esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 5 Solutions of the CPS Communication Hub
### Solutions for Data Plane Negotiation
#### 5.6.1 Prerequisite Steps  
Before data plane negotiation can occur, the XMPP client embedded in the physical resource must establish an XMPP session with the XMPP server that is hosting the virtual communication space of the owner. The minimum parameter set for the session establishment contains a valid XMPP account identified by the XMPP ID (JID) and a password. JID consists of a username, server, and resource parts. In the context of data plane negotiation, all three are considered mandatory, as they are needed to uniquely identify different physical devices of the M2 Mgrids system.  
Just after an XMPP session is established, the XMPP client of the physical resource should also send the initial presence to the XMPP server in order to express its willingness to communicate with someone else. A trust relationship is required to be created between the owners. Creation is based on XMPP's fundamental feature, called presence subscription. It is executed between two XMPP clients of physical resources belonging to different owners. Within the same owner, the trust relationship is considered to exist inherently and the presence subscription procedure need not be conducted.  
Presence subscription is a two-way procedure. Both involved XMPP clients should initialize it by sending \"subscribe\" request to the other endpoint. The recipient of the \"subscribe\" request should accept the subscription by sending a \"subscribed\" response to the sender. If, for any reason, the subscription cannot be accepted, the recipient should send an \"unsubscribed\" response. In that case, the trust relationship creation fails and data plane negotiation is cancelled.  
The status of the presence subscription between the two owners is maintained in the XMPP server. Actually, there are several states in which status can depend on which phases the two presence subscription procedures are in. Status \"both\" means the state in which both endpoints have accepted the presence subscription made by the other endpoint. In this case, the \"trust\" relationship has been entirely created. Any other status indicates that the \"trust\" relationship is not established.  
The status of the presence subscription is preserved in the XMPP server even if the XMPP client of the owner disconnects from the XMPP server. The XMPP client can also check the status. If the trust relationship exists, it need not be created again. Accordingly, if two endpoints have established a trust relationship between two owners, subsequent data plane negotiations between those owners can skip this phase.  
Accepting or declining the presence subscription request is based on the list of trusted JIDs. The list contains all XMPP JIDs that are trusted by the owner of the physical resource. The list contains bare JIDs, that is JIDs are in the format that contain the username and server, but does not include the resource field.
## 5 Experimental Results
### Ablation Study  
We also studied the effect of the use of keypoint information from two hands, body, and face. The experimental results summarized in Table 10 imply that the keypoint information from both hands is the most important among all the keypoint information from hands, face, and body.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **ROUGE-L** & **METEOR** & **BLEU** & **CIDEr** \\ \hline Body & 68.82 & 42.68 & 59.96 & 2.554 \\ Hand & 72.03 & 45.19 & 62.90 & 2.681 \\ Body, Face & 60.08 & 36.66 & 48.90 & 2.051 \\ Hand, Face & 69.43 & 43.26 & 59.92 & 2.538 \\ Hand, Body & **74.02** & **46.84** & **65.83** & **2.831** \\ Hand, Body, Face & 73.61 & 46.52 & 65.26 & 2.794 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation study on the contributions of keypoints from body, face, and hands. The results are obtained on the test set.  
Figure 5: Example frames of additional test set videos recorded with cluttered background and non-expert signers.  
Interestingly, the experimental result tells us that the keypoint information from face does not help to improve the performance in general. The performance even dropped when we added face keypoints in all cases. We suspect that the reason is partly due to the imbalanced number of keypoints from different parts. Recall that the number of keypoints from face is 70 and this is much larger than the number of the other keypoints.  
While the keypoints from both hands are definitely the most important features to understand signs, it is worth noting that the 12 keypoints from body boosted the performance. Actually, we lossy the information about relative positions of parts from each other as we normalized the coordinates of each part separately. For instance, there was no way to infer the relative positions of two hands with the normalized feature vectors from both hands. However, it was possible to know the relative position from the keypoints of body as there also existed keypoints corresponding to the hands.
# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
###### Abstract  
Accurate and timely disaster classification is critical for effective disaster management and emergency response. This study proposes an improved ResNet-50-based deep learning model to classify seven types of natural disasters, including earthquake, fire, flood, mudslide, avalanche, landslide, and land subsidence. The dataset was compiled from publicly available sources and partitioned into training and validation sets using an 8:2 split. Experimental results demonstrate that the proposed model achieves a classification accuracy of 87% on the validation set and outperforms the traditional VGG16 model in most evaluation metrics, including precision, recall, F1-score, AUC, specificity, and log loss. Furthermore, the model effectively mitigates the gradient vanishing problem, ensuring stable convergence and robust training performance. These findings provide a practical technical reference for multi-disaster classification tasks and contribute to enhancing the efficiency of disaster response and societal resilience.  
Keywords:disaster classification; machine learning; ResNet-50 +
Footnote †: journal: Applied sciences
# A Survey of Secure Time Synchronization
## 2 Time Synchronization
### Common Challenges for Time Synchronization
#### 2.3.2 Robustness  
Sensor networks are left unattended for large amounts of time in hostile environments [23]. In the case of some nodes' failures or link failures, synchronization schemes should be up and running for the remaining nodes. Mobile nodes move around which can disrupt the routing schemes and can create partitioning in the network. The TPSN is unable to handle topology changes efficiently [31]. In the case of root node failure, the whole process of synchronization must be repeated which increases energy consumption. The LTS protocol is not suitable for high precision apps because its accuracy reduces towards the depth of the tree [34]. However, the FTSP provides a high level of robustness as it selects its root node dynamically, which provides high precision and works fine even in the case of root node failure, but the downside is that it increases energy consumption [30].
## Data, Methodology and Results
### Data and Model
#### 2.1.1 TDS-1 Satellite Data  
The GNSS-R payload on the TDS-1 consists of the antennas receiving direct and reflection GPS signals and a remote sensing receiver. The receiver records the reflections' time and the corresponding spatial coordinates of the GPS satellites, the receiver and the specular reflection points, those positions are contained in the TDS-1 Level-1 metadata (obtained from: [[ftp://ftp.merrbys.co.uk](ftp://ftp.merrbys.co.uk)]([ftp://ftp.merrbys.co.uk](ftp://ftp.merrbys.co.uk))) [18]. In this study, we used a total of 14,768 reflections from 43 tracks in April 2018. According to the orbital altitude and the inclination of TDS-1 satellite, these tracks cover almost all the latitudes and longitudes except the polar zone with ice sheet [13], having a good coverage on the distributions of the ocean tidal amplitude and phase.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 3 Hybrid Representation Approach
### Route Level Classification  
This stage unifies the previously described components in order to obtain route level classification, as illustrated in Figure 1. The histogram generated from the CNN-based component (Section 3.1) or RNN-based component (Section 3.2) is concatenated with the one generated from the rule-guided component (Section 3.3), forming a feature vector aimed at reflecting overall driving behaviour. This feature vector is divided by overall route duration and normalized. Labelled samples of normalized feature vectors are used to train a standard SVM classifier, in order to assess driving behaviour at the route level. A preliminary version of the hybrid approach has been presented in [17]. This approach can be considered as a framework. Accordingly, the NN-based component, the rule-guided component or the SVM classifier could be replaced by alternatives.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Event Type**} & \multicolumn{3}{c}{**Threshold Sensitivity**} \\ \cline{2-4}  & **Low** & **Medium** & **High** \\ \hline Acceleration & \(0.1\) g \(<\) a\({}_{\text{z}}\)\(<0.2\) g & \(0.2\) g \(<\) a\({}_{\text{z}}\)\(<0.4\) g & \(0.4\) g \(<\) a\({}_{\text{z}}\) \\ Braking & \(-0.1\) g \(>\) a\({}_{\text{z}}\)\(>-0.2\) g & \(-0.2\) g \(>\) a\({}_{\text{z}}\)\(>-0.4\) g & \(-0.4\) g \(>\) a\({}_{\text{z}}\) \\ Turning & \(0.1\) g \(<\) 1 day\({}_{\text{y}}\)\({}_{\text{l}}\)\(<0.2\) g & \(0.2\) g \(<\) 1 day\({}_{\text{y}}\)\({}_{\text{l}}\)\(<0.4\) g & \(0.4\) g \(<\) 1 day\({}_{\text{y}}\)\({}_{\text{l}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Event thresholds [1].
# Herbal Support for the Nervous System: The Impact of Adaptogens in Humans and Dogs
## 3 Siberian Ginseng (_Eleutherococcus senticosus_
### Studies Related to Therapeutic Effects on Humans  
[PERSON] et al. [86] used a quantitative proteomics approach to study the effects of _E. semicosus_ extract on nitrosative stress and inflammatory response in BV-2 microglial cells stimulated by lipopolysaccharide-LPS. The results showed that _E. semicosus_ inhibits nitric oxide production induced by LPS, while exhibiting no significant toxicity in cells. These findings suggest that _E. semicosus_ can suppress LPS-induced nitrosative stress in BV-2 cells and may provide valuable insights into the molecular mechanisms underlying its potential neuroprotective effects. This may help improve the understanding of its preventive mechanisms against neurological diseases.  
Figure 3: Characteristics of the plant and its principal active compounds [76; 77; 78; 79] (search without time range in the Scopus database with Article title, Abstract, Keywords, searching documents with the following command: (adaptogen name) AND nervous AND human/dog, as of 30 March 2025 [60]).  
Studies have shown that an aqueous extract of _E. senticosus_ improves memory in mice, while its saponin fraction exhibits promising neuroprotective effects in vitro [87]. A series of memory and learning tests demonstrated a significant memory-enhancing effect. These studies confirm the neuroprotective properties of _E. senticosus_.  
Furthermore, as demonstrated by [PERSON] et al. [88] in mouse studies, the neuroprotective effects of spiny eleuthero extract administered at a dose of 45.5 mg/kg for 20 days are associated with protection against mitochondrial dysfunction and structural damage. Therefore, it is a promising candidate for preventing and treating mitochondrial neurodegenerative diseases, such as Parkinson's disease in humans.  
According to [PERSON] et al. [89], syringin is an active compound isolated from _E. senticosus_, which exhibits anti-inflammatory and neuroprotective properties. Syringin treatment significantly reduces infarct volume, brain water content, and improves neurological scores, while attenuating neuronal death. Additionally, syringin exerts a protective effect against brain damage by reducing inflammation associated with cerebral ischemia.
# Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks
## 5 Fatigue Reliability Modeling of Multi-Cracked Laminates
### Fatigue Life and Reliability Modeling
#### 5.2.2 Fatigue Reliability Modeling  
To evaluate the distribution properties of the data, a statistical analysis is conducted on the fatigue life of carbon fiber laminates with multiple initial cracks, as forecasted via the PSO-BP neural network model. The data are fitted to the Lognormal, Weibull, and Gamma distributions, with the results of the fitting presented in Figure 15 and the specific parameters listed in Table 10. An assessment of the goodness of fit using the determination coefficient R\({}^{2}\) for the fatigue life curves indicates that the Normal distribution aligns most closely with the fatigue life of the multi-cracked carbon fiber plate, rendering it the optimal choice for characterizing the fatigue reliability of such laminates.  
Figure 14: Scatter plot of predicted life results.  
Figure 15: Probability plot of distribution of carbon fiber laminates containing multiple cracks.  
By employing the Normal distribution to fit the data, we derive the probability density function of the carbon fiber plate.  
\[\mathrm{f(x)}\ =\ \frac{1}{\sqrt{11684.42\pi}}\mathrm{e}^{\left(-\frac{\left( \mathrm{x}-39148.9\right)^{2}}{2\times 5842.21^{2}}\right)} \tag{19}\]  
Through the process of integration, we arrive at the cumulative fatigue failure distribution function for the carbon fiber plate, expressed with the following formula:  
\[\mathrm{F(t)=}\frac{1}{5842.21\sqrt{2\pi}}\mathrm{f}_{0}^{\mathrm{t}}\mathrm{ e}^{-\left(\frac{\left(\mathrm{x}-39148.9\right)^{2}}{2\times 5842.21^{2}}\right)} \mathrm{dx} \tag{20}\]  
The formula for reliability is articulated as follows:  
\[\mathrm{R(t)}\ =\ 1-\mathrm{F(t)} \tag{21}\]  
Figure 16 illustrates that in the early stages of the fatigue life for carbon fiber laminates with multiple cracks, the rate of crack growth is initially slow. However, the decline in reliability becomes precipitous when it falls below the threshold of 0.9. This behavior suggests that while the crack propagation is gradual at the onset of fatigue damage, it accelerates as the process advances, with the cumulative effects becoming pronounced. Eventually, this leads to a considerable surge in the incidence of cracks within the carbon fiber plates, which greatly compromises the reliability of these plates within the wind turbine blades.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Distribution** & **Normal** & **Gamma** & **Weibull** \\
**Type** & **Distribution** & **Distribution** & **Distribution** \\ \hline R\({}^{2}\) & 0.99986 & 0.99794 & 0.99977 \\ \hline \end{tabular}
\end{table}
Table 10: Judgment coefficients of life fit curve.  
Figure 16: Cumulative probability of failure and reliability curves for carbon fiber laminates with multiple cracks.
# Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
## 3 Methodology
### Theoretical Analysis of Axiomatic Compliance  
DeepPrior-EG inherits the axiomatic guarantees of expected gradients while introducing critical enhancements through prior-guided baselines. The framework preserves three fundamental properties essential for reliable feature attribution:Implementation Invariance is maintained through the gradient integration mechanism inherited from EG. By computing gradients along the interpolation path between the prior-adjusted baseline \(x^{\prime}_{\text{prior}}\) and the input \(x\), the method remains invariant to functionally equivalent model architectures. This ensures consistent attributions regardless of implementation details, as gradient calculations depend solely on the model's functional behavior rather than its structural particulars.  
Sensitivity is enhanced through the prior knowledge integration mechanism. When a feature \(x_{i}\) exhibits significant deviation from its prior-adjusted baseline \(x_{i,\text{prior}}\), the term \(\left(x_{i}-x^{\prime}_{i,\text{prior}}\right)\)amplifies the corresponding gradient magnitude during path integration. Simultaneously, the prior distribution \(p_{\text{prior}}\) acts as a probabilistic filter that suppresses spurious correlations in background regions. This dual mechanism ensures that attribution scores \(\text{EGPrior}_{i}(x)\) proportionally reflect the true impact of discriminative features while maintaining robustness to noise.  
Completeness is preserved through the conservation relationship shown in Equation (19).  
\[\sum_{i=1}^{n}\text{EGPrior}_{i}(x)=f(x)-\mathbb{E}_{x^{\prime}_{\text{prior} }}\left[f(x^{\prime}_{\text{prior}})\right] \tag{19}\]  
This extends the original EG completeness property by replacing conventional baselines \(x^{\prime}\) with knowledge-enhanced counterparts \(x^{\prime}_{\text{prior}}\). The formulation guarantees that the total attribution magnitude corresponds precisely to the model's output shift between input samples and prior-informed reference points.  
The framework demonstrates theoretical consistency with standard EG under specific baseline conditions. When the prior distribution \(p\)prior becomes uninformative (e.g., approaches a uniform distribution \(\mathcal{U}(-\epsilon,\epsilon)\) with \(\epsilon\to 0\), the prior adjustment operation reduces to an identity transformation as in Equation (20).  
\[x^{\prime}_{\text{prior}}=x^{\prime}-p_{\text{prior}}\approx x^{\prime} \tag{20}\]  
Substituting this into the DeepPrior-EG formulation (Equation (9)) yields Equation (21).  
\[\text{EGPrior}_{i}(x)=\int_{x^{\prime}}\text{IG}_{i}(x,x^{\prime})p_{D}(x^{ \prime})dx^{\prime}=\text{EG}_{i}(x) \tag{21}\]  
This degeneration confirms that the proposed method generalizes standard EG, recovering its exact formulation when prior knowledge is absent. The preservation of EG's core properties ensures that DeepPrior-EG maintains rigorous theoretical foundations while enabling enhanced interpretability through domain-specific prior integration.
# A Review of Validation Methods for the Intracranial Response of FEHM to Blunt Impacts
## 2 Experimental Procedures Used in FEHM Validation
### Cadaver Experimentation
#### 2.1.2 Relative Brain Displacement Response
The simulated displacement was then applied to the nodes representing the NDT clusters, connected both by triangular and tetrahedral elements. The strains generated in these isolated clusters were compared to the strains recorded in the complete FEHM. The tetrahedral formation was found to show significantly better agreement with the FEHM simulation responses across all five model variations (peak strain difference \(<0.01\)) than with triad outputs, where the average peak difference was 0.03. It is therefore proposed that the Green-Lagrangian strain responses calculated from tetrahedral cluster formations should be used as the empirical strain reference data during FEHM validation [46]. An example comparison of the strain calculated by [PERSON] et al. [36] and [PERSON] et al. [45; 46] for impact test C380-T2 is shown in Figure 3b. With the exception of test C288-T3, only one NDT cluster per experiment had data suitable for recalculation and therefore only depict the strain response in one region of the brain. Across the 36 tests summarised in Table 3, only seven (tests C288-T3, C380-T1, -T2, -T3, -T4, -T6 and C393-T4) have complete data sets, with respect to brain motion and strain. The short duration of the data available for test C380-T5 limits its potential to be used in validation of FEHM. Of these, only test C288-T4 represents an impact primarily in the anterior-posterior direction due to its occipital location. The remaining six are lateral impacts to the temporal and parietal regions. There is, however, a spread of aligned and offset cases, and cases with and without a helmet present. Figure 3: Illustration of the relative brain displacement and strain measures reported through monitoring Neutral Density Targets (NDTs) for the impact C380-T2: A deceleration impulse to the parietal location, offset from anatomical axes with a helmet present (adapted from [36,45,46]). (**a**) Motion of NDT-4 and NDT-11, the targets at the centre of each cluster, presented as co-ordinate traces starting at the location indicated by the circular marker and following the path depicted by line trace. [PERSON] et al. [36] provide similar traces for all NDTs available in the anatomical plane. (**b**) The response history of the same key NDTs against time for the complete impact duration. The **top** panel shows the relative displacements of NDT-4 and NDT-11 in the anterior-posterior (\(x\)) and medial-lateral (\(y\)) directions. The **bottom** depicts the corrected mean strains in Cluster 1 (surrounding NDT-4) calculated by [PERSON] et al. [46] using a tetrahedral (tetra) formation compared to the triad based calculation [PERSON] et al. [45] and to the values originally reported by [PERSON] et al. [36] across the same time-scale. \begin{table}
# Nonlinear Vibration and Post-Buckling Behaviors of Metal and FGM Pipes Transporting Heavy Crude Oil
## 6 Numerical Results
### Validation of the Present Findings  
In this sub-section, we first validate the obtained results against previous research findings. Following this, we examine the post-buckling and nonlinear vibration behaviors of the FGM pipes transporting heavy crude oil. In this regard, in Table 1, non-dimensional natural frequencies (NDNFs) of a homogeneous pipe in the absence of flow (i.e., when \(\Gamma=0\)) are compared with the findings of [PERSON] et al. [36], [PERSON] [37], and [PERSON] et al. [38]. In addition, Table 2 provides a comparison of the NDNFs for the beam made of FGM with those obtained by [PERSON] and [PERSON] [39]. These comparisons demonstrate a strong correlation between the present results and the published data, confirming the accuracy of the proposed approach.  
To generate the results for the post-buckling and nonlinear vibration behaviors of the pipe made of FGM transporting heavy crude oil, the material properties and structural dimensions are demonstrated in Tables 3 and 4. The density (\(\rho_{f}\)) and velocity (\(\Gamma\)) of the heavy oil are 940 Kg/m\({}^{3}\) and 0.1 m/s.
# Contrast Enhancement for Topographic Imaging in Confocal Laser Scanning Microscopy
## 5 Test Chart and Application  
In the experiment low contrast structures which are slightly above the diffraction limit of our confocal setup are investigated. As a test target to analyze the resolution and basic characteristics of the optical system, a custom-built silica test structure is chosen. Different patterns in the range from 200 nm to 500 nm were etched in silica substrate by electron beam lithography, the depth of the composition is about 40 nm. The size of the structure is written in nanometer near the pattern, as shown in Figure 5.  
The size was verified using a scanning electron microscope. For instance the actual size of the 250 nm bars was typically measured to be 252 nm.  
The second sample used for the experiment is an Application Specific Integrated Circuit (ASIC) specifically developed for security evaluation of various cryptographic implementations. Further information about the application of the ASIC is given by [PERSON] et al. [30]. It is manufactured using a 90 nm CMOS Low Power MS RF process and consists of nine metal layers. As a proof of principle for the contrast enhancement method, the final back end of line layer which is one of the last processing  
Figure 4: Simulation of depth scans in z-direction in focus (black), with pinhole displacement far (blue) and close (red) using a 10 AU pinhole and ideal lenses.  
Figure 5: Sketch of a part of the used test chart, showing 300 nm and 350 nm structures.  
steps of the ASIC is examined. The layout of the back end of line layer is shown in Figure 6, the used material is copper.  
For our sample, the surface was intentionally damaged to simulate potential faults in manufacturing.