A computer-implemented method may include receiving, by a computing system on an autonomous vehicle, local weather data and routing data of the autonomous vehicle, the autonomous vehicle including a sensor platform mounted on the autonomous vehicle. The method may include based on the local weather data and the routing data, predicting, by the computing system using a thermal model, a thermal loading and a temperature of a component(s) of a sensor platform at one or more future times, the thermal loading and the temperature being during an operation of the autonomous vehicle. The method may include sending an overheating warning to a controller of the autonomous vehicle based on a determination that the thermal loading or the temperature of the component(s) exceeds a threshold. The method may include reporting a degraded state of the autonomous vehicle to park prior to a predicted overheating of the component(s).

A computer-implemented method may include receiving, by a computing system on an autonomous vehicle, local weather data and routing data of the autonomous vehicle, the autonomous vehicle including a sensor platform mounted on the autonomous vehicle. The method may include based on the local weather data and the routing data, predicting, by the computing system using a thermal model, a thermal loading and a temperature of a component(s) of a sensor platform at one or more future times, the thermal loading and the temperature being during an operation of the autonomous vehicle. The method may include sending an overheating warning to a controller of the autonomous vehicle based on a determination that the thermal loading or the temperature of the component(s) exceeds a threshold. The method may include reporting a degraded state of the autonomous vehicle to park prior to a predicted overheating of the component(s).

Provided is a setting method of a Wi-Fi doorbell (2), in which a Wi-Fi doorbell (2) comprising a camera capable of reading a two-dimensional code and a mobile communication terminal (4) comprising a display unit (6) capable of displaying the two-dimensional code are used. The setting method comprises steps of causing the display unit (6) to display a two-dimensional code in which password information for enabling the Wi-Fi doorbell (2) to connect to a network (5) via a Wi-Fi router (20) is embedded reading the two-dimensional code by the camera (9) of the Wi-Fi doorbell (2); and setting the Wi-Fi doorbell (2) to a state where the Wi-Fi doorbell (2) can perform communication with a remotely disposed server (3) via the Wi-Fi router (20), based on the read two-dimensional code.

Provided is a setting method of a Wi-Fi doorbell (2), in which a Wi-Fi doorbell (2) comprising a camera capable of reading a two-dimensional code and a mobile communication terminal (4) comprising a display unit (6) capable of displaying the two-dimensional code are used. The setting method comprises steps of causing the display unit (6) to display a two-dimensional code in which password information for enabling the Wi-Fi doorbell (2) to connect to a network (5) via a Wi-Fi router (20) is embedded reading the two-dimensional code by the camera (9) of the Wi-Fi doorbell (2); and setting the Wi-Fi doorbell (2) to a state where the Wi-Fi doorbell (2) can perform communication with a remotely disposed server (3) via the Wi-Fi router (20), based on the read two-dimensional code.

Apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score. More particularly, apparatuses, systems and methods are provided for generating a base-line probable roof loss confidence score based on hail data. The apparatuses, systems and methods may generate a probable roof loss confidence score. The apparatuses, systems and methods may generate verified probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance underwriting data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance claims data based on probable roof loss confidence score data. The apparatuses, systems and methods may generate property insurance loss mitigation data based on probable roof loss confidence score data.

Devices and systems for determining personal outdoor comfort are described herein. One device includes instructions executable to receive inputs corresponding to characteristics of a user associated with a mobile device, determine a location of the mobile device, communicate an indication of the characteristics and the determined location to a computing device, and receive an outdoor comfort determination from the computing device, wherein the outdoor comfort determination is particular to the user based on the characteristics of the user and particular to the location of the mobile device based on a plurality of environmental parameters associated with the location of the mobile device.

An environmental monitoring network has transportable, self-contained, environment sensing capsules, each capsule is water-proof, with first and second sections, the second section being hollow. Apertures in the capsule's housing enable fluid and gas entry wherein first sensor(s) disposed internal to the housing measure within the hollow, and second sensor(s) disposed external to the housing measure external to the housing. A controller and power system are connected to the first and second sensors and transmits measured data. An access entry way is on a side of the housing, enabling access to first sensors, controller system, power system, and the communication system. A central data server is configured to receive and analyze the measurement data sent from the capsules. There is a priority list of appropriate personnel for contact by the central data server in an event there is an emergency condition at a capsule location.

Systems and methods for providing irrigation water to a soil depth of a crop rootzone in a plurality of crop fields using a sensor network and soil moisture modeling are provided. In various embodiments methods include receiving data from a sensor network in a first crop field and determining a soil moisture model using data from the sensor network in the first field. Various embodiments further include determining a first field irrigation time using the soil moisture model, the first field irrigation time providing irrigation water to a soil depth of the crop rootzone above a Wilting Point (WP) and below a Field Capacity (FC) of soil in the first field, and applying the soil moisture model to a second field.

Systems and methods for providing irrigation water to a soil depth of a crop rootzone in a plurality of crop fields using a sensor network and soil moisture modeling are provided. In various embodiments methods include receiving data from a sensor network in a first crop field and determining a soil moisture model using data from the sensor network in the first field. Various embodiments further include determining a first field irrigation time using the soil moisture model, the first field irrigation time providing irrigation water to a soil depth of the crop rootzone above a Wilting Point (WP) and below a Field Capacity (FC) of soil in the first field, and applying the soil moisture model to a second field.

The present disclosure describes various embodiments of systems, apparatuses, and methods for large-scale processing of weather-related data. For one such system, the system comprises a database of weather-related data providing from at least one weather monitoring station and at least one processor for coordinating a data processing job for processing a set of input weather-related data from the database. Accordingly, the input data comprises sensor data from the at least one weather monitoring station positioned on an open shoreline during a hydrodynamic event, weather model data for the hydrodynamic event, and at least one of air-craft reconnaissance data or satellite reconnaissance data regarding the hydrodynamic event, wherein the at least one processor is configured to assimilate the input data and generate, using machine learning, an improved weather prediction model for the hydrodynamic event. Other systems, apparatuses, and methods are also provided.