cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/mhncmbv6jd.1
title: Comparison of hydrogen permeation curves for MS1700 and QP1180 AHSSs

description: Devanathan-Stachurski permeation tests were carried out on as-received grade 1700 Martensitic Advanced High Strength Steel (MS-AHSS) and grade 1180 Quenched and Partitioned (QP) AHSS, with the aim to obtain the effective diffusivity coefficient.

The hydrogen permeation curves of the two AHSSs are reported in terms of normalized time (s/m^2) VS normalized flux.

Financed by the European Union - NextGenerationEU (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033 - 17/06/2022, Spoke 11 - Innovative Materials & Lightweighting). The opinions expressed are those of the authors only and should not be considered as representative of the European Union or the European Commission’s official position. Neither the European Union nor the European Commission can be held responsible for them. 

Activity carried out within the framework of the MOST Closed Call for Scalability project MADE (Multiphase high strength steel forming and testing for robust Automotive lightweight DEsign).

Keywords: Spoke11 - CNMS MOST, Ferrous alloys, MADE
DOI: 10.17632/hk7mydst66.1
title: Good and Bad Classification of fenugreek leaves

description: Title: Good and Bad Classification of Fenugreek Leaves: Techniques, Parameters, and Perspectives

1. Introduction

Fenugreek (Trigonella foenum-graecum) is a widely cultivated herb in the Fabaceae family, used both as a spice and a medicinal plant. Its fresh leaves are commonly consumed as a leafy vegetable in many parts of Asia and the Middle East. Given its high nutritional and therapeutic value, the quality of fenugreek leaves is of paramount importance to ensure consumer satisfaction and safety.

One crucial step in the post-harvest handling of fenugreek leaves is the classification or sorting of good and bad leaves. Manual sorting is time-consuming and inconsistent, leading to the growing interest in automated classification using image processing and machine learning. This essay provides an in-depth analysis of the classification of fenugreek leaves into "good" and "bad" categories, covering the criteria, techniques, technologies, challenges, and future outlook.


---

2. Importance of Classification

Classification of fenugreek leaves is essential for several reasons:

Quality Control: Ensures only fresh, uncontaminated leaves reach consumers.

Market Value: Improves pricing and demand for higher-quality produce.

Shelf Life: Discarding spoiled or infected leaves extends the storage life of the batch.

Processing Efficiency: Reduces contamination during processing for frozen or dried leaf products.



---

3. Classification Criteria

To effectively classify fenugreek leaves, various quality parameters are considered:

a. Visual Appearance

Color: Good leaves exhibit a vibrant green color, while bad leaves may show yellowing, browning, or black spots.

Texture: Healthy leaves are turgid and crisp; bad ones are wilted, dry, or slimy.

Size and Shape: Good leaves maintain uniform shape; damaged, malformed, or shriveled leaves are considered bad.


b. Physiological Condition

Decay or Rot: Fungal or bacterial infection can cause soft rot, making the leaf unfit.

Pest Damage: Holes, discoloration, or patterns indicating pest attacks disqualify leaves from the good category.


c. Foreign Matter

Leaves contaminated with soil, dust, or foreign plant matter are categorized as bad unless cleaned and inspected.


d. Odor

A fresh herbal aroma indicates good leaves; any sour, foul, or fermented smell implies spoilage.



---

4. Manual Classification Techniques

Traditionally, manual sorting is conducted at collection centers or processing units. Workers rely on:

Visual inspection under natural or artificial light.

Touch and feel to identify wilting or softness.

Occasionally smell for spoilage detection.


Limitations:

Inconsistent results due to fatigue or subjective judgment.

Time- and labor-intensive.

Higher chances of contamination.
5. Automated Classification Using Image Processing

Advancements in machine vision and artificial intelligence have enabled the development of automated classification systems.

a. Image Acquisition
DOI: 10.17632/hk7mydst66.2
title: Good and Bad Classification of fenugreek leaves

description: Title: Good and Bad Classification of Fenugreek Leaves: Techniques, Parameters, and Perspectives

1. Introduction

Fenugreek (Trigonella foenum-graecum) is a widely cultivated herb in the Fabaceae family, used both as a spice and a medicinal plant. Its fresh leaves are commonly consumed as a leafy vegetable in many parts of Asia and the Middle East. Given its high nutritional and therapeutic value, the quality of fenugreek leaves is of paramount importance to ensure consumer satisfaction and safety.

One crucial step in the post-harvest handling of fenugreek leaves is the classification or sorting of good and bad leaves. Manual sorting is time-consuming and inconsistent, leading to the growing interest in automated classification using image processing and machine learning. This essay provides an in-depth analysis of the classification of fenugreek leaves into "good" and "bad" categories, covering the criteria, techniques, technologies, challenges, and future outlook.


---

2. Importance of Classification

Classification of fenugreek leaves is essential for several reasons:

Quality Control: Ensures only fresh, uncontaminated leaves reach consumers.

Market Value: Improves pricing and demand for higher-quality produce.

Shelf Life: Discarding spoiled or infected leaves extends the storage life of the batch.

Processing Efficiency: Reduces contamination during processing for frozen or dried leaf products.



---

3. Classification Criteria

To effectively classify fenugreek leaves, various quality parameters are considered:

a. Visual Appearance

Color: Good leaves exhibit a vibrant green color, while bad leaves may show yellowing, browning, or black spots.

Texture: Healthy leaves are turgid and crisp; bad ones are wilted, dry, or slimy.

Size and Shape: Good leaves maintain uniform shape; damaged, malformed, or shriveled leaves are considered bad.


b. Physiological Condition

Decay or Rot: Fungal or bacterial infection can cause soft rot, making the leaf unfit.

Pest Damage: Holes, discoloration, or patterns indicating pest attacks disqualify leaves from the good category.


c. Foreign Matter

Leaves contaminated with soil, dust, or foreign plant matter are categorized as bad unless cleaned and inspected.


d. Odor

A fresh herbal aroma indicates good leaves; any sour, foul, or fermented smell implies spoilage.



---

4. Manual Classification Techniques

Traditionally, manual sorting is conducted at collection centers or processing units. Workers rely on:

Visual inspection under natural or artificial light.

Touch and feel to identify wilting or softness.

Occasionally smell for spoilage detection.


Limitations:

Inconsistent results due to fatigue or subjective judgment.

Time- and labor-intensive.

Higher chances of contamination.
5. Automated Classification Using Image Processing

Advancements in machine vision and artificial intelligence have enabled the development of automated classification systems.

a. Image Acquisition
DOI: 10.5517/ccdc.csd.cc2n0xxj
title: CCDC 2443758: Experimental Crystal Structure Determination

description: Related Article: C. Nyapola, S.J. Zamisa, B. Omondi, E.M. Njogu|2025|IUCrData|10|x250337|doi:10.1107/S2414314625003372
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2n2182
title: CCDC 2444823: Experimental Crystal Structure Determination

description: Related Article: C.Y. Jiang, R.J. Staples, S.M. Biros|2025|Acta Crystallogr.,Sect.E:Cryst.Commun.|81|425|doi:10.1107/S2056989025003500
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.pck5g7
title: Occurrence Download

description: A dataset containing 30767 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Peucaea botterii (P.L.Sclater, 1858)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 30767 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026895-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2n31b5
title: CCDC 2445786: Experimental Crystal Structure Determination

description: Related Article: M. Nidhishree, S. Gomathi, J.S. Nirmalram, L. Mathivathanan|2025|IUCrData|10|x250364|doi:10.1107/S2414314625003645
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.17632/6n7z6hytrj.1
title: Data for: Associations of selected genetic variants with lipid profile variability in response to statins in Alzheimer’s disease

description: Excel sheet with demographic and clinical data from all patients.
DOI: 10.15468/dl.beatmh
title: Occurrence Download

description: A dataset containing 7004 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is South America",
 "Country is Argentina",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 7004 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026896-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rgnt2u
title: Occurrence Download

description: A dataset containing 91202 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Belgium",
 "DatasetKey is Waarnemingen.be - Non-native animal occurrences in Flanders and the Brussels Capital Region, Belgium",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Psittacula krameri (Scopoli, 1769)"
 ]
}

 The dataset includes 91202 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026891-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rg45zg
title: Occurrence Download

description: A dataset containing 8158055 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is one of (Coleoptera, Lepidoptera)",
 "Year 2012-2025"
 ]
}

 The dataset includes 8158055 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026888-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8w4pj76zvp.1
title: ESI. Mat. Lett. Bi2Se3 NPs

description: Nanoparticles of Bi2Se3 were prepared by hot injection method using bismuth nitrate and 1,3-dimethyl-1H-benzo[d]imidazole-2(3H)-selenone (a cyclic selenourea) synthesized in the laboratory as selenide source. This cyclic selenourea was produced with a yield of 67% by reacting elemental selenium powder with 1,3-dimethyl-1H-benzo[d]imidazol-3-ium iodide made from laboratory reagents. Different solvents: triethanolamine, oleylamine, and oleic acid used in the hot-injection processing affected the shape, composition, and crystallinity of the resulting Bi2Se3 nanoparticles. X-ray diffraction patterns confirm the rhombohedral crystalline structure of the material with an average crystallite size of 13 - 19 nm. FESEM reveals that the material was obtained as nanosheet. Optical diffuse reflectance spectrum of the material suggests an indirect bandgap of 0.78 eV for it
DOI: 10.17632/8w4pj76zvp.2
title: ESI. Mat. Lett. Bi2Se3 NPs

description: Nanoparticles of Bi2Se3 were prepared by hot injection method using bismuth nitrate and 1,3-dimethyl-1H-benzo[d]imidazole-2(3H)-selenone (a cyclic selenourea) synthesized in the laboratory as selenide source. This cyclic selenourea was produced with a yield of 67% by reacting elemental selenium powder with 1,3-dimethyl-1H-benzo[d]imidazol-3-ium iodide made from laboratory reagents. Different solvents: triethanolamine, oleylamine, and oleic acid used in the hot-injection processing affected the shape, composition, and crystallinity of the resulting Bi2Se3 nanoparticles. X-ray diffraction patterns confirm the rhombohedral crystalline structure of the material with an average crystallite size of 13 - 19 nm. FESEM reveals that the material was obtained as nanosheet. Optical diffuse reflectance spectrum of the material suggests an indirect bandgap of 0.78 eV for it
DOI: 10.17632/8w4pj76zvp.3
title: ESI. Mat. Lett. Bi2Se3 NPs

description: Nanoparticles of Bi2Se3 were prepared by hot injection method using bismuth nitrate and 1,3-dimethyl-1H-benzo[d]imidazole-2(3H)-selenone (a cyclic selenourea) synthesized in the laboratory as selenide source. This cyclic selenourea was produced with a yield of 67% by reacting elemental selenium powder with 1,3-dimethyl-1H-benzo[d]imidazol-3-ium iodide made from laboratory reagents. Different solvents: triethanolamine, oleylamine, and oleic acid used in the hot-injection processing affected the shape, composition, and crystallinity of the resulting Bi2Se3 nanoparticles. X-ray diffraction patterns confirm the rhombohedral crystalline structure of the material with an average crystallite size of 13 - 19 nm. FESEM reveals that the material was obtained as nanosheet. Optical diffuse reflectance spectrum of the material suggests an indirect bandgap of 0.78 eV for it
DOI: 10.17632/8w4pj76zvp.4
title: ESI. Mat. Lett. Bi2Se3 NPs

description: Nanoparticles of Bi2Se3 were prepared by hot injection method using bismuth nitrate and 1,3-dimethyl-1H-benzo[d]imidazole-2(3H)-selenone (a cyclic selenourea) synthesized in the laboratory as selenide source. This cyclic selenourea was produced with a yield of 67% by reacting elemental selenium powder with 1,3-dimethyl-1H-benzo[d]imidazol-3-ium iodide made from laboratory reagents. Different solvents: triethanolamine, oleylamine, and oleic acid used in the hot-injection processing affected the shape, composition, and crystallinity of the resulting Bi2Se3 nanoparticles. X-ray diffraction patterns confirm the rhombohedral crystalline structure of the material with an average crystallite size of 13 - 19 nm. FESEM reveals that the material was obtained as nanosheet. Optical diffuse reflectance spectrum of the material suggests an indirect bandgap of 0.78 eV for it
DOI: 10.15468/dl.7m8adn
title: Occurrence Download

description: A dataset containing 14476 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Digitalización y Sistematización de las Colecciones Biológicas Nacionales del Instituto de Biología, UNAM (vertebrados)",
 "OccurrenceStatus is Present",
 "PublishingOrg is ff90b050-c256-11db-b71b-b8a03c50a862",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 14476 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026898-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/b668kmn864.1
title: An Embedded IoT Architecture for Continuous Output Pressure Surveillance in Medical Oxygen Concentrators

description: The increasing demand for portable and reliable medical oxygen delivery systems has highlighted the need for continuous monitoring of key performance parameters, particularly output pressure, to ensure patient safety and device efficiency. Traditional oxygen concentrators often lack real-time monitoring capabilities, making it difficult to detect pressure anomalies that may compromise therapeutic effectiveness. To address this limitation, a cost-effective, IoT-enabled monitoring system has been developed. The proposed system integrates a 1.2 MPa atmospheric pressure sensor with an HX710B analog-to-digital converter to accurately measure output pressure levels. An ATmega328 microcontroller processes the data and displays real-time readings on an LCD screen, while an ESP8266 module transmits the information to the Blynk cloud platform for remote access and data logging. This architecture allows healthcare providers and users to receive immediate alerts when pressure deviates from safe thresholds, enabling timely maintenance and minimizing risks. The system demonstrates stable performance, reliable wireless communication, and effective pressure monitoring. Its low cost, ease of integration, and remote accessibility make it well-suited for home healthcare, telemedicine applications, and smart medical device development.
DOI: 10.17632/xrr4mrphsv.1
title: NORMATIVE REFERENCE VALUE OF SINGLE-LEG HOP TEST FOR A DISTANCE IN INDIAN POPULATION BETWEEN 18 TO 23 YEARS

description: The Single-Leg Hop Test for distance is a widely used as assessment of lower limb muscle power, coordination, and functional movement capability, which is broadly applicable across populations for fitness evaluation and injury assessment. However, normative reference values for this test are lacking among the Indian Population. This study aimed to establish normative values for the Single-Leg Hop Test for a distance among Indian men and women aged 18-23, a demographic prone for lower limb muscle weakness and sports injuries. The study also evaluated the Limb Symmetry Index (LSI) to determine baseline asymmetries and functional differences between lower limbs. Given racial, environmental, and lifestyle disparities, existing normative values from other country populations but not accurately represent Indian individuals. This study address the research gap by providing normative values for the Single-Leg Hop Test for distance among young Indian adults, contributing to fitness evaluation and sports injury prevention. This finding will assist clinicians and researchers in effectively assessing lower limb function within this demographic, providing a valuable reference point for both clinical evaluations and future research studies.
DOI: 10.17632/ffh4ydkk56.2
title: Workers Gaze dynamics in precision machining work process

description: Data Description
Participants and Skill Levels

The dataset includes performance data from three operators with distinct skill levels:
jss (Level 1) – Less than 1 year of experience
lhj (Level 2) – Approximately 2 years of experience
smc (Level 3) – More than 10 years of experience
Task Description

All operators performed a standardized precision machining task involving handwork processes.
The task was conducted under controlled conditions to minimize external variables and ensure consistency across trials.
Data Collection Method

Data was collected using Tobii Glasses 3 (eye-tracking device).
The device provided detailed gaze and head movement data, which were used to assess visual attention, focus, and cognitive workload during the task.
Data was preprocessed to remove noise and irrelevant artifacts, ensuring high-quality input for analysis.

Notable Findings
✅ Performance Differences by Skill Level
More experienced operators (smc) demonstrated higher accuracy, better consistency, and shorter completion times compared to less experienced operators.
Novice operators (jss) showed more variability in gaze patterns and hand movements, suggesting higher cognitive load and less automation in task execution.

✅ Visual Attention Patterns
Expert operators (smc) displayed more focused and predictable gaze patterns, indicating better situational awareness and motor control.
Novice operators exhibited more scattered gaze patterns, reflecting increased difficulty in maintaining focus and task execution efficiency.

✅ Learning Curve and Task Adaptation
How to Interpret and Use the Data
This dataset can be used to develop predictive models for operator performance based on eye-tracking and task execution patterns.
The data provides insights into how skill acquisition influences task efficiency, which can be applied to training program design and workflow optimization.
Comparative analysis of gaze patterns and hand movement consistency can help identify key behavioral markers of expertise in precision machining.
The findings can also support ergonomic improvements and process standardization to reduce errors and improve operator performance.
DOI: 10.17632/ffh4ydkk56.3
title: Workers Gaze dynamics in precision machining work process

description: Data Description
Participants and Skill Levels

The dataset includes performance data from three operators with distinct skill levels:
jss (Level 1) – Less than 1 year of experience
lhj (Level 2) – Approximately 2 years of experience
smc (Level 3) – More than 10 years of experience
Task Description

All operators performed a standardized precision machining task involving handwork processes.
The task was conducted under controlled conditions to minimize external variables and ensure consistency across trials.
Data Collection Method

Data was collected using Tobii Glasses 3 (eye-tracking device).
The device provided detailed gaze and head movement data, which were used to assess visual attention, focus, and cognitive workload during the task.
Data was preprocessed to remove noise and irrelevant artifacts, ensuring high-quality input for analysis.

Notable Findings
✅ Performance Differences by Skill Level
More experienced operators (smc) demonstrated higher accuracy, better consistency, and shorter completion times compared to less experienced operators.
Novice operators (jss) showed more variability in gaze patterns and hand movements, suggesting higher cognitive load and less automation in task execution.

✅ Visual Attention Patterns
Expert operators (smc) displayed more focused and predictable gaze patterns, indicating better situational awareness and motor control.
Novice operators exhibited more scattered gaze patterns, reflecting increased difficulty in maintaining focus and task execution efficiency.

✅ Learning Curve and Task Adaptation
How to Interpret and Use the Data
This dataset can be used to develop predictive models for operator performance based on eye-tracking and task execution patterns.
The data provides insights into how skill acquisition influences task efficiency, which can be applied to training program design and workflow optimization.
Comparative analysis of gaze patterns and hand movement consistency can help identify key behavioral markers of expertise in precision machining.
The findings can also support ergonomic improvements and process standardization to reduce errors and improve operator performance.