esa-sceva/satcom-chunk-collection huggingface dataset:
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 1 Introduction
Specifically, edge server resources are allocated using a greedy method, and a sequence-to-sequence neural network-based reinforcement learning algorithm is employed to obtain optimal task allocation decisions. The S2S neural network is a deep learning model that is implemented using multiple layers of a recurrent neural network. It is capable of transforming an input sequence into a corresponding output sequence. Furthermore, these two approaches are then combined to minimize task execution time. The main contributions of this paper are as follows. * We propose a two-stage resource allocation method in the context of dependent tasks. * In the first stage, we model the problem of incentivizing users to request resources from edge servers as a multivariate Stackelberg game. We analyze the uniqueness of SE under the scenario of information sharing. Furthermore, we investigate the incentive problem in the absence of information sharing, and we transform it into a partially observable Markov decision process for multiple agents. To solve the SE in this situation, we design a learning-based game-theoretic reinforcement learning algorithm. * In the second stage, to allocate resources effectively, we design a greedy-based deep reinforcement learning algorithm to minimize the task execution time. * Through experimental simulation, it is demonstrated that the reinforcement learning algorithm proposed in this paper, which is based on learning games, can achieve SE in scenarios without information disclosure, and that it outperforms the conventional A2C algorithm. The reinforcement learning algorithm, grounded in the principle of greediness, can significantly reduce the execution time of tasks. The rest of the paper is structured as follows. Section 2 describes related works. Section 3 presents our system model. Section 4 discusses the design of incentives in information sharing scenarios. Section 5 presents our game-theoretic learning algorithm in the context of non-information sharing. Section 6 details how greedy methods are used in the design of reinforcement learning algorithms. Section 7 evaluates the performance of the proposed algorithm through simulations. Finally, in Section 8, we provide a comprehensive summary of the article.
# Fast Calculation of Transient Thermal Stress in Surge Lines with Thermal Stratification Region
## 3 Fast Calculation of Thermal Stress Based on Green's Function Method
### Division of Thermal Zones and Working Conditions
#### 3.2.1 Material Properties  
Thermal stress Green's function is generally obtained by applying a certain step temperature on each thermal zone. However, the physical parameters of metal materials are greatly affected by temperature. [PERSON] and [PERSON] [16] confirmed that the temperature characteristics of materials have a significant influence on the maximum peak stress, and the temperature dependence of material properties affects the maximum stress range for fatigue evaluation; they used the weighting function method to make the Green's function  
\begin{table}
\begin{tabular}{c c c} \hline
**Flow Case** & **Judgments** & **Description** \\ \hline Case 1 & \(Q_{hl}=100\%Q_{n}\) & Main pump running at full power \\  & \(\text{Abs}(Q_{sig})\geq Q_{1}\)1  & Surge out with large flow \\  & \(\text{Without thermal stratification}\) \\  & \(Q_{hl}=100\%Q_{n}\) & Main pump running at full power \\ Case 2 & \(\text{Abs}(Q_{sys})<Q_{1}\) & Surge in (out) with small flow \\  & \(\text{T}_{hl}>\text{T}_{1}\) & With thermal stratification \\ Case 3 & \(\text{Abs}(Q_{sys})<Q_{1}\) & Surge in (out) with small flow \\  & \(\text{T}_{hl}<\text{T}_{1}\)1  & With thermal stratification \\ Case 4 & \(Q_{hl}=100\%Q_{n}\) & Surge in with large flow \\  & \(\text{Abs}(Q_{sys})>Q_{1}\) & Without thermal stratification \\ Case 5 & \(Q_{hl}=100\%Q_{n}\) & Main pump running at full power \\  & \(\text{Abs}(Q_{sys})>Q_{2}\)1  & Surge in with very large flow \\ Case 6 & \(Q_{hl}<10\%Q_{n}\) & Surge in with large flow \\  & \(\text{Abs}(Q_{sys})>Q_{1}\) & Without thermal stratification \\ Case 7 & \(Q_{hl}<10\%Q_{n}\) & No flow in the hot leg \\  & \(\text{Abs}(Q_{sys})<Q_{1}\) & Without thermal stratification \\ \end{tabular}
\end{table}
Table 1: Typical flow cases of the pressurized surge line.  
method more accurate. Therefore, it is necessary to consider this effect when using the Green's function method to monitor the fatigue damage of the actual operating conditions of nuclear power plants. Therefore the temperature-dependent material properties were defined in the current study. The surge line and the connected hot leg are Z3 CND20.09M and Z2 CND17.12, respectively, and the material properties were taken from RCC-M specifications, as shown in Figure 7. The Poisson's ratio is taken as a constant of 0.3, and the density is taken as a constant of 7930 kg/m\({}^{3}\), and the fluid physical parameters change with temperature, as shown in Figure 8.
## 2 Theoretical Background and Related Works
### Research Gap  
In IoT-based OSH, there are limited studies investigating the potential advantages of IoT-based professional safety and health solutions to society. More comprehensive knowledge-sharing research results are necessary to demonstrate the societal and economic benefits of utilizing IoT technologies in the working environment to guarantee OSH [75]. Furthermore, privacy and security issues cannot be separated from IoT-related techniques. Insufficient information about the data collection processes in the worksite forms results in distrust of the technology [67]. Due to data security being of the utmost importance for personal health information, the IoT network system should collect more subjects to validate and uplift the reliability and privacy level [74]. In addition, researching user-centered approaches in the occupational IoT for retaining OSH would prove significant. In CPS-related OSH, context-awareness has been used in applications for running CPS to guarantee workers' safety in the working environment. However, only a few studies have been published for this specific domain. For instance, more than 300 ICT solutions related to safety in the systematic inventory can be applied in the smart working environment; only a few of them are related to context awareness [93]. The result reveals that the development of CPS for OSH as an essential research field is still lacking. As CPS is a broad research area, further interdisciplinary research and revolutionary activities must offer bases in CPS's hardware and software components design. As a result, the main goal of ensuring OSH for workers can be achieved. In the domain of hand wearable device that is relevant to OSH, only a few worksite hand wearables have undergone rigorous field studies and comprehensive verification against standards. With insufficient peer-reviewed information, it could be challenging to confirm the efficacy and safety of hand wearable devices in the market. Moreover, very few studies have investigated the social resistance to adopting hand wearable devices. The degree of acceptability is more crucial than the related functions and benefits in modern times [67]. Consequently, exploring the appropriate methods to boost the acceptance of hand wearable technologies at work is a prerequisite for preserving OSH and getting rid of the research gap.
## 5 Comparative Analysis between the Two Weight Optimization Methods
### The Variation Trend of Topology Optimization Efficiency with Mesh Size  
To study the computational efficiency of topology optimization with the changing trend of mesh refinement, the mesh growth rate is defined as \(G_{1}\), which means the ratio of the number of elements between two mesh sizes, calculated as:  
\[G_{1}=\frac{Mesh_{i+1}-Mesh_{i}}{Mesh_{i}} \tag{3}\]  
The growth rate of material removal as a result of topology optimization is defined as \(G_{2}\), which means the ratio of the volume removed by the material of the topology optimization result between two mesh sizes, calculated as:  
\[G_{2}=\frac{Removal_{i+1}-Removal_{i}}{Removal_{i}} \tag{4}\]  
For the simulated mesh, it is hoped that the growth rate of material removal \(G_{2}\) could be as close as possible to the mesh growth rate \(G_{1}\), and \(G_{2}\) could even be greater than \(G_{1}\). This means that the computer resources have been used efficiently. The mesh efficiency \(\eta_{M}\), which means the ratio of \(G_{2}\) to \(G_{1}\), can be calculated as:  
\[\eta_{M}=\frac{G_{2}}{G_{1}} \tag{5}\]  
By re-arranging the equations of \(G_{1}\), \(G_{2}\), and \(\eta_{M}\), Figure 20 was obtained. In general, the process of mesh refinement could improve the material removal rate of topology optimization. However, as mesh is further refined, the increasing speed of removal rate decreases. The 2 mm mesh size had about 20,000 more elements than that of the 1 mm mesh size. In terms of the optimization results, the material removal rate of 1 mm mesh size was slightly higher than that of the 2 mm mesh size. Figure 20 clearly indicates that the growth rate of material removal from the default mesh size to the 5 mm mesh size and from the 4 mm mesh size to the 3 mm mesh size was higher than the growth rate of the mesh size, that is, \(\eta_{M}>1\). After the mesh was refined with a size smaller than 1 mm, the material could continue to be removed. However, the optimization gains during this period brought on by the mesh refinement were very limited.
## 2 Establishment and Definition of Train Model
### Wind Aerodynamic Forces  
The wind is classified into two types: mean and fluctuating winds. Mean winds are equivalent to static forces, while fluctuating winds represent turbulence and randomness in the atmospheric boundary layer. Turbulence is a complex problem in fluid dynamics that can regulate the vertical exchange of momentum, energy, and composition of the wind. The structure of the wind field caused by the train is complex, and the turbulence problem is inevitable. There are many studies on the turbulent atmospheric boundary layer [17; 18; 19; 20; 21; 22]. Large-scale detailed calculations of the flow field around trains require many computational resources. Therefore, some researchers use the quasi-steady calculation method to calculate the aerodynamic load around high-speed trains [23]. [PERSON] [24] found that the aerodynamic characteristics around the train calculated by the Chinese hat wind model were in good agreement with the wind speed. [PERSON] et al. [6] discussed the error of the two calculation methods and found that the error between the quasi-static calculation method and the unsteady calculation method was small. Therefore, a quasi-static method was used to calculate the wind load. The effects of turbulence are not considered.  
In this paper, to simplify the calculation, a steady wind in the time domain was used to describe the crosswinds, and wind loads are simulated as concentrated forces and moments. As is shown in Figure 4, the wind loads were exerted on the pneumatic center of the head car, middle car, and tail car. The specific calculation methods are given in Equations (1) and (2).  
\[F_{i}=\frac{1}{2}\rho S_{c}V_{res}^{2}C_{Fi}(\beta) \tag{1}\]  
\[M_{i}=\frac{1}{2}\rho S_{c}L_{c}V_{res}^{2}C_{Mi}(\beta) \tag{2}\]  
\[V_{res}^{2}=V_{c}^{2}+V_{w}^{2}-2V_{c}V_{w}cos(\pi-\alpha) \tag{3}\]  
where \(\rho\) is the air density, \(S_{c}\) is the area of the vehicle affected by crosswinds; \(C_{Fi}\) (\(\beta\)), and \(C_{Mi}\) (\(\beta\)) are aerodynamic coefficients that depend on the yaw angle \(\beta\), which we can get from [PERSON] et al. [2]. \(L_{c}\) is the height of the car. \(V_{res}\) is the relative speed of the wind to the train.  
The relationship between synthetic composed wind speed and vehicle speed is shown in Figure 5 and calculated in Equation (3) where \(V_{c}\) is the speed of the train, \(V_{w}\) is the speed of the wind, and \(\alpha\) is the wind attack angle.  
Figure 3: Crosswinds load for train curve passing. (**a**) Train running under crosswinds; (**b**) curve track composition.
## 4 Experiment Results and Comparison
### Results and Discussion
#### 4.5.3 Runtime performance  
Table 5 captures the runtime performance of the three semi-supervised algorithms; the test was performed five times to obtain the mean execution time. From Table 5, it can be observed that the PGM is much faster than KNNVWC and SAL algorithms, respectively, in both datasets. Furthermore, when compared to the other two semi-supervised models, the runtimes of the SAL algorithm are the slowest. Therefore, future work will be reserved to minimise the runtime of the SAL approach.
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Heuristic Algorithm
#### 4.1.9 Ant Colony Optimization Algorithm
[106] proposed an improved Ant Colony Optimization algorithm, which introduced the location allocation method and the new node selection strategy into the Ant Colony Optimization algorithm and solved the problem of slow trajectory planning optimization speed of the Ant Colony Optimization algorithm in formation transformation. [PERSON] et al. [107] proposed a hybrid meta-heuristic algorithm, which combined maximum-minimum Ant Colony Optimization and differential evolution to solve the problem of slow global convergence of traditional Ant Colony Optimization algorithms and maximum-minimum Ant Colony Optimization algorithms. [PERSON] et al. [108] proposed a system framework for multi-UAV collaborative task assignment and tracking planning. The framework uses a Particle Swarm Optimization algorithm based on a guidance mechanism to solve the combinatorial optimization model. Then, adaptive parameter adjustment, encounter point prediction, bidirectional search and online replanning are introduced into the Ant Colony Optimization algorithm for trajectory planning (SAP-ACO), which solves the cooperative task assignment and tracking planning problems of UAV formations facing moving targets. [PERSON] et al. [109] proposed a bionic optimization algorithm, which combines the maximum-minimum Ant Colony Optimization (MMACO) and the Cauchy mutant (CM) operator, and they use the CM operator to enhance the MMACO algorithm to solve the problems of slow convergence and possible local optimum in traditional ACO and MMACO. [PERSON] and [PERSON] [110] proposed a distributed trajectory planning algorithm based on dual decomposition of UAV communication chains. This algorithm improves the traditional Ant Colony Optimization algorithm (ACO) from the aspects of trajectory selection, pheromone update, rollback strategy, etc., and solves the problems of poor efficiency, adaptability and robustness of the ACO algorithm. [PERSON] et al. [111] proposed an asynchronous Ant Colony Optimization (AACO) algorithm. The visibility matrix and test track coverage matrix are added into the ACO algorithm. The search order of the population track primitive is changed from the current fitness value and the previous fitness value to the current fitness value. Finally, the incentive value is introduced to avoid track repetition, which solves the problem of optimal trajectory planning for multiple UAVs in three-dimensional space. [PERSON] and [PERSON] [112] proposed a multi-objective coverage flight trajectory planning algorithm, which added the fitting sensor footprint scanning (SFS) and sparse trajectory point graph (SWG) to the Ant Colony Optimization (ACO) algorithm. Traversing the area of interest (AOI) solves the problem of high cost of multi-UAV coverage trajectory planning in urban environments.
## 2 Materials and Methods
### Profiling Headspace Volatile Metabolites by HS-SPME-GC\(\times\)GC-ToFMS
#### 2.3.2 Instrumentation  
SPME fiber was manually introduced into the GC\(\times\)GC-ToFMS injector and exposed for thermal desorption into heated inlet at 250 \({}^{\circ}\)C. The inlet was lined with a 0.75 mm I.D. splitless glass liner and splitless injections mode were used (30 s). The LECO Pegasus 4D (LECO, St. Joseph, MI, USA) GC\(\times\)GC-ToFMS system was comprised by an Agilent GC 7890A gas chromatograph (Agilent Technologies, Inc., Wilmington, DE, USA), with a dual-stage jet cryogenic modulator (licensed from Zoex) and a secondary oven, as well as mass spectrometer equipped with a ToF analyzer. An Equity-5 column (30 m \(\times\) 0.32 mm I.D., 0.25 \(\upmu\)m film thickness, Supelco, Inc., Bellefonte, PA, USA) and a DB-FFAP column (0.79 m \(\times\) 0.25 mm I.D., 0.25 \(\upmu\)m film thickness, J&W Scientific Inc., Folsom, CA, USA) were used for first (\({}^{1}\)D) and second (\({}^{2}\)D) dimensions, respectively. Helium was employed as carrier gas at a constant flow rate of 2.50 mL min\({}^{-1}\). The following temperature programs were used: the primary oven temperature was ranged from 40 \({}^{\circ}\)C (1 min) to 140 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\), and then to 200 \({}^{\circ}\)C (1 min) at 7 \({}^{\circ}\)C min\({}^{-1}\). The secondary oven temperature program was 15 \({}^{\circ}\)C offset above the primary oven. Both the MS transfer line and MS source temperatures were 250 \({}^{\circ}\)C. The modulation period was 5 s, keeping the modulator at 20 \({}^{\circ}\)C offset above the primary oven, with hot and cold pulses by periods of 0.80 and 1.70 s, respectively. The ToF analyzer was operated at a spectrum storage rate of 100 spectra s\({}^{-1}\), with mass spectrometer running in the EI mode at 70 eV and detector voltage of \(-\)1480 V, using a \(m/z\) range of 35-300. ChromaTOF(r) (LECO) GC\(\times\)GC data processing software was used to process the total ion chromatograms at the signal-to-noise threshold of 200. For identification purposes, the mass spectrum and retention times (\({}^{1}\)D and \({}^{2}\)D) of the analytes were compared with standards, when available. Additionally, the identification process was done by comparing the mass spectrum of each peak with existing ones in mass spectral libraries, which included an in-house library of standards and two commercial databases (Wiley 275 and US National Institute of Science and Technology (NIST) V. 2.0--Mainlib and Replib). Moreover, additional information such as linear temperature programmed retention indexes (RI) were experimentally determined [40]. For this purpose, the C\({}_{8}\)-C\({}_{20}\)_n_-alkanes series (the solvent _n_-hexane was used as C\({}_{6}\) standard) was used for RI determination comparing these values with reported ones in existing literature for chromatographic columns similar to \({}^{1}\)D column (Table 1). The Deconvoluted Total Ion Current GC\(\times\)GC area data were used as an approach to estimate the relative content of each metabolite under study.
## 3 Results  
Building upon the methodology presented in the previous section, this section presents the results derived from its application in a real-world scenario. The findings are organized following the same four-step structure: (1) identification of needs, (2) design of the DT architecture and framework, (3) development of the implementation methodology, and (4) validation through a case study in a composting facility. This structure allows for a comprehensive evaluation of the proposed approach, demonstrating its feasibility, effectiveness, and potential for replication in other production and service systems.  
Step 1: Contextual Analysis and Problem Definition: Municipal solid waste represents a major environmental challenge on a global scale [6]. Agriculture and household activities are the most common sources of organic waste, and the primary challenge in organic waste management lies in transforming organic matter into usable products [3]. Among the processes for waste reduction and utilization, composting stands out as a potential technology capable of transforming organic substances into stable fertilizers, making it a viable recycling option [6]. The global increase in waste, driven by economic development and population growth, makes sustainable management essential. This approach must align with the circular economy, prioritizing reduction, reuse, and recycling before resorting to recovery [43]. Inadequ management of organic waste causes environmental and health issues, prompting municipalities to adopt strategies aimed at reducing improper disposal, as highlighted in specialized literature [44].  
Proper management of organic waste, which accounts for 28% to 64% of municipal solid waste, is crucial to preventing significant environmental impacts, such as gas emissions and leachate production in landfills [2]. Smart cities, driven by cyber infrastructure, promote sustainable development by integrating economic growth, improved quality of life, and efficient resource management [45]. To achieve this, Industry 4.0 drives full automation through cyber-physical systems, cloud computing, and IoT, fostering smart manufacturing. This approach enables the design and management of complex ecosystems with real-time information and autonomous interaction between systems [46]. In Industry 4.0, DTs stand out by representing the duality of systems: a physical version and a digital or informational counterpart of the system [47]. DTs offer a promising solution to enhance automation and advance towards smart manufacturing through a bidirectional connection between the physical and digital worlds [48]. Despite their potential in various fields, DTs lack a formal definition, a unified implementation process, and a framework to classify and define their use cases [49]. DTs has the potential to transform composting systems by optimizing processes, improving efficiency, and minimizing environmental impacts.  
Step 2: Definition, Design of the DTs Architecture and Framework
# ISAR Image Quality Assessment Based on Visual Attention Model
## 2 Proposed Method
### Inter-Region Attention Block  
The output features \(F^{N\times C\times L}\) of the CAB module are convolved and rearranged to obtain \(F^{N\times C\times H/8\times W/8}\), which is subsequently input into the IRAB. The IRAB module has three sub-modules consisting of IRL, Conv, and Coef. IRL includes the Intra-Region Attention layer, Multi-Layer Perceptron, and Inter-Region Attention layer. The Intra-Region Attention layer performs self-attention operation across a single region to improve interactions within one region. The Inter-Region Attention layer computes attention results  
Figure 2: CAB module.  
across different regions using a sliding window over all regions. Coef is a scaling parameter \(\alpha\). The formula is outlined as follows:  
\[F_{out}=\alpha\cdot H_{CONV}(H_{IRL}(\widetilde{F}_{0})+\widetilde{F}_{0} \tag{4}\]  
\(\widetilde{F}_{0}\) denotes \(F^{N\times C\times H/8\times W/8}\), which is input into the Irab. \(\widetilde{F}_{0}\) passes through the IRL convolution layer and relu layer, and then multiplies the scale factor by 0.8. The feature is added at the beginning of the \(\widetilde{F}_{i,0}\) to obtain \(F_{out}^{N\times C\times H/8\times W/8}\). This module further strengthens the connection between spatial regions and extracts fine features across regions for image quality assessment (Figure 3).