esa-sceva/satcom-chunk-collection huggingface dataset:
# Siamese Tracking with Adaptive Template-Updating Strategy
## 3 Our Approach
### Adaptive Template-Updating Strategy
#### 3.2.3 Template-Update Strategy  
We set the greatest response on the heat map as the criterion for strategic decision. When the greatest response was smaller than our threshold, it indicated that the similarity between the tracked target in the current frame and the template was low. There were mainly two kinds of conditions in our application scenarios that could lead to this phenomenon: deformation and occlusion. When the target is deformed, such as when a vehicle makes a turn, updating the template is necessary because previous templates may not be suitable for the same object from another view angle; on the contrary, when a target is temporarily occluded, such as when a vehicle passes through the jungle and is shielded by trees, updating the template during occlusion is not needed and may cause template pollution because of shield interference. Therefore, our system needs to differentiate between these two conditions and decides whether to update the template or not. Examples of deformation and occlusion in tracking are shown in Figure 7.  
After analyzing the characteristics of both conditions, we proposed an updating strategy that decided by the results of two separate networks: a contour- and a target-detection network. If the target had obvious appearance changes from one frame and activated the template-update network, both networks would output corresponding results within a neighboring target area, and a new appearance model would be generated. It needs to be denoted that our tracking method with shape-adaptive templates is able to deal with partial-occlusion conditions because there is less background interference compared with the traditional bounding-box template. Only severe occlusion may cause similarity low enough to activate the template-update network. Under severe occlusion conditions, the contour- and target-detection networks are not able to get consistent results in most cases. Figure 8 shows outputs from our template-updating network regarding the deformation and occlusion conditions.  
Figure 7: Results during (**a**) deformation and (**b**) occlusion. Bottom, tracking result in origin frame; top left, cropped image showing partial enlarged detail of tracking result; top right, corresponding heat map denoting similarity. Green boxes, ground truth; red, tracking results.  
In Figure 8a, which was under deformation, the target vehicle in the center of image and its contour were simultaneously detected, while in Figure 8b, which was under occlusion, neither the more severe shielded vehicle nor its contour were detectable. In order to improve the robustness of our system and avoid unknown conditions that may lead to false tracking, we preserved the template in the first frame, which was manually selected and had the highest confidence. This highest confidence template was utilized as a backup template to match with the search region during each template update except for the first time.
# Solar Filament Detection Based on an Improved Deep Learning Model
###### Abstract  
Solar filaments are good tracers of space weather and magnetic flux ropes in the corona. Identifying and detecting filaments helps to forecast space weather and explore the solar magnetic field. Many automatic detection methods have been proposed to process the large number of observed images. Current methods face issues of unreliable dataset annotations and poor anti-interference capability. First, to address the issue of unreliable dataset annotations, we built a solar filament dataset using a manual annotation method. Second, we introduced Transformer into Convolutional Neural Networks. Transformer, with the ability to extract more global features, can help counter interference. In addition, there is large disparity in the size of solar filaments. Therefore, a multi-scale residual block is designed to extract features across various scales. Deformable large kernel attention and a res path are used to better integrate encoder and decoder information. Results show that this method outperforms the existing solar filament detection methods (improved U-Net and improved V-Net), achieving an F1 score of 91.19%. In particular, our results show lower interference by sunspots and background noise than existing methods. The ability to counter interference is improved.  
solar filaments; deep learning; semantic segmentation +
Footnote †: journal: Applied  
0000-0001-8880-3880][PERSON] 1  
1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China. [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (M.S.)
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 4 Experimental Analysis  
The two techniques introduced in the previous section have been implemented and tested using an architecture representative of low-cost mass-market GNSS receivers, with the purpose of showing and proving the efficacy of spatial diversity techniques in this type of devices. In this section, the scheme used for the experiments conducted is detailed, together with a thorough analysis of the results obtained.
## 2 Numerical Simulation
### Modelling for Numerical Simulation  
In the process of modelling for numerical simulation, there are three main steps, namely, the establishment of geometric models, the meshing generation, and the setting of boundary conditions. Moreover, all the steps are implemented in LS-DYNA software. Figure 3 illustrates the established geometric model of HBS erosion by a submerged water jet. The size of HBS is \(4\times 4\times 5\) cm\({}^{3}\). The jet source, namely, the nozzle, is cylindrical with a length of 0.05 cm. The standoff distance is defined as the distance from the HBS surface to the nozzle. Furthermore, the simulation model of HBS erosion by the water jet is axisymmetric; to reduce the computation time (CPU) and increase computation efficiency, only 1/4 of the model is adopted for numerical calculation.  
The mapped meshing approach is used to mesh the simulation model of HBS erosion by a water jet. Obviously, there are local effects in the eroding process; therefore, mesh refinement is carried out in the center of the water domain to improve computation efficiency. Meanwhile, all of the components of the geometric model are meshed by ALE elements, including the jet source, the water domain, and the HBS. The finite element mesh consisted of the usual 8-node brick elements, and the total number of elements of HBS is 243,360. The model mesh of the jet source, water domain, and HBS are illustrated in Figure 4. In addition, the interaction between the two meshes is based on fluid-solid coupling, and the coupling is realized by defining the keyword \"CONSTRAINED-LAGRANGE-IN-SOLID [34].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Description** & **Value** & **Unit** & **Reference** \\ \hline \(\rho\) & Density & 1.9 & g/cm\({}^{3}\) & [PERSON] [27] \\ \(G_{\text{s}}\) & Specific gravity & 2.78 & & [PERSON] [40] \\ \(K\) & Bulk modulus & 9.4 & GPa & [PERSON] [27]; [PERSON] [36] \\ \(G\) & Shear modulus & 4.4 & GPa & [PERSON] [36] \\ \(\varphi\) & Friction angle & 0.218 & rad & [PERSON] [27] \\ \(c\) & Cohesion & 45 & kPa & [PERSON] [27]; [PERSON] [38] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the material model of hydrate-bearing sediment (HBS).  
Figure 3: A quarter geometric model of HBS erosion by a submerged water jet.  
With respect to the setting of boundary conditions, two symmetry planes are defined to realize the numerical computation and the displacement of the bottom of the model is limited to ensure that the HBS does not move during water jet impingement. Meanwhile, non-reflecting conditions are applied to the boundary of the simulation model to eliminate the reflection of the stress waves and simulate an unlimited boundary.
## 4 End-to-End SNR Correlation Analysis
\begin{split} T_{2}&=\,\int_{x_{n}}^{\infty}\cdots \int_{x_{n}}^{\infty}\prod_{j=l_{k}+1,j\
eq n}^{N_{k}}f_{\gamma_{k,j}}(x_{j}) dx_{j}\\ &=\,\sum_{i_{l_{k}+1}=0}^{m_{k,l_{k}+1}-1}\cdots\sum_{i_{n-1}=0}^{ m_{k,n-1}-1}\sum_{i_{l_{k}+1}=0}^{m_{k,n+1}-1}\cdots\sum_{i_{N_{k}}=0}^{m_{k,N_{k}}-1} \left(\prod_{\begin{subarray}{c}j=1_{k}+1,j\
eq n\\ j\
eq n\end{subarray}}^{N_{k}}\frac{\rho_{k,j}^{-i_{j}}}{\Gamma(i_{j}+1)} \right)\times e^{-\sum_{j=l_{k}+1,j\
eq n}^{N_{k}}\frac{x_{n}}{\rho_{k,j}}}\,x_{ n}^{\sum_{j=l_{k}+1,j\
eq n}^{N_{k}}i_{j}}.\end{split}  \tag{9}\]  
Multiplying Equations (8) and (9), then we obtain the first part of Equation (7) as follows:  
\[ \begin{split}&\sum_{n=l_{k}+1}^{N_{k}}\int_{0}^{\infty}x_{n}T_{1}T_{2 }f_{\gamma_{k,n}}(x_{n})dx_{n}\\ &=\sum_{n=l_{k}+1}^{N_{k}}\sum_{i_{l_{1}}=0}^{m_{k,1}-1}\cdots \sum_{i_{n-1}=0}^{m_{k,n-1}-1}\sum_{i_{l_{k}+1}=0}^{m_{k,n+1}-1}\cdots\sum_{i_{ l_{k}}=0}^{m_{k,N_{k}}-1}\frac{\rho_{k,n}^{-i_{k},n}}{\Gamma(m_{k,n})}\\ &\quad\times\left(\prod_{j=1,j\
eq n}^{N_{k}}\frac{\rho_{k,j}^{-i _{j}}}{\Gamma(i_{j}+1)}\right)\left\{\Gamma\left(\omega_{k,n}\right)\left(\sum_{j =1}^{N_{k}}\frac{1}{\rho_{k,j}}\right)^{-\omega_{k,n}}+\sum_{u=0}^{\sum_{j=1}^{ l_{k}}i_{j}}\frac{\Gamma\left(1+\sum_{j=1}^{l_{k}}i_{j}\right)}{\Gamma(u+1)}\left(\sum_{j =1}^{l_{k}}\frac{1}{\rho_{k,j}}\right)^{-1+u-\sum_{j=1}^{l_{k}}i_{j}}\right.\\ &\quad\times\left.\Gamma(\lambda_{k,n})\left(\sum_{j=1}^{N_{k}} \frac{1}{\rho_{k,j}}\right)^{-\lambda_{k,n}}\right\},\end{split}  \tag{10}\]where \(\omega_{k,n}=m_{k,n}+2+\sum_{j=1,j\
eq n}^{N_{k}}i_{j}\), and \(\lambda_{k,n}=u+m_{k,n}+1+\sum_{j=l+1,j\
eq n}^{N_{k}}i_{j}\). Using Equations (1) and (4), we derive the other part of Equation (7) as follows:  
\[ \begin{split}&\int_{0}^{\infty}\int_{y}^{\infty}\cdots\int_{y}^{ \infty}y^{2}f_{\gamma_{k}l_{k+1}}(x_{l+1})\cdots f_{\gamma_{k}N_{k}}(x_{N_{k}} )f_{q_{k,l_{k}}}(y)dx_{l_{k}+1}\cdots dx_{N_{k}}dy\\ &=\sum_{i_{1}=0}^{m_{k,1}-1}\cdots\sum_{i_{N_{k}}=0}^{m_{k,N_{k}} -1}\left(\prod_{j=1}^{N_{k}}\frac{\rho_{k,j}^{-i_{j}}}{\Gamma(i_{j}+1)}\right) \left\{\left(\sum_{j=1}^{l_{k}}\frac{1}{\rho_{k,j}}\right)\int_{0}^{\infty}y^{ 2+\sum_{j=1}^{N_{k}}i_{j}}e^{-\sum_{j=1}^{N_{k}}\frac{y}{\rho_{k,j}}}dy\right. \\ &\quad-\left(\sum_{j=1}^{k}i_{j}\right)\int_{0}^{\infty}y^{1+ \sum_{j=1}^{N_{k}}i_{j}}e^{-\sum_{j=1}^{N_{k}}\frac{y}{\rho_{k,j}}}dy\bigg{\}} \\ &=\sum_{i_{1}=0}^{m_{k,1}-1}\cdots\sum_{i_{N_{k}}=0}^{m_{k,N_{k}} -1}\left(\prod_{j=1}^{N_{k}}\frac{\rho_{k,j}^{-i_{j}}}{\Gamma(i_{j}+1)}\right) \left\{\left(2+\sum_{j=1}^{N_{k}}i_{j}\right)\left(\sum_{j=1}^{l_{k}}\frac{1}{ \rho_{k,j}}\right)\left(\sum_{j=1}^{N_{k}}\frac{1}{\rho_{k,j}}\right)^{-1} \right.\\ &\quad\left.-\sum_{j=1}^{l_{k}}i_{j}\right\}\Gamma\left(2+\sum_{j =1}^{N_{k}}i_{j}\right)\left(\sum_{j=1}^{N_{k}}\frac{1}{\rho_{k,j}}\right)^{- 2-\sum_{j=1}^{N_{k}}i_{j}}.\end{split}  \tag{11}\]  
Finally, \(E[q_{k}q_{k,l_{k}}]\) is yielded by the sum of Equations (10) and (11) as follows:  
\[
# Parameterized Algorithms in Bioinformatics: An Overview
## 2 Genome Comparison and Completion
### Common Partitions
#### 2.2.1 Minimum Common String Partition  
**Problem Description.** The most natural partition problem is Minimum Common String Partition[18] (see Figure 3), which admits slightly different formulations [26; 27]. It can be seen as a generalization of the breakpoint distance on balanced strings. Indeed, a common string partition may be seen as a mapping between the characters of both strings, i.e. a permutation, whose breakpoints correspond to the limits between consecutive blocks. Rather than a genomic distance, it can also be seen as a method for assigning orthologs across two genomes, assuming that all duplication events happened before their last common ancestor. For example, depending on how orthologs are matched, genomes abacd and acdab may be seen as permutations 12345 and 34512 (i.e., matching two blocks ab and acd), or 12345 and 14532 (matching four blocks a, b, a, and cd). Gene sequences alone may not be sufficient to distinguish both cases: in such a situation, the first option presumably reflects better the evolutionary history of these species, since it involves fewer large-scale events in the genome.  
**Results.** MCSP is FPT for several combinations of parameters, depending on the relative size of the blocks, the size \(k\) of the partition and/or the maximum number occ of occurrences of a character [28; 29]. It can also be solved in \(k^{O(k^{2})}\) time [30], although this is too slow to be of practical interest. Finally, MCSP admits a more practical algorithm for the parameter \(k+\texttt{occ}\), running in \(O(\texttt{occ}^{2k}kn)\) time [31].  
Figure 3: A minimum common string partition for the strings abcdabcaed and daedbcabca.  
This last algorithm also applies to unbalanced strings where the problem is extended as follows: excessive characters may be removed from their respective input strings, but only between consecutive blocks.  
Another possible parameter is the number of _preserved duos_, \(k^{\prime}=|G_{1}|-k\). Note that MCSP is denoted _Maximum-Duo Preserving String Mapping_ in the context of approximations and parameterized algorithms maximizing \(k^{\prime}\). This version is FPT as well, and admits a kernel of size \(O(k^{8})\)[32].
## 5 Signal Acquisition Approaches
### Dry Electrodes  
These are adhesive sensors that do not require conductive gel. They can be applied directly to the skin and provide a high signal-to-noise ratio [95]. The advancement in materials and technology enables these electrodes to function without conductive gel, often using micro-needle arrays or novel conductive materials. Their ease of use, comfort, and minimal preparation make them increasingly popular in portable EEG devices, suitable for long-term monitoring in telemedicine and consumer-based health applications.
## 2 Introduction of Elastic Navigation and Positioning System  
### System Composition  
Using all available navigation resources, the elastic navigation and positioning terminal elastically integrates sensors such as the navigation receiver, inertial sensor, Wi-Fi, UWB, vision and lidar. The types of sensors can be flexibly selected according to the actual application scenarios. The optimal fusion location model is matched according to the environment and various prior information, and the sensor data are elastically selected for multisource fusion location to realize the accurate estimation of the carrier position, see Figure 1.  
The elastic PNT terminal can receive the information of various navigation resources. This information is received and processed by the sensors in the elastic PNT terminal, and all data are unified under the same spatiotemporal benchmark. Then, the data quality of these sensor data is evaluated, and the optimal data combination involved in positioning is found in the redundant information for elastic intelligent hybrid positioning, The processing process of elastic intelligent positioning can be controlled by a cooperative positioning strategy and an a priori information model.
## 3 Shaking Table Test  
### Model Similarity  
Determining the similarity between the model and the original structure was the most critical task of the entire shaking table test. To make the model and the original structure meet the physical-mechanical similarity, the following four similitude laws are required: similar geometry, similar stress-strain relationship between the model material and the prototype material, similar mass and gravity, and similar initial and boundary conditions. The similarity between mass and gravity is the most flexible similitude law in model design. In shaking table tests, the mass similitude law is widely used because it can satisfy the similarity conditions of both gravity and horizontal inertia force [25]. According to the principle of dimensional coordination, the similarity coefficients of elastic modulus, density, length, and acceleration had the following relationships:  
\[\mathrm{S_{E}/S_{\mathrm{\rho}}S_{a}S_{L}}=1 \tag{1}\]  
where \(\mathrm{S_{E}}\), \(\mathrm{S_{\rho}}\), \(\mathrm{S_{a}}\), and \(\mathrm{S_{L}}\) are similarity scaling factors for elastic modulus, density, acceleration, and length.  
Based on the size of the shaking table and the space size of the laboratory, \(\mathrm{S_{L}}\) was 0.025. According to the similarity relationship requirements, fine aggregate concrete was used to simulate the prototype concrete, a galvanized iron wire was used to simulate the prototype reinforcement, and red copper was used to simulate the prototype steel member. According to the material performance test, \(\mathrm{S_{E}}\) was 0.2. Based on factors such as the bearing capacity of the vibration table and the maximum ground acceleration peak of the prototype structure, \(\mathrm{S_{a}}\) was 1.8. The density similarity coefficient \(\mathrm{S_{\rho}}\) was determined by the first three similarity coefficients through the formula (1). The remaining similarity scaling factors could also be calculated through the dimensional analysis method. The main similarity scaling factors for this test are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Seismic Wave** & **Time Delay (s)** & **Acceleration Amplification** \\ \hline AW1-X direction & 0.36 & 1.39 \\ AW1-Y direction & 0.06 & 1.44 \\ AW2-X direction & 0.51 & 1.72 \\ AW2-Y direction & 0.11 & 1.65 \\ AW3-X direction & 0.45 & 1.56 \\ AW3-Y direction & 0.07 & 1.83 \\ AW4-X direction & 0.43 & 1.48 \\ AW4-Y direction & 0.06 & 1.58 \\ AW5-X direction & 0.38 & 1.66 \\ AW5-Y direction & 0.05 & 1.42 \\ \hline Average value in X direction & 0.43 & 1.56 \\ Average value in Y direction & 0.07 & 1.58 \\ \hline \end{tabular}
\end{table}
Table 1: Seismic wave time delay and acceleration amplification.
## 2 Materials and Methods
### Damage-Sensitive Features
#### 2.1.4 Linear Stiffness Indicators  
Two additional DSFs are defined in the time-domain and are computed as (i) an approximation of the structural stiffness and (ii) the prediction error resulting from a linear approximation. The stiffness proxy, \(KPRX\), approximates the reaction force, which is defined as the sum of accelerations of all measured DOFs above the input DOF, \(i\), and the displacement, obtained as the relative displacement at the output DOF, \(o\), with respect to the input DOF, \(i\), as formulated in Equation (11):\[KPRX_{io,m}(t)=\frac{\sum_{dof=i}^{o}\ddot{x}_{dof}(t)}{\dot{d}_{o}(t)-\dot{d}_{i} (t)}\,, \tag{11}\]  
where \(\ddot{x}_{dof}\) is the acceleration at a given DOF, and \(\dot{d}_{i}(t)\) and \(\dot{d}_{o}(t)\) are the displacement approximations at time \(t\) obtained through numerical integration at the input and output DOF, respectively. Changes in the stiffness proxy, \(KPRX\), deliver a direct damage indicator and, when comparing against the \(KPRX\) values from healthy reference data, this DI provides a direct measure of stiffness loss. In order to mitigate known limitations and uncertainties induced by numerical integration schemes, prior wavelet-based filtering [77] is performed to reduce noise effects and increase precision.  
Another feature is the error induced by linear fitting of the output displacement estimate, \(\dot{d}_{o}(t)\), which is defined as \(LFIT_{io,m}\) in Equation (12):  
\[LFIT_{io,m}=\sqrt{\dot{d}_{o}-\dot{x}_{i}\cdot KPRX_{io,m}}\,, \tag{12}\]  
where \(\ddot{x}_{i}\) is the acceleration measured at the input DOF, \(i\); \(\dot{d}_{o}\) is the displacement estimate at the output DOF, \(o\), obtained from double integration; and \(KPRX_{io,m}\) is the stiffness proxy, which is defined in Equation (11).
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 4 Discussion
### Motion Correction and Filter  
Another important consideration for preprocessing is identifying and properly dealing with artifacts that can appear in the signal due to subject motion. With tight coupling of optical fibers, fNIRS is relatively resistant to motion, in comparison to fMRI, however, with increasing use in neonates and natural environments, motion correction is becoming a more important part of the fNIRS pre-processing pipeline. Systematic evaluation of various motion correction algorithms has been performed before [7; 10; 11] as discussed in the introduction. However here we wanted to show how even two very similar motion correction methods perform differently when used automatically. We found that these two particular methods were quite different (mean correlation between the two wavelet motion corrections was \(r\) = 0.78, Figure 6). The introduction of larger artifacts caused by one of the motion correction methods with recommended default setting (MC#2) is especially troubling, and can only be detected when visualized. Evaluating which method works on the specific dataset (length of data, specific motion artifacts including spikes as well as DC shifts), instead of trusting that a higher correction (correlation of 0.73 of BW#2 with no correction, versus correlation of 0.92 of BW#1 with no correction) means a better removal of artifacts, is critical and shows that even though there are various motion correction methods, we are not yet at an automatic pre-processing pipeline.  
For the bandpass filter, the difference between the bandpass filters we tested was minimal (Figure 7). However, choosing which filter to use can indeed be relevant to data collection and interpretation, as can be seen in the profiles where the FFT filter has very sharp edges and therefore isvery precise in the cutoff of the desired frequency in comparison to the Butterworth filter. However, sharp edges can create edge effects, for which it is important to have longer acquisition times before and after the actual task, to avoid these effects in the data itself. Decisions on which processing methods are desired should therefore be made beforehand.
## 2 Research Methods
### Imaging Analysis of Brain Function  
fMRI data were analyzed using SPM 12 software (Wellcome Department of Cognitive Neurology, London, UK). All functional images were aligned with the anatomical images using affine transformation routines built into the SPM 12 program. A time series of images acquired from the same participants was determined using a least-squares approach and a 6-parameter (rigid body) spatial transformation. The first image in the list specifiedby the user was utilized as the reference to which all subsequent scans were realigned. The realigned scans were co-registered with the participants' anatomic images obtained during each session and normalized to a template image in SPM 12 (using the space defined by the Montreal Neurologic Institute). Motion correction was performed using sinc interpolation. Time series data were filtered with a 240 s high-pass filter to remove artifacts due to cardiorespiratory and other cyclical influences. Moreover, co-registered T1 images were used via multichannel segmentation to extract probabilistic maps of the six following tissue classes: GM, WM, CSF, bone, soft tissue, and residual noise. The functional map was smoothed using an 8 mm isotropic Gaussian kernel prior to statistical analysis. Statistical analyses were performed at the group level using a general linear model and the theory of Gaussian random fields implemented in SPM 12. Group analyses were performed to extend the inferences of individual activation to the general population, from which the participants were drawn to list all clusters above the chosen level of significance, as well as the separate (>8 mm apart) maxima within each cluster, with the details of significance thresholds (\(p<0.05\)) and search volume presented underneath.  
Using the subtraction method, the areas of the brain activated under stress-inducing conditions in the moderate- and high-stress groups were identified and compared to the regions of the brain activated during the post-stress recovery state. Additionally, the number of voxels activated in each lobe during the stress and recovery phases was determined, and the number of activated voxels between the two groups was compared by phase using an independent \(t\)-test.