Responsiveness is a challenge for space systems to sustain competitive advantage over al-ternate non-spaceborne technologies.For a satellite in its operational orbit,in-orbit responsiveness is defined as the capabilit...Responsiveness is a challenge for space systems to sustain competitive advantage over al-ternate non-spaceborne technologies.For a satellite in its operational orbit,in-orbit responsiveness is defined as the capability of the satellite to respond to a given demand in a timely manner.In this paper,it is shown that Average Wait Time(AWT) to pick up user demand from ground segment is the ap-propriate metric to evaluate the effect of ground segment location on in-orbit responsiveness of Low Earth Orbit(LEO) sunsynchronous satellites.This metric depends on pattern of ground segment access to satellite and distribution of user demands in time domain.A mathematical model is presented to determine pattern of ground segment access to satellite and concept of cumulative distribution function is used to simulate distribution of user demands for markets with different total demand scenarios.Monte Carlo simulations are employed to take account of uncertainty in distribution and total volume of user demands.Sampling error and standard deviation are used to ensure validity of AWT metric obtained from Monte Carlo simulations.Incorporation of the proposed metric in the ground segment site location process results in more responsive satellite systems which,in turn,lead to greater customer satisfaction levels and attractiveness of spaceborne systems for different applications.Finally,simula-tion results for a case study are presented.展开更多
In an urban city,the daily challenges of managing cleanliness are the primary aspect of routine life,which requires a large number of resources,the manual process of labour,and budget.Street cleaning techniques includ...In an urban city,the daily challenges of managing cleanliness are the primary aspect of routine life,which requires a large number of resources,the manual process of labour,and budget.Street cleaning techniques include street sweepers going away to different metropolitan areas,manually verifying if the street required cleaning taking action.This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation.For the large volume of the process,the deep learning-based methods can be better to achieve a high level of classifica-tion,object detection,and accuracy than other learning algorithms.The proposed Histogram of Oriented Gradients(HOG)is used to features extracted while using the deep learning technique to classify the ground-level segmentation process’s images.In this paper,we use mobile edge computing to process street images in advance andfilter out pictures that meet our needs,which significantly affect recognition efficiency.To measure the urban streets’cleanliness,our street clean-liness assessment approach provides a multi-level assessment model across differ-ent layers.Besides,with ground-level segmentation using a deep neural network,a novel navigation strategy is proposed for robotic classification.Single Shot Mul-tiBox Detector(SSD)approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset.The SSD can classify and detect the garbage’s accurately and autonomously by using deep learning for garbage recognition.Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods.展开更多
This article presents and analyses the modular architecture and capabilities of CODE-DE(Copernicus Data and Exploitation Platform–Deutschland,www.code-de.org),the integrated German operational environment for accessi...This article presents and analyses the modular architecture and capabilities of CODE-DE(Copernicus Data and Exploitation Platform–Deutschland,www.code-de.org),the integrated German operational environment for accessing and processing Copernicus data and products,as well as the methodology to establish and operate the system.Since March 2017,CODE-DE has been online with access to Sentinel-1 and Sentinel-2 data,to Sentinel-3 data shortly after this time,and since March 2019 with access to Sentinel-5P data.These products are available and accessed by 1,682 registered users as of March 2019.During this period 654,895 products were downloaded and a global catalogue was continuously updated,featuring a data volume of 814 TByte based on a rolling archive concept supported by a reload mechanism from a long-term archive.Since November 2017,the element for big data processing has been operational,where registered users can process and analyse data themselves specifically assisted by methods for value-added product generation.Utilizing 195,467 core and 696,406 memory hours,982,948 products of different applications were fully automatically generated in the cloud environment and made available as of March 2019.Special features include an improved visualization of available Sentinel-2 products,which are presented within the catalogue client at full 10 m resolution.展开更多
基金Supported by the Research Council of Shahid Beheshti University,G. C.
文摘Responsiveness is a challenge for space systems to sustain competitive advantage over al-ternate non-spaceborne technologies.For a satellite in its operational orbit,in-orbit responsiveness is defined as the capability of the satellite to respond to a given demand in a timely manner.In this paper,it is shown that Average Wait Time(AWT) to pick up user demand from ground segment is the ap-propriate metric to evaluate the effect of ground segment location on in-orbit responsiveness of Low Earth Orbit(LEO) sunsynchronous satellites.This metric depends on pattern of ground segment access to satellite and distribution of user demands in time domain.A mathematical model is presented to determine pattern of ground segment access to satellite and concept of cumulative distribution function is used to simulate distribution of user demands for markets with different total demand scenarios.Monte Carlo simulations are employed to take account of uncertainty in distribution and total volume of user demands.Sampling error and standard deviation are used to ensure validity of AWT metric obtained from Monte Carlo simulations.Incorporation of the proposed metric in the ground segment site location process results in more responsive satellite systems which,in turn,lead to greater customer satisfaction levels and attractiveness of spaceborne systems for different applications.Finally,simula-tion results for a case study are presented.
文摘In an urban city,the daily challenges of managing cleanliness are the primary aspect of routine life,which requires a large number of resources,the manual process of labour,and budget.Street cleaning techniques include street sweepers going away to different metropolitan areas,manually verifying if the street required cleaning taking action.This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation.For the large volume of the process,the deep learning-based methods can be better to achieve a high level of classifica-tion,object detection,and accuracy than other learning algorithms.The proposed Histogram of Oriented Gradients(HOG)is used to features extracted while using the deep learning technique to classify the ground-level segmentation process’s images.In this paper,we use mobile edge computing to process street images in advance andfilter out pictures that meet our needs,which significantly affect recognition efficiency.To measure the urban streets’cleanliness,our street clean-liness assessment approach provides a multi-level assessment model across differ-ent layers.Besides,with ground-level segmentation using a deep neural network,a novel navigation strategy is proposed for robotic classification.Single Shot Mul-tiBox Detector(SSD)approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset.The SSD can classify and detect the garbage’s accurately and autonomously by using deep learning for garbage recognition.Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods.
基金funding from the German Federal Ministry of Transport and Digital Infrastructure(BMVI).
文摘This article presents and analyses the modular architecture and capabilities of CODE-DE(Copernicus Data and Exploitation Platform–Deutschland,www.code-de.org),the integrated German operational environment for accessing and processing Copernicus data and products,as well as the methodology to establish and operate the system.Since March 2017,CODE-DE has been online with access to Sentinel-1 and Sentinel-2 data,to Sentinel-3 data shortly after this time,and since March 2019 with access to Sentinel-5P data.These products are available and accessed by 1,682 registered users as of March 2019.During this period 654,895 products were downloaded and a global catalogue was continuously updated,featuring a data volume of 814 TByte based on a rolling archive concept supported by a reload mechanism from a long-term archive.Since November 2017,the element for big data processing has been operational,where registered users can process and analyse data themselves specifically assisted by methods for value-added product generation.Utilizing 195,467 core and 696,406 memory hours,982,948 products of different applications were fully automatically generated in the cloud environment and made available as of March 2019.Special features include an improved visualization of available Sentinel-2 products,which are presented within the catalogue client at full 10 m resolution.
