esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Approach to Classify AI-Generated and Human-Written Texts
## 2 Background and Related Literature
### Related Literature  
The detection of AI-generated text has gained considerable research attention in recent years due to the growing concerns around authenticity, misinformation, and ethical misuse. Studies such as [17] have highlighted that while AI-generated text is advancing rapidly, it still lacks the nuanced coherence and complexity found in human-authored content. These limitations have led to a diverse set of detection approaches spanning traditional machine learning, deep learning, and hybrid methods. Table 1 offers a concise overview of previous research findings, emphasizing the distinct contributions of our study in addressing existing gaps. Collectively, these studies showcase the efficacy of various deep learning models in identifying AI-generated content. Early detection tools have focused on linguistic cues and metadata. For example, the authors in [19; 20; 21] used features like syllable count, sentence structure, perplexity, and semantic similarity to differentiate between human and AI texts. Although these models achieved notable accuracy (up to 93%), their reliance on surface-level features makes them susceptible to evasion when AI models improve their mimicry of human style. Tools like GPTZero and ZeroGPT, though widely used, were found to underperform when compared with feature-enhanced models. Studies also integrated explainable AI (xAI) tools (e.g., LIME, SHAP) to interpret model decisions, thus increasing transparency [19]. However, while feature-based methods benefit from interpretability, they often struggle to generalize across domains and newer generative models [17; 22]. Several researchers have assessed traditional ML algorithms for text classification. The authors in [23] found that NuSVC outperformed other algorithms like random forest and KNN on a balanced dataset, while the authors in [24] compared decision trees, SVM, and logistic regression. These studies reveal the trade-off between simplicity and accuracy: while ML models offer fast computation and easier training, they tend to underperform on nuanced textual patterns, especially for large-scale and multilingual data. Recent literature has pivoted toward deep learning due to its superior performance in handling sequential data and capturing contextual nuances. The authors in [15] introduced a Deep Recurrent Neural Network (DRNN), while the authors in [14] conducted a broad comparison across CNN, ANN, RNN, LSTM, GRU, BiLSTM, and BiRNN. Among these, BiLSTM achieved the highest F1 score (98.77%), demonstrating its robustness in identifying subtle differences between AI-generated and human text. Similarly, the authors in [2; 9] showed that LSTM-based models, including hybrid variants like TSA-LSTM-RNN, achieved high accuracy (above 93%), although were still somewhat behind the best BiL
###### Abstract  
Introducing collaborative relay and simultaneous wireless information and power transfer (SWIPT) techniques into a cognitive wireless network, named the SWIPT-enabled cognitive relay network (CRN), is considered a promising approach to deal with insufficiency and the low utilization of spectrum resources, as well as the node's energy-constrained issues in wireless networks. In this paper, to improve the network spectrum efficiency (SE) and energy efficiency (EE) of the SWIPT-enabled CRN, we design an energy-efficient adaptive bidirectional transmission strategy. To be specific, we first select an energy-constrained best relay node with the consideration of signal-to-noise ratio and global channel gain to achieve a better bidirectional relay transmission (BRT). At the same time, we let the energy-constrained best relay node transmit a signal with the SWIPT technique, which can solve the node's energy-constrained issue and improve the network EE. Then, with the selected energy-constrained best relay node, we design a total transmit power threshold (TTPT) determining algorithm to find the TTPT, which lets the total transmission rate of the BRT be equal to the bidirectional direct transmission (BDT). Based on this TTPT, we further design an adaptive bidirectional transmission strategy and let the network achieve adaptive transmission between the BRT and BDT to obtain a higher network SE. Furthermore, to further achieve the energy-efficient transmission of the adaptive bidirectional transmission strategy, we optimize the nodes' power under the requirement of primary users' interference threshold and obtain the analytical expressions of the optimal power. Simulation results show that the transmission rate, the outage probability, and the EE of the designed energy-efficient adaptive bidirectional transmission strategy in the SWIPT-enabled CRN are, respectively, 3.01, 0.07, and 3.10 times that of the non-collaborative transmission, which show the effectiveness of our designed transmission strategy.  
2024246478[[https://doi.org/10.3390/s24196478](https://doi.org/10.3390/s24196478)]([https://doi.org/10.3390/s24196478](https://doi.org/10.3390/s24196478))
## 6 Results and Discussion
### TOPSIS Analysis for Identification of Best Tradeoff  
In this phase, data collected from the two sites were analyzed and the best tradeoff solution for each turbine was identified using the TOPSIS method for each site. Tables 11-15 given in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Turbine** & **Rated Power (KW)** & **Rated Speed (m/s)** & **Rotor Diameter** \\ \hline Acciona AW 70/1500 Class I & 1500 & 14 & 70 \\ Alstom ECO 100/2000 Class I & 3000 & 17 & 100 \\ DeWind D92 & 2000 & 13 & 93 \\ Dongfang DF110-2500 & 2500 & 10 & 110 \\ Doosan WinDS3000 & 3000 & 12.5 & 91.3 \\ Enercon E-82 E2/2000 & 2300 & 13 & 82 \\ Enercon E-82 E4/3000 & 3000 & 16 & 82 \\ Games G97-2.0 MW & 2000 & 14 & 97 \\ Hanjin HJWT2000-93 & 2000 & 12.5 & 93 \\ Leitwind LTW70-2000 & 2000 & 13 & 70.1 \\ Nordex NJ31/3000 & 3000 & 14 & 131 \\ Sinovel S3000/115 & 3000 & 11.5 & 115 \\ Vestas V110-2.0 MW & 2000 & 11.5 & 110 \\ Vestas 112-3.0 MW & 3075 & 11.5 & 112 \\ Windtec FC 3000-130 & 3000 & 10.5 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of turbines used in the study.  
Figure 2: Pseudo code of the TOPSIS method.  
Appendix A show the results for the 15 turbines for the two sites. The measurements for the decision criteria were taken at different hub heights. The best balance for each site is marked in boldface. As an example, consider Table A1. The first decision criterion, the hub height, is given in column 1. This criterion is common for both sites. Then, the values of other decision criteria are mentioned in columns 2-5 for Turaif and columns 9-12 for Wejh. The values of the output, \(R_{i}\), are given in column 8 for Turaif and column 15 for Wejh. The higher the value of \(R_{i}\), the betterthe selection. As an example, consider Table A1, where the maximum \(R_{i}\) for Turaif is 0.429. Similarly, in the same table, the maximum value of \(R_{i}\) for Wejh is 0.656. Both choices indicate the best selection for the respective sites.  
As far as Turaif is concerned, results in Tables A1-A15 indicate that the best tradeoff between the decision criteria was found at the hub height of 130 or 140 m, identified by the corresponding \(R_{i}\) values. There is one exception of turbine type Acciona AW 70/1500 Class I, for which the best tradeoff was found at 60 m. Similar trends are found with regard to Wejh; the best tradeoffs were found mainly at 140 m, with Dongfang DF110-2500 standing best at 130 m. Furthermore, there are instances where decisions at 130 and 140 meters were the same. Such instances are Dewind D92 and Doosan WinDS 3000 for Turaif, and Windtec FC 3000-130 for Wejh. In this scenario, it is the role of the decision-maker to pick any of the two solutions.
## 4 Frequency Response Function (FRF)
### FRF of the Beam Bridge Model with Three DOF
\begin{split}\overline{v}_{1}\equiv\overline{v}_{1}(q)=\frac{V_{ 1}(q)}{V_{bk}(q)},\ \ \overline{v}_{2}\equiv\overline{v}_{2}(q)=\frac{V_{2}(q)}{V_{bk}(q)},\ \ \overline{v}_{3}\equiv\overline{v}_{3}(q)=\frac{V_{3}(q)}{V_{bk}(q)}\end{split}  \tag{34}\]  
Divide each of the equations of motion by a value \(V_{bk}\)(\(q\)) and obtain:  
\[ \begin{split}&(k_{11}-m_{1}\cdot\omega^{2})\cdot\overline{v}_{1}+k_{ 12}\cdot\overline{v}_{2}+k_{13}\cdot\overline{v}_{3}=\frac{1}{4}k_{11}+\frac{ 2}{4}k_{12}+\frac{3}{4}k_{13},\\ & k_{21}\cdot\overline{v}_{1}+(k_{22}-m_{2}\cdot\omega^{2})\cdot \overline{v}_{2}+k_{23}\cdot\overline{v}_{3}=\frac{1}{4}k_{21}+\frac{2}{4}k_{ 22}+\frac{3}{4}k_{23},\\ & k_{31}\cdot\overline{v}_{1}+k_{32}\cdot\overline{v}_{2}+(k_{33}-m _{3}\cdot\omega^{2})\cdot\overline{v}_{3}=\frac{1}{4}k_{31}+\frac{2}{4}k_{32} +\frac{3}{4}k_{33}.\end{split}  \tag{35}\]  
By solving the system of Equation (35) in the frequency band \(0\div 40\) Hz, the amplitude and phase characteristics are calculated. A frequency step of \(0.01\) Hz was used for the calculation. The absolute value (magnitude) of the frequency response function at point \(1\) (\(|\)FF\({}_{1}\)\(|\)\(\equiv\)\(|\)\(\overline{v}_{1}\)\(|\)) and its phase (fi FRF\({}_{1}\)\(\equiv\)\(\varphi_{1}\)) are plotted in Figure 8. FRF is a complex function. In our particular case, the functions \(\overline{v}_{i}\) (\(i=1\), \(2\), \(3\)) are real functions. That is all right, because an undamped system can only vibrate in phase or in antiphase, \(\varphi_{i}\) = 0 or \(\pi\) (_i_) = 1, 2, 3). The vector \(\overline{v}_{i}\) (_i_ = 1,2,3), therefore, has only a real component, which represents the special case of a complex number. The spikes of the \(\left.\right|\overline{v}_{1}\left.\right|\) function correspond to the frequencies 4.04 Hz, 16.16 Hz, and 36.36 Hz. The function \(\left.\right|\overline{v}_{1}\left.\right|\) has points of discontinuity (vertical asymptotes) in these places. Their position corresponds to the values of natural frequencies of the system.
# Integrated Inundation Modeling of Flooded Water in Coastal Cities
## 5 Summary and Conclusions  
In coastal urban areas, various types of flooding occur in a singular or a complex form, causing extensive damage. However, previous studies [15; 16] usually applied a linear sum of individual inundation components to predict comprehensive flood phenomena; this approach does not consider weighted effects associated with the simultaneous occurrence of flooding with multiple sources. Therefore, in this study, a two-dimensional hydrodynamic numerical model for complex flood analysis was developed and verified against an urban inundation problem. The experimental measurements that reproduced a flood in New Castle, Australia in 2007 were utilized. The developed model provided accurate results in predicting inundation flow behavior, and as for inundation depth, the developed model performed 26% better than a commercial model.  
In this study, the single flood simulation refers to the numerical modeling of the flood phenomena considering only one submerging source among the inland flooding, river inundation, and storm surge, while the comprehensive flood simulation means the flood prediction by combination of multiple  
Figure 9: Comparison of inundation depth and flow velocity along the checking lines (\(t\) = 400 s) (**a**) Locations of checking line. (**b**) Comparison between individual sum and comprehensive modeling along checking lines.  
sources. When water that overflowed from the sea and the river converged with inland water in the comprehensive flooding simulation, the inundation depth increased from 20.4% to 63.6% and the flow velocity increased from 10.5% to 81.7% compared with the single flood simulation. The difference in flooded areas between the summation of inundated space identified by the single source flooding simulations and the submerged region identified by multisource flood simulations was as high as 20%. The single flood modeling or a linear sum of individual inundation components fails to capture the dynamic nature of inundation appropriately and can underestimate the flooding damage, which can cause higher error in the calculation results.  
For areas with frequent occurrences of comprehensive flooding such as estuaries, nearby rivers or coastal cities, various sources of the flooding must be considered simultaneously. The comprehensive inundation modeling approach can provide useful tools for coastal designing, predictive measures for flooding, and improvements of preparedness in evacuations. Thereby, coastal societies can better estimate their flood disaster risks. Further studies are now being prepared to extend the area of application domain by adaptive mesh reformulation strategy. In addition, the model performance on the computational speed must be improved for the practical purpose.  
Conceptualization, C.G.S. and D.S.R.; Formal Analysis, E.T.S.; Data Curation, E.T.S. and J.S.; Supervision, C.G.S.; Writing--Original Draft, E.T.S.; Writing--Review & Editing, J.S., D.S.R., H.-J.K., and C.G.S.  
This work was funded by an Incheon National University Research Grant in 2018.  
The authors declare no conflicts of interest.
## 3 Methodology  
This section outlines the methodology employed in the development of Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES), which is designed to enhance the extractive summarization process through the use of contextual hypergraphs. The methodology is divided into four main modules: Contextual Hypergraph Construction, Contextual Homogenization Module, Hypergraph Contextual Attention Module, and Extractive Read-out Strategy. In Figure 1, the general structure of the MCHES framework is summarized.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Technology Used** & **Performance/Advantages** & **Disadvantages** \\ \hline [PERSON] et al. (2021) [4] & Deep learning models & Superior performance on complex texts; high accuracy & High computational cost; require extensive training data \\ \hline [PERSON] et al. (2023) [10] & Graph-based models (TextRank and LexRank) & Capture relational information among sentences; improved coherence & May struggle with very large documents; computationally intensive \\ \hline [PERSON] and [PERSON] (2019) [12] & Hypergraph-based summarization & Flexible representation of relationships; better integration of document context & Complexity in construction and processing of hypergraphs; requires sophisticated algorithms \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._The Multi-Element Contextual Hypergraph Extractive Summarizer (MCHES) framework is designed to enhance text summarization by leveraging the rich contextual relationships among sentences. The framework consists of several key stages:  
1. **Input Document Processing**: The document is segmented into individual sentences, which are then transformed into high-dimensional vector representations by using pre-trained embeddings.
2. **Hypergraph Construction**: Sentences are represented as nodes in a hypergraph, with different types of hyperedges (semantic, narrative, and discourse) capturing various contextual relationships between sentences.
3. **Contextual Homogenization Module (CHM)**: This module harmonizes features from the different hyperedges to create a unified feature set for each node.
4. **Hypergraph Contextual Attention (HCA) Module**: A dual-level attention mechanism is applied to focus on the most relevant sentences and their contextual relationships.
5. **Extractive Read-out Strategy**: The most informative sentences are selected based on their relevance scores, ranked, and combined to form the final summary.
6. **Output Summary**: The final summary is generated, capturing the core themes and logical structure of the original document.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 8 Submerged HF Membrane Integrity and Failure  
Given the chemical and physical stresses experienced by submerged HF membranes during operation, the lifespan of the membrane fibers tends to be significantly shortened. The prorated warranty provided by membrane manufacturers can range from 3 to 10 years [305]; however, experience teaches that the effective membrane life can either exceed or fall short of the manufacturer's expectations. In one particular case, a UF plant treating wastewater from a manufacturer of cosmetics experienced rapid membrane failure resulting in an average membrane lifespan of less than 6 months (note that the membranes were cleaned once a week with an alkaline bleach product, and backwashed monthly) [306], and the main cause was found to be high local shear forces due to fibrous material in the wastewater. In another study performed by [PERSON] et al., the lifespan of the membranes was determined to be 13 years by extrapolating data based on 3 years of operation [307].  
Given that the paramount operating objective is to avoid any failure that could compromise quality and restrict capacity, the development of a strategy that relies exclusively on the manufacturer's warranty to estimate the membrane lifespan and replacement schedules is fraught with uncertainty. With the prevalent variability in the integrity and productivity of membrane modules, operators of full-scale plants would need to manage an inventory of several thousand membranes; thus, anticipating and scheduling activities for the replacement of these membranes in service becomes a unique challenge for drinking-water plants utilizing membrane technology.  
Even though membrane ageing and failure are closely related, a distinction should be made between these two factors. Membrane degradation is the result of ageing and the onset of its adverse effects, which in turn leads to membrane failure. Membrane failure, on the other hand, results in a loss of process removal efficiency, and a reduction in product-water throughput as well as product-water non-compliancy.
## 3 Remanence Measurement Method Based on Degaussing Hysteresis Loop
### Overall Methodology Flow  
The overall process of this method is illustrated in Figure 4, and the specific method flow can be divided into the following steps:  
1. Calculate the energy storage capacitance according to Equation (17).
2. Apply DC voltage to the energy storage capacitor to make the voltage reach \(U_{c}(0)\).
3. After the capacitor voltage is reached and stabilized, the attenuation oscillation current is released to the transformer winding.
4. Record the voltage and current data during the release process.
5. Convert the voltage and current data into a demagnetization hysteresis loop.
6. Calculate the remanence based on the demagnetization hysteresis loop.
7. Verify the calculation results based on the change in excitation inductance of the transformer.  
Figure 4: Flowchart of the proposed method.
## 2 Materials and Methods
### Interaction Effect  
Building on the vector autoregression (VAR) model, [PERSON] et al. [58] introduced the panel vector autoregression (PVAR) model to accommodate the panel data analysis. The PVAR model offers several advantages including the ability to analyze all variables as endogenous, thereby addressing issues of endogeneity without the need to distinguish between endogenous and exogenous variables. Additionally, it overcomes the high time-series length requirement of the traditional VAR model, making it well-suited for shorter time spans. By treating all variables as endogenous, the PVAR model eliminates concerns over causality between variables. To determine the dynamic causality between the DE and GD, the PVAR model was utilized. Generalized method of moments (GMM) estimation was employed following smoothness testing for parameter estimation with impulse response and variance decomposition, then we explored the dynamic relationship. The specific formula is as follows:  
\[Y_{it}=\theta_{0}+\sum\limits_{j=1}^{k}\theta_{j}Y_{i,t-j}+\gamma_{i}+\varepsilon _{it}, \tag{21}\]  
where \(Y_{it}\) denotes the column vector containing all endogenous variables of the model, \(\theta_{0}\) is the intercept term, \(\theta_{j}\) represents the parameter matrix at lag order \(j\), \(\gamma_{i}\) denotes the fixed effect, and \(\varepsilon_{it}\) is the random error.
## 2 Materials  
### Study Area  
A crop classification experiment for both virtual Sentinel-2 optical image generation and crop classification was conducted in the subarea in Illinois within the US Corn Belt. Illinois was selected because Korea mainly imports corn and soybean from the US [54] and Illinois is one of the core producing states of corn and soybean in the US [5; 55].  
In this study, the subarea in the southwest agricultural statistics district, where diverse crops are grown [56], was first selected as a pilot study area considering the diversity of land-cover types. Both the quantitative evaluations of the virtual Sentinel-2 imagery generated by the CGANs and the effect analysis of the generated virtual Sentinel-2 imagery on early crop mapping require multi-temporal cloud-free images. Hence, we finally selected the small pilot study area where cloud-free Sentinel-2 images in 2019 were available (Figure 1).  
From the cropland data layer (CDL) provided by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) [57; 58], the crops in the study area consist of five types, including corn, soybean, wheat, alfalfa, and double cropping of winter wheat and soybean (Figure 1). The non-crop class includes developed areas, deciduous forests, grassland, and water. Corn and soybean, as major crops in the study area, occupy approximately 71.6% of the study area.