Space Archaeology: Survey and Implementation of Deep Learning Methods for Detecting Ancient Structures

Remote sensing instruments are changing the nature of archaeological work. No longer are archaeological discoveries done by field work alone. Light Detection and Ranging, or LiDAR, optical imagery and different types of satellite data are giving opportunities for archaeological discoveries in areas which might be inaccessible to archaeologists. Different types of machine learning and deep learning methods are also being applied to remote sensing data, which enables automatic searches to large scale areas for detection of archaeological remains. In this thesis faster R-CNN object detection deep learning frameworks were used to train models and apply these to three types of archaeological remains. LiDAR based Digital Terrain Models were used to identify burial mounds in Norway. Optical imagery was used to identify fortress structures in Central Asia. Synthetic Aperture Radar data, or SAR, was used to detect archaeological settlement mounds in Central Asia. The success and limitations of these models are presented

Space Archaeology: Survey and Implementation of Deep Learning Methods for Detecting Ancient Structures

Remote sensing instruments are changing the nature of archaeological work. No longer are archaeological discoveries done by field work alone. Light Detection and Ranging, or LiDAR, optical imagery and different types of satellite data are giving opportunities for archaeological discoveries in areas which might be inaccessible to archaeologists. Different types of machine learning and deep learning methods are also being applied to remote sensing data, which enables automatic searches to large scale areas for detection of archaeological remains. In this thesis faster R-CNN object detection deep learning frameworks were used to train models and apply these to three types of archaeological remains. LiDAR based Digital Terrain Models were used to identify burial mounds in Norway. Optical imagery was used to identify fortress structures in Central Asia. Synthetic Aperture Radar data, or SAR, was used to detect archaeological settlement mounds in Central Asia. The success and limitations of these models are presented

Space Archaeology: Survey and Implementation of Deep Learning Methods for Detecting Ancient Structures

Remote sensing instruments are changing the nature of archaeological work. No longer are archaeological discoveries done by field work alone. Light Detection and Ranging, or LiDAR, optical imagery and different types of satellite data are giving opportunities for archaeological discoveries in areas which might be inaccessible to archaeologists. Different types of machine learning and deep learning methods are also being applied to remote sensing data, which enables automatic searches to large scale areas for detection of archaeological remains. In this thesis faster R-CNN object detection deep learning frameworks were used to train models and apply these to three types of archaeological remains. LiDAR based Digital Terrain Models were used to identify burial mounds in Norway. Optical imagery was used to identify fortress structures in Central Asia. Synthetic Aperture Radar data, or SAR, was used to detect archaeological settlement mounds in Central Asia. The success and limitations of these models are presented

Space Archaeology: Survey and Implementation of Deep Learning Methods for Detecting Ancient Structures

Remote sensing instruments are changing the nature of archaeological work. No longer are archaeological discoveries done by field work alone. Light Detection and Ranging, or LiDAR, optical imagery and different types of satellite data are giving opportunities for archaeological discoveries in areas which might be inaccessible to archaeologists. Different types of machine learning and deep learning methods are also being applied to remote sensing data, which enables automatic searches to large scale areas for detection of archaeological remains. In this thesis faster R-CNN object detection deep learning frameworks were used to train models and apply these to three types of archaeological remains. LiDAR based Digital Terrain Models were used to identify burial mounds in Norway. Optical imagery was used to identify fortress structures in Central Asia. Synthetic Aperture Radar data, or SAR, was used to detect archaeological settlement mounds in Central Asia. The success and limitations of these models are presented

Outstanding radiation tolerance of supported graphene: Towards 2d sensors for the space millimeter radioastronomy

We experimentally and theoretically investigated the effects of ionizing radiation on a stack of graphene sheets separated by polymethyl methacrylate (PMMA) slabs. The exceptional absorption ability of such a heterostructure in the THz range makes it promising for use in a graphene-based THz bolometer to be deployed in space. A hydrogen/carbon ion beam was used to simulate the action of protons and secondary ions on the device. We showed that the graphene sheets remain intact after irradiation with an intense 290 keV ion beam at the density of 1.5×1012 cm−2 . However, the THz absorption ability of the graphene/PMMA multilayer can be substantially suppressed due to heating damage of the topmost PMMA slabs produced by carbon ions. By contrast, protons do not have this negative effect due to their much longer mean free pass in PMMA. Since the particles’ flux at the geostationary orbit is significantly lower than that used in our experiments, we conclude that it cannot cause tangible damage of the graphene/PMMA based THz absorber. Our numerical simulations reveal that, at the geostationary orbit, the damaging of the graphene/PMMA multilayer due to the ions bombardment is sufficiently lower to affect the performance of the graphene/PMMA multilayer, the main working element of the THz bolometer, which remains unchanged for more than ten years

Low-energy spectra of 8He and 10He studied in (t,p) type reactions in inverse kinematics

Missing mass spectra for 8He and 10He obtained in the reactions 3H( 6He, p) 8He and 3H( 8He, p) 10He, respectively, are presented.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Soft dipole mode in 8He

The low-lying spectrum of 8He was studied in the 3H(6He, p)8He transfer reaction for small center of mass angles. The 0+ ground state (g.s.) of 8He and excited states, 2+ at 3.6-3.9 MeV and (1+) at 5.3-5.5 MeV, were populated with cross sections of 200, 100-250, and 90-125 μb/sr. Some evidence for the excited state at about 7.5 MeV can be found in the data. The possible nature of the near-threshold anomaly above 2.14 MeV in 8He is related to the population of a 1- continuum (soft dipole excitation) with a peak energy value at about 3 MeV. This assumption can probably resolve the problem of a large uncertainty existing in the experimental data on the 8He 2+ state energy. © Pleiades Publishing, Ltd. 2009.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Properties of very n-rich He isotopes

Low-energy spectra of the 8,10He nuclei were investigated in the (t, p) -type reactions at small centre-of-mass angles using ̃ 25 AMeV beams of 6He and 8He nuclei. The 0+ ground state (g. s.) of 8He and excited states, 2+ at 3. 6-3. 9MeV and (1+) at 5. 3-5. 5MeV, were populated with cross-sections of 200, 100-250, and 90-125μb/sr, respectively. To account for a near-threshold anomaly observed in the 8He spectrum the population of a 1- continuum (soft dipole excitation) was considered. The lowest-energy group of events in the 10He spectrum was observed at ̃ 3 MeV with a cross-section of ̃ 140 μb/sr. This result is consistent with the previously reported observation of 10He providing a new g. s. position for 10He at about 3MeV. © Societá Italiana di Fisica / Springer-Verlag 2009.SCOPUS: ar.jinfo:eu-repo/semantics/publishe