esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 CFD Numerical Model
### Model Descriptions  
The CFD simulation is conducted using a full-scale dimension, and the typical China railway high-speed train running on a viaduct is used as the geometric model. In order to possibly minimize the boundary effect and fully reproduce the wake flow, a fluid domain with a suitable size has to be chosen first. Referring to the related investigations conducted by [PERSON] et al. [5] and [PERSON] et al. [15], the flow domain is set as a parallelepiped in \(400\times 280\times 60\) m key dimensions for the simulation of a moving train in crosswinds, where the bridge is \(80\) m from the upstream and the train runs for \(240\) m on the bridge, and the size of the flow domain is also calibrated by several trial calculations, shown in Figure 1a. A uniform inlet velocity boundary condition with a mean velocity \(U\) and \(5\%\) low turbulence intensity is applied on the inlet of the fluid zone, and the outlet is a zero-gauge pressure outlet boundary. The top and side surfaces of the computational domain are defined as the no-slip wall, and the boundary effect on the surfaces of train and bridge and the ground is performed by the no-slip wall with a standard wall function.  
Figure 1: Configuration of numerical model (unit: m): (**a**) flow domain and boundary condition; (**b**) train geometry model; (**c**) train-bridge cross section.  
The complete CRH train in 200 m length is out of available computing resources. Thus, the total train length in the simulation is limited to 75 m assembly consisting of a head car, a middle car, and a tail car, and the full-scale width and height dimensions of a train are 3.4 and 3.7 m, respectively, as illustrated in Figure 1b,c. Besides, the train geometry in the calculation is further simplified to smooth curved surfaces without regard to the effects of wheelsets, mirrors, windshield wipers and other attached parts. The bridge geometry with a width of 13.4 m and height of 3.05 m is originated from the 32 m-span simply supported box beam widely used in China high-speed railways, and the attached structures on the bridge, such as railings, tracks and contact networks are also neglected.  
The hexahedral-dominated mesh is utilized to discretize the flow domain in this study. A proper resolution is required close to the train surface to correctly simulate the flow structure. Therefore, a finer mesh density adopting a minimum of 5 mm size is generated in the region near the train and bridge, and then the size is gradually running to 0.2 m in the coarse region, as shown in Figure 2a. It is noted that the boundary layer mesh of a 6 mm total thickness is arranged on the train and bridge surfaces to capture the wall flow accurately, in which the initial layer thickness is created to 0.6 mm and then it expands to 5 th layers at a ratio of 1.2, as shown in Figure 2c. The corresponding dimensionless wall distance y\({}^{+}\) is close to 1, and finally, the total mesh number in this numerical model is about 15 million.
## 4 Numerical Results
### Sensitivity Analysis  
#### 4.4.1 Criteria Weights  
To analyze the sensitivity of the criteria weights to prioritization, first, the criteria are classified into three groups--a cost-related group, a policy-related group, and a production  
Figure 5: SfRDMA effectiveness map.  
Figure 6: The ultimate score for moving high-tech manufacturing.  
condition-related group. For these groups, four different criteria weight allocation scenarios were constructed, as shown in Table 9. In scenario 1, the criteria were weighted equally. Meanwhile, scenario 2 focused on cost-related criteria. Scenario 3 was concerned with policy criteria, while scenario 4 was concerned with production conditions. As shown in Figure 7, the sensitivity analysis results showed that the weight of the criteria had a significant influence on the evaluation results of the proposed method. The ranking of countries did not show a big difference between the base scenario, where the weights of the criteria were determined by the proposed method, and scenario 1. Another finding was that the rankings of India, Thailand, and Vietnam were always at the top, regardless of differences in the allocation of weights. As the weight of the cost-related criteria decreased, Malaysia's ranking dropped significantly from 3 rd to 7 th place. In contrast, Singapore rose in rank as decision makers became more concerned about the criteria for production conditions.
# Clustering-Based Data Dissemination Protocol Using the Path Similarity for Autonomous Vehicles
## 2 Related Works and Problems
### Data Dissemination Scheme Based on Clustering and Probabilistic Broadcasting
In this paper, we will compare this with a simulation environment which depicts a real environment rather than efficiency based on simple arithmetic operation. Also, the authors did not take into account the probability that the route will change to link quality, an option which is favorable in existing environments such as highways, but can be extremely disadvantageous in urban areas with frequent path deviations. Clustering algorithms using a simple path loss model cannot guarantee future connectivity. When the cluster is disconnected frequently, the overhead is increased, and the message to disseminate is more likely to be lost. Moreover, since all nodes must continuously check the connectivity, flooding may occur due to discovery messages in the city where the vehicle is driven. This is considered to be unsuitable for future Internet environments where there are numerous nodes besides vehicles. In addition, the conventional algorithm does not consider roads with several lanes when determining the probability that a message is forwarded using density. In an actual driving environment, except for a highway, the lane serves as one of the critical variables that changes the route. Because of these problems, it seems that the existing algorithms are less effective in urban scenarios. In particular, if we look at the left side of Figure 2, it can be confirmed that only one-dimensional positional variables can be considered since all lanes can be regarded as straight lines when the highway is generalized. However, when we look at the right side of Figure 2 considering roads with many lanes in actual cities, it can be seen that the lane information of the road not only refers to the position information between the left and right, but also affects the density of the node. That is, the number of lanes also affects estimations that utilize density [36]. For example, a vehicle on a left-hand lane and a straight-ahead lane may have high connectivity, but in reality, it only takes a few seconds to move in a different direction. Clustering algorithms based solely on link probabilities are difficult to distinguish. However, if the exact path of each vehicle can be known in advance, it can be seen that the paths are compared with each other and the path changes soon. Thus, in this paper, we will discuss how to obtain the advantages and overcome the disadvantages of existing research if we assume the above situation and have path information for each node in the future.
## 3 Experimental Investigation
### Experimental Results
As we can see, in the right top corner of the SOM (Figure 6a) all these three texts are located in the same cell. The other texts also make some clusters which are formed according to the Table 5 described categories. The results of \(Q_{4}\) answers texts in the SOM (Figure 6b) shows that the texts \(D_{13}\) (near copy), \(D_{17}\) (light revision) are similar to the original text \(D_{20}\), but the other near copy texts \(D_{3},D_{6},D_{9}\) are located and grouped far away from original text cell. So in this case, the SOM recognize similarity partly. The similarity measures also confirmed similarity of three texts (Table 6) only, where overlap measure gave the highest values: \(D_{3}=105\%,D_{13}=99\%,D_{19}=96\%\). In this case the text \(D_{3}\) fully overlapped the original text so it is obviously plagiarism. The results of the last question \(Q_{5}\) answers texts similarity were almost all confirmed by calculated measures. According to Table 5, the near copy texts are \(D_{5}\), \(D_{7}\), \(D_{10}\), \(D_{14}\), and \(D_{16}\). All four measures proved that four of five texts are similar to the original text. As with previous results, the highest percent was obtained using the overlap measure: \(D_{5}=100\%\), \(D_{10}=97\%\), \(D_{14}=100\%\), and \(D_{16}=97\%\). Only one overlap measure confirmed the similarity of the text \(D_{5}\), with other measures the value is small. In the bottom left corner of the SOM (Figure 7), the original text \(D_{20}\) is located in the same cell with text \(D_{16}\). The other near copy texts are scattered over all map so in this case, it is hard to confirm similarity just using the SOM. Figure 5: \(9\times 9\) self-organizing maps: (**a**) first question (\(Q_{1}\)); (**b**) second question (\(Q_{2}\)). Figure 6: \(9\times 9\) self-organizing maps: (**a**) third question (\(Q_{3}\)); (**b**) fourth question (\(Q_{4}\)).
## 3 Results and Discussion
### THz Imaging of LCP
For the cross-Nicol configuration, the transmitted light intensity \(I\) can be expressed as:  
\[I=I_{0}\cdot\sin^{2}2\phi\cdot\sin^{2}\frac{\delta}{2}, \tag{2}\]  
where \(I_{0}\) is the input light intensity, and \(\varphi\) is the angle between the molecular long axis of the birefringent sample and the polarizer axis. Here, when the direction of the molecular long axis, with respect to the analyzer as, \(\alpha\), is set to \(\alpha\) = 90\({}^{\circ}-\varphi\), then:  
\[I=I_{0}\cdot\sin^{2}2\alpha\cdot\sin^{2}\frac{\delta}{2}. \tag{3}\]  
To detect the orientation of the LCP, we obtained THz images while rotating the LCP sample in the cross-Nicol configuration. We obtained THz images of the LPC sample when \(\alpha\) was varied from 0\({}^{\circ}\) to 45\({}^{\circ}\) in steps of 15\({}^{\circ}\) (10 \(\times\) 10 pixels, 0.4 mm steps). To obtain a better signal-to-noise ratio, we acquired and accumulated measurements a total of 40 times. From the obtained images, the average value and standard deviation were calculated. The normalized plots and theoretical results calculated by using Equation (3) are shown in Figure 6a. The experimental value was in good agreement with the theoretically calculated value. Figure 6b-e shows the transmitted images when the LCP sample was rotated in steps from 0\({}^{\circ}\) to 45\({}^{\circ}\) (\(\alpha\) = 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), respectively). As a result, we could find the orientation of LCP by measuring the THz transmission intensity. Figure 6: (**a**) Normalized transmitted THz intensity when rotating the LCP sample and theoretical results (solid line) calculated by using Equation (3). (**b–e**) Transmission images when rotating LCP sample in steps from 0\({}^{\circ}\) to 45\({}^{\circ}\) (\(\alpha\) = 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), respectively). Images were obtained with raster-scanning method.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Defining Message Format and Interval
#### 4.2.4 Global Pheromone Map and Location Information Message Interval  
A UAV sends the global pheromone map and location information whenever a new one-hop neighbor is added to the one-hop neighbor table. In addition, the pheromone map and location information are updated and shared with the neighboring UAVs when a UAV reaches a waypoint.
## 1 Introduction  
### Application of Pesticides and Soil Pollution  
Pesticides are widely used to prevent and control crop pests all over the world, ensuring the improvement of yield and quality and playing a great role in producing food to meet global demand [1]. However, there are benefits and drawbacks when using pesticides in agriculture. Increasingly, pesticides are applied to control various pests, diseases, and breeds, though most of these pesticides remain in the environment, posing a potential risk to the whole agricultural ecosystem [2]. Pesticides' environmental contamination is raising concerns because of their negative effects on nontarget organisms in the soil and even the entire ecosystem in the world [3]. Pesticides are a vital pollutant of nonpoint source pollution and can contaminate soil in a variety of ways [4]. After applying pesticides, only a small proportion remains in the plant, and the majority remains in the soil [5,6]. Excessive use of pesticides in the past several decades has caused the pesticides' residues accumulation problem, which often exceeds the self-purification capacity of the soil. This further leads to increasing soil pollution and soil quality deterioration. Due to their high solubility in water, pesticides with high water solubility have a high risk of leaching into the soil, which is the primary reason for pesticide misuse in agricultural fields [7].
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
###### Abstract  
Distributed estimation and tracking of interested objects over wireless sensor networks (WSNs) is a hot research topic. Since network topology possesses distinctive structural parameters and plays an important role for the performance of distributed estimation, we first formulate the communication overhead reduction problem in distributed estimation algorithms as the network topology optimization in this paper. The effect of structural parameters on the algebraic connectivity of a network is overviewed. Moreover, aiming to reduce the communication overhead in Kalman consensus filter (KCF)-based distributed estimation algorithm, we propose a network topology optimization method by properly deleting and adding communication links according to nodes' local structural parameters information, in which the constraint on the communication range of two nodes is incorporated. Simulation results show that the proposed network topology optimization method can effectively improve the convergence rate of KCF algorithm and achieve a good trade-off between the estimate error and communication overhead.  
Kalman consensus filter (KCF); network topology optimization; algebraic connectivity; communication overhead; convergence rate +
Footnote †: journal: Medical Science  
1
Footnote 1: email: _applied_  
Received: 24 June 2021  
Accepted: 27 July 2021  
Subject: 31 July 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Key words:**
# Influence of Right Triangular Prism Rough Beds on Hydraulic Jumps
## 2 Mathematical Method
### Dimensional Analysis for Hydraulic Jump Characteristics  
The conjugate depth (\(y_{2}\)) and jump length (\(L_{j}\)) are the two main parameters of the hydraulic jump phenomenon that directly affect the scale and cost of the stilling basin construction. Therefore, these parameters must be clearly defined in the design of irrigation works. Therefore, this section presents how to determine the relationship between these parameters, the upstream flow conditions, and the geometry characteristics of a rough bed configuration. Those relationships might be written as the following:  
\[y_{2}\text{ or }L_{j}=f_{1}(\rho,\
u,g,y_{1},V_{1},t,s,L_{s},m_{1},m_{2}) \tag{1}\]  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model Name** & **Schematic of New Rough Beds** &
\begin{tabular}{l} **Supercritical Flow** \\ **Parameters** \\ \end{tabular} & **Symbol** & **Values** \\ \hline M\_I & InFlow & rough bed region & Flow depth & \(y_{1}\) (cm) & 1.5, 2.0, 2.5, 3.0 \\ \hline M\_II & InFlow & \(y_{1}\) (cm) & Discharge per unit width & \(q\) (m\({}^{2}\) s\({}^{-1}\)) & 0.0276–0.1533 \\ M\_III & InFlow & \(y_{1}\) (cm) & Froude number & \(F_{1}\) (-) & 4.8–9.4 \\ M\_IV & InFlow & \(y_{1}\) (cm) & Reynolds Number & \(Re_{1}\times 10^{3}\) & 27.31–151.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Schematic of rough beds using the simulations and study parameters.  
Figure 1: Hydraulic jump on a right triangular prism rough bed.  
where \(\rho\), \(\
u\), and \(g\) are the mass density of water, water kinematic viscosity, and gravity acceleration; \(m_{1}\) and \(m_{2}\) are the upstream and downstream slope of roughness element, and \(V_{1}\) is the inflow velocity.  
In this study, we chose three repeated variables, including supercritical flow depth \(y_{1}\), initial flow velocity \(V_{1}\), and density of water \(\rho\), to perform a dimensionless analysis for both parameters \(y_{2}/y_{1}\) and \(L_{j}/y_{1}\). With the initial Reynolds number (\(27.3\times 10^{3}\leq Re_{1}\leq 151.8\times 10^{3}\)), the viscosity effect was neglected. By using the Pi theorem, Equation (1) can be rewritten as follows:  
\[\frac{y_{2}}{y_{1}}\;\sigma\;\frac{L_{j}}{y_{1}}=f_{2}(Fr_{1},\frac{t}{y_{1}}, \frac{s}{y_{1}},\frac{L_{s}}{y_{1}},m_{1},m_{2}) \tag{2}\]  
Similarly, we also obtained the relationship of energy loss (\(E_{L}/E_{1}\)) and shear force coefficient (\(\varepsilon_{f}\)) with flow conditions and rough bottom geometry as follows:  
\[\frac{E_{L}}{E_{1}}\;\sigma\;\varepsilon_{f}=f_{3}(Fr_{1},\frac{t}{y_{1}}, \frac{s}{y_{1}},\frac{L_{s}}{y_{1}},m_{1},m_{2}) \tag{3}\]
## 3 Model Development and Training
### Model Structure  
As shown in Figure 3, the model consists of a set of consecutive connected layers. The first layer has sixteen recurrent cells. The first layer is fully connected to the second layer, which has ten cells. The final layer has six cells, one for each human activity. The model systematically traverses the dataset. We used well-known algorithms called sliding windows [24]. The algorithm employs a predetermined window size and constant sliding steps. The purpose is to group subsequent records into one entity. Based on the literature, we picked the window size to be fifty records, corresponding to approximately 2.5 s at WISDM's 20 Hz sampling rate. While this fixed-window method is standard for WISDM, we acknowledge that different healthcare applications may require varying temporal resolutions from sub-second seizure detection to multi-minute rehabilitation tracking. Future work may consider adaptive windowing strategies to dynamically adjust window lengths based on signal characteristics or medical context.  
To maintain data integrity, we only include instances where the dominating activity constitutes at least 70% of the observations in the training phase. Including this percentage will ensure a higher degree of activity purity. The data was divided into three distinct portions. Precisely 10% was allocated for testing, and 90% was left for training. From the training data, 10% was reserved as a validation dataset to evaluate the model's performance  
Figure 2: WISDM dataset activities, walking and jogging counts, represent more than 50% of the data.  
while training, which will help the model avoid problems like overfitting, a scenario where the model performs well during training but poorly when applied to testing data.
## 4 Proposed Solution
### Trajectory Optimization Algorithm Based on DDPG
#### 4.3.4 Basic Idea  
The flowchart of the trajectory optimization algorithm based on DDPG designed in this paper is shown in Figure 4. The algorithm is composed of two nested loops. The outer loop iterates N times to train the model proposed in this paper. As the number of iterations in the outer loop increases, we can determine whether the training has reached convergence based on the trend of reward changes. The inner loop represents a single training process in a specific scenario, which continues until the energy of a UAV in the system is depleted. In the inner loop process, the algorithm performs steps such as action selection, collision avoidance, communication control, and reward calculation and updates the state space and network.  
Figure 4: Explanation of the flow of our algorithm.
## 3 Results and Discussions
### Throughput Comparison  
Figure 5 depicts the throughput comparison of the EMBLR, MP-OLSRv2, and MEQSA-OLSRv2 routing approaches. It can be observed that the results achieved through the proposed EMBLR approach are higher than the existing approaches in terms of network throughput. Both MP-OLSRv2 and MEQSA-OLSRv2 routing approaches exclude the link quality metric of the intermediate devices in the selection of the optimal route. Whereas the EMBLR routing scheme exploits the HETX value through the probe packets before data transmission, which minimizes the chance of link failure and enhances the network throughput. Moreover, the EMBLR routing scheme utilizes the Backpressure algorithm which forwards the packets toward the low congested device. Therefore, it balances the load among the devices that lead to the maximization of the network throughput. When the speed of devices increases from 10 to 60 m/s, the network throughput decreases. The EMBLR decreases from 58.45 to 49.64 kbps, the MEQSA-OLSRv2 decreases from 54.56 to 46.22 kbps, and the MP-OLSRv2 also decrease from 53.12 to 43.56 kbps. The EMBLR routing protocol has a higher throughput performance than both MEQSA-OLSRv2 and MP-OLSRv2 in all of the different device speeds.
## 3 Experimental Results and Discussion
### Load and Deflection Relationship
#### 3.2.4 Ductility  
Figure 11 compares the ductility index, which is defined as the ultimate deflection, \(\delta_{u}\), divided by deflection at yield load, \(\delta_{y}\). The ductility of T1O, T1M, T2, T2E, T2 AP, and T3 were 1.32, 2.08, 1.58, 1.41, 1.22 and 1.5, respectively. T1M showed higher ductility compared with other TRM specimens. Contrary to T1M, T2 AP with mechanical end anchorage had the lowest ductility while suppressing slippage. The ductility of the control specimen was 2.8, and all TRM specimens were less ductile than that of the control specimen, and thus failed to ensure sufficient ductility for structural safety.
## 2 Concept of Periodically-Distributed Bends Along a Microstrip Transmission Line
For the sharp edge design, when the angle of the bend was reduced from 56 degrees (<56 deg. ), it changed the cutoff frequencies. The cutoff frequency went higher in this case. Further, it significantly changed the radiation as the gain reduced with the increase in the beamwidths. When the angle was increased from 56 degrees (>56 deg. ), again, a similar kind of phenomenon has been observed and degraded the radiation performance. In the case of smooth edge sine wave design, when the angle of the bend was reduced from 56 degrees (<56 deg. ), the operating frequency region modified. The higher cutoff frequency for Mode-2 has been observed at 15 GHz. Further, there was no mode coupling at the 60-degree phase, which is one of the reasons for the modified operating frequency bands. When the angle was >56 degrees, the mode coupling was significantly less, and it occurred at the 120-degree phase. For both cases, broad beamwidths have been observed, which reduced the overall gain. The proposed principle, which was originally inspired by our analysis on a lattice model, has been validated with the dispersion analysis, the results of which have validated the measured propagation performance and radiation characteristics of the proposed right-hand/left-hand transmission line design with sharp corners and the right-hand transmission line design with smooth corners.