esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilization of Machine Learning Algorithms
## 1 Introduction
Several ML techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing. Least absolute shrinkage and selection operator (LASSO) models, logistic regression (LR), and random forest (RF) were used to accurately predict the HIV status of individuals based on nationwide electronic data in Denmark [15]. In the United States of America (USA), ML models discovered that old age, inpatient visits, alcohol use, male gender, condom use, sexual behavior, and substance use were key predictors of HIV risk and diagnosis [19; 20; 21]. In a similar study by [23] in the USA, the incidence of HIV diagnosis among women was accurately predicted via retrospective analysis of electronic health record (EHR) data using extreme gradient boosting (XGBoost) and LASSO models. Multiple ML algorithms predicted timely clinic attendance and the uptake of HIV testing among men who have sex with men (MSM) in China [24]. Fascinatingly, ML experts are even developing novel models that can be integrated into HIV testing devices to enhance automated testing and mitigate the rate of false-positive HIV results. [PERSON] et al. [25] applied deep learning techniques to detect 38% of false-positive HIV results in France. Additionally, in a South African pilot study, artificial intelligence (AI) conversational agents were developed on Android phones, enabling participants to conduct self-HIV counseling, risk determination, rapid testing, and linkage to care for positive cases [17]. According to the study participants, speaking with the AI agent felt normal, like talking with a human, and they felt more at ease taking an actual HIV test with the agent [17]. Beyond reasonable doubt, it is evident that ML methods significantly enhance HIV counseling and testing owing to their precise accuracy, preservation of privacy, rapidity, and facilitation of easy access to HIV testing. This review aims to understand to what extent ML algorithms can be employed to enhance the efficiency and accuracy of HIV testing interventions, as well as identify key outcomes and successes, gaps, opportunities, and limitations associated with their implementation.
## 4 Cryptographic Analysis
### Signal-to-Noise Ratio  
The signal-to-noise ratio (SNR) test is excellent for measuring speech signal intelligibility. SNR measurement sets have precise criteria for measuring the performance of optimal signal processing [28] which can be calculated as follows:  
\[SNR=10 log_{10}\frac{\sum_{i=1}^{N}x_{i}^{2}}{\sum_{i=1}^{N}\left[x_{i}-y_{i} \right]}dB, \tag{14}\]  
where \(x_{i}\) and \(y_{i}\) are the corresponding sample values from the original audio file and the encrypted audio file, and \(N\) is the number of samples.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 3 Our Proposed AMC Method
### Hybrid Data Augmentation
#### 3.3.2 RandMix  
The RandMix data augmentation method works on the signal dataset after augmentation using rotation and is useful as an effective regularization method to improve the generalization performance and robustness of the model. The principle of RandMix data augmentation is shown in Figure 8. For a given signal sample, it is first sliced into \(M\) signal slices with equal lengths based on its total length. These \(M\) signal slices are then randomly combined to create an augmented signal sample.
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
###### Abstract  
An inertially stabilized platform (ISP) is generally equipped with a position and orientation system (POS) to isolate attitude disturbances and to focus surveying sensors on interesting targets. However, rotation of the ISP will result in a time-varying lever arm between the measuring center of the inertial measurement unit (IMU) and the phase center of the Global Positioning System (GPS) antenna, making it difficult to measure and provide compensation. To avoid the complexity of manual measurement and improve surveying efficiency, we propose an automatic estimation method for the dynamic lever arm. With the aid of the ISP encoder data, we decompose the variable lever arm into two constant lever arms to be estimated on line. With a complete 21-dimensional state Kalman filter, we accurately and simultaneously accomplish navigation and dynamic lever arm calibration. Our observability analysis provides a valuable insight into the conditions under which the lever arms can be estimated, and we use the error distribution method to reveal which error sources are the most influential. The simulation results demonstrate that the dynamic lever arm can be estimated to within [0.0104; 0.0110; 0.0178] m, an accuracy that is equivalent to the positioning accuracy of Carrier-phase Differential GPS (CDGPS).  
position and orientation system (POS); lever arms; Kalman filter; error distributions +
Footnote †: journal: Sensors
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 5 Discussion  
The performance of the multi-objective path-planning algorithm is presented across different experimental scenarios, with a detailed analysis of each experimental result. By comparing the optimization effectiveness of paths generated by different algorithms, the effectiveness of the proposed method is verified. This experiment selected road network data within Wuhan as the study area, setting two geographical coordinates within Wuhan as the start and end points for path planning, with latitude and longitude coordinates of (114.2547391, 30.632697) and (114.3286448, 30.3069509), respectively. The geographic area of this experiment covers various road types, including highways, main roads, secondary roads, and residential roads, allowing the influence of different road types on various indicators to be reflected, thereby providing a more comprehensive test of the algorithm's performance in multi-objective trade-offs. In this experiment, 10 initial paths were generated using the A* algorithm with dynamic avoidance points, and optimized paths were selected using the multi-objective optimization algorithm. In different scenarios, the requirements and focus of path planning vary. Therefore, to evaluate the effectiveness of the algorithm in the multi-objective optimization process, we compared the changes in each indicator between the optimized paths and the initial population and conducted statistical analysis, as shown in Table 2. The path maps for each scenario are shown in Figure 2, where the red curve represents the optimized path, and the other colored curves represent the paths from the initial population. From the distribution of paths in each scenario shown in Figure 2, it can be observed that the dynamic avoidance point strategy significantly enhances the diversity of the population, providing a broad search space for the genetic algorithm. Table 2 presents the weighted cost and the three lowest-cost initial population paths, along with the optimized path for each scenario. In the \"Urban Commuting\" scenario, the optimized path has a total length of approximately 69 km, significantly shorter than the paths in the initial population. The total time of the optimized path is also lower than that of the initial population paths, meeting the primary requirement for speed and smooth traffic flow during peak hours. In the \"Energy-Efficient Driving\" scenario, the energy consumption of the optimized path is significantly lower than the average energy consumption level of the initial population, with fewer congested segments, further reducing energy usage. In the \"Holiday Travel\" scenario, the optimized path exhibits excellent performance in terms of total length, risk index, and energy consumption, balancing the trip's cost-effectiveness with driving safety. In the \"nighttime travel\" scenario, the optimized path performs well in terms of risk index and path length, ensuring safety while reducing congested segments.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 3 Preliminaries  
A graph with \(N\) nodes and \(M\) edges can be represented as \(\mathcal{G}=\{\mathcal{V},\mathcal{E}\}\), where \(\mathcal{V}\) is the node set \(\{v_{i}\}_{i=1}^{N}\) and \(\mathcal{E}\) is the edge set \(\{e_{i}\}_{i=1}^{M}\). Let \(A\in\{0,1\}^{N\times N}\) be the adjacency matrix of graph \(\mathcal{G}\); \(A_{ij}=1\) if and only if \((v_{i},v_{j})\in\mathcal{E}\), where \((v_{i},v_{j})\) represents the existence of an edge between node \(v_{i}\) and node \(v_{j}\). To prevent isolated nodes during training, \(\bar{A}\) stands for the adjacency matrix for a graph with added self-loops \(\mathbf{I}\); each node \(v_{i}\in\mathcal{V}\) is associated with a \(d\)-dimensional feature vector \(x_{i}\in\mathbb{R}^{d}\). Hence, for simplicity, an attributed graph can be described as \(\mathcal{G}=(\mathbf{X},A)\). Graph \(\mathcal{G}\) is isomorphic to graph \(\mathcal{G}^{\prime}\), and we use \(\mathcal{G}\sim\mathcal{G}^{\prime}\) to represent this relation. What is more, the isomorphism is transitive; if \(\mathcal{G}_{1}\sim\mathcal{G}_{2}\) and \(\mathcal{G}_{2}\sim\mathcal{G}_{3}\), then according to transitivity, \(\mathcal{G}_{1}\sim\mathcal{G}_{3}\). GAEs learn a parametric mapping function \(f_{\theta}\) that transfers the node feature matrix \(X\) into low-dimensional latent representations \(\mathbf{Z}\); formally, \(f_{\theta}(\mathcal{G})=\mathbf{Z}\in\mathbb{R}^{N\times d}\).
## 1 Introduction
Additionally, the material damping coefficient was assessed by means of non-destructive tests. The significance of the conducted research work relies on the fact that it puts forward data for the material properties of rubberized concrete (RuC) well beyond the standard testing time of 28 days. The main objective was to determine the values of the modulus of elasticity, compressive and tensile splitting strength and their evolution after a significantly longer time interval compared to the standard testing time. Material damping was another important factor considered in the research since it offers an insight into the damping properties of RuC. On the other hand, the data are limited by the number of selected mix proportions, type of aggregate that was replaced by rubber particles as well as storage conditions of the specimens.
## 6 Conclusions  
The load-notch-strain curve describes the relationship between the load on the component and the locally occurring strain when elastic-plastic material behaviour is taken into account. It is a practical tool for the efficient determination of the local stress-strain path, which is required for a service life estimation according to the notch strain approach. This contribution describes how the load-notch-strain curve can be calculated using an FEA with elastic-plastic material behaviour and how the efficiency of this procedure is increased. The following points must be considered in order to carry out the determination of the load-notch-strain curve for the application of the FKM guideline nonlinear [25] as efficiently and accurately as possible with FEA:  
1. The loads in the simulation are applied with a constant distance.
2. It is sufficient to determine the load-notch-strain curve for the hysteresis branch, from which the load-notch-strain curve for the initial load is derived.
3. The material law should be stored in the FE programme with \(n_{\text{M}}=50\) support points.
4. By using shell elements on the surface of the component in the area to be analysed, the Gauss points are shifted to the surface. This makes it possible to use a relatively coarse mesh despite the elastic-plastic material behaviour.
5. When using spline interpolation, it is possible to determine the load-notch-strain curve for the hysteresis branch with only \(n_{\text{H}}=5\) load steps with a good accuracy.  
The standard procedure in the FKM guideline nonlinear for determining the load-notch-strain curve is the estimation with the help of a notch root approximation. For both permitted notch root approximations, an FEA with elastic material behaviour must be carried out. In addition, the limit load factor \(K_{\text{P}}\) must be determined using an FEA with elastic-ideal-plastic material behaviour. The time required to determine the limit load factor makes the use of notch root approximations less attractive.  
In contrast, this contribution shows that the determination of load-notch-strain curves in an FEA with elastic-plastic material behaviour using methods for increasing the efficiency is a good alternative to notch approximation methods in terms of quality and efficiency.  
Conceptualisation, L.M., R.B. and M.W.; methodology, L.M.; software, L.M., R.B. and M.W.; validation, L.M.; writing--original draft preparation, L.M.; writing--review and editing, R.B., M.W. and A.E.; visualisation, L.M.; supervision, A.E.; project administration, M.W. and A.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
We acknowledge financial support by the Open Access Publishing Fund of Clausthal University of Technology.  
The authors declare no conflict of interest.
# Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
## 4 Font Recommendation System through Content Emotion Analysis
### Implementation of a Font Recommendation System  
The font recommendation system, based on the emotions of the content, recommends, fonts depending on the emotions extracted through deep learning emotion analysis APIs when content like images and text is input.  
In the case of an image input, the DeepFace API [20] was utilized to extract emotions based on facial expressions. The API employs a model to determine emotion values. The DeepFace API is an application programming interface that provides access to the DeepFace facial recognition technology. It is commonly employed for face recognition, face detection, and facial attribute analysis in machine learning and computer vision applications. Developed by Facebook AI Research, DeepFace is renowned for its deep neural network models that can identify and analyze faces in images and videos.  
Figure 2 below displays the list of recommended fonts and emotion numerical values based on the input image. The DeepFace API yields seven emotion values as results: 'angry', 'disgust', 'fear', 'happy','sad','surprise', and 'neutral.' In this instance, the'surprise' emotion value is 1, while the values for the other emotions tend to converge toward 0. Fonts with irregular handwritten designs, rather than those from the Ming and Gothic font families, are recommended for expressions manifesting such emotions. Basic Ming fonts are ranked the lowest in the recommendations.  
The font recommendation system can also be employed for text input, and for sentence emotion analysis, the IBM Watson API [21] is utilized. This model analyzes the emotion of sentences into five categories:'sadness', 'joy', 'fear', 'disgust', and 'anger.' An example of this is illustrated in Figure 3, where the input sentence results in the emotion 'JOY', and it displays the associated fonts' emotions.  
Figure 2: Displays the list of recommended fonts and corresponding emotion numerical values based on the input image.
# Time-Series Forecasting of Seasonal Data Using Machine Learning Methods
## 3 Development of the Models
### Long Short-Term Memory  
Long Short-Term Memory (LSTM) is an artificial neural network in artificial intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) can process single data points and entire data sequences. This characteristic makes LSTM networks ideal for processing and predicting data, including time series. The main idea of the method is presented in [21].  
To build the LSTM model, we used the keras library and a set of functions _Sequential_, _Dense_, and LSTM, which were used to set the structure of the neural network. The following parameters were chosen to build the model:  
* batch_size = 16--number of samples per gradient update;
* epochs = 200--number of epochs to train the model;
* units = 32--dimensionality of the output space.  
A description of the LSTM model obtained using the keras library is shown below.  
LSTM model summary Model: \"sequential\" Layer (type) Output Shape Param # lstm (LSTM) (None, 32) 4352 dense (Dense) (None, 1) 33  
Total params: 4,385 Trainable params: 4,385 Non-trainable params: 0 The result of forecasting with the LSTM model based on real and synthetic test data is shown in Figures 16 and 17 (see Appendix A).  
All models show good quality with a good R-squared metric > 0.9. The models with R-squared > 0.95 shows excellent accuracy. However, the second factor--time for preprocessing and training models and time to obtain the forecasting--will be used to choose the best model.  
The leaders in accuracy are three models: SARIMA, LSTM, and XGBoost. However, SARIMA and LSTM significantly lose the time required to create a new model, which is caused by the automatic procedure of selecting parameters. The SARIMA method has high accuracy, but its application in systems with non-stationary time series with seasonal variability can be complex because of the long time required for automatic model preparation. The LSTM method showed promising results on accurate data. Still, the accuracy decreases significantly on synthetic test data, i.e., the forecast horizon for natural systems will decrease to preserve the precision. This indicates that the method has good potential but requires more careful tuning, network structure choice, and input data optimization for training and model use. XGBoost requires less than 10 s for fully automatic model building. The time it takes to obtain a prediction when the control signal is applied once per second is 0.2 s, i.e., about 20%. In addition to Python, the XGBoost libraries are implemented in the C++/C# programming languages, making it easy to bring it into production. Therefore, the optimal choice for solving the task is the XGBoost method.  
It should be noted that this study has limitations: we assume that the sensors are working correctly. New data are always coming in. If the system is interrupted for more than two days, it will be necessary to accumulate new continuous historical data for the last two to three weeks before starting.
# EEG-Based Measurement for Detecting Distraction in Coal Mine Workers
## 3 Methods
### EEG Features Extraction
#### 3.2.2 Frequency Domain  
Spectral analyses are crucial for understanding brain dynamics and neural synchrony, transforming time-domain signals into frequency-domain spectra using mathematical techniques like the Fast Fourier Transform (FFT). This process intuitively displays the energy distribution across frequency components. Given that EEG signals are discrete, the Discrete Fourier Transform (DFT) method is typically used, where the DFT is defined for a signal of length \(N\) as Equation (1):  
\[X(k)=\sum_{n=0}^{N-1}x(n)\cdot W_{N}^{kn},W_{N}=e^{-j\frac{2\pi}{N}} \tag{1}\]  
where \(x(n)\) represents the input signal, \(N\) is the total number of samples, \(k\) is the frequency domain index, \(X(k)\) denotes the Fourier component at frequency \(k\), and \(W_{N}\) is the kernel function that represents the relationship between discrete frequency points. In this study, FFT was performed on EEG data from two groups, categorizing the resulting spectra into standard EEG frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz). We calculated the average power within each band to assess neural activity intensity. Additionally, we incorporated various power ratio indices across different frequency bands, such as \(\theta/\beta\), \(\theta/\alpha\), \(\alpha/\beta\), \((\alpha+\theta)/\beta\), \((\alpha+\beta)/\theta\), and \((\alpha+\theta)/(\alpha+\beta)\), to evaluate cognitive states [32; 33; 34]. Thus, these indices were also incorporated into our consideration of spectral features.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 5 Experimental Results
### Spatial Stream  
To assess the spatial stream, three scenarios are considered. First, we deployed the original architecture shown in Figure 3 and trained it from scratch on UCF101 with the same configuration as that of the temporal stream. This took a lot of time to train and showed poor results, with an accuracy of just 41.6%. Secondly, we adopted the enhanced spatial stream, for which we first evaluated the pre-trained models listed in Table 3 by training them on our dataset. Finally, we fine-tuned the best-performing model from Table 3 on the enhanced dataset.  
Figure 7 shows the performance of enhanced spatial stream by using different pre-trained models and then fine-tuning them on UCF101. We can see that MobileNet performs best, with an accuracy of 75.2%. Moreover, Figure 7 also gives us the idea that almost every pre-trained model we utilized performed better than the original model. Fine-tuning the enhanced spatial stream on UCF101 leads to improvements because the ImageNet and UCF101 datasets are slightly different, and the feature extraction part still needs to learn the dataset through fine-tuning.  
Based on the data given in Figure 7, we picked the MobileNet model since it has the best performance. After that, we trained classification layers and fine-tuned the entire  
Figure 6: Temporal stream loss curve.  
Figure 7: Pre-trained models’ performance.  
model with the augmented dataset this time. It can be seen in Table 5 that it outperformed the standard MobileNet model, with a 1.53% increase in accuracy.
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 3 HSI Classification Method in Explainable Two-Layer Mode
### Back-End Learning Layer for HSI Classification  
Front-end learning obtains HSI data re-expression in a lower dimension, while back-end learning starts up the classification procedure.  
KNN or SVM is used to complete the classification task for back-end learning. Given a new sample point to be classified, KNN searches for the nearest training samples in the training set and predicts the new sample point's category based on the neighbors' categories. In our numerical test, the distance between samples is calculated with Euclidean distance. The basic idea of SVM is to find hyperplanes that maximize the interval between different categories. In the numerical test, we call Matlab's function KKN-Classifier or SVM-Classifier to complete the classification work.
## 4 Transient Stability Analysis
### Transient Stability Analysis with the Phase Trajectory Method  
Figure 11 shows the phase trajectories of the full-order model in the \(\delta^{+}\)-\(x_{\text{pl}}\)+-\(\delta^{-}\) phase plane for Case 1.  
As seen in Figure 11, the phase trajectory of the full-order model converges, indicating the transient synchronization stability of the system. This is consistent with the electromagnetic transient simulation results shown in Figure 10. Additionally, Figure 11 also presents the phase trajectories of the fourth-order model. The phase trajectory of the fourth-order model diverges, indicating the transient synchronization instability of the system. Therefore, the theoretical analysis results shown in Figure 11 demonstrate that the dynamic interaction between PR-based current control, DSOGI//PNSC, and PLL control enhances the transient stability of the system.  
Figure 11: The phase trajectories of the full-order model and the fourth-order nonlinear model for Case 1.  
It is important to note that the analyses in [14; 15; 16] indicate that, under symmetrical fault conditions, the interaction between current control and PLL control can deteriorate the system's transient stability. However, the theoretical analysis shown in Figure 11 suggests that, under asymmetrical fault conditions, the interaction of multiple control loops enhances the system's transient stability.