esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Dropout Prediction for University Students Using Machine Learning
## 5 Conclusions and Future Work  
In this study, we implemented machine learning models to predict the dropout of university students and compared the performance of the models using academic records  
Figure 5: Change in F1-scores of the proposed prediction models before and after applying SMOTE, ADASYN, and Borderline-SMOTE.  
Figure 6: Distribution of grades and number of semesters on leave of absence for dropout and non-dropout students in the Department of Computer Engineering of Sahmyook University.  
collected from 20,050 students at Sahmyook University located in Seoul, Republic of Korea. Since the source records were stored in multiple tables separately to avoid redundancy, the records from the tables were merged into a single table with 150 attributes to perform learning. Among them, 10 attributes having a high correlation with student dropout were extracted and used for learning. To implement a prediction model, various machine learning algorithms, including Linear Regression, Decision Tree, Random Forest, Support Vector Machine, Deep Neural Network, and Light Gradient Boosting Machine (LightGBM), were used. The performance of the models was compared through experiments, and the LightGBM model provided the best performance with an F1-score of 0.840.  
We also examined the influence of the data imbalance on the prediction performance. For this purpose, we compared the performance of the proposed prediction models before and after applying oversampling techniques such as SMOTE, ADASYN, and Borderline-SMOTE. Interestingly, the performance of all models decreased after applying the oversampling techniques, except the RF model, and the performance improvement of the RF model was also not so significant. Among the oversampling techniques, SMOTE showed the best performance, whereas ADASYN performed the worst.  
Such performance degradation means that there was a lot of overlap in the data between the dropout and non-dropout classes. From this, new records augmented by the oversampling techniques acted as noise when the classification process was performed. Regarding this, we plan to study oversampling techniques that can reduce noise by detecting the class boundary or outliers more clearly. We will also continue research to develop a new model that can predict student dropout by semester.  
Methodology, C.H.C. and Y.W.Y.; Writing--original draft, H.G.K. All authors have read and agreed to the published version of the manuscript. This research was supported by the Sahmyook University Research Fund in 2023. Not applicable. Not applicable. Not applicable. Not applicable. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Parametric Analyses
### Influence of the Mechanical Parameters  
Under the combined action of bending moment \(M\) = 40 MN\(\cdot\)m and residual jacking force, we evaluated the LEBS of the shield tunnel by changing the elastic modulus of bolts and lining concrete grade in turns, and the remaining parameters are shown in Table 1.  
As shown in Figure 10a, when the effective ratio of LEBS is greater than 0.85, the elastic modulus of bolts has little influence on the effective ratio of LEBS. The LEBS and the effective ratio of LEBS for a shield tunnel increase with the increasing elastic modulus of bolts, albeit to a small extent. As shown in Figure 10b, the LEBS of the shield tunnel increases by approximately 2.88 \(\times\) 10\({}^{7}\) kN\(\cdot\)m\({}^{2}\) for every bolt's modulus increase by 10 MPa,  
Figure 8: Effects of bolts radius on the LEBS.  
Figure 9: Effects of bolt numbers on the LEBS.  
and the maximum increase in the LEBS of the shield tunnel reaches 57.5%. It is found that if bolt corrosion occurs during the tunnel operation, it will reduce the LEBS of the shield tunnel and increases the tunnel deformation.  
Figure 11 shows that the effective ratio of LEBS gradually decreases with increasing lining concrete grade; the difference between the maximum and minimum values of the effective ratio for LEBS is 8.14% when the residual jacking force is 0 MN. The LEBS of the shield tunnel increases with the increase in concrete grade, and the maximum increase in the LEBS of the shield tunnel is 9.48%. The greater the residual jacking force, the greater the increment of LEBS for different concrete grades. As shown in Figure 11c, according to the slope change point of the LEBS for the shield tunnel, it is suggested that the optimal value of the lining concrete grade is C45-C55.  
Figure 11: Effects of lining concrete grade on the LEBS.  
Figure 10: Effects of bolts elastic modulus on the LEBS.  
This study shows that the LEBS is nonlinearly proportional to the concrete grade, elastic modulus of bolts, and residual jacking force. The lining concrete grade meets the bearing capacity design requirements and can satisfy the requirements of LEBS and LEBS effective ratio. The LEBS and effective ratio of LEBS increase with increasing bolts' elastic modulus. If the bolts are corroded, the elastic modulus of the bolts will decrease, which will lead to some decrease in the LEBS of the shield tunnel. Thus, attention should be paid to leakage-induced bolt corrosion during tunnel operation.  
When a new tunnel passes through an existing tunnel, it is advised to increase the longitudinal compressive stress (residual jacking force) of the existing tunnel by setting the connecting beam or other engineering measures, which is conducive to improving the LEBS of the existing tunnel, reducing the longitudinal deformation of shield tunnel, and providing a theoretical basis for the reinforcement design of the existing tunnel.
## 5 Experiments and Results
### Experimental Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **CNN/DailyMail** & **Xsum** & **PubMed** \\ \hline LEAD-3 & 58,261 & 87,199 & 86,073 \\ \hline SummaRuNver & 56,060 & 84,004 & 81,392 \\ \hline NeuSum & 56,576 & 84,882 & 82,460 \\ \hline BanditSum & 56,830 & 85,567 & 82,784 \\ \hline JECS & 56,952 & 86,273 & 83,453 \\ \hline HIBERT & 57,421 & 86,491 & 83,570 \\ \hline BERTSUMEXT & 57,880 & 86,581 & 84,281 \\ \hline NeRoBERTa & 57,999 & 86,766 & 84,684 \\ \hline MatchSum & 58,130 & 87,036 & 85,566 \\ \hline HAHSum & 58,130 & 87,077 & 85,603 \\ \hline GenCompareSum & 58,661 & 87,257 & 86,180 \\ \hline MCHES (proposed model) & **59,995** & **88,424** & **89,285** \\ \hline MCHES-NoCHM & 58,862 & 87,299 & 87,121 \\ \hline MCHES-NoHCA & 59,094 & 87,784 & 87,342 \\ \hline MCHES-NoReadout & 59,153 & 87,833 & 87,980 \\ \hline MCHES-BasicGraph & 59,260 & 88,193 & 88,430 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: BERTScore values for the CNN/DailyMail, Xsum, and PubMed datasets. component adds value, their absence does not drastically diminish the overall semantic alignment with the references. This could suggest that the remaining components can compensate to an extent, preserving the semantic quality of the summaries. The MCHES-BasicGraph model demonstrates that even a simplified representation within the MCHES framework can achieve results comparable to more complex models. This implies potential scalability and adaptability of the framework to different summarization contexts, perhaps even suggesting a degree of robustness against model over-parameterization. To summarize the main findings of the empirical results in Figures 2-4, we present the main effects plots for R-1, BERTScore, and MoverScore values, respectively. To provide a comprehensive comparison, we implemented a simple TF-IDF-based summarization algorithm. The following table presents the comparison results for each dataset and each metric. Figure 4: Main effects plot for MoverScore values. Figure 3: Main effects plot for BERTScore values. As shown in Table 8, the TF-IDF algorithm, while simple and easy to implement, consistently lags behind the MCHES framework in terms of ROUGE and BERTScore metrics across all datasets. This comparison highlights the substantial improvements offered by our proposed MCHES framework, demonstrating its effectiveness in producing high-quality summaries.
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
## 3 Results
### Design Specification
#### 3.2.3. Diodes
Table 4 lists the most common types of diodes that are used in various rectifiers' designs.
## 4 Experimental Results
### Transfer Learning with Fine-Tuning on Our Dataset  
Our dataset used in this study has been tested on different transfer learning models based on fine-tuning. The baseline models selected for comparison--InceptionV3, MobileNet, ResNet50, VGG16, and VGG19--were chosen due to their widespread use and proven performance in various image classification and defect detection tasks. These architectures represent a diverse set of design philosophies: from lightweight models (e.g., MobileNet) suitable for real-time applications to deeper and more complex networks (e.g., ResNet50, InceptionV3) that have demonstrated high accuracy in noise-prone environments. The results obtained were compared experimentally. Table 7 shows comparisons with transfer learning models such as InceptionV3, MobileNet, ResNet50, VGG16 and VGG19.  
To ensure a fair and consistent evaluation, all baseline models were adapted using transfer learning with fine-tuning strategies. Specifically, the pre-trained weights (initialized on ImageNet) were partially frozen to retain generic visual features, while the final layers were retrained on the fabric defect dataset to adapt to domain-specific patterns. The relatively modest performance of some models--such as ResNet50 and MobileNet--can be attributed to a combination of factors, including the domain gap between natural images in ImageNet and the fine-grained texture variations in fabric images. Additionally, these models may not be well-suited to capture the subtle, noise-prone, and small-scale defect patterns typical in fabric inspection. In particular, MobileNet's lightweight architecture prioritizes computational efficiency over expressive capacity, which may lead to underfitting in complex defect detection tasks. On the other hand, models like VGG16 and VGG19, with deeper and more uniform convolutional structures, appear more capable of capturing the repetitive and localized nature of fabric defects, resulting in better precision and AUC values. This observation further validates the need for a specialized architecture, such as Log-DRSN, that integrates adaptive shrinkage and attention mechanisms tailored to noisy, domain-specific data.
## 1 Introduction
The primary objectives of the current review are to (i) identify and categorize shoreline detection and coastal erosion monitoring with open-access satellite applications, (ii) outline integrated workflows involving AI, UAV, GIS, and Ground Truth validation, and (iii) evaluate operational constraints and potential for the development of adaptive coastal monitoring systems based on open access satellite data.
## 4 Discussion
### Limitations of the Study  
This study has several limitations, including a cross-sectional design, limiting the establishment of causality, and a relatively small sample size, which may affect the generalizability of our findings. While our results indicate associations between shoulder muscle strength, JPS, and pain in individuals with SIS, it is important to recognize that our study design does not allow for the determination of causality. Future research should employ longitudinal designs and larger cohorts to further investigate the causal relationships between pain, muscle strength, and proprioception in individuals with SIS. Additionally, interventions targeting pain management and muscle strength improvement should be explored to provide more definitive insights into their clinical efficacy. Furthermore, our study focused on a specific age group (18-65 years), which may not represent the full spectrum of individuals with SIS, and future investigations should consider broader age ranges to better represent the SIS population. Lastly, we did not assess other potentially relevant factors, such as the influence of comorbidities or the duration of symptoms, which could impact shoulder function and warrant consideration in future studies.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Shear Stress on Membrane Surface by Non-Bubbling Techniques
#### 5.3.1 Vibrations
longitudinal vibration, with frequencies between 0-30 Hz and amplitudes of 0.2, 0.7, and 1.175 mm, on HF bundles with nominal pore diameters of 0.45 \(\upmu\)m, and found a general correlation between the critical flux (\(J_{crit}\)) and average shear rate (\(\gamma_{ave}\)) induced by vibration on the membrane surface as follows:  
\[J_{crit}=a\gamma_{ave}^{n} \tag{1}\]  
The correlations based on Equation (1) using different values of \(a\) and \(n\) for three aqueous suspensions are shown in Figure 5.  
To calculate the shear rate (\(\gamma\)) on the surface of a vibrating fiber, [PERSON] et al. [229] proposed the following equation:  
\[\gamma=A(2\pi f)^{1.5}v^{-0.5}\cos\left(\omega t-\frac{3\pi}{4}\right) \tag{2}\]  
where \(A\) is the amplitude, \(f\) is the frequency of the vibration, \(\
u\) is kinematic viscosity of the fluid, and \(t\) is the time. As expected, the shear rate displays a periodic behavior as a function of time since the membrane vibrates with a velocity of:  
\[u=2\pi\ A\ f\ \cos(\omega t) \tag{3}\]  
Equation (2) does not depend on the fiber diameter because the Navier-Stokes equation was solved under the assumption that the curvature of the fiber near the membrane surface is negligible [229]. However, recently, [PERSON] et al. [232] showed that the shear rate of a vibrating fiber is also a function of the fiber diameter. They showed that Equation (2) can have a relative error of 40% for a fiber with a diamter of 1 mm vibrating with an amplitude and frequency of 10 mm and 1 Hz, repectively. [PERSON] et al. [233] applied longitudinal vibrations to a bundle of silicon hollow tube membranes to enhance the mass transfer to the liquid on the lumen side of the membrane, and found that the mass-transfer coefficient was increased by a factor of 2.65 relative to that without vibrations. An analytical solution was developed for the velocity profile of the laminar flow within the vibrating tube. However, this solution is not applicable for commonly used submerged HF applications, since the external shear on the fiber is of more interest. However, it could be useful for novel submerged HF processes, such as forward osmosis (FO) and membrane distillation (MD) applications. Figure 5: Correlation between the critical flux (\(J_{crit}\)) and average shear rate (\(\gamma_{ave}\)) for yeast suspensions with concentration of 19 g/L; 1% Fungymal solutions; and 1% Fungymal + 5 g/L yeast [231]. Reproduced with permission from [231], Copyright Separation and Purification Technology, 2007.
# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 3 Framework Architectures
### Dynamic Cluster Radius Equation  
Cluster heads, which link nodes to the base station, deplete energy more rapidly than their intra-cluster counterparts. Energy consumption escalates with the distance of the cluster head from the base station. To optimize network energy efficiency, it is pivotal to limit the count of cluster heads situated far from the base station. Conversely, the vicinity near the base station should feature a higher density of cluster heads with a reduced cluster radius to foster efficient connectivity. Initiating with the goal of minimizing average energy usage within clusters, a formula for calculating a dynamic cluster radius is introduced. The energy expenditure within a cluster encompasses several components: \(E_{\rm TO\_BS}\), the energy utilized by a cluster head to transmit data to the base station; \(E_{A}\), the energy used in data aggregation; \(E_{\rm TO\_Head}\), the cumulative energy used by nodes to send data to their cluster head; and \(E_{R}\), the energy expended by the cluster head to receive data from nodes. The formula for calculating the energy a cluster head uses to transmit data to the base station is given by  
\[E_{\text{To\_BS}}=m\varepsilon D_{i}^{2}+mE_{\text{PX}} \tag{9}\]  
where \(D_{i}\) is the distance between node \(i\) and the base station, \(R_{i}\) is the node's cluster radius, and \(m\) is the data packet size. The energy required for a cluster head to aggregate \(m\) bits of data is  
\[E_{A}=\pi\rho R_{i}^{2}mE_{\text{DA}} \tag{10}\]  
where \(E_{\text{DA}}\) is the energy per bit used in data aggregation. Physical Interpretation of Energy Components: The energy \(E_{\text{To\_Head}}\) consists of two distinct parts:  
* Amplification energy (\(\epsilon mr^{2}\)), which depends on transmission distance \(r\), requiring spatial integration over \([0,R_{i}]\);
* Circuit energy (\(mE_{PX}\)), which is _independent of distance_ but proportional to the node count within cluster area \(A=\rho\pi R_{i}^{2}\) Thus the total circuit energy is \[\int_{0}^{R_{i}}2\pi r\rho\cdot mE_{PX}dr=mE_{PX}\cdot(\rho\pi R_{i}^{2})\] which appears in Equation (9) as \(m\rho\pi E_{PX}R_{i}^{2}\). While \(E_{PX}\) itself is distance-invariant, its cluster-wide summation scales with \(R_{i}^{2}\) due to area coverage. The energy consumed by a node to transmit data to the cluster head is  
\[E_{\text{To\_Head}}=\int_{0}^{R_{i}}2\pi r\rho\Big{(}m\varepsilon r^{2}+mE_{ \text{PX}}\Big{)}dr=m\rho\pi\bigg{(}\frac{1}{2}\varepsilon R_{i}^{4}+E_{\text {PX}}R_{i}^{2}\bigg{)} \tag{11}\]  
Here, \(\rho\) represents the average node density in a \(M\times M\) rectangular area where \(N\) nodes are deployed; it is calculated as  
\[\rho=\frac{N}{M\times M} \tag{12}\]  
Note: Global density \(\rho\) initializes local \(R_{i}\) calculations, but actual cluster formation adapts to non-uniform distributions through neighbor discovery beacons.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 3 Proposed RF Fingerprinting-Based Emitter Identification Method  
The RFEI algorithm is implemented as follows.  
* SF extraction: An SF is an RF signal that contains feature information for emitter ID identification. It can be any signal involved in the demodulation process for communication. However, the SF used in this study focused on analog SF, i.e., RT, SS, and FT signals.
* Time-frequency feature extraction: A feature is a set of values containing physical measurements that can ensure robust classification. Any feature having a physical meaning can be applied from statistical moments to a raw preamble signal. In this study, a spectrogram of the SF was considered.
* User emitter classification: Classification is a decision process in which an emitter ID can be estimated from an input feature. A classifier was trained and tested on a large set of extracted features. Subsequently, the emitter ID was estimated from the classifier output vector. In this study, we consider a discriminative classifier model from a support vector machine (SVM) to a DIN-based ensemble classifier.
* Attacker emitter detection: This detection process enables the classifier to search whether the input feature has been trained for the classifier. The difference between the classifier output characteristics of the trained and outlier samples can be utilized. In this study, a simple but effective threshold based approach was applied.  
The RFEI method can be formulated as a classification problem using the following expression  
\[\mathbf{y}=\mathrm{F}_{\mathrm{RFEI}}(\mathbf{s}) \tag{8}\]  
where \(\mathbf{s}=[s(T_{s}),s(2T_{s}),...,s(NT_{s})]\in\mathbb{C}^{N\times 1}\) is a baseband hop signal sampled by the sampling period \(T_{s}\). The vector representation of the signal is now used in this study for convenience. Further, \(N\) is the length of a complex-valued baseband hop signal, \(\mathrm{F}_{\mathrm{RFEI}}\) is a mapping function from the signal space to the ID space referencing the RFEI algorithm, and \(\mathbf{y}\in\mathbb{R}^{\mathbb{C}\times 1}\) is the output vector of the algorithm containing the emitter ID information, where \(\mathbb{C}\) is the number of emitters trained on the algorithm.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 6 Factors Affecting the Adsorption Process
### Synthetic Polymer Adsorbents
A study reported by [PERSON] and co-workers on ethylene-diamine, diethylenediamine, and tetraethylenedamine functionalised magnetic PGMA resins for mercury ions extraction and maximum sorption values were enhanced with molecular chains [204]. [PERSON] et al. was the first to report the metal ion exchange capacity of the sulphonated glycolidyl methacrylate-ethylenedylcopolymer [210]. It was observed that the reaction between sulphur trioxide monohydrate and GMA-EGDMA did not lose its ion exchange capacity even after 100 cycles. Glycidyl methacrylate type resins such as poly(glycidyl methacrylate-ethylene glycol dimethacrylate) have been discovered to be a suitable and versatile preliminary resource for the preparation of ion-exchange resins [211]. Amines react with epoxy moiety through ring-opening reaction and result in the formation of a polymer with suitable exchange kinetics for heavy metal ions because of the hydrophilic property of the obtained resin i.e., ring-opening produces an -OH moiety on the carbon atom \(\beta\) to the incoming amine [212]. A polymer having EDTA-type chelating units was able to adsorb heavy metal ions like iron, zinc, cadmium, lead, nickel, copper, and cobalt ions. The resin possesses a diethylene triamine tetra acetic acid group that was more efficient in the softening of water. [PERSON] et al. continued their work on the efficient removal of metal ions using polymer-supported pendant urea groups. This resin was prepared by reacting GMA-MMA-DVB resin with triethylenetetramine and subsequent reaction with acidic isocyanate [205]. The polymer obtained holds a urea moiety with a removal capacity of 7.8 mmol/g and showed a better affinity for mercury with a loading capacity > 6.7 mmol/g. [PERSON] and [PERSON] reported poly (GMA-MMA-EGDMA) based polymeric beads containing a ferric oxide group [213]. These magnetic beads were used for the removal of Hg ions and the best possible uptake of mercury took place at pH 5.5. The highest sorption ability of mercury ions by using this resin was 124.8 mg/g. The resins containing divinylbenzene (DVB) as a crosslinker were immobilised with tetra ethylene pentamine, result in an amine functionality in the resins GMA/DVB/TEP (R1-en) and GMA/MBA/TEP (R2-en) correspondingly in the presence of suspended magnetic particles [214]. The removal ability of two resins was investigated for molybdate anions and sorption values were found to be 4.24 and 6.18 mmol/g for (R1-en) and (R2-en) adsorbents. Renewal ability up to 90-96% was obtained by the use of ammonia buffer [215]. The resultant resin was found to sorb Cu or Pb ions considerably. However, resin showed more affinity for copper over lead ions in the mixture of both metal solutions. The removal of cadmium ions from aqueous solution by sulphonated poly(glycidyl methacrylate) SPGMA was also studied [216]. The maximum sorption capacity of cadmium onto synthesised SPGMA was