esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Polycaprolactone (PCL) is one of the most regularly used synthetic polymers for medical applications; this is due to the slow biodegradation and biocompatible characteristics that it displays [1; 2; 3; 4]. A combination of the PCL properties (biocompatibility and slow biodegradation) with the unique feature of nanofibrous structure emanating from electrospinning can result in a promising material for different applications including medical applications. A number of different techniques, including the use of physical, chemical, thermal and electrostatic fabrications are often employed to produce polymeric nanofibers. Over the past 25 years, amongst the above mentioned techniques, electrospinningning is the most extensively studied. Figure 1 demonstrates the number of annual publications related to the electrospinningning of polymeric fibers, which shows that since 2003, the number of the annual publications related to \"electrospinning\", has grown from 10 to 3000 articles whilst electrospinningning of PCL alone reached an approximation of 243 articles (for the year 2018).  
Electrospinning is a flexible and interesting technology for the production of ultrafine fibers with diameter ranging from a few micrometers to sub-nanometers [5]. This technique can produce nano- or microfibers by applying an electrostatic field to a polymer solution, propelled by the supply of high voltage between a needle tip and a collector. Several studies have investigated the electrospinning of PCL [6; 7; 8; 9; 10; 11; 12; 13; 14]. For medical applications, it is important for PCL fiber to be without beads. This is important for medical applications because fiber diameters need to imitate the natural extra cellular morphology and, thus promote optimal cell growth. PCL has been electrospun by using different solvents, such as: chloroform, dichloromethane, dimethylformamide and methanol or a combination of the above solvents [6; 7; 15]. Amongst the above-mentioned solvents, chloroform is often employed as a solvent for electrospinning PCL, even though it has often, produced microfibers rather than nanofibers. This review summarizes the different solvents for electrospinning of PCL and its composites, with the aim of producing nanofibers on a nano scale, rather than on a micro scale. Furthermore, the different properties of electrospun PCL and PCL composites are summarized for medical applications.
## 2 Simulation Setup  
The angle at which sunlight refracts from the primary optical element (POE) of the hybrid CPV module depends on the wavelength, which creates chromatic aberration [22]. In addition, infrared rays with wavelengths above 1.3 \(\upmu\)m are not absorbed in either TJ solar cells or silicon solar cells, due to current limits in the top cell and limited semiconductor bandgap energy, respectively. Therefore, we considered the effective wavelength range while we optimized the SOE of the hybrid CPV module. To analyze the hybrid CPV architecture, we used the TracePro (Lambda Research Corporation, Littleton, Massachusetts, USA) raytracing software, based on a Monte Carlo method with the xenon lamp spectrum (1000 W/m\({}^{2}\)), to create the three-dimensional model shown in Figure 1. Assuming modest-DNI regions, sunlight was designed with 80% DNI and 20% diffuse light. We selected a \(254\times 254\times 3\) mm\({}^{3}\) Fresnel lens made of polymethyl methacrylate (PMMA; refractive index = 1.49 at 500 nm) and an Al mirror (92% reflective at 500 nm) as the POE and SOE, respectively. The focal length of the POE was 250 mm, and the body size was \(254\times 254\times 253\) mm\({}^{3}\). We placed the silicon solar cell 8 mm away from the TJ solar cell in position #2, and the spacing between the silicon solar cells was 3 mm. For ease of cleaning the module, the flat side of the Fresnel lens faced the sun, and the stepped side faced the solar cells. We placed a high-performance TJ solar cell (\(10\times 10\) mm\({}^{2}\); concentration ratio = 645X) at the center of the hybrid CPV module to collect the concentrated DNI. Around the TJ solar cell, we spread 12 silicon solar cells (\(80\times 55\) mm\({}^{2}\)) to collect light that was scattered from the concentrator and diffuse solar irradiance.  
Figure 1: (**a**) The raytracing model and (**b**) raytracing results of the hybrid concentrator photovoltaic (CPV) module combined with low-cost silicon solar cells.
## 4 Numerical Experimentation and Results
In both cases, \(m=3\) and \(m=5\) were used for illustrative purposes. The intra-step points corresponding to these values of \(m\) are given in Table 2 below. Using \(m=3\) as an example, the matrix parameters for the optimal overlapping hybrid block approach are as follows:  
\[B_{0}= \begin{bmatrix}\frac{\sqrt{5}+11}{120}&0&0\\ \frac{11}{120}&0&0\\ \frac{1}{12}&0&0\end{bmatrix} ,\;\;B_{1}= \begin{bmatrix}\frac{25-\sqrt{5}}{120} &\frac{25-13\sqrt{5}}{120}&\frac{\sqrt{5}-1}{120}\\ \frac{13\sqrt{5}+25}{120}&\frac{\sqrt{5}+25}{120}&-\frac{\sqrt{5}-1}{120}\\ \frac{5}{12}&\frac{5}{12}&\frac{1}{12}\end{bmatrix} \]  
which is applied on the scheme:  
\[A_{1}Y_{1}=A_{0}Y_{0}\,+\,h(B_{0}F_{0}\,+\,B_{1}F_{1}) \tag{29}\]  
in the first interval \([t_{0},t_{1}]\) and  
\[\hat{B}_{0}= \begin{bmatrix}\frac{11\sqrt{5}+61}{600}&\frac{-5\sqrt{5}-9}{2200}& 0\\ \frac{49-11\sqrt{5}}{600}&\frac{5\sqrt{5}+9}{2200}&0\\ \frac{1}{12}&0&0\end{bmatrix} ,\;\;\hat{B}_{1}= \begin{bmatrix}\frac{55-4\sqrt{5 }}{300}&\frac{137-65\sqrt{5}}{600}&\frac{37\sqrt{5}-61}{600}\\ \frac{17\sqrt{5}+35}{150}&\frac{5\sqrt{5}+113}{600}&\frac{-37\sqrt{5}-49}{6600} \\ \frac{5}{12}&\frac{5}{12}&\frac{1}{12}\end{bmatrix} \]  
which is applied in \([t_{n},t_{n+1}]\), for the overlapping hybrid block scheme:  
\[A_{1}Y_{1}=A_{0}Y_{0}+h(\hat{B}_{0}F_{0}+\hat{B}_{1}F_{1}) \tag{30}\]  
for \(n=1,2,\ldots,N-1\). The proposed overlapping grid approach is referred to as the overlapping optimal hybrid block method (OOHBM) for the purposes of this paper. The original optimal hybrid block method (OHBM), without overlap, is constructed by applying Equation (29) to all the integration blocks \([t_{n},t_{n+1}]\) for \(n=0,1,2,\ldots,N-1\). It is important to note that the size of the matrices in both the original HBM scheme and the overlapping HBM scheme is the same. This indicates that the computational effort necessary to solve the IVPs will be roughly equivalent for both methods.
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 4 Steady-State Mode Process Monitoring Methods
### Unsupervised Process Monitoring Method
#### 4.2.2 Pattern Recognition Clustering Method
[69] proposed a modeling method referred to as a variable Bayesian Gaussian mixture model with canonical correlation analysis (VBGMM-CCA). The method uses VBGMM to identify the number of patterns of sample data, establish residuals, cluster the training data in each pattern and construct a local canonical correlation analysis (CCA) model. Finally, the parameters of each local model can be unified to establish a unified Bayesian inference probability (BIP) index monitoring statistic. This method can automatically identify the number of operation modes in certain modes.
## 1 Introduction
Currently, programs such as Scanalytics, GelcomparII, GelJ, Gel-Pro Analyzer, ImageJ, PyElph, TotalLab, PDquest, Proteomeaver, Dcyder 2D, imageMaster, Melanie, BioNumerics, Redfin, Gel IQ, Z3, and Delta2D Flicker are used to analyze images of DNA or protein gels [1; 10; 23; 24; 25; 26; 27; 28; 29]. These programs employ semi-automatic filters to remove noise, meaning the operator must manually select the column or band of interest. The user also adjusts the intensity changes and decides the threshold that reduces the background noise. However, due to the dependence on analysts with little knowledge of intensities and thresholding, this often results in poor gel analysis. In this article, the methodology described in reference [30] is used to obtain different concentrations of pure GPN protein added to a bacterial cell extract to create SDS-PAGE gels. The incorrect or excessive expression of some proteins may be associated with an imbalance in health. So, this research produces gels representing patient samples with different amounts of protein expression, including absence and different levels of overexpression, to emulate various stages of Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) Her2+ breast cancer. Therefore, identifying specific proteins can be helpful in disease diagnosis. Examples of such proteins are GTPases, such as Rho, Rab27, and GPN, which have been linked to the development of breast cancer [31]. Reference [30], an article by the same authors as the present article, describes a new methodology for obtaining pure GPN protein in high levels in homogeneous form. In the current research, an algorithm was proposed for a new method to identify the lanes and bands of samples present in an SDS-PAGE gel and the molecular weight, to identify the overexpressed protein related to breast cancer. For that, the obtained images were analyzed using the newly developed image analysis methodology to identify different levels of expression of the GPN protein expressed per sample. This methodology is based on detecting the pixel values of the white color of the histogram segmentation of images filtered by a binary mask.
## 5 Passive Dual-Band Phase-Gradient Metasurfaces
In Figure 5, we observe how an array of antenna elements with a phase difference from element to element equal to \(\phi\) resembles a metasurface of cells with a gradient phase profile having an adjacent phase difference of \(\phi_{step}\). The fundamental phase-shifting element in the PGM is a sub-wavelength cell. Based on structure and composition, the different types of cells available in the literature can be broadly classified, as shown in Figure 6.  
Figure 5: Analogy between an antenna array and array of cells arranged in a metasurface to exhibit a transmission phase gradient. The first step in designing any metasurface is to find a suitable phase transformation cell topology. Since a phase-shifting cell is a fundamental element that is repeated with variations in its geometric properties on the surface, the overall performance of the antenna system greatly depends on the design and analysis of the cell. This phase shifting cell is initially simulated using periodic boundary conditions in the \(x\) and \(y\) directions while assuming Floquet mode excitation in the \(z\) direction to analyze its transmission coefficient phase and magnitude response, and hence is referred to as a unit cell when simulated with periodic boundary conditions. Floquet mode excitation in the CST- Microwave Studio (MWS) is shown in Figure 7. The different types of dual-band unit cells that have been used in the literature using the different structural topologies are shown in Figure 8.  
Figure 6: Different phase transformation cell topologies based on their implemented structure. (**a**) An all-dielectric multiwavelength cell [83], (**b**) a dual-band all metal cell [31], and (**c**) a dual-band composite cell redrawn based on the structure reported in [32]. Figure 7: Unit cell simulations in CST, periodic in \(x\) and \(y\) axes, open in \(z\) axis with Floquet mode excitation.
# Shared Tuned Mass Dampers for Mitigation of Seismic Pounding
## 4 Conclusions  
This study revisits the idea of shared TMD for mitigation of seismic bounding between adjacent buildings. Dynamics of an optimal solution reported to be effective in [PERSON] et al. [28] is investigated in detail to arrive at the following conclusions.  
1. The solution proposed in the literature does not act like a tuned mass damper. The mass of the device is not tuned to the structure it is connected to; it is very stiff and as a result, the device does not move. Any reduction in response resulting from such devices is due to the viscous coupling of the two buildings rather than the tuned vibration of the STMD mass. Our results show that a similar level of effectiveness can be achieved without the TMD mass, simply by coupling the two buildings by a viscous dashpot. This eliminates the need for extra costs required to support the additional mass of the TMD.
2. The cost function for optimization of STMDs displays two minima, one near resonance of the device with one of the buildings, and the other at a frequency much larger than that of the buildings. The solution near the resonance is a tuned mass damper, while the other one is equivalent to the viscous coupling of two buildings.
3. For a large set of 462 ground motions, an optimal TMD placed on one of the buildings alone was found to be almost as effective as an STMD tuned to the building both in controlling roof displacement of the building and bounding distance with an adjacent building. The trouble of sharing the TMD with another building is therefore not worthwhile.  
All authors discussed and agreed upon the idea, and made scientific contributions. All authors have read and agreed to the published version of the manuscript.  
This research was funded by University of Iceland Research Fund.  
We acknowledge constructive and insightful comments and discussions from two anonymous reviewers and the editors, which helped improve the paper. The authors acknowledge support from the University of Iceland Research Fund.  
The authors declare no conflict of interest.
# An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
## 4 Performance Assessment
### Impact of Vehicle Density in Reliability of Data Dissemination
The brief summary of various performance metrics according to the impact of vehicle density in a realistic environment is presented in Table 4.  
The benefit for this proposed EEAPD protocol is that each vehicle will be chosen as suitable node among other nodes to be forwarder node according to five important factors: number of road segments within transmission range, distance from accident node, minimum waiting time to rebroadcast, message direction (further behind the receiver vehicle), and the nature of current network density. Therefore, each vehicle will determine the best decision for rebroadcasting the packet without the knowledge from neighboring nodes. Tables 3 and 4 give the performance metrics comparison between  
Figure 17: End-to-end vs. vehicle density. Figure 18: Relaying vehicles vs vehicle density. EEAPD protocol (highlighted) and other protocols. This indicates that the performance of the proposed protocol is better than others in terms of all metrics.
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 2 Related Work  
### Cambridge  
The Cambridge dataset [20] was collected using an Android/web application and includes the participants' cough, voice, and breath sounds as well as information on their medical history and symptoms. Participants record three coughing sounds and three breathing sounds after providing their age, gender, a brief medical history, and any symptoms they may be experiencing. A total of 4352 users of the web app and 2261 users of the Android app each contributed to the dataset, resulting in 5634 samples and 4352 samples, respectively. The participants' gender distribution was as follows: 4525 males, 2056 females, 26 non-respondents, and 6 others. Through the process of directly checking all the samples of COVID-19-positive test cases, 141 samples were retrieved.  
[PERSON] et al. [20] trained a classification model by extracting two types of features--handcrafted features and features from transfer learning--from the Cambridge dataset. Gradient boosting trees and support vector machines (SVMs) were used as a classification model. Raw sound waveform data were resampled at a frequency of 22 kHz before handcrafted characteristics were extracted. Handcrafted features were extracted at the frame level and segment level. A total of 477 handcrafted features were used, including duration, MFCC, \(\Delta\)-MFCC, \(\Delta^{2}\)-MFCC, onset, period, root mean square (RMS) energy, roll-off frequency, spectral centroid, tempo, and zero-crossing. The features from transfer learning used VGGish [30], which is a convolutional neural network designed for audio classification based on raw audio input. VGGish is a model trained using a large YouTube dataset, and the parameters of the model are public. A pre-trained VGGish model was used to extract 256 dimensional features, and the sampling rate was 16 kHz. As a result of training the SVM model by extracting two types of features using only the cough sound data, the area under the ROC curve (AUC) was 0.82 and the sensitivity was 0.72.
# Adversarial Data Augmentation on Breast MRI Segmentation
## 2 Methods
### Adversarial Generator
#### 2.2.2 CycleGAN  
The CycleGAN [22] is yet another iteration on the original Pix2 Pix architecture, especially suited for datasets with unpaired domain samples. Knowing that the most common situation with MRI datasets is not having the respective annotations, we experimented with the _cycle-consistency_ loss and framework to approach our task. The framework boils down to a GAN design, similar to the one presented in ResGAN (Figure 1), with the added complexity of duplicating the structure: two generators (\(G_{xy}\) & \(G_{yx}\)) and two discriminators (\(D_{y}\) & \(D_{x}\)).  
The concept behind it is to avoid having paired data by training a Conditional GAN (\(G_{xy}\) & \(D_{y}\)) to translate the input \(X\) to the output domain \(Y\), while a second, similar GAN (\(G_{yx}\) & \(D_{x}\)) converts that output \(Y\) back to the original domain \(X\) (Figure 2). By providing this closed loop processing scheme, one can add, to the classic loss, a cycle-consistency loss. This is done by comparing the original input to its reconstruction using a \(L_{1}\) distance:  
\[\begin{split}\mathcal{L}_{cyc}(G_{xy},G_{yx})=& \mathbb{E}_{x\sim p_{data}(x)}\big{[}\big{\|}G_{yx}(G_{xy}(x))-x \big{\|}_{1}\big{]}+\\ &\mathbb{E}_{y\sim p_{data}(y)}\big{[}\big{\|}G_{xy}(G_{yx}(y))- y\big{\|}_{1}\big{]}\end{split} \tag{1}\]  
The authors of the CycleGAN also introduced the idea of identity loss--n autoencoding loss component for the generator--with the goal of color correction.  
The cycle consistency term is controlled by \(\lambda_{1}\), while the identity term is factored by \(\lambda_{2}\), giving the full loss function present in Equation (2).  
\[\begin{split}\mathcal{L}(G_{xy},G_{yx},D_{y},D_{x})=& \mathcal{L}_{GAN}(G_{xy},D_{y},X,Y)+\mathcal{L}_{GAN}(G_{yx},D_{x},Y,X)+\\ &\lambda_{1}\mathcal{L}_{cyc}(G_{xy},G_{yx})+\lambda_{2} \mathcal{L}_{id}(G_{xy},G_{yx})\end{split} \tag{2}\]
## 3 Data Analysis
### Relationships between GNSS-R Observables and Vegetation Parameters  
In this section, the GNSS-R observables are analyzed as a function of one specific vegetation parameter, namely, the LAI of the plant cover. As in the case of soil moisture, two elevation angle intervals (50-70\({}^{\circ}\) cf. Figure 5 and 70-90\({}^{\circ}\) cf. Figure 6) and three observables (\(\Gamma_{RL}\), LHCP SNR, and polarimetric ratio) were considered. To avoid effects due to soil moisture, we considered data corresponding to dry conditions, with soil moisture lower than 0.1 m\({}^{3}\)/m\({}^{3}\). In the database used for the present study, since a small number of bare soil measurements were found to correspond to the wet moisture values (Mv), an additional second class, with Mv \(>\) 0.2 m\({}^{3}\)/m\({}^{3}\) was not considered.  
Figures 5a and 6a show the sensitivity of \(\Gamma_{RL}\) to the vegetation LAI for the reference fields with dry soil conditions (sets of 28 and 23 samples for the two elevation angle intervals (50-70\({}^{\circ}\) and 70-90\({}^{\circ}\)), respectively). Each sample represents a GNSS-R mean measurement on a reference field at a given date. There is a clear decrease in this GNSS-R observable intensity with increasing vegetation LAI, due to the influence of signal scattering and attenuation (increasing optical thickness). Dynamic ranges between 5 and 10 dB are observed for the 50-70\({}^{\circ}\) and 70-90\({}^{\circ}\) elevation angles, respectively. The observed higher sensitivity (Sv) at low elevation angles (Sv equal to \(-\)0.91 dB/(m\({}^{2}\)/m\({}^{2}\))) can probably be attributed to a decrease in the influence of the soil component (roughness and moisture) due to an increase in the vegetation optical depth. This sensitivity is accompanied by an increase in the correlation between GNSS-R signals and vegetation LAI, with R\({}^{2}\) equal to 0.38 at low elevations, and only 0.03 at high elevations for the case of reflectivity. A similar behavior is observed for the reflected signal SNR, as shown in Figures 5b and 6b, which reveals a dynamic range of approximately 5-10 dB, a sensitivity equal to \(-\)0.76 dB/(m\({}^{2}\)/m\({}^{2}\)) for the elevation angles 50-70\({}^{\circ}\) range and reduced to \(-\)0.17 dB/(m\({}^{2}\)/m\({}^{2}\)) for the elevation angles of 70-90\({}^{\circ}\).  
Figure 5: GNSS-R observables at 50-70\({}^{\circ}\) elevation angles as a function of LAI at dry conditions (wet moisture values (Mv) \(<\) 0.1 m\({}^{3}\)/m\({}^{3}\)): (a) \(\Gamma_{RL}\); (b) SNR; (c) PR.  
The PR observable is found to be correlated with the vegetation LAI (Figures 5c and 6c), with a high sensitivity to vegetation LAI equal to \(-1.1\) dB/(m\({}^{2}\)/m\({}^{2}\)) for low elevation angles and to \(-1.18\) dB/(m\({}^{2}\)/m\({}^{2}\)) for high elevation angles. This better performance of PR compared to its sensitivity to soil moisture can be explained by a high effect of vegetation on depolarization of the GNSS signals. Table 3 summarizes all the results of the GNSS-R observable sensitivities to LAI for the different configurations discussed above.
## 5 Discussion  
### Power Allocation  
In order to further improve the performance of the system, the efficiency of power can also be considered. The bottleneck of power allocation lies in the transmission power of the user node corresponding to the worst quality transmission channel in the system. Therefore, the method to improve this problem is to ensure that the worst channel user has enough transmission power to ensure that the signal power in each alignment direction is equal. Assume that the total transmission power of the user node in the MIMO-Y channel model is normalized to 1. Therefore, according to the above analysis, in order to improve system performance, the transmission power of each user node needs to meet the following conditions:  
\[\left\{\begin{array}{l}p_{1}+p_{2}+p_{3}=1\\ p_{1}\cdot\left|\mathbf{U}_{12}\right|^{2}=p_{2}\cdot\left|\mathbf{U}_{13} \right|^{2}\\ p_{1}\cdot\left|\mathbf{U}_{12}\right|^{2}=p_{3}\cdot\left|\mathbf{U}_{23} \right|^{2}\end{array}\right. \tag{40}\]  
where a is the transmission power of the user node. The modulus of the orthogonal alignment direction vector in the equation reflects the quality of the channel. Based on this, the transmission power of each user node is allocated. The user with the worst transmission quality gets more transmission power to improve the overall performance of the system.