Find research datasets worth reusing

opencc-by-4.0Oct 2023View details →

Radarcape ADS-B messages captured by antenna installed at the roof of the EETAC during Nov'2023

Messages collected from the ADS-B antenna at the roof of the EETAC-UPC school at Castelldefels, Barcelona, from 15th of Sep 2023 to 31st of Oct 2023

opencc-by-4.0Jan 2018View details →

Simulation of Fire Propagation in Cable Tray Installations - Data Set

This repository contains simulation data used for a conference paper at ISTSS 2018, with the title "<a href="https://www.researchgate.net/publication/323999819_Simulation_of_Fire_Propagation_in_Cable_Tray_Installations_for_Particle_Accelerator_Facility_Tunnels?ev=auth_pub">Simulation of Fire Propagation in Cable Tray Installations for Particle Accelerator Facility Tunnels</a>". Furthermore, the plots are provided, including the Python 3 scripts to create the plots, used in this paper. With the Fire Dynamics Simulator FDS, in the versions 6.3.2 and 6.5.3, simulations of cable fire tests have been performed. Experimental data from micro-combustion calorimetry and Cone Calorimeter tests were used to calibrate a material parameter set, aming to predict the fire spread in a cable tray installation. The simulations are based on experimental data from the CHRISTIFIRE Phase 1 campaign. The authors want to thank Kevin B. McGrattan for providing access to the CHRISTIFIRE data.   Some remarks on the usage: Unfortunately, for some unclear reason, Zenodo does right now not support the creation of folders within the repository. In an effort to maintain the structure of the data, ZIP archives have been created. Note that specifically the MT-3 simulations are quite large and take about 3.5 GB of space after extraction. It is only necessary to reproduce the file structure, if the user wants to utilise the provided Python scripts "as is". It is, of course, also possible to adjust the file pathes in the scripts to the users desire. To recreate the original file structure, one needs to copy all files of this repository into a single directory. The ZIP archives are sub-directories within that basic directory. The names of the archives contain the information of how the sub-directory structure looks like. Triple underscores '___' are placeholders indicating the file path, thus need basically changed to '/'. For example, the ZIP archive ''Cone___CoarseCone___ArrCHRISTIFIRE.zip' translates to the path 'Cone\CoarseCone\ArrCHRISTIFIRE\'.

opencc-by-4.0Jun 2024View details →

SoA of measuring devices installed in NG transmission and distribution networks

Deliverable D1.1 aims to design the state of the art of measuring devices in natural gas transmission and distribution networks.  Transporting green hydrogen into existing gas assets requires carefully assessing its effect on the existing components. Since several projects have already been completed or have planned research activities to answer still-existing technical questions, the THOTH2 project focuses on the existing measuring devices. Specifically, the focus of the project regards the identification of the existing gaps in normative standards and the suggestions for solutions to cover them (if any). To contribute the hydrogen readiness of the existing gas transport and distribution infrastructures, new methodologies and protocols have to be developed to perform validated tests for metering devices. Suggestions on the need to change the standards or develop new ones will be based on the results of these experimental tests. Despite the simplicity of the methodological approach, it would be very critical when applying it to measuring devices. Several technologies are available in the market to measure gas properties. Furthermore, the operators can select more than one configuration based on the expected field conditions. Since limited resources are available, testing all the possible configurations would be impossible. Prioritization is required. Task 1.1 aims to collect all the information to provide a clear overview of the measuring devices installed in the existing gas assets. Specifically, this document includes the state of the art of measuring devices installed in gas assets. Different technologies are available to measure gas parameters. For example, turbine, rotary piston, ultrasonic, diaphragm, thermal mass, orifice, and Coriolis meters are available to measure flow rate. These technologies differ not only for the operating principle but also for the material used, the size available on the market, and the effect that different conditions could have on the metrological performances like, for example, overload conditions, flow rate pulsations, leakages through the clearance and pressure drops. Furthermore, different maintenance activities are usually expected, resulting in different operative costs throughout the lifetime. To date, turbine, rotary piston gas, and ultrasonic meters are used for fiscal gas metering in transmission networks. Specifically, based on the data collected, turbine gas meters are the most installed technologies for medium to high flow rate, followed by rotary piston and ultrasonic (for high flow rate). Few cases of use of Coriolis meters have been found. Regarding distribution, a different situation results. Despite the fact that few answers have been received to date, and only from Italy, it appears that diaphragm gas meters are the prevailing technology installed, even if a greater penetration is expected for thermal mass meters. THOTH2 also includes other measurements like gas quality by chromatographs, pressure and temperature, and trace water dew point. Regarding temperature, it was assumed that since the sensor is not in contact with the fluid but is protected by the thermowell, it can be assumed that no problem would arise. However, further investigation should be performed to investigate if any effect of hydrogen on response time exists. Regarding pressure measurement, many models are commercially available, but attention should be given to the effect of hydrogen on the material with which the fluid is in contact. Specifically, identifying critical materials that can be affected by hydrogen among those available in commercial products should be the next step to identifying the products to be tested. Gas chromatographs are also present in different models and configurations in the existing networks. Usually, different columns are used based on the specific analysis to be performed. Even if the range of the concentration allowed for each molecule is usually known for each model, more details about the configuration of each gas chromatograph are needed to complete the analysis and check the capability to handle hydrogen. Only some models of trace water sensors have been identified in the investigated networks. Specifically, impedance sensors result in the most implemented devices. Other devices are also typically used in the networks. Electronic Volume Converters and Flow Computers convert measurements into standardized gas volumes for fiscal purposes. The main issues to be investigated are the implemented algorithms and their capability to consider hydrogen. The main algorithms are AGA8, SGERG, and AGA-NX19, and the Operators can check the hydrogen limits. The main issue is that many different models are installed in gas transmission and distribution networks. Furthermore, based on the conclusion about pressure and temperature sensors, the potential effects of hydrogen on the metrological performances of those devices that have these sensors integrated have to be carefully assessed not to overcome the limits on errors provided by the standards. Last, leak detection is essential to detect fugitive emissions to the atmosphere and to minimize the risk of failures or accidents . To date, many devices are supplied to the technicians on the field to verify the presence of hazardous substances. Since different sensors can be implemented in the same devices to measure different quantities, attention should be given in Task 2.1 to selecting those sensors that, on the current knowledge, appear to be most critical when being in contact with hydrogen.

opencc-by-4.0Feb 2021View details →

New datasets obtained from experimental installations with centralized control

The dataset contains the data that local controller 1 (LC1) received from local controller 2 (LC2) during normal system operation. The system was created and it is located in the Laboratory for Manufacturing Automation at the Faculty of Mechanical Engineering, University of Belgrade. The system is based on a smart sensor (electromagnetic linear encoder with local controller) and a smart actuator (rodless pneumatic cylinder with electro-pneumatic pressure regulator and local controller), and the main goal is to achieve the desired position of the piston on the pneumatic cylinder. The dataset includes 7 signals that represent a combination of different piston trajectories. Each signal was recorded during the 200 minutes of piston movement along a defined trajectory, where the length of each signal is 400,000 samples. Table 2 shows the list of collected signals, whereas a detailed description of the system can be found in [1]. This dataset was developed with the support of the Science Fund of the Republic of Serbia, Grant No. 6523109, AI - MISSION4.0, 2020-2022.

opencc-by-4.0Jul 2020View details →

Time series of electricity output for large grid connected photovoltaic installations in Chile

These data sets accompany the paper "Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?". They include capacity factors (values 0 to 1) in hourly temporal resolution of 103 large PV installations in Chile derived from official sources as well as simulated capacity factors using PV_LIB with ERA5-land and MERRA-2 reanalysis data. The data covers the period 2014-2018 and simulations were performed assuming either a "fixed" system with orientation towards north and inclination equal to the latitude (i.e. optimal inclination) or a horizontal single axis “tracking” system with backtracking. Furthermore, accuracy indicators (Pearson’s correlation, mean bias error and root mean square error) are provided, comparing the simulated time series with capacity factors derived from official sources. Capacity factors were calculated for the 103 installations and both alternative configurations (“fixed” and “tracking”) but only a subset of 23 installations has reference data of sufficient quality to allow for a validation (for a more detailed description, see the paper). Indicators were also calculated for all installations and configurations but should be only compared for installations inside a particular configuration: there are 14 systems classified as “tracking” and 9 as “fixed”. Further details are available in the paper and the entire Python and R code is available on github at <a href="https://github.com/inwe-boku/PV_from_era5">https://github.com/inwe-boku/PV_from_era5</a>.

opencc-by-4.0Oct 2020View details →

Oscillations of Offshore Wind Turbines undergoing Installation I: Raw Measurements

Overview This repository contains data from an offshore measurement campaign conducted during the installation of the offshore wind farm trianel wind farm borkum II (https://www.trianel-borkumzwei.de/). The wind farm consists of 32 Senvion 6XM152 turbines. The installation took place between August 2019 and May 2020. An offshore wind turbine undergoing installation is interesting from a research point of view for several reasons: <ul> <li>Simple geometry: turbine foundation and tower are both rotationally symmetric steel tubes. Rotational symmetry also leads to (approximate) isotropical structural characteristics in the plane normal to tower and foundation.</li> <li>High Reynolds number flow: Assuming a tower diameter of 6 m, and average wind speeds ranging from 5 m/s to 12 m/s under installation conditions, Reynolds numbers range from 4.5 million to 10.5 million.</li> <li>Wave loading under full-scale conditions.</li> <li>Practical relevance to improving the competetivity of offshore wind.</li> </ul> For fluid mechanics, closely monitoring offshore wind turbines under wind and wave loading thus compares to a full-scale experiment. Monitoring 32 turbines undergoing installation thus enables the measurement a broad spectrum of different states. The investigation into the data is ongoing, questions and contributions are welcome. The current data release still does not include all data. The dataset will thus be updated again in the future with more data to come. First analytic results can be found here: Sander, A, Haselsteiner, AF, Barat, K, Janssen, M, Oelker, S, Ohlendorf, J, & Thoben, K. "Relative Motion During Single Blade Installation: Measurements From the North Sea." Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. Volume 9: Ocean Renewable Energy. Virtual, Online. August 3–7, 2020. V009T09A069. ASME. <https://doi.org/10.1115/OMAE2020-18935> Sander, A, Meinhardt, C & Thoben, KD. "Monitoring of Offshore Wind Turbines under Wind and Wave Loading during Installation" Proceedings of the EuroDyn 2020 XI International Conference on Structural Dynamics. Volume 1. Virtual, Online. November 23-26, 2020. <https://generalconferencefiles.s3-eu-west-1.amazonaws.com/eurodyn_2020_ebook_procedings_vol1.pdf> Recordings of the conference presentations are available on youtube: <ul> <li>OMAE20: https://www.youtube.com/watch?v=QcAwdv6Z4e4</li> <li>EURODYN20: https://www.youtube.com/watch?v=iL-jAe0luTw</li> </ul> Physical Background <ol> <li>An offshore wind turbine under installation conditions can be simplified as a cantilevered beam (circular cross-section, rotationally symmetric wall thickness) with an eccentric mass (nacelle with generator) vibrating transversally (fore-aft and side-side in the reference system of the nacelle) under wind and wave loads.</li> <li>Both wind and wave loads are stochastic and are described using statistical models.</li> <li>Wave loads are a function of the sea state. For a sea state, the most important parameters are significant wave heigh H_m0 and Wave peak period T_P. To a lesser extend, wave direction, zero upcrossing period and maximum wave height are also important. Different statistical models can be used to describe the sea state and the relationship between significant wave heigh H_m0 and wave peak period. Most prominent in the North Sea is the JONSWAP spectrum.</li> <li>Wind loads are depending on wind speed, wind direction, shear factor and turbulence intensity. Different statistical models are available to describe the wind spectrum.</li> <li>Wind and wave loads trigger a structural response of the turbine. The structural response depends on the loading spectrum as well as the transfer function. Furthermore, the structural response is strongly depending on the damping and elasticity of the structure. In turbines, damping is typically very low (~ 0.5 - 1.5 %).</li> <li>The response is dominated by the first Eigenfrequency of the turbine. As the turbine is assumed to be rotationally symmetric, the fore-aft and side-side mode are extremely close together if not indistinguishable [1].</li> <li>The response has the characteristics of a narrow-band random vibration. A narrow-band random vibration is characterized by being dominated by a single, narrow frequency peak (here: first Eigenfrequency). The amplitude envelope follows a Rayleigh distribution and the phase angle is equally distributed between 0 and 2 pi.</li> <li>If viewed from above, the structural response describes a closed curve (orbit) which can be characterized by it shape (eccentricity), mean amplitude and direction. Mathematically speaking, this is a lissajous-figure, where the time series from one response direction is plotted as a function of the time series of the second response direction.</li> </ol> [1]: under installation condition Experimental Setup Several locations were used to record data during the installation of the wind farm. They are listed in the following table: <ul> <li>helihoist-{1,2}: data recorded from the helicopter hoisting platform atop the turbine nacelle. For most installations, two sensor boxes were deployed to ensure data availability.</li> <li>tp: Measurements from the transition piece</li> <li>sbitroot: Measurements from the blade lifting yoke's blade root side. The Z-axis is aligned to the blade main axis, X-Axis is perpendicular.</li> <li>sbittip: Measurements from the tip side of the blade lifting yoke. Z-axis aligned with the blade main axis, X-axis perpendicular</li> <li>damper: Measurements from the tuned mass damper used during single blade installation</li> <li>towertop: measurements from inside the turbine tower at the upper lift plattform</li> <li>towertransfer: measurements from atop the towers during sail out from the base harbour to the installation site </li> </ul> Organization of data For each turbine installation, a separate folder can be found, e.g. turbine-01 for the first and turbine-16 for the 16th turbine. Turbine numbering follows the order of installation. Different data sources are organized in subfolders for each turbine dataset. Unfortunately, not every data source is available for each turbine. Data sources are roughly sorted into categories. The following table lists these categories: <ul> <li>location / tom : data from custom build sensor boxes. Data includes acceleration, angular acceleration, magnetic field, gnss recording and rough estimates of the eulerian angles.</li> <li>waves / wmb-sued : Sea state statistics for the installatin period of the turbine.</li> <li>waves / fino : Sea state statistics from the german research platform FINO1 located approx. 6 km from the installation site.</li> <li>waves / waveradar : Sea state statstics, recorded by a wave rider wave laser. </li> <li>wind / lidar : high fidelity wind data recorded on the installation vessel during the installation of the wind farm.</li> <li>wind / scada : 10 min. mean wind statistisc recorded on wind turbines in the vicinity of the installation site. This data is used in case no LIDAR data is available.</li> <li>wind / anemometer : During some of the installations, anemometers were present on the installation vessel. These recordings are sorted into this sub-subfolder.</li> <li>wind / fino : Additonal wind statistics recorded by the FINO research station. Least recommended for investigations, as these recordings were taken approx. 6 km from the installation site.</li> </ul> The zenodo data set includes 16 zip archives (for 16 turbines) as well as one zip archive including environmental data. The following lists the folder structure of the turbine-04.zip archive (with most of the data files removed for clarity). <pre><code class="language-bash">└── turbines ├── turbine-04 │ ├── helihoist-1 │ │ └── tom │ │ └── clean │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-27-17_2019-09-01-11-54-00.csv │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-54-00_2019-09-01-12-20-44.csv │ ├── sbitroot │ │ └── tom │ │ └── clean │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-06-48-53_2019-09-07-07-17-16.csv │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-07-17-16_2019-09-07-07-45-59.csv │ ├── towertop │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-18-55-52_2000-01-06-20-30-10.csv │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-20-30-11_2000-01-06-22-04-31.csv │ ├── towertransfer │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-03-11-53_2019-08-31-04-00-09.csv │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-04-00-10_2019-08-31-04-48-19.csv │ └── tp │ └── tom │ └── clean │ ├── turbine-04_tp_tom_clean_2019-08-31-18-34-45_2019-08-31-19-07-52.csv │ ├── turbine-04_tp_tom_clean_2019-08-31-19-08-00_2019-08-31-19-40-43.csv </code></pre> The following list the contents of the environment.zip archive. Note that again most of the data files have been removed for clarity.  <pre><code class="language-bash">└── environment ├── waves │ └── wmb-sued │ ├── wmb-sued_2019-08-15.csv │ ├── wmb-sued_2019-08-16.csv │ ├── wmb-sued_2019-08-17.csv └── wind └── lidar ├── lidar_2019-08-03.csv ├── lidar_2019-08-04.csv ├── lidar_2019-08-05.csv </code></pre> TOM data description The abbreviation TOM referes to Tower Oscillation Measurement and the data that was acquired using a specific set of sensor boxes built by university of Bremen for this specific purpose. These Sensor Boxes were initially designed to measure accelerations and GPS tracks of offshore wind turbine towers undergoing installation. During the installation of the wind farm Trianel Windpark Borkum II they were subsequentially used to track the complete installation with a focus on single blade installation Data from the TOM devices comes as CSV files. The firmware of the boxes was designed, such that data was written into 10 MB sized txt files instead of one large txt file in order to circumvent data corruption due to power loss. However, this leads to a few milliseconds of missing data between log files. Additionally, jitter due to IO-Operations is present in the data as well and data should under all circumstance be resampled befor further analysis. Parameters provided by the TOM devices are listed in the following: <ul> <li>epoch : machine readable time stamp based on the unix epoch in UTC [s] </li> <li>runtime : time since last boot of the tom device [ms] </li> <li>latitude : Degrees latitude in decimal writing. For Trianel: [degree due North] </li> <li>longitude : Degrees longitude in decimal writing. For Trianel: [degree due East] </li> <li>elevation : elevation above mean sea level [m] </li> <li>rot_{x,y,z} : Rotational acceleration around the three cartesian axis {x,y,z} of the TOM box. [degree / s^2] </li> <li>acc_{x,y,z} : linear acceleration in each of the three cartesian axis {x,y,z} in the reference system of the TOM box [m/s^2] </li> <li>mag_{x,y,z} : magnetic field strength in each of the local cartesian TOM box axis {x,y,z} [micro-Tesla] </li> <li>roll : eulerian roll angle (angle around the x Axis of the tom box) [degree] </li> <li>pitch : eulerian pitch angle (angle around the y Axis of the tom box) [degree] </li> <li>yaw : eulerian yaw angle (angle around the z Axis of the tom box) [degree] </li> </ul> LIDAR wind data description A Leosphere WindCube LIDAR was mounted on the installation vessel Taillevent to record the atmospheric boundary layer during installation. The data recorded by the lidar was exported as csvs and provided by the vessel operator. The raw data includes the following parameters <ul> <li>epoch : time stamp as a unix epoch in UTC [s]</li> <li>wind_speed_N : The wind speed at the N'th return level [m/s]</li> <li>wind_dir_N : Wind direction at the N'th return level in the vessels reference frame [degree]</li> <li>wind_dir_N_corr : Wind direction at the N'th return level due North [degree due North]</li> <li>heigh_N : The height of the lidar return level [m] </li> </ul> Data is resampled to a 1 s return interval.  wave data description Wave data was recorded using a waverider wave buoy (DWR-G). The wave rider buoy was located at 54 00' 238'' degree North and 6 26' 553'' degree East. In decimals: 54.0031096 North, 6.4425532 East. Based on the raw data, sea state statistics were derived with a return period of 30 minutes. The data comes as csv, column oriented text files. The first line entails parameter names and units and is commented out by a hashbang (unix commentary). The data is a combination of two different data files as provided by the buoy: \*.HIS Data Text File (History of Spectrum parameters) and \*.HIW Data Text File (History of Wave Statistics). This results in the two timestamps in the data file because history of wave statistics data is available immediately after each measurement time period, whereas history of spectrum parameters take approx. 5 minutes to calculate by the buoy and thus have a slightly later time stamp. For actual postprocessing purposes, a third timestamp rounded to full half hour is used. Parameters included in the data files are: <ul> <li>epoch: number of seconds since 1st of January 1970 in UTC. Common time stamp format in computing. [s]</li> <li>Tp  Tp := 1 / fp, peak period, the frequency at which S(f) is maximal [s]</li> <li> Dirp  peak direction, the direction at f = fp [deg due North]</li> <li> Sprp  peak spread, the directional spread at f = fp [s]</li> <li> Tz  Tz := sqrt(m0 / m2), zero-upcross period [s]</li> <li> Hm0  Hm0 := 4*sqrt(m0), the significant waveheight [m]</li> <li> TI  TI := sqrt(m[-2] / m0), integral period [s]</li> <li> T1  T1 := m0 / m1, mean period [s]</li> <li> Tc  Tc := sqrt(m2 / m4), crest period [s]</li> <li> Tdw2  Tdw2 := sqrt(m[-1] / m1) [s]</li> <li> Tdw1  Tdw1 := sqrt(m[-1,2] / m0) [s]</li> <li> Tpc  Tpc := m[-2] * m1 / m0 ^ 2, calculated peak period [s]</li> <li> nu  nu := sqrt((T1 / Tz) ^ 2 - 1), band width parameter [-]</li> <li> eps  eps := sqrt(1 - (Tc / Tz) ^ 2), bandwidth parameter [-]</li> <li> QP  QP := 2 * m[1,2] / m0 ^ 2, Goda's peakedness parameter [-]</li> <li> Ss  Ss :=2 * pi / g * Hs / Tz ^ 2, significant steepness [-]</li> <li> TRef  TRef, reference temperature (25deg) [C]</li> <li> TSea  TSea, sea surface temperature [C]</li> <li> Bat  Bat, battery status (0..7) </li> <li> m[n]  m[n] := Integral from f=0 to f=Inf over S(f) * f ^ n </li> <li> m[n,2]  m[n,2] := Integral from f=0 to f=Inf over S(f) ^ 2 * f ^ n  </li> <li> Percentage  Percentage of data with no reception errors [%] </li> <li> Hmax  Height of the highest wave [cm] </li> <li> Tmax  Period of the highest wave) [s] </li> <li> H(1/10)  Average height of 10% highest waves [cm] </li> <li> T(1/10)  Average period of 10% highest waves [s] </li> <li> H(1/3)  Average height of 33% highest waves [cm] </li> <li> T(1/3)  Average period of 33% highest waves [s] </li> <li> Hav  Average height of all waves [cm] </li> <li> Tav  Average period of all waves) [s] </li> <li> Eps  bandwidth parameter </li> <li> #Waves  Number of waves</li> </ul> Note: the m's are moments of the power spectral density S(f).

opencc-zeroMar 2024View details →

Madrid Grid Area Buildings + Reachable Endpoints for Given RF Transmitter Location and Parameters, with and without RISs Installation.

1- The obstacles_save folder contains arrays defining the vertices locations (x,y) of buildings in the considered area in Madrid Grid. A transmitter is placed at the center of a square at location [600, 900]. Possible receiver (or relay trasnceivers) locations are defined as the vertices (i.e., corners) of buildings (from previous list). The goal of the simulation is to find how many hops are needed to reach, if possible, each location from the previously mentioned list of vertices, assuming a maximum allowed path loss value of 90 dB between any two consecutive hops.  2- The arrays in no_ris specify the vertices reachable within N sucessive hops, when no RIS is installed in the area. 3- Similarly, the arrays in double_ris give the coordinates of vertices reachable with N hops when a two RISs are installed in the middle square(as shown in related paper). The RIS beamforming gain is 20 dB (in Table 1 in the paper the gain should be 20 not 15 dB).

opencc-by-4.0Mar 2024View details →

Dataset: Results of the CRAFT-OA requirement survey for OJS installation and update toolkit

This dataset is the result of a  CRAFT-OA survey that collected requirements for an installation and update toolkit which aims at facilitating a state-of-the-art implementation and operation of the journal software OJS.

opencc-by-4.0Dec 2024View details →

1.3A (Ge337) calibration data for new Ge115 monochromator installed on Echidna Neutron Powder Diffraction Instrument

In early October 2024 the Echidna neutron powder instrument located at the OPAL reactor, ANSTO, installed a new monochromator with Ge115 cut. The present calibration data were collected shortly afterwards from a standard LaB6 sample in a 6mm diameter Vanadium can. The instrument was set to 140 degrees takeoff angle and monochromator angle 85.08 degrees, corresponding to the Ge337 reflection. Raw data in NeXus format are contained in ECH0034261.nx.hdf. These data were corrected for variable detector response using the information in eff_2024-10-06.cif and pixel vertical positions adjusted according to the table in vertical_offsets_2024-10-06.txt. Deviations from the ideal detector 1.25 degree angular spacing were applied using echidna-Apr2018.ang. The detector response was then recorrected based on overlapping measurements using the algorithm described in <a href="https://doi.org/10.1107/S1600576718014048">Avdeev and Hester (2018)</a> resulting in a 1D pattern suitable for fitting wavelength and peak shapes. This 1D pattern is provided here as a plain table (ECH0034261_LaB6.xyd) and as a pdCIF file (ECH0034261_LaB6.cif) including metadata on data collection and reduction. Details of data reduction are described in the above paper, and the data reduction routines used are included in the <a href="https://github.com/Gumtree/Echidna_scripts">Gumtree package as python code</a>.

opencc-by-4.0Feb 2022View details →

Spain's marginal electricity mix and its relevance for assessing the environmental performance of installations with variable load or power

This upload contains the Supplementary Information file and the underlying data as Excel-file for the Journal article with the same name. More specifically, it provides time series of the Spanish electricity generation mix for the years 2015-2020 for energy system analysis and the life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request.

opencc-by-4.0Apr 2019View details →

Installing a EC Sensor on a remote location.

1.Introduction The objective of this document is to provide a description of the dataset entitled “Installing an EC Sensor on a remote location”. The guidelines on how to use the files are included in this document. The dataset is part of the deliverables D9.4 (First data management plan) and D9.5 (Final data management plan). 2.Description of the data 2.1.Origin This dataset includes data collected from the experiments related to the task “Installing a EC Sensor on a remote location” as part of the WP8 “validation in the industrial scenario”. 2.2.Type The data consists of Eddy Current (EC) measurements. 2.3.Formats The acquired data are available in several formats. 2.3.1.*.sidata files These files are proprietary format that can be opened with the software “UPecView” supplied by Sensima Inspection (http://www.sensimainsp.com). This software provides an interface familiar to what expected by eddy-current inspectors. Each file includes all the relevant information that may be used for analysis: the measurements and the instrument configuration (ex. Excitation frequency of the probe) is contained in this file. 2.3.2.*.csv files The csv files contain an export of the measurements only (without instrument settings); a comma separator is used. Each row is composed of the following variables: Time (s), Signal (in-phase), Signal (out-of-phase), Channel/state, Extra signal (ADC), Encoder coordinate 1 (x), Encoder coordinate 2 (y), Encoder coordinate 3 (z), Encoder error status.     3.Measurements indexing Folder Filename Creation date Description Target Location   EXP001 0001A 12.03.2019 Calibration block scan Calibration block Seville, Spain   EXP001 0001B 12.03.2019 Manual reference scan on weld pipe Calibration block Seville, Spain   EXP001 0002A 12.03.2019 Drone overall scan inspection and sensor deployment Cement kiln Seville, Spain   EXP001 0002B 12.03.2019 Deployed sensor Cement kiln Seville, Spain   EXP001 0002C 12.03.2019 Deployed sensor Cement kiln Seville, Spain   EXP001 0002D 12.03.2019 Permanent sensor removal Cement kiln Seville, Spain

opencc-by-4.0Dec 2023View details →

Particle size and velocity distributions from a Thies Clima 3D Stereo disdrometer installed at the Casale Calore site in L'Aquila (Italy), monthly netCDF archive

Disdrometric data from a Thies Clima 3D Stereo disdrometer, with 22 size classes and 20 velocity classes, located at the instrumented site of Casale Calore in L'Aquila (Italy, 42.3831 N, 13.3148 E, 683 m a.s.l.), managed by the University of L'Aquila and the Center of Excellence Telesensing of Environment and Model Prediction of Severe Events (CETEMPS).  Mid values and widths of the classes and instrument ancillary data are provided. One-minute spectra are aggregated every 5 minutes and saved in monthly netCDF files. Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00">https://antarcticdatacenter.cnr.it/geonetwork/srv/eng/catalog.search#/metadata/27e2bd39-097e-4512-96f0-fb213cd59a00</a> -------------------------------------------------------------------- Example of netCDF file structure: <h2>File "LAQ_3DS_202301_5min.nc"</h2> <pre> dimensions: diameter = 22; velocity = 20; n_image = 20; y_image = 12; x_image = 12; time = UNLIMITED; // (8741 currently) variables: long time_UTC(time=8741); :description = "Measurement time. Timestamp indicates the end of the observation interval, e.g. 01-Mar-2020 00:05:00 represents the particle counts registered between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float diameters(diameter=22); :description = "Mid values of the size classes"; :units = "mm"; float velocities(velocity=20); :description = "Mid values of the velocity classes"; :units = "m s^-1"; float diameters_width(diameter=22); :description = "Width of the size classes"; :units = "mm"; float velocities_width(velocity=20); :description = "Width of the velocity classes"; :units = "m s^-1"; int spectrum(diameter=22, velocity=20, time=8741); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over 5 minutes."; :units = "counts"; :_ChunkSizes = 22U, 20U, 1U; // uint float PSD(diameter=22, time=8741); :description = "Particle size distribution, 5 minutes interval, normalized by the observed volume."; :units = "m^-3 mm^-1"; :_ChunkSizes = 22U, 1U; // uint double monthlySpectrum(diameter=22, velocity=20); :description = "Matrix of particle counts in each of the 22 diameter sizes and 20 velocity ranges over the entire month."; :units = "counts"; double monthlyPSD(diameter=22); :description = "Particle size distribution for the whole month, normalized by the observed volume."; :units = "m^-3 mm^-1"; int images(x_image=12, y_image=12, n_image=20, time=8741); :description = "Images of samples of the detected precipitating particles. Images are 48x12 pixel maximum, for a max of 4 stacked 12x12 images. Most of the time less than 4 images are provided."; :units = "0-255 pixel values"; :_ChunkSizes = 12U, 12U, 20U, 1U; // uint int image_count(time=8741); :description = "How many images are registred by the instrument in the minute."; :units = "0-4 count"; :_ChunkSizes = 1024U; // uint int precip_type(n_image=20, time=8741); :description = "Precipitation type as classified by the instument based on shape, size, velocity and presence of water, according to the following table with 11 entries (0-10): 0-reserved value, 1-false positive, 2-rain or graupel, 3-drizzle, 4-drizzle with rain, 5-rain, 6-rain with snow, 7-snow, 8-ice prisms, 9-graupel, 10-hail."; :units = "0-10 code"; :_ChunkSizes = 20U, 1U; // uint int particle_diam(n_image=20, time=8741); :description = "Main diameter of the particles shown in the images."; :units = "mm"; :_ChunkSizes = 20U, 1U; // uint // global attributes: :title = "Thies Clima 3D Stereo disdrometer data, aggregated to 5min, monthly netCDF archive."; :comment = "Particle counts diveded in 22 size classes and 20 velocity classes. Note that this data has been processed regardless of precipitation type."; :time_label = "Jan 2023"; :institution = "CNR-ISAC, Rome (IT)"; :contact_person = "Luca Baldini, CNR-ISAC, Rome, l.baldini@isac.cnr.it"; :source = "TC 3DS disdrometer at MZS (Antarctica)"; :location = "Mario Zucchelli Station (74°42\'S, 164°07\'E, 15 m a.s.l.)"; :author = "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :creation_date = "23-Oct-2024 11:13:22 UTC"; :coverage = "Monthly coverage (Jan 2023): 100 %"; :time_resolution = "5 minutes"; :history = "Created from raw TC telegram TDD 163, aggregated to 5min temporal resolution with a sum of the 1-minute counts if least 3 out of 5 are not NaN."; </pre>

opencc-by-4.0Feb 2019View details →

Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum

Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet.

opencc-by-4.0Feb 2019View details →

Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum

Operating diagram of hatching module in Zoug jars, this system consists of a 300-litre temperature-controlled isothermal enclosure containing 10 one-litre Zoug jars, each able to accommodate several hundred eggs. An ascending current holds the eggs in suspension and carries the larvae to the surface. Another bottle connected to this device collects the larvae. The water circulating in the jars is independent of that used in the filtration circuit. A cooling unit and UV sterilizer complete the installation.

opencc-by-4.0Dec 2018View details →

Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980]. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement

Рис. 9. Блок-схема прогноЗирования сроков установки коллекторов и оЖидаемого количества спата [Белогрудов, 1980]. Fig. 9. The block diagram of prediction timing for installation of collectors and the expected number of spat [Belogrudov, 1980].

opencc-by-4.0Dec 2021View details →

Wave prediction at MegaRoller installation site

Wave prediction at MegaRoller installation site. This dataset specify the wave characteristics at MegaRoller installation site for power performance and load estimation.

opencc-by-4.0Jun 2022View details →

FRIPON (MOROI) Stations installed in Romania and coordinates

In this table we present the coordinates all-sky cameras of The Fireball Recovery and Inter Planetary Observation Network (FRIPON) (Colas et al., 2020) installed over the Romanian territory with the use of the Meteorite Orbits Reconstruction by Optical Imaging (MOROI) network (Nedelcu et al. 2018). Those cameras were installed over the course of the year 2021. The detection of meteors is made with the use of fish-eye lens with the focal length of 1.25 mm and the detector Sony ICX445. Detections made by the FRIPON-MOROI cameras over the time period January 2021 - April 2022 are characterised in (Boaca et al. 2022) (https://iopscience.iop.org/article/10.3847/1538-4357/ac8542 ). If using this data, please reference the above publication.  The online presentation of the paper can be found on the AAS youtube channel (https://www.youtube.com/watch?v=Wx186QB5ZVE).

opencc-by-4.0Jul 2022View details →

SQLite3 DB of npm package versions using install hooks

SQLite3 database containing reduced metadata for all versions using at least one of the available install hooks Compressed using zstandard. You can decompress on Linux using these commands: <pre><code class="language-bash">zstd -d npm_registry.db.zst</code></pre>