esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Evaluation
### Fingerprint Model Identification Effect and Feature Importance Comparison  
Based on the WebRTC DTLS handshake fingerprint dataset collected and composed in the previous subsection, we conducted corresponding experiments on Snowflake-based Tor traffic identification. We carried out comparison experiments on different machine learning algorithms for extracting handshake fingerprint features to identify Snowflake traffic, and discovered that the fingerprint features extracted by this method perform well and have similar results for five machine learning algorithms, namely Random Forest, XGBoost, AdaBoost, LinearSVM, and KNN. The specific experimental results are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measures** & **Flows** \\ \hline Tor-Snowflake Ordinary & 5271 \\ Tor-Snowflake HS & 6150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tor-Snowflake dataset structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Snowflake** & **Facebook** & **Google** & **Discord** \\ \hline Flows & 1855 & 1580 & 1995 & 1989 \\ \hline \hline \end{tabular}
\end{table}
Table 3: WebRTC fingerprint dataset structure.  
The experimental results demonstrate that the fingerprinting method for Tor-Snowflake traffic is effective and the average accuracy of different algorithms achieved more than 99.8%. This method can be applied to a wide range of machine learning models, and the identification requires processing only a small number of DTLS handshake messages. The experiment on the dataset shows that the method consumes less time for extracting fingerprint features and model training for classification, and has better real-time identification of Tor-Snowflake traffic.  
The importance ranking of the fingerprint features in the experiment according to Random Forest is shown in Figure 6. The chosen cipher suite in Server Hello has the highest feature importance of 18%. The fragment length of Server Hello, the cipher suites, and the cipher suites length in Client Hello also have a great impact on the Snowflake traffic identification model. The first four features combined can achieve about 50% importance. From the server side of the Snowflake communication, the cipher suite chosen by the volunteers outside the censored area during the handshake negotiation largely reveals the application. The fragment length on the server side changes with the application form the experiment results. The cipher suites content and length of the Client Hello message often differ depending on client application. These fingerprint features can effectively guide the identification of Tor-Snowflake traffic.
# 4D-Flow MRI Characterization of Pulmonary Flow in Repaired Tetralogy of Fallot
## 3 Results
For VFA, delayed ejection showed a significant reduction in the rTOF patients versus the controls (20 \(\pm\) 13% vs. 41 \(\pm\) 13%, \(p\) < 0.001), and retained inflow showed a significant elevation in the rTOF patients when compared with the controls (32 \(\pm\) 6% vs. 15 \(\pm\) 6%, \(p\) < 0.001). A FVA comparison between a control and an rTOF patient is illustrated in Figure 9.  
Figure 9: Comparison of ventricular flow analysis in a healthy control and a patient with repaired Tetralogy of Fallot. Panel (**A**) shows the ventricular flow analysis in a healthy control at peak systole (right) and early diastole (left). Panel (**B**) shows the ventricular flow analysis in a patient with repaired Tetralogy of Fallot (rTOF) with mild pulmonary regurgitation (orange arrow) at peak systole and early diastole. Maximum and mean EL were also associated with the PA diameter (r = 0.645, \(p\) < 0.001 and r = 0.569, \(p\) = 0.001, respectively). Considering FVA, delayed ejection showed a negative association with PA diameter (r = \(-\)0.501, \(p\) = 0.004), whereas retained inflow showed an association (r = 0.502, \(p\) = 0.003). Maximum and mean EL were also associated with the pulmonary valve peak velocity (r = 0.812, \(p\) < 0.001 and r = 0.914, \(p\) < 0.001, respectively). For VFA, pulmonary valve peak velocity was associated with delayed ejection (r = \(-\)0.507, \(p\) = 0.001) and retained inflow (r = 0.618, \(p\) < 0.001). Maximum and average valve pressure also showed a moderate association with the regurgitant fraction (r = 0.387, \(p\) = 0.018 and r = 0.333, \(p\) = 0.044, respectively). For maximum and average EL, the values were r = 0.354, \(p\) = 0.032 and r = 0.387, \(p\) = 0.018, respectively. For FVA, regurgitant fraction was negatively associated with delayed ejection (r = \(-\)0.478, \(p\) = 0.003) and retained inflow (r = 0.531, \(p\) = 0.001). Right ventricular volumes and EF were associated with pulmonary regurgitant fraction: RVEDVi (r = 0.687, \(p\) < 0.001), RVESVi (r = 0.736, \(p\) < 0.001), and RVEF (r = \(-\)0.676, \(p\) < 0.001). Similarly, right ventricular volumes and EF were associated with maximum and average EL: RVEDVi (r = 0.404, \(p\) = 0.027 and r = 0.421, \(p\) = 0.020, respectively), RVESVi (r = 0.01, \(p\) = 0.028 and r = 0.405, \(p\) = 0.026), and RVEF (r = \(-\)0.435, \(p\) = 0.016 and r = \(-\)0.379, \(p\) = 0.039). For FVA, right ventricular volumes and EF were associated with delayed ejection and retained inflow: RVEDVi (r = \(-\)0.531, \(p\) = 0.003 and r = 0.668, \(p\) < 0.001, respectively), RVESVi (r = \(-\)0.554, \(p\) = 0.002 and r = 0.685, \(p\) < 0.001), and RVEF (r = 0.478, \(p\) = 0.008 and r = \(-\)0.516, \(p\) = 0.004).
# Improving Software Defect Prediction in Noisy Imbalanced Datasets
## 2 Background  
### Software Defect Prediction (SDP) and Metrics  
SDP is an increasingly important subject in software reliability research. Using historical data to link software metrics and defects, SDP supports the designing and testing of software by determining the defect tendency of software modules [28]. At present, SDP mainly uses machine learning algorithms to make binary classification judgments on whether a module is defective, and it is applied to software at different stages, including data acquisition, data processing, model training, and model evaluation. And a major focus of data collection is to characterize software features through metrics.  
The earliest software metric applied to SDP measured software code and its complexity. In 1991, [PERSON] and [PERSON] proposed the famous CK metric element [6], which became one of the standard metric tuples in the SDP field. From the perspective of object-oriented design, the CK metric tuple comprehensively considers the factors affecting software, such as the number of code lines, the degree of class cohesion, and the relationship between classes. Researchers also proposed applying to SDP the metrics of the code development process [7; 8; 9; 10] in order to address the macro-integrity of a software program and how its elements interact. Researchers also provided network metrics to measure codes and thus build a software network that can characterize software features. Many studies on network metrics have been conducted [11; 12; 13; 14; 15]. Later, [PERSON] proposed a distance metric based on cost-sensitive learning for reducing class imbalance and better differentiation of defect/clean samples [29].  
Besides software metrics, there are many studies that have applied machine learning for software defect prediction. [PERSON] removed clean samples surrounding defect samples using a filtering technique to enhance the performance of predicting software defects using SVMs [30]. [PERSON] represented the source code as an augmented code property graph and trained a software defect predictor with a graph neural network [31]. [PERSON] trained a code pre-trained language model using defect datasets as the corpus for code defect domain tasks [32].
# IWF-TextRank Keyword Extraction Algorithm Modelling
## 4 Discussion  
The proposed IWF-TextRank algorithm demonstrates significant improvements in keyword extraction for traffic accident texts, primarily due to three key enhancements: multi-feature weighting, BP neural network optimisation, and semantic enhancement through the CBOW model. Each of these factors is summarised below with supporting quantitative evidence.  
1. Multi-Feature Weighting Mechanism Unlike the traditional TextRank, which relies solely on word frequency, the IWF-TextRank incorporates additional features such as lexicality and word length to provide a more comprehensive weighting for each candidate keyword. The experimental results indicate that the inclusion of these features improves both the precision and recall of keyword extraction by approximately 7%, particularly by accurately capturing the relevance of nouns and verbs in accident descriptions. Additionally, the word-length feature enables the model to better extract keywords with longer lengths, further improving the extraction of key information.
2. Optimisation through BP Neural Network The BP neural network automatically balances the weights for word frequency, part of speech, and word length, eliminating subjective manual adjustments. The cross-validation results show that the BP optimisation enhances extraction accuracy with statistical signif  
Figure 6: Comparison of different extraction methods. (**a**) Comparison of \(p\)-values of different extraction methods; (**b**) comparison of R values of different extraction methods; (**c**) comparison of F-values of different extraction methods.  
icance (\(p<0.05\)), indicating the network's effective contribution to model stability and precision in identifying critical accident-related keywords.  
Semantic Enhancement via the CBOW Model  
Integrating the CBOW model from Word2 Vec allows the algorithm to capture deeper semantic relationships between words.  
Despite the advantages of IWF-TextRank in keyword extraction, certain limitations remain. First, the dataset is limited to traffic accident records from a single city in 2020, potentially limiting the model's generalisability across regions and timeframes. Second, while BP neural network optimisation performs well on small datasets, it may require additional tuning for larger datasets. Finally, although the CBOW model improves semantic understanding, it may face challenges with longer or multi-topic texts.
# High Resolution Brillouin Sensing of Micro-Scale Structures
## 4 Correlation-Based Brillouin Sensing
A low-coherence chaotic laser has also been employed to detect a 5-cm hot spot in an optical fibre based on the BOCDA principle [90]. In this method, the coherence of the random laser source is controlled by the feedback loop to the laser, which in turn determines the spatial resolution of the measurement. Table 1 provides a summary of BOCDA distributed sensing techniques on different platforms including their spatial resolution with an emphasis on mm- and sub-mm resolutions. Noticeable from this table is the change of platforms from optical fibres to on-chip waveguides as we move from cm-scale to mm and sub-mm spatial resolutions. It is also clear from Figure 2 that the introduction and evolution of the BOCDA technique have increased the detection capabilities of Brillouin distributed sensing and put more emphasis on applications such as mapping opto-acoustic responses along the waveguide [46; 48], in addition to the more traditional distributed strain and temperature sensing applications in optical fibres. The high spatial resolution achieved by BOCDA can be applied to the short-length scale micro-scale waveguides to map the opto-acoustic responses of those structures. In the next section, we look at the opto-acoustic interactions in micro-scale waveguides and provide an overview of the applications of Brillouin scattering in detecting very small transverse and longitudinal features in those structures using different setups.
## 2 Results and Discussion
### Antiviral Activity
#### 2.4.2 Hemagglutination Inhibition Assay (HI)  
The capability of compounds **4**, **9**, **16**, and **18** to inhibit hemaggluting activity was evaluated by HI. The inhibitory activity of peptides **1** and **2** is shown as a reference. The oseltamivir-resistant A/Parma/24/09 H1N1 virus strain was used. As shown in Table 2, compounds **4** and **16** prevented the hemagglutination of the oseltamivir-resistant strain A/Parma/24/09 H1N1 at quite similar concentrations to the reference peptides.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pep** & **Sequence** & \multicolumn{2}{c}{**IC\({}_{50}\)****b (nM)**} \\ \cline{3-3}  & & **A/Parma/24/09 H1N1** \\ \hline
**1**\({}^{\text{a}}\) & SLDC & \(6\pm 3\) \\
**2**\({}^{\text{a}}\) & SKHS & \(1.5\pm 0.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sequence and HI activity of peptides **1**, **2**, **4**, **9**, **16**, and **18**. The activity of peptides **1** and **2** is reported as a reference [23].  
Figure 3: Surface plasmon resonance (SPR) analysis. Sensorgrams to evaluate the binding affinity between HA (H1N1 hemagglutinin, Cat No. Z03181, GenScript, NE) and peptidomimetics **4**, **9**, **16**, and **18**. Each compound was injected at six different concentrations (0.32, 0.63, 1.25, 2.5, 5, and 10 \(\upmu\)M). The ratio between the kinetic dissociation (k\({}_{\text{off}}\)) and association (k\({}_{\text{on}}\)) constants was used to determine the equilibrium dissociation constants (K\({}_{\text{D}}\)).
## 2 Design of the Permanent Magnet TCV  
### Schematic of the Conventional TCV  
The currently widely used TCV for ESC has the structure shown in Figure 3. Among them, the armature, magnet core, upper case, and lower case are magnetic materials that are magnetized when power is applied. The magnetic body is made of materials with high magnetic flux density and low magnetic flux strength, and most of them are made of low carbon steel materials such as S10C. The armature, magnet core, and cases are magnetized by applying 3.4 A to the magnetic coil. This induces the armature to be adsorbed to the magnet core, pushing the plunger downwards to block the hole in the sol-seat. The hole in the sol-seat is blocked, and the flow of brake fluid is blocked. When the power supply is interrupted, the magnetic material is demagnetized. At this time, the plunger opens the flow path of the sol-seat by pushing the armature upward by the reaction force applied  
Figure 1: Configuration of the ESC: (**a**) Structure of ESC; (**b**) Solenoid valves of the ESC.  
Figure 2: Comparison of the conventional TCV and permanent magnet TCV operation methods: (**a**) Conventional TCV operation method; (**b**) Permanent magnet TCV operation method.  
by the spring. However, in order to block the flow of brake oil in sol-seat in TCV for ESC, current must be continuously supplied. Continuous current supply generates heat in the magnetic coil and increases power consumption. If the auto-hold function is used for 2 h a day while driving an electric vehicle, the power consumption is about 82.3 Wh. The heat generated by the magnetic coil rises up to a maximum of 200 \({}^{\circ}\)C, which in turn raises the temperature inside the ECU up to a maximum of 150 \({}^{\circ}\)C Therefore, in order to prevent the temperature rise of the magnetic coil, the initial current is lowered to 1.5 A after applying 3.4 A to prevent the temperature rise of the magnetic coil.  
Figure 4a shows a state in which the flow path of the sol-seat is open because power is not supplied to the magnetic coil. At this time, the distance between the armature and the magnet core is 0.47 mm and the distance between the plunger and the sol-seat is 0.30 mm. Figure 4b shows the closed state of the flow path of the sol-seat. When power is applied and the plunger closes the flow path of the sol-seat, the distance between the armature and the magnet core is 0.17 mm. When power is applied and the plunger closes the flow path of the sol-seat, the distance between the armature and the magnet core is 0.17 mm. The reason to keep the 0.17 mm gap between the armature and the magnet core is to prevent the noise caused by the contact of the two metal objects. It also has the responsiveness to quickly separate the residual electromagnetic force generated between the armature and the magnet core when the power is cut off.
## 3 Theoretical Performance Limits  
Some theoretical performance bounds of the UiL content delivery system will be evaluated in this section. For convenience and mathematical tractability, we consider a simplified scenario with relatively ideal assumptions. Specifically, we assume that users' interest profiles are independent from each other. The users' interest coefficient \(r_{uf}\) follows independent and identical distributions (i.i.d.) given by a truncated normal distribution in \([a,a+h]\) with mean \(\mu\) and variance \(\delta^{2}\). In addition, we suppose that content file sizes \(L_{f}\) follows a uniform distribution in \([b,b+g]\). Our subsequent analysis will try to derive the upper and lower bounds of the mean user interest.
# High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector
## 4 Improved Detection Method
### Kalman Filtering  
Kalman filtering is a time domain method which involves the conception of state space into stochastic estimation theory [14]. It regards the signal transformation as the output of a linear system under the effect of white noise [15]. Using the system state equation, measurement equation, process noise, and measurement noise to describe the input and output makes it become a filtering algorithm [16]. In general, Kalman filtering is used to forecast or filter in the situations we deal with time sequence and white noise or the system, which can be described as the similar model [12].  
In Kalman filtering, we consider describing the dynamic system using the following equation [17]:  
Figure 3: Improved detection method.  
\[X(k+1)=\Phi X(k)+\Gamma W(k) \tag{8}\]  
\[Y(k)=\mathrm{H}X(k)+V(k) \tag{9}\]  
Here, \(k\) is the discrete time, and the system state at time \(k\) is \(X(k)\in R^{n}\); \(Y(k)\in R^{m}\) is the measurement signal of the corresponding state; \(W(k)\in R^{r}\) is the input white noise; \(V(k)\in R^{m}\) is the measurement noise. Equation (8) is called state equation and (9) is called the measurement equation. \(\Phi\) is called state transition matrix and \(\Gamma\) is called measurement matrix. The recursion of Kalman filter is described as follows [18].  
\[\text{One step forecasting of state}:\ \hat{X}(k+1|k)=\Phi\hat{X}(k|k) \tag{10}\]  
\[\text{State updating}:\ \hat{X}(k+1|k+1)=\hat{X}(k+1|k)+K(k+1)\epsilon(k+1) \tag{11}\]  
\[\text{Filtering gain matrix}:\ K(k+1)=P(k+1|k)H^{T}\Big{[}HP(k+1|k)H^{T}+R \Big{]}^{-1} \tag{12}\]  
\[\text{One step forecasting of covariance matrix}:\ P(k+1|k)=\Phi P(k|k)\Phi^{T}+\Gamma Q \Gamma^{T} \tag{13}\]  
\[\text{Covariance matrix updating}:\ P(k+1|k+1)=[I-K(k+1)H]P(k+1|k) \tag{14}\]  
\[\hat{X}(0|0)=\mu_{0},\hat{P}(0|0)=P_{0} \tag{15}\]  
In one period, Kalman filtering have two information updating processes; these are the time updating process and the measurement updating process. Equation (10) shows the method to estimate and forecast the state of time \(k\) using the state of time \(k-1\); Equation (13) gives a quantified description for the quality being good or bad. Equation (10) and Equation (13) together describe the process of Kalman filtering updating. That is to say, we use the state \(k-1\) to forecast state \(k\) using Equation (10), but the forecasting is not accurate, so we use the variance and some known parameters to estimate the value of the state, which is closer to the real state value. With the increase of \(k\), which means an increase in the iteration steps, the values of state are slowly convergent. All the above steps describe the Kalman filtering method, where we reasonably use measurement value to estimate state value, which is unknown.
## 3 Results and Discussion
### Effects of Different DMG Substitution Ratios on the Dual-Fuel Engine
#### 3.2.1 Performance Characteristics  
The indicated thermal efficiency (ITE) and the indicated mean effective pressure (IMEP) are two critical indicators used to evaluate engine performance. The simulated ITE and IMEP results of the dual-fuel engine for different DMG substitution ratios are presented in Figure 3; both ITE and IMEP demonstrate an increase as the DMG substitution ratio increases. The ITE is 42.0% for the diesel engine, and it increases to 46.1% when the DMG substitution ratio increases to 20%. The growth is approximately 4%. The IMEP is 2.29 MPa for the diesel engine, and it increases to 2.50 MPa when the DMG substitution ratio increases to 20%. The reasons for the improvement in engine performance can be attributed to the enhanced premixed combustion facilitated by the presence of hydrogen and CO in the DMG. This enhancement contributes to a more uniform and efficient combustion process. Additionally, methanol's relatively lower cost compared to diesel is advantageous in terms of reducing overall fuel expenses.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Substitution Ratio** & **Diesel Consumption per Cycle (mg/cyc)** & **Methanol Consumption per Cycle (mg/cyc)** & **CH\({}_{3}\)OH Mass in DMG (mg/cyc)** & **CO Mass in DMG (mg/cyc)** & **H\({}_{2}\) Mass in DMG (mg/cyc)** \\ \hline
0\% & 237 & 0 & 0 & 0 & 0 \\
10\% & 213.3 & 49.716 & 27.264 & 19.877 & 2.575 \\
15\% & 201.45 & 74.574 & 40.897 & 29.815 & 3.863 \\
20\% & 189.6 & 99.432 & 54.529 & 39.753 & 5.151 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The fuel consumption per cycle results and DMG compositions at different substitution ratios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Substitution Ratio** & **O\({}_{2}\) Mass Fraction** & **N\({}_{2}\) Mass Fraction** & **CH\({}_{3}\)OH Mass Fraction** & **CO Mass Fraction** & **H\({}_{2}\) Mass Fraction** \\ \hline
0\% & 23\% & 77\% & 0 & 0 & 0 \\
10\% & 22.797\% & 76.319\% & 0.485\% & 0.354\% & 0.0458\% \\
15\% & 22.692\% & 75.968\% & 0.735\% & 0.536\% & 0.0694\% \\
20\% & 22.590\% & 75.626\% & 0.979\% & 0.735\% & 0.0924 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The initial in-cylinder compositions at different substitution ratios.