esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 5 Results and Discussion
### Flow Field Structure Analysis
#### 5.2.1 Velocity Field and Streamlines Analysis  
The velocity magnitude contours and the streamline diagram for the different spur dike spacing are shown in Figure 7. The left column shows the results of fixed bed tests, and the right shows the results of movable bed tests. The spacing from the top to the bottom is 2\(l\), 3\(l\) and 4\(l\), and there are nine velocity cross-sections. For the 2\(l\) spacing these are at x = \(-\)20, 0, 10, 20, 30, 40, 50, 60, and 70 cm; for the 3\(l\) spacing these are at x = \(-\)30, 0, 15, 30, 45, 60, 75, 90, and 105 cm; and for the 4\(l\) spacing, these are at x = \(-\)80, \(-\)40, \(-\)20, 0, 20, 40, 60, 80, and 100 cm. There are two typical longitudinal streamlines at y = 2 cm and 25 cm.  
Figure 7: The velocity magnitude contours and the streamline diagrams of different spur dike spacing. (**a**) fixed bed for 2\(l\) spacing; (**b**) fixed bed for 3\(l\) spacing; (**c**) fixed bed for 4\(l\) spacing; (**d**) movable bed for 2\(l\) spacing; (**e**) movable bed for 3\(l\) spacing; (**f**) movable bed for 4\(l\) spacing.  
The mainstream was squeezed due to the narrowing effect of the spur dike when the water flow was close to the spur dike. The single-width flow increased, and the flow rate increased sharply, resulting in intense scouring of the riverbed. In the three cases, the maximum velocity (which is twice that of the approach flow velocity) appeared in the main flow area between spur dike I and spur dike II in the fixed bed. Similar conclusions were drawn in [15]. The velocity in the backflow area decreased and sedimentation occurred. The maximum flow velocity in the main flow zone decreased from approximately 53 cm/s in the fixed bed to approximately 33 cm/s in the last configuration. This is largely because as the scouring pit grows, the cross-sectional area of the flow increases and the flow velocity decreases. As the high-velocity mainstream area is close to the spur dike head, the scour is most significant near this point.  
As can be seen from the streamlines of Figure 7, when the water flow was close to the spur dike, part of the flow blocked by the first spur dike was bent towards the head, resulting in a significant increase in flow velocity. This is the main source of sediment erosion. At the same time, a clockwise vortex was formed at the root of the spur dike, and backflow areas were formed between the spur dikes. The flow velocity in the backflow area decreased sharply, causing sediment to accumulate between spur dikes. The horizontal separation flow and the vertical downward flow existed simultaneously, which is also the reason why the flow field around the spur dike presents strong three-dimensional characteristics. The water flow around the first spur dike was divided into two parts. One part became the backflow area behind the spur dike, and the other part entered the mainstream and became part of the mainstream. The main flow was still downstream.
## 3 Problem Description
### Static Topology
#### 3.1.1 Coverage Analysis  
As shown in Figure 11, to satisfy the requirement of covering the entire target area, the distance between neighboring sensors should satisfy  
\[d\leq\sqrt{3}R_{s}. \tag{1}\]  
The overlap of coverage of the network is minimized at \(d=\sqrt{3}R_{s}\). We know that the overlap of coverage increases as the value of \(d\) decreases, which results in energy wastage. On the other hand, the larger \(d\) is, the more energy will be consumed to transmit a message in a single hop. This implies the necessity of choosing an optimal value for \(d\).
###### Abstract  
The carbon nanotube (CNT) cold cathode is an attractive choice for millimeter and terahertz vacuum electronic devices owning to its unique instant switch-on and high emission current density. A novel, dual-grafted, field emission architecture based on a CNT cold cathode is proposed here. CNTs are synthesized directly on the cathode surface. The first separating grid is attached to the CNT cathode surface to shape the CNT cathode array. The second separating grid is responsible for controlled extraction of electrons from the CNT emitters. The cathode surface electric field distribution has been improved drastically compared to conventional planar devices. Furthermore, a high-compression-ratio, dual-grafted, CNT-based electron gun has been designed to further increase the current density, and a 21 kV/50 mA electron beam has been obtained with beam transparency of nearly 100%, along with a compression ratio of 39. A 0.22 THz disk-loaded waveguide backward wave oscillator (BWO) based on this electron gun architecture has been realized theoretically with output power of 32 W. The results indicate that higher output power and higher frequency terahertz BWOs can be made using advanced, nanomaterial-based cold cathodes.  
carbon-nanotube; cold cathode; field emission; terahertz; vacuum electronic device; backward wave oscillator
## 1 Introduction
Similar to cross-validation, the error of matching point bidirectional optical flow is used to remove the mismatching point pair. Reverse optical flow can effectively reduce the inaccurate tracking of optical flow tracking and obtain correct tracking matching results. However, its shortcomings are also evident. First, the optical flow method is only suitable for slow-motion scenes. Secondly, the reverse optical flow is used to eliminate mismatches, which is equivalent to performing optical flow twice in the entire tracking and matching process, resulting in increased time consumption. RANSAC is another common method. However, some correct matching points will also be removed with RANSAC, which is unacceptable when the image quality is not high and the number of feature points tracked is small. Moreover, in similar areas where the image is blurred, RANSAC will fit some mismatched points. Overall, the descriptor can describe the feature points well, and can effectively eliminate the mismatched points in the case of blurred images, but it cannot eliminate the mismatched points with high regional similarity. The optical flow method reduces the computational complexity required for feature point matching and improves feature point tracking accuracy. However, the optical flow method requires that the camera move slowly and is susceptible to changes in light. In an environment with changing lighting conditions, where the image quality is drastically reduced, a single mismatch point pair elimination method is not effective in eliminating erroneous matches. Therefore, we propose a multi-constraint feature point mismatch removal method. By utilizing the complementary features among various constraints, we can more stably eliminate incorrect matching pairs. Firstly, feature point pairs are extracted using the optical flow method. Then, the BRIEF descriptor of the optical flow point pairs is calculated, and erroneous matches are eliminated through a global threshold method. Subsequently, RANSAC is used to further remove mismatched pairs. Furthermore, we use optical flow errors and feature descriptor errors to avoid correct matching point pairs eliminated by RANSAC. Finally, to retain high-qualitymatching points, we propose an adaptive image mask homogenization method that adaptively selects and retains the feature points extracted by optical flow tracking. The main contributions of this paper are summarized as follows:  
* In order to solve the problem of feature point mismatch, this paper proposes a feature point mismatch removal method that combines optical flow, descriptor, and RANSAC, suitable for complex environments. * We propose an improved mask homogenization method to optimize the quality of feature points and improve their distribution in the image. * We integrate the proposed feature point mismatch removal method and improve mask homogenization method in feature tracking. Experimental results on EuRoC [31] dataset demonstrate the robustness and accuracy of our method in various complex environments.
## 2 Experimental Section
### Testing and Analysis Instruments  
The thickness of the MAO layer was measured using a TT260 eddy current coating thickness tester; however, the MAO layer roughness was measured using a TR200 handheld roughness tester. The micromorphology of the MAO layer was observed and tested by Merlin Compact Zeiss field-emission scanning electron microscopy (FESEM). The porosity of the MAO layer was analyzed and calculated by using ImageJ software. The \"threshold\" tool was used to calculate the pore area and the porosity factor was obtained by dividing the pore area by the total area. The results of the porosity factor, thickness, and roughness were measured by the average of three measurements. XRD was used to measure the structural characteristics of the MAO layer.  
The adhesion strength was tested according to the ASTM D4541-17 standard test method for the pull-off strength of coats using portable adhesion testers and the test instrument was the Elometer 510.  
The action potential polarization curves of the 7050 aluminum alloy and its MAO layer were measured on a CHI760E electrochemical workstation. The corrosion solution was sodium chloride solution with a mass fraction of 3.5%.
# A Supernet-Only Framework for Federated Learning in Computationally Heterogeneous Scenarios
## 2 Background and Related Work
### Federated Neural Architecture Search (Fed-NAS)  
Fed-NAS [23] research primarily addresses two key challenges in terms of performance. The first focuses on developing high-performance neural networks from diverse client data while preserving privacy. The second emphasizes efficiently using NAS to tailor architectures for varying client capabilities under privacy and computational constraints.  
Some studies focus on the first challenge, performing device-aware or offline NAS on high-performance private clients while employing lightweight methods to reduce client burdens during the search process [24]. Some studies explore potential approaches to address the second challenge. However, the resulting sub-networks may become excessively downsized [25], or performance within each federation may be enhanced through client clustering methods [26; 27]. Most studies fail to effectively address the issue of computational heterogeneity.  
Recent research attempts to resolve these limitations by performing structure searches tailored to resource-constrained clients. For example, we optimize unique sub-networks of each client extracted from a supernet [28] or separate clients used for training or inference [29]. Other approaches partition the model into shared and personalized units, aggregating only the shared units during the federated process [30; 31]. However, these methods more or less face a trade-off between federation size and network size.
## 2 Materials and Methods
### Emissions Inventories and Processing
#### 2.2.1 2020 COVID Emissions Adjustments  
A bottom-up approach was employed to adjust the 2020 BAU emissions with source sector-specific COVID adjustment factors based on changes in activity. For some sectors, adjustments were developed at a more disaggregated level, namely by Source Classification Codes (SCCs), e.g., within on-road for heavy duty and light duty vehicles, using auxiliary information available within the ARB 2020 EI to determine fractions of subcategories within a sector. Detailed information regarding the data sources utilized, activity basis, geographic and temporal specificity, and derived adjustment factors is provided in the Supplementary Information Document, Section S3. The sector with the largest NOx reductions is on-road, and the primary data source used to derive the on-road scaling factor was the U.S. Energy Information Administration (EIA) refinery gasoline and diesel sales in California. EIA June and July 2020/2019 fuel sale ratios were 77% for gasoline and 84% for diesel. ARB's Emission FACtor (EMFAC) tool [27] was used to calculate Vehicle Miles Traveled (VMT) and appropriately apply scaling factors to the on-road fleet. Figure 3 provides a summary of the emissions for the three model scenarios for NOx and TOG in panels (a) and (b), respectively. Note that the TOG emissions adjustments are less than 1% between the three scenarios, because they are dominated by area sources that had a minimal COVID adjustment, and the change in on-road VOC (\(-\)27.2 tpd) is partially offset by an increase in the consumer products (+12.1 tpd).
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 3 Improved Deep Principal Component Analysis Method
### The Process of ID-PCA  
The structure diagram of the early fault detection method of rolling bearing based on ID-PCA is shown in Figure 1:  
The specific steps for early fault detection of rolling bearings based on ID-PCA are as follows:  
Offline modeling part:  
Step 1: Collect the vibration signal in a normal state, decompose it according to the formulas in Tables 1 and 2 to obtain the time-domain characteristic information of the vibration signal, and form a matrix \(X\) of these characteristics.  
Step 2: Select the appropriate order \(j\), and calculate the corresponding \(X_{i,j}\) according to Formula (7).  
Step 3: Calculate the number of pivot elements, eigenvalue matrix \(\Lambda_{i,j}\), and eigenvector matrix \(P_{i,j}\) of each \(X_{i,j}\).  
Step 4: Calculate statistics \(T_{i,j}^{2}\) and \(SPE_{i,j}\) according to Formulas (8) and (10).  
Step 5: Calculate the control limits \(TUCL_{i,j}\) and \(QUCL_{i,j}\) for \(T_{i,j}^{2}\) and \(SPE_{i,j}\) according to Formulas (9) and (11).  
Online monitoring part:  
Figure 1: The structure of ID-PCA.  
Step 1: Collect the vibration signal in the test state, decompose it according to the formulas in Tables 1 and 2 to obtain the time-domain characteristic information of the vibration signal, and form a matrix \(X_{t}\) of these characteristics.  
Step 2: Obtain the i-order subspace \(X_{t_{ij}}\) by Formula (7).  
Step 3: Calculate statistics \(T_{i,j}^{2}\) and \(SPE_{ij}\) according to Formulas (8) and (10).  
Step 4: Compare the obtained statistic with the corresponding control limit, and if the statistic is greater than the control limit, a fault occurs; otherwise, it is normal.  
The flowchart of the fault detection method based on ID-PCA is shown in Figure 2:
# FEM Modelling Approaches of Bolt Connections for the Dynamic Analyses of an Automotive Engine
## 2 Problem Description  
Tetrahedral quadratic elements were used to model components such as engine crankcase, cylinder head, crankshaft, gearbox, etc., whereas quadrilateral quadratic surface elements were used to model thin elements such as the exhaust pipe or the oil pan (Figure 1a). The average element size was set to 3-6 mm and the final full FEM model (Figure 1a) comprised nearly 5.7 \(\times\) 10\({}^{6}\) elements and 4.7 \(\times\) 10\({}^{6}\) nodes.  
Such engine numerical model was simplified for the dynamic analysis, switching from the configuration of Figure 1a to that of Figure 1b, in such a way to reduce the computational burden. The simplification adopted was judged as acceptable within the framework of the proposed problem, as the dynamics of bracket and accessory bracket are nearly insensitive to the relatively limited engine mass reduction operated with the subtraction of the cylinder head cover and related components. Consequently, such a simplified configuration was adopted for all calculations throughout this work.  
FRF analyses were adopted to evaluate the vibration behavior of the reduced model with all the bolt connections modelled in three different ways: \"full 3D bolt model\", \"Glue surfaces\", and \"1D model\".
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 5 Experimental Verification
### Fault Feature Extraction Based on ICEEMD-Time-Frequency Information Entropy  
First of all, the vibration signal collected by the sensor was preprocessed.  
Secondly, the ICEEMD algorithm was applied to decompose each fault mode. The threshold of multi-scale permutation entropy \(\theta_{0}\) was 0.6, embedded dimension m was 6, time delay factor \(\tau\) was 1, and the scale factor s was 5. The high frequency part of the vibration signal of the planetary gearbox contained the main fault signal. Therefore, the first six intrinsic modal components (IMFs) of the second CEEMD decomposition were taken.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation Condition Number** & **Speed (Hz)** & **Load (Nm)** \\ \hline Condition 1 & 20 & 0 \\ Condition 2 & 40 & 0 \\ Condition 3 & 60 & 0 \\ Condition 4 & 20 & 0.6 \\ Condition 5 & 40 & 0.6 \\ Condition 6 & 60 & 0.6 \\ Condition 7 & 10 & 1.2 \\ Condition 8 & 20 & 1.2 \\ Condition 9 & 30 & 1.2 \\ Condition 10 & 40 & 1.2 \\ Condition 11 & 50 & 1.2 \\ Condition 12 & 60 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The description of signal operation conditions.  
Figure 6: Test bed of planetary gearbox.  
From Figures 7-10, it can be seen that different fault types can be well separated under the same operation condition. There is no feature vector mixing under different fault states. Moreover, the clustering effect about the eigenvalues of the same fault type shows satisfactory performance and no divergence.
Remote Sens._**2013**, \(5\), 6284-6304; doi:10.3390/rs5126284  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
**A New Laser Based Approach for Measuring Atmospheric Greenhouse Gases**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 Exelis Geospatial Systems, 1919 Cook Rd., Fort Wayne, IN 46818, USA;  
E-Mails: [PERSON]S] (M.B.); [PERSON]SS] (N.B.)  
2 Atmospheric and Environmental Research, Lexington, MA 02421, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]S]; Tel.: +1-260-451-6652; Fax: +1-260-451-3310.  
_Received: 23 September 2013; in revised form: 12 November 2013 / Accepted: 18 November 2013 / Published: 25 November 2013_  
**Abstract:** In 2012, we developed a proof-of-concept system for a new open-path laser absorption spectrometer concept for measuring atmospheric CO\({}_{2}\). The measurement approach utilizes high-reliability all-fiber-based, continuous-wave laser technology, along with a unique all-digital lock-in amplifier method that, together, enables simultaneous transmission and reception of multiple fixed wavelengths of light. This new technique, which utilizes very little transmitted energy relative to conventional lidar systems, provides high signal-to-noise (SNR) measurements, even in the presence of a large background signal. This proof-of-concept system, tested in both a laboratory environment and a limited number of field experiments over path lengths of 680 m and 1,600 m, demonstrated SNR values \(>\)1,000 for received signals of \(\sim\)18 picoWatts averaged over 60 s. A SNR of 1,000 is equivalent to a measurement precision of \(\pm\)0.001 or \(\sim\)0.4 ppmv. The measurement method is expected to provide new capability for automated monitoring of greenhouse gas at fixed sites, such as carbon sequestration facilities, volcanoes, the short- and long-term assessment of urban plumes, and other similar applications. In addition, this concept enables active measurements of column amounts from a geosynchronous orbit for a network of ground-based receivers/stations that would complement other current and planned space-based measurement capabilities.  
**Keywords:** lidar; greenhouse gas; laser absorption spectroscopy
## 3 Results and Discussion
### Effects of Flux on Angular Distortion and Heat Input  
The angular distortion of the weldment is a result of the expansion and contraction of the weld metal and the adjacent base material during welding thermal cycles. Uneven heating through the thickness of a joint plate during welding causes non-uniform transverse shrinkage, yielding angular distortion of the weldment. The measured angular distortion in three different weld samples is shown in Figure 7a. Comparing with TIG welding without flux, activated TIG welding increases both the joint penetration and the weld depth-to-width ratio, which indicates a lower mechanical deformation during activated TIG welding. By combing the following input energy analysis, this can be contributed to a high localization of supplied heat, which prevents overheating of the base material and reduces the incidence of thermal stress and incompatible strain due to shrinkage in thickness. Therefore, the activated TIG welding can significantly reduce the angular distortion of the weldment.  
The arc voltage vs. time graph during welding with and without fluxes are shown in Figure 7b. It was observed that the average arc voltage increased with use of activating fluxes. The average arc voltages observed without flux, with microscale flux and with nanowire flux welding were 8.869 V, 12.843 V and 14.227 V, respectively. The activating flux coating on the plate surface is believed to act as an electric current insulator between the plate and the tungsten electrode because of the high electrical resistivity of the fluxes. Therefore, arc voltage increases to maintain the smooth flow of current. According to the heat input equation [43]:  
\[H=\frac{I*V}{S} \tag{2}\]  
Figure 6: Top: weld geometry photo of welded joints without fluxes (**a**), with Fe\({}_{2}\)O\({}_{3}\) microscale (**b**) and Fe\({}_{2}\)O\({}_{3}\) nanowire fluxes (**c**), welding speed of 60 mm/s. Below: weld bead geometry variation on different concentration fluxes (\(d/w\) ratio).  
where \(H\) = heat input per unit weld length (J/mm), \(I\) = welding current (A), \(V\) = welding voltage (V), \(S\) =welding speed (mm/s). Since the welding speed and current between three samples are the same, increasing the arc voltage will surely increase the heat input, thus increasing the welding performance. The heat input calculated during welding without flux, with microscale flux, and with nanowire flux were 1.774 kJ/mm, 2.568 kJ/mm, and 2.845 kJ/mm, respectively. Comparing with the previous study reported by [PERSON]'s group [43] with the heat input of 2.757 kJ/mm (with flux), the nanoflux slightly increases the heat input into the steel plate.
## 2 Materials and Methods
### Preprocessing  
#### 2.3.1 Bandpass Filtering  
ECG data often contains a significant amount of noise due to various factors such as external interference during measurement, electrode issues, muscle movements, and problems with the measuring equipment. Therefore, noise removal is essential for utilizing ECG data effectively. Bandpass filtering is widely used for noise reduction in ECG datasets, as it is effective at eliminating both high-frequency and low-frequency noise [21; 22]. The FFT graph in Figure 2 shows that noise occurs at frequencies above 50 Hz. To retain essential information while removing noise, a bandpass filter allowing only frequencies between 0 and 50 Hz to pass was used. Figure 3 shows noise-free ECG and FFT graphs after bandpass filtering, demonstrating that the noise was ideally removed after the bandpass filtering process.  
Figure 1: The system framework.  
Figure 3: After applying bandpass filtering, ECG graph (**top**) and FFT graph (**bottom**).  
Figure 2: ECG source graph (**top**), and FFT graph (**bottom**).
## 3 Application
### Thermochemical Conversion
#### 3.8.1 Gasification
Its strengths lie in its capability to track individual particle trajectories and model the interactions between particles and the gas flow, which is crucial for understanding and optimizing gasification efficiency. However, the method also has limitations, such as computational intensity and the need for accurate models to represent particle-wall interactions and transient phenomena. The future development of the CFD-DPM method is likely to focus on enhancing the accuracy of particle models, reducing computational demands and expanding the method's applicability to a wider range of gasification conditions, ultimately aiming to improve the design and operation of gasification systems.