Abstract: Discussed herein are devices, systems, and methods for determining an answer to a natural language question. A method can include receiving a question and a passage to be used to answer the question and executing (i) a first trained ML model, based on the passage and the question and in response to determining that the question, passage, or a combination thereof includes more than a first threshold number of out of vocabulary (OOV) words, relative to a general purpose language ML model, to generate a first answer to the question or (ii) executing a second trained ML model, based on the passage and the question and in response to determining the question, passage, or a combination thereof includes less than the first threshold number of out of vocabulary (OOV) words, relative to the general purpose language ML model, to generate a second answer to the question.

Abstract: A system detects a maneuver of at least one space object by receiving a first data set relating to orbital characteristics of at least one space debris object. The system trains a model, using the first data set, in order to model orbital behaviors of the at least one space debris object. The system then receives a second data set relating to orbital characteristics of the at least one space object, and detects a maneuver of the at least one space object using the trained model and the second data set.

Abstract: Devices, systems, and methods for improving results returned from a query. A method can include identify, based on a term embedding of a corpus of terms, expansion terms of a raw query term that are nearest the raw query term, normalize distances between the raw query term and the identified expansion terms, identify, based on the term embedding, expansion term neighbors of an expansion term that are nearest the expansion term; normalize distances between the expansion term and the identified expansion term neighbors, determine a WMA weight between the raw query term and the expansion term, and execute the query with the raw query terms and the expansion terms (determined based on the WMA weight) to generate query results.

Abstract: Devices, systems, and methods for improving results returned from a query. A method can include identify, based on a term embedding of a corpus of terms, expansion terms of a raw query term that are nearest the raw query term, normalize distances between the raw query term and the identified expansion terms, identify, based on the term embedding, expansion term neighbors of an expansion term that are nearest the expansion term; normalize distances between the expansion term and the identified expansion term neighbors, determine a WMA weight between the raw query term and the expansion term, and execute the query with the raw query terms and the expansion terms (determined based on the WMA weight) to generate query results.

Abstract: Discussed herein are devices, systems, and methods for determining an answer to a natural language question. A method can include receiving a question and a passage to be used to answer the question and executing (i) a first trained ML model, based on the passage and the question and in response to determining that the question, passage, or a combination thereof includes more than a first threshold number of out of vocabulary (OOV) words, relative to a general purpose language ML model, to generate a first answer to the question or (ii) executing a second trained ML model, based on the passage and the question and in response to determining the question, passage, or a combination thereof includes less than the first threshold number of out of vocabulary (OOV) words, relative to the general purpose language ML model, to generate a second answer to the question.

Abstract: A system detects a maneuver of at least one space object by receiving a first data set relating to orbital characteristics of at least one space debris object. The system trains a model, using the first data set, in order to model orbital behaviors of the at least one space debris object. The system then receives a second data set relating to orbital characteristics of the at least one space object, and detects a maneuver of the at least one space object using the trained model and the second data set.