esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Retrieval Technique for Trademarks Based on the Fuzzy Inference System
## 5 Experimental Results and Discussion
### Performance of Bull's Eye Score and the Retrieval Ranking  
In this experiment, all of the classes are used to evaluate performance using the Bull's eye score and the ANMRR value. Figure 12 and Table 1 show the Bull's eye score and the ANMRR value of the proposed technique and the two competitive approaches, ZMEG and ZMLDP. The results show that the average BES of the proposed technique exceeds ZMEG and ZMLDP by 19.56% and 30.58, respectively. In terms of ranking capability, our technique also provides better performance, where the ANMRR score improves by 0.167 and 0.236, respectively. As seen in the experiment results, the proposed technique is better than the two competitive approaches.  
However, the value of a TIR system is to search the trademark images that are highly isomorphic, invariantly rotated, and scaled to the query image. Therefore, we illustrate the comparison of the performance of the ranking capability of our technique with that of the competitive approaches. Here, the ranking capability is computed by the precision, and we set that \(n_{rt}\) as half of the number of classes in Equation (28). The largest precision represents the best retrieved result. Figure 13 shows the retrieval results of the query image in class 1 by using the proposed technique and the two competitive approaches, but the ranks of results do not include the query image. Here, we search the 43 images, half the class 1, in the database for the query image. The red Arabic numeral is presented as the non-relevant image. The results show that, comparatively, the proposed technique provides the better ranking performance for this image since its precision is 95.34%. It is observed that the first 40 retrieved images are correctly retrieved, and there are only two incorrect images in the first 43 retrieved images, half of the number of the first class. Additionally, the precision of ZMEG is 58.14%, as there are 18 non-related images in the first 43 retrieved images, and the precision of ZMLDP is 55.81%, as there are 19 non-related images in the first 43 retrieved images. In the above experiments, the results show that the proposed technique can effectively and accurately find the relevant images in a database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Bull’s Eye Score** & **ANMRR** \\ \hline Proposed technique & 72.26\% & 0.324 \\ ZMEG [15] & 52.7\% & 0.491 \\ ZMLDP [12] & 41.68\% & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Averages of the Bull’s eye and average normalized modified retrieval rank (ANMRR) scores.  
Figure 11: Comparison of the average precision and recall rates obtained by the three approaches.  
Figure 12: Comparison of the Bull’s eye and ANMRR scores of all classifications in the database. (**a**) The Bull’s eye scores. (**b**) The ANMRR scores.
## 2 Edge Computing
### Edge Architecture  
Several architectures are proposed for the deployment of the EC layer, but they lack clear definitions and distinctions among nodes. Recent surveys conducted by researchers on EC architectures contain numerous outlooks such as mobile edge cloud servers and networks, application specificity, and considerations regarding resource type, resource management objectives, resource location, and resource utilization. Also, architectural-related challenges like scalability and heterogeneity are elaborated. [PERSON] et al. classified all such edge architectures under three categories, i.e., based on the location of resource-ful servers from edge devices, resources from heterogeneous edge nodes, and classes of resources at edge and data centers [13].  
Figure 3 illustrates a fundamental three-layer architecture for EC. This structure establishes a connection from devices to an edge server, which in turn links to the entire network, encompassing both the cloud and data centers. Within this type of EC architecture, the edge server is situated in a fixed physical location and boasts significant computational capabilities, albeit less powerful than the conventional data centers employed in cloud computing. Furthermore, there is a discernible demarcation between the device level and the edge level, which includes the presence of edge servers [92; 93]. The lowest layer includes the IoT sensing nodes responsible for the ingestion of data and applications. It includes IoT devices like cameras, sensors, controllers, industrial machines, etc. The middle layer includes the edge computing infrastructure for data processing, routing, and computing operations. Data generated at the device layer undergo aggregation, analysis, and processing at the edge servers before being transmitted to the upper layer or returned to the device. Although edge computing servers have lower computational ability than cloud servers, they offer better quality of service (QoS) and lower latency than cloud servers. At the topmost layer, there are cloud data centers involving a central data center and interconnected regional data centers. Even in an EC architecture, cloud data centers persist to serve a crucial role as storage places of information. This layer is accountable for tasks such as data analytics, artificial intelligence, machine learning, visualization, and more.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Characteristics** & **Cloud Computing** & **Edge Computing** \\ \hline Deployment & Centralized & Distributed \\ Latency & High & Low \\ Computational & Unlimited & Limited \\ Storage & Unlimited & Limited \\ Scalability & High & Low \\ Privacy & High risk & Data stays at source \\ Security & A robust security plan and proactive & It requires, to a lesser degree, \\  & monitoring against attacks is required & a powerful security plan \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Cloud and Edge computing.
## 2 Materials and Methods
### Sample Quality Control and Correlation Analysis  
A quality control (QC) sample was prepared from a mixture of sample extracts to examine the repeatability of the sample under the same treatment conditions. Metabolite extraction and detection repeatability were evaluated by analyzing the overlapping display of total ion chromatogram (TIC) of different QC samples. The high stability of the instrument was related to the high repeatability and reliability of the data. The correlation between samples was established using the 'cor' function in R software (www.r-project.org (accessed on 4 November 2022)).
## 5 Key Enablers
### Software-Defined Controllers for Edge Cloud
#### 5.3.2 SDN Controller for Resource Allocation  
The growing number of user applications and their diverse nature highly rely on the computing, storage, and network resources of edge cloud networks. Therefore, to improve the performance of edge cloud networks, resource management is one of the key functions. SDNs can enhance the use of network resources by providing a global view of the network and automating resource allocation [52]. Keeping this in mind, [PERSON], [PERSON], and [PERSON] [52] proposed a resource allocation framework called infrastructure-as-a-service (IaaS) provisioning using SDNs. In this framework, they presented that node and link provisioning in the edge cloud can be better co-ordinated using an SDN controller. Figure 9 shows the simplified architecture of the SDN-based edge cloud resource allocation framework.  
Some of the major components in the above architecture are request handler, QoS classifier, orchestrator, routing and configuration engine, and optimizer. The request handler manages each IaaS request and generates detailed information about the request. Orchestration co-ordinates with other functional components to manage resources and provision the IaaS requests across the underlying data networks. Based on the QoS classifier and available resources, the routing and configuration engine finds a set of routing paths. The optimization engine finds the optimized link and node, and communicates to the orchestrator so that the orchestrator can allocate resources efficiently to the requested IaaS. There are many other research communities also working on resource allocation in the edge cloud environment [57; 65; 66].  
Figure 8: HomeCloud architecture [64].
## 2 Materials and Methods
### Convolutional Neural Networks  
As previously mentioned, Convolutional Neural Networks have demonstrated a high performance in image classification. The current problem of forecasting the magnitude of future earthquakes from a number of GIMs (refer to Figure 3, which shows the data used for the prediction) can also be understood as a problem of image classification (into the categories of Table 1) from a number of GIMs that are the analog of the 3-channel RGB images.  
In a previous work [26], the author presented an estimator with significant success for large earthquake prediction after the extensive testing of all earthquakes of magnitudes \(M_{w}\) of 4.0 or higher that occurred worldwide during the years 2011 to 2018 in the form of a nonlinear regression model. Unfortunately, the application to subsequent years has proved to be quite disappointing. Whatever the reason for this limitation, it is clear that some data typically change or evolve, as is the case for TEC values, which are certainly higher these days than between 2011 and 2018 (due to the solar cycle), and any approach, whatever it is, must evolve over time and allow for the incorporation of recent data and not remain frozen in the past.  
This is why, with the solution presented here--which has been trained with data from 1 January 2011 to 1 October 2024--it is understood that it should be trained periodically with updated data.  
We use a network of four convolutional layers, the first with 16, the second with 32, the third with 64, and the fourth with 128 feature maps, all of them with filters of sizes \(5\times 5\) and zero padding equal to one. There is also a fully connected layer which is connected to a softmax output layer, Figure 5. The Stochastic Gradient Descent with Momentum (SGDM)is used for the adjustment. The training, validation, and testing that will be later explained were all performed using the Matlab Deep Learning Toolbox [38].  
Although the solutions tested so far have by no means been exhausted, the CNN approach for regression testing was not successful, while the approach with the CNN for classification has been successful, as presented later. Many ideas to improve the network design may be possible. In the spirit of a collaborative effort of this presentation, the training and validation data (that is, both the three-dimensional matrices and the magnitudes of the subsequent earthquake magnitudes for the entire dataset) are provided in this paper along with the trained network (in Matlab format) for other researchers to try to improve the design of the network to enhance the prediction results that will be presented next.
## 3 Materials and Methods
### Step 2. Public Engagement Through a Web Application  
To support community awareness and improve accessibility to hazard information, an interactive web application was developed using the ArcGIS Experience Builder platform [25].  
First, the individual hazard maps were published on the ArcGIS Online platform [26], as web maps. Instead of using a ready-made template, a custom experience was built from scratch using Experience Builder to allow greater flexibility and control over the interface.  
The web application was designed to maximize accessibility, user engagement, and the effective dissemination of critical information. The design and functionality choices were guided by the following key considerations:  
* User-Centric Design: The app is designed with a user-friendly interface to ensure accessibility for a broad audience, including individuals with varying levels of technical expertise. The intuitive navigation and clear visual elements help users easily find and understand the information they need.
* Interactive Mapping: Leveraging the capabilities of ArcGIS, the app features interactive maps that allow users to visualize hazard data geographically. This spatial representation helps users comprehend the potential impact areas and the geographic distribution of various hazards.
* Comprehensive Information Layers: Multiple layers of information are available, allowing users to toggle between different types of hazards and related data. This layered approach enables a comprehensive understanding of the hazards and facilitates informed decision making.
* Responsive Design: Although ArcGIS Experience Builder provides tools to create responsive apps, ensuring compatibility across various devices, for the scope of the present paper only the desktop version is developed.
* Educational Resources: In addition to hazard data, the app provides educational resources to help users understand the nature of the hazards and recommended safety measures. This dual focus on information and education empowers users to take proactive steps in hazard preparedness.  
By combining spatial analysis with an engaging digital platform, this two-part approach aims to support both scientific understanding and public communication of natural hazards on Lefkas Island.
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 2 Environmental Vibration Model of Metro Depot
### Track Structure  
The material parameters of the track structure in the metro depot are shown in Table 2.  
The rails are connected to the sleepers through fasteners. In this study, spring-damper connectors are used to simulate the stiffness and damping of the fasteners. The specific parameters are shown in Table 3.
## 2 Routing Background  
In a large MANET, intermediate nodes acting as routers are needed, so that a node is able to communicate with a distant node out of its coverage range. Two things are required in order to make this multihop routing work: firstly, the network must be dense enough to guarantee that at least there is one path between the source and destination [11, 12]; secondly, a routing protocol is needed to find this path.  
Routing protocols for MANETs are different from routing protocols for fixed networks. In fixed networks, only a few nodes act as routers, but in MANETs, almost all of the nodes have to act as routers to maintain network connectivity among all of the nodes. Besides, due to the highly dynamic nature of MANETs, the low capacity of wireless links, the limited bandwidth and the power constraintsof mobile nodes, the routing strategies designed for wired networks are not suitable for a mobile _ad hoc_ environment.  
Two main different routing approaches are considered in mobile _ad hoc_ networks: (1) table-driven protocols or proactive protocols; and (2) on-demand protocols or reactive protocols.  
Proactive protocols are those where nodes continuously search for routing information within a network. Therefore, when a route is needed, it is already known. Nodes always have an updated table to know which path to use towards a destination. They offer fast response times and are adequate for real-time scenarios. Many proactive routing protocols have been proposed. The most representative one is the OLSR protocol.  
On the other side, reactive protocols do not take the initiative to find a route to the destination until a node requires it. This kind of protocol only attempts to discover routes on demand, when a node needs to send data to an unknown destination. Thus, a periodic update of topology information is avoided. This way, reactive routing protocols reduce control data overhead and consumed bandwidth in the network at the cost of increasing the route acquisition latency to find routes to the destination. AODV is the most representative routing protocol for reactive protocols.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 5 Results  
In this section, we present the results of our experiments for each of the research questions.  
RQ1: Can LLMs Generate Software Code That Meets Functional Requirements Directly from the Overall Requirements of Safety-Critical Software?  
According to the experimental results in Section 4.2, the answer to this question is negative. LLMs only create basic code frameworks when directly generating code for safety-critical software from overall requirements. The code does not meet the functional requirements. However, generating code from the overall requirements still has its significance. It can provide software developers with a starting point for structuring the overall framework of the code during the conceptual stage of software development. This approach is suitable for scenarios where the LLMs are used as a software development assistant tool.  
RQ2: Can LLMs Generate Software Code That Meets All Functional Requirements, Especially Domain-Specific Feature Requirements, from the Specific Requirements of Safety-Critical Software?  
According to the experimental results in Section 4.3, the answer to this question is partially negative. The current language understanding and generation capabilities of the LLMs are not sufficient to generate complete functional code from highly specialized domain requirements. However, there has been good progress in generating code for general control flow and input-output relationships. This generated code can directly provide development support to software developers.  
RQ3: How Can Augmented Prompts Be Provided to Enable LLMs to Generate Code That Meets All Functional Requirements and Complies with Industry Regulations?  
According to the experimental results in Section 4.4, the quality of generated code has significantly improved with augmented prompts. From the perspective of prompt enhancement, there are mainly two aspects. First, domain-specific functional requirements  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} Pass \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Robustness testing of generated code.  
are transformed into more general descriptions that are easier for LLMs to understand. Second, domain-specific standard requirements are extracted and transformed into non-functional constraints for code generation, thereby ensuring that the generated code meets higher domain requirements. Moreover, this augmented prompt information can usually be extracted from the requirement documents of safety-critical software. The detail procedures for conducting the experiments is shown in Appendix E Listing A10.
## 4 Case Study
### Results from Different Construction Scenarios
#### 4.4.1 Case 1: Working at Ground Level  
Several construction activities performed at ground level are influenced by the risk of FFH. For instance, when there are excavation works, or when the basement is built. The current case described a task situation with a worker placed far away from the virtual barrier of beacons. Beacons were placed near to the edge of the fall, to delimit the safety area. Figure 5 describes a graphical view of this scenario.  
The altitude from the level of the worker to the next lower level was approximately 1 m and the wind factor was considered medium (10 km/h). The harness was attached and the distance to the virtual fence was 150 cm. In the described situation, and using the described values as input data, the risk value estimated was 2.9999 (Figure 6). This scenario fired Rule 1 with a weight of 75% corresponding to a far distance with the virtual fence and Rule 2 (25% weight) corresponding to the harness connected; as a result, the risk was low. These estimated values are aligned with the expert panel values for this scenario.  
Previous authors pointed the influence in the risk levels of dangerous activities, such as assembly and disassembly of temporary structures [67; 68]. In the scenario analyzed, the absence of cited dangerous activities and the low altitude of the tasks performed, motivated low risk values. Then, risk values obtained by the system are aligned with previous results and safety experts' opinion.  
In this scenario, other technological solutions are possible. For instance, location of the worker using a GPS device could be applied in order to alert a dangerous situation of the worker near to the falling zone [69]; however, the configuration of the system will be complex, and the accuracy of the GPS signal could not be enough for an adequate management of the FFH risk [51].  
Figure 5: Visual example of fall from ground level to lower level.  
Figure 6: Risk levels estimated by the system for the scenario 1 (ground level to lower level).
# Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology
## 3 Results and Discussion
### Effective Wavelength Selection
#### 3.3.4 LS-SVM Discriminant Model  
Based on the characteristic wavelength extracted by RC, SPA, and CARS algorithm, the LS-SVM discriminant model for the defect category of _Cerasus humilis_ fruit was established. The model discrimination results are shown in Table 3.  
As can be seen in Table 3, when \(\gamma\) = 2.47 \(\times\) 10\({}^{3}\), \(\sigma^{2}\) = 1.19 \(\times\) 10\({}^{3}\), the prediction set classification accuracy of the RC-LS-SVM model is lowest; its value was 84.76%. This may be because the RC algorithm was the characteristic wavelength extracted based on PLS model, and so it was not necessarily suitable for nonlinear discriminant models. When \(\gamma\) = 5.82 \(\times\) 10\({}^{3}\), \(\sigma^{2}\) = 2.04 \(\times\) 10\({}^{3}\), the CARS-LS-SVM model was the best for the classification of _Cerasus humilis_ fruit defects. The classification accuracy of the calibration set and prediction set was 86.35% and 91.43%, respectively. It showed that the characteristic wavelength extracted by the CARS algorithm retained the important characteristic spectral information related to the defect category of _Cerasus humilis_ fruit to the greatest extent.  
In order to further analyze the discriminant accuracy of each type of defective _Cerasus humilis_ fruit samples, the prediction results of different defect categories under the CARS-LS-SVM model are shown in Figure 7. A total of nine samples were misjudged. Among them, four insect-damaged fruits were misclassified as intact fruits due to the small area of insect damage, three rust spot fruits were misclassified as intact fruits due to the small area of ruseting, and two cracked fruits were misclassified as intact fruits; this is probably due to the inconspicuous location of the crack on the surface of the fruit and the small size of the crack.
# New Evolutionary-Based Techniques for Image Registration
## 6 Experimental Results
From both accuracy and computation effort points of view, the results obtained by the proposed hybrid methods are significantly better as compared to the aforementioned optimizer in most of the cases. For instance, in case of the pair of images depicted in Figure 1a,b the register image using ES-APSO2 is presented in Figure 2a (MIR 0.8716, SNR 24.4441) while the best result obtained by One Plus One Evolutionary Optimizer is shown in Figure 2b (MIR 0.6772, SNR 19.8342).