Flux Zone Download
Mathilde Chisler
Howdy y'all! I play on a custom server, usually alone, and don't launch nukes often since I don't play long. I want to nuke a zone where I can get a good spread of all the fluxes. I can't beat Earle or SBQ myself (I've tried!) but I'm an excellent scrounger! Where can I go to get as much flora, irradiated fluids, etc. necessary for a good spread of all kinds of flux?
Right now I'm considering Abie's Bunker, but I'm open to alternatives.
Our HfS2 crystals are grown using ultra pure flux vapor technique to produce crystal that are free of halides and vacancy defects. The defect concentration remains lower than 1E9cm-2 for high optical and electronic performance. Our growth technique has been designed and optimized since 2011 to achieve perfect industrial semiconductor grade materials with: 1) excellent stoichiometry, 2) large single domain size, 3) single phase materials without any mixed phases or amorphous content, 4) perfect layered crystal ideal for exfoliation purposes with impressive mosaic spread 0.08 degrees, 5) unmatched purity -semiconductor grade (6N), 99.9999%.
Growth method matters> Flux zone or CVT growth method? Contamination of halides and point defects in layered crystals are well known cause for their reduced electronic mobility, reduced anisotropic response, poor e-h recombination, low-PL emission, and lower optical absorption. Flux zone technique is a halide free technique used for synthesizing truly semiconductor grade vdW crystals. This method distinguishes itself from chemical vapor transport (CVT) technique in the following regard: CVT is a quick (2 weeks) growth method but exhibits poor crystalline quality and the defect concentration reaches to 1E11 to 1E12 cm-2 range. In contrast, flux method takes long (3 months) growth time, but ensures slow crystallization for perfect atomic structuring, and impurity free crystal growth with defect concentration as low as 1E9 - 1E10 cm-2. During check out just state which type of growth process is preferred. Unless otherwise stated, 2Dsemiconductors ships Flux zone crystals as a default choice.
I am working with meteorological data and need to produce local statistics. For example, determining the low and high temperature within the local day. I can use flux to do this for a single local day, but I have not found the flux solution for processing months of data in a single operation.
As I mentioned, I can achieve my goal for a single day, but I cannot figure out how to get flux to shift UTC for the daily aggregates when I try to process months of data (data acquired every minute) on the local day to produce daily statistics. This is where I could really use some help.
Thanks for replying back. Given that the flux language can almost do the entire job very efficiently, I was hoping to avoid writing code to pull out data to calculate the statistics and then insert it back into InfluxDB. This is not a difficult chore.
This lack of this very simple time functionality in flux discourages the use of InfluxDB for a much broader class of IoT projects that must work on or provide metrics on local/standard time. Even I am beginning to wonder if I should start looking at other solutions if I have to start writing code for some simple tasks. This is not about being lazy, but it is just a practical consideration of the overall ROI given the current state of platform maturity. It is truly a disappointing thought given that the InfluxDB platform has so incredibly much offer (including the power of the flux language).
I looked at the various flux functions that could be used to offset time and ran quite a number of tests using each. The only way I found to reliably adjust UTC to local time was to use the timeShift function. Then I leveraged the aggregateWindow function with a 24-hour duration. This approach appears to work.
The downside is that the timeShift duration is fixed, and is not tied automatically to NTP. A simple built-in function to adjust for time zone is missing and it would make this time adjustment incredibly straight-forward. Deployment of sensors to multiple time zones is a bit more cumbersome.
The time in the InfluxDB do not match the submitted epoch times being added to the data point. There is always a difference of at least a few seconds. For example, if you try to submit the epoch for 2021-01-01T23:59:59 UTC, what is logged in InfuxDB is 2021-01-02T00:00:00 UTC. Unless I back off several seconds, the logged time is always to the next day. This should be precise.
Besides this only solves for my own local timezone - is there another approach, that is more agile? I have not found a function in Flux, that can convert UTC to another timezone (why?) - that would be the most simple solution for a workaround.
It seems as the only solution to aggregations above 1h. From the top of my head - if InfluxDB was the primary source of data (meaning the raw data are stored directly to InfluxDB), I would make a task in InfluxDB, that made aggregated values on an hourly basis. Then create a client-task, that made higher levels of aggregations and stores it back into InfluxDB with correct UTC start times. That way the InfluxDB UI, Grafana etc. will be able to aquire the correct aggregations from any locale (and very fast).
Convective rain ratio of experimental results for all four domains. The thick red (GSAS) and black (OSAS) lines are the revised SAS and original SAS simulations, respectively. The thin blue (GCMF), thin blue dashed (GCIN), and thin blue dotted (GDTR) lines are for the cloud-base mass flux, trigger, and convective liquid water detrainment simulations, respectively.
A set of sensitivity experiments with the Weather Research and Forecasting (WRF) Model is conducted for a heavy rainfall case over South Korea. The results show that the revised SAS CPS outperforms the original SAS. At 3 and 1 km, the precipitation core over South Korea is well reproduced by the experiments with the revised SAS scheme. On the contrary, the simulated precipitation is widespread in the case of the original SAS experiment and there are multiple spurious cores when the CPS is removed at those resolutions. The modified mass flux at the cloud base is found to play a major role in organizing the grid-scale precipitation at the convective core. A 1-month simulation at 3 km confirms that the revised scheme produces slightly better summer monsoonal precipitation results as compared to the typical model setup without CPS.
The representation of cumulus convection, generally called cumulus parameterization, has almost always been at the core of efforts to numerically model the atmospheric phenomena because cumulus convection plays a central role in most of the interactions between physical processes in the atmosphere (Arakawa 2004). This is because a cumulus parameterization scheme (CPS) should represent the impacts of convection in terms of environmental conditions, whereas a microphysics scheme (MPS) expresses the precipitation with grid-resolved variables. The MPS is assumed to be relatively robust, since it activates the precipitation processes when the grid-cell mean relative humidity is greater than 100%. It is noted that some MPSs produce condensates in the presence of subsaturation (e.g., Zhao and Carr 1997). The parameterizations of subgrid fluxes in CPSs conceptually differ from one scheme to another. The complexity of the convective subgrid-scale processes led to a wide variety of CPSs. The main classes are the adjustment type (Betts and Miller 1986), moisture convergence (Kuo 1974), and the mass-flux type (e.g., Arakawa and Schubert 1974; Tiedtke 1989; Kain and Fritsch 1990; Grell 1993).
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