esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Analysis of a Large LNG Tank Considering Different Site Conditions
## 4 Seismic Analysis of a Large Liquefied Natural Gas (LNG) Tank
### Modal Analysis  
Natural frequencies, such as the liquid-solid coupling frequency and the liquid sloshing frequency, are essential parameters in the design and analysis of LNG prestressed storage tanks. To determine the natural frequency, three methods are primarily used for extracting eigenvalues in ABAQUS: the subspace iteration method, the AMS eigensolver, and the block Lanczos algorithm. This study uses the Lanczos algorithm to calculate the first frequency of the empty tank as 3.753 Hz. According to specifications [36], the basic natural frequency of solid-liquid coupling is shown as:  
\[f_{c}=\sqrt{\frac{E\delta}{R^{2}H\rho}}\left[0.206082-0.0514\frac{H}{R}-0.00003 77\left(\frac{H}{R}\right)^{2}-0.001053432\left(\frac{H}{R}\right)^{3}\right] \tag{21}\]  
Figure 5: FEM of the LNG storage tank; (**a**) the entire LNG tank, (**b**) the outer tank, (**c**) the inner tank.  
where \(R\) is the radius of the inner tank; \(\rho\) is the density of the LNG liquid; \(E\) is elastic modulus of the inner tank; \(\delta\) is the thickness of the inner tank at 1/3 of the total height; \(H\) is the height from the tank bottom to the liquid level.  
In addition, according to specifications [36], the basic natural vibration period of liquid sloshing is shown in the following Equation:  
\[T=2\pi\sqrt{\frac{\mathrm{D}}{3.68\mathrm{g}\mathrm{c}\mathrm{t}\mathrm{d} \left(\frac{3.68\mathrm{H}}{\mathrm{D}}\right)}} \tag{22}\]  
where D is inner tank diameter; \(T\) is the basic period; \(\mathrm{c}\mathrm{t}\mathrm{h}\) is the hyperbolic tangent function; g is the acceleration of gravity. The basic natural frequency of solid-liquid coupling and the basic natural frequency of liquid sloshing under different liquid levels are listed in Tables 7 and 8.  
As shown in Tables 7 and 8, the simulation results of natural frequency of solid-liquid coupling and the basic natural frequency of liquid sloshing in large LNG storage tanks are in agreement with relevant theoretical results. More specifically, both simulation and theoretical results show that the basic natural frequency of solid-liquid coupling decreases with increasing liquid level, while the basic natural frequency of liquid sloshing increases with increasing liquid level. Therefore, the accuracy and feasibility of using numerical simulation for LNG tanks is demonstrated.
###### Abstract  
Ship wake detection plays a crucial role in compensating for target detection failures caused by defocusing or displacement in SAR images due to vessel motion. This study addresses the challenge of enhancing wake features in high-resolution spaceborne SAR by exploiting the distinct linear characteristics of wake echoes and the random motion of ocean background clutter. We propose a novel method based on sub-aperture image sequences, which integrates equivalent multi-channel technology to fuse wake and wave information. This approach significantly improves the quality of raw wake images by enhancing linear features and suppressing background noise. The Radon transform is then applied to evaluate the enhanced wake images. Through a combination of principle analysis, enhancement processing, and both subjective and objective evaluations, we conducted experiments using real data from the AS01 SAR satellite and compared our method with traditional wake enhancement techniques. The results demonstrate that our method achieves significant wake enhancement and improves the recognition of detail wake features.  
high-resolution spaceborne SAR; sliding spotlight; wake enhancement; Radon transform +
Footnote †: journal: Applied sciences  
Article
## 4 Discussion
### sT2  
sST2 is one of two isoforms of the gene suppression of tumorigenicity-2 (ST2), which is a member of the Toll-like/IL-1 receptor family [16]. The counterpart of the soluble isoform sST2 is the transmembrane form ST2L, which forms a ligand-receptor complex with interleukin (II-)33 to avert complex cardiac remodeling and fibrosis processes. Increased shear forces in the context of mechanical stress on the heart and lungs trigger increased secretion of sST2 from alveolar epithelial cells and cardiomyocytes. sST2 binds with markedly higher affinity to II-33 and thus inhibits the cardioprotective signaling cascade with the membrane-associated ST2L [17; 18].  
This knowledge of the molecular mechanism is important in understanding the results presented here. Severe AS patients with (sPAP \(\geq\) 40 mmHg) and without (sPAP \(<\) 40 mmHg) PH showed a similar curve progression as the overall cohort of [PERSON] et al. [19], who observed an additional increase in plasma concentration of sST2 in a time frame of 7 days after TAVR. In our study, the highest peak was measured after 24 h and then fell again after 96 h in patients with both an sPAP \(<\) 40 mmHg and \(\geq\)40 mmHg to then settle at this plasma level. The finding that, almost universally, patients with an sPAP \(\geq\) 40 mmHg had significantly higher plasma sST2 concentrations than the comparison cohort with an sPAP \(<\) 40 mmHg is likely of prognostic relevance. The exception here was the determined plasma concentrations after 12 months; however, even here a relevant trend emerged with a \(p\) = 0.092. Additionally, it could be shown in multivariate binary logistic regression that sST2 was a relevant independent predictor for the detection of potential PH. This has already been confirmed recently by our own working group using a collective of patients with severe AS and available right heart catheterization data, on the basis of which PH was verified by the absolute gold standard [20]. It may well be hypothesized that removal of the aortic valve stenosis decreases the corresponding left ventricular mechanical stress; however, the already fixed pulmonary hypertension leads to continued secretion of sST2 from cardiomyocytes during sustained right ventricular stress.
# TrojanDetector: A Multi-Layer Hybrid Approach for Trojan Detection in Android Applications
## 4 Experimental Validation
### Discussion on Results  
Experimental results on three publicly available datasets, i.e., CCCS-CIC-AndMal-2020, Contagio Mobile, and VirusShare, are promising. The analysis suggests that the TrojanDetector using SVM as the classifier attains the highest accuracy of 96.64%.  
This research exploits the decision tree classifier with a depth of 10--the value of min-weight-fraction-leaf to 3 and max-leaf-nodes to 5. DT is normally considered useful on a smaller dataset, which is not the case here. In a large dataset, this classifier normally overfits, resulting in over-prediction. Similarly, we applied RF with just five trees and used the default criterion of (mse, mae). Here, we set the max depth to 5, with min-samples-split to 3 and min-weight-fraction-leaf to 10.  
Though the accuracy increases with respect to the DT classifier, it is still lower than the top-performing SVM classifier. This work uses SVM with a Radial Basis Function (RBF) as its kernel function. This algorithm is commonly known as a maximum margin classifier and is utilised to tackle classification problems for a large dataset. There are several kernel selections available for the SVM method. We applied all the available kernels, and the RBF kernel yielded the highest accuracy. There are two tuning parameters for SVM-RBF: kernel parameter sigma (\(\sigma\)) and cost parameter (C) adjusted for repeated training. The sigma value plays a role in getting a good fit model to the data. The cost parameter is the penalty limit if the data point is miss-classified or oversteps the maximum margin. The SVM works better on the margins, and due to the clear margins, the SVM gives the highest accuracy.
## 3 Discussion
### Effect of Elastic Modulus on Axial and Radial Pressures
For cylinder diameters larger than 65 mm, the elastic modulus increases more rapidly with the axial expansive pressure. In the models with cylinder diameter of 90 mm, 113 mm, and 130 mm, the average elastic modulus is 3.162 GPa, 4.425 GPa and 3.585 GPa, respectively. When the diameter is 150 mm, the average elastic modulus increases to 6.132 GPa. The reason for above phenomenon is that the hydration process of SCA is a compaction process. During the compaction process of SCA, the elastic modulus will first increase with the pressure and then tend to a stable value. The above numerical results indicate that in models with smaller cylinder diameter, the effect of axial expansive pressure on selection of elastic modulus is trivial, while the effect is more prominent for models with larger cylinder diameter. The errors in axial expansive pressures obtained by physical tests are amplified for determination of elastic modulus. The amplification factor increase with increasing diameter. The analysis shows that: for the models with larger cylinder diameter (\(\Phi\geq\) 113 mm), the elastic modulus varies in a larger range, which is caused by the amplified errors in the measured axial expansive pressures. In consideration of the general mechanical performance of elastic body, the elastic modulus for models with large cylinder diameter is adjusted appropriately. The elastic modulus is considered to be stable when the diameter is greater than 90 mm, as shown by the fitting curve in Figure 7.  
First, error analysis is performed for the elastic modulus before and after fitting. Meanwhile, according to the fitted elastic modulus curve, the axial expansive pressure corresponding to a cylinder diameter of 90, 113, 130 and 150 mm, respectively, is determined. The errors are analyzed by comparing with the axial expansive pressure corresponding to the elastic modulus before adjustment. For four models with various cylinder diameters, the elastic modulus error varies in a large range. When the diameter is 150 mm, the error in elastic modulus reaches 16.7%. However, the corresponding axial expansive pressure error varies in a small range, with the maximum being less than 2.0%. Therefore, it is shown that,  
Figure 6: Relationship between simulated axial expansive pressure and elastic modulus. when the elastic modulus after adjustment is adopted for numerical simulation, the error in the axial expansive pressure is relatively small, as shown in Figure 8.
# Self-Sovereign Identity-Based E-Portfolio Ecosystem
## 2 Background and Related Work
### Access Control  
In the development of applications, the choice of an access control model is crucial and should align with the specific requirements of the scenario. Two common models are Role-Based Access Control (RBAC) [33] and Attribute-Based Access Control (ABAC) [29; 30]. RBAC simplifies permission assignment by predefined roles, facilitating swift access control for new users. However, its effectiveness diminishes when dealing with dynamic attributes such as temporal and spatial parameters. Conversely, ABAC offers increased adaptability in administering access control, accommodating various organizational roles and job responsibilities, and can include dynamic attributes in access control policies.  
Existing access control management systems, such as PKI [34], face scalability and granularity issues and are susceptible to attacks targeting certificate authorities. Blockchain-based solutions, with their transparency, nonrepudiation, and security features, emerge as attractive alternatives for access control. Many decentralized access control methods use blockchain to share access control policies or employ a multilayer blockchain structure to ensure reliability and efficient operations.  
For example, in [35], they suggested using blockchain to manage access control policies. [PERSON]. [36] employed AuthPrivacyChain to secure cloud services against unauthorized access. The author of [37] proposed a user rights management system using blockchain and smart contracts to facilitate relationships between users, data providers, and regulatory bodies. [PERSON]. [38] introduced a distributed ABAC system that employs blockchain to audit access attempts in digital libraries. Lastly, [PERSON]. [39] discussed a multi-stakeholder ABAC system in which blockchain smart contracts support relationships between users, data providers, and regulatory bodies. These various approaches highlight the versatility of blockchain technology in improving access control in different applications.
## 4 Performance Evaluation and Discussion
### Model Establishment
#### 4.1.1 Database Description  
Our model was established and evaluated based on a set of 36 distorted videos in LFOVIA Video QoE Database [12]. These videos have different playout patterns distorted by bitrate switching and rebuffering events. In this database, the overall QoE and the time-varying instantaneous QoE scores for those videos were obtained are in the range [0, 100], with score 0 being the worst and 100 being the best. The set of distorted videos was divided into training and testing sets with a training:testing ratio of 80:20. Accordingly, there were 28 videos in the training set and 8 videos in the testing set. The training and testing set were, respectively, used to obtain the model parameters described in Section 4.1.3 and evaluate the prediction performance of the model presented in Section 4.2.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture
### Anomaly Detection Approach
#### 3.2.3 Future Work: Exploring Lightweight Transformer Variants  
Future research will focus on evaluating lightweight transformer architectures optimized for edge deployment to further improve sequential anomaly detection while ensuring computational efficiency. These models offer a balance between accuracy and resource constraints, making them suitable for on-device anomaly monitoring in smart home environments.  
Potential candidates include the following:  
* **MobileViT--**A mobile-friendly vision transformer that reduces computational complexity while preserving accuracy [22].
* **TinyBERT--**A distillation-based transformer designed for low-power AI applications.  
By integrating quantized and optimized deep learning architectures, future iterations of this framework aim to achieve higher anomaly detection accuracy while maintaining real-time processing capabilities on embedded IoT hardware. Additionally, further evaluations will assess the trade-offs between computational overhead and on-device anomaly monitoring performance to ensure optimal deployment in resource-constrained environments.
## 3 Results and Discussion
### Quadrupole Time-of-Flight Mass Spectra
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**N\({}^{\text{o}}\)** & **Oligomer** & **Molecular Formula** & **CAS** & **Molecular Mass (Da)** & **Parent Molecular Ion Mass (m/z and Form)** & **Commercial Available** & **UPLC-QTOF-MS** \\ \hline \multirow{2}{*}{\(1\)} & cPET-EG & \multirow{2}{*}{C\({}_{20}\)H\({}_{16}\)O\({}_{8}\)} & \multirow{2}{*}{24388-68-9} & \multirow{2}{*}{384.34} & \multirow{2}{*}{385.09143 ([M + H]\({}^{+}\))} & \multirow{2}{*}{Yes} & Id. + quant. \\  & dimer & & & & & \\ \hline \multirow{2}{*}{\(2\)} & cPET-EG & \multirow{2}{*}{C\({}_{30}\)H\({}_{24}\)O\({}_{12}\)} & \multirow{2}{*}{7441-32-9} & \multirow{2}{*}{576.50} & \multirow{2}{*}{577.13398 ([M + H]\({}^{+}\))} & \multirow{2}{*}{Yes} & Id. + quant. \\  & trimer & & & & & \\ \hline \multirow{2}{*}{\(3\)} & cPET-EG & \multirow{2}{*}{C\({}_{40}\)H\({}_{32}\)O\({}_{16}\)} & \multirow{2}{*}{16104-96-4} & \multirow{2}{*}{768.67} & \multirow{2}{*}{769.17632 ([M + H]\({}^{+}\))} & \multirow{2}{*}{Yes} & Id. + quant. \\  & tetramer & & & & & \\ \hline \multirow{2}{*}{\(4\)} & cPET-EG & \multirow{2}{*}{C\({}_{50}\)H\({}_{40}\)O\({}_{20}\)} & \multirow{2}{*}{16104-97-5} & \multirow{2}{*}{960.84} & \multirow{2}{*}{983.19957 ([M + H]\({}^{+}\))} & \multirow{2}{*}{Yes} & Id. + quant. \\  & pentamer & & & & & \\ \hline \multirow{2}{*}{\(5\)} & cPET-EG & \multirow{2}{*}{C\({}_{60}\)H\({}_{48}\)O\({}_{24}\)} & \multirow{2}{*}{29644-29-9} & \multirow{2}{*}{1153.43} & \multirow{2}{*}{1153.25962 ([M + H]\({}^{+}\))} & \multirow{2}{*}{Yes} & Id. + quant. \\  & hexamer & & & & & \\ \hline \multirow{2}{*}{\(6\)} & cPET-EG & \multirow{2}{*}{C\({}_{70}\)H\({}_{56}\)O\({}_{28}\)} & \multirow{2}{*}{29668-12-0} & \multirow{2}{*}{1345.46} & \multirow{2}{*}{1367.32663 ([M + Na]\({}^{+}\))} & \multirow{2}{*}{Yes} & Id. + quant. \\  & heptamer & & & & & \\ \hline \hline \end{tabular} \({}^{*}\)Id. = identification; quant. = quantification.  
\end{table} 
Table 3: Target cyclic oligomers, molecular formula, ion found, measured mass and analytical standard availability, analyzed by the UPLC-QTOF-MS.  
Figure 4: Total Ion Chromatogram (TIC) QTOF-MS of the studied PET cyclic oligomers (2 \(\upmu\)g mL\({}^{-1}\) in HFIP): dimer (1), trimer (2), tetramer (3), pentamer (4), hexamer (5) and heptamer (6). On the contrary, for all odd-numbered cyclic oligomers (trimer, pentamer and heptamer) it was observed that they are not fragmented directly to the dimer nor via the trimer. Instead, they are fragmented via the formation of the dimer's ion (m/z 385.091), which is not present for the even numbered. This behavior clearly differentiates the higher number odd and even cyclic oligomers, but nothing can be concluded for the dimer and the cyclic trimer.
## 2 Materials and Methods
### The Basic Structure and Principle of ED Net
#### 2.2.3. Smooth Labeling of the Loss Function  
The loss function will have better resistance to overfitting after the smooth labeling improvement. The loss function is essentially designed to encourage the output of the model to approach the corresponding sample labels. In the network learning process of traditional one-hot encoded labels, the loss function encourages the model to predict the probability of being the target category to be close to 1, and the probability of being the non-target category to be close to 0. The traditional one-hot encoded label vector is \(y_{i}\) and is expressed as:  
\[y_{i}=\left\{\begin{array}{c}1,if(i=y)\\ 0,otherwise\end{array}\right. \tag{2}\]  
where \(y\) represents the target label and \(i\) represents the output value. However, this is likely to result in overfitting. If the output value is extremely close to the sample label, but this training sample is incorrect, this can be extremely detrimental to the model. Because a faulty sample must be learned during the training process and has a very high probability of occurring, this will have a significant effect on the posterior probability estimation, and sometimes the classes are not unrelated to one another, leading to a degree of overfitting if the output probability interval is large. Consequently, the probability interval can be appropriately reduced. The impact of incorrect labeling can be mitigated by adjusting the distance between the extremes. The new label vector \(\hat{y_{i}}\) replaces the conventional one-hot encoded label vector and is expressed as:  
\[\hat{y_{i}}=\left\{\begin{array}{c}1-\varepsilon,if(i=y)\\ \frac{\varepsilon}{M},otherwise\end{array}\right. \tag{3}\]  
where \(\varepsilon\) represents a small positive real hyperparameter (typically assumed to be 0.1), while \(M\) represents the total number of multicategories. Therefore, the extremes are \(1-\varepsilon\) and \(\varepsilon/M\), rather than 1 and 0.  
Smoothing the labels is equivalent to adding noise to the true distribution to prevent the model from being overconfident about the correct labels, resulting in a relatively small difference between the output values of the predicted positive and negative samples. This method prevents overfitting to some extent, reduces the impact of incorrect labels, and enhances the model's generalization ability.
## 3 Results and Discussion
### Method Validation  
The HPLC-DAD method was successfully validated and included linearity of calibration, repeatability, processed sample stability, long-term stability as well as the calculation of LOD and LOQ.  
Both calibration ranges (25-250 mg L\({}^{-1}\) and 250-500 mg L\({}^{-1}\)) showed a linearity in the examined concentration range with coefficient of determinations ranging from 0.9995-0.9999 and 0.9959-0.9985, respectively. For the calibration model 25-250 mg L\({}^{-1}\), the maximum relative standard deviation (RSD) value was 2.2%, the minimum bias \(-\)5.4 and the maximum bias 6.1%. The RSD as well as minimum and maximum bias for the calibration model 250-500 mg L\({}^{-1}\), were 0.9, \(-\)3.3 and 2.0%, respectively. All results are summarized in Tables S4 and S5.  
Repeatability of the applied HPLC-DAD method was investigated by calculating the interday and intraday RSD values over a period of eight days. All RSD values were far below 15%, demonstrating repeatability of the applied HPLC-DAD method. All results are given in Tables S6 and S7.  
Processed sample stability results showed that the concentration did not significantly change during a measurement period of 19 h at 10 \({}^{\circ}\)C in the autosampler. The maximum deviations from the initial value were ranging between 1.1 and 2.4%, respectively.  
The long-term stability was investigated and showed that all phenolic acids at a concentration of 50 and 500 mg L\({}^{-1}\) were stable for at least 14 days at 4 \({}^{\circ}\)C as well as 23 \({}^{\circ}\)C. The maximum deviations from the initial value at 4 \({}^{\circ}\)C were ranging from 0.9-1.8% and at 23 \({}^{\circ}\)C from 1.2-5.2%.  
The LOD and LOQ were calculated according to DIN 32645:2008-11 and gave values ranging from 0.02-0.04 mg L\({}^{-1}\) and 0.08-0.12 mg L\({}^{-1}\), respectively. All results are given in Table S8.
## 4 Results and Discussion
### Performance Evaluation of the LSTM Model
Figure 9 confirms that the model performance depends on the two hyperparameters--the larger the batch size, the lower the computational cost as \(R^{2}\) decreases. When the learning rate was \(10^{-2}\), the performance increased as the batch size increased up to 32. Figure 8: Scatter plots comparing the reference and predicted \(\mathrm{Vp}\) data (scaled) for the training, validation, and test data for the ANN and LSTM (Fold 3). The performance with a learning rate of \(10^{-5}\) was relatively inferior. A learning rate of \(10^{-3}\) and batch size of 32 had the highest \(R^{2}\) at an affordable computational cost. With these hyperparameter values, the \(R^{2}\) of each experiment was greater than 0.75, confirming both the consistency and generality of the LSTM model on well-logging data in the gas hydrate field. Thus, this hyperparameter setting was used for all experiments throughout this study.