esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### VOC Analysis of Apo Pickle  
During the fermentation of VBA and TBA, a total of 642 VOC were detected by GC-MS, which were divided into nine categories (The change of content ratio of each category during the fermentation process is shown in the Figure 2A), including 114 main VOC (esters (19), acids (18), phenols (11), aldehydes (18), ketones (6), alcohols (29), sulfides (5), isothiocyanates (8), and nitriles (2)). The content and standard deviation of individual substances are shown in Table S4. Isothiocyanates are the main odorants with pungent and distinctive odor in fresh _B. napu_[28]. From 0 d to 35 d, the proportion of isothi cyanates in VBA and TBA decreased from 12.51% to 2.19% and 0.85% (Figure 2A), respectively, mainly including the decrease in the proportion of 2-isothiocyanate-Butane, 2-Phenylethyl isothiocyanate, 1-Butene-4-isothiocyanate and n-Heplyl isothiamate (Table S3), which helped to make the aroma milder. Similar results could be obtained for other fermented vegetables [29]. It was shown that isothi cyanates were converted to nitriles during fermentation [1]. Sourness is a characteristic flavor of fermented vegetables. Throughout the fermentation process of VBA and TBA, the proportion of acids and phenols increased steadily (Figure 2A). Acetic acid was the main important VOC in mature Apo pickle, and its content in VBA and TBA fermentation increased from 0.27% (0d) to 18.06% (35 d) and 20.71% (35 d), respectively. In addition, trans-3-Hexenoic acid, tetradecanoic acid, octadecanoic acid, 4-ethylphenol, and maltol, which accounted for a large proportion at the conclusion of fermentation, also contributed to the unique flavor of mature Apo pickle (Table S4). Aldehydes are chemically active, and most of them are intermediate products. Thus, the content of aldehydes in the Apo pickle showed an increase and then a decrease (14.38-17.44-8.08% in VBA, 14.38-18.58-6.56% in TBA) (Figure 2A). According to PCA (Figure 2B), the two kinds of Apo pickle obviously overlap, which can confirm the types and changing trends of VOC in TBA were similar to those of VBA. In the mature stage of Apo pickle (35 d), except for some VOC which exist in both VBA and TBA and have similar proportions, such as acetic acid (20.17%, 18.06%), 4-ethylphenol (7.61%, 7.44%), 3-hexen-1-ol (3.36%, 4.14%), and ethanol (3.38%, 3.68%), which contributed sour/ pungent, pungent, bell pepper/ grass, alcohol/ floral odors, respectively, there are still many different flavor substances between VBA and TBA, which makes the overall flavor different. Figure 2C is the result of a semi-quantitative analysis of Apo pickle at the mature stage (35 d). It can be concluded that VBA has more volatile flavor substances than TBA, so it has a richer flavor.  
Figure 2: Variation of VOC during Apo pickle fermentation. (**A**) The proportion of VOC during the whole fermentation process of VBA and TBA; (**B**) The concentration of VOC in VBA and TBA on 35d; (**C**) The PCA of the VOC of VBA and TBA fermentation process. V represented VBA; T represented TBA; Numbers (0, 5,10, 15, 20, 25, 30, 35) represent fermentation time.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 2 Literature Review  
### Online Learning Supported by VR  
VR has become prevalent in online learning due to its immersive three-dimensional environments and interactive capabilities. [PERSON] [27] defined VR as a technology that allows users to maneuver and engage within a computer-generated world, underscoring attributes such as presence and interactivity [28]. When users wear HMDs and enter the virtual world, they experience a sense of presence in the virtual environment [29]. Users can also interact with objects in the virtual environment through natural means of communication such as gestures, body movements, and language [30]. These features make VR applicable to various fields, including gaming, healthcare, and real estate, as well as online learning.  
The continuous advances in VR have allowed its application to online learning to yield many benefits. First, several studies have experimentally demonstrated that VR can improve learning outcomes. [PERSON] et al. [31] compared online VR-based physics laboratories with traditional laboratories and found that students in the VR condition showed a better understanding of complex concepts, thus improving learning outcomes. Similar findings have been reported across different disciplines [32,33]. Second, it has been claimed that VR can significantly enrich the online learning experience [15,17]. For example, [PERSON] [34] conducted an experiment in which a VR platform was implemented for online music learning; students' academic achievements improved significantly as a result. However, the application of VR in online learning is not without challenges. Users may encounter physical discomfort such as eye strain and motion sickness [35].  
However, these effects are not consistent across all contexts. Differences across disciplines and educational levels may partly account for the variations in outcomes. VR has been shown to be particularly effective in natural science subjects, while learning outcomes have also been found to vary across academic domains in both K-12 and higher education settings [36,37]. Additionally, technological features also contribute to these discrepancies. Factors such as interactivity, visual fidelity, and level of immersion have been identified as critical to user engagement and knowledge retention [9,10]. Furthermore, instructional design is another key factor. The effectiveness of VR-based environments varies depending on pedagogical strategies, with inquiry-based and collaborative models often producing stronger results than didactic approaches [13].  
In sum, VR can enhance online learning by improving learning outcomes and experiences, but it also poses problems that require proper setup and application. The impact of the disciplinary domain, technological features, and instructional design on the effectiveness of VR-supported online learning also need to be considered [38].
# Investigation on Hydroplaning Behaviors of a Patterned Tire on a Steel Bridge Deck Pavement
## 2 Modeling of 3D-Patterned Tire
### Material Parameter Acquisitions of Tire
#### 2.1.1 Rubber Materials  
The hyperelastic property of rubber materials can be characterized by the Yeoh model [21; 22], as expressed in Equation (3).  
\[W=C_{10}(I_{1}-3)+C_{20}(I_{1}-3)^{2}+C_{30}(I_{1}-3)^{3} \tag{3}\]  
where \(W\) is the strain energy per unit volume; \(I_{1}\) is the strain invariant; \(C_{i0}\) (\(i=1,2,3\)) are the material parameters, which can be determined by fitting test data. In this study, the uniaxial tensile test was conducted to acquire the hyperelastic characterization parameters for different rubber materials [23], as shown in Figure 1. Through fitting the test data by the Yeoh model, the values of model parameters for different rubber materials are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Rubber Types** & **Tread** & **Cap Ply** & **Belt Ply** & **Carcass** & **Inside Liner** & **Sidewall** & **Bead Filler** \\ \hline Density (kg/m\({}^{3}\)) & 1150 & 1148 & 1205 & 1124 & 1254 & 1105 & 1216 \\ C10 & 0.7052 & 0.9950 & 1.0233 & 0.6711 & 0.3586 & 0.6582 & 1.3385 \\ C20 & \(-\)0.1802 & \(-\)0.2127 & \(-\)0.2860 & \(-\)0.1640 & \(-\)0.1037 & \(-\)0.1584 & \(-\)0.3187 \\ C30 & 0.0652 & 0.1168 & 0.1168 & 0.0564 & 0.0262 & 0.0448 & 0.1520 \\ \hline \end{tabular}
\end{table}
Table 1: Yeoh model parameters for different rubber materials.  
Figure 1: Uniaxial tensile test for different rubber materials (**a**) direct tensile testing machine; (**b**) test results of different rubber materials.
# Safety Verification of Driving Resource Occupancy Rules Based on Functional Language
## 4 CSP\({}_{\text{M}}\) Definition  
### The Syntax of CCSP  
For a system \(S\), the process variable set \(P\):=[\(X\),\(Y\),\(P\),\(Q\)... ) represents the process in the CSP, and the discrete variable set \(Y\):=[\(d\),\(t\),\(m\),\(n\),\(x\),\(y\),\(z\)... ) represents the system variable. The conditional communication sequence process CCSP can be defined as:  
\(STOP|SKIP|WAIT\)\(t|a\to Q|P;Q|P\square Q|P\cap Q|P\)\(\triangleright\)\(Q|PSQ|P\Delta Q|P\backslash A|f(X)|P/(K)Q\).  
\(STOP\): which means the interruption of a process. The process does not communicate with the outside, which can indicate deadlock or process non convergence;  
\(SKIP\): which indicates that a process does nothing except terminate;  
\(WAIT\)\(t\): which means that the process terminates after \(t\) time without doing anything;  
\(a\)\(\rightarrow\)\(Q\): prefix operation, which means that process \(Q\) is executed after event \(a\) is executed;  
\(P\),\(Q\): sequential composition, which means that the process \(Q\) is executed after the process \(P\) is executed;  
\(P\square Q\): external selection, which means that the execution process \(P\) or \(Q\) depends on the first event of the process execution;  
\(P\sqcap Q\): internal selection, which means that the execution process \(P\) or \(Q\) is determined by the process internally;  
\(P\)\(\triangleright\)\(Q\): timeout, if the two processes do not communicate within \(d\) time, it is considered timeout, and the control right is handed over from \(P\) to \(Q\);  
\(PSQ\): time interruption, regardless of whether the \(P\) process is completed after \(t\) time, interrupt process \(P\) and execute process \(Q\);  
\(P\triangle Q\): interruption, the execution of any event of \(Q\) can cause the interruption of \(P\);  
\(P\)[\(R\)]: change the mark, \(P\)[\(R\)] and process \(P\) have the same structure, but the event in \(P\) is mapped to another name through the relationship \(R\). For example, the following process  
Figure 1: CSP\({}_{\text{M}}\)-based modeling and verification framework for autonomous driving resource occupancy rules.  
\(P\) continuously executes event \(a\). Process \(Q\) is equal to all occurrences of \(a\) in process \(P\) and replaced with \(b\): \(P=a\!\rightarrow\!P\), \(Q=P[a\!\rightarrow\!b]\);  
\(P/A\): set hiding means that any event belonging to \(A\) in process \(P\) is not displayed;  
\(P\mid\mid Q\): synchronous concurrency, \(P\) and \(Q\) are concurrent in the events of the set \(C\), and the events of other sets are interleaved;  
\(P\mid\mid Q\): asynchronous concurrency, each event executed by the process is an event in the process \(P\) or \(Q\);  
\(\mu X\cdot f(X)\): \(f(X)\) is a prefix expression containing the process variable \(X\);  
\(P/(K)Q\): which is our extended condition operator, indicates that process \(P\) and process \(Q\) are not allowed to be executed under \(K\) at the same time. \(K\) is a set form, \(K\):= \(l(t_{0}\),\(k_{0}\)),(\(t_{1}\),\(k_{1}\))... (\(t_{n}\),\(k_{n}\))).
## 3 Modeling Methodology
### Decription of Numerical Models  
The software Fluent 14.5 was utilized to simulate the results in the present study. The combustion of pulverized coal mainly includes thermal decomposition, volatile gas phase combustion, char combustion, and other physical and chemical processes, which is very complex. Due to many factors involved, more reliable simulation results can be obtained by selecting numerical sub-models in Fluent. In the present study, the standard k-\(\varepsilon\) model was applied to calculate the turbulent flow in view of accuracy and stability. The interaction between particles and gases in radiation was included, and Lagrange method was adopted to track and calculate the particle movement. The kinetics/ diffusion-limited model was used to described the combustion of char, and the devolatilization ratio of pulverized coal was described by the two-competing-reactions model [29; 30]. The calculation of radiant heat transfer adopted the P-1 model, where the number of bands was selected to be zero. In other words, the radiation in furnace was approximately pure gray. The gas-phase turbulence-chemistry interaction was depicted by the finite-rate/eddy-dissipation model, and the reaction of gas phase is included the following four equations [26; 31]:  
\[\mathrm{CH_{y}O_{x}+(0.5+0.25y-0.5x)O_{2}\to CO+0.5 yH_{2}O} \tag{1}\]  
\[\mathrm{CO+0.5O_{2}\leftrightarrow CO_{2}} \tag{2}\]  
Figure 3: Evaluation of the grid independence.  
Figure 2: The mesh system of the wall fired boiler.  
\[\mathrm{CO}+\mathrm{H_{2}O}\leftrightarrow\mathrm{CO_{2}}+\mathrm{H_{2}} \tag{3}\]  
\[\mathrm{H_{2}}+0.5\mathrm{O_{2}}\leftrightarrow\mathrm{H_{2}O} \tag{4}\]  
It is generally believed that the main combustion process and the flow field are slightly affected by the NO\({}_{x}\) concentration in numerical simulations. Therefore, the calculation of NO\({}_{x}\) adopted a post-processing method based on the stable temperature field and flow field. This method greatly simplified the calculation work and could satisfy the precision requirement in practical applications [24]. According to the source and generation path of nitrogen, NO\({}_{x}\) can be divided into three types: prompt NO\({}_{x}\), fuel NO\({}_{x}\), and thermal NO\({}_{x}\). Among them, the production of prompt NO\({}_{x}\) in pulverized boilers is usually negligible [32; 33]. In the present calculation process, thermal NO\({}_{x}\) and fuel NO\({}_{x}\) were considered. The reaction of nitrogen and oxygen in the air mainly produced Thermal NO\({}_{x}\), and the formation of thermal NO\({}_{x}\) was explained by the mechanism of Zeldovich [24]. Fuel NO\({}_{x}\) is produced by nitrogen oxidation in fuels and the formation of fuel NO\({}_{x}\) originates from the oxidation of nitrogen in volatile and char. It was believed that the reaction process of nitrogen in coal was that fuel-N was first mainly converted to HCN and NH\({}_{3}\), and then NH\({}_{3}\) and HCN were reduced to N\({}_{2}\) or oxidized to NO through competitive reactions.
# Piezoelectric Electro-Mechanical Impedance (EMI) Based Structural Crack Monitoring
###### Abstract  
To detect small cracks in plate like structures, the high frequency characteristics of local dynamics were studied with the piezoelectric electro-mechanical impedance (EMI) method, and damages were monitored by the changes of the EMI. The finite element simulation model of EMI was established, and numerical analysis was conducted. The simulation results indicated that the peak frequency of the piezoelectric admittance signal is a certain order resonance frequency of the structure, and the piezoelectric impedance method could effectively detect the dynamic characteristics of the structure. The piezoelectric admittance simulation and experimental study of aluminum beams with different crack sizes were performed. Simulation and experimental results revealed that the peak admittance frequency decreases with the increase of crack size, and the higher resonance frequency is more sensitive to the small-scale damage. The proposed method has good repeatability and strong signal-to-noise ratio to monitor the occurrence and development of small-scale crack damage, and it has an important application prospect.  
c pacs: crack damage detection; piezoelectric impedance; piezoelectric admittance; peak frequency +
Footnote †: journal: Applied Physics Letters
# A Novel Coding Based Dimming Scheme with Constant Transmission Efficiency in VLC Systems
## 3 The Proposed Coding Scheme
### Dimming Decoder  
In this subsection, we will demonstrate the process of decoding the structure. To make our presentation clear, some notations are given as follows:  
1. After maximum likelihood (ML) detection, the received binary data can be regarded as the estimation of transmitted data, which is denoted as \(\hat{\mathfrak{c}}=[\hat{\mathfrak{c}}_{K},\cdots,\hat{\mathfrak{c}}_{2},\hat{ \mathfrak{c}}_{1}]\), where \(\hat{\mathfrak{c}}_{i}=[\hat{\mathfrak{c}}_{iK},\cdots,\hat{\mathfrak{c}}_{ 2},\hat{\mathfrak{c}}_{i1}]\) with \(i\in\{1,2,\cdots,K\}\). Then, the binary data sequence \(\hat{\mathfrak{c}}\) is taken as the input of the \"dimming decoding\" part.
2. We perform the bitwise OR operation on each subsection of received binary codeword \(\hat{\mathfrak{c}}_{i}\) to estimate the original input data string \(\hat{\mathfrak{b}}\).  
Consequently, we can summarize the above-mentioned encoding process as Algorithm 2 where \(\hat{\mathfrak{c}}\) denotes the operation of bitwise OR.  
```
received binary data \(\hat{\mathfrak{c}}\)
``` if\(0<\gamma\leq 0.5\) \(\hat{\mathfrak{b}}=[\hat{\mathfrak{c}}_{K}\bigoplus\cdots\bigoplus\hat{ \mathfrak{c}}_{2}\bigoplus\hat{\mathfrak{c}}_{1}]\) else\(0.5<\gamma<1\) \(\hat{\mathfrak{b}}=[\overline{\hat{\mathfrak{c}}_{K}}\bigoplus\cdots\bigoplus \overline{\hat{\mathfrak{c}}_{2}}\bigoplus\overline{\hat{\mathfrak{c}}_{1}}]\), where \(\overline{\hat{\mathfrak{c}}_{k}}=1-\hat{\mathfrak{c}}_{k}\) ```  
**Algorithm 2** Dimming Decoding  
By means of the above-mentioned encoding/decoding algorithms, we can simultaneously achieve dimming control and data transmission with a fixed transmission rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{g}_{3}\) & \(\mathbf{g}_{2}\) & \(\mathbf{g}_{1}\) & \(\gamma\) \\ \hline
100 & 010 & 001 & 1 \\
110 & 010 & 001 & 1 \\
111 & 111 & 001 & 1 \\
111 & 111 & 101 & 1 \\
111 & 111 & 111 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dimming code under different dimming levels with \(K=3\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{i}\) & \(\mathbf{b}\) & \(\mathbf{g}_{3}\) & \(\mathbf{g}_{2}\) & \(\mathbf{g}_{1}\) & \(\mathbf{c}\) \\ \hline
1 & 000 & 100 & 010 & 001 & 00000000 \\
2 & 001 & 100 & 010 & 001 & 00000001 \\
3 & 010 & 100 & 010 & 001 & 000010000 \\
4 & 100 & 100 & 010 & 001 & 10000000 \\
5 & 011 & 100 & 010 & 001 & 000010001 \\
6 & 101 & 100 & 010 & 001 & 100000001 \\
7 & 110 & 100 & 010 & 001 & 100010000 \\
8 & 111 & 100 & 010 & 001 & 100010001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The construction of generated codewords under dimming factor \(\gamma=\frac{1}{6}\) with \(K=3\).
## 6 Related Works
### Input Scheduling  
Coverage-based fuzzing: Honggfuzz [25] sequentially chooses the input to mutate from the candidate list, using the first-in, first-out strategy. LibFuzzer [24] prefers the inputs that can hit more new basic blocks, using SanitizerCoverage [33] to trace the information of basic block coverage. AFL [2] uses edge coverage information to choose the seed input, based on which seeds can find new edge coverage. Besides, it also prefers cases with a smaller size and is executed faster, to execute as many files as possible in a fixed time. AFLFast [26] observes that in the fuzzing process, most resources are wasted on repeatedly executing the same high-frequency paths, while low-frequency paths are rarely executed. To solve this problem, they used the Markov chain to discover low-frequency paths and prioritize the inputs that include those low-frequency paths. This work is based on AFL [2], using the same edge coverage hash algorithm as AFL to trace the frequency of paths. VUzzer [3] has a similar strategy, which is based on basic block coverage. This work defines the \"hard-to-reach\" paths by calculating the weights of different basic blocks in terms of executing probabilities. The error-handling basic blocks are excluded from the weight so that the inputs with these basic blocks will be considered as non-essential. If a basic block is executed too many times, then the weight of this basic block will also decrease. In summary, VUzzer chooses inputs with deeper paths to be executed and mutated by the fuzzer. Angora [5] uses this method by source code instrumentation to get the information of unexplored branches. In our work, we use a similar strategy, however on binary instrumentation, which is more challenging to achieve.  
Directed fuzzing: Normally, directed fuzzing is based on target locations that are discovered by static analysis or other information about the vulnerabilities. For input scheduling, most works choose to find inputs that are more likely to reach the target locations. AFLGo [34] selects seed inputs that are closer to the target location. QTEP [35] selects inputs that can discover more faulty code by statically analyzing the program, to increase the probability of finding new vulnerabilities. Both works rely on static analysis, which has a high false-positive problem. SAVIOR [36] is a bug-driven approach that prioritizes the concolic execution of the seeds that are likely to uncover more vulnerabilities by modeling faulty situations using SMT constraints. In our paper, we only use binary analysis to analyze the priority of different inputs. Furthermore, we focus on the coverage more than specific target locations. Therefore, our approach to input scheduling is closer to most coverage-based fuzzing works, which treat unexplored branches as their most important concerns.
## 4 Error Analysis and Processing
### Time Error Analysis and Digital Pre-Processing
Therefore, it is only necessary to find the corresponding output analog value when the time error is \(\Delta t\), and then change the corresponding input digital value, so that the output analog value of the corresponding DAC can be outputted as a sine wave after passing through the multiplexer. The value of the waveform. Figure 9: The output waveform after channel switching when the time error occurs. When DAC2, DAC3 and DAC4 output waveforms and the output time error after multiplexer switching are \(\Delta t2\), \(\Delta t3\) and \(\Delta t4\), respectively, the output waveforms of DAC2, DAC3 and DAC4 after passing through the multiplexer can be expressed as follows. The DAC2 output:  
\[ \begin{array}{rl}x_{2}(t)&=A\cdot\sin\left(2\cdot\pi\cdot\frac{1}{5\cdot T} \cdot\left(\frac{n\cdot T}{4}+\Delta t_{2}\right)\right)+b\\ &=A\cdot\sin\left(\frac{n\cdot T}{10}+\frac{2\cdot\pi\cdot\Delta t_{2}}{5\cdot T }\right)+b,\\ \frac{n\cdot T}{4}+\Delta t_{2}&\leq t<\frac{(n+1)\cdot T}{4}+\Delta t_{3};\ n=1,5,9,13,17; \end{array}  \tag{8}\]  
The DAC3 output:  
\[ \begin{array}{rl}x_{3}(t)&=A\cdot\sin\left(2\cdot\pi\cdot\frac{1}{5\cdot T} \cdot\left(\frac{n\cdot T}{4}+\Delta t_{3}\right)\right)+b\\ &=A\cdot\sin\left(\frac{n\cdot\pi}{10}+\frac{2\cdot\pi\cdot\Delta t_{3}}{5\cdot T }\right)+b,\\ \frac{n\cdot T}{4}+\Delta t_{3}&\leq t<\frac{(n+1)\cdot T}{4}+\Delta t_{4};\ n=2,6,10,14,18; \end{array}  \tag{9}\]  
The DAC4 output:  
\[ \begin{array}{rl}x_{4}(t)&=A\cdot\sin\left(2\cdot\pi\cdot\frac{1}{5\cdot T} \cdot\left(\frac{n\cdot T}{4}+\Delta t_{4}\right)\right)+b\\ &=A\cdot\sin\left(\frac{n\cdot\pi}{10}+\frac{2\cdot\pi\cdot\Delta t_{4}}{5\cdot T }\right)+b,\\ \frac{n\cdot T}{4}+\Delta t_{4}&\leq t<\frac{(n+1)\cdot T}{4};\ n=3,7,11,15,19; \end{array}  \tag{10}\]  
Therefore, the digital quantity in DAC2 needs to be updated, and the digital quantity corresponding to the analog waveform value in Formula (8) is given to DAC2, and then output by it, the output waveform can conform to the corresponding output waveform of DAC1. In the same way, the pre-stored waveform data of DAC3 and DAC4 also need to be updated accordingly. According to the previously measured time error \(\Delta t3\) and \(\Delta t4\), combined with Formulas (8) and (10), the simulation value is calculated and updated. After obtaining the analog voltage values required by the new DAC2, DAC3 and DAC4, use Formula (1) to obtain the digital quantity corresponding to each analog value, and then store it in the corresponding storage space to collect and output for the DAC.