Methods, systems, and apparatus for receiving a request for a risk assessment for a parcel, receiving a set of images for the parcel, the set of images including two or more images, each image having an image scale and an image resolution that is different from other images in the set of images, providing a first-level feature embedding and a second- level feature embedding, the first-level feature embedding being provided by processing a first-level image through a first-level machine learning (ML) model, and the second-level feature embedding being provided by processing a second-level image through a second- level ML model, determining a risk assessment at least partially by processing each of the first-level feature embedding and a second-level feature embedding through a fusion network, and providing a representation of the risk assessment for display.
Methods, systems, and apparatus for providing a ML model for inference, the ML model having been trained using a first set of training data to provide predictions associated with an adverse event, after training of the ML model, receiving data from one or more data sources, the data representative of characteristics relevant to predictions associated with the adverse event, providing a second set of training data, determining, by a trigger module, a trigger decision based on a set of signals at least partially determined from the second set of training data, the trigger decision indicating whether the ML model is to be one of updated and retrained based on the second set of training data, and selectively executing one of updating and retraining of the ML model using at least a portion of the second set of training data in response to the trigger decision.
Methods, systems, and apparatus for obtaining a first plurality of data elements, each data element representing a fire-related metric of a geographic region, determining, using at least a subset of the first data elements, one or more values representing one or more derived fire-related metrics, associating the one or more values with the first data elements, obtaining a second plurality of data elements, each data element representing a fire-related metric of the geographic region, and training a machine learning (ML) model using at least a subset of the first plurality of data elements, at least a subset of the second plurality of data elements, and values associated with the subset of the first plurality of data elements to provide a trained ML model.
Methods, systems, and computer-readable media that implement autonomous seagoing power replenishment watercraft. An example system includes a plurality of marine vessels; a plurality of watercraft, each watercraft of the plurality of watercraft including a rechargeable electrical power supply and being configured to operate in: a first mode in which the watercraft awaits an assignment to provide electrical energy to a marine vessel of the plurality of marine vessels; a second mode in which the watercraft performs operations including keeping station with an assigned marine vessel and providing electrical energy to the assigned marine vessel from the power supply; and a third mode in which the watercraft recharges the power supply from a charging station. The system includes a controller configured to perform operations comprising: transmitting, to a first watercraft, an instruction indicating an assignment of the first watercraft to provide electrical energy to a first marine vessel.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing recycled concrete aggregate (RCA). One of the methods includes obtaining first optical measurements of RCA particles; determining an initial characterization of the RCA particles; iteratively performing a carbonation process on the RCA particles, obtaining second optical measurements of the RCA particles, and determining a second characterization of the RCA particles, where conditions of the carbonation process are initially set based on the initial characterization, and the conditions of the carbonation process are adjusted based on the second characterization; iteratively performing a densification process on the RCA particles, obtaining third optical measurements of the RCA particles, and determining a third characterization of the RCA particles, where conditions of the densification process are initially set based on the initial characterization or the second characterization, and the conditions of the densification process are adjusted based on the third characterization.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium (406), that automate selection of configuration data. One of the methods includes obtaining meal configuration data relating to feeding livestock in an aquaculture environment that includes one or more feeding parameters relating to feeding farmed livestock and one or more criteria. Each of the one or more criteria can be associated with the one or more feeding parameters. Sensor data can be obtained that describes at least one property of the aquaculture environment. A selected set of feeding parameters can be selected using the obtained sensor data and the obtained feeding parameters. The selected set of feeding parameters can be provided to a feeding control subsystem (550).
This disclosure describes a system and method for generating images and location data of a subsurface object using existing infrastructure as a source. Many infrastructure objects (e.g., pipes, cables, conduits, wells, foundation structures) are constructed of rigid materials and have a known shape and location. Additionally these infrastructure objects can have exposed portions that are above or near the surface and readily accessible. A signal generator can be affixed to the exposed portion of the infrastructure object, which induces acoustic energy, or vibrations in the object. The object with affixed signal generator can then be used as a source in performing a subsurface imaging of subsurface objects, which are not exposed.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying wildfire in satellite imagery. In some implementations, a server obtains a satellite image of a geographic region and a date corresponding to when the satellite image was generated. The server determines a number of pixels in the satellite image that are indicated as on fire. The server obtains satellite imagery of the geographic region from before the date. The server generates a statistical distribution from the satellite imagery. The server determines a likelihood that the satellite image illustrates fire based on a comparison of the determined number of pixels in the satellite image that are indicated as on fire to the generated statistical distribution. The server can compare the determined likelihood to a threshold. In response to comparing the determined likelihood to the threshold, the server provides an indication that the satellite image illustrates fire.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a synthesized signal. A computer-implemented system obtains generator input data including at least an input signal having one or more first characteristics, processes the generator input data to generate output data including a synthesized signal having one or more second characteristics using a generator neural network, and outputs the synthesized signal to a device. The generator neural network is trained, based on a plurality of training examples, with a discriminator neural network. The discriminator neural network is configured to process discriminator input data that combines a discriminator input signal having the one or more second characteristics with at least a portion of generator input data to generate a prediction of whether the discriminator input signal is a real signal provided in one of the plurality of training examples or a synthesized signal.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a temporal range of a fire. In some implementations, a server obtains a date when a fire occurred within a region. The server obtains satellite imagery of the region from before the date when the fire occurred. The server generates a first statistical distribution from the satellite imagery. The server determines a start date of the fire using the first statistical distribution. The server obtains second satellite imagery of the region from before and after the start date. The server selects a second set of imagery from the second satellite imagery from before the start date. The server generates a second statistical distribution from the second set of imagery. The server determines an end date of the fire using the second statistical distribution. The server provides the start date and the end date for output.
A62C 3/02 - Fire prevention, containment or extinguishing specially adapted for particular objects or places for area conflagrations, e.g. forest fires, subterranean fires
H04W 4/021 - Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
The present disclosure relates to leveraging explainable machine learning methods and feature importance mechanisms as a mechanism for gene discovery and furthermore leveraging the outputs of the gene discovery to recommend ideal gene expression profiles and the requisite genome edits that are conducive to a desired phenotype. Particularly, aspects of the present disclosure are directed to obtaining gene expression profiles for a set of genes measured in a tissue sample of a plant, inputting the gene expression profiles into a prediction model constructed for a task of predicting a phenotype as output data, generating, using the prediction model, the prediction of the phenotype for the plant, analyzing, by an explainable artificial intelligence system, decisions made by the prediction model to predict the phenotype, and identifying a set of candidate gene targets for the phenotype as having a largest contribution or influence on the prediction based on the analyzing.
Methods, systems, and apparatus for receiving a request for a damage propensity score for a parcel, receiving imaging data for the parcel, wherein the imaging data comprises street-view imaging data of the parcel, extracting, by a machine-learned model including multiple classifiers, characteristics of vulnerability features for the parcel from the imaging data, determining, by the machine-learned model and from the characteristics of the vulnerability features, a damage propensity score for the parcel, and providing a representation of the damage propensity score for display.
Methods, systems (100) and computer programs encoded on computer-readable storage media for preparing a concrete mixture. The methods include controlling an ingredient metering system (108) to measure and add a plurality of ingredients from storage hoppers (112a, 112b,..., 112n) to a concrete mixture in a mixing vessel (110), measuring characteristics of at least one ingredient of the ingredients using a particle analyzer (104), determining an estimated rheometry measurement of the concrete mixture based on the measured characteristics, obtaining an actual rheometry measurement of the concrete mixture, and selectively controlling the ingredient metering system (108) to add one or more additional ingredients to the concrete mixture based on a comparison of the estimated rheometry measurement with the actual rheometry measurement.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for self-calibrating ultrasonic removal of sea lice. In some implementations, a method includes generating, by transducers (106) distributed in a sea lice treatment station (102), a first set of ultrasonic signals, detecting a second set of ultrasonic signals in response to propagation of the first set of ultrasonic signals through water, determining propagation parameters of the sea lice treatment station based on the second set of ultrasonic signals that were detected, obtaining an image of a sea louse on a fish in the sea lice treatment station, determining, from the image, a location of the sea louse in the sea lice treatment station, and generating a third set of ultrasonic signals that focuses energy at the sea louse.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for escape detection and mitigation for aquaculture. In some implementations, a method includes obtaining one or more images that depict one or more fish within an enclosure; generating, as a result of providing the one or more images to multiple detection models, a value that reflects a quantity of fish that are depicted in the images that are likely a member of each different type of fish; detecting an error condition relating to a possible opening of the enclosure based at least on the value; and in response to detecting the error condition relating to the possible opening of the enclosure, initiating one or more mitigation actions relating to the possible opening.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for fish weight estimation based on fish tracks identified in images. In some implementations, a method includes obtaining images of fish enclosed in a fish enclosure, identifying fish tracks shown in the images of the fish, determining a quality score for each of the fish tracks, selecting a subset of the fish tracks based on the quality scores, determining a representative weight of the fish in the fish enclosure based on weights of the fish shown in the subset of the fish tracks, and outputting the representative weight for display or storage at a device connected to the one or more processors.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for visually detecting a halocline. In some implementations, a method includes moving a camera through different depths of water within a fish enclosure, capturing, at the different depths, images of fish, determining that changes in focus in the images correspond to changes in depth that the images were captured, and based on determining that the changes in focus in the images correspond to the changes in depths that the images were captured, detecting a halocline at a particular depth.
Method (300), system (100) and apparatus for performing pultrusion moulding with concrete. One exemplary method (300) includes (i) conveying a textile material (120) through a concrete infusion system (104) to provide a concrete infused textile material (CITM); (ii) heating and shaping the CITM by conveying the CITM through a microwave chamber (106) comprising an electrically non-conductive die (110) disposed therein, the microwave chamber (106) operable to heat concrete infused in the textile material (120) by irradiating the CITM with microwave energy as the CITM is conveyed through the microwave chamber (106), and the die (110) operable to shape the CITM as the CITM is conveyed through the microwave chamber (106); (iii) cooling the CITM by conveying the CITM through a cool down chamber (112) operable to maintain heat in the CITM as the CITM is conveyed through the cooling chamber (112), allowing the concrete to cure while a temperature of the CITM is reduced.
In some embodiments, logic stored on a computer-readable medium, in response to execution, causes a computing system to conduct an inverse design process to generate a plurality of segmented designs corresponding to a plurality of device specifications, determine at least one highly impactful design area based on the plurality of segmented designs; and designate the at least one highly impactful design area as a static design area. In some embodiments, a product line comprising a plurality of physical devices is provided. Each physical device of the plurality of physical devices includes a design region that includes a static design area and a customized design area. The static design area for each physical device is the same for each physical device of the plurality of physical devices, and the customized design area for each physical device is different for each physical device of the plurality of physical devices.
G02B 6/12 - Light guides; Structural details of arrangements comprising light guides and other optical elements, e.g. couplings of the optical waveguide type of the integrated circuit kind
G02B 6/293 - Optical coupling means having data bus means, i.e. plural waveguides interconnected and providing an inherently bidirectional system by mixing and splitting signals with wavelength selective means
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sorting fish in aquaculture. In some implementations, one or more images are obtained of a particular fish within a population of fish. Based on the one or more images of the fish, a data element is determined. The data element can include a first value that reflects a physical characteristic of the particular fish, and a second value that reflects a condition factor of the particular fish. Based on the data element, the fish is classified as a member of a particular subpopulation of the population of fish. An actuator of an automated fish sorter is controlled based on classifying the particular fish as a member of the particular subpopulation of the population of fish.
A01K 61/95 - Sorting, grading, counting or marking live aquatic animals, e.g. sex determination specially adapted for fish
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
21.
CAMERA CONTROLLER FOR AQUACULTURE BEHAVIOR OBSERVATION
Methods, systems (100), and apparatus, including computer programs encoded on computer-storage media, for controlling a camera (102) to observe aquaculture feeding behavior. In some implementations, a method includes moving a camera (102) to a first position (210), obtaining an image captured by the camera at the first position (220), determining a feeding observation mode (230), and based on the feeding observation mode and analysis of the image, determining a second position to move the camera (240).
This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a lighting controller for sea lice detection. In some implementations, fish are contained within an elliptical tank filled with water. An imaging station located on the elliptical tank is used to capture an image of a fish from which image analysis can be performed to detect sea lice or other skin features, including lesions, on the fish. Pairs of imaging assemblies coordinate pulsing light of at least a first and a second color and capturing images of the fish while the fish is illuminated by at least the first and the second color. By using the captured images with different color light, computers can detect features on the body of a fish including sea lice, skin lesions, shortened operculum or other physical deformities and skin features. Detection results can aid in mitigation techniques or be stored for analytics.
Techniques for providing carbon dioxide include generating thermal energy, an exhaust fluid, and electrical power from a power plant; providing the exhaust fluid and the generated electrical power to an exhaust fluid scrubbing system to separate components of the exhaust fluid; capturing heat from a source of heat of an industrial process in a heating fluid; transferring the heat of the industrial process captured in the heating fluid to a carbon dioxide source material of a direct air capture (DAC) system; providing the generated electrical power from the power plant to the DAC system; providing the thermal energy from the power plant to the DAC system; and separating, with the transferred portion of the heat of the industrial process and the provided thermal energy, carbon dioxide from the carbon dioxide source material of the DAC system.
F01K 13/00 - General layout or general methods of operation, of complete steam engine plants
F25B 7/00 - Compression machines, plants or systems, with cascade operation, i.e. with two or more circuits, the heat from the condenser of one circuit being absorbed by the evaporator of the next circuit
H05K 7/20 - Modifications to facilitate cooling, ventilating, or heating
25.
SEA LICE MITIGATION BASED ON HISTORICAL OBSERVATIONS
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for sea lice mitigation. In some implementations, a method includes obtaining multiple observations of a population of reference fish across a period of time, generating, from the multiple observations, a record for each reference fish that indicates an extent of sea lice infestation for the reference fish across the period of time, training, based on the records, a model that determines a predicted health indicator for a fish, obtaining an image of a sample fish that is not in the population of reference fish, determining, based on the image of the sample fish and with the model, a predicted health indicator for the sample fish, and selectively initiating sea lice mitigation based on the predicted health indicator.
A method for controlling a sensor subsystem, the method including receiving one or more metrics representing one or more characteristics of livestock, including one or more livestock objects, contained in an enclosure and monitored by one or more sensors coupled to a winch subsystem. The method further includes determining a position to move the one or more sensors based on the metrics and determining an instruction that includes information related to a movement of the one or more sensors. The method further includes sending the instruction to the winch subsystem to change the position of the one or more sensors.
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for a lighting controller for sea lice detection. In some implementations, a pulse of red light and a pulse of blue light can be timed with the exposure of a camera to capture multiple images of a fish or group of fishes in both red and blue light. By using the captured images with different color light, computers can detect features on the body of a fish including sea lice, skin lesions, shortened operculum or other physical deformities and skin features. Detection results can aid in mitigation techniques or be stored for analytics. For example, sea lice detection results can inform targeted treatments comprised of lasers, fluids, or mechanical devices such as a brush or suction.
Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, mass, and health of fish are described. A pair of stereo cameras may be utilized to obtain off-axis images offish in a defined area. The images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the fish model to determine characteristics of the fish.
Quantum repeater systems and apparatus for quantum communication. In one aspect, a system includes a quantum signal receiver configured to receive a quantum field signal; a quantum signal converter configured to: sample quantum analog signals from a quantum field signal received by the quantum signal receiver; encode sampled quantum analog signals as corresponding digital quantum information in one or more qudits, comprising applying a hybrid analog-digital encoding operation to each quantum analog signal and a qudit in an initial state; decode digital quantum information stored in the one or more qudits as a recovered quantum field signal, comprising applying a hybrid digital-analog decoding operation to each qudit and a quantum analog register in an initial state; a quantum memory comprising qudits and configured to store digital quantum information encoded by the quantum signal converter; and a quantum signal transmitter configured to transmit the recovered quantum field signal.
Techniques for improving a blast pattern at a mining site include conducting an initial blast and recording the initial blast as a high speed optical video. The high speed optical video, and the blast pattern used in the initial blast are sent as inputs to a machine learning model, which correlates one or more characteristics of the region being blasted with measurements associated with characteristics of the region being blasted obtained from the high speed optical video. The machine learning model can then determine an improved blast pattern based on the correlation made. This improved blast pattern can be displayed on a user computing device, or transmitted to a drilling system to automatically drill the improved blast pattern for subsequent blasts.
A technique for simulating and optimizing the fabrication and design of a physical device is described. The technique includes executing a fabrication simulation of the physical device with a fabrication model that receives a fabrication specification as input and outputs a structural design for the physical device in response to the fabrication simulation. An operational simulation of the physical device is executed with a design model that simulates a field response propagating through a simulated environment of the physical device. The structural design output from the fabrication model is forward cascaded to the design model and an output from backpropagation of a performance loss error through the design model is reverse cascaded to the fabrication model.
Techniques for processing ore include the steps of causing an imaging capture system to record a plurality of images of a stream of ore fragments en route from a first location in an ore processing facility to a second location in the ore processing facility; correlating the plurality of images of the stream of ore fragments with at least one or more characteristics of the ore fragments using a machine learning model that includes a plurality of ore parameter measurements associated with the one or more characteristics of the ore fragments; determining, based on the correlation, at least one of the one or more characteristics of the ore fragments; and generating, for display on a user computing device, data indicating the one or more characteristics of the ore fragments or data indicating an action or decision based on the one or more characteristics of the ore fragments.
A method and system for optimizing structural parameters of an electromagnetic device is described that includes performing operations. The operations include performing a time-forward simulation of a field response in a simulated environment describing the electromagnetic device and extracting decomposition components from the field response to compute a loss value. The operations further include backpropagating the loss value backwards in time using the decomposition components to determine an influence of changes in the structural parameters of the electromagnetic device on the loss value. The operations further include generating a revised description of the electromagnetic device by updating the structural parameters to reduce the loss value.
G06F 30/20 - Design optimisation, verification or simulation
G06F 30/23 - Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
G02B 6/10 - Light guides; Structural details of arrangements comprising light guides and other optical elements, e.g. couplings of the optical waveguide type
G02B 27/00 - Optical systems or apparatus not provided for by any of the groups ,
A sensor positioning system, includes an actuation server for communicating with components of the sensor positioning system. The sensor positioning system additionally includes a first actuation system and a second actuation system, wherein each actuation system includes a pulley system for maneuvering an underwater sensor system. The sensor positioning system includes a dual point attachment bracket that connects through a first line to the first actuation system and connecting through a second line to the second actuation system. The underwater sensor system is affixed to the first pulley system, the second pulley system, and the dual attachment bracket through the first line and the second line.
A sensor positioning system, includes an actuation server for communicating with components of the sensor positioning system. The sensor positioning system additionally includes a first actuation system and a second actuation system, wherein each actuation system includes a pulley system for maneuvering an underwater sensor system. The sensor positioning system includes a dual point attachment bracket that connects through a first line to the first actuation system and connecting through a second line to the second actuation system. The underwater sensor system is affixed to the first pulley system, the second pulley system, and the dual attachment bracket through the first line and the second line.
A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.
A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.
A fish monitoring system deployed in a particular area to obtain fish images is described. Neural networks and machine-learning techniques may be implemented to periodically train fish monitoring systems and generate monitoring modes to capture high quality images of fish based on the conditions in the determined area. The camera systems may be configured according to the settings, e.g., positions, viewing angles, specified by the monitoring modes when conditions matching the monitoring modes are detected. Each monitoring mode may be associated with one or more fish activities, such as sleeping, eating, swimming alone, and one or more parameters, such as time, location, and fish type.
Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras (185) may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.
Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.
Methods, systems, and apparatuses, including computer programs encoded on a computer-readable storage medium for estimating the shape, size, and mass of fish are described. A pair of stereo cameras may be utilized to obtain right and left images of fish in a defined area. The right and left images may be processed, enhanced, and combined. Object detection may be used to detect and track a fish in images. A pose estimator may be used to determine key points and features of the detected fish. Based on the key points, a three-dimensional (3-D) model of the fish is generated that provides an estimate of the size and shape of the fish. A regression model or neural network model can be applied to the 3-D model to determine a likely weight of the fish.
A balloon may include an optical-communication component, which may have a pointing axis. A pointing mechanism could be configured to adjust the pointing axis. The optical-communication component could be operable to communicate with a correspondent balloon via a free-space optical link. For example, the optical-communication component could include an optical receiver, transmitter, or transceiver. A positioning system could be configured to acquire a first location, which could be based on the location of the balloon. A controller could be configured to acquire a second location, which could be based on a location of the correspondent balloon. The controller may determine an approximate target axis based on the first location and the second location. The controller may control the pointing axis of the optical-communication component within a scanning range based on the approximate target axis to establish the free-space optical link with the correspondent balloon.
patents
