esa-sceva/satcom-chunk-collection huggingface dataset:
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
###### Abstract  
Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers' failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage and environments with limited visibility continue to present elevated risks. The scarcity and difficulty of collecting data under such complex conditions pose significant challenges to the development of accurate detection systems. This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. This model is then applied to automatically annotate newly generated synthetic images, which are incorporated into the training dataset for subsequent rounds of model retraining and image generation. Through this iterative process, the model progressively refines its ability to simulate and recognize diverse contextual features, thereby enhancing detection performance under varying environmental conditions. The experimental results show that environmental variations--such as daytime, nighttime, and rainy conditions--substantially affect the model performance in terms of F1-score. Training with a balanced mix of real and synthetic images yields an F1-score comparable to that obtained using real data alone. These results suggest that CycleGAN-generated images can effectively augment limited datasets and enhance model generalization. The proposed system may be integrated with in-vehicle assistance platforms as a supportive tool for pedestrian-crossing detection in data-scarce environments, contributing to improved driver awareness and road safety.  
p edestrian; recognition; loop learning; automatic labeling; zebra crossing; computer vision +
Footnote †: journal: Applied sciences
# A Review of Light Shelf Designs for Daylit Environments
## 2 Overview
Figure 3: Various interior radiance renderings of a south oriented room with dimensions 4 \(\times\) 6 \(\times\) 3 m and a window to floor ratio equal to 20% equipped with: (**a**) No light shelf, under overcast sky; (**b**) Perfectly diffuse external horizontal light shelf with 0.5 m depth (reflectance 0.8), under overcast sky; (**c**) Perfectly diffuse external horizontal light shelf with 0.5 m depth, under clear sky conditions, sun’s elevation 37.8\({}^{\circ}\); (**d**) Like; (**c**) but with mirror external horizontal light shelf; (**e**) Similar to; (**d**) with the external inclined upwards by 15\({}^{\circ}\); (**f**) Similar \(\mathbf{\xi}\); (**e**) with a perfectly diffuse internal light shelf (depth 1.2 m). Therefore, the criteria used for the performance evaluation of a light shelf, which are common among daylighting systems, are the following:  
1. An increase in illuminance especially in non-daylit areas. 2. An increase in uniformity. 3. The improvement of visual comfort. 4. The provision of sufficient shading. Light shelves can be categorized according to their geometrical form. Therefore, in the present paper, research results are presented for (a) horizontal, (b) flat tilted, (c) curved and finally, (d) active light shelves.
# Semantic Lidar-Inertial SLAM for Dynamic Scenes
## 1 Introduction  
Simultaneous localization and mapping (SLAM) [1; 2] is of crucial significance for mobile robot navigation, micro aerial vehicles (MAVs), virtual reality (VR), and augmented reality (AR) since accurate pose estimation is fundamental for machine operation. In recent years, studies have proliferated regarding the theme of the fusion of inertial measurement units (IMU) with a single perceptual sensor. When the sensor is a lidar sensor, the system is called a lidar-inertial SLAM system [3]. Compared with other SLAM systems, the lidar-inertial SLAM system can acquire accurate ego-motion estimation and dense point cloud maps since the lidar sensor is invariant to changes in illumination and can provide distance measurements of the surrounding environments.  
Over the past few years, many impressive lidar-inertial SLAM systems [4; 5; 6; 7; 8; 9] have been developed and continue to perform well, such as LIO-mapping [6], LIO-SAM [8], SuMa [9], and so on. However, traditional lidar-inertial SLAM systems operate on the assumption that the scene is static. Most scenes in real-life, especially outdoor scenes, consist of dynamic environments. The feature points of the dynamic object are unstable, which affects the accuracy of localization and mapping. Furthermore, the typical lidar-inertial SLAM builds a map only with geometric information (points, lines, and planes), and lacks the semantic information of the surrounding scene that is needed by the robot to complete some advanced tasks.  
In this paper, we focus on avoiding the moving objects in dynamic scenes by combining point cloud semantic segmentation with lidar-inertial SLAM. We propose a point cloud semantic segmentation network that imports an attention mechanism to the PointConv [10] network to improve the capacity to predict details. We define a graph constructed from reference points and their neighbors and input the relative coordinates of the neighborscombined with their features. We build attention weight functions to dynamically distribute the weights of neighbors. After filtering the points belonging to moving objects through a semantic segmentation network, stable static feature points are extracted, and a static local map is built. Then, joint non-linear optimization is adopted within a sliding window to obtain the final state of the system. We conduct experiments on the SemanticKITTI dataset [11] and UrbanNav dataset [12]. The results demonstrate that, compared with other SLAM systems, the accuracy of the estimated trajectory can be improved to different degrees compared with other methods, and the global map of our system is refined, becoming clearer.
# A Nodal Analysis Based Monitoring of an Electric Submersible Pump Operation in Multiphase Flow
## 4 Materials and Methods
### The Homogenous Model  
The fluid densities at various GVFs were calculated using a homogenous model. The two-phase mixture (liquid and gas) was treated as a homogenous fluid [35]. The total mixture flow rate \(\mathrm{Q_{m}}\) is calculated as the sum of the in situ liquid (oil and water) flow rate \(\mathrm{Q_{l}}\) and the gas flow rate \(\mathrm{Q_{g}}\) (Equation (4)):  
\[\mathrm{Q_{m}=Q_{l}+Q_{g}} \tag{4}\]  
[PERSON] and [PERSON] [35] showed that the pumping head under two-phase flow (\(\mathrm{H_{tp}}\)) could be expressed by multiplying the head of the single liquid phase (\(\mathrm{H_{l}}\)) and the degradation factor (the two-phase specific gravity, \(\mathrm{SG_{m}}\)) for each individual GVF. Hence, ESP head degradation is given by Equation (8):  
\[\mathrm{\lambda_{g}=Q_{g}/Q_{m}} \tag{5}\]  
\[\mathrm{\rho_{m}=\lambda_{g}\rho_{g}+[1-\lambda_{g}]\rho_{l}} \tag{6}\]  
\[\mathrm{SG_{m}=\rho_{m}/\rho_{l}} \tag{7}\]  
Figure 1: Nodal analysis simulation model. **(Left)** Schematic diagram of the ESP horizontal oil well components. **(Right)** Shows the model representation in Schlumberger PIPESIM software (version 2017.01), developed by Schlumberger, located in Houston, TX, USA.  
\[\mathrm{H_{tp}}=\mathrm{SG_{m}H_{l}} \tag{8}\]  
where the new abbreviations are \(\rho_{l}\) for the liquid density, \(\rho_{g}\) for the gas density, and \(\lambda_{g}\) for the GVF.
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 2 Protoplanetary Disc Dynamics in Alpha Centauri
### Circumstellar Discs Setup  
We followed the disc implementation of Phantom and initialised two circumstellar discs for a binary with the same orbital parameters as Alpha Cen AB. Both discs are coplanar with the binary orbital plane. Each disc was modelled using 500,000 SPH particles assuming a total gas mass of \(0.01\,M_{\odot}\) akin to typical protoplanetary disc masses [17]. Initially, we set the inner and the outer edges of both discs at \(R_{\rm in}=0.5\) au and \(R_{\rm out}=10\) au, respectively. The surface density profiles followed the same prescription: \(\Sigma_{\rm A}(R)=\Sigma_{\rm B}(R)=2350\) g cm\({}^{-2}\)\((R/1\ {\rm au})\big{(}1-\sqrt{0.5\,{\rm au}/R}\big{)}\), where \(R\) is the cylindrical radial coordinate (from the host star) given in au. We adopted a mean Shakura-Sunyaev disk viscosity \(a_{\rm SS}\approx 0.005\) by setting a fixed artificial viscosity parameter \(\alpha_{\rm AV}=0.25\) and using the 'disk viscosity' flag of Phantom [18]. We further assumed that the disc is vertically isothermal, and we noted \(H\) as the pressure scale height. The disc aspect ratio \(H/R\) at a distance of 1 au was set to 0.05 for both discs, as is for typical protoplanetary discs. The temperature profiles are given by the following: \(T_{\rm A}(R)=690\,(R/1\ {\rm au})^{-0.5}\) K and \(T_{\rm B}(R)=581\,(R/1\ {\rm au})^{-0.5}\) K, thereby considering that stars A and B do not have the same mass and luminosity.
# Multi-View Network Representation Learning Algorithm Research
## 1 Introduction
This kind of optimization process transforms the low-order network representations into the high-order network representations, however, NEU has no influence on modeling procedure of network representation learning. We adopt a novel approach to jointly learn the network representations. For the final network representations, the different \(k\)-step networks should be given different weights. Therefore, we adopt the link probabilities of non-existing edges to weight different \(k\)-step network features. For the joint learning task, we use the inductive matrix completion algorithm to generate network representations  
Figure 1: Algorithm framework of multi-view network representation (MVNR). (**a**) Higher order neighboring relations acquisition; (**b**) Edge weight calculation. for each view. As the GraRep [4] algorithm, we concatenate different network representations from each view as the final representations. We believe that the \(k\)-step relations between different vertices reveal higher order relations and the global structural features, and it is essential to explicitly take full advantage of the different \(k\)-step network features for learning a better graph representation for various networks. Our contributions are as follows:  
1. We introduce a novel network representation learning (MVNR), which explicitly captures higher order neighboring relations and global features. In network classification task, the performance of MVNR is superior to that of network representation algorithms based on the features of single view, such as DeepWalk, Line and node2 vec. MVNR also outperforms the existing network representation learning algorithm based on the features of higher order, such as, GraRep and NEU. 2. We introduce the Matrix Forest Index (MFI) to evaluate the weights of different \(k\)-step networks, which gives different weights for the representation vectors of different \(k\)-step networks in common representation vector space. This operation makes up for the important deficiency of GraRep algorithm. In addition, MFI features are calculated based on the network structure features. Therefore, it can provide sufficient structure features and improve the sparse problem of structure feature matrix in sparse networks. 3. The visualization result of MVNR algorithm is better than that of node2 vec and DeepWalk algorithm. It shows stronger cohesiveness and clearer classification boundary. Therefore, MVNR can learn the discriminative representation vectors. Consequently, it also shows excellent performance in link prediction tasks, and its link prediction performance is better than that of the baseline algorithms used in this paper.
## 3 Acsa-Olta
### The Optimal Correlation Space Based on EWPRE
The value of EWPRE represents the mean equivalent relative measurement error among different grid sizes so that it can evaluate the measurement accuracy difference of these grid sizes even though it is not the estimation of the real mean measurement error. It is further verified by simulation in Section 4.1. Consequently, the optimal code search grid size of open loop tracking method for a given \(C/N_{0}\) is the one with the minimum EWPRE. After some algebraic transformation, the EWPRE is simplified as below. When the coherent integration length is 300 ms, the EWPRE of different grid sizes for different \(C/N_{0}\) is shown in Figure 3.  
\[S(\tau_{b})=\frac{\tau_{b}^{2}}{8T_{p}}+\frac{1}{T_{p}}\sum_{n=1}^{T_{p}/\tau_ {b}}[(e_{n}-\tau_{b}/4)\cdot\int_{\frac{2n-1}{2}\tau_{b}}^{\frac{2n+1}{2}\tau_ {b}}F(P_{f}>P_{r};x)\mathrm{d}x] \tag{31}\]Taking the \(C/N_{0}\) of 10 dBHz, 15 dBHz, 20 dBHz and 25 dBHz as examples, the EWPRE of different grid sizes is detailedly drawn in Figure 4.  
One can see that there is a grid size that minimizes the EWPRE for each \(C/N_{0}\) and that shall be the grid size with best accuracy. Taking the coherent integration length of 300 ms as an example, the optimal code search grid sizes of different \(C/N_{0}\) are shown in Figure 5. It indicates that when \(C/N_{0}\) is low, the grid size is enlarged to increase the power difference between adjacent grids so as to avoid the error brought by the false peak detection which is much more serious than the error within the  
Figure 4: The detailed EWPRE of different grid size with 300 ms coherent integration. Figure 3: The EWPRE of different grid sizes and \(C/N_{0}\) with 300 ms coherent integration. grid. When the \(C/N_{0}\) is high, as the detection performance is perfect, the optimal grid size is really small to reduce the measurement error in the grid. Therefore, the correlation space between the batch correlators of open loop tracking which is the code search grid size should be adjusted to the optimal size shown in Figure 5 as \(C/N_{0}\) varies to improve the measurement accuracy. Considering small grid size demands large numbers of correlators which means huge computational costs, the method to calculate the measurement with less correlators but high accuracy is studied in the next subsection. The verification of the optimal grid size is conducted in Section 4 with the help of Monte Carlo simulation and test.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Single-Channel MAC Protocols  
There is no centralized assistant for channel access. In a single-channel MAC, the access channel is not divided, so there can be interference chances. These are basically designed to resolve challenges related to packet allocation and resource allocation based on TDMA, CDMA, and SDMA. The main focus is on TDMA- and SDMA-based time-slot allocations and contention-based parameters for improving network throughput from that of IEEE 802.11p. To adapt to VANETs' dynamic change in topology, distributed MAC protocols are designed to minimize communication overhead.
## 4 Quadrotor Control
### Quadrotor Classical Control Using RM  
To use the linear RM technique for quadrotor control, the complete nonlinear Equations (1)-(6) should be simplified. The most used technique to simplify these nonlinear equations is toconsider weak angles variations (reduced gyroscopic effects) and retain the linear portion, the Equations (1)-(6) become:  
\[\left\{\begin{array}{l}I_{x}\ddot{\varphi}=lU_{2}\\ I_{y}\ddot{\theta}=lU_{3}\\ I_{z}\ddot{\psi}=U_{4}\\ \ddot{x}=\frac{\cos\varphi\cos\psi\sin\theta+\sin\varphi\sin\psi}{m}U_{1}- \frac{K_{f\eta x}}{m}\dot{x}\\ \ddot{y}=\frac{\sin\psi\sin\theta\cos\varphi-\cos\psi\sin\varphi}{m}U_{1}- \frac{K_{f\eta y}}{m}\dot{y}\\ \ddot{z}=-g+\frac{\cos\varphi\cos\theta}{m}U_{1}-\frac{K_{f\eta z}}{m}\dot{z} \end{array}\right. \tag{27}\]  
The inputs \(U_{2}\), \(U_{3}\), \(U_{4}\), \(U_{x}\), \(U_{y}\), and \(U_{z}\) affect respectively angles of roll, pitch, and yaw, and positions \(x,y\), and \(z\), the reason for their usage as the PID controllers' inputs. By applying the Laplace transform, we obtain the following transfer functions for angles (\(\phi\), \(\theta\), \(\psi\)) and positions (\(x\), \(y\), \(z\)).  
\[\left\{\begin{array}{l}G_{\varphi}(p)=\frac{\varphi\,(p)}{U_{2}\,(p)}=\frac {l}{l_{x}p^{2}}\\ G_{\theta}(p)=\frac{\theta\,(p)}{U_{3}\,(p)}=\frac{l}{l_{y}p^{2}}\\ G_{\psi}(p)=\frac{\dot{\psi}\,(p)}{U_{4}\gives the parameters of the quadrotor and Table 2 presents the suggested trajectories' dynamics and their conditions.
# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
## 3 Ant Colony Optimization Algorithm
### Background  
This paper's primary purpose is to design an algorithm based on the decentralized performance of ants, using their natural ability to find the shortest path between the origin and destination by moving in the network, which uses routing criteria as optimal target functions and optimizes routing on the Internet of Things. In order to solve various problems with the ant colony algorithm, their necessary behavior must be considered:  
- They likely choose the path that has the most pheromones, or in other words, the one more ants have already crossed.  
- On the other hand, the methods by which some insects select a leader for decision-making lead us to the formation of a computer algorithm that can play an essential role in the network efficiencies.  
The primary intellectual basis of the ant algorithm is: \"Among the obstacles and limitations in nature, ants always choose the most optimal way from different permutations to reach food.\"  
According to pheromones and ant's traveling, where more ants have already traveled, this leads to finding the shortest way to solve the routing problem and thus reduce energy consumption in this paper. Artificial ants are used as optimizing elements to implement ant colonies. Of course, these elements are fundamentally different from real ants, which are: memory, artificial barriers, life in a discrete environment [31].  
Let A is a nest, E is a food source, FC is a barrier. According to the obstacles, the ant goes only through F or C from A to E or E to A; the distance between the points is shown in Figure 1. For each time unit, there are 30 ants from A to B, and 30 ants from E to D. When the quality content hormone (information) for ease of calculation is extracted during the material immobility at the initial time, the paths DC, DF, BC, BF, which have no information, is placed in B and E and ants choose a random path. From a statistical point of view, we can say that the probability of choosing DF, BC, BF, and DC is the same. After the time unit, the amount of information on the BCD path is twice the BFD information's content. T is the red moment; there will be 20 ants from B to C, ten ants from B and D to F. Over time, ants choose the BC path with more probability. Finally, the path selection, D, is made from the nest to the food source to find the shortest path [31].  
In the following subsections, the functions and elements of the ant algorithm are included:
## 4 Results and Discussion
In comparing these results in the first scenario column in Table 2, it is observed that the MSFOA obtains the minimum best among the algorithms, which indicates that MSFOA has the most powerful optimization ability in the statistical sense. Figure 3 displays the experimental results of the second test scenario. For this test scenario, DE and IFFO come into the threat areas. From Table 2, it can be seen that MSFOA achieves the smallest mean cost value. Furthermore, the result of the third scenario is shown in Figure 4. It is obvious that MSFOA has the best path for UAVs. In the third scenario column of Table 2, the cost of MSFOA is significantly decreased in comparison to the other algorithms. Above all, these experimental results demonstrate that the MSFOA still maintains a higher performance than the FOA, PSO, DE, ABC, and two other modified FOA versions. The convergence curves of the average best cost values are displayed in Figures 5-7. In the first scenario, it can be seen that DE, FOA, and IFFO show inferior convergence. MFOA achieves a faster convergence speed and smaller cost than MSFOA in the early stages. However, in the later iterations when most algorithms come to stagnation, MSFOA still shows the ability to search for better solutions. The searching range decreases as the cost value decreases below the threshold, which can improve the local searching ability near the optimal solution, and thus leads to the best global convergence of the algorithms. In Table 3, the best, median, and average of the cost values of the sum for three UAVs are recorded. The best values are highlighted using boldface. From this table, it can be seen that MSFOA achieves the smallest cost value in the first and third scenario and the second smallest in the second scenario. MFOA obtains a smaller average and median in the second scenario, but
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 2 Methodology  
The workflow of the proposed damage detection method is presented in Figure 1. Steps 1 and 2, combined, form the basis of the vision-based measurement. Utilizing the vibration video captured in step 1, step 2 adopts phase-based optical flow estimation algorithm to acquire vibration signals and identifies high-spatial-resolution mode shapes of the structure through blind source separation (BSS) and independent component analysis (ICA). Step 3 involves the extraction of multi-scale damage features, with the acquired mode shapes being mapped into Gaussian multi-scale space to suppress the measurement noise. Local information entropy (LIE) is computed at each scale to characterize local damage-induced irregularities in mode shapes, resulting in the novel damage index MS-LIE, which manifests multi-scale damage information. Step 4 employs a data fusion algorithm based on D-S theory to fuse the damage information at all scales, thereby facilitating a global assessment of the damage state and enabling a definitive identification of the damage location.
## 3 Application to Immersed Tunnel
### Calculation Model  
ABAQUS [39] was used to establish a 3D HZM immersed tunnel model of 300 \(\times\) 67.5 \(\times\) 70 m\({}^{3}\) (Figure 8), and an overall model including three segments (S1-S3), soil, and overlying soil was established. The thickness of the gravel layer above the tunnel was 3.0 m, and the clay layer in the HZM immersed tunnel was defined as the soil layer of the model. Table 1 shows the material properties of the model, and the critical incident angle of SV wave was 34\({}^{\circ}\). Hexagonal reduced integration elements with eight nodes (C3D8R) were used to simulate the soil and immersed tunnel, and springs were used to simulate the rubber bearing between shear keys of segment joints. The conventional grid size of the soil was 2 m, and the soil grid near the immersed tunnel was locally refined with a size of 1 m. The maximum grid size of the immersed tunnel was 1 m. Meanwhile, the grid size near the joint of immersed tunnel was locally refined, with a grid size of 0.25 m. Relative slip and detachment may occur between the tunnel and soil under an earthquake; the penalty function contact algorithm [39] in ABAQUS was used to simulate the dynamic interaction, which can alleviate the difficulties in numerical calculation and improve the solving efficiency. Since the error of the pore pressure ratio between the single-phase media and two-phase media of clay under earthquake action is less than 5% [40], only single-phase media are considered in this paper.  
Figure 8: Whole FE model.  
Figure 7: Typical structural section of segment joint in immersed tunnel (unit m).