esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Analysis  
Statistical analysis for the apparent bulk density, powder flowability, and soup flowability was performed using the software JMP Pro 14.2 (SAS Institute, Cary, NC, USA). To test for significant differences, a one-way analysis of variance (ANOVA) followed by a multiple comparison Tukey HSD test (\(p<0.05\)) was conducted. In case of unequal variances, a Welch's test was used to determine if significant differences were present. Multivariate data analysis (MVDA) was performed on the GC-MS data. Pre-processing of GC-MS data, partial least squares discriminant analysis (PLS-DA) and the calculation of discriminant compounds (variable identification coefficients (VID)) were achieved as described by [PERSON] et al. (2014) and [PERSON] et al. (2012) [33; 34], using Automated Mass Spectral Deconvolution and Identification System (AMDIS) (version 2.72, 2014, National Institute of Standards and Technology, Gaithersburg, MD, USA) for peak deconvolution, Mass Profile Professional (MPP) (version B12.00, 2012, Agilent Technologies, Diegem, Belgium) for peak filtering and alignment, SOLO (version 8.7.1, 2020, eigenvector Research, Inc., Manson, WA, USA) for PLS-DA analysis, and OriginPro (OriginPro 2020, OriginLab Corporation, Northampton, MA, USA) for data visualisation.
# Augmenting CCAM Infrastructure for Creating Smart Roads and Enabling Autonomous Driving
## 7 Conclusions  
It is undeniable that autonomous driving is a complex and problematic technology, which requires an intelligent environment. In this paper, we focused on enabling an intelligent environment by augmenting the CCAM infrastructure to create smart roads for autonomous driving. Meaning thereby, autonomous driving advances rely heavily on the CCAM infrastructure. Therefore, we identified sensory infrastructure, integration platforms, and communication interfaces for interconnections between components of the infrastructure. In addition, we parameterized the road and network infrastructures for advancements and evaluations under various conditions. The CCAM solution approach is validated by a set of experiments, which considered machine learning algorithms, i.e., linear regression model, gradient boosted regression model, and random forest linear regression model, for vehicle count (for road traffic analysis), mobility monitoring (for parking spaces), and WiFi devices count (for foot traffic analysis) using the external information.  
The results, in the form of an accuracy metric, obtained by the above experiments for vehicle count predictions and WiFi device count predictions, are provided as follows: the linear regression model resulted with 65% accuracy, the random forest linear regression model resulted with 93.8% accuracy, and the gradient-boosted regression model resulted with 94.3% accuracy for vehicle count predictions; the linear regression model resulted with 41.8% accuracy, and the gradient boosted regression model resulted in 91% accuracy for WiFi devices count predictions. Hence, the traffic count patterns, traffic intensity, traffic flow density, foot traffic analysis, and mobility monitoring and management requirements can be accurately predicted given the right external information about traffic features and condition. Our goal is to make roads of United Arab Emirates University smarter by deploying our solution approach for augmented CCAM infrastructure. Therefore, we plan to find inter-stakeholder relationships and smarter collaborations between CCAM (on-road) and vehicle (on-board) infrastructures. Consequently, these relationships and collaborations will introduce new challenges and ask for new effective solutions from the relevant stakeholders, research communities, and industry.  
Conceptualization, M.A.K.; Investigation, M.J.K., M.A.K. and O.U.; Methodology, M.J.K., M.A.K., O.U. and S.M.; Project administration, M.J.K., M.A.K., F.I., H.E.-S. and S.T.; Supervision, M.A.K., F.I. and S.T.; Writing--original draft, M.J.K., M.A.K., O.U. and S.M.; Writing--review and editing, M.J.K., M.A.K., F.I., H.E.-S. and S.T. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by Emirates Center of Mobility Research (ECMR) UAEU, Sandooq Al Watan, and UAEU-ZU research project.  
Data Availability Statement: Not applicable.  
Figure 19: SHAP values for linear regression model to show features’ contributions.
###### Abstract  
Mixed matrix membranes (MMMs) are seen as promising candidates to overcome the fundamental limit of polymeric membranes, known as the so-called Robeson upper bound, which defines the best compromise between permeability and selectivity of neat polymeric membranes. To overcome this limit, the permeability of the filler particles in the MMM must be carefully matched with that of the polymer matrix. The present work shows that it is not sufficient to match only the permeability of the polymer and the dispersed phase, but that one should consider also the individual contributions of the diffusivity and the solubility of the gas in both components. Here we compare the gas transport performance of two different MMMs, containing the metal-organic framework CuNi-MOF in the rubbery Pebax(r)1657 and in the glassy poly(ether-ether-ketone) with cardo moiety, PEEK-WC. The chemical and structural properties of MMMs were investigated by means of FT-IR spectroscopy, scanning electron microscopy and EDX analysis. The influence of MOF on the mechanical and thermal properties of both polymers was investigated by tensile tests and differential scanning calorimetry, respectively. The MOF loading in Pebax(r)1657 increased the ideal H2/N2 selectivity from 6 to 8 thanks to an increased H2 permeability. In general, the MOF had little effect on the Pebax(r)165 membranes because an increase in gas solubility was neutralized by an equivalent decrease in effective diffusivity. Instead, the addition of MOF to PEEK-WC increases the ideal CO2/CH4 selectivity from 30 to -48 thanks to an increased CO2 permeability (from 6 to 48 Barrer). The increase in CO2 permeability and CO2/CH4 selectivity is maintained under mixed gas conditions.  
mixed matrix membrane; glassy polymer; rubbery polymer; PEEK-WC; Pebax(r)1657; gas separation; CuNi-MOF +
Footnote †: journal: Applied Sciences
## 1 Introduction
Through comparison of numerical simulations and indoor experiments, it was proposed that the 5.8 GHz microwaves' heating time and penetration depth were shortened by one quarter compared with the 2.45 GHz microwaves, and 5.8 GHz microwaves have better application prospects in road microwave deicing [19; 20]. [PERSON] et al. proposed the application of 5.8 GHz magnetrons in asphalt pavement maintenance. By comparing the price, temperature rise, and heating depth of 2.45 GHz and 5.8 GHz magnetrons, it was found that the 5.8 GHz magnetrons can be effectively used for asphalt pavement maintenance [21; 22]. [PERSON] et al. concluded that the microwave deicing efficiency is proportional to the microwave frequency and material dielectric loss using the CST (CST Studio Suite v2008 SP6, CST China Ltd., Shanghai, China), Matlab (Matlab2007b, MathWorks, Natick, MA, USA)., and ANSYS (ANSYS9.0, ANSYS, Pittsburgh, PA USA) simulation software. It was proposed that high frequency microwaves can improve the deicing efficiency [23]. In 2009, [PERSON] et al. conducted an in-depth study of the road performance and microwave absorption capability of the taconite asphalt pavement, and further analyzed the prospects of its application in highways [24; 25; 26]. In 2017, [PERSON] et al. used the reflection properties of metals for microwaves to incorporate steel slag into asphalt mixtures to increase the microwave de-icing efficiency of asphalt pavements [27; 28]. The research on microwave heating technology has been going on for more than 30 years since the 1980s. However, there are still many problems that need to be solved when the microwave heating technology is applied to removing snow and ice on the road. The research on microwave deicing is mainly about the analysis of the factors that affect the deicing efficiency. In this paper, the microwave deicing efficiency is defined as the heating rate of the concrete surface when heated to 0 \({}^{\circ}\)C. Based on the analysis of the mechanism of microwave heating and deicing, the effects of different microwave frequencies and different road structure materials on microwave deicing efficiency are analyzed. Based on the finite element theory, a simulation model is established to analyze the influencing factors of the deicing efficiency, and the simulation model is verified by an indoor experiment. The accuracy of the model is proved. It provides theoretical guidance and a practical basis for the popularization and application of microwave deicing technology.
# Microstructure-Based Prediction of the Elastic Behaviour of Hydrating Cement Pastes
## 6 Results and Discussion
### Parametric Study
#### 6.3.3 Influence of Elastic Properties of C-S-H  
Despite the Portlandite CH product, the crystal structure of C-S-H in the cement paste is not yet fully known, and there is still ongoing debate over its nanostructure. In the literature, C-S-H is typically divided into two different phases, with different packing densities, and consequently with two different Young's moduli. A wide range of measured values for the corresponding elastic properties is reported in the literature, mainly for the lower density C-S-H. The amount of porosity (categorised as gel porosity) in the C-S-H has significant influence on its stiffness. A range between 13 GPa and 26 GPa, in accordance with Table 4, was adopted for Young's modulus of the outer C-S-H, to evaluate its influence on the effective Young's modulus of the REV. The obtained results from these analyses are plotted in Figure 16a. The influence of C-S-H stiffness at very early ages is not significant since the hydration degree is low and not much C-S-H product is formed yet. However, when the minimum value of 13 GPa was adopted for the outer C-S-H, the results from the FEM model overestimated the test results at very early ages. By increasing the degree of hydration, the higher stiffness for the outer C-S-H results in higher values for the effective elastic modulus of the REV.  
To better show the influence of the C-S-H stiffness, particularly at very early ages, a single value was assumed for [PERSON]'s modulus of C-S-H volumes in the REV. Then, a back-analysis was undertaken to fit the effective elastic modulus obtained from the model to the corresponding experimental result by varying [PERSON]'s modulus of the C-S-H. The results of this back-analysis are plotted in Figure 16b. Additionally, the given value of the back-fitted Young's modulus of C-S-H is plotted as a secondary vertical axis in this figure. As shown, by decreasing [PERSON]'s modulus of C-S-H at very early ages,  
Figure 15: Influence of voxel resolution on the effective elastic Young’s modulus of REV.  
the effective Young's modulus from the model captures the experimental value. This can be an evidence that, at very early ages, only low dense C-S-H starts to form in the microstructure, which agrees with similar conclusions stated by other authors [38; 39].  
It should be noted that the volume ratio between the inner and outer C-S-H products is not constant during hydration, and it also has a dependency on the \(w/c\) ratio [39]. However, this influence is not controlled in the HYMOSTRUC3D hydration model, as almost the same volume ratio between the inner and outer C-S-H is assumed to form for any \(w/c\) ratio.
## 2 Materials and Methods
### Control Methods
#### 2.2.2 Fuzzy Logic Controller  
Many terms used randomly in daily life have a fuzzy structure. The verbal or numerical expressions used when we are describing a thing or an event, commanding, and in many other cases, are also fuzzy. Some of these terms can be said to be young, long, short, hot, cold, hot, cloudy, partly cloudy, sunny, fast, slow, very, small, little, little, too much, etc. When an event is explained and a decision is reached in a situation, terms that do not express such certainty are used. Thanks to the fuzzy logic controller, the examination of systems gained a new perspective.  
A FLC fundamentally consists of three parts. These are fuzzification, rule base inferencing, and defuzzification blocks. When modelling the FLC, error and error change signals are employed. A FLC block diagram is illustrated in Figure 7.  
In this study, in order not to exceed the frequency limits, a designed fuzzy rule table is given in Table 2. Here, a seven stage fuzzy rule table which belongs to triangular membership functions, was formed. The regions in Table 2 were formed as three main regions: positive, zero, and negative. They were placed as two axes adjacent to each other, and their names have been given as \(ACE\) and \(\Delta ACE\). Moreover, they were divided into three parts: large, medium, and small. The fuzzy logic controller has the ability to make decisions with reference to the rules in Table 2. For instance, if \(ACE\) is larger than the set value and \(\Delta ACE\) is rapidly increased, the output of the FLC is expected to be large.  
A Mamdani fuzzy interface was used when arranging the fuzzy logic blocks. Ultimately, the output of the system approached the desired value. All other rules were obtained in a similar way. The weights of the employed triangular membership functions are demonstrated in Figure 8. These weights were found by the trial and error method, to minimize the system error.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller** & \(\mathbf{K_{p}}\) & \(\mathbf{K_{i}}\) & \(\mathbf{K_{d}}\) \\ \hline PID & \(\frac{3\times K_{a}}{5}\) & \(\frac{6\times K_{a}}{5\times T_{a}}\) & \(\frac{3\times K_{a}\times T_{a}}{40}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: ZN method of detection parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{8}{c}{**Fuzzy Logic Rules for FLC**} \\ \hline \multirow{2}{*}{\(\mathbf{ACE}\)} & \multicolumn{8}{c}{\(\mathbf{\Delta_{ACE(k)}}\)} \\ \cline{2-9}  & \(\mathbf{N_{L}}\) & \(\mathbf{N_{M}}\) & \(\mathbf{N_{S}}\) & \(\mathbf{Z}\) & \(\mathbf{P_{S}}\) & \(\mathbf{P_{M}}\) & \(\mathbf{P_{L
# Dynamic Stiffness Effect of Mechanical Components on Gear Mesh Misalignment
## 6 Conclusions  
This study demonstrated that considering the elastic stiffness of mechanical components, such as housings, bearings, and shafts, in the gear design process can lead to significant effects on gear mesh misalignment. In particular, unlike the conventional approach of considering the static stiffness only, we consider the dynamic stiffness along with the gear mesh frequency. This approach is appropriate because in normal operation conditions a gear train is fundamentally under dynamic loading. In this work, we proposed a simple design process to compute the gear mesh misalignment by considering the dynamic stiffness under a quasi-static condition and examined the impact of this consideration using a two-stage parallel gearbox. We found that employing the dynamic stiffness can lead to different design results in terms of misalignment, face load factor, contact pattern, and transmission error when compared to cases in which only the static stiffness was considered. In particular, differences in the gear mesh misalignment between cases using static or dynamic stiffness increase with higher gear mesh frequency, and also larger applied torque. This numerical study allows us to conclude that, for improved design results, the dynamic stiffness should be considered in the gear design process because it can directly lead to improved tooth modification and whine noise control. We emphasize again that the aim of this work was not an NVH analysis, but was rather a consideration of the dynamic stiffness effect in the gear design process under a quasi-static condition. In this work, we only focused on the numerical investigation of the dynamic stiffness effects, but those should be carefully validated with the experimental study in the near future, and the NVH issues should also be tackled using the proposed dynamic stiffness approaches.  
[PERSON] designed the methods and validated the schemes. [PERSON] and [PERSON] analyzed the data. [PERSON] supervised the whole work. [PERSON] contributed simulation tools and wrote the paper. All authors contributed to this manuscript.  
This research was supported by the Basic Science Research Programs through the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning (NRF-2015R1C1A1A01051499) and the International Collaborative Research and Development Program funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea).  
The authors declare no conflict of interest.  
Figure 11: Transmission error depending on gear mesh frequency when torque is 140 Nm.
# Machine Learning for Child Oral Health: A Scoping Review
## 3 Results
### Dental Specialties and Study Characteristics  
The included studies were categorized across five different dental specialties: pediatric, general, orthodontics, dental public health, and forensic (Table 4). Notably, ML algorithms were predominantly tested in conditions related to pediatric dentistry (51.7%), followed by dental public health (27.6%) and general dentistry (13.7%). The purpose of the ML models was also categorized into prediction, diagnoses, and management, with prediction (65.5%) and diagnosis (34.5%) being the most common applications of ML algorithms in these studies. Within pediatric dentistry, a strong emphasis was placed on predictive models (66.7%), utilizing common machine learning models such as neural networks (17.9%) and random forests (28.6%). General dentistry studies focused primarily on diagnostics (75%) and predictive (25%) applications, employing clustering methods (50%), deep learning (25%), and neural networks (25%). All studies in dental public health (27.6%) concentrated on prediction, with random forests (42.9%) and gradient boosting (21.4%) as the preferred models. Orthodontics and forensic dentistry each accounted for 3.5% of the studies, with orthodontics exclusively employing deep learning for diagnosis, while forensic dentistry utilized clustering methods for diagnostics.
## 3 System Architecture
### Assumptions  
* All vehicles are equipped with a dedicated short-range communication (DSRC) device.
* All vehicles are equipped with a GPS-based navigation system that has a digital roadmap.
* All communications are done using multi-hop vehicle-to-vehicle communication model (V2V).
* Drivers input the destination into their GPS-based navigation system as a navigation request when they start traveling. The VVS computes the shortest non-congested path and reserves it for them.
* Vehicles report the updates about their travel experience in road segments and at intersections along their travel path to their corresponding VVS.
* Since the travel paths are very sensitive information, all the communications between DIFPOS-Client and DIFPOS-Server are encrypted.
* The road segments are reserved in the order of a first come first served policy.
# Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks
## 2 Related Works  
Traditionally, for lipreading, [PERSON] et al. [38] first applied the ASM model to lipreading, using a set of feature points to describe the inner or outer lip contour. This model has the disadvantage of manually labeling the training data. The quality of its feature extraction depends on the accuracy of the labeling, which requires more effort. The choice of the traditional lipreading system classifier depends on the task requirements. For large-scale continuous sentence recognition tasks, traditional methods generally use a decoding model based on GMM-HMM [8; 39].  
Figure 1: The general end-to-end sentence-level lipreading architecture can be divided into three steps. (1) Cut out the regions of the mouth based on the alignment of the face in each frame of the video; (2) The feature extractor extracts visual features from the input image sequence. (3) A text encoder predicts the text output from the hidden feature matrix.  
Research shows that recurrent networks (e.g., LSTM, GRU [28; 40]) are more suitable for modeling sequence models than convolutional neural networks. Refs. [22; 23; 24; 30] use CNN-RNN architecture, combining convolutional neural networks (CNNs) and recurrent networks (LSTM, GRU [28; 40]) and training an end-to-end lipreading system. However, the recurrent network (RNN) has the defects of slow convergence, disappearing gradient, and local overfitting.  
Deep learning practitioners commonly regard recurrent architectures as the default starting point for sequence modeling tasks. A well-regarded recent online course on \"Sequence Models\" focuses exclusively on recurrent architectures [41]. Recent studies [42; 43; 44] have shown that the convolutional architecture can achieve state-of-the-art performance accuracy in audio synthesis, word-level language modeling, and machine translation. This raises the question of whether these successes of convolutional sequence modeling are confined to specific application domains or whether a broader reconsideration of the association between sequence processing and recurrent networks is in order.  
Recent studies [36] have shown that TCN show superior performance compared to RNNs in most sequence modeling tasks, and overcome the shortcomings of RNNs, while demonstrating longer effective memory.TCN solves the defects of slow convergence, gradient explosion or disappearance, and local overfitting in RNN. In terms of lipreading, we should reconsider the common association between the lipreading architecture and the recurrent network, and regard TCN as the natural starting point for lipreading tasks.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 2 Related Work
### Multi Classification of Breast Cancer: Analysis and Discussion
The method's ACC rates for normal, benign, and malignant cancer are 95%, 94%, and 98%, respectively. SVM outperforms MLP and J48+K-mean clustering WEKA manual techniques in sensitivity, specificity, and ROC areas. The SVM, KNN, LDA, and Decision Tree results were 96.9%, 93.8 percent, 89.7%, and 88.7%, respectively. Finally, [PERSON] et al. [94] suggested classifying breast malignancies into subclasses such as lobular carcinoma. The model outperforms other models in multi-class breast cancer classification with %95.4 accuracy using a public dataset. A literature review shows the effectiveness and potential of deep learning techniques, including CNNs, RNNs, integration with radiomics, and transfer learning, in enhancing breast cancer detection. These approaches offer promising avenues for improving diagnostic accuracy, helping with early detection, and contributing to more effective treatment strategies.