esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Simulation Verification of Dewatering Optimization Scheme Based on GMS  
### Element Subdivision of Numerical Model and Determination of Boundary Conditions  
Based on the hydrogeological conditions in the study area, a numerical model is established using GMS software. The model is divided into two layers, each of which is divided into 22,500 element meshes of 150 \(\times\) 150. The first layer is a sandy loam layer (119.5-122.5 m), the second layer is a pebble layer (99.5-119.5 m), and the bottom is a claystone layer. The terrain is flat, so it is treated as a horizontal layer. The vertical thickness of the overall model is 23 m. The southwest of the foundation pit in the study area is close to the Yellow River, and the fixed water head boundary is set according to the measured water level of the Yellow River during the simulation period. The Xixiayuan reservoir dam is located in the northwest, which is regarded as the impermeable boundary treatment. The poor permeability of the bottom claystone layer is regarded as the impermeable boundary, and the remaining boundaries are determined as the constant water head boundary according to the influence radius of pumping, which is set to be equal to the initial head of the study area. The grid division of the 3D geological model in the study area is shown in Figure 11. Specifically, the water level values of all constant water head boundaries in the numerical model are consistent with those taken in the optimization algorithm.  
The hydrogeological parameters and physical mechanical parameters of each layer are obtained according to the survey reports and on-site slug test results, as shown in Table 11.  
Figure 11: Grid division of 3D geological model of the study area.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Aquifer**} & **Permeability** & & **Elastic Water** & **Inelastic Water** \\  & **Coefficient** & **Coefficient** & **Porsity** & **Storage Rate** & **Storage Rate** \\  & **K/(m/d)** & & & \(S_{\textit{skv}}\) **(1/m)** & \(S_{\textit{skv}}\) **(1/m)** \\ \hline Sandy loam soil layer & 0.35 & 0.05 & 0.3 & \(6\times 10^{-4}\) & \(1.2\times 10^{-3}\) \\ Pebble layer & 432 & 0.2 & 0.4 & \(1.2\times 10^{-4}\) & \(1.6\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Hydrogeological parameters and physical and mechanical parameters of each layer.
###### Abstract
AE monitoring technology is basically the same as the principle of microseismic monitoring and is used to monitor the damage and failure of rocks in indoor laboratories [33; 34; 35]. AE is a kind of elastic waveform that is emitted when the internal elements of the rock rupture, which is closely related to the damage process of rocks [36]. Therefore, conducting indoor experiments to study the AE characteristics during the process of rock damage and fracture under freezing and thawing environments is also of great significance for the stability prediction of rock engineering in cold regions. In this study, we quantitatively investigated the relationship between the initial damage \(D_{NT}\) caused by freeze-thaw cycles and the number of freeze-thaw cycles by measuring the P-wave velocity. At the same time, the influence of the lowest freeze-thaw temperature on \(D_{NT}\) was also obtained. According to the stress-strain curve of rocks after freeze-thaw treatment, the change law of the characteristic stress of rocks with the number of freeze-thaw cycles and freeze-thaw temperature was determined. Meanwhile, AE technology was used to monitor the rock fracture information for each sandstone sample after the freeze-thaw treatment to provide a theoretical basis for judging the damage and fracture of the rock in an actual engineering application.
# Copernicus Global Land Cover Layers--Collection 2
## 2 Methodological Overview
### Validation and Comparison  
The quality of the CGLS-LC100 product was assessed quantitatively and qualitatively in an independent validation approach (Figure 1). A detailed description of the methods can be found in the  
Figure 2: Training dataset used in the workflow showing the \(\sim\)141K training locations together with their sampled land cover class at 100 m. Inlay shows the land cover type distribution in percentage of the training data. Adapted from [25].  
validation report [39]. In short: For the quantitative evaluation, the CGLS-LC100 discrete map was assessed using an independent validation dataset which includes 21,752 sampling locations across the globe (2843 to 3616 samples per continent, Table 2 and see Figure 3 for geographical distribution). The sampling protocol followed the probability sampling scheme [20]. Similar to the training data collection, validation data was collected on the Geo-Wiki platform, but using an independent branch from the training data collection [37]. The validation data collection platform was only accessible to around 30 regional experts with knowledge on satellite-based LC analysis and image interpretation, who contributed to the validation data collection [39]. More detailed information on the validation data collection and datasets used for collection is provided in [39; 40]. Based on the collected reference information, fractions of LC types were calculated and used to validate the CGLS-LC100 cover fraction layers. Specifically, the mean absolute error (MAE) and root mean square error (RMSE) were calculated. The LC fractions were further translated to a CGLS-LC100 discrete map legend based on the legend descriptions and used to validate the discrete map. More specifically, a confusion matrix was calculated based on the mapped and reference LC types. The error matrix was corrected by sample inclusion probabilities [20].  
A detailed quantitative comparison of the CGLS-LC100 and existing global LC datasets can be found in the validation report [39]. In summary, for quantitative comparison, [PERSON] et al. [40] combined six available reference datasets consisting of around 25,000 points and checked the correspondence of the resulting dataset with the Globcover 2009, LC-CCI 2010, and MODIS2010 global LC maps and as well as the CGLS-LC100 product. Similarly, the CGLS-LC100 validation dataset was also used to assess the Globeland30 2010 for comparison with the CGLS-LC100 2015 Collection 2 maps.  
All 20 layers forming the CGLS-LC100 Collection 2 product are distributed via several channels for viewing, on-demand analyses and direct download. See Chapter 4 for more details on the data access channels.
## 1 Introduction
Subsequently, the HD-VLL incorporates an acceleration compensation module and a jerk compensation module within the traditional VLL loop filter, which compensate for the phase tracking error with the system clock. Regarding the hardware implementation of HD-VLL, the output of the HD-VLL loop filter is updated with the system clock, which may conflict with the multiplexing function of traditional filters in parallel signal processing. In light of the aforementioned contradiction and taking into account the fact that the multi-carrier NCO module is also updated with the system clock, we design an HD-VLL-based multi-carrier NCO (HD-VLL-NCO). We conduct simulation analyses on an HD-VLL and HD-VLL-NCO, and the results reveal the following: Firstly, the HD-VLL demonstrates superior dynamic adaptability and steady-state tracking performance. Secondly, with the appropriate truncation bit width set, the HD-VLL-NCO is capable of achieving accuracy comparable to that of HD-VLL.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 6 Conclusions  
An efficient CORDIC iteration design for LiDAR sensors and point-cloud map reconstruction technology are proposed in this paper. A new CORDIC architecture is designed for LiDAR sensors to improve the traditional CORDIC architecture by changing the rotation characteristics to achieve the goal of pre-selecting angles and reducing the number of iterations. With the proposed architecture, we can reduce the number of iterations by half. Regardless of the number of input N digits, the values of the trigonometric functions SIN and COS can be found in seven rotations. According to the SIN and COS values obtained and distance information, the 3D point-cloud image is obtained through the three-dimensional coordinate conversion system.  
To prove the performance of the presented LiDAR sensing circuit, we have completed a series of experiments. The proposed chip improves the traditional CORDIC calculator. The proposed chip takes only 0.012 ms to solve a packet and 0.912 ms to decode a frame. The fault coverage rate of the proposed chip is 99.44%. The total chip area is 1.93 mm \(\times\) 1.93 mm and the core area is 1.32 mm \(\times\) 1.32 mm. The number of logic gates is 129,688. The maximum operating frequency is 100 MHz, and the average power consumption is 237.34 mW. Compared with those in related literature, our proposed sensing circuit has smaller area and low power consumption.  
[PERSON] has investigated the ideas, system, algorithm, and methodology of the proposed techniques, and wrote the manuscript; [PERSON] and [PERSON] implemented the proposed system, conducted the experiments, analyzed the experimental data, and provided the analytical results, and wrote the manuscript with support from [PERSON]. All authors discussed the results and contributed to the final manuscript.  
This work was supported by the Ministry of Science and Technology of Taiwan under Grant MOST 108-2218-E-035-013.  
The authors gratefully acknowledge the Taiwan Semiconductor Research Institute (TSRI), for supplying the technology models used in IC design.  
The authors declare no conflict of interest.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 5 Modeling of Digital Twin for AMB
To ease data communication in real-time between the sensors and the simulation model executed in MATLAB, the implementation used dSPACE software modules, namely TargetLink (V.5.0). The synchronization between the simulation model and the hardware sampling rates was carefully implemented to mimic the real-time simulation capabilities. The integration enabled the interaction between the simulation model and the physical system using the collected sensor data. Figure 8 shows the design of the digital twin model for the AMB. \begin{table}
\begin{tabular}{c c c c} \hline
**Symbol** & **Parameter** & **Value** & **Unit** \\ \hline \(m\) & Rotor mass & 3.34 & kg \\ \(l_{p}\) & Momentum of inertia & 0.00187 & kg\(\cdot\)m\({}^{2}\) \\ \(k_{i}\) & Current stiffness & 99 & N/A \\ \(k_{x}\) & Displacement stiffness & 473 & N/mm \\ \(k_{s}\) & Gain of displacement sensor & 5 & V/mm \\ \(K_{P}\) & Proportional coefficient of PID & 1.4 & \\ \(K_{I}\) & Integral coefficient of PID & 30 & \\ \(K_{D}\) & Differential coefficient of PID & 0.001 & \\ \(k_{AD}\) & Gain of A/D conversion & 4096/5 & \\ \(k_{DA}\) & Gain of D/A conversion & 5/4096 & \\ \hline \end{tabular}
\end{table} 
Table 2: The AMB configuration parameters used in experiments.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 2 Methods  
### System Solutions  
Many navigation sensors can obtain navigation data of autonomous vehicles in a resilient integrated navigation system, such as IMU, GNSS and MAG, etc. Autonomous vehicles are assumed to be equipped with multi-rate sensors. Especially under interference and disturbance, the sensor is prone to error increase and even failure. However,in the complex environment of the urban canyon and the electromagnetic interference environment, it is easy to cause the solution failure of GNSS. Due to the wide distribution of satellites, there are problems in measuring the pseudo-range data of only one or several satellites by receivers. A new fault detection and isolation method is proposed in the paper to locate the fault pseudo-range measurements in the factor graph navigation system. The scheme of the FDI method and the factor graph fusion method is shown in Figure 1. The proposed method determines whether the fault exists in the pseudo-range measurement of GNSS, and then the system isolates the fault.  
Due to vibration and electromagnetic disturbance, some sensors may become inactive or faulty. It is an urgent problem to improve navigation systems' fault detection and isolation effect and location accuracy under interference and electromagnetic disturbance. The proposed method is to achieve more reliable navigation from multi-source sensor information in case of failure.  
After the FDI algorithm detects a sensor fault, it is helpful to isolate the measurement information of the fault sensor in time to ensure positioning accuracy. The main feature of a factor graph is abstracting the navigation sub-system and realizing the algorithm of rapid integration, reconstruction and filtering. Effective fault detection and isolation algorithms can help quickly find fault sensors and isolate fault measurements that may pollute the whole system. Plug-and-play capability is helpful to isolate the measurement information of the fault sensor in time. When the system has no fault, the factor graph algorithm adds a factor node to the factor graph framework by the sensor measurement. The factor graph algorithm isolates the fault measurement when the sensor fails by not adding factor nodes.
## 4 Simulation Results  
The passive components of the layout, excluding the dicing streets, were simulated using ADS Momentum (a 2.5D planar EM simulator) with a mesh density of 50 cells per wavelength over the DC-100 GHz frequency range. The generated EM model was then integrated with active device library models for circuit-field co-simulation. After rigorous stability verification, the developed PA's performance was assessed in continuous-wave (CW) mode.
###### Abstract  
This paper presents a novel and efficient system capable of transmitting medium-power electric energy through a solid metal wall and generating a regulated direct current (DC) output. The electric power is transmitted using radio frequency (RF) ultrasound without physical penetration. An RF alternating current to direct current converter with input impedance matching is used to convert the transmitted RF signal to a DC output, which is directly suitable as a power supply for electronic devices. The system is constructed from commercial off-the-shelf components, and a computer-controlled measurement method is designed to test the effectiveness of the full system. The measurement results show that the system is able to transfer power through a 40-mm-thick stainless steel plate and generate 5-V, 15.7-W regulated DC output power with an overall power transfer efficiency of 27.7%. In addition, the effectiveness of the system is successfully demonstrated by powering ARM(r) evaluation boards with liquid-crystal display panels. A discussion of potential enhancements that could be made to improve the transfer capability and efficiency of the system is also presented. This system could be applied to improve safety and preserve structural integrity in many industrial and military applications, such as submarines, space crafts, planes, nuclear storage facilities, etc.  
ultrasound; through metal wall; power transfer; piezoelectric transducer; AC-DC converter 2018 8, 692; doi:10.3390/app8050692
## 3 Network Models and Related Definitions
### Network Models
#### 3.1.3 Adjustable Communication Radius Model  
The maximum communication radius of the node is \(Rc\), and its initial value is \(Rc_{init}\), the adjustable level of communication radius is defined as \(\theta\) and has an initial value of 1. Therefore, we can obtain the relationship between \(Rc\) and \(\theta\) through the following formula:  
\[Rc=Rc_{init}+\left(1+\theta\right)B_{u} \tag{2}\]  
where \(B_{u}\) is the increasing communication range per communication level.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 6 Additional Results: Evaluating Proposed Methodology on Urban Material Classification
### Classification Map  
Finally, Figure 12 depicts the class map for the Pavia image obtained by the proposed methodology (2D-CNN 1 after post-processing).  
Figure 12a describes the true label map and Figure 12b depicts the predicted label map.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Pavia Image** \\ \hline Patch size CNN & 22 \(\times\) 22 \(\times\) 104 \\ Total samples & 1.8 K \\ Patch size RNN & 8 \(\times\) 8 \(\times\) 104 \\ Training samples & 1.62 K (90 \%) \\ Validation samples & 180 (10 \%) \\ Testing samples & 4.2 K \\ Learning rate & 32 \\ Optimizer & Adadelta \\ Cost function & Entropy \\ Metric & Accuracy \\ Activation function 1 & Relu \\ Activation function 2 & Softmax \\ Batch size & 32 \\ Epoch & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameter values for experiments applied to Pavia database.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & & \multicolumn{2}{c}{**Proposed**} & \multicolumn{2}{c}{**Proposed**} & \multicolumn{2}{c}{**RNN**} & \multicolumn{2}{c}{**Chen**[60]} \\ \cline{3-10}
**Class** & **\# of Pixels** & \multicolumn{2}{c}{**Methodology**} & \multicolumn{2}{c}{**Methodology - BP**} & \multicolumn{2}{c}{**} & \multicolumn{1}{c}{**} \\ \cline{3-10}  & & \(AA\)(\%) & \(OA\)(\%) & \(AA\)(\%) & \(OA\)(\%) & \(AA\)(\%) & \(OA\)(\%) \\ \hline Asphalt & 6.8 K & 75.61 & & 75.13 & 50.55 & 94.14 & \\ Meadows & 18 K & 93.75 & & 92.56 & 43.73 & 92.78 & \\ Gravel & 2.2 K & 75.94 & & 73.17 & 50.97 & 80.6 & \\ Trees & 3.4 K & 98.79 & & 98.72 & 87.72 & 83.42 & \\ Painted & 1.3 K & 99.99 & 91.82 & 99.92 & 90.95 & 89.44 & 49.21 & 99.13 & 93.88 \\ metal & & & & & & & \\ Bare soil & 5.1 K & 99.46 & & 98.78 & 44.99 & 95.62 & \\ Bitumen & 1.3 K & 99.09 & & 97.66 & 26.92 & 87.31 & \\ Bricks & 3.8 K & 97.12 & & 97.12 & 30.21 & 98.39 & \\ Shadows & 1 K & 96.62 & & 96.66 & 93.76 & 63.02 & \\ \hline Average & - & **92.92** & 91.82 & 92.19 & 90.95 & 57.55 & 49.21 & 88.19 & **93.88** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison results of the proposed methodology for the Pavia database. The best average values achieved by each method are highlighted.
## 3 Results
### SHS in the Squamous Region and RFA Treatment Response
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cumulative Metrics** & **Neo-SE** & **BE** & \(p\)**-Value** & **Neo-SE** & **BE** & \(p\)**-Value** \\ \hline
2D number & 1.04 \(\pm\) 0.74 & 0.75\(\pm\) 0.24 & 0.221 & - & - & - \\
3D number & 8.31 \(\pm\) 7.52 & 5.88 \(\pm\) 2.80 & 0.860 & - & - & - \\ Volume (mm\({}^{3}\)) & 2.52 \(\pm\) 2.20 & 1.61 \(\pm\) 0.83 & 0.210 & - & - & - \\ \hline
**Metrics on a per gland basis** & \multicolumn{4}{c}{**Mean of all SHSs**} & \multicolumn{4}{c}{**Mean of cross-sectional extreme**} \\ \hline Size (mm\({}^{2}\)) & 0.22 \(\pm\) 0.06 & 0.22 \(\pm\) 0.10 & 0.336 & 0.28 \(\pm\) 0.12 & 0.26 \(\pm\) 0.15 & 0.374 \\ Depth (mm) & 0.83 \(\pm\) 0.13 & 0.96 \(\pm\) 0.14 & 0.041 \(\,\)* & 0.92 \(\pm\) 0.13 & 1.03 \(\pm\) 0.13 & 0.064 \\ Intensity & 0.63 \(\pm\) 0.06 & 0.65 \(\pm\) 0.11 & 0.598 & 0.69 \(\pm\) 0.08 & 0.68 \(\pm\) 0.12 & 0.750 \\ STD & 0.08 \(\pm\) 0.01 & 0.08 \(\pm\) 0.02 & 0.507 & 0.09 \(\pm\) 0.01 & 0.09 \(\pm\) 0.02 & 0.385 \\ Orientation (degrees) & 11.2 \(\pm\) 4.0 & 8.9 \(\pm\) 4.4 & 0.121 & 15.3 \(\pm\) 7.8 & 10.9 \(\pm\) 5.0 & 0.185 \\ Eccentricity (0–1) & 0.82 \(\pm\) 0.07 & 0.86 \(\pm\) 0.05 & 0.241 & 0.87 \(\pm\) 0.06 & 0.87 \(\pm\) 0.06 & 0.849 \\ Extent (0–1) & 0.60 \(\pm\) 0.04 & 0.57 \(\pm\) 0.03 & 0.087 & 0.56 \(\pm\) 0.05 & 0.54 \(\pm\) 0.04 & 0.363 \\ Perimeter (mm) & 2.02 \(\pm\) 0.21 & 2.00 \(\pm\) 0.25 & 0.087 & 2.27 \(\pm\) 0.41 & 2.17 \(\pm\) 0.34 & 0.363 \\ Solidity (0–1) & 0.90 \(\pm\) 0.01 & 0.89 \(\pm\) 0.01 & 0.053 & 0.89 \(\pm\) 0.01 & 0.88 \(\pm\) 0.01 & 0.238 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison ofSubgroup analysis stratified by pathology revealed that SHSs in the squamous region correlate differently with post-RFA response in different patient groups. SHSs eccentricity is strongly correlated with larger reduction of post-ablation BE length for LGD patients (R = \(-\)0.829, \(p\) = 0.003, Table S6), but with less reduction of post-ablation BE length for HGD/IMC patients (R = 0.685, \(p\) = 0.029) (Table S6). Less vertical, higher intensity and STD subsquamous SHSs corresponded to less reduction of post-ablation BE length for HGD/IMC patients, but not for LGD patients (Table S6). Smaller perimeter subsquamous SHSs was correlated with less reduction of post-ablation BE length for LGD patients, but not for HGD/IMC patients (Table S6).
# Dual Enhancement Network for Infrared Small Target Detection
## 3 The Proposed Dual Enhancement Network
### Spatial Attention Enhancement Module  
Because detection in the IRSTD task has the characteristic of targets being specially sparse and tiny, we put forward a spatial attention enhancement (SAE) module to enhance and highlight the dim small targets in the infrared images. Figure 2 illustrates the structure of SAE in detail. In brief, SAE takes the concated features as input and produces one spatial weight map for each feature, and then the concated features are fused with the spatial weight map by the Hadamard product operation. After that, we adopt the concatenation operation to fuse the features. Finally, an MLP is employed to generate the final prediction. With the feature enhancement in space, the problem of targets being dim and small is well solved, and the detection performance is also highly improved.  
The SAE module aims to enhance spatial attention in infrared small target detection and works in detail as follows: firstly, it takes as input the feature maps extracted from the convolutional layers in front of the CNN, capturing the hierarchical features learnt by the network. Second, its core mechanism is the spatial attention mechanism, which consists of multiple sets of convolutional layers and activation functions that generate an attention map that emphasises important spatial regions in the feature map. Task-specific convolutional operations are also applied to further improve the detection performance. Thirdly, the attention map generated by the spatial attention mechanism is used to modulate the original feature map. This modulation is achieved through element-level multiplication or addition, where the attention maps are used as weights to amplify or suppress feature activations depending on their importance. Finally, the SAE module is seamlessly integrated into the CNN architecture, often as a component or add-on module in the residual block, enabling the network to learn spatially adaptive feature representations during training.  
The SAE module we designed has several advantages in infrared small target segmentation. Its spatial attention mechanism directs the network's attention to relevant spatial regions, enhancing the identification of relevant features of small infrared targets. By emphasising information specific to the target, better segmentation performance is formed. Secondly, the adaptive feature enhancement of the SAE module helps the network to better deal with background clutter and noise in infrared images by suppressing irrelevant background features, thereby improving segmentation accuracy in complex scenes. Finally, the SAE module dynamically adjusts the feature representations according to the spatial attention map, which helps to achieve scaling and rotation invariance, and it is crucial for accurate segmentation of small targets in different scenes.