Supersonic-combustion rocket

A supersonic combustion rocket is provided in which a small rocket motor is substituted for heavy turbo pumps in a conventional rocket engine. The substitution results in a substantial reduction in rocket engine weight. The flame emanating from the small rocket motor can act to ignite non-hypergolic fuels

Determining the Shallow Surface Velocity at the Apollo 17 Landing Site

Many studies have been performed to determine the shallow surface velocity model at the Apollo 17 landing site. The Lunar Seismic Profiling Experiment (LSPE) had both an active component with eight explosive packages (EPs) and a passive experiment collecting data at various time intervals. Using the eight EPs, the initial shallow surface velocity model was determined to be 250 m/s in the first layer of depth 248 m, 1200 m/s with a depth of 927 m in the second layer, and 4000 m/s down to a depth of 2 km in the third layer. Have performed variations on this study to produce new velocity models shown. Recent studies have also been reanalyzing the passive LSPE data and have found three different thermal moonquake event types occurring at different times within the lunar day. The current goal of the project is to collocate the thermal moonquakes to physical surface features to determine the breakdown of lunar rocks. However, to locate shallow surface events, an accurate velocity model is needed. Presented a thermal moonquake location algorithm using first order approximation, including surface events only. To improve these approximations, a shallow surface velocity is needed

Deep Learning Models for Planetary Seismicity Detection

Research in planetary seismology is fundamentally constrained by a lack of data. Seismo-logical science products of future missions can typically only be informed by theoretical signal/noise characteristics of the environment or likely Earth-analogues. Although objectives can be re-assessed after some initial data-collection upon lander arrival, transfer of high-resolution data back to Earth is costly on lander power usage. Over the last several years, development of GPU computing techniques and open-source high-level APIs have led to rapid advances in deep learning within the fields of computer vision, natural language processing, and collaborative filtering. These techniques are actively being adapted in seismology for a variety of tasks, including: earthquake detection, seismic phase discrimination, and ground-motion prediction. Until the recent detection of mars quakes during the Mars InSight mission, the only other measurements of seismicity recorded outside of Earth was on the Moon during the Apollo missions between 1969 to 1977. These unique data sets have been periodically revisited using new seismological methods, including ambient noise interferometry and Hidden Markov Models. Our objective is to develop a deep learning seismic detector and use it to catalog moonquakes from the Apollo 17 Lunar Seismic Profiling Experiment (LSPE) and compare the results with those obtained by other methods. Additionally, we will assess the accuracy tradeoff between using a training set of lunar data and one composed of Earth seismicity. In this document, we present preliminary results using a prototype classifier trained on a small set of earthquakes that was able to obtain detections for LSPE moonquakes with a greater accuracy than a recent study using Hidden Markov Models

Inventory of forest and rangeland resources, including forest stress

There are no author-identified significant results in this report

Inventory of forest and rangeland resources, including forest stress

There are no author-identified significant results in this report

Inventory of forest and rangeland resources, including forest stress

There are no author-identified significant results in this report

Inventory of forest and rangeland resources, including forest stress

There are no author-identified significant results in this report

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Eucalyptus tree stands killed by low temperatures in December 1972 were outlined by image enhancement of two separate dates of ERTS-1 images (January 22, 1973-I.D. 1183-18175 and April 22, 1973-I.D. 1273-18183). Three stands larger than 500 meters in size were detected very accurately. In Colorado, range and grassland communities were analyzed by visual interpretation of color composite scene I.D. 1028-17135. It was found that mixtures of plant litter, amount and kind of bare soil, and plant foliage cover made classification of grasslands very difficult. Changes in forest land use were detected on areas as small as 5 acres when ERTS-1 color composite scene 1264-15445 (April 13, 1973) was compared with 1966 ASCS index mosaics (scale 1:60,000). Verification of the changes were made from RB-57 underflight CIR transparencies (scale 1:120,000)

Inventory of forest and rangeland resources, including forest stress

There are no author-identified significant results in this report