esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Results of the Quantitative Analysis  
Figure 1 shows how students rated each of the survey statements on a five-point Likert scale. Average scores are in the range between 3.8 and 4.4, pointing to broadly positive views. Students were most enthusiastic about how clearly the tutor explained concepts, how relevant the feedback felt, and how much it sparked their curiosity.  
The error bars (\(\pm\)1 SD) highlight that, although most items exhibit relatively tight agreement (SD \(\approx\) 0.4), a few prompt-engineering and self-regulated learning questions show greater dispersion, suggesting more varied student experiences in those areas.  
Internal consistency was adequate for both composite variables. The 10-item GPTQual scale, describing the perceived GPT pedagogical quality, had a [PERSON]'s \(\alpha\) value of 0.844 (N = 49), and the 9-item StudCmp (describing the perceived improvement in competences) scale had a [PERSON]'s \(\alpha\) value of 0.830 (N = 51). Students rated the GPT tutor's pedagogical quality highly (M = 4.31, SD = 0.45, range 3.10-5.00) and reported substantial capability gains (M = 4.01, SD = 0.46, range 3.00-5.00).  
Two one-sample t-tests compared each composite against the neutral midpoint of 3:  
* GPT pedagogical quality: t(50) = 20.67, \(p\) < 0.001, mean difference = 1.3076, 95% CI [1.1806, 1.4347], [PERSON]'s d \(\approx\) 2.90;
* Student competencies: t(50) = 15.62, \(p\) < 0.001, mean difference = 1.0087, 95% CI [0.8790, 1.1384], [PERSON]'s d \(\approx\) 2.19.  
Both composites are significantly above neutral, with substantial effect sizes. [PERSON]'s d was computed as the mean difference over the sample standard deviation. For GPT pedagogical quality, d = (4.31-3)/0.45 = 2.90; for student competencies, d = (4.01-3)/0.46 = 2.19. According to conventional benchmarks, these represent considerable effects [33].  
The Pearson correlation revealed a moderate-to-strong positive association between GPT pedagogical quality and Student competencies, r(49) = 0.571, \(p\) < 0.001, indicating that students who perceived higher tutor quality also reported greater gains in generic capabilities.  
A simple linear regression tested whether the perceived GPT pedagogical quality predicts the achieved student competencies. The estimated OLS model had the following characteristics:  
* Model fit: R = 0.571, R\({}^{2}\) = 0.326, adjusted R\({}^{2}\) = 0.312, F(1,49) = 23.72, \(p\) < 0.001;* Coefficients: Intercept = 1.497 (SE = 0.518), t = 2.887, \(p\) = 0.006; GPTQual \(\rightarrow\) StudCmp: b = 0.583 (SE = 0.120), \(\beta\) = 0.571, t = 4.870, \(p\) < 0.001.  
Thus, perceived GPT tutor pedagogical quality accounts for 32.6% of the variance in students' self-reported capability gains. All statistical assumptions for the OLS regression were met, including a lack of significant deviations from normality or homoscedasticity (as determined by a visual check of the residual plots).
# Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change +
## 2 Methodology
### Gap Evaluation
#### 2.1.2 Gap Rating  
To determine which traffic gaps are more favorable, all available gaps are rated with an evaluation function. Based on the analysis of human decisions during a lane change driving [21], the score of each traffic gap is a weighted sum of vehicle dynamic parameters:  
\[S_{g}=w_{1}^{T}d_{FH}+w_{2}^{T}v_{F}+w_{3}^{T}d_{FR} \tag{10}\]  
where \(S_{g}\) is the evaluated score of a target traffic gap; \(d_{FH}\) is the longitudinal distance between the leading vehicle and the host vehicle \(V_{H}\); \(v_{F}\) is the speed of the leading vehicle at the target traffic gap; and \(d_{FR}\) is the longitudinal distance between the leading vehicle and the following vehicle, which indicating the size of the gap. It is noted that \(d_{FH}\),\(v_{F}d_{FR}\) are discrete series formulated as the column vectors within the prediction horizon. \(w_{1}\),\(w_{2}\),\(w_{3}\) are column vectors of weight coefficients. The parameters of evaluation function are shown in Figure 3.  
By adjusting the weight coefficients \(w_{i}\), the evaluation function can be designed based on the desired optimization objective. Because the prediction accuracy will deteriorate when the prediction horizon becomes longer, the weight coefficients are designed as  
\[w_{i}(k)=w_{i}(m+1)e^{\beta(k-m-1)},\ m+1\leqslant k\leqslant n \tag{11}\]  
where \(\beta\) controls the decaying rate of \(w_{i}\).
## 3 Proposed Procedure
### Texture Mapping Process
#### 3.3.3 UV Coordinate Calculation  
The coordinate system used in 3D models (world coordinates or local coordinates) differs from the 2D coordinate system used in GeoTIFF texture images. Therefore, converting 3D coordinates to the 2D UV coordinate system is necessary for proper texture mapping to the 3D model. The primary objective is to normalize and return to each vertex position.  
\[U=\frac{x-x_{min}}{x_{max}-x_{min}}\]  
\[V=\frac{y-y_{min}}{y_{max}-y_{min}}\]  
The UV coordinate transformation algorithm converts 3D model coordinates into GeoTIFF texture coordinates, optimizing texture mapping and enhancing mapping accuracy.
# Adaptive Cluster-Based Normalization for Robust TOPSIS in Multicriteria Decision-Making
## 3 Materials and Methods  
In this section, we introduce a new methodological approach that enhances both clustering and normalization within the TOPSIS framework while ensuring that the process remains straightforward and easy to implement. The proposed methods, Clustering Using Fuzzy Numbers and Centroid-Based Distance Allocation, and the integration of logarithmic normalization in TOPSIS, address key limitations in traditional techniques. By introducing a more flexible clustering process and an adaptive normalization approach, these methods allow for a more accurate representation of real-world data variability, improving decision-making outcomes.  
One of the fundamental challenges in decision models is that traditional clustering methods tend to assign alternatives to rigid categories, even when the data suggests a more nuanced classification. This can lead to misinterpretations, particularly when dealing with uncertainty or overlapping data points. To overcome this limitation, we propose Clustering Using Fuzzy Numbers and Centroid-Based Distance Allocation, which introduces a degree of membership for each alternative within a cluster instead of enforcing a strict assignment.  
This method acknowledges that alternatives may exhibit characteristics of multiple clusters, leading to a more precise, meaningful, and interpretable grouping of data.  
Similarly, conventional normalization techniques such as Min-Max and Z-score often fail to handle datasets with large numerical variations, highly skewed distributions, or extreme outliers. These issues can distort rankings in TOPSIS, as criteria with significantly larger values may disproportionately influence the final results. To address this, we propose the integration of logarithmic normalization in TOPSIS, which effectively smooths extreme variations, preserves relative differences, and ensures a more balanced influence across criteria. One of its most compelling advantages is that, despite its effectiveness in handling complex data distributions, it remains as easy to apply as traditional normalization methods, making it an accessible yet powerful enhancement for decision-makers.  
The strength of our proposed methodology lies in its ability to enhance accuracy, robustness, and adaptability while maintaining ease of implementation. Both the fuzzy clustering approach and logarithmic normalization are designed to seamlessly integrate into existing decision-making workflows without adding computational complexity. By introducing greater flexibility in clustering and a more adaptive approach to normalization, this study provides a scalable, practical, and efficient framework for improving multi-criteria decision-making. The following sections provide an in-depth explanation of how these methods work and their practical applications.
## 3 Evaluation of the CRLB with Calibration Targets
However, in the edge case where the BR measurement noise is very large, two CRLBs would tend to be the same. This is because in this case, the BR measurement noise dominates and effect of transmitter/receiver position error on the localization accuracy is relatively small. The CRLB curves versus the transmitter/receiver position error level \(\sigma_{\rm s}\) are plotted in Figure 3b where the BR measurement noise level and calibration target position error level are fixed at \(\sigma_{\rm r}=10\) m and \(\sigma_{\rm c}=10\) m respectively. A similar trend, i.e., two CRLBs would tend to be the same, appears in Figure 3b, when the transmitter/receiver position error is sufficiently small. A reasonable explanation is that, in this case, the transmitter/receiver positions are \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**TX** & \(x_{\rm Lum}^{\rm o}\) & \(y_{\rm Lum}^{\rm o}\) & \(z_{\rm Lum}^{\rm o}\) & **RX** & \(x_{\rm Lum}^{\rm o}\) & \(y_{\rm Lum}^{\rm o}\) & \(z_{\rm Lum}^{\rm o}\) & \(\alpha_{\rm r,a}^{\rm o}\) & \(\alpha_{\rm r,a}^{\rm o}\) & \(\alpha_{\rm r,a}^{\rm o}\) & \(\alpha_{\rm r,a}^{\rm o}\) \\ \hline
1 & 20,000 & 0 & 100 & 1 & 2000 & 2000 & 0 & 1 & 10,000 & 10,000 & 2500 \\ \hline
2 & 15,000 & 5000 & 1000 & 2 & 2000 & \(-\)2000 & 500 & 2 & 15,000 & 30,000 & 3000 \\ \hline
3 & 15,000 & \(-\)5000 & 2000 & 3 & 5000 & 5000 & 1000 & 3 & 20,000 & \(-\)10,000 & 3500 \\ \hline – & – & – & – & – & 4 & 5000 & \(-\)5000 & 1500 & – & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Positions (in meters) of the transmitters, receivers and calibration targets. Figure 3: Comparison of the CRLBs with and without using calibration targets: (**a**) for different BR measurement noise level \(\sigma_{\rm r}\); (**b**) for different transmitter/receiver position error level \(\sigma_{\rm s}\); (**c**) for different calibration target position error level \(\sigma_{\rm c}\). Figure 2: Localization scenario geometry for simulation. known very accurately and their influence on the localization accuracy can be ignored compared to the BR measurement noise. The CRLB comparison versus calibration target position error level \(\sigma_{\rm c}\) is provided in Figure 3c where \(\sigma_{\rm r}=10\) m and \(\sigma_{\rm s}=20\) m. Interestingly, the trend of CRLB curves implies that, even when the calibration target position error is extremely large, the CRLB with utilization of calibration targets are still remarkably below the one without. This justifies again the analysis under (35), and similar results have also been presented in previous studies [23; 24; 25; 26; 27; 28; 29; 30] on source localization and sensor network localization issues. Generally, from Figure 3, the use of calibration targets brings a significant improvement in the localization accuracy in the normal case, at least at the CRLB level.
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 3 Results
### Comparing Alder Enzyme Activities with Other Host Species  
Unequal distribution of root tips from other host species among successional stages restricted comparisons of alder enzyme activities with other hosts to specific sites. In early succession, we compared alder (\(n=52\)) and Salicaceae root tips, the latter including both balsam poplar (\(n=15\)) and _Salix_ sp. (\(n=17\)). Alder tended to have lower activities than Salicaceae root tips for all enzymes, although mixed-effects models indicated that differences were only significant for acid phosphatase and phosphodiesterase when measured at high concentrations (Figure 2a). Alder root tips from late successional stands (\(n=44\)) had significantly higher activities of acid phosphatase (\(p<\) 0.05) and tended to have higher phosphodiesterase when measured at low concentrations than did white spruce (\(n=51\)), while white spruce tended to have higher activities of phytase (Figure 2b). Mixed effects models failed to resolve differences in phytase activity between alder and Salicaceae, or alder and white spruce. However, Wilcoxon rank sum tests (Mann-Whitney U) indicated that alder had significantly lower phytase activity than Salicaceae (W = 532.5, \(p<\) 0.01) in early succession, and tended to have lower phytase activities than white spruce in late succession (W = 1350.5, \(p=\) 0.08).  
Figure 1: Mean \(\pm\) S.E. activities of acid phosphatase (**a**), phosphodiesterase (**b**), and phytase (**c**) for thin-leaf alder root tips harvested from organic and mineral horizons from early, mid, and late successful stages. Different lower- and upper-case letters above bars indicate significant differences (\(p<0.05\)) between stages and horizons, respectively. Marginal (\(R^{2}{}_{m}\)) and conditional (\(R^{2}{}_{c}\)) values generated for linear mixed effects models evaluate variance explained by fixed and fixed + random effects, respectively (see Statistics section above).
## 5 CFD Results
### Back-Layering Phenomenon and Smoke Stratification  
Figure 10 shows the back-layering phenomenon and smoke stratification predicted along the escape route after 10 min from the start of the fire of 100 MW (i.e., Scenarios 0, Ia, and IIa) or 15 MW (i.e., Scenarios Ib and IIb) located in the middle of the tunnel length. It shows that the last escaping user is always exposed to safe evacuation conditions, except for the 100 MW fire of Scenario IIa in which the aforementioned user (i.e., the green user) might be a victim of serious risks to human health due to smoke spread. Figure 10 shows that: (i) the natural ventilation due to the piston effect of moving vehicles is not able to prevent the back-layering phenomenon (i.e., the smoke propagation in the opposite direction of the natural ventilation); (ii) with increase in \(\text{HRR}_{\text{max}}\) (i.e., the 100 MW for Scenario Ia in contrast to the 15 MW for Scenario Ib, and the 100 MW for Scenario IIa versus the 15 MW for Scenario IIb), worse smoke conditions are found on the left sidewalk of the north tube and both sidewalks of the parallel tube used for two-way traffic, respectively; (iii) the smoke layering length is not symmetrical for Scenarios 0, Ia, and Ib due to the natural ventilation that pushes the smoke towards portal B (i.e., pressure difference of 5 Pa from portal A to portal B); (iv) the smoke distribution in the adjacent tube (i.e., Scenarios IIa and IIb) is almost symmetrical in the two directions upstream and downstream of the fire because of the reduced pressure difference between the tunnel portals (i.e., 0.5 Pa from portal C to portal D, or equivalently \(-0.5\) Pa from portal D to portal C, that very slowly pushes the air flow towards portal D).  
Figure 9: Longitudinal CO\({}_{2}\) concentration profiles predicted at 2 m height along the escape path after 10 min from the start of the 100 MW (i.e., Scenarios 0, Ia, and IIa) or 15 MW (i.e., Scenarios Ib and IIb) fire located in the middle of the tunnel length. Scenario 0: ordinary conditions of functionality of the tunnel system (i.e., both lanes of each tube are used for one driving direction) with a fire that occurs in the north tube; Scenario Ia: partial closure of the north tube (i.e., only the right lane is closed due to a traffic accident) with a fire that might occur on the left lane of the north tube; Scenario Ib: similar to Scenario Ia, but with the HGVs traveling towards the north tube diverted to an alternative itinerary; Scenario IIa: complete closure of the north tube (i.e., both lanes are closed due to a traffic accident) with a fire that might occur on the lane of the adjacent tube that is used by traffic traveling towards the north; Scenario IIb: similar to Scenario IIa, but with the HGVs traveling towards the north tube diverted to an alternative itinerary.  
Therefore, Figure 10 also confirms, with respect to smoke layering, that better environmental conditions are found with the partial closure of the tube rather than complete closure, and that additional advantages can be found through the activation of VMSs that suggest an alternative route for HGVs only.
# Comprehensive Evaluation of a Sparse Dataset, Assessment and Selection of Competing Models
## 1 Introduction
### Literature Review  
When analyzing crash frequency alone or combining that aspect with the severity of crashes, e.g., EPDO, various approaches have been employed to address the sparse nature of the data response. The following paragraph highlights some of the methodological approaches that have been followed to tackle the sparse nature and the presence of excess zeros in the dataset response.  
The negative binomial model was used for modeling EPDO crashes in South Korea [4]. One of the reasons for justifying the use of EPDO was to diminish the errors introduced by underreporting less severe crashes. Another study was conducted to examine the highway-rail grade crossing crash frequency model, which is characterized by under-dispersion [5]. Different models such as the Conway-Maxwell-Poisson, hurdle Poisson, and zero-inflated models were considered and compared based on various criteria such as Alkaille Information Criterion (AIC). Readers can refer to the literature review for more information about Conway-Maxwell-Poisson [6], hurdle Poisson [7], and zero-inflated models [8].  
The EPDO crash rate was modeled using various hurdle regression frameworks to accommodate the excess number of zeros in the dataset [9]. For the second layer of this model, crash counts greater than 0 and different distributions such as lognormal, gamma, and normal distributions were considered and compared. The results indicated that the lognormal hurdle model outperformed the other models.  
The distribution of traffic crash rates were evaluated with various distributions such as lognormal hurdle and Tobit. Kolmogorov-Smirnov tests, kernel density, and quantile-quantile (Q-Q) plots were used to identify an appropriate distribution [10]. The results show that lognormal hurdle can best fit a mixed and right-skewed crash rate.  
The quantile regression model is another method applied in the literature review to model EPDO and identify crash blackspots [11]; quantile regression used as crash data was highly skewed by the preponderance of zeros.  
Despite efforts to model EPDO, little work has been made to model EPDO by the application of hurdle models with various distributional count models and to compare those results with other two-component models, e.g., zero inflated, and single-component model, e.g., Poisson model. Additionally, no studies have used EPDO for traffic barrier crashes by incorporating barriers that did not satisfy the recommended heights and barriers that have not experienced any historical crashes. The goodness of fit of various models was evaluated by measures such as Alkaille Information Criterion (AIC), and rootogram plots.
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
## 6 Conclusions  
A method for assessing aircraft collision risks on aprons based on Petri nets is proposed. By using predictive calculations of aircraft operational data, the method accurately quantifies the collision risks between aircraft and identifies high-risk collision areas on the apron. The paper presents the results showing different collision probabilities corresponding to various risk zones, addressing the current gap in research on collision risk areas. The study employs relevant neural network training methods to classify and evaluate collision risks, with visual representation through color coding. This helps apron managers to precisely monitor current collision risks during operations. Furthermore, the paper optimizes aircraft taxi routes based on collision risks in specific areas, thereby reducing risks and ensuring operational safety. This approach enhances the practical significance of collision risk management in operations, allowing the risk values to be effectively applied in real-world scenarios.  
The research results indicate that areas with higher taxiway occupancy rates are more likely to become collision risk zones. On peak days, the collision risk scenarios are more complex due to the increased workload, leading to higher risk levels and making it more challenging to reduce collision risks through path planning. Therefore, during peak day operations, airports should focus on areas with higher taxiway occupancy rates and implement rational taxi path planning to ensure the safe operation of the apron.  
Conceptualization, Q.S. and X.T.; methodology, J.S.; software, J.S.; validation, J.S., Q.S. and X.T.; formal analysis, J.S. and Q.S.; investigation, X.T.; resources, X.T.; data curation, J.S. and Q.S.; writing--original draft preparation, J.S.; writing--review and editing, J.S. and Q.S.; visualization, J.S.; supervision, Q.S. and X.T.; project administration, X.T.; funding acquisition, Q.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China U2233208.  
Not applicable.  
The data used in the simulation experiments in this paper comes from flight data of a specific operational period at Guangzhou Baiyun Airport. The relevant experimental parameters are based on references, which have been mentioned in the text. All data has been presented in the article.  
This paper would like to express sincere gratitude to [PERSON] for his invaluable support throughout this project. He not only provided crucial data for this research but also contributed significantly to the analysis of the results. His participation in writing the data analysis section and his overall contributions were essential to the completion of this paper. We are deeply grateful for his expertise and assistance.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Kinematics of DTF and STF Decay Mechanisms
#### 2.1.1 One-Step Decay: Direct Ternary Fission  
The kinematic plot for DTF is shown in the top panel of Figure 1. The tripartition occurs suddenly. Energy conservation for three body decay allows having a velocity of the third fragment in the following way:  
\[v_{3}=\left[\frac{2(E_{p}+Q)-M_{1}v_{1}^{2}-M_{2}v_{2}^{2}}{M_{3}}\right]^{1/2}. \tag{1}\]  
The Q-value in the above equation is defined as  
\[Q=M_{p}+M_{t}-\sum_{i=1}^{3}M_{i},\]  
where \(M_{p}\), \(M_{t}\), and \(M_{i}\) are the masses of the projectile, target and of the \(i\)-th product nucleus, respectively.  
Figure 1: In-plane kinematic plots of direct ternary fission (DTF-**top**) and sequential ternary fission(STF-**bottom**). In the DTF a simultaneous formation of the \(A_{1},A_{2}\) and \(A_{3}\) fragments occurs. In STF the first binary scission produces the \(A_{1}\) and \(A_{23}\) fragments, the subsequent binary scission of the \(A_{23}\) fragment produces the final products \(A_{2}\) and \(A_{3}\).  
By considering momentum conservation law, the angle of the third fragment and the velocity of the second fragment can be expressed, respectively, as follows:  
\[\theta_{3} = cos^{-1}\left[\frac{M_{p}v_{p}-M_{1}v_{1}\,cos(\theta_{1})-M_{2}v_{2 }\,cos(\theta_{2})}{\sqrt{M_{3}\{2(E_{p}+Q)-M_{1}v_{1}^{2}-M_{2}v_{2}^{2}\}}}\right] \tag{2}\] \[v_{2} = -\frac{M_{1}v_{1}\,sin(\theta_{1})+M_{3}v_{3}\,sin(\theta_{3})}{M _{2}\,sin(\theta_{2})} \tag{3}\]  
So, replacing the values of \(v_{3}\) and \(\theta_{3}\), the solutions of \(v_{2}\) are as follows:  
\[v_{2}=\frac{-\beta\pm\sqrt{\beta^{2}-4\alpha\gamma}}{2\alpha}, \tag{4}\]  
where  
\[\alpha = M_{2}(M_{2}+M_{3})\] \[\beta = 2M_{2}\{M_{1}v_{1}\cos(\theta_{1}-\theta_{2})-\sqrt{2E_{p}M_{p}} \cos(\theta_{2})\}\] \[\gamma = M_{1}v_{1}(M_{1}v_{1}+M_{3}(v_{1}-2(E_{p}+Q))-\sqrt{8E_{p}M_{p} }\cos(\theta_{1}))+2M_{p}E_{p}\]  
Now, if we substitute the value of \(v_{2}\) found in term of known parameters in the \(v_{3}\) and \(\theta_{3}\) expressions we obtain the three equations for \(E_{2}\), \(E_{3}\) and \(\theta_{3}\) as function of the three input parameters (\(E_{1}\), \(\theta_{1}\) and \(\theta_{2}\)) for each ternary fragmentation.