The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft

The time and expense required to collect and label audio data has been a prohibitive factor in the availability of domain specific audio datasets. As the predictive specificity of a classifier depends on the specificity of the labels it is trained on, it follows that finely-labelled datasets are crucial for advances in machine learning. Aiming to stimulate progress in the field of machine listening, this paper introduces AeroSonicDB (YPAD-0523), a dataset of low-flying aircraft sounds for training acoustic detection and classification systems. This paper describes the method of exploiting ADS-B radio transmissions to passively collect and label audio samples. Provides a summary of the collated dataset. Presents baseline results from three binary classification models, then discusses the limitations of the current dataset and its future potential. The dataset contains 625 aircraft recordings ranging in event duration from 18 to 60 seconds, for a total of 8.87 hours of aircraft audio. These 625 samples feature 301 unique aircraft, each of which are supplied with 14 supplementary (non-acoustic) labels to describe the aircraft. The dataset also contains 3.52 hours of ambient background audio ("silence"), as a means to distinguish aircraft noise from other local environmental noises. Additionally, 6 hours of urban soundscape recordings (with aircraft annotations) are included as an ancillary method for evaluating model performance, and to provide a testing ground for real-time applications

Incorporation of uranium into hematite during crystallization from ferrihydrite

Ferrihydrite was exposed to U(VI)-containing cement leachate (pH 10.5) and aged to induce crystallization of hematite. A combination of chemical extractions, TEM, and XAS techniques provided the first evidence that adsorbed U(VI) (≈3000 ppm) was incorporated into hematite during ferrihydrite aggregation and the early stages of crystallization, with continued uptake occurring during hematite ripening. Analysis of EXAFS and XANES data indicated that the U(VI) was incorporated into a distorted, octahedrally coordinated site replacing Fe(III). Fitting of the EXAFS showed the uranyl bonds lengthened from 1.81 to 1.87 Å, in contrast to previous studies that have suggested that the uranyl bond is lost altogether upon incorporation into hematite the results of this study both provide a new mechanistic understanding of uranium incorporation into hematite and define the nature of the bonding environment of uranium within the mineral structure. Immobilization of U(VI) by incorporation into hematite has clear and important implications for limiting uranium migration in natural and engineered environments. © 2014 American Chemical Society

AeroSonicDB (YPAD-0523): Labelled audio dataset for acoustic detection and classification of aircraft

AeroSonicDB (YPAD-0523): Labelled audio dataset for acoustic detection and classification of aircraft Version 1.1.1 (September 2023) Publication When using this data in an academic work, please reference the DOI and version. Description AeroSonicDB:YPAD-0523 is a specialised dataset of ADS-B labelled audio clips for research in the fields of environmental noise attribution and machine listening, particularly acoustic detection and classification of low-flying aircraft. Audio files in this dataset were recorded at locations in close proximity to a flight path approaching or departing Adelaide International Airport’s (ICAO code: YPAD) primary runway, 05/23. Recordings are initially labelled from radio (ADS-B) messages received from the aircraft overhead, then human verified and annotated with the first and final moments which the target aircraft is audible. A total of 1,895 audio clips are distributed across two top-level classes, “Aircraft” (8.87 hours) and “Silence” (3.52 hours). The aircraft class is then further broken-down into four subclasses, which broadly describe the structure of the aircraft and propulsion mechanism. A variety of additional “airframe” features are provided to give researchers finer control of the dataset, and the opportunity to develop ontologies specific to their own use case. For convenience, the dataset has been split into training (10.04 hours) and testing (2.35 hours) subsets, with the training set further split into 5 distinct folds for cross-validation. These splits are performed to prevent data-leakage between folds and the test set, ensuring samples collected in the same recording session (distinct in time, location and microphone) are assigned to the same fold. Researchers may find applications for this dataset in a number of fields; particularly aircraft noise isolation and noise monitoring in an urban environment, development of passive acoustic systems to assist radar technology, and understanding the sources of aircraft noise to help manufacturers design less-noisy aircraft. Audio data ADS-B (Automatic Dependent Surveillance–Broadcast) messages transmitted directly from aircraft are used to automatically trigger, capture and label audio samples. A 60-second recording is triggered when an aircraft transmits a message indicating it is within a specified distance of the recording device (see “Location data” below for specifics). The resulting audio file is labelled with the unique ICAO identifier code for the aircraft, as well as its last reported altitude, date, time, location and microphone. The recording is then human verified and annotated with timestamps for the first and last moments the aircraft is audible. In total, AeroSonicDB contains 625 recordings of low-altitude aircraft - varying in length from 18 to 60 seconds, for a total of 8.87 hours of aircraft audio. A collection of urban background noise without aircraft (silence) is included with the dataset as a means of distinguishing location specific environmental noises from aircraft noises. 10-second background noise, or “silence” recordings are triggered only when there are no aircraft broadcasting they are within a specified distance of the recording device (see “Location data” below). These “silence” recordings are also human verified to ensure no aircraft noise is present. The dataset contains 1,270 clips of silence/urban background noise. Location data Recordings have been collected from three (3) locations. GPS coordinates for each location are provided in the "locations.json" file. In order to protect privacy, coordinates have been provided for a road or public space nearby the recording device instead of its exact location. Location: 0 Situated in a suburban environment approximately 15.5km north-east of the start/end of the runway. For Adelaide, typical south-westerly winds bring most arriving aircraft past this location on approach. Winds from the north or east will cause aircraft to take-off to the north-east, however not all departing aircraft will maintain a course to trigger a recording at this location. The "trigger distance" for this location is set for 3km to ensure small/slower aircraft and large/faster aircraft are captured within a sixty-second recording. "Silence" or ambient background noises at this location include; cars, motorbikes, light-trucks, garbage trucks, power-tools, lawn mowers, construction sounds, sirens, people talking, dogs barking and a wide range of Australian native birds (New Holland Honeyeaters, Wattlebirds, Australian Magpies, Australian Ravens, Spotted Doves, Rainbow Lorikeets and others). Location: 1 Situated approximately 500m south-east of the south-eastern end of the runway, this location is nearby recreational areas (golf course, skate park and parklands) with a busy road/highway inbetween the location and runway. This location features heavy winds and road traffic, as well as people talking, walking and riding, and also birds such as the Australian Magpie and Noisy Miner. The trigger distance for this location is set to 1km. Due to their low altitude aircraft are louder, but audible for a shorter time compared to "Location 0". Location: 2 As an alternative to "Location 1", this location is situated approximately 950m south-east of the end of the runway. This location has a wastewater facility to the north, a residential area to the south and a popular beach to the west. This location offers greater wind protection and further distance from airport and highway noises. Ambient background sounds feature close proximity cars and motorbikes, cyclists, people walking, nail guns and other construction sounds, as well as the local birds mentioned above. Aircraft metadata Supplementary "airframe" metadata for all aircraft has been gathered to help broaden the research possibilities from this dataset. Airframe information was collected and cross-checked from a number of open-source databases. The author has no reason to beleive any significant errors exist in the "aircraft_meta" files, however future versions of this dataset plan to obtain aircraft information directly from ICAO (International Civil Aviation Organization) to ensure a single, verifiable source of information. Class/subclass ontology (minutes of recordings) 0. no aircraft (202) 0: no aircraft (202) 1. aircraft (214) 1: piston-propeller aeroplane (12) 2: turbine-propeller aeroplane (37) 3: turbine-fan aeroplane (163) 4: rotorcraft (1.6) The subclasses are a combination of the "airframe" and "engtype" features. Piston and Turboshaft rotorcraft/helicopters have been combined into a single subclass due to the small number of samples. Data splits Audio recordings have been split into training (81%) and test (19%) sets. The training set has further been split into 5 folds, giving researchers a common split to perform 5-fold cross-validation to ensure reproducibility and comparable results. Data leakage into the test set has been avoided by ensuring recordings are disjointed from the training set by time and location - meaning samples in the test set for a particular location were recorded after any samples included in the training set for that particular location. Labelled data The entire dataset (training and test) is referenced and labelled in the “sample_meta.csv” file. Each row contains a reference to a unique recording, its meta information, annotations and airframe features. Alternatively, these labels can be derived directly from the filename of the sample (see below). The “aircraft_meta.csv” and “aircraft_meta.json” files can be used to reference aircraft specific features - such as; manufacturer, engine type, ICAO type designator etc. (see “Columns/Labels” below for all features). File naming convention Audio samples are in WAV format, with some metadata stored in the filename. Basic Convention “Aircraft ID + Date + Time + Location ID + Microphone ID” “XXXXXX_YYYY-MM-DD_hh-mm-ss_X_X” Sample with aircraft {hex_id} _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext} 7C7CD0_2023-05-09_12-42-55_2_1.wav Sample without aircraft “Silence” files are denoted with six (6) leading zeros rather than an aircraft hex code. All relevant metadata for “silence” samples are contained in the audio filename, and again in the accompanying “sample_meta.csv” 000000 _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext} 000000_2023-05-09_12-30-55_2_1.wav Columns/Labels (found in sample_meta.csv, aircraft_meta.csv/json files) train-test: Train-test split (train, test) fold: Digit from 1 to 5 splitting the training data 5 ways (else test) filename: The filename of the audio recording date: Date of the recording time: Time of the recording location: ID for the location of the recording mic: ID of the microphone used class: Top-level label for the recording (eg. 0 = No aircraft, 1 = Aircraft audible) subclass: Subclass label for the recording (eg. 0 = No aircraft, 3 = Turbine-fan aeroplane) altitude: Approximate altitude of the aircraft (in feet) at the start of the recording hex_id: Unique ICAO 24-bit address for the aircraft recorded session: Unique recording session by time, location and microphone. offset: Time stamp marking the start of the audio event. duration: Length of the recording (in seconds) file_length: Total length of the audio file in seconds. reg: Registration number of the aircraft airframe: Describes the mechanical structure of the aircraft (eg. Power Driven Aeroplane, Rotorcraft) engtype: Type of engine (eg. Piston, Turboprop, Turbofan, Turboshaft) engnum: Number of engines shortdesc: 3 character alpha-numeric code describing the airframe and engine configuration (eg. L1P, L4J, H2T) typedesig: ICAO type designator for make and model of aircraft (eg. PC12, C185, B738) manu: Aircraft manufaturer (eg. Boeing, Pilatus, Airbus) model: Aircraft model (eg. 737-800, A320-232, DHC-8-315) engmanu: Engine manufacturer (eg. Pratt & Whitney, CFM Interntional, Rolls Royce) engmodel: Engine model (eg. TRENT XWB, CFM56-7B24E, PT6E-67XP) engfamily: Family of the engine model (eg. TRENT, CFM56, PT6) fueltype: Fuel type used in the engine (eg. Gasoline, Kerosine) propmanu: Propeller manufacturer (eg. Hartzell Propellers, Hamilton Standard, "Aircraft Not Fitted With Propeller") propmodel: Propeller model (eg. HC-E5A-3A\/NC10245B, 14SF-15, "Not Applicable") mtow: Maximum take off weight (MTOW) in kilograms Environmental evaluation audio As a means for evaluating model performance on real-world data, a supplementary set of real-time environmental recordings have been included with AeroSonicDB(YPAD-0523). This additional dataset contains six, one-hour long recordings of continuous urban noise, and is accompanied by a CSV file (environment_class_mappings.csv) annotated with relevant class labels per 5-second interval. Due to the variable length of an aircraft audio event and the lack of distinct onset and outset moments, audio segments which transition between aircraft and silence periods are tagged with an “ignore” class. This is done to provide a clear boundary between silence and aircraft events, helping to avoid false misclassification at event boundaries and ensure meaningful evaluation results. Conditions of use Dataset created by Blake Downward. The AeroSonicDB (YPAD-0523) dataset is offered free of charge for non-commercial use under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) Acknowledgements Special thanks to Jon Nordby of Soundsensing AS - his contributions were pivotal in maximising the potential of this dataset for open-source release. Feedback Please send suggestions, feedback and comments to: Blake Downward: [email protected] Change log 1.1.1: Minor change to "sample_meta.csv" - replaced "6" with "test" in the "fold" column 1.1: Replaced truncated aircraft samples with the original full-length files and annotated the beginning and end of each audio event. Added ‘ignore’ statements to aircraft event boundaries in the environmental class mappings file. 1.0: Environmental audio and mappings added 0.3: locations.json file added, README updated 0.2: location information added to READM

AeroSonicDB (YPAD-0523): Labelled audio dataset for acoustic detection and classification of aircraft

AeroSonicDB (YPAD-0523): Labelled audio dataset for acoustic detection and classification of aircraft Version 1.1 (September 2023) Publication When using this data in an academic work, please reference the DOI and version. Description AeroSonicDB:YPAD-0523 is a specialised dataset of ADS-B labelled audio clips for research in the fields of environmental noise attribution and machine listening, particularly acoustic detection and classification of low-flying aircraft. Audio files in this dataset were recorded at locations in close proximity to a flight path approaching or departing Adelaide International Airport’s (ICAO code: YPAD) primary runway, 05/23. Recordings are initially labelled from radio (ADS-B) messages received from the aircraft overhead, then human verified and annotated with the first and final moments which the target aircraft is audible. A total of 1,895 audio clips are distributed across two top-level classes, “Aircraft” (8.87 hours) and “Silence” (3.52 hours). The aircraft class is then further broken-down into four subclasses, which broadly describe the structure of the aircraft and propulsion mechanism. A variety of additional “airframe” features are provided to give researchers finer control of the dataset, and the opportunity to develop ontologies specific to their own use case. For convenience, the dataset has been split into training (10.04 hours) and testing (2.35 hours) subsets, with the training set further split into 5 distinct folds for cross-validation. These splits are performed to prevent data-leakage between folds and the test set, ensuring samples collected in the same recording session (distinct in time, location and microphone) are assigned to the same fold. Researchers may find applications for this dataset in a number of fields; particularly aircraft noise isolation and noise monitoring in an urban environment, development of passive acoustic systems to assist radar technology, and understanding the sources of aircraft noise to help manufacturers design less-noisy aircraft. Audio data ADS-B (Automatic Dependent Surveillance–Broadcast) messages transmitted directly from aircraft are used to automatically trigger, capture and label audio samples. A 60-second recording is triggered when an aircraft transmits a message indicating it is within a specified distance of the recording device (see “Location data” below for specifics). The resulting audio file is labelled with the unique ICAO identifier code for the aircraft, as well as its last reported altitude, date, time, location and microphone. The recording is then human verified and annotated with timestamps for the first and last moments the aircraft is audible. In total, AeroSonicDB contains 625 recordings of low-altitude aircraft - varying in length from 18 to 60 seconds, for a total of 8.87 hours of aircraft audio. A collection of urban background noise without aircraft (silence) is included with the dataset as a means of distinguishing location specific environmental noises from aircraft noises. 10-second background noise, or “silence” recordings are triggered only when there are no aircraft broadcasting they are within a specified distance of the recording device (see “Location data” below). These “silence” recordings are also human verified to ensure no aircraft noise is present. The dataset contains 1,270 clips of silence/urban background noise. Location data Recordings have been collected from three (3) locations. GPS coordinates for each location are provided in the "locations.json" file. In order to protect privacy, coordinates have been provided for a road or public space nearby the recording device instead of its exact location. Location: 0 Situated in a suburban environment approximately 15.5km north-east of the start/end of the runway. For Adelaide, typical south-westerly winds bring most arriving aircraft past this location on approach. Winds from the north or east will cause aircraft to take-off to the north-east, however not all departing aircraft will maintain a course to trigger a recording at this location. The "trigger distance" for this location is set for 3km to ensure small/slower aircraft and large/faster aircraft are captured within a sixty-second recording. "Silence" or ambient background noises at this location include; cars, motorbikes, light-trucks, garbage trucks, power-tools, lawn mowers, construction sounds, sirens, people talking, dogs barking and a wide range of Australian native birds (New Holland Honeyeaters, Wattlebirds, Australian Magpies, Australian Ravens, Spotted Doves, Rainbow Lorikeets and others). Location: 1 Situated approximately 500m south-east of the south-eastern end of the runway, this location is nearby recreational areas (golf course, skate park and parklands) with a busy road/highway inbetween the location and runway. This location features heavy winds and road traffic, as well as people talking, walking and riding, and also birds such as the Australian Magpie and Noisy Miner. The trigger distance for this location is set to 1km. Due to their low altitude aircraft are louder, but audible for a shorter time compared to "Location 0". Location: 2 As an alternative to "Location 1", this location is situated approximately 950m south-east of the end of the runway. This location has a wastewater facility to the north, a residential area to the south and a popular beach to the west. This location offers greater wind protection and further distance from airport and highway noises. Ambient background sounds feature close proximity cars and motorbikes, cyclists, people walking, nail guns and other construction sounds, as well as the local birds mentioned above. Aircraft metadata Supplementary "airframe" metadata for all aircraft has been gathered to help broaden the research possibilities from this dataset. Airframe information was collected and cross-checked from a number of open-source databases. The author has no reason to beleive any significant errors exist in the "aircraft_meta" files, however future versions of this dataset plan to obtain aircraft information directly from ICAO (International Civil Aviation Organization) to ensure a single, verifiable source of information. Class/subclass ontology (minutes of recordings) 0. no aircraft (202) 0: no aircraft (202) 1. aircraft (214) 1: piston-propeller aeroplane (12) 2: turbine-propeller aeroplane (37) 3: turbine-fan aeroplane (163) 4: rotorcraft (1.6) The subclasses are a combination of the "airframe" and "engtype" features. Piston and Turboshaft rotorcraft/helicopters have been combined into a single subclass due to the small number of samples. Data splits Audio recordings have been split into training (81%) and test (19%) sets. The training set has further been split into 5 folds, giving researchers a common split to perform 5-fold cross-validation to ensure reproducibility and comparable results. Data leakage into the test set has been avoided by ensuring recordings are disjointed from the training set by time and location - meaning samples in the test set for a particular location were recorded after any samples included in the training set for that particular location. Labelled data The entire dataset (training and test) is referenced and labelled in the “sample_meta.csv” file. Each row contains a reference to a unique recording, its meta information, annotations and airframe features. Alternatively, these labels can be derived directly from the filename of the sample (see below). The “aircraft_meta.csv” and “aircraft_meta.json” files can be used to reference aircraft specific features - such as; manufacturer, engine type, ICAO type designator etc. (see “Columns/Labels” below for all features). File naming convention Audio samples are in WAV format, with some metadata stored in the filename. Basic Convention “Aircraft ID + Date + Time + Location ID + Microphone ID” “XXXXXX_YYYY-MM-DD_hh-mm-ss_X_X” Sample with aircraft {hex_id} _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext} 7C7CD0_2023-05-09_12-42-55_2_1.wav Sample without aircraft “Silence” files are denoted with six (6) leading zeros rather than an aircraft hex code. All relevant metadata for “silence” samples are contained in the audio filename, and again in the accompanying “sample_meta.csv” 000000 _ {date} _ {time} _ {location_id} _ {microphone_id} . {file_ext} 000000_2023-05-09_12-30-55_2_1.wav Columns/Labels (found in sample_meta.csv, aircraft_meta.csv/json files) train-test: Train-test split (train, test) fold: Digit from 1 to 5 splitting the training data 5 ways (else test) filename: The filename of the audio recording date: Date of the recording time: Time of the recording location: ID for the location of the recording mic: ID of the microphone used class: Top-level label for the recording (eg. 0 = No aircraft, 1 = Aircraft audible) subclass: Subclass label for the recording (eg. 0 = No aircraft, 3 = Turbine-fan aeroplane) altitude: Approximate altitude of the aircraft (in feet) at the start of the recording hex_id: Unique ICAO 24-bit address for the aircraft recorded session: Unique recording session by time, location and microphone. offset: Time stamp marking the start of the audio event. duration: Length of the recording (in seconds) file_length: Total length of the audio file in seconds. reg: Registration number of the aircraft airframe: Describes the mechanical structure of the aircraft (eg. Power Driven Aeroplane, Rotorcraft) engtype: Type of engine (eg. Piston, Turboprop, Turbofan, Turboshaft) engnum: Number of engines shortdesc: 3 character alpha-numeric code describing the airframe and engine configuration (eg. L1P, L4J, H2T) typedesig: ICAO type designator for make and model of aircraft (eg. PC12, C185, B738) manu: Aircraft manufaturer (eg. Boeing, Pilatus, Airbus) model: Aircraft model (eg. 737-800, A320-232, DHC-8-315) engmanu: Engine manufacturer (eg. Pratt & Whitney, CFM Interntional, Rolls Royce) engmodel: Engine model (eg. TRENT XWB, CFM56-7B24E, PT6E-67XP) engfamily: Family of the engine model (eg. TRENT, CFM56, PT6) fueltype: Fuel type used in the engine (eg. Gasoline, Kerosine) propmanu: Propeller manufacturer (eg. Hartzell Propellers, Hamilton Standard, "Aircraft Not Fitted With Propeller") propmodel: Propeller model (eg. HC-E5A-3A\/NC10245B, 14SF-15, "Not Applicable") mtow: Maximum take off weight (MTOW) in kilograms Environmental evaluation audio As a means for evaluating model performance on real-world data, a supplementary set of real-time environmental recordings have been included with AeroSonicDB(YPAD-0523). This additional dataset contains six, one-hour long recordings of continuous urban noise, and is accompanied by a CSV file (environment_class_mappings.csv) annotated with relevant class labels per 5-second interval. Due to the variable length of an aircraft audio event and the lack of distinct onset and outset moments, audio segments which transition between aircraft and silence periods are tagged with an “ignore” class. This is done to provide a clear boundary between silence and aircraft events, helping to avoid false misclassification at event boundaries and ensure meaningful evaluation results. Conditions of use Dataset created by Blake Downward. The AeroSonicDB (YPAD-0523) dataset is offered free of charge for non-commercial use under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/) Acknowledgements Special thanks to Jon Nordby of Soundsensing AS - his contributions were pivotal in maximising the potential of this dataset for open-source release. Feedback Please send suggestions, feedback and comments to: Blake Downward: [email protected] Change log 1.1: Replaced truncated aircraft samples with the original full-length files and annotated the beginning and end of each audio event. Added ‘ignore’ statements to aircraft event boundaries in the environmental class mappings file. 1.0: Environmental audio and mappings added 0.3: locations.json file added, README updated 0.2: location information added to READM

The cell surface glycoprotein CUB domain-containing protein 1 (CDCP1) contributes to epidermal growth factor receptor-mediated cell migration

Epidermal growth factor (EGF) activation of the EGF receptor (EGFR) is an important mediator of cell migration, and aberrant signaling via this system promotes a number of malignancies including ovarian cancer. We have identified the cell surface glycoprotein CDCP1 as a key regulator of EGF/EGFR-induced cell migration. We show that signaling via EGF/EGFR induces migration of ovarian cancer Caov3 and OVCA420 cells with concomitant up-regulation of CDCP1 mRNA and protein. Consistent with a role in cell migration CDCP1 relocates from cell-cell junctions to punctate structures on filopodia after activation of EGFR. Significantly, disruption of CDCP1 either by silencing or the use of a function blocking antibody efficiently reduces EGF/EGFR-induced cell migration of Caov3 and OVCA420 cells. We also show that up-regulation of CDCP1 is inhibited by pharmacological agents blocking ERK but not Src signaling, indicating that the RAS/RAF/MEK/ERK pathway is required downstream of EGF/EGFR to induce increased expression of CDCP1. Our immunohistochemical analysis of benign, primary, and metastatic serous epithelial ovarian tumors demonstrates that CDCP1 is expressed during progression of this cancer. These data highlight a novel role for CDCP1 in EGF/EGFR-induced cell migration and indicate that targeting of CDCP1 may be a rational approach to inhibit progression of cancers driven by EGFR signaling including those resistant to anti-EGFR drugs because of activating mutations in the RAS/RAF/MEK/ERK pathway