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opencc-by-4.0May 2022View details →

Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks

Research data of Modelling and Simulation in Engineering journal article “Designing of Fiber Bragg Gratings for Long-distance Optical Fiber Sensing Networks”. Most optical sensors on the market are optical fiber Bragg grating (FBG) sensors with low reflectivity (typically 7-40%) and low side-lobe suppression (SLS) ratio (typically SLS <15dB), which prevents these sensors from being effectively used for long-distance remote monitoring and sensor network solutions. This research is based on designing the optimal grating structure of FBG sensors and estimating their optimal apodization parameters necessary for sensor networks and long-distance monitoring solutions. Gaussian, sine and raised sine apodizations are studied to achieve the main requirements, which are - maximally high reflectivity (at least 90%) and side-lobe suppression (at least 20 dB), as well as maximally narrow bandwidth (FWHM<0.2 nm), FBGs with uniform (without apodization). Results gathered in this research propose high-efficiency FBG grating apodizations, which can be further physically realized for optical sensor networks and long-distance (at least 40 km) monitoring solutions.

opencc-by-4.0May 2022View details →

A 5000 km2 ASTER alteration map of the Oman–UAE ophiolite crust: Data archive and remote sensing toolkit

This archive contains data and maps accompanying the journal article "Multispectral discrimination of spectrally similar hydrothermal minerals in mafic crust: A 5000 km2 ASTER alteration map of the Oman–UAE ophiolite". The archive includes the full resolution, multi-format alteraton maps of hydrothermal alteration of the entire Oman–UAE ophiolite crust generated by ASTER remote sensing. Additional files necessary to reproduce or build on this work are also provided, constituting a remote sensing toolkit for the Oman–UAE ophiolite. A complete list of contents is provided within. Please contact TMB in case of compatibility issues.

opencc-by-4.0Jun 2022View details →

DATASET - Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals

Data for experiments presented in the paper "Improving Remote Sensing of Extreme Events with Machine Learning: Application to IASI LST Retrievals"

opencc-by-4.0Jun 2022View details →

Sensing images of Chutou gully during 2019 to 2021

We release three sets of sensing images, which were obtained on 7  February 2019, 26 February 2020, and 12 March 2021, in Chutou gully, , Miansi Town, Wenchuan County, Sichuan Province, Southwestern China. These UAV images both can be viewed and edited in ENVI software.

opencc-by-4.0Jun 2022View details →

Raw data: High-throughput screening of soybean di-nitrogen fixation and seed nitrogen content using spectral sensing

Symbiotic di-nitrogen fixation of grain legumes has a substantial impact on crop performance, harvest product quality, and nitrogen (N) balance of crop rotations, particularly under organic management regimes. In soybean breeding, selection for increased nitrogen fixation is desirable for improving seed protein content and N balance of cropping systems. However, the lack of high-throughput screening methods for direct measurement of N 2 fixation rates prohibits practical breeding efforts. Therefore, hyperspectral canopy reflectance measurement as a field-based phenotyping method was evaluated in three environments for indirect estimation of N fixation and uptake of soil nitrogen in a set of early maturity soybean genotypes exhibiting a wide range in seed protein content. Reflectance spectra were collected in repeated measurements during flowering and early seed filling stages. Subsequently, various spectral reflectance indices (SRIs) were calculated for characterizing nitrogen accumulation of individual genotypes. Moreover, prediction models for seed protein content as an end-of-season target trait were developed utilizing full spectral information in partial-least-square regression (PLSR) models. A number of N-related SRIs calculated from spectral reflectance data recorded at the beginning of the seed filling stage were significantly correlated to seed protein content. The best prediction of seed protein content, however, was achieved in PLSR models (validation R 2 =0.805 across all three environments). Environments lower in initial soil mineral N content appeared as more favorable selection sites in terms of prediction accuracy, because N fixation is not masked by soil N uptake in such environments. Hyperspectral reflectance data proved to be a valuable method for determining genetic variation in crop N accumulation, which might be implemented in high-throughput screening protocols for N fixation in plant breeding programs.

opencc-by-4.0Jul 2022View details →

Data for remote sensing tool calibration

This dataset contains the in-situ data and the extracted pixel band information used to calibrate and develop an open-source remote sensing tool. The remote sensing tool provides near real-time water quality conditions of lakes/reservoirs in the USA.

opencc-by-4.0Jul 2022View details →

Image sensing with multilayer, nonlinear optical neural networks

This data repository contains the information necessary to reproduce the main results of the paper “Image sensing with multiplayer, nonlinear optical neural networks”. This repository contains the data and the code for generating the figures in the manuscript "Image sensing with multilayer, nonlinear optical neural networks", including figures in the main text and in supplementary materials. The repository also contains the code for controling the experiment setup and running the experiments conducted in the paper: <ul> <li>Folder 'Data_Collection_Example' and 'Data_Extraction_Example' contain example scripts for instrument control and data collection using the multilayer optical-neural-network sensor.</li> <li>Other folders are organized according to the figure panels in the main text, each containing the data and the code required to reproduce the plots in a main figure panel and its associated supplementary figures. In each of these folders, there is a README.txt file that summarizes the role of each file in the folder. </li> </ul>

Data set: Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects

This is the data set accompanying the paper "Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects" by Sune L. Sørensen and Mikkel Baun Kjærgaard. Please refer to the paper for a description of the hardware used to record the data and how it is recorded. It consists of the following files: IoT camera images: RBG images, named img_aa_bbb_0.jpg, where aa is the setup ID, bb is the camera ID (101, 102, 103 or 104). Robot RGB images: RBG images, named aa0.png where aa is the setup ID. Robot point clouds: pcd-files, named aa0.pcd where aa is the setup ID. The transformation from the IoT coordinate system to the robot coordinate system is: robotTiot = np.array([[0.914428, 0.134934, -0.378832, 3.76475], [0.393661, -0.49845, 0.772371, 0.791051], [-0.0846896, -0.855336, -0.509056, 2.37154], [0.0, 0.0, 0.0, 1.0]]) Example, tranforming a pose in IoT coordinates to robot coordinates: p_rob = robotTiot * p_iot

opencc-by-4.0May 2022View details →

Vibration and IMU Sensing Human Activity Dataset

This dataset contains fine-grained human daily activity data collected by infrastructure vibration sensors and one on-wrist IMU sensor. This dataset is collected from six persons from two domestic homes, in total, there are 12 sub-datasets. For the naming, "p" means person and "l" means location. Each dataset has 11 columns, 1o of them stands for sensors' reading. * Due to the uploading platform, please ignore all files in the folder '__MACOSX', and files whose names start with '._'. These are computer system files, not parts of the shared dataset.  ** If you are going to use this dataset for any publications, we will appreciate you to cite this dataset properly. ************************************************************ The following content is copied from README.txt in the compressed folder: ----------------------- Labels: Keyboard typing 1 Using mouse 2 Handwriting 3 Cutting vegetables 4 Stir-frying vegetables 5 Wiping the table 6 Sweeping floor 7 Using vacuum to vacuum floor: 8 Open and close drawer: 9 None Activity: 10 ----------------------- 11 Columns: 1: Activity label 2: Vibration sensor put on the Living Area floor 3: Vibration sensor put on the Living Area table 4: Vibration sensor put on the Studying Area floor 5: Vibration sensor put on the Studying Area desk 6, 7, 8: Accelerometer X,Y,Z 9, 10, 11: Gyroscope X,Y,Z ----------------------- All signals are zero-meaned. The vibration sensors' sampling rate is roughly around 6500Hz, and the IMU sensors' original sampling rate is roughly around 235Hz. ************************************************************ New in Version 2: - Added extracted features from IMU data and vibration data for reference. - IMU signal is applied with a sliding window of 1.5 seconds with 0.75 seconds overlapping, then the feature is extracted in each window. The feature's description can be found here: https://dl.acm.org/doi/abs/10.1145/3410530.3414320 - The vibration signal is applied with event detection to extract events in the vibration signal. For each event, we normalize it by its energy, then extract 10~490 Hz frequency amplitude as the feature.   Disclaimer: Both event detection and feature extraction are empirical, we don't guarantee it is an optimal one.

Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica

This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile. 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator. 2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data. 3. Refraction P wave travel time from a geophone array refraction survey.

CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing

Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. Sci Data 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x   The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m3/m3 to 0.014 m3/m3 and on average is close to a desired SM product stability threshold of 0.01 m3/m3 per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m3/m3.

Non-acoustic speech sensing system based on flexible piezoelectric

The non-acoustic speech sensing system based on flexible piezoelectric is designed to satisfy specific needs around testing device models (in high-noise, complex environments). The system collected vibration signals from the jaws of six males and five females containing ten different control commands at 90 dB of background noise. The dataset is reliable with high intelligibility and is able to achieve 93.7% recognition accuracy by calculation. In general, this paper provides a non-acoustic speech dataset for Mandarin, including the parts collected, the number of people collected, and the environment. The dataset is available at: https://doi.org/10.5281/zenodo.7095762 The data descriptor paper with details of data collection and cleaning process is under submission. For proper citation of the manuscript, please refer to the latest version of this dataset which includes the details. This dataset and its descriptor paper were created by: Shiji Yuan, Ying Sun, Dezhi Zheng, Xinlei Chen,Ying Ding, Shuai Wang, Shangchun Fan For questions or suggestions, please e-mail Dezhi Zheng <zhengdezhi@buaa.edu.cn> Description: Ten common words were chosen as the core of the vocabulary in this dataset. These ten command words can be used for commands in IoT or robotics applications: "forward", "backward", "right", "left", "stop", "up", "down", "draw", "drop", and "reset". The recording was carried on by software named Adobe Audition 2022. We set monophonic recording, 16-bit storage format, and 16 kHz sampling frequency before recording and saved the recorded voice in wav format.  The dataset is provided with two storage rules, which are stored by subject number and command number as classification. In the first rule, the speech data of 11 subjects were stored in different folders with the subject serial number as the folder name. Each folder contains subfolders categorized by command. In the second rule, the speech data of ten commands are stored in different folders, and the names of the folders are the command contents.  The subject number, command number and record order are given for each data entry. For example, the data obtained when subject 1 recorded command 10 for the first time was labeled as "1-10_1". After the data collection process, a filtering algorithm for automatic detection of low non-acoustic speech data was designed to remove problematic data that were very short or very quiet.The script of the data filtering algorithm is provided in this repository.   For specific detail of the data filtering process, please refer to the script (speech data filtering algorithm in MATLAB) in this repository and the data descriptor paper. The dataset in this repository is the processed version. The raw dataset and removed audio files are not included in this repository. File list: Non-acoustic Speech Dataset.zip speech data filtering algorithm.zip Readme.txt

HiP-RI: High-resolution spatial assessment of precipitation using in-situ and remote sensing data in the Cordillera Blanca, Peru

The HiP-RI product was obtained from CHIRP, PERSIANN and GPM datasets, also vegetation products (NDVI-BOKU), topography (DEM SRTM) and data from 38 meteorological stations (2012-2020) were used to estimate precipitation in the Cordillera Blanca, northern sector of the Peruvian Andes. The observed data underwent quality control. A Gaussian filter, resampling and temporal homogenization at monthly scale were applied to the raster data. Subsequently, a linear regression model was built with the different datasets that served as predictors for precipitation spatialization. This allowed obtaining the best R2 values between the in situ data and those estimated with the model (HiP-RI). The results obtained were satisfactory with R2 values higher than 0.60 and an RMSE = 54%.

Figure 5 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 5. The relationship between UV Aerosol Index extracted from Sentinel-5 imagery and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).

Figure 6 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 6. The relationship between daily CHIRPS-precipitation and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).

Figure 9 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 9. The relationship between NDVI (10 m) provided form Sentinal-2 and density of spider mite during monitoring windows based on ANOVA for linear regression. The alphabetical letters indicate of the sequence windows from June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite distribution data).

Figure 4 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 4. Distribution maps of spider mite based on IDW model during monitoring windows, a–n are the sequence windows form June 9, 2020 to September 17, 2020 (First window, May 30 to June 9 was not spider mite population data).

Figure 8 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 8. The relationship between MODIS-Evapotranspiration and spider mite population (mean score of each window) from June 9, 2020 to September 17, 2020. (First window, May 30 to June 9 was not spider mite distribution data).

Figure 3 in Effects of climatic parameters on Tetranychus urticae (Acari: Tetranychidae) populations based on remote sensing in the southeastern Caspian Sea

Figure 3. Spider mite distribution throughout Golestan province; 6 (min.) × 6 (min.) grid cells in the DMS coordinate system (yellow points indicate the monitoring fields).

Non-acoustic speech sensing system based on flexible piezoelectric

The non-acoustic speech sensing system based on flexible piezoelectric is designed to satisfy specific needs around testing device models (in high-noise, complex environments). The system collected vibration signals from the jaws of six males and five females containing ten different control commands at 90 dB of background noise. The dataset is reliable with high intelligibility and is able to achieve 93.7% recognition accuracy by calculation. In general, this paper provides a non-acoustic speech dataset for Mandarin, including the parts collected, the number of people collected, and the environment. The dataset is available at: https://doi.org/10.5281/zenodo.7185663 The data descriptor paper with details of data collection and cleaning process is under submission. For proper citation of the manuscript, please refer to the latest version of this dataset which includes the details. This dataset and its descriptor paper were created by: Shiji Yuan, Ying Sun, Shuai Wang, Xinlei Chen,Ying Ding,Dezhi Zheng , Shangchun Fan For questions or suggestions, please e-mail Shuai Wang <wangshuai@buaa.edu.cn> Description: Ten common words were chosen as the core of the vocabulary in this dataset. These ten command words can be used for commands in IoT or robotics applications: "forward", "backward", "right", "left", "stop", "up", "down", "draw", "drop", and "reset". The recording was carried on by software named Adobe Audition2022. We set monophonic recording, 16-bit storage format, and 16 kHz sampling frequency before recording and saved the recorded voice in wav format.  The dataset is provided with two storage rules, which are stored by subject number and command number as classification. In the first rule, the speech data of 11 subjects were stored in different folders with the subject serial number as the folder name. Each folder contains subfolders categorized by command. In the second rule, the speech data of ten commands are stored in different folders, and the names of the folders are the command contents. The subject number, command number and record order are given for each data entry. For example, the data obtained when subject 1 recorded command 10 for the first time was labeled as "1-10_1". After the data collection process, a filtering algorithm for automatic detection of low non-acoustic speech data was designed to remove problematic data that were very short or very quiet.The script of the data filtering algorithm is provided in this repository.   For specific detail of the data filtering process, please refer to the script (speech data filtering algorithm in MATLAB) in this repository and the data descriptor paper. The dataset in this repository is the processed version. The raw dataset and removed audio files are not included in this repository. File list: Non-acoustic Speech Dataset.zip speech data filtering algorithm.zip Readme.txt