esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure-free (dataset E).  
Keywords:epileptic seizure detection; bioelectrical signals; local mean decomposition (LMD); mel-frequency cepstrum coefficients (MFCC) +
Footnote †: journal: Applied
## 1 Introduction
### Motivation  
In HetNets with Macro cell Base Stations (MBSs) and Pico cell Base Stations (PBSs), Cell Range Expansion (CRE) scheme is proposed to adjust the coverage of PBS and balance the load between MBSs and PBSs by adding a positive bias to their measured Reference Signal Received Power (RSRP) during cell association [9; 10]. The pico edge User Equipment (UE), however, will suffer significant interference from MBSs, even causing the outage of control signal. As a result, the downlink interference mitigation needs to be performed even for control channels, which becomes a fundamental requirement for the network EE improvement.  
Another feature called time domain Enhanced Inter-Cell Interference Coordination (elCIC) has been adopted as a baseline method for control channel interference mitigation in LTE-Advanced [11]. In eICIC schemes, each MBS remains silent for certain periods, termed as Almost Blank Subframe (ABS), over which PBS can schedule CRE area UEs with reduced interference [12]. On the basis of eICIC ABS technology, obtaining the optimal pico CRE bias will directly affect the number of UEs served by PBSs and eventually contribute to the network EE improvement. This is also one of the key issues to realize 5G green cellular networks [13; 14].  
Furthermore, to meet 1000 times wireless traffic volume increment in the near future, massive PBSs deployment will be necessary and the related EE problems will become severe due to the following reasons. First, the dense deployment of PBSs causes additional inter-tier and intra-tier interference, which is difficult to characterize. Second, most PBSs are typically deployed to satisfy the peak traffic volume, while the highly dynamic wireless traffic may result in low EE if massive PBSs are still awakened during low traffic periods such as midnight [15].
## 2 Materials and Methods
### Modelling
#### 2.6.4 Clustering  
Clustering refers to any class of unsupervised machine learning methods related to grouping data points together such that the degree of difference between variables within a cluster are minimised and therefore smaller than differences observed with data points outside of that cluster. K-means clustering is one such technique. Cluster centroids are defined based on the mean across the data points on each axis in a cluster.  
K-means clustering was facilitated by the SKlearn Cluster package. The two approaches considered were: (1) clustering over FeatureDS2 (clus-raw) and (2) clustering over Principal Components of FeatureDS1 (clus-PCA). Training patients were clustered over all data available using a random initial state of 42 and 300 maximum iterations with all possible values of K considered (i.e., 20 models). Validation patients were assigned to a cluster based on the cluster centroid that minimised the Euclidean distance. The mean weight over the training patients was considered the prediction weight for unseen patients assigned to that cluster.  
To aid the analysis of cluster performance, two metrics were calculated for each model formation: (1) the sum of the squared differences between each point and its cluster centroid (SSE) and (2) a silhouette coefficient--a value between \(-1\) and \(1\) that scores the goodness-of-fit of each formation based on average inter- and intra-cluster distances. SSE values close to zero and silhouette scores close to 1 indicate models that are well defined.
# Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics
### Contributions
#### 1.1.3 Contribution 3  
The expected value of the elevation angle and its measures of dispersion are relevant for planning and implementation of LEO satellite systems, since the received power varies according to the elevation angle. This document describes an analytical methodology to obtain the first and second order statistics of the elevation angle for different orbit configurations and ES locations, which, to the best of our knowledge are not available in the literature and were just utilized for a specific case in [9].
## 4 Discussion
### Methionine Absorption in Pig Jejunum  
In the present study, we tested the absorption of Met released from Met-ADS complex in porcine jejunum _ex vivo_. Exogenous Met is commonly added to diets that do not contain a sufficient amount of Met to meet the Met requirement by the pig. To be transported from the intestinal lumen into the blood, Met enter the intestinal absorptive epithelial cells (a.k.a. enterocytes) first through corresponding transporters present in the brush border membrane at the apical side and then exported from the epithelial cells to the bloodstream through the transporters present in the basolateral membrane [24]. Met is a neutral AA, and its uptake across the apical membrane of intestinal epithelial cells is almost entirely Na+-dependent [25; 26]. The major apical neutral AA transporter in the intestine is the B(0)AT1 (SLC6A19) system, which cotransports one Na+ per AA [27]. In pigs, Met is absorbed along the entire small intestinal tract.  
The Using system offers an _ex vivo_ measurement of electrophysiological measurements such as TEER (as a measurement of gut integrity) and short circuit current (Isc). Isc is a summation of all ionic currents across the epithelium, and it represents an estimation of actively transported ions across the epithelial membrane [17]. However, once absorbed, Met can be metabolised by the epithelial cells or released in the serosal side of the epithelium.  
lium. In order to estimate the quantity of Met released in the serosal side, Using-obtained data must be integrated with other techniques such as Mass Spectroscopy.  
In this study, we used jejunum segments because previous studies in pigs demonstrated that the rate of Met absorption in this region is comparable with or exceeds those of other regions [28]. During the 3 h exposure time of the jejunum segments, a small percentage of both free Met and Met-ADS were absorbed. The low percentage of both Met and Met-ADS absorbed during the trial could be explained by the saturable absorption of the Met. Concentrations tested in our study were chosen to ensure the saturation of the intestinal carriers. The maximum rates of saturable absorption of Met has been estimated in the range of nmol min\({}^{-1}\) mg tissue\({}^{-1}\) by the middle intestine [28]. However, the lack of statistically significant difference in the Met and Met-ADS-induced Alsc demonstrated that ADS did not affect the potential of the Met to be absorbed by the intestinal active transporters once released by the carrier. In agreement with the cytotoxicity test in vitro, no detrimental effects on the intestinal epithelial integrity were observed. Specifically, no differences in the TEER between CTR, Met and Met-ADS were found. In all the groups, the TEER remained nearly constant during the whole experiment demonstrating that the integrity lasted for the entire trial. The forskolin-induced Alsc values also confirmed the viability of the tissues at the end of the experiment, confirming the absence of detrimental effects of Met-ADS on jejunum integrity.
# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 3 Proposed Method
### Classification of Fundus Images  
The classification of segmented images is performed utilizing the ResNet model, which is a pre-trained network architecture. ResNet is one of the most extensively used pre-trained models in the CNN category. To avoid wasting processing capacity on the same activity, the notion of transfer learning (TL) is used. TL eliminates the need to develop a model from scratch because the network has previously been trained with a set of features on larger datasets. TL is used to add pre-trained networks that have already been trained to perform a certain task. To tackle the difficulty of training neural networks, ResNet is built on a residual block that bypasses connections. There may be 16, 18, 34, 50 or 101 layers in these structures. Conv1, Conv2x, Conv3x, Conv4x, Conv5x and a fully connected (FC) layer are the six modules that make up ResNet-101 [33]. Conv1 is the name of the network's first connected layer (CL). FC is in charge of learning and modifying weights to train better. The ResNet-101 model is used for the extraction of features and classification of DR and HR (Figure 5).  
The proposed network architecture is shown alongside the architecture of ResNet. More than 10 million similar images were used to train the pre-trained model. The network was trained using the dataset, and the previous model's FC layer was replaced with the newly proposed CL model. In an 80:20 ratio, the network is tested and trained. To ensure unbiased sampling, the model uses random retinal images from the dataset.  
Recent research has found that network depth improves classification accuracy [34]. However, as the network grows in depth, it becomes saturated, and performance begins to decline fast [35]. This is a problem that the ResNet framework can solve. At every three convolutional layer, shortcut connections are introduced to the deep network. These shortcut connections conduct identity mapping without adding additional parameters or increasing computational complexity, making network optimization easier during the  
Figure 5: The proposed ResNet-101 architecture.  
training phase. As a result, when undertaking image classification tasks, ResNet allows deeper networks to attain a higher accuracy than shallower networks [36].  
MATLAB is used to implement the proposed model for retinal image classification. The fundus images are taken from a publicly accessible dataset of around 6000 images. The ResNet-101 transfer learning model was used to train and test the suggested model. ResNet-101 is one of the best pre-trained models for categorizing various types of medical imaging at the moment. To extract features from images, the CL, pool5 and FC layers are employed, and the FC layer is enhanced in this study by adjusting the weights according to the dataset for improved training. Each image yields a total of 2048 unique features.
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 2 Quaternion Orthogonal Designs
### Symmetric-Paired Design 1: (Square QODs)  
This construction technique constructs QODs \(\mathbf{A}+\mathbf{B}j\) in which COD \(\mathbf{B}\) is obtained from \(\mathbf{A}\) through permutation of columns, where permutation operation on two columns \(m\) and \(n\) results in swapping the positions of these two columns with each other. For all CODs based on the permutation of CODs for a specific antenna dimension, the diversity order remains the same, therefore, without loss of generality we employ (2) to arrive at the following result.  
**Theorem 1**.: _For a given COD \(\mathbf{A}\) in (2) and its permuted version \(\mathbf{B}\), a complex amicable and symmetric-paired design can be constructed such that the following realization,_  
Figure 1: Quaternionic Nomenclature: Two symmetric-paired complex orthogonal designs (CODs) \(\mathbf{A}\) and \(\mathbf{B}\) generate a quaternion orthogonal design (QOD) \(\mathbf{Q}\), which gives rise to different quasi-codes \(\mathbf{C}_{\mathbf{Q}}\) and \(\mathbf{C}_{\mathbf{q}}\) with linear and decoupled decoders.  
\[\mathbf{Q}_{2^{l}}(z_{1},z_{2},\ldots,z_{l+1})=\mathbf{A}+\mathbf{B}j=\begin{bmatrix} \mathbf{G}_{2^{l-1}}+z_{l+1}\mathbf{I}_{2^{l-1}}j&z_{l+1}\mathbf{I}_{2^{l-1}}+ \mathbf{G}_{2^{l-1}}j\\ -z_{l+1}^{*}\mathbf{I}_{2^{l-1}}+\mathbf{G}_{2^{l-1}}^{H}j&\mathbf{G}_{2^{l-1}} ^{H}-z_{l+1}^{*}\mathbf{I}_{2^{l-1}}j\end{bmatrix}, \tag{3}\]  
_provides a QOD of dimension \(2^{l}\times 2^{l}\)._  
The following example illustrates the construction in which we start with an Alamouti code \(\mathbf{G}_{2}=\begin{bmatrix}z_{1}&z_{2}\\ -z_{2}^{*}&z_{1}\end{bmatrix}\). Using (2), we obtain a square matrix \(\mathbf{A}\) of order 4. Consequently, we have a QOD.  
**Example 1**.: _Using the permutation operation on \(\mathbf{A}\), as described above and representing it with \(\mathbf{B}\), we obtain following QOD \(\mathbf{Q}_{1}=\mathbf{A}+\mathbf{B}j\), to be used in the configuration of 4 dual-polarized antennas_  
\[\mathbf{Q}_{1}=\begin{bmatrix}z_{1}+z_{3}j&z_{2}&z_{3}+z_{1}j&z_{2}j\\ -z_{1}^{*
## 2 Materials and Methods
### Corrosion Behaviors  
#### 2.3.1 Potentiodynamic Polarization Curve  
The potentiodynamic polarization curves were plotted to characterize the electrochemical behaviors of different samples in simulated body fluid (SBF, compositions: NaCl, 8 g/L; KCl, 0.4 g/L; NaHCO, 0.35 g/L; CaCl\({}_{2}\), 0.14 g/L; Na\({}_{2}\)HPO\({}_{4}\), 0.06 g/L; KH\({}_{2}\)PO\({}_{4}\), 0.06 g/L; MgSO\({}_{4}\)-7H\({}_{2}\)O, 0.01 g/L; glucose, 1 g/L; Sinopharm Chemical Reagent Co., Ltd, Huai'an, China). The pH value of the corrosion medium was adjusted to 7.4 using an NaOH solution. The SBF solution was kept at 37 \(\pm\) 0.5 \({}^{\circ}\)C during the experiment. The experiments were carried out on a CHI660D electrochemical workstation (CHI Instruments, Inc., Shanghai, China). A three-electrode system (CHI Instruments, Inc., Shanghai, China) was used to measure the polarization curve. The sample, saturated Ag/AgCl electrode and the platinum electrode were the working electrode, reference electrode and the auxiliary electrode,respectively. Before the experiment, the samples were encapsulated by epoxy resin using Cu wire, as the conducting wire and the exposed area for each sample was about 1.13 cm\({}^{2}\). The sample was immersed into the SBF solution until the open circuit potential became stable. The polarization curves were measured with the scanning rate of 10 mV/s. The corrosion current and corrosion potential were determined according to the polarization curve.
## 2 Materials and Methods
### Data
#### 2.1.3 Weather Stations  
The present study is mainly based on meteorological factors and indices related to them. The data used in this study come from 39 weather stations (Figure 1) operated by the Murcian Institute of Agricultural Research and Development and Food. Each station comprises a database with records of daily rainfall (mm), temperatures (\({}^{\circ}\)C), relative humidity (%), and wind speed (m/s) during the 2000-2014 period. Annual precipitation in the region is on average 270 mm, average relative humidity ranges between 60% and 70%, and the average temperature varies between 13 \({}^{\circ}\)C in the northwest and the 18 \({}^{\circ}\)C in the southeast. These values are higher than in the rest of the region in Zone 4, especially in spring and summer favoring wildfires, which is also due to the scarcity of rainfall events. Wind speed depends on altitude, and the highest values are reached on mountain range peaks.  
A combination of the Global Climate Model (GCM) EC-EARTH and the Regional Climate Model (RCM) HIRHAM5 was downloaded from the EURO-CORDEX initiative [59] to simulate future scenarios and to assess climate change consequences in the region for the period 2071-2100 under two different representative concentration pathway (RCP) emission scenarios (RCP4.5 and RCP8.5). These scenarios assume pathways to different target radiative forcing at the end of the 21 st century [59]. For example, scenario RCP4.5 figures that an increase in radiative forcing of 4.5 W/m\({}^{2}\) can be produced by the end of the century relative to preindustrial conditions. [PERSON] et al. [60] applied the Fuzzy TOPSIS technique to rank a set of nine GCM-RCM combinations in the study area, obtaining that the combination of EC-EARTH and HIRHAM5 provides a good fit to the observed meteorological variables during the control period. A bias correction technique based on distribution mapping of variables was applied to the downscaled data to increase the accuracy of the results. This process adjusts the cumulative distribution function of the forecasts to the observed one by applying mapping functions between the corresponding quantiles. We used the R free software and the contributed package \"qmap\" [61], which can be obtained from the R project website.
## 3 Results and Discussion
### Dissolved Trace Elements
#### 3.2.3 Nickel  
The mean dissolved Ni concentration was significantly higher at site 1 than at all other sites. Sites 2-7 did not experience significant differences when compared to one another and remained rather constant during the 13-month sampling period. Comparison with pre-treatment concentrations of dissolved Ni at site 1 by [PERSON] et al. [19] showed that the content decreased drastically from 16 mg/L to a mean concentration of 0.391 mg/L in our study, still showing a high variability with concentrations ranging from 0.011-2.84 mg/L. The fate and decrease of Ni in treated MIW have been associated with the co-precipitation of the trace metal with iron [39]. Batch experiments by [PERSON] et al. [40] showed that interactions with surface sites of precipitated Fe and Al compounds and rising pH led to an increased removal of Ni. In this regard, some pathways in which Fe(III) hydroxides acted as a sink for Ni and other trace metals in MIW treatment have already been described in the literature, including adsorption, absorption, surface precipitation, and co-precipitation [38; 40; 41; 42]. The SA-WQG does not include data on Ni. After consulting the guidelines of the European Union as well as the Alberta Government, a solid assessment concerning the toxicity of dissolved Ni towards aquatic organisms in the freshwater system is not trivial because different guidelines suggest a wide concentration range [26; 27]. The reasoning for that is the reduced toxicity of this metal with the rising hardness of the water. Due to the extremely high content of Ca\({}^{2+}\) as well as Mg\({}^{2+}\) in the water of site 1, the immediate effect on aquatic environments is more difficult to predict. However, note that the temporarily reached concentrations are up to 100x higher than in typical surface freshwater bodies, underlining the burden of dissolved Ni in the MIW at site 1.
# Identification of Novel Markers of Prostate Cancer Progression, Potentially Modulated by Vitamin D
## 1 Introduction
The high serum vitamin D3 levels, estimated by measuring 25(OH)D3, play important roles in the prevention of various forms of cancer, including prostate cancer [15]. An in vitro study using prostate cancer LNCaPP cells reported that a potent analog of 1,25(OH)2D3 (EB108928) plays an inhibitory role on the growth of cancer cells [16]. In another study in immortalized human prostate cell lines (PZ-HPV-7 cells), 10 nM of 25(OH)D3 was reported to have growth inhibitory activity [16]. In 2018, a meta-analysis study, composed of seven eligible cohort studies with 7808 participants, suggested that higher 25(OH)D3 level was associated with a reduction of mortality in prostate cancer patients, suggesting that Vitamin D can be considered an important protective factor in the progression and prognosis of prostate cancer [17]. Recently, it has been shown that dietary Vitamin D causes a dose-dependent increase in serum 25(OH)D3 levels and a reduction in the percentage of mice with adenocarcinoma, but it did not improve bone mass. In contrast, high calcium did suppress serum 1,25(OH)2D3 levels, leading to an increased incidence of adenocarcinoma and improved bone mass. These data support the hypothesis that the loss of Vitamin D signaling accelerates the early stages of prostate carcinogenesis [18]. Furthermore, it is important to note that several factors could influence the anticancer action played by Vitamin D on PCa, such as the polymorphism of its receptor (VDR) [19] or the expression of the enzymes (CYP24A1 and CYP27B1) that regulate its metabolism [20]. The aim of this study was to find new target genes of PCa, and those potentially modulated by the action of Vitamin D (1,25D3 and 25D3). We explored several microarray datasets in order to investigate and identify possible cancer gene pathways hypothetically modulated by the action of Vitamin D.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 2 Families of Sequence-Specific Endoribonuclease TA Toxins
### YhaV  
_E. coli_ YhaV is a ribosome-independent endoribonuclease that digests mRNA, 16S rRNA and 23S rRNA in vitro in the absence of the ribosome [78]. However, its recognition sequence remains unknown. YhaV is a toxin that also belongs to the RelE superfamily, containing highly-conserved arginine residues [78]. The replacement of two of these residues results in the loss of enzymatic activity [78]. The antitoxin, PrIF, on the other hand, belongs to the same family as MazE and PemL (AbrB super family), suggesting the ancient genetic rearrangement between TA pairs and a swapping of cognate pairs [78; 79; 80].