Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity

5G New Radio proposes the usage of frequencies above 10 GHz to speed up LTE's existent maximum data rates. However, the effective size of 5G antennas and consequently its repercussions in the signal degradation in urban scenarios makes it a challenge to maintain stable coverage and connectivity. In order to obtain the best from both technologies, recent dual connectivity solutions have proved their capabilities to improve performance when compared with coexistent standalone 5G and 4G technologies. Reinforcement learning (RL) has shown its huge potential in wireless scenarios where parameter learning is required given the dynamic nature of such context. In this paper, we propose two reinforcement learning algorithms: a single agent RL algorithm named Clipped Double Q-Learning (CDQL) and a hierarchical Deep Q-Learning (HiDQL) to improve Multiple Radio Access Technology (multi-RAT) dual-connectivity handover. We compare our proposal with two baselines: a fixed parameter and a dynamic parameter solution. Simulation results reveal significant improvements in terms of latency with a gain of 47.6% and 26.1% for Digital-Analog beamforming (BF), 17.1% and 21.6% for Hybrid-Analog BF, and 24.7% and 39% for Analog-Analog BF when comparing the RL-schemes HiDQL and CDQL with the with the existent solutions, HiDQL presented a slower convergence time, however obtained a more optimal solution than CDQL. Additionally, we foresee the advantages of utilizing context-information as geo-location of the UEs to reduce the beam exploration sector, and thus improving further multi-RAT handover latency results.
On the Exponentiated Weibull Distribution

Abstract The important extension of the Weibull family—the exponentiated Weibull distribution—is reviewed with various new statistical measures. An explicit expression for the mode is derived and a comparison between the authors'and Mudholkar and Hutson's results are tabulated for various values of the parameters of the distribution. A general formula for the mean residual life function is obtained.
Peer-to-Peer Non-Bayesian Learning in Finite Time with a Finite Amount of Communication

We consider the problem of distributed non-Bayesian learning (or hypothesis testing) where a group of agents interacts over a peer-to-peer network to identify the true state of the world from a finite set of hypotheses, based on a series of stochastic signals that each agent receives. Prior work on this problem has provided distributed algorithms that guarantee asymptotic learning of the true state, with corresponding efforts to improve the rate of learning. In this paper, we first argue that one can readily modify existing asymptotic learning algorithms to enable learning in finite time, effectively yielding arbitrarily large (asymptotic) rates. Furthermore, we show that such finite-time learning can be achieved via a simple algorithm which only requires the agents to exchange a binary vector (of length equal to the number of possible hypotheses) with their neighbors at each time-step. Finally, we show that if the agents know the diameter of the network, our algorithm can be further modified to allow all agents to learn the true state and stop transmitting to their neighbors after a finite number of time-steps.
Geometric magnetic frustration in olivines

Polycrystalline samples of ${\mathrm{Mn}}_{2}{\mathrm{SiO}}_{4},$ ${\mathrm{Mn}}_{2}{\mathrm{GeO}}_{4},$ ${\mathrm{Co}}_{2}{\mathrm{SiO}}_{4},$ and ${\mathrm{Ni}}_{2}{\mathrm{SiO}}_{4}$ were prepared and studied in the context of a search for geometrically frustrated magnetic materials. These compounds are isostructural with olivine, a series of minerals commonly found in the terrestrial crust. The olivine structure possesses a distinctive magnetic lattice in which metal ions sit at the vertices of corner-sharing triangles, forming alternating sawtooth chains. Magnetic susceptibility experiments reveal that the manganese-based olivines exhibit spin-glass transitions and magnetic frustration, while the cobalt olivine exhibits antiferromagnetic order with no suppression of the N\'eel temperature. The magnetic susceptibility of nickel olivine exhibits a large deviation from Curie-Weiss behavior, suggesting the presence of a magnetic structure in which a sublattice possessing a spin gap interacts with surrounding local-moment ${\mathrm{Ni}}^{2+}$ ions.
A Provably Secure ECC-based Multi-factor 5G-AKA Authentication Protocol

Due to the constant penetration of various security attacks, it is highly important to secure the underlying communication networks between the IoT, Fog and Cloud in the next generation of mobile communication system (5G). Thus, secure authentication and key agreement protocol, namely 5G-AKA, has been proposed in the literature to safely and stably access the 5G mobile services. However, some recent findings reveal that 5G-AKA and its numerous versions based on symmetric or asymmetric encryption are either vulnerable to different attacks such as perfect forward secrecy violation, malicious Serving Network (SN), de-synchronization attack, privacy theft, stolen device, or are computationally intensive. Apart from that, these protocols use single-factor authentication. Considering the above demerits of these protocols and the necessity to provide enhanced security, we propose an Elliptic Curve-Cryptography (ECC)-based multi-factor 5G-AKA authentication protocol. It provides additional security and achieves cost-effectiveness in terms of computational, communication, storage costs and energy consumption. The formal security analysis using Real-Or-Random (ROR) logic has been done to confirm its security. Moreover, we evaluate the performance of the proposed protocol in terms of computational, communication, storage costs and energy consumption. The evaluation results show that the proposed protocol requires less cost than its counterparts, reducing computational cost by up to 57%, communication cost by up to 59%, storage cost by up to 52%, and energy consumption by up to 51%.
Epitaxial growth and planar dielectric properties of compositionally graded (Ba1−xSrx)TiO3 thin films prepared by pulsed-laser deposition

We have heteroepitaxially deposited compositionally graded (Ba1−xSrx)TiO3 (BST) thin films with increasing x from 0.0 to 0.25 on (100)-oriented MgO substrates using pulsed-laser deposition. The compositional gradients along the depth in the graded films were characterized by Rutherford backscattering spectroscopy. By using surface interdigital electrodes, the planar dielectric response of epitaxial graded BST films was measured as a function of frequency, temperature, and dc applied voltage. At room temperature, the dielectric constant of the graded BST film was about 450 with a dielectric loss, tan δ of 0.007 at 100 kHz. Measurements varying the dc bias voltage showed hysteresis of the dielectric response and a tunability of 25% at an applied electric field of 80 kV/cm. The graded BST films undergo a diffuse phase transition with a broad and flat profile of the capacitance versus temperature. Such behavior of the dielectric response in graded BST films is attributed to the presence of the compositional a.
Identifying temporary water bodies from drone images at real-time using deep-learning techniques

The detection and control of larval habitats—in particular, temporary water bodies—can play an important role in fighting against mosquito-transmitted diseases like malaria. In this paper, we explore the feasibility of identifying temporary water bodies from images taken by surveillance drones at real-time using the YOLOv4 algorithm. For our experiments, we use a dataset consisting of 782 drone-captured images, which includes 1522 temporary water bodies of different sizes and shapes, including puddles (i.e., small accumulations of water on a surface). Although still preliminary, our results are encouraging: the YOLOv4 trained models obtained a general score of 71.47(6.42)% mAP, and the processing time per image is 24.87 milliseconds, on average. In terms of size, the models can identify puddle objects as small as 23x23 cm on images taken from 120 m over surface-level.
Single-Snapshot Localization for Near-Field RIS Model Using Atomic Norm Minimization

Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.
Maximal Ellipsoid Method for Guaranteed Reachability of Unknown Fully Actuated Systems

In the face of an adverse event, autonomous systems may undergo abrupt changes in their dynamics. In such an event, systems should be able to determine their continuing capabilities to then execute a provably completable task. This paper focuses on the scenario of a change in the system dynamics following an adverse event, aiming to determine the system's guaranteed performance capabilities by finding a set of states that are provably reachable by the system. While it is obviously impossible to exactly determine the reachable set without full knowledge of the system dynamics, we present a method of determining its under-approximation while assuming only partial knowledge of the system structure. Our technical approach relies on showing that an intersection of infinitely many ellipsoids — available velocity sets for each system consistent with the partial knowledge of the dynamics — is the same as an intersection of some finite collection of ellipsoids. This result enables us to find a maximal ellipsoid lying in such an intersection, yielding a set of velocities that the system is provably able to pursue regardless of its exact dynamics.
An Extended Plant Circadian Clock Model for Characterising Flowering Time under Different Light Quality Conditions

Speed breeding has recently emerged as an innovative agricultural technology solution to meet the ever-increasing global food demand. In speed breeding, typically various light qualities (e.g., colour, duration, intensity) are modified to manipulate the circadian clock of the plants, which in turn alter the plant growth and enhance the productivity such as by reducing the flowering time. In order to develop a comprehensive framework describing plant growth, a model incorporating the effect of various light qualities on plant growth needs to be established. Recently a mathematical model of the plant circadian clock for Arabidopsis thaliana has been developed to characterise the hypocotyl growth subject to multiple light quality properties. This is a first step towards developing a more comprehensive model that links light quality, plant circadian clock and plant growth. In this work, we extend the model by adding the effect of various light qualities on the flowering time. The proposed model can capture the flowering time behaviours of plant when subject to red, blue, and mixed lights and can be used to guide experiment of light properties manipulation for optimised plant growth via hypocotyl growth and flowering time.
Closed-form Estimates of the LQR Gain From Finite Data

When dealing with unknown systems, data can be used to directly learn controllers with desirable features, thus bypassing system identification. In this paper we present strategies to design optimal controls for unknown linear systems directly trough closed-form functions of the data. In particular, when data is sufficiently informative, (i) we find the control input that minimizes a finite-horizon quadratic function of the states and inputs and (ii) we show how these inputs enable the estimate of the static feedback controller that minimizes an infinite-horizon quadratic function, i.e., the Linear Quadratic Regulator. Our formulas are closed-form, making them computationally efficient and of straightforward interpretation.
TDL-IDS: Towards A Transfer Deep Learning based Intrusion Detection System

With the development of Internet of Things (IoT), network security has become very important as cyber-attackers can easily compromise such distributed networks and systems. An intrusion detection system (IDS) is a basic and essential security mechanism to detect malicious traffic. In the literature, in addition to traditional machine learning algorithms, many deep learning schemes have been examined to enhance the detection performance. However, insufficient amounts of labeled samples are still a challenge for real-world implementation, especially in some scenarios such as smart home and Internet of Vehicles. To address this issue, we explore transfer learning as a promising solution. In this work, we develop TDL-IDS, a transfer deep learning based IDS that can work with limited labeled data items. Our approach first uses Long Short Term Memory (LSTM) to train a model on the source domain and then leverages transfer learning to continue the training process on the target domain. In the evaluation, we use NSL-KDD as the source domain, and AWID as the target domain. Our results indicate that TDL-IDS can outperform many similar approaches.
Sediment transport in nonlinear skewed oscillatory flows: Transkew experiments

New experiments under sheet flow conditions were conducted in an oscillating water tunnel to study the effects of flow acceleration on sand transport. The simulated hydrodynamic conditions considered flow patterns that drive cross-shore sediment transport in the nearshore zone: the wave nonlinearities associated with velocity and acceleration skewness and a negative mean current, the undertow. Net transport rates were evaluated from the sediment balance equation and show that (1) the acceleration skewness in an oscillatory flow produces a net sediment transport in the direction of the highest acceleration; (2) the net transport in the presence of an opposing current is negative, against the direction of the highest acceleration, and reduces with an increase in flow acceleration; and (3) velocity skewness increases the values of the net onshore transport rates.
Visual-Inertial Fusion on KITTI using MSF-EKF

In this article, we propose a loose-coupling approach between the accurate Parallax Beam (PB) algorithm and an Inertial Measurement Unit (IMU) for a robust, off-road and system agnostic navigation application. With the aim of achieving better robustness against image-shortage and motion-estimation errors while keeping PB accuracy, we propose the use of the Multi-Sensor Fusion Extended Kalman Filter (MSF-EKF). The latter uses asynchronous data provided by both the IMU and PB with different sampling rates to perform a high rate estimation able to ensure correct navigation during image shortage. The evaluation performed on the KITTI vision benchmark suite shows promising results with a good accuracy and is able to run with a sampling rate ten times faster than the sampling rate of Parallax Beam.
Dirichlet-to-Neumann Maps for Unbounded Wave Guides

Dirichlet-to-Neumann (DtN) boundary conditions for unbounded wave guides in two and three dimensions are derived and analyzed, defining problems that are suitable for finite element analysis. In the most general cases considered wave numbers may vary in arbitrary cross sections. The full DtN operator, in the form of an infinite series, is exact. Nonunique solutions may occur when this operator is truncated. Simple criteria for the number of terms in the truncated operator that guarantee unique solutions are presented. A simple modification of the truncated operator leads to uniqueness for any number of terms. Numerical results validate the performance of DtN formulations for wave guides and confirm the criteria for uniqueness.
Control of Misbehaving Multiagent Networks through Driver Nodes: The Directed Graph Case

In this paper, we study multiagent networks subject to misbehaving nodes (i.e., nodes subject to exogenous disturbances) over fixed, connected, and directed graphs. In contrast to previous studies, which apply feedback control signals to every node in the network to suppress the adverse effect of misbehaving nodes, we propose to apply feedback control signals to a subset of nodes (i.e., driver nodes) in the network due to constraints. In particular, proportional-integral feedback controllers are proposed to be executed by the driver nodes, which guarantee that the overall multiagent network is stable in the sense of input-to-state stability (i.e., they make the resulting closed-loop system matrix Hurwitz). Following that we present a system-theoretical approach to find the steady-state value of each node in the network. In addition, a graph-theoretical approach is utilized to allow users to find steady-state values of critical nodes without requiring the knowledge of the Laplacian matrix of the overall multiagent network. Based on the results presented in this paper, one can gain the understanding how to select the driver nodes to negate the effect of misbehaving nodes on the neighborhood of the critical nodes. Finally, illustrative numerical examples are also presented to demonstrate the efficacy of the proposed approaches.
Local identification in diffusively coupled linear networks

Physical dynamic networks most commonly consist of interconnections of physical components that can be described by diffusive couplings. Diffusive couplings imply symmetric cause-effect relationships in the interconnections and therefore diffusively coupled networks can be represented by undirected graphs. This paper shows how local dynamics of (undirected) diffusively coupled networks can be identified on the bases of local signals only. Sensors and actuators are allocated to guarantee consistent identification. An algorithm is developed for identifying the local dynamics.
TanDEM-X mission—new perspectives for the inventory and monitoring of global settlement patterns

Abstract. TerraSAR-X add-on for digital elevation measurement (TanDEM-X) is a German Earth observation mission collecting a total of two global coverages of very high resolution (VHR) synthetic aperture radar (SAR) X-band data with a spatial resolution of around three meters in the years 2011 and 2012. With these, the TanDEM-X mission (TDM) will provide a unique data set which is complementary to existing global coverages based on medium (MR) or high resolution (HR) optical imagery. The capabilities of the TDM in terms of supporting the analysis and monitoring of global human settlement patterns are explored and demonstrated. The basic methodology for a fully-operational detection and delineation of built-up areas from VHR SAR data is presented along with a description of the resulting geo-information product—the urban footprint (UF) mask—and the operational processing environment for the UF production. Moreover, potential follow-on analyses based on the intermediate products generated in the context of the UF analysis are introduced and discussed. The results of the study indicate the high potential of the TDM with respect to an analysis of urbanization patterns, peri-urbanization, spatio-temporal dynamics of settlement development as well as population estimation, vulnerability assessment and modeling of global change.
A molecular dynamics method for simulations in the canonical ensemble

A molecular dynamics simulation method which can generate configurations belonging to the canonical (T, V, N) ensemble or the constant temperature constant pressure (T, P, N) ensemble, is proposed. The physical system of interest consists of N particles (f degrees of freedom), to which an external, macroscopic variable and its conjugate momentum are added. This device allows the total energy of the physical system to fluctuate. The equilibrium distribution of the energy coincides with the canonical distribution both in momentum and in coordinate space. The method is tested for an atomic fluid (Ar) and works well.
Semi-empirical Modeling for DNA Nucleotides via Silicon Nanowire Field-effect Transistor Sensor with a Nanogap

Lately, Silicon Nanowire field-effect transistor have attracted massive attention, due to its high-sensitivity, selectivity, label-free and real-time detection capabilities. DNA detection has obtained significance increasingly through the years. Here, a novel Silicon Nanowire field-effect transistor sensor with a nanogap placed in the middle of the sensor is studied for DNA nucleotides detection purpose. The sensor results in a distinct electronic signature for each of the different DNA nucleotides (Adenosine, Guanosine Thymidine, and Cytidine). Semi-empirical model integrated with non-equilibrium Green’s function is used to analyze the DNA nucleotides detection process. The results indicate that the developed sensor could promote a novel sensing of DNA nucleotides with high accuracy.
Kernel based State-Space Kriging: Application to predictive control

In this paper, we extend the State-Space Kriging (SSK) modelling technique presented in a previous work by the authors so that non autonomous systems can be considered. The SSK method computes predictions as linear combinations of past outputs. In order to model nonlinearity, we develop here a new version of the SSK where kernel functions are used to model nonlinear dynamics instead of resorting on considerations about local data, which in practice is akin to a linearization. Also, a Kalman filter is used to improve the obtained predictions at each time step in the case of noisy measurements. A constrained Nonlinear Model Predictive Control (NMPC) scheme is built upon the black-box input-output system obtained by means of the SSK prediction method. Finally, a numerical example of a continuously stirred tank reactor (CSTR) is provided in order to assess the performance of the proposed controller.
In-Vehicle Network Delay Tomography

Due to the increased complexity of new in-vehicle networking architectures, which makes direct monitoring of internal network components intractable, alternative solutions are required to tackle this issue. One solution is to leverage the end-to-end measurements to estimate the internal network performance. To this end, we propose to employ network tomography as a monitoring approach for in-vehicle networks. Network tomography can infer the overall network performance by measuring only subset of the network. We investigate the use of network tomography in in-vehicle network by analysing network identifiability of three main architectures: bus-based, central-gateway, and Ethernet-based architectures. Our analysis results indicate the applicability of network tomography in in-vehicle networks based on certain topological and monitors' conditions. Furthermore, we validate our analytical results through simulation which shows a maximum error of only $174\mu s$. Moreover, we compare the proposed approach with one of existing solutions and show that network tomography achieves better bandwidth and latency performance with monitoring overhead saving up to 52.2% and $782.3\mu s$, respectively.