esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Analysis of Water Stability  
An immersion Marshall test and a freeze-thaw splitting test of different asphalt mixtures were carried out. They were conducted with reference to the \"Highway Engineering Asphalt and Asphalt Mixture Test Procedures\" (JTG E20-2011). The ratio of residual strength (TSR) and residual stability is shown in Figure 10. The freeze-thaw split was carried out 34 times, and the results are shown in Figure 11.  
Figure 9: Flexural strain energy density for different mixture types.  
It can be seen in Figure 10 that the residual stability of the eight gradation mixtures was greater than 85% in the immersion Marshall test, which satisfied the specification requirements. However, the residual stability and substitution ratio did not show obvious trends. By comparing Figures 10 and 11, it was found that for the same type of mixture, the failure load (after 50 compactions) and the damage load after freezing and thawing cycles were greater than 34 times those of the compaction test under the same conditions. According to the experimental data, the unfrozen and wrecked load of 34 compacted specimens was about 80% of the failure load of 50 compacted specimens under the same conditions. This was because the specimen compactness was better than 34 in 50 compaction cases.  
The second case was real, so the former had a higher ability to withstand damage. This indicated that the compactness of the test piece had a great influence on the water stability effect. Compared to virgin asphalt, the addition of a rubber reticle in the test piece could significantly improve the water stability. China's regulations stipulate that the TSR value of the mixture should be greater than 80%, while the TSR of the matrix asphalt was 82% under the standard number of compactions, and the performance increased to 90% after the rubber powder was added. At 34 compactions, the mixture  
Figure 11: Freeze–thaw split test results (done 34 times).  
Figure 10: Water stability test results.  
TSR was 77%. After adding CRT, it basically reached 85% or higher. Thus, it could be found that CRT could significantly improve the water stability of the mixture.
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 6 Performance Evaluation  
In this section, we evaluate the effectiveness and the efficiency of the proposed defense system against adversarial attacks in SV systems. Specifically, we first describe the experimental setup. We then measure the impact of the defense methods on the normal operations of SV systems and against FakeBob attacks. Next, we evaluate the performance of adaptive FakeBob attacks against the noise-adding defense method. By default, the noise-adding method applies Gaussian noise. Finally, we measure the effect of different types of noises for the noise-adding defense method.
## 4 Experiments and Results
### EEG-TCNTransformer with Different Hyperparameters
Table 21 is a comparison table of F1 score between baseline EEG-TCNet, baseline EEG-Conformer, and EEG-TCNTransformer. EEG-TCNTransformer improves the F1 score of subject 2 by about 10.23% and 6.67% compared with EEG-Conformer and EEG-TCNet, respectively. Moreover, EEG-TCNTransformer improves the F1 score of subject 1, subject 4, subject 5, subject 6, and subject 9 from 3.2% up to 4.44%. The average F1 score of EEG-TCNTransformer is 83.25%, which is higher by 3.63% than the average F1 score of EEG-Conformer and 7.23% than the average F1 score of EEG-TCNet. Besides that, Figure 13 provides the confusion matrices of 9 subjects of EEG-TCNTransformer below. Models of subject 3, subject 4, subject 5, subject 7, subject 8, and subject 9 perform well in classifying 4 classes: left hand, right hand, foot, and tongue. Subject 1 has an incorrect classification of about 9 to 10 samples in the foot and tongue classes. Subject 2 has some incorrect classifications between the tongue and other classes and between the foot and other classes. Subject 6 also has some incorrect classifications in the left hand and other classes and between the right hand and other classes. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EEG-TCNet** & **EEG-Conformer** & **EEG-TCNTransformer** \\ \hline
**S1** & 82.94 & 84.69 & **87.84** \\
**S2** & 61.31 & 58.66 & **71.28** \\
**S3** & 91.55 & 94.54 & **94.78** \\
**S4** & 64.30 & 78.14 & **82.46** \\
**S5** & 76.53 & 78.88 & **81.95** \\
**S6** & 59.74 & 63.98 & **70.47** \\
**S7** & **91.44** & 88.27 & 89.90 \\
**S8** & 81.98 & **89.51** & 88.64 \\
**S9** & 79.23 & 83.81 & **89.09** \\
**Average** & 76.56 & 80.16 & **84.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Comparisons in the recall of baseline EEG-TCNet, baseline EEG-Conformer, and EEG-TCNTransformer. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EEG-TCNet** & **EEG-Conformer** & **EEG-TCNTransformer** \\ \hline
**S1** & 84.60 & 83.92 & **87.90** \\
**S2** & 66.57 & 58.00 & **69.16** \\
**S3** & 91.98 & 94.47 & **94.50** \\
**S4** & 69.95 & 78.03 & **81.75** \\
**S5** & 79.21 & 78.67 & **81.88** \\
**S6** & 64.36 & 63.74 & **68.33** \\
**S7** & **91.97** & 88.90 & 89.65 \\
**S8** & 82.36 & 88.57 & **88.64** \\
**S9** & 80.62 & 82.60 & **89.02** \\
**Average** & 79.07 & 79.66 & **83.43** \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Comparisons in the precision of baseline EEG-TCNet, baseline EEG-Conformer, and EEG-TCNTransformer.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Sequential Estimation
#### 4.3.2 Results for the First Estimation Using AOA  
The results of the first estimation using AOA are shown in Figure 29. Among the three scenarios, the best localization accuracy was achieved with AOA \(\rightarrow\) HYBRID. In all three scenarios, there was no significant difference in the median localization error. This indicates that for nearly half of the sources, the choice of sequential estimation method had little impact on localization accuracy.  
However, the HYBRID approach resulted in a greater number of sources with high localization accuracy. This suggests that the inclusion of RSSI in the HYBRID approach allowed for the incorporation of distance-related features, which slightly enhanced localization performance. The benefit is particularly evident when the original AOA estimation does not fully capture positional nuances. Interestingly, around the CDF 90% region, there was no substantial difference between AOA \(\rightarrow\) RSSI and AOA \(\rightarrow\) HYBRID. This implies that adding RSSI in the second step has limited impact on correcting large localization errors, likely due to its high sensitivity to noise. This observation aligns with [PERSON] et al. (2019), who reported that adding distance-based metrics does not always yield further improvements when directional estimation is already stable.  
Furthermore, RSSI as a secondary update was effective in removing prominent outliers in several cases. [PERSON] and [PERSON] (2018) discussed similar effects in their indoor  
Figure 28: CDF comparison of second-stage sequential estimations after initial RSSI-based localization. Using AOA or HYBRID in the second stage significantly reduces large errors.  
Figure 29: CDF comparison of sequential estimation outcomes starting with AOA. The AOA\(\rightarrow\)HYBRID combination yields the best overall performance.  
positioning study, noting that appropriate weighting of RSSI and direction can suppress extreme deviations even under multipath conditions. Figure 29 illustrates that while the hybrid approach has potential in refining moderate errors, its impact on large-error sources appears limited when AOA is already used initially.
# Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
## Methods
### Regression Analysis  
We performed a regression analysis [24] to search for the physiological factors that determine individual differences in the LSCP model parameters, exploring the predictive power of several physiological factors of interest: Gender (male, female), Age (years), Weight (kg), BMI (kg/m\({}^{2}\)), State and Trait scores from the Spielberg State-Trait Anxiety Inventory (STAI) [25] and score in the Shirom-Melamed Burnout Questionnaire (SMBQ) [26; 27; 28]. Previous studies have validated the Swedish version of the STAI [29; 30; 31] and the SMBQ [32; 33]. It is interesting considering both STAI and SMBQ measures since burnout consists of a combination of physical fatigue, emotional exhaustion, and cognitive weariness, not interchangeable with depression and anxiety [33; 34].  
The numerical variables were also modelled as categorical. We describe those that became relevant in the analysis. The categorical variable Age Groups divides the TPs in three categories of age: 20-30, 30-40, and over 40 years old. The categorical variable Stress, based on the value of SMBQ, divides the TPs into the following three groups: Low-Stressed (SMBQ \(<1\)); In-Between (SMBQ \(\in[2.75,3.75)\)); pre-stage Exhaustion Disorder (Pre-ED, SMBQ \(>3.75\)). The variable Fit, based on BMI, has the following standard levels: Underweight (BMI \(<18.5\)); Normal (BMI \(\in[18.5,25)\)); Overweight (BMI \(\in[25,30)\)); Obese (BMI \(>=30\)).  
Simple regression (i.e., regression models with a single explanatory variable) was tested first to isolate the effect of every factor, and then multivariate models were evaluated based on the statistical significance of the predictors (significance level of 0.1), the adjusted coefficient of determination \(R_{\text{adj}}^{2}\), and the Akaike Information Criterion (AIC). Regression diagnostics included residual analysis, F-test for testing inclusion of variables, detection and treatment of outliers and influential observations.
## 2 Methodology, System Description, and Problem Formulation
### System Description  
As shown in Figure 2, the proposed 360\({}^{\circ}\) VR video service system consists of a cloud server, an MEC server, and multiple video users accessing the service. The cloud server provides functions such as video storage, tile encoding and selection, saliency map generation, and FoV prediction. To fully utilize the computational power of the MEC server, it is responsible for decoding, rendering, and re-encoding the proactively cached data packets that are hit by the users, and then sending them to the users. In addition to receiving data from the MEC server, each user may also need the cloud server to provide data packets that were not hit by the MEC server. For these data packets, the user needs to decode and render them independently, and ultimately combine the data obtained from both the MEC and cloud servers to construct a displayable image.  
Figure 1: An illustration of the methodology of this work.
## 2 Materials and Methods
### Point Cloud Data Collection and Generation
#### 2.2.3 UAV Image-Based Point Cloud Generation  
In addition to directly acquiring point clouds using the LiDAR system equipped on the UAV, we also reconstructed a 3D point cloud of the same area using images obtained from the high-resolution camera mounted on the UAV. Specifically, we used the DJI Matrice 350 RTK drone fitted with a SHAR 100M PRO optical camera (made by SHARE, Shenzhen, China) (Figure 2f). This RGB camera boasts a resolution of 11,648 \(\times\) 8736 pixels and a 70 nm focal length. To obtain clear RGB images of the maize plants, we employed a five-direction flying method that included a nadir and four oblique photos. The flight altitude for capturing both nadir and oblique images was set at 25 m (Figure 2g). For nadir images, the longitudinal and lateral overlap was set to 80% and 70%, respectively, achieving a ground sampling distance (GSD) of 0.13 mm per pixel. The gimbal angle was set at 45\({}^{\circ}\), with both longitudinal and lateral overlap at 70%, and a GSD of 0.19 mm per pixel.  
Additionally, Metashape (version 2.10) software was utilized to generate 3D point clouds from these 2D images and to export these point clouds for analysis of maize plants. To assess the potential suitability of UAV-derived point clouds for segmentation tasks, the point clouds were denoised using CloudCompare (version 2.13.1) software. Semantic annotation was then manually performed on the structurally intact maize point clouds at the organ level.
## 3 Molecular Mechanisms of Omega-3 Fatty Acids in Cancer
### Cell Cycle and Cell Death in Cancer Mediated by n-3 PUFA  
The regulation of the cell cycle involves intricate mechanisms governed by checkpoints and a diverse array of CDK complexes. Transcriptional modulation of these complexes occurs in accordance with the specific phase of the cell cycle and may be upregulated or downregulated. Additionally, there are regulatory proteins known as CDKIs, which are classified into two main families; the CDK interacting protein/Kinase inhibitory protein family, which includes p21, p27 and p57, targeting cyclin-CDK1 and -CDK2 complexes, and the inhibitors of CDK4 and CDK6 family, known as the INK4 family, comprising p15, p16, p18 and p19, which interact primarily with cyclin-CDK4 and -CDK6 complexes [51]. This cell cycle machinery is often dysregulated in cancer, which can lead to uncontrolled growth [52].  
[PERSON] et al. studied the effects of n-3 PUFAs in transgenic embryonic mouse stem cells exhibiting a prolonged G0/G1 phase with an upregulation of p21 and a consequent downregulation of CDK4; this was demonstrated once the inhibitory effect in CDK4 was reduced in a p21 inhibitor pretreatment group [53]. Similar results were found in a neuroblastoma cell line study that showed that DHA and EPA had growth-inhibitory effects, producing a prolonged G0/G1 phase accompanied by a decrease in CKD2 and cyclin E and activation of the intrinsic apoptosis pathway as well as almost no toxicity in non-tumorigenic cells both in murine rat and human cells [54]. [PERSON] et al. reported antiproliferative effects of DHA that induced a G1 phase cell cycle arrest and cellular senescence in three human gastric cancer cell lines with upregulation of p21 and p27, and downregulation of proliferating cell nuclear antigen, cyclin E and cyclin D1 in a dose-dependent manner [55] (Figure 4).
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 4 Performance Evaluation and Results
### Simulation Results
#### 4.3.3 Dynamic Multipath Scenario  
To further verify the ranging performance of the PAOC technique in the dynamic multi-path scenario, a slow-varying multipath signal is simulated. Figure 12 illustrates the variation of this multipath signal.  
Figure 13 shows the ranging errors of the four methods for the BOC(15, 2.5) signal in the dynamic multipath scenario. The carrier and subcarrier ranging errors of the PAOC-PAOC method are always smaller than those of the other three methods. Table 4 shows the ranging errors of the four methods in the dynamic multipath scenario. Both the RMSE and maximum ranging error of the PAOC-PAOC method are much smaller than those of the other three methods. In summary, the PAOC-PAOC method can effectively reduce the carrier and subcarrier ranging errors caused by the low-dynamic multipath.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Subcarrier** & **Subcarrier** & **Carrier** & **Carrier** \\  & **RMSE** & **Maximum Error** & **RMSE** & **Maximum Error** \\ \hline DBT & 0.49 m & 1.01 m & 9.65\({}^{\circ}\) & 23.06\({}^{\circ}\) \\ OC-P & 0.29 m & 0.77 m & 9.63\({}^{\circ}\) & 23.98\({}^{\circ}\) \\ OC-OC & 0.28 m & 0.76 m & 6.70\({}^{\circ}\) & 16.97\({}^{\circ}\) \\ PAOC-PAOC & 0.11 m & 0.41 m & 3.92\({}^{\circ}\) & 14.47\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ranging errors of the four methods for the BOC(15, 2.5) signal in the dynamic multipath scenario.  
Figure 12: Variations of the multipath relative delay.  
Figure 13: Ranging errors of various methods for the BOC(15, 2.5) signal in the dynamic multipath scenario: (**a**) subcarrier ranging error; (**b**) carrier ranging error.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 1 Introduction
### Mobility Management Framework in 5G NR  
In the next-generation-Node-B (gNB) of multi-beam architecture, there is a significantly increased number of measurements for network management including handover or beam switching. Multiple-beam architecture leads to independently small coverage with respect to each beam. Therefore, supporting seamless user-mobility based on multiple-beam operation scenarios is an important issue in massive MIMO networks [6; 7]. At the initial stage of 5G technologies, the research was focused to improve handover performance [8; 9; 10; 11; 12; 13; 14]. In the multi-beams architecture, mobility management has been defined based on \"cell-level mobility\" and \"beam-level mobility\" after release 15 [15]. Cell-level mobility is the handover process where the user establishes a new connection to a new cell for better quality-of-service (QoS). Beam-level mobility is the procedure in which the user establishes a new data-path to a new beam within the same cell coverage.
## 2 Related Work
### SEAPORT Method  
Manual portability assessment is inefficient, error-prone, and requires significant technical expertise in the domains of serverless and cloud computing. To simplify this process, a work from the University of Stuttgart [35] specifies a method called SEAPORT (_SEreverless Applications PORtability assessmentT_) that automatically evaluates the portability of serverless orchestration tools with respect to a chosen target provider or platform. The method can be optimized over time by testing more and more heterogeneous use cases.  
The SEAPORT method introduces a _CAnonical SEreverless_ (CASE) model, which is the result of transforming the obtained deployment model from a certain serverless orchestration tool into a provider-agnostic format. Yet, we find the represented CASE model far too abstract for our needs, making it non-reusable. For instance, the model does not detail the various composing elements of a function definition, it only specifies the function's programming language and the event category that triggers its execution. Furthermore, it does not illustrate the abstraction of different types of authentication mechanisms used by cloud orchestration tools in order to obtain access to resources from different providers. Lastly, the model represents some extra entities that are only convenient to facilitate the portability evaluation of a serverless application, making them unrelated to this work.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 6 Conclusions and Future Work  
In this work, we proposed an anomaly detection method based on dynamic graph embedding, and designed the AWMC system. In the anomaly detection method, we first constructed the dynamic graph by finding the relationship among multiple time series, then calculated the graph entropy, and lastly constructed a graph embedding model on the basis of graph entropy for anomaly detection. We discussed the AWMC system's design, implementation, functionalities, an illustrative example, and user interface. The system can help people in understanding abnormal weather and its causes by visualizing changes in weather anomalies and corresponding weather data. In addition, the system visualizes the weather anomalies of each city on a map to compare anomalies for each city. We applied Korean weather data to the system. The result shows that the proposed method reached average precision of approximately 90.9%, recall of 93.2%, and F1 score of 92.1% for all the cities. Lastly, the AWMC system was implemented via the AWMC website ([[http://awmc.caucse.club](http://awmc.caucse.club)]([http://awmc.caucse.club](http://awmc.caucse.club)), accessed on 2 October 2022).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** \\ \hline Seoul & 0.904 & 0.928 & 0.916 \\ Incheon & **0.918** & **0.938** & **0.928** \\ Busan & 0.901 & 0.924 & 0.913 \\ Daejeon & 0.908 & 0.935 & 0.922 \\ Daegu & 0.915 & 0.937 & 0.926 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results.  
However, due to limitations, we could not detect anomalies in the weather data in real time. Therefore, to improve this system, we aim to find ways that would allow for this system to be able detect anomalies in weather data in real time. In addition, to understand global weather changes, we will apply more countries.  
Conceptualization, Y.G., J.G., G.L. and J.J.J.; methodology, G.L. and J.J.J.; software, Y.G., J.G. and G.L.; validation, Y.G., J.G. and G.L.; formal analysis, J.J.J. and D.C.; investigation, Y.G and H.Y.; resources, J.J.J., S.A. and D.C.; data curation, [PERSON], J.G. and G.L.; writing--original draft preparation, Y.G.; writing--review and editing, J.J.J., S.A. and D.C.; visualization, [PERSON], J.G. and G.L.; supervision, J.J.J.; project administration, J.J.J. and D.C.; funding acquisition, J.J.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Chung-Ang University Research Grants in 2021. This work was also supported in part by Oracle Cloud credits and related resources provided by the Oracle for Research program.  
Not applicable.  
This research was supported by Chung-Ang University Research Grants in 2021. This work was also supported in part by Oracle Cloud credits and related resources provided by the Oracle for Research program.  
The authors declare no conflict of interest.
###### Abstract  
Left ventricular ejection time (LVET) is an important parameter for assessing cardiovascular disorders. In a medical office, it is typically measured using the Tissue Doppler Imaging technique, but new wearable devices have led to a growing interest in integrating this parameter into them, increasing accessibility to personalized healthcare for users and patients. In the cardiovascular domain, photoplethysmography (PPG) is a promising technology that shares two distinctive features with invasive arterial blood pressure (ABP) tracing: the tidal wave (TDW) and the dicrotic wave (DCW). In the early years of cardiovascular research, the duration of the dicrotic point was initially linked to the ending phase of left ventricular ejection. Subsequent studies reported deviations from the initial association, suggesting that the ejection period is related to the tidal wave feature. In this current study, we measured left ventricular ejection time in both ABP and PPG waveforms, considering recent research results. A total of 27,000 cardiac cycles were analyzed for both afore-mentioned signals. The reference value for ejection time was computed based on the T-wave segment duration from the electrocardiogram waveform. In lower blood pressure, which is associated with decreased heart contractility, the results indicated an underestimation of \(-29\pm 19\) ms in ABP and an overestimation of \(18\pm 31\) ms in PPG. On the other side of the spectrum, during increased contractility, the minimum errors were \(-3\pm 18\) ms and \(4\pm 33\) ms, respectively. Since the tidal wave feature is strongly affected by arterial tree compliance, the population evaluation results indicate a Pearson's correlation factor of 0.58 in the ABP case, and 0.53 in PPG. These findings highlight the need for advanced compensation techniques, in particular for PPG assessment, to achieve clinical-grade accuracy.  
Keywords:tidal wave; dicrotic wave; left ventricular ejection time; photoplethysmography; blood pressure; electrocardiogram; signal database +
Footnote †: journal: Applied Sciences