esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Time Spent on a** \\ **Model Build** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline AdaBoostM1 & 593.74 s & 0.862 & 0.961 & 0.883 & 0.920 \\ \hline Bagging & 14,089.45 s & 0.987 & 0.996 & 0.988 & 0.992 \\ \hline BayesNet & 438.8 s & 0.978 & 0.988 & 0.986 & 0.987 \\ \hline CDT & 991.14 s & 0.987 & 0.997 & 0.988 & 0.992 \\ \hline DTNB & 9329.23 s & 0.986 & 0.996 & 0.988 & 0.992 \\ \hline IB1 & 7 s & 0.978 & 0.986 & 0.987 & 0.987 \\ \hline J48 & 3003.97 s & 0.987 & 0.997 & 0.988 & 0.992 \\ \hline KStar & 1 s & 0.980 & 0.990 & 0.986 & 0.988 \\ \hline Local Knn & 445,905 s & 0.983 & 0.992 & 0.988 & 0.990 \\ \hline Logistic & 20,578 s & 0.946 & 0.971 & 0.964 & 0.968 \\ \hline LogitBoost & 2565 s & 0.975 & 0.987 & 0.983 & 0.985 \\ \hline Multilayer perceptron & 16,290 s & 0.956 & 0.974 & 0.974 & 0.974 \\ \hline Random Tree & 445.08 & 0.982 & 0.991 & 0.988 & 0.989 \\ \hline SMO & 35,787 s & 0.885 & 0.997 & 0.880 & 0.935 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of different algorithms using features selected by the Flower Pollination Algorithm (FPA). As can be seen in Table 8, after applying 14 different machine learning algorithms, according to the results obtained, we obtained the best accuracy in intrusion detection by applying the Bagging and J48 machine learning algorithms, of which 98.8% were able to detect different types of cyber threats and distinguish them from regular traffic; furthermore, regarding the shortest time to build the model, the construction time was 3 s using the Ant Colony Optimization feature selection method. According to the results obtained after selecting features using the Flower Pollination Algorithm by applying different machine learning algorithms, the best result was an accuracy of 98.7 in detecting regular traffic and different cyberattacks. Moreover, the shortest model-building time was one second using the KStar algorithm. Table 10 shows that the DTNB and J48 machine learning algorithms, combined with the Artificial Bee Colony feature selection approach, yielded the maximum accuracy of 98.6, and that this strategy took the smallest amount of time to develop the model at 2 s.  
Readers are encouraged to explore the following charts (Figures 5 and 6), portraying the accuracy and time needed for model construction employing various machine learning algorithms alongside diverse feature selection techniques. As shown in Figure 5, the highest accuracy among the three different feature selection methods, including Artificial Bee Colony (ABC), the Flower Pollination Algorithm (FPA), and Ant Colony Optimization (ACO), was that related to Ant Colony Optimization (ACO) with 98.8% using the Bagging and J48 machine learning algorithms. Moreover, the minimum model-building time was related to the Flower Pollination Algorithm (FPA), which was only 1 s using the KStar machine learning algorithm.
## 4 Moving Head Calibration  
The moving head alone is not (and can not) be aware of any other coordinate system other than its own, as it was not intended as a precision light spot projection system with Cartesian coordinate control. Additionally, it does not possess the necessary provisions to measure it's position and orientation externally. As discussed in Section 2, the calculation of the moving head position and orientation relative to a reference coordinate system has been performed with a calibration frame by [7], and the utilization of known points on a large structure workpiece was conducted by [37]. The scope of the following sections involves the combination of these ideas as well as the calculation of the position and orientation of the moving head relative to the workpiece coordinate system, denominated as the reference coordinate system. Figure 5 gives an overview of the main steps in the calibration process as well as references to the sections describing each step.  
The first phase of the calibration process is executed by illuminating distinctive points on the workpiece of which the exact coordinates in the reference coordinate system are known from the CAD construction data or, as shown later in the validation in Section 5.1, can be measured with external metrology equipment. The axis positions of the moving head are saved and used to calculate the moving head position and orientation relative to the reference coordinate system using an iterative process. The term \"Calibration\" is not used in a sense to improve the accuracy of the system as it is known in measurement systems, but as an analogy to camera calibration in computer vision. The derived transformation is comparable to the extrinsic camera parameters of camera calibration, which determine the position and orientation of the camera in a reference coordinate system.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 2 Related Work
### Simple Fog Generation  
Simple Fog Generation is a foundational approach to simulating fog in images, often based on uniform fog application techniques [17]. This method applies a uniform fog layer using a simplified physical model, making it computationally efficient and easy to implement. It has been used to generate synthetic foggy datasets for training dehazing algorithms [13]. However, its uniform application of fog overlooks scene depth variations and atmospheric heterogeneity, leading to unrealistic results in complex outdoor environments.
###### Abstract  
Since the classification methods mentioned in previous studies are currently unable to meet the accuracy requirements for fault diagnosis in large-scale chemical industries, these methods are gradually being eliminated and rarely used. This research offers a probabilistic neural network (PNN) based on feature selection and a bio-heuristic optimizer as a fault diagnostic approach for chemical industries using artificial intelligence. The sample characteristics are initially simplified using heuristic feature selection and support vector machine recursive feature elimination (SVM-RFE). Using PNN as the principal classifier of the fault diagnostic model and employing a modified salp swarm algorithm (MSSA) linked with the bio-heuristic optimizer to optimize the hidden smoothing factor (\(\sigma\)) of PNN further improves the classification performance of PNN. The MSSA introduces the Levy flight method, greatly enhancing exploration capabilities and convergence speed compared to the standard SSA. To validate the engineering application of the suggested method, a PSO-SVM-REF-MSSA-PNN model is created, and TE process data are utilized in tests. The model's performance is evaluated by comparing its accuracy and F1-score to other regularly used classification models. The results indicate that the data samples selected by PSO-SVM-RFE features simplify and eliminate redundant features more effectively than other feature selection techniques. The MSSA algorithm's optimization capabilities surpass those of conventional optimization techniques. The PNN network is more suitable for fault detection and classification in the chemical industry. The three considerations listed above make it evident that the proposed approach might greatly help identify TE process problems.  
fault diagnosis; probabilistic neural network; TE process; modified salp swarm algorithm; feature selection; SVM-RFE; PSO-SVM +
Footnote †: journal: Applied Sciences
## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **t-Statistic** & \(p\)**-Value** & **Mean Difference** & **Cohen’s d** \\ \hline ResNet + Grad-CAM + FS + SVM & \(1.2078\) & \(0.2616\) & \(-0.0118\) & \(-0.4026\) \\ ResNet + Grad-CAM + FS + XGBoost & \(-0.1551\) & \(0.8806\) & \(0.0016\) & \(0.0517\) \\ ResNet + Grad-CAM + FS + AdaBoost & \(0.3005\) & \(0.7715\) & \(-0.0029\) & \(-0.1002\) \\ ResNet + Grad-CAM + FS + RF & \(-0.4376\) & \(0.6732\) & \(0.0038\) & \(0.1459\) \\ ResNet + Grad-CAM + FS + BO-DNN & \(\mathbf{-3.4337}\) & \(\mathbf{0.0089}\) & \(\mathbf{0.0245}\) & \(\mathbf{1.1446}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Paired \(t\)-test results of various classification models against the ResNet model. Figure 10 shows the t-distribution and highlights the critical regions for a significance level of \(\alpha=0.05\). The vertical dashed lines denote the paired t-statistics for each model comparison against the ResNet baseline. Among the models, only ResNet + Grad-CAM + FS + BO-DNN falls within the critical region, with a t-statistic of \(-3.4337\). The other models, such as SVM, XGBoost, AdaBoost, and RF, remain outside the critical region, and this indicates that other models have no significant difference from the ResNet baseline.
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 3 Study Area and Datasets  
### Study Area  
The study area includes Lake Erie and Lake Ontario, two of the Laurentian Great Lakes of North America (Figure 1). Lake Erie covers an area of 25,655 km\({}^{2}\), with an average depth of 19 m. Lake Ontario with a surface area of 18,960 km\({}^{2}\) has an average depth of 86 m. The Great Lakes do not typically completely freeze over during ice seasons, although Lake Erie sometimes does due to its shallow depth. During winter 2014, because of persistent low temperatures, the Great Lakes recorded their second largest ice coverage since 1973 (NOAA/GLERL NOAA Great Lakes Environmental Research Laboratory-Historical Ice Cover [34].
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
###### Abstract
However, the challenge is to accurately determine whether the cause of fluctuations in the signal output from the sensor is a fault or a characteristic of the control target. Early fault detection is a trade-off with the incidence of false detection of faults due to noise and other factors, and from the standpoint of safety, even more accurate and early fault detection is a challenge [15]. Therefore, in this study, we will perform two tasks: detecting faults in temperature sensors and constructing a fault-tolerant control system using the microreactor system cooled by Peltier devices as the control target. For fault detection, the unsupervised fault detection system combining ChangeFinder and One-Class SVM was used. This is because we aimed to improve detection performance by providing explicit features from data analysis to the input of the One-Class SVM. A supervised classification system combining ChangeFinder and SVM was proposed in previous research [17]. Since the hyperparameters are not optimized in the previous method, the fault classification performance will be improved by optimizing the hyperparameters using a real-coded genetic algorithm. In addition, the fault detection system is effective for only one temperature sensor in the microreactor system in the conventional method. On the other hand, in this paper, a fault detection system is constructed for multiple sensors. Similarly, in designing the fault-tolerant control system, we will design a fault-tolerant control system that is effective for multiple sensors. The effectiveness of this system is confirmed by simulation and actual experiments, assuming that a sensor fault occurs during temperature control such that the sensor is damaged and shows a measured value that is different from the original temperature.
## 2 Signal Model
### Azimuth Transceiver Model  
The hypersonic platform SAR adopts the DPCAM technique, that is, multiple sub-apertures are configured in the along-track dimension, and each sub-aperture has the same beamwidth and illuminates the same observation scene. For the single transmitter and multiple receivers sampling mode of SAR system, each receiving sub-aperture can obtain the independent azimuth echo signal such that PRF can be reduced by adjusting the phase center spacing of the adjacent sub-apertures.  
Figure 1: SAR imaging model for hypersonic platform: (**a**) geometric diagram; (**b**) plane diagram.  
To meet a certain range of imaging bandwidth, as an example shown in Figure 2, azimuth multichannel sampling for hypersonic platform SAR adopts the mode of single transmitter and multiple receivers, that is, the middle sub-aperture transmits the signal and all of the sub-apertures receive the echo signal. For conventional airborne SAR, the array antenna configuration is adapted with the flight trajectory of the platform. However, due to the curved trajectory of the hypersonic platform, only the instantaneous position of the transceiver sub-aperture coincides with the current flight trajectory, while all other receiving sub-apertures deviate from the flight trajectory. Since the instantaneous turning radius of this platform is much larger than the phase center spacing of sub-apertures, the equivalent sampling position of each sub-aperture can be approximately processed along the curved trajectory. In this way, it is helpful that the azimuth receiving model could be simplified with some minor sampling position error.
## 5 CFD in Extrusion Processes
### Multiphase and Thermal Aspects in CFD Models
Most food matrices demonstrate shear-thinning characteristics, while protein-rich or fiber-containing formulations may also exhibit yield stress and viscoelasticity. CFD simulations incorporate rheological models such as the Herschel-Bulkley or Carreau-Yasuda equations, which are calibrated against experimental data over a wide range of shear rates and temperatures. In multiphase systems, each phase may require an independent rheological description, and effective bulk viscosity is often estimated as a function of phase volume fractions and interaction dynamics. Despite their advantages, multiphase and thermal CFD models present significant computational and experimental challenges. High-resolution meshes are required near screw elements and die regions to resolve steep gradients in velocity, temperature, and phase distribution. Thermal and rheological phenomena must be tightly coupled to maintain numerical stability, and validation is complicated by limited access to in-process data on internal pressure, temperature, and moisture. Often, only indirect validation is possible, for example by comparing predicted and observed expansion ratios, or final product moisture content. Fully coupled multiphase-thermal simulations are computationally demanding and may require model simplifications for practical use in industrial settings. Nevertheless, the predictive capabilities afforded by such simulations are increasingly valuable in food engineering. They support the design of advanced screw and die geometries, optimize process parameters for targeted structural and sensory outcomes, and accelerate product development through digital prototyping. Their relevance is particularly pronounced in the growing fields of plant-based foods, high-protein snacks, and functional extrudates, where precise control of phase and thermal dynamics is essential for product quality and consumer acceptance.
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
## 5 Discussion  
### Paleoglacial Morphology Mapping  
The combination of GIS and field mapping permitted the performance of a map of paleoglacial morphologies of the high mountains of Costa Rica. Various authors have explored glaciated landscapes over 3000 m in Costa Rica since the XIX century [71]. In addition, geomorphological mappings were made with diverse scales and techniques, and served different purposes. [PERSON] and [PERSON] [72] studied the Late Quaternary glacial phases using fieldwork and airborne imagery analysis. They reconstructed an ice cap of 35 km\({}^{2}\) in Chirripo National Park, and smaller glaciers on Cerro Kamuk, around Cerro de la Muerte, and probably other mountainous over 3200 m. In addition, in the Chirripo area [PERSON] et al. [73] mapped glacial landforms of the massif by combining Google Earth high-resolution imagery, a 30 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), and field inspections to map 22.1 km\({}^{2}\). [PERSON] and [PERSON] [74] mapped landforms which indicated evidence of past glacial extent, to interpret moraine complexes and reconstructed glacier surfaces. [PERSON] et al. [75] presented a study of the glacial constraint of tropical mountain height, and performed an LGM dating of Chirripo. Finally, [PERSON] et al. [76] mapped glacial lakes, till deposits, moraines and horns in the nearby Cerro Chirripo, and dated a major glacial event which occurred in this area from \(\sim\)25 ka, roughly synchronic with the worldwide LGM.  
The presented geomorphological map shows the glacially modeled areas of the top summits of Costa Rica during the LGM. As in other studies (e.g., [77; 78]), the combination of field mapping, remote sensing and GIS technologies in the present study has been demonstrated to be a useful technique to carry out a geomorphological map of a high mountain area. Despite previous studies of the glacial morphologies of Cerro Chirripo, there was no detailed scale mapping of glacial and/or periglacial features, as shown in [PERSON] et al. [68]. The classical geomorphology techniques provide a detailed analysis of the erosional and depositional morphologies. These approaches allow us to understand the LGM dynamics in the tropical highlands of Costa Rica, and can also provide the basis for different type of studies, i.e., geographical [73], ecological [79; 80], hydrological [81; 82; 83], climatological [72; 76], and geo heritage [70].
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 3 Related Work
### NTRUs (Nth Degree Truncated Polynomial Ring Units)  
Quantum computers can quickly solve public-key cryptography systems like RSA that rely on problems like integer factorization and discrete logarithms. By finding the period \(r\) in a function \(f(x)=a^{x}\bmod N\) involving modular arithmetic, where \(f(x+r)=f(x)\), one can rapidly determine the factors of \(N\). They can efficiently compute the quantum Fourier Transform in parallel processing, making it useful for discovering the period \(r\).  
However, the NTRU algorithm is based on solving problems such as finding the shortest vector in high-dimensional lattice structures or solving equations with added noise. These problems often involve searching every lattice point or finding solutions through random sampling, which results in exponential time complexity. Due to these characteristics, lattice-based encryption methods like NTRUs resist [PERSON]'s algorithm, which quantum computers utilize. NTRUs provide well-suited encryption and decryption processes with limited resources. Additionally, NTRUs' flexibility allows for customizable security levels, ensuring scalability and adaptability in various applications. There are two components in NTRUs: NTRUEncrypt, which is used for encryption, and NTRUSign, which is used for digital signatures. The patent for NTRUEncrypt was released into the public domain in 2017, while NTRUSign remains patented but is available under the GPL for software use [24].  
Based on a complex mathematical problem, NTRUs generate two polynomials, \(m(x)\) and \(r(x)\), from an initial polynomial \(f(x)\). These polynomials are then utilized in the encryption and decryption processes. Notable for its rapid processing speed and minimal memory requirements, NTRUs effectively balance efficiency and security, although increases in key length enhance computational complexity [25]. The NTRU algorithm includes a key generation phase that produces the polynomials \(r(x)\) and \(m(x)\), followed by encryption and decryption phases that utilize these polynomials. Predominantly, NTRUs enhance network communications across various domains to facilitate the secure transmission of data in services such as email, online banking, and file transfers.  
Distinguished by their reliance on the lattice-based shortest distance problem, NTRUs offer superior cryptographic strength to counter quantum computer threats. Their swift speed primarily results from polynomial manipulations and the potential for complexity reduction through methods such as the Fast Fourier Transform to \(n\log n\), thereby making NTRUs more efficient [14].
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Optimal Placement of Sensors  
In this section, the SNR for both the one-dimensional beam and the 2D frame structure is studied. The framework is the same for both configurations with the previously presented examples. A fixed set of possible receivers is initially assumed, each of them defined by the spatial location and the monitored DOF. The first initial receiver is chosen, to check for the SNR obtained using some specific monitored quantity. Then, the number of receivers is gradually increased, randomly chosen from the fixed set of possible ones, and the SNR is tracked. This way one is able to observe the influence of the increasing number of receivers on the SNR and to choose the optimal placement of receivers for a specific number of sensors in order to optimize the structural health monitoring system for a certain SNR measure.
# Detecting Arcing Faults in Switchgear by Using Deep Learning Techniques
## 2 Materials and Methods
### Proposed Methods
#### 2.1.2 Pre-Processing  
In this study, the pre-processing step was very important because it turned the data collected into a format that deep learning algorithms could use. This process involved converting the waveform audio data into an image format using the Mel-Spectrogram technique. Mel-Spectrogram is a widely used method to extract spectral features from audio signals. This process involves transforming the sound signal into a spectrogram, which is essentially a 2D matrix of intensities that represent the frequency spectrum of the sound signal. After transforming the data into images, the data was then further transformed into a matrix format to comply with the requirements of the Python programming language, which was used for the implementation of the machine learning algorithms. This transformation allowed the data to be organized and structured in a way that could be processed by the algorithms. The arcing and non-arcing data were combined and then split into three phases: training, validation, and testing. The training phase comprised the largest proportion of the dataset (70%), while the remaining 30% of the dataset was split equally for each of the validation (15%) and testing (15%) phases. It is important to note that the non-arcing data in this study was not limited to just one type of fault but rather included a range of other faults such as corona, tracking, mechanical, and normal faults. Additionally, the pre-processing and flowchart were implemented using the Google Colab environment.
# An Efficient Parallel Algorithm for Detecting Packet Filter Conflicts
## 3 Definition and Method of 5D Conflict Detection  
In this study, we define 5D conflict detection as the matching of five common fields in an IP header. The five fields are SA, DA, source/destination port (SP/DP), and protocol. Table 2 presents an example of a 5D filter database. In this section, we define how the two 5D filters encounter the conflict condition, then we explain our proposed simple and fast 5D filter detection algorithm.