bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Heterogeneity in Runoff Dynamics and Its Drivers in a Water Conservation Area of the Upper Yellow River Basin over the Past 35 Years
Authors: Zeng Biao , Zhang Fuguang , Zeng Weifeng , Yan Ke , Cui Chengyu
Published: August 2022

The upper Yellow River Basin (UYRB) is an important water source and conservation area. As well as a warming climate, the region has experienced long-term human interventions, such as grazing, farming and reservoir construction, since the mid-1980s. The runoff dynamics and its drivers in the UYRB remain poorly constrained, especially the differences between pastoral and agricultural areas, due to a lack of detailed measurements. Here, the contributions of climate change, land-use change and anthropogenic water consumption to runoff changes observed at hydrological stations were evaluated by combining the observations with scenarios simulated by a process-based hydrological model. Changes in the pastoral and agricultural areas of the UYRB were then compared at 10-year intervals during the period 1986–2020. The results indicated lower runoff in the UYRB over the past 35 years, when compared with that in the baseline period of 1965–1985, due to the negative influences of both climate change and anthropogenic water consumption. Meanwhile, land-use change had only a weak influence, due to stable land-use patterns. However, spatial differences in dominant drivers of runoff reductions were evident between the pastoral area and agricultural area. Specifically, runoff decreases were caused by the negative influences of climate change in the pastoral area and anthropogenic water consumption in the agricultural area. A shift in the dominant drivers after the period 2006–2015 was attributed to the positive influence of climate change and a weakening of agricultural water consumption and reservoir inflow during the same period. These changes initially caused a decrease in runoff, and later an increase. These findings contribute to a crucial evidence base for optimizing water resource management, ecological protection and high-quality development in the Yellow River Basin.
Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types
Authors: Liu Yantao , Zhou Wei , Gao Si , Ma Xuanlong , Yan Kai
Published: August 2022

In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.
A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements
Authors: Das Spandan , Wang Yiding , Gong Jie , Ding Leah , Munchak Stephen J. , Wang Chenxi , Wu Dong L. , Liao Liang , Olson William S. , Barahona Donifan O.
Published: August 2022

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.
Experimental Proof of Concept for the Hurricane Imaging Radiometer (HIRAD) Measurement of a Hurricane Ocean Surface Wind Speed Field
Authors: Coto Jonathan , Jones W. Linwood , Cecil Daniel J. , Biswas Sayak
Published: August 2022

The objective of the Hurricane Imaging Radiometer (HIRAD) is to produce wide-swath images of hurricane wind and rain fields during a single pass from a high-altitude aircraft. This instrument could be a prototype for the next generation of airborne hurricane remote sensors that operate on NOAA/USAF surveillance flights over named storms and hurricanes. The improved two-dimensional surface wind field measurements provided by the HIRAD approach are crucial to improved forecasts and warnings. For almost a decade, HIRAD has been used in research flights over hurricanes; however, because of various hardware issues, the scientific potential of its measurements has not been fulfilled. This paper presents a reanalysis of HIRAD measurements over Hurricane Gonzalo on 17 October 2014 that demonstrate remarkable results. The basis for this novel approach is to use coincident surface wind speed (WS) and rain rate (RR) measurements from another source to calibrate the HIRAD brightness temperature measurements. As a result, the HIRAD retrievals of WS and RR are in excellent agreement with the accompanying airborne remote sensors and in situ surface wind speed measurements, which validates the HIRAD technique proof of concept.
Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis
Authors: Guo Wenqi , Zhang Weixiong , Zhang Zheng , Tang Ping , Gao Shichen
Published: August 2022

The extensive amount of Satellite Image Time Series (SITS) data brings new opportunities and challenges for land cover analysis. Many supervised machine learning methods have been applied in SITS, but the labeled SITS samples are time- and effort-consuming to acquire. It is necessary to analyze SITS data with an unsupervised learning method. In this paper, we propose a new unsupervised learning method named Deep Temporal Iterative Clustering (DTIC) to deal with SITS data. The proposed method jointly learns a neural network’s parameters and the resulting features’ cluster assignments, which uses a standard clustering algorithm, K-means, to iteratively cluster the features produced by the feature extraction network and then uses the subsequent assignments as supervision to update the network’s weights. We apply DTIC to the unsupervised training of neural networks on both SITS datasets. Experimental results demonstrate that DTIC outperforms the state-of-the-art K-means clustering algorithm, which proves that the proposed approach successfully provides a novel idea for unsupervised training of SITS data.
Detecting Mountain Forest Dynamics in the Eastern Himalayas
Authors: Wang Chunling , Wang Jianbang , He Zhuoyu , Feng Min
Published: August 2022

Forest dynamics is critical to forested ecosystems, and considerable efforts have been devoted to monitoring long-term forest dynamics with the goals of sustainable management and conservation of forests. However, little attention has been given to mountain forests, which are more challenging to monitor due to complex topography, weather, and their distribution. We developed a 30-m resolution tree-canopy cover (TCC) and forest change dataset for the Eastern Himalayas from 1986 to 2021. The tree-canopy cover estimation was validated against estimates from the space-borne Global Ecosystem Dynamics Investigation (GEDI), demonstrating strong consistency (R-square greater than 0.81). A comprehensive assessment for the forest change dataset was performed using 448 visually interpreted points and reported high accuracy of the dataset, i.e., 97.7% and 95.9% for forest loss and gain, respectively. Higher producer and user accuracies were reported for forest loss (PA = 78.0%, UA = 60.9%) than these for forest gain (PA = 61.7%, UA = 56.7%). The results indicated that (1) the mean tree-canopy cover in the region increased by 2.76% over the past three decades, from 40.67% in 1990 to 43.43% in 2020, suggesting the forests have improved during the period; (2) forest loss was identified for a total area of 6990 km2 across the study area, which is less than the 10,700 km2 identified as forest gain; (3) stronger forest gains were found at elevations greater than 3000 m asl, indicating faster forest growth in high elevations likely influenced by the warming temperatures in the Eastern Himalayas.
Deep Contrastive Self-Supervised Hashing for Remote Sensing Image Retrieval
Authors: Tan Xiaoyan , Zou Yun , Guo Ziyang , Zhou Ke , Yuan Qiangqiang
Published: August 2022

Hashing has been widely used for large-scale remote sensing image retrieval due to its outstanding advantages in storage and search speed. Recently, deep hashing methods, which produce discriminative hash codes by building end-to-end deep convolutional networks, have shown promising results. However, training these networks requires numerous labeled images, which are scarce and expensive in remote sensing datasets. In order to solve this problem, we propose a deep unsupervised hashing method, namely deep contrastive self-supervised hashing (DCSH), which uses only unlabeled images to learn accurate hash codes. It eliminates the need for label annotation by maximizing the consistency of different views generated from the same image. More specifically, we assume that the hash codes generated from different views of the same image are similar, and those generated from different images are dissimilar. On the basis of the hypothesis, we can develop a novel loss function containing the temperature-scaled cross-entropy loss and the quantization loss to train the developed deep network end-to-end, resulting in hash codes with semantic similarity preserved. Our proposed network contains four parts. First, each image is transformed into two different views using data augmentation. After that, they are fed into an encoder with the same shared parameters to obtain deep discriminate features. Following this, a hash layer converts the high-dimensional image representations into compact binary codes. Lastly, a novel hash function is introduced to train the proposed network end-to-end and thus guide generated hash codes with semantic similarity. Extensive experiments on two popular benchmark datasets of the UC Merced Land Use Database and the Aerial Image Dataset have demonstrated that our DCSH has significant superiority in remote sensing image retrieval compared with state-of-the-art unsupervised hashing methods.
Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020
Authors: Cao Ruyin , Xu Zichao , Chen Yang , Chen Jin , Shen Miaogen
Published: August 2022

As the largest and highest alpine ecoregion in the world, the Qinghai–Tibetan Plateau (QTP) is extremely sensitive to climate change and has experienced extraordinary warming during the past several decades; this has greatly affected various ecosystem processes in this region such as vegetation production and phenological change. Therefore, numerous studies have investigated changes in vegetation dynamics on the QTP using the satellite-derived normalized-difference vegetation index (NDVI) time-series data provided by the Moderate-Resolution Imaging Spectroradiometer (MODIS). However, the highest spatial resolution of only 250 m for the MODIS NDVI product cannot meet the requirement of vegetation monitoring in heterogeneous topographic areas. In this study, therefore, we generated an 8-day and 30 m resolution NDVI dataset from 2000 to 2020 for the QTP through the fusion of 30 m Landsat and 250 m MODIS NDVI time-series data. This dataset, referred to as QTP-NDVI30, was reconstructed by employing all available Landsat 5/7/8 images (>100,000 scenes) and using our recently developed gap-filling and Savitzky–Golay filtering (GF-SG) method. We improved the original GF-SG approach by incorporating a module to process snow contamination when applied to the QTP. QTP-NDVI30 was carefully evaluated in both quantitative assessments and visual inspections. Compared with reference Landsat images during the growing season in 100 randomly selected subregions across the QTP, the reconstructed 30 m NDVI images have an average mean absolute error (MAE) of 0.022 and a spatial structure similarity (SSIM) above 0.094. We compared QTP-NDVI30 with upscaled cloud-free PlanetScope images in some topographic areas and observed consistent spatial variations in NDVI between them (averaged SSIM = 0.874). We further examined an application of QTP-NDVI30 to detect vegetation green-up dates (GUDs) and found that QTP-NDVI30-derived GUD data show general agreement in spatial patterns with the 250 m MODIS GUD data, but provide richer spatial details (e.g., GUD variations at the subpixel scale). QTP-NDVI30 provides an opportunity to monitor vegetation and investigate land-surface processes in the QTP region at fine spatiotemporal scales.
Automated Extraction of Annual Erosion Rates for Arctic Permafrost Coasts Using Sentinel-1, Deep Learning, and Change Vector Analysis
Authors: Philipp Marius , Dietz Andreas , Ullmann Tobias , Kuenzer Claudia
Published: August 2022

Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments.
Ground Penetrating Radar Measurements in Shallow Water Environments—A Case Study
Authors: Fediuk Annika , Wunderlich Tina , Wilken Dennis , Rabbel Wolfgang
Published: August 2022

In this study, we investigate GPR measurements in freshwater of less than 5 m at four different locations to derive rules of thumb in terms of depth penetration, resolution, and material contrasts of the method for 200 and 400 MHz antennas under field conditions. The objective is to improve the attractiveness of the method for archaeological issues in water, as there are hardly any studies on this subject so far. The depth penetration of 2–4 m is negligibly influenced by the choice of the 200 or 400 MHz antenna. Organic material in the water column also does not affect the water depth but offers new fields of applications for mapping and volume estimation of biomass in lakes with GPR. The horizontal resolution in the cm range in the direction of the profile and in the dm range across the profile could not be improved by the narrow antenna radiation pattern of <30° at the 3 dB level. In the crossline direction, the use of an antenna array would be necessary here. Still, the narrow antenna pattern reduces side reflections. Most common archaeological material contrasts can be resolved with the method. The method shows reflection coefficients >0.1 for materials of <80% porosity to the water column and for materials of <25% porosity and of >45% porosity to water-saturated sand. Large reflection coefficients also show, for example, granite to sand and gyttja to wood. The water column has a considerable effect on the data quality of the 400 MHz antenna from a depth of 2 m due to the antenna ringing. Furthermore, multiples must be expected in a water column <0.5 m. The method can especially complement the common geophysical methods of seismics and geoelectrics to exclude material ambiguities. The major advantage is the simple setting of the land equipment in the water.
Hyperbola Detection with RetinaNet and Comparison of Hyperbola Fitting Methods in GPR Data from an Archaeological Site
Authors: Wunderlich Tina , Wilken Dennis , Majchczack Bente Sven , Segschneider Martin , Rabbel Wolfgang
Published: August 2022

Hyperbolic diffractions in Ground Penetrating Radar (GPR) data are caused by a variety of subsurface objects such as pipes, stones, or archaeological artifacts. Supplementary to their location, the propagation velocity of electromagnetic waves in the subsurface can be derived. In recent years, it was shown that deep learning tools can automatically detect hyperbola in radargrams using data measured over urban infrastructure, which are relatively clear. In contrast, in this study, we used an archaeological dataset with diverse underground structures. In the first step we used the deep learning network RetinaNet to detect hyperbola automatically and achieved an average precision of 0.58. In the next step, 10 different approaches for hyperbola fitting and thus velocity determination were applied. The derived information was validated with manually determined velocities and apex points. It was shown that hyperbola extraction by using a threshold and a column connection clustering (C3) algorithm followed by simple hyperbola fitting is the best method, which had a mean velocity error of 0.021 m/ns compared to manual determination. The average 1D velocity-depth distribution derived in 10 ns intervals was in shape comparable to the manually determined one, but had a systematic shift of about 0.01 m/ns towards higher velocities.
Subpixel Multilevel Scale Feature Learning and Adaptive Attention Constraint Fusion for Hyperspectral Image Classification
Authors: Ge Zixian , Cao Guo , Zhang Youqiang , Shi Hao , Liu Yanbo , Shafique Ayesha , Fu Peng
Published: August 2022

Convolutional neural networks (CNNs) play an important role in hyperspectral image (HSI) classification due to their powerful feature extraction ability. Multiscale information is an important means of enhancing the feature representation ability. However, current HSI classification models based on deep learning only use fixed patches as the network input, which may not well reflect the complexity and richness of HSIs. While the existing methods achieve good classification performance for large-scale scenes, the classification of boundary locations and small-scale scenes is still challenging. In addition, dimensional dislocation often exists in the feature fusion process, and the up/downsampling operation for feature alignment may introduce extra noise or result in feature loss. Aiming at the above issues, this paper deeply explores multiscale features, proposes an adaptive attention constraint fusion module for different scale features, and designs a semantic feature enhancement module for high-dimensional features. First, HSI data of two different spatial scales are fed into the model. For the two inputs, we upsample them using bilinear interpolation to obtain their subpixel data. The proposed multiscale feature extraction module is intended to extract the features of the above four parts of the data. For the extracted features, the multiscale attention fusion module is used for feature fusion, and then, the fused features are fed into the high-level feature semantic enhancement module. Finally, based on the fully connected layer and softmax layer, the prediction results of the proposed model are obtained. Experimental results on four public HSI databases verify that the proposed method outperforms several state-of-the-art methods.
Object Tracking Based on Satellite Videos: A Literature Review
Authors: Zhang Zhaoxiang , Wang Chenghang , Song Jianing , Xu Yuelei
Published: August 2022

Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively.
Seasonal Investigation of MAX-DOAS and In Situ Measurements of Aerosols and Trace Gases over Suburban Site of Megacity Shanghai, China
Authors: Tanvir Aimon , Bilal Muhammad , Zhang Sanbao , Sandhu Osama , Xue Ruibin , Ali Md. Arfan , Zhu Jian , Qiu Zhongfeng , Wang Shanshan , Zhou Bin
Published: August 2022

Shanghai has gained much attention in terms of air quality research owing to its importance to economic capital and its huge population. This study utilizes ground-based remote sensing instrument observations, namely by Multiple AXis Differential Optical Absorption Spectroscopy (MAX-DOAS), and in situ measurements from the national air quality monitoring platform for various atmospheric trace gases including Nitrogen dioxide (NO2), Sulfur dioxide (SO2), Ozone (O3), Formaldehyde (HCHO), and Particulate Matter (PM; PM10: diameter ≤ 10 µm, and PM2.5: diameter ≤ 2.5 µm) over Shanghai from June 2020 to May 2021. The results depict definite diurnal patterns and strong seasonality in HCHO, NO2, and SO2 concentrations with maximum concentrations during winter for NO2 and SO2 and in summer for HCHO. The impact of meteorology and biogenic emissions on pollutant concentrations was also studied. HCHO emissions are positively correlated with temperature, relative humidity, and the enhanced vegetation index (EVI), while both NO2 and SO2 depicted a negative correlation to all these parameters. The results from diurnal to seasonal cycles consistently suggest the mainly anthropogenic origin of NO2 and SO2, while the secondary formation from the photo-oxidation of volatile organic compounds (VOCs) and substantial contribution of biogenic emissions for HCHO. Further, the sensitivity of O3 formation to its precursor species (NOx and VOCs) was also determined by employing HCHO and NO2 as tracers. The sensitivity analysis depicted that O3 formation in Shanghai is predominantly VOC-limited except for summer, where a significant percentage of O3 formation lies in the transition regime. It is worth mentioning that seasonal variation of O3 is also categorized by maxima in summer. The interdependence of criteria pollutants (O3, SO2, NO2, and PM) was studied by employing the Pearson’s correlation coefficient, and the results suggested complex interdependence among the pollutant species in different seasons. Lastly, potential source contribution function (PSCF) analysis was performed to have an understanding of the contribution of different source areas towards atmospheric pollution. PSCF analysis indicated a strong contribution of local sources on Shanghai’s air quality compared to regional sources. This study will help policymakers and stakeholders understand the complex interactions among the atmospheric pollutants and provide a baseline for designing effective control strategies to combat air pollution in Shanghai.
Generalized Asymmetric Correntropy for Robust Adaptive Filtering: A Theoretical and Simulation Study
Authors: Qu Hua , Wang Meng , Zhao Jihong , Zhao Shuyuan , Li Taihao , Yue Pengcheng
Published: August 2022

Correntropy has been proved to be effective in eliminating the adverse effects of impulsive noises in adaptive filtering. However, correntropy is not desirable when the error between the two random variables is asymmetrically distributed around zero. To address this problem, asymmetric correntropy using an asymmetric Gaussian function as the kernel function was proposed. However, an asymmetric Gaussian function is not always the best choice and can be further expanded. In this paper, we propose a robust adaptive filtering based on a more flexible definition of asymmetric correntropy, which is called generalized asymmetric correntropy that adopts a generalized asymmetric Gaussian density (GAGD) function as the kernel. With the shape parameter properly selected, the generalized asymmetric correntropy may get better performance than the original asymmetric correntropy. The steady-state performance of the adaptive filter based on the generalized maximum asymmetric correntropy criterion (GMACC) is theoretically studied and verified by simulation experiments. The asymmetric characteristics of queue delay in satellite networks is analyzed and described, and the proposed algorithm is used to predict network delay, which is essential in space telemetry. Simulation results demonstrate the desirable performance of the new algorithm.
Enhanced Semantic Information Transfer of Multi-Domain Samples: An Adversarial Edge Detection Method Using Few High-Resolution Remote Sensing Images
Authors: Xia Liegang , Yang Dezhi , Zhang Junxia , Yang Haiping , Chen Jun
Published: August 2022

Edge detection of ground objects is a typical task in the field of remote sensing and has advantages in accomplishing many complex ground object extraction tasks. Although recent mainstream edge detection methods based on deep learning have significant effects, these methods have a very high dependence on the quantity and quality of samples. Moreover, using datasets from other domains in detection tasks often leads to degraded network performance due to variations in the ground objects in different regions. If this problem can be solved to allow datasets from other domains to be reused, the number of labeled samples required in the new task domain can be reduced, thereby shortening the task cycle and reducing task costs. In this paper, we propose a weakly supervised domain adaptation method to address the high dependence of edge extraction networks on samples. The domain adaptation is performed on the edge level and the semantic level, which prevents deviations in the semantic features that are caused by the overgeneralization of edge features. Additionally, the effectiveness of our proposed domain adaptation module is verified. Finally, we demonstrate the superior edge extraction performance of our method in the SEGOS edge extraction network in contrast to other edge extraction methods.
A DLSTM-Network-Based Approach for Mechanical Remaining Useful Life Prediction
Authors: Liu Yan , Liu Zhenzhen , Zuo Hongfu , Jiang Heng , Li Pengtao , Li Xin
Published: August 2022

Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these methods still have the following weaknesses: (1) The correlation between the information collected by each sensor and the remaining useful life of the machinery is not sufficiently considered. (2) The accuracy of deep learning algorithms for remaining useful life prediction is low due to the high noise, over-dimensionality, and non-linear signals generated during the operation of complex systems. To overcome the above weaknesses, a general deep long short memory network-based approach for mechanical remaining useful life prediction is proposed in this paper. Firstly, a two-step maximum information coefficient method was built to calculate the correlation between the sensor data and the remaining useful life. Secondly, the kernel principal component analysis with a simple moving average method was designed to eliminate noise, reduce dimensionality, and extract nonlinear features. Finally, a deep long short memory network-based deep learning method is presented to predict remaining useful life. The efficiency of the proposed method for remaining useful life prediction of a nonlinear degradation process is demonstrated by a test case of NASA’s commercial modular aero-propulsion system simulation data. The experimental results also show that the proposed method has better prediction accuracy than other state-of-the-art methods.
Irrigated Crop Types Mapping in Tashkent Province of Uzbekistan with Remote Sensing-Based Classification Methods
Authors: Erdanaev Elbek , Kappas Martin , Wyss Daniel
Published: August 2022

Appropriate crop type mapping to monitor and control land management is very important in developing countries. It can be very useful where digital cadaster maps are not available or usage of Remote Sensing (RS) data is not utilized in the process of monitoring and inventory. The main goal of the present research is to compare and assess the importance of optical RS data in crop type classification using medium and high spatial resolution RS imagery in 2018. With this goal, Landsat 8 (L8) and Sentinel-2 (S2) data were acquired over the Tashkent Province between the crop growth period of May and October. In addition, this period is the only possible time for having cloud-free satellite images. The following four indices “Normalized Difference Vegetation Index” (NDVI), “Enhanced Vegetation Index” (EVI), and “Normalized Difference Water Index” (NDWI1 and NDWI2) were calculated using blue, red, near-infrared, shortwave infrared 1, and shortwave infrared 2 bands. Support-Vector-Machine (SVM) and Random Forest (RF) classification methods were used to generate the main crop type maps. As a result, the Overall Accuracy (OA) of all indices was above 84% and the highest OA of 92% was achieved together with EVI-NDVI and the RF method of L8 sensor data. The highest Kappa Accuracy (KA) was found with the RF method of L8 data when EVI (KA of 88%) and EVI-NDVI (KA of 87%) indices were used. A comparison of the classified crop type area with Official State Statistics (OSS) data about sown crops area demonstrated that the smallest absolute weighted average (WA) value difference (0.2 thousand ha) was obtained using EVI-NDVI with RF method and NDVI with SVM method of L8 sensor data. For S2-sensor data, the smallest absolute value difference result (0.1 thousand ha) was obtained using EVI with RF method and 0.4 thousand ha using NDVI with SVM method. Therefore, it can be concluded that the results demonstrate new opportunities in the joint use of Landsat and Sentinel data in the future to capture high temporal resolution during the vegetation growth period for crop type mapping. We believe that the joint use of S2 and L8 data enables the separation of crop types and increases the classification accuracy.
Engineer-Centred Design Factors and Methodological Approach for Maritime Autonomy Emergency Response Systems
Authors: Asplund Fredrik , Ulfvengren Pernilla
Published: August 2022

Commercial deployment of maritime autonomous surface ships (MASSs) is close to becoming a reality. Although MASSs are fully autonomous, the industry will still allow remote operations centre (ROC) operators to intervene if a MASS is facing an emergency the MASS cannot handle by itself. A human-centred design for the associated emergency response systems will require attention to the ROC operator workplace, but also, arguably, to the behaviour-shaping constraints on the engineers building these systems. There is thus a need for an engineer-centred design of engineering organisations, influenced by the current discourse on human factors. To contribute to the discourse, think-aloud protocol interviewing was conducted with well-informed maritime operators to elicit fundamental demands on cognition and collaboration by maritime autonomy emergency response systems. Based on the results, inferences were made regarding both design factors and methodological choices for future, early phase engineering of emergency response systems. Firstly, engineering firms have to improve their informal gathering and sharing of information through gatekeepers and/or organisational liaisons. To avoid a too cautious approach to accountability, this will have to include a closer integration of development and operations. Secondly, associated studies taking the typical approach of exposing relevant operators to new design concepts in scripted scenarios should include significant flexibility and less focus on realism.
Diseño e implementación de software y hardware de un prototipo de vehículo autónomo recolector de objetos basado en tecnología Arduino
Authors: Sánchez Rogel Kevin Damián , Ibarra Pincay Melanie Cristina , Tiviño Valdez Diego Lenin
Published: August 2022

En este artículo se presenta el diseño e implementación de software y hardware para elaborar un prototipo de vehículo autónomo recolector de objetos. Los componentes electrónicos necesarios se conectan y comunican con un Arduino Mega 2560, y estos son alimentados con baterías de ion de Litio, para definir el amperaje y voltaje necesario de estas baterías se realizaron cálculos teóricos del consumo de cada componente. En cuanto al algoritmo desarrollado, permite el movimiento del vehículo y del brazo robótico de forma manual y remota, por medio de un mando Wireless de PS2. Además de esto, gracias a la integración de sensores, se incorporó funcionalidades de detección de distancia, con esto se puede reconocer si hay un objeto o no delante del vehículo, para que así, el vehículo pueda recoger objetos de forma automática y almacenarlos en un contenedor. Con lo expuesto, se denota la particularidad de la implementación, el prototipo tiene más de una forma de uso, teniendo un control manual o automático y de forma remota. También se realizaron distintas pruebas de funcionalidad del prototipo respecto a la distancia entre el vehículo recolector y el objeto, obteniendo datos e interpretándolos. De esta forma y en conjunto a las funcionalidades programadas para controlar el vehículo, el brazo robótico y el control automático han dado como resultado un prototipo funcional de vehículo recolector de objetos controlado remotamente, que presenta una probabilidad de éxito inversamente proporcional, a mayor distancia se encuentra el objeto respecto al vehículo, menor será la probabilidad de éxito para recolectarlo. La metodología a seguir para la implementación de un vehículo autónomo recolector de objetos con Arduino Mega 2560, consiste en: 1) diseño del hardware, 2) implementación del hardware, 3) diseño del software y algoritmo, 4) implementación del software y algoritmo, 5) pruebas técnicas de funcionamiento. Este prototipo se puede tomar como base para la creación de equipos autónomos recolectores de objetos, para distintas áreas de aplicación. De aquí pueden partir soluciones para mejorar o desarrollar dispositivos que complementen una necesidad específica en el ámbito científico, investigativo o industrial.
Evaluación de competencia en cálculo diferencial en estudiantes de ingeniería
Authors: Cenas Chacón Fernando Ysmael , Silva Balarezo Mariana Geraldine , Minez Cuba Yamil Zenefelder
Published: August 2022

Este artículo expone los resultados en función de cómo la evaluación de las competencias referidas al cálculo diferencial, en la carrera de Ingeniería Civil, es importante no solamente para comprender las falencias que aquejan el bajo desarrollo de las mismas, sino también como estas pueden desagregarse en teóricas, metodológicas y sociales. El diseño fue cuantitativo, descriptivo y no empírico, de corte transversal. Se utilizó la encuesta y un test para poder evaluar las competencias teóricas y metodológicas y un cuestionario para evaluar las competencias sociales. La muestra participante fue de noventa (90) estudiantes que cursaban cálculo diferencial en Ingeniería Civil durante el II ciclo 2021. Como parte de los resultados se determinó que las competencias teóricas se encuentran en un nivel alto el 92%; la competencia metodológica en un 57%, en un nivel bajo; en cuanto a las competencias sociales, los estudiantes en un 67.8 % mencionaron que la participación en las sesiones de clase es muy buena y un 27.8% consideró que fue buena. De acuerdo a los resultados puede afirmarse que son las competencias teóricas las que se encuentran mucho más desarrolladas, a esto se suma con un reconocimiento importante las competencias sociales; sin embargo, el docente debe hacer énfasis en las metodológicas, puesto que es a través de las mismas que se logrará tener una mejor base para dar salida al planteamiento de problemas y aplicar los conocimientos fortaleciendo con ello la base de cálculo.
Geographical Indication (GI) as a means of craft tourism: a case of muga silk in Assam, India
Authors: Das Niranjan
Published: August 2022

Geographical Indications (GI) is one of the tools of Intellectual Property Rights (IPR) that protects name of a good as originating in the territory of a WTO member country, a region or locality in that territory, where a given quality, reputation or other characteristics of the good is essentially attributable to its geographical origin.  Muga Silk has been and continues to be an integral part of Assamese life and tradition. Assam is literally a ‘Silk country’ and silk culture is deep-rooted in the rural life and culture of Assamese people where Muga silk (Antheraea assamensis) is grown. There are 100 per cent of India’s Muga silk production is originated in Assam and hence Assam silk occupies a unique position in the sericulture map of the world. Considering the ecological conditions, food plant distribution, presence of eco-types and species of diverse nature in co-existence, it is speculated that this region is home of origin of Muga (Antheraea assamensis). Muga cloth has 85.8 per cent absorption capacity of ultra-violet ray of sunlight. Muga, the unique golden-yellow silk of Assam was granted the Geographical Indication (GI) registration in 2007. The present research will be highlighted the geo-environmental condition, craft tourism prospect of Muga silk as well as its contribution on livelihood generation in the Brahmaputra valley of Assam. This paper tries to emphasize the possibility of craft tourism in Muga rearing areas of Assam, so that the host community might earn their livelihood from tourism activity.
Trilhas ecológicas e interpretativas como estratégia para o ensino-aprendizagem de Geografia
Authors: Silva Lucas da , Nogueira da Silva Maria Wesla
Published: August 2022

O trabalho analisou as contribuições da aula de campo com o uso de trilhas ecológicas e interpretativas como estratégia de ensino de geografia. A pesquisa pautou-se em estudo teórico-metodológico quali-quantitativo, tendo como procedimento o estudo de caso com levantamento de dados por meio da realização de questionários aplicados aos discentes do curso de Licenciatura em Geografia do IFCE/Campus de Quixadá. Os resultados evidenciaram que a utilização de trilhas como estratégia de ensino nas aulas de geografia, corrobora significativamente na aprendizagem dos discentes, em relação as temáticas abordadas em sala, contribuindo para a flexibilização do uso de metodologias ativas, agregada aos temas transversais e interdisciplinares.
Extensão ou Comunicação? Perspectivas freireanas no Curso de Agroecologia do Projeto de Fortalecimento da Produção de Base Agroecológica do Estado de Sergipe
Authors: Santos Marcio Eric Figueira dos , Batista Kauane Santos , Costa Josefa Paula Santos , Barbosa Anézia Maria Fonsêca
Published: August 2022

Refere-se ao estudo das perspectivas freireanas aplicadas no curso de Agroecologia do projeto de Fortalecimento da Produção de Base Agroecológica do Estado de Sergipe. Classifica-se o estudo como uma  pesquisa científica de natureza aplicada, descritiva e explicativa, com abordagem qualitativa, abrangência de procedimentos bibliográficos, documental e com dados secundários obtidos por meio de ferramentas de diagnóstico. O estudo contribui para  ampliação do diálogo com a academia e sociedade em geral, por meio da publicização de toda esta trajetória, auxiliando no desenvolvimento doutras pesquisas/estudos que envolvam a agroecologia, a  Educação Ambiental e gestão ambiental.
Educação Ambiental e seus desdobramentos hoje no Brasil: uma revisão sistemática
Authors: Gonçalves Jozeli , De Oliveira Tiago , Gonçalves Maraisa
Published: August 2022

Este trabalho teve como objetivo realizar uma revisão sistemática de literatura que informasse sobre as práticas de Educação Ambiental no ambiente escolar que vem sendo reportadas pela comunidade científica. Nas seções analíticas analisou-se 35 estudos, a fim de avaliar se estão de acordo com as leis educacionais que versam sobre o tema no Brasil. Observou-se que embora existam muitos projetos, eles não envolvem todos os componentes curriculares de ensino, situação que pode ser compreendida como multidisciplinaridade dentro da escola básica. Além disso, analisar o envolvimento da comunidade escolar numa aprendizagem interacionista, ou seja, aprender fazendo junto em conformidade os objetivos de desenvolvimento sustentável segundo a agenda 2030.
Transcription Challenge Framework (TCF) Archiving Dossier Narrative
Authors: Morreale Laura , Albritton Benjamin L.
Published: August 2022

The Transcription Challenge Framework (TCF) is a sustainable scholarly model organized to support and promote collaborative transcription of digitized handwritten materials, including manuscripts from all time periods. TCF leadership issues an annual call for projects to sponsor two transcription events per year, supervised by our Advisory Board and supported by our tech partners, FromThePage. Transcription events follow the TCF workflow, and all project data is catalogued and preserved at partner repositories for long term access, citation, and preservation
Apixaban versus No Anticoagulation by P2Y12 Inhibitor Prescription Status in Dialysis Patients with Atrial Fibrillation
Authors: Mavrakanas Thomas A. , Charytan David M.
Published: August 2022

Key Points

In patients with atrial fibrillation on dialysis, the incidence of stroke was similar with apixaban or no anticoagulation, regardless of P2Y12 prescription.

In patients with atrial fibrillation on dialysis who were on a P2Y12 inhibitor, apixaban increased the risk of bleeding, compared with no anticoagulation.

The incidence of myocardial infarction or ischemic stroke was similar with apixaban or no anticoagulation, regardless of P2Y12 prescription status.
Level Of Sevice Jalan Profesor Doktor Hamka Kota Padang (Depan Kampus Universitas Negeri Padang-Simpang Tunggul Hitam)
Authors: Eko Eko Prayitno , Veronika  , Lusi Utama  , Indra Khaidir
Published: August 2022

Banyaknya hambatan samping, putar balik kendaraan yang tidak pada tempatnya, mobil yang melewati, atau mobil yang menempatkan mobil dibadan jalan membuat masalah kemacetan. Tujuan penelitian ini adalah menganalisis volume lalu lintas, hambatan samping, mengetahui kinerja jalan dan tingkat pelayanan jalan. Penelitian dilaksanakan diruas jalan Profesor Doktor Hamka, kota Padang. Survey dilakukan pada jam 6-8 waktu Indonesia barat, jam 12-14 waktu Indonesia barat, jam 16-18 waktu Indonesia barat. Survey dilaksanakan selama 2 hari, senin 08 februari 2021 dan sabtu 13 februari 2021. Nilai kapasitas jalan 1667,51 skr/jam, jalur kanan (padang-bukittinggi) dan 1639,55 skr/jam, jalur kiri (bukittinggi-padang). Nilai kepadatan tertinggi 352,36 hari sabtu jam 17.45-18.00 wib, jalur kiri (bukittinggi-padang), nilai kepadatan terendah 21,93 hari senin jam 06.00-06.15 wib, jalur kanan (padang-bukittinggi). Nilai derajat kejenuhan tertinggi 1,57, tingkat pelayanan jalan F, hari sabtu jam 17.45-18.00 wib jalur kiri (bukittinggi-padang), nilai derajat kejenuhan terendah 0,46, tingkat pelayanan jalan C, hari senin jam 06.00-06.15 jalur kanan (padang-bukittinggi).