X-SAR radiometric calibration and data quality

In April and October, 1994 the X-SAR was flown as part of the SIR-C/X-SAR space radar laboratory missions (SRL-1/2) on the Space Shuttle. Amongst other activities DLR is responsible for the calibration of all X-SAR data products and is running the German Processing and Archiving Facility (D-PAF). Calibration activities included three major parts. Before the first mission, the authors performed a detailed analysis of the overall system to localize the main error sources and developed algorithms and procedures to correct these errors. During the missions they concentrated their efforts on calibration campaigns at the Oberpfaffenhofen super test site. Post mission activities included the determination of the antenna pattern and the absolute calibration factor as well as detailed performance analyses. This paper describes the overall approach to radiometrically calibrate the X-SAR and provides information on system performance and data quality to users in the different application fields     

Validation and verification of expert systems

Validation and verification (V&V) are procedures used to evaluate system structure or behavior with respect to a set of requirements. Although expert systems are often developed as a series of prototypes without requirements, it is not possible to perform V&V on any system for which requirements have not been prepared. In addition, there are special problems associated with the evaluation of expert systems that do not arise in the evaluation of conventional systems, such as verification of the completeness and accuracy of the knowledge base. The criticality of most National Aeronautics and Space Administration (NASA) missions makes it important to be able to certify the performance of the expert systems used to support these missions. This paper presents recommendations for the most appropriate methods for integrating V&V into the Expert System Development Methodology (ESDM) and suggestions for the most suitable approaches for each stage of ESDM development.     

Spacelab life sciences-1 electrical diagnostics expert system

The Spacelab Life Sciences-1 (SLS-1) Electrical Diagnostic (SLED) expert system is a continuous, real-time knowledge-based system to monitor and diagnose electrical system problems in the Spacelab. After fault isolation, the SLED system provides corrective procedures and advice to the ground-based console operator. The SLED system uses the Unit (frame) of KEE to represent the knowledge about the electrical components and uses KEE-Bit-maps to represent the electrical schematics. The diagnostic logic, stored as a set of LISP structures, mirrors that in the malfunction procedures defined in the Spacelab Flight Data File Malfunction Procedures Handbook (JSC-18927) of NASA. The SLED system utilizes downlink telemetry data as input. The system performs some initial screening of the data in order to recognize patterns representing serious problems, and updates its knowledge about the status of Spacelab every 3 seconds. Important parameters are monitored via Active-Values within KEE. The Active-Value kicks off the diagnostic analysis to determine the source of the problems if any problem has been identified. The system supports multiprocessing of malfunctions and allows multiple failures to be handled simultaneously. The user can examine each of the reported problems and receive corrective advice related to each problem. Information which is readily available via a mouse click includes: general information about the system and each component, the electrical schematics, the recovery procedures of each malfunction, and an explanation of the diagnosis. A rich set of user interfaces is provided in SLED. Various tools have been included which allow a non-programmer to define new diagnostic procedures, define and update schematics of the electrical system, and to change the SLED model by changing the graphical representation. Each tool and function has explanatory prompts to aid the user.     

Space instrument neural network for real-time data analysis

A simple software implementation of an artificial neural network (ANN) was used to analyze up to 200 autocorrelation functions (ACFs) per second within the Shuttle Potential and Return Electron Experiment (SPREE) flown on the Shuttle STS46 mission, July 31, 1992. As all ACF data are stored onboard until postmission, this facility provided ground-based experimenters with their only access to ACF data in real time for optimum instrument control. ACFs contain data either as waveforms or as radar echoes. Operating directly on the ACF, the neural network identifies the type of data, ascertains the wave frequency or radar peak separation, and provides a score or measure of significance of its decision. An effective 16:1 data reduction is achieved and the data interpretation performance is comparable to that achieved by an expert data analyst. Erroneous analysis accounts for less than 1% of data analyzed     

SIR-C data quality and calibration results

The SIR-C/X-SAR imaging radar took its first flight on the Space Shuttle Endeavour in April 1994 and flew for a second time in October 1994. This multifrequency radar has fully polarimetric capability at L- and C-band, and a single polarization at X-band (X-SAR). The Endeavour missions were designated the Space Radar Laboratory-1 (SRL-1) and -2 (SRL-2). Calibration of polarimetric L- and C-band data for all the different modes SIR-C offers is an especially complicated problem. The solution involves extensive analysis of pre-flight test data to come up with a model of the system, analysis of in-flight test data to determine the antenna pattern and gains of the system during operation, and analysis of data from over fourteen calibration sites distributed around the SIR-C/X-SAR orbit track. The SRL missions were the first time a multifrequency polarimetric imaging radar employing a phased array antenna has been flown in space. Calibration of SIR-C data products involved some unique technical problems given the complexity of the radar system. In this paper, the approach adopted for calibration of SIR-C data is described and the calibration performance of the data products is presented     

软件产品保证--系统策划与分阶段任务

软件产品保证是产品保证的重要组成部分，较详细地阐明了软件产品保证的策划过程，系统地归纳了软件研制各阶段的软件产品保证活动，对软件开发人员和质量管理人员有一定的指导作用。     

EOS MLS Science Data Processing System: a description of architecture and capabilities

The Earth Observing System (EOS) Microwave Limb Sounder (MLS) is an atmospheric remote sensing experiment led by the Jet Propulsion Laboratory of the California Institute of Technology. The objectives of the EOS MLS are to learn more about stratospheric chemistry and causes of ozone changes, processes affecting climate variability, and pollution in the upper troposphere. The EOS MLS is one of four instruments on the National Aeronautics and Space Administration (NASA) EOS Aura spacecraft launched on July 15, 2004, with an operational period extending at least 5 years after launch. This paper describes the architecture and capabilities of the Science Data Processing System (SDPS) for the EOS MLS. The SDPS consists of two major components-the Science Computing Facility and the Science Investigator-led Processing System. The Science Computing Facility provides the facilities for the EOS MLS Science Team to perform the functions of scientific algorithm development, processing software development, quality control of data products, and scientific analyses. The Science Investigator-led Processing System processes and reprocesses the science data for the entire mission and delivers the data products to the Science Computing Facility and to the Goddard Space Flight Center Earth Science Distributed Active Archive Center, which archives and distributes the standard science products. The Science Investigator-led Processing System is developed and operated by Raytheon Information Technology and Scientific Services of Pasadena under contract with Jet Propulsion Laboratory.     

The SIR-C/X-SAR synthetic aperture radar system

The SIR-C/X-SAR synthetic aperture radar, a three-frequency radar to be flown on the Space Shuttle in September 1993, is described. The SIR-C system is a two-frequency radar operating at 1250 MHz (L-band) and 5300 MHz (C-band), and is designed to get four-polarization radar imagery at multiple surface angles. The X-band synthetic aperture radar (X-SAR) system is an X-band imaging radar operating at 9600 MHz. The discussion covers the mission concept; system design; hardware; RF electronics; digital electronics; command, timing, and telemetry, and testing     

Overview of results of Spaceborne Imaging Radar-C, X-Band SyntheticAperture Radar (SIR-C/X-SAR)

The Spaceborne Imaging Radar-C, X-Band Synthetic Aperture Radar (SIR-C/X-SAR) was launched on the Space Shuttle Endeavour for two ten day missions in the spring and fall of 1994. Radar data from these missions are being used to better understand the dynamic global environment. During each mission, radar images of over 300 sites around the Earth were obtained, returning over a terabit of data. SIR-C/X-SAR science investigations were focused on quantifying radar's ability to estimate surface properties of importance to understanding global change; and focused studies in geology, ecology, hydrology and oceanography, as well as radar calibration and electromagnetic theory studies. In addition, the second flight featured an interferometry experiment, where digital elevation maps were obtained by interfering data from the first and second shuttle flight, and from successive days on the second flight. SIR-C/X-SAR data have been used to validate algorithms which produce maps of vegetation type and biomass; snow, soil and vegetation moisture; and the distribution of wetlands, developed with earlier aircraft data     

An expert system that performs a satellite stationkeeping maneuver

The development and capabilities of a prototype expert system that provides real-time spacecraft system analysis and command generation are described. At present, ESSOC (Expert System for Satellite Orbit Control) is capable of performing the stationkeeping maneuver for a geostationary satellite. ESSOC guides the operator through the stationkeeping operation by recommending appropriate commands that reflect both the changing spacecraft condition and previous procedural action. Information regarding satellite status is stored in a knowledge base internal to the expert system. This knowledge base is continuously updated with processed spacecraft telemetry. Information on the procedural structure is encoded in production rules. The independence of the procedural rules from each other, and from the knowledge base, makes the system easy to maintain and expand. Particular attention is directed to distinctive features of the ESSOC system and its development, namely, the structured methods of knowledge acquisition, and the design and performance-enhancing techniques that enable ESSOC to operate in a real-time environment.     

Space/ground systems as cooperating agents

Within NASA and the European Space Agency (ESA), it is agreed that autonomy is an important goal for the design of future spacecraft, and that this requires on-board artificial intelligence. NASA emphasises deep space and planetary rover missions, while ESA considers on-board autonomy as an enabling technology for missions that must cope with imperfect communications. ESA's attention is on the space/ground system. A major issue is the optimal distribution of intelligent functions within the space/ground system. This article describes the multi-agent architecture for space/ground systems (MAASGS). A MAASGS agent may model a complete spacecraft, a spacecraft subsystem or payload, a ground segment, a spacecraft control system, a human operator, or an environment. The MAASGS architecture has evolved through a series of prototypes. This study recommends that the MAASGS architecture should be implemented in the operational Dutch Utilisation Centre.     

检察信息系统安全保障体系的研究

检察信息系统体系结构由网络层、数据层、应用支撑、应用层、门户和用户层五个层次和运行管理体系、信息安全保障体系、标准体系三大体系共同构成。论文在介绍检察信息系统的基础上,采用了面向SOA的风险分析方法,对信息系统进行了风险分析,以安全需求和风险分析为依据,提出了检察信息系统安全保障体系。     

海上测控任务数据中心一体化管理平台设计

根据海上测控任务数据和信息分布的特点,设计了海上测控任务数据中心的一体化管理平台.首先,平台设计采用基于无共享存储的高性能集群模式数据库架构,具备统一的计算与存储系统能力以及良好的动态可扩展性;其次,平台采用层次化设计,包括数据采集与处理、数据存储、数据管理和数据应用4层,实现统一的数据中心管理和应用.根据应用需求,重点设计了航天器遥测故障预警与诊断、测控设备远程监控与故障诊断、外测数据精度分析与数据挖掘等典型数据应用模块,实现数据核心价值.     

Development of Analog Optohybrid Circuit for the CMS Inner Tracker Data Acquisition System: Project, Quality Assurance, Volume Production, and Final Performance

The tracker system of the compact muon solenoid (CMS) experiment, will employ approximately 40000 analog fiber-optic data and control links. The optical readout system is responsible for converting and transmitting the electrical signals coming out from the front-end to the outside counting room. Concerning the inner part of the Tracker, about 3600 analog optohybrid circuits are involved in this tasks. These circuits have been designed and successfully produced in Italy under the responsibility of INFN Perugia CMS group, completing the volume production phase by February 2005. Environmental features, reliability, and performances of the analog optohybrid circuits have been extensively tested and qualified. This paper reviews the most relevant steps of the manufacturing and quality assurance process: from prototypes to mass-production for the final use in the CMS data acquisition system.     

基于油水井生产管理系统的程序设计技巧研究

详细阐述了使用ASP.NET开发油气水井生产数据管理系统(简称A2系统)管理专栏、数据接口程序、厂矿生产日报处理程序时需要注意的若干方法和技巧,这些方法包括使用代码支持文件、减少表单回送、存储过程、Session对象等。这些数据管理及接口程序的应用,为A2系统在单轨运行提供了坚实的基础。     

基于B/S结构的编辑部稿件处理系统设计

介绍了一种基于asp动态网页技术的远程稿件处理系统。该系统以Access数据库作为数据支持,以asp技术为主要开发平台。从某学院学报编辑部的实际需求入手,设计了一个基于浏览器/服务器的两层结构的稿件远程处理系统,实现了科技期刊从作者在线投稿/查询稿件,编辑人员的在线编辑/选择评审专家,送审稿件到评审专家在线审理等一系列...     

Automating reuse of software for expert system analysis of remotesensing data

Systems involving remote sensing analysis for airborne and satellite data in combination with geographic information systems are large and complex. The Canada Centre for Remote Sensing (CCRS) has created an expert system shell and several expert systems in order to provide image analysis programs with the necessary knowledge to solve difficult image processing problems, such as updating a forest inventory geographic information system. An interactive task interface (ILTI) provides an expert system with a Prolog module designed to answer queries from the image analysis program by retrieving knowledge from an image analysis knowledge base, the analyst advisor. Image analysis experts currently create ILTI's. They have found this to be a time-consuming task. An incremental/adaptive planner has been developed that will create a plan that emulates the ILTI's behavior by analyzing image processing session dialogues between a human expert and an image analysis program for several cases of forest updates. The planner relies on a knowledge base in order to generalize and modify plans acquired from session dialogues. The planner speeds and simplifies the creation of new expert systems     

Pioneer Venus Multiprobe Entry Telemetry Recovery

The Entry Phase of the Pioneer Venus Multiprobe Mission involved data transmission over only a two-hour span. The criticality of recovery of those two hours of data, coupled with the fact that there were no radio signals from the Probes until their arrival at Venus, dictated unique telemetry recovery approaches on the ground. The result was double redundancy, use of spectrum analyzers to aid in rapid acquisition of the signals, and development of a technique for recovery of telemetry data without the use of real-time coherent detection which is normally employed by all other NASA planetary missions.