Abstract: A structure and control system for changing the attitude of an object, such as a spacecraft, is provided. An example attitude control system includes a panel coupled to the object. The panel includes a first panel section coupled to the object by way of a first joint, a second panel section coupled to the first panel by way of a second joint, and a third panel section coupled to the second panel by way of a third joint.

Abstract: A structure for changing the attitude of a spacecraft is provided including an elongated member and a plurality of actuators coupled to the elongated member. The elongated member is coupled to the spacecraft. The elongated member has a length and an angular position relative to an axis. The plurality of actuators are configured to deflect the angular position of the elongated member relative to the axis and to alter the length of the elongated member.

Abstract: A structure for changing the attitude of a spacecraft is provided including an elongated member and a plurality of actuators coupled to the elongated member. The elongated member is coupled to the spacecraft. The elongated member has a length and an angular position relative to an axis. The plurality of actuators are configured to deflect the angular position of the elongated member relative to the axis and to alter the length of the elongated member.

Abstract: An indication that a virtual machine is starting is received. Requested data blocks associated with the virtual machine are identified. Based on identifiers of the requested data blocks, a trained learning model is used to predict one or more subsequent data blocks likely to be requested while the virtual machine is starting. The one or more subsequent data blocks are caused to be preloaded in a cache storage. It is determined that the one or more predicted subsequent data blocks are incorrect. It is determined that an end of a boot sequence associated with the virtual machine has been reached. In response to a determination that the end of the boot sequence associated with the virtual machine has been reached, the boot sequence associated with the virtual machine is used to update the trained learning model.

Abstract: An indication that a virtual machine is starting is received. Requested data blocks associated with the virtual machine are identified. Based on identifiers of the requested data blocks, a trained learning model is used to predict one or more subsequent data blocks likely to be requested while the virtual machine is starting. The one or more subsequent data blocks are caused to be preloaded in a cache storage. It is determined that the one or more predicted subsequent data blocks are incorrect. It is determined that an end of a boot sequence associated with the virtual machine has been reached. In response to a determination that the end of the boot sequence associated with the virtual machine has been reached, the boot sequence associated with the virtual machine is used to update the trained learning model.

Abstract: An indication that a virtual machine is starting is received. Requested data blocks associated with the virtual machine are identified. Based on identifiers of the requested data blocks, a trained learning model is used to predict one or more subsequent data blocks likely to be requested while the virtual machine is starting. The one or more subsequent data blocks are caused to be preloaded in a cache storage.

Abstract: An indication that a virtual machine is starting is received. Requested data blocks associated with the virtual machine are identified. Based on identifiers of the requested data blocks, a trained learning model is used to predict one or more subsequent data blocks likely to be requested while the virtual machine is starting. The one or more subsequent data blocks are caused to be preloaded in a cache storage.

Abstract: An indication that a virtual machine is starting is received. Requested data blocks associated with the virtual machine are identified. Based on identifiers of the requested data blocks, a trained learning model is used to predict one or more subsequent data blocks likely to be requested while the virtual machine is starting. The one or more subsequent data blocks are caused to be preloaded in a cache storage.

Abstract: A structure and control system for changing the attitude of an object, such as a spacecraft, is provided. An example attitude control system includes a panel coupled to the object. The panel includes a first panel section coupled to the object by way of a first joint, a second panel section coupled to the first panel by way of a second joint, and a third panel section coupled to the second panel by way of a third joint.

Abstract: An indication that a virtual machine is starting is received. Requested data blocks associated with the virtual machine are identified. Based on identifiers of the requested data blocks, a trained learning model is used to predict one or more subsequent data blocks likely to be requested while the virtual machine is starting. The one or more subsequent data blocks are caused to be preloaded in a cache storage.