esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
## 1 Introduction
Most theory-based controllers are designed based on the optimizing control theory. [PERSON] in [6], [PERSON] in [10], and [PERSON] in [11] all designed controllers based on the LQR (linear quadratic regulator) theory. [PERSON] designed an optimal controller based on the SDRE (state dependent Riccati equation) technique and implemented the controller in a test vehicle in [12]. [PERSON] designed a MIMO (multiple input multiple output) LQR controller by a three-state bicycle model with saturated rear tires to realize vehicle drift motions in steady states in [13; 14]. The MPC theory also is an optimizing control theory and has a great ability to handle linear constraints and future prediction in the design process, which is popular in path tracking as shown by [PERSON] in [15] and [PERSON] in [16]. The optimizing controllers can obtain the optimum solutions based on accurate models without disturbances, and the anti-jamming capability of optimizing controllers is worse than robust controllers which direct at uncertain problems. The robust theory, in recent years, is used to design vehicle controllers to follow paths and track, which provides a reference for the controller design. [PERSON] designed controllers by LQ (linear quadratic) H-infinity robust in LMI (linear matrix inequation) using the LPV (linear parameter varying) vehicle models to achieve path tracking in [17]. [PERSON] designed an H-infinity controller with an uncertainty model and shown the effectiveness by simulation experiments in [18]. He designed a robust H-infinity coordination controller based on LMI with the consideration of uncertain external disturbance to realize vehicle path tracking in [19]. [PERSON] designed an H-infinity controller with a T-S (Takagi-Sugeno) fuzzy model to control the front-wheel steering for path tracking with parametric uncertainties and nonlinearities in [20]. The H-infinity control is one kind of robust control and is suitable to deal with vehicle modeling uncertainty and external disturbance as [21] mentioned. Considering that there are uncertain external disturbances in practice, the paper designs the drift controller based on the H-infinity control. From the above analysis, the paper studies how the vehicle work in drift conditions by analyzing the vehicle motion mechanism with the reference of the human operation. The vehicle dynamics model is established by considering longitudinal, lateral, roll, and yaw motions and the rolling safety with the nonlinear tire model UniTire. The drift motion mechanism is analyzed in steady and transient states to obtain vehicle main status parameters. The paper designs the drift controller based on the H-infinity and LMI with uncertain external disturbances and the controller is proved by simulations.
###### Abstract  
Within the frame of this work, the synthesis of silver nanoparticles (Ag NPs) and silver chloride nanoparticles (AgCl NPs) as mediated by microbes has been investigated. The nanoparticles were reduced from a silver nitrate precursor by the presence of bacteria, like _Raoultella planticola_ and _Pantoa agglomerans_. The results show that the characteristic surface plasmon resonance absorption band occurs at about 440 nm. Nanoparticles were also characterized with the help of scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), and X-ray diffraction (XRD), which showed the formation of spherical Ag/AgCl NPs with a centered cubic crystal structure and a mean particle size of around 10-50 nm. Assays for antimicrobial activity of the biosynthesized nanoparticles demonstrated meaningful results against microorganisms such as _Staphylococcus aureus_, _Streptococcus pyogenes_, _Salmonella_, and _Bacillus amyloidateaciens_. Furthermore, this study shows that the combination of the obtained nanoparticles with standard antibiotics may be useful in the fight against emerging microbial drug resistance.  
silver chloride; nanoparticles; _Pantoa agglomerans_; _Raoultella planticola_; antibacterial properties 1  
11 March 202125 March 202125 April 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Vol. 2021**, _11_, 3134. [[https://doi.org/10.3390/app11073134](https://doi.org/10.3390/app11073134)]([https://doi.org/10.3390/app11073134](https://doi.org/10.3390/app11073134))
###### Abstract  
The digital twin solution is an industry 4.0 specific tool that has grown in the past decade, stemming from the modelling and simulation approaches that existed before, complemented by new sensor capabilities, cloud processing, big data analytics, and implementation mechanisms. As it is being used mostly in the present by manufacturing companies, the primary focus of the solution is to enhance productivity and reduce costs by optimizing processes and enabling real-time problem-solving, sometimes based on decision-making systems and artificial intelligence. However, as companies are being faced with an increasingly steep list of environmental requirements and regulations, ranging from the classical pollution control and waste recycling to full-scale economic models based on circular economy and transformative carbon dioxide elimination programs, the features of the manufacturing digital twins must also evolve to provide an appropriate answer to these challenges. In this paper, the authors propose a framework for building better digital twins for production systems by incorporating environmental-related functions. The demarches start from analysing existing solutions presented in literature from the point of view of environmental suitability, based on the use of the MoScOW method for differentiating attributes (into Must have, Should have, Could have, Will not have elements) and determining development alternatives based on the employment of Multi-Criteria Decision Analysis (MCDA) for feature selection, and the TRIZ method (Theory of Inventive Problem-Solving) for application guidelines. The MCDA was performed within a focus group of nine production specialists from regionally successful sectors. We arrive at the conclusion that environmental-related functions are poorly implemented in the digital twins of the present (although more so in integrated solutions and custom-built applications) and that the development of the proper tools, databases, and interpretation keys should proceed immediately in the fields of production engineering, industrial ecology, and software development to support them.  
digital twin; digital production; production system; environmental management; multi-criterial decision analysis +
Footnote †: journal: Applied Sciences
## 3 Methods
### Features of EEG Signals
#### 3.1.1 Average Power in the Time Domain  
In the case of a seizure-free state, the energy and power of the iEEG signal are usually lower than during a seizure. Under normal conditions, the brain exhibits regular electrical activities, resulting in lower energy of the iEEG signal. During an epileptic seizure, the iEEG signal often shows an increase in energy. This is caused by intense and abnormal electrical discharges in the brain that characterize a seizure [56]. These abnormal discharges lead to increased neuronal activity in the brain, resulting in higher energy of the iEEG signal [57]. This increase in energy can be observed across different frequencies, such as delta, theta, alpha, beta, and gamma, depending on the type of seizure [58].  
To calculate the energy of the signal in the specific frequency bands (delta, theta, alpha, beta, and gamma), digital filters can be used. For this purpose, filters are designed for each frequency range. For example, for alpha waves, a frequency range of 8-12 Hz can be selected, for beta waves 12-35 Hz, for gamma waves 35-100 Hz, for theta waves 4-8 Hz, and for delta waves 0.5-4 Hz. An example of an iEEG signal recorded during a seizure, after passing through the filters responsible for the alpha, beta, gamma, theta, and delta bands, is presented in Figure 3. Applying signal filtration enables the computation of features for each frequency band. The average power in the time domain was then calculated for each second of the signal and each channel [59]:  
\[P_{x}=\frac{1}{N}\sum_{n=0}^{N-1}|x[n]|^{2} \tag{1}\]  
where \(N\) is the number of samples in the window and \(x[n]\) is the value of the \(n\)th sample. The calculated values have a unit of \(\mu\)V\({}^{2}\). The average power in the time domain is typically calculated for time windows of equal length and is proportional to the signal energy.  
Figure 3: An iEEG signal and the result of signal filtering in the alpha, beta, gamma, theta, and delta bands.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 3 Methods
### Performance Metrics  
The performance of MLT models was evaluated using a coefficient of determination (R\({}^{2}\)) and two prediction errors: root mean squared error (RMSE) and mean absolute percentage error (MAPE).  
R\({}^{2}\) is a measure applied to characterize the quality of the predictive model. It reflects the effectiveness of the regression model in fitting the data. The coefficient of determination, R\({}^{2}\), was calculated using Equation (3) [48].  
\[R^{2}=\frac{\sum_{i=1}^{N}(p_{i}-\overline{r})^{2}}{\sum_{i=1}^{N}(r_{i}- \overline{r})^{2}} \tag{3}\]  
where \(r_{i}\) is the observed data, \(p_{i}\) is the predicted data, \(\overline{r}\) is the observed data average, and \(N\) is the size of the dataset. \(R^{2}\) represents the fraction of target variable variability that has been explained by the model [49]. The range is \(R^{2}\in\langle 0,1\rangle\), and values close to one indicate a high-quality model.  
Figure 3: The schema of the applied five-fold cross-validation procedure.  
Errors were used to characterize the quality of prediction, and their low values indicated high-quality prediction.  
RMSE calculates the average squared difference between the observed and predicted data, as given by Equation (4).  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(p_{i}-r_{i})^{2}} \tag{4}\]  
where \(r_{i}\) is the observed data, \(p_{i}\) is the predicted data, and \(N\) is the size of the dataset.  
RMSE is expressed in the same units as the predicted variable; so, its magnitude is easy to interpret as low or high for a particular physical quantity. On the other hand, RMSE use is limited when it comes to comparing the error of determining distinct quantities. This error measure is sensitive to outliers due to the squaring mechanism [49].  
MAPE calculates the absolute difference between the observed and predicted data and views it as a fraction of the observed data, as given by Equation (5) [48].  
\[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{|p_{i}-r_{i}|}{r_{i}}\cdot 100\% \tag{5}\]  
where \(r_{i}\) is the observed data, \(p_{i}\) is the predicted data, and \(N\) is the size of the dataset. MAPE represents the percentage error, providing an understanding of how off the predictions are in relative terms. In this work, it was chosen as the performance metric to overcome the limitation of RMSE in comparing predictions of different air parameters.
## 2 Materials and Methods
### Numerical Model and Methods
#### Reduction of Mesh Resolution  
To reduce the computational costs of the numerical larynx model, the mesh resolution was reduced in this study. The aim was to answer the question of how far it is possible to decrease the mesh resolution and still obtain relevant and significant results from the numerical model. Starting with the finest and base mesh (MB), three meshes (M1-M3) were generated with systematically coarsened spatial resolution. The resulting mesh parameters and total numbers of cells are summarized in Table 2. For validation, the results obtained with the coarse meshes were compared with those obtained with the base mesh.  
As mentioned by [PERSON] et al. [22], for LES modeling the mesh size should be of the order of the Taylor micro-scale (\(\lambda_{\mathrm{T}}\)), which is a measure of the length scale of turbulence. By considering the integral length scale (a measure of the large-scale eddies) to be one order of magnitude smaller than the geometric characteristic length, which in the larynx model is the glottal width (D\({}_{\mathrm{g}}\)), 10% turbulence intensity and the maximum axial velocity \(\mathrm{u}_{\mathrm{xmax}}\) from the simulation with the base mesh, the Taylor micro-scale can be calculated as follows [22; 65]:  
\[\lambda_{\mathrm{T}}=\sqrt{\frac{15\
u 0.1\mathrm{D}_{\mathrm{g}}}{0.1\mathrm{u}_{ \mathrm{xmax}}}}. \tag{1}\]  
The mean value of the Taylor micro-scale for the numerical simulation with the base mesh was approximately \(\lambda_{\mathrm{T}\mathrm{e}\mathrm{m}\mathrm{e}\mathrm{m}}\approx 0.085\) mm. Therefore, the mesh size in the glottal duct, which contains the finest mesh, was not increased above this value during the process of reduction of the mesh resolution.  
Furthermore, in order to exclude the impact of the CFL number variation, the time step size was increased synchronously to the increasing mesh size with the same factor.  
The requirement for some cells between the vocal folds in the closed phase of the cycle was mentioned in Section 2.2.4. The number of remaining cell layers in this area depends on the mesh size in the glottal duct. Considering this, the number of cell layers in the minimum glottal area of M3 was lower than the others, which caused deactivation of some of these cells by the overset mesh solver. As a result, the deactivated cells were replaced by wall boundary conditions and flow through them was prevented. Therefore, it was partially working like a blockage of the flow at the time of glottal closure.  
Figure 4: Principle structure of the finest mesh. Gray: cells with base size. Yellow: Cells with basic refinement. Red: Cells with glottis refinement.
# Using Low Salinity Waterflooding to Improve Oil Recovery in Naturally Fractured Reservoirs
## 1 Introduction
This is called the effective salinity range [24; 27]. It is also observed that there is occasionally a change to the maximum relative permeability of water. These changes resulted in an increment in oil mobility and, hence, improve oil recovery. In the simulation, two sets of relative permeability should be used to represent the regions of high salinity and low salinity brine, respectively. A weighting factor is used to calculate relative permeability in the mixing zone [24]. In this paper, we presented an approach to simulate the impact of low salinity waterflooding on reservoir behaviour. These steps include:  
1. A new weighting factor has been proposed based on a 1D tracer model. 2. The results of reported coreflood experiments were utilised to generate the relative permeability sets of the flooded regions by low salinity brine. 3. Various scales were employed to demonstrate the impact of the low salinity waterflooding in fractured reservoirs as well as to highlight the influence of the dual-porosity model on the simulation outcome. Initially, we collected and evaluated the published results of coreflood experiments to conclude a range of change to the system wettability (in the form of saturation exponents) and the residual oil saturation (e.g., the published results of [20; 21; 23; 25; 26]). Then, we proposed a new approach to determine the weighting factor using a tracer model, which can improve the simulation accuracy in the mixing zone. On the other hand, it is challenging to model the chemical interactions that have been proposed to occur during low salinity water flooding at sector or field scales accurately. Therefore, we utilised the relative permeability curves as a proxy for any chemical and physical interactions that have not been represented explicitly in the simulation model. The generated sets of relative permeability enable us to emulate the observed behaviour of the low salinity coreflood in the laboratory using various modelling scales. Besides, the impact of the change to the relative permeability on the fractional flow displacement in naturally fractured reservoirs has been presented to illustrate how the alteration process to the flow curves can delay the water flow; and, hence, reduce the water cut as well as boost oil recovery.
_Article_  
Correlation between the Golden Ratio and Nanowire Transistor Performance +  
Footnote †: journal: Applied Physics Letters B  
[PERSON] 1,2 2  
1 School of Engineering, University of Glasgow, Glasgow G12 8 LT, UK; [EMAIL_ADDRESS]; Tel.: +44-0141-330-4792  
2 Electrical Engineering Department, College of Engineering, Al-Mustansiriyah University,  
Baghdad 10052, Iraq  
+ This paper is an extended version of: [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] Does a Nanowire Transistor Follow the Golden Ratio? A 2D Poisson-Schrodinger/3D Monte Carlo Simulation Study. In Proceedings of the 2017 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD), Kamakura, Japan, 7-9 September 2017.  
Received: 25 November 2017; Accepted: 22 December 2017; Published: 2 January 2018  
An observation was made in this research regarding the fact that the signatures of isotropic charge distributions in silicon nanowire transistors (NWT) displayed identical characteristics to the golden ratio (Phi). In turn, a simulation was conducted regarding ultra-scaled n-type Si (NWT) with respect to the 5-nm complementary metal-oxide-semiconductor (CMOS) application. The results reveal that the amount of mobile charge in the channel and intrinsic speed of the device are determined by the device geometry and could also be correlated to the golden ratio (Phi). This paper highlights the issue that the optimization of NWT geometry could reduce the impact of the main sources of statistical variability on the Figure of Merit (FoM) of devices. In the context of industrial early successes in fabricating vertically stacked NWT, ensemble Monte Carlo (MC) simulations with quantum correction are used to accurately predict the drive current. This occurs alongside a consideration of the degree to which the carrier transport in the vertically stacked lateral NWTs are complex.  
silicon nanowire transistors; quantum confinement; golden ratio; technology computer aided design (TCAD); drift diffusion; Poisson-Schrodinger; simulations; 3D-Monte Carlo
## 1 Introduction
The cache size allocation scheme proposed in this paper adopts a proportional distribution method, that is, the size of the cache space of each node is proportional to its importance. The existing proportional division methods are mainly based on the network topology but ignore the content transmission process. The consequent mismatch in terms of communication semantics between \"where\" (location of the node) and \"how\" (the content dissemination) is at the origin of a number of significant deficiencies of these schemes. Hence, we analyze the actual transmission process of content in given network topology, then calculate the importance of each node according to its role in the actual transmission, and finally, allocate the given total cache space to each network node. The main contributions of this paper are as follows:  
* We implement a hierarchy partitioning method based on content dissemination to simplify the network topology with the arbitrary graph structure, while the content transmission process is clarified. * We quantify the importance of nodes from the content transmission process and network topology by formulating a set of calculation methods for weights at each level, which resolves the mismatch between content dissemination and topology location. * We use real-world network topology to evaluate the performance of the cache size allocation scheme proposed. The results show that our scheme performs better in different aspects. The rest of this article is organized as follows. In Section 2, we introduce the background and related work of ICN caching. Section 3 is the problem statement. In Section 4, we introduce the basis and method of network hierarchy division. In Section 5, we introduce the calculation method of weight and the specific allocation method of cache. Section 6 gives the results of quantitative experiments. We discuss this in Section 7. Finally, we summarize the paper in Section 8.
# Classification of Defective Fabrics Using Capsule Networks
## 3 Method  
Deep learning-based methods tend to trend up in fabric defect detection studies. In recent years, the majority of publications in this field have discussed defect detection strategies. In this paper, the Capsule Network, as a deep learning method proposed by [PERSON] et al., is used. The route followed in this paper is shown in Figure 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Data** & **Classes** & **Acc. 1** & **Ref. 2** \\ \hline \multirow{2}{*}{Multi-scale CNN} & TILDA & 6: Strain, Hole, Weft formed, Slub, Stitching, Color change & 95.56\% & [27] \\ \hline Autoencoders & Custom Dataset & 2: Defective and defect-free & 88\% & [28] \\ \hline Pretrained AlexNet & Custom Dataset & 2: Defective and Defect-free & 98.68\% & [29] \\ \hline Combination of compressive sensing and convolutional neural network (CS-CNN) & \multirow{2}{*}{Custom Dataset} & 10: Normal, Mispick, Broken pick, Double flat, Slub, Fletter, Drawback, Sundries, Broken end, Oil stain & 97.9\% & [30] \\ \hline Transfer learning and faster RCNN & Custom Dataset & 2: Holes \& Cuts, Stain & 95.48\% & [31] \\ \hline U-Net and Convolutional Neural Network & Custom Dataset & 2: Defective and defect-free & 96.62\% & [32] \\ \hline Faster Regional-based CNN & Custom Dataset & 6: Broken pick, Felter, Drawback, Sundries, Broken end, Oilstains & 95.8\% & [33] \\ \hline VGG19 & TILDA & 7: nondefect, hole, knitting, stain, thread, weave, shaded & 94.65\% & [34] \\ \hline \multirow{7}{*}{Visual Long Short Term Memory (VLSTM)} & \multirow{2}{*}{DHU-FD-500, Aliyun-FD-10500} & 10 defect types for DHU-FD-500 and DHU-FD-1000: Mispick, Broken pick, Double Flat, Slub, Felter, Draw-back, Sun-dries, Broken end, Oil stain & 99.47\% & [35] \\ \cline{1-1}  & & 7 defect types for Aliyun-FD-10500: Normal, Broken end, Broken picks, Hole, Stain, Crack, Felter & \\ \hline Fisher Criterion Based Autoencoder & Custom Dataset & 2: Defect-free and defective fabrics. & 98.6\% & [36] \\ \hline Autoencoder and MXNet & TILDA Patterned fabric & 5: Broken end, Hole, Netting multiple, Thick bar, and Thin bar & 98\% & [37] \\ \hline Deep Denoising Convolutional Autoencoder (DDCAE) & Custom Dataset & 2: Defective and NonDefective & 97.75\% & [38] \\ \hline \end{tabular}  
* Accuracy, 2 Reference.  
\end{table}
Table 1: Publication details for deep-learning based fabric defect detection literature.