esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
###### Abstract
In the field of image registration, these also show good results. By training the neural network model, the similarity standard between moving image and fixed image is calculated, and the transformation model is continuously optimized to realize the registration between image pairs. [PERSON] et al. [41] proposed classifying the features in the image using a convolutional neural network. Compared with the traditional SIFT descriptor, the effect of feature point detection and matching has been significantly improved. Different from the traditional feature-based method, [PERSON] et al. [42] proposed using a deep convolutional neural network to estimate the relative homography between image pairs, and training the neural network to directly perform geometric transformation to achieve the registration of two images. In summary, most of the current image registration methods are limited to medical images, remote sensing images, pedestrian detection, etc. In the field of fire monitoring for electrical equipment, the quality of the images to be registered is often low, and the scenes tend to be complex. Furthermore, there is a critical need for real-time registration in such cases. Unfortunately, existing image registration methods have proven inadequate in meeting these demands. Therefore, a two-stage registration strategy for substation thermal-visible images based on multi-modal PIIFD is proposed, which solves the registration task in special scenes of low image quality and complex images, and provides key preliminary work for subsequent image fusion analysis.
## 2 Bypass Line Design Requirements
In order to avoid the risk of catastrophic loss of the insulation vacuum in the whole SIS100 ring by a single leak, the SIS100 vacuum space was divided into fifteen sections separated by vacuum barriers, which are located in the bypass lines. In the case of a helium leak in any vacuum section, the BPL vacuum vessel might cool down by approximately 100 K and shrink due to thermal contraction. The vacuum barriers will be loaded with the pressure forces of about 20 kN each, which have to be entirely captured by BPL without transfer of the forces to the magnet's cold structures. Process pipes in the SIS100 ring are protected against excessive pressure build-up by the safety valves, also located in the bypass lines. For those reasons, the bypass line has to be designed to be flexible, allowing free movement of the magnet during its adjustment and simultaneously capturing all pressure forces and thermal contractions. The requirement of zero-force exerted on the magnets means that the bypass line needs to be designed with a very limited set of external fixed supports. The design of such a structure is challenging from a mechanical point of view. The general layout of the SIS100 bypass line is shown in Figure 4. The specification of the design parameters of the process pipes is presented in Table 1. The support system is divided into three parts:  
* external support of the vacuum vessel;
* supports of the thermal shield inside the vacuum vessel;
* supports of process pipes and superconducting busbars inside the thermal shield. The parameters of the process pipes--temperatures, operating and design pressures--are presented in Table 1. The busbar system is hydraulically connected between He-supply magnet pipes (A1) and He-return magnet pipes (B). The operating temperature of the superconducting busbars (BBs) is 4.5 K.
# Gating Hysteresis as an Indicator for Silicon Nanowire FET Biosensors
## 3 Results and Discussion
### Detection of Thrombin: Optimizing the Conditions  
To achieve the measurable signal changes upon thrombin attachment, the experiments were performed in slightly acidic conditions with pH of about 5.7, based on the isoelectric point (pl) of thrombin (pl = 7.0-7.6, Haematologic Technologies, Inc., Essex Junction, VT, USA). The acidity of the solution thus ensures the positively charged state of the protein molecules are able to influence the surface potential of the NW FETs [25; 26; 47].  
Figure 2: (**a**) Semi logarithmic transfer characteristics and (**b**) output characteristics of the Si NW FETs. (**c**) Biochemical functionalization of the aluminum oxide surface. Glycidoxypropyltrimethoxysilane (GOPMS) was used as a linker molecule to covalently attach the aptamer via amino-alcohol bond formation. The further thrombin recognition by the aptamer leads to semiconducting channel modulation (dashed yellow arrows).  
For the specific attachment of thrombin via the aptamers (Figure 2c), functionalization of the GOPMS modified sensor area was further performed by injecting the solution containing the TBA into the microfluidic channel, and left incubating for 20 min. A randomized control sequence (RDM, 3'-CATCCAACTGTCTCACAC-(CH\({}_{2}\))\({}_{6}\)-NH\({}_{2}\)-5') was immobilized on another NW FET as a nonspecific control test. After the removal of loosely attached strands, covalently bound DNA was incubated with 10 mM Tris buffer (pH 7.4) containing K\({}^{+}\) ions to promote the proper folding of the aptamer structure [48; 49; 50]. Further, 1 mM pure sodium phosphate buffer (SPB) buffer at pH 5.7 was injected into the channel prior to detection experiments to establish the reference baseline for further experiments.  
Finally, we inject the SPB buffer solutions with the different concentrations of thrombin into the channel step by step with the flow rate 100 uL/min. The infusion of each concentration was accompanied with a pumping pause during 200 s for the incubation purpose. Subsequent pumping of pure buffer removes loosely bound thrombin, while at the same time delivering the signal level (after rinse level). A concentration range from 200 pM to 200 nM of thrombin was tested in the following experiments.
## 3 Experimental Results
### Quantitative Evaluation
#### 3.4.2 IOU Results on 20 Test Samples  
The IOU results of TongueNet and REF are displayed in Figure 12. The proposed TongueNet achieved an IOU of over 90% on almost all samples with the highest value reaching 96.38% (the 10 th sample). This indicates that most of the pixels in the tongue region were predicted precisely. Furthermore, TongueNet outperforms REF on all samples with the most significant difference being 33.7% (the 14 th sample). To present an overall perspective of IOU, the mean IOU (MIOU) is 93.11% for TongueNet, while the MIOU of the REF method is 83.1%. In general, more pixels in the tongue region will be predicted correctly by TongueNet than REF.
## 4 Energy Measurement Results
### Transferred Energy Results
#### 4.2.2 Energy Measurement Results of the Aluminum Honeycomb Type of Device
Finally, the maximum compression speed of the aluminum honeycomb is calculated as 8.333 m/s. [PERSON] et al. [18] pointed out that the crushing stress of aluminum honeycomb is a stable mechanical parameter, and the geometric size of the sample has no effect on this parameter. According to Equation (9), the mean plastic crushing stress at 8.333 m/s is 1.104 MPa. The corresponding cross-sectional area of the aluminum honeycomb with \(M\)= 9 is 3117.691 mm\({}^{2}\). The mean plastic crushing force of the aluminum honeycomb can be calculated by multiplying the stress and area as 3117.691 \(\times\) 1.104 = 3441.931 \(M\). Therefore, the sample statistical data of transferred energy can be obtained, as shown in Table 6.
###### Abstract  
Due to the high power-to-weight ratio and robustness, hydraulic cylinders are widely used in the actuation area of the legged robot systems. Most of these applications are focused on the motion stability, gait planning, and impedance control. However, the energy efficiency of the legged robotic system is also a very important point to be considered. Hopping locomotion requires a fast extension of the tibia leg at the end of the take-off phase, which causes a continuous increment of the cylinder velocity under the normally direct attachment geometry (DAG) of the cylinder. This leads to a high flow requirement, large pressure drop, and low energy efficiency. Therefore, we propose a four-bar mechanism attachment geometry (FMAG) to improve the energy efficiency by refining the relationship between the joint angle and cylinder displacement trend. The kinematic and dynamic models of the bionic one-legged robot are built to calculate the hopping process during the take-off phase. Based on the established dynamic models, the design parameters in both the DAG and FMAG are optimized to maximize the hopping height, respectively. The hopping experiments are conducted to verify the effectiveness of the new attachment geometry. The experimental results show that the robot hopping energy at the end of the take-off phase increases 14.8% under the FMAG.  
bionic one-legged robot; cylinder attachment geometry; vertical hopping height; energy efficiency; four-bar mechanism +
Footnote †: journal: Applied Sciences
# Effect of Rotational Speed Variation on the Flow Characteristics in the Rotor-Stator System Cavity
## 1 Introduction
In order to be capable of monitoring the distribution of physical quantities within the turbulent boundary layer, shear strain rate data were extracted close to the rotor wall for processing and ultimately quantifying the flow parameters within the boundary layer. Under the action of high-speed rotation and wall shear stress in the rotor-stator system, the clearance flow characteristics directly affect the aerodynamic performance and efficiency of the entire machine. This is of vital importance for the optimal design of fluid machinery with a clearance flow.
## 3 Results and Discussion
### XRD and SAXS Measurements of PDMSB Homopolymers  
In Figure 4, the X-ray diffraction (XRD) pattern of the PDMSB sample with an average molar mass of \(5.6\;\mathrm{kg\;mol}^{-1}\) is shown, which has been isothermally crystallized at 283 K after annealing at 353 K for 10 min. The scattering pattern consists of a series of narrow Bragg peaks. Obviously, the sample is partially crystalline. The peak positions are the same as those reported previously by [PERSON] _et al._[34]. Only the relative intensity of the peaks is different. This can be caused by a different orientation of crystallites in the samples as a consequence of the sample preparation procedure.  
Figure 3: Differential scanning calorimetry (DSC) diagram of PDMSB with an average molar mass of \(5.6\;\mathrm{kg\;mol}^{-1}\).  
We now turn to the crystalline/amorphous mesostructure of the sample. Figure 5 shows the Lorentz-corrected SAXS profile for the melt-crystallized sample at 283 K for 120 min. The results show one long range order peak, which relates to a long period of 10.7 nm. This differs from observations in the isothermal crystallization behavior of PEO homopolymers [48]. For PEO, it has been found that at first, a scattering peak was observed during isothermal crystallization, followed by a decrease of the scattering peak, and the appearance of two other scattering peaks developed gradually. Isothermal thickening and/or thinning processes lead to the formation of the final IF chain crystals for low molecular weight PEO, which can be used to explain the multiple melting peaks phenomenon. However, for PDMSB, this is not the case, as only one scattering peak is observed after crystallization.  
Figure 4: X-ray diffraction pattern of PDMSB with an average molar mass of 5.6 kg mol\({}^{-1}\) isothermally crystallized at 283 K after annealing at 353 K for 10 min.  
Figure 5: Lorentz-corrected small-angle X-ray scattering (SAXS) profile evaluated for the melt-crystallized sample at 283 K for 120 min. \({q_{m}}^{*}\) denotes the scattering vector corresponding to the reciprocal of the long period.  
To sum up, for all results derived by DSC, XRD and SAXS measurements for low molar mass PDMSB homopolymers, we assume that the first melting peak corresponds to crystal lamellae formed during isothermal crystallization, while the second one is caused by recrystallization during the heating process. DSC measurements show that when isothermal crystallization temperature, \(T_{c}\), is low enough, there will be two melting peaks, but only one melting peak when \(T_{c}\) is high. Recent DSC curves of isothermal crystallized PDMSB samples also show that there is only one peak without any shoulders. This interesting crystallization behavior for low molar mass PDMSB will be explicitly discussed in a prospective work.  
Investigations regarding the crystallization of PDMSB homopolymers with higher molar masses show a fast and distinct crystallization, which shows PDMSB to be an excellent candidate for studying the influence of the crystallization of anchored polymers on a curved substrate, as described in the following sections.
# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 2 Materials and Methods
### Feature Extraction
#### 2.4.2 Differential Entropy  
Differential entropy (DE) is grounded in the concept of information-theoretic entropy and aims to measure the amount of information within a random variable. [PERSON] [25] demonstrated that in emotion classification tasks based on EEG, DE exhibited higher accuracy compared to traditional energy spectrum features. The DE algorithm adopted by the MBPPE is based on related studies by [PERSON] [26]. They found that when EEG signals were band-pass filtered in the range of 2 Hz to 44 Hz with a step size of 2 Hz, and then subjected to the Kolmogorov-Smirnov test, the probability of each sub-band signal conforming to a Gaussian distribution exceeded 90%. In a fixed-length sequence of EEG data, the differential entropy estimation for a specific frequency band I corresponds to the logarithmic energy spectrum of that band, as outlined in Equation (2).  
\[\begin{split} h_{i}(X)&=\frac{1}{2}\log\Bigl{(}2 \pi e\sigma_{i}^{2}\Bigr{)}\\ &=\frac{1}{2}\log\Bigl{(}N\sigma_{i}^{2}\Bigr{)}+\frac{1}{2}\log \Bigl{(}\frac{2\pi e}{N}\Bigr{)}\\ &=\frac{1}{2}\log(P_{i})+\frac{1}{2}\log\Bigl{(}\frac{2\pi e}{N} \Bigr{)}\end{split} \tag{2}\]  
Herein, \(h_{i}\) and \(\sigma_{i}^{2}\) denote the differential entropy and variance of the EEG signal within frequency band \(i\), respectively. \(P_{i}\) can be viewed as the energy spectrum, equivalent to the product of \(\sigma_{i}^{2}\) and a constant \(N\) ( \(N\) is the length of the fixed time window).  
The design of MBPPE reflects this relationship, implementing differential entropy calculations via the logarithmic energy spectrum approach. First, we cut a signal segment every second according to the sampling rate using a Hanning window. Then, we perform an FFT for each signal segment, converting the EEG data into a frequency-domain representation. After that, we focus on five frequency ranges: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-31 Hz), and gamma (31-50 Hz). For these frequency ranges, we use [PERSON]'s theorem to calculate the energy values of each segment separately, and the differential entropy is the log representation of these energies.  
Notably, for input data of size \(n\times m\), the output adopts a three-dimensional structure of \(n\times(m\div window)\times 5\). Here, \(n\) denotes the number of channels, \(m\) signifies the length of the time series data, \(window\) represents the fixed length into which the EEG data is segmented (by default, equal to the data sampling rate), and 5 corresponds to the five primary frequency bands: delta, theta, alpha, beta, and gamma.
## 6 Conclusions  
In this research, we proposed a BO-LSTM model that integrates crop phenology and meteorological and remote sensing data to predict county-level winter wheat yields. Yield estimation performance was then compared using three predictive models, including linear regression, machine learning, and deep learning. The conclusions are as follows:  
1. Using Bayesian optimization of LSTM neural network model hyperparameters can achieve identification of the optimal combination of hyperparameters in a shorter period of time.
2. Multi-temporal remote sensing data based on BO-LSTM model combined with meteorological data can provide effective information to obtain more accurate yield prediction models to estimate regional scale winter wheat yield.
3. Among the three prediction models, BO-LSTM achieves higher yield estimation accuracy relative to Lasso and SVM.
4. There is some spatial variation in the estimated yield advantage in different areas, and our method is more suitable for places where crop cultivation is concentrated, far from urban building sites and with less residential land.  
[PERSON] and [PERSON] designed the methodology and obtained funding for the research. [PERSON] contributed to compiling and analysing the data and wrote the manuscript. [PERSON] and [PERSON] conducted statistical analysis. [PERSON], [PERSON] and [PERSON] participated in the data analysis and supervised the writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by High resolution Earth observation System Project, grant number 09-Y30F01-9001-20/22 and 09-H30G02-9001-20/22, and the earmarked fund for CARS-08.  
Figure 7: Spatial distribution of winter wheat in Hengshui City, 2016-2019.
# Numerical Technique for Study of Noise Grating Dynamics in Holographic Photopolymers
## 2 Methods
### Index Development  
The index of the photopolymer is then calculated from these chemical distributions. The local volume fraction of polymer is first found from the fractional conversion and the known polymerization shrinkage. The volume fraction of the rubbery solid matrix is found assuming perfect mixing and incompressibility as one minus the monomer and polymer volume fractions. Finally, the index of the three-component mixture is calculated by the Lorentz-Lorenz equation:  
\[\frac{n^{2}-1}{n^{2}+2}=\sum_{i}\frac{n_{i}^{2}-1}{n_{i}^{2}+2}\phi_{i}, \tag{5}\]  
where the refractive indices \(n_{m},\ n_{P},\ n_{M}\) of the monomer, polymer and matrix, respectively, are found using prism-coupler measurements of mixtures of the components. These are typically not the same as measurements of pure compounds. For example, vitrification typically limits complete shrinkage upon polymerization. Conversely, in the rubbery host, the writing monomer can typically fully convert with minimal shrinkage stress. This yields a greater shrinkage and refractive index than exhibited by neat polymer at full conversion. Taking the fractional volume shrinkage upon polymerization to be \(\sigma\), the associated volume fractions of each component are:  
\[\begin{gathered}\phi_{m}=\phi_{m0}m(\overrightarrow{r},t),\\ \phi_{P}=\phi_{m0}(1-\sigma)P(\overrightarrow{r},t),\\ \phi_{M}=1-\phi_{m0}[m(\overrightarrow{r},t)+(1-\sigma)P( \overrightarrow{r},t)],\end{gathered} \tag{6}\]  
where \(\phi_{m0}\) is the monomer volume fraction before exposure and \(m=[m]/[m_{0}]\) and \(P=[P]/[m_{0}]\) are the concentration of monomer and polymer normalized to the initial monomer concentration. A cross-section of the typical resulting index is shown in Figure 2c.  
An important aspect of this calculation is that index change and shrinkage upon polymerization result in finite index contrast immediately upon polymerization. Subsequent diffusion amplifies this response. Since the development of haze is hypothesized to be self-reinforced noise gratings that form during exposure, the time dynamics of the index formation process are critical.
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
## 1 Introduction
In summary, the joint distribution has an important influence on the mechanical response and the failure mode of the coal mass. The simulation of laboratory-scale coal rocks with built-in simple structural planes (joints) has been well studied. However, most of the numerical models used do not take into account the actual joint distribution in the coal rocks and focused on studies utilizing single or multiple preformed cracks embedded within the specimens model. [PERSON] et al. [5; 6] made an attempt to address this issue by establishing numerical models of coal masses with realistic joint distribution to investigate the effects of the confining pressure. Nevertheless, the number of particles in the model established by [PERSON] et al. [5] was more than one million, resulting in a computational process that was exceedingly slow and inefficient. This limitation could not satisfy the comparative analysis of the mechanical response of different coal masses with different joint distributions and loading conditions. Therefore, there is a need for more efficient and scalable numerical models for practical applications. To address the above problems, this paper proposed a model construction approach for engineering-scale coal mass that simultaneously fulfills the computational efficiency and the accurate calibration of the parameters, and on the basis of which, an in-depth study of the mechanical response characteristics of the coal mass was carried out.
# Research on an Optimal Path Planning Method Based on A* Algorithm for Multi-View Recognition
## 1 Introduction
The single-source shortest path problem of the fused directed graph based on the A* algorithm is solved to complete the optimal observation path planning, satisfying the shortest distance requirement. 3. Observation path planning experiments are implemented based on the UR5 robot manipulator with different observation viewpoints and different path starting points. The results demonstrated the effectiveness of the A* algorithm for perspective-seeking path planning. The effect of viewpoint number on recognition results is analyzed and discussed. The remainder of this paper is structured as follow. Section 2 presents the related research on multi-view target recognition. Section 3 establishes the target observation model, determines the objective function characterizing the viewpoint recognition performance and shortest path, and studies in detail the A* algorithm-based multi-viewpoint recognition optimal path planning. Different path starting points are selected for perspective-seeking path planning experiments in Section 4. The research content of this paper is summarized in Section 5.
## 5 Numerical Examples  
To demonstrate the efficiency and accuracy of the present iterative DOA, several examples are presented. All the numerical computations were carried out in Microsoft Developer Studio with an Intel Core I7-3770, CPU 2.80 GHz and 8 GB memory. The precision is \(10^{-16}\).