SEIS: Insight's Seismic Experiment for Internal Structure of Mars.

By the end of 2018, 42 years after the landing of the two Viking seismometers on Mars, InSight will deploy onto Mars' surface the SEIS (Seismic Experiment for Internal Structure) instrument; a six-axes seismometer equipped with both a long-period three-axes Very Broad Band (VBB) instrument and a three-axes short-period (SP) instrument. These six sensors will cover a broad range of the seismic bandwidth, from 0.01 Hz to 50 Hz, with possible extension to longer periods. Data will be transmitted in the form of three continuous VBB components at 2 sample per second (sps), an estimation of the short period energy content from the SP at 1 sps and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams will be augmented by requested event data with sample rates from 20 to 100 sps. SEIS will improve upon the existing resolution of Viking's Mars seismic monitoring by a factor of ∼ 2500 at 1 Hz and ∼ 200 000 at 0.1 Hz. An additional major improvement is that, contrary to Viking, the seismometers will be deployed via a robotic arm directly onto Mars' surface and will be protected against temperature and wind by highly efficient thermal and wind shielding. Based on existing knowledge of Mars, it is reasonable to infer a moment magnitude detection threshold of M w ∼ 3 at 40 ∘ epicentral distance and a potential to detect several tens of quakes and about five impacts per year. In this paper, we first describe the science goals of the experiment and the rationale used to define its requirements. We then provide a detailed description of the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors. We conclude by describing the experiment ground segment, including data processing services, outreach and education networks and provide a description of the format to be used for future data distribution.Electronic supplementary materialThe online version of this article (10.1007/s11214-018-0574-6) contains supplementary material, which is available to authorized users

On Maximum Weight Clique Algorithms, and How They Are Evaluated

Maximum weight clique and maximum weight independent set solvers are often benchmarked using maximum clique problem instances, with weights allocated to vertices by taking the vertex number mod 200 plus 1. For constraint programming approaches, this rule has clear implications, favouring weight-based rather than degree-based heuristics. We show that similar implications hold for dedicated algorithms, and that additionally, weight distributions affect whether certain inference rules are cost-effective. We look at other families of benchmark instances for the maximum weight clique problem, coming from winner determination problems, graph colouring, and error-correcting codes, and introduce two new families of instances, based upon kidney exchange and the Research Excellence Framework. In each case the weights carry much more interesting structure, and do not in any way resemble the 200 rule. We make these instances available in the hopes of improving the quality of future experiments

Deriving information from sampling and diving

We investigate the impact of sampling and diving in the solution of constraint satisfaction problems. A sample is a complete assignment of variables to values taken from their domain according to a a given distribution. Diving consists in repeatedly performing depth first search attempts with random variable and value selection, constraint propagation enabled and backtracking disabled; each attempt is called a dive and, unless a feasible solution is found, it is a partial assignment of variables (whereas a sample is a \u2013possibly infeasible\u2013 complete assignment). While the probability of finding a feasible solution via sampling or diving is negligible if the problem is difficult enough, samples and dives are very fast to generate and, intuitively, even when they are infeasible, they give some statistic information on search space structure. The aim of this paper is to understand to what extent it is possible to help the CSP solving process with information derived from sampling and diving. In particular, we are interested in extracting from samples and dives precise indications on how good/bad are individual variable-value assignments with respect to feasibility. We formally prove that even uniform sampling could provide precise evaluation of the quality of variable-value assignments; as expected, this requires huge sample sizes and is therefore not useful in practice. On the contrary, diving seems to be much better suited for assignment evaluation purposes. Three dive features are identified and evaluated on a collection of Partial Latin Square instances, showing that diving provides information that can be fruitfully exploited. Many promising direction for future research are proposed

A novel human-centred approach using Axiomatic Design and Kansei engineering for designing physically and cognitively safe human-robot collaborative workstations

Human-centred design of collaborative human–robot (HRC) workspaces is central to Industry 5.0. While proximity with collaborative robots offers productivity and flexibility gains, it also raises concerns for both physical safety and cognitive ergonomics. Although physical safety is well addressed, few studies integrate cognitive and physical well-being into workstation design. This research presents a novel approach that combines Kansei Engineering (KE) with Suh’s Axiomatic Design (AD) to support physically and cognitively safe HRC workstations. Unlike existing studies that rely solely on Suh’s Axiom 1 (maintain independence), this work also takes into account Axiom 2 (minimise information) to select between equally independent physical and cognitive design parameters. The approach is demonstrated through a case-study workstation, visually illustrating the relationship between functional and physical metrics. This study advances the field by providing a novel replicable, human-centred approach that unites cognitive and physical ergonomics,bridging theory and practical application for both academic and industrial contexts