esa-sceva/satcom-chunk-collection huggingface dataset:
The model achieved an average estimation accuracy of 99.2% and performed well in future voluntary motion prediction with up to 100 steps ahead.The proposed model also demonstrated an average tremor reduction of 93.8% \(\pm\) 1.5% in real-time experimental setups, outperforming the commonly used weighted Fourier linear combiner estimator by 25%. Additionally, the proposed model achieved a prediction time as low as 1.5 ms for 100 steps ahead. In their work, [PERSON] and [PERSON] [17] introduced novel adaptive digital filtering algorithms that are based on internal model control (IMC) and aim to improve the stability, robustness, and tracking performance of active vibration and noise attenuation schemes. The proposed methods involve the use of a process model to estimate the dynamic behavior of the target plant and can be combined with an internal model controller to create a feedback control system based on IMC. The algorithms are designed to manage multi-spectral signals effectively and have been numerically verified in several cases with single sine waves and multi-spectral signals. The performance of the proposed system is evaluated in terms of time-domain tracking and estimation errors in both time and frequency domains. Table 1 presents a comparison of techniques for passive vibration control in wearable assistive devices [13] and active vibration control [14; 15; 16; 17], considering the last 7 years of literature. However, no evidence has been found of other publications using adaptive filters to control vibrations associated with ET and PD. Notably, in the reviewed studies, the authors worked with signal acquisition processes--which have difficult reproducibility of the results--or used mathematical models distinct from ET and PD models. Consequently, comparing algorithms that use different signal acquisition methods or specific purpose mathematical models is highly complicated unless differences between them are carefully accounted for in the evaluation process.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 4 An Objective Model for Coded Spatial Audio QoE Prediction
### ViSQOLAudio  
The Virtual Speech Quality Objective Listener Audio (VISQOLAudio) is a signal-based full reference quality metric that uses a spectro-temporal measure of similarity between a reference and a test audio signal [18]. The assessment method has three major processing stages: pre-processing, time-alignment, and similarity assessment. In the pre-processing stage the test signal is scaled to match the reference signal's sound pressure level. In the time-alignment stage, two spectrogram representations of the reference and test signals are created using a Gammatonegram filter bank  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Ambisonics** & **Bit Rate** & **Bit Rate Per** \\
**Type** & **Order** & **(kbps)** & **Channel (kbps)** \\ \hline Reference & 3 & 12,288 & 768 \\
3 OA 512 & 3 & 512 & 32 \\
3 OA 384 & 3 & 384 & 24 \\
3 OA 256 & 3 & 256 & 16 \\ FOA 128 & 1 & 128 & 32 \\ FOA 96 & 1 & 96 & 24 \\ FOA 64 & 1 & 64 & 16 \\ FOA 32 (anchor) & 1 & 32 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Encoding/compression schemes used with multiple point audio sources.  
(with 32 critical frequency bands uniformly spaced on the Equivalent Rectangular Bandwidth scale between the lowest central frequency of 50 Hz and highest central frequency of 14,064 Hz respectively). Then the reference spectrogram is segmented into patches 480 ms long (30 frames, each 16 ms) and each reference patch is aligned with the corresponding test patch. Spectrogram patches are treated as images and a metric called Neurogram Similarity Index Measure (NSIM) [31] is used for time-alignment and similarity assessment. This NSIM metric is a simplified version of the Structural Similarity Index Measure (SSIM) which was developed to assess JPEG images quality relative to reference uncompressed versions of the same images [32]. In this work, the resulting similarity scores (i.e., NSIM scores between 0 and 1) are calculated for each spectrogram patch separately and averaged over the patches to yield the overall similarity metric for the test signal. Figure 5 shows spectrogram patches of the reference and test signals, similarity map, and corresponding NSIM scores.
## 2 Materials and Methods
### Isotope Production Irradiation on a Medical Cyclotron
#### 2.6.1 FBG-Based Temperature Probe and Pressure Transducer  
For this experiment, a custom FBG-based temperature probe was developed that employed the RAD configuration FBG as a temperature sensor. In order to collect fluid temperature in the isotope production target with the FBG sensor, the fiber had to be mechanically protected during irradiation from the rapidly mixing liquid. Furthermore, it needed to be mounted in a leak-tight configuration. The custom FBG-based temperature probe used in the experiments mimicked the form generally used in handheld thermocouple sensors. A 1/16'' outer diameter closed end Inconel protection tube was slid over the fiber. The FBG sensor was located at the closed end of this protection tube with the FBG's measurement location placed \(<\)10 mm from the tip of the probe. The protection tube was 150 mm long and terminated with a fitting through which the fiber transitioned into a fiber optic cable via a connector. The FBG temperature probe was calibrated using a Fluke 9103 dry well and a Guildline 9540a resistance temperature detector with a reported measurement uncertainty of \(\pm\)0.2 \({}^{\circ}\)C over the operating range of the test. The FBG temperature probe was inserted into the production target and sealed with a 1/16'' brass compression fitting.  
A custom FBG-based pressure transducer employing two RAD configuration FBGs was also developed. One acted as a strain gauge, the second as a temperature sensor. A stainless-steel mechanism with a diaphragm exposed to the pressurized fluid (Figure 3) was designed. This diaphragm was laser welded to a shaft in the diaphragm's center, oriented perpendicular to its surface. When the diaphragm was deflected by an increase in fluid pressure, the shaft was compressed. The FBG was attached to the shaft and thereby experienced a contraction of its length. A second FBG was embedded into the housing of the pressure transducer to provide a temperature compensation device. This FBG allowed for the measurement of the temperature of the transducer's body and was used during the calibration process and during operation to compensate the pressure output signal for temperature fluctuations. When applied fluid pressure increased, a negative wavelength shift was observed by the strain FBG. When the transducer body temperature increased, both the strain and temperature FBGs experienced a positive wavelength shift. The implementation of Equation (1) facilitated the mathematical comparison of the signals conducted in post processing which produced temperature compensated pressure readings. Due to differences in the thermal time constants of the strain and temperature FBGs, thistemperature compensation worked effectively at steady state conditions. The pressure transducer was calibrated using a Fluke 2271A industrial pressure calibrator and a Tenney environmental chamber. The transducer was calibrated from 0 to 500 psi with a reported measurement uncertainty of \(\pm\)1.25 psi (\(\pm\)0.25% PSO).
## 3 Experiment
### Performance Analysis of TDCP-Aided GNSS with Method A
Although, the velocity error was corrected back to the stationary state, and averted to influence the state estimation further as the vehicle started to move, this erroneous position result was still unable to adjust until the end of stationary. Moreover, even if method A was implemented, an unsatisfactory result was still observed in the previously discussed GNSS-outage environment (area I of scenario E, during time 1945-1954 s). The proposed odometer-aided FDE did not detect the false result, because the lateral errors during time 1947-1954 s did not surpass the threshold. Specifically, the effectiveness of the detection is vulnerable to the quality of the initial position, such as an inaccuracy result at time 1946 s. Although the result was relatively stable than the GNSS solutions without method A in this area (cyan and orange result), the maximum error presented an unsatisfactory outcome (9.94 m and 5.36 m in the horizontal and vertical directions) due to the limited effectiveness of method A. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Forward/Lateral**} \\ \cline{2-6}
**Unit m** & **Conventional** & **TDCP-Aided** & **TDCP-Aided** & **TDCP-Aided** & **TDCP-Aided** \\
**GNSS** & **GNSS** & **GNSS +** & **GNSS +** & **GNSS + Method** \\
**Method A** & **Method B** & **A Method B** & **A Method B** \\ \hline
**Mean** & \(-0.41/-0.47\) & 0.39/\(-0.54\) & \(-0.49/-0.61\) & \(-0.47/-0.55\) & \(-0.37/-0.49\) \\
**Max** & 25.95/17.21 & 14.92/10.05 & 12.6/9.30 & 15.86/10.60 & 10.75/12.75 \\
**RMSE** & 2.98/2.47 & 2.21/2.07 & 1.87/2.10 & 2.09/2.16 & 1.63/1.95 \\
**RMSE** & - & 25.8\%/16.2\% & 37.2\%/15.0\% & 29.9\%/12.6\% & 45.3\%/21.1\% \\
**Improvement** & - & 25.8\%/16.2\% & 37.2\%/15.0\% & 29.9\%/12.6\% & 45.3\%/21.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis for GNSS solution in vehicular path tracking. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**3D**} \\ \cline{2-6}
**Unit m** & **Conventional** & **TDCP-Aided** & **TDCP-Aided** & **TDCP-Aided** & **TDCP-Aided** \\
**GNSS** & **GNSS** & **GNSS +** & **GNSS +** & **GNSS + Method** \\
**Method A** & **Method B** & **A Method B** \\ \hline
**Mean** & 5.84 & 4.75 & 4.16 & 4.80 & 3.65 \\
**Max** & 37.24 & 19.42 & 12.45 & 15.00 & 8.88 \\
**RMSE** & 8.35 & 5.68 & 4.87 & 5.57 & 4.06 \\
**Improvement** & - & 32.0\% & 41.7\% & 33.3\% & 51.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance analysis for the stationary condition.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 1 Introduction
### Definitions
#### 1.1.1 Reliability  
Reliability is a dynamic concept which is applicable to many fields, i.e., is not only strictly technical. For it, a possible definition could be expressed in terms of probability, and in particular, as \"the probability that a system, subsystem or part is able to perform its specific function in a pre-established time and under pre-established conditions\". One of the most important reliability metrics is represented by the Mean Time Between Failures (MTBF), expressed in hours of activity; the higher the value, the more reliable the equipment. For a part or a single subsystem, the MTBF is often expressed as the reciprocal of reliability. For instance, the MTBF gives information about the level of unreliability, and it typically shows the number of failures of a piece of equipment over an established time, i.e., 10,000 h [4].  
The Failure In Time (FIT) rate of a device represents the number of expected failures in one billion (\(10^{9}\)) device-hours of operation. This parameter is widely diffused in the semiconductor industry [5; 6; 7] and international standards.
# Fault Imaging of Seismic Data Based on a Modified U-Net with Dilated Convolution
## 7 Conclusions  
We developed a modified U-Net-based method to automatically image faults in the sandstone reservoirs in China. The proposed network containing GCM and GSM modules is designed and trained to enhance the ability of the network to select multi-scale information. The GCM and GSM module can select multi-scale information obtained by convolution of different dilation rates between groups, enhance the consistency of receptive field and fault recognition target region, and jointly improve the recognition ability of micro-faults. The field data applications demonstrate the effectiveness of this approach. For sandstone oil and gas reservoirs in China with abundant faults, this method has great advantages in improving fault imaging accuracy, but further research is needed in improving computational efficiency and optimizing network architectures.  
Conceptualization, J.W. and Y.S.; methodology, W.W.; software, Y.S.; validation, J.W.; formal analysis, Y.S.; investigation, W.W.; resources, J.W.; data curation, Y.S.; writing--original draft preparation, J.W.; writing--review and editing, J.W.; visualization, W.W.; project administration, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of National Natural Science Foundation of China (41930431), China Postdoctoral Science Foundation (2020M680840) and Northeast Petroleum University's special fund (1305021889).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: (**a**) A time slice is displayed with faults that are imaged via (**b**) the trained U-Net model and (**c**) the trained modified U-Net model.
## 4 Physical and Chemical Treatments to Improve SvGIs Production
### Elicitors to Enhance Synthesis and Gene Expression of SvGls  
Elicitors such as hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), chitosan, methyl jasmonate (MeJA), and salicylic acid have shown improved SvGls (Reb A) ratios in the field. The foliar application of 20 mg/L chitosan and MeJA can increase the gene expression of _UGT85C2_, _UGT76G1_, and _KO_ by 14-15%, respectively, while a higher concentration of yeast extract is required to increase the expression of _UGT85C2_, and _UGT76G1_ genes [32]. Uptake of organic fertilizer enriched with nutrients in stevia plants also increases the SvGls synthesis, in particular, Stevioside and Reb A, Reb C [33]. Another study elucidated the application of NO\({}_{3}\)\({}^{-}\) on the increased SvGls synthesis in stevia leaves, although the nitrogen-induced accelerated SvGls synthesis mechanism needs to be explored further for its application in gene editing approaches [34]. Elicitors such as salicylic acid are observed to complement microelements (Fe, Zn)-enriched exogenous application on the synthesis of SvGls. Although many elicitors function by sending signals to the recipient, it seems that hormones act indirectly on gene expression and eventually biosynthesis of SvGls. However, the exact mechanisms remain unclear and require further investigations. A summary of the various conventional approaches involving physical and chemical processes used to enhance SvGls production in _S. rebaudiana_ is represented in Table 1.
## 5 Application in Real-World HDS System
### Settings of the Case Study  
We implement our algorithm of the industrial microcontroller in Huade Co., Ltd. to plan a proper shipping path and then evaluate the operability of the planned path by executing the four-way shuttle with a conveyor to finish the whole shipping operation along the planned path. Settings of the industrial microcontroller are listed in Table 1. A time window of 120 s is assigned to cover the time it takes to transport the cargo along the planned path, as well as the extra time consumption from other necessary shipping operations. The other necessary shipping operations, as well as the parameters that may cause extra time consumption, are system initialization (microcontroller bootup and shipping equipment warm up), shipping speeds with and without cargo loaded, operations of load and turn, the size of shipping equipment (same length/width for four-way shuttles and conveyors), and the size of the storage position. The necessary shipping operations with the parameters are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specifications** \\ \hline CPU & NXP i.MX 6 ULL ARM \\ Cortex-A7 processor \\ Memory & 512 MB RAM \\ Storage & 8 GB onboard storage (eMMC/Flash) \\ Operations Temperature & \(-\)40 \({}^{\circ}\)C to +80 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: The setting of the industrial microcontroller for the case study of Huade Co., Ltd.  
Figure 11: The mesh-grid tree map topological representation of the case study.  
Similarly to numerical studies, scenarios without obstacles and with randomly settled obstacles at 5%, 10%, and 20% occupations are considered. The average and standard deviation of 'Searching Time' and 'Number of Traversed Nodes' \(\overline{t}_{\text{search}}\), std\({}_{\text{search}}\), \(\overline{n}_{\text{search}}\), and std\({}_{\text{node}}\) are calculated to evaluate the optimality of the proposed algorithm. The 'Completion Rate' \(r_{c}\) is adopted to analyze the feasibility and robustness of our method. The operational feasibility for industrial production is checked by executing the cargo transportation along the planned path, and the average and standard deviation of the shipping time, \(\overline{t}_{\text{ship}}\) and std\({}_{\text{ship}}\), are the two metrics. The three benchmark algorithms are A-star (\(l_{1}\)), BFS (\(b_{w}^{*}=0.7\)), and RRT-star (\(\eta=3\)). All of the simulations are executed 100 times with the same seeds.
## 5 Simulation Case Analysis  
### Simulation Conditions  
The hardware-in-the-loop (HIL) testing system for the energy storage controller, built on the MT 6040 Simulator platform, is shown in Figure 5. Real-time simulation is used to collect measured data, including positive-sequence voltage, active and reactive power, and active and reactive current at the ESS grid-connected inverter port, for analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Voltage Magnitude (p.u.)** & **Voltage Dip Duration (ms)** \\ \hline
0 & 100–150 \\
0.20 & 600–700 \\
0.50 & 1100–1300 \\
1.20 & 9800–10,100 \\
1.25 & 970–1020 \\
1.30 & 450–550 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Voltage fault scenarios.  
Figure 4: Parameter identification process.  
The hardware-in-the-loop (HIL) testing system for the energy storage controller, built on the MT 6040 Simulator platform, is shown in Figure 5. Real-time simulation was used to collect measured data, including positive-sequence voltage, active and reactive power, and active and reactive current at the ESS grid-connected inverter port, for analysis.  
To validate the effectiveness and accuracy of the proposed parameter identification method, a fault ride-through test model for the energy storage system was established using the BPA. The model employs the identified voltage fault ride-through control strategy to regulate system behavior. The energy storage system described in this study has a rated capacity of 3.76 \(\mathrm{MV}\cdot\mathrm{A}\) and a rated voltage of 35 kV, connected via a step-up transformer to a 220 kV transmission line and an infinite bus system.  
Voltage faults are applied at the 35 kV bus to replicate measured voltage deviations at the grid connection point. Simulation data, including three-phase voltages, active power, reactive power, active current, and reactive current, were recorded. To verify the validity of the identification results, a subset of operating conditions was used as the experimental group for parameter identification. In contrast, the remaining conditions serve as the validation group to test the correctness of the identified parameters.
## 4 Result and Discussion
### Sensitivity Ranking for the PDSI  
In compilation of the overall statistical analyses, such as agreement, error estimation, and uncertainty analysis, it was found that the EVI, which indicates the biomass of vegetation while ignoring the soil background and atmospheric effects, showed the most promising result in terms of long-term sensitivity to the PDSI in all five years (Table 2). However, it was also observed that the NDVI (greenness indicator) showed an overall similar result to the EVI in the uncertainty analysis and gave values very near to the EVI in the error estimation analysis. Though the NDVI could not outperform the EVI in the overall context, its sensitivity remained pretty impressive throughout the analyses, making NDVI the second-most sensitive vegetation parameter after the EVI, according to the study. Table 2 shows the best performances of vegetation parameters in all three sta  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**YEAR** & **PDSI-GPP** & **PDSI-LAI** & **PDSI-fAPAR** & **PDSI-NDVI** & **PDSI-EVI** \\ \hline
2019 & 70.10047 & 11.23408 & 16.35537 & 0.22529 & 0.07128 \\
2018 & 85.00281 & 14.707568 & 14.64304 & 0.26018 & 0.07962 \\
2017 & 8  
tistical analyses. Detailed results of the three statistical analyses are provided in the Appendix (See Table A1 of Appendix A).
# Feasibility of Using Low-Cost Dual-Frequency GNSS Receivers for Land Surveying
## 3 Results and Discussion
### RTK and NRTK
#### 3.3.2 Positioning Accuracy  
In the last field experiment, the horizontal and vertical accuracy of RTK/NRTK was evaluated by using three different configurations in the urban environment. Each set of coordinates was determined from a fixed solution (as indicated by the receiver). The highest horizontal accuracy was achieved in RTK mode by using a geodetic-grade base station (Figure 10a). The RMSE of horizontal positioning error was 20 mm and the highest error was 33 mm. In the NRTK mode, the corresponding values were 32 mm and 56 mm (Figure 10b), which was in agreement with the horizontal positioning accuracy of 3 cm declared by the service provider.  
In the configuration including a base station and a mobile receiver, both constituted by low-cost receivers (Figure 10b), the RMSE was 60 mm and the highest error reached 104 mm. The horizontal positioning precision, measured as the standard deviation of position errors, was 32 mm. The baselines were shorter than 0.5 km, thus achieving a positioning precision worse by a factor of 2 rather than the that declared by the manufacturer, i.e., 1 cm + 1 ppm.  
The positioning error in height determination varied from \(-\)40 to +80 mm (Figure 11). For RTK with respect to the geodetic-grade base station, an average height error of 52 mm was recorded, but the standard deviation of height error was only 10 mm, indicating the high precision of the vertical component determination. In the NRTK mode, height was overestimated by 27 mm \(\pm\) 23 mm. When two low-cost receivers are used for RTK, the positioning error for height was 0 mm \(\pm\) 18 mm. The results indicate that both low-cost receivers have similar vertical PCO, which, however, is non-zero and causes overestimation of heights in NRTK or RTK modes with respect to a geodetic-grade base station.  
Figure 9: Positioning error: (**a**) as a function of azimuth for baseline length; (**b**) as a function of azimuth for height difference; (**c**) as a function of antenna locations for height difference (color-coded), with indicated changes in the rotation direction. CW—clockwise, CCW—counter-clockwise.