esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Gap  
However, most of the research on CAS focuses on small-scale production environments (e.g., assembly work benches) [6], hindering the direct applicability of the current approaches. Nevertheless, some of the core approaches of CASs with visual information output are generally applicable. These include augmented reality glasses, lasers, and video and light projectors (moving head spots). In view of the requirements described above, light projectors may be a cost-effective solution and are fundamentally suitable for use in aircraft production (note: more information about pricing of selected devices can be found in Appendix B). Nevertheless, there is not a significant number of papers on light projectors in the literature, which is due to the fact that they are mainly used in event technology, and no applications in the manufacturing industry are known. What exists are only a few papers touching on this technology's potential as an information channel for a CAS [7; 8]. So far, the integration of the moving head spot in the industrial laboratory environment has been demonstrated in principle. The commissioning, which includes the calibration of the device, has been solved using a frame so that a projection within a previously measured two-dimensional plane is enabled. Thus, there is a lack of a calibration methodology for three-dimensional space and, on the other hand, a methodology that does not require additional mechanical calibration jigs. To overcome this deficit, this paper presents a workpiece-based calibration method for moving head spots, which calculates the transformation between the moving head spot and a given reference coordinate system using [PERSON]'s method. This not only simplifies the calibration process, but also makes it easier to work in three-dimensional space, enabling the illumination of curved objects such as those found in aircraft production.
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 3 System Model for the Navigation System
### Measurement Model of the System
#### 3.2.2 Barotmetric Altimeter Measurement Equation  
The barometric altimeter is used to measure barometric pressure and translate it into an output of voltage ranging from zero to five volts. The barometric errors vary in accordance with the environment. The observation model of the barometer sensor is given by  
\[h_{pres}=h+\omega_{pres} \tag{4}\]  
where \(h_{pres}\) is the altitude measured by the barometer, \(h\) is the true altitude, and \(\omega_{pres}\) is the white noise of the barometer.  
The height measured by the barometric altimeter is relative to the sea level, while the height measured by the GPS receiver is based on the WGS84 coordinate system. There are some differences between them. In practical application, the height has to be converted to a uniform standard and then fused. The sea level parameter correction method is often used to compensate the barometric altimeter bias. Actual sea level parameters can be calculated by using the atmospheric parameters of the location when the altitude is known. With the modified sea-level parameters, the barometric altimeter bias will be compensated.
## 4 Results and Discussion
### Time-Averaged Unsteady Simulation Results
#### 4.1.3 Investigation of the Flow Field in the Different Diffusers
Figure 9: Distribution of static pressure on diffuser meridional direction at low mass flow rate operating point (\(\varphi=0.7\ Q_{\mathrm{d}}\)). inner product of vorticity vectors and relative velocity (\(-1\) means that the rotation direction of the vortex structure is opposite to that of the impeller, while \(1\) indicates that the rotation direction of the vortex structure is the same as the rotation direction of the impeller) [9]. A longitudinal vortex is observed on the suction surface of the diffuser, as indicated by a white ellipse dashed line in Figure 12a. The vortex is generated by secondary flow passing through the vane root (indicated by a black arrow in Figure 12b) and is almost stationary. Consequently, the secondary flow caused by this vortex toward the suction surface of the vane (indicated by a white arrow in Figure 12b) prevents the growth of the boundary layer and the development of flow separation on the suction surface of the diffuser vane. The vortex structures of the TVD leading-edge are also obtained using \(Q\)-definition and colored by nondimensional helicity, as shown in Figure 13. These vortex structures are mainly composed of horseshoe vortices, tornado-like vortices, and transverse vortices. The horseshoe vortices are primarily located at the shroud side and hub side of the diffuser vane inlet. The separation vortex is enlarged due to the relatively low flow angle of the diffuser leading-edge generating a tornado-like vortex that connects the diffuser suction-side surface and shroud wall. Under the action of meridional curvature of the centrifugal impeller blade, low-momentum flow is driven to radial migration from hub side to shroud side [22]. The high adverse pressure gradient reverses flow near the shroud side on the suction surface of the diffuser. Therefore, two transverse vortices (I and II) originate near the shroud side on the suction surface of the diffuser, and the two transverse vortices rotate in opposite directions. Vortex I is clockwise; it is affected by the tornado-like vortex and impacts the diffuser suction surface. Vortex B is counterclockwise; it rotates towards the pressure surface of the adjacent diffuser vane, blocks the mainstream in the diffuser passage, leads to increased flow loss, and even causes the diffuser to stall [23]. Comparing the vortex structures at the leading edges of TVD and SVD, it can be seen that the flow in the passage of the TVD diffuser is more complicated than that in SVD.
# Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
## 2 Materials and Methods
### Probabilistic Slope Stability of Unsaturated Soils  
#### 2.4.1 Infinite Slope Stability  
The infinite slope model is used widely to study shallow landslides, which often occur as rainfall-induced events with shallow depths. Natural slopes have long slope lengths and shallow soil depths, leading to failure behavior similar to that of infinite slopes. [PERSON] et al. (2016) proposed the calculation of shear strength in unsaturated soils by considering the matric suction, with the factor of safety (\(F_{S}\)) of the infinite slope model for unsaturated soils expressed as  
\[F_{S}=\frac{c^{\prime}+\sigma^{\prime}\text{tan}\varnothing^{\prime}+(u_{a}-u_{ w})\text{tan}\phi^{b}}{W\text{ sin}\beta\text{ }cos\beta} \tag{1}\]  
where \(c^{\prime}\) is effective cohesion (kPa); \(\sigma^{\prime}\) is effective normal stress (kPa); \(\phi^{\prime}\) is effective friction angle (\({}^{\circ}\)); \(u_{a}\) is pore air pressure (kPa), and \(u_{w}\) is the pore water pressure (kPa). Here, the difference between \(u_{a}\) and \(u_{w}\) is defined by matric suction, and \(\phi^{b}\) is the angle of shearing resistance that increases with matric suction. \(W\) is the weight of the soil slice per unit area (kPa), and \(\beta\) is the slope angle (\({}^{\circ}\)). An increase in the soil shear strength owing to matric suction of unsaturated soil can be expressed as follows ([PERSON] et al., 2016):  
\[\text{tan}\phi^{b}=\text{tan}\phi^{\prime}\left(\frac{\theta-\theta_{r}}{ \theta_{s}-\theta_{r}}\right) \tag{2}\]  
where \(\theta\) is volumetric water content (%) at the potential failure surface, and \(\theta_{s}\) and \(\theta_{r}\) are saturated and residual volumetric water contents (%), respectively. The soil-water characteristic curve (SWCC) used for estimating the shear strength increase in unsaturated soil was adopted from [PERSON] and [PERSON] (2015).
## 5 Conclusions  
The high data transmission with minimum losses is the key demand of current users because the online markets and jobs have increased the pressure on the present nature of LDOTNs. It was studied in this paper how NLPns are the main reason for limiting the outcomes of multichannel optical networks. Thus, to consider these NLPns, a soft decoding strategy was utilized in this paper. With the existence of NLPns in LDOTNs, the data transmission was improved up to a 600 km path cover. Low-dispersion fiber (G-652.D and G-657.A1) is employed in order to support multichannel and high-capacity propagation. Furthermore, the advanced modulation formats DP-16/64 QAM boost the proposed model capacity. The presented model constellation diagram-based analysis highlights how NLPns factors are reduced multiple times instead of standard modulation schemes and fibers. Additionally, the advanced NMI measuring parameter is utilized to evaluate the proposed structure's performance. With regard to future works, the transmission reach  
Figure 19: Constellation diagrams: (**a**) affected signals’ performance at reaching side after covering 600 km path (DP-16 QAM); and (**b**) affected signals’ performance at the reaching side after covering a 600 km path (DP-64 QAM).  
Figure 20: Constellation diagrams: (**a**) purified signals from NLPns at reaching side after covering 600 km path (DP-16 QAM) using soft detecting technique; and (**b**) purified signals from NLPns at the reaching side after covering 600 km path (DP-64 QAM) using the soft detecting technique.  
and bandwidth volume can further improve the suggested model especially in terms of accuracy through deep learning methodologies such as transfer-learning-assisted neural-network nonlinear compensation (TL-NN-NLC). Table 3 shows the comparison between the proposed LDOTN and the current model in terms of applied techniques.  
**Funding:** This work was funded by the Deanship of Scientific Research at Jouf University under grant No. (DSR-2021-02-03103).  
**Acknowledgments:** This work was funded by the Deanship of Scientific Research at Jouf University under grant No. (DSR-2021-02-03103).  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Results
### Comparison of Spatiotemporal Change Detection and Single-Date Classification  
All of the newly added wilted pine trees with canopy width greater than 3 m were collected to assess the relationship between the accuracies and the thresholds of \(\alpha\) and \(N\). The specific producer accuracy as well as user accuracy under different \(\alpha\) and \(N\) values are listed in Tables 11 and 12 in Appendix A, respectively. Due to the trade-off between the producer and the user accuracies, the \(\alpha\) was set to 0.015 and the \(N\) was set to 16 for further analyses. We compared the advantages and disadvantages of the proposed spatiotemporal change detection method and the single-date image classification method. The validation results for the each field site are presented in Tables 3 and 4. Compared to the single-date classification, the spatiotemporal change detection showed better user accuracies for the mixed forest (Sites A, B, C, and D). Overall, the producer accuracies for the two method were relatively close (84.7% and 82.6%), while the user accuracy for the spatiotemporal change detection (81.2%) was significantly better than that of the single-date classification (67.7%).  
The detection results for the two methods at Site A are shown in Figure 9. The spatiotemporal change detection correctly detected 11 of 15 wilted pine trees and missed Nos. 2, 9, 14, and 15, and there was only one commission error (Figure 9a). The single-date classification correctly detected 12 wilted pine trees and missed Nos. 2, 9, and 14, but there were eight false detections, significantly more than that for spatiotemporal change detection (Figure 9b). The eight commission errors included four discolored deciduous trees, specifically Nos. i, ii, iii, and v, along with four barren and mixed vegetation-soil targets. Meanwhile, the detection result of the spatiotemporal change detection did not include discolored deciduous trees at Site A.
## 4 Conclusions  
In this study, we proposed a high-resolution and low-computational-cost technique for an AT device. We estimated the number of propagation modes using information theoretic criteria and the DL technique. We proposed a method to estimate the optimal DL value required for guided wave estimation. The proposed method did not involve processes of high computational cost. The proposed method was evaluated experimentally using 4-mm-thick copper and bone-mimicking plates. The estimation error was less than 4% and the calculation time when using the proposed method on a laptop computer was less than 0.5 s. While the processor is different and is in fact faster than that which was used in the previous conventional study, the calculation time is less than 1% of that of the conventional method. We believe that our proposed method thus has the potential to accurately characterize the elastic properties of cortical bone.  
This work was supported in part by a Grant-in-Aid for Scientific Research (A) (Grant No. 25249057), a Grant-in-Aid for JSPS Fellows (Grant No. 15J05687), and the PEPS program (15R03051 AMETCARMAT) from the Universite Paris-Est. This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Program (Grant Agreement No. 682001, project ERC Consolidator Grant 2015 BoneImplant). We thank [PERSON], MSc, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript.  
[PERSON] analyzed the data and wrote the paper; [PERSON] contributed to the basic conception and discussion and gave feedback on the manuscript; [PERSON] contributed to the feedback on the signal processing algorithm and the manuscript; [PERSON] contributed to the discussion and gave feedback on the manuscript from an application perspective; [PERSON] contributed to the discussion and provided feedback on the manuscript; [PERSON] contributed to the design of the signal processing algorithm and gave feedback on the manuscript.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 11: Wavenumbers estimated for differently sized sub-arrays. Red cross marks and blue circles show the results obtained for \(N_{\text{sub}}=5\) and \(26\), respectively. Black lines show the theoretical curves.
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### FalconDB
#### 4.3.1 Authenticated Data Structure  
Recognising malicious server nodes in outsourced databases is a function of Authenticated Data Structures (ADS) [43], where databases are uploaded to a cloud server and used remotely. ADS provides clients the opportunity to verify server results; thus, it can validate query and update requests. ADS has five key functions for querying, validating, and calculating the digest. The digest is generated for the current database content.  
The correct execution of the request can be validated through running verification and proof validation over the results and proofs received from the server node. The validation is successful if the server executed the appropriate functions of the ADS.  
**Limitation of ADS:** ADS has two major limitations, which are solved by blockchain architecture:  
1. outdated, malicious digest causes issues, as the user must have the most recent valid digest.
2. an attack can cause changes to the data, for example inserting or deleting records, this data cannot be recovered by ADS, nor can the attacker be identified.  
The first limitation is solved with a BFT consensus mechanism, which synchronises the digest among the clients easily. The second limitation is solved through immutable and transparent property.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 2 Datasets
### Base Map Image: Landsat-8  
Landsat-8's Operational Land Imager (OLI) is a mid-resolution satellite optical sensor launched in 2013. As published OLI images have already been projected onto a map coordinate, the latitude  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Wavelength (\(\upmu\)m)** & **Pixel Scale (degree/pixel)** & **GSD (m)** & **Image Width (km)** & **Bit Depth (bit)** \\ \hline BOL & 8–14 & 0.0142 & 156 & \(100\times 75\) & 12 \\ VIS & 0.4–1.0 & 0.00774 & 85 & \(109\times 87\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the un-cooled bolometer imager (BOL) and the visible monochromatic camera (VIS) onboard UNIFORM-1. At the given ground sampling distances (GSDs) and image widths, UNIFORM-1 observes the ground from a 628 km altitude under the nadir condition.  
Figure 1: Example of map projection of a VIS image onto latitude–longitude coordinates. The attitude information is deduced from the onboard sensors alone (a gyroscope, a magnetic sensor, and Sun sensors) without the STT. The contrast of the VIS image is enhanced. The background image (base map) is taken from Landsat-8 OLI images (Band 4).  
and longitude of each pixel in the images can be determined from the map projection information distributed by the United States Geological Survey (USGS). Additionally, the elevation of each pixel can be determined by a digital elevation model (DEM). The reported accuracy of the map projection is 18 m [5]. Given these characteristics, the OLI images provide a suitable base-map for the VIS images.  
In this study, we used Band 4 images as the base-map images, whose wavelength is included in the VIS coverage. The specifications of OLI (Band 4) are summarized in Table 2. Pan-chromatic images constructed from multi-band integration might better correspond to the observed wavelength, but the proposed method using Band 4 images was sufficiently effective in experiments (see Section 4). The DEM was generated from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, with a GSD of 30 m [16; 17], identical to that of OLI Band 4.
## 2 Materials and Methods
### Model Wastewater  
The procedure for optimizing pharmaceutical extraction and extract clean-up was carried out on model (synthetic) wastewater. They were prepared to reflect the qualitative composition of actual municipal wastewater. To this end, enriched broth (152 mg\(\cdot\)L\({}^{-1}\)) and casein peptone (226 mg\(\cdot\)L\({}^{-1}\)) were used to reflect the source of carbon compounds and organic nitrogen; NH\({}_{4}\)Cl (20 mg\(\cdot\)L\({}^{-1}\)) was used as a source of ammonium nitrogen, and KH\({}_{2}\)PO\({}_{4}\) (16 mg\(\cdot\)L\({}^{-1}\)) and K\({}_{2}\)HPO\({}_{4}\) (40 mg\(\cdot\)L\({}^{-1}\)) were used as a source of phosphorus. CH\({}_{3}\)COONa (212 mg\(\cdot\)L\({}^{-1}\)) was added to increase COD. The model wastewater was prepared using tap water filtered on an activated carbon column to preserve macro- and micronutrients and remove chlorine and organic pollutants [56]. Their BOD and COD values were approximately 300 mg\(\cdot\)L\({}^{-1}\) and 500 mg\(\cdot\)L\({}^{-1}\), respectively. The model wastewater was stored at \(-20\) \({}^{\circ}\)C until analysis.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Compound and CAS Number** & **Molecular Formula** & **Structural Formula** & **Molar Mass [g mol\({}^{-1}\)]** & **Bioavailability** & **Lowest ENResurater** & **PKa** & **Water Solubility [mg mL\({}^{-1}\)]** & **logP** & **Melting Point [\({}^{-1}\)C]** \\ \hline Antidiabetics & & & & & & & & \\ \hline metformin 657-24-9 & C\({}_{4}\)H\({}_{1}\)N\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\) & 129.2 & 50–60\% & 160 & 12.4 & 1.38 & \(-2.6\) & 223–226 \\ \hline Beta-blockers & & & & & & & & \\ \hline atenol 29122-687 & C\({}_{4}\)H\({}_{2}\)N\({}_{2}\)O\({}_{3}\) & & & & & & & \\ \hline bisoprolol 6672-44-9 & C\({}_{5}\)H\({}_{1}\)N\({}_{4}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\) & 266.3 & 50–60\% & 150 & 9.6 & 0.7 & 0.2 & 158–160 \\ bisoprolol 6672-44-9 & C\({}_{5}\)H\({}_{1}\)N\({}_{4}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\) & 325.5 & \(>\)90\% & 92 & 9.5 & 0.07 & 2.2 & 100–103 \\ micropol 5138-51-1 & C\({}_{15}\)H\({}_{2}\)N\({}_{5}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\) & 267.4 & 50–70\% & 8.6 & 9.7 & 0.40 & 2.2 & 120 \\ \hline Diuretics & & & & & & & & \\ \hline indapamide 26807-65-8 & C\({}_{4}\)H\({}_{4}\)CH\({}_{3}\)O\({}_{5}\)S & NH\({}_{4}\)N\({}_{2}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\) & 365.8 & nd & 1.29 & 8.8 & 0.03 & 2.5 & 160–162 \\ torasemide 56211-40-6 & C\({}_{4}\)H\({}_{2}\)N\({}_{4}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\)O\({}_{5}\) & NH\({}_{4}\)N\({}_{2}\)O\({}_{5}\) & 348.4 & 80–90\% & nd & 7.1 & 0.06 & 3.4 & 163–164 \\ \hline Hormones & & & & & & & & \\ \hline levonorgetrel 797-63-7 & C\({}_{21}\)H\({}_{3}\)O\({}_{2}\) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 1 Introduction
When the L2A ratio exceeds this optimal level, the BER escalates because the positive impact of the increasing extinction ratio is outweighed by the negative impact of the increasing interference. Ambient light is uncontrollable in outdoor environments where VOCC systems operate. However, the light output from LEDs can be adjusted to the optimal L2A ratio to achieve the lowest BER at the same data rate. This paper shows that the optimal level of the L2A ratio is not fixed, but varies according to the interval between LEDs in the panel. The contribution of this paper is multifold. Firstly, to the best of our knowledge, this paper is the first work in literature to discuss the effect of the L2A ratio on BER in VOCC systems. Through theoretical analysis, this paper elucidates the L2A ratio and LED interval as the two primary parameters governing the BER of a system. This analysis also reveals that the lowest BER can be achieved through maximizing the ratio of the between-class variance to the within-class variance (B2W) of the On and Off LED features in received images. Based on that, we formulate a constrained optimization problem to finding the optimal L2A ratios in order to maximize the B2W variance ratio subject to different LED intervals. The optimization result, which is the optimal ranges of L2A ratios at different LED intervals, is achieved through simulation. In practice, the L2A ratio optimization process is performed and the optimal values of L2A ratios are obtained at the hands of the manufacturers in the offline phase before the VOCC components are released to the market. This process can be accomplished using the method of maximizing the B2W variance ratio as proposed in the paper. In the online phase when the VOCC system is operating, the luminance of the LEDs can be adjusted to the optimal level corresponding to the current ambient light and LED intervals. Note that the level of ambient light can be estimated easily using built-in light metering systems in commercial cameras. In this paper, the simulation result shows that by using the optimal L2A ratio, a much lower BER of the VOCC system can be achieved without having to change the data rate.