esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Global Fast Sliding Mode Control for Steer-by-Wire System Road Vehicles
###### Abstract  
A steer-by-wire (SbW) system, also known as a next-generation steering system, is one of the core elements of autonomous driving technology. Navigating a SbW system road vehicle in varying driving conditions requires an adaptive and robust control scheme to effectively compensate for the uncertain parameter variations and external disturbances. Therefore, this article proposed an adaptive global fast sliding mode control (AGFSMC) for SbW system vehicles with unknown steering parameters. First, the cooperative adaptive sliding mode observer (ASMO) and Kalman filter (KF) are established to simultaneously estimate the vehicle states and cornering stiffness coefficients. Second, based on the best set of estimated dynamics, the AGFSMC is designed to stabilize the impact of nonlinear tire-road disturbance forces and at the same time to estimate the uncertain SbW system parameters. Due to the robust nature of the proposed scheme, it can not only handle the tire-road variation, but also intelligently adapts to the different driving conditions and ensures that the tracking error and the sliding surface converge asymptotically to zero in a finite time. Finally, simulation results and comparative study with other control techniques validate the excellent performance of the proposed scheme.  
adaptive global fast sliding mode (AGFSM); adaptive sliding mode observer (ASMO); Kalman filter (KF); Steer-by-Wire (SbW) +
Footnote †: journal: Applied Sciences
## 2 The Waveform Superposition Algorithm
### Waveform Superposition
The normalized array factor is shown in Figure 6, and the azimuth and elevation angles of the target are \(0^{\circ}\) and \(60^{\circ}\), respectively. The amplitude of the main lobe is significantly higher than that of the side lobe. Compared with the uniform array, the random array shows a more focused distribution of the field pattern. According to the pattern multiplication theorem, the beampattern of the sparse array is equal to the product of the node's field pattern and the array factor:  
\[ \begin{split} B(\theta,\phi)&=f(\theta,\phi)\cdot F _{array}(\theta,\phi)\\ &=\sum_{i=1}^{n}e^{-jk(\mathbf{u}-\mathbf{u_{q}})\cdot\mathbf{x_{ i}}}\cdot\sum_{k=0}^{N-1}e^{-jk(d_{k}-d_{k0})}\end{split}  \tag{20}\]  
Therefore, the gain of the array is expressed as  
\[G(\theta,\phi)=4\pi\eta_{a}\frac{|B(\theta,\phi)|^{2}}{P_{total}} \tag{21}\]  
where \(\eta_{a}\) and \(P_{total}\) are the efficiency factor and total radiated power of the sparse array, respectively. \(P_{total}\) is expressed as  
\[P_{total}=\frac{2\pi^{2}}{N_{e}N_{a}}\sum_{i=1}^{N_{e}}\sum_{j=1}^{N_{a}}|B( \theta_{i},\phi_{j})|^{2}\sin\theta_{i} \tag{22}\]
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 7 Micromegas for Neutron Detection
### Micromegas for Neutron Time-of-Flight Measurements
an auto-trigger mode and stores the time difference between the strips recording a signal, allowing for the reconstruction of the track of the charged particles in the detector gas and, therefore, the interaction point of the incident neutrons, as illustrated in Figure 22.  
While the GET electronics is very versatile for low count-rate experiments, some shortcomings related to a relatively low sampling rate of 100 MHz, a small buffer size, and the absence of simultaneous read/write operations, have been experienced at higher count rates. Dedicated multi-channel electronics, capable of signal sampling with a pace of 1 ns or lower over a repeating 100 ms time window with high local count rates, are essential for time-of-flight experiments combined with TPC-like particle tracking. The VMM3 chip [102] was developed and put in production for the readout of the ATLAS-NSW Micromegas detectors of the muon spectrometer [15]. This is a promising candidate to be used for neutron-beam Micromegas strip detectors for which typically 256 acquisition channels are sufficient. A single VMM3 chip consists of 64 channels, where each channel has an analogue peak detector and three Analog-to-Digital Converters (ADCs) for data digitization: a 10-bit ADC with a 200 ns adjustable conversion time for peak amplitude conversion, a 6-bit ADC single stage conversion, which completes within 25 ns from the peak, and an 8-bit ADC together with a 12-bit Gray-code counter for a 20-bit timing information within a clock cycle. The full timing information together with the amplitude information is constructed and processed on the chip as final events and buffered for the readout over the ethernet connection. This ongoing development of a multipurpose, portable detector for the measurements of energy-dependent neutron fluxes, neutron-induced reaction cross sections, and angular distributions of (n,f) and (n,lcp) reactions brings together the latest techniques for Microbulk production and the dedicated data acquisition system optimized for Micromegas detectors and suited for time-of-flight measurements. The neutron transparency of the detector, only possible with the Microbulk variant of Micromegas-based neutron detectors, will allow both for stacking detectors and for the setup of down-stream experiments.
## 3 Results
### Dense Image Matching (DIM)
#### 3.2.2 Analysis Based on DSM Profiles of Ground Areas  
The available LiDAR DSM (Section 2.1) was used to geometrically verify the DIM results. However, the time difference between the available LiDAR and the image-matching DSMs (ca 26 years) suggested performing comparisons based on profiles in unchanged areas. This would avoid considering vegetation growth, landscape changes, urbanization, demolishment, or the installation of new facilities. Three sample areas, sectioned by 30 m lines, were selected: (1) an unaltered asphalt road characterized by an unambiguous and relatively even surface plane (Figure 10), (2) a concrete open-air basketball court (Figure 11) and (3) an unaltered parking lot (Figure 12). In all three areas, height differences along the profiles show large discrepancies with respect to the reference LiDAR. Table 6 reports the RMSEs of the differences with values up to 50 cm in the case of the consumer-grade scanner dataset. Profiles from the photogrammetric scanner dataset are generally less noisy, highlighting the importance of the scanning capabilities. Table 6 also shows that  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Image Matching Scenario** & \begin{tabular}{c} **Mean** \\ **Value [m]** \\ \end{tabular} & \
\begin{tabular}{c} **Min** \\ **Value [m]** \\ \end{tabular} &
\begin{tabular}{c} **Max** \\ **Value [m]** \\ \end{tabular} & **RMSE [m]** \\ \hline \multirow{4}{*}{Metashape} & Photogrammetric dataset—\(2\times\) GSD & \(-0.026\) & \(-0.649\) & \(0.615\) & \(0.200\) \\  & Photogrammetric dataset—\(4\times\) GSD & \(-0.027\) & \(-0.819\) & \(0.716\) & \(0.207\) \\ \cline{2-6}  & Consumer-grade dataset—\(2\times\) GSD & \(-0.001\) & \(-0.925\) & \(0.929\) & \(0.307\) \\  & Consumer-grade dataset—\(4\times\) GSD & \(0.001\) & \(-0.929\) & \(0.879\) & \(0.291\) \\ \hline \multirow{4}{*}{Inpho} & Photogrammetric dataset—\(1\times\) GSD & \(-0.010\) & \(-0.936\) & \(0.766\) & \(0.222\) \\  & Photogrammetric dataset—\(3\times\) GSD & \(0.020\) & \(-0.950\) & \(0.788\) & \(0.225\) \\ \cline{1-1} \cline{2-6}  & Consumer-grade dataset—\(1\times\) GSD & \(0.006\) & \(-0.748\) & \(0.530\) & \(0.207\) \\ \cline{1-1}  & Consumer-grade dataset—\(3\times\) GSD & \(0.056\) & \(-0.789\) & \(0.861\) & \(0.237\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparing vertical values of the four DEMs with respect to GNSS measurements (110 points).  
Figure 9: Histogram of vertical differences between the four DEMs and the GNSS measurements (110 points).  
high-density image matching generally produces more noisy results. Note that these reported results are restricted to the tested scanners and depend on the specific software and version evaluated.  
Figure 11: Analysis based on DSM profile (**a**) no. 2 (park and basketball pitch): orthophoto from 1986 (**b**) and 2012 (**c**).  
Figure 10: Analysis based on profile (**a**) no. 1 (road): orthophoto from 1986 (**b**) and 2012 (**c**).
## 2 Results and Discussion
### Optimization of Sample Preparation and Extraction Conditions  
In previous work using SPMESH-DART-HRMS/MS for rapid analyses of IBMP reported repeatability (RSD (relative standard deviation) = 32%) and limits of detection (LOD = 4 ng/L, or 3.6 ng/L assuming a homogenate density of 1.1 g/mL) when quantifying native IBMP in grape homogenate [13], worse than what can be achieved with the gold standard HS-SPME-GCMS approach (RSD = 2-14%, LOD = 1.2 ng/L or ng/kg in wines) [11]. During SPMESH sheet extraction, the sheet is placed in a stainless-steel holder, and the wells are isolated from each other via a Teflon gasket containing water jet cut holes to expose the mesh. The stainless-steel sheet holder and gaskets are placed above the well plate prior to sealing the extraction apparatus with bolts at the corners of the apparatus. The original SPMESH sheet holder allowed for modest levels of leakage between adjacent wells and averaged 1.2% per well [13]. For central wells, which are adjacent to four other wells, average crosstalk is 4 \(\times\) 1.2% = 4.8%, which limits the dynamic range of samples that could be analyzed on a single plate. This original SPMESH holder design was used for a previous validation study with IBMP in grape samples [13]. Recently, a new SPMESH holder design was described to allow for 'direct-immersion' SPMESH in which the sample holder can be inverted without leakage [15]. Briefly, the new extraction apparatus adds a second solid Teflon gasket on top of the SPMESH holder and below the metal cover plate. Additionally, a vise clamp is used in place of butterfly nuts to ensure that uniform pressure is applied across the central and peripheral well plates. The new SPMESH holder design limited cross talk to <0.03% per well for IBMP analyses, but other analytical figures of merit were not reported.  
Using IBMP spiked at 5 ng/kg into grape homogenate (\(n=8\)), the repeatability of measurements was determined to be 18% RSD. This result is a considerable improvement over the repeatability reported (32% RSD, Figure 2) using the older SPMESH holder design but otherwise identical conditions [12]. Further improvements in limit of detection and repeatability were achieved by increasing the mass of grape homogenate loaded into each well from 3.5 to 5 g, which resulted in a 41% increase in signal at 5 ng/kg (\(n=8\), \(p<0.05\)). Because the increase in signal was roughly the same as the increase in sample size, a likely explanation is that the increased sample mass led to a proportional increase in the mass of IBMP extracted (10 pg vs. 14 pg) [9]. This sample-size-dependent signal is more commonly  
Figure 1: Overview of SPMESH-DART-MS/MS extraction and analysis approach including optimized sample preparation, SPMESH extraction, and DART-MS/MS parameters in this study.  
observed with thin-film geometries such as SPMESH as compared to SPME fibers due to the higher sorbent volume of the former [16; 17]. This change, in combination with apparatus and sample preparation optimization, led to a decrease in the limit of detection by more than half 3.6 ng/kg vs. 1.5 ng/kg).
## 2 Study Area and Data Sources
### Data Sources
#### 2.2.3 Phenological Data  
Distinct phenological profiles across crop types not only serve as critical determinants for optimal satellite data acquisition windows but also form the fundamental basis for constructing crop-specific vegetation index time series [15; 43]. Phenological observations from 2019 to 2023 were obtained from China Agrometeorological Station ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/))). A total of nine agrometeorological stations within or near Zhangjiakou were used, covering diverse altitudes and cropping systems. In this region, oat is typically sown in early to mid-May and harvested in late September, whereas maize cultivation spans early April to early September; potato and sesame share a sowing window in early May but differ in harvest periods--mid-to-late September for potato versus early September for sesame. As seen in Figure 2 and depending on the crop, a considerable amount of time of the growing season is fallow, with potential to be integrated into crop rotation.
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 4 Determining the Voltage Ratio and Measurement Uncertainty
### Measurement Uncertainty of Pulsation \(\omega\)  
According to the pulsation formula, the only factor that determines the uncertainty of determining this parameter is the measurement of the time \(T\):  
\[\omega=2\pi\frac{1}{T} \tag{13}\]Time measurement is also performed with the DrDAQ signal acquisition card. The limit error \(E_{T}\) of the time measurement provided by the manufacturer in the technical specification is described as follows:  
\[E_{T}=\pm\left(0.2\cdot S_{i}+0.05\cdot W_{i}+10^{-4}\cdot T_{MEAS}\right) \tag{14}\]  
where \(T_{MEAS}\) is the measured value of time \(T\), \(S_{i}\) is the time interval between samples (depends on the sampling frequency), and \(W_{i}\) is the waveform interval (depends on the record length).  
Thus, the standard uncertainty \(u(T)\) of the time measurement, after taking into account the limit error of the time measurement \(E_{T}\) and the error resulting from the resolution \(d\) of this measurement, can be described by the formula:  
\[u(T)=\sqrt{\left(\frac{E_{T}}{\sqrt{3}}\right)^{2}+\left(\frac{d}{2\sqrt{3}} \right)^{2}} \tag{15}\]  
Finally, the standard uncertainty of determining the pulsations \(u(\omega)\)--after calculating the differential from the Equation (13) and taking into account the measurement uncertainty of the time \(u(T)\)--can be determined using the formula:  
\[u(\omega)=\frac{2\pi}{T^{2}}\cdot u(T) \tag{16}\]
# Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm
## 4 Results
### Analysis of Ablation Experiments  
Figure 6 shows the point and line diagrams of ablation experiments, involving LSTM, FFL, FELA, and FFLSTMEA algorithms, which correspond to the indicators of MAE and RMSE, MSE, R-squared, and MAPE from (a) to (d), respectively, and illustrate the variations in each algorithm's performance using point and line diagrams in the relevant indicators, which makes it easier to analyze the particular impacts of the algorithms in the ablation tests.  
Table 3 demonstrates the results of the ablation experiments of the FFLSTMEA algorithm proposed in this paper, and the performance of each metric (MAE, MSE, RMSE,  
Figure 6: Graph of comparative experimental effect.  
R-squared, and MAPE) in different cases is observed by setting the three modules, namely, feature fusion, attentional mechanism, and data enhancement, with or without any modules. Without any modules: The corresponding values are follows: MAE is 4.11, MSE is 39.54, RMSE is 6.29 (light green), R-squared is 0.73, and MAPE is 22.38 (blue). Among the metrics, this is the initial base performance case. Adding feature fusion only: MAE is reduced to 3.81 (light green), MSE is reduced to 27.83 (blue), RMSE is 5.28 (blue), R-squared is increased to 0.79 (light green), and MAPE is 22.73. After adding the feature fusion module, it is evident that the majority of the indexes have been optimized and that the outcome has improved when compared to the initial state. Adding feature fusion and the attention mechanism: MAE is further reduced to 3.58 (blue), MSE is reduced to 24.41 (red), RMSE reaches 4.94 (red), R-squared is improved to 0.81 (blue), and MAPE is 22.19 (red). This shows that with the addition of the attention mechanism, the indicators are further optimized and the effect continues to improve compared to when only feature fusion is included. With the addition of feature fusion, the attention mechanism, and data enhancement: MAE is 3.50 (red), MSE is 27.92 (light green), RMSE is 5.28 (blue), R-squared reaches 0.87 (red), and MAPE is 24.40, which shows that after adding the data enhancement module, the overall performance of some indicators can still be maintained at a high level or even continue to be optimized, especially in the case of the R-squared indicator, and the overall effect is gradually improved in the process of adding modules.  
From the ablation experiment results, it can be clearly seen that as the three modules of feature fusion, attention mechanism, and data enhancement are added in turn, most of the evaluation indexes show a gradual positive trend, which fully reflects the positive role of each module for the algorithm to improve the effect of the algorithm, but also provides indirect evidence of the algorithm's general rationality and effectiveness, emphasizing how the modules work together to produce a cumulative algorithmic impact. It also serves as evidence of the algorithm's overall efficacy and rationale, emphasizing how the combined activity of all modules steadily improves the algorithm impact.
# Wavelength Selection for Satellite Quantum Key Distribution
## 2 Transmittance and Spectral Radiance
### Key Assumptions and Limitations of MODTRAN Simulations
#### 2.1.2 Scenario 2: Waterford, Ireland  
In contrast, at noon on the winter solstice (21 December), the atmosphere for the Irish coastal region of Waterford is reconstructed using 23 km visibility rural aerosols and farm albedos. Similarly, the transmittance (green) and spectral radiance (red) is also plotted in Figure 1 for a direct comparison with data from Tucson. At this time of year, Waterford is often covered by clouds. Therefore, a cirrus cloud model was used, and more opaque variants were also considered later.
## 3 Results and Discussion
### Geometry Creation & Computer-Aided Design  
It is clear from Figure 9 that the results from the optimisation could not simply be printed. \"Design interpretation\" is a key stage of this process; using some of the optimisation results directly may implement too high a level of mass reduction which may not perform functionally. In order to account for this, common features present in all results were identified. These could then be traced in CAD, at which stage each feature could be easily modified using FEA guidance to reach the optimal level of mass reduction. These common features included the 4 primary \"limbs\" as well as the \"auxiliary spars\" stemming from these (highlighted yellow and pink, respectively, in Figure 10).  
The design interpretation produces beam-like structures which can fail through fracture or deflection, determined by their strength and stiffness, respectively. In pure bending, the value for second moment of area (I) is key, where a greater I decreases stress and deflection. Torsion, however, is dependent upon the polar second moment of area (J), where a greater J reduces shear stress and twist. J should be maximised for strength and stiffness [27]. Application of this theory identified three design variables:  
1. Tube Diameter 'D': Increased D results in increased J and M
2. Wall Thickness 'WT': Increased WT results in reduced J and increased M
3. Auxiliary Tubing: Addition as per topology results D = tube diameter, J = polar second moment of area, M = tube mass, WT = tube wall thickness.  
As this was a feasibility assessment with regards to building the component, a \"worst-case\" of these variables was used.  
Design for Additive Manufacturing  
The current design is produced entirely through focusing on performance; modifications have to be made with regards to Design for Additive Manufacture (DfAM) (Figure 11). Due to its complex geometry, support material would likely be removed by hand and thus be time-consuming and uneconomical. Therefore, all proceeding design work aimed to require no further support material for manufacture (now only required at the pedal spindle, BB taper, and base), reducing waste material, cost, and build time.  
A feature was added to support the bottom of the pedal spindle boss (Figure 11). To enable powder removal from inside the part, holes were routed through the component.  
Figure 10: Design Interpretation.  
The model was heavily filleted both internally and externally to ease stress concentrations and reduce the risk of manufacturing defects. The fillet radius was dependent upon the geometry of the feature, and external radii were matched by corresponding internal fillets to achieve uniform wall thickness throughout.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 5 Discussion
### Changing Spatial Patterns of Electricity Consumption in Cambodia  
Timely and accurate access to data on the spatial distribution of electricity consumption is essential in the construction of electrical infrastructure and for assessing the achievement of SDG7. Total night-light data from 2012 to 2019 were used to obtain the spatial pattern of electricity consumption in Cambodia at a 500 m resolution (Figure 6 shows three years as examples).  
The analysis of the spatial pattern of electricity consumption in Cambodia discloses that in 2012, electricity was mainly consumed in the capital city of Phnom Penh, the southwestern port city of Sihanoukville, and the northwestern cities of Battambang and Siem Reap. Interestingly, these four cities are the most economically developed regions in Cambodia and have the highest electricity consumption. From 2012 to 2019, the electricity consumption in Cambodia has been spreading outwards from these four cities, demonstrating the phenomenon of urban sprawl. The rapid increases in electricity consumption in the vicinity of the two main transport routes along the western part of Cambodia indicate that transport has made a huge boost to Cambodia's development. Additionally, the country's electricity coverage has improved considerably in recent years, with its electricity consumption slowly spreading from the major cities to the vast rural areas. The most rapid growth in electricity consumption has occurred in the southwestern port city of Sihanoukville and the southeastern province of Svay Rieng, which are inextricably linked to Cambodia's national development strategy and Chinese investment.