esa-sceva/satcom-chunk-collection huggingface dataset:
# Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
## 2 Related Work
### Residual Structure  
The residual structure is a significant method used in neural networks to deal with gradient vanishing problems during deep network training and problems with complex model training. Its fundamental concept is to create residual connections between the input and output of network levels, directly incorporating prior knowledge into the calculation of succeeding layers. Networks may be trained more easily and more generally thanks to this method.  
Deep Convolutional Neural Networks (CNNs) were initially the subject of a proposal and use of the residual structure. Researchers led by [PERSON] introduced the ResNet (Residual Network) model [32], which included residual blocks, in a study from 2015. The input data was passed through a convolutional layer, an identity mapping (a skip connection), and then another convolutional layer in each residual block. These skip connections made it easier to train the network and improved gradient flow during backpropagation, which prevented gradient vanishing. The ImageNet image classification challenge saw the ResNet model perform well, proving the value of residual structures in deep networks.  
Since then, residual structures have found widespread use in fields like natural language processing and Graph Neural Networks in addition to their effectiveness in image classification challenges. In Graph Neural Networks, residual structures can be successfully added to the node update process to improve the model's learning power while maintaining crucial node information. When tackling challenges involving few-shot learning and sophisticated graph structures, this results in exceptional performance.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 4 Results and Discussion
### Dataset  
In this study, the Sleep-EDF Expanded Database is considered, which is widely recognized in EEG-based sleep research [72]. This dataset contains 197 full PSG recordings in EDF format, with multiple sessions from 78 subjects (healthy and with sleep disorders) between 25 and 101 years of age. These data were collected during approximately 20 consecutive hours of sleep monitoring in a home environment [33]. The recordings comprise several physiological signals: two EEG signals, EOG, submental EMG, event makers, respiration, and body temperature, all stored in EDF format with a 16-bit resolution. Each EDF file is accompanied by metadata, including patient information, recording settings, signal specifications, and administrative details. The EEG channels (Fpz-Cz and Pz-Oz) follow the 10-20 international system, covering the frontal and occipital regions, as suggested by the AASM. EEG and EOG signals were sampled at 10 Hz, while the remaining signals were sampled at 1 Hz [33]. The recordings are divided into 30 s epochs and each labeled according to the R&K guidelines as \"W\", \"1\", \"2\", \"3\", \"4\", \"R\", \"movement\", and unlabeled segments.
# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
###### Abstract  
Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Uptum estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0-9 days, and R square about 0.65-0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5-10 days, bias about 0-8 days, and R square about 0.6-0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization.  
crop phenology; PhenoCam; PlanetScope; high resolution; near-surface
## 6 Applications and Simulations
### Peer-to-Peer Greedy Routing
#### 6.1.2 Load Distribution  
We have also calculated the traffic load distribution among the sensor nodes, as illustrated in Figure 11. The traffic load under our proposed scheme is well balanced, with more than 40% of the  
Figure 10: (**a**) More routes under the proposed scheme have low stretch factors than random-walk does; (**b**) with the increase of the actual path length between source and destination, the stretch factor decreases under our proposed scheme, while the stretch factor of random-walk grows noticeably.  
nodes involved in less than 30 routes and around 60% in less than 50 routes. Random-walk performs greedy routing first and then randomly searches along the boundaries when a dead-end is reached. Thus, the nodes near boundaries (especially inner boundaries) usually experience heavy load.
## 3 Fatigue Crack Growth in Hydrogen Environment
### Mechanisms of Fatigue Crack Initiation and Growth
The other was an in situ observation of intergranular fatigue crack growth in a hydrogen environment. They concluded that the credible mechanism for intergranular fatigue crack propagation is due to the increased number of slip repetitions enhanced by hydrogen, which damages the grain boundaries ahead of the crack tip. Therefore, in a hydrogen environment, fatigue cracks are more likely to propagate along the grain boundaries. In addition to the effects of hydrogen atom diffusion, in many cases, chemical reactions may occur between certain metals and hydrogen atoms [90; 91; 92], resulting in the formation of hydrides or hydride phases, which can further affect the physical and mechanical properties of the metal. This chemical reaction accelerates the process of crack growth and metal damage. This further affects the fatigue life of the material. [PERSON] et al. [12] investigated the  
Figure 6: Fatigue crack growth rate data: (**a**) fatigue crack growth rate data for X100 steel; (**b**) fatigue crack growth rate data for X52 [88]. corrosion fatigue crack growth mechanism of high strength steel bar HRB400 in different corrosive environments. Fatigue crack growth tests were also conducted under different fatigue load types, environments and stress ratios. The results show that hydrogen embrittlement plays a dominant role in the corrosion fatigue process of HRB400 bars. The contribution of anodic dissolution to the FCG rate increases with the level of aggressiveness. The contribution of hydrogen embrittlement to the FCG rate increased with increasing stress ratio and stress intensity factor range. Understanding the mechanisms of these fatigue cracks generation and propagation is crucial to predict and protect against hydrogen embrittlement. Therefore, through experiments and theoretical simulations, scientists have endeavoured to reveal the interactions between hydrogen and metals in order to develop effective protective measures and material design strategies to ensure the safety and reliability of metallic structures in hydrogen environments [18]. There are some important issues and challenges regarding the existing mechanisms of hydrogen embrittlement fatigue crack generation and extension. The current understanding of the mechanism of hydrogen embrittlement assisted fatigue crack generation and extension is not deep and comprehensive enough, especially the mechanism of crack behaviour under complex stress states has not been fully clarified. Secondly, most of the existing studies focus on the macroscopic level, and there is a lack of in-depth research on the effects of hydrogen atom diffusion and aggregation in materials on cracking behaviour at the microscopic scale. In addition, the mechanism of hydrogen embrittlement fatigue crack generation and propagation is affected by a variety of factors, including the nature of the material itself, the stress state at the crack tip, the hydrogen environment, etc., and the complex interactions between these factors have not been fully understood.
## 3 Preliminary Study
### Result of Noise Reduction  
This section presents the results of noise removal using a DAE model trained with Gaussian random noise. The Gaussian noise was generated with a mean of 0 and a standard deviation of 0.1, as illustrated in Figure 5. This synthesized noise was added to the raw data and used as the input to the DAE model. The encoder and decoder were structured to enable the reconstruction of the original signal from the noisy input.  
Figure 4: (**a**) Normal data (FFT). (**b**) Failure data (FFT).  
Figure 3: (**a**) Normal data. (**b**) Failure data.  
To improve model stability and noise reduction performance, the data was normalized to a range between 0 and 1 during training. The denoised output obtained through the trained model is shown in Figure 6. Compared to the original raw data, the overall vibration amplitude was reduced, and the results indicate that some peak components were retained during the noise removal process.  
For the preprocessing based on the Wavelet Transform, MATLAB's Wavelet Toolbox was utilized. Given that the selection of a suitable wavelet function should reflect the characteristics of the target signal, the Daubechies 4 (db4) wavelet--known for its similarity to vibration signals--was adopted. The denoising process involved four levels of decomposition, and soft thresholding, which is commonly employed in most related studies, was applied at each level to suppress noise [23]. The resulting denoised signal is illustrated in Figure 7.  
Figure 5: Gaussian noise.  
Figure 6: (a) Noise reduction normal data (DAE). (b) Noise reduction failure data (DAE).
###### Abstract  
Herein, we report the performances of crosslinked facilitated transport membranes based on carboxymethylated nanofibrils of cellulose (cmNFC) and polyvinylamine (PVAm) with the use of 3-(2-Aminoethylamino) propyltrimethoxysilane (AEAPTMS) as second fixed carrier for CO\({}_{2}\) selectivity and permeability. The grafting of AEAPTMS on cmNFC was optimized by following the hydrolysis/condensation kinetics by \({}^{29}\)Si Nuclear Magnetic Resonance (NMR) analyses and two different strategies of the process of membrane production were investigated. In optimized conditions, around 25% of the -COOH functions from cmNFC have crosslinked with PVAm. The crosslinked membranes were less sensitive to liquid water and the crystallinity of PVAm was tuned by the conditions of the membrane elaboration. In both processes, CO\({}_{2}\) selectivity and permeability were enhanced especially at high water vapor concentration by the use of PVAm and AEAPTMS suggesting the existence of a facilitation effect due to amine-CO\({}_{2}\) interaction, while the mechanical integrity of the swollen membranes remained intact.  
Keywords:amine-based fixed site carrier crosslinked facilitated transport membranes; carboxymethylated nanofibrils of cellulose; carbon capture utilization and storage +
Footnote †: journal: Applied Sciences
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 2 Use Case Mobile Robotics and Requirements  
Mobility is an essential requirement of flexible lineless assembly systems to meet the market demand for highly customized products. Recent advancements in robotics,sensor systems, and communication infrastructure push toward ultra-flexible production systems [16]. Industrial mobile platforms enable temporary production cells in assembly stations. Mobile robots enable new sets of use cases, and to realize them, reliable real-time communication is a must for mobile robots. Reliable real-time communication between the mobile robots and the factory cloud system enables collaborative decision-making tasks and advanced AI/ML methods, which was impossible with onboard processing on the mobile robots. In the simplest case, a mobile robot consists of a power supply, a platform, a manipulator, a computing and control unit, and several sensors. Depending on the applications and use cases, the mobile robot can be equipped with specific components, such as a loading unit or conveyor belt for transport. Mobile robots for more complex industrial applications are often equipped with a manipulator, enabling several possible assembly tasks such as pick and place, handling (storage, placing, holding, setting, etc.), and supporting processes (marking, cleaning, inspection, etc.). Recent publications [17; 18] highlight the following advantages of mobile robots compared to fixed robots:  
* Greater reconfigurability and flexibility of the production system;
* Increased production efficiency with high product variability;
* Greater reliability and less downtime.
###### Abstract  
The coal mine's underground heading roadway suffers from a lack of effective temporary support, which contributes to low efficiency and inadequate safety in excavation operations. This paper presents a novel approach for roadway excavation, support, and anchoring, utilizing the twin-link stepping temporary support method. To address the stability requirements of the roadway roof in this support method, a numerical model of the excavation face was developed using the BBM (Bonded Block Model) method in 3 DEC 7.0 software. Through numerical simulation, the repeatability of the twin-link stepping temporary support process was assessed for various roadway roof rock types, thicknesses, and burial depths. By considering aspects such as roof fragmentation, roof convergence, and relative displacement between the direct roof and the bedrock, the range of roadway roof repeatability was determined. The proposed twin-link stepping temporary support process, along with its applicable roof conditions, establishes a theoretical foundation for implementing the parallel operation mode in coal mine underground heading roadways, thereby significantly enhancing safety and efficiency.  
roadway excavation; temporary support; repeat support; numerical simulation; discrete element block
# A Machine Learning Framework for Student Retention Policy Development: A Case Study
## 4 Experimental Results and Discussion
### Dropout Prediction Using Machine Learning Algorithms
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Models** & **Accuracy** & **Precision** & **Recall** & **Dropout F1** & **Grad F1** & **Macro F1** & **Weighted F1** \\ \hline
**Support Vector Classifier (SVC)** & \(\uparrow\)0.76 & \(\uparrow\)0.75 & \(\uparrow\)0.74 & 0.67 & 0.81 & \(\uparrow\)0.74 & 0.75 \\ \hline
**K-Nearest Neighbors (KNN)** & 0.66 & 0.64 & 0.63 & 0.54 & 0.73 & 0.63 & 0.65 \\ \hline
**Logistic Regression (LR)** & \(\uparrow\)0.60 & 0.30 & 0.50 & 0.0 & 0.75 & \(\uparrow\)0.38 & 0.45 \\ \hline
**Gaussian Naive Bayes (NB)** & 0.61 & 0.58 & 0.53 & 0.23 & 0.74 & 0.49 & 0.54 \\ \hline
**Artificial Neural Network (MLP)** & 0.81 & \(\uparrow\)0.81 & \(\downarrow\)0.79 & 0.75 & 0.85 & 0.80 & 0.81 \\ \hline
**Random Forest (RF)** & 0.82 & \(\uparrow\)0.82 & 0.80 & 0.76 & 0.86 & 0.81 & 0.82 \\ \hline
**Categorical Boosting (CatBoost)** & **0.83** & **0.83** & **0.81** & **0.77** & **0.87** & **0.82** & **0.83** \\ \hline
**Classification and Regression Trees (CART)** & \(\uparrow\)0.81 & \(\uparrow\)0.81 & \(\uparrow\)0.80 & 0.75 & 0.85 & \(\uparrow\)0.80 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance measures of ML models with optimal parameters using all features. An upward arrow (\(\uparrow\)) indicates an increase in performance, while a downward arrow (\(\downarrow\)) denotes a decrease. Table 6 shows the performance of the classifiers using only the most important four features identified for each model on the test dataset. With the optimal settings, the highest Accuracy and F1-score were achieved by the CatBoost classifier with an Accuracy of 82% and an F-score of 81%. From the analysis, it is evident that MLP, RF, CatBoost, and CARTs classifiers consistently achieve high-performance metrics, indicating their suitability for dropout prediction. These models' robustness in handling complex relationships within the data makes them ideal for developing early prediction frameworks. However, models like Logistic Regression and Gaussian Naive Bayes showed significantly lower performance, highlighting the need for advanced feature engineering and the potential benefits of ensemble methods. In conclusion, the results presented in Tables 5 and 6 indicate that CatBoost, CARTs, MLP, and RF have the highest Accuracy, dropout F1-scores, and Macro F1-scores. Thus, in response to Research Question 2, these classifiers are identified as suitable for dropout prediction using limited registration data. For the framework presented in this study, the highest performing classifier CatBoost was used to predict the at-risk students that are discussed in Section 4.3. Furthermore, an analysis of Figure 12 shows that a comparable performance was achieved using only the top five features instead of all the features. This is an indication that the ML models will benefit from a systematic feature elimination approach such as recursive elimination.
## 5 Simulation Results and Discussion
### Case 1--Frequency Restoration  
In Case 1, two different control methods for frequency restoration are compared. They adopt an integral controller and the proposed controller, respectively, for grid-feeding units for frequency restoration. The purpose of this case is to test whether the grid-feeding units restore the frequency without causing any problems, such as the hunting effect, while frequency measurement error among grid-feeding units is simulated. To simulate this measurement error, 0.001 Hz is added to the frequency measured by DG4. The load is changed after 0.8 s of the simulation to cause a frequency deviation. Figure 5 shows the simulation results achieved using an integral controller.  
As shown in Figure 5, the frequency drops at 0.8 s due to the load increment. Focusing closely on the zoom in part of the frequency, a measured frequency error among DGs can be observed. The active power of the grid-forming units immediately follows the load change at 0.8 s and hence changes their  
Figure 4: One-line diagram of the studied microgrid.  
Figure 5: Frequency restoration results achieved using the integral controller.  
output frequency. Since grid-feeding units adopt an integral controller to restore the frequency, their active power increases after frequency deviation. However, even after the frequency is restored back to nearly 60 Hz, the active power of DG2 and DG4 does not remain as the steady-state value, but is instead increased and decreased, respectively. This is due to the measurement error, which exists in the real world. Since DG2 and DG4 measure smaller and larger values than 60 Hz, respectively, they keep increasing and decreasing the active power, respectively. Eventually, the system will collapse. Furthermore, if there are set point errors in frequency reference and/or controller gain, the system frequency will not be maintained at a steady-state value.  
To overcome this real world problem, new controllers are proposed and the simulation results obtained using the proposed controller are shown in Figure 6. After the frequency deviation, grid-feeding units wait for the \(\Delta f_{int}\) signal to restore the frequency. Slightly after 1.2 s of simulation time, the \(\Delta f_{int}\) of DG2 activates earlier than that of DG4, since DG2 measures a smaller frequency value than DG4. At 1.5 s, the \(\Delta f_{int}\) of DG4 is turned off earlier than that of DG2, since DG4 measures a larger frequency value than DG2. By using this control scheme, the active powers of DG2 and DG4 can reach the steady-state value (see the zoom in part of the active power in Figure 6), even if there is a frequency measurement error (see the zoom in part of the frequency in Figure 6).  
Figure 6: Frequency restoration results obtained using the proposed controller.
## 3 Simulated Phased Array with P-CRLH Phase Shifters
### Main Beam at \(15^{\circ}\) Off-Broadside  
To scan the main beam at \(15^{\circ}\) off-broadside of the array axis, the inter-element adjacent phase difference is calculated using the following expression [7]:  
\[\Delta\varnothing=-\mathrm{fidisun}\hat{\ }_{0}, \tag{3}\]  
where \(\theta_{0}\) is the direction of main beam from the \(z\)-axis, \(d=\lambda/2\) is the center spacing between the antenna elements in Figure 8 and \(\beta=\frac{2\times p!}{\lambda}\) is a wave number. For \(\theta_{0}=15^{\circ}\), \(\Delta\varnothing=-45^{\circ}\). This phase shift is achieved using the unit cell P-CRLH phase shifter discussed in Section 2.1. The four patch antenna elements in the array of Figure 8 are excited with unit cell and cascaded unit cells P-CRLH phase shifters having phases of \(-46.7^{\circ}\), \(-92.2^{\circ}\),\(-137.8^{\circ}\) and \(-183.9^{\circ}\), respectively (see Figure 5). The P-CRLH-excited far-field radiation at 5.5 GHz is shown in Figure 11. Then, for comparison, using Equation (3), the patch antenna elements in the array are directly excited with \(-45^{\circ}\), \(-90^{\circ}\), \(-135^{\circ}\) and \(-180^{\circ}\), and the far-field pattern for the directly excited array is also shown in Figure 11. For both excitation phases, the value of peak gain is 11.5 dB at a \(15^{\circ}\) scan angle and there is a difference of 0.85 dB for the first sidelobe level values.  
Figure 10: Simulated main beam at broadside \(\theta_{0}=0^{\circ}\) with directly excited and P-CRLH-excited phases.