esa-sceva/satcom-chunk-collection huggingface dataset:
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 5 Experiment and Result Analysis
### Experimental Comparison
#### 5.2.2 Average Success Rate of Faulty Link Restoration  
To verify the fault link recovery performance of the BPDM-GCN backup path algorithm, the experiment uses the fault link recovery success rate as the evaluation criterion. The experiment is carried out in six IEEE benchmark test power system communication network topologies, and sets the link load network topology environment of 20%, 40%, 60%, and 80%. The ratio of the number of links successfully recovered during the experiment to the total number of failed links in the topology is defined as the recovery success rate. The failure link recovery success rates of the BPDM-GCN backup path algorithm and the failure link recovery algorithms of the NRLF_RL algorithm, FR_VLAN method, and LIR_LFR method are compared experimentally. The experimental results are shown in Figure 5.  
As depicted in Figure 5, across diverse link loads in communication network topologies of six IEEE benchmark power systems, the BPDM-GCN backup path algorithm consistently achieves an average fault recovery success rate of 95.2%. Conversely, the other three fault link recovery algorithms exhibit varying degrees of decline in their average fault recovery success rates with increasing link loads in the network topologies. The LIR_LFR fault link recovery method is deficient in optimizing under varying link load conditions. As link load increases, this algorithm exhibits the lowest recovery success rate among comparable fault recovery methods, clearly demonstrating a downward trend. The FR_VLAN fault recovery method employs data aggregation for fault recovery, but its performance diminishes in network topologies with high link loads. Despite this, its average recovery  
Figure 4: Average fault link recovery delay under different link loads.  
success rate for faulty links is higher compared to the LIR_LFR fault link recovery method. The NRLF_RL algorithm for large-scale fault network recovery utilizes reinforcement learning techniques to assess link performance within network topologies. However, it suffers from suboptimal backup path optimization, leading to a need for improvement in the average recovery success rate of faulty links across varying network loads. Traditional techniques such as shortest path algorithm and static/dynamic weight setting have limitations, while BPDM-GCN technology learns network features through GCN to achieve adaptive weight determination and multi-objective optimization, thereby more accurately reflecting network status and designing better backup paths. The BPDM-GCN backup path algorithm modifies the link weights in the network topology based on the GCN improved DDPG algorithm, and utilizes the incremental shortest path tree to calculate the backup path, thereby enabling the backup path to cope with network topologies with varying link load conditions. In comparison to the NRLF_RL fault recovery algorithm, which exhibits superior performance, the fault link recovery success rate has been enhanced by approximately 5.24%.
## 2 ARD Framework
### Docker and Parallel Processing  
The pipeline, structured around the _Metaflow_[59] Python framework for the design and management of data science projects, ensures that the multiple steps required to process each satellite product are carried out in the correct order, whilst providing the flexibility to allow end users to extend the framework to shape the ARD for a particular application. Where tasks are able to be processed independently--such as the calibration, filtering and terrain correction of each Sentinel-1 product, or the collocation of matching Sentinel-1 and Sentinel-2 products--the framework can scale horizontally, to fully utilize computing resources and reduce the required processing time, i.e., tasks will be deployed in parallel across each available CPU core.  
The generation of ARD is characterised by a number of dependencies. Data sources must be queried, data downloaded, images processed and collocated and optionally cut into separate patches of suitable size for specific applications, e.g., training ML models. The framework specifies all dependencies via a _Dockerfile_, which can be created and deployed using _Docker_[60], provisioning a consistent environment managing all necessary dependencies for the preparation of ARD. _Docker_ images are suitable for use on either a personal or office-scale computing infrastructure, and can be readily converted to container images suitable for deployment on a high-performance computing (HPC) infrastructure, such as _Singularity_[61].
## 3 System Model
### Link Quality  
Due to the rapid movements of the UAVs, the network topology changed frequently, making wireless links unreliable. Other factors also influenced the quality of the received signal, such as signal fading, attenuation, and Doppler shifts. These factors led to packet loss. Therefore, the accurate evaluation of wireless link quality was vital for designing routing protocols that could guarantee network performance.  
The link quality estimators were divided into hardware-based and software-based estimators. The hardware-based link quality estimators were obtained from the physical layer module. The software-based link quality estimators were obtained by calculating the expected transmission count from the data link layer. In order to obtain a more comprehensive and accurate evaluation of the link quality, we considered the SNR and the expected transmission count in our calculations. As mentioned earlier, the path losses and Doppler shifts in the UANET were key factors that influenced the SNR.
## 3 Results
### Evaluation of Results
#### 3.3.1 Crystalline Silicate-Reinforced Ceramics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**FS (MPa) 8–10 min**} \\ \hline
**Authors** & **Ceramics/Manufacturer** &
\begin{tabular}{c} **Heat** \\ **Teatment/Furnace** \\ **Manufacturer** \\ \end{tabular} & **Time** & **FS (MPa)** & **FS (MPa) SD** \\ \hline [PERSON], Monteiro JB & Vita Suprinity & 840 \({}^{\circ}\)C Vita & & \\ et al. 2018 [38] & VITA Zahnfabrik & Vacumat 6000 MP & 8 min & 191 & x \\ [PERSON], Monteiro JB & Vitra Zahnfabrik & Vita Zahnfabrik & & & \\ \hline [PERSON], Monteiro JB & Celtra Duo & 830 \({}^{\circ}\)C Multimat & & \\ et al. 2018 [38] & Dentsply Sirona & Dentsply Sirona & & & \\ [PERSON], Dal Piva AMO et al. & IPS e.max CAD & 850 \({}^{\circ}\)C & & \\
2023 [33] & Ivoclar Vivadent & Programat P100 & 10 min & 358 & 73 \\ \hline [PERSON], [PERSON]. & Vita Suprinity & 840 \({}^{\circ}\)C Programat & & & \\
2020 [37] & VITA Zahnfabrik & P310 & 8 min & 267 & 32 \\ \hline \hline \end{tabular} SD: Standard deviation; MPa: Megapascals; FS: Flexural strength.  
\end{table} 
Table 4: FS results of the silicate ceramics group in a time range of 8–10 min. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**MH (GPa) 8–10 min**} \\ \hline
**Authors** & **Ceramics/Manufacturer** &
\begin{tabular}{c} **Heat** \\ **Teatment/Furnace** \\ **Manufacturer** \\ \end{tabular} & **Time** & **Vickers (GPa)** & **Vickers (GPa) SD** \\ \hline [PERSON] et al. 2019 [40] & Vita Suprinity & 840 \({}^{\circ}\)C Kota Evo & & \\ VITA Zahnfabrik & Dorjen Company & & & \\ \hline [PERSON] et al. 2019 [40] & IPS e.max CAD & 850 \({}^{\circ}\)C Kota Evo & & \\ Ivoclar Vivadent & Dorjen Company & & & \\ \hline [PERSON], [PERSON] et al. & Vita Suprinity & 840 \({}^{\circ}\)C Programat & & \\
2016 [39] & VITA Zahnfabrik & EP 510/Ivoclare & 8 min & 7.6 & 0.7 \\  & VIVadent & & & \\ \hline [PERSON], [PERSON] & Vita Suprinity & 840 \({}^{\circ}\)C Vita & & \\ et al. 2018 [38] & VITA Zahnfabrik & Vacumat 6000 MP & 8 min & 6.8 & 16 \\  & Vita Zahnfabrik & & & \\ \hline [PERSON], [PERSON] & Celtra Duo & 830 \({}^{\circ}\)C Multimat & & & \\ et al. 2018 [38] & Dentsply Sirona & Dently Sirona & & & \\ \hline \hline \end{tabular} SD: Standard deviation; GPa: Gigapascals; MH: Microhardness.  
\end{table} 
Table 5: MH results in the silicate ceramics group in a time range of 8–10 min. It is important to note that only one study focused on evaluating the MH and FT of the Celtra Duo material. This study utilized a treatment time of 1.5 min at a temperature of 820 \({}^{\circ}\)C, using a Kota Evo furnace (Brazil, Sao Paulo, Rua Iris Memberg-Dorjen Company). The results obtained were as follows: A microhardness of 6.78 \(\pm\) 0.18 GPa and a fracture toughness of 1.40 \(\pm\) 0.12 K\({}_{\text{IC}}\)[40].
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Delay Variance Attacks
#### 5.2.3 Throughput  
Figure 15 shows a comparison of throughput depending on various numbers of delay variance attacker nodes. In delay variance attacks, the throughput decreases whenever the number of attackers increases. The results show that, for any number of attacker nodes, the proposed SecE-V2X has a higher throughput value than that of the other algorithms. For three malicious nodes, the proposed protocol performs 617.42 packets/s, better than [37], 2017 (455.41 packets/s), and [20], 2018 (562.19 packets/s). The throughput of [37], 2017,  
Figure 14: End-to-end delay under Jellyfish delay variance attacks [20,37].  
Figure 13: Packet delivery ratio under Jellyfish delay variance attacks [20,37].  
drastically reduces to 298.36 packets/s when the number of attacker nodes is increased to nine, whereas [20], 2018, has a throughput of 400.86 packets/s, and our protocol retains the best throughput of 448.76 packets/s. The results demonstrated that SecE-V2X performs better than [37], 2017, and [20], 2018, in terms of throughput, demonstrating that our proposed protocol remains effective against jellyfish delay variance attacks.
## 4 Experiments and Results
### Evaluation of the Flash Flood Forecasting Method
Following this method, the assessment of the predictive capacity of the SENDI system is shown in Figure 11 with a confidence of 95%, by using the t-student test. As noted in Tables 6 and 7 and Figure 11, the accuracy of both alerts exceeds 65% and the red alert has a higher percentage of accuracy (80%) than the yellow alert (66%). Similarly, the percentage of false positives of the red alert is well below that of the yellow alert. However, an in-depth analysis was conducted of the percentage of false negatives, where there is the occurrence of flooding that has not been forecasted by the system. In this respect, the red alert far outweighs the yellow alert and has a much lower failure rate. Although the situation described involves a scenario where there are several failures in SENDI and in a better scenario the distribution and combination of data from other sensors can be used to improve the results of both alerts, as seen in [19]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{**True Class**} & **Positive** & 28 & 4 \\  & **Negative** & 6 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage of hits and misses in the forecasts (red alert). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{**True Class**} & **Positive** & 20 & 14 \\  & **Negative** & 2 & 11 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Percentage of hits and misses in the forecasts (yellow alert). Figure 11: Evaluation of the results obtained by SENDI.
# Climate Effects on Tallgrass Prairie Responses to Continuous and Rotational Grazing
## 2 Materials and Methods
### Landsat Images and the Enhanced Vegetation Index (EVI)  
As Landsat 8 data (started from 2013) does not cover the whole study period (2008-2017), we also used the Landsat 7 data, which has been operating without the scan line corrector (SLC) since 2003. Fortunately, our study area is located within the central part of the image tile (Path28/Row35) and is not affected by the strips in Landsat 7 imagery caused by SLC-off. Landsat 7 and Landsat 8 surface reflectance products (Tier 2) covering the study area were downloaded from the USGS EarthExplorer ([[http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)]([http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov))) to provide an 8-day temporal resolution and 30 m spatial resolution.  
False color composite images around early May and late August were used to represent the vegetation conditions in late spring and late summer of each year, depending on the data availability and its quality (Landsat 7 or Landsat 8). A commonly used vegetation index, the enhanced vegetation index (EVI) [40], was calculated using the Landsat images following the equation below (Equation (1)). EVI is linearly correlated with the green leaf area index (LAI) in crop fields [41] and it remains sensitive to canopy variations in high chlorophyll content [40]. Only clear pixels were included in analysis based on the quality assessment band.  
\[\text{EVI}=\ 2.5\times\frac{\rho_{\mathit{nir}}-\ \rho_{\mathit{red}}}{\rho_{ \mathit{nir}}\ +\ 6.0\times\ \rho_{\mathit{red}}\ -\ 7.5\times\ \rho_{\mathit{blue}}\ +\ 1} \tag{1}\]  
where \(\rho_{\mathit{blue}}\), \(\rho_{\mathit{red}}\), and \(\rho_{\mathit{nir}}\) represent surface reflectance from blue, red, and near infrared bands (841-876 nm), respectively.
## 1 Introduction
In addition, as it is expected that more batteries will be introduced into power systems in the future, the battery energy storage system (BESS) is also considered in this paper, and a cooperative frequency control system with an ASG is designed. In this paper, to evaluate the validity of the proposed cooperative control, a degree of the smoothing effect of system frequency is investigated by simulation analyses with respect to various conditions of the small-scale power system, and then a possible amount of wind power installation is assessed. The simulation analysis is carried out using the PSCAD/EMTDC simulation program.
# Life-Cycle Assessment of Biofortified Productions: The Case of Selenium Potato
## 1 Introduction  
The micronutrient deficiency typical of many populations' diets, especially in developing countries, is spreading rapidly in different areas of the world. This phenomenon is not simply indicated as \"hunger\", but as \"hidden hunger\". Recent FAO (Food and Agriculture Organization) studies show that around two billion people suffer from micronutrient deficiencies, mainly due to poor diets [1]. Micronutrient malnutrition is known as \"hidden hunger\" because its symptoms have few visible warning signs. Caused by a deficiency of critical micronutrients such as vitamin A, iron (Fe), zinc (Zn), and selenium (Se) hidden hunger impairs the mental and physical development of children and adolescents, thereby generating long-term effects on their livelihoods [2, 3]. When people cannot afford to diversify their diets with adequate amounts of fruits, vegetables, or animal-source foods that contain large amounts of micronutrients, deficiencies are inevitable [4]. The enrichment of food through other elements, particularly selenium, allows limiting the onset of widespread diseases such as heart disease, the reduction of immune defenses, male fertility, and hypothyroidism. In addition to nutritional requirements, there are food trends in Western countries in relation to the consumption pattern evolution. The consumer at the time of purchase is driven by interest in the health properties of food, preferring products with a high nutritional content that provide the elements they need. In conjunction, there is a growing demand to find more sustainable products on the market that are made with environmentally friendly practices. To achieve both goals, i.e., to enrich foods with micronutrients and at the same time to obtain a sustainable product, the strategy that can be adopted for some products is the biofortification of crops. This research is based on the environmental impact assessment of the biofortified potato through the application of selenium, and the tool used to quantify emissions is the internationally recognized life-cycle assessment, regulated by ISO 2006 a,b standards [5, 6]. Environmental life-cycle analysis (LCA) provides a comprehensive method of analysis that allows comparing alternativeproduction systems and identifying the greatest consumption of resources and emissions into the environment, highlighting where improvements in techniques are most needed [7].  
The aim of the study was to evaluate the sustainability performance of a crop that is subject to a biofortification process, the selenium potato, in a typically suitable area, in order to guide public and private stakeholders in the choice of a strategy that takes due account of the two requirements: favoring a paradigm of green and sustainable agriculture, while respecting the need to support local populations interested in a diet rich in micronutrients
## 5 Evaluation: A Healthcare Scenario  
The proposed attack path approach is evaluated using a real healthcare case study scenario. This section presents an overview of the scenario, incorporating the implementation of the attack path process. The studied context may identify the potential attacks and take necessary measures to tackle the attacks and related vulnerabilities. The aims of this evaluation are to: demonstrate the applicability of the proposed attack path generation method into a real healthcare scenario; highlight the usefulness of CVSS metrics and exploitability for attack path generation; and display the benefits of the KB rules and IoC for analysing the attack path.
# Impacts of Micro-Deviations of Aperture on the Characteristics of Collision Atomization Field
## 4 Experimental Setup  
The experimental system for the internal flow field of the impinging nozzle is shown in Figure 7. The nitrogen bottle outputs high-pressure nitrogen to the sealed water tank, and the injection pressure of 0-1.0 MPa can be provided by adjusting the pressure reducing valve. Before entering the tributary, a flow sensor and a pressure sensor detect the total flow value and injection pressure value in real time. After entering the tributary, before reaching the nozzle, a flow sensor and a filter were installed to detect the branch flow and purify water quality. The measurement accuracy of flow and pressure sensors were \(\pm\)0.5% (1.5-100 L/h) and 0.25% (0-1.0 MPa) of full scale, respectively. The image acquisition system is provided with stable background light by 15 W white LED light source and diffusion plate. The high-speed CCD camera of MotionBLITZ EoSens Cube7 combined with a large depth of field continuous zoom microscope lens which can be adjusted manually can be used to acquire high-quality images of flow field and atomization field in the orifice. The system is also applied to the measurement of the atomization cone angle, as shown in Figure 6.  
Figure 6: Scattered light imaging of a single droplet. (**a**) Original image. (**b**) Processing image. (**c**) Atomization cone angle.  
Figure 7: Schematic diagram of the whole device of the visualization experimental system for the internal flow field of the impinging nozzle (Figure adapted from [7]).  
For the purpose of this paper, an atomizer with a replaceable orifice is designed. The collision structure consists of two identical atomizers. Since the diameters of the two jets are relatively small, in order to realize the two jets can collide together or adjust the collision angle, the azimuth adjustment system needs to be able to accurately adjust the direction of the two jets. Therefore, the two atomizers are, respectively, installed on two 360\({}^{\circ}\) rotating cloud disks with different adjustable angles. The collision angle of the two jet orifices is adjusted by the cloud disk. The adjustment range is between 0\({}^{\circ}\) and 180\({}^{\circ}\) without directly adjusting the positions of the two atomizers. The structure of a single injector is shown in Figure 8. It consists of three parts: body, replaceable transparent orifice and a small part. The main body is symmetrically mounted on the rotating cloud disk. Transparent orifice is processed by poly (methyl methacrylate) to visualize the flow field inside the orifice. The small part is to install the orifice on the main body through the internal thread to achieve the purpose of fixing the orifice. The flow orifice and its upstream flow channel are exposed, so that the internal flow field can be observed clearly.
# Evaluation of an Optical Sorter Effectiveness in Separating Maize Seeds Intended for Sowing
## 2 Materials and Methods
### Experimental Design
Figure 3 exemplifies three phases during the evaluation: Figure 3a is an image of the corn seeds impurities with sunflower and soybean seeds; Figure 3b depicts the manual verification of the separation efficiency; Figure 3c shows the seeds remaining after the separation. The evaluation was carried out by taking 20 samples from each batch, which were weighed using an electronic precision scale (Partner, precision 0.5 kg). In the context described above, the following aspects were considered:  
* In the case of the first analysis method (by probing), samples were taken from the \"basic material\" being processed and then 30 samples were taken from the main sorts resulting from the processing, respectively from the \"accept\" and from \"reject\". The results obtained are of a nature to appreciate the quality of the materials from the three categories of products. A comparative analysis of the values showed the influence exerted by the machine during processing. The analysis was performed on the following elements of seed quality: good grains, broken grains, fusarium grains, grains with other defects, and mineral impurities. The data obtained for these indicators regarding the quality of the material used in the test (base material) and the data regarding the quality of the sorts resulting after processing (accept and reject) indicated, by comparison, the work performance of the optical sorter. * In the second case, the artificial contamination method was used, in which a known amount of artificial impurified product was introduced (seed from another species of agricultural plants) into the working system of the machine) so that after the processing, this impurity could be manually extracted from the \"reject\" sort, where the workload was smaller. After weighing, this value indicated exactly how many artificial impurities the optical equipment managed to be removed in the \"reject\", and by comparing (from the total amount introduced into the system), the exact amount of artificial impurities remaining in the \"accept\" was obtained. In this working system, the sum of product inputs must be equal to the sum of product components leaving the machine. For artificial impurities, sunflower seeds and soybean seeds were used (soybean was closer to the color characteristics of corn seeds).