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12 results for “Speed limit”
RoadTrafficMARKS: Dataset of 10057 images (256x256 pixels) of Curved arrow, Straight arrow, Pedestrian crossing, Straight-right merge arrow, Merge arrow, Left arrow, Yield, Stop, Right arrow, Straight-left merge arrow, Speed limit and Straight-right-left merge arrow markings and their BoundingBox labels
<p><strong>The dataset consists of 10057 PNG images (256x256 pixels) of </strong><strong>high resolution aerial orthoimages </strong><strong>taggged with </strong><strong>twelve classes of traffic signals</strong><strong>: (1) Curved arrow, (2) Straight arrow, (3) Pedestrian crossing, (4) Straight-right merge arrow, (5) Merge arrow, (6) Left arrow, (7) Yield, (8) Stop, (9) Right arrow, (10) Straight-left merge arrow, (11) Speed limit and (12) Straight-right-left merge arrow markings, together with their corresponding Bounding Boxes. The dataset has been created in the framework of the SROADEX project to train an identification process based on artificial neural networks.</strong></p> <p><strong>The dataset was created by manually tagging the twelve class of marks on orthoimage tiles of 256x256 pixels </strong><strong><strong>with the LabelMe tool</strong>. After the semantic labeling (manual digitalization of the contour of the signals), a transformation and random splitting process has been carried to prepare the data for the neural networks</strong><strong><strong> training</strong>. It resulted in 80% of the images for training (8031), 10% for validation (1000) and 10% for testing (1021).</strong></p> <p><strong>The next table presents the number of images of each class on the </strong><strong><strong>"train", "valid" and "test" </strong>sets.</strong></p> <table> <tbody> <tr> <td> </td> <td>Train</td> <td>Valid</td> <td>Test</td> <td>Total</td> </tr> <tr> <td>CURVED ARROW</td> <td>319</td> <td>43</td> <td>48</td> <td>410</td> </tr> <tr> <td>STRAIGHT ARROW</td> <td>6730</td> <td>806</td> <td>843</td> <td>8379</td> </tr> <tr> <td>PEDESTRIAN CROSSING</td> <td>3881</td> <td>465</td> <td>485</td> <td>4831</td> </tr> <tr> <td>STRIGNT-RIGHT MERGE ARROW</td> <td>1373</td> <td>187</td> <td>177</td> <td>1737</td> </tr> <tr> <td>MERGE ARROW</td> <td>550</td> <td>91</td> <td>59</td> <td>700</td> </tr> <tr> <td>LEFT ARROW </td> <td>238</td> <td>33</td> <td>31</td> <td>302</td> </tr> <tr> <td>YIELD</td> <td>356</td> <td>47</td> <td>40</td> <td>443</td> </tr> <tr> <td>STOP</td> <td>141</td> <td>19</td> <td>15</td> <td>175</td> </tr> <tr> <td>RIGHT ARROW</td> <td>836</td> <td>93</td> <td>111</td> <td>1040</td> </tr> <tr> <td>STRIGNT-LEFT MERGE ARROW</td> <td>152</td> <td>19</td> <td>21</td> <td>192</td> </tr> <tr> <td>SPEED LIMIT</td> <td>392</td> <td>50</td> <td>45</td> <td>487</td> </tr> <tr> <td>STRIGNT-RIGHT-LEFT MERGE ARROW</td> <td>82</td> <td>9</td> <td>9</td> <td>100</td> </tr> </tbody> </table>
Center-of-mass and minimal speed limits of the great hammerhead
The great hammerhead is denser than water, and hence relies on hydrodynamic lift to compensate for its lack of buoyancy, and on hydrodynamic moment to compensate for a possible misalignment between centers of mass and buoyancy. Because hydrodynamic forces scale with the swimming speed squared, whereas buoyancy and gravity are independent of it, there is a critical speed below which the shark cannot generate enough lift to counteract gravity, and there are anterior and posterior center-of-mass limits beyond which the shark cannot generate enough pitching moment to counteract the buoyancy-gravity couple. The speed and center-of-mass limits were found from numerous wind-tunnel experiments on a scaled model of the shark. In particular, it was shown that the margin between the anterior and posterior center-of-mass limits is a few tenths of the product between the length of the shark and the ratio between its weight in and out of water; a diminutive 1% body length. The paper presents the wind tunnel experiments, and discusses the roles that the cephalofoil and the pectoral- and caudal- fins play in longitudinal balance of a shark.
Data from: Weather conditions determine attenuation and speed of sound: environmental limitations for monitoring and analysing bat echolocation
Echolocating bats are regularly studied to investigate auditory-guided behaviours and as important bioindicators. Bioacoustic monitoring methods based on echolocation calls are increasingly used for risk assessment and to ultimately inform conservation strategies for bats. As echolocation calls transmit through the air at the speed of sound, they undergo changes due to atmospheric and geometric attenuation. Both the speed of sound and atmospheric attenuation, however, are variable and determined by weather conditions, particularly temperature and relative humidity. Changing weather conditions thus cause variation in analysed call parameters, limiting our ability to detect and correctly analyse bat calls. Here, I use real-world weather data to exemplify the effect of varying weather conditions on the acoustic properties of air. I then present atmospheric attenuation and speed of sound for the global range of weather conditions and bat call frequencies to show their relative effects. Atmospheric attenuation is a non-linear function of call frequency, temperature, relative humidity and atmospheric pressure. While atmospheric attenuation is strongly positively correlated with call frequency, it is also significantly influenced by temperature and relative humidity in a complex non-linear fashion. Variable weather conditions thus result in variable and unknown effects on the recorded call, affecting estimates of call frequency and intensity, particularly for high frequencies. Weather-induced variation in speed of sound reaches up to about ±3%, but is generally much smaller and only relevant for acoustic localisation methods of bats. The frequency- and weather-dependent variation in atmospheric attenuation has a three-fold effect on bioacoustic monitoring of bats: it limits our capability (1) to monitor bats equally across time, space, and species, (2) to correctly measure frequency parameters of bat echolocation calls, particularly for high-frequencies, and (3) to correctly identify bat species in species-rich assemblies or for sympatric species with similar call designs.
ArcGIS GDB for CPBS Report 23UCB01 - A Context-sensitive Street Classification Framework for Speed Limit Setting
<p>ArcGIS .gdb geodatabase files and a data dictionary in Excel format.</p>
Data for: Tensile cracks can shatter classical speed limits
<p class="MsoNormal"><span>Brittle materials fail by means of rapid cracks. At</span> <span>their tips, tensile cracks dissipate elastic energy </span><span>stored in the surrounding material to create newly</span> <span>fractured surfaces, precisely maintaining `energy </span><span>balance' by exactly equating the energy flux with</span> <span>dissipation. Using energy balance, fracture</span> <span>mechanics perfectly describes crack motions;</span> <span>accelerating from nucleation to their maximal speed </span><span>of c</span><span><sub>R</sub>,</span><span> </span><span>the Rayleigh wave speed. Beyond c</span><sub><span>R</span></sub><span>, tensile</span> <span>fracture is generally considered to be impossible</span><span>. B</span><span>y the use of brittle hydrogels,</span> <span>we experimentally demonstrate that a wholly </span><span>new and different class of tensile cracks</span><span> that move faster than the shear wave speed, c<sub>s</sub>, </span><span>exists. The principle of energy</span> <span>balance no longer dictates their dynamics; this new </span><span>branch of cracks smoothly surpasses </span><span>c<sub>s</sub> </span><span>to reach</span> <span>unprecedented speeds that approach the speed of </span><span>dilatation waves. The transition from `classical'</span> <span>cracks to these `supershear' cracks takes place at </span><span>critical values of applied strains. We, furthermore,</span> <span>show that the values of these, rather moderate (12–14%), critical strains are intimately related to the</span> <span>microscopic material structure.</span> <span>This new mode of tensile</span> <span>fracture represents a fundamental paradigm shift in</span> <span>our understanding of 'how things break'.</span></p>
Speed Limits: Food Intake and Eating Behaviour of Ultra-processed and Unprocessed Foods
ClinicalTrials.gov study NCT04280146. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data for: Tensile cracks can shatter classical speed limits
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Data from: Weather conditions determine attenuation and speed of sound: environmental limitations for monitoring and analysing bat echolocation
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Center-of-mass and minimal speed limits of the great hammerhead
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Data from: The travel speeds of large animals are limited by their heat-dissipation capacities
<div> <h2>Purpose</h2> <p>This is an extension of the travel speed dataset published by Dyer et al. 2023 in PLOS Biology that includes additional metadata. Data follow the same aggregation to species level by grouping records across unique combinations of study and species while preserving variation across important categorical covariates (listed below). </p> </div> <div> <h2>Scripts</h2> <ul> <li><code>scripts/modelling/fit_model.R</code>: Fits mechanistic locomotion models to aggregated data using RStan. Plots predictions and outputs parameter estimates of competing models.</li> </ul> </div> <div> <h2>Data Columns</h2> <p>Each heading below describes a column of the travel speed dataset.</p> <p><strong>move_speed_ref</strong>: Unique reference number for each study reporting animal travel speed. The corresponding references are contained within the supplementary reference list file.</p> <p><strong>move_mass_ref</strong>: Unique reference number for each study reporting animal body mass. The corresponding references are contained within the supplementary reference list file.</p> <p><strong>scientific_name</strong>: Scientific name of the species according to the taxonomy of the Global Biodiversity Information Facility (accessed via GBIF.org during 2022). A small number of studies that report only the genus name or common name of the species are reported in our dataset as <em>Genus sp.</em> (e.g. <em>Gazella sp.</em>). One study, which estimated the travel speed and body mass of an unknown species of mouse via camera traps, is reported in our dataset as <em>Amazon mouse sp.</em></p> <p><strong>move_movement_mode</strong>: Categorical value indicating whether the reported travel speed corresponds to an animal engaged in flying, running, or swimming. This allows species to be accommodated that are capable of multi-modal locomotion (e.g. northern elephant seal, <em>Mirounga angustirostris</em>).</p> <p><strong>taxon_group</strong>: Categorical value indicating membership to one of eight animal groups (amphibian, arthropod, cnidarian, bird, fish, mammal, mollusc, reptile).</p> <p><strong>thermo_reg</strong>: Categorical value indicating thermoregulatory strategy, i.e. the contribution of metabolic heat production to the maintenance of core body temperature during rest:<br>- <strong>ectotherm</strong>: negligible contribution, body temperature matches ambient temperature<br>- <strong>mesotherm</strong>: weak contribution with partial thermal stability, as in tunas, lamnid sharks, and leatherback sea turtles<br>- <strong>endotherm</strong>: strong contribution with metabolic stability even when ambient temperatures are significantly below body temperature</p> <p><strong>move_medium</strong>: Categorical value indicating whether the animal’s travel speed was measured during locomotion within the terrestrial realm (air) or aquatic realm (water).</p> <p><strong>move_speed_method</strong>: Categorical value indicating whether travel speed was estimated directly (i.e. instantaneously via direct observation in real-time, animal-attached speedometer, or video recording) or indirectly from higher-resolution telemetry data (i.e. from changes in an animal’s spatial coordinates at intervals < 30 minutes apart).</p> <p><strong>move_study_condition</strong>: Categorical value indicating whether travel speed was estimated under natural field conditions or under a controlled laboratory setting such as within an aquarium or mesocosm. Travel speeds of the smallest animals (e.g. arthropods) can only feasibly be estimated within a controlled setting.</p> <p><strong>move_avgspeed_value</strong>: Categorical value indicating whether average travel speed was reported as a mean or median within the original study.</p> <p><strong>move_bodymass_kg</strong>: Continuous value indicating the average adult body mass (units: kilograms) of the species. In cases where the study’s reference numbers <code>move_speed_ref</code> and <code>move_mass_ref</code> differ, we referred to secondary literature sources to assign the average adult body mass of the species. In cases where only body length was given, we used published allometric equations to estimate the wet body mass.</p> <p><strong>move_avgspeed_ms</strong>: Continuous value indicating the average (geometric mean) travel speed (units: metres per second) of the species reported within the study.</p> <p><strong>move_speed_source</strong>: Categorical value indicating the sampling level at which travel speed measurements were reported within the study.<br>- <code>indv</code>: travel speed reported from an individual animal. - <code>avgs</code>: travel speed reported as an average (mean or median) across multiple individuals from the same species.</p> <p><strong>move_individuals_n</strong>: Integer value indicating the number of individual animals from which travel speed measurements were obtained within the study.</p> <p><strong>move_measurements_n</strong>: Integer value indicating the number of travel speed measurements taken across individuals within the study. This value can exceed <code>move_individuals_n</code> when repeated measurements were made from the same individuals.</p> <p><strong>move_ygeo_min</strong>: Continuous value indicating the minimum reported latitude (decimal degrees) of the study location(s).</p> <p><strong>move_ygeo_max</strong>: Continuous value indicating the maximum reported latitude (decimal degrees) of the study location(s).</p> <p><strong>move_xgeo_min</strong>: Continuous value indicating the minimum reported longitude (decimal degrees) of the study location(s).</p> <p><strong>move_xgeo_max</strong>: Continuous value indicating the maximum reported longitude (decimal degrees) of the study location(s).</p> <p><strong>move_date_min</strong>: Earliest reported date of data collection within the study (format: YYYY-MM-DD).</p> <p><strong>move_date_max</strong>: Latest reported date of data collection within the study (format: YYYY-MM-DD).</p> <p><strong>move_study_country</strong>: Country in which the study was conducted.</p> <p><strong>move_study_location</strong>: A description of the study location.</p> </div> <div> <h2>Version Notes</h2> <p>This updated version of the travel speed dataset extends the aggregated dataset published in <br>Dyer et al. 2023, PLOS Biology<strong> </strong>through the inclusion of additional metadata fields.<br>Metadata follow the same aggregation to species level by grouping records across unique combinations of study and species while preserving variation across important categorical covariates (listed above).</p> <div> <h3>Key Updates</h3> <ul> <li><strong>Added spatial metadata</strong>: minimum and maximum latitude/longitude, study country, and study location.</li> <li><strong>Added temporal metadata</strong>: earliest and latest reported dates of data collection (YYYY-MM-DD).</li> <li><strong>Refined sampling information</strong>: number of individuals (<code>move_individuals_n</code>) and number of speed measurements (<code>move_measurements_n</code>) are now reported, where available.</li> </ul> </div> </div>
Reduce Speed Limits From 90km/h to 80 km/h on French Roads: Impact on Mortality
ClinicalTrials.gov study NCT03798990. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A case study for the implementation of an integrated variable speed limit (VSL) control strategy in a freeway section of I-80 based on SUMO simulations
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