esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
(1) Based on the basic principle of the SFS algorithm combined with the analytic method, a new three-dimensional (3D) reconstruction method is introduced to recover machined surface topography. By using the gray gradient as the constraint condition, the relationship between the surface gradient and gray gradient is described, and the analytic model of the surface gradient is developed. Moreover, the problem of SFS can evolve into a regularization problem using the proposed reconstruction principle.  
(2) The quantization error and ambiguity of the gray scale has a significant influence on the reconstruction accuracy of machined surface topography. Hence, we apply the wavelet denoising algorithm and the image processing technology to effectively solve those problems, and develop the 3D reconstruction algorithm for machined surface topography.  
Figure 14: Comparison of measured and reconstructed profiles: (**a**) Turing surface; (**b**) Milling surface; and (**c**) Boring surface.  
(3) Experimental results on synthetic images and machined surface topography images reveal that the proposed algorithm is effective, and the profiles of the reconstructed topography are very close to the measured profiles with a relative error of less than 10%.  
(4) The proposed three-dimensional reconstruction method solves the problem of accuracy and smoothness of the reconstructed surface. However, there must be a \"more or less\" limitation that the geometric features of specimen surface topography are relatively good. Hence, it is quite valuable to extend the measured surface.  
Conceptualization, W.-C.S. and J.-M.Z.; methodology, W.-C.S.; software, X.-B.L.; validation, W.-C.S., J.-M.Z. and Y.L.; formal analysis, W.-C.S.; investigation, W.-C.S.; resources, W.-C.S.; data curation, W.-C.S.; writing--original draft preparation, W.-C.S.; writing--review and editing, W.-C.S.; supervision, Y.L.; project administration, J.-M.Z.; funding acquisition, J.-M.Z.  
This research was funded by Natural Science Foundation Research Project of Shaanxi Province (51575237).  
Thank the Shaanxi Provincial Natural Science Foundation. Thanks to [PERSON] for guiding the article. Thanks to other authors for their help with the article.  
The authors declare no conflict of interest.
# An Enhanced Neural Word Embedding Model for Transfer Learning
## 5 Experiments
### Comparison with Previous Techniques

Table 6: Performance comparison of different feature extractors in the text classification of Bangal sentiment analysis classification benchmark dataset corpus. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature Extraction Methods**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**Performance Score**} \\ \cline{3-7}  & & **Accuracy** & **Hamming Loss** & **Recall** & **Precision** & \(f\)**1-Score** \\ \hline
**Word2 Vec** & KNN & 0.5677 & 0.4322 & 0.6620 & 0.5908 & 0.5908 \\  & XGB & 0.5952 & 0.4041 & 0.7969 & 0.5953 & 0.6815 \\  & SVM & **0.6057** & **0.3942** & 0.7288 & 0.6155 & 0.6674 \\  & RF & 0.5824 & 0.4175 & 0.9471 & 0.5693 & **0.7111** \\  & LR & 0.5989 & 0.4013 & 0.6369 & 0.6294 & 0.6326 \\ \hline
**FastText** & KNN & 0.6211 & 0.3788 & 0.6388 & 0.6753 & 0.6188 \\  & XGB & 0.6694 & 0.3314 & 0.7389 & 0.6798 & 0.6815 \\  & SVM & 0.6848 & 0.3151 & 0.8095 & 0.6747 & **0.7262** \\  & RF & 0.6621 & 0.3378 & 0.8297 & 0.6566 & 0.7231 \\  & LR & 0.6186 & 0.3813 & 0.3217 & 0.6825 & 0.6324 \\  & LSTM & **0.7459** & **0.2132** & 0.6822 & 0.7113 & 0.6946 \\  & CNN & 0.7326 & 0.2321 & 0.5423 & 0.7636 & 0.6225 \\ \hline
**BanFastText** & KNN & 0.6418 & 0.3581 & 0.6469 & 0.6532 & 0.6499 \\
**(CBOW)** & XGB & 0.6838 & 0.3032 & 0.7495 & 0.6815 & 0.7137 \\  & SVM & 0.7033 & 0.2966 & 0.7901 & 0.6966 & **0.7491** \\  & RF & 0.6803 & 0.3196 & 0.8215 & 0.6663 & 0.7361 \\  & LR & 0.6295 & 0.3709 & 0.6387 & 0.6646 & 0.6514 \\  & LSTM & **0.7625** & **0.2124** & 0.6667 & 0.7245 & 0.6734 \\  & CNN & 0.7411 & 0.2245 & 0.5612 & 0.7876 & 0.6467 \\ \hline
**BanFastText** & KNN & 0.6328 & 0.3672 & 0.6578 & 0.6534 & 0.6435 \\
**(Skip-gram)** & XGB & 0.6825 & 0.3191 & 0.7583 & 0.6729 & 0.7296 \\  & SVM & 0.7195 & 0.2879 & 0.8035 & 0.9829 & **0.7427** \\  & RF & 0.6712 & 0.3245 & 0.8198 & 0.6721 & 0.7382 \\  & LR & 0.6287 & 0.3711 & 0.6455 & 0.6712 & 0.6629 \\  & LSTM & **0.7505** & **0.2142** & 0.6845 &For the Bengali News Comment dataset, the accuracy of BanFastText and FastText is 71.95% and 68.48%, and the \(f1\)-score is 74.27% and 72.62%, respectively. However, the Hamming loss for BanFastText is 0.28, which is also low. For the LSTM model, the accuracy of BanFastText (CBOW) outperforms Facebook's FastText model in terms of accuracy, but both techniques produce a nearly identical \(f1\)-score and Hamming loss, where the accuracies are 76.25% and 74.59%, and the \(f1\)-scores are 69.21% and 69.46%, respectively, and the Hamming loss is 0.21 for both the BanFastText and FastText models. For CNN, BanFastText outperforms FastText both in accuracy and \(f1\)-score, where the accuracy is 74.11% for BanFastText and 73.26% for FastText, the \(f1\)-score is 64.67% in BanFastText and 62.25% for FastText, and the lowest Hamming loss of 0.22 is found for BanFastText.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 2 Results
### Summarizing the Effects of UV Radiation on Wines by PCA
Ac1, ethanolic acid; Ac2, hexanoic acid; Ac3, nonanoic acid; Ac4, octanoic acid; Al1, 3-methyl butanol; Al2, 2-methyl-1-butanol; Al3, 1-hexanol; Al4, phenyl ethyl alcohol; Cat, catechin; Cut, coumaroyl tartaric acid; Cya, cyanidin-(6’-acetyl)glucoside; Cyc, cyanidin-(6’-p-coumaroyl)glucose; Cyg, cyanidin-glucose; Dea, delphinidin-(6’-acetyl)glucose; Dec, delphinidin-(6’-p-coumaroyl)glucose; Deg, delphinidin-glucoside; Eca, epicatechin; Ecc, caffeic acid ethyl ester; Egl, epigallocatechin; Es1, ethyl butanoate; Es2, methyl hexanoate; Es3, ethyl hexanoate; Es4, ethyl heptanoate; Es5, methyl octanoate; Es7, ethyl octanoate; E8, ethyl nonanoate; Es9, ethyl decanoate; Es10, ethyl dodecanoate; Es11, ethyl hexylsali-cylate; Es13, 2-methylbutyl acetate; Es16, octanoic acid, 3-methylbutyl ester; Es17, hexadecanoic acid, ethyl ester; Es18, tetradecanoic acid ethyl ester; Gal, gallic acid; Gca, gallocatechin; Hy2, tetradace; Igu, isorhannetin-glucuron; Kgl, kaempfer-glucose; Kgu, kaempfer-glucose; Kar, laricitrin; Lgl, laricitrin-glucosidase; Maa, malvidin-(6’-acetyl)glucose; Mag, malvidin-glucose; Mgl, myricetin-glucose; Mgu, myricetin-glucuron; PB1, procyanidin B1; PB2, procyanidin B2; Pea, peonidin-(6’-acetyl)glucose; Peg, peonidin-glucose; Pro, protocatechuic acid; Pta, petunidin-(6’-acetyl)glucose; Ptc, petunidin-(6’-p-coumaroyl)glucose; Ptg, petunidin-glucose; Qga, quercetin-galactoside; Qgl, quercetin-glucose; Qgu, quercetin-glucuron; Res, resveratrol; Rgl, resveratrolol-glucose; Sgl, syringeetin-glucose. The different families of compounds are shown in different colors: stilbenes in light green, flavanols in red, flavonols in orange, phenolic acids in deep blue, anthocyanins in violet, alcohols in yellow, hydrocarbons in light blue, esters in pink, and fatty acids in deep green. Axis I is the horizontal one, and axis II is the vertical one. Each tick on the axes represents one unit.
## 3 Multiaccess Resource Allocation Algorithms and Power Control
### Resource-Allocation Algorithm
Within the algorithm, the assignment of the subcarrier \(v\) to VUEi is denoted by the expression allocated \(VUE(i,\ v)=1\), whereas the assignment of subcarrier \(v\) to \(CUEj\) is denoted by the expression allocated \(CUE(j,\ v)=1\). The algorithmic stages are presented in Algorithm 1. Figure 2: Flowchart of the resource-allocation algorithm. ```
Input:\(E_{v}\): set of CUEs using subcarrier \(v\), F: set of subcarriers \(B_{v}\): set of VUEs using subcarrier \(v\) \(B_{fv}\): set of VUEs that do not use subcarrier \(v\) Output: allocation of resources
1. Begin
2. Prepare Initialization: \(W_{1}^{v}=W_{1}^{max}\), \(W_{1}^{v}=\frac{W_{1}^{max}}{V}\), \(\Delta\Psi_{j}=\Psi_{j}^{size}-\Psi_{j}\)
3. While F is not empty or any CUE's data rate requirement is not satisfied then
4. Select\(j*=\max\left(\Delta\Psi_{j}\right)\), that is, the \(j^{*}\) th CUE
5. Select\(v*=max\left(a_{j}^{v}*\right)\), that is, allocate the \(v^{*}\) th subcarrier to the CUEj \({}^{*}\)
6. Else Recalculate \(\Psi_{j},\Delta\Psi_{j}\), and remove subcarrier \(v^{*}\) from set F
7. Endwhile
8. When F is not empty do
9. Allocate the subcarrier with the best channel gain for VUEs
10. End When
11. For each subcarrier \(v\)do
12. Select the VUE with the largest channel gain ratio in \(B_{fv}\)
13. If\(E_{v}\) is not empty then
14. Calculate the data rate of CUE using subcarrier \(v\)
15. Else if the CUE's data rate requirement is still satisfied then
16. Allow reuse, check the next subcarrier
17. else the next VUE user until the cumulative interference makes the CUE's data rate requirement unsatisfied, then
18. Check the next subcarrier
19. End elseif
20. \(E_{v}\) is empty
21. Allow the VUE to use subcarrier \(v\)
22. End for end If
23. End
```  
**Algorithm 1**Pseudocode for multiaccess resource allocation
## 3 The Experimental Results and Analysis
### Accuracy Analysis  
To objectively verify the accuracy of the proposed method, the comparison experiment between FTP and the proposed method has been accomplished. Figure 11 shows the 550 th column sectional view of the reconstructed 3D profile of the object. The blue dashed line is the result of FTP, and the red dashed line is the result of proposed method. It can be seen from Figure 11a that the result of the two fast real-time 3D shape measured technique are relatively close. However, from the details shown in Figure 11b, it can be clearly seen that on the boundary near the hollow, the outline of the hollow reconstructed by the proposed method is smooth and much closer to the real object than the result of the FTP algorithm.  
Figure 9: Knife measuring experiment: (**a**) the knife; (**b**) captured deformed patterns; (**c**) the binary deformed patterns; (**d**) extracted sinusoidal deformed patterns; (**e**) wrapped phase; and (**f**) reconstructed result.  
Figure 11: The 540 th column data of the reconstructed object: (**a**) the sectional view of the 3D profile of the object; (**b**) the magnified view of the hollow boundary area of (**a**).  
Figure 10: Real-time experiment for the workpiece: (**a–c**) the four grayscale deformed patterns captured online; (**d–f**) the corresponding wrapped phases; (**g–i**) the corresponding reconstructed results in high speed; and (**j–l**) the reconstructed results in lower shutter speed.  
Moreover, in order to analyze the online measuring accuracy of the proposed method, the workpiece is measured at a different moving speed. As shown in Table 1, the Root Mean Squared Error (RMSE) of the height reconstructed by the proposed method remains between 0.034 and 0.048 mm. The height RMSE is very small compared to the known height of 4.47 mm. Thus, the quaternary grating projection PMP method is optional.
# Real-time Neural Networks Implementation Proposal for Microcontrollers
## 1 Introduction
With three inputs, five hidden neurons, and an output neuron on the MLP, the application showed complete suitability for a \(\mu\)C-based approach. The results comparing the \(\mu\)C implementation showed almost no difference in Mean Square Error (MSE) after 30 iterations of the training algorithm. The work presented in [20] shows an embedded MLP with two layers on the microcontroller. The proposal uses an external tool for training and embedding a trained ANN. In [21], the authors implemented a classification application for the MNIST dataset. This implementation processes the 10 digits and full classification with 99.15% testing accuracy. In addition, this is implemented with a knowingly highly resource-hungry ANN model, the Convolutional Neural Network (CNN), using less than 2 KB of SRAM memory and also 6 KB of program memory, FLASH. This work was embedded on a Arduino Uno development kit that is composed of a breakout board for the 8-bit ATMega328p \(\mu\)C, with 32 KB of program memory and 2 KB of work memory, running at 16 MHz. In [22], the authors present an approach of reordering the operations on an ANN, reducing the memory usage for a CNN. They deployed a SwiftNet Cell with 250 KB of parameters into a Cortex-M7, 32-bit \(\mu\)C, running at 216 MHz, with 512 KB of SRAM. The results of reordering the operations showed an increase in inference time and energy use of less than 1% and a decrease of approximately 53% of peak memory usage, thus showing that such applications can be made in 32-bit \(\mu\)Cs. The reference works presented above demonstrate different aspects of implementing ANNs on \(\mu\)Cs. In [17; 18; 19], you will find application proposals showing MLP-ANNs trained with the backpropagation (BP) algorithm implemented on \(\mu\)Cs with good results; however, none of these talk about memory usage and processing-time parameters that vary according to the MLP hyperparameters. In [16; 23; 24], the authors present some results regarding processing time, but none are dependent on the number of artificial neurons or even comparing the time required to train the ANN in real time or not. Therefore, this work proposes an implementation of an MLP-ANN that can be trained with the BP algorithm into an ATMega2560 8-bit \(\mu\)C in the C language to show that many applications with ANNs are suitable on the \(\mu\)C platform. This work also presents two implementations regarding a model that is trained on the \(\mu\)C platform in real time and another implementation that is trained with MatLab and then ported to the same architecture to execute classification in real time. In addition to the implementation proposal, the parameters of the processing time for each feedforward step and backward steps in the training and classification process are presented. In addition, the variation of these parameters is shown due to the variation of the hyperparameters of the MLP. The validation of the classification and training results on a Hardware-In-the-Loop (HIL) strategy also are presented in this work.
## 2 Related Work
### Packet-Based Attack Identification Methods  
Packet-based attack identification methods use packet header information, such as IP address and header size, to infer the degree of anomaly. [PERSON] et al. [5] used packet header information and correlation between the target IP address and port of edge routers for identification. LUCID [6] represented the packet header data sequence as an image and used CNN for feature learning. Kitsune [11] was a packet-based method using an integrated autoencoder (AEs) as a detector. It extracted the source host, channel (source and target host pair), and socket characteristics and performed attack detection using an unsupervised learning model. The result showed that deep learning could improve the learning of host interaction patterns. In addition, related studies treated packet header information as words in natural language and used embedding methods to generate numerical representationsfor detection. A study in [12] mentioned an IP2 Vec applicable to logging flows for learning embeddings of IP addresses, port numbers, and protocols.  
Although packet header information is easy to extract, it has low confidence in dynamic and high-speed networks, which is also ineffective in detecting attacks with complex behaviors, and large-scale packet-level identification incurs huge resource and time costs. Especially the features of packets are microscopic representations of traffic and cannot globally express complex attack behavior patterns.
## 5 Case Study
### Application of the Framework to the Case Study
One of the key considerations during this phase was the integration of RPA tools with monitoring and feedback systems to ensure that automation results could be continuously evaluated and adjusted, guaranteeing maximum efficiency and regulatory compliance. The modularity of the system also allowed for the easy incorporation of new processes into the automation system in the future, without the need for major reengineering. Phase 4: Testing Phase  
The testing phase was conducted according to the guidelines set by the FRIDA, ensuring that all performance and compliance requirements were met before the system was launched into production. The RPA team developed and executed a series of test scenarios to validate the functionalities of data entry, document validation, and financial compliance verification. Through rigorous testing, any process failures were identified, and we ensured that the robot operated according to specifications, both in terms of performance and accuracy. Additionally, the testing phase included simulations of production environments, allowing for an evaluation of the system's resilience and its ability to handle load peaks, such as those experienced at the end of each fiscal quarter. The test results indicated a significant reduction in the processing time for credit applications, with automation managing to process an application in an average of 20 min, a substantial improvement from the previous two hours. Phase 5: Implementation and Hypercare  
Following the successful completion of testing, the robot was deployed into production, initiating the hypercare phase, during which its performance was closely monitoredand corrective interventions were made if necessary. During this phase, the RPA team closely monitored the system's behavior, collecting user feedback to adjust the automation and resolve any unexpected issues that arose. Real-time monitoring, aligned with FRIDA practices, enabled a proactive approach to resolve issues before they affected the business continuity. Moreover, during the hypercare phase, continuous update and maintenance strategies were implemented for the automated system, ensuring that automation remained aligned with regulatory compliance requirements and ever-evolving business needs. * Phase 6: Go-live and Support  
In the go-live phase, the automated system was fully integrated into the company's daily processes, enabling the complete automation of credit application processing, with minimal human intervention. Automation provided greater consistency in processes and reduced operational errors, allowing the company's team to focus on higher-value tasks, such as analyzing more complex cases. The ongoing support phase involved a robust monitoring system, allowing the RPA team to adjust and improve the system as necessary. The key performance indicators (KPIs) defined, such as processing time per application and error rate, were closely monitored to ensure that efficiency and accuracy objectives continued to be met over time.
###### Abstract  
As treated wastewater is increasingly used for agricultural purposes; questions remain about the microbiological quality of produce irrigated by these waters. This study conducted a quantitative microbial risk assessment (QMRA) using microbial data collected from North Carolina Type 2 reclaimed waters, which have been proposed as supplemental irrigation waters. Reclaimed waters were collected from four different water reclamation facilities located in central North Carolina and evaluated for five representative pathogens from the three groups of microorganisms (bacteria, virus, and protozoan parasites). Using these data, produce consumption scenarios were evaluated using a variety of irrigation techniques, including spray irrigation, drip irrigation, and subsurface drip irrigation, and the disability adjusted life years (DALYs) that result from illness by each pathogen as a result of produce consumption were compared to the acceptable level set by the World Health Organization. Based on the types of crop irrigation examined in this study using NC Type 2 reclaimed water, there were irrigation conditions and certain pathogens for which the annual risk of infection was not always reduced below the acceptable DALY risk level of <1 \(\times\) 10\({}^{-6}\) set by the WHO. The risks of viral infection by adenoviruses groups A-F were below the acceptable risk level; however, for _Salmonella_ spp., _Cryptosporidium_, and _Giardia_, the annual risk of infection was sometimes greater than would be considered acceptable.  
reclaimed water; water reuse; water-supply systems; agro-wastewater; risk assessment Article  
Type 2 Reclaimed Water for Agricultural Reuse  
[PERSON]  
1 Department of Environmental Sciences and Engineering, Gillings School of Global Public Health,  
The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA 12 Department of Public Health, College of Pharmacy & Health Sciences, Campbell University,  
Buies Creek, NC 27506, USA 2
## 1 Introduction
However, for the initiation mechanism of the impact of nano-particles on the modification effect and streamer morphology, no consensus has been reached. In addition, the above models were discussed by a simulating analysis which lacks the necessary experimental evidence. Obviously, analysis of the relationship among nano-particles, streamer morphology, and breakdown strength may provide a valuable insight and experimental evidence to explain the modification effect of nano-liquids. Moreover, it is helpful to provide an improved understanding of the macroscopic breakdown phenomena and microscopic development mechanism. In this paper, we experimentally investigated the streamer morphology and modification effect in pure transformer oil and nano-liquids. We first set up a platform for microsecond pulse breakdown strength testing and streamer shooting, which is composed of a microsecond pulse generator, breakdown test cell, and high-speed camera. Then, we prepared transformer oil nano-liquids with the addition of Al\({}_{2}\)O\({}_{3}\) nano-particles and TiO\({}_{2}\) nano-particles in different volume fractions. In particular, we shot the streamer with a high-speed camera, and measured the breakdown strength under microsecond pulse. By comparing the streamer morphology and modification effect of pure transformer oil and nano-liquids, we found that the addition of nano-particles can significantly affect the streamer morphology and modification effect. The paper is organized as follows: In Section 2, we briefly introduce the preparation of the nano-liquids, microsecond-pulse breakdown strength test platforms, and explain how a breakdown experiment is performed in this system. In Section 3, by measuring and comparing the streamer morphology and breakdown strength in nano-liquids, we found that the addition of nano-particles significantly affects the streamer morphology and breakdown strengths. In Section 4, we discuss the behavior of nano-particles in liquid mediums to explain the variation in streamer morphology and breakdown strength. Finally, conclusions are drawn in Section 5.