esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Details
### Fabrication of SG Polishing Pads  
The SG polishing pads were fabricated using sodium alginate for polishing experiments. As shown in Figure 2, the manufacturing process of the SG polishing pad mainly includes four steps: 1. uniform dispersion of abrasive particles within the sodium alginate solution; 2. flattening the mixed solution on a flat plate to create a thin film; 3. immersion of the thin film and flat plate in a Ca\({}^{2+}\) solution to facilitate gelation; and 4. drying the gel film. Figure 3a shows a typical SG polishing pad with a grain size of 20 \(\upmu\)m. The microstructure of the SG polishing pad in Figure 3b indicated the diamond grains were evenly distributed on the pad. The average tensile strength and shore hardness of the formed SG polishing pad are 32.7 MPa and 63.4 HA, respectively [24].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Marble** & **Density (g/cm\({}^{3}\))** & **Water Absorption (\%)** & **Tensile Strength (MPa)** &
\begin{tabular}{c} **Uniaxial Compressive** \\ **Strength (MPa)** \\ \end{tabular} \\ \hline Volakos (VO) & 2.76 & 0.28 & 18.1 & 140.0 \\ Sofitel King (SK) & 2.71 & 0.13 & 7.5 & 121.2 \\ Castle Gray (CG) & 2.66 & 0.15 & 14.0 & 168.6 \\ Yinding Gray (YG) & 2.65 & 0.22 & 11.4 & 72.7 \\ Focus Cream (FC) & 2.68 & 0.11 & 14.8 & 166.0 \\ Crema Marfil (CM) & 2.72 & 0.45 & 12.7 & 125.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some physical and mechanical properties of the marble tiles investigated [25].  
Figure 1: Natural marble tiles used in this study: (**a**) Volakos (VO), (**b**) Sofitel King (SK), (**c**) Castle Gray (CG), (**d**) Yinding Gray (YG), (**e**) Focus Cream (FC), and (**f**) Crema Marfil (CM). The optical images were taken by a camera.  
Figure 2: Schematic of the fabrication process of the SG polishing pads.
## 3 Experiments and Comparison
### Quantitative Analysis of the Nonuniformity Correction Effect of the Algorithms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence\textbackslash\{Method}** & **Original** & **[PERSON] [16]** & **[PERSON] [17]** & **[PERSON] [18]** & **Proposed Method** \\ \hline bus & 0.0741 & 0.0765 & 0.0787 & 0.0652 & **0.0740** \\ people & 0.0942 & 0.0962 & 0.0975 & 0.0863 & **0.0943** \\ building & 0.0621 & 0.0646 & 0.0662 & 0.0591 & **0.0623** \\ hand & 0.0877 & 0.0939 & 0.0936 & 0.0868 & **0.0891** \\ stairs & 0.0977 & 0.1009 & 0.1045 & 0.0939 & **0.0984** \\ dataset & **0.0834** & 0.0851 & 0.0876 & 0.0786 & **0.0841** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Roughness of [PERSON] [16], [PERSON] [17], [PERSON] [18], and our method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence\textbackslash\{Method}** & **[PERSON] [16]** & **[PERSON] [17]** & **[PERSON] [18]** & **Proposed Method** \\ \hline bus & 40.16 & 41.18 & 31.90 & **46.24** \\ people & 40.80 & 42.72 & 36.19 & **45.30** \\ building & 42.35 & 43.48 & 39.17 & **47.20** \\ hand & 38.49 & 44.48 & 43.21 & **46.65** \\ stairs & 34.78 & 38.19 & 29.77 & **38.98** \\ dataset & 39.85 & 42.84 & 34.79 & **45.74** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PSNR results of [PERSON] [16], [PERSON] [17], [PERSON] [18], and our method. According to Table 5, the algorithm in this paper has the closest energy of vertical gradient of the corrected image to the original infrared image in most cases. The vertical gradient energy of the corrected image produced by the [PERSON] [18] method is the lowest and the images are smoother. This is consistent with the image in Figure 8.  
From Figure 10, we can see that the proposed algorithm has the highest PSNR value of the corrected images, which indicates the best effect. It is worth noting that the PSNR of the corrected image of [PERSON]'s [18] algorithm with a variance \(\sigma\) of 0.01 is smaller than that of the noisy image without denoising treatment. The main reason for this is that the algorithm applies TV regularization to the DC component of the image after the wavelet transform, causing the overall pixel value to alter. It can be seen from the effect in Figure 7 that the [PERSON] [18] algorithm has the highest vertical smoothing degree for the image, and a better correction effect for horizontal stripes than the algorithms of [PERSON] [16] and [PERSON] [17]. Therefore, the PSNR of the corrected image varies slowly as the variance increases.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 2 Literature Review
### Travel Time Prediction Techniques
The cell state (\(C_{l}\)) is a memory from a previous unit (\(C_{t-1}\)) that is updated and sent to the next unit, where the gates contain the combination of the current input (\(x_{t}\)) and the previous output (\(h_{t-1}\)) activated by either the sigmoid function (\(sig\)) or the hyperbolic tangent function (_tanh_). In addition, the LSTM can be improved by the addition of attention, such as self-attention, so it becomes a self-attention LSTM (SA-LSTM). The mechanism of an SA-LSTM involves the learning of the association between the current batch of data and the previous parts in a sequence of data. In the case of travel time prediction, this method attempts to choose near-term data points that have a high correlation with 1 [13; 14; 16]. In addition to the original LSTM, Convolutional Long Short-Term Memory (ConvLSTM) is also used in the travel time prediction problem [14]. The ConvLSTM is an extension of LSTM that uses convolutional layers to process input. This approach is suitable for large input matrices, such as images. There are studies, such as [14], that have taken advantage of this method to predict the travel time of buses and have found that the ConvLSTM prediction model provided the best performance. In addition, the work in [22] also used the ConvLSTM in a travel time prediction experiment. Figure 1: The architecture of a Long Short-Term Memory (LSTM) network. Moreover, to improve the analytic method, we often required filters to transform the data into a good shape for good-quality inputs for training the model. Thus, methods to clean, denoise, reduce demotion, and transform the dataset were needed. To achieve these goals, some filters were applied, such as a Kalman filter with a neuron network or LSTM [23; 24]. As time-series data can be viewed as signal data, some well-known filters, such as the Kalman filter, the Savitzky-Golay filter, and the Butterworth low-pass filter, have been researched [27; 28]. First, there is the Kalman filter [21], which has often been used for travel time prediction [23; 24]. This filter calculates a joint probability distribution across the variables for each period to smooth the signal data. Second, the Savitzky-Golay filter uses a low-degree polynomial and the linear least-squares method to smooth data points based on adjacent points. Last, the Butterworth low-pass filter is a well-known signal processing filter that operates in a frequency domain. This method uses an algorithm to screen the signal by cutting off the high-frequency signal pass, so it generally helps denoise signal data. Thus, we also attempted to adopt these filter methods and selected one of them to improve the performance of the travel time prediction model.
# An Observation Scheduling System for Radio Telescope Array
###### Abstract  
The \(4\times 4.5\) m radio telescope array at Guizhou Normal University is an astronomical observation facility in operation, mainly aiming at the scientific detection of pulsars and fast radio bursts. To adequately address the observational requirements of this telescope array, we developed an observation scheduling system. This system is able to predict and plot the elevation change curves of the observed targets in relation to the telescope array during the whole day. In addition, for multiple targets, it provides intelligent scheduling and processing according to the observable time. The system also offers a comprehensive database of calibrators for the flux calibration of the targets. Moreover, it can generate pre-configured array \(uv\) coverage maps, which assist in determining the optimal configuration of the array. This system has been operating during the daily observations of the \(4\times 4.5\) m radio telescope array and has successfully detected two typical pulsars. It has also been tested for applicability in target-observation prediction in other radio telescope arrays.  
Keywords:telescope array; trajectory prediction; intelligent scheduling; flux calibration; \(uv\) coverage; software development +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Multivariate Data Analysis
#### 2.5.2 K-Nearest Neighbor  
K-nearest neighbor (KNN) is a widely used pattern recognition method [19]. It calculates the distances between an unknown sample and the samples in the predefined training set. The number of nearest samples (\(k\)) to the unknown sample is manually defined. The category of the unknown sample is determined by the categories of its \(k\) nearest samples in the training set. The determination of \(k\) is crucial for KNN. In this study, \(k\) was optimized by comparing KNN models using \(k\) from 3 to 20 with a step of 1.
###### Abstract  
This paper proposes a novel hybrid approach that combines unsupervised feature extraction through clustering and unsupervised feature selection for data reduction, specifically targeting high-dimensional data. The proposed method employs K-means clustering for feature extraction, where cluster membership serves as a new feature representation, capturing the inherent data characteristics. Subsequently, the K-Nearest Neighbors (KNN) and Random Forest algorithms are utilized for supervised feature selection, identifying the most relevant feature to enhance model performance. This hybrid approach leverages the strengths of both unsupervised and supervised learning techniques. The new algorithm was applied to 13 different tabular datasets, with 9 datasets showing significant improvements across various performance metrics (accuracy, precision, recall, and F1-score) in both KNN and Random Forest models, despite substantial feature reduction. In the remaining four datasets, we achieved substantial dimensionality reduction with only negligible performance decreases. This improvement in performance while reducing dimensionality highlights the potential of the proposed method within the procedure, where datasets are treated without prior knowledge or assumptions. The proposed method offers a promising solution for handling high-dimensional data, enhancing model performance while maintaining interpretability and ease of integration within the proposed frameworks, with the ability to be irrespective of supervised or unsupervised designation datasets while reducing the dependency on a target or label features.  
data reduction; feature selection; K-means; KNN; random forest 2025  
1
# Impact of Sound and Image Features in ASMR on Emotional and Physiological Responses
## 2 Materials and Methods
### Materials
#### 2.1.5 Electroencephalography (EEG)  
EEG signals were recorded using a 12-channel electrode cap aligned with the international 10-20 electrode placement system, within a 32-channel configuration using the TruScan Family of EEG systems by DEYMED Diagnostic (Hronov, Czech Republic). The sampling rate was set at 250 Hz, and the impedance between the electrodes and the skin was maintained below 5 k\(\Omega\), with the GM electrode serving as the ground and the Cz electrode as the reference.  
Previous research on ASMR and brain responses has shown that activation is primarily observed in the frontal lobe, although other studies have reported brain activation in other regions [19; 20]. To comprehensively analyze brain responses to ASMR stimuli across differ ent regions, this study selected electrode sites representing the frontal, central, occipital, parietal, and temporal lobes. As a result, EEG signals were recorded from 12 electrode sites (Fp1, Fp2, F3, F4, C3, C4, P3, P4, T3, T4, O1, O2).  
Brain wave activity was recorded across four frequency bands: delta (0.0-4.0 Hz), theta (4.0-8.0 Hz), alpha (8.0-12.0 Hz), and beta (12.0-30.0 Hz), with reference to the Cz channel. Theta, alpha, and beta frequencies were selected based on previous studies indicating that ASMR stimuli predominantly affect these bands, while delta frequencies were included for a more detailed analysis of ASMR's relaxation effects. Bandpass filters (BPF) were applied to all EEG data to remove noise.  
To assess hemispheric asymmetry in brain activity, an asymmetry index (AI) was calculated for EEG variables that exhibited significant differences. The AI was defined as follows [21]:  
\[\text{AI}=\frac{\text{Right}-\text{Left}}{|\text{Right}|+|\text{Left}|} \tag{1}\]  
This ratio-based method was employed to compare the relative differences between the left and right hemispheres. To ensure the accuracy of the calculations, the denominator was treated as an absolute value, given that the data included both negative and positive values.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 2 Application Scenario
### Sampling and Mining Rovers  
Based on energy availability, distance and Estimated Time of Arrival (ETA) to a location of interest, the swarm of rovers negotiates which rover(s) will collect samples or mine resources. In addition, the rovers communicate their availability to the swarm of autonomous vehicles. Upon task completion, a transporting rover will be selected similarly, which then collects the samples or mined resources and moves them to the Martian base, where other rovers can use them to analyze samples or to build infrastructure. When there is insufficient energy available, rovers will negotiate new task allocations, requesting others to take over. When the 'next shift' arrives, the rover stops its task and proceeds to recharge itself.
## 3 Methods  
The present work addresses the estimation of backscattering coefficients at the spatial scale of the plot. The steps implemented in the estimation of backscattering coefficients are illustrated in the following synopsis (Figure 6). Microwave satellite images are first processed to obtain backscatter coefficients at the plot scale (processing steps described in Section 2.3). The backscattering coefficients are then estimated using the two statistical algorithms described below (i.e., multiple linear regression or random forest). These algorithms were implemented by considering all collected soil measurements, allowing the analysis of variable importance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mission** & **Mode** & **Acquisition Date (mm, dd, yy)** & **Pass** & **Incidence** & **Pixel** & **Polarization** \\  & & & **(\({}^{\circ}\))** & **(m)** & **States** \\ \hline TS-X & Spotlight & 15 March 2010 & D & 28.7 & 2 & HH \\ TS-X & Spotlight & 14 April 2010 & A & 32.3 & 2 & HH \\ TS-X & Spotlight & 8 April 2010; 30 April 2010; 29 August 2010 & A & 45.5 & 1.75 & HH \\ TS-X & Spotlight & 5 March 2010; 21 May 2010; 18 August 2010 & D & 53.3 & 1.5 & HH \\  & & 30 September 2010; 11 September 2010; & & & \\  & & 22 September 2010 & & & & \\  & & 2 November 2010; 13 November 2010; 24 November 2010 & & & \\ TS-X & StripMap & 21 February 2010; 26 March 2010; 9 May 2010; & D & 27.3 & 2.75 & HH \\  & & 20 May 2010 & D & 27.3 & 2.75 & HH \\  & & 16 August 2010; 29 September 2010; 10 October 2010 & & & \\  & & 21 October 2010; 12 November 2010; 21 November 2010 & & & \\ TS-X & StripMap & 15 September 2010 & A & 31.8 & 2.75 & HH \\ TS-X & StripMap & 27 February 2010; 31 July 2010 & D & 41.7 & 3 & HH \\ RS-C & FQ5 & 5 March 2010; 24 November 2010 & A & 24.3 & 5 & Full \\ RS-C & FQ6 & 21 October 2010; 14 November 2010 & D & 25.6 & 5 & Full \\ RS-C & FQ10 & 26 February 2010; 15 April 10; 9 May 2010; & A & 30.0 & 5 & Full \\  & & 30 September 2010 & A & 30.0 & 5 & Full \\ RS-C & FQ11 & 26 March 2010; 17 August 2010 & D & 31.1 & 5 & Full \\ RS-C & FQ15 & 15 March 2010; 8 April 2010; 2 May 2010; 30 August 2010; & A & 35.1 & 5 & Full \\  & 17 October 2010 & D & 36.2 & 5 & Full \\ RS-C & FQ16 & 20 May 2010; 31 July 2010; 11 October 2010 & D & 36.2 & 5 & Full \\ RS-C & FQ20 & 3 November 2010 & A & 39.9 & 5 & Full \\ RS-C & FQ21 & 20 February 2010; 16 March 2010 & D & 40.8 & 5 & Full \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean features of the TerraSAR-X and Radarsat-2 acquisitions.
## 2 Materials and Methods
### Detection Methods
#### 2.2.1 SEM and XRD  
SEM analysis was performed using scanning electron microscope model JSM-IT500 LV of Changsha Mining and Metallurgy Institute Testing Company, with images acquired at 20 kV and 40 uA current, a working distance of generally 10 mm and image magnification from \(\times\)5 to \(\times\)300,000. XRD physical phase analysis was performed using X-ray diffractometer model D8 ADVANCEX of Changsha Mining and Metallurgy Institute Testing Company, with a WANT detector, better than 25% resolution, minimum step size of 0.0001 and maximum output power of 3 kW.
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 4 Applications
### Cost Reduction  
Paper [17] presented a DT of a ship electrical power and propulsion system, consisting of two diesel generators (DG1 and DG2), two propulsion electric motors, a fuel supply system, switchboards, and AVR units. The simulation experiments were performed to simulate events in a consecutive order, enabling conclusions about the operation of the ship's power system. The experiment confirmed expected system dynamics by means of frequency and voltage fluctuations after increasing and decreasing load, as well as the controller dynamics when sharing loads between generators. OSP enables the connection of models of various subsystems, usually covering different engineering fields to form an overall model of a system, thus enabling the collaboration of experts from various fields of engineering on a single platform, which results in time and money savings. The advantage over other simulator solutions lies in the fact that the OSP is an online open-source standardised and collaborative environment for model exchange in maritime industry.
# Performance Assessment of Predictive Control--A Survey
## 3 Control Performance Assessment
Model-based methods: * minimum variance and normalized Harris index [75], Control Performance Index [76], and other variance benchmarking methods [77]; * all types of the model-based measures [78], derived from close loop identification, such as aggressive/oscillatory and sluggishness indexes [79]; * frequency methods starting from classical [PERSON], Nyquist and Nichols charts with phase and gain margins [69] followed by deeper investigations, such as with the use of Fourier transform [80], sensitivity function [81], reference to disturbance ratio index [82], and singular spectrum analysis [83]; and * alternative indexes using neural networks [84] or support vector machines [85]. Figure 4: CPA techniques classification. Data-driven methods:  
* integral time measures, e.g., Mean Square Error (MSE), Integral Absolute Error (IAE) [86], Integral Time Absolute Value (ITAE) [87], Integral of Square Time derivative of the Control input (ISTC) [88], Total Squared Variation (TSV) [89], and Amplitude Index (AMP) [71];
* correlation measures, such as oscillation detection index [90] or relative damping index [91];
* statistical factors utilizing different probabilistic distribution function (standard deviation, variance, skewness, kurtosis, scale, shape, etc.) [92], variance band index [93], or the factors of other probabilistic distributions [94; 95; 96];
* benchmarking methods [97]; and
* alternative indexes using wavelets [98], orthogonal Laguerre [99] and other functions [65], Hurst exponent [100], persistence measures [101; 102], entropy [103; 104; 105], multifractal approaches [106], or fractional-order [107; 108]. Apart from the above items, there is a group of methods utilizing hybrid or mixed approaches:  
1. fusion CPA measures using sensor combination [109] or the Exponentially Weighted Moving Averages (EWMA) evaluated for other indexes [110];
2. graphic visualization and patter recognition methods [91; 111; 112]; and
3. case-specific business Key Performance Indicators (KPIs), e.g., number of alarms or human interventions, time in manual mode [113], and many other currency-based units [67].
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 3 The Design of Simulation Environments and Simulation Tasks
### Simulation of GPS Faults  
(1) Simulation parameter configuration when simulating GPS failure  
When performing software-in-the-loop simulations of UAVs, the parameters can be changed to simulate the effects of wind speed, wind direction, sensor failure, etc., on UAVs. For example, set \"SIM_GPS_DISABLE\" from 0 to 1 to simulate a GPS failure during the operation of the drone. Some parameters for simulating UAV failure are shown in Table 1.  
(2) GPS failure simulation  
The trajectory of the UAV at the ground station QGroundControl is shown in Figure 9. From point A to B and from point C to D, the drone experienced a GPS failure; from point B to C, the drone's GPS returned to normal. In the simulation environment, after losing the GPS signal, the drone's response is to keep the current running position and continue to fly forward. After the GPS signal is restored, the drone will move toward the received GPS coordinates at a speed different from normal flight until it reaches the designated waypoint.  
In order to understand the impact of GPS failure on the drone, we extracted the drone's information from the log. Figure 9(a) shows that after the GPS failure, the UAV stalled, and the UAV ran at a relatively fast speed when the TimeStamp is 500,000 ms. When the TimeStamp was 600,000 ms, the GPS failure of the UAV recovered, and there was a short pause. Since the flight mission had to be continued, it returned to the original waypoint at a relatively fast speed. From Figure 9(b),c, due to the change of velocity, the Circular Angle and acceleration were abnormal. It can be seen from Figure 9(d) that after the GPS failure, the UAV lost its accurate judgment on its position, so the roll angle and pitch angle of the UAV were abnormal.