esa-sceva/satcom-chunk-collection huggingface dataset:
proposed a model based on robot inclination angle obtained from an odometer. [PERSON] [15] used current values of joint electricity and force sensor data to recognize terrain categories. [PERSON] [16] proposed a neural network model for terrain classification based on robot body acceleration and angular velocity of the inertial measurement unit (IMU). These kinds of information are quite different from visual information and so special methods are needed.  
Most of the existing methods have good classification accuracy on single-type terrain, but few are suitable for mixed terrain, which is common in the natural environment. To solve this problem, [PERSON] [17] used a sliding window technique for heterogeneous terrain images. [PERSON] [18] compiled an algorithm for complex terrain classification based on multisensor fusion. [PERSON] [19] proposed a learning method to predict the environment by consecutive distance- and shape-related feature extraction. However, most of these methods have poor robustness because they require high-resolution images. Mixed terrain has different features than single-type terrain and sometimes the edges of the terrain cannot be recognized clearly, which makes identification of mixed terrain difficult. In order to enhance the recognition rate of detecting mixed terrain and terrain with obstacles, a systematic classification method for complex terrain is proposed in this paper. The following aspects were studied: Terrain information collected by a Kinect 3D vision sensor. Herein, we established a fast and effective terrain classifier based on speeded up robust features and binary robust invariant scalable key points (SURF-BRISK) features and support vector machine (SVM). A segmentation method for complex terrain images based on super-pixels is proposed, which can effectively segment complex terrain images into single terrain images. An image infilling method for terrain with obstacles and mixed terrain is also proposed. The local features are magnified to help the recognition of different complex terrains. Experiments on classifying terrain with obstacles and mixed terrain are conducted. The proposed system is validated by the multilegged robot.  
This paper is organized as follows: In Section 2, the hexapod robot and SURF-BRISK-based image infilling method are introduced. In Section 3, the experimental results are presented and analyzed. Section 4 summarizes and concludes the paper.
# Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine
## 3 The Proposed Algorithm
### Reflection Mutation Strategy Based on Sine Cosine  
In the proposed algorithm, reflection mutation strategy based on sine cosine is a key operator. Inspired by Nelder-Mead method and SCA, the strategy is proposed. After the operator, a new trial individual is generated to guide the research direction. It is similar to reflection mutation strategy in [43], but the difference is that sine and cosine math functions are introduced into the proposed strategy. First, four individuals are selected from the current population or historical population randomly to construct a unit simplex. The center of the unit simplex is computed using Equations (9) and (10).  
\[X_{o} =\omega_{1}\cdot X_{a}+\omega_{2}\cdot X_{b}+\omega_{3}\cdot X_{c} \tag{9}\] \[\omega_{1} =\frac{f\left(X_{a}\right)}{f\left(X_{a}\right)+f\left(X_{b}\right) +f\left(X_{c}\right)}\] \[\omega_{2} =\frac{f\left(X_{b}\right)}{f\left(X_{a}\right)+f\left(X_{b} \right)+f\left(X_{c}\right)}\] (10) \[\omega_{3} =\frac{f\left(X_{c}\right)}{f\left(X_{a}\right)+f\left(X_{b} \right)+f\left(X_{c}\right)}\]  
where \(a,b\) and \(c\) are the index of three different individuals randomly selected from population to construct a unit simplex. \(\omega_{1},\omega_{2}\) and \(\omega_{3}\) are the weights of three vertexes in the unit simplex. \(X_{a},X_{b}\) and \(X_{c}\) are three vertexes of the unit simplex. \(X_{o}\) is the center of the unit simplex.  
Based on the center, the best individual found so far, and sine cosine are introduced into the strategy to balance exploration and exploitation. The best individual provides the better search direction to improve exploration ability of the algorithm, while sine and cosine math models are used to enhance diversity ability and the center of the unit simplex also provides more diversity information. The formulation of the strategy is listed as follows:  
\[V_{i,j}=\left\{\begin{array}{l}X_{best,j}+\eta\cdot\sin\left(r_{1}\right) \left(r_{2}X_{o,j}-X_{m,j}\right)\text{ if }r_{3}<0.5\\ X_{best,j}+\eta\cdot\cos\left(r_{1}\right)\left(r_{2}X_{o,j}-X_{m,j}\right) \text{ otherwise}\end{array}\right. \tag{11}\]  
\[\eta=\text{a}\cdot\left(1-\frac{t}{MaxGen}\right) \tag{12}\]  
where \(r_{1}\) and \(r_{3}\) are random number in [0, 1]. \(r_{2}\) is a random number in [0, 2]. \(X_{best,j}\) indicates the \(j\)th dimension of the best individual. \(X_{m,j}\) is the \(j\)th dimension of the \(m\) individual randomly selected from population. \(t\) is the current iteration and \(MaxGen\) is the maximum iteration. \(a\) is a perturbation parameter and is usually set to 2.0.
## 4 Experimental Results
### Driving Performance Evaluation
#### 4.2.1 Case 1: Lane Keeping  
In Figure 7, the maneuver of the proposed method and the baseline method is shown within the same simulation environment, under identical maneuver conditions of nearby vehicles. Despite this uniformity, noticeable discrepancies in the paths generated by our method and the baseline method are observed. To assess the lane-keeping performance of each path, Figure 8 zooms into a segment spanning from 1100 to 1500 m. The Center of the Lane (CoL) represents the position where the vehicle should be located, determined by the sub-goal output from the DM module referenced in Section 3.2.1. The positional deviation is computed using the mean square error between CoL and the vehicle's lateral position. Based on the maneuver data depicted in Figure 7(a), TD3 + CER demonstrates an average deviation of 0.06 m and a maximum deviation of 0.07 m, accompanied by stable steering angles as illustrated in Figure 7(c). In comparison, TD3 exhibits an average deviation of 0.49 m and a maximum deviation of 0.7 m under similar conditions. Figure 7(d) highlights the fluctuation of steering angles. Our method achieves minimal lane deviation through smooth steering control inputs.  
Figure 6: Reward curves of baseline method (TD3) and proposed method (TD3 + CER). The colored areas on each graph represents the boundaries of each measure, with averages calculated per 10 episodes, while lines represent averages per 1000 episodes.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### MRI-to-PET Translation:
#### 3.1.4 Contrast Enhancement  
In MR imaging, different sequences (or modalities) can be acquired that provide valuable and distinct knowledge about brain disease, for example, T1-weighted, T2-weighted, proton density imaging, diffusion-weighted imaging, diffusion tensor imaging, and functional MRI (fMRI) [62; 63; 64]. The imaging process can highlight only one of them. Multiple scan acquisition processes and long scan times for capturing all contrasts can give rise to the cost and discomfort of the patient. The enhancement process that generates different contrasts from the same MRI sequences is helpful for overcoming the data heterogeneity [65]. The contrast enhancement methods can be divided into three categories, as shown in Figure 5.
# Dimensional Control of Aircraft Transmission Bodies Using CNC Machines and Neuro-Fuzzy Systems
## 2 Aircraft Transmission Bodies  
### General Characteristics of the Bodies  
Aircraft transmission bodies are designed for mounting and mutual arrangement of various elements, assemblies and mechanisms, mainly bearings, shafts and cogwheels. The housings are made as light alloy components, i.e., aluminum or magnesium alloys. They are characterized by a large variety of shapes, dimensions and semi-products used to make them. Due to the required reliability and flight safety, ADT are characterized by specific technological and constructional requirements. They are connected, among others, with the need to minimize the mass of the airframe propulsion unit, high dimensional and shape accuracy and limited space for the housings of the power unit.  
The shape complexity of the transmission results from the limited space for its housing. It is also a consequence of the fact that the external shapes of the bodies are not much different from the outlines of the elements inside. In addition, the wall thicknesses of the bodies are small in relation to the dimensions of the transmissions, which results from the weight reduction requirement. Therefore, ADT bodies are classified as so-called thin-walled components. The methods of manufacturing, processing and measuring thin-walled aircraft components are one of the most difficult issues in aircraft production and require the use of special, individualized approaches [4; 5; 6; 7; 8].  
The aircraft transmission bodies are made as castings with technological allowances. During the machining process carried out in order to give the workpiece the required dimensional and shape features, changes occur in the stressed state resulting from:  
* Release of stresses in the semi-product originating in the casting process [9];
* Introduction of deformations resulting from the cutting process [10].  
For this reason, after removing from the holder, the transmission bodies are subject to geometric deformations.
## 3 Optimization Procedure by PDG
### Constraint Conditions
The pipeline levels are classified as follows:  
Class 1 pipeline: From the load point to the node of another or more class \(N(N\geq 1)\) pipelines, where the pipe is called class 1 pipeline, shown as black line in Figure 10;  
Class 2 pipeline: Starting from nodes of two or more Class 1 pipelines to nodes of another or more Class \(N(N\geq 2)\) pipelines, where the pipelines are called stage 2 pipelines, shown as a green line in Figure 10;  
...  
Class \(i\) pipeline: Starts from the nodes of two or more Class \((i-1)\) pipelines to nodes of another or more Class \(N\) (\(N\geq 1)\) pipelines, where the pipelines are called class \(i\) pipelines. Pipe diameter grading is based on flow balance, as expressed in Equation (19):  
\[\dot{Q}^{N}=\sum_{i=1}^{n}\dot{Q}_{i}^{N-1}+\sum_{i=1}^{n}\dot{Q}_{i}^{N-2}+ \ldots+\sum_{i=1}^{n}\dot{Q}_{i}^{1}, \tag{19}\]  
where \(\dot{Q}^{N}\) refers to the Class-\(N\) pipe flow rate to the same node; Equation (20):  
\[\dot{Q}^{N}\geq\dot{Q}_{i}^{m}, \tag{20}\]  
By classifying the pipes at different levels, different diameter characteristics can be assigned to them, so that all edge weights \(w(u,v)\) can be changed from distance \(l_{i}\) to cost \(D_{i}\) in the connected graph G. Then, the Prim algorithm of the minimum spanning tree can be used to find the optimal scheme of the minimum cost for further optimization. Figure 10: Pipe Diameter Grading when the classification of the pipe is 3. It should be noted that the cost is estimated based on the minimum pipeline length. The next step is to study and analyze the target area, classify the different pipe segments, and perform further optimization with minimum cost as the target.
###### Abstract  
Fiber-reinforced polymer (FRP) has become increasingly popular in repairing existing steel-reinforced concrete (RC) members or constructing new structures. Although the quasi-static axial compression performance of FRP-confined concrete (FCC) has been comprehensively studied, its dynamic compression performance is not well understood, especially the dynamic compressive behavior of FRP-confined high-strength concrete (FCHC). This paper presents an experimental program that consists of quasi-static compression tests and Split-Hopkinson Pressure Bar (SHPB) impact tests on FRP-confined high-strength concrete. The effects of the FRP types, FRP confinement stiffness, and strain rate on the impact resistance of FCHC are carefully studied. The experimental results show that the strain rate effect is evident for FRP-confined high-strength concrete and the existence of the FRP greatly improves the dynamic compressive strength of high-strength concrete. An existing strength model is modified for impact strength of FCHC and the predicted results are compared with the test results. The results and discussions show that the proposed model is accurate and superior to the existing models.  
Keywords:fiber-reinforced polymer (FRP); high-strength concrete; Split Hopkinson Pressure Bar (SHPB); impact load; confinement; high strain rate; dynamic strength of concrete +
Footnote †: journal: Applied Soft. 2019, 9, 2830; doi:10.3390/app9142830
## 3 Results and Discussion
### Correlation Analysis
#### 3.4.3 Non-Linear Regression  
Referring to the correlations listed in Table 9 and depicted in Figure 10, the backscatter of PALSAR-2 HV polarization gave better \(R^{2}\) as compared to the HV as well as Sentinel-1A VV and VH.  
Figure 9: Scatter plots of simple linear correlations between AGB (\(y\)-axis) and image variables (\(x\)-axis).  
The HV backscatter ranged from \(-1\) to \(-20\) dB and the saturation point concentrated \(-12\) dB. Rapid increment occurred, especially at lower biomass level (i.e., up to 200 Mg ha\({}^{-1}\)), and then decreased towards higher AGB. The trend line became almost constant when the AGB exceeded 200 Mg ha\({}^{-1}\). It was obvious that the estimation uncertainties are larger at AGB \(>\) 200 Mg ha\({}^{-1}\). The results were even worse for HH polarization.  
It has been reported that, at a given polarization and incidence angle, the saturated backscatter value for forest was within a small range of backscatter [11]. The dynamic range is determined primarily by the backscatter at low levels of AGB. It increases with decreasing frequency and it is higher at HV compared to HH polarization. Similarly, Sentiel-1A polarizations saturated quickly at AGB lower than 100 Mg ha\({}^{-1}\). The Sentinel-1A VV and VH backscatter ranged from \(-18\) to \(-3\) dB and \(-24\) to \(-8\) saturated at \(-8\) dB and \(-14\) dB, respectively. This was because the C-band wavelength is shorter than the L-band, and thus not very sensitive to the AGB at high level. Figure 11 illustrates how L- and C-bands interact with the forest canopy structure that influence the strength of the backscatter at high biomass forest.  
Figure 10: Scatter plots of non-linear correlations between AGB and single polarization of (**a**) PALSAR-2 HH; (**b**) PALSAR-2 HV; (**c**) Sentinel-1A VV; and (**d**) Sentinel-1A VH.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Polarization** & **Prediction Equation** & \(\mathbf{R^{2}}\) \\ \hline \multirow{2}{*}{PALSAR-2} & HH & \(y=1043\times\phi^{0.1215\mathrm{x}}\) & 0.2058 \\  & HV & \(y=3114.2\times\phi^{0.173\mathrm{x}}\) & 0.3502 \\ \hline \multirow{2}{*}{Sentinel-1A} & VV & \(y=644.1\times\phi^{0.0664\mathrm{x}}\) & 0.1596 \\  & VH & \(y=718.72\times\phi^{0.0469\mathrm{x}}\) & 0.0749 \\ \hline \hline \end{tabular} All correlations are significant at \(p<\) 0.05.  
\end{table}
Table 9: Summary of non-linear correlation between AGB and individual polarization.  
The accuracy was also mostly influenced by the tree density, soil surface roughness, soil moisture, tree sizes, and the layering effects of the SAR itself [35]. An experiment has found that the backscattering intensity interacted only with trees of dbh larger than 15 cm. These stands are actually dominating the higher canopies, which gave the best response to backscatter in HV polarization of L-band [13]. Other factors--such as orientation of the forest, polarimetry, incidence angle, and crown structure--also play important role in the estimated biomass [36; 37].
## 3 Three-Dimensional OSD Finite Element Model
### IIW SSE Method  
As described in the previous section, IIW SSE methods (i.e., QSE, LSE 410, and LSE 515) were used for the evaluation of SHSS at the weld-toe of the RD joint. Therefore, after measuring the stresses from the numerical model, the SHSS was calculated using Equations (1)-(3), and results are summarized in Figure 7a-d considering different types of elements used in this study. Figure 7a-d show the SHSS results at the weld-toe under the influence of bending and membrane stress produced in the deck-plate and the rib-plate, respectively. The deck and the rib-toe results varied from 55-72 MPa and 11-17 MPa, respectively. This window reduced to 55-65 MPa in the case of the deck-toe when the nodal averaging feature was used. The stress results were not normalized to differentiate them easily from each other.  
Figure 6: (**a–h**) Different meshing techniques used for the evaluation of SHSS methods.  
The QSE method gave higher SHSS results at the deck-toe and lower results at the rib-toe as compared to LSE methods. The difference was quite evident for the LSE 515 method. It was largely due to the presence of highly non-linear stresses at the deck-toe as compared to the rib-toe, and the QSE method generally gives better results in the stress concentration zone due to its parabolic stress distribution. At the deck-toe, all the elements showed a smooth decreasing trend in SHSS results as we moved from QSE to LSE 515, except for the 2-CH8 element. The rib-toe results had two distinguished lines, the solid elements (1/2-CH20/8 and 1/2-CH20E/8E) had lower SHSSs except for 1/2-CH8E elements, and their results were similar to shell elements (1/2-CQ4/8). Moreover, the solid parabolic elements (1/2-CH20) had lower results, whereas shell linear elements (1/2 CQ4) had higher SHSS results as compared to other elements in both cases (i.e., deck and rib-toe).  
Nodal averaging did not have much influence on the parabolic elements (1/2-CH20 and 1/2-CQ8), whereas linear elements (1/2-CH8/8E and 1/2-CQ4) showed a slight decrease in SHSS results, as shown in Figure 7a-d. The reduction in SHSS was more pronounced in the 2-CH8 element. The coarser mesh (i.e., 4/8-CQ4/8) was only applicable to LSE 515, as other SSE methods have mesh size limitations, but their results were not convincing, especially for linear elements (4/8-CQ4). Moreover, nodal averaging had a major influence on their SHSS results. Overall solid elements (1/2-CH20/8 and 1/2-CH20E/8E) showed lower and slightly scattered results, especially in linear elements (1/2  
Figure 7: (**a–d**) Variation of SHSS evaluation methods for different meshing techniques.  
CH8/CH8E), whereas shell elements (1/2-CQ8/4) showed higher and consistent SHSS results for both cases (i.e., deck and rib-toe).
## 1 Introduction
It first vectorizes the vehicle-type attributes to word encodings and applies the multi-head attention to embed them into vehicle-state vectors to generate vehicle-state embeddings (VSEs). Then, VSEs are padded and converted into lane segment states, which act as the initial encoding for the encoder-decoder structure. Meanwhile, CeT fuses the temporal attributes and dynamic topology graph to the spatial-temporal embeddings (STEs), which guide the extraction of spatial-temporal dependencies and correlations and the generation of future traffic states [7]. We utilize the trajectory data from pNEUMA and the map from OpenStreetMap (OSM) to establish two traffic scenarios, containing two-phase signalized intersections and four-phase signalized intersections [8]. Then, a map converter is employed to convert the OSM maps to Lanelet2 maps with appropriately sized lane segments [9]. These lane segments can be considered cell space providing discretized spatial-temporal data for CeT. After training, CeT is compared with baseline models for predictive performance and subjected to ablation studies to investigate the contributions of its key components. Furthermore, the influences of hyperparameters and model sensitivity are also analyzed to verify the robustness of CeT. The contributions of CeT are summarized as follows:  
* This study presents a novel prediction framework that integrates the Cell Transmission Model with Transformer-based methodologies. By leveraging structured HD map data, the framework forecasts traffic states within discrete spatial-temporal systems, providing a robust solution for modeling dynamic traffic flows at signalized intersections. * The proposed method encodes vehicle-type attributes into vehicle-state embeddings, allowing the model to distinguish between different vehicle types and enhance prediction accuracy. By representing signal phase variations as dynamic connections in a directed graph and synthesizing spatial-temporal embeddings, CeT effectively captures the intricate interactions between spatial and temporal traffic dynamics. * The prediction performance of CeT is rigorously validated using real-world traffic data from the pNEUMA dataset. Experimental results demonstrate its superior predictive accuracy and robustness across various scenarios, highlighting the significant contributions of its key components to traffic state forecasting. The remainder of this paper is structured as follows. Section 2 reviews the relative research. Section 3 introduces the conventional CTM and the architecture of CeT. Section 4 describes the experiments and predictive performance evaluation methods. Section 5 presents the experimental results and corresponding analysis. Finally, Section 6 summarizes this paper and looks forward to further research.