esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application in Large-Scale Aircraft Measurement
The skin contour, which is applied as a registration constraint, will be matched with the extraction of Ck feature points. This method not only fixes the local convergence problem but also ensures both accuracy and speed in point cloud registration. To detect the deformation of large aircraft skin, [PERSON] et al. [148] suggested a seam structure-aware ICP (SSA-ICP) that is suitable for the original point clouds of aircraft with noise and outliers' overload. This method can effectively locate the scanning point cloud in the whole aircraft skin directly on the original scanning point cloud. To acquire comprehensive data on the sealing quality of an aviation fuel tank, multiple viewpoints of the aircraft fuel tank point cloud must be registered. Nevertheless, numerous repeating features are found in airplane fuel tanks, including the regularly spaced rivets. They might result in mismatches in the point clouds of the tanks if conventional registration techniques are applied. A unique 3D descriptor named LP-PPF was constructed by [PERSON] et al. [149] employing the point and line characteristics of point clouds created from aviation fuel tanks to overcome the challenge. The suggested descriptor has the ability to achieve precise alignment between neighboring point cloud pairs. This technique handles point cloud registration in recurrent and challenging scenarios well. Furthermore, [PERSON] et al. extended this approach to other scenes in the aerospace area. Figure 10a illustrates the evaluation on the registration of airplane skin, airplane flap, and helicopter skin, respectively. The registration results in Figure 10b further demonstrate the promising results of this approach. Among them, the ground truth of the raw scanned data is obtained by structured-light 3D scanner with laser tracker. Figure 10: The pairwise registration results on aerospace applications [149]. (**a**) Different applications including airplane flap, airplane skin, and helicopter skin. (**b**) The registration results of different types of point clouds. Today, as aircraft manufacturing technology gathers momentum, the demand for large-scale aircraft measurement is rising by leaps and bounds. Accordingly, the registration algorithm of point clouds obtained by the measurement is also playing an increasingly significant role. As far as the measurement of large-scale aircraft is concerned, the registration algorithm of massive point clouds is the core factor affecting the practical application of this technology. In terms of the current research progress, the point cloud registration algorithm for large-scale aircraft is still in the laboratory research stage. Furthermore, there is still a long way to go before it can be applied in aviation manufacturing enterprises.
## 5 Conclusions  
In this study, the geospatial accuracy of photogrammetric products obtained by the FEIMA D2000 direct georeferencing method was evaluated using ground-measured data.  
In addition, we evaluated the effect of the quantity and spatial distribution of GCPs on the quality of the results. The research results are summarized as follows:  
1. UAV SfM is a flexible and efficient method to obtain high-resolution topographic data. The direct georeferencing method based on the RTK/PPK fusion difference to obtain high-accuracy image positions has potential for improving the accuracy of the products, especially when GPS measurements are difficult, as well as reducing the dependence on the GCPs in the bundle adjustment, and decreasing the field work time and cost.
2. The research results show that the vertical _RMSE_ of the DSM obtained by direct georeferencing was 0.087 m, approximately equal to 5.12 GSD. The horizontal _RMSE_ of the DOM was 0.041 m, approximately equal to 2.41 GSD. Both values reached the centimeter positioning accuracy and achieve the application research of decimeter-error scale. The accuracy of UAV direct georeferencing could be guaranteed through careful flight planning, an appropriate survey, and accurate data post-processing. In the study of terrain change detection, we suggest evenly deploying two to three GCPs to achieve a good compromise between appropriate accuracy, repeatability and efficiency.
3. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain to reduce the dome effect. With an increase in the number of GCPs in the bundle adjustment, both the horizontal error and vertical error decreased, and the horizontal error was always lower than the vertical error. When the density of the GCPs was greater than 12 GCP/km\({}^{2}\) and 10 GCP/km\({}^{2}\), respectively, the decrease in the vertical and horizontal errors was not obvious. The minimum vertical and horizontal _RMSE_ were 0.032 (-1.88 GSD) and 0.015 m (-0.88 GSD), respectively.  
Conceptualization, X.L. ([PERSON]) and X.L. ([PERSON]); methodology, X.L. ([PERSON]) and X.L. ([PERSON]); software, F.W.; validation, F.W., Y.H. and Y.Z.; formal analysis, X.L. ([PERSON]); investigation, [PERSON] ([PERSON]); resources, [PERSON] ([PERSON]) and W.Y.; data curation, Y.Z.; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] ([PERSON]); visualization, Y.H.; supervision, [PERSON] ([PERSON]); project administration, W.Y.; funding acquisition, [PERSON] ([PERSON]) and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 42101414 and 51704205; the Basic Applied Research Projects of Shanxi Province, grant number 201901D111466; the Key projects of Shanxi coal based low carbon joint fund of National Natural Science Foundation of China, grant number U1810203.  
Not applicable.  
We would like to thank the reviewers for their helpful comments.  
The authors declare no conflict of interest.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### O-AODV Topologies and Testbed Design  
To evaluate the proposed O-AODV protocol in this research, we developed a testbed as depicted in Figure 2 and the topology diagrams in three formats: linear topology with ten nodes as shown in Figure 3, multipath topology with six nodes, and partial mesh multipath topology with ten nodes, as depicted in Figures 4 and 5.  
Furthermore, it is important to note that we increased the complexity of the Scenario 3 topology from the six-node topology in order to evaluate the O-AODV protocol's efficiency and robustness. Therefore, we included traffic results for the same four paths for O-AODV and AODV in the Experimental Results Section to demonstrate clearly the protocols' efficiency.  
For simulation, Renode software was the only option for simulating the Zephyr RTOS application; it has support for a variety of boards [34]. Although the nRF52840 board is supported by Renode, it currently lacks a radio connectivity feature. Consequently, this paper focused on the experimental evaluation of the proposed protocol, which has the additional advantage of providing insights into various implementation constraints of the protocol in actual devices.
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 4 Results
### Model Performance Metrics
Such results reinforce the limited reliability of Encoder-only transformer blocks in EEG-based clinical classification when used without architectural modifications. In contrast, the CNN-GRU-LSTM model consistently demonstrated high and stable performance across repeated trials, with minimal standard deviation in all core metrics (e.g., F1-score: 0.996 \(\pm\) 0.0004, AUC: 0.9998 \(\pm\) 0.0001). Its robustness in capturing temporal dynamics of EEG signals highlights its practical value for clinical applications, particularly where interpretability and reliability are prioritized. Among the hybrid architectures, the spatiotemporal transformer achieved 99.88% \(\pm\) 0.11 accuracy, with nearly perfect AUC and F1-score values, benefiting from the use of 2D positional encoding to model electrode topology and time-space dependencies. The proposed hybrid CNN-RNN-transformer model outperformed all alternatives, reaching 99.99% \(\pm\) 0.005 accuracy and F1-score 0.9999 \(\pm\) 0.00003, with negligible variance, demonstrating exceptional generalization capacity and robustness to train/test split fluctuations. Finally, a comparative analysis of standard deviation in test accuracy across models further supports the importance of performance consistency. The transformer encoder (2\(\times\)) and (3\(\times\)) models yielded standard deviations of \(\pm\)1.59% and \(\pm\)2.11%, respectively, while CNN-LSTM showed moderate variability (\(\pm\)0.79%). In contrast, CNN-GRU-LSTM, hybrid CNN + GRU + LSTM, and spatiotemporal transformer maintained deviations below \(\pm\)0.11%, with the hybrid CNN-RNN-transformer achieving a remarkably low \(\pm\)0.005%. These results confirm that high-performing architectures must be evaluated not only by average performance but also by their stability and reliability across random train-test partitions, particularly in high-stakes clinical decision-making scenarios.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 3 Formation Control: Virtual Linkage Approach  
The idea of using a virtual linkage to solve the problem of moving in formation is much the same as virtual structures: the agents are controlled to fit the virtual linkage and then the virtual linkage tracks the predefined trajectory. The real position and orientation of the virtual linkage are determined by the individual virtual links, which in turns are determined by the agents' position. In this paper, each agent is assumed to know its position in a global coordinate frame. In real robotic systems, this assumption can be realized by \"localization\" technics such as using global position systems, motion capture system or other perception methods. With knowing the position of virtual linkage, the desired position for each agent is given. In other words, there exists a hierarchical architecture and bi-directional control as shown in Figure 5.  
Figure 4: Formation reconfiguration example.  
In this section, the virtual linkage approach will be carefully illustrated to solve the formation control problem. Firstly, the overall control architecture of the virtual linkage approach and its advantages are derived. Then, the state representation of the virtual link and virtual linkage is illustrated. Finally, the detailed method is introduced to enable a group of agents to move in formation and facilitate formation reconfiguration ability.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 5 Partial Fake Speech Against Machines
### PF Attack Against Resemblyzer
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**PF (TTS-Based)—Fake Segment Location**} \\ \cline{2-4}  & **2F, 4R** & **4R, 2F** & **2R, 2F, 2R** \\ \hline AWS (M) & 0\% & 13.44\% & 5.5\% \\ AWS (F) & 0\% & 8.64\% & 0\% \\ Azure (M) & 0\% & 36.44\% & 9.28\% \\ Google (M) & 0\% & 1.75\% & 4.82\% \\ Google (F) & 0\% & 3.48\% & 0\% \\ Tortoise (M) & 74.95\% & 93.26\% & 86.61\% \\ Tortoise (F) & 3.48\% & 95.15\% & 74.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attack success rate against Resemblyzer using AWS, Azure, Google Cloud, and Tortoise TTS generation methods. Below, 2F refers to two seconds of fake segment, 4R is four seconds of real segment, and 2R is two seconds of real segment. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**PF (VC-Based)—Fake Segment Location**} \\ \cline{2-4}  & **2F, 4R** & **4R, 2F** & **2R, 2F, 2R** \\ \hline FreeVC (M) & 6.39\% & 79.68\% & 60.73\% \\ FreeVC (F) & 1.62\% & 82.44\% & 49.83\% \\ DiffVC (M) & 0\% & 28\% & 9\% \\ DiffVC (F) & 0\% & 35.45\% & 5.45\% \\ ppgVC (M) & 0\% & 47.12\% & 9.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Attack success rate against Resemblyzer using FreeVC, DiffVC, and ppgVC open-source VC tools. Next, we discuss the results and the impact on the attack success rate of fake segment location, synthetic speech generation methods, gender, and target dialect in greater detail.
## 5 Empirical Study Design
### Dataset  
In this study, a subset of the TAWOS [35] dataset has been utilized, encompassing 31,960 issues extracted from 26 projects developed using Scrum as the software development methodology. These projects originated from 13 diverse open-source repositories. Derived from a reputable source, the TAWOS dataset ensures a broad representation of scenarios across these projects, enhancing the reliability of the data. The dataset includes crucial information such as 'title', 'description', 'issue type', 'components', and 'SPs' for each issue within the projects. The first four features pertain to textual data, while the last one consists of numeric values representing SPs. Notably, the SPs in the dataset are estimated by software experts, adding to the dataset's quality and reliability. Table 1 provides descriptive statistics of the data. Figure 2 visually illustrates the comparative average SPs across various projects, clearly highlighting the differences in workload or complexity as represented by SPs. It offers a succinct overview of how SPs vary from one project to another, potentially reflecting the diverse nature of tasks or challenges inherent in each project. Figure 3 visually illustrates the distribution of SPs across all projects, showing a clear concentration of SPs in the lower range, with a significant drop-off as the SPs increase. This suggests that most tasks are of lower complexity, with fewer tasks requiring a higher number of SPs, which could indicate more complexity or effort required. For each project, the issues are arranged in ascending order based on their creation time and are subsequently divided into two subsets-training and validation--with an 80% and 20% ratio, respectively. This standard 80-20% split is a widely accepted practice in ML, ensuring that models are trained on a representative sample and validated on a separate set to assess their generalization ability. The textual data of issues for each project in both the training and the validation subsets are amalgamated, resulting in a single text dataset for each project. This amalgamation ensures consistency in model training and validation, highlighting the meticulous approach to the analytical process. These are recorded as two datasets, namely 'train_text' and 'validation_text'. The 'train_text' dataset is utilized for the training phase. During this phase, the dataset is further divided into two subsets--train and text--with an 80% and 20% ratio, respectively. Meanwhile, the 'validation_text' dataset(comprising 20% of the initial TAWOS subset) serves the purpose of validating the trained model's performance. This comprehensive approach, considering both textual and numeric features, results in a well-defined training process followed by a robust evaluation and validation. By leveraging the comprehensive nature of this approach, the methodology's robustness is underscored, and alignment with established data science practices is ensured, guaranteeing the accuracy of SP estimation across multiple projects without the need for separate models for each project.
# Brillouin Frequency Shift Sensing Technology Used in Railway Strain and Temperature Measurement
## 3 Methods of Test and Verification
### The Separation of Temperature and Strain  
The measured Brillouin frequency shift was a combination of two variables: the strain (\(\epsilon\)) and temperature (\(T\)). To obtain the strain of the tested rail, we tried to obtain its temperature measurement first.  
The reference track was set without fasteners or sleepers so that it could be freely retractable without being restricted by external forces. There was no strain difference between the optical fibers on the reference and tested tracks (\(\Delta\epsilon=0\)), and the temperature of the reference track can be calculated using Equation (4):  
\[\begin{array}{l}\Delta v_{B}\ =C_{\epsilon}(\alpha\times\Delta T)+C_{T} \Delta T\\ \Delta T=\Delta v_{B}\ \ /\  
We downloaded weather observation data from the C0R170 weather station [10] on the same day as the field test for a comparison with measurement data. The results are shown in Figure 9. The rail and atmospheric temperature rose and fell with the same trend.  
Both the heat radiation under sunshine in the daytime and the specific heat capacity of the rail being larger than that of air caused the rail temperature to be 12.1 \({}^{\circ}\)C higher than the atmospheric temperature, and there was a lag time difference of about 1.5 h. In addition, the lowest temperature of the rail was about 3.0 \({}^{\circ}\)C lower than the atmospheric temperature due to rainfall and radiation cooling effects in the early morning, e.g., 5:30 a.m. For inspectors who are concerned about the extension characteristics of rails, this measurement result can be used as a reference for determining the inspection time when performing personnel inspection tasks.
## 2 Materials and Methods
### Intelligent Data-Driven Model--Clustering Algorithm  
The clustering method, which was adopted in this study, is an unsupervised learning method that takes input features and data and does not require proper labels to predict and evaluate them. It is a data analysis technique for identifying intriguing patterns in data, such as fault patterns and groupings. It provides a quick summary of the data that could be utilized to make inferences. Since the purpose of a clustering task is to find data structures, the clustering method must therefore be able to determine the number of structures/groups in the data and how the features are distributed within each group.  
Clustering, for example, can be used to detect defects, faults, and anomalies in a system by using the system's database or historical data; moreover, the locations of the faults or defects in the system, as well as the area where errors occur more frequently, can also be determined using the clustering method. There are several clustering methods that can be used for this task, including K-mean clustering [17], mini-batch K-means clustering [18], spectral clustering, Gaussian mixture clustering [17], birch clustering [19], density-based clustering [20; 21], hierarchical clustering, and random forest clustering [22]. All of these methods can be successfully implemented to address the reliability problems in the sensor. In this thesis, however, the K-mean clustering model algorithm was proposed, and the other methods are used for the comparison of the final simulation results.
# Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids
## 5 Case Studies  
### Profiles  
In this section, the proposed method is implemented to the planning of the ESS, in a low-voltage distribution system, as shown in Figure 5. The parameters of the low-voltage case study distribution system are shown in the Table 1 of Appendix A. There were sixty-one nodes, in this case study network, and the base voltage was 10 kV, with the tight upper and lower limits of the voltage magnitude being 1.03 p.u. and 0.97 p.u., respectively. The node 451 was the slack bus and the node 150 was the standby node in the network. In this region, the time-of-use pricing mechanism was adopted. The electricity prices and time periods were: 0.976 V/(kW\(\cdot\)h) from 08:00 to 21:00 (peak hours), 0.294 V/(kW\(\cdot\)h) from 00:00 to 07:00, and 22:00 to 24:00 (off-peak hours). The sampling period was 1 h. The daily profiles of the loads and the output of PV are shown in Figure 6.  
The Li-ion battery was selected as the ESS, in this study. The upper and lower limits of the SOC were 90% and 10%, respectively, and the overall round-trip efficiency of the ESS was set to 95%. The efficiency of the power conversion system \(\eta_{\text{IPCS}}\) was 95% [21]. According to Reference [17], the project life cycle was 20 years, without considering the annual average decline rate of the battery-installation cost. For this study, the cost of unit energy was 3224 V/kW\(\cdot\)h, the cost of unit power was 1085 V/kW, and the operation and maintenance cost was 155 V/kW\(\cdot\)year. The specific disposal cost of the battery was 1582 V/kW [11] and the cost for the necessary supporting facilities was ignored, in this study, due to the use of the Li-ion battery, which did not require complicated supporting facilities. In addition, the discount rate was set as 10%.  
Figure 5: A low-voltage case study distribution system.
## 2 Literature Review
### Geospatial Feature Extraction  
Light detection and ranging (LiDAR) is a technology that can measure distances by shooting light pulses and recording the time they require to bounce back [24]. It is often used for creating accurate 3D maps or models, commonly in the form of point clouds (datasets composed of 3D points representing the surfaces of objects). While identifying changes in spatial entities, this involves comparing different scans or time frames of the same location to detect changes. These include structural changes in a building, vegetation growth or reduction, and shifts in land topography.  
These 3D LiDAR data often must be processed using specialized software or libraries to be analyzed. Two libraries commonly used for this purpose are the Point Cloud Library (PCL) and Open3D.  
* PCL (Point Cloud Library): This is a comprehensive, open-source library specifically designed for the processing of 3D point cloud data [25]. It provides many features and functions such as point filtering, surface normal or curvature estimation, disparate point cloud alignment, and shape recognition. It can support a variety of formats, making it suitable for various applications.
* Open3D: This is another open-source library aimed at processing 3D data. It is known for its speed, and it provides easy-to-use pipelines for processing point clouds,meshes, and depth images [26]. Open3D also includes functions for 3D visualization, 3D reconstruction, and 3D registration (aligning different 3D data sets).