esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of E-Nose Response during BBP-Meju Fermentation with Different Salt Concentrations  
The E-nose can acquire the flavor characteristics of the sample to be tested by simulating the human olfactory system. Thus, it can avoid the subjectivity of sensory evaluation and is widely used in flavor identification [46]. In this study, an E-nose was used to analyze the flavor characteristics of BBP-meju during fermentation with different salt concentrations. Subtle differences in flavor compounds were captured by differences in sensor response values. Principal component analysis (PCA) was first conducted on the E-nose data, and the results are shown in Figure 4. The results indicated that PC1 and PC2 contributed 10.3% and 81.8% of the variance, respectively, explaining 92.1% of the total variance. Therefore, these two principal components were sufficient to reflect the flavor response information of different sensors for different samples [47]. According to the PCA score plot, samples L35, M35, and H35 were distributed in the first, fourth, and third quadrants, respectively. However, all three samples on day 7 were distributed in the second quadrant (Figure 4a). On day 7, the flavor characteristics of the three samples with different salt concentrations were similar. As the fermentation progressed, the flavor differences among meju samples with different salt concentrations became more pronounced.  
The flavor profiles of the different meju samples were further elucidated more intuitively and clearly by radar charts. Overall, the radar chart profiles of all meju samples were relatively similar. However, there were differences in the response of each sensor for different samples (Figure 4b). Sensors W1C, W3C, W6S, W5C, W1W, and W3S showed weak responses to all the samples, while sensors W5S, W1S, W2S, and W2W had strong responses. As fermentation progressed, sensors W5S, W1S, W2S, and W2W significantly enhanced in response to meju samples, and their responses decreased with increasing salt concentration. The results indicated that the various flavor compounds in meju continuously accumulated during fermentation, ultimately forming the rich aroma of BBP-meju. Additionally, salt-reduced fermentation could further increase the level of the flavor compounds, enhancing the characteristic flavor of BBP-meju.
###### Abstract  
The quick access recorder (QAR), as an airborne device used to monitor and record flight parameters, has been widely installed on various types of aircraft. Based on QAR data, research on runway overrun, a typical flight safety incident, has attracted widespread attention in recent years. However, existing runway overrun risk models generally suffer from oversimplified risk metrics or insufficient consideration of risk dynamics. In this paper, we propose a new dynamic runway overrun risk assessment model based on QAR data. We first consider the noise of aircraft trajectory data in the QAR parameters and present a landing trajectory correction method combining ground speed and runway position information. Second, to improve the accuracy of the risk assessment model, we design an algorithm to automatically recognize the aircraft autobrake level during the landing phase, based on which a new dynamic risk assessment model is developed. Finally, feature engineering is performed to extract the relevant contributing factors of runway overrun risk, based on which classification and regression models are applied to identify risky flights and predict the risk values. The proposed risk assessment model was evaluated using QAR data from an airline in China. The results show that the automatic deceleration rate, the way that the aircraft approaches the runway, the touchdown distance, and the kinetic energy at 50 ft are key factors in the risk of runway overrun during the landing phase.  
QAR data; runway overrun; risk assessment model; trajectory correction; E learning
# Enhancing Online Learning Through Multi-Agent Debates for CS University Students
## Appendix A Pre-Test and Post-Test Questions
### Numerical Approximation of Gradients Pre-Test Questions  
1. What is the main purpose of gradient numerical approximation? 1. Directly calculate the derivative of a function at a certain point 2. Test the accuracy of the derivative by approximation method * Taylor series expansion of the solution function (d) Computational efficiency of optimization function
* What kind of function is the gradient numerical approximation method mainly suitable for? (a) Differentiable continuous functions (b) Functions that are not differentiable (c) Discrete function (d) Any function
* Which of the following formulas is the standard formula for the two-sided difference method? (a) \((f(x+h)-f(x))/h\) (b) \((f(x)-f(x-h))/h\) (c) \((f(x+h)-f(x-h))/2h\) (d) \((f(x+h)+f(x-h))/2h\)
* What is the main difference between unilateral difference and bilateral difference in gradient numerical approximation? (a) Computational complexity (b) The magnitude of the approximation error (c) Type of function used (d) Whether a derivation formula is needed
* In the gradient test, which one is more effective? (a) Unilateral difference (b) Bilateral difference
* Please briefly describe basic principles and differences of unilateral difference and bilateral difference.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.6 Controller Node  
The controller node is placed in a fixed location in a given map at simulation runtime, as it can occur in a real-world scenario. This node is equipped with a CSMA net device to directly communicate with the RSUs and a point-to-point wired connection between PGW and the controller node to communicate with the vehicular nodes through LTE.
# Telepresence Social Robotics towards Co-Presence: A Review
## 1 Introduction
The method for this literature review and article selection consisted of retrieving and collecting review studies on social presence, co-presence, and the principles and heuristics of human-robot interaction (HRI), with emphasis on teleoperated telepresence robots. Searches were performed on bibliographic scientific databases, such as ACM's digital library, Google Scholar, MIT Press Direct, Elsevier's ScienceDirect, IEEE's Xplore, PubMed, Scopus, and Springer. Queries included general keywords such as social robots, social robots survey, co-presence taxonomy, copresence or co-presence robots, telepresence robots, adaptive systems, and more specifically, the compositions of these keywords. The selection of papers for in-depth reading was determined by the number of citations, being a recent publication, being a journal (e.g., IEEE transactions, Elsevier's, or Springer), being a book, including user evaluations studies, or being an article in a reputable conference in the field (e.g., ACM HRI conferences, IEEE Robotics and Automation Society, ICRA, or the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)). Citations in these papers directed new readings and paper selections. Figure 1(a) depicts the article's citation distribution per main topic, Figure 1(b) is from the _Co-Presence Taxonomy/Preditors_ topic, Figure 1(c) is from the _From Telepresence to Co-Presence Design_ topic, and Figure 1(d) shows the article citation distribution per year. This survey presents an overview of social robotics systems and focuses on how to enhance the sense of co-presence via robot mediation. It reviews the literature to define social presence and co-presence, identifies predictors, and proposes a taxonomy for \"co-presence\" and \"user-adaptive systems\" mechanisms. It provides technical methods to support roboticsocial interactions. The structure of this article is composed of four parts. Section 2 identifies potential predictors for social presence, suggesting a taxonomy for \"co-presence\". Section 3 presents several robotic telepresence systems currently available in the market or used in research. It also reviews autonomous user-adaptive systems for social robots, aiming for a taxonomy, and additionally provides design guidelines for mechanisms that enhance the sense of co-presence in communications through a teleoperated telepresence robot. It includes guidelines for the evaluation of these systems, having as reference the face-to-face interaction. Finally, Section 4 presents the conclusions and future work.
## 6 Conclusions  
This article utilizes complex functions and wave function expansion methods to study the scattering and DSCC issues of steady-state SH waves in piezoelectric materials with non-circular openings. The results show that the shape of the opening, the number of incident waves and the incidence angle have significant effects on the structure. Compared to the theory of elastic media, this method successfully generalizes the mature theory of elastic piezoelectric media to this field, providing a new approach for analyzing the dynamic problems of piezoelectric materials.  
Based on the calculation results of a piezomagnetic material structure model with an elliptical opening, the following conclusions can be drawn:  
1. The DSCC value around the elliptical opening is related to the illumination region. In the case of incident waves with the same frequency, a larger illumination region will generate a larger DSCC. Therefore, elastic waves traveling along the minor axis of the elliptical opening will cause a larger DSCC than those traveling along the major axis.
2. Elastic waves in the low-frequency range are more significant to consider. In most instances, the DSCC produced by low-frequency waves is higher than that produced by high-frequency waves. Moreover, the DSCC in the illumination region is generally higher than in the shadow region.
3. The DSCC changes differently at different locations in the piezoelectric material with an elliptical opening. The DSCC in specific areas can be much higher than in other places, making them more prone to damage.  
Figure 4: The relationship between DSCC and dimensionless wave number, \(e=0.7\).  
Figure 5: The relationship between DSCC and incidence angle, \(e=0.7\).  
**Author Contributions:** Conceptualization, Z.W. and C.Z.; methodology, J.W.; validation, N.H., R.Z. and Y.G.; formal analysis, W.P.; investigation, J.W. and N.H.; data curation, W.P.; writing--original draft preparation, J.W.; writing--review and editing, Z.W.; supervision, Y.G.; project administration, C.Z.; funding acquisition, N.H. and R.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Sichuan Science and Technology Program (No. 2022 YFG0274).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The authors confirm that the data supporting the findings of this study are available within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results  
### SeNPs-CV Particles' Characterization  
The green synthesis of SeNPs was confirmed by the transformation of the reaction mixture from clear to deep ruby red. SeNPs-CV had a mean diameter of 134.7 nm and a mean zeta potential of \(-\)26.8 mV (Figure 1A,B). The particle size distribution of the selenium nanoparticles was determined by the polydispersity index (PDI), and PDI was found to be 0.48, suggesting homogeneity and a uniform dispersion of SeNPs. These results are in agreement with [PERSON] et al. [26] who found that the green-synthesized SeNPs have an average size of 146 nm with a low PDI of 0.521. The morphology of SeNPs-CV was observed by TEM (Figure 1E). The nanoparticles displayed a spherical shape with a particle size less than 200 nm, confirming the Zetasizer findings. The FT-IR spectrum of artificially produced SeNPs-CV and CV alone is shown in Figure 1C,D: the O-H group is represented by a wide peak at 3326.70 cm\({}^{-1}\) with a slight downshift from 3338.31 cm\({}^{-1}\) that presented in CV; the branched alkane C\(\subseteq\)C stretching band can be seen at 2137.65 cm\({}^{-1}\); and the C=C stretching band at 1635.07 cm\({}^{-1}\). These two bands are modified bands at 2127.39 cm\({}^{-1}\) and 1638.21 cm\({}^{-1}\) of CV. A new band at 455.41 cm\({}^{-1}\) is present due to C-X stretch carbon compounds with Se, as an indicator of a successful preparation process and conjugation between Se and CV. These findings point to the presence of several functional groups that could be responsible for the reduction and stability of SeNPs-CV.
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
### Power Consumption
#### 5.2.2 TX Power Consumption  
In energy-constrained IoT networks, minimizing radio transmission costs (TX costs) is crucial for maximizing node lifetime. Data transmission, especially frequent transmissions in large-scale deployments, significantly contributes to TX energy consumption. This is because intermediate nodes expend energy forwarding received packets. Therefore, mitigating the transmission of illegitimate packets can substantially extend network longevity.  
As evidenced in Figures 10 and 11, our mitigation strategy effectively reduces the transmission of illegitimate packets by node 2. This is reflected in the near-identical TX energy consumption observed between the mitigated scenario and the baseline. This finding aligns with the observation that the Bloom filter of node 2 drops approximately 95% of the packets received from the attacker nodes, with the remaining 5% constituting false positives.  
Figure 8: CPU power consumption of target node in scenario with one attacker.  
Figure 9: CPU power consumption of target node in scenario with two attackers.
## 3 Results  
### Meteorological Data  
Temperature (\({}^{\circ}\)C), relative humidity (\(\%\)) and wind speed (km/h) observations were recorded during the flowering seasons of _Z. nummularia_ from June-July (season I) and from August-October (season II). The observations revealed hot temperatures (with means of 38 and 36 \({}^{\circ}\)C and ranges of 26-46 \({}^{\circ}\)C and 21-44 \({}^{\circ}\)C in seasons I and II, respectively) and dry weather (with mean RH values of 9\(\%\) and 11\(\%\) and ranges of 4-20\(\%\) and 4-30\(\%\) in seasons I and II, respectively), with mean wind speeds of 13 and 10 km/h (ranging from 0-28 km/h and from 0-30 km/h) during flowering seasons I and II, respectively (Figure 1A).  
The hourly weather pattern revealed that the temperatures were significantly higher at noon (N), afternoon (AN) and sunset (SS) than at sunrise (SR) and forenoon (FN) during both flowering seasons. In both flowering seasons, the highest temperatures were recorded at the AN time and the lowest temperatures were recorded at the SR time. The relative humidity (RH) was significantly high at SR and gradually decreased to the lowest level later in the day during both seasons. The wind speed was significantly higher at N, AN and SS than at SR and FN during both flowering seasons (Figure 1B).  
The monthly pattern of weather revealed that temperature was equally high during June, July, and August but was significantly lower during September and October (Figure 2A). RH was equally low during June, July, and August but significantly higher during September and October. The wind speed was significantly higher during July and August than during June, September and October (Figure 2A). Figure 2B shows the seasonal patterns of temperature, RH and wind speed in each month of the studied flowering seasons.  
Figure 1: Meteorological temperature (\({}^{\circ}\)C), relative humidity (\(\%\)) and wind speed (km/h) data recorded during the flowering seasons of \(Z\). _nummularia_ in central Arabia. (**A**) Seasonal patterns of weather factors (mean \(\pm\) SEM) in each flowering season (season I and season II). (**B**) Daily patterns of weather factors (mean \(\pm\) SEM) registered at different times of day (SR: sunrise, FN: forenoon, N: noon, AN: afternoon, and SS: sunset). Different letters indicate significant differences among the mean values of a single weather factor at different times of day at \(p<0.05\). SEM: standard error of the mean. Flowering season I was from June-July, and flowing season II was from August–October.
## 3 Results and Discussion
### Regression Model  
In this study, GBRT, RF, LR, and SVMR models were used for the regression. Based on the CARS-SCA characteristic band selection algorithm, the characteristic bands with correlation values greater than 0.6 were used as the independent variables, and the soil As content was used as the dependent variable to construct the soil As model. It can be seen from Figures 8 and 9 that the eight models all obtain a good accuracy. The closer the scatter plot of the measured value and predicted value to the 1:1 line, the higher the inversion accuracy. Most of the samples are closely distributed around the 1:1 line, indicating that the inversion models are accurate. Among them, the GBRT regression model shows the smallest deviation from the 1:1 line, and the degree of fitting is the highest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Area** & **Characteristic Band** & **Modeling Method** & \(R_{P}^{2}\) & \(RMSE_{P}\) & \(MAE_{P}\) \\ \hline \multirow{4}{*}{Honghu} & 401.9, 405, 408.1, 437.3, 813.6, 817.3, 818.6, & GBRT & \(-\)0.9364 & 4.9147 & 3.6577 \\  & 822.3, 823.5, 827.2, 978.9,997.6,998.5, 999.4, & RF & \(-\)0.7230 & 4.6360 & 3.7935 \\  & 1000.3, 1896.8, 1929.2, 1942.9, 2303.257.7 & LR & \(-\)0.3553 & 4.1116 & 3.2008 \\  & 2362.4, 2376.4, 2385.7 & SVMR & \(-\)0.5136 & 4.3452 & 3.5838 \\  & 401, 402, 420, 566, 577, 676, 677, 688, 689, & GBRT & 0.2140 & 0.8555 & 0.7089 \\  & 690, 697, 705, 706, 707, 822, 826, 827, 871, & RF & 0.4131 & 0.7392 & 0.6488 \\  & 957, 984, 1826, 1906, 2224, 2232, 2344, & LR & 0.3028 & 0.8057 & 0.6568 \\  & 2382, 2393, 2394 & SVMR & 0.0046 & 0.9628 & 0.7804 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy validation of the different models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Area** &
\begin{tabular}{c} **Characteristic Band** \\ **Wavelengths (nm)** \\ \end{tabular} & **Modeling Method** & \(R_{P}^{2}\) & \(RMSE_{P}\) & \(MAE_{P}\) \\ \hline \multirow{4}{*}{Honghu} & GBRT & 0.9711 & 0.6007 & 0.4990 \\  & RF & 0.9557 & 0.7432 & 0.6714 \\  & LR & 0.8914 & 1.1638 & 0.9212 \\  & SVMR & 0.9111 & 1.0531 & 0.8139 \\  & GBRT & 0.9796 & 0.1379 & 0.1078 \\  & RF & 0.9542 & 0.2035 & 0.1562 \\  & LR & 0.9018 & 0.3023 & 0.2269 \\  & SVMR & 0.8691 & 0.3491 & 0.1935 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy validation of the different models.  
Figure 8: A comparison between the measured values and predicted values of the different regression models for Honghu. (**a**) Gradient boosting regression tree model. (**b**) Random forest regression model. (**c**) Linear regression model. (**d**) Support vector machine regression model.  
Figure 9: _Cont._
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Resilience Capacity Decomposition for the Arctic Northeast Route
#### 3.3.1 Absorptive Capacity  
The absorptive capacity can be defined as the extent to which the Arctic Northeast Route security system is able to automatically defend against shocks due to disruptions. As shown in Figure 6, the absorptive capacity is considered the first line of defense to resist the adverse impact of the disruption. Inspired by the \"inherent resilience\" theory proposed by [69], the absorptive capacity can be further divided into the defense capacity under ordinary circumstances and the emergency response capacity under crisis conditions. Finally, a list of seven contributing factors for the absorptive capacity of the Arctic Northeast Route security system was obtained, and it is presented in Table 4.  
Figure 6: Disruptions to safe navigation along the NSR.  
Figure 7: Resilience decomposition designed for the NSR.
## 2 Related Works
### Progress in Large-Scale Geological Data Rendering  
In the field of 3D geological model rendering optimization, research mainly focuses on improving the loading and display efficiency of large-scale data. To address this issue, several optimization strategies have been proposed. First, the use of a half-edge folding algorithm based on quadratic error metrics (QEMs), combined with a hierarchical Level of Detail (LOD) model and a tiled hierarchical structure tree, optimizes the display performance of 3D models, allowing for the efficient loading of 3D Tiles files on the Cesium platform ([PERSON] et al., 2024) [35]. Second, [PERSON] et al. (2023) [36] effectively alleviated data transmission pressure and improved rendering efficiency through data caching, asynchronous calls, error evaluation functions, visibility culling, and parallel rendering techniques based on data parallelism. [PERSON] et al. proposed the VDEC-HRT index structure, which, combined with pre-scheduling and culling techniques, optimized data retrieval and object traversal, reducing computational load during rendering ([PERSON] et al., 2022) [37]. [PERSON] et al. (2020) [38] also implemented a viewpoint-based LOD strategy to render multiscale reservoir models quickly while addressing gaps between meshes. [39] (2018) [40] used GPU-based ray-tracing technology and stack representation to enhance the rendering efficiency of volumetric terrain and geological structures and optimize loading speed by organizing texture data with space-filling curves.  
The above discussion shows that by employing techniques such as the half-edge folding algorithm based on quadratic error metrics, hierarchical LOD models, and tiled layered structures, along with data caching, asynchronous calls, error evaluation, visibility culling, and parallel rendering, the loading and display efficiency of large-scale 3D geological models has been dramatically enhanced. These methods have achieved significant progress in data simplification, transmission, rendering acceleration, and the application of GPU technology but still face challenges in data consistency, real-time performance, and dynamic load balancing.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 4 Experimental Results
### Comparing LEO vs. Receiver Dynamics  
The focus of this paper is on LEO dynamics; however, we found it of interest to compare the reference static case with a simple dynamic scenario: circle, duration = 500 s, radius = 50 m, constant speed = 50 km/h. Figure 10a illustrates the frequency rate for this circle scenario, where the user dynamic is visible like a perturbation on top of the LEO profile. The carrier phase jerk induced by the user motion and LEO orbits are shown in Figures 10b and 10c, respectively.  
Figure 10: LEO satellite vs. user dynamics for a circular scenario: (**a**) frequency rate; (**b**) rate of change of acceleration due to user motion; (**c**) rate of change of acceleration due to satellite motion.  
Figure 9: Results for CN0 ramp: (**a**) aiding OFF; (**b**) aiding ON; (c) CDF of position error.