esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Applications to Cardiovascular Medicine
### Arterial Wall
#### 6.4.2 Atherosclerotic Plaques
Kalman filters were used to estimate material parameters for each section while minimizing the diastolic-to-systolic displacements. Each iteration included the estimation of diastolic stress distribution by a backward increment method assuming a pressure load of 80 mmHg and population average-based axial stretch. From this preloaded state, a forward inflation problem to systolic pressure was then solved. Parallelization techniques were employed to reduce computer processing times achieving convergence between 12 h and three days. Sensitivity of the results to numerical and model parameters was carried out, finding that perivascular elastic properties have a significant effect on material parameter predictions. The estimated material parameters agreed with the magnitudes reported from available experimental data.
## 1 Introduction
In this way, the future scenarios on climate change imply a global increase in the ocean's temperature, a decrease in total primary productivity, and an increase of salinity [16; 17; 18]. The fishing sector is strongly affected by extreme climate conditions [19]. In Mexico, the shrimp is a high priority fishing resource due to its high demand and value on the market, providing a considerable source of currency and job generation for an important proportion of the fisheries' population [20]. Climate change could have severe repercussions on Mexico, since it will not only be temperature increases, but the frequency of extreme hydrometeorological events will increase too, having severe impacts on some species and causing their extinction if temperature variations surpass their adaptive capability [21]. Therefore, information is needed regarding spatial and temporal variability of this type of fishery to provide a view on the changes that shrimp populations can expect [22], and to provide useful scenarios to integrate strategies for climate change adaptation and thereby reduce potential future impacts in the fishing industry and fishers. The habitat suitability model (HSM) portrays information about relationships between species and environmental variables; therefore, it must be considered as a useful and improvable approach, and as an applicable tool to other problems such as the forecast of potential environmental impacts on species distribution [23; 24; 25]. These models are in full development and expansion, with new methods that allow evaluations on environmental variations impacts, which could ease the development of strategies that help to elaborate scientific answers on how the species react to the changes that global warming causes [26]. Many algorithms exist for HSM creation, but the Maximum entropy (MaxEnt) model is the most used because it implements one of the algorithms of precise mathematical formulation and only requires presence data and environmental variables [27]. Considering what was discussed previously, this work had the purpose of using HSM to analyze the historical distribution of brown shrimp (_F. californiensis_), blue shrimp (_L. stylirostris_), and white shrimp (_L. vannanei_) to project the models in function of the possible variations on oceanographic conditions caused by climate change, using the scenarios 2.6, 4.5, and 8.5 of the Representative Concentration Pathways (RCP) proposed by the Intergovernmental Panel on Climate Change (IPCC). This effort generated unpublished information about the potential effects on the distribution of the main species of the fishery in the face of climate change, and identified the variables with the greatest contribution to the modelling process.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Resource Accumulation
#### 4.1.1 Thuyg-20  
THUYG-20 [112] is a Uyghur speech recognition database published by Tsinghua University and Xinjiang University in 2017. It consists of 20 h of training speech recorded by 348 speakers (163 males and 185 females) and 2 h of test speech recorded by 23 speakers (13 males and 10 females). The recording device is a table microphone connected to a desktop. All the speech data were collected in an office environment, and the speakers uttered the sentences in the reading style. Besides speech data, the research group also published dictionaries (phoneme dictionary and morpheme dictionary), language models, and all the source codes ([[https://github.com/wangdong99/kaldi/tree/master/egs/](https://github.com/wangdong99/kaldi/tree/master/egs/)]([https://github.com/wangdong99/kaldi/tree/master/egs/](https://github.com/wangdong99/kaldi/tree/master/egs/))thuyg20, accessed on 25 December 2022). THUYG-20 is the first complete and open-source Uyghur database.
# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
## 4 Instantiation of the Proposed Framework to SoC Estimation and RUL Prediction
### Process for Battery State Prediction Instantiated for SoC and RUL
#### 4.2.1 Data Ingestion  
This implementation facilitates data acquisition from two primary sources: (i) the battery management system (BMS) connected with the battery of an EV and (ii) cyclers used in laboratory settings for battery testing procedures.  
The BMS is a system located in the EV and is designed to monitor and optimize battery behavior. It is also capable of handling different cell types and chemical compositions and managing from a single module up to hundreds of them. The BMS software is designed to be accessed and updated remotely in order to provide remote maintenance functionality and to anticipate faulty situations beyond the testing phase. The BMS is a highly customized solution for monitoring, protecting, and controlling all types of batteries. It is designed to receive measurements not only from the battery pack (BP) but also from the constituent components, including the modules and cells, through sensors. Specifically, one of the main functionalities of the BMS is to measure voltage, current, and temperature, as it has restricted the capacity of the sensors that are used for measurements. EVs have a high-voltage battery pack that consists of individual modules and Li-Ion cells organized in series and parallel, as illustrating in Figure 4. A cell is the smallest, packaged form a battery can take, and a module consists of several cells connected in either series or parallel.  
A single cell can store and release energy. A BP is then assembled by connecting modules together in a similar way.  
Multi-channel cyclers are used for cycling aging tests in labs. In each channel one cell is attached, and the cycler controls the way of charging and discharging. The cycler is wrapped from an environmental chamber in order to perform the tests in a stable environmental temperature. It can also provide measurements for voltage, current, temperature, charge and discharge capacity, energy, and power at the cell level during cycling. Some additional features that arise from these conditions are the internal resistance and inclination of the voltage curve dV/dt. Furthermore, it is able to collect metadata, including the temperature of the chamber, the charging protocol applied to the test, the cycle life of the cell, the overall operational time, the start and end times of the test, and the sampling frequency. Finally, the cell's barcode and the number of cells that are used in the test can be considered as additional features that describe the experiment. In aging lab tests, the cells are charged and discharged periodically following a capacity threshold. The cyclers provide a controlled environment for testing by applying different C-rates (defining the charging protocol), as well as values of current and temperature [27].
# Experiment and Analytical Model for Pore Structure of Early-Age Composite Cement Pastes by LF-NMR
## 1 Introduction
Moreover, SEM is unable to recognize gel pores due to the limitations of microscope magnification. NMR reflects the characteristics of pore structure by testing hydrogen protons' relaxation time of water in pores [26; 27; 28]. Compared to the methods mentioned above, nuclear magnetic resonance has the advantages of being non-invasive and non-destructive, since NMR methods do not require drying of the paste, which would destroy the C-S-H gel structure, and can observe the gel pores. As a new method for investigating the pore structure of cementitious materials, NMR has been widely adopted. [PERSON] et al. [29; 30] applied progressive drying to determine the surface relaxation rate and tested the pore structure of cementitious materials by NMR. Their comprehensive descriptions of the pore distribution are instructive. [PERSON] [31] investigated the pore structure of cement pastes with high-volume fly ash and highlighted that nuclear magnetic resonance (NMR) technology has distinct advantages over mercury intrusion porosimetry (MIP) in analyzing capillary and gel pores. In particular, NMR exhibits significantly higher sensitivity than MIP when examining fine pores and gel pores. [PERSON] et al. [32] studied the effect of the drying method on water content and pore size of cementitious materials by NMR, and compared this with the results by MIP and N2 gas adsorption measurements. The results showed that water content decreased significantly as the pore size distribution varied. NMR was able to reveal the weak change in pore size distribution, while MIP and N2 gas adsorption failed. In this study, low-field nuclear magnetic resonance (LF-NMR) technology was used to investigate the effects of water-to-cement ratio, curing time, and mineral admixture content (such as fly ash, slag, and silica fume) on the pore structure of early-age cement pastes. Tomore accurately fit the experimental data, an improved bimodal model was introduced, combining the Shimomura and Maekawa models with the Weibull distribution function. This model provides a better description of the influence of various admixtures on the pore structure of cement pastes. Using this model, the evolution of the pore structure of cement pastes, both with and without the incorporation of mineral admixtures like fly ash, slag, and silica fume, was analyzed.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 2 Group Communication
### CoAP-Based Group Communication  
There are two main approaches to allow CoAP to be used for the interaction with groups. The first approach relies on IPv6 multicasts and was adapted by the IETF CoRE working group. We have proposed an alternative approach in [9] that relies on IPv6 unicasts. These two communication methods have different characteristics that heavily influence the respective approaches that use them. In this subsection, we discuss the different approaches to realize CoAP-based group communication, after briefly introducing the key features of the CoAP protocol.
###### Abstract  
Measurement while drilling (MWD) data reflect the drilling rig-rock mass interaction; they are crucial for accurately classifying the rock mass ahead of the tunnel face. Although machine-learning methods can learn the relationship between MWD data and rock mechanics parameters to support rock classification, most current models do not consider the impact of the continuous drilling-sequence process, thereby leading to rock-classification errors, while small and unbalanced field datasets result in poor model performance. We propose a novel deep neural network model based on Bi-directional Long Short-Term Memory (BILSTM) to extract information-related sequences in MWD data and improve the accuracy of the rock-mass classification. Two optimization modules were designed to improve the model's generalization performance. Stratified K-fold cross-validation was used for model optimization in small and unbalanced datasets. Model validation is based on the MWD dataset of a highway tunnel in Yunnan, China. Multiple metrics show that the prediction ability of the network is significantly better than those of a multilayer perceptron (MLP) and a support-vector machine (SVM), while the model exhibits an improved generalization performance. The accuracy of the network can reach 90%, which is 13% and 15% higher than the MLP and SVM, respectively.
## 3 Fuzzy Harmony Search Algorithm with Dynamic Parameter Adaptation  
The value of the _HMR_ parameter is constant during the process of improvisation of the traditional HS algorithm; based on this it was decided to make it a fuzzy parameter for achieving dynamic parameter adaptation. The proposed method is based on the original harmony search algorithm described in Section 2, and specifically focuses on step 3, which is improvisation and uses fuzzy logic to perform the dynamic adjustment of the _HMR_ parameter as the number of iterations or improvisations advance to achieve an improvement in the convergence of the algorithm and obtain better results. To represent this idea, this parameter is converted into a fuzzy parameter. In this method, this value is considered to be fuzzy as it is updated within the FHS progress, and is determined by Equation (4), where _HMR_ has a changing value in the range 0-1:  
\[HMR=\ \frac{\sum_{i=1}^{n_{\mathit{I}}}h^{\mathit{I}}_{i}mr\ (\mathit{I}mr_{1i})}{\sum_{i=1}^{n_{ \mathit{I}}}h^{\mathit{I}}_{i}mr}, \tag{4}\]  
where _HMR_ is the memory considerations; \(r_{\mathit{I}mr}\) is the number of rules of the fuzzy system corresponding to _hmr_; \(h\mathit{n}r_{1i}\) is the output result for rule \(i\) corresponding to _hmr_; and \(h^{\mathit{I}}_{i}mr\) is the membership function of rule \(i\) corresponding to _hmr_. To represent this idea in a concise way, the pseudocode of the proposed method is shown in Figure 1:  
Figure 1: Flowchart of the FHS.  
The Type-1 and Type-2 FHS methods use an output that is _HMR_ parameter and the input is the iterations as presented in Figures 3 and 5, respectively.  
The representation of the input and the output variables in the fuzzy systems are shown in Figures 3 and 5. The input is known as the \"iteration\", and is granulated into three triangular membership functions, which are low, medium, and high. The output called \"_HMR_\" uses values from 0 to 1, the output is granulated into three triangular membership functions, low, medium, and high, and the fuzzy systems are of Mamdani type and contain three rules.  
The rules are designed based on the study of parameters of the algorithm, so that the initial iterations explore and the final iterations exploit the search space. In this case the rules have an increasing trend, as shown in Table 1. These rules are the same in the Type-1 and interval Type-2 fuzzy systems.  
Table 1 represents the idea of increasing the rules as iterations are progressing. To represent the linguistic variable of iterations, the following equation is used:  
\[Iterations=\ \frac{Current\ Iteration}{Maximum\ of\ iterations}. \tag{5}\]  
This phase considers a percentage of elapsed iterations to find the values of _HMR_. It initializes with low values of _HMR_ so that the algorithm has diversification and then achieves intensification.
## 1 Introduction
By calculating the micro deflecting angle \(\delta\) of every moment in a period, the micro deflecting motion trajectory (MDMT) of the gate-rotor is available. The MDMT of the gate-rotor is similar to the journal bearing center track to some extent. For problems such as deflection trajectory or center track, some scholars have conducted relative research. [PERSON] and [PERSON] established a model of the tool tip ellipse trajectory deflection control, measured it and analyzed the relation between tool tip ellipse trajectory deflection and the cutting quality [21]. [PERSON] defined a new concept of instantaneous whirling speed of axis orbit and studied its new perspective for the vibration analysis of cracked rotors [22]. To bore elliptical hole, [PERSON] and [PERSON] applied Gauss pseudospectral method to obtain the relation between load capacity and servo system, then made the shaft center orbit quickly get close to the designed elliptical hole [23]. To improve the stability of the journal bearing in twin-screw compressors, [PERSON] presented a homogeneous two-phase flow model to calculate bearing axis orbit and analyzed the impact from evaporating temperature and different built-in volume ratio [24]. By analyzing the MDMT, the working state of the gate-rotor and the tribology information can be obtained. The lubrication characteristics of the MPPs in SSCs can be evaluated by the MDMT comprehensively. Figure 5: The schematic drawing of the micro deflecting motion. In this paper, a mathematical model of the MDMT is established. Based on the model, numerical calculations have been carried out to evaluate the lubrication performance of different MPPs in machines with different sizes or under different working conditions.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
## 2 Principle of Random Truncation Small-Sample Reliability Analysis
### Model Hypothesis Testing  
#### 2.4.1 Linear Correlation Test  
Using linear correlation coefficient \(\hat{\rho}\) to test the linear correlation between linear models, the formula for calculating the correlation coefficient is as follows [22]:  
\[\hat{\rho}=\frac{\sum\limits_{i=1}^{p}x_{i}y_{i}-n\overline{x}\,\overline{y}} {\sqrt{\left(\sum\limits_{i=1}^{p}x_{i}^{2}-p\overline{x}^{2}\right)\left(\sum \limits_{i=1}^{p}y_{i}^{2}-p\overline{y}^{2}\right)}} \tag{9}\]  
where \(x_{i}=\ln t_{i}\), \(y_{i}=\ln[\ln(1/(1-F(t_{i})))]\), \(t_{i}\) is time of failure, \(\overline{x}\) and \(\overline{y}\) are average values of \(x_{i},y_{i}\) respectively, and \(p\) is the number of sample points.  
When \(\dot{\rho}>\rho_{a}\), it was believed that there was a linear correlation between \(x\) and \(y\), indicating the validity of the parameter estimation. \(\rho_{a}\) is the minimum value of the correlation coefficient (\(\alpha\) is the significance level, and the value of the significance level in engineering is usually 0.1, that is, \(\alpha=0.1\)) and \(\rho_{a}\) is obtained by the critical values table of linear correlation coefficients.
## 3 Outage Rate Analysis
### Selective Combining  
In case that selective combining is used, the conditional CDF of \(\gamma_{sc}\) is derived as  
\[F_{\gamma_{sc}}(x|\Omega_{1},\Omega_{2}) =\Pr[\gamma_{sc}\leq x]=\Pr[\gamma_{1}\leq x]\cdot\Pr[\gamma_{2} \leq x] \tag{18}\] \[=\left[1-\exp\left(-\frac{x}{\Omega_{1}}\right)\right]\cdot \left[1-\exp\left(-\frac{x}{\Omega_{2}}\right)\right]\]  
where \(x\geq 0\). Therefore, the outage rate for SC is expressed as  
\[P_{out}^{sc} =\int_{0}^{\infty}\int_{0}^{\infty}F_{\gamma_{sc}}(\gamma_{th}| \Omega_{1},\Omega_{2})f_{\Omega_{1},\Omega_{2}}(\Omega_{1},\Omega_{2})d\Omega _{1}d\Omega_{2} \tag{19}\] \[=\int_{0}^{\infty}\int_{0}^{\infty}\frac{\xi^{2}}{2\pi\sigma^{2} \Omega_{1}\Omega_{2}\sqrt{1-\rho^{2}}}\left[1-\exp\left(-\frac{\gamma_{th}}{ \Omega_{1}}\right)\right]\cdot\left[1-\exp\left(-\frac{\gamma_{th}}{\Omega_{2} }\right)\right]\] \[\cdot e^{\frac{1}{\rho^{2}-1}\left(\frac{(10\log_{10}\Omega_{1}- \mu_{1})^{2}}{2\sigma^{2}}+\frac{(10\log_{10}\Omega_{2}-\mu_{2})^{2}}{2\sigma^ {2}}-\frac{\rho}{\sigma^{2}}(10\log_{10}\Omega_{1}-\mu_{1})(10\log_{10}\Omega _{2}-\mu_{1})\right)}d\Omega_{1}d\Omega_{2},\]  
which can be numerically calculated through
# Molecular Subtyping and Outlier Detection in Human Disease Using the Paralique Algorithm
## 2 Methodology  
Clique-centric methods have long been used in a wide variety of applications [31]. On real and noisy data, however, clique finders may be inherently prone to high false negative rates. Indeed, an entire clique may be missed if even a single edge is lost. Thus, the paraclique algorithm is an effort to ameliorate difficulties posed by noise. Its essential strategy is first to isolate a maximum clique, and then expand it by glomming onto any new vertex that is adjacent to all but some predefined number of vertices already in this clique. This number is termed the glom term, \(g\). An illustration of paraclique construction with \(g=2\) is provided in Figure 1. Paraclique details and a thorough discussion of clique selection, edge weights, densities, and other important algorithmic features can be found in [32; 33]. Web-based versions of paraclique and related tools are available to the community via GrAPPA [34].  
In this effort, we were mainly concerned with case-control transcriptomic data, for which we applied an initial filtering step to limit the effects of confounding factors. False discovery rate adjusted \(p\)-values for the differential expression of genes between case data and control data were calculated using the Benjamini-Hochberg method [35] accessed via the EntropyExplorer R package [36]. Only those genes with \(p\)-values less than or equal to 0.1 were retained. The motivation for such a filter was to restrict attention to genes of potential interest in the differential diagnosis of disease. After all, we wanted to concentrate on potential disease subtypes and not be distracted by irrelevant subgroups such as age, ethnicity, or hair color. Once filtering was complete, we focused our attention only on case data, and reversed the roles of variables and correlations. We therefore calculated pairwise Pearson correlation coefficients between samples (not genes), and across their corresponding lists of expression levels (not patients). We then thresholded the resultant correlation matrix using spectral methods as in [37] and constructed an unweighted graph with vertices representing samples and edges between highly correlated sample pairs. Once this graph had been created, we invoked the paraclique algorithm to extract dense, noise resilient subgraphs. Thus, each such subgraph represented a putative subtype or outlier.  
For consistency, and because this work mainly represents a proof of concept, we set the glom term to \(g=1\) throughout this effort. Depending on the data under study, however, crisper results may naturally be anticipated with fine tuning. In [27], for example, a glom term of \(g=5\) was found to produce superior ontological enrichments when studying non-obese diabetic mice as a model of type 1 diabetes mellitus.