esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
## 3 Prediction Model for Dementia Diagnosis
### Feature Selection  
Feature selection is a method of extracting the most relevant features from data with a certain pattern. It can be used to remove irrelevant data, eliminate redundancy, and identify what features contribute to a model with high accuracy. When creating a model, it is important to write as few features as possible, so that one can reduce the number of features through feature selection. In this paper, feature selection is performed using chi square and information gain.  
The chi-square test is used to analyze categories, variables, or relationships, and is useful for studying categorical variables such as regional and political preferences, and the relationship between food and obesity. The chi-square test can be used in two broad contexts: as a fitness test to test whether the observed data follows a predicted distribution, and as an independence test to test whether two random variables are independent of each other. Independence means that there is no cause or effect relationship between them. The following Equation (2) indicates the chi-square feature selection.  
\[\text{chi square}=\frac{\left(observed-expected\right)^{2}}{expected} \tag{2}\]  
Information gain means that when an attribute is selected, the data is well distinguished because of its attribute. Information gain is the value obtained by subtracting the entropy value of the lower node from the entropy of the upper node. Equation (3) is used for calculating the information gain amount when the attribute A is selected, and the entropy of the original node is obtained. This is the result of subtracting the value divided by the smallest m nodes. The larger the value of Gain (\(A\)), the greater the information gain and the better the discriminative power; see the study conducted by [PERSON] et al. [18].  
\[\text{Gain}\left(A\right)=\text{I}(s_{1},s_{2},\ldots,s_{m})-\text{E}(\text{ feature}\ A) \tag{3}\]  
In this study, feature selection was performed in two distinct phases, in which Phase 1 dealt with MMSE-KC data, while Phase 2 selected features among data from both MMSE-KC and CERAD-K.
### The Correction of the Reference Length for the Selected Parts' Center
A correction of the reference length by its rotation to the perpendicular axis can be calculated as follows:  
\[RL_{un,un+1}=RL_{vert}.\ \text{cos}\ \beta\ [\text{mm}] \tag{5}\]  
A coefficient scale of a reduction of \(SC\) measures that object at a point \(\text{S}_{n,n+1}\) against the real measure, and can be calculated as follows:  
\[SC=\frac{RL_{un,un+1}}{RL} \tag{6}\]  
The perpendicular distance between the reference point \(\text{RP}_{n}\) and \(\text{RP}_{n+1}\) in the photomontage \(s_{vertphoto}\) can be determined by measurement in the photomontage. The distance \(s_{vertphoto}\) must be adjusted by a correction that originates from its rotation in the perpendicular axis by the angle \(\beta\) (Figure 12). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(k_{hor}\) [1] & 0–0.066 & 0.067–0.132 & 0.133–0.265 & 0.266–0.397 & 0.398–0.529 & 0.530–0.618 & 0.619–0.779 & 0.780–0.897 \\ \hline \(c_{hor}\) [1] & 1.0 & 1.0 & 1.0 & 0.99911 & 0.99866 & 0.99822 & 0.99755 & 0.99666 \\ \hline \end{tabular}
\end{table} 
Table 1: Dependence of coefficient \(c_{hor}\) on the interval of distance \(k_{hor}\). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(k_{vert}\) [1] & 0–0.086 & 0.087–0.171 & 0.172–0.343 & 0.344–0.390 & 0.391–0.429 & 0.430–0.514 & 0.515–0.686 & 0.687–0.800 \\ \hline \(c_{vert}\) [1] & 1.0 & 0.99844 & 0.9971 & 0.98552 & 0.98463 & 0.98418 & 0.97171 & 0.95678 \\ \hline \end{tabular}
\end{table} 
Table 2: Dependence of the coefficient \(c_{vert}\) on the interval of distance \(k_{vert}\). The real vertical distance between the reference points \(\rm RP_{n}\) and \(\rm RP_{n+1}\)\(s_{vertral}\) can be determined as follows:  
\[s_{vertral}=s_{vertphoto}\cdot\cos\beta\cdot\frac{1}{SC}[\rm mm] \tag{7}\]  
Figure 11: Correction of the reference length for a rotation in the vertical axis (source: The authors of this article). Figure 12: Determination of the vertical distance between the reference points \(\rm RP_{n}\) and \(\rm RP_{n+1}\) (source: The authors of this article).
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 5 Experiment and Result
### Runtime Performance  
In order to comprehensively evaluate the performance of BLSTM neural network, we gather statistics on the time consumption in each of the stages. We randomly select 2000 unique samples, half of which are benign and half of which are malicious and that recorded the time consumption. As for abstract code representation, Table 7 shows the minimum, maximum, average, and median time consumption at each stage, for one file. As we can see, deobfuscation takes the least amount of time, and a file takes only 18 ms on average. The generation of AST is divided into two steps, one is to parse JavaScript files with Esprima [33], the other is to construct the AST, which makes the process take a little longer, with an average of 96 ms per file. The more time-consuming stage is to generate the PDG, which largely depends on the size of the AST, because we have to traverse it several times to add data dependencies. The generation of _semantic slices_ takes the most time, with an average of 233 ms, because this process requires traversing the PDG and finding the corresponding source code statements at the same time. In general, the more complex code representation causes higher overhead, but this also makes the code abstract representation preserve more semantic information.  
Since the input vector dimensions of BLSTM neural networks are fixed, the time consumption per sample is similar. Table 8 shows the average time of each stage of learning the BLSTM neural network, for one file. As we can see, in the learning phase, the model spends an average of 0.241 ms processing a file, and in the classification phase, the model spends an average of 2.765 ms process a file.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Min(ms)** & **Max(s)** & **Mean(ms)** & **Median(ms)** \\ \hline deobfuscation & 0.012 & 0.761 & 18.214 & 9.001 \\ \hline AST & 0.138 & 2.414 & 96.231 & 20.587 \\ \hline CFG & 0.013 & 1.211 & 37.091 & 4.635 \\ \hline PDG & 0.142 & 12.261 & 210.258 & 9.621 \\ \hline _semantic slice_ & 0.271 & 13.957 & 232.712 & 11.243 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Time consumption to generate abstract code representations.  
Figure 8: The influence of the number of BLSTM layers.  
For one JavaScript sample, the detection time is approximately the sum of the average time for generating the abstract code representation (from deobfuscation to _semantic slice_ generation) and the average time for the classifier. Based on the data in Tables 7 and 8, the detection time is 595 ms. Considering that the average size of JavaScript samples is 32 KB, we think this overhead is reasonable.
# A Massively Parallel SMC Sampler for Decision Trees +
## 4 Bayesian Decision Trees
### Sequential Monte Carlo
280-288. * (38) [PERSON]; [PERSON] A Very Simple Safe-Bayesian Random Forest. _IEEE Trans. Pattern Anal. Mach. Intell._**2015**, _37_, 1297-1303. [CrossRef]
* (39) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] A Shared Memory SMC Sampler for Decision Trees. In Proceedings of the 2023 IEEE 35 th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Porto Alegre, Brazil, 17-20 October 2023; pp. 209-218. [CrossRef]
* (40) [PERSON]; [PERSON]; [PERSON]; [PERSON] _The Elements of Statistical Learning: Data Mining, Inference, and Prediction_; Springer: Berlin/Heidelberg, Germany, 2009; Volume 2. * (41) [PERSON]; [PERSON]; [PERSON] Resampling Methods for Particle Filtering: Classification, implementation, and strategies. _IEEE Signal Process. Mag._**2015**, _32_, 70-86. [CrossRef]
* (42) [PERSON] Efficient Metropolis-Hastings Proposal Mechanisms for Bayesian Regression Tree Models. _Bayesian Anal._**2016**, _11_, 885-911. [CrossRef]
* (43) [PERSON]; [PERSON]; [PERSON] Bayesian CART: Prior Specification and Posterior Simulation. _J. Comput. Graph. Stat._**2007**, _16_, 44-66. [CrossRef]
* (44) [PERSON]; [PERSON]; [PERSON] Efficient Scan Operator Methods on a GPU. In Proceedings of the 2014 IEEE 26 th International Symposium on Computer Architecture and High Performance Computing, Paris, France, 22-24 October 2014; pp. 190-197. [CrossRef]
* (45) [PERSON]; [PERSON]; [PERSON] Efficient GPU-Implementation for Integer Sorting Based on Histogram and Prefix-Sums. In Proceedings of the 50 th International Conference on Parallel Processing, New York, NY, USA, 9-12 August 2021; ICPP '21, pp. 1-11. [CrossRef]* (46) [PERSON] Optimal and Efficient Algorithms for Summing and Prefix Summing on Parallel Machines. _J. Parallel Distrib. Comput._**2002**, _62_, 517-543. [CrossRef]
* (47) [PERSON] Efficient Hierarchical MCMC for Policy Search. In Proceedings of the Twenty-First International Conference on Machine Learning, New York, NY, USA, 4-8 July, 2004; ICML '04, pp. 97-104. [CrossRef]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
# Seasonal Variability of Volatilome from _Dictyota dichotoma_
###### Abstract  
Dictyotaceae, the large family of brown algae with the genus _Dictyota_ as the richest one among them, produce a significant number of secondary metabolites, like diterpenes. The aim of this study was to investigate the variations in the composition of the volatile organic compounds (VOCs) of the brown alga _Dictyota dichotoma_ collected from the Adriatic Sea. For the first time, both seasonal changes and the impact of air drying were examined. Headspace solid-phase microextraction (HS-SPME) on two fibres with different polarities and hydrodistillation (HD), followed by gas chromatography and mass spectrometry (GC-MS) analysis, was performed on both fresh (FrDd) and air-dried (DrDd) _D. dichotoma_. The major compounds of HS-FrDd were pentadecane and oct-1-en-3-ol. The percentage of pentadecane in HS-DrDd was increased up to 7.8 times in comparison with HS-FrDd. Principal component analysis (PCA) identified differences between the variability of data among fresh and dried samples over months and clearly dissociated the fresh May samples from the others in the HS-SPME results. The most abundant group of VOCs in HD were terpen, with diterpenes isopachydictylO A and cembra-4,7,11,15-tetra-3-ol as the major compounds. Diterpene pachydictylO A was also found and among sesquiterpenes and gleenol was the most abundant. Based on the dominant compound analyses, the PCA showed distinct separation of the fresh and dried samples, indicating similarities between the samples and allowing the establishment of typical VOCs significant for the chemotaxonomy of _D. dichotoma_.  
brown macroalga; volatiles; gas chromatography and mass spectrometry; pentadecane; isopachydictylO A; pachydictylO A; cembra-4,7,11,15-tetra-3-ol +
Footnote †: journal: _m molecules_
## 3 Results and Discussion
### Correlation Analysis between Volatile Organic Compounds (VOCs) and Aroma Profiles  
Partial least square (PLS) regression model analysis has been previously used to determine the relationship between the sensory and volatile profiling of durian [18], peach [30], and melon [31]. Thus, in this study, PLS model was performed to correlate the sensory scores with the volatile organic compounds of the wild edible durians that were detected using chromatography. The sensory data set was defined as the y-variable, and the amount of the volatile organic compounds was determined as the x-variables under the hypothesis that the volatile organic compound profile might primarily influence the sensory perception of durian. As can be seen in the PLS loading biplot (Figure 5), the seven genotypes were grouped into four following quadrants.  
This result was confirmed with QDA data in which _D. gravelens_, yellow-fleshed and orange-fleshed, in Quadrant I, grouped as having alcohol, nutty, fruity, and sweet aroma as there is the presence of various volatile organic compounds. The compounds such as 3-methyl\(-\)1\(-\)butanol, 2-methyl\(-\)1\(-\)propanethiol strongly correlate to alcohol aroma (0.887 and 0.920, respectively), 2-methyl\(-\)3-hexanol and 2,7\(-\)dimethyl\(-\)4,5\(-\)octanediol strongly correlate with fruity aroma (0.789 and 0.779, respectively) and 1,4,7,10,13,16-hexaacyclocatedacane strongly correlate with nutty aroma (0.785), where all of these compounds might contribute to the various aroma in _D. gravelens_, yellow and orange-fleshed. This PLS result grouped _D. gravelens_ (red-fleshed) and _D. zibethinus_ (Terung iban) in Quadrant II because both genotypes had presented of high relative amount of diethyl disulfide (H5), and it was a strongly positive correlate (0.890) (Table 5) with sulfur aroma. Some sulfur-containing compounds, such as ethanethiol, ethyl 1\(-\)methylethyl disulfide, diethyl trisulfide and 3,5\(-\)dimethyl\(-\)1,2,4-trithiolane were correlated strongly to the sulfury aroma, with correlation value between 0.890 to 0.901. _Durio kutejensis_ and _Durio oxleyanus_ grouped together in Quadrant III due to their mild aroma, which was proven in the QDA correlation. _Durio dulcis_ (Quadrant IV) was shown to have a different aroma profile than other genotypes. The aldehyde compounds, 2-methyl-propanol (B1) and 5-hydroxymethylfurfural (B5), which were described as having green and floral odor  
Figure 5: Partial least square (PLS) biplot of seven genotypes of wild edible durians.  
were strongly correlated (0.759 and 0.852, respectively) with grassy aroma (Table 5). These compounds were probably separating \(D.\)_dulcis_ from other genotypes. Butanoic acid, 2-methyl-, ethyl ester (D6), an ester compound, has a high amount in \(D.\)_dulcis_ but did not correlate well with a fruity aroma. This is in agreement with the findings by [PERSON] et al. [16] and [PERSON] et al. [18], where the concentration of butanoic acid, 2-methyl\(-,\) ethyl ester did not increase linearly with an increase in the intensity of fruity aroma.
# A Novel Visual SLAM Based on Multiple Deep Neural Networks
## 4 Experiments
### Runtime Experiment  
In order to show the running speed of our proposed system, we measured the duration of each image frame from reading to deriving the pose of this image frame as the time consumed to process this image frame. The running speed, denoted as \(v_{t}\), is defined by the averaged time by an image sequence:  
\[v_{t}\triangleq\frac{\sum\limits_{n=1}^{N_{f}}t_{n}}{N_{f}}, \tag{7}\]  
where \(N_{f}\) is number of frames in the image sequence, \(t_{n}\) is the time used for processing the \(n\)th frame.  
From Table 4, it is obvious that our proposed system only takes 81.8 ms to process an image frame with the GeForce RTX 3060 Mobile GPU running the SuperPoint, SuperGlue model and the Intel Core i7-11800H CPU running the rest of the system. Since the sampling period of the sequence is 100 ms, our system can achieve the performance of real-time operation under the above equipment conditions. According to the results published on the corresponding website of [40], the speed of our system (0.08 s per frame) already exceeds that of many existing SLAM systems.  
Figure 8: Comparison of SuperPoint+SuperGlue and SuperPoint+EuclideanDistance matching results in the same pair of images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **Our** & **SuperPoint-SLAM** \\ \hline
00 & **1.0** & X \\
03 & **0.5** & 1.0 \\
04 & **0.2** & 0.4 \\
05 & **0.6** & 3.7 \\
06 & **0.7** & 14.3 \\
07 & **0.4** & 3.0 \\
09 & **3.1** & X \\
10 & **0.8** & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with SuperPoint-SLAM in the KITTI odometry dataset using RMSE (m).
# Sex-Based Differences in Bronchial Asthma: What Are the Mechanisms behind Them?
## 3 Endotypes and Phenotypes of Bronchial Asthma

Table 2: Potential clinical and biochemical biomarkers for the eosinophil, neutrophil, and paucigranulocytic endotypes of bronchial asthma (modified from [1; 2; 3; 8]). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Classified Drugs** & **Examples of Drugs Used** & **References** \\ \hline \multicolumn{3}{l}{**Eosinophilic asthma**} \\ Currently available treatments: & & \\ \hline \multicolumn{3}{l}{Inhaled corticosteroids (ICSs)} & Budesonide, fluticasone & [12; 14; 15] \\ Oral corticosteroids (OCSs) & Prednisolone & [12; 14] \\ Bronchodilators (SABA, LABA, SAMA, LAMA) & Salbutamol, formoterol, ipratropium, tiotropium & [12; 15] \\ Xanthine derivatives & Theophylline & [16; 17] \\ Anti-IgE & Omalizumab & [18; 19] \\ Anti-IL-5/IL-5R & Mepolizumab, benarlizumab & [20; 21] \\ Anti-IL-4R & Dupilumab & [22; 23] \\ Anti-TSLP & Tezepelumab & [23; 24] \\ Anti-leukotrienes & Montelukast & [25; 26] \\ Prospective treatments: & & \\ \hline \multirow{4}{*}{Anti-IL-13 Tyrosine kinase inhibitors} & Talokinumab & [27] \\  & Dasatinib & [28; 29] \\ \cline{1-1}  & Simvastatin, atorvastatinatin & [30; 31] \\ \cline{1-1}  & Azithromycin & [32] \\ \cline{1-1}  & Fevipiprant & [33] \\ \hline \multicolumn{3}{l}{**Neutrophilic asthma**} \\ Currently available treatments: & & \\ \cline{1-1}  & Macrolide antibiotics & Azithromycin & [32; 34] \\ \cline{1-1}  & Bronchodilators (LAMA) & Tiotropium & [35; 36] \\ \cline{1-1}  & Xanthine derivatives & Theophylline & [16; 17] \\ \cline{1-1} Prospective treatments: & & \\ \cline{1-1}  & PDE4 inhibitors & Roflumilast & [37; 38] \\ \cline{1-1}  & Situs & Simvastatin, atorvastatinatin & [30; 31] \\ \cline{1-1}  & Tyrosine kinase inhibitors & Dasatinib & [28; 29] \\ \cline{1-1}  & CXCR2 antagonists & AZD5069 & [39; 40] \\ \cline{1-1}  & Anti-TNF\(\alpha\) & Etanercept & [41; 42] \\ \cline{1-1}  & Anti-IL-17 & Brodalumab & [43; 44] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Currently available and prospective treatments for eosinophil, neutrophil, and paucigranulocytic endotypes of bronchial asthma.
## 4 Next Forwarding Node Selection in UWSNs
### Void-Aware
#### 4.2.2 Non-Localization Routing Protocols  
Non-localization routing protocols did not fully use location information for selecting the next forwarding nodes during the routing process. To find the group of forwarder nodes toward the sink, routing protocols in this category use other information such as physical distance, hop-count, layering, dynamic address, and depth of nodes. Non-localization category is divided according to data collection methods into two subcategories: beacon-based and pressure-based. In beacon-based, a group of next forwarding nodes toward the sink is identified based on special information about the network such as addressing based and physical distance that is provided by sending periodic beacon messages from sink nodes to the ordinary nodes from the surface of the water to the bottom.  
On the other hand, depth information is the main factor used in the pressure-based subcategory to find the position of each node. All of the nodes are equipped with inexpensive pressure sensors that can calculate the depth manually and locally by changing the pressure of the water in different depths in the water current. The next forwarding node is the node that has less depth than the sender node. This process is continuously repeated in a hop-by-hop manner until reaching the sink node located on  
Figure 6: Packet forwarding in DFR.  
the water surface in UWSNs. The pressure-based method does not need expensive and full location information compared to the location-based and beacon-based methods. The location-based category needs complete information about a node's location, and the beacon-based category requires sending expensive beacon messages. Conversely, a pressure-based category just employs depth information that is obtained locally without high cost and extra overhead. Based on the selection mechanism of forwarding nodes, non-localization routing protocols can be divided into four categories: addressing based, energy-efficient, physical distance, and void-aware. The following paragraphs discuss these subcategories in detail regarding selecting the next forwarding nodes.
# Laboratory Measurements of Zeta Potential in Fractured
###### Abstract
The study simulated the expected SP response from a mineralogically homogenous reservoir with permeability anisotropy, which did not intercept the well or could not be detected by seismic surveys. Hence, the study reported results from the so-called forward modelling so that a thorough inversion of the SP signal along with information on expected spatial variation of mineralogy would be required to quantify the exact distance of the permeability anisotropy from the well and the expected flow velocities across the reservoir from the measured SP signal. Nonetheless, even without such information, SP was shown to be useful in qualitatively predicting the waterfront shape (e.g., [22, 28]) and quantitatively locating it relative to the instrumented well. However, the inversion of the SP field measurements (as explained above) and gain of both qualitative and quantitative interpretation of the groundwater flow (i.e., preferential flow paths, anisotropy of flow and hydrodynamic properties, pressure gradients, local flow velocities) requires values of a key petrophysical property, termed the zeta potential, which characterizes electrochemical interactions that take place at the mineral-water interface. The zeta potential depends on the aquifer lithology, mineralogy, pore space topology (e.g., [29, 30]) as well as on groundwater properties, specifically ionic composition, pH and concentration of aqueous solutions [31]. Numerous studies have reported zeta potentials measured in sandstones (e.g., [31, 32, 33]), carbonates (e.g., [34]) and granites [35] in contact with aqueous solutions of varying ionic strength and composition. However, no such measurements have been reported for gries-sose rocks, which form large parts of crystalline basement regions, and are the focus of this study. Therefore, the aim of this study is to thoroughly investigate the zeta potential of fractured gneiss samples saturated with simple and complex aqueous solutions under a range of confining pressures.
# A Feasibility Study of a Novel Piezo MEMS Tweezer for Soft Materials Characterization
## 1 Introduction
A new mathematical model to measure the stiffness, the equivalent Young's modulus, and the viscous damping coefficient of the soft tissue has been developed. The model has been tested on three different soft test specimens and the results were in good agreement with those obtained by COMSOL finite elements code.