esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 4 Overview of Proposed Protocol
### Overall Operation  
The overall operation of the proposed protocol consists of system setup, periodic credential generation, on-duty token generation and accident Reporting procedures.  
* System setup: TA sets up its master secret key and its corresponding public key. Each vehicle provides its real identity and TA generates the corresponding pseudo identity from which a partial private key is computed. DMVs and RSCs also provide their real identities and TA computes their partial private keys. Each vehicle registers with the TA through the DMV, the designated vehicles such as the police or ambulance also register with the TA through the DMV.
* Periodic credential generation: Periodically, a vehicle request for credential in order report the recorded videos. DMV generates the periodic credential to vehicles along with the set of attributes corresponding to the type of request. We assume that based on some criteria such as accident record or reckless driving, DMV can decide to give different set of attributes to participating vehicles.
* On-duty token generation: The designated vehicles also received on-duty tokens. These tokens will be used by the designated to retrieve reported videos from the RSCs.
* Accident Reporting: Periodically, a vehicle registers for road reporting services. During the registration, the vehicle specifies the types of services to be reported such as accident or abnormal scene (we assume that the camera of a vehicle is not limited to report road's accidents only). An incentive technique based on point accumulation could be considered in order to motivate the vehicle users to participate in video reporting. Whenever an accident or abnormal scene occurs the camera records the scene and upload the files to the RSC using mmWare technology or D2D communication. The designated vehicles would later on acquire the report from the RSCs as long as they possess enough access structure to recover the secret.
## 5 Conclusions and Discussion
These experimental results demonstrate that CSNGAN-ASPP effectively recovers the spatial distribution patterns of regionalized variables within the reservoir domain. The simulation outcomes consistently adhere to the given conditioning data constraints, with high accuracy in reconstructing the facies attribute information at the conditioning point locations. Additionally, quantitative assessments also reveal that CSNGAN-ASPP can preserve the spatial variability and channel connectivity of facies attribute information, with the statistical and structural characteristics of the simulation results closely aligning with the reference model. The trained generator parameters are saved, allowing for the generation of multiple reservoir reconstruction results within 1-2 s by inputting conditioning data with varying distribution patterns. This enables efficient and automatic modeling of reservoir geological bodies under sparse data constraints. While CSNGAN-ASPP effectively characterizes categorical reservoir models with sparse conditioning data constraints, its performance is somewhat limited for models with continuous attributes, and it is also influenced by the type of conditioning data. To further improve model robustness and address the limitations of sparse conditioning data, future research will focus on incorporating more diverse data types to support modeling, such as seismic data and dynamic monitoring data between wells. Additionally, in the future, we will investigate adjustments to the network structure, facilitating the efficiency of automatic reconstruction of complex reservoir models with continuous properties under various constraints. Conceptualization, S.W. and G.L. ; methodology, S.W., G.L. and Q.C. ; software, S.W. ; validation, S.W., G.L., Z.W. and Q.C. ; formal analysis, S.W., G.L., Z.W., Q.C., J.X., Z.C. and H.F.; investigation, S.W., G.L., Z.W., Q.C., J.X., Z.C. and H.F.; resources, G.L., Z.W. and Q.C. ; data curation, S.W., J.X., Z.C. and H.F.; writing--original draft preparation, S.W. ; writing--review and editing, G.L., Z.W. and Q.C. ; visualization, S.W. ; supervision, G.L., Z.W. and Q.C. ; project administration, G.L. ; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (42372345) and the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (KF-2023-08-25).
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Fluctuation Test
#### 4.3.8 Tuning the IMU against Electronic Induced Offsets  
The IMU measures the earth's magnetic field, so it needs to avoid being affected by the CubeSat's own magnetic fields. To measure the earth's magnetic field correctly, the IMU needs to be calibrated to remove the impact of the CubeSat dipoles. The CubeSat produces two types of magnetic offsets: hard iron offsets and soft iron offsets. The hard offsets are the magnetic fields that come from the electronics onboard. The hard offsets are a constant value that can just be subtracted from the IMU reading. The soft iron offsets, however, are more complicated because they vary depending on the controller. The offsets come from the magnetic fields created by the magnetic torquers.  
To explain the effects of these better, the hard iron offsets are displacement vectors. The offsets shift the three-dimensional readings linearly. The soft iron offsets, on the other hand, are distorted by a transformation matrix that depends on the power of each magnetic torque coil. See Figure 46 below.  
Moreover, the CubeSat's temperature had fairly big impacts on the readings from the magnetometer. Our model would also need to consider these effects.  
Figure 45: Convergence criteria. \(n\) number of points within an m% region of convergence, and the endpoint must lie in the region of convergence. (\(n=5\) and \(m=10\)).  
The magnetic torque coils produce soft iron offsets that will taint the IMU readings. The soft iron offsets differ from hard iron in that they fluctuate as a function of the power of the system. Each of the three torque coils will affect the magnetometer reading independently based on the current through the coil and its relative position with respect to the magnetometer. The controller constantly changes the current in each of the three coils, and the magnetic fields of the torque coils cause offsets that need to therefore be continuously re-evaluated. These values are then taken away from the IMU magnetometer reading in real time. However, it turned out that current was not the only thing that influenced the magnetic field through the coils. Experimentation and system verification showed that the magnetic offsets vary as the battery drains because of the lowered voltage. So, a model was created that would give soft iron offset values based on the current input to the X, Y, and Z coils and the current battery voltage.
## 4 Acoustic Testing and Results
### Damping Performance of Acoustic Liner  
Figure 8 shows the comparison of the averaged dissipation coefficients for the no-flow case (\(M=0\)). The reference HR liner with rigid walls (reference liner) was prepared the same with alternating active and inactive cell rows in main flow direction.  
Most of the FlexiS configurations demonstrate a significant increase of the dissipation in the frequency range from 400 to 800 Hz. The material 1170 A, 0.3 mm shows even three local dissipation  
Figure 8: Comparison of the averaged dissipation coefficients for the no flow case (\(M=0\)).  
maxima at 600, 1000 and 1350 Hz. However, the widening of the bandwidth goes along with a reduction in peak performance compared to the reference liner. The dissipation maximum of the sample with the 1195 A, 0.1 mm material is shifted from 940 Hz (Reference) to around 750 Hz.  
In Table 3, the benefit is quantified by introducing an artificial threshold (\(\Delta>0.3\)) for which the lower and upper frequency limits are given. Furthermore, the area between the threshold and the damping curve is compared to the reference liner. A significant increase in the frequency range, a shift towards lower frequencies, and an increase of \"integral\" damping is observed for three FlexiS liners (1170 A, 0.3 mm, 1195 A, 0.1 mm). The 1195 A, 0.5 mm liner exhibits only a small shift towards lower frequencies, but does not yield additional benefits. In the case with grazing flow (not shown here) the observed behaviour holds with improved acoustic damping for the FlexiS liner.
## 3 Mathematical Model of Gas-Liquid Separation
### Characteristic Parameters of Multiphase Flow in Production Profile Logging
#### 3.1.2 Oil-Gas-Water Multiphase Flow  
Under the bottom hole condition, the gas's density is 0.01-0.22 g/cm\({}^{3}\). The oil density is 0.6-0.98 g/cm\({}^{3}\). The density of water is about 1.0 g/cm\({}^{3}\). Therefore, regardless of each item's contents, the gas flow velocity in the oil-gas-water multiphase flow mixing system is the highest. The water's flow velocity is the lowest. The oil's flow rate is between that of gas and water [17].  
\[v_{m}=v_{sw}+v_{so}+v_{sg} \tag{5}\]  
\[Y_{o}+Y_{w}+Y_{g}=1 \tag{6}\]  
\[v_{sgw}=v_{g}-v_{w}=\frac{v_{sg}}{Y_{g}}-\frac{v_{sw}}{Y_{w}} \tag{7}\]  
\[v_{sow}=v_{o}-v_{w}=\frac{v_{so}}{Y_{o}}-\frac{v_{sw}}{Y_{w}} \tag{8}\]  
\[v_{sgo}=v_{g}-v_{o}=\frac{v_{sg}}{Y_{g}}-\frac{v_{so}}{Y_{o}} \tag{9}\]After sorting out the above equations, we get the following results:  
\[v_{sw}=Y_{w}(v_{m}-Y_{g}v_{sgw}-Y_{o}v_{sow}) \tag{10}\]  
\[v_{sg}=Y_{g}\Big{[}v_{m}+(1-Y)_{g}v_{sgw}-Y_{o}v_{sow}\Big{]} \tag{11}\]  
\[v_{so}=Y_{o}\big{[}v_{m}-Y_{g}v_{sgw}+(1-Y_{o})v_{sow}\big{]} \tag{12}\]  
and  
\[v_{so}=v_{m}-v_{sw}-v_{sg} \tag{13}\]  
where \(v_{sow}\), \(v_{sgw}\) and \(v_{sgo}\) are the slippage speed of oil-water, gas-water, and gas-oil, respectively. \(Y_{g}\), \(Y_{o}\) and \(Y_{w}\) are holdup of gas, oil, and water, respectively. \(v_{so}\), \(v_{sow}\) and \(v_{sg}\) are boundary velocities of oil, water, and gas, respectively.  
\[C_{w}=Y_{w}\bigg{(}1-\frac{Y_{g}v_{sgw}+Y_{o}v_{sow}}{v_{w}}\bigg{)} \tag{14}\]  
\[C_{g}=Y_{g}\bigg{(}1+\frac{(1-Y_{g})v_{sgw}-Y_{o}v_{sow}}{v_{m}}\bigg{)} \tag{15}\]  
\[C_{o}=1-C_{g}-C_{w} \tag{16}\]  
Equation (14) shows that the higher the flow rate, the more \(C_{w}\) tends toward. In the limit case, it is \(C_{w}\approx Y_{w}\), \(C_{o}\approx Y_{o}\), \(C_{g}\approx Y_{g}\). At the same time, the water holdup is greater than the water content, and the gas holdup is less than the gas content.  
In this study, oil, gas, and water are each regarded as incompressible fluids in this paper. Therefore, according to the continuous principle of incompressible fluids, the flow velocity of the fluid entering the instrument through the inlet can be obtained as follows:  
\[v_{1}\Delta S_{1}=v_{2}\Delta S_{2} \tag{17}\]  
where \(\Delta S_{1}\) is the cross-sectional area of Pipe 1, \(\Delta S_{2}\) is the cross-sectional area of Pipe 2,\(v_{1}\) is the velocity through Section 1, and \(v_{2}\) is the velocity through Section 2.
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 4 Experimental Results and Analyses
### The Evaluation of Traffic Information Estimation Methods
#### 4.2.1 The Evaluation of Traffic Flow Estimation  
For the evaluation of traffic flow estimation, the simulated amounts of HOs and NLUs from Cell\({}_{i}\) are collected as CFVD to generate \(q_{i,h}\) and \(q_{i,n}\). NLU is only performed with MS entering new LA, so the estimated traffic flows are consistence in the same LA in the proposed approach. For instance, the amount of HOs in Cell\({}_{1}\) (\(h_{1}\)) was 126 at 8 a.m. and 9 a.m., so the traffic flow of the first road segment (\(q_{1,h}\)) could be estimated as 7560 car/h by utilizing Equation (3) and MS communication behaviors (e.g., the expected value (1/\(\upmu\)) of call holding time is 1 min/call). Furthermore, because 6672 NLUs in Cell\({}_{1}\) were performed at 8 a.m. and 9 a.m., the traffic flow of the first road segment (\(q_{1,n}\)) could be estimated as 6672 car/h according to Equation (4). This study calculated the accuracies of traffic flow estimation as \(1-\dfrac{\left|Q_{i}-q_{i,h}\right|}{Q_{i}}\) and \(1-\dfrac{\left|Q_{i}-q_{i,n}\right|}{Q_{i}}\). Table 2 shows that the average accuracies of traffic flow estimation of the first road segment between 8 a.m. and 22 p.m. are 89% for the amount of HOs and 100% for the amount of NLUs. As shown in Table 3, the comparison of the traffic flow estimation comparisons between \(q_{1,h}\) and \(q_{i,n}\) indicates that the amount of NLUs is more suitable for traffic flow estimation than the amount of HOs.  
Figure 6: Trace-driven simulation for the traffic information estimations.  
Figure 7: The distribution of cells, location areas and vehicle detectors (VDs) in the simulation experiments.
# Empowering Advanced Parametric Modes Clustering from Topological Data Analysis
## 2 Methods
### On the Surface Topology
#### 2.2.1 Geometric Features  
In order to describe the geometry of the data-set \(\mathbb{M}\), we first identify four types of geometrical features associated with \(\mathbb{M}\):  
* A vertex \([\mathbf{x}_{m}]\) is generated by an individual point \(\mathbf{x}_{m}\in\mathbb{M}\);
* A segment \([\mathbf{x}_{m},\mathbf{x}_{n}]\) joins two vertex \([\mathbf{x}_{m}],\ [\mathbf{x}_{n}]\in\mathbb{M}\) \[[\mathbf{x}_{m},\mathbf{x}_{n}]:=\Big{\{}\mathbf{x}\in\mathbb{R}^{3}:\mathbf{ x}=\lambda\mathbf{x}_{m}+(1-\lambda)\mathbf{x}_{n}\text{ where }0\leq\lambda\leq 1\Big{\}};\]  
Figure 2: Reference surface (**left**) and nodes distribution on it (**right**).  
* A triangle is generated by three different vertexes \([\mathbf{x}_{m}],\ [\mathbf{x}_{n}],\ [\mathbf{x}_{l}]\in\mathbb{M}\), such that \(\mathbf{x}_{m}-\mathbf{x}_{n}\) and \(\mathbf{x}_{m}-\mathbf{x}_{l}\) are linearly independent, and then: \[[\mathbf{x}_{m},\mathbf{x}_{n},\mathbf{x}_{l}]:=\Big{\{}\mathbf{x}\in\mathbb{ R}^{3}:\mathbf{x}=\lambda_{m}\mathbf{x}_{m}+\lambda_{n}\mathbf{x}_{n}+\lambda_{l} \mathbf{x}_{l}\Big{\}},\] where \(\lambda_{m},\ \lambda_{n}\) and \(\lambda_{l}\) are the barycentric coordinates, with \(\lambda_{m}+\lambda_{n}+\lambda_{l}=1\);
* A tetrahedron is generated by four different vertices \([\mathbf{x}_{m}],\ [\mathbf{x}_{n}],\ [\mathbf{x}_{l}],\ [\mathbf{x}_{p}]\in\mathbb{M}\), such that \(\mathbf{x}_{m}-\mathbf{x}_{n},\ \mathbf{x}_{m}-\mathbf{x}_{l}\), and \(\mathbf{x}_{m}-\mathbf{x}_{p}\) are linearly independent, and then: \[[\mathbf{x}_{m},\mathbf{x}_{n},\mathbf{x}_{l},\mathbf{x}_{p}]:=\Big{\{} \mathbf{x}\in\mathbb{R}^{3}:\mathbf{x}=\lambda_{m}\mathbf{x}_{m}+\lambda_{n} \mathbf{x}_{n}+\lambda_{l}\mathbf{x}_{l}+\lambda_{p}\mathbf{x}_{p}\Big{\}},\] where \(\lambda_{m},\ \lambda_{n},\ \lambda_{l}\) and \(\lambda_{p}\) are the barycentric coordinates, with \(\lambda_{m}+\lambda_{n}+\lambda_{l}+\lambda_{p}=1\).  
The vertices represent the dimension-0 features, segments the dimension-1 features, and triangles the dimension-2 features and tetrahedron dimension-3 features.
## 5 Conclusions  
The main objective of this study was to evaluate the potential of the LUEc enhanced four-scalar light use efficiency approach of [PERSON] et al. (2013) to provide regional GPP estimates. By incorporating MODIS imagery and gridded weather data into the LUEc, daily GPP was estimated over the entire growing season in seven U.S. Corn Belt agricultural sites in four Midwestern US locations. Overall RMSE between measured GPP and modeled GPP for all field years was 3.5 g C m\({}^{-2}\) d\({}^{-1}\). This is an increase in RMSE of 0.9 g C m\({}^{-2}\) d\({}^{-1}\) compared to the in situ-based study ([PERSON] et al., 2017). However, the MNB of 30.5% is of particular concern. Generalized scalars were developed for maize and soybeans when the model was calibrated with remotely sensed derived gLAI data and gridded weather data from all seven sites. GPP estimates were better at some sites (Mead, NE) than at others. Accuracy of the LUEc estimates decreased when remotely sensed and gridded weather data inputs were used compared to the results of [PERSON] et al. (2013). While accuracy decreased, so did the amount of resources required to run the model. The findings of this study indicate that the LUEc can provide acceptable daily GPP estimates for regions outside eastern Nebraska (for which the model was originally tested)  
Figure 9: AmeriFlux measured APAR as a function of Daymet and MODIS derived APAR for all three Nebraska sites for (**a**) all growing season days, (**b**) all green-up days, (**c**) all post green peak days.  
using gridded and remotely sensed data. The LUEc performs better than a LUE model that does not incorporate stressors, supporting the need to include meteorological data (stressors) in estimating daily GPP. Future research should be conducted to determine if more generalized coefficients can be derived to allow the model to function accurately at broader regional scales.  
Conceptualization, A.S. and E.A.W.-S.; Formal analysis, G.M.-M. and A.L.N.-R.; Methodology, G.M.-M., A.S. and E.A.W.-S.; Writing--original draft, G.M.-M. and E.A.W.-S.; Writing--review and editing, A.L.N.-R., T.J.A. and E.A.W.-S. All authors have read and agreed to the published version of the manuscript.  
This project is based on research that was partially supported by the Nebraska Agricultural Experiment Station with funding from the Hatch Act [Accession Number 1002649] through the USDA National Institute of Food and Agriculture. Funding for AmeriFlux data resources was provided by the U.S. Department of Energy's Office of Science.  
We acknowledge the following AmeriFlux sites for their data records: US-Ne1, US-Ne2, US-Ne3, US-Ro1, US-Bo1, US-Br1, and US-Br3. In addition, we acknowledge the assistance of colleagues in improving the manuscript.  
The authors declare no conflict of interest.
###### Abstract  
This study explores the utilization of Neural Radiance Fields (NeRFs), with a specific focus on the Instant NeRFs technique. The objective is to represent three-dimensional (3D) models within the context of the industrial metaverse, aiming to achieve a high-fidelity reconstruction of objects in virtual environments. NeRFs, renowned for their innovative approach, enable comprehensive model reconstructions by integrating diverse viewpoints and lighting conditions. The study employs tools such as Unity, Photon Pun2, and Oculus Interaction SDK to develop an immersive metaverse. Within this virtual industrial environment, users encounter numerous interactive six-dimensional (6D) models, fostering active engagement and enriching the overall experience. While initial implementations showcase promising results, they also introduce computational complexities. Nevertheless, this integration forms the basis for immersive comprehension and collaborative interactions within the industrial metaverse. The evolving potential of NeRF technology promises even more exciting prospects in the future.  
3D reconstruction; metaverse; artificial intelligence +
Footnote †: journal: Medical  
a significant impact on design and simulation processes, enhancing the way objects are represented and visualized.  
Another relevant aspect is the ability to promote collaboration and communication among teams and professionals. By having a shared virtual representation of objects and scenes in an industrial metaverse, remote collaboration and joint decision-making are facilitated. This overcomes geographical limitations and allows different users to interact with and visualize the same data, which is particularly useful in industrial environments where collaboration between teams is essential.
###### Abstract
We extend the XQIFFT approach by replacing the brute force search with a cheaper and more accurate search algorithm that leverages Fibonacci search and Simpson quadrature to achieve optimal search speed and controlled numerical accuracy. Invariance properties of the XQIFFT are studied and it is shown that linear and logarithmic scalings can be considered special cases of the XQIFFT. We extend the noise analysis to a proper study relating estimator mean-squared error to signal-to-noise ratio. We compare the proposed estimator to two state-of-the-art estimators and the Cramer--Rao bound for unbiased estimators, and analyze the systematic bias due to noise. A case study on Hann-windowed signals shows that the optimal power-scaling coefficient is related to window length in a structured way. In another study, we compare the XQIFFT to the state-of-the-art estimators of [PERSON] [14] and [PERSON] [15] as well as the linear- and logarithmically-scaled versions of the QIFFT for twelve common windowing functions. Although [PERSON] [14] and [PERSON] [15] do not directly compare their methods to the QIFFT family, our study shows that indeed their methods usually outperform the linear- and logarithmically-scaled versions of the QIFFT. Our main finding is that the XQIFFT outperforms the state-of-the-art estimators of [PERSON] [14] and [PERSON] [15] at high and low SNRs for ten out of twelve common windowing functions. The structure of the remainder of this article is as follows. Section 2 defines the problem of single sinusoidal parameter estimation. Section 3 reviews previous work on extracting sinusoidal parameters from the three spectral bins surrounding a magnitude peak using parabolic interpolation. Section 4 introduces our proposed method: the XQIFFT. Section 5 contains an error definition, a study on the error properties, and a heuristic method for optimizing the power scaling of the XQIFFT. In Section 6, we run experiments to study different aspects of the XQIFFT and compare it to two state-of-the-art estimators.