bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast and Versatile Chromatography Process Design and Operation Optimization with the Aid of Artificial Intelligence
Authors: Mouellef Mourad , Vetter Florian Lukas , Zobel-Roos Steffen , Strube Jochen
Published: December 2021

Preparative and process chromatography is a versatile unit operation for the capture, purification, and polishing of a broad variety of molecules, especially very similar and complex compounds such as sugars, isomers, enantiomers, diastereomers, plant extracts, and metal ions such as rare earth elements. Another steadily growing field of application is biochromatography, with a diversity of complex compounds such as peptides, proteins, mAbs, fragments, VLPs, and even mRNA vaccines. Aside from molecular diversity, separation mechanisms range from selective affinity ligands, hydrophobic interaction, ion exchange, and mixed modes. Biochromatography is utilized on a scale of a few kilograms to 100,000 tons annually at about 20 to 250 cm in column diameter. Hence, a versatile and fast tool is needed for process design as well as operation optimization and process control. Existing process modeling approaches have the obstacle of sophisticated laboratory scale experimental setups for model parameter determination and model validation. For a broader application in daily project work, the approach has to be faster and require less effort for non-chromatography experts. Through the extensive advances in the field of artificial intelligence, new methods have emerged to address this need. This paper proposes an artificial neural network-based approach which enables the identification of competitive Langmuir-isotherm parameters of arbitrary three-component mixtures on a previously specified column. This is realized by training an ANN with simulated chromatograms varying in isotherm parameters. In contrast to traditional parameter estimation techniques, the estimation time is reduced to milliseconds, and the need for expert or prior knowledge to obtain feasible estimates is reduced.
Development of a General PAT Strategy for Online Monitoring of Complex Mixtures—On the Example of Natural Product Extracts from Bearberry Leaf (Arctostaphylos uva-ursi)
Authors: Jensch Christoph , Knierim Larissa , Tegtmeier Martin , Strube Jochen
Published: December 2021

For the first time, a universally applicable and methodical approach from characterization to a PAT concept for complex mixtures is conducted—exemplified on natural products extraction processes. Bearberry leaf (Arctostaphylos uva-ursi) extract is chosen as an example of a typical complex mixture of natural plant origin and generalizable in its composition. Within the quality by design (QbD) based process development the development and implementation of a concept for process analytical technology (PAT), a key enabling technology, is the next necessary step in risk and quality-based process development and operation. To obtain and provide an overview of the broad field of PAT, the development process is shown on the example of a complex multi-component plant extract. This study researches the potential of different process analytical technologies for online monitoring of different component groups and classifies their possible applications within the framework of a QbD-based process. Offline and online analytics are established on the basis of two extraction runs. Based on this data set, PLS models are created for the spectral data, and correlations are conducted for univariate data. In a third run, the prediction potential is researched. Conclusively, the results of this study are arranged in the concept of a holistic quality and risk-based process design and operation concept.
Life Cycle Assessment of Functional Food: Improving Sustainability in the Biotechnology Industry through Transparency
Authors: Lee Alisha , Wang Yini , Lo Shih-Fang
Published: December 2021

With the advancement of biotechnology, consumers are demanding more scientifically advanced products as well as being more concerned with the environmental impact of products. A life cycle carbon footprint assessment is an important tool in reaching net-zero carbon production goals. This paper presents the greenhouse gas emissions of functional foods by highlighting TCI Co., Ltd. as a case study. TCI conducted life cycle assessments (LCAs) on two mainstream products that utilize common manufacturing processes to examine their carbon footprint and gain a better understanding of their environmental impact. The main finding shows that while the manufacturing stage accounts for around 20–30% of emissions, the raw material stage accounts for 70–80% for both products. This suggests that TCI needs to work closely with its suppliers to ensure a low emissions supply chain and to achieve its net-zero target. Not only do these LCAs allow TCI to increase the products’ transparency, but they can also be offered as a reference to other businesses producing similar products. Sharing of knowledge and practices in the biotechnology industry benefits the entire ecosystem and improves corporate sustainability.
An Analytic Solution for an Expanding/Contracting Strain-Hardening Viscoplastic Hollow Cylinder at Large Strains and Its Application to Tube Hydroforming Design
Authors: Lang Lihui , Alexandrov Sergei , Rynkovskaya Marina
Published: December 2021

This paper presents a semi-analytic rigid/plastic solution for the expansion/contraction of a hollow cylinder at large strains. The constitutive equations comprise the yield criterion and its associated flow rule. The yield criterion is pressure-independent. The yield stress depends on the equivalent strain rate and the equivalent strain. No restriction is imposed on this dependence. The solution is facilitated using the equivalent strain rate as an independent variable instead of the polar radius. As a result, it reduces to ordinary integrals. In the course of deriving the solution above, the transformation between Eulerian and Lagrangian coordinates is used. A numerical example illustrates the solution for a material model available in the literature. A practical aspect of the solution is that it readily applies to the preliminary design of tube hydroforming processes.
Dirty South Feminism: The Girlies Got Somethin’ to Say Too! Southern Hip-Hop Women, Fighting Respectability, Talking Mess, and Twerking Up the Dirty South
Authors: Johnson Adeerya
Published: December 2021

Within southern hip-hop, minimal credit has been given to the Black women who have curated sonic and performance narratives within the southern region. Many southern hip-hop scholars and journalists have centralized the accomplishments and masculinities of southern male rap performances. Here, dirty south feminism works to explore how agency, location, and Black women’s rap (lyrics and rhyme) and dance (twerking) performances in southern hip-hop are established under a contemporary hip-hop womanist framework. I critique the history of southern hip-hop culture by decentralizing male-dominated and hyper-masculine southern hip-hop identities. Second, I extend hip-hop feminist/womanist scholarship that includes tangible reflections of Black womanhood that emerge out of the South to see how these narratives reshape and re-inform representations of Black women and girls within southern hip-hop culture. I use dirty south feminism to include geographical understandings of southern Black women who have grown up in the South and been sexually shamed, objectified and pushed to the margins in southern hip-hop history. I seek to explore the following questions: How does the performance of Black women’s presence in hip-hop dance localize the South to help expand narratives within dirty south hip-hop? How can the “dirty south” as a geographical place within hip-hop be a guide to disrupt a conservative hip-hop South through a hip-hop womanist lens?
Ritual and Thought: Spirituality and Method in Philosophy of Religion
Authors: Connelly William L.
Published: December 2021

This paper outlines a strain of French Spiritualism, a philosophical tradition extending from Maine de Biran, Félix Ravaisson, and Jules Lachelier to their reception in the work of Maurice Blondel and his protégé Henry Duméry. In receiving and transforming this tradition, Blondel and Duméry have helped to provide a distinct philosophical paradigm in philosophy of religion, capable of providing insight into the spiritual nature of the human being, both in how spirituality relates to the advanced stages of religious culture in addition to its primitive presence in spontaneous action. As a tradition consecrated to the study of human consciousness, and the operations of the mind [l’esprit], the French spiritualist tradition provides a rich conceptual matrix for analyzing the nature of human thinking and its relationship to action. In such an analysis of human thought, Maurice Blondel set up a moral psychology and metaphysical anthropology, highlighting how the consciousness of the human being is linked to the objective order of existence, both in its material form and in the intelligible realities behind the nature of existence. This philosophical matrix helps to show how religious practices, through embodied engagement with the material world, are effective at generating a consciousness of metaphysical or transcendent realities. As such, this philosophical paradigm provides the means for constructing a theory of ritual, where ritual acts with symbols and signs may be rendered intelligible as the sensible means for the cognitive expression of spiritual activity.
Mosque Architecture in Cyprus—Visible and Invisible Aspects of Form and Space, 19th to 21st Centuries
Authors: Kiessel Marko , Tozan Asu
Published: December 2021

A comprehensive analysis of Cypriot mosque architecture between the 19th and 21st centuries, from the Ottoman and British colonial periods to the present, does not exist. The phase after 1974, after the division of the island into a Turkish Cypriot, predominantly Muslim north and a Greek Cypriot, mainly Christian south, is especially insufficiently studied. This paper aims to interpret Cypriot mosque architecture and its meaning(s) through a comparative analysis, considering cultural, religious, and political developments. Based on an architectural survey and studies about Muslim Cypriot culture, this study investigates formal and spatial characteristics, focusing on the presence/absence of domed plan typologies and of minarets which, as visual symbolic markers, might express shifting cultural-religious notions and/or identities. Inconspicuous mosques without domes and minarets dominate until 1974. However, with the inter-communal tensions in the 1960s, the minaret possibly became a sign of Turkish identity, besides being a cultural-religious marker. This becomes more obvious after 1974 and is stressed by the (re)introduction of the dome. Since the late 1990s, an ostentatious and unprecedented neo-Ottoman architecture emphasizes visible and invisible meanings, and the Turkish presence in Cyprus stronger than before. The new architectural language visually underlines the influences from Turkey that North Cyprus has been experiencing.
Going Hands-Free: MagnetoSuture™ for Untethered Guided Needle Penetration of Human Tissue Ex Vivo
Authors: Mair Lamar O. , Chowdhury Sagar , Liu Xiaolong , Erin Onder , Udalov Oleg , Raval Suraj , Johnson Benjamin , Jafari Sahar , Cappelleri David J. , Diaz-Mercado Yancy , Krieger Axel , Weinberg Irving N.
Published: December 2021

The application of force in surgical settings is typically accomplished via physical tethers to the surgical tool. While physical tethers are common and critical, some internal surgical procedures may benefit from a tetherless operation of needles, possibly reducing the number of ports in the patient or the amount of tissue damage caused by tools used to manipulate needles. Magnetic field gradients can dynamically apply kinetic forces to magnetizable objects free of such tethers, possibly enabling ultra-minimally invasive robotic surgical procedures. We demonstrate the untethered manipulation of a suture needle in vitro, exemplified by steering through narrow holes, as well as needle penetration through excised rat and human tissues. We present proof of principle manipulations for the fully untethered control of a minimally modified, standard stainless steel surgical suture needle.
Improving the iGNSS-R Ocean Altimetric Precision Based on the Coherent Integration Time Optimization Model
Authors: Sun Xuezhi , Zheng Wei , Wu Fan , Liu Zongqiang
Published: December 2021

Improving the altimetric precision under the requirement of ensuring the along-track resolution is of great significance to the application of iGNSS-R satellite ocean altimetry. The results obtained by using the empirical integration time need to be improved. Optimizing the integration time can suppress the noise interference from different sources to the greatest extent, thereby improving the altimetric precision. The inverse relationship between along-track resolution and signal integration time leads to the latter not being infinite. To obtain the optimal combination of integral parameters, this study first constructs an analytical model whose precision varies with coherent integration time. Second, the model is verified using airborne experimental data. The result shows that the average deviation between the model and the measured precision is about 0.16 m. The two are consistent. Third, we apply the model to obtain the optimal coherent integration time of the airborne experimental scenario. Compared with the empirical coherent integration parameters, the measured precision is improved by about 0.1 m. Fourth, the verified model is extrapolated to different spaceborne scenarios. Then, the optimal coherent integration time and the improvement of measured precision under various conditions are estimated. It was found that the optimal coherent integration time of the spaceborne scene is shorter than that of the airborne scene. Depending on the orbital altitude and the roughness of the sea surface, its value may also vary. Moreover, the model can significantly improve the precision for low signal-to-noise ratios. The coherent integration time optimization model proposed in this paper can enhance the altimetric precision. It would provide theoretical support for the signal optimization processing and sea surface height retrieval of iGNSS-R altimetry satellites with high precision and high along-track resolution in the future.
Urban Functional Zone Recognition Integrating Multisource Geographic Data
Authors: Chen Siya , Zhang Hongyan , Yang Hangxing
Published: December 2021

As the basic spatial unit of urban planning and management, it is necessary to understand the real development trend of urban functional zones in time and carry out reasonable planning adjustment. Because of the complexity of urban functional zones, the automatic recognition of urban functional zones has become a significant scientific problem in urban research. Urban functional zones contain natural and socioeconomic characteristics, but the existing identification methods fail to comprehensively consider these features. This paper proposes a framework that integrates multisource geographic data to recognize urban functional zone. We used high-resolution remote sensing imagery, point-of-interest (POI) data and high-spatial-resolution nighttime light imagery to extract both natural and socioeconomic features for urban functional zone accurate interpretation. Various features provide more accurate and comprehensive description for complex urban functional zone, so as to improve the recognition accuracy of urban functional zone. At present, there are few studies on urban functional zone recognition based on the combination of high-resolution remote sensing image, POI and high-resolution nighttime light imagery. The application potential of the combination of these three geographical data sources in urban function zone recognition needs to be explored. The experimental results show that the accuracy of urban functional zone recognition was obviously improved by the three data sources combination, the overall accuracy reached 80.30% and a comprehensive evaluation index reached 68.26%. This illustrate that the combination of the three data sources is beneficial to the urban functional zone recognition.
Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method
Authors: Chen Zhiwei , Zheng Wei , Yin Wenjie , Li Xiaoping , Zhang Gangqiang , Zhang Jing
Published: December 2021

Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5° to 0.25°, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5° to 0.05° by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at −20.86 mm/yr and −21.79 mm/yr (−14.53 mm/yr and −15.46 mm/yr), respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products.
Potential Factors That Trigger the Suspension of Calcium Carbonate Sediments and Whiting in a Semi-Enclosed Gulf
Authors: Shanableh Abdallah , Al-Ruzouq Rami , Gibril Mohamed Barakat A. , Khalil Mohamad Ali , AL-Mansoori Saeed , Yilmaz Abdullah Gokhan , Imteaz Monzur Alam , Flesia Cristina
Published: December 2021

Whitings, the manifestation of high levels of suspended fine-grained calcium carbonate particles in the water, have been reported and studied worldwide. However, the triggering mechanism of whiting occurrences remains uncertain. The current study attempted to analyze potential factors that might account for whiting occurrences in a semi-enclosed gulf (namely the Arabian/Persian Gulf, hereinafter called the Gulf). First, spatial and temporal variability of whiting events and different potential driving factors (i.e., whiting seasonality, wind-induced mixing, sea surface temperature, and bathymetry) were explored and examined for five years (2015–2020). Second, as a general indicator of whiting occurrences in the Gulf, a whiting index (WI) was developed using time-series analysis and decision tree (DT) classification algorithm. Third, the correlation between the proposed WI and the spatial coverage of various whiting events was examined. Time-series analysis showed that whiting events during the winter season are associated with high winds that lasted for several days. Nevertheless, whiting events were rarely observed despite high wind speed and increased potential for CaCO3 precipitation in summer. This finding suggests that wind-driven forces might be potential sources for mixing water columns, resuspension of CaCO3 particles, and the appearance of whiting in the Gulf. The DT classification algorithm demonstrated that a minimum WI value of 1.1 can explain the initiation of most summer and winter whiting events. Furthermore, a Pearson correlation coefficient of 0.73 was measured between WI and the extent of whiting along the UAE and Qatar coastlines in the Gulf. The proposed WI shows a simple yet effective method for identifying and estimating the extent of whiting in the Gulf.
Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks
Authors: Chen Hanlin , Niu Fei , Su Xing , Geng Tao , Liu Zhimin , Li Qiang
Published: December 2021

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.
Public Participation as a Tool for Solving Socio-Spatial Conflicts of Smart Cities and Smart Villages in the Sustainable Transport System
Authors: Ogryzek Marek , Krupowicz Wioleta , Sajnóg Natalia
Published: December 2021

The article presents modern international approaches to public participation in Sustainable Transport System planning. It discusses the causes of social conflicts during the implementation of transport infrastructure projects using the example of implementation of several Polish strategic road infrastructure projects. It provides the assessment of the form, scope, and scale of stakeholders’ involvement in the decision-making process. Among mitigation measures, the authors propose a model solution based on a comprehensive approach to public participation in road infrastructure planning in smart cities and smart villages within a Sustainable Transport System. The proposed idea involves a model of multi-criteria spatial analysis using the analytic hierarchy process (AHP) developed in the geographical information systems (GIS) environment, which—apart from technical-functional, environmental, cultural, economic, financial, and social criteria—also encompasses preferences expressed by local community representatives. The model includes eight stages of public participation in the decision-making process, involving all the rungs of a ladder of citizen participation. The presented solution departs from typical social participation methods used in road infrastructure planning processes.
Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin
Authors: Tang Senlin , Wang Hong , Feng Yao , Liu Qinghua , Wang Tingting , Liu Wenbin , Sun Fubao
Published: December 2021

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.
High Chlorophyll-a Areas along the Western Coast of South Sulawesi-Indonesia during the Rainy Season Revealed by Satellite Data
Authors: Wirasatriya Anindya , Susanto Raden Dwi , Setiawan Joga Dharma , Ramdani Fatwa , Iskandar Iskhaq , Jalil Abd. Rasyid , Puryajati Ardiansyah Desmont , Kunarso Kunarso , Maslukah Lilik
Published: December 2021

The southern coast of South Sulawesi-Indonesia is known as an upwelling area occurring during dry season, which peaks in August. This upwelling area is indicated by high chlorophyll-a (Chl-a) concentrations due to a strong easterly wind-induced upwelling. However, the investigation of Chl-a variability is less studied along the western coast of South Sulawesi. By taking advantages of remote sensing data of Chl-a, sea surface temperature, surface wind, and precipitation, the present study firstly shows that along the western coast of South Sulawesi, there are two areas, which have high primary productivity occurring during the rainy season. The first area is at 119.0° E–119.5° E; 3.5° S–4.0° S, while the second area is at 119.0° E–119.5° E; 3.5° S–4.0° S. The maximum primary productivity in the first (second) area occurs in April (January). The generating mechanism of the high primary productivity along the western coast of South Sulawesi is different from its southern coast. The presence of river runoff in these two areas may bring anthropogenic organic compounds during the peak of rainy season, resulting in increased Chl-a concentration.
Multi-Source Remote Sensing Image Fusion for Ship Target Detection and Recognition
Authors: Liu Jinming , Chen Hao , Wang Yu
Published: December 2021

The active recognition of interesting targets has been a vital issue for remote sensing. In this paper, a novel multi-source fusion method for ship target detection and recognition is proposed. By introducing synthetic aperture radar (SAR) sensor images, the proposed method solves the problem of precision degradation in optical remote sensing image target detection and recognition caused by the limit of illumination and weather conditions. The proposed method obtains port slice images containing ship targets by fusing optical data with SAR data. On this basis, spectral residual saliency and region growth method are used to detect ship targets in optical image, while SAR data are introduced to improve the accuracy of ship detection based on joint shape analysis and multi-feature classification. Finally, feature point matching, contour extraction and brightness saliency are used to detect the ship parts, and the ship target types are identified according to the voting results of part information. The proposed ship detection method obtained 91.43% recognition accuracy. The results showed that this paper provides an effective and efficient ship target detection and recognition method based on multi-source remote sensing images fusion.
Study of Persistent Haze Pollution in Winter over Jinan (China) Based on Ground-Based and Satellite Observations
Authors: Li Hui , Shi Rui , Jin Shikuan , Wang Weiyan , Fan Ruonan , Zhang Yiqun , Liu Boming , Zhao Peitao , Gong Wei , Zhao Yuefeng
Published: December 2021

A comprehensive study of the formation process of haze events in the Jinan area of China during winter is conducted based on the ground-based and satellite observation data from 1 December 2020 to 12 January 2021. According to variation of pollutant concentrations, two typical types of haze pollution are found in the Jinan area. During the type 1 haze pollution, the PM2.5 concentrations are greater than 75 μgm−3 and less than 115 μgm−3 with a short duration. The haze is mainly caused by local pollutant emissions and the accumulation of pollutants transported from areas around Jinan. By contrast, type 2 haze pollution episodes have a long duration and peak PM2.5 concentrations between 150 μgm−3 and 250 μgm−3, which is considered heavy pollution. Type 2 haze pollution is mainly caused by a mixture of long-range transported dust with locally emitted pollutants. Moreover, the unfavorable meteorological factors such as stable inversion layer, continuous cold high-pressure system, high relative humidity, and low wind speed play an important role in the formation of both types of haze pollution. In addition, there are significant photochemical processes during the haze pollutions. According to satellite data, the AOD in Jinan and surrounding areas is maintained at a high-level during haze pollution. It indicates that the local pollution is often accompanied by regional pollution during haze pollution events. This study reveals the formation process of haze pollution and promotes the study of regional climate change, which can provide guidance to the government in the prevention and control of haze pollution in East China.
A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform
Authors: Cui Langfu , Zhang Qingzhen , Yang Liman , Bai Chenggang
Published: December 2021

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.
Efficient SAR Azimuth Ambiguity Reduction in Coastal Waters Using a Simple Rotation Matrix: The Case Study of the Northern Coast of Jeju Island
Authors: Choi Joon Hyuk , Won Joong-Sun
Published: December 2021

Azimuth ambiguities, or ghosts on SAR images, represent one of the main obstacles for SAR applications involving coastal monitoring activities such as ship detection. While most previous methods based on azimuth antenna pattern and direct filtering are effective for azimuth ambiguity suppression, they may not be effective for fast cruising small ships. This paper proposes a unique approach for the reduction of azimuth ambiguities or ghosts in SAR single-look complex (SLC) images using a simple rotation matrix. It exploits the fact that the signal powers of azimuth ambiguities are concentrated on narrow bands, while those of vessels or other true ground targets are dispersed over broad bands. Through sub-aperture processing and simple axis rotation, it is possible to concentrate the dispersed energy of vessels onto a single axis while the ghost signal powers are dispersed onto three different axes. Then, the azimuth ambiguities can be easily suppressed by a simple calculation of weighted sum and difference, while preserving vessels. Applied results achieved by processing TerrSAR-X SLC images are provided and discussed. An optimum weight of 0.5 was determined by Receiver Operating Characteristic (ROC) analysis. Capabilities of ship detection from the test image were significantly improved by removing 93% of false alarms. Application results demonstrate its high performance of ghost suppression. This method can be employed as a pre-processing tool of SAR images for ship detection in coastal waters.
Analysis on the Spatio-Temporal Changes of LST and Its Influencing Factors Based on VIC Model in the Arid Region from 1960 to 2017: An Example of the Ebinur Lake Watershed, Xinjiang, China
Authors: Amantai Nigenare , Ding Jianli
Published: December 2021

LST (Land surface temperature) is an important indicator for monitoring dynamic changes in the earth’s resources and environment. However, the complexity of obtaining long-term, continuous LST data hinders the development of research on LST responses to meteorological factors or LUCC in areas where data is lacking. The objective of this research was to use the VIC-3L (Variable Infiltration Capacity) based on multi-source remote sensing data to simulate and explore spatio-temporal changes in the LST, to analyze the relationship between the LST and meteorological elements by using cross-wavelet transform (XWT) and wavelet coherence (WTC), the relationship between the LST and LUCC by using three-phase remote sensing images of LUCC. The following results were obtained. The annual average LST of the study area is increasing at a rate of 0.027 °C per year. The annual average LST level is relatively high in the central and eastern regions. The average temperature has an important influence on LST, which is mainly reflected in the period scale of 1~4a in 1963–1972, 1980–1996, and 2004–2010. The sharp decline in open shrubs may have exacerbated the increase in LST in the study area. This study provides a scientific reference for studying LST in arid areas.
A DNN-Based UVI Calculation Method Using Representative Color Information of Sun Object Images
Authors: Ga Deog-Hyeon , Oh Seung-Taek , Lim Jae-Hyun
Published: December 2021

As outdoor activities are necessary for maintaining our health, research interest in environmental conditions such as the weather, atmosphere, and ultraviolet (UV) radiation is increasing. In particular, UV radiation, which can benefit or harm the human body depending on the degree of exposure, is recognized as an essential environmental factor that needs to be identified. However, unlike the weather and atmospheric conditions, which can be identified to some extent by the naked eye, UV radiation corresponds to wavelength bands that humans cannot recognize; hence, the intensity of UV radiation cannot be measured. Recently, although devices and sensors that can measure UV radiation have been launched, it is very difficult for ordinary users to acquire ambient UV radiation information directly because of the cost and inconvenience caused by operating separate devices. Herein, a deep neural network (DNN)-based ultraviolet index (UVI) calculation method is proposed using representative color information of sun object images. First, Mask-region-based convolutional neural networks (R-CNN) are applied to sky images to extract sun object regions and then detect the representative color of the sun object regions. Then, a deep learning model is constructed to calculate the UVI by inputting RGB color values, which are representative colors detected later along with the altitude angle and azimuth of the sun at that time. After selecting each day of spring and autumn, the performance of the proposed method was tested, and it was confirmed that accurate UVI could be calculated within a range of mean absolute error of 0.3.
Estimation of Lower Limb Kinematics during Squat Task in Different Loading Using sEMG Activity and Deep Recurrent Neural Networks
Authors: Zangene Alireza Rezaie , Abbasi Ali , Nazarpour Kianoush
Published: December 2021

The aim of the present study was to predict the kinematics of the knee and the ankle joints during a squat training task of different intensities. Lower limb surface electromyographic (sEMG) signals and the 3-D kinematics of lower extremity joints were recorded from 19 body builders during squat training at four loading conditions. A long-short term memory (LSTM) was used to estimate the kinematics of the knee and the ankle joints. The accuracy, in terms root-mean-square error (RMSE) metric, of the LSTM network for the knee and ankle joints were 6.774 ± 1.197 and 6.961 ± 1.200, respectively. The LSTM network with inputs processed by cross-correlation (CC) method showed 3.8% and 4.7% better performance in the knee and ankle joints, respectively, compared to when the CC method was not used. Our results showed that in the prediction, regardless of the intensity of movement and inter-subject variability, an off-the-shelf LSTM decoder outperforms conventional fully connected neural networks.
Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study
Authors: Erhan Laura , Di Mauro Mario , Anjum Ashiq , Bagdasar Ovidiu , Song Wei , Liotta Antonio
Published: December 2021

Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.
Double Antiresonance Fiber Sensor for the Simultaneous Measurement of Curvature and Temperature
Authors: Pereira Diana , Bierlich Jörg , Kobelke Jens , Ferreira Marta S.
Published: December 2021

Antiresonant hollow core fibers (ARHCFs) have gained some attention due to their notoriously attractive characteristics on managing optical properties. In this work, an inline optical fiber sensor based on a hollow square core fiber (HSCF) is proposed. The sensor presents double antiresonance (AR), namely an internal AR and an external AR. The sensor was designed in a transmission configuration, where the sensing head was spliced between two single mode fibers (SMFs). A simulation was carried out to predict the behaviors of both resonances, and revealed a good agreement with the experimental observations and the theoretical model. The HSCF sensor presented curvature sensitivities of −0.22 nm/m−1 and −0.90 nm/m−1, in a curvature range of 0 m−1 to 1.87 m−1, and temperature sensitivities of 21.7 pm/°C and 16.6 pm/°C, in a temperature range of 50 °C to 500 °C, regarding the external resonance and internal resonance, respectively. The proposed sensor is promising for the implementation of several applications where simultaneous measurement of curvature and temperature are required.
Experimental Validation of Real-Time Ski Jumping Tracking System Based on Wearable Sensors
Authors: Link Johannes , Guillaume Sébastien , Eskofier Bjoern M.
Published: December 2021

For sports scientists and coaches, its crucial to have reliable tracking systems to improve athletes. Therefore, this study aimed to examine the validity of a wearable real-time tracking system (WRRTS) for the quantification of ski jumping. The tracking system consists of wearable trackers attached to the ski bindings of the athletes and fixed antennas next to the jumping hill. To determine the accuracy and precision of the WRRTS, four athletes of the German A or B National Team performed 35 measured ski jumps. The WRRTS was used to measure the 3D positions and ski angles during the jump. The measurements are compared with camera measurements for the in-flight parameters and the official video distance for the jumping distance to assess their accuracy. We statistically evaluated the different methods using Bland–Altman plots. We thereby find a mean absolute error of 0.46 m for the jumping distance, 0.12 m for the in-flight positions, and 0.8°, and 3.4° for the camera projected pitch and V-style opening angle, respectively. We show the validity of the presented WRRTS to measure the investigated parameters. Thus, the system can be used as a tracking system during training and competitions for coaches and sports scientists. The real-time feature of the tracking system enables usage during live TV broadcasting.
Wildfire Smoke Classification Based on Synthetic Images and Pixel- and Feature-Level Domain Adaptation
Authors: Mao Jun , Zheng Change , Yin Jiyan , Tian Ye , Cui Wenbin
Published: December 2021

Training a deep learning-based classification model for early wildfire smoke images requires a large amount of rich data. However, due to the episodic nature of fire events, it is difficult to obtain wildfire smoke image data, and most of the samples in public datasets suffer from a lack of diversity. To address these issues, a method using synthetic images to train a deep learning classification model for real wildfire smoke was proposed in this paper. Firstly, we constructed a synthetic dataset by simulating a large amount of morphologically rich smoke in 3D modeling software and rendering the virtual smoke against many virtual wildland background images with rich environmental diversity. Secondly, to better use the synthetic data to train a wildfire smoke image classifier, we applied both pixel-level domain adaptation and feature-level domain adaptation. The CycleGAN-based pixel-level domain adaptation method for image translation was employed. On top of this, the feature-level domain adaptation method incorporated ADDA with DeepCORAL was adopted to further reduce the domain shift between the synthetic and real data. The proposed method was evaluated and compared on a test set of real wildfire smoke and achieved an accuracy of 97.39%. The method is applicable to wildfire smoke classification tasks based on RGB single-frame images and would also contribute to training image classification models without sufficient data.
Dependable Sensor Fault Reconstruction in Air-Path System of Heavy-Duty Diesel Engines
Authors: Taherkhani Ashkan , Bayat Farhad , Mobayen Saleh , Bartoszewicz Andrzej
Published: December 2021

This paper addresses the problem of robust sensor faults detection and isolation in the air-path system of heavy-duty diesel engines, which has not been completely considered in the literature. Calibration or the total failure of a sensor can cause sensor faults. In the worst-case scenario, the engines can be totally damaged by the sensor faults. For this purpose, a second-order sliding mode observer is proposed to reconstruct the sensor faults in the presence of unknown external disturbances. To this aim, the concept of the equivalent output error injection method and the linear matrix inequality (LMI) tool are utilized to minimize the effects of uncertainties and disturbances on the reconstructed fault signals. The simulation results verify the performance and robustness of the proposed method. By reconstructing the sensor faults, the whole system can be prevented from failing before the corrupted sensor measurements are used by the controller.
Complex Permittivity Measurements of Steel Fiber-Reinforced Cementitious Composites Using a Free-Space Reflection Method with a Focused Beam Lens Horn Antenna
Authors: Cho Kyeongyong , Jo Sunghui , Noh Yeong-Hoon , Lee Namkon , Kim Sungwook , Yook Jong-Gwan
Published: December 2021

To measure the electromagnetic properties of steel fiber-reinforced concrete (SFRC) in the X-band, 1-port measurements were performed using a lens horn antenna in a free-space measurement system. Free-space 1-port calibration with translations of the position of the reflector regarding the characteristics of the focused beam lens horn antenna was applied. The intrinsic impedance and complex permittivity of the SFRC were obtained from the measured reflection characteristics. The steel fiber content increased and the electromagnetic properties of the SFRC gradually changed from a dielectric to a conductor, even in very low frequencies compared to the plasma frequencies of general metal, which are optical frequencies. This is considered to be the plasmon effect of the metallic structure formed by the steel fiber. This result is applicable for analyses of the electromagnetic phenomenon of large structures with fiber content.