esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
First, the model's high sensitivity to the weather attention component indicates a potential brittleness that could be problematic if weather data quality degrades. Second, our training data come from a single airport (KBTP), and generalization to airports with different traffic patterns, terrain, or operational characteristics requires further validation. Third, the computational overhead compared to simpler baselines may limit deployment in resource-constrained environments. Fourth, our current approach does not explicitly model rare events or emergency scenarios, which, while infrequent, are critical for comprehensive safety analysis. Finally, the probabilistic outputs, while valuable for uncertainty quantification, require careful interpretation by end users who may be more familiar with deterministic systems. These limitations define important directions for future research and development before widespread operational deployment. Looking ahead, several promising research directions emerge. First, exploring adaptive approaches that can dynamically adjust between deterministic and stochastic modeling based on data density could further improve performance across varying sampling rates, as our results indicate that stochastic modeling benefits dense sampling rates, but may introduce unnecessary complexity at sparser rates. Second, incorporating additional contextual information such as pilot experience, aircraft specifications, and airspace regulations could improve the precision of the prediction in complex scenarios. Finally, extending our approach to handle rare events and anomalous flight patterns remains a challenging but important area for future investigation, particularly as general aviation continues to grow and integrate with broader air traffic management systems. Conceptualization, Y.Y. and Q.X. ; methodology, H.H. ; software, H.H. ; validation, H.H. and Y.Y. ; formal analysis, Q.X. ; data curation, H.H. ; writing--original draft preparation, H.H. ; writing--review and editing, Y.Y. ; visualization, Y.Y. ; supervision, Q.X. ; funding acquisition, Q.X. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Science Foundation of Jiangsu Province: [Grant Number BK20230893] and China Postdoctoral Science Foundation: [Grant Number 2024M754130]. Not applicable. Data available in a publicly accessible repository: [[https://theairlab.org/trajair/](https://theairlab.org/trajair/)]([https://theairlab.org/trajair/](https://theairlab.org/trajair/)) (accessed on 8 June 2025) [30]. The authors declare no conflicts of interest.
## 5 Results and Discussion
### Comparisons with Euclidean Representations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method and [Ref] and DC (\(\checkmark\))**} & \multirow{2}{*}{**Metric**} & \multicolumn{5}{c}{**Datasets and (\(|\Psi_{REF}|\))**} \\ \cline{3-8}  & & & **CEDAR** & **MCYT** & **GPDS300** & **BANGLA** & **HINDI** \\ \hline Graph edit distance (MCS) [77] & \(EER_{SF}^{user}\) & 5.91(10) & 3.91(10) & & & \\ \hline Surroundedness [78] & \(EER_{SF}^{user}\) & 8.33(1) & - & 13.7(1) & - & - \\ \hline Region Deep Metric (MSDN) [79] & \(EER_{SF}^{user}\) & 1.75(10) & - & - & - & - \\ \hline Point2 Set DML [80] & \(EER_{SF}^{user}\) & 5.22(5) & 9.86(5) & - & - & - \\ \hline DMML(with HOG) [81] & \(EER_{SF}^{user}\) & - & 13.4(5) and & - & - & - \\ \hline Partially ordered sets [37]\(\checkmark\) & \(EER_{SF}^{user}\) & 2.90(5) & 3.50(5) & 3.06(5) & & \\ \hline DCCM and Feat. Diss. Thresh. [82] & \(AvE_{SF}^{user}\) & 2.10(5) & - & 18.4(5) & - & - \\ \hline SURDS [35] & \(AvE_{SF}^{user}\) & - & - & - & 12.6(8) & 10.5(8) \\ \hline TransOSV [19] & \(EER_{SF}^{user}\) & - & - & - & 9.90(1) & 3.24(1) \\ \hline Sim. Dist. Learn. (SPD) [55]\(\checkmark F_{intra}\) & \multirow{2}{*}{0.37(10)} & \multirow{2}{*}{0.96(10)} & \multirow{2}{*}{-} & \multirow{2}{*}{0.26(10)} & \multirow{2}{*}{0.77(10)} \\  100\%RF, a = 4 (\(\mathcal{D}_{1}\)) & & & & & \\ \hline Sim. Dist. Learn. (SPD) [55]\(\checkmark F_{intra}\) & \multirow{2}{*}{0.38(10)} & \multirow{2}{*}{1.02(10)} & \multirow{2}{*}{-} & \multirow{2}{*}{0.25(10)} & \multirow{2}{*}{0.78(10)} \\  0\%RF, a = 4 (\(\mathcal{D}_{1}\)) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Direct comparisons (DCs, denoted with \(\checkmark\)) and comparative summary of WI-SV systems. Performance metrics are either the average (per user and dataset) equal error rate \(EER_{SF}^{user}\) or the average (per user and dataset) error rate \(AvE_{SF}^{user}\). The number in the parentheses denotes the number of reference samples during the testing stage.
# UAV Image Multi-Labeling with Data-Efficient Transformers
## 4 Experimental Results
### Results
#### 4.4.2 Experiment 2: The Role of Encoder's Layers  
To assess the role of each layer in the encoder, we repeated the experiments with a different number of encoder layers. Table 4 and Figure 4 summarize the experimental results of using a different number of layers on the Trento dataset, and Table 5 and Figure 5 show the results on the Civezzano dataset. In general, better results were obtained with a higher number of layers. However, adding more layers increased the complexity of the encoder architecture and hence, increased the time required to train the model. Figures 4 and 5 show visually that performance started to plateau after layer 10 in both datasets. Therefore, for UAV multi-labeling, an encoder with ten layers would be sufficient to achieve good results. In detail, the results of the Trento dataset showed a consistent improvement in all metrics as the number of layers increased. However, the performance started to decrease after the tenth layer. At the same time, the results of the Civezzano dataset show a slight decrease in few metrics such as the recall, the F1-score, and the average after layer 10. We further visualized the attention heat map learned by each layer of our model. Figures 6 and 7 show the attention heat maps generated by the first, sixth, and last layers of the encoder for the two UAV datasets. The red color in the attention heat maps highlights the areas with maximum attention weight, while the blue color represents the areas with lower attention weight. The attention heat maps clearly show that the self-attention component in our model was able to capture the long-range relations across the entire scene. Moreover, the attention heat maps show that as the number of layers increased, the model improved its ability to focus on the regions corresponding to objects existing in the scene. This can be highly noticed in the initial layers from one to six, which played a key role in highlighting the discriminative regions. In contrast, the later layers of the encoder (i.e., from 7 to 12) had the role of refining the attention heat maps resulted from the previous layers and detecting the details of the objects. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Layers**} & \multirow{2}{*}{**Sp**} & \multirow{2}{*}{**Re**} & \multirow{2}{*}{**Pr**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{**F2**} & \multirow{2}{*}{**Avg**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**HL**} & \multirow{2}{*}{**RL**} & \multicolumn{2}{c}{**Time**} \\  & & & & & & & & & & **(Seconds)** \\ \hline
1 & 93.55 & 53.73 & 59.07 & 56.27 & 57.92 & 73.64 & 38.57 & 12.33 & 45.31 & 629 \\
2 & 95.06 & 63.14 & 68.90  
Figure 4: Results of our model on the Trento multi-label dataset. Figure 5: Results of our model on the Civezzano multi-label dataset.
## 6 Uncertainties Associated with Distances Derived from Point Clouds
### Distance Measurement: Uncertainty Contribution from Probe Qualification Effects  
We assume that probe qualification effects are modelled as in Section 4.2:  
\[\mathbf{e}_{PQ,k}\in\mathcal{N}(\mathbf{0},\sigma_{PQ,k}^{2}I).\]  
If \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) are measured using the same probe, then the uncertainty contribution to the distance \(d_{ij}\) from probe qualification effects is zero. Otherwise,  
\[u^{2}(d_{ij})=\sigma_{PQ,k(i)}^{2}+\sigma_{PQ,k(j)}^{2}.\]  
The uncertainty contribution associated with \(d_{ij}-d_{rs}\) arises from the term  
\[(\mathbf{e}_{k(i)}-\mathbf{e}_{k(j)})^{\top}\mathbf{n}_{ij}-(\mathbf{e}_{k(r)}-\mathbf{e}_{k(s)})^{ \top}\mathbf{n}_{rs}.\]  
If \(k(i)=k(j)\) and \(k(r)=k(s)\), then \(u^{2}(d_{ij}-d_{rs})=0\). If \(k(r)=k(s)\) but \(k(i)\
eq k(j)\), then  
\[u^{2}(d_{ij}-d_{rs})=u^{2}(d_{ij})=\sigma_{PQ,k(i)}^{2}+\sigma_{PQ,k(j)}^{2}.\]  
If \(k(i)=k(r)\) and \(k(j)=k(s)\) but \(k(i)\
eq k(j)\), then  
\[u^{2}(d_{ij}-d_{rs})=(2-2\mathbf{n}_{ij}^{\top}\mathbf{n}_{rs})\Big{(}\sigma_{PQ,k(i)} ^{2}+\sigma_{PQ,k(j)}^{2}\Big{)},\]  
so that  
\[0\leq u^{2}(d_{ij}-d_{rs})\leq 4\Big{(}\sigma_{PQ,k(i)}^{2}+\sigma_{PQ,k(j)} ^{2}\Big{)},\]  
depending on the angle between \(\mathbf{n}_{ij}\) and \(\mathbf{n}_{rs}\). The uncertainty contribution is zero if \(\mathbf{n}_{ij}=\mathbf{n}_{rs}\), e.g., when two gauge blocks are measured parallel to each other with both left faces measured by one probe and both right faces by the other. The uncertainty contribution is maximised when \(\mathbf{n}_{ij}=-\mathbf{n}_{rs}\), e.g., when the left face of one gauge block is measured by one probe and the left face of a second parallel gauge block is measured by the other probe with the probes interchanged for the right face. If all four measurements are undertaken by different probes, then  
\[u^{2}(d_{ij}-d_{rs})=u^{2}(d_{ij})+u^{2}(d_{rs})=\sigma_{PQ,k(i)}^{2}+\sigma_{ PQ,k(j)}^{2}+\sigma_{PQ,k(r)}^{2}+\sigma_{PQ,k(s)}^{2}.\]
## 4 Conclusions  
To investigate the self-assembly behavior of **UBD** containing an amphiphilic chain in aqueous solution, three **UBD (UBD-1-3)** with different alkyl chain lengths, namely, hexyl, heptyl, and octyl, respectively, were prepared. All **UBD** formed identical spherical nanoparticles at concentrations above the CAC. Moreover, the size of the nanoparticles increased with increasing temperature because of the enhanced hydrophobic interactions due to dehydration of the ethylene glycol chains. In addition, the transmittance of the **UBD** solutions abruptly decreased, indicating an LCST behavior. A comparison of the CAC and LCST of the three **UBD** revealed that **UBD-3** exhibited the smallest CAC and the largest LCST values, suggesting a relationship between the hydrophobicity of the **UBD** and its self-assembly behavior. Studies with varying concentrations of **UBD** (Figure 5, left scheme) revealed an equilibrium between the monomer and the nanoparticles. On the other hand, by varying the temperature (Figure 5, right scheme), the dehydration process was responsible for the formation of larger particles, which were in equilibrium with the nanoparticles. In the future, we plan to synthesize nanoparticles having functional groups for bioimaging [30] and investigate the self-assembly behavior of nanoparticles bearing different numbers of amphiphilic side chains.  
Figure 5: Predicted mechanism of the self-assembly of **UBD** in aqueous solution.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/8/7/1080/s1](http://www.mdpi.com/2076-3417/8/7/1080/s1)]([http://www.mdpi.com/2076-3417/8/7/1080/s1](http://www.mdpi.com/2076-3417/8/7/1080/s1)), Concentration dependence of the \({}^{1}\)H NMR spectra of **UBD-2** and **-3**. Temperature dependence of the \(D_{\text{H}}\) values and transmittance changes for **UBD-1**. Temperature dependence of the LCST behavior of **UBD-2** and **-3**. FTIR spectra of the freeze-dry samples of **UBD-1**, **-2**, and **-3**.  
**Author Contributions:** [PERSON] designed the experiments, conceived and supervised the project, analyzed the final data, and wrote the manuscript. [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments and analyzed the data to investigate the properties and mechanistic behavior of the materials. [PERSON] checked the data and the manuscript, and discussed the results with the corresponding author.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors thank [PERSON] for the helpful discussions. This work was partially supported by the PRESTO Program on Molecular Technology from the Japan Science Technology Agency (JST).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Exploring Benefits of Using Blending Splines as Transition Curves
## 3 Method
### Blending Spline Fitting
#### 3.2.3 B-Function  
The B-funtion used in the study is the one presented in (2), an ERB-function. This function can be varied through the following parameters: \(\alpha>0\), \(\beta>0\), \(\gamma>0\), and \(0\leq\lambda\leq 1\), where \(\alpha,\gamma\in\mathbb{N}\) and \(\beta,\lambda\in\mathbb{R}\). The default ERB-function is given by \(\alpha=1\), \(\beta=1.0\), \(\gamma=1\), and \(\lambda=0.5\), as seen in Figure 7. \(\alpha\) and \(\gamma\) are called the asymmetric tightening parameters. Increasing them tightens the function so that its shape goes toward a linear function on parts of the domain. Figure 8 shows plots of ERB-functions with varying tightening values. \(\beta\) is called the slope parameter. This adjusts the steepness of the function.  
Figure 9 shows ERB-functions with varying slope values. \(\lambda\) is called the balance parameter. Varying this moves the function along the x-axis. Figure 10 gives plots of ERB-functions with varying balance values. For the comparisons in this paper, the parameters were adjusted independently, modifying one while keeping the default values for the rest. The tested values for each parameter are  
Figure 5: An example of two local arc curves (dashed and dotted) starting in \([p_{3},q_{3}]\).  
Figure 6: An example of how the last control point \(cp_{l}\) is moved to the start of the local curve in the opposite end \(cp_{n}\), for the left side local Bézier curve starting in \(p_{1}\). The dashed curve is the first Bézier approximation of the arc, and the solid curve is the one used in the blending.  
given in Table 2. They are selected based on the shape differences that they bring to the B-function, so that a range of contrasting blending functions are tested.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\alpha\) & \(\gamma\) & \(\beta\) & \(\lambda\) \\ \hline
**Default** & \(1\) & \(1\) & \(1.0\) & \(0.5\) \\
**Tightening 1** & \(4\) & \(1\) & \(1.0\) & \(0.5\) \\
**Tightening 2** & \(10\) & \(1\) & \(1.0\) & \(0.5\) \\
**Slope 1** & \(1\) & \(1\) & \(8.0\) & \(0.5\) \\
**Slope 2** & \(1\) & \(1\) & \(50.0\) & \(0.5\) \\
**Balance 1** & \(1\) & \(1\) & \(1.0\) & \(0.2\) \\
**Balance 2** & \(1\) & \(1\) & \(1.0\) & \(0.8\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Expo-rational blending function (ERB-function) parameter values used in the comparisons.  
Figure 8: Plots of Expo-rational blending functions (ERB-functions) with varying tightening parameters. The solid function is the default ERB (\(\alpha=\gamma=1\)), the dashed function has \(\alpha=2\) and \(\gamma=1\), and the dotted function has \(\alpha=10\) and \(\gamma=1\).  
Figure 7: Plot of the default ERB-function.
###### Abstract  
This paper discusses the application of piezoceramic bender elements (BEs) for measurement of shear wave velocity in the time and frequency domain in a triaxial cell under different isotropic confinement. Different interpretation methods were used in the tests and their results were finally compared with each other. Two types of anthropogenic material were tested: pure Recycled Concrete Aggregate (RCA) and RCA-rubber chips mixtures (15% of rubber addition). Presented study is an attempt to describe dynamic properties, in terms of shear wave velocity (\(V_{S}\)), of the aforementioned anthropogenic material using the technique commonly applied for natural soil. Although some research is currently being carried out, in order to evaluate physical, chemical and mechanical properties of RCA and rubber-soil mixtures, still little is known of their dynamic properties. Hence, this work will provide the experimental results of shear wave velocity of RCA and its modified version. The results show that tires chips significantly decrease the \(V_{S}\) values of modified RCA. They help to reduce the near field effect, but the received parameters are more incoherent. The \(V_{S}\) values were found to be influenced by interpretation technique, mean effective stress and wave's propagation period. The maximum \(V_{S}\) values were obtained mostly from the frequency domain method, although time domain analysis gives the results that are more coherent.  
**Keywords:** anthropogenic soil dynamics; bender elements tests; shear wave velocity; recycled concrete aggregate; rubber chips +
Footnote †: journal: Applied
## 2 Preliminaries  
### Induction Motor Mathematical Model  
The stator windings of electrical machines with AC are intended to generate a sinusoidally distributed conductor density, so the distributed density in the air gap will be sinusoidal. Considering a squirrel-cage rotor, the density in the bars is uniform; however, the currents present in the same bars produce a magnetomotive force (mmf), which is sinusoidally distributed, allowing, it to replace the stator and rotor windings of the induction motor, respectively, with an equivalent of sinusoidally distributed balanced three-phase windings [25; 26].  
To obtain the mathematical model considered, we consider the following assumptions.  
1. Magnetic circuits are linear and three-phase windings are balanced.
2. The magnetic material in the stator and rotor operates in its linear region, and it has a very high value for the permeability factor; saturation, iron losses, end-windings, and slot-effects are ignored.
3. The machine is symmetric.
4. The stator resistance is constant.  
According to these assumptions, based on the general equations that describe the mathematical model of the three-phase motor [27] (pp. 135, 170), [26], the Clarke transform is applied to convert from a three-phase system to a two-phase system, obtaining as a result a fifth-order model that describes the dynamics of a balanced induction motor. This model is expressed in a fixed reference frame attached to the stator \((a,b)\)[26] and is given as follows:  
\[\frac{d\omega}{dt}=\mu(\psi_{ra}i_{sb}-\psi_{rb}i_{sa})-\frac{T_{L}}{J},\]  
\[\frac{d\psi_{ra}}{dt}=-\alpha\psi_{ra}-\omega\psi_{rb}+\alpha L_{m}i_{sa},\]  
\[\frac{d\psi_{rb}}{dt}=-\alpha\psi_{rb}+\omega\psi_{ra}+\alpha L_{m}i_{sb},\]  
\[\frac{di_{sa}}{dt}=-\gamma i_{sa}+\frac{v_{sa}}{\sigma}+\beta\alpha\psi_{ra}+ \beta\omega\psi_{rb},\]  
\[\frac{di_{sb}}{dt}=-\gamma i_{sb}+\frac{v_{sb}}{\sigma}+\beta\alpha\psi_{rb}- \beta\omega\psi_{ra}, \tag{1}\]  
where \(\sigma=L_{s}-\frac{L_{m}}{L_{r}}\), \(\beta=\frac{L_{m}}{\sigma L_{r}}\), \(\gamma=\frac{R_{s}}{\sigma}+\beta\alpha L_{m}\), \(\alpha=\frac{R_{r}}{L_{r}}\), \(\mu=\frac{L_{m}}{L_{r}}\), \(T_{L}\) is the load torque, \(R_{s}\) and \(R_{r}\), are the resistances of the stator and rotor, \(L_{s}\) and \(L_{r}\) the self-inductances of the stator and rotor, and \(L_{m}\) is the mutual inductance. The state variables \([\omega,\psi_{ra},\psi_{rb},i_{sa},\) and \(i_{sb}]\) denote the speed of the rotor, the components of the rotor flux, and the rotor current, respectively, and the input variables \(v_{sa},v_{sb}\) are the voltage input to the machine. The Clarke inverse transformation [26] is applied to obtain the three-phase signal in AC, then this signal is converted to a root mean square voltage, which is equivalent to the calculated control signal for each phase. Then, it is adjusted with a proportionality constant to maintain the V/F relation according to the characteristics of VFD Delta VFD007 EL11A [28]. Although the VFD Delta VFD007 EL11A cannot directly control the motor torque, the scalar control tries to maintain the maximum torque available based on the V/F relation [28].
## 3 Hardware Architecture
### Antenna Design  
For localization within the horizontal plane, the signal must be emitted omnidirectionally in azimuth, in order to fulfill the system requirements for robustness and efficiency in an underwater environment. For this purpose, a half-wavelength dipole antenna was designed for underwater use, avoiding effects of air-water interfaces, as well as significantly reducing its length compared to terrestrial counterparts. A detailed build instruction is provided at [[https://youtu.be/muQLCZgodWk](https://youtu.be/muQLCZgodWk)]([https://youtu.be/muQLCZgodWk](https://youtu.be/muQLCZgodWk)). In consideration of good maintainability, simple sealing and future applications onboard \(\mu\)AUVs, the sleeve-dipole design is favored over a dipole T-shape. The underwater antenna is made of RG316 coax cable and matched to a \(50\,\Omega\) coaxial transmission line. The medium of propagation noticeably affects the signal wavelength and hence influences the antenna length. In the freshwater experimental environment, the best signal transmission is observed with an antenna length of \(160\,\mathrm{mm}\). Note, that the antenna length depends strongly on the conductivity of the surrounding water. The connection between antenna and feeder is realized through a dielectric tube in which an industrial PVC cable-sealing fixes the antenna. The antenna build-up is illustrated in Figure 7 with a photo and a schematic.  
Figure 8 shows the Smith chart of the deployed antennas in freshwater with a conductivity of \(\sigma=0.031\,\mathrm{S/m}\) at \(18\,^{\circ}\mathrm{C}\).
## 5 Performance Evaluation and Comparative Analysis  
In 5G VANETs, where network resources including hardware and software are distributed across different geographical locations, it is imperative to match vehicular requests to the right resources that can guarantee the required QoS. Furthermore, in a fully virtualised 5G communication infrastructure for critical VANETs applications, where network resources including the virtual Radio Access Network (vRAN) are collectively managed by the SDN Controller, the granular matching of vehicular requests to network functions presents novel opportunities for ensuring optimal QoS for critical operations. The disintegration of the application, control, and user planes in SDN enables the seamless matching of network functions across the three planes.  
To evaluate the performance of the proposed framework, we applied queueing and delay theories to queue vehicular data (events) across a chain of distributed and virtualised network resources (queues), including communication links. The proposed event-driven multi-queue and delay broker framework (Figure 12) employs a broker (SDN controller flow rules, R1-RN) to process vehicular data on a chain of queues (VNFs) including the SDN gateway, VXLAN links to gNB, gNB, and links to edge application servers.  
Traditionally, VANET frameworks rely on layered communication architectures that apply a fixed set of rules at each layer. This rigid structure limits scalability and dynamic access, making it difficult to achieve optimal QoS in 5G VANETs. In contrast, event-driven software architectures are distributed and asynchronous, offering high scalability and adaptability to single-purpose event-processing components [21]. Event-driven software models consist of highly decoupled processing units that enable the granular and strategic management of processing units using pre-defined sets of rules.