esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## Inverse Models  
Ground displacements measured by InSAR and GNSS were modeled using a 3-D Mixed Boundary Element forward method [45; 46] which assumes that the medium is linear, elastic and homogeneous. Fast and precise computations are produced by the combination of a direct method and a displacement discontinuity method used for the ground surface and fractures, respectively. The model can take tensile cracks and shear fractures under a realistic topography into account [47; 48; 49]. Boundaries were meshed using triangular elements. To avoid edge effects, the ground surface mesh was five times larger than the deformed area. To provide the best compromise between computation time and accuracy, the most deformed boundaries were finely meshed, while away from the deformation zone, coarser meshes were used. A Young's Modulus of 5 GPa and a Poisson's ratio of 0.25 were assumed for the Piton de la Fournaise edifice, following [PERSON] and [PERSON] [46], [PERSON] et al. [48].  
To take advantage of the whole data set acquired during this eruption, we developed a strategy for inversion that consisted of several steps (Figure 4). First, we performed a global inversion that is, an inversion of the cumulated InSAR and GNSS displacements recorded between two dates: one before the eruptive crisis onset, that is, before the onset of the magma propagation and one after the eruption had ended. In this study, we refer to this global inversion as the \"static inversion\" as it is intended to reproduce the final geometry of the magma that was trapped at depth after the eruption had resumed. Second, we performed a temporal inversion, that is, inversions of the cumulated displacements for 29 time intervals between 20:40 on 25 May and 4:00 on 26 May, from GNSS time series. This temporal inversion was used to track the dynamics of the magma propagation before it reached the surface and the eruption began. The last step was a validation of the methods used for the temporal inversion and of the model family used as an a priori. It compared the misfits and models inverted at a date for which an intermediate InSAR acquisition was available, allowing the displacement of the first part of the eruption to be measured.
## 4 Proposed Hybrid FVIMDE Algorithm
### Population Size and Role of the Four Vectors  
In the FVIM phase, the population size remains the same as in the DE phase and is denoted as \(N\).  
Position Update  
During the FVIM phase, the position of each particle is updated based on the positions of the Alpha, Beta, Gamma, and Delta particles. The new position for each particle is computed as the average of four intermediate positions, as given in Equation (8):  
\[\mathbf{X}_{i,j}=\frac{\mathbf{X}_{i,j}^{1}+\mathbf{X}_{i,j}^{2}+\mathbf{X}_{ i,j}^{3}+\mathbf{X}_{i,j}^{4}}{4} \tag{8}\]  
where \(\mathbf{X}^{1},\mathbf{X}^{2},\mathbf{X}^{3},\mathbf{X}^{4}\) are position updates influenced by the Alpha, Beta, Gamma, and Delta particles, respectively, as shown in Equation (9).  
Each intermediate position \(\mathbf{X}_{i,j}^{k}\) is calculated as follows:  
\[\mathbf{X}_{i,j}^{k}=\begin{cases}\mathbf{P}_{k,j}+a\times(2\times\text{rand} (0,1)-1)\times|\text{rand}(0,1)\times\mathbf{P}_{k,j}-\mathbf{X}_{i,j}|&\text {if }\text{rand}(0,1)<0.5\\ \mathbf{P}_{k,j}-a\times(2\times\text{rand}(0,1)-1)\times|\text{rand}(0,1) \times\mathbf{P}_{k,j}-\mathbf{X}_{i,j}|&\text{otherwise}\end{cases} \tag{9}\]  
where \(k\in\{\text{Alpha, Beta, Gamma, Delta}\}\), and \(a\) is a linearly decreasing coefficient calculated as shown in Equation (10):  
\[a=2-\text{iteration}\times\frac{2}{\text{Max\_iter}-\text{DE\_iter}} \tag{10}\]  
where iteration is the current iteration number, and \(\text{DE\_iter}=\frac{\text{Max\_iter}}{2}\).
# Rapid and Precise Zoom Lens Design Based on Voice Coil Motors with Tunnel Magnetoresistance Sensors
## 4 Experimental Verification  
The rapid zoom lens prototype is assembled after completing the VCM structural design and magnetic-scale closed-loop control system, a photograph of which is shown in Figure 12. The core component of the VCM is shown in the red dashed box, where the zoom group, energized coil, permanent magnet, and magnetic yoke are also visible. Rapid zooming and precise positioning were our goals; thus, experiments to measure the zoom time and positioning accuracy were performed. In the zoom time test experiment, the full-range zoom time from the wide-angle end to the telephoto end was measured to evaluate the response speed of the rapid zoom. In the positioning accuracy test experiment, the deviation between the actual position of the lens group and the target position was repeatedly measured to evaluate the system's accuracy and stability.  
Figure 11: Diagram of closed-loop control process.  
Figure 12: Photographs of rapid zoom lens and VCM.  
In the zoom experiment, as presented in Figure 13, we utilized a VCM and a traditional step motor to drive the 40\(\times\) zoom optical system. In the zoom speed test experiment, zoom lenses with the same magnification were driven from the wide-angle end to the telephoto end. As shown in Figure 13a and Supplementary Movie S1, clear high-magnification images were obtained using a rapid zoom lens with VCMs at \(t=0.1\) s, and it took less than 0.2 s to complete the zooming process. As a comparison, a slow and continuous zoom was achieved using a traditional zoom lens with a step motor from \(t=0\) s to \(t=20\) s, as shown in Figure 13b and Supplementary Movie S2. The striking contrast demonstrates that the rapid zoom lens with the VCMs effectively increases the zooming speed and decreases the time required.  
Additionally, experiments to measure the positioning accuracy were performed under ISO 230-2-1997 [30], and the results are listed in Table 1. Two target positions (\(i=1,2\)) were set at 0 mm and 38 mm, respectively, and five cycles (\(j=1,2,3,4,5\)) were carried out to obtain the positioning deviation on a bi-directional axis. Herein, \(\downarrow\) and \(\uparrow\) refer to different movement directions when a zoom lens group approaches a target position in either direction along the axis. For \(j=4\), the maximum positioning deviation was \(-0.0048\) mm at Target Position 1. Therefore, it can be seen that our positioning control system achieves high positioning repeatability with a positioning deviation of less than 5 \(\upmu\)m.
## 2 Materials and Methods
### Data Processing Procedure  
Figure 5 describes the procedure of processing the measured signals. Before analysis, the vibration signals from each axis were standardized by comparing their maximum amplitudes. To further refine the data and remove any low-frequency noise or other unwanted signal components, a third-order Butterworth high-pass filter with a cutoff frequency of 100 Hz was applied to the signals. After standardization and filtering, the vibration signals were subjected to wavelet denoising using a Symlet 4 mother wavelet and level 9 decomposition. The denoising method employed the empirical Bayesian technique with a level-dependent noise estimator. Subsequently, a SAWP time series was derived from the processed signal. Finally, a set of features was extracted from the SAWP. The two most important features were selected after analyzing the Laplacian score. These features were then utilized in unsupervised machine learning clustering algorithms to group similar signals together. This approach can assist in identifying patterns or anomalies in the vibration data.
## 3 Results and Discussions
### Case Study-1, 3 HP Induction Motor  
Figure 5 shows the FRA signatures measured for this motor for normal stator winding and when various SC faults are staged on the winding. Figure 5 reveals that the FRA signature is characterized by the main resonance frequency. At the high-frequency range, additional peaks and valleys can be observed due to the contribution of the motor's capacitive components. Figure 5 shows that turn-to-turn SC fault slightly shifts the resonance frequencies at 50 kHz and 200 kHz to the left with a slight increase in the magnitude over the medium and the high-frequency range. The coil-to-coil SC as shown in Figure 5 affects the FRA signature in a similar way as the effect of turn-to-turn SC fault but is more obvious. The turn-to-ground and coil-to-ground faults effects on the FRA signature are shown in Figure 5,d, respectively. It can be seen that turn-to-ground fault does not affect the magnitude of the FRA signature but slightly reduce the frequency of the main resonance point. On the other hand, the coil-to-ground fault shifts the main resonance frequency at 50 kHz towards the higher frequency range. The neutral-to-ground SC fault in Figure 5 shows a drop in the response' magnitude within the frequency range of 50 kHz to 500 kHz. The phase-to-phase fault shows the largest variation in the FRA signature, as shown in Figure 5, in which the FRA magnitude increases and shifts towards the high-frequency range.  
Figure 4: Schematic diagram for FRA cables connections (**a**) signature for U–N phase (**b**) signature for U–V phases.
## 5 Discussion
### Relationship between Flow Volume and TN and TP Discharge Loads  
The flow volume, which was strongly linked to precipitation, exhibited a positive correlation with both annual TN and TP discharge loads (Figure 7). Intensive winter rain events are responsible for accelerated erosion processes, both in the sloped subbasins and  
Figure 8: (**a**) The relationship between the subbasin area for 25 subbasins and the corresponding mean annual TN discharge load for 18 years (2004–2021). (**b**) The relationship between the subbasin area for 25 subbasins and the corresponding mean annual TP discharge load for 18 years (2004–2021).  
at the riverbanks. Such a correlation is consistent with other studies, e.g., [84; 85; 86; 87; 88; 89], that detected a very strong relationship between flow volume and N and P discharge loads. Thus, in the future, nutrient flux changes in the basin will result from changes in land cover and precipitation. Moreover, the impact of land cover and precipitation changes can be coupled; for example, [PERSON] [90] found that agricultural lands leach relatively high nutrient amounts, but they do so even more during heavy rains. Therefore, large-scale land cover changes, for example, could alleviate the increase in nutrient loading in the basin due to precipitation increases. Land cover alteration can include, for instance, a substantial reduction in agricultural land cover and/or changes in agricultural practices and intensity in cultivated areas, as well as installing wastewater treatment facilities in urban areas.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 4 Experimental Studies
### Amplification Case Analysis
For all cases, the total number of samples per speaker was kept consistent at 100 to isolate the effects of the GAN-to-real sample ratio. The network configuration, including hyperparameters such as learning rate, batch size, and the number of training epochs, remained the same across all cases to ensure a fair comparison.
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Qualitative Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AP_{S}\) & \(AP_{M}\) \\ \hline Faster R-CNN & 0.258 & 0.722 & 0.073 & 0.255 & 0.339 \\ + BDAM & **0.262** & **0.741** & **0.093** & **0.258** & **0.352** \\ + CBAM & 0.258 & 0.722 & 0.087 & 0.255 & 0.342 \\ + EAM & 0.257 & 0.727 & 0.093 & 0.250 & 0.346 \\ + BCA & 0.235 & 0.675 & 0.070 & 0.230 & 0.352 \\ \hline RetinaNet & 0.213 & 0.607 & 0.072 & 0.206 & 0.363 \\ + BDAM & **0.223** & **0.638** & **0.075** & **0.219** & 0.352 \\ + CBAM & 0.211 & 0.599 & 0.069 & 0.204 & **0.385** \\ + EAM & 0.218 & 0.632 & 0.063 & 0.214 & 0.333 \\ + BCA & 0.207 & 0.598 & 0.058 & 0.203 & 0.324 \\ \hline SSD & 0.158 & 0.488 & 0.045 & 0.148 & 0.325 \\ + BDAM & **0.169** & **0.517** & **0.045** & **0.160** & **0.343** \\ + CBAM & 0.162 & 0.517 & 0.042 & 0.157 & 0.298 \\ + EAM & 0.139 & 0.476 & 0.030 & 0.133 & 0.301 \\ + BCA & 0.052 & 0.210 & 0.006 & 0.052 & 0.122 \\ \hline YOLOv4 & 0.284 & 0.819 & **0.092** & 0.280 & 0.402 \\ + BDAM & **0.287** & **0.830** & 0.087 & **0.284** & **0.408** \\ + CBAM & 0.284 & 0.826 & 0.090 & 0.280 & 0.405 \\ + EAM & 0.273 & 0.809 & 0.088 & 0.270 & 0.408 \\ + BCA & 0.256 & 0.789 & 0.074 & 0.252 & 0.400 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of different algorithms on dataset LS-SSDD. LS-SSDD does not contain large ships, so the metrics of \(AP_{L}\) do not apply to it. Bolded data represent the best and underlined data represent the second best.
# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 2 Materials and Methods
### Algorithms for Orchestration and Analytics
the digital training twin system to operate as a fully adaptive, privacy-compliant, and audit-ready training framework that dynamically evolves with both learner performance and regulatory demands. The reinforcement learning agent shown in Figure 5 operates in a discrete action space, where each action corresponds to the selection of a specific training resource \(r\) for a given learner profile \(\mathbf{C}_{t}\). The environment returns a scalar reward signal \(R_{t}\) after each action based on the learner's updated competence and the efficiency of the training session. The reward function is defined as follows:  
\[R_{t}=\lambda_{1}\cdot\Delta_{gap}-\lambda_{2}\cdot\Delta_{time}\]  
where \(\Delta_{gap}=\|\mathbf{C}_{t}-\mathbf{T}_{r}\|-\|\mathbf{C}_{t+1}-\mathbf{T}_ {r}\|\) measures the improvement in alignment with the target competence vector, and \(\Delta_{time}\) represents the session duration or training cost. Coefficients \(\lambda_{1}\) and \(\lambda_{2}\) are tunable parameters that balance pedagogical gains and time/resource efficiency. This reward structure encourages the reinforcement learning agent to recommend resources that maximize learning gains with minimal resource expenditure. As system usage grows and learner diversity increases, the Q-learning policy can be extended to a Deep Q-Network that handles continuous state representations and enables policy generalization across a broader range of competence profiles. For federated learning, the system adopts a horizontal FL architecture, where each edge node trains a local model on learner interaction data (such as xAPI logs and competence updates). Local model weights are periodically aggregated on the central cloud aggregator using the FedAvg algorithm. All communication between edge nodes and cloud infrastructure is conducted over a secure Kafka-MQTT messaging backbone, ensuring compliance with data privacy regulations such as GDPR, while enabling global model refinement without direct sharing of raw learner data.
## 6 Performance Analysis and Discussion
Our experimental results show that NetCodCCN and IDEANE, which utilize network coding technology, can process the information on each node in the network data transmission process, and then transmit it to downstream nodes. The intermediate nodes encode or process signals, and then deduce and restore the network data at the final node. Network coding reduces transmission duplication, dramatically improves the transmission efficiency, and enhances network robustness. IDEANE can obtain more required content in the event of partial link failure by distinguishing interest packet requests by identity identifiers. Network encoding consumes a small amount of computing power when encoding and decoding content chunks, thus increasing the network latency accordingly. In order to improve the performance of network coding, we reduce the computational complexity by splitting content into many _generations_. In order to reduce the pressure on resource-constrained Industrial Internet objects, we only perform encoding at the nodes adjacent to the content provider and decode the encoding block at the edge nodes in close proximity to the content requesters. In order to reduce the network latency, intermediate nodes do not perform encoding and decoding operations. In our edge computing system integrated with linear network coding, the end user and the edge server are single-hop network connections, and the latency is directly related to the wireless transmission technology used. Especially in a 5G network environment, the delay in wireless communication can be reduced to one millisecond, which makes edge computing virtually indistinguishable from local computing in terms of delay. Network coding does not compress data; instead, it restores the original data by collecting sufficient encoded blocks for linear combination, which can facilitate the collaboration of multiple edge nodes. Two different edge nodes allow for redundant encoded packets. At the same time, if the original data are divided into \(K\) data blocks and encoded into \(M\) coded blocks (\(M\geq K\)), \(M-K\) transmission errors can be allowed in network transmission. Such fault tolerance can alleviate the impact of data loss in network transmission during the movement of content requesters. On the other hand, during the movement process of the content requester, there may already be linearly independent coding blocks of the same content that have been cached in the different edge nodes that it is connected to, thereby reducing repeated content requests.
## 4 Results and Discussion
### Experimental Settings  
Considering that the proposed UPI-LT model considers multiple attribute characteristics of nodes, comparative methods are designed that consider only specific factors to evaluate the effectiveness of each factor. The proposed information dissemination models are then compared against these methods using two real-world datasets. To evaluate the impact of individual factors on information propagation, different factors are set as the activation thresholds in the LT model and assigned names accordingly, as shown in Table 3.  
* The LT model that only considers the node's importance (PageRank) is named PR-LT.
* The LT model that only considers the local clustering coefficient is named CC-LT.
* The LT model that only considers the structural hole factor is named SH-LT.
* The classic LT model with random threshold assignments and the IC model are used as baseline models.  
The number of activated users is utilized as the metric to assess the effectiveness of the proposed UPI-LT model. Influence diffusion was compared across two real-world networks, namely Wiki-Vote ([[http://snap.stanford.edu/data/wiki-vote.html](http://snap.stanford.edu/data/wiki-vote.html)]([http://snap.stanford.edu/data/wiki-vote.html](http://snap.stanford.edu/data/wiki-vote.html)) (accessed on 7 January 2024)) and Facebook ([[http://snap.stanford.edu/data/ego-Facebook.html](http://snap.stanford.edu/data/ego-Facebook.html)]([http://snap.stanford.edu/data/ego-Facebook.html](http://snap.stanford.edu/data/ego-Facebook.html)) (accessed on 7 January 2024)), from publicly available Stanford datasets. During the experiment, seed nodes were randomly selected, with the number of seed nodes incrementally increasing from 5 to 35.
## 3 Results
### Dataset Description
#### 3.1.2 Experiment Paradigm Design in Dataset  
Each subject took part in three calibration runs. In this paper, we used the calibration-run data to train and test the ability of proposed method for target and non-target sub-trials classification. Each calibration runs contained nine trials, and each of the nine types of sound was used as the target stimulus in one run, as shown in Figure 6. In addition, an exercise run without EEG data recording (run 0) was initially performed. Before the start of each trial, the current target number was presented to the subject three times, and the corresponding number on the \(3\times 3\) grid was highlighted on the screen.  
In the calibration phase, each test consists of 13 or 14 pseudo-random sequences of all nine auditory stimuli. No visual stimulation was given in these tests. When using the last 12 sequences to train the classifier, ignore the first or two sequences to ensure a balanced distribution of stimuli in the calibration data. A representation of a tonal stimulus and corresponding EEG data (time interval up to 800 milliseconds after the stimulus) is called a sub-trial. Therefore, a single experiment provides \(9\times 12\) trial epochs (12 target trial epochs and \(8\times 12\) non-target trial epochs) for classifier training. The combined training data for all runs include \(108\times 27=2916\) trials per subject.  
The subjects listened to a sequence of audio stimuli in different combination of tone and channel. One of the nine combinations would be the target. When the subject listened to the target stimulus, the EEG data recorded in this phase would be used to train the BCI classification system to detect the target ERP waveform. When the BCI system detected target ERP waveform from EEG signal, it would virtually trigger a press on the keypad in Figure 5, like typing a text message on traditional mobile phones, before the smartphone era. In the testing phase, the task would change to actually input a sentence. The subject would imagine the keypad key they want to press. Therefore, when a subject was presented the stimulus representing the target key, among other eight no-target stimuli, the trained system would detect the target waveform in EEG data and virtually press the target key. Like a real mobile phone, to type a word would require multiple presses on a keypad, so the trial would repeat multiple times as well.