This dataset contains mobile wireless download speed test results and areas where the PSD (Vermont Public Service Department) challenged mobile wireless service asserted by wireless carriers.DOWNLOAD SPEED TEST RESULTSResults from download speed tests that were conducted in September-December 2018 are contained by 6 point feature-classes, each with results for a particular carrier.PSD staff employed the android smartphone application G-NetTrack to conduct download speed tests at approximately 300 meter intervals along all federal-aid highways.The point feature-classes are very detailed and more suitable when zoomed into the neighborhood scale. All point feature-classes have the same field schema, which includes these fields: timestamp: Date and time at which the data point was collected. signal_str: Signal strength (RSRP in dBm). download_s: Download speed (in Mbps). latency: The round-trip time for a request to a website, in milliseconds.DRIVE-TEST BLOCKSDrive-test blocks (Utility_DriveTest_poly_Blocks) is a polygon feature-class that is composed of 1-kilometer blocks; it has a field for each of the 6 carriers; the fields show the average download speed recorded in each block for each carrier.The fields also include a composite field (All_) that contains averages of all carriers, masking variation in coverage between individual carriers. "999" indicates no test was conducted for the carrier in that block.Drive-test blocks are generalized information and are suitable when zoomed at various scales. A BLOCK DOES NOT INDICATE SERVICE THROUGHOUT A BLOCK; use the point feature-classes for detailed data and judge accordingly.WIRELESS CHALLENGE BLOCKSWireless Challenge Blocks (Utility_DriveTest_poly_VTMFCIIChallengeBlocks) depicts the status of each block in the submission of the PSD in the FCC Mobility Fund Phase II Challenge process. It shows challenges to mobile wireless service asserted by wireless carriersA value of 0 in the Area_1 field indicates that the challenge was rejected, either because a) the block is already largely eligible, or b) because no tests below 5 Mbps were submitted.DISCLAIMERVCGI and the State of VT make no representations of any kind, including but not limited to the warranties of merchantability or fitness for a particular use, nor are any such warranties to be implied with respect to the data.

The information presented in this data set is based on records of dockets, petitions, tower share requests, and notices of exempt modifications received and processed by the Council.

This database is not an exhaustive listing of all wireless telecommunications sites in the state in that it does not include all information about sites not under the jurisdiction of the Siting Council.

Although the Connecticut Siting Council makes every effort to keep this spreadsheet current and accurate, the Council makes no representation or warranty as to the accuracy of the data presented herein.

The public is advised that the records upon which the information in this database is based are kept in the Siting Council’s offices at Ten Franklin Square, New Britain and are open for public inspection during normal working hours from 8:30 a.m. to 4:30 p.m. Monday through Friday.

Note to Users: Over the years, some of the wireless companies have had several different corporate identities. In the database, they are identified by the name they had at the time of their application to the Siting Council. To help database users follow the name changes, the list below shows the different names by which the companies have been known. Recent mergers in the telecommunications industry have joined companies listed as separate entities. AT&T Wireless merged with Cingular to do business as New Cingular. Sprint and Nextel have merged to form Sprint/Nextel Corporation.

Cingular: SNET, SCLP, and New Cingular after merger with AT&T

T-Mobile: Omni (Omnipoint), VoiceStream

Verizon: BAM, Cellco

AT&T: AT&T Wireless, New Cingular after merger with Cingular, then Cingular rebranded as AT&T

Nextel: Smart SMR

The recent growth in the use of Autonomous Aerial Vehicles (AAVs) has increased concerns about the safety of the autonomous vehicles, the people, and the properties around the flight path and onboard the vehicle. Much research is being done on new regulations, more robust systems are designed to address the concerns, and new methods and algorithms are introduced to detect the potential hardware and software issues. This dataset presents several fault types in control surfaces of a fixed-wing Unmanned Aerial Vehicle (UAV) for use in Fault Detection and Isolation (FDI) and Anomaly Detection (AD) research. Currently, the dataset includes processed data for 47 autonomous flights with 23 sudden full engine failure scenarios and 24 scenarios for seven other types of sudden control surface (actuator) faults, with a total of 66 minutes of flight in normal conditions and 13 minutes of post-fault flight time. It additionally includes many hours of raw data of fully-autonomous, autopilot-assisted and manual flights with tens of fault scenarios. The ground truth of the time and type of faults is provided in each scenario to enable the evaluation of new methods using the dataset. We have also provided the helper tools in several programming languages to load and work with the data and to help the evaluation of a detection method using the dataset. A set of metrics is proposed to help to compare different methods using the dataset. Most of the current fault detection methods are evaluated in simulation and as far as we know, this dataset is the only one providing the real flight data with faults in such capacity. We hope it will help advance the state-of-the-art in Anomaly Detection or FDI research for Autonomous Aerial Vehicles and mobile robots to enhance the safety of autonomous and remote flight operations further. Hardware: The platform used for collecting the dataset is a custom modification of the Carbon Z T-28 model plane. The plane has 2 meters of wingspan, a single electric engine in the front, ailerons, flaperons, an elevator, and a rudder. We equipped the aircraft with a Holybro PX4 2.4.6 autopilot, a Pitot Tube, a GPS module, and an Nvidia Jetson TX2 onboard computer. In addition to the receiver, we also equipped it with a radio for communication with the ground station.Software: The Pixhawk autopilot uses a custom version of Ardupilot/ArduPlane firmware to control the plane in both manual and autonomous modes and to create the simulations. The original firmware is modified from ArduPlane v3.9.0beta1 to allow disabling control surfaces during the flight. The onboard computer uses Robot Operating System(ROS) Kinetic Kame on Linux Ubuntu 16.04 (Xenial) to read the flight and state information from the Pixhawk using MAVROS package (the MAVLink node for ROS). More Information and Supplemental ToolsPlease visit http://theairlab.org/alfa-dataset for more information. It includes the description of each flight sequence, alternative download locations to view and download each individual flight sequence, correct citations to the relevant publications, supplemental code, and an open-source published method using the dataset.The corresponding paper explaining the dataset in more detail is currently under review in the International Journal of Robotics Research (IJRR). The pre-print (arXiv) of the paper can be accessed from our website at http://theairlab.org/alfa-dataset .The supplemental tools for reading and working with the dataset in C++, MATLAB and Python languages can be accessed from https://github.com/castacks/alfa-dataset. The repository also includes a C++ ROS-based tool for evaluating the new methods and all the ROS message type definitions for working directly with the ROS bags. Citing the WorkPlease refer to our website at http://theairlab.org/alfa-dataset to find the correct citation(s) if you are using this dataset.