esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wideband Metal-Only Patch Antenna for CubeSat
## 2 Antenna Configuration and Simulation Results  
### Antenna Structure  
We selected the folded patch feeding approach with air substrate for our proposed antenna to broaden its impedance bandwidth [19]. Figure 1 shows the geometry of the proposed metal-only patch antenna for CubeSat communications designed to operate at a dominant resonant frequency of 2.45 GHz. The proposed antenna also incorporates shorting pins and a lower folded patch feed. The antenna is mounted on a square ground plane of 100 mm \(\times\) 100 mm at a distance h\({}_{1}\), uses the air as a substrate and is fed by a 50 \(\Omega\) coaxial probe at the position (x, y). The presence of the shorting pins in the antenna structure can be considered as adding a shunt inductance in the radiating structure, which enlarges its electrical size by keeping the same physical size and enhancing its radiation performance [16]. The three shorting pins connect the upper patch with the ground plane in order to adjust the lower operating frequency of 1.6 GHz adjacent to the dominant resonant frequency of 2.45 GHz, hence ensuring coupling between the two resonances and wideband operation [25]. The diameter of the shorting pins that provides the optimal antenna bandwidth is 3.64 mm. Moreover, the folded patch technique is used and fed at h\({}_{2}\), to reduce the coaxial probe length and inductance at the feed section. By doing that, a thicker substrate can be used which leads to an improved overall impedance bandwidth [13]. In addition, the use of metal-only patch antenna with air substrate improves the gain of the proposed antenna and ensures zero dielectric losses. The width of the arm W\({}_{1}\) and length of the slot L\({}_{2}\) have a significant effect on the \(-\)10 dB bandwidth and the reflection coefficient of the proposed antenna. Therefore, the parametric study is provided using the High Frequency Simulator Structure (HFSS) to determine their optimal values. Table 1 lists the optimal parameters of the proposed antenna. The effect of the shorting pins on the patch is also investigated through the surface current distribution at different frequencies.  
The F-shaped patch can be considered as a resonant RLC circuit with \(R\_\)loss representing the antenna radiation and losses, as shown in Figure 2a, containing a slot with width W\({}_{1}\) and length L\({}_{2}\). This changes the resonant characteristics of the antenna by introducing an additional current path around the slot [13], which generates an additional lower operating frequency. In other words, this introduces a second RLC resonant circuit with a series inductance \(L\_low\) generated by the slot on the patch as shown in Figure 2b. Therefore, the antenna can be visualized as two RLC resonant circuits, which, when coupled together, can achieve a wider bandwidth.  
Figure 1: Proposed antenna: (**a**) top view and (**b**) side view.
# Cardiac Nuclear Imaging Findings in Atypical Variants of Takotsubo Cardiomyopathy
## 3 Results
### Analysis of the Evidence
#### 3.3.1 Cardiac Nuclear Imaging Findings in Midventricular TTC
After a coronary angiogram showed no organic stenosis, a ventriculogram revealed akinesi in the middle portion of the left ventricle and transthoracic echocardiography (TTE) confirmed the abovementioned finding, 201 Tl scintigraphy and 123I-mIBG scintigraphy were performed to exclude a possible previous myocardial injury by [PERSON] et al. [80], who published an interesting image in 2014. The mismatch images obtained by subtracting the 201 Tl images showing no defects from the corresponding 123-I BMIPP images showing metabolic abnormalities revealed a ring-shaped defect on the polar maps in this 74-year-old woman with mid-ventricular ballooning syndrome. In 2012, [PERSON] et al. [81] also reported a \"doughnut-like\" circular uptake reduction on bull's eye imaging in 123I-mIBG scintigraphy performed on the sixth day in an 83-year-old woman referred to their institution. The involved area coincided with the ballooning region demonstrated through left ventriculography but not with any epicardial coronary artery territory.
One goal of this research is to develop a systematic model that provides insights into characteristics upon which the cost of a data breach can be based. Using the data provided by the Ponemon Institute, we concluded that our portrayal of Ponemon's model as simple is both accurate and entirely fair. In this analysis, we show that the approach used by Ponemon is not only overly simple but also misleading; it may even be harmful to organizations using the Ponemon research in their risk analyses. Using only the number of records lost in a breach is not an accurate indication of the impact of that breach. However, this model has an advantage in its simplicity. The end-user can multiply the number of records in their system by a fixed dollar figure and get an estimate of loss. In this paper, we propose computational models for both the cost and likelihood of security breaches. The cost is divided into a few partial costs, and a model is proposed for each. The proposed model is based on the available data; it formalizes and consolidates the computational approaches used by the existing calculators. A preliminary modeling approach had been proposed [3] using a linear relationship between the breach size (number of records involved in the breach) and the cost of the breach. A careful examination of the data (when the breach sizes span several orders of magnitude) suggests that a power regression model (considered here) is more appropriate than a linear model. Quantitative assessment of cybersecurity risks for managing security costs and mitigating data breaches in the business system allows us to maintain the security issues of data management considered in the business environments. Cybersecurity risks are studied using datasets related to cyber-attacks, which occur in many data handling organizations. The paper is organized as follows. Section 2 presents the literature review. In Section 3, the applicability of the existing models is examined. Section 4 presents an estimation approach using an economy of scale since the concept of cost per record is misleading for breaches of different magnitudes. In Section 5, a formal model for data breach costs is introduced. In Section 6, an approach for estimating the likelihood of a data breach within a year for an organization is discussed. Existing challenges and limitations are discussed in Section 7. Finally, in Section 8, conclusions are presented with further work.
# An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators
## 3 Scoring by the Needleman-Wunsch Algorithm
### Calculation of the Operation Score  
The similarity between two sequences can be calculated based on the backtracking results of sequence alignment. Let the total length of the backtracked sequence be \(n\) and  
Figure 6: Processing results of the backtracking: (**a**) backtracking paths shown by arrows; (**b**) backtracking results.  
the number of matched elements be \(m\). The similarity score \(P\) between the two sequences is calculated as  
\[P=m/n\times 100. \tag{4}\]  
If the standard operation sequence of the operational problem does not include specified critical steps, the trainee's operation score can be directly calculated using a similarity score, which can be determined by Equation (4).  
If the standard operation sequence of the operational problem includes specified critical steps, it is necessary to align the matching status of all critical steps in sequence. If there are any unmatched key steps, corresponding deductions should be made. The operation question specifies the deduction value for each critical step, and the total deduction score is calculated using Equation (5):  
\[D=\sum_{i=0}^{n}d_{i},\quad d_{i}=\begin{cases}0,&S_{i}\text{ is not a key step, or matched}\\ d,&S_{i}\text{ is a key step, and mismatched}\end{cases}. \tag{5}\]  
In this case, the trainee's operation score is \((P-D)\).
## 3 Results  
### Materials Characterization  
Several combinations of QAS and HBD, including TEAAC, TEAB, Glycine, and Tetraethylammonium Iodide with Glycerol and MEG, were tested for this research work. However, this paper only discussed  
Figure 1: Experimental schematic diagram of rocking cell hydrate reactor [42].  
Based on Figures 3a and 5a, comparing TEAAC:Glycerol and TEAB:Glycerol with pure Glycerol, it was observed that the FTIR spectrum of DES produced a lower intensity of OH stretch (3400-3200 cm\({}^{-1}\)) in comparison to pure Glycerol spectrum. N-H stretching was also observed to be overlapped with the O-H between 3000 and 3400 cm\({}^{-1}\), similar to previous works in the literature [44]. The lower intensity was also noticed in the aliphatic stretching vibration between 3000 and 2800 wavelength. By comparing the FTIR spectrum of TEAAC:MEG and TEAB:MEG with the spectrum of pure MEG, as shown in Figures 4a and 6a, respectively, a similar was observed. The intensity of OH stretch and aliphatic stretching vibration was weaker when DES was formed. The reason for this was due to the decrease in the melting point when DES was developed. The reduction in the melting point produced a weaker spectrum of FTIR. However, it was also observed that Glycerol-based DES produced a more vigorous intensity of C-O stretching in comparison to the pure Glycerol spectrum [44]. In other contexts, the MEG-based DES gave almost similar intensity of C-O stretch as the pure MEG.  
FromFigures 3b and 4b, it was found that the pure TEAAC spectrum showed a weaker intensity of OH stretch at the wavelength of 3400-3200 when compared to the IR spectrum of TEAAC:Glycerol and TEAC:MEG. High intensity of C=O stretch at the wavelength of 1650-1590 cm\({}^{-1}\) shifted to the high intensity of C-O stretch. Besides, Figures 5b and 6b show the pure TEAB to have no OH- stretch in its spectrum, while a peak of OH- stretch could be observed in the DES. The appearance of the OH-stretch indicated that the DES formulated had successfully formed a hydrogen bonding when TEAB was mixed with MEG or Glycerol. Besides, the CH bend from pure TEAB spectrum also shifted to the right where a high intensity of C-O stretch was produced instead. Thus, it could be concluded that the CH stretch was produced in the same way as the CH stretch.  
Figure 5: The FTIR comparison of TEAB:Glycerol with **(a)** pure Glycerol and **(b)** pure TEAB. TEAB, Tetraethylammonium Bromide.  
Figure 6: The FTIR comparison of TEAB:MEG with **(a)** pure MEG and **(b)** pure TEAB.  
from the shifts in the peaks that the change in the chemical bonding occurred when the Deep Eutectic Solvent was formed.
## 5 Challenges and Future Suggestions  
Commercializing mannosylerythnt lipid (MEL) biosurfactant production from acid oil involves significant challenges, including high production costs, scalability issues, and stringent regulatory requirements. The complexity of optimizing microbial strains and fermentation conditions for efficient MEL production adds to these challenges. Strategies to overcome these hurdles include engineering robust microbial strains to enhance yields, optimizing bioreactor designs for large-scale production, and developing cost-effective downstream processing methods. Collaborating with industrial partners for pilot-scale validation and securing regulatory approvals through rigorous testing are crucial. Market acceptance can be improved by demonstrating the environmental benefits and superior performance of MEL compared to synthetic surfactants.  
MEL (mannosylerlythritol lipid) production faces challenges due to low productivity (0.59 g/L/h) and high costs, with downstream processing making up 60% of the total cost [37]. Although production costs for related glycolipids, SLs (sophorolipids) and RLs (rhammolipids), are estimated at USD 2.95/kg and USD 20-25/kg, MELs are expected to be even more expensive [38]. Efforts to reduce these costs continue, but bioprocess costs remain higher than those for chemical surfactants (USD 1-3/kg). Despite this, recent advancements have demonstrated the scalability of MEL production to 1 m\({}^{3}\), a significant step toward commercial viability [39; 37; 38].  
Future research on extracting MEL from acid oils should focus on optimizing the purification processes to handle the variability and impurities in acid oils effectively. Investigating microbial strains with higher tolerance to impurities and enhanced biosurfactant production capabilities can improve yields and consistency. Additionally, exploring cost-effective and scalable purification techniques will be essential in order to address the limitations of current methods. Research on the environmental impact and lifecycle analysis of using acid oils for MEL production can provide insights into sustainable practices. Finally, developing standardized protocols for the consistent supply and quality of acid oils will support the scalability and industrial application of this approach.  
Figure 8: Molecular phylogenetic tree of some MEL-producing microorganisms. [Adapted from open-access article, Ref. [36], available at: _Biomolecules_**2024**, _14_(5), 557; [[https://doi.org/10.3390/biom14050557](https://doi.org/10.3390/biom14050557)]([https://doi.org/10.3390/biom14050557](https://doi.org/10.3390/biom14050557))].
###### Abstract  
Currently, machine learning techniques are successfully applied in biometrics and Electrocardiogram (ECG) biometrics specifically. However, not many works deal with different physiological states in the user, which can provide significant heart rate variations, being these a key matter when working with ECG biometrics. Techniques in machine learning simplify the feature extraction process, where sometimes it can be reduced to a fixed segmentation. The applied database includes visits taken in two different days and three different conditions (sitting down, standing up after exercise), which is not common in current public databases. These characteristics allow studying differences among users under different scenarios, which may affect the pattern in the acquired data. Multilayer Perceptron (MLP) is used as a classifier to form a baseline, as it has a simple structure that has provided good results in the state-of-the-art. This work studies its behavior in ECG verification by using QRS complexes, finding its best hyperparameter configuration through tuning. The final performance is calculated considering different visits for enrolling and verification. Differentiation in the QRS complexes is also tested, as it is already required for detection, proving that applying a simple first differentiation gives a good result in comparison to state-of-the-art similar works. Moreover, it also improves the computational cost by avoiding complex transformations and using only one type of signal. When applying different numbers of complexes, the best results are obtained when 100 and 187 complexes in enrolment, obtaining Equal Error Rates (EER) that range between 2.79-4.95% and 2.69-4.71%, respectively.  
ECG biometrics; machine learning; pattern recognition +
Footnote †: journal: Applied Sciences
## 3 System Functional Modules
### Sensor Node Module Hardware and Software Design
#### 3.2.1 Design of the Interface with the Sensor  
Considering the real environment, the circuit board of node is tailored, and the interfaces compatible with a potentially hostile environment are selected. The battery set and the circuit board are encapsulated in separate packages, making it convenient for battery replacement and circuit protection (Figure 5).
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 4 Results and Discussion  
The UBFC-RPPG dataset [20] was selected to assess the performance of the neural network presented in Section 3. From the initial dataset, we manually removed image sequences in which the participant presented no distinct PPG signal (due particularly to wide head movements) or in case of corrupted ground-truth signals. In total, 1312 pulse rate values from 15 participants were extracted from the initial dataset.  
The benchmark methods (presented hereafter) operate more efficiently with prior skin detection, robust face tracking, or when pixels of interest are segmented beforehand [46]. Therefore, and in order to provide fair comparisons, the forehead or the cheeks (when the forehead was covered with hair) were manually selected as regions of interest.
## 2 Energy Storage, Materials, and Construction of Electrochemical Capacitors
The maximum voltage of the supercapacitors (SCs; ECC or HSC) is dependent primarily on the electrolyte used. The ionic conductivity of the electrolyte is an important factor in determining the resistance of the supercapacitors. The characteristics [5; 6; 7] of electrolytes that can be used in SCs are shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Electrolyte** & **Diameter of Molecule** & **Density** & **Ionic Conductivity** & **Max Operating** \\  & **(Å)** & **(g/cm\({}^{3}\))** & **(mS/cm)** & **Voltage** \\ \hline
**Aqueous** & & & & \\ KOH & 1.8 & 1.29 & 540 & 1 \\ KCL & 1.8 & 1.09 & 210 & 1 \\ H\({}_{2}\)SO\({}_{4}\) & 2.9 & 1.2 & 750 & 1 \\ Na\({}_{2}\)SO\({}_{4}\) & 2.9 & 1.13 & 91 & 1.2\(-\)1.5 \\ Li\({}_{2}\)SO\({}_{4}\) & 2.9 & 1.2 & 75 & 1.5\(-\)2.0 \\ LiNO\({}_{3}\) & 2.6 & 1.2 & 150 & 1.5\(-\)2.0 \\
**Organic** & & & & \\ TEABF\({}_{4}\)/PC & 4\(-\)5 & 1.2 & 15 & 2.5\(-\)3 \\ TEABF\({}_{4}\)/CAN & 4\(-\)5 & 0.78 & 60 & 2.5\(-\)3 \\
**Ionic liquids** & & & & \\ Imidazolium/[EMIM]\({}^{*}\)[BF\({}_{4}\)]\({}^{-}\) & 5\(-\)6 & 1.3\(-\)1.5 & 8 (25 C) & 4.3 \\ Tetraalkylammonium [mPrMe\({}_{3}\)N]\({}^{*}\) & 5\(-\)6 & 1.3\(-\)1.5 & 1.4 (25 C) & 5.3 \\
[N(CF\({}_{3}\)SO\({}_{2}\))]\({}^{-}\) & 4\(-\)5 & 1.3\(-\)1.5 & 1.9 (25 C) & 3.4 \\ Pyridinium/[BuPyr]\({}^{*}\) & 4\(-\)5 & 1.3\(-\)1.5 & 1.5 & 5.6 \\
[N(CF\({}_{3}\)SO\({}_{2}\))]\({}^{-}\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The characteristics of various electrolytes used in supercapacitors. Figure 2: Schematic of an HSC electrochemical capacitor. Most commercial SCs use organic electrolytes because, even for EDLCs, the rated voltages can be 2.5-3 V rather than 1-1.5 V for aqueous electrolytes. The higher rated voltages result in a much higher energy density than for devices using aqueous electrolytes. Aqueous electrolytes have much higher ionic conductivity than organic electrolytes, resulting in lower resistances in devices using aqueous electrolytes. Ionic liquid electrolytes (4-5 V) have much lower ionic conductivity than organic electrolytes, and for that reason ionic liquids have been used to a very limited extent in SCs, except for in high-temperature applications, even though they permit the design of devices with significantly higher rated voltages.
## 3 Proposed Method
### \(F_{0}\) Contour Modeling
#### 3.1.2 Sampling-Based Representation of \(F_{0}\)
Then, values of \(F_{0}\) in each syllable are sampled. The \(F_{0}\) contour of utterance \(F=[f_{1},f_{2},\ldots,f_{T}]\) is represented by concatenating the sequence of the sampled points of \(N\) syllables \(C=[c_{1}c_{2}...c_{N}]\). The values of \(F_{0}\) in each syllable are sampled as follows:  
\[c_{i}=[f_{(b_{i}+D_{i}/K)},f_{(b_{i}+2D_{i}/K)},...,f_{(b_{i}+(K-2)D_{i}/K)},f_ {(b_{i}+(K-1)D_{i}/K)},f_{(b_{i}+D_{i})}] \tag{1}\]  
where \(f\) denotes the smoothed \(F_{0}\) contour, \(b_{i}\) and \(D_{i}\) are the starting frame position and duration of syllable \(i\), respectively, and \(K\) is the number of sampling points per syllable. The output vector \(C\) contains the \(KN\) of the sampled values of \(F_{0}\). To improve the performance of the model, additional features are used because using only the sampled values of \(F_{0}\) does not guarantee the continuity of the \(F_{0}\) contour between adjacent points of syllables. Thus, we use dynamic features computed from the sequence of sampled values of \(F_{0}\). We expect dynamic features within and across syllables to improve the continuity of the generated \(F_{0}\) contour. The sampled \(F_{0}\) vector with dynamic features \(O=[o_{1},o_{2},\ldots,o_{KN}]\) contains a sequence of sampled values of \(F_{0}\), including delta and delta-delta, as follows:  
\[O = \begin{bmatrix}C&\Delta C&\Delta^{2}C\end{bmatrix}  \tag{2a}\] \[= \begin{bmatrix}CW_{0}&CW_{1}&CW_{2}\end{bmatrix}  \tag{2b}\]  
where C is the vector of the sampled sequence of log \(F_{0}\) and \(W_{n}\) is a window matrix for calculating the \(n\)-th dynamic feature described in [42]. Syllable-level features \(Y=[y_{1},\ldots,y_{N}]\) are prepared by reshaping the sampled \(F_{0}\) vector \(O\) as follows:  
\[y_{i}= \begin{bmatrix}o_{(i-1)K+1},o_{(i-1)K+2},\ldots,o_{ik}\end{bmatrix} . \tag{3}\]  
In our method of representation, an appropriate number of sampling points \(K\) is required because an inappropriate \(K\) can eliminate detail or create unnecessary values of \(F_{0}\). We tuned the model by setting \(K\) to the approximately mean of the duration of all syllables in the Isarn speech corpus. We also explore the effects of the number of sampling points in Section 4.4.3.
## 2 Materials and Methods
### Spatial Interpolation, Analysis, and Flood Hazard Map  
The data collected in the study, such as the physicochemical properties of surface water, hydrogeologic parameters, flood hazard, and Mn concentration of both surface water and soil, underwent spatial interpolation. Spatial interpolation has been used in many environmental studies [55; 56] to predict values of spatial phenomena in unsampled locations [57]. In spatial interpolation, the inverse distance weighting (IDW) method is a deterministic method widely used by geoscientists and geographers [58]. The IDW method assumes that the properties of an unsampled location are the weighted average of known values in the area, and the weights are inversely correlated to the distances between the prediction location and sampled locations. IDW was used in this study as the interpolation method to develop spatial data in the floodplains of the two rivers. The value at the unknown location in the floodplain was considered as the weighted sum of the values of \"\(N\)\" known points, as shown in Equation (1) [59].  
\[C_{P}=\frac{\frac{\frac{N}{\sigma}}{\underline{\underline{\underline{\alpha} }}}w_{i}C_{i}}{\begin{array}{c}w_{i}=\frac{\frac{1}{d_{i}}}{\frac{N}{\sigma}} \end{array}} \tag{2}\]  
where \(C_{P}\) denotes the unknown concentration data, \(C_{i}\) signifies the known data, \(N\) is the number of sampling stations, \(w_{i}\) is the weighting of individual stations, and \(d_{i}\) is the distance from every station to the unknown point.  
The flood hazard map used in this study was developed by Phil-Lidar 1 using Lidar technology to accurately derive elevation data and define the floodplain terrain of the area. Flood depths identified by the hazard map were then used to identify whether these events, specifically flood heights, affect or stimulate Mn accumulation in soils near the two rivers.