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opencc-by-4.0Apr 2023View details →

Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion

Dataset IntroductionThe advent of neural networks capable of learning salient features from variance in the radar data has expanded the breadth of radar applications, often as an alternative sensor or a complementary modality to camera vision. Gesture recognition for command control is arguably the most commonly explored application. Nevertheless, more suitable benchmarking datasets than currently available are needed to assess and compare the merits of the different proposed solutions and explore a broader range of scenarios than simple hand-gesturing a few centimeters away from a radar transmitter/receiver. Most current publicly available radar datasets used in gesture recognition provide limited diversity, do not provide access to raw ADC data, and are not significantly challenging. To address these shortcomings, we created and make available a new dataset that combines FMCW radar and dynamic vision camera of 10 aircraft marshalling signals (whole body) at several distances and angles from the sensors, recorded from 13 people. The two modalities are hardware synchronized using the radar's PRI signal. Moreover, in the supporting publication we propose a sparse encoding of the time domain (ADC) signals that achieve a dramatic data rate reduction (>76%) while retaining the efficacy of the downstream FFT processing (<2% accuracy loss on recognition tasks), and can be used to create an sparse event-based representation of the radar data. In this way the dataset can be used as a two-modality neuromorphic dataset.Synchronization of the two modalitiesThe PRI pulses from the radar have been hard-wired to the event stream of the DVS sensor, and timestamped using the DVS clock. Based on this signal the DVS event stream has been segmented such that groups of events (time-bins) of the DVS are mapped with individual radar pulses (chirps).Data storageDVS events (x,y coords and timestamps) are stored in structured arrays, and one such structured array object is associated with the data of a radar transmission (pulse/chirp). A radar transmission is a vector of 512 ADC levels that correspond to sampling points of chirping signal (FMCW radar) that lasts about ~1.3ms. Every 192 radar transmissions are stacked in a matrix called a radar frame (each transmission is a row in that matrix). A data capture (recording) consisting of some thousands of continuous radar transmissions is therefore segmented in a number of radar frames. Finally radar frames and the corresponding DVS structured arrays are stored in separate containers in a custom-made multi-container file format (extension .rad). We provide a (rad file) parser for extracting the data out of these files. There is one file per capture of continuous gesture recording of about 10s.Note the number of 192 transmissions per radar frame is an ad-hoc segmentation that suits the purpose of obtaining sufficient signal resolution in a 2D FFT typical in radar signal processing, for the range resolution of the specific radar. It also served the purpose of fast streaming storing of the data during capture. For extracting individual data points for the dataset however, one can pool together (concat) all the radar frames from a single capture file and re-segment them according to liking. The data loader that we provide offers this, with a default of re-segmenting every 769 transmissions (about 1s of gesturing).Data captures directory organization (<a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a>)The dataset captures (recordings) are organized in a common directory structure which encompasses additional metadata information about the captures.dataset_dir/<stage>/<room>/<person>-<gesture>-<distance>/ofxRadar8Ghz_yyyy-mm-dd_HH-MM-SS.radIdentifiers<ul><li>stage [train, test].</li><li>room: [conference_room, foyer, open_space].</li><li>subject: [0-9]. Note that 0 stands for no person, and 1 for an unlabeled, random person (only present in test).</li><li>gesture: ['none', 'emergency_stop', 'move_ahead', 'move_back_v1', 'move_back_v2', 'slow_down' 'start_engines', 'stop_engines', 'straight_ahead', 'turn_left', 'turn_right'].</li><li>distance: ['xxx', '100', '150', '200', '250', '300', '350', '400', '450'] (in cm). Note that xxx is used for none gestures when there is no person present in front of the radar (i.e. background samples), or when a person is walking in front of the radar with varying distances but performing no gesture.</li></ul>The test data captures contain both subjects that appear in the train data as well as previously unseen subjects. Similarly the test data contain captures from the spaces that train data were recorded at, as well as from a new unseen open space.Files List<a href="https://zenodo.org/api/records/10359770/draft/files/radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z/content">radar8Ghz-DVS-marshaling_signals_20220901_publication_anonymized.7z</a>This is the actual archive bundle with the data captures (recordings).<a href="https://zenodo.org/api/records/10359770/draft/files/rad_file_parser_2.py/content">rad_file_parser_2.py</a>Parser for individual .rad files, which contain capture data.<a href="https://zenodo.org/api/records/10359770/draft/files/loader.py/content">loader.py</a>A convenience PyTorch Dataset loader (partly Tonic compatible). You practically only need this to quick-start if you don't want to delve too much into code reading. When you init a DvsRadarAircraftMarshallingSignals class object it automatically downloads the dataset archive and the .rad file parser, unpacks the archive, and imports the .rad parser to load the data. One can then request from it a training set, a validation set and a test set as torch.Datasets to work with.  <a href="https://zenodo.org/api/records/10359770/draft/files/aircraft_marshalling_signals_howto.ipynb/content">aircraft_marshalling_signals_howto.ipynb</a>Jupyter notebook for exemplary basic use of loader.pyContactFor further information or questions try contacting first M. Sifalakis or F. Corradi.

opencc-by-4.0Jun 2024View details →

Marshall Arthur Wier (w3264)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Marshall Arthur Wier musiXplora-ID: w3264 musiXplora-URI: <a href="https://musixplora.de/mxp/w3264">https://musixplora.de/mxp/w3264</a> Gender: m First Mentioned: 11 March 1879 Last Mentioned: 20 September 1895 Sectors: Instrumentenbau Professions (Historical): Patentinhaber Professions (Non-Musical): Ingenieur Other Places of Activity: Kingston, London Nachweis: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Erwähnungen</td><td>Erwähnung</td><td>Brummkreisel als Vorläufer der Plattenspieldose</td><td><a href="https://musixplora.de/mxp/5020579">5020579</a></td></tr></tbody></table> Patentrecht: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>ErfinderInnen</td><td>Erfinder</td><td>Patentschrift. Neuerungen an Kreiseln mit Vorrichtungen zur Hervorbringung von Tönen. Anmeldedatum. 1879 Mar 11</td><td><a href="https://musixplora.de/mxp/5081101">5081101</a></td></tr><tr><td>ErfinderInnen</td><td>Erfinder</td><td>Patentschrift. Neuerungen an Drehkreiseln. Anmeldedatum. 1881 Dez 31</td><td><a href="https://musixplora.de/mxp/5081102">5081102</a></td></tr><tr><td>ErfinderInnen</td><td>Erfinder</td><td>Patentschrift. Musik- bzw. Farbkreisel. Anmeldedatum. 1895 Sep 20</td><td><a href="https://musixplora.de/mxp/5081103">5081103</a></td></tr><tr><td>ErfinderInnen</td><td>Erfinder</td><td>Patentschrift. Improvements in Spinning Tops. Anmeldedatum</td><td><a href="https://musixplora.de/mxp/5081119">5081119</a></td></tr><tr><td>ErfinderInnen</td><td>Erfinder</td><td>Patentschrift. Musical Top or Gyrophone. Anmeldedatum</td><td><a href="https://musixplora.de/mxp/5081120">5081120</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-zeroAug 2024View details →

National Checklists 2017: Marshall Islands Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Marshall Islands collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

National Checklists 2019: Marshall Islands Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from Marshall Islands collected using effechecka and geonames polygons

opencc-by-4.0Feb 2024View details →

Fig. 13. A–C. Calliotropis eucheloides Marshall, 1979. A in The Vetigastropoda (Mollusca) of Walters Shoal, with descriptions of two new genera and thirty new species

Fig. 13. A–C. Calliotropis eucheloides Marshall, 1979. A. Apertural view showing duplex tooth at base of columella, Walters Shoal, stn DW4904, diameter 12.0 mm (MNHN-IM-2013-67241). B–C. Apical and basal views, Walters Shoal, stn DW4892, diameter 10.6 mm (MNHN). D–F. Calliotropis velata Vilvens, 2006, Walters Shoal, stn CP4910, diameter 15.9 mm (MNHN-IM-2013-67242). G. Calliotropis eucheloides, living animal, shell diameter 12.2 mm (MNHN-IM-2013-67240), image courtesy of Alain Barrère/MNHN. H–J. Spinicalliotropis lepidota sp. nov., holotype, height 2.9 mm (MNHN- IM-2000-36293).

Figures 9–17 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figures 9–17: Comparative adult specimens of Cerurina marshalli (all ANHRT, unless otherwise indicated), arrows indicate diagnostic generic characters. 9. Syntype ♀, Zimbabwe, Mashonaland (NHMUK), a. defined antemedial fascia, b. almost completely deleted subterminal fascia; 10. Zambia, Mutinondo (ANHRTUK00152995, LG5366♀); 11. Zambia, Mutinondo (ANHRTUK00152996, LG5357♀); 12. Zambia, Lukwakwa (ANHRTUK00061207, ANHRT Gen. slide no. 00519♀); 13. Zambia, Lukwakwa (ANHRTUK00061232, LG5358♂); 14. Zambia, Kitwe (ANHRTUK00050387, ♂); 15. Ivory Coast, Mt. Tonkoui (ANHRTUK00043082, ANHRT Gen. slide no. 00315♂); 16. Tanzania, Livingstone Mts. (ANHRTUK00081591, ANHRT Gen. slide no. 00520♂); 17. D. R. Congo, Nord Kivu (ANHRTUK00158203, LG5367♂).

Figures 39‒42 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figures 39‒42 ‒ Male genitalia of Afrocerura spp. (all ANHRT), arrow indicates a diagnostic generic character. 39. A. cameroona (Bethune-Baker, 1927), Gabon, Ivindo N.P. (ANHRTUK00044930, ANHRT slide no. 00521♂); 40. A. cameroona, Zambia, Kafue N.P. (ANHRTUK00081592, ANHRT slide no. 00522♂); 41. A. cameroona, Zambia, Hillwood, Ikelenge (ANHRTUK00073571, LG5359♂); 42. A. thomensis (Talbot, 1929), São Tomé, Bom Successo (ANHRTUK00041722, LG5362♂), a. dorsal crest on the uncus.

Figures 32‒35 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figures 32‒35: Male genitalia of Cerurina marshalli (Hampson, 1910) (all ANHRT), arrows indicate diagnostic generic characters. 32. Zambia, Lukwakwa (ANHRTUK00061232, LG5358♂), a. bifid uncus tip and lateral denticulations, b. socii well developed with 2‒3 denticulations, c. valvae club-like and apically rounded; 33. Ivory Coast, Mt. Tonkoui, (ANHRTUK00043082, ANHRT Gen. slide no. 00315♂); 34. Tanzania, Livingstone Mts. (ANHRTUK00081591, ANHRT Gen. slide no. 00520♂); 35. D.R. Congo, Nord Kivu (ANHRTUK00158203, LG5367♂).

Figures 36‒38 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figures 36‒38 ‒ Male genitalia of Afrocerura spp. (all ANHRT), arrows indicate diagnostic generic characters. 36. A. leonensis (Hampson, 1910), Guinea, Dalaba (ANHRTUK00103029, LG5368♂), a. narrow uncus tip without denticulate margins, b. relatively short, slender, slightly arched socii without denticulations; 37. A. bifasciata bifasciata (Janse, 1920), Zambia, Kasanka N.P. (ANHRTUK00073572, LG5363♂), c. narrow valvae; 38. A. bifasciata bifasciata, Zambia, Kankonde Camp, Mutinondo Stream (ANHRTUK00073573, LG5364♂).

Figure 48 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figure 48: Geographical proximity of the type locality of Afrocerura cameroona (HT♀), the type locality of Cerura argentina Schultze, 1916 (ST♀♂) and the collecting locality of the ANHRT Gabon specimen (ANHRTUK00044930, ♂), all believed here to be conspecific with A. cameroona.

Figure 47 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figure 47: Locality map for material examined of Cerurina and Afrocerura spp., showing areas where species are known to occur sympatrically. C. marshalli (ST♀); A. leonensis (HT♀); A. bifasciata bifasciata (HT♀); A. bifasciata tanganyikae (HT♂); A. cameroona (HT♀); Cerura argentina Schultze, 1916 (ST♀); A. thomensis (HT♂).

Figures 18–31 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figures 18–31: Adults of examined Afrocerura spp. (all ANHRT, unless otherwise indicated), arrows indicate diagnostic generic characters. 18. A. leonensis, holotype ♀, Sierra Leone (NHMUK), a. antemedial fascia often interrupted or deleted entirely, b. subterminal fascia almost always present; 19. A. leonensis, Guinea, Dalaba (ANHRTUK00103029, LG5368♂); 20. A. bifasciata bifasciata, holotype ♀, Zimbabwe, (Type no. 1655) (TMSA); 21. A. bifasciata bifasciata, Zambia, Kasanka N.P. (ANHRTUK00073572, LG5363♂); 22. A. bifasciata tanganyikae, holotype ♂ [without holotype label] Kenya, Mombassa (NHMUK); 23. A. thomensis, holotype ♂, São Tomé (NHMUK); 24. A. thomensis, paratype (allotype) ♀, São Tomé (NHMUK); 25. A. thomensis, São Tomé, Bom Successo (ANHRTUK00041722, LG5362♂); Comparative specimens of A. cameroona: 26. A. cameroona, Gabon, Ivindo N.P. (ANHRTUK00044930, ANHRT slide no. 00521♂); 27. A. cameroona, holotype ♀, Cameroon, Bitye, (NHMUK); 28. A. cameroona, Zambia, Kitwe (ANHRTUK00081593, ANHRT slide no. 00523♀); 29. A. cameroona, Zambia, Kafue N.P. (ANHRTUK00081592, ANHRT slide no. 00522♂); 30. ♀(ZMHB) & 31. ♂ (ZMHB) A. cameroona, (=Cerura argentina Schultze, 1916 syntypes), Süd-Kamerun, Molundu.

Figures 43‒46 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figures 43‒46 ‒ Female genitalia of Cerurina and Afrocerura spp. (all ANHRT). 43. C. marshalli (Hampson, 1910), Zambia, Mutinondo (ANHRTUK00152996, LG5357♀); 44. C. marshalli, Zambia, Mutinondo (ANHRTUK00152995, LG5366♀); 45. C. marshalli, Zambia, Lukwakwa (ANHRTUK00061207, ANHRT Gen. slide no. 00519♀); 46. A. bifasciata bifasciata (Janse, 1920), Zambia, Kitwe (ANHRTUK00042917, ANHRT Gen. slide no. 00316♀).

Figure 1 in Notes on the life history and taxonomy of Cerurina marshalli (Noctuoidea: Notodontidae: Cerurinae)

Figure 1 – Habitat at Mutinondo wilderness area in Zambia showing vast Miombo woodland and granite inselberg habitats.

opencc-zeroAug 2024View details →

National Checklists: Marshall Islands Species List

Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>

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Ecoregion and community structure influences on the foliar elemental niche of balsam fir (Abies balsamea (L.) Mill.) and white birch (Betula papyrifera Marshall)

Context: Changes in foliar elemental niche properties, defined by axes of carbon (C), nitrogen (N), and phosphorus (P) concentrations, reflect how species allocate resources under different environmental conditions. For instance, elemental niches may differ in response to large-scale latitudinal temperature and precipitation regimes that occur between ecoregions and small-scale differences in nutrient dynamics based on species co-occurrences at a community level. Methods: at a species level, we compared foliar elemental niche hypervolumes for balsam fir (Abies balsamea (L.) Mill.) and white birch (Betula papyrifera Marshall) between a northern and southern ecoregion. At a community level, we grouped our focal species using plot data into conspecific (i.e., only one focal species is present) and heterospecific groups (i.e., both focal species are present) and compared their foliar elemental concentrations under these community conditions across, within, and between these ecoregions. Between ecoregions at the species and community level, we expected niche hypervolumes to be different and driven by regional biophysical effects on foliar N and P concentrations. At the community level, we expected niche hypervolume displacement and expansion patterns for fir and birch, respectively – patterns that reflect their resource strategy. Results: at the species level, foliar elemental niche hypervolumes between ecoregions differed significantly for fir (F = 14.591, p-value = 0.001) and birch (F = 75.998, p-value = 0.001) with higher foliar N and P in the northern ecoregion. At the community level, across ecoregions, the foliar elemental niche hypervolume of birch differed significantly between heterospecific and conspecific groups (F = 4.075, p-value = 0.021) but not for fir. However, both species displayed niche expansion patterns, indicated by niche hypervolume increases of 35.49% for fir and 68.92% for birch. Within the northern ecoregion, heterospecific conditions elicited niche expansion responses, indicated by niche hypervolume increases for fir of 29.04% and birch of 66.48%. In the southern ecoregion we observed a contraction response for birch (niche hypervolume decreased by 3.66%), and no changes for fir niche hypervolume. Conspecific niche hypervolume comparisons between ecoregions yielded significant differences for fir and birch (F = 7.581, p-value = 0.005 and F = 8.038, p-value = 0.001) as did heterospecific comparisons (F = 6.943, p-value = 0.004, and F = 68.702, p-value = 0.001, respectively). Conclusions: our results suggest species may exhibit biogeographical specific elemental niches – driven by biophysical differences such as those used to describe ecoregion characteristics. We also demonstrate how a species resource strategy may inform niche shift patterns in response to different community settings. Our study highlights how biogeographical differences may influence foliar elemental traits and how this may link to concepts of ecosystem and landscape functionality.

opencc-zeroAug 2022View details →