This experiment was performed in order to empirically measure the energy use of small, electric Unmanned Aerial Vehicles (UAVs). We autonomously direct a DJI ® Matrice 100 (M100) drone to take off, carry a range of payload weights on a triangular flight pattern, and land. Between flights, we varied specified parameters through a set of discrete options, payload of 0 , 250 g and 500 g; altitude during cruise of 25 m, 50 m, 75 m and 100 m; and speed during cruise of 4 m/s, 6 m/s, 8 m/s, 10 m/s and 12 m/s. We simultaneously collect data from a broad array of on-board sensors. The onboard sensors used to collect these data are * Wind sensor: FT Technologies FT205 UAV-mountable, pre-calibrated ultrasonic wind sensor with accuracy of $\pm$ 0.1 m/s and refresh rate of 10 Hz.; * Position: 3DM-GX5-45 GNSS/INS sensor pack. These sensors use a built-in Kalman filtering system to fuse the GPS and IMU data. The sensor has a maximum output rate of 10Hz with accuracy of $\pm$2 m RMS horizontal, $\pm$5 m RMS vertical. * Current and Voltage: Mauch Electronics PL-200 sensor. This sensor can record currents up to 200 A and voltages up to 33 V. Analogue readings from the sensor were converted into a digital format using an 8 channel 17 bit analogue-to-digital converter (ADC). The number of flights performed varying operational parameters (payload, altitude, speed) was 196. In addition, 13 recordings were done to assess the drone’s ancillary power and hover conditions.

Repository for UML-English data This repository contains the data used for "Extraction of UML Class Diagrams from Natural Language Specification" (Yang et al. 2022) ## Getting the dataset To get the entire dataset, you must download the release containing dataset.tar.gz. ## Structure of the dataset * dataset.tar.gz: archive that contains all the files * fragments.csv: file that lists UML fragments and their characteristics * labels.csv: file that contains the labels received in the crowdsourcing effort * models.csv: file that lists UML class diagrams and their characteristics * zoo/: folder that contains all the UML data itself, such as pictures and UML encodings ## Making use of the dataset Unzip the tarball first. ### Opening the image of a certain UML model Open models.csv to read the list of available models. Copy its name and search in the zoo/ folder for .png files starting with that name. For example, the ACME model has an image in the zoo/ folder called ACME.png. bash ls zoo/ACME.png code zoo/ACME.png # any other image visualizer ### Opening the image of a certain fragment Fragment files are named in the following pattern. Class fragments: (ModelName)_(class)(number).png Relationship fragments: (ModelName)_(rel)(number).png Similarly, you can visualize them. bash code zoo/CFG_class0.png ### Finding the image of a fragment starting from a label 1. Browse through labels.csv and find the line that has the label of interest. 2. Every label has a fragment_id, which can be indexed in fragments.csv. Find the ID for the label of interest. 3. Inside fragments.csv, search for the line where the column value of unique_id equals fragment_id from Step 2. 4. Proceed like in the previous section