Miracle-dz/UniFAR-FTUs huggingface dataset:
Intersection Mobility for Polaris Car

Autonomous Vehicles society is blooming and evolving as driving safety is expected to improve by autonomous vehicles (AVs) compared with human driving. Intersection mobility is one of the challenging tasks for AVs; thus, this paper proposes two algorithms. The first proposed algorithm uses a driving space to generate a motion trajectory based on LiDAR data and road geometry. The other algorithm provides C2 smooth path Bézier curve by joining two cubic Bézier curve segments. Information pieces from Polaris car’s position during turning are utilized to compute the intersection road’s corner radius. The proposed algorithms have been tested in multiple simulation environments with different traffic conditions in Gazebo using Polaris car. Simulation results show that Polaris car can turn continuously and smoothly through the intersection square, demonstrating the effectiveness of the proposed algorithms.
Energy-Efficient Multi-Tier Caching and Node Association in Heterogeneous Fog Networks

Caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. In this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements.
Ship Detection in Radar Image Series Based on the Long Short-Term Memory Network

Ship detection is one of the important ocean applications of radar images. However, researches for ship detection in low-resolution images are relatively scarce. To improve the ship detection performance in low-resolution conditions, the paper adopts the range-Doppler (RD) images and takes advantage of the multi-frame information with the long short-term memory (LSTM) network. In this paper, the interpolation method and the LSTM method are proposed, which have the advantages of speed and precision respectively and show strong anti-interference ability.
A Comparative Survey of Connectivity Models in Vehicular Networks

Connectivity probability is an important indicator of the planning, design, and evaluation of vehicle ad networks (VANET). As an important part of Intelligent Transportation System (ITS), VANET realizes the wide-scale sharing of information. It makes full use of the information, effectively reduces the occurrence of road traffic accidents, and greatly improves the traffic efficiency of roads in real life. This paper firstly summarizes the characteristics of VANET and the key parameter characteristics that affect its connectivity, and then classifies the system models of related literatures and introduces the various models. The connectivity probabilities of VANET under different system models are pointed out and analyzed. Finally, the connected probability features of different models are compared by simulation analysis.
Pathloss attenuation analysis for D2D communication in 5G mmWave networks

The effect of pathloss attenuation on D2D communication in mmWave underlaying 5G networks is unprecedented. Pathloss attenuation is catered through a variety of sources into the wireless communication system. It is imperative to model a scheme through which the attenuation can be minimized. This paper focuses on a scheme based on the trade-off between pathloss attenuation and distance between the D2D users for enhancing QoS of the cellular as well as D2D users. This is achieved by mode assignment through reuse and dedicated or cellular mode. The decision is made based on the distance between the D2D users and also the pathloss attenuation attached to it as pathloss plays an equally pivotal role for a mmWave network. Finally, a comparison is also made with other existing schemes to prove its efficacy.
Scaling Trends of Digital Single-Event Effects: A Survey of SEU and SET Parameters and Comparison With Transistor Performance

The history of integrated circuit (IC) development is another record of human challenges involving space. Efforts have been made to protect ICs from sudden malfunctions due to single-event effects (SEEs). These effects are triggered by only a single strike of particle radiation, such as an $\alpha $ -ray or cosmic ray, originating from our solar activity and galactic events including supernovas. This article explores how SEEs have evolved along with the progress in complementary metal-oxide-semiconductor (CMOS) digital IC technology, or device scaling, from the early micrometer-scale generations to the current nanometer-scale generations. For this purpose, focusing on basic digital elements, that is, inverters and static random access memories (SRAMs), this study collected more than 100 sets of data on four characteristic parameters of single-event upsets (SEUs) and single-event transients (SETs), both of which are undesired flips in digital logic states. The results show that all the examined parameters, such as the SEU critical charge, decrease with the device feature size. Analysis involving structure classification, such as bulk versus silicon-on-insulator (SOI) substrates and planar versus fin channels, reveals relationships between the examined SEE parameters and other device features such as the power supply voltage. All the data collected in this survey are explicitly given in tables for future exploration of IC reliability.
High Definition Map Distribution in Named Data Networking Based VANETs

High Definition (HD) map is a key technology for autonomous driving, and how to quickly distribute HD map is an open issue. Named Data Networking (NDN) is an information-centric architecture and naturally supporting consumer mobility. NDN shows strong potential in HD map distribution for au-tonomous driving. However, there are still some problems in the autonomous driving area, such as the use of Vehicle-to-everything (V2X) cooperative communication for map data delivery, and V2X interface handover. In this paper, we propose an NDN HD map distribution scheme based on Vehicle Ad-hoc Network (VANET), which improves the map distribution efficiency in the case of limited bandwidth. The scheme is divided into three parts, a fast content delivery mechanism using multiple data sources, a probabilistic handover strategy for V2X interface handover, and an Interest sending rate control scheme for different interfaces. The evaluations show that the proposed scheme can improve by about 51% of average throughput and reduce up to 50% packet loss.
Influence of Electrical Stimulation, Cooling Rates and Aging on the Shear Force Values of Chilled Lamb

An investigation using high voltage (800 VRMS, 1140 V peak at 14.3 Hz) electrical stimulation (ES) showed that lambs should be stimulated within 30 min of slaughter for 60–120 sec for maximum effect on Warner-Bratzler shear force values. Shear force values of lamb muscles chilled at different rates were significantly (P<0.001) reduced by ES, compared with nonstimulated controls. However, aging at temperatures between 0° and 9°C, for only 2–3 days, educed shear force values of nonstimulated lamb subjected to moderate chilling rates to those of ES samples at 1 day postmortem. Increasing aging temperature significantly (P<0.001) increased aging rate but duration of aging had the greater effect.
Interpretability of Recurrent Neural Networks in Remote Sensing

In this work we propose the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for multivariate time series of satellite data for crop yield estimation. Recurrent nets allow exploiting the temporal dimension efficiently, but interpretability is hampered by the typically overparameterized models. The focus of the study is to understand LSTM models by looking at the hidden units distribution, the impact of increasing network complexity, and the relative importance of the input covariates. We extracted time series of three variables describing the soil-vegetation status in agroe-cosystems -soil moisture, VOD and EVI- from optical and microwave satellites, as well as available in situ surveys on crops across Continental U.S. to perform the experiments. Firstly, the models were validated in error terms. Secondly, the trained models were visualized and, thirdly, some useful statistics were extracted from the hidden unit activation heatmaps, accounting for redundancy and cluttering of activation responses. Results reveal how networks assign most of the relevance to soil moisture and focus on two phenological stages of crop growth.
A Fuzzy Approach to Individual Tree Crown Delineation in Uav Based Photogrammetric Multispectral Data

Multispectral remote sensing from Unmanned Aerial Vehicle (UAV) platforms is fast becoming an economical approach to gather Very High Resolution (VHR) spatial data at the individual tree level. However, detecting and delineating tree crowns are challenging due to the intrinsic intra-class spectral variability, shadow and noise in VHR images. In addition to the unrealistic assumption that tree crowns do not overlap in forests, most state-of-the-art algorithms using photogrammetric multispectral data rely maximally on the spectral information to delineate tree crowns, resulting in crown delineation errors. The joint use of both the spectral and the spatial contextual information in multispectral data in classification mitigates the effects of intra-class spectral variance and noise, and hence has the potential to accurately delineate crowns. Thus, we proposed an approach to detect and delineate individual tree crowns by performing a Markov Random Field (MRF) based spatial-contextual modelling on a fuzzy classification framework, to achieve accurate crown delineation. The results obtained prove the proposed method to be effective.
Introduction of a 5G-Enabled Architecture for the Realization of Industry 4.0 Use Cases

The increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industry 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators, but new use cases such as moving robots or drones demand a higher flexibility on communication services. Wireless technologies, especially 5th generation wireless communication systems (5G) are best suited to address these new requirements. Furthermore, this facilitates the renewal of brownfield deployments to enable a smooth migration to Industry 4.0. This paper presents results from the Tactile Internet 4.0 (TACNET 4.0) project and introduces a tailored architecture that is focused on the communication needs given by representative Industry 4.0 use cases while ensuring parallel compliance to latest developments in relevant standardization.
Multi-Access Edge Computing Architecture, Data Security and Privacy: A Review

Multi-Access Edge Computing (MEC) is an extension of cloud computing that aims to provide computation, storage, and networking capabilities at the edge of the network in close proximity to end-users. The MEC architecture supports applications and services that bridge between cloud computing and end-users. The architecture includes devices and systems that are interconnected, layered, and flexibly deployed. As a result of the technological advancements, MEC is facing a myriad of highly sophisticated threats. This paper provides a review of MEC Architecture, use cases, conceptual guidelines for MEC security architecture, security and privacy techniques, and identifies current and future challenges, their implications, and approaches to overcome the challenges. This research examined significant threats, described the MEC architecture, identified the susceptible functional layers, the different categories of threats, and the potential security safeguards. The research recommends that MEC providers should implement multiple layers of security controls to mitigate targeted attacks.
Predicting the Onset of Asphaltene Precipitation from Refractive Index Measurements

Despite the complexity of asphaltene chemistry, a simple relationship exists between the onset of precipitation and the refractive index of the mixture in which precipitation is first observed. Separation of asphaltenes from oil is a colloidal phenomenon. Because aggregation and separation of the asphaltenes are primarily dependent on the magnitude of van der Waals forces between nonpolar species, the mixture refractive index can be used to quantify the ability of a crude oil to disperse its asphaltene fraction. In some crude oils, asphaltenes are very close to the onset of precipitation, while in other oils, the asphaltenes are relatively stable. The value of the refractive index of an oil sample and the refractive index at which precipitation is first observed are distinctive properties of each crude oil. The difference between these two values is a measure of the stability of the asphaltenes in their respective crude oils. Mixture refractive index correctly predicts the onset of asphaltene precipitatio.
Improving Perceived QoS of Delay-sensitive Video Against A Weak Last-mile: A Practical Approach

We present a novel approach to improve the perceived QoS for video-feeds in delay-sensitive interactive applications under practical last-mile impairments. Conventional approaches consume additional communication and computation resources in the effort to regain the PSNR of the frames by trying to recover the lost information. Such approaches undermine the strict latency requirements leading to eventual frame-drop and reduction in overall system QoE. Instead, we aim to create an approximation of the corrupted frame by regaining the structural information and minimizing the effect of the lost packet(s). The objective is to minimize the possibility of frame drop under weakened last-mile connection so that the remote user can have a better perceived QoS at the application level and can have better probability of reacting to remote scene despite lossy condition. We prove the efficiency of the system under reallife situations with impairments in real field deployment. The benchmarking is performed in terms of full referential video quality metrics against established state-of-the art like WebRTC and RTP.
A Local Reaction Anti-Jamming Scheme for UAV Swarms

Unmanned aerial vehicle (UAV) swarms (or UAV networks) are vulnerable to jamming attacks due to the shared wireless transmission medium. In order to address this difficulty, frequency hopping based anti-jamming schemes are commonly used in the literature, however, their performance is usually limited due to the unique mobility feature of UAV swarms. In this work, a practical local reaction anti-jamming (LRAJ) scheme is proposed to reduce the packet transmission delay when the jammed nodes are dynamically changing. In the proposed scheme, the jammed nodes and their one-hop neighbors determine their node types at each frequency (channel) by exchanging information about the states of their corresponding frequencies, and performing adaptive frequency hopping (AFH) accordingly. In the mean time, the unjammed nodes can still maintain their normal operations. Therefore, with the aid of the proposed LRAJ scheme, the considered UAV swarm is able to resist malicious jamming attacks in the local area. Simulation results validate the effectiveness of the proposed scheme.
Regeneration of Used Lubricant Oil by Polar Solvent Extraction

Solvent extraction is one of the cheapest and more efficient processes for waste oil recycling. In this work the action of some solvents (2-propanol, 2-butanol, 2-pentanol, methyl ethyl ketone, and methyl n-propyl ketone) on both yield and quality of the recovered oil has been investigated. The quality has been assessed through the measurement of metallic, polymeric, and oxidation compound concentrations in the extracted oil. Experimental results have shown that extraction yields increase with increasing solvent/oil ratios up to a point at which they stabilize. When comparing alcohols and ketones it has been found that yields obtained with solvents of equal numbers of carbon atoms are similar and increase with increasing solvent molecular weight for both families. On the other hand, metallic and oxidation compound removal was similar for alcohols and ketones of equal numbers of carbon atoms, but alcohols were more efficient than ketones when polymeric additive elimination was considered. All these results.
Joint Transceiver Design for Full-Duplex Amplify-and-Forward Cooperative Systems with Frequency-Selective Fading Channels

To boost the spectral efficiency of cooperative communication systems, full duplex relays (FDRs) have been widely considered due to the concurrent signal transmission and reception at the relay. However, the self-interference (SI) is always a main problem that deteriorates the system performance. Most conventional FDR designs focus on narrowband transmission and treat SI as a harmful signal, consequently aiming to cancel SI as clear as possible. However, the relay transceiver can be inherently modeled as an infinite impulse response (IIR) filter by recognizing SI as a delayed desired signal. Based on this concept, we propose a novel design where the finite-length source, FDR filters, and the linear minimum mean-squared error (MMSE) equalizer are jointly optimized for frequency-selective fading channels. Simulations demonstrate the effectiveness of our design that preserving partial SI indeed enables further performance improvement.
Theory of Space-borne Linear Antennas Fed at Arbitrary Position in an Ionospheric Plasma

In this paper, the current distribution and input impedance of a very low frequency (VLF) antenna fed at the arbitrary position in an anisotropic ionosphere are determined. By considering the ionospheric anisotropy in the VLF band, we obtain the current along the antenna fed at the arbitrary position through the integral equation, with the contributions from both the ordinary and extraordinary waves. This work helps to improve the efficiency of space-borne antennas by analyzing their electrical characteristics.
Optimal Dispatch of Power Electronic Transformer Embedded AC/DC Hybrid Distribution System

With the development of power electronics and the higher penetration of renewable energy sources, power electronic transformer (PET) becomes a prospective device for future AC/DC hybrid distribution system. Based on second order cone programming, this paper proposes a multi-period dynamic optimal dispatch model for the multi-port PET embedded AC/DC hybrid distribution system, which focuses on the convex relaxation of the power flow constraints. The steady-state mathematical model considering the exact power loss of PET is established, multiple control modes and corresponding control variables of PET are given. The proposed two-stage joint optimization of PET AC/DC ports and active-reactive power of schedulable resources aims at improving the flexibility of power flow control to minimize the operation cost and power losses in the hybrid distribution system. The security operation of PET and the system is also considered. Case study results verify the necessity and superiority of the proposed optimal dispatch model and the renewable energy source curtailment is decreased.
A Distribution Controllable Simulation Method of Remote Sensing Sea-Ice Images

In the case of sailing out in the sea-ice areas, it is instructive for route planning to research the distribution characters of ice in the target sea. Existing deep learning methods have shown their strength on sea-ice images processing like image classification. Due to the complex environment around sea-ice area, capturing large quantities of images is not easy. Besides, it's often hard to guarantee the abundance of sea-ice distribution of each different scene class, which causes unsatisfactory classification results. Therefore, it is of considerable practical value to research on sea-ice images simulation. In this paper, a distribution controllable simulation method is proposed based on generative adversarial networks for remote sensing sea-ice images. This research can help settle the problem of small sea-ice samples, as well as can provide a practical method for optical image simulation and similar type problems.
Hyperspectral Image Classification Based on Tensor-Train Convolutional Long Short-Term Memory

In recent years, deep learning models have shown great advantages for hyperspectral images (HSIs) classification, in which long short-term memory (LSTM) has attracted plenty of attentions for its characteristic of modeling long-range dependencies. However, for the 2-D extended architecture of it (namely 2-D convolutional LSTM, ConvLSTM2D), it is the special gate structures of ConvLSTM2D that leads to a large number of training parameters and high requirements for device storage. To address this shortcoming, in this paper, a lightweight ConvLSTM2D cell is developed by using tensor-train decomposition (TTD) for the compression of training parameters, which is named TT-ConvLSTM2D and further applied to two state-of-the-art ConvLSTM2D-based HSI classification models for verifying its superiority. Experiments on a widely-used Indian Pines HSI data set are conducted, whose results demonstrate that the proposed TT-ConvLSTM2D cell can effectively reduce the number of the parameters and memory requirements of the whole models within a small range of accuracy degradation.
A Flexible Dual-Band Rectenna With Full Azimuth Coverage

This paper presents a novel cylindrical shaped dual-band flexible rectenna capable of harvesting RF energy from number of RF sources available in the entire azimuth plane. The proposed configuration is an array of four identical dual-band rectenna subsystems, printed on the lightweight flexible substrate. Here, each rectenna subsystem comprises of a dual-ring shaped dual-band monopole antenna and a dual-band rectifying circuit. The designed topology is good for popular bands of LTE 1.8 GHz and Wi-Fi 2.45 GHz. The robustness of the designed antenna in the proposed cylindrical shaped rectenna is also verified by testing it for different bending angles in the H-plane by simulation as well as measurement. The bending performance is tested for the antenna using S11 and farfield radiation pattern. Further, for extracting maximum RF signals in the entire azimuth plane, these four identical antennas are backed by a wide-band artificial magnetic conductor (AMC) placed in the inner region of the cylinder. The fabricated single rectenna unit is capable of converting power with 40% efficiency at very low input RF power −12 dBm. Finally, the overall rectenna array is fabricated and validated through measurements using two different test antennas as a RF sources in the real environment of lab. The proposed light weight, rectenna printed on a flexible substrate can extract RF signals from a number of randomly distributed RF sources simultaneously, while maintaining the overall compact configuration.