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Figs. 112–125. Macrostemum hestia. 112 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 112–125. Macrostemum hestia. 112, right forewing; Male genitalia: 113, 115, lateral; 114, 116, segment X dorsal; 117, phallus lateral; 118, phallus tip. Macrostemum indistinctum. 119, right forewing; Male genitalia: 120, lateral; 121, segment X dorsal; 122. phallus lateral. Macrostemum luteipes. Male genitalia: 123, lateral; 124, segment X dorsal; 125, phallus lateral. Scale: 112, 119 = 2 mm; 113–118, 120–125 = 0.02 mm (123–125 redrawn from Kimmins, 1955).

Figs. 95–111. Macrostemum fenestratum. 95–101 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 95–111. Macrostemum fenestratum. 95–101, right forewing variants; Male genitalia: 102, lateral; 103–104, segment X variants dorsal; 105, phallus lateral; 106, phallus tip. Macrostemum floridum. 107, right forewing; Male genitalia: 108, lateral; 109, segment X dorsal; 110, phallus lateral; 111, phallus tip. Scale: 95–101, 107 = 2 mm; 102–106, 108–111 = 0.02 mm.

Figs. 70–84. Macrostemum dione. 70 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 70–84. Macrostemum dione. 70, right forewing; Male genitalia: 71, lateral; 72, segment X dorsal; 73, phallus lateral; 74, phallus tip. Macrostemum distinguendum. 75, right fore- and hind wing; Male genitalia: 76, lateral; 77, segment X dorsal; 78, phallus lateral; 79, phallus tip. Macrostemum dohrni. 80, right forewing; Male genitalia: 81, lateral; 82, segment X dorsal; 83, phallus lateral; 84, phallus tip. Scale: 70, 75, 80 = 2 mm; 71–74, 76–79, 81–84 = 0.02 mm.

Figs. 85–94. Macrostemum eleanora. 85 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 85–94. Macrostemum eleanora. 85, right forewing; Male genitalia: 86, lateral; 87, segment X dorsal; 88, phallus lateral; 89, phallus tip. Macrostemum fastosum. 90, right fore- and hind wing; Male genitalia: 91, lateral; 92, segment X dorsal; 93, phallus lateral; 94, phallus tip. Scale: 85, 90 = 2 mm; 86–89, 91–94 = 0.02 mm.

Figs. 17–36. Amphipsyche gratiosa. 17 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 17–36. Amphipsyche gratiosa. 17, right forewing; Male genitalia: 18, lateral; 19, segment X dorsal; 20, phallus lateral; 21, phallus tip. Amphipsyche magna. Male genitalia: 22, lateral; 24, phallus lateral; 25, phallus tip; 23, thorax. Amphipsyche meridiana. 26, right forewing; Male genitalia: 27, lateral; 28, segment X dorsal; 29, phallus lateral; 30, phallus tip. Amphipsyche parva. Male genitalia: 31, lateral; 32, phallus lateral; 33, phallus tip. Amphipsyche petiolata. 34, lateral; 35, phallus lateral; 36, phallus tip. Scale: 17, 26 = 2 mm, 18–25, 27–36 = 0.25 mm (22–25, 27–36 redrawn from Barnard, 1984).

Figs. 59–69. Macrostemum caliptera. 59, right forewing. Macrostemum centrotum. 60 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 59–69. Macrostemum caliptera. 59, right forewing. Macrostemum centrotum. 60, right forewing; Male genitalia: 61, lateral; 62, segment X dorsal; 63, phallus lateral; 64, phallus tip. Macrostemum dairiana. 65, right forewing; Male genitalia: 66, lateral; 67, segment X dorsal; 68, phallus lateral; 69, phallus tip. Scale: 59, 60, 65 = 2 mm; 61–64, 66–69 = 0.02 mm (59 redrawn from Banks, 1931c).

Figs. 8–16. Amphipsyche bifasciata. 8 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 8–16. Amphipsyche bifasciata. 8, right forewing; Male genitalia: 9, lateral; 10, phallus lateral; 11, phallus tip. Amphipsyche exsiliens. 12, right fore- and hind wing; Male genitalia: 13, lateral; 14, segment X dorsal; 15, phallus lateral; 16, phallus tip. Scale: 8, 12 = 2 mm; 9–11, 13–16 = 0.25 mm (8–16 redrawn from Barnard, 1984).

Figs. 37–41. Macrostemum albardanum. 37 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 37–41. Macrostemum albardanum. 37, right forewing; Male genitalia: 38, lateral; 39, segment X dorsal; 40, phallus lateral; 41, phallus tip. Scale: 37 = 2 mm; 38–41 = 0.02 mm.

Figs. 1–7. Aethaloptera sexpunctata. 1 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 1–7. Aethaloptera sexpunctata. 1, forewing; 2, hind wing; Male genitalia: 3, lateral; 4, segment X dorsal; 5, phallus lateral; 6, phallus tip; 7, Head dorsal. Scale bars: 1–2 = 2 mm, 3–6 = 0.25 mm, 7 = 1 mm.

Figs. 42–58. Macrostemum bacham. 42 in Review of the filter-feeding caddisfly subfamily Macronematinae (Trichoptera: Hydropsychidae) in tropical Southeast Asia

Figs. 42–58. Macrostemum bacham. 42, right forewing; Male genitalia: 43, lateral; 44, segment X dorsal; 45, phallus lateral; 46, phallus tip. Macrostemum bellerophon. 47, right forewing; Male genitalia: 48, lateral; 49, segment X dorsal; 50, phallus lateral; 51, phallus tip. Macrostemum bellum. 52, right forewing. Macrostemum bifenestratum. 53, right forewing. Macrostemum boettcheri. 54, right forewing; Male genitalia: 55, lateral; 56, segment X dorsal; 57, phallus lateral; 58, phallus tip. Scale: 42, 47, 52–53 = 2 mm; 43–46, 48–51, 55–58 = 0.02 mm (43–46 redrawn from Malicky, 2010; 52 redrawn from Banks, 1916; 56 redrawn from Navás, 1929).

PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTDOC Observations of LCROSS

This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTDOC instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" Remote Sensing 15, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a> This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.

PCA Filtering of Magdalena Ridge Observatory 2.4m PHOTGJON Observations of LCROSS

This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the PHOTGJON instrument on the Magdalena Ridge Observatory 2.4m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" Remote Sensing 15, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a> This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.

Raw Data and PCA Filtering of Apache Point Observatory NMSU 1m StellaCam Observations of LCROSS

This archive contains the raw data and data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the StellaCam instrument on the Apache Point Observatory NMSU 1m telescope. Full details about the raw data are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. J. Geophys. Res. (Planets) 116, E08003 (2011). <a href="https://doi.org/10.1029/2010JE003761">https://doi.org/10.1029/2010JE003761</a> We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" Remote Sensing 15, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a> This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.

PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS

This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" Remote Sensing 15, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a> This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.

PCA Filtering of MMT Observatory 6.5m CCD47 Observations of LCROSS

This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the CCD47 instrument on the MMT Observatory 6.5m telescope. We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal. Full details are available in the published journal article: Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" Remote Sensing 15, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a> This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.

Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations

The published database is related to the calculations presented in the paper "Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations". The main catalogue contains three folders named as GNSS, Campaign, and DInSAR. In the GNSS folder, the time series of XYZ coordinates and uncertainties estimated in the post-processing scenario for RES1, PI02, PI03, PI04, PI05, and PI16 permanent stations are provided. The GNSS calculations were performed at the Wrocław University of Environmental and Life Sciences in the ITRF2014 reference frame. The Campaign folder contains the results of epoch-based GNSS measurements and was used in the article as a verification data source. The Campaign results, prepared by the Military University of Technology, were used in the quality analyses. In order to co-locate the permanent PI02, PI04, PI05, and PI16 receivers with the nearest campaign points, a cross-reference was performed. The epoch-based time series of XYZ coordinates and uncertainties are provided in the ITRF2014 reference frame. The DInSAR interferograms were prepared at the Wrocław University of Environmental and Life Sciences and the results were stored in two directories named as Ascending and Descending. To perform a point-based unification of DInSAR and GNSS techniques, it was necessary to acquire the data from pixels intersected by the GNSS permanent station's locations. The DInSAR time series contain displacements (DSP), incidence angles (INC_ANG), heading angles (HEAD_ANG), and coherence (COH) data.

opencc-by-4.0May 2022View details →

Portable two-filter dual-flow-loop 222Rn detector: stand-alone monitor and calibration transfer device

Little overlap exists in the required capabilities of 222Rn (radon) monitors for public health and atmospheric research. The former requires robust, compact, easily transportable instruments to characterise daily to yearly variability >100 Bq m−3, whereas the latter requires static instruments capable of characterising sub-hourly variability between 0.1 and 100 Bq m−3. Consequently, detector development has evolved independently for the two research communities, and while many radon measurements are being made world-wide, the full potential of this measurement network can't be realised because not all results are comparable. Development of a monitor that satisfies the primary needs of both measurement communities, including a calibration traceable to the International System of Units (SI), would constitute an important step toward (i) increasing the availability of radon measurements to both research communities, and (ii) providing a means to harmonize and compare radon measurements across the existing eclectic global network of radon detectors. To this end, we describe a prototype detector built by the Australian Nuclear Science and Technology Organisation (ANSTO), in collaboration with the EMPIR 19ENV01 traceRadon Project and Physikalisch-Technische Bundesanstalt (PTB). This two-filter dual-flow-loop radon monitor can be transported in a standard vehicle, fits in a 19′′ instrument rack, has a 30 min temporal resolution, and a detection limit of &sim;0.14 Bq m−3. It is capable of continuous, long-term, low-maintenance, low-power, indoor or outdoor monitoring with a high sensitivity and an uncertainty of &sim;15 % at 1 Bq m−3. Furthermore, we demonstrate the successful transfer of an SI traceable calibration from this portable monitor to a 1500 L two-filter radon monitor under field conditions.

Puma concolor occurrence points (filtered data)

Puma concolor occurrence points (duplicates removed) in Canada until December 2021.  Used in Maxent habitat suitability model (performed in R).

opencc-by-4.0Jan 2023View details →

Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"

Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle".   Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022. Three directories can be downloaded: DataOBS :  AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022).  FigPaper : Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282).  MY_SRC_PISCES_NEMO_3.6 : Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)