esa-sceva/satcom-chunk-collection huggingface dataset:
# Multipath Error Fusion Modeling Methods for Multi-GNSS
## 5 Conclusions  
In order to realize the application of the SF method in multi-GNSS multipath elimination, in this research, we analyzed the time shifts of orbit repeat periods for different systems from different days in 2018 and 2020. The time shifts of orbit repeat period for satellites in the same types of constellation are relatively stable in the long-term statistical results. The orbit repeat periods of the satellites in BDS2 are consistent with those of satellites in the same type of constellation in BDS3. After numerical investigations, we found that in the BDS system, the time shifts \(t\) in (2) can be set as 246s, 246s, and 1700s for GEO, IGSO, and MEO satellites, respectively, and the time shift of the Galileo satellites can be set to a mean value of 2424s. Through the above statistical results, we have successfully expanded the SF method to both BDS3 and Galileo systems, and realized the multi-GNSS multipath error mitigation in the observation domain.  
Furthermore, considering the inconvenience of utilizing the SF method for multi-GNSS multipath error modeling, we present a space domain multipath error mitigation method called MHGM. The space domain MHGM demonstrates similar correction effects as the SF method in the observation domain, but the former is more flexible for multipath error modeling in multi-GNSS data processing. Moreover, modeling the multipath error using multi-day data can further improve the effectiveness of the MHGM to a certain degree.  
Conceptualization, X.Z., Y.W. and W.T.; methodology, Z.L.; software, Y.W.; validation, Y.L. and R.F.; writing--original draft preparation, Y.W. and Y.L.; writing--review and editing, C.D. and J.C.; project administration, W.T. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (grant number 2017 YFB0503702), the National Natural Science Foundation of China (grant numbers 41804028 and 41874037), the Wuhan science and technology project (grant number 2018010401011271), and the Joint Fund of the Ministry of Education of China for Equipment Pre-research (grant number 6141A02022372).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.
# Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane
## 2 Method
### Physical Setup  
The listening test was performed in a double-walled ITU-R BS. 1116-compliant listening room (6.2 m \(\times\) 5.2 m \(\times\) 3.5 m) at the Applied Psychoacoustics Laboratory of the University of Huddersfield. The listening room has a short reverberation time (RT = 0.25 s) and a low noise level (NR = 12). Early reflections within 15 ms after direct sound generated by any loudspeaker used in this study were attenuated by minimum 22 dB for side walls and minimum 14 dB for the floor, which exceeded the requirement of the ITU-R BS. 1116 recommendation for critical listening room design. Therefore, it was considered that the room acoustics would have a minimal influence on the SRT measurement conducted in this study.  
Genelec 8331A co-axial loudspeakers were mounted at \(-30^{\circ}\), \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\) and \(90^{\circ}\) in the median plane. The distance between each loudspeaker and the listening position was 2 m, except for the \(90^{\circ}\) loudspeaker (1.4 m). All loudspeakers were level-matched and their room responses were equalised at the listening position using Genelec GLM 3.0 software.  
The stimuli were reproduced via custom listening test software created in Max-MSP, through a Merging Technology (Puidoux, Switzerland) Horus audio interface. The target was always played from the \(0^{\circ}\) loudspeaker, while the masker was played from one of the five loudspeakers in each test trial. The target was presented initially at 44 dB L\({}_{\text{Aeq}}\) and varied across the course of the test, while the level of the masker was held constant at 68.6 dB L\({}_{\text{Aeq}}\). This was calibrated using a DPA 4006A omni-directional microphone placed at a position corresponding to the subject's head.
## 3 Results
### VOC Correlation Coefficients  
The correlation coefficients for particular VOCs released from wheat plants have been determined. All correlation coefficients were found to be positive (Figures 1-4). For the 1 SL treatment, we observed statistically significant results between all pairs of VOCs, except for (\(Z\))-3-hexen-1-yl acetate and linalool, and (\(Z\))-3-hexene-1-yl acetate and (\(E\))-\(\beta\)-farnesene (Figure 1). For the 3 SL treatment, we observed statistically significant results between all pairs of VOCs except for (\(Z\))-3-hexen-1-yl acetate and linalool, (\(Z\))-3-hexen-1-yl acetate and \(\beta\)-carophyllene, and (\(Z\))-3-hexen-1-yl acetate and (\(E\))-\(\beta\)-farnesene (Figure 2). For the MJ-V treatment, no significant correlations were observed between (\(Z\))-3-hexen-1-yl acetate and the following four VOCs: (\(Z\))-\(\beta\)-ocimene, linalool, \(\beta\)-carophyllene, and (\(E\))-\(\beta\)-farnesene (Figure 3). For the MJ-S treatment, all pairs of VOCs were found to be correlated (Figure 4).  
Regarding the intensities (emission rates) of particular VOCs at the three time points chosen, the majority of the VOCs showed the highest release at 48 h after the stress induction. The clearest trend was observed for the \"Serenada\" variety with 3 SL and MJ-S treatment. In addition, for the \"Serenada\" variety, and with MJ-V treatment, the highest released levels for C6 aldehydes and the corresponding alcohols were observed.  
The correlation coefficients demonstrate that the emission levels of the C6 aldehydes and alcohols, classified as Green Leaf Volatiles (GLVs), are well correlated, and especially so for those associated with mechanical wounding that imitated Bishop's Mitre shieldbugs feeding on wheat. Of note is that the monitoring of MeJA application efficiency may be done by observing a single compound from (\(Z\))-\(\beta\)-ocimene, linalool, \(\beta\)-carophyllene, and (\(E\))-\(\beta\)-farnesene group.  
1. (_D_)-3-hexesesal
2. (_E_)-hexesal
3. (_D_)-3-hexes-1-cl
4. (_E_)-2-hexes-1-cl
5. (_D_)-3-hexes-1-ylacetate
6. (_E_)-p-ocinese
7. Inanol
8. p-caryophilene
9. (_E_)-6-farmesene  
Figure 3: Heatmap for Pearson’s correlation coefficients between observed VOCs resulting from MJ-V treatment (\({}^{***}\)\(p<0.001\)).  
Figure 2: Heatmap for Pearson’s correlation coefficients between observed VOCs resulting from 3 SL treatment (\({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\); \({}^{***}\)\(p<0.001\)).
# Fiber-Optic System for Monitoring Pit Collapse Prevention
## 2 Materials and Methods
### Experimental Installation
The data processing device (DPD) contains an optical radiation source, which is a semiconductor laser with the wavelength of 650 nm and power of 30 mW. In the DPD, there is a high-resolution photomatrix that perceives all the changes in the light wave during mechanical impact on the OF. The principle of operation of the data processing unit is based on controlling the changes in the properties of the light wave passing through the OF, followed by the analysis of changes in its intensity. The laser beam passes from the radiation source through the optical splitter (OS) and connecting patch cord to FOSS. The OF ends diverge inside the ferrule and form additional losses in the event of a displacement, which leads to changing the intensity of the light spot incident on the surface of the photomatrix. Multiple plastic sheaths and Kevlar thread were used to better protect the optical fiber from mechanical damage. This thread protects the fiber from breaking. The optical fiber was coated on top with a layer of acrylic varnish 100 microns thick, then a plastic sheath 0.9 mm thick and an additional sheath with a diameter of 3 mm were placed. This scheme for protecting the sensor from mechanical damage has been tested many times in practice and is quite effective. In the future, it is planned to improve this scheme by using protective metal armored sleeves located from the sensor to the data block. This protection scheme is more effective and applies to many different external factors (weather conditions, animals, natural disasters, etc.) in comparison with existing protection schemes. At the same time, an alarm is triggered if the fiber-optic patch cord is damaged. It is built into the system to check the functionality of the sensor. Because quarrying takes place in open areas away from residential areas, wild animals may be nearby and cause damage, for example, foxes may chew through the fiber-optic patch cord.
## 4 Research on the Influence of Track Structure Stiffness Change on Track Deformation
### Lateral Stiffness under Block  
According to the calculation results of the simulation conditions of different lateral stiffness under block, the maximum values of the vertical displacement of the rail, the lateral displacement of the rail, and the gauge expansion are summarized in Table 11, the changes are shown in Figure 13.  
According to Table 11 and Figure 13, when the lateral support stiffness under the block increases from 80 kN/mm to 240 kN/mm, the vertical displacement of the rail changes little, and the lateral displacement and gauge expansion change simultaneously, and then show a decreasing trend. The range of amplitude change is 0.933\(\sim\)1.129 mm, 1.659\(\sim\)1.773 mm, and the relative change rates are 9.49%, 6.87%, respectively. The lateral and vertical displacement of the rail and the amplitude of the gauge expansion are all within the specified limits.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Vertical stiffness under rail (kN/mm) & 40 & 60 & 80 & 100 & 120 & 140 & 160 \\ Vertical displacement (mm) & 2.268 & 1.775 & 1.516 & 1.365 & 1.26 & 1.179 & 1.127 \\ Lateral displacement (mm) & 0.937 & 0.966 & 0.94 & 0.908 & 0.97 & 0.955 & 0.993 \\ Gauge expansion (mm) & 1.655 & 1.708 & 1.656 & 1.603 & 1.714 & 1.676 & 1.745 \\ \hline \end{tabular}
\end{table}
Table 10: Summary table of maximum values of track structure deformation control coefficients.  
Figure 12: Variations in the deformation coefficients under different vertical stiffness under block. (**a**) Wheel-rail vertical displacement change: left-time history waveform, right-maximum, (**b**) Wheel-rail lateral displacement change: left-time history waveform, right-maximum and (**c**) Change in gauge expansion: left-time history curve, right-statistical distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Vertical stiffness under rail (kN/mm) & 80 & 100 & 120 & 140 & 160 & 180 & 200 & 220 & 240 \\ Vertical displacement (mm) & 1.128 & 1.128 & 1.125 & 1.125 & 1.122 & 1.124 & 1.127 & 1.129 & 1.127 \\ Lateral displacement (mm) & 1.021 & 1.015 & 0.964 & 0.978 & 0.973 & 0.974 & 0.955 & 0.933 & 0.934 \\ Gauge expansion (mm) & 1.763 & 1.773 & 1.683 & 1.707 & 1.708 & 1.710 & 1.689 & 1.661 & 1.659 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Summary table of maximum values of track structure deformation control coefficients.  
Analysis on Dynamic Response Sensitivity of Vehicle-Track System under the Change of Track Structure Parameters
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 2 System Framework and Operation Process
### Brief Introduction of Fault Diagnosis Real-Time Processing System  
Real-time processing system mainly deals with accident-level information, including protection action information and circuit breaker tripping information, which is reported by substations in real-time. Therefore, this subsystem at the dispatching center takes a real-time response to the information and diagnoses grid fault rapidly. By using this subsystem, circuit breakers and protections with maloperation or refuse-operation and faulty elements are obtained, and the loss of accident-level information is analyzed.
## 3 Data and Preprocessing
### Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline
**Image Group** & **1** & **2** & **3** & **4** & **5** \\ \hline Image number & 1904 & 1472 & 464 & 1196 & 656 \\ Patient number & 119 & 92 & 29 & 80 & 41 \\ Slice thickness (mm) & 6 & 6 & 6 & 6 & 4 \\ Pulse repetition time/Echo time/ & 8000/102/2370 & 9000/1022500 & 8500/1022439 & 8800/152/2100 & 8525/142/2100 \\ Inverse time (ms/ms/ms) & & & & & \\ Imaging frequency (MHz) & 123.1678 & 123.2407 & 123.2463 & 127.7692 & 127.7706 \\ Pixel bandwidth (Hz/pixel) & 287 & 287 & 287 & 195.312 & 195.312 \\ Pixel spacing (mm) & 0.4492 & 0.4492 & 0.4492 & 0.4688 & 0.4688 \\ Receive coil & 12-channel & 12-channel & 12-channel & 8-channel & 8-channel \\ head coil & head coil & head coil & head coil & head coil \\ \hline  & & & & & \\ \hline
**Image Group** & **6** & **7** & **8** & **9** \\ \hline Image number & 924 & 548 & 644 & 384 \\ Patient number & 46 & 30 & 32 & 20 \\ Slice thickness (mm) & 5 & 5 & 5.5 & 5 \\ Pulse repetition time/Echo time/ & 8500/90/2439 & 8500/91/2439 & 9000/83/2500 & 9000/128/2500 \\ Inverse time (ms/ms/ms) & & & & & \\ Imaging frequency (MHz) & 123.2587 & 123.2622 & 123.2001 & 123.2292 \\ Pixel bandwidth (Hz/pixel) & 289 & 287 & 201 & 285 \\ Pixel spacing (mm) & 0.6875 & 0.4297 & 0.8984 & 0.8984 \\ Receive coil & 12-channel & 12-channel & 12-channel & 32-channel \\ head coil & head coil & head coil & head coil & head coil \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The details of the patient images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image Group** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Pulse sequence & Axial T2W & T1W FLAIR & T2W FLAIR & Axial T2 PRO- & T1 FLAIR & T2 \\ Slice thickness (mm) & 6 & 6 & 6 & 6 & 6 \\ Pulse repetition time/Echo time/ & 3500/95/NA & 2000/17/857 & 8000/102/2370 & 4582/96/NA & 1872/22/720 & 8525/146/2100 \\ Inverse time (ms/ms/ms) & 123.1678 & 123.1678 & 123.1678 & 127.7705 & 127.7705 & 127.7705 \\ Pixel bandwidth (Hz/pixel) & 287 & 287 & 287 & 195.312 & 195.312 & 195.312 \\ Pixel spacing (mm) & 0.8984 & 0.8984 & 0.4492 & 0.4688 & 0.4688 & 0.4688 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The details of the volunteer images.
# Gamma-ray Bursts: 50 Years and Counting!
## 3 The BATSE Era (1991-1996)
In 1997, two weeks before the discovery of the first X-ray and optical afterglows from BeppoSAX, they published a seminal paper in which they systematically predicted the multiwavelength afterglows of GRBs in a self-consistent manner [21]. In those years, new candidates for the sources of GRBs were also proposed. [PERSON] proposed that the collapse of a rapidly rotating Wolf-Rayet star (massive star whose outer hydrogen envelope is stripped away by a stellar wind), leading to a supernova, now known as the collapsar model for long GRBs, could be an appropriate progenitor of long-duration GRBs with a complex time profile [22]. The first mention of a millisecond magnetar (a newborn, rapidly spinning, highly magnetized neutron star) as a possible central engine [23] was made for the first time as an alternative to the standard hyper-accreting black hole scenario adopted by most modelers of the time. The tension between extragalactic models, favored by the observation of the spatial distribution of GRBs but with surprisingly high energy demands (at a distance of 1 Gpc, the energy required would be \(\sim\)10\({}^{51}\) erg, with a flow of \(\sim\)10\({}^{-7}\) erg cm\({}^{-2}\) s\({}^{-1}\)), and galactic models, with decidedly more \"reasonable\" energy budgets (\(E\sim 10^{43}\) erg at a distance of 100 kpc) but disfavored by observations, culminated in the so-called \"Great Debate\" of 1995, where [PERSON] and [PERSON] showed evidence and arguments as to why a scale distance should be preferred to the other. As we will see, the solution to this debate later came thanks to precise localization measurements from the BeppoSAX satellite. BATSE reported 2704 GRBs in its last catalog [24]. The failure of one of the three gyroscopes responsible for the maneuvering of CGRO determined the conclusion of the mission, and the satellite was de-orbited on 4 June 2000. This era can be summarized as follows:  
* BATSE observed a large number of GRBs, leading to better modeling of GRB properties. * GRBs come in all shapes and sizes, but two subgroups exist: short hard bursts and long soft bursts. * GRBs are distributed isotropically in the sky. This marked the beginning of the end of galactic models. Still, they were not ruled out completely, and this led to the Great Debate in 1995.
## 2 Datasets
### Visible/Infrared MSG SEVIRI Data  
The Meteosat Second Generation satellites have an altitude of about 36,000 km above the equator at 0\({}^{\circ}\) W and 41.5\({}^{\circ}\) E. With respect to its precursor instrument MVIRI (Meteosat Visible and InfraRed Imager), the EUMETSAT Spinning Enhanced Visible and InfraRed Imager (SEVIRI) [22] provides significantly increased information due to an imaging-repeat cycle of 15 min (30 min for MVIRI) and 12 spectral channels (3 channels for MVIRI), quantization with 10 bits per pixel (8 for MVIRI), and image sampling distances of 3 km at nadir for all channels except the high-resolution visible with 1 km (for MVIRI, 5 and 2.5 km, respectively). These enhanced time-space resolution performances allow to retrieve timely information on rapid weather development and are particularly helpful to rainfall retrieval algorithms. The high resolution visible channel has an IFOV of 1.67 km, and the oversampling factor is 1.67 that corresponds to a sampling distance of 1 km at nadir. The corresponding values for the eight thermal IR and the other three solar channels are 4.8 km IFOV, with an oversampling factor of 1.6 and a sampling distance of 3 km for nadir view. Eight of the channels are in the thermal infrared, providing, among other information, permanent data about the temperatures of clouds, land, and sea surfaces. One of the channels is called the High Resolution Visible (HRV) channel and has a sampling distance at nadir of 1 km, as opposed to the 3 km resolution of the other visible channels. Using radiation with wavelengths physically and significantly interacting with ozone, water vapor and carbon dioxide molecules, MSG satellites allow meteorologists to analyze the characteristics of atmospheric air masses in the three dimensions. The improved horizontal image resolution for the visible light spectral channel (1 km as opposed to 2.5 km) also helps weather forecasters in detecting and predicting the onset or end of severe weather. The crucial properties to detect rain (optical thickness, droplet size, cloud phase, cloud top temperature, and water vapor content) can be retrieved using the SEVIRI channels [19; 23]. This aspect will be more deeply addressed in Section 3.1 dealing with the selection of the radiances measured by SEVIRI to be used as input for our NN algorithms.
###### Abstract
The proposed system enhances the data rate and performance of the orthogonal transform division multiplexing (OTDM) technique and is based on using space-time coding (STC) with OTDM, to increase the system diversity and consequently the system performance. Simulation results provided by the authors show that the proposed technique achieves better performance when compared with other multicarrier techniques. The 15 th paper, authored by [PERSON], [PERSON], and [PERSON], analyzes the intrinsic interference of filter bank-based multicarrier systems with isotropic orthogonal transfer algorithm pulse-shaping [15]. Such intrinsic interference is treated as a parity symbol and an iterative soft-in-soft-out detector, which is based on a message-passing algorithm, is proposed to exploit the useful information of the intrinsic interference. Finally, the last paper, authored by [PERSON], [PERSON], and [PERSON], presents an augmented Lagrangian-based alternating-direction inexact Newton (ALADIN) method in order to estimate the Laplacian spectrum in a decentralized scheme for dynamic controlled networks [16]. The key feature of this paper is a direct solution to non-convex optimization for Laplacian spectrum estimation using the proposed ALADIN method. **Funding:** This research received no external funding. **Acknowledgments:** We would like to thank and congratulate all the authors for their valuable contributions to this Special Issue. We also thank the peer reviewers for their comments and suggestions, which helped the authors improve their papers. Finally, we would like to express our gratitude to the editorial team of MDPI _Applied Sciences_ for their continuous support. **Conflicts of Interest:** The authors declare no conflict of interest.
# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 5 Conclusions  
Since the malfunction of WTs directly impacts their power production capacity, characterizing the relationship between wind and power has attracted much interest. This dependence, however, is complex and non-linear since factors of a very varied nature (mechanical, electromagnetic, aerodynamic, control systems, etc.) are involved. One powerful way to express this relationship is through the wind-power curve. From the result of this work, after analyzing five WTs of a small WF, we note, after the estimation of their power curves from SCADA data, that:  
1. Estimated curves substantially differ from the curve that one can deduce from the data provided by the manufacturer.
2. Given that the locations of the WTs are not identical, the environmental conditions of each turbine are also slightly different. Consequently, the estimated curves for each WT reflect such differences and have slight differences.
3. Once the curve of one WT is determined from the training data, it is reproduced very accurately when re-estimated from the test data.
4. The anomaly filtering methodology introduced permits consistently estimating the power curves using simple ANNs.
5. When there is a problem in one of the WTs, such is the case of the WT84, where a fault is documented at one point in the test phase, the problem becomes visible in its power curve when estimated by test data.  
**Author Contributions:** Conceptualization, P.M.-P. and M.S.-S.; Methodology, P.M.-P.; Software, P.M.-P., J.A.H. and M.S.-S.; Validation, P.M.-P.; Investigation, P.M.-P., J.A.H. and M.S.-S.; Resources, J.S.-C.; Writing--original draft, P.M.-P. and M.S.-S.; Visualization, J.A.H.; Supervision, P.M.-P. and J.S.-C.; Project administration, J.S.-C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministerio de Ciencia e Innovacion of the Spanish Government (ref: PID2020-120314 RB-I00/AEI/10.13039/501100011033).  
Not applicable.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ANFIS & Adaptive network fuzzy inference system \\ ANN & Artificial neural network \\ GP & Gaussian Process \\ IQR & Interquartile range \\ LSE & Least-square estimate \\ LSOM & Linear Self-organized map \\ RMSE & Root mean squared error \\ SCADA & Supervisory control and data acquisition \\ SOM & Self organized map \\ WF & Wind farm \\ WT & Wind turbine \\ WT80 & Wind turbine 80 \\ WT81 & Wind turbine 81 \\ WT82 & Wind turbine 82 \\ WT83 & Wind turbine 83 \\ WT84 & Wind turbine 84 \\ \end{tabular}