esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation
### IoT Nodes
#### 4.1.1 Actuator Nodes

Table 7: Sensors used by ZiWi’s nodes. Figure 6 shows the final prototype of an actuator node. The node is powered with a 5 V 700 mA AC-DC converter that provides enough current to power all the components and the relay module. Moreover, an aluminum separator was introduced as noise insulation screen to avoid the possible noise generated by the switched power supply, which is connected to ground to derive any possible interference. This modification was made because it was observed that the selected low-cost AC-DC converter introduced electrical noise and, since the WiFi module is close to the converter, the separator prevented potential interference in the communications. Regarding the relays, they are on a separate board and consist of eight relays that are powered through the external switched supply, since the NodeMCU is not able to provide enough current to power them. It is also worth mentioning that current sensors use three-wire connectors that are soldered directly to the corresponding pins of the CMOS analog selector, as well as to the temperature sensor. Such a sensor cannot be seen in Figure 6, since it is actually glued to the back of a power outlet, as it can be seen in Figure 7.  
Figure 5: Electronic schematic of the actuator node. Figure 6: Final prototype of the actuator node.
# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 3 Experimental Results and Analysis
### Qualitative Evaluation
#### 3.1.2 Result on Orbbec Depth Camera Data  
Orbbec is a domestic company specializing in 3D cameras and computer vision technology. It is dedicated to the research and development, as well as the production, of high-performance, high-precision, and cost-effective 3D cameras and sensors. In this study, we utilize the Orbbec Astra S monocular structured light depth camera, which is suitable for scenes with less stringent accuracy requirements and within a distance range of 40 cm to 200 cm.  
Due to the hardware design of the Orbbec camera, there are data missing in several leftmost columns of the captured depth image, as shown in Figure 5b. From the details observed in Figure 5c,d, it can be deduced that the MECI algorithm successfully accomplishes the hole recovery, yet disparities from the ground truth depth map persist. The contours of objects exhibit a certain degree of blurring. In comparison, the algorithm presented in this paper achieves more accurate object contours in most cases and demonstrates superior performance in filling the holes within the depth map. However, the depth restoration of transparent objects is not optimal in our algorithm, as illustrated in the restoration details of the \"Corner2\" scene in Figure 5d. The repaired values for the missing region in the computer host area are close to the depth values of the curtain behind it, which is not appropriate for that region.
# Innovation in Company Labor Productivity Management: Data Science Methods Application
## 2 Methodology of Research  
### Conceptual Approach for Labor Productivity Management  
We propose the technology based on step-by-step data processing and modeling. It reflects demographic, social factors and factors about the personnel health quality. The conceptual diagram of this approach is shown in Figure 1 and is aimed to labor productivity management. The technology is implemented by stages.  
Stage 1. Qualitative analysis of the personnel health status, technical and economic data acquisition. A continuous examination of the company's employees is carried out using medical examinations and questionnaires. The result of this stage is objective data about health state of employees as well as subjective information about the health state and its quality.  
Stage 2. Selection and substantiation of factors affecting the personnel individual productivity. An exploratory data analysis is carried out, an assessment of the influence of factors reflecting the social and demographic personnel characteristics as well as factors determining the influence of the health state and its quality on the labor productivity is conducted on the basis of correlation and regression analysis methods.  
Stage 3. Employers' clustering (grouping) into homogeneous groups and composition typical personnel profiles for each cluster. The personnel with similar demographic, social factors and health quality are united into homogeneous groups. The result of this stage is the personnel typical profiles by the clusters.  
Stage 4. Development of management decisions aimed at improving the state and quality of personnel health for each homogeneous employers' cluster.
## 3 Numerical Results and Discussion
### Entropy Combinations
#### 3.2.2 NNetEn as a Paired Feature in Signal Classification  
The problem of separating signals is addressed through the definition of classification accuracy \(A_{\text{RKF}}\) described in Section 2.6.3. Figure 10a shows the \(A_{\text{RKF}}\) dependencies using the NNetEn(D1) and NNetEn(D2) features separately. NNetEn(D1) has a higher classification accuracy than NNetEn(D2), reaching the maximum of \(A_{\text{RKF}}\) \(\sim\)0.837 at \(N\)_set_ = 34. It is consistent with Figure 7 where the F-ratio also reached its maximum at \(N\)_set_ = 34, with Dataset 1 showing higher values than Dataset 2.  
Figure 9: F-ratio distribution for three variants of entropy differences: NNetEn(D1)-NNetEn(D1) (**a**), NNetEn(D2)-NNetEn(D2) (**b**), NNetEn(D2)-NNetEn(D1) (**c**). The figure demonstrates the relationship between the F-ratio and the difference in entropies. The entropy difference under certain settings can have high efficiency (F-ratio) when used as a feature.  
We used \(\mathrm{SampEn}(m=2,\,r=0.2d)\) together with NnetEn(D1) and NnetEn(D2). According to Figure 10b, a single \(\mathrm{SampEn}\) gives recognition accuracy \(A_{\mathrm{RKF}}=0.8845\). By combining entropies, \(A_{\mathrm{RKF}}\) can either increase, reaching values of \(\sim\)0.9145 or decrease to \(\sim\)0.8735. The combination of \(\mathrm{SampEn}\) and NNetEn entropies can significantly increase classification accuracy. Figure 10c illustrates a quantitative assessment of the synergistic effect. At _Nset_ = 2, 27, 35, 50, 59, the D1 base achieves the highest \(\mathrm{Ksyn}\). The settings numbers correspond to M1 and M3 reservoir filling methods. A two-dimensional diagram is shown in Figure 11 for the combination [NNetEn(D1, _Nset_ = 2), \(\mathrm{SampEn}\)]. As can be seen, there is a selective extrusion of points along the ordinate axis as indicated by the sign NNetEn(D1, _Nset_ = 2). In this way, it is possible to more clearly separate the classes; in the figure, a blue straight line indicates a conditional separation between the classes. A slanted blue line separates classes better than a vertical or horizontal line, and it proves the effectiveness of using NNetEn and \(\mathrm{SampEn}\) in pairs.  
Figure 10: Classification accuracy \(A_{\mathrm{RKF}}\) dependences using NNetEn(D1) and NNetEn(D2) features separately (**a**), their combination with \(\mathrm{SampEn}\) (**b**) and dependence of the synergistic effect coefficient (**c**). The figure shows the efficiency of using NNetEn as a feature pair to \(\mathrm{SampEn}\).
## 1 Introduction
China's Gaofen-5 satellite (GF5-02), equipped with the Advanced Hyperspectral Imager (AHSI), is specifically designed to detect methane point sources within a confined area (60 \(\times\) 60 km\({}^{2}\)) using a fine pixel resolution (30 m) and relatively high accuracy (3-18 percent) [22]. It aims to detect methane plumes with a spectral resolution of 10 nm and a pixel resolution of 30 m, offering a significant advantage in detecting methane point source emissions due to its high signal-to-noise ratio (approximately 500 in shortwave Infrared, SWIR) [23; 24; 25]. The Environmental Mapping and Analysis Program (EnMAP) mission, initiated in April 2022 (DLR/GFZ, Potsdam, Germany), also features similar spatial and spectral characteristics (30 m resolution, 30 km swath, and about 8 nm spectral sampling at 2300 nm). Despite a similar spectral resolution and sampling, research on EnMAP's capability to map methane has been relatively limited due to its more recent launch [26]. Frequent revisits require a satellite constellation system due to spatial coverage and mission scheduling constraints. The Sentinel-2, with its twin-satellite configuration, achieves global coverage every five days and a revisit rate of every two to three days in mid-latitude regions [27]. Therefore, in this study, we leverage the combined capabilities of Sentinel-2 and EnMAP/GF5-02-AHSI for methane plume detection. We apply a match filter algorithm based on EnMAP/AHSI to identify and quantify abnormal point source emissions in methane hotspots in Shanxi, China, and the Permian Basin, USA. In addition, we conduct the high-frequency monitoring of methane emissions from two facilities, the Sanyuan Nan Yao Coal Mine in Changzhi, Shanxi, China, and the EOG Shale Gas Well in Eddy County, New Mexico, using the MBMP (multi-band multi-pass) method. Our findings suggest that the emissions from these methane plumes are not related to seasonal changes, offering a unique perspective on the variability and intermittency of large point source methane emissions. This work demonstrates how satellite-based multispectral imaging instruments can facilitate the global high-frequency mapping of large methane point sources by combining a fine pixel resolution with rapid revisit rates.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 2 Ranging System
### Conventional Cross-Correlation TOA Estimator  
Signal arrival time needs to be obtained from reference signals. In Long Term Evolution (LTE) Release 9, positioning reference signals (PRS) were used to improve TOA-based positioning. PRS are pseudo-random sequences with good autocorrelation. With the help of the autocorrelation characteristics of the PRS sequence, it is easier to find the direct path in the environment of multipath transmission. The cross-correlation expression is:  
\[\begin{split} R_{xy}&=\sum_{n=1}^{N}\overline{x^{m}[ n-\tau]}y^{m}[n]\\ &=h_{1}[nT_{s}]\mathrm{e}^{-j2\pi f_{c}\tau_{1}[nT_{s}]}R_{m}^{ xx}[\tau-\tau_{1}[nT_{s}]]+\sum_{i=2}^{L}h_{i}[nT_{s}]\mathrm{e}^{-j2\pi f_{c} \tau_{i}[nT_{s}]}R_{m}^{xx}[\tau-\tau_{i}[nT_{s}]]\\ &+R_{m}^{xx}[\tau],\tau\in[1,...N];\end{split} \tag{7}\]  
where \(\overline{(.)}\) denotes the complex conjugate function, \(x[n]\)is the replica of the transmitted PRS. Furthermore, based on the autocorrelation of the PRS series, we have:  
\[\begin{split} R_{m}^{xx}[\tau]&=\sum_{n=1}^{N} \overline{x^{m}[n-\tau]}x^{m}[n]=\delta[\tau]\\ R_{m}^{xx}[\tau]&=\sum_{n=1}^{N}\overline{x^{m}[n- \tau]}w^{m}[n]\approx 0\end{split}. \tag{8}\]  
where \(w^{m}[n]\) is the downsampled additive noise. From Equation (7), it can be seen that the magnitude of the correlation function is affected by the carrier phase \(2\pi f_{c}\tau_{1}[nT_{s}]\). To exclude this effect, we use \(|R_{xy}[\tau]|\) instead of \(R_{xy}[\tau]\) for TOA estimation. Taking the threshold method as an example, TOA is determined by estimating the time delay of the first (earliest) peak in the magnitude of the normalized cross-correlation function above a certain threshold [4].  
\[\hat{\tau}=\underset{\tau}{\arg\min}\Bigg{\{}\frac{|R_{xy}[\tau]|}{\max\{|R_ {xy}|\}}\geq\zeta\Bigg{\}}, \tag{9}\]  
here, \(\zeta\) is the preset threshold. Correlation profile-based methods can estimate the propagation delay of the first path in a multipath environment. Still, due to the limited sampling rate of the system, the measurement accuracy of this method is low. Rewriting \(\hat{\tau}\) to \(T_{r}^{i}\) and introducing terminal \(r\) and the BS \(i\), then the estimated TOA can be modeled as [25]:  
\[T_{r}^{i}=(d_{r}^{i}+w_{r,T}^{i})/c. \tag{10}\]* \(T_{r}^{i}\) (known) is the TOA measurement from terminal \(r\) to BS \(i\) (units).
* \(c\) is the speed of radio waves in vacuum, 299,792,458 (unit: m/s).
* \(d_{r}^{i}=\sqrt{\left(x_{i}-x\right)^{2}+\left(y_{i}-y\right)^{2}}\) (unknown) is the geometric distance between the antennas of transmitter \(i\) and receiver \(r\) (unit: m).
* \((x_{i},y_{i})\) (known) is the two-dimensional vector giving the coordinates of BS \(i\).
* \((x,y)\) (unknown) is the location of the terminal to be solved.
* \(w_{r,T}^{i}\sim\mathcal{N}\left(0,\sigma_{r,i}^{2}\right)\) (unknown) is a random Gaussian variable accounting for the residual estimation error (unit: m).
## 5 Development of the EnMAP Science Plan
This includes research on optical models for high absorbing coastal waters [67] and on hyperspectral absorption of dissolved salts [68], as well as the influence of particle composition and particle size to the mass-specific absorption of light. The differentiation of phytoplankton groups using derivative analysis of hyperspectral data is also being investigated. * Urban areas: Since the turn of the century, more people are thought to live in cities than in rural areas. Airborne and spaceborne remote sensing approaches are frequently used to provide spatial information to characterize, monitor and manage urban areas. Imaging spectroscopy started to be used for urban studies mainly based on airborne sensor data in the 1990s and has subsequently led to detailed and automated urban surface material mapping using material specific spectral characteristics [69]. Recent analysis of simulated EnMAP data showed its potential for more detailed and accurate maps on urban fraction cover than, for example, at Landsat spectral resolution [64]. However, due to the lack of spaceborne imaging spectrometers, the potential for urban studies has not yet been fully explored. Still, the relative coarse geometric resolution of sensors like EnMAP is not sufficient to resolve most of the small-scale urban objects, such as buildings and streets. Thus, combining EnMAP spectral information with other high spatial resolution information, such as multispectral satellite data (e.g., Sentinel-2, WorldView), digital surface models and spectral libraries of surface materials, is thought to be useful [70]. Spectral mixture analysis approaches will be developed accounting for the high spectral heterogeneity of urban areas. The derived surface material abundance maps can then be used to generate maps of surface imperviousness, detect hazardous materials or as input to urban micro-climate models. EnMAP's lifetime, spatial coverage and revisit time will allow repeatedly observing large cities on the globe and the continuous reporting of changes.
# Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization
## 3 Simulation Environment  
The simulation environment is based on the MATLAB autonomous driving toolbox. The whole algorithm is made in MATLAB.m file. Since previous research proved no significant differences between S shape roads and straight roads, in this paper, only the S shape road with the 50 m range of perception limit was used, as shown in Figure 2(a). The three-dimensional features are generated randomly along the road, as shown in Figure 2(b).  
The noise setting parameters are shown in Table 2. The GPS sensor data is used for the vehicle position initialization and particle initialization. The noise is generated by the normal distribution and transformed with sinusoidal function [29; 30]. The onboard noise values are empirical values. The perception error is modeled as a normal distribution in x, y, z-directions. In this paper, we assumed that the perception was performed by a LiDAR sensor. These simulation noise parameters only provide reference results. The perception error depends on the algorithm of the perception module; therefore, it should also be tuned to the actual sensors. The velocity was assumed to be constant. The test velocities of the vehicle were 60 km/h, 70 km/h, 80 km/h, 90 km/h, 100 km/h, 110 km/h, and 120 km/h.  
\begin{table}
\begin{tabular}{c c} \hline
**Order** & **Process** \\ \hline
1 & Start one sample time iteration \\
2 & Initialization N particles \\
3 & For 1 to N do \\
4 & \(\mathrm{\widetilde{X}_{k+1}=\ \mathrm{prediction\ model}(X_{k},\ \mathrm{u_{t}})}\) \\  & End for \\
5 & \(\mathrm{\widetilde{x_{PF}}=\ \mathrm{argmax}_{\begin{subarray}{c}x,y,\theta \\ x,y,\theta\end{subarray}}\mathrm{P_{p,i},\ \mathrm{y_{p,i}},\ \mathrm{z_{p,i}} \end{subarray}}}\) \\
6 & For 1 to naug do \\
7 & \(\mathrm{X_{k+1}=\ \mathrm{unscented\ transform}\ (\lambda,\ x_{k},\sigma)}\) \\
8 & \(\mathrm{\widetilde{x}_{pauk,k+1\mathbf{j}}=\ \mathrm{CTRV\ model\-based\ state\ prediction}}\) \\
9 & \(\mathrm{\widetilde{z}_{pauk,k+1\mathbf{j}}=\ \mathrm{A}(\mathrm{\widetilde{x}_{pauk,k+1 \mathbf{j}}})}\) for measurement prediction \\
10 & \(\mathrm{\widetilde{x}_{pauk,\mathrm{P}}}\) \\
11 & End one sample time iteration of extended PAUKF \\ \hline \end{tabular}
\end{table}
Table 1: The pseudocode of extended PAUKF.  
The random seed was fixed to 50 for repeatable simulation. The sample time was set as 0.05 s, by considering the perception time. These parameters provide a reference for the performance of the extended PAUKF algorithm. These empirical noise parameters should therefore be tuned with the actual sensor characteristics. We also ran the algorithm in a huge perception error environment to confirm the performance of the algorithm.
# Automatic Docking Trajectory Design-Based Time-Varying-Radius Dubins for Unmanned Surface Vessel
## 2 Preliminaries and System Description  
The descriptions of the main notations in this paper are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(W\) & The width of region \\ \(L\) & The length of region \\ \(V_{min}\) & The minimal speed of USV \\ \(V_{max}\) & The maximal speed of USV \\ \(V_{i}\) & The speed of USV \\ \(k\) & Radius influence factor \\ \(R_{min}\) & The minimal radius of USV \\ \(R_{max}\) & The maximal radius of USV \\ \(R_{i}\) & Each segment’s radius \\ \(\zeta_{i}\in\Omega\) & Each segment’s in the trajectory \\ \(|\zeta_{i}|\) & Each segment’s length \\ \(t_{i}\) & Each segment’s time \\ \(T(\Omega)\) & Total trajectory time \\ \(n\) & The maximal number of iterations \\ \(m\) & The actual number of iterations \\ \(\theta_{i}\) & The current heading of USV \\ \(h\) & The number of different speed selection \\ \(H\) & The set of minimal turning radius intervals \\ \(e\) & Iteration termination conditions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of the main notations.
## 3 Earth Surface Effects on GNSS-R Relevant to Land Applications
### Regional Scale  
This section provides a regional scale analysis (Figure 10) with focus on Africa-Congolian tropical rainforests, Sahara and Kalahari Desert (Figures 11-13), Northern Asia Boreal forests (Figures 14-16), and the Greenland ice sheet (Figures 17 and 18). These three regions have been selected because they provide a wide overview of different scattering properties over the Earth's surface. The information provided by the seasonal variations of the PR, SNR, and the spreading of the waveforms is complemented with maps of Soil Water Index (SWI) from METOP-ASCAT [38], and Normalized Difference Vegetation Index (NDVI) from MODIS [39]. Additionally, the effect of larger AGB on tropical rainforests as compared to boreal forests is derived from the lower levels of SNR for higher AGB, the lower PR, and the lower spreading of the waveforms, as compared to different terrain types (Figure 10).  
Figure 9: SMAP over-land Signal-to-Noise Ratio at LHCP [dB] using one-year averaged values.  
Figure 11: _Cont_.  
Figure 10: Annual evolution (mean and standard deviation) of main waveforms’ parameters: (**a**) Signal-to-Noise Ratio H-Pol (dB); (**b**) Signal-to-Noise Ratio V-Pol (dB); (**c**) trailing edge width (m); (**d**) leading edge width (m); and (**e**) Polarimetric Ratio (dB).  
Figure 11: Regional study over Africa. Polarimetric Ratio (**a**) Summer; (**b**) Winter. Signal-to-Noise Ratio (**c**) Summer; (**d**) Winter. Trailing edge width (**e**) Summer; (**f**) Winter. Soil Water Index (**g**) Summer; (**h**) Winter. Normalized Difference Vegetation Index. (**i**) Summer; (**j**) Winter.  
Figure 14: Map of forest height over Northern Asia produced from NASA’s ICESAT/GLAS, MODIS, and TRMM sensors.  
Figure 12: Close up over Northern Africa. Polarimetric Ratio (**a**) Summer; (**b**) Winter. Signal-to-Noise Ratio (**c**) Summer; (**d**) Winter. Trailing Edge Width (**e**) Summer; (**f**) Winter.  
Figure 13: Close up over Southern Africa. Polarimetric Ratio (**a**) Summer; (**b**) Winter. Signal-to-Noise Ratio (**c**) Summer; (**d**) Winter. Trailing Edge Width (**e**) Summer; (**f**) Winter.  
Figure 15: Regional study over Norther Asia. Polarimetric Ratio (**a**) Summer; (**b**) Winter. Signal-to-Noise Ratio (**c**) Summer; (**d**) Winter. Trailing edge width (**e**) Summer; (**f**) Winter. Soil Water Index (**g**) Summer; (**h**) Winter. Normalized Difference Vegetation Index. (**i**) Summer; (**j**) Winter.  
Figure 16: Close up of Signal-to-Noise Ratio seasonal changes over central Russia: (**a**) Summer; (**b**) Winter; and over Eastern Russia: (**c**) Summer; and (**d**) Winter.  
Figure 17: View of the extent and frequency of surface melt for the Greenland ice sheet. Data from the National Snow and Ice Data Center.  
Figure 18: Regional study over North America and Greenland. Polarimetric Ratio (**a**) Summer; (**b**) Winter. Signal-to-Noise Ratio (**c**) Summer; (**d**) Winter. Trailing edge width (**e**) Summer; (**f**) Winter. Soil Water Index (**g**) Summer; (**h**) Winter. Normalized Difference Vegetation Index; (**i**) Summer; (**j**) Winter.
# Numerical Simulation of Non-Isothermal Mixing Flow Characteristics with ELES Method
## 2 Turbulence Model and CFD Methodology
### Boundary Conditions and Numerical Setting  
The vortex method [34] was adopted to generate turbulent fluctuations at RANS-LES interfaces in the simulations. The number of vortices was set to be large enough to affect all spots on the face zone. For the inlet boundaries, the profiles of velocity and turbulence quantities obtained from precursor RANS simulations were specified. For the solid boundaries, the pipe walls were set under non-slip and adiabatic conditions.  
As recommended in reference [19], the time steps should be chosen to achieve a Courant number of CFL\(\sim\)1 in the LES portion. Therefore, the time step was set as 0.001 s in our simulations, which leads to CFL\(\sim\)1 in the central mixing zone. The mass conservation was most concerned in the process of convergence, and the mass residuals decreased by more than 10 orders of magnitude each time step with 20 inner iteration loops. Other numerical settings and related information in the current simulations are listed in Table 3.  
As for the physical properties of the water, the density, specific heat capacity, and coefficient of thermal conductivity change slightly with the temperature, hence, the three physical properties are treated as constant. However, the dynamic viscosity changes a great deal with the temperature and it seems that the dynamic viscosity is approximately linear with a temperature between 33 \({}^{\circ}\)C and 48 \({}^{\circ}\)C; therefore, a UDF was constructed, and the detailed code can be seen in Appendix A.
# Real-Time Analysis of Facial Expressions for Mood Estimation
## 4 Performance Evaluation
### Results  
The results of the comparison are presented in Table 6. At the first level (Positive and Negative), there was a 94% match between the estimated mood and the self-rated values from the SAM questionnaire. At the second level (Exalted, Calm, Anxious, and Bored), a 73% match was observed. Finally, at the third level (Elated, Dependent, Relaxed, Docile, Hostile, Anxious, Bored, and Disdainful), the match between the annotations and the estimated mood was 64%. It is noticeable that, in general, regarding the cases for which there is no match, we can mainly observe tracking errors caused by the presence of facial artifacts such as beards or glasses, and a lack of facial expressiveness.