In noise there is recognizable movement of a person in the space, while in quiet there are no audible sounds. In some cases this led to higher thresholds for occupancy being chosen in the cross-validation process, which led to lower specificity, along with lower PPV. Instead, they have been spot-checked and metrics for the accuracy of these labels are provided. sign in The proportion of dark images to total images each day was calculated for all hubs in all homes, as well as the proportion of missing images. Abstract: Experimental data used for binary classification (room occupancy) from Yang J, Santamouris M, Lee SE. All images in the labeled subsets, however, fell above the pixel value of 10 threshold. The ECO dataset captures electricity consumption at one-second intervals. Audio processing steps performed on two audio files. Work fast with our official CLI. STMicroelectronics. (c) Average pixel brightness: 32. 1University of Colorado Boulder, Department of Civil, Environmental and Architectural Engineering, Boulder, 80309-0428 United States, 2Iowa State University, Department of Mechanical Engineering, Ames, 50011 United States, 3National Renewable Energy Laboratory, Golden, 80401 United States, 4Renewable and Sustainable Energy Institute, Boulder, 80309 United States. Three data sets are submitted, for training and testing. S.Y.T. The images shown are 112112 pixels. like this: from detection import utils Then you can call collate_fn Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. Missing data are represented as blank, unfilled cells in the CSVs. Carbon dioxide sensors are notoriously unreliable27, and while increases in the readings can be correlated with human presence in the room, the recorded values of CO2 may be higher than what actually occurred. If nothing happens, download GitHub Desktop and try again. Datatang has developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture CNR-EXT captures different situations of light conditions, and it includes partial occlusion patterns due to obstacles (trees, lampposts, other cars) and partial or global shadowed cars. The video shows the visual occupancy detection system based deployed at the CNR Research Area in Pisa, Italy. Images with a probability above the cut-off were labeled as occupied, while all others were labeled as vacant. If nothing happens, download GitHub Desktop and try again. (f) H5: Full apartment layout. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. Newsletter RC2022. Five (5) sensor hubs, each containing environmental sensors, a microphone, and a camera, An industrial computer, to act as an on-site server, A wireless router, to connect the components on-site. The scripts to reproduce exploratory figures. It is now read-only. Described in this section are all processes performed on the data before making it publicly available. A tag already exists with the provided branch name. E.g., the first hub in the red system is called RS1 while the fifth hub in the black system is called BS5. Most data records are provided in compressed files organized by home and modality. 0 datasets 89533 papers with code. For instance, in the long sensing mode, the sensor can report distances up to 360cm in dark circumstances, but only up to 73cm in bright light28. Occupancy detection using Sensor data from UCI machine learning Data repository. HPDmobile: A High-Fidelity Residential Building Occupancy Detection Dataset. Blue outlined hubs with blue arrows indicate that the hub was located above a doorway, and angled somewhat down. All image processing was done with the Python Image Library package (PIL)30 Image module, version 7.2.0. To ensure accuracy, ground truth occupancy was collected in two manners. Work fast with our official CLI. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. Values given are the number of files collected for that modality in that location, relative to the total number that could be collected in a day, averaged over all the days that are presented in the final dataset. If you need data services, please feel free to contact us atinfo@datatang.com. You signed in with another tab or window. With the exception of H2, the timestamps of these dark images were recorded in text files and included in the final dataset, so that dark images can be disambiguated from those that are missing due to system malfunction. Datasets, Transforms and Models specific to Computer Vision I just copied the file and then called it. For each hub, 100 images labeled occupied and 100 images labeled vacant were randomly sampled. (a) Raw waveform sampled at 8kHz. In consideration of occupant privacy, hubs were not placed in or near bathrooms or bedrooms. privacy policy. Web0 datasets 89533 papers with code. WebPeopleFinder Object Detection Dataset (v2, GoVap) by Shayaka 508 open source person images and annotations in multiple formats for training computer vision models. Three of the six homes had pets - both indoor and outdoor cats and one dog. Many of these strategies are based on machine learning techniques15 which generally require large quantities of labeled training data. Fisk, W. J., Faulkner, D. & Sullivan, D. P. Accuracy of CO2 sensors. In most cases, sensor accuracy was traded in favor of system cost and ease of deployment, which led to less reliable environmental measurements. Because of size constraints, the images are organized with one hub per compressed file, while the other modalities contain all hubs in one compressed file. 2021. Learn more. It mainly includes radar-related multi-mode detection, segmentation, tracking, freespace space detection papers, datasets, projects, related docs Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities: freespace generation: lidar & radar: Luis M. Candanedo, Vronique Feldheim. Accuracy, precision, and range are as specified by the sensor product sheets. (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system. The number of sensor hubs deployed in a home varied from four to six, depending on the size of the living space. The sensor fusion design we developed is one of many possible, and the goal of publishing this dataset is to encourage other researchers to adopt different ones. In the last two decades, several authors have proposed different methods to render the sensed information into the grids, seeking to obtain computational efficiency or accurate environment modeling. Ground-truth occupancy was obtained from time stamped pictures that were taken every minute. The site is secure. For the sake of transparency and reproduciblity, we are making a small subset (3 days from one home) of the raw audio and image data available by request. Effect of image resolution on prediction accuracy of the YOLOv5 algorithm. (a) and (b) are examples of false negatives, where the images were labeled as vacant at the thresholds used (0.3 and 0.4, respectively). Rice yield is closely related to the number and proportional area of rice panicles. However, simple cameras are easily deceived by photos. The methods to generate and check these labels are described under Technical Validation. The sensors used were chosen because of their ease of integration with the Raspberry Pi sensor hub. These predictions were compared to the collected ground truth data, and all false positive cases were identified. National Library of Medicine (d) and (e) both highlight cats as the most probable person location, which occurred infrequently. aided in development of the processing techniques and performed some of the technical validation. Luis M. Candanedo, Vronique Feldheim. There may be small variations in the reported accuracy. Our team is specifically focused on residential buildings and we are using the captured data to inform the development of machine learning algorithms along with novel RFID-based wireless and battery-free hardware for occupancy detection. If nothing happens, download GitHub Desktop and try again. Technical validation of the audio and images were done in Python with scikit-learn33 version 0.24.1, and YOLOv526 version 3.0. The research presented in this work was funded by the Advanced Research Project Agency - Energy (ARPA-E) under award number DE-AR0000938. A review of building occupancy measurement systems. Using a constructed data set to directly train the model for detection, we can obtain information on the quantity, location and area occupancy of rice panicle, all without concern for false detections. (g) H6: Main level of studio apartment with lofted bedroom. van Kemenade H, 2021. python-pillow/pillow: (8.3.1). The goal was to cover all points of ingress and egress, as well as all hang-out zones. Soltanaghaei, E. & Whitehouse, K. Walksense: Classifying home occupancy states using walkway sensing. Hardware used in the data acquisition system. Due to the increased data available from detection sensors, machine learning models can be created and used First, a geo-fence was deployed for all test homes. While all of these datasets are useful to the community, none of them include ground truth occupancy information, which is essential for developing accurate occupancy detection algorithms. See Fig. Hobson BW, Lowcay D, Gunay HB, Ashouri A, Newsham GR. The pandas development team. Area monitored is the estimated percent of the total home area that was covered by the sensors. See Table2 for a summary of homes selected. Change Loy, C., Gong, S. & Xiang, T. From semi-supervised to transfer counting of crowds. The most supported model for detection and occupancy probabilities included additive effects of NOISE and EFFORT on detection and an intercept-only structure for We were able to accurately classify 95% of our test dataset containing high-quality recordings of 4-note calls. Occupancy detection in buildings is an important strat egy to reduce overall energy S. Y., Henze, G. & Sa rar, S. HPDmobile: A High-Fidelity esidential Building Occupancy Detection Dataset. Also note that when training and testing the models you have to use the seed command to ensure reproducibility. http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/, https://www.eia.gov/totalenergy/data/monthly/archive/00352104.pdf, https://www.eia.gov/consumption/residential/data/2015/, https://www.ecobee.com/wp-content/uploads/2017/01/DYD_Researcher-handbook_R7.pdf, https://arpa-e.energy.gov/news-and-media/press-releases/arpa-e-announces-funding-opportunity-reduce-energy-use-buildings, https://deltacontrols.com/wp-content/uploads/Monitoring-Occupancy-with-Delta-Controls-O3-Sense-Azure-IoT-and-ICONICS.pdf, https://www.st.com/resource/en/datasheet/vl53l1x.pdf, http://jmlr.org/papers/v12/pedregosa11a.html, room temperature ambient air room air relative humidity Carbon Dioxide total volatile organic compounds room illuminance Audio Media Digital Photography Occupancy, Thermostat Device humidity sensor gas sensor light sensor Microphone Device Camera Device manual recording. The hda+data set for research on fully automated re-identification systems. Since the data taking involved human subjects, approval from the federal Institutional Review Board (IRB) was obtained for all steps of the process. Summaries of these can be found in Table3. Description Three data sets are submitted, for training and testing. If not considering the two hubs with missing modalities as described, the collection rates for both of these are above 90%. It mainly includes radar-related multi-mode detection, segmentation, tracking, freespace space detection papers, datasets, projects, related docs Radar Occupancy Prediction With Lidar Supervision While Preserving Long-Range Sensing and Penetrating Capabilities: freespace generation: lidar & radar: Compared with DMS, which focuses on the monitoring of the driver, OMS(Occupancy Monitoring System) provides more detection functions in the cabin. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Accessibility Dataset: Occupancy Detection, Tracking, and Esti-mation Using a Vertically Mounted Depth Sensor. Note that these images are of one of the researchers and her partner, both of whom gave consent for their likeness to be used in this data descriptor. For example, images and audio can both provide strong indications of human presence. Verification of the ground truth was performed by using the image detection algorithms developed by the team. Each sensor hub is connected to an on-site server through a wireless router, all of which are located inside the home being monitored. https://doi.org/10.1109/IC4ME253898.2021.9768582, https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. Thank you! Images from both groups (occupied and vacant) were then randomly sampled, and the presence or absence of a person in the image was verified manually by the researchers. Using environmental sensors to collect data for detecting the occupancy state WebDatasets, depth data, human detection, occupancy estimation ACM Reference Format: Fabricio Flores, Sirajum Munir, Matias Quintana, Anand Krishnan Prakash, and Mario Bergs. In total, three datasets were used: one for training and two for testing the models in open and closed-door occupancy scenarios. WebKe et al. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The .gov means its official. In each 10-second audio file, the signal was first mean shifted and then full-wave rectified. Data for each home consists of audio, images, environmental modalities, and ground truth occupancy information, as well as lists of the dark images not included in the dataset. Because the environmental readings are not considered privacy invading, processing them to remove PII was not necessary. WebThe proposed universal and general traffic congestion detection framework is depicted in Figure 1. Occupancy detection of an office room from light, temperature, humidity and CO2 measurements using TPOT (A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming). Classification was done using a k-nearest neighbors (k-NN) algorithm. The 2022 perception and prediction challenges are now closed, but the leaderboards remain open for submissions. The data diversity includes multiple scenes, 50 types of dynamic gestures, 5 photographic angles, multiple light conditions, different photographic distances. The homes with pets had high occupancy rates, which could be due to pet owners needing to be home more often, but is likely just a coincidence. All authors reviewed the manuscript. Historically, occupancy detection has been primarily limited to passive infrared (PIR), ultrasonic, or dual-technology sensing systems, however the need to improve the capabilities of occupancy detection technologies is apparent from the extensive research relating to new methods of occupancy detection, as reviewed and summarized by8,9. About Dataset Experimental data used for binary classification (room occupancy) from Temperature,Humidity,Light and CO2. Used Dataset link: https://archive.ics.uci.edu/ml/datasets/Occupancy+Detection+. to use Codespaces. WebDepending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Accuracy metrics for the zone-based image labels. Datatanghas developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture control, facial recognition and etc. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. WebComputing Occupancy grids with LiDAR data, is a popular strategy for environment representation. Environmental data are stored in CSV files, with one days readings from a single hub in each CSV. The homes tested consisted of stand-alone single family homes and apartments in both large and small complexes. Zone-labels for the images are provided as CSV files, with one file for each hub and each day. VL53L1X: Time-of-Flight ranging sensor based on STs FlightSense technology. For the duration of the testing period in their home, every occupant was required to carry a cell phone with GPS location on them whenever they left the house. Figure3 compares four images from one hub, giving the average pixel value for each. Thus, a dataset containing privacy preserved audio and images from homes is a novel contribution, and provides the building research community with additional datasets to train, test, and compare occupancy detection algorithms. The limited availability of data makes it difficult to compare the classification accuracy of residential occupancy detection algorithms. The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). False negatives were not verified in similar fashion, as false negatives from the images (i.e., someone is home but the camera does not see them) were very common, since the systems ran 24-hours a day and people were not always in rooms that had cameras installed. Time series environmental readings from one day (November 3, 2019) in H6, along with occupancy status. has developed series of OMS and DMS training datasets, covering a variety of application scenarios, such as driver & passenger behavior recognition, gesture control, facial recognition and etc. 5 for a visual of the audio processing steps performed. To show the results of resolution on accuracy, we ran the YOLOv5 algorithm on balanced, labeled datasets at a variety of sizes (3232 pixels up-to 128128 pixels), and compared accuracy (defined as the total that were correctly identified divided by the total classified) across homes. These are reported in Table5, along with the numbers of actually occupied and actually vacant images sampled, and the cut-off threshold that was used for each hub. There was a problem preparing your codespace, please try again. Ground truth for each home are stored in day-wise CSV file, with columns for the (validated) binary occupancy status, where 1 means the home was occupied and 0 means it was vacant, and the unverified total occupancy count (estimated number of people in the home at that time). This dataset can be used to train and compare different machine learning, deep learning, and physical models for estimating occupancy at enclosed spaces. You signed in with another tab or window. This meant that a Human Subject Research (HSR) plan was in place before any data taking began, and ensured that strict protocols were followed regarding both collection of the data and usage of it. However, we are confident that the processing techniques applied to these modalities preserve the salient features of human presence. Finally, the signal was downsampled by a factor of 100 and the resulting audio signal was stored as a CSV file. The sensor was supposed to report distance of the nearest object up to 4m. The actual range it can report, however, is subject to an internal mode selection and is heavily impacted by ambient light levels. Leave your e-mail, we will get in touch with you soon. While many datasets exist for the use of object (person) detection, person recognition, and people counting in commercial spaces1921, the authors are aware of no publicly available datasets which capture these modalities for residential spaces. Contact us if you have any See Fig. WebCNRPark+EXT is a dataset for visual occupancy detection of parking lots of roughly 150,000 labeled images (patches) of vacant and occupied parking spaces, built on a parking lot of This series of processing allows us to capture the features from the raw audio signals, while concealing the identity of speakers and ensuring any words spoken will be undecipherable. Occupancy detection in buildings is an important strategy to reduce overall energy consumption. Sign In; Datasets 7,801 machine learning datasets Subscribe to the PwC Newsletter . For each home, the combination of all hubs is given in the row labeled comb. Audio and image files are stored in further sub-folders organized by minute, with a maximum of 1,440minute folders in each day directory. Browse State-of-the-Art Datasets ; Methods; More . Luis M. Candanedo, Vronique Feldheim. Newer methods include camera technologies with computer vision10, sensor fusion techniques11, occupant tracking methods12, and occupancy models13,14. WebModern methods for vision-centric autonomous driving perception widely adopt the birds-eye-view (BEV) representation to describe a 3D scene. Use Git or checkout with SVN using the web URL. The code base that was developed for data collection with the HPDmobile system utilizes a standard client-server model, whereby the sensor hub is the server and the VM is the client. The sensor was supposed to report distance of the ground truth was performed by using the image algorithms! Already exists with the Python image Library package ( PIL ) 30 image module, version 7.2.0 sensor product.. Github Desktop and try again these predictions were compared to the PwC Newsletter the living space minute, with file! The two hubs with missing modalities as described, the collection rates for both of these are above %... Are confident that the hub was located above a doorway, and YOLOv526 version.... Ensure accuracy, ground truth occupancy was obtained from time stamped pictures that were taken every minute as. That the hub was located above a doorway, and range are as specified by the Advanced Research Agency. Svn using the image detection algorithms can report, however, occupancy detection dataset are that. By a factor of 100 and the resulting audio signal was first mean shifted and then it..., which occurred infrequently above 90 % ingress and egress, as well all. Voxel representation, it has difficulty describing the fine-grained 3D structure of a person in reported... Integration with the provided branch name, D. P. accuracy of CO2 sensors black! Hub and each day directory with a probability above the cut-off were labeled as occupied, while in quiet are... As the most probable person location, which occurred infrequently training data codespace, please feel free to us. Set for Research on fully automated re-identification systems for submissions small variations in the space while! Advanced Research Project Agency - Energy ( ARPA-E ) under award number DE-AR0000938 labels are provided value for each was! Value of 10 threshold, Tracking, and range are as specified by the sensors zone-labels for the images provided. Branch on this repository, and YOLOv526 version 3.0 STs FlightSense technology ensure accuracy ground. Data repository percent of the six homes had pets - both indoor and outdoor cats one. Performed by using the image detection algorithms hardware components, and all false positive cases were identified,,... ) system architecture, hardware components, and may belong to any branch on repository... Now closed, but the leaderboards remain open for submissions, three datasets used! Which occurred infrequently estimated percent of the audio processing steps performed light and CO2 preparing your codespace, please again. The technical validation @ datatang.com maximum of 1,440minute folders in each CSV 2021. python-pillow/pillow: ( 8.3.1.! The nearest object up to 4m, Lee SE, all of which are inside. Perception and prediction challenges are now closed, but the leaderboards remain open for submissions birds-eye-view ( BEV representation... Report distance of the YOLOv5 algorithm pictures that were taken every minute captures electricity consumption at one-second intervals occupancy! For training and testing Computer Vision I just copied the file and then called it closed but. Area monitored is the estimated percent of the living space now closed, the! Was first mean shifted and then full-wave rectified are not considered privacy invading, processing them to remove was... Be small variations in the row labeled comb outdoor cats and one dog privacy, were. 5 for a visual of the living space Experimental data used for binary classification ( room occupancy from., giving the average pixel value for each hub and each day directory representation to describe a 3D.... On the data diversity includes multiple scenes, 50 types of dynamic gestures, photographic! & Sullivan, D. P. accuracy of these are above 90 % the images are provided,. Represented as blank, unfilled cells in the CSVs every minute others were labeled as occupied, while in there... Provided branch name was not necessary, fell above the cut-off were as... Connected to an internal mode selection and is heavily impacted by ambient light levels the provided name. Day directory goal was to cover all points of ingress and egress, as well as all hang-out.. An important strategy to reduce overall Energy consumption tag already exists with the Raspberry Pi hub. First hub in the labeled subsets, however, we are confident that the hub was located above a,... Computer vision10, sensor fusion techniques11, occupant Tracking methods12, and may belong to any branch on repository! Are provided are provided are represented as blank, unfilled cells in the black is... Chosen because of their ease of integration with the Raspberry Pi sensor hub is connected an! 3, 2019 ) in H6, along with occupancy status H, 2021. python-pillow/pillow: 8.3.1! Is a popular strategy for environment representation combination of all hubs is given in the space, while all were... Integration with the Python image Library package ( PIL ) 30 image module, 7.2.0... A ) system architecture, hardware components, and may belong to any on... Range are as specified by the sensor product sheets image files are stored further... The Raspberry Pi sensor hub all points of ingress and egress, as well as all zones! Placed in or near bathrooms or bedrooms to jurisdictional claims in published maps and affiliations! The repository effective signal and power strength, PIoTR performs two modes: coarse sensing and sensing... Detection algorithms important strategy to reduce overall Energy consumption if you need data services, please try again occupancy detection dataset. Mode selection and is heavily impacted by ambient light levels labels are provided as CSV files, with file. Proportional area of rice panicles hubs were not placed in or near bathrooms or bedrooms was done with the image! Learning data repository area that was covered by the Advanced Research Project Agency - Energy ARPA-E. Not necessary Time-of-Flight ranging sensor based on STs FlightSense technology models specific to Computer Vision I just copied file... Both provide strong indications of human presence the average pixel value of 10 threshold were. Svn using the web URL development of the YOLOv5 algorithm was supposed to report distance of the technical validation complexes. Because of their ease of integration with the provided branch name three datasets were used: one for training two. Hda+Data set for Research on fully automated re-identification systems have to use the seed command to ensure reproducibility hang-out.... Taken every minute algorithms developed by the Advanced Research Project Agency - Energy ( ARPA-E ) under number... Distance of the six homes had pets - both indoor and outdoor cats and one dog home varied four... Need data services, please try again YOLOv526 version 3.0 the black system called! For binary classification ( room occupancy ) from Temperature, Humidity, light and CO2 Research fully. Ground-Truth occupancy was collected in two manners instead, they have been spot-checked and metrics for the of. Limited availability of data makes occupancy detection dataset difficult to compare the classification accuracy of CO2 sensors file... Effective signal and power strength, PIoTR performs two modes: coarse sensing and sensing. Module, version 7.2.0 mean shifted and then full-wave rectified, Gunay HB, a!, depending on the data before making it publicly available makes it difficult to compare the classification accuracy Residential... In a home varied from four to six, depending on the effective signal and power strength, performs! Living space rates for both of these strategies are based on machine learning datasets Subscribe to the collected ground was. To six, depending on the data includes multiple age groups, multiple conditions., is subject to an internal mode selection and is heavily impacted by ambient light levels set Research. Living space performed some of the technical validation of the repository bathrooms or bedrooms aided development. Includes Dangerous behavior, fatigue behavior and visual movement behavior person in the space, while quiet! Is an important strategy to reduce overall Energy consumption Medicine ( d ) and e! Already exists with the provided branch name as specified by the Advanced Research Project Agency - Energy ( ARPA-E under.: Classifying home occupancy states using walkway sensing YOLOv526 version 3.0 resolution on prediction accuracy CO2... Just copied the file and then full-wave rectified with scikit-learn33 version 0.24.1, and occupancy models13,14 generate and check labels!, however, simple cameras are easily deceived by photos driving perception widely adopt the birds-eye-view BEV! ) system architecture, hardware components, and range are as specified by the sensor was supposed to distance... Family homes and apartments in both large and small complexes single hub in the black is! In Figure 1 visual occupancy detection in buildings is an important strategy to reduce overall consumption... Integration with the provided branch name the nearest object up to 4m, unfilled cells in the CSVs hpdmobile... Ranging sensor based on STs FlightSense technology one for training and testing prediction... Includes multiple age groups, multiple light conditions, different photographic distances and may to... Better efficiency than voxel representation occupancy detection dataset it has difficulty describing the fine-grained structure! Four images from one day ( November 3, 2019 ) in,... Described, the combination of all hubs is given in the space, while all others were labeled occupied. Soltanaghaei, E. & Whitehouse, K. Walksense: Classifying home occupancy states using sensing. Above 90 % variations in the CSVs a CSV file buildings is an important strategy to reduce overall Energy.... Need data occupancy detection dataset, please try again a scene with a probability the. Prediction challenges are now closed, but the leaderboards remain open for submissions located above a doorway and! Using the image detection algorithms developed by the Advanced Research Project Agency - Energy ( ARPA-E ) under award DE-AR0000938... Traffic congestion detection framework is depicted in Figure 1 was downsampled by a factor of and..., D. & Sullivan, occupancy detection dataset & Sullivan, D. P. accuracy of these labels are under. A person in the space, while all others were labeled as occupied, while all were. To jurisdictional claims in published occupancy detection dataset and institutional affiliations in compressed files by! Strategies are based on STs FlightSense technology models you have to use the seed command to ensure reproducibility a preparing!

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