods (e.g., /smart/NICs, SR/IOV devices, GPUs) and requires the hardware vendor to implement the device plugin.  
To run TSN functions on a general server, specialized NICs might be required to guarantee bounded latency and jitter for time-sensitive traffic. As shown in the integrated edge-TSN-FRER architecture in Section 4.2.1, the application and the TSN functions are split; therefore, only some nodes of the edge cloud must have these NICs. Those nodes can be labeled, and Kubernetes provides mechanisms for the placement of the application components presented in the following subsection.
###### Abstract  
The prediction aptitude of an artificial neural network (ANN) is improved by incorporating two novel metaheuristic techniques, namely, the shuffled frog leaping algorithm (SFLA) and wind-driven optimization (WDO), for the purpose of soil shear strength (simply called shear strength) simulation. Soil information of the Trung Luong national expressway project (Vietnam) including depth of the sample (m), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content, wet density (kg/m\({}^{3}\)), liquid limit (%), plastic limit (%), plastic index (%), liquidity index, and the shear strength (kPa) was collocated through a field survey. After constructing the hybrid ensembles of SFLA\(-\)ANN and WDO\(-\)ANN, both models were optimized in terms of complexity using a population-based trial-and error-scheme. The learning quality of the ANN was compared with both improved versions to examine the effect of the used metaheuristic techniques. In this phase, the training error dropped by 14.25% and 28.25% by applying the SFLA and WDO, respectively. This reflects a significant improvement in pattern recognition ability of the ANN. The results of the testing data revealed 25.57% and 39.25% decreases in generalization (i.e., testing) error. Moreover, the correlation between the measured and predicted shear strengths (i.e., the coefficient of determination) rose from 0.82 to 0.89 and 0.92, which indicates the efficiency of both SFLA and WDO metaheuristic techniques in optimizing the ANN.  
SFLA-multilayer perceptron (MLP); WDO-MLP; soil shear strength; soft computing; metaheuristic science +
Footnote †: journal: Applied Sciences
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 2 Overview and Theoretical Foundations
### Computing Paradigms and Storage Architectures for Industrial IoT
For instance, a predictive maintenance system might retain raw vibration data at the edge for only 24 h (to support real-time anomaly detection), while shipping compressed features to fog nodes for medium-term trend analysis, and eventually archiving aggregated statisticsin cloud storage for long-term model retraining. This hierarchical approach exemplifies the Industrial Data Fabric concept, which seeks to optimize data placement and movement across the entire computing continuum. The emergence of 5G/6G-enabled industrial networks has further complicated this architectural landscape by introducing new possibilities--and challenges--for distributed storage systems. Network slicing capabilities allow for dedicated virtual networks with a guaranteed quality of service (QoS), enabling novel use cases like distributed erasure coding across edge devices. However, these advancements also exacerbate existing tensions between consistency and availability in distributed storage systems, particularly in safety-critical applications where stale data could have catastrophic consequences. Recent research has explored hybrid consistency models that employ physical-time-based reconciliation, leveraging synchronized industrial clocks (e.g., via the IEEE 1588 Precision Time Protocol) to resolve conflicts in sensor data replication. Looking ahead, the convergence of storage-class memory (e.g., Intel Optane) and near-data processing architectures promises to further reshape industrial computing paradigms. These technologies enable novel approaches to real-time analytics, such as executing streaming algorithms directly on the memory bus or performing the in-storage processing of time-series data. For industrial IoT systems, this could dramatically reduce the latency and energy overhead associated with traditional von Neumann architectures, particularly for applications like high-frequency condition monitoring or adaptive process control. However, these innovations also demand corresponding advances in programming models and system software to fully realize their potential in industrial settings.
## 4 Results and Discussion  
### Determination of the Cutting Frequency of the Cutter Unit  
Table 3 shows the performance of the cutter unit under different driving motor speeds/cutting frequencies. The cutting capability of the cutter unit generally improved with an increase in the driving motor speed. Under the lowest speed 450 r/min, the cutter unit could only cut off the thinnest branches. Under the highest speed 1000 r/min, the cutter unit could cut off 4.5 mm branches in one run, while branches thicker than 5 mm were too strong for the cutter unit. As reported in [26], the cutting energy required for cutting _Miscantlus x giganteus_ stems was proportional to the stem diameter and the cutting energy from the blade was directly proportional to the cutting speed. For the cutter unit in this study, a higher cutting frequency meant a faster cutting speed of the moving blade, and thus provided more cutting energy.  
Generally, the branch diameters of small shrub plants are smaller than 5 mm, and the designed cutter unit will be capable of trimming them. A shrub plant with branches thicker than 5 mm would be a plant too large for the trimming machine designed in this work. Since the cutter unit performed best under the highest driving motor speed, 1000 r/min, or 16.7 Hz cutting frequency was determined to be the optimal working parameter for the cutter unit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plant** & \
\begin{tabular}{c} **Average Diameter of** \\ **Branches (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Average Moisture Content of** \\ **Branches (\%)** \\ \end{tabular} \\ \hline _Aglaia odorata_ & 3.61 & 47.07 \\ _Murraya paniculate_ & 3.54 & 50.26 \\ _Camellia oleifera_ & 3.73 & 40.92 \\ _Osmanthus fragrans_ & 3.74 & 39.52 \\ _Radermachera sinica_ & 3.65 & 45.03 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average diameters and moisture contents of the branches from the five shrub plants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Rotating** & **Cutting** & \multicolumn{4}{c}{**Branch Diameter (mm)**} \\ \cline{3-6}
**Speed (r/min)** & **Frequency (Hz)** & **1.2** & **2.5** & **3.4** & **4.5** & **5.4** \\ \hline \multirow{3}{*}{450} & \multirow{3}{*}{7.5} & \multirow{3}{*}{Cut off} & \multirow{3}{*}{Cut off by multiple runs} & \multirow{3}{*}{No cut off} & \multirow{3}{*}{No cut off} & \multirow{3}{*}{No cut off} \\  & & & & & & \\ \cline{3-6}
# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
###### Abstract  
During the last decades, there have been significant changes in science that have provoked a big increase in the number of articles published every year. This increment implies a new difficulty for scientists, who have to do an extra effort for selecting literature relevant for their activity. In this work, we present a pipeline for the generation of scientific literature knowledge graphs in the agriculture domain. The pipeline combines Semantic Web and natural language processing technologies, which make data understandable by computer agents, empowering the development of final user applications for literature searches. This workflow consists of (1) RDF generation, including metadata and contents; (2) semantic annotation of the content; and (3) property graph population by adding domain knowledge from ontologies, in addition to the previously generated RDF data describing the articles. This pipeline was applied to a set of 127 agriculture articles, generating a knowledge graph implemented in Neo4j, publicly available on Docker. The potential of our model is illustrated through a series of queries and use cases, which not only include queries about authors or references but also deal with article similarity or clustering based on semantic annotation, which is facilitated by the inclusion of domain ontologies in the graph.  
knowledge graph; property graph; semantic web; digital publishing; literature search Article
## 2 Materials and Methods
### Docking of Ceruchinol against Selected Targets  
To validate the prediction made during the ligand-based virtual screening campaign using PS, ceruchinol was docked against the selected targets using Dockthor [40]. The following steps were applied:  
* The selected target was prepared for the docking protocol (missing side-chains, duplicated amino acids, pH).
* For some of the targets, different PDB structures were found in the PDB database. Structures with the best resolution were selected. In the case of flexible regions, the most representative structure was chosen.
* Protonation state and tautomerization were calculated.
* Co-crystallized compounds were docked against their own target to validate the docking protocol, evaluating its capacity to retrieve the same pose available in the PDB.
* If multiple cavities were found, compounds were docked in each of them. To evaluate and select the best docking poses, four different terms were used:
* The interaction energy (E\({}_{\text{I}}\)), which is given by the sum of the van der Waals and electrostatic interaction energies.
* The total energy (E\({}_{\text{tot}}\)), which is calculated by combining (i) the intermolecular interaction energy calculated as the sum of the van der Waals and electrostatic potentials between the protein-ligand atom pairs, (ii) the intramolecular interaction energy calculated as the sum of the van der Waals and electrostatic potentials between the 1-4 atom pairs, and (iii) the torsional term of the ligand.
* The affinity prediction or docking score (S), which is evaluated using an empirical function that considers the Total Energy of the top-ranked pose.  
Figure 5: Workflow for the ligand preparation, ligand-based virtual screening, and molecular docking. \({}^{*}\)_Selection of ligands based on chemical structure and available biological information on potential targets._* The root-mean-square deviation (RMSD), which is calculated by comparing the atomic coordinates of each binding mode with those of a reference ligand and measures the average distance between the atoms of the two molecules. Additionally, and to support the previous information, a visual evaluation of each pose was done to assess the interactions (hydrogen bonds, polar, hydrophobic, etc.) between the compound and the residues in the binding pocket as well as the similarity of the pose to the co-crystallized compound.
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 5 Evaluation of Simplification Effect
### Geometrical Properties
#### 5.1.1 Shape Similarity  
The shape distribution involves the distribution of the geometric characteristics of the model that are evaluated with a shape function, and is used to quantitatively describe and compare the 3D geometry [30; 31]. There are numerous shape distributions that apply various shape functions, such as the angle (A3), distance (D1, D2), area (D3), and volume (D4); however, the D2 shape distribution is known to be most suitable for model comparison and classification [32]. The D2 shape distribution is defined as the distribution of the distance between two arbitrary points on a model surface. In practice, it is evaluated based on a histogram of the distance between the two points that are sampled randomly from the surface.  
The similarity between the shape distributions was evaluated using the Bhattacharyya coefficient. For distributions \(p_{1}(x)\) and \(p_{2}(x)\) (\(x\in X\)), the Bhattacharyya coefficient (\(\rho\)) is calculated as follows [33]:  
\[\rho(p_{1},p_{2})=\sum_{x\in X}\sqrt{p_{1}(x)\;p_{2}(x)} \tag{8}\]  
The Bhattacharyya coefficient is equal to the area of the overlapping region between the two distributions. It has a range of \(0\leq\rho\leq 1\), and can be interpreted as a standardized similarity between the distributions.
# Compact Single-Frequency MOPA Using a Silica Fiber Highly Doped with Yb\({}^{3+}\)
###### Abstract  
We report on a single-frequency fiber master oscillator power amplifier utilizing a polarization-maintaining step-index fiber with an Al/Ce/F core-glass composition doped with a very high Yb concentration (0.25 at.%). This design made it possible to use a very short fiber (\(\sim\)1 m) and to coil it in a tight radius (4 cm in the amplifier, while 2 cm gave similarly negligible bending loss) so that the packaged system is one of the most compact reported to date (\(\sim\)0.6 L). The use of a short fiber increased the threshold for stimulated Brillouin scattering well above 100 W while maintaining near-ideal beam quality. The fiber was pumped with a diode-pumped solid-state laser and cooled passively by spooling it on a grooved aluminum mandrel. The amplifier produced a strongly linearly polarized output at 1064 nm in the fundamental mode (\(M^{2}\leq\) 1.2) with a 150 kHz linewidth and a power of 81.5 W for 107 W of launched pump power. No deleterious effects from the elevated thermal load were observed. The residual photodarkening loss resulting from the high Yb concentration, found to be small (\(\sim\)0.7 dB/m inferred at 1064 nm) with accelerated aging, reduced the output power by only \(\sim\)20% after 150 h of operation.  
Yb-doped fibers; single frequency; fiber amplifier; photodarkening; compact fiber amplifier +
Footnote †: journal: Applied Physics Letters  
15 September 2021  
Accepted: 19 October 2021  
Subject headings:25 October 2021