bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Structure and Particle Size of Iron, Cobalt and Ruthenium Catalysts on Fischer–Tropsch Synthesis
Authors: Ma Wenping , Dalai Ajay K.
Published: March 2021

This review emphasizes the importance of the catalytic conversion techniques in the production of clean liquid and hydrogen fuels (XTF) and chemicals (XTC) from the carbonaceous materials including coal, natural gas, biomass, organic wastes, biogas and CO2. Dependence of the performance of Fischer–Tropsch Synthesis (FTS), a key reaction of the XTF/XTC process, on catalyst structure (crystal and size) is comparatively examined and reviewed. The contribution illustrates the very complicated crystal structure effect, which indicates that not only the particle type, but also the particle shape, facets and orientation that have been evidenced recently, strongly influence the catalyst performance. In addition, the particle size effects over iron, cobalt and ruthenium catalysts were carefully compared and analyzed. For all Fe, Co and Ru catalysts, the metal turnover frequency (TOF) for CO hydrogenation increased with increasing metal particle size in the small size region i.e., less than the size threshold 7–8 nm, but was found to be independent of particle size for the catalysts with large particle sizes greater than the size threshold. There are some inconsistencies in the small particle size region for Fe and Ru catalysts, i.e., an opposite activity trend and an abnormal peak TOF value were observed on a Fe catalyst and a Ru catalyst (2 nm), respectively. Further study from the literature provides deeper insights into the catalyst behaviors. The intrinsic activity of Fe catalysts (10 nm) at 260–300 °C is estimated in the range of 0.046–0.20 s−1, while that of the Co and Ru catalysts (7–70 nm) at 220 °C are 0.1 s−1 and 0.4 s−1, respectively.
Crowdfunding Salafism. Crowdfunding as a Salafi Missionising Method
Authors: Sorgenfrei Simon
Published: March 2021

As is also the case in other parts of the world, Salafi interpretations of Islam appear to be on the rise in Sweden, especially among young people turning to Islam. One of the most active and visible missionising Salafi organisations in Sweden is called islam.nu. It is based in Stockholm but has a national outreach programme and a very active online presence. This article focuses on islam.nu and a dawa campaign called #karavanen (the Caravan) and how it was advertised and developed on the social media platform Instagram from March 2018 to March 2020. By using market and consumer value theories to analyse the Instagram content related to the #karavanen, the article is an explorative attempt to approach contemporary Salafi missionising and growth from a new perspective.
God and the Problem of Evil: An Attempt at Reframing the Debate
Authors: Wilmot Brett
Published: March 2021

This article attempts to reframe the traditional account of the problem of evil for God’s existence. The philosophical debates about the problem of evil for the existence of God within the traditional framework do not exhaust the available options for conceiving of God’s perfection, including our understanding of God’s power and God’s relationship to the world. In responding to the problem of evil, rational theists should seek a reformulation of divine perfection consistent with God’s existence as both necessary and as morally relevant to human life in a manner that does not collapse in the face of the problem of evil. The neoclassical account of God’s nature as developed in the tradition of process philosophy is presented as an alternative that meets these requirements.
Transcripts of Gender, Intimacy, and Islam in Southeast Asia: The “Outrageous” Texts of Raja Ali Haji and Khatijah Terung
Authors: Musa Mohd Faizal
Published: March 2021

It is generally perceived that Malays—who are predominantly Muslims—are comparable with the notion of politeness, aligned with moral axioms, and behave in ways copiously guided by religion. Casual sex and other forms of sexual “deviance” are typically attributed to foreign influences, most popularly, Westernisation. New social trends among this community, such as the emphasis on male dominance, changing prescriptions about the functions and expectations of sex, receptiveness towards the body and emotion, exposure to sex education, and openness to sexual discourse are often attributed to the “immoral” West. Yet, forms of sexual behaviours depicted in the writings of notable Malay religious and literary personages reveal surprising insights into the Malay-Muslim milieu of 19th-century Riau. A variety of sexual practices and relations are expressed through these writings. This article adopts a historical-sociological framework to examine the “artisan tools” of textual materials as in the Kitab Pengetahuan Bahasa (Book of Linguistic Knowledge) by Raja Ali Haji and Perhimpunan Gunawan bagi Laki-Laki dan Perempuan (A Compendium of Charms for Men and Women) by Khatijah Terung. The “outrageous” sexual depictions in these texts are discussed and analysed, in part to debunk the idea of a “sexual revolution” or “sexual licentiousness” as emanating from an external culture.
An Improved Index for Urban Population Distribution Mapping Based on Nighttime Lights (DMSP-OLS) Data: An Experiment in Riyadh Province, Saudi Arabia
Authors: Alahmadi Mohammed , Mansour Shawky , Martin David , Atkinson Peter M.
Published: March 2021

Knowledge of the spatial pattern of the population is important. Census population data provide insufficient spatial information because they are released only for large geographic areas. Nighttime light (NTL) data have been utilized widely as an effective proxy for population mapping. However, the well-reported challenges of pixel overglow and saturation influence the applicability of the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) for accurate population mapping. This paper integrates three remotely sensed information sources, DMSP-OLS, vegetation, and bare land areas, to develop a novel index called the Vegetation-Bare Adjusted NTL Index (VBANTLI) to overcome the uncertainties in the DMSP-OLS data. The VBANTLI was applied to Riyadh province to downscale governorate-level census population for 2004 and 2010 to a gridded surface of 1 km resolution. The experimental results confirmed that the VBANTLI significantly reduced the overglow and saturation effects compared to widely applied indices such as the Human Settlement Index (HSI), Vegetation Adjusted Normalized Urban Index (VANUI), and radiance-calibrated NTL (RCNTL). The correlation coefficient between the census population and the RCNTL (R = 0.99) and VBANTLI (R = 0.98) was larger than for the HSI (R = 0.14) and VANUI (R = 0.81) products. In addition, Model 5 (VBANTLI) was the most accurate model with R2 and mean relative error (MRE) values of 0.95% and 37%, respectively.
Multiscale Decomposition Prediction of Propagation Loss in Oceanic Tropospheric Ducts
Authors: Dang Mingxia , Wu Jiaji , Cui Shengcheng , Guo Xing , Cao Yunhua , Wei Heli , Wu Zhensen
Published: March 2021

The oceanic tropospheric duct is a structure with an abnormal atmospheric refractive index. This structure severely affects the remote sensing detection capability of electromagnetic systems designed for an environment with normal atmospheric refraction. The propagation loss of electromagnetic waves in the oceanic duct is an important concept in oceanic duct research. Owing to the long-term stability and short-term irregular changes in marine environmental parameters, the propagation loss in oceanic ducts has nonstationary and multiscale time characteristics. In this paper, we propose a multiscale decomposition prediction method for predicting the propagation loss in oceanic tropospheric ducts. The prediction performance was verified by simulating propagation loss data with noise. Using different evaluation criteria, the experimental results indicated that the proposed method outperforms six other comparison methods. Under noisy conditions, ensemble empirical mode decomposition effectively disassembles the original propagation loss into a limited number of stable sequences with different scale characteristics. Accordingly, predictive modeling was conducted based on multiscale propagation loss characteristic sequences. Finally, we reconstructed the predicted result to obtain the predicted value of the propagation loss in the oceanic duct. Additionally, a genetic algorithm was used to improve the generalization ability of the proposed method while avoiding the nonlinear predictor from falling into a local optimum.
Large-Scale Analysis of the Spatiotemporal Changes of Net Ecosystem Production in Hindu Kush Himalayan Region
Authors: Guo Da , Song Xiaoning , Hu Ronghai , Zhu Xinming , Jiang Yazhen , Cai Shuohao , Zhang Yanan , Cui Xiaoyong
Published: March 2021

The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie–Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC∙m−2∙yr−1, and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai–Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000–5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage.
A Sensitivity Analysis on the Spectral Signatures of Low-Backscattering Sea Areas in Sentinel-1 SAR Images
Authors: Corcione Valeria , Buono Andrea , Nunziata Ferdinando , Migliaccio Maurizio
Published: March 2021

Satellite synthetic aperture radar (SAR) is a unique tool to collect measurements over sea surface but the physical interpretation of such data is not always straightforward. Among the different sea targets of interest, low-backscattering areas are often associated to marine oil pollution even if several physical phenomena may also result in low-backscattering patches at sea. In this study, the effects of low-backscattering areas of anthropogenic and natural origin on the azimuth autocorrelation function (AACF) are analyzed using VV-polarized SAR measurements. Two objective metrics are introduced to quantify the deviation of the AACF evaluated over low-backscattering areas with reference to slick-free sea surface. Experiments, undertaken on six Sentinel-1 SAR scenes, collected in Interferometric Wide Swath VV+VH imaging mode over large low-backscattering areas of different origin under low-to-moderate wind conditions (speed ≤ 7 m/s), spanning a wide range of incidence angles (from about 30° up to 46°), demonstrated that the AACF evaluated within low-backscattering sea areas remarkably deviates from the slick-free sea surface one and the largest deviation is observed over oil slicks.
Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images
Authors: Geng Xiaomeng , Shi Lei , Yang Jie , Li Pingxiang , Zhao Lingli , Sun Weidong , Zhao Jinqi
Published: March 2021

Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.
UAS Identify and Monitor Unusual Small-Scale Rhythmic Features in the Bay of Cádiz (Spain)
Authors: Talavera Lara , Benavente Javier , Del Río Laura
Published: March 2021

Unusual shore-normal and barred-like rhythmic features were found in Camposoto Beach (Bay of Cádiz, SW Spain) during a monitoring program using unmanned aerial systems (UAS). They appeared in the backshore and persisted for 6 months (October 2017–March 2018). Their characteristics and possible formation mechanism were investigated analyzing: (1) UAS-derived high-resolution digital elevation models (DEMs), (2) hydrodynamic conditions, and (3) sediment samples. The results revealed that the features did not migrate spatially, that their wavelength was well predicted by the edge wave theory, and that they shared characteristics with both small-scale low-energy finger bars (e.g., geometry/appearance and amplitude) and swash cusps (e.g., wavelength, seaward circulation pattern, and finer and better sorted material in the runnels with respect to the crests). Our findings pinpoint to highly organized swash able to reach the backshore during spring tides under low-energy and accretionary conditions as well as backwash enhanced by conditions of water-saturated sediment. This study demonstrates that rhythmic features can appear under different modalities and beach locations than the ones observed up to date, and that their unusual nature may be attributed to the low spatiotemporal resolution of the traditional coastal surveying methods in comparison with novel technologies such as UAS.
Planetary Wave Spectrum in the Stratosphere–Mesosphere during Sudden Stratospheric Warming 2018
Authors: Wang Yuke , Milinevsky Gennadi , Evtushevsky Oleksandr , Klekociuk Andrew , Han Wei , Grytsai Asen , Antyufeyev Oleksandr , Shi Yu , Ivaniha Oksana , Shulga Valerii
Published: March 2021

The planetary wave activity in the stratosphere–mesosphere during the Arctic major Sudden Stratospheric Warming (SSW) in February 2018 is discussed on the basis of microwave radiometer (MWR) measurements of carbon monoxide (CO) above Kharkiv, Ukraine (50.0° N, 36.3° E) and the Aura Microwave Limb Sounder (MLS) measurements of CO, temperature and geopotential heights. From the MLS data, eastward and westward migrations of wave 1/wave 2 spectral components were differentiated, to which less attention was paid in previous studies. Abrupt changes in zonal wave spectra occurred with the zonal wind reversal near 10 February 2018. Eastward wave 1 and wave 2 were observed before the SSW onset and disappeared during the SSW event, when westward wave 1 became dominant. Wavelet power spectra of mesospheric CO variations showed statistically significant periods of 20–30 days using both MWR and MLS data. Although westward wave 1 in the mesosphere dominated with the onset of the SSW 2018, it developed independently of stratospheric dynamics. Since the propagation of upward planetary waves was limited in the easterly zonal flow in the stratosphere during SSW, forced planetary waves in the mid-latitude mesosphere may exist due to the instability of the zonal flow.
GRACE-Derived Time Lag of Mekong Estuarine Freshwater Transport in the Western South China Sea Validated by Isotopic Tracer Age
Authors: Ma Zhongtian , Fok Hok Sum , Zhou Linghao
Published: March 2021

Estuarine freshwater transport has a substantial impact on the near-shore ecosystem and coastal ocean environment away from the estuary. This paper introduces two independent methods to track the Mekong freshwater-induced mass transport by calculating the time lag (or equivalently, the phase) between in situ Mekong basin runoff and the equivalent water height (EWH) time series over the western South China Sea from a gravity recovery and climate experiment (GRACE). The first method is the harmonic analysis that determines the phase difference between annual components of the two time series (called the P-method), and the other is the cross-correlation analysis that directly obtains the time lag by shifting the lagged time series forward to attain the highest cross-correlation between the two time series (called the C-method). Using a three-year rolling window, the time lag variations in three versions of GRACE between 2005 and 2012 are computed for demonstrating the consistency of the results. We found that the time lag derived from the P-method is, on average, slightly larger and more variable than that from the C-method. A comparison of our gridded time lag against the age determined via radium isotopes in September, 2007 by Chen et al. (2010) revealed that our gridded time lag results were in good agreement with most isotope-derived ages, with the largest difference less than 6 days. Among the three versions of the GRACE time series, CSR Release 05 performed the best. The lowest standard deviation of time lag was ~1.6 days, calculated by the C-method, whereas the mean difference for all the time lags from the isotope-derived ages is ~1 day by P-method. This study demonstrates the potential of monitoring Mekong estuarine freshwater transport over the western South China Sea by GRACE.
Potential Associations between Low-Level Jets and Intraseasonal and Semi-Diurnal Variations in Coastal Chlorophyll—A over the Beibuwan Gulf, South China Sea
Authors: Liu Shuhong , Tang Danling , Yan Hong , Ning Guicai , Liu Chengcheng , Yang Yuanjian
Published: March 2021

Low-level jet (LLJ) significantly affects the synoptic-scale hydrometeorological conditions in the South China Sea, although the impact of LLJs on the marine ecological environment is still unclear. We used multi-satellite observation data and meteorological reanalysis datasets to study the potential impact of LLJs on the marine biophysical environment over the Beibuwan Gulf (BBG) in summer during 2015–2019. In terms of the summer average, the sea surface wind vectors on LLJ days became stronger in the southwesterly direction relative to those on non-LLJ days, resulting in enhanced Ekman pumping (the maximum upwelling exceeds 10 × 10−6 m s−1) in most areas of the BBG, accompanied by stronger photosynthetically active radiation (increased by about 20 μmol m−2 s−1) and less precipitation (decreased by about 3 mm day−1). These LLJ-induced hydrometeorological changes led to an increase of about 0.3 °C in the nearshore sea surface temperature and an increase of 0.1–0.5 mg m−3 (decrease of 0.1–0.3 mg m−3) in the chlorophyll-a (chl-a) concentrations in nearshore (offshore) regions. Intraseasonal and diurnal changes in the incidence and intensity of LLJs potentially resulted in changes in the biophysical ocean environment in nearshore regions on intraseasonal and semi-diurnal timescales. The semi-diurnal peak and amplitude of chl-a concentrations on LLJ days increased with respect to those on non-LLJ days. Relative to the southern BBG, LLJ events exhibit greater impacts on the northern BBG, causing increases of the semi-diurnal peak and amplitude with 1.5 mg m−3 and 0.7 mg m−3, respectively. This work provides scientific evidence for understanding the potential mechanism of synoptic-scale changes in the marine ecological environment in marginal seas with frequent LLJ days.
ZoomInNet: A Novel Small Object Detector in Drone Images with Cross-Scale Knowledge Distillation
Authors: Liu Bi-Yuan , Chen Huai-Xin , Huang Zhou , Liu Xing , Yang Yun-Zhi
Published: March 2021

Drone-based object detection has been widely applied in ground object surveillance, urban patrol, and some other fields. However, the dramatic scale changes and complex backgrounds of drone images usually result in weak feature representation of small objects, which makes it challenging to achieve high-precision object detection. Aiming to improve small objects detection, this paper proposes a novel cross-scale knowledge distillation (CSKD) method, which enhances the features of small objects in a manner similar to image enlargement, so it is termed as ZoomInNet. First, based on an efficient feature pyramid network structure, the teacher and student network are trained with images in different scales to introduce the cross-scale feature. Then, the proposed layer adaption (LA) and feature level alignment (FA) mechanisms are applied to align the feature size of the two models. After that, the adaptive key distillation point (AKDP) algorithm is used to get the crucial positions in feature maps that need knowledge distillation. Finally, the position-aware L2 loss is used to measure the difference between feature maps from cross-scale models, realizing the cross-scale information compression in a single model. Experiments on the challenging Visdrone2018 dataset show that the proposed method draws on the advantages of the image pyramid methods, while avoids the large calculation of them and significantly improves the detection accuracy of small objects. Simultaneously, the comparison with mainstream methods proves that our method has the best performance in small object detection.
MSDRN: Pansharpening of Multispectral Images via Multi-Scale Deep Residual Network
Authors: Wang Wenqing , Zhou Zhiqiang , Liu Han , Xie Guo
Published: March 2021

In order to acquire a high resolution multispectral (HRMS) image with the same spectral resolution as multispectral (MS) image and the same spatial resolution as panchromatic (PAN) image, pansharpening, a typical and hot image fusion topic, has been well researched. Various pansharpening methods that are based on convolutional neural networks (CNN) with different architectures have been introduced by prior works. However, different scale information of the source images is not considered by these methods, which may lead to the loss of high-frequency details in the fused image. This paper proposes a pansharpening method of MS images via multi-scale deep residual network (MSDRN). The proposed method constructs a multi-level network to make better use of the scale information of the source images. Moreover, residual learning is introduced into the network to further improve the ability of feature extraction and simplify the learning process. A series of experiments are conducted on the QuickBird and GeoEye-1 datasets. Experimental results demonstrate that the MSDRN achieves a superior or competitive fusion performance to the state-of-the-art methods in both visual evaluation and quantitative evaluation.
Global Assessment of the GNSS Single Point Positioning Biases Produced by the Residual Tropospheric Delay
Authors: Yang Ling , Wang Jinfang , Li Haojun , Balz Timo
Published: March 2021

The tropospheric delay is one of the main error sources that degrades the accuracy of Global Navigation Satellite Systems (GNSS) Single Point Positioning (SPP). Although an empirical model is usually applied for correction and thereby to improve the positioning accuracy, the residual tropospheric delay is still drowned in measurement noise, and cannot be further compensated by parameter estimation. How much this type of residual error would sway the SPP positioning solutions on a global scale are still unclear. In this paper, the biases on SPP solutions introduced by the residual tropospheric delay when using nine conventionally Zenith Tropospheric Delay (ZTD) models are analyzed and discussed, including Saastamoinen+norm/Global Pressure and Temperature (GPT)/GPT2/GPT2w/GPT3, University of New Brunswick (UNB)3/UNB3m, European Geostationary Navigation Overlay System (EGNOS) and Vienna Mapping Functions (VMF)3 models. The accuracies of the nine ZTD models, as well as the SPP biases caused by the residual ZTD (dZTD) after model correction are evaluated using International GNSS Service (IGS)-ZTD products from around 400 globally distributed monitoring stations. The seasonal, latitudinal, and altitudinal discrepancies are analyzed respectively. The results show that the SPP solution biases caused by the dZTD mainly occur on the vertical direction, nearly to decimeter level, and significant discrepancies are observed among different models at different geographical locations. This study provides references for the refinement and applications of the nine ZTD models for SPP users.
UAV Recognition Based on Micro-Doppler Dynamic Attribute-Guided Augmentation Algorithm
Authors: Zhao Caidan , Luo Gege , Wang Yilin , Chen Caiyun , Wu Zhiqiang
Published: March 2021

A micro-Doppler signature (m-DS) based on the rotation of drone blades is an effective way to detect and identify small drones. Deep-learning-based recognition algorithms can achieve higher recognition performance, but they needs a large amount of sample data to train models. In addition to the hovering state, the signal samples of small unmanned aerial vehicles (UAVs) should also include flight dynamics, such as vertical, pitch, forward and backward, roll, lateral, and yaw. However, it is difficult to collect all dynamic UAV signal samples under actual flight conditions, and these dynamic flight characteristics will lead to the deviation of the original features, thus affecting the performance of the recognizer. In this paper, we propose a small UAV m-DS recognition algorithm based on dynamic feature enhancement. We extract the combined principal component analysis and discrete wavelet transform (PCA-DWT) time–frequency characteristics and texture features of the UAV’s micro-Doppler signal and use a dynamic attribute-guided augmentation (DAGA) algorithm to expand the feature domain for model training to achieve an adaptive, accurate, and efficient multiclass recognition model in complex environments. After the training model is stable, the average recognition accuracy rate can reach 98% during dynamic flight.
Design and Analysis of a Continuous and Non-Invasive Multi-Wavelength Optical Sensor for Measurement of Dermal Water Content
Authors: Mamouei Mohammad , Chatterjee Subhasri , Razban Meysam , Qassem Meha , Kyriacou Panayiotis A.
Published: March 2021

Dermal water content is an important biophysical parameter in preserving skin integrity and preventing skin damage. Traditional electrical-based and open-chamber evaporimeters have several well-known limitations. In particular, such devices are costly, sizeable, and only provide arbitrary outputs. They also do not permit continuous and non-invasive monitoring of dermal water content, which can be beneficial for various consumer, clinical, and cosmetic purposes. We report here on the design and development of a digital multi-wavelength optical sensor that performs continuous and non-invasive measurement of dermal water content. In silico investigation on porcine skin was carried out using the Monte Carlo modeling strategy to evaluate the feasibility and characterize the sensor. Subsequently, an in vitro experiment was carried out to evaluate the performance of the sensor and benchmark its accuracy against a high-end, broad band spectrophotometer. Reference measurements were made against gravimetric analysis. The results demonstrate that the developed sensor can deliver accurate, continuous, and non-invasive measurement of skin hydration through measurement of dermal water content. Remarkably, the novel design of the sensor exceeded the performance of the high-end spectrophotometer due to the important denoising effects of temporal averaging. The authors believe, in addition to wellbeing and skin health monitoring, the designed sensor can particularly facilitate disease management in patients presenting diabetes mellitus, hypothyroidism, malnutrition, and atopic dermatitis.
Design of a Sensitive Balloon Sensor for Safe Human–Robot Interaction
Authors: Kim Dongjin , Han Seungyong , Kim Taewi , Kim Changhwan , Lee Doohoe , Kang Daeshik , Koh Je-Sung
Published: March 2021

As the safety of a human body is the main priority while interacting with robots, the field of tactile sensors has expanded for acquiring tactile information and ensuring safe human–robot interaction (HRI). Existing lightweight and thin tactile sensors exhibit high performance in detecting their surroundings. However, unexpected collisions caused by malfunctions or sudden external collisions can still cause injuries to rigid robots with thin tactile sensors. In this study, we present a sensitive balloon sensor for contact sensing and alleviating physical collisions over a large area of rigid robots. The balloon sensor is a pressure sensor composed of an inflatable body of low-density polyethylene (LDPE), and a highly sensitive and flexible strain sensor laminated onto it. The mechanical crack-based strain sensor with high sensitivity enables the detection of extremely small changes in the strain of the balloon. Adjusting the geometric parameters of the balloon allows for a large and easily customizable sensing area. The weight of the balloon sensor was approximately 2 g. The sensor is employed with a servo motor and detects a finger or a sheet of rolled paper gently touching it, without being damaged.
Review of Dissolved CO and H2 Measurement Methods for Syngas Fermentation
Authors: Dang Jie , Wang Ning , Atiyeh Hasan K.
Published: March 2021

Syngas fermentation is a promising technique to produce biofuels using syngas obtained through gasified biomass and other carbonaceous materials or collected from industrial CO-rich off-gases. The primary components of syngas, carbon monoxide (CO) and hydrogen (H2), are converted to alcohols and other chemicals through an anaerobic fermentation process by acetogenic bacteria. Dissolved CO and H2 concentrations in fermentation media are among the most important parameters for successful and stable operation. However, the difficulties in timely and precise dissolved CO and H2 measurements hinder the industrial-scale commercialization of this technique. The purpose of this article is to provide a comprehensive review of available dissolved CO and H2 measurement methods, focusing on their detection mechanisms, CO and H2 cross interference and operations in syngas fermentation process. This paper further discusses potential novel methods by providing a critical review of gas phase CO and H2 detection methods with regard to their capability to be modified for measuring dissolved CO and H2 in syngas fermentation conditions.
Recent Progress in Distributed Brillouin Sensors Based on Few-Mode Optical Fibers
Authors: Kim Yong Hyun , Song Kwang Yong
Published: March 2021

Brillouin scattering is a dominant inelastic scattering observed in optical fibers, where the energy and momentum transfer between photons and acoustic phonons takes place. Narrowband reflection (or gain and loss) spectra appear in the spontaneous (or stimulated) Brillouin scattering, and their linear dependence of the spectral shift on ambient temperature and strain variations is the operation principle of distributed Brillouin sensors, which have been developed for several decades. In few-mode optical fibers (FMF’s) where higher-order spatial modes are guided in addition to the fundamental mode, two different optical modes can be coupled by the process of stimulated Brillouin scattering (SBS), as observed in the phenomena called intermodal SBS (two photons + one acoustic phonon) and intermodal Brillouin dynamic grating (four photons + one acoustic phonon; BDG). These intermodal scattering processes show unique reflection (or gain and loss) spectra depending on the spatial mode structure of FMF, which are useful not only for the direct measurement of polarization and modal birefringence in the fiber, but also for the measurement of environmental variables like strain, temperature, and pressure affecting the birefringence. In this paper, we present a technical review on recent development of distributed Brillouin sensors on the platform of FMF’s.
Advancing Ground-Based Radar Processing for Bridge Infrastructure Monitoring
Authors: Michel Chris , Keller Sina
Published: March 2021

In this study, we further develop the processing of ground-based interferometric radar measurements for the application of bridge monitoring. Applying ground-based radar in such complex setups or long measurement durations requires advanced processing steps to receive accurate measurements. These steps involve removing external influences from the measurement and evaluating the measurement uncertainty during processing. External influences include disturbances caused by objects moving through the signal, static clutter from additional scatterers, and changes in atmospheric properties. After removing these influences, the line-of-sight displacement vectors, measured by multiple ground-based radars, are decomposed into three-dimensional displacement components. The advanced processing steps are applied exemplarily on measurements with two sensors at a prestressed concrete bridge near Coburg (Germany). The external influences are successfully removed, and two components of the three-dimensional displacement vector are determined. A measurement uncertainty of less than 0.1 mm is achieved for the discussed application.
Recognition of Bathroom Activities in Older Adults Using Wearable Sensors: A Systematic Review and Recommendations
Authors: Zhang Yiyuan , D’Haeseleer Ine , Coelho José , Vanden Abeele Vero , Vanrumste Bart
Published: March 2021

This article provides a systematic review of studies on recognising bathroom activities in older adults using wearable sensors. Bathroom activities are an important part of Activities of Daily Living (ADL). The performance on ADL activities is used to predict the ability of older adults to live independently. This paper aims to provide an overview of the studied bathroom activities, the wearable sensors used, different applied methodologies and the tested activity recognition techniques. Six databases were screened up to March 2020, based on four categories of keywords: older adults, activity recognition, bathroom activities and wearable sensors. In total, 4262 unique papers were found, of which only seven met the inclusion criteria. This small number shows that few studies have been conducted in this field. Therefore, in addition, this critical review resulted in several recommendations for future studies. In particular, we recommend to (1) study complex bathroom activities, including multiple movements; (2) recruit participants, especially the target population; (3) conduct both lab and real-life experiments; (4) investigate the optimal number and positions of wearable sensors; (5) choose a suitable annotation method; (6) investigate deep learning models; (7) evaluate the generality of classifiers; and (8) investigate both detection and quality performance of an activity.
Occluded Grape Cluster Detection and Vine Canopy Visualisation Using an Ultrasonic Phased Array
Authors: Parr Baden , Legg Mathew , Bradley Stuart , Alam Fakhrul
Published: March 2021

Grape yield estimation has traditionally been performed using manual techniques. However, these tend to be labour intensive and can be inaccurate. Computer vision techniques have therefore been developed for automated grape yield estimation. However, errors occur when grapes are occluded by leaves, other bunches, etc. Synthetic aperture radar has been investigated to allow imaging through leaves to detect occluded grapes. However, such equipment can be expensive. This paper investigates the potential for using ultrasound to image through leaves and identify occluded grapes. A highly directional low frequency ultrasonic array composed of ultrasonic air-coupled transducers and microphones is used to image grapes through leaves. A fan is used to help differentiate between ultrasonic reflections from grapes and leaves. Improved resolution and detail are achieved with chirp excitation waveforms and near-field focusing of the array. The overestimation in grape volume estimation using ultrasound reduced from 222% to 112% compared to the 3D scan obtained using photogrammetry or from 56% to 2.5% compared to a convex hull of this 3D scan. This also has the added benefit of producing more accurate canopy volume estimations which are important for common precision viticulture management processes such as variable rate applications.
Accurate Spectral Estimation Technique Based on Decimated Linear Predictor for Leak Detection in Waterworks
Authors: Lay-Ekuakille Aimé , Telesca Vito , Visconti Paolo , Giannoccaro Nicola Ivan
Published: March 2021

Rural pipelines dedicated to water distribution, that is, waterworks, are essential for agriculture, notably plantations and greenhouse cultivation. Water is a primary resource for agriculture, and its optimized management is a key aspect. Saving water dispersion is not only an economic problem but also an environmental one. Spectral estimation of leakage is based on processing signals captured from sensors and/or transducers generally mounted on pipelines. There are different techniques capable of processing signals and displaying the actual position of leaks. Not all algorithms are suitable for all signals. That means, for pipelines located underground, for example, external vibrations affect the spectral response quality; then, depending on external vibrations/noises and flow velocity within pipeline, one should choose a suitable algorithm that fits better with the expected results in terms of leak position on the pipeline and expected time for localizing the leak. This paper presents findings related to the application of a decimated linear prediction (DLP) algorithm for agriculture and rural environments. In a certain manner, the application also detects the hydrodynamics of the water transportation. A general statement on the issue, DLP illustration, a real application and results are also included.
Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment
Authors: Roh Donggeun , Shin Hangsik
Published: March 2021

The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as ‘good’ and ‘poor’ classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘poor’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process.
Plug Regime Flow Velocity Measurement Problem Based on Correlability Notion and Twin Plane Electrical Capacitance Tomography: Use Case
Authors: Mosorov Volodymyr , Rybak Grzegorz , Sankowski Dominik
Published: March 2021

In this paper, the authors present the flow velocity measurement based on twin plane sensor electrical capacitance tomography and the cross-correlation method. It is shown that such a technique has a significant restriction for its use, particularly for the plug regime of a flow. The major issue is with the irregular regime of the flow when portions of propagated material appear in different time moments. Thus, the requirement of correlability of analyzed input signal patterns should be met. Therefore, the checking of the correlability should be considered by such a technique. The article presents a study of the efficiency of the original algorithm of automatic extraction of the suitable signal patterns which has been recently proposed, to calculate flow velocity. The obtained results allow for choosing in practice the required parameters of the algorithm to correct the extraction of signal patterns in a proper and accurate way. Various examples of the application of the discussed algorithm were presented, along with the analysis of the influence of the parameters used on the quality of plugs identification and determination of material flow.
The Cognitive-Emotional Design and Study of Architectural Space: A Scoping Review of Neuroarchitecture and Its Precursor Approaches
Authors: Higuera-Trujillo Juan Luis , Llinares Carmen , Macagno Eduardo
Published: March 2021

Humans respond cognitively and emotionally to the built environment. The modern possibility of recording the neural activity of subjects during exposure to environmental situations, using neuroscientific techniques and virtual reality, provides a promising framework for future design and studies of the built environment. The discipline derived is termed “neuroarchitecture”. Given neuroarchitecture’s transdisciplinary nature, it progresses needs to be reviewed in a contextualised way, together with its precursor approaches. The present article presents a scoping review, which maps out the broad areas on which the new discipline is based. The limitations, controversies, benefits, impact on the professional sectors involved, and potential of neuroarchitecture and its precursors’ approaches are critically addressed.
Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy
Authors: Corrêdo Lucas de Paula , Maldaner Leonardo Felipe , Bazame Helizani Couto , Molin José Paulo
Published: March 2021

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.
Detecting Nuclear Materials in Urban Environments Using Mobile Sensor Networks
Authors: Flanagan Robert , Brandt Logan , Osborne Andrew , Deinert Mark
Published: March 2021

Radiation detectors installed at major ports of entry are a key component of the overall strategy to protect countries from nuclear terrorism. While the goal of deploying these systems is to intercept special nuclear material as it enters the country, no detector system is foolproof. Mobile, distributed sensors have been proposed to detect nuclear materials in transit should portal monitors fail to prevent their entry in the first place. In large metropolitan areas, a mobile distributed sensor network could be deployed using vehicle platforms such as taxis, Ubers, and Lyfts, which are already connected to communications infrastructure. However, performance and coverage that could be achieved using a network of sensors mounted on commercial passenger vehicles has not been established. Here, we evaluate how a mobile sensor network could perform in New York City using a combination of radiation transport and geographic information systems. The geographic information system is used in conjunction with OpenStreetMap data to isolate roads and construct a grid over the streets. Vehicle paths are built using pickup and drop off data from Uber, and from the New York State Department of Transportation. The results show that the time to first detection increases with source velocity, decreases with the number of mobile detectors, and reaches a plateau that depends on the strength of the source.
Development of a Guidance System for Motor Imagery Enhancement Using the Virtual Hand Illusion
Authors: Jeong Hojun , Kim Jonghyun
Published: March 2021

Motor imagery (MI) is widely used to produce input signals for brain–computer interfaces (BCI) due to the similarities between MI-BCI and the planning–execution cycle. Despite its usefulness, MI tasks can be ambiguous to users and MI produces weaker cortical signals than motor execution. Existing MI guidance systems, which have been reported to provide visual guidance for MI and enhance MI, still have limitations: insufficient immersion for MI or poor expandability to MI for another body parts. We propose a guidance system for MI enhancement that can immerse users in MI and will be easy to extend to other body parts and target motions with few physical constraints. To make easily extendable MI guidance system, the virtual hand illusion is applied to the MI guidance system with a motion tracking sensor. MI enhancement was evaluated in 11 healthy people by comparison with another guidance system and conventional motor commands for BCI. The results showed that the proposed MI guidance system produced an amplified cortical signal compared to pure MI (p < 0.017), and a similar cortical signal as those produced by both actual execution (p > 0.534) and an MI guidance system with the rubber hand illusion (p > 0.722) in the contralateral region. Therefore, we believe that the proposed MI guidance system with the virtual hand illusion is a viable alternative to existing MI guidance systems in various applications with MI-BCI.