esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 2 Current Research Status
### Prediction Model Based on a Combination Forecasting  
In 1969, [PERSON] et al. [17] first proposed the concept of combined models, which combine more than two models to predict traffic flow for the complex traffic conditions of different roads in different cities, which can overcome the limitations of a single prediction model to the greatest extent and organically combine the advantages of a single model. [PERSON] et al. [18] used Bayesian networks to combine BP neural network models, differentially integrated moving average autoregressive models based on wavelet analysis, and BP neural network models based on wavelet analysis to make them more accurate than single prediction models. [PERSON] et al. [19] developed a deep learning-based approach that includes two single models, CNN and LSTM, for real-time traffic flow prediction at road intersections. [PERSON] et al. [20] proposed an LSTM-weighted model that combines a normal distribution with a time window to enhance the ability to predict traffic flows. [PERSON] et al. [21] combined LSTM and SVR to construct a short-term traffic flow prediction model, and the results show that the accuracy of the prediction model was higher than that of LSTM and CNN.
## 3 Result and Discussion
### Relation between Parameters
#### 3.5.2 Relation between Porosity and Fractal Dimensions  
Figure 8 displays the relationship between porosity and fractal dimensions of sandstone immersed in acid solution under different F-T cycles. As can be seen from Figure 8, porosity is linearly correlated with fractal dimension, while the correlation coefficients are low. In Figure 8a, the correlation between porosity and meso pores fractal dimensions for sandstone immersed in solutions of pH equaling 7.0, 5.6, 4.2 and 2.8 are 0.578, 0.557, 0.866 and 0.817, respectively. It indicates that samples immersed in low acidity solution have low linear correlation between porosity and D\({}_{2}\), while samples immersed in high acidity solution have stronger linear correlation between porosity and D\({}_{2}\). That is because the distribution of meso pores is complicated by F-T cycles, which show rapid growth of D\({}_{2}\), causing low linear correlation between porosity and D\({}_{2}\). With sandstone treated by high acidity solutions, the distribution of meso pores is complicated by acidic erosion during the F-T cycles, the gap of D\({}_{2}\) between different F-T cycles reduced and the correlation between porosity and D\({}_{2}\) enhanced.  
For the correlation between porosity and D\({}_{3}\), their linear correlation coefficients are quite low, which indicates that there is no strong relationship between porosity and macro pores fractal dimensions. This is mainly because sandstone tends to peel off when macro pores are highly developed on its surface, resulting in D\({}_{3}\) fluctuates in porosity.  
Figure 7: Correlation between porosity and permeability for sandstone immersed in different acidic solutions.
## 2 Materials and Methods
### EEG Recording  
The EEG data were continuously sampled at 128 Hz from 16 active silver-silver chloride electrodes attached to an electrode cap (Brain Products Nation9128w, Shanghai, China), placed according to the international 10-20 system [28]. The 16 electrodes were placed on left frontal pool (Fp1), right frontal pool (Fp2), left frontal (F3), right frontal (F4), left central (C3), right central (C4), left parietal (P3), right parietal (P4), left occipital (O1), right occipital (O2), left anterior temporal (F7), right anterior temporal (F8), left mid-temporal (T3), right mid-temporal (T4), left posterior temporal (T5), and right posterior temporal (T6) regions, respectively. The reference electrodes were placed on the bilateral earlobes with interelectrode impedance kept below 5 k\(\Omega\) to minimize noise artifacts.  
During the EEG recording, subjects were asked to take a relaxed sitting position in the room with the light off, closing their eyes and avoiding any movement. After a 10 min warm-up with or without the music intervention, a 3 min EEG was recorded. Each dataset was visually checked for excessive noise. If more than 25% of the recording was determined to be noisy, the entire dataset was rejected. All EEG signals were preprocessed in MATLAB to remove artifacts and noise. Ocular and cardiac artifacts were removed using the independent components analysis (ICA) from the EEGLAB toolbox. Subsequently, all signals were digitally filtered with a band pass Butterworth filter between 0.5 and 50 Hz. For both tests, 30 s artifact-free data were selected for analysis, and the cortical EEG power spectrum energy and energy percentage were calculated. The power density was calculated for the following bands: \(\delta\) (0.1-3 Hz), \(\theta\) (4-8 Hz), \(\alpha\) (8-12 Hz), and \(\beta\) (12-28 Hz) bands [29]. In addition, we also calculated \(\theta/\beta\) and (\(\theta+\alpha\))/(\(\alpha+\beta\)), which are related to fatigue [30; 31].
## 4 Experimental Setup
### Data Collection  
This experiment collected several images to fine-tune the YOLOv8 object detection model in which the LED matrix is included in the images and captured several images in which the LED matrix was placed at different angles and positions. This technique is employed so that the object detection model is not overfitted and adapts to various LED placement scenarios, which is due to the irregular position of the LED matrix in real-world situations. Since different people's heights are not always the same, the placement of the LED matrix will also be different.  
We captured around 950 images for training and testing. An example of the training and testing data is shown in Figure 8. The LED matrix in the captured images will have different types of the SN set on the LED to simulate data readings on multiple patient schemes. We labeled the image using the CVAT tool by drawing a bounding box on the LED matrix. We split the images into training and validation with a ratio of 70% and 30%, respectively. The training is conducted on the hardware and software specified in Table 1. The training time is up to 45 min. To prevent overfitting, before taking images, we placed the LED matrix on several distance and angle variations. In addition, some images are set to have only a single LED matrix and some images are set to have two LED matrices at the same time.  
For the health monitoring simulation, health monitoring data by simulating OCC communication in an indoor scenario were obtained. Two cameras are employed, and each camera has its identifier used to obtain time difference data and patient position data. The Python program that controls the camera is set to capture images at a rate of 30 frames per second. All health monitoring data, patient location, and timestamp data will be obtained  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Learning rate & 0.01 \\ \cline{2-3} Hyperparameters & Momentum & 0.95 \\ \cline{2-3}  & Weight decay & 0.0005 \\ \cline{2-3}  & Batch size & 1 \\ \cline{2-3}  & Epoch & 100 \\ \hline  & CPU & Intel i7 12 th gen \\ \cline{2-3} Hardware & GPU & RTX 3060 Ti \\ \cline{2-3}  & RAM & 16 GB \\ \hline  & Framework & PyTorch and ONNX \\ \cline{2-3} Software & OS & Ubuntu 22.04 \\ \cline{2-3}  & LED controller & Arduino \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameters, hardware, and software specifications for experiment.  
Figure 8: Example of training and testing image data.  
and sent to the local server. The patient's time and positioning calculation is performed on the server.
## 4 Discussion  
Figure 5 graph shows the relationship between impedance and 1 st peak current in the adult and pediatric modes. The experimental results show an inverse relationship between current and impedance in the adult mode and pediatric mode. In other words, the higher the impedance, the lower the output current. The low current indicates that although the total energy may not change much, the current density is lowered due to the increase in impedance during the energy transfer process. This means that when an electric shock is delivered to the patient, the amount of energy transferred is inaccurate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Impedance** & \begin{tabular}{c} **First Peak** \\ **Voltage** \\ **(v)** \\ \end{tabular} & \begin{tabular}{c} **First Peak** \\ **Current** \\ **(A)** \\ \end{tabular} & \begin{tabular}{c} **Second Peak** \\ **Voltage** \\ **(V)** \\ \end{tabular} & \
\begin{tabular}{c} **Second Peak** \\ **Current** \\ **(A)** \\ \end{tabular} &
\begin{tabular}{c} **Amount of** \\ **Energy** \\ **(J)** \\ \end{tabular} \\ \hline
25 & 1411 & 56.4 & 1044 & 29.8 & 156.0 \\
50 & 1423 & 28.5 & 848 & 17.0 & 148.4 \\
75 & 1467 & 19.6 & 906 & 12.1 & 153.5 \\
100 & 1462 & 14.6 & 914 & 9.1 & 150.8 \\
125 & 1468 & 11.7 & 925 & 7.4 & 151.3 \\
150 & 1471 & 9.8 & 930 & 6.2 & 150.7 \\
175 & 1474 & 8.4 & 935 & 5.3 & 150.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test result table for electric shock energy in adult mode.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Impedance** & \begin{tabular}{c} **First Peak** \\ **Voltage** \\ **(v)** \\ \end{tabular} & \begin{tabular}{c} **First Peak** \\ **Current** \\ **(A)** \\ \end{tabular} & \begin{tabular}{c} **Second Peak** \\ **Voltage** \\ **(V)** \\ \end{tabular} & \
\begin{tabular}{c} **Second Peak** \\ **Current** \\ **(A)** \\ \end{tabular} &
\begin{tabular}{c} **Amount of** \\ **Energy** \\ **(J)** \\ \end{tabular} \\ \hline
25 & 807 & 32.2 & 420 & 16.8 & 50.4 \\
50 & 829 & 16.5 & 489 & 9.8 & 49.6 \\
75 & 840 & 11.2 & 513 & 6.8 & 49.4 \\
100 & 844 & 8.4 & 522 & 5.2 & 49.6 \\
125 & 843 & 6.7 & 525 & 4.2 & 49.0 \\
150 & 846 & 5.6 & 528 & 3.5 & 49.2 \\
175 & 847 & 4.8 & 532 & 3.0 & 49.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test result table for electric shock energy in pediatric mode.
## 2 Materials and Methods
### Mammography Image Segmentation
#### 2.3.1 Segmentation via Global Thresholding  
The technique of global thresholding is a common method that does not need many computational resources. The algorithm aims to find the optimal threshold value by searching in the spatial domain. An iterative process is followed that analyzes the parameters of the enhanced image and finds the global threshold. Parameter \(\lambda\) is a pre-defined constant [34]. The iterative process is:  
* Initializing the threshold value using the function \(T=0.5\cdot(I_{max}+I_{min})\), where \(I_{max}\) and \(I_{min}\) are the maximum and minimum values of the image.
* Thresholding the image with the value \(T\). This way, the pixels are separated into two groups: \(I_{1}\), which contains the pixels with values \(\geq T\), and \(I_{2}\), which contains the pixels with values \(<T\).  
* Finding the average \(m_{1}\) and \(m_{2}\) from the groups \(I_{1}\) and \(I_{2}\) that were calculated previously.
* Finding a new threshold value using the function \(T^{\prime}=0.5(m_{1}+m_{2})\) to compare it with the previous thresholding value.
* If \(|T-T^{\prime}|>\lambda\), then the steps 2 to 4 must be repeated. Otherwise, the threshold value is \(T^{\prime}\)[34].
# Molecular Dynamics-Based Study of Graphene/Asphal Mechanism of Interaction
## 2 Models Establishment and Simulation Methods
### Molecular Dynamics Simulation Methods
#### 2.2.1 Solubility Parameter  
The solubility parameter is an important indicator used for identifying the interaction between material molecules and the compatibility of reaction mixture systems [24]. In this study, the solubility parameter characterization (\(\delta\)) was introduced to characterize the storage stability of modified bitumen. As shown in Equation (1), the solubility parameter (\(\delta\)) can be calculated by the square root of the cohesive energy density (\(CED\)); the smaller the difference in \(\Delta\delta\), the better the compatibility between asphalt and modifier.  
\[\delta=\sqrt{CED} \tag{1}\]
###### Abstract  
Vessel segmentation in fundus images is crucial for diagnosing eye diseases. The rapid development of deep learning has greatly improved segmentation accuracy. However, the scale of the retinal blood-vessel structure varies greatly, and there is a lot of noise unrelated to blood-vessel segmentation in fundus images, which increases the complexity and difficulty of the segmentation algorithm. Comprehensive consideration of factors like scale variation and noise suppression is imperative to enhance segmentation accuracy and stability. Therefore, we propose a retinal vessel segmentation method based on multi-scale feature extraction and decoupled representation. Specifically, we design a multi-scale feature extraction module at the skip connections, utilizing dilated convolutions to capture multi-scale features and further emphasizing crucial information through channel attention modules. Additionally, to separate useful spatial information from redundant information and enhance segmentation performance, we introduce an image reconstruction branch to assist in the segmentation task. The specific approach involves using a disentangled representation method to decouple the image into content and style, utilizing the content part for segmentation tasks. We conducted experiments on the DRIVE, STARE, and CHASE_DB1 datasets, and the results showed that our method outperformed others, achieving the highest accuracy across all three datasets (DRIVE0.9690, CHASE_DB1.0.9757, and STARE0.9765).  
vessel segmentation; deep learning; U-Net; multi-scale features; disentangled representation
# Securing SCADA Energy Management System under DDs Attacks Using Token Verification Approach
## 2 Related Work
### The Authentication for DoS Defense in IoT Environment  
[PERSON] et al. [25] pointed out that the major threats in the SCADA system are its application of a lightweight private key exchange mechanism and the absence of an attack defense service; therefore, establishing a prevention mechanism for SCADA should adopt encrypted keys, which is important for authentication and authorization of access. [PERSON] et al. [26] proposed employing selective authentication-based geographic opportunistic routing (SelGOR) against DoS attacks, which ensures the integrity of data on the basis of entropy information, isolates DoS attacks, and reduces the calculation cost through distributive coordinated authentication. In addition to employing the identity authentication protocol, [PERSON] et al. [27] also adopted users' misbehavior and the received signal strength indicator to achieve solutions for energy saving, prevent DoS attacks, and locate the attacker in the applied IoT scenario. Regarding the token-based proposal, tokens are remarkably resilient to achieve the authentication and authorization mechanism. In addition, tokens are issued by a third-party authentication center, which could improve its reliability and security. Moreover, tokens feature great privacy protection, and ensure that sensitive information would not be disclosed easily. Ref. [28] created Token-based Lightweight User Verification (TBLUA) to enhance the strength and security of identity authentication. Therefore, the keys to achieving IIoT security include low calculation costs, low communication and storage costs, achieving identity authentication, and preventing attacks. In this context, this paper proposed an encrypted authentication mechanism with a Trusted Encrypted Validator Module (TEVM), based on token authentication and the transport layer security (TLS) protocol, to improve SCADA's defense against DoS attacks.
## 4 Experiment and Evaluation
### Experimentation and Analysis Based on Actual Car Training Data  
After training the end-to-end intelligent driving model on the PreScan dataset, the well-performing model is solidified and retrained using actual environment data for transfer learning [30]. The features learned in the first few layers of the neural network are usually generic. The features learned in the later, deeper network layers are specific to a particular task environment. The network layers that learn generic features are transferred, while those that learn specific features are finetuned. Finetuning is a method that adjusts the weight parameters of neural network models that others have trained for new learning tasks.  
The actual dataset is used to finetune and retrain the end-to-end intelligent parking model trained on the PreScan dataset using a transfer learning approach to close the environment gap between the actual and simulated environments. The finetuning strategy for transfer learning in this paper is to fix the weights of the ResNet50 and LSTM layers, initialize the fully connected layers for training, and retrain the neural network weights using the backpropagation method. The learning rate is set to 0.001, the training algorithm is the Nadam algorithm, and the model is trained until the error no longer decreases. The loss function curve of the CNN-LSTM network trained on the actual parking environment dataset is shown in Figure 14, and the neural network converges during the training process.  
The prediction performance of the model was evaluated. The mean square error of the CNN-LSTM model was 0.069 and 0.082 for the steering wheel angle and speed in the test environment, which indicates that the CNN-LSTM network model has a good prediction of the steering wheel angle and speed after retraining by transfer learning on the actual environment dataset, and the completed model has good generalization after training.  
Figure 13: Vehicle speed curve predicted by CNN-LSTM model.
# Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications
## 3 Measured Results  
We designed a three-stage differential PA using a 65 nm RFCMOS, as seen in Figure 10 which shows a photograph of the designed PA. The total size of the PA, including the pads, was 0.90 \(\times\) 0.50 mm\({}^{2}\). and the core size of PA, excluding the pads and decoupling capacitors, was 0.74 \(\times\) 0.16 mm\({}^{2}\). The s-parameter and power of the designed PA was measured using Rohde-Schwarz ZNA43 vector network analyzer.  
Figure 11 shows the S-parameter measurement results for the designed PA, which was measured in all three stages with a V\({}_{\text{DD}}\) of 1 V and gate bias voltages of 330/350/350 mV. The measured standby current of the PA was 47 mA, and the resulting current density of the transistor used was 40 \(\mu\)A/\(\mu\)m. From S\({}_{21}\), the gain at 3 dB bandwidth is 4 GHz (26.5-30.4 GHz). The measured S\({}_{11}\) and S\({}_{22}\) were below \(-\)10 dB, ranging between 26.5-29.2 GHz and 26.8-32.2 GHz, respectively; the difference between the measurement and simulation results were due to variation in the process, and the two results were similar because of rigorous EM simulations. The measurement results of the CW signal under the specified bias conditions are shown in Figure 12. The PA has a saturated output power of 16.1 dBm at 28.5 GHz and 13.1 dBm at P1 dB with a gain of 31.5 dB. The maximum PAE was 29.7%, whereas the PAE at P1 dB was 22%. The measurement results of the CW signal are similar to that in the preceding chapter; however, it differs in current and gain due to variation in the process, resulting in a higher gain and PAE than that in the simulation. Figure 13 shows the measurement results of CW signals with frequencies ranging between 26.5-29.5 GHz. The maximum PAE of 30.1% was achieved at 29 GHz, over 27% in the higher bandwidths, and between 20.5-23% at P1 dB. The output power P1 dB ranged between 12.8-13.7 dBm, and the saturated output power ranges between 15.4-16.4 dBm.  
Figure 10: Chip photograph of designed three-stage PA.  
Figure 9: Simulation results of matching networks: (**a**) input conjugate matching, (**b**) conjugate matching for first and second stages, (**c**) conjugate matching for second and power stages, and (**d**) output matching for \(Z_{L}\) with \(Z_{opt}\) through load-pull simulation.  
Figure 11: Measured s-parameters (solid) and simulated s-parameters (dot).  
Figure 12: Measured gain and PAE versus output power with CW signal.  
Figure 13: Results of CW signal measurements by frequency.
## 1 Introduction  
Berry fruit wastes, mainly pomace obtained during juice pressing or wine production, are nowadays considered as a promising source of bioactive compounds. On a large scale, they are processed into animal feed or fertilizers. However, due to polyphenol content or seed oil abundance, the waste can have a high added value through the extraction of these valuable components [1]. The proximate analysis of berry fruit pomaces shows a high abundance of carbohydrates (40-70%), followed by protein (5-10%) and fat (2.5-12.5%) [2; 3; 4]. Alternative extraction methods, e.g., ultrasound-assisted, pulsed electric field-assisted, and microwave-assisted may accelerate the process and also be beneficial in terms of financial and environmental savings when compared to conventional extraction procedures, like solid/liquid or Soxhlet methods [5].  
Special emphasis is put on ultrasound-assisted extraction (UAE) as it is an easy, quick, and efficient extraction method. It utilizes cavitation phenomenon, which involves forming and collapsing cavitation bubbles in liquid media and results in rapid local events such as mixing, turbulent liquid movement, particle breakdown, sonoporation, fragmentation, deteturation, and erosion in the surface of solid material [6]. All these effects contribute to improved diffusivity and mass transfer, which are crucial for extraction efficiency.  
UAE was used in numerous studies to obtain oil from by-products with effective yield and with beneficial properties. [PERSON] et al. [7] applied UAE to obtain oil from apple seeds. Compared to conventional extraction and supercritical extraction, UAE results in oil with higher unsaturated fatty acid content and increased concentration of antioxidant compounds. It was also stated that UAE was time efficient when compared to other extraction methods. [PERSON] et al. [8] extracted oil from winery grape by-products. UAE was found to be more efficient than cold pressing in terms of oil yield, antioxidant capacity, and abundance of \(\alpha\)-tocopherol in grape seed oil. Oxidative stability of oil obtained in UAE was also improved. [PERSON] et al. [9] studied the UAE of avocado seed oil. The main effects of US application improved oxidative stability, acidity, peroxide, and iodine indexes. The time/yield ratio was also significantly improved by US.  
Taking into account all the possible benefits of UAE, the aim of the study was to attempt US application in the oil extraction process from blackberry (BB), chokeberry (CH), and raspberry (RB) seeds, considered as by-products of the fruit industry. The objective of the work was also optimization of UAE variables (extraction time and amplitude level) in order to obtain oil with highest possible yield and longest oxidation induction time. Oils obtained in optimized conditions with control samples obtained in conventional solid/liquid extraction process were additionally subjected to detailed chemical analyses: fatty acid profile and distribution determination, oxidation kinetics, melting and crystallization characteristic studies. The results of the study were expected to point out opportunities to valorize food side streams.