esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Catboost**} & & \\
**DLCN Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \(<\)0, \(\geq\) & 0.625 & 0.667 & 0.286 & 0.400 \\ \(<\)3, \(\leq\)5- & 0.812 & 0.828 & 0.686 & 0.750 \\ \(<\)6, \(\approx\) & 0.857 & 0.889 & 0.615 & 0.727 \\
9 and above & 0.800 & 0.714 & 0.714 & 0.714 \\ \hline  & & \multicolumn{2}{c}{**Voting Cat-RF-SVM**} & & \\
**DLCN Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \(<\)0, \(\geq\) & 0.750 & 1.000 & 0.429 & 0.600 \\ \(<\)3, \(\leq\)5- & 0.800 & 0.800 & 0.686 & 0.738 \\ \(<\)6, \(\approx\) & 0.881 & 0.900 & 0.692 & 0.783 \\
9 and above & 0.900 & 0.857 & 0.857 & 0.857 \\ \hline  & & \multicolumn{2}{c}{**Stacking Cat-SVM**} & & \\
**DLCN Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \(<\)0, \(\geq\) & 0.625 & 0.667 & 0.286 & 0.400 \\ \(<\)3, \(\leq\)5- & 0.835 & 0.862 & 0.714 & 0.781 \\ \(<\)6, \(\approx\) & 0.881 & 0.900 & 0.692 & 0.783 \\
9 and above & 0.850 & 0.833 & 0.714 & 0.769 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of the evaluation of selected model ensembles regarding the decision into groups of FH likelihood assessed based on the DLCN score. Figure 3: Comparison of confusion matrices calculated for the test dataset. The figure illustrates the changes between classification using a voting algorithm for model ensembles between Categorical Boosting (Cat), Extreme Gradient Boosting (XGB), classification by Support Vector Machine (SVM), and Random Forest (RF). A subset accuracy measure restricted to positive cases was introduced to evaluate how the classification models compared to the accuracy of the DLCN score. This metric represents the proportion of true positives among all instances where the model predicted a positive outcome. The results of this comparison are presented in Table 5.  
To evaluate the performance of the selected models (CatBoost, Voting Cat-RF-SVM, and Stacking Cat-SVM), it was necessary to establish a baseline derived from the DLCN score. A threshold of DLCN > 5 was used to transform the DLCN score into binary outcomes. Another method used to provide a standard reference point was logistic regression applied to the DLCN score. Likewise, the binary outcome of DLCN score, the predictions generated by logistic regression, served as the baseline for assessing whether the machine-learning models offered statistically significant improvements. [PERSON]'s test was then employed to compare the paired predictions based on the DLCN score and each machine-learning model. This non-parametric test is particularly suitable for evaluating differences in classification performance, as it analyzes the disagreement between models' predictions on the same dataset. The results of the statistical analysis are presented in Table 6.
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
###### Abstract  
Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based model is proposed in this study. This model consists of three stages. In the first phase (cultivation), the proposed model tried to plant crops in the most optimized way by using an automized algorithmic approach (Sand Cat Swarm Optimization algorithm). In the second stage (control and monitoring), the growing processes of the planted crops was tracked and monitored using Internet of Things (IoT) devices. In the third phase (harvesting), a new method (Reverse Ant Colony Optimization), inspired by the ACO algorithm, was proposed for harvesting by autonomous robots. In the proposed model, the most optimal path was analyzed. This model includes maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, the optimal path for autonomous robots, finding the best time to harvest, and consuming the least power. According to the results, the proposed model performs well compared to many well-known methods in the literature.  
Keywords:autonomous robots; remote sensing; smart agriculture; climate change; environmental protection; drone; metaheuristic; Internet of Things +
Footnote †: journal: Applied sciences
## 7 Results and Analysis
### Mitigation Strategies  
To address these biases and enhance the reliability of the findings, the experimental methodology incorporated multiple hybrid validation techniques. One approach involved enriching the DAWN dataset's adversarial scenarios with synthetic attack vectors, including gradient inversion attacks and adversarial examples specifically crafted to target vulnerabilities in O-RAN's E2 and A1 interfaces. This augmentation allowed for the stress testing of the proposed framework under previously unmodeled conditions, thereby providing a more comprehensive evaluation of its resilience. In parallel, a subset of critical experiments, such as Distributed Unit (DU) sleep scheduling under data poisoning attacks, was replicated within a physical testbed environment. This testbed comprised commercial O-RUs, O-DUs, and near-RT RIC instances, facilitating the observation of hardware-in-the-loop behaviors. The findings revealed a 5% to 8% divergence in energy savings between the simulation and physical deployment, underscoring the necessity of incorporating real hardware elements in the evaluation process. Additionally, Monte Carlo simulations were deployed to assess the framework's robustness under stochastic network conditions, including fluctuating Ultra-Reliable Low-Latency Communication (URLLC) traffic loads. This probabilistic modeling approach ensured that the framework's performance remained resilient in the face of unmodeled operational variability and randomness inherent in live networks.  
A summary of the identified limitations, their potential impacts on the evaluation outcomes, and the mitigation strategies adopted in this study is provided in Table 15. This table also highlights how these biases were systematically addressed through synthetic attack injection, hardware-in-the-loop validation, and robustness testing under stochastic conditions to ensure a more realistic assessment of the framework's resilience.
## 5 Strain/Load Prediction Based on Deep Learning Model
### Dimensionality Reduction by Neural Networks  
An AE network model consists of two parts, the encoder and the decoder networks, as shown in Figure 8. The encoder maps the input data to the latent space (or low dimensional space), while the decoder maps the latent data back to the original dimensional data space. Typically, the encoder contains one or more hidden layers with a decreasing number of nodes that ultimately generate the latent representation. The structure of a decoder is usually the opposite of an encoder, containing one or more hidden layers with a progressively increasing number of nodes. The objective of an AE model optimization is to minimize the reconstruction error between the input data and the reconstruction data. The most common objective function is Mean Squared Error (MSE), which minimizes the reconstruction error by adjusting the weights of the encoder and decoder.  
The settings of some methods can be found in SI Note 3, respectively. Using 18 flight parameters such as altitude, Mach number, center of gravity normal overload, attack angle, aileron deflection, roll angular velocity, and others as inputs to the neural network model for dimensionality reduction, the target dimensionality after reduction was set to 13. Figure 9 displays the visualization results obtained by transforming the data reduced by AE using local linear interpolation method into a new three-dimensional space. Clustering was also performed, with colors representing the classes to which each data point belongs; different colors indicate different clusters. Figure 9 illustrates the Mean Squared Error (MSE) data of the reconstructed data \(\hat{X}_{i}\) by the encoder, denoted as  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\bigl{(}X_{i}-\hat{X}_{i}\bigr{)}^{2}\]  
which represents the numerical value of information loss during the encoding and decoding process. Notably, the maximum MSE does not exceed 0.068, meeting the requirements.  
Figure 8: The structure of AE model.  
From these dimensionality reduction graphs, it is evident that the AE exhibits the most accuracy and stability among all dimensions based on Figure 9c.  
The reconstruction MSEs of the LLE and PCA models, both of which are higher than that of the AE model, are shown in Figures 10a and 10b, respectively, under the same parameter settings (detailed settings can be found in Appendix A.3 Note 3). This demonstrates that the AE model achieves the highest accuracy in reconstructing the lower-dimensional parameters back to their original dimensional space.
## 2 Background
### Chromatic Aberration  
The refractive index of glass varies depending on its material, whereas that of light differs by its wavelength. After entering an optical system, a ray of white light produces light in different color bands when refraction occurs. Chromatic aberration occurs when different colored light is focused at different positions. Chromatic aberration is positive when the focus point of light with long wavelengths is left of that of light with short wavelengths. Conversely, chromatic aberration is negative when the focus point of the light with long wavelengths is right of that of light with short wavelengths. Generally, red light is longer in wavelength and blue light is shorter in wavelength. In addition, chromatic aberration can be categorized into axial and lateral. As depicted in Figure 6,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Press, Chapter 2 Ocean optics, P.R.C.**} \\ \hline
**Wavelength (nm)** & **Refractive Index** & **Wavelength (nm)** & **Refractive Index** \\ \hline
250 & 1.3773 & 486 & 1.3371 \\ \hline
308 & 1.3569 & 589 & 1.3330 \\ \hline
359 & 1.3480 & 768 & 1.3289 \\ \hline
400 & 1.3433 & 1000 & 1.3247 \\ \hline
434 & 1.3403 & 1250 & 1.3210 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Refractive indices of light at different wavelengths at a water temperature of 20 \({}^{\circ}\)C [10].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Salinity**} & \multicolumn{4}{c}{**Temperature (\({}^{\circ}\)C)**} \\ \cline{2-5} (\%, ppt) & **0** & **10** & **20** & **30** \\ \hline
0 & 1.33400 & 1.33369 & 1.33298 & 1.33194 \\
5 & 1.33498 & 1.33463 & 1.33390 & 1.33284 \\
10 & 1.33597 & 1.33557 & 1.33482 & 1.33374 \\
15 & 1.33595 & 1.33652 & 1.33573 & 1.33464 \\
20 & 1.3 axial chromatic aberration indicates that the difference between the focus points of light at different wavelengths is on the optical axis after refraction in an optical system. As illustrated in Figure 7, lateral chromatic aberration refers to a difference between the focus points of light at different wavelengths on the ideal imaging surface after refraction in an optical system.  
Figure 5: (**a**) Positive and (**b**) negative distortion.  
Figure 6: Axial chromatic aberration. Such chromatic aberration typically affects the displacement of the focus points of different colored light in different spectra and enables underwater cameras to produce absolute images.  
Figure 7: Lateral chromatic aberration. Such chromatic aberration typically occurs at the edges of images and is commonly used with a haze to produce an effect similar to flare. Although nonconsequential in this study, the selection of glass using the genetic algorithm increases image quality and reduces flare.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 4 Results and Discussion
### Comparison with DL Approaches  
Compared to selected DL approaches, our results are reported in Table 4. We included the best performing FASEgeNet [19] (\(F_{1}\) = 90.9%), the intermediate scoring UNet [47] (\(F_{1}\) = 90%), and the worst performing so far HRNet [48] (\(F_{1}\) = 88.3%). Although we do not outperform any DL method, UFS-HT-REM scores in close proximity. We are lower by only 3.7% (9.2%) of the HRNet that performs the worst and 6.6% (11.8%) of the FASEgeNet in accuracy (F1-score), respectively. It is a good compromise taking into account the simplicity of our methodology.  
Our method does not require image pre-processing or normalization. The input consists solely of the acquired color image, which can have any resolution and size. In contrast, DL approaches require large training datasets, as well as validation and test sets, and typically necessitate pre-processing and normalization of all input data. Labeling these training and validation sets is time-consuming and prone to human annotation errors. Moreover, data augmentation, essential for training in DL approaches, is unnecessary in our method. Additionally, achieving excellent results with a deep neural network (DNN) trained on one dataset does not guarantee the same performance on a different dataset, often requiring retraining or the use of transfer learning to maintain result quality. Finally, while DL approaches involve thousands to millions of trainable parameters, the proposed method does not involve any.  
Figure 12: The average values of \(ACC\), \(REC\), \(PR\), \(F_{1}\), and \(\overline{F_{1}}\) computed on the Flood Area dataset for different values of **(a)**\(T_{L}\) (with \(T_{H}\) = 0.75) and **(b)**\(T_{H}\) (with \(T_{L}\) = 0.01).
## 3 Background and Motivation
### Smart Administration  
Smart administration has been studied since 2010, with sensors enabling approaches [38] and the use of AI algorithms to assist in the running of organizations. Several organizations are working on educational leadership tools; however, Microsoft [34] is proposing new tools that integrate with new technologies for educational leadership. Their products, such as Microsoft Teams, are used to manage staff, share directions, and manage virtual meetings. Similarly, Google [39] is providing leading services for the management of educational leadership.  
Managing resources in institutions is challenging, particularly when resources are limited. The authors of in [40] used IoT technology to manage classroom furniture according to the strengths of students.  
Similarly, the authors in [41] used radio frequency identification (RFID) for a student attendance system. The RFID-embedded cards were issued for experimentation. Attendance was also automatically marked as a student entered the class. The same technologies were used by [42] to protect the students in school.  
This concept was further explored by the authors in [43], where they introduced the concept of a flexible classroom that enabled students to easily adjust their seating and orientations, thus facilitating better interaction. The instructor taught Introduction to Electronic Circuits (IEC) for one year in a traditional classroom and the following year in a flexible classroom. They found that the results of the flexible classroom were better than those of the traditional classroom. Extending the same concept, the authors in [44] focused on addressing the under-utilization of classrooms on a college campus to bridge the gap between enrollment and attendance. Various IoT sensors in real-time were used to effectively use classrooms. With the aid of AI (to predict attendance and optimal class allocation), they minimized space wastage.  
The smart campus is widely discussed in academia. The authors in [45] extend this concept and investigate the smart university concept. They used IoT to manage the university environment (i.e., parking, lighting, tracking, and inventory, etc.). Smart security is the focus of smart cities and smart campuses. The authors in [5] proposed a smart security framework that uses smart sensors to monitor the institutions' premises. In case of a security breach, the concerned authorities are informed. For monitoring purposes, the authors of [22] provide a comprehensive review of the use of video images, deep learning neural networks, and a new Pleasure, Arousal, and Dominance (PAD) emotion model to assess student concentration in online classrooms. The authors used the DAiSEE dataset, a new sampling script, and a neural network to test the accuracy of the system. The authors have thoroughly explored the potential of deep learning and PAD emotion models in assessing student concentration, and the results of the study are promising. Similarly, the authors in [46] have used IoT to reduce educational costs and maximize performance.
###### Abstract  
The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital for maintaining the sustainability of the ATM sector. Flight efficiency is closely tied to adherence to assigned airport arrival and departure slots, which helps minimize primary delays and prevents cascading reactionary delays. Significant deviations from scheduled arrival times--whether early or late--negatively impact airport operations and air traffic flow, often requiring the imposition of Air Traffic Flow Management (ATFM) regulations to accommodate demand fluctuations. This study leverages a data-driven machine learning approach to enhance the predictability of in-block and landing times. A Bidirectional Long Short-Term Memory (BLSTM) neural network was trained using a dataset that integrates flight trajectories, meteorological conditions, and airport operations data. The model demonstrated high accuracy in predicting landing time deviations, achieving a Root-Mean-Square Error (RMSE) of 8.71 min and showing consistent performance across various long-haul flight profiles. In contrast, in-block time predictions exhibited greater variability, influenced by limited data on ground-level factors such as taxi-in delays and gate availability. The results highlight the potential of deep learning models to optimize airport resource allocation and improve operational planning. By accurately predicting landing times, this approach supports enhanced runway management and the better alignment of ground handling resources, reducing delays and increasing efficiency in high-traffic airport environments. These findings provide a foundation for developing predictive systems that improve airport operations and air traffic management, with benefits extending to both short- and long-haul flight operations.  
airport; flight; delay; model; prediction; predictability +
Footnote †: journal: _Aerospace_  
0024202411991[[https://doi.org/10.3390/aerospace11120991](https://doi.org/10.3390/aerospace11120991)]([https://doi.org/10.3390/aerospace11120991](https://doi.org/10.3390/aerospace11120991))
###### Abstract  
Accurate dew point estimation is crucial for measuring water condensation in various fields such as environmental studies, agronomy, or water harvesting, among others. Despite the numerous models and equations developed over time, including empirical and machine learning approaches, they often involve trade-offs between accuracy, simplicity, and computational cost. A major limitation of the current approaches is the lack of balance among these three factors, limiting their practical applications under diverse conditions. This research addresses these key challenges by developing a new, streamlined equation for dew point estimation. Using the [PERSON] equation, deemed as the most reliable equation, as a benchmark, and by applying a process of non-linear regression fitting and parametric optimization, a new equation was derived. The results demonstrate high accuracy with a streamlined implementation, validated through extensive data and computational simulations. This study highlights the importance of accurate dew point modeling, especially under variable environmental conditions, provides a reliable solution to existing limitations, paving the way for enhanced efficiency in related processes and research endeavors, and offers researchers and practitioners a practical tool for more effective modeling of water condensation phenomena.  
dew point estimation; water condensation modeling; parametric optimization Article
# Enhanced Tone Mapping Using Regional Fused GAN Training with a Gamma-Shift Dataset
## 5 Conclusions  
When using the CycleGAN model to generate HDR tone mapping images, the color of the resulting image is distorted and the global tones only change slightly. Therefore, the use of a GAN training optimization model for converting LDR images into HDR images is proposed in this study. First, a gamma shift training method with an extended luminance range is proposed. Second, a weighted loss map trains for tone compression in the local area of images. Third, an RFT method is used to restore the speed of local tone training by balancing the training method with the regional weight map. As the generated module performs well in bright images, mean gamma tuning is applied to the luminance channel of the test image, which is then fed into the module. The proposed model was tested on foggy images, images with dark surroundings, images with bright surroundings, and high-contrast images, and the proposed model outperformed conventional models in the comparative analysis. The HDR-VDP2, MS-SSIM, and PIQE methods were used as evaluation indices. All the evaluation indices showed that the proposed model had an excellent performance on average. The proposed model complements the performance of Mask R-CNN, a prominent object detection model, even in a real night environment. The proposed model successfully detected an object that Mask R-CNN could not detect. Furthermore, the proposed model restored the tone of the surrounding background to an appropriate level. Thus, it has potential applications in commercial CCTV surveillance systems and security industries.  
Conceptualization, S.-H.L.; Methodology, S.-H.L.; Software, S.-W.J.; Supervision, S.-H.L.; Writing--original draft, S.-W.J.; Writing--review and editing, H.-J.K. All authors have read and agreed to the published version of the manuscript.  
Figure 21: Metric scores for parameter optimization: (**a**) perception-based image quality evaluator, (**b**) multiscale structural similarity, (**c**) HDR-VDP2 quality, and (**d**) HDR-VDP2 visibility.  
**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) and the BK21 Four project funded by the Ministry of Education, Korea (NRF-2019R1D1A3A03020225, NRF-2020R11I1A101074234, 4199990113966).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this paper.