cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/f9n9s3p99t.3
title: MontadoDB: A Comprehensive Dataset of Pasture Parameters in the Sourthern Region of Portugal

description: This data comprises 705 samples, including collection date and day of the year (Date and DOY), sample region (Sample), geographical coordinates (Lat and Long), and the parameters collected from the laboratory: Pasture Measure Content, Crude Protein, Neutral Detergent Fiber, Fresh Weigth, Dry Matter (PMC (%), PB (g/100g), NDF (g/100g), Kg MS/ha), with its BoxCox transformation (PMC (%)_BoxCox, PB (g/100g)_BoxCox, NDF (g/100g)_BoxCox, Kg MS/ha_BoxCox) for data normalization.

Multispectral and weather data were collected for each sample coordinate. The spectral data comes from Google Earth Engine on four satellites (Landsat-8, Sentinel-2, Sentinel-3, and MODIS) and their respective collections and tiers. 

- Sentinel-2 Surface reflectance : B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, B12
- Sentinel-2 Top-of-atmosphere: B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12
- Landsat-8 Surface reflectance tier 1: SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7
- Landsat-8 Top-of-atmosphere (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11
- Landsat-8 Raw (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11
- Sentinel-3: Oa02, Oa03, Oa04, Oa05, Oa06, Oa07, Oa08, Oa09, Oa11, Oa12, Oa13, Oa14, Oa15, Oa16, Oa17, Oa18, Oa19, Oa20, Oa21
- MODIS: NDVI, EVI

The spectral bands columns are preceded by a column that indicates the date of the collected image. Every column name highlights the type of satellite, its collection, and tier, e.g., Landsat-8_SR_1, which refers to the Landsat-8 satellite, surface reflectance collection, and tier 1. 

The weather data can be acquired from Open weather and Open Meteo APIs, fetching the following weather conditions: Maximum and Minimum Temperature acquired (TEMP_MAX (°C), TEMP_MIN (°C)), Average Solar Radiation (RAD_SOL (J/M2)), Average rainfall recorded during the day (RAIN (mm)), Average Wind Speed (WIND_SPD (km/h)), Average Estimated Soil Evapotranspiration (EVAPOT (mm)), Average Atmospheric Pressure (PRES_ATM (hPa)) and Average Relative Humidity (HUM_REL (%)).

The "dataset" folder includes eight files: "Experimental_data.csv", "Sentinel-2.csv", "Sentinel-3.csv", "Landsat-8.csv", "Modis.csv", "Complete_data.csv", "Weather_data.csv", and "Satellite_info.json". "Experimental_data.csv" and "Weather_data.csv" contain only samples and weather data, respectively. "Each satellite CSV includes Forage parameters, spectral bands, and weather data. The "Complete_data.csv" merges all the other files. "Satellite_info.json" contains auxiliary satellite data for GEE and dataset build.

The "scripts" folder contains two necessary modules, one for GEE functions - "multispectral_api.py", and another for Weather API calls - "weather_api.py", and a notebook Python file ("build_dataset.ipynb") that uses both modules to fetch the data and build the dataset.
DOI: 10.17632/f9n9s3p99t.4
title: MontadoDB: A Comprehensive Dataset of Pasture Parameters in the Sourthern Region of Portugal

description: This data comprises 705 samples, including collection date and day of the year (Date and DOY), sample region (Sample), geographical coordinates (Lat and Long), and the parameters collected from the laboratory: Pasture Measure Content, Crude Protein, Neutral Detergent Fiber, Fresh Weigth, Dry Matter (PMC (%), PB (g/100g), NDF (g/100g), Kg MS/ha), with its BoxCox transformation (PMC (%)_BoxCox, PB (g/100g)_BoxCox, NDF (g/100g)_BoxCox, Kg MS/ha_BoxCox) for data normalization.

Multispectral and weather data were collected for each sample coordinate. The spectral data comes from Google Earth Engine on four satellites (Landsat-8, Sentinel-2, Sentinel-3, and MODIS) and their respective collections and tiers. 

- Sentinel-2 Surface reflectance : B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, B12
- Sentinel-2 Top-of-atmosphere: B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12
- Landsat-8 Surface reflectance tier 1: SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7
- Landsat-8 Top-of-atmosphere (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11
- Landsat-8 Raw (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11
- Sentinel-3: Oa02, Oa03, Oa04, Oa05, Oa06, Oa07, Oa08, Oa09, Oa11, Oa12, Oa13, Oa14, Oa15, Oa16, Oa17, Oa18, Oa19, Oa20, Oa21
- MODIS: NDVI, EVI

The spectral bands columns are preceded by a column that indicates the date of the collected image. Every column name highlights the type of satellite, its collection, and tier, e.g., Landsat-8_SR_1, which refers to the Landsat-8 satellite, surface reflectance collection, and tier 1. 

The weather data can be acquired from Open weather and Open Meteo APIs, fetching the following weather conditions: Maximum and Minimum Temperature acquired (TEMP_MAX (°C), TEMP_MIN (°C)), Average Solar Radiation (RAD_SOL (J/M2)), Average rainfall recorded during the day (RAIN (mm)), Average Wind Speed (WIND_SPD (km/h)), Average Estimated Soil Evapotranspiration (EVAPOT (mm)), Average Atmospheric Pressure (PRES_ATM (hPa)) and Average Relative Humidity (HUM_REL (%)).

The "dataset" folder includes eight files: "Experimental_data.csv", "Sentinel-2.csv", "Sentinel-3.csv", "Landsat-8.csv", "Modis.csv", "Complete_data.csv", "Weather_data.csv", and "Satellite_info.json". "Experimental_data.csv" and "Weather_data.csv" contain only samples and weather data, respectively. "Each satellite CSV includes Forage parameters, spectral bands, and weather data. The "Complete_data.csv" merges all the other files. "Satellite_info.json" contains auxiliary satellite data for GEE and dataset build.

The "scripts" folder contains two necessary modules, one for GEE functions - "multispectral_api.py", and another for Weather API calls - "weather_api.py", and a notebook Python file ("build_dataset.ipynb") that uses both modules to fetch the data and build the dataset.
DOI: 10.15468/dl.2pxkd2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius argus Peters, 1854",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.628 -23.023,35.6007 -22.9702,35.6007 -23.165,35.628 -23.023))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033593-250525065834625/datasets/export for details.
DOI: 10.17632/f9n9s3p99t.5
title: MontadoDB: A Comprehensive Dataset of Pasture Parameters in the Sourthern Region of Portugal

description: This data comprises 705 samples, including collection date and day of the year (Date and DOY), sample region (Sample), geographical coordinates (Lat and Long), and the parameters collected from the laboratory: Pasture Measure Content, Crude Protein, Neutral Detergent Fiber, Fresh Weigth, Dry Matter (PMC (%), PB (g/100g), NDF (g/100g), Kg MS/ha), with its BoxCox transformation (PMC (%)_BoxCox, PB (g/100g)_BoxCox, NDF (g/100g)_BoxCox, Kg MS/ha_BoxCox) for data normalization.

Multispectral and weather data were collected for each sample coordinate. The spectral data comes from Google Earth Engine on four satellites (Landsat-8, Sentinel-2, Sentinel-3, and MODIS) and their respective collections and tiers. 

- Sentinel-2 Surface reflectance : B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11, B12
- Sentinel-2 Top-of-atmosphere: B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, B12
- Landsat-8 Surface reflectance tier 1: SR_B1, SR_B2, SR_B3, SR_B4, SR_B5, SR_B6, SR_B7
- Landsat-8 Top-of-atmosphere (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11
- Landsat-8 Raw (tier 1 / 1 + realtime): B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11
- Sentinel-3: Oa02, Oa03, Oa04, Oa05, Oa06, Oa07, Oa08, Oa09, Oa11, Oa12, Oa13, Oa14, Oa15, Oa16, Oa17, Oa18, Oa19, Oa20, Oa21
- MODIS: NDVI, EVI

The spectral bands columns are preceded by a column that indicates the date of the collected image. Every column name highlights the type of satellite, its collection, and tier, e.g., Landsat-8_SR_1, which refers to the Landsat-8 satellite, surface reflectance collection, and tier 1. 

The weather data can be acquired from Open weather and Open Meteo APIs, fetching the following weather conditions: Maximum and Minimum Temperature acquired (TEMP_MAX (°C), TEMP_MIN (°C)), Average Solar Radiation (RAD_SOL (J/M2)), Average rainfall recorded during the day (RAIN (mm)), Average Wind Speed (WIND_SPD (km/h)), Average Estimated Soil Evapotranspiration (EVAPOT (mm)), Average Atmospheric Pressure (PRES_ATM (hPa)) and Average Relative Humidity (HUM_REL (%)).

The "dataset" folder includes eight files: "Experimental_data.csv", "Sentinel-2.csv", "Sentinel-3.csv", "Landsat-8.csv", "Modis.csv", "Complete_data.csv", "Weather_data.csv", and "Satellite_info.json". "Experimental_data.csv" and "Weather_data.csv" contain only samples and weather data, respectively. "Each satellite CSV includes Forage parameters, spectral bands, and weather data. The "Complete_data.csv" merges all the other files. "Satellite_info.json" contains auxiliary satellite data for GEE and dataset build.

The "scripts" folder contains two necessary modules, one for GEE functions - "multispectral_api.py", and another for Weather API calls - "weather_api.py", and a notebook Python file ("build_dataset.ipynb") that uses both modules to fetch the data and build the dataset.
DOI: 10.15468/dl.jtxmcd
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Myanmar, Nepal, Bhutan, Bangladesh, Pakistan, Afghanistan)",
 "TaxonKey is Colotis vestalis (Butler, 1876)"
 ]
}

 The dataset includes 84 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033594-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kwhq4s
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius argus Peters, 1854",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.6306 -23.844,35.6007 -23.8125,35.6007 -24.0157,35.6306 -23.844))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033595-250525065834625/datasets/export for details.
DOI: 10.17632/4k36n4773c
title: Artificial Intelligence, Labor Market Polarization and Income Inequality：Evidence from China

description: this research builds a two-way fixed-effects and spatial econometric model using provincial panel data from 30 Chinese provinces between 2003 and 2022. The findings are as follows: (1) Artificial intelligence exacerbates income inequality by expanding the wage gap and employment demand between low-skill industries and high-skill industries relative to middle-skill industries, thereby further intensifying income inequality. (2) Heterogeneity analysis shows that artificial intelligence significantly exacerbates income inequality in the central and western regions, while significantly alleviating income inequality in the western region. (3) The phenomenon of artificial intelligence exacerbating income inequality through "labor market polarization" exists in a positive spatial spillover effect.
DOI: 10.17632/4k36n4773c.1
title: Artificial Intelligence, Labor Market Polarization and Income Inequality：Evidence from China

description: this research builds a two-way fixed-effects and spatial econometric model using provincial panel data from 30 Chinese provinces between 2003 and 2022. The findings are as follows: (1) Artificial intelligence exacerbates income inequality by expanding the wage gap and employment demand between low-skill industries and high-skill industries relative to middle-skill industries, thereby further intensifying income inequality. (2) Heterogeneity analysis shows that artificial intelligence significantly exacerbates income inequality in the central and western regions, while significantly alleviating income inequality in the western region. (3) The phenomenon of artificial intelligence exacerbating income inequality through "labor market polarization" exists in a positive spatial spillover effect.
DOI: 10.15468/dl.vc4wnd
title: Occurrence Download

description: A dataset containing 137 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Lophuromys ansorgei de Winton, 1896",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 137 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033596-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15608325
title: Data and scripts for "Disentangling temperature and Reynolds-number effects in quantum turbulence"
DOI: 10.5281/zenodo.15608326
title: Data and scripts for "Disentangling temperature and Reynolds-number effects in quantum turbulence"
DOI: 10.17632/fbfczg4fyh
title: Supplementary Material for The Impact of Biological Treatments in Dermatology on the Risk of Cardiovascular Disease: A Systematic Review and Single-Arm Meta-Analysis

description: Supplementary Material for The Impact of Biological Treatments in Dermatology on the Risk of Cardiovascular Disease: A Systematic Review and Single-Arm Meta-Analysis
DOI: 10.17632/fbfczg4fyh.1
title: Supplementary Material for The Impact of Biological Treatments in Dermatology on the Risk of Cardiovascular Disease: A Systematic Review and Single-Arm Meta-Analysis

description: Supplementary Material for The Impact of Biological Treatments in Dermatology on the Risk of Cardiovascular Disease: A Systematic Review and Single-Arm Meta-Analysis
DOI: 10.15468/dl.vdetyv
title: Occurrence Download

description: A dataset containing 474 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Androsace tapete Maxim."
 ]
}

 The dataset includes 474 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033597-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xnpefk
title: Occurrence Download

description: A dataset containing 1617 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Trigona pallens (Fabricius, 1798)"
 ]
}

 The dataset includes 1617 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033598-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15581149
title: RevDecode: Frankenbinary Evaluation Dataset

description: This artifact contains evaluation data for the Frankenbinary dataset used in the paper, "RevDecode: Enhancing Binary Function Matching with Context-Aware Graph Representations and Relevance Decoding," to be presented at USENIX Security 2025.

Further details are provided in the dataset's README file.
DOI: 10.5281/zenodo.15581150
title: RevDecode: Frankenbinary Evaluation Dataset

description: This artifact contains evaluation data for the Frankenbinary dataset used in the paper, "RevDecode: Enhancing Binary Function Matching with Context-Aware Graph Representations and Relevance Decoding," to be presented at USENIX Security 2025.

Further details are provided in the dataset's README file.
DOI: 10.15468/dl.b8dw6v
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hyperolius horstockii (Schlegel, 1837)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033599-250525065834625/datasets/export for details.
DOI: 10.17632/kyxytvgd2m
title: ESC voting dataset

description: Eurovision song contest voting data
DOI: 10.17632/kyxytvgd2m.1
title: ESC voting dataset

description: Eurovision song contest voting data
DOI: 10.5281/zenodo.15607776
title: A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase II – Active Wake Control

description: Phase II of the Blind Test campaign introduces active individual blade pitch control to the upstream turbine, challenging participants to simulate advanced wake dynamics. This data set ("Data files.zip") contains the wind tunnel measurement data based on wind tunnel experiments organised under the TWEET-IE project (www.tweet-ie.eu).

The experiments were carried-out in the large section 2.5 ×3.5×12 m^3 (H× W × L) of the closed-circuit wind tunnel at NTUA. The set up consistsof two identically scaled wind turbine models (G1 models) which are placed in line with a longitudinal distance of 5D. In the context of Phase II, the upstream turbine was initially tested in its baseline configuration before being subjected to dynamic helix motion, with amplitude ranges of 3° and helix frequency of 𝑓ℎ𝑒𝑙𝑖𝑥 = fβ/fr ∈ [0.7,1.3]. The inflow conditions include both Low and High Turbulence Intensities, while Particle Tracking Velocimetry (PTV) measurements were also conducted within the context of Phase II, in a specified dimensionless helix frequency of 𝑓ℎ𝑒𝑙𝑖𝑥 = 0.82.

The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1m. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.docx").

File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.
DOI: 10.5281/zenodo.15607777
title: A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase II – Active Wake Control

description: Phase II of the Blind Test campaign introduces active individual blade pitch control to the upstream turbine, challenging participants to simulate advanced wake dynamics. This data set ("Data files.zip") contains the wind tunnel measurement data based on wind tunnel experiments organised under the TWEET-IE project (www.tweet-ie.eu).

The experiments were carried-out in the large section 2.5 ×3.5×12 m^3 (H× W × L) of the closed-circuit wind tunnel at NTUA. The set up consistsof two identically scaled wind turbine models (G1 models) which are placed in line with a longitudinal distance of 5D. In the context of Phase II, the upstream turbine was initially tested in its baseline configuration before being subjected to dynamic helix motion, with amplitude ranges of 3° and helix frequency of 𝑓ℎ𝑒𝑙𝑖𝑥 = fβ/fr ∈ [0.7,1.3]. The inflow conditions include both Low and High Turbulence Intensities, while Particle Tracking Velocimetry (PTV) measurements were also conducted within the context of Phase II, in a specified dimensionless helix frequency of 𝑓ℎ𝑒𝑙𝑖𝑥 = 0.82.

The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1m. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.docx").

File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.
DOI: 10.5281/zenodo.15631979
title: TABLE 1 in A new diplodocine sauropod from the Morrison Formation, Wyoming, USA

description: TABLE 1. Measurements of the cervical vertebrae of Ardetosaurus viator MAB 011899 (in mm).
Dimension1314Anteroposterior length507*545*Greatest height (left/right)316*/342*416*/369*Centrum length484*541*Centrum minimum width7674Width across diapophyses250*250*Width across prezygapophyses165176*Width across postzygapophyses154181*Pleurocoel length (left/right)262/285302/295Pleurocoel height (left/right)63/5173/69Cotyle width119152Cotyle height136186Condyle width85*91*Condyle height136*144*Neural spine height190*248*Centrum length minus condyle length418441Neural arch height146*148*
Notes: Asterisks indicate estimates. Measurements are influenced by shear and transverse compression. Centrum length includes condyle length. Neural arch height is measured from the dorsal margin of the cotyle rim vertically to an horizontal line joining both posterior margins of the postzygapophyses. Neural spine height is measured vertically from the dorsal margin of the cotyle rim to the neural spine tip.
description: Published as part of van der Linden, Tom T. P., Tschopp, Emanuel, Sookias, Roland B., Wallaard, Jonathan J. W., Holwerda, Femke M. & Schulp, Anne S., 2024, A new diplodocine sauropod from the Morrison Formation, Wyoming, USA, pp. 13 in Palaeontologia Electronica (a 49) 27 (3) on page 13, DOI: 10.26879/1380, http://zenodo.org/record/14661518
DOI: 10.5281/zenodo.15631981
title: TABLE 2 in A new diplodocine sauropod from the Morrison Formation, Wyoming, USA

description: TABLE 2. Measurements of the dorsal vertebrae of Ardetosaurus viator MAB011899 (in mm).
Dimension123456789Greatest height462511*568*583*580*550*535*Centrum length417*360266255*251*255257*Pleurocoel length (left/right)102/14257/84121/59*117/101118/123135/122101/11372/11897/103Pleurocoel height (left/right)53/6720/4647/48*78/5369/6556/5162/5350/3344/28Cotyle width159150*156184173214199*214*194*Cotyle height162174*151118142150106136*130*Condyle width176*130*110*147*186*192*181*212*201*Condyle height150*151*92*118*106*142*110*114Neural spine height195*210234*251327*302295*314Centrum length minus condyle length322/308247/268207*/205*193/199197/171215/224217/228200/208200/202Neural arch height153*170*157170151151145129
Notes: Asterisks indicate estimates, often influenced by shear and transverse compression of the vertebrae. Centrum length includes condyle length. Neural arch height is measured from the dorsal margin of the cotyle rim vertically to an horizontal line joining the posterior-most edge of the postzygapophyses.
description: Published as part of van der Linden, Tom T. P., Tschopp, Emanuel, Sookias, Roland B., Wallaard, Jonathan J. W., Holwerda, Femke M. & Schulp, Anne S., 2024, A new diplodocine sauropod from the Morrison Formation, Wyoming, USA, pp. 35 in Palaeontologia Electronica (a 49) 27 (3) on page 35, DOI: 10.26879/1380, http://zenodo.org/record/14661518