esa-sceva/satcom-chunk-collection huggingface dataset:
# QiOi: Performance Isolation for Hyperledger Fabric
## 5 Evaluation
### Support for Different Chaincodes  
Now, because Fabric performance and resource usage are different depending on the chaincodes of Fabric, we investigate the effectiveness of QiQi when Fabric runs different chaincodes. For experiments, we run simple and marbles chaincodes [32]. Simple chaincode executes simple queries such as checking and storing values. Marbles chaincode consists of a series of transactions transferring marbles between multiple marble owners and stores data as various data structures such as JSON format. We measured the performance and CPU utilization of Fabric and sysbench containers, respectively, when the Fabric components ran with 16 sysbench containers.  
Figure 10 illustrates the impact of QiQi when simple chaincode is executed in Fabric. Figure 10 shows Fabric throughput (left \(y\)-axis as bars) and latency (right \(y\)-axis as a solid line) compared between _w/o QiQi_ and _w/QiQi_. When QiQi runs with Fabric, Fabric throughput increases by 18% and latency decreases by 1.8 times. In addition, Figure 10 shows that the CPU utilization of Fabric (left y-axis as bars) increases by 12% and throughput of sysbench containers (right \(y\)-axis as a solid line) only decreases by 2%. This is because QiQi minimizes scheduling delay by increasing the _CPU share_ of orderers and peers. As a result, QiQi lets the CPU utilization of Fabric increase and then improves Fabric performance. Note that CPU utilization of sysbench containers (left \(y\)-axis as bars) decreases by 9%.  
Figure 10: Impact of QiQi on Fabric using simple chaincode.  
Figure 11 shows the result of marbles chaincode. Fabric throughput increases by 17%, and latency decreases by 2 times. Figure 11 illustrates that performance degradation of sysbench containers is negligible in marbles chaincode as well (decreasing by 1%) and CPU utilization of Fabric and sysbench containers increases by 13% and 14%, respectively.  
The results show that there are some differences in the performance improvement rate between Fabric chaincode (i.e., smallbank, simple, and marbles). In terms of Fabric throughput, the improvement rate is high in the order of smallbank (see Section 5.1), simple, and marbles (increasing by 22%, 18%, and 17%, respectively). In terms of Fabric latency, the improvement rate is high in the order of smallbank, marbles, and simple (decreasing by 2.5 times, 2 times, and 1.8 times, respectively). Each chaincode has different program code and is simulated on the peers. Fabric performance depends on the complexity of the chaincode, the time required for simulation, and the micro-architecture metrics required [40]. Regardless of chaincodes, QiOi focuses on minimizing the scheduling delay of components. For this reason, there is a difference in the performance improvement rate for each chaincode. Smallbank has the highest improvement rate in both throughput and latency. Simple and marbles have a similar improvement rate.  
In summary, this experiment shows that the QiOi can mitigate performance degradation and improve latency in various chaincodes of Fabric.
## 3 Results and Discussion
### Oral Processing Characteristics
Before swallowing, correlations are very weak to weak. Particle size fragmentation that happens during mastication and/or before swallowing is in relation with water content and mechanical properties of food [37]. Therefore, it is not surprising that hot sauce did not significantly affect particle size distribution (Figure 2). However, based on the Figure 2a-c, it is obvious that: (i) the frequency of large particles decreases over time (for all three pre-defined mastication points) and (ii) the frequency of small particles increases with the mastication process. All boluses had between 31.68% and 40.36% of the area occupied by big particles (>100 mm\({}^{2}\)) regardless of the type of sample and number of strokes. Further analysis showed that small particles (1-10 mm\({}^{2}\)) occupied between 10.5% and 19.6% of the area, while medium-sized particles (10-100 m\({}^{2}\)) occupied between 46.6% and 52.8% of the area. Concerning particle size fragmentation, there was a statistically significant association observed between samples after 25 strokes (\(\chi^{2}\) = 82.059; _p_! 0.05) and before swallowing (\(\chi^{2}\) = 119.094, _p_! 0.05). After 10 strokes, the level of statistical significance was not observed (\(\chi^{2}\) = 39.579, _p_! 0.05). This may be caused by the fact that pungency sensation does not start immediately after the first bite, so particle size was more affected by the texture of meat than by the pungency sensation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Number of Chews**}} & **Consumption** & **Number of** & **Chewing Rate** & **Eating Rate** \\  & **Time** & **Swallows** & **Chewing Rate** & **Eating Rate** \\ \hline
10 strokes & \(-\)0.197 & \(-\)0.154 & \(-\)0.216 * & 0.190 & 0.195 \\
25 strokes & \(-\)0.076 & \(-\)0.045 & \(-\)0.072 & \(-\)0.376 ** & 0.005 \\ Before swallowing & 0.063 & 0.121 & 0.224 * & \(-\)0.092 & \(-\)0.076 \\ \hline \hline \end{tabular} Legend: * Correlation is significant at the 0.05 level. ** Correlation is significant at the 0.01 level (2-tailed).  
\end{table} 
Table 3: Spearman’s Rho correlation coefficient between oral processing parameters and saliva incorporation. Figure 2: Particle size distribution (frequency vs. area): (**a**) after 10 strokes; (**b**) after 25 strokes; (**c**) before swallowing.
## 2 Experimental Works  
### Specim Preparation  
The studied rock was obtained from a mine located near Antofagasta, which is the capital and largest city of the region of Antofagasta in northern Chile. The marine calcareous rock is dated in the lower cretaceous era (between the Hauterivian and Barremian ages) and is a fine limestone with a variable content of CaCO\({}_{3}\) between 78% and 90%.  
Beginning from some rock samples obtained in the mine, 13 cylindrical specimens (30.9 mm in diameter and a length-to-diameter ratio of 2.1 [4]) were obtained in order to perform UCS tests. Geometry requirements of the specimens [5] were achieved by means of careful drilling and a grinding machine. All the specimens showed no visible discontinuities and were tested in order to obtain density, porosity, ultrasonic velocity, and uniaxial compressive strength, following the current standards [5,6]. The results of these tests are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{2}{c}{**Density**} & \multicolumn{2}{c}{**P-Wave Velocity**} & \multicolumn{2}{c}{**S-Wave Velocity**} & \multicolumn{2}{c}{**Uniaxial Compressive**} \\
**Specimen** & **Diameter** & **Height** & **Porosity** & \multicolumn{2}{c}{\(\rho\) (g-cm\({}^{-3}\))} & \multicolumn{2}{c}{\(\
u_{\rho}\) (km-s\({}^{-1}\))} & \multicolumn{2}{c}{\(\
u_{\rm s}\) (km-s\({}^{-1}\))} & \multicolumn{2}{c}{**Strength**} \\  & **D (mm)** & **H (mm)** & **n (\%)** & \multicolumn{2}{c}{**n (\%)**} & \multicolumn{2}{c}{**n (\%)**} & \multicolumn{2}{c}{**n (\%)**} \\ \hline  & & & & **Dry** & **Sat.** & **Dry** & **Sat.** & **Dry** & **Sat.** \\ \hline
1 & 30.830 & 60.548 & 2.46 & 2.64 & 2.66 & 5.351 & 5.996 & 2.992 & 2.976 & 76.30 \\
2 & 30.853 & 60.335 & 3.15 & 2.59 & 2.62 & 5.263 & 5.505 & 2.844 & 2.871 & 41.37 \\
3 & 30.968 & 60.135 & 2.26 & 2.63 & 2.63 & 5.505 & 5.825 & 3.176 & 3.061 & 77.59 \\
4 & 30.860 & 60.588 & 1.95 & 2.64 & 2.64 & 5.996 & 5.865 & 3.211 & 3.211 & 106.19 \\
5 & 30.923 & 60.345 & 2.55 & 2.61 & 2.61 & 5.962 & 5.825 & 3.282 & 3.077 & 77.10 \\
6 & 30.858 & 60.6718 & 2.46 & 2.63 & 2.65 & 5.596 & 5.865 & 3.297 & 3.128 & 91.43 \\
7 & 30.933 & 60.395 & 2.43 & 2.62 & 2.62 & 5.505 & 5.505 & 3.226 & 3.000 & 55.93 \\
8 & 30.193 & 60.650 & 2.29 & 2.64 & 2.64 & 5.263 & 5.825 & 2.970 & 3.000 & 73.14 \\
9 & 30.843 & 95.855 & 3.27 & 2.60 & 2.60 & 5.310 & 5.556 & 2.804 & 3.158 & 49.24 \\
10 & 30.853 & 60.518 & 2.95 & 2.61 & 2.61 & 5.446 & 5.505 & 3.177 & 3.177 & 42.15 \\
11 & 30.975 & 60.155 & 2.26 & 2.66 & 2.68 & 5.556 & 5.714 & 2.956 & 2.927 & 61.24 \\
12 & 30.808 & 60.223 & 2.32 & 2.63 & 2.63 & 5.596 & 5.922 & 3.081 & 3.050 & 94.32 \\
13 & 30.915 & 60.458 & 2.77 & 2.62 & 2.62 & 5.310 & 5.310 & 2.800 & 2.804 & 50.80 \\ \hline Mean & 30.832 & 60.379 & 2.55 & 2.62 & 2.63 & 5.481 & 5.678 & 3.063 & 3.029 & 68.99 \\ SD & 0.199 & 0.241 & 0.38 & 0.02 & 0.02 & 0.194 & 0.192 & 0.179 & 0.116 & 20.77 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geometry of specimens, \(\
u_{\rm p}\), \(\
u_{\rm s}\), \(\rho\), and n results.
# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
## 5 Results and Discussion of MTM-CW
### Statistical Analysis of MTM-CW  
To statistically evaluate the results obtained by the proposed MTM-CW, the Friedman test was performed [56]. Figure 9 illustrates the post hoc Friedman test where \(p\) values show the significance level. We found that the difference between results produced by all the models was statistically significant. The results of the proposed model (MTM-CW) were found statistically significant. The MTM-CW was also statistically significant against the MTM-CNN and its variants except for the MTM-CNN\({}^{\star}\)\({}^{in}\) (GENIA-POS and CoNLL used at the innermost layer).  
The statistical analysis was also extended with the pairwise comparison of different models to see which model was statistically better than the others. The graphical representation of the pairwise comparison is shown in Figure 10. The MTM-CW was found statistically better than the rest of the approaches on its right side. It can be seen that the MTM-CNN was statistically worse compared to the other models.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **[PERSON] et al. [35]** & **MTM-CNN** & **MTM-CW** & **MTM-CW\({}^{\text{w/o}}\)** \\ \hline AnatEM & 86.0 & 86.9 & **87.5** & 86.9 \\ BC2 GM & 78.8 & 80.8 & **81.5** & 81.2 \\ BC4 CHEMD & 88.8 & 87.3 & **89.2** & 87.4 \\ BC5 CDR & 88.1 & 87.8 & **88.5** & 88.1 \\ BioNLP09 & 88.0 & 88.7 & 88.5 & **89.3** \\ BioNLP11 EPI & 83.1 & 84.7 & **85.3** & 85.0 \\ BioNLP11 ID & 83.2 & 87.6 & 87.1 & **88.1** \\ BioNLP13 CG & 82.4 & 84.2 & **84.9** & 84.6 \\ BioNLP13 GE & 79.8 & 79.8 & 80.9 & **82.2** \\ BioNLP13 PC & 88.4 & 88.8 & **89.1** & 89.0 \\ CRAFT & 82.8 & 83.1 & **85.2** & 83.4 \\ Ex-PTM & 80.1 & 80.9 & 81.7 & **82.4** \\ JNLPBA & 72.2 & **74.0** & 72.1 & 72.0 \\ LINNAEUS & **88.8** & 87.7 & 88.1 & 88.6 \\ NCBI-disease & 85.5 & **85.6** & 85.0 & 85.1 \\ Average & 83.7 & 84.5 & **85.0** & **84.9** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multitask models comparison where CW represents character and word, respectively.  
Figure 10: Graphical representation of the [PERSON] test for MTM-CW vs. MTM-CNN. The arrows show models that are statistically significant with each other.  
Figure 9: Post hoc pairwise analysis of [PERSON]’s test for MTM-CNN vs. MTM-CW.
## 1 Introduction
### Motivation Example  
Human-to-human spoken dialogue analysis is becoming more popular in the literature. Advances in this research area are described in [18]. Topic identification, for which a state-of-the-art review is in [19], represents the basic component of the present study. The application considered in this paper deals with the automatic analysis of dialogues between a call center agent who can solve problems defined by the application domain documentation and a customer whose behavior is unpredictable [20].  
Real-world human-to-human telephone calls, in which an agent asks a customer to formulate an issue and then attempts to solve it, are considered. The customer is expected to seek information or formulate complaints about the energy supplier system and services. The issues in this domain application can be summarized as follows: First, real-world customers have an unpredictable language style. Second, there is a verbose nature in conversations in a customer support center. Customers frequently describe their problems in factual terms, while agents use a broad introduction followed by a concise formal explanation that includes theme-specific terminology and phrases. Finally, the conversations are in the Italian natural language only, which is rarely considered in the literature on NPL approaches.  
The objective is to investigate a customer problem over some time. Survey data are used to create problem percentages to track user satisfaction and prioritize problem-solving initiatives. TM is essential for assembling precise reports that can be used to conduct a reliable survey. A computerized system was put in place, with a voice recognition module for obtaining automatic transcriptions of the discussions. The main challenges are as follows: One or more subjects may be discussed in the conversations to be examined. Valuable issues may be interspersed with irrelevant comments. Mentions might be incomplete or erroneous due to repetition, ambiguity, and language and pronunciation errors. Discussions about an application-related issue may become irrelevant in some cases.
# A Novel Method to Mitigate the Multipath Error for BDS-2 Dam Deformation Monitoring
## 2 Materials and Methods  
When the receiving antenna is located in a plane horizontal reflector and its transmission is only affected by a single reflected signal, the multipath error effect on the carrier phase can be expressed as follows [29]:  
\[\Delta=\frac{\lambda}{2\pi}\tan^{-1}\frac{\alpha\sin\left(4\pi\frac{H}{ \lambda}\sin\varepsilon\right)}{1+\alpha\cos\left(4\pi\frac{H}{\lambda}\sin \varepsilon\right)} \tag{1}\]  
where \(H\) is the antenna height; \(\varepsilon\) and \(\alpha\) are the incidence angle and the reflection coefficient of the reflected signal, respectively and \(\lambda\) is the wavelength of the satellite signal.  
The main steps of SD observation domain filtering include the following. Firstly, the BDS-2 data of the reference day are processed in static mode, and then the final baseline can be achieved after the ambiguities are resolved. Second, the integer ambiguities and baseline solution are brought back to the BDS-2 DD observation, and the DD residuals including the multipath error can be obtained. Based on the simple \"zero mean\" assumption, the SD residuals are recovered from DD residuals through a transfer matrix at each epoch [19]. Finally, the multipath model of BDS after smoothing the SD residuals can be established. However, since the orbital repetition period of the MEO and GEO/IGSO is different and the carrier phase multipath errors of GEO satellites are systematic, the application of the simple \"zero mean\" assumption introduces new errors to BDS-2 positioning. Therefore, learning from the multipath hemispherical map of [PERSON] et al. (2016), the MHGM was proposed and developed.
# Review of Weed Detection Methods Based on Computer Vision
## 4 Weed Detection and Identification Methods Based on Deep Learning
### Weed Detection and Identification Methods Based on Semi- and Unsupervised Feature Learning  
Supervised deep neural networks rely on artificially annotated data; even with the use of rotation and cropping data enhancement techniques, at least hundreds of annotated images are still required for supervised training. Relevant scholars began to study semi-supervised learning with only a small amount of labeled data and unsupervised feature learning without data labeling [142; 143]. [PERSON] et al. [34] proposed a new image-based deep learning architecture called Graph Weed Network (GWN). The purpose is to identify multiple types of weeds from RGB images collected from complex pastures. GWN can be regarded as a semi-supervised learning method, which alleviates the complex annotation task. The evaluation on the DeepWeeds dataset reached the highest accuracy of 98.1% at the time. [PERSON] et al. [144] proposed semi-supervised GCN-ResNet101 to improve the recognition accuracy of crops and weeds in a limited labeled dataset, combining the advantages of CNN features and the semi-supervised learning capability of the graph. [PERSON] et al. [145] combined k-means unsupervised feature learning with the advantages of multilayered and refined CNN parameters as a pretraining process for the identification of weeds in soybean seedlings. This method replaces the random initialization weights of traditional CNN parameters, which effectively proves that this method is more accurate than randomly initialized convolutional networks. [PERSON] et al. [146] proposed an automatic learning method for weed detection in the UAV images of bean and spinach fields, which was based on CNN and an unsupervised training dataset. Experimental results proved that the performance of this method was close to that of supervised data labeling. [PERSON] et al. [33] tested two latest unsupervised deep clustering algorithms by using two public weed datasets. They proposed to use semiautomatic data labeling for weed identification. Compared with manually marking each image, semiautomatic data labeling could reduce the marking cost by hundreds of times. Then, NMI and unsupervised clustering accuracy indexes were used to evaluate the performance of pure unsupervised clustering. The use of unsupervised learning and clustering on agricultural issues will continue to be the direction of continuous development.
## 3 Modeling of Subsurface Drainage of a Two-Dimensional PFC Based on Finite Element Analysis
### Governing Equations and Variational Formulation  
In this study, transient unsaturated seepage was utilized to model the subsurface drainage of the PFC. In the analysis, it was assumed that the water has a constant volume and is incompressible. The governing equation of the two-dimensional water flow is illustrated in Equation (3):  
\[\frac{\partial}{\partial x}\left[k_{x}\frac{\partial h}{\partial x}+k_{\text{ roll}}\frac{\partial u_{w}}{\partial x}\right]+\frac{\partial}{\partial y} \left[k_{y}\frac{\partial h}{\partial y}+k_{\text{ roll}}\frac{\partial u_{w}}{ \partial y}\right]=-\gamma_{w}m_{2}^{w}\frac{\partial h}{\partial t} \tag{3}\]  
where \(k_{x}\) and \(k_{y}\) are the hydraulic conductivities in the horizontal and vertical directions, respectively, \(h\) is the total water head, \(k_{\text{ roll}}\) is the vapor conductivity, \(u_{w}\) is the pore water pressure, \(\gamma_{w}\) is the unit weight of water, \(m_{2}^{w}\) is the coefficient of water storage obtained from the derivative of the soil-water characteristic curve (SWCC), and \(t\) denotes time.  
It is of note that this study excludes the consideration of vapor flow and assumes isotropic permeability for the PFC pavement. Consequently, the partial differential equation in Equation (3) simplifies to the following:  
\[\
abla\cdot(k\
abla h)+f=-\gamma_{w}m_{2}^{w}\frac{\partial h}{\partial t} \tag{4}\]  
The SWCC presents the non-linear relationship between the volumetric water content and the suction in the PFC. In the current study, [PERSON]'s equation [21; 22] was used to extract the SWCC, as shown in Equation (5):  
\[\theta=\theta_{r}+\frac{\theta_{s}-\theta_{r}}{\left[1+\left(a\psi^{n}\right) \right]^{m}} \tag{5}\]  
where \(\theta\) represents volumetric water content, \(\theta_{s}\) is the saturated volumetric water content, \(\theta_{r}\) is the residual volumetric water content, \(\Psi\) signifies the soil suction, and \(a\), \(n\), and \(m\) are material (fitting) parameters.  
The drainage process within the PFC is treated as a time-dependent problem governed by the partial differential equation (PDE) presented in Equation (4). To solve this, the time derivative using an implicit Euler approximation was discretized, leading to Equation (6):  
\[\
abla\cdot\left(k\
abla h^{n+1}\right)+f^{n+1}=-\gamma_{w}m_{2}^{w}\left( \frac{h^{n+1}-h^{n}}{\Delta t}\right) \tag{6}\]  
where \(n\) denotes the time level and \(\Delta t\) is the time discretization parameter. This equation can be effectively solved using the finite element method (FEM). The weak formulation of Equation (6) is expressed in Equation (7):  
\[\int\limits_{\Omega}hvd\Omega+\Delta tk\int\limits_{\Omega}(\
abla h\cdot \
abla v)d\Omega=\int\limits_{\Omega}vh^{n+1}d\Omega+\Delta t\int\limits_{ \Gamma}f^{n+1}vd\Gamma \tag{7}\]  
where, for all \(v\) belonging to a suitable function space, \(\Omega\) represents the domain, and \(d\Omega\) is the differential volume element. In this study, an FEM solver was developed using FEniCS [23] and implemented entirely in Python programing language.
## 5 Conclusions  
This paper discussed the implementation and optimization of the LBM based on the Sunway TaihuLight SW26010 heterogeneous multi-core processor architecture and the simulation computation of NACA0012 2D airfoil flow. In parallel computing, the focus is on two-layer parallel design, the parallelism between CGs, and the parallelism between MPEs and CPEs. Parallelism between CGs is achieved by MPI, and that between MPEs and CPEs is achieved by the accelerating thread library. We adopted the double-buffer mode to reduce the communication time and adjust the discrete storage structure along with other optimization methods; these achieved a good parallel efficiency and speedup ratio for the Sunway TaihuLight. In terms of airfoil computation, the pressure and velocity distributions at different angles of attack were calculated respectively; and the average pressure coefficient and lift coefficient were obtained for an angle of attack of 8\({}^{\circ}\). The results were in good agreement with the literature.  
Future work will focus on optimizing procedures, improving efficiency--especially the efficiency of CPEs--exploring 3D models, more large-scale numerical computation of airfoils, and increasing the number of MPEs to one million. We will also explore the application of the LBM in flow noise. A common method for solving flow noise is to solve the Frowcs Williams and Hawkings (FW-H) equation. High accuracy of the flow field information is required for solving the FW-H equation. The LBM is a discrete format of the Boltzmann equation. Computation results from the LBM can reflect the fine structure of the flow field and provide accurate numerical results for the computation of aerodynamic noise. Long-term research shows that the LBM is indeed suitable for the computation of aerodynamic noise [26; 27].  
Conceptualization, L.W. and W.Z. ([PERSON]); Formal analysis, X.Z.; Funding acquisition, W.Z. ([PERSON]) and W.Z. ([PERSON]); Investigation, L.W.; Methodology, L.W., W.W. and X.C.; Project administration, W.Z. ([PERSON]); Resources, W.Z. ([PERSON]) and K.X.; Supervision, L.W.; Validation, K.X.; Visualization, W.W.; Writing--original draft, L.W.; Writing--review & editing, L.W.  
This research was funded by the National Natural Science Foundation of China major research project key project (91630206).  
Firstly, we would like to thank [PERSON], [PERSON] and [PERSON] for providing us with the basic theoretical knowledge of LBM and financial support. We learned a lot from [PERSON] about airfoils flow and aerodynamics. We express our since thanks to [PERSON] for his help in achieving better parallel performance on the Sunway TaihuLight supercomputer. Finally,we should express our gratitude to [PERSON] and [PERSON] for the implementer of the experiment and writing.  
The authors declare no conflict of interest.  
Figure 14: (**a**) Average pressure coefficient (\(C_{p}\)) distribution at an angle of attack of 8\({}^{\circ}\); (**b**) The trend of the lift coefficient (\(C_{L}\)) with the angle of attack.