Sensing Aided Reconfigurable Intelligent Surfaces for 3GPP 5G Transparent Operation

Can reconfigurable intelligent surfaces (RISs) operate in a standalone mode that is completely transparent to the 3GPP 5G initial access process? Realizing that may greatly simplify the deployment and operation of these surfaces and reduce the infrastructure control overhead. This paper investigates the feasibility of building standalone/transparent RIS systems and shows that one key challenge lies in determining the user equipment (UE)-side RIS beam reflection direction. To address this challenge, we propose to equip the RISs with multi-modal sensing capabilities (e.g., using wireless and visual sensors) that enable them to develop some perception of the surrounding environment and the mobile users. Based on that, we develop a machine learning framework that leverages the wireless and visual sensors at the RIS to select the optimal beams between the base station (BS) and users and enable 5G standalone/transparent RIS operation. Using a high-fidelity synthetic dataset with co-existing wireless and visual data, we extensively evaluate the performance of the proposed framework. Experimental results demonstrate that the proposed approach can accurately predict the BS and UE-side candidate beams, and that the standalone RIS beam selection solution is capable of realizing near-optimal achievable rates with significantly reduced beam training overhead.Comment: The RIS dataset and script files will be available soon. arXiv admin note: text overlap with arXiv:2211.0756

A qualitative study of airborne minerals and associated organic compounds in southeast of Cairo, Egypt

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This study is concerned with the identification of the mineralogical composition of dust fall samples collected from southeast of Cairo, Egypt. The mineralogical identification was conducted by means of the polarizing microscope, infra-red spectroscopy (IR), and X-ray diffraction (XRD). The relationship between the mineralogical composition of dust fall samples and 10 rock samples from the surrounding terrains were investigated. The major mineralogical species existing in the atmosphere of the study area are: carbonates mainly in the form of calcite in addition to the appearance of the dolomite form in traces overall the study area, but with considerable observation in the southern region; quartz which is less than calcite in its abundance; sulphates in the form of gypsum which may also be present as traces in the anhydrite form. Trace constitution of feldspars; clay minerals in the form of kaolinite, illite, and montimorillonite; and halite are also observable in the same samples. Organic compounds are present in the atmosphere of the area mainly as alkanes with presence of traces of phosphines. This study qualitatively shows the mineralogy of air particulate over rock processing area and the obtained results indicates that the main pollution source in the study area is the industrial activities with minor contribution of the natural sources, especially erosion and dust carried by winds from the surrounding terrains Cairo in the southern direction. This study provides useful results for the contribution of rock processing activities to the mineral composition of atmospheric particulates

Review of specific features and challenges in the current Internet of Things systems impacting their security and reliability

The current development of the Internet of Things (IoT) technology poses significant challenges to researchers and industry practitioners. Among these challenges, security and reliability particularly deserve attention. In this paper, we provide a consolidated analysis of the root causes of these challenges, their relations, and their possible impacts on IoT systems’ general quality characteristics. Further understanding of these challenges is useful for IoT quality engineers when defining testing strategies for their systems and researchers to consider when discussing possible research directions. In this study, twenty specific features of current IoT systems are discussed, divided into five main categories: (1) Economic, managerial and organisational aspects, (2) Infrastructural challenges, (3) Security and privacy challenges, (4) Complexity challenges and (5) Interoperability problems

Quality and reliability metrics for IoT systems:a consolidated view

Quality and reliability metrics play an important role in the evaluation of the state of a system during the development and testing phases, and serve as tools to optimize the testing process or to define the exit or acceptance criteria of the system. This study provides a consolidated view on the available quality and reliability metrics applicable to Internet of Things (IoT) systems, as no comprehensive study has provided such a view specific to these systems. The quality and reliability metrics categorized and discussed in this paper are divided into three categories: metrics assessing the quality of an IoT system or service, metrics for assessing the effectiveness of the testing process, and metrics that can be universally applied in both cases. In the discussion, recommendations of proper usage of discussed metrics in a testing process are then given

Machine learning based IoT Intrusion Detection System:an MQTT case study (MQTT-IoT-IDS2020 Dataset)

The Internet of Things (IoT) is one of the main research fields in the Cybersecurity domain. This is due to (a) the increased dependency on automated device, and (b) the inadequacy of general-purpose Intrusion Detection Systems (IDS) to be deployed for special purpose networks usage. Numerous lightweight protocols are being proposed for IoT devices communication usage. One of the distinguishable IoT machine-to-machine communication protocols is Message Queuing Telemetry Transport (MQTT) protocol. However, as per the authors best knowledge, there are no available IDS datasets that include MQTT benign or attack instances and thus, no IDS experimental results available. In this paper, the effectiveness of six Machine Learning (ML) techniques to detect MQTT-based attacks is evaluated. Three abstraction levels of features are assessed, namely, packet-based, unidirectional flow, and bidirectional flow features. An MQTT simulated dataset is generated and used for the training and evaluation processes. The dataset is released with an open access licence to help the research community further analyse the accompanied challenges. The experimental results demonstrated the adequacy of the proposed ML models to suit MQTT-based networks IDS requirements. Moreover, the results emphasise on the importance of using flow-based features to discriminate MQTT-based attacks from benign traffic, while packet-based features are sufficient for traditional networking attacks

Synthesis, spectroscopic and thermal characterization of Fe(III)-mixed ligand complexes and spectrophotometric determination of Fe(III) in various samples

The aim of the present study is to find a non time consuming, economical and reliable spectrophotometric procedures using commercially available spectrophotometric reagents for the determination of Fe(III) ions. The methods are based on the formation of colored ternary complexes using, 1,10-phenanthroline and eriochrome black T or tartrazine mixed reagents and improved using a cationic surfactant, cetyltrimethyl ammonium bromide. This surfactant interacts with the complex to build up true ternary complex. The most suitable conditions for determination of Fe(III) ions and the parameters affecting the reactions including pH, time, temperature, stoichiometric ratios and reagents concentrations are optimized. The effect of different interfering ions is studied together with the suitable masking agents. The developed methods are used for the determination of Fe(III) ions in the presence of cetyltrimethyl ammonium bromide in different types of water (polluted industrial waste, ground, river Nile and drinking water). The synthesis and spectroscopy studies of two Fe complexes were reported. Thermal analysis was carried out in order to give an idea about the thermal stability of the complexes

Synthesis And Characterization Of 3D-Printed Functionally Graded Porous Titanium Alloy

This study aims to 3D print titanium alloy constructs incorporating gradient of porosities, from the fully dense core to the porous outer surface. Gradient porous specimens were prepared using selective laser melting (SLM). Fully dense specimens fabricated by SLM were used as the control group. Characterization of samples was done using X-ray tomography, uniaxial compression testing, and optical and scanning electron microscopes. The biocompatibility of fabricated samples was investigated using human periodontal ligament stem cells via assessment of cell attachment, viability, and proliferation by direct and indirect assays. The data were analyzed using ANOVA and Tukey’s post hoc test. Characterization of constructs reveals interconnected gradient porosities and higher contact angle in porous samples. The introduction of porosity leads to a significant decrease in compression strength. However, Young’s modulus of the samples with gradient porosity was more similar to the natural bone modulus. The surface microstructure consists of loosely bonded spherical particles. Biocompatibility of the dense and porous samples is appropriate. Although the porosity size led to a reduced cell proliferation rate in the gradient sample, the extract of the gradient sample results in more cell proliferation than the dense sample’s extract. The study demonstrates that a biocompatible functionally graded porous titanium structure can be well fabricated by SLM, and this structure leads to a good match of Young’s modulus to that of the bone

Nano-Scale Stiffness and Collagen Fibril Deterioration: Probing the Cornea Following Enzymatic Degradation Using Peakforce-QNM AFM

Under physiological conditions, the cornea is exposed to various enzymes, some of them have digestive actions, such as amylase and collagenase that may change the ultrastructure (collagen morphology) and sequentially change the mechanical response of the cornea and distort vision, such as in keratoconus. This study investigates the ultrastructure and nanomechanical properties of porcine cornea following incubation with α-amylase and collagenase. Atomic force microscopy (AFM) was used to capture nanoscale topographical details of stromal collagen fibrils (diameter and D-periodicity) and calculate their elastic modulus. Samples were incubated with varying concentrations of α-amylase and collagenase (crude and purified). Dimethylmethylene blue (DMMB) assay was utilised to detect depleted glycosaminoglycans (GAGs) following incubation with amylase. Collagen fibril diameters were decreased following incubation with amylase, but not D-periodicity. Elastic modulus was gradually decreased with enzyme concentration in amylase-treated samples. Elastic modulus, diameter, and D-periodicity were greatly reduced in collagenase-treated samples. The effect of crude collagenase on corneal samples was more pronounced than purified collagenase. Amylase was found to deplete GAGs from the samples. This enzymatic treatment may help in answering some questions related to keratoconus, and possibly be used to build an empirical animal model of keratoconic corneas with different progression levels.</jats:p