esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 3 Results
### Test Results and the Comparison of the Result Accuracy in On-Line Mode
#### 3.3.3 Influence of the Laboratory Structure to the Quality of the Localization  
During the research, no specific factor or object was taken into consideration individually; however, several factors could influence the quality of the localization. The accuracy of the localization was determined at different segments of the laboratory instead. Based on the results of this analysis, the critical segments regarding the efficiency of the localization were determined. Based on this, insight was obtained regarding algorithm improvements and/or RSSI data filtering.  
Figure 11: Comparison of the localization quality for different number of anchors for the industrial laboratory.  
Figure 10: Comparison of localization accuracy for points with unknown RSSI values.  
In the case of the industrial laboratory, the highest influence was determined due to metal surfaces. In the reminder of this chapter, the segments with higher error values were analyzed. For the analysis, test samples with \"known\" RSSI values were used. In Figure 12, the localization error is presented if a neural network is used, for different numbers of neurons in the hidden layer.  
In Figure 13, accuracy of localization by using WkNN for different numbers of neighboring points is presented.  
Figure 12: Accuracy of localization by using neural networks for different number of neurons in the hidden layer. (**a**) _HN_ = 100; (**b**) _HN_ = 200; (**c**) _HN_ = 300.
###### Abstract  
Background. To evaluate the radiological features of B3 lesions in patients with genetic mutations to establish an anatomo-radiological correlation. Methods. A total of 227 women with a histological diagnosis of B3 breast lesion were enrolled. Breast images of 21 patients with genetic test positivity for mutations in genes associated with breast cancer were analyzed. Results. BRCA1 was the most frequent mutation (\(n=12\)) followed by ATM (\(n=6\)) and BRCA2 (\(n=3\)). The histological findings showed nine atypical ductal hyperplasia (ADH), six lobular neoplasia (LN) including lobular carcinoma in situ (LCIS), three flat epithelial atypia (FEA) and three radial scar (RS) lesions. The results showed a significance difference between B3 lesion distribution in the three subgroups of mutations. LN and FEA showed the highest malignancy correlation. Patient age and risk anamnesis were factors that significantly influenced the malignancy rate. By mammography, 90.5% of lesions appeared as microcalcifications. By ultrasound, 13 lesions were observed as hypoechoic lesions. On breast MRI, 16 lesions were detected as a mass enhancement in all groups. DWI and kinetic curves significantly correlated with the risk of cancer. Conclusions. The radiological features of B3 lesions may help in the diagnosis of breast cancer malignancy. The high malignancy rate of cancer in our sample suggests they should always be surgically excised.  
B3 lesions; breast cancer; breast biopsy; mammography; breast ultrasound; MRI +
Footnote †: journal:  
high-risk (BRCA1, BRCA2, TP53, STIK11, PTEN, CDH1), moderate-risk (ATM, CHEK2, PALB2, BRIP1) and low-risk (RAD51C, RAD51D, Mitochondrial DNA) variants. The most common genes involved in the hereditary predisposition of breast cancer are BRCA1 (BReast CAncer gene one) and BRCA2 (BReast CAncer gene two) [8,9]. BRCA1 is located on chromosome 17 while BRCA2 is located on chromosome 13 [10].  
To the best of our knowledge, this is the first study evaluating the incidence, radiological features and clinical impact of B3 lesions in patients with genetic mutations. Thus, the aim of our study is to evaluate the radiological features of B3 lesions in patients with genetic mutations to establish an anatomo-radiological correlation that is potentially useful to guide the therapeutic approach in this specific subgroup of patients.
# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
###### Abstract  
Nowadays, the plastic injection molding industry is ever-growing, crucial, and its plastic products can be seen everywhere. However, the mold damage problem still frustrates operators because of its high maintenance price and time-consuming maintenance process. This damage is commonly caused by foreign bodies in mold area, and the conventional mold protection method is insufficient for high-performance injection molding machines because of the uncertainty from many setting parameters. To improve detection precision of mold protection driven by a toggle mechanism (\(TM\)), this paper puts forward \(EMP\), i.e., an extended Kalman filter (\(EKF\)) based self-adaptive mold protection method, wherein the \(EKF\) is used in current curve optimization, and the self-adaptive method (\(SAM\)) is proposed to gain an safety range of current curve. The \(EMP\) was verified in a 140-ton electric injection molding machine. Compared with a general method, the proposed method decreases the detected distance of mold protection by 22% under different thickness foreign bodies.  
mold protection; toggle mechanism; EKF; servo system; self-adaptive Article
## 3 Results and Discussion
### Rheological Properties of GPC Slurry  
The results reveal that the increase in the concentration of \(R\)-additive has led to the increase in rheological properties of the slurry, \(PV\) and \(YP\). As shown in Figure 5, as the concentration of \(R\)-additive was raised from 10% to 15%, 20% and 25%, \(PV\) increased from 48 cP to 74, 83 and 104 cP, respectively. In view of the difficulties experienced during the pumping of slurry with \(PV\) that exceeds 100 cP through the wellbore, as elaborated by [PERSON] et al. (1996) and [PERSON] et al. (1997), the addition of \(R\)-additive at concentrations of not more than 20% is recommended. In parallel with the impact on \(PV\), as presented in Figure 6, as the concentration of \(R\)-additive was raised from 10% to 15%, 20% and 25%, \(YP\) also increased from 3.8 N/m\({}^{2}\) to 6.7, 10.1 and 12.3 N/m\({}^{2}\), respectively. The increase in \(PV\) and \(YP\) with respect to the increase in concentration of \(R\)-additive is in agreement with the findings of [PERSON] et al. (2018), which employed dual-coated polyacrylamide (DPAM) as the additive, where it was deduced that the higher \(PV\) was obtained as a result of the gelation characteristics of the slurry, and the concentration of DPAM of 16% resulted in optimal rheological characteristics, as further additions of DPAM beyond 16% did not exhibit further variations of \(PV\).  
Figure 4: Density (\(\rho\)) of geopolymer cement (GPC) slurry at varying concentrations of elastomeric expandable additive (\(R\)-additive).
# Subsea Methane Hydrates: Origin and Monitoring the Impacts of Global Warming
## 4 Full Waveform Inversion for Viscoelastic Media
### Numerical Experiments  
For numerical experiments, we use the same vertically inhomogeneous model as before (see Figure 4); however, we introduce an additional layer with P- and S-wave attenuation of \(q_{p}\) and \(Q_{s}\) at the depth of 200 m (see Figure 6).  
When performing numerical experiments for a viscoelastic medium, we set the main task to study the coupling of parameters; therefore, we allowed for variability of both velocities and quality factors. By the connection of parameters, we mean the possibility of their false variability. Let us explain what is meant. Suppose there is a model of a viscoelastic medium with four parameters: P- and S-velocities \(V_{p}\) and \(V_{s}\), and P- and S-absorption \(Q_{p}\) and \(Q_{s}\).  
Figure 6: Q-factor distribution for P-(**top**) and S-(**bottom**) waves.  
Further, we assume that we know the initial approximation, which coincides with the true distribution of parameters in the medium, except for one of them, for example, \(Q_{p}\). Then, the presence of the coupled parameters means that, in the process of solving the inverse problem, we obtain a perturbation not only for \(Q_{p}\) but also for other parameters.  
Naturally, the coupling of parameters is not allowed when monitoring the state of gas hydrates, since here it is necessary to be sure that the results obtained are true and that the obtained change in the quality factor is really associated with a change in the physical properties of the geological object under study. As can be seen from the presented results of implementation the FWI in a viscoelastic medium, our approach is free from the mentioned drawbacks.  
First, let us analyze the results of Q-factor restoration for P- and S-waves as presented in the two upper panels of Figure 7. We note the confident coincidence of the true and reconstructed quality factors, both in terms of their spatial localization and absolute values as well as the absence of manifestations of their influence on the other elastic parameters. This allows us to assert that these two groups of parameters are not coupled when solving the inverse problem for viscoelastic media by the FWI method.  
Figure 7: The parameters of a viscoelastic medium recovered by multi-parameter FWI.  
To conclude this section, we note the monotonic decrease in the objective functional when minimizing by the conjugate gradient method (see Figure 8). This ensures that the iterations will be not stopped before the minimum is reached.
# Dual-ISM: Duality-Based Image Sequence Matching for Similar Image Search
## 4 Experimental Results
### Comparative Experiment of Similar Image Search Accuracy--Low-Dimensional Transformation Methods  
In this experiment, a comparative experiment of similar image searching accuracy for the proposed Dual-ISM method was conducted. In this paper, we define accuracy as the probability of including an image with the same class among images that were determined as similar images, and the equation is as follows.  
\[accuracy=\frac{\textit{the number of images with same class}}{\textit{the number of similar images}} \tag{10}\]  
The methods were compared for searching for similar images as follows:  
* 128 d+ED: Euclidean distance calculation that is based on the original feature vector
* LDA+ED: Euclidean distance calculation after LDA low-dimensional transformation
* Dual-ISM (LDA): the proposed method using LDA low-dimensional transformation
* PCA+Dual-ISM (PCA): the proposed method using PCA low-dimensional transformation
* NMF+Dual-ISM (NMF): the proposed method using NMF low-dimensional transformation  
As a result, as shown in Figure 4a, for the Flowers-10 dataset, two methods, LDA+ED and Dual-ISM (LDA), found eight images such as the query sequence and searched for most similar images. In the case of the ILSVRC dataset in Figure 4b, the two methods similarly recorded the highest accuracy. Based on the experimental results, LDA recorded the highest accuracy among the three low-dimensional transformation methods.  
Although the two methods had the same number of similar images that were searched, there was, however, a difference in the real distance between the sequences. In this paper, we define precision by scoring the distance ranking of images of the same class among similar images and the equation is as follows. Here \(n\) is the number of similar images and the rank score was calculated higher as it was closer to the query image sequence.  
\[precision=\frac{\sum\textit{the rank score of same class image}}{\sum_{1}^{n}i} \tag{11}\]  
As the result, the precision of the Dual-ISM was 45% and LDA+ED was 23%, and the actual distance calculation results are shown in Tables 1 and 2. The lines that are shaded in Tables 1 and 2 are image sequences that have the same class among the results of searching  
Figure 4: Comparison of similar image search accuracy. (**a**) Accuracy comparison for the Flowers dataset and (**b**) the accuracy comparison for the ILSVRC dataset.  
Figure 7 shows the actual image results. A query image is given at the top of the table, and among the methods that were used in the experiment, four methods that had satisfactory results were compared. In the proposed method, only images of the same class as the actual image were searched, and, when comparing with the actual images, it was confirmed that they were similar. The remaining three methods show that images with different classes from the query image were derived from the top-three results, and the actual images were also significantly different.
# Agent-Based Model for Automaticity Management of Traffic Flows across the Network
## 2 Autonomic Networking Management  
Today, people are using networks to communicate, and networks have become bigger and more complex. The network management is a complicated task that goes beyond technical support and device configuration. To simplify the network management, the automation of the management tasks are critical. The International Business Machines Corporation (IBM) has proposed five levels according to the degree of automaticity in the network management [4; 5].  
* Level 1: Refers to the continuous monitoring of the elements of the system by means of software tools handled by people who provide manual support in the event of a failure.
* Level 2: Consists of a set of monitoring applications that intelligently collect information to reduce the workload of the network administrator, and is known as the management level.
* Level 3: This level can recognize certain behavior patterns of the network in addition to suggesting actions to be taken by the support staff; this level is also called predictive.
* Level 4: Uses level 3 tools but attempts to minimize human intervention by helping with Service Level Agreements (SLAs); this level is known as adaptive.
* Level 5: Business policies and objectives dynamically manage the system at this level. Its disadvantage is that the automaticity is very closed only based on certain rules, and this level is recognized as autonomous.  
The proposed agent-based model is a step forward to reach the fifth level of automaticity, by the integration of intelligent agents that determine the path that will follow the data flows when a congested node situation emerges. The rules used for these agents rely on the preferences of each network node.
# Investigation of Ytterbium Incorporation in Lithium Niobate for Active Waveguide Devices
## 3 Results and Discussion
### Diffusion Theory
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **D0 (\(\times\) 10\({}^{8}\) cm\({}^{2}\)/s)** & **E\({}_{\mathbf{A}}\) (eV)** \\ \hline Diffusion in z-direction & 9.7 \(\pm\) 0.8 & 1.46 \(\pm\) 0.07 \\ Diffusion in x-direction & 24.1 \(\pm\) 1.9 & 1.64 \(\pm\) 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Diffusion constants and activation energies for Yb diffusion in LiNbO\({}_{3}\). Figure 6: Arrhenius plot of the solubility data of Yb in LiNbO\({}_{3}\). The line is a fit of the data using Equation (8). Figure 7: **(a)** Simulated Yb profile in LiNbO\({}_{3}\) for the in-diffusion of two 22 nm thick layers evaporated under \(\pm 60^{\circ}\) and in-diffused for 216 h at 1125 \({}^{\circ}\)C. **(b)** The Yb profile in x-direction in the middle of the ridge (red line). The results for a plane layer of Yb diffused in from the surface only are included as a blue line. **(c)** Simulated (left) and measured (right) intensity distribution for the fundamental mode at 980 nm for a 10 \(\upmu\)m-wide ridge-waveguide formed by three-side Ti in-diffusion. Figure 7c shows the expected intensity distribution of the quasi-TE modes at 800 nm and the intensity distribution measured at 796 nm. It is clearly visible that a good overlap between the Yb profile and the intensity distribution of the mode is achieved. The simulated concentration profiles shown in Figure 7a,b prove that ridge waveguides can be almost homogeneously doped by three-side in-diffusion while avoiding any surface damage that might be caused by exceeding the maximum solubility of Yb in LiNbO\({}_{3}\). The transmission spectrum of such a 10 \(\upmu\)m wide and 2.2 cm long ridge waveguide was measured using unpolarized white light coupled into the waveguide by means of a 40\(\times\) microscope objective. At the output facet, the transmitted light was collected and coupled into a spectrometer. To measure absorption as a function of polarization, a polarizer was placed in front of the collimator of the spectrometer. The measured absorption spectra (\(\uppi\) and \(\upsigma\) polarization) show three main peaks at 918, 956, and 980 nm (see Figure 8a), which is in good agreement with the absorption spectra reported for bulk-doped Yb:LiNbO\({}_{3}\)[22] and Yb:Ti:LiNbO\({}_{3}\) channel waveguides [14]. The losses of the waveguide of 0.8 dB/cm were determined at 1064 nm using the Fabry-Perot method. Fluorescence spectra (\(\uppi\) and \(\upsigma\) polarization) were measured for pumping with a titanium-sapphire laser at 918 nm. Figure 8b shows the polarized fluorescence spectrum. In agreement with previously reported results for Yb:LiNbO\({}_{3}\) waveguides [14,20], we observed the expected four main peaks around 960, 980, 1008, and 1062 nm.
## 6 Conclusions and Future Work  
This work presents a hybrid-optimization-enabled deep learning method for Sentiment Classification and Review Rating Prediction. Here, Yelp 2013, Yelp 2014, IMDB and social media app review datasets were taken as the input. The feature extraction process is more imperative for further Sentiment Classification and Review Rating Prediction processes. The GRU model was employed for Sentiment Classification and the HAN model was applied for Review Rating Prediction. Furthermore, the HAN and GRU models were trained by the hybrid optimization algorithm to enhance the detection and classification process. The experimental results show that the TD-Spider TaylorChoA-GRU and Jaya-TDO-HAN models obtained better results than existing methods. The proposed Jaya-TDO technique attained a better performance of 0.9425, 0.9654 and 0.9538, and TD-Spider Taylor ChoA achieved 0.9524, 0.9698 and 0.9588 in terms of the precision, recall and F-measure. In the future, the developed Sentiment Classification and Review Rating Prediction model can be further improved by extracting other features.  
[PERSON] designed and wrote the paper; [PERSON] supervised the work; [PERSON] performed the experiments with advice from [PERSON], [PERSON] and [PERSON] organized and proofread the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in the experiments are publicly available. Details have been given in Section 4.1.  
The authors appreciate and acknowledge anonymous reviewers for their reviews and guidance.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ChOA  
Chimp Optimization Algorithm  
DCBVN  
Demand-aware Collaborative Bayesian Variational Network  
DECOR  
Deep-Learning-Enabled Course Recommender System  
DNN  
Deep Neural Networks  
Figure 11: Performance analysis based on (**a**) accuracy and (**b**) loss for Review Rating Prediction using social media app review dataset.
# Effect of Mineral Additives on the Behavior of an Expansive Soil for Use in Highway Subgrade Soils
## 5 Conclusions  
In this research, the physical-mechanical properties of BCS stabilized with lime, VA, and their combination were investigated using compaction, Atterberg limits, CBR, and UCS tests. pH, EC, XRD, and TEM tests were conducted to observe the change in mineral phases. Test roads were constructed replacing BCS with the stabilized soil and gravel soil to investigate the moisture change and soil movement in the BCS foundation. The following conclusions are drawn:  
1. BCS is characterized as having high clay mineral content, plasticity index, and swelling potential, which indicate that BCS is a typical expansive soil.
2. Lime can vastly improve the physical-mechanical properties of BCS. VA can also decrease the liquid limit and plasticity index of BCS and increase the CBR and UCS of BCS, but has no significant effect. BCS stabilized with combinations of lime and VA presents a better effect than a single stabilizer.
3. According to JTG D30-2015, BCS stabilized with 3% lime + 15% VA meets the performance requirements of roadbed materials and could become one option for treating BCS in Kenya.
4. The consumption of stabilizers indicates that BCS reacts with stabilizers to form new minerals, as observed by XRD and TEM.
5. BCS stabilized with 3% lime + 15% VA shows a better effect in controlling both the moisture change and soil movement of the foundation BCS.  
[PERSON] and [PERSON] proposed the research method of analyzing the effect of lime and natural pozzolans on BCS stabilization. The experiments were performed and the manuscript was written by [PERSON].  
This research was funded by the project of China Communication and Construction Co. entitled \"To Develop Sets of Techniques for Highway Construction in Black Cotton Soil Regions, East African\", grant number 2013-ZJKJ-02.  
Figure 12: Vertical displacement of the BCS foundation during the period July 2016–November 2016. Replacement of BCS with (**a**) soil stabilized with lime and natural pozzolans and (**b**) gravel soil.  
**Acknowledgments:** The authors thank the engineering technicians of China Road and Bridge Corporation (Kenya) for their assistance in the collection of materials for this research and the anonymous reviewers for their careful review and critique, which led to the improvement of the article.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this paper.
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
###### Abstract
In summary, modulus backcalculation based on FWD deflection is an important part of pavement structure maintenance, and an accurate backcalculation of the asphalt layer modulus is affected by the temperature-sensitive characteristics of the asphalt mixtures. However, a uniform deflection temperature correction model for semi-rigid asphalt pavements has not been fully developed. Therefore, in this paper, in order to establish an accurate deflection temperature correction model, continuous deflection tests were performed on in-service highway asphalt pavement in different seasons, and the detected pavement temperature was recorded. Then, the representative temperature of the asphalt layer and the temperature's effective influence distance on the deflection basin were determined. Finally, a temperature correction model of asphalt pavement deflection based on the deflection change rate was also established.