esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Model
### Loss Function
\[DDA=\frac{TP+TN}{TP+TN+FP+FN} \tag{15}\]

\[DDR=\frac{TP}{TP+FN} \tag{16}\]

\[FDR=\frac{FP}{TP+FP} \tag{17}\]

where _TP_ is the number of correctly detected sample defects in the testing process, _TN_ is the number of correctly detected true defect-free samples, _FP_ is the number of incorrectly detected true defect-free samples as defective samples, and _FN_ is the number of incorrectly judged defects as true sample backgrounds. The IOU of the detection model in the testing process is set to 0.25. The results of the validation set of (a)-(j) are shown in Tables 4-14. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**YOLOv5**} & \multicolumn{2}{c}{**SSD**} & \multicolumn{2}{c}{**Faster-RCNN**} \\ \cline{2-10}  & **DDA** & **DDR** & **FDR** & **DDA** & **DDR** & **FDR** & **DDA** & **DDR** & **FDR** \\ \hline Class 1 & 88.89\% & 66.67\% & 0.00\% & 81.67\% & 46.67\% & 3.45\% & 87.78\% & 63.33\% & 0.00\% \\ Class 2 & 93.89\% & 81.67\% & 0.00\% & 92.22\% & 76.67\% & 0.00\% & 91.67\% & 75.00\% & 0.00\% \\ Class 3 & 89.44\% & 73.33\% & 6.38\% & 88.33\% & 71.67\% & 8.51\% & 90.56\% & 75.00\% & 4.26\% \\ Class 4 & 79.44\% & 46.67\% & 15.15\% & 81.11\% & 50.00\% & 11.76\% & 80.56\% & 48.33\% & 12.12\% \\ Class 5 & 86.67\% & 68.33\% & 10.87\% & 85.00\% & 66.67\% & 14.89\% & 86.11\% & 68.33\% & 12.77\% \\ Class 6 & 90.56\% & 73.33\% & 2.22\% & 88.89\% & 70.00\% & 4.55\% & 89.44\% & 71.67\% & 4.44\% \\ Class 7 & 88.89\% & 71.67\% & 6.52\% & 87.78\% & 70.00\% & 8.70\% & 88.89\% & 71.67\% & 6.52\% \\ Class 8 & 84.44\% & 65.00\% & 15.22\% & 81.11\% & 58.33\% & 20.45\% & 84.44\% & 65.00\% & 15.22\% \\ Class 9 & 83.89\% & 68.33\% & 19.61\% & 84.44\% & 70.00\% & 19.23\% & 85.56\% & 71.67\% & 17.31\% \\ Class 10 & 92.22\% & 76.67\% & 0.00\% & 92.78\% & 78.33\% & 0.00\% & 92.22\% & 76.67\% & 0.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Indicators for each detection model in De-Test A. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Train Dataset**} & \multirow{2}{*}{**Test Dataset**} & \multicolumn{2}{c}{**De-Test B**} & \multicolumn{2}{c}{**De-Test C**} \\ \cline{3-10}  & & & **DDA** & **DDR** & **FDR** & **DDA** & **DDR** & **FDR** \\ \hline \multirow{4}{*}{YOLOv5} & \multirow{2}{*}{De-Train B} & Tree-CycleGAN [42] & 88.46\% & 69.35\% & 4.44\% & 90.06\% & 73.77\% & 4.26\% \\  & & CycleGAN-TSS [43] & 88.40\% & 68.85\% & 4.55\% & 90.61\% & 75.41\% & 4.17\% \\  & & PreCaCycleGAN & **89.62\%** & **71.43\%** & **2.17\%** & **92.86\%** & **80.65\%** & **1.96\%** \\ \cline{1-1} \cline{2-10}  & & Tree-CycleGAN [42] & 90.11\% & 72.58\% & 2.17\% & 92.27\% & 78.69\% & 2.04\% \\ \cline{1-1}  & & CycleGAN-TSS [43] & 91.21\% & 74.19\% & 0.00\% & 93.37\% & 80.33\% & 0.00\% \\ \cline{1-1}  & & PreCaCycleGAN & **91.89\%** & **76.92\%** & **0.00\%** & **94.54\%** & **84.13\%** & **0.00\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Class 1 test result.
## 4 Breakdown Pressure Prediction with a Multi-Layer Neural Network
### Relative Importance of Influencing Variables
This property is especially beneficial for rocks with high tensile strength, where conventional fluids are less effective in initiating fractures due to limited penetrative ability. Additionally, tensile strength significantly influences the pressure threshold for fracture initiation. Higher tensile strength requires increased breakdown pressure, which SC-CO\({}_{2}\) mitigates through its superior infiltration capability and stress-reducing properties. These characteristics enable SC-CO\({}_{2}\) to overcome the resistance posed by high tensile strength, enhancing fracturing performance in formations that are challenging for use in traditional hydraulic fracturing. By incorporating various fluid types, flow rates, and stress conditions, this study provides a more nuanced understanding of how different factors contribute to fracturing outcomes. Moreover, the use of data-driven, deep learning techniques offers a flexible and adaptable approach to predicting breakdown pressure, particularly in the context of SC-CO\({}_{2}\) fracturing, where traditional models often fall short. The trained neural network model serves as a decision-support tool, enabling engineers to simulate fracturing outcomes under various parameter settings before field implementation. This application minimizes trial-and-error in parameter selection and improves operational efficiency by predicting optimal conditions for SC-CO\({}_{2}\) fracturing, such as ideal injection rates and pressure levels. By accurately predicting breakdown pressure, the model ensures safe, cost-effective fracturing operations while maximizing resource extraction. Figure 12: The relative importance of influencing variables.
# A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search
## 5 Conclusions and Future Work  
In this paper, we proposed exact and approximate methods to address the VM placement problem in cloud data centers, focusing on an MOILP model and a TS algorithm. The model offers optimal solutions, while the TS algorithm provides near-optimal solutions in a more computationally efficient manner. Both methods address three critical objectives: maximizing the number of accepted VMs, minimizing resource wastage, and reducing energy consumption. Simulation results showed that the TS algorithm achieved results close to the optimal ones provided by the MOILP model, offering a practical trade-off between accuracy and computational efficiency. The TS algorithm consistently outperformed traditional methods in the literature, making it a robust approach for real-time VMP in large-scale cloud environments.  
Future work will expand this research to the optimization of VM and container placement in cloud-native environments. Containers, unlike VMs, are more lightweight and share the host operating system, which presents unique challenges and opportunities in resource allocation. Key factors such as inter-container communication, resource isolation, and fault tolerance need to be addressed for effective placement strategies in containerized environments. To adapt the TS algorithm for this new context, modifications would involve incorporating container-specific constraints, such as affinity and anti-affinity rules, which define which containers should be placed together or kept apart for optimal performance and fault isolation. Additionally, the model would need to account for dynamic scaling  
Figure 16: Average CPU execution time for various values of \(N\).  
requirements to handle the elasticity of containerized workloads in real time. Further, load balancing and network management for containerized applications must be integrated into the placement strategy to ensure efficient resource utilization and minimize communication latency between containers. By exploring these challenges and developing hybrid placement strategies, this work aims to enhance resource efficiency, operational agility, and performance in modern, cloud-native infrastructures, thus bridging the gap between traditional VM environments and emerging container-based systems.  
Conceptualization, M.K. and R.R.; methodology, M.K., R.R., A.S.K., M.N. and F.B.; software, M.K. and R.R.; validation, M.K., A.S.K., M.N. and F.B.; formal analysis, M.K., A.S.K., M.N. and F.B.; investigation, M.K. and R.R.; resources, M.K., R.R., A.S.K., M.N. and F.B.; data curation, M.K. and R.R.; writing--original draft preparation, M.K.; writing--review and editing, M.K., R.R., A.S.K., N.M. and F.B.; visualization, M.K.; supervision, M.K., A.S.K., M.N. and F.B.; project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data supporting the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
# The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
## 3 Pipeline Residual Strength Assessment Methods  
[PERSON] et al. [40] showed that corrosion defects subjected to axial compressive stress result in noticeable failure pressure differences. Therefore, simplifications made to corrosion assessment methods whereby axial compressive stress is considered to have little or no effect on pipe failure are inaccurate. For instance, [PERSON] et al. [41] showed that ASME B31G did not consider the effects of end loads (i.e., tensile loads during close-ended tests), which would result in conservative failure pressure predictions.  
The exclusion of axial compressive stress influence in conventional standards and codes for the corrosion assessment methods of pipe bends also results in a highly conservative and inaccurate evaluation. Existing assessment methods only incorporate internal pressure during corrosion assessments. In addition, the research on developing corrosion assessment methods for pipe bends subjected to internal pressure and axial compressive stress is minimal. Hence, for such cases, FEM is used. In recent studies, FEM has been incorporated with ANN to develop new assessment methods that are highly accurate for corroded pipe components subjected to internal pressure and axial compressive stress [2; 3; 4; 8; 9; 11; 12; 42; 43; 44; 2; 45].
## 4 Risk Analysis for Current Protocols
### TCP/IPv6
#### 4.1.2 Spoofing  
TCP/IPv6 networks are known to be vulnerable to spoofing at the Data-Link layer (second layer in the 7-layer TCP model) [23, 24]. Devices do not typically authenticate the  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **DoS** & **Spoofing** & **Packet Sniffing** & **Relay/Man in the Middle** \\ \hline TCP/IPv6 & Medium & Medium & Medium & High \\ \hline CAN Bus/NMEA 2000 & High & High & High & High \\ \hline NMEA 0183 & High & High & High & High \\ \hline AIS & High & High & High & High \\ \hline OneNet & Medium & High & Low & Low/High \\ \hline \end{tabular}
\end{table}
Table 2: Summary of marine network protocols and their respective security risk level.  
validity of a claimed Media Access Control (MAC) address, so impersonation of an existing MAC address can be easily implemented by an adversary. Additionally, IPv6 addresses can be spoofed although this may lead to routing and packet-switching errors.  
Internet Protocol Security (IPSec) can be implemented to prevent eavesdropping on the communication channel and impersonation [25]. IPSec creates a boundary between unprotected and protected interfaces. Network traffic traversing this boundary is subject to access controls specified by the user or administrator responsible for the IPSec configuration. These controls indicate whether packets cross the boundary unimpeded or are discarded. Typical security services are ensured via an Authentication Header or Encapsulating Security Payload. IPSec ensures transparency for higher network layers (i.e., all software runs unaffected of IPSec). However, an infected computer (e.g., remote or local malware infection) can easily spy on the network's MAC and IPv6 addresses.
## 3 Datasets  
The implementation of our hybrid dimensionality reduction procedure was tested on 13 diverse datasets from the UCI Machine Learning Repository. Table 1 presents the key characteristics of these datasets.  
All datasets were downloaded from the UCI Machine Learning Repository ([[https://archive.ics.uci.edu/](https://archive.ics.uci.edu/)]([https://archive.ics.uci.edu/](https://archive.ics.uci.edu/))), accessed on 10 March 2024. Preprocessing steps included the removal of non-numerical features, identifier columns (e.g., \"caseid\" from US Census Data), and existing target variables to ensure consistent treatment as unsupervised datasets in our experimental framework.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Dataset** & **Observations** & **Original Features** & **Features After Preprocessing** & **Description** \\ \hline
1 & US Census Data [49] & 2,458,285 & 69 & 68 & Demographic data from the 1990 US Census \\ \hline
2 & Taiwanese Bankruptcy Prediction [50] & 6819 & 96 & 95 & Financial ratios and bankruptcy status of Taiwanese companies \\ \hline
3 & Anuran Calls (MFCCs) [51] & 7195 & 26 & 22 & Acoustic features extracted from frog call recordings \\ \hline
4a & Wine Quality-Red [52] & 1599 & 12 & 11 & Physicochemical properties and quality scores of red wines \\ \hline
4b & Wine Quality-White [52] & 4898 & 12 & 11 & Physicochemical properties and quality scores of white wines \\ \hline
5 & Gas Turbine CO and NOx & 36,733 & 12 & 11 & Gas turbine parameters and resulting emissions \\ \hline
6 & HTRU2 [54] & 17,898 & 9 & 8 & Pulsar candidates from radio telescope data \\ \hline
7 & Image Segmentation [55] & 210 & 19 & 19 & Features of image segments for classification \\ \hline
8 & Letter Recognition [56] & 20,000 & 17 & 16 & Eating habits and physical condition data related to obesity \\ \hline
9 & Obesity Levels [57] & 2111 & 17 & 16 & Eating habits and physical condition data related to obesity \\ \hline
10 & Online Shoppers & 12,330 & 18 & 17 & Features from e-commerce website sessions \\ \hline
11 & Travel Reviews [59] & 980 & 11 & 10 & User reviews of travel destinations \\ \hline
12 & Waveform Database Generator [60] & 5000 & 22 & 21 & Generated waveform data with noise \\ \hline \multicolumn{6}{c}{Note: Features After Preprocessing refers to the number of features used in our analysis after removing non-numerical features, identifier columns, and target variables (if any) to treat all datasets as unsupervised according to our methodology.} \\ \end{tabular}
\end{table}
Table 1: Summary of datasets.
# Effect of Soil Washing with Ferric Chloride on Cadmium Removal and Soil Structure
## 3 Results and Discussion
### Applications and Suggestions  
Although most of the research of soil washing with ferric chloride on cadmium removal has been conducted in laboratory experiments, there have been some studies on slurry reactor soil and other practical equipment, such as a circulating washing system device and soil leaching machine. [PERSON] conducted batch and slurry reactor soil washing experiments to explore the removal of Cd, Cr, Pb, and Zn using seven agents. Citric acid (CA) and ferric chloride (FeCl\({}_{3}\)) exhibited an obvious synergistic effect on the removal of heavy metals [33]. This technology is already in use in some countries, such as Japan.  
The side-effect of its application has raised concern. [PERSON] studied a comparison extraction of Cd and Pb with EDTA, DTPA, citric acid, and FeCl\({}_{3}\), as well as their effects on soil fertility. Results indicated that slight changes in soil fertility, including pH, cation exchange capacity, organic matter, and soil extractable phosphorus, were observed [34]. Furthermore, if overcommitted, ferric chloride will become a new contaminant in the soil. Hence, in the process of application, the amount of FeCl\({}_{3}\) should be controlled. [PERSON]'s research focused on how to treat the wastewater after soil washing since water pollution is a severe threat to the water security of China. Ca(OH)\({}_{2}\) and two kinds of biochars (biochar and ZnCl\({}_{2}\)-modified biochar) were tested to treat the waste FeCl\({}_{3}\) washing effluent [35].  
Figure 4: Al release from differently treated soils into solutions. Lowercase letters compare different treatments at the equilibrium stage. Bars followed by the same letter do not differ significantly a-ccording to [PERSON]’s test (\(p\) < 0.05).  
All of these issues need further study. Furthermore, the mechanism of ferric chloride for the removal of Cd should be studied in the future to support the application of this technology.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 1 Introduction
However, the excessive overhead limits the transmission efficiency. At the transport layer, most multipath transmission methods establish multiple parallel paths through multiple network interfaces of the terminal device to improve network bandwidth utilization [13; 14]. However, these methods can only be applied in the scenario in which the terminal device has a multi-homing feature. At the application layer, researchers propose deploying overlay networks to construct virtual paths to enhance the flexibility and efficiency of end-to-end transmission [15; 16]. However, these methods suffer from low network resource utilization and high complexity due to the chimney-style overlay deployment. In addition, multipath transmission technology has been widely studied in Wireless Sensor Networks (WSN) and Network on Chip (NoC) networks. In WSN, researchers have proposed some multi-hop relay routing algorithms to improve network performance and reduce energy consumption [17; 18]. In order to improve the fault tolerance and recovery ability of NoC, researchers propose to apply the self-organization algorithm in WSN to NoC and provide the corresponding rules to bypass the blocked network sections [19]. Although such algorithms can enhance transmission performance, we believe that mechanically applying them to ICN cannot achieve significant performance gains. In this context, we introduce a multipath transmission method combining the functions and characteristics of ICN networks and design a scheduling strategy based on path correlation to enhance transmission efficiency and robustness. The main contributions of this paper are as follows:  
* We introduce a multipath transmission method applied to ICN which establishes multiple end-to-end relay paths by treating ICN routers as relays between the source and the destination. * We propose a relay-node-selection algorithm based on path correlation to minimize the impact of overlapping links. Moreover, we comprehensively calculate the path state value by combining round-trip time (RTT) and packet loss rate and propose a multipath data-scheduling algorithm based on the path state value. * We evaluate the proposed method through a series of simulation experiments. Experimental results show that the proposed method can maintain high bandwidth utilization while reducing the number of out-of-order packets. The rest of this paper is organized as follows. In Section 2, we discuss the related technologies of multipath transmission technologies and data-scheduling strategies. Then, the ID-based multipath transmission method is introduced in Section 3. Section 4 presents a relay-node-selection algorithm and multipath data-scheduling algorithm. Simulation experiments are carried out in Section 5. In Section 6, we finally conclude our contributions.
# A Workflow for Creating Gastric Computational Models from SPARC Scaffolds
## 2 Materials and Methods
### Slow Wave Simulations  
The three computational models were validated through whole-organ slow wave simulations. A slow wave activity with a normal propagation pattern through the tissue network was simulated by solving the monodomain continuum model [30; 31] using  
\[A_{m}\left(C_{m}\frac{\partial V_{m}}{\partial t}+I_{ion}\right)-\
abla\cdot( \sigma\
abla V_{m})=0 \tag{5}\]  
where \(A_{m}\) corresponds to the surface-to-volume ratio, \(C_{m}\) denotes the membrane capacitance, \(V_{m}\) is the transmembrane potential, \(t\) is the time, \(I_{ion}\) is the total current from the cell models, and \(\sigma\) represents the conductivity along the fiber directions (\(f\)) computed by Equation (4). Based on the previous work [10], the values of 2000 cm\({}^{-1}\) and 3 \(\mu\)F/cm\({}^{2}\) were used for \(A_{m}\) and \(C_{m}\), respectively, and a conductivity value of 0.7 mS/cm was used in the solutions along the fiber orientations.  
The fundus region, which is known to be quiescent [32; 33], was defined using \(u_{L}\) obtained from the Laplace solution in the longitudinal direction, and the cells in this region were assigned to a passive cell model available in Chaste. Assuming the muscle activation is controlled by ICC initiated slow waves, the remaining cells were described as ICC using an established cell model capable of modeling the self-excitatory behavior and standard morphology of gastric slow waves [34]. The nodes in a semi-circular region with a 1 mm radius on the greater curvature (the green region in Figure 2b) were defined as the dominant pacemaker region. The pacemaker location was chosen in accordance with the initiation of contractile patterns experimentally observed in the rat stomach [35]. The cells in the pacemaker region had an intrinsic pacing frequency of 4.4 cycles-per-minute initiating a slow wave every 14 s, which is within the range of experimentally observed values [32; 36].  
Simulations were solved for 200 s using Chaste on a high-performance computing server with 144 Intel Xeon Gold 6254 CPUs (Intel Corporation, Santa Clara, CA, USA). The ordinary differential equation time-step of 0.1 ms and the partial differential equation time-step of 1 ms were used for numerical solutions. The activation time (AT) of each cell within slow wave cycles was determined by finding the time point corresponding to the local maxima of the gradient of \(V_{m}\) (i.e., the sharpest upstroke) resulting from the simulations. The reliability of the simulations in the 3 FEM models was analyzed using a temporal convergence test, where ATs of nodes were compared against the period of defined pacemaker activity. The ATs were also compared across the three FEM models to determine the impact of anatomical features on slow wave propagation profiles.
## 2 Preprocessing
### Features Selection
#### 2.2.4 Calculating Frequency  
To analyze EEG signals, techniques such as fast Fourier Transform (FFT) is used, since this is a mathematical algorithm that transforms time-domain EEG signals into the frequency domain, allowing the identification of predominant frequency components. Wavelet transform: Useful for assessing frequency changes over time, providing a time-frequency representation of EEG data.