The GOES-R PLT ER-2 Flight Navigation Data dataset consists of multiple altitude, pressure, temperature parameters, airspeed, and ground speed measurements collected by the NASA ER-2 high-altitude aircraft for flights that occurred during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). ER-2 navigation data files in ASCII-IWG1 format are available for March 21, 2017 through May 17, 2017.

The dataset covers: - over 30 countries in Europe, - over 300 airports and - over 8.000 routes.

The flights are scheduled for June to August 2025 and will be operated by low-cost airlines.

This data contains the flight plans for the NSF/NCAR HIAPER Gulfstream V (GV) aircraft flown during the O2/N2 Ratio and CO2 Airborne Southern Ocean (ORCAS) Study. Data covers two test flights and 13 research flights between 5 January and 29 February 2016. The data is comprised of text, csv, kml, png, and html files. It also includes an R-based flight plan tool for viewing the data, with associated instructions and configuration files.

Scheduled services operated by international airlines to and from Australia. Covers operated flights and seats by city, airline, route, country and region.\r Please read the Notes (text file) for explanation of items.

GNSS-R data used in paper "Sea surface height measurements by UAV altimeters using LiDAR and low-cost GNSS Reflectometry"F1, F2, F3 are for Flight 1, 2, 3 respectively. Time, H_U, h_L, h_G, H_B are includedF1vSats includes All, GPS only, GLONASS only, BeiDou only analysis for Flight 1Tide is the SSH variations recorded by a bottom pressure gauge

This survey data is from a study exploring the potential to promote lower-emissions air travel by providing consumers with information about the carbon emissions of possible flight choices in the context of online flight search and booking. We surveyed over 450 faculty, researchers, and staff at the University of California, Davis, and asked them to choose among hypothetical flight options for a domestic and an international university-related business trip. Emissions estimates for different flight alternatives were displayed as prominently as price; this simple intervention has been promoted in several demonstration projects, including GreenFLY, a demo we created at UC Davis.

Methods The flight choice experiment involved an online survey in which UC Davis employees were asked to make a series of binary discrete choices between roundtrip flight alternatives, that varied in terms of cost, carbon emissions, layovers (0 or 2: one layover each way), and airport (SMF or SFO), for two hypothetical UC Davis-related business trips, one to Washington, DC and the other to London. We based these hypothetical scenarios (trip destinations and attribute levels of flight alternatives) on data about actual UC Davis employee air travel.

For the layover flight alternatives, we created eight possible cost-carbon combinations, using each cost level and each carbon level twice, and not repeating any pairing. There are many ways to do this, and we chose one which tended to pair high cost with low carbon, to create trade-offs. Our eight layover flights to DC appear in Table 2. The same cost-carbon pairings were used for layover flight alternatives from SFO.

We organized the flight alternatives into sets of two for the choice experiment questions. Criteria for pairing flight alternatives were as follows: 1. Every flight alternative should appear roughly the same number of times in the survey, 2. The distribution of kinds of flights in the questions (eg. layover out of SFO) should match the distribution in the entire set, 3. Avoid questions in which the two flights have the same cost, or the same carbon, and 4. Focus on pairs that might have competitive utility (e.g. an alternative that is lower cost, lower carbon, nonstop and out of SMF is likely to be selected in most cases, so it is not useful for understanding potential trade-offs).

From this, we created seven "buckets" of questions for Washington, and seven for London, and asked each participant a randomly-chosen question from each bucket. We made an error in the online questionnaire-design software, which caused a random bucket to be skipped for London. Nonetheless, each flight option appears freqently (between 40 and 120 times) in the questions we asked.

The original data output from Qualtrics was processed into a format suitable for processing with the mlogit package in R.

Data and R script of analyses in support of the study: "Visual guidance of forward flight in hummingbirds reveals control based on image features instead of pattern velocity"Study authors: Roslyn Dakin, Tyee K. Fellows, and Douglas L. Altshuler

Dataset Overview: Flight Price Dataset of Bangladesh

Introduction

The "Bangladesh Flight Fare Dataset" is a synthetic dataset comprising 57,000 flight records tailored to represent air travel scenarios originating from Bangladesh. This dataset simulates realistic flight fare dynamics, capturing key factors such as airline operations, airport specifics, travel classes, booking behaviors, and seasonal variations specific to Bangladesh’s aviation market. It is designed for researchers, data scientists, and analysts interested in flight fare prediction, travel pattern analysis, or machine learning/deep learning applications. By combining real-world inspired statistical distributions and aviation industry standards, this dataset provides a robust foundation for exploring flight economics in a South Asian context.

Dataset Purpose

This dataset aims to: - Facilitate predictive modeling of flight fares, with "Total Fare (BDT)" as the primary target variable. - Enable analysis of travel trends, including the impact of cultural festivals (e.g., Eid, Hajj) and booking timings on pricing. - Serve as a training resource for machine learning (ML) and deep learning (DL) models, with sufficient sample size (50,000) and feature diversity for generalization. - Provide a realistic yet synthetic representation of Bangladesh’s air travel ecosystem, blending domestic and international flight scenarios.

Data Collection and Methodology

The dataset is synthetically generated using Python, with its methodology rooted in real-world aviation data and statistical principles. Below is a detailed breakdown of its construction:

1. Data Components

• Airlines:
  • Count: 25 airlines (21 international, 4 domestic).
  • Source: Compiled from Bangladesh Civil Aviation Authority and Airline History, including major carriers like Emirates, Qatar Airways, and Biman Bangladesh Airlines.
  • Selection: Random uniform choice per flight record to reflect operational diversity.
• Airports:
  • Source Airports: 8 domestic airports (e.g., DAC - Hazrat Shahjalal International Airport, Dhaka).
  • Destination Airports: 20 airports (8 domestic + 12 international, e.g., DXB - Dubai International Airport).
  • Coordinates: Sourced from World Airport Codes, used for distance calculations.
  • Full Names: Added for readability, mapped via a dictionary (e.g., "DAC" → "Hazrat Shahjalal International Airport, Dhaka").
• Travel Classes: Economy, Business, First Class, standard across the industry, randomly assigned with uniform distribution.
• Booking Sources: Direct Booking, Travel Agency, Online Website, reflecting common methods, per Statista, with uniform random selection.
• Aircraft Types: Boeing 777, Airbus A320, Boeing 737, Boeing 787, Airbus A350, assigned based on flight distance, sourced from Boeing and Airbus.

2. Key Calculations

• Distance:

  • Method: Haversine formula calculates great-circle distance: a = sin²(Δφ/2) + cos(φ₁) cos(φ₂) sin²(Δλ/2) c = 2 arctan2(√a, √(1-a)) d = R · c, R = 6371 km

• Purpose: Determines flight duration, aircraft type, and stopovers.

• Source: Wikipedia - Haversine Formula.

• Flight Duration:

• Formula: Duration = max(d/s · U(0.9, 1.1), 0.5), where s is speed (300 km/h for <500 km, 600 km/h for 500-2000 km, 900 km/h for >2000 km), and U is uniform random variation.

• Source: Speeds adjusted from World Atlas, ensuring realism (e.g., DAC to CGP ~45 minutes).

• Fares:

• Base Fares:

• Domestic: Economy (2000-5000 BDT), Business (5000-10000 BDT), First Class (10000-15000 BDT).

• International: Economy (5000-70000 BDT), Business (15000-150000 BDT), First Class (25000-300000 BDT).

• Source: Derived from Trip.com and Expedia, e.g., DAC to LHR ~$380-600 (~41800-66000 BDT at 1 USD = 110 BDT).

• Adjustments:

• Seasonal multipliers (Regular: 1.0, Eid: 1.3, Hajj: 1.5, Winter: 1.2), per demand trends from Timeanddate.com.

• Days Before Departure: 20% discount (60+ days), 10% discount (30-59 days), 20% surge (<5 days), per Skyscanner.

• Taxes: Domestic: 200 BDT; International: 2000-6000 BDT + 15% base fare, per [Bangladesh Civil Aviation Authority](https://www.dgca.g...

This dataset contains relevant MATLAB/SIMULINK(R) code and supporting data in relation to CHAPTER 3 of the dissertation 'Advances in Dynamic Inversion-based Flight Control Law Design: Multivariable Analysis and Synthesis of Robust and Multi-Objective Design Solutions' by T.S.C. Pollack (2024) [DOI: 10.4233/uuid:28617ba0-461d-48ef-8437-de2aa41034ea]. It concerns multi-loop robust synthesis of Incremental Nonlinear Dynamic Inversion (INDI) based control laws against mixed uncertainty. Data is generated using MATLAB's Control System Toolbox routines, such as SYSTUNE. For more information, we refer to the respective thesis chapter / publication.

The following datasets are related:

1) MATLAB/SIMULINK(R) Code and Supporting Data for Analysis of Inversion Strategy on Robustness of IDI-based Control Laws (DOI: https://doi.org/10.4121/1c3b2fe8-15ee-4c93-9de3-9e5dddaca6a0)

2) MATLAB/SIMULINK(R) Code and Supporting Data for Assessment of Commonalities between Hybrid INDI-based and PID-based Flight Control Design (DOI: https://doi.org/10.4121/1c425f5d-943c-4e9c-8c6b-4e026dba20ca)

3) MATLAB/SIMULINK(R) Code and Supporting Data for Design of Multi-objective Incremental Control Allocation-based Flight Control Laws (DOI: https://doi.org/10.4121/b265ae09-64ef-4faf-bd77-d18712c11239)

Dataset contains aerial images captured in a simulated 3D environment. Ortho photo images from USGS Aerial Imagery dataset was used as ground view. Gazebo simulator with PX4 flight controller software and a plane model was used to simulate flights at different altitude and trajectories over different maps.

Each dataset is provided in a zip file, named under two character and number abbreviation, which can be interpreted by the first letter for map type (U - urban, F - forest), second letter stands for trajectory type (L - straight line, C - circular trajectory, R - rectangular trajectory) and the number stands for altitude in meters, e.g. FC-300, means forest map, circular trajectory at 300 meters altitude. Alongside image data, a text file containing CSV data is included, which contains aircraft attitude and geographical information of each image.

Additionally, maps used for simulation environment are included in this dataset, maps of the same area captured on different years are also included, which can be used to evaluate algorithms matching against maps.

ROS Publisher node is available in GitHub: https://github.com/jureviciusr/AIRDatasetPublisher

These digital images were taken over an area of the Potomac River in White's Ferry, Maryland using 3DR Solo unmanned aircraft systems (UAS) on October 23, 2019. These images were collected for the purpose of evaluating UAS assessment of river habitat data such as water depth, substrate type, and water clarity. Each UAS was equipped with a FLIR Vue Pro R 640 13mm radiometric thermal camera that provides temperature data embedded in every pixel. Some photographs contain black and white targets used as ground control points (GCPs), which were surveyed by a field crew with a high-precision (GNSS) Global Navigation Satellite System and/or containing internal post processing kinematic (PPK) GPS system. This data release includes the original images from FLIR Vue Pro R 640 13mm radiometric thermal camera of the Potomac River in White's Ferry, Maryland.

Dataset

This dataset was created by victor

Released under MIT

Contents

Clearwing moths are known for their physical resemblance to hymenopterans, but the extent of their behavioural mimicry is unknown. We describe zigzag flights of sesiid bee mimics which are nearly indistinguishable from those of sympatric bees, whereas sesiid wasp mimics display faster, straighter flights more akin to those of wasps. In particular, the flight of the sesiids Heterosphecia pahangensis, Aschistophleps argentifasciata and Pyrophleps cruentata resembles both Tetragonilla collina and T. atripes stingless bees and, to a lesser extent, dwarf honey bees Apis andreniformis, whereas the sesiid Pyrophleps sp. resembles Tachysphex sp. wasps. These findings represent the first experimental evidence for behavioural mimicry in clearwing moths.

Usage Notes I. Tables 3-11 results of ANOVA and Tukey-Kramer tests for flight parameters by speciesTables 3–11: results of ANOVA and Tukey-Kramer tests for flight parameters by species. From "Moving like a model: mimicry of hymenopteran flight trajectories by clearwing moths of Southeast Asian rainforests" Skowron Volponi, Marta, McLean, Donald, Volponi, Paolo, Dudley, RobertI_Tables_pdfs.zipII. R code. Flight trajectories calculationsThis is a complete R code used for flight trajectory analysis described in "Moving like a model: mimicry of hymenopteran flight trajectories by clearwing moths of Southeast Asian rainforests" Skowron Volponi, Marta, McLean, Donald, Volponi, Paolo, Dudley, RobertII R Code.zipIII. Calculated flight trajectories parametersComplete calculated parameters of all analysed flight trajectories in study "Moving like a model: mimicry of hymenopteran flight trajectories by clearwing moths of Southeast Asian rainforests" Skowron Volponi, Marta, McLean, Donald, Volponi, Paolo, Dudley, RobertIII_Parameters_Complete.csvIV. Minimums, maximums and means of calculated variablesMinimums, maximums and means of calculated variables from "Moving like a model: mimicry of hymenopteran flight trajectories by clearwing moths of Southeast Asian rainforests" Skowron Volponi, Marta, McLean, Donald, Volponi, Paolo, Dudley, RobertIV_Parameters_Means_Species.csvV. (x,y) coordinates of digitized flight trajectories(x,y) coordinates of digitized flight trajectories from "Moving like a model: mimicry of hymenopteran flight trajectories by clearwing moths of Southeast Asian rainforests" Skowron Volponi, Marta, McLean, Donald, Volponi, Paolo, Dudley, RobertV_xy_coordinates_analysed_trajectories.zip

Due to the rapid aging of bridges in Japan, there is a need for an advance and more reliable structural health monitoring techniques. This study conducted an unmanned aerial vehicle (UAV) semi-autopilot path flight controlled method by utilizing the DJI Software Development Kit (SDK) for inspection of Global Navigation Satelite System (GNSS)-denied parts of a bridge specifically in-between girders and other semi- closed and narrow areas. A mini- UAV’s path planning was pre-programmed under a known environment using waypoints and python programming language. A miniature low cost camera was attached as a payload and captured the images underneath the bridge deck aside from the captured images along the line of sight of the UAV. The UAV test flight was done in a pedestrian bridge located at Saitama University. The UAV successfully inspected underneath the bridge deck and some narrow parts in semi-autopilot mode. After that, corrosion, spalling, and crack damages were detected using two different vision based deep learning methods, YOLOv3 and Mask R-CNN. In addition, since the flight path plan was pre- programmed by measured commands, the location of the captured damages were easily located. To visualize the damage location, a 3D model underneath the decks was generated using structure from motion (SfM) and open sourced softwares. Due to the UAV’s small size and the ability to have a semi-autopilot controlled flight path, it eliminated the GNSS dependent problem in bridge damage inspection and been able to inspect narrow areas which were difficult to access. The combination of semi-autopilot inspection under gps-denied parts of bridge inspection, damage detection, and 3D model construction were the main focus of this research.

Data and code for: "Hummingbirds use distinct control strategies for forward and hovering flight" by Baliga VB, Dakin R, Wylie DR, and Altshuler DL; Proceedings of the Royal Society B: Biological SciencesQuestions/concerns/comments: Vikram Baliga (vikram.baliga@ubc.ca)Data:- All data required to re-create analyses and figures are stored within /processed_data_exports. Note that these are not original (Flydra) data; due to file size constraints, Flydra data cannot be included here.- The /lookup_and_metadata directory contains several files that describe the metadata and other important characteristics of the original data and subjectsCode:- All code required to re-create analyses and figures are provided as .R files.- The two "master_script_" files are required to be run prior to recreating figures. Objects that figure creation relies on are generated by the master scripts- Files to support the master scripts are house in /data_import_scripts, but do not need to be called directly by the user. Rather, the master scripts will source these auxiliary files automatically as needed.- Code for each figure is provided within separate .R files. Multi-panel plots are created wherever possible, but might differ slightly in presentation from their final format due to rendering differences across platforms.

These digital images were taken over an area of the Potomac River in Point of Rocks, Maryland using 3DR Solo unmanned aircraft systems (UAS) on October 24, 2019. These images were collected for the purpose of evaluating UAS assessment of river habitat data such as water depth, substrate type, and water clarity. Each UAS was equipped with a FLIR Vue Pro R 640 13mm radiometric thermal camera that provides temperature data embedded in every pixel. Some photographs contain black and white targets used as ground control points (GCPs), which were surveyed by a field crew with a high-precision (GNSS) Global Navigation Satellite System and/or containing internal post processing kinematic (PPK) GPS system. This data release includes the original images from FLIR Vue Pro R 640 13mm radiometric thermal camera of the Potomac River in Point of Rocks, Maryland.

These digital images were taken over an area of the Potomac River in Brunswick, Maryland using 3DR Solo unmanned aircraft systems (UAS) on October 22, 2019. These images were collected for the purpose of evaluating UAS assessment of river habitat data such as water depth, substrate type, and water clarity. Each UAS was equipped with a FLIR Vue Pro R 640 13mm radiometric thermal camera that provides temperature data embedded in every pixel. Some photographs contain black and white targets used as ground control points (GCPs), which were surveyed by a field crew with a high-precision (GNSS) Global Navigation Satellite System and/or containing internal post processing kinematic (PPK) GPS system. This data release includes the original images from FLIR Vue Pro R 640 13mm radiometric thermal camera of the Potomac River in Brunswick, Maryland.

https://www.hackerearth.com/challenges/hackathon/enter-the-travel-verse/

Identify trends with Airline ticketing data

The past few years represent the best and worst in air travel in decades. 2019 saw the best year for air travel this century while the pandemic brought long periods of extreme swings in demand. ARC’s data is the world’s largest single source of airline ticketing data.

The goal is to identify a trend that leads to a new prediction using ARC’s data to incorporate it into a marketable data product within the B2B or B2B2C space.

Your task is to find creative ways to apply the vast data store from historical trends mapped into predictive analytics to specific recommendations for consumers and suppliers of air travel — the potential has no limit.

The Challenge - Review the provided airline ticketing dataset below Identify a problem in the travel and tourism industry where advanced awareness of current and future trends using airline ticketing data will solve. - Identify the audience in the B2B or B2B2C space that would find value in the solution. - Using Machine learning, data science technologies and/or advanced analytics to develop a solution that solves the problem that you have identified and defined. (For example, recommender systems, predictive analytics, etc.) - Create an application prototype (program, website, API etc.) and/or visual aid (such as a dashboard or video presentation) to demonstrate the business value of the proposed solution.

The GOES-R PLT Mission Reports dataset consists of various reports filed by the scientists during the GOES-R Post Launch Test (PLT) field campaign including flight reports, weather forecasts, mission scientist reports, and plan-of-day reports. The campaign took place from March to May of 2017 in support of post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The GOES-R PLT Mission Reports dataset contains reports from March 13 through May 17, 2017 in PDF, PNG, Microsoft Excel and Word (.xlsx and .docx) format, and KMZ format for display in Google Earth.

Flying insects often forage among cluttered vegetation that forms a series of obstacles in their flight path. Recent studies have focused on behaviors needed to navigate clutter while avoiding all physical contact, and as a result, we know little about flight behaviors that do involve encounters with obstacles. Here, we challenged carpenter bees (Xylocopa varipuncta) to fly through narrow gaps in an obstacle course to determine the kinds of obstacle encounters they experience, as well as the consequences for flight performance. We observed three kinds of encounters: leg, body, and wing collisions. Wing collisions occurred most frequently (in about 40% of flights, up to 25 times per flight) but these had little effect on flight speed or body orientation. In contrast, body and leg collisions, which each occurred in about 20% of flights (1-2 times per flight), resulted in decreased flight speeds and increased rates of body rotation (yaw). Wing and body collisions, but not leg collisions, were more likely to occur in wind versus still air. Thus, physical encounters with obstacles may be a frequent occurrence for insects flying in some environments, and the immediate effects of these encounters on flight performance depend on the body part involved. Methods Freely flying female carpenter bees (Xylocopa varipuncta, n = 15) were collected from the University of California, Davis campus and subjected to flight challenges in a laboratory flight tunnel. Individual bees were placed in the flight tunnel (20 x 19 x 115 cm; width x height x length), which included a series of vertical columns (hereafter ‘obstacles’) that spanned the middle of the tunnel (obstacle diameter = 7 mm, space between obstacles = 34.44 ± 2.80 mm; mean ± SD). The bees’ wing spans (45.19 ± 2.11 mm, from tip to tip) were larger than the size of the gaps between obstacles; thus, bees could not fly straight through gaps, but instead needed to rotate their body (e.g., yaw) in order to pass through. Obstacles were attached to a mechanical arm that oscillated laterally (amplitude = 21 mm, frequency = 2 Hz) or remained stationary. Fans at each end of the tunnel could be turned on to produce a gentle breeze (mean velocity = 0.54 m/s) or off for still air. Wind direction was constant: bees flying in one direction experienced headwinds and in the other direction tailwinds. Up to 12 flights through the obstacles were elicited from each bee, using full spectrum lights that were alternately turned on and off at each end of the tunnel. Obstacle motion (stationary versus oscillating) was fixed for a given bee, but all bees experienced both wind and still air, with the order of the wind condition switched after approximately six flights. Thus, four different flight conditions were tested on the group of bees: still air with stationary obstacles (n = 40 flights), still air with oscillating obstacles (n = 34), wind with stationary obstacles (n = 42), and wind with oscillating obstacles (n = 29). Flights were filmed with two synchronized Phantom v611 cameras (Vision Research, Inc., Wayne, NJ, USA) sampling at 1500 frames/s and positioned 30º from the vertical on opposite sides of the obstacles. Cameras were calibrated using a standard checkerboard calibration method and built-in MATLAB functions. In each video, the positions of the bee’s head (midpoint between antennae), thorax (approximating the body centroid), and wing tips were tracked with the machine-learning software DeepLabCut. Tracked points were checked and manually corrected, and obstacle positions were labeled using DLTdv6 in MATLAB. Labeled positions were converted from two-dimensional coordinates in each camera view into three-dimensional space using built-in MATLAB functions. We classified and counted each physical encounter that occurred between the bees and obstacles. The most common encounters were body collisions (the head, thorax, or abdomen contacted the obstacles), leg collisions (one or more forelegs contacted the obstacles), and wing collisions (the distal half of one or more forewings contacted the obstacles).

We next assessed how encounters with obstacles affected flight performance. In each video, we identified the first occurrence of each body, leg, and wing collision, and defined a 20-ms period before and after each encounter. This temporal window allows us to quantify performance immediately before and after obstacle encounters, as in, to determine how flight performance changes during encounters. Some of these pre- and post-encounter periods contained additional collisions with obstacles, but they were included in the analysis to reflect the true nature of flight in clutter (i.e., flights with just one obstacle encounter were rare). From this analysis, videos yielded either a single encounter type (n = 42 flights), two encounter types (n = 26), or all three encounter types (n = 9). For each encounter, we measured the change in horizontal ground speed and yaw between the pre- and post-collision periods, as well as the post-collision yaw rate, where yaw was the body angle about the vertical axis. To calculate kinematics, we smoothed the three-dimensional position data with cubic smoothing spline curves via the ‘smooth.spline’ function in the R package stats. Horizontal ground speed was calculated as the change in x-y position (lateral and longitudinal motions, omitting vertical motion) per time. Yaw was calculated by converting the Cartesian coordinates of the head and thorax to spherical coordinates via the ‘cart2sph’ function in the R package pracma and finding the horizontal angle between the two body points and the long axis of the tunnel. The yaw rate was calculated over the 20 ms post-collision by taking the derivative of yaw with respect to time.