esa-sceva/satcom-chunk-collection huggingface dataset:
# MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane
###### Abstract  
The MEthane Remote sensing Lidar missioN (MERLIN) aims at demonstrating the spaceborne active measurement of atmospheric methane, a potent greenhouse gas, based on an Integrated Path Differential Absorption (IPDA) nadir-viewing LIght Detecting and Ranging (Lidar) instrument. MERLIN is a joint French and German space mission, with a launch currently scheduled for the timeframe 2021/22. The German Space Agency (DLR) is responsible for the payload, while the platform (MYRIADE Evolutions product line) is developed by the French Space Agency (CNES). The main scientific objective of MERLIN is the delivery of weighted atmospheric columns of methane dry-air mole fractions for all latitudes throughout the year with systematic errors small enough (<3.7 ppb) to significantly improve our knowledge of methane sources from global to regional scales, with emphasis on poorly accessible regions in the tropics and at high latitudes. This paper presents the MERLIN objectives, describes the methodology and the main characteristics of the payload and of the platform, and proposes a first assessment of the error budget and its translation into expected uncertainty reduction of methane surface emissions.  
MERLIN; space mission; IPDA Lidar; atmospheric methane; CH\({}_{4}\) emissions; global methane budget +
Footnote †: journal: Remote Sensing
# LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution
## 3 Proposed Method
### Degradation Module  
Let \(\mathbf{X}\in\mathbb{R}^{W\times H\times C}\) represent the desired target HSI, where \(W\) and \(H\) are the width and height and \(C\) is the number of HSI bands, and \(\mathbf{Y}\in\mathbb{R}^{W\times H\times C}\) represent the input MSI, which has the same spatial size as \(\mathbf{X}\) and where \(c\) is the number of MSI bands. The paired MSI-HSI reference is denoted by \(\mathbf{Y}_{ref}\in\mathbb{R}^{W\times H\times c}\) and \(\mathbf{X}_{ref}\in\mathbb{R}^{W\times H\times C}\). For convenience, we call the reference MSI and HSI \"ref-MSI\" and \"ref-HSI\", respectively. The relationship between the target \(\mathbf{X}\) and observation \(\mathbf{Y}\) can be formulated as:  
\[\mathbf{Y}=\mathbf{R}\mathbf{X}+\mathbf{E}, \tag{1}\]  
where \(\mathbf{R}\in\mathbb{R}^{C\times c}\) denotes the SRF, representing the transformation matrix from the hyperspectral sensor to the multispectral sensor, and \(\mathbf{E}\) is the residual.  
The SSR task aims to recover the target \(\mathbf{X}\) from the observation \(\mathbf{Y}\). According to (1), the spectral reconstruction (i.e., obtaining \(\mathbf{X}\) from \(\mathbf{Y}\)) is an inverse task, which is an ill-posed problem. Consequently, we want the recovery task to have more constraints and extra information in order to improve the reconstruction results. The spectral transformer module is key to transforming the spectral information from the extra reference image.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 3 Nodal Accuracy Improvement Technology for Linear Elements  
### Nodal Accuracy Improvement for 1D Problems  
It is observed that it is the second-order convergence accuracy of \(u_{i}^{h}\) that restricts the higher accuracy of \(u^{*}\) for linear elements. In order to get rid of the restriction, higher accuracy of nodal displacements should be sought [28].  
For 1D problems, consider the following problem:  
\[\begin{cases}Lu\equiv-(pu^{\prime})^{\prime}+qu=f,&0<x<1\\ u(0)=\overline{u}_{0},&u^{\prime}(1)=\overline{u}_{1}^{\prime}\end{cases} \tag{10}\]  
After the FE solution \(u^{h}\) and EEP solution \(u^{*}\) of problem (10) are obtained, the error \(e^{*}=u-u^{*}\) of the EEP solution \(u^{*}\) is substituted back to problem (10), leading to the following supplementary problem:  
\[\begin{cases}Le^{*}=f-Lu^{*},&0<x<1\\ e^{*}(0)=0,e^{*\prime}(1)=0\end{cases} \tag{11}\]  
Then, the FE solution \(\varepsilon^{h}\) to problem (11) is solved by using the same FE mesh and trial space as used when solving problem (10). It is noted that, in the process of FE solution to problem (10) and problem (11), the global stiffness matrix \(\mathbf{K}\) does not change. Therefore, after the equivalent nodal load vector \(\delta\mathbf{P}\) (also called residual nodal load vector) of problem (11) is calculated, a simple back-substitution process produces the nodal increment displacement vector \(\delta\mathbf{D}\) as shown in the following form:  
\[\delta\mathbf{D}=\mathbf{K}^{-1}\delta\mathbf{P} \tag{12}\]  
Since neither \(\mathbf{K}\) nor \(\mathbf{K}^{-1}\) change, the cost of obtaining a nodal increment solution \(\delta\mathbf{D}\) is much less than a full FE analysis. For 1D problems, numerous numerical experiments show that the improved nodal displacement \(\mathbf{D}+\delta\mathbf{D}\) achieves super-convergence \(O(h^{4})\), which doubles the original \(O(h^{2})\).
# FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection
## 1 Introduction
Compared with the traditional frequency-domain InSAR imaging algorithm, the proposed algorithm introduces the antenna position information and improves the imaging and phase accuracy. Compared with the time-domain InSAR imaging algorithm, it avoids the repetitive calculation of index and improves imaging efficiency. Therefore, the proposed algorithm is adopted to SAR imaging system, the interferometry system and bistatic system with larger scene. However, due to the complex geometric relationship of bistatic system, the range from transmit-system and receive system to the target scene is calculated separately, which leads to the uniform orthogonal distribution of the frequency grid. Thus, it is necessary to improve the algorithm to process the data from the bistatic system. In this paper, the feasibility of the proposed algorithm is verified by the simulation and measured data of different airborne interferometry system. The rest of this paper is organized as follows: The second section describes the model of InSAR imaging and the principle of TDBP. The proposed FDBPA for InSAR imaging is introduced in the third section. In the fourth section, the simulations and experiments are applied to demonstrate the effectiveness of the proposed algorithm. The fifth part analyzes the imaging characteristics of FDBPA. Finally, the conclusions and future work are illustrated in the sixth section.
## 4 Model Estimation and Results
### Measurement Models  
#### 4.1.1 Construct Validity  
[PERSON] [65] states that construct validity must be examined when a latent variable explains a statistically significant part of the variation of its constructs. In Figure 3, we can observe the coefficients related to the variables in the measurement model, obtained using the SEM-PLS equations.  
[PERSON] [66] considers that the constructs for which the coefficient values are less than 0.5 should be excluded from the measurement model. As a consequence, from the model  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Latent**} & \multirow{2}{*}{**AVE**} & \multicolumn{6}{c}{**The Square Root of the Correlation**} \\  & & \multicolumn{6}{c}{**Coefficients between Latent Variables**} \\ \cline{3-8}  & & **SV** & **Reglem** & **Compet** & **Oper** & **Resp** & **Inov** \\ \hline VS & 1.000 & 1 & & & & & \\ REGLEM & 1.000 & 0.621 & 1 & & & & \\ COMPET & 0.825 & 0.679 & 0.512 & 1 & & & \\ OPER & 0.748 & 0.528 & 0.491 & 0.531 & 1 & & \\ RESP & 0.729 & 0.683 & 0.608 & 0.596 & 0.612 & 1 & \\ INOV & 0.688 & 0.602 & 0.543 & 0.580 & 0.602 & 0.548 & 1 \\ \hline \end{tabular} Source: Data analysis was performed by the authors using the SmartPLS software 3.2.8.  
\end{table}
Table 4: Correlation matrix of the latent variables.  
Figure 3: Measurement PLS-SEM 1 model. Source: Data analysis was performed by the authors using the SmartPLS software 3.2.8.  
we will exclude the variables: vs_4, compet_2, oper_3, and resp_3. After these variables were removed from the model, using the SEM-PLS equations and using the SmartPLS 3.2.8 software, we obtained the second measurement model.  
As it can be seen in the Figure 4, the values of all the coefficients of the reflective variables in the model are greater than 0.5, from which it follows that all these coefficients are valid. Thus, the construct validity of the model is confirmed.
## Abbreviations  
Tables presents all the acronyms used in the paper. Definitions of all acronyms mentioned in the paper.  
\begin{tabular}{l l} AES & Advanced Encryption Standard \\ AppSkey & Application Key \\ BPSK & Binary Phase-Shift Keying \\ CDMA & Code Division Multiple access \\ CKA & Compromised Key Attack \\ CSS & Chirp-spread-spectrum \\ DPSPK & Differential Binary Phase Shift Keying \\ DL & Downlink \\ DoS & Denial of Service \\ DSSS & Direct Sequence Spread Spectrum \\ EDHOC & Ephemeral Diffie-Hellman Over COSE \\ EEPROM & Erasable Programmable ROM \\ FEC & Forward Error Correction \\ FHMA & Frequency Hop Multiple Access \\ FSK & Frequency-shift keying \\ GPSK & Gaussian Frequency-Shift Keying \\ GMSK & Gaussian Minimum Shift Keying \\ HD & Hamming Distance \\ IDS & Intrusion Detection System \\ IoT & Internet of Things \\ ICMP & Internet Control Protocol \\ ICT & Information Communication and Technology \\ ISM & Industrial Scientific and Medical \\ Kbps & Kilobits Per Second \\ KDF & Key Derivation Function \\ KLD & Kullback Leibler Divergence \\ LoRa & Long-Range \\ LoRaWAN & Long Range Wide Area Network \\ LoS & Line of Sight \\ LPWAN & Low Power Wide Area Network \\ MITM & Man-in-the-middle \\ M2M & Machine-to-Machine \\ NB-IoT & Narrow Band-Internet of Thing \\ NwKSkey & Network Key \\ OFDMA & Orthogonal Frequency Division Multiple Access \\ OTA & Over-The-Air \\ PKI & Public Key Infrastructure \\ QPSK & Quadrature Phase Shift Keying \\ RPMA & Random Phase Multiple Access \\ RSA & Rivest-Shamir-Adleman \\ R-FDMA & Random Frequency Division Multiple Access \\ SE & Secure Elements \\ SeLPC & Secure Low Power Communication \\ TDD & Time Division Duplex \\ UL & Uplink \\ UNB & Ultra-Narrowband \\ \end{tabular}
## 2 Related Works
### Organization Pattern of Network Structure
#### 2.3.1 Non-Virtual Structure Based Pattern  
Some algorithms collect data without virtual structure. In [30], [PERSON] et al. propose the minimum Wiener index spanning tree (MWST). This algorithm can get a routing topology which is suitable for WSNs with mobile sink. As finding a spanning tree with the minimum Wiener index from a weighted graph is NP-hard, the authors propose a new way to solve this problem. They find that the minimum spanning tree (MST) has the Wiener index as close as that of the MWST through extensive experiments. The algorithm to find the MST from a given graph has low time complexity. Hence, the authors choose the Wiener index of the minimum spanning tree of a given graph as the initial upper bound. This algorithm provides a way to establish routing path more efficient without virtual structure, however the problem is that this algorithm does not give the way to find the current location of sink when the sink is moving.  
In [25], [PERSON] et al. propose a milestone-based predictive routing protocol which solves the problem mentioned in MWST. The algorithm consists of three parts. The first part is estimation of mobile sink's future location. The mobile sink regularly broadcasts its updated location, calculated by the Formula (1), to its neighboring nodes. The \(\vec{Sink}\) represents the mobile sink's current moving speed and direction. If the mobile sink changes its direction, it estimates its future location by using the Formula (2). The \(t_{c}\) is the current time and \(t_{l}\) is the time when the sink has changed its moving direction recently.  
\[Sink_{updated}(X,Y)=Sink_{current}(X,Y)+\overrightarrow{Sink} \tag{1}\]  
\[Sink_{estimated}(X,Y)=Sink_{current}(X,Y)+\overrightarrow{Sink}\times(t_{c}-t_{l}) \tag{2}\]  
The second part is about milestone nodes and the way to spread sink's location information. This part introduces the concept of a milestone node. Its duty is to spread the estimated sink's future location to the nodes located in the vicinity of the recent trail of the mobile sink. When sink changesits direction, it will choose a new milestone node. The third part is routing strategy. Sensory data is relayed to sink according to these milestone nodes. This algorithm gives the way to find current location of sink, and milestone nodes is the tool. However, the problem is that too many milestone nodes are needed when sink moves longer. The data routing path will be longer and longer, and control packets of routing path consume much energy. So the way to find current location of sink is always inefficient.  
According to the algorithms mentioned above, we can find that the network without virtual structure always has higher complexity in routing data and sink location discovery.
## 3 Materials and Methods
### Analysis of Dispersion Properties  
#### 3.2.1 Measured Quantities  
The presence of agglomerates and the occurrence of sedimentation are indicators of an unstable nanoparticle-based dispersion. Agglomerates can amplify the process of sedimentation, since larger and heavier particles settle faster. Both of the indicators can be quantified by various measurands (see Table 1). These quantities primarily comprise viscosity, zeta potential, particle size distribution, and a quantity resulting from an optical evaluation, such as the average gray value.  
ViscosityAssuming that they are of the same composition and preparation, a well-dispersed sample has a lower viscosity than a poorly dispersed one that contains agglomerates. Agglomerations occur when the forces acting between the particles--in particular, the Brownian, hydrodynamic, and colloidal forces--are not balanced, and attractive forces predominate in the dispersion [34]. These agglomerations form a three-dimensional network, and the dispersion becomes gel-like and it has a higher viscosity [34].  
A high standard deviation is another indication of the presence of agglomerates, as they have a strong effect on the reproducibility of the viscosity values. The measuring gap must be significantly larger than the biggest particles in the dispersions to determine viscosity using a rheometer [35]. However, agglomerations exhibit diameters many times larger than single particles. As a result, the measuring gap during the measurement may not be sufficiently large, in which case the viscosity of the agglomerate is measured instead of that of the dispersion [36]. Because of the rotation of the rheometer, this can occur with varying frequency and intensity, which results in large standard deviations [36].  
Figure 1: Flow chart showing the preparation process of the particle-water mixtures.  
A decrease in viscosity implies the occurrence of sedimentation. The more the particles settle, the smaller their influence on the viscosity. According to the equation that was formulated by [PERSON] and [PERSON], the viscosity of a dispersion increases as the volume fraction of the particles in the liquid phase increases [37]. However, sedimentation causes particles to settle at the bottom of the vessel, which reduces the volume fraction of particles in the remainder of the dispersion [37]. The viscosity then approaches that of the pure fluid, which is lower [37].
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 3 Results
### Testing of Different MCPs  
Following the initial assessment, the proposed method was ultimately applied to evaluate the performance of different MCPs. Notably, in addition to testing three actual MCP products, an area-blocking strategy was employed to create a series of \"simulated\" MCPs with a broader range of OARs. Specifically, a rectangular blocking plate was used to partially cover the incident surface of the MCP, thereby reducing its active area, as illustrated in Figure 6a. The tapered circular MCP was selected for this experiment to generate a series of OARs ranging from 0% to 67.2%. However, since OARs below 40% are generally not practically significant, the analysis focused on data from larger OARs. Figure 6b presents the relationship curve between the measured pulse intervals and the \"simulated\" OARs, revealing a nonlinear correlation. Unfortunately, the underlying mechanisms responsible for this nonlinearity remain unclear. One potential factor is the non-uniform distribution of energy and density among the incident photoelectrons. That is, the UV lamp's non-uniform intensity may create an uneven electron beam. Additionally, edge effects of the electric field may introduce deviations in both the energy and trajectory of the electrons. These factors result in non-uniform energy and density distributions of incident electrons collected by the MCP. Nevertheless, it is worth noting that pulse intervals measured from different types of MCPs under the same experimental conditions align closely with this curve. The pulse interval of the photoelectron signal collected by the MCP with a larger OAR is shorter, demonstrating its higher CE. These findings suggest that the method proposed in this study is feasible for evaluating the CE of MCPs and further highlights the superior performance of MCPs with larger OARs.  
Figure 5: Influence of (**a**) operating voltage and (**b**) incident angle on the average signal intensity and pulse interval output by the MCP.
## 3 Research Model and Hypotheses
### External Variables of TAM  
#### 3.2.1 Telepresence in VR  
Telepresence is a multi-dimensional concept consisting of vividness and interactivity [25]. While vividness is determined by the sensory breadth and depth perceived by the user, interactivity is determined by the response speed, range, and mapping of the VR technology. In summary, higher levels of telepresence could indicate that the users perceived the virtual environment as reflecting the real world. Prior studies have utilized these two dimensions of telepresence to analyze its effect on flow experience or satisfaction [28].  
Furthermore, recent studies focusing on the acceptance of VR or AR have utilized TAM, and they have observed the effect of telepresence on user belief. For example, a study on the acceptance of AR interactive technology, in which consumers were allowed to try on clothing products using AR, confirmed the significant effect of presence on the perceived usefulness and ease of use for both consumers with high and low cognitive innovation [37]. Similarly, [PERSON] and [PERSON] [38] confirmed the effect of presence on perceived usefulness and perceived ease of use when adopting haptic enabling technology (HET), which allowed users to control a virtual environment using touch.  
Therefore, to study the effect of telepresence on user beliefs in the VR construction safety training context, we present the following hypotheses:  
**H4(a).**: _Vividness has a positive effect on perceived usefulness._
**H4(b).**: _Vividness has a positive effect on perceived ease of use._
**H5(a).**: _Interactivity has a positive effect on perceived usefulness._
**H5(b).**: _Interactivity has a positive effect on perceived ease of use._
# Vegetation Dynamics and Recovery Potential in Arid and Semi-Arid Northwest China
## 4 Materials and Methods
### Model and Methodology  
#### 4.3.1. LJJ Model  
The LPJ (Lund-Potsdam-Jena) model, as a dynamic global vegetation model (DGVM), can simulate the vegetation distribution and carbon and water exchange processes in ecosystems [15; 59]. Vegetation distribution in the LPJ model was based on bioclimatic indicators, and carbon was stored in leaves, roots, heartwood, sapwood, and above-ground and below-ground apoplastic pools, as well as in two soil carbon pools [60]. The LPJ model simulates the carbon and water cycles and acts out the terrestrial carbon and water exchange processes one by one on a grid basis. The water cycle module of the model takes into account precipitation, snowmelt, runoff, vegetation retention, and vegetation transpiration to simulate vegetation-soil-atmosphere water exchange. The carbon cycle in the model mainly takes into account the processes of respiration, photosynthesis, the decomposition of fallen material, and the growth and death of vegetation and introduces the perturbation mechanism of fire, thus simulating the flow of carbon dioxide between vegetation, soil, and the atmosphere [61; 62].  
The input data for the LPJ model were monthly average temperature, monthly precipitation, monthly cloudiness, monthly wet days, soil texture, CO\({}_{2}\) concentration, etc. The output data were net primary productivity, leaf area index, soil carbon stock, biomass, evapotranspiration, runoff, etc. In the LPJ model, the NPP, leaf area index, soil carbon stock, biomass, evapotranspiration, and runoff were all used [63]. In the LPJ model, NPP (net primary productivity) was calculated by subtracting the amount of carbon consumed by growth respiration and maintenance respiration from the Gross Primary Production (GPP), which was calculated by coupling photosynthesis and water balance schemes [64]. The formula for calculating NPP was as follows:  
\[NPP=0.75\times(GPP-R_{m}) \tag{3}\]  
where \(GPP\) was the \(TPP\) (Total Primary Productivity) in gC-m\({}^{-2}\)-y\({}^{-1}\); \(R_{m}\) was the amount of carbon required for maintenance respiration in gC-m\({}^{-2}\)-y\({}^{-1}\); and 0.75 means that 25% of \(GPP-R_{m}\) was consumed for vegetative growth and maintenance respiration.
# Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net
## 3 Methodology  
### Proposed Vegetation Removal Method by 2D Segmentation and Photogrammetry  
There are two possible vegetation removal approaches on the 3D point cloud reconstruction using the 2D image segmentation as shown in Figure 4. Approach number (#) 1 is to perform the 2D image segmentation to generate mask files to be fed to photogrammetry procedure. A mask file of an input image is a file with the same number of pixels of the input image, but filled with 0 or 1 instructing the photogrammetry procedure whether to ignore each pixel or not. COLMAP does have such functionality for structure from motion, but not for multi-view stereo. Thus, this approach was not implementable using COLMAP.  
This paper proposes an alternative, but a simpler approach as shown as approach #2 in Figure 4. The photogrammetry procedure is done without any vegetation consideration, to produce a point cloud with vegetation (Figure 5a). Each point in the point cloud is re-projected into the image coordinates (Figure 5c) using the transformation matrix identified by structure from motion. the corresponding point in the image coordinates can be determined if it belongs to vegetation or not, using the 2D vegetation segmentation by U-net on the image (Figure 5b). If it belongs to vegetation, it is deleted from the point cloud. Otherwise, it is kept. This process is repeated for every point in the point cloud to create a point cloud without vegetation (Figure 5d).  
For a robust 2D segmentation, this study used a deep learning based 2D vegetation segmentation using U-Net to overcome the limitations of the colour index method, as described in Section 3.2. However, the use of the colour index method is still useful especially for green vegetation, and it is proposed to use the colour index method to minimise efforts for preparing a segmented image dataset as shown in Figure 6. Original images are segmented by the colour index method first, followed by segmentation quality check by human. Images without any apparent incorrect segmentation are fed to the segmented image dataset. Images with incorrect segmentation are manually trimmed and corrected by humans, and fed to the segmented image dataset.  
Figure 4: Proposed vegetation removal approach #2.  
Figure 5: Illustration of the proposed vegetation removal approach: (**a**) 3D Point Cloud with vegetation, (**b**) Original Image, (**c**) Vegetation Mask, and (**d**) 3D Point Cloud without vegetation.