Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometrical-Empirical Channel Propagation Model for Human Presence at 60 GHz

Previous efforts to develop simulation and/or measurement-based channel propagation models for the effect of human blockage on millimeter-wave communication systems have yielded important results, but lack either accuracy, generality, or simplicity. To fill that void, in this paper we propose a hybrid geometrical-empirical model for human presence; we refer to it as human presence because reflection from the body before and after blockage occurs in addition to diffraction around the body during blockage (as in previous efforts) is incorporated. Specifically, propagation is modeled as the superposition of the main transmission path and reflected and/or diffracted paths from the body; the geometrical component of the model accounts for the phase of each path while the empirical component accounts for its amplitude. To validate the proposed model and extract its empirical parameters, an exhaustive measurement campaign with 120 blockage scenarios, comprising varying human subjects and transmitter-human-receiver configurations, was conducted; a total of 180,000 channel acquisitions was recorded with our precision, state-of the-art 60-GHz channel sounder. The overall model is shown to be computationally efficient yet general enough to accurately represent a wide range of scenarios.
SCMA System Design With Index Modulation via Codebook Assignment

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) scheme for massive machine-type communications for 5G-and-beyond communication systems. Innovative use of orthogonal frequency division multiplexing with index modulation (OFDM-IM) takes advantage of using tone indices in data transmission. In this study, we propose a novel multiplexing and multiple access scheme, namely SCMA based on OFDM-IM (SCMA-IM). SCMA-IM method can be implemented in uplink NOMA systems in an efficient way with similar computational complexity as SCMA. In this method constellation differences and diversity are obtained via SCMA codebooks (CBs) and the employment of CBs is transferred to index modulation (IM). Performance improvement is achieved via CBs so that the number of data bits carried by indices increases more than one bit with different constellations, and also with the number of CBs assigned. In order to determine the indices used in IM, the receiver structure is simplified by the message passing algorithm and log-likelihood ratio detection process used in SCMA receivers without indexing the receiver. Thus, a new NOMA scheme is obtained by coding with IM. Bit error rate performance of the system is studied for 150% and 200% overloading factors.
Detection of Faults in Solar Panels Using Deep Learning

Renewable energies, carbon neutrality, and sustainable practices have become an important aim for many countries. Solar power generation has drawn much consideration where maintenance of solar panels is an essential task due to the natural and other mechanical circumstances. In this paper, we have proposed a deep learning (DL) approach for the detection of faults in solar panels. The proposed system uses an unmanned aerial vehicle (UAV) equipped with a thermal camera and GPS for acquiring thermal images and localization of the fault in solar panels. An improved version of You only look once (YOLOv3-tiny) is employed as a DL model for the detection of the fault and then transmitted that information using Long-Term Evolution (LTE) to a remote server for visualization. The performance of the proposed model is compared with the current default YOLOv3-tiny, where high performance was achieved by the proposed DL model.
Using Megavoltage Computed Tomography to Estimate Radiotherapy Dose for High-Density Metallic Implants

Streak artifacts in kilovoltage computed tomography (kVCT), originated from metallic implants, are reduced by megavoltage CT (MVCT). This study aimed to establish the image value-to-density table (IVDT) of MVCT and validate dose accuracy. The IVDT of MVCT was established with different physical densities (0.290–19.427 g/cm3) and compared with kVCT for doses. The Acuros external beam (AXB) algorithm was compared with the analytical anisotropic algorithm (AAA) for heterogeneity correction in MVCT. Based on the experimental results, the IVDT of MVCT showed good linearity. Dose overestimations for the metallic hip prosthesis in a water phantom were reduced from 2.6%/2.8% in kVCT_AAA, 1.8%/−0.7% in MVCT_AAA and to −0.7%/−0.3% in MVCT_AXB with 6-MV/10-MV photons, respectively. The predictive formulas were validated and highly correlated with the measured doses ( $R^{2}= 0.9933$ /0.9862 for 6-/10-MV photons, respectively). On average, the dose differences with a metallic eye shield on the Alderson radiation therapy phantom using 6-MV photon were reduced from 3.6% in kVCT_AAA to −1% in MVCT_AAA. In patient studies, the reductions of dose overestimations were from 28.6% and 7.4% to −3.9% with dental fillings, from 13.6% and −9% to −9.1% with the unilateral hip prosthesis, and from −25.3% and −5.9% to −4.3% with bilateral hip prostheses in kVCT_AAA, MVCT_AAA, and MVCT_AXB, respectively. In conclusion, MVCT with the AXB algorithm reduces metal artifacts on kVCT and plans accurate doses.
Information Sources Estimation in Time-Varying Networks

Identifying information sources plays a significant role in network science and engineering. However, existing source identification approaches generally focus on static networks without considering the temporal features of networks. To this end, we comprehensively study the problem of identifying single and multiple information sources in time-varying networks. Specifically, we first represent the time-varying networks by time aggregated graph (TAG), and employ a microcosmic susceptible-infected-recovered (SIR) model to characterize the diffusion dynamics of each node. Second, in the case of single-source, we exploit a TAG-based reverse infection (RI-TAG) algorithm to specify a set of suspect nodes, which not only reduces the scope of seeking the source but also ensures the feasibility of path calculation. Then, a novel computationally efficient algorithm is proposed to estimate the information source and diffusion time simultaneously. Subsequently, in the case of multi-source, we design a multi-source estimation algorithm, which divides the set of infected nodes into various partitions, and then runs our single-source estimation algorithm in each partition. Moreover, we present an effective algorithm to estimate the number of sources. Finally, experimental results on various synthetic and empirical time-varying networks demonstrate the effectiveness of the proposed algorithms.
Harmonic-Based Optimal Motion Planning in Constrained Workspaces Using Reinforcement Learning

In this work, we propose a novel reinforcement learning algorithm to solve the optimal motion planning problem. Particular emphasis is given on the rigorous mathematical proof of safety, convergence as well as optimality w.r. t. to an integral quadratic cost function, while reinforcement learning is adopted to enable the cost function's approximation. Both offline and online solutions are proposed, and an implementation of the offline method is compared to a state-of-the-art RRT$^{\star }$ approach. This novel approach inherits the strong traits from both artificial potential fields, i.e., reactivity, as well as sampling-based methods, i.e., optimality, and opens up new paths to the age-old problem of motion planning, by merging modern tools and philosophies from various corners of the field.
Opportunities for Improving the Learning/Teaching Experience in a Virtual Online Environment

The COVID-19 pandemic triggered unprecedented adoption of online education in universities and digital technologies are increasingly becoming an essential part of the learning and teaching experience. From within an ongoing pandemic ‘lockdown’, this study investigates the current landscape of online education with a focus on the learning and teaching experience in virtual classrooms compared to physical classrooms. We found that instructors typically combine multiple technologies to create a holistic learning experience for students. The benefits of virtual classrooms included self-paced learning, increased opportunities to gain procedural knowledge, diversified communication channels, and greater opportunities for learning analytics. Compared to physical classrooms, the drawbacks of virtual classrooms included the lack of both timely access to all information through a single interface and natural interpersonal interaction. Based on our findings we recommend instructors to remain flexible when selecting technologies and to adapt the level of offline learning to fit their students’ learning styles and abilities. We also identified opportunities for virtual reality technology to mitigate the drawbacks of online education by helping to translate in-class experience to virtual spaces.
Exploring the Sequences of Synthetic Facial Expressions and Type of Problems Solved in a Personal Instructing Agent using Lag Sequential Analysis

This study attempted to determine and visualize the sequences of synthetic facial expressions (SFE) and problems solved in a personal instructing agent named PIA of 75 grade 7 students. PIA is a pedagogical agent that exhibits SFE and provides hints as feedback while students solve an algebra problem. PIA exhibited 8,879 SFE and 83% of which was a happy SFE. Students mostly solved easy problems (63%). Lag-sequential analysis revealed that students tend to stick on the level of difficulty they are comfortable with. When PIA exhibited a happy SFE, it is more likely that the students will answer the problem correctly. However, on the other hand, when PIA exhibited a sad SFE, there is a high probability that PIA will exhibit a cycle of sad SFE, which may eventually lead to the abandonment of the problem. It can be concluded that sequences of SFEs can be utilized to detect students persistence (or lack thereof) in a PIA and can be utilized to improve the software. Design implications to personalization of a learning environment are recommended.
Practice-Oriented Smartphone Security Exercises for Developing Cybersecurity Mindset in High School Students

Advancements in technology and the increase in Internet usage through mobile devices have led to greater visibility of organizations and individuals to cybercrimes. Teenagers being easy targets of these cybercrimes, there is a need to educate them on cybersecurity trends since training students on existing cyberattacks is viewed as a powerful tool to teach cybersecurity. We present a pedagogical approach to train students to identify new threats and respond to mitigate them. This is accomplished through observatory, experiential, and real-life practice-oriented cybersecurity exercises. Seven malicious android applications targeting malware class and phishing, namely Email-Lite-Scare, Shop-Shock-Struck, CyberSafe Practices, Play-Read-Disrupt, Fish-A-Phish, Chat-Phish, and Spy-The-Trojan, have been developed. Psychological learning is emphasized in this approach by exercising the application extensively. The underlying goals of this work are to develop a security mindset, spread awareness on threats associated with smartphone/tablet usage, and to inculcate interest in cybersecurity careers among high school students.
Low-Rank Matrix Factorization Method for Multiscale Simulations: A Review

In this paper, a review of the low-rank factorization method is presented, with emphasis on their application to multiscale problems. Low-rank matrix factorization methods exploit the rankdeficient nature of coupling impedance matrix blocks between two separated groups. They are widely used, because they are purely algebraic and kernel free. To improve the computation precision and efficiency of low-rank based methods, the improved sampling technologies of adaptive cross approximation (ACA), post compression methods, and the nested low-rank factorizations are introduced. <inline-formula> <tex-math notation="LaTeX">${\mathrm {O}}(N)$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\mathrm {O}}(N \log N)$ </tex-math></inline-formula> computation complexity of the nested equivalence source approximation can be achieved in low and high frequency regime, which is parallel to the multilevel fast multipole algorithm, <italic>N</italic> is the number of unknowns. Efficient direct solution and high efficiency preconditioning techniques can be achieved with the low-rank factorization matrices. The trade-off between computation efficiency and time are discussed with respect to the number of levels for low-rank factorizations.
Corrosion inhibitive properties and adsorption behaviour of ethanol extract of Piper guinensis as a green corrosion inhibitor for mild steel in H 2 SO 4

The corrosion inhibition of mild steel by ethanol extract of Piper guinensis (EEPG) has been studied using gravimetric, gasometric and thermometric methods. The results of the study reveals that the different concentrations of ethanol extract of Piper guinensis (EEPG) inhibit mild steel corrosion. Inhibition efficiency of the extract is found to vary with concentration, temperature and period of immersion. Values of activation energy of the inhibited corrosion reaction of mild steel are greater than the value obtained for the blank. Thermodynamic consideration reveals that adsorption of P. guinensis extract (EEPG) on mild steel surface is spontaneous and occurs according to Langmuir adsorption isotherm. Physical adsorption mechanism has been proposed for the adsorption of the inhibitor from the trend of the inhibition efficiency with temperature and the values of some kinetic and thermodynamic parameters obtained.
Music Emotion Visualization through Colour

The possibility of retrieving colours relevant to musical emotions is an emerging multidisciplinary research concept. It can be considered as the initial starting point for music visualization. But due to the novice nature of previous findings and limited availability of emotionally annotated musical databases, obtaining accurate colours for emotions is a computationally challenging task. The purpose of this research is to obtain the most suitable emotional colour for a given song segment considering Russell's Circumplex Emotional Model. MediaEval Database for Emotional Analysis of Music (DEAM data-set) has been annotated in previous studies considering the aforementioned emotional model. In this research, a linear regression approach was used with the WEKA machine learning tool for the DEAM data-set. Effectiveness of the results compared with several linear regression models available in WEKA. Then the predicted emotion can use with Itten's colour model to obtain relevant colour for the emotion. According to the above comparison, the random forest linear regression approach provided the most reliable results compared to other models (accuracy of 81% for arousal and 61% for valence). This study unveiled that Russell's Circumplex model and Itten's colour model can effectively sync emotions in music and colours in arts. Therefore, it can be used to obtain effective music visualizations and musically synced artistic patterns considering emotions.
Composition of the essential oils of wild and cultivated Satureja khuzistanica Jamzad from Iran

The essential oils of the wild and cultivated Satureja khuzistanica Jamzad (Lamiaceae) from Iran were isolated by hydrodistillation in yields of 0.6% and 1.2% respectively. The chemical composition of the essential oils was examined by GC and GC–MS. Twelve components of the wild plants' oil, representing 98.5% of the total oil, and 21 components of the cultivated plants' oil, representing 99.2%, were identiﬁed. The main components of the wild plants' oil were carvacrol (93.9%), eugenol (1.0%), p-cymene (0.8%) and thymol (0.6%). The major constituents of the cultivated plants' oil were carvacrol (80.6%), p-cymene (4.8%), myrcene (1.5%), γ-terpinene (2.1%) and terpinene-4-ol (2.1%). In both the wild and cultivated plants the main component of the oil was carvacrol. Copyright © 2004 John Wiley & Sons, Ltd.
Ferroelectric phase transition in triglycine selenate: an interpretation based on its structure and its comparison with triglycine sulphate

Triglycine selenate (TGSe) is a hydrogen-bonded ferroelectric, which undergoes a structural phase transition at T c = 295 K. It is isomorphous to triglycine sulphate (TGS) which is a very well studied order–disorder ferroelectric extensively used in infrared detection. The crucial molecular unit from the point of view of ferroelectric phase transition in these crystals is the group of one of the three glycine ions GI, which has two equivalent positions in an asymmetric unit. This group gets disordered between its two equivalent positions for temperatures above T c . The potential energy of this group as a function of the distance between its equivalent positions was modelled, and the phase transition in TGSe was interpreted using “the coupled anharmonic oscillator model” of ferroelectrics proposed by Y. Onodera. Similarities as well as differences between the ferroelectric phase transition in TGS and TGSe are discussed.
Electroanalytical Characterization of Verapamil and its Voltammetric Determination in Pharmaceuticals and Human Serum

Abstract The electrooxidative behavior and determination of Verapamil HCl, one of the class IV anti‐arrhythmic agent, on a glassy carbon disc electrode were investigated for the first time by using cyclic, differential pulse (DPV), and Osteryoung square wave voltammetry (OSWV). Verapamil showed an irreversible oxidation behavior at all pH values and buffers studied. From the electrochemical response, the main oxidation step was found to be related to the methoxy group on the phenyl ring. DPV and OSWV were used to generate peak current versus concentration curves for verapamil. A linear response was obtained in the range comprised between 8×10−7 and 1×10−4 M for both techniques with detection limit of 1.61×10−7 M for DPV and 1.33×10−7 M for OSWV. The repeatability and reproducibility of the methods for all investigated media (such as supporting electrolyte and serum samples) were determined. Precision and accuracy were also checked in all media. The methods were proposed for the determination of verapamil in dosage forms adopting both DPV and OSWV modes. The methods were extended to the in vitro determination of verapamil in spiked serum samples. No electroactive interferences from the endogenous substances were found in human plasma.
Pencil and Shaped Beam Patterns Synthesis Using a Hybrid GA/l₁ Optimization and Its Application to Improve Spectral Efficiency of Massive MIMO Systems

The synthesis of pencil beam and arbitrarily shaped beam patterns of linear antenna arrays (LAA) using reduced number of antenna elements attracts the attention of researchers in recent years. In this paper, a hybrid beamforming technique based on the combination of the genetic algorithm (GA) optimization technique and the <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> minimization method denoted as (GA/<inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>) is introduced for LAAs synthesis. The proposed GA/<inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> beamforming technique optimizes both the elements excitations and interelement spacing to synthesize the desired LAA pattern with a minimum number of antenna elements. The GA/<inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> technique provides an excellent approximation to the desired radiation pattern with high accuracy and low complexity (less number of iterations and computational time) compared to the other synthesis approaches introduced in the literature. In addition, as an application of this work, the proposed GA/<inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> technique is used to build up a proposed hybrid precoding and beamforming (HP-BF) structure for Massive Multi-input Multi-output (M-MIMO) systems. In this structure, the transmit antenna array is synthesized for maximum gain realization using the existing number of antenna elements. In the HP-BF structure, the proposed GA/<inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> technique is used to make full use of the existing transmit array elements to synthesize the radiation pattern of much larger size and higher gain arrays without the need for additional elements. Thereby, significant savings in the number of antenna elements and their corresponding radio frequency (RF) chains are achieved, which reduces the system complexity. In addition, the array gain maximization will maximize the received signal to noise ratio (SNR) giving rise to higher system performance in terms of spectral efficiency (SE) and power utilization.
Production of colloidal organic carbon and trace metals by phytoplankton decomposition

Colloids are important intermediates and can considerably influence the biogeochemical cycling of organic carbon and trace metals in aquatic systems. Previous studies have determined the release of carbon and trace elements from decomposing phytoplankton debris, but the degradation of biogenic particles to colloids and their significance in metal and carbon cycling remain unknown. In this study, we measured the release of carbon and trace elements (Cd, Cr, Se, and Zn) from the debris of two phytoplankton (diatom Thalassiosira pseudonana, and dinoflagellate Prorocentrum minimum) into a colloidal phase (operationally defined as 5 kDa–0.2 mm). In general, Cd, Se, and Zn were released at a faster rate and Cr was released at a slower rate than the release of C from the decomposing debris into the dissolved phase (<0.2 mm). Release of trace metals appeared to closely follow C release. The release rate coefficients of Cd, Cr, Se, Zn, and C in the two phytoplankton debris with microbial addition were 1.67–1.68 d−1 for Cd, 0.147–0.239 d−1 for Cr, 0.712–0.845 d−1 for Se, 0.765–1.14 d−1 for Zn, and 0.443–0.512 d−1 for C. The percentages of trace elements (Cr, Se, Zn) and C associated with the colloidal phase were relatively high within the first 5 d ofdecomposition. After 5 d, the fraction of colloidal trace elements in the <0.2–µm dissolved phase was 5–10% for Cd, 13–52% for Cr, 13–28% for Se, 14–30% for Zn, and 16–41% for C in the diatom decomposing experiment; and 4–9% for Cd, 15–62% for Cr, 15–31% for Se, 3–27% for Zn, and 22–38% for C in the dinoflagellate decomposing experiment, respectively. The partitioning of metals in the colloidal phase was not greatly affected by the microbial activity. Although the overall percentage of organic carbon release was related to that of metals from decomposing debris, no significant correlation between the percentages of colloidal metals and colloidal organic carbon was observed, implying that organic C and metals have different partitioning behavior during their release from decomposing phytoplankton debris. Our study demonstrated that the decomposition of biogenic particles may contribute considerably to the production of colloids in marine environments.