esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 NFV and SFC Frameworks Taxonomy
### Resilience and Fault Recovery Frameworks
#### 4.3.2 FTC Framework  
[PERSON] et al. [88] presented a framework for fault-tolerant chaining (FTC). FTC uses a different approach, which opposes existing solutions where middle-box snapshots are taken for the purpose of replicating state, or approaches where the state of middle-boxes is stored in a fault-tolerant data store. The design requirements of the FTC framework are (i) correctness of middlebox recovery, (ii) quick recovery from failures with low processing overhead, and (iii) efficient use of servers hosting middleboxes. Middlebox state information is added to the packets as they traverse the SFC chain, which is replicated in the host servers. The deployment of fault-tolerant chains is handled by the ONOS controller, which serves as a centralised orchestrator for the management of NF and chain life cycles.  
To achieve fault tolerance, FTC makes use of replicas, which comprise data and control plane modules for interacting with the orchestrator. New threads are spawned by the control module in fail-over scenarios [88]. To optimise the amount of memory used for service replication, updates that have been added to the standby middleboxes are removed. FTC middleboxes are built using Click [77] elements that interact with the ONOS controller. Parameters such as replication factor, time required for failure recovery, throughput, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Framework** & **Problem** & **Solution(s)** & **Processing** & **Testbed/PoC** \\ \hline [PERSON] et al. [48] & SFC Resilience & Runtime traffic reroute for fail-over and fault recovery. & VM & OpenStack/OpenDalight \\ CN-SFC [76] & Traffic Steering & NSM and network-aware steering. & Containers & Kubernetes \\ FTC [88] & SFC resilience & vNF state piggybacking and resource management. & Click & Server cluster \\  & & Single and multiple nodes failure & & \\ REINFORCE [90] & SFC failures & recovery, remote and local & & \\  & & redundancy, link and node failure & & \\  & & detection. & & \\ [PERSON] et al. [91] & SFC Resilience and placement & Single Link/Node failures. & VM & ILP Models \\  & & Slave and Master vNFs deployment, & & \\ FtVNF [92] & NF fault tolerance & vNF failure recovery and fault & Click & Commodity servers \\  & & tolerance. & & \\ [PERSON] et al. [93] & SFC Resilience & Controller-independent HA scheme. & VM & OpenFlow/OpenStack \\ \hline \hline \end{tabular}
\end{table}
Table 5: Resilience and fault recovery frameworks.  
latency were all measured while varying the length of the service chains deployed first in a cluster of 12 servers and second on distributed servers on the cloud. The FTC was compared with FTMB [94] and NF, which is a baseline framework designed with no fault tolerance. FTC produces a much higher throughput (with an increase in service chain size) with a chain-wide overhead that is less than that obtained with FTMB.
# Reduction of Coupling Interface Degrees of Freedom in Mixed-Interface Component Mode Synthesis
## 5 Numerical Examples
### Example 3  
A simplified dam-foundation system is introduced here to study the accuracy and computational efficiency of the MCMS-rid method when the DOFs of the FE model is huge and the material properties of the substructures are different. The dimensions of the FE model are shown in Figure 12, and the dam is defined as constrained substructure \"1\" while the foundation is free substructure \"2\". The material properties and the numbers of elements, nodes and DOFs of the substructures are listed in Tables 4 and 5 respectively. Figure 13 shows the shape of the coupling interface and two interpolating schemes are adopted, in which 6 IBNs with 18 coupling interface DOFs (Figure 13b) and 12 IBNs with 36 coupling interface DOFs (Figure 13c) are chosen for substructure docking.  
The number of the truncated normal modes for substructure \"1\" is 100 while for substructure \"2\" is 200, so that the DOFs kept for mode synthesis are reduced down to 828 (MCMS-fid), 318 (MCMS-rid:b) and 336 (MCMS-rid:c) respectively, which are almost the 4.15%, 1.60% and 1.69% of the original FE models size. The computational costs for the MCMS-fid, MCMS-rid:b and MCMS-rid:c method are shown in Table 6, which indicate that reducing the number of interface DOFs is responsible for the further saving of computational efforts when compared to FEM.  
The first fifty natural frequencies and their relative errors between the MCMS-fid and MCMS-rid method are plotted in Figures 1
## 4 Experiment and Results
### Training Results
#### 4.6.1 Comparison Analysis Under Different CO\({}_{2}\) Emission Factors for Electric Vehicles  
Due to the uncertainty in the future changes in CO\({}_{2}\) emission factors, this section selects the CO\({}_{2}\) emission factors for the years 2021, 2022, and 2023, as published by the Ministry of Ecology and Environment of China, to analyze their impact on the model results. The ratio of fuel vehicles to electric vehicles remains consistent with the assumptions, and the corresponding CO\({}_{2}\) emission factors are 556.8 g CO\({}_{2}\)/kWh, 536.6 g CO\({}_{2}\)/kWh, and 570.3 g CO\({}_{2}\)/kWh, respectively. The changes in average vehicle waiting time and average CO\({}_{2}\) emissions under different CO\({}_{2}\) emission factors are shown in Figure 10. It can be clearly observed that, with variations in the CO\({}_{2}\) emission factor, there is no significant change in vehicle average waiting time, and the change in average CO\({}_{2}\) emissions is also relatively small. When the CO\({}_{2}\) emission factor is at its lowest value of 536.6 g CO\({}_{2}\)/kWh, the Rainbow DQN-based optimization scheme shows the largest change in average CO\({}_{2}\) emissions, with variations reaching 3.46 g and 2.69 g, respectively. However, overall, there is no significant change. The average waiting time only changes by 0.3 s. This may be because, in the scenario where the proportion of electric vehicles is only 29%, changing the CO\({}_{2}\) emission factor has little impact on the vehicle's average waiting time, and the change in CO\({}_{2}\) emissions is also minor. Furthermore, the logic of the reward function in this study involves dynamically weighting the differences in vehicle CO\({}_{2}\) emissions and waiting times between two consecutive time steps. When only the CO\({}_{2}\) emission factor for electric vehicles is altered, the relative differences between the two time steps are relatively small, so the reward function has little effect, and no significant change is observed.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Rainbow** & **D3 QN\_CNN** \\  & **DQN\_AM-CO2 Reduction** & **DQN\_CNN** \\ \hline Average waiting time & 27.58\% & 12\% \\ Average CO\({}_{2}\) emissions & 7.34\% & 3.5\% \\ \hline \end{tabular}
\end{table}
Table 8: Comparison of model optimization levels.
## 6 Simulation Results
### MOEA Termination Criterion  
The MOEA termination adopted in this work is similar to that used in [30], in the sense that we also maintained an external archive of non-dominated solutions obtained at some predefined steps at earlier generations, and it was subject to be updated some generations later. However, instead of computing the ratio of the number of solutions in the archive that were dominated by the new ones of the current generation and the ratio of the number of solutions that were also present in the new set of non-dominated solutions, we computed the ratio of new solutions, which were not present in both dominated and non-dominated sets of the archive, and we used it to define our stopping criterion. We used \(\tau=0.05\) as the cut-off value for the new solutions. However, the choice of the exact cut-off value may depend on the problem and may require some trial and error. Figure 6 shows the ratio of new solutions at every tenth generation (i.e., step = 10). The ratio was significantly high in the first generation when the algorithm was evolving and decreased with the generation, as new solutions were not frequent. We also observed that depending on \(\mu\), NSGA-II took a different number of generations to achieve the cut-off value. In fact, the value of \(\mu\) affected the cardinality of \(Q\), hence increasing or decreasing the search space, i.e., the higher the cardinality of \(Q\), the higher was the number of generations to achieve the cut-off value. On the other hand, the lower the cardinality of \(Q\), the lower was the number of generations to achieve the cut-off value. These results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline NSGA-II Population Size & 80 \\ NSGA-II \(p_{c}\) & 0.9 \\ NSGA-II \(p_{m}\) & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: NSGA-II setup parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Rate (Mbits/s)** & **Min. Sensitivity (dBm)** & \(\mathcal{D}_{i}\) **(m)** \\ \hline
6 & \(-82\) & 892.24 \\
9 & \(-81\) & 803.58 \\
12 & \(-79\) & 651.81 \\
18 & \(-77\) & 528.70 \\
24 & \(-74\) & 386.23 \\
36 & \(-70\) & 254.11 \\
48 & \(-66\) & 167.19 \\
54 & \(-65\) & 150.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum achievable distance of each transmission mode based on the minimum sensitivity of the receiver antenna.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Transmit Power (\(P_{i}\)) & 23 dBm \\ Antenna model & Omni-directional \\ Propagation model & Log-distance \\ Area \(\mathcal{A}\), (\(X_{max}\times Y_{max}\)) & 5000 m \(\times\) 5000 m \\ No. of GNs & 100 \\ \(\alpha\) & 2.2 \\ \(d_{0}\) & 1 m \\ \(c\) & \(3\times 10^{8}\) m/s \\ \(f\) & \(2.412\times 10^{9}\)\(H_{z}\) \\ \(P_{r}(d_{0})\) & \(-47\) dBm \\ \(\mu\) & [0.15, 0.30, 0.45] \\ \(\mathcal{D}\) & 892.24 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters in each scenario.
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
## 1 Introduction
[PERSON] et al. achieved integrated signal decomposition and modal parameter identification using improved EMD and COV-SSI methods [23]. In recent years, machine learning methods have been heavily utilized for structural abnormal pattern recognition. These methods mainly include sparse Bayesian learning methods and deep convolutional neural network methods. [PERSON] et al. utilized sparse Bayesian learning methods to improve the robustness of structural damage recognition [24]. [PERSON] et al. introduced the Laplace prior probability model into sparse Bayesian learning, obtaining a more efficient computational model for structural damage recognition and reducing the model's limitation relating to the dataset [25]. [PERSON] et al. defined the residual as the structural damage indicator by comparing the predicted output of the convolutional neural network with the true response in the healthy state of the structure [26]. [PERSON] et al. proposed a method combining Kriging and Artificial Neural Networks to build an evaluation model and improve its prediction accuracy [27]. However, the current methods for identifying abnormal states in structural dynamic characteristics all have their own limitations, and many of the applications for identifying anomalies in structural dynamic characteristics have not been tested in practical bridges. Further research is still needed in this area. In order to ensure the structural safety of in-service bridges, this paper conducts research on a structure abnormal pattern detection method based on real-time health monitoring data analysis. Taking a large-span arch bridge as the research object, the paper analyzes the temporal variation patterns and correlation relationships of the monitoring data, extracts characteristic indicators, and establishes Lasso regression models and their regression variables considering the characteristics of the monitoring data under different time scales. Based on the regression residuals, a bridge structure abnormal pattern detection method is proposed, and verification analysis is conducted in combination with practical cases.
# LR-MPIBS: A LoRa-Based Maritime Position-Indicating Beacon System
###### Abstract
In this paper, we design a LoRa-based Maritime Position-Indicating Beacon System (LR-MPIBS), which integrates LoRa technology [18; 19; 20] and BeiDou satellite navigation technology to address the challenges faced by existing PIBS in terms of endurance, false alarm rate, and deployment convenience. The LR-MPIBS helps to further maintain the safety of ship navigation, especially for ships with difficulties in searching and rescuing personnel who have fallen into the sea.
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 4 Results
### Network Throughput  
The network shows cooperative communication among vehicles, infrastructure, and RSUs. Network throughput shows the successful content pre-caching under lower delay in VANET. The performance of the proposed network infrastructure is explained using network throughput. Maximizing the network throughput explains less network congestion using the proposed technique. The cache hit ratio improved in Section 4.3, effectively showing the utilization of the network caching, which overcomes the burden of vehicles to search for the content which causes network congestion. Once the vehicles join the zone using algorithm 1, it utilizes the pre-cached content easily using Algorithm 2, which enhances the network throughput, ultimately reducing congestion inside the network. The proposed protocol reduces the data request collision probability by setting up the required features. The network uses all the protocols defined inside the network with the ability to  
Figure 11: Average Delay of proposed technique compared with RPSS, MLCP, CCMP, PCZS + PCNS.  
Figure 12: Average delay on the number of nodes for the proposed methodology.  
participate in overcoming network congestion. Figure 13 shows the network throughput of the proposed technique. The correlation between vehicle speed and throughput is when the vehicle speed grows.  
Then the cache hit ratio and services delivery performance (i.e., throughput) impact the network's overall performance as speed matters in highway scenarios where the pre-caching is a significant challenge resolved in this problem and a high value of throughput is achieved. Network throughput explains the average ratio of the average delay, pre-caching and prediction accuracy, and cache hit ratio when vehicles were moving along the highway. Figure 4 shows the throughput with network performance, like average delay, pre-caching, accuracy, and cache hit ratio. According to the results in Figure 4, our proposed technique matches the vehicle speed. In response, the RPCC and PCZN + PCNS techniques only show results when the vehicle speed is slower. Under high speed, their throughput goes down. According to the results, it is clear that when the vehicle moves at high speed, the throughput of these techniques is compromised. The results also show that the throughput of accessing the content caching and network congestion control enhances with time. The increase in the throughput is the availability of content data and the running network smoothly.
## 2 Results and Discussion
### Screening of XO Inhibitors from Clerodendranthus spicatus  
Our previous studies indicated that the extract of _Clerodendranthus spicatus_ showed inhibition activities on XO. In this experiment, the inhibitory activity of the extract of _Clerodendranthus spicatus_ was also tested to ensure the smooth implementation of this work. The results showed that the extract of _Clerodendranthus spicatus_ exhibited inhibition on XO with the IC\({}_{50}\) value at 147.0 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, which indicated there were plenty of potential XO inhibitors in the extract. Therefore, the analysis of XO inhibitors from extract of _Clerodendranthus spicatus_ was meaningful.  
The chromatograms of _Clerodendranthus spicatus_ in aqueous and eluent solutions after screening using Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles are shown in Figure 6. In order to obtain the optimum chromatograms for analysis, various gradient elution modes, several flow rates and column temperatures were evaluated. Finally, acceptable separation was achieved within of 60 min analysis. The chromatogram of _Clerodendranthus spicatus_ extract can be found in Figure 6a. It can be seen that most of the peaks in the sample reached baseline separation and that the peak shape presented well.  
When Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles were incubated with extract of _Clerodendranthus spicatus_, the potential XO inhibitors would bind to Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles based on affinity interaction. Meanwhile, the other components in the extract would not have any specific or chemical interaction with the Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles. Finally, those bound potential XO inhibitors would be eluted by an organic solution and analyzed for further experiments. It can be seen in Figure 6b that two peaks appeared in the chromatogram of the eluent after screening with active Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles; they are marked with numbers in Figure 6.  
Deactivated Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles, after high temperature process, were also used in screening to exclude nonspecific binding between active compounds and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO  
Figure 5: (a) Effect of screening time on the screening capacity. (b) Reusability of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles.  
nanoparticles, and thus confirm the accuracy of the screening results. Figure 6c shows the chromatogram of eluent after screening with deactivated Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles. Obviously, no peak was observed in the chromatogram at the same retention time of peak 1 and 2. Therefore, it could be ensured that the two compounds marked with numbers, which needed further identification and analysis, were specifically bound to Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-XO nanoparticles.
## 2 Materials and Methods
### Methodologies  
According to the above analysis, this study first utilized the GEE cloud platform, and employed the random forest algorithm to conduct supervised classification on Landsat remote-sensing images from the years 2000, 2010, and 2020 within the research area, thereby obtaining land-use data for three distinct periods. And then, the geo-informatic Tupu model and a newly revised method of benefit transfer were primarily employed for investigating the geographic features of LUTs and their ecological effects from 2000 to 2020. A detailed description of each method is shown below.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 4 Discussion
The values read by the ultrasonic sensor are processed, and emergency alerts are sent to predefined email addresses based on the level of risk. This functionality is crucial for flood prevention and response, providing timely information to mitigate potential damage. As missing data management is an important research topic in hydrology and water resources, the performance of the proposed system has also been evaluated in cases of missing data. In the current study, data loss during the data collection process is very limited. However, in the case of rare data losses, the system's resilience is maintained, and data reliability is enhanced. Minor discrepancies in discharge values were mitigated through optimized measurement intervals, with the system performing two measurements per minute to improve data accuracy and continuity. Future studies are planned to address missing data by integrating statistical methods and artificial intelligence-based approaches to ensure a more robust data management system. These enhancements, along with the system's ability to address minor discrepancies in discharge values, underline its potential as a reliable tool for river level monitoring.
# Bootstrap-CURE: A Novel Clustering Approach for Sensor Data--An Application to 3D Printing Industry
## 2 3D Printing Process  
The term, _additive manufacturing_ (commonly known as _3D printing_), has been drawing attention from different sections of the industries by allowing the digitization of physical models. In [7], a review of the main 3D printing technologies can be found.  
One of the major advantages of 3D printing is the ability to produce a monolithic structure of complex geometry. This is possible by building parts through a stack of thin cross sections in an additive manner. Hence, this type of printing also saves print material, which is often wasted in traditional subtractive manufacturing. Some of the key 3D printing technologies are as follows [8]:  
1. **Fused deposition modeling**: Machine lets the plastic filament melt and extrude through nozzles onto the bed platform, where it is cooled and solidified.
2. **Binder jetting**: Machine distributes a layer of powder onto a build platform, and a bonding agent helps to fuse the parts. The process keeps repeating until the parts are built up in the powder bed.  
3. **Laser sintering**: Uses a laser as a power source to aim at points in 3D space to sinter the powder material and create a solid part.
4. **Laser melting**: Machine uses laser(s) to melt metal powder.
5. **Stereo-lithography**: Machine builds parts out of liquid photopolymer through polymerization activated by a UV laser.  
This paper focuses on finding interpretable models to describe the work done on anonymized sample sensor measurements from _HP Multi-Jet 3D Fusion_ printers. According to the overview provided in _additive manufacturing_[7], the _jet-fusion_ technology is a further improvement developed by HP, on the fused deposition modeling and polyjet technologies. A brief technical introduction of multi-jet fusion technology is provided in the work [9]. Such 3D printers are equipped with dozens of sensors placed across different components to measure and control factors, such as temperature, humidity, velocity, pressure, etc. A 3D printing process is a complex job, where many parameters must align among themselves for the success of the job. A brief technical introduction of multi-jet fusion technology is provided in the work by [9].  
The data collected through the sensors network of the printer are produced in a streaming mode and sent to a cloud-based server for further processing. Analyzing the sensor-based network is challenging in the sense that it is required to run the analysis on the fly and be able to ingest large amount of sensor data rather quickly.