Climate Change/Environmental

The data used in this work is collected using the AirBox Sense system developed to detect six air pollutants, ambient  temperature, and ambient relative humidity. The pollutants  are Nitrogen Dioxide (NO2), surface Ozone (O3), Carbon  Monoxide (CO), Sulphur Dioxide (SO2), Particulate Matter  (PM2.5, and PM10). The sensors monitor these pollutants in real-time and store them in a cloud-based platform using a cellular module. Data are collected every 20 seconds, producing  4320 readings each day.

This work presents a specialized dataset designed to advance autonomous navigation in hiking trail and off-road natural environments. The dataset comprises over 1,250 images (640x360 pixels) captured using a camera mounted on a tele-operated robot on hiking trails. Images are manually labeled into eight terrain classes: grass, rock, trail, root, structure, tree trunk, vegetation, and rough trail. The dataset is provided in its original form without augmentations or resizing, allowing end-users flexibility in preprocessing.

This dataset, titled "Synthetic Sand Boil Dataset for Levee Monitoring: Generated Using DreamBooth Diffusion Models," provides a comprehensive collection of synthetic images designed to facilitate the study and development of semantic segmentation models for sand boil detection in levee systems. Sand boils, a critical factor in levee integrity, pose significant risks during floods, necessitating accurate and efficient monitoring solutions.

Data Collection Period: Both datasets cover the period from July 1, 2022, to July 31, 2023. This one-year span captures a full cycle of seasonal variations, which are critical for understanding and forecasting air quality trends.

Data Characteristics

- Temporal Resolution: The data is recorded at 15-minute intervals, offering detailed temporal resolution.

- Missing Data: Both datasets contain missing values due to sensor malfunctions or communication issues. These missing values were handled using imputation techniques as part of the preprocessing phase.

We investigated the long term functionality of the designed PMCS in a practical use case where we monitored plant growth of classical horticulture Dianthus flowers (Dianthus carthusianorum) under the effect of plastic mulching over a period of 4 months. This use case represents a common phenological monitoring case that can be used in agricultural studies. For this, we integrated our PMCS in an embedded vision camera equipped with the openMV H7 Plus board in a waterproof housing [26]. The system shown in Fig.

These datasets are gathered from an array of four gas sensors to be used for the odor detection and recognition system. The smell inspector Kit IX-16 used to create the dataset. each of 4 sensor has 16 channels of readings.  Odors of different 12 samples are taken from these six sensors

1- Natural Air

2- Fresh Onion

3- Fresh Garlic

4- Black Lemon

5- Tomato

6- Petrol

7- Gasoline

8- Coffee 

9- Orange

These datasets are gathered from an array of six gas sensors to be used for the odor recognition system. The sensors those used to create the data set are; Df-NH3, MQ-136, MQ-135, MQ-8, MQ-4, and MQ-2.

odors of different 10 samples are taken from these six sensors 

1- Natural Air

2- Fresh Onion

3- Fresh Garlic

4- Fresh Lemon

5- Tomato

6- Petrol

7- Gasoline

8- Coffee 1,2

9- Orange

10- Colonia Perfume