esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Media-Based Communication Strategies
### WOM Case Study  
A goal of the WOM project is to communicate conservation messages about the Mekong region to Cambodian, regional, and international audiences. To achieve this goal, the WOM project has maintained relationships with several media outlets since 2017 to promote stories about Mekong issues, including National Geographic. As one of the most prominent media brands in the world, with an online audience that outweighs its print and television distribution, National Geographic claims to reach more than 450 million people across all media platforms. With 167 million followers (as of June 2021), it is the most followed noncelebrity brand on Instagram. WOM project leader Zeb Hogan (paper co-author) has a long-standing affiliation with the organization as a National Geographic Explorer and host of the National Geographic television show \"Monster Fish\", which helped forge the project's media relationship. Between March 2017 and August 2020, National Geographic published 17 articles focused on Mekong issues, wonders, and threats (Table 2), of which seven included an accompanying short video story. All but one of the stories were written by WOM project member Stefan Lovgren (paper co-author), who has been a contributor to National Geographic since 2003. Based on traffic data from the National Geographic website, the most popular story was about the giant stingray (Table 2), which reflects the popularity of stories about large-bodied animals [87]. The next three most viewed stories all relate to the crisis of low water levels in the Mekong River. National Geographic also shares stories on Facebook, where it has more than 48 million followers as of June 2021. The Mekong story that generated the most Facebook engagements was about the extinction risk of large freshwater species (Table 2), again reflecting the popularity of charismatic megafauna. The story's 4000 shares are more than four times higher than the next most shared story. The stories with the next highest engagement on Facebook included hopeful stories about increasing populations of other charismatic species, giant softshell turtles (_Pelochelys cantorii_) and Irrawaddy river dolphins.
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 3 HIF Detection in Eigenvalue Space
### Framework for Eigenvalue Identification  
The first step of this solution is to estimate the eigenvalues of the powerline being monitored. Figure 5 is an example of the estimation result. Using PMUs, voltage readings from one end of the line can be integrated with current measurements at the opposite end of the line. These two measurements are then sent to an estimator. Physically, the estimator can reside at either a centralized location, such as a control center or database, or at a secure distributed location such as a substation. This versatility regarding the location of the equipment adds to the generality of the solution.  
Figure 4: RLC circuit schematic.  
Figure 3: Diagram of the proposed configuration.  
As discussed in the previous section, Subspace Estimation was used to realize the eigenvalues of the system, however, other algorithms can be used to process the data collected by the PMUs and estimate the eigenvalues. The choice of the estimation algorithm is not critical, as long as the output provided by the estimator is reliable and consistent. This work is not concerned with identifying the parameters of the system with great accuracy with respect to the real (physical) values. The algorithms used in this work establish a benchmark of acceptable operating regions in the Eigenvalue Space.
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 3 The Proposed Critical Action Information Extraction Network
### Feedback Attention-Guided Module (FAGM)
#### 3.2.1 Temporal Attention Module (TAM)  
As shown in Figure 2, within FAGM, the features before and after the convolution \(1\times 1\) operation are divided into low-level and high-level features, denoted as \(\mathbf{F}_{l}\in\mathbb{R}^{N\times C_{l}\times H_{l}\times W_{l}}\) and \(\mathbf{F}_{h}\in\mathbb{R}^{N\times C_{h}\times H_{l}\times W_{h}}\), respectively. For the low-level features \(\mathbf{F}_{l}\), as illustrated in Figure 5, we first perform a feature reshape to expand \(\mathbf{F}_{l}\) to \(\mathbf{F}_{l}^{\prime}\in\mathbb{R}^{N_{1}\times T\times C_{l}\times H_{l} \times W_{l}}\) (\(N_{1}=N/T\)). Secondly, \(W_{l}\in\mathbb{R}^{N_{1}\times T\times 1\times 1\times 1}\) is obtained through the operation of average pooling \(\mathbf{F}_{l}^{\prime}\) along the temporal dimension and processing it with a sigmoid function. Finally, after performing element-wise multiplication on \(W_{l}\) and \(\mathbf{F}_{l}^{\prime}\), the result is added to \(\mathbf{F}_{l}^{\prime}\) and reshaped into the feature \(\mathbf{F}_{l}^{T}\in\mathbb{R}^{N\times C_{l}\times H_{l}\times W_{l}}\), which contains key frame information. The high-level features \(\mathbf{F}_{h}\) are processed in the same way to obtain the feature \(\mathbf{F}_{h}^{T}\). The specific formulas are as follows:  
\[\mathbf{F}_{l}^{\prime}=\text{reshape}(\mathbf{F}_{l}) \tag{8}\]  
\[W_{l}=\sigma(\text{AvgPool}(\mathbf{F}_{l}^{\prime})) \tag{9}\]  
\[\mathbf{F}_{l}^{T}=\text{reshape}\big{(}W_{l}\odot\mathbf{F}_{l}^{\prime}+ \mathbf{F}_{l}^{\prime}\big{)} \tag{10}\]  
After processing with TAM, the model can focus on video frames' rich motion information, reducing the impact of frames with less noticeable motion changes on video action recognition.
# Lamb Behaviors Analysis Using a Predictive CNN Model and a Single Camera
## 2 Materials and Methods
### Object Detection
#### 2.2.4 Evaluation Procedures and Metrics  
This subsection shows how the YOLO\({}_{V4}\) deep learning system evaluated model performance using the confusion matrix terminology [37] below:  
* True positive (TP).
* False positive (FP).
* False negative (FN).
* True negative (TN).  
In object detection, the annotation and the expected shape of a bounding box do not match completely, so an extra parameter is required to calculate the variables in question. This parameter is called intersection over union (IoU) and defines the relative \(\alpha\) obligatory overlap of the shapes of the \(B_{p}\) bounding boxes and the \(B_{g}\)\(t\) truth map, as defined by [32]:  
\[\alpha=\frac{area\left(B_{p}\cap B_{gt}\right)}{area\left(B_{p}\cup B_{gt} \right)} \tag{6}\]  
The default value of this parameter is 0.5 [38]. Using the terminology of the confusion matrix and IoU, the following metrics were calculated [37]):  
Average precision (\(AP\)) is a measure of an object detector's performance related to a specific class in the object detection task. The procedure to calculate the \(AP\) was as follows:  
1. Based on the confidence score, sort all detections.
2. Take detections with the highest confidence scores and match them to the ground truth until a recall \(r\) higher than the expected \(r\) level is reached.
3. Calculate precision values based on each level of recall \(r\).
4. Interpolate the precision \(P_{interp}\) by the maximum precision obtained for a recall level \(r\). This was defined by [38]:  
\[AP_{interp}(r)=\underset{rr\geq r}{\text{max}}(r) \tag{7}\]  
For this study, eleven recall levels were used, \(r\in\{0,0.1;\ldots,1\}\), with a consistent step size. Finally, the \(AP\) is the arithmetic mean of the precision \(P_{interp}\) at different levels of recall [38], as shown in Equation (8):  
\[AP=\frac{1}{11}\sum_{r\in\{0,0.1;\ldots,1\}}P_{interp}(r) \tag{8}\]  
Furthermore, the mean average precision (mAP) (Equation (9)) is the mean of the AP values for each class object [39], and the higher the value, the better the result of detection of temperature distribution.  
\[\text{mAP}=\frac{\sum_{c=1}^{C}AP(c)}{C} \tag{9}\]  
where \(C\) is the number of detection categories. For the specific case of this study, \(C=3\).  
Tracking Metrics  
This subsection shows the lamb behavior metrics generated by YOLO\({}_{V4}\). These were frame number, detection ID, label, accuracy, bounding box data (\(Xmin\), \(Ymin\), \(Xmax\), \(Ymax\), \(CentroidX\), \(CentroidY\)), distance traveled by object based on the previous and current frame, inference time, and date (Table 2).
## 4 Experimental Results and Analysis  
### Quorum, Results and Analysis  
As shown in Figure 3, the throughput for Memory and Calldata with 2 KB messages (blue and orange lines), which replicates the average compressed transaction size, lies primarily around 300 tps, with occasional spikes above 600 tps. Transactions are fully processed after 280 s. For State (green lines), results are around 200 tps, with short bursts of lower throughput. Transactions are fully processed after 440 s. The results show that storage methods that store transactions in history (Calldata and Memory) outperform the storage method that saves data to the State. The figure also shows that Calldata and Memory are almost identical in performance, with only marginal differences.  
The 10 KB tests in Figure 3 (red, purple and brown lines) show similar trends to the 2 KB test, where Memory and Calldata significantly outperform writing data to the State. The same pattern is observed for the 10 KB test, however ranging between 70 and 140 tps for Memory and Calldata, while between 20 and 40 tps results for State. When increasing the volume of transactions and the number of concurrent users, the same phenomena is observed with an increase in the difference between average highs and lows. During tests, we observed that the network often requires spin-up time. This is very visible in the results for 2 KB in Figure 3, where the first 20 to 50 s are not used to process transactions. This is because transaction data are passed to the nodes, which create and sign the transactions before they are distributed into the network.  
The results of the Quorum tests are presented in Table 3. The conclusion from the Quorum tests is that the results would satisfy the national B2B/B2G use-case for a country the size of Denmark where there is a demand for 130 to 135 tps throughput. However, the requirement from the entire Euro-zone of 9000 to 10,000 tps is not satisfied.
## 4 Conclusions  
In this paper, a single-step prediction method for short-term building heat loads, driven by a multi-component fusion LSTM Ridge Regression Ensemble Model (ST-LSTM-RR), is designed and implemented. This method is applied to the single-step prediction of short-term building heat loads for eight groups in a large mountain hotel park. The experimental results show that the introduction of trend and seasonal components has an effect on the LSTM prediction results; the three basic models of the proposed ST-LSTM-RR model are diverse yet effective in different ways; the overall prediction results of the ST-LSTM-RR model are better and more stable than those of the comparison models. The prediction results of the proposed method can be used to guide energy deployment, improve energy efficiency, and then reduce the carbon emissions of buildings.  
Conceptualization, Y.Z. and G.C.; methodology, Y.Z. and G.C.; software, G.C.; validation, Y.Z.; formal analysis, G.C.; investigation, Y.Z. and G.C; resources, Y.Z.; data curation, Y.Z. and G.C.; writing--original draft preparation, Y.Z. and G.C.; writing--review and editing, Y.Z. and G.C.; visualization, G.C.; supervision, G.C.; project administration, Y.Z. and G.C.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Error** & **Model** & **Rg1** & **Rg2** & **Rg3** & **Rg4** & **Rg5** & **Rg6** & **Og1** & **Og2** \\ \hline \multirow{3}{*}{RMSE} & ConvLSTM & 64.419 & 48.049 & 53.009 & 38.214 & 35.013 & 44.749 & 6.238 & 21.651 \\  & TCN & 58.189 & 37.044 & 39.808 & 27.939 & 30.347 & 38.851 & 15.582 & 32.418 \\  & Proposed model & **40.935** & **25.533** & **34.790** & **17.907** & **20.843** & **24.739** & **3.809** & **13.397** \\ \hline \multirow{3}{*}{MAE} & ConvLSTM & 54.764 & 41.755 & 43.825 & 29.303 & 26.798 & 37.101 & 2.985 & 9.522 \\  & TCN & 45.521 & 30.418 & 29.702 & 23.554 & 24.415 & 32.130 & 12.438 & 18.090 \\ \cline{1-1}  & Proposed model & **30.924** & **20.638** & **26.792** & **14.688** & **15.058** & **19.172** & **2.655** & **8.328** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results of different models.  
**Funding:** This research was funded by the State Key Laboratory for GeoMechanics and Deep Underground Engineering & Institute for Deep Underground Science and Engineering, grant number XD2021021, and the Postgraduate education and teaching quality improvement project of Beijing University of Civil Engineering and Architecture, grant number J2022003.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Research on the Precision Measurement Method of Flat Screen Gap Based on Mobile Vision
## 1 Introduction
introduced a precision visual measurement system for micro-small optical components by proposing a dark-field scattering illumination scheme that combined dome light and coaxial light [13]. [PERSON] et al. utilized a vision measurement system comprising a CMOS camera and coaxial telecentric parallel light source. They proposed a high-precision measurement method of thread parameters based on contour corner detection [14]. According to the above study, the precision measurement method based on global imaging usually collects a high-resolution image of an object in a relatively fixed small field of view and then calculates the size of the object through the algorithm. This method is only suitable for the precision measurement of small-sized objects due to the limitation of the size of the image sensor. When measuring large-sized objects, the field of view has difficulty covering the entire object. Even if the entire object can be covered, the limitation of pixel accuracy will reduce the accuracy of the measurement. Especially for the telecentric lens commonly used for precision measurement, it is difficult to obtain a larger field of view and higher pixel accuracy at the same time. Therefore, the traditional method cannot complete the measurement task or achieve high-precision measurement when measuring large-size objects. In this paper, the linear motor module and the telecentric monocular vision system are combined into a mobile vision system. The um-level high-precision positioning of the linear motor and the low distortion characteristics of the telecentric camera are used to realize the precision measurement of the vision system in a large working range, which solves the problem where the traditional method could not measure the large-size object accurately. Based on the mobile vision system, in order to increase the effectiveness of the actual screen gap measurement process and take into account the accuracy of the measurement, a large-scale object precision measurement method using fusing system positioning information and multiple local images is suggested. In order to resist interference and defects in local images and properly locate the outer edge of the screen gap, a quick region segmentation algorithm is proposed based on pixel statistical theory, and an adaptive line detection algorithm is created by combining dichotomy and Hough transform. The remainder of this article is structured as follows: The structure of a mobile vision system and the principle of full-size measurement are elaborated in Section 1. Section 2 presents the methods of region segmentation and adaptive line detection. Experimental data and measurement results are interpreted in Section 3. In Section 4, we discuss the research of this paper. Finally, our conclusions and future works are summarized in Section 5.
## 4 Conclusions  
This study evaluated the effects of the gradation, especially mineral filler content, on the structural and functional performances of PAC through comprehensive laboratory tests. Based on the outcome of this study, the following findings can be obtained:  
1. When the mineral filler content is increased, more asphalt binder is needed to form the asphalt mastic, which can improve the strength of PAC10 mixtures.
2. Novel PAC10 (without 0.075-3 mm aggregate) with 6% mineral filler can have equivalent moisture susceptibility and durability compared with conventional PAC13.
3. PAC13 showed better rutting resistance than the conventional PAC10, but the high-temperature stability of the novel PAC10 can be equivalent to or even better than PAC13 by increasing the mineral filler content.
4. Conventional PAC10 showed better low-temperature cracking resistance than PAC13. The low-temperature performance of PAC10 is compromised when 0.075-3 mm aggregate is excluded, but it can be improved by increasing the mineral filler content.
5. The permeability of the conventional PAC10 is obviously worse than PAC13, but excluding the 0.075-3 mm aggregate from PAC10 is effective in improving the permeability of PAC10.
6. Six percent mineral filler is recommended for the novel gradation design to keep the balance of durability and functionality.  
For PAC mixtures, it is important to balance their structural performance and permeability. To achieve such balance, this study developed a novel gradation for PAC10 by considering the following two facts: on one hand, the permeability of PAC is improved by excluding the aggregate in the size range of 0.075-3 mm; and on the other hand, the durability of PAC10 is maintained by increasing the mineral filler content. The results of this study indicated that the novel PAC10 mixture containing 6% mineral filler could provide equivalent durability and permeability compared with the conventional PAC13 mixture, making PAC10-I6 a suitable porous mixture especially suitable for thin overlays. With increasing the mineral filler content, the OAC of PAC10 increases, and the price of asphalt mixture is closely related to the OAC. Therefore, compared with the conventional PAC10-C, the recommended PAC10-I6 will increase the cost slightly. In the study, several kinds of PAC mixtures with different gradation were compared. However, the type of bitumen has significant influence on the performance properties of PAC mixture, this is the further research will be conducted in future work.  
Conceptualization, X.M. and P.Z.; methodology, X.M.; validation, X.M. and H.W.; formal analysis, H.W.; investigation, H.W. and P.Z.; data curation, X.M.; writing--original draft preparation, X.M. and P.Z.; writing--review and editing, X.M. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the Hebei Provincial Transportation Scientific Research Project (NO. TH1-202013) and the Priority Academic Program Development of Jiangsu Higher Education Institutions in China.  
The authors declare no conflict of interest.
## 2 Finite Element (FE) Model and Verification
### FE Model  
To simulate the cyclic behavior of the timber frame with straight mortise-tenon joints, a 3D finite element model is built based on ABAQUS as shown in Figure 6. The columns and the beam are all modelled by 8-node reduced-integration solid element (C3D8R).  
Figure 3: Damages of the mortise-tenon joint: (**a**) Gap between the mortise and tenon; (**b**) Damage in the tenon.  
Figure 4: The test model.  
Figure 5: The loading pattern.  
The post-yield behavior of timber is simulated through [PERSON]'s potential function, which is a simple extension of the Mises function and can be expressed in terms of rectangular Cartesian stress components as follows:  
\[f(\sigma)=\sqrt{F(\sigma_{22}-\sigma_{33})^{2}+G(\sigma_{33}-\sigma_{11})^{2}+H( \sigma_{11}-\sigma_{22})^{2}+2L\sigma_{23}{}^{2}+2M\sigma_{31}{}^{2}+2N\sigma_ {12}{}^{2}} \tag{1}\]  
where \(F\), \(G\), \(H\), \(L\), \(M\), \(N\) are constants obtained by tests of the material in different orientations, \(\sigma_{ij}(i,j=1,2,3)\) are different stress components. For simplicity, the post-yield moduli are set as zero in this study. The plastic material parameters of the FE model are presented in Table 2.  
The interaction between the mortise and the tenon plays a significant role in the seismic performance of traditional timber structures since it can dissipate a vast amount of energy through friction between these two parts. In ABAQUS, the interaction between two surfaces is simulated by surface-to-surface contact which includes the normal and the tangential behaviors. The normal behavior is set as 'HARD' contact which can transfer the normal stress between the two surfaces and allow these two surfaces to  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\mathbf{E_{1}}\)/MPa & \(\mathbf{E_{2}}\)/MPa & \(\mathbf{E_{3}}\)/MPa & \(\mathbf{G_{12}}\)/MPa & \(\mathbf{G_{13}}\)/MPa & \(\mathbf{G_{23}}\)/MPa & \(\mathbf{v_{12}}\) & \(\mathbf{v_{13}}\) & \(\mathbf{v_{23}}\) \\ \hline
4720 & 378 & 236 & 337 & 33.7 & 317 & 0.37 & 0.47 & 0.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elastic material parameters of the finite element (FE) mode.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Direction** & \(\mathbf{\sigma_{cy}}\)\({}^{1}\)/MPa & \(\mathbf{\sigma_{ty}}\)\({}^{2}\)/MPa & \(\mathbf{\tau_{y}}\)\({}^{3}\)/MPa \\ \hline
1 & 21 & 30 & 4.2 \\
2 & 2.1 & 2.1 & 4.2 \\
3 & 2.1 & 2.1 & 4.2 \\ \hline \hline \end{tabular} \({}^{1}\) The yield compressive stress; \({}^{2}\) The yield tensile stress; \({}^{3}\) The yield shear stress.  
\end{table}
Table 2: Plastic material parameters of the FE mode.  
Figure 6: Finite element (FE) model.  
separate after contact. The tangential behavior is realized by Coulomb friction to simulate the lateral sliding behavior at the interface of the mortise and tenon.