Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agent Deep Reinforcement Learning based Interdependent Critical Infrastructure Simulation Model for Situational Awareness during a Flood Event

The paper proposes a Multi-Agent Deep Reinforcement Learning (MADRL) simulation model that is useful in understanding the status of Critical Infrastructures (CI) during extreme events. The simulation model can be used to understand the spatiotemporal nature of the event and evaluate and predict the propagation of cascading failure scenarios in the critical infrastructure network. Multi agent-based modeling is performed by interconnecting multiple agents, which are autonomous computational entities. Geospatial based intelligent agents are developed, such that each agent registers with a CI such as a Healthcare infrastructure agent, Transportation agent, etc. These agents check for an infrastructure state change (e.g. the roads which lead to the hospital are blocked due to debris), and if there is a state change then they would reason about the impacts of these events upon other dependent infrastructures. Deep reinforcement learning approach helps the geospatial based CI agents in making a rapid and an optimal decision based on its spatiotemporal environment, during a flood event. The utility of the approach is evaluated using a real-world case study. Real-time information simulation would help disaster response personnel to respond to the question, ‘what if something else happens?
Analysis of wireless device-to-device networks under Rician fading using stochastic geometry

This paper investigates device-to-device (D2D) communications where two nearby users are enabled to communicate directly with each other. A network analysis under Rician fading is proposed to account for a strong line-of-sight (LOS) component typical for this technology. Semi closed-form expressions for the cellular (CC) and underlaid D2D communication outage probabilities are derived relying on stochastic geometry theory. Schemes where D2D communications reuse the uplink (UL) or the downlink (DL) CC bands are compared. The choice between them is shown to strongly depend on the base stations (BS) and user equipments (UE) transmit powers.
Multi Objective Barnacle Mating Optimization for Control Design of a Pendulum System

This paper presents a MultiObjective Barnacle Mating Optimization (MOBMO) and its application to optimize controller parameters for an inverted pendulum system. The algorithm is an extended version of a single-objective Barnacle Mating Optimization (BMO). In terms of solving a complex problem that has two conflicting objectives, a multiobjective type BMO is needed. Therefore, in the proposed MOBMO, nondominated sorting and crowding distance approaches are incorporated into BMO as a technique to formulate the multiobjective algorithm. The proposed algorithm is tested on various multiobjective benchmark functions. Its performance in terms of accuracy and diversity attainment to find a theoretical pareto front solution is analyzed. Moreover the proposed MOBMO is applied to optimize control parameters for PD controls of a pendulum system. The performance of the proposed MOBMO is compared with Multiobjective Water Cycle Algorithm (MOWCA). Result of the benchmark functions test shows that the proposed algorithm has attained a higher accuracy and a competitive diversity in locating the theoretical front solution. For its application to optimize PD control parameters, both MOWCA and MOBMO have successfully attained a good pareto front solution and controlled the pendulum sufficiently good. Overall performance, the proposed MOBMO has outperformed MOWCA for accuracy attainment and achieved the same level of diversity performance.
A Vehicle-Aided Data Collection Scheme for Wireless Rechargeable Sensor Networks

Extensive use of wireless rechargeable sensor net-work (WRSN) in various real-life detection and monitoring applications requires collection of environmental data periodically from the sensors. Some researchers try to solve the problem through multi-hop communication, where sensors that are located close to the base station (BS) will collect and forward data to the base station. However, these sensors run out of energy quickly, and data collection severely affected. Some researchers use mobile vehicles for data collection from all the sensors. Due to limited battery capacity of these vehicles, they may run out of their energy in the middle of the tour resulting into an ineffective data collection schedule. In this paper, we have formulated an optimization problem in WRSN for data collection with multiple vehicles, whose objective is to collect data from as many sensors as possible while minimizing the average travel distance of all the vehicles. We prove the problem to be NP-complete, design an effective data collection scheme while evaluating the performance of the proposed algorithm with respect to the existing schemes.
Deep Convolutional Neural Network for Mangrove Mapping

Updated information on the spatial distribution of mangrove forests is of high importance for management plans. Yet, access to mangrove distribution maps is limited, even-though remote sensing data is currently freely available and deep learning algorithms score high performances in automatic classification tasks. The methodologies developed in this paper are based on a deep convolutional neural network and have been tested on WorldView 2 and Sentinel-2 images. The obtained results are highly satisfactory and open perspectives for automatically mapping mangrove distribution over large areas.
Effect of CO2 and H2O on the synthesis of single‐walled CNTs

Two different floating catalyst synthesis methods were used to investigate the effect of CO2 and H2O vapor on the growth of single‐walled carbon nanotubes (CNTs). As a carbon source and catalyst material, carbon monoxide and iron nanoparticles were used. The catalyst particles were prepared by evaporation from a resistively heated iron wire (hot wire generator) and by thermal decomposition of ferrocene vapor. The essential role of CO2 and H2O in the CNT formation was demonstrated. The addition of small amounts of CO2 and H2O vapor in the reactor resulted in an increase in CNT length. Also, the CO2 introduction was found to decrease the minimum temperature of the CNT synthesis from 890 °C to below 600 °C. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Compact MIMO Slot antenna for UWB Applications with single Notched-band

In this paper a compact MIMO slot antenna for UWB applications with single notched band is presented. The proposed antenna has a very compact size $30\times 30\times 1.6\ \text{mm}^{3}$. A stepped slot is integrated in the ground plan to obtain an UWB band width. L-shaped slot is integrated near the stepped slot to create notched band. Acceptable results are obtained in terms of mutual coupling and ECC between the antenna elements. The radiation pattern is omnidirectional for H-Plan and bidirectional for E-Plan.
Comparative Study of Selector Device Design for Sneak Current in 3D Crosspoint ReRAM

Resistive Random Access Memory (ReRAM) is an emerging high density storage device in today’s world. The crosspoint connection of the ReRAM cell will have the advantage of eliminating the expensive transistors in the design. But the challenge in the design is the selection of the selector device. A review of selector device design on 3D crosspoint ReRAM is presented in this article. The number of ReRAM devices are connected with the wordline and bitline in the crossbar array to have high density storage. This article illustrates the methods for the stacking of ReRAM memory cells and selector devices. The flow of sneak current and the formation of sneak path are discussed in the article. The sneak path will consume the storage space in the memory device unwantedly, so it has to be reduced for the efficient use of the storage cell. The review focuses on the reason for the design of selector device and the methods that are available to compensate for the sneak current problem in the crossbar ReRAM memory and to improve the endurance and resistance ratio of the device are also discussed.
Winter Wheat Mapping from Landsat NDVI Time Series Data Using Time-Weighted Dynamic Time Warping and Phenological Rules

Winter wheat occupies an important position in agricultural production and national economy. In this study, we proposed a new method of winter wheat mapping that combined time-weighted dynamic time warping (TWDTW) and phenological rules from Landsat NDVI time series data. The proposed method was evaluated in winter wheat mapping using Landsat time series data in Beijing and Hebei Province, China. The experimental results obtained showed that the proposed method outperformed the existing method in mapping winter wheat with an acceptable accuracy.
Heterogeneous Multi-Operator Spectrum Sharing Architecture for Massive IoT Access with NOMA

For the massive access of Internet-of-Things (IoT) devices in 5G or beyond 5G (B5G) wireless networks, how to support the coexisting of cellular users and massive IoT devices with quality-of-service (QoS) guarantees over limited spectrum is challenging. In this paper, inspired by the active radio access network (RAN) sharing standard of 3GPP, we present a heterogeneous multi-operator spectrum sharing architecture by leveraging the spectrum trading to support the coexistence of QoS-guaranteed cellular users and massive IoT devices with non-orthogonal multiple access (NOMA). In the architecture, we formulate the spectrum trading between the wireless spectrum provider (WSP) and mobile network operators (MNOs) as a Stackelberg pricing game. Meanwhile, for each MNO, the cellular users and massive IoT devices can be both serviced in the uplink NOMA networks with QoS guarantees. For the pricing game, we prove the uniqueness of the equilibrium solution, which can maximize the payoffs of MNOs and WSP simultaneously. Moreover, we propose an iterative pricing algorithm to achieve the equilibrium solution with theoretical optimality guarantees. Simulation results demonstrate that our framework can support more IoT devices and reach the optimal spectral bandwidth price.
Compressed Multiple Random Access with Energy Modulation

Massive machine-type communications (mMTC) are expected to support bunches of low cost devices, which are stochastically active. By utilizing the simple while efficient noncoherent energy-based transmission scheme, this work aims to jointly detect the user activity and the data in mMTC. First, by exploiting the sparse characteristics of the user activity, approximation message passing (AMP) algorithm is proposed to suppress the multi-user interference. Then, maximum a posteriori (MAP) criterion is adopted to approximately detect the user activity and the desired data. By minimizing the symbol error probability of the above two-step algorithm, the power constellations at each user are designed. Finally, the scaling behavior of the considered system is analyzed, and it is shown that to guarantee reliable communications, the number of the receiver antennas per user should vanish in the order of $O(\frac{1}{\text{log}(N)})$ with $N$ being the number of the total users.
Camera-based Smart Traffic State Detection in India using Deep Learning Models

In the age of smart cities, it is more than necessary that a real-time road accident detection system exists that can automatically identify the vehicle, predict its level of damage, and notify emergency services for quick perusal. In this paper, we build an intelligent traffic monitoring system using state-of the-art deep learning models that aims to detect traffic accidents, burning vehicles, and traffic state (congestion/sparse traffic) using a single model for real-time monitoring purpose. Web-crawling was done to create a custom-built dataset for Indian traffic conditions. The developed models were found to perform decently in Indian traffic conditions with more than 90% accuracy showing the efficacy of the models in Indian conditions.
High-Resolution Remote Sensing, In-Situ Observations, and Modeling of Low-Salinity Lenses in the Presence of Oil Slick

River runoff on the Louisiana Coast produces shallow, low-salinity lenses. Due to the presence of a leaking oil platform (Taylor Energy), the fine structure of these lenses is visible in synthetic aperture radar (SAR) imagery. In this paper, we report results of the comprehensive study including high-resolution remote sensing and in-situ observations of low-salinity lenses on the Louisiana Coast as well as modeling the lens using a computational fluid dynamics (CFD) model. Our results indicate that these transient near-surface lenses create significant horizontal density gradients and spread as gravity currents, influencing the propagation of oil slicks.
MFENet: An 1D CNN-based Model for Automatic Feature Extraction and Fusion

As Android phones become the mainstream of smartphones, more and more malicious applications are being developed to attack the system and steal user information. Many researches based on machine learning can automatically detect Android malware currently. In spite of that, these methods require manual feature selection, which relies on experience and may loss major features. In this paper, we propose a tiny Android malware detection model named MFENet. This model is based on the one-dimensional convolutional neural network (1D CNN). The core of the model is MFEBlock, which can extract key features and fuse them automatically. This model got 94.62% accuracy on the test dataset, which achieves the outstanding results for Android malware detection on an open dataset.
Evaluation of the Relationship Between IASI NH3R-I Total Column and Terrestrial Vegetation Conditions

The IASI NH3R-I total column products provide a new perspective for monitoring global atmospheric ammonia (NH3) concentration. However, there are few evaluations on its correlation with vegetation conditions. In this paper, we use binning and resampling to evaluate the relationship between IASI-retrieved NH3 concentration and MODIS-retrieved vegetation indices from 2008 to 2016. As the results show, there is a significant positive correlation between NH3 concentration and vegetation conditions. The relationship has a clear intraannual pattern, while it has great spatial heterogeneity. Another finding is that nighttime NH3 observations generally have a better correlation with vegetation conditions. Still, deeper investigations and model simulations are needed to explain the mechanism behind this relationship and its spatial and temporal patterns.
Non-intrusive Load Monitoring Using Inception Structure Deep Learning

Non-intrusive load monitoring is an important technology to realize energy efficiency tracking and smart electricity consumption, in which the load identification method is very important. In order to realize non-intrusive load monitoring, we pro pose a method based on deep neural network. The dataset is derived from the total energy consumption, sub-consumption and weather data collected from the buildings in the commercial building park in Shanghai, China. First, we perform data cleaning to mi ne the basic characteristics of the commercial building load. Second, we propose a deep learning model based on the Inception structure that combines a Multi-Layer Perceptron, Convolutional Neural Networks, and Long Short-Term Memory to address the problems of overfitting and long computation time of the deep learning model. Finally, a comparison with a common deep learning network model is made on a test data set to verify the effectiveness and accuracy of the proposed method.
KPIs-Based Clustering and Visualization of HPC Jobs: A Feature Reduction Approach

High-Performance Computing (HPC) systems need to be constantly monitored to ensure their stability. The monitoring systems collect a tremendous amount of data about different parameters or Key Performance Indicators (KPIs), such as resource usage, IO waiting time, etc. A proper analysis of this data, usually stored as time series, can provide insight in choosing the right management strategies as well as the early detection of issues. In this paper, we introduce a methodology to cluster HPC jobs according to their KPI indicators. Our approach reduces the inherent high dimensionality of the collected data by applying two techniques to the time series: literature-based and variance-based feature extraction. We also define a procedure to visualize the obtained clusters by combining the two previous approaches and the Principal Component Analysis (PCA). Finally, we have validated our contributions on a real data set to conclude that those KPIs related to CPU usage provide the best cohesion and separation for clustering analysis and the good results of our visualization methodology.
Steady-state Security Region-based Chance-constrained Optimization for Integrated Energy Systems

The power injection uncertainties caused by renewable power generations and demand response dramatically affect the operation optimization of integrated energy systems (IESs). Then, how to coordinate the energy hubs in the systems to meet this challenge has become a key issue. Although the chance-constrained method is a workable solution, the determination for chance-constraints is complex, which leads to great difficulty in solving the model. Therefore, the steady-state security region (SSSR) method is applied into the optimization in this paper. First, a SSSR-based model for IESs is established. Second, the power inputs of energy hubs are set as variables and linear chance-constrained expressions are fast generated by using a Cornish-Fisher expansion-based method. Because the variables in both constraints and the objective are identical, the computation of optimization is greatly reduced. Third, a linear model is established with the minimum total cost of IESs, and the calculation speed is very fast when using the linear programming method. Optimization results for the test combined IEEE 33-node power system with 13-node gas system and the test combined PG&E 69-node power system with Belgian gas system are given to verify the effectiveness of the proposed method.
Short-term Wind Power Prediction Based on IPSO-GSA Optimized Neural Network

Accurate and reliable short-term wind power forecasting is very important for coping with wind power fluctuation and optimizing wind farm operation. Due to the complexity and variability of wind farm surroundings, traditional forecasting methods are difficult to meet the requirements of refined and dynamic modeling, and there are problems such as easy to fall into local optimum, convergence speed and forecast accuracy to be improved. In response to the above requirements, this paper established an intelligent wind power prediction method based on the IPSO-GSA-BP model Firstly, in view of the defects of the universal gravitation algorithm (GSA) such as the lack of jumping out of the local optimal mechanism and the group memory function, the Inproved Particle Swarm optimization algorithm (IPSO) is used to improve the universal gravitation search algorithm from the aspects of particle inertial mass and global search to improve its optimal precision. Then, the BP neural network is optimized by the optimal parameters obtained by the IPSO-GSA. Finally, a simulation experiment was carried out using the operating data of a wind farm in southern China, and compared with other models. The results showed that the method proposed in this paper has higher accuracy and higher reliability in short-term wind power forecasting, and can realize refined and dynamic modeling, so as to provide a new technological path for optimal scheduling management in wind farm.
A Geographically Weighted Total Composite Error Analysis for Soft Classification

Errors in land cover classification are often spatially heterogeneous even though a soft classification model such as spectral unmixing is implemented to mitigate a mixed pixel problem. The estimated land covers are fractions of targeted classes with the restriction of the sum to one and being non-negative. To assess the classification with considering a spatial heterogeneity, we propose a geographically weighted total composite error analysis. By using the USGS global reference database, we assessed errors of spectral unmixing classification of ALOS AVNIR-2 data into 4 land cover classes. Results yield a spatial surface of local errors by the Aitchison distance and address that the error magnitude across space is associated with the complexity of land covers.
Efficient Distribution of Key Chain Commitments for Broadcast Authentication in V2V Communications

Road safety applications such as intersection collision warning, emergency brake warnings, etc., rely on the periodic broadcast of messages by vehicles and roadside infrastructure. PKI-based approaches ensuring the integrity of messages and the legitimacy of the sender are computationally expensive and result in long messages. Approaches based on hashed key chains such as Timed Efficient Stream Loss-tolerant Authentication (TESLA) offer an alternative solution. Because they use symmetric-key cryptography, the messages are shorter and less expensive to verify. However, they bring their own challenges. This paper focuses on one challenge, the problem of distributing key chain commitments required for message verification. We propose and evaluate two techniques, respectively involving periodic broadcast of commitment keys by the vehicles themselves and selective unicasting by a central V2X Application Server (VAS). We find that the VAS-centric solution has advantages over the vehicle-centric solution and a related solution proposed by other researchers.
Accelerating Sustainable Development with Earth Intelligence

The global community is increasingly recognizing the value of scalable and accurate Earth Intelligence in the pursuit of the United Nations Sustainable Development Goals (SDGs). This paper reviews several case studies where public and private sector organizations are leveraging Earth Intelligence, a combination of satellite imagery, big data analytics, and artificial intelligence-based approaches, in support of the SDGs.
A Video Object Detector Based on Deep Reinforcement Learning and Correction Module

As a basic research topic of computer vision, object detection is facing the challenges of being extended for video object detection. For deformation anomaly, low-quality candidate regions feature can be caused by motion blur, rare poses and video defocus, resulting to poor detection performance in videos. In this paper, we propose a video object detector, which consists of an image-based detector using deep reinforcement learning and a correction module using objects tracking algorithms. We treat videos as continuous images. The agent will search on the feature map of each image by a policy network to generate high-quality candidate regions. These candidate regions will be corrected by a correction module. The bounding box at the current image from the tracklet proposed by the object tracking algorithms will correct the candidate regions. Experiments are conducted on the MOT 15 dataset. Our detector achieves better detection performance, exactly 68.28% with 5.01 points improvement compared to the Faster-R CNN as the single-frame baseline.