esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing Layer
#### 2.2.2 GPR Electromagnetic Data Processing  
2D electromagnetic scans recorded in the GPR survey require digital processing of recorded data to accurately determine the location, depth, size, and type of object or utility. Several state-of-the-art data-processing techniques have been developed over the years, such as deconvolution, attribute analysis, and noise suppression [41]. The basic purpose of data processing is to enhance electromagnetic signals and reduce noise, expunge system-recorded data irregularities, and correct geometrical error irregularities (Figure 5a). In this research, GPR data processing is based on four basic GPR data processing steps: (1) data editing, in which error related to field data acquisition is treated to remove the data redundancy; (2) primary editing, in which low-frequency components are ignored--normally referred to as \"dewowing the data\"; (3) advance data processing, in which well-known seismic operations are applied to enhance the visible weaker signals and add artifacts that can lead to misleading results; (4) visual interpretation processing, in which, on the basis of expert knowledge, the location, depth, size, and type of object or utility is determined (Figure 5b).  
Figure 4: (**a**) Orthomosaic, the corresponding sparse with marked GCPs (ground control points) and their distribution. (**b**) Digital surface model (DSM) before densification. (**c**) Image overlapping density for accurate point cloud generation. (**d**) High-density, full-colored 3D point cloud with camera ray points.
# Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery
## 2 Materials and Methods
### Data Analysis
#### 2.3.1 Preprocessing  
Before feature extraction and classification, a batch normalization operation was performed for each input image to solve the problem of internal covariate shift. The application of batch normalization generally stabilizes the training process, even with a higher learning rate [17]. In our CNN architecture, preprocessing is a normalization step that fixes the mean and variances of input image.
# K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data
## 3 Results  
To verify the performance of K-RBBSO, its stochastic search algorithm based on the validated data and the current evaluation value were compared with those of RBBSO without k-means. Windows 10, 64-bit processor, Intel(r) i5-1240P CPU, 1.7 GHz, and 16 GB RAM were used as part of the experimental environment. The cluster engine was developed using C++ 2022 (64-bit) version 17.3.5. As verification data, the dataset of the widely used UCI machine learning repository was employed (Figure 1). It includes Iris, Wine, Glass, Vowel, Cloud, and CMC, which have been utilized in data clustering research. The data used in this study are summarized in Table 1.  
The number of habitats of the K-RBBSO and RBBSO algorithms was set to 10. In addition, the number of emigration, immigration, and mutation habitat was set to 1. Each habitat choice function was applied according to steps 5, 6, and 7. The large t of the S.A. algorithm was set to 1, and the delta t was set to 0.99. The delta t in the S.A. algorithm represented the falling temperature in every calculation. The small t represented the calculation iteration duration for a calculation time of delta t. In the A.B.C. algorithm, the numbers of pop and bob were set to 5. Moreover, each iteration limit was set to 100. Every parameter between S.A. and A.B.C. was a measurement obtained from the empirical experiment in this study. To ensure validity and reliability during the data selection process, various numbers of data points and attributes were considered. Moreover, as the aim was the verification of effectiveness, the number of clusters K was established for each data type by referring to previous research. For instance, if a previous study on Iris data had used three clusters, for instance, K was taken to be 3 in this study. Using the aforementioned data, the performances of SA [13], ABC [14], and RBBSO without K-means algorithms were compared with that of the proposed algorithm. Additionally, the termination condition for all algorithms was set to \"terminate when the same result value is obtained corresponding to the number of rows of the presently running data\". For instance, the algorithm was programmed to terminate if the evaluation value of the execution result was produced 1473 times during the analysis of CMC data. In addition, the silhouette coefficient and computation times were concurrently monitored and compared for effectiveness analysis. Each algorithm was executed ten times for each data type, and the mean, standard deviation, and minimum and maximum values were recorded. The outcomes of each analysis are presented in Tables 1 and 2, respectively. As is evident from Table 1, the K-RBBSO algorithm generated a lower silhouette coefficient corresponding to fewer data compared to the other algorithms and generated a
# Two-Stage Beamforming Technique for GNSS Applications +
## 3 Experimental Results and Discussion
### Experimental Setup  
A record and playback system consisting of four synchronized Vector Signal Transceivers (VSTs) were used to generate and acquire GNSS signals on four RF ports; the local oscillators (LO) of the VSTs were shared from a common LO source to ensure the phase and amplitude coherency among the RF chains and across the recorded bandwidth, as shown in Figure 2a. The deployed VST system was connected to four antenna elements that were distributed as a square with a side length of 10 cm, as shown in Figure 2b. The recorded bandwidth, the sampling rate and the center frequency were, respectively,  
Figure 1: A schematic representation of the proposed technique. The RF chains of the N antenna elements will be digitized and subjected to the acquisition process. During the acquisition, the complex symbols (from all the N antennas) that are associated with the codephase and the doppler frequency shift (\(\Delta f_{k}\)) will pass to the SAGE algorithm. The SAGE algorithm estimates the direct and reflected signals’ parameters. The elevation (\(\theta_{k}\)) and the azimuth (\(\Phi_{k}\)) of the direct path, which is aimed in the direction of the k-th satellite, and the reflected signal covariance matrix \(\mathbf{R}_{\text{xx}}^{\text{m}}\) will be passed to the MVDR beamformer. The MVDR algorithm will estimate the weight vector (\(\mathbf{w}_{k}\)) and pass it to the tracking process. Afterwards, a typical GNSS processing is conducted to provide the PNT solution.  
32 MHz, 40 Msps, and 1575.42 MHz. A total of 5 min was recorded; however, due to the lengthy analysis using MATLAB R2024a, only the result for 1 min is shown. Furthermore, a 16 ms integration time was used during the acquisition stage, of which 4 ms period was the coherent integration.
# Quantification of Optical Chirality in Cellulose Nanocrystal Films Prepared by Shear-Coating
## 3 Results and Discussion  
### Visual Appearance of CNC Films  
Figure 2a shows an image of a CNC film taken through a left circular polarizer. As can be seen, a bright blue color extends over areas of several cm\({}^{2}\) with border effects. In a visual test of the selective Bragg reflection, it is observed that the sample appears dark when viewed through a right circular polarizer, as shown in Figure 2b. This optical chirality is indicative of a of left-handed chiral structure in the CNC film. The reflection of the left-handed polarization implies the transmission of the right-handed complementary polarization. The quantification of these effects is discussed below in Section 3.2. Figure 2c shows a POM image of the center of the film. At this scale, the color domains are blue and cover areas of hundreds of square micrometers. The small variation of the color of the domains is indicative of a small variation in pitch. In summary, although the deposition method used in this work applies a shear stress on the suspension, it is not strong enough to break the tactoids in the chiral nematic liquid crystal phase, but might cause elongation in the direction in which the stress was exerted [7].  
The fabrication of supported CNC films by shear-coating methods has been reported to produce films with ordered/oriented CNC [15; 16; 22]. Doctor blade coating of a highly concentrated CNC suspension (11.8 wt%) aligned the CNC along the direction of the applied stress, producing birefringent films with a thickness of about 30 \(\upmu\)m [15]. In another report, ultrathin films (thicknesses in the range 17-85 nm) with ordered CNC were shear-coated using a low volume (10 \(\upmu\)L) of a ramie CNC suspension at a concentration 2.5% [16]. Other authors reported a study of blade coated films from sonicated CNC suspensions using hydrolyzed filter paper [22]. In that work, the films prepared from a suspension in the biphasic regime (6.98 vol%) lost the birefringent effect in a few minutes, relaxing to a polydomain structure. However, the spectroscopic properties were not reported. A suspension in the anisotropic phase (11.6 vol%) was required in order to produce a permanent alignment of CNC [22]. In the present work, a non-s  
Figure 2: Photography of a CNC film through (**a**) left and (**b**) right circular polarizers. (**c**) Polarized optical microscopy (POM) image in reflection mode. Scale bars are 1 cm in (**a**,**b**), and 250 \(\upmu\)m in (**c**).  
biphasic regime (6.5 wt%) stored for 18 days was used to produce CNC chiral films. It is important to emphasize the importance of the storage time of the suspension as the films produced with a fresh suspension did not show colors. This proves that the formation of the anisotropic phase takes time after preparation from a non-sonicated CNC suspension. A detailed study on the effect of the storage time on non-sonicated suspensions will be reported elsewhere.
## 3 Experimental Methods
### Experimental System
#### 3.1.3 AE Monitoring System  
An 8-channel AE monitoring system was utilized to capture the real-time evolution of AE energy and the spatial location of AE events. As shown in Figures 4 and 5, it consisted of AE sensors, AE pre-amplifiers, an AE data acquisition instrument, and an AE analysis software.  
In this experiment, four cylindrical AE sensors with a diameter of 18 mm were mounted slightly on the surface of the sandwiched coal-rock specimen with tape to acquire the AE signals and parameters. According to the method suggested by International Society for Rock Mechanics for laboratory AE monitoring [28], the arrangement position of AE sensors was shown in Figure 5. The linking line of sensor 1 and sensor 2 was vertical mutually to that of sensor 3 and sensor 4. To enhance the connections and improve signal transmission, silicon grease was applied as the coupling agent between the AE sensors and the sandwiched coal-rock specimen. The data acquisition frequency and amplitude threshold were set at values of 2.5 MHz and 40 dB, respectively.
## 4 Results  
Figure 5 shows that all sensor channels of original signals formed a subject after baseline wandering removal, data truncation, and cycle alignment. Each subfigure represents data in multiple cycles of every channel of a PPS sensor. Channel 11 with a red subtitle has the maximum calculated energy. All channels were then reordered according to the signal energy from high to low. For example, channel 11 on the spatial arrangement was renumbered as channel no. 1.  
Figure 4: Four key physiological points of the radial pulse wave in a complete heartbeat cycle: (**a**) onset: aortic valve open; (**b**) P\({}_{1}\): ventricular contraction; (**c**) P\({}_{2}\): reflected wave; (**d**) P\({}_{3}\): aortic valve closure; (**e**) P\({}_{4}\): diastolic wave. \(t\) represents time, and \(h\) represents the amplitude of each key physiological point.  
As described in the above section, a convolutional neural network was constructed to determine the channel-scaled factors of each sensor channel. The neural network architecture of the CSF estimation module is shown in Figure 6a. We implemented a custom nine-layer CNN to predict the CSF values. The size of the input pulse data was \(1\times 72\). Train sets and test sets selected from our 3 DPI dataset were split into 80/20 ratio, respectively. In addition, we use the Adam ([PERSON] and [PERSON] 2014) optimizer with a mini-batch size of 27 for a fixed number of training epochs.  
Figure 6b shows the value ranges of weighting factors in each channel during the iteration of our algorithm. As the channel number increased from 1 to 25, the signal pattern  
Figure 5: Original signals of 25 channels. Each subplot represents aligned and scaled cycles of each channel. Channel 11 shown in red has the maximum energy.  
Figure 6: Overview of our CSF estimation module and the calculated weighting factors of each reordered channel in iterations 1–1000. (**a**) Model architecture; (**b**) the calculated weighting factors for 25 channels.  
of each channel varied. Meanwhile, the weighting factors of each channel showed an increasing trend along the channel axis.
## Appendix A  
In evaluating language models, various metrics are employed to measure their performance and alignment with human expectations. Below is a list of abbreviations for these metrics:  
* **HV**: Human Validation--assesses how well the model's outputs align with human judgment.
* **F1**: F1-Score--measures the balance between precision and recall, indicating the model's accuracy [66].
* **Prec.**: Precision--gauges the correctness of the model's positive predictions [67].
* **Cos. Sim.**: Semantic Textual Similarity--calculates the similarity between two vectorized texts [68].
* **Fact. Cons.**: Factual Consistency--verifies the accuracy of the information provided by the model [69].
* **Coh.**: Coherence--evaluates the logical and smooth flow of text [70].
* **Cons.**: Consistency--checks for uniform responses to similar inputs [71].
* **Flu.**: Fluency--assesses the smoothness and naturalness of the text [72].
* **Relev.**: Relevance--measures how well the model's outputs correspond to the input prompts [73].  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Model** & **Rank** & **HV** & **F1** & **Prec.** & **Recall** & **Cos. Sim.** & **Fact. Cons.** & **Coh.** & **Cons.** & **Flu.** & **Relev.** \\ \hline GPT4 & 1 & 0.92 & 0.93 & 0.92 & 0.94 & 0.78 & 0.70 & 0.45 & 0.54 & 0.41 & 0.62 \\ GPT4 & Raw & 2 & 0.82 & 0.91 & 0.89 & 0.93 & 0.80 & 0.74 & 0.55 & 0.77 & 0.24 & 0.56 \\ Mistral & 3 & 0.52 & 0.88 & 0.90 & 0.85 & 0.58 & 0.59 & 0.43 & 0.69 & 0.57 & 0.48 \\ Llama\_2 & Raw & 4 & 0.44 & 0.87 & 0.90 & 0.85 & 0.55 & 0.57 & 0.41 & 0.70 & 0.48 & 0.46 \\ Mistral Clean & 5 & 0.44 & 0.87 & 0.90 & 0.84 & 0.56 & 0.54 & 0.27 & 0.37 & 0.54 & 0.44 \\ Llama\_2 Clean & 6 & 0.36 & 0.87 & 0.89 & 0.84 & 0.51 & 0.51 & 0.23 & 0.36 & 0.49 & 0.40 \\ Bard & 7 & 0.23 & 0.83 & 0.81 & 0.84 & 0.25 & 0.23 & 0.53 & 0.63 & 0.62 & 0.37 \\ Bard Clean & 8 & 0.16 & 0.83 & 0.82 & 0.85 & 0.30 & 0.24 & 0.33 & 0.38 & 0.56 & 0.36 \\ GPT3\_5 Clean & 9 & 0.21 & 0.86 & 0.86 & 0.86 & 0.51 & 0.29 & 0.21 & 0.32 & 0.31 & 0.29 \\ GPT3\_5 Raw & 10 & 0.10 & 0.82 & 0.83 & 0.82 & 0.24 & 0.14 & 0.25 & 0.50 & 0.33 & 0.23 \\ \hline \hline \end{tabular}  
* Based on the above table, it is noted that the GPT4 (catalog) model with raw data is the top-ranked model for the dataset criteria based on the MCDA metrics and the GPT4 (catalog) model with clean data has the highest human validation score of 0.56.  
\end{table}
Table 5: Evaluation metrics for different models for platelets criteria.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Model** & **Rank** & **HV** & **F1** & **Prec.** & **Recall** & **Cos. Sim.** & **Fact. Cons.** & **Coh.** & **Cons.** & **Flu.** & **Relev.** \\ \hline \hline GPT4 & Raw * & 1 & 0.51 & 0.93 & 0.93 & 0.93 & 0.86 & 0.48 & 0.58 & 0.73 & 0.87 & 0.63 \\ GPT4 & Clean * & 2 & 0.56 & 0.94 & 0.94 & 0.94 & 0.88 & 0.53 & 0.44 & 0.55 & 0.87 & 0.67 \\ GPT3\_5 Clean & 3 & 0.47 & 0.92 & 0.93 & 0.91 & 0.
## 4 Exploring the TES Performances Improvement by a Numerical Parametric Study  
The heat transfers during melting and crystallization have been widely studied [11; 18; 31; 32; 33; 34; 35], showing that the driving phenomenon is the conductive transfer during the crystallization, while natural convection may also be involved during melting. Convection modeling may be avoided by considering the effective thermal conductivity [36; 37; 38]. Based on the purely conductive heat transfer hypothesis, the resource-consumption modeling of the mobile phase change may be side-stepped by considering the enthalpy rather than the temperature in the energy conservation equation in the PCM [39; 40; 41; 42; 43].  
At the system level, and more precisely concerning spherically encapsulated PCM systems, a review showed that despite the case studies' diversity, models based on energy conservation equations for HTF and PCM are suitable for most of the systems [44]. Due to its ease of implementation and its demonstrated viability [45], the enthalpic method is one of the more widely used [33]. Thus, it was selected for the numerical part of this study, based on a previous work [37] adapted to the particularity of the prototype presented in paragraph 3.  
The aim of this numerical study is to explore the influence of monitoring parameters, materials' properties, and system size on the global system's performances. Those performances were assessed by the following parameters:  
* the storage responsiveness \(\tau_{95\%}\) and efficiency \(\eta_{\textit{stored/discharged}}\) as previously defined;
* the storage density, defined as the amount of energy stored by unit of volume;
* the valuable energy, defined as the energy discharged under industrial conditions, meaning above a temperature or power threshold.
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
###### Abstract  
Nowadays, visible light communication (VLC) systems have become one of the candidate technologies for high data rate indoor communications. However, the main challenge to develop a high data rate VLC system is the narrow modulation bandwidth of light-emitting diodes (LEDs). Power domain non-orthogonal multiple access (PD-NOMA) is a promising scheme to enhance the spectral efficiency of downlink VLC systems. In this paper, we introduce cooperative PD-NOMA to the system to improve the signal reception for the far users. We evaluate the bit error rate (BER) and achievable rate performance of non-cooperative and cooperative PD-NOMA under perfect channel state information (CSI). Moreover, we drive analytic expressions for the BER and provide a Monte Carlo simulation results for verifying the validity of the derived analytical BER results. The results show that cooperative PD-NOMA outperforms non-cooperative PD-NOMA by 8.2 dB at BER \(10^{-6}\) and by achievable rate 14.1 bit/s/Hz at 45 dB in a two-user scenario.  
visible light communication (VLC); power domain non-orthogonal multiple access (PD-NOMA); cooperative PD-NOMA; bit error rate (BER); achievable rate +
Footnote †: journal: Applied
## 1 Introduction
With obtaining the dynamic response of ABA up to 350 km/h, the results have shown that high-frequency vibration exists in ABA. Figure 1: Rail joint (**a**) and rail weld (**b**). The above studies focused mainly on testing ABA. It is difficult to carry out tests under extreme conditions due to many unknown factors in the measured work and due to safety considerations. Consequently, it is vital to develop a numerical model for an in-depth investigation. In the past, problems of dynamics were widely processed by adopting a rigid multi-body dynamics model [18]. Results from the modal analysis introduced to the rigid model are regarded as a way to further consider high-frequency dynamic effects of wheel/rail high-speed rolling contact. However, upon considering the vibration mode, the wheel/rail contact geometry relationship becomes complex and difficult to solve because of the bending and torsion of the wheel/rail systems. In recent years, a new method--the explicit finite element (FE) method--has been used to solve problems of high-frequency wheel/rail rolling contact [6; 15; 19; 20; 21; 22; 23]. This method abandons three basic assumptions including infinite half space, steady rolling, and linear elastic materials of the classic contact algorithms such as [PERSON]'s CONTACT [22]. [PERSON] and [PERSON] [19; 20] developed a 3D explicit FE model based on this method to simulate the change in ABA at short-wave irregularities of rail at a speed of 140 km/h; their results agreed with that of the test site. It should be noted that [PERSON] and [PERSON]'s main purpose for developing such a model was to monitor squat defects on the rail surface and the degrees of damage. Therefore, the relationship between ABA and wheel/rail force was not given and we cannot estimate degradation of the defect through ABA as the evaluation index is always wheel/rail force or geometry size. The operating speeds of high-speed railway in China have reached 300 km/h, bringing higher requirements for safety. This paper aims to provide a new theoretical basis and analysis method to monitor and control welds at at such high speeds. First, the S transform was used to analyze vibration characteristics of ABA in the time-frequency domain at welds based on the measured data of 300 km/h. Then, a validated 3D wheel/rail rolling contact FE model was employed to simulate dynamic responses of ABA induced by rail welds under 300 km/h through investigating the influence of weld geometry on ABA. A quantitative relationship between ABA and wheel/rail force has also been established. Based on these results, an approach to estimate rail weld is proposed. The differences in ABA induced by rail weld, rail corrugation, and wheel flat are then discussed.
# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 2 Generation of Radar Chart
### Radar Chart Drawing
It is noted that the closed area defines the size of the image converted later. The radar charts generated are shown in Figure 3 below. The numbers 1-7 in the figure represent the 7 fault indicator variables, respectively. Through drawing the graphs, the changes of the fault indicator variable values and the correlation between the fault indicator variables can be intuitively reflected. Figure 3a shows radar charts of the fault indicator variables data under normal operation of the converter; Figure 3b presents radar charts of the fault indicator variables data under faulty operation status of the converter. In Figure 3a, the charts were plotted corresponding to the wind speeds of 3.1 and 6.4 m/s, respectively. In the case of normal operation of the converter, the fault indicator variables data do not change much and are relatively stable. Although the amount of data is large, the number of curves shown in the graph is relatively small because of the phenomenon of data overlap, and the graph is relatively regular. In Figure 3b, the charts were plotted under the operation condition with wind speeds of 4.3 and 6.5 m/s, respectively. In the case of faulty status of the converter in operation, the fault indicator variables data change remarkably, resulting in poor regularity of the graph and irregular lines. When the system fails, some fault indicator variable data appear to be 0, such as the power data, so that incomplete graphics are observed in the radar chart. It can be seen from the figure that although there is a certain overlap in the radar chart of the fault  
Figure 3: Radar charts covering the fault features. (**1**) Wind speed; (**2**) turbine rotor position; (**3**) active power; (**4**) reactive power; (**5**) generator rotor speed; (**6**) grid side converter voltage; (**7**) generator torque set-point. indicator variables data, there is still a big difference from the radar charts drawn by using the normal operation data.