esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Works Combining GNSS and InSAR in Europe
## 5 Discussion
The three main products provided by the EGMS are: (i) the ground deformation maps and time-series along the LOS direction; (ii) an advanced ground deformation map in LOS direction combined with the GNSS data of the EUREF permanent network (EPN) [227]; and (iii) the two main deformation components derivable from the combination of the two orbits, i.e., the horizontal East-West and the vertical up-down deformation. All these products will be updated annually and will open several other opportunities for investigation with a consequent advancement in the knowledge in various fields, e.g., ground deformation prediction [228], mapping [224; 225; 229], and monitoring [230] of natural hazards, and atmospheric delay analysis. Another use of GNSS data, which has become increasingly widespread over recent years, is aimed at the retrieval of 3D surface displacement, thus overcoming the limitations associated with DInSAR LOS displacement measurements, e.g., References [92; 128]. Some limits of the geodetic and InSAR approaches can be overcome by the combination of GNSS and InSAR data, but some limitations cannot be removed. In the latest year, GNSS data were mostly used for completing SAR-derived information and filling in points were data were missing, thus improving the temporal coverage of the deformation time series, as performed in a pervious study [100], or for investigating the \"real\" ground deformation velocity, thereby remedying the geodetic displacement that affects the ordinary InSAR measurements. Furthermore, the areal coverage of results can be improved by joining GNSS stations and derived-PS points by PSI approaches, which can obtain deformation data over areas invisible to SAR systems, e.g., Reference [39]. It enables the obtainment of information in areas that are not covered by direct measurements or by the use of a single technique. On the other hand, GNSS stations are placed in strategic positions to create an approximatively regular network and cannot completely overpass the InSAR limits, such as the coverage of shadow areas due to the interaction between the topography and the LoS of SAR satellites. To overcome this limitation, a traditional survey with an operator by GPS must be conducted, reducing the easy and fast repeatability and considerably enlarging the time and monetary expenses. The substantial improvement of both technical and technological approaches in recent decades, as well as the demonstrated useful and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, by more than 180 scientific contributions suggests that the incremental use of these approaches in the future will be further enhanced and widespread. The free availability of Sentinel-1 images and InSAR products by GMS will help in the increasing application in different fields and the relative spreading and dissemination.
# A Bayesian Control Chart for Monitoring Process Variance
## 6 An Example for Demonstration

Table 8: The service time from 10 counters in a bank branch. In order to illustrate the detecting power for the variance change, ten new samples of size 10 were collected from a new automatic service system of the bank branch. Installing an automatic service system makes more consistent service time. The process variance will be reduced significantly. Table 9 lists the service time of the ten new out-of-variance-control samples, \(M_{t}\), and \(EWMA_{t}\). Note that the out-of-control process variance is estimated with \(\hat{\sigma}^{2}\) = 2.9437, showing a significant downward shift in the process variance. Hence, the observed \(M_{t}\)s are all 0 and the observed \(EWMA_{t}\)s decrease with \(t\). We then plotted the ten new observed statistics \(EWMA_{t}\)s in order on the proposed control chart, see Figure 3. The proposed Bayesian EWMA variance chart detected out-of-control signals from the first sample onward (samples 1-10). That is, our chart can quickly detect an out-of-control signal when the variability of the new service time is significantly reduced because of the improved new automatic service system. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \(t\) & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \(X_{4}\) & \(X_{5}\) & \(X_{6}\) & \(X_{7}\) & \(X_{8}\) & \(X_{9}\) & \(X_{10}\) & \(S^{2}\) & \(M\) & \(EWMA\) \\ \hline
1 & 3.54 & 0.01 & 1.33 & 7.27 & 5.52 & 0.09 & 1.84 & 1.04 & 2.91 & 0.63 & 5.83 & 0 & 0.8602 \\
2 & 0.86 & 1.61 & 1.15 & 0.96 & 0.54 & 3.05 & 4.11 & 0.63 & 2.37 & 0.05 & 1.62 & 0 & 0.8172 \\
3 & 1.45 & 0.19 & 4.18 & 0.18 & 0.02 & 0.7 & 0.8 & 0.97 & 3.6 & 2.94 & 2.30 & 0 & 0.7764 \\
4 & 1.37 & 0.14 & 1.54 & 1.58 & 0.45 & 6.01 & 4.59 & 1.74 & 3.92 & 4.82 & 4.15 & 0 & 0.7375 \\
5 & 3 & 2.46 & 0.06 & 1.8 & 3.25 & 2.13 & 2.22 & 1.37 & 2.13 & 0.25 & 1.10 & 0 & 0.7007 \\
6 & 1.59 & 3.88 & 0.39 & 0.54 & 1.58 & 1.7 & 0.68 & 1.25 & 6.83 & 0.31 & 4.11 & 0 & 0.6656 \\
7 & 5.01 & 1.85 & 3.1 & 1 & 0.09 & 1.16 & 2.69 & 2.79 & 1.84 & 2.62 & 1.85 & 0 & 0.6324 \\
8 & 4.96 & 0.55 & 1.43 & 4.12 & 4.06 & 1.42 & 1.43 & 0.86 & 0.67 & 0.13 & 3.02 & 0 & 0.6007 \\
9 & 1.08 & 0.65 & 0.91 & 0.88 & 2.02 & 2.88 & 1.76 & 2.87 & 1.97 & 0.62 & 0.75 & 0 & 0.5707 \\
10 & 4.56 & 0.44 & 5.61 & 2.79 & 1.73 & 2.46 & 0.53 & 1.73 & 7.02 & 2.13 & 4.70 & 0 & 0.5422 \\ \hline \end{tabular}
\end{table} 
Table 9: The new service time from 10 counters in a bank branch. Figure 2: The Bayesian EWMA variance chart under the in-control process.
# Differences in Body Fat in Athletes Categorized by Resting Metabolic Rate
## 2 Materials and Methods
### Procedures
#### 2.3.2 Body Composition  
A Seca(r) physician's scale and stationeter were used to determine body mass and height per standard clinical procedures. Air displacement plethysmography (BODPOD, Cosmed, Concord, CA, USA) was used to assess body composition for the determination of body fat and fat-free mass (FFM). Prior to each testing session, calibration procedures were completed in accordance with manufacturer guidelines using a calibration cylinder of a standard volume (49.55 L) and an empty chamber. Participants were instructed to wear a formfitting sports bra (females), spandex shorts, and swim cap, and remove all jewelry, in accordance with standard operating procedures to reduce air displacement. Manufacturer default settings were used to predict thoracic gas volumes for each participant. Fat mass and FFM were then determined based on the participant's body mass and measured body volume using the Brozek equation [17]. Test-to-test reliability in our lab is high for BM (r = 0.999), body fat percent (r = 0.994), and FFM (r = 0.998). Male and female athletes were stratified into groups based on relative RMR values. For each sex, tertiles were determined as used to create low, moderate, and high relative RMR groups as follows: low (M: <26 kcal/kg; F: <24 kcal/g), moderate (M: 26.1-29 kcal/kg; F: 24.1-27 kcal/kg), and high (M: >29.1 kcal/kg; F: >27.1 kcal/kg).
## 4 Results and Discussion
### Triboelectric Charging of PES Films Doped with RUT  
Figure 4 shows the relationship between \(V_{\text{sat}}\) and the relative humidity for the PES films prepared using different RUT doping levels. At all the tested doping levels, \(V_{\text{sat}}\) shifted negatively with increasing humidity, a behavior that follows the CWP model. In addition, at a constant relative humidity, the absolute value of \(V_{\text{sat}}\) decreased with the increasing RUT doping level. This was attributed to the increase in the relative permittivity, \(\varepsilon\), of the film with increasing TiO\({}_{2}\) doping level, as shown by its measured values in the Supplementary Materials (Table S1). Another possible factor for the decrease in the absolute value of \(V_{\text{sat}}\) is the decrease in the film electrical resistivity, \(\rho\), due to TiO\({}_{2}\) doping; however, as shown in the Supplementary Materials (Table S2), \(\rho\) was near-identical for all the tested films and was thus not considered a factor in this study. This may be attributed to the fact that continuous conductive channels of TiO\({}_{2}\) nanoparticles are not formed at doping levels of 10 wt% or less. More important findings are that the 95% CI values in Figure 4 (0.12-0.30, see Table 1) are smaller than those in Figure 3b (0.50, corresponding to 0 wt% doping level in Table 1) and that the humidity dependence decreased as the RUT doping level increased. A discussion of these findings is provided in Section 4.3.  
Figure 3: (**a**) Triboelectric charging behavior of the polyester (PES) single film when charged with iron powder beads under various humidity conditions. (**b**) Plot of saturated surface potential (\(V_{\text{sat}}\)) versus relative humidity.
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 4 Cluster Division Solution Based on Adaptive GA
### Solution Procedure  
The active distribution network cluster division process based on the adaptive GA proposed in this paper was implemented in MATLAB 2022. The flowchart is shown in Figure 4, and the pseudo-code is given in Algorithm 1 to clarify the overall procedure for applying the proposed cluster division framework. The specific steps are as follows:  
Step 1: The actual distribution system retains the key information to be distilled into a nodal graph, which reads the line impedance, capacity of various power sources, generation power, and other required parameters described in Section 3. The GA parameters are also initialized.  
Step 2: The pass-through relationship between every two nodes is represented by 0 and 1 through the neighbor-matrix-based genetic algorithm presented in Section 4. In this case, each set of collocation matrices represents an individual chromosome.  
Step 3: The construction of the index system described in Section 3 assesses the size of the individual frequency regulation potential and the ability to consume renewable energy, through which the current individual can be scored.  
Step 4: It is determined whether the algorithm satisfies the iteration termination condition, i.e., whether the current number of individual score calculations reaches the set value. If the termination condition is satisfied, the loop ends and we go to step 6; if the relevant condition is not satisfied, the number of iterations is increased by one and we move to step 5.  
Step 5: The fitness of the current individual is judged to determine whether it is greater than the average value; if it is, return to step 3, and if not, modify the crossover and variance probability in accordance with Equations (14) and (15), and return to step 3.  
Step 6: The best individual that satisfies the fitness function during the iteration process is selected. The optimal chromosome currently found, i.e., the adjacency matrix, is reduced to one division of the distribution system to obtain the final division.  
```
Input: distribution network system and operation data. Output: cluster division result. Initialize the parameters of the adaptive GA.  Conduct coding based on the adjacency matrix and obtain the initial population. While\(I<I_{\max}\)do  calculate the fitness \(f\) of the current individual using (13) and  update the current adjacency matrix according to the optimal fitness. If\(f<f_{ave}\),then  change the values of \(p_{c}\) and \(p_{m}\) using (14) and (15);  cross and mutation according to current \(p_{c}\) and \(p_{m}\). If\(f\geq f_{ave}\),then  cross and mutation according to normal \(p_{c}\) and \(p_{m}\). End if. End while.  Decode the adjacency matrix. Return the optimal cluster division result.
```  
**Algorithm 1**Pseudo-code of cluster division based on adaptive GA.  
Figure 4: Flowchart of proposed method.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 7 Conclusions  
For the image segmentation of the reflected laser lines during on line monitoring of weld pool surface, both accuracy and the efficiency are important. In this paper, we propose eight approaches to segment the reflected laser lines by combining different image processing algorithms. We evaluate their accuracy and efficiency extensively with both qualitative and quantitative results. Experimental results ranked the accuracy and the efficiency of the proposed approaches objectively. The quantitative results showed that the recently proposed image processing methods, including the difference method, the threshold selection method, the gradient detection method and the spline fitting method, are the most effective steps to form the effective segmentation approaches. The quality of the captured image is mainly determined by the welding process. During monitoring weld pool with violent changes, e.g.,  
\begin{table}
\begin{tabular}{c c} \hline
**Approaches** & **F-Measure** \\ \hline Approach 1 & 0.5380 \\ Approach 2 & 0.2074 \\ Approach 3 & 0.2276 \\ Approach 4 & 0.5011 \\ Approach 5 & 0.8546 \\ Approach 6 & 0.4743 \\ Approach 7 & 0.4469 \\ Approach 8 & 0.9176 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of computation accuracy for eight methods.  
\begin{table}
\begin{tabular}{c c} \hline
**Approaches** & **Computation Time** \\ \hline Approach 7 & 0.05 s \\ Approach 3 & 0.045 s \\ Approach 2 & 0.042 s \\ Approach 8 & 0.031 s \\ Approach 6 & 0.028 s \\ Approach 5 & 0.0145 s \\ Approach 4 & 0.0138 s \\ Approach 1 & 0.01 s \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of computation time for eight methods.  
GMAW weld pool, all these recently proposed image processing methods should be combined as an approach to achieve the required accuracy. During monitoring gently changing weld pool, e.g., GTAW weld pool, only the difference method, the gradient detection method and the threshold selection method are required to form approach 5 that could meet the required accuracy while achieving higher efficiency.  
Conceptualization, Z.W. ([PERSON]); Methodology, Z.W. ([PERSON]); Software, Z.P. and Z.W. ([PERSON]); Validation, X.Q., L.L. and J.P.; Formal Analysis, C.Z. and S.M.; Investigation, Z.W. ([PERSON]); Resources, Z.W. ([PERSON]); Data Curation, [PERSON] ([PERSON]); Writing-Original Draft Preparation, [PERSON] ([PERSON]); Writing-Review & Editing, C.Z., Z.P., [PERSON] ([PERSON]), X.Q., L.L., J.P. and S.M.; Visualization, [PERSON] ([PERSON]); Supervision, [PERSON] ([PERSON]); Project Administration, [PERSON] ([PERSON]); Funding Acquisition, [PERSON] ([PERSON]).  
The authors declare no conflict of interest.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
###### Abstract  
This review paper discusses the complete evolution of free-space optical (FSO) communication, also known as unguided optical communication (UOC) technologies, all the way back to ancient man's fire to today's machine-learning-supported UOC systems. The principles, significance, and developments that have happened over the past several decades, as well as installation methodologies, technological limitations, and today's challenges of UCCs are presented. All the subsets of UOC: FSO communication, underwater optical wireless communication (UOWC), and visible light communication (VLC), with their technology/system developments, potential applications, and limitations are reviewed. The state-of-the-art developments/achievements in (i) FSO channel effects and their mitigation techniques; (ii) radio-over-FSO techniques; (iii) wavelength division multiplexing and sub-carrier multiplexing techniques; (iv) FSO for worldwide interoperability for microwave access applications; (v) space optical satellite communication (SOSC); (vi) UWOC; (vii) photoacoustic communication (PAC); (viii) light-fidelity; (ix) VLC; (x) vehicular VLC (V\({}^{2}\)LC); and (xi) optical camera communication are reviewed. In addition, the current developments on emerging technologies such as artificial intelligence (to improve the performance of UOC systems), energy harvesting (for the effective utilization of UOC channels), and near-future communication network scenarios (mandatory for secured broadband digital links) are covered. Finally, in brief, to achieve the full potential of UOC systems, challenges that require immediate research attention are summarized.  
FSO communication; environmental effects; radio-over-FSO; visible light/space optical communications; artificial intelligence in FSO systems; energy harvesting from FSO channel; Internet of Things (IoT); optical camera communications; underwater optical communications; photoacoustic communications; UOC research challenges +
Footnote †: journal: Electronics Letters  
0
Footnote †: journal: Electronics Letters  
0
Footnote 0: 0
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 7 GNSS HO-BOC Signals vs. Galileo E5 Positioning Performance Results  
Finally, to complete the discussion, we summarize in Figure 14 the SPP and RTK positioning results using the four HO-BOC signals (see Section 2.5), which were compared against the Galileo E5 signal performance. We only show the results for a good satellite geometry because in the bad GDOP case, the conclusions were the same as for the Galileo E5 signal in Section 6.2.  
* First, notice that for the SPP, the same time-delay threshold behavior was obtained, the L1-M and E6-PRS HO-BOC signals being the ones that provided a the best robustness with a slight performance degradation w.r.t. E5. The E1-PRS and B1A signals achieved a similar E5 asymptotic performance, but needed the better [3; 4; 4] dB of SNR at the output of the MLE.
* In the RTK positioning case, \(\text{CRB}_{real/integer}\), which mainly depended on the carrier signal wavelength \(\lambda_{c}\), was slightly better for the HO-BOC signals, which operated at a higher frequency in the L1/E1/B1 band. Under nominal conditions, this implied that the carrier phase-based solution was improved by the L1-M and E6-PRS signals, because they preserved the same threshold behavior. In contrast, as for the SPP case, the threshold for the E6-PRS and B1A signals was degraded [1; 2; 3] dB.
* Notice that for a bad GDOP and because the RTK convergence was driven by the time-delay estimation performance (refer to Figure 13), the four HO-BOC signals behaved similar to or worse than the Galileo E5.  
We could conclude that the L1-M and E6-PRS signals are recommended, in scenarios with bad satellite geometries for SPP and for nominal conditions in the RTK case. In the latter case and considering an SPP solution, the E1-PRS and B1A signals provided a performance similar to Galileo E5.  
Figure 13: Non-nominal bad GDOP scenario RTK RMSE/CRB (m) convergence at high SNR.
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 3 Multiplexing Concurrent OFDM Signals
### Discussion
In all the approaches mentioned above, the signal multiplexing carried out at the transmitter side is transparent to the PHY receivers, meaning that the radio stack at the receivers does not need to be modified. As a limitation, the proposed hypervisor architecture supports the operation of multiple concurrent RATs as long as they are OFDM-based signals, have subcarrier spacings that are integer multiples of the smallest adopted frequency spacing and CP lengths that are integer multiples of the maximum number of vPHYs, which is given by \(M\), i.e., RATs with numerologies that are multiple integers of one another, which are also referred here as RATs with correlated numerology. This limitation was chosen at design-time so that the proposed architecture could get rid of several digital signal processing tasks (e.g., up/down-conversion, filtering, etc.) necessary by a BS to handle uncorrelated RAT numerologies (e.g., LTE, Wi-Fi, ZigBee, etc.) but that would increase the processing time and complexity of a given solution for multiplexing these RATs. Therefore, we have chosen this limitation for the sake of processing time and complexity optimization. Our approach is novel when compared to other ones (please, refer to Section 5) that support the multiplexing of RATs with uncorrelated numerology by performing several additional digital signal processing tasks that affect the processing performance of a BS, especially when the number of multiplexing signals increases. With our proposed approach, we wanted to focus on correlated RATs (i.e., RATs with numerologies that are multiple integers of one another) and with that have an optimized architecture for such RATs. Therefore, based on the correlated numerology limitation, it is not possible to employ the proposed architecture to multiplex the signal of RAT technologies with uncorrelated numerologies such as LTE, Wi-Fi and ZigBee. As mentioned in the use cases given in Section 4, the proposed architecture is meant for multiplexing concurrent signals of the same RAT or correlated RATs (e.g., LTE/LTE, LTE/NB-IoT, NB-IOT/NB-IoT, Wi-Fi/Wi-Fi, ZigBee/ZigBee, etc.). Consequently, if the different RATs have correlated numerologies, then, as analytically proved in Section 3, these different and concurrent RAT signals can be orthogonally multiplexed successfully.
# Review of Functionally Graded Thermal Sprayed Coatings
## 3 Principal Properties of Thermal Spray Coatings and Methods of Their Characterizations
### Mechanical Properties
#### 3.1.1 Adhesion (Bond Strength of Sprayed Coatings)  
The state-of-the-art of coatings adhesion evaluation methods are shown by [186; 187]. The most common method is tensile adhesion test (TAT), which is specified in the ASTM C633 ([[https://www.astm.org/Standards/A633.htm](https://www.astm.org/Standards/A633.htm)]([https://www.astm.org/Standards/A633.htm](https://www.astm.org/Standards/A633.htm)) checked on 10 July 2020) or ISO 14916:2017 ([[https://www.iso.org/standard/60995.html](https://www.iso.org/standard/60995.html)]([https://www.iso.org/standard/60995.html](https://www.iso.org/standard/60995.html)) checked on 10 July 2020) standards. The tests are used in research and in industry [1; 7; 10]. An example of application of TAT method could be found in [188]. The modification of conventional TAT test were proposed for flat and wide substrates. For example, [PERSON] and [PERSON] [189] developed single bar and double bar methods. There also are many shear tests useful in determining the adhesion strength [190; 191; 192; 193; 194]. Many tests of adhesion were developed basing on fracture mechanics [195]. Other methods of adhesion characterization in macroscopic and microscopic scale include the tests such as: tubular coating tensile, peel adhesion, pin, laser-shock, interfacial indentation, and scratch one [196; 197; 198; 199; 200; 201; 202; 203; 204; 205]. The cohesive strength of a coating can be determined by tests of tensile adhesion, pin, shear load, and scratch one [206].