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Multi-protocol Messaging and Streaming Broker for Grasshopper (RabbitMQ_GH)
No full textRabbitMQ GH is a Grasshopper plugin for implementing RabbitMQ messaging capabilities to exchange data between external applications and Grasshopper scripts in real time, with a high degree of flexibility and scalability.
INSTALLATION
Erlang installation
Download the current Erlang version installer and follow the default setup in the installation wizard.
Add Erlang to the Environment variables. To do it, go to Control Panel>System and Security>System. A new window will open. Click on Advanced system settings under the Device specifications collapsable panel. When the System Properties window appears, click on the Environment Variables button at the bottom of the Advanced tab.
On the User variables section use the New... button to add a new variable with ERLANG_HOME as the Variable name and the installation path as the variable value (By default the installation path should be C:\Program Files\Erlang OTP).
To verify that the variable was correctly added, open a command prompt and enter the ECHO %ERLANG_HOME% command. If the installation path is printed on the screen, the variable is correctly added.
RabbitMQ installation
On RabbitMQ´s GitHub page go to the Releases section on the right-hand side of the window. Click on the latest release and navigate to the assets section and download the rabbitmq-server-windows-<version>.zip file, where<version> corresponds to the latest release version number. Extract the compressed files to the directory where you want RabbitMQ to be stored.
To run RabbitMQ, open a terminal window and go to the installation directory. Once there, run the rabbitmq-server.bat command. The current Erlang version and the plugins being used should be printed on the screen.
In Windows, RabbitMQ does not run on startup by default. To change that, the Startup type of the RabbitMQ Server must be set to Automatic
If the plugins are not working properly, follow the same procedure described in the following section to activate them.
Management plugin activation
To activate the management plugin, open a terminal window and go to the installation directory. After that, run the rabbitmq-plugins enable rabbitmq_management command. This should enable the plugin and it should be working the next time RabbitMQ is launched. The same command works for activating other plugins by replacing rabbitmq_management with the plugin's name after it has been downloaded into the plugin's directory located on RabbitMQ's installation directory.
For further information, follow this link.
Grasshopper plugin installation
Copy the folder Installation_Files/Rabbit_MQ_GH into your Grasshopper component folder. If you don´t know the location of this folder on your computer, you can find it using Grasshopper's interface. Go to File>Special Folders>Components Folder and a file explorer window will open in the correct directory.
In general, downloaded .gha files can be blocked by Windows OS, so verify that all the downloaded files are unblocked. To do so, find each file in the file explorer and right-click on it. In the context menu select Properties. This will open a new window. Go to the lower part of the general tab and check the checkbox labeled Unblock.
For the use of the plugin, even after closing the .gh files, the connections created in the session will remain open if the Run toggle is set to True. To prevent this, make sure to set the toggle to False before closing the file.
Additional resources
Linux machine deployment
AWS deployment
MANAGEMENT PLUGIN
To open the management plugin, in your web browser navigate to localhost:15672 If no credentials have been established, use guest as the default username and password to log in.
USE PATTERNS
Example files covering several use patterns for Grasshopper, Python, and C# are located in the folder Tutorial/Examples/.
Most of them build on top of the consumer-producer pattern. It has two main components: A producer that sends messages to a RabbitMQ queue and a consumer that receives the messages from the queue and processes them.
These are all the patterns covered in the example files with a detailed explanation in Tutorial/Examples/readme.md:
Basic consumer
Competing consumers
Publisher-subscriber
Request-reply
Basic routing
Topic routing
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Prozessparameter und Farbwerte für einen Laserprozess auf Edelstahl
No full textBei diesem Versuch wurde eine Edelstahlplatte mit unterschiedlichen Prozessparametern eines Lasersystems belichtet und die resultierende Farbe mit einem Mikroskop ausgewertet.
Die zentralen Komponenten der Laseranlage sind ein Galvanometerscanner (MINISCAN III mit einer Strahleintrittsapertur von 14 mm) der Firma Raylase, ein f -Theta Objektiv (Brennweite 160 mm) und ein gepulster Laser (TruPulse 2002 nano - FK10-RM mit einer mittleren Ausgangsleistung von 20 W und einer Wellenlänge von
1064 nm) der Firma Trumpf.
Jedes belichtete Feld hatte eine Größe von 2 mm.
In Summe wurden 625 Felder mit einer einzigartigen Parameterkombination belichtet. Die verwendeten Parameter sind dabei:
Geschwindigkeiten in mm/min: 30000, 20000, 15000, 10000, 5000
Laserleistung in ‱ (1 pro Zehntausend) von 20 W: 6000, 5000, 4000, 3000, 2000
Pulsrate in μs: 2, 3, 4, 5, 6
Hatch-Abstand in mm: 0.01, 0.005, 0.0025, 0.002, 0.001
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CSI Dataset espargos-0001: Four antenna arrays in indoor lab room
No full textDataset containing WiFi channel state information (CSI) alongside ground truth data (position tags, timestamps) measured with ESPARGOS antenna arrays. Measurement parameters and machine-readable file format descriptions are provided in a JSON file (spec.json).
Four antenna arrays are pointed at a small measurement area in a lab room. Only line-of-sight channels
Supplemental Material for: Testing the Test: Observations When Assessing Visualization Literacy of Domain Experts
No full textThese documents contain supplemental material for the paper "Testing the Test: Observations When Assessing Visualization Literacy of Domain Experts" which was conditionally accepted at the BELIV 2024 Workshop in conjunction with the IEEE VIS 2024 Conference.
The CSV file contains the summaries of the important parts of the interview, each assigned to a category.
The PDF document contains the interview questions, the defined categories, and the questionnaire.
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SFB/Transregio 161 Data Management Plan 2023-2027
No full textThe participating universities in SFB/Transregio 161 acknowledge the general importance of re-search data management as a vital issue for all of their work and provide increasing central sup-port for long-term accessibility and reusability of data, documentation of methods and tools and privacy protection. However, technical and organisational offerings for data management can only be effective as far as researchers are aware of them and can make informed decisions on which solution is the right one for which data, which in turn requires an overall knowledge of the types and amounts of data collected or produced in the project. Furthermore, ensuring long-term availability of data for reproducibility of research results, which is one of the key ideas be-hind the research efforts of SFB/Transregio 161 and even more important in the final funding period of the project, requires planful actions as serving data beyond the lifetime of a project involves allocation of technical and organisation resources.
This data management plan (DMP) identifies the research data collected or produced in SFB/Transregio 161, assesses their value for the goals of reusability and reproducibility and states the timeframes in which SFB/Transregio 161 and/or the participating universities guarantee their future availability. It furthermore describes the technical and organisational means for storing and publishing research data which the researchers of SFB/Transregio 161 can make use of. Such means might be provided by the project and funded by DFG or by the universities. The DMP states minimum requirements for metadata that must be provided for research data, briefly ad-dresses privacy and ethics issues and provides the researchers with guidelines for GPDR-compliant handling of personal data which might be collected in the projects.
The DMP is a living document and therefore will be updated as necessary.</p
Implementation details and source code for CaΣoS: A nonlinear sum-of-squares optimization suite
No full textThis dataset contains detailed information, source code, and benchmark tests for CaΣoS: A nonlinear sum-of-squares optimization suite.
Please refer to the "supplementary.pdf" for more information
Replication Data for: Iaroslavtceva et al. "A consistent MMC-LES approach for turbulent premixed flames" (2024) Proc. Combust. Inst.
No full textA Consistent MMC-LES Approach for Turbulent Premixed Flames
This data set contains case setups to reproduce the simulation results presented in Iaroslavtceva et al. (2024) Proceedings of the Combustion Institute. In this publication, a set of freely propagating turbulent premixed flames with varying equivalence ratios is used to test the performance of Multiple Mapping Conditioning (MMC) model. MMC coupled with Large Eddy Simulation (LES) provides a closure
of the filtered chemical source term. Results of MMC-LES are compared with reference Direct Numerical Simulations (DNS) that include full chemistry. Additional simulations were carried out where MMC is coupled with quasi-DNS where the flow field is fully resolved but the reaction progress variable field is thickened and its source term is closed with a flamelet approach. This allows to isolate the different modelling assumptions invoked in MMC-LES.
Data structure
The setups for the following cases are presented:
Equivalence ratio = 0.6 (MMC-quasi-DNS): eqRatio0_6/MMC-DNS
Equivalence ratio = 0.6 (MMC-ATF-DNS): eqRatio0_6/MMC-ATF-DNS
Equivalence ratio = 0.6 (MMC-LES): eqRatio0_6/MMC-LES
Equivalence ratio = 0.6 (DNS): eqRatio0_6/DNS
Equivalence ratio = 1.0 (MMC-LES): eqRatio1/MMC-LES
Equivalence ratio = 1.0 (DNS): eqRatio1/DNS
Equivalence ratio = 0.25 (MMC-LES): eqRatio0_25/MMC-LES
Equivalence ratio = 0.25 (DNS): eqRatio0_25/DNS
Instructions on how to run the cases can be found in each case folder in README file.
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In situ micro-XRCT data set of an open-cell polyurethane foam sample under uniaxial compression load
No full textThis dataset contains seven micro X-Ray Computed Tomography (micro-XRCT) scan data sets (projection, reconstructed, and segmented images, respectively) of an open-cell reticulated Polyurethane (PUR) foam specimen with 10 pores per inch under different uniaxial deformation states. The initial diameter and length of the unloaded cylindrical sample are 52 mm and 50 mm. The sample was glued on two aluminum sample holders. Scans of the sample were performed for seven successive time steps (t0, t1, t2, t3, t4, t5, t6) under uniaxial displacements of {0, 5, 10, 15, 20, 25, 0} mm imposed at the bottom part of the sample (fixed upper sample holder). Before starting the image acquisition at the different deformation states, the sample was relaxed for 20 min each time. The relaxation curves (including relaxation during the scan) are provided in the files "t*_utm_data.csv". The scans were performed displacement-controlled.
The image data sets are labeled according to the time step the micro-XRCT scan was performed (t0, t1, t2, t3, t4, t5, t6) followed by a specification of the image data set ("_projections", "_reconstructed", "_segmented"). Due to an eccentricity error between the upper and lower rotational table, compensation in the reconstruction was necessary. That was restricted to the foam part and not to the sample holders. Besides the projection images and the 3d reconstructed images, segmented images (into foam skeleton and void space) are provided. To reduce the data size, the foam sample was extracted by a cylinder of 52.36 diameter. Due to strong beam hardening in the aluminum sample holders, superimposed with cone-beam angle artifacts in those areas, and additional contaminated foam by the used glue in the transition region, only 44.26 mm in the axial direction of the foam sample at t0 (undeformed state) is given. For all other deformation states, the first and last slices of the segmented image stacks correspond roughly to them.
After the image acquisition and a sufficiently long relaxation of the sample, a uniaxial compression test up to 30 mm maximum displacement was performed using the same sample. Loading/unloading was performed displacement controlled with |0.5| mm/s. The data is provided in the file "utm_data.csv"
D1244 sensor data (July 2023)
No full text
General information:
This dataset contains measurements from the adaptive high-rise demonstrator building D1244, built in the scope of the CRC1244. This 36m high building is equipped with 24 hydraulic actuators providing the basis for its structural adaptation. Strain gauges, pressure sensors and position encoders are mounted throughout the building and used for state estimation and monitoring.
Structure of the dataset:
Each zip-file contains measurements of one day in the hdf5 format.
The hdf5-files in each zip-file contain an array of 244 signals sampled over 10^6 time steps at approximately 200Hz.
labels.csv contains auxiliary information on all measured signals, including the sensor type and the sensor's location in the building
File contents:
Each hdf5-file contains signals of the following types, arranged as stated in labels.csv:
strain: strain (in mm/m) in columns and diagonal bracing elements, measured by strain gauges.
pressure: pressure (in bar) in the piston side or the rod side chamber of a hydraulic actuator. For actuators in the diagonal bracing, the rod side chamber is permanently connected to the tank.
posenc: displacement (in meters) of each actuator, measured by a position encoder.
optic: optically measured displacement (in meters) of emitters attached to the building's facade.
The building consists of four modules spanning three stories each, and all sensors within a module are connected to a control cabinet, from which all measurements are transmitted. The cameras of the optical measurement system are placed outside the building and transmit their data separately from the sensors within the building. Therefore, there are an additional two signals per module (or camera):
timestamp: unix timestamp (seconds since 1st January 1970) of the control cabinet
numvars: number of measured variables
Missing measurements are marked as NaN. The optical measurement system is currently undergoing maintenance, which is why the corresponding signals are all NaN.
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Models and Prepared Datasets for the Second Stage
No full textModels trained with Heat Plume Prediction
and datasets prepared with Heat Plume Prediction into reasonable format + normalization etc, used for training these models. Last relevant git commit: 5d6c5eae5b00e438.
Based on raw data from doi:darus-3651 and doi:darus-3652