esa-sceva/satcom-chunk-collection huggingface dataset:
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 5 Experiments
### Comparison with Other Works  
QUIC traffic classification results of our GBT, XGBT, and LGBM are compared with the other works available in the literature. For the comparison, to be more realistic and fairer, we chose the work that closely matched our objective of QUIC traffic classification and used the same dataset as we used. Additionally, we considered the papers that used accuracy as an evaluation metric to maintain the uniformity of the measurement. It should be emphasized that we did not re-run (replicate) their experiments gain, but the results were taken from each of their best-performing models. More details about their evaluation methods can be read in the respective papers shown in Table 11. From Table 11, it is observed that the classification accuracy of our work is either significant or closely comparable with that of other related works in the literature. In particular, from the confusion matrix of XGBT and LGBM, it is interesting to note that only three samples are misclassified from among five classes overall, where each class has 100 samples. In detail, out of each of the 100 testing samples from Google Drive and Google Music traffic, LGBM misclassified only 2 samples and one sample, respectively. In contrast, for all the other three traffics, classification accuracy is 100%. The same trend is observed for the case of XGBT, but with misclassification traffic in Google Drive and YouTube. The overall highest accuracy and F1-score are 99.40% each. One advantage of this approach is its simplicity since it only requires the packets captured from the traffic as input and does not require any additional feature extraction. The next section is rendered for performance sensitivity analysis of different ensemble techniques in light of the number of packets used and the training scenario used.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **10-Fold Cross-Validation Acc.** & **Training Time (s)** \\ \hline RF & 96.93 & 96.80 & 96.78 & 96.80 & 98.24 & 14.67 \\ \hline ET & 97.48 & 97.40 & 97.40 & 97.40 & 97.43 & 8.17 \\ \hline GBT & 99.40 & 99.40 & 99.40 & 99.40 & 98.66 & 523.32 \\ \hline
**XGBT** & 99.40 & 99.40 & **99.40** & **99.40** & 98.92 & 156.47 \\ \hline
**LGBM** & 99.41 & 99.40 & **99.40** & **99.40** & **99.12** & 69.46 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results on test set 120 packets.
## 2 Materials and Methods
### Validation of HPLC Method  
The HPLC-UV bio-analytical method of EPA and DHA was validated for linearity, accuracy, precision, limit of detection (LOD), and the limit of quantification (LOQ). The linearity was identified after injecting standard solutions of EPA and DHA at different concentration levels of 1, 5, 10, 25, and 50 \(\upmu\)g/mL. Calibration curves for every compound with the correlation coefficient (r\({}^{2}\)) were calculated using the regression equation in Microsoft Excel 365. The calculation for the LOD and LOQ was conducted using the standard deviation (SD) of the y-intercept of the regression (r) and the slope (S) using the following equation: LOD = 3.3 \(\times\)\(\alpha\)/S, LOQ = 10 \(\times\)\(\alpha\)/S. The accuracy was expressed as the degree of agreement between the measured concentration and the nominal concentration. The repeatability of EPA and DHA was expressed as the relative standard deviation (RSD) in standard solutions of each component prepared for linearity. The precision was assessed based on the results for EPA and DHA standard solutions, and the average RSD of the values gained in the three replicates was calculated.
## 3 Methodology and Materials
### Study Area
Home locations were determined by identifying the grid cell with the highest cumulative dwell time during nighttime hours (21:00-08:00). Workplace locations were similarly determined by locating the grid cell exhibiting the longest cumulative daytime dwell time during weekdays (09:00-17:00), excluding home locations. If daytime and nighttime locations coincided--indicative of individuals working from home or those without distinct external workplaces--the second-most frequent daytime location was evaluated. If this secondary location represented a substantial proportion of daytime presence (set by a predefined time threshold), it was designated as the workplace. In cases where no suitable secondary daytime location existed, users were categorized as having no distinct workplace, reflecting patterns characteristic of retirees or home-based occupations. Additionally, other significant activity locations such as shopping areas, educational facilities, or recreational venues were identified using a dwell-time threshold criterion (continuous presence exceeding one hour). Figure 4: Location of Jurong City between Nanjing and Zhenjiang. By sequencing these locations chronologically, complete daily mobility chains for each user (e.g., Home \(\rightarrow\) Destination \(1\rightarrow\) Destination \(2\rightarrow\) Home) were constructed, facilitating comprehensive OD trip identification. * Extraction of intercity OD flows: In the final step, we extracted intercity OD trips involving Jurong and its neighboring cities, Nanjing and Zhenjiang. Specifically, trips were selected where either the origin or destination (or both) was located within the administrative boundary in Jurong, with the corresponding origin/destination located in Nanjing or Zhenjiang. Intra-city movements (origin and destination within Jurong only) and trivial movements involving non-core visitors were excluded. This filtering procedure isolated intercity mobility flows within the tri-city area (Jurong-Nanjing, Jurong-Zhenjiang, and, for contextual reference, Nanjing-Zhenjiang). The resulting dataset comprised approximately 1.54 million OD records over a representative week-long sampling period (10-16 November 2019). Among these, approximately 1.22 million OD records directly involved the core users in Jurong. Each OD record included information on the origin grid, destination grid, and an anonymized user identifier, enabling detailed spatial aggregation and mobility pattern analyses. This refined OD dataset enabled the quantification of precise intercity mobility metrics such as daily commuter volumes from Jurong to Nanjing and Zhenjiang, reciprocal inflows from these cities to Jurong, and the spatial distribution patterns of regional interactions. Consequently, these data provide robust empirical support for analyzing integration dynamics, administrative fragmentation impacts, and spatial-functional roles as an emerging urban interstice in Jurong.
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 2 Overview of Classifiers
### k-Nearest Neighbors  
One of the most simple and efficient classifiers is \(k\)NN, which identifies the distance between the classified data point and its \(k\) nearest neighbors. The main advantage of this classifier is that it is a very fast algorithm that does not require much time for computation and classification. The distance metric used for \(k\)NN classifier can be cosine distance, Euclidean distance, Manhattan distance, etc. Apart from the distance metric, an important hyperparameter is the number of neighbors, which is compared to the classified data point. For the binary classification problem, it is necessary to ensure that \(k\) is an odd number, to avoid the state when the classifier is equally voting for both classes.  
By trying different hyperparameters when training on data collected from healthy subjects, the best result was provided by Manhattan distance, computed as  
\[d(X_{j},X_{i})=\sum_{m=0}^{m=M-1}|x_{jm}-x_{im}|, \tag{2}\]  
where the classified EEG vector \(X_{j}\) of length \(M\) was compared to its \(k\) neighbors. Here \(X_{i}\) denotes the \(i\)th neighbor's vector and \(x_{im}\) denotes the \(m\)th data point of this vector. The best number of \(k\) was evaluated using GS. The classifier reached a promising result of F-score \(=98.6\%\) for \(k=3\). Moreover, the best computational complexity was provided by using \(k=3\).
## 4 Results and Analysis
### Reliability Analysis  
A reliability analysis was conducted to determine whether the data are reliable and whether the respondents truthfully answered the questions. This was carried out on 12 items of the four dimensions of the PSC scale (see Table 5). The corrected item-total correlation (CITC) of the corrected items is greater than 0.5. [PERSON]'s \(\alpha\) being greater than 0.70 for each dimension and 0.938 overall indicates that the reliability of the questionnaire is high. In addition, when any item was deleted, [PERSON]'s \(\alpha\) was lower than the original value, indicating that none of the 12 items should be deleted and the questionnaire data have high stability.
# Denoising Directional Room Impulse Responses with Spatially Anisotropic Late Reverberation Tails
## 3 Plane Wave Decomposition and Spatial Analysis
### Covariance Analysis in the Spatial Domain
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Arrangement** & **Mean Separation** & **Min. Separation** & \(\mathbf{\sigma_{W}}\) (dB) & \(\mathbf{\Gamma_{iso}}\) & \(\mathbf{\Gamma_{aniso}}\) \\ \hline Hyperinterpolation & 40.5\({}^{\circ}\) & 39.5\({}^{\circ}\) & 0.477 & 0.884 & 0.751 \\ Fliege & 40.9\({}^{\circ}\) & 39.6\({}^{\circ}\) & 0.400 & 0.896 & 0.743 \\ Spherical Covering [24] & 38.7\({}^{\circ}\) & 36.8\({}^{\circ}\) & 0.456 & 0.890 & 0.743 \\ Spherical Maximal Volume [24] & 40.1\({}^{\circ}\) & 38.6\({}^{\circ}\) & 0.437 & 0.893 & 0.743 \\ Spherical Packing [24] & 41.6\({}^{\circ}\) & 41.6\({}^{\circ}\) & 0.552 & 0.871 & 0.741 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of 5 different arrangements of 25 look directions over the sphere. The mean and minimum separations refer to the angular distance between nearest-neighbor look directions. \(\sigma_{W}\) is the standard deviation in the total combined directivity \(W(\Omega)\) (Equation (13)), and \(\Gamma\) is calculated using Equation (15). As can be expected analytically, the SHD covariance matrix only tends to the identity matrix for a spatially isotropic field (see Figure 2, also shown by [PERSON] and [PERSON] [23]), confirming the necessity of analyzing incoherence in the PWD domain for highly anisotropic cases. Despite the additional \"background\" coherence inevitably generated by the finite-order PWD's overlapping directivity lobes (see Figure 1 and Figure 2c,d), these investigations show that the use of a set of look directions chosen according to the \"maximum independent coverage\" criteria detailed in the above sections enables a spatial representation with a high degree of independence between signals, even in such an extremely anisotropic example.
## 3 Results  
### The Experimental Setup  
Figure 2 shows the experimental setup of the PS bitloading VLC radar fusion system. In the training phase, the SNR table has not been obtained at first. The 4-QAM training signal is DMT modulated and then sent to the AWG. The two identical analog signals that output from the AWG are equalized by the hardware equalizers (EQU), amplified by the electric amplifiers (EA) and coupled with the DC voltage by the bias tees. Two LED headlights are driven by the biased signal and emit the light. The power of the LED headlights is approximately 2.5 W each. A millimeter wave radar working on the band of 76 to 81 GHz is set at the same position with the headlights to measure the distance between the transmitter and the receiver. The receiver, which is located on the same track with the transmitter, consists of the lenses and an optical filter filtering the yellow fluorescent light. The remaining blue light is converted to the electrical signal by a photo diode (PD) array. The two headlights and the PD array form the MIMO VLC system. The PD array outputs two separate signals, which are amplified by EAs and attenuated by two adjustable attenuators (ATT). An oscilloscope (OSC) is used as the analog-to-digital converter acquiring the received signal. The maximum distance supported by the track is 5 m. The list of the experimental equipment is shown in Table 1. Therefore, the calibration points at 1 m, 2 m, 3 m, 4 m, and 5 m are selected evenly within the distance range as the distance between two adjacent points is 1 m. The received signal is processed to estimate the SNR of all the 250 subcarriers at the calibration points referencing the distance measured by the radar. The SNR at these points is recorded and then interpolated to generated the SNR table.  
In the testing phase, the distance between the transmitter and the receiver is firstly measured by the radar. An estimated SNR can be looked up from the SNR table according to the distance. Then, the data sequence to be transmitted is processed by the 256-QAM PS bitloading modulation process with the estimated SNR. The \(R_{c}\) is 0.82, and the error-free threshold of NGMI is 0.92. At the receiver side, the received signal is demodulated to verify the AIR. The modulation parameters are listed in Table 2.
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 1 Introduction
Research on the diffusion mechanism of fracture grouting is late in development, and there is still no unified identified formula for the diffusion radius of fracture grouting as a theoretical basis. Most of the existing research on the diffusion of fracture grouting is based on Newtonian slurries and power-law slurries, and there are fewer research results on the fracture diffusion mechanism of Bingham fluid. However, cement slurry, cement clay slurry, and composite cement slurry commonly used in grouting engineering with a water-cement ratio of 0.6-1.0 are all Bingham fluids [42]. It can be seen that the mechanism of fracture grouting diffusion of soil-rock mixture under the Bingham fluid is an urgent problem to be solved. Analyzing the above, it can be noted that the diffusion radius of fracture grouting is a very topical issue. Therefore, the purpose of this study is to establish the diffusion formula of soil-rock mixture fracture grouting under a Bingham fluid; to achieve this, it is necessary to solve the following:  
1. Derivation of the Bingham fluid flow equation. 2. Establishing the diffusion formula of soil-rock mixture fracture grouting under Bingham fluid. 3. Conducting field grouting tests and indoor model tests under different working conditions. 4. Adaptation verification of the fracture grouting diffusion equation using grout diffusion radius data from field grouting tests and indoor model tests.
# Combining Supervised and Reinforcement Learning to Build a Generic Defensive Cyber Agent
## 1 Introduction
As cyber attacks grow in sophistication and frequency, organizations need agile defenses that can quickly adapt to new threats and vulnerabilities. Autonomous systems can analyze and learn from each encounter, continuously refining their algorithms and strategies to stay ahead of emerging threats. Reinforcement learning (RL) has been explored in various areas of computer network security, including penetration testing [7; 8], malware detection [9; 10], and anti-jamming communication systems [11; 12]. Existing research demonstrates that RL can play a crucial role in networked systems' security, making it a promising machine learning paradigm for enhancing ACOs. In RL for ACOs, a computer network environment is modeled as a game involving blue and red agents with defined actions, network states, and rewards. This abstracted environment allows the agents to learn effective decision-making processes. However, computer networks often consist of numerous and varied entities and are dynamic, resulting in a large state space. Additionally, agents have access to a wide range of possible actions, further enlarging the state space. This makes training effective agents for ACOs a challenging task. Moreover, optimizing hyper-parameters (HPs) is essential to enhance the performance of RL algorithms. Large action and state spaces make HP optimization more challenging and time-consuming, requiring numerous training runs to identify the optimal HPs. The contribution of this paper are as listed as follows:  
* A framework for generic blue agent training capable of dealing with various types of red agents;
* An enhanced version of the framework that incorporates a custom feed-forward neural network based on the principles of a variational autoencoder (VAE) into the original generic blue agent training framework;
* An examination of the limitations of retraining blue agents against different types of red agents in an attempt to develop a generalized blue agent;
* Experimental results demonstrating the effectiveness of the proposed framework. The rest of this paper is organized as follows: Section 2 presents the background and related work. Section 3 details the framework for generic blue agent training. Section 4 provides the performance evaluation results. Finally, Section 5 offers the conclusions.
## 3 System Integration
### MATLAB Integration  
After modeling the chain's blocks using MATLAB, a NPDSCH transmitter is designed using MATLAB built-in functions based on 3 GPP standard Rel-14. Then, the channel model ETU is added with a maximum Doppler shift of 5 Hz, 9 taps, and a maximum excess tap delay of 5 \(\upmu\)s [32]. A noise model using AWGN is applied with the minimum required SNR for the design, \(-\)12.6 dB [20]. Finally, frequency and time offsets are applied, where the range of frequency offset is [\(-\)35:35] kHz and time offset range is [0:192,000] samples [20]. In addition, the receiver's chain is added in a generic format to satisfy all TBSs and repetition ranges. The system is tested for repetitions of 1, 32, 64, 128, 256, 512, and 1024 under SNR ranges [\(-\)14:20] dB. The designed model is compared with the reference model by MathWorks [36] as shown in Figure 14 [36]. The two models are not completely identical, as the MathWorks model uses the following:  
* A perfect synchronization.
* A perfect frequency correction using exponential, but cordic in the presented model.
* A perfect channel estimation.
* A soft symbol de-mapper.
* A soft decoding.
# Power Control Techniques for Interference Management-
## 2 Systematic Review Method
### Selection and Data Extraction
#### 2.3.3 Quality Assessment  
Quality assessment (QA) is a stage that follows the screening for the inclusion stage. In this stage, researchers obtain full texts of the studies under review. The QA acts as a final stage to ensure that the full-text articles from each study are ready for data extraction and synthesis. In this study, we are not analyzing studies; our review is on power control in 5G networks. Thus, QA is only limited to the articles we obtained from the databases [25]. The authors read through the full text to carefully examine each journal article against the quality criteria (inclusion/exclusion). The full-text review also provided an opportunity for a final check on inclusion/exclusion. Studies that did not satisfy the inclusion criteria previously specified were excluded from the final literature list [25]: \"the list of excluded papers should be retained for record-keeping, reproducibility, and crosschecking\". Finally, 770 journal articles related to the defined research questions were considered.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Autonomous Flight Path Planning Algorithm  
This step is responsible for planning the flight path of our drone system. The overall block diagram of the flight planning algorithm is shown in Figure 5. An image of the whole PV power station downloaded from Google Maps. In the absence of internet service, an alternate approach for getting the image of the entire PV power station is to use image stitching. The drone is flown over the entire PV power station and images corresponding to various parts of the power station are captured. The captured images are stitched using image stitching technique, which results in an image mosaic that includes the whole power station, as shown in Figure 6b. After obtaining the image (see Figure 7a), we need to segment the solar panels from the image.  
Since solar panels have homogenous color, we opted for color-based segmentation. For color-based segmentation, the image is converted into HSV color space in order to separate chroma information from intensity information. This is followed color thresholding in the HSV (Hue, Saturation, Value) color space to extract the region of interest (ROI), which in our case is the solar panel area. A threshold with a lower limit of (80, 65, 65) and an upper limit of (140, 255, 255) was selected based on the fact that most of the solar panels are blue in color.  
Figure 5: Block diagram of the flight planning algorithm.  
The segmented image \(I_{ROI}\) as shown in Figure 7b is smoothed to remove noise before inputting it to the canny edge detection algorithm for edge detection. After edge detection, we find contours in the edge image \(I_{e}\) (see Figure 7c) and filter out contours below a certain threshold area. Bounding boxes (see the red boxes in Figure 7d) are generated for each of the remaining contours. The left and right vertical lines of the bounding box are selected as the start (marked blue in Figure 7e) and end (marked red in Figure 7e) path points for each contour. The obtained waypoints are input to the drone GCS and set to perform the automatic flight path.  
Figure 6: (**a**) Aerial images captured by drone; and (**b**) image of PV power station after image stitching.  
Figure 7: (**a**) Image from Google maps; (**b**) segmented image; (**c**) detected contours; (**d**) filtered contours; and (**e**) final image with markers.  
The drone captured images of the entire PV station at an altitude of 10 m while moving with a velocity of 3 m/s. RGB and Thermal images were captured at an interval of 1 s with a resolution of \(1024\times 768\) and \(640\times 480\), respectively. The captured images were stored in internal SD card. A trigger signal from the main controller was used to capture images from thermal and RGB camera simultaneously.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 2 Wet Methods for Black Silicon Fabrication
### Metal-Assisted Chemical Etching (MACE)
#### 2.1.1 Silver-Assisted Chemical Etching
Concurrently, [PERSON] et al. conducted a study on Si substrates to generate inverted pyramids of varying sizes from the micron to sub-micron scale via MACE with temperature control [29]. Utilizing a mask-free silver-assisted chemical etching process, succeeded by a subsequent step to reconstruct nanostructures, they effectively demonstrated this method as a viable light-trapping mechanism. Both studies use innovative approaches to structuring Si for improved light absorption in solar cell applications, with significant implications for enhancing PV efficiency [30].