Current developments in electrochemical storage systems for satellites

The need for batteries with greater power capacity and service life for power satellites is examined. The Ni/Cd and Ni/H batteries now being used must be upgraded to meet advanced space requirements. Improvements in power capacity, service life, and cycle count for various satellites in LEO and GEO orbits are discussed. The Ni/Cd and Ni/H cell reactions are explained, and the solubility and volume changes for various charged and uncharged masses are described. A chart of the energy content and cycle count for various cell systems is presented, and the factors which cause aging and failure in the Ni/Cd and Ni/H cell systems are discussed. The advantages of the Ni/H battery are given and the need for more developed electrochemical storage systems because of an increase in the mass of satellites is explained. The requirements for space batteries and the work currently done by NASA and West Germany on advanced batteries are discussed

Development works on nickel/hydrogen cells

Experiments were performed to reduce the costs for NI/H2 cells by using nickel oxide electrodes with high capacity per unit area. No maintenance requirements, long cycle life, insensitivity to overcharge and cell reversal, and high power capability were revealed

For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances

The Quantum Approximate Optimization Algorithm, QAOA, uses a shallow depth quantum circuit to produce a parameter dependent state. For a given combinatorial optimization problem instance, the quantum expectation of the associated cost function is the parameter dependent objective function of the QAOA. We demonstrate that if the parameters are fixed and the instance comes from a reasonable distribution then the objective function value is concentrated in the sense that typical instances have (nearly) the same value of the objective function. This applies not just for optimal parameters as the whole landscape is instance independent. We can prove this is true for low depth quantum circuits for instances of MaxCut on large 3-regular graphs. Our results generalize beyond this example. We support the arguments with numerical examples that show remarkable concentration. For higher depth circuits the numerics also show concentration and we argue for this using the Law of Large Numbers. We also observe by simulation that if we find parameters which result in good performance at say 10 bits these same parameters result in good performance at say 24 bits. These findings suggest ways to run the QAOA that reduce or eliminate the use of the outer loop optimization and may allow us to find good solutions with fewer calls to the quantum computer.Comment: 16 pages, 1 figur

Indoor Navigation with MEMS sensors

AbstractAccurate positioning becomes extremely important for modern application like indoor navigation and location-based services. Standalone GPS cannot meet this accuracy. In this paper a method to couple GPS and a high resolution MEMS pressure sensor is presented to improve vertical as well as horizontal (in urban canyon environment) positioning. Further, a step counter based on an accelerometer is improved with an altimeter for stair detection and automatic step length adaptation for dead reckoning inside buildings. Finally, a stand-alone system accurately tracks floor levels inside buildings, using only a pressure sensor

The Implementation of the Global Minimum Tax (GloBE): The Need for an Effective Dispute Prevention and Resolution Mechanism

The successful implementation of the Global Anti-Base Erosion (GloBE) rules on aglobal scale cannot be achieved without an international effective dispute prevention and reso-lution mechanism. However, the development of a dispute prevention and resolution frameworkfor the GloBE rules faces significant challenges. This article offers two possible options for aneffective dispute prevention and resolution mechanism: a model based on reciprocal domesticlegislations and the multilateral convention model

Structural and Magnetic Investigations of Single-Crystals of the Neodymium Zirconate Pyrochlore, Nd2Zr2O7

We report structural and magnetic properties studies of large high quality single-crystals of the frustrated magnet, Nd$_2$Zr$_2$O$_7$. Powder x-ray diffraction analysis confirms that Nd$_2$Zr$_2$O$_7$ adopts the pyrochlore structure. Room-temperature x-ray diffraction and time-of-flight neutron scattering experiments show that the crystals are stoichiometric in composition with no measurable site disorder. The temperature dependence of the magnetic susceptibility shows no magnetic ordering at temperatures down to 0.5 K. Fits to the magnetic susceptibility data using a Curie-Weiss law reveal a ferromagnetic coupling between the Nd moments. Magnetization versus field measurements show a local Ising anisotropy along the axes of the Nd$^{3+}$ ions in the ground state. Specific heat versus temperature measurements in zero applied magnetic field indicate the presence of a thermal anomaly below $T\sim7$ K, but no evidence of magnetic ordering is observed down to 0.5 K. The experimental temperature dependence of the single-crystal bulk dc susceptibility and isothermal magnetization are analyzed using crystal field theory and the crystal field parameters and exchange coupling constants determined.Comment: 10 pages, 6 figures, 4 tables. Accepted for publication in Physical Review

SpF: Enabling Petascale Performance for Pseudospectral Dynamo Models

Pseudospectral (PS) methods possess a number of characteristics (e.g., efficiency, accuracy, natural boundary conditions) that are extremely desirable for dynamo models. Unfortunately, dynamo models based upon PS methods face a number of daunting challenges, which include exposing additional parallelism, leveraging hardware accelerators, exploiting hybrid parallelism, and improving the scalability of global memory transposes. Although these issues are a concern for most models, solutions for PS methods tend to require far more pervasive changes to underlying data and control structures. Further, improvements in performance in one model are difficult to transfer to other models, resulting in significant duplication of effort across the research community.We have developed an extensible software framework for pseudospectral methods called SpF that is intended to enable extreme scalability and optimal performance. High-level abstractions provided by SpF unburden applications of the responsibility of managing domain decomposition and load balance while reducing the changes in code required to adapt to new computing architectures. The key design concept in SpF is that each phase of the numerical calculation is partitioned into disjoint numerical kernels that can be performed entirely in-processor. The granularity of domain-decomposition provided by SpF is only constrained by the data-locality requirements of these kernels. SpF builds on top of optimized vendor libraries for common numerical operations such as transforms, matrix solvers, etc., but can also be configured to use open source alternatives for portability. SpF includes several alternative schemes for global data redistribution and is expected to serve as an ideal testbed for further research into optimal approaches for different network architectures.In this presentation, we will describe the basic architecture of SpF as well as preliminary performance data and experience with adapting legacy dynamo codes. We will conclude with a discussion of planned extensions to SpF that will provide pseudospectral applications with additional flexibility with regard to time integration, linear solvers, and discretization in the radial direction

Bayesian Optimal Experimental Design for Simulator Models of Cognition

Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data. Recent work in cognitive science considered BOED for computational models of human behavior with tractable and known likelihood functions. However, tractability often comes at the cost of realism; simulator models that can capture the richness of human behavior are often intractable. In this work, we combine recent advances in BOED and approximate inference for intractable models, using machine-learning methods to find optimal experimental designs, approximate sufficient summary statistics and amortized posterior distributions. Our simulation experiments on multi-armed bandit tasks show that our method results in improved model discrimination and parameter estimation, as compared to experimental designs commonly used in the literature.Comment: Accepted as a poster at the NeurIPS 2021 Workshop "AI for Science