Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the possibility to combine LOFAR and GNSS measurements to sense ionospheric irregularities

Inhomogeneities within the ionospheric plasma density affect trans-ionospheric radio signals, causing radio wave scintillation in the amplitude and phase of the signals. The amount of scintillation induced by ionospheric irregularities typically decreases with the radio wave frequency. As the ionosphere affects a variety of technological systems (e.g., civil aviation, financial operations) as well as low-frequency radio astronomy observations, it is important to detect and monitor iono- spheric effects with higher accuracy than currently available. Here, a novel methodology for the detection and characterization of ionospheric irregularities is established on the basis of LOFAR scintillation measurements at VHF that takes into account of the lack of ergodicity in the intensity fluctuations induced by scintillation. The methodology estimates the S 4 scintillation index originating from irregularities with spatial scales in the inertial sub-range of electron density fluctuations in the ionosphere. The methodology is illustrated by means of observations that were collected through the Polish LOFAR stations located in Bałdy, Borówiec and Łazy: its validation was carried out by comparing LOFAR VHF scintillation observations with independent GNSS observations that were collected through a high-rate receiver located near the LOFAR station in Bałdy as well as through geodetic receivers from the Polish ASG-EUPOS network. Two case stud- ies are presented: 31 March 2017 and 28 September 2017. The comparison between LOFAR S4 observations and independent ionospheric measurements of both scintillation and rate of change of TEC from GNSS reveals that the sensitivity of LOFAR and GNSS to ionospheric structures is different as a consequence of the frequency dependency of radio wave scintillation. Furthermore, it can be noticed that observations of LOFAR VHF scintillation can be utilised to detect plasma structures forming in the mid-latitude ionosphere, including electron density gradients occurring over spatial scales that are not necessarily detected through traditional GNSS measurements: the detection of all spatial scales is important for a correct monitoring and modelling of ionospheric processes. Hence, the different sensitivity of LOFAR to ionospheric structures, in addition to traditional GNSS ionospheric measurements, allows to expand the knowledge of ionospheric processes.
Model-Free Path Following for Autonomous Vehicles Based on GRU Predictive Control within Parallel Links

Path following is a basic functionality for the safe driving of autonomous vehicles and has aroused much attentions in recent years. However, most of the researches fully or partly regard the vehicle model as a priori while acquiring these models are expensive and time-consuming. This paper presents a lateral control framework to endow the cars with accurate tracing to the desired paths without considerations of vehicle model parameters and also with a self-tuning mechanism to stabilize the tracking errors in an adaptive way. Specifically, the proposed controller is composed of two parallel links that are designed to regulate the distance errors and heading errors respectively. Both of the links use a gated recurrent unit (GRU) to predict a critical intermediate variable that is correlated with the stabilizations of associated errors. Then the Bayesian optimization (BO) is applied to search for the optimal allocations of link signals so as to improve the entire control behavior in a black-box manner. Simulation results demonstrate the tracking accuracy and fairly low-cost usage of the proposed method over traditional control approaches.
Incidence of Prediabetes/Diabetes among Women with Prior Gestational Diabetes and Non-Alcoholic Fatty Liver Disease: A Prospective Observational Study

Background and Objectives: This prospective longitudinal study aims to evaluate and compare the incidence of prediabetes/diabetes among women stratified at the baseline postpartum visit according to the prior GDM and NAFLD status. Methods: Of the 309 women with baseline postpartum assessment at a median of 16 months following the index delivery, 200 (64.7%) [GDM: 137 (68.5%), normoglycaemia: 63 (31.5%)] were available for the follow-up analysis (performed at median of 54 months following the index delivery) and were participants for this study. We obtained relevant demographic, medical and obstetric details and performed a 75 g OGTT with glucose estimation at 0 and 120 min. NAFLD status was defined by ultrasonography at the baseline visit. Participants were divided into four groups: no NAFLD and no prior GDM (group 1), NAFLD but no prior GDM (group 2), prior GDM but no NAFLD (group 3), and NAFLD and prior GDM (group 4). Results: The mean age of study participants (n = 200) was 32.2 ± 5.1 years, and the mean interval between the two visits was 34.8 ± 5.5 months. A total of 74 (37%) women had progression to prediabetes/diabetes [incidence rate of 12.8/100 woman-years]. The incidence rates (per 100 woman-years) were 8.6, 8.9, 13.4 and 15.3 in groups 1, 2, 3 and 4, respectively. The adjusted hazard ratio for incident (new-onset) prediabetes/diabetes in group 4 (reference: group 1) was 1.99 (95% CI 0.80, 4.96, P = 0.140). Among women with baseline NAFLD (irrespective of GDM status), the risk of incident prediabetes/diabetes increased with an increase in the duration of follow-up (3.03-fold higher per year of follow-up, P = 0.029) and was significantly higher in women who were not employed (6.43, 95% CI 1.74, 23.7, P = 0.005) and in women with GDM requiring insulin/metformin during pregnancy (4.46, 95% CI 1.27, 15.64, P = 0.019). Conclusion: NAFLD and GDM increased the risk for glycaemic deterioration in young Indian women. Future studies should focus on evaluating the effectiveness of lifestyle and behavioural interventions in such high-risk women.
Performance Investigation on Tunable EM Wave Absorber Composed of Varactor Diode-loaded Open Ring Resonator

This paper presents the performance investigation of tunable electromagnetics (EM) wave absorber which is composed of open ring resonator loaded by varactor diode. The proposed EM wave absorber configuration is mainly constructed by an array of 2-dimensionally grounded open ring resonators on a dielectric substrate. The varactor diode with varied reverse direct current (DC) bias voltages is loaded into the open ring resonator of EM wave absorber configuration for achieving the tunable frequency operation. As the basic pattern, a unit cell of 1.5mm width open ring resonator is configured on a 1.6mm thick FR4 epoxy dielectric substrate with the dimension of 25mm × 25mm. The characteristics of varactor diode loaded open ring resonator are analyzed through its equivalent circuit, whereby its consists of an inductor connected in series with a shunt capacitor which is connected in parallel with a series of resistor and junction capacitor. Here, the values of junction capacitor are set from 2.67 pF to 0.63 pF which correspond to the reverse DC bias voltage of varactor diode. By using those junction capacitor values, the EM wave absorber yields the tunable frequency operation from 2.54 GHz to 2.70 GHz with varied reflection coefficient values from −12.81 dB to −18.89 dB, respectively. The result shows that the loading varactor diode into open ring resonator can tune the frequency operation of EM wave absorber configuration.
Quality of Life in HIV-Positive People in Poland Treated in the City of Bialystok: A Cross-Sectional Study

The quality of life is one of the key factors in assessing the health status of HIV-positive individuals, with its improvement considered an important goal of treatment. Assessment of the quality of life helps accurately evaluate the impact of diseases and treatment on the patient’s life. The aim of this study was to assess the quality of life of HIV-positive people in Poland using the example of patients treated in the Observation and Infection Clinic with the Subunit for HIV/AIDS Patients of the University Clinical Hospital in Bialystok, based on the evaluation of HIV-positive status acceptance in HIV patients as well as sociometric variables such as age, gender and marital status. A total of 147 patients participated in this study, including 104 men (70.7%) and 43 women (29.3%). This study was conducted between May 2019 and January 2020 in the Observation and Infection Clinic with the Subunit for HIV/AIDS Patients with the Consultation and Diagnostic Centre at the Teaching Hospital of the Medical University of Bialystok. This study used a diagnostic survey method with a modified questionnaire “Psychosocial Situation of People Living with HIV/AIDS in Poland” by Dr. Magdalena Ankiersztejn-Bartczak and the following standardised psychometric tools: the World Health Organization Quality of Life (WHOQOL-BREF), Short-Form Health Survey (SF-36), Acceptance of Illness Scale (AIS) and Satisfaction with Life Scale (SWLS). The majority of respondents (60%) reported no significant changes in their lives as a result of HIV infection. Gender was not a differentiating factor in the quality of life of people living with HIV. The variation in psychometric measures within the female and male groups was far greater than the difference between them. Marital status clearly differentiated the quality of life. The following conclusions were drawn from this study: The surveyed HIV patients presented a moderate level of quality of life, which was mainly determined by marital status. Higher quality of life was presented by married persons. Duration of infection was not correlated with quality of life. The level of acceptance of HIV infection was relatively high among respondents. A higher level of HIV acceptance was associated with a higher quality of life. The respondents presented a relatively poor level of satisfaction with life. Changing jobs, going on disability, relationship breakdown, not having a family of their own and losing friends were the key HIV-related changes in the lives of the respondents.
Plastic Deformation Analysis of a Heat Treated Vibrating Screen Bracket

Vibrating screens have significantly improved the particle separation process in the mining industry. However, they operate under demanding conditions. Components such as the cross bracket, used to transmit the excitation from the actuation motors to the screen frame, are prone to failure. To improve the welded joints, a newly developed vibrating screen mounting bracket was heat-treated in the oven at 580˚C to relieve the residual stresses in the weld. The air that was trapped inside the interior of the vibrating screen bracket expanded due to the heating and this cased the permanent deformation. This necessitated detailed analysis of the behavior of the bracket under elevated temperature conditions to mitigate the permanent deformations while maintaining expected performance dictated structural integrity. This work aimed to determine the load that caused the permanent deformation of the vibrating screen mounting bracket using experimental, analytical, and numerical methods. The pressure that caused the permanent deformation was determined to be 0.510 MPa and 0.6235 MPa analytically and numerically respectively. This showed a percentage difference of approximately 18%. The solution for avoiding the expansion of the vibrating screen mounting bracket was to introduce holes onto the structure of the vibrating screen so that no air would be trapped inside the bracket.
Matrix-Completion-Based False Data Injection Attacks Against Machine Learning Detectors

False data injection (FDI) attacks can manipulate power system measurements, leading to system economic losses and security issues. Although machine-learning (ML) detectors can effectively detect FDI attacks, the current methods used to construct FDI attacks do not take into account the presence of ML detectors. To tackle this problem, we propose novel convex matrix-completion-based FDI (MC-FDI) attacks on DC and AC power flow models from an attacker’s perspective, accounting for the temporal correlation between compromised and historical measurements. The proposed attacks minimize the nuclear norm of the compromised measurement matrix to make the compromised measurement consistent with the historical measurements, and also maximize the L1-norm of the incremental voltage angle to ensure a sufficient negative impact on the power system operation. Moving target defense (MTD) is proposed to detect the proposed MC-FDI attacks from the defender’s standpoint. The idea is to actively change the line impedance to corrupt the spatial and temporal correlation of the compromised measurements in the MC-FDI attacks. Numerical results on the IEEE 14-bus and IEEE 118-bus systems show the stealthiness of the proposed attacks to both the Chi-square detector and ML detectors as well as the efficacy of MTD in detecting the MC-FDI attacks.
The Effectiveness of Edge Detection Evaluation Metrics for Automated Coastline Detection

We analyse the effectiveness of RMSE, PSNR, SSIM and FOM for evaluating edge detection algorithms used for automated coastline detection. Typically, the accuracy of detected coastlines is assessed visually. This can be impractical on a large scale leading to the need for objective evaluation metrics. Hence, we conduct an experiment to find reliable metrics. We apply Canny edge detection to 95 coastline satellite images across 49 testing locations. We vary the Hysteresis thresholds and compare metric values to a visual analysis of detected edges. We found that FOM was the most reliable metric for selecting the best threshold. It could select a better threshold 92.6% of the time and the best threshold 66.3% of the time. This is compared RMSE, PSNR and SSIM which could select the best threshold 6.3%, 6.3% and 11.6% of the time respectively. We provide a reason for these results by reformulating RMSE, PSNR and SSIM in terms of confusion matrix measures. This suggests these metrics not only fail for this experiment but are not useful for evaluating edge detection in general.
IoT Challenges, Solution Framework And Implementation

The Internet of Things is a vast growing technology that is almost applicable in all fields and has simplified our lives. IOT is being used everywhere these days, including in smart cities, smart environments, security, smart business processes, smart agriculture, healthcare, and many more fields. Being able to cover various fields, is very challenging and presents various problems. This paper highlights IOT components, building blocks, enabling technologies, characteristics, applications, and products, as well as the issues encountered by them and lastly their causes. It brings forward seven steps of proposed solutions for handling and eliminating the problems encountered by almost all IOT devices and applications. The main aim of this paper is to provide the solution for all the issues which have been talked about for years but could not be able to resolve.
CycleMVAE: Benchmarking End-to-End Cycle-Consistent Multi-Task Variational Autoencoder for EEG-Based Emotion Recognition

Affective computing, particularly the identification of emotions from multichannel electroencephalography (EEG) signals, has gained importance. In this study, we propose a novel deep neural network model called Cycle Consistent Multi-Task Variational Autoencoder (CycleMVAE) to simultaneously investigate pairwise translation of emotional features, signal reconstruction, and emotion classification across two different EEG recording samples. CycleMVAE comprises two Variational Autoencoders (VAEs) and a supervised classifier. Each VAE consists of an encoder and a decoder. The encoder of the first VAE transfers emotional properties from EEG sample X to a compact latent space Z, while the decoder retrieves these features from Z to transfer them to EEG sample Y. Similarly, the second VAE uses the compact latent space Z’ to transfer emotion features from EEG sample Y to EEG sample X. This forms a cyclic translation of feature among EEG sample recordings. The model is trained using reconstructed loss, cycle consistency loss, and latent vector regularization loss, with a supervised classifier used to categorize emotions into arousal, valence, and dominance categories. The proposed approach improves EEG classification performance while reducing pre-processing complexity. The effectiveness of the proposed approach has been validated by experimental findings on the multimodal DREAMER emotional database.
MDF-Net: A Multi-Scale Dynamic Fusion Network for Breast Tumor Segmentation of Ultrasound Images

Breast tumor segmentation of ultrasound images provides valuable information of tumors for early detection and diagnosis. Accurate segmentation is challenging due to low image contrast between areas of interest; speckle noises, and large inter-subject variations in tumor shape and size. This paper proposes a novel Multi-scale Dynamic Fusion Network (MDF-Net) for breast ultrasound tumor segmentation. It employs a two-stage end-to-end architecture with a trunk sub-network for multiscale feature selection and a structurally optimized refinement sub-network for mitigating impairments such as noise and inter-subject variation via better feature exploration and fusion. The trunk network is extended from UNet++ with a simplified skip pathway structure to connect the features between adjacent scales. Moreover, deep supervision at all scales, instead of at the finest scale in UNet++, is proposed to extract more discriminative features and mitigate errors from speckle noise via a hybrid loss function. Unlike previous works, the first stage is linked to a loss function of the second stage so that both the preliminary segmentations and refinement subnetworks can be refined together at training. The refinement sub-network utilizes a structurally optimized MDF mechanism to integrate preliminary segmentation information (capturing general tumor shape and size) at coarse scales and explores inter-subject variation information at finer scales. Experimental results from two public datasets show that the proposed method achieves better Dice and other scores over state-of-the-art methods. Qualitative analysis also indicates that our proposed network is more robust to tumor size/shapes, speckle noise and heavy posterior shadows along tumor boundaries. An optional post-processing step is also proposed to facilitate users in mitigating segmentation artifacts. The efficiency of the proposed network is also illustrated on the “Electron Microscopy neural structures segmentation dataset”. It outperforms a state-of-the-art algorithm based on UNet-2022 with simpler settings. This indicates the advantages of our MDF-Nets in other challenging image segmentation tasks with small to medium data sizes.
Design of a Fully Pulley-Guided Wire-Driven Prismatic Tensegrity Robot: Friction Impact to Robot Payload Capacity

The tensegrity structure was initially created as a static structure, but it has gained significant attention among robotics researchers due to its benefits, including high payload capability, shock resistance, and resiliency. However, implementing tensegrity structures in robotics presents new technical challenges, primarily related to their wire-driven structure, such as wire-routing and wire-friction problems. Therefore, this research letter proposes a technical solution for the aforementioned problems. The main contribution of this research is the design of frictionless pulley-guided nodes. To validate the proposed concept, we conducted comparative experiments between a common tensegrity prototype and a pulley-guided prototype, evaluating wire tension distribution and payload capacity.
Eisler's Encyclopedia of Environmentally Hazardous Priority Chemicals

Thousands of inorganic and organic chemicals and their metabolites enter the biosphere daily as a direct result of human activities. Many of these chemicals have serious consequences on sensitive species of natural resources, crops, livestock, and public health. The most hazardous of these were identified by a panel of environmental specialists from the U.S. Fish and Wildlife Service; these chemicals are the focus of this encyclopedia.For each priority group of chemicals, information is presented on sources, uses, physical and chemical properties, tissue concentrations in field collections and their significance, lethal and sublethal effects under controlled conditions. This includes effects on survival, growth, reproduction, metabolism, carcinogenicity, teratogenicity, and mutagenicity - and proposed regulatory criteria for the protection of sensitive natural resources, crops, livestock, and human health. Taxonomic groups of natural resources covered include terrestrial and aquatic plants and invertebrates, fishes, amphibians, reptiles, birds, and mammals. This book is the only product that centers on the most hazardous environmental chemicals to sensitive natural resources and is the only single volume compendium on the subject, allowing ease in consulting, written by a noted national and international authority on chemical risk assessment to living organisms.
A Scalable N-Step Equally Split SSHI Rectifier for Piezoelectric Energy Harvesting With Low-Q Inductor

In piezoelectric energy harvesting, the large inherent capacitor ( $C_{\text {P}}$ ) of a piezoelectric transducer (PT) results in significant charge loss and low-power extraction. To improve the power extraction, various interface circuits using a flip process have been proposed, such as synchronized switch harvesting on inductor (SSHI) rectifier and synchronized switch harvesting on capacitor (SSHC) rectifier. The flip process reduces the influence of large $C_{\text {P}}$ and the charge loss by using external elements. However, to extract high power, each rectifier requires a bulky inductor with a high quality factor (Q) and numerous external capacitors, respectively, which increases the system’s volume. Therefore, to solve the tradeoff issue between the extracted power and the volume of the system, this article proposes a scalable N-step equally split synchronized-switch harvesting-on-inductor (ES-SSHI) rectifier. By splitting the flip process into equal N-step, the ES-SSHI rectifier reduces an inductor RMS current and conduction loss. Moreover, the duty signals of each split phase are regular and symmetric, which enables a single controller to generate multiple flip duty signals and allows for predicting the zero-crossing point of the inductor current, ultimately reducing the controller loss. The proposed system was fabricated in the 180-nm CMOS process. The measured results demonstrate that the proposed ES-SSHI rectifier achieves a power extraction improvement of 1170% over the full-bridge rectifier (FBR) even with a subcubic millimeter scale low-Q inductor, reducing the system’s volume.
Self-Powered Flexible Triboelectric Nanogenerators (TENGs) Based on Lateral Sliding Mode

A sliding triboelectric nanogenerator (TENG) was successfully designed, fabricated and tested at various surface contact displacements. The sliding-TENG consists of polyvinylidene fluoride (PVDF) and polyethylene terephthalate (PET) as bottom and top triboelectric layers, respectively. Glass was utilized as a supporting layer for the top and bottom triboelectric layers. Aluminum (AI) tapes were used as electrodes and sandwiched between glass and triboelectric layers. The performance of the fabricated TEN G was investigated in terms of open circuit voltage $(\mathrm{V}_{\text{oc}})$ and short circuit current $(\mathrm{I}_{\text{sc}})$. TENG was evaluated by subjecting it to $\sim 0.5$ m/s sliding velocity. The $\mathrm{V}_{\text{oc}}, \mathrm{I}_{\text{sc}}$, power and power density were measured and calculated as $26.63 \mathrm{V}_{\text{pp}},8.08 \mu \mathrm{A},213.84\ \mu \mathrm{W}$ and $8.55 \mu \mathrm{W}/\text{cm}^{2}$, respectively.