esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 1 Introduction  
Dynamic magnetic measurement can be applied to geological structure analysis, resource exploration, magnetic anomaly detection, geomagnetic navigation, and other fields and is usually realized by attaching to a mobile carrier [1; 2; 3]. The three-axis fluxgate magnetometer is the main sensor for mobile magnetic measurement. Its measurement accuracy is key for magnetic measurement and its applications. The error of the magnetometer consists of static measurement error and dynamic measurement error. The existing error calibration and compensation methods for the three-axis fluxgate magnetometer are all established for static measurement errors [4; 5; 6; 7], including scale factor error, zero bias error, and nonorthogonal error, etc. For static magnetic measurement, the measured magnetic signal is in a relatively stable state and can be considered as an approximately time-invariant \"staticsignal\". Moreover, the existing static compensation methods can realize error correction and complete restoration of the real signal well [8].  
However, when the fluxgate magnetometer is in the mobile measurement state, the magnetic field input induced by its sensitive axis changes dynamically with time. Due to the limitation of the magnetometer's sensitive materials, magnetic core structure, signal conditioning circuit, and other reasons [9; 10; 11], the wording frequency band of the magnetometer has a certain range and cannot completely cover all the frequency components in the dynamic input magnetic field, resulting in dynamic measurement error. A dynamic error is a function of time and varies with the frequency of the input signal, which cannot be corrected by traditional static compensation methods and reduced by the dynamic compensation method. At present, dynamic compensation methods are mainly aimed at pressure sensors, temperature sensors, and acceleration sensors, while dynamic error compensation for fluxgate magnetometers is still in a blank [8; 12; 13; 14; 15; 16].  
To solve this problem, a series dynamic compensator based on system identification is constructed in this paper, and a hybrid optimization method of the genetic algorithm (GA)-simulated annealing algorithm (SA) is proposed to solve the model parameter optimization problem of the dynamic compensator [17; 18; 19]. The hybrid optimization method cannot only expand the search scope effectively and improve the search efficiency, but also can avoid the problem that the genetic algorithm easily falls into the local optimum. Finally, the dynamic compensator model is established through the parameters determined by the hybrid optimization algorithm, and the dynamic error compensation of the fluxgate magnetometer is realized.
## 4 Experimental Analysis
### GNSS/LEO Combined POD during a Solar Storm
From Table 5, we can see that the average ODOP value of the three types of BDS satellites derived from the S4 scheme is smaller than that from the S2 scheme; however, the BDS orbit solutions for the two schemes show nearly no difference. This can be explained by investigating the inner accuracy, which is computed as \(D_{xx}=Q_{xx}\cdot\sigma_{0}^{2}\), where the calculation of the coefficient matrix \(Q_{xx}\) is identical to that of the ODOP and both are related to the geometric strength, and the variance of the unit weight \(\sigma_{0}^{2}\) is calculated based on the residuals. Larger observational errors will result in larger amplitude residuals and, in turn, larger \(\sigma_{0}^{2}\). Considering the POD accuracy is co-affected by both the ODOP and the observational errors, the BDS orbit solutions in the S4 scheme present identical results to those in the S2 scheme and the lowest ODOP and smallest observational errors after the adjustment of the S6 scheme show the best solutions.
# Expansion of Image Space in Enhanced-NA Fresnel Holographic Display
## 1 Introduction
We analyzed an angular view of a holographic image, dependent on the hologram numerical aperture, and an expansion of image space. Secondly, optical experiments were conducted to obtain the reconstructed image without an interference of high-order noises. Subsequently, we studied the method for reconstructing a holographic image with an extended image space, at a larger viewing angle.
## 6 Results
### Comparison Between Bottling Planning and Real Performance  
The analysis of the 10 production batches and their initial assignment, based on the weekly coordination meeting, is represented in Table 3. The initial production plan considered a total time of 565 min for Line 1 and 507 min for Line 2. For Line 1, 431 min were allocated to the bottling process, with the remaining 134 min designated for format transitions, cleaning, and product changes. Similarly, for Line 2, 431 min were allocated to bottling and the remaining 76 min for transitions and adjustments. It is important to note that the times assigned for adjustments between batch changes do not vary, as only the difference between the planned and actual bottling times is recorded.  
Due to the nature of the wine in terms of fouling capacity and hydraulic properties, deviations of 63 min were observed in the bottling plan for Line 1 and 37 min for Line 2. These deviations represent an approximate 9.3% increase in the daily production plan, resulting in additional costs for the company in terms of labor hours and energy consumption.  
It is worth noting that these discrepancies between planned and actual observations are common, and the company has internalized the economic impact of this scenario, incorporating it into its projections and operational strategies.  
The difference in filtration areas between the systems of lines 1 and 2 has a significant impact on their performance. Line 1 has a filtration area of 12 m\({}^{2}\), while Line 2 features a larger capacity with an area of 18 m\({}^{2}\). This difference in filtration capacity allows Line 2 to handle wines with higher fouling properties more efficiently, as the larger surface area reduces cumulative obstruction and helps maintain a more stable flow rate during the process.  
However, despite the larger capacity of Line 2, a deviation of 63 min was observed in its bottling plan, compared to the 61-minute deviation in Line 1. This suggests that although the larger filtration area partially mitigates the effects of fouling capacity, it does not completely eliminate the hydraulic limitations caused by the wine's properties. These observations are consistent with findings in the literature, which emphasize that larger filtration areas provide greater flow stability, but the cumulative impact of suspended particles can still affect process efficiency.  
The comparison between the lines highlights the need to carefully consider the relationship between filtration area and specific wine properties, such as viscosity and particle content, when planning and optimizing bottling processes.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
###### Abstract  
In order to improve the continuity and smoothness of transpolar flight and optimize autonomous navigation performance, a SINS/SRS/CNS (strapdown inertial navigation system/spectral redshift navigation system/celestial navigation system) multi-information fusion global autonomous navigation method based on parameter conversion was studied for this article. The global autonomous navigation scheme based on multi-information fusion was designed. The principle of spectral redshift navigation was studied. On this basis, the system equations of the SINS/SRS/CNS multi-information fusion global autonomous navigation system were established in the middle-low latitudes and high latitudes. Furthermore, the navigation and filter parameter conversion relationships between the geographic navigation coordinate frame and the grid navigation coordinate frame were derived. The simulation and experiment verified that the SINS/SRS/CNS multi-information fusion global autonomous navigation method with parameter conversion can effectively improve the accuracy and smoothness and realize non-oscillation switching in transpolar navigation. In the vehicle experiment, the proposed algorithm improved the horizontal position accuracy by more than 29% compared with the multi-information fusion global autonomous navigation method without filter parameter conversion.  
Global autonomous navigation; transpolar flight; filter parameter conversion; spectral redshift navigation system +
Footnote †: journal: Applied  
1  
A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation MethodBin Zhao  
1  
2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Experimental Setup
### Signal Processing  
The thermographic image acquisition is carried out using the IRBIS software (IRBIS professional 3.1.100) of the camera manufacturer InfraTec, while the Python software (3.9.18) is used for image processing. The correction of the image orientation is substantial for the application of the signal processing approaches in free field measurements. The correction of the image alignment includes the following image processing steps:  
1. Background removal and outlier removal.
2. Detection of the rotor blade edges with Canny edge detection [32].
3. Assignment of the edges (leading, trailing) via clustering and standard deviation within a cluster.
4. Horizontal alignment of the leading edge by means of image rotation.
5. Compensation of the interpolation effects on the image edges due to image rotation. These preprocessing steps allow an evaluation of a thermogram both with (Section 2.2) and without (Section 2.1) previous knowledge for comparison reasons.
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 5 L2-CalSat Operation
### Access Time and Viewed Detectors
Taking into account that over a 3-year period of CMB mission, the total observation time of Jupiter (the most powerful celestial intensity calibrator) is of about one hour, and provide a NF range of about \(-40\) to \(-70\) dB from 40 to 400 GHz respectively, from Table 3 is easy to conclude that L2-CalSat can provide NF values better than \(-72\) dB in all the frequency range, and in only 6 h (in fact, it is estimated that about 90 min of access time would be enough to characterise the FSL of all the sensors at the reported level of accuracy). Additionally, given the high SNR provided by the calibration sources of L2-CalSat, similar periods of time should be enough for both, intensity (beam pattern, non-linearity, bandwidth...) and polarization (beam pattern, polar angle...) in-flight calibration. Finally, it is important to note that the electrical power system of the ancillary satellite allows to emit calibration signals only during a 40% of the calibration session period, because the rest of the time is necessary to recharge the battery module. However, this  
Figure 12: Total access time according to the position of L2-CalSat relative to the CMB satellite for a FoV of 30\({}^{\circ}\) and throughout 6 (**a**), 12 (**b**) and 24 h (**c**), and for a FoV of 120\({}^{\circ}\) and throughout 12 h (**d**). limitation does not represent an issue because, from the previous figures, it is estimated that for a calibration session of 12 h only about 2.5 h (1.5 for the beam characterization and 1 h for the polar angle, bandwidth, etc.) are required for calibration, which is about a 20% of the total calibration session time. As a consequence, during the periods of time in which the calibration signal is not reaching any sensor, the source will be turn-off and the battery will be recharged, in order to provide the required power to all the systems of L2-CalSat during the emission of the calibration signals.
## 3 Experimental Results
### Comparative Evaluation of Detection Performance
combining SRCNN for image resolution enhancement with a bilateral filter to suppress noise, NSSRD effectively minimized object information loss and performed accurate drone detection. These findings highlight the advantages of NSSRD over the comparison models in detecting drones under similar conditions. Figure 9 shows the drone detection results at an altitude of 20 m and a distance of 60 m. As the distance between the sensor and the object increased, the boundaries of the object became blurred, resulting in failed detections by all existing models except NSSRD. For the models of [PERSON] et al. [30], [PERSON] et al. [13], and [PERSON] et al. [31], the magnified images revealed blurred resolution and indistinct boundaries, leading to detection failure. Even when a Laplacian filter was applied, the object boundaries remained ambiguous, highlighting the reasons for their inability to detect the drone. [PERSON] et al. [16] attempted to improve the object resolution using SRCNN; however, this process also amplified the noise surrounding the object, preventing accurate detection. In contrast, NSSRD successfully addressed these challenges by applying a bilateral filter, effectively removing the noise around the object that hindered detection in the model of [PERSON] et al. [16]. This approach allowed NSSRD to restore clearer object boundaries and achieve accurate drone detection, demonstrating its robustness under challenging conditions. Figure 10 shows the drone detection results at an altitude of 40 m and a distance of 60 m in an environment affected by clouds. Similar to the detection results at a 20 m altitude and a 60 m distance, all existing models except NSSRD, failed to detect the drone. In particular, [PERSON] et al. [31] attempted to remove noise around the object using a bilateral filter; however, this resulted in excessive object information loss compared with the other models. This also led to a blurring of the drone shape; thus, false alarms occurred in areas that  
Figure 8: Drone detection results at altitude of 20 m and distance of 10 m: (**a**) [PERSON] et al. [30]; (**b**) [PERSON] et al. [13]; (**c**) [PERSON] et al. [31]; (**d**) [PERSON] et al. [16]; (**e**) NSSRD. were not the object. In contrast, NSSRD combined a bilateral filter with SRCNN, thereby effectively suppressing unnecessary noise around the object, enhancing the resolution, and enabling the accurate restoration of the object boundaries. These results provide strong evidence that NSSRD can minimize the impact of noise while accurately detecting drones. Figure 10: Drone detection results at altitude of 40 m and distance of 60 m in environments affected by clouds: (**a**) [PERSON] et al. [30]; (**b**) [PERSON] et al. [13]; (**c**) [PERSON] et al. [31]; (**d**) [PERSON] et al. [16]; (**e**) NSSRD. Figure 9: Drone detection results at altitude of 20 m and distance of 60 m: (**a**) [PERSON] et al. [30]; (**b**) [PERSON] et al. [13]; (**c**) [PERSON] et al. [31]; (**d**) [PERSON] et al. [16]; (**e**) NSSRD.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 3 Results
### Common Wearable Sensors Used in Gait, Balance, and RoM Analysis
#### 3.1.3 Electromyography (EMG) Sensors  
Electromyography (EMG) sensors use electrodes to record electrical activity from subject's muscle tissue. There are two types of EMG sensors; surface electrodes and needle electrodes. Needle electrode exams are more specific and accurate. EMG sensors can detect whenever a muscle is at rest or active; a negative electrical potential difference is maintained across the muscle when the muscle is resting, while a positive potential travels along the length of the fiber when the nerve activates the muscle fiber [80].  
EMG is an essential tool in diagnostic evaluation of patients with peripheral neurologic disorders, such as peripheral neuropathy, Guillain Barre or ALS [81]. EMG has contributed in multiple clinical areas to enhance the management of patients with neuromuscular disorders. including neurology, neurosurgery and orthopedics [82]. EMG is often used in combination with nerve conduction velocity tests. EMG and nerve conduction velocity provide different information about the peripheral nervous system, but when analyzed together, aid in accurate diagnosis [81]. Unfortunately, EMG has limitations and considerations [81; 83; 84]:  
* technical limitations may be present in cases of obesity or advanced age,
* EMG cannot be used for all muscles for all activities,
* EMG does not give RoM information,
* electrode placement is vital,
* traditional EMG cannot detect passive movements,
* for surface EMG (SEMG), skin must be cleaned and static charges on the skin can alter the signals.  
EMG and SEMG have been used to identify certain gait characteristics, distinguish compensatory balance responses, and develop and improve methods used to assess balance. Continuous EMG analysis in patients with neurological disorders provide relevant diagnostic contributions in terms of nosological classification, localization of focal impairments, detection of pathophysiological mechanisms, and functional assessment to supplement the clinical evaluation of neuromuscular disorders [31; 52; 69; 73].
# The Forecast of the Wind Turbine Generated Power Using Hybrid (LTC + XGBoost) Model
## 5 Results
### Application of Filtering to Predicted Values  
The study was carried out to assess the impact of wind speed and gusts on LTC, XGBoost, and Hybrid models' predictions with respect to wind turbine characteristics. It is important since if the wind speed is too low, there is not enough energy to turn on the generator. If the wind is too strong, it can cause some damage, and the generator must be shut down. The power curve of the Enercon E82/2000 wind turbine in Figure 8 shows that the minimum wind speed required to start the turbine is 2 m/s, and the turbine is stopped at wind speeds above 25.5 m/s. The maximum generated power is reached at a wind speed of 12.5 m/s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **nMAE** & **nRMSE** & **MAE** & **RMSE** & **Training** \\ \hline LSTM & 0.2119 & 0.2437 & 423.8 & 487.4 & 16,250.4 \\ Exponential Smoothing & 0.1059 & 0.1345 & 211.8 & 269.0 & 41.56 \\ ARIMAX & 0.1052 & 0.1336 & 210.4 & 267.2 & 52.54 \\ Random Walk & 0.1046 & 0.1331 & 209.2 & 266.2 & 35.39 \\ with Drift & & & & & \\ LTC & 0.1020 & 0.1264 & 204.0 & 252.8 & 695.0 \\ XGBoost & 0.1052 & 0.1258 & 210.4 & 251.6 & 57.6 \\
**Hybrid** & **0.0856** & **0.1092** & **171.2** & **218.4** & **30.8 (783.4)1** \\
**(XGBoost + LTC)** & & & & & \\ \hline \hline \end{tabular} \({}^{1}\) With XGBoost and the LTC model’s training took 783.4 s.  
\end{table}
Table 5: Experimental (n)MAE and (n)RMSE values describing wind power prediction accuracy of analyzed models.  
The experiment tested whether wind speed falls within the range of the wind turbine power curve and whether wind speed, together with wind gusts, remains within the range. In this context, LTC, XGBoost, and Hybrid forecast values were filtered and predictions adjusted (see Table 6). When filtering by wind speed, nMAE errors decreased across all models by \(\sim\)6.5-10%, while nRMSE increased by \(\sim\)6-10%.  
This shows that filtering worked as a trade-off between nMAE and nRMSE errors. While the predictions improved on average, they worsened in extreme cases. Filtering by wind speed and wind gust reduced nMAE errors only by \(\sim\)3-5%, while nRMSE errors increased by \(\sim\)7-12%. This also shows that additional filtering smooths variations but does not improve overall performance, as it may introduce new biases or amplify large errors.
###### Abstract  
Various methods have been proposed to estimate the tissue speed of sound (SOS) of propagating medium using the curvature of received channel waveform or the analysis of resultant image quality. In our previous study, baseband delay-multiply-and-sum (DMAS) beamforming methods have been developed for multi-angle plane-wave (PW) imaging which relies on signal coherence among transmit events (Tx-DMAS) or receive channel (Rx-DMAS) or both (2D-DMAS) to suppress low-coherence clutters. In this study, we further extend our DMAS beamforming to quantify the level of signal coherence for determining the average SOS in multi-angle PW imaging. The signal coherence in multi-angle PW imaging is represented as the DMAS coherence factor (DCF) which can be easily estimated from the magnitude ratio of the pixel value of DMAS image to that of DAS image. By searching the beamforming velocity that provides the highest signal coherence of echo matrix, the average tissue SOS of the imaged object can be determined. For the PICMUS experimental dataset, the optimal beamforming velocity (C\({}_{opt}\)) estimated by the proposed DCF method does provide the best image quality. For the Prodigy dataset, the estimated tissue SOS is 1426 \(\pm\) 6 m/s which is very close to the actual tissue SOS of 1427 m/s and the estimated SOS also corresponds to the \(C_{opt}\) with the minimal \(-\)6 dB lateral width and the maximal contrast within an error of 10 m/s. Estimation of tissue SOS in the proposed DCF method is also robust even in the presence of transmit delay error due to deviation of SOS.  
Keywords:baseband delay-multiply-and-sum; multi-angle plane-wave imaging; phase coherent factor; estimation of tissue speed of sound; optimal beamforming velocity +
Footnote †: journal: Applied
## 2 Structure/Function in Electrocatalysis
### Layered Rim-Edge-Plane Activities  
The predominant theory behind the reactivity of MoS\({}_{2}\), TiS\({}_{2}\), and WS\({}_{2}\) has been the rim-edge model, in which the basal planes of the material are inactive. The activity of the edge/rim sites of the catalyst has been well studied [76; 77; 78; 79]. The Metal and sulfur atoms on the rim or edge are the only species to participate in the reactivity of the catalyst [76; 77; 78; 79]. A representation of layered TMS (showing the MoS\({}_{2}\) lattice) and the metal- and sulfur-rich edges is shown in Figure 1[80].  
The rim and edge model for the activity indicates that the basal plane of the TMS is non-active or has very low activity [76; 77; 78; 79]. From hydrogenation of C-S bonds in the HDS reactions, it has been shown that stacks of 4-5 layers of MoS\({}_{2}\) planes show activity. However, the basal plane is inactive towards the reaction where the reactivity occurs at the edge or rim of the MoS\({}_{2}\) stack. At the rim sites the active hydrogenation of the C-S bonds occurs, whereas on the edge site the C-S-bond breaking occurs [76; 77; 78; 79]. This edge-rim activity observed in HDS reactions is preserved in electrocatalytic reduction reactions of CO\({}_{2}\) where the basal plane is inactive and the reactivity is observed only on the rim/edge of the stacked planes. Figure 2 shows the different sites for in the MoS\({}_{2}\) rim and edge models and the respective reactions.  
The activities on the rim and edges sites translate to the layered TMS, which in turn translate into the CO\({}_{2}\) reduction reactions. For the MoS\({}_{2}\)-, TiS\({}_{2}\)-, and WS\({}_{2}\)-based electrocatalys, the edge sites are far more reactive than the basal planes. However, there has been recent research which has shown creating sulfur deficiencies on the surfaces/basal planes produces reactivity [81; 82; 83]. The non-layered TMS typically have the metal located on the surfaces of the crystalline phase and not blocked by the other atoms [84; 85; 86]. Figure 3 shows examples of Fe\({}_{3}\)S\({}_{4}\), Fe\({}_{7}\)S\({}_{8}\), and CuS crystal structures, which illustrate the availability of the metal ions for reaction [87; 88; 89].  
Figure 1: Structure of layered TMS dichalcogenides (based on the molybden structure), the metal-rich edge sites, and the sulfur-rich edge sites.  
Figure 2: Break-down of the layered TMS structure into the basal planes, rim sites, and edge sites, and the HDS reaction associated with the rim and edge sites.