Abstract: A method implemented by computer for generating a model for anomaly detection in a system includes obtaining a learning data matrix corresponding to a normal operation of the system, decomposing the learning data matrix into singular values of the matrix of learning data, calculating a new base, defining a maximum Mahalanobis distance threshold representing a limit of the normal operation of the system, and defining an anomaly detection model from the new base and from the maximum Mahalanobis distance threshold.

Abstract: A user equipment (UE) may receive a configuration message including a configuration for a transmission of a set of positioning signals associated with a plurality of transmission (Tx) power settings. The set of positioning signals may include a first subset of positioning signals and a second subset of positioning signals. The UE may transmit the first subset of positioning signals using a first Tx power setting of the plurality of Tx power settings. The UE may transmit the second subset of positioning signals using a second Tx power setting of the plurality of Tx power settings.

Abstract: This disclosure provides systems, methods, and devices for wireless communication that support sensing assisted positioning. In a first aspect, a method of wireless communication includes receiving, from a first management function of a network, a first configuration that indicates a first parameter associated with a sensing operation operable to measure a characteristic of an object within an environment. The method also includes receiving, from a second management function of the network, a second configuration that indicates a second parameter associated with a positioning operation operable to identify a position of the object within the environment based on the characteristic The method further includes transmitting a sensing report based on the sensing operation and that indicates the characteristic, and transmitting a positioning report. Each of the positioning report and the sensing report indicating the position of the object within the environment.

Abstract: Aspects presented herein may enable a UE and a network entity to have a common understanding for AI/ML models used in association with AI/ML positioning, thereby improving the performance and efficiency of AI/ML positioning. In one aspect, a UE transmits, to a network entity, a list of UE-supported AI/ML positioning functionalities. The UE receives, from the network entity, an indication of a set of network-supported AI/ML positioning functionalities that are supported by the network entity. The UE transmits, to the network entity, a PRS-based measurement or an estimated location of the UE that is based on using at least one AI/ML model associated with at least one UE-supported AI/ML positioning functionality in the list of UE-supported AI/ML positioning functionalities or at least one network-supported AI/ML positioning functionality in the set of network-supported AI/ML positioning functionalities.

Abstract: Disclosed are techniques for communication. In an aspect, a radio access network (RAN) node transmits, to a network entity, a set of machine learning positioning capabilities supported by the RAN node, wherein the set of machine learning positioning capabilities includes a list of identifiers of a set of machine learning positioning functionalities supported by the RAN node, wherein the set of machine learning positioning functionalities is associated with a set of machine learning models that support the set of machine learning positioning functionalities, and wherein each machine learning positioning functionality of the set of machine learning positioning functionalities is associated with one or more machine learning models of the set of machine learning models, and transmits, to the network entity, one or more measurements of one or more sounding reference signal (SRS) resources transmitted by a user equipment (UE).

Abstract: Disclosed herein are exosomes, compositions, and methods for the treatment of subjects infected with, or at risk for infection with a coronavirus, such as SARS-COV-2, HCoV-NL63, or SARS-COV. The disclosed exosomes comprise ACE2 protein and typically display ACE2 protein on the exosome surface. In some embodiment, the exosomes optionally are loaded with one or more additional therapeutic agents for treating an infection by a coronavirus, such as remdesivir. In some embodiments, compositions comprising the exosomes are administered to a subject in need thereof, e.g., to a subject diagnosed with, or suspected of having a SARS-COV-2 infection, an HCoV-NL63 infection, or a SARS-COV infection. In some embodiments, administration is via inhalation. In some embodiments, administration is via injection.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) receives, from a location server, a configuration of a plurality of positioning frequency layers (PFLs) and one or more positioning reference signal (PRS) resources in each of the plurality of PFLs, obtains a first set of measurements of the one or more PRS resources in each of the plurality of PFLs, and applies a machine learning model to the first set of measurements to obtain positioning information associated with the first set of measurements.

Abstract: A method of producing a 3D printed proppant comprises providing a 3D printing apparatus that produces the 3D printed proppant; distributing a layer of build material within a build chamber of the 3D printing apparatus, wherein the build material comprises metal, polymer, ceramic, composite, or combinations thereof; depositing a layer of binder material on the layer of build material; curing the binder material within the 3D printing apparatus; and repeating as necessary to produce the 3D printed proppant, wherein the 3D printed proppant has a particle size from 8 mesh to 140 mesh.

Abstract: A wireless device may receive a set of positioning signals. The wireless device may measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The wireless device may output the measured set of positioning signals for training a positioning model. The wireless device may output the measured set of positioning signals for training a positioning model by training the positioning model at the wireless device based on the measured set of positioning signals or may transmitting, to a training entity, the measured set of positioning signals for training the positioning model. The training entity may train the positioning model based on the measured set of positioning signals. The wireless device may include a user equipment (UE) or a network node.

Abstract: Aspects presented herein may improve the efficiency and performance of artificial intelligence (AI)/machine learning (ML) (AI/ML) positioning by enabling a network node (e.g., a base station, a transmission reception point (TRP), etc.) to compress uplink (UL) reference signal measurements to reduce reporting overhead for the UL reference signal measurements. In one aspect, a network node performs at least one channel impulse response (CIR) measurement or at least one channel frequency response (CFR) measurement for a set of sounding reference signals (SRSs). The network node compresses the at least one CIR measurement or the at least one CFR measurement for the set of SRSs. The network node reports, for a network entity, one or more of the at least one compressed CIR measurement or the at least one compressed CFR measurement for the set of SRSs.

Abstract: Aspects presented herein may improve the efficiency and performance of artificial intelligence (AI)/machine learning (ML) (AI/ML) positioning by enabling a user equipment (UE) to compress downlink (DL) reference signal measurements to reduce reporting overhead for the DL reference signal measurements. In one aspect, a UE performs at least one channel impulse response (CIR) measurement or at least one channel frequency response (CFR) measurement for a set of positioning reference signals (PRSs). The UE compresses the at least one CIR measurement or the at least one CFR measurement for the set of PRSs. The UE reports, for a network entity, one or more of the at least one compressed CIR measurement or the at least one compressed CFR measurement for the set of PRSs.

Abstract: Disclosed are techniques for positioning. In an aspect, a network entity receives, from at least one network node, a measurement report including one or more radio frequency fingerprint (RFFP) measurements, wherein the one or more RFFP measurements include at least one RFFP measurement of at least one sidelink channel between a first user equipment (UE) and a second UE, determines one or more locations of a target UE based on the one or more RFFP measurements and a machine learning module, wherein the machine learning module is trained based on previously collected RFFP measurements of one or more downlink channels, RFFP measurements of one or more uplink channels, RFFP measurements of one or more sidelink channels, locations of one or more sidelink anchor UEs, locations of one or more base stations, or any combination thereof.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A sequence of data records is accessed, each data record comprising wireless channel measurements and inertial measurement unit (IMU) data. Known position information corresponding to at least a first data record is accessed. A first sequence of positions is determined by processing the sets of IMU data and known position information using a forward operation. A second sequence of positions is determined by processing the sets of IMU data and known position information using a backward operation. An IMU adjustment parameter is generated using the first and second sequences of positions. A pseudo-label is generated for a second data record using the IMU adjustment parameter and the sets of IMU data. A machine learning model is trained, using the second data record and the pseudo-label, to predict positions using one or more wireless channel measurements.

Abstract: In one aspect, a drone is disclosed for inspecting a wellhead having a transceiver and adjusting an electronic choke. The drone includes a plurality of propellers and a plurality of electric motors where each of the plurality of electric motors is connected to one or more of the plurality of propellers. The drone further includes a camera that captures a survey of the wellhead, a processor coupled to a memory that controls the plurality of electric motors based on the survey to position the drone within a transmission range of the transceiver of the wellhead, and a transceiver of the drone coupled to the processor that receives wellhead data from the transceiver of the wellhead and stores the wellhead data in the memory. In other aspects, a method for operating a drone and a non-transitory computer readable medium (CRM) storing instructions for performing operation of a drone are also disclosed.

Abstract: The present disclosure relates to a single-ply tissue paper material having a basis weight less than 40 gsm and a GMT tensile strength of at least 60 N/m, comprising a non-wood tissue ply, comprising non-wood cellulose pulp fibres being present in an amount of at least 10% by dry weight of the of the non-wood tissue ply. The disclosure also relates to a single-ply tissue paper product and to a multi-ply tissue paper product comprising at least one non-wood tissue ply.

Abstract: Disclosed are techniques for wireless communication. In an aspect, a network device may operate according to a cell-level discontinuous transmission (cell DTX) cycle that comprises one or more active times and one or more inactive times. The network device may transmit positioning reference signal (PRS) at one or more scheduled PRS occasions during the cell DTX cycle regardless of whether the one or more scheduled PRS occasions coincide with the one or more inactive times.

Abstract: A method of using a truffle-derived biomass for producing 1,3-diolein includes obtaining a mycelial biomass from truffles, preparing an extract from the mycelial biomass, and isolating the 1,3-diolein from the extract. In an embodiment, isolating the 1,3-diolein from the extract can include using column chromatography for eluting a portion of a first extract with a mixture including chloroform and methanol into a plurality of fractions, and using column chromatography for eluting at least one of the fractions with methanol to provide 1,3-diolein.

Abstract: Amaranthus hybridus silver nanoparticles can be synthesized by mixing an extract of Amaranthus hybridus with a silver nitrate solution to provide a mixture including Amaranthus hybridus silver nanoparticles. The Amaranthus hybridus silver nanoparticles can have an average particle size ranging from about 45 nm to about 100 nm. The AH-AgNPs can have antifungal properties and can be particularly effective agents against Madurella mycetomatis, for the treatment of eumycetoma.

Abstract: A wireless device may receive a set of positioning reference signals (PRSs). The wireless device may measure the set of PRSs. The wireless device may output at least one of a set of measurements based on the measured set of PRSs, a set of labels associated with the measured set of PRSs, or assistance information associated with the measured set of PRSs for training a positioning model. In one aspect, the wireless device may transmit, to a network entity, the set of measurements for the positioning model. In another aspect, the wireless device may include the positioning model. The wireless device may train the positioning model based on at least one of the set of measurements, the set of labels, or the assistance information. The wireless device may include a user equipment (UE) or a positioning reference unit (PRU). The network entity may include a location management function (LMF).

Abstract: A network node may receive a set of sounding reference signals (SRSs) from a wireless device. The network node may measure the set of SRSs. The network node may output at least one of a set of estimated positioning labels, training associated information, or labeling assistance information associated with the set of measured SRSs for a positioning model. In one example, the network node may output the data by training the positioning model based on at least one of the set of estimated positioning labels, the training associated information, the labeling assistance information, or the set of measured SRSs. In another example, the network node may output the data by transmitting, for a network entity, at least one of the set of estimated positioning labels, the training associated information, or the set of measured SRSs for training the positioning model.