Harsh Scenarios Research Articles

Bio‐Inspired Hybrid Laser Direct Writing of Interfacial Adhesion for Universal Functional Coatings

AbstractEnhancing interfacial adhesion between functional coatings and target surfaces facilitates long‐term stable service by mitigating interferences of mechanical mismatches. Design of mechanical interlocks affords an effective strategy to strengthen the interfacial bonding with durability and compatibility, but the in‐depth investigations are still lacked. Herein, a gecko‐inspired hierarchical strategy realized by hybrid laser direct writing is proposed, which incorporates an armored frame scale for surface protection and a riveted anchor scale for interlocks. Such dual‐scale configurations endow the functional coatings with the stronger adhesion to the targets than the pristine and mono‐scale cases, resulting in 2 orders of magnitude enhancement resistant to tape peeling tests. Utilizing this scheme, a laser‐induced integrated deicing system is in situ manufactured on thermoplastics, primarily comprising superhydrophobic structures, carbon‐based sensors as well as adhesive copper (Cu) interconnects and heaters, where Cu‐based devices exhibit superior resistance to water impacts and stress fatigue. Interfacing with signal processing modules, such an all‐in‐one system demonstrates real‐time temperature monitoring and high efficiency in deicing (4.24 folds faster than the control group). The facile route for intensified adhesion holds promise in the interfaces within advanced equipment and under harsh scenarios.

Biomimetic Mechanical Robust Cement-Resin Composites with Machine Learning-Assisted Gradient Hierarchical Structures.

Biological materials relying on hierarchically ordered architectures inspire the emergence of advanced composites with mutually exclusive mechanical properties, but the efficient topology optimization and large-scale manufacturing remain challenging. Herein, this work proposes a scalable bottom-up approach to fabricate a novel nacre-like cement-resin composite with gradient brick-and-mortar (BM) structure, and demonstrates a machine learning-assisted method to optimize the gradient structure. The fabricated gradient composite exhibits an extraordinary combination of high flexural strength, toughness, and impact resistance. Particularly, the toughness and impact resistance of such composite attractively surpass the cement counterparts by factors of approximately 700 and 600 times, and even outperform natural rocks, fiber-reinforced cement-based materials and even some alloys. The strengthening and toughening mechanisms are clarified as the regional-matrix densifying and crack-tip shielding effects caused by the gradient BM structure. The developed gradient composite not only endows a promising structural material for protective applications in harsh scenarios, but also paves a new way for biomimetic metamaterials designing.

Inverse fitting direct absorption spectroscopy Technology: Simplified implementation and enhanced performance

The conventional direct absorption spectroscopy (DAS) technique has been plagued by the difficulty of obtaining accurate baseline, which is caused by photoelectric drift and the absence of non-absorbing regions in the transmitted light intensity signal. An inverse fitting direct absorption spectroscopy (IF-DAS) technique has been proposed to address this difficulty. The technique leverages the intrinsic nonlinear intensity response of tunable lasers to achieve baseline-free concentration measurements. It offers the advantages of being straightforward to implement, baseline-free, calibration-free, and resistant to photoelectric signal drift. Its efficacy was validated using an example under ambient temperature and atmospheric pressure conditions. The performance of the IF-DAS technique was compared with that of the conventional DAS technique through standard experimental tests. The results demonstrate that the IF-DAS technique is less susceptible to fluctuations in light intensity, exhibits superior linearity and accuracy, with an R2 value of 0.99986 and an overall error of less than 2%. This technique shows potential for application in harsh scenarios such as reactive flow fields and long-term engineering applications.

Wiener Filter Using the Conjugate Gradient Method and a Third-Order Tensor Decomposition

In linear system identification problems, the Wiener filter represents a popular tool and stands as an important benchmark. Nevertheless, it faces significant challenges when identifying long-length impulse responses. In order to address the related shortcomings, the solution presented in this paper is based on a third-order tensor decomposition technique, while the resulting sets of Wiener–Hopf equations are solved with the conjugate gradient (CG) method. Due to the decomposition-based approach, the number of coefficients (i.e., the parameter space of the filter) is greatly reduced, which results in operating with smaller data structures within the algorithm. As a result, improved robustness and accuracy can be achieved, especially in harsh scenarios (e.g., limited/incomplete sets of data and/or noisy conditions). Besides, the CG-based solution avoids matrix inversion operations, together with the related numerical and complexity issues. The simulation results are obtained in a network echo cancellation scenario and support the performance gain. In this context, the proposed iterative Wiener filter outperforms the conventional benchmark and also some previously developed counterparts that use matrix inversion or second-order tensor decompositions.

Bionic dual-scale structured films for efficient passive radiative cooling accompanied by robust durability.

Passive radiative cooling (PRC), as an energy-free cooling approach, is ingeniously harnessed for certain natural organisms to withstand extreme high-temperature climates, which has inspired numerous bionic designs. However, it is a great challenge to enhance the durability of the designed materials in practical scenarios while inheriting the natural biological principles. We demonstrate bionic dual-scale structured (BDSS) films for efficient passive radiative cooling accompanied by robust durability after discovering the excellent thermoregulatory properties of the inner surface of Hawaiian scallop shell. We found that the inner surface of the shell consists of large-scale triangular ridges scattered with small-scale terrace steps. This dual-scale structure can enhance the reflectivity of sunlight by efficient Mie scattering and increase the emissivity in the mid-infrared range by lengthening the propagation of photons, thereby decreasing the surface temperature. Underpinned by this finding, we developed a BDSS film that features a strong solar spectrum reflectivity of 0.95 and a high mid-infrared emissivity of 0.98, achieving a sub-ambient cooling of 10.8 °C under direct sunlight. Additionally, the designed films possess robust durability including excellent self-cleaning, flexibility, mechanical strength, chemical stability, and anti-ultraviolet radiation, which is promising for thermal thermoregulation in various harsh scenarios.

Locational detection of the false data injection attacks via semi-supervised multi-label adversarial network

False data injection attacks (FDIAs) pose a significant threat to the healthy and safe operation of smart grids. Traditional fdia detection methods are difficult to deal with complex and harsh scenarios. Considering the increasing demand for attack location services in practical power grid management, this paper proposes a new adversarial scheme called Semi-Supervised Multi-Label Adversarial Network (SMAN). To better adapt to harsh learning conditions with only a few labeled samples, this scheme combines semi-supervised mechanisms with state-of-the-art generative adversarial networks. The showdown between generator and discriminator aims to optimize the model for high detection accuracy in semi-supervised training. Among them, for the non-Euclidean structure of the power system, we propose a graph attention-based generation mechanism to improve the generator and enhance the authenticity of the generated samples. Furthermore, the classification network based on label correlation of FDIAs is proposed to capture the inconsistency and co-occurrence dependency in the measurements due to the potential attacks, which is applied to detect the exact injection locations with high dimension data. To this end, we perform label transformation and hierarchical training on the localization task to quickly localize data attacks based on multi-label classification. Extensive simulations and comparisons implemented on IEEE 14-bus and 118-bus power systems demonstrate the superiority of this scheme in localization detection. And it has been proved that it has high robustness and generalization ability under harsh conditions.

Analysis of synchronous localization systems for UAVs urban applications

Unmanned-Aerial-Vehicles (UAVs) represent an active research topic over multiple fields for performing inspection, delivery and surveillance applications among other operations. However, achieving the utmost efficiency requires drones to perform these tasks without the need of human intervention, which demands a robust and accurate localization system for achieving a safe and efficient autonomous navigation. Nevertheless, currently used satellite-based localization systems like GPS are insufficient for high-precision applications, especially in harsh scenarios like indoor and deep urban environments. In these contexts, Local Positioning Systems (LPS) have been widely proposed for satisfying the localization requirements of these vehicles. However, the performance of LPS is highly dependent on the actual localization architecture and the spatial disposition of the deployed sensor distribution. Therefore, before the deployment of an extensive localization network, an analysis regarding localization architecture and sensor distribution should be taken into consideration for the task at hand. Nonetheless, no actual study is proposed either for comparing localization architectures or for attaining a solution for the Node Location Problem (NLP), a problem of NP-Hard complexity. Therefore, in this paper, we propose a comparison among synchronous LPS for determining the most suited system for localizing UAVs over urban scenarios. We employ the Cràmer–Rao-Bound (CRB) for evaluating the performance of each localization system, based on the provided error characterization of each synchronous architecture. Furthermore, in order to attain the optimal sensor distribution for each architecture, a Black-Widow-Optimization (BWO) algorithm is devised for the NLP and the application at hand. The results obtained denote the effectiveness of the devised technique and recommend the implementation of Time Difference Of Arrival (TDOA) over Time of Arrival (TOA) systems, attaining up to 47% less localization uncertainty due to the unnecessary synchronization of the target clock with the architecture sensors in the TDOA architecture.

Computer Vision-Aided mmWave UAV Communication Systems

Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter wave (mmWave) communication with its intrinsic line-of-sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this paper, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

Enhancement of a State-of-the-Art RL-Based Detection Algorithm for Massive MIMO Radars

In the present article, a reinforcement learning (RL)-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive</i> algorithm to optimize the transmit beampattern for a colocated massive multiple-input multiple-output (MIMO) radar is presented. Under the massive MIMO regime, a robust Wald-type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-driven</i> scheme for the selection of the hyperparameters involved in the RL algorithm. Such an adaptive selection makes the Wald-RL-based detector independent of any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> , and potentially suboptimal, manual tuning of the hyperparameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.

Inside Back Cover: Volume 3 Issue 4

Inside back cover image: In this work, the bottlenecks of low cost polypyrrole in term of practical application, such as insufficient electrochemical performances and poor cycling stability, have been solved by an optimized pulse-potential polymerization strategy. This strategy effectively removed the concentration polarization effect during the electrodeposition process, leading to shorter molecular structure and homogeneous stress distribution of polypyrrole. In addition, the enhanced protonation level further boosted the charge transfer kinetics in polypyrrole. As a result, the optimized polypyrrole electrodes achieved outstanding specific capacitance and long cycle life simultaneously. The fabricated pouch supercapacitors of large size can exhibit a stable energy output and strong compressive endurance when suffering harsh scenarios of simulating real-world, which provides a promising commercial prospect for polypyrrole-based electrode materials. (DOI: https://doi.org/10.1002/smm2.1116)

A real-time algorithm for continuous navigation in intelligent transportation systems using LiDAR-Gyroscope-Odometer integration

Abstract Real-time positioning in suburban and urban environments has been a challenging task for many Intelligent Transportation Systems (ITS) applications. In these environments, positioning using Global Navigation Satellite Systems (GNSS) cannot provide continuous solutions due to the blockage of signals in harsh scenarios. Consequently, it is intrinsic to have an independent positioning system capable of providing accurate and reliable positional solutions over GNSS outages. This study exploits the integration of Light Detection and Ranging (LiDAR), gyroscope, and odometer sensors, and a novel real-time algorithm is proposed for this integration. Real field data, collected by a moving land vehicle, is used to test the presented algorithm. Three simulated GNSS outages are introduced in the trajectory such that each outage lasts for five minutes. The results show that using the proposed algorithm can achieve a promising navigation performance in urban environments. In addition, it is shown that the denser environments, that existed over the second and third outages, can provide better positioning accuracies as more features are extracted. The horizontal errors over the first outage, with less density of surroundings, reached 7.74 m (0.43%) error with a mean value of 3.15 m. Moreover, the horizontal errors in the denser environments over the second and third outages reached 4.97 m (0.28%) and 3.99 m (0.23%), with mean values of 2.25 m and 1.89 m, respectively.

Articulating Resilience: Adaptive Locomotion of Wheeled Tensegrity Robot

Resilience plays an important role in improving robustness for robots in harsh environments such as planetary exploration and unstructured terrains. As a naturally compliant structure, tensegrity presents advantageous flexibility for enhancing resilience in robotic applications according to existing research. However, tensegrity robots to date are normally based on monolithic morphologies and are slow in locomotion. In this paper, we demonstrate how we adopt such flexibility to improve the robustness of wheeled robots by articulating modules with tensegrity mechanisms. The test results reveal the robot is resistant and resilient to external hazards in a fully passive manner owing to the continuous elasticity in the structure network. It possesses a good number of DoFs and can adapt to various terrains easily either with actuation or not. The robot is also capable of crawling locomotion aside from wheeled locomotion to traverse uneven surfaces and provide self-recovery from rollovers. It demonstrates good robustness and mobility at the same time compared with existing tensegrity robots and shows the competitiveness with conventional rigid robots in harsh scenarios. The proposed robot presents the capability of tensegrity robots with resilience, robustness, agility, and mobility without compromise. In a broader perspective, it widens the potential of tensegrity robots in practical applications.

Reliable detection of pulse arrival time for partial discharge location in HV cable based on improved Allen-Bear algorithm

In multi-sensor data based partial discharge (PD) source location in high voltage (HV) cable, poor reliability of detection of pulse arrival time against noises, waveform distortion, amplitude attenuation is the major barriers to practical application. In this paper, an improved Allen-Bear PD pulse detection algorithm, which combines the advantage of the characteristic functions of the two time-window energy ratio based detection algorithms, is proposed for robust PD location. The most critical contribution of proposed method is the extension of frequency domain component compared to the characteristic functions of the original algorithm. Simulation and physical experiment results demonstrate that the proposed method not only ensures the detection accuracy of the PD pulse arrival time, but also improves the reliability under harsh practical scenarios. It performs significantly better than the existing detection algorithm in terms of anti-noise, anti-waveform distortion, time window length variation, etc. It has significant value for practical applications.

Influence of oil deposition on the measurement accuracy of a calorimetric flow sensor

The calorimetric flow sensor is easily contaminated by dust and oil vapor particles present in the flow for some harsh scenarios, and its measurement accuracy is thus affected. This paper deals with the impact evaluation of oil deposition on the calorimetric flow sensor's measurement accuracy by simulation and experiment. An oil vapor deposition experiment was proposed to present the process and distribution of oil deposition and quantitatively measure the sensor output variation after contamination. In detail, a 3D simulation model is firstly established to investigate the influences of the parameters such as the thickness, width, and location of the oil film. Accordingly, a manual oil deposition experiment by smearing oil with a fine fiber is implemented to study the effect of the oil film on the measurement and to verify the simulation results. The experiment and simulation results consistently demonstrate that the oil deposited on the heater of the sensor has the most significant influence on the measurement, followed by the oil upstream of the heater. The measurement error exceeds 40% when there is only 7 or 8 μm thick oil film on the heater.

Research on Tightly Coupled Multi-Antenna GNSS/MEMS Single-Frequency Single-Epoch Attitude Determination in Urban Environment

GNSS-only attitude determination is difficult to perform well in poor-satellite-tracking environments such as urban areas with high and dense buildings or trees. In addition, it is harder to resolve integer ambiguity in the case of single-frequency single-epoch process mode. In this contribution, a low-cost MEMS gyroscope is integrated with multi-antenna GNSS to improve the performance of the attitude determination. A new tightly coupled (TC) model is proposed, which uses a single filter to achieve the optimal estimation of attitude drift, gyro biases and ambiguities. In addition, a MEMS-Attitude-aided Quality-Control method (MAQC) for GNSS observations is designed to eliminate both the carrier multipath errors and half-cycle slips disturbing ambiguity resolution. Vehicle experiments show that in GNSS-friendly scenarios, the Ambiguity Resolution (AR) success rate of the proposed model with MAQC can reach 100%, and the accuracy of attitudes are (0.12, 0.2, 0.2) degrees for heading, pitch and roll angles, respectively. Even in harsh scenarios, the AR success rate increases from about 67% for the GNSS only case to above 90% after coupling GNSS tightly with MEMS, and it is further improved to about 98% with MAQC. Meanwhile, the accuracy and continuity of attitude determination are effectively guaranteed.

Recycling municipal solid waste incineration fly ash in fired bricks: An evaluation of physical-mechanical and environmental properties

Municipal solid waste incineration fly ash is usually listed as hazardous waste because of inorganic contaminants (heavy metals) and organic contaminants (dioxin). This study examined influences of fly ash introduction in physical, mechanical, porosity and environmental risk in the production of fired clay brick. The introduction of fly ash declined physical and compressive strength of products, and involvement of soluble salts (NaCl and KCl) in fly ash also negatively impacted physical and mechanical properties. The morphology of fired bricks shows coarse and irregular due to the addition of fly ash containing soluble salts, while became smooth and homogeneous when soluble salts were removed by washing. Meanwhile, the introduction of fly ash containing soluble salts increased surface area and pore size of products remarkably. The desalination by washing process is thus necessary for obtaining a high performance fired brick. Mineral phase analysis indicated that soluble salts hindered the formation of complex silicoaluminates (e.g. mullite), which impacted the combination of raw materials. The environmental risk of fired bricks containing fly ash are evaluated by simulated environmental leaching test, including acid rain leaching, underground water leaching and seawater leaching. Leaching results suggested fired brick incorporating 10 wt% of washed fly ash can satisfy regulation limitation, and can be used in the harsh scenarios. It is showed fly ash could be introduced in the production of fired brick after removing soluble salts by washing, and release risk of heavy metals meet regulation requirements completely.

Letter to the editor

Editor, “Inside the word emerging is emerge, from an emergency new things come forth. The old certainties are crumbling first, but danger and possibility are sisters.’’1 The population burden of SARS-CoV-2, in terms of number of new cases, has declined significantly since the peak of the outbreak. Concerns rightfully persist regarding a possible second wave of infection. We feel the impact upon acute medicine warrants further discussion. Acute medicine by definition; the immediate management of life-threatening medical emergencies, the initial treatment (first 72 hours) of all presenting general medical ailments, and the provision of ambulatory care2 witnessed almost overnight changes in patient cohorts, emergency department attendance numbers,3 service delivery and staffing models. Of the patients presenting with COVID pneumonitis, in our hospital, the sickest were manged by our intensivist colleagues, while those needing respiratory support were managed by respiratory consultants and members of the acute medicine multidisciplinary team. New ways of providing care emerged4 with innovation powered on by collaborative spirit and an infectious ‘yes culture’. Allowing those clinicians with frontline experience to drive through changes to better deliver care for our patients, is the heart of what many of those in acute medicine just want to get on and do. Even against the harsh clinical scenarios witnessed, for many, accomplishing what acute medicine delivered was a silver lining of opportunity, not many will see again. Where does all that tremendous effort leave us now? How exactly do we run two versions of the NHS – a hot and cold, a red and green or a COVID and non COVID – for patients presenting with acute medical problems. How do we ensure we get the right person to the right place, first time?5 Do we need to re-explore our relationship with primary care and the walls within which acute medicine is delivered? Even our medicine has changed – the sequalae of COVID pathology, the delayed presentation of disease states, the impacts of social isolation and the economic fall-out, the tidal wave of undiagnosed, late presentation cancer that is about to befall us. And we haven’t even mentioned winter. Acute Medicine has always been composed of talented, committed people, skills and attributes. Examples of good practice are often shared throughout the acute medicine community, critiqued and then taken home to implement on our own ‘turf’. It is vital that at this time of significant change and reconfiguration that this continues to happen in some way. Dr Sue Crossland (President of SAM) and Dr Nicholas Scriven (Immediate Past President) outlined some of the challenges facing our specialty to the Health and Social Care Committee.6 We hope that by acknowledging that COVID 19 has impacted on the delivery of Acute Medicine, and that by starting a dialogue about how different AMUs have managed such extraordinary times; the good, the bad, and the ugly of COVID 19 can be used to harness the best for Acute Medicine going forwards and ensure it’s place in the heart of unscheduled care.

BSS: A Burst Error-Correction Scheme of Multipath Transmission for Mobile Fog Computing

In the scenario of mobile fog computing (MFC), communication between vehicles and fog layer, which is called vehicle-to-fog (V2F) communication, needs to use bandwidth resources as much as possible with low delay and high tolerance for errors. In order to adapt to these harsh scenarios, there are important technical challenges concerning the combination of network coding (NC) and multipath transmission to construct high-quality V2F communication for cloud-aware MFC. Most NC schemes exhibit poor reliability in burst errors that often occur in high-speed movement scenarios. These can be improved by using interleaving technology. However, most traditional interleaving schemes for multipath transmission are designed based on round robin (RR) or weighted round robin (WRR), in practice, which can waste a lot of bandwidth resources. In order to solve those problems, this paper proposes a novel multipath transmission scheme for cloud-aware MFC, which is called Bidirectional Selection Scheduling (BSS) scheme. Under the premise of realizing interleaving, since BSS can be used in conjunction with a lot of path scheduling algorithms based on Earliest Delivery Path First (EDPF), it can make better use of bandwidth resources. As a result, BSS has high reliability and bandwidth utilization in harsh scenarios. It can meet the high-quality requirements of cloud-aware MFC for transmission.

A Joint Scheduling and Beamforming Scheme for RoF-Aided MC-SSN

Applied to harsh scenarios, the mission critical sensors and sensor network (MC-SSN) is expected to be of high capacity, density, and low cost. Both the industry and research community have a consensus on the need for energy and spectrum saving. Radio over fiber (RoF) system is promising for low latency and flexible system of high efficiency. In this paper, we consider the RoF-aided MC-SSN system serving the sensors with massive multiple-input multiple-output (MIMO) equipped. Based on this model, the base stations, relays, and other network elements can set up connections with each other via optical fiber or wireless links. Aiming to maximize the energy efficiency, this paper proposes a scheme including two aspects: 1) scheduling of the sensors based on the direction of arrival sensing of interference an 2) massive MIMO beamforming matrices generation based on a codebook. The numerical results show that the proposed scheduling and resource allocation method can achieve sufficient throughput with reduced power consumption for the RoF-aided MC-SSN system.

Reliable Storage System With Priority Filter and Load Balance Collection Model for Large Scale Sensor Networks

Wireless sensor network are widely used in various types of environmental monitoring and there are also many applications in the industrial field, we usually deploy sensor nodes in the corresponding areas to monitor and collect relevant data, waiting for a sink node to collect the stored data for further analysis and decision. But in some extremely harsh scenarios, the living environment of the sensor node is very bad, hence the data on the nodes will always lose. Generally, we use LT Codes to improve data persistence. However, some difficult problems with regard to the efficiency of coding and decoding of data can not be resolved by traditional solutions. In terms of the above problems, this paper improves the traditional coding method by reducing the number of encoded packets, so that the network congestion can be alleviated under the premise of successful data coding. What's more, we propose an edge layered collection model and a new coding strategy Edge Layering Fountain Codes (ELFC) based on the model, each sensor node inside the network sends its data to the edge nodes which are relatively safe to ensure the secure collection of data and allocate the resource consumption of the sensor nodes around the sink node. On this basis, the Ordered Edge Layering Fountain Codes (OELFC) is further proposed. The OELFC can collect data packets in a low degree-to-high degree manner, which greatly improves the efficiency of data decoding.