esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Power and Communication Bus Using HIL and Computational Intelligence
## 5 HIL-Based Optimization Method for Bus Prototyping
### Single or Multi-Objective Evolutionary Algorithm  
Evolutionary algorithms are based on the natural selection process that mimics biological evolution. In order to apply such an optimization technique for finding a solution of the problem it is necessary to define the following properties of the algorithm [30]: the representation of the individuals, the fitness function, selection and succession methods, crossover and mutation operators. It is assumed that the number of individuals in the population is fixed at each epoch of the evolutionary process and that individuals are composed of genes representing real numeric and integer values of adjustable hardware and software parameters of the bus. The length of the chromosome is dependent on the number of these parameters and equals the length of the vector \(p\):  
\[\mathbf{chr}=\mathbf{p}=[p_{1}\ p_{2}\ \ldots\ p_{d}]. \tag{12}\]  
The initial population is generated using the normal distribution. The fitness value of an individual is computed using the objective function (2) or (10). The best fitness value for a population is the smallest fitness value for every individual in the population. Stochastic uniform is applied to choose parents for the next generation, whereas succession operations are realized by defining the reproduction rules characterized by two parameters: elite count (\(\delta_{s}\)) and crossover fraction (\(p_{c}\)). The first parameter is the number of individuals with the best fitness values in the current generation that are guaranteed to survive to the next generation. The second one is the fraction of individuals in the next generation, other than elite children, that are created by crossover. It is decided to use a simple heuristic crossover operator. On the basis of two individuals, \(\mathbf{p}_{1}\) and \(\mathbf{p}_{2}\), in the case of \(C(\mathbf{p}_{1})<C(\mathbf{p}_{2})\), then the new one \(p_{3}\) is created according to the formula mentioned below  
\[\mathbf{p}_{3}=\mathbf{p}_{2}+\lambda_{h}(\mathbf{p}_{1}-\mathbf{p}_{2}), \tag{13}\]  
where \(\lambda_{h}\) is a fraction pointing at the better adapted individual.  
A mutation operator is responsible for generating heterogeneous individuals. This operator is based on an adaptive feasible method, and it randomly generates directions with respect to the last successful or unsuccessful generation. The mutation operator finds a direction and step length satisfying bounds and linear constraints. The algorithm is also described by two important parameters such as the population size and the number of generations. The values of these parameters are arbitrarily selected during optimization experiments. In this study, it is decided that the total number of evaluation of the fitness function is constant, and it is equal to \(\Psi\). Hence, the number of individuals in a population is calculated using the following formula:  
\[\text{Population}=\frac{\Psi}{\text{Generations}}. \tag{14}\]
## 1 Introduction
A typical diagram for clustering-hierarchical routing protocols is shown in Figure 1. However, the most important challenge for these routing protocols is energy conservation. Moreover, the existing cluster-based routing protocols focus on efficient CH selection in homogeneous WSNs where all the deployed SNs have the same initial energy level [19; 20; 22; 23]. These protocols have also been extended to two-tier WSNs [21] and three-tier heterogeneous WSNs [24], where two and three different types of SNs with distinct initial energy levels are deployed, respectively. However, these protocols assume that the BS is located within the FoI. Practically, the FoI may lie away from the BS, leading to significant challenges. Using existing routing protocols in such cases can result in an unstable WSN, as the CHs may experience early battery depletion (i.e., early death of CH) due to the longer transmission distances. This can lead to reduced data gathering efficiency and impact network performance. In such scenarios, unmanned aerial vehicle (UAV)-based networks have recently received the attention of the research community due their numerous features such as fast and easy deployment, high-chance of line of sight (LoS) link establishment, reliable connectivity, higher mobility, scalability, and flexibility. In addition, UAVs can survey and scan areas that are difficult to reach by human beings (e.g., oceans and mountains) [25]. Furthermore, due to the adjustable altitude and mobile nature of UAVs, they can move towards the ground users to minimize the transmission distance, and establish a reliable link with low transmit power requirement [26; 27; 28; 29]. Although UAVs as aerial base stations (ABSs) improve the performance of such IoT sensor networks/WSNs significantly, especially in human inaccessible regions such as mountains, oceans, and rural areas, they perform better in collection of data from ground SNs in an energy-efficient way. However, there is a crucial requirement to design an energy-efficient and reliable routing protocol for data collection and delivery through UAVs from remotely deployed SNs.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 4 Proposed DRL-Based Method
### Introduction of Algorithm
 \tag{8}\] \[w^{*} =\underset{A^{\prime}}{argmin}\frac{1}{n}\sum_{i=1}^{n}\left[Q_{w}( S_{t},a)-(R_{t}+\gamma Q_{t+1})\right]^{2} \tag{9}\]  
```
1 Randomly initialize value network \(Q_{w}(S_{t},a)\) with weights \(w\);
2 Initialize target network \(Q_{w^{-}}(S_{t},a)\) with weights \(w^{-}\);
3 Initialize experience replay buffer \(B\) and environment;
4 for epoch \(m\):= 1,..., \(M\)do
5 Initialize state \(S_{0}\);
6 for episode \(e\):= 1,...,\(E\)do
7 Select the random position for the first element;
8 for element \(t\):= 1,...,\(T\)do
9 Select element type and generate \(S_{t}\);
10 Predict the value matrix \(Q_{t}\) by \(Q_{w}(S_{t},a)\);
11 According to \(Q_{t}\), action \(a\) is adopted by \(\epsilon\)-greedy strategy;
12 Obtain reward \(R_{t}\) from environment;
13\(S_{t}\) transforms to \(S_{t+1}\);
14 Store data \(\{S_{t},a_{t},R_{t},S_{t+1}\}\) into \(B\);
15 endfor
16 endfor
17 Sample a random batch \(H\) of data \(\{S_{t},a_{t},R_{t},S_{t+1}\}\) from \(B\);
18 Calculate the next value \(Q_{t+1}\) by TD;
19 Calculate the TD target \(R_{t}+\gamma Q_{t+1}\);
20 Calculate the loss by Equation (9);
21 Update \(w\) by minimizing the loss;
22 If\(m\) modulo updating interval \(P\) equals 0, then
23 Duplicate \(w\) to \(w^{-}\);
24 endif
25 endfor
```  
**Algorithm 1**Details of the Algorithm
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 3 The IACO-Based Spectrum Allocation Method
### Transform for the Spectrum Allocation Problem  
The graph-theoretic model based spectrum allocation problem is NP-hard, so we introduce the IACO to obtain the efficient assignment for cognitive users. We convert the assignment issue into the shortest paths finding in a special undirected graph using IACO, for example, the model in Figure 1 is transformed into an undirected graph (Figure 2d) by the transform process shown in Figure 2.  
The transform process takes three steps: first, given the high computational complexity of resources allocation, IACO names only the non-zero elements in matrix \(K\) into binary sequences (the process of a to b in Figure 2); second, according to the row-major principle, we encode all letters into a row vector \(P=[A,B,C,\ldots,L,M]\) with the length of \(l\) that is the non-zero elements in \(K\) or \(K^{\prime}\) (the process of b to c in Figure 2); third, based on the row vector \(P\), we transform the letters into vertices of a special undirected graph except for the starting point \(SS\) and the ending point \(EE\) (the process of c to d in Figure 2). The ants move back and forth between \(SS\) to \(EE\) in the special graph to obtain the shortest paths (the optimal assignment scheme).
# Hyperledger Fabric Access Control for Industrial Internet of Things
## 2 Preliminary
### Access Control
#### 2.5.3 Role-Based Access Control (RBAC)  
RBAC divides the permissions of users in the system into roles, providing a simple, easy-to-manage, and fine-grained access control method [21]. In RBAC, each role is associated with one or multiple sets of permissions, and each permission may be assigned to multiple role groups too. Roles are assigned to users which eventually associate users with permissions. Compared to previous access control models, RBAC has greater flexibility in providing access rights rather than providing access to each user individually. All-access rights associated with a user can be easily audited by checking the permissions associated with the associated role. It also makes it easy to identify risk exposures associated with users.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Usle
#### 3.1.5 Determination of Conservation Practice Factor (\(P\))  
The term \(P\) in Equation (1) is the non-dimensional support practice factor, which reflects the relationship between the soil losses with a determined work practice: plow in terraces, in contour or crop in contour strips and irrigated furrows [1,13,19]. Soil losses under the USLE plowing conditions are given by [PERSON] and [PERSON] (1978) [PERSON] and [PERSON] (1978). USLE can only be used to calculate erosion by rain and cannot take into account snowmelt or rainfall on a frozen area.
###### Abstract  
The paper focuses on the phenomenon of phase transitions related to changes taking place directly in a network structure; i.e., in physical topology. Although these changes are related to the physical layer, they directly affect the functioning of higher layers, and thus the quality and efficiency of network transmission. The research presented herein was focused in particular on phase transformations in MESH type wireless topologies. The dependence between the values of radius (transmitter power) nodes and the coherence of the MESH network is presented. These considerations are important for understanding the dynamics of processes occurring in topological structures, and for optimizing the use of power sources for individual nodes. The paper also presents the use of phase transition analysis for early detection of network coherence loss at the physical layer level. A new mechanism for assessing the level of network communication and an active method of preventing its failure using the arbiter and a set of limited diagnostic information are described. The solution presented can be used both in Internet of Things (IoT) systems and Industry 4.0 to create a high level of reliability wireless connection infrastructure between mobile and fixed devices.  
wireless networks; MESH; phase transitions; transmitter power; IoT; Industry 4.0; network management; computer network Article
## 1 Introduction
In this regard, the International Institute of Welding (IIW) has made significant progress in the standardization of local stress approaches and compiled a designer guideline for the fatigue analysis of the plate-type welded structures. This guide provides an overview for the fatigue life assessment of welded connections using different structural hot-spot stress (SHSS) methods and also provides application with examples using FEA [1]. However, these guidelines are not widely accepted in the field or the design office, because there are some uncertainties present in their usage. The SHSS methods are generally considered to be practical for fatigue analysis of welded structures when using FEA for assessment, but there are some questions that remain open when applying these methods to complex welded details, such as the effect of element type, mesh size, nodal-averaging, etc. In addition, the fatigue life prediction may be strongly affected by large variations in the local weld profile, which should be kept in mind when assessing the reliability of these methods. Furthermore, the practicality is very important for industrial application. In this respect, the testing engineer in the field and the structural designer at the office must have some well-established methods for the measurement of local strains and stresses to evaluate the fatigue life of the structures. Therefore, to overcome these uncertainties, an extensive study was carried out by considering a complex welded detail in an orthotropic steel deck (OSD) that connects the steel deck to the longitudinal-rib and usually known as a rib-to-deck (RD) joint [9,10]. The OSD models were simulated and tested on a finite element method (FEM) based software Siemens NX.12.0. The limitations and the accuracy of these methods were investigated under different element types and meshing techniques. Moreover, the effect of the nodal-averaging feature was studied on each mesh variation. Two types of governing stresses were produced in the RD joint by the application of the Eurocode fatigue load model-4 [11], i.e., non-linear stress in the deck-plate and linear stress in the rib-plate. At the end of this paper, a guideline is proposed to evaluate SHSS by using these methods. The details of the methods used in this study are given in the next section.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 4 Vitamin D Deficiency and Cardiovascular Risk
### Vitamin D Deficiency and Heart Failure  
Many studies also correlate myocardial infarction with Vitamin D deficiency. In particular, a significant increase in biomarkers linked to heart failure, apoptosis, and inflammation was observed in VDR KO mice. The administration of paralcictid (PC), an activated Vitamin D analog, promotes cardioprotection through the activation of anti-apoptotic and anti-inflammatory mechanisms [58]. In fact, it has been shown that Vitamin D deficiency correlates with an increase in inflammatory cytokines (TNF-\(\alpha\), IL-6, and IL-1\(\beta\)), which decrease when Vitamin D is supplemented. Alongside this role it seems that Vitamin D also has an anti-fibrotic and anti-hypertrophic role at the cardiac level.  
In fact, mesenchymal multipotent cells (MMCs) exposed to 5'-azacytidine to induce a fibrotic phenotype, and then treated with active Vitamin D (1,25D), presented a decreased expression of fibrotic genes [59]. Epidemiological studies also confirm the importance of Vitamin D in subjects suffering from heart failure. Increased mortality in patients with heart failure (due to left ventricular dysfunction) appears to be associated with low levels of 25-hydroxy Vitamin D [60] A recent study published in 2023 shows how Vitamin D can be used today as a prognostic biomarker in heart failure. In particular, the authors demonstrate that in 70 patients with left ventricular heart failure there was also a significant drop in 25(OH)D levels [61]. All these results clarify the importance of Vitamin D in correct cardiovascular function and underline the need to study new types of Vitamin D supplementation and dietary models in relation to heart failure. Table 1 summarizes the possible role of Vitamin D for various cardiovascular disease (CVD) risk factors.
## 4 Results
### Spatial and Temporal Evolution of Pore Pressure  
Upon the onset of fault rupture, a stress wave is generated and propagates outward, causing the contraction and dilation of the pore volume. Due to the poro-elastic effect, the pore pressure changes as the volume changes. Figure 9 shows the temporal and spatial distribution of the pore pressure at 5, 10, and 15 s following the initiation of fault rupture. The high-pore-pressure zones align closely with areas of stress increase caused by the right-lateral fault slip, emphasizing the strong coupling between fault mechanics and pore fluid behavior during seismic events. At 5 s, the pore pressure response is highly localized around the fault rupture zone. The highest-pore-pressure regions (indicated by red and orange) are concentrated along the fault traces where the stress increases due to the right-lateral fault slip. This localized pressure rise reflects the immediate transfer of stress changes into the pore space. The right-lateral motion causes compressional stresses on one side of the fault and extensional stresses on the other, and the high-pore-pressure zones correspond to the compressional side where the stress increases the most. This finding seems consistent with the interpretation of GWL rises and drops caused by the 2009 Wenchuan earthquake [16]. By 10 s, the high-pressure regions expand outward from the fault, following the spatial distribution of stress increases caused by the fault motion. The propagation of pore pressure changes is influenced by the fault's slip mechanics, with the most significant increases remaining aligned with areas of high compressional stress. This shows that the pore pressure response is not uniform, but strongly tied to the stress perturbations induced by the fault slip. At 15 s, the high pore pressure zones continue to expand, but remain concentrated near regions of maximum stress increase. These zones still align with areas affected by the right-lateral slip dynamics, particularly where compressional forces dominate. While the pressure gradients start to stabilize and diffuse into the surrounding medium, the sustained high-pressure regions near the fault emphasize the prolonged impact of stress-induced pore pressure changes.
## Materials and Methods
### Data Analysis
#### 2.4.4 Center of the Sound Scattering Layer (SSL)  
The center of the SSL was quantitatively measured and defined based on the averaged MVBS and vertical velocity around the detected zooplankton area [32]. For the MVBS echogram, the SSL was determined by the shape and length of the high acoustic scattering in the water column. MVBS contrast between pixels in echogram with high value was compared with the background pixels outside the SSL, which have relatively low value. The threshold of the SSL was set at a low MVBS value (threshold \(-100\) dB was used in this research). The averaged MVBS (dB) for each ensemble was then plotted in the echogram. The average MVBS calculations were done for each ensemble in moored ADCP data. The identification of the center of the SSL was calculated as stated below [32]:  
\[SSL=\ \left\{\begin{array}{ll}1,&\quad px>\mu\\ 0,&\quad px\leq\mu\end{array}\right., \tag{5}\]  
where \(SSL\) is a Boolean variable with \(1\) is for bins that are deemed to belong to an SSL while \(0\) is for those that are not, \(px\) is the specified MVBS value in one bin, \(\mu\) is the MVBS value threshold (\(-100\) dB). After the SSL range was identified by extracting the bin that scored \(1\), the background noise outside the range that has a low MVBS value was eliminated. The upper and lower border and the size of the SSL can vary depending on the acoustic backscattering distribution in the SSL. For example, the midnight time may have a different range from the twilight time caused by the migration of zooplankton. The center of the SSL was determined based on the average around the identified SSL range over time. The center of the SSL line has the purpose to gain better insight into the nature of the SSL within the observed area, enabling us to understand the pattern of the daily zooplankton migration over time.  
The average vertical velocity was calculated on the SSL by analyzing the vertical velocity each bin that have different contrasts value with other bin. The procedure was identifying the upward and downward movement from the vertical velocity profiles time series, analyzing the highest velocity in some range of depth, and averaging the velocity. The averaged velocity values were then plotted into the vertical velocity profiles over time.
# Optimal Prefix Free Codes with Partial Sorting +
## 1 Introduction  
Given \(n\) positive weights \(W[1..n]\) coding for the frequencies \(\left\{W[i]/\sum_{j=1}^{n}W[j]\right\}_{i\in[1..n]}\) of \(n\) messages (We use the conveniently concise and general terminology of messages for the input and symbols for the output, as introduced by [PERSON] [1] himself, which should not be confused with other terminologies found in the literature, of input symbols, letters, or words for the input and output symbols or bits in the binary case for the output), and a constant number \(D\) of (output) symbols\({}^{3}\), an optimal prefix free code [1] is a set of \(n\) code strings on the alphabet \([1..D]\), of variable lengths \(L[1..n]\) such that no string is prefix of another, and the average length of a code is minimized (i.e., \(\sum_{l=1}^{n}L[i]W[i]\) is minimal). The particularity of such codes is that even though the code strings assigned to the messages can differ in lengths (assigning shorter ones to more frequent messages yields compression to \(\sum_{l=1}^{n}L[i]W[i]\) symbols), the prefix free property insures a non-ambiguous decoding.  
Such optimal codes, known since 1952 [1], are used in \"all the mainstream compression formats\" [2] (e.g., PNG, JPEG, MP3, MPEG, GZIP, and PKZIP). \"[PERSON]'s algorithm for computing minimum-redundancy prefix-free codes has almost legendary status in the computing disciplines\" ([PERSON] [3]). The concept is \"one of the fundamental ideas that people in computer science and data communications are using all the time\" ([PERSON] [4]), and the code itself is \"one of the enduring techniques of data compression. It was used in the venerable PACK compression program, authored by [PERSON] in 1978, and remains no less popular today\" ([PERSON] et al. [5] in 1997).  
Even though some compression methods use only precomputed tables coding for an Optimal Binary Prefix Free Code \"built-in\" the compression method, there are still many applications thatrequire the computation of such codes for each instance (e.g., BZIP2 [6], JPEG [7], etc.). In the current state of the art, the running time of such algorithm is almost never the bottleneck of the compression process, but nevertheless worth studying if only for theoretical sake, and potentially for future applications less directly related to compression: [PERSON] [8; 9; 10] described an adaptive version of merge sort which scans for sorted runs in the input, and merge them according to a scheme which is exactly the [PERSON] code tree; and [PERSON] and [PERSON] [11] described how to use such code tree to compute the optimal shape of a wavelet tree for runs-based compression of permutations. This, in turns, has applications to the compression of general texts, via the compression of the permutations appearing in a Burrows Wheeler's transform of the text.