esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 5 Experiments  
### Dataset and Metrics  
All of the training and testing data are provided by Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) [16]. The KITTI computer vision benchmark contains a large number of datasets for different tasks, such as the object detection dataset consisting of 7481 training images and 7518 test images and the tracking dataset consisting of 21 training sequences and 29 test sequences. Cars, pedestrians, and cyclists are annotated with 2D bounding boxes. We use synthetic datasets that consist of training images in both detection and tracking benchmarks. Since we study multi-vehicle tracking, annotations and images containing objects such as pedestrians are removed. Then, schemes such as image augmentation are used to enlarge the dataset during training, which improves the training results. In the following, the tracking performance is evaluated on the test sequence of the object tracking benchmark. Following the submission policy of the KITTI team, we submitted our test results to them and obtained metric scores.  
Due to the complexity of multi-object tracking, there are a large number of metrics to evaluate its performance. From all video sequences, we compute more than 20 metrics by using evaluation tool that official organization provides, such as the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [17], the CLEARMOT metrics (MOTA, MOTP, MT, ML, Frag, etc.) [18], identity metrics (IDs, IDSW, etc.), and fragmentation (Frag) [19] metrics. HOTA is able to comprehensively evaluate the performance of detection and data association in the TBD paradigm. While Frag focuses more on association performance, MOTA evaluates detector capabilities and focuses more on detection performance.
## 3 Results and Discussion
### Case Study of Numerical Simulation
Three numerical experiments were designed to test the viability of our algorithm for the detection of common tunnel lining defects, as shown in Figure 6. In the RPCA-extracted sparse matrix (the fourth column in Figure 6), it is shown that our method is highly effective in detecting common tunnel lining defects in GPR data, the shapes and positions of the lining defects is more obvious. However, the cracks of the crack model were not fully extracted, as the imaging results of cracks are not sufficiently sparse. In this case, it is necessary to combine the RPCA results with raw migration imaging data to identify the cracks in the tunnel lining. Figure 5: The model of defects with different reflectance. (**a**) the distribution of relative permittivity; (**b**) 2-D GPR profile; (**c**) back projection imaging; (**d**) RPCA anomaly extraction.
# Bulk and Rayleigh Waves Propagation in Three-Phase Soil with Flow-Independent Viscosity
###### Abstract  
The flow-independent viscosity of the soil skeleton has significant influence on the elastic wave propagation in soils. This work studied the bulk and Rayleigh waves propagation in three-phase viscoelastic soil by considering the contribution of the flow-independent viscosity from the soil skeleton. Firstly, the viscoelastic dynamic equations of three-phase unsaturated soil are developed with theoretical derivation. Secondly, the explicit characteristic equations of bulk and Rayleigh waves in three-phase viscoelastic soil are yielded theoretically by implementing Helmholtz resolution for the displacement vectors. Finally, the variations of the motion behavior for bulk and Rayleigh waves with physical parameters such as relaxation time, saturation, frequency, and intrinsic permeability are discussed by utilizing calculation examples and parametric analysis. The results reveal that the influence of soil flow-independent viscosity on the wave speed and attenuation coefficient of bulk and Rayleigh waves is significantly related to physical parameters such as saturation, intrinsic permeability, and frequency.  
bulk wave; Rayleigh wave; viscoelasticity; unsaturated soil; wave speed; attenuation coefficient Article  
Bulk and Rayleigh Waves
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 6 Discussions  
In this chapter, we discuss the feasibility in a real-world scenario of this study. Q-learning faces a problem of memory and high computation requirements if the combination of states and actions are too large. In this paper, network simulation was performed based on 10 GVNs and 1 URN. Computational operations related to Q-learning were performed entirely by URN and there was no problem in running the simulation. However, if the size of the network increases and the number of Q-learning actions increases, the size of the Q table becomes extremely large. In this case it may not be possible to apply the Q-learning algorithm because of the URN's computational power. In particular,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Traffic (r/s)** & **Traffic Type** & **Packet Delivery Ratio (\%)** & **Network Utilization (\%)** & **Latency (s)** \\ \hline U2 RV & 10 & USM & 79.2 & & 1.24 \\  & & RTS & 76.3 & 42.8 & 1.31 \\  & & COP & 78.1 & & 1.35 \\ \hline  & 20 & USM & 68.5 & & 2.11 \\  & & RTS & 67.6 & 47.7 & 2.35 \\  & & COP & 68.1 & & 2.42 \\ \hline  & 30 & USM & 64.3 & & 2.72 \\  & & RTS & 66.5 & 47.9 & 2.77 \\  & & COP & 66.8 & & 2.83 \\ \hline Q-LBR & 10 & USM & 88.9 & & 0.81 \\ (w/o QL) & & RTS & 86.5 & 64.9 & 0.92 \\  & & COP & 75.4 & & 0.98 \\ \hline  & 20 & USM & 76.3 & & 1.98 \\  & & RTS & 72.7 & 67.4 & 2.37 \\  & & COP & 64.5 & & 2.61 \\ \hline  & 30 & USM & 69.2 & & 2.57 \\  & & RTS & 65.2 & 67.3 & 2.81 \\  & & COP & 60.7 & & 2.99 \\ \hline Q-LBR & 10 & USM & 96.7 & & 0.44 \\  & & RTS & 94.2 & 60.8 & 0.56 \\  & & COP & 64.3 & & 0.99 \\ \hline  & 20 & USM & 87.4 & & 1.18 \\  & & RTS & 83.5 & 72.5 & 1.22 \\  & & COP & 62.2 & & 2.76 \\ \hline  & 30 & USM & 77.5 & & 1.39 \\  & & RTS & 74.1 & 75.9 & 1.98 \\  & & COP & 61.9 & & 2.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results for varying the traffic request rate (speed = 0 s).  
Figure 11: Performance comparison for traffic request rate: (**a**) total PDR, (**b**) total network utilization and (**c**) total latency.  
the communication hardware mounted on URN is an embedded system and there are limitations on memory and power. As a solution to this, deep reinforcement learning (DRL), which combines deep learning and reinforcement learning, is considered to be an effective alternative. For example, multistep learning- Deep Q-learning Network (DQN) [24] proposed the concept of using multilayered compensation after a one-step bootstrap when calculating the target Q value. If Q-learning is performed in advance by using the reward information after an n-step bootstrap, it is expected that the amount of computation required for learning can be greatly reduced.
## 2 Background
### The Goals of Network Traffic Analysis for SOCs  
While there are various types and sizes of SOCs, NTA is performed to achieve different purposes by security experts in SOCs whose role includes preliminary threat detection, triage of events, and incident response [43]. While in this paper we focus on malware detection and malware family classification only, we have listed three main goals of security experts in SOCs associated with network security monitoring as follows:  
* _Malware detection_: In malware (traffic) detection, NTA is used to detect network traffic containing various types of malicious content, or contributing to malicious applications. Traditionally, detection of malicious traffic is analyzed according to pre-configured rules for known attacks, but machine learning-based detection has been proposed as a complement of the signature-based network intrusion detection systems [44]. Malware detection methods typically utilize accumulated attack knowledge so that collecting and regularly updating the knowledge base is important in SOCs [2].
* _(Network) Anomaly Detection_: Network anomaly detection, or anomaly based intrusion detection is the problem to detect exceptional patterns in network traffic which can be distinguished from the expected normal network traffic pattern [45]. A broad range of anomaly detection techniques such as statistical, unsupervised, and rule-based techniques have been proposed in literature [46]. Furthermore, deep learning-based anomaly detection systems are actively discussed [47]. However, in real-world SOCs, the potential of human security experts may be more trusted than the automated methods so that some SOCs utilize or develop practical machine learning-based anomaly detection solutions combined with information visualization [48; 49], which is out of our scope.
* _Application identification_: NTAs for application identification identify the network traffic from particular applications, including unauthorized applications. This can be used for specific policy enforcement in SOCs (e.g., block Amazon traffic during work hours). Recently, especially for mobile traffic, there are several machine learning-based solutions where mobile application identification and even user actions can be identified [21; 22], which is sometimes called user behavior analytics (UBA) in the context of SOCs [50]. While malware family classification can be seen as a variant of the conventional application identification problem, to the best of our knowledge, there is no NTA method to identify fine-grained behavior of malware from encrypted traffic.
## 6 Time Frequency Transforms
### Continuous Wavelet Transform  
The continuous wavelet transform (CWT) uses inner products to measure the similarity between a signal (i.e., the radar signal in a fading channel) and an analyzing function (i.e., a specific wavelet, also called a mother wavelet). The CWT compares the signal to shifted and compressed or stretched versions of a wavelet. The stretching operation is called dilation, while the scaling operation is referred to the physical notion of scale. The result of the CWT is obtained by comparing the signal to the wavelet at various scales and positions. If the wavelet is complex-valued, the CWT is a complex-valued function of scale and position. If the signal is real-valued, the CWT is a real-valued function of scale and position.  
In this study, we used the CWT MATLAB implementation from the WAVELAB library [21], and we used the pre-defined mother wavelets in the library: Sombrero, Derivative Gaussian, Gaussian, and Morlet. Each of the mother wavelets used in this study is defined in the following equations:  
\[\psi(t)(Sombrero)=t^{2}e^{\frac{-t^{2}}{2}} \tag{3}\]  
\[\psi(t)(DerGauss)=ite^{\frac{-t^{2}}{2}} \tag{4}\]  
\[\psi(t)([PERSON])=e^{\frac{-t^{2}}{2}} \tag{5}\]  
\[\psi(t)([PERSON])=e^{\frac{-(t-t_{0})^{2}}{2}}-e^{-\frac{t^{2}+t_{0}^{2}}{2}} \tag{6}\]  
The CWT is used in this study because it was adopted in [12] for LOS/NLOS channel identification to improve indoor positioning, and it has demonstrated superior performance to other time-frequency representation in other wireless communication problems in [18].
## 2 Materials and Methods
### Quantification of Phenolic Acids and Flavonoids by HPLC  
Quantification of phenolic acids and flavonoids compounds was carried out by RP-HPLC, as reported by [PERSON] et al. [15] with few modifications. The sample was freshly prepared by redissolving the 10 mg dried extract of each sample into 1 mL methanol and filtered. The HPLC profile for the phenolic acids and flavonoids was carried out by Chromera HPLC (Perkin Elmer, 520 South Main St., Suite 2423, Akron, OH, USA), a binary solvent gradient system equipped with a C-18 column (250 \(\times\) 4.6 mm internal diameter, 5 \(\upmu\)m particle size), a UV/Vis LC detector and a non-linear gradient system containing two solvent system like solvent A (methanol: acetonitrile; 30:70) and solvent B (0.5% glacial acetic acid and distilled water). The UV/Vis spectra were recorded at 275 nm where the maximum absorption was observed. Quantification was done by standard addition method by matching the retention times and spikes of known concentrations of standards (2.5, 5, 10 ppm).
# Performance Evaluation of Classification Algorithms to Detect Bee Swarming Events Using Sound
## 4 k-NN, SVM, and U-Net CNN Algorithms Evaluation
### Experimental Preprocessing Results  
The main differences in bees' behavior during swarming events are detected in the frequency range of 400-500 Hz. In addition, there are some minor noticeable differences in the frequency ranges of 200-300 Hz. The first part of our experiments was to prove that the critical range when swarming events are detected is, according to previous studies, 400-500 Hz.  
Figure 4 shows the Fast Fourier Transformation results during swarming in the 150-600 Hz range. Experiments have shown that for such small differences, the classifiers cannot distinguish between swarming and non-swarming events, leading to wrong predictions and very low accuracy (below 70%). Therefore, the experimentation of SVM, k-NN classifiers, and training focuses only on the critical range of 300-600 Hz, where the swarming events can be easily recognized (see Figure 5). Conversely, the U-Net CNN classifier uses the entire LPF-filtered spectrum as input.  
As shown in Figure 5, the frequencies of the bees' sound during swarming events rapidly shift to 400-500 Hz, providing information to detect the event. Thus, to detect the swarming event early, we needed to examine the frequency's changes ten days before swarming. The results are shown in Figure 6 and explained below.  
As is shown in Figure 6, ten days before the swarming event, there are some noticeable differences in frequency bins between the two cases (swarming, non-swarming). However, there are many points where the two cases match. This affects the classifiers' discrimination, reducing their accuracy.  
Figure 4: Frequencies 5 days before a swarming event in the frequency band of 150–600 Hz.
## 3 Materials and Methods
### Automated Detection  
#### 3.3.1 Related Work  
Deep learning-based solutions require large amounts of labeled data, which is time- and resource-wise costly to obtain. This is aggravated in object detection, where each object needs to be localized and classified. Using synthetic data from simulators is a viable alternative; although developing a 3D model is challenging and time consuming, it is a one-time investment that can produce an unlimited amount of automatically labeled data [10,31] and the resulting model can be used for visualization.  
Notwithstanding, there is an inherent domain gap between simulated and real data, that can be bridged by several strategies [32], including image-to-image translation. An unpaired translation model converts one domain into the other, reducing distribution shift such that the detector performs better on the target data [33, 34, 35]. However, the detector never sees images from the target domain, given that they are usually unlabeled. Pseudo labeling is able to leverage the unlabeled instances by fine-tuning the detector on highly-confident predictions, progressively improving them [32,36].  
[PERSON] et al. [37] combine both paradigms to perform cross domain object detection. However, traditional pseudo labeling was not designed for object detection, so [PERSON] et al. [9] propose an unbiased mean teacher to tackle the inherent foreground/background imbalance of these tasks. After training on labeled source data, a teacher model generates pseudo labels for the unlabeled target domain, which are used as supervision for a student model, that, in turn, updates the teacher with what it has learned via Exponential Moving Average (EMA). The process is repeated so both evolve jointly and the pseudo labels given to the student are continuously being improved. Thus, the teacher can be regarded as a temporal ensemble of the student at different time steps.  
The teacher-student learning paradigm suffers from bias towards the source domain, so [PERSON] et al. [38] convert target images using a Cycle Generative Adversarial Network (CycleGAN) [7] to give as inputs to the teacher, while the student receives original target images, to perform knowledge distillation. The student is also trained with both original and target-like source images, mitigating its bias towards the source domain. Although powerful, this approach requires translation in both directions, implying having either a cyclic model or training two translation models. Therefore, we hypothesize that combining the approach from [37] with an object detection-specific pseudo labeling method such as [9] might be more suitable for our particular application.  
Despite the plethora of works on semi-supervised cross domain object detection using simulated data, this is, to the best of our knowledge, the first work to incorporate all these concepts and methods to automatic quality control in the automotive industry.
# Demand Analysis of Lubricating Oil in Spur Gear Pairs
## 6 Conclusions  
The theoretical calculation of the oil volumes for lubrication and cooling is established in this study. Lubricating oil demand during the meshing process is obtained by using different gear parameters under different working conditions. The following conclusions can be drawn from this study.  
Figure 10: Oil volume for lubrication (**a**), friction coefficient (**b**), and oil volume for cooling (**c**) at different input torques during the meshing process.  
During the meshing of gears, two equal maxima of the oil volume for lubrication at the end of first two tooth meshing and second two tooth meshing areas exist in a meshing circle. Meanwhile, the two maxima of the oil volume for cooling of the gear are located at the start of the first two tooth meshing and the second two tooth meshing areas.
2. At the beginning and end of meshing, gears are prone to failure.
3. The maximum lubricating oil demand during the meshing process should be used as the oil supply for lubrication system design.
4. Oil for lubrication increases, but oil for cooling decreases as the modulus increases.
5. As the number of teeth increases, the oil volume for lubrication increases, but the oil volume for cooling decreases.
6. The rangeability of the oil volume for lubrication along the mesh line (i.e., the slope of the curve of the oil volume for lubrication) increases as the transmission ratio increases. The oil volume for cooling decreases as the transmission ratio increases. However, the oil volume for cooling at the start of meshing and at the intersection of the single tooth meshing and the second two tooth meshing areas initially decreases and then increases as the transmission ratio increases.
7. The oil volume for lubrication increases as the input speed increases but decreases as the input torque increases. The oil volume for cooling increases as the input speed and torque increase.  
Conceptualization, F.J. and Y.L.; methodology, F.J.; Project administration, J.H.; writing--original draft preparation, F.J.; writing--review and editing, Y.F.; investigation, B.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (2018 YFB0104901) and Science and Technology Project of Qingdao(18-1-2-17-zhc).  
The authors declare no conflict of interest.
## 3 Results
### Scoping Review  
#### 3.2.1 Real-Time Monitoring of Workers  
The main challenge in construction safety is preventing accidents, with safety performance focusing on maintaining safe workplaces. This can be achieved through the safety culture of an organisation, the safety behaviour of workers, or by minimising accidents and occupational injuries on site [35]. Safety performance can be improved through both proactive and reactive approaches. Reactive technologies collect data in real-time, but require subsequent processing to extract meaningful information. Proactive technologies, on the other hand, operate in real time and provide immediate warnings and alerts to workers about hazards as they occur [36].  
Several studies have examined proactive accident prevention techniques based on real-time workplace and worker monitoring technologies to improve workplace safety [12]. These technologies include the use of wearable sensors, surveillance cameras with video analytics, GPS tracking devices, and artificial intelligence-based warning systems. These tools make it possible to identify risky behaviour, hazardous environmental conditions, and potential security breaches before serious incidents occur [12].  
In addition, integrating these technologies with ongoing training programmes and promoting a robust safety culture helps to reduce accidents. The active involvement of management and workers in identifying and mitigating risks is fundamental to the success of these approaches. In this way, the combination of proactive technologies and effective  
Figure 6: Keywords co-occurrence in fall from height research.  
safety management practices creates a safer, more resilient working environment, adapted to the dynamic demands of the construction industry [36].
## 3 Experimental Section
### Characterization  
The Fe\({}_{3}\)O\({}_{4}\) nanoparticles were observed in a transmission electron microscope (TEM, JEOL JEM-3010, Tokyo, Japan). A scanning electron microscope (SEM, XL-30, Philips Corp., Eindhoven, Holland) was used to examined the morphology of EDA-MPMs. Optical microscope (Olympus, Japan) was used to determine the diameter and diameter distribution of the MPMs. The FT-IR spectra of MPMs and the EDA-MPMs were measured using Fourier transform infrared (FT-IR) spectroscopy (IR Prestige-21, Shimadzu, Tokyo, Japan) in the range from 4000 to 500 cm\({}^{-1}\). The vibrating sample magnetometer (VSM, LakeShore 7407, Westerville, OH, USA) was applied to investigate the magnetic samples at room temperature. The X-ray diffraction (Model D/Max2500 PC Rigaku, Tokyo, Japan) patterns were taken from 15\({}^{\circ}\) to 85\({}^{\circ}\) (20 value) using Cu K\(\alpha\) radiation. The elemental analysis of MPMs and EDA-MPMs was performed with a Vario EL Cube elemental analyzer (Elementar Analysen Systeme GmbH Vario E1, Hanau, Germany)