esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In developing countries, heavy-duty trucks play an important role in transportation for infrastructure construction. However, frequent truck accidents cause great losses. Previous studies have mainly focused on passenger drivers; to date, little has been done to assess the driving behavior of heavy truck drivers. The overall objective of this study is to classify driving styles at intersections, analyze the impacts of differing types of traffic control at intersections on driving styles, and identify potentially risky intersections. We selected 11 heavy-duty truck drivers and collected kinematic driving parameters (including driving speed and both lateral and longitudinal acceleration) from field experiments in Nanjing for our study. Our study on driving styles followed the following steps. First, we reduced data size and extracted data features on the basis of time windows in Python. Second, driving styles were classified into three driving styles: cautious, normal, and aggressive, based on the K-means clustering method, and the corresponding thresholds for each category were obtained. Kinematic driving parameters were used as driving style measurements. Third, according to classifications of driving style, the impacts of four different intersection traffic control types: two-phase signalized, multiphase signalized, stop, and yield intersections, on driving styles have been analyzed using the multinomial logit model. Moreover, based on the above analysis, potentially risky intersections were identified. The results suggest that different types of traffic control at intersections lead to variations in driving styles and have different influences on driving styles. In terms of accuracy, our method, which uses driving speed, both lateral and longitudinal acceleration, and jerk as features, performs better than traditional methods which only use speed and acceleration. The results of the study allow us to analyze the driving data of heavy-duty trucks and identify drivers who drive more aggressively during a trip. In addition, the results show that aggressive driving styles mostly occur at stop intersections and in the dilemma zones of signalized intersections. Therefore, early-warning interventions can be provided during a driver's trip by analyzing the different types of traffic control at intersections on the route in advance. Finally, the cumulative analysis of driving styles at intersections over multiple trips can be used to identify potentially high-risk intersections. It is possible to eliminate potential risks in these areas through measures such as early warnings and by improving traffic management control methods.  
driving style; driving behavior; K-means clustering; traffic control types of intersections; heavy-duty trucks +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Curve Decomposition Tool
If after the next 9 iterations (30% of the maximum iterations number) the stopping criterion is still unmet, then rgenoud is enabled, and the system works in the sequence nloptr, rgenoud, and BFGS. Next, if no improvement is achieved, GenSA is added to the top of the sequence at the iteration no. 25 (50% of the maximum iterations number). Last, optimx is added after iteration no. 40 (80% of the maximum iterations number), resulting in the sequence of optimization algorithms: GenSA, nloptr, rgenoud, optimx, and BFGS. The R peak decomposer reports its results in the form of a text file formatted for an easy import into the spreadsheet. The elements of this report include the raw data of each Gaussian function, parameters of Gaussians, and areas of each Gaussian function. As the real DSC signal is never ideally positioned in parallel to the temperature axis, a baseline is computed to compensate for this signal distortion. The baseline is expressed in the form of a linear function, whose coefficients may be calculated with two methods: (1) linear regression using signal boundaries at the ends of the temperature axis, (2) non-linear optimization of both coefficients of the linear function carried out jointly with Gaussian functions' coefficients. The latter method was used in this work. The areas above the curves were computed considering the calculated baselines and used as indicators of the amounts of the respective fractions of water. In the fitting procedure, the following criteria were kept:  
* following Occam's razor, the number of components curves should be minimal
* since the ice-melting curves were asymmetrical, they were deconvoluted using bi-Gaussian functions
* the theoretical curve given by the sum of the individual ones was best fitted to the experimental DSC curve
* the goodness-of-fit criterion was normalized root-mean-squared error (\(NRMSE\)) calculated according to the formula in Equation (2); an empirical rule of \(NRMSE<0.1\%\) was employed as an algorithm stopping criterion. \[NRMSE=\frac{RMSE}{y_{MAX}-y_{MIN}}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}^{PRED}-y_{i}^{\text{OS}}\right)^{2 }}{n}} \tag{2}\]  
where:  
\(NRMSE\)--normalized root-mean-squared error  
\(RMSE\)--root-mean-squared error  
\(yiMAX\)--maximum value of the observed output variable  
\(yiMIN\)--minimum value of the observed output variable  
\(yPRED\)--output variable predicted by the model  
\(yOBS\)--observed output variable  
\(i\)--ith index of the output variable \(n\)--number of the data points
# Test Method for Single Satellite's Inter-Satellite Link Pointing and Tracking via Ground Station
## 5 Conclusions  
The test method for a single satellite's pointing and tracking abilities with respect to the inter-satellite link is studied. The proposed method is available for multiple types of transmission equipment, such as microwaves and lasers. The rotation angle and off-axis angle for the inter-satellite link pointing and tracking of local and target satellites are derived. The virtual target satellite is simulated by two ground stations. The orbit of the virtual satellite is obtained according to the desired orbital altitude. The time difference between the two ground stations' pointings is solved through several iterations. The pointing and tracking abilities of the local satellite are evaluated by comparing the actual time differences between the ground stations receiving the signals to the theoretical time difference, which achieves an independently in-orbit test of inter-satellite performance.  
According to the simulation results, the final error in the time difference in the virtual target satellite is 5 ms, which suggests that two ground stations can simulate the motion of a virtual target satellite. The time windows for establishing links with ground stations A and B are about 56.9 s and 52.8 s, respectively. The off-axis angle and rotation angle for pointing to station A are about 6.6\({}^{\circ}\) and 5.2\({}^{\circ}\), while those for pointing to station B are about 5.2\({}^{\circ}\) and 5.0\({}^{\circ}\), respectively. Considering a 0.3\({}^{\circ}\) half-beam angle, the time difference is about 390 s, which approximates the theoretical value of 400 s. These results demonstrate that the pointing and tracking abilities of the satellite satisfy the demands for establishing an inter-satellite link.  
Methodology, Z.H., L.Y. and S.L.; Software, Z.H., Z.B. and Y.Y.; Validation, Z.B.; Investigation, Z.H., X.H., L.Y. and Y.Y.; Writing--original draft, X.H.; Supervision, S.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Science and Technology on Space Intelligent Control Laboratory (Grant Nos. HTKJ2024 KL502001 and HTKJ2024 KL502006).  
Data is contained within the article.  
The authors declare no conflict of interest.
# Depth-Optimized Quantum Circuit of Gauss-Jordan Elimination
## 1 Introduction
### Contributions  
In summary, our work involves the following:  
1. **Quantum Circuit of Gauss-Jordan Elimination in ISD**. We present the implementation of quantum circuit of Gauss-Jordan elimination, which is one of the crucial modules in ISD.
2. **Depth-Optimized Quantum Circuit Implementation**. Our focus is on minimizing both the Toffoli depth and the overall depth in our implementation of the quantum circuit for Gauss-Jordan elimination. To achieve this while keeping the number of qubits reasonable, we employ several methods, including duplicating pivot elements, designing parallel swap and elimination steps in Gauss-Jordan elimination, and using reverse operations to initialize qubits (i.e., set to \(|0\rangle\)), in order to reuse and reduce the qubit count.
3. **Concrete Estimates of Required Quantum Cost**. Using the quantum programming tool ProjectQ [10] ([[https://github.com/ProjectQ-Framework/ProjectQ](https://github.com/ProjectQ-Framework/ProjectQ)]([https://github.com/ProjectQ-Framework/ProjectQ](https://github.com/ProjectQ-Framework/ProjectQ)) (accessed on 19 September 2024.)), we verify our quantum circuit implementation and analyze the needed quantum resources in detail. By decomposing high-level quantum gates (specifically Toffoli gates in this work), we estimate the number of Clifford and \(T\) gates needed.
# Shear Sonic Prediction Based on DELM Optimized by Improved Sparrow Search Algorithm
## 4 Practical Application and Result Analysis
### Comparative Analysis of Model Prediction Effect  
The ISSA-DELM model and the other proposed models, such as ELM, DELM, PSO-DELM, GA-DELM, and SSA-DELM, are all used to predict the \"missing\" DTS of Well N.  
Figure 6: RMSE variation curve of different characteristic numbers.  
Figure 7: Error reduction rate in various iterations of the optimization algorithms (ISSA, SSA, PSO, and GA) for training DELM using Well M.  
The quantitative results of nested cross-validation are shown in Table 3. It can be observed from Table 3 that the correlation coefficient (R-square) of DELM is better than that of ELM, indicating that the addition of DELM can also improve the prediction accuracy. The R-square of the DELM optimized by the intelligent optimization algorithms (GA, PSO, SSA, and ISSA) is higher than that of the DELM without being optimized. At the same time, it can be seen that the mean absolute error (MAE) and root mean square error (RMSE) of the prediction results of the ISSA-DELM are the lowest, while the R-square is the highest, indicating that compared with other optimization algorithms, the ISSA can obtain more suitable parameters.  
The comparisons between the outputs of ISSA-DELM and other models for DTS estimation in Well N and measured DTS values in Well N are shown in Figure 8. When the log curve changes smoothly, the output of the ISSA-DELM model is not very different from that of other models. When the logging curve local mutation (green area) occurs, the results predicted by ISSA-DELM are closer to the measured data than the results of the other models. The reason is that the addition of DELM and ISSA improves the prediction precision and enhances the feature extraction ability. The above analysis proves that the ISSA-DELM model is more suitable for DTS prediction than the ordinary machine learning model.  
The cross-plots of original DTS vs. predicted DTS using ISSA-DELM, SSA-DELM, PSO-DELM, GA-DELM, DELM, and ELM in Well N are shown in Figure 9. It can be observed that the predicted results of ISSA-DELM are closer to the original DTS than other models, indicating that the ISSA-DELM is better than other models in fitting multiple local details of DTS.  
Figure 8: Visual results of the RHOB prediction results of the Well N.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **R-Square** \\ \hline ELM & 7.3150 & 5.8558 & 0.9582 \\ DELM & 6.6490 & 4.3293 & 0.9626 \\ GA-DELM & 6.5488 & 4.1938 & 0.9698 \\ PSO-DELM & 6.5266 & 4.2075 & 0.9806 \\ SSA-DELM & 6.2341 & 4.1615 & 0.9860 \\ ISSA-DELM & 6.1255 & 4.1369 & 0.9916 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons between the ELM, DELM, PSO-DELM, GA-DELM, SSA-DELM, and ISSA-DELM models for DTS estimation in Well N.  
In conclusion, the ISSA-DELM has excellent prediction effects on both the whole and local mutations of the DTS, fully demonstrating its advantages in predicting the missing DTS.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 5 Conclusions  
In this study, we present a novel approach for detecting objects in RSI, ProSAF-Net. First, we introduce the shallow detail aggregation module (SDAM) to extract rich shallow-detail information and spatial structure features. Then, we design the deep semantic fusion module (DSFM) to effectively capture deep semantic information. By integrating SDAM and DSFM, our method facilitates the interaction between fine-grained shallow details and high-level semantic features, thereby enhancing the model's ability to perform object classification and localization. Unlike conventional unidirectional feature propagation methods, which may lead to mismatches between semantic and detailed information, our progressive feature fusion mechanism allows shallow features to be refined under semantic guidance, while deep features are enriched with detailed information. This allows the model to acquire richer and more distinctive feature representations. Considering the complex backgrounds in RSI, we further design the spatial context awareness module (SCAM), which fully leverages both global and local contextual information through interactive mechanisms. This helps effectively distinguish foreground objects from background clutter, enhancing the detection robustness of the model. Finally, we propose a new loss function named ADIoU, which combines the benefits of Inner-IoU and SDIoU. ADIoU adapts the impact of scale and position losses based on object scale variations, while also integrating an auxiliary bounding box mechanism to speed up the bounding box regression. Experimental outcomes show that our model achieves mAP scores of 97.12% on RSD, 96.56% on NWPU VHR-10, and 76.1% on DIOR, outperforming all comparison algorithms. The hierarchical integration of shallow details, deep semantics, and spatial context information in ProSAF-Net establishes a novel design paradigm for multi-scale object detection in complex remote sensing scenarios, demonstrating strong robustness and generalization capability. In the future, we plan to extend ProSAF-Net to multi-modal data and investigate lightweight variants for deployment on resource-constrained platforms.  
Conceptualization, L.L. and J.W.; methodology, L.L. and Y.L.; software, L.L. and W.Q.; validation, L.L. and Y.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L. and J.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Project of the Jiangxi Provincial Department of Education under Grant GJJ190240 and the Key Research and Development Project of the Jiangxi Provincial Department of Science and Technology under Grant 20192 BBEL50039.  
All datasets used for training and evaluating the performance of our proposed approach are publicly available and can be accessed from [45, 46, 47].  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Omission Test
All of the omission solutions without (E)-\(\beta\)-damascenone (4-1, \(p<0.001\)), eugenol (4-5, \(p<0.001\)), both of the terpenoids (4, \(p<0.001\)), 3-methylbutanoic acid (5-1, \(p<0.001\)), 2-methylpropanoic acid (5-2, \(p<0.001\)), or both of the acids (5, \(p<0.001\)) were significantly different from the complete reconstitution, which means the floral and acidic properties were indispensable to the aroma profile of pervaporation produced peach spirit. Lactones have been proved to be key aroma compounds of peach fruit and peach wine [58]. The omission tests results proved this conclusion again. Lack of \(\gamma\)-butyrolactone (7, \(p<0.001\)) or \(\gamma\)-decalactone (8, \(p<0.001\)) made the intensity of the typical peach aroma decreased. 1,1-Diethoxyethane or furfural omitted also influenced the overall aroma compared to the complete reconstitution. In addition, ethyl isobutyrate, isobutyl acetate, 1,1-diethoxyethane, \(\gamma\)-butyrolactone, (E)-\(\beta\)-damascenone, and 3-methylbutanoic acid were proved to be key active aroma components in peach spirit for the first time.
## 3 Methodology
### Preprocessing Raw Data
#### 3.1.4 Amplitude Normalization (AN)  
The ASR implementation in EEGLAB was configured with the following parameters, selected experimentally: BurstCriterion of 20, WindowCriterion of 0.25, WindowCriterionTolerances set to [-Inf 7], Euclidean distance for artifact reconstruction, and with FlatlineCriterion, ChannelCriterion, LineNoiseCriterion, and Highpass set to off. These parameters were chosen to balance between preserving valid EEG data and mitigating artifacts. The artifact reconstruction process used the Euclidean distance metric with a conservative threshold to retain physiological activity.  
This plays a critical role in enhancing the reliability, interpretability, and efficiency of EEG signal analysis, particularly in neuroscience, clinical applications, and BCI systems [31; 32].  
In this study, the clean EEG data, denoted as \((x_{clean}[n])\), were processed to find the minimum (min) and maximum (max) values of \(x_{clean}[n]\). AN was then applied to scaling the signals to the desired range [\(A\) to \(B\)], which in this case was selected as [\(-\)1 to 1]. The normalized signal \(x_{nor}[n]\) was calculated using Equation (1):  
\[x_{nor}[n]=\left((B-A)\times\left(\frac{x_{clean}[n]-min(x_{clean})}{max(x_{ clean})-min(x_{clean})}\right)\right)+A \tag{1}\]  
Figure 2: The ASR results: red segments show excluded artifacts, while blue segments represent the reconstructed, artifact-free signals for clean EEG analysis.  
This ensured that the normalized signal (\(x_{nor}[n]\)) ranged between [\(-1\) and 1], effectively centering the signal around zero and removing the DC component. Therefore, this normalization process maintained the relative relationships between the signal features, which were crucial for further analysis and classification.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 4 DTN Routing Protocols in Disjointed Satellite Networks
### DTN Flooding-Based Techniques
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Methods** & **Category** & **Decision Criteria** & **Advantages** & **Limitations** & **Delivery Ratio** & **Average Delay** & **Overhead** & **Complexity** \\ \hline MaxProp [58] & History & - & Loss messages traffic & - & High-day path induced. & High-day power consumption & Low-of resources unlimited & High-day & (O+m \(\times\)) \\ \hline MV [59] & Flooding & History & - & Loss messages traffic. & - & High-day path of resources & High-day resources unlimited & Low-of resources unlimited & High-day & O+m \(\times\)) \\ \hline PREZ [60] & History & - & Loss messages traffic & - & High-day ratio of resources & High-day ratio of resources & High-day & O+m \(\times\)) \\ \hline Spray and Wiki [61] & None & - & Simple and resource friendly & - & High-day ratio of resources & High-day ratio of resources & High-day & O+m \(\times\)) \\ \hline Spray and Wiki [61] & None & - & Simple and resource friendly & - & High-day ratio of resources & High-day ratio of resources unlimited & Low-of resources unlimited & Medium & O+m \\ \hline Spray and Focus [62] & None & - & Simple and resources friendly & - & High-day ratio of resources & High-day ratio of resources unlimited & Low-of resources unlimited & Medium & O+m \\ \hline OURAR [63] & Quota & History & - & Simple and resource friendly & - & High-day ratio of resources & High-day ratio of resources unlimited & Low-of resources unlimited & Medium & O+m \\ \hline [PERSON] et.al. [64] & None & - & Simple, no prior knowledge required & - & High-day ratio of resources & High-day ratio of resources & High-day ratio of resources unlimited & Low-of resources unlimited & Medium & O+m \\ \hline UDM [55] & History & - & Resource friendly & - & High-day ratio of resources & High-day ratio of resources & High-day ratio of resources unlimited & Low-of resources unlimited & Medium & O+m \\ \hline MCSN [64] & History & - & Resource friendly & - & High-day ratio of resources & High-day ratio of resources & High-day ratio of resources unlimited & Low-of resources unlimited & Medium & O+m \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment--Using MEC/SDN
## 3 IoT/5G System Structure  
The proposed system deploys D2D communications, MEC and SDN technologies to integrate heterogeneous IoT networks with 5G networks. Figure 1 provides an end-to-end system structure of the proposed IoT/5G. The system can be viewed into two main parts; radio access networks, RAN, and core networks, CN. RAN comprises three subsystems; heterogeneous IoT networks, D2D-based networks, and RAN of the cellular networks. However, core networks deploy an SDN network with a multi-controller scheme.
## 3 Preliminaries
### DAS (Data Aggregation Scheduling)  
The DAS (data aggregation schedule) is a sequence of sender sets \(\{\sigma_{1},\sigma_{2},\sigma_{3},\ldots,\sigma_{l}\}\) satisfying the following conditions:  
1. \(\sigma_{i}\cap\sigma_{j}=\varnothing\), \(\forall i\
eq j\)
2. \(\bigcup_{i=1}^{l}\sigma_{i}=V-\{\textit{Sink}\}\)
3. Data are aggregated from \(\sigma_{k}\) to \(V-\bigcup_{i=1}^{k}\sigma_{i}\) at time slot k, for all \(k\ =\ 1,2,\cdots,l\) and finally all the data are aggregated to the sink in time slot \(l\).  
Reference [19] defines DAS as strong DAS and weak DAS. The main difference between these forms is that the strong DAS requires a node to have all of its neighbors closer to the sink with a slot higher than its own, while a weak DAS only requires at least one such neighbor to transmit later.