esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Optimization
### Effects of System Geometry  
The optimized results use argon (Ar) molecules as the working gas to reduce computational expenses as plasma behavior is similar to those employing molecular gas species with complex chemical reactions. The reactions and collision formulas of argon are listed in Table 1.  
In order to see the effects of system geometry, Figure 5 shows (a) the electron density, (b) the ion density, (c) the electric field, and (d) the electron temperature as functions of the height of the sample holder. It is apparent that the holder height significantly changes the plasma density. After analyzing the electric field, electron density, electron temperature, and ion density through COMSOL modeling, it is found that the distribution of energy inside the cavity is optimized at the stage height of 118 mm. The local electric fields at the  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Formulas** \\ \hline
1 & \(\mathrm{e}+\mathrm{Ar}\rightarrow\mathrm{e}+\mathrm{Ar}\) \\
2 & \(\mathrm{e}+\mathrm{Ar}\rightarrow\mathrm{e}+\mathrm{Ar}^{*}\) \\
3 & \(\mathrm{e}+\mathrm{Ar}\rightarrow\mathrm{2e}+\mathrm{Ar}^{+}\) \\
4 & \(\mathrm{e}+\mathrm{Ar}^{*}\rightarrow\mathrm{2e}+\mathrm{Ar}^{+}\) \\
5 & \(\mathrm{e}+\mathrm{Ar}^{*}\rightarrow\mathrm{e}+\mathrm{Ar}\) \\
6 & \(\mathrm{Ar}^{*}+\mathrm{Ar}^{*}\rightarrow\mathrm{e}+\mathrm{Ar}+\mathrm{Ar}^{+}\) \\
7 & \(\mathrm{Ar}^{*}+\mathrm{Ar}\rightarrow\mathrm{e}+\mathrm{Ar}+\mathrm{Ar}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Collisions and chemical reactions of argon (Ar) used in the MPJCVD model.  
Figure 4: (a) Reactor geometry and (b) the FEM adaptive meshing of the MPJCVD system.  
antenna for different heights of the stage at the same microwave power of 700 W, the gas pressure of 70 Torr, and the microwave frequency of 2.45 GHz are compared. At a height of 118 mm, the electron and ion densities are relatively concentrated at \(10^{18}\) m\({}^{-3}\), while the electron temperature is 30 eV. Figure 6 shows the contours of electron density, ion density, electric field, and electric temperature for the optimized case.
## 3 Analysis of the Collaboration in AR Context
### Definition of Collaboration in AR Context and Its Main Characteristics
* _Content alignment and profiled visualization_: Digital elements may have different views tailored to the specifications of different users. Thus, the visual aspect presented by AR assets may not be exactly the same for all users. Among these characteristics are the object position, scale and rotation as well as more advanced ones such as color or shape. For example, let us consider a 3D AR cube that is positioned on a real, physical table. If the AR content is _aligned_, the cube will be seen by all participants in the same exact position (setting aside possible drift errors). On the contrary, if the content is _not aligned_ the same cube will be represented in different positions for each user. For example, one may see it on the table while another user may see it on the floor. However, both representations will refer to the same cube. In this case, being aligned refers not only to the position, but also to characteristics such as rotation and scale (e.g., it is possible that each uses sees the cube in the same position but with different sizes). On the other hand, the information displayed to one user may not be visible to another, or it may show added or removed elements. In this sense we say that the content has been adapted to the user's profile; in other words it has been _profiled_. * _Collaboration model_: the collaboration model defines how users organize themselves to visualize and interact with AR content. This organization may come from themselves (they agree by speech), due to implemented control mechanisms (e.g., if the element of interaction has to be transferred) or by high-level rules (e.g., it is defined that they have to work in shifts).
## 3 Method
### BTDM Decoder
#### 3.2.1 Entity Extraction  
Our proposed BTDM is a bidirectional extraction framework. Due to the space limitation, we only introduce the details of BTDM in the _s2p_ direction, and the details of the model in the backward direction _o2p_ are similar.  
The subject tagger module extracted the subject for the _s2p_ direction. Following prior research [17; 18], we employed the span-based tagging strategy to effectively identify the subject and its position. In particular, the entity extraction module employed two binary classifiers to identify the start and end positions of the subject by assigning a binary(0/1) tag to each token. The binary tag indicates whether the current token position is the start or the end of a subject. If the token at the present position is the start or end of a subject, then Tag 1 is assigned; otherwise, Tag 0 is assigned. The detailed operations of the entity extraction on each token in the sentence are as follows:  
\[\begin{split} p_{i}^{s\_start}&=\sigma(\textbf{{W} }_{s\_start}\textbf{{hs}}_{i}+\textbf{{b}}_{s\_start})\\ \ p_{i}^{s\_end}&=\sigma(\textbf{{W}}_{s\_end} \textbf{{hs}}_{i}+\textbf{{b}}_{s\_end})\end{split} \tag{3}\]  
Figure 2: The structure of our the proposed approach, displaying the processing for handling one sentence that contains three relational triples (_[PERSON], born in, Beijing_), (_[PERSON], nationality, China_), and (_Beijing, capital of, China_). In this example, BTDM first identifies entity pairs by translating and decoding in both directions, and then aligns them. Then, the relation is judged by linking a (entity pairs–relations) bipartite graph. Please note that all invalid links have been removed for the sake of clarity.  
where \(p_{i}^{s,start}\) and \(p_{i}^{s,end}\) represent the possibility that the \(i\)-th token in a sentence is the start or end position of a subject, respectively. If the possibility exceeds the threshold, we set the predefined value; then, the current token is assigned Tag 1. Otherwise, the current token is assigned Tag 0; \(\sigma\) denotes an activation function.  
The process of the \(o2p\) direction is similar and will not be introduced here. At this stage, we obtain all possible sets of subjects \(Y_{s}(S)\)and sets of objects \(Y_{o}(S)\), respectively.
## 3 Results and Discussion
### Study on Air Leakage Law of Shallow Buried Coal Seam  
1. On 7 June 2021, SF\({}_{6}\) was released from the observation hole of the sealed wall of contact lane 5106 and 5104, with a discharge flow of 800 mL/min and a release time of 3.5 h. On the 12 th of the day: After 30 min of release, SF\({}_{6}\) gas was received at the corner of return air and 15 m away from the corner of 5106 working face. Through gas sample analysis, the maximum SF\({}_{6}\) concentration was 0.00345 ppm, and the SF\({}_{6}\) background concentration measured in the inlet lane was 0.00565 ppm. It can be considered that there is no air leakage between the goaf 5104 and the working face 5106.
2. SF\({}_{6}\) was released in the 5106 transport trough on 8 June 2021, with a discharge flow of 800 mL/min and a release time of 1 h. The release begins at 13:57 and ends at 15:07. After 15 min of release, samples were taken at each measuring point successively, and analyzed by SF6 special chromatograph. It was found that the air leakage between the inlet and return air lanes was 20 m\({}^{3}\)/min.
3. On 8 June 2021, SF\({}_{6}\) was released at the detection hole of 5106 transportation lane and 1112 goaf with a discharge flow of 25 L/min and a release time of 1.5 h. In order to accelerate the diffusion of SF\({}_{6}\) gas in the 1112 goaf after the release, nitrogen was injected into the detection hole for 30 min. SF\({}_{6}\) gas was measured at the corner of the return air with SF\({}_{6}\) portable instrument and the concentration signal was the strongest. The gas was taken when the SF\({}_{6}\) gas was detected by SF\({}_{6}\) portable instrument and the concentration signal was the strongest. It is 380 times of the SF\({}_{6}\) background concentration measured in the inlet roadway, which is 0.00565 ppm. It can be determined that there is air leakage between the goaf 1112 and the working face 5106. In addition, through field observation, there is an obvious gas outpouring phenomenon in the detection hole, and the pressure in the hole is 240 pa higher than that in the roadway, which also proves that the 5106 working face is connected with the 1112 goaf after mining.
4. In order to verify whether there is air leakage between the ground crack and the 5106 working face, SF\({}_{6}\) was released at the ground crack on 15 June 2021, and the SF6 portable instrument was used for simultaneous detection at the return air corner of the 5106 working face. The release began at 11:05 on the same day with a discharge flow of 10 L/min. SF\({}_{6}\) gas was received at the return air corner of 5106 working face at 11:29 after 24 min of release, and the concentration signal was very strong. Therefore, it could be concluded that there was air leakage between the ground crack and 5106 working face.
## 4 Numerical Experimentation
### Experimental Results for Datasets
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Model** & **RMSE** & **MAE** & **MAPE (\%)** & **RMSE** & **MAE** & **MAPE (\%)** \\ \hline  & Wind turbine 1 & \multicolumn{4}{c}{Wind turbine 3} \\ \hline EEMDCSSOVM & 0.3513 & 0.1815 & 4.79 & 0.2463 & 0.2120 & 2.69 \\ EEMDPSOVM & 0.4652 & 0.2159 & 5.63 & 0.3027 & 0.2436 & 2.82 \\ COSMV & 0.8294 & 0.2975 & 10.67 & 0.5121 & 0.4251 & 5.08 \\ PSOSVM & 1.0135 & 0.7100 & 12.18 & 0.7928 & 0.6362 & 9.70 \\ \hline  & Wind turbine 2 & \multicolumn{4}{c}{Wind turbine 4} \\ \hline EEMDCSSOVM & 0.2342 & 0.1841 & 3.07 & 0.2679 & 0.3028 & 3.91 \\ EEMDPSOSVM & 0.2744 & 0.2191 & 3.24 & 0.3147 & 0.1869 & 4.44 \\ COSMV & 1.1381 & 0.1927 & 7.63 & 0.9204 & 0.6137 & 9.27 \\ PSOSVM & 0.6680 & 0.5165 & 8.58 & 1.1478 & 0.8190 & 10.15 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the value of RMSE, MAE, and MAPE between the proposed model and some related models. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Model** & **WI** & **E\({}_{\text{NS}}\)** & **E\({}_{\text{LM}}\)** & **WI** & **E\({}_{\text{NS}}\)** & **E\({}_{\text{LM}}\)** \\ \hline  & Wind turbine 1 & \multicolumn{4}{c}{Wind turbine 3} \\ \hline EEMDCSSOVM & 0.9838 & 0.9332 & 0.7600 & 0.9918 & 0.9670 & 0.8268 \\ EEMDPSOSVM & 0.9723 & 0.9251 & 0.7148 & 0.9744 & 0.9310 & 0.7201 \\ COSMV & 0.9152 & 0.6858 & 0.4460 & 0.9667 & 0.8720 & 0.6625 \\ PSOSVM & 0.8622 & 0.5367 & 0.3599 & 0.8239 & 0.4950 & 0.1197 \\ \hline  & Wind turbine 2 & \multicolumn{4}{c}{Wind turbine 4} \\ \hline EEMDCSSOVM & 0.9929 & 0.9713 & 0.8388 & 0.9839 & 0.9350 & 0.7565 \\ EEMDPSOSVM & 0.9876 & 0.9424 & 0.7950 & 0.9786 & 0.9234 & 0.7159 \\ COSMV & 0.9206 & 0.7011 & 0.5134 & 0.8274 & 0.4885 & 0.2514 \\ PSOSVM & 0.9174 & 0.6852 & 0.4812 & 0.7490 & 0.3355 & 0.1253 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of the value of WI, \({\rm E_{NS}}\), and \({\rm E_{LM}}\) between the proposed model and some related models.
## 1 Introduction
Figure 1: Number of peer-reviewed journal articles discussing AI transparency in healthcare. Using the Web of Science, we searched and counted the works containing (Transparency, Ethics, Equity, Interpretability, Explainability, Security, Privacy, Intellectual property, Trust, Reliability) and (Healthcare, Medicine, Diagnosis, Prognosis, Prediction, Health, Care, Medical) and (Artificial intelligence) in their abstracts. The common denominator in these and other studies is that there are serious transparency problems that have yet to be addressed, as well as a mismatch between academics' desire to publish research papers showcasing sophisticated AI techniques versus the actual application of such research in healthcare. Stakeholder perspectives on AI trust have also come to the forefront of the literature [28; 35; 36]. [PERSON] et al. [28] surveyed 77 medical doctors in The Netherlands, Portugal, and the U.S. about the ethics surrounding Health AI. They found that physicians (i) recognise the need to automate elements of the decision-making process but also state that they must be the ones making the final decision, (ii) acknowledge that, while private tech companies seek profit, it is fundamental to assess AI to the highest standards and hold corporations responsible for any harm caused by their tools, and (iii) highlight the importance of engaging doctors--as end-users--throughout the development process. [PERSON] [35] collected and analysed responses from 307 individuals in the USA about their perceptions of AI medical devices with clinical decision-support characteristics. The authors found that higher concerns, especially regarding performance anxiety and communication barriers, coincide with higher perceived risk and lower benefit, implying that when patients do not comprehend how AI devices work, they distrust them the most. The authors advise carrying out and publishing studies on the benefits and drawbacks of using AI devices such that customers are well aware of them and may have more trust in AI. Identifying the challenges that professionals in computing and healthcare perceive with AI in healthcare practice might serve as a springboard for additional education, research, and policy development [4; 30]. Thus, the question this paper seeks to answer is: _what challenges do experts and professionals in computing and healthcare identify concerning transparency of AI in healthcare_? Here we respond to this question based on recent literature discussing this topic, and on a global online survey, we sent to professionals working in computing and healthcare and potentially within AI. Based on these, we propose a set of recommendations to enhance AI transparency in healthcare.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 2 Related Work
### Object Detection  
Object detection is a core task in computer vision that involves identifying and localizing objects within an image, playing a vital role in road sign recognition by enabling systems to detect and classify signs, symbols, and other relevant features. Over the years, significant advancements in deep learning have introduced highly effective object detection models, with Faster-RCNN and YOLO being among the most prominent. Faster-RCNN is a two-stage object detection framework that integrates region proposal generation and object classification into a unified architecture. The model employs a Region Proposal Network (RPN) to identify potential object regions, which are then passed to subsequent convolutional layers for classification and bounding box refinement [1; 2]. This approach excels in detecting small and overlapping objects, making it particularly suitable for complex visual environments such as road signs, where elements like directional arrows, road numbers, and other small-scale features must be identified [6].  
In contrast, YOLO represents a single-stage object detection approach that processes the entire image in one pass through a neural network. By dividing the image into a grid and predicting bounding boxes and class probabilities simultaneously for each cell, YOLO achieves high-speed detection suitable for real-time applications such as autonomous vehicles and traffic monitoring systems [3; 4]. However, YOLO's grid-based prediction method may encounter challenges with small or densely packed objects, such as textual elements or arrows frequently found in road signs [7]. Despite this limitation, YOLO remains a popular choice for tasks requiring efficiency and scalability due to its real-time processing capabilities. Together, these models form the foundation of the modern object detection techniques used in road sign recognition systems, with each offering distinct advantages tailored to specific requirements and scenarios.  
The effectiveness of Faster-RCNN for road sign detection has been well-documented in previous studies, demonstrating superior accuracy for small-object detection compared to single-stage detectors like YOLO. This study builds upon such findings and prioritizes the integration of detected sign components with user-adapted reasoning rather than conducting a direct benchmark of detection models. Nevertheless, Faster-RCNN was selected due to its ability to handle overlapping objects and small text regions, as highlighted in related research.
# A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer
## 3 The Proposed Method
### Dual Attention Network
#### 3.6.1 Position Attention Module  
The location attention module is introduced to build a rich context model based on local features. The location attention module encodes broad context information into local features, thereby enhancing their representation capabilities. As shown in Figure 1(a), the given local feature \(A\in R^{\mathbb{C}\times H\times W}\) is first input into a convolutional layer to generate two new feature maps \(B\) and \(C\), where \(\{B,C\}\in R^{\mathbb{C}\times H\times W}\). Then they are reshaped into \(R^{\mathbb{C}\times N}\), where \(N=H\times W\) is the number of pixels. Next, matrix multiplication is performed between the transposition of \(C\) and \(B\), and the softmax layer is applied to calculate the space. Note that the figure \(S\in R^{N\times N}\).  
\[s_{ji}=\frac{\exp(B_{i}\times C_{j})}{\sum_{i=1}^{N}\exp(B_{i}\times C_{j})} \tag{8}\]  
where \(s_{ji}\) represents the influence of the \(i\)-th position on the \(j\)-th position. As the similarity of the feature representations of two locations increases, the correlation between them gets close.  
Additionally, feature A is sent into the convolutional layer to generate a new feature map \(D\in R^{\mathbb{C}\times N}\), and then the obtained feature map is reshaped into \(R^{\mathbb{C}\times N}\). Next, matrix multiplication is performed between the transpose of \(D\) and \(S\), and the result is reshaped to \(R^{\mathbb{C}\times H\times W}\). Finally, the reshaped result is multiplied by the scale parameter \(\alpha\) and an element-wise summation is performed by using feature \(A\) to obtain the final output \(E\in R^{\mathbb{C}\times H\times W}\) as follows.  
\[E_{j}=\alpha\sum_{i=1}^{N}\left(s_{ji}D_{i}\right)+A_{j} \tag{9}\]  
where \(\alpha\) is initialized to 0, and then more weights are gradually assigned by learning. According to Equation (9), the resulting feature \(E\) of each location is the weighted sum of the features of all locations and the original feature. Therefore, there is a global context view, and the contexts based on the spatial attention map are selectively aggregated. Similar semantic features are mutually improved, thereby improving intra-class compactness and semantic consistency.  
Figure 2: The details of the position attention module and channel attention module.
## 4 Results and Analysis
### Analysis of Influencing Factors
#### 4.2.5 Construction Principle of TCC  
To sum up, construction principles for the application of TCC in practice are proposed as follows. First of all, the position of TCC had little influence on the improvement effect of RWT. So it should be determined after considering the complex terrains, economic condition, waterway transport, and other specific cases. Secondly, \(Pr\) should be set between 65% and 90%, whereby the larger the \(Pr\) chosen, the higher RWT was. Thirdly, the \(OSD\) and \(RQ\) had little effect on RWT, so in practical engineering its variation will not influence the improving effect of TCC on RWT.
###### Abstract  
Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety. A novel procedure for the classification of maize seed varieties based on HSI was proposed in this study. The optimal wavelengths for the classification of maize seed varieties were selected using the successive projections algorithm (SPA) to improve the acquiring and processing speed of HSI. Subsequently, spectral and imaging features were extracted from regions of interest of the hyperspectral images. Principle component analysis and multidimensional scaling were then introduced to transform/reduce the classification features for overcoming the risk of dimension disaster caused by the use of a large number of features. Finally, the integrating features were used to develop a least squares-support vector machines (LS-SVM) model. The LS-SVM model, using the integration of spectral and image features combined with feature transformation methods, achieved more than 90% of test accuracy, which was better than the 83.68% obtained by model using the original spectral and image features, and much higher than the 76.18% obtained by the model only using the spectral features. This procedure provides a possible way to apply the multispectral imaging system to classify seed varieties with high accuracy.  
hyperspectral imaging; maize seed; classification; SPA; LS-SVM; feature transformation methods
## 3 Method for Estimating the NMF Rank
### Noise Variance Estimation
Even if multiple notes are played simultaneously at the first onset-it is generally a chord-\(\mathbf{R_{v}}(N_{rank1})\) can be expected to be a rank-1 matrix because the notes possess the same time basis vector as shown in Figure 8[9]. Therefore, \(\mathbf{R_{v}}(N_{rank1})\) has \((N_{rank1}-1)\) noise eigenvalues of \(2\leq k\leq N_{rank1}\), which correspond to the remaining components. Figure 7: Eigenvalues of \(\mathbf{R_{v}}\) for all of the data (\(N_{p}=87\)): (**a**) Full-scale display, (**b**) scaled display for noise eigenvalues, where “x” denotes a signal eigenvalue and “o” denotes a noise eigenvalue. From the relationship of (26) or (27), we can estimate the noise variance by using either the maximum or the median values of the noise eigenvalues. Consequently, we propose two algorithms. In the first, the noise variance \(\sigma_{e}^{2}\) is estimated as follows:  
\[\sigma_{e}^{2}=\frac{l_{2}}{(1+\gamma^{-1/2})^{2}}, \tag{29}\]  
using the maximum value of the noise eigenvalues. In the second version, the noise variance \(\sigma_{e}^{2}\) is estimated as follows:  
\[\sigma_{e}^{2}=\frac{med(l_{2},\cdots,l_{\rho})}{F_{\gamma,1}^{-1}\left(1/2 \right)}, \tag{30}\]  
using the median value, because the MP distribution has the property of \(F_{\gamma,\sigma_{e}^{2}}^{-1}\left(x\right)=\sigma_{e}^{2}F_{\gamma,1}^{-1} \left(x\right)\)[16]. Algorithm 1 shows a summary of the proposed algorithm. Because the proposed methods were developed based on the characteristics of the AMT method, they may not be applied to other NMF-related tasks. However, the key idea is to determine the noise variance using part of the data. This is possible when the task's characteristics can be used to find a suitable rank for part of the signal. Therefore, if we can find the proper characteristics of a target task, the proposed methods can be extended to other NMF-related tasks. ```
0: Spectrogram \(\mathbf{V}\), second onset frame number \(N\). 1. Set the rank-1 frame number \(N_{rank1}\) as a value smaller than the second onset frame number. 2. Calculate the rank-1 covariance matrix \(\mathbf{R_{v}}(N_{rank1})\) using (28). 3. Determine \(l_{2}\), the second largest eigenvalue of \(\mathbf{R_{v}}(N_{rank1})\), and estimate the noise variance using (29) (version 1) or (30) (version 2). 4. Calculate the covariance matrix \(\mathbf{R_{V}}=\mathbf{V}\mathbf{V}^{T}/N\). 5. Calculate the SURE risk function \(\mathfrak{R}_{R}\) with the eigenvalues of \(\mathbf{R_{V}}\) using (22). 6. Determine the number of bases \(\hat{R}\) in accordance with \[\hat{R}=\arg\min_{R}\mathfrak{R}_{R}.\]
```  
**Algorithm 1** Rank Estimation with SURE (RESURE)  
An onset detection algorithm must be used to determine the first and second onset frames. Many studies have proposed successful onset detection algorithms [28; 29], but developing such an algorithm is beyond this study's scope. Figure 8: Illustrative diagram of the NMF model for the case in which a chord is played at the first onset.