esa-sceva/satcom-chunk-collection huggingface dataset:
# MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy
## 4 Functionalities
### Electromatomical Map Analysis
#### 4.4.2 Radiotherapy Planning  
Stereotactic body radiotherapy (SBRT) has recently been applied to treat refractory ventricular tachycardia [9,10]. MUSICardio provides an environment for an in-depth analysis and SBRT session planning.  
CT and MR images are segmented, creating a 3D structural model comprising a detailed whole heart anatomy, myocardial scar maps generated from CT-derived wall thickness or late-enhancement MRI analysis, and the structures at risk during the interventions.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline
**Tool** & **Description** \\ \hline Binary operations & Intersection, union and difference between two meshes. \\ \hline Convert mesh to mask & Convert mesh into mask to work on it and convert back. \\ \hline Mesh cleaning & Filling holes in meshes, remove unused vertices, merge very close points. \\ \hline Mesh interactors & Selecting and deleting selected cells. \\ \hline Mesh manipulation & Apply transformation to mesh: translation, rotation, scaling. \\ \hline Mesh registration & Registration of a mesh to another mesh using landmarks or the iterative closest point method. Registration of a mesh to an image using landmarks. \\ \hline Merge volumes & Concatenate meshes. \\ \hline Projection mapping and interpolation & Projection of data on meshes, scalar or vector field, linear and nonlinear interpolation. \\ \hline Remeshing & Decimate, refine, smooth meshes. \\ \hline \end{tabular}
\end{table}
Table 4: List of tools dedicated to mesh enhancement.  
Figure 7: Annotations of 1D electrophysiological signals.  
For patients who previously underwent catheter radio-frequency ablation, electroanatomical maps can be imported and co-registered with the CT and MR segmented meshes to allow the user to perform the 3D rendering of combined structural information and EP data.  
The ablation targets and organs at risk can be delineated, creating contours to be exported in the DICOM RT-struct format. Figure 8 shows an example of such contours.
## Appendix A Phytoplankton Assemblage Variability in the EAP Model
### Justification for Using Measurement-Derived Refractive Indices across Wide Size Ranges  
The main light-harvesting pigments in typical diatom and dinoflagellate assemblages (fucoxanthin and peridinin, respectively)--while chemotaxisonomically distinct--display the typical broad, featureless absorption spectra characteristic of carotenoids, with peaks centered around 500 nm [87] and vary well within the natural variability of phytoplankton absorption (Figure 11). They consequently have similar refractive indices [27] and so these types were combined into a generalised set of diatom/dinoflagellate IOPs, as no significant difference was found between the dinoflagellate and diatom groups in terms of their optics that could not be attributed to the respective particle sizes (see also [38; 88]). This group of IOPs should correctly be referred to as Chl \(a\)-carotenoid IOPs.  
While the measurements and refractive index derivations [89] were performed for cells of approximately 12 to 20 \(\upmu\)m, it should be made clear that the imaginary refractive index characterises the absorption of the intracellular material and has absolutely no dependency on cell size. It can be inferred therefore that these refractive indices can be used to represent a range of phytoplankton sizes displaying dominant Chl \(a\) and carotenoid pigments. Southern Ocean nanophytoplankton comprise mainly diatoms and dinoflagellates, but also chlorophytes and happophytes in smaller proportions [90; 91]. The latter two phytoplankton groups are generally dominated by Chl \(a\) and the fucoxanthin pigment derivatives 19'-hex-fucoxanthin and 19'-but-fucoxanthin [91], which display somewhat elevated absorption peaks located at slightly shorter wavelengths with respect to fucoxanthin [87; 92]. The optical influence of these derivative pigments is assumed to be negligible in the context of the case studies for two reasons. Firstly, in the nanophytoplankton group as a whole, the fucoxanthin-derived pigments occur in far lesser concentrations than that of fucoxanthin itself [91], which is represented by the refractive indices. This is reinforced by the derivation of a nanophytoplankton group of refractive indices in non-bloom conditions (\(D_{eff}=2\)\(\upmu\)m), whose impact on the IOPs of small cells was not sufficiently different from the diatom-dinoflagellate group to warrant its routine use for small cells. Secondly, these pigments act to increase phytoplankton absorption around the 450 nm spectral region, identified the spectral region as very vulnerable to small variability in \(a_{gd}\), and to large satellite \(R_{rs}\) measurement uncertainty, so the conclusions regarding the spectral regions containing the most useful signatures for PFT detection still hold.  
EAP sensitivity testing has indicated that increasing the intracellular Chl \(a\) density \(C_{i}\) may be appropriate for small cells, but this is not explored here.
## 2 Field Experiment and WRF Model Setup
### Description of the Case and Ambient Conditions
Precipitation was observed over the Mushi site and Ulasitai site, and there was strong vertical wind shear at approximately 4 km. In the next section, the characteristics of the observed cloud and precipitation will be analyzed, and the role of vertical wind shear will be discussed. Figure 3: Time series of (**a**) temperature near surface (red) and liquid water path (black); (**b**) Freezing level height (black) and LCL height (red) from 0:00 to 14:00 UTC derived from the radiometer measurements. The vertical line in each panel indicates the starting time of precipitation over the Mushi site. Figure 2: (**a**) Visible image observed from the FY4A satellite and streamlines at 800 hPa; (**b**) Cross-section of wind vectors along the red line shown in (**a**); (**c**,**d**) Geopotential height (black lines), wind barbs, and relative humidity (colored) at 700 hPa and 500 hPa at 9:00 UTC on 27 July 2019. S and N below (**b**) indicate south and north of the cloud radar.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 3 Practical Application of RF-CNN-LSTM Model
### Random Forest to Fill in Missing Values  
The data is fed into the Random Forest model for missing value checking and filling, and the specific process is shown in Figure 5.  
(1) Characterization construction:  
The year and month are extracted as features from the time index of the failure rate data.  
(2) Data segmentation:  
The data is categorized into two parts, without and with missing values, which are used as training and test sets, respectively.  
(3) Model training:  
A random forest regression model is trained using the training set data. In this model, year and month are the independent variables and the 'cumulative failure rate' is the dependent variable; the model learns a complex nonlinear mapping from time (year and month) to cumulative failure rate. The setup parameters are shown in Table 2.  
From the table, n_estimators denotes the number of trees in Random Forest, while random_state is used to control the random state to ensure that the results are reproducible.  
(4) Missing value prediction:  
Figure 4: Data on the number of failures for vehicle type A.  
Figure 5: Flowchart showing the process of using Random Forest to fill in missing values.  
Predicting missing values with a trained Random Forest model.
* Data post-processing: Because the lapse rate becomes progressively larger with time during the cycle, it is necessary to ensure that the predicted cumulative lapse rate is not smaller than the previous valid data value while not larger than the latter valid value, in order to comply with the non-decreasing nature of the cumulative lapse rate.
* Integration of results Fill in missing values and remove auxiliary features. The results of data filling are shown in Figure 6.  
As can be seen from the figure, 20 missing values were identified through Random Forest and filled in, which shows that Random Forest has a good filling effect on incomplete failure rate data.
# Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network
## 3 Workflow
### Station Address Seeking  
With WSN serving as the network centering on data, all nodes transfer the information they collect to station for summary. Based on this working mode, the closer a node is to station, the higher the data forwarding frequency and the more frequent node couple appears will be, which makes it possible for the platform to seek for base efficiently. On WSNs using location-based routing protocol, the direction line of node couple is always pointing at the general base direction. Moreover, the closer a node couple is to station, the stronger its directional directivity will be. Our method is to seek for station address based on this characteristic. The specific method: Before platform U captures node couple after entering network area, it moves at a constant speed according to the pre-determined trace within the network, and detects surrounding nodes. When the platform captures node couple, upstream nodes are determined according to the address code of the destination node in the MAC frame of data packet (nodes of the data packet whose receiving destination is station or nodes whose sending destination is data packets of other nodes are upstream nodes). In early platform detection stage, destination node address of data packets is forcefully recognized as station address. As an increasing number of data packets are captured in the later stage, the address code that appears most frequently is station address code. After upstream node is determined, the platform moves to upstream node quickly after it is determined and moves forward along the direction line. If no new node couple is captured, the platform will keep moving along this direction, and detect surrounding nodes. If meeting network boundary (if no data packet is captured, and no node responds to the hello information or handshake information sent out within a period of time, it is thought boundaries are met), the platform changes direction after boundaries are reflected, and then keeps moving forward along the new direction line. If data packets are captured again, the platform will change position and direction line with the above-mentioned method until it finds station. In actual work with network, routing node address in the MAC frame of data packets that the station receives is the same as destination address, and data packets that the station sends to terminals have different frequencies and protocols with the data packets inside network, helping recognize the station of non-cooperative network (shown in Figure 7).  
Figure 6: How the platform captures data packets and determines node couple.
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
## 4 Conclusions  
By considering the aforesaid tool wear and cutting force modeling, conclusions can be drawn as follows:  
* A simulation model based on DEFORM 3D is built to simulate the process of micro-milling aluminum alloy LF 21 and the prediction of tool wear is achieved. The results proved that the Usui wear rate model can effectively predict the tool wear in the micro-milling process. * The arc radius of the cutting edge increases due to tool wear; the friction between the cutter and the workpiece also increases. In this paper, the average tool wear rate is used to solve the cutting edge arc radius dynamically. In addition, considering the effects of tool run-out, material elastic recovery and cutter vibration displacement, the instantaneous cutting thickness model is established. * Based on the instantaneous cutting thickness and minimum cutting thickness values, we established the force model of aluminum alloy LF 21 micro-milling process based on shear effect and plough effect and verified the validity of the force model by experiment. * We also propose a model of force coefficients mainly caused by the increase in arc radius of the cutting edge considering cutting dead zone. The experiment results verify that the cutting forces prediction results and experiment results are well matched. Conceptualization, X.L. and C.C. ; methodology, C.C. ; software, P.H. ; validation, C.C., K.X. and P.H. ; formal analysis, X.L. ; investigation, C.C. ; writing--original draft preparation, X.L. ; writing--review and editing, C.C. ; visualization, K.X. ; supervision, S.Y.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (Grant No. 51875080), the Fundamental Research Funds for the Central Universities (Grant No. DUT20 ZD204), DalianScience and Technology Innovation Fund (Grant No. 2020)J26 GX041). The financial contributions are gratefully acknowledged. The study did not report any data. The authors declare no conflict of interest.
# Action Recognition Algorithm of Spatio-Temporal Differential LSTM Based on Feature Enhancement
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.3 Comparison of ST-LSTM and ST-D LSTM in Terms of Stability and Accuracy  
For stability experiments, the ST-LSTM and ST-D LSTM units, which are both stacked five-layers, were applied to the LRCN network for three repeated experiments. The average accuracy was calculated as the final result. The standard deviation was calculated to compare the stability of the ST-LSTM unit and ST-D LSTM unit. The average accuracy and standard deviation of the three repeated experiments are plotted. As shown in Figure 7, in three different datasets, the accuracy of the ST-D LSTM unit is higher than that of the ST-LSTM unit, but the standard deviation is not higher than that of the ST-LSTM unit. Therefore, the ST-D LSTM unit has good stability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UCF-101** & **HMDB-51** & **Hollywood2** \\ \hline
1 layer & 70.47\% & 40.39\% & 46.12\% \\
2 layers & 71.32\% & 42.51\% & 47.41\% \\
3 layers & 73.44\% & 43.61\% & 47.54\% \\
4 layers & 74.48\% & 44.01\% & 48.21\% \\
5 layers & 75.70\% & 44.11\% & 49.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The accuracy comparison of stacked ST-D LSTM units with different layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UCF-101** & **HMDB-51** & **Hollywood2** \\ \hline
1 layer & 38 & 35 & 56 \\
2 layers & 31 & 24 & 42 \\
3 layers & 27 & 17 & 34 \\
4 layers & 16 & 10 & 23 \\
5 layers & 11 & 10 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The training speed comparison of stacked ST-D LSTM units with different layers (in frames per second).  
Figure 7: The comparison of accuracy and standard deviation between the ST-LSTM and ST-D LSTM.  
In order to further verify the performance of the ST-D LSTM unit, the ST-D LSTM unit is compared with other deep learning algorithms. The experiment is performed on the UCF-101 and HMDB-51 datasets and results are shown in Table 4.  
The ST-D LSTM is compared with the two-stream convolutional network, the LRCN network with an attention mechanism, and the LRCN network with BiLSTM. Due to differential calculation, the ST-D LSTM unit is more sensitive to action changes and can achieve high accuracy on the UCF-101 and the HMDB-51 datasets.
## 4 Examples
* The parameter estimation errors given by the BC-PSE algorithm become small with \(k\) increasing; see Tables 2 and 3 and Figures 1 and 6. * A low noise variance leads to higher accuracy of parameter estimates; see Figures 1 and 6. * As the data length \(k\) increases, the parameter estimates approach their true values; see Figures 2, 7 and 8. * The estimated states are close to the actual states of the system model; see Figures 3, 4 and 9-11. * The predicted outputs match well with the actual outputs; see Figures 5 and 12. Figure 11: The true state \(x_{3}(k)\) and the estimated state \(\hat{x}_{3}(k)\) versus \(k\) for Example 2 (\(\delta=0.25\)). Figure 10: The true state \(x_{2}(k)\) and the estimated state \(\hat{x}_{2}(k)\) versus \(k\) for Example 2 (\(\delta=0.25\)). Figure 9: The true state \(x_{1}(k)\) and the estimated state \(\hat{x}_{1}(k)\) versus \(k\) for Example 2 (\(\delta=0.25\)).
## 5 Uncertainty
### Uncertainty in ToF and TDoA Measurements  
Using binary cross correlation for ToA/ToF measurements with the chirp settings of Figure 3, the random component was tested to be normal distributed with a standard deviation of 400 ns or converted with the speed of sound at room temperature about 140 \(\upmu\)m. The random component is a complex function of the electronics noise, microphone self-noise, acoustic noise (Brownian motion), sampling rate, length and bandwidth of the used chirp, which shall all be subsumed in above measure.  
ToF and ToA measurements are modeled according to Equation (11) where in case of ToA the term \(\tt{t_{0}}\) described the unknown transmission time. In the case of ToF, the term \(\tt{t_{0}}\) contains any electrical and acoustic delays.  
\[\rm{ToF}/\rm{ToA}=\tt{t_{t}}+\tt{t_{0}}+\overline{\rm{N_{t}}}\,\rm{with}\, \overline{\rm{N_{t}}}=0,s_{\overline{\rm{N_{t}}}}{}^{2}\ =(400\ \rm{ns})^{2} \tag{11}\]  
Time difference measurements are obtained by using one ToA as a reference. As measurements are uncorrelated the variances add.  
\[\rm{TDoA}=\tt{t_{t1}}-\tt{t_{t2}}+\overline{\rm{N_{t}}}\,\rm{with}\,\overline{ \rm{N_{t}}}=0,s_{\overline{\rm{N_{t}}}}{}^{2}\ =(400\ \rm{ns})^{2}+(400\ \rm{ns})^{2} \tag{12}\]  
Assuming a homogenous temperature, distances, pseudo-distances and distance differences can be calculated by scaling the ToF, ToA or TDoA measurements by the speed of sound. The temperature in this case is modeled assuming a systematic and a random component.
# C-RAN Traffic Aggregation on Latency-Controlled Ethernet Links
## 4 Numerical Results
In option 6, instead, reasonable values of \(L_{F}\) seem to be not less than 1000 bytes which give an average number of segments less than 3 for any \(L_{B}\), with a resulting overhead around 10%, which is reasonable as well. However, working with high \(L_{F}\) increases the encapsulation delay, which in the worst case is 18.3 \(\upmu\)s for CPRI option 1 and 1.83 \(\upmu\)s for CPRI option 6. Figure 8: Overhead for backhaul (BH) packets as a function of payload length \(L_{F}\) for different BH packet length \(L_{B}\) using CPRI opt. 1. Solid lines for the case with segmentation (S), dashed lines for the no-segmentation case (P). Figure 7: Backhaul (BH) throughput, normalized to the output link capacity, as a function of payload length \(L_{F}\) for different BH packet length \(L_{B}\) using CPRI opt. 6. Solid lines for the case with segmentation (S), dashed lines for the no-segmentation case (P). Maximum normalized capacity by fronthaul (FH) is shown by a solid line.
# A Comprehensive Review of Computer Vision in Sports:
## 8 Research Directions in Sports Vision
### Future Research Trends according to Methodologies in Sports Vision  
In this section, we aim to set forth the methodological approach to various components of detection, classification, and tracking in sports. By considering the deep analysis of sports studies, it is clear that the performance of the algorithm depends on the type (annotation parameters) of dataset used, which is carried out based on loss functions and evaluation metrics. The major difficulties in the real-time use of AI algorithms in various research areas of sports are accuracy, computation speed, and size of the model. Considering all these aspects, the development of future trends based on contemporary ideas are presented below.  
* Due to the continuous movements of the player, jersey numbers encounter serious deformation, and various image sizes and low resolution make it difficult to read the jersey number [205]. Players' similar appearances and severe occlusions make it difficult to track and identify players, referees, and goalkeepers reliably, which causes the critical problem of identity switching among players [212]. To solve these challenges, a framework is needed to propose that learning objects' identities with deep representations and improve tracking using identity information is necessary.
* The algorithms employed to detect and track the state of the ball such as whether it is controlled by a player (dribbling), moving on the ground (passed from one player to another player), or flying in the air to categorize the movement as a rolling pass or lobbing pass are not robust concerning the size, shape, and velocity of the ball and other parameters under different environmental conditions. A few researchers have come forward with novel ideas to deal with the above-mentioned aspects [62; 68; 69; 107; 203; 210; 285]; however, the state-of-the-art research is still at a nascent stage.
* Conventional architectures of detecting, classifying, and tracking are replaced with more promising and potential modern learning paradigms such as Online Learners and Extreme Learning Machines.
* Various activities in sports such as players' fatigue information can be acquired from wearable sensors and monitoring health conditions of each player in the play-field, tracking players and the ball, predicting the trajectory of the ball, game play analysis and evaluations; identifying players' actions and other fundamental elements can be accomplished with the help of big data and information technologies. This improves the sports industry's operational efficiency and leverages its immense potential. Due to advancements in big data analysis [298; 299], and the Internet of Things (IoT) [300],personalized care monitoring will become a new direction and breakthrough in the sports industry.