esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Examples and Results
### Algorithm Parameters  
A \(\mu+\lambda\) Genetic Algorithm has been employed to address the optimization problem developed (The complete code can be found in [[https://github.com/atapiaco/run_of_river_plant_3D_optimization](https://github.com/atapiaco/run_of_river_plant_3D_optimization)]([https://github.com/atapiaco/run_of_river_plant_3D_optimization](https://github.com/atapiaco/run_of_river_plant_3D_optimization))), using the coordinates of the nodes of the layout \(\Gamma\) as genes. To find an adequate value of the hyperparameter on the generation scale \(\sigma\), a search has been performed. A total of 10 thousand individuals have been generated and evaluated for values of \(\sigma\) ranging from 0 to 10, with the results shown in Figure 11. The best fitness was obtained with \(\sigma=4.2\), and thus this value has been employed for the simulation.  
Given the similarity of the mutation scheme to that proposed in the discrete version of the problem [23], the optimal probabilities that were determined have been considered in this work. These are  
\[p_{mut,mov}=0.01,\quad p_{mut,01}=0.05,\quad p_{mut,10}=0.20,\quad\beta_{mut }=0.5\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(P_{min}\) (KW) & 7 \\ \(D_{noz}\) (m) & 0.022 \\ \(E\) (GPa) & 200 \\ \(S_{y}\) (MPa) & 250 \\ \(K_{sup}\) (c.u.) & 9 \\ \(K_{exc}\) (c.u./m\({}^{3}\)) & 8 \\ \(X_{sup}\) (1/m) & 0.2 \\ \(\beta_{exc}\) (\({}^{\circ}\)) & 10 \\ \(a_{0}\) (c.u./m) & 13.14 \\ \(a_{1}\) (c.u./m\({}^{2}\)) & 99.76 \\ \(a_{2}\) (c.u./m\({}^{3}\)) & 616.10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the scenario parameters considered for the example.  
Finally, the algorithm has been executed for different values of the mutation and crossover probabilities, with 10 trials for combination. A summary of the main parameters of the GA is shown in Table 2.
## 5 System Overview
### Functional Features  
1. Serverless architecture as preferred instead of using name node : The term \"serverless\" does not mean that servers are not used, or there is no server. This only means that we no longer have to think too much about them [44]. At the same time, the data node at the same level are considered serverless hierarchically. Each of the four nodes used in this study serves file storing operations for different types of client applications. When any of these nodes is disabled, the system will automatically route the client to the other data nodes. In this way; * The \"serverless\" schema does not mean servers are no longer involved. It simply means that developers no longer should think that much about them. Computing resources get used as services without having to operate around physical capacities or limits. * It is possible to use different sources (more than one hard disk) on the same machine.
2. Corporations need small and medium block sizes: Hadoop and the others are generally configured for at least a 64 MB block size, this approach makes it difficult to acquire an excellent choice for small and medium-sized big data, this phenomenon results in as those not available for small and medium size big data problems.
3. Name node crash recovery problem is solved. The fault tolerance of the system is very high because there is no name node.
4. An easy to manage virtual file system has been produced on the underlying operating system. A mixture of both bitmap and linked list structure was used for managing the bunches which have a fixed size of 10 MB.
5. The start address of the file is saved in client applications that give us sufficient performance gain.
6. The most important era of our system is any of the data nodes may be a server for any application. In other words, any machine in the system can be a file server.
7. All the IP lists which contain data nodes and replica IPs and secure connection IPs are available for all nodes in the cluster. Non-functional specifications that KUSDFS have:  
1. Performance (reading): when we compare data processing speed with other systems, we have achieved very good performance.
2. Scalability: due to the features it has, it is sufficient to install only the service program to add new nodes into the system. In this way, the system is designed to be able to serve thousands of nodes, while it can work with several nodes according to the institutional needs.
3. Survivability: survivability is that the system survives despite all the negativities and maintains its minimum functions. In the developed system, even if at least one of the nodes is active, the system is still able to work.
4. Availability: availability is that the system delivers its services successfully every time. This has been done with the developed system. Especially since there is no name node, the survival of the system is quite high.  
Security: static IP and synchronous encryption method were used to strengthen the security level of the system.
6. Minimum cost: data nodes used in the system are ordinary machines and do not have an extra feature.
# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 3 Proposed Approach
### Proposed Approach  
In order to improve RPL protocol, four objective functions are proposed to be used at the routes establishment stage: DQCA-OF1, DQCA-OF2, DQCA-OF3 and DQCA-OF4. Each one of these objective functions is based on the fusion of metrics for 6 LoWPAN environments, used by the RPL and described in RFC 6551. The context information is used as a parameter for selecting the one that best suits a given application, among the four available objective functions.  
The APPLICATIONS module represents the context of each application. The DATABASE module contains the objective functions which will be used for generating routes that meet the requirements of each application.  
Figure 1 illustrates the functioning of the proposed approach, which is based on the relationship among three nodes.
# Optical Diffraction Tomography Using Nearly In-Line Holography with a Broadband LED Source
## 3 Experimental Setup and 3D RI Reconstructions
The holograms presented here (and also in the work of [PERSON] and [PERSON]) were recorded with a reference beam, which was the portion of the incident beam that was not scattered, which was chosen to be at an angle just enough to separate the signal beam from the twin image (the conjugate term) in the Fourier space. Since the object in this case is a weakly scattering phase only object, the effects of the modulation of the incident beam by the sample did not appreciably affect the reconstructions. Fringes were not visible in the recording shown in Figure 4a due to low contrast and sampling by the camera, which was close to the Nyquist rate. The fringes were weak, but they were surely there, since the 2D Fourier transform shown in Figure 4b contains two orders. To retrieve the illumination angle, we adopted a previously developed algorithm, which retrieved the illumination angle by detecting the boundaries and distance between the center of the circle and the Zero-order term [30]. After acquiring different projections by illuminating individually and sequentially the LEDs (see the Supplementary Video), the 3D RI reconstruction was retrieved by mapping the filtered Fourier transform shown in Figure 2 into the 3D Fourier-space. This was followed by taking the 3D inverse Fourier transform of the scattering potential in the Fourier-space to calculate the 3D RI distribution. Figure 5 shows the 3D RI reconstruction for the cheek cell shown in Figure 4 (see also Supplementary Video). Figure 6 shows another 3D RI reconstruction for a Human Cheek cell where the bacterial structures are clearly detected with their refractive index higher than the cytoplasm of the cell agreeing with results reported in the literature [17; 31]. Figure 5: 3D RI reconstruction at different depths the left column is the highlighted regions at z = \(-1\) μm (dark blue) and z = 0 μm (red). The images show the high resolution due to the use of a partially incoherent LED source with the speckle noise highly reduced.
## 2 Materials and Methods
### Bayesian Network Analysis  
Bayesian networks provide a graphical representation of how random variables probabilistically influence one another, combining graph theory with probability theory. In the network structure, vertices correspond to random variables, while directed edges signify conditional dependency relationships between them, and the probability distribution of a node takes into account the conditional probability distribution of that node given its parent. Bayesian networks can effectively deal with uncertainty, provide flexible and accurate modeling for many practical problems, help to understand the probabilistic dependencies of complex systems and support inference and decision-making.  
The 3 fundamental topological configurations of Bayesian networks are illustrated in Figure 5.  
Where in the V-shaped structure of Figure 5a, one feature is dependent on the others, i.e., \(x_{1}\) is dependent on \(x_{2}\) with \(x_{3}\). When the values of the sub-features are unknown, the parent features are statistically independent, i.e., when \(x_{1}\) is unknown, \(x_{2}\) is independent of \(x_{3}\), with the following equation:  
\[\sum_{x_{1}}P(x_{1},x_{2},x_{3})=\sum_{x_{1}}P(x_{2})P(x_{3})P(x_{1}|x_{2},x_{3}) \tag{5}\]  
\[\begin{array}{ll}P(x_{2},x_{3})&=P(x_{2})P(x_{3}){\sum_{x_{1}}}P(x_{1}|x_{2}, x_{3})\\ &=P(x_{2})P(x_{3})\times 1\\ &=P(x_{2})P(x_{3})\\ \end{array} \tag{6}\]  
In the same parent structure of Figure 5b, multiple features depend on one feature, i.e., \(y_{2}\) and \(y_{3}\) depend on \(y_{1}\). \(y_{2}\) is independent of \(y_{3}\) when \(y_{1}\) is known, as in Equation (7):  
\[\begin{array}{ll}P(y_{2},y_{3}|y_{1})&=\frac{P(y_{1},y_{2},y_{3})}{P(y_{1})} \\ &=\frac{P(y_{1})P(y_{2}|y_{1})P(y_{3}|y_{1})}{P(y_{1})}\\ &=P(y_{2}|y_{1})P(y_{3}|y_{1})\\ \end{array} \tag{7}\]  
In the sequential structure of Figure 5c, the first feature depends on the second feature, and the second feature depends on the third feature, i.e., \(z_{1}\) depends on \(z_{2}\), and \(z_{3}\) depends on \(z_{1}\). \(z_{2}\) is independent of \(z_{3}\) when \(z_{1}\) is known as in Equation (8):  
\[\begin{array}{ll}P(z_{2},z_{3}|z_{1})&=\frac{P(z_{1},z_{2},z_{3})}{P(z_{1})} \\ &=\frac{P(z_{2})P(z_{1}|z_{2})P(z_{3}|z_{1})}{P(z_{1})}\\ &=\frac{P(z_{1},z_{2})P(z_{3}|z_{1})}{P(z_{1})}\\ &=P(z_{2}|z_{1})P(z_{3}|z_{1})\\ \end{array} \tag{8}\]  
The Bayesian network constructed in this paper basically consists of these 3 basic structures.
# Study on the Combustion Behavior and Kinetic Characteristics of Semi-Coke from Oil Shale
## 3 Results and Discussion
### Characterization of Combustion Residues Analysis
#### 3.3.2 XRD Characterization  
To reveal the changes in mineral composition and crystalline structure during the combustion process of oil shale, X-ray diffraction (XRD) analysis was performed on both raw oil shale and pyrolyzed semi-coke samples. The results are shown in Figure 7.  
As shown in Figure 7, the XRD pattern of the oil shale sample (Figure 7a) indicates distinct characteristic peaks for quartz (Q) and dolomite (M), suggesting that these minerals are present in significant amounts. The strong peak intensity of calcite (C) indicates that it is a major carbonate mineral in the oil shale sample. The presence of characteristic peaks for pyrite (P) confirms that the sample contains a certain amount of sulfides. Multiple sharp diffraction peaks in the spectrum indicate that the minerals in the oil shale have good crystallinity. Organic matter does not show obvious features in the XRD pattern, as it is primarily amorphous.  
The XRD pattern of the semi-coke sample (Figure 7b) shows that the quartz (Q) peaks remain present and their intensity significantly increases, indicating the relative enrichment of quartz during combustion. The intensity of the calcite (C) peaks decreases sharply, suggesting that the calcite decomposed at high temperatures, producing CO\({}_{2}\) and leading to a reduction in content. The pyrite (P) peaks weaken, indicating that some sulfides may have decomposed or transformed. New characteristic peaks for potassium feldspar (K) appear in the semi-coke sample, likely due to mineral recrystallization during high-temperature reactions.
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 3 Method
### Cross-Frame Association Module
#### 3.3.2 Grouped Cross-Attention Mechanism  
To balance computational efficiency and feature representation capability, we adopt a grouped attention strategy, splitting the 256-dimensional feature space into four 64-dimensional subspaces for parallel processing as follows:  
\[\text{Attention}_{k}(Q,K,V)=\text{Softmax}\bigg{(}\frac{Q^{(k)}(K^{(k)})^{T}}{ \sqrt{d_{k}}}\odot M_{\text{temp}}\bigg{)}V^{(k)},\quad k=1,...,4 \tag{11}\]  
Here, \(Q^{(k)}\), \(K^{(k)}\), and \(V^{(k)}\) represent the query, key, and value matrices of the \(k\)-th subspace, respectively; \(d_{k}=64\) is the subspace dimension; \(M_{\text{temp}}\) is a temporal mask matrix; and \(\odot\) denotes element-wise multiplication. The temporal mask matrix \(M_{\text{temp}}\) effectively prevents reverse temporal matching, ensuring temporal consistency in associations, and is defined as follows:  
\[M_{\text{temp}}(i,j)=\begin{cases}1,&\text{if target $j$ exists at time $t-1$}\\ 0,&\text{otherwise}\end{cases} \tag{12}\]  
The attention results from each subspace are concatenated and normalized to form the final output as follows:  
\[f_{\text{out}}^{t}=\text{LayerNorm}(\text{Concat}[\text{Attention}_{1},\text{ Attention}_{2},\text{Attention}_{3},\text{Attention}_{4}]) \tag{13}\]  
This grouping strategy significantly reduces computational complexity from \(O(n^{2}d)\) to \(O(n^{2}d/g)\), where \(n\) is the number of targets, \(d\) is the feature dimension, and \(g=4\)is the number of groups. Additionally, the multi-head design allows the model to learn complementary attention patterns from different subspaces, enhancing the diversity of feature representation.
# A Review of Artificial Intelligence Research in Peer-Reviewed Communication Journals
## 2 Literature Review
### The Evolution of Artificial Intelligence as a Research Area in the Communication Field
#### 2.1.1 AI and Journalism  
AI's influence on journalism has proven to be deeply impactful. In the initial stages, automated journalism experiments primarily concentrated on rudimentary data-driven reporting. However, with the progression of AI technologies, their capabilities have expanded exponentially [23]. Presently, AI algorithms possess the ability to analyze extensive datasets, discern emerging trends, and promptly generate insightful articles and reports. This not only streamlines the reporting process but also ensures the rapid and accurate dissemination of news to the public. Moreover, the potential of AI-driven content generation lies in its capacity to cater to individual reader preferences, providing a personalized news experience [24].  
AI's ascendancy in communication research has gained momentum with the advent of natural language processing and machine learning. These technological breakthroughs have paved the way for the implementation of chatbots, sentiment analysis tools, and automated content generation [25]. In the domain of journalism, AI-driven algorithms have become pivotal in the large-scale production of news articles and reports [26].
# Experimental Investigation of Controlled and Uncontrolled Rectifiers for Low-Power Wind Turbines
## 6 Silicon-Controlled Rectifier
The firing angle delay continued to increase by 15 degrees until a maximum delay of 120 degrees was achieved with the reduction in output DC voltage to 3.4 VRMS. This validates the fact that the output voltage of a three-phase rectifier can be controlled by firing angle delay. The results also correspond to the theoretical analysis and simulations. \begin{table}
\begin{tabular}{c c c c c} \hline
**Firing Angle** & **Delay (ms)** &
\begin{tabular}{c} **Theoretical** \\ **Voltage** \\ \end{tabular} & **Sim Voltage (V)** & **Exp Voltage (V)** \\ \hline
0 & 0 & 32.747 & 32.66 & 28.65 \\
15 & 0.694 & 31.631 & 30.75 & 28.48 \\
30 & 1.388 & 28.356 & 27.91 & 26.28 \\
45 & 2.083 & 23.8 & 24.239 & 23.40 \\
60 & 2.777 & 19.9066 & 19.899 & 20.20 \\
75 & 3.472 & 14.013 & 15.152 & 16.36 \\
90 & 4.166 & 9.4533 & 10.3259 & 12.48 \\
105 & 4.861 & 5.537 & 5.814 & 7.64 \\
120 & 5.555 & 2.533 & 2.674 & 3.4 \\ \hline \end{tabular}
\end{table} 
Table 4: Theoretical simulation and experimental measurements for the three-phase fully controlled rectifier. Figure 13: Firing angle delay vs. output voltage.
## 2 Materials and Methods
### Radionuclide Release Modeling  
The diffusion-controlled release conceptual model implies that a radionuclide is discharged at the surface and that the entire 6.2 m thickness of the concrete waste vault is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Radionuclide**} & \multirow{2}{*}{**Half-Life (Vears)**} & \multicolumn{2}{c}{**Distribution Coefficient (cm\({}^{3}\)/g)**} & \multirow{2}{*}{**Diffusion Coefficient (m\({}^{2}\)/yr)**} \\ \cline{3-4}  & & **State II** & & **State IV** \\ \hline \({}^{14}\)C & 5700 & 5000 & 100 & \(1\times 10^{-12}\) \\ \({}^{90}\)Sr & 28.8 & 30 & 1 & \(5\times 10^{-11}\) \\ \({}^{97}\)Tc & 211,000 & 3000 & 1 & \(1\times 10^{-8}\) \\ \({}^{137}\)Cs & 30 & 20 & 0 & \(5\times 10^{-10}\) \\ \hline \hline \end{tabular} The distribution coefficient (K\({}_{d}\)) data for all radionuclide was obtained from [12]. As for Ds, \({}^{14}\)C and \({}^{137}\)Cs data was obtained from [11] while \({}^{90}\)Sr and \({}^{99}\)Tc data is from [20].  
\end{table}
Table 1: Sorption data of radionuclides in states II and IV of concrete degradation.  
evenly contaminated. In the RESRAD code, the contaminated medium represents the inner composition of the concrete waste vault, and the contaminated zone represents the area of the 84 by 10 m concrete vault. A total mass of 44,100 kg of concrete and waste is assumed, together with a volume of 17,640 cubic meters. Concrete is conservatively estimated to have a porosity of 25 percent and a density of 2380 kg/m\({}^{3}\). The unsaturated and saturated zones of the near-surface disposal site are positioned beneath the contaminated concrete waste vault, where radionuclides discharged from the contaminated medium come into contact with groundwater. Additional site-specific RESRAD-OFFSETITE input parameters are listed in Table 2.  
This study considers four radionuclides (with half-life greater than 20 years) from the Gyeongju LILW site source term inventory (\({}^{14}\)C, \({}^{137}\)Cs, \({}^{90}\)Sr and \({}^{99}\)Tc), which represent a fair percentage of the radionuclide inventory of the Gyeongju LILW site. The diffusion coefficient of concrete is determined by the water-to-cement ratio used in its creation, as well as its aggregates. This analysis employs the sorption values of typical Portland cement concrete with a water-to-cement ratio of 0.45 [7]. To model the effect of concrete degradation, the sorption values for states II and IV are selected. State I is not considered because its duration is only three years. States II and III are considered to be the same state of degradation due to the fact that their sorption values are not significantly different. State IV is the most limiting because most radionuclides have low K\({}_{\rm d}\) values. The sorption values for the selected radionuclides are listed in Table 1. A sensitivity analysis was conducted on the distribution coefficient, the diffusion coefficient, and the dry bulk density to further understand the parameters that influence the release of radiation from degraded concrete.
# Modelling of Failure Behaviour of 3D-Printed Composite Parts
## 2 Materials and Methods
### Computational Modelling
The finite element models of RVE are developed in order to apply a numerical homogenization method for determining the coefficients of effective stiffness matrix \(C\) for the layers of 3D-printed parts. More details on the computational models for linear material modelling are provided in [39]. Similar RVE models are developed in the present work for computational modelling of nonlinear behaviour of the parts. Experimental investigations [6] revealed two types of macro failures in 3D-printed parts; these are breakages of extrudates and the debonding between adjacent extrudates. As well as the above two failure modes, fibre pullout is also observed in the case of composite 3D-printed parts [16]. Experimental work also revealed that the failure in 3D-printed parts is not brittle, but the printed parts experienced both linear and nonlinear behaviour which are seen in the stress-strain graphs of bidirectionally 3D-printed parts. Furthermore, experimental works uncovered that the polymeric material mainly contributes to the nonlinear behaviour in the parts, although the material used in the 3D printing is composite. It was also observed that the fibre reinforcements in the composites do not undergo deformation. Therefore, only ABS polymer's nonlinear material behaviour is considered in the present work on damage modelling of 3D-printed parts. Further, the fibre reinforcement material is treated to remain elastic during the deformation. The following computational methodologies are developed for nonlinear material modelling of 3D-printed parts.
# Neutron and Gamma Pulse Shape Discrimination by Robust Determination of the Decay Shape
## 2 Materials and Methods
### Discrimination Performance Criteria  
To evaluate the performance of neutron/gamma separation of the scintillator with the different methods, it is customary to use the figure of merit (FOM) value, which is given by  
\[FOM=\frac{S}{FWHM_{n}+FWHM_{\gamma}}=\frac{|\mu_{n}-\mu_{\gamma}|}{2.35( \sigma_{n}+\sigma_{\gamma})} \tag{15}\]  
where \(S\) is the separation between neutron and gamma-ray maxima for the variable of the particular method (for example, the charge ratio in the case of the charge comparison method), and \(\text{FWHM}_{\text{n}}\) and \(\text{FWHM}_{\gamma}\) represent the full widths at half maximum of the neutron and gamma-ray contributions, respectively, Figure 3. Both components are assumed to be able to be adjusted by Gaussian functions, so that \(\mu_{n}\) and \(\mu_{\gamma}\) are the mean positions and \(\sigma_{n}\) and \(\sigma_{\gamma}\) are the standard deviations, respectively, of the neutron and the gamma ray contributions.  
The crossing line (CL) to separate gamma-ray and neutron pulses can be set, following [11], as  
\[CL=\frac{\left(\mu_{\gamma}+3\sigma_{\gamma}\right)+\left(\mu_{n}-3\sigma_{n} \right)}{2} \tag{16}\]