Thermal Recovery of Multi-Limbed Robots with Electric Actuators

The problem of finding thermally minimizing configurations of a humanoid robot to recover its actuators from unsafe thermal states is addressed. A first-order, data-driven, effort based, thermal model of the robots actuators is devised, which is used to predict future thermal states. Given this predictive capability, a map between configurations and future temperatures is formulated to find what configurations, subject to valid contact constraints, can be taken now to minimize future thermal states. Effectively, this approach is a realization of a contact-constrained thermal inverse-kinematics (IK) process. Experimental validation of the proposed approach is performed on the NASA Valkyrie robot hardware

Modular Autonomous Systems Technology Framework: A Distributed Solution for System Monitoring and Control

The Modular Autonomous Systems Technology (MAST) framework is a tool for building distributed, hierarchical autonomous systems. Originally intended for the autonomous monitoring and control of spacecraft, this framework concept provides support for variable autonomy, assume-guarantee contracts, and efficient communication between subsystems and a centralized systems manager. MAST was developed at NASA's Johnson Space Center (JSC) and has been applied to an integrated spacecraft example scenario

Energy self-sufficient systems for monitoring sewer networks

Underground infrastructure networks form the backbone of vital supply and disposal systems. However, they are under-monitored in comparison to their value. This is due, in large part, to the lack of energy supply for monitoring and data transmission. In this paper, we investigate a novel, energy harvesting system used to power underground sewer infrastructure monitoring networks. The system collects the required energy from ambient sources, such as temperature differences or residual light in sewer networks. A prototype was developed that could use either a thermoelectric generator (TEG) or a solar cell to capture the energy needed to acquire and transmit ultrasonic water level data via LoRaWAN. Real-world field trials were satisfactory and showed the potential power output, as well as, possibilities to improve the system. Using an extrapolation model, we proved that the developed solution could work reliably throughout the year.Comment: To be published in proceedings of the conference "21. ITG/GMA- Fachtagung Sensoren und Messsysteme 2022", 10.-11. Mai 2022, N\"urnberger CongressCenter, Nuremberg, Germany, or IEEE explor

The accretion disc in the quasar SDSS J0924+0219

We present single-epoch multi-wavelength optical-NIR observations of the "anomalous" lensed quasar SDSS J0924+0219, made using the Magellan 6.5-metre Baade telescope at Las Campanas Observatory, Chile. The data clearly resolve the anomalous bright image pair in the lensed system, and exhibit a strong decrease in the anomalous flux ratio with decreasing wavelength. This is interpreted as a result of microlensing of a source of decreasing size in the core of the lensed quasar. We model the radius of the continuum emission region, sigma, as a power-law in wavelength, sigma lambda^zeta. We place an upper limit on the Gaussian radius of the u'-band emission region of 3.04E16 h70^{-1/2} (/M_sun)^{1/2} cm, and constrain the size-wavelength power-law index to zeta<1.34 at 95% confidence. These observations rule out an alpha-disc prescription for the accretion disc in SDSS J0924+0219 with 94% confidence.Comment: 8 pages, 5 figures. Accepted for publication in MNRA

Exploring the dust content of SDSS DR7 damped Lyman alpha systems at 2.15≤zab<\le z_{ab}< 5.2

We have studied a sample of 1084 intervening absorption systems with 2.15$\le z_{ab}\le$5.2, having log(N$_{\rm HI}$) $>$ 20.0 in the spectra of QSOs in Sloan Digital Sky Survey (SDSS) data release 7 (DR7), with the aim of understanding the nature and abundance of the dust and the chemical abundances in the DLA absorbers. Composite spectra were constructed for the full sample and several subsamples, chosen on the basis of absorber and QSO properties. Average extinction curves were obtained for the samples by comparing their geometric mean composite spectra with those of two samples of QSOs, matching in z$_{em}$ and i magnitude with the DLA sample, one sample without any absorbers along their lines of sight and the other without any DLAs along their lines of sight irrespective of the presence of other absorption systems. While the average reddening in the DLA sample is small, we find definite evidence for the presence of dust in subsamples based on absorber properties, in particular the strength of metal absorption lines. DLAs along lines of sight to QSOs which are not colour selected are found to be more dusty compared to those along the lines of sight to the more numerous colour selected QSOs. From these studies and from the strengths of absorption lines in the composite spectra, we conclude that $\le$ 10% of the DLAs in SDSS DR7 cause significant reddening, have stronger absorption lines and have higher abundances as compared to the rest of the sample. The rest of the sample shows little reddening. Due to the dominant color selection method used to target QSOs in the SDSS DR7, this fraction of 10% likely represents a lower limit for the global fraction of dusty DLAs at high-z.Comment: 12 pages, 9 figures. To appear in MNRA

137,138,139^{137,138,139}La(nn, γ\gamma) cross sections constrained with statistical decay properties of 138,139,140^{138,139,140}La nuclei

The nuclear level densities and $\gamma$-ray strength functions of $^{138,139,140}$La were measured using the $^{139}$La($^{3}$He, $\alpha$), $^{139}$La($^{3}$He, $^{3}$He$^\prime$) and $^{139}$La(d, p) reactions. The particle-$\gamma$ coincidences were recorded with the silicon particle telescope (SiRi) and NaI(Tl) (CACTUS) arrays. In the context of these experimental results, the low-energy enhancement in the A$\sim$140 region is discussed. The $^{137,138,139}$La($n, \gamma)$ cross sections were calculated at $s$- and $p$-process temperatures using the experimentally measured nuclear level densities and $\gamma$-ray strength functions. Good agreement is found between $^{139}$La($n, \gamma)$ calculated cross sections and previous measurements

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation