esa-sceva/satcom-chunk-collection huggingface dataset:
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 4 Evaluation  
This section presents the evaluation of the VILE mechanism for redundancy mitigation on the CAV scenario compared to baseline (ETSI CPM generation rules [32]). In addition, this section presents the scenario description, detailing the setup and simulation and the results obtained from CPM generated and received. The baseline approach is used to evaluate the behavior of the proposal, which serves as the main benchmark for the analysis of collective perception approaches. From a collective perception services perspective, we also evaluate the redundancy of objects and the number of detected pedestrians and vehicles perceived by the mechanisms.
# Integrating Cork Biochar as a Green Filler via Photopolymerization
## 3 Results and Discussion
### Thermal Analysis of Cork Biochar-Based Composites  
#### 3.2.1 Thermogravimetric Analysis  
Thermal analysis can provide valuable information about the composition, stability, and thermal properties of manufactured CBPCs. Applying TGA to biochar-based composites, the thermal stability and decomposition behavior of the composites were analyzed. In Figure 3, the TGA results of the CBPCs are shown.  
Three areas can be differentiated in the TGA curves: the first, marked in green, corresponds to the loss of moisture naturally present in the samples. The temperature range of this first zone goes up to 150 \({}^{\circ}\)C. This area is very common in the TGA analyses of biomass composites, as can be found in the literature [41,42]. The second area, marked by blue arrows, extends from 150 \({}^{\circ}\)C to 450 \({}^{\circ}\)C. The active pyrolysis zone is where the major percentage of mass is liberated. The circles 1 and 2 in the TGA curves of Figure 3A indicate the interest zone inside the active pyrolysis area, and an enlarged image of this area is shown Figure 3B. In the area marked as circle 1, it was noted that CBPCs containing up to 0.35\(\%w/w\) of cork biochar exhibited slightly smaller stability compared to those containing a higher cork biochar concentration in the polymer matrix. This difference between the samples was smaller than 3%, which could also be explained by the precision of the measurements. Advancing into the active pyrolysis zone (circle 2, Figure 3), a slight difference in mass loss was observed between the composites and the neat resin, but as in the previous discussion, it was not quite significant. These results indicate that the inclusion of cork biochar, which acts as a thermally stable network within the polymer matrix, could increase thermal resistance and prevent degradation at high temperatures in the composite matrix, enhancing its heat resistance and improving its ability to retain its shape at elevated temperatures. Other research has produced comparable findings on biochar composites [43].  
Continuing with the TGA curve analysis, the DTG curves are shown in Figure 3C. In the DTG graph, the two peaks correspond to the two main areas of the TGA curves. The first region is located at approximately 100 \({}^{\circ}\)C, which corresponds to the moisture loss observed in the TGA curves, and the second region agrees with the active pyrolysis area observed in the TGA graphs.  
At the beginning of the pyrolysis process, the temperature at which the weight-loss rate occurs is slightly less pronounced in CBPCs than in neat resin. The inflection point occurred around 350 \({}^{\circ}\)C, in which the weight loss was slightly higher for CBPCs than for CBPCs. The temperature at which the weight loss was less pronounced is higher than in the rest of the TGA curve.  
the neat resin. However, as it was discussed in the TGA analysis, these differences are not significant because they did not exceed 3% of the difference in the samples.
## 5 Evaluation
### Experimental Setup
#### 5.1.2 Methodology  
Based on the initialization Algorithm 1 and iteration Algorithm 2, we obtain the distributed deployment plan using Python 3.6 and then obtain simulation results on the CloudSim platform. We adopt two pre-trained PyTorch models from the ImageNet database, VGG16 and Alexnet, as the CNN models of our experiments. The ptfops tool is used to obtain their MACs, which is not only for the whole model but also for each network layer. The computation workload distribution is shown in Figure 7. We set the workload as the image classification task on one ImageNet [55] image. The average inference latency, computation intensity, and transmission intensity of one hundred runs are taken as the results.
# FlexBFT: A Flexible and Effective Optimistic Asynchronous BFT Protocol
###### Abstract  
Currently, integrating partially synchronous Byzantine-fault-tolerant protocols into asynchronous protocols as fast lanes represents a trade-off between robustness and efficiency, a concept known as optimistic asynchronous protocols. Existing optimistic asynchronous protocols follow a fixed path order: they execute the fast lane first, switch to the slow lane after a timeout failure, and restart the fast lane after the slow lane execution is completed. However, when confronted with prolonged network fluctuations, this fixed path sequence results in frequent failures and fast lane switches, leading to overhead that diminishes the efficiency of optimistic asynchronous protocols compared with their asynchronous counterparts. In response to this challenge, this article introduces FlexBFT, a novel and flexible optimistic asynchronous consensus framework designed to significantly enhance overall consensus performance. The key innovation behind FlexBFT lies in the persistence of slow lanes. In the presence of persistent network latency, FlexBFT can continually operate round after round within the slow lane--the current optimal path--until the network conditions improve. Furthermore, FlexBFT offers the flexibility to combine consensus modules adaptively, further enhancing its performance. Particularly in challenging network conditions, FlexBFT's experimental outcomes highlight its superiority across a range of network scenarios compared with state-of-the-art algorithms. It achieves a performance with 31.6% lower latency than BDT, effectively merging the low latency characteristic of deterministic protocols with the robustness inherent in asynchronous protocols.  
blockchain; decentralization; Byzantine fault tolerance; asynchronous consensus; optimistic path
## 1 Introduction
With the development of remote sensing technology, a large number of remote sensing time series data have been collected. In the meantime, the free medium resolution remote sensing image data is increasingly accessible. Huanjing-1 A/B (HJ-1 A/B) are two small satellites launched by China in 2008 for environment and disaster monitoring and forecasting [16]. Each of them carries two charge-coupled device (CCD) multi-spectral sensors with the same design principle. The scan width of a single sensor is 360 km and the spatial resolution is 30 m. The images acquired by the HJ-1 A/B multi-spectral sensor consist of four multi-spectral bands (B1: 0.43-0.52 \(\upmu\)m; B2: 0.52-0.6 \(\upmu\)m; B3: 0.63-0.69 \(\upmu\)m; and B4: 0.76-0.9 \(\upmu\)m) which are similar to the Thematic Mapper (TM) sensor bands 1 to 4. The orbits of the HJ-1 A and HJ-1 B satellites are exactly the same, and the revisiting period after networking is only two days [16]. HJ-1 A/B data have proven useful in monitoring environmental and ecological changes [17; 18]. The data is currently available for free. As the HJ-1 A/B has a spatial resolution of 30 m and a revisit frequency of two days, it will be a useful complement to other medium resolution remote sensing data. This paper takes Fuling District of Chongqing as the study area. Because the interval of a single source time series is very long due to the consistent cloud and fog in this region, we propose a multi-source time series remote sensing image fusion method based on MulTiFuse. In this approach, Landsat data and HJ-1 A/B data which has a good spatial resolution are combined to form a time series imagery of shorter time intervals. The fused time series data are then used in the subsequent dynamic monitoring process to detect forest land change in the study area.
## 3 Results and Discussion
### XGBoost Comparison with Other ML Algorithms  
We compared the predictive performance of the XGBoost with other machine learning algorithms, including K-Nearest Neighbors (KNN), RandomForest (RF), and Support Vector Machine (SVM) algorithms. For comparison, the three machine learning algorithms described above replaced XGBoost in the predictor and retrained the prediction predictor, which was then evaluated by 10-fold cross-validation on the six datasets. The performances of the four models are depicted in Figure 6. It indicates the performances in terms of the four metrics (SP, SN, ACC, MCC). It can be observed that, for the two algorithms of KNN and RF, both ACC and MCC are far inferior to XGBoost in the six species, and on the metrics of SN and SP, the KNN algorithm is very unstable and has large deviations. Taking _E. coli_ as an example, the SN metric of KNN reached the highest value of 95.4%, while the SP metric was very low at only 74.7%. On the contrary, in the _G. pickertigii_, the SP metric of KNN had a higher level of 91.9%, but the SN metric was the lowest among the four ML algorithms and does not exceed 80%. However, for the SVM of the _C. elegans_, _D. melanogaster_, and _G. subterruneus_ species, the performance of SVM and XGBoost were almost the same, the MCC value of _D. melanogaster_ and _G. subterruneus_ on XGBoost was only 0.2% higher than SVM. For the other three species, the ACC values of the XGBoost models for _A. thaliana_, _E. coli_, and _G. pickertigii_ outperformed the corresponding SVM models by 1.1%, 1.4%, and 1.5%, respectively, and the MCC for the XGBoost improved by an average of 1.1-3.2%. Therefore, the overall performance of the four classifiers, the XGBoost was the best. Hence, the XGBoost was used to establish computational tools in the following.  
Figure 5: t-SNE visualization of the _E.coli_, _G. subterruneus_, and _D. melanogaster_ datasets in a two-dimensional feature space.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture
### IoT Sensors
#### 3.1.2 Optical Sensors in Aquaculture  
Optical sensors play an important role in monitoring water quality in aquaculture, providing real-time information on turbidity and water condition, two key indicators of aquatic systems' environmental health. Turbidity, which results from suspended particles such as organic matter, sediments, and plankton, can negatively impact fish health by reducing light penetration, disrupting oxygen levels, and altering overall ecological situations [52]. Elevated turbidity is associated with phytoplankton blooms or pollutants, which are responsible for increasing fish stress and susceptibility to disease [55]. Optical turbidity sensors analyse the interaction of light with suspended particles, typically measuring either light scattering or absorption to assess water clarity.  
Beyond turbidity monitoring, optical systems, particularly cameras integrated with computer vision algorithms, have gained prominence in aquaculture for behavioural analysis. These systems enable continuous observation of fish's spatial distribution and movement inside the tanks, offering valuable data for assessing stress levels and early disease detection [61]. Research indicates that erratic movements, changes in swimming speed, or fish turning frequency are early indicators of environmental stresses or health problems, allowing for timely intervention [62]. Furthermore, computer vision-based monitoring has been instrumental in optimising feeding practices by correlating fish behaviour with feeding responses, thereby minimising feed waste and promoting sustainability [13].  
Recent advancements, such as self-cleaning optical surfaces and hydrophobic coatings, have been developed to mitigate biofouling and extend sensor lifespan in long-term aquaculture applications [52]. Integrating optical sensors with other monitoring systems, such as dissolved oxygen and pH sensors, provides a more comprehensive water quality assessment. This multi-sensor approach enhances data reliability and enables more effective environmental control in aquaculture [52]. Regardless of their advantages, optical sensors face challenges like biofouling, which may restrict optical pathways and degrade measurement accuracy over time [52]. Nevertheless, their ability to deliver precise, low-energy measurements makes them a cost-effective and practical solution for water quality management in aquaculture. By detecting slight variations in water clarity, these sensors facilitate proactive environmental monitoring, helping prevent adverse effects on fish health and ensuring the sustainability of aquaculture operations. Emerging technologies, including AI-driven image analysis and hyperspectral imaging, further enhance optical sensors' capabilities, allowing for more precise and automated aquaculture monitoring [61].
# Control-Flow Integrity: Attacks and Protections
## 5 Protection Techniques
### CCFI: Cryptographically Enforced CFI (CCFI)  
CCFI possesses new pointer arrangements, which cannot be imposed with static approaches [46]. It comprises two prime attributes. First, it recategorizes function pointers at run-time to boost typecasting. Second, it restricts swapping of two valid pointers which consist of the same type. Static analysis does not resemble dynamic characteristics; hence, CCFI involves dynamic CFI mechanism which uses cryptographic primitives. It is a fine-grained compiler enhancement where Message authentication codes (MACs) are implemented on every control flow pointer and impose various run-time, as well as language attributes. CCFI is designed to protect return addresses, frame pointer, function pointers, vtable pointers, and exception handlers. A MAC key produces random values at the beginning of a program to be stored in the registers. Random offsets are added to each malloc and stack frame in order to randomize each memory and stack frame.  
Nevertheless, with all the implemented features, CCFI comprises an average overhead of 52% on all benchmarks and is vulnerable to replay attacks [48]. CCFI only detects calls to print doubles function. It is not address taken and comprises dissimilar non-variadic prototype than square. CCFI permits all types of forbidden calls [54]. Besides reporting the highest overhead compared to other CFI techniques, it requires the Operating Systems to maintain and restore the MAC key while the context switches take place [55]. CCFI is unable to protect function pointers studded in C structures [56]. It also fails to identify structure pointers, and it is possible to disrupt the control flow by altering the current pointer with the old pointer. CCFI mainly focuses on defending the user-level program and does not include kernel-level security [46]. The security features are not focused on protecting the heap and stack addresses. However, to overcome such attacks, randomness can be implemented by injecting entropy, causing a high memory consumption.
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Model** & **Group** & \multicolumn{4}{c}{**FTR**} & \multicolumn{4}{c}{**TPR**} & \multicolumn{4}{c}{**Loss**} \\ \hline  & & **Pre-Adjust** & **Post-Adjust** & **Post-Adjust** & **Pre-Adjust** & **Post-Adjust** & **Post-Adjust** & **Post-Adjust** & **Post-Adjust** \\  & & **Ods** & **Opportunity** & **Ods** & **Opportunity** & **Ods** & **Opportunity** & **Ods** & **Opportunity** \\ \hline ResNet152 & White & 0.8889 & 0.8889 & 0.8652 & 0.8667 & 0.8165 & 0.8652 & 0.2732 & 0.3161 & 0.2752 \\  & non-White & 0.6786 & 0.7500 & 0.7778 & 0.8214 & 0.8148 & 0.8889 & 0.2732 & 0.3161 & 0.2752 \\ Wide\_ResNet101 & White & 0.4444 & 0.5565 & 0.3745 & 0.5556 & 0.6367 & 0.6255 & 0.5683 & 0.4033 & 0.4142 \\  & non-White & 0.1071 & 0.5714 & 0.0000 & 0.5714 & 0.6296 & 0.5926 & 0.5683 & 0.4033 & 0.4142 \\ VGG & White & 0.8000 & 0.8000 & 0.8989 & 0.8000 & 0.8989 & 0.8989 & 0.2077 & 0.2371 & 0.2289 \\  & non-White & 0.8214 & 0.6786 & 1.0000 & 0.7500 & 0.8889 & 0.9259 & 0.2077 & 0.2371 & 0.2289 \\ ResNet50 & White & 0.5333 & 0.5333 & 0.6742 & 0.5333 & 0.6742 & 0.6742 & 0.2842 & 0.3706 & 0.3651 \\  & non-White & 0.5357 & 0.4286 & 0.9630 & 0.5357 & 0.6296 & 0.5926 & 0.2842 & 0.3706 & 0.3651 \\ ResNet18 & White & 0.7556 & 0.7756 & 0.7790 & 0.7556 & 0.7790 & 0.7790 & 0.2896 & 0.3188 & 0.3324 \\  & non-White & 0.8571 & 0.7500 & 0.9630 & 0.6786 & 0.8148 & 0.7037 & 0.2896 & 0.3188 & 0.3324 \\  & non-White & 0.7556 & 0.7556 & 0.8165 & 0.7556 & 0.7865 & 0.8165 & 0.2896 & 0.3188 & 0.2970 \\  & non-White & 0.7143 & 0.7500 & 0.7778 & 0.7143 & 0.7778 & 0.8148 & 0.2896 & 0.3188 & 0.2970 \\ RegNet & White & 0.8667 & 0.8667 & 0.824 & 0.9778 & 1.0000 & 0.8240 & 0.3333 & 0.3661 & 0.3106 \\  & non-White & 1.0000 & 0.8214 & 1.000 & 1.0000 & 0.9630 & 0.8148 & 0.3333 & 0.3661 & 0.3106 \\ ConvNeXt & White & 0.5111 & 0.5111 & 0.6704 & 0.5111 & 0.6704 & 0.6704 & 0.3497 & 0.3678 & 0.3488 \\  & non-White & 0.6786 & 0.3571 & 1.0000 & 0.5000 & 0.6296 & 0.7407 & 0.3497 & 0.3678 & 0.3488 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model performance before and after fairness adjustments in the binary classification setting. We compare the FPR, TPR, and loss values before adjustment and after applying two post-processing procedures optimized for equalized odds and equalized opportunity.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 4 Numerical Preparations
### Grid Generation  
The vertical pipe, with an inner diameter of 100 mm and a length of 5000 mm, was used for numerical simulation. The flow domain in the pipe was discretized by structured grids. A grid-independence examination was performed, and five grid schemes were devised. The same numerical setting was applied for the five grid schemes, and the total pressure drop was monitored. According to the obtained results, as the total grid number  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{Pipe parameters} \\ \hline Diameter (m) & 0.1 \\ Length (m) & 5 \\ Poisson’s ratio & 0.3 \\ Density (kg/m\({}^{3}\)) & 7980 \\ Shear modulus (Pa) & \(7.0\times 10^{7}\) \\ Particle–wall restitution coefficient & 0.52 \\ Particle–wall static friction coefficient & 0.16 \\ Particle–wall rolling friction coefficient & 0.01 \\ Liquid property \\ Liquid density (kg/m\({}^{3}\)) & 1044 \\ Liquid temperature (\({}^{\circ}\)C) & 24 \\ Liquid viscosity (Pa\(\cdot\)s) & \(1.01\times 10^{-3}\) \\ Solid particle property \\ Poisson’s ratio & 0.4 \\ Density (kg/m\({}^{3}\)) & 2040 \\ Shear modulus (Pa) & \(2.13\times 10^{7}\) \\ Particle-particle restitution coefficient & 0.45 \\ Particle–particle static friction coefficient & 0.28 \\ Particle–particle rolling friction coefficient & 0.01 \\ CFD reference pressure (Pa) & 101,325 \\ CFD time step (s) & 0.001 \\ CFD turbulence model & RNG \(k\)-\(\epsilon\) \\ DEM time step (s) & 0.0001 \\ Total simulation time (s) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings for EDEM and ANSYS Fluent [27].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Operating**} & \multirow{2}{*}{**No.**} & **Initial SVF** & **Particle Diameter** & **Initial Mixture** \\
**Parameters** & & **C\({}_{\mathrm{n,0}}\) (\%)** & **\(d_{s}\) (mm)** & **V\({}_{mix,0}\) (m/s)** \\ \hline Effects of \(C_{\mathrm{p,0}}\) & Case 1 & 1 & 15 & 4 \\  & Case 2 & 5 & 15 & 4 \\  & Case 3 & 10 & 15 & 4 \\ \hline Effects of \(V_{mix,0}\) & Case 4 & 5 & 15 & 3 \\  & Case 5 & 5 & 15 & 4 \\  & Case 6 & 5 & 15 & 4.5 \\ \hline Effects of \(d_{s}\) & Case 7 & 5 & 5 & 4 \\  & Case 8 & 5 & 15 & 4 \\  & Case 9 & 5 & 30 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Operating conditions for present simulations.  
increases, \(I_{v}\) decreases continuously, as shown in Figure 3. Furthermore, as the total grid number exceeds 5 million, the relative difference of \(I_{v}\) between neighboring grid schemes is less than 5%. In consideration of both numerical accuracy and economy, the grid scheme with the total grid number of 5,115,844 was selected in subsequent simulations.
## 5 Results and Discussions
### Successful of Finding a Solution
#### 5.2.1 Simulations for 10 Nodes  
For the ten nodes cases, we applied four different configurations of connectivity, as shown in Table 3, i.e., CONFIGs 10-1, 10-2, 10-3, and 10-4. CONFIG 10-1 is the graph with the connectivity visualized in Figure 3. CONFIG 10-2 is constructed from CONFIG 10-1 with the elimination of the edge connecting Nodes 5 to 8. CONFIG 10-3 is constructed from CONFIG 10-3, with the elimination of the edge connecting Nodes 5 to 9. CONFIG 10-4 is constructed from the CONFIG 10-1, with the addition of the edges connecting Nodes 1 to 4, 3 to 6, and 4 to 7. Table 3 describes the degree of each node. The averages of the degree of nodes for CONFIG 10-1, 10-2, 10-3, and 10-4 are 3.20, 3.00, 2.80, and 4.00.  
We investigate the relationship between the nodes' average degrees and the number of vehicles to the possibility of successfulness of finding a solution. Here, we introduce a parameter \(\phi\) defined as follows:  
\[\phi=\frac{N_{\rm u}}{r_{\rm avg}\,N_{\rm v}}, \tag{12}\]  
where \(r_{\rm avg}\) is defined as the average degree of all nodes. From Table 3, it can be seen that the solution can be found for \(\phi\leq 0.19\). It can be concluded that, from the 10 trials, at the range of the \(\phi\), there exists at least one successful trial.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**(1)** & **(2)** & **(3)** & **(4)** & **(5)** \\ \hline \(t_{\rm occ}\)(s) & \(n_{\rm success}\) & \(T_{\rm avg}\)(s) & \(\sigma_{\rm T}\)(s) & \(T_{\rm min}\)(s) \\
10.00 & 10 & 1754.16 & 320.69 & 1482.50 \\
50.00 & 9 & 2648.04 & 663.91 & 1896.30 \\
100.00 & 8 & 3412.84 & 1191.16 & 1961.10 \\
150.00 & 8 & 3314.01 & 841.83 & 1684.20 \\
300.00 & 5 & 3735.32 & 1180.45 & 2851.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation results for various \(t_{\rm occ}\) for three vehicles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**(1)** & **(2)** & **(3)** & **(4)** & **(5)** \\ \hline \(\rho\) & \(n_{\rm success}\) & \(T_{\rm avg}\)(s) & \(\sigma_{\rm T}\)(s) & \(T_{\rm min}\) \\
0.1 & 9 & 2977.72 & 732.50 & 1949.00 \\
0.2 & 7 & 3317.33 & 636.00 & 2189.30 \\
0.3 & 6 & 2269.77 & 403.38 & 1744.00 \\
0.4 & 8 & 3213.81 & 777.59 & 1966.40 \\
0.5 & 9 & 2929.99 & 609.94 & 2064.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation results for various evaporate constants at \(t_{\rm occ}>150\) s.