esa-sceva/satcom-chunk-collection huggingface dataset:
# AE-DTI: An Efficient Darknet Traffic Identification Method Based on Autoencoder Improvement
## 3 AE-DTI Model Design
### CNN-Based Dark Network Traffic Recognition Model Design  
A convolutional neural network (CNN) consists of key components such as the convolutional layer, activation function, pooling layer, fully connected layer, and loss function. Utilizing large-scale training data and back-propagation algorithms, CNN has the ability to automatically adjust network parameters, enabling it to learn optimal feature representations and adapt to diverse tasks and data. Building upon this concept, this paper designs a one-dimensional convolutional model, known as traffic identification-CNN (TI-CNN), specifically designed for traffic classification. The TI-CNN network structure is designed as follows: in the convolutional pooling computation stage, TI-CNN is designed with three layers of convolution and three layers of pooling; in the classification stage, TI-CNN adds a dropout layer after two fully connected layers. The network structure is shown in Figure 5.  
**Convolutional pooling calculations**  
**Classification**  
**Figure 5. One-dimensional convolutional neural network structure (TI-CNN).  
The TI-CNN convolution process is shown in Formula (5) where \(f\) denotes the original one-dimensional image, \(g\) denotes the convolution kernel, and \(o\) denotes the position in the output result. \(\cdot\) denotes the dot product operation, where two numbers are multiplied. \(z\) is the offset inside the convolution kernel, and the convolution kernel \(g\) is slid over the original image \(f\). For each position \(o\), the output result \(f*g[o]\) is obtained by dot multiplying and summing the convolution kernel \(g\) with the corresponding position of \(f\) at \(o\).  
\[(f*g)[o]=\sum_{k=-\infty}^{\infty}f[z]\cdot g[o-z] \tag{5}\]  
To reduce feature size and computational complexity, TI-CNN employs average pooling. Additionally, to address the issue of vanishing gradients associated with the _Sigmoid_() and _Tanh_() functions, TI-CNN adopts the _ReLU_() function as the activation function, as in Formula (6). When the input \(x\) is greater than or equal to 0, the output of the _ReLU_() function is \(x\), otherwise the output is 0.  
\[f(x)=max\ (0,x) \tag{6}\]  
To prevent overfitting and improve the generalization ability and robustness of the model, TI-CNN designs a dropout layer after the fully connected layer. Some neurons are discarded with a certain probability and propagated forward, as in Formula (7). \(E\) is the input, the dropout ratio is \(p\), \(D\) is a binary matrix of the same size as \(E\), indicating the sign of whether to discard each neuron, \(D_{i,j}\) takes the value of 0 or 1, and the probability of satisfying \(D_{i,j}=1\) is \(1-p\), \(D_{i,j}=0\) is the probability of \(p\).  
\[Y=\frac{1}{1-p}\cdot D\cdot E \tag{7}\]
# Physical Simulation Experiment for Visualizing Pulverized Coal Transport in Propped Fractures
## 3 Results and Discussion  
### Characterization of Pulverized Coal Deposition in Propped Fractures  
1. Pulverized coal accumulation method  
The pores formed between the stacked proppants serve as pathways for both pulverized coal and the transporting fluid. As the mixed solution of pulverized coal enters the propped fractures, the fluid tends to traverse cracks with lower resistance, creating a flow line. First, when pulverized coal is deposited along the flow line but has not yet caused a blockage, it forms a strip at the deposition front, dispersing in the flow direction. With the progression of displacement experiments, increasing amounts of pulverized coal deposit along the flow line, eventually resulting in blockage formation. This evolution progresses from \"block deposition\", to \"flame accumulation\", \"linear accumulation\", \"dispersed accumulation\", and other accumulation patterns (Figure 2). However, under the same conditions, significant variations exist in the accumulated amount of pulverized coal within the observation unit, showing the trend of dispersed fracturing fluid > distilled water > active water fracturing fluid > agglomerated fracturing fluid.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type of Carrying Fluid** &
\begin{tabular}{c} **Surface Tension** \\ **(mN\(\cdot\)m\({}^{-1}\))** \\ \end{tabular} & **Contact Angle (\({}^{\circ}\))** & **Viscosity (mPa\(\cdot\)s)** \\ \hline Distilled water & 61.75 & 56.13 & 0.5 \\ \hline Active water fracturing fluid & 66.76 & 67.50 & 0.2 \\ \hline Agglomerated fracturing fluid & 25.08 & 8.00 & 0.1 \\ \hline Dispersed fracturing fluid & 27.06 & 18.25 & 0.4 \\ \hline \end{tabular}
\end{table}
Table 1: Types and properties of carrier fluids.  
(2) Bonding between pulverized coal and proppant  
As the mixed solution of pulverized coal infiltrates the propped fracture, some pulverized coal is either deposited or adheres to the proppant due to reduced flow rates or entrapment by the proppant. The choice of carrier fluid influences the degree of pulverized coal adhesion to the proppant, consequently affecting crack permeability. During the experiment, the bonding between fine pulverized coal particles and proppant was more pronounced in distilled water and active water fracturing fluid, whereas it was relatively minimal in dispersed fracturing fluid and agglomerated fracturing fluid (Figure 3).
# Adsorption of Ibuprofen from Water Using Waste from Rose Geranium (_Pelargonium gravelens_) Stems
## 3 Characterisation
### Adsorption Results
#### 3.5.2 Temperature Effect and Thermodynamics of SPG  
The removal of IBU was studied at different temperatures to evaluate the effect on uptake. The temperature effect was studied between 25 and 45 \({}^{\circ}\)C, and the graph is shown in Figure 6. The removal of IBU increased significantly between 25 and 35 \({}^{\circ}\)C, showing that uptake favours an endothermic system [24]. However, the removal decreased slightly between 35 and 45 \({}^{\circ}\)C, indicating an exothermic nature and that a higher temperature was unfavourable for uptake. This is due to the IBU molecules gaining kinetic energy in the solution, resulting in higher mobility and reduced uptake [25].  
The temperature effect data were used to determine the thermodynamic parameters, which are listed in Table 3. The values for \(\Delta H^{o}\) were between 25 and 35 \({}^{\circ}\)C endothermic and between 35 and 45 \({}^{\circ}\)C exothermic. When the value of \(\Delta H^{o}\) is in the range of 0 and \(-\)20 KJ/mol, the reaction is physisorption, while \(\Delta H^{o}\) between \(-\)80 and \(-\)400 KJ/mol means chemisorption [26]. The \(\Delta H^{o}\) value shows that the adsorption of IBU on SPG was controlled by physisorption due to weak van der Waals forces. The obtained values of \(\Delta G^{o}\) are negative, indicating that the uptake of IBU was spontaneous. The value of \(\Delta S^{o}\) is positive, indicating that there was increased freedom when equilibrium was reached.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\Delta H^{o}\) (KJ/mol) & \(\Delta S^{o}\) (KJ/mol) & **Temperature (K)** & \(\Delta G^{o}\) (KJ/mol) \\ \hline \(-1\times 10^{-3}\) & \(3\times 10^{-3}\) & 25 & \(-\)4.88 \\  & & 30 & \(-\)2.87 \\  & & 35 & \(-\)2.10 \\  & & 40 & \(-\)1.97 \\  & & 45 & \(-\)1.89 \\ \hline \end{tabular}
\end{table}
Table 3: Thermodynamic parameters of SPG.  
Figure 6: Temperature effect on SPG for ibuprofen.
## 5 Performance Evaluation and Analysis
### Performance Evaluation of Each ACO Model
#### 5.1.3. Source Update  
In this section, the performance of Ant System and Ant Colony System algorithms with _Source Update_ was measured.  
First, this is the experimental result of AS with _Source Update_. When the pheromone evaporation coefficient was increased from 0.1 to 0.9, it was confirmed that the optimized pheromone evaporation coefficient(\(\rho\)) was about 0.3. This is a different coefficient(\(\rho\)) compared with AS only. The average path length was 14.998210. This performance overwhelmed the previous cases. The time series graph of Figure 17a shows that it is very fatly approaching the optimal path than AS only. The histogram of Figure 17b shows a totally different result compared with a result of AS only. Almost ant packets passed the optimized path.  
Second, this is the experimental result of ACS with _Source Update_. When the pheromone evaporation coefficient was increased from 0.1 to 0.9, it was confirmed that the optimized pheromone evaporation coefficient(\(\rho\)) was about 0.3. It is a different result compared with the previous cases. These results imply that if Source Update is applied, then the optimized pheromone evaporation coefficient(\(\rho\)) will be larger. The average path length is 14.219080 which was the best result than any other case. The time series graph of Figure 18a shows that it is very fatly approaching the optimal path than ACS only. After about 2 evaporation cycles, the Optimized path was established. The histogram of Figure 18b shows a totally different result compared with the result of ACS only. Almost ant packets passed the optimized path except for a very small number.  
Figure 16. Ant Colony System with simple backtracking algorithm. (**a**) Time series graph; (**b**) Histogram.  
Figure 17: Ant System, Source Update. (**a**) Time series graph; (**b**) Histogram.  
Figure 18: _Cont._
# Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential
###### Abstract  
Aiming at the interactive behavior of vehicles changing lanes in the intelligent network traffic environment, the relative position of vehicle, speed, acceleration, and steering angle are introduced to correct the molecular interaction potential, and the vehicle interaction potential model is established. The spatial distribution map of the vehicle interaction potential field under different motion states was drawn, and the changing law of the vehicle interaction potential field was described. By analyzing the longitudinal critical distance of the lane changing safe critical time, the expression of acceptable clearance for lane changing is defined, and the relationship between acceptable clearance and the speed and acceleration of the interactive vehicle is verified by simulation. Combined with the acceptable clearance of lane changing, a lane changing behavior model based on the vehicle interaction potential is established, and the situation of lane changing vehicles subjected to the interaction potential and potential field forces generated by the surrounding vehicles is analyzed, and the driving decision of lane changing vehicles under the action of different potential field forces is given. Compared with the molecular dynamics lane changing model and LS2015 lane changing model, the results show that the vehicle action potential lane changing model can effectively improve the running speed and stability of traffic flow. The research has theoretical significance for the lane changing behavior decisions of networked autonomous vehicles in the future.  
networked autonomous vehicles; interaction potential; lane changing behavior; acceptable gap
## 3 Results and Discussion
### TGA (Thermogravinetric Analysis)  
The thermogravimetric analysis (TGA) results for the different formulations are summarized in Table 3 and illustrated in Figure 2. The analysis was conducted to evaluate the thermal stability of TPS/PBAT and TPS/PBAT-PBSA bioplastic films, as well as the impact of the Vinnex and Joncryl additives in structural terms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Melt Density (g/cm\({}^{3}\))** \\ \hline TPS/PBAT & 1.20 \(\pm\) 0.00 \\ TPS/PBAT-V & 1.20 \(\pm\) 0.01 \\ TPS/PBAT-J & 1.21 \(\pm\) 0.02 \\ TPS/PBAT-V/J & 1.22 \(\pm\) 0.01 \\ TPS/PBAT-PBSA & 1.20 \(\pm\) 0.02 \\ TPS/PBAT-PBSA-V & 1.23 \(\pm\) 0.02 \\ TPS/PBAT-PBSA-J & 1.20 \(\pm\) 0.02 \\ TPS/PBAT-PBSA-V/J & 1.23 \(\pm\) 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Melt density of the different formulations.  
Figure 2: Thermogravimetric curves of TPS/PBAT and TPS/PBAT-PBSA formulations. Insert zooms in the initial 50% mass-loss until 400 \({}^{\circ}\)C.  
The TGA curves reveal that all samples exhibited a gradual weight loss as the temperature increased. The thermal decomposition process occurs in four main stages, characterized by distinct changes in the rate of weight loss:  
1. First stage (up to \(\sim\)200 \({}^{\circ}\)C, T10%): This stage corresponds to the evaporation of residual moisture and the initial thermal degradation of glycerol, a plasticizer commonly used in TPS blends. This stage is critical for assessing the onset of thermal degradation, as it represents the temperature at which 10% of the sample's initial mass is lost. The T10% values varied between 176 \({}^{\circ}\)C and 208.4 \({}^{\circ}\)C, indicating minor differences in the initial thermal stability among the formulations.
2. Second stage (\(\sim\)200-300 \({}^{\circ}\)C, TmaxI): The primary decomposition of thermoplastic starch occurs here, including the breakdown of glycosidic linkages and depolymerization. The TmaxI values ranged from 207 \({}^{\circ}\)C to 225 \({}^{\circ}\)C, reflecting the impact of compatibilizers like Joncryl on delaying the degradation of starch by improving interfacial interactions and phase dispersion.
3. Third stage (\(\sim\)300-400 \({}^{\circ}\)C, TmaxII): This stage is dominated by the degradation of PBAT and PBSA. The TmaxII values, ranging from 298 \({}^{\circ}\)C to 305 \({}^{\circ}\)C, showed relatively stable temperatures across all formulations, with PBSA contributing to slightly higher thermal resistance compared to TPS/PBAT blends.
4. Fourth stage (\(\sim\)400 \({}^{\circ}\)C and above, TmaxIII): The final stage involves the degradation of carbonaceous residues, including any cross-linked or charred material. The TmaxIII values (389 \({}^{\circ}\)C to 400 \({}^{\circ}\)C) decreased slightly in formulations with Joncryl and Vinnex, suggesting minor effects on high-temperature stability.  
The residue percentage after complete thermal decomposition ranged from 0.7% to 1.7%, indicating a minimal amount of remaining material. The final residue values primarily include contributions from partial decomposition of talc and carbonaceous residues [53; 54].
# Geometric Accuracy Evaluation Method for Subway Stations Based on 3D Laser Scanning
## 4 Case Study
### Point Cloud Structure Element Extraction  
To evaluate the geometric accuracy of each surface and structural column, structural element extraction from subway station point cloud data is needed. A spatial octree is established for voxel down-sampling of the point cloud data. The structural elements of the subway station are extracted after region growth. The angle threshold \(\theta_{\text{th}}\) is set to 15\({}^{\circ}\) and 30 nearest neighbors were used (k = 30); the residual threshold \(r_{\text{th}}\) is calculated by the 98 th percentile of the plane fitting residuals. The results of the structural element extraction of the subway station are shown in Figure 14, where each surface is distinguished by a different color, and corresponding features of the interior of the station are obtained, including 4 facades, 2 slabs, and 50 structural columns. Table 1 shows the results of the quantitative analysis of the structural elements of the point cloud; the point cloud is dense enough to accurately display the geometric characteristics of the components. Then, all the structural elements extracted are evaluated in order according to the method proposed in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline
**Structural Element** & **Number of Points** & **Point Density (pts/m\({}^{2}\))** \\ \hline East facade & 755,424 & 5502 \\ West facade & 902,986 & 4386 \\ North facade & 15,022,981 & 6351 \\ South facade & 14,858,647 & 6533 \\ Roof slab & 19,793,397 & 5182 \\ Base slab & 6,799,713 & 3742 \\ Structural column & 2,252,290 & 5513 \\ \hline \end{tabular}
\end{table}
Table 1: Quantitative analysis results of the point cloud model.  
Figure 13: Fine registration results between the point cloud and BIM.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 2 Related Works  
Multipath transmission technologies have been extensively studied due to their ability to improve transmission robustness and efficiency. In this section, we will provide a detailed introduction to the multipath transmission technologies and multipath data-scheduling strategies.
## 2 Experimental
### Catalyst Characterization  
The crystalline structure of the reduced catalyst was determined by X-ray diffraction (High-Resolution X-ray Diffractometer brand Bruker D8 Advance, BRUKER AXS INC., Fitchburg, WI, USA) using a Cu K\(\alpha\) radiation at 40 kV and 30 mA. Diffraction angles were measured in steps of 0.02\({}^{\circ}\), at 1 s/step in the range of 20 to 80 (20). The size of the metal crystallites was calculated from line broadening with the help of the Scherrer equation [33]. The total surface area of catalysts was measured using the multipoint BET-N\({}_{2}\) on a surface area analyzer (Micromerities, Gemini 2360, Norcross, GA, USA). Prior to the analysis, the samples were degassed at 200 \({}^{\circ}\)C for 2 h to remove unwanted components on the surface before outgassing the samples.  
A chemisorption analyser (Micromerities, Chemisorb 2720, Norcross, GA, USA), was used to evaluate the reducibility of the active metals (nickel and cobalt) on the support by applying a temperature-programmed reduction of hydrogen (TPR-H\({}_{2}\)). Before the reduction, 25 mg of the catalyst was treated at 300 \({}^{\circ}\)C under high purity 99.99% helium with the flow rate of 20 mL/min for an hour to remove moisture and other gas impurities. The TPR-H\({}_{2}\) profile was obtained by ramping up the temperature at 10 \({}^{\circ}\)C/min, 20 mL/min of 10% (vol.) H\({}_{2}\)/Ar between 50 \({}^{\circ}\)C and 800 \({}^{\circ}\)C.  
Thermogravimetric analysis (TGA) profiles were carried out in a Perkin Elmer TGA instrument (Waltham, MA, USA) operated under nitrogen flowing in the heating rate of 10 \({}^{\circ}\)C/min. Exothermic weight loss was observed at the temperature range between 30 and 950 \({}^{\circ}\)C.
## 4 Simulation and Numerical Results
### Radar Performance
#### 4.1.3 The Complexity of Parameter Estimation  
Figure 8 shows the complexity of the proposed parameter estimation method. In the simulation, \(10^{4}\) Monte Carlo experiments were carried out. According to Figure 8a, when \(H=1\), the algorithm does not reduce the dimension, and the number of complex multiplications is large. Having said that, the complexity of the proposed method can be effectively reduced by increasing \(H\), as seen in Figure 8a. In addition, Figure 8b shows that when \(H=4\), the complexity of the proposed system will be slightly higher than that of the MIMO-OFDM system. However, when \(H\) is increased to eight, the complexity of the proposed system is greatly reduced, which is lower than the complexity of the improved algorithm in the traditional MIMO-OFDM system. Ultimately, the \(H\) to be chosen should be a reasonable compromise between the algorithm complexity and parameter estimation performance.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
###### Abstract  
Breast cancer is a leading cause of mortality among women worldwide. Early detection is crucial for increasing patient survival rates. Artificial intelligence, particularly convolutional neural networks (CNNs), has enabled the development of effective diagnostic systems by digitally processing mammograms. CNNs have been widely used for the classification of breast cancer in images, obtaining accurate results similar in many cases to those of medical specialists. This work presents a hybrid feature extraction approach for breast cancer detection that employs variants of EfficientNetV2 network and convenient image representation based on phase features. First, a region of interest (ROI) is extracted from the mammogram. Next, a three-channel image is created using the local phase, amplitude, and orientation features of the ROI. A feature vector is constructed for the processed mammogram using the developed CNN model. The size of the feature vector is reduced using simple statistics, achieving a redundancy suppression of 99.65%. The reduced feature vector is classified as either malignant or benign using a classifier ensemble. Experimental results using a training/testing ratio of 70/30 on 15,506 mammography images from three datasets produced an accuracy of 86.28%, a precision of 78.75%, a recall of 86.14%, and an F1-score of 80.09% with the modified EfficientNetV2 model and stacking classifier. However, an accuracy of 93.47%, a precision of 87.61%, a recall of 93.19%, and an F1-score of 90.32% were obtained using only CSAW-M dataset images.  
breast cancer detection; phase image features; convolutional neural networks (CNNs); EfficientNetV2; classifier ensembles Article
# Hybrid Wind-Redox Flow Battery System for Decarbonizing Off-Grid Mining Operations
## 4 Design, Sizing, and Modeling of the Wind Energy and Storage System  
A MATLAB-based simulation model was developed to evaluate the performance of the Raglan mine energy network following the integration of additional wind turbines and energy storage. This model enables the simulation of various operational scenarios by incorporating multiple input parameters, including  
* Wind energy production;
* Electricity and heat generation based on generator operation;
* Diesel fuel consumption;
* Energy storage dynamics (charging and discharging cycles).  
The model operates on an hourly time scale over an entire year, allowing for a detailed analysis of key performance indicators such as  
* Renewable energy penetration rate;
* Wind energy curtailment losses;
* Diesel fuel savings;
* Greenhouse gas (GHG) emissions reduction;
* Economic performance metrics (including financial feasibility analysis).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **20 MW, Raglan** & \\
**Parameters** & **4 h** & **6 h** & **8 h** \\ \hline
**Electrochemical system (M CAD)** & 117.0 & 152.7 & 188.6 \\ \hline
**Area required (m2)** & 4000 & 6000 & 8000 \\ \hline
**Shed price (M CAD)** & 32.5 & 48.8 & 65.0 \\ \hline
**Total cost (M CAD)** & 149.5 & 201.4 & 253.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Final costs of redox batteries.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **10 MW, Raglan** & \\
**Parameters** & **4 h** & **6 h** & **8 h** \\ \hline Network Integration & 9.0 & 6.0 & 4.5 \\ \hline Total CAPEX & 312.5 & 271.8 & 251.8 \\ \hline Total OPEX & 35.3 & 33.9 & 33.3 \\ \hline Total LCOS & 347.8 & 305.8 & 285.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._The MATLAB-based dynamic model provides a crucial tool for predicting system behavior under various configurations, eliminating the need for physical testing. It enables the identification of optimal energy management strategies while ensuring grid stability and cost-effectiveness.  
The simulation framework is structured into four interdependent sub-models, which operate in an iterative process to capture the complex interactions between energy production, storage, and demand, as shown in the flowchart in Figure 10.