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Memorial sobre el estado de los soldados en las misiones de California, 1775 marzo 11
No full textMemorial del Sargento Juan Ruiz y el Cabo Miguel Pericas sobre el estado deplorable de los soldados en las misiones de California y los servicios prestados en la expedición de San Carlos de Monterrey bajo el teniente Pedro Fages. Piden favor real a la corona española por los sacrificios que han hecho en la expedición y en las misiones. La carta detalla el numero de soldados y el racionamiento de alimentos, ropa y otros artículos básicos que tuvieron que hacer en cada misión para sobrevivir en los últimos cinco años. Los autores afirman ser representadores de los soldados de la compañía franca de voluntarios de Cataluña. —— Account of Sergeant Juan Ruiz and Corporal Miguel Pericas on the dire state of the soldiers in California's missions and services rendered in the San Carlos de Monterrey expedition under the command of Lieutenant Pedro Fages. They are requesting royal favor from the Spanish crown in light of the sacrifices they have made for the expedition and in the missions. The letter details the number of soldiers stationed and the rationing of food, clothes, and other staples they had to do in each mission to survive in the last five years. The authors claim to be representatives of the soldiers of the Free Company of Volunteers of Catalonia. 2 f. (4 p.
Replication Data for: Southard, P. J., Johnson, J. P. L., Rempe, D. M., & Matheny, A. M. (2022). Impacts of vegetation on dryland river morphology: Insights from spring-fed channel reaches, Henry Mountains, Utah. Water Resources Research, 58, e2021WR031701. https://doi.org/10.1029/2021WR031701
No full textThis dataset contains five zipped folders of files (zipped using the built-in function in Windows 10 Pro). DSW is the downstream Woodruff Canyon reach, TRL is the Trail Canyon reach, and USW is upstream Woodruff Canyon reach. Each folder contains a README.txt file that gives information for interpreting the data (especially useful for the "Pickled" python data for channel geometry).
In the "Model_Results" folder, models ending in "_r" describe the variable roughness model runs (the other folders and subfolders are the uniform hydraulic roughness runs (Manning's n=0.04). Data on modeled flow depth, velocity, and momentum are provided as TIF images with associated world files and projection information. "fbe" (in each filename) indicates that the model was run on the "filled bare earth elevation" topography (every model was run using this topography).
The "Processed_LiDAR_Data" folder contains TIFs of topography (fbe) and also of LVI (our LiDAR vegetation index). "Tabular_Data" contains xlsx spreadsheets for cross section-averaged discharge and related flow modeling data, and also tables of statistical fits. The "Xsections_wetted" folder contains png images of plots approximating channel cross sections. In particular and as also stated in the README.txt file, these cross sections will be close to the measured topography, but not exact, because these were calculated assuming a flat water surface, even though AnUga allows variations in water surface elevation to develop. This is just based on how we pulled the data initially (relative to water surface at a given location, rather than pulling topography across the cross section and water surface elevation separately)
Supporting Data for "The Martian Boulder Automatic Recognition System, MBARS"
No full textSupporting data for "The Martian Boulder Automatic Recognition System, MBARS". Data here include Boulder maps generated for several HiRISE images using MBARS as well as other techniques. These data serve as initial tests, verification, and demonstrations of the MBARS toolkit. Images used to make this data can be found via NASA's Planetary Data System, or at https://www.uahirise.org
Replication Data for: Imbrication and erosional tectonics recorded by garnets in the Sikkim Himalayas
No full textThis site has supplementary data for "Imbrication and erosional tectonics recorded by garnets in the Sikkim Himalayas" submitted to the journal Geosciences (MDPI)
Topographic and bathymetric lidar, orthophotos, sonar, and GPR data of and along the Devils River from Juno to Lake Amistad, Texas (Lidar Rasters 01 of 01)
No full textData collected in and along the Devils River from Juno to Lake Amistad, Texas include: Topographic and bathymetric lidar data in both 1m raster (BIL) and point (LAS) formats; Orthophotos in geoTiff format; and an index map (SHP) depicting data extents. For an area of more detailed study--a pool two kilometers long from Finnegan Springs to Dolan Falls--we're providing Sonar and GPR point (LAS) data and a 1m composite (lidar + GPR) raster (BIL) dataset. 0README.txt provides additional information
1-day Rainfall Block Maxima Data
No full textCSV files of quality controlled 1-day rainfall block maxima for each pooled region. Each file, as titled based on pooled region (i.e., AL1, AL2, etc), contains the pooled regions' county composites. The rainfall values are per year, if the year had a block maximum in precipitation
Moose Sores and Molt
No full textThermal imagery, molt scoring, histological and genomic data from investigation of sores in moose on the Kenai Peninsula, Alask
Socially Compliant Navigation Dataset (SCAND)
No full textThis dataset provides human-teleoperated socially compliant navigation demonstrations.
Social navigation is the capability of an autonomous agent to navigate in a socially compliant manner such that it recognizes and reacts to the objectives of other navigating agents, at least somewhat adjusting its own path in response, while also projecting signals that can help the other agents reciprocate. Some examples of socially compliant navigation behavior include sticking to the right of the road, following a crowd, overtaking a crowd, etc.
The image above shows two socially compliant navigation strategies around human crowds: a) moving with traffic, and b) sticking to the right of the road.
SCAND contains 25 miles and 8.7 hours of robot driven trajectories through a variety of social environments around the University of Texas at Austin campus.
The image below shows the area within the UT Austin campus where data was collected by teleoperating the robots.
SCAND CONTENTS
138 robot-driven trajectories.
15 days of social navigation data on 2 robots: a wheeled Clearpath Jackal, and a legged Boston Dynamics Spot.
Indoor and outdoor environments @ UT Austin campus.
2 highly crowded football game days (including a concert at the same time!)
Mild to heavily crowded environments.
4 different human demonstrators.
Each SCAND trajectory includes: RGB Azure Kinect camera, velodyne pointclouds, stereo camera, visual odometry (Spot), wheel odometry (Jackal), leg positions (Spot), joystick commands issued by the demonstrator, and monocular cameras (Spot). All data is stored in the ROSBAG data format http://wiki.ros.org/Bags/Format.
For each trajectory the accompanying video contains a speed up version of the robot's camera view, intended to provide a quick overlook of the scene in a particular trajectory for the end user.
SCAND files follow the following file naming convention : {Demonstrator tag}_{Robot name}_{Start location}_{End location}_{Day}_{Month}_{Date}_{Trajectory number}.bag/.mp4/.avi
The wheeled Jackal and legged Spot robots used to collect the data are shown below along with the multi-modal sensors implemented in both.
The image below illustrates five example scenarios from SCAND showing the RGB image and accompanying LIDAR. The scenarios have tags indicating the characteristics present in the trajectories such as: street crossing, narrow doorway, navigating through large crowds, vehicle interaction and crossing stationary queue.
In SCAND, we provide twelve different labels of socially compliant navigation events, along an entire trajectory for all the trajectories in the dataset. To label the dataset, the trajectories were manually annotated by monitoring the camera information. The labels are included in the AB_README.txt file alongside their frequency of appearance and in the metadata corresponding to each file published in this dataset. Note that the labels provided are coarse because they are not associated with a timestamp.
Detailed information about the trajectories, the human demonstrators, the robots, the sensors, and the software used to collect and process the data is also included in the AB_README.txt file published along with the data.
In Related Materials we point to software and hardware resources utilized in this project.
CAMERA PARAMETERS
Some Spot bag files contain parameters from Spot's built-in camera. The bag files lack the camera parameters from the Azure Kinect camera used for recording. To overcome this limitation, users have used camera intrinsic from Azure Kinect cameras that they have in the lab:
camera_matrix = np.array([[608.1159057617188, 0., 639.1864013671875], [0., 607.8717041015625, 363.0690612792969], [0., 0., 1.]])
dist_coeffs = np.array([0.6671934723854065, -3.1600592136383057, 0.0004678548430092633, -0.0004770300292875618, 1.8223274946212769, 0.5343461036682129, -2.9515035152435303, 1.7315576076507568])
Robot camera extrinsics were not recorded. Users may be able to empirically estimate the extrinsic values based upon the observed positions of the camera and LiDAR from the ifollowing mage of the robots.
Karnan, Haresh; Nair, Anirudh; Xiao, Xuesu; Warnell, Garrett; Pirk, Soeren; Toshev, Alexander; Hart, Justin; Biswas, Joydeep; Stone, Peter, 2022, "AB_robots_sensors.png", Socially Compliant Navigation Dataset (SCAND), https://doi.org/10.18738/T8/0PRYRH/7LIACN, Texas Data Repository, V5
DATA USAGE EXAMPLES
In Related Publications, users can find references to publications describing this study including examples of experiments conducted with the dataset.
The objective behind the SCAND dataset is to provide a rich set of socially compliant robot navigation demonstrations. An imitative policy was trained using the behavior cloning algorithm. Below is an example figure of how the behavior cloned policy trained using SCAND was tested using real-world deployments in a human participant study. Results were perceived to be socially compliant in comparison to the classical move_base navigation stack.
This dataset can be used to develop autonomous mobile robots that can navigate within human crowds in a socially compliant manner, and to analyze human reactions and behaviors in the presence of mobile robots of different morphologies.
DOWNLOADING LARGE DATASETS
The entire SCAND is ~ 400 gig of size. A few individual rosbag files are larger than 4 gig. These characteristics present limitations for downloading the entire dataset as well as individual large files through the DataVerse interface. Users that want to download data in bulk can use the download_data. py script provided in this dataset.</p
Ideal Baseband HE-SU 802.11 ax Downlink WiFi Signals with 20 MHz Bandwidth
No full textThis dataset consists IQ samples of ideal, single-user (HE-SU), 20 MHz bandwidth, downlink 802.11ax WiFi signals. The signals are generated using the WLAN toolbox in MATLAB (version R2021b). The sampling rate used for data generation is 20 MHz. The payload consists of random bits and each signal (data file) consists of five packets with no idle time between each packet. The IQ samples from each signal are stored as complex column vector in file WLAN802-11ax_data_#, where # is an integer used to index the file name. The corresponding file WLAN802-11ax_parameters_# consists of the unique configuration parameters used to generate the data file. If multiple transmit antennas (multiple data streams) are present, then each data stream is entered as a column vector in the data file. Details on the configuration settings are listed in the Readme available with this dataset