esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Preprocessing on Arabic Document Categorization
## 1 Introduction  
Given the tremendous growth of available Arabic text documents on the Web and databases, researchers are highly challenged to find better ways to deal with such huge amount of information; these methods should enable search engines and information retrieval systems to provide relevant information accurately, which had become a crucial task to satisfy the needs of different end users [1].  
Document categorization (DC) is one of the significant domains of text mining that is responsible for understanding, recognizing, and organizing various types of textual data collections. DC aims to classify natural language documents into a fixed number of predefined categories based on their contents [2]. Each document may belong to more than one category.  
Currently, DC is widely used in different domains, such as mail spam filtering, article indexing, Web searching, and Web page categorization [3]. These applications are increasingly becoming important in the information-oriented society at present.  
The document in text categorization system must pass through a set of stages. The first stage is the preprocessing stage which consists of document conversion, tokenization, normalization, stop word removal, and stemming tasks. The next stage is document modeling which usually includes the tasks such as vector space model construction, feature selection, and feature weighting. The final stage is DC wherein documents are divided into training and testing data. In this stage, the training process uses the proposed classification algorithm to obtain a classification model that will be evaluated by means of the testing data.  
The preprocessing stage is a challenge and affects positively or negatively on the performance of any DC system. Therefore, the improvement of the preprocessing stage for highly inflected language such as the Arabic language will enhance the efficiency and accuracy of the Arabic DC system. This paper investigates the impact of preprocessing tasks including normalization, stop word removal, and stemming in improving the accuracy of Arabic DC system.  
The rest of this paper is organized as follows. Related works are presented in Sections 2 and 3 provides an overview of the Arabic language structure. An Arabic DC framework is described in Section 4. Experiment and results are presented in Section 5. Finally, conclusion and future works are provided in Section 6.
## 1 Introduction
This study was implemented for simulation and measurement and showed capability of only 62 channels in receive to make 4D volumetric imaging. To do SA the entire volume must be beamformed with every emission making real-time difficult at best. [PERSON] et al. [25] improved SA tensor velocity estimates for RC arrays by motion compensation of low-resolution volumes. [PERSON] et al., implemented Power Doppler estimation on a large 128 + 128 elements RCA with plane wave emissions [26]. The same method was used in functional brain imaging [27]. However, Power Doppler provides no directional information and gives only qualitative measure of blood flow, and one can not get the velocity components by this method. Moreover, in this paper were used coherently-compounded orthogonal plane waves, investigated by [PERSON] et al. [28]. For that purpose is necessary to reconstruct the whole volume for every emission, which gives the similar computational complexity as SA imaging mentioned above. This paper presents volumetric tensor velocity imaging (TVI) simulations of parabolic flow using RC addressed array and shows that TVI can be done with the same computational complexity as conventional 2D imaging and without ECG gating. Directional TO [29] and interleaved emission sequences [30] are combined and adapted for use with RC arrays to achieve full volumetric 3D TVI. The interleaved sequence introduced to reduce time difference between correlated signals in SA flow from \(N\)\({}^{\star}\)\(T_{prf}\) to \(T_{prf}\). Achieved by interleaving two identical sequences. In this work, it is adapted to non-SA flow. The interleaved sequence is compared to a non-interleaved sequence with the same parameters. Section 2 describes the methods including the RC array, the emission sequence, and the velocity estimator. Section 3 presents the results, and Sections 4 and 5 discusses the results and presents conclusions.
# Antioxidant, Antimicrobial, Antidiabetic and Cytotoxic Activity of _Crocus sativus_ L. Petals
## 1 Introduction  
According to the World Health Organization, infectious diseases are one of the major threats to human beings [1]. The prevalence of species susceptible to various antimicrobial agents has increased, resulting in increased morbidity, mortality and total treatment costs [2]. The overuse of antibiotics contributes to the rapid development of multidrug-resistant pathogens [3]. The continued rise in the magnitude of multidrug resistance to traditional antibiotics is a biggest concern of researchers and practitioners globally and it has become a worldwide problem in recent decades [4]. The increasing number of incidents of infectious diseases associated with the emergence of antimicrobial resistance helps in the discovery of new antimicrobial agents [5].  
Natural products have a significant role in the health care sector and have been used for the prevention of several infectious diseases since ancient times [6]. Such products including medicinal plant extract, are important source for the development of novel disease therapies [7, 8]. Over recent decades, the use of medicinal plants has become increasingly integral to the world's primary healthcare needs. Medicinal plants are the cornerstone of traditional complementary and alternative medical practice, as many current drugs and their derivatives are isolated from these plants [9]. Plants are known as being the most effective antimicrobial agents due to the presence of polyphenolic compounds [10]. Plants may contain a number of biologically active compounds, and all these compounds have the potential for effective therapeutic effect [11; 12].  
_Crocus sativus_ L. (Saffron) (family Iridaceae) is often used as a spice in various parts of the world and as a therapeutic agent for a range of diseases [13]. The name of saffron was derived from the Arabic word \"zafaran\", meaning yellow [14]. Literature survey revealed that more than 100 bioactive compounds has be isolated from saffron stigma mainly flavonoids [15], terpenoids [16], anthraquinones [17]. Saffron has been used in folk medicines to treat multiple conditions, including cough, gastrointestinal, menstrual irregularities and cardiovascular ailments [18]. Recent pharmacological analysis of saffron stigma and petals showed a broad range of biological activities like antioxidant [19], anticancer [20], neuroprotective [21], antidiabetic [22], aphrodisiac [23], hypolipidemic [24], anti-inflammatory [25], antimicrobial activities [26], etc.  
The objective of this work was to evaluate the antioxidant, antimicrobial, antidiabetic and cytotoxic effects of C. _sativis_ L. petals using various screening tests.
## 7 Conclusions
\begin{tabular}{l l} \(p_{\text{rated}}^{\text{LOLP}}\) & Rated probability of LOLP \\ \(P_{\text{rated}}^{\text{ESP}}\) & Rated probability of RSP \\ \(P_{\text{r}}^{R}\) & Renewable generation at period \(t\) \\ \(P_{t}^{tR}\) & Forecast power of renewable generation at period \(t\) \\ \(P_{t,\text{max}}^{R}\) & Upper limit of renewable generation at period \(t\) \\ \(P_{t,i}^{G}\) & Output power of unit \(i\) at period \(t\) \\ \(f_{\text{C}_{i}}(P_{i,t}^{G})\) & Quadratic cost function of unit \(i\) at period \(t\): \(c_{2,i}(P_{i,t}^{G})^{2}+c_{1,i}P_{i,t}^{G}+c_{0,i}\) \\ \(C_{1,t}\) & Generation cost part of the total cost at period \(t\) \\ \(r_{i,t}^{u}\) & Upward reserve of unit \(i\) at period \(t\) \\ \(r_{i,t}^{d}\) & Downward reserve of unit \(i\) at period \(t\) \\ \(c_{t}^{u}\) & Cost coefficient of unit \(i\) for upper reserve \\ \(c_{t}^{t}\) & Cost coefficient of unit \(i\) for downward reserve \\ \(C_{2,t}\) & Reserve cost part of the total cost at period \(t\) \\ \(T\) & The number of dispatch periods \\ \(G\) & The set of synchronous generator plants \\ \(R\) & The set of renewable generation plants \\ \(D\) & The set of demand \\ \(u\) & Upward \\ \(d\) & Downward \\ \(p_{\text{ramp},i}^{u}\) & Upward ramp rate \\ \(p_{\text{ramp},i}\) & Downward ramp rate \\ \(\Delta T\) & Dispatch interval \\ \(T_{r}\) & Reserve dispatch interval \\ \(p_{\text{pert}}\) & Penetration of renewable generation \\ \(P_{RG}\) & Renewable output in a time step \\ \(L_{\text{served}}\) & Load served in a time step \\ \(3\) Acronyms & \\ DSO & Distribution system operator \\ ED & Economic dispatch \\ IBG & Inverter-based generation \\ IIR & Instantaneous inertial response \\ IR & Inertial response \\ LOLP & Loss of load probability \\ MPP & Maximum power point \\ MPPT & Maximum power point tracking \\ PEI & Power electronic interfaces \\ PFR & Primary frequency response \\ RES & Renewable energy sources \\ RG & Renewable generation \\ ROCOF & Rate of change of frequency \\ RSP & Renewable spillage probability \\ SFD & Second frequency droop \\ SFR & Secondary frequency regulation \\ SG & Synchronous generator \\ SO & System operator \\ TFR & Tertiary frequency regulation \\ TSO & Transmission system operator \\ VSG & Virtual synchronous generator \\ VSI & Voltage-sourced inverters \\ \end{tabular}
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 6 Conclusions  
In this paper, we develop an optimised 2D U-Net model to perform prostate segmentation, without the need of any enhancing pre- or post-processing. We also establish the importance of individual components within the U-Net model to perform prostate segmentation. Compared to transposed convolution, we found that interpolation and convolution results in a better performance for upsampling. Within each convolution block, the combination of feature maps with a skip connection is only beneficial with a concatenation operation. For pooling, the use of average pooling brings significant improvement as compared to the strided-convolution, max, RMS or L2 pooling. Including a batch normalisation layer before the activation layer also brings further improvement in the model performance. We show that the optimised model in this paper outperforms traditional segmentation methods on the private dataset by approximately 6% and 7% in median and mean DSC score respectively. Furthermore, it outperforms (in terms of DSC) other comparable state-of-the-art 2D models on the PROMISE12 public dataset. In addition, we discuss the intra-and inter-expert label variability and the effect on the model performance, as well as provide suggestions to reduce the associated errors.  
Figure 8: Box-and-whisker plots. (**a**) Inter-expert DSC scores to highlight the inter-expert variability problem; (**b**) DSC scores of the 3 experts and the model against the majority voting label to show that the optimised model performance is within the performance range of the experts.  
**Author Contributions:** Data, review, P.G.; Methodology, software, analysis, validation, writing, I.P.A.; Main supervision, review, editing, J.S.W.; Review, S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** [PERSON] has been awarded a 2016 University of Newcastle Scholarship provided by UNIPRS and UNRSC 50:50.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Bending Behavior of Corroded H-Shaped Steel Beam in Underground Environment
## 3 Analysis of Results
### Simplified Calculation of Residual Bearing Capacity of Corroded H-Shaped Steel Beams  
#### Assumptions  
To simplify the calculation, the following assumptions were made:  
1. The web heights and the flange widths were assumed to be the same after corrosion;
2. The stress-strain relationship of the corroded steel was simplified as a double-linear model, as shown in Figure 14, and the influence of corrosion on the yield load was considered;
3. The corrosion was only expected to reduce the thickness, and both the uneven steel surface and stress concentrations resulting from corrosion were ignored.
4. It was assumed that the corrosion degrees of the upper flange, lower flange, and web were the same.
5. A simple method is provided without consideration to the influence of sustained loading.
## 2 Results
### DEGs Involved in Terpenoid biosynthesis
_AACT_, _AcetylCoA C-acetyltransferase_; _HMGS_, _hydroxymethylgluturd-CoA synthase_; _HMGR_, _hydroxymethylgluturd-CoA reductase (NADPH)_; _MVK_, _mealonate kinase_; _PMK_, _phosphonechanonate kinase_; _MVD_, _diphosphonechanonate decarboxylase_; _DXS_, _1-deoxy-D-xylulose-5-phosphate synthase_; _DXR_, _1-deoxy-D-xylulose-5-phosphate reductio isomerase_; _MCT_, _2-C-methyl-D-erythhitol 4-phosphate cytidyltransferase_; _CMK_, _4-diphosphocridylyl-2-C-methyl-D-erythhitol kinase_; _MDS_, _2-C-methyl-D- erythroid12_, _4-cycloidphosphate synthase_; _HDS_, _(E)-4-hydroxy-3-methylbut-2-enyl-diphosphatesuthinase_; _HDR_, _4-hydroxy-3-methylbut-2-en-1-yl diphosphate reductase_; _IDI_, isopentenyl-diphosphate Delta-isomerase_; _DMAPP_, _dimethylallyl diphosphate_; _GPPS_, _geranyl diphosphate synthase_; _FPPS_, _farresyl diphosphate synthase_; _GGPS_, _geranylgeranyl diphosphate synthase_; _NRS_, _(35,6E)-neroidal synthase_; _GERD_, _(-)-geracrene D synthase_; _FAR-DT_, _farresyl diphosphate farresyltransferase_; _SQUIS_, _squalene monoxygenase_; _AMYS_, _beta-amyrin synthase_; _CINS_, _1-8-cineole synthase_; _NEOD_, _(+)-nenomethol dehydrogenase_; _CPS_, _ent-copalydiphosphate synthase_; _KS_, _ent-kaurene synthase_; _KO_, _ent-kaurene oxidase_; _KAO2_, _ent-kaurene acid oxidase_; _ZA3_, _gibberellin 3-beta-dioxygenase_; _GA2_, _gibberellin 2-oxidase_; _GA20_, _gibberellin 20-oxidase_; _MAS_, _monilactone-A synthase_. As key enzymes in the synthesis of terpenoids, 49 DEGs encoding terpenoid synthase (TPS) were found, including 15 genes involved in sesquiterpene and triterpene synthesis (ko00909), 26 genes involved in diterpene synthesis (ko00904), and 8 genes involved in monoter synthesis (K07385 and K15095) (Table S5). Correlation network analysis was used to model the relationship of the selected 49 genes encoding TPS with the detected terpenoids (Figure 6). As shown in the figure, 40 TPS genes and 20 terpenoids showed high correlation, for example, (-)-germacrene D synthase (\(GERD3\)) was positively correlated with five terpenoids, including Triacetate (Imjp04533), 12,13-Dihydrotusolic acid (Zmjp01361), Progesterone (Lmqp010784), 2\(\alpha\),3\(\alpha\),23-trihydroxyolean-12-en-28-oic acid (Smpn009230), and Madasiatic acid (Hmjn003948), with correlation coefficients varying from 0.83 to 0.97. Gibberellin 2 beta-dioxygenase (GA2-5), 1,8-cineole synthase (CINS3), and ent-kaurene oxidase (KO) were positively correlated with the same three terpenoids. 24,30-Dihydroxy-12(13)-enolupinol (pmn001700) was positively correlated with nine TPS genes and negatively correlated with four TPS genes. Beta-amyrin synthase (AMYS1, AMYS2, AMYS3) exhibited a strong positive correlation with 24,30-Dihydroxy-12(13)-enolupinol (pmn001700) and 12,13-Dihydrotusolic acid (Zmjp013616).
# Special Issue on Ultrasonic Modeling for Non-Destructive Testing
###### Abstract  
This Special Issue of Applied Sciences focuses on advancing modeling methods for the ultrasonic Non-destructive Testing (NDT) of materials. Ultrasonic techniques are employed for non-destructive purposes to evaluate the properties and damage states of structures devised for numerous applications (engineering, building materials, medicine, etc.). The advantages and inconveniences of ultrasonic testing compared to other NDT methods have been widely reviewed [1]. The coexistence of all the different NDT methods can allow the inspection of different kinds of structures and damages [2]. The scope of this Special Issue ranges from ultrasonic wave techniques for classical non-destructive evaluation, the structural health and condition monitoring of structures, existing or novel methods for imaging, ultrasonic characterization, non-linear acoustics, acoustic emission, laser ultrasonics, additive manufacturing, medical applications, and sensors, to, signal and noise analysis.  
The current Special Issue notably aims to explore advances in ultrasonic modeling methods for understanding or predicting NDT inspections. Different authors present novel achievements in the understanding and modeling of ultrasonic waves for NDT applications. High-quality research and review papers on theoretical, practical, and validation aspects were accepted, leading to a collection of 20 published papers. These are briefly reviewed here, classified into different topics. A brief overview of the main developments in the field is recalled in each topic.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Passive BCI
#### 4.2.1 Signal Processing and Feature Extraction  
Different types of filters were used in this category to preprocess the EEG signals. Ref. [47] used a lowpass filter at 50 Hz along with a highpass filter at 1 Hz and a notch filter at 50 Hz. Ref. [48] employed a bandpass filter between 3 and 45 Hz and a notch filter at 50 Hz, while ref. [50] used a spatial filter. Finally, refs. [46,49] did not mention their preprocessing phase.  
All of the researchers employed different approaches to extract features for their classifiers. Ref. [47] used PSD, ref. [48] employed FBCSP, and ref. [50] used spectral estimation.
## 5 Conclusions  
* In this paper, an adaptive inversion control method is proposed for the nonlinear problem of the multilevel electromechanical servo system. Through joint simulation with Simulink and ADAMS software, the displacement, torque, and speed results of a multistage electromechanical servo system under an adaptive inversion controller and a traditional PID controller are compared, to verify the feasibility and reliability of the designed controller. The designed control algorithm was debugged on the experimental platform, and the system step signal tracking and sinusoidal signal tracking tests were carried out. The test results verify that the designed adaptive inversion multistage EMA driving algorithm can respond quickly, has better tracking performance, and has better robustness and stability than the traditional PID controller.
* Next, the future work is to replace the analog sensor with a digital sensor and change the loading mode to improve the experimental accuracy.  
Conceptualization, Y.L. and Y.Z.; methodology, Y.L.; software, Y.Z.; validation, Y.L., Y.Z. and J.Z.; investigation, J.Z. and S.W.; data curation, J.Z. and S.W.; writing--original draft preparation, Y.L.; writing--review and editing, Y.Z. and S.M.; project administration, S.M.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key R&D projects in Shaanxi Province, grant number 2021 ZDLGY10-08, National Natural Science Foundation of China, grant number 51875458, 51905428.  
Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sinusoidal** & & & **Traditional PID Control** & & **Adaptive Inversion** \\
**Displacement** & **Frequency** & & & & \\
**Command** & **(Hz)** & & **Lag Time** & & **Lag Time** & \\
**(mm)** & & **(s)** & & **(s)** & **(s)** & **Amplitude Decay** \\ \hline
1100–1900 & 0.05 & 2.2 & 9.4\% & 1.2 & 4.9\% \\ \hline
1300–1700 & 0.1 & 1.2 & 6.8\% & 0.5 & 3.5\% \\ \hline
1400–1600 & 0.2 & 0.5 & 3.7\% & 0.3 & 2.8\% \\ \hline
1460–1540 & 0.5 & 0.2 & 1.7\% & 0.1 & 0.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Displacement following performance parameters of multilevel electromechanical system.  
Figure 9: Sine 0.5 Hz following performance test **(a)** Traditional PID control method; **(b)** adaptive inversion control method.
## 3 Results
### Patients' Characteristics and Interventions Received in the First and Second Wave  
Of the 1593 included in the analyses, 690 (43.3%) were admitted during the first wave and 903 (56.7%) during the second wave. We reported baseline characteristics at the admission of the two cohorts of patients including demographic, clinical, and laboratory findings in Table 1. Patients in the second wave were a median of six years older and had significantly more pre-existing comorbidities than those hospitalized in the first wave. Additionally, the proportion of women was higher in the second wave. Laboratory characteristics confirmed the substantial difference between these two cohorts of patients with significantly different values in lymphocytes, eosinophils, hemoglobin, interleukin-6, serum ferritin, c-reactive protein, gamma-glutamyl transpeptidase, creatine kinase, triglycerides, international normalized ratio, and fibrinogen, Table 1. The new internal organization resulted in a lower proportion of patients admitted to the ICU in the second wave (first vs. second wave, 16.9%, and 8.75%, \(p\) < 0.001) mostly substituted by a sub-intensive type of care. The proportion of patients receiving invasive oxygen therapy (3.8% vs. 0.23%, \(p\) < 0.001) was significantly lower in the second wave, while the proportion of those receiving other types of oxygen support was significantly higher (54.3% vs. 63.1%, \(p\) = 0.001). The use of CPAP was not significantly different between the two waves (11.9% vs. 9.1%, \(p\) = 0.189). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **First Wave (n = 690)** & **Second Wave (n = 903)** & _p-Value_ \\ \hline \multicolumn{3}{c}{**Mean \(\pm\) SD; Median (IQR) or n (\%)**} & **Mean \(\pm\) SD; Median (IQR) or n (\%)** & \\ \hline \multicolumn{3}{c}{**Demographic Characteristics**} \\ \hline Age (years) & 66.21 \(\pm\) 14.44; 67.00 (56.00–77.00) & 71.17 \(\pm\) 14.30; 73.00 (61.00–82.00) & \textless{}0.01 \\  & (n = 690) & (n = 903) & \\ \hline \multirow{2}{*}{\(\geq\)65} & 296 (42.9) & 272 (30.1) & \multirow{2}{*}{\(p\)-Value} \\  & (n = 690) & (n = 903) & \\ \hline \multirow{2}{*}{\(<\)65} & 394 (57.1) & 631 (69.9) & \multirow{2}{*}{\(p\)-Value} \\  & (n = 690) & (n = 903) & \\ \hline Gender & & & 0.006 \\ \hline \multirow{2}{*}{Female} & 221 (32.0) & 350 (38.8) & \multirow{2}{*}{\(p\)-Value} \\  & (n = 690) & (n = 903) & \\ \hline \multirow{2}{*}{Male} & 469 (68.0) & 553 (61.2) & \multirow{2}{*}{\(p\)-Value} \\  & (n = 690) & (n = 903) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic, clinical, and laboratory characteristics of hospitalized COVID-19 patients (n = 1593).