Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Time-To-Collision: Correlation of Driver Gaze and Surrounding Objects

Observing surrounding traffic is one of the primary responsibilities of a driver and research has shown that a proportion of accidents could have been prevented if the driver had reacted faster to, e.g., braking vehicles. One reason for slowed reaction is missing visual attention, used, for instance, to set up the navigation system or operate the multimedia system. This reduces driver's ability to perceive other vehicles. In this work, we propose a method to quantify the effect of missing perception of leading vehicles. We extend Time-To-Collision (TTC) to a probabilistic TTC (PTTC) model considering the (additional) perception of the driver instead of the vehicle's sensor system only. The underlying theory is that whenever the driver's gaze is off the road, he cannot observe whether vehicles ahead apply the brake. The position of leading vehicles is modeled probabilistically, according to the gaze-off-road time. Results from a naturalistic driving study (vehicle sensors, driver's face) indicate, e.g., a reduced environmental perception during lane change situations, but with little impact on road safety. Data further show that in situations with high TTC, drivers tend to operate controls in the center console. PTTC will now be used as a more realistic, driver related, TTC variant.
An evolutionary algorithm for reducing fear of crime

A fundamental aspect of the perception of security is the fear of crime, which is the concern of being a crime victim. The fear of crime has negative social consequences, including neighborhood deterioration, physical and behavioral health outcomes, among others. Different interventions allow fear of reduction, including crime reduction, an increase of police presence, and improvement of social cohesion, among others. However, there are no quantitative approaches to guide the selection of policies for reducing the fear of crime. This article proposes a novel method based on optimization for finding policies aimed to decrease fear of crime by using mathematical models and evolutionary algorithms. Results suggest that policies that promote interactions among members of different groups may enhance community cohesion resulting in reductions of the fear of crime for the most susceptible members in the group.
Design of a Microstrip Rectenna for WLAN Wireless Energy Harvesting

A microstrip rectenna for WLAN wireless energy harvesting is proposed in this paper. The rectenna is composed of a microstrip antenna and a rectifier. The operating frequency of the receiving microstrip antenna is from 2.418GHz to 2.458GHz which the reflection coefficient is below −10dB. The circuit of rectifier is based on single stage double voltage rectifying structure. The working bandwidth of the rectifier is 40MHz. When the input power is −15dBm and the load resistance is $4\mathrm{K}\Omega$, the output voltage of the rectenna at 2.44GHz is 0.11V.
Estimation of Crystal Orientation Fabric from Airborne Polarimetric Ice Sounding Radar Data

Information on ice sheet crystal orientation fabrics (COF) is valuable, as it tells about past deformation and current flow properties, which can potentially improve ice flow modelling. Due to the birefringence of ice sheets, ice sounding radars can provide the desired COF information if multiple polarizations are employed. Until now such data have mostly been acquired with ground-based single-polarization radars by rotating the antenna between multiple data takes. In this paper, a novel method based on polarimetric radar data, polarization synthesis, and complex coherence is proposed and tested with data from an airborne, polarimetric ice sounding radar. The results are consistent with ice core analyses, ice sheet physics, and existing radar results.
Ultrasound-Mediated Chemical Sensing Using Titanium Dioxide (TiO2) Nanoparticles-Embedded Hydrogel with Possibilities of Performance Enhancement Using Machine Learning

In situ chemical sensing, such as temperature, pH, or a specific molecule, is important in healthcare and environmental monitoring [1]. The principle of chemical sensing usually involves multiple elements, including receptor, transducer, and a complex readout circuit. The receptor selectively detects chemical information, which is then translated into electrical signals by the transducer. However, the receptor is usually consumable, and the transducer requires frequent calibration due to the accumulation of chemical species (e.g., protein, biofilm). More recently, hydrogel-based sensing mechanisms based on the incorporation of micro/nanoparticles onto the polymeric network of hydrogels have been reported [2]. For instance, Holtz et al. demonstrated crystalline colloidal particles polymerized within a hydrogel allowed the change of color in response to glucose [3]. Another effort by our group was embedding silica bead in a pH-sensitive hydrogel, which can be interrogated by ultrasound imaging [4].
Identification for Recycling Polyethylene Terephthalate (PET) Plastic Bottles by Polarization Vision

The recycling of disposed PET plastic bottles has significant meaning for environmental pollution control and resource recycling. The reuse value heavily depends on the purity of the recycled PET. In order to improve the purity of PET recycling, a new means of material identification of PET plastic bottle based on polarization vision is proposed. To identify PET plastic bottles on conveyor belt, an effective region segmentation method for plastic bottle location and feature extraction has been put forward. The polarization of the scattered light on the surface of the object contains the information of its material. To obtain robust material characteristics of the PET plastic bottle, an approximate refractive index calculation method based on the degree of polarization and phase angle is introduced. The mean, standard deviation and coefficient of variation of the approximate refractive index and intensity distribution are used to construct the feature vector. The support vector machine (SVM) algorithm was used for PET identification. The experimental results show that the mean accuracy of PET plastic bottles material identification of recycled bottles is up to 92.06%.
Understanding the Personality of Contributors to Information Cascades in Social Media in Response to the COVID-19 Pandemic

Social media have become a major source of health information for lay people. It has the power to influence the public's adoption of health policies and to determine the response to the current COVID-19 pandemic. The aim of this paper is to enhance understanding of personality characteristics of users who spread information about controversial COVID-19 medical treatments on Twitter.
CO2 Sequestration and Fate of Organic Matters within Seagrass (Zostera marina) Ecosystem

The purpose of this study was to clear the fate of organic carbon and nitrogen in seawater and sediment, especially the difference between organic carbon and nitrogen in dissolved and particulate forms and to determine carbon sequestration by seagrass ecosystem. We had carried out four types experiments: the diel variation for 24 hour, and seasonal changes of DOC (Dissolved Organic Carbon)/DON (Dissolved Organic Nitrogen) and POC (Particulate Organic Carbon)/PON (Particulate Organic Nitrogen) in outside and inside of seagrass communities, and the enclosed chamber experiment for evaluation of release of organic matters from seagrass exuadation and the sediment, decomposition experiments in laboratory and in situ. POM (Particulate Organic Matter) of seagrass leaf litter is resistance to the biological degradation under 25°C. (The rate constant of decomposition is 0.0083 d -1 , namely remineralization time is about 120 days.) While POM in water column shows POC is more rapidly consumed to PON, and POC/PON ratio decresed with time of decomposition. DOM in water column shows DON is more rapidly consumed to DOC and DOC/DON ratio increased with time of decomposition. This suggests that rate of solubilization of DON from PON may dominate a rate of remineralization of nutrients in seagrass communities. We developed a kinetic model of the seagrass decomposition process (SDP) changes in POC/PON ratio with time as well as phytoplankton decomposition model (Fujii et al., 2002). The SDP model was adapted to the results of three kinds of decomposition experiments where the initial C/N ratio and the volume ratio of POM and DOM differed by more than a factor of three. The apparent zeroth-order decrease in POC and the constant PON in the first labile decomposition process are well expressed by the SD model, and consequently, the calculated POC/PON ratio shows good agreement with the experimental result. A realistic model for CO 2 sequestration was also developted using net organic production (net organic production=gross organic production-respiration), sedimentation inside seagrass bed and sedimentation at the open ocean (=export flux). We calculated net sequestration of carbon into deep water after 100 years under two different sinking velocities which are for 6 m/d and 24 m/d. These results showed that 120 t-C for 6 m/d and 150 t-C for 24 m/d during 100 years were accumulated into deepwater, suggesting that 1.2 to 1.5 t-C/year for 31 t-C of gross production of seagrass/km 2 are accumulating into deep water. Seagrass ecosystem is very effective as one of the CO 2 sequestration.
An Interferometric W-BAND Radar for Large Structures Monitoring

Interferometric radars are widely used for static and dynamic displacement monitoring of large structures as bridges, culverts, wind turbine towers, chimneys, masonry towers, stays cables, buildings, and monuments. The most of these radars operates in Ku-band (17 GHz). Nevertheless, a higher operative frequency could allow to design smaller and lighter equipment. The aim of this paper is to verify if a commercial 77 GHz radar designed for automotive applications could be used as interferometric radar for static and dynamic monitoring of large structures. The preliminary results obtained in controlled conditions are very promising.
AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles

The realization of vehicle-to-everything (V2X) communication enhances the capabilities of autonomous vehicles in terms of safety efficiency and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this regard, open-source software plays a significant role in prototyping, validation, and deployment. Specifically, in the developer community, Autoware is a popular open-source software for self-driving vehicles, and OpenC2X is an open-source experimental and prototyping platform for cooperative ITS. This paper reports on a system named AutoC2X to enable cooperative perception by using OpenC2X for Autoware-based autonomous vehicles. The developed system is evaluated by conducting field experiments involving real hardware. The results demonstrate that AutoC2X can deliver the cooperative perception message within 100 ms in the worst case.
Monitoring of the Cross-Calibration Biases Between the S-NPP and NOAA-20 VIIRS Sensor Data Records Using Goes Advanced Baseline Imager as a Transfer

To provide near-real time monitoring of Suomi-National Polar-orbiting Partnership (S-NPP) and NOAA-20 data inter-sensor biases, this study extends the Geosynchronous Equatorial Orbit – Low-Earth Orbit (GEO-LEO) intercalibration method established in [1] to the Visible Infrared Imaging Radiometer Suite (VIIRS) Sensor Data Record (SDR) data at six reflective solar bands (RSBs) and three thermal emissive bands (TEBs) bands via the STAR Integrated Calibration and Validation System (ICVS) framework. The Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) is used as a transfer to calculate double difference (DD) of VIIRS-ABI Simultaneous Nadir Overpass (SNO) pairs for the S-NPP and NOAA-20 VIIRS SDR cross-calibration biases. A series of sensitivity analyses on the dependence of the results to view geometries, spectral difference corrections, latitudinal variation, and cloud screening are conducted for more accurate bias estimations. The findings are further verified by the other independent approaches, namely the 32-day average difference method (32Day-AD) [2] and DD with radiative transfer model as a transfer (RTM-DD) method.
Intelligent Wristbands - Fabrication of Wearable RFID Solutions by 3D Printing Pen

We present RFID (radio frequency identification)-based passive UHF (ultra-high frequency) wristbands fabricated with a 3D printing pen. The used 3D printing material is flexible and biodegradable, and thus suitable for fabrication of such inexpensive and widely useful wearable wireless platforms. The tag antenna (that is fabricated from conductive thread) and the RFID IC (integrated circuit) are embedded into the platform by using 3D-printed fasteners. This paper presents the wristband fabrication, ensures their reliability, and carries out practical testing in normal use environments. Based on the achieved results, the studied combination of conductive thread and 3D printing pen is a suitable fabrication solution for intelligent wearable platforms. The fabricated wristbands show high moisture reliability and excellent wireless performance in practical testing situations.
Investigation and Improvement of VGG based Encoder-Decoder Architecture for Background Subtraction

Object detection in motion pictures is always a challenging task due to the presence of dynamic background. Deep learning architectures especially encoder-decoder type has shown promising performance in segmenting foreground objects against the background in video sequences. Thus, in this work, a VGG-16 based encoder-decoder architecture is investigated and several modifications are proposed to improve the efficiency the model. The modified models are evaluated on two different standard databases— CDNet 2014 and SBI2015 with various scenes and achieved the highest precision of 0.99 which is competitive in nature with the current schemes in the state-of-the-art.
Discriminative Semi-Supervised Generative Adversarial Network for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection has been facing great challenges in the field of deep learning due to high dimensions and limited samples. To address these challenges, a novel discriminative semi-supervised generative adversarial network (GAN) method with dual RX (Reed-Xiaoli), called semiDRX, is proposed in this paper. The main contribution of the proposed method is to learn a reconstruction of background homogenization and anomaly saliency through a semi-supervised GAN. To achieve this goal, firstly, the coarse RX detection is performed to obtain a background sample set with potential anomalous pixels being removed. Secondly, the obtained coarse background set learns more comprehensive background characteristics through the network. The original hyperspectral image (HSI) is fed into the learned network to obtain reconstructions with homogeneous backgrounds and salient anomalies. The refined detection results are generated by a second RX detector. Experiments on three HSIs over different scenes demonstrate its advancement and effectiveness.
MIMO Detector Selection for Multiple High-Order Modulations with Unified Neural Network

We propose a unified multi-layer perceptron (MLP) network to select an appropriate multiple-input, multiple-output (MIMO) detector for high-order quadrature amplitude modulations (QAMs) such as 64-, 256-, and 1024-QAM. The network is trained to select a low-complexity detector dynamically for 64-, 256-, and 1024-QAM from a set of candidate detectors, while simultaneously maintaining a block error rate (BLER) close to the highest complexity candidate detector. We train the network on a combined data set collected under various environments such as multiple modulation orders, channel profiles, and signal-to-noise ratios. For offline training, a selective back-propagation method is proposed wherein samples of specific $M$-QAM are used to update modulation specific weights between the hidden layer and the output layer of the MLP network. The common weights between the input layer and the hidden layer are updated using all the samples in the data set. Thus, a single unified network can be deployed for multiple modulation orders together. The performance is evaluated with different channel profiles including channels for which the network was not trained. Simulation results show that the proposed algorithm maintains the BLER close to that of the most complex candidate detector even under un-trained channel profiles. The proposed algorithm also reduces the computational complexity of the MIMO detection block up to 10×.
A management system for forestry remote sensing images based on the global subdivision model

In this research, we proposed a data management system for forestry remote sensing images. The methodological foundation of this research is the global subdivision theory. The storage and management of massive remote sensing data have become one of the key issues in the field of spatial information science, public service and commercial activities. The proposed management system for forestry remote sensing images is based on GeoSOT global subdivision model. This management system simplifies the process for forestry remote sensing data collection, storage, analysis and display procedures; besides, it demonstrates the potential to be applied to several forestry related applications.
ADSORPTION OF SUBSTITUTED PHENOLS BY SOIL

I evaluated the effect of introducing other functional groups to phenol on adsorption by soil and developed equilibrium adsorption isotherms for phenol, catechol, resorcinol, and the ortho, meta, and para isomers of cresol, methoxyphenol, nitrophenol, and chlorophenol. Introducing —CH3, —OCH3, —NO2, or —Cl groups to phenol resulted in increased adsorption. With the exception of o-nitrophenol, adsorption of the phenols studied occurred by H-bonding to sites on soil surfaces. Adsorption was generally higher than would be predicted for hydrophobic sorption, presumably due to H-bonding. Ortho-nitrophenol was apparently adsorbed hydrophobically, and H-bond formation was not indicated. Adsorption of the meta- and para- substituted phenols was related to substituent effects on the basicity of the phenolic hydroxyl. Adsorption was enhanced by electron-donating substituents, indicating that the phenolic —OH group formed H-bonds by acting as a proton acceptor. Ortho substitution of —CH3, —OCH3, and —Cl resulted in decreased adsorption compared with para- substitution, due to steric hindrance of H-bonding. In the case of catechol, the formation of multiple H-bonds by adjacent functional groups enhanced adsorption. I also measured the di- and trichlorinated phenols. Increasing the degree of chlorination of phenol resulted in increased adsorption. Competitive effects of adsorption from solutions containing two or three phenolic compounds were evaluated. Adsorption was adversely affected by the presence of these other solutes. The competitive effect became more pronounced with an increasing number of compounds in solution.
A Regional L-Band High Biomass Estimation Framework Leveraging Spaceborne Lidar and Interferometric Data to Overcome Backscatter Saturation

We propose a framework to estimate high above ground biomass (AGB) from L-band SAR imagery leveraging spaceborne lidars such as GEDI or ICESat-2 and repeat-pass coherence. Our results indicate we are able to overcome model saturation typically associated with purely backscatter methodologies. We validate our approach using lidar-derived AGB maps from the AfriSAR datasets at Mondah, Ogooue, and Lope. We apply our framework to UAVSAR and ALOS-2 imagery to obtain 50 meter resolution biomass maps. We obtain < 60% nRMSE (in some cases much better) with negligible relative bias using a multiscale random forest model. We illustrate that the inclusion of coherence can significantly improve high AGB estimation particularly at the coastal site Mondah.
Data Augmentation for Ship Detection using Kompsat-5 Images and Deep Learning Model

This paper proposes a useful scheme to augment the amount of training database (DB) for ship detection using Korea multi-purpose satellite–5 (KOMPSAT–5) images and deep learning. The proposed scheme utilizes electromagnetic numerical analysis technique to generate SAR chips of ship targets. In addition, two sea clutter models are adopted to simulate realistic SAR patches containing various SAR chips. Then, the simulated SAR patches are directly used to train single shot multi-box detector (SSD) model, leading to the improvement of ship detection performance.
Evaluation of SMAP and SMOS Soil Moisture Products Using Distributed Ground Observation Network in Cold and Arid Regions in the Northwest of China

Validation of existing satellite soil moisture (SM) products are critical for retrieval algorithms refinement and high-quality SM datasets achievement. Based on distributed ground observations, accuracy performance of two L-band (1.41GHz) passive microwave remotely sensed SM products of SMAP enhanced level 3 (L3) (SMAP-L3) and the newly published SMOS-INRA-CESBIO (SMOS-IC) are evaluated in Heihe River Basin (HRB) over various land surface types in Northwest China. Results reveals that: both two L-band SM products underestimate over grassland and cropland land cover types in HRB, and the underestimation obviously increases with the increasing vegetation effect. Totally, the SMAP L3 product behaves better in capturing the SM variation with the averaged ubRMSE values of 0.03m3/m3 and high R values above 0.6 in ascending orbits for different land cover types in HRB, while the SMOS-IC retrievals are strongly affected by the severe RFI contamination and algorithm uncertainties which need to be handled before application, especially for the descending product.
Vulnerability Assessment Method of Electrical Cyber–Physical Interdependent Network Considering Node Heterogeneity

The vulnerability assessment of the electrical cyber-physical interdependent network is of great significance, which is conducive to the reliable energy supply and safe operation of the system. Due to the difference in topological location and tasks undertaken, the importance of each node in power grid and cyber network are different. However, previous vulnerability methods only focused on the coupling relationship between the networks, but did not consider the heterogeneity between them, which make the evaluation less accurate. In response to the above problem, this paper—from the perspective of energy flow and information flow, respectively— analyzes the node heterogeneity of the power grid and cyber network, then proposes a vulnerability assessment indicator based on the node importance—to evaluate the impact of failures to the system robustness. Case study on the standard and modified IEEE 39-bus system integrated with cyber network shows the effectiveness of the proposed method.
Multi Seasonal Deep Learning Classification of Venus Images

Deep neural networks (NNs) trained on hyperspectral images are employed typically for the classification of new images collected from the same sensor, assuming similar characteristics to those of the training images. Creating, however, high-quality ground truth (GT) for training is rather complex, especially when attempting to classify multi-temporal images over seasonal changes. To overcome this difficulty, we propose a novel method that utilizes an additional, one-time collection of hyperspectral FENIX images in the Spring along with ground observations from the end of the Fall. The hyperspectral data are then used for simulation of GT for training. At the same time, the field campaign allows for fine-tuning of the NN to achieve enhanced, multi-seasonal hyperspectral image classification. Indeed, we demonstrate how the proposed method successfully classifies new VEN $\mu \mathrm{S}$ images obtained during different seasons.