bluuebunny/crossref_metadata_2025 huggingface dataset:
First Hydrogen Fuel Sampling from a Fuel Cell Hydrogen Electrical Vehicle–Validation of Hydrogen Fuel Sampling System to Investigate FCEV Performance
Authors: Bacquart Thomas , Moore Niamh , Mattelaer Vincent , Olden James , Morris Abigail Siân Olivia , Storms Ward , Murugan Arul
Published: August 2022

Fuel cell electric vehicles (FCEV) are developing quickly from passenger vehicles to trucks or fork-lifts. Policymakers are supporting an ambitious strategy to deploy fuel cell electrical vehicles with infrastructure as hydrogen refueling stations (HRS) as the European Green deal for Europe. The hydrogen fuel quality according to international standard as ISO 14687 is critical to ensure the FCEV performance and that poor hydrogen quality may not cause FCEV loss of performance. However, the sampling system is only available for nozzle sampling at HRS. If a FCEV may show a lack of performance, there is currently no methodology to sample hydrogen fuel from a FCEV itself. It would support the investigation to determine if hydrogen fuel may have caused any performance loss. This article presents the first FCEV sampling system and its comparison with the hydrogen fuel sampling from the HRS nozzle (as requested by international standard ISO 14687). The results showed good agreement with the hydrogen fuel sample. The results demonstrate that the prototype developed provides representative samples from the FCEV and can be an alternative to determine hydrogen fuel quality. The prototype will require improvements and a larger sampling campaign.
Assessment of Public Opposition to Construction and Demolition Waste Facilities: A Case Study in Australia
Authors: Ghafoor Soheila , Shooshtarian Salman , Maqsood Tayyab , Wong Peter
Published: August 2022

The purpose of this paper is to investigate the nature of public opposition (PO) to the siting of construction and demolition (C&D) waste management facilities in Australia. A qualitative case study of PO to the development proposal for the Gunnedah waste facility, in the state of New South Wales (NSW), was conducted. The waste facility is promised to process up to 250 kilotons of waste materials, much of which is C&D waste intended for use in road constructions after processing. Using a content analysis approach, the study analysed 86 public submissions that were lodged within the allocated development application exhibition period to systematically analyse the arguments used by the submitters about the establishment of the facility. The case study revealed five broad perceived risk classes to the siting of the Gunnedah waste facility, namely location, environmental, human health, financial and process risks. It was also shown that while not-in-my-back-yard (NIMBY) attitudes may have played a part in the PO to the sitting at the facility, the PO was heavily affected by the poor public participation process. The study outlines key strategies for an effective public participation process that may assist with the management of PO to the siting of C&D waste management facilities in Australia. The study contributes to the theory and practice of effective C&D waste management, enhancing the social acceptance of such facilities toward a more circular economy in the built environment.
Therapeutic but Not Therapy: Using Critical Spirituality to Engage with Traumatic Experiences
Authors: Gardner Fiona
Published: August 2022

Participants in critical reflection workshops and peer supervision often comment that the process feels therapeutic, enabling them to engage with challenging experiences, even although it is clearly not therapy. This reflective article explores these comments, particularly how the processes of critical reflection embedded in critical spirituality can foster a deep exploration of traumatic experiences that undermine the sense of self and the ability to act with agency. While spirituality is broadly defined as that which gives life meaning including a sense of the transcendent, the ‘critical’ aspect includes the influence of the person’s own and the broader social context. Using two participant examples for illustration, key aspects of the process are identified: unearthing and naming deeply held, limiting and often longstanding assumptions influencing the person’s sense of who they are and how they operate. Next, understanding the prevailing social context can generate liberating new perspectives. Asking what is meaningful and why given the person’s spirituality can foster new, freeing and enabling assumptions, values and beliefs and experimenting with new ways of being and acting. What often emerges is that participants come to recognise the depth of meaning that transforms their perception of their experience and sense of themselves.
Music as a Liberal Art: The Poetry of the Universe
Authors: Aquila Dominic A.
Published: August 2022

This article explores the place of music in the classical liberal arts curriculum, which consists of the trivium (the arts of language) and the quadrivium (the arts of number). Music is part of the quadrivial disciplines and studied as applied arithmetic. However, as argued in this article, it is also a bridge to the discipline of rhetoric, which is part of the trivium. The article begins with a brief review of St. Augustine’s De Musica, the first in a planned (but unrealized) series of dialogs on the value of the classical liberal arts to the emerging Christian culture of Antiquity. It proceeds to a discussion of music and its relation to the contemporary American liberal arts curriculum. Two case studies follow that address the ontological reality of music as a time-bound medium, and attempts to mute this reality in the service of creating a sense of timelessness. Thus subsuming the temporal into the Divine–what Augustine called “a poem of the universe”. The first case study is Dante Alighieri’s Paradiso, which is a journey through the heavens with each planet representing one of the seven liberal arts as preparatory to the Beatific Vision. The second case study focuses on J.S. Bach’s attempts to create a sense of timelessness in the St. Matthew Passion by the use of musical forms and musical rhetorical devices that tend to abolish time. The article concludes with suggestions on how teachers can use 20th and 21st-century movements in Western art music as pathways to appreciate music’s pivotal role in the Catholic liberal arts tradition.
Liturgical Gratitude to God
Authors: Cockayne Joshua , Salter Gideon
Published: August 2022

Gratitude to God is a core component of Christian liturgy; along with the countless hymns which express attitudes of thanks to God, Christian liturgy often includes acts of spoken gratitude, as well as prayers of thanksgiving. We argue that two aspects of liturgical gratitude distinguish it from gratitude more generally. First, liturgical gratitude is always scripted, leading to the worry that those who express gratitude do so disingenuously. Secondly, liturgical gratitude is always social in some way, as our gratitude is drawn into the worship of the community of the Church. The paper provides an account of liturgical gratitude that explores these two key distinctive features.
Safeguarding Places of Worship during the Prophetic Era: Assessment of Early Islamic Covenants and Their Impacts on Early Muslim Polities
Authors: Mehfooz Musferah
Published: August 2022

Treaties and covenants have been the most important instruments of international relations in both ancient and modem times, playing a significant role in the promotion of religious freedom, peaceful coexistence, and interfaith harmony. The rapid spread and broad appeal of early Islam brought matters of international relations and cosmopolitan state governance to a cadre of Muslim leaders whose main political experience had been with parochial Arabian tribalism. The foremost issue was the position, rights, and responsibilities of non-Muslim religious communities within the Arab-Islamic empire. Consequently, numerous covenants and treaties were devised with subjects and with foreign states during the expansion of the Muslim world. This study examines the protection of non-Muslim places of worship under the rule of the Prophet Muḥammad ﷺ and his successors, including future caliphs and generals. It explores the practical application of the covenants by the successors of the Prophet Muḥammad ﷺ during early Islamic history, exploring the extent to which these covenants and treaties were effective in maintaining peaceful co-existence in a multi-faith society. In sum, for the sake of concision, only specific segments of the covenants and treaties are examined, which were devised with the non-Muslims for the protection of their worship places during the early Muslim Conquests.
Measuring the Level of Intercultural Competence (IC) among Muslim Religious Leaders in Sri Lanka
Authors: Ramzy Mohammad Ismath , Alshighaybi Mohammed Salem , Rislan Mohamed
Published: August 2022

As far as the religious leaders are influential in Sri Lankan society, their intercultural competence (IC) level matters for social harmony in the country. The existing literature found a lack of IC among religious leaders and highlighted their damaging role in a long history of ethnic violence in Sri Lanka. Referring to the 2019 easter attack, some social activists questioned the level of IC of Muslim religious leaders and the relevance of madrasa education. This research, therefore, evaluates the level of IC among the graduates of Sri Lankan madrasas. For this purpose, the Developmental Model of Intercultural Sensitivity (DMIS) was adopted. This study used a mixed method of data collection. A quantitative research instrument was administered among 26 madrasa alumni, and only 101 Muslim religious leaders responded. Out of the total participants, 72 (71.3%) were males and 29 (28.7%) were females. In the qualitative approach, 06 Madrasa principles accepted the invitation to the interview. They shared information about the curriculum, madrasa environment, institution policy, and their experience in dealing with issues of IC. This research found ‘minimization level or third level of IC in DMIS among all participants.’ A total of 65% of participants obtained acceptance and adaptation levels of IC. Since these madrasas have no other means except Islamic religious courses to develop this level of IC, researchers concluded that the religious education in Sri Lankan madrasas does not oppose multiculturalism. Furthermore, Islamic religious education can potentially develop IC among Muslim religious leaders. Since this research was conducted among Muslim religious leaders who graduated more than ten years ago, research is needed to ensure the current situation.
Generation of Combined Daily Satellite-Based Precipitation Products over Bolivia
Authors: Saavedra Oliver , Ureña Jhonatan
Published: August 2022

This study proposes using Satellite-Based Precipitation (SBP) products and local rain gauge data to generate information on the daily precipitation product over Bolivia. The selected SBP products used were the Global Satellite Mapping of Precipitation Gauge, v6 (GSMaP_Gauge v6) and the Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The Gridded Meteorological Ensemble Tool (GMET) is a generated precipitation product that was used as a control for the newly generated products. The correlation coefficients for raw data from SBP products were found to be between 0.58 and 0.60 when using a daily temporal scale. The applied methodology iterates correction factors for each sub-basin, taking advantage of surface measurements from the national rain gauge network. Five iterations showed stability in the convergence of data values. The generated daily products showed correlation coefficients between 0.87 and 0.98 when using rain gauge data as a control, while GMET showed correlation coefficients of around 0.89 and 0.95. The best results were found in the Altiplano and La Plata sub-basins. The database generated in this study can be used for several daily hydrological applications for Bolivia, including storm analysis and extreme event analysis. Finally, a case study in the Rocha River basin was carried out using the daily generated precipitation product. This was used to force a hydrological model to establish the outcome of simulated daily river discharge. Finally, we recommend the usage of these daily generated precipitation products for a wide spectrum of hydrological applications, using different models to support decision-making.
Spatial Analysis of a Rapid Intrusion Event of the East Australian Current Using High Frequency Radar Data
Authors: Xie Senyang , Wang Xiao Hua , Hu Yuwei , Huang Zhi
Published: August 2022

The East Australian Current (EAC) is a highly dynamic western boundary current of the South Pacific Gyre. The EAC frequently encroaches shoreward, drives upwelling, changes coastal bio-physical dynamics, and thus exerts significant impacts on coastal marine ecosystems. In this study, with high frequency (HF) radar and mooring data, for the first time accurate daily mapping and tracking of a rapid EAC intrusion event was conducted and the impacts of the EAC intrusion on the shelf water off Coffs Harbor were monitored. The results show that, during the event, the EAC was highly dynamic with a mean daily onshore/offshore movement of ~5 km/day. In addition, we found that the bottom ocean temperature and the surface current speed on the shelf varied linearly with the EAC-to-coast distance. This study thus demonstrates the value of HF remotely sensed data for the ongoing quantitative monitoring of the highly dynamic EAC fluctuations.
Cloud Macro- and Microphysical Properties in Extreme Rainfall Induced by Landfalling Typhoons over China
Authors: Zhao Dajun , Yu Yubin , Li Ying , Xu Hongxiong , Chen Lianshou
Published: August 2022

Extreme rainfall induced by landfalling typhoon (ERLTC) can cause destructive natural disasters throughout China. Cloud properties in ERLTC are not yet well understood and parameterized, which limits the forecast accuracy of ERLTC to some extent. The 99th percentile intensity of daily rainfall associated with LTC is objectively defined as ERLTC and using the CloudSat tropical cyclone (CSTC) dataset from 2006 to 2018, cloud macro- and microphysical characteristics are statistically investigated. Results show that the proportion of single-layer (double-layered) clouds increases (decreases) significantly on the occurrence day of ERLTC. In the TC inner core region, the proportion of deep convective cloud at 2–10 km is the highest, reaching 50%. In the TC envelop region, deep convective cloud at the height of 3–8 km and cirrus at the height of 12–14 km account for the highest proportions. For the TC outer region, cirrus around 13 km has the highest proportion. During the ERLTC period, the ice-water content is mainly distributed in 5–18 km, and is mostly distributed in the TC inner core, followed by the envelop region. A large number of smaller ice particles are gathering in the upper troposphere at 13–18 km, while a small number of larger ones is gathering in the middle levels around 8–10 km. These results are useful for evaluating the ERLTC simulations and are expected to provide new forecasting factors for ERLTC in cloud macro- and microphysical perspectives.
Development of a Multi-Scale Tomato Yield Prediction Model in Azerbaijan Using Spectral Indices from Sentinel-2 Imagery
Authors: Psiroukis Vasilis , Darra Nicoleta , Kasimati Aikaterini , Trojacek Pavel , Hasanli Gunay , Fountas Spyros
Published: August 2022

This paper presents the development and update of a multi-scale yield prediction model for processing tomatoes. The study was carried out under the EU-funded programme “Support to Development of a Rural Business Information System (RBIS)”, and the performance of the updated crop-specific yield prediction models and their generated predictions at regional and national levels are presented. The model was built using Sentinel-2 satellite imagery to obtain cumulative values of six (6) selected vegetation indices (VIs). The data were collected on five (5) different dates for processing tomato fields in the Khachmaz region of Azerbaijan during summer 2021 (June to August) at 10- to 13-day intervals. In addition, a targeted field sampling campaign was conducted on selected Khachmaz pilot fields towards the end of the growing season to assess the potential of Sentinel-2 data to determine yield variability in tomato fields. Finally, actual recorded yields were collected at the field level to build the yield prediction regression model and evaluate its performance at different spatial scales, ranging from single field to national level, as well as under different data availability scenarios (number of consecutive Sentinel-2 images used). The results showed a high degree of correlation between all implemented VIs and processing tomato yield, with a coefficient of determination of up to 0.89 for the NDVI, providing valuable information for future estimates of tomato production across multiple spatial scales. The developed prediction model could also be used in the agri-food sector for national yield estimates to support policy and regulatory decisions at the national level.
Evaluation of Cotton Defoliation Rate and Establishment of Spray Prescription Map Using Remote Sensing Imagery
Authors: Chen Pengchao , Xu Weicheng , Zhan Yilong , Yang Weiguang , Wang Juan , Lan Yubin
Published: August 2022

The site-specific management of cotton fields is necessary for evaluating the growth status of cotton and generating a defoliation prescription map. The traditional assessment method of pests and diseases is based on spot surveys and manual participation, which is time-consuming, labor-intensive, and lacks high-quality results. The RGB and multispectral images acquired by drones equipped with sensors provide the possibility to quickly and accurately obtain the overall data for a field. In this study, we obtained RGB and multispectral remote sensing images to calculate the spectral index of the target area. At the same time, ground survey data were obtained by tracking and investigating the defoliation rate of cotton after spraying. With the help of data analysis methods, such as univariate linear regression, multiple linear regression models, neural network models, etc., a cotton defoliation effect monitoring model based on UAV remote sensing images was constructed. The results show that the BP neural network based on the VARI, VDVI, RSI, NGRDI, NDVI index has an R2 value of 0.945 and RMSE value of 0.006. The R2 values of the multiple linear regression model are 0.844 based on the RSI and NGRDI indexes and RSI and VARI indexes. Additionally, based on the model, the cotton defoliation of the whole farmland was evaluated, and the spray prescription map of the UAV sprayer was obtained.
BDS-3/GPS/Galileo OSB Estimation and PPP-AR Positioning Analysis of Different Positioning Models
Authors: Li Bo , Mi Jinzhong , Zhu Huizhong , Gu Shouzhou , Xu Yantian , Wang Hu , Yang Lijun , Chen Yibiao , Pang Yuqi
Published: August 2022

With the completion of the BeiDou Global Navigation Satellite System (BDS-3), the multi-system precise point positioning ambiguity resolution (PPP-AR) has been realized. The satellite phase fractional cycle bias (FCB) is a key to the PPP-AR. Compared to the combined ionosphere-free (IF) model, the undifferenced and uncombined (UDUC) model retains all the information from the observations and can be easily extended to arbitrary frequencies. However, the FCB is difficult to apply directly to the UDUC model. An observable-specific signal bias (OSB) can interact directly with the original observations, providing complete flexibility for PPP-AR for multi-frequency multi-GNSS. In this study, the OSB product generation for the GPS (G), Galileo (E), and BDS-3 (C) systems is performed using 117 globally distributed multi-GNSS experiment (MGEX) stations, and their performances are evaluated. Then, the PPP-AR comparison and analysis of the two positioning models of the UDUC and IF are conducted. The results show that the stability of OSB products of the three systems is better than 0.05 ns. For the precise point positioning (PPP) ambiguity fixed solution, with comparable positioning accuracy and convergence time to the products of both the Wuhan University (WUM) and the Centre National d’Etudes Spatials (CNES) institutions, an average fixed-ambiguity rate is over 90%. Compared to the PPP float solution, the PPP-AR has the most significant improvement in positioning accuracy in the E-direction. The average improvements in the positioning accuracy under the IF and UDUC models in the static and kinematic modes are higher than 45% and 40%, respectively. The convergence times of the IF and UDUC models are improved on average by 48% and 60% in the static mode and by 40% and 55% in the kinematic mode, respectively. Among the IF and UDUC positioning models, the former has slightly better positioning accuracy and convergence time than the latter for the PPP float solution. However, both models have comparable positioning accuracy and convergence time after the PPP-AR. The GCE multi-system combination is superior to other system combinations. The average convergence time for the static PPP fixed solution is 8.5 min, and the average convergence time for the kinematic PPP fixed solution is 16.4 min.
Ten Years of VIIRS On-Orbit Geolocation Calibration and Performance
Authors: Lin Guoqing , Wolfe Robert E. , Zhang Ping , Dellomo John J. , Tan Bin
Published: August 2022

The first innovative Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been in operation for 10 years since its launch on 28 October 2011. The second VIIRS sensor aboard the first Join Polar Satellite System (JPSS-1) satellite has been in operation for 4 years since its launch on 18 November 2017, which became NOAA-20. Well-geolocated and radiometrically calibrated Level-1 sensor data records (SDRs) from VIIRS are crucial to numerical weather prediction (NWP) and Level-2+ environmental data record (EDR) algorithms and products. The high quality of Level-2+ EDRs is a requirement for the continuity of NASA Earth science data records (ESDRs) and climate data records (CDRs), one of the two objectives of the SNPP mission and one of the three elements in the JPSS mission objective. The other objective of the SNPP mission is risk reduction for the follow-on JPSS missions. This paper summarizes the on-orbit geolocation calibration and validation (Cal/Val) activities for both VIIRS sensors onboard SNPP and NOAA-20 in the past 10 years. These activities include nominal geolocation Cal/Val activities, risk reduction activities, and improvements for the on-orbit VIIRS sensor operations. After these activities, sub-pixel geolocation accuracy is achieved. Nadir equivalent geolocation uncertainty is generally within 75 m (1-σ), or 20% imagery band pixels, in either the along-scan or along-track direction for both SNPP and NOAA-20 VIIRS sensors. The worst 16-day measured geolocation errors (radial, 3-σ) are 280 m and 267 m, respectively, in the latest SNPP and NOAA-20 VIIRS data collections, which are better than the required accuracy of 375 m (radial, 3-σ). The risk reduction activities also improved VIIRS builds for JPSS-3 and JPSS-4 satellites, and provide lessons learned for other VIIRS-like sensor builds.
Mapping Flash Flood Hazards in Arid Regions Using CubeSats
Authors: Wang Zhaocheng , Vivoni Enrique R.
Published: August 2022

Flash flooding affects a growing number of people and causes billions of dollars in losses each year with the impact often falling disproportionally on underdeveloped regions. To inform post-flood mitigation efforts, it is crucial to determine flash flooding extents, especially for extreme events. Unfortunately, flood hazard mapping has often been limited by a lack of observations with both high spatial and temporal resolution. The CubeSat constellation operated by Planet Labs can fill this key gap in Earth observations by providing 3 m near-daily multispectral imagery at the global scale. In this study, we demonstrate the imaging capabilities of CubeSats for mapping flash flood hazards in arid regions. We selected a severe storm on 13–14 August 2021 that swept through the town of Gila Bend, Arizona, causing severe flood damages, two deaths, and the Declaration of a State of Emergency. We found the spatial extent of flooding can be mapped from CubeSat imagery through comparisons of the near-infrared surface reflectance prior to and after the flash flood event (ΔNIR). The unprecedented spatiotemporal resolution of CubeSat imagery allowed the detection of ponded (ΔNIR ≤ −0.05) and flood-affected (ΔNIR ≥ +0.02) areas that compared remarkably well with the 100-year flood event extent obtained by an independent hydraulic modeling study. Our findings demonstrate that CubeSat imagery provides valuable spatial details on flood hazards and can support post-flood activities such as damage assessments and emergency relief.
PGNet: Positioning Guidance Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Images
Authors: Liu Bo , Hu Jinwu , Bi Xiuli , Li Weisheng , Gao Xinbo
Published: August 2022

Semantic segmentation of very-high-resolution (VHR) remote sensing images plays an important role in the intelligent interpretation of remote sensing since it predicts pixel-level labels to the images. Although many semantic segmentation methods of VHR remote sensing images have emerged recently and achieved good results, it is still a challenging task because the objects of VHR remote sensing images show large intra-class and small inter-class variations, and their size varies in a large range. Therefore, we proposed a novel semantic segmentation framework for VHR remote sensing images, called Positioning Guidance Network (PGNet), which consists of the feature extractor, a positioning guiding module (PGM), and a self-multiscale collection module (SMCM). First, the PGM can extract long-range dependence and global context information with the help of the transformer architecture and effectively transfer them to each pyramid-level feature, thus effectively improving the segmentation effectiveness between different semantic objects. Secondly, the SMCM we designed can effectively extract multi-scale information and generate high-resolution feature maps with high-level semantic information, thus helping to segment objects in small and varying sizes. Without bells and whistles, the mIoU scores of the proposed PGNet on the iSAID dataset and ISPRS Vaihingn dataset are 1.49% and 2.40% higher than FactSeg, respectively.
Double-Stack Aggregation Network Using a Feature-Travel Strategy for Pansharpening
Authors: Li Weisheng , He Maolin , Xiang Minghao
Published: August 2022

Pansharpening methods based on deep learning can obtain high-quality, high-resolution multispectral images and are gradually becoming an active research topic. To combine deep learning and remote sensing domain knowledge more efficiently, we propose a double-stack aggregation network using a feature-travel strategy for pansharpening. The proposed network comprises two important designs. First, we propose a double-stack feature aggregation module that can efficiently retain useful feature information by aggregating features extracted at different levels. The module introduces a new multiscale, large-kernel convolutional block in the feature extraction stage to maintain the overall computational power while expanding the receptive field and obtaining detailed feature information. We also introduce a feature-travel strategy to effectively complement feature details on multiple scales. By resampling the source images, we use three pairs of source images at various scales as the input to the network. The feature-travel strategy lets the extracted features loop through the three scales to supplement the effective feature details. Extensive experiments on three satellite datasets show that the proposed model achieves significant improvements in both spatial and spectral quality measurements compared to state-of-the-art methods.
Improving the Accuracy of the Cross-Calibrated Multi-Platform (CCMP) Ocean Vector Winds
Authors: Mears Carl , Lee Tong , Ricciardulli Lucrezia , Wang Xiaochun , Wentz Frank
Published: August 2022

The Cross-Calibrated Multi-Platform (CCMP) Ocean vector wind analysis is a level-4 product that uses a variational method to combine satellite retrievals of ocean winds with a background wind field from a numerical weather prediction (NWP) model. The result is a spatially complete estimate of global ocean vector winds on six-hour intervals that are closely tied to satellite measurements. The current versions of CCMP are fairly accurate at low to moderate wind speeds (<15 m/s) but are systematically too low at high winds at locations/times where a collocated satellite measurement is not available. This is mainly because the NWP winds tend to be lower than satellite winds, especially at high wind speed. The current long-term CCMP version, version 2.0, also shows spurious variations on interannual to decadal time scales caused by the interaction of satellite/model bias with the varying amount of satellite measurements available as satellite missions begin and end. To alleviate these issues, here we explore methods to adjust the source datasets to more closely match each other before they are combined. The resultant new CCMP wind analysis agrees better with long-term trend estimates from satellite observations and reanalysis than previous versions.
Assessment of Socioeconomic Dynamics and Electrification Progress in Tanzania Using VIIRS Nighttime Light Images
Authors: Zhu Changjun , Li Xi , Ru Yuanxi
Published: August 2022

Tanzania is one of the fastest-growing countries in the world, but it still faces many challenges of unbalanced development. However, Tanzania’s economic assessment studies based on traditional statistics are mostly conducted at the national level, which leaves the details of regional economic disparity and electrification unknown. Despite experiencing one of the fastest urbanizations in the world, there is a lack of research on the match between urbanization and electrification in Tanzania. This study accesses the socioeconomic dynamics in Tanzania using nighttime light images from the Visible Infrared Imaging Radiometer Suite (VIIRS), providing spatiotemporal details for Tanzania’s development. We examined the ability of nighttime light data to evaluate the socioeconomic dynamics in Tanzania and studied regional economic disparity based on the total nighttime light (TNL). Furthermore, the land electrification rate (LER) was defined to study the relationship between urbanization and electrification in Tanzania’s major cities. We found that the LER was less than 0.9 in 2019 and had decreased from 2015 to 2019 in most cities, indicating that the power infrastructure gaps were widespread and growing in major cities. Additionally, we found a negative correlation between the change rate of land electrification and the urban expansion rate, indicating that the construction of power infrastructure has lagged behind the urbanization. We concluded that nighttime light data can effectively provide spatiotemporal details for socioeconomic dynamics in Tanzania. Additionally, our data mining method may be applied to other data-poor countries.
Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas
Authors: Deng Hui , Wu Xiantan , Zhang Wenjiang , Liu Yansong , Li Weile , Li Xiangyu , Zhou Ping , Zhuo Wenhao
Published: August 2022

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.
“Cool” Roofs as a Heat-Mitigation Measure in Urban Heat Islands: A Comparative Analysis Using Sentinel 2 and Landsat Data
Authors: Mushore Terence , Odindi John , Mutanga Onisimo
Published: August 2022

Urban growth, characterized by expansion of impervious at the cost of the natural landscape, causes warming and heat-related distress. Specifically, an increase in the number of buildings within an urban landscape causes intensification of heat islands, necessitating promotion of cool roofs to mitigate Urban Heat Islands (UHI) and associated impacts. In this study, we used the freely available Sentinel 2 and Landsat 8 data to determine the study area’s Land Use Land Covers (LULCs), roof colours and Land Surface Temperature (LST) at a 10-m spatial resolution. Support Vector Machines (SVM) classification algorithm was adopted to derive the study area’s roof colours and proximal LULCs, and the Transformed Divergence Separability Index (TDSI) based on Jeffries Mathussitta distance analysis was used to determine the variability in LULCs and roof colours. To effectively relate the Landsat 8 thermal characteristics to the LULCs and roof colours, the Gram–Schmidt technique was used to pan-sharpen the 30-m Landsat 8 image data to 10 m. Results show that Sentinel 2 mapped LULCs with over 75% accuracy. Pan-sharpening the 30-m-resolution thermal data to 10 m improved the spatial resolution and quality of the Land Surface map and the correlation between LST and Normalized Difference Vegetation Index (NDVI) used as proxy for LULC. Green-colour roofs were the warmest, followed by red roofs, while blue roofs were the coolest. Generally, black roofs in the study area were cool. The study recommends the need to incorporate other roofing properties, such as shape, and further split the colours into different shades. Furthermore, the study recommends the use of very high spatial resolution data to determine roof colour and their respective properties; these include data derived from sensors mounted on aerial platforms such as drones and aircraft. The study concludes that with appropriate analytical techniques, freely available image data can be integrated to determine the implication of roof colouring on urban thermal characteristics, useful for mitigating the effects of Urban Heat Islands and climate change.
A Batch Pixel-Based Algorithm to Composite Landsat Time Series Images
Authors: Li Jianzhou , Ma Jinji , Ye Xiaojiao
Published: August 2022

Compositing is a fundamental pre-processing for remote sensing images. Landsat series optical satellite images are influenced by cloud coverage, acquisition time, sensor types, and seasons, which make it difficult to obtain continuous cloud-free observations. It limits the potential use and analysis of time series images. Therefore, global change researchers urgently need to ‘composite’ multi-sensor and multi-temporal images. Many previous studies have used isolated pixel-based algorithms to composite Landsat images; however, this study is different and develops a batch pixel-based algorithm for composing continuous cloud-free Landsat images. The algorithm chooses the best scene as the reference image using the user-specified image ID or related parameters. Further, it accepts all valid pixels in the reference image as the main part of the result and develops a priority coefficient model. Development of this model is based on the criteria of five factors including cloud coverage, acquisition time, acquisition year, observation seasons, and sensor types to select substitutions for the missing pixels in batches and to merge them into the final composition. This proposed batch pixel-based algorithm may provide reasonable compositing results on the basis of the experimental test results of all Landsat 8 images in 2019 and the visualization results of 12 locations in 2020. In comparison with the isolated pixel-based algorithms, our algorithm eliminates band dispersion, requires fewer images, and enhances the composition’s pixel concentration considerably. The algorithm provides a complete and practical framework for time series image processing for Landsat series satellites, and has the potential to be applied to other optical satellite images as well.
Investigating NUCAPS Skill in Profiling Saharan Dust for Near-Real-Time Forecasting
Authors: Kuciauskas Arunas , Reale Anthony , Esmaili Rebekah , Sun Bomin , Nalli Nicholas R. , Morris Vernon R.
Published: August 2022

Dust outflows off Northwest Africa often propagate westward across the North Tropical Atlantic Basin (NTAB) into the greater Caribbean and US. From a health perspective, weather forecasters in these regions often monitor hazardous air quality associated with this dust. However, forecasters can be constrained by sparse data observations upwind over the Atlantic of the impacted populated areas. Global satellite sounding retrievals can potentially augment and enhance the operational forecasting toolkit for monitoring Saharan dust episodes. The focus of this paper was to examine the skill of the NOAA Unique Combined Atmospheric Processing System (NUCAPS) temperature and water vapor profiles within the dust and non-dust conditions during the March 2019 NOAA Aerosols and Ocean Science Expedition (AEROSE). During this time, the NOAA Ron Brown research ship launched radiosondes to coincide with satellite overpasses that served as independent ground truth data for evaluating NUCAPS. Compared to RAOBs from the Ron Brown, the SNPP and NOAA-20 NUCAPS-derived soundings showed skill in profiling atmospheric conditions supporting Saharan dust monitoring. Outside of dust regions, the NOAA-20 NUCAPS surface temperature bias peaks at 2.0 K; the surface water vapor bias is minimal (~1000 hPa), with a small cold bias that peaks at −50% between 742 and 790 hPa. Corresponding temperature RMS values are less than 2.0 K; water vapor RMS values are generally below 70%. Within the dust regions, NOAA-20 NUCAPS temperature soundings show a cold bias peak of 2.6 K at 918 hPa and 113% of a moist bias peak at the same level. Corresponding temperature RMS values maximize at 3.5 K at 945 hPa; the water vapor RMS shows a peak value of 106% at the same level. Weather forecasters can apply NUCAPS across the NTAB in issuing timely and accurate hazardous air quality warnings and visibility alerts to health officials and the general public.
Quantifying Two-Dimensional Surface Displacements Using High-Resolution Cosmo-SkyMed, TerraSAR-X and Medium-Resolution Sentinel-1 SAR Interferometry: Case Study for the Tengiz Oilfield
Authors: Bayramov Emil , Tessari Giulia , Kada Martin
Published: August 2022

The present study was aimed at comparing vertical and horizontal surface displacements derived from the Cosmo-SkyMED, TerraSAR-X and Sentinel-1 satellite missions for the detection of oil extraction-induced subsidence in the Tengiz oilfield during 2018–2021. The vertical and horizontal surface displacements were derived using the 2D decomposition of line-of-sight measurements from three satellite missions. Since the TerraSAR-X mission was only available from an ascending track, it was successfully decomposed by combining it with the Cosmo-SkyMED descending track. Vertical displacement velocities derived from 2D Decomposition showed a good agreement in similar ground motion patterns and an average regression coefficient of 0.98. The maximum average vertical subsidence obtained from the three satellite missions was observed to be −57 mm/year. Higher variations and deviations were observed for horizontal displacement velocities in terms of similar ground motion patterns and an average regression coefficient of 0.80. Fifteen wells and three facilities were observed to be located within the subsidence range between −55.6 mm/year and −42 mm/year. The spatial analyses in the present studies allowed us to suspect that the subsidence processes occurring in the Tengiz oilfield are controlled not solely by oil production activities since it was clearly observed from the detected horizontal movements. The natural tectonic factors related to two seismic faults crossing the oilfield, and terrain characteristics forming water flow towards the detected subsidence hotspot, should also be considered as ground deformation accelerating factors. The novelty of the present research for Kazakhstan’s Tengiz oilfield is based on the cross-validation of vertical and horizontal surface displacement measurements derived from three radar satellite missions, 2D Decomposition of Cosmo-SkyMED descending and TerraSAR-X ascending line-of-sight measurements and spatial analysis of man-made and natural factors triggering subsidence processes.
Affordable Open-Source Quartz Microbalance Platform for Measuring the Layer Thickness
Authors: Matusiak Adrian , Żak Andrzej Marek
Published: August 2022

The layer thickness measurement process is an indispensable companion of vacuum sputtering and evaporation. Thus, quartz crystal microbalance is a well-known and reliable method for monitoring film thickness. However, most commercial devices use very simple signal processing methods, offering only a readout of the frequency change value and an approximate sputtering rate. Here, we show our concept of instrument, to better control the process parameters and for easy replication. The project uses open-source data and its own ideas, fulfilling all the requirements of a measuring system and contributing to the open-source movement due to the added value and the replacement of obsolete technologies with contemporary ones. The device provides an easy way to expand existing sputtering machines with a proper controller based on our work. The device described in the paper can be easily used in need, being a proven project of a fast, inexpensive, and reliable thin-film thickness monitor.
Composing Optimized Embedded Software Architectures for Physics-Based EKF-MPC Smart Sensor for Li-Ion Battery Cell Management
Authors: Madsen Anne K. , Perera Darshika G.
Published: August 2022

Efficient battery technology is imperative for the adoption of clean energy automotive solutions. In addition, efficient battery technology extends the useful life of the battery as well as provides improved performance to fossil fuel technology. Model predictive control (MPC) is an effective way to operate battery management systems (BMS) at their maximum capability, while maintaining the safety requirements. Using the physics-based model (PBM) of the battery allows the control system to operate on the chemical and physical process of the battery. Since these processes are internal to the battery and are physically unobservable, the extended Kalman filter (EKF) serves as a virtual observer that can monitor the physical and chemical properties that are otherwise unobservable. These three methods (i.e., PBM, EKF, and MPC) together can prolong the useful life of the battery, especially for Li-ion batteries. This capability is not limited to the automotive industry: any real-world smart application can benefit from a portable/mobile efficient BMS, compelling these systems to be executed on resource-constrained embedded devices. Furthermore, the intrinsic adaptive control process of the PBM is uniquely suited for smart systems and smart technology. However, the sheer computational complexity of PBM for MPC and EKF prevents it from being realized on highly constrained embedded devices. In this research work, we introduce a novel, unique, and efficient embedded software architecture for a PB-EKF-MPC smart sensor for BMS, specifically on embedded devices, by addressing the computational complexity of PBM. Our proposed embedded software architecture is created in such a way to be executed on a 32-bit embedded microprocessor running at 100 MHz with a limited memory of 128 KB, and still obtains an average execution time of 4.8 ms.
Automatic Modulation Classification for MASK, MPSK, and MQAM Signals Based on Hierarchical Self-Organizing Map
Authors: Li Zerun , Wang Qinglin , Zhu Yufei , Xing Zuocheng
Published: August 2022

Automatic modulation classification (AMC) plays a fundamental role in common communication systems. Existing clustering models typically handle fewer modulation types with lower classification accuracies and more computational resources. This paper proposes a hierarchical self-organizing map (SOM) based on a feature space composed of high-order cumulants (HOC) and amplitude moment features. This SOM with two stacked layers can identify intrinsic differences among samples in the feature space without the need to set thresholds. This model can roughly cluster the multiple amplitude-shift keying (MASK), multiple phase-shift keying (MPSK), and multiple quadrature amplitude keying (MQAM) samples in the root layer and then finely distinguish the samples with different orders in the leaf layers. We creatively implement a discrete transformation method based on modified activation functions. This method causes MQAM samples to cluster in the leaf layer with more distinct boundaries between clusters and higher classification accuracies. The simulation results demonstrate the superior performance of the proposed hierarchical SOM on AMC problems when compared with other clustering models. Our proposed method can manage more categories of modulation signals and obtain higher classification accuracies while using fewer computational resources.
Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
Authors: Gromada Krzysztof
Published: August 2022

The scarcity of open SAR (Synthetic Aperture Radars) imagery databases (especially the labeled ones) and sparsity of pre-trained neural networks lead to the need for heavy data generation, augmentation, or transfer learning usage. This paper described the characteristics of SAR imagery, the limitations related to it, and a small set of available databases. Comprehensive data augmentation methods for training Neural Networks were presented, and a novel filter-based method was proposed. The new method limits the effect of the speckle noise, which is very high-level in SAR imagery. The improvement in the dataset could be clearly registered in the loss value functions. The main advantage comes from more developed feature detectors for filter-based training, which is shown in the layer-wise feature analysis. The author attached the trained neural networks for open use. This provides quicker CNN-based solutions implementation.