esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Particle Size on Mechanical Property of Bio-Treated Sand Foundation
## 4 Conclusions  
A series of mixed levels orthogonal experiments were conducted to examine the effect of particle size range, bacterial suspension concentration, and cementation fluid concentration on efficacy of bio-treated sand columns. The following conclusions can be drawn from this study:  
* In the present research, the increase of sand particle size is beneficial to improve the effect of bio-treated sand, which may be related to the compact bulk density of sand particles. At low concentration bacterial suspension and low concentration cementation reagent, more calcium carbonate is formed, which increases the unconfined compressive strength.
* As the sand particle size increased, the uniformity of calcium carbonate precipitation was better along its height of the bio-treated column. The coefficient of variation was reduced by up to 52.0%.
* Smaller and denser calcium carbonate crystals are precipitation in the pore matrix when using low concentration cementation reagent for bio-treated loose sand particles.  
Conceptualization, D.Y. and G.X.; methodology, G.X. and Y.D.; validation, D.Y. and G.X.; formal analysis, Y.D.; investigation, D.Y. and Y.D.; resources, G.X. and Y.D.; data curation, D.Y.; writing--original draft preparation, D.Y.; writing--review and editing, D.Y. and G.X.; visualization, D.Y.; project administration, G.X; funding acquisition, G.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All workers from the State Key Laboratory of Hydraulic Engineering Simulation and Safety of Tianjin University are acknowledged. The authors are also grateful for the assistance of the anonymous reviewers.  
The authors declare no conflict of interest.  
The abbreviations are used in this manuscript:  
\begin{tabular}{l l} MICP & Microbiology induced calcite precipitation \\ OD\({}_{600}\) & The optical density at a wavelength of 600 nm \\ \(d\) & Particle size range (\(\mu\)m) \\ \(\rho\) & Compact bulk density (g/cm\({}^{3}\)) \\ \(\rho^{\prime}\) & Apparent density (g/cm\({}^{3}\)) \\ \(\rho_{0}\)\({}^{\prime}\) & Loose bulk density (g/cm\({}^{3}\)) \\ H & The height of the sand room (mm) \\ D & The diameter of the sand room (mm) \\ \(m\) & The diameter of the tube for injecting liquid (mm) \\ \(n\) & The wall thickness of the plexiglass tube (mm) \\ \(C_{b}\) & Concentration of bacterial (mol\(\cdot\)L\({}^{-1}\), M) \\ \(C_{c}\) & Concentration of cementation reagent \\ BS & Bacterial suspension \\ DW & Distilled water \\ CR & Cementation reagent \\ PV & Pore volume (cm\({}^{3}\)) \\ \(\lambda\) & The total batch number of biological treatments (times) \\ \(q_{\mathbf{u}}\) & The unconfined compression strength of bio-treated soil specimens (kPa) \\ \(\overline{\delta}\) & The average calcium carbonate content (\%) \\ R & Rang \\ \(C\)\(V\) & The coefficient of variation \\ XRD & X-ray diffraction \\ SEM & Scanning electron microscopy \\ Q & Quartz \\ C & Calcite \\ \end{tabular}
# The Influence of Feature Representation of Text on the Performance of Document Classification
## 2 Background
### Dimensionality Reduction  
The major characteristic of the document representation models that include features corresponding to words/terms is the high dimensionality of the feature space: the size of the vocabulary can be tens or hundreds of thousands of terms for an average-sized document collection. In such situations, to reduce the vocabulary size, the documents are first preprocessed by removing non-informative terms (stop words). Also, document frequency thresholding [5] and dimensionality reduction methods are used to further reduce the number of features obtained with the document representation models relying on features stemming from words.  
Document frequency thresholding [5] observes the number of documents in which a term occurs, i.e., the document frequency of a term. In turn, it removes terms with document frequency below some predetermined threshold. The method is based on the assumption that rare terms are not informative for document classification and are often an artefact of noise (misspelling) in the text data.  
Dimensionality reduction is also used to cope with the vast number of features produced by document representation models [2]. [PERSON] et al. [23] propose the use of principal component analysis (PCA) and the variants thereof for document classification. PCA methods transform the set of numerical features in the original training data space into a set of orthogonal, linearly uncorrelated features, referred to as principal components. The latter are computed in such a manner that the first principal component explains as much variance in the training data set as possible, the second explains as much of the remaining variance as possible, etc. When using PCA as a dimensionality reduction method, one will opt for the first \(p\) principal components of the training data, reducing the original dimensionality of the feature space to \(p\). There are different heuristics for selecting the value of \(p\)We have used a commonly used heuristic that selects the value of \(p\) in such a way that the first \(p\) principal components explain 80% of the variance of the training data set [59].
# A New Tooth Profile Modification Method of Cycloidal Gears in Precision Reducers for Robots
## 2 Theoretical Tooth Profile and Pressure Angle of a Cycloid Gear
### Expression of the Modified Profile of a Cycloidal Gear  
Tooth profile modification is a necessary step in the design of cycloidal gear. The pressure angle distribution of the cycloidal profile determines the meshing force transmission state of the gear teeth and affects the motion accuracy of the reducer [7]. Therefore, in this study, the profile of the cycloidal gear is modified according to the distribution characteristics of the pressure angle of the tooth profile curve as well as the reference point, the tooth tip and root clearance defined by the transmission accuracy index. After superimposing the modifications \(\Delta L\) into the theoretical tooth profile along the common normal direction, the modified tooth profile of the cycloidal gear can be expressed as:  
\[\mathbf{R}_{c}(\varphi)=\mathbf{R}_{c0}-\mathbf{n}_{c}\cdot\Delta L=\left[ \begin{array}{c}\left(r_{p}-\left(r_{rp}+\Delta L\right)/S\right)\sin\varphi- \left(a-k_{1}\left(r_{rp}+\Delta L\right)/S\right)\sin\left(z_{p}\varphi \right)\\ \left(r_{p}-\left(r_{rp}+\Delta L\right)/S\right)\cos\varphi-\left(a-k_{1} \left(r_{rp}+\Delta L\right)/S\right)\cos\left(z_{p}\varphi\right)\\ 0\\ 1\end{array}\right]. \tag{4}\]
# A Survey of the Effects of Vehicle Configuration on Urban Air Mobility +
## 2 Aerial Urban Vehicle Configurations and Concepts
### Vertical Take-Off and Landing (VTOL)
#### 2.2.3 Cyclocopter
One of the key challenges in the field of cyclocopters is the complexity involved in designing and manufacturing the propulsion unit, which typically utilizes mechanical systems such as gears or belt transmitters to control the blade angle of attack and aircraft motion. This complexity can lead to issues with stability and control, particularly if any of the mechanical components fail. In such situations, the aircraft may experience uncontrollable yaw, roll, or pitch rotations due to the gyroscopic moment produced by the propulsion unit. Additionally, the large size and weight of the propulsion unit make the aircraft vulnerable to high winds or collisions with birds, which can result in immediate crashes. However, by implementing a Disturbed Propulsion (DP) system, the aircraft can better handle these situations and land safely. To reduce the risk of structural failure during the flight from the propulsion unit gyroscopic moment force, a strong body structure is necessary to support the heavy and bulky propulsion unit [61; 62; 63].
# Predictors of Analgesic Consumption in Orthodontic Patients
## 4 Discussion  
### Summary of Main Findings  
This investigation focused on analyzing the factors that could predict the need for analgesics consumption in orthodontic patients treated with fixed appliances. All the patients included in the study were divided into two groups--patients that consumed analgesics during the first day of orthodontic treatment and those who did not. Several variables were investigated: general characteristics, intensity of pain, pain location, pain triggers, and pain description. Our study has revealed that age, intensity of pain, and headaches proved to be predictors for analgesic consumption. The older the patients are, the higher the intensity of pain and if a headache is present, there is a higher chance that orthodontic patients will consume analgesics in order to alleviate pain caused by fixed orthodontic appliance treatment.
# Health Impact Assessment from Rice Straw Production in Cambodia
## 1 Introduction
Without proper treatment, rice straw is left to burn in the field. After harvest, roughly 10 million tons of rice straw are produced in Cambodia, of which 3 million tons are burned [19]. Therefore, this practice results in higher greenhouse gas emissions, contributes to air toxicity, and is harmful to human health. Several alternative techniques for rice straw use have been proposed in Cambodia, such as its reuse as feed for mushroom cultivation, vegetable production, and the feeding of livestock. To improve facilities for rice straw waste management, Cambodia aims to (1) design and develop a prototype machine for collection purposes; (2) improve technology for mushroom and ruminant farming; (3) improve bioenergy technology; (4) conduct sustainability research and educate rice straw farmers on sustainable rice production; (5) produce a rice straw production and waste database, and a GIS map; and (6) introduce mechanized collection methods and supply chain models [19]. There was a study of pollutants emission from agricultural residue open burning in Southeast Asia, including Cambodia, where it was found that 117 Tg/year of rice straw was burned and rice straw made up 85-98% of the total CROB emissions, which was a significant contribution [20]. In recent years, there was a study of Health Impacts and Cost Assessment of Fine Particulate Matter Formation from Rice Straw Utilization in Thailand where straw open burning was the highest contributor to health impacts [21]. We had three main objectives in assessing the environmental impact of particulate matter (PM\({}_{2.5}\)), of both primary and secondary formation, from rice straw production in Cambodia: (1) to quantify the amount of rice straw subjected to open burning in Cambodia; (2) to estimate the emission inventory (EI) from rice production processes and open rice straw burning in the 25 provinces of Cambodia; and (3) to calculate endpoint impact categories for Cambodia's rice production and straw burning processes and convert these to human health impact units in disability-adjusted life years (DALYs), with a focus on air pollutants SO\({}_{2}\), NO\({}_{x}\), NH\({}_{3}\), and PM\({}_{2.5}\).
###### Abstract
The experimental results indicate that the model surpasses other methods in terms of accuracy and efficiency. [PERSON] et al. [32] proposed a novel hybrid machine learning model called GSVR (Group Support Vector Regression) for predicting the heating and cooling loads of residential buildings. This model combines the GMDH (Group Method of Data Handling) and SVR (Support Vector Regression) models. The study validated the model using datasets of 12 different building shapes simulated in Ecotect software. The experimental results show that the GSVR model can accurately predict heating and cooling loads, demonstrating high correlation coefficients and low statistical errors. [PERSON] et al. [33] proposed a decomposition-integration prediction model combining VMD (Variational Mode Decomposition) and GRU (Gated Recurrent Unit) to accurately predict building thermal loads in the absence of meteorological parameters. The model operates through four steps: data cleaning, modal decomposition, GRU prediction, and result integration, proving its superiority over traditional models. The main advantage of the model is its ability to make effective predictions without meteorological data. Through analyzing the current trends in building heat load research, it has been observed that an increasing number of scholars are adopting machine learning, deep learning, various decomposition algorithms, and optimization algorithms for hybrid modeling to forecast short-term building heat loads. However, few researchers start from the data itself to analyze the inherent time series characteristics of heat load data, such as trends, periodicity, seasonality, randomness, volatility, and stationarity. These characteristics add to the complexity of the data. Choosing appropriate data processing methods and forecasting models based on the data's characteristics can further enhance the accuracy of predictions. This article conducts a thorough investigation into this subject. By analyzing the characteristics of the heat load data, it is found to exhibit complex trend changes, significant seasonal variations, and randomness. To accommodate these characteristics, this paper designs and implements a multi-component fusion LSTM Ridge Regression Ensemble Model (ST-LSTM-RR) for the short-term single-step prediction of the building heat load. First, the STL seasonal decomposition algorithm is used to extract the trend and seasonal components of the heat load, which are then integrated into the original data to construct three diverse datasets. Second, the original LSTM is trained for basic prediction, the trend LSTM is employed to capture peak changes in the heat load, and the seasonal LSTM is used to capture cyclic variations. Lastly, a Ridge Regression model is trained to integrate the predictive values of the three basic models, thereby enhancing the overall model's prediction accuracy and stability and reducing the impact of randomness on the prediction results. The experimental results show that the proposed method achieves the smallest average RMSE and average MAE across eight groups.
## 4 Discussion  
### Seasonal \(p\)Co\({}_{2}\) Differences  
The difference in riverine \(p\)CO\({}_{2}\) between seasons can likely be explained by comparing the difference in ambient water and atmospheric parameters between seasons and analyzing their relationships with \(p\)CO\({}_{2}\). \(p\)CO\({}_{2}\) demonstrated positive correlations with river discharge and turbidity and inverse relationships with water temperature and pH (Table 2).  
Figure 5: Comparison of daily outgassing calculations. “15:00” data represents extrapolating CO\({}_{2}\) flux rates at 15:00 CST over the 24-h period. “Linear Interpolation” data represents daily flux calculated by linear interpolation between 3-h intervals.  
We postulate the difference in \(p\)CO\({}_{2}\) between seasons is primarily driven by two processes regulated by river discharge. First, the dissolution of soil pore-water CO\({}_{2}\) during the spring high-flow period caused higher \(p\)CO\({}_{2}\) levels in spring than summer. As soils become wetted and their temperatures begin to rise in spring months, conditions become more favorable for CO\({}_{2}\) producing microbial processes as found by [PERSON] et al [42], resulting in soil \(p\)CO\({}_{2}\) concentrations generally much greater than atmospheric \(p\)CO\({}_{2}\)[43]. The flushing of soil \(p\)CO\({}_{2}\) into rivers via baseflow and interflow has been documented by [PERSON] et al. [4] as a significant piece of the riverine CO\({}_{2}\) cycle. Second, the respiration of organic material delivered to the river during the spring high-flow period likely resulted in higher \(p\)CO\({}_{2}\) values. Organic carbon concentrations and community respiration are strongly correlated with discharge in the Mississippi River, which in-turn correlate with the greater production of \(p\)CO\({}_{2}\) and greater net heterotrophy in spring [26; 33]. Despite a slower river flow velocity, greater water temperatures, and lower turbidity in summer providing an environment more conducive of biological processing [44], the lack of organic inputs associated with lower discharge likely limited \(p\)CO\({}_{2}\) production, resulting in a greater net removal of \(p\)CO\({}_{2}\). This phenomenon is reflected in significantly higher pH in the river during the summer and the inverse relationship between \(p\)CO\({}_{2}\) and river pH (Table 2).
###### Abstract  
Plasma-activated water (PAW), enriched with reactive oxygen and nitrogen species (RONS), presents oxidative and antimicrobial characteristics with potential in cosmetic applications. This study examined the effects of two PAW formulations--nitrate-rich (PAW-N) and peroxide-rich (PAW-P)--on human hair types classified as straight (Type 1), wavy (Type 2), and coily/kinky (Type 4). The impact of PAW on hair structure and chemistry was evaluated using Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), UV-Vis spectrophotometry, and physicochemical analyses of the liquids (pH, ORP, conductivity, and TDS). PAW-N, with high nitrate content (\(\sim\)500 mg/L), low pH (2.15), and elevated conductivity (6244 \(\mu\)S/cm), induced significant damage to porous hair types, including disulfide bond cleavage, protein oxidation, and lipid degradation, as indicated by FTIR and EDS data. SEM confirmed severe cuticle disruption. In contrast, PAW-P, containing \(>\)25 mg/L of hydrogen peroxide and exhibiting milder acidity and lower ionic strength, caused more localized and controlled oxidation with minimal morphological damage. Straight hair showed greater resistance to both treatments, while coily and wavy hair were more susceptible, particularly to PAW-N. These findings suggest that the formulation and ionic profile of PAW should be matched to hair porosity for safe oxidative treatments, supporting the use of PAW-P as a gentler alternative in hair care technologies.  
plasma-activated water; reactive oxygen and nitrogen species; hair morphology; oxidative stress; cosmetic applications +
Footnote †: journal: Applied sciences  
article
## 3 Field Quantization
### Linearized Quantization
#### 3.2.3 Squeezing Hamiltonian  
When the optical and mechanical frequencies do not differ by orders of magnitude so that neither \(\omega\gg\Omega\) nor \(\omega\ll\Omega\) hold, then the linearized Hamiltonian could be recast as  
\[\mathbb{H}_{4}=\hbar G_{4}^{+}\left(\hat{b}^{\dagger}+\hat{b}\right)\left(e^{ i\varphi}\hat{a}^{\dagger}+e^{-i\varphi}\hat{a}\right)+\hbar G_{4}^{-}\left(\hat{b}^{ \dagger}-\hat{b}\right)\left(\hat{a}^{\dagger}+\hat{a}\right), \tag{54}\]This can be written as  
\[\mathbb{H}_{4}=\hbar G\Big{(}\hat{a}\hat{\mathbb{B}}^{\dagger}+\hat{a}^{\dagger} \hat{\mathbb{B}}\Big{)}=\hbar G\Big{(}\hat{b}\hat{\mathbb{A}}^{\dagger}+\hat{b} ^{\dagger}\hat{\mathbb{A}}\Big{)}, \tag{55}\]  
where  
\[\begin{gathered} G_{4}=\sqrt{G_{4}^{+}G_{4}^{-}},\hat{\mathbb{A}} =\hat{a}^{\dagger}\sin\mathrm{h}\rho+\hat{a}\cos\mathrm{h}\rho,\hat{\mathbb{B }}=\hat{b}^{\dagger}\cos\mathrm{h}\rho+\hat{b}\sin\mathrm{h}\rho,\\ \rho=\tan\mathrm{h}^{-1}\bigg{(}\frac{G_{4}^{+}-G_{4}^{-}e^{i \theta}}{G_{4}^{+}+G_{4}^{-}e^{i\theta}}\bigg{)}.\end{gathered} \tag{56}\]  
It is here to be noticed that \(\hat{\mathbb{B}}\) and \(\hat{\mathbb{A}}\) are in the standard form of Bogoliubov squeezing operator [54; 55]. It may be noted that the equation  
\[G_{4}^{-}=R\frac{\Omega^{2}}{\omega^{2}}G_{4}^{+}, \tag{57}\]  
is actually a function of \(\theta\) by definition of \(G_{4}^{+}\) and \(G_{4}^{-}\). Simplifying the above gives the expression for squeeze ratio as  
\[\rho=\ln\biggl{(}\frac{\omega}{\sqrt{R}\Omega}\biggr{)}-i\frac{\varphi}{2}. \tag{58}\]  
This shows that quadratic interactions give rise to squeezed mechanical or optical states unless \(\omega=\sqrt{R}\Omega\) and of course \(\varphi=0\).
# MFCC Selection by LASSO for Honey Bee Classification
## 2 Materials and Methods
### Information Criterion  
An information criterion is a statistical measure used in model selection and comparison. It quantifies the trade-off between the complexity of a model and its goodness-of-fit to the data. The key objective of calculating an information criterion is to choose the most suitable model among a set of competing models. There are various information criteria available, but some of the commonly used include the Akaike Information Criterion (AIC) [41] and the Bayesian Information Criterion (BIC) [42]. These criteria penalize com  
Figure 4: Regularization path by LASSO: the sequential calculation of coefficients \(\hat{\beta}^{LASSO}=(\beta_{1},\beta_{2},\ldots,\beta_{p})\) for the decreasing in logarithmic scale values of the regularization parameter \(\alpha\). Each color represents a particular path for \(\beta_{i}(\alpha)\) for \(i=1,\ldots,p\).  
plex models that have more parameters, preferring simpler models that achieve comparable goodness-of-fit. In this section, we discuss the criteria for feature vector size optimization. In short, models with lower information criterion value have better predictive power.  
The Akaike Information Criterion was developed by [PERSON] [41], and its objective is to balance the predictive power of a model by minimizing both the number of model parameters \(p\) and the prediction error by maximizing the estimated maximum likelihood \(L\):  
\[AIC=2p-2\ln(L). \tag{8}\]  
The Bayesian Information Criterion, also known as the Schwarz criterion [42], is based on a Bayesian approach and is calculated as follows:  
\[BIC=p\ln(n)-2\ln(L), \tag{9}\]  
where \(p\) is the number of model parameters, \(L\) is the maximum likelihood estimated by the model, and \(n\) is the number of observations.  
By calculating an information criterion for different models (i.e., different sets of features chosen from the **MFCC + Delta + DeltaDelta** set via LASSO), one can compare their relative performance and select the model that strikes the optimal balance between model complexity and fit to the data. This process helps prevent overfitting, where a model performs very well on the training data but struggles with new data, and enables the identification of the most suitable model for predicting or explaining the underlying phenomenon. In other words, using the information criterion combined with LASSO feature selection allows us to find the best set of features chosen from **MFCC + Delta + DeltaDelta**, which are used for neural network training in the next step.
# Application of Computational Fluid Dynamics Analysis after Bimaxillary Orthognathic Surgery
###### Abstract  
Bimaxillary orthognathic surgery is widely used to treat skeletal class III malocclusion. Changes in jaw position affect the shape of surrounding soft tissues. We used computational fluid dynamics (CFD) simulation to observe changes in airways observed in a patient who underwent bimaxillary orthognathic surgery. For CFD simulation, we performed cone beam computed tomography (CBCT) preoperatively (T0), 3 days postoperatively (T1), and 7 months postoperatively (T2). The values of velocity, pressure drop (\(\Delta P\)), and wall shear stress all increased 7 months after surgery (Vmax 7.038 m/s to 12.054 m/s, \(\Delta P\) -7.723 Pa to -53.739 Pa, WSSmax 4.214 Pa to 14.323 Pa). Locations where the velocity and pressure gradients are large included the velopharynx, oropharynx, and epiglottis, with narrow cross-sectional areas. Wall shear stress was also observed at these locations. The velopharynx, oropharynx, and epiglottis are structures most vulnerable to morphological changes, that is, they can easily become obstructed.  
computational fluid dynamic simulation; airway change; bimaxillary orthognathic surgery; 2 jaw surgery 2020; Accepted: 11 February 2020; Published: 2 March 2020