esa-sceva/satcom-chunk-collection huggingface dataset:
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 3 Point Line Feature Processing
### Reprojection Error Model of Line Feature  
As shown in Figure 5, the projection line segments of line \(L\) on the image plane are \(L_{1}\), and \(l_{1}^{\prime}\) is the observation line segment. One must let the end points of \(l_{1}^{\prime}\) segment \(X_{1}=[x_{1},y_{1},1]^{T}\) and \(X_{2}=[x_{2},y_{2},1]^{T}\), and the projection segment \(L_{1}=[l_{1},l_{2},l_{3}]\).  
Then, the distance between the two endpoints and the projected line segment is as follows:  
\[e_{l}=\begin{bmatrix}d_{1}&d_{2}\end{bmatrix}^{T}=\left[\frac{x_{1}^{T}L_{1}} {\sqrt{l_{1}^{2}+l_{2}^{2}}}\frac{x_{2}^{T}L_{1}}{\sqrt{l_{1}^{2}+l_{2}^{2}}} \right]^{T} \tag{9}\]  
The Jacobian matrix of the camera pose increment can be solved according to the chain rule, which is as follows:  
\[J_{L}=\frac{\partial e_{l}}{\partial\delta_{\zeta}}=\frac{\partial e_{l}}{ \partial L_{1}}\frac{\partial L_{1}}{\partial L_{c}}\frac{\partial L_{c}}{ \partial\delta_{\zeta}} \tag{10}\]  
\(L_{1}\) and \(L_{c}\) can be obtained from Equations (5) and (6), and the three items on the right of Equation (10) are as follows:  
\[\begin{array}{l}\frac{\partial e_{l}}{\partial L_{1}}=\left[\begin{array}[ ]{cc}\frac{-l_{1}x_{1}^{T}L_{1}+x_{1}(l_{1}^{2}+l_{2}^{2})}{(l_{1}^{2}+l_{2} ^{2})^{\frac{3}{2}}}&\frac{-l_{2}x_{1}^{T}L_{1}+y_{1}(l_{1}^{2}+l_{2}^{2})}{( l_{1}^{2}+l_{2}^{2})^{\frac{3}{2}}}&\frac{1}{\sqrt{l_{1}^{2}+l_{2}^{2}}}\\ \frac{-l_{1}x_{1}^{T}L_{1}+x_{2}(l_{1}^{2}+l_{2}^{2})}{(l_{1}^{2}+l_{2}^{2})^{ \frac{3}{2}}}&\frac{-l_{2}x_{1}^{T}L_{1}+y_{2}(l_{1}^{2}+l_{2}^{2})}{(l_{1}^{ 2}+l_{2}^{2})^{\frac{3}{2}}}&\frac{-l_{2}x_{1}^{T}L_{1}+y_{2}(l_{1}^{2}+l_{2}^ {2})}{(l_{1}^{2}+l_{2}^{2})^{\frac{3}{2}}}&\frac{-l_{2}x_{1}^{T}L_{1}+y_{2}(l_ {1}^{2}+l_{2}^{2})}{(l_{1}^{2}+l_{2}^{2})^{\frac{3}{2}}}&\frac{1}{\sqrt{l_{1}^ {2}+l_{2}^{2}}}\\ \frac{\partial L_{1}}{\partial L_{c}}=\frac{\partial e(x_{c})}{\partial L_{c}}= \left[\begin{array}{cc}\kappa&0\end{array}\right]_{3\times 6}\\ \frac{\partial L_{c}}{\partial\delta_{\zeta}}=\left[\begin{array}{cc}-(R_{ cw}w_{cw})^{\wedge}-(l_{cw}^{\wedge}R_{cw}w_{w})^{\wedge}&-(R_{cw}w_{w})^{ \wedge}\\ -(R_{cw}w_{w})^{\wedge}&0\end{array}\right]_{6\times 6}\end{array} \tag{11}\]
## 3 Methods
### Model Validation  
Both the LME models and the linear fixed effects model were validated against ground measurements using the following statistics: R\({}^{2}\), root-mean-square error (RMSE), mean prediction error (MPE), temporal R\({}^{2}\), and spatial R\({}^{2}\)[35]. MPE was estimated as the mean absolute differences between predicted and measured soil moisture. Temporal R\({}^{2}\) was calculated by regressing Delta SM against Delta predicted where: Delta SM is the difference between measured soil moisture at site \(i\) on day \(j\) and the mean measured soil moisture at site \(i\), and Delta predicted is defined similarly for the predicted values generated from the models. Spatial R\({}^{2}\) was calculated by regressing the mean measured soil moisture at site \(i\) against the mean predicted soil moisture at site \(i\). Furthermore, a cross-validation method was adopted to test the stabilities of the linear mixed and the fixed effects models in predicting the soil moisture index. Specifically, the data of one site (test site) were separated from those of the other 14 sites (calibration sites). The models were trained with the data from the calibration sites and then predicted soil moisture indices for the test site. This process was repeated until each of the 15 sites was tested, and cross-validated R\({}^{2}\) values between predicted and measured soil moisture indices were then calculated.
## 4 Engineering Examples and Results of Statistical Analyses  
### Overview of the Mining Area  
To further validate the performance of the IGGIII-BFGS algorithm, the engineering application example of the Banji coal mine is selected to compare the accuracy of parameter inversion between the BFGS algorithm and the IGGIII-BFGS algorithm. Banji coal mine is located in Huainan City, Anhui Province, and 110801 working face is selected for the experimental analysis.  
The 110801 first mining face adopts the integrated mechanized coal mining method of strike-longwall-based backward one-time full-height mining, and the roof is managed by the all-caving method. This working face has the characteristics of thick loose layer, large mining depth, and thin bedrock. Trial mining began on 1 April 2021, and mining ended on 15 October 2022, with 562 days of mining time, an average strike length of 1300 m and a tendency length of 279 m, with the top plate of the coal seam mostly sandy mudstone and the bottom plate mostly mudstone and sandy mudstone, with an average inclination angle of 8\({}^{\circ}\), an average coal thickness of 2.2 m, and an average mining thickness of 2.5 m, an average thickness of the loose layer in the monitoring area of 600 m, and an average mining depth of 767 m. The schematic diagram of the 110801 working face is shown in Figure 7.
## 3 Determining Optimal LUT
### Simulation Model  
We use the fixed point toolbox of Matlab for quantization simulations. The simulation model used is shown in Figure 7. As we see in the simulation model signal, noise and interference pass through the channel select filter. The variable gain amplifier (VGA), upon getting feedback from digital portion of the receiver, re-sizes signal levels to full scale of ADC. Noise levels are controlled by the SNR control to maintain a SNR\({}_{FE}\) at the input of ADC. Amplitude and time resolutions of ADC and digital baseband sections are variable.
# A 60 GS/s Two-Stage Sampler with a Linearity Calibration Loop for PAM-8 Receivers
## 2 Track-And-Hold Circuit
### Circuit Implementation  
The circuit described in [13], which implements the first sampling stage (TH buf1), samples the signal on one of eight capacitances at a time. One input signal sample is taken every 16.7 ps. A buffer (TH buf2) is then connected to each sampling capacitance. To implement the second stage interleaving operation, eight parallel switches are connected to the output of the second buffer. The switches are operated to sample the signal on one of the eight sampling capacitances at a time. The buffers composing the second stage sampler can be implemented with the circuits in [14; 15], composed of an inverter followed by another one closed in a diode configuration. These circuits show good linearity because while the inverter decompresses the signal, the second one has an opposite behavior, compensating for each other's distortions. This approach has some limitations across process corners and temperatures stemming from the different variations of the linearity of the two blocks. To deal with this problem, we use the buffer topology depicted in Figure 3. The simple gm-gm topology is improved by varying the diode nonlinearity through the voltage \(v_{corr}\), as in [8]. An optimal value of the gate voltage can be chosen to minimize the distortions of the front end.  
Figure 2: Block diagram of a 64 TI-TH circuit with in blue the first stage sampler and in green the second state, using a DAC for the second-stage buffer bias voltage control [8].
## Nomenclature  
\begin{tabular}{l l} Term & Description \\ \(BF\) & Bifaciality factor (\%) \\ \(BG\) & Bifacial gain (\%) \\ \  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Model** & **Monofacial** & **Bifacial** & **Unit** \\ \hline \(I_{SC,STC}\) & DeSoto & 9.745 & 9.642 & A \\  & PVsyst & 9.573 & 9.629 & \\ \(I_{O}\) & DeSoto & 0.007 & 0.005 & nA \\  & PVsyst & 1.381 & 5.682 & \\ \(R_{SER}\) & DeSoto & 0.304 & 0.382 & \(\Omega\) \\  & PVsyst & 0.192 & 0.255 & \\  & DeSoto & \(3.539\times 10^{2}\) & \(1.891\times 10^{3}\) & \(\Omega\) \\ \(R_{SHINT}\) & PVsyst & \(2.035\times 10^{3}\) & \(3.821\times 10^{3}\) & \\ \(a\) & DeSoto & 1.533 & 1.518 & \\ \(A_{djust}\) & DeSoto & 9.010 & 6.311 & \\ \(R_{SHINT,0}\) & PVsyst & \(8.856\times 10^{3}\) & \(2.971\times 10^{4}\) & \(\Omega\) \\ \(Gamma\) & PVsyst & 1.126 & 1.21 & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Thermal coefficients used in simulations. The NOCT of 42 \({}^{\circ}\)C was used because this value gave a comparable thermal response as the selected U\({}_{0}\)/U\({}_{1}\) coefficients with the 1-year of meteorological data used in this study.  
**Table A4.** Bifacial-specific coefficients used in the simulations.  
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Source** \\ \hline Bifaciality & 0.6700 & DTU I-V Measurements \\ Transmission fraction & 0.0375 & DTU Calculations \\ Mismatch loss factor 1 & 0.0025 & [19] \\ Structure shading factor & 0.0070 & [52] \\ Front PV surface reflectivity & 0.0100 & [9] \\ Rear PV surface reflectivity & 0.0300 & [9] \\ \hline \hline \end{tabular}  
* \({}^{1}\) Includes front side irradiance contribution.  
\end{table}
Table A5. DC losses used in the simulations.
## 1 Introduction
As a kind of improved method, channel-adapted spatial modulation (CASM) activates several LEDs to transmit information simultaneously [14], reducing inter-beam interference by finding the optimal LED combination. But there may still be significant interference between different combinations. In this paper, we have comprehensively considered the interference issues in multi-beam NOMA systems and propose an adaptive transmit diversity strategy. Firstly, a multi-beam system is constructed using LED arrays with a semi-spherical structure. It can generate directional multi-cluster spatial beams based on user relative positions. We design an algorithm for adaptive selection and merging beams based on the degree of interference between beams in space, which merges LED beams with high interference. The low-interference LED clusters continue to derive spatial reuse gains. Within the same beam, we use an OFDM-NOMA scheme to match and group the users. Signals from different user groups are sent through different subcarriers to improve resource utilization. This enhances access capacity while reducing NOMA inter-user interference. Through simulations, we substantiate the feasibility and superiority of the proposed strategy in the context of the multi-beam NOMA system. Our main contributions are summarized in the following:  
* In order to solve the serious inter-beam and intra-beam interference issues caused by multiple space-division beams and multiple access users, we propose an adaptive transmit diversity strategy. We design an adaptive selection merge algorithm to combine different LED beams according to the degree of interference. Diversity techniques are used at the transmitter to reduce inter-beam interference. * Based on the adaptive transmit diversity strategy for underwater optical multi-beam NOMA systems, a model and method for evaluating beam interference are proposed. The method can find the optimal merging scheme. * Further, we propose an OFDM-NOMA scheme with an adaptive transmit diversity strategy, where users are grouped according to their channel gains and different user groups are assigned to transmit on different subcarriers. This will resist the influence of interference within the beam.
## 3 Results and Discussion
### Performance Test
#### 3.2.2 Permeation Flux Efficiency  
Figure 7 shows the permeation flux of membranes with different loadings of sulfur measured at pressures of 0.5 and 1.0 bar in order to study the function of operating pressure on the permeation flux efficiency. It was found that pure NBR membrane with 1.0 and 1.5 phr sulfur did not show any water flux due to the lack of hydrophilicity of the membrane [58]. In pure NBR membrane, there is absence of GO, hence, this leads to absence of hydrophilic property in the membrane. The pure NBR membrane and NBR/GO membrane with 0.5 phr of sulfur content broke or failed during the testing under pressure. As evidenced from the SEM images and UTS results, a continuous and strong membrane was not formed at 0.5 phr sulfur loading. Only NBR/GO membrane with 1.0 and 1.5 phr sulfur loading showed water permeation. GO is hydrophilic in nature; thus, with addition of GO, the overall hydrophilicity of the membrane will increase which then enables the passing of a permeate stream [59]. From Figure 7, the membrane with 1.0 phr of sulfur shows the higher permeation flux of 789.4 and 834.1 L/m\({}^{2}\cdot\)hr at 0.5 and 1.0 bar, respectively, as compared with the membrane with 1.5 phr of sulfur that only shows the permeation  
Figure 6: Elongation at break of the membrane.  
flux of 329.9 and 559.6 L/m\({}^{2}\)-hr. This is because the higher the sulfur loading, the higher the crosslink density that causes the chains between the NBR polymeric system to become more compact. Consequently, the filtration ability of the membrane will reduce. Moreover, the SEM images revealed that wrinkles and ridges in membranes with 1.0 phr sulfur are well formed compared to other membranes. The membrane with 1.5 phr sulfur loading shows less wrinkles and ridges which shows a less effective area for contact with water.  
The permeation flux of the membranes for 1.0 to 1.5 phr of sulfur increases as the operating pressure increases. This is because at higher pressure, the water droplets are able to pass through the membrane more readily [14]. As compared to the literature, the permeation flux of NBR/GO membranes achieved in this work is much higher compared to other membranes. For instance, there was only 153.2 Lm\({}^{-2}\)h\({}^{-1}\) permeation flux reported for filtration of oily wastewater by [PERSON] et al. [11] using Kaolin/MnO\({}_{2}\) bi-layer composite dynamic membrane. Besides, the permeation flux of PVC-bentonite membrane and PAN ultrafiltration membrane were reported as only 186 Lm\({}^{-2}\)h\({}^{-1}\) and 96.2 Lm\({}^{-2}\)h\({}^{-1}\), respectively, for oily wastewater filtration at a pressure of 4 bars [4,16]. Although the permeation flux of the NBR/GO membrane is much higher than that reported in the literature, the ultimate choice will still be based on the oily wastewater rejection rate. The oily wastewater rejection rate is crucial because the regulatory body requirement must be followed before the wastewater is discharged to the water body.
# Modelling and Experimental Testing of Passive Magnetic Bearings for Power Loss Reduction
## 2 PMB Model
The radial stiffness \(k_{r}\), linearised about the rotor centred position, is expressed in Equation (9) as the derivative of the radial force \(F_{r}\):  
\[k_{r}(\Delta z)=\left.-\frac{\partial F_{r}(\Delta z)}{\partial r}\right|_{r=0}. \tag{9}\]  
Figure 1: Surface charge interaction between two PMB rings (with blue and red colours identifying the magnetic north and south poles, respectively). Figure 2: Normalised axial force \(F_{z}/F_{z,max}\) as function of the normalised radial \(\Delta r/\delta\) and axial \(\Delta z/h\) offsets. An alternative approach for the evaluation of \(k_{r}\) with the derivative of \(F_{z}\) is proposed. According to Equation (10), which is related to the Earnshaw theorem and derived from the steps detailed in Appendix B, the sum of PMB stiffnesses in the \(x,y,z\)-directions of a Cartesian reference system is zero. \[k_{x}+k_{y}+k_{z}=0 \tag{10}\]  
Consequently, if a Cartesian reference system is considered with its \(z\)-axis aligned with the axis of the PMB, then under the hypothesis of null radial displacement of the floating ring, both stiffness \(k_{x}\) and \(k_{y}\) are equal to \(k_{r}\) due to the bearing axial symmetry and depending only on \(\Delta z\), resulting in Equation (11):  
\[k_{x}=k_{y}=k_{r}=-\frac{1}{2}k_{z} \tag{11}\]  
where  
\[\
abla\mathbf{\cdot}\mathbf{B}=0. \tag{2}\]0  
This approach avoids the evaluation of \(F_{r}\) and requires only \(F_{z}\) for the calculation of both axial force and radial stiffness; requiring only one of the components of \(\mathbf{F}\) decreases the computational time associated with the demanding numerical solution of the integral in Equation (8) for each axial offset. Furthermore, Equation (11) highlights that the bearing is always unstable since a positive radial stiffness corresponds to a negative axial stiffness and vice versa. Thus, a rotor with PMBs used in the thrust-radial configuration needs an auxiliary bearing to stabilise the axial degree of freedom. The values of the axial force \(F_{z}\) from Figure 2 are considered at the null radial offset as functions of the PMB ring axial offset, and the corresponding radial stiffness \(k_{r}\) is calculated as a function of the axial offset with Equations (11) and (12). Both \(F_{z}\) and \(k_{r}\) are normalised with respect to their maximum value, respectively, and plotted in Figure 3 as functions of the axial offset. The intersection of the two axial offset ranges with upward axial force and positive stiffness, respectively, is equal to \(\sim\)0 \(\div\) 0.5 and corresponds to the condition where the PMB works in the thrust-radial configuration. Finally, the trends of \(F_{z}\) and \(k_{r}\) in Figure 3 highlight that in the PMB thrust-radial configuration, the higher the thrust axial force for the rotor weight compensation, the lower the radial stiffness. \[\
abla\mathbf{\cdot}\mathbf{B}=0. \tag{2}\]1 Identification of the axial offset range for the PMB thrust–radial configuration through the analysis of axial force and stiffness.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 5 Conclusions  
This paper proposes a dynamic phase comparison algorithm for the planar direction finding of radiation sources on a high-speed moving satellite radio receiver. Based on the phase interferometer algorithm, this algorithm treats the high-speed moving antenna as equivalent to single-baseline array antennas. According to the electromagnetic wave frequency and satellite's velocity, it can adjust the baseline length to avoid phase ambiguity indirectly. Furthermore, compared with the amplitude comparison algorithm used by dual channel receivers, it offers a larger direction-finding range. Finally, by integrating both algorithms, the simulations demonstrate improved accuracy, showing an approximate 6 % improvement over the dynamic phase comparison algorithm.  
Conceptualization, Z.W. and K.Y.; methodology, Z.W. and M.M.; software, Z.W. and J.X.; validation, J.X. and Z.Z.; formal analysis, Z.W. and M.M.; investigation, Z.W.; resources, Z.W.; data curation, M.M. and J.X.; writing--original draft preparation, Z.W.; writing--review and editing, M.M., J.X., Z.Z. and K.Y.; visualization, Z.W.; supervision, Z.Z. and K.Y.; project administration, Z.W. and K.Y.; funding acquisition, Z.Z. and K.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Natural Science Foundation of Jiangxi under Grant 20224 BAB212027, in part by the Financial Contract of Science and Technology Project of Jiangxi Province under Grant ZBG20230418032, and in part by the Interdisciplinary Innovation Fund of Natural Science from Nanchang University under Grant 9167-28220007-YB2104.  
Not applicable.  
The raw data and source codes supporting the conclusions of this article will be made available by the authors on GitHub [[https://github.com/wzz91225/DF_Simulations_of_Letter](https://github.com/wzz91225/DF_Simulations_of_Letter)]([https://github.com/wzz91225/DF_Simulations_of_Letter](https://github.com/wzz91225/DF_Simulations_of_Letter)) (accessed on 11 April 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RPI & Radio plasma imager \\ EM Waves & Electromagnetic waves \\ VLF & Very-low frequency \\ VHF & Very-high frequency \\ DF & Direction finding \\ DOA & Direction of arrival \\ SNR & Signal-to-noise ratio \\ \end{tabular}
###### Abstract  
The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrivevement of fine scale structures while still been computationally efficient comparing to model based data assimilation schemes. However, coupling this data-driven priors to classical filtering schemes limits their potential representativity. From this point of view, the recent advances in machine learning and especially neural networks and deep learning can provide a new infrastructure for dynamical modeling and interpolation within a data-driven framework. In this work we adress this challenge and develop a novel Neural-Network-based (NN-based) Kalman filter for spatio-temporal interpolation of sea surface dynamics. Based on a data-driven probabilistic representation of spatio-temporal fields, our approach can be regarded as an alternative to classical filtering schemes such as the ensemble Kalman filters (EnKF) in data assimilation. Overall, the key features of the proposed approach are two-fold: (i) we propose a novel architecture for the stochastic representation of two dimensional (2D) geophysical dynamics based on a neural networks, (ii) we derive the associated parametric Kalman-like filtering scheme for a computationally-efficient spatio-temporal interpolation of Sea Surface Temperature (SST) fields. We illustrate the relevance of our contribution for an OSSE (Observing System Simulation Experiment) in a case-study region off South Africa. Our numerical experiments report significant improvements in terms of reconstruction performance compared with operational and state-of-the-art schemes (e.g., optimal interpolation, Empirical Orthogonal Function (EOF) based interpolation and analog data assimilation).  
Keywords:data assimilation; dynamical model; Kalman filter; neural networks; data-driven models; interpolation +
Footnote †: journal: