esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description
### Optimization Objective  
The goal of Trinity is to search for a model parallel strategy that optimizes training performance based on reinforcement learning. We first establish a directed acyclic computational graph and device topology diagram based on the neural network data flow and cluster topology, and give the optimization objective.  
**Definition 1** (Computational Graph \(\mathcal{G}(O,E)\)).: _According to the deep learning data flow, define the computational graph \(G(O,E)\). \(\hat{O}\) represents the operator sets, and the node \(o_{i}\in O\) represents the operator (such as multiple, reshape, and pooling, etc.). \(E\) is the set of directed edges between nodes, including the data dependency between operators._  
**Definition 2** (Device Topology Diagram \(\mathcal{D}(V,C)\)).: _According to the cluster device topology information, define the cluster device topology diagram \(\mathcal{D}(V,C)\), where the node \(v_{i}\in V\) represents a device (such as CPU or GPU). Edge \(c_{ij}=(v_{i},v_{j})\) represents the communication between \(v_{i}\) and \(v_{j}\). Communication methods include NVLink, PCIE, etc._Based on the above definitions, the optimization objectives of this paper are as follows:  
\[\big{(}\pi_{g},\pi_{s}\big{)}=argmax_{\pi_{g},\pi_{s}}f(R;\mathcal{G},\mathcal{D}). \tag{1}\]  
where \(R\) represents the execution performance of the parallel strategy. We pose the strategy selection as a maximization optimization problem \(f\) on the condition of given \(\mathcal{G}\) and \(\mathcal{D}\). The strategy herein can be seen as two parts, namely \(\pi_{g}\) and \(\pi_{s}\), where \(\pi_{g}\) is the partition strategy, which divide the computational graph nodes \(O\) into \(k\) submodels, denoted as \(\mathcal{G}=\{g_{1},g_{2},\ldots,g_{k}\}\). Each submodel \(g_{i}\) contains multiple operators, which form disjoint subsets. The partition strategy is denoted as \(\pi_{g}\).  
\(\pi_{s}\) is schedule strategy, which schedules the submodels in G to be executed on different devices. The schedule process denoted as \(\pi_{s}:=\{s_{1},s_{2},\ldots,s_{k}\}\). Intuitively, it can be described as \(s_{i}:=g_{i}\to d_{j}\), the correspondence of group and devices.  
The goal of Trinity is to search for the strategy combination (partition strategy \(\pi_{g}\) and schedule strategy \(\pi_{s}\)), that can maximize the \(R(\pi_{g},\pi_{s})\) through reinforcement learning. Here, \((\pi_{g},\pi_{s})\) is the optimal parallel strategy that we seek.  
Optimize Formula (1) needs to solve two core problems:  
1. We need to characterize the performance evaluation model \(R\), evaluate the policy performance, and guide the solution of optimization problems.
2. We also need to build an agent optimization model. Use reinforcement learning to solve the optimal value in the model-parallel space.  
We then give the definition of model parallelism and analyze the factors that affect the performance of model parallelism.
## 2 Results and Discussion
### Total Phenolic Compounds and Antioxidant Capacity  
The total phenolic (TPC), flavonoid (TFC), and anthocyanin (TAC) contents, as well as the scavenging ability of ABTS and DPPH free radicals, of six fermented strawberry wines are shown in Figure 1A-E. The TAC in CJ strawberry wine was significantly higher, but the TPC and TFC were the lowest. A similarly low result was observed for the scavenging ability of ABTS and DPPH free radicals in CJ strawberry wines. This suggested that fermentation from juice without maceration was favorable for the extraction and preservation of anthocyanin in strawberry wine production, but not for the leaching of other phenolic substances. The TPC, TFC, and scavenging ability of ABTS and DPPH free radicals of the NJ and 50J strawberry wines were 12.33-25.42%, 23.45-42.18%, 20.77-35.81%, and 22.08-39.68% lower, respectively, than those values of NM wines fermented with pulp, which is significant. It was indicated that the vinicification of juice could not replace the traditional fruit pulp steeping fermentation, as more phenolic substance could be transferred from pulp to strawberry wine. Compared to NJ, thermovinification at 50 \({}^{\circ}\)C (50) increased the TPC by 21.78% and the TPC by 13.02%, respectively. Similarly, increases were discovered in the scavenging ability of ABTS and DPPH free radicals, where the increase rateswere 27.25% and 11.28%, respectively. Inconsistently, thermovinification noticeably decreased the TAC of the 50J sample from 22.79 \(\pm\) 0.87 mg CGE/L to 14.41 \(\pm\) 0.68 mg CGE/L compared with maceration at a normal temperature (NJ). These findings are in agreement with the red wine study, which resulted in similar TPC, TFC, and TAC of wine samples fermented through thermovinification [27; 28].  
The TPC, TAC, and scavenging ability of ABTS and DPPH free radicals in 50J strawberry wine were higher than those in PJ ones, but there was no significant difference between them. The TFC of PJ strawberry wine added with pectinase was less than that of 50J wine. However, there are wines produced via enzymatic pre-treatment which have shown higher concentrations of total polyphenols and anthocyanins [27].
# Defect Detection and Error Source Tracing in Laser Marking of Silicon Wafers with Machine Learning
###### Abstract  
Laser marking on wafers can introduce various defects such as inconsistent mark quality; under- or over-etching, and misalignment. Excessive laser power and inadequate cooling can cause burning or warping. These defects were inspected using machine vision, confocal microscopy, optical and scanning electron microscopy, acoustic/ultrasonic methods, and inline monitoring and coaxial vision. Machine learning has been successfully applied to improve the classification accuracy, and we propose a random forest algorithm with a training database to not only detect the defect but also trace its cause. Four causes have been identified as follows: unstable laser power, a dirty laser head, platform shaking, and voltage fluctuation of the electrical power. The object-matching technique ensures that a visible image can be utilized without a precise location. All inspected images were compared to the standard (qualified) product image pixel-by-pixel, and then the 2D matrix pattern for each type of defect was gathered. There were 10 photos for each type of defect included in the training to build the model with various labels, and the synthetic testing images altered by the defect cause model for laser marking defect inspection had accuracies of 97.0% and 91.6% in sorting the error cause, respectively  
random forest; error source tracing; laser marking +
Footnote †: journal: Applied sciences  
1
## 3 Results and Discussion
### Yield
Outlet temperature is a result of the heat and mass balance in the drying chamber [35] and, therefore, a consequence of gas inlet temperature, aspirator rate, pump performance (feed flow rate) and concentration of the feed solution [34; 46]. Outlet temperature can also be described as the highest temperature to which a product may be heated [47] and is an indication of drying speed. In the work of [PERSON] et al. [48] it was found that temperatures in different locations of the drying chamber were closer to the outlet temperatures than to the inlet temperatures, suggesting that the outlet temperature determined the droplet drying speed [14; 48]. Moreover, [PERSON] et al. determined in their work [48] that an increase in outlet temperature is directly proportional to the inlet temperature and spray flow rate which, in turn, corresponds to a higher drying rate, and thus leads to higher yields [14]. However, in the work of [PERSON] et al., for the spray drying of carboxymethyl cellulose, it was found that using high inlet temperatures, and consequently high outlet temperatures, could cause an accumulation of particles on the chamber wall, consequently leading to lower yields [35; 48; 49]. Another contributing factor may be the influence of the glass-transition temperature of CMC:PVA used (between 75 and 80 \({}^{\circ}\)C) [50]. Since the glass-transition temperature of the polymeric matrix is lower than the outlet temperature, the rubbery polymeric matrix may stick to the walls of the drying chamber, affecting the product yield [51]. In what concerns the influence of the spray flow rate (drying air rate) on the yields obtained, it is only noticeable for inlet temperatures of 180 and 190 \({}^{\circ}\)C. For 170 \({}^{\circ}\)C, the obtained yields are similar (considering the experimental error). Spray flow rate influences the rate of water evaporation, and thus the drying rate, of the droplets formed [14]. Increasing the spray flow rate leads to smaller drying times (faster drying), and consequently, to higher yields [49], which is in line with what was observed in the presented results (Table 4).
## 4 MPPT with the Modified Levy Flight Algorithm  
### Variables and Equations of the Proposed MPPT  
There are few modifications in incorporating the modified Levy flight algorithm in MPPT. Initially, the assigned particles are needed to determine the search space. The particles represent the duty cycle, D, i.e., \(\mathrm{D_{i}}\) (i = 1, 2,... n). Here, n denotes the total number of particles. Next, the step size, \(\alpha\) needs to be determined. Then, the fitness function, \(\mathrm{f^{\prime}}\), is the power of the PV.  
At the start, the initialization is first performed, where all particles are executed in the search space, and the initial value of the fitness function is the power at all respective particles. The maximum power is considered initially, and the best duty cycle at the respective power is considered the best particle sample. Soon after, the next stage is executed following the equation for Levy flight as shown in Equation (11) [38,41]; the new particles at the respective power are considered  
\[\mathrm{D_{i}^{k+1}=D_{i}^{t}+\alpha\oplus Levy(\lambda)} \tag{11}\]  
where \(\alpha=\alpha_{o}(v_{best}-v_{i})\).  
A simplified modified Levy distribution is given by Equation (12).  
\[q^{\prime}=\alpha_{o}(v_{best}-v_{i})\oplus Levy(\lambda)\approx Px\left( \frac{u}{|v|^{\frac{1}{\beta_{1}}}}\right)(v_{best}-v_{i})+Px\left(\frac{u}{ |v|^{\frac{1}{\beta_{2}}}}\right)(G_{best}-v_{i}) \tag{12}\]  
Figure 4: Trajectory of Levy flight.  
\(\beta=1.5\) (\(0\leq\beta\leq 2\)), and P is the Levy multiplying coefficient. The coefficients \(\mathrm{u}\) and \(\mathrm{\upsilon}\) are obtained from normal distribution curves as in Equation (13) [38,41].  
\[\mathrm{u}\approx\mathrm{N}\Big{(}0,\sigma_{\mathrm{u}}^{2}\Big{)}\mathrm{ \upsilon}\approx\mathrm{N}\Big{(}0,\sigma_{\mathrm{v}}^{2}\Big{)} \tag{13}\]  
Variables \(\sigma_{\mathrm{u}}\) and \(\sigma_{v}\) are expressed in Equation (14) with symbol \(\Gamma\) to denote the integral gamma function.  
\[\sigma_{u}=\left(\frac{\Gamma(1+\beta)x\sin(\pi x\beta/2)}{\Gamma\Big{(}\frac {1+\beta}{2}\Big{)}x\beta\kappa 2^{(\frac{\beta-1}{2})}}\right)\text{ and }\sigma_{v}=1 \tag{14}\]  
The power of the PV module is calculated and compared for the respective particle. The power in the respective duty cycle is compared, and the new best particle is selected. The best particle is once again compared with the global best particle at the respective power. If the best particle is not the current best particle, the new global best particle is assigned. Consequently, the iterative process is executed for all particles, and the current global best particle is evaluated over f'. This iterative process is executed until all particles achieve the maximum power point (MPP).
# Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
## 4 Experimental Analysis  
### Dataset and Knowledge Base  
#### 4.1.1 Dataset  
To evaluate the model that is proposed in this paper, experiments were conducted on the Airline Travel Information System (ATIS) dataset. The ATIS dataset [36] is the most-used dataset for NLU research. The dataset consists of sentences that people used when they made flight reservations. The training set contains 4978 utterances, and the test set contains 893 utterances. There were 18 different intent types and 127 distinct slot labels in total.  
In our experiment, the ATIS dataset was partitioned into a training set, a validation set, and a test set according to a ratio of 7:1:2. The training set was used to train the model, the validation set was used to adjust the hyperparameters, and the test set tested the generalization performance of the model. An example sentence, \"What are the flights from Tacoma to San Jose on Wednesday the nineteenth?\" is demonstrated in Table 1. This sentence follows the popular IOB (in-out-begin) format for representing the slot tags. The domain of the sentence is airline travel and the intent is to find a flight. The word \"Tacoma\" is labeled as the departure city and \"San Jose\" is labeled as the arrival city. In addition, the word \"nineteenth\" is labeled as the departure date.  
To verify the generality of the proposed model, we used another NLU dataset collected by Snips for model evaluation. This dataset is collected from the Snips personal voice assistant. The training set contains 13,084 utterances and the test set contains 700 utterances. Compared with the single-domain ATIS dataset, Snips is more complicated mainly because  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Entity** & **Relation** \\ \hline  & what & O \\  & are & O \\  & the & O \\  & flights & O \\  & from & O \\  & Tacoma & B-fromloc.city\_name \\ atis\_flight & to & O \\  & San & B-toloc.city\_name \\  & Jose & I-toloc.city\_name \\  & on & O \\  & Wednesday & B-depart\_date.day\_name \\  & the & O \\  & nineteenth & B-depart\_date.day\_number \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example utterance from the ATIS dataset.  
of the intent diver. The number of slot labels and intent type are 72 and 7, respectively. These seven intents include Search Creative Work, Get Weather, Book Restaurant, Play Music, Add to Playlist, Rate Book, and Search Screening Event.
## 2 Materials and Methods
### Primer and probe design  
TaqMan gene expression assays (containing both primers and probes) were manually designed with Geneious Pro 5.3 for 40 target genes and for six reference genes based on the complete genome sequences of _L. rhamnus_ R0011 and the EPS gene clusters of _L. rhamnus_ R, ATCC 9595, and RW-9595M (NCBI). Putative gene functions were obtained from the NCBI database and from the pathway databases BioCyc and Genomes (KEGG) (NCBI Resource Coordinators, 2015) (December 2015, Tables S1 to S34). With Geneious Pro 5.3 the presence of hairpins, self-dimers, and pair-dimers in the TaqMan gene expression assays was predicted. The TaqMan gene expression assays were synthesized by Life Technologies. The PCR amplification was simulated with both primers ([[http://insilico.ehu.es/PCR/](http://insilico.ehu.es/PCR/)]([http://insilico.ehu.es/PCR/](http://insilico.ehu.es/PCR/))). To confirm gene- and strain-specificity of the TaqMan gene expression assays, qPCR amplification was simulated in silico by Primer-Blast [29] and performed on genomic DNA of both strains (Ct results were at least 10 Ct higher for the strains of _L. rhamnus_, respectively; Table S2) [28].
# Intersatellite and Downlink Agile Attitude Maneuvers
## 6 Attitude Maneuvering for Intersatellite Connection
### Monte Carlo Analysis  
A Monte Carlo analysis is also performed for the interlink attitude maneuver. The stochastic initial conditions are generated by using the same approach described in Section 5.3. The mean value and the standard deviations of two quantities are reported in Table 4, i.e., (a) average value of the pointing error (angle \(\Phi_{e}\)) and (b) average magnitude of the angular velocity error. Figures 30 and 31 illustrate these two quantities for all the simulations. The average pointing error is less than 0.3\({}^{\circ}\) for the great majority of the simulations, whereas the average magnitude of the angular velocity error never exceeds 6\(\cdot\)10\({}^{-3}\)\({}^{\circ}\)/s and is less than 5\(\cdot\)10\({}^{-4}\)\({}^{\circ}\)/s in most cases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\tilde{\omega}\),[\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{   }}}}}}}}}}}}}\)] & \(\sigma_{\tilde{\omega}_{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}\)] & \(\tilde{\Phi}\),[\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{ \mathbf{ \mathbf{ }}}}}}}}}}}}}}}\)] & \(\sigma_{\tilde{\Phi}}\),[\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{  }}}}}}}}}}}}}}}}\)] \\ \hline
3.05\(\cdot\)10\({}^{-4}\) & 4.51\(\cdot\)10\({}^{-4}\) & 9.89\(\cdot\)10\({}^{-2}\) & 6.64\(\cdot\)10\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Intersat: numerical results from the Monte Carlo campaign.  
Figure 29: Errors on estimated inertia moments and products \(\tilde{J}_{11}\)(t), \(\tilde{J}_{22}\)(t), \(\tilde{J}_{33}\)(t), \(\tilde{J}_{12}\)(t), \(\tilde{J}_{13}\)(t), \(\tilde{J}_{23}\)(t)[kgm\({}^{2}\)] during the entire time interval.
# Numerical Investigation of the Relationship between
## 5 Conclusions  
In this paper, we aimed to address bypass graft failures by minimizing hemodynamic disturbances through the investigation and optimization of anastomosis angles for ridged spiral flow bypass grafts. We hypothesized that due to the significant influence of the junction angle in local flow fields within the anastomosis region, it can largely affect blood flow, and optimizing it could provide a synergistic effect with the spiral flow in stabilizing local hemodynamics. Through CFD, we were able to determine that the anastomosis angle indeed heavily influences the local hemodynamics in bypass graft anastomosis and thus can be used in enhancing bypass graft patency and longevity. Primarily, we observed that smaller anastomosis angles tend to minimize hemodynamic disturbances associated with ridged spiral flow grafts. The smaller anastomosis angle minimized the pressure drop and areas affected by recirculation and abnormally high WSS, all of which are believed to cause bypass graft failure. This fills the research gap on how the anastomosis angle can play a role in the hemodynamic performance of ridged spiral flow grafts. Additionally, we have confirmed that the use of spiral flow bypass grafts can outperform conventional, straight flow bypass grafts as they can offer higher, atheoprotective shear conditions. Our study demonstrates how important optimizing and understanding the relationship among different geometric properties are in designing long-lasting bypass grafts. Future studies in bypass graft designs must therefore ensure that each geometric property is optimized individually and with respect to its relationship to other properties.  
Several simplifying assumptions were used to reduce the complexity and computational cost of solving the problem. Geometries were idealized, so future studies may include imperfections in their geometric models, or if computational resources permit, use more accurate imaging and patient-specific models. The study did not include the proximal anastomosis region in the analysis, so it can be looked into in future research. Fluid-structure interaction (FSI) analysis may be used in future iterations to take into consideration the reciprocal effects of hemodynamics and vessel wall deformation which were neglected in this study.  
Conceptualization, J.J.A., L.T. and J.H.; formal analysis, J.J.A.; funding acquisition, L.T.; investigation, J.J.A.; methodology, J.J.A., L.T. and J.H.; software, J.J.A.; supervision, L.T. and J.H.; validation, J.J.A.; visualization, J.J.A.; writing--original draft, J.J.A.; writing--review and editing, J.J.A., L.T. and J.H. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Frequency distribution of TAWSS values in the artery.  
Funding: This study was published through a grant provided by Mapia University. Data Availability Statement: The data presented in this study are available in the article. Conflicts of Interest: The authors declare no conflict of interest.
# The XMUSPEECH System for Accented English Automatic Speech Recognition
## 3 Experimental Results  
### Data Sets and Augmentation  
Our experiments were conducted on the accented English data sets (16 kHz) of eight countries provided by Datatang ([[https://www.datatang.com](https://www.datatang.com)]([https://www.datatang.com](https://www.datatang.com)) (accessed on 24 August 2021)), including American (US), British (UK), Chinese (CHN), Korean (KR), Japanese  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Layer Type** & **Context** & **Size** \\ \hline Frame1 & TDNN & \(\{\text{t}-4:\text{t}+4\}\) & 512 \\ Frame2 & TDNN & \(\{\text{t}-2,\text{t},\text{t}+2\}\) & 512 \\ Frame3 & TDNN & \(\{\text{t}-3,\text{t},\text{t}+3\}\) & 512 \\ Frame4 & TDNN & \(\{\text{t}\}\) & 512 \\ Frame5 & TDNN & \(\{\text{t}\}\) & 1500 \\ Stat pool & & \(\{\text{0,T}\}\) & \(2\times 1500\) \\ Segment6 & Affine & \(\{\text{0}\}\) & 512 \\ Segment7 & Affine & \(\{\text{0}\}\) & 512 \\ Softmax & & \(\{\text{0}\}\) & Num.accent/spk \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accent/spk-xvector architecture.  
Figure 2: The overall framework of the system.  
(JPN), Russian (RU), Portuguese (PT), Indian (IND), Spanish (SPA), and Canadian (CA), where the official training set does not include the Spanish and Canadian accents to evaluate the generalization of the ASR system. Each accented speech data were collected from 40 to 110 speakers and recorded by Android devices or iPhones in a quiet house acoustic environment. The speakers were gender-balanced, age 20 to 60. Each country's accented data consisted of about 20 h. The speech content consisted of daily communication and interaction with smart devices. The training set, development set, and evaluation set were about 148 h, 14 h, and 21 h, respectively.  
We augmented the training data by changing the speed of the audio signal, producing three versions of the original signal with speed factors of 0.9, 1.0, and 1.1 [26] and then applied volume perturbation. All the systems share the same type of data augmentation techniques. In addition, SpecAugment, which provides masks on the spectrogram of input utterances, was performed during training.
## 2 Materials and Methods
### Method Validation  
#### 2.4.1 Specificity  
The elution time was found, and the separation capacity for DEHP was checked. Specifically, a standard solution of 0.7 mg/L DEHP in a matrix of ethyl alcohol 40% \(v/v\), a pure solvent solution of ethyl alcohol 40% \(v/v\), and a real sample of Ouzo were measured following all the analytical procedure described.  
Ouzo matrix effect check was conducted. The main test of the specificity of the method should be to demonstrate that the peak area (signal), at the elution time, is due solely to DEHP and not to some other substance present in the matrix of the actual sample [6]. To check the effect of the matrix of a real \"ouzo\" sample, the following tests were performed:  
* In a real sample of Ouzo, 4 independent spikes of DEHP were made at concentrations of 0.3, 0.7, 1.1, and 1.5 mg/L and measured following the entire analytical procedure. Additionally, unspiked \"ouzo\" was measured. The spike was conducted by adding a small volume of a dense standard to avoid dilution of the sample.
* In pure solvent ethyl alcohol 40% \(v/v\), 4 independent spikes of DEHP were made at concentrations of 0.3, 0.7, 1.1, and 1.5 mg/L and were measured following the entire analytical procedure. The pure solvent without spike was also measured.