esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parameter Estimation
### Optimal Lower Bound
Hence, by using the energy constraint in Equation (16) together with the equal precision condition in Equation (15), we obtain a simple expression for the QFIM associated with estimating the parameters \(a\) and \(b\) from the state \(|\alpha_{1}\rangle\otimes|\alpha_{2}\rangle\), namely  
\[F^{(\alpha_{1}\alpha_{2})}=\left( \begin{array}{cc}4&0\\ 0&4\end{array} \right). \tag{17}\]  
This imposes three conditions on the eight encoding constants \((\epsilon_{k,l},\eta_{k,l})\). From the QFIM in Equation (17), one can deduce the QCRB on the variance of the estimators \(\tilde{a}\) and \(\tilde{b}\) of the classical parameters \(a\) and \(b\) for a pair of input coherent states  
\[\Delta^{2}\tilde{a}\geq 1/4,\qquad\Delta^{2}\tilde{b}\geq 1/4. \tag{18}\]  
Before we find an encoding and estimation strategy such that [PERSON] can optimally estimate both \(a\) and \(b\) (see Section 4), let us discuss the attainability of the QCRB.
## 4 Results and Discussion  
The proposed four-port flexible smartphone MIMO antenna is fabricated and measured. The fabricated antenna is demonstrated in Figure 4. Each element is given a separate feed through Sub Miniature version A (SMA) to UFL connector concerning the common ground. The S parameters and radiation patterns are measured using the Keysight vector network analyzer and shielded anechoic chamber. While measuring the S-parameters, when two antenna elements are considered as an active radiator, the other two antenna elements are terminated with a load impedance of 50 \(\Omega\), as shown in Figure 4b. The inset view is illustrated in Figure 4c in order to clearly showcase the fabricated antenna geometry.  
Figure 3: Reflection coefficient S11 (dB) of evolution steps of mobile MIMO antenna.  
Figure 2: Evolution steps of the proposed mobile MIMO antenna element.
## 5 Conclusions  
In this study, conducted at an uncontrolled intersection in the rural-urban fringe area, we simulated and evaluated four viable, low-cost, and adaptable speed-calming measures that function to provide appropriate warning and guidance to drivers. These measures included anti-collision barrels, traffic-channelizing lane markings, a combination of anti-collision barrels and traffic-channelizing lane markings, and the integration of speed bumps with cross-hatched intersection markings. The collected data included observations of driving behavior and analysis of participants' eye movements as they approached and departed the uncontrolled intersection. As anticipated, these findings suggest that the installation of speed-calming measures has significant potential to improve traffic safety.  
In contrast with existing studies examining the effects of speed-calming measures, this paper presents an analysis and evaluation of the effects of four tested speed-calming measures on drivers' behavior and eye movements near an uncontrolled intersection; such intersections are prevalent in China, yet they have not received sufficient attention from researchers. The study's findings can be summarized as follows.  
Analyses of both vehicle motion and drivers' visual focus revealed that the implementation of anti-collision barrels, traffic-channelizing lane markings, and their combination had a statistically significant impact. However, acceleration noise measurements indicate that these speed-calming measures may have adverse effects on driving stability and driving comfort. Overall, the impact of lane markings on the direction of drivers' visual attention and on vehicle deceleration is acceptable.  
In conclusion, this study suggests that the four experimentally simulated speed-calming measures have the potential to effectively promote driving at reduced speeds and foster safety-oriented behavior. Our study successfully elucidates the impact of various speed-calming measures on drivers' perceptions and behavior, providing insights into the cognitive processes involved in drivers' visual perceptions of such measures. These low-cost temporary speed-calming measures can be used as a feasible method of effectively reducing vehicle speed at unsignalized intersections. In addition, when traffic authorities have sufficient funds to install traffic signal lights, left-turn diversion lines can be retained, attracting the attention of drivers and regulating their left-turning driving paths.
## 3 Watermarking in Tensor Domain
### Procedures of Watermark Extraction  
This part mainly introduces the specific procedures of watermark extraction, as shown in Figure 8. The extracting process of the watermark is as follows. The watermarked image is geometrically rectified before the watermark is extracted. The technique of geometric correction can improve the watermark correct extraction rate, as shown in Table 8.  
**Step1** : Obtain the Zernike moment of the watermarked image \(I_{w}\) with size \(X\times Y\times 3\), the six-order features of Zernike moments as the input of trained machine learning network MLS-SVR to correct geometric distortion, and the corrected watermarked image \(I^{{}^{\prime}}_{w}\) is obtained.  
**Step2** : Divide the corrected watermarking image \(I^{{}^{\prime}}_{w}\) into blocks, with a block size of \(8\times 8\times 3\), the numbers of the blocks are \(\frac{X\times Y}{X\times K}\).  
**Step3** : Construct a pure quaternion \(f_{w}(x,y)=R_{w}(x,y)i+G_{w}(x,y)j+B_{w}(x,y)k\) using the three RGB channels of the color image block. We can obtain a real component \(A_{w}(\lambda,v)\) and three imaginary components \(C_{w}(\lambda,v)\), \(D_{w}(\lambda,v)\), and \(E_{w}(\lambda,v)\) of each color block by QDFT.  
**Step4** : Construct a third-order tensor \(T_{w}\) with dimensions of \(8\times 8\times 3\) using \(C_{w}(\lambda,v)\), \(D_{w}(\lambda,v)\), and \(E_{w}(\lambda,v)\) of each color block.  
**Step5** : Operate Tucker decomposition on \(T_{w}\) and then the core tensor \(K_{w}\) is obtained.  
**Step6** : Perform logistic mapping for all core tensor \(K_{w}\) blocks, and then, the odd-even quantization technique is used to extract a bit watermark in position \(K_{w}(1,1,1)\) of each \(K_{w}\), the specific extraction rules are as follows:  
\(K(1,1,1)\)=\(|(K(1,1,1)|,\)\(\eta=round(K(1,1,1)/Q)\),  
\[K(1,1,1)=\begin{cases}w=1&if\mod\left(\eta,2\right)=0,\\ w=0&if\mod\left(\eta,2\right)=1.\end{cases} \tag{26}\]  
where \({}^{\prime}|*|^{\prime}\) is the functions abs.  
**Step7** : Obtain complete watermark \(w_{e}\) through the odd-even quantization rule.
# GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization
## 6 Conclusions  
Graph condensation is an important technique for efficient GNN training and deployment. In this paper, we showed the limitations of previous graph condensation methods in exacerbating subgroup unfairness through empirical evaluations. Starting from the intuition that condensation GNNs act as an information bottleneck, we propose GCARe, which utilizes adversarial learning to regularize condensation GNNs during the condensation process. Extensive experiments demonstrated GCARe's ability in simultaneously improving accuracy and fairness for condensed graphs.
## 3 Results
### Spatial Downscaling of the SM Dataset
This result indicates that errors in input coarse resolution data greatly affect the quality of spatial downscaling results, like in the case of the Landsat dataset. Although there were no significant differences in correlation for the three regression models, RF-based ATPRK with TC normalization (C3_RF) showed the highest correlation coefficient value (0.996 for MAE and 0.990 for MRAE). Other RF-based predictions (C2_RF and C1_RF) also had strong correlations to input errors, which implies that RF-based predictions are most susceptible to input errors. In contrast, the prediction accuracy values of C1_SVR and C2_SVR showed the lowest correlation to the input errors. Consequently, C1_SVR achieved the best prediction accuracy for most cases. Despite the insignificance of the correlation between accuracy and \(R^{2}\) values, a further interpretation was made. The moderately negative correlation between the accuracy values of LR-based predictions and the \(R^{2}\) values (\(-0.59\) and \(-0.61\) for C1_LR and C2_LR, respectively) indicates that the higher explanatory power of TC estimation may lead to a slight improvement in accuracy. In addition, as shown in Table 6, a positive correlation of the \(R^{2}\) value of SVR to the mean of the observations might contribute to better prediction performance, particularly in the summer season. However, as found in the  
Figure 4: Correlation coefficients between accuracy measures and input errors. The case name can be referred to in Table 2.  
Figure 3: The radar chart of mean relative absolute errors for all spatial downscaling results for the SM dataset. The case name can be referred to in Table 2.  
Landsat dataset, the highest \(R^{2}\) values of RF in most cases did not lead to better prediction accuracy. Furthermore, TC estimation with very low explanatory power showed worse prediction accuracy. Based on the results of the experiment on the real SM dataset, it can be concluded that the use of the TC estimate as a downscaling result exhibited the best prediction performance. In addition, as input errors increased, prediction errors increased accordingly. A clear correlation between the explanatory power of regression models and prediction accuracy was not observed. However, it is evident that higher explanatory power cannot always guarantee improvement in prediction accuracy.
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 3 Results
### Accuracy of Operations and Procedural Adherence  
Operational accuracy was assessed based on the participants' ability to follow correct procedural steps, such as adjusting control settings and responding to emergencies. The VR + eye-tracking group demonstrated a 15% increase in accuracy compared to the VR-only group (t(58) = 2.98, \(p\) = 0.0011, [PERSON]'s d = \(-\)0.350), suggesting that gaze-assisted feedback helped users maintain focus on critical procedural elements.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 1: Multi-Source Input
#### 2.1.4 Airborne LiDAR and Aerial Imagery  
The integration of airborne LiDAR and aerial imagery has recently gained popularity in tree-level urban forest inventories. It has great potential not only for enhancing estimation accuracy but also for increasing the number of identifiable tree species. However, the limited coverage and expensive flight costs of LiDAR hamper its use at a large scale. [PERSON] et al. (2019), for example, found that by using fused HSR hyperspectral AVIRIS imagery with high point density LiDAR data, 29 common urban tree species at crown level could be clearly identified. This was a substantial improvement compared to 15 species identified using AVIRIS imagery alone, as reported in an earlier study ([PERSON] et al., 2017). Even though the addition of LiDAR data could increase the overall accuracy by only 4.2%, it was able to enhance the classification accuracy of certain species, especially small crowned species, by more than 10%. Later, [PERSON] et al. (2019) found that fused airborne LiDAR and aerial imagery could outperform field-plot data when measuring the uneven distribution of canopies across the city. Compared to field-plot data, the consistency of airborne fused data is very suitable for measuring hard-to-access urban areas or for large-scale measurements that require replication. These studies confirm the merits of using hyperspectral imagery and LiDAR data fusion in mapping crown-level tree species in a highly heterogeneous urban environment.  
[PERSON] et al. (2017) designed a framework to integrate airborne LiDAR data and aerial images for a tree-level urban forest inventory. Above-ground points were separated from ground points in the LiDAR data. The authors innovatively used NDVI values derived from hyperspectral images to separate vegetation points from non-vegetation points. The ground points and vegetation points were used to isolate individual trees with a treetop detection algorithm and a tree boundary extraction algorithm. Lastly, tree species could be delineated in the hyperspectral images. Although this method was able to increase the accuracy of tree-level identification, it was computationally demanding and not scalable to larger spatial scales. Future work should focus on algorithm optimisation and computing hardware upgrade to improve data processing efficiency.  
The high point density of airborne LiDAR coupled with the high spectral resolution of aerial imagery is an exciting approach for fine estimation. High-density 3D point clouds and high spatial and spectral resolutions together can ensure high estimation accuracy. More importantly, unlike satellite imagery, the quality of the fused data is not compromised by synoptic conditions, because the data are obtained via active sensors. However, expensive flight costs and time-consuming data processing limit its tractability for large-scale applications.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
###### Abstract  
Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a snapshot. This makes spectral-based methods inefficient. Spectral methods, such as maximum likelihood (ML) methods, multiple signal classification (MUSIC), and beamforming methods, are theoretically and systematically investigated in this study. MUSIC is an approach used for frequency estimation and radio direction finding, ML is a technique used for estimating the parameters of an assumed probability distribution for given observed data, and PWD applies a Fourier transform to the capture response and produces them in the frequency domain. Although they have been previously adapted and used to estimate DoA of EM signals impinging on linear and planar antenna array configurations, this paper investigates their suitability and effectiveness for a spherical antenna array. Various computer simulations were conducted, and plots of root-mean-square error (RMSE) against the square root of the Cramer-Rao lower bound (CRLB) were generated and used to evaluate the performance of each method. Numerical experiments and results from measured data show the degree of appropriateness and efficiency of each method. For instance, the techniques exhibit identical performance to that in the wideband scenario when the frequency f = 8 GHz, f = 16 GHz, and f = 32 GHz, but f = 16 GHz performs best. This indicates that the difference between the covariance matrix of the signal is coherent and that the steering vectors of signals impinging from that angle are small. MUSIC and PWD share the same problems in the single-frequency scenario as in the wideband scenario when the delay sample d = 0. Consequently, the DoA estimation obtained with ML techniques is more suitable, less biased, and more robust against noise than beamforming and MUSIC techniques. In addition, deterministic ML (DML) and weighted subspace fitting (WSF) techniques show better DoA estimation performance than the stochastic ML (SML) technique. For a large number of snapshots, WSF is a better choice because it is more computationally efficient than DML. Finally, the results obtained indicate that WSF and ML methods perform better than MUSIC and PWD for the coherent or partially correlated signals studied.  
Cramer-Rao lower bound (CRLB); direction-of-arrival (DoA) estimation; maximum likelihood (ML); spherical antenna array (SAA); root-mean-square error (RMSE) IEEEexample:BSTcontrol
# In-Vitro Experimental Modeling of Oscillatory Respiratory Flow in a CT-Scanned OSAHS Tract
## 5 Conclusions  
In the present research, particular efforts were made to experimentally mimic the respiratory flow conditions in an in-vitro experimental model to measure the oscillatory respiratory flow velocity inside a CT-scanned ETA model with OSAHS by using PIV technique. Four representative phases were highlighted to clarify the respiratory flow patterns and the associated flow structures in the ETA model.  
With realistic non-sinusoidal respiratory flow rate and CT-scanned ETA model, the respiratory flow patterns at the reversal phases can neither be regarded as the pattern of inspiration nor that of expiration.  
Flow patterns at the inspiratory and expiratory phases are very different. High-speed jet flows generally appear at the opposite sides of the oropharyngeal, laryngopharyngeal and tracheal regions at the inspiration and expiration phases. The expiration process is not a simple reversal process of inspiration.  
The respiratory flow patterns and the associated flow structures have clinical implications for breathing tract blockage. The area of stenosis is apt to be blocked at the peak inspiration phase due to potential wall collapse.  
To the best of the authors' knowledge, this has been the first research devoted to measuring oscillatory respiratory flows inside CT-scanned ETA model with OSAHS. The PIV measurements of unsteady flow velocity will be used to calibrate the computational fluid dynamics method, which can be further used to capture even more detailed flow fields. Funding acquisition, C.Z., Y.J.; project administration, Y.J.; formal analysis, Z.Z.; investigation, Z.Z.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, Y.J. and C.Z. All authors have read and agreed to the published version of the manuscript. This research was financially supported by the National Key Research and Development Project of China under Grant No. 2016 YFB0200901, National Natural Science Foundation of China under Grant No. 51176146, and Shaanxi Key Research and Development Project under Grant No. 2018 KWZ-01.  
The authors declare that for this study they have no conflicts of interest.
## 3 Data Collections
### Flux Tower Observations
It comprises over 900 flux tower sites globally (See [[https://fluxnet.fluxdata.org/](https://fluxnet.fluxdata.org/)]([https://fluxnet.fluxdata.org/](https://fluxnet.fluxdata.org/)), last accessed on 14 October 2020.). Data were originally released in December 2015 and subsequently updated in July 2016 and November 2016, following the procedures and quality controls described in [68; 69]. The ERA-Interim global reanalysis data set of the European Centre for Medium-Range Weather Forecasts (ECMWF) is used to fill extended gaps of micrometeorological variable records. These data are distributed under the CC-BY-4.0-based data policy (See [[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), last accessed on 14 October 2020.). The reliability of phenological information derived from satellite remote sensing observations is evaluated by confronting them to flux tower results [70; 71]. The availability of both remote sensing and field measurements at the Skukuza site thus offers a unique opportunity to characterize the phenology of that biome.
###### Abstract  
Brain-Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (\(84.0\pm 15.0\%\) and \(74.4\pm 34.5\%\) for the first and second day, respectively) and high path efficiency (\(88.9\pm 11.7\%\) and \(92.2\pm 9.6\%\)). Subjects took on average \(439.7\pm 203.3\) s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed.  
alpha rhythms; assistive robotic manipulator; BCI; EEG Article
## 1 Introduction
The proposed method can obtain the DCCF with a higher integral gain, which ensures the performance of parameter estimation. In addition, the proposed method simplifies parameter estimation to peak detection through the SAC algorithm and 2D-FFT, avoiding parameter search. In this paper, focus imaging is performed on the raw data. So, clutter suppression, target detection and target signal extraction are necessary before refocusing the moving target. After clutter suppression and target detection, the background is sufficiently uncluttered. The proposed method can be summed up five steps. Step I: Clutter suppression and target detection based on multi-channel and target signal extraction. Step II: The RCMC is realized by Hough transform and SOKT. Step III: Constructing DCCF to reduce the third-order phase in the slow-time domain to second-order; then the parameter estimation is converted into peak detection with the SAC algorithm and 2D-FFT, which simplify the parameter estimation process. Step IV: The moving-target focusing imaging is performed by the estimated third-order phase coefficient. Finally, based on the estimated parameters, the target is relocated. The remainder of this paper is structured as follows. In Section 2, the third-order signal model of the uniformly accelerated target is constructed. In Section 3, an improved ground moving-target parameter estimation and imaging method for multichannel high-resolution SAR is proposed. In Section 4, the imaging process of a moving target based on the measured data is given. In Section 5, the effectiveness of the proposed method is verifiedby simulation and measured data. In Section 6, the proposed method is summarized and discussed.