esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis
## 2 Wind Data and Preprocessing  
We used the publicly available database obtained from the National Renewable Energy Laboratory's National Wind Technology Center (NWTC) [26]. The NWTC is located approximately 8 km (5 miles) south of Boulder, Colorado, 36 km (20 miles) northwest of Denver (United States). We use both M2 and M4 tower datasets. The M2 tower is located at the western end, while M4 is located at the southwest corner of the NWTC grounds. The data from M4 were obtained at 20 Hz, including various atmospheric properties (i.e., wind speed, wind direction, temperature, dew point, pressure, acceleration, and precipitation) collected from different heights (3 to 134 m). On the other hand, the data from M2 were taken every two seconds and averaged over one minute. All data were measured at different heights, starting from 2 to 80 m. Finally, based on the data quality, one-year data from each tower were selected for this study from the years 2004 and 2018 for M2 and M4, respectively.  
The wind speed data were preprocessed by imputing the not-a-number (NaN) and encoded values using a sliding window average. Let the wind speed data \(y\) have missing data at time t. The average of the sliding window \(y=[y_{t-2},y_{t-1},y_{t+1},y_{t+2}]\) was used for imputations, considering the location (head or tail of data) and consecutive missing values. The high-resolution (20 Hz) wind speed data were then averaged over different time intervals (i.e., one, two, and three minutes) to investigate the wind speed and power output variability. The World Meteorological Organization (WMO) standard for estimating the mean wind is the 10-min average [27]. For smaller intervals, WMO has recommended wind speed conversion gust factors for four different land classes. The recommended gust factors are higher than 1 for the averaging intervals implemented in this study. Therefore, in this paper, the average wind speed is calculated by using a simple scalar average of the wind speed observations, regardless of gust effects. Figure 1 shows the one-minute average wind speed data for both the M2 and M4 towers. It is worth noting that the wind speed data for the M4 tower have some missing observations for long periods (sometimes days at a time, as shown in Figure 1b). However, we visually inspected the data and only considered the observations with fewer missing values and less noise.  
Figure 1: Example of one minute of wind speed data: (**a**) M2 tower; (**b**) M4 tower.
## 4 Engineering Example  
### Overview of the Project  
The data utilized in this research were obtained from an automated corrosion monitoring system implemented in a city's concrete sewer pipelines. The data collection and transmission flowchart is shown in Figure 7. The monitoring equipment adopts a buried multi-probe corrosion sensor, which integrates the collector and multi-functional corrosion monitoring probe into one and can monitor multiple corrosion parameters in real time. The sensor cable is introduced into the special junction box reserved in the pipe section through the pre-embedded pipe and distributed along the longitudinal direction of the pipe section at multiple points. Due to the fluidity of the corrosive medium in the pipe, the closer monitoring points may collect the relevant results. This study selected a section with neighboring local monitoring points M1-M6 as an example of engineering data. The frequency of data collection is once every hour, and the collection period is from 21 July 2021 to 26 July 2024, with a total of 24,796 periods of data obtained.
## 4 Results and Discussion
### Geographical Distribution of Carbon Footprint Studies  
Research on carbon footprints is concentrated in different regions globally, with this geographical distribution varying according to research capacity, level of economic development, and regional environmental policies. According to the literature review results, the countries that contribute most to carbon footprint studies are the United States, China, and European Union countries.  
The United States is one of the leading countries in this field, producing a vast amount of literature. Research on energy efficiency, sustainable production, and the integration of renewable energy sources is particularly supported by U.S. environmental policies and renewable energy incentives. China, due to its industrialization and increasing energy demand, has gained significant prominence in carbon footprint studies, with a focus on efforts to achieve environmental sustainability goals. The studies are typically directed toward optimizing industrial production processes and improving energy efficiency.  
European Union countries, such as Germany, France, and the United Kingdom, stand out for their policies aimed at reducing carbon emissions and play a significant role in carbon footprint research. Studies in Europe focus on topics like LCA and green supply chain management. There has also been an increase in carbon footprint research in developing countries such as India, Brazil, and South Korea. These studies focus on efforts to reduce energy consumption and carbon emissions in production processes.  
Overall, the geographical distribution of carbon footprint research is directly linked to the environmental policies and economic development levels of countries. Developed countries lead carbon footprint research thanks to their technological and academic capacities, while developing countries seek solutions to the environmental challenges they face during industrialization.  
The data in Table 6 highlights the primary focus areas and contributions of countries in carbon footprint research. The United States plays a leading role in energy efficiency (30), sustainable production (25), and renewable energy (45), while China focuses on industrial processes (50) and energy demand optimization (50).  
Among European Union countries, Germany emphasizes Life Cycle Assessment (30), France focuses on green supply chain management (20), and the UK prioritizes emission reduction policies (40). Developing countries like India, Brazil, and South Korea are also making significant contributions, with India focusing on energy consumption (35), Brazil on emission reduction in production (25), and South Korea on optimizing energy processes (20).  
Figure 6: Integration of ERP Systems, Industry 4.0, and Sustainability in Carbon Footprint Management: Holistic Approaches and Solutions.  
This distribution shows that research priorities align closely with the environmental policies and economic goals of each country.
# Light Harvesting and Optical-Electronic Properties of Two Quercitin and Rutin Natural Dyes
## Results and Discussion
### Electrochemical Characteristics  
Electrochemical characteristics can be applied to evaluate the photoelectric properties of molecules. The electrochemical characteristics of S-Q and S-R are measured by cyclic voltammetry measurements [44], as shown in Figure 3. By intersecting the two tangent lines corresponding to the rising current curve and the starting current curve, one can obtain the onset oxidation and the reduction potentials of S-Q are \(-0.26\) eV and \(0.59\) eV, and those of S-R are \(-0.37\) eV and \(0.62\) eV, respectively. When the working electrode is a glassy carbon electrode, and the reference electrode is an Ag/AgCl electrode, the HOMO energy and the LUMO energy can be calculated with the following calculation formulas: \(E_{HOMO}~{}=~{}-(E_{ox}+4.4)~{}eV\), \(E_{LUMO}~{}=~{}-(E_{rad}+4.4)~{}eV\), where \(E_{ox}\) represents the onset oxidation potential and \(E_{rad}\) represents the reduction potential [45]. Therefore, the HOMO energy is S-Q (\(-4.99\) eV) > S-R (\(-5.02\) eV), and LUMO energy is S-Q (\(-4.14\) eV) < S-R (\(-4.03\) eV). The HOMO energy of S-Q is over S-R, and a high molecular HOMO energy means a better molecular electron donating ability. \(E_{g}\) of S-Q is smaller than that of an S-R, and small \(E_{g}\) shows that molecules need less energy to be excited. In view of those results, S-Q has a greater electron donating ability, and can thus be easier to excite when compared to S-R.  
To get a better understanding of the experimental results in depth, the energy levels of S-Q and S-R were theoretically calculated. Table 1 shows that the HOMO energy levels are S-Q (\(-\)5.834 eV) \(>\) S-R (\(-\)5.907 eV) and the LUMO energy levels are S-Q (\(-\)1.862 eV) \(<\) S-R (\(-\)1.715 eV); the trend of \(E_{g}\) is S-Q (3.972 eV) \(<\) S-R (4.192 eV). The theoretically calculated trends of HOMO, LUMO, and E\({}_{g}\) of S-Q and S-R are consistent with the results of the experiment, which demonstrated that the theoretical calculation could have a reliable prediction for the microscopic properties of molecules. The HOMOs of S-Q and S-R are below the redox energy level of (\(I^{-}/I_{3}^{-}\)), and the value of (\(I^{-}/I_{3}^{-}\)) is around \(-\)4.8 eV [46], which shows that S-Q and S-R can receive electrons from the redox couple when they lose electrons. The LUMO energy levels of S-Q and S-R are higher than the (CB) of TiO\({}_{2}\) (\(-\)4.0 eV) [47], which contributes to a smooth electron injection into the TiO\({}_{2}\) electrode. Small \(E_{g}\) is beneficial to the red-shift of absorption spectra and S-Q has a smaller \(E_{g}\) than S-R. Therefore, S-Q has red-shifted when compared to S-R from the theoretical simulated results, which is beneficial for S-Q to harvest more light. The small energy gap also shows that S-Q is more easily excited.
# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
## 3 Data Collection
### Dataset  
Table 2 displays the raw data collected in ground tests. Due to the sampling frequency of the rover's position/attitude (20 Hz) having inconsistency with the sampling frequency of the motor current/velocity (1 Hz), the current and angular velocity data needed to be interpolated by simple linear interpolation. The step time of the research was set to 0.05 s [22]. On TYII-2 and JLU Mars-2, approximately 7500 and 4600 data points were obtained, respectively. The hold-out method was used to randomly split the ground test data into the training set (66.7%) and testing set (33.3%). In this paper, each ground type was used to obtain a single trained model. Therefore, we would like to compare which model is more applicable to the Mars conditions, so that we can see which type of simulated soil is more similar to the soil in the Zhurong landing areas.
# A Review and Experimental Evaluation on Split Learning
## 1 Introduction
Generally, we categorize them into three types: model-split-only, weight aggregation-based, and intermediate data \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain** & **Dataset Name** & **Data Partitioning** & **References** \\ \hline \multirow{8}{*}{Vision} & MNIST [45] & IID and Non-IID & [18; 46; 47; 48] \\  & Fashion-MNIST [57] & IID and Non-IID & [18; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60] \\  & CIFAR [61] & IID and Non-IID & [17; 50; 51; 52; 53; 54; 59; 65; 66; 67; 68; 69; 70] \\  & HAM1000 [71] & IID and Non-IID & [67; 68] \\ \cline{2-4}  & CINIC-10 [72] & IID and Non-IID & [68; 69] \\ \cline{2-4}  & ImageNet [73] & IID & [13; 48] \\ \hline \multirow{8}{*}{Healthcare} & CheXpert [74] & IID & [14] \\  & IID and Non-IID & [16; 75] \\ \cline{2-4}  & ECG [76] & IID and Non-IID & [19; 52] \\ \cline{2-4}  & Diabetic Retinopathy [78] & IID & [14] \\ \hline \multirow{2}{*}{Finance} & Bank [79] & IID & [80] \\ \cline{2-4}  & Adult [81] & IID & [80] \\ \hline \multirow{2}{*}{Graph} & CORA [82] & IID and Non-IID & [83] \\ \cline{2-4}  & PubMed [84] & IID and Non-IID & [83] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of datasets and partitioning methods used in recent SL studies. aggregation-based approaches. We annotate them along four dimensions and provide suggestions for which scenarios are suitable for each of them. * Detailed Experimental Evaluation of Existing SL Paradigms in Different Scenarios: We conducted extensive experiments to compare existing SL paradigms in different scenarios using the SLPerf framework. In the remainder of this paper, we first review the SL paradigms in Section 2. In addition, we present an extensive experimental evaluation of SL paradigms in Section 3. Finally, we conclude the entire article in Section 4.
## 2 Materials and Methods
### Genetic Analysis of Microbial Population
#### 2.5.3 Bioinformatic Analysis  
The output sequencing data were analyzed using the CLC Genomics Workbench 8.5, with the CLC Microbial Genomics Module 1.2 software (Qiagen, Hilden, Germany). Readings were trimmed, demultiplexed, and paired ends were joined. Then, the chimeric readings were identified and removed. The data were clustered independently against two reference databases at 97% similarity of operational taxonomic units (OTU). The SILVA v119 database [29] was used for taxonomy annotation and biodiversity analysis, and the Green-Genes 13.5 database [30] was used for PICRUST analysis. Selected biodiversity indices were determined: OTU number, Simpson's index, and phylogenetic diversity. Raw sequence data were deposited in the Sequence Read Archive (SRA) as project no. PRJNA831882.  
In order to better characterize the analyzed microbiome, a linear discriminant analysis (LDA) effect size (LESe) [31] was performed. It allowed for the identification and selection of the most differentially abundant taxa. The microbial biomarkers that best describe the differences between the groups differing in the level of supplementation with nitrogen compounds were assessed. The following groups were determined: deficiency of nitrogen compounds: variants NP0 and NP1; and no deficiency (sufficient level: variants NP2 and NP3). LDA was coupled with effect size measurements: a non-parametric Kruskal-Wallis rank-sum test (\(p<0.05\)), and the Wilcoxon rank-sum test (\(p<0.05\)). A threshold of 3.5 for the logarithmic LDA score for discriminative attributes was established as the cut-off value.
###### Abstract
Therefore, the key point of the PCSSR algorithm is the distance information and how to generate it properly. In the previous study, however, researchers only apply another SR process to compute out the distance information, which only focuses on the spectral information but fails to utilize the spatial information. Despite the highly discriminative capability to achieve high classification accuracy, PCSSR suffers from the limitation for neglecting the spatial information of HSI. Since sample pixels have the characteristics of spatial continuality, failing to consider spatial information would miss such important characteristics that are beneficial for enhancing classification capability. Referring to the classification results in PCSSR paper, within a wide range of land covers for a certain class, we may observe mislabeled pixels that have been classified into a wrong class. Therefore, we conclude that the classification results by using only spectral information would lack spatial continuality and smoothness. In order to address the above-mentioned limitation, this work aims to incorporate the spatial distance information into PCSSR to improve the discriminative capability of PCSSR. In addition, for better estimating the spatial distance, we propose a new measurement method for spatial distance called class adjusted spatial distance (CASD). This new method takes into account both the spatial distance and class difference between each two pixels. By such means, we can obtain appropriate discriminative information for pixels belonging to the same class but with long spatial distance, by assigning a relatively small CASD value. The effectiveness of employing CASD for the regularization process in PCSSR was thoroughly verified by the experimental results. Experimental results on different datasets demonstrate that the proposed method can significantly improve the classification accuracy by incorporating the spatial information in the CASD metrics. Compared with state-of-the-start classification models, the proposed method achieves the highest overall accuracies of 99.71%, 97.13%, and 97.07% on BOT, KSC, and truncated IND PINE datasets, respectively, with a small number of training samples selected from each class. Specifically, the main contributions of this paper include the following two aspects. 1. We propose the concept of the CASD. The calculation of the CASD based mainly on the planar Euclidean distance and the shortest path algorithm. The CASD takes the class similarity between samples into consideration, which can make the measurement of distance more accurate and reasonable. 2. We apply the CASD to estimate the distance information needed in the PCSSR algorithm. The results show that, this approach can enhance the performance of the PCSSR algorithm when enough training samples are provided. We achieve the highest improvement of classification accuracy of 8.65% and 3.85% on the KSC and the BOT dataset when the number of labeled samples selected from each class reaches 20, and achieve 15.97% on the truncated IND PINE dataset when the number of labeled samples selected from each class reaches 15.
# Highly Curved Lane Detection Algorithms Based on Kalman Filter
## 2 Research Method
### Top View Image Transformation  
The second step in our algorithm is to create a top view image of the road. The output image is the top view or bird's view of the road where lanes will be parallel or close to parallel after this transformation. Also, this transformation converts from pixels in the image plane to the world coordinate metric. If necessary we can measure distance using that transformed image.  
Figure 3 illustrates the geometry of the top-view image transformation. For the transformation, we need some parameters, where \(\theta_{h}\) is the horizontal view angle of the camera, \(\theta_{v}\) is the vertical view angle of the camera, \(H\) is the height of the camera located, and \(\alpha\) is the tilt angle of the camera.  
The height of the camera located in the vehicle is measured in the metric system. We can create two types of top-view image, one is measured by metric using \(H\) parameter, another is measured by pixel using \(H_{pixel}\) parameter. \(V\) is the width of the front view image \(P_{i}(U_{i},V_{i})\) and is proportional to \(W_{min}\) of the top view image field illustrated in Figure 3. Equations (4) and (5) show the relationship between the \(H\) measured by metric and \(H_{pixel}\) measured by pixel.  
\[\left\{\begin{array}{l}L_{min}\ =\ H*\tan(\alpha)\\ W_{min}\ =\ 2*L_{min}*\tan(\theta_{h}/2)\\ K\ =\ V/W_{min}\end{array}\right., \tag{4}\]  
Figure 3: Top view image transformation.  
Coefficient \(K\) is used to transform the metric into the pixel data.  
\[\left\{\begin{array}{l}H_{pixel}\ =\ H{\cdot}K\\ \gamma\ =\ \theta_{v}\Big{(}\frac{U-U_{i}}{U}\Big{)}\\ x_{i}\ =\ L_{i}-L_{0}\ =\ H_{pixel}*\tan(\alpha+\gamma)-H_{pixel}*\tan(\alpha) \end{array}\right. \tag{5}\]  
According to the geometrical description shown in Figure 3, for each point \(P_{i}(U_{i},V_{i})\) on the front view image, the corresponding sampling point \(P_{t}(U_{i},V_{i})\) on the top view image can be calculated by using the previous Equations (4)-(6).  
\[\left\{\begin{array}{l}\beta\ =\ \theta_{h}\Big{(}\frac{V-V_{i}}{V}\Big{)}\\ y_{i}\ =\ L_{i}{\cdot}\tan(\theta_{h}-\beta)\end{array}\right. \tag{6}\]  
Then RGB color data are copied from the \((U_{i},V_{i})\) position of the front view camera image to the \((x_{i},y_{i})\) position of the top view image.  
After top-view image transformation, line detection becomes a simple process, which only detects parallel lines that are generally separated by a given, fixed distance. The next step is to detect a straight lane using the Hough transform.
## 3 Analysis of Key Technologies  
### Registration Method of Axial Line of Engine Based on Least Squares Principle  
In order to locate the axial line of engine quickly and accurately, a fixture for blade clamping as shown in Figure 5 is designed in this study.  
The fixture is composed of three exact centrosymmetric components: the mounting column, the base platform and the pressing plate. The high-precision positioning and clamping of blade is achieved using an adjustable dovetail groove structure formed by sides of the pressing plate and base platform. In addition, the dowel datum planes of the measured blade are closely clamped with the sides of pressing plate, which guarantees the parallel relation between axial line of engine and side of base platform. Therefore, the precise adjustment of the engine axis can be realized by measuring and rotating the side of base platform.  
The registration method of axial line of engine based on the fixture for blade clamping shown in Figure 5 is described as follows:  
(1) Establishment of workpiece coordinate system. Firstly, a set of coordinates are obtained by measuring the selected profile of the mounting column with the probe system along the \(X\)-axis direction. Then, the center coordinates and radius of the mounting column are solved based on the least squares circle fitting for the collected coordinate data. Finally, through motion control, the workpiece coordinate system \(O\)-\(XYZ\) is established on the axis of the mounting column as shown in Figure 5.  
(2) Registration of the engine axis. As shown in Figure 6, the axial line of the engine is matched and aligned with the \(X\) axis in three steps. Firstly, a line with a length of 50 mm on the side of base  
Figure 5: Fixture for blade clamping.  
platform in _X_-axis direction is selected, and a set of coordinates are collected by measuring the planned profile with the probe system in spacing of 2 mm. Then, the slope and intercept are calculated based on least squares linear fitting for the measured data and the registration angle \(\alpha\) between the normal line on the side of the base platform and \(Y\) axis is obtained. Finally, the angle \(\alpha\) is adjusted to zero by the rotary motion of the measuring machine to realize the registration of engine axis.
## 9 Evaluation
### Channel Interference on Normal Public Wireless Transmitters  
When the safe wireless transmitter is far away from the normal public APs, there is no channel interference between them. In order to choose an appropriate distance between the safe wireless transmitter and the other normal public APs, we carry out experiments on different distances, and in the experiment, the channel spacing between the safe wireless transmitter and the other normal public AP is one.  
From Figure 10a,b, we know that when the distance between the safe wireless transmitter and other normal public wireless APs is from 0.5 m-2 m, the interference value of the received packets and the reference value of the received packets are almost the same. This is because the safe wireless transmitter and the other normal public APs are close, and even if there is no channel interference, it will also affect the normal communication. As the distance between them increases, the influence of channel interference will be weakened. As can be seen from Figure 10, when the channel interference between the safe wireless transmitter and the other normal public AP is more than 3 m, the channel interference between them will be weak.  
In Figure 10c,d, when the distance is less than 2 m, \(IS\) is high and \(AR\) is relatively low; when the distance is more than 3 m, \(IS\) decreases and \(AR\) increases sharply. Therefore, in order not to interfere with the other normal public APs, the distance between the safe wireless transmitter and the other normal public APs would be better when it is more than 4 m. When the distance is 4 m, the channel switch will not influence the communication of normal public APs. The longer the distance between the safe wireless transmitter and other normal public APs is, the smaller the impact of channel switching will be on the communication of normal public APs.  
Figure 10: Four parameters under different distances.  
Figure 9: The parameters to characterize the channel interference for different wireless transmitters.