Abstract: An edge computing (EC) entity belongs to an EC system, and a mobile communication core MCC entity belongs to a MCC system, like a 3GPP system. The EC entity is configured to obtain topology information of the EC system, and expose the topology information to another network, in particular to a MCC system. The MCC entity is configured to receive topology information of an EC system from the EC entity, and provide the topology information to a network exposure function (NEF) or to a policy control function (PCF) of the MCC system.

Abstract: A control plane network entity for providing a multicast/broadcast service for one or more user equipments in a cellular communication network is provided. The control plane network entity is configured to: receive a session setup request for a multicast/broadcast session for the user equipments, wherein the session setup request comprises one or more identifiers identifying the user equipments and/or one or more spatial regions the user equipments are located in; select one or more access and mobility management function entities of the cellular communication network based on the one or more identifiers for providing the multicast/broadcast session to the user equipments; and send a user plane session setup request to the selected one or more access and mobility management function entities. Moreover, the disclosure relates to a user plane network entity and a radio access network node for anchoring and providing a multicast/broadcast service respectively.

Abstract: An edge computing (EC) entity belongs to an EC system, and a mobile communication core MCC entity belongs to a MCC system, like a 3GPP system. The EC entity is configured to obtain topology information of the EC system, and expose the topology information to another network, in particular to a MCC system. The MCC entity is configured to receive topology information of an EC system from the EC entity, and provide the topology information to a network exposure function (NEF) or to a policy control function (PCF) of the MCC system.

Abstract: A control plane network entity for providing a multicast/broadcast service for one or more user equipments in a cellular communication network is provided. The control plane network entity is configured to: receive a session setup request for a multicast/broadcast session for the user equipments, wherein the session setup request comprises one or more identifiers identifying the user equipments and/or one or more spatial regions the user equipments are located in; select one or more access and mobility management function entities of the cellular communication network based on the one or more identifiers for providing the multicast/broadcast session to the user equipments; and send a user plane session setup request to the selected one or more access and mobility management function entities. Moreover, the disclosure relates to a user plane network entity and a radio access network node for anchoring and providing a multicast/broadcast service respectively.

Abstract: Embodiments of the disclosure include methods and devices in which a flight parameter vector for an aircraft is automatically estimated and then compared to a threshold to determine whether the flight parameter vector is accurate. The detection device estimates a coefficient vector in an observation window, calculates an estimated flight parameter value, and minimizes an error associated with the observation window when calculating the estimated flight parameter value of the flight parameter vector. Comparisons may be performed by a failure detector or a flight control computer to determine failures impacting the flight parameter vector.

Abstract: The data frequency analysis method comprises: a step for inputting signals coming from a first sensor; a step for inputting signals coming from at least a second sensor, each second sensor being positioned close to the first sensor so that the signals coming from each second sensor are strongly correlated with the signals coming from the first sensor; a step of estimating, for each sensor, a transfer function or model established from the combination of the signals from the first sensor and from each second sensor; and a step of extracting the structural properties of the system from each of the estimated models.

Abstract: Automatic estimation process and device for a flight parameter vector in an aircraft, as well as detection methods and assemblies for a failure affecting such a vector. The detection device (1) comprises means (5) for estimating, on an observation window, a coefficient vector allowing to determine a linear relationship between the searched flight parameter vector and explanatory values, by implementing a PLS regression, and means (5) to use such a coefficient vector so as to calculate, thru a linear modeling, an estimated value of said flight parameter vector.

Abstract: Systems and methods for optimizing travel criteria for a meeting are disclosed. A set of attendee originating cities may be specified along with the number of attendees from each attendee city. The system then determines various travel scenarios for each destination city in a database. An ordered list of scenarios is presented. Ordering of the list may be determined by various criteria such as the travel cost of the scenario, total mileage for travel to the destination city in the scenario, or total CO2 emissions for the scenario. Additionally, the costs may include telepresence costs associated with using a telepresence facility for the meeting.

Abstract: The data frequency analysis method comprises: a step (310) for inputting signals coming from a first sensor; a step (315) for inputting signals coming from at least a second sensor, each second sensor being positioned close to the first sensor so that the signals coming from each second sensor are strongly correlated with the signals coming from the first sensor; a step of estimating, for each sensor, a transfer function or model established from the combination of the signals from the first sensor and from each second sensor; and a step (320) of extracting the structural properties of the system from each of the estimated models.

Abstract: Method for analysing the frequency in real-time of a non-stationary signal and corresponding analysis circuit. According to the invention, the signal is sampled, it is digitised, it is broken up into sub-bands. In each sub-band, the signal is modelled by an auto-regressive filter the transfer function of which is of the form 1/A(z). By an adaptive method that is recursive in time and in order, all the polynomials A(z) that have a degree between 1 and a maximum value are calculated. The order of the model is estimated and the polynomial that has this order is retained. The roots of this polynomial are calculated and the components are monitored. The frequency and the amplitude of the sinusoidal components of the signal are thus obtained. Application in aeronautics, electromagnetic, mechanics, seismic prospecting, zoology, etc.

Abstract: According to the invention, the signal is sampled, it is digitised, it is broken up into sub-bands. In each sub-band, the signal is modelled by an auto-regressive filter the transfer function of which is of the form 1/A(z). By an adaptive method that is recursive in time and in order, all the polynomials A(z) that have a degree between 1 and a maximum value are calculated. The order of the model is estimated and the polynomial that has this order is retained. The roots of this polynomial are calculated and the components are monitored. The frequency and the amplitude of the sinusoidal components of the signal are thus obtained.