esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Arc Faults in Transformer Windings via Transient Signal Analysis
## 3 Fast Fourier Transform  
Fast Fourier Transform (FFT) is an efficient algorithm for calculating the Discrete Fourier Transform (DFT). It decomposes the data represented in the DFT into components at different frequencies and is applied in various fields. Discrete Fourier Transforms are extremely useful because they reveal the periodicity in the input data and the relative strength of the periodic components [27]. In many studies conducted in the past to detect faults and fault points, it has been observed that fast and reliable results were obtained [28,29].  
As an efficient computational method, FFT is widely used in signal processing, spectrum analysis and other areas. Consider a real-valued signal sequence \(x[n]\) with dimension \(N\), where \(0\leq n\leq N-1\) represents discrete data points in the time domain [30]. The DFT formula is expressed as follows:  
\[X[k]=\sum\
olimits_{n=0}^{N-1}x[k]e^{-j(2\pi/N)k\pi} \tag{5}\]  
The DFT allows a signal to be decomposed into the amplitude and phase information of its different frequency components. In this context, \(X[k]\) represents the signal in the frequency domain after the Fourier transform, \(x[n]\) is the nth data point in the time domain, \(N\) is the total number of data points, and \(j\) is the imaginary unit. The DFT facilitates the analysis of the properties of these frequency components by transforming the signal from the time domain to the frequency domain. The notation \(\mid X[k]\mid\) denotes the amplitude at frequency \((2\pi/N)k\). After applying the DFT, we can convert the digital signal from the frequency domain back to the time domain using the Inverse Fourier Transform. The formula for the inverse Fourier transform is as follows:  
\[x[k]=\frac{1}{N}\sum\
olimits_{n=0}^{N-1}x[k]e^{j(2\pi/N)k\pi} \tag{6}\]  
In this study, FFT is applied to the transformer end currents and end voltages obtained through a combination of ANSYS (version R2017b) and MATLAB (version 2023R1). The generation of an arc in the transformer windings is detected by analyzing the resulting spectrum. The detailed results and their evaluation are presented in the following section.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 2 Problem Analysis and Formulation
### Existing Work of the S-WTA Problem
#### 2.1.1 Framework A: The Independent S-WTA Framework  
As far as we know, the mathematical models of the S-WTA problem were first proposed by [PERSON] and [PERSON] [19]. According to the benefit of the weapon assignment and sensor assignment, they proposed an independent model. The independent model is shown as  
\[\left\{\begin{array}{l}\max\sum\limits_{k=1}^{N}\sum\limits_{i=1}^{M}a_{ik} \cdot x_{ik}+\max\sum\limits_{k=1}^{N}\sum\limits_{j=1}^{W}b_{jk}\cdot y_{jk}\\ \text{s.f.}\ \left\{\begin{array}{l}\sum\limits_{i=1}^{M}x_{ik}\leq 1,\forall k \in\{1,2,\cdots,N\}\\ \sum\limits_{k=1}^{N}x_{ik}\leq 1,\forall i\in\{1,2,\cdots,M\}\\ \sum\limits_{j=1}^{W}y_{jk}\leq 1,\forall k\in\{1,2,\cdots,N\}\\ \sum\limits_{k=1}^{N}y_{jk}\leq 1,\forall j\in\{1,2,\cdots,W\}.\end{array}\right.\end{array}\right. \tag{1}\]  
As shown in Equation (1), the objective function is composed of two independent parts. The first part is the total benefit of sensor assignment. The second part is the total benefit of weapon assignment. Therefore, no matter how sensors are assigned, the results of their assignment will not affect the benefit of the weapon assignment.This S-WTA model describes an independent framework. It is summarized and shown in Figure 1.  
As shown in Figure 1, as the sensors and weapons are independent, the information of sensors and weapons is not shared. Furthermore, the objective function includes two parts: the benefits of sensor assignment and the benefits of weapon assignment. Therefore, the sensor assignment and the weapon assignment is directly impact on the battlefield situation. As a result, as the scheme of sensor assignment and the scheme of weapon assignment do not affect each other, their effectiveness can be clear analyzed separately. However, the relationship between sensor and weapon is neglected. Therefore, the framework is unable to provide the accurate sensor and weapon assignment scheme.
###### Abstract  
Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients' lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogleNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features' dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival.  
deep learning; ANN; tuberculosis; SVM; GLCM; DWT; LBP +
Footnote †: journal: Applied Sciences
# Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion
## 2 Exosuit Functionality and Design
### Exosuit Design
#### 2.2.2 Actuation  
The actuation part is composed of a pneumatic double-acting cylinder (stroke: 50 mm, diameter 25 mm, max. 300 N), a 5/3 solenoid valve for air injection and release, a Bowden cable which is connected to the cylinder to transmit force and a compressed air tank to operate autonomously. The generated torque actuates the ankle joint in the direction of dorsiflexion and plantarflexion. Due to the double-acting mechanism of the cylinders, it is possible to use one cylinder for actuating two different directions of torque. Furthermore, a pneumatic system ensures high dynamics. This is important due to the short time of about 200 ms while the foot is lifted in swing phase.  
For preventing additional stress to distal joints due to the weight of system components, the actuation module was stored in a hip belt. To locate the majority of the system weight near the centre of the body, it also comprises the 12 V battery for powering an Arduino control device and the valve for the pneumatic cylinder.
## 3 Results
### Predominant Fungal Genera  
At the genus level, a total of 75 genera were detected with more than 10 sequences from all samples combined. The proportions of the 50 most abundant fungal genera are shown in Figure 5. The predominant genera detected against both the UNITE and RefSeq databases were _Leptospora_ (12.0%), _Cystobasidium_ (7.7%), _Pyrenochaetopsis_ (6.5%), _Trichoderma_ (4.0%), _Alternaria_ (3.8%), and _Neodeviesia_ (2.9%). Except for _Wickerhamomyces_ (2.5%), these results were reproducible regardless of the selection of the reference databases (Figure 6). Lower relative abundances were observed for fungal genera commonly found in homes in temperate countries, e.g., _Aspergillus_ (0.053%), _Candida_ (0.53%), _Cladosporium_ (0.055%), _Epicoccum_ (0.020%), _Fusarium_ (0.13%), _Penicillium_ (0.77%), _Rhodotorula_ (0.14%), and _Stachybortys_ (0.74%).  
Figure 4: Fungal compositions at the class level. The “others” category includes ambiguous sequences.  
Figure 3: Fungal compositions at the phylum level. The “others” category includes ambiguous sequences.  
Figure 5: Relative abundances at the genus level. The 50 most abundant genera are shown. The tree represents between-genus similarity of log-transformed relative abundances based on Euclidean distance. Double asterisks (**) indicate genera detected against both the UNITE and RefSeq databases. Single asterisks (*) indicate genera detected only against the UNITE database that were absent in the RefSeq database. The genera without asterisks are those detected only against the UNITE database, even though they were present in the RefSeq database. n.d.: not detected.
# Detection of Malicious Websites Using Symbolic Classifier
## 2 Materials and Methods
### Over-Sampling Methods
#### 2.4.4 KMeansSMOTE  
The KMeansSMOTE [33] algorithm is another extension of the original SMOTE algorithm that uses the KMeans clustering method on dataset samples before applying SMOTE algorithm. Using the clustering process the algorithm groups the samples and generates new samples based on clustering density. The first step is to randomly select \(k\) points in all samples \(D=x_{1},x_{2},\ldots,X_{n}\) and using them creates cluster centers \(C_{1},C_{2},\ldots,C_{k}\). The next step is to calculate the distances between each dataset sample and cluster centers using the expression:  
\[d=\sqrt{\sum_{i=1}^{n}{(x_{i}-C_{k})}^{2}},\quad x1,x_{2},\ldots,x_{i}\in D\quad C _{1},C_{2},\ldots,C_{k}\in C. \tag{11}\]  
After distance calculation, the next step is to assign each sample to its closest center and after that recalculate the cluster centers using the expression which can be written as:  
\[\mu_{i}=\frac{1}{|C_{i}|}\sum_{x\in C_{i}}x. \tag{12}\]  
The distance calculation, assigning samples to the nearest cluster centers and recalculating cluster centers are repeated until there is no change in cluster centers. The next step is to filter the cluster centers and select clusters with more minority class samples which will be used to generate new synthetic samples of the minority class. The final step is to perform oversampling of cluster centers using the expression:  
\[X_{new}=x_{c}+rand(0,1)\times(\hat{x}-x_{c}), \tag{13}\]  
where \(rand(0,1)\), \(X_{new}\), \(x_{c}\), and \(\hat{x}\) represent a function that randomly selects a number in 0 to 1 range, new synthetically generated negative class sample, the negative class that is randomly selected from nearest neighbors of filtered clusters, and negative samples of filtered clusters except nearest neighbors, respectively.  
After the application of ADASYN, SMOTE, BorderlineSMOTE, and KMeansSMOTE on the original dataset, a total of four new balanced datasets were obtained. With the application of these balancing methods, the balance in the number of samples of majority and minority classes was equalized. In all four datasets, the total number of samples is 3,053,238, and the number of samples in class 0 and class 1 is equal to 1,526,619.
## 3 Materials and Methods
### Interference and Network Optimization Difficulties
#### 3.9.2 Interference in 5G
The combination of technologies such as 5G, IoT, and others will enable big data, offering complex services to the community, adding members in smart cities, and ensuring compatibility. However, it should be noted that interference must be characterized. When talking about smart cities using Wi-Fi because of the use of the millimeter band, physical obstacles such as walls are things to keep in mind, in addition to channel overlapping and inter-carrier interference [96].
## Appendix A  
To assess the propagation loss of printed waveguide samples of different lengths, having supposedly common loss factors, such as coupling and end-facet reflections, we slightly modified the fringe contrast analysis technique of [29; 30] by introducing the fringe visibility metric, and separated the different contributions of the Fabry-Perot etalon due to the waveguide end-facet reflections. The normalized output intensity of the etalon is provided by:  
\[\frac{\text{I}_{\text{out}}}{\text{I}_{\text{in}}}=\frac{\left(1-\text{R} \right)^{2}\text{G}}{\left(1-\text{GR}\right)^{2}+4\text{GR}\sin^{2}(\varphi/2)} \tag{1}\]  
Here, \(\text{I}_{\text{out}}\) is the transmitted intensity and \(\text{I}_{\text{in}}\) is the input intensity (or powers), \(\text{R}\) is the Fresnel intensity reflection at the two end facets (under the relevant assumption that they are equal), \(\text{G}=\text{e}^{-\alpha\text{L}}\) is the transmission efficiency via the waveguide of length L and loss a (per unit length), and \(\varphi\) is the cavity phase delay. The Fabry-Perot transmission curve has distinct maximum or minimum values, at wavelengths where the cavity phase delay is interfered constructively or destructively (depending on the \(\sin^{2}(\varphi/2)\) term being zero or one), hence:  
\[\left[\frac{\text{I}_{\text{out}}}{\text{I}_{\text{in}}}\right]_{\text{max}} =\frac{(1-\text{R})^{2}\text{G}}{(1-\text{GR})^{2}},\qquad\left[\frac{\text{ I}_{\text{out}}}{\text{I}_{\text{in}}}\right]_{\text{min}}=\frac{\left(1-\text{R} \right)^{2}\text{G}}{\left(1-\text{GR}\right)^{2}+4\text{GR}}=\frac{(1-\text{ R})^{2}\text{G}}{\left(1+\text{GR}\right)^{2}} \tag{2}\]  
The oscillatory transmission curve exhibiting the interference effect is normally characterized with the dimensionless visibility metric, \(\
u\), defined as:  
\[\
u\triangleq\frac{\text{I}_{\text{max}}-\text{I}_{\text{min}}}{\text{I}_{ \text{max}}+\text{I}_{\text{min}}}=\frac{\left(1+\text{GR}\right)^{2}-\left(1 -\text{GR}\right)^{2}}{\left(1+\text{GR}\right)^{2}+\left(1-\text{GR}\right)^ {2}}=\frac{2\text{GR}}{1+\left(\text{GR}\right)^{2}} \tag{3}\]  
Since \(\text{G}\leq 1\) and \(\text{R}\ll 1\) (certainly for waveguide materials with \(\text{n}<2\)), then \(\
u\cong 2\text{GR}\). We can now separate the propagation and reflection contributions:  
\[\text{G}=\text{e}^{-\alpha\text{L}}=\frac{\
u}{2\text{R}} \tag{4}\]  
Applying a natural logarithm and simplifying, we have:  
\[\alpha\text{\textminus}\text{L}-\ln(2\text{R})=-\ln(\
u) \tag{5}\]  
Equation (5) is a linear curve relating the waveguide length to the observed visibility, with two unknowns that can be estimated using regression analysis: the slope, or loss, \(\alpha\), and the offset, or Fresnel reflection, R.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 4 Results
### Model Development and Performance Evaluation
#### 4.3.1.1 Model Training and Validation  
To develop our model, we performed hyperparameter tuning using grid search with five-fold cross-validation to minimize the impact of randomness on experimental outcomes. The grid search explored the number of trees (\(n\)) from 50 to 400 in increments of 50 and minimum leaf sizes (\(l\)) from 1 to 5 in increments of 1. As shown in Figure 12, the model achieved its highest score of 0.857 with \(n\) set to 200 and \(l\) set to 1, indicating this as the optimal parameter combination for model performance.  
To validate our feature selection methods, we conducted a comparative evaluation of model performance using three distinct feature subsets: all EEG features, features selected through statistical significance testing, and features optimized via algorithms. The results are summarized in Figure 13, where the model using statistically significant features outperformed the baseline model employing all EEG features. This improvement suggests that removing insignificant features enhances the model's ability to identify crucial ones. Notably, the model trained on optimized features achieved the highest performance across all metrics, reaching an accuracy of 84%. It also demonstrated balanced precision and recall, indicating reduced false positives and negatives without overfitting.  
Figure 11: Model accuracy by number of features.  
Figure 12: Results of the grid search.  
Furthermore, we assessed the discriminative ability of models corresponding to the three feature subsets by plotting ROC curves (Figure 14). When trained using all EEG features, the model achieved an AUC of 0.62, suggesting predictive ability slightly better than random chance and limited reliability for some applications. In contrast, using features selected through statistical significance testing increased the model's AUC to 0.73, indicating a significant improvement in distinguishing between positive and negative samples compared to using all features. Notably, employing features optimized by the algorithm boosted the AUC to 0.86, underscoring the crucial role of our feature selection strategy in enhancing the model's discriminative performance.  
To account for individual variability, we conducted comparative validation by evaluating the performance of two models on independent datasets from all participants: one model was trained using optimized features, while the other model was trained with all features. Performance evaluation results for both models are summarized in Table 3, demonstrating a notable enhancement in overall classification performance with the optimized feature set. This approach illuminates performance variability across individuals, thereby assessing the model's flexibility and applicability.  
Figure 14: ROC curves of models with different feature combinations.  
Figure 13: Performance metrics of models with different feature combinations.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 2 User Stories
### Story: Finding a Lost Person  
Seniors with deteriorating mental health, including dementia, can wander away from their residence and get lost. A holistic approach for identifying when the person left their residence and identify the current location is needed for timely intervention by rescue teams or search groups from other residents in the area. In a rural area, people might live alone and have infrequent visitations from other community residents, and they might not be identified as missing for many days. Chances of finding the person can be increased by equipping everyday used clothing items such as a winter jacket, outdoor shoes, a belt, or a personal item with a battery device. This device will send a signal that can be triangulated or obtain and transmit GPS coordinates. Such a signal should be received by any other healthcare gateways in the area. A healthcare gateway used by the person's residence should have an algorithm for detecting and raising the alarm when certain events occur that indicate the house resident is expected to be at home but is not present and should be considered lost [8]. For privacy reasons, the healthcare gateway should not transmit such data if the person was away due to normal daily activities depending on the time of day and has returned within the expected time.  
A wireless healthcare service system for seniors with dementia is proposed in [9]. The authors suggest using various wireless solutions and rely on the availability of a GSM network to send emergency signals. In a rural environment, a GSM network might not be available. However, any proposed system should support all necessary technology to locate the person.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
###### Abstract  
Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This paper introduces a detailed MATLAB (R2024a)/Simulink-based simulation platform that models a benchmark low-voltage industrial installation, including a six-pulse controlled rectifier, linear loads, and a capacitor bank for power factor correction. Systematic simulations are performed with the platform to examine the sensitivity of notch characteristics to key parameters within plausible ranges, such as short-circuit power at the point of common coupling, commutation reactance, firing angle, snuber circuits, and rated power of the rectifier. In addition, parameters such as the rated power of linear loads and the compensation power of the capacitor bank are examined. Other influencing parameters including background voltage unbalance and distortion are also modeled and considered. A comparative analysis with field measurements from German industrial LV networks validates the plausibility and suitability of the simulations. Building upon this platform, a Monte Carlo simulation approach is adopted to generate extensive datasets of realistic voltage notch waveforms by randomly varying these key parameters. A case study conducted under conditions typical of German LV networks demonstrates the applicability of the simulations. To support further research, the simulation platform and exemplary synthetic waveforms are provided alongside the paper, serving as a valuable tool for testing and designing strategies for analysis, detection, and monitoring of voltage notches.  
low-voltage (LV) network; power quality disturbance (PQD); six-pulse controlled rectifier; voltage notch; waveform distortion +
Footnote †: journal: applied sciences  
0000-0001-6582-2885][PERSON]