esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Coarray Parallel Processing Technique  
KIESSI-3D currently uses the OpenMP method and the Intel/MKL PARDISO direct sparse solver (2019 version). It calculates mass and stiffness matrices of the finite elements, and mass and stiffness matrices of the infinite elements and effective seismic load for each frequency to perform the frequency domain analyses, as illustrated in Figure 3.  
Figure 1: Parallel processing example using OpenMP and CAF: (**a**) Shared memory of OpenMP; (**b**) Distributed memory of CAF.  
Figure 2: Modeling of soil-structure interaction system using KIESSI-3D program utilizing finite and dynamic infinite elements: (**a**) Modeling concept; (**b**) Modeling examples.  
Furthermore, it stores the results for all frequency points shared after carrying out all the analyses on the hard drive. Because the code was parallelized using the OpenMP, the analysis rate varies depending on the number of cores. However, since the analysis rate achieved by the OpenMP is not linearly proportional to the number of cores, it was necessary to introduce a new parallel processing method, and thus CAF was adopted in this study. The source code was optimized to carry out an efficient analysis using this method. In addition, the analysis procedure was established and the program was modified so that the CAF functions could be maximized. A conceptual analysis diagram of our proposed new version of KIESSI-3D incorporating CAF is shown in Figure 4.  
The modified KIESSI-3D consists of OpenMP parts running on a single image and two OpenMP+CAF parts running on multiple images, as shown in Figure 4. The OpenMP parts oversee the information required for all single-image analyses and distribute the information to the other images. At this time, each individual image acquires data in parallel  
Figure 4: Conceptual diagram of the proposed KIESSI-3D with CAF incorporated (e.g., with a three coarray, 12-core computer system): Orange boxes represent accelerated parallelization by CAF combined with OpenMP, gray boxes represent parallelization using OpenMP only. NF denotes the number of frequency points to be analyzed.  
Figure 3: Conceptual analysis diagram of the existing KIESSI-3D.  
for the system equation corresponding to the pre-allocated frequency points, analyzing the data using OpenMP. The proposed technique can be summarized, as follows:  
* Step 1. Assign frequency points to be analyzed in each image.
* Step 2. Run all images simultaneously to construct element matrices of infinite elements and effective seismic loads (OpenMP+CAF part).
* Step 3. Construct system matrices by running a single image and save them to the hard drive frequency by frequency.
* Step 4. Run all images simultaneously for the pre-assigned frequency points to solve the matrix equation and save the solution to the hard drive (OpenMP+CAF part). At this time, OpenMP is used to run on each image.
* Step 5. Construct frequency domain responses by running a single image, which is done by collecting and interpolating solutions at the frequency points for further post-processing.
# Transcranial Direct Current Stimulation in the Treatment of Chronic Knee Pain: A Scoping Review
## 2 Methods
### Search Strategy  
An initial limited search of MEDLINE was conducted via the PubMed interface to identify relevant articles on the topic. The search strategy included specific keywords, MeSH terms, and Boolean operators to ensure comprehensive coverage. The following search strings were used:  
MEDLINE: ((\"transcranial direct current stimulation\"[MeSH Terms] OR \"tDCS\"[Title/ Abstract]) AND (\"chronic knee pain\"[Title/Abstract] OR \"knee osteoarthritis\"[Title/ Abstract] OR \"OA\"[Title/Abstract]) AND (\"efficacy\"[Title/Abstract] OR \"safety\"[Title/ Abstract] OR \"mechanisms of action\"[Title/Abstract]).  
Cochrane Central: ((\"transcranial direct current stimulation\" OR \"tDCS\") AND (\"chronic knee pain\" OR \"knee osteoarthritis\" OR \"OA\") AND (\"efficacy\" OR \"safety\" OR \"mechanisms of action\")).  
Scopus: TITLE-ABS-KEY((\"transcranial direct current stimulation\" OR \"tDCS\") AND (\"chronic knee pain\" OR \"knee osteoarthritis\" OR \"OA\") AND (\"efficacy\" OR \"safety\" OR \"mechanisms of action\")).  
PEDro: (\"transcranial direct current stimulation\" OR \"tDCS\") AND (\"chronic knee pain\" OR \"knee osteoarthritis\" OR \"OA\") AND (\"efficacy\" OR \"safety\" OR \"mechanisms of action\").  
The searches were performed on 31 August 2023, without any date restrictions. Full-text availability was required for inclusion.
## 3 Results and Discussion
### Hg\({}^{0}\) Removal Mechanism Analysis  
Figure 6 shows the analysis of the Hg\({}^{0}\) removal process under a N\({}_{2}\) + 6%O\({}_{2}\) atmosphere by the 15%MnO\({}_{\rm x}\)-15%CeO\({}_{2}\)/CNFs(OX) sorbent. The Hg\({}^{0}\) in flue gas was almost totally removed by the sorbent during the 9 h reaction. Inset picture (a) displays the Hg-TPD profile of the spent sorbent. Two main desorption peaks were observed in the temperature range of 200-300 \({}^{\circ}\)C, indicating that Hg\({}^{0}\) was mainly captured on the sorbent surface via chemical adsorption [12; 31]. By integrating the Hg-TPD profile, -81% of total Hg\({}^{0}\) was adsorbed on the sorbent, while the rest of the Hg\({}^{0}\) was removed by catalytic oxidation and released to the flue gas, as shown in inset picture (b).  
Figure 7 shows the XPS analysis of O 1s, Mn 2p, and Ce 3d with spent 15%MnO\({}_{\rm x}\)-15%CeO\({}_{2}\)/CNFs(OX). For spent 15%MnO\({}_{\rm x}\)-15%CeO\({}_{2}\)/CNFs(OX), the ratio of Mn\({}^{4+}\)/Mn\({}^{3+}\) was 0.3633, and the ratio of Ce\({}^{3+}\)/Ce\({}^{4+}\) was 0.2371. Both of these values are lower than those of the fresh sorbent, so it can be inferred that Mn\({}^{4+}\) and Ce\({}^{3+}\) participated in the catalytic adsorption and oxidation of Hg\({}^{0}\).  
In summary, for 15%MnO\({}_{\rm x}\)-15%CeO\({}_{2}\)/CNFs(OX), two approaches of adsorption and catalytic oxidation existed for Hg\({}^{0}\) removal, of which the chemical adsorption approach occupied the main proportion. The active components such as active oxygen species, Mn\({}^{4+}\) species, and Ce\({}^{3+}\) species greatly benefited the chemical adsorption and oxidation of Hg\({}^{0}\). The relatively high loading capacity of the highly dispersed Mn and Ce provided more active components for Hg\({}^{0}\) removal.
## 4 Results and Discussion
### Simulation Result Analysis
#### 4.1.2 Sinusoidal Tracking Response  
Set channel 1, channel 2 and channel 4 of the MCCS system to work, load the simulation ball valve opening at 100% with no interference, and only open the main controller. The main pressure-setting value is a sinusoidal signal with a frequency of 0.5 Hz and a varied range of [0.015, 0.035] MPa. The tracking effect is shown in Figure 14. The main pipe pressure under the three control methods has a certain lag and error. It can be seen from the figure that the changing trend of the tracking error also shows a sinusoidal trend. Moreover, the tracking error of the variable universe fuzzy PID control is compared with the tracking error of the adaptive fuzzy PID and the traditional PID, and is slightly smoother. It is proven that the variable universe fuzzy PID control has good dynamic characteristics and high tracking accuracy.  
Figure 14: Sinusoidal tracking response curve.  
Figure 13: Step response curve.
# Auditory Localization in Low-Bitrate Compressed Ambisonic Scenes
## 2 Background  
### Human Auditory Localization  
The basic property of spatial hearing is the ability to derive information about the sound source location based on the phase and level differences in left and right ear signals. Additional sound source localization information is provided by pinna-related spectral cues. An in-depth review of published research on human auditory localization can be found in [4; 5]. A concise summary is provided here. The performance of human auditory localization varies depending on the direction of incidence of the sound wave, distance, level, and frequency characteristics of the sound source. The misplacement between the perceived location of the sound source and its actual location introduces a _constant localization error_, which can be interpreted as the _accuracy_ of auditory localization. The variability of listener's perception and listening conditions contributes to the _random localization error_, which represents the _precision_ of auditory localization. This concept of localization accuracy and precision is illustrated in Figure 1.  
Figure 1: Auditory localization accuracy and precision. Adapted from [5].  
The directional spatial resolution (precision) of the auditory system can be associated with the minimum difference in the direction of the sound source that causes a change in the perceived position of an auditory event. [PERSON] describes this attribute as _directional auditory localization blur_. It is also referred to as Minimum Audible Angle (MAA), when obtained in sound-source-discrimination experiments. The lower limit of the MAA in the horizontal plane is about \(1^{\circ}\)[6,7] and it is observed for sound sources located in front of the listener. The horizontal MAA increases to about \(10^{\circ}\) for the lateral directions. This confirms that spatial auditory resolution in the horizontal plane depends on the discrimination thresholds of interaural time and level differences, which change more rapidly in the azimuth for sources located ahead of the listener in comparison to the lateral region.  
The reported MAA threshold in the median plane for sound sources placed in front of the listener is approximately \(3^{\circ}\)[7]. In this case, the auditory localization performance depends on the directional filtering of the pinna and body. For sources located on the diagonal planes spatial resolution depends on both interaural differences and spectral changes [7,8]. Auditory localization performance degrades in the presence of other sound sources [9].
## 2 Theory and Method
### The Governing Equations
#### 2.2.5 Fluid Flow Equations  
The injection rate is usually above 150 m\({}^{3}\)/h, and Reynold number ranges from 625 to 1.22 \(\times\) 10\({}^{6}\). In the previous studies, turbulence flows occur during the process of initial first stage construction [32]. The Navier-Stokes \(\kappa\)-\(\varepsilon\) equations describe the complex turbulence flow in the horizontal salt cavern.  
The mass conservation equation [32; 40]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})=0 \tag{5}\]  
The momentum equation:  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\mu\frac{\partial^ {2}u_{i}}{\partial x_{j}\partial x_{j}}+\frac{\partial}{\partial x_{j}}\Big{(} -\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\Big{)} \tag{6}\]  
The turbulent kinetic energy equation:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{ i})=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k}} \right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho\varepsilon- Y_{M} \tag{7}\]  
The turbulent dissipation rate equation:  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}}( \rho\varepsilon u_{i})=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{ \mu_{t}}{\sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{\partial x_{ j}}\right]+C_{1\varepsilon}\frac{\varepsilon}{\bar{k}}(G_{k}+C_{3\varepsilon}G_{b})-C_{2 \varepsilon}\rho\frac{\varepsilon^{2}}{k} \tag{8}\]  
where \(u_{i}\) is the brine velocity component in the \(i\) direction, m/s, \(i\) = 1, 2, 3; \(u_{j}\) is the brine velocity component in the \(j\) direction, m/s, \(j\) = 1, 2, 3; \(p\) is the pressure, Pa; \(\mu_{t}\) is the turbulent viscosity, Pa\(\cdot\)s; \(u_{i}^{\prime}\) is the fluctuating velocity component in \(i\) direction, m/s, \(i\) = 1, 2, 3; \(u_{j}^{\prime}\) is the fluctuating velocity component in the \(j\) direction, m/s, \(j\) = 1, 2, 3; \(k\) is the turbulence kinetic energy, m\({}^{2}\)/s\({}^{2}\); \(\varepsilon\) is the turbulence dissipation rate, m\({}^{2}\)/s\({}^{3}\); \(G_{k}\) is the turbulence kinetic energy due to the mean velocity gradients, kg/(m\(\cdot\)s\({}^{3}\)); \(G_{b}\) is the turbulence kinetic energy due to the buoyancy, kg/(m\(\cdot\)s\({}^{3}\)); \(Y_{M}\) is the contribution of the fluctuating dilatation to the dissipation rate, kg/(m\(\cdot\)s\({}^{3}\)); \(C_{1\varepsilon}\) is the empirical coefficient measured by experiment and is dimensionless; \(C_{2\varepsilon}\) is the empirical coefficient measured by experimenters and is dimensionless; \(C_{\mu}\) is the experimental coefficient and is dimensionless; \(\sigma_{k}\) is the turbulent Prandtl number for kinetic energy and is dimensionless; \(\sigma_{\varepsilon}\) is the turbulent Prandtl number for the dissipation rate and is dimensionless; and \(u_{g}\) is the velocity component in the gravity direction, m/s.
## 3 Materials and Methods
### Trajectory Segmentation  
Trajectory segmentation is the process of dividing the trajectory into a number of parts (segments), such that each part satisfies a given criterion (discussed below). In ReconTraj4 Drones, we provide three different ways to segment a trajectory into parts. As the reconstructed trajectory consists of a set of recording points, where each of these is a geo-tagged photo, the trajectory is split/segmented based on the time interval between two consecutive geo-tagged photos. The time interval is defined by the user, and the module splits the reconstructed trajectory into parts whenever the time interval between two consecutive photos (recording points) exceeds a given threshold.  
Similarly, the module also considers, as an additional splitting criterion, the distance between two consecutive geo-tagged photos. The haversine distance between every two consecutive points is calculated. Whenever this distance is greater than a given threshold,  
Figure 5: Returned data from Meteorat for a given latitude, longitude, timestamp, and altitude.  
the trajectory is segmented. As in the first case, the threshold is defined by the users according to the domain and their specific needs.  
In addition to the time interval and haversine distance segmentation criteria, a third case is the segmentation of the reconstructed trajectory based on the criterion of altitude variations of a drone during its flight. The drone's flight altitude is recorded in geo-tagged photos, and this information is extracted during the trajectory reconstruction phase. Thus, the altitude variations are computed between every two consecutive photos. Whenever these variations exceed the threshold (provided by users), the trajectory is segmented.  
In all three above cases (segmentation criteria), the produced trajectory parts are returned as a set of (\(i,j\)), where \(i\) is the \(i\)-th (recording) point defining the beginning of that segment, and \(j\) is the \(j\)-th (recording) point defining the end of that segment. In cases in which the segmentation criterion is not satisfied for any point on the trajectory, the framework returns the entire trajectory as one segment, and the points (\(i,j\)) correspond to the start and end points of the trajectory. Finally, if the trajectory segmentation process is executed before the interactive visualization process, then the visualization of these segments is available.
## 2 Numerical Analysis
### Parametric Study  
Parametric studies were conducted to investigate the influence of the pull-out behavior on the tunnel-type anchorage based on the geometric and rock joint characteristics. From the numerical analysis results, the load-horizontal displacement curves of the tunnel-type anchorage were analyzed based on the geometric and rock joint characteristics.  
A series of FE analyses on the tunnel-type anchorage in rock was performed using the influential parameters: the size of the enlarged part, anchorage spacing, joint dip/dip direction, joint spacing (s), the internal friction angle of the joint (\(\phi\)), and the cohesion of the joint (c). These values are summarized in Table 3. The cable pull-out loads were applied using displacement control. As the displacement changes incrementally in a displacement-controlled analysis, a load-displacement curve could be derived.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material Type**} & **Unit Weight** & **Elastic Modulus** & **Poisson’s Ratio** & **Cohesion (kN/m\({}^{2}\))** & **Internal Friction** \\  & **(kN/m\({}^{3}\))** & **(kN/m\({}^{2}\))** & **Angle (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** \\ \hline Soil & 18.0 & 20,000 & 0.35 & 10 & 30.0 \\ Rock & 22.0 & 1,101,000 & 0.25 & 200 & 33.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil and rock parameters used for this study [26; 27].  
Figure 3: Convergence study for three different mesh densities: load-horizontal displacement curves for tunnel-type anchorages.  
Figure 5 shows the enlarged height and spacing of the tunnel-type anchorage used for numerical analysis. The diameter of the tunnel-type anchorage constructed in the Ulsan Grand Bridge is 10.85 m. The enlarged heights applied to the 3D numerical analysis were H = 0.0 m, H = 2.0 m, H = 4.0 m, H = 6.0 m, and H = 8.0 m. Additionally, the installation spacings of the tunnel-type anchorage applied to the 3D numerical analysis were 18.5 m, 23.5 m, and 28.5 m.  
Figure 4: Plane view of an FE mesh for the rock joint: (a) joint set #1, (b) joint set #2, and (c) joint set #3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Cases** \\ \hline Enlarged part (m) & 0, 2, 4, 6, and 8 \\ Anchorage spacing (m) & 18.5(1.7D), 23.5(2.2D), and 28.5(2.6D) \\ Joint dip/dip direction & 60/162, 60/342, and 55/252 \\ Joint spacing (m) & s: 10, 15, and 20 \\ Cohesion for rock joint (kPa) & c: 18.5, 23.5, and 33.5 \\ Internal friction angle for rock joint (\({}^{\circ}\)) & \(\phi\): 25, 30.5, and 35 \\ \hline D: the diameter of the tunnel-type anchorage. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the numerical analyses conducted.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 3 Results
### Study Characteristics  
The datasets used in the 15 selected articles for further assessment were categorized into three types: (1) in vivo data; (2) phantom data (including in vitro, ex vivo, and tissue-mimicking datasets--both self-made and commercial); and (3) simulation data. For studies comparing multiple deep learning algorithms or loss functions, the best-performing algorithm or loss function and its corresponding performance was reported. Characteristics of the included articles are shown in detail in Table 2. Most articles [33, 37, 38, 41, 42, 43] used Plane Wave Imaging (PWI) for data collection by mapping low-quality images from one or a few angles to high-quality compounded images from multiple angles. The next most common ultrasound mode for data collection involved low-quality input data from handheld POCUS devices and high-quality reference images from high-end ultrasound devices [6, 14, 15, 35]. Additionally, all articles used (a combination of) a CNN and/or a GAN as a deep learning network. More variety in loss functions was observed, even though often (albeit in combination with other loss functions) the MSE and SSIM loss were used.
## 5 Conclusions  
In this paper, ERF-IMCS was proposed, which integrates visual topological localization aided by CNN-based image retrieval with a probabilistic approach of Monte Carlo localization. The image retrieval strategy is responsible for calculating the similarity between current view image and keyframes in topological nodes based on a pre-trained faster RCNN model, which is injected into the next image-based Monte Carlo localization stage to compute the weights of the possible location of the robot. Moreover, during the topological mapping stage, we stored visual features and scene properties in topological nodes, which is conductive to judge whether it has reached the goal location with more flexibility for the mobile robot. Systematic experiments were conducted on a real mobile robot with a monocular camera. The results indicate that ERF-IMCS exhibits great performance in efficiency and robustness compared to existing navigation methods.  
[PERSON] and [PERSON] designed the study; [PERSON] and [PERSON] conducted the experiments; [PERSON] implemented the system and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China (Grant No. 2019 YFB1310802).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: The topological navigation path of the robot.  
Figure 12: Time cost of each navigation method. Each trial was performed 5 times.
# Long-Term EEG Component Analysis Method Based on Lasso Regression
###### Abstract  
At present, there are very few analysis methods for long-term electroencephalogram (EEG) components. Temporal information is always ignored by most of the existing techniques in cognitive studies. Therefore, a new analysis method based on time-varying characteristics was proposed. First of all, a regression model based on Lasso was proposed to reveal the difference between acoustics and physiology. Then, Permutation Tests and Gaussian fitting were applied to find the highest correlation. A cognitive experiment based on 93 emotional sounds was designed, and the EEG data of 10 volunteers were collected to verify the model. The 48-dimensional acoustic features and 428 EEG components were extracted and analyzed together. Through this method, the relationship between the EEG components and the acoustic features could be measured. Moreover, according to the temporal relations, an optimal offset of acoustic features was found, which could obtain better alignment with EEG features. After the regression analysis, the significant EEG components were found, which were in good agreement with cognitive laws. This provides a new idea for long-term EEG components, which could be applied in other correlative subjects.  
emotion cognition; Lasso regression; EEG component analysis; long-term EEG; affective computing 20211427114271142  
only contain perceptual information, but also cognitive information, which can better express an emotional experience. Compared with EMG, EEG is the brain's natural reflection of information, which can accurately analyze the dynamic activities of the brain. Therefore, EEG signals are selected to study long-term emotions. However, the research on EEG emotion is still in the primary stage, and the research objects are simple notes. The lack of intrinsic features and calculation models of long-term EEG greatly limits the development of affective computing. Therefore, a long-term EEG signal analysis method is necessary, which could more scientifically and accurately reflect emotional changes, and deeply explore the cognitive principle of emotion and brain.  
In this article, a long-term EEG component analysis method based on Lasso regression is proposed, which could analyze long-term and continuous EEG signals, and find emotion-related components.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture
### Read Operation  
During the read state, when the cell stores logic '1' (Q = S1 = 1, QB = S0 = 0), BL and BLB are pre-charged to VDD, and WL and WWL are then asserted as HIGH, turning on transistors N7 and N8 to connect the internal nodes to the bit lines. BL remains at VDD through N8 and BLB discharges through N7, producing a voltage differential (\(\Delta\)V > 50 mV). As a result, the sense amplifier is triggered by the differential and the stored data is output. During read, storage nodes remain undisturbed so that the original data is preserved. Operational waveforms of WRTU-16T during the hold, read, and write modes are identified in Figure 3.