esa-sceva/satcom-chunk-collection huggingface dataset:
# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 2 Related Works
### Generic Semantic Segmentation  
Traditional semantic segmentation algorithms usually extract the color value, gray value and geometric shape features of the image to segment the image into several non-intersecting regions and then annotate these segmented regions to get the image segmentation. This kind of segmentation effect is often not ideal. Compared with traditional semantic segmentation algorithms, the semantic segmentation algorithm based on deep learning can not only extract the color, texture and shape information of the image, but also extract the high-level semantic information of the image. This kind of algorithm often has higher segmentation accuracy. Classical semantic segmentation algorithms based on a convolutional neural network include FCN [12], PSPNet [13], U-Net [14], DeepLab series [15; 16; 17], etc.  
FCN [12] replaces the fully connected layer in the CNN network with a fully convolutional layer to construct an end-to-end, pixel-to-pixel semantic segmentation network. FCN opens a new idea for the field of semantic segmentation, but the network does not consider the relationship between pixels and lacks spatial consistency, which will cause the misjudgment of categories and the segmentation results to not be fine enough. PSPNet [13] improves the segmentation problem of the FCN network and proposes a spatial pyramid module to extract the context information and multi-scale information on the dilation backbone, reducing the probability of false segmentation of image categories in the FCN network. DeepLab series algorithm [15; 16; 17] is a semantic segmentation model developed by the Google team based on CNN. The latest algorithm is DeepLab v3+ [17]. DeepLabv3+ does not use full connection CRF and proposes deep separable convolution. The algorithm combines atrous spatial pyramid pooling (ASPP) with the Encoder-Decoder model and applies Xception and depthwise separable convolution to ASPP and a decoder, which can greatly reduce the amount of computation while maintaining the performance. Both the extended backbone network and the encoder-decoder structure can learn detailed information and high-level semantic information at the same time, but this method often has a high computational cost. In this paper, we propose an efficient architecture that achieves a good balance between speed and accuracy.
## 4 The Multi-Resolution Pre-Stack Migration Imaging Method
### The Finite-Difference Migration Imaging Method
Let \(\mathbf{u}=\mathbf{u}_{i,j+1/2}^{n+1/2}\), \(\frac{\partial\mathbf{u}}{\partial z}=\frac{\mathbf{u}_{i,j+1/2}^{n+1}-\mathbf{u}_{i,j+1/ 2}^{n}}{\Delta z}\), then Equation (20) can be simplified as  
\[\frac{\partial^{2}\mathbf{u}}{\partial z\partial t}=\frac{1}{\Delta z\Delta t} \Big{(}\mathbf{u}_{i,j+1}^{n+1}-\mathbf{u}_{i,j}^{n+1}-\mathbf{u}_{i,j+1}^{n}+\mathbf{u}_{i,j }^{n}\Big{)}. \tag{23}\]  
For the \(\frac{\partial^{2}\mathbf{u}}{\partial x^{2}}\), let \(\mathbf{u}=\left(\mathbf{u}_{i,j+1/2}^{n+1}+\mathbf{u}_{i,j+1/2}^{n}\right)/2\), then  
\[\frac{\partial^{2}\mathbf{u}}{\partial x^{2}}=\frac{1}{2\Delta x^{2}}\Bigg{(} \begin{aligned} \mathbf{u}_{i,j+1/2}^{n+1}-2\mathbf{u}_{i,j+1/2}^{n+1}+\mathbf{u}_{i-1,j+1/2}^{n+1}\\ +\mathbf{u}_{i+1,j+1/2}^{n}-2\mathbf{u}_{i,j+1/2}^{n}+\mathbf{u}_{i-1,j+1/2}^{n} \end{aligned} \Bigg{)}, \tag{24}\]  
In summary, Equation (20) can be written in the following form:  
\[\left(\mathbf{u}_{i,j+1}^{n+1}-\mathbf{u}_{i,j}^{n+1}-\mathbf{u}_{i,j+1}^{n}+\mathbf{u}_{i,j}^ {n}\right)=\frac{-v\Delta z\Delta t}{8\Delta x^{2}}\Bigg{(} \begin{aligned} \mathbf{u}_{i+1,j+1/2}^{n+1}-2\mathbf{u}_{i,j+1/2}^{n+1}+\mathbf{u}_{i-1,j+1/2}^{n+1}\\ +\mathbf{u}_{i+1,j+1/2}^{n}-2\mathbf{u}_{i,j+1/2}^{n}+\mathbf{u}_{i-1,j+1/2}^{n} \end{aligned} \Bigg{)}, \tag{25}\]  
Figure 6: Schematic diagram of the symmetric implicit difference scheme. culating \(\mathbf{u}_{j}^{n+1}\), it is known that \(\mathbf{u}_{j+1}^{n+1}\) is zero. Therefore, as long as matrix \(\mathbf{A}\) is invertible, Equation (29) is solvable, that is:  
\[\mathbf{u}_{j}^{n+1}=\mathbf{A}^{-1}\mathbf{B}\mathbf{u}_{j}^{n}-\mathbf{u}_{j+1}^{n}. \tag{33}\]  
Since \(\mathbf{A}\) is a diagonally dominant matrix and \(\alpha\) is always greater than zero, we have \(|1+2\alpha|>2|\alpha|\), which implies that \(\det(\mathbf{A})\
eq 0\). Therefore, the matrix \(\mathbf{A}\) is invertible [33]. The wavefield at the grid nodes in the subsurface can be extrapolated downward using the aforementioned method. Subsequently, the wavefield values at the grid nodes, corresponding to a travel time of \(2z/v\), are extracted as imaging data. These data are then arranged in both the horizontal and depth directions, ultimately yielding the imaging results of the electrical interface.
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
## 2 Results
### Prediction of Subcellular Localization of SmIS1 and SmIS2  
We predicted the subcellular localization of SmIS1 and SmIS2 using WOLF PSORT, Predotar and ProtComp. We compared the prediction results in Table 2. WOLF PSORT predicted that SmIS1 was possibly localized to the endoplasmic reticulum or chloroplast. However, the corresponding scores were not high, and the Predotar and ProtComp predictions were inconsistent with the WOLF PSORT  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Enzyme** & \multicolumn{6}{c}{**Substrate**} \\ \hline  & \multicolumn{3}{c}{**8-Oxogeranial**} & \multicolumn{3}{c}{**NADPH**} \\  & **Km** & **Kcat** & **Kcat/Km** & **Km** & **Kcat** & **Kcat/Km** \\  & **(μM)** & **(s\({}^{-1}\))** & **(s\({}^{-1}\)M\({}^{-1}\))** & **(μM)** & **(s\({}^{-1}\))** & **(s\({}^{-1}\)M\({}^{-1}\))** \\ SmIS1 & 7.29 \(\pm\) 1.59 & 0.11 \(\pm\) 0.007 & 15,559.6 \(\pm\) & 77.94 \(\pm\) 6.03 & 0.135 \(\pm\) 0.01 & 1731.7 \(\pm\) 11.3 \\ SmIS2 & 54.37 \(\pm\) 4.29 & 0.034 \(\pm\) 0.001 & 628.6 \(\pm\) 75.9 & 41.83 \(\pm\) 7.2 & 0.026 \(\pm\) 0.01 & 628 \(\pm\) 104.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compared kinetic parameters of recombinant of SmIS1 and SmIS2.  
Figure 7: Expression pattern of the SmIS1 and SmIS2 genes in planta. **(A)** Semi-quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) analysis in leaf, stems, and flower. Actin gene was used as a loading control; **(B)** Real-time quantitative PCR (RT-qPCR) analysis in leaf, stems, and flower.  
prediction results. The two programs predicted that SmIS1 was possibly localized to the cytoplasm. WOLF PSORT and ProtComp predicted that SmIS2 was possibly localized to the cytoplasm.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
## 2 System Model
### Power Allocation Scheme  
In NOMA, different users are separated on the basis of their respective power, making the power allocation coefficient a major factor in affecting the performance of the system. In proposed Q-CNOMA-based system, the ordering of users is based on their respective user rate \(\text{B}_{i}\) not on their channel conditions, as users are placed very close to each other. The power allocation scheme employed in our system uses rates of receivers to assign the power allocation coefficient which can be calculated as follows according to [18]:  
\[\beta_{i}=\frac{1/\text{B}_{i}}{\sum_{j=1}^{\text{U}}\frac{1}{\text{B}_{j}}} \tag{6}\]  
where \(U\) is the total number of users in a cell and \(\text{B}_{i}\) is the rate of the \(i\)th user. In order to maintain the fairness among users, inverse rates are used for power allocation so that the user with a high target rate does not experience a high \(SINR\) as compared to users with low rate requirement.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 1 Introduction
For machine learning methods, classification accuracy can be defined by considering the viewpoint position as a category, and using classification accuracy to define the accuracy of viewpoint estimation [10]. Viewpoint estimation is not entirely equivalent to classification [16]; for example, in Figure 1, Figure 1,b belongs to the same class in the view of image classification. However, if inferred based on the viewpoint of the image, the viewpoints of Figure 1,c are the same and differ greatly from the viewpoint of Figure 1b. In addition, in engineering applications, such as taking photos or installing surveillance cameras, even if there is a slight angular deviation between the actual viewpoint and the optimal viewpoint it is often acceptable, because adjacent viewpoints still contain some accuracy information when characterized by cosine distance. Another example is  
Figure 1: Three chairs displayed from different viewpoints. (**a**) A chair displayed from the upper right viewpoint; (**b**) The chair displayed from the upper left viewpoint; (**c**) Another chair displayed from the upper right viewpoint. three-dimensional reconstruction, where 15-degree angle perturbations to the estimated viewpoint of the input image are acceptable [17]. Considering the differences between image-based viewpoint estimation and image classification, it is necessary to establish a publicly available image dataset with viewpoint direction annotation for viewpoint estimation problems and new accuracy evaluation metrics. In this paper, we contribute to addressing these two issues. The highlights of this paper are as follows:  
1. We propose an image-based viewpoint estimation method that demonstrates the complete process from creating a dataset to evaluating the results. 2. In the viewpoint position selection module, we integrate the initial viewpoint moving strategy into the spherical viewpoint uniform sampling based on analytical methods to enhance the effectiveness of spherical uniform random sampling. 3. A new dataset has been established and made available on the GitHub website. Based on the new dataset learning, we implemented image-based viewpoint estimation using a simple CNN. The viewpoint estimation results are evaluated based on the new accuracy calculation formula. 4. We use the viewpoint estimation method to analyze the chair display angles on a furniture website, illustrating the display angle preferences of merchants displaying items. The remainder of the paper is organized as follows. Section 2 reviews related work. Section 3 describes the methods. Section 4 introduces the dataset and the implementation details. Section 5 concludes the paper by summarizing our study and future works.
## 5 Conclusions and Future Work  
In this study, we propose an effective model for AF detection from short ECG recordings. The model can adequately capture rhythmic features in short single-lead ECG recordings for classification. Through a comparative analysis of multiple experiments, the model can meet the daily testing requirements of wearable devices. The experimental results show that the F1 score of the MCNN-BLSTM model in the CinC2017 testing set is 84.56%, and the F1 score of N, AF, and O class is 92.02%, 82.03%, and 79.62%, respectively. The classification results of the proposed model are higher than that of the model MCNN-ULSTM and 1 BCNN-BLSTM. Our proposed method achieves competitive results compared to most current state-of-the-art methods. The MCNN-BLSTM model is universal to different databases. In the CPSC2018 database, the average score of F1 is 82.01%, and the F1 score  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source Reference** & **Model** & \(\mathbf{F1_{Normal}}\) & \(\mathbf{F1_{AF}}\) & \(\mathbf{F1_{Other}}\) & \(\mathbf{F1_{Overall}}\) \\ \hline [PERSON] [6] & CNN-LSTM & 90.28 & 82.21 & 73.24 & 81.91 \\ [PERSON] [18] & LSTM & 91.00 & 84.00 & 70.00 & 81.67 \\ [PERSON] [7] & BIT-CNN & 89.73 & 81.06 & 74.45 & 81.75 \\ [PERSON] [8] & VGG & 90.00 & 83.00 & 75.00 & 82.67 \\ [PERSON] [36] & ECG-RCLSTM-Net & - & - & - & 82.40 \\ Our work & MCNN-BLSTM & 92.02 & 82.03 & 79.62 & 84.56 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between the related work and the method proposed in this work.  
Figure 3: Scatter diagram based on features learned from different branch. Panels (**a–d**) correspond to the features extracted by the first branch, the second branch, the third branch and the mdoel MCNN-BLSTM, respectively.  
for N, AF, and O classes is 69.41%, 85.47%, and 91.15%, respectively. In addition, a visual analysis of the ECG features learned by the proposed model is performed to verify the effectiveness of learned features in screening AF patients.  
In future research, we plan to consider other atrial arrhythmia types (atrial premature beats, atrial flutter, etc.) as follow-up studies. As atrial flutter has the same clinical significance as atrial fibrillation, subsequent studies can be considered a beneficial incremental effect.  
Conceptualization, H.Z.; investigation, H.Z., H.G. and J.G.; methodology, H.G. and J.G.; resources, H.Z.; supervision, P.L. and Z.W.; validation, G.C.; writing--original draft, H.G. and J.G.; writing--review and editing, H.Z. and H.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research was partly supported by the Key Research, Development, and Dissemination Program of Henan Province (Science and Technology for the People) [grant no. 182207310002], and the Key Science and Technology Project of Xinjiang Production and Construction Corps [grant no. 2018 AB017].  
The authors declare no conflict of interest.
## 2 System Methodology for Coexisting Public Safety Networks
### Priority Gates Model of the PS-LTE Network  
The priority gates model for the PS-LTE is the procedure that divides the process required for a user's device to connect to the network into three steps, as shown in Figure 2[20]. To access and use the network, device users must pass the criteria presented at each step. The priority gates model's first step is the access priority. The user's priority is classified, as shown in Table 1, according to the access class in the device's chip [21].  
Consequently, the access priority step distinguishes public safety users accessing the PS-LTE network to obtain resources first, according to the access class of the chip embedded in the device. The third-generation partnership project (3 GPP) prioritized use of the same frequency band into public and non-public safety users. However, in Korea, the frequency band of public safety networks is the same, so the access priority step has to be changed.  
The admission priority step uses allocation and retention priority (ARP) to prioritize resources according to preset pre-emptions. ARP consists of three parameters: ARP priority level, pre-emption capability indicator, and pre-emption vulnerability. ARP is only involved in creating or rejecting a new  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User Priority** & **User Identification** & \begin{tabular}{c} **Traffic** \\ **Class** \\ \end{tabular} & \begin{tabular}{c} **Barring** \\ **(RACH)** \\ \end{tabular} & \begin{tabular}{c} **Establishment** \\ **Cause** \\ \end{tabular} \\ \hline PS First Responders & \begin{tabular}{c} PS Emergency: \\ PS1 to PS5 \\ Commercial User \\ Emergency \\ Commercial User \\ Emergency \\ Commercial User \\ Non-Emergency \\ \end{tabular} & 12–14 & Barring for Special & \
\begin{tabular}{c} High-priority \\ Access \\ \end{tabular} \\ \hline
\begin{tabular}{c} 12-14 \\ Commercial User \\ Emergency \\ Commercial User \\ Non-Emergency \\ \end{tabular} & 10 & Barring for Special & Emergency \\ \hline \hline \end{tabular}
\end{table}
Table 1: User priorities in 3 GPP ProSe LTE-A.  
Figure 2: The priority gates model of the public safety-long-term evolution (PS-LTE) network.  
EPS bearer. ARP is not involved in the packet forwarding priority in situations in which packet data are sent once an EPS bearer is created [22]. Users can access the PS-LTE network after the last step (scheduling priority). In scheduling priority, the base stations use QoS class identifier (QCI) parameters to control the priority of packet transmissions.  
In this paper, Step 1 (access priority) is not considered, because it is used to distinguish between public safety and non-public safety users, and we consider only public safety users. Since we do not consider the other users of commercial networks, Step 2 (admission priority) is not applied. Because we are managing resource allocation using scheduling, we employ Step 3 (scheduling priority) in our simulations to check the feasibility of our proposed scenario.
# Preconception Health Attitudes and Behaviours of Women: A Qualitative Investigation
## 3 Results
### Information Preferences  
#### 3.5.1 Preferred Topics  
Women suggested a range of preferred topics for preconception health information. In particular, food safety, the recommended types and intensity of physical activity and which brand of pre-pregnancy supplements should be taken.  
\"There is a lot of foods that you cannot eat while pregnant, um, but it probably would have been helpful to have some information on that maybe before pregnancy, just in case you were pregnant and you did not know it yet and you were eating all these wrong foods and stuff. That would have been helpful information to have.\"--(P15, pregnant, 34 years old, no children).  
\"I guess they do say like if you, if you exercise and all that sort of thing, continue doing it. But I guess if you um, like I have, I have done CrossFit stuff before, so that is a lot of weights and what-not. You know, is that still okay to do, or do I need, or would I need to dial it down? Obviously exercise you are doing it to do, but yeah, if, depending on what you do, do you need to drop things down? Like if you play a contact sport, is that still okay to do, or that sort of thing.\"--(P4, preconception, 28 years old, no children).  
\"I think one thing that I searched for was like multivitamins and thinking what is the best one to take, because you hear about like Elevit and brands that are advertised, but how is that better than like when you go to the shop and you stand there and you look, and you are like, oh my gosh. So, information about yeah that and like when is best to start that as well, I think that is important.\"--(P3, pregnant, 30 years old, no children).  
Additionally, many women felt that it would be helpful to receive a breakdown of what preconception health actions were important to take and when these should be taken.  
\"Even like you know a checklist of things you should start, and you know, when you should start. Probably always good to have a healthy diet and lifestyle, but you know when it really is important, you know just think about your drinking and you know when you really do need to start taking supplements, or when it is best to go to your GP and get things checked and all those kind of things like that... like a checklist would be really good, of timings of when you need to be doing things.\"--(P2, preconception, 33 years old, no children).  
Other topics of interest included men's pre-pregnancy health, mental health, general health check-up, financial implications of pregnancy (including difference between public and private health care pathways) and changes to expect during pregnancy.
# Sensorless Current Pulsation Compensation in a Hybrid Energy Storage
## 3 Implementation and Experimental Verification  
### Verification of the Calculation of the AC Component in the DC Link  
The pulsation calculator was implemented as depicted in Figure 7, on a Texas Instruments F28379D Delfino microcontroller, connected to another, identical one, which controlled the pulsation compensator (DAB2). In this approach, the symmetrical decomposition is performed by two second-order general integrator filters (SOGI 1 and SOGI 2), which generate signals shifted by \(\pi/2\) for both \(I_{\alpha}\) and \(I_{\beta}\) calculated by the Clarke transform. Based on the four output signals, the positive sequences \(I_{\alpha\text{ps}}\) and \(I_{\beta\text{ps}}\) are calculated. Then, two Park transforms are performed: one for \(I_{\alpha}\) and \(I_{\beta}\) calculated directly from the inverter output currents, and the other for \(I_{\alpha\text{ps}}\) and \(I_{\beta\text{ps}}\), calculated by SOGI filters. The difference between the \(I_{d}\) results of both transforms is the AC component of the DC link pulsation.  
Before implementation, the calculation method was verified using MATLAB. The code was split into two sections: a pulsation calculator based on instantaneous current and voltage values, and a pulsation calculator based on SOCI filtering. The results are presented in Figure 8.  
The magnitude of the extracted pulsation (Figure 8e) matches the magnitude of the pulsation in the DC link current (Figure 8b). The positive sequence \(I_{d}\) current is used to remove the DC component from the DC link current. Based on the MATLAB code, the C-code for the microcontroller was developed and implemented in the AC/DC converter controller. The extracted AC pulsation value is then sent by the SPI protocol to the DC/DC converter.  
Figure 7: Control algorithm with DC link pulsation estimation algorithm.
## 4 Application of the Model
### Level 5 Evaluation: Communication Performance
#### 4.5.1 Communication Distance  
For the provided example, we first computed the communication distance of the armored vehicle in free space and on actual terrain and compared it with the communication distance in the absence of interference to determine the reduction in the communication distance, which are shown in Figure 11.