Abstract: Examples of techniques for threat detection in an industrial process system are described herein. An aspect includes determining a plurality of subsystems of an industrial process system. Another aspect includes, for each of the plurality of subsystems, constructing and training a respective deep autoencoder (DAE) model of the subsystem based on data corresponding to the industrial process system. Another aspect includes monitoring the industrial process system using the plurality of DAE models corresponding to the plurality of subsystems. Another aspect includes, based on the plurality of DAE models, determining a cyberattack in a subsystem of the plurality of subsystems.

Abstract: A lossy compression algorithm is described for performing data compression of high-frequency floating point time-series data, for example. The compression algorithm utilizes a prediction engine that employs at least one of a linear prediction model or a non-linear prediction model to calculate one-step-ahead prediction of a current data value at current sampling time t using N previous quantized data values, where N is the model order. A prediction error is determined between the predicted value and an actual value, and the prediction error is quantized. A quantized current data value is determined from the predicted value and the quantized prediction error. The quantized prediction error is sent from an edge device to a data decompressor on a cloud device. The decompressor reconstructs the quantized current data value using the received quantized prediction error and by generating the same predicted value as the compressor.

Abstract: A method of processing air quality data includes: receiving air indicator data collected by one or more air quality sensors, wherein the one or more air quality sensors are detachably coupled to a hardware base of the air quality monitoring station via one or more data communication interfaces, and the hardware base is communicably coupled to a remote server over a network; generating air quality data according to the air indicator data, wherein the air quality data is derived from air indicator data received from one or more air quality sensors over a distance traveled by the one or more air quality sensors; and sending the air quality data, via the hardware base, to the remote server.

Abstract: A method of processing air quality data includes: receiving air indicator data collected by one or more air quality sensors, wherein the one or more air quality sensors are detachably coupled to a hardware base of the air quality monitoring station via one or more data communication interfaces, and the hardware base is communicably coupled to a remote server over a network; generating air quality data according to the air indicator data, wherein the air quality data is derived from air indicator data received from one or more air quality sensors over a distance traveled by the one or more air quality sensors; and sending the air quality data, via the hardware base, to the remote server.

Abstract: A movable touch module and an electronic device using the same are provided. The movable touch module is disposed at a main body and includes a suspending element, a touch unit, a four-direction sensing element, and at least two rotation sensing elements. The suspending element is disposed at the main body and is movable relative to the main body on a plane. The suspending element has a plurality of elastic portions against the main body. The touch unit is fastened to the suspending element. The four-direction sensing element and the rotation sensing elements are fastened to the main body. When the suspending element moves along a direction on the plane, the suspending element triggers the four-direction sensing element along the direction. When the suspending element rotates on the plane, the suspending element correspondingly triggers one of the rotation sensing elements.