esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology  
### Establishment of Simulation Model  
A simulation model was established to simulate the environment of PM\({}_{2.5}\) to further examine the air environment in the stadium. SketchUp was used for auxiliary modelling, and ICEM CFD was used for model refinement and mesh division. The number of meshes was approximately two million; 99.9% of the mesh quality ranged between 0.95 and 1, and the mesh quality satisfied the requirements. Standard k-\(\varepsilon\) turbulence was used to simulate the PM\({}_{2.5}\) concentration using Fluent19.0 [27,28]. The indoor particulate matter movement is a quite complex process. In order to simplify the model for simulation, the following assumptions are made to the model:  
1. The indoor supply air volume and return air volume are stable values. 2. Not considering the indoor PM\({}_{2.5}\) reduction rate. 3. Indoor PM\({}_{2.5}\) is uniformly distributed. 4. Assuming that the indoor PM\({}_{2.5}\) emission intensity is constant, and does not change over time. 5. Neglecting the dust generation in ventilation ducts and the PM\({}_{2.5}\) deposition inside the ducts. Generally speaking, the indoor PM\({}_{2.5}\) diffusion in curling venues occurs in turbulent flow fields. Therefore, the indoor pollutants' convective diffusion law should be based on turbulent diffusion theory, and an accurate numerical simulation of the convective diffusion process should be carried out on this basis. A reasonable turbulence model has a significant impact on the accuracy of the simulation. The commonly used turbulence models can be divided into two categories: One is eddy viscosity models, including zero equation models, one equation model, and two equation models. Another type is second-order moment closure models based on Reynolds stress transport equations, including Reynolds stress models and algebraic stress models. The standard k-epsilon is a dual equation model developed on the basis of the k-equation model that characterizes turbulent pulsation kinetic energy and has wide applicability. Although in terms of accuracy, RNG k-\(\varepsilon\) The model has more advantages, and many scholars have also achieved good results in using the standard k-\(\varepsilon\) model to simulate the diffusion of atmospheric pollutants. For example, [27] used the standard k-\(\varepsilon\) model to simulate natural ventilation and the diffusion and distribution of pollutants in street valleys, and the simulation results were consistent with the actual  
Figure 1: National Aquatics Center (Ice Cube) curling venue. situation. In [28], the authors used the standard k-epsilon model to simulate the effect of temperature on the three-dimensional block flow field and pollutant diffusion, which showed good consistency with actual measurement data. Compared with on-site detection data, the two were in good agreement, indicating that the standard k-epsilon model can be applied to simulate the diffusion of pollutants in different types of environments. In the standard model, k and \(\varepsilon\) are two basic unknowns.
# Topology Optimization of Anisotropic Materials with Smooth Fiber Orientation
## 3 Numerical Results
### Analysis of Structural Stiffness and Fiber Continuity
This adjustment will inevitably affect the mechanical properties of the structure to a certain extent. Our design goal is to ensure that fiber continuity is improved while minimizing the sacrifice of the overall structural stiffness. Scheme (3) is based on this goal. By combining the fiber angle constraint strategy and adaptive filtering technology, the structural stiffness is maintained to the greatest extent while improving fiber continuity so as to achieve the purpose of optimization. Figure 14: Trend of the fiber continuity rate of the bridge girder with different schemes. Combined with the analysis of Figures 8 and 9, it is concluded that adaptive filtering can maintain the continuity of fibers. Its more prominent advantage is that compared with the optimization scheme without adaptive filtering, it optimizes the topology with higher stiffness, which is the embodiment of both manufacturability and mechanical properties. Figure 10 shows the optimization results of the boundary conditions of the double-load cantilever beam with different schemes. It is a diagram enlarged by the results of (b-1), (b-2), and (b-3) in Figure 7, which is intended to analyze the differences in topology and fiber angle distribution. From the perspective of fiber angle distribution, the angle in scheme (1) is discontinuous, especially at the node and support of the bar,  
by constraints and gradually converges to 85.82% in concurrent optimization; scheme (2) introduced constraints at the sixth iteration, and the fiber continuity rate increased significantly, began to fluctuate, and finally converged to 98.00%. Scheme (3) introduces constraints at the beginning of the 34 th iteration, and the fluctuation is small. Finally, it also converges at 98.01%. Figure 15 shows the changing trend of the structural compliance of bridge girders with different optimization schemes. In the diagram, it can be seen that scheme (1) is not affected by constraints, the compliance eventually converges to 19.75, and the overall structural stiffness is large; the compliance change in scheme (2) fluctuates greatly and finally converges to 61.65, and the overall structural stiffness is small. The compliance fluctuation of scheme (3) is very small, converging to 27.96, and the structural stiffness is greatly improved compared with scheme (2). In summary, in the concurrent optimization considering fiber angle constraints, the results show a smoother fiber distribution. However, this mandatory constraint sacrifices the mechanical properties of the material to a certain extent, resulting in a decrease in the overall stiffness of the structure. In order to solve this problem, we introduce adaptive filtering technology on the basis of fiber angle constraints. Through the application of this technology, not only is the continuity of the fiber significantly improved but also the overall stiffness of the structure is effectively maintained, and the balance between the optimization goal and the mechanical properties is achieved.
# Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments
## 4 Experiments and Results
### Platform and Environment  
In order to reflect the effect of curve-localizability-SVM active localization algorithm, we selected a campus grassland scene with obvious slope changes for our experiments. The aerial image of the scene is shown in Figure 5(a). The robot platform used in our experiment is Jiao Long Intelligent Wheelchair platform as shown in Figure 5(b) and main sensors include Rslidar RS-LIDAR-16 3D Lidar Sensor, Inertial Measurement Unit(IMU), and Odometer. The performance and observation principle of RS-LIDAR-16 is basically the same as VLP-16. The industrial computer of this intelligent wheelchair is equipped with Intel Core i7-8550U 1.8 GHz CPU and 16G memory, our algorithms are running under the ROS Kinetic System.
# Stationary Target Identification in a Traffic Monitoring Radar System
## 3 Signal Measurement and Target Detection Using Traffic Monitoring Radar
### Detection Results from Radar Signal Processing  
Using the signal processing method described in Section 2, the target information, such as \(R_{l}\), \(v_{l}\), and \(\theta_{l}\) of the target, can be extracted in our traffic-monitoring radar system. The \(R_{l}\) and \(v_{l}\) information obtained here indicates the range and velocity in the radial direction, as shown in Figure 6.  
Thus, using the estimated values of \(R_{l}\) and \(\theta_{l}\), we can express the position of the \(l_{th}\) target in the Cartesian coordinate system as:  
\[(x_{l},\,y_{l})=(R_{l}\sin\theta_{l},\,R_{l}\cos\theta_{l}). \tag{14}\]  
For example, Figure 7 shows the detection result obtained from the traffic monitoring radar installed on the overpass. This figure shows a two-dimensional range map converted to a Cartesian coordinate system using \(R_{l}\) and \(\theta_{l}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters of Our Radar System** & **Parameter Values** \\ \hline Center frequency, \(f_{c}\) & 24.15 GHz \\ Bandwidth, \(\Delta B\) & 200 MHz \\ Detectable range & 2 to 300 m \\ Range resolution & 80 cm \\ Detectable velocity & \(-250\) to 250 km/h \\ Velocity resolution & 1.2 m/s \\ Field of view & \(-15^{\circ}\) to \(15^{\circ}\) \\ Frame time & 33 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the traffic monitoring radar system.  
The experimental data were acquired in the same environment as in Figure 1. A vehicle was approaching the radar, and guardrails were located on the left and right sides of the road. There was a forest on the left side of the guardrail. In this road environment, a number of points corresponding to a target vehicle and guardrails on either side of the road are detected. In addition, radar clutter also occurs due to trees in the forest off the road. In this case, if a meaningful moving target can be identified, more stable detection performance can be achieved.
# A Gesture-Based Teleoperation System for Compliant Robot Motion
## 3 Gesture-Based Teleoperation System for Robot Motion
### Active Force Control  
In order to guarantee the safety of the robotic system, the operator's motion command is filtered in the inner force control loop according to the following law.  
\[{}^{\Sigma_{B}}\mathbf{\bar{r}}_{t}=K_{f}^{\Sigma_{B}}\times\mathbf{r}_{t}+ \left(I-K_{f}^{\Sigma_{B}}\right)\times\mathbf{r}_{t-1}, \tag{8}\]  
where  
\[K_{f}^{\Sigma_{B}}=\left[\begin{array}{ccc}k_{x}&0&0\\ 0&k_{y}&0\\ 0&0&k_{z}\end{array}\right],\]  
is a weight matrix indicating whether the given command is acceptable. The elements are  
\[k_{i}=\left\{\begin{array}{cc}1&F_{i}<F_{threshold}\\ 0&F_{i}\geq F_{threshold}\end{array}\right.\;\;i=\left\{x,y,z\right\}, \tag{9}\]  
where \(F_{threshold}\) is the predefined threshold contact force. It can be determined by the robot capacity and part materials. Also, the scaling coefficient \(s\) is related to this value. If the material is stiff, it is better to use a smaller \(s\) to ensure the contact force will not exceed \(F_{threshold}\) in a single step.  
As shown in Figure 4, there is a connection between the force controller and the robot controller. It is a protective mechanism in extreme conditions. The force controller continuously monitors the analog force signals. Once the contact force exceeds the predefined value \(F_{max}\), a \"STOP ROBOT\" signal is sent to the robot controller immediately. Different from the above active force control, it is a reactive action with minimum delay. After triggering, the robot will not respond to the operation until performing a manual recovery.  
This extreme state may be triggered when the robot moves with a large step size toward an obstacle. It can be avoided by using fine motion mode and moving the hands slowly.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 5 Three-Dimensional Navigation  
Autonomous robot navigation is a complex task that involves several sub-tasks, including localization, path planning, and motion control, as illustrated in Figure 13. In order to successfully navigate an environment, the starting and destination points must first be defined. Once these points are established, a map of the environment must be constructed to enable the implementation of pathfinding algorithms. Localization and path planning are two critical sub-tasks in the navigation process. The robot must be able to accurately determine its position within the environment and plan a path from its current location to the destination point. Motion control is also an important consideration, as the robot must be able to follow the planned path while being controlled by twist commands.
# Avian Influenza Virus Strain Specificity in the Volatile Metabolome
###### Abstract  
Background/Objectives: Outbreaks of highly pathogenic avian influenza virus (AIV) result in significant financial losses and the death or depopulation of millions of domestic birds. Early and rapid detection and surveillance are needed to slow the spread of AIV and prevent its spillover to humans. The volatile metabolome (i.e., the pattern of volatile metabolites emitted by a living subject) represents one such source of health information that can be monitored for disease diagnosis. Indeed, dogs have been successfully trained to recognize patterns of \"body odors\" associated with many diseases. Because little is known regarding the mechanisms involved in the alteration of the volatile metabolome in response to health perturbation, questions still arise regarding the specificity, or lack thereof, of these alterations. Methods: To address this concern, we experimentally infected twenty mallard ducks with one of two different strains of low-pathogenic AIV (ten ducks per strain) and collected cloacal swabs at various time points before and after infection. Results: Headspace analyses revealed that four volatiles were significantly altered following infection, with distinct profiles associated with each viral strain. The volatiles that differed between strains among post-infection sampling periods included ethylbenzyl ether (\(p\) = 0.00006), 2-phenoxyethanol (\(p\) = 0.00017), 2-hydroxybenzaldehyde (\(p\) = 0.00022), and 6-methyl-5-hepten-2-one (\(p\) = 0.00034). Conclusions: These findings underscore that AIV-induced changes to the volatile metabolome are strain-specific, emphasizing the need for disease-specific profiling in diagnostic development.  
metabolome; influenza; ducks; diagnostic Article
## 3 Results
### First Screening
), but never circular and with no radial symmetry, two lateral plastids..................3
* short cells, the filaments are easily separated in small groups of cells, valves symmetric both on the frontal and lateral faces, septa are lacking in the frustules.................._Diatoma_
* long cells, valves with a circular shape (only visible in valvar view of isolated cells), the cells form filaments often found inside the shady fountains.................._Melosira_
* both valves with a raphe (structure composed by two slits or fissures), usually with a striation from the raphe to marginal part..................4
* both valves without a raphe..................14
* the raphe is located in the middle of both valves..................5
* the raphe is located in a different part..................12
* the raphe is located on both valves..................6
* the raphe is located along the margin of the valves..................11
* bi-symmetric valves, central raphe..................7
* mono-symmetric valve, central or lateral raphe..................9
* striation narrow or not very wide that is not visible..................8
* very wide striations, the raphe bent at both ends..................Pinnularia
* thin striation, visible in dead cells, valve elliptical to boat-shaped.................._Navicula_
* very thin striations (horizontal and vertical), difficult to see even in dead cells...._Prustulia_
* the valves are longitudinally asymmetric, raphe marginal..................10
* the valves are transversely asymmetric, with the upper part broader than the lower one, with a trapezoidal girdle view..................Gombonema
* the valve margins are different, one convex and the other straight or slightly curved, raphe parallel to the straight margin and striations do not strongly radiate in the center..................Cymbella
* elliptic or oblong valves vision, intercalary bands and striations are impereceptible.................._Amplora_
* the raphe is present on two sides of each valve, with thick striation and a big central channel..................Stirrella
* the raphe is present on one side of each valve..................13
* hyaline valves, usually linear with lateral refractory dots (fibulae)..................Nitzschia
* the raphe forms a peak in the ventral part of the valve..................Epithemia
* linear-elliptic valve, with a bent girdle view..................Achnantes
* elliptic and almost circular valve, straight girdle view..................Cococoneis
* needle-shaped valves with parallel and thin striations, lacking septa (a silica internal sheet, occluding a portion of the frustula)..................Synedra (Ullnaria)
* the frustules present septa, observed in girdle view, forming filaments...._Tabellaria_
## 3 Results
### Evolution Characteristics of Heavy Rain Disaster-Causing Factors
#### 3.1.3 Spatial Variation  
The frequency of heavy rain days is defined as the ratio of the number of heavy rain days to the total number of precipitation days at each meteorological station [38], reflecting the frequency of heavy rain occurrences in a region. This paper calculates and analyzes the spatial distribution characteristics of the frequency of heavy rain days. The classification of the frequency levels of heavy rain days using the natural breaks method in ArcGIS10.8 (Esri, Redlands, CA, USA) is shown in Figure 5. The number of heavy rain days decreases from the southeast to the northwest, with significant geographical variation. High-frequency areas are located in Tangshan, Qinhuangdao, southern Chengde, Miyun District in Beijing, Tianjin, eastern Cangzhou, and Langfang. Areas with relatively high frequencies include Shijiazhuang, Baoding, Xingtai, Handan, and most of western Beijing. In the northern part of the region, specifically most of Zhangjiakou and northern Chengde, the frequency of heavy rain days is low due to the blocking effect of the Yanshan Mountains, which reduces moisture transport. The lowest frequencies are observed in Yu County and Huailai County in Zhangjiakou.  
In summary, the analysis of the spatiotemporal evolution characteristics of heavy rain disaster-causing factors revealed an overall increase trend in the number of heavy rain days and total rainfall in the Beijing-Tianjin-Hebei region from 2000 to 2017. This suggests a potential increase in the risk of heavy rain disasters in the future. Furthermore, based on the spatial distribution of the frequency of heavy rain days, areas such as the southeastern part of the region experience higher frequencies of heavy rain events. Therefore, it is necessary to further investigate disaster risk factors by integrating the underlying surface conditions and socio-economic data. This will help to understand the distribution of heavy rain risks across various urban areas and provide effective support for formulating disaster prevention and mitigation measures under different scenarios.  
Figure 5: Spatial distribution map of the frequency of heavy rain days in the area.
## 7 Results
### Scenario PSC 4  
Based on the results in Table 21, the ANN provided an excellent trade-off between accuracy and convergence time, achieving 91.75% efficiency with fast response (0.1938 s). The DQN achieved the lowest MSE but at the cost of higher convergence time. Hybrid GA-based methods also performed well, while traditional algorithms like P&O and InC lagged behind with reduced efficiency and greater tracking error.
## 3 Materials and Methods  
Since our method is based on a two-stage Mask R-CNN, we first provide a brief review of this network. The Facebook AI Research group developed Mask R-CNN in 2017 [7]. It is an extension to Faster R-CNN [25], where an extra head is added to the network to perform instant segmentation, Faster R-CNN starts with a backbone for feature extraction. The backbone is followed by a region proposal network (RPN), and two head branches, one for bounding box regression. The other is for classification, the added branch in Mask R-CNN uses the region of interest (ROI) in a fully convolutional network to predict a mask for that ROI. All of the three heads utilize features extracted by the backbone network, which is usually a ResNet [12]. The ROIs are extracted using the region proposal network (RPN). The weights of the network are adjusted during the training using five loss values with a total loss calculated as follows.  
\[\begin{array}{l}\textit{loss}=w_{1}\times\textit{rpn\_class\_loss}+w_{2} \times\textit{rpn\_bbox\_loss}\\ +w_{3}\times\textit{class\_loss}+w_{4}\times\textit{bbox\_loss}\\ +w_{5}\times\textit{mask\_loss}\end{array} \tag{1}\]  
The RPN class loss and RPN bounding box loss are related to the output of the region proposal network. The RPN class loss is a binary classification loss; it is considered positive if the intersection over union (_loU_) of the proposed region with a ground truth bounding box is >0.7. The RPN bounding box loss is a regression loss between the four corner points of the ground truth bounding box and the proposed region bounding box. The RPN produces multiple regions which may have a significant overlap; a non-maximum suppression algorithm is applied to these regions to discard regions with overlap and produce regions of interest. The features maps corresponding to the ROI are cropped from the backbone and aligned before using them in the three heads to produce the bounding boxes, classification, and segmentation masks.  
Recall that our main problem is segmenting the optic cup and optic disc from the fundus images, then calculating the cup to disc ratio from the segmentation masks. The optic cup and optic discs are in the optic nerve head. The size of the optic nerve head is tiny compared to the fundus image, and the optic cup is even smaller; hence finding and segmenting the optic cup is hard. Using Mask R-CNN on the entire fundus image will localize the ONH well but cannot segment the optic cup adequately and sometimes fails to detect it.
## 2 Materials and Methods
### Thermal Reduction Tests  
Reduction trials were carried out by means of a TG-DSC (thermograviometry-differential scanning calorimetry) instrument, both in argon and air atmosphere (flow rate: 2 Nl/h), in order to understand the chemical/phase transformations and mass loss that take place during the reduction process. The samples were heated up to 1200 \({}^{\circ}\)C (with a ramp of 10 \({}^{\circ}\)C/min) and then cooled to room temperature with a ramp of 30 \({}^{\circ}\)C/min. A muffle furnace was used to carry out the tests on massive samples (2 to 5 g). The samples were brought to 1200, 1300, and 1500 \({}^{\circ}\)C respectively, and maintained at those temperature for 15 min and then cooled to 40 \({}^{\circ}\)C in a furnace. Graphite crucibles were used to contain the blends.  
At the end of the tests, the mass loss was measured. For each condition, three repetitions were performed for its subsequent characterization.  
To verify the assumptions derived from thermal reduction tests, theoretical mass loss was calculated, based on stoichiometric reduction reactions. The reliability of each reaction was evaluated through the Ellingham diagram in standard conditions, as a function of the reference temperature (1200 or 1500 \({}^{\circ}\)C) [61]. The main reactions considered are:  
1. \(2C+O_{2}\to 2 CO\)
2. \(2 Fe_{2}O_{3}+CO\to 2 Fe_{3}O_{4}+CO_{2}\)
3. \(Fe_{2}O_{3}+3 CO\to 2 Fe+3 CO_{2}\)
4. \(ZnO+CO\to Zn+CO_{2}\)
5. \(PbO+CO\to Pb+CO_{2}\)
6. \(P_{2}O_{5}+5 CO\to 2P+5 CO_{2}\)
7. \(SO_{3}+3 CO\to S+3 CO_{2}\)
8. \(Cr_{2}O_{3}+3 CO\to 2 Cr+3 CO_{2}\)
9. \(MnO+CO\to Mn+CO_{2}\)
10. \(V_{2}O_{5}+5 CO\to 2V+\,5 CO_{2}\).  
Potassium, chlorine, zinc and lead were considered lost for evaporation, whereas Na\({}_{2}\)O, being blind in complex phases, was assumed staying in the slag [62]. All the remaining oxides having an oxygen potential below V-V\({}_{2}\)O\({}_{5}\) equilibrium (SiO\({}_{2}\), TiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), MgO, CaO) were considered not reducible and passing integrally to the slag.  
Specific boundary conditions are reported in the Results and Discussion session.