esa-sceva/satcom-chunk-collection huggingface dataset:
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 2 Materials and Results
### The HR Pipeline
#### 2.3.5 SQI-Filtered Heart Rate  
The SQI step of the pipeline was evaluated using the specificity and sensitivity metrics (Equations (2) and (3)) applied to Dataset C, a very longform dataset (length \(\geq\) 50 min) captured outside of a lab environment, taken at 250 Hz. These factors were combined to provide high levels of noise in the dataset, while still being clean enough to have areas of signal that should be preserved. A set of filtering parameters that minimised the residual error in the processed HR (AT: 1.3, FW: 31) were used as the base for the SQI algorithm (Figure 12b). An example visualisation of the SQI algorithm applied to a noisy HR is shown in Figure 13, with the results of the analysis shown in Figure 14.  
Figure 14: A demonstration of the SQI algorithm on a noisy dataset. Each dot represents one recording. The algorithm has a very good true positive rate (sensitivity), but a slightly worse false positive rate (1—specificity), implying it will often identify good areas, but may not reject areas of bad HRs as often as it should.  
As seen in Figure 13, the SQI algorithm is not designed to label an HR signal as poor where only individual beats are missing. However, where multiple peaks are missing and the underlying median filter is more unreliable, the HR is designed to be labelled as not usable. Figure 14 shows the general success rate of the SQI algorithm when applied to noisy datasets. Overall, the SQI has a high true positive rate, meaning almost no clean signal is excluded from the recording. The SQI has a higher false positive rate for some of the recordings, meaning some noisy signals can make it into the final analysis, which  
Figure 13: A demonstration of SQI on Dataset C. Top: derived HR signal. Brown and green lines represent the raw HRs. Brown HR is an HR that falls within a local median, while a green HR falls outside the local median. The red HR line represents the HR following a local linear interpolation to remove outlier HR points. The purple line represents the SQI vector indicating whether a time point has a good (=1) or poor (=0) signal quality. This vector indicates a noisy period when the HR data can be considered unreliable and thus excluded from further analyses. Bottom: the corresponding ECG signal (light purple), with the detected R-peaks shown as green dots. Individual missing peaks can be approximated by interpolations, but a noisier period (SQI = 0) becomes harder to recover and so is considered unreliable.  
may require these recordings to be excluded. However, this does represent a significant improvement over the baseline of including all HR signals and is also shown to work very well for the cleaner signals (top left in Figure 14).
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 1 Introduction
### WGS-84 Coordinate System to Gaussian Plane Coordinate System  
To establish the relationship between the WGS-84 coordinate system (L,B) and the Gaussian plane coordinate system (_x,y_), the difference \(l\) between geodetic longitude \(L\) and central meridian longitude \(L\)0, and isometric latitude \(q=\int_{0}^{B}\frac{M}{N\cos\text{B}}\text{dB}\) are introduced, and the relationship between (_l,q_) and (_x,y_) are established; that is:  
\[\begin{array}{l}x=x(l,q)=m_{0}+m_{2}l^{2}+m_{4}l^{4}+\dots\\ y=y(l,q)=m_{1}l+m_{3}l^{3}+m_{5}l^{5}+\dots\end{array} \tag{1}\]  
where \(M=a(1-e^{2})/\left(1-e^{2}\sin^{2}B\right)^{3/2}\) is the radius of curvature in the meridian and \(N=a/\left(1-e^{2}\sin^{2}B\right)^{1/2}\) is the radius of curvature in the prime vertical, where \(a=6399698.9018\) is the radius of the ellipsoid's major axis and \(e^{2}=0.0066943799013\) is the first eccentricity of the ellipsoid. The Gaussian projection satisfies the Cauchy-Riemann condition, as shown in Equation (2).  
\[\begin{array}{l}\frac{\partial x}{\partial q}=\frac{\partial y}{\partial l} \\ \frac{\partial x}{\partial l}=-\frac{\partial y}{\partial q}\end{array} \tag{2}\]  
Since the Gaussian projection does not change the length of the central meridian, the following equation exists:  
\[x=m_{0}=X \tag{3}\]  
Figure 3: Flowchart of Pavement Construction Area (PCA) boundary recognition.  
where \(X=6367558.49686*B/57.29577951308\) is the meridian arc length from equator to geodetic latitude \(B\). The relationship between the WGS-84 coordinate system (\(L\),\(B\)) and the Gaussian plane coordinate system (\(x\),\(y\)) can be inferred from Equations (1)-(3), as shown in Equation (4):  
\[\begin{array}{l}x=X+\frac{Nt}{2}\cos^{2}Bl^{2}+\frac{Nt}{24}\cos^{4}B(5-t^{2 }+9\eta^{2}+4\eta^{4})l^{4}+\frac{Nt}{720}\cos^{6}B(61-58l^{2}+t^{4})l^{6}\\ y=N\cos Bl+\frac{N}{6}\cos^{3}B(1-t^{2}+\eta^{2})l^{3}+\frac{N}{120}\cos^{5}B( 5-18t^{2}+t^{4}+14\eta^{2}-58\eta^{2}t^{2})l^{5}\end{array} \tag{4}\]  
where \(t=\tan B\),\(\eta^{2}=e^{\prime 2}\cos^{2}B\) and \(e^{\prime 2}=0.00673949674227\) is the second eccentricity of the ellipsoid.
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
###### Abstract  
Real-time robotic applications encounter the robot on board resources' limitations. The speed of robot face recognition can be improved by incorporating cloud technology. However, the transmission of data to the cloud servers exposes the data to security and privacy attacks. Therefore, encryption algorithms need to be set up. This paper aims to study the security and performance of potential encryption algorithms and their impact on the deep-learning-based face recognition task's accuracy. To this end, experiments are conducted for robot face recognition through various deep learning algorithms after encrypting the images of the ORL database using cryptography and image-processing based algorithms.  
cloud robotics; image face recognition; deep learning algorithms; security; encryption algorithms +
Footnote †: journal:  
using convolutional neural network (CNN) algorithms by simply connecting to a cloud service [7].  
However, this solution faces security problems. Indeed, it is essential to ensure the security of the facial images to be sent through the network. An interesting solution for this problem is the use of a cryptographic system allowing for avoiding network attacks able to recover these data.  
In the present work, we focus on the two environments: robot and cloud. Certainly, the confidentiality of robots is essential. This is because private information is shared in public clouds. As a result, there is a clear risk of abuse or at least misuse of private data. The robot contains and uses private information. They should be safeguarded and treated with respect for confidentiality and privacy.  
The contribution of this paper is threefold:  
* We provide a security analysis of the potential encryption algorithms that can be used to encrypt images stored on the cloud.
* We present a comparative and experimental study of several CNN based secure robotic facial recognition solutions.
* We study the impact of encryption algorithms on the performance of the CNN based robot face recognition models.  
The experimental done in this paper includes several combinations of various encryption algorithms and deep learning algorithms that have been tested and have shown an improvement in recognition speed and accuracy without impacting privacy issues when executed on cloud compared to their execution in robot environment.  
The remainder of this paper is structured as follows: Section 2 presents an overview of the main CNN based robot face recognition models. Then, Section 3 highlights the different encryption techniques that can be used for images encryption. Section 4, provides a security analysis of the encryption algorithms studied. The performance of the CNN based robot face recognition and the impact of the encryption algorithms on their performance were presented and discussed in Section 5. Later, The benefit of outsourcing computation to the cloud for face recognition algorithms is shown before concluding the paper.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 4 Hansel Services
### JASON Based Positioning Relay Service (POS)  
HANSEL offers to the _testbed_ users the possibility to process GNSS data using Rokubun's Jason Service ([49]) using the POS service (which is a relay service that interfaces with the Jason service in the cloud). Any user that has a set of GNSS measurements in the RINEX format can use this service to obtain an accurate navigation solution in post process using one of the following methods:  
* Post-Processing Kinematics (PPK), which is the post-facto version of the Real Time Kinematic (RTK) differential technique. In this case, Jason will look and fetch the GNSS measurements from the closest reference GNSS station, chosen among a collection of more than 10,000 stations, spread among several public Continuous Operating Reference Station (CORS) networks;
* Precise Point Positioing (PPP). Depending on the age of the data set, a set of precise orbits and clocks (i.e., SP3 files from the International GNSS Service Analysis Centers) will be fetched instead of using the broadcast ephemeris. In current operations, PPP is only available after 1 or 2 weeks as it uses final products. This latency could be reduced to a few hours if ultra-rapid products were used instead.  
[PERSON] works on a best effort basis, trying to provide the best possible solution. If PPK cannot be performed (by e.g., closest reference station too far), PPP will be used instead. If everything else fails, a Single Point Positioing (SPP) solution will be provided.
# Adaptive MPC Based Real-Time Energy Management Strategy of the Electric Sanitation Vehicles
###### Abstract  
Electric sanitation vehicles have increasingly been applied to cleaning work due to the requirement of air pollution control. The power distribution and energy management strategy (EMS) influence the vehicle's performance a lot both in the aspects of cleaning effect and electricity consumption. Aiming to improve energy economy and ensure clean tasks, first, the electricity consumption percentages of the vehicle onboard devices are analyzed and the main contributors are clarified, and the power requirement model of the working motor is built based on experimental data. Second, a universal modeling method of garbage distribution on the road surface is proposed, which implements a nonlinear autoregressive neural network as the predictor. Third, an adaptive model predictive control (AMPC)-based EMS is proposed and verified. The results show the AMPC method can accurately predict the garbage density and the proposed EMS can approximate the energy consumption of the DP-based EMS with little deviation. Compared to conventional rule-based EMS, the AMPC-based EMS achieved a 15.5% decrease in energy consumption as well as a 14.6% decrease in working time.  
adaptive model predictive control (AMPC); nonlinear autoregressive neural network; electric sanitation vehicle; energy management strategy; dynamic programming +
Footnote †: journal: Medical Engineering  
0000-0001-9998-2277-2888][PERSON]  
1,498
# Quality Assessment of Photogrammetric Methods--A Workflow for Reproducible UAS Orthomosaics
## 3 Results
### Orthomosaic Reproducibility  
To evaluate the reproducibility of orthomosaics, the images of the 4 th forest flight were computed 25 times with identical photogrammetric parameters. The 300 pixel-wise correlation analysis between the 25 orthomosaics were performed within a testing area of 600 by 650 pixels showing the forest canopy (Figure 4a). Pixels with correlation coefficients higher than 0.95 were considered reproducible between the orthomosaics. Highly reproducible pixels are characterized by consistently high correlation coefficients and therefore high values in the summed up layer shown in Figure 4b. Non-reproducible regions appear mainly in forest clearings or at dead trees. The actual canopy appears stable across multiple computations.  
Using only five identical orthomosaics (i.e., 10 pairwise correlation analyses, Figure 4c) revealed the same patterns as the 300 correlation layers. Hence, only five repetitions are considered enough for subsequent reproducibility analyses.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Controlpoint Error (m)**} & \multicolumn{4}{c}{**Checkpoint Error (m)**} \\ \hline
**Flight** & **XYmean** & **XYsd** & **Zmean** & **Zsd** & **XYmean** & **XYsd** & **Zmean** & **Zsd** \\ \hline Forest 01 & 0.0149 & 0.0003 & 0.0207 & 0.0003 & 0.0220 & 0.0002 & 0.0591 & 0.0010 \\ Forest 02 & 0.0082 & 0.0008 & 0.0217 & 0.0006 & 0.0377 & 0.0008 & 0.1989 & 0.0019 \\ Forest 03 & 0.0140 & 0.0001 & 0.0264 & 0.0012 & 0.0565 & 0.0003 & 0.1765 & 0.0030 \\ Forest 04 & 0.0112 & 0.0004 & 0.0122 & 0.0006 & 0.0529 & 0.0034 & 0.0861 & 0.0090 \\ Forest 05 & 0.0176 & 0.0005 & 0.0215 & 0.0005 & 0.0362 & 0.001 & 0.1344 & 0.0022 \\ Forest 06 & 0.0120 & 0.0002 & 0.0173 & 0.0003 & 0.0595 & 0.0053 & 0.1845 & 0.0223 \\ \hline Grassland 2013 & 0.0001 & \textless{}0.0001 & 0.0001 & \textless{}0.0001 & 0.2917 & 0.0012 & 2.0691 & 0.0041 \\ Grassland 2015 & 0.0009 & \textless{}0.0001 & 0.0001 & \textless{}0.0001 & 0.1837 & 0.0006 & 1.3638 & 0.0095 \\ Grassland 2017 & 0.0040 & \textless{}0.0001 & 0.0001 & \textless{}0.0001 & 0.0700 & \textless{}0.0001 & 0.0007 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Controlpoint and checkpoint error of the five computations of the six forest flights. The images of from each flight were computed five times with identical settings.  
Figure 4: Pixel-wise correlations of the RGB values of a 600 by 650 pixels area of the canopy (**a**) of identical orthomosaic processes. (**b**) The sum of a binary classification over 25 identical computations, hence 300 pairwise correlations. For the binary classification, a pixel-wise correlation coefficient of 0.95 or greater was used. High values (yellow) denote high reproducibility of the RGB values in this pixel over the 25 images. Low values (blue) indicate non-reproducible orthomosaics since the correlation coefficient between the 25 computations is consistently below 0.95. (**c**) The results of only 5 identical computations, hence the sum of 10 correlation layers.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 5 Experimental Results
### Power Failure Time Detection  
The predictive power management algorithm has a risk of over-assigning energy that possibly leads to power shortage. Therefore, we need to further investigate the power failure time \(t_{f}\). In this section, we compare the proposed system with D-PEA and sta-PEA in reference [38], the test scenario is just the same as step B, and the duty cycle ratio \(D_{C}\)'s dynamic variation is shown in Figure 12 primarily (for a direct perception, we classify the ratio into 5 sorts: 0%, 25%, 50%, 75%, and 100%):  
The figure shows the detailed work/sleep ratio variations from day 1 to day 7, apparently the ratio rises to 100% under a continuous wind blowing and drops to 0% when in breezeless, the average ratio is lower than 40% in the first four days due to the incomplete work of WCSAD, and improves to over 60% in the last three days because of the prediction management's proper functioning. From these data, we could infer that the proposed energy management algorithm can effectively charge the EH-node that is given at a lower initial energy, and make it work reliably.  
Then we detect and compare the power failure time, here we consider that system power failure is caused by the depletion of the supercapacitor's remaining energy; i.e., its terminal voltage \(V_{SC}\) drops below the start-up threshold of Buck-Boost converter (0.85 V). Figure 13 shows the measurement result.  
We can see that D-PEA and the proposed scheme achieve consistently lower \(t_{f}\) compared with sta-PEA. The performance margin increases with the time elapsed particularly for our proposed strategy because of the three efficient and effective integrated submodules. It demonstrates that the proposed scheme can best react to the changing wind speed and avoid depleting the energy storage device.  
Figure 11: Line diagram of the power distributed in the proposed wind-powered sensor.  
Figure 12: Changes of duty cycle ratio (with 20% initial electricity).
## 10 Relationship between Land Ownership and the Right to Groundwater  
### Two Theoretical Approaches and the Supreme Court Rulings  
Clear setting of the relationship between land ownership and right to the groundwater is another key legal issue, because a clean underground reservoir and its groundwater artificially created by ASWQE project can be critically influenced by the legal rights of the property ownership. Herein, the relationship between land ownership and the right to groundwater are examined by focusing on (i) the view in which groundwater is considered a component of land and (ii) the view in which the two elements are considered individual entities.  
According to the former, the right to groundwater is also part of land ownership and an object of land ownership. The landowner may use groundwater exclusively. In addition, Article 212 of the Civil Act can be applied to all cases regardless of whether the legal status of groundwater refers to hot springs, mineral springs, or mineral water, and regardless of how deep the groundwater is in relation to the surface of land [29]. There may be cases in which someone that is not the landowner has a separate right to use the groundwater, which is one of the limitations of land ownership by the legal principle of the adjoining neighbor relationship, stated as follows in Article 235 of the Civil Act: \"Rightto Use Water for Common Use: Adjoining neighbors have the right to use the wellspring or water supply for common use in proportion to their demands as long as they do not disturb the other's use of water\".  
As the issue of water deficiency is becoming more severe every day, interest in groundwater is also rapidly increasing worldwide. Consequently, active arguments are being held to distinguish the right to use groundwater from land ownership. There are various legal grounds for acknowledging groundwater as an independent object of real rights separate from the land. These different views have certain differences in perspective in terms of the argument that sees water rights for groundwater and land ownership as two individual rights. However, these views ultimately claim the two as rights with independent characteristics depending on the public nature of groundwater or special assets, the social need for groundwater as an important national resource, and the need to mediate conflicts of rights among stakeholders ([PERSON], 2018).
###### Abstract  
Leaf pigment contents, such as chlorophylls \(a\) and \(b\) content (C\({}_{ab}\)) or carotenoid content (Car), and the leaf area index (LAI) are recognized indicators of plants' and forests' health status that can be estimated through hyperspectral imagery. Their measurement on a seasonal and yearly basis is critical to monitor plant response and adaptation to stress, such as droughts. While extensively done over dense canopies, estimation of these variables over tree-grass ecosystems with very low overstory LAI (mean site LAI \(<\) 1 m\({}^{2}\)/m\({}^{2}\)), such as woodland savannas, is lacking. We investigated the use of look-up table (LUT)-based inversion of a radiative transfer model to retrieve LAI and leaf C\({}_{ab}\) and Car from AVIRIS images at an 18 m spatial resolution at multiple dates over a broadleaved woodland savanna during the California drought. We compared the performances of different cost functions in the inversion step. We demonstrated the spatial consistency of our LAI, C\({}_{ab}\), and Car estimations using validation data from low and high canopy cover parts of the site, and their temporal consistency by qualitatively confronting their variations over two years with those that would be expected. We concluded that LUT-based inversions of medium-resolution hyperspectral images, achieved with a simple geometric representation of the canopy within a 3D radiative transfer model (RTM), are a valid means of monitoring woodland savannas and more generally sparse forests, although for maximum applicability, the inversion cost functions should be selected using validation data from multiple dates. Validation revealed that for monitoring use: The normalized difference vegetation index (NDVI) outperformed other indices for LAI estimations (root mean square error (RMSE) = 0.22 m\({}^{2}\)/m\({}^{2}\), \(R^{2}\) = 0.81); the band ratio \(\frac{\rho_{0.750\mu\mathrm{m}}}{\rho_{0.850\mu\mathrm{m}}}\) retrieved C\({}_{ab}\) more accurately than other chlorophylls indices (RMSE = 5.21 \(\mu\)g/cm\({}^{2}\), \(R^{2}\) = 0.73); RMSE over the 0.5-0.55 \(\mu\)m interval showed encouraging results for Car estimations.  
AVIRIS; DART; leaf pigments; Mediterranean forest; tree-grass ecosystem
## 5 Conclusions  
This study addresses the challenge of multi-phase terminal trajectory optimization for anti-ship missiles by proposing a 3D multi-phase trajectory optimization method based on MOPSO-h. The algorithm combines the efficient global search capability of PSO with a multi-objective optimization framework, incorporating an adaptive learning mechanism based on HMS to replace traditional mutation operations. This innovative approach enables missiles to achieve optimal trade-offs among various performance metrics in complex operational scenarios. The simulation results demonstrate that, in typical scenarios, the trajectory optimized by MOPSO-h achieves superior overall performance and significantly enhances terminal guidance accuracy compared to conventional algorithms. The state curve analysis indicates that MOPSO-h facilitates automatic speed control and overload adjustment for anti-ship missiles under constrained conditions. Additionally, the research examines the influence of angle-tracking errors on trajectory optimization performance, uncovering a nonlinear relationship between the two and offering valuable insights for improving missile guidance performance in complex battlefield environments.  
Conceptualization, J.S., S.Y. and D.H.; methodology, Z.X.; software, P.W. and Z.Y.; validation, J.S., S.Y. and D.H.; resources, J.S.; supervision, S.Y.; writing--original draft, J.S., S.Y. and D.H.; writing--review and editing, Z.X., P.W. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The relationship between MMD and the elevation angles error and azimuth angles error.  
Figure 7: The relationship between MMD and angular binding errors and seeker range errors.  
**Funding:** This work was supported in part by the Jiangxi Provincial Natural Science Foundation (no. 20242 BAB212004) and the National Natural Science Foundation of China (no. 62365014).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON] and [PERSON] were employed by the company 8511 Research Institute of China Aerospace Science and Industry Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.