Miracle-dz/UniFAR-FTUs huggingface dataset:
LoRaWAN Networks: a More Precise Assessment of the Energy Consumption

In this paper, we evaluate the network energy consumption (NEC) of a LoRaWAN network based on the pure ALOHA protocol. First, we define a reference scenario, which limits the energy consumption to the energy consumed by the radio transceiver. Then, we consider a second scenario in which we add to the energy consumption the energy consumed by the different operation modes of the end-devices after transmission of a packet. Finally, we simulate and compare the energy consumption of both scenarios. Our simulations results show that the energy consumption increases significantly when considering the different operation modes of the end-device.
Application of Additive Manufacturing Based Thermal Printing Techniques for Realization of Electronically Rewritable Chipless RFID Tags on Flexible Substrates

In this article, we present the design and realization of an electronically rewritable RF encoding particle (REP) for chipless RFID applications. This (REP) resonator geometry is integrated with two conductive bridging random access memory (CBRAM) based non-volatile RF switches, and could be electronically programmed using DC pulses to any of possible four resonant states to represent two bits of information. Emerging additive manufacturing (AM) technique based thermal transfer printing is utilized for the realization of this resonator on flexible and low cost Polyethylene terephthalate (PET) substrate. At present the size of the REP is such that five such REPs could be arranged on a standard credit card size tag. Also this REP encodes the most number of bits reported among non-volatile electronically rewritable REPs for chipless RFID applications - 2 bits, till date.
Are BERT embeddings able to infer travel patterns from Twitter efficiently using a unigram approach?

Public opinion is nowadays a valuable data source for many sectors. In this study, we analysed the transportation sector using messages extracted from Twitter. Contrasting with the traditional surveying methods that are high-cost and inefficient used in transportation sector, social media are popular sources of crowdsensing. This work used BERT embeddings, an unsupervised pre-trained model released in 2018, to classify travel-related terms using tweets collected from three distinct cities: New York, London, and Melbourne. In order to understand if a simple model can have a good performance, we used unigrams. A list of 24 travel-related words was used to classify the messages. Popular words are train, walk, car, station, street, and avenue. Between 3% to 5% of all messages are classified as traffic-related, while along the typical working hours of the day the values is around 5-6%. A high model performance was obtained, with precision and accuracy higher than 0.80 and 0.90, respectively. The results are consistent for all the three cities assessed.
Intelligent Grid Shepherd: Towards a Resilient Distributed Energy Resources Control System

The recent flourish of diversified distributed energy resources (DERs) such as generators, consumers, and prosumers brings indispensable cybersecurity challenges for the smart grid controller. Therefore, to assure a resilient smart grid operation, in this paper, we study the problem of continuous-time consensus policy-based DERs control mechanism for the smart grid controller. In particular, we propose an intelligent grid shepherd for the smart grid controller in the power grid framework. That can autonomously detect the abnormal behavior of the received status message from each DER and apply control decisions into the smart grid controller. To do this, first, we propose a continuous-time Markov decision process problem by formulating a resilient control system for the intelligent grid shepherd. Second, we design a data-informed policy-based model-free reinforcement learning framework to find the optimal consensus policy for each DER control decision (i.e., remain connected with the main grid or disconnected). Thus, we devise a distributed energy resources control algorithm for the intelligent grid shepherd. Particularly, we design an advantage actor-critic scheme under the continuous-time domain with the shared neural network mechanism. Finally, experimental results show the efficiency of the proposed intelligent grid shepherd in terms of accuracy and robustness towards a resilient DERs control.
A Novel Grant-Based Pilot Access Scheme for Crowded Massive MIMO Systems

We propose a novel grant-based pilot access scheme to tackle the pilot collision in crowded massive multiple-input multiple-output (MIMO) systems. The proposed scheme divides the cell into several annuluses. Then, weaker user equipments (UEs) in each annulus, judged by a decision rule in a strongest-user collision resolution (SUCRe) scheme, send their reselected or allocated idle pilots to a base station (BS). We call this scheme enhanced SUCRe combined idle pilots access (E-SUCR-IPA). We analyze the number of successful UEs and the successful access probability of one UE with respect to its distance to the BS for the E-SUCR-IPA scheme. Numerical results show that the E-SUCR-IPA scheme significantly increases the number of successful UEs while ensuring fairness among UEs compared to baselines.
A Quick Look to 6G Antenna Concepts as seen from the Deep Physical Layer Level

The race for the 6G has started, and new exciting ideas on 6G antennas are on the table. This contribution gives a look to the 6G ideas from a novel angle of view, the Deep Physical Layer (DPL), a layer added at the very botton level of the OSI stack, in which the analysis is carried out at the electromagnetic field level. It is suggested that the performance of the communication system can be quantified using the space-time Number of Degrees of Freedom (ST-NDF), allowing a simple comparison among the different solutions.
Design and Verification of IR-UWB Localisation Sensor Node with Cavity-Backed Textile Antenna

This paper handles the design and verification of a localisation sensor with a cavity-backed slot antenna. The sensor node uses a Decawave DW1000 chipset, together with a basic sensor, a microcontroller and a flash memory card. It is designed on a mechanically flexible Printed Circuit Board (PCB), where component edges and vias are aligned forming rigid lines. As antenna a Substrate Integrated Waveguide (SIW) design is proposed: a cavity-backed slot antenna combining vulcanised rubber foam and electrotextile. Impulse-Radio Ultra-Wideband (IR-UWB) localisation is analysed by the pulse deformation in the System Fidelity Factor (SFF) and the phase shift in the Distance Estimation Error (DEE). On-body measurements show that everything works fine.
Design of New Compact Multi-Layer Quint-Band Bandpass Filter

In this paper, we propose the new compact multilayer quint-passband bandpass filter. The multilayer substrate technique is used for further miniaturizing the circuit size. The filter has quint passband at 1.8, 2.5, 3.3, 3.8 and 4.5 GHz. Coupled resonators such as the stepped impedance resonators (SIRs), stub-loaded uniform impedance resonators (UIRs), and quarter-wavelength uniform impedance resonators are applied to produce the quint-passband of the filter simultaneously. The stub-loaded uniform impedance resonators and stepped impedance resonators can easily obtain a filter with closed passbands. Using a multilayer substrate technique, the filter can provide multipath propagation to improve the frequency response and achieve a compact circuit size. The proposed quint-passband bandpass filter shows a simple configuration, a practical design method, and a small circuit size.
A contactless measurement of the surface impedance of a thin sheet of material

Surface impedance value represents a crucial parameter for the characterization of thin sheets of material. A method for the estimation of this quantity that does not require a direct contact with the sample under test is proposed. The surface impedance is calculated through an inversion procedure that exploits the scattering parameters obtained from the proposed waveguide measurement setup. An inversion procedure based on the representation of the waveguide-air section-waveguide section as a T junction is employed. It is also shown that the absence of contact of the sample under test and the waveguide determines the leakage of electric fields which invalidate the inversion procedure. An improved configuration including an electromagnetic band-gap (EBG) structure is used to make the estimation of the surface impedance more accurate.
Joint Reconstruction of Conductivity and Velocity in Two-Phase Flows Using Electromagnetic Flow Tomography and Electrical Tomography: A Simulation Study

Characterization of two-phase flows is a common problem in the process industry. Due to complexity of flows, having accurate measurements of velocity fields, phase fractions, and the volumetric flow rates is challenging. Even though several approaches have been developed, accurate measurements of the flow quantities remain a challenge. Metering volumetric flow rate requires information on the local phase fraction and velocity field in the pipe cross section to be imaged. These data are commonly obtained using two individual measurement systems, the so-called dual-modality system. This article considers a dual-modality consisting of electromagnetic flow tomography (EMFT) and electrical tomography (ET) imaging, which provide information on the velocity field and electrical conductivity distribution, respectively; the combination of EMFT and ET reconstruction can be further used for inferring the volumetric flow rate. The aim of this article is to improve the accuracy of the EMFT and ET reconstructions—and the resulting flow rate estimate—by enhanced modeling of the unknown velocity and conductivity fields. More specifically, the proposed approach is based on modeling the joint statistics of the velocity and conductivity with a cross-covariance matrix which is based on a representative ensemble of velocity–conductivity image pairs obtained, for example, from fluid dynamical modeling or empirically. The cross-covariance matrix is incorporated in the joint reconstruction within the Bayesian inverse problems framework as an additional prior model. The proposed image reconstruction approach is tested with a set of numerical simulations. The results show that the joint reconstruction approach (JRA) with a cross-covariance model is capable of improving the accuracy of the estimates compared to the approaches that are currently in use.
Phase-Only Nulling for Uniform Linear Array via Kronecker Decomposition

This paper presents a novel beam-forming algorithm and its application to phase-only nulling. The proposed algorithm aims to achieve the interference nulling only by modulate phases so that we can reduce cost when setting phase shifter. This algorithm is based on the Kronecker decomposition. We can decompose the steering vector and weight vector into the form of Kronecker product because of their Vandermonde structures. With Kronecker decomposition, the weight vector design can be transformed into the design for several sub-vectors. According to the theory of beam forming and interference nulling, we are able to get analytical solutions when designing the desired weight vector and its complexity is low. Simulation results are provided to demonstrate the performance of the proposed algorithm.
Polynomial chaos within the frame of non-destructive testing

Inverse scattering problems can be seen as an optimization problem in which a cost function has to be minimized. The cost function measures the discrepancy between a physical model written as a function of the sought parameters and the measured data. Due to the high computational cost of physical models, metamodels (also known as surrogate models) have gradually appeared as alternatives. Polynomial chaos (PC) expansions are a type of metamodel based on an explicit spectral decomposition of the physical model, allowing for an exact computation of the gradient of the metamodel. The gradient of the metamodel is then used to improve the performance of the global algorithm Particle Swarm Optimization (PSO).
An Industrial Workbench for Test Scenario Identification for Autonomous Driving Software

Testing of autonomous vehicles involves enormous challenges for the automotive industry. The number of real-world driving scenarios is extremely large, and choosing effective test scenarios is essential, as well as combining simulated and real world testing. We present an industrial workbench of tools and workflows to generate efficient and effective test scenarios for active safety and autonomous driving functions. The workbench is based on existing engineering tools, and helps smoothly integrate simulated testing, with real vehicle parameters and software. We aim to validate the workbench with real cases and further refine the input model parameters and distributions.
Robust Recursive Regulator for Systems Subject to Polytopic Uncertainties

We present a robust recursive framework for the regulation of discrete-time linear systems subject to polytopic uncertainties. Based on regularized least-squares with a penalty parameter, we formulate a convex optimization problem and weight the polytope vertices altogether. In this sense, the main contribution of this paper consists of a robust recursive framework for the computation of stabilizing feedback gains. The solution does not require numerical optimization packages and relies ultimately on a single penalty parameter which is easily tuned. Moreover, the gains are obtained recursively through algebraic expressions, as opposed to related works which employ linear matrix inequalities. Under observability and controllability conditions, we demonstrate convergence and stability of the closed-loop system in terms of an algebraic Riccati equation. We provide numerical and real-world examples to validate the proposed approach and for comparison with a robust H∞ controller.
Ionospheric behavior statistics from the Livingston station

The existing statistics of the Antarctic ionosphere parameters behavior is supplemented by the results from the Livingston station data for the summer periods of 2005–2019, 35 months in total. The climatology of the parameters behavior came to the constancy of the values of the critical frequency foF2 in the local daytime and evening times and significant differences at night, reaching factor 2. The highest values are in December, the lowest in March. For the maximum height hmF2, the spread of values is small. A comparison of the values of foF2 and hmF2 of the IRI model with observational data is carried out. A certain tendency of an increase in deviations with an increase in solar activity was found. For foF2, dispersion σ does not exceed 25%, for hmF2 −17%. The average values for the entire period are: $\vert\Delta \text{foF}2\vert=0.73$ MHz, σ(foF2) =0.86 MHz and 13.6%, $\vert\Delta \text{hmF}2\vert=22.5$ km, σ(hmF2)=27.5 km and 9.65%. These magnitudes are close to those for stations in the northern hemisphere. The possibility of using the total electron content for determining foF2 is shown.
Neural Network-based Forecasting of Decodability for Early ARQ

Forecasting the decodability of a received packet of a given decoder is a hard task as many State-of-the-Art (SoTA) decoders are of high complexity and not easy to analyse in an analytical fashion. Gathering this forecast on the other hand would enable to save computational complexity and latency as a decoder execution can be saved if it is unlikely that the received packet is decoded correctly. On top, we can provide early feedback for Automatic Repeat Request (ARQ) schemes before actually running the decoding chain. Guided by this motivation, several approaches of classifying the received packet before the actual decoding process have been discussed. We propose to use neural networks (NN) in the context of forecasting received packets for a given receiver chain. We evaluate the performance of the NN by evaluating different performance metrics and perform an efficiency analysis of ARQ.
Internal Electric Field Reconstruction and SAR Estimation of In-Body Antenna Using Inverse Equivalent Current Method

In this article, we reconstruct the internal electric field due to a dipole antenna embedded in a dielectric phantom for developing a noninvasive specific absorption rate (SAR) measurement system. The reconstruction method is based on the surface equivalent theorem and boundary conditions, which relate the external field radiated from the embedded antenna to equivalent electromagnetic surface currents on the human body. The electric field data sampled at spatial points on a surface enclosing the phantom are used in the inverse calculation. All field integrals are discretized and numerically solved to obtain the electromagnetic currents on the phantom's surface. The internal electric field distribution and SAR value in a phantom can be calculated from the reconstructed surface currents if the electric properties of the phantom are known. The validity of the method was demonstrated numerically and experimentally using a dipole antenna embedded in the lossy rectangular phantom, which has a dielectric constant close to human skin tissue at 2.5 GHz. Comparison with forward numerical simulations has shown that the surface current and electric field distribution can be predicted with great precision. Carefully performing the experiments using a self-made phantom validated our numerical demonstration and provided satisfactory reconstruction results.
Stimulation of a Layered Medium by $N$ External Dipoles

A layered spherical medium is excited by a number of external electric dipoles. Boundary-value problems for the involved individual and overall electromagnetic fields are formulated. The direct scattering problem is solved by a T-Matrix method. Explicit formulas for the far-field, the overall cross section and the total interaction cross sections are derived. Some preliminary numerical results on the variations of these cross sections are presented.
Designing Highly Sensitive Surface Plasmon Resonance Sensor with Dual Analyte Channels

The ease of controlling waveguide properties through unparalleled design flexibility has made the photonic crystal fiber (PCF) an attractive platform for plasmonic structures. In this work, a dual analyte channel’s highly sensitive PCF bio-sensor is proposed based on surface plasmon resonance (SPR). In the proposed design, surface plasmons (SPs) are excited in the inner flat portion of two rectangular analyte channels where gold (Au) strip is deposited. Thus, the surface roughness that might be generated during metal deposition on circular surface could be effectively reduced. Considering the refractive index (RI) change in the analyte channels, the proposed sensor is designed and fully characterized by the finite element method based COMSOL Multiphysics software. Improved sensing characteristics including wavelength sensitivity (WS) of 186,000 nm/RIU and amplitude sensitivity (AS) of 2,792.97 RIU-1 in the wide RI range of 1.30 to 1.43 is obtained. In addition, the proposed sensor exhibits excellent resolution of 5.38 × 10-7, signal to noise ration (SNR) of 13.44 dB, figure of merits (FOM) of 2188.23, detection limit (DL) of 101.05 nm, and detection accuracy (DA) of 0.0204 nm-1. Outcomes of the analysis indicate that the proposed sensor could be suited for accurate detection of organic chemicals, bio-molecules, and biological analytes.
Direct-path Delay Estimation under Closely-spaced Multipath Interference

For direct sequence spread spectrum (DSSS) signal, the minimum mean square error (MMSE) algorithms based on reiterative inverse filters have good delay resolution under closely-spaced multipath interference. However, the limited density of the correlator function dictionary leads to a model error of the filter. Under the inexact model, the performance of delay estimation decreases. To address this problem, we add a group of correction parameters into the dictionary matrix and estimate them in the reiterative filtering. Based on the sparsity of correction parameters, a threshold decision is adopted to sift the parameters that need to be estimated by maximum likelihood (ML) search. The parameters that fail to pass the threshold are set to zero. Simulation results show that, compared with least squares (LS) and MMSE algorithms, the proposed algorithm can improve the performance of delay estimation.
Cloaking Analysis by Accelerated-Particle-Swarm-Optimization Techniques

Cloaking behavior of spherically-layered media is analyzed by means of a chaotic accelerated particle-swarm-optimization (chaotic APSO) algorithm. The optimization problem concerns the determination of the radii, the permittivities, and the permeabilities of a small number of magneto-dielectric layers covering a PEC core. The results of some preliminary numerical experiments are presented.
A High-Gain Leaky-Wave Antenna Using Resonant Cavity Structure With Unidirectional Frequency Scanning Capability for 5G Applications

This paper presents a high-gain leaky-wave antenna based on a resonant cavity (RC) structure with the capability of unidirectional beam scanning over predefined frequencies and desired beam angles. Frequency scanning is achieved by implementing a properly-designed single-layer partially reflective surface (PRS) based on a simple ray tracing-based design procedure. In the proposed structure, two techniques are combined to make the beam scanning unidirectional. First, a metallic wall is placed at a proper distance from the radiator to suppress the antenna propagation at undesired directions. Second, a feed antenna with a tilted beam is designed as the main radiating element inside the cavity to radiate over desired beam angles. A prototype of the proposed antenna is fabricated and measured demonstrating a beam scanning from <inline-formula> <tex-math notation="LaTeX">$12^{o}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$46^{o}$ </tex-math></inline-formula> over the frequency band from 25 to 31 GHz. Moreover, a maximum gain of 15.4 dBi is achieved at 26 GHz on the elevation plane with tilted angle of <inline-formula> <tex-math notation="LaTeX">$22^{o}$ </tex-math></inline-formula>. The proposed leaky-wave antenna is suitable for various wireless applications, in particular the <inline-formula> <tex-math notation="LaTeX">$5^{th}$ </tex-math></inline-formula> generation of cellular networks and small cell base station antenna (BSA) applications.
Network Data Analytics Function for IBN-based Network Slice Lifecycle Management

Networks slicing in 5G network enables the network operators to accommodate the different quality of service (QoS) to their customers. Moreover, the data analytics in 5G mobile network can be seen as a robust solution to transform the challenging features of 5G into a reality. So, for that, the Third Generation Partnership Project (3GPP) has been introduced a network data analytics function (NWDAF) in 5G service-based architecture (SBA). NWDAF collects the data from different core and management domains and performs analytics on that historical data. It also enables the network operators (NOs) to train their Machine Learning (ML) techniques and use various third-party solutions. On the other side, the automation and management of the end - to-end (e2e) network slicing in a multi -domain environment is a critical task. Therefore, in this manuscript, we have designed a closed-loop Intent-based Networking (IBN) platform, which automatically ensures the commissioning, activation, run-time monitoring, and decommissioning of the network slices. Moreover, we have integrated the newly introduced 3GPP NWDAF with the IBN platform for efficient e2e network slice lifecycle management. By implementing different ML models in the NWDAF function, we can predict the slice load, user mobility, traffic forecasts, and anomaly detection from the network slices.