esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Applications to Cardiovascular Medicine
### Hemodynamics  
In general, computational modeling of hemodynamics is more resource-consuming than tissue mechanics, as simulations need to account for transient effects and deal with the difficulties introduced by the non-linearities of convection and momentum dissipation. This makes the application of inverse modeling to hemodynamics a challenging task.  
The use of simplified 0D (lumped) and 1D models can significantly reduce the computational cost. These simplified models have been used on a patient-specific basis and implemented onto inverse modeling approaches to provide useful systemic information about flow distribution, vascular resistance, and the systemic effect of drug treatments [297; 298]. However, these approaches cannot exploit the detailed features offered by modern image-based kinematics as they only deal with 2D integrated or averaged metrics. Furthermore, despite all assumptions and simplifications, inverse approaches to lumped and 1D models are still prone to solution multiplicity [119]. With our focus on inverse modeling based on image-based kinematics, these approaches employing 1D simplified models fall outside the scope of this review.  
To deal with the computational expense of the forward problem on inverse hemodynamics, [PERSON] et al. proposed a method for parametrizing the Navier-Stokes equations and patient-specific geometries to reduce the basis of the partial differential equations. The parameterized model is iteratively solved until the algorithm is close to the final solution. At this point, the inverse method then switches to the solution of the full-forward problem using FVM. This method was tested using deterministic and Bayesian optimization algorithms showing promising results on the solution of test cases involving rigid-wall and FSI simulations [103]. Herein, we review some of the existing research on inverse hemodynamics separating approaches that assume rigid-wall flow boundaries from those using FSI approaches.
# 3-D People Counting with a Stereo Camera on GPU Embedded Board
## 4 Conclusions  
In this paper, we proposed a people-counting method using a stereo camera in a NVIDIA Jetson TX2. The people-counting method using a mono camera is problematic as the counting accuracy is low given that detection is not performed due to occlusion. In the proposed method, two IMX 185 cameras were used to acquire 3-D information. The problem of non-detection due to occlusion was solved by projecting the side-view onto the top-view using this three-dimensional information. The detected person was tracked using the Kalman filter-based tracker to determine whether the person crossed the counting line. Compared with other methods, the proposed method showed a higher counting accuracy. Furthermore, it showed that the proposed method could operate real-time processing with rapid measurements. In the future, we will study ways to exclude user input at the initial stage by  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Resolution**} & \multicolumn{2}{c}{**Side-View**} \\ \cline{3-4}  & & **FPS** & **Occupancy (\%)** \\ \hline \multirow{2}{*}{CPU} & QVGA (320 \(\times\) 240) & 15.4 & 21 \\ \cline{2-4}  & VGA (640 \(\times\) 480) & 9.6 & 27 \\ \hline \multirow{4}{*}{GPU} & HD (1280 \(\times\) 720) & \multirow{2}{*}{12.5} & GPU: 63 \\  & & CPU: 18 \\ \cline{1-1} \cline{2-4}  & FHD (1920 \(\times\) 1080) & & 5.4 & GPU: 65 \\ \cline{1-1} \cline{2-4}  & & CPU: 19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: FPS and resource occupancy per resolution of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Mono Camera**} & \multicolumn{2}{c}{**Stereo Camera**} \\ \cline{2-5}  & **GoogleNet–SSD** & **MobileNet–SSD** & **Ours (VGA)** & **Ours (HD)** & **Ours (FHD)** \\ \hline Accuracy & 76.2\% & 83.0\% & 95.59\% & 98.95\% & 98.55\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison for counting accuracy.  
automatically estimating the ground. In addition, we will increase the number of counting lines to four to consider various directions.  
Conceptualization, G.-c.L. and J.Y.; Methodology, G.-c.L.; Software, G.-c.L. and S.-h.L.; Validation, G.-c.L.; Formal Analysis, G.-c.L.; Investigation, G.-c.L. and S.-h.L.; Resources, J.Y.; Data Curation, G.-c.L.; Writing--Original Draft Preparation, G.-c.L.; Writing--Review & Editing, G.-c.L.; Visualization, G.-c.L.; Supervision, J.Y.; Project Administration, J.Y.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Methods
#### 3.2.1 Data Characterisation  
Carefully analysing the data is useful not only to understand the profile of the calling citizens but also for the SPMS team to get new insights over the phone calls received along the years and the way those are being handled. Thus, it was agreed to analyse the following information: distribution of calls per clinical pathway, per gender, per relation of the caller with the citizen, per district, per age of citizen (general and for the 10 most frequent pathways), per hour of the day, day of week and month of year (general and for the the 10 most frequent pathways). Moreover, an analysis of the distribution of referrals per pathway was also done.
# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 5 Conclusions  
In this work, we propose a Siamese framework with a group convolution and pixel-level correlation for visual object tracking, with training from end to end, using multi-layer feature fusion and attention mechanisms to improve the feature extraction capability of the network, which works well under fast motion, occlusion, etc. We designed two attention modules: a multi-scale channel attention block (MCA) and a global-to-local spatial attention block (GSA), which enable the network to extract more meaningful features in the classification and regression sub-network. During tracking, pixel-level correlation reduces background interference and provides more refined target boundaries, and it decomposes the template features from the channel and spatial dimensions and uses every pixel feature to correlate the template and search regions. Furthermore, in order to improve the inference speed, our tracker uses a group convolution, which reduces the number of  
Figure 12: Comparisons of tracking results from different trackers. Targets including person, car, boat, UAV, and images present challenging attributes such as low resolution, occlusion, fast motion, and size variation. Green boxes denote ground truth, yellow boxes are results from SiamCAR, and red boxes are our model results.  
parameters in the network, as well as the use of activation functions and normalization in the backbone. The final inference speed reaches 40 FPS, nearly 9 FPS faster than that of SiamCAR. Our model achieved a 65.5% success rate and an 85.2% precision rate on the UAV123 dataset, outperforming SiamCAR by 4%; a 70.1% success rate and a 91.4% precision rate on the OTB100 dataset; and a 57.2% success rate on LaSOT, outperforming Ocean by 1.2%. Accordingly, our tracker performs better than other trackers and effectively improves the results under lighting changes and occlusion, showing its effectiveness and generalizability.  
Conceptualization, L.L. and B.H.; methodology, L.L.; software, L.L.; validation, L.L., Y.L. and T.N.; formal analysis, G.L.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, B.H., G.L. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China, grant number 62105328.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### The Flexible Excavation Assessment (EXCASS) System  
In rock engineering projects such as slopes, dams, and tunnels, the excavation of rock masses is a fundamental requirement. The excavatability of rock masses is primarily governed by the structure of the rock mass, including its discontinuities, as well as the mechanical properties of the rock material. Various excavation methods, including diggers, rippers, hammers, and blasting, are employed depending on these characteristics. Since selecting the appropriate excavation method during the design phase is crucial for cost efficiency, numerous valuable studies on rock mass excavatability can be found in the literature [12; 13; 14; 15].  
The EXCASS system, developed by [PERSON] et al. [9], utilizes an artificial neural network (ANN) learning algorithm, making it highly adaptable. This flexibility is attributed to the incorporation of two key parameters: the excavation power index (\(EPI\)) and the excavation performance rating (\(EPR\)). As illustrated in Figure 1, the optimum excavation method can be determined by evaluating the optimum excavation power index (\(EPI_{opt}\)), which ranges from zero to 100 (Equation (15)).  
\[EPI_{opt}=0.77\left(GSI^{2}x\sqrt{I_{s50}}\right)^{0.52} \tag{15}\]  
where \(GSI\) is the geological strength index of the rock mass and \(I_{s50}\) is the point load index of the rock material.  
[PERSON] et al. [9] stated that \(EPR\) was incorporated into the EXCASS system to account for deviations from \(EPI_{opt}\), either in the positive or negative direction. When \(EPR\) takes positive values, the selected excavation method will be more powerful than the optimum method determined by \(EPI_{opt}\). Positive \(EPR\) values are particularly relevant when a very large volume of rock mass needs to be excavated within a given operational time frame.  
As explained in detail by [PERSON] et al. [9], a positive \(EPR\) indicates that the excavation method is stronger than the optimal choice, which is necessary when handling substantial rock mass volumes within a specific time constraint. In such cases, the degree of disturbance in the rock mass will inevitably increase due to the selection of a more intensive excavation method. Conversely, when a negative \(EPR\) value is used, a weaker excavation method is preferred, resulting in minimal disturbance to the rock mass. However, this also means that the volume of excavated rock per unit of time will be lower. The incorporation of the \(EPR\) enhances the practical applicability of the EXCASS system, adding flexibility by allowing adjustments based on excavation volume and operational requirements.
## 2 Model Marginal Distribution of Load and Wind Speed Using SNMM  
For an arbitrary PDF, there is no unique methodology to model it accurately enough, which propels the study on GMM to provide a rather satisfactory approximation. On this basis, SNMM was developed as an improvement of GMM by covering the skewness of the PDF, observed in the expression of SNMM PDF later.  
Therefore, SNMM inherits almost all the merits of GMM. For example, any distribution can be represented by as a convex combination of several skew-normal (SN) distributions with respective means and variances. If infinite components are adopted, SNMM can inerrably depict distribution of any kind. Besides, due to the improvement, SNMM is capable of representing the asymmetry, heavy tail and multimodality of PDFs, which is superior to GMM [32].  
Because a PDF must be nonnegative and the integral of a PDF over the sample space of RVs must evaluate to unity, the mixture weights must be nonnegative and the sum of all the weights must be equal to one. Hence, for the univariate case, the parametric form of the PDF of SNMM \(f(x_{lm})\) is:  
\[f(x_{lm})=\sum_{m=1}^{n_{SN}}\frac{\omega_{m}}{\sigma_{m}}f_{SN,m}(x_{lm}), \tag{1}\]  
where \(x_{lm}\) represents the RV of load or wind speed. \(lw\) is the index for the sites of load and wind speed. \(\omega_{m}\) is the weight of the \(m\)-th component of the SNMM. The sum over \(\omega_{m}\) is 1. \(n_{SN}\) is the number of components in SNMM. \(f_{SN,m}(\cdot)\) is the PDF of a single SN distribution, whose expression is shown as:  
\[f_{SN,m}(x_{lm})=2\varphi(\frac{x_{lm}-\mu_{m}}{\sigma_{m}})\Phi(\lambda_{m} \frac{x_{lm}-\mu_{m}}{\sigma_{m}}), \tag{2}\]where \(\varphi\) is the PDF of standard Gaussian distribution. \(\Phi\) is the corresponding cumulative distribution function (CDF) of \(\varphi\). \(\mu_{m}\), \(\sigma_{m}\) and \(\lambda_{m}\) are the parameters reflecting the location, scale and skewness of the \(m\)-th SN distribution, respectively.  
The estimation of all parameters is conducted by the expectation maximization (EM) algorithm, which is a very mature and steady method applied in various mixture models [33].  
According to [34], the CDF of SNMM \(F(x_{lm})\) can be expressed by the addition of the CDFs of the SN components, shown as:  
\[F(x_{lm})=\sum_{m=1}^{n_{SN}}\omega_{m}F_{SN,m}(x_{lm}), \tag{3}\]  
where the CDF of a single SN distribution \(F_{SN,m}(x_{lm})\) is:  
\[F_{SN,m}(x_{lm})=\Phi(\frac{x_{lm}-\mu_{m}}{\sigma_{m}})-2T(\frac{x_{lm}-\mu_ {m}}{\sigma_{m}},\lambda_{m}), \tag{4}\]  
where \(T(\cdot)\) is the [PERSON]'s T function.  
Based on (3) and (4), we derived the theoretical expression of the CDF of SNMM. Therefore, when we draw samples from SNMM, we can directly use the inverse CDF (iCDF) of SNMM, although the iCDF may not be an explicit expression [35], i.e., \(x_{lm}=F^{-1}(U)\), where \(U\) is uniformly distributed in \([0,1]\).
## 2 Experimental Setup and Detection Method
### Data Collection Process  
#### 2.2.1 Data Collection of Fruit Flowers  
As briefly explained in Section 2.1, the photos and video for the dataset will be collected using the Azure Kinect DK camera from Microsoft. This camera has multiple different sensors implemented into the camera to help with different AI applications, such as a depth sensor, spatial microphone array, gyroscope and accelerometer [30]. It is also capable of regular color or RGB capturing as well as an infrared camera [30]. With these features in mind, this camera is capable of obtaining very detailed information for a wide variety of applications.  
For the first collection of the fruit flower data for this specific case, the camera was connected to a 2015 MacBook Air running Ubuntu due to limitations with resources at the time. The camera was used to take zoomed-out photos and close-up photos, similar to Figures 1 and 3, respectively. For the zoomed-out photos, the Azure Kinect DK camera was placed on top of a tripod for a stable photo. The purpose of these photos was to capture the entire tree, while the close-up photos were used to obtain a more detailed view of the fruit flowers. The close-up photos were obtained by taking a video of the fruit flowers and walking up and down the rows of the orchard. Then, using a video splicing tool, separate photos were obtained which were then filtered to take out any photos that had visual issues, such as blurry photos. These steps were repeated for 3 trips at the Simcoe Research Station until a satisfactory amount of fruit flower photos were obtained for labelling.  
When more resources were available, such as the development of the mobile vehicle by another Graduate Student, more data was collected the following year. This consisted of driving the mobile vehicle up and down the orchard rows at a set distance to obtain zoomed-out photos of the apple trees. Multiple trips were completed to ensure enough data was collected. Manual counting of the fruit flower clusters was also conducted to have a comparison for the performance of the model when fully trained.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
###### Abstract  
The dynamic-scheduling problem of transmission tasks (DSIT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy algorithm for task allocation is poor. In the case of more tasks and equipment and smaller time division, the precise algorithm cannot complete the calculation within an effective timeframe. In order to solve this problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO), builds a DSTT mathematical model suitable for the DPSO, solves the problem that particle swarm operations are not easy to describe in discrete problems, and redefines the particle motion strategy and adds random disturbance operation in its probabilistic selection model to ensure the effectiveness of the algorithm. In the comparison experiment, the DPSO achieved much higher success rates than the greedy algorithm (GR) and the improved genetic algorithm (IGA). Finally, in the simulation experiment, the result data show that the accuracy of the DPSO outperforms that of the GR and IGA by up to 3.012295% and 0.11115%, respectively, and the efficiency of the DPSO outperforms that of the IGA by up to 69.246%.  
task dynamic scheduling; discrete particle swarm optimization algorithm; probability selection model; random disturbance; schedule; evaluation of transmission effect +
Footnote †: journal: Applied Sciences  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Analysis of the Unbalanced Powers in a Three-Phase Four-Wire Linear System  
Figure 1 shows a linear unbalanced load that is connected to a three-phase four-wire system with unbalanced voltages.  
The apparent power in the bus is determined by Equation (1).  
\[\underline{S}=\underline{S_{a}}+\underline{S_{b}}+\underline{S_{c}} \tag{1}\]  
where  
* \(\underline{S}\) is the complex apparent power of the system defined in the classical theories.
* \(\underline{S_{a}}\), \(\underline{S_{b}}\) and \(\underline{S_{c}}\) are the complex powers in each of the phases.  
In contrast, the total apparent power of Buchholz \(S_{T}\) of a three-phase four-wire system, which is expressed in symmetrical components and related to the apparent power \(S\), is determined by Equation (2).  
\[S_{T}=3\sqrt{\left(V_{+}^{2}+V_{-}^{2}+V_{0}^{2}\right)\left(l_{+}^{2}+l_{-}^{ 2}+l_{0}^{2}\right)}=\sqrt{S^{2}+S_{uT}^{2}}=\sqrt{P^{2}+Q^{2}+S_{uT}^{2}} \tag{2}\]  
where \(S_{uT}\) is the unbalanced apparent power caused by voltages and currents of different sequences. Its value is obtained from Equation (3).  
\[\begin{array}{c}S_{uT}^{2}={S_{u+}^{-}}^{2}+{S_{u+}^{0}}^{2}+{S_{u-}^{+}}^{2 }+{S_{u-}^{0}}^{2}+{S_{u0}^{+}}^{2}+{S_{u0}^{-}}^{2}-2(P_{+}P_{-}+P_{+}P_{0}+ P_{-}P_{0})-\\ 2(Q_{+}Q_{-}+Q_{+}Q_{0}+Q_{-}Q_{0})\end{array} \tag{3}\]  
Here,  
* \(P_{+}\), \(P_{-}\) and \(P_{0}\) are the positive-, negative- and zero-sequence active power, respectively.
* \(Q_{+}\), \(Q_{-}\) and \(Q_{0}\) are the positive-, negative- and zero-sequence reactive power, respectively.  
The values of these unbalanced apparent powers are calculated from Equations (4)-(9), for \(z=\{a,b,c\}\).  
\[S_{u+}^{-}=3V_{+}I_{-} \tag{4}\]  
\[S_{u+}^{0}=3V_{+}I_{0} \tag{5}\]  
\[S_{u-}^{+}=3V_{-}I_{+} \tag{6}\]  
\[S_{u-}^{0}=3V_{-}I_{0} \tag{7}\]  
\[S_{u0  
consumed by the load constant. \(S_{u-}^{+}\) and \(S_{u0}^{+}\) cannot be compensated, since in an infinite short-circuit power network, negative-sequence voltages and zero-sequence voltages are imposed by the network itself, although its value will change depending on the new values of positive-sequence currents.
## 4 Establishment of CFD Model
### Grid Sensitivity Analysis  
The sensitivity analysis results are obtained by simulation to determine the appropriate overall size of mesh, as demonstrated in Table 1.  
With the mesh refinement, the accuracy of physical quantity solution increases, but the convergence speed decreases. When the mesh base size is greater than 0.75, an error of float point overflow starts to occur due to poor volume mesh quality in narrow spaces, and the physical quantities fluctuate greatly. On the flip side, after the basic size of the grid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Base Size/m**} & **Total Number of Cell/10\({}^{\mathbf{d}}\)** & **Side Wing** & **Side Wing** & **Lift of** & **Convergence after** & **Convergence after** \\  & **of Cell/10\({}^{\mathbf{d}}\)** & **Lift/N** & **Drag/N** & **Vehicle/N** & **1000 Steps** & **2000 Steps** \\ \hline
0.75 & 106 & \(-\)98.9914 & 29.1466 & \(-\)468.235 & **no** & **yes** \\
0.6 & 189 & \(-\)91.9932 & 30.9724 & \(-\)470.751 & **yes** & **yes** \\
0.5 & 298 & \(-\)88.627 & 28.9974 & \(-\)462.26 & **yes** & **yes** \\
0.4 & 466 & \(-\)89.2804 & 29.106 & \(-\)462.313 & **no** & **yes** \\
0.25 & 1907 & \(-\)88.4492 & 28.6526 & \(-\)462.126 & **no** & **no** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grid sensitivity analysis results.  
Figure 10: Mesh operation results and three different zones (body, wake, side wing) that are densified.  
is reduced to 0.25 m, the number of convergence steps exceeds 2000 steps, which actually reaches more than 3000 steps, and the time consumption increases geometrically (after 3055 steps, the fluctuation of lift of vehicle with in 100 iterations starts to be smaller than 2%, and before 3055 steps, other judging values including side wing lift and side wing drag have already reached the same convergence standard of an 100 steps fluctuation smaller than 2%). Therefore, 0.5 m is selected as the basic size of the grid in balance of accuracy and convergence speed.
## 2 Materials and Methods
### Evma Control Charts  
The EWMA control charts, first proposed by [PERSON] et al. [7], are especially preferred for single observations and Phase II applications, but they can also be used for rational subsets. The EWMA statistics (\(z_{i}\)) are defined in Equation (1).  
\[z_{i}=\lambda x_{i}+(1-\lambda)z_{i-1} \tag{1}\]  
where \(x_{i}\) is the value of \(i\)th observation for \(i(i=1,\ldots,n)\) and \(0<\lambda\leq\)1 is smoothing constant. To calculate value \(z_{1}\), we need to provide the starting value \(z_{0}\) which is equal to the process target value \(\mu\) given in Equation (2).  
\[z_{0}=\mu. \tag{2}\]  
Sometimes, the average of preliminary data is used as the starting value of the EWMA, so that \(z_{0}=\bar{x}\), where \(\bar{x}\) is the sample mean. To illustrate that the \(z_{i}\) represents a weighted average of all previous sample means, replacing \(z_{i-1}\) by \([\lambda x_{i-1}+(1-\lambda)z_{i-2}]\) then Equation (3) is obtained.  
\[z_{i}=\lambda x_{i}+\lambda(1-\lambda)[\lambda x_{i-1}+(1-\lambda)z_{i-2}]= \lambda x_{i}+\lambda(1-\lambda)x_{i-1}+(1-\lambda)^{2}z_{i-2} \tag{3}\]After performing a recursive substitution, \(z_{i}\) is obtained as shown in the following Equation (4).  
\[z_{i}=\lambda\sum_{j=0}^{i=1}(1-\lambda)^{j}x_{i-j}+(1-\lambda)^{j}z_{0} \tag{4}\]  
The weights sum to unity since \(\lambda(1-\lambda)^{j}\) decreases geometrically as \(j\) increases. Hence, the sum converges to one given in Equation (5).  
\[\lambda\sum_{j=0}^{i=1}(1-\lambda)^{j}=\lambda\Big{(}\frac{1-(1-\lambda)^{i}}{ 1-(1-\lambda)}\Big{)}=1-(1-\lambda)^{i}. \tag{5}\]  
If the sample data consist of independent random variables \(x_{i}\) with a variance of \(\sigma^{2}\), then the variance of \(z_{i}\) is given in Equation (6).  
\[\sigma_{z_{i}}^{2}=\sigma^{2}\Big{(}\frac{\lambda}{2-\lambda}\Big{)}[1-(1- \lambda)^{2i}]. \tag{6}\]  
Hence, the control chart of EWMA is created by using the \(z_{i}\) values against the sample number \(i\). The upper control limit (UCL) and the lower control limit (LCL) of the EWMA control chart are defined in Equations (7) and (8).  
\[UCL=\mu+L\sigma\sqrt{\frac{\lambda}{(2-\lambda)}[1-(1-\lambda)^{2i}]} \tag{7}\]  
\[LCL=\mu-L\sigma\sqrt{\frac{\lambda}{(2-\lambda)}[1-(1-\lambda)^{2i}]} \tag{8}\]  
where \(L\) is the width of the control limits, and the center line (CL) is \(\mu\).