esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Comparison on Different Subcategories of Mechanical Components  
In this section, we compare the accuracy of our model to DGCNN on different categories of components. We divided the mechanical components into the following categories: 1--nuts; 2-- bearings; 3--pins; 4--washers; 5--screws; and 6--gear. We compared our model's performance to the DGCNN's performance, as shown in the table below as well as in the visualization.  
As shown in Figure 6 and Table 1, our model behaves better than the DGCNN in every category, showing the effectiveness of the attention and geometric-aware module in classifying those objects.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Component Category** & **DGCNN** & **MC Classifier** \\ \hline Nuts & 0.426 & 0.764 \\ Bearings & 0.549 & 0.844 \\ Pins & 0.760 & 0.958 \\ Washers & 0.822 & 0.943 \\ Screws & 0.870 & 0.947 \\ Gears & 0.499 & 0.530 \\ Rest & 0.478 & 0.691 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of accuracies.  
Figure 5: Comparison between our model and the DGCNN in accuracy. The X axis is number of iterations, and the Y axis is the test accuracy for the models.
## 2 Materials and Methods
### Multi-Resolution Analysis
In this study, we apply the modified mapping method described above to the selected Sentinel-2 tiles at a spatial resolution of 10 m, 20 m, 40 m, 60 m, 100 m, 300 m, 600 m and 1000 m to systematically evaluate the impact of spatial resolution on the identified irrigated area. In order to investigate how the Sentinel-2 and PROBA-Vegetation (PROBA-V) spectral coverage compares when using the selected irrigation mapping approach and in order to link the results of the varying-resolution Sentinel-2 mapping with the operational PROBA-V (300 m) mapping of irrigated area, the same irrigation mapping method is also applied to the available PROBA-V data sets of the same period and regions. PROBA-V was developed as successor of SPOT5 to ensure the continuation of low-resolution vegetation products and was successfully launched in 2013. The spectral range is similar to SPOT5 and provides 4 bands (BLUE, RED, NIR, SWIR) in a spatial resolution from 100 to 300 m [37; 38]. The Sentinel-2 and PROBA-V results are compared at a spatial resolution of 300 m.  
Sentinel-2 is a multi-spectral satellite and is part of the EU's Copernicus program. The spatial resolution depends on the spectral band. The bands (band 8 (NIR) and band 4 (RED)) used in this study are available at a resolution of 10 m. We use the Top-Of-Atmosphere reflectance (TOA) Sentinel-2 data that are corrected for atmospheric effects to Top-Of-Canopy (TOC) reflectance data at 10 m using an inverse radiative transfer approach based on MODTRAN radiative transfer simulations [39]. During the atmospheric correction process, a cloud and snow mask is automatically derived from the images. All available unmasked data of all available dates of 2016 for the selected tiles are used for our analysis. Figure 2 shows the average number of valid observations per pixel for each month. Figure 2: The average number of valid Sentinel-2 observations per pixel for each month in the study regions. The Normalized Difference Vegetation Index (NDVI) is calculated from the available Sentinel data where RED is TOC reflectance in band 4 and NIR is TOC reflectance in band 8 as:  
\[ \begin{split}\text{NDVI}&=\frac{\text{NIR(band8)}- \text{RED(band4)}}{\text{NIR(band8)}+\text{RED(band4)}}\\ \text{RED}&=\text{TOC reflectance in band 4}\\ \text{NIR}&=\text{TOC reflectance in band 8}\end{split}  \tag{1}\]  
This results in a spatially distributed multi-temporal 10 m-resolution temporal course of NDVI covering the year 2016. To calculate multi-temporal NDVI data at 20 m, 40 m, 60 m, 100 m, 300 m, 600 m and 1000 m, the TOC reflectance values of the spectral bands RED and NIR are separately rescaled using a moving window which averages the reflectance of the pixel within the respective area of the coarser resolution. The upscaled reflection value is then used to calculate the NDVI according to Formula 1. For each spatial resolution data set, irrigation maps are created using the identical adapted threshold method to map irrigated areas [7].
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## 4 System Model
### Proposed Semi-Markov Model
Thus, \(H_{2i+5}\) can be given as follows:  
\[H_{2i+5}=p_{2i+5,2i+6}*T_{ON}+\sum_{j=1}^{T_{ON}}j*p_{j}^{ON}+T_{sh}*p_{s}= \frac{e^{-\lambda(T_{SC}-T_{ON})}-e^{-\lambda T_{SC}}}{1-e^{-\lambda}}. \tag{17}\]  
On the other hand, when the device is within the on duration state of the long DRX cycle, there are also three cases. In the first case, the packet arrives after the expiry of the on duration with the probability \(p_{L+1,L}\). In the second case, the packet arrives at the \(j^{th}\) subframe of the on duration with probability \(p_{L}^{ION}\). In the last case, the packet arrives during the long sleep period with probability \(p_{LS}\). Therefore, \(H_{L+1}\) is given as follows:  
\[H_{L+1}=p_{L+1,L}*T_{ON}+\sum_{j=1}^{T_{ON}}j*p_{j}^{ION}+T_{sh}*p_{LS}=\frac{e ^{-\lambda(T_{LC}-T_{ON})}-e^{-\lambda T_{LC}}}{1-e^{-\lambda}}. \tag{18}\]  
Additionally, when the device is within the active states, there are two cases. In the first case, the packet arrives after the expiry of the DRX inactivity timer with the probability \(p_{25}\), \(p_{35}\) or \(p_{45}\), whereas, in the second case, the packet arrives at the \(m^{th}\) subframe of the DRX inactivity timer with the probability \(p_{m}=e^{-\lambda(m-1)}-e^{-\lambda m}\). Thus, \(H_{2}\), \(H_{3}\) and \(H_{4}\) can be calculated as follows:  
\[H_{2}=H_{3}=H_{4}=T_{m}+\sum_{i=1}^{T_{m}}i*\frac{p_{i}}{p_{25}}=\frac{e^{- \lambda T_{m}}-1}{1-e^{-\lambda}}. \tag{19}\]
## 4 Experimental Results
In particular, when only two satellites are connected, the average TTRD is around eleven seconds, whereas when three or more satellites are corrected, the mean TTRD is always below eight seconds, and values of around six seconds are achieved with five connected satellites. It is worth noting that these values are significantly lower than the update rate of the corrections broadcast by the Galileo HAS.
## 5 Experimental Results and Analysis
### Data Source and Preprocessing
#### 5.1.1 Vehicle Speed Dataset  
After preliminary cleaning of the original ETC transaction data, the abnormal data caused by external factors is removed, and then the ETC data and topology data are combined to calculate the average speed of each vehicle segment, thereby constructing a expressway vehicle speed dataset. As shown in Figure 8, the dataset for the section of Fu-xia on 1 May 2021 is presented. Taking Figure 8a as an example, it represents the 24 h section speed data of QD\({}_{340257-340259}\), where the horizontal axis corresponds to every hour, the vertical axis represents the size of the interval speed, each box represents the overall distribution of vehicle speed within QD\({}_{340257-340259}\) an hour, and the small black dots represent outlier speed data.
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 4 Case Study: 3D Printing
### KG Application: Cypher Queries
#### 4.8.3 Influence of Engineering Decisions on Production Phase  
To investigate the dependencies and influences of early engineering decisions on subsequent production phases, we executed the query depicted in Listing 7:  
```
MATCH (startNode)-[rel:InfluencesPropertiesOfPhase]->(endNode) RETURNstartNode.nameASStartNode,rel.propertyASInfluence, endNode.nameASEndNode
```  
**Listing 7** Cypher Query for Identifying Influences in 3D Printing.  
This query retrieves pairs of nodes connected by the InfluencesPropertiesOfPhase relationship, indicating how specific decisions or parameters influence downstream phases of the product life cycle. The results are summarized in Table 7.  
These results illustrate how engineering decisions, such as the selection of a volume model or CAM software parameters, propagate downstream and influence multiple phases of the 3 DP product life cycle. For example, the \"VolumeModel\" directly impacts the \"Production Phase\" by determining the mass of the product, which affects material consumption and energy requirements during manufacturing. Additionally, it influences the complexity of assembly and disassembly processes, which are critical for the post-processing and disposal phases. Similarly, decisions made in the \"Material Selection\" phase influence the transport distance required in the \"Transport Process\", highlighting the interdependencies between design choices and logistical considerations.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Start Node** & **Influence** & **End Node** \\ \hline Volume model & Mass & Production phase \\ Material selection & Transport distance & Transport process \\ CAM software & Support structure & Post-processing and sale process \\ CAM software & Printing time, & Production phase \\  & material consumption, & \\  & energy consumption & \\ Volume model & Assembly complexity & Post-processing and sale process \\ Volume model & Disassembly complexity & Disposal phase \\ \hline \hline \end{tabular}
\end{table}
Table 7: Illustration of influence from start node to end node in the 3 DP process.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Scenario 1: Cement (CEM IV 42.5)**} \\ \hline
**Phase Name** & **Emissions (kg CO\({}_{2}\)-eq)** \\ Production phase—material & 68.8 \\ Production phase—energy & 40.0 \\ Transport phase & 15.0 \\ Total emissions & 123.8 \\ \hline \multicolumn{2}{c}{**Scenario 2: Cement (CEM II 42.5)**} \\ \hline
**Phase Name** & **Emissions (kg CO\({}_{2}\)-eq)** \\ Production phase—material & 79.5 \\ Production phase—energy & 40.0 \\ Transport phase & 10.0 \\ Total emissions & 129.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of emissions for different scenarios in 3 DP phases.  
Understanding these relationships allows engineers to anticipate the downstream effects of early decisions, enabling the optimization of product design for efficiency and sustainability.
## 3 Test Results and Discussion  
### Crack Morphology and Deformation Characteristics  
In the loading test, the failure characteristics of ordinary concrete and rubber concrete specimens were similar: under the cyclic action, the surface cracks of the specimens were longitudinal microcracks, and the end cracks were dense. Under cyclic loading, the main crack of ordinary concrete specimens appeared later, and then the microcrack propagated along the weak part of the cement base. When rubber particles were added, cracks appeared  
Figure 3: Pattern of cyclic loading.  
Figure 2: Testing process.  
earlier, but microcracks no longer appeared, which means crack resistance was improved. The change in concrete crack under the addition and unloading is shown in Figure 4, and the yellow box represents the variation in cracks during loading and unloading.  
Concrete material was composed of hardened cementitious material, coarse aggregate, and interfacial transition zone. Due to the fact that the original defects such as pores, cracks, and bubbles often exist in the interface transition zone, these weak parts were damaged due to stress concentration during loading. Although the addition of rubber particles created many original defects in the interior of the test block due to the interface with the cementitious material, and the macroscopic mechanical properties would be weakened [28], the rubber particles, as the aggregate of the test piece, played the role of energy absorption and dissipation during the loading and unloading process, and tended to restrain the crack tip and prevent further expansion. To sum up, the crack and deformation resistance performance of rubber concrete was better than that of ordinary concrete.  
This article conducts research and analysis on concrete materials using scanning electron microscopy (SEM). From Figure 5, it can be seen that, although the cement base inside the concrete material is thin and loose, the cement base inside the ordinary concrete is laminated and the surface is uneven, indicating that the rubber particles play an inhibitory role in the extension and development of the internal cracks of the concrete. With the increase in rubber particle size, the specific surface area of rubber particles increases. Due to its hydrophobicity, the bonding between cement and rubber particles is not tight enough. The interior becomes loose with the increase in rubber particle size, and more pores generate micro-cracks and are interconnected, which will lead to the decrease in the mechanical properties of the concrete material and the increase in the damage performance.
# Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube
## 5 Conclusions  
This study developed a compressible MHD solver, mhdF, and a magnetic diffusion solver on the OpenFOAM platform. These solvers are integrated into a multi-region coupling framework to create a multi-region coupled compressible MHD solver, mhdMRF, aimed at investigating the dynamic processes of CT in drift tubes and the influence of finite-resistivity walls. The solvers' accuracy and engineering applicability in addressing MHD and magnetic diffusion problems were validated. By implementing custom interface coupling boundary conditions, the magnetic field coupling between fluid regions and finite-resistivity wall regions was achieved. The research further explored the effects of the geometric structure of the inner electrode tip and finite-resistivity walls on the MHD behavior of the CT. Numerical results demonstrate that during the transition of the CT from the acceleration zone to the drift tube, the geometric structure of the inner electrode tip plays a critical role in regulating the distributions of the magnetic and flow fields. Introducing a conical transition section to the traditional cylindrical inner electrode significantly reduces local velocity gradients, enhances magnetic field structural integrity, and improves CT confinement. These findings provide crucial design guidelines for optimizing device configurations. The resistivity of finite-resistivity walls directly impacts the magnetic field diffusion rate. A lower resistivity slows magnetic field dissipation and suppresses plasma accumulation near the walls, thereby enhancing CT stability. Although the solver achieved ideal numerical simulation results for CT magnetohydrodynamics processes, limitations remain in capturing transonic shock phenomena and incorporating coupled effects of magnetic fields on material properties (e.g., dynamic viscosity, thermal conductivity). Future work will focus on algorithm improvements, coupled effect modeling, and experimental validation.  
[PERSON] performed numerical modeling, program development, numerical simulations, and manuscript writing; [PERSON] supervised the entire research work and manuscript review; [PERSON], [PERSON] and [PERSON] provided experimental data and theoretical guidance. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National MCF Energy R&D Program under Grant No. 2024 YFE03130000, the Institute of Energy, Hefei Comprehensive National Science Center No. 24 KZS303.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 5 Protocol and Interfaces
### Control Plane Interfaces
#### 5.2.1 Control Plane Application Interfaces  
The control plane application is the main application in the architecture, but, from a service interface perspective, it only has two main interfaces towards the orchestration layer. Interfaces that the control plane application implements are perceived as interfaces held by other services, explained in the following sections. The main interfaces for the control plane application are:  
* An NSD interface for incoming requests from the orchestration plane. This includes the NSDs for SFC, the VNFs and the Transport Links.
* A service capability interface to get information about the Transport Links to inform the orchestration layer whether the Transport Links exist and how they are established. This service is reflected from the orchestration plane and proxies the BGP route table to the orchestration plane as an NSD.
# Properties of the Quadratic Transformation of Dual Variables
## 6 Solving Test Problems  
Tables 2-5 show the results for dimensions \(n=1000\), \(m=100\). Each table contains the results of calculations for three functions with two ways of dual variable transformation. The number \(mb\) of active constraints is varied. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(\Delta\psi\)} & \multirow{2}{*}{\(\Delta f\)} & **Number of** & **Number of** & **Number of Evaluations** \\  & & & **Iterations** & **of Function and** \\  & & & & **Gradient** \\ \hline \hline  & \multicolumn{3}{c}{Function LP\_R\_Y2 \(mb=5\), \(ma=10\), \(md=10\)} \\ sub & 4.860 \(\times\)\(10^{-1}\) & 1.098 \(\times\)\(10^{-5}\) & 104 & 208 \\ Shor & 4.860 \(\times\)\(10^{-1}\) & 1.561 \(\times\)\(10^{-5}\) & 323 & 644 \\ yv & 4.860 \(\times\)\(10^{-1}\) & 1.081 \(\times\)\(10^{-7}\) & 64 & 130 \\ MsqrPPR & 4.860 \(\times\)\(10^{-1}\) & 2.023 \(\times\)\(10^{-8}\) & 259 & 559 \\ BFGS & 4.860 \(\times\)\(10^{-1}\) & 1.770 \(\times\)\(10^{-5}\) & 102 & 263 \\  & \multicolumn{3}{c}{Function LP\_R\_Y1 \(mb=5\), \(ma=10\), \(md=10\)} \\ sub & 4.860 \(\times\)\(10^{-1}\) & 7.999 \(\times\)\(10^{-4}\) & 438 & 878 \\ Shor & 4.860 \(\times\)\(10^{-1}\) & 3.335 \(\times\)\(10^{-5}\) & 1471 & 3127 \\ yv & 4.860 \(\times\)\(10^{-1}\) & 1.747 \(\times\)\(10^{-4}\) & 443 & 843 \\  & \multicolumn{3}{c}{Function L\_Y2 \(mb=5\), \(ma=10\), \(md=10\)} \\ sub & 3.011 \(\times\)\(10^{-9}\) & 1.402 \(\times\)\(10^{-3}\) & 100 & 202 \\ Shor & 1.223 \(\times\)\(10^{-11}\) & 1.327 \(\times\)\(10^{-5}\) & 279 & 563 \\ yv & 1.380 \(\times\)\(10^{-12}\) & 1.019 \(\times\)\(10^{-6}\) & 58 & 120 \\ MsqrPPR & 2.328 \(\times\)\(10^{-8}\) & 2.394 \(\times\)\(10^{-4}\) & 107 & 242 \\ BFGS & 9.582 \(\times\)\(10^{-12}\) & 5.955 \(\times\)\(10^{-6}\) & 73 & 183 \\  & \multicolumn{3}{c}{Function L\_Y1 \(mb=5\), \(ma=10\), \(md=10\)} \\ sub & 1.635 \(\times\)\(10^{-6}\) & 3.738 \(\times\)\(10^{-3}\) & 427 & 856 \\ Shor & 7.250 \(\times\)\(10^{-9}\) & 5.664 \(\times\)\(10^{-4}\) & 1418 & 3001 \\ yv & 4.695 \(\times\)\(10^{-9}\) & 2.193 \(\times\)\(10^{-4}\) & 506 & 975 \\  & \multicolumn{3}{c}{Function 1/\(\times\)\_Y2 \(mb=5\), \(ma=10\), \(md=10\)} \\ sub & 9.478 \(\times\)\(10^{-13}\) & 3.952 \(\times\)\(10^{-7}\) & 95 & 192 \\ Shor & 1.307 \(\times\)\(10^{-11}\) & 1.086 \(\times\)\(10^{-6}\) & 69 & 130 \\ yv & 2.215 \(\times\)\(10^{-13}\) & 7.348 \(\times\)\(10^{-8}\) & 49 & 100 \\ MsqrPPR & 2.489 \(\times\)\(10^{-15}\) & 8.978 \(\times\)\(10^{-11}\) & 62 & 128 \\ BFGS & 0.129 \(\times\)\(10^{-12}\) & 1.825 \(\times\)\(10^{-7}\) & 40 & 86 \\  & \multicolumn{3}{c}{Function 1/\(\times\)\_Y1 \(mb=5\), \(ma=10\), \(md=10\)} \\ sub & 8.000 \(\times\)\(10^{-5}\) & 1.142 \(\times\)\(10^{-4}\) & 212 & 427 \\ Shor & 4.560 \(\times\)\(10^{-7}\) & 1.228 \(\times\)\(10^{-5}\) & 737 & 1560 \\ yv & 6.589 \(\times\)\(10^{-6}\) & 3.316 \(\times\)\(10^{-5}\) & 173 & 323 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of calculations for dimensions \(n=1000\), \(m=100\) with \(mb=5\).
## 1 Introduction
### Research Objective  
Understanding that corrosion has a significant impact on the seismic vulnerability of CI, this paper aims to present a conceptual framework for evaluating seismic risk, accounting for the time-dependent degradation effects of corrosion on reinforced concrete (RC) elements. This research addresses a crucial gap in current seismic vulnerability assessments, which often overlook the progressive deterioration of structural integrity due to corrosion processes. This study aims to  
1. Develop a methodology for simulating the progression of corrosion in RC structures over time, considering different corrosion intensities;
2. Quantify the impact of corrosion-induced degradation on the seismic performance of CI components, particularly focusing on changes in structural capacity;3. Integrate corrosion effects into seismic fragility analyses by proposing adjustments to traditional fragility curves, thereby providing a more accurate representation of the structural vulnerability throughout the infrastructure's lifecycle;
4. Establish a risk assessment framework that combines adjusted seismic fragility with hazard data to estimate the expectancy of the total lifecycle risk cost, considering the corrosion-induced deterioration.  
The significance of this research lies in its potential to enhance the accuracy and reliability of long-term seismic risk assessments for critical infrastructure. By incorporating the effects of corrosion, this study provides a more realistic evaluation of infrastructure vulnerability, crucial for informing maintenance strategies, improving resource allocation, and enhancing the safety and reliability of essential services, particularly in seismically active regions with corrosive environments.
## 5 Evaluation
### Metrics and Scoring
#### 5.2.5 F1-Score  
The F1-score is not merely the mean of precision and recall; it is frequently regarded as the harmonic mean of those two metrics. In the event of the data being strongly biased, for example, due to an imbalance in the amount of malicious and benign documents, one of the metrics would be likely to produce a very low result, resulting in the F1-score falling below the mean of the two. The definition is as follows:  
\[\text{F1-score}=\frac{2\times(\text{Precision}\times\text{Recall})}{\text{ Precision}+\text{Recall}}\]  
Inserting our test results, we receive the following F1-score:  
\[\text{F1-score}=\frac{2\times(0.929\times 0.929)}{(0.929+0.929)}=0.929\]  
The F1-score represents an effective metric for the comparison of different classification models. In comparison to metrics such as precision alone, the F1-score is less susceptible to biases present in the sample data, as it considers both precision and recall. Given that the F1-score of 0.929 does not appear to be particularly exceptional when compared to other machine learning detection methods, it is important to consider the absolute error rate of one for each classification when assessing the relative error.