esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### EEG/dEEG Recordings in Depression  
Various studies are investigating the use of Electroencephalography (EEG) and Quantitative Electroencephalography (qEEG) in treating depression by detecting hemispheric asymmetry in brain electrical activity in people with MDD compared to healthy controls.  
These may serve as potential risk indicators for depression and other emotional disorders, as well as in tracking the effectiveness of various therapeutic approaches.  
Even if more and more studies are performed in this area, using qEEG as a predictor of the efficacy of antidepressant treatment remains a challenging task for clinicians. The primary benefit that qEEG can bring in the treatment of major depression is the possible diminution of the number of typical unsuccessful treatment options explored by a psychiatist before the right antidepressant is found.  
In the qEEG analysis, an increase in alpha power in the left hemisphere and a decrease in beta values in the left parietal-occipital cortex, along with hypercoherence in the right anterior region, were identified in individuals with unipolar depression.  
Clinicians can use the coronary artery disease (CAD) system as a nonlinear method of depression diagnosis to detect and confirm the disorder early [21].  
In addition to the traditional depression scales used to assess a patient's level of depression subjectively, qEEG is an emerging technique that can objectively identify changes in brain abnormalities when compared to usual population standards and can be used to monitor treatment effects.  
The potential biomarker capabilities of EEG used to increase the chances of antidepressant efficacy [22] have been identified as a method that can influence treatment outcomes and could significantly increase the remission rate in the treatment of depression.  
qEEG is still not considered a clinically reliable predictor of a specific antidepressant treatment, as current studies still lack scientific validity and insufficiently replicate previous findings. Future studies will need to remedy the current limitations before this method can be proposed as a guideline for psychiatrists [23].
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.5 Floor Plan Prediction/Generation
They created a system of transmitters and radars that allowed room structure reconstruction thanks to the signal reflections and their processing. Floor plan prediction task analysis was concluded with articles by [PERSON] et al. [62], [PERSON] et al. [172], and [PERSON] et al. [68]. The first one [62] presented a space partitioning approach to floor plan generation from the point cloud, similar to and compared with the already mentioned work of [PERSON] et al. [71]. The second article [172] focused on processing CAD floor plans exported from architectural software as DXF files. It was desired to skip the rasterization part of floor plan generation and work with a graph structures recognized from the CAD primitives. The last article [68] proposed to process an input point cloud and generate an accurate floor plan using geometric priors--instead of relying solely on the density of points to recognize the building structure, they proposed combining them with indoor area recognition, normal information, and other geometric analysis.
## 3 Results  
Control and alternatively cured sausages differed in terms of sodium chloride (NaCl) concentrations, and a higher NaCl content was noted in AC sausages. A higher residual nitrite concentration was determined in control sausages, which was expected since these sausages were produced with the direct addition of sodium nitrite. However, nitrite was also detected in the alternatively cured sausages (Table 2). The products did not differ in terms of water activity (Table 2).  
Changes in _L. monocytogenes_ counts in products during storage are shown in Figure 1. The counts were affected by both product type (\(p\) < 0.001) and storage day (\(p\) < 0.05),  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Attribute** & **Control** & **AC** \\ \hline NaCl (\%) & 1.90 b (0.03) & 2.32 a (0.02) \\ Residual nitrite (mg/kg) & 44.89 a (0.50) & 12.10 b (0.38) \\ Water activity & 0.9638 a (0.0023) & 0.9653 a (0.0032) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the control (produced with the direct addition of nitrite) and alternatively cured (AC) sausages (mean values and standard deviation in the brackets).  
but an interaction between these two factors was not noted (\(p>0.05\)). In the control sausage \(L\). _monocytogenes_ counts did not differ during storage. In AC samples there were no significant differences in \(L\). _monocytogenes_ counts up to the 4 th day of storage, but at the 6 th, 8 th and 10 th day the number was significantly higher compared to day 0 (Figure 1). Control and AC products did not differ significantly at the 0, 2, 4 and 10 th day, whereas at the 6 th and 8 th day higher \(L\). _monocytogenes_ counts were noted in AC sausages compared with the control (Figure 1).  
Calculated \(L\). _monocytogenes_ growth potential is shown in Table 3. The highest number of \(L\). _monocytogenes_ (mean value) was noted in AC sausages at the 8 th day (3.30 log\({}_{10}\) CFU/g), whereas in the case of control sausages the highest number of the pathogen was noted at the end of the storage period (10 th day, 2.96 log\({}_{10}\) CFU/g), and these values were used to calculate the growth potential coefficient. The growth potential coefficient was higher in AC sausages, and moreover, it was higher than a threshold value of 0.5 [18], which indicated the possibility of \(L\). _monocytogenes_ growth in alternatively cured products. In contrast, the coefficient obtained for control sausages was below the threshold, indicating that these products do not support the growth of \(L\). _monocytogenes_ at 6 \({}^{\circ}\)C.
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 2 Numerical Model
### Sediment-Transport Model
#### 2.2.1. _Suspended-Load Transport_
The correction factor \(\beta_{s}\), the lag between the flow and sediment velocities, can be ignored for fine sediments, though for coarse sediments it is nonnegligible. According to [42], based on the logarithmic velocity distribution and the Rouse distribution for suspended load, the correction factor relation to the Rouse number \(R\) and the Chezy's coefficient \(C_{h}\) is defined as:  
\[\beta_{s}=\left\{ \begin{array}{l}0.0289R^{3}-0.0448R^{2}-0.2977R+0.994\ (C_{h}=40)\\ 0.0222R^{3}-0.0433R^{2}-0.1825R+0.9924\ (C_{h}=60)\\ 0.0189R^{3}-0.044R^{2}-0.122R+0.9924\ (C_{h}=80)\\ 0.0113R^{3}-0.0227R^{2}-0.1051R+0.9918\ (C_{h}=100)\end{array} \right. \tag{10}\]  
where \(R=\omega_{s}/\left(\kappa U_{*}\right)\) is the Rouse number with \(x\) the van Karman constant (=0.41) and \(U_{*}=\sqrt{\tau_{0}/\rho}\) is the shear velocity (m/s). Interpolation may be performed with Equation (10) for the other \(C_{h}\) values.
## 4 Results and Discussion
### Corpus of Crypto-Ransomware Binaries  
The corpus of crypto-ransomware binaries used in this study were downloaded from [[http://www.virusshare.com](http://www.virusshare.com)]([http://www.virusshare.com](http://www.virusshare.com)) (accessed on 14 March 2023) public repository [30; 31; 32; 33]. The corpus consists of 8152 samples. Those samples represent different families such as Cerber, TeslaCrypt, CryptoWall, Petya, and WannaCry. The samples were collected during the period from September 2016 to August 2017. In addition, 1000 benign programs were downloaded from informer.com [32; 33; 34; 35], a popular Windows-based applications repository. Those files are called ransomware binaries, often referred to as Portable Executable (PE) files. Those PE files are a central focus in the field of malware analysis as they serve as a common format for executables, object code, and DLLs in Windows operating systems. When analyzing ransomware, PE files are closely inspected statically or dynamically to understand the malicious software's structure, functionalities, behavior, and potential impact. By dissecting the PE file, we can extract valuable metadata, identify suspicious or known malicious signatures, and gain insight into the ransomware's behavior, such as encryption methods, communication with command and control servers, or persistence mechanisms. Analyzing these binaries is pivotal in developing countermeasures, creating detection signatures, and understanding the evolving tactics of ransomware authors. In this study, the dynamic analysis, which involves executing the PE file as the sandbox, is adopted.
# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 2 Acoustic Emission Testing Methods
### Acoustic Emission Sources
Recent studies have highlighted the multiplicity of information that can be drawn from the use of these techniques. Through the adoption of AE tests, we can obtain much information:  
* Damage phenomena origin: Having noted the arrival times of the acoustic waves to each sensor and the speed of propagation of the waves in the material, it is possible to locate an AE event within the structure of the material with great accuracy. * Fractured area: The set of points relating to all events located in each time interval allows obtaining the density of the damage in the material. When the geometric distribution of the localized points identifies a well-defined path, the progressive formation of a crack is observed. Furthermore, the speed of seismic waves in damaged areas is significantly lower than that in intact areas. * Damage mechanism: The shape of the waves recorded by each sensor is a function of the source mechanism and depends on the path taken by the acoustic wave when it travels from the source to receiver. The analysis of the shape of the waves allows distinguishing between the different mechanisms of propagation of the cracks. * Stress state: There is a relationship between areas with high-speed anomaly and regions subject to a high state of stress and, therefore, potentially damaged. * Material properties: The frequency and amplitude of a wave traveling in a material directly depend on its properties. Measurements of seismic velocity, anisotropy, and attenuation are, therefore, sensitive to any variation in the properties of the materials. * Time-dependent behavior: Through continuous monitoring, information is obtained on the evolution of the mechanical response of a material and, therefore, on the phenomena of degradation and progressive formation of cracks.
# A Twitter-Based Comparative Analysis of Emotions and
## 3 Methodology
### Capturing Tweets  
We captured all the tweets written in Arabic and Spanish languages that were posted during the period of the Argentina vs. Saudi Arabia football match in the FIFA World Cup in Qatar, in 2022. We chose 22 November 2022 as the starting date, which was the day of Argentina vs. Saudi Arabia match (the day when the trend \"Where is [PERSON]\" in the Arabic language started), and 23 November 2022 as the ending date, which was one day after the match. We developed a Python script to communicate programmatically with Twitter via its Application Programming Interface (API) for developers. In this method, the script takes as input a key phrase, a start date, and an end date, and returns all tweets containing the specified key phrase throughout the specified duration. The Tweet-gathering period was marked by a high level of tension among both Arab speakers and Spanish speaker users. To facilitate the cross-cultural analysis by examining Twitter tweets, we retrieved the Twitter data using Python script with specific filtering. Therefore, only users watching Argentina vs. Saudi Arabia football match from hundreds of countries and regions were represented. As the aim of this work was to study emotions and sentiments on Twitter for Arabic- and Spanish-language speakers only, we needed to rule out tweets in any other language. Therefore, we parsed all the tweets posted by unique Arabic and Spanish users in the selected period. We crawled all the Arabic and Spanish Twitter data throughout the collection period with the key phrase \"Where is [PERSON]\" written in either Arabic or Spanish. [PERSON] is a well-known Argentinian football player known to football fans worldwide.
# Parametric Analysis of Outrigger Systems for High-Rise Buildings with Different Geometric Shapes
## 2 Simplified Approach for Outrigger Systems  
### Background  
Outrigger systems in tall buildings are frequently analyzed using simplified continuum or discrete approaches. Early idealized continuum models, like those proposed by [PERSON] [39], represent the core as a cantilever beam with rotational springs at the outrigger levels, assuming infinite flexural stiffness for outriggers and belt-trusses. Subsequent research expanded upon this foundation. For example, [PERSON] et al. [27; 40; 41] proposed a cantilever beam model with outriggers rigidly connected to the core and pinned to the exterior columns, assuming linear elastic behavior, axial forces in columns, and constant sectional properties along the building's height. [PERSON] et al. [42; 43] employed a graphical method with simply supported outriggers (pinned to both core and columns) for the preliminary design of high-rise steel structures with an RC core, outrigger-breaced system, and non-fixed foundation conditions. [PERSON] et al. [44; 45; 46] developed a refined two-dimensional model incorporating shear deformation in both outriggers and the core. Assumptions included linear elastic behavior, uniform member properties, rigid core-outrigger connections, pinned outrigger-column connections, and a fixed core base. Later research highlighted the significance of accurately representing core-outrigger relative stiffness. [PERSON] et al. [47] demonstrated that assuming infinite outrigger rigidity affects horizontal deflection and base moments, thus highlighting the significance of using actual outrigger stiffness in optimization analyses. [PERSON] et al. [48] investigated the load-transfer mechanism between outriggers and the core using the strut-and-tie method. [PERSON] et al. [49; 50] developed an approximate method for framed tube, shear-core, and outrigger belt-truss systems, focusing on the optimal location. [PERSON] (2018) employed a simplified model with an elastic core shear wall, rigid outriggers, and elastic columns to analyze dynamic behavior. More recently, [PERSON] et al. (2018) proposed an idealized analytical model simplifying the outrigger frame system into a central cantilever core and a concentrated restoring moment from the outrigger.  
This body of research demonstrates the evolution of modeling techniques, progressing from simple cantilever models to more sophisticated approaches. It is important to highlight that simplified analytical models provide a valuable basis for understanding the global behavior of outrigger systems and precede detailed finite element analyses. However, two main limitations can be identified. First, outriggers are typically discretized as elements with infinite flexural stiffness and assumed to be simply pin-connected to the columns. Second, these methods are primarily suited for estimating optimal outrigger placement for the drift minimization of regular structures. Consequently, such methods exhibit limited applicability to complex configurations involving geometric or material variations.
# Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics
## 2 Materials and Methods
### Modeling Methods  
Partial least squares-discriminant analysis (PLS-DA) is a supervised learning feature extraction method frequently used in spectral analysis. PLS-DA predicts the class for each sample based on PLSR step [34]. The optimum number of latent variables (LVs) at minimum value of the corresponding prediction errors of sum of squares (PRESS) will be selected. In current study, to reduce the overfitting risk, calibration and prediction sets were randomly divided based on the ratio of 3:1 (300 samples vs. 100 samples). The optimized number of LVs in developed PLS-DA models were determined based on the value of RMSE under two-fold'venetian blinds' cross-validation. The variety values herein were replaced using pseudo-variables of 1 and 2 (1 for Ningguo walnut, and 2 for Lin'an walnut).  
The \(k\)-nearest neighbor (KNN) algorithm is a commonly used supervised pattern recognition method, which automatically describes nonlinear relationship according to the K values [35]. The general steps are (1) evaluate the distance between the sample to be classified and other samples in calibration set, (2) look for the K samples closest to the targeted sample, (3) see the classification performance of these K samples, and finally (4) take the group with the most occurrences as the category of the sample to be classified. In our study, Euclidean distance evaluation method was employed, and the range of K values was set from 1 to 10 with a cross-validation step of two based on'venetian blinds' method.  
Support vector machine (SVM) is a kernel-based nonlinear method which has been proven to exhibit good performance in spectral modeling. In SVM classification, a hyperplane is explored to segment nonlinear spectral data of prepared samples [36]. In this study, radial basis function (RBF) was used as the kernel function of SVM, which has strong ability in addressing nonlinear problems. The parameters of \(c\) (the penalty coefficient) and \(g\) (the radial width of the kernel function) for RBF-SVM were chosen in automatic optimization process. The value ranges were set to \(2^{-8}\) to \(2^{8}\) for both \(c\) and \(g\) searching. All the three modeling procedures were performed in Matlab software with PLS-DA, KNN and SVM toolboxes.
# Raman Spectroscopy to Monitor the Delivery of a Nano-Formulation of Vismodegib in the Skin
## 3 Results and Discussion
### Further Discussion  
UV-visible spectroscopy, whether using a conventional instrument or as part of an HPLC detector, has disadvantages for the measurement of VDG, due to its low absorbance and fluorescence. For these reasons, when using a UV-visible detector, organic extractions are necessary to obtain samples from the tapes, and grouping of these samples is required to obtain VDG concentrations that can be measurable by the detectors. On the other hand, when employing Raman, the acquisition of information is directly performed on the skin. In the context of diagnostic applications, and as a complement to therapeutic applications, the near-infrared wavelength used is harmless, with good skin penetration [26]. The acquisition mode is not only fast, but Raman detectors can also be constructed in a portable manner [27]. All these factors contribute to the significant biomedical potential of this method in relation to skin diseases.  
The acquired Raman spectra allowed for the identification of active vibrational modes of the target species by comparing them with the spectra of solid VDG and transfersomes in aqueous suspension, obtained using a DXR Raman microscope [24]. A wavelength of 785 nm, corresponding to the near-infrared range, was chosen, utilizing 100% of its emission power (approximately 150 mW). This selection was made because many molecules exhibit sufficiently high fluorescence signals that can interfere and overlap with the Raman signal, especially in complex environments such as the skin, where melanin absorbs light in the visible range. In contrast, at this wavelength, there is a significant reduction in fluorescence intensity, allowing for efficient observation of Raman-active vibrational mode peaks [28]. On the other hand, the use of a grating with 600 grooves per mm implied a lower spectral resolution but a wider spectral range for scanning analysis. The peaks corresponding to the characteristic vibrational modes of transfersomes and VDG were previously described [24]. They were found at 1152 cm\({}^{-1}\) and 1437 cm\({}^{-1}\) for transfersomes, and at 350 cm\({}^{-1}\), 415 cm\({}^{-1}\), and 770 cm\({}^{-1}\) for VDG.
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 7 ANN Results with Simulated Degradation on _I-V_ Curves
### Comparison with Other ANN Solutions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **ANN Structure** & **Inputs** & **Data for the** & **PV** & & \\  & & & **Training Phase** & **Granularity** & **Detected Faults** & **Complexity** \\ \hline
[9] & MLP-ANN or & \(V_{app}\), \(I_{app}\), & simulated & & shadow, cell in open circuit, & Medium \\  & RBF-ANN & \(V_{ac}\) & \(I\)-\(V\) curves & PV string & shunted bypass diode, & \\  & & & in faulty conditions & & short circuit & \\ \hline
[10] & KELM + SLFN & \(V_{app}\), \(I_{app}\), & experimental and & PV string, & partial shading & High \\  & & & simulated -\(V\) curves in & PV array & string degradation, & \\  & & & healthy and faulty operation & & short circuit, open circuit & \\ \hline
[11] & RBF-ANN & PV power, & long-term measure & & PV panel disconnection, & Low \\  & & irradiance & of PV production, & PV array & partial shading & \\ \hline
[34] & Deep Learnig & down sampled & simulated or & & \(R_{c}\) degradation, & High \\  & ANN & \(I\)-\(V\) curves, & experimental & PV array & partial shading, & \\  & & \(G\), \(T\) & \(I\)-\(V\) curves & & short circuit, open circuit & \\ \hline
[12] & PNN & per unit values of & simulated and & & & \(R_{c}\) degradation, & High \\  & & \(V_{app}\), \(I_{app}\) & experimental & PV array & partial shading, & \\  & & \(V_{ac}\), \(I_{ac}\) & \(I\)-\(V\) curves & & short circuit, open circuit & \\ \hline
[13] & 2 PNN & irradiance, & experimental & large & number of & Medium \\  & & \(V_{app}\),\(I_{app}\) & & healthy -\(V\) curves, & PV array & short-circuited panels & \\  & & & simulated -\(I\)-\(V\) curves & & & \\  & & & in faulty conditions & & & \\ \hline
**Proposed method** & 2 MLP-ANN & 3 points & Datasheet curves or & & panel degradation, & Low \\  & & around MPP, & healthy -\(V\) curves, & & partial shading, & \\  & & irradiance & simulated -\(V\) curves & & hot spots, & \\  & & panel temperature & in degraded conditions & & by-pass diode failure & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: ANN techniques for PV diagnosis and faults identification.