Pathfinder parachuted through the Martian nocturnal boundary layer during the northern hemisphere summer. Encased in airbags the lander bounced across the surface in darkness coming to a stop ...surrounded by a rugged terrain. Studying the motion of Pathfinder during its descent and landing offers a unique opportunity to study the Martian winds at night in the stable boundary layer. We simulate the atmospheric descent of Pathfinder using a trajectory model together with winds imported from a global climate model. The bouncing trajectory of the airbags was investigated using atmospheric pressure observations made during the landing and a Digital Terrain Map (DTM).
Reconstruction of the parachute and airbag trajectories together with accelerometer data indicates that there was possibly strong wind shear in the nocturnal boundary layer prior to the firing of the braking rockets. This is unexpected from atmospheric models. Atmospheric pressure data and the DTM indicate the first airbag impact was on the side of a low hill north of the deployed lander. From this point our airbag model indicates the airbags would have bounced downhill towards the south by about one hundred metres and descended several metres in elevation before arriving on level ground where they would have slowed to a stop.
We use modern observations and analytical tools to reconstruct the descent and landing of Pathfinder and provide information on the atmospheric and surface conditions during the night-time landing. In addition we provide clues to the behaviour of the nocturnal boundary layer on Mars.
•Pathfinder is the only lander to descend through the nocturnal boundary layer on Mars.•Strong wind shear appears to have caused its braking rockets to tilt as they fired.•Accelerometer data and trajectory modelling indicate a drop in wind speed at 100 m altitude.•Topography data suggests the first airbag impact was on a low hill north of the lander.•The airbags bounced down hill then changed direction to approach from the east.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We report the first results of the LISA Pathfinder in-flight experiment. The results demonstrate that two free-falling reference test masses, such as those needed for a space-based gravitational wave ...observatory like LISA, can be put in free fall with a relative acceleration noise with a square root of the power spectral density of 5.2 +/- 0.1 fm s(exp -2)/square root of Hz, or (0.54 +/- 0.01) x 10(exp -15) g/square root of Hz, with g the standard gravity, for frequencies between 0.7 and 20 mHz. This value is lower than the LISA Pathfinder requirement by more than a factor 5 and within a factor 1.25 of the requirement for the LISA mission, and is compatible with Brownian noise from viscous damping due to the residual gas surrounding the test masses. Above 60 mHz the acceleration noise is dominated by interferometer displacement readout noise at a level of (34.8 +/- 0.3) fm square root of Hz, about 2 orders of magnitude better than requirements. At f less than or equal to 0.5 mHz we observe a low-frequency tail that stays below 12 fm s(exp -2)/square root of Hz down to 0.1 mHz. This performance would allow for a space-based gravitational wave observatory with a sensitivity close to what was originally foreseen for LISA.
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CMK, CTK, FMFMET, IJS, NUK, PNG, UM
This paper proposes a new meta-heuristic algorithm called Pathfinder Algorithm (PFA) to solve optimization problems with different structure. This method is inspired by collective movement of animal ...group and mimics the leadership hierarchy of swarms to find best food area or prey. The proposed method is tested on some optimization problems to show and confirm the performance on test beds. It can be observed on benchmark test functions that PFA is able to converge global optimum and avoid the local optima effectively. Also, PFA is designed for multi-objective problems (MOPFA). The results obtained show that it can approximate to true Pareto optimal solutions. The proposed PFA and MPFA are implemented to some design problems and a multi-objective engineering problem which is time consuming and computationally expensive. The results of final case study verify the superiority of the algorithms proposed in solving challenging real-world problems with unknown search spaces. Furthermore, the method provides very competitive solutions compared to well-known meta-heuristics in literature, such as particle swarm optimization, artificial bee colony, firefly and grey wolf optimizer.
•A new heuristic algorithm has been proposed.•The method is a swarm-based algorithm and different in mathematical model.•The proposed method has been tested on some test beds.•The proposed method showed a superior performance to find global optima.•Also, it has been applied to a real engineering problem and found good results.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The Au mineralization in the Kubi Gold Mining Area in the Birimian of Ghana is associated with garnet (about 85 vol.%), magnetite, pyrrhotite, arsenopyrite, and sulfide minerals, as well as quartz ...with gold and calcite. These minerals and the included elements can act as indicator minerals or pathfinder elements. For the present work, we collected samples from drill holes at different depths, from the alluvial zone (0–45 m) to the ore zone (75–100 m). The distributions of minerals and elements in the rocks that act as indicator minerals and pathfinder elements in the concession area were investigated along the drill hole cross sections. X-ray diffraction shows that the samples contain garnet, pyrite, periclase, and quartz as the main indicator minerals. By energy-dispersive X-ray spectroscopy, Fe, Mg, Al, S, O, Mn, Na, Cu, Si, and K are identified as corresponding pathfinder elements. The results indicate that the Au mineralization in the Kubi Mine area correlates mostly with the occurrence of garnet, pyrite, goethite, and kaolinite in the host rocks, which show towards the surface increasingly hematitic and limonitic alteration in form of Fe(oxy-)hydroxides.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
As a kind of large volume underground vehicles, the subway greatly relieves the traffic pressure of the city. However, the main defect is a closed space can easily lead to high casualty once there is ...a fire. In this paper, Pathfinder software was utilized to simulate the evacuation in special subway station. The evacuation in dissimilar status was analyzed by setting up the fire scenario and changing the flow rate in the station. The results showed that the pressure of evacuation is mainly at stairway entrance and the width of exit has little effect on relieving the evacuation pressure. Besides, the number of the people in platform need to be restricted to 500 when the train is full; when the number of passengers in the train is 1542, that of people on the platform has to be limited to 480; when the number of passengers in the train is 1224, no more than 800 people on the platform need to be controlled.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The algorithm to produce the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4.0 (Ed4) Energy Balanced and Filled (EBAF)-surface data product is explained. The algorithm forces computed ...top-of-atmosphere (TOA) irradiances to match with Ed4 EBAF-TOA irradiances by adjusting surface, cloud, and atmospheric properties. Surface irradiances are subsequently adjusted using radiative kernels. The adjustment process is composed of two parts: bias correction and Lagrange multiplier. The bias in temperature and specific humidity between 200 and 500 hPa used for the irradiance computation is corrected based on observations by Atmospheric Infrared Sounder (AIRS). Similarly, the bias in the cloud fraction is corrected based on observations by Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat. Remaining errors in surface, cloud, and atmospheric properties are corrected in the Lagrange multiplier process. Ed4 global annual mean (January 2005 through December 2014) surface net shortwave (SW) and longwave (LW) irradiances increase by 1.3 W m−2 and decrease by 0.2 W m−2, respectively, compared to EBAF Edition 2.8 (Ed2.8) counterparts (the previous version), resulting in an increase in net SW + LW surface irradiance of 1.1 W m−2. The uncertainty in surface irradiances over ocean, land, and polar regions at various spatial scales are estimated. The uncertainties in all-sky global annual mean upward and downward shortwave irradiance are 3 and 4 W m−2, respectively, and the uncertainties in upward and downward longwave irradiance are 3 and 6 W m−2, respectively. With an assumption of all errors being independent, the uncertainty in the global annual mean surface LW + SW net irradiance is 8 W m−2.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Assessments of the Clouds and the Earth's Radiant Energy System Edition 4 (Ed4) cloud retrievals are critical for climate studies. Ed4 cloud parameters are evaluated using instruments in the A-Train ...Constellation. Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) retrievals are compared with Ed4 retrievals from the Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) as a function of the CALIOP horizontal averaging (HA) scale. Regardless of the HA scale, MODIS daytime (nighttime) water cloud fraction (CF) is greater (less) than that from CALIOP. MODIS ice CF is less than CALIOP overall, with the largest differences in polar regions. Ed4 and CALIOP retrieve the same cloud phase in 70%-98% of simultaneous observations depending on the time of day, surface conditions, HA scales, and type of cloud vertical structure. Mean cloud top height (CTH) differences for single-layer water clouds over snow-/ice-free surfaces are less than 100 m. Base altitude positive biases of 170-460 m may be impacted by CPR detection limitations. Average MODIS ice CTHs are underestimated by 70 m for some deep convective clouds and up to ~2.2 km for thin cirrus. Ice cloud base altitudes are typically underestimated (overestimated) during daytime (nighttime). MODIS and CALIOP cirrus optical depths over oceans are within 46% and 5% for daytime and nighttime observations, respectively. Ice water path differences depend on the CALIOP retrieval version and warrant further investigation. Except for daytime cirrus optical depth, Ed4 cloud property retrievals are at least as accurate as other long-term operational cloud property retrieval systems.
Pathfinder algorithm (PFA) for finding the best food area or prey based on the leadership of collective action in animal groups is a new metaheuristic algorithm for solving optimization problems with ...different structures. PFA is divided into two stages to search: pathfinder stage and follower stage. They represent the exploration phase and mining phase of PFA respectively. However, the original algorithm also has the problem of falling into a local optimum. In order to solve this problem, the teaching phase in the teaching and learning algorithm is added to the pathfinder stage in the text. In order to balance the exploration and mining capabilities of the algorithm, the learning phase of the teaching and learning algorithm is added to the follower phase in the article. In order to further enhance the depth search ability of the algorithm and increase the convergence speed, the exponential step is given to the followers. Therefore, a teaching-learning-based pathfinder algorithm (TLPFA) is proposed. 19 benchmark functions of four different types and six engineering design problems are used to test of the TLPFA exploration and exploiting capabilities. The experimental results show that the proposed TLPFA algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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