esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Border Gateway Protocol Security Using Public Blockchain
## 7 Conclusions and Future work  
As discussed in the introduction, BGP has some security deficiencies. It is possible to hijack prefixes to influence the routing of traffic. To increase resistance against this attack, we modified the original architecture for use with edge routers of ASes. Our modified architecture provides decentralized and immutable storage for prefixes allocated to an AS. Finally, we have implemented a set of smart contracts to manage BGP configurations for edge routers of the AS.  
Our solution enables verification of the prefixes before deployment to routers and limits the number of fraudulent BGP origin announcements. Moreover, it keeps the irrefutable history of all changes through blockchain. Related works employing blockchain concentrate on reactive verification of prefix information in BGP update packets when they arrive. Our solution's key novelty is that we focus on the proactive prevention of fraudulent BGP origin announcements in addition to reactive BGP origin AS validation. We achieve this by deploying verifiably correct configurations to routers in origin AS.  
As future work, we want to focus our research on a different environment for this architecture. We want to test this architecture in more extensive topologies to obtain results more relevant to a large scale of the Internet. Further advances should target reducing attacks on BGP by following the AS paths and take into account an extended list of BGP attributes. A possible avenue of improvement is to add information about links to other peers to the transaction. We also aim to enhance the periodic checking of AS updates in the current implementation. This process could be replaced by sending notification messages when relevant changes have been propagated through the blockchain. The last improvement to the architecture we are aware of is a more efficient consensus algorithm [31]. Current implementation uses Proof of Work. If we replaced it, we could decrease the duration time of the uploading subconfiguration.  
Conceptualization, L.M., M.G. and P.H.; methodology, L.M.; software, L.M.; validation, L.M., M.G. and P.H.; formal analysis, P.H.; investigation, L.M. and M.G.; resources, L.M.; data curation, L.M.; writing--original draft preparation, L.M.; writing--review and editing, P.H. and M.G.; visualization, L.M.; supervision, I.K.; project administration, I.K. and M.R.; funding acquisition, M.R. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank for financial contribution from the STU Grant scheme for Support of Young Researchers. This research was supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic, Incentives for Research and Development, Grant No.: 2018/14427:1-26C0.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract
Finally, compared to the case without an external magnetic field, the heat exchange increased by 20% at \(\varphi\) = 0.05. In addition, in the case of \(\varphi\) = 0.05 and Re = 500, the heat transfer performance was improved by 86%. [PERSON] et al. [10] experimentally studied laminar forced convection using a ferrofluid by applying an external magnetic field around a small heat sink device. The magnetic field area was visually confirmed using the commercial program COMSOL Multiphysics. In addition, the temperature of the heat sink was measured by using thermocouple wire and a data logger. The magnetic field strength and Re were set as variables to analyze the heat transfer performance by the ferrofluid. In the presence of an external fixed magnetic field, the magnetic nanoparticles were maintained in a constant arrangement towards the magnetic field in the base fluid. Finally, the thermal conductivity of the ferrofluid was increased by the magnetic field, thereby improving the heat transfer performance. [PERSON] et al. [11] set the magnetic field strength and position as variables and studied the heat transfer performance of high temperature ferrofluid when it flowed over a cold wall. The magnetic field strength was set to five variables and the location to six variables. The higher the magnetic field strength, the better the heat transfer performance. The heat exchange was most active when all magnets were located on the cold wall. This is because small circulation is combined around the magnet to create a big vortex, and the temperature exchange is activated by this vortex. Finally, the cooling rate was increased. Studies have been recently conducted comparing the heat transfer performance of ferrofluids under different arrangements of magnets. However, studies comparing the performance of ferrofluids based on optimization techniques are insufficient [12]. Therefore,the experimental results were analyzed according to the arrangement of external permanent magnets after containing the ferrofluid in a glass tube. Based on this, an arrangement of permanent magnets with optimal heat dissipation performance was found by utilizing optimization techniques such as design of experiments (DOE) and response surface method (RSM).
## 5 Determination of Extrusion Deformation Limit Value of Tunnel Face
### Analysis of Test Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{2}{c}{**Monitoring Time/min**} & **8** & **12** & **14** & **16** \\ \hline \multirow{4}{*}{Condition \(\Gamma_{1}\)} & Extrusion deformation & \multirow{2}{*}{1.06} & \multirow{2}{*}{2.53} & \multirow{2}{*}{3.67} & \multirow{2}{*}{3.98} \\  & of tunnel face/mm & & & \\  & Risk probability/\% & 0.27 & 0.64 & 0.93 & 2.14 \\  & Risk level & 2 & 2 & 2 & 3 \\ \hline \multirow{4}{*}{Condition \(\Gamma_{2}\)} & Extrusion deformation & \multirow{2}{*}{0.8} & \multirow{2}{*}{1.72} & \multirow{2}{*}{2.24} & \multirow{2}{*}{2.34} \\  & of tunnel face/mm & & & \\  & Risk probability/\% & 0.2 & 0.4 & 0.57 & 0.59 \\  & Risk level & 2 & 2 & 2 & 2 \\ \hline \multirow{4}{*}{Condition \(\Gamma_{1}+\Gamma_{2}\)} & Extrusion deformation & \multirow{2}{*}{1.08} & \multirow{2}{*}{2.65} & \multirow{2}{*}{3.92} & \multirow{2}{*}{5.6} \\  & of tunnel face/mm & & & \\  & Risk probability/\% & 0.27 & 0.67 & 10 & 90 \\ \cline{1-1}  & Risk level & 2 & 2 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Risk assessment of tunnel face collapse and instability. Figure 4: Deformation of tunnel face under condition \(\Gamma 1+\Gamma_{2}\). (**a**) Lateral view; (**b**) Front view. In working condition \(\Gamma_{1}+\Gamma_{2}\), when the test is carried out from 12 min to 14 min and from 14 min to 16 min, the extrusion deformation of the tunnel face only increases by 1.27 mm and 1.68 mm, but the risk of face collapse increases sharply, and the risk level jumps from level 2 to level 3 and level 3 to level 4, respectively. According to the calculation method of risk factor coupling coefficient in Section 2.2, the risk probability obtained from the test of two risk factors of full section excavation and initial support not closed in time is substituted into Formula (16) for calculation. \(\beta\)(\(\Gamma_{1}\),\(\Gamma_{2}\)) = 90%\(-\)2.14%/90% = 0.97, indicating that the coupling coefficient of the two risk factors of full section excavation and initial support not closed in time is 0.97, which belongs to strong coupling. The two risks are positively correlated and have coupling amplification effect. The simultaneous occurrence of these two risk factors should be avoided in tunnel construction.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
###### Abstract  
The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%.  
edge computing; energy-efficient system; roadside unit; vehicular ad hoc network; vehicle-to-infrastructure; vehicle-to-vehicle; sixth-generation wireless communication 2023235817. [[https://doi.org/10.3390/s23135817](https://doi.org/10.3390/s23135817)]([https://doi.org/10.3390/s23135817](https://doi.org/10.3390/s23135817))  
22
## 5 Numerical Analysis Results and Discussion
### PLAXIS Analysis
#### 5.3.1 Case Simulation of Site 1
As can be seen from the table, SF \(<\) 1.07 indicates frequent slope instability, 1.07 \(<\) SF \(<\) 1.25 indicates slope instability that has occurred, and SF \(>\) 1.25 indicates rare collapse events. Figure 21 shows Site 1's \(\Sigma\)_Msf_ curve in different scenarios. The ordinate value after the curve in the figure reaches the stable stage is the SF. As can be seen from Table 9, the SF of Scenario 1 was 1.627, the SF of Scenario 2 was 1.626, and the SF of Scenario 3 was 1.223. According to the analysis results, in three different scenarios, the SF value of the slope of Site 1 was greater than the design specification value [11]. This shows that the slope protection project at Site 1 is working effectively. Furthermore, according to [PERSON] safety factor standard [46], the slope of Site 1 is stable in three different scenarios. Nonetheless, the stability study shows that the slope's incremental displacement is mostly localized near the slope's summit. Localized harm could result from this, so possible weak spots need to be addressed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Safety Factor** & **Slope Condition** & **Meaning** \\ \hline SF \(<\) 1.07 & Frequent occurrence of slope collapse & Labile \\
1.07 \(<\) SF \(<\) 1.25 & Slope collapse sometimes occurs & Critical \\ SF \(>\) 1.25 & Slope collapse events are rare & Stable \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Safety factor and meanings of slope condition. Figure 20: The shading distribution diagram of the potential sliding surface based on safety analysis in Scenario 3 at Site 1. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Safety Factor**} \\ \cline{2-3}
**Scenarios** & **Simulation Result** & **Design Specification** \\ \hline Scenario 1: normal groundwater level & 1.627 & \(\geq\) 1.5 \\ Scenario 2: high groundwater level & 1.626 & \(\geq\) 1.2 \\ Scenario 3: normal groundwater level and & 1.223 & \(\geq\) 1.1 \\ pseudo-static analysis & & \\ \hline \hline \end{tabular} Note: Initial value of safety factor for Site 1 at normal groundwater level without slope protection facilities = 1.286.  
\end{table} 
Table 9: Safety factors for Site 1 in various scenarios.
# Influence of Right Triangular Prism Rough Beds on Hydraulic Jumps
## 2 Mathematical Method
### Numerical Flow-3D Model Theory  
This paper used the CFD method to simulate the hydraulic jump phenomenon over right triangular prism rough beds by combining the volume of fluid (VOF) method and the RNG \(k\)-\(\varepsilon\) turbulent model. The RNG \(k\)-\(\varepsilon\) turbulent model is more accurate than the standard \(k\)-\(\varepsilon\) model for simulating hydraulic jumps on rough beds because it can consider the effect of small-scale vortex motion [5,13]. The volume of fluid (VOF) method is a free-surface modeling approach, i.e., a numerical method for tracking and locating the free surface (air-fluids interface) [3,5,6,13-15]. Equations utilized in this CFD technique include continuity and momentum equaitons expressed as:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{i})}{\partial x_{i}}=0 \tag{4}\]  
\[\frac{\partial(\rho u_{j})}{\partial t}+\frac{\partial(\rho u_{i}u_{j})}{ \partial x_{i}}=-\frac{\partial P}{\partial x_{j}}+\frac{\partial}{\partial x _{i}}\Bigg{[}(\mu+\mu_{t})\Bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+ \frac{\partial u_{j}}{\partial x_{i}}\Bigg{)}\Bigg{]}+\rho g_{j}\]  
where \(t\) is time; \(u_{i}\) and \(u_{j}\) are velocity components; \(x_{i}\) and \(x_{j}\) are coordinate components; \(\mu\) is the molecular viscosity coefficient; \(\mu_{t}\) is the turbulent viscosity coefficient; and \(P\) is the hydrostatic pressure.  
Equations for the RNG \(k\)-\(\varepsilon\) turbulent model are  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{ i}}=\frac{\partial}{\partial x_{i}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{ \varepsilon_{k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}- \rho\varepsilon \tag{5}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho\varepsilon u _{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+ \frac{\mu_{t}}{\varepsilon_{t}}\bigg{)}\frac{\partial\varepsilon}{\partial x_ {j}}\Bigg{]}+C_{1\varepsilon}\frac{\varepsilon}{k}G_{k}-C_{2\varepsilon} \rho\frac{\varepsilon^{2}}{k}\]  
where \(k\) is the turbulent kinetic energy; \(\varepsilon\) is the turbulent dissipation rate; \(G_{k}\) is the production of turbulent kinetic energy, the constants: \(\sigma_{k}\), \(\sigma\varepsilon\), \(C_{1\varepsilon}\), and \(C_{2\varepsilon}\) is introduced by [PERSON] and [PERSON] [16].  
The VOF transport [17] equation is described by Equation (6). Here, \(F\) denotes the fraction function. A free surface must be cells having \(F\) values between unity and zero.  
\[\frac{\partial F}{\partial t}+\frac{1}{V_{F}}\Bigg{[}\frac{\partial(FA_{x}u)} {\partial x}+\frac{\partial(FA_{y}v)}{\partial y}+\frac{\partial(FA_{z}w)}{ \partial z}\Bigg{]}=0 \tag{6}\]  
where \(V_{F}\) is the flow volume; and \(A_{x}\), \(A_{y}\), and \(A_{z}\) are the flow areas.
## 7 Recommended Framework
### IoT Network Design Concepts  
IoT networks frequently face challenges such as bottlenecks, single points of failure, and suboptimal routing protocols that contribute to increased energy consumption. To enhance energy efficiency, it is essential to thoughtfully design IoT networks with a focus on robust architectures and effective routing mechanisms. Key network design concepts include the following:  
* Centralised vs. Distributed Networks: In centralised networks, data processing occurs through a single gateway, which can create bottlenecks and single points of failure. Conversely, distributed networks utilise multiple gateways, enhancing flexibility, scalability, and resilience while reducing latency. Transitioning to distributed architectures is recommended for achieving robust, energy-efficient IoT networks capable of handling diverse and dynamic applications.
* Routing Mechanisms: Effective routing protocols are essential for minimising energy consumption by optimising data flow between devices. By implementing adaptive routing algorithms that dynamically select the shortest and most efficient paths, IoT systems can reduce communication delays and energy usage. Developing adaptive protocols that can respond to real-time changes in network conditions is critical for maintaining energy-efficient operations.
## 2 Materials and Methods
### Reaction Kinetics  
To characterize the reactor's performance during dye degradation with and without UV light exposure, the dye concentration was monitored using UV-Vis spectrophotometry within a wavelength range of 300 to 700 nm. Samples were collected from the reactor every 30 min.  
The fraction of degraded dye (f\({}_{\text{deg}}\)) was calculated using Equation (1), where n represents the dye moles of Tartrazine, Erythrosine, and Brilliant Blue, and it is the fraction (\(f_{n}\)) of the remaining dye (Equation (2)) yet to be degraded. Each fraction (Tartrazine, Erythrosine, and Brilliant Blue) was determined by monitoring the decrease in absorbance at the peaks of 428 nm, 530 nm, and 630 nm, respectively.  
The fraction of degradation for each dye without UV light was fitted using a zero-order equation (Equation (3)), while with UV light, Equation (4), proposed in this work based on the proposed reaction mechanism, was employed. Here, n is the number of moles, j is the consumed current density, A is the electrode area, F is Faraday's constant, z is the number of transferred electrons, n is the dye mass, k\({}_{\text{elec}}\) is the degradation constant expressed in mol units, and k\({}_{\text{O}\text{x}\text{1}}\) and k\({}_{\text{O}\text{x}\text{2}}\) are oxidation constants according to the proposed reaction mechanism.  
Experimental data were nonlinearly fitted by iteration using the Levenberg-Marquardt algorithm implemented in the Origin 8 computational program. This enabled a thorough understanding of the degradation kinetics and provided valuable insights into the efficiency of the proposed photocatalytic system. The corresponding set of kinetic expressions is presented in Equations (1)-(4):  
\[f_{deg}=\sum_{n=n_{0}}\frac{1}{3}(1-f_{n}) \tag{1}\]  
\[f_{n}=\frac{[n]_{t}}{[n]_{0}} \tag{2}\]  
\[f_{deg}=\frac{jA}{zF[n]_{0}}K_{el
# Research on Retinal Vessel Segmentation Algorithm Based on a Modified U-Shaped Network
## 3 Experiment and Results
### Evaluation Metrics  
The segmentation performance of the MU-Net model is evaluated from four metrics: accuracy (_Acc_), sensitivity (_Sen_), specificity (_Spe_), [PERSON]'s correlation coefficient (_Mcc_) [47], and area under the receiver operating characteristic curve (_AUC_). The calculation formulas for the first four evaluation metrics are as follows:  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{13}\]  
\[Sen=\frac{TP}{TP+FN} \tag{14}\]  
\[Spe=\frac{TN}{TN+FP} \tag{15}\]  
\[Mcc=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{16}\]  
where _TP_ is true positive, which means the number of correctly segmented vessel pixels. _TN_ is true negative, which means the number of correctly segmented background pixels. _FP_ is false positive, which means the number of pixels that recognize the background as blood vessels. _FN_ is false negative, which means the number of pixels that recognize the blood vessels as background. Accuracy represents the percentage of correctly segmented pixels among all pixels. Sensitivity is exploited to measure the ability to correctly detect  
Figure 5: Examples of the image pre-processing: (**a**) original image, (**b**) green channel image, (**c**) CLAHE, and (**d**) gamma correction.  
vessel pixels. Specificity means the ability to recognize non-vessel pixels. The model's performance is evaluated using the _Mcc_ to prevent data imbalance [48]. A perfect prediction is achieved when the _Mcc_ value reaches 1. The receiver operating characteristic curve (ROC) takes the false positive rate as the horizontal axis and the true positive rate as the vertical axis. The closer the _AUC_ value is to 1, the better the segmentation effect.
## 2 Materials and Methods
### Measurement of Yield and Quality Parameters  
The measurements of spikes m\({}^{-2}\) (n = 12 per treatment), spike length (n = 36 per treatment), and the number of grains per spike (n = 62 per treatment) were registered at physiological maturity (pre-harvest). At the harvest, discarding the furrows of the edges, wheat biomass was collected manually using sickles, from the center of each plot. Every sheaf of wheat (per plot/replicate) was weighed on a scale (to estimate the total aboveground biomass), and then their grains were weighed after using a wheat threshing machine (to record grain weight per sample (n = 12 per treatment), considering the percentage of moisture at harvest, which was 8.8%). Then, straw yield and grain yield (adjusted to 14% moisture) were estimated, extrapolating each weight per area to tons per hectare. The harvest index was calculated by dividing grain yield by above-ground biomass (n = 12 per treatment). Hectoliter weight (n = 12) and 1000-grain weight of grains samples of each plot (n = 12) were estimated with a SeedCount Digital Image Analysis System SC5000 (Next Instruments, Sydney, Australia). On the other hand, a DA 7200 NIR analysis system (Perten Instruments, Sweden) was used to measure the percentage of grain (n = 12) protein at 12.5% humidity by near-infrared spectroscopy, which was calibrated based on official methods AACC 39-10.01 and 45-15.01 [22]. Additionally, wholemeal yellowish (Minolta b* value) was estimated utilizing a colorimeter (Konica Minolta, Tokyo, Japan) (n = 12); the SDS sedimentation index (n = 12) was calculated by dividing the SDS sedimentation volume by the sample protein content; sodium dodecyl sulfate (SDS) sedimentation volume (n = 12) was determined according to the method of [PERSON] et al. (1990) [23].