Crimson and Clover are now on the road to recovery at Oakland Zoo in California. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Crimson (left) was rescued shortly after Clover(right). Breakthroughs, discoveries, and DIY tips sent six days a week. Mountain lions (, cougars, pumas, among its many other names) are carnivorous, sharp-toothed and clawed big cats.

While this could be done using the synthesis formulation, we demonstrate that this leads to slower performances.

The preliminary spacecraft trajectory design phase can be posed as a parameterized global search problem for optimal spacecraft trajectories. At each stage of the preliminary design, the mission objectives, requirements, and constraints may change, resulting in variations of the global search problem parameters. Parameters may also change to represent increased modeling fidelity. The aim at any stage of the preliminary design is to solve for a large set of high quality spacecraft trajectories with diverse, or similarly qualitatively different, features. High quality is naturally defined by the value of a solution's objective value relative to the best known. Examples of qualitatively different features may include trajectories that have a different number of revolutions around a central body, a different number or sequence of gravity assist flybys, solutions that avoid radiation belts or other hazards, or solutions that depart the original or target orbital planes. The benefit of having different qualitative solutions is that it allows mission designers to trade different priorities in their design and reflects the fact that not all relevant objectives and constraints can be incorporated into the optimal spacecraft trajectory problem so early or readily in the design phase (i.e., without prior knowledge of what is relevant and when designing at a quick cadence). In the simplest of cases, a mission designer's past experience may be sufficient to guide them in finding a high quality set of solutions.

Accurate predictions of base stations' traffic load are essential to mobile cellular operators and their users as they support the efficient use of network resources and sustain smart cities and roads. Traditionally, cellular network time-series have been considered for this prediction task. More recently, exogenous factors such as points of presence and other environmental knowledge have been introduced to facilitate cellular traffic forecasting. In this study, we focus on smart roads and explore road traffic measures to model the processes underlying cellular traffic generation with the goal to improve prediction performance. Comprehensive experiments demonstrate that by employing road flow and speed, in addition to cellular network metrics, cellular load prediction errors can be reduced by as much as 56.5 %. The code and more detailed results are available on https://github.com/nvassileva/DataMatters.

This paper presents a robust path-planning framework for safe spacecraft autonomy under uncertainty and develops a computationally tractable formulation based on convex programming. We utilize chance-constrained control to formulate the problem. It provides a mathematical framework to solve for a sequence of control policies that minimizes a probabilistic cost under probabilistic constraints with a user-defined confidence level (e.g., safety with 99.9% confidence). The framework enables the planner to directly control state distributions under operational uncertainties while ensuring the vehicle safety. This paper rigorously formulates the safe autonomy problem, gathers and extends techniques in literature to accommodate key cost/constraint functions that often arise in spacecraft path planning, and develops a tractable solution method. The presented framework is demonstrated via two representative numerical examples: safe autonomous rendezvous and orbit maintenance in cislunar space, both under uncertainties due to navigation error from Kalman filter, execution error via Gates model, and imperfect force models.