esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Analysis and Sludge Volume Index  
The size, \(d_{P}\), of a floc of irregular shape was calculated in terms of the equivalent diameter by [17]  
\[d_{P}=(4A/\pi)^{1/2} \tag{1}\]where \(A\) was the projected area of the floc. Then the average size and the size distribution of the flocs in three images within each compartment were used to reflect the floc size at the corresponding moment.  
The two-dimensional fractal dimension was used to characterize the fractal characteristics of the flocs. The fractal dimension \(D_{f}\), defined how the projected area (\(A\)) of the particles scales up with the length of the perimeter (\(P\)) according to [18; 19]  
\[A\propto P^{D_{f}} \tag{2}\]  
\(D_{f}\) depicted the surface morphology of the sludge floc in a two-dimensional projection; its values ranged from \(D_{f}=1\) for the projected area of a sphere (a circle) to \(D_{f}=2\) for a line (e.g., a chain of particles) [20]. According to the conceptual model of flocs, the fractal dimension was not determined by the size of the flocs. Therefore, here the fractal dimension of flocs was measured by big flocs, instead of the whole fractal dimension of flocs, including small flocs. According to the result by [PERSON] et al., the statistical fractal dimension was the same for the big flocs and smaller flocs in a jar test experiment [21].  
The sludge volume index (\(SVI\), mL/g) referred to the slurry mixed liquid corresponding to the volume occupied by dry sludge for 1 g after 30 min of static precipitation and the calculation formula was as follows:  
\[SVI=SV\times 1000/MLSS\times 100 \tag{3}\]  
where \(SV\) was the precipitation rate which refers to the volume occupied of sludge blanket for 100 mL mixed liquid after 30 min of precipitation, and \(MLSS\) was the mixed liquid suspended solids.  
\(SVI\) could better reflect the loose degree and the sedimentation performance of the activated sludge. The smaller the value of \(SVI\) was, the better the sedimentation performance of the sludge was, but if the value of \(SVI\) was too low, it might be caused by the high content of inorganic impurities in the sludge and in this case, the activity of the sludge was low. If the value of \(SVI\) was too high, it usually caused poor performance of the sedimentation process and it might be in danger of sludge expansion.
## 3 Boundary Conditions and Material Models
### Multiscale Failure Analysis
#### 3.3.2 Elastic-Plastic Material Model for Epoxy Resin  
This elastic-plastic material model is intended to describe the mechanical performance of the pure resin, such as, the matrix in micromechanical model and the resin-rich zone in mesomechanical model. The elastic properties of resin (Epoxy resin Epikote 828) are obtained from Reference [13]. The plastic deformation and failure data of Epikote 828 are obtained from References [23,24]. In summary, the matrix properties utilized in this paper are presented in Table 2.  
To account different yielding behavior under uniaxial tension, uniaxial compression and shear, the von Mises criterion and the Drucker-Prager yield criterion are chosen. The von Mises are used to define the yield and inelastic flow behavior of a metal at relatively low temperatures. In this paper, this criterion is applied to describe the tensile behaviors of the epoxy resin. The Drucker-Prager criterion is applied to model the failure behavior of the pure matrix materials under compression and shear load. [PERSON] [2] has proved that using this criterion to predict failure of matrix materials is feasibility, and the two criteria are readily available in Abaqus.
## 2 Materials and Method
### Method for Estimation of Damping Ratio  
The damping ratio was estimated by applying linear vibration theory with a single degree-of-freedom system with viscous damping by using Equations (5) and (6). The equation of motion is given by [47]:  
\[m\ddot{x}+c\dot{x}\ +kx\ =c\dot{u}\ +ku, \tag{3}\]  
where \(m\) is the mass of the moving object, \(c\) is the linear viscous damping coefficient, \(k\) is the linear elastic stiffness coefficient, and \(x\) and \(u\) are the response displacement and the excitation displacement, respectively. The transmissibility value (\(T_{r}\)) for a linear spring-mass system with a single degree-of-freedom system with viscous damping was calculated using the following equation, a Fourier transformation of Equation (3) [46]:  
\[T_{r}\ =\sqrt{\frac{1\ +\ \left[2\ \xi\left(\frac{f_{f}}{f_{n}}\right)\right]^{ 2}}{\left[1-\left(\frac{f_{f}}{f_{n}}\right)^{2}\right]^{2}\ +\ \left[2\ \xi\left(\frac{f_{f}}{f_{n}}\right)\right]^{2}}}, \tag{4}\]  
where \(f_{f}\) is the forcing frequency [Hz], \(f_{n}\) is the natural frequency [Hz], and \(\xi\) is the damping ratio. When \(f_{f}\cong\ f_{n}\), the damping ratio can be estimated using Equation (4) [47]:  
\[\xi\ =\ \frac{1}{2}\ \times\ \sqrt{\frac{1}{T_{r}^{2}\ -1}}, \tag{5}\]  
When \(T_{r}\ \gg 1\), the damping ratio can be estimated using Equation (5) [47]:  
\[\xi\ \approx\frac{1}{2T_{r}}, \tag{6}\]  
Transmissibility curves of damped systems are shown in Figure 6.  
The evaluation of the effects of temperature on vibration transmission are arranged in sequence below:  
1. Preparation of the samples and mass blocks (size, weight)
2. Conditioning of the elements (temperature, time)
3. Assembly of the measurement system (fixture, static stress)
4. Parameter setting and setup of the vibration measurement (frequency band, sweep rate, amplitude, frequency range)
5. Expression of the measured and calculated results (applied equations, units).  
Figure 6: Transmissibility curves with varying damping.
## 3 Application
### Build Monitoring  
#### 3.3.1 Data Collector Monitors  
Data collector monitors are destined to obtain numerical data from a model and accomplish statistical calculations for the retrieved data, i.e., to construct one or more performance metrics [20]. We define data collector monitors to obtain the different mean times, the mean time to first failure: MTTFF, the mean time between failures: MTBF, and the mean time to repair: MTTR. For instance, MTTFF monitors shall record the component's first failure time. They are applied to the different failure transitions for the components.  
Figure 6: Master cylinder: timed CPN specifications model.
## 4 Results and Discussion
### pH Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**RPD**} & \multicolumn{2}{c}{**RER**} \\ \cline{2-5}  & **Mean** & **95\% CI** & **Mean** & **95\% CI** \\ \hline FSR & 2.747 & (2.617; 2.877) & 10.950 & (10.451; 11.450) \\ RR & 2.921 & (2.784; 3.059) & 11.654 & (11.108; 12.200) \\ LASSO & 2.915 & (2.780; 3.052) & 11.635 & (11.071; 12.199) \\ EN & 2.947 & (2.814; 3.081) & 11.762 & (11.214; 12.311) \\ PCR & 2.939 & (2.799; 3.079) & 11.726 & (11.163; 12.289) \\ KSVR linear & 2.393 & (2.278; 2.509) & 9.536 & (9.098; 9.974) \\ PLS & 2.838 & (2.698; 2.978) & 11.321 & (10.773; 11.869) \\ iPLS & 2.701 & (2.561; 2.840) & 10.755 & (10.259; 11.250) \\ RF & 2.548 & (2.417; 2.679) & 10.146 & (9.670; 10.621) \\ BoTR & 2.501 & (2.378; 2.624) & 9.965 & (9.491; 10.438) \\ KPCR polynomial & 2.687 & (2.555; 2.819) & 10.724 & (10.177; 11.272) \\ KPCR rbf & 2.893 & (2.753; 3.034) & 11.546 & (10.979; 12.114) \\ KPLS polynomial & 2.640 & (2.510; 2.770) & 10.626 & (10.022; 11.030) \\ KPLS rbf & 2.835 & (2.698; 2.970) & 11.312 & (10.759; 11.865) \\ KSVR polynomial & 2.586 & (2.467; 2.705) & 10.310 & (9.843; 10.777) \\ KSVR rbf & 2.540 & (2.420; 2.660) & 10.129 & (9.654; 10.604) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RPD and RER results in the anthocyanin parameter for the external validation sets—Mean and associated 95% confidence intervals obtained for each regression method. of the variability that can be expected from different models built under slightly different conditions. Concerning the scientific literature on this subject, most of the works from Table 1 did not identify the important spectral regions for the anthocyanin concentration, nevertheless, the results obtained here are in agreement with those previously reported in [10].
## 3 Application Examples
### Calculation of Fracture Parameters
#### 3.1.2 Fracture Width  
Identify fractures by analysing wall core and imaging logging data. Calculate the hydrodynamic width of fractures by utilising Equation (4). According to the statistical analysis results shown in Figure 4, the average hydrodynamic width of the fracture is mainly between 0.02 and 0.1 mm.
###### Abstract  
In modern tunnel construction, complex environments with high geothermal gradients and abundant groundwater are frequently encountered. To investigate the damage and failure mechanisms of sandstone under the combined effects of temperature and water, uniaxial compression tests were conducted on sandstone at different temperatures (25 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, 85 \({}^{\circ}\)C, and 95 \({}^{\circ}\)C) and soaking durations (0.5 h, 1 h, and 3 h). The acoustic emission (AE) signals and energy evolution during the damage and failure processes were analyzed, revealing the damage characteristics and failure mechanisms of sandstone. The results indicate the following: (1) As the temperature increases, under the 3 h condition, the water content of sandstone is highest at 55 \({}^{\circ}\)C, reaching 3.01%, and the thermal expansion effect of sandstone is not obvious. Under the conditions of 85 \({}^{\circ}\)C and 95 \({}^{\circ}\)C, the thermal expansion effect leads to a decrease in the water content, enhances the water absorption softening effect, increases the plastic deformation capacity of sandstone, and weakens its brittle failure capacity. (2) When soaked for 0.5 h and 1 h, the maximum acoustic emission ring count and maximum acoustic emission energy of sandstone increase initially, then decrease, and subsequently increase again as the temperature rises, while the cumulative acoustic emission ring count gradually increases with temperature. Under the 3 h soaking condition, the maximum ring count, maximum energy, and cumulative ring count of sandstone at all temperatures show a consistent increasing trend with temperature. (3) The increase in soaking time reduced the damage variable of sandstone, with the largest reduction of 54.17% under the 3 h condition. At different temperatures, the damage variable of sandstone was smallest at 55 \({}^{\circ}\)C, only 0.33. (4) Sandstone primarily experiences tensile failure under different temperatures and soaking times. The extension of soaking time promotes the development of shear cracks, while the increase in temperature can effectively promote the expansion of tensile cracks. The research results provide certain theoretical references for the damage and failure of surrounding rock in modern tunnel construction.  
Keywords:temperature water; acoustic emission; energy evolution; damage and failure +
Footnote †: journal: Applied sciences
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 1 Introduction
* (Task 2) TaSAC-BP: Alignment and validation of speech signals with partial and inaccurate text transcriptions, organized by the University of the Basque Country (UPV/EHU), which consists of aligning text and audio extracted from a plenary session of the Basque Parliament. * Search on Speech Challenge (SoSC), organized by Universidad San Pablo-CEU and AuDIaS from Universidad Autonoma de Madrid, which consists of searching in audio content a list of terms/queries. The main novelty of the IberSpeech-RTVE 2022 Challenges compared to previous evaluations [11; 12] relies on the two following features: (1) A text and speech alignment challenge has been proposed for the very first time in the Albayzin evaluation series, including two different sub-tasks for two possible applications; and (2) new and more challenging databases have been released for all the evaluation tasks, covering different domains but focusing on broadcast media content, some of them, such as the RTVE and the Basque Parliament databases, being specifically created for these challenges. A total of 7 teams from industry and academia registered to participate in the IberSpeech-RTVE 2022 challenges, with 20 different systems submitted in total. Compared to previous evaluations, the number of participants has significantly decreased. In the 2018 edition,16 teams submitted 85 systems in the different challenges. In the 2020 edition, 12 teams submitted a total of 31 systems. We present in this paper an overview of the IberSpeech-RTVE 2022 challenges along with the data supplied by the organization to the participants and the performance metrics. The overview includes a detailed description of the systems showcased for evaluation, their corresponding results, and a comprehensive set of conclusions derived from the 2022 evaluation campaign and previous campaigns in 2018 [11] and 2020 [12]. This paper will serve as reference for anyone wanting to use the datasets provided in the evaluation. Up to the date of writing this paper, more than 50 international research groups have asked for access to the RTVE database. The paper is organized as follows: Section 2 presents the databases used in the different challenges; Section 3 describes the four IberSpeech-RTVE 2022 challenge tasks: speech-to-text transcription, speaker diarization and identity assignment, text and speech alignment, and search on speech, along with with the performance metrics used in each challenge; Section 4 provides a brief description of the submitted systems; Section 5 presents and discusses the results; and finally, a summary of the paper, conclusions and future work are outlined in Section 6.
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 4 Methodology  
This paper introduces a methodology for the automatic translation of Mixtec-Spanish texts. The proposed approach is divided into four phases (shown in Figure 3), each addressing specific aspects of the translation process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool Name** & **Language** & **Total Languages** \\ \hline Microsoft Bing & Otomi, Maya & 111 \\ Apertium & Hindi, Esperanto & 51 \\ Wixarika-traductor & Wixarika & 2 \\ Mainumby & Guarani & 2 \\ ModernMT & Aymara, Guarani, Igbo & 200 \\ Google Translate & Guarani, Aymara & 134 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Translation tools incorporating low-digital-resource languages. We detail the names of the tools, examples of low-resource languages they support, and the total number of languages available in each tool.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Translation Techniques** & **Tools** & **Metric** \\ \hline
[26] & Transformer, & Fairseq & BLEU 36.73, ChrF 54.40, \\  & POS tagging & & WER 49.30 \\
[27] & Transformer & Not specified & BLEU 51.11, P 51.11, \\  & & R 53.49\%, F 52.27\%. \\
[28] & Transformer & Not specified & BLEU 25.6, ChrF 0.55 \\
[29] & Transformer & Not specified & BLEU 48.62 \\
[30] & Bilingual dictionaries, & Not specified & BLEU 51.6 \\  & Syntactic rules & & WER 0.77 \\
[31] & Morphosyntactic parsers & Apertium & WER 0.77 \\
[34] & RNN, transformer, SMT & OpenNMT-py, & BLEU 11.7, ChrF 0.47 \\  & & Moses & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Research summary on translation methodologies utilizing a combination of corpora, highlighting the integration of Statistical Machine Translation (SMT), Recurrent Neural Networks (RNNs), and other techniques. The table outlines the reference works, the translation techniques applied, the computational tools used, and a range of metrics for evaluating translation performance, including Precision, Recall, F-Measure, BLEU score, Word Error Rate (WER), and character F-measure (ChrF).
# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 4 Results
### Model Performance  
#### 4.2.1 Complete Dataset  
The first model analyzed was developed with the complete dataset, including all the observations deemed valuable (see Figure 3 in Data Preparation). The accuracy of the model was 0.9113, exceptionally high when compared to the results obtained by other studies. The AUC for this model obtained from the ROC curve was 0.8938 with a 95% CI of 0.8929-0.8948, also a better result than any observed at the time of arrival to the ED in any previous study.  
Figure 9 shows the ROC curve obtained for this first model, varying the discrimination threshold from 1 (bottom left corner) to 0 (upper right corner). As always sought in ROC curves, the objective is to have the curve as close to the upper left corner as possible, which implies a high true positive rate (sensitivity), with a low false positive rate (1--specificity). All the ROC curves for the following models had the same shape, so they have not been included in the report to avoid repetition.  
Figure 8: Top 10 symptoms (CCS) in adults.  
Figure 9: ROC curve complete dataset.  
The importance of the variables in the model was obtained with the summary function of the \"gbm\" package and it showed that the symptom classified with the CCS method was the variable providing the higher information gain at every tree split (76.98%), followed by the triage level (16.68%) and age (6.08%). The four remaining variables (accumulated number of visits during the year, days of age for babies, gender, and frequency of appearance for CCS symptoms) had an almost non-significant contribution to the model adding up to 0.25% of the explanation of it, with the frequency of CCS symptoms showing a null relative influence. This information was used to create a model including only the first three variables and to run the analysis again. This yielded results similar to these of the complete model, with an accuracy of 0.9112 and an AUC of 0.8936 (95% CI 0.8927-0.8946). Variable importance is shown in Figure 5.  
The initial model was tested for overfitting, but the results clearly showed that using three levels of depth for the trained trees and a 0.01 shrinkage parameter, 1000 trees were an optimal amount and not large enough to run into overfitting. Figure 10 shows the error on both the train (black line) and test (green line) subsets. It is clear that the performance on the test set is not being jeopardized by overfitting to the training set characteristics. The dotted blue line shows the optimal number of iterations (trees) given the input parameters.
## 4 Discussion  
### The Necessity of Considering Variety and Water Stress  
The current models from existing research have not accounted for differences in varieties and water stress. Therefore, further validation is needed to determine whether the _f_PAR estimation model based on the vegetation index will produce accuracy differences under different varieties and water stress. For instance, the estimation model conducted by the variety of T3 should be applied to T11 to test its suitability among different varieties. Similarly, the models based on one IS also should be assessed by other ISs to ensure the necessity of considering water stress or not.  
The correlation model between the vegetation index MNDVI and _f_PAR based on T3 is Equation (5).  
\[f\text{PAR}=1.4198\times\text{MNDVI}-0.4126, \tag{5}\]  
It was found that the model achieved R of 0.710 when used to estimate T3, as shown in Figure 9a. However, when this model was used to estimate the fPAR of variety T11, some plots had estimated _f_PAR values less than 0, as shown in Figure 9b, which is an incorrect estimation result. Similarly, Equation (6) is based on IS1 to establish a correlation model between MNDVI and _f_PAR.  
\[f\text{PAR}=0.3647\times\text{MNDVI}+0.535, \tag{6}\]  
When using the measurement value of IS-A for accuracy verification, the RMSE is 0.077, as shown in Figure 9c, while when using IS-G for verification, the RMSE is 0.245, as shown in Figure 9d. It can be seen that without considering the differences in variety and water stress, there will be significant errors in the estimation results and even unreasonable estimation values. This indicates the need to consider the differences in variety and water stress.  
Figure 8: Comparison of MNDVI estimation results and true values on satellite scale in HB.
## 3 Methodology
### Data Pre-Processing
#### 3.2.1 Data Filtration  
Data from heterogeneous platforms in real-world environments often contain incomplete and inconsistent values, which can significantly affect the effectiveness of machine learning classifiers. In the \(D2\) dataset, the 'Flow Packets/s' feature includes abnormal values like 'NaN' and 'Infinity', which can adversely affect the classification accuracy of predictive models. To address these issues, we implemented basic data cleaning procedures, including the removal of infinite values and incomplete samples from the datasets.
# Experimental Study on Mechanical Properties of Highway
## 4 Discussion  
Scholars around the world have conducted extensive research on the mechanics and deformation mechanisms of railway subgrades in cold regions. These studies have utilized advanced testing techniques and instruments to measure the comprehensive effects of temperature, water content, freeze-thaw cycles, and other factors on the mechanical properties of subgrade soil in cold regions. However, the focus of these studies has been primarily on subgrade soil in a frozen state, with limited research conducted on subgrade soil in a melted state. It is important to consider the stability of subgrades in both frozen and melted states. This study examines the highway subgrade soil under the melted state in cold regions. The research found that the number of freeze-thaw cycles, water content, and confining pressure significantly influenced the mechanical properties of subgrade soil after thawing, while the freezing temperature had a minimal effect. The main reason for this is that during the freezing process, water consolidates into ice, resulting in larger pores between soil particles, which affects soil strength. The study investigated the effects of several factors on the initial tangent modulus, ultimate deviatoric stress, and failure ratio of the soil. In practical engineering, it is crucial to consider the influence of these factors on subgrade stability and take corresponding measures to improve the bearing capacity and durability of the subgrade. However, this study has certain limitations, such as experimental conditions and the complexity of soil mechanical properties, which need further exploration and improvement in future studies. This includes taking into account temperature changes throughout the four seasons and vehicle loads.