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1. Coordinates and Dynamic Pressure of High-Speed Magnetosheath Jets Measured by THEMIS Satellites, 2012-2018

   https://doi.org/10.5281/zenodo.10531586  

   Collins, Kristina (January 2024, Zenodo)

   This dataset adds satellite parameters and dynamic pressure calculations to the event list at https://osf.io/7rjs4/ You can reference this data set as follows: Plaschke, F., Hietala, H., & LaMoury, A. T. (2020, October 27). THEMIS magnetosheath jet data set 2012-2018. Retrieved from osf.io/7rjs4. The description of the identification processes of the THEMIS data sets is published in: Plaschke, F., Hietala, H., and Angelopoulos, V.: Anti-sunward high-speed jets in the subsolar magnetosheath, Ann. Geophys., 31, 1877–1889, https://doi.org/10.5194/angeo-31-1877-2013, 2013. The original THEMIS list should be downloaded locally from the link above before running the python code in the Jupyter notebook. The columns contain:    column 1: jet number    column 2: observing spacecraft (A: THEMIS-A, ..., E: THEMIS-E)    column 3: start of identified jet interval in UT    column 4: time of maximum dynamic pressure ratio in UT    column 5: end of identified jet interval in UT In the final version of the list, the column is indexed by "Max" (col 4 in original), and the other columns are named 'Jet Number', 'Ref Spacecraft', 'Start', and 'End'. From CDAWeb, we add the following columns:     SM_LAT, SM_LON: Latitude and longitude in GSM coordinates    SM_X, SM_Y, SM_Z: Cartesian position in GSM coordinates    GEO_X_1, GEO_Y_1, GEO_Z_1: Cartesian position in geographic coordinates    DIST_FROM_P93_BOW_SHOCK: Distance from the P93 Bow Shock    DIST_FROM_MAGNETOPAUSE: Distance from the RS93 Magnetopause    DIST_FROM_T95_NS: Distance to the Tsyganenko 1995 model Neutral Sheet    L_VALUE: Dipole L value    INVAR_LAT: Dipole Invariant Latitude    MAGNETIC_STRENGTH: Magnetic Field Strength    Dynamic Pressure (nPa): Peak dynamic pressure of jet     Electron density:     Vx, Vy, Vz: Cartesian coordinates of ion velocity. Used in computing dynamic pressure These are pulled from orbit parameters and on-board moment data, using ai.cdas and the second using pyspedas respectively. 

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2. Hydrologic Alteration and Enhanced Microbial Reductive Dissolution of Fe(III) (hydr)oxides Under Flow Conditions in Fe(III)-Rich Rocks: Contribution to Cave-Forming Processes

   https://doi.org/10.3389/fmicb.2021.696534  

   Calapa, Kayla A.; Mulford, Melissa K.; Rieman, Tyler D.; Senko, John M.; Auler, Augusto S.; Parker, Ceth W.; Barton, Hazel A. (July 2021, Frontiers in Microbiology)

   null (Ed.)

   Previous work demonstrated that microbial Fe(III)-reduction contributes to void formation, and potentially cave formation within Fe(III)-rich rocks, such as banded iron formation (BIF), iron ore and canga (a surficial duricrust), based on field observations and static batch cultures. Microbiological Fe(III) reduction is often limited when biogenic Fe(II) passivates further Fe(III) reduction, although subsurface groundwater flow and the export of biogenic Fe(II) could alleviate this passivation process, and thus accelerate cave formation. Given that static batch cultures are unlikely to reflect the dynamics of groundwater flow conditions in situ , we carried out comparative batch and column experiments to extend our understanding of the mass transport of iron and other solutes under flow conditions, and its effect on community structure dynamics and Fe(III)-reduction. A solution with chemistry approximating cave-associated porewater was amended with 5.0 mM lactate as a carbon source and added to columns packed with canga and inoculated with an assemblage of microorganisms associated with the interior of cave walls. Under anaerobic conditions, microbial Fe(III) reduction was enhanced in flow-through column incubations, compared to static batch incubations. During incubation, the microbial community profile in both batch culture and columns shifted from a Proteobacterial dominance to the Firmicutes, including Clostridiaceae, Peptococcaceae, and Veillonellaceae, the latter of which has not previously been shown to reduce Fe(III). The bacterial Fe(III) reduction altered the advective properties of canga-packed columns and enhanced permeability. Our results demonstrate that removing inhibitory Fe(II) via mimicking hydrologic flow of groundwater increases reduction rates and overall Fe-oxide dissolution, which in turn alters the hydrology of the Fe(III)-rich rocks. Our results also suggest that reductive weathering of Fe(III)-rich rocks such as canga, BIF, and iron ores may be more substantial than previously understood. 

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3. Interpreting summertime hourly variation of NO ₂ columns with implications for geostationary satellite applications

   https://doi.org/10.5194/acp-24-12687-2024  

   Chatterjee, Deepangsu; Martin, Randall V; Li, Chi; Zhang, Dandan; Zhu, Haihui; Henze, Daven K; Crawford, James H; Cohen, Ronald C; Lamsal, Lok N; Cede, Alexander M (January 2024, Atmospheric Chemistry and Physics)

   Abstract. Accurate representation of the hourly variation in the NO2-column-to-surface relationship is essential for interpreting geostationary observations of NO2 columns. Previous research indicated inconsistencies in this hourly variation. This study employs the high-performance configuration of the GEOS-Chem model (GCHP) to analyze daytime hourly NO2 total columns and surface concentrations during summer. We use measurements from globally distributed Pandora sun photometers and aircraft observations over the United States. We correct Pandora total NO2 vertical columns for (1) hourly variations in effective temperature driven by vertically resolved contributions to the total column and (2) changes in local solar time along the Pandora line of sight. These corrections increase the total NO2 columns by 5–6 × 1014 molec. cm−2 at 09:00 and 18:00 across all sites. Fine-scale simulations from GHCP (∼12 km) reduce the normalized bias (NB) against Pandora total NO2 columns from 19 % to 10 % and against aircraft measurements from 25 % to 13 % in Maryland, Texas, and Colorado. Similar reductions are observed in NO2 columns over the eastern US (17 % to 9 %), the western US (22 % to 14 %), Europe (24 % to 15 %), and Asia (29 % to 21 %) when compared to 55 km simulations. Our analysis attributes the weaker hourly variability in the total NO2 column to (1) hourly variations in column effective temperature, (2) local solar time changes along the Pandora line of sight, and (3) differences in hourly NO2 variability from different atmospheric layers, with the lowest 500 m exhibiting greater variability, while the dominant residual column above 500 m exhibits weaker variability. 

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4. Relational Memory: Native In-Memory Accesses on Rows and Columns

   Roozkhosh, Shahin; Hoornaert, Denis; Mun, Ju Hyoung; Papon, Tarikul Islam; Sanaullah, Ahmed; Drepper, Ulrich; Mancuso, Renato; Athanassoulis, Manos (March 2023, International Conference on Extending Database Technology (EDBT'23))

   Analytical database systems are typically designed to use a column-first data layout to access only the desired fields. On the other hand, storing data row-first works great for accessing, inserting, or updating entire rows. Transforming rows to columns at runtime is expensive, hence, many analytical systems ingest data in row-first form and transform it in the background to columns to facilitate future analytical queries. How will this design change if we can always efficiently access only the desired set of columns? To address this question, we present a radically new approach to data transformation from rows to columns. We build upon recent advancements in embedded platforms with re-programmable logic to design native in-memory access on rows and columns. Our approach, termed Relational Memory (RM), relies on an FPGA-based accelerator that sits between the CPU and main memory and transparently transforms base data to any group of columns with minimal overhead at runtime. This design allows accessing any group of columns as if it already exists in memory. We implement and deploy RM in real hardware, and we show that we can access the desired columns up to 1.63× faster compared to a row-wise layout, while matching the performance of pure columnar access for low projectivity, and outperforming it by up to 2.23× as projectivity (and tuple reconstruction cost) increases. Overall, RM allows the CPU to access the optimal data layout, radically reducing unnecessary data movement without high data transformation costs, thus, simplifying software complexity and physical design, while accelerating query execution. 

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5. Bulk Glass Reinforced Composite Columns: Physical Testing Results, Analysis, and Discussion

   https://doi.org/10.3390/jcs7060241  

   Cotter, John; Guldiken, Rasim (June 2023, Journal of Composites Science)

   Glass-reinforced composite columns (GRCCs) may provide an economical alternative to conventional construction materials due to the superior cost to strength provided by bulk glass. Prior to this study, no GRCCs had been physically tested, having previously relied on simulation to predict the behavior of the columns. This study utilizes polyurethane resin bonds in place of sizing agents for adherence between materials, a key requirement for the development of the structural system of the columns. The unreinforced control column failed at a load of 11.2 kN while the maximum GRCC load was 30.8 kN. This indicates that glass can be loaded to 123 MPa before the onset of delamination failure of the GRCCs. Maximum shear stress of 53 MPa was reached, exceeding the 11 MPa required for practical GRCCs. Buckling of the columns occurred at 30.8 kN, below the theoretical maximum of 64.4 kN. Through gradual delamination, the column slowly transferred to an unbonded condition, causing buckling failure. Delamination is unlikely to occur in practical GRCCs due to the lower required shear strengths. 

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