esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate, Efficient, and Stable Perspective-n-Point Algorithm in 3D Space
## 2 Methods
### The Application of Hidden Variable Method to Solve Pose Parameters
\begin{cases}(cW_{11}+dW_{12}+W_{13})d=(cW_{31}+dW_{32}+W_{33})c\\ (cW_{31}+dW_{32}+W_{33})d=(cW_{21}+dW_{22}+W_{23})c\\ (cW_{31}+dW_{32}+W_{33})^{2}=(cW_{11}+dW_{12}+W_{13})(cW_{21}+dW_{22}+W_{23}) \end{cases}  \tag{17}\]  
Equation (17) can be expanded as follows:  
\[ \begin{cases}s_{11}c^{2}+s_{12}cd+s_{13}c+s_{14}d^{2}+s_{15}d=0\\ s_{21}c^{2}+s_{22}cd+s_{23}c+s_{24}d^{2}+s_{25}d=0\\ s_{31}c^{2}+s_{32}cd+s_{33}c+s_{34}d^{2}+s_{35}d+s_{36}=0\end{cases}  \tag{18}\]  
where \(s_{11}=-W_{31},s_{12}=W_{11}-W_{32},s_{13}=-W_{33},s_{14}=W_{12},s_{15}=W_{13},\)  
\(s_{21}=-W_{21},s_{22}=W_{31}-W_{22},s_{23}=-W_{23},s_{24}=W_{32},s_{25}=W_{33},\)  
\(s_{31}=W_{31}^{2}-W_{11}W_{21},s_{32}=2W_{31}W_{32}-W_{12}W_{21}-W_{11}W_{22},\)  
\(s_{33}=2W_{31}W_{33}-W_{13}W_{21}-W_{11}W_{23},s_{34}=W_{22}^{2}-W_{12}W_{22},\)  
\(s_{35}=2W_{32}W_{33}-W_{13}W_{22\[\left[ \begin{array}{ccc}h_{11}&h_{12}&h_{13}\\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&h_{33}\end{array} \right]\left[ \begin{array}{c}c\\ d\\ 1\end{array} \right]=0\Leftrightarrow H(b)\left[ \begin{array}{c}c\\ d\\ 1\end{array} \right]=0 \tag{22}\]  
According to Equation (22),  
\[det(H(b))=0 \tag{23}\]  
After the operation and arrangement of Equation (23) with Matlab software, a polynomial equation, with the highest degree of 8 and only the unknown \(b\), can, finally, be obtained:  
\[E_{b}b^{8}+E_{7}b^{7}+E_{6}b^{6}+E_{5}b^{5}+E_{4}b^{4}+E_{3}b^{3}+E_{2}b^{2}+E _{1}b=0 \tag{24}\]  
where \(E_{i}\) (\(i=0\), 1, 2, 3,..., 8) is the known coefficient. The eigenvalue method is used to calculate the root of \(b\), which has at most 8 roots. The value of \(c\) and \(d\) can be obtained by substituting \(b\) back into Equations (19)-(21), and, thus, clarifying the value of the rotation matrix \(R\) and translation vector \(t\).
## 4 Discussion
### Limitations and Future Directions  
The accuracy of SPEI generated by the interpolation of site-based precipitation and temperature data might have some limitations. SPEI was calculated based on precipitation and potential evapotranspiration in this study. Many studies have reported that the increase in potential evapotranspiration will exacerbate the aridity of a regional environment [60]. Therefore, how the complex interactions between different environmental factors affect vegetation growth need to be further examined. In addition, the construction of the VAR model requires the data to pass the stability test, which will result in partial data loss. Due to the difficulties of this model for large-scale data analysis [61], we synthesized 8-day MODIS data into monthly-scale time series data. In a future study, other data processing methods such as Toda and Yamamoto procedure [62] can be tried to improve the efficiency of data utilization and analysis so as to meet the requirement of quantitatively revealing the relationship between drought and ecosystem function response at fine scale.
# Adsorption of Coxsackievirus in Sediments: Influencing Factors, Kinetics, and Isotherm Modeling
###### Abstract  
Drinking groundwater contamination by pathogenic viruses represents a serious risk to worldwide public health, particularly for enteric viruses, which exhibit high prevalence and occurrence during outbreaks. Understanding how enteric viruses adsorb in groundwater is essential to protecting human health and ensuring the sustainable use of water resources. The adsorption properties of Coxsackievirus A16 (CA16), a common gastrointestinal virus that spreads through groundwater, were investigated in this work. A typical batch equilibrium approach was used to investigate CA16 adsorption and factors that influence it. In a laboratory recognized nationally as a biosafety level 2 facility, stringent research protocols were followed to guarantee compliance with experimental standards. The variables that were investigated included the size of the sediment particles, the starting concentration of the virus, temperature, pH level, and humic acid content. The findings showed that the CA16 virus was more strongly attracted to finer sediment particles and that its adsorption increased as the size of the sediment particle decreased. Furthermore, it was discovered that higher temperatures improved the CA16 virus's ability to bind to sediment particles. The pH of the aqueous environment has a significant effect on the effectiveness of virus adsorption; higher effectiveness was seen in acidic environments. Furthermore, it was found that the presence of humic acid decreased the ability of clay to adsorb CA16, suggesting that humic acid has a detrimental influence on clay's ability to adsorb viruses. The examination of kinetic models demonstrated that, in every scenario examined, the adsorption process of CA16 adhered to the pseudo-second-order kinetics model. Additionally, the Langmuir and Freundlich isotherm models were used to assess the equilibrium data that were collected in this investigation. The outcomes amply proved that the most accurate representation of the adsorption equilibrium was given by the Langmuir isotherm model. The study offered a solid scientific foundation for treating groundwater and creating plans to stop the spread of viruses.  
CA16; sediments; adsorption; influencing factors; kinetics modeling; adsorption isotherm
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 4 Background and Related Work
It is required that client satisfaction is to be measured based on a metric that accounts for joint energy and QoS performance optimization, and hence, a pragmatic satisfaction between the fog jobs of the cloud environment and service providers is met. Such satisfactions are to be assessed by deriving a performance metric that measures the QoS of fog jobs so as to penalize the amounts of energy consumption and violations of service and subsequently formulare schedules across the cloud-fog computing environment.
## 4 Discussion  
A methodology to model subsurface objects, integrating completion strategies and confidence levels, is proposed. Confidence levels can be chosen by the user and are visualized by bounding volumes, called \"secondary objects\". Only simple geometries are used, since uncertainties related to object geometry and positioning would decrease possible advantages of a more precise representation. The methodology has been integrated into the workflow of the InnoSubsurface project and validated on two subsurface volumes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **PAV Sector** & **Cornavin Sector** & **Berlin [33]** \\ \hline surface, km\({}^{2}\) & 0.31 & 0.32 & \\ primary volumes, m\({}^{3}\) (000 000) & 0.49 & 0.42 & \\ UUS m\({}^{3}\)/m\({}^{2}\) (in cm) & 156.8 & 133.7 & 128.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Urban underground space (UUS) for PAV (Arve river) and Cornavin zones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Colour** & **Object** & **Colour** & **Object** \\ \hline olive & tree root & red & electricity network \\ blue & natural water & green & telecommunication \\ magenta & wastewater & yellow & gas \\ light grey & geothermal drills & dark grey & subsurface buildings \\ \hline \hline \end{tabular}
\end{table}
Table 3: Color codes used in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & **Completeness Ratio** & \
\begin{tabular}{c} **Refined** \\ **Completeness Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Gross Geometric** \\ **Parameters** \\ \end{tabular} \\ \hline Cornavin & 80.22\% & 72.33\% & 387\({}^{\prime}\)282 \\ PAV & 79.36\% & 73.76\% & 147\({}^{\prime}\)468 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Description of data completeness ratios for PAV and Cornavin zones.  
The concept of primary and secondary objects provides an intuitive way for users to evaluate the degree of saturation of subsurface volumes. The project has been presented at several occasions on a Swiss national level and practitioners in private companies and administration were very receptive to the concept of representing confidence levels together with data, which is a novel approach.  
The analysis of missing topographical and geometrical attributes showed that it is always possible to define reasonable hypotheses to complete primary objects and to model secondary objects indicating a level of confidence. However, a quality measure related to the number of missing object attributes, indicating if the application of meaningful results is possible, still needs to be established.  
The methodology has been generalized for all subsurface objects represented in the taxonomy and successfully applied for the 3D representation of subsurface objects. We are aware that our approach might still lack precision. There is potential to improve the underlying hypothesis for data completion and the calculation of confidence levels. Nevertheless, we are convinced that this non-intrusive and inexpensive method will help decision makers to obtain a first insight into available subsurface space and serve as a useful tool for subsurface planning.
# A 3D Vision-Based Weld Seam Extraction Method for Arbitrarily Placed Spatial Planar Weldments
## 2 Improved RANSAC for Plane Segmentation
### Improved RANSAC
#### 2.2.4 Optimization for Final Output  
Rule 3 and Rule 4 generally perform well for normal situations; however, there are instances where they fall short, particularly when the angle \(\alpha\) in Figure 2(d) is smaller than the \(\sigma_{a}\) defined in Rule 3. As illustrated by \(S_{1}\) and \(S_{2}\) in Figure 4, the current RANSAC algorithm would only segment one plane (e.g., plane \(S^{\prime}\) in Figure 4b) since the inliers of both \(S_{1}\) and \(S_{2}\) fall within the distance and angle thresholds. This outcome inevitably introduces additional segmentation errors in the resulting fitted planes and weld seams.  
The points in \(S_{1}\) and \(S_{2}\) can be easily clustered into two groups using Euclidean clustering: Point sets \(\Phi_{1}\) and \(\Phi_{j}\) are two clusters, if  
\[\min\bigl{(}\text{distance}\bigl{(}p_{i},p_{j}\bigr{)}\bigr{)}\geq\sigma_{d} \tag{13}\]  
holds for any \(i\) and \(j\), where \(\sigma_{d}\) is the distance threshold [22]. This definition also indicates that for any point \(p_{i}\) in a cluster that has more than one element, there exists at least one point that distanced less than \(\sigma_{d}\) to \(p_{i}\).  
Rule 5. Final refinements.  
After inlier validation, Euclidean clustering on the inliers is performed, resulting in \(m\) clusters of inliers \(\left(p_{1}^{1},\ldots,p_{l_{1}}^{1}\right)\), \(\left(p_{1}^{2},\ldots,p_{l_{2}}^{2}\right)\), \(\left(p_{1}^{m},\ldots,p_{l_{m}}^{m}\right)\). Choose only one cluster as the inliers for the final segmented plane according to the following steps.  
1. Least-square fitting is performed on each cluster of inliers, leading to a corresponding number of plane models. That is to say, \[S_{j}=\text{argmin}\sum_{i=1}^{l}\bigl{\|}\text{distance}\bigl{(}p_{i},S_{j} \bigr{)}\bigr{\|}^{2},\] (14) where \(p_{i}\) annotates the \(i\)th inlier of plane \(S_{j}\).
2. Choose a plane as the final output that satisfies \[S_{j}=\text{argmin}_{j}\bigl{(}e_{j}\bigr{)},\] (15) where \(e_{j}\) is defined as \(e\) in (11). The inliers that do not belong in the selected cluster return to the point pool for sampling in the next iteration.  
Figure 5 illustrates how Rule 5 affects the segmentation results of the case in Figure 4. Without the refinements in Rule 5, spatially isolated planes with a small angle are recognized as a single plane. Also, the segmented planes may look incomplete if there are points (that should be inliers of the segmented planes) distanced farther than \(\sigma_{d}\) to them. Both problems are eliminated with the introduction of Rule 5.  
Figure 4: Segmentation error brought by similar planes: (**a**) 3D view; (**b**) top view. In the top view, \(S_{1}\) and \(S_{2}\), represented with blue and green solid lines respectively, are the planes to be segmented. In contrast, the mistakenly segmented plane \(S^{\prime}\) is represented with a dotted red line. \(\sigma_{d}\) is the distance threshold for inlier selection in RANSAC.
## 2 Data
### Meteorological Data  
Modern Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2), developed by the updated Goddard Earth Observing System Version 5 (GEOS-5) from the Global Modeling and Assimilation Office (GMAO) National Aeronautical and Space Administration (NASA), provides hourly and daily data with a spatial resolution of 0.5 \(\times\) 0.625\({}^{\circ}\) from 1980-2023 and passes verification using field-observed data; thus, it is widely used for the estimation of GPP [55,56]. Variables selected from MERRA-2 for the study included T\({}_{\rm amin}\), dew point temperature (T\({}_{\rm d}\)), average air temperature (T\({}_{\rm a}\)), and surface incoming shortwave flux (SSF). Then, these  
Figure 1: Site distribution of GPP measurement eddy covariance towers across North America in different vegetation types obtained from the MODIS Year Land Cover Version 061 (MCD12 QI.061) product. There are 35 sites from the AmeriFlux dataset indicated by orange rectangles, and 49\(-\) sites from the FLUXNET2015 dataset represented by red circles.  
variables were aggregated to an eight-day scale and interpolated to a spatial resolution of 0.05 \(\times\) 0.05\({}^{\circ}\) by the modified cosine function [57], which was effective in eliminating abrupt changes from one side to the other [58]. The VPD (hPa) was calculated based on T\({}_{\text{d}}\) (\({}^{\circ}\)C) and T\({}_{\text{a}}\) (\({}^{\circ}\)C):  
\[VPD=SVP\times(1-RH) \tag{2}\]  
\[SVP=6.112\times e^{\frac{17.675\times 10}{243.5+10}} \tag{3}\]  
\[RH=e^{\frac{17.675\times 17}{243.0+11}-\frac{17.675\times 10}{243.0+10}} \tag{4}\]  
where \(SVP\) is saturated vapor pressure (hPa) and \(RH\) is relative humidity [12].
# Performance Analysis of Ocean Surface Topography
## 1 Introduction
[17] used AirSWOT (an airborne near-nadir InSAR which was developed by Jet Propulsion Laboratory) collected near-nadir InSAR data to retrieve water surface elevation of the Tanana River, Alaska. Verified by in situ measurements, results showed that the root-mean-square error (RMSE) was 9 cm for water surface elevation averaged over 1-km\({}^{2}\) areas. The interferometric imaging radar altimeter (InIRA), as an experimental payload, was developed by the National Space Science Center, Chinese Academy of Sciences. InIRA is borne on Tiangong II, the Chinese space laboratory, which was launched on 15 September 2016 and runs in a non-sun-synchronous orbit at an altitude of about 400 km. InIRA is the second spaceborne InSAR that can perform single-pass interferometry after SRTM. Its two antennas work at Ku-band and form a baseline of 2.3 m. As shown in Figure 1, due to the wide range of the incidence (1\({}^{\circ}\)-8\({}^{\circ}\)), a swath larger than 40 km can be achieved along the right side of the orbit track. The usual SAR incidence varies from 20\({}^{\circ}\) to 60\({}^{\circ}\), which determines that Bragg scattering is prevalent among the received signal [18]. While under the near-nadir incidence, specular-reflected signal from ocean surface dominates, which makes InIRA acquired data quite unique from any other SAR images. In this paper, we analyze the performance of InIRA on ocean surface topography altimetry to evaluate its capabilities on exploiting various oceanic processes; in Section 2, the measurement principle of near-nadir InSAR is briefly introduced and the altimetric precision is simulated based on the system parameters of InIRA; in Section 3, after a series of data processing procedures, the altimetric precision on a 1-km grid is estimated from the multi-look interferometric phase; in Section 4, the intensity boundary phenomenon of near-nadir InSAR image is first found and discussed; some recommendations of parameter design for future near-nadir InSAR are given based on the performance of InIRA in Section 5.
# A Choreography-Based and Collaborative Road Mobility System for L'Aquila City
## 1 Introduction
Service choreographies support the reuse-based service-oriented philosophy in that they represent a powerful and flexible approach to realize systems by (possibly) reusing services and devices, and composing them in a fully distributed way. The need for choreographies was recognized in the Business Process Modeling Notation 2.0 ([[http://www.omg.org/spec/BPMN/2.0.2/](http://www.omg.org/spec/BPMN/2.0.2/)]([http://www.omg.org/spec/BPMN/2.0.2/](http://www.omg.org/spec/BPMN/2.0.2/))) (BPMN2), which introduced _Choreography Diagrams_ to offer choreography modeling constructs. During the last decade, with the aim of bringing the adoption of choreographies to the development practices adopted by IT companies, we focused our research and development activity on practical and automatic approaches to support the realization of reuse-based service choreographies [12; 14; 15; 16; 17; 18; 19; 20; 21; 22]. This activity has been mainly funded by two EU projects: the FP7 CHOReOS and its follow-up H2020 CHOReVOLUTION (www.choreos.eu--www.chorevolution.eu). The city of L'Aquila, after the devastating earthquake of 6 April 2009, is facing a massive reconstruction process, which has acquired an even more challenging connotation in the face of new technological and social challenges that are becoming real urban regeneration. In this context, the INCIPICT ([[http://incipict.univaq.it](http://incipict.univaq.it)]([http://incipict.univaq.it](http://incipict.univaq.it))) (INnovating City Planning through Information and Communication Technologies) project aims at creating a metropolitan network consisting of a fiber optic ring dedicated to the Public Administration, Schools, and Universities. This infrastructure enables the NGI vision, and hence it can be exploited to provide innovative services for citizens. This paper presents a case study that has been developed within one of the INCIPICT application scenarios and that employs the CHOReVOLUTION approach to realize a Collaborative Road Mobility System (CRMS) for L'Aquila city. The CRMS allows vehicles and transport infrastructure to interconnect, share information and use it to coordinate their actions. Furthermore, it provides traffic coordination services exploited through a mobile app for assisting drivers in the most eco-friendly and comfortable driving experience [9; 23]. The paper is structured as follows. Section 2 discusses related work. Section 3 overviews the CHOReVOLUTION approach by discussing the choreography realizability enforcement problem it solves, and by describing underlying technologies in Section 3.1. Section 4 presents the CRMS case study, and Section 4.1 describes a realization of it obtained by using the CHOReVOLUTION approach at work. Section 4.2 reports the outcomes of the experimental evaluation performed on the CRMS case study. Conclusions and future research directions are given in Section 5.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 2 System Hypotheses and Signal Modeling
### Signal Model for 3D Positioning  
In Equation (8), the signal \(\mathbf{x}\) is only one snapshot from the \(\mathbf{K}\) signal sources. Furthermore, the receiving signal \(\mathbf{y}\) only contains the azimuth and elevation angles. In order to integrate the delay information contained in the SRS for 3D positioning (Section 2.2.3), the \(\mathbf{x}\in\mathbb{C}^{K\times 1}\) in Equation (8) is replaced by the multipath propagated SRS \(\mathbf{S}(\mathbf{\tau})\in\mathbb{C}^{K\times W}\). \(W\) is the length of the SRS. \(\mathbf{S}=[\mathbf{s}_{1},\mathbf{s}_{2},...,\mathbf{s}_{k}]^{T}\). Then, Equation (8) can be rewritten as:  
\[\mathbf{Y}=\mathbf{A}(\theta,\phi)\mathbf{S}(\mathbf{\tau})+\mathbf{N} \tag{9}\]  
where \(\mathbf{Y}\in\mathbb{C}^{MN\times W}\), and \(\mathbf{N}\in\mathbb{C}^{MN\times W}\) are the receiving SRS and noise, respectively, \(\mathbf{\tau}=[\tau_{1},\tau_{2},...,\tau_{k}]\). \(\mathbf{Y}\) contains the azimuth, elevation angles, and delay information. To facilitate the 3D positioning, we further define the 3D manifold vector:  
\[\mathbf{u}(\theta,\phi,\tau)=\mathbf{A}(\theta,\phi)\otimes\mathbf{g}(\tau) \tag{10}\]  
where \(\mathbf{u}(\theta,\phi,\tau)\in\mathbb{C}^{MN\times 1}\) is the 3D manifold vector. Similarly, the receiving signal \(\mathbf{Y}\) is vectorized to \(\mathcal{Y}=vec(\mathbf{Y})\).
###### Abstract  
A combined experiment and numerical analysis was performed to determine whether the flow field has a significant impact on the simultaneous verification of multi-ultrasonic flowmeters (UFMs) in tandem arrangement (SVUFMST) and whether the SVUFMST is practical for flowrate measurement of pressurized natural gas. In practical testing, SVUFMST can highly improve the efficiency of UFMs' calibration. Two types of UFMs of different nominal diameter were studied: DN250 and DN150. When it comes to DN250, data obtained from the actual-flow experiments were first used to verify the simulated results for \(n\leq 3\) (number of tested UFMs) and then the verified numerical method was extended to simulate the internal flow of four and five tested UFMs. Comparison analysis shows that all the numerical results agree remarkably well with the available experimental data and more than two tested UFMs can induce an overall shift in the \(E\)-\(Q\) curve (relative measurement error \(E\) plotted as a function of flowrate \(Q\)). For the slight difference among the tested UFMs, the practicability of the SVUFMST at \(n\leq 4\) (DN250) and \(n\leq 3\) (DN150) are thereby proved, whereas the striking difference in \(E\) (\(\sim\)1.0%) between the most downstream UFM and the other UFMs for \(n=5\) (DN250), associated with the outstanding collapse onto the velocity profile along the acoustic paths, results in the invalidation of the SVUFMST.  
simultaneous verification of Multi-Ultrasonic-Flowmeters; natural gas; actual flow experiments; CFD +
Footnote †: journal: Applied Physics Letters  
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**Keywords:** simultaneous verification of Multi-Ultrasonic-Flowmeters; natural gas; actual flow experiments; CFD
###### Abstract  
This paper presents a method of implementing a 4 \(\times\) 4 correlation matrix for evaluating the uplink channel properties of multiple-input multiple-output (MIMO) antennas using an over-the-air measurement system. First, the implementation model used to determine the correlation coefficients between the signals received at the base station (BS) antennas via the uplink channel is described. Then, a methodology is introduced to achieve a 4 \(\times\) 4 correlation matrix for a BS MIMO antenna based on Jakes' model by setting the initial phases of the secondary wave sources in the two-dimensional channel model. The performance of the uplink channel for a four-element MIMO terminal array antenna is evaluated using a two-dimensional bidirectional fading emulator. The results show that the measured correlation coefficients between the signals received via the uplink channel at the BS antennas using the proposed method are in good agreement with the BS correlation characteristics calculated using Monte Carlo simulation and the theoretical formula, thereby confirming the effectiveness of the proposed method.  
uplink channel; 4 \(\times\) 4 correlation matrix; initial phase; Jakes' model; multiple-input multiple-output (MIMO); over-the-air (OTA); bidirectional fading emulator; channel capacity +
Footnote †: journal: 15 September 2021  
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