esa-sceva/satcom-chunk-collection huggingface dataset:
# Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
## 3 Algorithm Proposed to Conceal a Hole
### Summary of the Proposed Algorithm  
Figure 7 summarizes the proposed algorithm. In the first step, the hole is found using the algorithm described in Section 3.1, where the hole \(B_{L}^{c}\) and the neighboring blocks \(B_{L}^{l}\), \(B_{L}^{r}\), \(B_{R}^{c}\), \(B_{R}^{l}\), and \(B_{R}^{r}\) are defined according to the relationship between their coordinates. In the second step, we derive \(K(B_{R}^{c})\), R(\(B_{R}^{c})\), R(\(B_{L}^{c})\), and \(K(B_{L}^{c})\) using the algorithm proposed in Section 3.4. In the third step, each pixel in the hole \(B_{L}^{c}\) is filled with the corresponding pixel in the right ERP, where the \(H(B_{L}^{c}\to B_{R}^{c})\) of (11) provides the coordinate of the corresponding pixel. When the hole is in the right VR image, \(H(B_{L}^{c}\gets B_{R}^{c})\) of (12) is used to fill the hole.  
The main characteristics of the proposed algorithm are as follows: First, the proposed algorithm conceals the holes in stereoscopic VR images, whereas most conventional algorithms [16; 17; 18; 19; 20; 21; 22; 23; 24; 29] for filling holes were proposed for non-VR images. In particular, certain concealment algorithms [30; 31; 32] were proposed to fill the holes in depth images, where the holes have arbitrary shapes and some of them are small particles. Second, our algorithm can derive the location of the most similar pixel for each pixel in a hole. In our scenario, we use the data of the camera stream of another view to fill the holes, where the exact location of the most similar part should be calculated to increase the objective and subjective quality of the filled region. The position is estimated by analyzing Euler angles of extrinsic matrices of the related neighbor blocks. In the analysis of Euler angles, we decompose the angles along x, y, and z axes and then merge them to find the exact location of the reference data. The relevant explanation is provided in Section 3.4.  
Figure 7: Flowchart of the proposed algorithm.
## 2 Methodologies
### Feature Extraction  
#### 2.2.1 Sample Entropy  
Sample Entropy [23] was introduced for time-sequence analysis, complexity measurement in particular, which has been applied in physiological signal analysis [9]. For discrete time sequence \(X=\{x(1),x(2),...,x(N)\}\), the sample entropy can be calculated as follows:  
1. Construct a set of \(m\)-length vector series \(\{X_{m}(1)\), \(X_{m}(2)\),..., \(X_{m}(N-m+1)\}\), where \(X_{m}(i)=\{x(i),x(i+1),...,x(i+m-1)\}\), \(1\leq i\leq N-m+1\).
2. Define \(d[X_{m}(i),X_{m}(j)]\) to be the distance between \(X_{m}(i)\) and \(X_{m}(j)\), which takes the following form. \[d[X_{m}(i),X_{m}(j)]=\underset{k=0,1,...,m-1}{max}\,|x(i+k)-x(j+k)|.\] (1)
3. For a given \(X_{m}(i)\), assign \(B_{i}\) to be the number of \(j\) which meets the following condition: \(d[X_{m}(i),X_{m}(j)]\leq r\) and \(j\
eq i\), where \(r\) is the tolerance parameter. Then, define: \[B_{i}^{m}(r)=\frac{1}{N-m-1}B_{i}.\] (2)
4. Define \(B^{(m)}(r)\) to be: \[B^{(m)}(r)=\frac{1}{N-m}\sum_{i=1}^{N-m}B_{i}^{m}(r).\] (3)
5. Increase the length of vector series from \(m\) to \(m+1\). Then repeat step _(1)_ to _(4)_ to obtain \(B^{(m+1)}(r)\).
6. The sample entropy of time sequence X is: \[SameEn(m,r)=\lim_{N\rightarrow\infty}-ln\left(\frac{B^{(m+1)}(r)}{B^{(m)}(r)}\right).\] (4)  
When N is a finite value, sample entropy can be estimated by the following formula:  
\[SameEn(m,r)=-ln\left(\frac{B^{(m+1)}(r)}{B^{(m)}(r)}\right). \tag{5}\]  
Figure 2: DTDWT flow chart. \(L_{Ti}\) and \(H_{Ti}\) denote low-pass and high-pass analysis filters in the \(i\)th wavelet tree, respectively. The operator “\(\downarrow\) 2” denotes the 1/2 down-sampling operation. \(A_{T\bar{T}l,j}\) and \(D_{T\bar{T}l,j}\) denote the approximation and detail components of level \(j\) decomposition in the \(i\)th wavelet tree respectively.
# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 4 Experiments
### The Impact of Iteration Times  
In this section, the relationship between the recognition accuracy, CPU time, and classifier iteration times of the RPREC is mainly analyzed. The specific relationship curve between the recognition accuracy and the number of iterations of the classifier in the RPREC is depicted in Figure 8. We conducted three repetitive experiments based on the same dataset described in Section 4.2. Then, the classifier was updated with 200, 500, 1000, and 1500 iterations in each experiment. Finally, the statistical results of each experiment, including recognition accuracy and CPU time, are recorded in Table 7.  
It can be seen that once the recognition accuracy of the RPREC reaches a certain level, it will not significantly improve with an increase in iteration times. As shown in Figure 8, the recognition accuracy of targets, clutter, and uncertainty is always difficult to reach 0.98. When the number of iterations exceeds 1000, it will approach its upper limit of 0.976.  
Table 7 shows that the CPU time is basically proportional to the number of iterations in each experiment. When the number of iterations reaches 500 from 200, the recognition accuracy can be quickly improved, from around 0.35 to over 0.93. When the number of iterations increased from 500 to 1000, the improvement in recognition accuracy slowed down by about 10 percentage points. When the number of iterations exceeds 1000, there is no significant change in recognition accuracy as the number of iterations increases. For example, in these three experiments, they remained around 0.951, 0.935, and 0.961, respectively. This indicates that the classifier initially improves its ability with the learning of radar plots and then reaches a certain level of performance that cannot be further improved with iterative learning, which can only increase CPU time. Therefore, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{5}{c}{**Recognition Accuracy/CPU Time (s)**} \\ \cline{2-7}  & **PSO-SVM** & **PSO-MLP** & **FNN** & **CNN** & **RNN** & **RPREC** \\ \hline S1 & 0.852/2.12 & 0.833/3.43 & 0.821/4.89 & 0.841/7.08 & 0.843/8.32 & 0.923/52.55 \\ S2 & 0.856/1.31 & 0.852/2.98 & 0.842/4.09 & 0.863/5.53 & 0.894/6.21 & 0.936/35.39 \\ S3 & 0.873/1.24 & 0.881/2.44 & 0.877/3.47 & 0.903/4.03 & 0.925/5.92 & 0.931/29.27 \\ S4 & 0.871/1.03 & 0.906/1.41 & 0.895/1.98 & 0.927/2.61 & 0.931/4.53 & 0.929/18.31 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The recognition accuracy and CPU time of different algorithms.  
reasonable setting of parameters requires a comprehensive requirement for recognition accuracy and timeliness based on specific scenarios. Of course, it is necessary for us to further carry out research on adaptive parameter configurations that can adapt to different radar plots in the future.
## 3 Materials and Methods
### Measurement by the TLS and MLS Methods
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{Technology} & 3D laser scanner with an & & 3D handheld laser scanner \\  & integrated system for & 3D laser scanner with SLAM technology & with SLAM technology, an \\  & capturing HDR panoramic & with GNSS and an IMU system that & integrated IMU system, and an \\  & images and a VIS system for & ensures real-time registration of & RTK module, due to which it is \\  & registering data clouds in & data clouds & possible to georeference point \\  & real time & & clouds during measurement \\ \hline Mobility & \begin{tabular}{c} Terrestrial \\ (placed on a tripod) \\ \end{tabular} & Mobile (placed on a carrier: car, backpack) & \begin{tabular}{c} Mobile \\ (handheld measurement) \\ \end{tabular} \\ \hline Weight & 5.35 kg (without batteries) & 1.2 kg & 1.6 kg (without batteries) \\ \hline Range & 0.5 m–130 m & 0.05 m–300 m & 0.05 m–120 m \\ \hline Waterproof/ & & IP54 & IP67 & IP54 \\ dustproof & & & \\ \hline Operating temperature & \(-5\)\({}^{\circ}\)C to +40 \({}^{\circ}\)C & \(-5\)\({}^{\circ}\)C to +50 \({}^{\circ}\)C & \(-10\)\({}^{\circ}\)C to +45 \({}^{\circ}\)C \\ \hline Dimensions & 120 mm \(\times\) 240 mm \(\times\) 230 mm & 125 mm \(\times\) 125 mm \(\times\) 150 mm & 372 mm \(\times\) 163 mm \(\times\) 106 mm \\ \hline GNSS & \begin{tabular}{c} External GNSS set with an \\ RTK module \\ \end{tabular} & External GNSS set with an RTK module & \
\begin{tabular}{c} External GNSS set with an \\ RTK module \\ \end{tabular} \\ \hline Scanning point & Up to 2,000,000 pts/s & 640,000 pts/s & 320,000 pts/s \\ \hline \multirow{4}{*}{Accuracy} & Angular: 18\({}^{\prime\prime}\) & \multirow{4}{*}{\(+/-1\) cm} \\  & Distance: & & \\ \cline{1-1}  & 1.0 mm + 10 ppm & & \\ \cline{1-1}  & 3D points: & & \\ \cline{1-1}  & 1.9 mm @ 10 m & & \\ \cline{1-1}  & 2.9 mm @ 20 m & & \\ \cline{1-1}  & 5.3 mm @ 40 m & IMU &
\begin{tabular}{c} 0.010/0.005/0.005 \\ (\({}^{\circ}\)) \\ \end{tabular} \\ \hline Resolution & 3/6/12 mm @ 10m & - & - \\ \hline Field of view & 360\({}^{\circ}\)\(\times\) 300\({}^{\circ}\) & 360\({}^{\circ}\)\(\times\) 40.3\({}^{\circ}\) & 360\({}^{\circ}\)\(\times\) 270\({}^{\circ}\) \\ \hline Price & 80,000 \(\epsilon\) & 60,000 \(\epsilon\) & 40,000 \(\epsilon\) \\ \hline \multirow{4}{*}{Camera} & 36 MPx 3-camera system & & \\ \cline{1-1}  & 432 MPx raw data for & & \\ \cline{1-1}  & calibrated panoramic & & \\ \cline{1-1}  & 360\({}^{\circ}\)\(\times\) 300\({}^{\circ}\) image & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specifications of the laser scanners used in the study [40; 41; 42; 43; 44].
###### Abstract  
Modeling pedestrian-vehicle interaction behaviors not only helps better predict the intentions and actions of traffic participants but also contributes to generating more realistic pedestrian trajectories for testing autonomous vehicles. Most existing pedestrian-vehicle interaction models use repulsive forces toward target directions to avoid collisions. However, pedestrian agents in these models lack the ability to plan avoidance routes based on their positions when facing conflicting vehicles, leading to poor simulation effects at unsignalized intersections. By analyzing the crossing trajectories of pedestrians at unsignalized intersections through video data, we observed that when participants reject a current vehicle gap, they may tend to move toward the vehicle's rear to start crossing the traffic flow earlier, thereby obtaining a safer opportunity to cross the road. In contrast, most previous pedestrian-vehicle interaction models only simulated pedestrians' avoidance by moving away from vehicles. In response, we propose a pedestrian-vehicle interaction model incorporating pedestrian avoidance tendencies, which is based on the social force framework. Our improvements include refining the vehicle's influence on pedestrians in lateral and longitudinal dimensions. The pedestrian agents in this model can make appropriate crossing decisions and select collision avoidance paths according to traffic conditions. This model can simulate pedestrian-vehicle interaction scenarios at unsignalized intersections and can be extended to pedestrian safety testing for autonomous vehicles.  
usignalized intersection; pedestrian-vehicle interaction; traffic simulation; social force model; autonomous driving testing Article
## 6 Simulation Results and Discussion
### Simulation Parameters  
In the simulation environment, nodes varying from (150-450) are randomly deployed in UWSN environment of dimensions 1500 m \(\times\) 1500 m \(\times\) 1500 m (see Figure 15) with nine sinks (at sea surface). We kept the transmission range, DR and PS of the DP as 250 m, 50 Kbps, and 200 bytes, respectively. The initial energy of the sensor nodes is kept 100 J, where the transmission and receiving the power of DP is kept at 2 W and 0.1 W. To handle the mobility of nodes, we have considered the node speed in the horizontal direction 2 m/s. Moreover, the propagation speed of the acoustic wave is kept 1500 m/s along with bandwidth of 4 kHz. Meanwhile, the size of beacon is kept 50 bits. We compared the existing work by increasing the node density to show the efficacy of the proposed work. The simulations are conducted in Aqua-Sim (NS-2-based UWSN simulator). The above-mentioned parameters are taken from [23] and listed in Table 4.  
Figure 15: Nodes deployment.
## 3 State-of-the-Art
### Machine Learning  
Machine learning can be defined as the application of artificial intelligence and computer systems to learn from the environment, improve itself from experience without the need for any explicit programming and call for action that does not require human intervention. Machine learning focuses on enabling algorithms to learn from the data provided, gather insights and make predictions on previously unanalyzed data using the information gathered [37].  
The shipping industry has been keeping an eye on the development of ML that can customize container freight and overcome tough operational problems encountered in everyday operations. For instance, ML can be used to forecast estimated travel time (ETA), even if there are congestions at transshipments points, weather-related difficulties, overbooking issues, and equipment paucity. The computation learns from the past data,thus providing a much more definite forecast [38]. To improve fuel efficiency and vessel performance at sea, an algorithm was developed for an intelligent fuel oil consumption monitoring system that can propose an optimal trim condition to minimize the ship resistance during voyage [39]. Fujitsu Laboratories has developed a new artificial technology and teamed up with Mitsui O.S.K. Line, Ltd. (MOL) in improving fuel efficiency and reducing CO\({}_{2}\) emission of a ship through the use of operational big data [40]. ML is also used for fouling analysis in predicting a more accurate dry docking, cleaning and coating schedules as the vessel fuel consumptions are affected by the vessel speed and the fouling of the ship [41]. An ML approach was also developed by the Technical University of Denmark in predicting the main energy consumption under realistic operational conditions [42]. An artificial neural network (ANN)-based decision support system has also been developed for cargo vessel operation by employing a combination of traditional statistical analysis and ANNs [27].
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Datasets
#### 4.1.3 DIOR Dataset  
The DIOR dataset serves as a comprehensive open-access benchmark for object detection in high-resolution aerial imagery. This extensively utilized resource contains 23,463 images (800 \(\times\) 800 pixels) with 192,472 meticulously annotated instances across 20 land-use categories: windmill (WM), train station (TS), tennis court (TC), storage tank (ST), stadium (SD), ground track field (GF), ship (SP), golf course (GC), overpass (OP), harbor (HB), vehicle (VH), expressway toll station (ES), expressway service area (EA), dam (DM), chimney (CM), bridge (BG), basketball court (BC), baseball field (BF), airport (AT), and airplane (AL). Following standardized evaluation protocols, the dataset is organized into three subsets: 5862 training samples, 5863 validation samples, and 11,738 testing samples, maintaining methodological rigor and cross-study comparability.
## 2 Classification of Electromagnetic Surfaces
### Homogeneous Effective and Spatially Dispersive  
Two-dimensional surfaces are always composed of inhomogeneous unit cells, like the periodically distributed square patches shown in Figure 4. However, if the inhomogeneity scale is much smaller than wavelength \(\lambda\), the surfaces can be modelled as homogeneous effective mediums [17], whose characteristic parameters are constants along surfaces. Homogeneous effective surfaces whose period, \(p\), is usually much smaller than \(\lambda\) can be characterized by effective surface susceptibilities \(\overline{\overline{\chi}}_{ee}\) and \(\overline{\overline{\chi}}_{mm}\)[7; 21]. As frequency increases, \(\lambda\) will gradually become comparable to \(p\), and surfaces cannot be regarded as homogeneous mediums any more. Then \(\overline{\overline{\chi}}_{ee}\) and \(\overline{\overline{\chi}}_{mm}\) become functions of space coordinates, and surfaces with wavelength-comparable period are spatially dispersive [17]. FSS is a typical application of spatially dispersive surfaces, whose period is usually designed close to \(\lambda/2\).
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 3 MPC Controller Design Based on Stability Boundary
### Solve MPC Controller
The formula can be derived as follows:  
\[\mathbf{U}_{\mathrm{min}}\leq A\Delta\mathbf{U}(t)+\mathbf{U}(t)\leq\mathbf{U}_{\mathrm{max}}\]  
By substituting Equation (8) into the objective function expressed in Equation (15), the output deviation within the prediction horizon is obtained as follows:  
\[\left\  
tively. \(y_{\text{h,min}},y_{\text{h,max}},y_{\text{s,min}},y_{\text{s,max}}\) are the limit values of the hard constraint output and soft constraint output, respectively.  
The system obtains the control increment sequence through QP solution in each control time domain:  
\[\Delta\boldsymbol{U}(t)^{*}=\left[\Delta\boldsymbol{u}_{t}^{*},\Delta \boldsymbol{u}_{t+1}^{*},\cdots,\Delta\boldsymbol{u}_{t+N_{c-1}}{}^{*}\right]^ {\text{T}} \tag{17}\]  
By inputting the first element in Equation (17) into the system as the actual control increment, it can be obtained that  
\[\boldsymbol{u}(t)=\boldsymbol{u}(t-1)+\Delta\boldsymbol{u}_{t}^{*} \tag{18}\]  
Upon entering the subsequent control time domain, the system perpetually iterates through the aforementioned process, thereby enabling closed-loop tracking of the reference path. The devised MPC control algorithm is implemented via an S-function, and an intelligent vehicle path tracking controller is established on the CarSim/Simulink co-simulation platform. Certain controller parameters are detailed in Table 2.  
The following is an explanation for the determination of the controller parameters in Table 2.  
(1) Determination of Weighting Matrices \(\boldsymbol{Q}\) and \(\boldsymbol{R}\):  
Q Matrix (State Weighting): The relative importance of lateral deviation, heading deviation and yaw rate in path tracking was analyzed. Initial values were referenced from classical MPC literature, followed by iterative tuning via CarSim/Simulink co-simulation under typical conditions (e.g., double lane change, serpentine curves). The final selection, \(Q=\) diag [200, 100, 100], prioritizes lateral tracking accuracy. \(R\) Matrix (Control Input Weighting): To prevent abrupt front wheel steering, a high weight (\(R=\) [5 \(\times\) 10\({}^{4}\)]) was set based on vehicle dynamic stability constraints, ensuring smooth control inputs. (2) Relaxation Factor Weight (\(\rho\)):  
To balance constraint violations and optimization objectives, \(\rho=\) 1000 was selected through genetic algorithm testing across multiple scenarios, ensuring solver feasibility even when tire slip angles approach non-linear regions. (3) Sampling Period (\(T\)):  
Considering both real-time performance (computational load) and control precision, \(T=\) 0.05 s was chosen based on vehicle dynamics time constants (0.05-0.1 s) and hardware capabilities.
## 5 Experimental Setup
### Mel-Spectrogram and Raw Waveforms  
In the mel-spectrogram experiments, window sizes of \(3^{6}\), \(3^{5}\) and \(3^{4}\) are used to match up to the filter sizes in the first convolution layer of the raw waveform model as shown in Table 2. FFT size was set to 729 (=3\({}^{6}\)) in all experiments. When the window is less than the FFT size, we zero-padded the windowed frame. The linear frequency in the magnitude spectrum is mapped to 128 mel-bands and the magnitude compression is applied with a nonlinear curve, \(\log(1+C|A|)\) where \(A\) is the magnitude and \(C\) is set to 10. Also, we conducted the input normalization simply by dividing the standard deviation after subtracting mean value of entire input data. On the other hand, we did not perform the input normalization for raw waveforms.  
As described in Section 3.3, \(m\) refers to the filter size (which can be compared to a window size of FFT in the spectrogram) or pooling size (which also can be compared to a hop size of FFT in the spectrogram) of the intermediate convolution layer modules, and \(n\) refers to the number of the modules. In our previous work, we adjusted \(m\) from 2 to 5 and increased \(n\) according to the configuration of \(m^{n}\)-SampleCNN [13]. Among them, \(3^{9}\)-SampleCNN model with 59049 samples as input worked best and thus we fix our baseline model to it. In this configuration, we can increase the filter size and stride size in the first layer by decreasing the layer depth to conduct comparison experiments between the frame-level models and the sample-level model. For example, if the hop size or the stride size of the first convolutional layer is 729 in either the frame-level mel-spectrogram model or the frame-level raw waveform model, 4 convolutional modules with 3-sized filters are added when the input size is 59,049 samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**3\({}^{n}\) Models,** & & & & \\
**59,049 Samples as Input** & \(n\) & **Window Size** & **Hop Size** & **AUC** \\ \hline \multirow{4}{*}{Frame-level (mel-spectrogram)} & 4 & 729 & 729 & 0.9000 \\  & 5 & 729 & 243 & 0.9005 \\  & 5 & 243 & 243 & 0.9047 \\  & 6 & 243 & 81 & 0.9059 \\  & 6 & 81 & 81 & 0.9025 \\ \hline \multirow{4}{*}{Frame-level (raw waveforms)} & 4 & 729 & 729 & 0.8655 \\  & 5 & 729 & 243 & 0.8742 \\ \cline{1-1}  & 5 & 243 & 243 & 0.8823 \\ \cline{1-1}  & 6 & 243 & 81 & 0.8906 \\ \cline{1-1}  & 6 & 81 & 81 & 0.8936 \\ \cline{1-1}  & 7 & 27 & 27 & 0.9002 \\ \cline{1-1}  & 8 & 9 & 9 & 0.9030 \\ \cline{1-1}  & 9 & 3 & 3 & 0.9055 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of three CNN models with different window size (filter size) and hop size (stride size). \(n\) represents the number of intermediate convolution and max-pooling layer modules, thus \(3^{n}\) times hop (stride) size of each model is equal to the number of input samples.