esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Flow around the DrivAer Model
modifies the side flow field: (_i_) vortices are created at the wheelhouses discontinuities and convected downstream where they merge into the wake; (_ii_) the front-wheel influences the A-pillar region, as it modifies the direction of the A-pillar vortex (the low pressure region on the side window visible in Figure 13 disappears). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method** & **Elements** & **DoFs** & \(\mathbf{c_{D}}\) & \(\mathbf{c_{L}}\) \\ \hline \multirow{4}{*}{[PERSON] et al. [13]} & LES & \(24\times 10^{6}\) (full) & \(24\times 10^{6}\) & 0.244 & 0.005 \\  & LES & \(80\times 10^{6}\) (full) & \(80\times 10^{6}\) & 0.234 & 0.005 \\  & LES & \(130\times 10^{6}\) (full) & \(130\times 10^{6}\) & 0.228 & 0.005 \\  & LES & \(55\times 10^{6}\) (half) & \(110\times 10^{6}\) & 0.274 & 0.005 \\  & LES & \(65\times 10^{6}\) (half) & \(130\times 10^{6}\) & 0.231 & 0.005 \\ \hline \multirow{4}{*}{[PERSON] et al. [12]} & WMLES & \(9\times 10^{5}\) (full) & \(9\times 10^{5}\) & 0.293 & 0.036 \\  & WMLES & \(6.5\times 10^{6}\) (full) & \(6.5\times 10^{6}\) & 0.253 & 0.057 \\  & WMLES & \(53\times 10^{6}\) (full) & \(53\times 10^{6}\) & 0.246 & 0.083 \\  & WMLES & \(79\times 10^{6}\) (full) & \(79\times 10^{6}\) & 0.251 & 0.069 \\ \hline \multirow{4}{*}{[PERSON] et al. [6]} & RANS SA & \(80\times 10^{6}\) (full) & \(80\times 10^{6}\) & 0.260 & 0.136 \\  & RANS \(k\)-\(\epsilon\) & \(80\times 10^{6}\) (full) & \(80\times 10^{6}\) & 0.244 & 0.085 \\  & RANS \(k\)-\(\omega\) SST & \(80\times 10^{6}\) (full) & \(80\times 10^{6}\) & 0.260 & 0.124 \\  & RANS \(k\)-\(\epsilon\) B-EVM & \(80\times 10^{6}\) (full) & \(80\times 10^{6}\) & 0.243 & 0.116 \\  & RANS EB-ESM & \(80\times 10^{6}\) (full) & \(80\times 10^{6}\) & 0.248 & 0.075 \\  & SST IIDES & \(80\times 10^{6}\) (full) & \(80\times 10^{6}\) & 0.268 & 0.011 \\  & SST IIDES & \(100\times 10^{6}\) (full) & \(100\times 10^{6}\) & 0.261 & 0.024 \\ \hline \multirow{2}{*}{[PERSON] et al. [4]} & RANS EARSM & \(38.6\times 10^{6}\) (full) & \(38.6\times 10^{6}\) & 0.254 & 0.079 \\  & SST DES & \(38.6\times 10^{6}\) (full) & \(38.6\times 10^{6}\) & 0.266 & 0.024 \\ \hline \multirow{2}{*}{[PERSON] et al. [11]} & RANS \(k\)-\(\omega\) SST & \(19.8\times 10^{6}\) (full) & \(19.8\times 10^{6}\) & 0.241 & - \\  & Experiment & - & - & 0.243 & - \\ \hline \(DG-\mathbb{P}^{n}\) & ILES & \(406\,304\) (half) & \(4.22\times 10^{6}\) & 0.255 & 0.034 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DrivAer model—Drag, \(c_{D}\), and downforce, \(c_{L}\), coefficients in comparison with other reference numerical results and experimental data. DoFs are the degrees of freedom, i.e., the unknown solutions. All the reference solvers are based on cell-centered finite-volume methods, i.e., the DOFs are equal to the mesh elements. DoFs of the simulations for half model are reported projected to the full model simulation in order to have an easy comparison. Figure 20: DrivAer model—Details of the streamlines coloured with the \(x\)-component of the velocity for the time-averaged solution near the rear-end (**left**) and the A-pillar (**right**) region.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 3 Results
### Validation of Up-Scaled GPP at Footprint Scale  
#### 3.2.1 Footprint of GPP at Flux Tower Sites  
The climate footprints of GPP at flux tower sites in the study area are shown in Figure 6. In the upstream area, we found that the footprint distances in the northwest to southeast directions were about 500 m to 1000 m, while the footprint distance was less than 300 m in the other directions at Arou (grassland). Moreover, the GPP footprint distances were less than 500 m, and were mainly distributed in the northwest, southeast, and southwest at Dashalong (grassland). In the middle stream area, the prevailing winds were southwest in Huazhaizi (bare land), southeast and northwest in Shidi (wetland), west and northwest in Daman (cropland), and north and southwest in Bajitan (bareland). The GPP footprint distances ranged from 500 m to 1000 m. In the downstream area, the GPP footprint distances at Huyanglin (DBF) and Hunhelin (MF) ranged from 1200 m to 1500 m, and the GPP footprints ranged from 800 m to 1200 m. As the prevailing wind directions were west and northwest in the downstream, the footprints were also mainly distributed in those directions.  
The height of the eddy covariance systems was one of the main influence factors of the GPP footprint [7; 9]. Heights of the eddy covariance systems were the highest in Huyanglin (22.00 m) and Hunhelin (22.00 m), in which the footprint distances were more than 800 m. While the height of the eddy covariance systems in Huazhaizi (2.85 m) and Arou (3.50 m) were the lowest, the footprint distances were less than 200 m. We analyzed the changes in the GPP footprint distance at flux tower sites, with the changes in measurement height (Figure 7). The footprint distance would decrease by about 43.9-65.2% when the height of  
Figure 5: Time series of up-scaled GPP using five machine learning models in (**a**) Sidaoqiao, (**b**) Huazhaizi, (**c**) Huyanglin, (**d**) Hunhelin, (**e**) Dashalong, (**f**) Daman, (**g**) Shidi, (**h**) Bajintan, (**i**) Arou. These figures demonstrate time series of 8-day average GPP at flux tower sites and 8-day average up-scaled GPP from May to September in 2014 using five machine learning methods.  
the eddy covariance systems decreased by 50%. While the footprint distance would increase by about 56.5-74.9% with the 50% increase in the height of the eddy covariance systems.
## 1 Introduction  
One of the remarkable developments in geosynthetic reinforced soil (GRS) technology is the geosynthetic-reinforced soil integrated bridge system (GRS-IBS). The GRS-IBS was initially developed by the Federal Highway Administration (FHWA) in the United States and now has several variations. One of those variations was developed in Japan using a GRS retaining wall (RW) as bridge abutment with a staged-construction full height rigid (FHR) facing and an integral bridge system (IBS). This GRS RW with stage-construction FHR facing was first implemented in the mid-1980s and developed in the 2000s as a bridge abutment, integrating a continuous reinforced concrete (RC) girder on top of the FHR facing without the use of bearings. The GRS integral bridge (GRS-IB) technology alleviates the technical difficulties of conventional bridges by solving problems related to RC structures by eliminating the bearings and joints. It has also solved several problems involving backfill using geosynthetics reinforcement. Moreover, the GRS-IB technology has been proven highly cost-effective in construction and long-term maintenance while remaining stable under static and seismic loading conditions [1, 2, 3, 4].  
Geosynthetics (geostetile) have been used as reinforcement since the early 1970s. One of the first geostetile reinforced walls in the United States was built in Siskiyou National Forest, Oregon. In 1971, the first geostetile-reinforced soil was constructed in France for improving embankment stability. A similar structure was constructed in the United States in 1974. Around the early 1980s, geogrids were developed and used as soil reinforcements.  
In 1981, the first geogrids-reinforced soil was used. The extensive use in the United States began around 1983 [5; 6; 7; 8; 9; 10]. The technology has been widely used as temporary support on permanent roadway embankments. Since then, there has been a significant increase in the application of geosynthetics reinforced soil. The use of geosynthetics as reinforcement improves the mechanical properties of the soil by providing tensile strength lacking in soil and rigidity against shear and bending forces. Geosynthetic reinforcement increases the bearing capacity of the soil and restrains the deformation in soil subjected to external forces. The common applications of geosynthetics in geotechnical engineering can be found in the improvement and stabilization of soft or weak grounds, slopes, embankments, and retaining walls [7; 11; 12; 13; 14]. Some successful applications of geosynthetics also include the protection of buried pipes [15; 16]. Geosynthetics reinforcement is a growing field, and its continued development remains a high priority for many private and government institutions worldwide.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 3 Preliminary: Pseudo-Plane Coefficients  
Given a depth map and camera intrinsic parameters, we can use the pinhole camera model to back-project each pixel into 3D space. Assuming the focal lengths \(f_{x}\) and \(f_{y}\), and the principal point \((u_{0},v_{0})\), each pixel \(p=(u,v)\) can be mapped to a 3D point \(P\) as follows:  
\[Z=D(u,v),\quad X=\frac{Z(u-u_{0})}{f_{x}},\quad Y=\frac{Z(v-v_{0})}{f_{y}} \tag{1}\]  
where \(D(u,v)\) represents the depth value at pixel \((u,v)\).  
Assuming \(P=(X,Y,Z)^{T}\) are the three-dimensional coordinates of a point, the point-normal equation of the tangent plane at point \(P\) can be written as  
\[\vec{n}\cdot P+d=0 \tag{2}\]  
where \(\vec{n}=(a,b,c)^{T}\) is the normal vector to the plane, and \(-d\) is the distance from the origin to the plane. Substituting \(P\) from Equation (1) into Equation (2), we obtain the following equation:  
\[\frac{1}{Z}=\frac{-a}{f_{x}d}u+\frac{-b}{f_{y}d}v+\frac{1}{d}(\frac{a}{f_{x}}u _{0}+\frac{b}{f_{y}}v_{0}-c) \tag{3}\]  
Assume that  
\[\begin{split}\alpha^{\prime}&=\frac{-a}{f_{x}d}\\ \beta^{\prime}&=\frac{-b}{f_{y}d}\\ \gamma^{\prime}&=\frac{1}{d}(\frac{a}{f_{x}}u_{0}+ \frac{b}{f_{y}}v_{0}-c)\\ \rho&=\sqrt{(\alpha^{\prime})^{2}+(\beta^{\prime})^{ 2}+(\gamma^{\prime})^{2}}\end{split} \tag{4}\]Then, we normalize these three values, \(\alpha^{\prime},~{}\beta^{\prime},~{}\gamma^{\prime}\), to obtain  
\[\alpha=\frac{\alpha^{\prime}}{\rho},~{}~{}~{}\beta~{}~{}~{}=\frac{\beta^{\prime }}{\rho},~{}~{}~{}\gamma=\frac{\gamma^{\prime}}{\rho}. \tag{5}\]  
Therefore, the above Equation (3) can be simplified to the following Equation (6):  
\[Z=[(\alpha u+\beta v+\gamma)\rho]^{-1} \tag{6}\]  
In this way we encoded the camera intrinsic parameters and the three-dimensional plane into the plane coefficients \(C=(\alpha,~{}\beta,~{}\gamma,~{}\rho)\) and refer to them as pseudo-plane coefficients.  
In the following proposed modules, we first predict the pseudo-plane coefficient for each pixel. Subsequently, we use Equation (6) to calculate the specific depth values based on the two-dimensional coordinates of each pixels. The pseudo-plane coefficient not only significantly reduces calculation complexity but also reduces the model's dependency on camera parameters.
## 4 Consistency Enhancement Strategy of Ahp in Group Decision-Making  
After completing the construction of the comprehensive index system, determining the weight score of each index in the process of specific evaluation is the next key research content. We score the weight of each index based on the principle of AHP in group decision-making. The scoring matrix obtained by comparing the importance of each indicator pairwise is also called the judgment matrix. The authors of [31] explain how the existing traditional methods would reduce the consistency of the judgment matrix:  
1. The expert evaluation scoring input has a certain subjectivity, which could cause the unreliability of the initial input value of the judgment matrix.
2. The discrete integer constraint of the traditional scaling mechanism will further amplify and diffuse the initial input error.  
For the first point, even if some experts have strong authority in a certain field, they still inevitably have knowledge limitations. Especially in the context of some interdisciplinary assessments, the subjective limitations of experts become more prominent. In addition, each expert may intentionally make a biased evaluation because of different preferences or representative interests.  
Regarding the scaling problem in the second point, we introduce the concept of transitivity for the convenience of explanation [32]. For any indicator a, b, or c, transitivity of importance between indicators can be described by \(a\succ b,b\succ c\ \ \Rightarrow a\succ c\). Moreover, when \(a\succ b,b\succ c\), if there are elements in the judgment matrix satisfying \(c\succ a\), it indicates that there is a contradictory evaluation in the judgment matrix. In AHP, the detection of transitivity and the ranking of index importance are also a method to find the contradictory elements of the judgment matrix. In addition, this often means that it is difficult for the traditional scale to accurately describe the importance between indicators during the optimization process of the judgment matrix. For example, _when_\(a\succ b\succ d\), \(a\succ c\succ d\), \(if\ a/d=3\ andb\succ c\). At this time, it is inappropriate to set both the b/d and c/d scales to 2 due to the [1; 2; 3; 4; 5; 6; 7; 8; 9] integer scale--the classic scale in the AHP [33]. It is more suitable to set b to 2.5. Therefore, during the processing of the judgment matrix in this paper, the fractional scale will not be immediately reduced to the integer scale, thereby weakening the influence of the scale on the consistency.  
Based on the above analysis, we decompose the AHP consistency problem in the group evaluation scenario into two sub-problems: (1) How to enhance the consistency of the judgment matrix that does not meet the consistency conditions; (2) how to effectively aggregate judgment matrices from multiple experts. For problem 1, we propose a consistency correction strategy with a better initial value and a faster decline. For problem 2, we give a method for generating the aggregation coefficient combining expert authority and initial deviation. The overall group evaluation strategy process is illustrated in Figure 5.
## 2 Materials and Methods
### Experimental Design  
To evaluate the performance of the coagulant, four control factors, that is, turbidity, COD, TOC and phenols, were considered. Coagulant dosage and control factors were selected based on a literature review [5], and the raw olive mill wastewater characterization is shown in Table 1. The coarse particles of OMW were separated using a sieve at 0.3 mm as a screening step. At a time (batch 1:100 mg/L-600 mg/L: alum dosage), 6 glass beakers with an 8000 mL volume were used, and 500 mL aliquots were added in tanks for the coagulation-flocculation process, performed through a jar test (shown in Figure 1); in the second batch, another 6 glass beakers were used (batch 2:700 mg/L-2000 mg/L: alum dosage). The coagulant alum concentration was varied in the range 100-2000 mg/L, and the coagulation time was 60 min at fixed pH (4.65) without any pH adjustments, initially with 3 min rapid agitation at 150 RPM and then slowly at 30 RPM for 20 min. The sedimentation step (60 min) followed the coagulation and flocculation process, where an aliquot of OMW solution was withdrawn and then pH, COD, TOC, turbidity and total phenols were determined.  
All the parameters were analyzed according to the Standard Methods for the Examination of Water and Wastewater [21]. The turbidity was measured by an HF Scientific(tm) Micro 100 turbidimeter and the units were recorded as nephelometric turbidity units (NTUs), whereas the pH was measured with a Crison pH meter; COD was measured according to the closed reflux colorimetric method and the phenol concentration was measured by a UV-VIS spectrophotometer at 270 nm by observing the change in the absorbance at the maximum absorption wavelength (T80+, PG Instruments, Ltd., Wibtoff, UK) and then the concentration was calculated from a calibration curve; TOC values were determined using the TOC-L analyzer (Shimadzu) using a standard method [5].  
The OMW was characterized by an initial COD value of 24,892 mg/L, TOC of 3200 mg/L, initial turbidity of 3200 NTU and phenols of 260 mg/L, presented in Table 1. Coagulation tests were conducted at room temperature, OMW was not diluted and pH was not adjusted.
## 1 Introduction
### Related Work
Network lifetime is defined as the time until the proportion of dead nodes exceeds a certain threshold, which is concluded to potentially fail in providing coverage, connectivity or both in a certain region. The proposed heuristic is called location-wise pre-determined heterogeneous node deployment (LPHND). It is compared with the relay node deployment with a Gaussian distribution (RNDGD) and the LPHND with placement error (LPHNDE) schemes. The problem of controlling sink mobility in event-driven applications to achieve maximum network lifetime is the focus of [17]. The authors propose a convex optimization model inspired by the support vector regression technique to determine an optimal trajectory of the mobile sink. The network lifetime is defined as the elapsed time since the beginning of the network operation till the first active sensor node dies. Three optimization models are proposed and compared to a tabu search-based algorithm. The network is divided into zones that have one mobile sink. The paper reports results on the difference of average lifetime between the proposed convex models and the tabu search algorithm. Approaches that act mainly on the energy spent during the task of communication among sensor nodes or between sensor nodes and sink nodes can be found in [5; 17; 18; 19; 20; 21]. For example, transmission power control techniques aiming at managing energy consumption during communications are discussed in [18]. Some of these techniques use a single transmission power for the whole network; others apply local power management algorithms that do not involve MAC protocols. Additionally, time-division multiple access (TDMA) with collision avoidance and duty cycle strategies are also discussed in [18]. In [5], the authors propose a technique to mitigate the interference between WBANs using convex optimization in order to optimize the overall transmission power. Still in the context of optimization techniques, the authors of [22] consider the power consumption, throughput and delay as metrics related to the objective function of the optimization problem. It is shown in [20] that duty cycle control and network coding techniques can be integrated to utilize the network resources efficiently in the so-called bottleneck zone, which is the area around the sink node. A clustering algorithm based on the consumed energy in each time when a node becomes the cluster head is proposed in [19]. In [21], based on solid guidelines for lifetime improvement, a greedy algorithm that uses both the real-time channel state and the residual energy information is proposed for controlling the communications between the sensor nodes and the sink node.
## 4 Results
### Performance Analysis by Video Type
#### 4.2.6 Fast Motion (FM)  
This collection of 30,000 images features high-velocity drones.  
GELAN demonstrates strong performance in fast-motion scenarios, indicating its robustness in detecting drones under dynamic conditions (Table 9). YOLO also exhibits acceptable performance; however, it is inferior to GELAN, suggesting that its speed may not be sufficient to compensate for the complexity of these scenarios. Refer to Figure 9 for an example of this category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **IoU** & **AP** & **F1-Confidence** \\ \hline YOLO & 64\% & 70.6\% & 74\% at 0.046 \\ \hline GELAN & 80\% & 80.7\% & 81\% at 0.107 \\ \hline DETR & 81.3\% & 81\% & 84\% at 0.137 \\ \hline ViTDet & 70.4\% & 68.6\% & 76\% at 0.062 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results for video type Thermal and Infrared Crossover (TC).  
Figure 8: Detection results for Scale Variation (SV).
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
## 3 Guidance and Navigation  
This section describes the guidance and navigation scheme used for the rendezvous of the two satellites. The guidance law is an iterative algorithm that calculates the steering maneuvers to achieve the final position of the target, using the available information on the position and velocity of both vehicles. The navigation subsystem estimates the information needed to calculate the guidance command by using the measurements from the on-board sensors. In this paper, it is assumed that an ISL and a camera provide range and bearing measurements.
# Evaluation and Prediction of Higher Education System Based on AHP-TOPSIS and LSTM Neural Network
## 3 Higher Education Evaluation System
### Evaluation of National Higher Education for Statistical Countries  
Combining with the weight matrix obtained from AHP, we apply TOPSIS to the original data collected from the data source and calculate the quantitative evaluation scores of each research object from 2014 to 2018 as shown in Figure 2. In this way, the evaluation model is created based on both AHP and TOPSIS.  
As shown in Figure 2, the higher education levels in developed countries are always in a leading position, while those in developing countries are relatively backward. Therefore, the evaluation results of this model present a good fit to reality. By analyzing the specific performance of each country, we find that the US has maintained the highest level over the whole five years. However, the evaluation score of the US presents a declining trend. In fact, due to the characteristics of TOPSIS reflecting relative levels, as time goes on, the gap between the US and any other country in the study presents a decreasing trend, as well. Furthermore, Britain, Australia and France are under the same level; the gap among them is very small. Moreover, Colombia's level lies in the middle and has been relatively stable. The evaluation results of Egypt and Vietnam are not ideal, staying in the lowest level throughout the five years. However, in 2018, these two countries both presented obvious upward trends relative to the prior year. This indicates that countries with lower levels of higher education have considerable potential to develop. By examining Vietnam's higher education policies [26], we realize that international cooperation provides opportunities for the development of higher education in a world of globalization and economic integration. Therefore, the Vietnamese government supports the expansion of international exchanges and encourages foreign capitals to invest in the research projects of the higher education system in Vietnam and beyond. The policies which the Vietnamese government adopt provide a valuable experience for the development of higher education in other countries.  
In our model, China's higher education has been significantly promoted. During just five years, China has developed from the same echelon as Colombia to the one close to some developed countries. In comparison with the US, which has the most advanced higher education system, there are still many areas for China to improve. In fact, data analyses show that the development of higher education in China is unbalanced, with some of the indicators remaining in a high level while others still staying low.