Abstract: A method and system includes subject participants, a verifier participant and a certifier participant of a communication network exchanging information about ESG data, wherein a subject participant provides a proof derived from an ESG credential and the verifier participant receives the proof via the communication network, where the participant receives the ESG credential from the certifier participant, the proof assigns authenticity of the certifier participant providing the ESG credential, wherein the proof additionally assigns integrity of the ESG data, parts or an attribute of the ESG data, wherein the system further includes nodes of a decentralized registry that manages a public key of the certifier participant, where the decentralized registry provides a public key of the certifier participant corresponding to the proof to the verifier participant so that the verifier participant can cryptographically verify the authenticity and integrity with the public key via a computing component of the verifier pa

Abstract: Various embodiments of the teachings herein include a computer aided method for generating training data for a neural network designed to determine a pollutant concentration at a measurement station from a pollutant emission. The method may include: providing a measurement series of the concentration containing a value above a threshold; providing a measurement series for a variable related to the pollutant concentration; providing a transmission model modelling a relationship between the pollutant emission, the measured variable, and the pollutant immission at the measurement station; computing a first value of the pollutant immission using the transmission model using a value of the measured variable; computing a second value of the pollutant immission using the transmission model by numerically altering the measured value of the measured variable; and generating a synthetic measurement series as training data by an alteration based at least in part on a relative change in computed values.

Abstract: Various embodiments of the teachings herein include a computer aided method for generating training data for a neural network designed to determine a pollutant concentration at a measurement station from a pollutant emission. The method may include: providing a measurement series of the concentration containing a value above a threshold; providing a measurement series for a variable related to the pollutant concentration; providing a transmission model modelling a relationship between the pollutant emission, the measured variable, and the pollutant immission at the measurement station; computing a first value of the pollutant immission using the transmission model using a value of the measured variable; computing a second value of the pollutant immission using the transmission model by numerically altering the measured value of the measured variable; and generating a synthetic measurement series as training data by an alteration based at least in part on a relative change in computed values.

Abstract: Various embodiments include a method for verifying sensors for a sensor network including a reference sensor comprising: identifying a first sensor; locating the identified sensor; comparing a measured value recorded by the identified sensor to a measured value recorded at the same time and within a defined spatial environment using the reference sensor; determining a deviation of the measured value of the identified sensor from the measured value of the reference sensor; and assigning the sensor to precisely one of at least two categories depending on an amount of the deviation.

Abstract: Various embodiments of the teachings herein include a computer-aided method for generating training data for a neural network used to determine a pollutant concentration from a pollutant emission. The method may include: providing a first series of the pollutant concentration with one reading above a defined threshold value; providing a second series for a physical measured variable related to the pollutant concentration; providing a model for a relationship between the two; computing a first value of the pollutant emission with the model using a value of the measured variable related to a value of the pollutant concentration; computing a second value of the pollutant emission with the model by numerically altering the measured value of the measured variable; and generating a synthetic measurement series as training data using an alteration of the value of the measured series, using the relative change in the computed values of the pollutant emissions.

Abstract: A method and system includes subject participants, a verifier participant and a certifier participant of a communication network exchanging information about ESG data, wherein a subject participant provides a proof derived from an ESG credential and the verifier participant receives the proof via the communication network, where the participant receives the ESG credential from the certifier participant, the proof assigns authenticity of the certifier participant providing the ESG credential, wherein the proof additionally assigns integrity of the ESG data, parts or an attribute of the ESG data, wherein the system further includes nodes of a decentralized registry that manages a public key of the certifier participant, where the decentralized registry provides a public key of the certifier participant corresponding to the proof to the verifier participant so that the verifier participant can cryptographically verify the authenticity and integrity with the public key via a computing component of the verifier pa

Abstract: Various embodiments include a method for verifying sensors for a sensor network including a reference sensor comprising: identifying a first sensor; locating the identified sensor; comparing a measured value recorded by the identified sensor to a measured value recorded at the same time and within a defined spatial environment using the reference sensor; determining a deviation of the measured value of the identified sensor from the measured value of the reference sensor; and assigning the sensor to precisely one of at least two categories depending on an amount of the deviation.