esa-sceva/satcom-chunk-collection huggingface dataset:
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 3 Taxonomy of Resilience Evaluation Methods
### Resilience Metric Computational Method
#### 3.3.2 Multi-Criteria  
This method combines various parameters (such as service performance, topology, topography, and event characteristics) to output resilience metrics. Different analytical tools are used to aggregate all these parameters into final metrics [67; 92; 115].
## 2 Related Work  
The original Hough (line) transform was utilized to detect straight lines on images [37; 38]. Since then, several variants and generalizations of the original HT have been proposed and applied in various fields [39]. The different versions of HT have also been successfully applied in various localization schemes. The bearing trace HT was used to process sonar data for ship tracking and monitoring [40]. In the acoustic shot localization system [41], the HT of the time-stamped angle of arrival measurements was used to detect projectile trajectories. HT was also utilized to detect and localize reflector surfaces to prevent suboptimal solutions in indoor localization [42]. An HT-based solution was proposed for hyperbolic localization in [31], and the model was also extended to contain various types of measurements [34]. We will refer to the variant of HT adapted for processing TDOA measurements as hyperbolic HT (HHT).  
Various solutions were proposed to decrease the processing time of HT: probabilistic HT [43] and randomized HT [44] reduce the computational cost by randomly decreasing the number of processed pixels, while fast HT [45] uses a hierarchical approach, which increases resolution only where detection is possible.  
To increase the processing speed of HHT, in [31], two approaches were proposed. The randomized HHT (R-HHT) uses randomly selected points of the parameter space. This approach, however, still requires a high number of samples for reasonable accuracy (in [31] as much as 50% and 75% of the grid points were utilized); thus, the increase in speed is modest. The hybrid HHT (H-HHT) is performed in two stages: in stage 1, an R-HHT is executed. In stage 2, a grid search is performed around the maximum value found in step 1, using a small grid size. According to the experiments, this approach provides faster evaluation, but the speed gain is still moderate. It is worth noting that neither R-HHT nor H-HHT guarantees that the global optimum is indeed found.  
In this paper, a fast HHT (F-HHT) is introduced that significantly decreases the required amount of computation and guarantees convergence to the global maximum of the parameter space on a predefined grid.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 2 Related Work
### Research Gap  
Most prior studies focused on single-server FL, cluster-based architectures [17], or cloud-centric designs [10]. A few multi-server FL approaches have been proposed, but they target relatively static environments (e.g., cellular edge servers [18; 19] or IoT healthcare scenarios [20]) and do not account for the high mobility and dynamic nature of vehicular networks.  
Existing multi-server schemes generally aggregate all received model updates without evaluating their quality. In a dynamic vehicular setting, outdated or compromised updates from peer servers can introduce bias, slow convergence, or even degrade the global model's accuracy. Prior work on trust-based multi-server frameworks [21] selects only the single \"most trusted\" model from clients' perspectives, potentially discarding useful diversity.  
Moreover, vehicular clients frequently enter and leave RSU coverage areas, causing non-IID data distributions and uneven update rates. Yet, most multi-server FL frameworks assume stable client populations or rely on static overlap regions for model exchange, overlooking the impact of vehicle mobility on training consistency and convergence.  
We introduce MS-FL, a multi-server FL framework for vehicular edge networks. First, each server shares its model with neighboring servers and vehicles, enabling efficient peer-to-peer model exchange across overlapping coverage areas and distributing the current model to vehicles for local training. Next, every server evaluates incoming peer updates on its own validation set, filters outliers, and weights each contribution by its assessed quality before aggregation. Finally, our handover protocol preserves any \"lost\" updates--when a vehicle crosses into a new server's coverage, it forwards its pending update rather than dropping it. Together, these mechanisms dynamically adapt to changing client populations, reduce bias from stale or anomalous updates, and integrate multiple reliable models in each round--yielding faster convergence, higher accuracy, and greater robustness in vehicular scenarios.  
Table 1 provides a concise comparison of representative FL schemes. We contrast traditional single-server FL, hierarchical FL, and two prior edge-only multi-server approaches [18; 21] against our proposed MS-FL framework. Key dimensions of comparison include the communication topology (edge-only vs. edge + cloud), support for client handover, whether models are evaluated before aggregation, and whether aggregation occurs at the server level. For each method, we also summarize its primary advantages and limitations in handling non-IID data, client dropout, overlapping coverage, and end-to-end latency.
# Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality
## 2 Design of Agricultural Product Quality and Safety Evaluation System
### Construction of Evaluation Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item (Unit)** & **Sample Size** & **Average Value** & **Standard Deviation** \\ \hline Soil pH & 200 & 4.46 & 0.432 \\ Soil organic matter content & 200 & 33.76 & 0.493 \\ (g/kg) & & & \\ Soil cation exchange & 200 & 14.67 & 0.539 \\ capacity (cmol/kg) & 200 & 0.05 & 0.398 \\ Cadmium content (mg/kg) & 200 & 0.12 & 0.425 \\ Arsenic content (mg/kg) & 200 & 4.45 & 0.512 \\ Lead content (mg/kg) & 200 & 37.62 & 0.563 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average value and standard deviation of agricultural product samples. in fertilizers, heavy metal content in the atmosphere, and heavy metal content in irrigation water. The weight coefficients of each secondary indicator were obtained after scoring by experts. The risk assessment model Equation (4) obtained after regression analysis is as follows:  
\[Os=3h+4i+j+2k \tag{4}\]  
In Equation (4): \(h\): content of heavy metals in pesticides; \(i\): heavy metal content in fertilizer; \(j\): heavy metal content in the atmosphere; \(k\): heavy metal content in irrigation water. If heavy metal pollution also exists in other external environments other than soil, the risk of heavy metal residues in agricultural products will be increased, and the risk value can be increased by up to 10 points. Finally, Equation (5) of the agricultural product quality and safety risk assessment model is obtained as follows:  
\[EM=a\times(5b+3c+2d)\times(0.6e+0.2f+0.2g)+(3h+4i+j+2k) \tag{5}\]  
The risk early warning value of the agricultural product quality and safety risk assessment score (\(EM\)) is 60 points. When the \(EM\) value is early warning, that is, the risk early warning score exceeds 60 points, it can be considered that under the influence of heavy metals, the quality and safety risk of agricultural products is high, which should be traced and disposed of.
## Materials and Methods  
### Digital Twin  
The digital twin is one of the promising digital technologies now under development to support digital transformation and decision making in multiple industries [27]. It is essentially a digital replica of a physical entity created by combining data from various sources such as sensors, software, and simulations [28]. Digital twins are often used in manufacturing, engineering, and construction industries to create a virtual model of a physical model or system [29]. A digital twin can test different scenarios, predict how the physical system will behave under various conditions, and optimize its performance without needing physical prototypes [30]. Moreover, digital twins can be used to monitor and analyze the performance of physical assets in real-time, enabling predictive maintenance, and improving efficiency [31]. By continuously monitoring and analyzing the data, the digital twin can provide insights into potential issues, allowing preventative measures to be taken before any significant problems arise [32].  
Overall, digital twins are a powerful tool for improving the design, performance, and maintenance of physical assets, enabling greater efficiency, cost savings, and innovation. A simulation digital twin uses computer simulations to represent a physical system [33]. It involves modeling the physical system in a digital environment and simulating the system's behavior under various conditions [34]. Digital twin simulations are often used in manufacturing to evaluate and improve the performance of a physical system before it is built. Simulating the system's behavior can test different design configurations, predict how the system will behave under different conditions, and optimize its performance. This can save time and money by avoiding the need for physical prototypes [35; 36]. Digital twin simulations may also be used to monitor and assess a physical system's efficiency in real-time. The system's performance, ability to predict future problems, and ability to take preventative action before difficulties occur can be found by merging real-time data from sensors and other sources with the simulation model [37; 38]. Integrating and fusing data is a critical aspect of creating a digital twin. The virtual model is directed by the information and behavior generated by the physical model, and this can generate a substantial amount of data. Several data processing techniques, such as data mining, monitoring, fusion, and optimization may be necessary to manage and analyze these data effectively. By following this method, it is possible to guarantee that the virtual representation accurately and constantly reflects the physical one [39; 40].
## 4 Discussion
### Relations between Parameters
#### 4.1.3 Carbon Isotope Ratios (\(\delta^{13}\)C\({}_{\text{CO2}}\))
Under 0.2% of CO\({}_{2}\), the atmospheric endmember has a contribution that may become significant as may be deduced from the relative contribution of soil CO\({}_{2}\) and atmospheric CO\({}_{2}\) reported on Figure 6A. Such contamination was mainly stated during winter acquisitions (2/3 of the data) and may result from sampling bias linked to defective sealing of the sampling probe or the presence of water in soil that hampers the correct pumping of soil gas (half of the anomalies are from the December 2009 campaign). Hypothetical mantle or the lower crust CO\({}_{2}\) degassing (\(\delta^{13}\)C\({}_{\rm CO2}\) ranging from \(-4\) to \(-8\%\) VPDB; [79]) is not supported by \({}^{4}\)He measured values. Finally, CO\({}_{2}\) produced by the dissolution of carbonate rocks or soil carbonates will have  
Figure 6: Carbon-isotope ratios (\(\delta^{13}\)C\({}_{\rm CO2}\)) as a function of the inverse of CO\({}_{2}\) concentration in soil gas (semi-logarithmic scale). (**A**) three atmospheric-biological mixing lines are calculated, using 0.04% CO\({}_{2}\) and \(-8.4\%\) Vienna Pee Dee Belemnite (VPDB) standard for the atmospheric end-member, and 10% CO\({}_{2}\) and \(-15\) to \(-28\%\) VPDB for the biological end-member. Variation ranges of literature data are reported on the Y-axis. (**B**) mixing curves between two biological end-members (\(-15\) and \(-28\%\)) and hypothetical deep CO\({}_{2}\) end-member (taken at \(-40\%\)). a \(\delta^{13}\)C\({}_{\text{CO2}}\) around 0\(\%\) VPDB and a contribution of carbonate dissolution may also explain part of the low depleted data but this hypothesis cannot be tested using our data. Scattering of the \(\xi^{13}\)C\({}_{\text{CO2}}\) data is strongly reduced when low CO\({}_{2}\) data are filtered from the measurements (Figure 3). There was only little variability throughout the monitoring period: the mean baseline value is \(-23.9\%\), the mean injection value is \(-24.3\%\) and the mean post-injection value is \(-24.6\%\). The apparently progressive decrease of ratios that is observed when using the whole \(\xi^{13}\)C\({}_{\text{CO2}}\) dataset is thus the result of more frequent atmospheric contamination of the samples during the first sampling sessions, as no selection based on CO\({}_{2}\) concentration was made. The selectivity used afterwards, especially during post-injection acquisitions (CO\({}_{2}\) >2%), strongly limits this risk. In conclusion, the data provided by carbon-isotope ratio measurements point to a near-surface biological origin for CO\({}_{2}\) gas in the soil, and thus to CO\({}_{2}\) flux at the soil/atmosphere interface.
# Application of Virtual Reality for Learning the Material Properties of Shape Memory Alloys
## 5 Conclusions  
In this study, a teaching module is developed using VR technology and the research results of SMA products as the learning contents for learning the SMA properties, shaping and restoration processes, and real-life applications in an immersive and interactive way. It is helpful in enhancing students' understanding of the SMA's material properties and applications. A teaching experiment has been performed for investigating students' learning achievements and attitudes after using the teaching module. The experimental results reveal that:  
1. The teaching module is effective in improving the learning achievement.  
Both groups made significant improvement after the teaching activities, indicating the shaping experiment using real SMA wire as well as the teaching module are both effective for improving the learning achievement. However, the ANCOVA results show that the learning effectiveness of the teaching module is significantly higher than that of the real shaping experiment.
2. The students held positive attitudes towards the teaching module.  
The questionnaire results show that students were mostly satisfied with the teaching module and they held positive attitudes towards the learning contents, interface design and operational experience. They considered the teaching module easy to operate, useful in increasing their learning interest, and effective in helping them understand the properties and applications of SMAs.  
3. The teaching module can save the cost and time of conducting real experiments.  
Using the teaching module, there is no need to use the TEM to observe the SMA's crystal structures. Additionally, a high-temperature oven up to 500 \({}^{\circ}\)C is required in the shaping experiment and it takes more than 5 h for the shaping process to complete. Therefore, the teaching module is a useful tool in learning the basic concepts and applications of SMAs for educational purposes.  
**Author Contributions:** Formal analysis; C.-J.C.; investigation: C.-M.L.; methodology: C.-Y.L.; resources: Y.-J.L.; writing--review and editing: W.T.  
**Funding:** This research was funded by the Ministry of Science and Technology (MOST), Taiwan under the grant number 105-2514-S-007-004.  
**Conflicts of Interest:** The authors declare no competing interests regarding the publication of this paper.
# Assessment of the RACPC Performance under Diffuse Radiation for Use in BIPV System
## 5 Conclusions  
Different analyses of the performance of the RACPC for use in BIPV systems under diffuse light conditions were carried out in this project. Simulation work was carried using ZEMAX(r) software with the aim of determining the optical concentration gain under diffuse irradiance. The angle of incidence of the rays does not affect the results due to the fact that rays from diffuse radiation come from various directions having an optical concentration gain under diffuse irradiance of 2.12. In order to validate experimentally the results from the simulations, a single device with the RACPC-PV was integrated with a 1 cm by 1 cm solar cell. The design was tested outdoors with the aim of comparing the results obtained on an overcast day with the optical concentration gain obtained from the ZEMAX(r) simulation, which has a value of 2.20. This means a deviation of 3.8% in the opto-electronic gain.  
Figure 8: Results from outdoor experiments during an overcast day.  
Figure 7: The \(\mathrm{k_{T}}\) and diffuse irradiance at the horizontal plane values during the outdoor experiment.  
The results from this work allow for more accurate way of predicting the actual annual performance of any concentrator technology, especially the RACPC installed at any location; by taking into account the contribution from both direct and diffuse radiations.  
Conceptualisation, S.F., R.R.-I. and D.F.R.; Data curation, S.F.; Formal analysis, S.F.; Funding acquisition, F.M.-S., R.R.-I., S.H.A.-B., N.A.B. and J.A.A.-R.; Investigation, S.F. and J.D.-G.; Methodology, F.M.-S., R.R.-I., D.F.R. and S.H.A.-B.; Resources, R.R.-I.; Software, S.F., F.M.-S., R.R.-I., D.F.R. and S.H.A.-B. Supervision, F.M.-S., R.R.-I. and D.F.R.; Validation, F.M.-S. and R.R.-I.; Visualisation, S.F., F.M.-S. and S.H.A.-B.; Writing--original draft, S.F.; Writing--review and editing, S.F., F.M.-S., R.R.-I., D.F.R., J.D.-G., S.H.A.-B., N.A.B., A.B.M., A.A.M. and J.A.A.-R.; All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank Ministry of Higher Education (MOHE), Malaysia, Universiti Teknologi Malaysia (UTM) (Research cost centre no. Q.K130000.3556.06G43), the support of the Agencia Nacional de Investigacion y Desarrollo (through the project Fondecyt regular 1200055 and the project Fondecyt fD19101065), project PI_m_19_01 (UTFSM) and School of Computing, Engineering and Built Environment, Glasgow Caledonian University for funding this research project.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Risk Characterization Model and Visualization System in Aluminum Production
## 2 Risk Characterization Model
### Spatial Search Model
#### 2.3.1 Space Search Model of Explosion Accident  
For an explosion accident, the system first calculates the accidental release of energy, then converts the accidental energy release into a TNT equivalent. The damage range was determined according to the TNT equivalent. The exposed people and equipment were obtained through a space search according to the damage range [50; 51]. Formula (10) gives the TNT equivalent:  
\[W_{TNT}=\frac{E_{exp}}{Q_{TNT}} \tag{9}\]  
In order to estimate the possible casualties caused by explosion accidents, the scope of personal injury caused by explosion accidents is usually divided into three areas (death, serious injury, and minor injury). The accidental release of energy also causes great damage to surrounding structures. Thus, the property loss radius \(R_{d}\) is introduced, which indicates the range of secondary damage to buildings under the action of an explosion shock wave.  
1. Death If there are no protective measures, few people will survive within this range, and the death radius is recorded as R\({}_{1}\). The calculation formula is as follows: \[R_{1}=13.6\bigg{(}\frac{W_{TNT}}{1000}\bigg{)}^{0.37}\] (10) (2) Serious injury If there are no protective measures for personnel, the personnel in the area are likely to be seriously injured, with only a few slightly injured. The radius of serious injury is recorded as R\({}_{2}\), which requires that the peak overpressure of the shock wave is 44 kPa. The radius of serious injury can be calculated according to the overpressure value, as shown in the following formula: \[\triangle\mathrm{P}=0.137Z^{-3}+0.119Z^{-2}+0.267Z^{-1}-0.019\] (11) \[Z=R_{2}\bigg{(}\frac{P_{0}}{E}\bigg{)}^{\frac{1}{3}}\] (12) \[\triangle\mathrm{P}=\frac{44000}{P_{0}}\] (13) (3) Minor injury This area has little impact on people, most of whom are only slightly injured and rarely die. The minor injury radius is R\({}_{3}\) and the required peak overpressure of the shock wave is 17 kPa. The minor injury radius can be calculated according to the overpressure value, as shown in the following formula: \[\triangle\mathrm{P}=0.137Z^{-3}+0.119Z^{-2}+0.267Z^{-1}-0.019\] (14) \[Z=R_{3}\bigg{(}\frac{P_{0}}{E}\bigg{)}^{\frac{1}{3}}\] (15) \[\triangle\mathrm{P}=\frac{17000}{P_{0}}\] (16) (4) Equipment and property loss The accidental release of energy also causes great damage to surrounding structures. Therefore, the property loss radius R\({}_{d}\) is introduced, which indicates the range of secondary damage to buildings under the action of an explosion shock wave. According to the overpressure impulse criterion, the property loss radius R\({}_{d}\) is calculated according to the following formula: \[R_{d}=\frac{4.6W_{TNT}^{\frac{1}{3}}}{\bigg{[}1+\bigg{(}\frac{3175}{W_{TNT}} \bigg{)}^{2}\bigg{]}^{\frac{1}{6}}}\] (17)
## 2 Novel Technologies of 6G for Smart Grid Applications
### AI-Based Secured Edge/Cloud Computing  
The current cloud and edge computing frameworks are not capable of processing huge numbers of small data sets, as they are designated to handle big data sets. To overcome this drawback, next-generation 6G communication networks will require AI-based edge computing infrastructure to appropriately process such vast quantities of small data. This will necessitate the development of new machine learning methods that go beyond the traditional big data analysis approach [8]. These advanced AI-based techniques will have a vital role in enhancing network functions, as well as enabling the provision of novel services to reduce the processing delay of the edge devices envisioned for future communication networks [8]. In the future, network configurations will emphasize energy efficiency improvement when offloading substantial volumes of small data to edge computing centers.  
Ensuring data privacy and security in current IoT-based networks is a crucial priority. The Internet of Everything (IoE) interconnects a huge number of devices. Thus, the need for decentralized AI technologies becomes evident to train data sets that are spread unevenly across multiple edge/cloud devices for IoE networks. However, this distributed approach also introduces potential security vulnerabilities, including poisoning attacks and authentication issues [98].  
The integration of AI and the blockchain is a potential solution in which to address the abovementioned challenges. Future communication networks must incorporate adaptive security solutions based on AI and blockchain principles [76; 99]. These advanced security measures will play a critical role in securing IoE networks from potential threats and ensuring the transmitted data integrity, security, as well as the process, across the communication network [8]. The novel technologies of 6G wireless communication provide significant improvements in KPIs for various applications. However, future network technologies will raise concerns about cost, security, and privacy [98; 100]. The technology of 6G can address security issues by introducing AI, the blockchain, and federated learning (FL) [76; 77; 99]. Blockchain-incorporated FL prevents malicious activities that can persist in conventional blockchain-based applications. Moreover, the introduction of appropriate algorithms for blockchain- and FL-based 6G networks could provide significant security and privacy enhancements for various applications [98; 100].