esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 SLIPI-Scan for Extinction Coefficient (\(\mu_{\mathrm{e}}\)) Mapping
### SLIPI-Scan Realization in Sprays  
The recorded stack of 2D-SLIPI images (see Figure 6) can be described as a 3D matrix of voxels defined by the number of pixels in the X- and Y-direction and the number of planes, \(m\), in the Z-direction. Along with any row \(l\) through the spray in a given plane m, the irradiance \(I\) follows the Beer-Lambert's law:  
\[I(k+1,l,m)\ =\ I(k,l,m)e^{-\mu_{e}(k,l,m)dx} \tag{4}\]  
where \(k\) denotes the column and \(dx\) its corresponding width in the image. For the first column in each plane, incident irradiance is determined from the reference image at plane \(m=0\). Alternatively, the irradiance in each voxel in the spray can be expressed as the difference between the incident irradiance in the previous voxel along the direction of propagation and the irradiance lost through scattering (or LIF in this case) \(I_{s}\) in that previous voxel:  
\[I(k+1,l,m)\ =\ I(k,l,m)-I_{s}(k,l,m) \tag{5}\]  
The combination of Equations (4) and (5) yields:  
\[\mu_{e}(k,l,m)\ =\ -ln\Bigg{(}\frac{(I(k,l,m)-I_{s}(k,l,m)}{I(k,l,m)}\Bigg{)} \frac{1}{dx} \tag{6}\]  
The scattered irradiance, \(I_{s}\), is calculated as given in references [25,26]:  
\[I_{s}(k,l,m)\ =\ \frac{S(k,l,m)\Big{(}I_{i}(l,m)-I_{f}(l,m)\Big{)}}{(1-a(k,l,m ))\sum_{k=\ 1}^{k_{\rm max}}\frac{S(k,l,m)}{(1-a(k,l,m))}} \tag{7}\]  
where \(S\) is the detected SLIPI signal in the images; \(I_{i}\) and \(I_{f}\) are the incident and final irradiance obtained from the images of the reference cuvette at plane \(m=0\) and the corresponding plane \(m\), respectively; and \(a\) describes the signal attenuation that signal, from a particular voxel \((k,l,m)\), experiences on its way to the camera. It is calculated as:  
\[a(k,l,m)\ =\ 1-exp\Bigg{(}-\sum_{m^{\prime}}^{m-1}\mu_{e}(k,l,m^{\prime}) dz\Bigg{)} \tag{8}\]  
For the calculation of \(I_{s}(k,l,1)\), it is assumed that the signal attenuation is negligible for that plane, i.e., \(a(k,l,1)\ =\ 0\) For the calculation of \(I_{s}(k,l,1)\), it is assumed that the signal attenuation is negligible for that plane, i.e., \(a(k,l,1)\ =\ 0\)[25,26]. Hence, all parameters in Equation (7) are available and the extinction coefficient can be calculated according to Equation (6) for the first plane of the sample. Then, the \(\mu_{e}\) determined in the first plane is used to calculate the signal attenuation \(a(k,l,2)\) in the second plane. From there, \(I_{s}(k,l,2)\) can be calculated according to Equation (7), which in return allows calculation of \(\mu_{e}(k,l,2)\) according to Equation (6). Continuing this iterative process in a plane-wise manner allows the calculation of \(\mu_{e}\) for all planes of the spray imaged. Figure 7 shows the resulting 2D image of local \(\mu_{e}\) deduced at plane \(m=30\) (Z = 15 mm).  
Figure 7: Image of local extinction coefficient \(\mu_{e}\) at the plane \(m=30\) corresponding to 15 mm inside the spray volume. In this case, \(\mu_{e}\) is in the range of 0.01 to 0.06 mm\({}^{-1}\).
###### Abstract  
The aim of this study was to evaluate the impact of groundwater level, soil temperature and general soil chemistry on greenhouse gas (GHG)--carbon dioxide (CO\({}_{2}\)), methane (CH\({}_{4}\)) and nitrous oxide (N\({}_{2}\)O)--fluxes from tree stems in deciduous stands with nutrient-rich naturally wet and drained peat soils. In total, nine sample plots were established in the central and north-eastern part of Latvia. The studied tree species were silver birch (_Betula pendula_ [PERSON]) and black alder (_Alnus glutinos_ (L.) [PERSON]). Tree stands of different ages and tree dimensions were selected for the study. GHG fluxes were measured with a circular-type non-transparent chamber of fixed area and volume, which was connected to the \"Gasmet DX404\" mobile spectrometer. Ambient and soil temperature at a depth of 10 cm were measured, soil parameters (pH and content of carbon (C), nitrogen (N), potassium (K), phosphorus (P)) down to 30 cm depth were analyzed, and groundwater levels and weather conditions (wind, cloudiness, precipitation) were determined. The study found that CO\({}_{2}\) fluxes from tree stems show a distinct seasonal pattern and a strong positive correlation with soil temperature. Significant differences in CO\({}_{2}\) fluxes were found between temperature ranges below and above 5 \({}^{\circ}\)C, indicating that this temperature represents a threshold value. CH\({}_{4}\) emissions from the tree stems increased with increasing groundwater levels. The impact of groundwater level becomes insignificant if the depth of the groundwater exceeds 30 cm. No significant N\({}_{2}\)O fluxes from tree stems were detected for most of the study period, except for March, April and June in black alder stands. As with CH\({}_{4}\), N\({}_{2}\)O emissions exhibit an increase as groundwater levels rise. The C and N contents in soil have a significant impact on N\({}_{2}\)O fluxes from tree stems. There is a tendency for the N\({}_{2}\)O flux to increase along with increasing C and N contents in soil.  
tree stem; GHG fluxes; temperature; groundwater level; soil N content; soil C content Article  
Carbon Dioxide, Methane and Nitrous Oxide Fluxes from Tree Stems in Silver Birch and Black Alder Stands with Drained and Naturally Wet Peat Soils
## Processing Setup and Description
### Uncertainty Modeling  
The estimation of uncertainties attributed to POD poses a challenging task since the accuracy of an orbit in an absolute sense is difficult to determine, but measures for orbit uncertainty can be derived through validation. Hence, as part of the RO processing at WEGC the novel POD setup of the rOPS provides a capability to compute estimated _systematic_ and _random_ position and velocity uncertainties for LEO and GNSS satellites from orbit inter-comparison (internal solutions, Table 2), satellite laser ranging residuals, and propagated random errors in LEO results.  
In a conservative approach all systematic uncertainty estimates of an orbit found to be below an a-priori defined _conservative bound_ uncertainty (\(u_{\rm CB}\)) are set to this low-bounding value. Otherwise, if exceeding this bound, they are kept at the derived value (as described below). In addition, the overall smaller estimated random uncertainties contribute in a root-mean-squares (RMS) sense to the total combined uncertainty budget for receiver (Rx) and transmitter (Tx) satellites respectively, as follows:  
\[u_{c}^{\rm Sat}=\sqrt{\left(u_{s}^{\rm Sat}\right)^{2}+\left(u_{r}^{\rm Sat} \right)^{2}}\qquad\text{for}\quad\text{Sat}=\text{Rx},\text{Tx}, \tag{2}\]  
where \(u_{r}\) denotes the estimated random uncertainty and \(u_{s}\) the total estimated systematic uncertainty. \(u_{s}\) is per definition always \(\geq u_{\rm CB}\) as described for the LEO satellites next.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 6 Analysis of Simulation Results
### Simulation Analysis of Step Characteristics  
PID control, fuzzy PID control, and PID control optimized by the GA were applied to the control system under the condition that the input was an 8\({}^{\circ}\) step signal simulating the load. The sampling time was set to 0.01 s. The dynamic and static indices of the system under different control strategies were analyzed (see Figure 10 for simulation results).  
Table 4 shows the simulation results of the step response. The rise time of the system under the control of a fuzzy PID controller optimized by the GA was reduced by about 37.91% compared with the classical PID control under the loaded step response. It was about 23.39% shorter than the fuzzy PID control. The adjustment time of the system controlled by the fuzzy PID controller optimized by the GA was about 32.46% shorter than that of the PID control and about 22.32% shorter than that of the fuzzy PID control. The steady-state error of the system under genetic fuzzy PID control was 88.67% less than that of the classical PID control, and 7.18% less than that of the fuzzy PID control in the steady-state response.  
Figure 10: (a) Step response; (b) local enlarged view.  
Figure 9: Iterative curve of the GA.
## 3 Digital Twin Calibration and Validation
This calibration process confirms the digital twin's fidelity and supports its utility for structural assessment, simulation, and future diagnostic applications. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Frequency Values for S1, S2, and S3 Spans (Hz)**} \\ \cline{2-10}
**Mode No.** & **Initial** & \multicolumn{2}{c}{**S1**} & \multicolumn{2}{c}{**S2**} & \multicolumn{2}{c}{**S3**} & \multicolumn{2}{c}{**Max. Diff.**} \\ \cline{3-10}  & **FEM** & **Before** & **After** & **Before** & **After** & **Before** & **After** & **(\%)** \\  & **(Looding)** & **(Looding)** & **(Looding)** & **(Looding)** & **(Looding)** & **(Looding)** & **(Looding)** & **(Looding)** \\ \hline
1 & 0.99 & 1.12 & 1.11 & 1.12 & 1.11 & 1.12 & 1.11 & 11.61 \\
2 & 1.64 & 1.87 & 1.89 & 1.89 & 1.88 & 1.89 & 1.86 & 13.22 \\
3 & 1.90 & 2.12 & 2.12 & 2.13 & 2.13 & 2.12 & 2.07 & 10.80 \\
4 & 2.35 & 2.53 & 2.57 & 2.51 & 2.54 & 2.56 & 2.51 & 8.56 \\
5 & 2.82 & 2.75 & 2.78 & 2.75 & 2.75 & 2.74 & 2.75 & 2.48 \\
6 & 3.15 & 3.34 & 3.35 & 3.35 & 3.34 & 3.35 & 3.34 & 5.97 \\
7 & 3.78 & 4.03 & 4.07 & 4.04 & 4.03 & 4.04 & 4.02 & 7.13 \\
8 & 4.34 & 4.28 & 4.25 & 4.28 & 4.25 & 4.25 & 4.25 & 1.40 \\
9 & 4.42 & 4.58 & 4.55 & 4.57 & 4.53 & 4.55 & 4.55 & 3.49 \\
10 & 5.13 & 5.26 & 5.23 & 5.25 & 5.23 & 5.28 & 5.23 & 2.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of operational frequency values for S1, S2, and S3 Spans (Hz) finite-element model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Frequency Values for S1, S2, and S3 Spans (Hz)**} & \multicolumn{2}{c}{**S3**} & \multicolumn{2}{c}{**Max.**} \\ \cline{3-10}
**Mode No.** & **Updated** & \multicolumn{2}{c}{**Before**} & \multicolumn{2}{c}{**After**} & \multicolumn{2}{c}{**Before**} & \multicolumn{2}{c}{**After**} & \multicolumn{2}{c}{**Before**} & \multicolumn{2}{c}{**After**} & \multicolumn{2}{c}{**Diff. (\%)**} \\ \cline{3-10}  & **FEM** & **Looking** & **Looking** & **Looking** & **Looking** & **Looking** & **Looking** & **Looking** & **Looking** \\ \hline
1 & 1.08 & 1.12 & 1.11 & 1.12 & 1.11 & 1.12 & 1.11 & 4.17 \\
2 & 1.80 & 1.87 & 1.89 & 1.89 & 1.88 & 1.89 & 1.86 & 5.00 \\
3 & 2.03 & 2.12 & 2.12 & 2.13 & 2.13 & 2.12 & 2.07 & 4.93 \\
4 & 2.57 & 2.53 & 2.57 & 2.51 & 2.54 & 2.56 & 2.51 & 2.33 \\
5 & 2.88 & 2.75 & 2.78 & 2.75 & 2.75 & 2.74 & 2.75 & 4.86 \\
6 & 3.41 & 3.34 & 3.35 & 3.35 & 3.34 & 3.35 & 3.34 & 2.05 \\
7 & 4.07 & 4.03 & 4.07 & 4.04 & 4.03 & 4.04 & 4.02 & 1.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of frequency values for S1, S2, and S3 Spans with the updated finite-element model.
## 2 Background: Electron Localization in Disordered Medium
### Interacting Disordered Systems: Beyond the Single-Particle Description  
The interplay of strong electronic interactions and disorder and its relevance to the metal-insulator transition, remains an open and challenging question in condensed matter physics. There was an exciting revival of the field after the pioneering experiments by Kravchenko et al. in low-density high mobility MOSFETs [114; 115; 116; 117]. These experiments provided a clear evidence for a metal-insulator transition in such 2D systems, which contradicted the paradigmatic scaling theory of localization according to which the absence of metallic behavior is expected in non-interacting disordered electron systems in \(D\leq 2\).  
Incorporating electron-electron interactions into the theory has been problematic mainly due to the fact that when both disorder and interactions are strong, the perturbative approaches break down. Perturbative renormalization group calculations found indications of metallic behavior, but in the case without a magnetic field or magnetic impurities, the runaway flow was towards a strong coupling region outside of the controlled perturbative regime and hence the results were not conclusive [118; 119; 120; 121; 122; 123; 124].  
Numerical methods for the study of systems with both interactions and disorder are rather limited. Accurate results are largely based on some variants of exact diagonalization on small clusters. Given this difficulty, the effective medium DMFT-like approaches for localization would be particularly helpful. In particular, the approaches which employ the TDOS in the DMFT present a new opportunity for the study of interacting disordered systems. Consequently, interesting questions which are controversial in the effective field theory approach, can be studied from an entirely different perspective. These include the DOS of the disordered Fermi liquid at low dimensions, the existence of a direct metal to Anderson insulator transition, and the criticality in the transition between the metallic phase and the Anderson phase.  
In Refs. [125; 126; 127] the generalized DMFT, using the numerical renormalization group as the impurity solver, was used to study the Anderson-Hubbard model. Here, a typical medium calculated from the geometric averaged DOS instead of the usual linear averaged DOS as that in the CPA [126], was used to determine the effective medium. The effect of disorder and interactions on the Mott and Anderson transitions is investigated, and it is shown that the TDOS can be treated as an order parameter even for the interacting system. However, all these calculations were performed with a local single-site approximation. In Section 5.5 we show that the cluster extension, within the TMDCA framework can treat the effects of disorder and interaction on an equal footing. It thus provides a new framework for the study of interplay between Mott-Hubbard and Anderson localization.
## 4 Results and Discussion
### Overall Comparison of Conventional Supervised Machine Learning Classifiers on Deep Features  
We conducted experiments with nine different conventional supervised classifiers trained on deep features, such as the features extracted by Alexnet and ResNet-18 models. Figure 6 shows that SVM and K-NN achieve the highest overall accuracy of 94.97% when using Alexnet and ResNet-18 features, respectively. An important observation is that 'LPBoost' and DS achieve an accuracy of less than 65.5%, while all other classifiers achieve an accuracy of over 76.80%. Among the conventional supervised classifiers, K-NN and SVM perform the best, while the second-highest accuracy is achieved by LDA. Additionally, it is worth noting that K-NN achieves the highest accuracy of 94.97% with ResNet-18 features, where the feature vector size is 1 \(\times\) 512; on the other hand, SVM achieves the same accuracy with Alexnet features, where the feature vector size is 1 \(\times\) 4096. K-NN processes a smaller feature vector and therefore requires less memory and computing power. Overall, Alexnet features provide the highest accuracy compared to ResNet-18, except K-NN, where ResNet-18 features achieve the highest accuracy as shown in Figure 6.  
Figure 5: Performance comparison of LDA trained with different discriminants on AlexNet and ResNet-18 Deep features.  
Figure 6: Performance comparison of conventional supervised machine learning classifiers on AlexNet and ResNet-18 Deep features.
## 1 Introduction
Secondly, the ability of LSMs to simulate water and carbon fluxes varies significantly across different plant functional types (PFTs), and balancing universal versus PFT-specific parameters remains a research challenge [21; 22; 23]. Detailed evaluations of complex models like Noah-MP across more than ten major PFTs are rare, yet essential for identifying structural biases and guiding parameter optimization. Therefore, this study aims to systematically evaluate the performance of Noah-MP in simulating ET and GPP across 12 vegetation types based on the global FLUXNET2015 in situ dataset. We focus on the model's performance at half-hourly (capturing diurnal cycles), monthly, and annual scales, analyzing simulation biases across different vegetation types to identify the strengths and weaknesses of Noah-MP in water and carbon flux simulations. The results of this study will not only provide a scientific basis for the further development and parameter optimization of the Noah-MP model but will also contribute to improving the accuracy of land-atmosphere interaction simulations in Earth system models and, consequently, to more reliable predictions of carbon and water cycle dynamics under future climate change scenarios.
## 5 Orbit Evaluation
### Orbit Overlapping  
We conducted a comparison by using the second day from the 3-day arc orbit with the first day from the subsequent 3-day arc orbit as the orbit overlapping. Figure 6 illustrates the RMS of the orbit overlapping for BDS-3 in the radial direction, along direction, cross direction, and 1D directions, employing B1I/B3I combination observations. Figure 7 depicts the RMS of the BDS-3 orbit overlapping using B1C/B2a combination observations. The figure indicates that the mean 1D RMS for B1C/B2a orbit overlapping of the BDS-3 satellites are 1.61 cm and 3.04 cm for MEO and IGSO constellations, respectively. The higher RMS for IGSO satellites can be attributed to the inferior observing geometry compared with MEO. From Figure 6, we can see that the 1D RMS values of the orbit overlapping using the B1I/B3I combination observations are slightly larger than those using B1C/B2a. This discrepancy may be due to the fact that the B1C/B2a solutions achieve a higher rate of ambiguity fixing. In both the B1I/B3I solution and the B1C/B2a solution, the RMS values of orbit overlapping from BDS-3 MEO SECM are within 2 cm, demonstrating smaller RMS compared with BDS-3 MEO CAST. Notably, the most significant orbit overlapping RMS value for BDS-3 IGSO can reach about 4 cm.  
Figure 6: The RMS of overlapping orbits for BDS-3 of every satellite from B1I/B3I observations in radial, direction, along direction, cross direction, and 1D direction, respectively.  
Table 6 presents the RMS for B1C/B2a BDS-3 orbit overlapping; the radial component is 1.58 cm, the along component is 2.09 cm, the cross component is 1.86 cm, and the 1D component is 1.86 cm. In detail, the 1D RMS for the BDS-3 orbit overlapping from the B1C/B2a combination is 3.05 cm for IGSO, 1.80 cm for MEO CAST, and 1.30 cm for MEO SECM. Comparatively, the RMS values for the BDS-3 orbit overlapping using B1I/B3I combination observations are slightly larger than those using B1C/B2a combination. This difference may be attributed to the higher percentage of ambiguity fixing to integer achieved by the B1C/B2a solution.
## 2 Preliminaries
### Alternating Direction Method of Multipliers  
Alternating the direction method of multipliers (ADMM) [60] is a method of the proximal splitting algorithm that can treat convex optimization problems of the form  
\[\min_{\mathbf{x}\in\mathbb{R}^{N_{1}},\,x\in\mathbb{R}^{N_{2}}}F(\mathbf{x}) +G(\mathbf{z})\ \ \text{s.t.}\ \ \mathbf{z}=\mathbf{L}\mathbf{x}, \tag{3}\]  
where \(F\) and \(G\) are usually assumed to be quadratic and proximable functions, respectively, and \(\mathbf{L}\in\mathbb{R}^{N_{2}\times N_{1}}\) is a matrix with full-column rank. For any \(\mathbf{x}^{(0)}\in\mathbb{R}^{N_{1}},\mathbf{z}^{(0)}\in\mathbb{R}^{N_{2}}, \mathbf{b}^{(0)}\in\mathbb{R}^{N_{2}}\) and \(\gamma>0\), the ADMM algorithm is given by  
\[\begin{split}\mathbf{x}^{(\tau+1)}&=\arg\min_{ \mathbf{x}}\biggl{\{}F(\mathbf{x})+\frac{1}{2\gamma}\|\mathbf{z}^{(\tau)}- \mathbf{L}\mathbf{x}-\mathbf{b}^{(\tau)}\|_{2}^{2}\biggr{\}},\\ \mathbf{z}^{(\tau+1)}&=\arg\min_{\mathbf{z}}\biggl{\{} G(\mathbf{z})+\frac{1}{2\gamma}\|\mathbf{z}-\mathbf{L}\mathbf{x}^{(\tau+1)}- \mathbf{b}^{(\tau)}\|_{2}^{2}\biggr{\}},\\ \mathbf{b}^{(\tau+1)}&=\mathbf{b}^{(\tau)}+\mathbf{L} ^{(\tau+1)}-\mathbf{z}^{(\tau+1)}.\end{split} \tag{4}\]When the optimization problem is convex, the sequence generated by (4) converges to an optimal solution of (3) under the existence of a saddle point.
# Concept for Generating Energy Demand in Electric Vehicles with a Model Based Approach
## 4 Simulation and Model Environment
### Vehicle Model
#### 4.1.4 DC/DC Converter Model  
For modelling the bidirectional DC/DC converter, a semi-active configuration was used in which the supercapacitor is connected in series with the DC/DC converter and the battery is connected directly to the DC bus [36], as shown in Figure 9. This topology has the following advantages [10]:  
* Semiactive control strategies can be implemented;
* The operating range of the energy storage components can be extended to improve the performance of the HESS;
* It provides flexibility to reduce the size/voltage of some of the energy storage components.  
A bidirectional converter was chosen because the energy is transferred from the supercapacitor to the DC bus in boost mode and the supercapacitor charges with the vehicle's braking energy in buck mode. To perform the mathematical model of the bidirectional DC/DC converter, the circuit presented in [10] was used. To perform the analysis of the circuit, it is necessary to divide it into its two modes of operation.  
The boost mode is represented as follows:  
\[\frac{diL}{dt}=\frac{1}{L}[iL(-R_{SC}-R_{L}-R_{D}(1-d)-R_{SW}d)+(1-d)(-V_{C}-V_ {D})+V_{SC}] \tag{9}\]  
\[\frac{dV_{C}}{dt}=\frac{1}{C}[\frac{1}{R_{b}}(V_{b}-V_{c})-I_{dmd}+iL(1-d)] \tag{10}\]  
Figure 8: Interpolated efficiency map of the BMW i3 based on Oak Ridge National Laboratory based on [35].  
The buck mode is represented as follows:  
\[\frac{diL}{dt}=\frac{1}{L}[iL(-R_{SC}-R_{L}-R_{D}(1-d)-R_{SW}d)-V_{C}d+V_{D}(1-d)+ V_{SC}] \tag{11}\]  
\[\frac{dV_{C}}{dt}=\frac{1}{C}[\frac{1}{R_{b}}(V_{b}-V_{c})-I_{dmd}+iLd] \tag{12}\]
###### Abstract  
A novel technique to enhance the phase shifting range of a liquid crystal (LC)-based, substrate-integrated waveguide (SIW) phase shifter by inserting inductive posts (IPs) is presented for the first time. The IPs inserted in the LC-based SIW phase shifter produce a phase advance based on the relative permittivity of the LC, resulting in an additional differential phase shift. At 28 GHz, the proposed structure with IPs achieves a ratio of maximum differential phase shift (\(\Delta\phi_{max}\)) to maximum insertion loss (\(IL_{max}\)) (FoM1) = 52.82 \({}^{\circ}\)/dB and ratio of maximum differential phase shift to length (FoM2) = 2.62 \({}^{\circ}\)/mm. Compared with conventional LC-based SIW phase shifters that lack an IP and use the same amount of LC, the FoM1 increased by 16% and the FoM2 increased by 55%. In addition, compared to the typical structure that uses additional LCs instead of IPs, the FoM1 decreased by 7%, and FoM2 increased by 21%. Therefore, inserting IPs into the LC-based SIW phase shifter can reduce the dimensions of the phase shifter and the amount of LCs required to achieve the desired differential phase shift. We believe this work can contribute to the design of compact and efficient SIW phase shifters for future telecommunication systems.  
SIW; LCs; mmWave; tunable phase shifter; phase tuning; cylindrical inductive post +
Footnote †: journal: Applied Physics Letters