esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual Saliency Detection Using a Rule-Based Aggregation Approach
## 2 Methodology
### Learning a Saliency Model  
Our goal is to take advantage of the outcomes from individual saliency detection methods to construct a saliency prediction model by using a rough-set-based learning algorithm. In Figure 2 we present the illustration of the learning process.  
Figure 2: Illustration of the training stage. Firstly, saliency maps are computed. Secondly, sampling is carried out by using labeled annotations from ground truth. Then, the learning step is performed by the MODLEM algorithm and the result is a rule-based saliency detection model.  
Basically, the learning process has been divided into three main stages. As the initial stage of our proposed method, we perform a feature extraction process on a given dataset. To clearly define the feature extraction process, let us consider four output images from state-of-the-art individual models: \(F00(x,y),F01(x,y),F02(x,y)\), and \(F03(x,y)\). They are all SMs with \(K\) different gray levels and \((M\times N)\) pixel size. For each pixel \(P\) located in the coordinates \((x,y)\), \((x,y)\in D,D=\{0,\ldots,M-1\}\times\{0,\ldots,N-1\}\), we obtain its corresponding gray value from each saliency map in such a way that each pixel can be represented by four values or features: \(P(x,y)=\{F00,F01,F02,F03\}\). Secondly, we label each \(P(x,y)\) taking into account the ground truth. Since the ground truth is provided as a binary map, we can classify pixels as non-salient or salient using values of 0 and 1, respectively.  
Thirdly, to make the training faster, we randomly sample the saliency maps. One hundred samples per image gave us the best result. We chose 90% of the samples corresponding to the non-salient class. Conversely, the 10% of the samples belong to the salient class. The set of samples are taken and submitted to the learning process by using the MODLEM algorithm. In Figure 3, we show an example of a set of the rules obtained; this set of rules is used in a later stage to saliency prediction.
## 2 Materials and Methods
### Methods  
Chemical analysis was performed by Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray microanalysis (EDX). Energy Dispersive X-ray Spectroscopy (EDS or EDX) calculated element concentrations based on the energy measurement of X-rays. The X-Rays are produced after an electron beam strikes the given point or area. Electrons from inner shells are released, and outer shell electrons cover their positions. The X-ray has a specific energy that is equal to the energy difference between the two shells and is characteristic to each element. The spectrum is taken at the same time for all energies. All the peaks are being evaluated, and those that have a value above the detection limit are presented in the results. The detection limit is not strictly dictated, and it depends on the area or the element; commonly it is somewhere between 0.1 and 0.2%, but it could be more or less than these values. Samples were measured through a Jeol JEE-4X, with an attached Rontec EDX microanalysis system equipped with a windowless Si (Li) detector, using acceleration voltage 15 kV and 20 kV. Before the measurement, small parts of each glass were mounted in epoxy resin blocks, then polished with a 1 \(\upmu\)m diamond paste and, last, to ensure conductivity, samples were coated with a thick carbon layer. Conductivity is necessary for observation, photography and analysis in an SEM-EDS. Standardization for the EDS analysis is achieved by a daily Co analysis. The EDS spectra is taken at the same time for all elements. The same peaks (K, L or M) are placed linearly. Each peak has on its right the next heavier element's same peak, and on its left the previous lighter element's same peak (K\(\alpha\), L\(\beta\), etc.). By analyzing Co we are taking a standard point for our spectra, and all other peaks are positioned accordingly. For each glass sample, we measured 5 different areas/angles and we present the mean value as a result.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 5 Conclusions  
This study developed a high-performance 10-A viscoelastic asphalt modifier through molecular engineering of conventional SBS-modified asphalt, establishing an optimized formulation with enhanced preparation parameters. Three key technological breakthroughs were systematically demonstrated:  
1. The novel preparation protocol enables precise control over viscoelastic balance, with modifier content demonstrating a critical threshold effect. At 9% dosage, the modified asphalt achieves optimal engineering performance, showing 16.6% and 9.3% simultaneous improvement in high-/low-temperature indices compared to 10-B asphalt mixture.
2. The redesigned modifier architecture enhances rutting resistance and water stability through strategic antioxidant-resin ratio optimization. This breakthrough resolves the traditional trade-off between fatigue resistance and thermal stability.
3. Friction dynamics analysis reveals that the PA-10 mixture's unique stabilizer-antioxidant synergy enables speed-adaptive friction characteristics, maintaining BPN > 85 even after 8 h abrasion cycles. This provides a theoretical foundation for smart pavement design in extreme climates.  
This study's limitations include unvalidated long-term aging performance beyond 5-year service and pending economic feasibility analysis. Future work should focus on life-cycle cost modeling and multi-scale performance prediction frameworks. Industrial-scale production trials are recommended to verify field applicability.  
W.C.: Writing--original draft, Z.Z. and W.C.: Data curation. W.W., C.G. and Z.Z.: Methodology. J.W., Y.S., W.W. and X.Z.: Project administration. J.W., Y.S. and X.Z.: Investigation. J.W.: Supervision. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Shandong Provincial Transport Department--Research on Key Technologies for Testing and Evaluation of Accelerated Abrasion of Road Surface Functions [grant numbers 2020-MS1-047]; the Science and Technology Plan of Shandong Transportation Department [grant number 2020B28].  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the Shandong Transportation Research Institute for their support.  
The authors declare no conflicts of interest.
## 3 SENDI: System for Detecting and Forecasting Natural Disasters Based on IoT
### System Architecture  
The nodes of Tier 1/WSN which were modeled based on the WSN that can be found in Sao Carlos--SP, Brazil, form the basis of SENDI. Thus, the nodes do not possess a limited supply of energy, although they have a solar battery charger. In this tier, the basic function of the nodes is data collection. In effect, these nodes have several sensors that are able to pick up different features of the monitored environment and send these data to the Tier 2/fog computing. The nodes of this tier also have audio and visual devices (such as sirens and lights) that are able to warn the local population of the risks of a disaster occurrence.  
The nodes of Tier 2/fog computing are in turn responsible for the aggregation of data collected by the sensor nodes and the dispatch of the data to Tier 3/_Cloud_. It should be noted that a node from Tier 2 is only responsible for collecting data from Tier 1 when they are neighbors. Two nodes can be defined as neighbors when they are able to communicate with each other directly without the need for hops. As shown in Figure 1, a node from Tier 2 is responsible for the nodes from Tier 1 that lie within its range of direct communication.  
The nodes of Tier 2 are characterized by having a unlimited supply of energy (being that the only difference with respect to Tier 1 nodes) and help reduce the energy consumption of the sensor nodes (Tier 1/WSN) while messages are being sent to Tier 3/_Cloud_. Thus, like the nodes of Tier 1/WSN and for reasons of redundancy, the nodes of Tier 2/fog computing also have audio and visual devices that are able to warn the local population of the risk of a disaster incident.  
Finally, Tier 3 is formed by _Cloud_, which provides the necessary resources for the data to be stored, combined with other online information about the monitored environment and processed by means of the forecasting models created via ML. If a risk of disaster is forecast, the _Cloud_ also send warnings online and to the nodes of Tier 2/fog computing, which passes on these warnings to the nodes of Tier 1/WSN.  
This configuration adds considerable flexibility to SENDI, since it possesses the simplest nodes that only carry out data readings, together with those that support the processing of large amounts of data and combinations of data, while at the same time making forms of energy saving available. This architecture can be easily modified depending on the monitored environment and the resources available in the locality.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 4 Experimental Simulation and Result Analysis
### Evaluation Indicators for Prediction Performance  
The prediction performance of the neural network model was evaluated by calculating the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the test sequence profile data using Equations (15) and (16):  
\[RMSE=\sqrt{\frac{1}{M}\sum_{m=1}^{M_{test}}\sum_{n=1}^{N}\sum_{h=1}^{H}\sum_{w =1}^{W}\left(f^{-1}(\hat{I}_{m,n})-G_{m,n}^{D}\right)^{2}} \tag{15}\]  
\[MAPE=\frac{100\%}{M}\sum_{m=1}^{M_{test}}\sum_{n=1}^{N}\sum_{h=1}^{H}\sum_{w =1}^{W}\left|\frac{f^{-1}(\hat{I}_{m,n})-G_{m,n}^{D}}{G_{m,n}^{D}}\right|, \tag{16}\]  
where \(M=M_{test}\times N\times H\times W\); \(M_{test}\) = 276 represents the number of test sequences; \"\(n\)\" denotes the profile grid point data of the \(n\)th frame in the \(m\)th test sequence, with \"\(n\)\" and \"\(w\)\" representing the vertical and horizontal grid points corresponding to the profile; and \(f^{-1}(\hat{I}_{m,n})\) indicates the projection of the predicted pixel value of the \(n\)th frame in the \(m\)th test sequence to the predicted profile grid point value, using Equation (10).
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 2 Strategies
### Satellite and Correction Data Selection  
In order to obtain PPK solutions, observation files from nearby base stations and satellite ephemeris files are required. The ephemeris files were pulled from the International GNSS Service (IGS) website. The files chosen were BRDM files that contain navigation parameters for all GNSS constellations over the course of the data collection day. The BRDM files are generated as part of the Multi-GNSS experiment (MGEX), where the signals from multiple constellations, including GPS, GLONASS, BeiDou, Galileo, QZSS, NAVIC, and SBAS are merged into combined broadcast ephemeris files [19]. The base station observation files were pulled from the National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey (NGS) website for the Stanford Linear Accelerator Center (SLAC) station. SLAC, currently known as the National Accelerator Laboratory, is one of the 17 Department of Energy national laboratories, and is located at an approximate distance of at most 35 km from the route, which is within the maximum recommended range of 50 km for decimeter-level accuracy RTK or PPK [7]. The provision of GPS, GLONASS, and Galileo observation data, along with the SLAC station's relative proximity to the data collection routes, made it a desirable site to utilize when attempting to eliminate atmospheric and clock errors in the PPK solution.
# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 4 Experiment
### Evaluation Indicators  
In order to evaluate the effectiveness of the method proposed in this paper on the segmentation of these two data sets, this paper analyzes the sensitivity, specificity, accuracy and F-measure evaluation indicators by generating a confusion matrix.  
It should be noted that the F-measure indexed here is the same as the dice score in the disc and cup segmentation fields. In addition, in the evaluation of the Drishti-GS1 data set, this paper adds an evaluation index which is widely used in the industry: boundary distance localization error (BLE) [42]. BLE is used to measure the error (in pixels) between the predicted cup boundary (\(C_{o}\) ) and the true label boundary (\(C_{g}\)), which is defined as follows:  
\[BLE(C_{g},C_{o})=\frac{1}{n}\sum_{\theta=0}^{\theta_{n}}|r_{\theta}^{g}-r_{ \theta}^{o}| \tag{8}\]  
where \(r_{\theta}^{g}\) represents the radial Euclidean distance from the predicted boundary point to the centroid in the direction \(\theta\), and \(r_{\theta}^{o}\) represents the radial Euclidean distance from the boundary point of the real label to the centroid in the direction \(\theta\). In the evaluation, 24 equidistant points are considered \(n=24\). The ideal value for BLE is 0.
# Code Similarity and Location-Awareness Automatic Program Repair
## 6 Related Work
### Genetic Programming-Based Repair Approaches  
[PERSON] et al. [2] proposed GenProg. The basic idea is that the code is redundant, so the patch in the reuse project has the probability of generating the correct patch. During the patch search process, mutation and crossover operations are used to increase the patch search space. The population is selected by the number of test cases passed by the patch to iterates until there is a patch that passes all test cases. This work has important implications for automatic program repair and has prompted more researchers to explore automatic program repair. However, GenProg is in the initial stage of the automatic repair approach, and the content has not yet matured. [PERSON] et al. [14] believe that the fitness selection function in genetic programming does not play a significant role in candidate patch selection, so random search is used instead of genetic search. It was found that the random search consumed less time to generate the patches generated by GenProg. [PERSON] et al. [4] proposed a new code modification representation to further optimize the genetic algorithm. They encoded the code representation method on the same chromosome with the mutation operator and the code element corresponding to the operator. This method can realize different mutation operations on the same code, which is more flexible than the traditional genetic algorithm and can improve the patch generation capability of repair tools. [PERSON] et al. [25] accelerated the repair process by filtering the test cases in the program, and [PERSON] et al. [26] improved the original GenProg repair method by optimizing the population initialization and mutation processes of the genetic algorithm. Then, [PERSON] et al. [27] proposed an approach that combines search-based automatic program repair techniques with a neural machine translation-based approach, and [PERSON] et al. [28] used novel search algorithms to optimize GenProg's search and avoid getting trapped in local optimal solutions. Because of the limited variety and simplicity of structures in bytecode, the patch space covered by the mutation operator is larger. In addition, bytecode modification can be run directly on the JVM without additional compilation, so it is more efficient. The actual verification results also show that this method is more efficient than the existing techniques.  
The major difference between SLARepair and GenProg lies in the candidate patch stage, where we improve patch generation efficiency by filtering candidate patches using code similarity. In order to improve search efficiency, RSRepair [14] uses random search and abandons the use of fitness functions to guide patch generation. While SLARepair optimizes the above aspects, it also improves population initialization. The methods proposed by [PERSON] et al. and [PERSON] et al. are to improve patch quality by optimizing the way to guide patch generation. SLARepair not only optimizes bootstrap patch generation but also focuses on the efficiency of patch generation.
###### Abstract  
The trend of increasing the power output and nominal load capacities of wind turbines (WT) over time has been driving the construction of testing facilities with increasing load capacities for testing WT drivetrain components prior to field deployment. Due to the high investment and operational costs of such facilities, a need exists to design accelerated tests that cover load situations corresponding to expected field conditions while maintaining high time-efficiency. This investigation addresses this need by presenting a methodology to achieve the following goals. Firstly, identifying ranges and combinations of WT 6-degree of freedom (6-DOF) rotor loads is to be expected in the field. This is achieved using aeroelastic multibody simulations (MBS) of an MBS WT model being subjected to simulated wind fields covering the design load cases outlined in the IEC 61400-1 standard and by analyzing the simulated time-series data to design accelerated tests that efficiently and realistically cover the design space of the variables, e.g., 6-DOF rotor loads, to be applied during WT drivetrain testing. The designed tests are to take place on a purpose-built test rig that allows for the application and control of the 6-DOF drivetrain input loads and rotational speed. Using the proposed method, accelerated tests were designed that efficiently cover load combinations within the realistic regions of the design space. A comparison with a full factorial design of experiments shows a significant (95+ %) reduction in total test time as well as the ability of the proposed method to help to avoid unsustainable and unrealistic load conditions within the design space that could result in costly, unintended drivetrain failures during testing.  
multivariate data analysis; wind turbine testing; drivetrain simulation; wind simulation; wind energy +
Footnote †: journal: Applied  
Article  
From Simulations to Accelerated Testing: Design of Experiments for Accelerated Load Testing of a Wind Turbine Drivetrain Based on Aeroelastic Multibody Simulation Data
# Human Activity Classification Based on Angle Variance Analysis Utilizing the Poincare Plot
## 2 Materials and Methods
### Laying and Sitting-Standing Classification  
Laying and sitting-standing activities were classified with the mean angle from the static activities. After the initial orientation, where laying data were considered as a reference, it shows the best accuracy (100%), sensitivity (100%), and specificity (100%). Overall, the count-based classification result shows accuracy (98.26%), sensitivity (98.14%), and specificity (98.31%). All the results were analyzed with 10-fold cross-validation. The ROC curve for laying and other static classification with the Actigraph count and mean angle-based classification is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Static/Dynamic Classification**} \\ \hline  & \multicolumn{2}{c}{**Accuracy (\%)**} & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline Actigraph count & X & 100 & 100 & 100 \\ \hline  & Y & 99 & 98 & 100 \\ \cline{2-5}  & Z & 98 & 98 & 99 \\ \cline{2-5}  & X, Y, Z & 100 & 100 & 100 \\ \hline  & Poincare plot & SD1 & 100 & 100 & 100 \\ \cline{2-5}  & SD2 & 100 & 100 & 100 \\ \cline{2-5}  & SDRR & 100 & 100 & 100 \\ \cline{2-5}  & SD1, SD2, SDRR & 100 & 100 & 100 \\ \cline{2-5}  & \multicolumn{2}{c}{**Laying-Sitting/Standing Classification**} \\ \hline  & \multicolumn{2}{c}{**Accuracy (\%)**} & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline Actigraph count & X & 98 & 98 & 98 \\ \hline  & Y & 71 & 34 & 74 \\ \cline{2-5}  & Z & 69 & 66 & 69 \\ \cline{2-5}  & X, Y, Z & 98 & 98 & 98 \\ \hline Angle & Mean Angle & 100 & 100 & 100 \\ \hline  & \multicolumn{2}{c}{**Sitting-Standing Classification**} \\ \hline  & \multicolumn{2}{c}{**Accuracy (\%)**} & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline Actigraph count & X & 48 & 50 & 0 \\ \hline  & Y & 55 & 73 & 52 \\ \cline{2-5}  & Z & 46 & 48 & 43 \\ \cline{2-5}  & X, Y, Z & 57 & 67 & 54 \\ \hline Angle & Mean Angle & 88 & 97 & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy with count-based and angle-based classification for the HAR dataset.  
Figure 10: ROC curve for laying and other static classification. The left panel denotes the ROC curve for the Actigraph count-based laying and other static classification. The right panel denotes the mean angle-based static and dynamic activities’ classification.
## 4 Results
### Qualitative Study of Acceptability According to the Role Assumed
Furthermore, the initial positive impression that probably led participants to take part in the study at D0 (55.6% of responses) suffers from the dysfunction of the technology, since 62.2% of the categorized responses at D21 are negative (of which 81.4% concern a judgment of the technology--\(\chi^{2}\) (466) = 23.12; \(p<0.001\)). However, it is mainly the change in the participant's self-assigned role that provides an explanation for the decrease in the intention to use. In fact, if 95% of the participants position themselves at D0 in the role of a future user \"U\", 61.1% of them changed roles at D + 21 and positioned themselves in the role of tester \"T\" of the system (\(\chi^{2}\) (18) = 11.00; \(p<0.001\)). This role change is, therefore, particularly marked during the 21 days. This change is reflected in the migration of the majority of responses given by participants, U+ at D0 (for 85% of participants) to T\(-\) at D + 21 (72%--\(\chi^{2}\) (38) = 16.1; \(p<0.001\)). While the decrease in the number of favorable mentions of the technology with regards to participant's projection as users is not significant between D0 and D21 (\"U+\": 5.4 vs. 4.6; t (19) = -1.35; ns), it appeared to be highly significant with regard to the increase in the number of mentions unfavorable to technology as a tester (\"T--\": 2.0 vs. 6.5; t (19) = 7.43; \(p<0.001\)--see Figure 4). Finally, participants who take on the role of future user consider the device to be more useful than those who only project themselves in the role of tester (4.37 vs. 3.19, cf. Table 3). This difference is probably due to the real and ongoing malfunctions of the technology tested. However, even more problematic, the latter (those who see themselves as testers) have less intention of using the device (3.93) than the former (those who see themselves as future users, 5.25). Figure 3: Dispersion by measurement time of judgments about the system, categorized according to the role taken and the valence of the response (SHERPAM project).
## 4 Rectification with Checkerboard Pattern  
For the measurements presented in this work, two different types of checkerboard grids are used. In both cases, a single calibration image with the grid is taken before the measurements. This calibration image is then used to create a transformation, which is applied to the subsequent measurements. This transformation warps the checkerboard grid so that all the chessboard squares are the same size. The goal is to transform images and sequences in such a way that the field of view becomes rectified without any distortion due to the perspectivity or due to the curvature of the surface.