esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 4 Evaluation, Result and Analysis
### Evaluation Result
#### 4.3.2 Comparison with Other Model  
To validate the performance of our proposed method, we compare its performance with various state-of-the-art methods in network-encrypted traffic classification. For accurate performance validation, it is essential to compare methodologies using the same dataset with identical preprocessing methods in a consistent environment. However, direct comparisons of different methodologies are often impractical due to various constraints. Therefore, we took the performance presented by each methodology and used them for the comparison. The methods are categorized into the following: (1) statistical feature-based, (2) ML- and DL-based, and (3) pretraining-based, and a total of 17 methodologies are compared.  
(1) Statistical feature-based methodologies: AppScanner [54], CUMUL [44], BIND [45]  
(2) ML- and DL-based methodologies: Deep Fingerprinting (DF) [46], FS-Net [15], Graph-DApp [38], TSCRNN [17], DeepPacket [10], 1D-CNN [26], FastTraffic [39], MATEC [47]  
(3) Pretraining-based methodologies: PERT [21], ET-BERT (flow) [34], ET-BERT (packet) [34], XENTC [33], BFCN [22], Flow-MAE [25], YaTC [24]  
Most studies do not perform task #1 (Encapsulation) on the ISCX 2016 VPN/Non-VPN data; rather, they perform tasks #2 (Category) and #3 (Application) to classify categories and applications. Therefore, we compare these methods and approaches that target task #2 and #3, as these tasks are more commonly addressed. The results of our experiments are shown in Tables 3 and 4. As each method varies in terms of metrics, number of classes, and targeted tasks, we only summarize the information presented by each study.  
The proposed method achieves about 96-98% accuracy on tasks #2 and #3, outperforming most of the existing research methods. Although several methodologies exhibit slightly better performance (i.e., accuracy 0.69-1.67% in task #2 and accuracy 1.31-2.98% in task #3), it is noteworthy that the existing approaches are designed for STL-based single task classification, while the proposed method is capable of classifying three tasks simultaneously. This capability to address multiple tasks simultaneously is remarkable. Through this multi-task classification, the proposed method not only maintains high performance but also proves to be more efficient than conventional approaches in handling the classification of multiple tasks concurrently.
## 3 Bioactivity of Pigments and Lipids from Plastids of Marine Phytoplankton
### Pigments
#### 3.1.1.1 Anti-Oxidant, Anti-Inflammatory and Anti-Mutagenic Activities  
Many diseases such as cancer, diabetes and, neurodegenerative and cardiovascular diseases have in common oxidative stress as a major cause of inflammatory events. The anti-oxidant and anti-inflammatory activities of phytoplankton pigments are widely demonstrated and evidenced in numerous _in vitro_ free and _in vivo_ radical scavenging assays. The presence of a metal-chelate strongly enhances the anti-radical capacity closed tetrapyrcles. Protoporphyrin methyl ester and its magnesium chelated derivative, as well as pheophorbide b and pheophytin b, were also identified as strong anti-oxidant molecules [65]. The ability of the porphyrin ring to transfer electrons explains the anti-oxidant activity of Chls and derivatives. The highest anti-oxidant activity was obtained with of pheophorbide b, compared to pheophorbide \(a\), suggesting that the presence of the aldehyde function may be critical to this activity [47]. This was confirmed in a study that investigated the action mechanisms of natural and synthetic porphyrins regarding their anti-inflammatory effects _in vitro_ and _in vivo_[66]. Unfortunately, no structure related to the Chl familly of compounds has been tested leaving uncertain the effects of plant pigments on porphyrins targets such as the tumor necrosis factor alpha production, the Fyn tyrosine kinase inhibitors [66]. However, on the basis of the study, some potential for Chl and Chl-derivatives can be hypothesized. More generally, it should be recall that the antioxidant properties of Chls and Chl-derivatives disappear in the presence of light. Actually, the accumulation of free tetrapyroles in photosynthetic cells leads to a dramatic oxidative stress upon light illumination [67, 68, 69]. Metal-free and metallo-Chl derivatives have also anti-mutagenic activities, as demonstrated using a bacterial mutagenesis assay [47, 70]. Similar properties are exhibited by open-chain tetrapyroles (anti-oxidative: allophycocyanin [71], phycocyanin [72], phycoerythrin [73] and anti-inflammatory: phycocyanin [74]) _in vitro_. As a conclusion, most microalgal pigments exert strong _in vitro_ anti-oxidant activity, but additional intervention trials are required to precise their absorption, metabolism and potential as natural anti-oxidant, anti-inflammatory and anti-mutagenic compounds _in vivo_. Very importantly, the capacity of phycocyanin to quench oxygen radical species is preserved in food supplement [75].
## 3 Experiments  
This section evaluates our algorithm on eight satellite videos, sets the relevant parameters, examines its tracking effect utilizing evaluation metrics, and challenges its performance against several classic algorithms.
# Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time
## 4 Research Framework  
Deep learning requires no labeled information to identify its next move. The CNN-LSTM method reduces the need for time-consuming feature engineering, which is needed for shallow machine learning techniques but might not be able to pull out enough useful features for classification tasks, as well as relationships between variables including dependency, consistency, and structural information in the collected data [24]. When trained on the same data, CNNs and RNNs may pick up representations from different angles and capture different attributes at higher levels. Convolutional neural networks (CNNs) excel at capturing local spatial correlation, but recurrent neural networks (RNNs) are particularly strong at gathering temporally sequential data. The CNN's strength in this area lies in its ability to extract features at each position in the sequence while sliding over the entire series via convolution operations between filters and the sequence, making it ideal for modeling the API call sequence. Long short-term memory (LSTM) can automatically figure out long-term relationships from a set of random data points [25].  
An overall representation of the suggested mixed deep model is depicted in Figure 3, combining both the temporal and geographical interactions of the CNN-LSTM, at the micro and macro levels. The CNN-LSTM model solves the problem of classifying malware by automatically abstracting and expressing high-level n-gram API requests as sequential feature maps.
# A High Throughput Hardware Architecture for Parallel Recursive Systematic Convolutional Encoders
## 3 Results
### Impact of the Parallelism Degree on the Source Utilization: Case Study  
In this section, we describe a methodology to estimate the dependency of the number of slice LUTs depending on the parallelism degree by considering as case study the RSC encoder shown in Equation (24).  
By using a similar approach to the one described in Section 3.2, \(N_{0}\) and \(N_{1}\) parameters of Equation (23) were estimated by using a MSE approach. In particular, \(\Omega_{D^{\prime}}[k]\) was measured by using the estimator described in Equation (27) and by exploiting the values of the learning parameters described in Table 3. The interpolation was performed by using data on slice LUTs utilization obtained by the multiple implementation of the RSC encoder on board Zynq 7000 xc7z010 clg400-1 FPGA; such data are shown in Table 5.  
\begin{table}
\begin{tabular}{l l} \hline \hline \(T_{A}\) **[s]** & 3.25590031598599e-10 \\ \hline \(T_{B}\) **[s]** & 7.99924552672264e-10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(T_{A}\) and \(T_{B}\) values derived through a mean square error (MSE) interpolation.  
Figure 7: Estimated \(f_{clk_{MAX}}[k]\) trend relative to the \(D^{\prime}\) matrix.  
Table 6 shows the \(N_{0}\) and \(N_{1}\) values found.  
Owing to the real nature of the \(N_{0}\) and \(N_{1}\) parameters, the result was rounded to obtained an integer estimation. Figure 8 shows the estimated dependency of the number of slice LUTs on the parallelism degree.
## 3 Experimental Study on the Coefficient of Restitution of Wheat Grains
### Measurement Accuracy Determination  
In this study, the accuracy of the collision-force measurements obtained with a piezoelectric sensor was evaluated. The dominant contribution to the uncertainty stems from the instrument's calibration and specifications (Type B); the statistical component (Type A) is minor. Each parameter was analysed separately under a rectangular probability distribution [27; 40; 41]:  
\[S_{B}(F_{\mathrm{C}})=\frac{\Delta_{gr}}{\sqrt{3}} \tag{13}\]  
where \(\Delta_{gr}\) is the calibration uncertainty specified by the manufacturer. Additional contributions--non-linearity (1%) and temperature sensitivity (0.054%)--were combined with Equation (13) by the root-sum-of-squares (RSS) method. The expanded uncertainty \(U\) was then obtained with a coverage factor \(k=2\) (\(\approx\)95% confidence):  
\[U=k\cdot S_{B}(F_{\mathrm{C}}) \tag{14}\]  
This procedure ensures that the reported force values carry well-defined confidence intervals and are comparable with those of other studies.  
Velocity was measured with a high-speed camera, so its uncertainty depends on both distance and time. The camera frame-rate uncertainty, \(S_{FR}=0.057\)\(FRS\), provides the temporal component, whereas the distance uncertainty, \(S_{\phi}\), combines calibration accuracy, operator repeatability, optical aberrations and lens distortion.  
Because velocity is a ratio of distance to time, the combined standard uncertainty was obtained by propagating \(S_{\phi}\) and \(S_{FR}\) with Equation (15):  
\[S_{\mathrm{P}}=\sqrt{\left(\frac{\partial v}{\partial\phi}\cdot S_{\phi} \right)^{2}+\left(\frac{\partial v}{\partial\Delta t}\cdot S_{\Delta t} \right)^{2}} \tag{15}\]  
The motivation for this detailed uncertainty calculation was to ensure that the experimental data accurately represent the true collision forces and velocities and to provide clear confidence intervals for every measurement. By accounting for instrument calibration, non-linearity, temperature drift and device precision, the results become more robust and comparable with other studies, thereby promoting reproducibility.  
As an example, the maximum collision force was 22.49 N, with an expanded uncertainty of \(\pm 0.56\) N (\(\approx\)2.49%). The highest impact velocity, 4.76 m\(\cdot\)s\({}^{-1}\), carried a standard uncertainty of \(\pm 0.03\) m\(\cdot\)s\({}^{-1}\) (\(\approx\)0.63%). These figures demonstrate the high confidence in the force and velocity data and underscore the importance of rigorous uncertainty analysis in experimental work.
## 3 Materials and Methods
### Methodology  
#### 3.2.1 Development of PDSI  
PDSI was developed by [PERSON] in 1965 and uses readily available temperature and precipitation data, as well as the locally available water content of the soil, to estimate the relative dryness. The step-wise retrieval of PDSI was discussed by [PERSON], 1965 [1]. It is a standardized drought index that spans -4 (extremely dry) to +4 (extremely wet). It has been reasonably successful at quantifying long-term drought, as it uses local temperature and rainfall data and a physical water balance model for estimation. It can also capture the basic effect of global warming on drought through changes in potential evapotranspiration. Monthly PDSI values do not capture droughs on time scales less than about 12 months.
## 4 Evaluation and Results
### Feature Computation
In this work, following [18], we decided to consider an epoch corresponding to the last 35 min before delivery. Once the features are computed, the data are standardized using \(z\)-score based normalization. As a result, each group of feature data has a mean value equal to zero and a standard deviation equal to one. After the feature extraction operation, we perform a feature elimination step based on a Wilcoxon rank-sum test [67], also employed in Section 4.1. Making use of this test, features that show a statistically significant difference (\(p\)-value) are selected, whereas the remaining non-significant features are excluded for the analysis. This operation is performed for the complete set of features (including CS, DD, and DR based features), independently for each feature. As a result of this operation from the CS, DD, and DR, we obtained 38, 23, and 27 significant features, respectively, which are presented in Tables 2-4. In these tables, the significant features are grouped in _feature sets_ according to their category; i.e., one feature set for each IMF and each computed indicator. \begin{table}
\begin{tabular}{c l l c} \hline \hline
**Feature Set** & **Significant Features** & **Type** \\ \hline \(CS_{1}\) & IMF\({}_{1}\)-\(E\) & \(\mu\), M, RMS & \\ \(CS_{2}\) & IMF\({}_{1}\)-\(E^{c}\) & \(\mu\), M, RMS & \\ \(CS_{3}\) & IMF\({}_{2}\)-\(E^{c}\) & ApEn, SampEn & \\ \(CS_{4}\) & IMF\({}_{4}\)-\(E^{c}\) & ApEn, SampEn & \\ \(CS_{5}\) & IMF\({}_{6}\)-\(E\) & \(\mu\), \(\sigma\), mad, RMS & \\ \(CS_{6}\) & IMF\({}_{6}\)-\(E^{c}\) & \(\mu\), \(\sigma\), mad, RMS & modal-spectral \\ \(CS_{7}\) & IMF\({}_{8}\)-\(E\) & \(\mu\), \(\sigma\), mad, RMS & \\ \(CS_{8}\) & IMF\({}_{8}\)-\(E^{c}\) & \(\mu\), \(\sigma\), mad, RMS & \\ \(CS_{9}\) & IMF\({}_{10}\)-\(E\) & ApEn, SampEn & \\ \(CS_{10}\) & IMF\({}_{10}\)-\(E^{c}\) & ApEn, SampEn & \\ \hline \(CS_{11}\) & FHR signal & \(\sigma\), mad, ApEn, SampEn & \\ \(CS_{12}\) & PBL & \(\sigma\), mad & conventional \\ \(CS_{13}\) & IMF\({}_{6}\) & ApEn & \\ \(CS_{14}\) & IMF\({}_{7}\) & ApEn & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature sets and corresponding significant features extracted from the complete signal (CS).
## 5 Proposed Model Architecture
### Model Training and Validation  
Utilizing a stratified methodology, we divided our dataset into distinct training, validation, and test segments. A significant portion, around 80%, was dedicated to training, empowering the model to decipher intricate patterns and correlations intrinsic to the data. The validation set, representing 10%, was instrumental in fine-tuning hyperparameters and optimizing the model's architecture, which was further enriched through cross-validation techniques. The remaining 10%, designated as the test set, served as a definitive measure to gauge our model's proficiency on novel data.
# Time-Varying Reliability Evaluation of Concrete Based on Carbonation Depth
## 4 Distribution Fitting of the Carbonation Depth  
To develop a prediction model for the carbonation depth, the probability distribution of the carbonation depth considering service age should be determined. In this paper, the data of carbonized concrete were used to investigate the probability distribution. Since the prediction models based on large data are more accurate than those based on limited data [36], 732 data (carbonated specimen) were divided into 7 groups corresponding to 2 years according to service ages to increase the accuracy of the prediction, as presented in Table 2.  
The MATLAB software was used for the analysis of the fitting distributions of data with the sample significance level a = 0.05. Gamma distribution, lognormal distribution, exponential distribution, and normal distribution were used to describe the probability distribution. It was found that the exponential distribution had the best agreement with the probability density distribution, as shown in Figure 7.  
The probability distributions of carbonation depth at different service ages are plotted in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Age (Year)** & **Representative Service Age (Year)** & **Total Sample Size** \\ \hline
37–38 & 37 & 88 \\
39–40 & 39 & 65 \\
41–42 & 41 & 106 \\
43–44 & 43 & 173 \\
45–46 & 45 & 104 \\
47–48 & 47 & 135 \\
49–50 & 49 & 61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Service age, representative age, and sample size.  
Figure 7: Distribution fitting of carbonation depth.  
Figure 8: _Cont._  
Different from what determined by the existing prediction models, the distribution of carbonation depth was found to be represented by an exponential distribution function. This is because numerous existing studies have been carried out using the statistical analysis of accelerated experiments, in which the variables represented by environmental factors are not considered. Additionally, the tested component in this experiment had the characteristic of long service life. Furthermore, the specimens in accelerated experiments are all carbonized, but, actually, numerous concretes are uncarbonized, as shown by the experiment in this paper in natural environment.
## 5 Conclusions  
In this paper, a novel six-degree-of-freedom (6-DOF) hybrid active-passive vibration isolation platform was proposed and developed. The APIVI platform features a three-dimensional orthogonal decoupled configuration, with each axis independently integrated with piezoelectric active actuator units and rubber passive isolation modules. A nonlinear dynamic model incorporating electromechanical coupling effects is established using a modified Lagrangian formulation, effectively characterizing the frequency-dependent characteristics of the hybrid isolation system. To address the parameter perturbations caused by manufacturing tolerances and component aging in practical engineering applications,  
Figure 16: Acceleration response of APIVI platform with passive control and hybrid control under random excitation signal.  
Figure 17: The transfer function of the APIVI platform with different control strategies under vertical sine signal disturbance.  
a structured uncertainty model is constructed. An innovative \(\upmu\)-synthesis robust control methodology is employed to design an H\({}_{\infty}\) controller, achieving broadband vibration suppression while ensuring system stability. Experimental investigations under simulated on-orbit microgravity conditions demonstrate that the APIVI platform exhibits outstanding vibration isolation performance above 18 Hz. Compared with purely passive isolation systems, the active control strategy successfully eliminates the first-order resonance peak with an attenuation of 18 dB.  
Methodology, Y.Z.; simulated analysis, T.Z.; validation, Z.Z. and Y.Z.; resources, W.F.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z. and W.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Suzhou Basic Scientific Research Project, grant number SSD2024013.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
APIVI Active-passive integrated 6-dimensional orthogonal vibration isolation PID Proportional integral derivative MAI Multi-target active vibration isolation platform MR Magnetorheological
## 2 Materials and Methods
Inductively coupled plasma mass spectrometry (ICP-MS) analyses were carried out using a Bruker Aurora M90 equipped with a frequency matching RF-generator. The basic parameters were as follows: radiofrequency power 1.30 kW; plasma argon gas flow rate 16.5 L min\({}^{-1}\); auxiliary gas flow rate 1.65 L min\({}^{-1}\); and sheath gas flow rate 0.18 L min\({}^{-1}\). All isotopes were measured without skimmer gas. No interference corrections were applied to the selected isotopes. The analysis mode used was peak hopping with 3 points per peak. Gas blank values were subtracted. The quantification was performed with a method using direct ratios to count rates of the standard similar to those of [PERSON] (2012) and [PERSON] et al. (2021) [28; 29]. The NIST612 standard was run before and after batches of three samples in order to monitor the stability of the beam and to act as a standard. A relative error of ca. 10% is estimated from these measurements mostly due to mineral heterogeneity of the samples. The following isotopes were analysed for: \({}^{29}\)Si, \({}^{44}\)Ca, \({}^{49}\)Ti, \({}^{52}\)Cr, \({}^{55}\)Mn, \({}^{57}\)Fe, \({}^{59}\)Co, \({}^{60}\)Ni, \({}^{65}\)Cu, \({}^{75}\)As, \({}^{85}\)Rb, \({}^{88}\)Sr, \({}^{107}\)Ag, \({}^{111}\)Cd, \({}^{121}\)Sb, \({}^{197}\)Au, \({}^{205}\)Ti, \({}^{208}\)Pb, \({}^{232}\)Th and \({}^{238}\)U. The maximum firing temperature was estimated according to a method developed by the CHART-group at the University of South Denmark which relies on measuring the magnetic susceptibility on a stepwise re-fired sample [30]. The susceptibility data were plotted as a function of the re-heating temperature. In order to establish the firing temperature more accurately, the square of the first derivative was plotted as a function of the re-heating temperature. The first derivative was calculated as \((\text{S}_{\text{i}}-\text{S}_{\text{i}-1})/\delta\text{T}\), where \(\text{S}_{\text{i}}\) and \(\text{S}_{\text{i}-1}\) are two consecutive susceptibility measurements and \(\delta\text{T}\) is the temperature difference between the heating steps. A sudden break in this curve is normally observed when the initial firing temperature has been surpassed. Under some circumstances the temperatures of two firing events can be distinguished.
# Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors
## 1 Introduction
While the addition of an external device can enhance the ignition reliability of the cavity-strut structure, it also introduces extra external equipment, thereby increasing the system's complexity. In contrast to existing approaches relying on external devices, this paper introduces a novel structure--the tail-groove strut. It aims to overcome these limitations by exclusively adjusting the internal structural configuration, eliminating the need for additional external equipment. The innovation incorporates a concave groove and a concave fin at the rear of the normal strut, aiming to improve the flame propagation performance of the cavity and addressing the challenges associated with ignition and stability. This study aims to explore the impact of the novel strut on the afterburner ignition characteristics. Herein, the kernel generation and flamelet growth in different cavity-strut structures are studied using a high-speed camera and CFD. The results aim to reveal how the tail-groove strut enhances fuel atomization and flame stability, offering a simplified yet effective solution for improving ignition reliability in advanced combustion systems.