esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations
### Performance Metrics  
In this subsection, we introduce the performance metrics that are measured in the simulations. The performance metrics measured are as follows:  
(i) _Diameter of the DG Tree_--The maximum distance (number of hops) from any leaf node to the root node of the DG tree.  
(ii) _Minimum Aggregation Delay_--The minimum number of time slots it takes for the data to get aggregated, starting from the leaf nodes and propagating to the root node of the DG tree  
(iii) _Hop Count per Path_--The average of the number of hops per path from every node (other than the root node) to the root node of the DG tree.  
(iv) _Number of Child Nodes per Intermediate Node_--The average of the number of immediate child nodes per intermediate node, considered across all the intermediate nodes (including the root node) of the DG tree.  
(v) _Fraction of Nodes as Leaf Nodes_--The ratio of the total number of leaf nodes (nodes without any child nodes) at all levels divided by the total number of nodes in the network.
# Ru(terpy)-Based Conducting Polymer in Electrochemical Biosensing of Epinephrine
## 2 Materials and Methods
### Epinephrine Sensing  
The epinephrine sensing tests were carried out at room temperature in an open-air atmosphere in a cell containing 30 cm\({}^{3}\) of phosphate buffer (0.1 mol dm\({}^{-3}\), pH 7.0). The electrochemical tests were performed by differential pulse voltammetry (DPV) and by chronoamperometry (CA). Differential pulse voltammograms were recorded between \(-\)0.6 V and +0.6 V (step potential: 0.004 V, amplitude: 0.05 V). According to the voltammetric responses, the chronoamperometric measurements were recorded while a constant potential of 0.18 V was applied and the solution was gently and constantly stirred. When the background current reached a constant value, incremental amounts of epinephrine aqueous solution were injected into the cell with a time interval of about 200 s, and the current/time response was recorded in a concentration range from 1 \(\upmu\)mol dm\({}^{-3}\) to 1 mmol dm\({}^{-3}\).
## 2 Methodology
### Delineation of Retrieval Workflow  
Figure 2 gives an overview of the complete processing workflow. Three well-defined processing blocks are depicted, starting with an S2 and S1 imagery acquisition and pre-processing section, followed by MOGP best model selection, and the retrieval of VWC applying MOGP to S1 and S2 time series datasets. To sum up, the implemented processing workflow consisted of the following six main steps:  
1. Building of VWC time series applying a GP model trained with in situ data of the BVCR 2020 crop campaign to S2 imagery, and pre-processing of RVI time series for S1 orbit 68 and orbit 141 imagery, respectively;
2. Assembling the S1 & S2 dataset containing multitemporal VWC retrieved values and S1 post-processed RVI data for a specific ROI of the BVCR study site;  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **North** & **West** & **South** & **East** & **Qty-x** & **Qty-y** & **Area** \\ \hline
**ROI-1** & \(-\)39.398 & \(-\)62.645 & \(-\)39.404 & \(-\)62.636 & 10 & 12 & 1.2 \\
**ROI-2** & \(-\)39.391 & \(-\)62.618 & \(-\)39.392 & \(-\)62.616 & 12 & 13 & 1.56 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ROI boundaries in geographic coordinates (WGS84), x-pixels quantity (Qty-x), y-pixels quantity (Qty-y), and ROI area selected for training the MOGP models with the S1 & S2 dataset. ROI-1 belongs to the winter wheat 2020 site whereas the ROI-2 to 2021 site.  
Setting up the MOGP kernels with Q = 4 and initializing the parameters using SM;
4. Training the MOGP models with the S1 & S2 dataset using the Adam optimizer and assessing the regression statistics error metrics (MAE, MAPE, RMSE, and NRMSE) for best model selection;
5. Multi-seasonal mapping of VWC retrieved given the best evaluated MOGP model and S1 & S2 stacked datasets at pixel level over two distinct bounded fields and corresponding process performance;
6. Reconstructing of artificially removed S2 GP VWC data gaps over winter wheat cropland considering the BVCR 2020 and 2021 crop campaigns.  
Figure 2: Illustration of the processing workflow to obtain maps of vegetation water content over irrigated winter wheat, as described in Section 2.7. The ordinal numbers in the graph refer to the workflow processing steps. The maps show the output obtained by our MOGP VWC models over the BVCR study site in Argentina.  
The entire software framework was implemented in Python version 3.6.13 ([[https://www.python.org/downloads/release/python-3613/](https://www.python.org/downloads/release/python-3613/)]([https://www.python.org/downloads/release/python-3613/](https://www.python.org/downloads/release/python-3613/)), accessed on 22 September 2022) utilizing the geemap package version 0.19.1 ([[https://github.com/giswqs/geemap/](https://github.com/giswqs/geemap/)]([https://github.com/giswqs/geemap/](https://github.com/giswqs/geemap/)), accessed on 11 January 2023) for mapping interactively with Google Earth Engine.
## 1 Introduction  
A huge and ever-growing amount of scientific findings is reported in scholarly articles. A scholarly article often discusses multiple research issues. The clustering of scholarly articles based on their research issues can facilitate timely and comprehensive analysis of research issues (and their findings) reported in scientific literature, which is a task that is routinely conducted by scientists. For example, CTD (Comparative Toxicogenomics Database), GHR (Genetic Home Reference), and OMIM (Online Mendelian Inheritance in Human) recruit many scientists to routinely update their article databases in terms of their associations with specific biomedical entities (information about how CTD, GHR, and OMIM update their databases can be respectively found at [1; 2; 3]). This _issue-based clustering_ task is a kind of overlapping clustering (i.e., partitioning articles into non-exclusive clusters), as an article may discuss multiple issues, and hence, often needs to be clustered into multiple clusters. Issue-based clustering is challenging as well, as it is difficult to identify the research issues in order to cluster the articles.  
In this paper, we investigate how existing clustering techniques can be improved to conduct the issue-based clustering of scholarly articles. Figure 1 illustrates the main idea. An article may discuss multiple issues (e.g., article \(a_{1}\) discusses issues I1, I2, and I3 in Figure 1), and for each issue it cites different references (e.g., \(a_{1}\) cites different references related to II to I3 in Figure 1). Therefore, multiple articles (e.g., \(a_{1}\) and \(a_{2}\)) can be clustered into a cluster if they cite those references that are related to the same issue (e.g., Issue I3 in Figure 1). The clustering task is thus based on citations (out-link references) in the articles. The clusters can provide an organized view of \"cited issues\" in scientific literature.  
More specifically, we present a technique ICRT (Issue-based Clustering with Reference Titles), which employs titles of the references cited by articles to conduct issue-based clustering. Section 2 discusses the technical contributions of ICRT when compared with previous related studies. The development of ICRT is presented in Section 3. It is based on an observation that a scholarly article often discusses several research issues, and for each issue, cites related references whose titles may indicate the subject. ICRT works as a post-processor for various clustering systems. Section 4 presents experiments to evaluate ICRT. The results show that, when collaborating with those clustering systems that employ state-of-the-art similarity measures for scholarly articles, ICRT successfully improves these systems in identifying clusters of articles having the same research focuses as judged by domain experts. The contribution is of technical and practical significance to the exploration and curation of research issues reported in scientific literature.
## 3 Simulation Setup and Performance Evaluation
### Average Energy Consumption  
We measure the energy consumption of each protocol using node failure probability. We observe that the energy consumption of each protocol increases when the node failure probability increases. However, PT show slightly lower energy consumption. The reason of the lower energy consumption for PT is the use of dynamic topology as depicted in Figure 7. The energy consumption could affect the Quality of service (QoS) provisioning. We also observe that the routing protocols consume additional energy because of node failure probability. The node failure probability could be improved using the optimized approaches.
# Machine Learning-Assisted Secure Random Communication System
## 1 Introduction
The proposed methodology for presenting simulation results can be used as a benchmark to compare any ML-RCS proposed in the future. The remainder of this paper is organized as follows. The theoretical background, practical utility of \(\alpha\)-stable noise, and associated research are presented in the first part of Section 3. This is followed by a general description of decision trees and their ability to address noise signals in the second part. The design of the proposed system and some of the primary results are presented in Sections 4 and 5. In Section 6, we review the necessary performance criteria used to evaluate RCSs. This is followed by the complete performance and analysis of the proposed ML-RCS through Monte Carlo simulations. Finally, we provide concluding remarks and directions for future research in Section 7.
## 3 Materials and Methods
### Databases and Image Pre-Processing
#### 3.1.1 ISIC Database  
As stated in reference [36], the complete ISIC database comprises a total of 93,083 images, with 71,372 designated for training purposes and 21,711 earmarked for testing, constituting the most extensive publicly available collection of dermoscopic skin lesion images [35]. In our previous study conducted in 2022 [37], we demonstrated that ISIC is the preferred database among researchers for developing high-performance melanoma detection systems. This is due to the meticulous expert labeling of images in the field and the annual challenge that offers monetary rewards.  
One of ISIC's primary objectives is to foster the discovery of optimal computational solutions for skin lesion detection, achieved by offering training and testing databases. The repository primarily encompasses several smaller databases, such as MSK, HAM10000, and BCN2000 [37; 38]. As previously mentioned, this current paper employs training and testing data drawn from the ISIC 2020 database. This repository contains 33,126 images of skin lesions designated for system training and 10,982 images intended for system testing. These images have been sourced from various institutions situated in diverse global regions and involve over 2000 patients [36; 39; 40].  
For our study, we selected a subset of 1000 images from the ISIC 2020 database, out of which 700 images were utilized for training the proposed architectures, while the remaining 200 images were used for testing purposes. Examples of images from the ISIC 2020 Database are presented in Figures 2 and 3.  
Figure 1: Architecture for the proposed method for the study.
# Simulation of the Infiltration of Fractured Rock in the Unsaturated Zone
## 2 Mathematical Model
### Pressure Equations and Saturation Equation  
An implicit pressure explicit saturation scheme (IMPES) resolves pressure and saturation sequentially. Despite its time-step limitations, this scheme has shown its effectiveness for dealing with highly dynamic systems, see [PERSON] and [PERSON] [55], [PERSON] et al. [56], and [PERSON] and [PERSON] [57]. To weaken the coupling between phase pressure and phase saturation, the global pressure formulation is used. This eliminates the capillary pressure term from the implicitly solved pressure equation, Chavent and Jaffre [58].  
The total velocity is taken as the sum of the phase velocities \(\mathbf{v}_{t}=\mathbf{v}_{n}+\mathbf{v}_{w}\). In this form of extended [PERSON]'s law, the total velocity is given by  
\[\mathbf{v}_{t}=-k\lambda_{t}\
abla p+\mathbf{k}(\lambda_{n}\rho_{n}+\lambda_{w}\rho_{w })\mathbf{g} \tag{5}\]  
where \(p\) is the global pressure. Volume conservation is expressed as  
\[\
abla\cdot(\mathbf{k}\lambda_{t}\
abla p-\mathbf{k}(\lambda_{n}\rho_{n}+\lambda_{w} \rho_{w})\mathbf{g})=q_{t} \tag{6}\]  
The total volume source term \(q_{t}\) is the sum of the phase volume source terms \(q_{\pi}\).  
The global pressure formulation is suitable for simulation regardless of whether the primary transport variable is the saturation of the wetting or non-wetting phase. The wetting-phase saturation is updated after the transport step by applying the fractional flow formulation  
\[\phi\frac{\partial S_{w}}{\partial t}+\
abla\cdot\left(f_{w}\mathbf{v}_{t}+\bar{ \lambda}\mathbf{k}(\
abla p_{c}-(\rho_{w}-\rho_{n})\mathbf{g})\right)=q_{w} \tag{7}\]  
where \(f_{\pi}=\lambda_{\pi}/\ \lambda_{t}\) is the fractional flow, and \(\bar{\lambda}=\lambda_{n}\lambda_{w}/\ \lambda_{t}\) is mobility product. This scheme is wide spread in the simulation of two-phase flow in porous media ([PERSON] and [PERSON] [53], [PERSON] et al. [59], [PERSON] and [PERSON] [60]). While this formulation is applied to the rock matrix, fracture flow and fracture-matrix transfer receive a special treatment.
## 4 Results and Discussion
### Techniques in Modeling GWL Changes  
Groundwater level changes over the years have been predicted with ML [2] and MM [56] approaches. This section examines how both strategies have been employed in research. According to our data (main studies), ML techniques accounted for 45.3 percent (53) of the publications, MM approaches for 39.3 percent (46) of the articles, and 5.1 percent (6) were cases where both ML and MM approaches were used in a study. The MODFLOW technique, which aids in the accurate, dependable, and efficient simulation of groundwater flow, accounted for 10.3% (12) of the primary publications (Figure 8).  
Figure 6: Distribution by country, based on origin of corresponding author.  
Figure 7: Proportion of top five countries from the primary studies.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 3 System Model
### Beamforming Models
See Table 3 for a list of notations for the beamforming models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Variable** \\ \hline \(n_{\text{MS}}\) & A single antenna unit at MS \\ \(N_{\text{MS}}\) & Number of antennas at MS \\ \(a\) & Radius of the UCA \\ \(r_{\text{MS}}\) & A single RF chain at MS \\ \(R_{\text{MS}}\) & Total number of RF chains at MS \\ \(c_{j}^{t}\) & Combining vector at MS at index \(I\) at (\(t\)) th time step \\ \(i\) & Index \(i\) representing one direction at MS \\ \(I\) & The number of pointing directions at MS \\ \(n_{\text{BS}}\) & A single antenna element at BS \\ \(N_{\text{BS}}\) & Total number of antennas at BS \\ \(r_{\text{BS}}\) & A single RF chain at BS \\ \(R_{\text{BS}}\) & Total number of RF chains at BS \\ \(j\) & Index \(j\) representing one direction at MS \\ \(J\) & Total number of directions at BS \\ \(p_{j}^{t}\) & Beamforming vectors \\ CMG & MS combining matrix \\ \(p_{j}^{t}\) & Beamforming vectors \\ \(P_{\text{BS}}\) & BS beamforming matrix \\ \(A_{\text{MS}}\) & AF here for MS \\ \(l_{n}\) & \(n\)-th antenna amplitude at MS \\ \(v\) & Wave-number \\ \(\lambda\) & Wavelength \\ \(\alpha\) & Speed of light \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of notations for the beamforming model. Figure 3: Beam indices at the MS.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Challenges of IoMT
#### 3.2.1 Data Privacy  
Ensuring sufficient cyber safety for health monitoring systems is one of the key issues for IoMT implementation. Security of the vast amount of private health information that is moved between systems is a problem that has to be solved [49]. Blockchain technology is used by \"CoviChain\" to address security and privacy concerns and prevent the exposure of personal information while gaining greater data storage capacity [50].
# Device Modeling of Quantum Dot-Organic Light Emitting Diodes for High Green Color Purity
## 1 Introduction  
Among the displays currently in mass production, organic light-emitting diodes (OLED) are the only flexible devices which can be manufactured in various forms, unlike LCDs or other displays [1, 2]. It is also being developed into stretchable and wearable form, so it looks to be very scalable [3, 4, 5]. In particular, OLED has a fast response speed, high contrast ratio, lightness, and flexible characteristics, so its application product range is very wide, from small-sized devices through medium-sized devices such as notebooks and monitors to large-sized OLED TVs [6, 7, 8]. Among these OLEDs, currently, quantum dot (QD)-OLED is now attracting considerable attention due to the high color gamut, wide viewing angle, and its many other advantages [9, 10].  
QD-OLED is a display made of OLED and semiconductor nanocrystals called quantum dots (QD) which produce pure monochromatic colors. In other words, the red QD and the green QD are stacked on a blue OLED that is used as an incident light source to express the desired colors of red and green by means of light conversion [11]. Several studies on QD unit devices are in progress, although few studies have reported on how to proceed from the device or the process architecture to improve the overall color gamut [12, 13].  
Therefore, in this study, how to improve the color purity of QD-OLED was systematically reported in terms of device and process architecture. Firstly, in order to obtain high color gamut, which is known as one of the main advantages of QD-OLED, it is necessary to systematically investigate the influence of QD layer components, that is, QD density and QD layer thickness. In order to achieve this purpose, in particular, this paper was conducted to optimize the emission spectrum of green QD(G-QD) according to the optimal process architecture by modeling from the QD layer's viewpoint and overall device optimization. Through this whole investigation, the optimal device structure of a green color with high color purity characteristics was found.  
The SEmiconducting Thin Film Optics Simulation (SETFOS) tool was used for this optimal device modeling. This tool follows the Monte Carlo numerical analysis of carriertransport between organic semiconductor layers between two electrodes [14]. Using this tool, the emission spectrum of high color purity was systematically analyzed, and the optimal structure of the QD-OLED layer structure with excellent color purity was developed.
# Improved Altitude Control Algorithm for Quadcopter Unmanned Aerial Vehicles
## 1 Introduction  
In recent decades, unmanned aerial vehicles (UAVs) have gained popularity in the scientific community due to their numerous advantages such as high maneuverability, diverse applicability, reliability, and economy. UAVs are used in a broad range of applications such as scientific research, civil engineering, military applications, aerial mapping, search and rescue operations, and risk zone inspection [1; 2; 3; 4]. Quadcopters are an important class of UAVs that are superior in many ways: they have a simple structure, are quick to manufacture, and are inexpensive [4; 5]. Controlling their position, in general, and altitude, in particular, has been the subject of numerous studies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] with ever more advanced algorithms having been proposed to address the quadcopter control problem.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction
### Amino-Acids neurotransmitters
#### 1.1.2 L-Aspartate  
L-aspartate is a conjugate base of the aspartic acid, whose role as a neurotransmitter in the central nervous system has been subject to controversy [27; 28]. Some researchers have proposed that L-aspartate may be a neurotransmitter in the visual cortex and in the cerebellum [29; 30; 31; 32], and others have shown that, like L-glutamate, L-aspartate may be an excitatory neurotransmitter and a neuropeptide-like modulator in the hippocampus [27; 33].  
The synaptic terminations that contain aspartate vesicles are co-localized with neurons containing glutamate and GABA vesicles. Thus, it seems that L-aspartate can play a role in both excitatory and inhibitory pathways. Synaptic termination with only aspartate vesicles has not been discovered in the hippocampus. A role for aspartate as co-neurotransmitter with glutamate was previously reported in the rat corticostriatal neurons and its release may be modulated by dopamine [34; 35; 36; 37].
## 3 Methods
### Assessment Metrics and Methods
#### 3.2.2 Social Metrics  
The impact of the implementation of the technologies on the community is also important to assess for energy master planning. To measure the impact of the technologies on local communities and society as a whole, the most common social indicators, such as job creation and social acceptability, were selected [47]. Job creation (number of jobs per year/MW) refers to the estimated total number of jobs that would be created over the lifecycle of the system or technology. In this paper, the number of jobs created per technology employed only considered the construction and installation (C&_I_) and operations and maintenance (O&_M_) [48] of the technology and was expressed through Equation (1). The number of jobs created is an important criterion as it provides benefits for nearby residents in terms of uplifting their socioeconomic status.  
\[Jobs\;Created=C\&I+O\&M \tag{1}\]  
where C&_I_ refers to the jobs that are created during the construction and installation period only. O&_M_ refers to created jobs that are associated with operating and maintaining the technology throughout its operational lifetime.  
Social acceptability is a qualitative performance indicator that is evaluated based on the typical opinions or preferences of local populations in many developed countries regarding the hypothesised realisation of technology projects that are under review [8]. Public opinion is crucial and needs consideration as it may influence the time that is required to proceed with and complete an energy project. The qualitative description of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bottom Lines (Level 1)** & **Indicators (Level 2)** \\ \hline Environmental & Capital costs \\  & Annual operations and maintenance costs \\ Social & Job creation \\  & Social acceptance \\ Economic & Embodied GHG emissions \\  & Cumulative avoided GHG emissions \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of indicators for the triple bottom line (3 BL).  
social acceptability that was used in this study is shown below. For quantitative values, a conversion was assumed and set as a qualitative scale:  
* High (100%): the majority of the local community highly accepts that the project is beneficial;
* Moderate (50%): half of the local community accepts the project but believes that it does not necessarily provide maximum benefits;
* Low (0%): the local community does not accept the project.