esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE) and coefficient of determination of prediction (R\({}^{2}\)P) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (\(-\)0.23, \(-\)0.27, and \(-\)0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE\({}_{\text{P}}\) of 3.718 and highest R\({}^{2}\)P of 0.391, followed by SVM (RMSE\({}_{\text{P}}\) = 3.753, R\({}^{2}\)p = 0.361) and PLSR (RMSE\({}_{\text{P}}\) = 4.087, R\({}^{2}\)P = 0.283). The SLR model was the worst model, with the lowest R\({}^{2}\)P of 0.281 and highest RMSE\({}_{\text{P}}\) of 3.930. ANN and SVM were better than OK (RMSE\({}_{\text{P}}\) = 3.727, R\({}^{2}\)p = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.  
**Keywords:** NDVI time series; soil organic carbon; digital soil mapping; prediction model; machine learning methods
## 4 Methodologies and Implementation
### Challenges and Limitations in Implementing DDPM for Biomedical Image Segmentation
#### 4.3.1 Computational Complexity  
DDPM involves complex mathematical operations, including iterative sampling and updating of diffusion processes. This can lead to high computational demands, making real-time or near-real-time applications challenging [97; 98].
## 5 Performance Evaluation
### Results
This work uses the \(N_{rtx}\) parameter to evaluate the performance of the proposed error recovery mechanism in terms of energy efficiency and data delivery delay. Retransmissions cause extra energy, as well as delay in a WBSN. Therefore, the reduction in \(N_{rtx}\) helps to make the proposed scheme energy efficient and to reduce delay in WBSNs. Figure 11 presents the performance analysis of the proposed mechanism in terms of the number of retransmissions needed by it. As presented in Figure 11, both the critical and the non-critical sensors in the proposed scheme require a smaller number of retransmissions in comparison to the MP's mechanism or without any error recovery method. This is because the probability of packet loss in this mechanism is lower than the others (see Figure 10). Due to the lower loss probability, the body sensors are required to retransmit their data less. If each retransmission consumes \(T\) units of time and \(E\) units of energy, it can be said that at 70% PER in MP's mechanism, the set of 12 sensors consumes about \(8T\) units of time and \(8E\) units of energy, and in the mechanism without error recovery, they consume \(9T\) units of time and \(9E\) units of energy. For the same settings, in the proposed mechanism, the sensors consume (overall) \(4T\) units of time and \(4E\) units of energy, where, individually, the set of non-critical sensors consume \(3T\) units of time and \(3E\) units of energy, and the set of critical sensors consume only one unit of time and one unit of energy. Figures 12 and 13 present the results for the energy efficiency and data delivery delay of the proposed mechanism considering the TelosB [47] as sensor nodes and the Imote2 [48] as relay nodes. We also considered that the distance between the relay nodes and personal server is around 20 meters. These figures clearly show the potential of the proposed mechanism in terms of energy efficiency and data delivery delay improvement. Trends in Figures 12 and 13 are very similar to Figure 11, as the energy costs and the delays are mainly contributed by the retransmissions. Figure 10: Performance comparison in terms of reliability. Figure 11: Performance comparison in terms of the number of retransmissions needed. Figure 12: Energy consumed due to the retransmissions. Figure 13: Data delivery delay due to the retransmissions.
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 4 FEA Results
### Shear Capacity Comparison between Model and Test  
The load-slip behavior in the loading process of studs is closely related to the shear behavior. The modeling result of the FEM HS-MS is compared with the testing results, and their load-slip curves are shown in Figure 11. We can see from Figure 11, that the FEM modeling result is in good agreement with the testing results. The ultimate load for HS-MS is 41.6 kN, the maximum ultimate load in the test specimen HS14-1 is 44.4 kN, and the minimum ultimate load in the test specimen HS14-2 is 43 kN. Therefore, the modeled load-slip curve is consistent with the testing one, which indicates the modeling method in this paper is accurate and reliable. The experimental values match well with the finite element simulation results, with an error of less than 6.7%.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 4 Enhanced Multi-Objective Salp Swarm Algorithm (EMSSA)
### Enhanced Multi-Objective Salp Swarm Algorithm (EMSSA)
#### 4.2.4 Enhanced Multi-Objective Salp Swarm Algorithm (EMSSA)  
Based on the description of MSSA and the three improvement measures mentioned above, the pseudo code for EMSSA can be seen in Algorithm 1. The EMSSA algorithm first initializes the population of salps with Logistics chaotic mapping. Then, it calculates the objective values for each salp and finds the non-dominated solutions. These non-dominatedsolutions are updated to the repository based on the repository update mechanism. Next, a food source is selected from the non-dominated solutions in the repository with the least crowded neighborhood. The next step involves updating the position of leading/follower salps using the respective formulas. If a salp goes out of bounds during the position update process, it is brought back within the boundary. Finally, all the above steps, except for the initialization step, are repeated until the termination condition is met.  
```
Data: Population size \(N\), maximum number of population iterations \(\mathbf{T}\). Result: repository
1 Initialize the salp population \(x_{i}(i=1,2,\cdots,n)\) with Logistics chaotic mapping while\((t<T)\)do
2 Calculate the fitness of each search agent (salp swarm);
3 Determine the non-dominated salp swarms;
4 Update the repository ifthe repository becomes fullthen
5 Call the repository maintenance procedure to remove one repository resident ;
6 Add the non-dominated salp to the repository
7 end if
8 Choose a source of food from repository: \(F_{d}=SelectFood(repository)\) Update \(c_{1}^{\prime}\) by Equation (17)
9 foreachsalp \((x_{i})\)do
10 if\(x_{i}=1\)then
11 Update the leader position by Equation (18)
12 else
13 Update the follower position by Equation (20)
14
15 end if
16
17
18 end for
19 Amend the salps based on the upper and lower bounds of variables
20
21 end for Return repository
```  
**Algorithm 1**Pseudo code of the EMSSA algorithm.
###### Abstract  
Assessment of the accuracy of an implant guide system is essential, yet the reliability of postoperative methods for locating the implant position has still not been clarified. This study therefore sought to evaluate the accuracy of postoperative methods for locating the actual position of implants in terms of their linear and angular deviations. The implant position in a dendiform model was located using the following three methods: manual matching on a cone-beam computed tomography (CBCT) image (MC group), manual matching on a mesh model of CBCT (MM group), and automatic matching on a scan abutment of a scan image (AS group). Thirty clinicians adopted each method, and the estimated position of the implant in each group was compared three-dimensionally with the reference implant position using image analysis software in terms of the linear, vertical, and angular deviations. One-way analysis of variance (ANOVA) and [PERSON]'s post-hoc test were used for statistical analyses (\(\alpha\) = 0.05). In general, the deviations were the largest in the MC group, followed by the MM group and the AS group. The ANOVA results suggested that all deviations values were markedly smaller in the AS group than in the MC group (\(p\) < 0.001). The interoperator measurement variability of all deviations was relatively smaller in the AS group than in the other two groups. The automatic matching method using scan abutments was more accurate than the manual matching methods using CBCT and mesh images in assessing the deviations that existed between the planned and actual positions of the implant. The use of scan abutments is recommended for the postoperative assessment of an implant's placement location.  
validation; accuracy; implant; position; postoperative; analysis method
## 2 Model Test Design
### Model Test System  
Figure 1 shows the tunnel horizontal freezing model test system, which includes a model box, freezing system and temperature measurement system.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Similarity Ratio** \\ \hline Geometry (m) & 30 \\ Time (s) & 900 \\ Temperature (\({}^{\circ}\)C) & 1 \\ Humidity (\%) & 1 \\ Density (kg/m\({}^{3}\)) & 1 \\ Thermal conductivity (kcal-m\({}^{-1}\).d-\({}^{-1}\).\({}^{\circ}\)C\({}^{-1}\)) & 1 \\ Specific heat (kcal-kg\({}^{-1}\).\({}^{\circ}\)C\({}^{-1}\)) & 1 \\ Latent heat of phase change (kcal-kg\({}^{-1}\)) & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Similarity ratios.  
Figure 1: Three-dimensional diagram of model test system.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Density** & **Thermal Conductivity** & **Specific Heat** & **Latent Heat of Phase Change** & **Freezing Temperature** \\
**Type** & **/(kg/m\({}^{3}\))** & **/(kcal-(m\({}^{\circ}\)C-d))** & **/(kcal-(kg\({}^{\circ}\)C))** & **/(kcal/kg)** & **/(\({}^{\circ}\)C)** \\ \hline
**Mild** & Unfrozen & 23.00 & 0.357 & \\
**Clay** & Frozen & 38.06 & 0.240 & 8.93 & \(-1\) \\ \end{tabular}
\end{table}
Table 2: Soil parameters.  
(2) Freezing system  
As shown in Figure 2a,d, the freezing equipment adopted the low-temperature thermostat produced by Jiangsu Hengnuo Instrument Manufacturing Co., Ltd. (Nanjing, China), which allowed alcohol to maintain a temperature of \(-21\)\({}^{\circ}\)C for cycle operation. The test time was set as 72 min, where the positive time was 60 min, and the design thickness of the frozen wall was set as 40 mm. In this model test, 4 freezing pipes with an outer diameter of 8 mm were determined by the equivalent heat transfer and similarity criteria, and these pipes were symmetrically distributed above the tunnel at an angle of 28\({}^{\circ}\). The structure of the freezing pipe is a coaxial sleeve, and the circulation mode of alcohol is entering from the inner pipe and leaving from the outer pipe, and its flow rate is 1.2 m\({}^{3}\)/h, as shown in Figure 3.  
Figure 3: Temperature measurement points.  
Figure 2: Tunnel horizontal freezing model test system: (**a**) the control panel of low-temperature constant temperature bath; (**b,c**): TDS-630 multi-point data acquisition instrument; (**d**) low-temperature thermostat; (**e**) model box; (**f**) mild clay and freezing pipes in the model box.  
Temperature measurement system  
A TDS-630 multi-point data acquisition instrument in Figure 2b,c was selected to record temperature, and the acquisition frequency was set as 1 min/time. In this model test, three temperature measurement sections were set along the length direction of the freezing pipe, that was 50 mm, 250 mm and 450 mm from the orifice of the freezing pipe. At each temperature measurement section, there were 23 measurement points in the soil and 4 measurement points on the freezing pipe wall, as shown in Figure 3.
## 7 Anomaly Detection Algorithm and Architecture  
The anomaly detection system integrates the CYO with a Multi-Layer Perceptron (MLP) neural network to detect malicious activities in real-time. The system architecture is designed to process and analyze network traffic within a corporate network, capturing various features such as packet size, protocol types, IP addresses, and connection durations. Initially, the system preprocesses the raw network traffic data, removing noise and irrelevant information. Features are then extracted and normalized to ensure consistency and relevance for the MLP training process.  
The preprocessed data are then fed into the MLP, where the CYO optimizes the neural network's weights and biases. The optimizer dynamically adjusts parameters such as scaling factors (SFs) and crossover rates (CRs) using its adaptive differential evolution mechanism, ensuring that the training process continuously evolves to enhance anomaly detection. The optimizer's archival mechanism retains successful solutions from previous generations, allowing for their reintegration, a process that mirrors cybersecurity strategies that leverage historical data to strengthen current defenses.  
In real-world implementations, the MLP continuously monitors network traffic in real-time. Once trained, the MLP classifies incoming network traffic as either normal or anomalous. Alerts are triggered when anomalies are detected, enabling network administrators to respond to potential security threats quickly. This architecture was deployed in a corporate network environment over several months, where the system monitored  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **MSE (Average)** & **MSE (STD)** & **Classification Rate** \\ \hline Cybersecurity-MLP & 0.003200 & 0.002400 & 97.4\% \\ Chimp-MLP & 0.004500 & 0.002800 & 96.0\% \\ CPO-MLP & 0.003800 & 0.002600 & 97.2\% \\ ROA-MLP & 0.004100 & 0.002700 & 96.1\% \\ WOA-MLP & 0.004700 & 0.002900 & 95.9\% \\ MFO-MLP & 0.003400 & 0.002500 & 94.3\% \\ WSO-MLP & 0.004000 & 0.002700 & 95.1\% \\ SHIO-MLP & 0.005100 & 0.003000 & 94.7\% \\ ZOA-MLP & 0.005600 & 0.003100 & 96.6\% \\ DOA-MLP & 0.006200 & 0.003300 & 97.4\% \\ HHO-MLP & 0.006700 & 0.003400 & 96.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Experimental results for the CSE-CIC-IDS2018 dataset.  
live traffic. The MLP was periodically retrained using newly collected data, ensuring it could detect evolving types of attacks. This real-world implementation demonstrated the system's efficiency in anomaly detection and its robustness against various types of attacks.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 2 Overview and Related Work
### Related Work
#### 2.2.1 Perceptual Quality  
As shown in Table 1, previous studies have investigated the impacts of various factors on the perceptual quality of 360 video such as gender, encoding parameters, rendering device, delay, and content motion. In [16], it is found that the delay (i.e., Motion-to-High-Resolution Latency) has a considerable effect on the perceptual quality, whereas the gender does not.  
Previous studies have shown significant impacts of encoding parameters on the perceptual quality, such as quantization parameter (QP) [25], resolution [16; 17; 23; 24; 25], framerate [24], and bitrate [23; 24; 26]. In comparing the impacts of encoding parameters, a conclusion in [24] is that the resolution and frame rate have very similar impacts on the perceptual quality, and their impacts are more significant than that of the bitrate.  
In [17], the authors compare two rendering devices of Oculus Rift and HTC Vive in terms of perceptual quality. Experimental results show that the Oculus Rift provides a slightly worse perceptual quality than the HTC Vive. However, the difference is not statistically significant. Therefore, the impact of device type seems trivial.  
With respect to the factor of content motion, its considerable effect on the perceptual quality has been found in [17; 26]. However, analyses and discussions on the impacts of specific content motions such as camera motion, object motion, and background motion are not presented.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
## 3 Real-Time Health Assessment Algorithm
### Health Assessment Based on Nonlinear Weighting and Time-Series Dependence  
The uncertainty and ambiguity of the space environment enable a nonlinear evaluation of the satellite health state, considering that in a swarm system, the components of a single satellite, intersatellite communication link, and swarm effectiveness affect each other; moreover, the health degree \(H_{swarm}(t)\) of the current moment of the swarm is influenced by the health state of the previous moment, i.e., there is a time-series dependence relationship. Therefore, a nonlinear weighting method [21] is used to construct the swarm health model under temporal dependence as follows.  
\[\Theta=H_{sat}(v_{i})^{k_{1}}\cdot H_{link}{}^{k_{2}}\cdot E_{swarm}{}^{k_{3}} \tag{23}\]  
\[H_{swarm}(t)=\Theta(H_{sat},H_{link},E_{swarm})+\sigma(H_{swarm}(t-1)) \tag{24}\]  
\[=H_{sat}{}^{k_{1}}\cdot H_{link}{}^{k_{2}}\cdot E_{swarm}{}^{k_{3}}+\sigma(H _{swarm}(t-1))\]  
where \(\sigma\) is the time series function and \(k_{1},k_{2},k_{3}\) is the influence parameter obtained from the degree of influence of each part of the swarm system. The primary exponential smoothing method was chosen and fitted to the health degree values obtained from several consecutive trials.  
\[H_{swarm}(t)=H_{sat}{}^{k_{1}}\cdot H_{link}{}^{k_{2}}\cdot E_{swarm}{}^{k_{ 3}}+(1-\alpha)H_{swarm}(t-1) \tag{25}\]  
where \(\alpha\in[0,1]\) is the weighting factor, which is generally obtained by fitting with historical data and satisfies \(k_{1}+k_{2}+k_{3}=\alpha\). The complete swarm health state assessment algorithm is shown in Figure 2. The process is as follows: firstly, single satellite, intersatellite communication and effectiveness assessment parameters are entered. Next, the health is calculated by the health assessment algorithm. Then, failure analysis is performed by variable weights. Finally, the health assessment results are output by the nonlinear synthesis.
## 1 Introduction
[8] proposed a multi-scale gradual integration CNN, which fused three scales of nodules gradually according to two different rules. This method used the 3D nodule information as a channel and used a 2D convolution kernel. Thirdly, network parallel connections can improve false-positive performance. [PERSON] et al. [10] proposed a multi-scale parallel 3D-CNN, which extracted blocks of three different scales for each candidate nodule. The classification calculations were made for each nodule block separately; then, the  
Figure 1: Examples of pulmonary nodules and non-nodules carrying similar structures. Each example is a two-dimensional (2D) transverse plane extracted from the location of the candidate nodule. three classification results were combined according to a set of weights. The weights were determined manually, not trained from the dataset. In this paper, we propose a multi-scale heterogeneous 3D CNN (MSH-CNN) for false-positive reduction of pulmonary nodules, which differs from other methods mainly in (1) using the 3D method. By making full use of 3D spatial information, our network can learn representative features with higher discernment than that of 2D CNN. (2) In order to cope with the large variations of nodules and more clearly distinguish them from other similar organizations, we use multi-scale blocks of the nodule as input. According to the characteristics of the nodule and organs surrounding the nodule, a novel heterogeneous network was designed to extract the expressions features. (3) We use a set of weights which are determined by back propagation to fuse the expression features. The experimental performance on the Lung Nodule Analysis 2016 (LUNA16) dataset demonstrates the effectiveness of our method and shows a brighter prospect on large datasets. Our main contributions are as follows:  
(1) We propose a novel 3D CNN for false-positive reduction of pulmonary nodules. (2) We evaluate the effect of multi-scale heterogeneous 3D CNN with different structures on the reduction of false-positive nodules. (3) Our method is tested on the Lung Nodule Analysis 2016 (LUNA16) [19] dataset, and the measure of performance surpasses most documented methods. The rest of this paper is organized as follows: Section 2 introduces the existing methods in the literature. Section 3 describes our method. We introduce the algorithm performance comparison method and evaluate the impact of different structures on the results in Section 4. In Section 5, we discuss the reasons for the effectiveness of our method and the future directions for improvement. The conclusions are drawn in Section 6.
## 4 Security Issues and Solutions for CAVs
### Cyber-Attacks on Inter-Vehicle Systems
#### 4.2.3 Open Issues of Inter-Vehicle System Security
Cyber-attacks on inter-vehicle systems can be from either the outside or inside of the platoon. In most cases, the cyber-attacker is outside the platoon. In [102], the jammer was mounted on a drone flying over the platoon. Theoretically, it is not very difficult to recognize attacks from the outside. Some state-of-the art measures can effectively detect and limit the capabilities of an outside attacker, but it is still challenging to effectively defend against inside attackers. In addition, due to the coupling of physical and cyber aspects of the inter-vehicle system, the co-attacks of a malicious vehicle inside the platoon which takes disturbing accelerations and an attacker outside the platoon would be greatly threatening. It is considerably challenging to identify the attacks sources, to cut off the cooperation of two attackers, and to eliminate the insider vehicle as well as re-stabilize the platoon. Some researchers proposed maintaining a lower and upper bound of inter-vehicle spacing to possibly mitigate the impact of cyber-attacks [102], which is a trade-off between traffic efficiency and safety. However, given that the message within a platoon contains control parameters, if an insider attacker directly tampers the message content when the message is transmitted from head to the end, it would directly cause collisions without noticeably degrading the communicate quality. Therefore, security mechanisms are urgently needed to detect and defend against insider attacks and co-attacks.