Experiment of Diffuse Reflection Laser Ranging to Space Debris and Data Analysis

Space debris has been posing a serious threat to human space activities and is needed to be measured and cataloged. As a new technology of space target surveillance, the measurement accuracy of DRLR (Diffuse Reflection Laser Ranging) is much higher than that of microwave radar and electro-optical measurement. Based on laser ranging data of space debris from DRLR system collected at SHAO (Shanghai Astronomical Observatory) in March-April 2013, the characteristics and precision of the laser ranging data are analyzed and its applications in OD (Orbit Determination) of space debris are discussed in this paper, which is implemented for the first time in China. The experiment indicates that the precision of laser ranging data can reach 39cm-228cm. When the data is sufficient enough (4 arcs of 3 days), the orbit accuracy of space debris can be up to 50m.Comment: 11 pages, 8 figure

The Mechanism of HDNS and Study on Programme Optimization

The shallow and thin layer heavy oil reservoir of Pai-601 block in Xinjiang adopts HDNS (horizontal well+ dissolver + nitrogen gas+ steam injection) to develop the heavy oil reservoir. It has made a significant effect, solving production problems of the block, such as: low natural energy, less thermal loss, and less oil cycle production. In order to improve the next process scheme, we have conducted a thorough study of its mechanism. The best HDNS’s parameters of Pai-601 block were chosen as follows: steam injection is 9.5~10.5 m3/m, nitrogen gas injection is 140~160 Nm3/m and thinning agent’s optimum dosage is 0.10 t/m.Key words: Shallow and thin layer; Super heavy oil; The technology HDNS; Mechanism; Scheme optimizatio

Wireless communication protocol architectures for nanosensor networks

Thesis (M.S.) University of Alaska Fairbanks, 2004Recent developments in micro fabrication and nanotechnology will enable the inexpensive manufacturing of massive numbers of tiny computing elements with sensors. New programming paradigms are required to obtain organized and coherent behavior from the cooperation of large numbers of sensor nodes. The individual nodes are identical, randomly placed and unreliable. They communicate with a small local neighborhood via wireless broadcast. In such environments, where individual nodes have limited resources, aggregating the node into groups is useful for specialization, increased robustness, and efficient resource allocation. In this paper, an application-specific self-organization protocol stack is developed. The clustering process is divided into phases. The first phase is to know the neighbor nodes. The second phase is to set up the cluster and routing. A 'find maximum clique algorithm' is used to set up clusters. A back off method is used to set up the hop field and routing. Group leaders set up a TDMA schedule for steady state operation. This schedule ensures that there is no conflict among in the same cluster and between clusters. Direct-sequence spread spectrum (DS-SS) is used to avoid inter-group conflict. The limited power resource is a challenge in nanosensor networks. This paper uses two different ways to analyze energy consumed in nanosensor networks, energy cost field and bit flow method. Sensor node deployment, cluster size, and propagation condition effect are discussed in this paper by those two methods respectively

Show and Write: Entity-aware Article Generation with Image Information

Many vision-language applications contain long articles of text paired with images (e.g., news or Wikipedia articles). Prior work learning to encode and/or generate these articles has primarily focused on understanding the article itself and some related metadata like the title or date it was written. However, the images and their captions or alt-text often contain crucial information such as named entities that are difficult to be correctly recognized and predicted by language models. To address this shortcoming, this paper introduces an ENtity-aware article Generation method with Image iNformation, ENGIN, to incorporate an article's image information into language models. ENGIN represents articles that can be conditioned on metadata used by prior work and information such as captions and named entities extracted from images. Our key contribution is a novel Entity-aware mechanism to help our model better recognize and predict the entity names in articles. We perform experiments on three public datasets, GoodNews, VisualNews, and WikiText. Quantitative results show that our approach improves generated article perplexity by 4-5 points over the base models. Qualitative results demonstrate the text generated by ENGIN is more consistent with embedded article images. We also perform article quality annotation experiments on the generated articles to validate that our model produces higher-quality articles. Finally, we investigate the effect ENGIN has on methods that automatically detect machine-generated articles