The primary trade-off here is that xESM provides a higher fidelity model for multi-segmented missions given that it includes the costs for all mission segments where an item is carried, while the ALSSAT’s ESM calculation method does not include preceding mission segments ALSSAT. This result is especially important considering downstream bio-manufacturing options which show a reduced xESM metric in scenarios where predeployment is leveraged to reduce the cost associated with the transit. Additionally, our “bring everything” mission which does not rely on bio-manufacturing yields larger costs overall from increased stored food. All three scenarios have equivalent tr2 ESM and xESM; this shows that in the last leg of the journey, or in a segment that is not influenced by future operations, ESM equals xESM. While simplified, this captures many of the critical features necessary to demonstrate the need for ESM extension. In cases where inventory from one segment can be used to satisfy constraints in another segment, the ESM summation of separately optimized mission segments can be less optimal than an ESM optimized with an objective function that accounts for both segments and constraint functions containing both terms from both segments. Given that system mass analyses are often used in the preliminary evaluation of technologies, it becomes more important when considering bio-manufacturing platforms to leverage the xESM formulation to provide higher fidelity and more favorable metric. However, we also must clarify that the aim of exploring this example is not to make claims about a specific technology,hydroponic vertical garden but rather to provide an example for differentiating ESM and xESM.
So far, we have looked at the xESM framework for calculating segmented costs. Based on the scenario chosen, the xESM metric is ultimately determined based on some set of specific technologies that are used. Simpler cases, as the ones given in the examples assume that the behavior of a particular system is fully known on Mars and the operation of the systems is undisturbed by external factors. Although several systems can reliably be considered deterministic in this scope, effects such as micro-gravity might affect the dynamics of specific processes in a bio-manufacturing context. Moreover, each process possesses a set of faulty states, i.e., technical issues may cause a system to underperform significantly. Detailed analysis of novel systems, e.g., in the bio-manufacturing case, requires the description of the operation of systems using mathematical models. To this end, the xESM framework can be used both to analyze the cost of individual processes as well as the cost of integrated processes in any desired segment, as they operate in time. A simulation-based analysis, either some cost analysis of specific elements or some end-to-end optimization procedure, makes use of models to simulate the systems, the environment, and associated costs for achieving the mission objectives. As a remark, we should note that the sophistication of the simulated case study can vary. For instance, higher-level decisions can be optimized without the need for detailed models for individual components, while exact scheduling and operational decision-making should involve dynamical models for the various subsystems. This principle has been widely adopted in manufacturing settings for design and control. Parts of the costs not commonly accounted for in cost calculations for space missions like ESM are uncertainty and risk. The latter are important factors during the design phase as we need to ensure safety in a robust, worst-case setting.The use of the xESM framework helps guide the development and implementation of software for a reference mission architecture for long-duration human exploration of Mars. We recognize that this extension of ESM as a metric for mission scenario comparison is preliminary and not exhaustive in its scope.
We also note that no single analytical result such as ESM or xESM will be the sole factor in the technical specification or platform decision-making. The differences presented are important but modest and are in scale with the uncertainty of the quantities used as the inputs. In addition to the incorporation of mission parameters, specific constants and terms in our formulation are required, such as a more precise calculation of equivalency factors for cooling, power, volume, and crew time and distillation of the specifics for risk fractions. Future endeavors include a comprehensive optimization problem formulation and solution based on the xESM framework both for biologically and non-biologically driven missions. Moving forward, we hope that our extension of ESM provides the basis for continued systems engineering and analysis research for a more quantitative and inclusive design and optimization of long-term human exploration missions.Calcium in plants has essential roles affecting tissue mechanical strength and tolerance to biotic and abiotic stresses . Understanding Ca translocation and partitioning to the different plant parts with time and the factors affecting it has a high agronomic and economical value as it will allow improving Ca nutrition practices to give higher quality end products. Ca was shown to accumulate mainly in transpiring organs in a process affected by various environmental conditions at both the canopy and root level, and is considered to be coupled to water movement driven by transpiration although controversies still arise in that relation . Furthermore, as Ca moves mainly in the xylem, a transport conduit under negative pressure, any attempt to sample it en-route will cause cessation of flow. As a result, the use of cumbersome destructive methods, which has limited research scope due to time and space constraints, has brought only fragmented and/or circumstantial evidence . For example, using pressurized stem exudation and leaf bleeding Siebrecht et al.showed either diurnal pattern or spatial distribution but not both together. Looking into various nondestructive methods it was found that Ca nuclides are either incompatible or inapplicable.
As Strontium was found to behave in similar ways in plants as well as in the more complex environment of human clinical research , it was chosen to serve as Ca tracer. Having a high energy gamma emitting nuclide that can be detected outside the plant, remote sensing became feasible. Tomato plants were grown in the phytotron of the Hebrew University under controlled climate of day/night temperatures of 28/18°C and RH of 40/65% respectively. Each plant was grown in a 5 L container containing half-strength modified Hoagland solution and was continuously aerated. After three months, reaching approximately a height of 1.60 m and having three fruit bearing trusses, eight plants were transferred each to a 2 L cylinder filled with nutrient solution and moved to a growth room subjected to temperature of 24/16°C and RH of 40/80% during the day and night respectively. Air temperature and RH at plants vicinity were recorded continuously and VPD was calculated according to Lowe . Light was supplied between 08:00 to 20:00 by two cool mercury lamps at 400 µmole m-2 s -1 PAR. Plants were arranged in four pairs with the 1st plant of each pair placed on a weighing lysimeter and monitored continuously with momentary whole plant transpiration derived from weight loss. The 2nd plant was installed with an array of five gamma radiation detectors ,vertical vegetable tower each with a custom-made lead shield. The shielded detectors were mounted on a moveable platform positioned to target the following locations: 1) main stem below the 1st fruit truss; 2) main stem below 2nd fruit truss; 3) main stem below 3rd fruit truss; 4) first fruit of 2nd truss; 5) leaf petiole adjacent to 2nd fruit truss. The detectors were connected to a PC via a custom-made communication device and radiation activity was measured continuously. More details of the system can be found in Wengrowicz et al. . Radiation readings were resampled to one minute and filtered in parallel to the transpiration data to eliminate noise. After three days of acclimatization, radio-Sr solution with an activity of 0.25 mCi was diluted in 10 mL of distilled water containing 4 mM Sr 2; Merck, Germany and added to the nutrient solution of the 2nd plant around noon.Every few days the radiation measuring system was detached and moved to the 2nd plant of the next pair. An example of radiation readings from one plant on the day of application is shown in Fig. 1. Within 30 minutes after adding the mixed Sr and radio-Sr solution to the nutrient solution, a sharp increase in radioactivity was noticed in the lower-most stem detector . A similar pattern yet with about half the rate was observed 30 minutes later in the middle stem detector and another 30 minutes took the radio-Sr to reach the upper-most stem detector with half the rate of the previous. Starting at the top of the plant root system and accounting for the distances between the detectors along the stem, radio-Sr velocity is estimated to be 0.154 mm s-1 , 0.143 mm s-1 and 0.125 mm s-1 at the 1st, 2nd and 3rd stem detectors respectively. Fruit and leaf petiole detectors showed a slow radiation increase and as no clear arrival time was seen, velocity could not be defined. To emphasize changes in radiation activity, and omit background levels, time derivative of radiation readings were calculated. On the day following application , radiation rate increased already before lights were switched on , starting at the low stem detector and followed by middle and top stem detectors around 03:10, 04:20 and 05:30 respectively. Fruit and leaf petiole radiation rate increased around the same time however with a much lower rate. Initial daily rate was highest at the lower-most stem detector and decreased the further the stem detector was from the source, with fruit and leaf petiole the lowest.
Maximum rates were achieved around 10:00 following the same order of both timing and rates, excluding the fruit detector which showed a 2-fold rate compared to leaf petiole. Thereafter radiation rates dropped quickly only to show a 2nd smaller wave peaking towards 18:00 and subsiding towards evening. A third wave was clearly observed at the three stem locations after lights were switched off, with rates decreasing the further the detector is from the source. Transpiration rate pattern of a neighbor plant showed low rates during dark periods except from a noticeable swell starting around 03:30. During light hours, a rate increase with three distinct peaks can be seen which correlated nicely with room VPD . It should be noted that transpiration rate correlated with radiation rate patterns only until the 10:00 peak, suggesting thereafter a more complex relationship between sap transport and radio-Sr translocation. As time passed, radiation readings at the top-most stem, fruit and leaf petiole detectors increased. The middle stem detector showed in-large a saturation curve pattern, while the lower-most stem sensor, which measured the first few days the highest radiation increase, showed later a decline to level lower then those detected at above stem positions . To shed some light on the accumulative patterns, radiation rates on the 10th day after application are presented in Fig. 4. Lower-most stem detector exhibited negative predawn and morning rates yet a morning peak was still present. Rates climbed slowly towards zero during light hours and proceeded with an after-dark positive peak. The middle stem detector showed a similar pattern although being positive till the predawn drop to later “surface” above zero in the afternoon. The top-most stem as well as fruit and leaf petiole detectors showed positive rates throughout the day with a similar pattern as the other stem detectors. The sequential arrival of root applied radio-Sr to stem locations on the day of application clearly maps its flow path, whereas its decreased velocity along it suggests sap loss as it is being directed towards side organs as leaves and to a probably lesser extent, fruit trusses. As radio-Sr translocation rates were also reduced along the path, it is assumed that Sr was embedded in plant tissue, absorbed on cation exchange sites, and/or unloaded off the xylem causing sap Sr dilution. Throughout the following days, daily transpiration rate showed predawn increases with a possible link to circadian stomata opening resulting in a sap flush within the plant. Predawn translocation rate pattern depended on time that passed from application and detector location. On the first days, when Sr was still accumulating on available cation exchange sites within the stem tissue, translocation rates exhibited significant increase at all locations.