esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[24] conducted cyclic loading tests on coal gangue concrete columns to study the ductility and energy dissipation capacity of coal gangue concrete columns under different axial compression ratios. [PERSON] et al. [25; 26] tested the bending performance of the spontaneous combustion coal gangue sand light concrete unidirectional composite slab and proved its feasibility as a floor slab. [PERSON] et al. [27] and [PERSON] et al. [28] studied the flexural performance and shear performance of coal gangue concrete beams with a variable coal gangue replacement rate. The results show that the mechanical properties of reinforced concrete beams are less affected by coal gangue concrete, and there is a certain safety factor [27; 28]. [PERSON] et al. [29] made GFRP tube-coal gangue concrete-steel tube hollow columns by replacing conventional concrete with coal gangue concrete and conducted axial compression tests with GFRP tube wall thickness, hollow ratio, steel tube wall thickness, and concrete type as variables. A low-cycle reciprocal load was applied to coal gangue concrete frame columns and frame middle joints by [PERSON] et al. [30; 31] to study the seismic performance. At present, most of the experimental studies on the seismic performance of coal gangue concrete shear walls focus on low shear walls, and few research were conducted on medium-high shear walls. Moreover, the test variables are mainly set with shear span-to-depth ratio, concrete strength and axial compression ratio and so on, without involving coal gangue replacement rate. In addition, the study of seismic performance is limited to some indicators and is not systematic. Based on these, in this paper, the coal gangue replacement rate is used as the research parameter. Through a lateral cyclic loading test of shear walls with coal gangue replacement rates of 0%, 50%, and 100%, the influence of the coal gangue replacement rate on the failure process and seismic performance of shear walls is analyzed, and the feasibility of using coal gangue concrete to replace ordinary concrete to make shear walls is verified. The theoretical basis for the structural design of coal gangue concrete shear walls is provided by the experimental data.
# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 1 Introduction
The main contributions of this paper are as follows:  
* A simulation system for streaming media services under the LEO satellite communication environment is built to simulate the initial buffering, rebuffering, playback, and idle state of the user during the service and can reflect the initial buffering duration, rebuffering duration, number of rebuffering, etc., which affect the user's QoE during the service. * QoE-driven resource allocation algorithms for access and handover in LEO satellite systems, leveraging channel capacity prediction to estimate buffer occupancy changes and allocate resources, ensuring uninterrupted streaming while preserving bandwidth for critical handovers under network congestion. * For cases where there are too many users leading to a shortage of LEO communication resources, we design the power occupation handover algorithm to allow other users to consume data in their own buffers instead of forcing the user whose playback is about to be stopped to continue to consume data in his or her own buffer.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs
### Differential Absorption LiDARs  
Differential absorption LiDAR (DIAL) is designed to detect and identify the atmospheric or surface constituents (such as ozone or water vapor) with high precision and vertical resolution throughout the troposphere and lower stratosphere [91; 92]. The operational principle of DIALs is as follows: two laser pulses with different wavelengths are emitted, where one wavelength is absorbed by the investigated substance more strongly than the other. The differential molecular absorption coefficient (\(\alpha_{mol,abs}\)) can be determined by comparing the absorption at these two wavelengths. With knowledge of \(\alpha_{mol,abs}\), it is possible to calculate the concentration of gas atoms or molecules in the investigated substance [93; 94]. DIAL systems are effective for atmospheric gases due to their ability to provide continuous high-resolution vertical profiles of gas concentration [91; 92].
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 3 Algorithm Improvement
### Path Planning of Dual-Arm Based on GA_RRT  
The disadvantage of the traditional RRT algorithm is that it is a random exploration of the whole space, and does not consider the path cost. This paper provides some knowledge of goal probability bias and path cost.  
Some principles of the goal probability bias and A* cost function are mainly introduced in this section. Due to the computational complexity of collision detection for dual-arm robots, collision detection is mainly performed using the method of the bounding sphere.
## 4 Application of AI in Food Integrity
### AI and ML in the Detection of Food Fraud
The proposed methodology, which employs the random forest algorithm with artificial outlier generation as a one-class classifier, showed improved performance in comparison to the PLS-DA and SIMCA methods. Specificity values of 0.9988 and 0.9286 were observed for evening primrose oil and ground nutmeg, respectively. [PERSON] et al. [113] employed deep learning techniques to differentiate between 10 different types of plant oils by examining the fatty acid profiles in the oils. Their ML approach consisted of supervised end-to-end learning methodologies to ascertain the oil composition of various oil mixes. The model was trained using a substantial dataset of simulated oil mixes and showed effectiveness during validation with an independent test dataset. [PERSON] et al. [114] presented a partial least square model for predicting oil adulteration with errors below 2%. The researchers further employed ANN and SVM algorithms to categorize pure and mixed oils, specifically focusing on differentiating between olive, rapeseed, peanut, and blend oils. The study determined that by utilizing their approach, a classification accuracy of 100% was attainable. [PERSON] and [PERSON] [115] utilized different ML algorithms to distinguish between pure beef and mixed beef. This distinction was made by analyzing various beef parameters, including temperature, strain, and humidity. By employing an electronic nose equipped with electrochemical and air sensors (capable of detecting gaseous emissions), data were collected and used to generalize the ML models to determine whether the beef is blended or unadulterated. The model performance, ranked from highest to lowest accuracy at room temperature, was as follows: KNN with an average ROC value of 0.886, Naive Bayes with an average ROC value of 0.856, Random Forest with an average ROC value of 0.839, and SVM with an average ROC value of 0.821. Table 2 summarizes some of the studies reviewed herein on the applications of AI and ML in the domain of food safety and integrity.
## 4 Experimental Results and Discussion
### Convergence Process  
Figures 9 and 10 describe the convergence processes of the proposed SEResNet-BiGRU model on HAPT and MobiAct v2.0, respectively. The loss rate of the proposed model (figures on the right side) decreased gradually, and the accuracy rate increased smoothly without any appearance of dilemma. These findings demonstrate that the SEResNet-BiGRU model learns correctly and without any overfitting problems.  
\begin{table}
\begin{tabular}{c c c c} \hline
**SE Module** & **BiGRU Module** & **Dataset** & **Accuracy (\%)** \\ \hline - & - & HAPT & 97.87\% \\ ✓ & - & HAPT & 97.89\% \\ - & ✓ & HAPT & 97.95\% \\ ✓ & ✓ & HAPT & 98.03\% \\ \hline - & - & MobiAct v2.0 & 98.84\% \\ ✓ & - & MobiAct v2.0 & 98.90\% \\ - & ✓ & MobiAct v2.0 & 98.89\% \\ ✓ & ✓ & MobiAct v2.0 & 98.92\% \\ \hline \end{tabular}
\end{table}
Table 8: Comparison results of ResNet-based models with or without the SE and BiGRU modules.  
\begin{table}
\begin{tabular}{c c c c} \hline
**SE Module** & **BiGRU Module** & **Dataset** & **Mean Prediction Time (ms.)** \\ \hline - & - & HAPT & 0.1304 \\ ✓ & - & HAPT & 0.3080 \\ - & ✓ & HAPT & 1.1696 \\ ✓ & ✓ & HAPT & 1.3208 \\ \hline - & - & MobiAct v2.0 & 0.1276 \\ ✓ & - & MobiAct v2.0 & 0.2857 \\ - & ✓ & MobiAct v2.0 & 1.8489 \\ ✓ & ✓ & MobiAct v2.0 & 2.0047 \\ \hline \end{tabular}
\end{table}
Table 9: Comparison of mean prediction times obtained from ResNet-based models with or without the SE and BiGRU modules.
## 4 Experiments And Analysis  
### Experimental Setup  
The simulation platform employed for the experiments was CoppeliaSim Edu version 4.2. For the implementation of the algorithm, the reinforcement learning frameworks PyTorch and OpenAI's Gym were utilized with the code developed in Python. The inverse kinematics module was provided by MATLAB. With the support of CoppeliaSim's Remote API, network communication between Python and the CoppeliaSim platform was achieved, as illustrated in Figure 5. The state information of the robotic arm was obtained by parsing the feedback data from the CoppeliaSim Edu simulation platform. Regarding the hardware, a PC (CPU [EMAIL_ADDRESS] GHz, RAM 32 GB, GPU RTX 3070 8 GB) was used to support the training of the algorithms.  
As shown in Figure 6, each environment included two cylindrical obstacles with a base diameter of 0.15 m and a height of 0.3 m. In environment 1, the two obstacles remained stationary while the state of the obstacles in environment 2 changed; namely, the obstacles moved randomly at every step. Their maximum linear velocity was set to 150 mm/s to simulate real-time environmental changes. The initial positions of the two obstacles relative to the robot base were set as [900, \(-\)400, 300] mm for Obstacle 1 and [1050, \(-\)300, 300] mm for Obstacle 2. Similarly, the target point was located at [100, \(-\)350, 300] mm relative to the robot base.In both cases, the robot arm had to bypass the obstacles and reach the target point to be considered successful. The location of the target point was randomly generated after the robot arm reached it. To ensure numerical stability and generalizability, all robot configurations during training and testing were selected to be sufficiently far  
Figure 4: Training flowchart after applying the dynamic exploration guidance mechanism.  
from singularities and joint limits. Training in the first environment was conducted for 5000 episodes.
# Genetic Algorithm-Based Model Updating in a Real-Time Digital Twin for Steel Bridge Monitoring
## 3 Real Bridge: Dynamic Identification  
### Extraction of Free Vibrations  
An accelerogram recorded by a sensor can be segmented into three distinct phases based on the following vibration characteristics: (1) ambient vibrations (low-amplitude background noise); (2) train-induced vibrations (high-amplitude pseudo-stationary signals during train transit); (3) free vibrations (decaying oscillations post-train passage).  
The effective duration of an accelerometric signal is defined as the time interval \(t_{eff}=t_{2}-t_{1}\) between two instants \(t_{1}\) and \(t_{2}\) during which the central 90% fraction of the total Arias energy develops, defined as:  
\[A_{tot}=\frac{\pi}{2g}\int_{0}^{T}a^{2}(t)dt \tag{1}\]  
where \(a(t)\) is the acceleration time history, \(g\) is the gravity acceleration and \(T\) is the total duration of the signal.  
The acceleration signal is then divided into three segments: ambient vibrations (\(t<t_{1}\)), high-amplitude pseudo-stationary vibrations from the passing train (\(t_{1}\leq t\leq t_{2}\)), and free vibrations (\(t>t_{2}\)).  
As illustrated in Figure 3 the proposed method effectively identifies the three phases. The free vibration segment \(t>t_{2}\) is particularly critical for analyzing structural damping and natural frequencies, as it represents the system's unforced response.  
Figure 3: Identification of ambient, transient, and free vibrations.  
Figure 2: Sensor placement on the steel bridge.
# Enhancement of Mechanical Properties of PCL/PLA/DMSO2 Composites for Bone Tissue Engineering
## 3 Results
### Fractography Analysis
[66] demonstrated that the FTIR spectra for PLA/PCL composites do not show the presence of any chemical bonding between PLA and PCL. In the results, no new peaks were observed for the PLA/PCL composites in the wavenumber  
Figure 5: As-molded SEM images: (**a**) pure PLA, (**b**) pure PCL, (**c**) PLA/PCL composites, (**d**) PLA/PCL/10 wt% DMSO\({}_{2}\) composites, (**e**) PLA/PCL/20 wt% DMSO\({}_{2}\) composites, and (**f**) PLA/PCL/30 wt% DMSO\({}_{2}\) composites. range of 4000-400 cm\({}^{-1}\), indicating that PLA and PCL had no chemical interactions or chemical decomposition [66; 67; 68; 69; 70]. The bands associated with hydrogen bonding were observed in FTIR spectra analysis for PLA/PCL/DMSO\({}_{2}\) composites. According to previous studies [71; 72; 73; 74], DMSO or SO\({}_{2}\) composites exhibited a -COOH or -OH hydrogen band in a range of 1320-1210 cm\({}^{-1}\) or 1366-1294 cm\({}^{-1}\), respectively, in the FTIR analysis. In this study, -COOH bands and -COH bands, corresponding to hydrogen stretching vibrations at 1320-1210 cm\({}^{-1}\) and 1127-1042 cm\({}^{-1}\), respectively, were observed for PLA/PCL/DMSO\({}_{2}\) composites [64; 71; 72; 75]. The FTIR results confirmed that the hydrogen bonding mechanisms were formed in PLA/PCL/DMSO\({}_{2}\) composites. The spectra for the PLA/PCL/DMSO\({}_{2}\) composites re  
Figure 6: Fractography SEM images: (**a**) pure PLA, (**b**) pure PCL, (**c**) PLA/PCL composites, (**d**) PLA/PCL/10 wt% DMSO\({}_{2}\) composites, (**e**) PLA/PCL/20 wt% DMSO\({}_{2}\) composites, and (**f**) PLA/PCL/30 wt% DMSO\({}_{2}\) composites (red circle: PCL particle debonded and blue circle: PCL particle pulled out). vealed the presence of hydrogen bands, and as the DMSO\({}_{2}\) content increased, the intensity of these hydrogen bands also increased. Consequently, ductility and yield strength increased, attributed to the enhanced intensity hydrogen bands with an increase in DMSO\({}_{2}\) content [76]. Additionally, SO\({}_{2}\) can be copolymerized with other polymers to obtain poly-sulfides, which exhibit excellent properties due to the enriched diversity of polymers [64]. Therefore, the polymerized SO\({}_{2}\) could enhance the interface adhesion between PLA and PCL, resulting in improved mechanical properties. However, to identify the detailed bonding mechanisms, further studies are needed through the blend melt mixing process of the composites.
# Fluid Flow-Based Vibration Energy Harvesters: A Critical Review of State-of-the-Art Technologies
## 7 Enhancement Methods to Optimize and Increase the Efficiency of FBEHs
### Nonlinear FBEHs
#### 7.1.2 Nonlinear Galloping PE-FBEHs
The findings demonstrate that, as compared to the traditional harvester with fixed bluff body, this PE-FBEH exhibits exceptional energy harvesting performance. A similar nonlinear approach of asymmetric magnetic coupling was introduced by [PERSON] et al. [167]. The piezoelectric beam experienced simultaneous bending and torsional vibration due to the eccentric distance. Experiments at various eccentric distances and wind speeds demonstrated the optimal external load resistance. The asymmetric magnetically coupled galloping energy harvester had a lower threshold wind speed of 2 m/s. The maximum output power at 6.4 m/s was about 5.5 mW at an optimum resistance of 100 k\(\Omega\) for the reported harvester. To improve the functional performance of aeroelastic energy harvesters in environments with varying wind speeds, a dual-beam piezoelectric wake-induced EH was reported in [168]. The device consisted of two piezoelectric beams connected by magnets (creating upstream and downstream EHs), with each beam attached to a cylindrical bluff body. When wind speed exceeded a crucial value, the displacement and voltage amplitudes increased significantly. The wind speed threshold value was about 1.25 m/s. When the wind speed and magnet separation were 10.2 m/s and 20 mm, respectively, the system's output power reached 0.49 mW.
# Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
## 3 System Design
### Reward  
Rewards are the measurements that can be used to evaluate the results of an agent's actions. The calculation begins when the agent has completed a one-time green light duration, which is when the agent switches from state \(\mathrm{s_{t}}\) to state \(\mathrm{s_{t+1}}\). The selection of the parameters used in calculating the reward is paramount because of the necessity of using agents to choose actions and apply them for additional learning.  
Figure 5: The directions of traffic signals phase.  
We used the waiting time to calculate rewards because the main objective of this study is to reduce traffic congestion, improve traffic control efficiency, and minimize waiting time at intersections. However, a problem was discovered in the prototype Deep Q-network with fuzzy logic [29] related to the prototype environment. In particular, the reward function calculates the total waiting time for all cars.  
Consequently, left-turning vehicles experience a long waiting time for traffic lights. Due to the reward process, it is reasonable that giving vehicles a signal to go straight can lead to better outcomes. As shown in Figure 6, groups 1, 3, 5, and 7 are left-turn lane groups, and groups 2, 4, 6, and 8 are straight lane groups. Calculating the accumulated waiting time in the straight lane groups for all cars would be higher than that in the left-turn lane groups because there were three lanes. Therefore, the accumulated waiting time in each lane was applied to solve this problem, where the highest value is selected from the lanes on each arm that use the same traffic signal. This differs from the prototype Deep Q-network with fuzzy logic [29], which calculates the accumulated waiting time for all cars. The accumulated waiting time increases when the vehicle travels at a velocity lower than 0.1 m/s.  
The waiting time calculation in each lane is shown in Equation (4).  
\[\mathrm{Twt_{t}}=\sum_{i=1}^{4}G_{2i-1,t}+\sum_{i=1}^{4}G_{2i,t} \tag{4}\]  
In Equation (4), \(\mathrm{Twt_{t}}\) is the total waiting time at time step t, \(G_{2i-1,t}\) is the total waiting time in the left-turn lane group at time step t, and \(\mathrm{max}G_{2i,t}\) is the highest total waiting time in an array of lane groups to go straight and turn right at time step t, as shown in Figure 6.  
The reward function is shown in Equation (5).  
\[\mathrm{r_{t}}=\mathrm{Twt_{t-1}-Twt_{t}} \tag{5}\]
## 5 Conclusions  
A hybrid algorithm based on the APF method and IRRT* algorithms was proposed to solve the path planning of mobile robots. By introducing the virtual force field of APF into the search tree expansion stage of the IRRT* algorithm, the guidance of the proposed algorithm was greatly improved, which greatly reduces extended nodes and further shortens the path length and search time. In addition, since the APF method is a local path planning algorithm which can construct an estimated map of the environment in real time, the proposed algorithm can be used both in known static scenarios and partially dynamic scenarios.  
The simulations were performed in different static scenarios and a dynamic scenario. For the static scenarios, the results show that the hybrid APF-IRRT* algorithm, compared with RRT*, IRRT* and P-RRT* algorithms, has the smallest values in search time and path length. For the dynamic scenario, the APF-IRRT* also finds a feasible path to avoid collision with unknown obstacles. Therefore, the proposed APF-IRRT* algorithm exhibits great potential in real path planning applications.  
As for future work, we will focus on the following aspects: (1) we are going to find a solution to obtain the optimal parameters automatically. In our experiment, the gain parameters of the APF were set heuristically, which leads to non-optimal parameters. (2) We will extend the proposed algorithm for the mobile robot path planning to a 3D environment since this work only considered a 2D environment. (3) We are considering improving the proposed algorithm in a maze-like environment since the success rate is not high comparing with that in other environments  
Conceptualization, D.W. and G.W.; methodology, G.D.; software, G.D.; validation, D.W. and L.T.; formal analysis, D.W.; investigation, D.W.; resources, D.W.; data curation, G.D.; writing--original draft preparation, D.W.; writing--review and editing, L.T. and L.W.; visualization, G.D.; supervision, G.W.; project administration, D.W.; funding acquisition, D.W. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Experiment of avoidance of a dynamic obstacle of APF-IRRT* algorithm in a real scenario.  
**Funding:** This work was supported by the Key Program of Natural Science Foundation of Anhui Provincial Education Department (KJ2020A0349, KJ2020 ZD39), the Priming Scientific Research Foundation for the Introduction Talent in Anhui Polytechnic University (No: 2018 YQQ008), the Advance Research Program for National Science Foundation in Anhui Polytechnic University (No: Xjky02201904) and the Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University (No: DTES2020A05, DTESD2020A02).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 2 Materials and Methods
### Methods of Morph Attack Detection  
Morphed images can be detected using various methods based on the available data and computation capability. Morph attack detection (MAD) methodologies can be divided into two major categories.  
* Single Image MAD Method  
Single image MAD methods only rely on the morphed image for analysis and classify the input image as bonafide or morphed. It exploits the fact that morphing an image leaves some traces and artifacts. These traces are used for the detection of morphing. Texture analysis, like binary statistical image features (BSIF) [19], can be very helpful in finding these artifacts. Moreover, deep learning is also a very useful tool for detecting such morphs.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Datasets** & **Publication** & **No. of** & **No. of** & **Images** & **Fear** & **Distance** & **Research** & **Morphing Tools** \\  & **Year** & **Images** & **Subjects** & **Subject** & & **Reference** & & \\ \hline AMSL [25] & 2018 & 6592 & 52 & variable & Gender & [26] & OpenCV, FaceMorpher, StyleGAN 2, WebMorpher & \\ \hline \multirow{2}{*}{SOTAMD [27]} & \multirow{2}{*}{2020} & 5748 & \multirow{2}{*}{150} & \multirow{2}{*}{Variable} & \multirow{2}{*}{Gender, ethnicity} & \multirow{2}{*}{[28]} & FaceMorpher, FaceFusion, FaceMorph, \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._But such methods require a reliable supply of data to train from. The quality of data determines the quality of MAD methods.
* Differential MAD Method  
The differential MAD method requires two images to process. One is the query image, and the other is the traveler's live image. Both images take part in the analysis and detection of morphing. Feature vectors of both specified images are obtained for assessment [1, 12, 13, 14, 15, 16, 17]. Demorphing is the process of identifying the accomplice. Demorphing can also be possible with the two above-mentioned images. Accomplice image can be retrieved by subtracting the query image and the live photo.  
The differential morph attack detection method is extensively utilized in different studies for morph attack detection. The following techniques are prevalent in literature.