This FWAwPRSSMreadme.txt file was generated on 2021-06-21 by Yagiz Bayiz GENERAL INFORMATION 1. Title of Dataset: Flapping Wing Aerodynamics with PRSSM 2. Author Information A. Principal Investigator Contact Information Name: Bo Cheng Institution: Penn State University Department: Mechanical Engineering Email: buc10@psu.edu 3. Date of data collection (single date, range, approximate date) : 2020-01 4. Geographic location of data collection : State College, PA 5. Information about funding sources that supported the collection of the data: SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: 2. Links to publications that cite or use the data: https://arxiv.org/abs/2103.07994 3. Links to other publicly accessible locations of the data: 4. Links/relationships to ancillary data sets: 5. Was data derived from another source? yes/no A. If yes, list source(s): 6. Recommended citation for this dataset: DATA & FILE OVERVIEW 1. File List: flapping_wing_aerodynamics.mat flapping_wing_aerodynamics_lasso_fit.mat predict_train_n_test.mat 2. Relationship between files, if important: Input files: flapping_wing_aerodynamics.mat, flapping_wing_aerodynamics_lasso_fit.mat Output file: predict_train_n_test.mat 3. Additional related data collected that was not included in the current data package: 4. Are there multiple versions of the dataset? yes/no A. If yes, name of file(s) that was updated: i. Why was the file updated? ii. When was the file updated? METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: The aerodynamics data used to train and test the Probabilistic Recurrent State-Space Model (PRSSM) was generated by the dynamically scaled robotic wing designed by the BioRob-InFL lab. The data set includes all 548 trajectories used in the analysis. The measured positions, forces, and moments were denoised and post-processed by the robotic wing target PC to remove the sensor bias, buoyancy, and gravitational effects. Furthermore, the data was downsampled and organized in the postprocessing to have a 25Hz sampling time and 470 data points for each trajectory. 2. Methods for processing the data: See the manuscript: https://arxiv.org/abs/2103.07994 3. Instrument- or software-specific information needed to interpret the data: https://arxiv.org/abs/2103.07994 4. Standards and calibration information, if appropriate: 5. Environmental/experimental conditions: 6. Describe any quality-assurance procedures performed on the data: 7. People involved with sample collection, processing, analysis and/or submission: DATA-SPECIFIC INFORMATION FOR: flapping_wing_aerodynamics.mat 1. Number of variables: 4 2. Number of cases/rows: 548 3. Variable List: • [Euler angles of the wing] - ds_pos (stroke, deviation, rotation), • [The kinematic variables derived from ds_pos] - ds_u_raw (7 variables, see the paper for the definitions and the order of variables), • [Aerodynamic forces and moments] - ds_y_raw (5 variables, see the paper for the definitions and the order of variables), • [The standardized versions of ds_u_raw and ds_y_raw] - ds_u and ds_y with the mean ds_mean_u and ds_mean_y and the standard deviation "ds_std_u" and ds_std_y vectors. Only the training data is considered when normalizing. DATA-SPECIFIC INFORMATION FOR: flapping_wing_aerodynamics_lasso_fit.mat 1. Number of variables: 4 2. Number of cases/rows: 548 3. Variable List: • [The kinematic features derived from ds_u_raw] - ds_uLR_raw (11 variables, see the paper for the definitions and the order of variables), • [Aerodynamic forces and moments] - ds_y (identical to the variable above), • [Lasso Model] - LRmodel (the lasso model fitted to ds_y with ds_uLR_raw as the input), • [Lasso Predictions] - lr_y (the the lasso predictions for all trajectories). DATA-SPECIFIC INFORMATION FOR: predict_train_n_test.mat 1. Number of variables: 9 2. Number of cases/rows: 548 3. Variable List: • [Prediction means] - gp_mean_training for training (512 trajectories) and gp_mean_test for test (36 trajectories), • [Prediction variations] - gp_var_training for training (512 trajectories) and gp_var_test for test (36 trajectories), • [Latent state means] - latent_mean_training for training (512 trajectories) and latent_mean_test for test (36 trajectories), • [Latent state variations] - latent_var_training for training (512 trajectories) and latent_var_test for test (36 trajectories), • [Ground truth, partitioned version of "ds_y"] - gt_training for training (512 trajectories) and gt_test for test (36 trajectories), • [Inputs, partitioned version of "ds_u"] - in_training for training (512 trajectories) and in_test for test (36 trajectories), • [Corresponding positions, partitioned version of "ds_ps"] - pos_training for training (512 trajectories) and pos_test for test (36 trajectories), • [Mean and standard deviation vectors that denormalize the inputs] - mean_in (identical to ds_mean_u from input data) and std_in (identical to ds_std_u from input data), • [Mean and standard deviation vectors that denormalize the predictions and ground truth] - mean_out (identical to ds_mean_y from input data) and std_out (identical to ds_std_y from input data).