esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Macro-Fine Mechanical Behavior of Ore Flow Based on the Discrete Element Method
## 3 Construction of the Numerical Model  
### Determination of the Contact Model and Detailed Parameters  
In the two-dimensional simulation of particle flow, particles are usually simplified to disc-shaped objects, which is the basis of model iterative calculation and the precondition of mechanical equilibrium. Because the ore-rock particles in nature are mostly irregular blocks with sharp edges, this will lead to large errors in mechanical simulation calculation between ore-rock particles. The calculation time can be shortened, and the results can be achieved close to the irregular block motion, by adjusting the rolling friction coefficient between the particles and restricting the rotational ability of the particles [26; 27]. Based on this, the anti-rotation linear contact model and the linear contact model built-in PFC were selected, respectively, for the ore-rock particles and the wall in this paper.  
The anti-rotation contact model, which is based on the linear contact model, mainly considers the effective modulus, the normal and tangential stiffness ratio \(K^{*}\), and the anti-rotational friction coefficient of the particles. Specifically, the effective modulus of the ore material in this study is determined to be \(E^{*}\) at 350 MPa according to the elastic modulus, and the normal and tangential stiffness ratio \(K^{*}\) is 1.0. The collapse measurement model established by PFC was used for determining the anti-rotational friction coefficient, which is based on the same value of the numerical model's natural repose angle obtained according to the physical test. After adjusting the anti-rolling friction coefficient and different mesoscopic parameters many times, the anti-rotational friction coefficient was finally determined to be 0.7. The established collapse measurement model is shown in Figure 3, and the mesoscopic parameters are shown in Table 2.  
Figure 2: Physical similarity model.  
It should be pointed out that the ore-rock bulk density given in Table 2 is the same as the physical experimental value. The main reason for this is that the gravity density of the waste-rock block measured by the laboratory is 3050 kg/m\({}^{3}\). According to the loosening coefficient of 1.50 of the ore-rock bulk provided by the mine, the ore-rock bulk density is calculated to be 2033 kg/m\({}^{3}\) simply by the gravity density of the waste rock being divided by the loosening coefficient. Considering that there may be some errors in this treatment method, the ore-rock bulk density is simply adjusted to 2050 kg/m\({}^{3}\), as shown in Table 2, and this is just a coincidence.
# 3D User Interface Design and Usability for Immersive VR
## 3 Methodology
### Gesture-Based Interaction  
In the gesture-based UI, the user performs a certain set of hand gestures to manipulate the common desktop tasks in the immersive VR environment. The natural UI should always be designed on specific users and with use context in mind [3]. The aim is to focus on the common natural hand gestures that users know and can apply from their lives; taking advantage of their existing knowledge. Introducing completely new gestures is out of the scope of this work as it would enforce a new learning curve that might be hard for the users to learn and remember. The design took into consideration that the same hand gestures could have different meanings in different cultures [11]. Therefore, the aim was to choose the most common natural hand gestures based on the people's level of familiarity with mobile devices and touch screen input. For example, people can directly perform an in-air tap gesture, similar to the tap gesture on the touch screen. Additionally, pinch to zoom and slide to scroll are very familiar gestures. Finally, the only novel gesture is closing a window by closing the fists. It does not directly relate to a touch-based gesture, but it conforms to the task description and consequently should result in a quicker recall.  
An example of the user interaction in the gesture-based UI, showing the visualization of the user's hands as well, is found in Figure 8. Each task in the interface is assigned a different hand gesture. The proper script is activated when the user's hand is recognized performing the intended gesture. The Leap Motion device is used for hand gesture recognition. A summary of the tasks and their corresponding hand gestures interactions using the gesture-based UI is found in Table 2. The users can display and access the menu that contains the different types of objects by flipping their left hand only.  
Figure 12 shows the employed gesture recognition algorithm. After acquiring and processing the data from the Leap Motion controller, the system is able to recognize the hand gestures without the need to have predefined gestures or calibration. The gesture classification is subject independent, which means that obtaining subject specific parameters during the training phase is not needed to be used in the testing phase. In addition, given the nature of the gestures, and the data acquired from the Leap Motion, the gestures are recognized using heuristics rather than a classification method.
## 4 Features and Issues of Existing Applications  
In this section, the relevant research papers are investigated in more detail, in order to analyze the features and issues of existing human-focused DT applications for OSH within workplaces.  
The first subsection discusses what the existing applications are for, by analyzing their target industries, physical assets, application scenarios, functional objectives, etc. In contrast, the other three subsections are dedicated to important components of these existing applications. With the increasing interest in DT, a number of researchers have proposed reference models or architectures for DT applications, where important components and functionalities are specified [16; 22; 55; 56; 57; 58]. Different reference models or architectures have different contents; however, there are three commonly identified modules: data collection, digital representation, and analysis and feedback [15].  
As mentioned earlier, this paper focuses on applications that collect digital data about human workers and provide visual representations for them. Thus, devices, technologies, and tools for collecting digital data about human workers and rendering their visual representation will be investigated in this section. Data collection and digital representation modules are required to create a DT for a physical asset itself. However, this is not the ultimate goal of the DT application. A DT application should enable the users to make decisions related to workplace operations management more systematically. In this context, the analysis and feedback module is also an essential part of DT application, and a wide range of approaches, from domain knowledge to advanced techniques such as simulation and AI, can be applied to develop this module [4; 16; 49]. The features of the analysis and feedback modules of the existing applications are also investigated in this section.
## 1 Introduction
The thermal stability of the probe is thus a critical issue when the developed probe is applied for long-term displacement measurement or surface profile measurement where long-time scanning is required. In this paper, experimental verification of the thermal stability of the first prototype femtosecond laser chromatic confocal probe [24] is carried out where a considerably large thermal instability of the probe for displacement measurement is verified. The possible reasons for the thermal instability of the probe, such as the thermal instability of the refractive index of the confocal lens and the thermal expansion of mechanical jigs of the probe, are then analyzed quantitatively. Based on the analysis results, a second prototype femtosecond laser chromatic confocal probe is newly designed and constructed to improve the thermal stability for displacement measurement where the optical path length of the laser beam is minimized and a low thermal expansion material is employed for mechanical jigs in the optical setup. Experimental results for demonstrating the improved thermal stability of the second prototype probe are also presented. It should be noted that the thermal stability of the overall chromatic confocal measurement setup is contributed by not only the thermal stability of the chromatic confocal probe itself but also the thermal stabilities of the sample, the sample jigs, and the mounting plate. This paper is focused on the thermal stability of the chromatic confocal probe since the thermal stabilities of the sample, the sample jigs, and the mounting plate change when the probe is employed for different applications.
## 4 Evolutionary Algorithms in VANETs  
The main areas of research are routing protocols, broadcasting systems, and optimizing clustering parameters in VANETs. Broadcast algorithms have recently become a popular study area because they represent one of VANET's significant applications [29]. For instance, if a car wants to transmit a signal to all other cars in the network, it should ensure that this signal reaches all other cars by applying a collision avoidance approach. This characteristic becomes crucial for VANETs in broadcast algorithms. In VANET, power consumption is not a parameter of concern, as cars can fuel themselves by using a built-in self-rechargeable battery. The IEEE 802.11p-based Wireless Access for Vehicle Environment (WAVE) employs RSUs to increase the network's overall efficiency [30]. Numerous biological systems seen in nature that are products of evolution continue to provide answers to problems such as scheduling, mobility, self-organization, stability, collective efficacy, and others [31]. According to common belief, evolutionary algorithms (EA) are optimization methods that may be specifically designed to address NP-complete problems. The EAs (individuals) are generated by applying some stochastic operators to the solution. Following that, a precise fitness function is used to assess the options.  
For the next generation, most fit solutions are maintained. Generations will process iteratively until an optimal solution is identified or a maximum number of generations is reached. Depending on the EA approach chosen, different operators will be added to each iteration. Essential biological needs such as finding food, following the herd, reproducing, moving around, etc., make up the underlying idea of computational methodologies. These core requirements were shown to be strongly related to identifying computer networks and communication options. This article describes the participation of the current evolutionary computation in fixing traditional ad hoc vehicle network issues.
## 3 System Model
### Mobile Devices
#### 3.3.2 Transmission  
When a task arrives at an edge node, it can be further offloaded to another edge node if it is was not received from an edge node. This limitation is in place for the simplicity of the network. Edge nodes, however, can maintain connections to multiple other edge nodes at the same time. In our simulation, the data rate between each two connected edge nodes \(r(e_{i},e_{j})\) is calculated using Equation (5). The time required for re-transmission of a task \(t_{i}\) from its current edge \(e_{c}\) to another edge node \(e_{k}\) is  
\[d_{r,s}(e_{c},e_{k},t_{i})=l(t_{i})/r(e_{c},e_{k}) \tag{18}\]  
and the amount of time the task \(t_{i}\) must wait in the transmission queue \(t(e_{c},e_{k})\) is  
\[d_{r,s}(e_{c},e_{k},t_{i})=\alpha.d_{r,s}(e_{c},e_{k},t_{c})+\sum_{t_{i}\in t( e_{c},e_{k})}d_{r,s}(e_{c},e_{k},t_{j}) \tag{19}\]  
where \(t_{c}\) is the current task being transmitted. If no task is being transmitted, \(\alpha.d_{r,s}(e_{c},e_{k},t_{c})\) should evaluate to zero. The total transmission time for task \(t_{i}\) is  
\[d_{r,st}(e_{c},e_{k},t_{i})=d_{r,s}(e_{c},e_{k},t_{i})+d_{r,sq}(e_{c},e_{k},t_{ i}) \tag{20}\]  
Consequently, the total time to run a task \(t_{i}\) after transmission to a second edge node can be calculated as  
\[d_{r,o2}(m_{j},e_{c},e_{k},t_{i})=d_{l,st}(m_{j},t_{i})+d_{r,st}(e_{c},e_{k},t _{i})+d_{r,el}(e_{k},t_{i}) \tag{21}\]
## 2 Materials and Methods
### Atomic Absorption Spectrometry  
The calibration curves were prepared using lead (Pb), zinc (Zn), and cadmium (Cd) (Ecroskhim Co., Ltd., Moscow, Russia) with atomic absorption stock solutions (1.0 g/L) by making successive dilutions. A working calibration solution between 1 mg/L and 10 mg/L was prepared. For the preparation of the solutions, ultrapure water obtained from a Milli-Q system (Merck, Millipore) was used. All chemical reagents used were of standard analytical grade, including nitric acid (65%) and hydrogen peroxide (30%).  
For validation of the analytical procedure, reference standard solutions were used, and the results should lay within \(\pm\)1 of the certified values. The method was validated in terms of linearity and range of the calibration curves. Linearity was validated using reference standard solutions by repeating the process five times for each metal. The limit of detection (LOD) and limit of qualification (LOQ) values for each metal were calculated for each solution: 1 mg/L, 2 mg/L, 5 mg/L, and 10 mg/L concentrations for Pb, Zn, and Cd, respectively.  
Pb, Zn, and Cd concentrations were determined using a flame atomic absorption spectrometer (Analytik Jena AG, novAA 350, Jena, Germany) equipped with a hollow cathode lamp and an air-acetylene flame. The wavelengths (nm) used for the determination of the analyses were Pb 283.3 nm, Zn 232.0 nm, and Cd 228.8 nm. The gas flow was 50 dm\({}^{3}\)/h, and the aspiration rate was 5 cm\({}^{3}\)/min. Single-element hollow cathode lamps (Hamamatsu Photonics K.K., Hamamatsu, Japan) of Pb, Zn, and Cd were used as light sources.
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 5 Ablation Study  
Since we performed FME recognition using a transformer, we need to know the influence of optical flow and distinguish it from the influence of the transformer. Therefore, we will compare the performance of CNN-based [27], transformer-based [28], and CNN-like transformer-based [20] models. The CNN-based model represents locality inductive  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**SMIC**} & \multicolumn{2}{c}{**CASME II**} \\  & & **UF1** & **Acc. (\%)** & **UF1** & **Acc. (\%)** \\ \hline
2013 & LBP-TOP [1] & - & 48.78 & - & - \\
2014 & DLSTD [3] & - & 68.29 & - & 63.41 \\
2016 & CNN-LSTM [12] & - & 53.6 & - & 47.3 \\
2016 & BI-WOOF [4] & 0.6200 & 62.20 & 0.6100 & 57.89 \\
2018 & HICO [5] & - & 67.21 & - & 68.29 \\
2018 & H-STLBP-P [6] & 0.6126 & 60.78 & 0.6110 & 63.83 \\
2019 & 3 DCNN [8] & - & 55.49 & - & 99.11 \\
2019 & STRCN [9] & 0.6950 & 72.30 & 0.7470 \(\dagger\) & 80.30 \(\dagger\) \\
2020 & LFM [10] & 0.7134 & 71.34 & 0.7165 & 73.98 \\
2021 & GEME [11] & 0.6158 & 64.63 & **0.7354** & **75.2** \\
2021 & **Proposed \(\star\)** & **0.7447** & **73.17** & 0.7106 & 70.68 \\ \hline \hline \end{tabular} Note: **Bold** show the best performance in each metric. \(\dagger\) was measured using only 4 classes in CASME II. \(\star\) From result of Late Fusion in Table 6.  
\end{table}
Table 2: Comparison to other methods on SMIC and CASME II.  
Figure 2: Validation confusion matrices of our method. Each matrix is measured using LOSO. (**a**) is the result of SMIC and (**b**) is the result of CASME II.  
bias, the transformer-based model represents inductive bias of global receptive field, and the CNN-like transformer-based model represents intermediate inductive bias between them. Inductive biases, broadly speaking, encourage the learning algorithm to prioritize solutions with certain properties. The comparison with these three models will be a clue to which inductive bias is more suitable for FME recognition. Furthermore, since the FME datasets consist of high-speed videos, 100 Hz or 200 Hz, it is different from regular videos with large changes in each frame. Therefore, we empirically investigate the proper way to use optical flow.  
In ablation 5.1 and 5.2, we do not use the LF method because the LF requires too much computation. For the model detail, see Table 3. We represent the amount of computation for each model in multiply-accumulate operations (MACs), which is a common step that computes the product of two numbers and adds that product to an accumulator.
# On the Modeling of Automotive Security: A Survey of Methods and Perspectives
## 4 Discussion
This reflects one of the major differences of EVITA from the other methods, that is, EVITA starts the analysis from the attacker's point of view, while other methods are from the designer's point of view. Moreover, the qualitative and quantitative methods from traditional IT security are referenced in the automotive security models. Qualitative methods are used as systematic tools to investigate and state the security threats in a descriptive language. Three of the listed automotive security models have used STRIDE method to identify the threats. Since STRIDE is well-structured and well applied in IT security, it helps engineers to frame the threats and limit the kinds of threats. The importance of the threat models is to identify the protected goals and the security requirements for the further design in the security process, other than enumerating all the threats. Thus, the qualitative methods are preferred for the automotive security models at this stage. Quantitative modeling methods are used to derive numerical description for security properties. In the method of EVITA, the method of attack tree is used to analyze the potential attacks from the dark scenarios and to calculate the probability of the attack. This also reflects a common fact in the field of security. It is difficult to characterize security in quantitative terms due to the subjective nature of security. Experts' opinions and descriptive forms are often needed. The method of SINA used STRIDE model firstly to derive the threats then used attack trees to determine a minimal set of attacks. Generally, the probabilistic approaches are not well developed and seldom applied in automotive security. Besides the inherent difficulties of assessing the security, the automotive security focuses more on the risk level, which will determine the following actions. In addition, different security properties are considered for different targets. One of the important objectives of security is to guard the safety functions for vehicles. If the safety is a primary requirement, the above methods except TVRA considered it in the security modeling. To implement the threat modelling for automotive security, the introduction of use cases is required for most methods. For the method of SAHARA, the outcomes of safety analysis are needed as well. The other methods referred safety as an impact factor. They are all designed from the security perspective. Different from the traditional security models, the methods used for automotive security include threat or severity levels for risk determination. These numerical parameters are defined by considering the impact factors, which are typical for the automotive industry. They are different from the quantitative security methods introduced in chapter 2. However, there is still no unified criteria to standardize the risk level to guide the industry. To develop a security assurance level for automobiles is one of the industry's biggest demands currently.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 5 Discussion
### Result Analysis
This improvement is particularly notable in complex scenarios such as sudden lane changes by surrounding vehicles, where the hybrid approach demonstrates superior decision-making in obstacle avoidance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Path Length (km)** & **Planning Time (s)** & **Risk Coefficient (0-1)** & **Dynamic Obstacle Avoidance Success Rate** \\ \hline A* & 31.5 \(\pm\) 0.2 & 0.3 \(\pm\) 0.05 & 0.9 \(\pm\) 0.05 & 10 \(\pm\) 5\% \\ \hline DQN & 38.2 \(\pm\) 1.8 & 2.1 \(\pm\) 0.3 & 0.3 \(\pm\) 0.1 & 88 \(\pm\) 4\% \\ \hline A*-DQN & 32.8 \(\pm\) 0.6 & 0.8 \(\pm\) 0.1 & 0.4 \(\pm\) 0.08 & 95 \(\pm\) 3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of experimental results.
## 2 Literature Review
### Fuzzy-Based Image Enhancement  
Fuzzy logic is to handle the imprecision and uncertainty of information. Fuzzy systems consist of three subsystems: fuzzifier, inference system, and defuzzifier. They employ fuzzy set theory, which includes various rules defined in a linguistic information-set format using If-then-Else constructs. The fuzzy method can be used to handle uncertainty, imprecision, and vagueness in data and decision-making processes. The fuzzy method, which incorporates fuzzy rules and membership functions, is applied to model complex relationships between input and output variables.  
Fuzzy was a useful method to deal with image problems with various applications. Fuzzy inference uses fuzzy logic to map inputs to outputs that achieve image-quality enhancement and reduced computational complexity [36], and it involves F-transform, which is used to deal with image compression [37] and image enhancement [38]. In the studies of fuzzy logic-based applications, the focus was on image enhancement. [PERSON] et al. [39] determined the number of sub-histograms based on the peaks enables contrast enhancement without compromising the image. [PERSON] and [PERSON] [40] used the fuzzy method to enhance the contrast of the low resolution. Based on the fuzzy method, the enhanced images are obtained by equalizing using a histogram-weighting method [41], histogram equalization (HE) method [42], bi-histograms [43], linear regression [44], and neural network [45]. More advanced methods were used to enhance images by adaptive gamma transform [46], structural information for homogeneous background images [47], and image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) [48]. The fuzzy technique exhibits lower error and retains brightness better compared to the traditional methods [49]. The comparison demonstrates that fuzzy enhancement outperforms HE, adaptive histogram equalization (AHE) [50; 51; 52], further studies on features, and weight adjustment to handle low-contrast images [53; 54; 55; 56; 57]. The above studies focused on handling broad aspects of image processing and did not explore further multiple-resolution videos. Although the above methods have been used to improve images, these were not suitable, especially in practice where the composition of mobility vehicle videos is relatively complex and is affected by the trucks driving in the surrounding environment of the road or alley. Therefore, this study proposed improving the enhancement method to adjust parameters for handling moving images with multiple resolutions.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 3 Experimental Verification
### Column Concentration Inversion under Different Environments
#### 3.3.2 Inversion of Gas Column Concentration under a Dynamic Environment
The peak of the gas column concentration is 4587 ppm\(\cdot\)m. It can be inferred from the experimental results that the inversion effect decreases with a decrease in the background temperature and gas column concentration. The Adaptive InfraRed Imaging Spectro radiometer (AIRIS) from Physical Science Corporation (PSI) in the United States can achieve a detection limit of 1000 ppm\(\cdot\)m for methane gas when the temperature difference between the background and target is greater than 2 K [35]. This system is lower than this method on the detection line, mainly due to the high noise of uncooled detectors, and when the column concentration of the target gas is too small, it will be submerged in the noise. Therefore, improving the sensitivity of the detector in this method can effectively improve the detection sensitivity of the system.