The electron density values listed in this file are derived from the IMAGE Radio Plasma Imager (B.W. Reinisch, PI) data using an automatic fitting program written by Phillip Webb with manual correction. The electron number densities were produced using an automated procedure (with manual correction when necessary) which attempted to self-consistently fit an enhancement in the IMAGE RPI Dynamic Spectra to either 1) the Upper Hybrid Resonance band, 2) the Z-mode or 3) the continuum edge. The automatic algorithm works by rules determined by comparison of the active and passive RPI data [Benson et al., GRL, vol. 31, L20803, doi:10.1029/2004GL020847, 2004]. The manual data points are not from frequencies chosen freely by a human. Rather the human specifies that the computer should search for a peak or continuum edge in a certain frequency region. Thus even the manual points are determined, in part, by the automatic algorithms. Of course that does not guarantee that the data points are right, but it does eliminate some human bias.

Measures of monthly UK inflation data including CPIH, CPI and RPI. These tables complement the consumer price inflation time series dataset.

The Regional Price Index contrasts the cost of a common basket of goods and services at a number of regional locations to the Perth metropolitan area. The RPIs were commissioned to assist with the calculation of the Western Australian State Government’s regional district allowance, and it has been used to assist in policy decision-making. Show full description

MotivationThe Dataset for Unmanned Aircraft System (UAS) Cellular Communications, short DUCC, was created with the aim of advancing communications for Beyond Visual Line of Sight (BVLOS) operations. With this objective in mind, datasets were generated to analyse the behaviour of cellular communications for UAS operations.

MeasurementA measurement setup was implemented to execute the measurements. Two Sierra Wireless EM9191 modems possessing both LTE and 5G capabilities were utilized in order to establish a connection to the cellular network and measure the physical parameters of the air-link. Every modem was equipped with four Taoglas antennas, two of type TG 35.8113 and two of type TG 45.8113. To capture the measurements a Raspberry Pi 4B is used. All hardware components were integrated into a box and attached to a DJI Matrice 300 RTK. A connection to the drone controller has been established to obtain location, speed and attitude. To measure end-to-end network parameters, dummy data was exchanged bidirectionally between the Raspberry Pi and a server. Both the server as well as the Raspberry Pi are synchronized with the GPS time in order to measure the one-way packet delay. For this purpose, we utilised Iperf3 and customised it to suit our requirements. To ensure precise positioning of the drone a Real Time Kinematik (RTK) station was placed on the ground during the measurements.

The measurements were performed at three distinct rural locations. Waypoint flights were undertaken with the points arranged in a cuboid formation maximizing the coverage of the air volume. Thereby, the campaigns were conducted with varying drone speeds. Moreover, for location A, different flight routes with rotated grids were implemented to reduce bias. Finally, a validation dataset is provided for location A, where the waypoints were calculated according to Quality of Service (QoS) based path-planning.

Dataset Structure and UsageThe dataset's structure consists of:-- Dataset |-- LocationX |-- RouteX (in case different routes at LocationX were created) |-- LocXRouteX.kml (file containing the waypoints in the kml format) |-- SpeedXMeterPerSecond (folder containing the datasets recorded with a specific drone speed) |-- YYYY-MM-DD hh_mm_ss.s.pkl.gz (Dataset file) |-- RouteY |-- ... |-- ...

The dataset files can be loaded using the pandas module in python3. The file "load.py" provides a sample script for loading a dataset as well as the corresponding .kml file which contains the predefined waypoints. In the file "Parameter_Description.csv" each parameter measured is further explained.

LicenseAll datasets are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us at automotive@oth-aw.de and we will immediately remove the respective data from our server.

AchnowledgementThe authors gratefully acknowledge the following European Union H2020 -- ECSEL Joint Undertaking project for financial support including funding by the German Federal Ministry for Education and Research (BMBF): ADACORSA (Grant Agreement No. 876019, funding code 16MEE0039).

The dataset include the data of 903 discrete samples taken from sediment core Co1401. It comprises the mass and magnetic susceptibility, natural remanent magnetization (NRM), and alternating field (AF) demagnetization of NRM performed at peak fields of 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, and 80 mT. Please note that the core was AF demagnetized using a peakfield of 15 mT before samples were collected.Additionally shown are the anhysteretic remanent magnetization (ARM) imparted imparted by 80 mT peak AF, 100 µT bias field, the AF demagnetization of the ARM at peak fields of 10 mT and 60 mT, and the isothermal remanent magnetization (IRM) imparted at 0.9 T. Measurements were performed at the cryogenic magnetometer laboratory of the Geological Survey of Norway in Trondheim (NGU).The relative palaeointensity (RPI) is calculated using the partial NRM (pNRM) between the 30 and 50 mT AF step and the magnetic susceptibility, ARM and IRM as normalizers. The charachteristic remanet magnetization (ChRM), RPI and PSV is not given for samples discarded during evaluation. The presented age model results from correlation of RPI(ARM) features to the most recent version of GLOPIS-75. Please note: the RPI age model does not consider possible influences of the lock-in depths.

Construction Output Price Indices (OPIs) from January 2014 to December 2024, UK. Summary.