Abstract: A lung phantom unit for radiotherapy according to an embodiment of the present disclosure may arrange, at a location not affected by a magnetic field, a phantom driving cylinder and a driving device which may move a lung mimic and a tumor mimic in a lung simulation block. The lung phantom unit may be used for general purpose even as a phantom for MRI-based and CT image-based radiotherapy. Since lung and tumor motions are implemented by air injection, it may be possible to precisely measure the motion and volume change of a lung according to subtle changes in air pressure so that customized radiotherapy suitable for a patient having various breathing patterns is possible.

Abstract: A method of a terminal may comprise: receiving, from a serving cell, a first message including measurement report configuration information; in response to transmission of a measurement report message being triggered based on the first message, generating a measurement report message; and in response to a measurement report message transmission condition configured by the first message being satisfied, transmitting the measurement report message to the serving cell.

Abstract: The present disclosure relates to a cell stacking assembly and a battery module including the same, the cell stacking assembly including: a plurality of battery cells stacked along a preset stacking direction and each including a first lead tab portion and a second lead tab portion; a first busbar electrically connected each of the first lead tab portions; a first busbar frame coupled with the first busbar to support the first busbar; a second busbar electrically connected each of the second lead tab portions; a second busbar frame coupled with the second busbar to support the second busbar; a first protrusion protruding from the first busbar frame and inserted into a first insertion hole positioned on the first busbar; and a second protrusion protruding from the second busbar frame and inserted into a second insertion hole positioned on the second busbar.

Abstract: An embodiment mounting and demounting device for a wireless electronic device includes a pair of holder wings arranged symmetrically to each other and configured to press both sides of the wireless electronic device, a holder wing adjusting device configured to apply a force to adjust a gap between the holder wings using a driving force of a drive motor, a lifting implementing device configured to interlock with the holder wing adjusting device to lift the wireless electronic device upward in a case in which the holder wings are spread apart by a predetermined reference gap or more, a lower housing wrapped around a lower side of the holder wings, the holder wing adjusting device, and the lifting implementing device, and an upper housing coupled to an upper side of the lower housing and configured to allow the wireless electronic device to pass through in an up and down direction.

Abstract: A lighting device of a steering wheel and an assembling method of the lighting device improving an assembly process and mass productivity of the lighting device by simplifying an assembly structure thereof, includes a grip body forming a rim of the steering wheel and including a body groove portion formed in a shape in which the body groove portion is cut away along the rim, a bezel fitted to the body groove portion and assembled to the grip body so that the body groove portion is covered by the bezel, the bezel including a center portion provided with a light transmission hole, and includes a light emitting body assembled to the bezel to overlap the light transmission hole from between the body groove portion and the bezel, the light emitting body being configured to emit light through the light transmission hole.

Abstract: A method of identifying AI/ML functionalities/models supported for mobile communication operated in mobile communication systems including a base station and one or more terminals may comprise: identifying, by at least one of the base station or the one or more terminals, information related to the AI/ML functionalities supportable by the one or more terminals; and identifying, by at least one of the base station or the one or more terminals, AI/ML model information supportable by the one or more terminals.

Abstract: A method of a terminal may comprise: receiving a reference signal from a base station; generating a first latent vector based on the reference signal by using an artificial neural network, the first latent vector being common CSI for M resource regions, and M being a positive integer; generating a second latent vector based on the reference signal by using the artificial neural network, the second latent vector being per-resource region CSI for each of the M resource regions; and transmitting structured CSI including the first latent vector and the second latent vectors to the base station.

Abstract: Disclosed are a method and an apparatus for improving mobility management performance by reducing unnecessary handovers in a communication system. a method of a terminal may comprise: receiving cell prediction configuration information; generating measurement results by performing measurements on a serving cell and neighbor cell(s) based on signal strength measurement configuration information; and generating a result of predicting a best cell by performing best cell prediction based on the measurement results.

Abstract: Disclosed are CSI transmission method and apparatus. A method of a terminal may comprise: receiving, from a base station, information on a measurement period for at least one channel state information-reference signal (CSI-RS) and a reporting period for transmitting at least one CSI; receiving the at least one CSI-RS during the measurement period; generating at least one measured CSI based on the at least one CSI-RS; generating at least one predicted CSI through a CSI prediction model based on the at least one CSI-RS and the at least one CSI; and transmitting the at least one measured CSI and the at least one predicted CSI to the base station.

Abstract: A method of a terminal may comprise: receiving augmentation assistance information from a base station; generating augmented channel data by applying the augmentation assistance information to original channel data; and training an artificial neural network using the augmented channel data.

Abstract: A method of a terminal may comprise: transmitting channel state prediction-related information to a base station; receiving a channel prediction request signal for a first time instance from the base station; receiving auxiliary reference signal(s) (RS(s)) and a first RS from the base station based on the channel state prediction-related information; and transmitting channel prediction information for the first time instance to the base station, wherein the channel state prediction-related information includes information indicating that channel state information prediction of the terminal is possible, and the channel prediction request signal includes configuration information of the auxiliary RS(s) and configuration information of the first RS.

Abstract: A method of a communication node may comprise: transmitting required network configurations for applying each of artificial neural network models to a network node; and transmitting a status report of the first model including a model identifier field and a model information field for each of the artificial neural network models to the network node to activate at least one artificial neural network model among the artificial neural network models, wherein each of the required network configurations includes a configuration identifier and network configuration information.

Abstract: A method of a terminal may comprise: receiving a channel state information (CSI) request message from a base station, the CSI request message including first indication information indicating one of quantization configuration information of a first artificial intelligence (AI) model configured in the terminal to encode and transmit CSI; quantizing generated CSI feedback information based on the first indication information; and transmitting a CSI report message including the quantized CSI feedback information to the base station.

Abstract: A method of a terminal may comprise: receiving, from a CU of a base station, configuration information of one or more candidate cells and configuration information for measurement; performing L1 measurement on the candidate cells based on the configuration information for measurement; acquiring uplink synchronization for the candidate cells by performing an uplink synchronization management procedure for the candidate cells based on the configuration information of the candidate cells; reporting a result of the L1 measurement to a DU of the base station; receiving, from the DU of the base station, information on a target cell, being the target cell to switch among the candidate cells, based on the result of the L1 measurement; and applying the uplink synchronization acquired through the uplink synchronization management procedure to the target cell.

Abstract: A method of monitoring and managing performance of an artificial neural network model for an air interface may comprise: receiving, by a network (NW) including a communication node performing a function of monitoring and managing performance of an artificial neural network model, a performance metric of the artificial neural network model from a user equipment (UE); and controlling, by the communication node, activation or deactivation of the artificial neural network model according to the performance metric, wherein the artificial neural network model is activated to improve a main performance metric of a mobile communication system including the communication node and the UE connected through an air interface.

Abstract: A method of a terminal may comprise: receiving a reference signal from a base station; generating channel information based on the reference signal; generating wavelet-transformed channel information by applying wavelet transform to the channel information; generating compressed channel information by compressing the wavelet-transformed channel information; and transmitting the compressed channel information to the base station.

Abstract: A method of a terminal may comprise: receiving a first message including state information of a base station from the base station; identifying preliminary inactive state information included in the first message when the state information of the base station indicates a preliminary inactive state; and performing a cell selection based on the preliminary inactive state information.

Abstract: A handover method of a first terminal in a wireless communication system may comprise: transmitting a first measurement report message to a first cell that is a primary cell (PCell); receiving, from the first cell, one or more first configuration messages for one or more target cells determined according to the first measurement report message; adding each of the one or more target cells as a candidate PCell based on the one or more first configuration messages; and performing data transmission and reception with at least one cell among the first cell and the one or more candidate PCells.

Abstract: An operation method of a first communication node may include: inputting first input data including first feedback information to a first encoder of a first artificial neural network corresponding to the first communication node; generating first latent data based on an encoding operation in the first encoder; generating a first feedback signal including the first latent data; and transmitting the first feedback signal to a second communication node, wherein the first latent data included in the first feedback signal is decoded into first restored data corresponding to the first input data in a second decoder of a second artificial neural network corresponding to the second communication node, and the first input data includes first common input data included in a common input data set previously shared between the first communication node and the second communication node.