Miracle-dz/UniFAR-FTUs huggingface dataset:
Instrumental development of NanoCarb, a new spectro-imaging sensor

Estimating emissions of the main greenhouse gases (such as CO2 and CH4) is a challenge in studying climate change. The European project called SCARBO, which involves several partners, aims at evaluating the feasibility of a low-cost constellation of small satellites onboarding small instruments, which could be capable of quantifying greenhouse gases. In this context, we propose NanoCarb, a miniature snapshot Fourier-transform imaging spectrometer concept dedicated to the measurement of two greenhouse gases: CO2 and CH4. Its compact design makes it a good candidate for small satellites. It is based on a multi-channel architecture which provides a sparse interferometric sampling strategy. This multi-channel design only involves two specific optical components: a microlens array and an interferometric plate, which is a stepcase phase plate. We have developed and assembled two NanoCarb prototypes which have been experimentally validated through a dedicated airborne campaign. This paper focuses on the design and laboratory calibrations of the two prototypes. The tests include both imaging and spectral features of the instruments. An adequate laboratory calibration is essential to process the data acquired by the prototypes during the airborne campaign. Experimental results show the good quality of the prototypes.
Performance analysis of an adaptive optics based optical feeder link ground station

Space data highway networks are currently being deployed to provide high data rate connectivity to non-fibre-connected areas around the world and to prepare the incoming growth of Internet Of Things market. Among other solutions, GEO optical feeder links are seen as the backbone of the future globalized very high data rate satellite-based telecommunication networks. The FEEDELIO experiment developed for ESA in April 2019 demonstrated the relevance of angular decorrelation models to assess the statistical characteristics of the pre-compensated uplink irradiance that is deeply affected by anisoplanatism. Based on the conclusion drawn from this experiment, we present the results of a preliminary design study of an Adaptive Optics (AO) pre-compensated optical feeder links ground station. Exploiting end-to-end simulations under relevant turbulence conditions, time-correlated fading statistics are investigated to provide typical fading durations for AO pre-compensated optical channel, taking into account the impact of point-ahead mispointing errors.
Improvement in performance of CZTSSe solar cell by using cadmium free buffer layers

Cadmium Sulphide(CdS) has been the most preferred n-type buffer layer and Indium Tin Oxide(ITO) is the popular window layer in kesterite solar cells. Cadmium being toxic and Indium being a rare earth element, continuous efforts are being made to replace these materials from kesterite solar cells structure. In this work, ZnS, ZnSe, and Zn0.8Sn0. 2O are considered as possible alternatives for CdS. Similarly, Aluminium doped Zinc Oxide(AZO) is considered as an alternative for ITO. Firstly, a cell model with CdS and ITO (Mo/CZTSSe/CdS/ZnO/ITO) is developed using SCAPS-1D software. To optimise the performance parameters namely open-circuit voltage(Voc), short-circuit current density(Jsc), fill factor(FF), and the power conversion efficiency (PCE) for irradiation under normal working conditions, thickness and the composition ratio of the absorber layer(CZTSSe) are evaluated through numerical simulations. PCE of 14.51% is achieved for a 40% of Sulphur content and 2 um thickness of Cu2ZnSn(SxSe1-x)4 when CdS is used as the buffer layer. For the same structure, replacing ITO with AZO results in a PCE of 14.62%. Use of Cadmium-free buffer layers ZnS, ZnSe, and Zn0.82Sn0. 18O with ITO as window layer result in PCE of 13.98%, 14.28%, and 14.53%, respectively. For the Cadmium-free buffer layers, an improvement in PCE is achieved when ITO is replaced by AZO, with the highest being 14.62% for Zn0.82Sn0. 18O. This can be attributed to the smaller conduction band offset, which reduces the recombination of photogenerated carriers and improves the carrier transport in the solar cell. The above results indicate that the Zn0.8Sn0. 2O and AZO can be potential candidates for the buffer layer and window layer, respectively, for high-performance and cheap kesterite solar cells.
Water flow and solute transport model of potentially toxic elements through unsaturated zone at regional wellfield Kosnica

The objective of this work was to build a prognostic water flow model and potentially toxic elements (lead, cadmium, zinc) transport model in the unsaturated zone. Research was conducted in the catchment area of Kosnica regional wellfield, where the unsaturated zone is characterised by Fluvisol. Lower sorption capacities were determined in the first horizons for all three potentially toxic elements. Correlation coefficient of the measured and simulated values of tracer concentration is 0.58 for the AC horizon and 0.84 for the 2C/C1 horizon. Based on calibrated water flow and transport parameters, a prognostic water flow model and potentially toxic elements (lead, cadmium, zinc) transport model in the unsaturated zone was built. In case of an accidental spill of potentially toxic elements with concentrations of 1000 mg/l, the risk of contamination of the aquifer is present. Copyright © 2016 John Wiley & Sons, Ltd.
Double-overdamped-oscillator model of terahertz complex dielectric permittivity of human brain tissues

In the paper, the double-Debye and double-overdamped-oscillator models are considered for parametrization of the terahertz (THz) dielectric response of human brain tissues. Experimental data can be accurately reproduced for the intact tissues and gliomas using both models. While the double-Debye dielectric model is widely used in THz biophotonics, the double-overdamped-oscillator model appears to be more physically rigorous, since it satisfies the sum rule. In our opinion, the described double-overdamped-oscillator model is important for further research and development in THz biophotonics and neurodiagnosis of tumors.
Does the instability of economic development affect the elasticity of the labour market?

Abstract Despite extensive research, the estimates of changes in employment are heterogeneous in different conditions of economic development. In this study, we examined the impact of the instability of economic growth on the elasticity of the labour market in a set of EU27 member states in the period 2000Q1–2019Q4.The sensitivity of the labour market was quantified in parallel by two available methods which are used for this purpose – by calculating the values of the arc elasticity coefficient and by regression analysis. Logarithmic linear regression models were compiled according to the analysis criteria individually for each member state. By comparing the values of the obtained elasticity indicators, differences in the responses of the labour market were identified. Our results show that the heterogeneity of opinions is to some extent natural. The elasticity of the labour market determined by calculating the values of the elasticity coefficient is characterized by a high variability of values. Similarly, the values of the regression coefficient reflect the nature of the macroeconomic development of the period under review. According to our findings, the frequent short-term trends of negative economic development result in a reduced sensitivity of the labour market to the changes in economic performance, manifested by a lower employment elasticity compared to its values in economies with a stable development trend. Based on this, we formulate the connection between the elasticity of the labour market and the positive and negative economic development. We condition the sensitivity of the labour market to the changes in the trend of economic development with the occurrence of longer-lasting trends of negative economic development.
The quality of intrapartum cardiotocography in preterm labour

Abstract Objectives The aim of this study is to determine the quality of the foetal heart rate (FHR) recording, defined as signal loss, during preterm labour below 28 weeks gestational age (GA) and contribute to the discussion if cardiotocography (CTG) is of value for the extreme preterm foetus. Methods From January 2010 to December 2019 a retrospective study was conducted with data of 95 FHR recordings of singletons born between 24 and 28 weeks GA at the Amsterdam University Medical Centre, location VUmc. FHR tracings had a duration of at least 30 min and were obtained via external ultrasound mode. Data of all recordings were divided in two groups according to gestation (24–26 weeks and 26–28 weeks). Signal loss was analysed. Statistical significance was calculated by non-parametric tests and chi-square tests. The median signal loss and the proportion of cases exceeding the International Federation of Gynaecology and Obstetrics Guidelines (FIGO) threshold of 20% signal loss were calculated. Results One-third of the recordings exceeded the 20% FIGO-criterion for adequate signal quality during the first stage of labour. In the second stage, this was nearly 75%. Similarly, the median signal loss was 13% during the first and 30% during the second stage of labour (p<0.01). Conclusions The quality of FHR monitoring in the extreme preterm foetus is inadequate in a large proportion of the foetuses, especially during the second stage. FHR monitoring is therefore controversial and should be used with caution.
Text Mining in Cybersecurity

The growth of data volume has changed cybersecurity activities, demanding a higher level of automation. In this new cybersecurity landscape, text mining emerged as an alternative to improve the efficiency of the activities involving unstructured data. This article proposes a Systematic Literature Review (SLR) to present the application of text mining in the cybersecurity domain. Using a systematic protocol, we identified 2,196 studies, out of which 83 were summarized. As a contribution, we propose a taxonomy to demonstrate the different activities in the cybersecurity domain supported by text mining. We also detail the strategies evaluated in the application of text mining tasks and the use of neural networks to support activities involving unstructured data. The work also discusses text classification performance aiming its application in real-world solutions. The SLR also highlights open gaps for future research, such as the analysis of non-English content and the intensification in the usage of neural networks.
Methods of management of bottom sediments from selected water reservoirs – a literature review

Abstract Sediment accumulation is a process that is typical of all types of water reservoirs. The rate and pattern of such accumulation are related to processes taking place in catchments that produce the sediments and to those within reservoirs that determine the percentage of the inflowing load that is trapped and where it is deposited. To keep reservoirs in working order requires desilting and managing of such bottom sediments once they are removed. The choice of strategy for sediment management depends on chemical and physical properties which result from both natural and anthropogenic processes. To varying degrees, these sediments may be contaminated with chemical compounds, especially trace metals. Therefore, research is needed in order to assess the quality of sediments, which will allow to opt for the proper management strategy. Based on an analysis of the available literature, the possibility of using sediments from reservoirs has been determined, using quality criteria and in accordance with applicable law and regulations.
Efficiency of Air Purifiers at Removing Air Pollutants in Educational Facilities: A Preliminary Study

Infectious diseases caused by airborne bacteria and viruses are a major problem for both social and economic reasons. The significance of this phenomenon is particularly noticeable during the time of the coronavirus pandemic. One of the consequences is the increased interest in the air purifier (AP) market, which resulted in a significant increase in sales of these devices. In this study, we tested the efficiency of APs in removing bacterial air contamination in the educational context in the Upper Silesia region of Poland during the “cold season” of 2018/2019. During the 6 months of measuring microbiological air quality, an 18% decrease in the concentration of microbiological pollutants as a result of the action of the APs was recorded. Additionally, the results of the particle size distribution of the bacterial aerosols showed a reduction in the share of the respirable fraction (particles with an aerodynamic diameter below 3.3 µm) by an average of 20%. The dominance of gram-positive cocci in the indoor environment indicates that humans are the main source of most of the bacteria present in the building. We conclude that the use of APs may significantly decrease the level of concentration of microbiological air pollutants and reduce the negative health effects of indoor bioaerosols; however, further work that documents this phenomenon is needed.
Safety, Tolerability, and Pharmacokinetics of a Novel Mitochondrial Modulator, TRC150094, in Overweight and Obese Subjects: A Randomized Phase-I Clinical Trial

TRC150094, a novel mitochondrial modulator, can restore metabolic flexibility by improving insulin resistance in preclinical studies. This study primarily aims to evaluate the safety, tolerability, and pharmacokinetics (PK) of oral TRC150094 after conducting two double-blind, randomized, Phase-I studies, single ascending dose (SAD) and multiple ascending dose (MAD), with n = 46, in overweight/obese adult and elderly subjects. In addition, the effect of TRC150094 on pharmacodynamic (PD) efficacy markers was evaluated. PK assessments, including maximum concentration (Cmax), area under the plasma concentration (AUC), time to Cmax (Tmax), and elimination half-life (t½), were assessed at pre-specified time points. PD assessments included apolipoprotein B (ApoB), triglycerides, hepatic fat by magnetic resonance spectroscopy (MRS) and cardiopulmonary exercise testing (CPET) parameters. TRC150094 was rapidly absorbed, and the AUC of TRC150094 increased in a dose-dependent manner across all doses in non-elderly and elderly cohorts. Cmax was more than the dose-proportional for all doses in all cohorts. Tmax ranged from 0.25 to 4 h, and t½ ranged from 15 to 18 h, making TRC150094 suitable for once-daily dosing. Food did not interfere with the overall absorption of the drug. The metabolites of TRC150094 were glucuronide and sulfate conjugates, and 20% of the drug was excreted unchanged in the urine. TRC150094 at 50 mg showed an improving trend in triglycerides. A significant reduction in Apo B was observed after 50 mg dose (−2.34 vs. 13.24%, p = 0.008), which was, however, not the case after 150 mg (8.78 vs. 13.24%, p = 0.1221). Other parameters such as hepatic fat and insulin sensitivity indices (HOMA-IR, MATSUDA Index derived from OGTT) showed an improving trend for the dose of 50 mg. In terms of safety, all the AEs reported were mild to moderate in severity. None of the adverse events was considered definitely or probably related to treatment, and there were no abnormal laboratory findings. In conclusion, the PK of TRC150094 was linear with no clinically significant food effect. TRC150094 and its metabolites suggest a lesser likelihood of drug-drug interactions. Overall, TRC150094 ensured safety and exhibited suitability for all subjects. Clinical Trial Registration: EUDRA CT: 2009-014941-10 (SAD) and CTR-India registration: CTRI/2009/091/000601 (MAD)
Enhanced Dense Space Attention Network for Super-Resolution Construction From Single Input Image

In some applications, such as surveillance and biometrics, image enlargement is required to inspect small details on the image. One of the image enlargement approaches is by using convolutional neural network (CNN)-based super-resolution construction from a single image. The first CNN-based image super-resolution algorithm is the super-resolution CNN (SRCNN) developed in 2014. Since then, many researchers have proposed several versions of CNN-based algorithms for image super-resolution to improve the accuracy or reduce the model’s running time. Currently, some algorithms still suffered from the vanishing-gradient problem and relied on a large number of layers. Thus, the motivation of this work is to reduce the vanishing-gradient problem that can improve the accuracy, and at the same time, reduce the running time of the model. In this paper, an enhanced dense space attention network (EDSAN) model is proposed to overcome the problems. The EDSAN model adopted a dense connection and residual network to utilize all the features to correlate the low-level feature and high-level feature as much as possible. Besides, implementing the convolution block attention module (CBAM) layer and multiscale block (MSB) helped reduce the number of layers required to achieve comparable results. The model is evaluated through peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics. EDSAN achieved the most significant improvement, about 1.42% when compared to the CRN model using the Set5 dataset at a scale factor of 3. Compared to the ERN model, EDSAN performed the best, with a 1.22% improvement made when using the Set5 dataset at a scale factor of 4. In terms of overall performance, EDSAN performed very well in all datasets at a scale factor of 2 and 3. In conclusion, EDSAN successfully solves the problems above, and it can be used in different applications such as biometric identification applications and real-time video applications.
Interaction With Gaze, Gesture, and Speech in a Flexibly Configurable Augmented Reality System

Multimodal interaction has become a recent research focus since it offers better user experience in augmented reality (AR) systems. However, most existing works only combine two modalities at a time, e.g., gesture and speech. Multimodal interactive system integrating gaze cue has rarely been investigated. In this article, we propose a multimodal interactive system that integrates gaze, gesture, and speech in a flexibly configurable AR system. Our lightweight head-mounted device supports accurate gaze tracking, hand gesture recognition, and speech recognition simultaneously. The system can be easily configured into various modality combinations, which enables us to investigate the effects of different interaction techniques. We evaluate the efficiency of these modalities using two tasks: the lamp brightness adjustment task and the cube manipulation task. We also collect subjective feedback when using such systems. The experimental results demonstrate that the Gaze+Gesture+Speech modality is superior in terms of efficiency, and the Gesture+Speech modality is more preferred by users. Our system opens the pathway toward a multimodal interactive AR system that enables flexible configuration.
Potential of Human Wharton’s Jelly Mesenchymal Stem Cells (hWJMSCs) Secretome for COVID-19 Adjuvant Therapy Candidate

SARS-CoV-2 is a virus that causes a serious life-threatening disease known as COVID-19. Patients with COVID‑19 can develop acute respiratory distress syndrome (ARDS) and multiorgan failure requiring safe and effective therapy. The human Wharton’s Jelly Mesenchymal Stem Cells (hWJMSCs) can act as an anti-inflammatory and regenerative properties due to their secretion such as cytokine, growth factor, antimicrobial, and chemokine. A natural way of producing the secreted proteins of a cell (secretome) is by culturing mesenchymal stem cells (MSCs) under starvation or serum deprivation condition. This research was performed to measure the level of Fibroblast Growth Factor-7 (FGF-7), antibacterial protein LL-37 (LL-37), interleukin 1 Receptor Antagonist (IL-1ra), respectively, in the secretome of hWJMSCs. The secretome levels at: non-starvation, 24-, 48-, and 72-hours starvation periods were measured using ELISA method. The hWJMSCs secreted levels of FGF-7, LL-37, and IL-1ra were 46.74 – 72.13 pg/mL, 1.70 – 4.14 ng/mL, 1.35 – 8.32 pg/mL, respectively. The longer the starvation period of cells, the higher the protein levels produced. The hWJMSCs secrete FGF-7, LL-37, IL-1ra, longer starvation periods of cells will increase secretome levels. The hWJMSCs could potentially serve as adjuvant therapy for COVID-19.
Size-Reduction of a Dual-Band Circularly Polarized Dielectric Resonator Antennas

In this paper, the size reduction of a dual-band circularly polarized (CP) Y-shaped dielectric resonator antenna (YDRA) is performed. Unlike the conventional CP DRAs, the circular polarization in the YDRA is achieved by the combination of linearly varying E-fields in the two arms of unequal length. Therefore, a PEC boundary condition based technique is applied to the long arm of the dielectric resonator (DR). As a result, a 33.18% reduction in the size is achieved after the optimization of the DR for the required frequency bands. A sample of the proposed design is fabricated to perform measurements. From the results, the antenna obtains a wide impedance bandwidth for $|S_{11}| \leq -10$ dB of 53.5% (2.3–3.98 GHz), 3 dB axial ratios bandwidths (ARBWs) of 4.08% (2.4–2.50 GHz) and 10.91% (3.38–3.77 GHz) with respective peak gains of 4.83 dBic and 4.81 dBic. The attained 3 dB ARBWs entirely cover the WLAN 2.4 GHz and WiMAX 3.5 GHz frequency bands.
Toward Optimal LSTM Neural Networks for Detecting Algorithmically Generated Domain Names

Malware detection is a problem that has become particularly challenging over the last decade. A common strategy for detecting malware is to scan network traffic for malicious connections between infected devices and their command and control (C&C) servers. However, malware developers are aware of this detection method and begin to incorporate new strategies to go unnoticed. In particular, they generate domain names instead of using static Internet Protocol addresses or regular domain names pointing to their C&C servers. By using a domain generation algorithm, the effectiveness of the blacklisting of domains is reduced, as the large number of domain names that must be blocked greatly increases the size of the blacklist. In this paper, we study different Long Short-Term Memory neural network hyperparameters to find the best network configuration for algorithmically generated domain name detection. In particular, we focus on determining whether the (complex) feature engineering efforts required when using other deep learning techniques, such as Random Forest, can be avoided. In this regard, we have conducted a comparative analysis to study the effect of using different network sizes and configurations on network performance metrics. Our results show an accuracy of 97.62% and an area under the receiver operating characteristic curve of 0.9956 in the test dataset, indicating that it is possible to obtain good classification results despite avoiding the feature engineering process and additional readjustments required in other machine learning techniques.
Interpreting externally expressed intentions of an autonomous vehicle

With the imminent addition of autonomous vehicles to traffic, it is becoming more vital to look at different alternatives to non-verbal communication between the driver and the pedestrian, so that the pedestrian would understand the intentions of autonomous vehicles. The aim of this paper is to evaluate different approaches to communicate the intent of an autonomous vehicle. A survey study was conducted among Estonian people to analyze their understanding of animations of more prominent explicit external interaction modalities. The study revealed that participants may not understand the vehicle intent if they have no prior knowledge about the displayed signals.