Miracle-dz/UniFAR-FTUs huggingface dataset:
Long term simulations for bi-static radar and test bench design

Instruments like CONSERT aboard Rosetta mission, is a technology capable to study the interior of small bodies (asteroids and comets) in the Solar System. Nowadays the knowledge of internal structure is based in remote sensing and theoretical modeling. This approaches cannot give enough details to completely unveil the evolution of the body. Bi-static radar instead offers this possibility. This type of radar demands high accuracy time synchronization between both electronics. One way to improve science is to understand the clock generation signals and how this will affect the measurement to propose compensation techniques. In this paper we present first the bi-static radar concept, we then expose the time analysis and propose a modification of the transponder structure to measure the clock drift between both clocks. We develop a Kalman filter to improve the estimation and compensate the time error. To verify our models and evaluate them, we present a test bench design to collect data from real clocks.
Assessment of plug-in electric vehicles charging impacts on residential low voltage distribution grid in Hungary

The growing concerns over greenhouse gas emissions coupled with the improvement of electric vehicle technology have encouraged the electrification of the transportation sector. Plugin electric vehicles (PEVs) are becoming an alternative to vehicles powered by an internal combustion engine. This leads to environmental benefits, but these PEVs represent new challenges facing the power grid. Therefore, the power grid must be prepared for these challenges. This paper provides a comprehensive study of PEVs charging impact on the low voltage distribution grid. For this purpose, a typical Hungarian low voltage grid was modeled in DIgSILENT Power Factory. Two charging scenarios have been studied, uncoordinated charging and delayed charging. Three penetration levels of PEVs are considered in this paper as follows, 20%, 40%, and 60% Time sweep analysis is used to investigate transformer loading, feeders loading, voltage deviation and system total losses. The results show that at 60% penetration level with uncoordinated charging, both the distribution transformer and feeders thermal loading violation is experienced. On the contrast, with the delayed charging, all the grid components function at their normal rating at all penetration levels and no network upgrade is required.
A deep neural network based on kernel function and auto-encoder for bearing fault diagnosis

Intelligent diagnosis technology has been widely applied in the classification of rotating machinery condition. The determination of the effective features from raw vibration data and the provision of accurate fault diagnosis results are important tasks for a bearing fault diagnosis. In this paper, a deep neural network based on Gaussian radial basis kernel function and auto-encoder (AE) is proposed. Firstly, a new AE is constructed with a kernel function to enhance the feature learning capability. Subsequently, a stacked AE network is developed with one new AE and T AEs. Finally, an error back-propagation algorithm is used to fine-tune the model parameters and then the deep characteristics are used to train the intelligent diagnosis model. Experimental data for an aircraft-engine inter-shaft bearing are used to verify the method. The results show that the proposed method has a better feature extraction capability, requires fewer iterations, and has a higher accuracy than standard methods using a stacked AE.
A High-Performance Plasmonic Nanosensor Based on an Elliptical Nanorod in an MIM Configuration

In this paper, we propose and numerically investigate a surface plasmon resonance-based nanophotonic biosensor structure providing an extremely high sensitivity and transmission efficiency with a high figure-of-merit (FOM). The sensor structure consists of a 1-D metalic grating, rectangular nanoslabs, and single elliptical nanorod (NR) in a metal–insulator–metal configuration. The spectral response of the proposed sensor is evaluated numerically by using the finite-difference-time-domain computation method for its operation in the near-infrared region of the spectrum. The results show strong enhancement and very high spectral sensitivity of the local electromagnetic fields near the tip of the NR. The performance parameters of the nanosensor are then optimized as a function of the shape, size, cavity length, and other parameters of the nanostructures. The optimized sensitivity of this device is found to be 1960 nm/RIU with a transmission efficiency of 82.30% and an FOM of 42.26, which are very promising as compared to those of the contemporary nanosensors. As a specific biosensing application, the proposed sensor device is numerically simulated with different concentrations of satellite tobacco mosaic virus (STMV) solutions and found to be capable of sensing the virus with extraordinary optical sensitivity up to 0.245 nm/(g/l) making the nanosensor sensitive even to the smallest of changes in concentrations of the STMV solutions. This highly sensitive nanosensor may have aspiring applications in biomedical and environmental sensing mechanisms and nanophotonic integrated circuits.
A Distance-Based Mode Selection Scheme for D2D-Enabled Networks With Mobility

In this paper, a new mode selection scheme for device-to-device (D2D)-enabled cellular communications with mobility is proposed and evaluated. The new scheme is based on an average threshold D2D distance between two given users to trigger the D2D mode. Besides, a tractable analytical framework considering relative movement for mode selection in moving D2D scenarios is presented. Under the proposed model, the expressions of the average threshold D2D distance, the probability of using D2D mode, the successful transmission probability, and the minimum D2D mode residence time are derived, which are the key parameters to evaluate the performance of D2D-enabled networks. Monte Carlo simulations are conducted to confirm the analytical results and the advantages of the proposed scheme over previously proposed ones. They show also the importance of studying the dynamic performance of the system in terms of user mobility as articulated in the calculated minimum D2D residence time.
Development of a new EMAT for multi-helical SH guided waves based on magnetostrictive effect

Electromagnetic acoustic transducers (EMATs) have the advantage of non-contact and have been widely used in nondestructive testing (NDT). However, the propagation of the guided wave generated by present transducers is either concentrated with narrow beam directivity or omni-directional with disperse energy. To overcome these disadvantages, a new EMAT is proposed to generate multi-helical shear horizontal (MHSH) guided waves. The waves can propagate in multiple helical angles within a certain range while the energy is still concentrated. In this paper, the excitation and reception process of the guided wave are modeled based on the magnetostrictive effect, and then Finite Element Method (FEM) is applied to confirm the models and to further study the wave radiation pattern. Series of experiment are carried out to evaluate the performance of the proposed EMAT. Finally, the influences of two key design parameters on the beam divergence angle are discussed.
Fast and low power optical CO2-sensors for medical application: New sensor designs for main- and side-stream CO2-sensors are presented in comparison with state of the art capnometers

The measurement of carbon dioxide in medical application is well established for a long time. Medical key parameters such as the end tidal CO2 concentration are well known and very useful in day-to-day clinical work. It is a non-invasive method for monitoring the patient's pulmonary function in emergency or intensive care as well as in anesthesia. The combination of a respiratory CO2-measurement with a volumetric breathing measurement enables the detection of the minute volume of CO2. This is an important parameter in respiratory medicine. The combination with an additional respiratory oxygen measurement is used for indirect calorimetry and enables the characterization of the metabolism. Such measurement methods are used in a wide range of applications, for instance in medicine, home car as well as in sports medicine. Although capnometers are state of the art for a long time and well developed, this paper shows a new design approach to improve sensor parameters such as power consumption, start-up-time, measurement time and robustness. Prototype sensors are realized for main- and side-stream application. CO2-measurements of the newly developed sensors are presented in comparison with results of commercial devices.
Potential implementation of subject areas in Malaysia's research assessment Co-word analysis study

In the education environment, subject area is one of important matter since primary school to higher learning institution. Subject area exists in other developed countries for research assessment. But, apparently, some of country didn't apply the subject area in its research assessment's framework. It is important to have the subject area in the assessment's framework in order to know the development and progress of subject area. Therefore, in this study, one of mechanism used to produce potential subject area is by manipulated selected bibliometric element which called as co-words analysis. This study is focused on 20 year time span started from 1995 and 2015 for Malaysian's researcher's publication. All data were extracted from selected database, Scopus. In Scopus, data were sorted from the highest cited publication. We used a powerful tool namely as Vosviewer to help us to extract the occurrence and potential words that hidden in publication. As a result, our result produced 7 clusters of potential subject area. In this study, we performed correlation analysis between our clusters with selected ranking in order to relationship between these two variable. The analysis results gave us a valuable information that there are potential to implement the subject area in the assessment. Another valuable information from our result is the potential to combine two method in assess the assessment, which is by using the bibliometric element with human judgement. The details for our research were divided into sections.
A soft sensor for the estimation of ionic electroactive actuator motion based on deep learning

In this paper, a black-box model, suitable for being used as a Soft Sensor for an Ionic Polymer-Metal Composite, is introduced. Applications of such materials, in many fields, such as bio-inspired robotics, medicine, and aerospace, have been reported. The proposed Soft Sensor is a data-driven Nonlinear Finite Impulse Response model, implemented by using a Deep Neural Network, where the output is estimated only on the basis of input past samples. The model has been selected with the aim of eliminating any hardware position sensor and, therefore, reducing the complexity of the positioning system.
ECT image reconstruction based on alternating direction approximate newton algorithm

As a simple linearization of a highly nonlinear problem of image reconstruction in Electrical Capacitance Tomography (ECT), this approach suffers from drawbacks such as mismatches of position and size of the objects being imaged. To solve the ill-posed and nonlinear inverse problem of ECT image reconstruction, an alternating direction approximate Newton (ADAN) method is developed. The algorithm is based on the alternating direction method of multipliers (ADMM) and an approximation to Newton's method in which a term in Newton's Hessian is replaced by a Barzilai-Borwein (BB) approximation. The numerical experiments show that ADAN has the advantages of fast convergence speed and high imaging accuracy. Compared with the Landweber method, Newton's method and conjugate gradient algorithm, ADAN algorithm is a more stable process, image reconstruction quality is improved significantly. Especially for the cross model and sunrise model, compared with Newton iterative algorithm, image relative error reduced by 0.585 and 0.369, image correlation coefficient increased by 0.498 and 0.431.
The OLCF GPU Hackathon Series : The Story Behind Advancing Scientific Applications with a Sustained Impact

The OLCF GPU Hackathons are a one-week code-development/learning event to better enable attendees to utilize GPUs. It only took three years to grow from a ``Let's give this a try''-event to a repeatedly copied format with several spin-offs that inspired HPC centers around the world. Sticking to a few fundamental principles---work on your own code, learn from your mentors just what you need and when you need it, stay flexible in achieving your goal---the week-long hackathon format created at Oak Ridge Leadership Computing Facility (OLCF) has been just the spark needed by many groups of scientists to light the fire of a wider GPU adoption in leading-edge as well as university-scale HPC environments. Most interestingly, the format enabled both ends of the experience spectrum---graduate students vs. postdoc fellows---the same kind of progress and chance of success.
Hybrid Precoding With Rate and Coverage Constraints for Wideband Massive MIMO Systems

Hybrid architecture is recognized as complexity and cost effective for the implementation of massive multi-input multi-output (MIMO) systems, where analog precoder is used to form narrow beams toward users for data transmission with reduced number of radio frequency chains. However, besides transmitting the user-specific data, the system also needs to broadcast control signaling intended for all users simultaneously, which requires wide beams to ensure the coverage. In this paper, we study hybrid precoder design for downlink space-division multiaccess and orthogonal frequency-division multiplexing wideband massive MIMO systems, aimed at minimizing the total transmit power of the base station, subject to both the coverage constraint of signaling and data rate requirements of users. We first derive the coverage probability of massive MIMO systems with hybrid architecture under general spatially correlated channels, based on which an alternating optimization algorithm and a low-complexity algorithm with channel statistics based analog precoder are, respectively, proposed to optimize the hybrid precoder. Simulation results show the performance gain of the proposed hybrid precoder over the method that only considers data rate requirements or coverage constraints and also demonstrate the necessity of separating data and signaling planes at high frequencies.
Fiber Bragg grating based oil film pressure measurement in journal bearings

We proposed and demonstrated the measurement of the oil film pressure of a journal bearing based on Fiber Bragg Gratings (FBGs) for the first time. FBG packaged by rubber was inserted into the bearing, which was used to sensing the oil film pressure, and the temperature in the oil film was measured at the same time. The experimental results of the oil film pressure were compared with the theoretical results for the shaft rotation speed from 140 r/min to 1400 r/min, showing its capability of doing our sensing scheme. FBG is expected to have the potential requirement for the oil film pressure measurement with a strong electromagnetic environment.
Convolutive blind separation of bearing faults using peak-based wavelet transform

In the health monitoring of rotating machinery, there often coexists multiple fault sources; thus a multi-source composite fault signal will be collected by sensors. Moreover, the vibration signal of rotating machinery is usually submerged by the environmental noise in practical engineering occasions. These increase the difficulty of separation from the composite fault signal. However, the effect of traditional wavelet analysis method for denoising to enhance features of fault signal is limited, because of the energy dispersion of fault signals. To overcome these problems, we propose a convolutive blind separation method based on peak transform using wavelet analysis. With this strategy, the original signal energy in high frequency is concentrated into that in low frequency band, through some non-linear transformations. Next, the large wavelet coefficients, which represent the main features of the signal, are retained to reconstruct a feature-enhanced signal. The convolutive fixed-point algorithm based on maximization of non-Gaussianity, is carried out to separate source signals from the feature-enhanced signal. Experimental results of rolling bearing show that the proposed strategy can effectively separate source fault signals from the composite signal and detect the fault features.
Salient object detection in digital images based on superpixels and intrinsic features

Human visual system research and object detection in digital images have taken great relevance in the last years due to the different applications where it can be used, such as watermarking techniques to protect information, auto-focus on digital cameras, image compression, auto navigation, etc. This paper describes a saliency object detection method that uses intrinsic features of digital images: intensity, color, and texture. Also, Super Pixel Linear Iterative Clustering (SLIC) is employed to find the saliency object. The results have shown an accurate detection of the saliency object in digital images via comparison with well-known methods of the state-of-the-art. The results were evaluated through a subjective test using Mean Opinion Score (MOS).
Cross-Cultural Adaptation of Research Tools: A Study on the Cultural Intelligence Scale Adaptation in Slovenian

The article examines the cross-cultural transferability of a widely accepted cross-cultural assessment tool – the Cultural Intelligence Scale (CQS) – using research conducted in Slovenia and insights from the American and Slovenian researchers who translated the tool into Slovene and adapted it for use in Slovenia. In the context of a qualitative focus group based study, the researchers look at the specific characteristics of CQS perceptions within the Slovenian sample (two focus groups – one in the capital and the other in the south of the country) and identify barriers to these perceptions and the specific characteristics of the perceptions of Slovenian citizens regarding cross-cultural interaction.
Probabilistic assumptions of software reliability growth models

The article describes research of probabilistic assumptions of software reliability growth models, that the time between two successive moments of defects detection is a random variable with a known distribution law. Experimental researches of the distribution law for time series of defect detection in 500 software systems has been performed. For increase the authenticity of the results, software systems written in 24 modern programming languages with different subject areas has been selected. The hypotheses that the series conform the normal, exponential, and uniform distribution laws, as well as the Poisson distribution, has been tested. The results of the experiment showed, that only 21% of tested software systems have one of the above listed time distributions between two consecutive time moments of detection of defects. Thus, the assumptions of the reliability models about a distribution law are not performs for 79% researched software systems. This fact can explain the absence of a single universal reliability mod-el, which with reasonable accuracy could describe the dynamics of defect detection in all software systems. The results of the research also testify about the necessity to revise the conceptual basis of the modern theory of software reliability.
Optimally Probing Channel in Opportunistic Spectrum Access

This letter studies an opportunistic communication system in which a transmitter probes <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> out of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> two-state Markov channels and accesses one channel to communicate with a receiver each time. The objective is to maximize the transmitter’s throughput over a finite time horizon. A joint probing and accessing policy is proposed in which the transmitter probes <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> channels from the second best and accesses the best if all <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> channels are found to be bad. Further, the policy is theoretically shown to be optimal for the case of <inline-formula> <tex-math notation="LaTeX">$k+2=N$ </tex-math></inline-formula> and conjectured to be optimal for <inline-formula> <tex-math notation="LaTeX">$k+2<N$ </tex-math></inline-formula> under certain conditions. The results show that the optimality of the proposed policy has a tight coupling with the initially available probabilities of those channels.
Energy Efficient Learning-Based Indoor Multi-Band WLAN for Smart Buildings

Broadband Internet access in the building has fundamentally changed almost every aspect of our lives. As the growing use of indoor portable systems and devices, saving their energy consumption becomes an interesting and important issue for smart buildings. Recently, multi-band WLAN where 2.4/5-GHz and 60-GHz bands coexist is a promising solution to offer both ultra-high speed and robust wireless connections. For a multi-band WLAN end device, detecting the available service areas in an energy efficient way is of great importance. In the existing systems, the RF units of device need to be turned on and kept listening all the time, which leads to substantial energy consumption overhead. To solve this problem, this paper proposes an energy efficient learning-based indoor multi-band WLAN system, in which the end device predicts the distinct service areas by learning the influences of reflected waves in buildings. We have performed extensive experiments in different indoor environments, and the evaluation results demonstrate that the proposed mechanism could substantially improve the performance compared with the existing approaches.
Resource Management for Device-to-Device Communication: A Physical Layer Security Perspective

As a promising technology for 5G networks, device-to-device (D2D) communication can improve spectrum utilization by sharing the resources of cellular users (CUs). However, this is at the cost of generating interference to the CUs. While most existing works focused on eliminating or suppressing the interference between the D2D links and the CUs, such interference could in fact be beneficial for improving the security of cellular communication. Specifically, D2D links may, in return for reusing cellular resources to achieve high spectral efficiency, act as friendly jammers and help the CUs against malicious wiretapping. To reach this win-win situation, D2D resource management has to be designed from a physical layer security perspective. In this paper, we consider the joint optimization of power allocation and channel assignment of the D2D links and the CUs with the aim to provide security to the CUs and improve the spectral efficiency of the D2D links simultaneously. We focus on the challenging downlink resource sharing problem and investigate both single-channel and multi-channel D2D communications. The resulting resource management design problems turn out to be difficult nonlinear mixed integer problems. Nevertheless, by exploiting the inherent properties of the formulated optimization problems, we are able to analytically characterize the optimal power allocation of the CUs and D2D links, and develop efficient methods for joint optimization of their channel assignments. Simulation results show that the proposed resource management policies outperform several baseline schemes and can indeed achieve the desired twofold objective.