esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### GC-MS Profiles of Different Taxigenic Strains

Table 1: Volatile metabolites of four strains extracted from the CYA culture medium. The esters generated by the four strains include 7 fatty acid methyl esters and two other esters, methyl benzoate and methyl-2-phenylacetate. The fatty acid methyl esters may derive from enzyme catalyzed reactions between alcohols and acyl-CoA [41]. Methyl-2-phenylacetate is an important flavor compound in wine, which contributes to the fruity notes of wine aroma [42] and it was first detected in _A. carbonarius_ incubated in CYA medium. Considering the hydrocarbons, 18 compounds were identified, including styrene, 17 alkanes and isoalkanes, of which the carbon backbone ranged from C11 to C18. Styrene is an 8-carbon compound and is derived from phenylalanine by the shikimic acid pathway [43; 44]. It has been found in some species of _Penicillin_ and could be a potential indicator of food spoilage, capable of producing off-flavors [45]. Alkanes and isoalkanes were found in _A. carbonarius_ and their diversity was mainly determined elsewhere by the different carbon source used in the culture medium [46]. There were 12 terpenids found in four strains, including limonene, _p_-cymene and 10 sesquiterpenes. Limonene is a commonly identified metabolite generated by _P. glabrum, P. roqueforti, A. flavus,_ and _A. ochraceus_[5; 47] and it was found in all the strains. Sesquiterpenes are regarded as representative compounds showing different characters with different toxicien strains, such as _Fusarium sambucinum_[13], _A. flavus_[7] and _P. roqueforti_[12]. In our study, 10 sesquiterpenes were detected and in particular, _p_-cedrene, _p_-chamigrene, _p_-himachalene and cuparene were only detected in AF strains. By contrast, _p_-farmesene was absent in AF strains. Of the other compounds, 3-furanacetic acid, 4-hexyl-2,5-dihydro-2,5-dioxo- was found in all strains and it was first detected in _A. carbonarius_ in our previous work [37]. The content of this compound reached a maximum at the 2 nd day, and sharply declined from the 3 rd to the 10 th day. This compound may not be regarded as a specific compound for different toxicien strains because it showed the same trend in both MT and HT strains. In summary, the volatile profile of these two groups were similar except the AF strain, which has a unique sesquiterpenes pattern. The differences between them were confusing and the procedure of qualitative analysis and quantitative analysis is complicated and time-consuming. Therefore, further analysis is necessary to explore the useful information which can be used to distinguish them reliably.
# Forecasting the Impacts of Prescribed Fires
## 2 Methodology
### PB Emission Estimation  
Once the location and the size of the burns are forecast, the next step in PB impact prediction is the estimation of emissions from the burns. Emission estimation starts with estimation of the fuel load and fuel consumption. A detailed description of the procedure used for emission estimation can be found in [PERSON] et al. (2010). We currently use the Fuel Characteristic Classification System (FCCS) fuel-bed maps to determine the fuel loads in burn locations assigned to each county. In the future, more up-to-date satellite products can be used to estimate the fuel loads in each grid cell where the burns are located. As for the determination of the fuel moistures required for the calculation of fuel consumption, we use the previous day's fuel moistures reported by the statewide Fire Weather Network. In the future, these fuel moistures can be modified according to the weather forecast for the burn day. Emissions are calculated by multiplying the forecast burn area with the fuel loads per unit area, the combustion efficiency and the emission factors (i.e., mass of PM\({}_{2.5}\) and other pollutants emitted per unit mass of fuel burned) for Southeastern fuels published by the US Forest Service (2016).  
Since most prescribed fires in the Southeastern US are low intensity fires (Southeastern, 2016), we assume that the plumes do not penetrate the free troposphere. The resulting emissions are currently mixed into the planetary boundary layer (PBL) portion of a vertical column of 4 km \(\times\) 4 km grid cells over the burn location. This procedure was designed after conducting a sensitivity study to understand the influence of the vertical distribution of emissions ([PERSON], 2016). Figure 10 in [PERSON] et al. (2016) shows that modeled PM\({}_{2.5}\) concentrations at a receptor are not too sensitive to the vertical distribution of fire emissions below the PBL height.
## 2 Results  
### Characterization of _CpMYC2_ and _bHLH13_  
Using the Illumina RNA-Seq technique, a transcriptomic library (NCBI Sequence Read Archive (SRA) accession number PRJNA492170) was constructed from the full open flower stage of the Wintersweet H29 cultivar (Figure 1), and out of 16 identified members of the _bHLH_ family, the desired sequence of two transcription factors _CpMYC2_ (2858 bp) and _bHLH13_ (2425 bp) was selected for further study [45]. Based on the NCBI ORF finder, the open reading frame of 1884 bp and 1779 bp nucleotide, encoding 628 and 593 amino acids, were observed in _CpMYC2_ and _CpbHLH13_, respectively. The theoretical isoelectric point (pI) and the molecular weight (Mw) of the _CpMYC2_ and _CpbHLH13_ proteins were Pi/Mw: 5.65/68707.29 and Pi/Mw: 6.43/65502.53, respectively. Multiple sequence alignment of wintersweet _CpMYC2_ and _CpbHLH13_ proteins with their homologous protein sequences in other species, especially _A. thaliana_, _Nicotiana tabacum_, _V. vinifera_, _Cinnamomum micranthum_, _Solanum lycopersicum_, and _Zea mays_ revealed the presence of a basic helix-loop-helix DNA-binding domain (446-493 amino acids for _CpMYC2_ and 429-476 amino acids for _CpbHLH13_) with an MYC-N terminal, which is a common feature of MYC family proteins (Figures 2 and 3). The three-dimensional protein structure of _CpMYC2_ and _CpbHLH13_ is shown in Supplementary Figures S1 and S2. Phylogenetic analysis demonstrated that _CpMYC2_ was closely related to the transcription factor Arabidopsis MYC2 (_AT1G32640_), whereas _CpbHLH13_ was most homologous to _AtbHLH13_ (_AT1G01260_), also known as JASMONATE-ASSOCIATED MYC2-like2 (JAM2) (Supplementary Figures S3 and S4). Further phylogenetic analysis with different species revealed that _CpMYC2_ had a close relationship with the predicted protein of _Cinnamom micranthum_ (_CmMYC2_) with 65.13% identity and _A. thaliana_ (_AtMYC2_) with 54%, while _CpbHLH13_ was mostly homologous to _C. micranthum_ (_CmbHLH13_) with 69.77% identity followed by _Nelumbo uncifera_ (_NntMYC2_) with 62.46% identity and _A. thaliana_ _AtbHLH13_ with 42% identity, although they were not in the same clade. Phylogenetic analyses revealed that _CpbHLH13_ had a close relationship with the _A. thaliana_ _AtbHLH13_/JAM2 (At1g01260) and _AtbHLH17_/AM1 (At2g46510) transcription factors (See Supplementary Figures S5 and S6). A summary of the homology of wintersweet _CpMYC2_ and _CpbHLH13_ with identity% and functions of the genes from various plants is described in Tables 1 and 2. Figure 1: Developing stages of the flower of the Wintersweet (_Climonanthus praccox_) H29 (Huazhong 29) genotype. Abbreviations: FB, flower bud; DP, display petal; POF, partially-open flower; OF, open flower stage; SF, senescing flower. Scale bar = 1 cm.
## 3 Computational Methods  
The supersonic jet was modeled using a 2D axisymmetric computational fluid dynamics solver. The modeled fluid domain begins in the small cylindrical region immediately upstream of the showerhead nozzle and ends downstream of the plenum past the filter, as shown in Figure 5, with component dimensions, as shown in Figure 2.  
The two filters were modeled using porous baffle interfaces. The porous viscous resistance values used in the solver, whose unit is m/s, were derived by dividing Equation (1) by \(\rho\) and Equation (2) by \(\rho\
u\).  
A steady-state solver was used, as supported by the quasi-steady nature of the jet. Evidence of this quasi-steady nature is abundantly available in the experimental results that follow; see, for example, the interval between 0.12 s and 0.24 s.  
Simulations were performed using the commercially available code STAR-CCM+ version 2020.1. A coupled implicit solver was employed, utilizing the realizable \(k-\varepsilon\) two-layer RANS turbulence closure [15]. The ideal gas equation of state and a transport equation for energy were also used.  
The transport equation for turbulent kinetic energy (\(k\)) is given by:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{\partial x_{ i}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}} \bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}+G_{b}-\rho\varepsilon -Y_{M}+S_{k}.\]  
The transport equation for the turbulent dissipation rate (\(\varepsilon\)) is:  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial(\rho\varepsilon u _{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+ \frac{\mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{ \partial x_{j}}\Bigg{]}+\rho C_{1}S\varepsilon-\rho C_{2}\frac{\varepsilon^{2 }}{k+\sqrt{\
u\varepsilon}}+C_{1}\varepsilon\,\frac{\varepsilon}{k}\,G_{b}+S _{\varepsilon}.\]  
The turbulent viscosity (\(\mu_{t}\)) is calculated using:  
\[\mu_{t}=\rho\,C_{\mu}(k,\varepsilon,S)\,\frac{k^{2}}{\varepsilon},\quad\text{ where}\quad C_{\mu}=\frac{1}{A_{0}+A_{s}U^{*}k/\varepsilon}.\]  
For detailed definitions of the terms and coefficients, please refer to [PERSON] et al. [15]. A 2D polyhedral grid mostly made of hexagons is used for spatial discretization, with a thin near-wall prism layer sufficient to ensure \(y^{+}<1\) at the wall. Mach number values for three different grid sizes are shown in Figure 6, which highlights how finer shockwaves appear with a smaller mesh base size. A grid base size of 0.5 mm was adopted in the present work.  
Figure 5: CFD simulation domain visually compressed in the axial direction.
## 4 Discussion
### Vehicle Mobility  
The mobility of vehicles is a major problem in the cooperative perception between edge nodes and autonomous vehicles. The highspeed movement of vehicles will lead to the fast movement of communication nodes, frequent changes in network topology, and unstable communication link times, resulting in the frequent connection and disconnection of communication links. Eventually, the link established between V2I and V2V fails, causing the transmission of communication and computing tasks to fail. Secondly, the rapid movement of vehicles will cause rapid changes in the surrounding environment, resulting in varying degrees of interference in the communication between edge nodes and autonomous vehicles. This leads to changes in data transmission efficiency and transmission quality, the degradation of communication quality, and difficulty in ensuring data integrity. In addition, the mission will fail if the vehicle is out of communication range while data uploading or message reception is in progress. At present, the communication quality problem caused by vehicle mobility cannot be effectively avoided. However, the interference of vehicle mobility on communication quality should be minimized to ensure the effectiveness and timeliness of cooperative perception. [PERSON] et al. [114] considered the frequent changes in the network topology caused by the mobility of vehicles and proposed using regional base stations to uniformly coordinate and manage vehicles within the communication range. When the vehicle with the computing task leaves the communication range, the task scheduling is performed again, which effectively improves the minimum service delay level and service quality. The disadvantage is that additional switching time overhead is required. [PERSON] et al. [115] utilized a vehicle's MEC network architecture to seamlessly switch computing tasks through the MEC network routing instead of simply offloading them to edge servers when the vehicle's communication range changed. This ensures the effective execution of computing tasks and effectively improves the scalability of computing and services. As in [114], it will also increase the time overhead. By introducing a penalty term for the failed tasks in the optimization objective, the task failures caused by vehicle movement can be effectively reduced, but the failures cannot be completely avoided [116; 117]. Based on the above research and analysis, it is concluded that the problems caused by vehicle mobility, such as mission failure, poor communication links, and unstable transmission quality, should still be the focus of future research.
## 3 Results and Discussion
### Viscosity Measurement  
Viscosity measurement is considered to confirm the difference in texture. The change in the viscosity of each sample solution with temperature was measured (Figure 1X), and the apparent activation energy (\(\Delta\)E) was calculated using the Andrade equation. The viscosities of Samples A, B, and C at 10 \({}^{\circ}\)C were 2.81 \(\pm\) 0.03 mPa-s, 2.74 \(\pm\) 0.06 mPa-s, and 2.69 \(\pm\) 0.03 mPa-s, respectively, and those of 15% ethanol and distilled water were 2.34 \(\pm\) 0 mPa-s and 1.36 \(\pm\) 0.03 mPa-s, respectively. The higher viscosity values of the wine samples were due to hydrogen bonding between alcohol and water. The value for Sample A was higher than that for Sample C, probably because of stronger intermolecular interactions in the solution caused by the included components. Thus, the Andrade equation, which expresses the relationship between viscosity and temperature, was used to evaluate the quality of the biofuels. The values of \(\Delta\)E for the wine samples obtained from the Andrade equation are useful for quantifying the internal structural changes in solution and evaluating Japanese sake (rice wine) [36]. To calculate \(\Delta\)E from the Andrade equation, a graph of the logarithm of viscosity as a function of the reciprocal of absolute temperature was plotted (Figure 1Y). The \(\Delta\)E values for Samples A, B, and C, calculated from the Andrade equation, were 22.3 \(\pm\) 1.1 kJ/mol, 22.0 \(\pm\) 0.2 kJ/mol, and 21.3 \(\pm\) 0.5 kJ/mol, respectively. The slope of the straight line corresponding to Sample C is more gradual than that for Samples A and B. Sample C showed a lower \(\Delta\)E value than A and B even when the temperature changed from 10 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. This suggests that wine quality is less likely to change with temperature. Moreover, the differences in the internal structural changes in the rice wine solutions were evident.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Brix (\%)** & **pH** & **Surface Tension (mN/m)** \\ \hline A & 10.3 \(\pm\) 0.1 **.**\# & 4.05 \(\pm\) 0.02 **,**\# & 43.20 \(\pm\) 0.33 \# \\ B & 10.0 \(\pm\) 0.0 \(\mathrm{\SIUnitSymbolMicro}\) & 4.24 \(\pm\) 0.03 \(\mathrm{\SIUnitSymbolMicro}\) & 44.08 \(\pm\) 0.23 \\ C & 9.8 \(\pm\) 0.0 & 4.32 \(\pm\) 0.03 & 43.90 \(\pm\) 0.33 \\ \hline \hline \end{tabular}  
* \(p<\) 0.01: A vs. B; \(p<\) 0.05, **\(p<\) 0.01: A vs. C; \(\mathrm{\SIUnitSymbolMicro}\)\(p<\) 0.05: B vs. C.  
\end{table}
Table 4: Brix, pH, and surface tension of Samples A, B, and C.
## 1 Introduction
[14] used ultrasound to measure growing-finishing pigs' backfat thickness and established a machine learning model of growing-finishing pigs' fat mass and backfat thickness. However, the use of the ultrasound method not only causes a stress response in sows but is also limited by the level and equipment of professionals. [PERSON] et al. [15] used image processing technology instead of manual methods to achieve accurate measurements of pork backfat thickness, which improved the efficiency but is limited to only carcasses. [PERSON] et al. explored the correlation betweentop-view 3D images and the backfat thickness of fattening pigs using deep learning and a manual means of extracting image features of fattening pigs, respectively. The experimental results showed that the R\({}^{2}\) of the predicted and measured backfat thickness obtained by the deep learning model and the mean absolute scaled error (MASE) were 0.45 and 13.56 mm. The best backfat thickness detection model was obtained by deep learning [16]. Thus, the deep learning algorithm can be utilized to construct a contactless detection model of pig backfat thickness. The essence of deep learning is to train neural networks. In the field of intelligent pig breeding research, generally, only end-to-end research of neural networks is conducted, focusing only on the detection accuracy of the network [17]. This method lacks the understanding of its internal working mechanism, resulting in the inability to determine the image region that affects the decision making of the neural network and to understand the phenomenon of the high detection accuracy of the neural network, thus failing to improve the credibility of the application of neural network models. By visualizing image features extracted by the neural network, the research object and non-research object image regions affecting the neural network decision making can be studied. The former can study the feature map corresponding to image regions affecting the network decision making, providing ideas for understanding the neural network detection performance. The latter can guide the separation of which images are non-research object regions, obtain new image data, and study possible methods of data processing for improving the accuracy of the model test. The residual network is a kind of deep learning. It is the pioneering image classification algorithm, and it solves the degradation and training problem of very deep networks, improves the neural network feature extraction ability [18; 19], and is widely used as the feature extraction network part of computer vision tasks, which are widely used in the research field of livestock and poultry breeding [20; 21; 22]. Therefore, to address the problem that real-time and low-cost contactless detection of sow backfat thickness cannot be realized, and to explain the backfat thickness detection model, this paper explores the relationship between sow back images and backfat thickness based on residual networks to construct a contactless detection model for sow backfat thickness.
### Bandwidth of 5G NR Signals  
Table 1 summarizes several 5G NR (5G New Radio) bands allocated (in the U.S.) by the Federal Communications Communications Commissions, and the bandwidths of channels in each band defined by the 5G NR standard. Also shown are the power penetration depths and variation in L across the widest channel in each band. The instantaneous bandwidth of the channels (which is at most the channel width) is always a small fraction of the carrier frequency, which constrains the rise time of any pulsed signals to nonzero values. At the frequencies used by 5G NR, the power penetration depth at the center of a band L is small, and its variation \(\Delta\)L across each channel/band is small relative to L. The narrow bandwidth of 5G NR limits, which are due to several constraints and must be rigorously maintained to allow for the system to work properly, prevents the formation of Brillouin precursors from 5G NR devices.
# A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators
## 5 Results and Discussion  
### Validation Using the Experimental Setup  
Figure 7 shows the bit error rate versus the optical signal to noise ratio (OSNR) for both the offline MATLAB obtained in [15] and real-time FPGA implementation of KNN. The dotted lines are the FPGA results, and the continuous lines are Matlab results. The colors of the lines represent different channel spacing. Matlab implementation used a 64-bits double-precision format for the data. Instead, the FPGA implementation used a  
Figure 6: Experimental setup of the Gridless Nyquist-WDM system affected by Inter-Channel Interference (ICI).  
32-bit single-precision floating-point format; nonetheless, the difference is negligible below FEC limit.  
Table 1 shows the characteristics of the experimental setup data. The sizes of the training data set for each label are different and total of 3240. The column Cluster-Blocks presents the clusters for each of the labels. In this way, it is possible to determine the number of operations that should be carried out with the training data, such as calculating distances and orders. The reduction in operations is significant for all sizes of Cluster-Block. The reduction percentage for 4-size clusters was 75%; for 6-size, it was 62.4% and 43.4% for 9-size Cluster-Block.
# Deep Directional Network for Object Tracking
## 5 Experiment  
### Implementation Details  
In this paper, VOT2013, VOT2014 and VOT2015 video sequence datasets are used for pretraining. The initial weight of the convolutional layer is from VGG-M [26]. The test standard used in the experiment is the object tracking benchmark (OTB) [4]. Our algorithm is implemented on the MATLAB platform on Windows 10. The network construction uses the MatConvNet toolbox [28]. The hardware platform uses Intel i7 8700K CPU, 8 GB of RAM and NVIDIA GTX 750 TI GPU. In the pretraining phase, all network weights of the convolutional layer and the fully connected layer are updated, but in the tracking phase, the convolutional layer is only used for feature extraction, and the fully connected layer's weights are fine-tuned online. Based on a large number of experiments, setting \(\alpha\) to 0.06, \(\gamma\) to 0.9, \(\lambda\) to 1.1, the setting of \(\theta\) to 0.9, and \(\sigma\) to 0.6 was determined to maximize the robustness of the tracking effect of the entire network.  
To better verify the performance of the proposed algorithm, 58 challenging video sequences were selected for experimentation. These video sequences include illumination variation (IV), scale variation (SV), object occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-plane rotation (OPR), out-of-view (OV), background clutter (BC), low resolution (LR) and other challenges. In the experiment, 7 excellent recent algorithms were selected for comparison: CNT [29], HCF [30], SiamFC [14], SAMF [31], DeepSRDCF [32], CREST [33] and Staple [34]. The following comparison will help us perform a qualitative analysis of the proposed algorithm.