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Future NASA satellite detector systems must be cooled to the 0.1 K temperature range to meet the stringent energy resolution and sensitivity requirements demanded by mid-term astronomy missions. The development of adiabatic demagnetization refrigeration (ADR) materials that can efficiently cool from the passive radiative cooling limit of ∼30 K down to sub-Kelvin under low magnetic fields (H⩽3 T) would represent a significant improvement in space-based cooling technology. Governed by these engineering goals, our efforts have focused on quantifying the change in magnetic entropy of rare-earth garnets and perovskites. Various compositions within the gadolinium gallium iron garnet solid solution series (GGIG, Gd3Ga5−XFeXO12, 0.00⩽X⩽5.00) and gadolinium aluminum perovskite (GAP, GdAlO3) have been synthesized via an organometallic complex approach and confirmed with powder x-ray diffraction. The magnetization of the GGIG and GAP materials has been measured as a function of composition (0.00⩽X⩽5.00), temperature...

Today’s space physics data systems are faced with a vast quantity of data and researchers need to have ready access to the data. These needs are compounded by the variety of ways in which researchers do their work. One way to provide ready access to data is through a central store. Even when there is an adequate central store of the data, researchers usually require local access to the data for performance reasons. Since the volume of data which may be distributed could be large it is very beneficial to distribute a data system with the data. Since system resources vary from researcher to researcher a data system must be scalable to the size of the resources on the system.Developers and researchers at the Planetary Plasma Interactions (PPI) Node of the Planetary Data System (PDS) have addressed this problem and have designed and implemented a scalable data system which uses incremental data sets. This system allows collections of data to be brought on‐line from archival media and merged with other collect...

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g., spectral densities or running averages).This paper describes the PIPE system. PIPE provides intelligent assistance developing scientific data preparation plans developed using Master Plumber, a general language for scientific data processing plans. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developers’ attention upon tho...

Members of the Planetary Plasma Interactions Node of the Planetary Data System (PDS/PPI) have designed and implemented a data system which allows for distributed access to centralized data, centralized access to distributed data, and distributed access to distributed data. While these types of data access might seem like completely different problems, they are not. The only differences are the number of access pathways to and from the data. By adopting a client/server model of system design we have been able to provide transparent access to both distributed and centralized data. In addition, by carefully choosing the location of the division between the client and server, the response of the system as a whole can be greatly enhanced. For example, the user interface is a stand‐alone application which configures itself according to information stored with the data sets and the data sets may be local or distributed. All keystroke interactions are handled on the local machine and the end results of menu selec...

The management of large databases can be a time consuming and tedious task without tools to automate the process. In order to facilitata the process, a database design philosophy which is consistent with application objectives must be adopted. The Planetary Plasma Interactions (PPI) Node of the Planetary Data System (PDS) provides users with a software application which allows for rapid retrieval (for examination or extraction) of datasets which are distributed and which may exist in more than one location simultaneously. In addition, the application software, which interacts with both a local and a centralized database management system (dbms) via a client‐server architecture, also is distributed. The basic problem lies in keeping all of the information current in a dynamic data environment so that the application software, regardless of its location, can access any data in the system with the greatest possible throughput. In addressing this problem the PDS/PPI Node has adopted a standardized architecture [2] and has chosen to package data management information with the data themselves as a set of detached relational flatfiles. At the PDS/PPI Node automated dbms tools are being developed which update both local and central management systems by using the information provided along with the datasets. This information can be stored in any relational database system or it can be accessed and manipulated outside of a dbms. Science data are not maintained under direct dbms control.