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6,588 results for “Blocks”
Raw microclimate data from plots at burned areas from the 2020 Holiday Farm fire in the Andrews Experimental Forest and Hagan Block, 2022-2024
This dataset includes a suite of microclimate sensor data from areas burned by the 2020 Holiday Farm fire within the McKenzie River basin. A total of 42 microclimate sensor suites were installed in July and August of 2022 distributed across RS01, RS08, RS15, WS02, WS09, WS01, HGBK. All sensor locations are within the Holiday Farm Fire footprint and within Permanent Sample Plots (PSPs). An additional sensor was placed within the Primary meteorological station (PRIMET) of the HJ Andrews Experimental Forest for comparison and calibration between open-air measurements. Sites are stratified across three treatment variables including 1) fire severity: high or low; 2) management: managed or unmanaged; 3) water balance: moister or drier topographic positions. This resulted in eight treatment blocks, each with 5 sensor suite replicates. Each microclimate site includes a suite of measurements: Hobo temperature and relative humidity sensor installed at 1.5m within a gill shield (recording at 30 minute interval), one TOMST TMS-4 temperature and soil moisture sensor was located within 1 m of plot center (recording at 15 minute interval). The TOMST sensors include air temperature sensors at 15cm, 2cm, and soil temperature at -6cm in soil near-surface. Soil moisture is measured across the ~10cm near surface zone.
The dynamics of marsh-channel slump blocks: an observational study using repeated drone imagery
We analyzed the spatial and temporal dynamics of slump blocks within Dean Creek, a creek located on Sapelo Island (GA) and surrounded by salt marsh. To accomplish this, we utilized 11 images of Dean Creek captured between March 2020 and March 2021, which were acquired by using a DJI Matrice 210 UAV. For each image, we manually digitized the perimeters of slump blocks, which have the distance from intact marsh boundary exceeding 0.3 m. These digitized perimeters were subsequently converted into polygons to determine the size, number and cumulative area of slump blocks in each image. Temporal changes to the slump blocks between subsequent images were also analysed. The coordinate system of these polygon data is WGS 1984 UTM Zone 17N.
Lake Sediment Pollen from Fresh Pond on Block Island RI from 13581 BP to Present
Aim We analyzed a dataset composed of multiple palaeoclimate and lake-sediment pollen records from New England to explore how postglacial changes in the composition and spatial patterns of vegetation were controlled by regional-scale climate change, a subregional environmental gradient, and landscape-scale variations in soil characteristics. Location The 120,000-km2 study area includes parts of Vermont and New Hampshire in the north, where sites are 150-200 km from the Atlantic Ocean, and spans the coastline from southeastern New York to Cape Cod and the adjacent islands, including Block Island, the Elizabeth Islands, Nantucket, and Martha’s Vineyard. Results Boreal forest featuring Picea and Pinus banksiana was present across the region when conditions were cool and dry 14,000-12,000 calibrated 14C yrs before present (ybp). Pinus strobus became regionally dominant as temperatures increased between 12,000 and 10,000 ybp. The composition of forests in inland and coastal areas diverged in response to further warming after 10,000 ybp, when Quercus and Pinus rigida expanded across southern New England, while conditions remained cool enough in inland areas to maintain Pinus strobus. Increasing precipitation allowed Tsuga canadensis, Fagus grandifolia, and Betula to replace Pinus strobus in inland areas during 9000-8000 ybp, and also led to the expansion of Carya across the coastal part of the region beginning at 7000-6000 ybp. Abrupt cooling at 5500-5000 ybp caused sharp declines in Tsuga in inland areas and Quercus at some coastal sites, and the populations of those taxa remained low until they recovered around 3000 ybp in response to rising precipitation. Throughout most of the Holocene, sites underlain by sandy glacial deposits were occupied by Pinus rigida and Quercus. Main conclusions Postglacial changes in the composition and spatial pattern of New England forests were controlled by long-term trends and abrupt shifts in temperature and precipitation, as well as by
newbi4fmri2020 Variant1 Suboptimal Block Designs
Open the record for dataset details and reuse information.
Nitrogen fixation (acetylene reduction) and denitrification (acetylene block) data from streams across ecoclimatic domains in the United States, 2017-2019
We conducted a cross-ecoregion study to test the hypothesis that N-fixation and denitrification would co-occur in streams and rivers across a range of reactive N concentrations. Between 2017 and 2019, we sampled 30 streams in 13 ecoregions, using chambers to quantify N-fixation using acetylene reduction and denitrification using acetylene block. 25 of the study streams were part of the National Ecological Observatory Network or the StreamPULSE network, which provided data on water temperature, light, nutrients, discharge and metabolism. Although N-fixation and denitrification occur under contrasting environmental conditions, we found that they co-occurred in ca. 40% of stream ecosystems surveyed, and microbes capable of carrying out each process were found in all surveyed streams. This dataset includes the chamber data used to calculate nitrogen fixation and denitrification rates, stream substrate information used to scale rates from substrate to whole-reach scale, and a variety of reach-to-landscape scale covariates used to evaluate predictors of rates across the study streams.
Stream cross-section profiles in the Andrews Experimental Forest and Hagan Block RNA 1978 to 2011
This database consists of periodically resurveyed cross-section profiles for 5 sites (stream reaches) located in the H.J. Andrews Experimental Forest on 3rd to 5th-order stream channels over the period 1978-present, and also includes historical data from Shorter Creek within the Andrews and two sites (North and South) on the Hagan Creek in the Hagan Block RNA. Each of these sites includes 10-20 cross sections spaced about 10-50 m apart. Observations include cross-sectional geometry based on surveys relative to a horizontal line between permanent stations on opposite sides of the stream, substrate type based on pebble counts and notes on extent of bedrock, and photo-documentation of the sites. Sampling was primarily done annually until 2000 and opportunistically since then depending on funding and noted geomorphic changes in response to floods. Sampling at least every 5 years is desired. Data tables cover 1. elevation and distance measurements between fixed end points, 2. pebble counts for particle size, 3. measures of extent of exposed bedrock, 4. vegetation (1984 only) in terms of % cover by spp., % cover of substrate types, and plant measurements for making biomass estimates with allometric equations, but such estimates are not presented. The original raw cross-section data for these sites has been adjusted to align profiles for consecutive year comparisons at each cross-section location to correct for imprecision in the survey methodology. John Faustini, in 1998, further corrected this cross-section data from 1978-1998 for the five main sites in database GS019. Arianna Goodman, in 2021, performed additional corrections on the data, including more recent collections, 1978-2011, also in database GS019. The Shorter Creek cross-section surveys took place in 1978-1982 only. The Shorter Creek cross-section surveys took place in 1978-1982 only.
Block summaries of biomass, carbon, nitrogen, and phosphorus allocation among tissue types, species, and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment harvests: 2000 and 2015, Toolik Lake Field Station, Alaska.
A complete accounting of biomass, C, N, and P allocation both among tissue types (leaves, stems, rhizomes, roots) and among species and plant functional types from Arctic LTER 1981 Moist Acidic Tussock (MAT81) long-term experiment’s untreated control plots and plots that were fertilized annually, harvested after 20 and 35 years, near Toolik Lake Field Station, Alaska. Data are gram per meter squared summarized by block.
blocking_6
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
blocking_7
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
blocking_3
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
blocking_2
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
blocking_1
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
blocking_5
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
blocking_8
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
blocking_4
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
MSP430FR5969 Basic Block Worst Case Energy Consumption (WCEC) and Worst Case Execution Time (WCET) dataset
<p>This dataset contains around 30 000 basic blocks whose energy consumption and execution time have been measured in isolation on the MSP430FR5969 microcontroller, at 1MHz. Basic blocks were executed in a worst case scenario regarding the MSP430 FRAM cache and CPU pipeline. The dataset creation process is described thoroughly in [1].</p> <h2>Folder structure</h2> <p>This dataset is composed of the following files:</p> <ul> <li><code>basic_blocks.tar.xz</code> contains all basic blocks (BB) used in the dataset, in a custom JSON format,</li> <li><code>data.csv</code>/<code>data.xlsx</code> contains the measured energy consumption and execution time for each basic block</li> </ul> <p>We first details how the <code>basic_blocks.tar.gz</code> archive is organized, and then present the CSV/XSLX spreadsheet format.</p> <h2>Basic Blocks</h2> <p>We extracted the basic blocks from a subset of programs of the <em>AnghaBench</em> benchmark suite [2]. The <code>basic_blocks.tar.gz</code> archive consist of the extracted basic blocks organized as <code>json</code> files. Each <code>json</code> file correspond to a C source file from <em>AnghaBench</em>, and is given a unique identifier. An example json (<code>137.json</code>) is available here:</p> <div> <pre><code>{ "extr_pfctl_altq.c_pfctl_altq_init": [ # Basic block 1 [ # Instruction 1 of BB1 [ "MOV.W", "#queue_map", "R13" ], # Instruction 2 of BB1 [ "MOV.B", "#0", "R14" ], # Instruction 3 of BB1 [ "CALL", "#hcreate_r", null ] ], # Basic block 2 [ .... ] ] }</code></pre> </div> <p>The json contains a dict with only one key pointing to an array of basic blocks. This key is the name of the original C source file in <em>AnghaBench</em> from which the basic blocks were extracted (here <a href="https://github.com/brenocfg/AnghaBench/blob/master/freebsd/sbin/pfctl/extr_pfctl_altq.c_pfctl_altq_init.c">extr_pfctl_altq.c_pfctl_altq_init.c</a>). The array contains severals basic blocks, which are represented as an array of instructions, which are themselves represented as an array <code>[OPCODE, OPERAND1, OPERAND2]</code>.</p> <p>Then, each basic block can be identified uniquely using two ids : its <em>file id</em> and its <em>offset</em> in the file (<code>id=<file id>_<offset></code>). In our example, the basic block 1 can be identified by the json file id (<code>137</code>) and its offset in the file (<code>0</code>). Its ID is <code>137_0</code>. This ID is used to make the mapping between a basic block and its energy consumption/execution time, with the <code>data.csv</code>/<code>data.xlsx</code> spreadsheet.</p> <h2>Energy Consumption and Execution Time</h2> <p>Energy consumption and execution time data are stored in the <code>data.csv</code> file. Here is the extract of the csv file corresponding to the basic block <code>137_0</code>. The spreadsheet format is described below.</p> <pre><code>bb_id;nb_inst;max_energy;max_time;avg_time;avg_energy;energy_per_inst;nb_samples;unroll_factor 137_0;3;8.77;7.08;7.04;8.21;2.92;40;50</code></pre> <p>Spreadsheet format :</p> <ul> <li><code>bb_id</code>: the unique identifier of a basic block (cf. <a>Basic Blocks</a>)</li> <li><code>nb_inst</code>: the number of instructions in the basic block</li> <li><code>max_energy</code>: the maximum energy comsumption (in nJ) measured during the experiment</li> <li><code>max_time</code>: the maximum execution time (in us) measured during the experiment</li> <li><code>avg_time</code>: the average execution time (in us) measured during the experiment</li> <li><code>avg_energy</code>: the average energy comsumption (in nJ) measured during the experiment</li> <li><code>energy_per_inst</code>: the average energy consumption per instruction (correspond to <code>avg_energy</code>/<code>nb_inst</code>)</li> <li><code>nb_samples</code>: how much time the basic block energy consumption/execution time has been measured</li> <li><code>unroll_factor</code>: how much time the basic block was unrolled (cf Basic Block Unrolling)</li> </ul> <h3>Basic Block Unrolling</h3> <p>To measure the energy consumption and execution time of the msp430, we need to be able to handle the scale difference between the measurement tool and the basic block execution time. This is achieved by duplicating the basic block multiple times while making sure to keep the worst-case memory layout as explained in the paper. The number of time the basic block has been duplicated is called the <code>unroll_factor</code>.</p> <p>Values of energy and time are always given per basic block, so they have already been divided by the unroll factor.</p> <p> </p> <h2>Dataset description</h2> <h3>Features</h3> <p>The selected features after PCA analysis for both energy and time model are listed here: <strong> MOV.W_Rn_Rn, MOV.W_X(Rn)_X(Rn), CALL, MOV.B_#N_Rn, ADD.W_Rn_Rn, MOV.W_@Rn_Rn, MOV.W_X(Rn)_Rn, ADD.W_#N_Rn, PUSHM.W_#N_Rn, MOV.W_X(Rn)_ADDR, CMP.W_#N_Rn, MOV.W_&ADDR_X(Rn), MOV.W_Rn_X(Rn), BIS.W_Rn_Rn, RLAM.W_#N_Rn, SUB.W_#N_Rn, MOV.W_&ADDR_Rn, MOV.W_#N_X(Rn), CMP.W_Rn_Rn, BIT.W_ADDR_Rn, MOV.W_@Rn_X(Rn), ADD.W_#N_X(Rn), MOV.W_#N_Rn, AND.W_Rn_Rn, MOV.W_Rn_ADDR, SUB.W_Rn_Rn, MOV.W_ADDR_Rn, MOV.W_X(Rn)_&ADDR, MOV.W_ADDR_ADDR, JMP, ADD_#N_Rn, BIS.W_Rn_X(Rn), SUB_Rn_Rn, MOV.W_ADDR_X(Rn), ADDC_#N_X(Rn), MOV.B_Rn_Rn, CMP.W_X(Rn)_X(Rn), ADD_Rn_Rn, nb_inst, INV.W_Rn_, NOP__, ADD.W_X(Rn)_X(Rn), ADD.W_Rn_X(Rn), MOV.B_@Rn_Rn, BIS.W_X(Rn)_X(Rn), MOV.B_#N_X(Rn), MOV.W_#N_ADDR, AND.W_#N_ADDR, SUBC_X(Rn)_X(Rn), BIS.W_#N_X(Rn), SUB.W_X(Rn)_X(Rn), AND.B_#N_Rn, ADD_X(Rn)_X(Rn), MOV.W_@Rn_ADDR, MOV.W_&ADDR_ADDR, ADDC_Rn_Rn, AND.W_#N_X(Rn), SUB_#N_Rn, RRUM.W_#N_Rn, AND_ADDR_Rn, CMP.W_X(Rn)_ADDR, MOV.B_#N_ADDR, ADD.W_#N_ADDR, CMP.B_#N_Rn, SXT_Rn_, XOR.W_Rn_Rn, CMP.W_@Rn_Rn, ADD.W_@Rn_Rn, ADD.W_X(Rn)_Rn, AND.W_Rn_X(Rn), CMP.B_Rn_Rn, AND.W_X(Rn)_X(Rn), BIC.W_#N_Rn, BIS.W_#N_Rn, AND.B_#N_X(Rn), MOV.B_X(Rn)_X(Rn), AND.W_@Rn_Rn, MOV.W_#N_&ADDR, BIS.W_Rn_ADDR, SUB.W_X(Rn)_Rn, SUB.W_Rn_X(Rn), SUB_X(Rn)_X(Rn), MOV.B_@Rn_X(Rn), CMP.W_@Rn_X(Rn), ADD.W_X(Rn)_ADDR, CMP.W_Rn_X(Rn), BIS.W_@Rn_X(Rn), CMP.B_X(Rn)_X(Rn), RRC.W_Rn_, MOV.W_@Rn_&ADDR, CMP.W_#N_X(Rn), ADDC_X(Rn)_Rn, CMP.W_X(Rn)_Rn, BIS.W_X(Rn)_Rn, SUB_X(Rn)_Rn, MOV.B_X(Rn)_Rn, MOV.W_ADDR_&ADDR, AND.W_#N_Rn, RLA.W_Rn_, INV.W_X(Rn)_, XOR.W_#N_Rn, SUB.W_Rn_ADDR, BIC.W_#N_X(Rn), MOV.B_X(Rn)_ADDR, ADD_#N_X(Rn), SUB_Rn_X(Rn), MOV.B_&ADDR_Rn, MOV.W_Rn_&ADDR, ADD_X(Rn)_Rn, AND.W_X(Rn)_Rn, PUSHM.A_#N_Rn, RRAM.W_#N_Rn, AND.W_@Rn_X(Rn), BIS.B_Rn_X(Rn), SUB.W_@Rn_Rn, CLRC__, CMP.W_#N_ADDR, XOR.W_Rn_X(Rn), MOV.B_Rn_ADDR, CMP.B_X(Rn)_Rn, BIS.B_Rn_Rn, BIS.W_X(Rn)_ADDR, CMP.B_#N_X(Rn), CMP.W_Rn_ADDR, XOR.W_X(Rn)_Rn, MOV.B_Rn_X(Rn), ADD.B_#N_Rn</strong></p> <h3>Code</h3> <p>The trained machine learning model, tests, and local explanation code can be generated and found here: <a href="https://colab.research.google.com/drive/1zaCIkbKDRDtwzhY-r3Iff0OpOSkOUibW?usp=sharing" target="_blank" rel="noopener">WORTEX Machine learning code</a> </p> <h2>Acknowledgment</h2> <p>This work has received a French government support granted to the Labex CominLabs excellence laboratory and managed by the National Research Agency in the “Investing for the Future” program under reference ANR-10-LABX-07-01</p> <h2>Licensing</h2> <p>Copyright 2024 Hector Chabot Copyright 2024 Abderaouf Nassim Amalou Copyright 2024 Hugo Reymond Copyright 2024 Isabelle Puaut</p> <p>Licensed under the Creative Commons Attribution 4.0 International License</p> <h2>References</h2> <p>[1] Reymond, H., Amalou, A. N., Puaut, I. “WORTEX: Worst-Case Execution Time and Energy Estimation in Low-Power Microprocessors using Explainable ML” in 22nd International Workshop on Worst-Case Execution Time Analysis (WCET 2024) </p> <p>[2] Da Silva, Anderson Faustino, et al. “Anghabench: A suite with one million compilable C benchmarks for code-size reduction.” 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2021.</p>
Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"
<p>Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"</p>
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-1 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-1 site automated weather station and associated soil substation at several temporal scales. Air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths. The nearest precipitation data is available from CSIS Block-2 site automated weather station located 309m distance.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-2 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-2 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-3 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-3 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
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