Thanks to the Lunar Reconnaissance Orbiter (LRO) and Lunar Orbiter Laser Altimeter (LOLA) teams for providing the Lunar Digital Elevation Maps. This work was directly supported by the NASA Solar System Exploration Research Virtual Institute cooperative agreement number 80ARC017M0006, as part of the Network for Exploration and Space Science (NESS) team.

1. Software setup
<ol>
	<li>First, go to https://deepblue.lib.umich.edu/data/concern/data_sets/bg257f178?locale=en and download the software package. This software has only been tested in a UNIX environment, and may not fully function in other environments. The README in this package will help guide one through the rest of the software needed and its uses.</li>
	<li>Make sure python 2.7 or greater is installed. A link is provided in the README. Several common python libraries are also needed including numpy, matplotlib, pylab, scipy, subprocess, ephem, and datetime.</li>
	<li>Make sure CASA 4.7.1 or greater is installed. A link in provided in the README.</li>
	<li>Make sure gcc 4.8.5 or greater is installed. A link is provided in the README.</li>
	<li>Make sure the C toolkit for SPICE is installed. This software is used to align different astronomical reference frames and track the relative positions of planets, moons, and satellites. A link to download this software is also included in the README.
	<ol>
		<li>Download several kernels that contain information on astronomical and lunar reference frames, as well as the orbital dynamics of the Moon, Earth, and Sun. The specific kernels needed are listed in the README alongside a link of where to download them.</li>
	</ol>
	</li>
	<li>Obtain the final prerequisite data needed: Digital Elevation Models (DEMs) of the lunar surface created from LRO LOLA measurements. The specific file needed is listed and linked in the README.</li>
</ol>
2. Creating the array configuration
<ol>
	<li>Customize the createArrayConfig.py script.
	<ol>
		<li>Choose the configuration of the array by providing a list of Longitude and Latitude coordinates for each antenna. 
		NOTE: The script is currently formatted for a 10 km diameter array with 1024 elements, 32 arms with 32 log space spaced antenna each, using a constant factor to convert between meters and degrees of longitude/latitude near 0 degrees latitude. The site of the array, (-1.04&#176;, -0.43&#176;), was chosen because it is the center of the 10x10 km patch with the lowest elevation variation (&#963; = 5.6 m) close to the sub-Earth point (0&#176;, 0&#176;) in the Moon ME frame.</li>
		<li>Change the lunarPath variable in the script to reflect the new download location of the Digital Elevation Model containing the elevation data of the lunar surface.</li>
	</ol>
	</li>
	<li>Run the createArrayConfig.py script with &#8220;python createArrayConfig.py&#8221;. This will use the lunar Digital Elevation Model to solve for the elevation at each longitude and latitude for each antenna. Save the longitude, latitude, and elevation to files and print to the screen for easy copying and pasting into the next script. Make figures showing the array configuration on top of the local lunar topography (Figure 1).</li>
</ol>
3. Using SPICE to align coordinates
<ol>
	<li>Customize the eqArrOverTimeEarth.c script.
	<ol>
		<li>Take the output from the previous script, the Longitude, Latitude, and elevation of each antenna and copy them over into the corresponding lists in the script, also updating the variable &#8216;numsc&#8217; with the number of receivers and corresponding coordinates. 
		NOTE: Since C does not have dynamic array allocation, there was no easy way to flexibly read in the data automatically, so manual copying must be done.</li>
		<li>Update the lunar_furnsh.txt included in the package with the new path names for the required frame and ephemeris files.</li>
		<li>Specify what set of dates to observe on. This will inform the ephemerides within SPICE to accurately track where the Earth and Sun are in relation to the defined array for those dates. In the script currently 48 dates occurring roughly weekly over the year 2025 are selected.</li>
		<li>Specify the targeted area of the sky for the array to track and image. Currently the script saves the RA Dec of the Earth as seen from the lunar surface, but one may easily just put in static RA Dec coordinates instead.</li>
	</ol>
	</li>
	<li>Compile the eqArrOverTime.c script
	<ol>
		<li>Compile the script using the gcc command in the comment at the top of the script. It will be something like &#8220;gcc eqArrOverTimeEarth.c -o eqArrOverTimeEarth -I/home/alexhege/SPICE/cspice/include /home/alexhege/SPICE/cspice/lib/cspice.a -lm -std=c99&#8221;. Change the paths to reflect where the cspice libraries are located.</li>
	</ol>
	</li>
	<li>Run the eqArrOverTime executable with &#8220;./eqArrOverTime&#8221;. This should result in a number of files each with a set of variables in them. Most important are the XYZ position of each antenna in J2000 coordinates, and the Right Ascension and Declination (RA and Dec) coordinates of the targeted area in the sky (currently those of the Earth from the Moon&#8217;s perspective). The output variables are saved to .txt files containing the data for all the requested dates.</li>
</ol>
4. Using CASA to simulate array response
<ol>
	<li>Customize the LunarEarthPicFreqIntegration.py script.
	<ol>
		<li>Specify the observing frequency for the array to make an image at. This is currently set to 0.75 MHz.</li>
		<li>Specify a CASA compatible truth image (or create from a .fits image file) with Jansky/pixel values for the array to reconstruct (e.g., Figure 2). Constants (res, res1, width, arcMinDiv) in the code will need to be changed to reflect the size and resolution of the input truth image. 
		NOTE: If using&#160;the SPICE method to provide&#160;the RA Dec coordinates, one&#160;may comment out the &#8216;import&#160;ephem&#8217; statement in this&#160;script. This library requires the&#160;use of casa-pip from the casa-python package to install, but 
		allows for tracking of other&#160;astronomical objects within&#160;python.</li>
	</ol>
	</li>
	<li>Run the LunarEarthPic.py script. Commented at the top of the script are examples on how to run the script. The following command is one example on how to run the script from the command line: 
	&#8220;nohup casa --nologger --nologfile --nogui --agg -c LunarEarthPicFreqIntegration.py -outDir . -correlate True -numSC 1024 | tee earth.out &#38;&#8221; 
	The -numSC flag is used to inform the code how many antenna/receivers are being used, and helps unpack the data from the .txt files containing the receiver coordinates. 
	NOTE: The antenna baseline vector, measured in units of the observing wavelength (&#955;), has length D&#955; and components (&#965;, &#957;, w) = (&#8710;x,&#8710;y,&#8710;z)/&#955; . The pipeline then calculates the visibilities, or observed cross correlated voltages for each pair of antennas. Here the small field of view approximation is used to calculate the visibilities, following the standard formula from Thompson et al.2 for an infinitesimal bandwidth at frequency &#957;. 
	<img alt="Equation 2" src="/files/ftp_upload/61540/61540equ02.jpg" /> 
	The sky coordinates of the target the array is imaging is deemed the phase center, to which the z, or w, axis of the frame is pointed. (l, m, n) are the direction cosines from the (U, V, W) coordinate system. The sky brightness pattern around the source under observation is I&#957;(l, m). Spectral flux density is often presented in the derived unit 1 Jansky (Jy) = 10&#8722;26 W/m2/Hz. Spectral brightness is simply Jy/steradian to represent the amount of flux coming from a particular area in the sky. A&#957;(l, m) is the normalized antenna primary beam pattern, or how sensitive it is to radiation coming from that point in the sky. 
	This script calculates the antenna separations in the appropriately projected reference frame from the coordinates output from the previous script. It then uses Equation 2 to calculate the visibility data for each pair of antenna. The resulting visibilities are stored alongside the baselines in a CASA Measurement Set file (.ms). This MS file is the primary output of this script.</li>
</ol>
5. Imaging the data &#8211; noiseless and noisy
<ol>
	<li>Customize the noiseCopies.py script.
	<ol>
		<li>Set the System Equivalent Flux Density (SEFD), referred to as avNoise in the script. The SEFD is a convenient way to talk about the total noise of a radio antenna since it ties in both the system temperature and the effective area, and provides a way to directly compare the signal and the noise. It is currently set to 1.38e7 Jansky, which is an optimistic noise level for 0.75 MHz. 
		NOTE: For the low frequency radio regime, there are three main sources on constant noise: amplifier noise, quasithermal noise from free electrons (estimated by Meyer-Vernet et al.24 to be 6.69e4 Jy at 0.75 MHz, using a electrically short dipole approximation), and Galactic background radiation from the Milky Way (estimated by Novacco &#38; Brown25 to be 4.18e6 Jansky at 0.75 MHz for the full sky, of which a lunar array will only see some portion). This optimal noise level of 1.38e7 Jy assumes that amplifier noise dominates the other terms. See Hegedus et al. for a more detailed discussion.</li>
		<li>Set the bandwidth being integrated over in variable &#8216;noise&#8217; line 200. Set to 500 kHz.</li>
		<li>Set the integration time in variable &#8216;noise&#8217; line 200.</li>
	</ol>
	</li>
	<li>Run the noiseCopies.py script with &#8220;nohup casa --nologger --nologfile --nogui --agg -c noiseCopies.py | tee noise.out &#38;&#8221;.
	<ol>
		<li>The script will first create an image from the noiseless visibility data, calling standard radio astronomy algorithm CLEAN26 to create an image like Figure 3.</li>
		<li>The script will then create copies of the MS and add the appropriate noise level to the complex visibility data and image it using CLEAN. The script currently makes images for a range of integration times up to 24 hours and over several robust weighting scheme values. Depending on the configuration of the array, image quality may vary with the choice of data weighting schemes. These noisy images will look something like Figure 4, which used an integration time of 4 hours. 
		NOTE: The noise is added with standard Signal to Noise formulas. From Taylor2 the interferometric noise for a single polarization is 
		<img alt="Equation 3" src="/files/ftp_upload/61540/61540equ03.jpg" /> 
		Here, &#951;s is the system efficiency or correlator efficiency, which has been set to a conservative value of 0.8. Nant is the number of antennas in the array (Nant = 2 for each individual visibility), &#8710;&#957; is the bandwidth being integrated over in Hz, and &#8710;t is the integration time in seconds.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

Each step of the simulation pipeline is necessary and feeds into the next, taking an array configuration on the lunar surface, aligning the reference frame correctly to orient the array to the target area in the sky, calculating the visibility data, adding the appropriate noise levels, and running imaging algorithms on the resulting data.
For each step, customizations may be made. In step 2, the user defined array configuration may be any list of longitudes and latitudes. This then feeds into the SPICE script in step 3, where one may choose the exact time of the planned measurements, as well as where in the sky the array should be focused. In step 3, one can specify the simulated truth emission that the array is attempting to image by providing a suitable CASA .truth file. Then in step 4 one may change the expected level of noise depending on the observing frequency and expected hardware capabilities. This set of codes constitutes a flexible simulation framework that may be used to iterate array design for any number of uses, depending on the targeted science. These codes can all be run on an average laptop or workstation, though computation time increases with number of antennas. The slowest parts of the process are predicting the visibilities, followed by imaging. For small arrays, the entire process can be done in minutes, while for larger arrays of a few hundred or thousand receivers, hours or days may be needed.
Some next steps that could be taken with this pipeline to increase its realism include adding a channel-dependent foreground removal system. This requires building up a global sky model, dominated at low frequencies by galactic synchrotron emission and a few bright sources like Cas A, tracking which part of the sky is visible to the receivers, and convolving that brightness pattern with the primary beam, with the phase center of the array aligned towards the imaging target. For longer integration times, tracking the apparent motion of the sky is also an issue. Another improvement that could be added is a transient event/radio frequency interference (RFI) flagging system that can remove flagged channels from normal imaging, and send them to a specialized pipeline that images and characterizes the flagged data. This transient event pipeline could then use special algorithms like uvmodelfit that can take advantage of the high signal to noise ratio of these events to characterize them better than the normal resolution of the array27.
There are also additional effects that need to be taken into account for a full array calibration, one of which is mutual coupling. As discussed in Ellingson28, this can lead to a decrease in sensitivity in arrays if they have receivers that are within a few wavelengths of each other. This is seen in a decrease in sensitivity for the array, or equivalently, an increase in the SEFD. This is especially true for beams greater than 10 degrees away from zenith. The example array in this work targets Earth, which is always near zenith by design, so mutual coupling should not affect this particular imaging target, but studies of the SEFD over the full range of elevation angles and frequencies will need to be done in commissioning for any real array to unlock its full potential. Another shortcoming of this array simulation pipeline lies in the imperfect lunar surface maps used. DEMs from LRO LOLA measurements have at best a resolution of 60x60 meters/pixel in the 512 pixels/degree maps. One can interpolate these data for simulated arrays, but for real arrays there will need to be a commissioning/calibration period where sources with a known position will be used to determine the relative separations between all the antenna to high precision. Possible calibration sources include Cas A, periodic low frequency emission from Jupiter or Earth, or potentially the Lunar Gateway29.
There is also the response of the lunar surface to consider. There is a layer of lunar topsoil called the regolith that acts like a lossy dielectric that can reflect incoming emission with some efficiency, above the lunar bedrock which can also reflect incoming emission with some better efficiency30,31. This response is dependent on the ambient temperature and incoming frequency, as well as the chemical composition of the regolith. Studies30,31 have found that at lower temperatures below 100 K, the regolith is nearly transparent to radio emission, and reflection occurs at the bedrock level with a reflection coefficient of around 0.5-0.6. At higher temperatures 150-200 K, the regolith can absorb emission and reflect incoming radiation at the surface with a reflection coefficient of around 0.2-0.3. At temperatures above 200 K, it is found that the dielectric properties of the regolith are diminished, and variation from reflection can be ignored. These effects can reduce the effective area of the array, reducing sensitivity and requiring longer integration times. This effect can be modeled with electromagnetic simulation software packages such as NEC4.232 given models of relative permittivity/dielectric constant as a function of lunar depth. This will output the SEFD of a receiver for a given frequency, which can be given to the array simulation pipeline to calculate the correct noise to add to the simulated signal. Adding a grounding grid between the receiver and the lunar surface may help diminish the effect of reflected waves, but adds its own set of complications in the form of deployment.
Many of the hypothetical or fuzzy details around the implementation of a radio receiver on the lunar surface will finally solidify into reality with recent funding of single low frequency antenna projects like Radio wave Observations on the Lunar Surface of the photoElectron Sheath (ROLSES) and the Lunar Surface Electromagnetics Experiment (LuSEE)33. LuSEE was recently funded by NASA as part of the Commercial Lunar Payload Services program. Both suites of antenna will consist mainly of flight spares for past instruments like STEREO/WAVES or PSP FIELDS and are planned for a 2021 delivery. Measurements from these receivers will finally solidify the level of quasithermal noise from the photoelectron sheath from ionized dust on the lunar surface and how it changes over the course of a lunar day. These measurements will also characterize the level of reflection and absorption from the lunar surface, and quantify how it changes the SEFD of the receiver. They will also provide statistics on the number of transient events or RFI that are received on the lunar surface. These missions will pave the way for arrays of antennas that will finally be able to make a multitude of novel scientific observations such as low frequency emission from solar radio bursts, far away galaxies, and planetary magnetospheres. The simulation pipeline described in this work provides a flexible way to iterate the design of these future arrays for a variety of scientific targets.

The field of radio astronomy began in 1932 with the accidental detection of galactic radio emission by Karl G. Jansky1 at 20 MHz, in a range now commonly called the low frequency radio. Ever since then, radio astronomy has grown rapidly, catching up with higher frequency optical observations that have been going on for centuries longer. Another breakthrough was the utilization of radio interferometry, where groups of antenna separated by large distances are used to create a synthetic aperture, providing a way to scale up the sensitivity and resolution of radio observations2,3. This can intuitively be thought of as an extension of the regular resolution formula for optical observations:
<img alt="Equation 1" src="/files/ftp_upload/61540/61540equ01.jpg" />
For an observing dish of size D meters, and an observing wavelength of &#955; meters, &#920;HPBW is the angular size in radians of the Half Power Beam Width (HPBW), defining the resolution on the sky. This process of synthesizing a fraction of a large full dish with only scattered points across a mostly empty area is also called aperture synthesis. In the realm of radio interferometry, the resolution of an array is determined by the furthest distance between any two receivers in the array, and this distance is used as D in Equation 1.
The mathematics behind interferometry has been well documented in classic texts like Thompson&#8217;s Interferometry and Synthesis in Radio Astronomy3. The basic insight can be communicated informally as &#8220;(for planar arrays observing a small field of view) the cross correlation of signals between any 2 receivers (a visibility) will yield information about a 2D Fourier coefficient of the sky brightness pattern.&#8221; What Fourier mode is sampled depends on the separation of the receivers (the baseline), normalized by the observing wavelength. Receivers that are further apart (in the standard UVW coordinate system oriented towards the imaging target) sample higher spatial frequency features, yielding higher resolution details at smaller scales. Conversely, receivers that are close together in the same UVW frame sample lower spatial frequencies, giving information of larger scale structures at a lower resolution.
For the lowest radio frequencies, free electrons in Earth&#8217;s ionosphere prevent radio waves below 10 MHz from travelling from space to the ground, and vice versa. This so-called &#8220;ionospheric cutoff&#8221; has long prevented ground-based observations of the sky for this frequency range. The obvious answer to this limitation is to put radio receivers into space where they can record data free of the influence of Earth&#8217;s atmosphere and free electrons in its ionosphere. This has been done before with single antennas on spacecraft like Wind4 and STEREO5, which have revealed many astrophysical processes that produce emissions in this low frequency radio range. This includes emissions from the interactions of electrons with the Earth&#8217;s magnetosphere, electron acceleration from solar eruptions, and from the galaxy itself. Single antenna observations can measure the total flux density of such events, but cannot pinpoint where the emission is coming from. In order to localize this low frequency emission and make images in this frequency regime for the first time, many antennas will have to be sent to space and have their data combined to make a synthetic aperture.
Doing this would open a new window through which humankind can observe the universe, enabling a number of scientific measurements that require images of the sky in these lowest frequencies. The Moon is one possible site for a synthetic aperture in space, and it comes with pros and cons when compared to free flying orbiting arrays. The lunar far side has a unique radio quiet zone that blocks all of the usual interference coming from man-made signals, while the near side provides a static place for Earth observing arrays, and if constructed at the lunar sub-Earth point, the Earth will always be at the zenith of the sky. With a static array, it is easier to obtain short baselines to measure large scale emissions, as they are in no danger of colliding, unlike free flying arrays. The drawbacks of a lunar array are chiefly difficulties in cost and power. A large-scale array on the Moon would require a substantial amount of infrastructure and money, while smaller orbiting arrays would require far fewer resources. There is also the issue of power; most places on the Moon are exposed to sufficient sunlight for solar power generation for 1/3 of each lunar day. Surviving the large swings in temperature from lunar day to night is also an engineering concern. Putting aside these difficulties, there is still the problem of making sure that the proposed array design is suitable for its specified science target(s). The response of any given array is dependent on the structure of the emission being observed along with the configuration and sensitivity of the array.
Several conceptual arrays to go on the lunar surface have been drawn up over the decades. Early designs were not the most detailed, but still recognized the scientific advances that could be attained by such arrays6,7,8,9,10. More arrays have also been put forth in recent years, some of which, like FARSIDE11, DEX12, and DALI13 seek to measure the absorption troughs of the redshifted neutral hydrogen 21-cm signal in the 10-40 MHz range to probe the so called &#8220;Dark Ages&#8221; and constrain cosmological models of the early universe. Others like ROLSS14 call out tracking bright solar type II radio bursts far into the heliosphere to identify the site of solar energetic particle acceleration within coronal mass ejections as their compelling science case. Smaller scale arrays have also been described like the 2-element interferometer RIF15, which would use a single lander and a moving rover to sample many baselines as it moves outward from the lander. RIF focuses on the ability to make a sky map of these low frequencies for the first time, and calculates the UV coverage and synthesized beam for integrated observations.
Space-based radio arrays could also enable low frequency imaging of distant radio galaxies to determine magnetic fields and astrometric measurements16. Low frequency images of these bodies would provide a more complete picture of the physics governing these systems, in particular yielding synchrotron emission data for the lower end of the electron energy distribution. There are also a range of various magnetospheric emissions that occur at these low frequencies, providing both global (constant synchrotron emission) and local (bursts, auroral kilometric radiation) signatures of electron dynamics that are not detectable from the ground17. The brightest recorded emissions of these types have come from Earth and Jupiter, as these are the nearest planets with strong magnetospheres. However, arrays with sufficient sensitivity and resolution could observe magnetospheric emission from other outer planets, or even extrasolar planets18. This topic in particular was called out as an area of interest at the recent Planetary Sciences Vision 2050 workshop.
This work focuses on simulating the response of radio arrays on the Moon consisting of anywhere from just a few antennas, to hundreds or thousands of antennas. This simulation framework is useful for iterating array design for imaging any given scientific target in a small field of view (a few square degrees) but does not currently support all sky imaging. Accurate estimates of the predicted brightness maps along with realistic noise profiles must be used to ensure that a given array size/configuration is sufficient to observe the target to a certain noise level or resolution. The geometry of the array must also be known to a high degree so that the baselines are computed accurately to enable correct imaging of the data. Currently, the best maps of the Lunar surface are Digital Elevation Models (DEMs) from Lunar Reconnaissance Orbiter&#8217;s (LRO&#8217;s)19 Lunar Orbiter Laser Altimeter (LOLA)20. The simulation pipeline accepts longitude latitude coordinates for each receiver and interpolates the elevation at these points from existing DEMs to calculate the full 3D position.
From these coordinates the baselines are computed and inserted into a Common Astronomy Software Applications (CASA)21 Measurement Set (MS) file. The MS format can be used with many existing analysis and imaging algorithms, and holds information about the array configuration, visibility data, and alignment with the sky. However, many of these software routines are hard coded to work with arrays that rotate with the Earth&#8217;s surface, and do not work for orbiting or Lunar arrays. To circumvent this, this pipeline manually calculates the baselines and visibilities for a given array and imaging target, and inserts the data into the MS format. The SPICE22 library is used to correctly align the lunar and sky coordinate systems and track the motions of the Moon, Earth, and Sun.
The simulation framework described here follows Hegedus et al.17, and the software is archived by the University of Michigan library in the Deep Blue archive23, stored at https://deepblue.lib.umich.edu/data/concern/data_sets/bg257f178?locale=en. Any patches or updates to this archived software can be found at https://github.com/alexhege/LunarSynchrotronArray. The following section will describe the requirements for this software, and walk through the process of forming an array, setting the appropriate noise levels, feeding the array a simulated truth image of the targeted emission, and simulating the array&#8217;s noiseless and noisy reconstructions of the emission using a CASA script.

<table>
	<tbody>
		<tr>
			<td>No physical materials are needed, this is a purely computational work.</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

simulating imaging of large scale radio arrays on the lunar surface

In recent years there has been a renewed interest in returning to the Moon for reasons both scientific and exploratory in nature. The Moon provides the perfect training ground for building large scale bases that one may apply to other planets like Mars. The existence of a radio quiet zone on the lunar far side has promise for early universe studies and exoplanet searches, while the near side provides a stable base that may be used to observe low frequency emissions from Earth&#8217;s magnetosphere that may help gauge its response to incoming space weather. The construction of a large-scale radio array would provide large scientific returns as well as acting as a test of humanity&#8217;s ability to build structures on other planets. This work focuses on simulating the response of small to large-scale radio arrays on the Moon consisting of hundreds or thousands of antennas. The response of the array is dependent on the structure of the emission along with the configuration and sensitivity of the array. A set of locations are selected for the simulated radio receivers, using Digital Elevation Models from the Lunar Orbiter Laser Altimeter instrument on Lunar Reconnaissance Orbiter to characterize the elevation of the receiver locations. A custom Common Astronomy Software Applications code is described and used to process the data from the simulated receivers, aligning the lunar and sky coordinate frames using SPICE to ensure the proper projections are used for imaging. This simulation framework is useful for iterating array design for imaging any given scientific target in a small field of view. This framework does not currently support all sky imaging.

Following the software pipeline should be fairly straightforward, and it should be obvious that each step is working as it should. Running createArrayConfig.py from step 2 should create a figure resembling Figure 1, where configuration of the defined array is plotted on top of the local topography of the lunar surface, as derived from the LRO LOLA derived Digital Elevation Model.
Step 3 should give key output files eqXYZ_EarthCentered.txt, RAs.txt, and Decs.txt, among others. Examples of these files are located in the downloaded package.
Step 4 should create a truth image that is similar to Figure 2, which is then used to calculate the visibility data. It should also output a CASA Measurement Set (.ms) file that one may browse with the usual CASA command of casabrowser to see that both the baselines and visibility data were calculated and saved.
Step 5 should output figures similar to Figure 3 and Figure 4 for the noiseless and noisy images respectively. The noisy images should look less clear than the noiseless image.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61540/61540fig01.jpg" /> 
Figure 1: Configuration of the array over elevation map of the lunar surface. 
This is an example array configuration consisting of a logarithmically spaced circular array over 10 km. The configuration has 32 arms of 32 logarithmically spaced antenna for a total of 1024 antenna. The site of the array, (-1.04&#176;, -0.43&#176;) was chosen because it is the center of the 10x10 km patch with the lowest elevation variation (&#963; = 5.6 m) close to the sub-Earth point (0&#176;, 0&#176;) in the Moon Mean Earth (ME) frame. The elevation data was obtained from a Digital Elevation Map derived from LRO LOLA measurements. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61540/61540fig02.jpg" /> 
Figure 2: Truth image of synchrotron emission from radiation belts at lunar distances. 
This is an example of a science target for the array to image. The recovered image is then compared to this input to determine the performance of the array. The brightness map was created from Salammb&#244; electron simulation data and run through a calculation for determining the synchrotron emission that would be observed at lunar distances. The 1.91&#176; Earth is added in for a scale indicator. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61540/61540fig03.jpg" /> 
Figure 3: Noiseless response of 10 km diameter array to input truth image. 
This is one of the outputs from Step 5, applying standard radio astronomy imaging algorithm CLEAN, using a Briggs weighting scheme with a robustness parameter of &#8722;0.5. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61540/61540fig04.jpg" /> 
Figure 4: Noisy response of 10 km diameter array to input truth image. 
This is one of the outputs from Step 5, applying standard radio astronomy CLEAN, using a Briggs weighting scheme with a robustness parameter of &#8722;0.5. For this image, a System Equivalent Flux Density of 1.38e7 Jansky was used, an integration bandwidth of 500 kHz, and an integration time of 4 hours. The noise was also reduced by a factor of 16 to simulate the response of a 16K antenna array instead of a 1K antenna array. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface at JoVE.com

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

A simulation framework for testing the imaging capabilities of large-scale radio arrays on the lunar surface is presented. Major noise components are discussed, and a software pipeline is walked through with details on how to customize it for novel scientific uses.

In recent years there has been a renewed interest in returning to the Moon for reasons both scientific and exploratory in nature. The Moon provides the ...

simulating-imaging-of-large-scale-radio-arrays-on-the-lunar-surface

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4.7K Views.  University of Michigan. A simulation framework for testing the imaging capabilities of large-scale radio arrays on the lunar surface is presented. Major noise components are discussed, and a software pipeline is walked through with details on how to customize it for novel scientific uses.

Video: Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

X-ray microtomography was used to image, at a resolution of 6.6 &#181;m, the pore-scale arrangement of residual carbon dioxide ganglia in the pore-space of a carbonate rock at pressures and temperatures representative of typical formations used for CO2 storage. Chemical equilibrium between the CO2, brine and rock phases was maintained using a high pressure high temperature reactor, replicating conditions far away from the injection site. Fluid flow was controlled using high pressure high temperature syringe pumps. To maintain representative in-situ conditions within the micro-CT scanner a carbon fiber high pressure micro-CT coreholder was used. Diffusive CO2 exchange across the confining sleeve from the pore-space of the rock to the confining fluid was prevented by surrounding the core with a triple wrap of aluminum foil. Reconstructed brine contrast was modeled using a polychromatic x-ray source, and brine composition was chosen to maximize the three phase contrast between the two fluids and the rock. Flexible flow lines were used to reduce forces on the sample during image acquisition, potentially causing unwanted sample motion, a major shortcoming in previous techniques. An internal thermocouple, placed directly adjacent to the rock core, coupled with an external flexible heating wrap and a PID controller was used to maintain a constant temperature within the flow cell. Substantial amounts of CO2 were trapped, with a residual saturation of 0.203 &#177; 0.013, and the sizes of larger volume ganglia obey power law distributions, consistent with percolation theory.

We present a methodology for the imaging of multiple fluid phases at reservoir conditions by the use of x-ray microtomography. We show some representative results of capillary trapping in a carbonate rock sample.

X-ray microtomography was used to image, at a resolution of 6.6 &#181;m, the pore-scale arrangement of residual carbon dioxide ganglia in the pore-space ...

Visualization of High Speed Liquid Jet Impaction on a Moving Surface

Two apparatuses for examining liquid jet impingement on a high-speed moving surface are described: an air cannon device (for examining surface speeds between 0 and 25 m/sec) and a spinning disk device (for examining surface speeds between 15 and 100 m/sec). The air cannon linear traverse is a pneumatic energy-powered system that is designed to accelerate a metal rail surface mounted on top of a wooden projectile. A pressurized cylinder fitted with a solenoid valve rapidly releases pressurized air into the barrel, forcing the projectile down the cannon barrel. The projectile travels beneath a spray nozzle, which impinges a liquid jet onto its metal upper surface, and the projectile then hits a stopping mechanism. A camera records the jet impingement, and a pressure transducer records the spray nozzle backpressure. The spinning disk set-up consists of a steel disk that reaches speeds of 500 to 3,000 rpm via a variable frequency drive (VFD) motor. A spray system similar to that of the air cannon generates a liquid jet that impinges onto the spinning disc, and cameras placed at several optical access points record the jet impingement. Video recordings of jet impingement processes are recorded and examined to determine whether the outcome of impingement is splash, splatter, or deposition. The apparatuses are the first that involve the high speed impingement of low-Reynolds-number liquid jets on high speed moving surfaces. In addition to its rail industry applications, the described technique may be used for technical and industrial purposes such as steelmaking and may be relevant to high-speed 3D printing.

Two experimental devices for examining liquid jet impingement on a high-speed moving surface are described: an air cannon device and a spinning disk device. The apparatuses are used to determine optimal approaches to the application of liquid friction modifier (LFM) onto rail tracks for top-of-rail friction control.

Two apparatuses for examining liquid jet impingement on a high-speed moving surface are described: an air cannon device (for examining surface speeds ...

Emission Spectroscopic Boundary Layer Investigation during Ablative Material Testing in Plasmatron

Ablative Thermal Protection Systems (TPS) allowed the first humans to safely return to Earth from the moon and are still considered as the only solution for future high-speed reentry missions. But despite the advancements made since Apollo, heat flux prediction remains an imperfect science and engineers resort to safety factors to determine the TPS thickness. This goes at the expense of embarked payload, hampering, for example, sample return missions. 
Ground testing in plasma wind-tunnels is currently the only affordable possibility for both material qualification and validation of material response codes. The subsonic 1.2MW Inductively Coupled Plasmatron facility at the von Karman Institute for Fluid Dynamics is able to reproduce a wide range of reentry environments. This protocol describes a procedure for the study of the gas/surface interaction on ablative materials in high enthalpy flows and presents sample results of a non-pyrolyzing, ablating carbon fiber precursor. With this publication, the authors envisage the definition of a standard procedure, facilitating comparison with other laboratories and contributing to ongoing efforts to improve heat shield reliability and reduce design uncertainties.
The described core techniques are non-intrusive methods to track the material recession with a high-speed camera along with the chemistry in the reactive boundary layer, probed by emission spectroscopy. Although optical emission spectroscopy is limited to line-of-sight measurements and is further constrained to electronically excited atoms and molecules, its simplicity and broad applicability still make it the technique of choice for analysis of the reactive boundary layer. Recession of the ablating sample further requires that the distance of the measurement location with respect to the surface is known at all times during the experiment. Calibration of the optical system of the applied three spectrometers allowed quantitative comparison. At the fiber scale, results from a post-test microscopy analysis are presented.

Development of new ablative materials and their numerical modeling requires extensive experimental investigation. This protocol describes procedures for material response characterization in plasma flows with the core techniques being non-intrusive methods to track the material recession along with the chemistry in the reactive boundary layer by emission spectroscopy.

Ablative Thermal Protection Systems (TPS) allowed the first humans to safely return to Earth from the moon and are still considered as the only solution ...

Dynamic Pore-scale Reservoir-condition Imaging of Reaction in Carbonates Using Synchrotron Fast Tomography

Underground storage permanence is a major concern for carbon capture and storage. Pumping CO2 into carbonate reservoirs has the potential to dissolve geologic seals and allow CO2 to escape. However, the dissolution processes at reservoir conditions are poorly understood. Thus, time-resolved experiments are needed to observe and predict the nature and rate of dissolution at the pore scale. Synchrotron fast tomography is a method of taking high-resolution time-resolved images of complex pore structures much more quickly than traditional &#181;-CT. The Diamond Lightsource Pink Beam was used to dynamically image dissolution of limestone in the presence of CO2-saturated brine at reservoir conditions. 100 scans were taken at a 6.1 &#181;m resolution over a period of 2 hours. The images were segmented and the porosity and permeability were measured using image analysis and network extraction. Porosity increased uniformly along the length of the sample; however, the rate of increase of both porosity and permeability slowed at later times.

Synchrotron fast tomography was used to dynamically image dissolution of limestone in the presence of CO2-saturated brine at reservoir conditions. 100 scans were taken at a 6.1 &#181;m resolution over a period of 2 h.

Underground storage permanence is a major concern for carbon capture and storage. Pumping CO2 into carbonate reservoirs has the potential to dissolve ...

Nanostructured Ag-zeolite Composites as Luminescence-based Humidity Sensors

Small silver clusters confined inside zeolite matrices have recently emerged as a novel type of highly luminescent materials. Their emission has high external quantum efficiencies (EQE) and spans the whole visible spectrum. It has been recently reported that the UV excited luminescence of partially Li-exchanged sodium Linde type A zeolites [LTA(Na)] containing luminescent silver clusters can be controlled by adjusting the water content of the zeolite. These samples showed a dynamic change in their emission color from blue to green and yellow upon an increase of the hydration level of the zeolite, showing the great potential that these materials can have as luminescence-based humidity sensors at the macro and micro scale. Here, we describe the detailed procedure to fabricate a humidity sensor prototype using silver-exchanged zeolite composites. The sensor is produced by suspending the luminescent Ag-zeolites in an aqueous solution of polyethylenimine (PEI) to subsequently deposit a film of the material onto a quartz plate. The coated plate is subjected to several hydration/dehydration cycles to show the functionality of the sensing film.

A protocol for the synthesis of moisture-responsive luminescent Ag-zeolite composites is described in this report.

Small silver clusters confined inside zeolite matrices have recently emerged as a novel type of highly luminescent materials. Their emission has high ...

Experimental Implementation of a New Composite Fabrication Method: Exposing Bare Fibers on the Composite Surface by the Soft Layer Method

The bipolar plate is a key component in proton exchange membrane fuel cells (PEMFCs) and vanadium redox flow batteries (VRFBs). It is a multi-functional component that should have high electrical conductivity, high mechanical properties, and high productivity.
In this regard, a carbon fiber/epoxy resin composite can be an ideal material to replace the conventional graphite bipolar plate, which often leads to the catastrophic failure of the entire system because of its inherent brittleness. Though the carbon/epoxy composite has high mechanical properties and is easy to manufacture, the electrical conductivity in the through-thickness direction is poor because of the resin-rich layer that forms on its surface. Therefore, an expanded graphite coating was adopted to solve the electrical conductivity issue. However, the expanded graphite coating not only increases the manufacturing costs but also has poor mechanical properties.
In this study, a method to expose fibers on the composite surface is demonstrated. There are currently many methods that can expose fibers by surface treatment after the fabrication of the composite. This new method, however, does not require surface treatment because the fibers are exposed during the manufacture of the composite. By exposing bare carbon fibers on the surface, the electrical conductivity and mechanical strength of the composite are increased drastically.

A protocol to expose bare fibers on the composite surface by eliminating resin rich area is presented. The fibers are exposed during fabrication of the composites, not by the post surface treatment. The exposed carbon composites exhibit high electrical conductivity in the through-thickness direction and high mechanical property.

The bipolar plate is a key component in proton exchange membrane fuel cells (PEMFCs) and vanadium redox flow batteries (VRFBs). It is a multi-functional ...

Preparation and High-temperature Anti-adhesion Behavior of a Slippery Surface on Stainless Steel

Anti-adhesion surfaces with high-temperature resistance have a wide application potential in electrosurgical instruments, engines, and pipelines. A typical anti-wetting superhydrophobic surface easily fails when exposed to a high-temperature liquid. Recently, Nepenthes-inspired slippery surfaces demonstrated a new way to solve the adhesion problem. A lubricant layer on the slippery surface can act as a barrier between the repelled materials and the surface structure. However, the slippery surfaces in previous studies rarely showed high-temperature resistance. Here, we describe a protocol for the preparation of slippery surfaces with high-temperature resistance. A photolithography-assisted method was used to fabricate pillar structures on stainless steel. By functionalizing the surface with saline, a slippery surface was prepared by adding silicone oil. The prepared slippery surface maintained the anti-wetting property for water, even when the surface was heated to 300 &#176;C. Also, the slippery surface exhibited great anti-adhesion effects on soft tissues at high temperatures. This type of slippery surface on stainless steel has applications in medical devices, mechanical equipment, etc.

Slippery surfaces provide a new way to solve the adhesion problem. This protocol describes how to fabricate slippery surfaces at high temperatures. The results demonstrate that the slippery surfaces showed anti-wetting for liquids and a remarkable anti-adhesion effect on soft tissues at high temperatures.

Anti-adhesion surfaces with high-temperature resistance have a wide application potential in electrosurgical instruments, engines, and pipelines. A ...

Radio Frequency Magnetron Sputtering of GdBa2Cu3O7&#8722;&#948;/ La0.67Sr0.33MnO3 Quasi-bilayer Films on SrTiO3 (STO) Single-crystal Substrates

Here, we demonstrate a method of coating ferromagnetic La0.67Sr0.33MnO3 (LSMO) nanoparticles on (001) SrTiO3 (STO) single-crystal substrates by radio frequency (RF) magnetron sputtering. LSMO nanoparticles were deposited with diameters from 10 to 20 nm and heights between 20 and 50 nm. At the same time, (Gd) Ba2Cu3O7&#8722;&#948; ((Gd) BCO) films were fabricated on both undecorated and LSMO nanoparticle decorated STO substrates using RF magnetron sputtering. This report also describes the properties of GdBa2Cu3O7&#8722;&#948;/ La0.67Sr0.33MnO3 quasi-bilayer films structures (e.g., crystalline phase, morphology, chemical composition); magnetization, magneto-transport, and superconducting transport properties were also evaluated.

Radio Frequency Magnetron Sputtering of GdBa2Cu3O7ÃƒÂ¢Ã‹â€ &#039;ÃƒÅ½ ´/ La0.67Sr0.33MnO3 Quasi-bilayer Films on SrTiO3 STO Single-crystal Substrates

Here, we present a protocol to grow LSMO nanoparticles and (Gd) BCO films on (001) SrTiO3 (STO) single-crystal substrates by radio frequency (RF)-sputtering.

Here, we demonstrate a method of coating ferromagnetic La0.67Sr0.33MnO3 (LSMO) nanoparticles on (001) SrTiO3 (STO) single-crystal substrates by radio ...

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Optical phased arrays (OPAs) can produce low-divergence laser beams and can be used to control the emission angle electronically without the need for moving mechanical parts. This technology is particularly useful for beam steering applications. Here, we focus on OPAs integrated into SiN photonic circuits for a wavelength in the near infrared. A characterization method of such circuits is presented, which allows the output beam of integrated OPAs to be shaped and steered. Furthermore, using a wafer-scale characterization setup, several devices can easily be tested across multiple dies on a wafer. In this way, fabrication variations can be studied, and high-performance devices identified. Typical images of OPA beams are shown, including beams emitted from OPAs with and without a uniform waveguide length, and with varying numbers of channels. In addition, the evolution of output beams during the phase optimization process and beam steering in two dimensions is presented. Finally, a study of the variation in the beam divergence of identical devices is performed with respect to their position on the wafer.

Here, we describe the operation of a SiN integrated photonic circuit containing optical phased arrays. The circuits are used to emit low divergence laser beams in the near infrared and steer them in two dimensions.

Optical phased arrays (OPAs) can produce low-divergence laser beams and can be used to control the emission angle electronically without the need for ...

Fabrication of Surface Acoustic Wave Devices on Lithium Niobate

Manipulation of fluids and particles by acoustic actuation at small scale is aiding the rapid growth of lab-on-a-chip applications. Megahertz-order surface acoustic wave (SAW) devices generate enormous accelerations on their surface, up to 108 m/s2, in turn responsible for many of the observed effects that have come to define acoustofluidics: acoustic streaming and acoustic radiation forces. These effects have been used for particle, cell, and fluid handling at the microscale&#8212;and even at the nanoscale. In this paper we explicitly demonstrate two major fabrication methods of SAW devices on lithium niobate: the details of lift-off and wet etching techniques are described step-by-step. Representative results for the electrode pattern deposited on the substrate as well as the performance of SAW generated on the surface are displayed in detail. Fabrication tricks and troubleshooting are covered as well. This procedure offers a practical protocol for high frequency SAW device fabrication and integration for future microfluidics applications.

Two fabrication techniques, lift-off and wet etching, are described in producing interdigital electrode transducers upon a piezoelectric substrate, lithium niobate, widely used to generate surface acoustic waves now finding broad utility in micro to nanoscale fluidics. The as-produced electrodes are shown to efficiently induce megahertz order Rayleigh surface acoustic waves.&#160;

Manipulation of fluids and particles by acoustic actuation at small scale is aiding the rapid growth of lab-on-a-chip applications. Megahertz-order ...