esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
The data processing strategy includes the following steps. The MOD09A1 data tiles were first mosaicked, reprojected using the MODIS Reprojection Tool and masked with the XUAR boundary at the pre-processing step. Per-pixel QA information in MODIS products allows for removal of most contamination of the NDVI signal related to clouds, aerosol and snow. The time series of vegetation indices, including NDVI, EVI and SAVI, were then calculated from 2001 to 2012. The spatiotemporal patterns of monthly and annual variation were compared for different VIs. Based on the smoothed VI time series, phenological metrics were detected. The regional phenology detection results over the study area were finally generated and compared.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 6 Conclusions  
This paper presents a contactless sleep head movement monitoring system, named Wi-Senser, using the existing ubiquitous WiFi signals. Firstly, the theoretical model of using WiFi signals for detecting human body movements is analyzed, which approximates the changes in CSI amplitude caused by body movement as the sum of a series of DC components and cosine signals whose frequencies vary with the dynamic path velocity and amplitudes vary with the dynamic path length. To obtain fine-grained CSI amplitude information, Wi-Senser first constructs a filtering channel composed of a Hampel filter, wavelet filter, and mean filter to filter out the outliers and noises existing in the raw  
Figure 10: The results of human head movement detection under different carrier selection methods.  
CSI measurements. Then, Wi-Senser proposes a new metric of carrier sensitivity based on standard deviation and kurtosis and then selects an optimal subcarrier for accurately recording the change in human head movements from the 30 candidate subcarriers. Finally, Wi-Senser designs a peak-finding algorithm to capture the real peak set so that our proposed contactless and accurate system for monitoring head movement during sleep can be achieved. Extensive experiments involving six volunteers demonstrate that Wi-Senser is able to achieve an average detection accuracy of higher than 97.95%. Compared with the traditional carrier selection method, the new metric we proposed to select an optimal carrier improves the average detection accuracy of the system by 6%. We envision that Wi-Senser will not only provide monitoring of sleep activities, but that it can also be extended to various novel applications of smart monitoring.  
Conceptualization W.L.; methodology, W.L.; software, W.L.; validation, W.L., Y.F. and S.Z.; formal analysis, W.L. and S.Z.; investigation, W.L.; resources, W.L.; data curation, W.L.; writing--original draft preparation, Y.F.; writing--review and editing, Y.F.; visualization, Y.F.; supervision, W.L.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant 62202001.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data that support the findings of this paper are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 4 Results
### Multilevel Analysis
#### 4.4.3 Correlating Aggregated Results on School Level  
The findings from the multilevel analysis show that a considerable amount of the variation between the schools can be explained by the analysis. In order to look further into the school level, data is aggregated with the school as the analytical unit.  
The correlations between Internet safety awareness, cultural capital, language integration and reading proficiency on the school level show high [PERSON]'s \(r\) (Table 5). When looking at the relationship between reading proficiency and social background, a regression analysis (\(r=0.91\), \(R^{2}=0.81\), \(p<0.01\)) on the school level shows that cultural capital explains 81% of the variation in reading proficiency scores on the school level.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Variables** & **1** & **2** & **3** & **4** & **5** \\ \hline
1. Internet safety & 1 & & & & \\
2. Reading proficiency & 0.92 ** & 1 & & & \\
3. Cultural capital & 0.90 ** & 0.90 ** & 1 & & \\
4. Language at home & 0.84 ** & 0.88 ** & 0.93 ** & 1 & \\
5. Academic aspirations & 0.73 ** & 0.80 ** & 0.79 ** & 0.67 ** & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bivariate Correlation between Internet Safety, Reading Proficiency, Cultural capital, Language at Home and Academic Aspirations (\(n=23\) upper secondary schools).  
The results presented in Table 5 also show that Internet safety on the school level has a strong positive correlation with cultural capital (\(r=0.9\), \(p<0.01\)), language integration (\(r=0.84\), \(p<0.01\)), academic aspirations (\(r=0.73\), \(p<0.01\)) and reading proficiency (\(r=0.92\), \(p<0.01\)).
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 5 Performance Evaluation  
This section aims to validate the work proposal by discussing use cases that benefit from MEC, detailing the validation environment, and presenting the results. These results will be compared with the characteristics of applications that benefit from MEC to discuss the solution's viability.  
The proposed work will evaluate the service results using Key Performance Indicators (KPIs) described in the literature. The evaluation scenario will include e-health and IoT use cases. For the health area, scenarios such as remote surgery, remote consultation, paramedic support, critical health events, vital signs, and location tags will be observed, using the minimum KPIs required for each application, based on previous studies [39, 40]. For IoT scenarios, characteristics of smart homes, wearable IoT (WIoT), and farms will be analyzed using the information described in [41]. The KPIs considered for comparison will be mainly related to latency, although other information can be used if necessary. The KPIs are presented in Table 2.
# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 3 Development of Land-Used LNG Loading and Unloading Arms
### Overall Structure of the Land-Used Loading Arm
#### 3.1.2 Cryogenic Breakaway Valve
In addition to this, sealing is also a crucial aspect to be considered in the future development of LNG breakaway valves, that is, ensuring effective sealing of the two separated parts after disconnection. For example, [PERSON] et al. [60] developed a unique soft seal to replace the metal seal, reducing the leakage of the shut-off valve, selecting low-temperature-resistant stainless-steel material and subjecting it to cryogenic treatment to be used as the valve housing, using low-temperature-resistant polytetrafluoroethylene as the sealing material [61], lining it with an elastic stainless-steel skeleton, and combining it with an appropriate structural design to ensure no LNG leakage.
## 2 Problem Description and System Models
### Constrained Markov Decision Process
#### 2.4.4 Reward Function  
The reward function is designed to reflect the optimization objectives, including the voltage deviation from reference values, active power losses, and the cost of device regulation. The objective of the agent's action is to maintain the voltage within a secure range around the reference level, while ensuring that inverters and capacitor banks provide appropriate reactive power for voltage regulation. The reward function can be defined as follows:  
\[R=\mathbb{E}\left[\sum_{t=0}^{T}\gamma^{t}r^{t}\right] \tag{5}\]  
\[r=-\min\sum_{t=1}^{T}\left(\frac{1}{N}\sum_{i}^{N}\lvert v_{i,t}-v_{\text{ref} }\rvert+\sum_{(i,j)\in\Omega}r_{ij}l_{ij}^{2}\,+\sum_{a}c_{a}1(d_{t}^{a})\right) \tag{6}\]  
where \(\gamma\) is the discount factor at time \(t\), which determines the importance assigned to future rewards. \(r_{t}\) is the value of the reward function at time \(t\).
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.4 Homomorphic Encryption  
Homomorphic Encryption allows data to be processed while remaining encrypted, enabling privacy-preserving computations. This technique is particularly useful in off-chain solutions for industries like healthcare and IoT, where sensitive data must be analyzed without decryption [110; 125; 120]. By maintaining data confidentiality, it allows external systems to perform secure computations [116]. However, its high computational complexity makes it resource intensive [135]. Homomorphic Encryption is primarily suited for off-chain data processing and can be integrated with blockchain-based applications [121; 141]. In hybrid models, encrypted data are processed off-chain, while only computation results or cryptographic references are stored on-chain [142]. A practical example includes IoT-generated data being processed securely off-chain, with summary results stored on the blockchain [136].
# Differential Signalling in Free-Space Optical Communication Systems
## Appendix A
### BER Expression in Weak Turbulence with PE  
The instantaneous BER of the considered scheme is given by Label (A13). The PDF of \(h_{i}=h_{a}h_{p}\) for weak turbulence with boresight PE is evaluated as  
\[f_{h_{i}}(h_{i})=\int_{\frac{h_{i}}{\pi_{0}}}^{\infty}f_{h_{p}}\left(\frac{h_ {i}}{h_{a}}\right)f_{h_{a}}(h_{a})dh_{a}, \tag{111}\]  
where \(f_{h_{a}}(x)\) is given in Label (15) and  
\[f_{h_{p}}(x)=\ \frac{\gamma^{2}}{A_{0}^{\gamma^{2}}}x^{\gamma^{2}-1}\,\ 0\leq x \leq A_{0}. \tag{112}\]  
In addition,  
\[E_{h_{i}}\bigg{[}Q\bigg{(}\frac{\eta h_{i}}{\sqrt{2}\sigma_{n}}\bigg{)}\bigg{]} =\int_{0}^{\infty}E_{h_{i}}\bigg{[}Q\bigg{(}\frac{\eta h_{i}}{\sqrt{2}\sigma_{ n}}\bigg{)}\bigg{]}f_{h_{i}}(h_{i})dh_{i}. \tag{113}\]  
Since there is no closed-form available of the integral Label (A23), we evaluated this integral using MATLAB (R2016b, US). On substituting the result of Label (A23) into Label (A13), the average BER of the considered system can be obtained.
## 5 Application of MSWOA in Fault Diagnosis of Analog Circuits
### Fault Diagnosis of Sallen-Key Bandpass Filter Circuit
#### 5.1.3 Experimental Results and Analyses  
In this subsection, MSWOA was compared with two other SI algorithms (PSO and WOA) by optimizing the SVM parameters for fault diagnosis. These three algorithms adopted the same initialization parameters in the experiments: The population size and the maximum number of iterations were set to 20 and 100, respectively. For a fair comparison, each of the three algorithms was executed 20 times independently to eliminate the contingency of one experimental result. The statistical results are demonstrated in Figure 17, where the B.V., W.V., and A.V. mean the best value, the worst value, and the average value of the 20 experimental results, respectively.  
As we can see from Figure 17, due to the strong searchability of MSWOA, the optimal value of the diagnosis result of SVM optimized by MSWOA was the greatest. Regarding the worst value, the result obtained by the MSWOA-SVM method was increased about 1.94% and 0.83% compared to that of PSO-SVM and WOA-SVM. And when it comes to the average value, the result of MSWOA-SVM was 1.07% and 0.64% higher than that of PSO-SVM and WOA-SVM, which indicates that the MSWOA algorithm improved its capacity of escaping the local optimal solution. In addition, Figure A2 presents the box plots, which include the median value, the upper and lower edges, and the 3/4 and 1/4 values in 20 results. It can be observed that the whole distribution of MSWOA was more concentrated than its competitors, which means MSWOA has stronger stability and robustness.  
Figure 18 shows the diagnostic accuracy curves of SVM optimized by PSO, WOA, and MSWOA, respectively. In each of those three figures, the mean fitness curve and the best fitness curve were  
Figure 16: Normalized fault features.  
Figure 17: Diagnosis results of the Sallen–Key band pass filter circuit.  
obtained by the whole population in one optimization process (100 iterations). From those figures, we can conclude that the SVM optimized by the proposed MSWOA had the best performances in accuracy and speed.
## 2 Materials and Methods  
### Datasets  
This study uses the Chinese butterfly image dataset [22] and the Leeds Butterfly Dataset [24]. Table 1 lists the scientific names of 30 species of butterflies and number of images per butterfly species. The Chinese butterfly dataset includes specimen images of butterflies (indoor photos) and images of butterflies in the ecological environment (outdoor photos/natural images). Ecological images of butterflies come from field photography and donations from butterfly lovers. The sizes of many of the images reaches \(5184\times 3456\) and the butterfly is not segmented from the background. The mimicry of butterflies also brings great challenges to the detection and recognition of butterflies in ecological images. Therefore, this paper only studies the problem of butterfly classification in ecological images. This study uses 20 species of butterfly images from the Chinese butterfly dataset. There are 10 images in the smallest category and 61 images in the largest category, for a total of 467 images. Figure 1 shows sample images of some Chinese butterfly species. As you can see from the image, the posture of the butterfly and the background of the image are very different. Some butterflies are similar in color to the background. These all pose challenges for classification.  
The Leeds Butterfly Dataset contains 10 categories of butterfly images. There are 55 images in the smallest category and 100 images in the largest category, for a total of 832 images, as shown in Figure 2 and Table 1.  
In this experiment, a total of 1299 butterfly images were used and the size of the images was adjusted to \(224\times 224\times 3\). For each category, we randomly split the dataset using ratios of approximately 60%, 21% and 19% for training, validation, and testing. There are 768 training samples, 280 validation samples, and 251 testing samples, respectively. The data distribution is listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Species** & **Quantity** & \begin{tabular}{c} **Train** \\ **Dataset** \\ \end{tabular} & \
\begin{tabular}{c} **Val** \\ **Dataset** \\ \end{tabular} &
\begin{tabular}{c} **Test** \\ **Dataset** \\ \end{tabular} \\ \hline Speyeria aglaja & 61 & 36 & 13 & 12 \\ Achillidesbianor Cramer & 43 & 25 & 10 & 8 \\ Limenitis helmanni & 35 & 21 & 7 & 7 \\ Aphantopus hyperanthus & 35 & 21 & 7 & 7 \\ Elymmias hypermensetra & 28 & 16 & 7 & 5 \\ Suatsus gremins & 26 & 15 & 6 & 5 \\ Celastrina oreas & 25 & 15 & 5 & 5 \\ Papilio paris & 23 & 13 & 6 & 4 \\ Papilio xuthus & 23 & 13 & 6 & 4 \\ China Butterfly & Eurema hecabe & 22 & 13 & 5 & 4 \\ Dataset & Papilio alcmenor Felder & 21 & 12 & 5 & 4 \\ Iambrix salsala & 20 & 12 & 4 & 4 \\ Polygonia c-aureum & 20 & 12 & 4 & 4 \\ Pyygusmacultatus [PERSON] & 18 & 10 & 5 & 3 \\ Abisara echerius & 13 & 7 & 4 & 2 \\ [PERSON] & 13 & 7 & 4 & 2 \\ Thecha betulae & 11 & 6 & 3 & 2 \\ Albulina orbitula & 10 & 6 & 2 & 2 \\ Menelaides protenor & 10 & 6 & 2 & 2 \\ Euploea midamus & 10 & 6 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The scientific names of butterfly species and distribution of dataset.
# Factorial Design Analysis for Localization Algorithms
## 4 Results
### Impact of the Path-Loss Exponent  
Figure 2 shows the performance graphs of the MSE as the path-loss exponent \(\eta\) varies. Figure 2a shows that the Hyperbolic and Multilateration range-based algorithms show greater MSE variation than the range-free algorithms, considering a noise level \((\sigma_{dB}=2\text{ dB})\), a node density \((\rho=1)\) and 5 RNs. Figure 2b shows an increase in the node density \((\rho=5)\) and the same behavior is seen in Figure 2a. Consequently, the path-loss exponent factor shows greater impact on the range-based algorithms than on the CL and REWL range-free algorithms. According to the log-normal shadowing model, the greater the path-loss exponent, the less the RSS value is affected by the Gaussian random variable \((\chi_{\sigma})\), meaning that this factor has less impact on the RSS between the NOI and the RNs than on the distance separating the NOI and the RNs, which is observed using Equation (20), i.e., the error of the actual and estimated distance between the NOI and the RNs is greater than the error from the actual RSS to the estimated RSS between the NOI and the RNs. Therefore, greater localization error variation can be seen in the range-based algorithms than in the range-free algorithms, as shown in Figure 2.  
\[\text{RSS}_{dB}=A-10\eta\log(d)-\chi_{\sigma} \tag{20}\]  
where \(\text{RSS}_{dB}\) is the power received in dB, \(A\) is the average power received at a reference distance \(d_{0}\) and \(\chi_{\sigma}\) is the Gaussian random variable with zero mean and standard deviation \(\sigma\) in dB.
###### Abstract
Firstly, incorporating an excessive variety of data can impede the model's ability to discern meaningful signals. Redundant data introduces additional noise, thereby diminishing the model's accuracy. Secondly, as the dimensionality of the data increases, the number of required data points grows exponentially, which heightens the risk of overfitting and reduces the model's generalizability. Furthermore, the increased computational burden renders the model impractical for deployment in local healthcare settings due to the associated high computational costs. These issues present significant barriers to the integration of artificial intelligence technologies into regional medical practice. Consequently, research on simplified data feature extraction and selection methods, based on filtering techniques commonly employed in EMG signal processing, is of critical importance as it holds the potential to alleviate these barriers and enhance clinical applicability. Numerous traditional predictive studies have reported lifestyle-based stroke risk factors [27; 28; 29; 30]. More recently, research has focused on predicting abnormal movements in stroke patients using EMG and machine learning (ML) [31; 32; 33]. However, ML-based stroke modeling studies that use the activation levels of multiple muscles as biomarkers remain limited [34]. Predictive modeling using muscle activity as training data could provide valuable insights into data features suitable for the rapid assessment of the neurophysiological state in patients at the early or pre-stroke stages. Therefore, this study aims to examine the impact of the statistical features of muscle activity of the major leg muscles during gait as predictive factors across various models to differentiate between stroke patients and healthy individuals.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 5 Results and Comparative Analysis
### Comparative Analysis of Routing Protocols with Heterogeneous Nodes  
This analysis highlights the importance of fixed nodes in small-scale deployments and their potential to optimize network efficiency in more complex configurations. Table 15 below represents the performance metrics across all the different heterogeneous scenarios.  
The outcomes of adopting various scenarios of heterogeneous nodes are displayed in Figures 13-16. Analyzing these situations helps identify bottlenecks, comprehend the scaling structure, and choose and optimize protocols with knowledge. Heterogeneous communication systems can be developed and enhanced through research.  
This comprehensive performance evaluation of eight MANET routing protocols using OMNeT++ 6.0.1 with INET-4.5.0 provides valuable insights into the strengths and weaknesses of each protocol type across different network conditions. The analysis highlights that:  
* Proactive Protocols (OLSR, DSDV): These protocols perform efficiently in smaller, stable networks by maintaining low delay, jitter, and energy consumption due to their constant route updates. However, they become less efficient in larger, dynamic networks because of the increased overhead of maintaining routing tables.
* Reactive Protocols (AODV, DSR, AOMDV, and TORA): Characterized by higher delay and jitter due to on-demand route discoveries, reactive protocols demonstrate effective scalability in moderately dense networks. They are suitable for networks with moderate to high node densities, where dynamic route management reduces overhead.
* Hybrid Protocols (ZRP, GRP): Combining the strengths of proactive and reactive approaches, hybrid protocols offer balanced performance across all metrics. They are particularly effective in highly dense network environments, maintaining high throughput, moderate delay, and energy efficiency while ensuring low packet loss rates and high packet delivery ratios.  
The evaluation of heterogeneous scenarios, with varying combinations of mobile and fixed nodes, underscores the adaptability of each protocol type. Proactive protocols are most suitable for small-scale deployments, reactive protocols for moderately dense networks, and hybrid protocols for large-scale, heterogeneous networks. This nuanced understanding of protocol performance across different network conditions is critical for optimizing the design and deployment of MANETs in real-world applications, enhancing overall efficiency and reliability. These findings provide a comprehensive guide for selecting the optimal routing protocol based on specific network requirements, ensuring efficient and reliable network performance under diverse conditions.
## 2 Materials and Methods
### Chameleon Method Routing Protocol (CM-Routing)  
CM-Routing combines the optimization mechanism described with the switching mechanism for two routing protocols (AODV and GPSR), as shown in Figure 4. The optimization mechanism evaluates the optimum configurations for a network scenario, and stores them as routing profiles in the RSU. The switching mechanism is added to the OBU for adopting the best routing profile based on the current attached network. In CM-Routing, the RSU is responsible to find the best configuration profile and store it; OBU selects the nearest RSU as its parent RSU. The nearest RSU is chosen based on distances in GPSR and numbers of hops in AODV. Moreover, OBUs obtain the optimized configurations profile from their parent by sending a profile request message. The RSU responds to the profile request message with the freshest profile stored.