esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Details of the Proposed Algorithm
### Computational Complexity  
The proposed algorithm does not include matrix inversion and matrix-matrix multiplication. In the \(\mathbf{x}\)-update procedure, the most computationally expensive part of both monotone and non-monotone strategies is the evaluation of the gradient \(\
abla f_{1}(\cdot)\), which takes two matrix-vector multiplications involving \(\boldsymbol{\Phi}\) and \(\boldsymbol{\Phi}^{H}\), and each multiplication has a complexity of \(\mathcal{O}(MN)\). The complexity of the soft-thresholding operator \(S_{t_{x}/\rho}(\cdot)\) in (24) is \(\mathcal{O}(N)\). When the extrapolation point \(\mathbf{u}\) in (25) does not meet the requirement, the non-monotone strategy will trigger an additional evaluation of gradient vector and soft-thresholding operator. Computation of gradient \(\
abla g_{1}(\cdot)\) only requires performing the matrix-vector product once, which has the same complexity as \(\mathcal{O}(MN)\). The evaluation of the soft-thresholding operator in the \(\mathbf{z}\)-update has complexity of \(\mathcal{O}(M)\). The remaining parts of the proposed algorithm only include vector-vector operations. The overall computational complexity per iteration is \(\mathcal{O}(MN)\).
## 5 Conclusions  
This paper proposes a new method for combining multiaxial stress criteria and cumulative fatigue damage theory to evaluate the fatigue strength of three typical welded joints (butt-welded joints, T-shaped fillet-welded joints, and lap fillet-welded joints) in an aluminium alloy head car body. The results indicate that, for the aforementioned car body configuration and load cases, the T-shaped fillet-welded joint exhibited excellent fatigue failure resistance, as it holds the lowest utilization degree and the highest fatigue life expectation. In contrast, the lap-welded joint showed the worst capability to resist fatigue failure, as it has the highest utilization degree and the lowest fatigue life. Regardless, all the welded joints tested in this paper complied with the fatigue safety requirements. This method is accurate and efficient, since it not only considers the material utilization degree in multiple stress states, but also accounts for the estimated fatigue life of the car body. Designers can determine the safety margin of a structure by comparison with a utilization degree of 1. In addition, the weak area of weld fatigue can be accurately predicted, and the structure's fatigue life can be determined. This method provides certain guidance and a theoretical basis for the fatigue evaluation of subway car body welded structures. The fatigue strength of welded structures can be improved in the following ways:  
1. The welding type can be changed, and the notch line grade can be improved to increase the allowable fatigue stress. For instance, lap fillet-welded joints can be changed to butt-welded joints, partial penetration can be changed to full penetration, and single-sided welding can be changed to double-sided welding. By doing so, a smaller value of index \(x\) can be achieved, which corresponds to higher allowable fatigue stress (Equations (7)-(13)) and a lower utilization degree (Equations (3)-(5)), both of which contribute to a safer weld bounding. 2. The structure can be optimized to reduce the stress range of the welded joints. The notch line grade can be improved through weld processing techniques, such as weld grinding. 3. In the design stage, the reinforcement can be arranged in the appropriate position of the fatigue assessment danger point to reduce the peak stress of the dangerous area and reduce the utilization of the weld material. 4. In the construction process, the level of post-welding treatment in dangerous areas should be improved, and more detailed flaw detection should be carried out. 5. In daily operation and maintenance, we should also focus on whether fatigue cracks occur in this area to ensure driving safety. The author's contributions are as follows: [PERSON] performed the conceptualization, methodology, supervision, and writing--review in charge of the whole trial. [PERSON] carried out the simulation and wrote the manuscript. [PERSON] performed the data processing and assisted with the theoretical analysis. [PERSON] assisted with the theoretical analysis. All authors have read and agreed to the published version of the manuscript.
## 2 The Intelligent Fault Diagnosis System Model
### Communication Model of MEC Servers and Intelligent Terminals  
In the considered network of intelligent fault diagnosis systems, the communications are operated in an orthogonal frequency division multiple access framework, and a dedicated subchannel with bandwidth \(B\) is allocated for each intelligent terminal for the partial task offloading. Supposing that intelligent terminal \(k\) communicates with MEC server \(m\), the received signal at MEC \(m\) receiver can be represented as  
\[y_{m,k}=h_{m,k}\sqrt{p_{k}^{o}(t)}s_{k}+n_{m,k}, \tag{1}\]  
where \(s_{k}\) denotes the symbols transmitted from intelligent terminal \(k\), \(p_{k}^{o}(t)\) is the utilized power at intelligent terminal \(k\), and \(n_{m,k}\) denotes the received additive Gaussian noise with power \(N_{0}\). Here the channel gains \(h_{m,k}(t)\) follows the finite-state Markov chain (FSMC), and thus the communication rate between MEC server \(m\) and intelligent terminal \(k\) is give by  
\[r_{m,k}^{o}(t)=B\log_{2}\biggl{(}1+\frac{p_{k}^{o}(t)|h_{m,k}(t)|^{2}}{N_{0}} \biggr{)}. \tag{2}\]  
Figure 4: The working principle of intelligent fault diagnosis system in this paper. The intelligent terminal collects the fault diagnosis data and then requests a policy from the agent server.
## 4 Conclusions  
This study employs a methodological approach that combines detailed geometric modeling with advanced numerical methods for evaluating the performance of a centrifugal radial turbine under variable operating conditions. By addressing critical factors such as tip clearance, blade geometry, and thermodynamic properties of the working fluid, the simulations provide valuable insights into the turbine's behavior beyond the design point, informing potential redesign and optimizations for integration into ORC systems utilizing variable waste heat sources.  
The findings indicated that the efficiency and power output of the turbine are significantly influenced by the rotational speed, inlet pressure, and outlet pressure. When operating beyond the design point, discrepancies were observed between the expected and actual performances, indicating that the turbine might encounter difficulties adapting to fluctuating waste heat conditions. It was determined that aerodynamic losses, including profile losses on rotor blades, wake formation in stator blades, and high exit velocities, were the primary factors contributing to the observed reduction in efficiency.  
In order to address these challenges, a number of recommendations have been proposed. Modifications to the operating conditions, particularly a reduction in inlet pressure, can align the turbine's performance with the design specifications, ensuring that the mass flow rates and power outputs remain within the expected ranges. A comprehensive redesign of the blade geometry may prove an effective means of mitigating aerodynamic losses, enhancing flow characteristics, and improving overall efficiency. Moreover, the meticulous selection and optimization of mechanical components, such as thrust bearings, are crucial to accommodating the augmented axial forces resulting from variable operating conditions. It is also imperative to regulate the outlet pressure by optimizing condenser conditions to maintain the desired pressure ratio across the turbine and maximize energy production.  
In conclusion, the effective integration of the turbine into ORC systems with variable waste heat sources necessitates both operational adjustments and design modifications. By addressing the identified issues, the efficiency and performance of the turbine can be enhanced, thereby improving energy recovery and the overall viability of ORC applications.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in this article; further inquiries can be directed to the author.  
Calculations were carried out at the Academic Computer Centre in Gdansk.  
The author declares no conflicts of interest.
# Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties
## 2 Mathematical Model  
### Planning Layer Model  
Due to the long time scale of the planning layer, it can be divided evenly into several time periods. A discrete-time linear programming model is established at the planning layer, with the maximization of profits as the objective function. Chance constrained programming is introduced to describe the uncertainty of demand. Through a series of mathematical transformations, the uncertain model with chance constraints is transformed into a deterministic model with a specified confidence level. At last, the transformed deterministic model is solved.
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 6 Datasets and Tools
### Overview
These comparisons assess the ability of GNN models to outperform conventional approaches in key performance metrics like latency, throughput, and energy efficiency. Studies often use simulated or real-world datasets to evaluate how well GNNs handle diverse network scenarios, including unseen topologies or dynamic environments. Latency minimization, throughput maximization, and energy efficiency--especially in resource-constrained networks like UAVs or satellite systems--are critical performance indicators. GNN-based methods, due to their ability to model complex topological relationships and adapt to changing network states, frequently show superior performance in terms of reducing end-to-end latency and increasing throughput compared to traditional algorithms. However, the scalability and real-time application of GNNs remain challenges, and their performance heavily depends on the quality and variety of the training data, with some studies demonstrating only marginal improvements over conventional methods in highly controlled environments.
## 3 IoT Standards and Protocols for Precision Agriculture
### IoT Standards for Precision Agriculture (Rq1)
#### 3.1.1 IoT Standard Requirements for Precision Agriculture  
The IoT devices used in precision agriculture monitoring systems often have different technical specifications, set by manufacturers, which allow interaction through sending and receiving messages regardless of the physical communication protocol used [10]. However, these specifications introduce challenges within the system. In addition to having to understand the communication methods and syntax of each type of device, it is also necessary to account for any variations in the technical specifications introduced by the manufacturer to resolve specific issues or support new functionalities [3]. To achieve seamless communication, independent of the types of devices used, the proposed system introduces a logical layer of **interoperability** at the data storage level, allowing data originating from sensors to be effectively utilized. The fundamental requirements for an IoT sensor standard, that must be met to implement the logical layer introduced in the project, are litest below:  
* Lightweight data flow
* Simplicity and ease of use
* Easily adaptable based on use cases
* Open platform
* Support for geolocation
* Support for various types of data
* Availability of open-source implementations
* Suitable for the needs of the precision agriculture sector
## 3 Results and Discussion
### Peak Interfacial Friction Coefficient
#### 3.2.2 Relationship between Particle Size and Peak Interfacial Friction Coefficient  
The relationship between particle size and peak interfacial friction coefficient for different surface hardness values is given in Figure 8. According to Figure 8, there was a linearly increasing relationship between the peak interfacial friction coefficient and particle size for harder surfaces (HV = 660, 350), while the peak interfacial friction coefficient decreased linearly with increasing particle size for softer surfaces (HV = 95). Additionally, a linear relationship between the peak interfacial friction coefficient and particle size was established for different surface hardness values, as shown in Equations (2)-(4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sand Type** & **Particle Size _d/mm_** & **Model File** & **Surface Hardness HV** &
\begin{tabular}{c} **Peak Interface Friction** \\ **Coefficient** \\ \end{tabular} \\ \hline \multirow{3}{*}{Fine sand} & \multirow{3}{*}{0.11} & \#45 steel pile & 660 & 0.16 \\  & & Aluminum alloy pile & 350 & 0.22 \\  & & Polymethyl methacrylate pile & 95 & 0.45 \\ \hline \multirow{3}{*}{Medium sand} & \multirow{3}{*}{0.41} & \#45 steel pile & 660 & 0.17 \\  & & Aluminum alloy pile & 350 & 0.25 \\  & & Polymethyl methacrylate pile & 95 & 0.43 \\ \hline \multirow{3}{*}{Coarse sand} & \multirow{3}{*}{0.69} & \#45 steel pile & 660 & 0.18 \\  & & Aluminum alloy pile & 350 & 0.27 \\ \cline{1-1}  & & Polymethyl methacrylate pile & 95 & 0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of the peak interface friction coefficient.  
\[\mu_{\text{pa}}=0.21d+0.09 \tag{2}\] \[\mu_{\text{ps}}=0.16d+0.03\] (3) \[\mu_{\text{pp}}=-0.13d+0.47 \tag{4}\]
###### Abstract  
Two as-cast low-density steels grades (austenite-based duplex Fe-30.9 Mn-4.9 Al-4.5 Cr-0.4C and austenitic Fe-21.3 Mn-7.6 Al-4.3 Cr-1C) with an initial dendritic microstructure were subjected to hot working conditions to understand the influence of deformation parameters on flow behavior and microstructural evolution. The alloys were produced using electric arc melting, and their phase constituents were determined using optical microscopy and X-ray diffraction analysis. This was then corroborated with the phase predicted from Thermo-Calc simulation. The as-cast alloys were machined to 10 \(\times\) 10 \(\times\) 7 mm specimen configurations for rectangular axial testing on the Gleeble 3500 thermomechanical simulator. The samples were deformed to a total strain of 0.5 at different deformation temperatures (800, 900, and 1000 \({}^{\circ}\)C) and strain rates (0.1 and 5 s\({}^{-1}\)). Thereafter, a hardness test was conducted on the deformed samples, and post-deformed microstructures were analyzed using optical and scanning electron microscopes. The results showed that the alloys' dendritic structures were effectively transformed at temperatures below 1000 \({}^{\circ}\)C regardless of the strain rate. At all deformation conditions, the peak flow stress of Fe-21.3 Mn-7.6 Al-4.3 Cr-1C alloy was at least 50% higher than that of Fe-30.9 Mn-4.9 Al-4.5 Cr-0.4C alloy owing to the higher carbon content in the austenitic low-density stainless steel. The hardness of all the deformed samples was superior to that of the as-cast samples, which indicates microstructural reconstitution and grain refinement in the alloys. Dynamic recrystallization, dynamic globularization, and dynamic recovery influenced the softening process and the microstructural changes observed in the alloys under different deformation conditions.  
lightweight steels; thermomechanical processing; dynamic recovery; dynamic recrystallization; Gleeble 3500 +
Footnote †: journal: Applied Sciences
## 2 Theory
Thus, the following approximations were obtained in mathematics:  
\[\exp(-\alpha L_{\rm{ave}})\approx 1-\alpha L_{\rm{ave}},\ \exp(-\alpha L_{0}) \approx 1-\alpha L_{0}. \tag{8}\]  
\[1-\alpha\bigg{(}L_{0}+\frac{1}{1-\rho(1-f)}L_{\rm{ave}}\bigg{)}\approx\exp \bigg{(}-\alpha\bigg{(}L_{0}+\frac{1}{1-\rho(1-f)}L_{\rm{ave}}\bigg{)}\bigg{)}. \tag{9}\]  
Thus, Equation (4) can be expressed as:  
\[ \begin{array}{ll}\Phi_{\rm{out}}&=f\Phi_{0}[1-\alpha(L_{0}+L_{\rm{ave}})] \frac{1/\rho-(1-f)-(1-f)\alpha L_{\rm{ave}}}{[1/\rho-(1-f)]^{2}-(1-f)^{2} \alpha^{2}l_{2\rm{ave}}^{2}}\\ &\approx\frac{f}{1/\rho-(1-f)}\Phi_{0}\Big{[}1-\alpha\Big{(}L_{0}+\frac{1}{1- \rho(1-f)}L_{\rm{ave}}\Big{)}\Big{]}\\ &=\Phi^{\prime}{}_{0}\exp\Big{[}-\alpha\Big{(}L_{0}+\frac{1}{1-\rho(1-f)}L_{ \rm{ave}}\Big{)}\Big{]}.\end{array}  \tag{10}\]  
Here,  
\[\Phi^{\prime}{}_{0}=\frac{f}{1/\rho-(1-f)}\Phi_{0}. \tag{11}\]  
Detailed derivations for Equations (4)-(10) are presented in \"Appendix A\". These indicate that Equation (10) is in the same form as the Beer-Lambert law. Comparing Equation (10) with the Beer-Lambert law, the expression for the EOPL for an arbitrary diffuse cavity can be obtained as:  
\[\text{EOPL}=L_{0}+\frac{1}{1-\rho(1-f)}L_{\rm{ave}}. \tag{12}\]  
Obviously, Equation (12) is similar to the previously reported EOPL expression [13; 14]. The only difference is the coefficient \(\rho\) in the numerator, which was replaced by 1 in the previous results. This slight difference was usually smaller than measurement errors and could be ignored. According to Equation (12), the approximate condition of Equation (7) can be transformed into  
\[\alpha\cdot\text{EOPL}<<1. \tag{13}\]  
Here, we define Equation (13) as the weak absorption condition of the diffuse integrating cavity. Thus, the absorbance can be expressed as Equation (14), according to Equation (10). \[A=\alpha\bigg{(}L_{0}+\frac{1}{1-\rho(1-f)}L_{\rm{ave}}\bigg{)}=\sigma\cdot N \cdot\text{EOPL}. \tag{14}\]  
Here, the particle number density, \(N\), denotes the concentration of the test gas and \(\sigma\) is the absorption cross-section. In other words, the absorbance can be expressed as Equation (14) under the weak absorption condition of Equation (13), where the absorbance depends linearly on the gas concentration. On the contrary, if the weak absorption condition was not satisfied, the absorbance will present non-linearly on the gas concentration.
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
###### Abstract  
Due to the continuous increase in data demanded by end-users, an energy-efficient base station (BS) is a vital topic of interest that would not only result in a substantial economic impact on service providers, but would also reduce the carbon footprint of operating a network. In this regard, we propose the structure and systematic operation of a BS with a large-scale (LS) antenna system that can increase the energy efficiency (EE) of cellular systems. The proposed BS structure includes various power-related units, such as a central management apparatus, power controller, EE calculator, radio site-dependent parameter space (RSD-PS) and determin. With the information provided from each unit, the decision unit determines how to adjust each component of the BS in order to maximize the EE. Extensive simulations show that the proposed BS improves the EE performance by about 83.05% relative to the reference BS.  
energy efficiency (EE); large-scale (LS) antenna system; base station (BS) Article
## 3 Methodology
### Processing
#### 3.2.3 Sliding Windows  
Following the application of the triggers, a 5 s SW with 50% overlap was applied. The SW approach consists of sectioning signals into fixed-size time windows, which may or may not be overlapped, thus reducing temporal complexity [50]. SW is particularly advantageous when dealing with data with short collection intervals and small sample sizes by allowing the collection of more data than non-overlapping SW (which in turn makes it less likely to miss significant events). Having this in consideration, overlapping SW were applied to all considered features, resulting in several time-series along the blocks. In the EMG activation interval calculation (EMG_front_IntervalActivation, EMG_zygo_IntervalActivation and EMG_corr_IntervalActivation), only intervals whose onset occurred within the sliding window value range were taken into account. Since the interval between odour presentation and blocks varied between participants (e.g., no time limit for the subjective assessment of the odours), the number of SW also varied. Therefore, each data segment was described by the median values of the features.
## 4 Analyses of the Applications of Digital Twin Models
### Manufacturing
#### 4.1.1 Application and Adoption of Digital Twin in Manufacturing
The advantage of this approach is that it obtains a large amount of real-time data from physical assets, which leads to the rapid training of the error prediction algorithm. The performance of this approach is higher than that of detection methods such as SWAT (Smart Workflow Automation Technology), WADI (Wireless Automation and Digital Integration), and BATADAL (Blockchain-Augmented Technologies for Advanced Data Analytics and Logistics). Among the challenges and limitations noted are the high level of complexity in establishing communications between the Digital Twin and the corresponding CPS to obtain real-time operating data, as well as the high probability of attacks occurring during the start-up phase of a CPS. In Ref. [60], [PERSON] et al., pointed out that the development of Digital Twin models allows horizontal and vertical integration of CPS components. Various parameters, such as depth, connectivity, validation, and variability of implementation in the context of human-based modeling, were considered in the proposed traffic management case study. Consequently, the use of Digital Twins in the context of CPS enables the dynamic allocation of physical and virtual entities based on operational conditions, leading to organizational and technological innovation. [PERSON] et al. [61] proposed an ontology for DT modeling in the context of CPS, by which simulation, monitoring, and management were identified as benefits. Different rule-based simulation models were created on the simulation side. Monitoring is performed with the help of a communication interface that allows the exchange of data between physical and virtual assets to make decisions and update the status of the devices. In addition, a management system can be created at the DT level to ensure continuous monitoring of access, authentication, security of stored data, and information obtained using the prediction algorithms used. As a challenge, the lack of semantic models was identified as a starting point for the design of an ontology, but also for the design models of the Digital Twin. In ref. [62], [PERSON] et al., proposed a Digital Twin-based CPS for automotive bodywork production lines, thus having the possibility of checking whether production could be carried out according to the plan. In addition, through the Digital Twin, various changes were proposed in terms of product ordering, verifying both the degree of adaptability to the abnormal scenarios that may occur and the monitoring of the behavior of the production lines. Although the Digital Twin ensures the interoperability of the entire system, some challenges are also mentioned. Among these, he mentioned the development of a Digital Twin to facilitate general production, not just for body production lines.