esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Polymer Nanocomposites as Adsorbent
### Polymer Nanocomposites as Adsorbent for Dye Removal
#### 3.2.2 Adsorbent Made from Polymer Carbon Nanotube for Dye Removal
[167] employed polyethyleneimine-functionalized carboxylated multi-walled carbon nanotubes as an adsorbent for light green dye removal from solution. The adsorption of light green dyes solution attained equilibrium in 5 h of contact time with a removal of 97%. [PERSON] et al. [168] reported a unique and straight forward biomimetic technique for Congo red removal. This was done by modifying the surface of carbon nanotubes with poly(ionic liquids). The data obtained revealed that the capacity of adsorption of the modified composite for Congo red removal was 50 mg L\({}^{-1}\) higher than the capacity of adsorption of the carbon nanotube that was not modified. The modified composite for Congo red removal also possesses adsorption capacity of about 178 mg g\({}^{-1}\) at pH of 7 [168]. Furthermore, the adsorption performance of some carbon nanotubes (such as pristine carbon nanotubes) is basically restricted owing to their poor dispersibility and without functional groups. Pristine nanotube possess some sidewall curvatures which are conjugated and exhibit an extremely hydrophobic surface; hence, the need for modification. [PERSON] et al. [169] conducted their study on dopamine comprising of copolymers prepared via the ring-opening reaction amid dopamine and poly(styrene cooperate maleic anhydride) that were directly utilized for modifying carbon nanotubes with the aim of attaining functionalized carbon nanotubes composites (carbon nanotubes@poly (poly(styrene cooperate maleic anhydride) -dopamine)). Under several adsorption parameters, the functionalized (carbon nanotubes@poly (poly(styrene cooperate maleic anhydride) -dopamine)) composites adsorbents were employed for the removal of methylene blue. The results they obtained depicted that the capacity of adsorption of these modified carbon nanotubes towards methylene blue removal was close to four-fold when likened to pristine carbon nanotubes [169]. [PERSON] [170] used pristine and oxygen functionalized multiwalled carbon nanotubes adsorbent to remove methyl orange dye. However, the results showed that the adsorption characteristics of the methyl orange dye are extremely affected by the medium pH. All the studies reviewed in this section have demonstrated that modified carbon nanotubes adsorbents with polymers aid the improvement of adsorption efficiency.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 5 Results
### Calibration and Forecasting  
The developed model enables the prediction of the delay rate (PDR) using a calibrated formula, which is as follows:  
\[PDR=\frac{1}{1+e^{-(-0.034573\cdot\text{Score}+42.913279)}} \tag{1}\]  
Formula (1) is derived from historical data and serves as a reliable tool to estimate delay rates based on the given score. The calibration ensures that the predicted PDR aligns closely with the observed delay rates (DR) over time.  
The robustness of this formula has been validated by testing it against historical data in increments of ten years. In each case, the predicted PDR has consistently demonstrated a strong correspondence with the actual observed DR values, confirming the formula's accuracy and reliability. This historical consistency underscores the model's effectiveness in predicting delay rates under varying conditions. The results are presented in Table 14.  
Very similar values for DR and PDR suggest that all necessary information to predict delays in flights are included in the model.
# The Use of Attentive Knowledge Graph Perceptual Propagation for Improving Recommendations
## 4 Methodology  
This section provides an overview of the AKGP framework and describe, in detail, each of the components, including user-item initial embedding representation, the multilayer perceptual propagation and attention network, aggregated prediction and hierarchical sampling optimization. The overall schematic of the framework is shown in Figure 2.  
AKGP takes the user-item historical interaction information and the user-item related knowledge information in the knowledge graph as input, and models the structured knowledge at a fine-grained level using the TransD [44] and hierarchical modeling methods. The user-item information is projected onto multiple vector spaces to obtain multifaceted fine-grained embedding vectors. To explore the higher-order interests of users, item \(v\) is associated with entity \(e\) in the knowledge graph, and user preferences are propagated on the knowledge graph through correspondence between the user embedding representation and entity \(e\) in the knowledge graph. In the propagation process, an attention network is used to obtain the weights of different relationships in the knowledge graph that affect the user's interest, and the relationships are used as propagation factors over a certain number of neighbors to generate user-embedded representations and item-embedded representations. These embedded representations are aggregated for Click-Through Rate (CTR) prediction and Top-K recommendation. Based on the prediction results, the model is optimized using a stratified sampling strategy to reduce sample bias.  
Figure 2: The general structure of the AKGP model. Upon inputting user–item interaction information and knowledge graph, the model calculates the weights of entities with different relationships through attention networks and propagates them in the knowledge graph as propagation factors. This enables us to aggregate and predict user preferences.
# A Transformer-Less Buck-Boost Grid-Tied Inverter with Low Leakage-Current and High Voltage-Gain
## 3 Operation Principles  
The investigated transformer-less buck-boost inverter is controlled by SPWM. Figure 4 is a conceptual diagram of the input voltage \(V_{in}\), the mains voltage \(v_{s}\)(t), the operation modes, and the switch driving-signals in a line-cycle, where the peak value \(V_{M}\) of \(v_{s}\)(t) is higher than \(V_{in}\). \(T_{ac}\) is the period of the mains voltage. As can be seen from Figure 4, based on the level of \(V_{in}\) and \(v_{s}\)(t), the investigated buck-boost inverter works in step-down or step-up mode, respectively. In the following, with regard to these two modes, their working principles in steady-state and the equivalent circuits of high-frequency switching will be provided in sequence.
## 3 Experimental
### Electrical Measurement Procedure  
Initially, 1 mL of each of the four different solution samples (DI water and phosphate-buffered saline (PBS) with three concentrations: 300 mOsm, 1500 mOsm, and 3000 mOsm) was prepared for the chip characterization measurement. Each sample was loaded into a syringe, and five measurement cycles were conducted to measure all samples. The sample was driven through the microchannel using  
Figure 2: A schematic diagram of the experimental set-up.  
a syringe pump with the flow rate of the syringe pump kept constant (60 \(\upmu\)L/min). The microneedles connected to impedance analyzer were used to measure the impedance of each solution. Impedance spectra (impedance and phase vs. frequency) were measured using an AC signal frequency range from 100 Hz to 1 MHz with an applied voltage of 1 Volt to differentiate variations of solution samples. Between each PBS sample measurement, the microchannel chip was flushed by DI water for 3 min. The data were recorded with the impedance analyzer GUI (graphical user interface) software (v3.10, Hioki E.E. Corporation, Nagano, Japan) and post-processed in MATLAB (R2011, MathWorks Inc., Natick, MA, USA).  
Meanwhile, for yeast cell concentration detection measurement, 1 mL of each of seven different concentrations of sample from 10\({}^{2}\) to 10\({}^{9}\) cfu/mL was prepared for the measurement. The sample was driven through the microchannel at a flow rate of 6 \(\upmu\)L/min. An AC signal frequency range from 100 kHz to 5 MHz with an applied voltage of 1 Volt was utilized for cell concentration detection measurement. The impedance change during the passage of yeast cells at the sensing area was measured. Impedance at three frequencies (100 kHz, 500 kHz, and 1 MHz) was measured to observe the changing of impedance for each sample.  
In order to perform the measurement, the impedance analyzer was self-calibrated by using short and open standard calibration. Furthermore, to calibrate the chip, impedance of 1 \(\times\) PBS (1 time the physiologic concentration of 300 mOsm) solution was measured at the 20 \(\upmu\)m electrode gap. All experiments were conducted at room temperature, and three microfluidic devices were utilized for reproducibility testing on the device. To validate the equivalent circuit model, impedance of the medium between microneedle was measured. Two separate solutions with different conductivities were used. The solutions were sterilized DI water and PBS (phosphate-buffered saline) with conductivities 6 mS/m and 1.4 S/m, respectively.
## 3 Methodology
### Decoder  
The decoder adopts the same architecture as the Parseq [53] decoder and employs two MHA modules. The inputs include the query vector \(q\) (trainable vector), context information \(c\) (utilized with real labels during training), and image features \(z\) from the encoder. The query vector \(q\) and context information \(c\) in the first MHA module are inputs. The primary objective of this module is to determine the most relevant context information for the current decoding step through the self-attention mechanism. This lets the decoder effectively utilize previous context information during decoding, enhancing the model's performance.  
\[h_{c}=q+\text{MHA}(q,c,c,m)\in R^{(T+1)\times d_{model}} \tag{5}\]  
\(T\) represents the length of contextual information, and \(m\) denotes an optional attention mask. During the training process, we drew inspiration from PARSeq's Permutation Language Modeling [53] approach to enhance the correlation between characters at different positions. We employed a strategy of random masking, achieving different decoding sequences through various masking methods. During training, masks were generated from random permutations. However, we followed the conventional approach during the inference and testing phases, masking in a left-to-right sequence.  
The input to the second MHA includes the output from the previous MHA and image features \(z\) from the encoder. Notably, the second MHA module does not use a mask when processing inputs, ensuring comprehensive information consideration.  
\[h_{i}=h_{c}+\text{MHA}(h_{c},z,z)\in R^{(T+1)\times d_{model}} \tag{6}\]  
Subsequently, we take the output of the second MHA as input, and it undergoes computation through an MLP, following Equation (7).  
\[h_{dec}=h_{i}+\text{MLP}(h_{i})\in R^{(T+1)\times d_{model}} \tag{7}\]  
In the end, we obtained the final output through a linear model.  
\[y=\text{Linear}(h_{dec})\in R^{(T+1)\times(S+1)} \tag{8}\]  
The size of the character set used for training is denoted as \(S\). Due to the inclusion of a character end marker, the character length and category length are processed with an additional increment of 1.
## 4 Application Examples
### Sunspot Numbers  
The time series analyzed comprises the total monthly number of sunspots, _i.e._, the number of dark spots on the solar surface caused by interactions between the solar magnetic field and the Sun's plasma [42]. The time series comprises 3151 values and spans a period from January 1749 to July 2011. The data was downloaded from the website of the Solar Influences Data Analysis Center (SIDC) of the Royal Observatory of Belgium (SIDC-team) [43]. The results of the analysis are given in Figure 5. All 23 peaks were correctly detected (no false negative errors) and no false detection occurred (no false positive errors).  
Figure 4: Results of applying the AMPD algorithm to the simulated type B data sets. (**a**) no noise; (**b**) SNR = 25 dB; (**c**) SNR = 10 dB; (**d**) SNR = 5 dB; (**e**) SNR = 0 dB.
## 3 Materials and Methods
### Machine Learning Algorithms  
The SVMs are advanced non-parametric statistical learning kernel-based algorithms commonly used in classification of remote sensing data [44; 45]. Training data are projected into a higher-dimensional space using a linear/kernel-based function to optimally separate classes [43]. Parameters that optimally define the linear/non-linear hyperplane to separate the target classes are determined through an optimization problem. New data are evaluated based on the defined hyperplane constraints and categorized accordingly. The SVM requires regularization parameters that assist in tuning the model. These are C and gamma values,  
Figure 2: The mean raw VV, VH, and VV/VH backscatter profiles. The extracted polarizations are for maize crops and other classes, which refers to aggregated bare soil and grasslands.  
which were determined by the grid search method. In this study, the regularization parameter was 100, the gamma value was 0.01, and a Radial Basis Function (RBF) kernel was used. A comprehensive review of the tuning method can be found in [PERSON] et al. [46].  
The Xgboost is part of the classification and regression ensemble gradient boosting machines (e.g., Gradient Boosting and AdaBoost). This boosting technique is an improved version of Gradient Boosting and AdaBoost because it has a higher computational efficiency and improved capacity to deal with over-fitting. For example, Xgboost grows trees parallel to each other, whereas the original Gradient Boosting model builds the trees in a series configuration [23; 36; 47]. Boosting uses many weak classifiers to produce a powerful classifier in an additive manner. The classifiers are trained on the weighted versions of the training sample; misclassified data are given more weight during the iteration process so that the next step focuses on the misclassified data [23]. The predictions improve over time and the final predictions are decided through a majority voting process to create vigorous predictions. This algorithm contains a rigorous number of regularization parameters that can be tuned to improve predictions and minimize overfitting [48]. These parameters are also determined using a grid search method.
# Research on Gangue Detection Algorithm Based on Cross-Scale Feature Fusion and Dynamic Pruning
## 3 Model Lightweighting Based on Global Dynamic Channel Pruning
### Improved Network Model for Gangue Detection Algorithm  
This study refines the coal and gangue detection algorithm based on the YOLOv5s network model. The modified network structure of the improved gangue detection algorithm is illustrated in Figure 5.  
Figure 4: Comparing static and dynamic trimming. (**a**) Static pruning. (**b**) PAGCP dynamic pruning.  
Firstly, to address the issue of suboptimal detection for small targets in coal and gangue, the feature extraction in the Backbone network is replaced with the ConvNext module, enhancing the network's ability to perceive small targets. This replacement enhances the network's ability to perceive small targets, and, furthermore, aims to incorporate more semantic and low-level information into the feature fusion process. This is achieved by assigning appropriate weights to the features, facilitating rapid and efficient cross-scale fusion to enhance detection performance for coal and gangue, thereby addressing instances of missed detections. Subsequently, to address the issue of mismatched angles between predicted and ground truth bounding boxes, the localization loss function adopts the ESIOU loss function to expedite network convergence. This approach enhances localization precision, and improves the overall accuracy of the model in detecting coal and gangue. Finally, global channel pruning is applied to eliminate redundant channels, leading to model lightweighting, and thereby improving the inference speed.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 4 Experimental Results
### Baseline Performance Evaluation
#### 4.2.3 Impact of Multiframe Insertion Strategy  
This experiment evaluated how the temporal distribution of watermark bits affects both robustness and perceptual quality. We compared the proposed scene-based insertion strategy--where each bit is embedded at the temporal midpoint of five detected scenes--with a random frame selection baseline. In both cases, a 128-bit watermark was embedded using adaptive quantization and \(r=5\) redundancy.  
Scene segmentation was performed using histogram-based shot boundary detection, ensuring that bits were embedded in visually stable regions. In contrast, the random strategy placed bits in arbitrary frames, regardless of scene content.  
As shown in Table 4, the structured insertion method yielded significantly lower BER and synchronization error, along with higher VMAF scores. The benefits were especially notable in dynamic content, where random insertion is more likely to coincide with scene cuts or motion peaks.  
These results confirm that temporal awareness during embedding improves both robustness and perceptual stability, particularly in videos with complex motion or frequent scene changes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Redundancy** & **Avg. BER (\%)** & **Frames Used** & **Recovery Accuracy** \\ \hline
1 & 5.2 & 128 & 94.8\% \\
2 & 3.1 & 256 & 96.9\% \\
3 & 1.8 & 384 & 98.2\% \\
4 & 1.1 & 512 & 98.9\% \\
5 & 0.2 & 640 & 99.8\% \\ \hline \hline \end{tabular} \({}^{1}\)Average across all videos.  
\end{table}
Table 3: Effect of redundancy factor on BER and watermark recovery rate \({}^{1}\).
# Elevator Fault Diagnosis Method Based on IAO-XGBoost under Unbalanced Samples
## 6 Conclusions  
In the IAO-XGBoost-based elevator fault diagnosis method under unbalanced samples proposed in this study, the IAO algorithm improves the ability of the algorithm to jump out of the local optimum compared with the traditional swarm intelligence algorithm. The model optimized by the algorithm has a better combination of hyperparameters, improving the accuracy and reliability of the model. At the same time, taking into account the fact that the elevator fault samples are small and the sample categories are unbalanced, the classification performance of fault diagnosis is improved. Combined with the example analysis, the conclusions obtained are as follows:  
1. The method of RFE-SMOTE-Tomek reduces the influence of redundant features on the generation of new samples by SMOTE-Tomek, obtains better sample balanced data, and improves the ability of subsequent fault diagnosis;
2. The AO algorithm is improved by introducing Tent chaotic mapping, nonlinear inertia weights, and nonlinear convergence factors, which can effectively improve the convergence speed and optimization- seeking performance of the algorithm;
3. With the proposed method of sample balancing, RFE-SMOTE-Tomek, combined with the elevator fault diagnosis model of IAO-XGBoost, which accurately identifies the state of the elevator, fault diagnosis accuracy can reach 99.06%.  
The method proposed in this article has achieved good results with respect to sample imbalance in elevators. Although this article mainly focuses on elevator fault diagnosis, the proposed method can be extended to the long-term operational health monitoring of elevators in the future. Through real-time monitoring and diagnosis, potential faults can be better predicted, and preventive maintenance measures can be taken, thereby extending the service life of elevators and improving their reliability.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fault Diagnosis Model** & **Accuracy/\%** & **Kappa Factor** & **Time/s** \\ \hline RF & 96.69 & 0.958 & 0.6673 \\ LightGBM & 96.93 & 0.961 & 0.1374 \\ XGBoost & 97.88 & 0.973 & 0.0522 \\ IAO-XGBoost & 99.06 & 0.988 & 0.0442 \\ \hline \end{tabular}
\end{table}
Table 7: Diagnosis results of different fault diagnosis models.  
**Author Contributions:** Investigation, P.Z. and X.Z.; Resources, M.L.; Writing--original draft, C.Q.; Writing--review & editing, L.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Natural Science Foundation of Xinjiang Uygur Autonomous Region grant number 2022D01C.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 3 Example Analysis
### Experimental Analysis and Verification
#### 3.2.1 Time-Related mRMR Feature Selection
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Number of Features** & **Accuracy** & **Removed Feature Number** & **Removed Feature Name** \\ \hline
23 & 0.9617 & Non & Non \\
22 & 0.9630 & 1 & total\_fiat NUMERIC \\
21 & 0.9633 & 14 & std\_flowiat NUMERIC \\
20 & 0.9636 & 5 & max\_fiat NUMERIC \\
19 & 0.9623 & 8 & mean\_biat NUMERIC \\
18 & 0.9614 & 7 & mean\_fiat NUMERIC \\
17 & 0.9610 & 0 & duration NUMERIC \\
16 & 0.9636 & 13 & mean\_flowiat NUMERIC \\
15 & 0.9503 & 3 & min\_fiat NUMERIC \\
14 & 0.9488 & 2 & total\_biat NUMERIC \\
13 & 0.9376 & 4 & min\_biat NUMERIC \\
12 & 0.9380 & 18 & std\_active NUMERIC \\
11 & 0.9388 & 17 & max\_active NUMERIC \\
10 & 0.9312 & 11 & min\_flowiat NUMERIC \\
9 & 0.9312 & 21 & max\_idle NUMERIC \\
8 & 0.9313 & 16 & mean\_active NUMERIC \\
7 & 0.9150 & 9 & flowPktsPerSecond NUMERIC \\
6 & 0.8794 & 12 & max\_flowiat NUMERIC \\
5 & 0.8772 & 20 & mean\_idle NUMERIC \\
4 & 0.8658 & 15 & min\_active NUMERIC \\
3 & 0.8514 & 19 & min\_idle NUMERIC \\
2 & 0.7038 & 6 & max\_biat NUMERIC \\
1 & 0.4016 & 10 & flowBytesPerSecond NUMERIC \\
0 & 0 & 22 & std\_idle NUMERIC \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The effect of the features number on the accuracy of the model. Figure 8: Effect of number of features on model accuracy. Figure 11: Coefficient correlation heat map after feature selection. Figure 10: Heat map of coefficient correlation before feature selection. Figure 9: Importance of data features. When the absolute value of Pearson's correlation coefficient is close to 0, the correlation between features is low, whereas the correlation between features close to 1.0 is higher. The mRMR feature selection removes feature numbers 1 and 14, respectively. It can be seen from Figure 11 that the correlation between feature numbers 1 and 2 is 0.9, and the correlation between feature numbers 14 and 13 is 0.9. Both pairs of features have a high correlation, which proves the feasibility of the mRMR feature selection results. The correlation between features of the encrypted traffic data after mRMR feature selection is lower than the correlation between features before going through this experiment. So mRMR feature selection on this dataset can make the redundancy between features reduced while ensuring the maximum correlation between features and classes. This method also solves the problem of feature redundancy that exists in the original data.