This repository contains all the raw data and raw images used in the paper titled 'Highly multi-mode hollow core fibres'. It is grouped into two folders of raw data and raw images. In the raw data there are a number of .dat files which contain alternating columns of wavelength and signal for the different measurements of transmission, cutback and bend loss for the different fibres. In the raw images, simple .tif files of the different fibres are given and different near field and far field images used in Figure 2.

gearboxes in real industrial settings often operate under variable working conditions

It is sometimes said that reliability field data is the “real reliability data” because they reflect actual reliability performance of a product or system. Reliability field data areobtained, most commonly, from warranty returns (combined with production/sales records to provide information on units that were not returned) and maintenance databases. For some products (e.g., medical devices), careful field tracking is done, providing detailed information about all units deployed into the field. Reliability field data are almost always multiply censored because many units had not failedat the time the data were analyzed. In addition to failure times, sometimes failure mode information is also available for units that have failed. Other complications like truncation also arise in some field reliability data sets.

Wind Spacecraft:

The Wind spacecraft (https://wind.nasa.gov) was launched on November 1, 1994 and currently orbits the first Lagrange point between the Earth and sun. A comprehensive review can be found in Wilson et al. [2021]. It holds a suite of instruments from gamma ray detectors to quasi-static magnetic field instruments, Bo. The instruments used for this data product are the fluxgate magnetometer (MFI) [Lepping et al., 1995] and the radio receivers (WAVES) [Bougeret et al., 1995]. The MFI measures 3-vector Bo at ~11 samples per second (sps); WAVES observes electromagnetic radiation from ~4 kHz to >12 MHz which provides an observation of the upper hybrid line (also called the plasma line) used to define the total electron density and also takes time series snapshot/waveform captures of electric and magnetic field fluctuations, called TDS bursts herein.

WAVES Instrument:

The WAVES experiment [Bougeret et al., 1995] on the Wind spacecraft is composed of three orthogonal electric field antenna and three orthogonal search coil magnetometers. The electric fields are measured through five different receivers: Low Frequency FFT receiver called FFT (0.3 Hz to 11 kHz), Thermal Noise Receiver called TNR (4-256 kHz), Radio receiver band 1 called RAD1 (20-1040 kHz), Radio receiver band 2 called RAD2 (1.075-13.825 MHz), and the Time Domain Sampler (TDS). The electric field antenna are dipole antennas with two orthogonal antennas in the spin plane and one spin axis stacer antenna.

The TDS receiver allows one to examine the electromagnetic waves observed by Wind as time series waveform captures. There are two modes of operation, TDS Fast (TDSF) and TDS Slow (TDSS). TDSF returns 2048 data points for two channels of the electric field, typically Ex and Ey (i.e. spin plane components), with little to no gain below ~120 Hz (the data herein has been high pass filtered above ~150 Hz for this reason). TDSS returns four channels with three electric(magnetic) field components and one magnetic(electric) component. The search coils show a gain roll off ~3.3 Hz [e.g., see Wilson et al., 2010; Wilson et al., 2012; Wilson et al., 2013 and references therein for more details].

The original calibration of the electric field antenna found that the effective antenna lengths are roughly 41.1 m, 3.79 m, and 2.17 m for the X, Y, and Z antenna, respectively. The +Ex antenna was broken twice during the mission as of June 26, 2020. The first break occurred on August 3, 2000 around ~21:00 UTC and the second on September 24, 2002 around ~23:00 UTC. These breaks reduced the effective antenna length of Ex from ~41 m to 27 m after the first break and ~25 m after the second break [e.g., see Malaspina et al., 2014; Malaspina & Wilson, 2016].

TDS Bursts:

TDS bursts are waveform captures/snapshots of electric and magnetic field data. The data is triggered by the largest amplitude waves which exceed a specific threshold and are then stored in a memory buffer. The bursts are ranked according to a quality filter which mostly depends upon amplitude. Due to the age of the spacecraft and ubiquity of large amplitude electromagnetic and electrostatic waves, the memory buffer often fills up before dumping onto the magnetic tape drive. If the memory buffer is full, then the bottom ranked TDS burst is erased every time a new TDS burst is sampled. That is, the newest TDS burst sampled by the instrument is always stored and if it ranks higher than any other in the list, it will be kept. This results in the bottom ranked burst always being erased. Earlier in the mission, there were also so called honesty bursts, which were taken periodically to test whether the triggers were working properly. It was found that the TDSF triggered properly, but not the TDSS. So the TDSS was set to trigger off of the Ex signals.

A TDS burst from the Wind/WAVES instrument is always 2048 time steps for each channel. The sample rate for TDSF bursts ranges from 1875 samples/second (sps) to 120,000 sps. Every TDS burst is marked a unique set of numbers (unique on any given date) to help distinguish it from others and to ensure any set of channels are appropriately connected to each other. For instance, during one spacecraft downlink interval there may be 95% of the TDS bursts with a complete set of channels (i.e., TDSF has two channels, TDSS has four) while the remaining 5% can be missing channels (just example numbers, not quantitatively accurate). During another downlink interval, those missing channels may be returned if they are not overwritten. During every downlink, the flight operations team at NASA Goddard Space Fligth Center (GSFC) generate level zero binary files from the raw telemetry data. Those files are filled with data received on that date and the file name is labeled with that date. There is no attempt to sort chronologically the data within so any given level zero file can have data from multiple dates within. Thus, it is often necessary to load upwards of five days of level zero files to find as many full channel sets as possible. The remaining unmatched channel sets comprise a much smaller fraction of the total.

All data provided here are from TDSF, so only two channels. Most of the time channel 1 will be associated with the Ex antenna and channel 2 with the Ey antenna. The data are provided in the spinning instrument coordinate basis with associated angles necessary to rotate into a physically meaningful basis (e.g., GSE).

TDS Time Stamps:

Each TDS burst is tagged with a time stamp called a spacecraft event time or SCET. The TDS datation time is sampled after the burst is acquired which requires a delay buffer. The datation time requires two corrections. The first correction arises from tagging the TDS datation with an associated spacecraft major frame in house keeping (HK) data. The second correction removes the delay buffer duration. Both inaccuracies are essentially artifacts of on ground derived values in the archives created by the WINDlib software (K. Goetz, Personal Communication, 2008) found at https://github.com/lynnbwilsoniii/Wind_Decom_Code.

The WAVES instrument's HK mode sends relevant low rate science back to ground once every spacecraft major frame. If multiple TDS bursts occur in the same major frame, it is possible for the WINDlib software to assign them the same SCETs. The reason being that this top-level SCET is only accurate to within +300 ms (in 120,000 sps mode) due to the issues described above (at lower sample rates, the error can be slightly larger). The time stamp uncertainty is a positive definite value because it results from digitization rounding errors. One can correct these issues to within +10 ms if using the proper HK data.

*** The data stored here have not corrected the SCETs! ***

The 300 ms uncertainty, due to the HK corrections mentioned above, results from WINDlib trying to recreate the time stamp after it has been telemetered back to ground. If a burst stays in the TDS buffer for extended periods of time (i.e., >2 days), the interpolation done by WINDlib can make mistakes in the 11th significant digit. The positive definite nature of this uncertainty is due to rounding errors associated with the onboard DPU (digital processing unit) clock rollover. The DPU clock is a 24 bit integer clock sampling at ∼50,018.8 Hz. The clock rolls over at ∼5366.691244092221 seconds, i.e., (16*224)/50,018.8. The sample rate is a temperature sensitive issue and thus subject to change over time. From a sample of 384 different points on 14 different days, a statistical estimate of the rollover time is 5366.691124061162 ± 0.000478370049 seconds (calculated by Lynn B. Wilson III, 2008). Note that the WAVES instrument team used UR8 times, which are the number of 86,400 second days from 1982-01-01/00:00:00.000 UTC.

The method to correct the SCETs to within +10 ms, were one to do so, is given as follows:

Retrieve the DPU clock times, SCETs, UR8 times, and DPU Major Frame Numbers from the WINDlib libraries on the VAX/ALPHA systems for the TDSS(F) data of interest.

Retrieve the same quantities from the HK data.

Match the HK event number with the same DPU Major Frame Number as the TDSS(F) burst of interest.

Find the difference in DPU clock times between the TDSS(F) burst of interest and the HK event with matching major frame number (Note: The TDSS(F) DPU clock time will always be greater than the HK DPU clock if they are the same DPU Major Frame Number and the DPU clock has not rolled over).

Convert the difference to a UR8 time and add this to the HK UR8 time. The new UR8 time is the corrected UR8 time to within +10 ms.

Find the difference between the new UR8 time and the UR8 time WINDlib associates with the TDSS(F) burst. Add the difference to the DPU clock time assigned by WINDlib to get the corrected DPU clock time (Note: watch for the DPU clock rollover).

Convert the new UR8 time to a SCET using either the IDL WINDlib libraries or TMLib (STEREO S/WAVES software) libraries of available functions. This new SCET is accurate to within +10 ms.

One can find a UR8 to UTC conversion routine at https://github.com/lynnbwilsoniii/wind_3dp_pros in the ~/LYNN_PRO/Wind_WAVES_routines/ folder.

Examples of good waveforms can be found in the notes PDF at https://wind.nasa.gov/docs/wind_waves.pdf.

Data Set Description

Each Zip file contains 300+ IDL save files; one for each day of the year with available data. This data set is not complete as the software used to retrieve and calibrate these TDS bursts did not have sufficient error handling to handle some of the more nuanced bit errors or major frame errors in some of the level zero files. There is currently (as of June 27, 2020) an effort (by Keith Goetz et al.) to generate the entire TDSF and TDSS data set in one repository to be put on SPDF/CDAWeb as CDF files. Once that data set is available, it will supercede

Data sets consists of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different engine i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data is contaminated with sensor noise. The engine is operating normally at the start of each time series, and develops a fault at some point during the series. In the training set, the fault grows in magnitude until system failure. In the test set, the time series ends some time prior to system failure. The objective of the competition is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. Also provided a vector of true Remaining Useful Life (RUL) values for the test data. The data are provided as a zip-compressed text file with 26 columns of numbers, separated by spaces. Each row is a snapshot of data taken during a single operational cycle, each column is a different variable. The columns correspond to: 1) unit number 2) time, in cycles 3) operational setting 1 4) operational setting 2 5) operational setting 3 6) sensor measurement 1 7) sensor measurement 2 ... 26) sensor measurement 26 Data Set: FD001 Train trjectories: 100 Test trajectories: 100 Conditions: ONE (Sea Level) Fault Modes: ONE (HPC Degradation) Data Set: FD002 Train trjectories: 260 Test trajectories: 259 Conditions: SIX Fault Modes: ONE (HPC Degradation) Data Set: FD003 Train trjectories: 100 Test trajectories: 100 Conditions: ONE (Sea Level) Fault Modes: TWO (HPC Degradation, Fan Degradation) Data Set: FD004 Train trjectories: 248 Test trajectories: 249 Conditions: SIX Fault Modes: TWO (HPC Degradation, Fan Degradation) Reference: A. Saxena, K. Goebel, D. Simon, and N. Eklund, ‘Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation’, in the Proceedings of the 1st International Conference on Prognostics and Health Management (PHM08), Denver CO, Oct 2008.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including

1. Time series mean annual BAWAP rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009).

As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

There are 4 csv files here:

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion.

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset Citation

Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Grid Substation Transformers, also known as Bulk Supply Points, that typically step-down voltage from 132kV to 33kV (occasionally down to 66 or more rarely 20-25). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.Care is taken to protect the private affairs of companies connected to the 33kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.This dataset provides monthly statistics data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow data for a transformer, use the ‘tx_id’ that can be cross referenced in the Grid Transformers Half Hourly Dataset.If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Grid Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional informationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks

This dataset (in .csv format), accompanying codebook and replication code serve as supplement to a study titled: “Does the mode of administration impact on quality of data? Comparing a traditional survey versus an online survey via a Voting Advice Application” submitted for publication to the journal: “Survey Research Methods”). The study involved comparisons of responses to two near-identical questionnaires administered via a traditional survey and through a Voting Advice Application (VAA) both designed for and administered during the pre-electoral period of the Cypriot Presidential Elections of 2013. The offline dataset consisted of questionnaires collected from 818 individuals whose participation was elicited through door-to-door stratified random sampling with replacement of individuals who could not be contacted. The strata were designed to take into account the regional population density, gender, age and whether the area was urban or rural. Offline participants completed a pen-and-paper questionnaire version of the VAA in a self-completing capacity, although the person administering the questionnaire remained present throughout. The online dataset involved responses from 10,241 VAA users who completed the Choose4Cyprus VAA. Voting Advice Applications are online platforms that provide voting recommendations to users based on their closeness to political parties after they declare their agreement or disagreement on a number of policy statements. VAA users freely visited the VAA website and completed the relevant questionnaire in a self-completing capacity. The two modes of administration (online and offline) involved respondents completing a series of supplementary questions (demographics, ideological affinity & political orientation [e.g. vote in the previous election]) prior to the main questionnaire consisting of 35 and 30 policy-related Likert-type items for the offline and online mode respectively. The dataset includes all 30 policy items that were common between the two modes, although only the first 19 (q1:q19) appeared in the same order and in the same position in the two questionnaires; as such, all analyses reported in the article were conducted using these 19 items only. The phrasing of the questions was identical for the two modes and is described per variable in the attached codebook.

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible. Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information:Open Data Portal Reuses — UK Power Networks

Datasets with dissolved gases concentrations in power transformer oil for remaining useful life (RUL), fault detection and diagnosis (FDD) problems.

Introduction

Power transformers (PTs) are an important component of a nuclear power plant (NPP). They convert alternating voltage and are instrumental in power supply of both external NPP energy consumers and NPPs themselves. Currently, many PTs have exceeded planned service life that had been extended over the designated 25 years. Due to the extension, monitoring the PT technical condition becomes an urgent matter.

An important method for monitoring and diagnosing PTs is Chromatographic Analysis of Dissolved Gas (CADG). It is based on the principle of forced extraction and analysis of dissolved gases from PT oil. Almost all types of equipment defects are accompanied by formation of gases that dissolve in oil; certain types of defects generate certain gases in different quantities. The concentrations also differ on various stages of defects developing that allows to calculate RUL of the PT. At present, NPP control and diagnostic systems for PT equipment use predefined control limits for concentration of dissolved gases in oil. The main disadvantages of this approach are the lack of automatic control and insufficient quality of diagnostics, especially for PTs with extended service life. To combat these shortcomings in diagnostic systems for the analysis of data obtained using CADG, machine learning (ML) methods can be used, as they are used in diagnostics of many NNP components.

Data description

The datasets are available as .csv files containing 420 records of gas concentration, presented as a time dependence. The gasses are 𝐻2, 𝐶𝑂, 𝐶2𝐻4 и 𝐶2𝐻2. The period between time points is 12 hours. There are 3000 datasets splitted into train (2100 datasets) and test (900 datasets) sets.

For RUL problem, annotations are available (in the separate files): each .csv file corresponds to a value in points that is equal the time remaining until the equipment fails, at the end of record.

For FDD problems, there are labels (in the separate files) with four PT operating modes (classes): 1. Normal mode (2436 datasets); 2. Partial discharge: local dielectric breakdown in gas-filled cavities (127 datasets); 3. Low energy discharge: sparking or arc discharges in poor contact connections of structural elements with different or floating potential; discharges between PT core structural elements, high voltage winding taps and the tank, high voltage winding and grounding; discharges in oil during contact switching (162 datasets); 4. Low-temperature overheating: oil flow disruption in windings cooling channels, magnetic system causing low efficiency of the cooling system for temperatures < 300 °C (275 datasets).

Data in this repository is an extension (test set added) of data from here and here.

FDD problems statement

In our case, the fault detection problem transforms into a classification problem, since the data is related to one of four labeled classes (including one normal and three anomalous), so the model’s output needs to be a class number. The problem can be stated as binary classification (healthy/anomalous) for fault detection or multi class classification (on of 4 states) for fault diagnosis.

RUL problem statement

To ensure high-quality maintenance and repair, it is vital to be aware of potential malfunctions and predict RUL of transformer equipment. Therefore, it is necessary to create a mathematical model that will determine RUL by the final 420 points.

Data usage examples

• Dataset was used in this article.
• Dataset was used in this research by Katser et.al. that solves the problem proposing ensemble of classifiers.

This dataset contains a consolidated view of Official Utilisation figures across all transport modes (train, metro, bus, ferry and light rail). Opal daily tap-on/tap-off data is aggregated to a total monthly figure representing the estimated number of trips across all transport modes. Starting July 1, 2024, the methodology for calculating trip numbers for individual lines and operators will change to more accurately reflect the services our passengers use within the transport network. This new approach will apply to trains, metros, light rail, and ferries, and will soon be extended to buses. Aggregations between line, agency, and mode levels will no longer be valid, as a passenger may use multiple lines on a single trip. Trip numbers at the line, operator, or mode level should be used as reported, without further combinations. The dataset includes reports based on both the new and old methodologies, with a transition to the new method taking place over the coming months. As a result of this change, caution should be exercised when analysing longer trends that utilise both datasets. More information on NRT ROAM can be accessed here

Below are frequency comparisons of different models with experiment Note Modeshapes aren't very descriptive for higher modes. There is coupling between them so this is just an approximate naming scheme. See modeshape plots for more details. PDF files are provided with figures of the modeshapes for selected FEM TET10 model (Nov 2011) (CASE 10) Hex8 Modeshapes (CASE 4) TET10 no modelcart (CASE 5) HIRENASD TET model with modelcart - new OML HIRENASD HEX 8 Wing only model Mode 1 Mode 1 Mode 2 Mode 2 Mode 3 Mode 3 Mode 4 Mode 4 Mode 5 Mode 5 Mode 6 Mode 6 Mode 7 Mode 7 Mode 8 Mode 8 Mode 9 Mode 9 Mode 10 Mode 10 Mode 11 Mode 12

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Primary Substation Transformers, that typically step-down voltage from 33kVto 11kV (occasionally from 132kV to 11kV). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables. This dataset provides half-hourly current and power flow data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.Care is taken to protect the private affairs of companies connected to the 11kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted. To find which transformer you are looking for, use the ‘tx_id’ that can be cross referenced in the Primary Transformers Monthly Dataset, which describes by month what transformers were triaged, if they could be made public, and what the monthly statistics are of that site. If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Primary Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. Where the primary transformer has 5 or fewer customers, we redact the dataset.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.

Quality Control Statement The data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.

Assurance Statement Creating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

Additional information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Download dataset information: Metadata (JSON)

We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power Networks

This dataset contains concurrent airborne DopplerScatt radar retrievals of surface vector winds and ocean currents from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. DopplerScatt is a Ka-band (35.75 GHz) scatterometer with a swath width of 24 km that records Doppler measurements of the relative velocity between the platform and the surface. It is mounted on a B200 aircraft which flies daily surveys of the field domain during deployments, and data is used to give larger scale context, and also to compare with in-situ measurements of velocities and divergence. Level 2 data includes estimates of surface winds and currents. The V1 data have been cross-calibrated against SIO-DopVis leading to the 'dopvis_2021' current geophysical model function. It is expected that additional DopVis data will lead to a reprocessing of this data set and it should be regarded as provisional, to be refined after future S-MODE deployments. Data are available in netCDF format.

This dataset contains Level-2, Wave mode (WV) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Level-2 data consists of geolocated geophysical products derived from Level-1.

From WV modes, the OCN product will only contain Ocean Swell Spectra (OSW) and Surface Radial Velocity (RVL).

The OSW component is a two-dimensional ocean surface swell spectrum and includes an estimate of wind speed and direction per swell spectrum. The OSW component provides continuity measurement of SAR swell spectra at C-band. OSW is estimated from Sentinel-1 SLC images by inversion of the corresponding image cross-spectra.

The OSW is generated from Stripmap and Wave modes only and is not available from the TOPSAR IW and EW modes. For Stripmap mode, there are multiple spectra derived from the Level-1 SLC image. For Wave mode, there is one spectrum per vignette.

Ocean wave height spectra are provided in units of m4 and given on a polar grid of wavenumber in rad/m and direction in degrees with respect to North.

The OSW product also contains one estimate of the wind speed in m/s and direction in degrees (meteorological convention) per ocean wave spectrum, as well as parameters derived from the ocean wave spectra (integrated wave parameters) and from the imagette (image statistics).

The spatial coverage of the OSW product is equal to the spatial coverage of the corresponding Level-1 WV SLC or Level-1 SM SLC product, limited to ocean areas.

The RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.

The Level-2 Doppler is computed on a grid similar to the OWI component grid and provides an estimate of the Doppler frequency and the Doppler spectral width. For TOPS, one grid is provided by swath (additional dimension in the NetCDF). The uncertainties of the estimates are also provided for both the Doppler and radial velocity. The Doppler frequency and the Doppler spectral width are estimated based on fitting the azimuth spectral profile of the data to the antenna model taking into account additive noise, aliasing, and sideband effects. The Doppler frequency provided in the product is the pure Doppler frequency estimated from the SLC data without correcting for geometry and mispointing errors.

Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. These data are available via CEDA to any registered CEDA user.

This article provides an adequate statistic for testing the rationality of point predictions of categorical outcomes under a subjective median or mode assumption. The test improves upon previous approaches by accounting for all the required moment inequalities and their respective covariance matrix in a single statistic. Monte Carlo simulations show that the test has an improved Type I error, especially in cases with multiple modes. Applying these tests to a dataset with parents' and students' predictions of school scores, I find that respondents are biased towards expecting higher grades and that students' expectations are correlated with the time of study.

Modes Of Transport

## Overview

Modes Of Transport is a dataset for object detection tasks - it contains Cars Bikes annotations for 401 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

These visualisations feature Opal Trips for all modes of Public Transport by week, month and year. Visualisations for each of the modes show the number of ticketed trips based on operator, line, contract area (where applicable) and card type.

An Opal trip describes where an Opal or Contactless card is used to tap-on and tap-off, including where a single tap-on or tap-off is recorded. All other travel is not included.

As of 1 July 2024, the methodology for calculating trip numbers for individual Lines and Operators has changed to better reflect the services our passengers use on the transport network. The new approach applies to Train, Metro and Light Rail and will soon be extended to Ferry and Bus. Aggregations between Line, Agency and Mode levels are no longer valid as a passenger may use several lines on a single trip. Trip numbers at Line, Operator or Mode level should be used as provided without further combination.

This dataset has reports based on both the new and old methodology with reports progressively moved to the new method in the coming months. Due to the change in method care should be taken when looking at longer trends that utilise both datasets.