esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Network Architecture
#### 3.3.2 Stable Decoder with Lyapunov Constraints  
The decoder generates future trajectory distributions conditioned on the latent variable \(z\). It employs an LSTM network with autoregressive prediction:  
\[h_{t}=\text{LSTM}([\hat{y}_{t-1},z],h_{t-1}) \tag{10}\]  
\[\mu_{t}=W_{\mu}h_{t}+b_{\mu} \tag{11}\]  
\[\log\sigma_{t}^{2}=W_{\sigma}h_{t}+b_{\sigma} \tag{12}\]  
A key innovation in our decoder is the incorporation of Lyapunov stability principles to ensure physically plausible trajectories. We introduce a Lyapunov function \(V(h_{t})\) parameterized by neural networks:  
\[V(h_{t})=W_{2}\cdot\text{ReLU}(W_{1}h_{t}+b_{1})+b_{2} \tag{13}\]  
For stability, we enforce that this function decreases over time by penalizing positive differences:  
\[\Delta V_{t}=V(h_{t})-V(h_{t-1}) \tag{14}\]  
This ensures that the predicted trajectories converge to established flight patterns while accommodating reasonable deviations, effectively balancing adherence to standard procedures with real-world variability, as illustrated in Figure 3.  
Figure 3: Illustration of Lyapunov stability applied to flight trajectories. The Lyapunov function (shown as contours) creates basins of attraction around standard flight patterns, ensuring that predicted trajectories naturally converge to standard flight patterns while allowing for reasonable variations.  
Figure 2: Weather attention mechanism architecture. The model learns to focus on relevant weather features by computing attention weights between hidden states and weather inputs across multiple attention heads.
## 4 Results and Discussion
### Future Works  
Based on our research experience, we offer the following recommendations for future work to enhance the accuracy of diagnosis, identify robust biomarkers, and support clinical evaluation of the disorder:  
* Gather comprehensive and diverse datasets to account for the heterogeneity of ASD data and develop a robust, generalizable, and more clinically useful model.
* Conduct longitudinal studies to understand the developmental trajectory of ASD-related brain changes, identify predictive biomarkers, and improve early detection and intervention strategies.
* Investigate multimodal imaging-based classification by combining sMRI data with other methods (fMRI, EEG, genetic data) to provide a more comprehensive understanding of the neurobiological mechanisms underlying ASD, reveal additional biomarkers and improve the accuracy of diagnosis [28].
* Perform fine-grained analysis of subtypes or phenotypic variations within the ASD spectrum to identify unique biomarkers and enable personalized diagnostic approaches and targeted interventions.
* Validate ML-based diagnostic systems in real-world clinical settings to assess their feasibility, acceptability, and clinical utility.
* DL methods differ from traditional ML models as they eliminate the need for manual feature extraction and minimize information loss. However, training DL networks and uncovering intricate patterns requires extensive datasets. In our research, we intend to investigate DL models using large datasets, employing techniques such as data augmentation, generative adversarial networks, and transfer learning [5; 27].
* Integrate Explainable AI (XAI) techniques to improve the interpretability of DL algorithms in the diagnostic process, enhancing their clinical utility and gaining trust and acceptance from clinicians and stakeholders. XAI explores the decision-making process, provides explanations for system behavior, and offers insights into future performance [67].
* Use I7 scans to obtain accurate and clinically useful biomarkers for ASD diagnoses.
* Explore the potential of ML techniques for early classification of infants at risk for ASD, addressing challenges associated with processing and interpreting MRI images in pediatric brains.
* Explore the use of BrainSuite and DL-based FastSurfer tools as a more efficient alternatives to FreeSurfer for neuroimaging data processing, whose high efficiency has been demonstrated in numerous studies, such as epilepsy [68; 69].
# Feasibility Study of Real-Time Monitoring of Pin Connection Wear Using Acoustic Emission
## 3 Results and Analyses
### Analyses Based on Surface Micrographs  
As shown in Figure 7, the surfaces of the four worn specimens had grooves that were visible to the naked eye. Detailed information is summarized in Table 2. There were similar distributions of grooves on the middle of the first, third and fourth specimens surface (Figure 7). However, the number of grooves was different. The first, the third, and the fourth specimens, respectively, had 12, three, and four obvious grooves. Considering the size and location of the U-shaped part and the circular part, it was found that wear occurred in the contact area between the circular part and the pin. In contrast, three obvious grooves were distributed on the end of the second specimen surface (Figure 7), which means that wear occurred in the contact area between the U-shaped part and the pin. Rough surface and uneven lubricant distribution may cause the phenomenon.  
Figure 6: The signal strength and the accumulative signal strength: (**a**) The first specimen; (**b**) the second specimen; (**c**) the third specimen; (**d**) the fourth specimen.  
Figures 8-11 are micrographs showing the wear track of the pin specimens. The different wear forms, including abrasive wear and adhesive wear, can be identified from these micrographs, which verify the prior prediction of different wear forms at the end of Section 3.1. There were some obvious grooves in the third and the fourth specimens (Figures 10 and 11), and these grooves were along the rotation direction of pins, and they distributed on the surface of the pins. Obviously, the characteristics of these wear were similar to that of abrasive wear that has been described in the introduction; therefore the type of damage was abrasive wear, and the average depths of wear in the third and fourth specimens were 2060.7 um and 2235.3 um, respectively. On the other hand, the surface of the first and the second specimens (Figures 8 and 9) were rough, and the trails of the transfer particles adhering to the surface could be seen, and different depth pits distributed here. The reason for this was that during the process of rotation, the surfaces of pins were repeatedly squeezed, and some debris would be peeled off from the surface. Meanwhile, grooves could also be found on the surfaces of the first and second specimens. That meant that the damage of the first and the second specimens were associated with not only abrasive wear but also with adhesive wear. Moreover, the average depths of wear in the first and second specimens were 2190.375 um and 2137.925 um, respectively. Comparing the average depths of wear in all four specimens, it was found that the average depth of wear was similar in this experiment.  
Figure 8: Micrographs of the first specimen: (**a**) 2D micrographs; (**b**) 3D micrographs.  
Figure 10: Micrographs of the third specimen: (**a**) 2D micrographs; (**b**) 3D micrographs.  
Figure 9: Micrographs of the second specimen: (**a**) 2D micrographs; (**b**) 3D micrographs.
## 4 Discussion
### System Usability and Clinical Acceptance  
LLM recommendations matched clinician decisions in 75.8% of reviewed cases, indicating intuitive behavior. End-to-end latency was 1-3 --effectively invisible in the ED workflow--so nurses saw guidance as soon as vitals or notes were entered.  
The modular rule engine lets sites localize thresholds without retraining; e.g., one hospital elevated chest-pain patients > 50 y to ESI-2, while another used stricter vital-sign cut-offs. Underlying FL-trained models still generalized across sites, with rules providing quick protocol customization and preserving consistency.
## 1 Introduction
Theoretical studies [3] have shown that in the case of an even number of flexible elements, some of the perturbations studied in [22] are mutually compensated. Therefore, the algorithms presented in [44] cannot be effectively used for such small spacecraft. This paper aims to study the disturbing effect of temperature shock on small spacecraft with a symmetrical arrangement of flexible elements for the subsequent development of effective control algorithms that neutralize temperature shock. In other areas, the considered problem is also important. For example, the study of parameters affecting the characteristics of sound transmission by a two-layer multilayer magnetoelectroelastic plate with transverse layers based on a viscoelastic medium under high-temperature conditions [50] shows the importance of the problem being solved. Thus, the results of the work can be applied in other areas. This paper has the following structure: the thermoelasticity problem is considered, within which approximate analytical dependences are obtained. They differ from those obtained earlier because they take into account various initial deflections of the flexible element. Then, the assessment of disturbing factors from temperature shock is carried out. Unlike previous works, it also takes into account various forms of initial deflection of the flexible element. Finally, numerical simulation is carried out for the schemes of two small spacecraft. They have two and four flexible elements, respectively. Conclusions are drawn about the effect of temperature shock on the dynamics of small spacecraft orbital motion.
# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
## 2 Methods and Equipment
### DHC Software
It includes particles whose size is less than 0.2 mm, and the section shape is close to a circle, i.e., the height-to-width aspect ratio of the circumscribed rectangle is \(M\geq 0.9\). Their size and concentration are used to calculate the turbidity of water. Taxon 9 (_Marine snow_) includes particles that satisfy the definition of marine snow. It includes particles of aggregated phytoplankton, feces, and other detritus components [43; 44; 45]. To determine the size and concentration of floating bubbles in areas where methane is released from bottom sediments, the DHC software uses data on the _Bubble_ taxon in the studied volume. Here we include data on particles that represent gas bubbles larger than 0.2 mm and whose section shape is close to a circle, i.e., \(M\geq 0.9\) (size \(H\) for the _Bubble_ taxon means the diameter of a gas bubble). Offshore the East Siberian Arctic Seas, bubble methane is released because of the loss of continuity of submarine permafrost, which subsequently creates talks and destabilizes hydrates [31; 32; 33; 34]. Note that the diffusional transfer occurs during the upward movement between the primary methane bubble (in a thin bottom layer) and nitrogen and oxygen dissolved in water, thus increasing the nitrogen and oxygen fractions as the bubble approaches the water-air interface [33; 46; 47]. The processing results of each hologram are registered as a 2D image with sharp images of particles and a table with data for each particle detected by the algorithm in this hologram. Examples of such data are shown in Figure 5 and Table 2. \begin{table}
\begin{tabular}{c c c c} \hline
**Taxa** & **Presence of Outgrowths** & **H, mm** & \(M\) \\ \hline
1. _Chaetognatha_ & YES & \textgreater{}0.2 & 0–0.2 \\
2. _Coppeda_ & YES & \textgreater{}0.2 & 0.2–0.5 \\
3. _Appalachuria_ & YES & \textgreater{}0.2 & 0.5–0.66 \\
4. _Caldacera_ & YES & \textgreater{}0.2 & 0.66–0.9 \\
5. _Other_ & YES & \textgreater{}0.2 & 0.9–1 \\
6. _Rotifera_ & YES & \(\leq\)0.2 & 0–0.9 \\
7. _Phytoplankton chain_ & NO & ANY & 0–0.25 \\
8. _Suspension_ & NO & \(\leq\)0.2 & 0.9–1 \\
9. _Marine snow_ & NO & ANY & 0.25–0.9 \\
10. _Bubble_ & NO & \textgreater{}0.2 & 0.9–1 \\ \hline \end{tabular}
\end{table} 
Table 1: Features of taxa for automatic classification of particles: \(H\)—length of the rectangle circumscribed around the particle image; \(M\)—morphological parameter (width-to-length ratio of the rectangle). Table 2 shows the degree of completeness and level of detail of the data recorded in a digital holographic experiment in situ.
# Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction
## 2 Improved DeepFM Recommendation Algorithm Incorporating Deep Feature Extraction
### DeepFM Model  
The DeepFM model consists of two parallel neural networks, an optimization of the Wide & Deep model. It inherits the advantages of the Wide & Deep model and replaces the LR model of the Wide part with an _FM_ model. The model structure is shown in Figure 3. It has a Factorization Machine (FM) part on the left and a Deep Neural Network (_DNN_) part on the right, forming end-to-end training and avoiding the work of manual feature extraction engineering. The _FM_ part and the _DNN_ part share feature vector parallel training and learn low-order feature crossover and high-order feature crossover simultaneously, which has good memory characteristics and generalization ability.  
Figure 3: DeepFM model.  
Figure 2: PV-DM model.  
The _FM_ part is used to extract the low-order features of the input data, which is based on the linear regression model to introduce cross-terms. It trains the weights of the combined features by learning a hidden weight vector for each feature. The inner product of the two vectors as a weight can not only alleviate the data sparsity problem but also effectively learn the feature combination. The calculation formula is as follows:  
\[y(x)=\omega_{0}+\sum_{i=1}^{n}\omega_{i}x_{i}+\sum_{i=1}^{n}\sum_{j=i+1}^{n} \omega_{ij}x_{i}x_{j} \tag{1}\]  
where \(\omega_{ij}\) is the weight of the feature combination \(x_{i}x_{j}\).  
On the right side is the _DNN_ (Deep Neural Networks) part, which forms end-to-end training and avoids the manual extraction of feature engineering. The _DNN_ network is a feed-forward neural network for learning higher-order feature crossover, and in the fully connected layer, let the feature vectors be combined to form higher-order features.  
The input of the _DNN_ layer is  
\[a^{(0)}=[e_{1},e_{2},\cdots,e_{m}]\]  
where \(e_{i}\) is the \(i\)th feature vector, \(m\) is the total number of features, and then \(a^{(0)}\) is fed back to the _DNN_, and the forward processing is as follows:  
\[a^{(l+1)}=\sigma\Big{(}W^{(l)}a^{(l)}+b^{(l)}\Big{)} \tag{2}\]  
where \(\sigma\) denotes the activation function, \(a^{(l)}\) denotes the output of the \(l\)-layer, \(W^{(l)}\) denotes the \(l\)-layer weight matrix, and \(b^{(l)}\) denotes the bias matrix.  
The final output of the _DNN_ model is shown in Equation (3)  
\[y_{DNN}=\sigma\Big{(}W^{|H|+1}a^{H}+b^{|H|+1}\Big{)} \tag{3}\]  
where \(H\) denotes the number of network layers of the model.  
The output of the final model is the sum of the two  
\[y=\textit{sigmoid}(y_{\textit{FM}}+y_{\textit{DNN}}) \tag{4}\]  
where \(y_{\textit{FM}}\) is the output of the _FM_ model part and \(y_{\textit{DNN}}\) is the output of the _DNN_ model.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Body Risk Parameters
#### 2.1.5 Grounding Issues  
Poorly designed or faulty cable grounding systems can lead to leakage or shock hazards. Cable grounding is the process of connecting the metal portion of a cable to the earth's electrical system, with the primary purpose of preventing safety incidents such as electric shock or fire by safely directing fault currents to ground through the grounding path in the event of a cable fault.
## 3 Time-Based Localization Method and Localization Performance in Numerical Examples  
### Time-Based Localization Method  
Based on wave propagation characteristics analyzed in the previous section, the time-based localization method is proposed. We focused on the monitoring points deployed on the ground surface (\(X\)-\(Y\) plane) where the propagation of a single surface wave (R wave) was assumed due to the vibratory driving source on a construction site. The difference in wave arrival time (\(\Delta\)ToA) between two monitoring points can be presented as:  
\[\frac{\sqrt{\left(x-x_{i}\right)^{2}+\left(y-y_{i}\right)^{2}}-\sqrt{\left(x- x_{j}\right)^{2}+\left(y-y_{j}\right)^{2}}}{v_{r}}=t_{ij} \tag{6}\]  
where \(t_{ij}\) represents the \(\Delta\)ToA between the \(i\)th monitoring point \(P_{i}\) (\(x_{i}\), \(y_{i}\)) and the \(j\)th monitoring point \(P_{j}\) (\(x_{i}\), \(y_{j}\)), \(x\) and \(y\) are unknown coordinates of the vibratory driving source, and \(v_{r}\) refers to the surface wave velocity. Similar relationships can be built among other monitoring points. The coordinate of the vibratory driving source (\(x\), \(y\)) can be calculated by solving these nonlinear equations considering the known coordinates of monitoring points and \(\Delta\)ToAs among them, without knowing the wave velocity. This study adopts the optimization function, _fsolve_ function in MATLAB [31], for the optimization calculations and the cross-correlation function (Equation (5)) for the determination of \(\Delta\)ToA (\(t_{ij}\)) among multiple monitoring points.  
Figure 6: Particle motions of soil nodes at different plan distances and depths when pile toe is pre-embedded 10 m below the ground surface.  
Once the vibratory driving source is localized, the measured vibration intensities at estimated plan distances to the localized driving source can be utilized to develop a site-specific empirical prediction model. As such, a global vibration intensity assessment can be achieved on the entire construction site.
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 3 Applying AI/ML into 5G RAN: Proposed Methodology
### Input Data--Classification Analysis
#### 3.2.1 Key Performance Indicators for 5G Traffic Types
As an example, the network function parametrizing radio data collection for an ML algorithm may admit receiving measurement data from a specific service (Table 1), referring also to a network slice (e.g., an _eMBB_ slice, a _URLLC_ slice, or an _mMTC_ slice). For the purpose of training a service-specific ML model, the input data function may target data set \(A\) to be denoted as:  
\[A=\{\emph{eMBB},\emph{URLLC},\emph{mMTC}\}, \tag{1}\]  
where _eMBB_, _URLLC_, and _mMTC_ denote key performance measurements taken per traffic type. The network function parametrizing radio data collection for an ML algorithm for the particular service (e.g., _eMBB_), may be targeting measurement results taken only for the _eMBB_ service, which is a subset of dataset A:  
\[a_{\emph{MBB}}=\{\emph{PeakDataRate},\emph{UserExpDataRate}\}, \tag{2}\] \begin{table}
\begin{tabular}{c c c} \hline \hline _eMBB_ & _URLLC_ & _mMTC_ \\ \hline Peak data rate & Latency & Connection density \\ User experience data data & Mobility interruption time & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Key performance indicators of 5G per traffic type. Consequently, the network function parametrizing radio data collection for an ML algorithm for combined traffic (e.g., _eMBB_ and _URLLC_) may be targeting measurement results taken for both services, that is a dataset denoted as:  
\[a_{MBB,\textit{URLLC}}=\{\textit{PeakDataRate},\textit{UserExpDataRate}\}\cup \textit{[Latency, MobilityInterTime]} \tag{3}\]  
As a result, any metric or metrics combinations collected from dataset \(A\) will represent key performance indicators of 5G traffic or a slice.
## 5 Results and Discussion
### Computational Efficiency Analysis  
Evaluating the computational efficiency of 2D versus 3D finite element (FE) models is essential for their practical application in engineering. This analysis compares the processing time and memory usage required to simulate single-segment, three-segment, and five-segment cable configurations using both 2D and 3D models, referencing the hardware specifications from Section 4.
## 4 The Performance Analysis
### The MIGM Performance Analysis  
To compare the performance of the MIGM with an existing vehicle gateway, conversion time and an error rate are measured when 4000 messages are converted to other messages. The experiment was conducted when a CAN message was converted to a FlexRay or a MOST message and vice-versa. The existing in-vehicle gateway and the MIGM were implemented in the C language and the experiment was conducted in a Host PC.  
Figure 13(a) shows that in the CAN-To-FlexRay conversion, the MIGM was improved by 33.3% in conversion time more than the existing in-vehicle gateway, in the CAN-To-MOST conversion, the MIGM was improved by 20.9% more than the existing in-vehicle gateway, in FlexRay-To-CAN conversion, the MIGM was improved by 29.2% more than the existing in-vehicle gateway, and in the MOST-To-CAN conversion, the MIGM was improved by 31.3% more than the existing in-vehicle gateway. Therefore, the conversion time of the MIGM was improved more than that of the existing in-vehicle gateway by about average 28.67%.  
Figure 13(b) shows that in the CAN-To-FlexRay conversion, the existing in-vehicle gateway causes an error rate, 1.55% and the MIGM an error rate, 1.55%, in the CAN-To-MOST conversion, the existing in-vehicle gateway causes an error rate, 1.66% and the MIGM an error rate, 0.45%, in FlexRay-To-CAN conversion, the existing in-vehicle gateway causes an error rate, 1.94% and the MIGM an error rate, 1.59%, and in the MOST-To-CAN conversion, the existing in-vehicle gateway causes an error rate, 2.59%. Therefore, the error rate of the MIGM was lower than that of the existing in-vehicle gateway by about 0.5%. When a CAN message was converted to another protocol message, it had higher performance than other cases by 1%.
## 2 Materials and Methods
### Extraction of PAHs and PCBs from Neritic Matrices
#### 2.4.1 Water Column  
Extraction was performed via solid-phase extraction (SPE) using an SPE Vacuum manifold and a polymeric reversed-phase (RP) cartridge (STRATA-XL-100 \(\upmu\)m; Phenomenex, Torrance, CA, USA). The cartridge was conditioned (20 psi) with 5 mL CH\({}_{2}\)Cl\({}_{2}\), 5 mL 2-propanol, and 5 mL H\({}_{2}\)O. Then, 200 mL water sample added with 20 mL 2-propanol was loaded (50 psi) on the SPE cartridge. The water sample container was subsequently washed with 20 mL 2-propanol-water solution (10 + 90, \(v/v\)). After loading, the cartridge was washed (20 psi) with 5 mL H\({}_{2}\)O, and 5 mL 2-propanol-water solution (85 + 15, \(v/v\)). The cartridge was dried for 10 min, and the analytes were finally eluted with two aliquots of 1.0 mL CH\({}_{2}\)Cl\({}_{2}\). The eluted extract was spiked with the internal standard solution of PAHs and PCBs to achieve a final concentration of 5 \(\upmu\)g/L and injected for GC-MS analysis. To evaluate the extraction recoveries of PAHs and PCBs, before extraction, the water samples were spiked with surrogates (as detailed in \"Reagents\" section) to achieve a final concentration of 2 \(\upmu\)g/L.