OVERVIEW This document presents a parametric model to help design an Interferometric Array. It focuses in the value vs. cost trade-off inherent to many of its architecture definitions. This is a Multiple Objective problem. This document describes design parameters to consider in § [sec:var] and a set of equations for research and cost objectives in § [sec:obj]. A spreadsheet that uses these design parameters and produces a CSV file for analysis of the emerging Pareto Front is introduced in § [sec:spreadsheet]. This output enables the of Multiple Objective Visual Analytics (MOVA) for complex engineered systems as proposed in . PARAMETERS This section presents selected design parameters that influence selected objectives in § [sec:obj]. We will select design parameters that are specification agnostic. As an example of this, the parameters will be relevant to multiple antenna specifications, including offset Gregorian and symmetric Cassegrain. Antenna Parameters Antenna Collecting Area We will use A in this document as each array element collecting area (thus we could also write π ⋅ D², with D being the dish diameter). Antenna Efficiency We will use ηa in this document as the antenna efficiency with \eta_a = } \cdot } \cdot } \cdot } as defined in . Antenna Quantity We will use N in this document as the number of array elements. Antenna Pad Parameters Pad Quantity We will use P in this document as the number of pad built for the array. In case re-configuration of the array is envisioned, there might be a bigger number of pads ready for aperture connection to the system. Pad Position We will use the geographic latitudes and longitudes to establish pad location in this document. We will calculate the length of the possible baselines using pad positions. We will also calculate length and complexity of the roads, fiber and power networks needed using pad positions. We will use B as the maximum array element separation in any single configuration. Reciever System Parameters Number of Receivers per Array Element We will use R as the number of frequency bands, being Ri the different frequency bands. If the array bandwidth is λmax − λmin, it is useful for our analysis to use wavelength λ = λmin. Notes: high bandwidth ration: up to 7 might be practical, but could compromise Ae/Tsys. High absolute bandwidth is challenging for digitalization. up to 20GHz might be practical. Receivers Efficiency Signal Processing and Transmision Parameters Notes: directly at RF (no reference), single sideband down conversion (LO and timing reference), double sideband (IQ) down conversion (two LO, two references, LO tunable. Bits per sample (dynamic range) Correlator Aspects Position We will geographic latitude and longitude to establish correlator location in this document. We will calculate fiber, power and road network aspects based in this information. Efficiency We will use ηc as correlator efficiency in this document, with \eta_c(t_{int}) = }{ t_{int}} as defined in . OBJECTIVES This section aims to include array performance objectives that might be influenced by design variables in § [sec:var]. Fourier Plane Coverage As derived in , the antenna diameter determines its beam size $ \approx {D}$. If the plane area ${\lambda}$ is divided in cells of size ${\lambda}$ then N_{occ} \leqslant \pi ({D})^2 Point Source Sensitivity An overall measure of performance is the System Equivalent Flux Density, SEFD, defined in as the flux density of a source that would deliver the same amount of power: SEFD = {}{{2k_B}}} in units of Janskys where Tsys is the system temperature including contributions from receiver noise, feed losses, spillover, atmospheric emission, galactic background and cosmic background, and kB = 1.380 × 10−23 Joule K−1 is the Boltzmann constant. According to , if we assume N apertures with the same SEFD, observing the same bandwidth Δν, during the same integration time tint, then weak-source limit in the sensitivity of a synthesis image of a single polarization is \Delta I_m = {{\eta_s }}{{}}} in units of Janskys per synthesized beam area, with ηs most important factor being correlator efficiency ηc. Surface Brightness Sensitivity Operations Costs Components reliability Maintenance complexity Calibration Software Costs Calibration Hardware Costs Power Consumption Cost Re-configuration Systems Operation Cost Up-front Costs Cost of Antennas Construction According to , a commonly used rule of thumb for the cost of an antenna is that it is proportional to Dα, where α ≈ 2.7 for values of D from a few meters to tens of meters. For N antennas of diameter D meters with accuracy ${16}$, where λ is in millimeters we could use as an upper limit for Antenna construction cost. = {10})^{2.7}}{(\lambda^{0.7})} + 500 in K$. Cost of Front-end system For M frequency bands, each 30% wide, and dual polarization we could use as an upper limit for Front-End System Cost: = 45MN + 200M in K$. Cost of LO system We could use as an upper limit for LO System Cost: = 80N+100 in K$. IF Transmission Cost We could use as an upper limit for IF Transmission Cost: = 8BN + 30N + 400 in K$. Correlator Cost We could use Correlator Cost as an upper limit: = 2N^2 + 112N +1360 in K$. Cost of Re-configuration Systems Construction DATA FOR VISUAL ANALYTICS - SPREADSHEET IMPLEMENTATION This section presents a spreadsheet that produces data in the right format for performing visual analytics, consistent with variables in § [sec:var] and objectives in § [sec:obj]. VISUALIZATION TOOL NOTES CONVERSATION NOTES Engineering cost vs. Calibration cost Tricky because you can compensate antenna quality with software. So the equations must capture this trade off.

TO DO - identify data set - complete outline - book room? - come up with quiz questions to identify suitable students

6.14 a Proving Lemma 6.4.1: $(\I,r,m) \vDash C_S \varphi$ iff $(\I,r',m') \vDash \varphi$ for all points (r′,m′) that are S-reachable from (r, m).

INTRODUCTION TO VERSION CONTROL Many scientists write code as part of their research. Just as experiments are logged in laboratory notebooks, it is important to document the code you use for analysis. However, a few key problems can arise when iteratively developing code that make it difficult to document and track which code version was used to create each result. First, you often need to experiment with new ideas, such as adding new features to a script or increasing the speed of a slow step, but you do not want to risk breaking the currently working code. One often utilized solution is to make a copy of the script before making new edits. However, this can quickly become a problem because it clutters your filesystem with uninformative filenames, e.g. analysis.sh, analysis_02.sh, analysis_03.sh, etc. It is difficult to remember the differences between the versions of the files, and more importantly which version you used to produce specific results, especially if you return to the code months later. Second, you will likely share your code with multiple lab mates or collaborators and they may have suggestions on how to improve it. If you email the code to multiple people, you will have to manually incorporate all the changes each of them sends. Fortunately, software engineers have already developed software to manage these issues: version control. A version control system (VCS) allows you to track the iterative changes you make to your code. Thus you can experiment with new ideas but always have the option to revert to a specific past version of the code you used to generate particular results. Furthermore, you can record messages as you save each successive version so that you (or anyone else) reviewing the development history of the code is able to understand the rationale for the given edits. Also, it facilitates collaboration. Using a VCS, your collaborators can make and save changes to the code, and you can automatically incorporate these changes to the main code base. The collaborative aspect is enhanced with the emergence of websites that host version controlled code. In this quick guide, we introduce you to one VCS, Git (git-scm.com), and one online hosting site, GitHub (github.com), both of which are currently popular among scientists and programmers in general. More importantly, we hope to convince you that although mastering a given VCS takes time, you can already achieve great benefits by getting started using a few simple commands. Furthermore, not only does using a VCS solve many common problems when writing code, it can also improve the scientific process. By tracking your code development with a VCS and hosting it online, you are performing science that is more transparent, reproducible, and open to collaboration . There is no reason this framework needs to be limited only to code; a VCS is well-suited for tracking any plain-text files: manuscripts, electronic lab notebooks, protocols, etc.

Problem Statement A random walk on the 2-dimensional integer lattice begins at the origin. At each step, the walker moves one unit either left, right, or up, each with probability $\frac13$. (No downward steps ever.) A walk is a success if it reaches the point (1, 1). What is the probability of success? Note: One can vary the problem by varying the target point. Eg., use (1, 0) or (0, 1) instead. Perhaps there is a good method to resolve the general case of target (a, b). Source: Bruce Torrence, Randolph-Macon College

Small satellites can enable a new kind of mission architecture: inspecting larger satellites on orbit in close proximity without mechanical contact. Induction coupling is a new actuation technology that can augment on-orbit servicing by exploiting eddy-current forces and torques. Current technologies for applying forces and torques between two spacecraft share a glaring disadvantage: they require direct contact or propellant. By using the forces between a magnetic field and the electric currents it induces in a target, an induction coupler can control the relative position and orientation between a chaser spacecraft and a target without physical contact. A system utilizing these eddy-current effects places relatively few requirements on the target and chaser compared to other proposed electromagnetic actuation concepts. This paper presents a system overview of a contactless induction coupler, outlines those requirements through the analysis of an inspection mission on the International Space Station, and traces them to flight applications through ongoing experimental work.

INTRODUCTION Biofilms are diverse and complex microbial consortia, and, the biofilm lifestyle is the rule rather than the exception for microbes in many environments. Large and small-scale biofilm architectural features play an important role in their ecology and influence their role in biogeochemical cycles . Fluid mechanics impact biofilm structure and assembly , but it is less clear how other abiotic factors such as resource availability affect biofilm assembly. Aquatic biofilms initiate with seed propagules from the planktonic community . Thus, resource amendments that influence planktonic communities may also influence the recruitment of microbial populations during biofilm community assembly. In a crude sense, biofilm and planktonic microbial communities divide into two key groups: oxygenic phototrophs including eukaryotes and cyanobacteria (hereafter “photoautotrophs”), and heterotrophic bacteria and archaea. This dichotomy, admittedly an abstraction (e.g. non-phototrophs can also be autotrophs), can be a powerful paradigm for understanding community shifts across ecosystems of varying trophic state . Heterotrophs meet some to all of their organic carbon (C) requirements from photoautotroph produced C while simultaneously competing with photoautotrophs for limiting nutrients such as phosphorous (P) . The presence of external C inputs, such as terrigenous C leaching from the watershed or C exudates derived from macrophytes , can alleviate heterotroph reliance on photoautotroph derived C and shift the heterotroph-photoautotroph relationship from commensal and competitive to strictly competitive . Therefore, increased C supply should increase the resource space available to heterotrophs and increase competition for mineral nutrients decreasing nutrients available for photoautotrophs (assuming that heterotrophs are superior competitors for limiting nutrients as has been observed ). These dynamics should result in the increase in heterotroph biomass relative to the photoautotroph biomass along a gradient of increasing labile C inputs. We refer to this differential allocation of limiting resources among components of the microbial community as niche partitioning. While these gross level dynamics have been discussed conceptually and to some extent demonstrated empirically , the effects of biomass dynamics on photoautotroph and heterotroph membership and structure has not been directly evaluated in plankton or biofilms. In addition, how changes in planktonic communities propagate to biofilms during community assembly is not well understood. We designed this study to test if C subsidies shift the biomass balance between autotrophs and heterotrophs within the biofilm or its seed pool (i.e. the plankton), and, to measure how changes in biomass pool size alter composition of the plankton and biofilm communities. Specifically, we amended marine mesocosms with varying levels of labile C input and evaluated differences in photoautotroph and heterotrophic bacterial biomass in plankton and biofilm samples along the C gradient. In each treatment we characterized plankton and biofilm community composition by PCR amplifying and DNA sequencing 16S rRNA genes and plastid 23S rRNA genes.

ABSTRACT Biological soil crusts (BSC) are key components of ecosystem productivity in arid lands and they cover a substantial fraction of the terrestrial surface. In particular, BSC N₂-fixation contributes significantly to the nitrogen (N) budget of arid land ecosystems. In mature crusts, N₂-fixation is largely attributed to heterocystous cyanobacteria, however, early successional crusts possess few N₂-fixing cyanobacteria and this suggests that microorganisms other than cyanobacteria mediate N₂-fixation during the critical early stages of BSC development. DNA stable isotope probing (DNA-SIP) with ¹⁵N₂ revealed that _Clostridiaceae_ and _Proteobacteria_ are the most common microorganisms that assimilate ¹⁵N₂ in early successional crusts. The _Clostridiaceae_ identified are divergent from previously characterized isolates, though N₂-fixation has previously been observed in this family. The Proteobacteria identified share >98.5 %SSU rRNA gene sequence identity with isolates from genera known to possess diazotrophs (e.g. _Pseudomonas_, _Klebsiella_, _Shigella_, and _Ideonella_). The low abundance of these heterotrophic diazotrophs in BSC may explain why they have not been characterized previously. Diazotrophs play a critical role in BSC formation and characterization of these organisms represents a crucial step towards understanding how anthropogenic change will affect the formation and ecological function of BSC in arid ecosystems. KEYWORDS: microbial ecology / stable isotope probing / nitrogen fixation / biological soil crusts

INTRODUCTION 1.Soil moisture patterns are important to predicting water quality of the runoff generated. Much attention in hydrology is paid to soil moisture as a risk indicator of surface runoff generation e.g. . Surface runoff quickly transports water to streams causing pulses of high flow that increase flooding potential, impact wildlife habitat, and transport sediment and dissolved contaminants from the land surface. The ability to predict where and when runoff is likely to be generated guides the planning and implementation of management practices that reduce these negative impacts. Traditionally, the temporal and spatial variability of soil moisture patterns is identified through two approaches. Complex, distributed hydrologic models such as the Soil Moisture Routing model predict patterns fairly well, but require climate and landscape data at fine resolutions to be reliable. In contrast, indices that draw on watershed-level hydrologic drivers such as terrain and soil properties can provide fast, simple estimates of soil moisture patterns. These topographic indices, originally developed by , can be used to quickly estimate patterns in regions where soil moisture spatial variability is driven by topographic changes and shallow soil depths. In the Northeastern United States, topographic indices have been shown to work well to predict soil moisture patterns . Soil moisture measurements can be collected rapidly and easily in the field, however due to signfiicant spatial and temporal variability, achieving a coverage for watersheds is a time-intensive endevor. Remote sensing techniques are in development, but the resolution of their predicitions is too coarse for meaningful guidance in management practices . In this project we are interested in using geostatistics and the topographic index models to extroplate soil moisture measurements for comparison with patterns predicted by a more complicated, uncalibrated hydrologic model. To do this, we use a geostatistical tool for characterizing spatial patterns, the semivariogram model. Semivariograms quantify the change in variance between two points in a field based on their distance. The sill represents the distance at very large distances (large being relative to the data). The distance at which the sill is stable is considered the range. At this distance, spatial correlation no longer exists. The variance in repeated measurements at the same location is given as the nugget of the semivariogram. Kriging is a geostatistical technique that makes use of the semivariogram for interpolating data where it does not exist. Kriging with additional information, such as the output of the topographic index models, can be used to influence the spatial pattern. This is particularly useful where linear distance is not the only important correlation for a spatial pattern. In the case of soil moisture, a location in a stream may be continually saturated to a similar degree as a location far downstream that is also along a high accumalting flowpath. A location much closer in spatial distance to the saturated area may be significantly drier, perhaps because of a steep slope that transmits water away quickly. For this reason, straight kriging with the soil moisture data will present limited spatial results. In some variograms the range will occur at very close distances or spatial correlation will not be apparent. In this project we investigate the locations and dates where good spatial correlation is evident and use topographic index information to improve kriging predictions.

The steady increase in white-tailed deer populations and largely unnoticed invasion of earthworms are changing some of the important underlying processes that support North American forest understory communities . Amidst a rapidly-changing set of ecological drivers, earthworm invasion and increased browse pressure from white tailed deer are two of the most important stressors to forest plant populations . Northern forests in North America have developed in the absence of earthworms, at least as far back as the last glaciation . Human activity has facilitated the introduction and dispersion of earthworms to even remote forested areas through logging roads, dumping of fishing bait, and the relocation of fill or horticultural materials . Whereas uninvaded forest soils often build up a thick leaf-fermenting-humus (LFH) layer through slow, localized decomposition of organic material, earthworm-dominated forests are characterized by bare mineral soil, and can completely lack a stratified soil profile . Many native plants require the unique habitat that the forest floor provides for protection of slowly germinating seedlings, nutrient and water retention, access to mycorrhizal symbionts and temperature buffering . Drivers of change in understory plant communities are also occurring above-ground. In much of North eastern North America, browse pressure from rapidly growing white-tailed deer (_Odocoileus virginianus_) populations is dramatically altering forest understory communities, restricting regeneration and causing extinction of native plants at the local level . Long-term datasets show that overall species diversity decreases with high deer browse pressure, and selective browsing disproportionately affects palatable species, while releasing unpalatable plants competition . Deer browse impacts are compounded in slow-growing perennials, as they preferentially browse the largest individual plants that maintain growth rates of the plant population . To look at the combined and individual effects of deer and earthworms, I set up experimental plantings of a large number of native forest understory species (n=15) in spring 2012. Because earthworm and deer impacts on seedlings are the net effect of a myriad of factors, I selected species with many different traits that could conceivably respond to these factors. Thus far I have been recording seedling survival, and have been surprised to find earthworms decrease seedling survival in 11 of 15 species, and had no effect on 4 species. This is surprising, because field surveys have found several plant species included in my study are associated with earthworm-invaded environments . However, these snapshots of plant communities do not provide information on the impacts of deer and earthworms on plant population growth rates. As my seedlings become reproductive, I can build a stage-structured model to determine how deer and earthworms impact native plant population growth, both individually and together. I expected that, 1) most plants growing in earthworm-invaded areas will be slower to progress through the vegetative stages 2) plant species that appear to benefit from earthworm invasion (sedges, etc.) will have higher seed set (theta will be large) 3) unfenced plots will see more regressions to smaller stages due to deer browse 4) deer and earthworms will have a synergistic effect on decreasing population growth.

INTRODUCTORY PARAGRAPH We explored the microbial contributions to decomposition using a sophisticated approach to DNA Stable Isotope Probing (SIP). Our experiment evaluated the dynamics and ecological characteristics of functionally defined microbial groups that metabolize labile and structural C in soils. We added to soil a complex amendment representing plant derived organic matter substituted with either ¹³C-xylose or ¹³C-cellulose to represent labile and structural C pools derived from abundant components of plant biomass. We found evidence for ¹³C-incorporation into DNA from ¹³C-xylose and ¹³C-cellulose in 49 and 63 operational taxonomic units (OTUs), respectively. The types of microorganisms that assimilated ¹³C in the ¹³C-xylose treatment changed over time being predominantly _Firmicutes_ at day 1 followed by _Bacteroidetes_ at day 3 and then _Actinobacteria_ at day 7. These ¹³C-labeling dynamics suggest labile C traveled through different trophic levels. In contrast, microorganisms generally metabolized cellulose-C after 14 days and did not change to the same extent in phylogenetic composition over time. Microorganisms that metabolized cellulose-C belonged to poorly characterized but cosmopolitan soil lineages including _Verrucomicrobia_, _Chloroflexi_ and _Planctomycetes_.