esa-sceva/satcom-chunk-collection huggingface dataset:
# Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers
## 4 Experiments
### Results  
For the goal estimation evaluation, two performance criteria are considered: accuracy and estimation speed. Accuracy represents how accurate the estimation can be performed, whereas estimation speed depicts how early the goal is anticipated. It is impractical to correctly conduct the estimation even if the goal is determined just before performing lane-changing. If the goal estimation is correctly conducted in sufficient time, drivers of the mainstream lane have sufficient reaction time with respect to the lane-changing of the target vehicle. The accuracy can be calculated by comparing the estimation result and the ground truth manually labeled. The evaluation was performed until the target vehicle crossed the center line in each case of testing data.  
To evaluate the effectiveness of the interaction between drivers, the performance was compared to the method excluding the interaction between drivers and only using the remaining distance to the end of the acceleration lane. Figure 9 shows the comparison result. This graph shows the average accuracy 4 s before the target vehicle crosses the center line using the entire testing data. The red line indicates the accuracy with the proposed method, the blue line represents the method excluding the interaction, and the green line shows the result with the GAM. First, the proposed method achieved the outperformed performance compared to that of the GAM. As the GAM is designed to judge whether the lane-changing to enter the space between the lead and rear vehicles is possible or not, this model cannot handle the case that the target vehicle overtakes the lead vehicle or waits the rear vehicle passing through. In addition, the GAM has the limitation caused by the variety of individual driving styles [21]. Second, it can be confirmed that the proposed method outperforms the method that does not consider the interaction. The accuracy of the method excluding the interaction did not reach 80% even 1 s before. Most of the time, the accuracy was under 70% between 0 and 2 s before the crossing. Conversely, the proposed method achieved accuracy with almost 80% between 0 and 2 s before the crossing. The previous study reported that the reaction time of the driver is in the range of 0.92 and 1.94 s [22]. Hence, this range is significant when the goal estimation performance is evaluated. The proposed method outperformed the method excluding the interaction in this range, and it indicates that the approach to consider the interaction between drivers is significant for the goal estimation of the lane-changing vehicle.  
Figure 8: Description of measurement area: the proposed method was trained and evaluated using a real traffic dataset acquired at US-101. The measurement area was 2100 feet long, and there are five mainstream lanes. The acceleration lane is 698 feet long. On and off-ramps exist within the measurement area. If drivers on the acceleration lane fail to perform the lane-changing until the end of lane, they should go out through off-ramp.
## 2 Results and Discussion
### Discussion
Regarding the aMSCs, the main drawback is the need to perform minor surgery under general anesthesia for harvesting adipose tissue, with the advantage being the greater therapeutic benefit achieved. The possibility of optimizing this treatment is the creation of a bank of autologous cells, allowing for their availability without the necessity of further surgeries. This would bring the treatment into the outpatient clinical setting [76]. Minimally invasive methods for the treatment of OA in dogs are appealing to both veterinarians and pet owners, particularly when compared with surgical alternatives such as joint replacement. The main limitations of this study include the lack of a placebo group, although recent studies have demonstrated that a single intra-articular injection of Saline Solution in OA dogs, has no effect in reducing pain or improving functionality [32]. It could be a very good option to have performed an MRI or a biopsy of the cartilage, and an arthrocentesis to collect synovial fluid at each visit. These complementary assessments were not performed due to the complications they implied for the owners. It is also true that the differences in the protocols makes it impossible to blind the treatment to the owner (dogs received a biopsy for the fat sample), but the evaluation of the dogs status and disease progression was performed by blinded physicians. So, to fully demonstrate the potential of these two therapies in the treatment of OA, more double-blind randomized controlled trials should be performed as histological results could be very important for the future of these treatments. These studies should include the determination of biological markers of articular catabolism and anabolism in the serum and synovial fluid, and arthroscopic and histological evaluation of the cartilage.
# Thermal Buckling and Vibration Analysis of SMA Hybrid Composite Sandwich Beams
## 1 Introduction
[26] analyzed the free vibration of hybrid laminated composite cylindrical shells reinforced with SMA by applying the [PERSON]'s one-dimensional constitutive law to calculate the thermo-mechanical properties of SMAs. According to the study, the fundamental frequency was increased by adding a proper quantity of SMA wires to the cylindrical shells. At the same time, [PERSON] and [PERSON] [27] showed that the SMA wire heating induces an axial stress in the graphite-epoxy composite beam, where the introduction of 15% volume fraction of SMA wires is capable of increasing the overall natural frequency. [PERSON] et al. [28] studied the free vibration of thermally pre/post-buckled shear deformable SMA hybrid composite beams. The problem was tackled theoretically based on the FSDT, von Karman geometrical non-linearity, and extended Hamilton principle. The authors, in their work, verified a general improvement of the buckling temperature and post-buckling path of the shape memory alloy hybrid composite (SMAHC) beam by concentrating the SMA fibers in the center of the laminated beam. In addition, [PERSON] et al. [29] analyzed the free and forced vibration of sandwich beams made of a functionally-graded porous core and composite face layers embedding SMAs, immersed within an elastic foundation, modeled according to the Vlasov basics. Once again, it was found that the temperature, volume fraction of SMA, and porosity distribution are important parameters affecting the overall response of sandwich beams. Starting with the available literature on the topic, the present work is aimed at investigating the vibration and buckling response of sandwich beams with SMA reinforced external layers and a FGM core, under thermal conditions. More specifically, two types of thermal distribution are considered, namely, a linear and a uniform distribution. The governing equations for the thermal buckling and vibration rely on a high order theory, combined with the Minimum Potential Energy principle or Hamilton principle, respectively. To verify the accuracy of the proposed analytical method, our results are successfully compared to well-known references from literature. A large sensitivity analysis is, thus, performed to check for the effect of some important parameters such as types of thermal load, volume fraction, and initial pre-strained SMA wires, as well as different geometrical properties for both the face sheets and the core of the selected sandwich structure.
## 3 Materials and Methods
### Image Fusion Methods
#### 3.2.1 Infrared and Visible Image Fusion Using a Deep Learning Framework  
This method, proposed by [PERSON] et al. [29] in 2018, presents a DL-based framework for the fusion of visible and infrared images. The output of this method is a grayscale image containing the features present in both the source visible and infrared images.  
First, the authors decompose the original images into base and detail parts. Next, [PERSON] et al. fuse the base parts through weight-averaging. Regarding the detail parts, the authors employ a _VGG19_ DCNN pre-trained on the ImageNet [73] to extract multi-layer features. The authors use L1 normalization and a weighted average strategy on the extracted features to generate candidates for the fused detail content. They use a max selection strategy to pick the final fused candidate, with the final output image constructed through the combination of the obtained detail and base contents. This method has the advantage of using only selected pre-trained layers of a _VGG19_ network, thus not needing further training on application-specific datasets. However, since it also employs traditional computer vision and image processing techniques, it is not an end-to-end model, increasing its implementation complexity. Additionally, the final fused images lose color information. This is relevant due to the color being one of the most used features in traditional visible image-based fire detection methods [3]. Throughout this paper, we will be referring to this method as the _VGG19_ method.  
Figure 4 shows the framework of the method proposed by [PERSON] et al. [29] with a sample image pair from the Corsican Fire Database.
## 2 Materials and Methods
### Synchronous Filtering Concept
The loop gain \(LG(z)\) is simulated for \(V_{r}=200\) LSB, \(k_{i}=2^{-23}\), and \(F_{s}=2\) kHz, and the simulated magnitude and phase responses are shown in \[V_{SIG}=V_{s}\sin(\omega\mathrm{t}) \tag{1}\]7 These simulations confirm the detailed analysis of the stability of the system. Replacing \(f_{3 dB}=0.7\) Hz and \(f_{nf}=50\) Hz in Equation (6), the quality factor of the SF algorithm is estimated to be Q = 35.7. Further decrement of \(k_{i}\) would lead to a higher Q but also to a slow reaction time. Note that 0.7 Hz corresponds to a time constant of 0.23 s and to a settling time from 10% to 90% of 2.2 \(\times\) 0.23 s \(\approx 0.5\) s, which is a reasonable start-up time. Further, the coefficient \(k_{i}\) should be selected with a tradeoff between Q and the limited speed of the closed-loop response. For sine wave mixing, as in our case, the two averagers are optional, and if they are not included, the phase margin of the system would always be  
\[V_{SIG}=V_{s}\sin(\omega\mathrm{t}) \tag{1}\]8 Magnitude and phase response of \(LG(z)\) for \(F_{s}=2\) kHz, \(V_{r}=200\) LSB, and \(k_{i}=2^{-23}\). \(90^{\circ}\) in a large variation in \(k_{i}\), allowing higher values of \(k_{i}\) to be selected. For example, if \(k_{i}\) is incremented four times, i.e., \(k_{i}\) = \(2^{-21}\), the Q factor becomes: Q = \(35.7/4\approx 9\).
# A Frame-to-Frame Scan Matching Algorithm for 2D Lidar Based on Attention
## 3 Attention Area Selection Module
### Reference Frame Attention Area Selection
The translation of the constraints of point \(p_{i}\) on the \(X\)-axis and the translation on the \(Y\)-axis are shown in Equations (4) and (5), respectively:  
\[c_{ix} = |n_{i}\cdot X_{i}|, \tag{4}\] \[c_{iy} = |n_{i}\cdot Y_{i}|, \tag{5}\]where \(X_{i}\) and \(Y_{i}\) represent the direction vector of the \(X\)-axis and the direction vector of the \(Y\)-axis, respectively. When the normal vector of a point is parallel to the coordinate axis, the constraint on that axis reaches its maximum value; when the normal vector of the point is perpendicular to the coordinate axis, the constraint on this axis is the smallest. Therefore, it is feasible to evaluate the contribution of a point to the translation constraint using the inner product of the vector with the direction vector of the coordinate axis. Based on the above description, Figure 2 depicts the flow diagram and the schematic diagram of the selected attention area of the reference area. First, the point clouds of the reference frame are clustered by the region growing segmentation method [28], and the clusters with too few points are removed; secondly, the number of points in each cluster and the average contribution of the point clouds to the \(X\)-axis constraints and \(Y\)-axis constraint is calculated; finally, the cluster that does not meet the threshold is removed, and the cluster that is the farthest from the origin of the reference frame among all of the clusters that meet the threshold is selected as the new landmark. The area corresponding to the landmark is the selected attention area. In Figure 2b, the red triangle represents the position of the robot, the red dots represent the lidar data, the black squares represent an obstacle in the environment, and the purple circle represents the selected attention area. It is clear that the selected attention area has sufficient constraints and is far enough away from the robot's position.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 5 Obstacle-Avoided Nearest Neighbour (OANN)-Based Approach  
The problem of energy-aware path planning is tackled in this section, which also takes into account the constraints that are imposed by some sensor pairs because of their proximity to one another. When there are obstacles situated between sensors, there is a possibility that they will hinder the direct movement of data from one sensor to the other. The autonomous ground vehicle (AGV) does not wish to travel directly from one sensor to the second sensor for a variety of reasons. Each of these reasons is discussed in more detail below. They include potentially hazardous objects, routes that are limited or muddy between the two sensors, and temperatures that are constantly changing. Within the context of this particular situation, the AGV will make its way to a sensor or sensors that are situated in the middle of the two sensors.  
A modified version of the Nearest Neighbour algorithm, which we refer to as the Obstacle-Avoided Nearest Neighbour (OANN)-based approach, is what we propose as a solution to the problem of three-dimensional path planning. This strategy aims to address minuscule obstructions that frequently develop in the space between some sensor pairs.  
\[D=\begin{bmatrix}d_{11}&d_{12}&\dots&d_{1n}&d_{1n}\\ d_{21}&d_{22}&\dots&d_{1n}&d_{1n}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ d_{(n-1)1}&d_{(n-1)2}&\dots&d_{(n-1)(n-1)}&d_{(n-1)n}\\ d_{n1}&d_{n2}&\dots&d_{n(n-1)}&d_{nn}\end{bmatrix}. \tag{2}\]  
If travelling from node \((n-1)\) to node \(n\) is impossible due to obstacles or some other blockage, then \(d_{(n-1)n}=M\), where \(M\) is a very large number.  
By giving a large number \(M\) such that \(d_{(n-1)n}=d_{n(n-1)}=M\), the modified distance cost matrix \(D^{OA}_{mod}\) obtained before using the Nearest Neighbour approach for \(n\) nodes is  
\[D^{OA}_{mod}=\begin{bmatrix}d_{11}&M&\dots&d_{1n}&d_{1n}\\ M&d_{22}&\dots&d_{1n}&d_{1n}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ d_{(n-1)1}&d_{(n-1)2}&\dots&d_{(n-1)(n-1)}&M\\ d_{n1}&d_{n2}&\dots&M&d_{nn}\end{bmatrix}. \tag{3}\]  
By considering this modified distance cost matrix \(D^{prac}_{mod}\), we use a 3D Nearest Neighbour algorithm. Hence, we proposed the Obstacle-Avoided Nearest Neighbour (OANN)-based approach, which has the same computational complexity as the Nearest Neighbour-based approach.
## 2 Materials and Methods
### Empirical Temperature-Based Models
#### 2.3.4 Proposed Empirical Model  
The proposed model originated from observing the scatter plot between the clearness index and the daily temperature difference in each AWS. We concluded that the logistic model would offer useful results, because this model has successfully studied human growth, animal and biological processes, energy use patterns, and atmospheric applications [39; 40]. The logistic regression commonly describes the relationship between binary variables and a predictor [41]. Although the relationship between extraterrestrial and terrestrial solar insolation does not offer binary results, it varies in a range between 0 and 1. The use of this technique _\"arises in estimating relationships in which the dependent variable is continuous_, but is limited in range\" [42]. [PERSON]'s statement describes the relationship between the dependent and independent variables in the proposed model because solar insolation relationships are continuous, and the temperature range is defined by the minimum and maximum temperatures. Logistic regression does not assume that  
\begin{table}
\begin{tabular}{c c} \hline \hline \(K_{T}\)**Range** & **Day Type** \\ \hline \(0.00<K_{T}\leq 0.20\) & Cloudy/Overcast \\ \(0.20<K_{T}\leq 0.60\) & Partially cloudy \\ \(0.60<K_{T}\leq 0.75\) & Sunny \\ \(0.75<K_{T}\leq 1.00\) & Very sunny \\ \hline \hline \end{tabular}
\end{table}
Table 4: Daily clearness index classification ranges.  
neither variables nor predictors have a normal distribution; this affirmation was another criterion to choose this approach [43].  
The logistic regression model is part of the Generalized Linear Model (GLM), which describes the relationship between the mean of the dependent and independent variables with a more complex relationship than \(y=a+bx\). In logistic regression, which is a statistical method, the coefficients have a similar meaning to linear regression; namely, \(a\) is the log-odds of success at \(x=0\), while \(b\) is the change in the log-odds of success corresponding to a one-unit increase in \(x\)[44]. This regression also assumes a linear relationship between the dependent and independent variables' log-odds [40].  
The logistic function came from the logistic model and is represented as follows:  
\[y=\frac{1}{1+e^{-z}},\]  
where \(y\) is the predicted variable, and \(z\) is a linear sum \(a+\sum b_{i}x_{i}\) where \(x_{i}\) is the independent variable, and \(a\) and \(b\) are constants [41].  
\[y=\frac{1}{1+e^{-(a+bx)}}.\]  
In this case, the temperature change \(\Delta T\) is the predictor variable. Therefore, the proposed model is defined as follows.  
\[\frac{H}{H_{0}}=\frac{1}{1+e^{-(a+b\Delta T)}}\]
# Input-to-State Stability of Variable Impedance Control for Robotic Manipulator
## 3 ISS of Variable Impedance Model
### Selection Guideline for Stiffness and Damping Matrices  
In this section the selection guidelines of stiffness and damping matrices are suggested from the sufficient conditions in Theorem 2. The variable impedance control should be considered in two aspects such as the constrained motion with contact and the free motion without any contacts as follows:  
(Case 1, when \(d=0\) to denote the free motion) Assume that any external forces and torques are not exerted to robotic manipulator, then the impedance parameters such as stiffness and damping are set up as the constants. Since \(d=0\) and \(\dot{K}=0\) in Equation (19), we have  
\[\dot{V}\leq-x_{1}^{T}\left[\frac{(1-\alpha)}{2}K\right]x_{1}-x_{2}^{T}\left[ \frac{(1-\alpha)}{2}K+D-\frac{1}{2}\dot{H}(q)\right]x_{2} \tag{33}\]  
where \(0<\alpha<1\) and \(K>0\). Now we can find the condition to guarantee \(\dot{V}<0\) as follow:  
\[\frac{(1-\alpha)}{2}K+D>\frac{1}{2}\dot{H}(q). \tag{34}\]  
From Equation (34), we have  
\[\left\|\frac{(1-\alpha)}{2}K+D\right\|_{l}\geq\left\|\frac{1}{2}\dot{H}(q)\right\| \tag{35}\]  
where \(\|\bullet\|_{l}\) is the lower bound of matrix \(\bullet\). To calculate the time derivative of inertia matrix, Equation (24) could be used. On the other hand, as presented in [17], since the upper bound of time derivative of inertia matrix \(\dot{H}(q)\) has following properties:  
\[\lambda_{M}\left(\dot{H}(q)\right)\leq\ \sup_{q\in\mathbb{R}^{n}}\left\{\sum_{i=1}^{n} \left\|\frac{\partial H(q)}{\partial q_{i}}\right\|\right\}\left\|q\right\|, \tag{36}\]  
the condition of Equation (35) can be simplified to:  
\[\left\|\frac{(1-\alpha)}{2}K+D\right\|_{l}\geq\sup_{q\in\mathbb{R}^{n}}\frac{ 1}{2}\left\{\sum_{i=1}^{n}\left\|\frac{\partial H(q)}{\partial q_{i}}\right\| \right\}\left\|q\right\|. \tag{37}\]  
Above inequality provides some insight to us in selecting the stiffness and damping matrix, for example, the faster robotic motion is required, the bigger stiffness and damping are required to guarantee the stability.  
(Case 2, when \(d\
eq 0\) to denote the constrained motion with contacts) In the case of nonzero external joint torques yielded from the interaction between the robotic manipulator and the environment or human operator, the time-varying stiffness \(\dot{K}(t)\) can be used to guarantee the stability only if the sufficient conditions of Equations (12) and (13) in the Theorem 2 are satisfied. In detail, Equation (26) is used to update the stiffness according to the variations of external joint torques yielded from environmental changes. As we can see in the Equation (26), smaller \(\alpha\) makes the stiffness update be faster.
# Facial Micro-Expression Recognition Based on Deep Local-Holistic Network
## 4 Experiments
### Parameter Analysis and Ablation Study
#### 4.3.2 Ablation Study  
To demonstrate the effectiveness of each of our proposed modules as well as combinations, we conduct the following ablation experiments, i.e., data validation on HCRNN,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(a\) & **0.1** & **0.2** & **0.3** & **0.4** & **0.45** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** \\ \hline Fold1 & 55.38 & 52.31 & 55.38 & 53.85 & 53.85 & 58.46 & **58.46** & 56.92 & 55.38 & 53.85 \\ Fold2 & 66.15 & 64.62 & 69.23 & **70.77** & **70.77** & **70.77** & 69.23 & 67.69 & 63.08 & 63.08 \\ Fold3 & 60 & 60 & 61.54 & 61.54 & 61.54 & **63.08** & 61.54 & 63.08 & 60 & 58.46 \\ Fold4 & 61.54 & 63.08 & 63.08 & **66.15** & **66.15** & 64.62 & **66.15** & 66.15 & 64.62 & 63.08 \\ Fold5 & 56.92 & 56.92 & 55.38 & **60** & **60** & 58.46 & 58.46 & 58.46 & 56.92 & 56.92 \\ Fold6 & 63.08 & 63.08 & **64.62** & 63.08 & **64.62** & 63.08 & 58.46 & 61.54 & 61.54 & 60 \\ Fold7 & **55.38** & 53.85 & 52.31 & 53.85 & 47.69 & 41.54 & 41.54 & 41.54 & 41.54 & 41.54 \\ Fold8 & **60** & 58.46 & **60** & **60** & 58.46 & 58.46 & 53.85 & 52.31 & 50.77 & 52.31 \\ Fold9 & 52.31 & 52.31 & 52.31 & 53.85 & 53.85 & 56.92 & 53.85 & 53.85 & **56.92** & **56.92** \\ Fold10 & **63.08** & **63.08** & **63.08** & 61.54 & 60 & 61.54 & 52.31 & 52.31 & 52.31 & 52.31 \\ \hline Mean & 59.385 & 58.769 & 59.692 & 60.308 & **60.309** & 60.308 & 57.385 & 57.385 & 56.308 & 55.847 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Facial micro-expression recognition accuracy (%) of our proposed DLHN with different parameter \(a\) in 10-fold cross-validation dataset. The maximum value in each fold and the maximum mean value are bolded.  
Figure 8: Network iteration curves.  
RPRNN, and the combination of both, DLHN, based on the same experimental setup. The experimental results are listed in Table 5. The mean accuracies of the HCRNN and the RPRNN are 55.08% and 59.53%, respectively. The fusion model, i.e., DLHN, obtains the best performance by combined local abundant features extracted by HCRNN and holistic sparse features extracted by RPRNN and achieves a 60.31% mean accuracy. In addition, RPRNN obtained the best performances in three folds (fold7, fold8, and fold10), which demonstrates the efficiency of the holistic sparse spatio-temporal feature extraction capacity of the RPRNN.