Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Phase-Unwrapping Method for Unwrapped Phase based RFID Localization

The localization accuracy of RFID tag is limited by phase noise and unsuccessful phase unwrapping. In this paper, a preprocessed method of phase data is proposed to improve the localization accuracy of the nonlinear optimization based RFID localization method. Specifically, the principle of phase change prediction is proposed and designed, by which simultaneous phase unwrapping and phase noise reduction filtering are carried out. Compared with the existing localization method, the proposed method is that it can solve the problem of unwrapping failure and reduce the influence of phase noise. The experimental tests indicate that the proposed method can improve the localization accuracy by about 30% compared with the SOTA methods.
An Analysis of Engineering Stream Segregated Simultaneous Units Converter

The availability of an accurate and efficient unit converter has always been a pressing issue. With the advancement in technology, there has been a surge in the number of digital unit converters available. However, the unit converters available currently have certain limitations, such as the need for simultaneous unit conversions, resulting in continuous tab-Switching to be able to gain an accurate understanding. Thus, a digital unit converter is devised, allowing users to convert units of measurement simultaneously. HTML is a popular web development language, coupled with CSS and JavaScript, making it an ideal choice for developing this project as a web service. This solution converts numerous measurements and quantities with the highest accuracy. The uniqueness of this unit converter is derived from the simultaneous conversion of units and their stream-wise segregation/ segregation according to the chosen stream. The overarching goal in engineering this user-Friendly multi-unit platform is to convert units with minimum effort and error.
IoT-based Smart Home Automation Systems for Energy Conservation

This research study explores the numerous components of smart home automation systems, including actuators, sensors, and controllers, and the integration of these components with Internet of Things (IoT) technology. To demonstrate the system's ability to reduce energy consumption, this study involves an experimental analysis of a prototype system. In order to scientifically evaluate the system's performance, the test data must be statistically analysed. The results of this research study have the potential to contribute towards the enhancement of energy efficiency and sustainability of the proposed system by offering insights for the construction of sustainable residential settings. This study investigates the effects of the residential environments. The use of complex and innovative systems has the potential to significantly improve the efforts to save energy and promote a sustainable future.
ENVIRONMENTAL ASSESSMENT OF DISSOLVED HEAVY METALS IN VLORA BAY, ALBANIA

ABSTRACT This study was carried out in Vlora Bay, Adriatic Sea, to evaluate the environmental status and estimate the pollution of different contamination sources in this area. Vlora Bay is a half-closed bay, positioned from the mouth of the Vjosa River delta to Karaburuni Peninsula. The total dissolved metal concentrations (Cd, Cu, Cr, Fe, Hg, Mn, Ni, Pb, Zn) of seawater samples were analyzed by graphite furnace AAS after the pre-concentration with CHELEX-100. The samples from 8 stations of Vlora Bay coastal areas, Albania, were collected during February 2012. There is little information available regarding the nu-trient and heavy metal contamination level of Vlora Bay. Our analytical data were subjected to statistical analysis. Good correlations between Ni and Pb; Mn and Zn; Fe and Zn; Hg and Zn were found. Statistical analysis of the data was carried out using EXCEL and MINITAB-15 package programs. Based on the heavy metals concentrations and UK standards for the protection of saltwater life (SACs Pro-ject 2001) [32], and Sweden Environmental Quality Crite-ria (2002) [33], the seawater quality of Vlora Bay is quite good. The levels of trace metals were found to be lower than the Swedish guidelines (Cu, Cr, Mn, Ni and Zn) for coastal water. The seasonal weather conditions of Febru-ary were characterized by heavy rains, strong wind and high seawater waves. These conditions are distinguished as the main factors that affect the concentration of trace elements in coastal water of Vlora Bay during our moni-toring period.
Identifying Breast Cancer's Nuclei Features for Machine Learning Prediction and Risk Factor Analysis

Breast cancer is a leading cause of cancer-related deaths in women worldwide. Early detection and diagnosis of breast cancer can significantly improve the chances of successful treatment and survival and thus we wanted to prove this by using several datasets and seeing whether machine learning had a part to play for early diagnosis based on its accuracy and practicality. One dataset showcases the presence of malignant tumors based on the dimensions of a breast cancer nuclei, the other shows the presence of breast cancer based on the risk factors held by a person such as their BMI. The results showed that SVM was the best machine learning algorithm for the dataset with the breast nuceli and had an accuracy of over 98% with best features whereas the dataset for the risk factors had an 84% accuracy with logistic regression. For the features that determined breast cancer the most, from the first it was the: concavity, area, perimeter and radius that showed the highest deterministic features for breast cancer whereas for the risk factors, it was: fat and working status. Overall, our study demonstrates the potential of machine learning algorithms for predicting the risk of breast cancer and highlights the importance of early detection and diagnosis in improving the chances of successful treatment and survival.
A Geomorphic Interpretation of Shoreline Change Rates on Reef Islands

ABSTRACT Mann, T. ; Bayliss-Smith, T. , and Westphal, H. , 2016. A geomorphic interpretation of shoreline change rates on reef islands. Recent-past shoreline changes on reef islands are now subject to intensified monitoring via remote sensing data. Based on these data, rates of shoreline change calculated from long-term measurements (decadal) are often markedly lower than recent short-term rates (over a number of years). This observation has raised speculations about the growing influence of sea-level rise on reef island stability. This observation, however, can also be explained if we consider two basic principles of geomorphology and sedimentology. For Takú Atoll, Papua New Guinea, we show that natural shoreline fluctuations of dynamic reef islands have a crucial influence on the calculation of short-term rates of change. We analyze an extensive dataset of multitemporal shoreline change rates from 1943 to 2012 and find that differing rates between long- and short-term measurements consistently reflect the length of the observation interval. This relationship appears independent from the study era and indicates that reef islands were equally dynamic during the early periods of analysis, i.e. before the recent acceleration of sea-level rise. Consequently, we suggest that high rates of shoreline change calculated from recent short-term observations may simply result from a change in temporal scale and a shift from geomorphic equilibrium achieved over cyclic time toward an apparent disequilibrium during shorter periods of graded time. This new interpretation of short- and long-term shoreline change rates has important implications for the ongoing discussion about reef island vulnerability, showing that an observed jump from low to high rates of change may be independent from external influences, including but not limited to sea-level rise.
An Approach for the Supervision and Management of Power for the Solar Photovoltaic and BESS Based DC Microgrid Integrated System

The paper presents the supervisory monitoring of sources like solar photovoltaic cell and battery energy storage system (BESS) for the various loading conditions in the DC micro-grid integrated system. The non-synchronization of power, control of frequency etc. are the advantages of DC- micro grid over AC system. The conventional control techniques like droop control, dc bus signaling control were unable to provide the robust solution in DC micro grid integrated system. This paper proposes a supervisory control technique for a microgrid comprising a number of PVs and batteries. The dc bus voltage is used as a measure for supply-demand power balance. Depending upon the magnitude of dc bus voltage, the control is divided into levels. The proposed technique divides dc bus voltage into three levels: Level 1, Level 2 and Level 3. Under normal operating conditions, there is a communication between different terminals of the microgrid, the microgrid operates in Level 1. The dc bus operates in Level 3 in case of abnormal conditions like sudden shedding or increase in loads or faults in PVs etc. Simulations are performed in MATLAB for validating the proposed research for the estimation of the various performance parameters.
Detection and Classification of Copra Meat Dryness Using Faster Region-Based Convolutional Neural Network with Inception v2 Architecture

Sun Dried coconut meat, also known as Copra is used mainly on coconut oil extraction and its by-products for livestock use. The grade of copra is set by the Philippine National Standard for Copra and one of the criteria for the grading/classification is dryness of the copra meat as indicated by the meat color. This study investigates the use of Faster Region-Based Convolutional Neural Network with Inception v2 model in detecting and classifying the level of dryness of copra images as either Optimally Dry, Under-Dry or Over-Dried levels of copra meat. The researchers trained the model and tested it using a validation set which yielded an accuracy rate of 90% for Under Dry, 92% for Optimally Dry copra and 81% for Over Dry, with an overall accuracy rate of 87.67%.
Optimisation Of Lasing Metasurfaces With Symmetry Constraints On The Modes

Metasurfaces that are infiltrated with active materials, such as dyes or up-converting nano-particles, offer many advantages for novel lasing systems, such as high directionality and low lasing threshold. However, such metasurface lasers require the optimisation of two metasurface modes that are resonant at both the absorption and lasing wavelength bands. The absorption band also requires a weak angular dispersion to allow for a large acceptance angle for the absorption. Here we propose and numerically demonstrate a novel technique to quantity and optimise these bands. Our technique is based on symmetry analyses of the eigenmodes of the metasurface and allows us to optimise mode volume into the active media, as well as the directionality of the lasing emission.
Impact of Blockage on the Sensing Performance Using Distributed MIMO Architecture

This paper considers the impact of blockage in distributed radar systems. It is shown, using an integrated radar and communication setup, that monostatic sensing systems are preferred over bistatic systems when the performance is blockage limited. Furthermore, the GLRT and the multistatic detector are compared, where it is shown that the multistatic detector is preferred under blockage, despite being SNR suboptimal.
On Some Properties of the Sandpile Model of Self-Organized Critical Systems Sobre Algunas Propiedades del Modelo de la Pila de los Sistemas Críticamente Auto Organizados

In this paper we analyze the sandpile model proposed by Bak, Tang and Wiesenfeld as the canonical example of self-organized critical systems. We find that the sandpile-model can reproduce staircase graphics and also that the distribution of large avalanches recurrence times in this model is log-normal. We also find that the slope of cumulative activity characterize a “province” of generation of avalanches in the same way as the seismic or evolutionary provinces do.
Data-Driven Approaches for Distribution Transformer Health Monitoring: A Review

Distribution transformers are the key components in distribution systems to maintain reliability of the system operation and reduce power outages. In this paper, a literature review is conducted on data-driven methods of the distribution transformer health monitoring by classifying the research streams and emphasizing advancements in machine learning, artificial intelligence and hybrid approaches in this area. The significance of data-driven methods is highlighted, demonstrating their ability to overcome traditional analytic limitations by providing real-time monitoring, prediction, and adaptability. As the distribution system continues to expand due to the increasing penetration of distributed energy resources (DERs) and electric vehicles (EVs), data-driven techniques emerge as a dependable and adaptable option for the effective transformer health monitoring.
Distributed ISAC Systems – Multisensor Radio Access and Coordination

Integrated sensing and communication (ISAC) qualifies mobile radio systems for detecting and localizing of passive objects by means of radar sensing. Advanced ISAC networks rely on distributed infrastructure, multisensor uplink and downlink, or meshed sidelink access. In this way, ISAC develops into a MS-MIMO (multisensor multiple input multiple output) network which constitutes a distributed MIMO radar network. Multisensor link coordination and synchronization are becoming crucial. Many multisensor access and signaling techniques find their communication counterpart in multiuser MIMO and cooperative multilink communications (CoMP) and can be adopted from there.
Environment Propagation Scanning using Identical UHF RFID tags configuration

This paper presents an experimental setup utilizing a set of 39 UHF RFID tags to scan the propagation channel environment. The main focus of the study is to exploit the power profiles obtained from each tag, utilizing a distance and power variation technique to get access to the reciprocal propagation channel. By examining the power profiles of these 39 tags positioned in a row and moving along a rail, the study demonstrates this capability to characterized the propagation channel. The knowledge of this channel called α is of interest for further advanced applications aiming to enhance read-range.
Optimizing the 12T Hybrid 1-Bit Full Adder Circuit for Low Energy Applications

This paper introduces a novel design for a 1-bit 12T full adder, utilizing a hybrid approach. The design was implemented using Cadence Virtuoso software with 90-nm technology. The performance metrics, including propagation delay, power consumption, power delay product (PDP), and layout area, were assessed and compared against various established designs like transmission gate (TG) adder, complementary pass-transistor logic (CPL) adder, transmission function adder (TFA), hybrid pass-logic (HPL) adder, and others. When operating at a supply voltage of 1.2-V, the proposed design demonstrated notably low average power consumption (61.9 nW), coupled with moderately low delay (427.495 ns) and an impressively compact layout area (99.225 Comparative analysis with existing adder designs revealed substantial enhancements in terms of speed, power efficiency, and energy consumption.
Chiral Harmonic Generation by Quasi-Bound States in the Continuum

Quasi-bound states in the continuum (quasi-BICs) of dielectric metasurfaces, selectively coupled to circularly polarized waves, provide maximum linear-optical chirality. We consider how they can empower chiral nonlinear-optical processes. We demonstrate that using arbitrarily polarized pumping wave one can generate circularly polarized harmonics at the chiral quasi-BIC wavelength. Conversely, pumping at such a wavelength results in giant harmonic circular dichroism. We design maximum-chiral metasurfaces based on different semiconductor materials and simulate generation of second and third harmonics. We compare various scenarios and reveal feasible limits of generation enhancement and chirality.
Optimal Duty Ratio Assisted PFM Control for VHF Isolated Class E DC-DC Converter

This article presents an optimal duty ratio assisted pulse frequency modulation control for very high frequency isolated class E dc–dc converter to achieve both continuous power flow and optimal switch timing. The continuous power flow significantly reduces output ripple of the converter, while the optimal switch timing guarantees consistent zero-voltage-switching (ZVS) and minimized reverse conduction loss. To pave the way for the proposed control, a resonant fitting model is established for deriving the optimal duty ratio and output power under dynamic switching frequency. The optimal duty ratio is calculated according to zero-crossing points of the switching node voltage (vds), and it aims to turn on the power switch once vds reaches zero. This ensures consistent ZVS while preventing reverse conduction of gallium nitride (GaN) high electron mobility transistor (HEMT) when the switching frequency changes. Furthermore, the proposed control is integrated with on/off modulation to extend load range of the converter. A 10–18 MHz prototype is built to verify effectiveness of the proposed control. Compared with conventional PFM control, the proposed control reduces the power loss by 35.9% at 40% load while maintains continuous power flow.
Use of novel compounds to reduce methane production and in pre-harvest strategies to decrease foodborne pathogens

Use of Novel Compounds to Reduce Methane Production and in Pre-Harvest Strategies to Decrease Foodborne Pathogens. (May 2008) Hector Gutierrez Bañuelos, B.S., Universidad Autonoma de Zacatecas; M.S., Universidad Autonoma Agraria “Antonio Narro” Chair of Advisory Committee: Dr. Gordon E. Carstens The first aim of this study (Chapter III), the effects of chlorate and nitroethane on foodborne pathogens and rumen fermentation were evaluated. The experimental chlorate product, reduced (P < 0.001) fecal, but not ruminal (P > 0.05) E. coli concentrations by 1000and 10-fold by 24 and 48 h after chlorate feeding when compared to pre-treatment concentrations (> 5.7 log10 colony forming units/g). Nitroethane treatment decreased (P < 0.01) ruminal (8.46, 7.91 and 4.74 ± 0.78 μmol/mL h) and fecal (3.90, 1.36 and 1.38 ± 0.50 μmol/g h) methane-producing activity for treatments 0, 80 and 160 mg nitroethane/kg body weight per day, respectively. Whole animal methane emissions, expressed as L/d or as a proportion of gross energy intake (%GEI) were unaffected by nitroethane treatment (P > 0.05). The second aim of this study (Chapter IV) was conducted to examine the effects of nitroethane and monensin on ruminal fermentation and nitro-metabolizing bacterial populations in vitro. The addition of nitroethane decreased methane production (μmol/mL) by at least 90%. The most probable number (MPN) of nitro-metabolizing
A Triple-Tap Hybrid Load Balancing System (TTHLB) for Health Monitoring System

With the rapid development in innovation and a rising need for efficient healthcare services, innovative approaches to healthcare system design are required. In recent years, integrating cloud computing, edge computing, and fog computing has emerged as a promising solution to enhance the efficiency and effectiveness of healthcare systems. The paper proposes a triple-tap hybrid load balancing method (TTHLB) that leverages the capabilities of cloud computing, edge computing, and fog computing to provide seamless and efficient healthcare services. The TTHLB architecture comprises three layers: the cloud layer, the edge layer, and the fog layer. The cloud layer is the central repository for storing and processing large volumes of healthcare data. It provides on-demand access to computational resources and supports advanced data analytics techniques for decision-making and predictive analysis. The edge layer, consisting of edge devices such as wearable’s and sensors, collects real-time patient data and performs initial data processing. It enables real-time monitoring, early detection of health issues, and timely intervention. Between the cloud and edge layers, the fog layer acts as an intermediate data processing and storage layer. It reduces latency and bandwidth requirements by performing data processing closer to the edge devices. The TTHLB leverages the benefits of cloud computing, such as scalability, flexibility, and cost-effectiveness, by offloading resource-intensive tasks to the cloud layer. It also utilizes the capabilities of edge and fog computing to provide low-latency, real-time data processing and analysis, ensuring timely and personalized healthcare services. Integrating these computing paradigms enables seamless data exchange and collaboration among healthcare providers, patients, and other stakeholders, improving diagnosis, treatment, and patient outcomes. Finally, the TTHLB presented in this research work provides a comprehensive solution to traditional healthcare systems' challenges. It uses cloud, edge, and fog computing to deliver efficient, personalized, and timely healthcare services. The TTHLB can transform the healthcare industry and improve overall patient care quality.
Reliability Assessment of JLFinFET Based Biosensor for Neutral Biomolecule Detection Under Temperature Variation from 250K to 350K

In this paper, the reliability of the Junctionless FinFET Biosensor has been analysed under diversified temperature conditions i.e., from 250K to 350K temperature range. Various neutral biomolecules have been studied for their label-free detection namely, Protein, Aptes, Zein, Keratin and Gelatin. A remarkable dynamic produced has been captured as the variation in different sensing parameters such as Drain Current, OFF current sensitivity, change in the ratio of ON to OFF current, sensitivity in terms of the ratio of ON to OFF current, threshold voltage and transconductance. The attributes of the studied device structure have also been evaluated by adding an oxide layer over the channel in the cavity created throughout and beneath the metal gate. The studied JLFinFET device structure has been compared with UGDMJLFinFET. The results obtained show that the studied device possesses improved OFF current sensitivity and the absence of an ambipolar effect for lower gate voltages.
A BERT-based Empirical Study of Privacy Policies’ Compliance with GDPR

Since its implementation in May 2018, the General Data Protection Regulation (GDPR) has prompted businesses to revisit and revise their data handling practices to ensure compliance. The privacy policy, which serves as the primary means of informing users about their privacy rights and the data practices of companies, has been significantly updated by numerous businesses post-GDPR implementation. However, many privacy policies remain packed with technical jargon, lengthy explanations, and vague descriptions of data practices and user rights. This makes it a challenging task for users and regulatory authorities to manually verify the GDPR compliance of these privacy policies. In this study, we aim to address the challenge of compliance analysis between GDPR (Article 13) and privacy policies for 5G networks. We manually collected privacy policies from almost 70 different 5G MNOs, and we utilized an automated BERT-based model for classification. We show that an encouraging 51% of companies demonstrate a strong adherence to GDPR. In addition, we present the first study that provides current empirical evidence on the readability of privacy policies for 5G network. we adopted readability analysis toolset that incorporates various established readability metrics. The findings empirically show that the readability of the majority of current privacy policies remains a significant challenge. Hence, 5G providers need to invest considerable effort into revising these documents to enhance both their utility and the overall user experience.