esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feature Selection Algorithms
### Wavelet Transform  
The wavelet transform (WT) decomposes a signal into a series expansion where the number of wavelet functions is multiplied by a set of coefficients representing the original signal. Similar to Fourier transform (FT), WT expresses a signal in more specific located-in-time functions, but in the case of WT, the original signal can be non-periodic. In particular, WT analysis of a signal can bring frequency information and its position in time. Wavelet-based techniques include the discrete wavelet transform (DWT), the wavelet packet transform (WPT), the continuous wavelet transform (CWT), and the stationary wavelet transform (SWT). Table 3 shows the combination of WT-based techniques with ML models for structural damage identification. In data-based models, WT techniques serve as dimensionality reduction techniques, noise removal filters, and damage-sensitive feature generators, thereby improving SHM systems' accuracy and robustness.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 8 User Study and Real-World Validation
### Observations and Limitations  
While the MR-assisted system significantly improved identification accuracy and efficiency, several challenges were noted. In scenarios where the vials were partially obscured or positioned at extreme angles, the detection model occasionally misclassified the object, indicating limitations in occlusion handling. Some users reported mild discomfort when wearing the HoloLens 2 for extended periods beyond 20 min, suggesting the need for a more ergonomic device for long-term usage. Furthermore, performance slightly degraded under extreme lighting variations, and the system struggled in environments with excessive  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **Manual** & **Barcode** & **MR-Assisted (Proposed)** \\ \hline Identification accuracy (\%) & 72.4 & 88.6 & 95.3 \\ \hline Average identification time (s) & 7.8 & 5.2 & 3.1 \\ \hline Error rate (\%) & 14.6 & 7.5 & 3.2 \\ \hline User experience score (1-5) & 2.8 & 3.7 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of vial identification methods.  
glare or dim lighting. These issues indicate the need for further model enhancements to improve robustness under variable conditions.
# Coordinated Multi-Platooning Planning for Resolving Sudden Congestion on Multi-Lane Freeways
## 4 Proposed Platoon-Based Coordination Scheme to Minimize Traffic Congestion
### The Overall Platoon Protocol and Algorithm
#### 4.6.2 Lane-Changing Maneuver  
Lane changing is a challenge for platoon control. Accurate control for each platoon member is critical as the inter-vehicle distance is very short and vehicles are moving at a very high velocity. The proposed lane-changing control is described as follows:  
* Lane Change Condition: The platoon leader is responsible for evaluating the safety condition for lane-changing maneuvers for the entire platoon. To perform the evaluation, the platoon leader verifies Equations (12) and (13) based on the platoon status information and status information of neighboring platoons. If the conditions in these two equations are met, the platoon leader will decide to perform a platoon lane change and executes the operation described in the next step.
* Lane Change Execution: To perform platoon lane change for the entire platoon, the platoon leader sends messages to its platoon members and leads them to complete lane changing. Simultaneously, each platoon member follows the received instructions from the platoon leader and its preceding vehicle to perform the lane changing.
* Lane Change Completion: The platoon leader is able to calculate the completion time of the platoon lane changing. When the lane changing is completed, it will groupcast a platoon status message as a completion and confirm the message to its platoon members.
## 2 Performance Analysis of the WADGPS System
The variance of the position estimate is shown in Equation (4) as well:  
\[\left(G^{T}WG\  
\(d_{(i)(j)}^{2}\) is covariance in the (_i_) and (_j_) directions, where \(E\) is east, \(N\) is north, \(U\) is up, and \(T\) is time.  
The Horizontal Protection Level (HPL) is:  
\[HPL=K_{H}\cdot\sqrt{\frac{d^{2}_{\textit{cant}}+d^{2}_{\textit{north}}}{2}+ \sqrt{\left(\frac{d^{2}_{\textit{cant}}-d^{2}_{\textit{north}}}{2}\right)^{ 2}+d^{2}_{\textit{EN}}}} \tag{5}\]  
where, the \(K_{H}\) equals to 6.18 for Non-Precision Approach (NPA). This is a standard deviation multiplier of the horizontal position error. Through the zero mean Gaussian [13], the multiplier ensures that the horizontal position error exceeds the HPL at most one time in ten million (\(10^{-7}\)), the tolerable probability of Hazardously Misleading Information (HMI). The protection level calculation is specified in the WAAS MOPS Appendices A and J [8]. The HPL is very important and will be used to determine the performance of WADGPS system in this paper. On the other hand, this paper uses a triangular chart as our WADGPS system performance indicator, called the Stanford Chart [9]. The Stanford Chart helps us visualize the performance of the corrections and error bounds of a GPS integrity messaging system. The chart helps evaluate availability, accuracy, and integrity. The performance is evaluated in the user position domain in a two-dimensional space. The horizontal axis represents the true position error magnitude, and the vertical axis represents the estimated protection level described above. One Stanford Chart example is shown in Figure 2. There are three major regions: _System Available, System Unavailable,_ and _Hazardously Misleading Information (HMI)_. If the estimated protection level is smaller than the alert limit of a designed operation mode, the service will be _available_. If the estimated protection level is larger than the alert limit of a designed operation mode, the service will be _unavailable_. If the true error is larger than the estimated protection level, it is _hazardously misleading information_, because the protection level is meant to bound the true error and users have no knowledge of the excessive true error. This situation should occur with a probability of less than one in ten million operations or \(10^{-7}\)and should be avoided. As a result, this Stanford Chart can intuitively present system performance in terms of accuracy, availability and integrity. The availability of the system can be determined by examining the percentage of points that lie within the service available region [14]. Figure 2: The Stanford Chart.
## 2 Driving Theory Analysis
### Through Performance Analysis
#### 2.4.1 Obstacle Crossing Analysis  
An important measure of the prototype's barrier-passing ability is the maximum height of a vertical step that it can pass over. The center of mass position and the related track chassis design parameters will affect the prototype's through performance. When the prototype passes over the step, the front end of the track comes into contact with the step, and the track keeps driving, driving the friction between the track teeth and the step, so that the prototype climbs forward, and the front section of the supporting wheels are lifted up, and when the center of mass reaches the maximum height, the prototype's tail end is lifted up, and the center of mass is lowered, so that the vehicle passes over the step and travels forward, and the prototype passes over the step, as shown in the diagram in Figure 7, with the expression of the height of the prototype \(H\) being as follows:  
\[H=(L-L_{1}-h\text{tan}\,\gamma)\text{sin}\,\gamma \tag{21}\]  
where \(H\) is the vertical height of the step, and \(\gamma\) is the tilt angle of the crawler conveyor.  
The two important through performance indexes of the prototype are the approach angle and departure angle, and the larger the values of each, the better the through performance. The larger the approach angle, the larger the track grounding length, and the lower the grounding specific pressure, and at the same time, the number of teeth in contact between the track and the drive wheel decreases, resulting in greater stress on the drive wheel and increased wear on the drive wheel, meaning that the prototype should adopt the appropriate approach and departure angles. Factors affecting the prototype include the radius of the driving wheel, the center of gravity's position, the track grounding length and the ground conditions. The longer the grounding length and the further forward the center of gravity position, the higher a vertical step that the prototype can pass over.
# Design and Experiment of a Moving Magnet Actuator Based Jetting Dispenser
## 4 Experimental Set-Up  
A fully experimental system was set up as illustrated in Figure 9. The MMA dispenser (1) was attached to the 2D jetting system. An accelnet Panel ADP-090-09 amplifier (3) from Copley Controls Corporation generated a square voltage pulse. The Accelnet amplifier was similar to a high-frequency switch. CME 2 tm software was used to communicate between the amplifier and the computer via  
Figure 8: Open loop control.  
Figure 7: Voltage control scheme. (**a**) Single pulse; and (**b**) double pulse. T1: Rising stage; T2: Opening stage; T3: Falling stage; T4: Dispensing stage.  
a CANopen network or an RS-232 serial port. The output voltage and current value provided by the Accelnet amplifier were measured on the Scope tool of the CME 2\({}^{\text{TM}}\) software interface. The oscilloscope HMO1024 (5) was used to double-check the output signal from the amplifier.  
It should be noted that the MMA generates heat during the period when there is a current in the coil. Under this thermal conduction, the MMA heat transfers to the housing and fluid body. As a result, the droplet diameter was affected when the adhesive inside the fluid body was heated. To reduce the heat, one 12 V, 0.23 A, 2500 rpm DC fan (2) is attached onto the top of the MMA dispenser holder. The airflow from the fan caused the heat to disperse into the environment. The fluid body always stays at a temperature ranging from approximately 40-45 \({}^{\circ}\)C. The fluid temperature is checked at point A located on the outside of the fluid body surface, as shown in Figure 4. The TM-747 DU thermometer (4) with a resolution of 0.1 \({}^{\circ}\)C records the temperature data at the tested point during the operating process.  
A glycerin material with physical viscosity of about 950 cP at 20 \({}^{\circ}\)C is used in the experiment, which is conducted under the various working conditions, including the pneumatic pressure/backpressure (\(P_{\text{gy}}\)), ball-needle displacement (\(z_{\text{m}}\)), operating frequency (\(F\)), and fluid temperature (\(T\)). Specifically, the changes in droplet diameter under the effects of the former parameters are investigated in detail. The parameters (\(P_{\text{gy}}\), \(z_{\text{m}}\), \(F\), and \(T\)) are set up for four different experiments, as shown in Table 2. Only one of the four parameters is changed while the others remain constant for each experiment.
## 5 Discussion
### Model Governance and Permissions in Capella  
In the \({}^{3}\)ColStar project, model governance in Capella was carefully managed to ensure integrity and traceability throughout the development process. The governance structure assigned specific roles and permissions to manage modifications effectively within the unified model.  
1. System Engineering Oversight: Two designated System Engineers were assigned critical roles in model governance. The Lead System Engineer, supported by a secondary System Engineer, held the authority to implement changes across the system model. This hierarchical structure ensured that modifications were aligned with the mission's high-level requirements and facilitated overall integration.  
Subsystem Management: Each subsystem within the satellite platform was managed by specific representatives who were granted permissions to modify their respective areas. These representatives were tasked with ensuring that their designs adhered to the mission requirements and that any changes were accurately reflected in the model.
3. Cross-Subsystem Coordination: To manage interactions between subsystems, regular cross-functional meetings were organized. These meetings included representatives from the affected subsystems and the System Engineering team. The purpose was to discuss and implement modifications that involved multiple subsystems, ensuring that the integrated model remained consistent and coherent.
4. View-Only Permissions: For team members not directly involved in model modifications, view-only permissions were implemented. This approach preserved the model's integrity by restricting editing capabilities to authorized personnel only.
5. Project Management Oversight: A team of five Project Managers was responsible for authorizing changes that had financial implications or were critical to the project. Their oversight ensured that modifications were evaluated for their impact on both technical aspects and project budgets.
6. Weekly Status Meetings: Weekly meetings were held to provide updates on the project's status and discuss recent changes. These meetings were essential for keeping all team members, including those with view-only access, informed about ongoing developments and modifications to the model.  
This structured approach to model governance in Capella ensured that all subsystem designs were integrated and traceable to the mission's high-level requirements. By delineating roles and permissions and implementing a collaborative process for managing changes, the \({}^{3}\)ColStar project effectively maintained model consistency and supported continuous verification and validation throughout the project.
## 5 Latency Mitigation Strategies
### Network Optimisation Approaches  
These optimisation methods target the latency during data transmission at the network level. [PERSON] et al. [135] provide a survey on latency reduction solutions for 5G networks. They divide the approaches into radio access network (RAN) solutions (e.g., short frame/packets, new waveform designs, mmWave aggregation), core network solutions (e.g., high-speed backhaul, mobile edge computing (MEC)/fog-network architectures), and caching solutions (e.g., distributed, centralised caching).  
Various approaches have been proposed to optimise the network for reduced latency. For instance, [PERSON] et al. [136] use redundant messages to exploit randomness across multiple paths, leading to low latency. Similarly, [PERSON] et al. [137] consider a redundancy-aware federated learning architecture for efficient and cooperative vehicular networking and improved data quality accuracy. [PERSON] et al. [138] propose intelligent infotainment caching models in autonomous vehicles, where they use deep learning to predict contents that need to be cached. Their method can minimise content downloading delays. [PERSON] et al. [38] reduce latency by using multiple links, enabling rate control and scheduling in combination with slice-by-slice video processing. [PERSON] et al. [139] develop a smart-contract-based secure edge computing architecture, which offers low-cost services for vehicles in 6G networks. [PERSON] et al. [140] focus on reducing video streaming latency in vehicle teleoperation through network factors. They apply UDP and the Real-Time Messaging Protocol (RTMP), tune the encoder, and further apply data compression. In another representative work, [PERSON] et al. [141] utilise DetNet for telesurgery, integrating time-sensitive networking (TSN), software-defined network (SDN), and other technical features for a more deterministic network with low latency and jitter. [PERSON] et al. [142] propose deep Q-learning to predict the optimal minimum contention window (MCW) for improved vehicular communication and age fairness. [PERSON] et al. [100] analyse the requirements of QoS predictions and discuss the architecture of a prediction model for 5G V2X applications, specifically for remote driving use cases. Further to that, [PERSON] et al. [143] use LSTMs for QoS prediction of 5G networks for CAVs.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
## 5 Conclusions  
We evaluated an inherent microstructure of the TLPS joint, skeleton-shaped Cu-IMC-resin microstructure, using FE simulations based on a 3D image reconstruction model to elucidate its macro- and micro-deformation properties. Macroscopically, the Cu-IMC-resin microstructure showed temperature-stable and low-stiffness mechanical characteristics because the Cu and IMC primarily acted as the load-carrying phases, and the embedded easily-deformable resin acted as the spacer. The homogenized Young's modulus of the TLPS Cu-IMC-resin microstructure (11 GPa, \(-\)89%) was significantly lower than that of Cu\({}_{6}\)Sn\({}_{5}\) (102 GPa, reference). Microscopically, the local stresses were broadly distributed on the IMC parts within the TLPS microstructure under uniaxial tensile simulation. This micro-deformation behavior indicated that the TLPS microstructure can exhibit highly  
Figure 11: Distribution of Mises stress obtained from the FE analysis of the 50-μm-sized Cu-IMC-resin model against uniaxial displacement at \(-\)55 \({}^{\circ}\)C. (**a**) FE model, and the models with uniaxial macroscopic strain of (**b**) 0.1%, (**c**) 0.4%, (**d**) 0.6%, and (**e**) 1.0%. The areas, whose Mises stress exceeded the tensile strength of Cu\({}_{6}\)Sn\({}_{5}\) (approximately 1000 MPa), are shown by white dotted circles.  
reliable performance under a specific macroscopic strain condition in spite of the poor ductility of the IMCs. SiC-DBC die-attached numerical model using the homogenized mechanical properties of the Cu-IMC-resin predicted its excellent reliability during thermal cycling, as the deformation behavior showed both quite low strain and stress from the macroscopic view, and lower local-stress than the tensile strength of Cu\({}_{6}\)Sn\({}_{5}\) from the microscopic view. This prediction was supported by the experimental results.  
The proposed microstructure deformed under low-stiffness due to its inherent skeleton-like shape. However, a balance between low stiffness and high thermal conductivity was crucial for an efficient operation. Therefore, the future scope of this work includes further optimization of the skeleton microstructure to alleviate this compromise.  
Conceptualization, H.T., Y.K. and A.H.; Investigation, H.T.; Methodology, H.T., T.M. and T.S.; Project administration, H.Y.; Resources. H.T. and H.Y.; Supervision, Y.K. and A.H.; Validation, H.T. and H.Y.; Visualization, H.T.; Writing--original draft, H.T.; Writing--review and editing; H.T., T.M., T.S., Y.K. and A.H.  
This research received no external funding.  
The authors would like to thank [PERSON] for technical assistance with the finite element analysis.  
The authors declare no conflict of interest.
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.5 Discussion  
In all demonstrated experiments, our proposed BAGCN model significantly outperformed other methods. BiLSTM-based approaches are likely to be limited because they only learn sentence sequence information. On the other hand, most dependency-based methods do not make full use of the dependency information because they only consider the connectivity of the dependency tree and ignore the important information in the dependency tree. Our proposed model can overcome these two limitations by stacking BiLSTM sentence encoders and GCN dependency tree encoders to automatically extract the hidden features of sentences. In addition, the model incorporates a novel pruning operation and a multi-head attention mechanism to improve performance. At the same time, we propose new pruning operations that also contribute to model performance. Compared with some single-pruning operations, our pruning operation well preserves the important words in the dependency tree. Additionally, it avoids the possibility of pruning removing useful information. Compared with other baseline models, our model combines their advantages and has better prediction performance.
## 5 Analysis and Discussion
### Pre-Warning of Threat  
The risk performance reasoning model constructed in this paper is based on the normal conditions of cargo, ship, and environment for Handy bauxite carriers. The data of factors for scenarios No. 2 to No. 5 were input into the HMM for risk performance reasoning based on Scenario No. 1, where the log-likelihood probability of the output could provide a guide to abnormal risk monitor. A larger log-likelihood would denote a greater probability of low risk performance. Figure 13 demonstrates the log-likelihood value of 15 time series for scenarios No. 1 to No. 5.  
The logarithm likelihood values of scenario No. 3 with unsatisfactory ship conditions and of scenario No. 4 with a terrible environment were close to those of the normal scenario No. 1.  
Figure 13: Sensitivity evaluation of reasoning for risk performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Scenario No. 2** & **Scenario No. 3** & **Scenario No. 4** & **Scenario No. 5** \\ \hline FA-based DOS & 88.962 & 35.789 & 3.118 & 89.567 \\ PFA-based DOS & 96.234 & 80.398 & 0.001 & 96.854 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Degree of sensitivity (DOS) of HMM for risk performance reasoning.  
The log-likelihood value of scenario No. 2 with worse cargo conditions more obviously deviated from that of scenario No. 1 than that of Scenario No. 3 and Scenario No. 4, indicating that the risk performance of the Handy bauxite carrier in scenario No. 2 was worse, and that the deterioration of cargo with respect to ship conditions and the environment had a greater impact on the total safety of the bauxite shipping process.  
Scenario No. 5 had a more serious deviation from the normal condition. This deviation was more significant than the worst scenario of single-category factors such as the cargo, ship, or environment, which all contribute to the total risk of bauxite shipping process. It was found that the coupling effect of risk factors produced a coupling risk, which aggravated the total risk performance during the shipping process.  
When using the PFA-based risk reasoning model to detect the performance, the log-likelihood value of individual time points was negative infinite, indicating that the risk behavior at this time was seriously inconsistent with the normal scenario. The risk performance at this time point can be determined as the highest rating, which is unacceptable.  
In scenarios No. 3, 4, and 5, the log-likelihood probability decreased gradually, showing that the total performance deviated from the normal condition and deteriorated gradually, thereby achieving the state monitoring and issuing a pre-warning threat during the shipping process.