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76 results for “TLS”
Text-fig. 16. Photomicrographs of thin sections of specimen BP/16/1734, Terminalioxylon mozambicense sp. nov. from Mhengere Hill, Gorongosa, Mozambique. a: TS with round vessels, scanty paratracheal to vasicentric parenchyma, diffuse and narrow terminal or initial bands; b: TS at higher magnification, note the very narrow rays; c: TLS, vessels with small alternate pits, and partly tylosed; d–g: TLS with crystals (small white arrows) in the parenchyma cells, medium to thick-walled fibres and uniseriate, low rays; h: TLS, rays up to 20 cells high; i: RLS rays with procumbent body cells and 1–2 rows of marginal upright cells. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 16. Photomicrographs of thin sections of specimen BP/16/1734, Terminalioxylon mozambicense sp. nov. from Mhengere Hill, Gorongosa, Mozambique. a: TS with round vessels, scanty paratracheal to vasicentric parenchyma, diffuse and narrow terminal or initial bands; b: TS at higher magnification, note the very narrow rays; c: TLS, vessels with small alternate pits, and partly tylosed; d–g: TLS with crystals (small white arrows) in the parenchyma cells, medium to thick-walled fibres and uniseriate, low rays; h: TLS, rays up to 20 cells high; i: RLS rays with procumbent body cells and 1–2 rows of marginal upright cells.
Text-fig. 15. Photomicrographs of thin sections of holotype BP/16/1738, Sorindeioxylon gorongosense gen. et sp. nov. from Muaredzi site 5, Gorongosa, Mozambique. a: TS, note the irregularly spaced and very narrow bands of parenchyma and mostly solitary vessel elements; b: TS at higher magnification with narrow rays; c: radial longitudinal section (RLS), rather oblique but shows the alternate, small-to-medium inter-vessel pits; d: tangential longitudinal section (TLS), rays are 1–3 cells wide but maintain the same width. Small arrow towards the right hand ray indicates a prismatic crystal in the ray cell; e: TLS rays with fibres in between; f: RLS showing mixed ray cells (upright, square and procumbent) poorly preserved. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 15. Photomicrographs of thin sections of holotype BP/16/1738, Sorindeioxylon gorongosense gen. et sp. nov. from Muaredzi site 5, Gorongosa, Mozambique. a: TS, note the irregularly spaced and very narrow bands of parenchyma and mostly solitary vessel elements; b: TS at higher magnification with narrow rays; c: radial longitudinal section (RLS), rather oblique but shows the alternate, small-to-medium inter-vessel pits; d: tangential longitudinal section (TLS), rays are 1–3 cells wide but maintain the same width. Small arrow towards the right hand ray indicates a prismatic crystal in the ray cell; e: TLS rays with fibres in between; f: RLS showing mixed ray cells (upright, square and procumbent) poorly preserved.
Dataset: Telos Corporation (TLS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
TLS 1.3 Handshake Data Collected By Lumen
<p>The data file <code>ccr_tls_release.csv.xz</code> contains handshake records and extension information from the TLS connections from the Lumen dataset up to early November 2019.</p>
Global dataset of co-incident TLS-derived and harvested tree biomass
<p>This dataset was used to producee the figures and statistics of the publication "Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions".</p> <p>This dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns. Elaborate method descriptions can be found in the publication or in the following original publications:</p> <ul> <li>Burt, A., Boni Vicari, M., da Costa, A. C. L., Coughlin, I., Meir, P., Rowland, L., et al. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science 8, 201458. doi:10.1098/rsos.201458</li> <li>Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., et al. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution 6, 198–208. doi:10.1111/2041-210X.12301</li> <li>Demol, M., Calders, K., Krishna Moorthy, S. M., Van den Bulcke, J., Verbeeck, H., and Gielen, B. (2021). Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning. Trees 35, 671–684. doi:10.1007/s00468-020-02067-7</li> <li>Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., et al. (2018). Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution 9, 223–234. doi:10.1111/2041-210X.12904</li> <li>Hackenberg, J., Wassenberg, M., Spiecker, H., and Sun, D. (2015). Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6, 1274–1300. doi:10.3390/ f6041274</li> <li>Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., and Mathys, L. (2021). Aboveground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 1–16doi:10.1093/aob/mcab002</li> <li>Lau, A., Calders, K., Bartholomeus, H., Martius, C., Raumonen, P., Herold, M., et al. (2019). Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana. Forests 10, 527. doi:10.3390/f10060527</li> <li>Momo Takoudjou, S., Ploton, P., Sonke, B., Hackenberg, J., Griffon, S., Coligny, F., et al. (2018). Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods in Ecology and Evolution 9, 905–916.<br> doi:10.1111/2041-210X.12933</li> <li>Stovall, A. E., Vorster, A. G., Anderson, R. S., Evangelista, P. H., and Shugart, H. H. (2017). Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sensing of Environment 200, 31–42. doi:10.1016/j.rse.2017.08.013<br> </li> </ul> <p><br> </p>
Individual tree scans by TLS: the Italian dataset
<p>The dataset contains single tree scans collected in different forests, with different forest managment, mainly during leaf off conditions. Forests belong to Italian ecosystems. The objective of this dataset is to publicly share the effort made from the Laboratory of forest geomatics (ForGeoLab) of CREA Research centre for forestry and wood.</p> <p>Such measures can be profitably used to measure tree volume, tree biomass, and tree architectural traits, without tree felling.</p> <p><a href="https://zenodo.org/api/files/f5a90e06-48e2-4987-a149-d08399754963/TLS_SingleTrees_CREA_202209.ods">TLS_SingleTrees_CREA_202209.ods</a> is a metadata file contains some info on evrey single tree of this dataset.</p> <p>The ascii.zip file contains single tree scans as reported in metadata file.</p>
Vertical plant profiles for Dassenbos (NL, 2014-2018, TLS); Wytham Woods (UK, 2022, LEAF) & Northern Australia (2021-2022, LEAF)
<p>This dataset was described and used for the analysis of the following publication:<br> <em>StrucNet: A global network for automated vegetation structure monitoring. Brede, B., Newnham, G., Culvenor, D., Armston, J., Bartholomeus, H., Griebel, A., Hayward, J., Junttila, S., Lau, A., Levick, S., Morrone, R., Origo, N., Pfeifer, M., Verbesselt, J. & Herold, M. Remote Sensing in Ecology and Conservation (accepted)</em></p> <p><strong>Any use of this dataset should cite the paper above </strong>(Creative Commons Attribution 4.0 International Public License).</p> <p>Contact: kim.calders@ugent.be</p> <p> </p> <p>================================================<br> Dataset<br> ================================================</p> <p>1) TLS vertical plant profiles Dassenbos. Five-year dynamics of forest structure for the four sampling locations in Dassenbos. Data were collected using the same measurement protocol and data analysis using https://www.pylidar.org/ as described in Calders et al. (2015) using a zenith range of 35-70 degrees for 184-186 (some scans were discarded for quality purposes) measurement days during the period from February 2014 to November 2018. The data repository contains the vertical plant profiles and plotting code (Fig 1 in paper)</p> <p>2) One-year dynamics of vegetation structure for a tropical savanna site in Northern Australia (Fig 2 in paper) and Wytham Woods (Fig 3 in paper). The data repository contains the vertical plant profiles derived from LEAF data and plotting code.</p>
Malthi2015_TLS_RawData
<p>Malthi provides a nearly unique example of a fully excavated Middle Hellenic (MH) settlement. The excavated remains include a series of houses, storage facilities, entrance ways, and possible public architecture, enclosed by a settlement wall. Malthi is perhaps the first MH site at which a major restructuring of the settlement architecture was observed, as proposed by Valmin. The PIs research focuses on the socio-cultural motivations for the decision to make substantial revisions to the urban layout, as well as impacts on the life of the settlement and the surrounding area. PIs Rebecca Worsham, Prof. Donald Haggis, and Prof. Michael Lindblom collaborated with SPARC researchers to produce an accurate and analysis-ready plan of the exposed standing remains and a digital elevation model (DEM) of the settlement at Malthi, using a combination of scanning and structure from motion techniques. </p> <p>This work was embedded in a larger project, and aimed to reconsider the architecture of the site including prior identifications of room types (by Valmin), and to attempt to identify coherent buildings. This improved survey, mapping and interpretation exercise was intended to support a rethinking about the settlement’s organization, and re-organization, as a whole. The production of a local DEM of the site was intended to aid in a consideration of access routes and forms a starting point for establishing the role of Malthi in the larger Soulima Valley network, which may function as a major corridor.</p> <p>This upload contains the TLS raw data created during the Malthi project in 2015. See the Index file for a list of files and folders.</p>
Dataset Railway sinkhole (leveling, GPR, TLS)
<p>Dataset of the paper titled: Sinkhole Subsidence Monitoring Combining Terrestrial Laser Scanner and High-Precision Leveling</p>
Dataset for TLS understory characterization: an exploratory study on hazel grouse in Italian Alps
<p>This dataset contains data collected in the middle of June 2021 with a mobile terrestrial laser scanner (mobile ZEB TLS) in 10 squared areas, of approximatively 20x20m each, in Adamello Brenta National Park. Data have been normalized using TreeLS package in R.</p> <p>This research was funded with the contribution of the Italian Ministry of Agricultural, Food, and Forestry Policies (MiPAAF) sub-project “Precision Forestry” (AgriDigit program) (DM 36503.7305.2018 of 20/12/2018).</p>
Terrestrial laser scanner (TLS) and destructive measurements in short-rotation woody crops (SRWCs) in NE of Romania
<p>The data are obtained from hybrid poplar crops installed in NE Romania, managed in short rotation (SRWCs) between 5, 6, and 7 growing seasons. Planted every spring, outside the growing season, at a depth of 0.6 m in the ground with two clones: AF8 and Pannonia. Rods (2 m long cuttings) were used as planting material for a density of 1667 trees/ha (3 x 2 m). For the estimation of volume and biomass, scans and gravimetric measurements were performed on tree component parts (trunk/stem and branches). Scanning of the sample areas (3 x 10 trees for each variant) was carried out using the Z+F Imager 5010. The scanning period was outside the growing seasons. The destructive (gravimetric) method of estimating biomass involves weighing the trees (184 in total) by component parts: trunk and branches (10 g accuracy). The moisture content of the components was determined by drying the samples (wood discs and branches) at a temperature of 105 °C to a constant mass. The QSM Model in MatLab was used for tree volume reconstruction. Where d is the diameter (cm) - measured at the base and at 1 m height, h (m) is the total height - measured after harvesting by roulette and by QSM reconstruction (after TLS scan). B (kg) is the dry mass of the whole tree (tot = branches + stem) and stem, and V (litres/ dm3) is the tree volume (tot) for the whole tree and stem. Branch weight results by difference for both variables.</p>
TLS data of forest plot
<p>TLS data acquired with a Riegl VZ-2000i within a forest plot in the Zirbenwald near Obergurgl, including 23 scanning positions using the one-touch-mode. Ground classification was done based on progressive TIN densification (Axelsson 2000). Projection: UTM 32N (EPSG 32632), fine registration based on the ALS-data from 2017.</p>
TLS Handshake Data Collected By Lumen
<p>This dataset contains TLS handshakes collected from Android devices running the privacy-enhancing app called Lumen between 2015 and 2017. It was used to conduct the first study of TLS usage in Android apps at scale, in a paper titled "Studying TLS Usage in Android Apps" published at ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) 2017.</p> <p>It contains anonymized TLS handshake messages (Client Hello, Server Hello), server certificate chains, and general device information and default TLS settings where available.</p> <p> </p>
Dataset Using TLS Fingerprints for OS Identification in Encrypted Traffic
<p>The dataset consists of data from three different sources; flow records collected from the university backbone network, log entries from the two university DHCP (Dynamic Host Configuration Protocol) servers and a single RADIUS (Remote Authentication Dial In User Service) accounting server. The data was collected from 2019-07-12 00:00 to 2019-07-16 23:59 with a few hours overhead on both sides of the interval for the log entries to cover long connection sessions overlapping to and from the time frame.</p> <p>We measured the flow data from the university uplink to the Internet. In the dataset, we kept only flows with source IP addresses from university wireless networks (Eduroam). The flow data was then enriched with information from DHCP and RADIUS servers to contain ID of the RADIUS session and operating system od the transmitting device as derived from DHCP logs.</p> <p>The dataset is in the form of CSV file with the following information fields important for OS identification:</p> <ul> <li>Basic flow features <ul> <li>Date flow start - timestamp of flow start</li> <li>Date flow end - timestamp of flow end</li> <li>Src IPv4 - source IPv4 address</li> <li>sPort - source L4 port</li> <li>Dst IPv4 - destination IPv4 address</li> <li>dPort - destination L4 port</li> </ul> </li> <li>Extended TCP/IP parameters <ul> <li>SYN size - the size of the initial SYN packet of a TCP connection (in bytes)</li> <li>TCP win - value of TCP Window size parameter</li> <li>TCP SYN TTL - observed TTL value</li> </ul> </li> <li>HTTP parameters <ul> <li>HTTP Host - hostname from the HTTP request</li> <li>HTTP UA OS - OS identification based on user-agent</li> <li>HTTP UA OS MAJ - OS identification based on user-agent</li> <li>HTTP UA OS MIN - OS identification based on user-agent</li> <li>HTTP UA OS BLD - OS identification based on user-agent</li> </ul> </li> <li>TLS parameters <ul> <li>TLS SNI - Server Name Indication field</li> <li>TLS SNI length - length of SNI in bytes</li> <li>TLS Client Version - TLS client hello Version field</li> <li>Client Cipher Suites - list of supported cipher suites</li> <li>TLS Extension Types - list of extension IDs</li> <li>TLS Extension Lengths - list of extension lengths</li> <li>TLS Elliptic Curves - list of supported curves (or supported groups in TLS1.3)</li> <li>TLS EC Point Formats - list of EC formats</li> </ul> </li> <li>Log based extensions <ul> <li>Session ID - ID of the session to match flows from one device</li> <li>Ground Truth OS - OS name derived from log data</li> </ul> </li> </ul> <p>The observed network traffic contains privacy-sensitive information. Hereby, we declare that the monitored data used for our research were processed in accordance with the EU General Data Protection Regulation 2016/679. The published dataset was anonymized with cryptographic means using Crypto-PAn algorithm to preserve both the scientific value and user privacy.</p> <p>When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{lastovicka2020using, title={Using TLS Fingerprints for OS Identification in Encrypted Traffic}, author={La{\v{s}}tovi{\v{c}}ka, Martin and {\v{S}}pa{\v{c}}ek, Stanislav and Velan, Petr and {\v{C}}eleda, Pavel}, booktitle = {2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)}, doi = {http://dx.doi.org/10.1109/NOMS47738.2020.9110319}, keywords = {OS fingerprinting;passive monitoring;IPFIX;TLS}, isbn = {978-1-7281-4973-8}, pages = {1-6}, publisher = {IEEE Xplore Digital Library}, year = {2020} }</code></pre> <p> </p>
Rocktopo Sensing Mountains Summer School 2024: UAV images, pointcloud and TLS pointclouds
<p>This dataset contains high-resolution 3D scans of an outdoor rock climbing wall, captured using both <strong>Unmanned Aerial Vehicle (UAV)</strong> photogrammetry and <strong>Terrestrial Laser Scanning (TLS)</strong>. The dataset includes images from the UAV, the reconstructed point cloud derived from photogrammetry, and the point cloud from TLS. These scans were conducted on the following dates:</p> <p>The used TLS was a Trimble X7 and the UAV a DJI Spark.</p> <ul> <li> <p><strong>TLS (Terrestrial Laser Scanning)</strong>: Provided in <code>TLS_240924</code> or <code>TLS_240925</code>, this folder contains the point cloud data captured via TLS, offering highly accurate and detailed 3D geometry of the climbing wall surface. In LAS format.</p> </li> <li> <p><strong>UAV Images: </strong>Stored in <code>UAV_240924</code> or <code>UAV_240925</code>, these subdirectories contain a set of aerial images taken from the UAV, which were later used to reconstruct the 3D point cloud of the rock climbing wall. In JPEG format.</p> </li> <li> <p><strong>Photogrammetric Point Cloud (LAS)</strong>: The resulting 3D point cloud from UAV photogrammetry is stored in <code>LAS_240924</code> or <code>LAS_240925</code>. These files represent the spatial data and geometry derived from processing the UAV images using photogrammetry techniques. In LAS format.</p> </li> <li><strong>Aligned Point Cloud (ICP)</strong>: The ICP aligned clouds that were used for analysis are stored in <code>ICP</code>. These files represent the spatial data and geometry derived from aligning the TLS and UAV clouds. In LAS format.</li> <li><strong>Potree Point Cloud (folder)</strong>: Octree converted pointclouds used in the <a href="https://github.com/jurriandoorbos/potree-rocktopo" target="_blank" rel="noopener">Potree rocktopo</a> visualization.</li> <li><strong>Route Points</strong>: Polylines of every route in the lower location, in .poly (x y z) format.</li> </ul> <p>The combination of both photogrammetric and laser scanning data provides a comprehensive and high-fidelity 3D representation of the climbing wall, useful for geospatial analysis, surface reconstruction, and outdoor modeling applications. The pointclouds are not georeferenced.</p> <p><strong>Please note: </strong>the TLS and UAV were taken simultaneously: the same location is therefore:<strong> UAV/LAS_240924 fits with TLS_250924</strong> and vice-versa.</p> <p> </p>
Laerdalselvi TLS
<p>Topographic dataset covering parts of Lærdalselva, Norway.<br> Dataset acquired using a Leica ScanStation P50 terrestrial laser scanner.</p>
TLS measurements in the Qinghai-Tibet Engineering Corridor
<p>Deformation monitoring was performance using a TLS with network RTK (Real-Time Kinematic) service provided by national geodetic control network (NGCN) for the China geodetic coordinate system 2000 (CGCS 2000) of permanent reference stations for GNSS (Global Navigation Satellite System).</p> <p>As a supplement to the TLS point cloud data, we prepared the Sentinel-1 deformation data for the freeze-thaw stage in the study area from 2014 to 2020 using Interferometric Synthetic Aperture Radar (InSAR) technology.</p> <p> </p>
140511 3DPC of a roundabaout, A7 Alenda Golf, Alicante (Spain) TLS C10
<p>3DPC of an excavated <a href="https://www.google.com/maps/search/alenda+golf/@38.3497605,-0.6695302,152m/data=!3m1!1e3">roundabout under A7 highway</a> close to Alenda Golf in Alicante, Spain. </p> <p>It was scanned using a Leica C10 scanstation and registered by Cyclone.</p>
Multiway Registration Capability Study in Increasing the Accuracy of Registration Results for Infrastructure and Mining Pits Terrestrial Laser Scanner (TLS) Data Point Cloud
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
TLS_input_data_Ciarella_et_al_2023
<p>Preprocessed pairs for TLS search</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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