esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Most studies are based on the fact that the ego vehicle can safely avoid obstacles while maintaining the current speed. However, in the actual obstacle avoidance process, obstacles are usually dynamic. In this situation, the safest obstacle avoidance strategy is to require the ego vehicle to decelerate or even stop. Considering the travel time cost or convenience, we hope that the ego vehicle can avoid obstacles in accordance with the predetermined requirements under the premise of ensuring safety. In other words, if the vehicle can avoid obstacles at a constant speed or by acceleration, deceleration is unnecessary, which requires that the speed of the ego vehicle and trajectory re-planning is real-time controllable during obstacle avoidance. Focusing on such problems, this research proposes a novel obstacle avoidance strategy that contains behavior re-planning based on post-encroachment time (BR-PET) and trajectory re-planning and tracking based on a two-layer MPC system. The BR-PET module will indicate the ego vehicle's speed change based on the initial in-formation of ego vehicle and the static obstacle and the dynamic obstacle and the speed and acceleration limits of the ego vehicle and the uncertain dynamic obstacle. At the same time, after selecting different dynamic vehicle models of different complexity for two different MPCs used in the path re-planning layer and tracking layer, the real-time performance is also guaranteed. The remainder of this paper is organized as follows: the scenario targeted by the strategy and the challenges of this research are presented in Section 2. Section 3 gives the principle of behavior re-planning and a detailed description about the design of the TL-MPC on trajectory re-planning layer and tracking layer. The performance of the proposed strategy is tested by CarSim-Matlab/Simulink co-simulation in Section 4. Section 5 is the conclusions.
## 3 Results and Discussion
### Analysis of Flow Characteristics  
#### 3.1.1 Discussion on the Velocity Vector  
Due to insertion of the graphene layer amidst the flow path, the direction of fluid flow is altered. This results in the flow being pointed towards the lower wall spot neighboring the graphene layer's foot in the post-graphene region (PG region). This can be observed from figures on velocity profiles (Figures 20-32). Due to the deviated velocity vector, the stress exerted by the flow stream on the vessel wall increases leading to the weakening of the endothelial layer of the arteriole wall. This could possibly result in spread of the existing pathology along the arteriole domain. Analysis of velocity vector questions the usage of the graphene layer in regulating stenosis or aneurysms. For blood, the velocity magnitude is more when compared to water even though the critical porosity is less for blood. In addition, for porosities greater than the critical porosity, velocity is not strongly dependent on porosity. For values below the critical porosity, velocity depends strongly on porosity and increases significantly with a small decrease in porosity. Figures 46-51 represent the formation of recirculation zones along the flow stream. While the recirculation zones are considered, the number of recirculation zones seems to increase with the increase in porosity due to the increased flow, evident from Figures 46-51. Again the fact of the increased number of recirculation zones goes against the purpose of our study. These recirculation zones take shelter near the foot of the graphene layer introduced.  
Figure 46: Recirculation zones in blood flow through 3.5% porous graphene (Case: Blood flow).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Condition** **Assumed** & **Fluid** & \begin{tabular}{c} **Porosity** \\ **of the** \\ **Graphene** \\ **Layer** \\ \end{tabular} & **DoF** & \begin{tabular}{c} **Number** \\ **of** \\ **Iterations** \\ \end{tabular} & \begin{tabular}{c} **Linear** \\ **Error** \\ \end{tabular} & \begin{tabular}{c} **Linear** \\ **Residue** \\ \end{tabular} \\ \hline \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{Blood} & \(C_{\text{\tiny{$\varepsilon$}}}=7.2\%\) & \multirow{3}{*}{2841} & 22 & \(7.7\times 10^{-12}\) & \(2.2\times 10^{-14}\) \\ \cline{3-6}  & & 4.5\% & & 28 & \(1.2\times 10^{-12}\) & \(1.9\times 10^{-14}\) \\ \cline{3-6}  & & 6.5\% & & 33 & \(3.3\times 10^{-12}\) & \(1.2\times 10^{-14}\) \\ \hline \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{Blood} & \(C_{\text{\tiny{$\varepsilon$}}}=7.2\%\) & \multirow{3}{*}{2841} & 85 & \(3.9\times 10^{-12}\) & \(1\times 10^{-14}\) \\ \cline{3-6}  & & 4.5\% & & 129 & \(2\times 10^{-12}\) & \(1.3\times 10^{-14}\) \\ \cline{3-6}  & & 6.5\% & & 49 & \(2.3\times 10^{-12}\) & \(7.1\times 10^{-14}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Figure 47: Recirculation zones in blood flow through 4.5% porous graphene (Case: Blood flow).  
Figure 48: Recirculation zones in blood flow through 6.5% porous graphene (Case: Blood flow).
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 3 Realistic Scenarios and Modelling Issues
### World Size and Simulation Time
#### 3.1.1 Simulation Time  
The time element is often not considered in simulations used for MANET protocol evaluation. In this context, the time referred to is the length of time that the MANET would operate in the real world (i.e., the simulated time), rather than the time it may take for the simulation to run. This is the time considered within the simulation that is the period of simulated time. However, some researchers argue about the time issue in terms of whether it addresses the time for MANET to reach a steady state or not ([PERSON] et al., 2012, [108]; [PERSON] and [PERSON], 2012, [109]). Many papers either have not mentioned time at all or have an arbitrary time. For example, [PERSON] et al. (2009) [68] give a detailed performance comparison of a number of vehicular ad hoc network simulators conducted by running the same scenario and protocol over a number of simulation packages. They tabulated the parameters set in this model, including street spacing and overall area covered, but there was no mention of the simulation time. [PERSON] and [PERSON] (2012) [110] run simulations to evaluate the effect of caching on MANET routing, and whilst they discussed the mobility models used, there was no mention of simulation time or of any other significant parameters. [PERSON] and [PERSON] (2010) [111] discuss and make use of a number of common mobility models in their evaluation of their AODV-related research, but they merely listed the simulation time as 160 s. Because they were looking at VANETS, a simulation time of less than three minutes does not seem to fit with any relevant application. [PERSON] and [PERSON] (2012) [112] use a simulation time of 1000 s in their investigation of the performance of VANET broadcast techniques but have not justified the simulation time. [PERSON] and colleagues (2005) [66] state that the majority of simulations surveyed have not specified whether the simulations are in a warming up, steady, winding, or closing down state.  
Generally, papers have not used simulation time that is akin to the actual time a MANET would operate in the real world. The context of the application may dictate the relevant simulation time. For example, if a VANET is covering a motorway, it makes sense to discuss steady state and to run it for a significant length of time. In search and rescue operations, each scenario will have its own elapsed time, but the time to cover a given area in a systematic search pattern will be governed by the pattern, the area covered, and the speed of the search vessels. Military operations will have planned timings and may be over very quickly, so there may not be sufficient time to wait for a progression from warming up to steady state or even for the entire MANET to connect. Such issues should be considered when setting simulation times.
# Long-Tailed Metrics and Object Detection in Camera Trap Datasets
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Model** & **AP\({}_{\text{VS}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{SM}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{ML}}\)** & **AP\({}_{\text{L}}\)** & **AP\({}_{\text{VL}}\)** & **AP\({}_{\text{SL}}\)** \\ \hline \multirow{2}{*}{SWG} & DINO & 0 & 0 & 7.2 & 16.5 & **43.5** & 57.3 & 78.7 & 84.2 \\  & DINO+BF & 0 & 0 & **9.2** & **17.4** & 42.3 & **60** & **80** & **86.7** \\ \hline \multirow{2}{*}{WCS} & DINO & **1.6** & 20.4 & 35.8 & 51.8 & 66.6 & 79.9 & 86.7 & 92.2 \\  & DINO+BF & 1.2 & **22** & **38.8** & **55.1** & **67.8** & **81.1** & **88** & **93.6** \\ \hline \multirow{2}{*}{CBL} & DINO & - & - & **22.3** & 42.6 & 57.6 & 70.9 & 80.9 & 87.9 \\  & DINO+BF & - & - & 21 & **43.6** & **57.7** & **73.2** & **82.7** & **90** \\ \hline \end{tabular} Note: Bold numbers are used to highlight better results.  
\end{table} 
Table 8: The results for DINO and DINO with BatchFormer on various-sized objects of three datasets. improved the performance of DINO by up to 2.9 % on Rare, 2.4% on Common, and 1.7% on Frequent. On the object/box-level scale imbalance, the BatchFormer module can also improve the performance of DINO by up to 3.3 % on eight types of object sizes, while the AP on very small, small, and small-medium objects is too low, demonstrating that it cannot make up for the shortcoming of too few and too small objects. Exploiting sample relationships is a simple yet effective way to promote long-tailed deep learning problems for camera trap dataset solving. Finally, due to the limitations of the experimental environments, this study did not make generalization experiments to test the performance on new camera trap data. Practically, the images from camera traps situated at new locations not included in training sets have different backgrounds (grasslands, forest, etc. ), different prominent objects (tree stumps, rocks, etc. ), and different environmental conditions (day, night, season, etc. ), and should be considered as different domains. The deep learning models generalization performance declines in new locations [14]. In future, we will leverage the approaches of few-shot and zero-shot learning, to improve the generalization. Except for the imbalance and generalization problem, the classification and detection of nocturnal animals, such as rodents, are considerably more challenging, due to issues such as low light, fast movement, small body size, etc. Data augmentation methods such as deblur, colorization, low-light enhancement, etc., can be implemented to increase the quality of night-time images, further improving classification and detection accuracy [56; 57; 58].
# Decoding _Scn2a_ Variants: Bridging Genetics and Phenotypes in Autism Spectrum Disorder
###### Abstract  
**Background:** Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a rising prevalence, driven by multifactorial genetic and environmental factors. Among the genetic contributors identified, _SCN2A_, a critical gene encoding the Nav1.2 sodium channel, has been implicated in ASD and other related neurological conditions. This systematic review aims to explore the relationship between _SCN2A_ mutations and ASD phenotypes. **Methods:** This review systematically analyzed data from studies reporting _SCN2A_ mutations in individuals diagnosed with ASD. The primary focus was on the characterization of mutation types, associated clinical features, and phenotypic variability. **Results:** The mutations identified were predominantly _de novo_ missense mutations and were associated with a spectrum of neurological and developmental challenges, including seizures, intellectual disability, movement disorders, and repetitive behaviors. A notable finding was the significant phenotypic variability observed across individuals. Gender differences emerged, suggesting a potentially greater impact on females compared to trends typically seen in ASD genetic studies. Specific mutations, such as c.2919+4 deIT, and mosaicism were identified as novel contributors to the observed heterogeneity. **Conclusions:** The review highlights the clinical significance of _SCN2A_ mutations in ASD and highlights their relevance in genetic counseling and the development of targeted therapies. Understanding the diverse genotype-phenotype correlations associated with _SCN2A_ can drive progress in personalized medicine, paving the way for precision therapies tailored to individuals with _SCN2A_-related ASD.  
**Keywords:** autism spectrum disorder; _SCN2A_; Nav1.2 sodium channel; neurodevelopmental disorders; genotype-phenotype correlation; mosaicism; targeted therapy; genetic counseling
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
###### Abstract  
Droughs are natural disasters that have significant implications for agricultural production and human livelihood. Under climate change, the drought process is accelerating, such as the intensification of flash droughs. The efficient and quick monitoring of droughs has increasingly become a crucial measure in responding to extreme drought events. We utilized multi-imagery data from the geostationary meteorological satellite FY-4A within one day; implemented the daily Maximum Value Composite (MVC) method to minimize interference from the clouds, atmosphere, and anomalies; and developed a method for calculating the daily-scale Temperature Vegetation Drought Index (TVDI), which is a dryness index. Three representative drought events (Yunnan Province, Guangdong Province, and the Huanghuai region) from 2021 to 2022 were selected for validation, respectively. We evaluated the spatial and temporal effects of the TVDI with the Soil Relative Humidity Index (SRHI) and the Meteorological Drought Composite Index (MCI). The results show that the TVDI has stronger negative correlations with the MCI and SRHI in moderate and severe drought events. Meanwhile, the TVDI and SRHI exhibited similar trends. The trends of drought areas identified by the TVDI, SRHI, and MCI were consistent, while the drought area identified by the TVDI was slightly higher than the SRHI. Yunnan Province has the most concentrated distribution, which is mostly between 16.93 and 25.22%. The spatial distribution of the TVDI by FY-4A and MODIS is generally consistent, and the differences in severe drought areas may be attributed to disparities in the NDVI. Furthermore, the TVDI based on FY-4A provides a higher number of valid pixels (437 more pixels in the Huanghuai region) than that based on MODIS, yielding better overall drought detection. The spatial distribution of the TVDI between FY-4A and Landsat-8 is also consistent. FY-4A has the advantage of acquiring a complete image on a daily basis, and lower computational cost in regional drought monitoring. The results indicate the effectiveness of the FY-4A TVDI in achieving daily-scale drought monitoring, with a larger number of valid pixels and better spatial consistency with station indices. This study provides a new solution for drought monitoring using a geostationary meteorological satellite from different spatial-temporal perspectives to facilitate comprehensive drought monitoring.  
Temperature Vegetation Drought Index; FY-4A; drought monitoring; spatial-temporal analysis +
Footnote †: journal: _sciences_
## 6 Instability Predicting and Verification
### Practical Engineering Application  
Taking a tunnel of the Fangshifang expressway as an example, the instability prediction model of tunnel arch collapse is established based on the actual engineering situation and field-measured data, and the collapse time of the tunnel arch is predicted, and the prediction model of sharp point mutation is tested.  
The convergence history curve of the tunnel vault is shown in Figure 6. Through regression analysis, the time history function of the vault settlement of the tunnel can be obtained:  
\[g(t)=1.73\times 10^{-3}t^{4}+0.02t^{3}-0.51t^{2}+5.74t+10.20 \tag{25}\]  
Substituting the parameters obtained into Equation (20) derives  
\[\overline{g}=1.73\times 10^{-3}x^{4}-0.55x^{2}+8.26x-5.91 \tag{26}\]  
Substituting the fitted parameters into Equation (20), and performing further standardization according to Equation (21), the following parameters can be obtained: \(\mu=-15.98\), \(v=1192.20\). Substituting all parameters into Equations (24) and (25), the result is obtained as \(t\approx 24\) d. In the actual tunnel construction process, the tunnel vault collapse occurred 25 days after construction. The comparison demonstrates that the forecasted instability time by the model aligns well with the actual situation, affirming the efficacy of the prediction model derived in this study. This validation offers valuable insights for tunnel design and construction in similar conditions.
## 4 Results and Discussion
### Results  
In order to improve the understanding of the data obtained from the model, the results are grouped as follows:  
* _Model operation_ explains how the model meets the energy load through the available resources and shows the role of the storage devices.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **1 h** & **6 h** & **12 h** & **24 h** & **72 h** \\ \hline
**MINLP [s]** & 1.79 & 25.86 & 20,038.7 & – & – \\
**OF [kWh/pr.]** & 0.1 & 0.65 & 1.32 & – & – \\
**Status** & ok & ok & ok & – & – \\
**MILP [s]** & 0.15 & 0.26 & 0.892 & 4.12 & 58.38 \\
**OF [kWh/pr.]** & 0.1 & 0.69 & 1.41 & 3.79 & 534,496.43 \\
**Status** & ok & ok & ok & ok & ok \\ \hline
**OF error** & 0\% & 6.2\% & 6.8\% & – & – \\ \hline \end{tabular}
\end{table}
Table 1: Performance of the MINLP and MILP.  
Figure 3: The four consecutive scenarios of 72 h. The vertical axis is the percentage of the total capacity that can be used by the WT/PV or the percentage of the energy demand for every hour.  
* _The weakest points_ identify the nodes with a high probability of ENS. This probability considers any percentage of non-compliance from the total load for a certain bus at every hour.
* _Flexibility Market_ presents the amount of ENS for every node at every hour and compares the total ENS when DSR is considered and when it is not.
* _Dynamic topologies_ show the topology scheduling that must follow the ADN to pass from an unbalanced system to a balanced system, with lower load compliance than the original.
###### Abstract  
The concrete lining of high-pressure water conveyance tunnels permeates under high-pressure water. Dense and hydrophobic coating can effectively improve the impermeability of concrete. However, the coating exhibits varying impermeability in different high-pressure environments, which can even lead to coating detachment or damage. The objectives of this study are to improve the high-pressure impermeability of concrete by using a polymer coating, and to study the varying impermeability through experiments. This study applied a polymer coating called SCU-SD-SP-II (SSS) to concrete surfaces, and it formed a composite protective layer with an epoxy-modified silicone (EMS) coating. A series of high-pressure impermeability tests were conducted to study the seepage regulation of the coated concrete and the failure mechanism of the SSS coating under cracks in the concrete. The results indicate that the SSS coating has excellent impermeability. Pressurized water of 3 MPa could not permeate the SSS coating with a thickness of 0.5 mm within 24 h. Under both external and internal water pressure conditions, the SSS coatings improved concrete impermeability. Additionally, the average seepage height and relative permeability coefficient of the latter decreased by 49.6% and 71.2%, respectively, compared with the former. After concrete cracking, the SSS coating could withstand 3 MPa pressure on crack surfaces smaller than 1 mm. When the crack width was greater than 2 mm, the SSS coating deformed under 1 MPa pressure. As the pressure increased to 2 MPa or even 3 MPa, the SSS coating was punctured or torn due to stress concentration. This study provides new insights into the impermeability of concrete under high water pressure.  
high water pressure; coating; impermeability test; concrete; crack Article
## 3 Method  
### Wind-Speed Reconstruction  
Since LiDAR provides 1D LOS velocities, we assume that horizontal and vertical wind directions are constant within the considered time interval (T) of the flow field. Thus, the streamwise wind-speed component (\(u\)) can be constructed relative to an external source of wind direction:\[u=\frac{\phi_{LOS}}{\cos(\phi^{\prime})\cos(\theta^{\prime})}, \tag{14}\]  
where \(\phi^{\prime}\) is the difference between the horizontal wind direction, \(\Phi\), and the azimuth angle, \(\phi\), and \(\theta^{\prime}\) is the difference between the vertical wind direction, \(\Theta\), and the elevation angle, \(\theta\), as illustrated in Figure 3.  
With nacelle-based measurements, we can assume that the wind direction tracking of the turbine is sufficiently good if no intentional yaw misalignment is present. Within the synthetic data, the lateral wind-speed component (\(v\)) and the vertical component (\(w\)) are assumed to average out to zero, though this assumption does not apply in the near-wake region (\(\chi D^{-1}\leq 3\)). Alternatively, the PPI scans we performed can be used to determine the wind direction at different downstream distances if we use the horizontal velocity azimuth display (VAD) fitting approach. In other cases, \(\Phi\) and \(\theta\) can be determined with external wind direction instruments, such as vertical profilers using VAD or Doppler-beam-swing (DBS).  
Below, we will calculate the average velocity and its standard deviation in the \(u\) direction. This calculation applies Equation (14) to the time series of LOS velocities of each point within the measurement area. The resulting statistics do not allow us to reconstruct the exact streamwise wind-speed component statistics. Even so, the implied assumptions are unavoidable when using LiDAR data without other external information or reconstructions based on turbulence spectra. We are aware that our calculation of the standard deviation of the projected wind-speed component (\(\sigma_{u}\)) differs from the standard deviation of the LOS velocities (\(\sigma_{\tilde{v}_{LOS}}\)) for \(\phi^{\prime}\
eq 0\) and \(\theta^{\prime}\
eq 0\) due to the non-linear dependence of the standard deviation of the LOS velocities (\(\sigma_{\tilde{v}_{LOS}}\)) on the standard deviations of the main wind-speed components (\(\sigma_{u}\), \(\sigma_{v}\), \(\sigma_{w}\)). Since the standard deviations of all wind-speed components in LiDAR measurements (\(\sigma_{u}\), \(\sigma_{v}\), \(\sigma_{w}\)) represent unknowns with no additional external measurements, Equation (14) represents a pragmatic approach for projecting the LOS velocity time series onto the main wind direction, from which the standard deviation (\(\sigma_{u}\)) can be compared with the standard deviation of the LES (\(\sigma_{u_{LES}}\)).  
The LOS velocities were projected scan-wise, and individual scans were interpolated onto a Cartesian (x-\(\gamma\) or \(\gamma\)-\(\zeta\)) coordinate grid using the natural-neighbour interpolation [40].