esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 FIL Component Quantitative Analysis: Experimental Testbed Design
### Evaluation Methodology  
With its intended goal of serving as tool for facilitating semantic interoperability in an Industry 4.0 scenario, the FIL component will need to be able to handle efficiently the different traffic generated by the infrastructure, devices, and components deployed in a generic industrial shop floor. In a deployment such as the one depicted in Figure 3, the FIL component receives data updates sent by PLCs along the production line. The PLCs handle various data, including sensor readings, temperature, pressure, and timing responses. These updates may include multiple configurations changing in real time, implying a significant growth in the amount of data updates that have to be handled by the component.  
Figure 3: Architecture of the proposed testbed scenario used to compare FIL component with its direct competitor F4I IDAS OPC-UA. In this figure, the FIL component is represented, but the same configuration was deployed for the IDAS OPC-UA Agent.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable Node Class Attributes** & **FIWARE Entity Class Attributes** \\ \hline Node ID & id \\ Display Name & name \\ Description & description \\ Value & value:value \\ Data Type & value:type \\ \hline \hline \end{tabular}
\end{table}
Table 3: OPC UA Variable Node to FIWARE Entity Attribute Mapping.  
To assess FIL's performance in managing substantial updates from OPC UA servers and its comparison with the F4I OPC UA Agent, we constructed a comprehensive testbed capable of simulating PLC data loads. In the proposed scenario, we simulated a production line requiring data sharing from a set of PLCs to automate response and control mechanisms. These data, essential for other components depicted as Component 1 and Component 2 in Figure 3, involve some PLCs connected through common middleware, in our case, FIWARE, and others externally consuming data via standard HTTP/HTTPS protocols. Figure 3 sums up the architecture of the scenario deployed to test the FIL component. The same structure but with the F4I OPC UA Agent was implemented to perform a comparative analysis of the solutions.  
The following design objectives were considered for the testbed:  
1. Performance observability, that is the ability to accurately capture, analyse, and present information about the performance of the proposed component. In a real shop floor, PLCs interact with the deployed components and it is necessary to balance the need for performance data against the cost of obtaining these data. As productivity and efficiency are core KPIs in industrial processes [57], we have used the Prometheus monitoring system [58] and cAdvisor [59] to collect extensive performance metrics;
2. Variable and massive load generation to generate the heterogeneity and overload situation that could happen in a real shop floor scenario with multiple PLCs working in multiple working stations. Apache JMeter [60] was used to generate large rates of new active nodes.
## 4 Results and Discussion
### Determination of BCI in Bridge No. 5
Despite the positive points mentioned in the previous paragraph, the proposed method has a limitation named the subjective influence of inspectors. Visual inspection is a measurement mechanism implemented by humans. Accordingly, variability influences the reliability of this mechanism. In fact, the visual examination gives valuable data on bridge health, but it is not always guaranteed since it depends mainly on the inspector's experience and knowledge. Of course, this issue can hardly be avoided. One solution for such an issue is to use auxiliary analyses, such as non-destructive tests (NDTs). Although this solution can confine the subjective influence of inspectors, it will confront the authors with another limitation. Most of the local organizations in Iran are deprived of NDT equipment or similar tools. Moreover, the authors intend to present an easy-to-use, applicable method for all organizations in Iran, including local organizations. Eventually,  
Figure 23: Distresses of pavement and safety problems in bridge No. 5.  
the authors decided to base their methodology according to direct inspection by inspectors. In their opinion, the advantages of being more usable of the methods conquer possible disadvantages of subjective influence of inspectors. For solving or reducing the problem of inspectors' influence, they suggest that organizations should focus on the personal selection and better training of inspectors. As part of future works, the authors are investigating three different research objectives. First, the authors are very interested in connecting new technologies/tools/methods with their methodology. One of these new technologies is remote sensing (RS). RS analyzes different objects on the earth's surface by data received from a device that is not in contact with those objects. Another tool is machine learning (ML). ML originated from artificial intelligence (AI) and has been used in recent years in various scientific areas. Increasing applications of RS [48; 49; 50; 51; 52; 53; 54] and ML [55; 56; 57; 58; 59] in structural health monitoring of infrastructures in recent years motivates the authors to conduct this idea. Second, the authors intend to extend the proposed methodology for other types of bridges, including steel and stone (old) bridges. The latter idea can help enlarge the application dominance of the methodology. Finally, by increasing the number of experts asked to fill the questionnaire, the accuracy and efficiency of the methodology will be more reliable.
## 4 Discussion
### Future Opportunities  
The framework created in this research provides a universal guide for designing and evaluating the PEDF system with various stakeholders. However, it may not fully meet all stakeholder needs and requires further development. Specifically, tailored control strategies and an improved optimization framework are necessary to better address daily working conditions. This enhancement will enable the system to quickly respond to faults and changes, preventing loss of control and increasing efficiency.  
To better serve the diverse stakeholders and improve configuration optimization for the PEDF system, it is essential to develop a software tool that integrates a PEDF configuration optimization and evaluation framework. This tool will adapt to various application scenarios, allowing for automated analysis, standardized planning and configuration, and optimized evaluation of configurations. By using this software, users can efficiently design systems that are economic, low-carbon, and reliable. Additionally, it will provide a platform for testing the PEDF system's performance in different scenarios, offering valuable data for decision making in real projects. This research team has already started designing and developing this software, using case studies from this research as examples. In summary, this software tool will significantly enhance the application of the PEDF system in building energy management, promoting green, intelligent, and sustainable development.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Energy Scheduling Strategy** & \begin{tabular}{c} **Configuration** \\ **Results (kW)** \\ \end{tabular} & \begin{tabular}{c} **Levelized Cost of** \\ **Energy (CNY/kW)** \\ \end{tabular} & \
\begin{tabular}{c} **Carbon Emission** \\ **Reduction (kgCO\({}_{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Loss of Power Supply** \\ **Probability (\%)** \\ \end{tabular} \\ \hline
100\% grid connection & [1600, 0] & 0.4787 & 1,083,578 & 70.35 \\
50\% self-use and & [1300, 0] & 0.4852 & 905,760 & 75.66 \\
50\% grid connection & [800, 0] & 0.4988 & 978,156 & 83.25 \\ Spontaneous self-use surplus & [800, 0] & 0.4988 & 978,156 & 83.25 \\ electricity grid connection & [1628, 300] & 0.4019 & 1,731,700 & 44.29 \\ Planning and operating a & & & & \\ dual-layer model & [1628, 300] & 0.4019 & 1,731,700 & 44.29 \\ (low-carbon priority) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of results of different energy scheduling strategies.
## 2 Search Strategy  
All literature examining sweat electrolyte adaptations, following heat training utilizing the WBW technique and/or regional sweat patches, was searched between December 2022 and May 2024 in PubMed, Scopus, and Google Scholar. Keywords included 'heat training', 'heat acclimation', 'acclimatization', 'heat adaptation', 'thermal adaptation', 'passive heating', 'electrolyte','sodium', 'potassium', 'chloride', and'sweat'. Subject headings were used independently and in combination. With the initial article search, only full-text, peer-reviewed articles with human participants and published in the English language were selected. Furthermore, review articles, abstracts, case/single studies, unpublished theses, conference papers, and editorials were not included. During abstract and full-text reviewing, only articles utilizing the WBW technique or regional sweat patches to analyze sweat electrolyte adaptation from heat training were included for discussion.
## 2 Related Research
### Access to Public Datasets  
Today, a few public datasets provide access to Android GNSS measurements. The review from [11] was based on a dataset published by Google, in the context of the Google Smartphone Decimeter Challenge (GSDC) that happened in both 2021 and 2022. The data used for the competition is still accessible online and can be downloaded upon acceptance of the competition rules [23; 24]. The data from 2021 are composed of dynamic tracks acquired in a car scenario and performed with different Android device models. Additional details can be found in [25].  
Other interesting datasets were published by the EUSPA (former GSA) as part of the \"GNSS Raw Measurements Task Force\" [26]. The data are accessible through a Google Doc, summarizing the campaigns. As of today, two campaigns are accessible through the document. One campaign from 2020 focuses on different wearables devices but provides access only to positions and not to raw GNSS measurements. Another campaign from 2021 contains data acquired in different scenarios (i.e., static, dynamic pedestrian/bike/car) and environments (i.e., open-sky, forest, suburban, urban, highway) from six Android devices. The accessibility and diversity of the data make the EUSPA dataset very interesting for research purposes. However, we did not use this dataset in this research, as we wanted to focus on a newer generation of Android devices including smartwatches, and assess the potential improvements in the GNSS measurement quality.  
To summarise, access to raw GNSS measurements in open datasets is still scarce and limited to a few providers. Indeed, all the studies mentioned before that compared several devices have shown the large discrepancies that can exist in terms of measurement quality. Thus, to mitigate the impact of the data over the proposed methods, it is necessary to compare it on a variety of datasets and devices. Given the amount of new Android devices released every year, keeping up with new datasets for every device might seem vain. Yet, crowd-sourcing and publication of the datasets used in studies could enable more device comparison without the need for each research team to purchase a specific device.
# Financial Market Correlation Analysis and Stock Selection Application Based on TCN-Deep Clustering
## 3 Experimental Settings and Results
### Parameter Settings  
The network parameters of the TCN encoder-decoder structure for this experiment are shown in Table 2. The features of the input layer used the stock closing price divided by the closing price of the previous day, which was then fed into the encoding and decoding layer structure of the network. The encoding layer corresponds to rows two to eight in Table 2, which mainly performed a dimension-raising operation on the data. Furthermore, the decoding layer corresponds to rows ten to eighteen in Table 2, which mainly performed a dimension-lowering operation on the data. The hidden state dimension was set to 320 dimensions. The number of iterations of the encoder was set to 100 and the Adam optimizer was adopted during the training process. Meanwhile, the initial learning rate was set to 0.001. The total number of parameters to be trained for the network reached 309,644.
## 4 Transient Fuel Consumption Model and Its Validation

Table 3: The values of coefficient \(D\) obtained from regression analysis. Finally, the developed model with TCF was simulated for three drive cycles. Figure 4 shows plots for \(\dot{m}_{ss\_m}\), \(\dot{m}_{exp}\) and \(\dot{m}_{TCF}\) on the UDDS cycle. It can be seen that after introducing TCF, the fuel consumption result of \(\dot{m}_{TCF}\) is more accurate with \(\dot{m}_{exp}\) compared with steady-state prediction. Figure 5 demonstrates the cumulative sum of the fuel rate: \(fc_{ss\_m}\), \(fc_{model}\), and \(fc_{exp}\) over the cycle. The results show that overall fuel consumption with transient correction (\(fc_{TCF}\)) and experimental total fuel usage (\(fc_{exp}\)) were matched better. Thus, it can be concluded that the developed TCF model allows for effectively including the transient characteristics of the engine. Table 4 summarizes the overall fuel consumption results for different driving cycles. It shows a good fit of \(fc_{TCF}\) with \(fc_{exp}\). In addition, the last column indicates the difference between \(fc_{TCF}\) and \(fc_{ss\_m}\), the sum of all extra fuel usage during the transient phases. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Driving Cycle** & \(fc_{exp}\) & \(fc_{TCF}\) & \(fc_{TCF}-fc_{ss\_m}\) \\ \hline UDDS & 741.6 [g] & 743 [g] & 51 [g] \\ \hline HW & 787 [g] & 785 [g] & 35 [g] \\ \hline US06 & 1005 [g] & 1006 [g] & 66 [g] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of total fuel consumption calculated from the model and measured from the experiments.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 6 Simulation Analysis
### Routing Performance Evaluation Indicators  
The packet delivery rate measures the probability of a packet successfully reaching its destination. End-to-end delay reflects the transmission delay of data packets from source to destination. The packet loss rate indicates whether data packets are lost during transmission. This article combines these three indicators to evaluate routing performance from multiple aspects.  
(1) Packet delivery rate  
The packet delivery rate can effectively reflect the maximum throughput of the network. The calculation expression is:  
\[Pdelivery=\frac{P_{R}}{P_{S}} \tag{4}\]  
In Formula (4), \(P_{R}\) represents the data packets that successfully reach the destination node, and \(P_{S}\) represents the total number of data packets sent from the source node.  
(2) Average end-to-end delay  
The average end-to-end delay is the average of the total delay of all data packets from leaving the source node to arriving at the destination node, which reflects the speed of data packet transmission on the network and the effectiveness of the link. The calculation expression is:  
\[Audelay=\frac{1}{N}\sum_{i=0}^{N}(R_{time}(i)-S_{time}(i)) \tag{5}\]  
In Formula (5), \(N\) represents the number of successfully transmitted data packets, \(R_{time}(i)\) represents the time when the \(i\)-th packet reaches the destination node, and \(S_{time}(i)\) represents the sending time of the \(i\)-th packet.  
(3) Packet loss rate  
Packet loss rate defines the percentage of data packets that are not successfully received by the destination node. During the operation of the entire network, some data packets will be lost due to network congestion, route breaks and other reasons. The packet loss rate reflects the reliability of network transmission very well. The lower the packet loss rate of data packets, the higher the reliability of the network.  
\[Ploss=\frac{P_{l}}{P_{S}} \tag{6}\]  
In Formula (6), \(P_{l}\) represents the number of lost data packets, and \(P_{S}\) represents the total number of data packets sent from the source node.
## 5 Results and Discussions
### Concentration Profile  
The effect of thermophoresis and Brownian motion parameters on the concentration profile is depicted in Figure 6. The concentration profile grows and declines by, respectively, rising thermophoresis and Brownian motion parameters. The rise in thermophoresis forces transfers particles from the plate to its vicinity, leading to a rise in the concentration profile. The variation of the Schmidt number and reaction rate parameters on the concentration profile is displayed in Figure 7. By looking at this Figure 7, it can be deduced that the concentration profile decays by escalating the Schmidt number and reaction rate parameter. The decay in the concentration profile due to the increment in the Schmidt number is the consequence of the decline in the mass diffusivity that slows down the diffusion process, resulting in a decline in the concentration profile. The enhancement in the reaction rate parameter produces breaking and forming of new substances, which slows down the concentration profile.
# Fast Conflict Detection for Multi-Dimensional Packet Filters
## 5 Experimental Results
### Comparing Time Performance with Extended 5D Algorithms
Table 14 shows that constructing an additional data structure for non-prefix fields costs considerable time and memory compared to our proposed algorithm, which cost little time and memory to set up 6-bit information for each filter and 1.5 KB lookup table. For applications that require frequent filter updates, the performance impact of the SBV algorithm is even greater. For example, each filter costs an average of 30.79 microseconds to build auxiliary information. In other words, it takes 30.79 microseconds to update each filter which is more than the time it takes to detect all conflicts in FW4. All the algorithms that use the trie-based data structure are affected in the same way. That is the reason we only compared our algorithm with the SBV algorithm.
## 4 Results and Discussion
### Aerodynamic Noise Characteristics
#### 4.2.2 Aerodynamic Noise Radiation and Attenuation Characteristics
To predict the OASPL in the region from the pantograph to the far field, it is found that the logarithm function, OASPL = OASPL\({}_{0}\) - b x ln(x + c), is fitted appropriately using the values of the OASPL under the relation between the OASPL and the distance in the range of \(0\leq y\leq 30\) m. The four correlations obtained by the fitted curves are mathematically expressed as follows: (1) OASPL = \(107.3-8.9\times\ln(\mathrm{x}-0.50)\) for the down-pantograph with the flush base; (2) OASPL = \(105.5-8.8\times\ln(\mathrm{x}-0.03)\) for the up-pantograph with the flush base; (3) OASPL = \(102.9-9.1\times\ln(\mathrm{x}-0.12)\) for the down-pantograph with the sunken base; and (4) OASPL = \(97.1-7.9\times\ln(\mathrm{x}-0.46)\) for the up-pantograph with the sunken base. Another observation is that the differences between the OASPL obtained by the up- and down-pantograph with the flush configuration base at each position are about 1-2 dB, whereas the differences between the OASPL obtained by the up- and down-pantograph with the sunken base at each position are about 4-5 dB. Besides, under the same configuration base, the OASPL obtained by the down-pantograph is higher  
Figure 17: Noise predictions at the far field monitoring points. (**a**) Noise of down-pantograph with flush base; (**b**) Noise of up-pantograph with flush base; (**c**) Noise of down-pantograph with sunken base; and (**d**) Noise of up-pantograph with sunken base. than that by the up-pantograph. To evaluate the OASPL caused by the pantograph in the far-field region, it is acceptable if the distance from the sound source is more than 25 m, the sound source is assumed as a point source [13]. Therefore, the sound pressure propagation can be considered as the spherical surface from the point source center and the attenuation value \(\Delta\)L of the OASPL from the point source in the far-field regions is computed by the equation: \(\Delta\)L = 20 log(r/r0). This can be used to the predict the geometric attenuation of noise. Figure 18b shows the OASPL along the x direction at y = 25 m. The down-pantograph always produces higher noise than the up-pantograph for both configuration bases. Besides, Figure 18a,b indicates that the OASPL in the far field along the x and y directions are always found that for all x and y positions the down-pantograph with the flush base provides the highest OASPL. The lowest OASPL is obtained by the up-pantograph with the sunken base. In addition, the OASPL caused by the up-pantograph with the flush base is higher than that by the down-pantograph with the sunken base.
## 6 Recommendations for Cybersecurity Maintenance in the Vehicle
### Cybersecurity Integration Throughout the Entire Vehicle Lifecycle
#### 6.1.2 Compliance Assessment in Automotive Cybersecurity  
Compliance assessment is critical for ensuring that automotive systems meet established cybersecurity standards and regulatory requirements. Given the complexity of modern vehicles and the increasing integration of digital technologies, compliance with cybersecurity standards is necessary to ensure the safety and security of both the vehicle and its users.  
The compliance assessment process typically involves the following:  
1. Standards Evaluation: Ensuring that the vehicle's cybersecurity features align with relevant standards, such as ISO/IEC 27001 (for information security) and ISO/SAE21434 (specific to automotive cybersecurity). These standards provide a framework for managing security risks and implementing necessary security controls.
2. Auditing and Testing: Independent audits and testing are conducted to evaluate the effectiveness of the implemented cybersecurity measures. This can involve penetration testing, code audits, and hardware inspections to ensure that the system is secure and robust.
3. Certification: Upon successfully meeting the required standards, the system is certified as compliant by an accredited body. This certification is a crucial step in verifying that the vehicle meets both industry and regulatory cybersecurity requirements.  
Compliance assessment is not a one-time process but requires ongoing audits and evaluations as part of the vehicle's lifecycle. Changes in the regulatory landscape, technological advancements, and emerging threats require OEMs and suppliers to continuously update and reassess compliance efforts to ensure sustained cybersecurity integrity.