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FIGURES 145–149 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 145–149. Male calling song at two different speeds (air temperature at recording shown; time scale below): 145—I. bureschi (BG: Pirin Mts, Chalin Valog); 146—I. yaraligozi (TR: Yaraligoz Mt.); 147—I. tosevski (MK: Moklishte Village); 148, 149—I. andreevae (148—BG: Eleshnitsa Locality; 149—BG: Kresna Gorge).
FIGURES 139–144 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 139–144. SEM of taxonomically important structures of Isophya: 139—I. bureschi (BG: Pirin Mts, Chalin Valog); 140—I. yaraligozi (TR: Yaraligoz Mt.); 141—I. tosevski (MK: Dojran Lake); 142, 143—I. andreevae (142—BG: Kresna Gorge; 143—BG: Eleshnitsa Locality); 144—I. clara (BA: Sarajevo). A—male stridulatory file (139—scale 200 μm; othersscale 1 mm); B—ventro-apical view of apex of male cerci (scale 100 μm); C—female stridulatory apparatus (139—scale 200 μm; 141, 144—scale 1 mm; 142—scale 500 μm).
FIGURES 135–138 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 135–138. Male calling song at two (136–138) or three (135) different speeds (air temperature at recording and time scale below each figure): 135—I. hospodar (RO: Gura Dobrogei); 136—I. rectipennis (BG: Karandila Locality); 137—I. pavelii (BG: Chernogorovo Locality); 138—I. thracica (TR: Elmali Village).
FIGURES 105–129 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 105–129. Female abdominal terminalia of the Balkan Isophya and I. yaraligozi (localities as in Figs 31–55 unless specifically given); figures of the whole ovipositor are followed by enlarged figure of the ovipositor base: 105—I. hospodar (BG: Dolni Glavanak, 25.04.2005, CC); 106—I. rectipennis (BG: Byala Vill., 27.06.2002, CC); 107—I. pavelii (BG: Chernogorovo, 29.05.2006, CC); 108—I. thracica; 109—I. bureschi (BG: Bansko, 8.08.2006, CC); 110—I. yaraligozi; 111—I. tosevski; 112—I. andreevae (BG: Kresnensko Hanche, 19.04.2006, CC); 113—I. clara; 114—I. miksici (BG: Gorski Dom Lodge, 24.06.2006, CC); 115—I. plevnensis (BG: Levishte, 14.07.2009, CC); 116—I. longicaudata adamovici (BG: Karandila, 24.06.2008, CC); 117—I. l. longicaudata (BG: Sofia University Botanical garden, 26.06.–1.07.2004, CC); 118—I. m. modesta (=intermedia); 119—I. rhodopensis leonorae (BG: Livade, 9.08.2006, CC); 120—I. rh. rhodopensis (BG: Bachkovo, 600 m, 23.05.2004, CC); 121—I. rh. petkovi (BG: Gluhite Kamani, 23.06.2008, CC); 122—I. modestior (BG: Bankya, 24.05.2006, CC); 123—I. dobrogensis; 124—I. zubowskii; 125—I. aff. camptoxypha (data as in Fig. 75); 126— I. gulae; 127—I. obtusa (BG: Ravnets Ridge, 14.07.2009, CC); 128—I. amplipennis (as in Fig. 78); 129—I. speciosa (BG: Bankya, 24.05.2006, CC). Indications on Fig. 127 show, respectively: G—gonangulum; L—lamella; P—lateral pit between lamella and gonangulum. Scale (if present) = 5 mm.
FIGURES 130–134 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 130–134. SEM of taxonomically important structures of Isophya: 130, 131—I. hospodar (130—BG: Dolni Glavanak; 131—BG: Sofia Kettle); 132—I. rectipennis (132A, C, BG: Karandila Locality; 132B, TR: "TURKEY: Bolu. | Ala Dagi. 2000 m. | Kartal Kaya Tepe. | 15.VII.1962 | Guichard & Harvey. | B.M.1962–299."); 133—I. pavelii (BG: Chernogorovo Locality); 134—I. thracica (TR: Elmali Village). A—male stridulatory file (scale 200 μm); B—apical part of male stridulatory file (scale 20 μm); C—apex of male cerci (scale 100 μm) (130, 132–134—ventro-apical view; 131—dorsal view); D—female stridulatory apparatus (scale 100 μm).
FIGURES 56–79 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 56–79. Morphology of female head, pronotum and tegmina of the Balkan Isophya and I. yaraligozi (localities as in Figs 31–55 unless specifically given): 56—I. hospodar; 57—I. rectipennis; 58—I. pavelii (paratype of I. rammei Peshev); 59— I. thracica; 60—I. bureschi; 61—I. yaraligozi; 62—I. tosevski; 63—I. andreevae (paratype); 64—I. clara; 65—I. miksici (paratype); 66—I. plevnensis (paratype); 67—I. longicaudata adamovici (paratype, BG: Sliven, 1000 m, NMNHS); 68—I. l. longicaudata; 69—I. rhodopensis leonorae (BG: "Alibotush, 1500 m", 13.07.1959, NMNHS); 70—I. rh. rhodopensis (as in Fig. 46 but 17.07.1956); 71—I. rh. petkovi; 72—I. modestior; 73—I. dobrogensis; 74—I. zubowskii; 75—I. aff. camptoxypha (MK: Bistra Mt., 2.08.2004, CC); 76—I. gulae (paratype, BG: Dolna Topchiya, 10.07.1974, NMNHS); 77—I. obtusa (BG: Vezhen Peak, 15.07.1969, NMNHS); 78—I. amplipennis (TR: "Turkey | 1960 | Sureya Bey", NHM); 79—I. speciosa (BG: Sakar Mt., 14.06.1952, NMNHS). Scale (if present; black line right of the specimen) = 10 mm.
FIGURES 19–30 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 19–30. General appearance of some Balkan Isophya; 19—I. modestior (male); 20, 21—I. dobrogensis (20—male, 21—female); 22, 23—I. zubowskii (22—male, 23—female); 24, 25—I. camptoxypha (24—male, 25—female); 26, 27—I. gulae (26—male, 27—female); 28, 29—I. obtusa (dark—28, and green—29 colour forms; males); 30—I. speciosa (malebelow, and female—above).
FIGURES 80–104 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 80–104. Male abdominal terminalia of the Balkan Isophya and I. yaraligozi (localities as in Figs 31–55): 80—I. hospodar; 81—I. rectipennis; 82—I. pavelii; 83—I. thracica; 84—I. bureschi; 85—I. yaraligozi; 86— I. tosevski; 87—I. andreevae; 88—I. clara; 89—I. miksici; 90—I. plevnensis; 91—I. longicaudata adamovici; 92—I. l. longicaudata; 93—I. m. modesta (=intermedia); 94—I. rhodopensis leonorae; 95—I. rh. rhodopensis; 96—I. rh. petkovi; 97—I. modestior; 98—I. dobrogensis; 99—I. zubowskii; 100—I. aff. camptoxypha; 101—I. gulae; 102—I. obtusa; 103—I. amplipennis; 104—I. speciosa. Scale (if present) = 2 mm.
FIGURES 1–18 in Review of the Balkan Isophya (Orthoptera: Phaneropteridae) with particular emphasis on the Isophya modesta group and remarks on the systematics of the genus based on morphological and acoustic data
FIGURES 1–18. General appearance of some Balkan Isophya. 1, 2—I. hospodar (1—male, 2—female); 3— I. thracica (male); 4, 5—I. rectipennis (4—male, 5—female); 6, 7—I. pavelii (6—male, 7—female); 8, 9—I. bureschi (8—male, 9— female); 10, 11—I. andreevae (10—male, 11—female); 12, 13—I. plevnensis (12—male, 13—female); 14, 15—I. m. modesta (=intermedia) (14—male, 15—female); 16— I. l. longicaudata (male); 17—I. rhodopensis leonorae (male); 18—I. rh. petkovi (male) (Figures 6–11, 17, 18—photo M. Langourov).
Research Data for FiHi: Fusion of inertial and high-resolution acoustic data for privacy-preserving human activity recognition
<h1><strong>Description</strong></h1> <div>This dataset contains information on 20 different activities collected from 15 participants (20-55 years old) using Wit-motion smart inertial sensors and Double Acoustics guitar pickups. Each participant performs these daily activities in an unrestricted environment, with each activity lasting at least 60 seconds and repeated twice.</div> <div> </div> <div>If you use the dataset in an academic work, please cite: </div> <div> </div> <div><code>@ARTICLE{10980212,</code><br><code> author={Yang, Zhe and Zhang, Ying and Li, Yanjun and Huang, Linchong and Hu, Ping and Lin, Yuexiang},</code><br><code> journal={IEEE Transactions on Instrumentation and Measurement}, </code><br><code> title={Fusion of Inertial and High-resolution Acoustic Data for Privacy-Preserving Human Activity Recognition}, </code><br><code> year={2025},</code><br><code> volume={74},</code><br><code> number={},</code><br><code> pages={1-20},</code><br><code> keywords={Human activity recognition;Sensors;Acoustics;Feature extraction;Privacy;Microphones;Biomedical monitoring;Wireless fidelity;Sensor phenomena and characterization;Sensor fusion;Human activities recognition;inertial sensing;Hi-res audio;attention mechanism},</code><br><code> doi={10.1109/TIM.2025.3565250}}</code></div> <h1><strong>DataSet Information</strong></h1> <h2><strong>1.Original_data.zip</strong></h2> <div>The data was annotated by manually reviewing the audio clips and assigning appropriate activity labels. Timestamping the inertial sensor data with the start time of the audio device recorded by the experimenter ensured correct segmentation and synchronization of the inertial and acoustic data. The total data length for</div> <div>all participants is over 10 hours.</div> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The inertial data (accelerometer and gyroscope) is sampled at 100 Hz and transmitted by Bluetooth to the host computer. These reviewed and annotated original samples from 15 participants are placed in separate csv files. The arrangement of information in each csv file is:</div> <div>Col 1-3: 3D-acceleration data (g)</div> <div>Col 4-6: 3D-gyroscope data (°/s)</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic data are sampled at 192 kHz by a Steinberg sound card and transmitted by USB cable to the host computer. These original samples are placed in separate wav files, with each file name containing all the necessary information regarding the contents of the file.</div> <div><strong>For example:</strong></div> <div>100801_sitting</div> <div>Participant ID (1-4 digits): 1008, Session ID (5-6 digits): 01, Activity ID: sitting.</div> <div> </div> <h2><strong>2、Processed_data.zip</strong></h2> <div>The last two columns of each file are as follows:</div> <ul> <li> <div>participant_id: such as 100101, 100102, 100201 ...... The last two digits are the Session ID, representing the two sessions from the same participant for the same activity.</div> </li> <li> <div>activity_id: Refer to the ACTIVITY SET below</div> </li> </ul> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The original inertial data is individually aligned with the processed Audio data based on their start times, with any excess data rows at the end being trimmed. Then, these inertial data files are augmented with activity_id and participant_id for identification, and consolidated into a single csv file.</div> <div>The continuous motion signal is segmented into sliding windows, each with a duration of 3 seconds and a step size of 3 seconds. Given the IMU’s sampling rate of 100 Hz, each window of inertial data consists of 300 time steps, with 6 channels of information (3 axes each for accelerometer and gyroscope). Consequently, a single inertial sample is represented by a 300 × 6 dimensional matrix.</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic signals are processed using the Short Time Fourier Transform (STFT) with a window length of 1024 points and an overlap of 256 points, , which generates n linear spaced frequency bins between n kHz frequency range in the frequency domain. The output contains the estimate of the short-term, time-localized frequency patterns. We examine two levels of privacy protection: 8 ∼ 96 kHz for non-speech sound and 20 ∼ 96 kHz for inaudible sound, with 88 and 76 linear spaced frequency bins, respectively.</div> <div>Under a sample rate of 192 kHz for the original acoustic signal, there are (192000 - 256)/(1024 - 256) ≈ 250 frequency features within a second, while each feature has 88 and 76 dimensions for non-speech (8∼96 kHz) and inaudible (20∼96 kHz) feature, respectively.</div> <h1><strong>ACTIVITY </strong><strong>SET</strong></h1> <div>The activityIDs and corresponding activities are listed in the following:</div> <div>0: use microwave</div> <div>1: brush teeth</div> <div>2: browse video</div> <div>3: drink water</div> <div>4: fry</div> <div>5: lie down</div> <div>6: flush</div> <div>7: go downstairs</div> <div>8: go upstairs</div> <div>9: sit</div> <div>10: stand</div> <div>11: manipulate door</div> <div>12: type</div> <div>13: jump</div> <div>14: run</div> <div>15: walk</div> <div>16: wash hands</div> <div>17: write</div> <div>18: operate light</div> <div>19: eat</div>
Figure 9. A–D in A new genus of African Acrometopini (Tettigoniidae: Phaneropterinae) based on morphology, chromosomes, acoustics, distribution, and molecular data, and the description of a new species
Figure 9. A–D, left cercus of male Altihoratosphaga species. A, Altihoratosphaga hanangensis sp. nov. B, Altihoratosphaga nou. C, Altihoratosphaga montivaga. D, Altihoratosphaga nomima. All specimens were collected from Ruaha National Park. Scale bar: 2 mm. E–H, subgenital plates of male Altihoratosphaga species. E, A. hanangensis sp. nov. F, A. nou. G, A. montivaga. H, A. nomima. All specimens were collected from Ruaha National Park. Scale bar: 2 mm. I, right tegmen of male A. hanangensis sp. nov. Scale bar: 0.5 cm. J, subgenital plate of female A. hanangensis sp. nov. Scale bar: 1 mm. K, ovipositor of A. hanangensis sp. nov. Scale bar: 1 mm.
Figure 8. A–D in A new genus of African Acrometopini (Tettigoniidae: Phaneropterinae) based on morphology, chromosomes, acoustics, distribution, and molecular data, and the description of a new species
Figure 8. A–D, lateral view of the pronotum and dorsal view of the abdominal apex of male Altihoratosphaga species. A, Altihoratosphaga hanangensis sp. nov., paratype. B, Altihoratosphaga nou, paratype. C, Altihoratosphaga montivaga. D, Altihoratosphaga nomima, holotype. E–H, dorsal view of abdominal apex of male Altihoratosphaga species. E, A. hanangensis sp. nov., paratype. F, A. nou, paratype. G, A. montivaga. H, A. nomima, holotype I, dorsal aspect of male pronotum of A. hanangensis sp. nov. Note the triangle-shaped posterior part of the pronotum. J, lateral view of abdominal apex of female A. hanangensis sp. nov., paratype. K, reduced alae of female A. montivaga. Scale bars: 2 mm.
Figure 6 in A new genus of African Acrometopini (Tettigoniidae: Phaneropterinae) based on morphology, chromosomes, acoustics, distribution, and molecular data, and the description of a new species
Figure 6. Bayesian inference (BI) of the phylogeny of the species included in the study, based on a fragment of the cytochrome oxidase subunit I (COI) gene. Priors were set to match a GTR + I + G model. The neighbour-joining (NJ) and maximum-parsimony (MP) analyses both resulted in the same tree topology shown here. Monticolaria was used as the out-group in all analyses. Support values indicated on the tree are from (upper to lower) the BI, NJ, and MP analyses, respectively. A total of 1000 bootstrap replicates were conducted in the NJ and MP analyses.
Figure 4 in A new genus of African Acrometopini (Tettigoniidae: Phaneropterinae) based on morphology, chromosomes, acoustics, distribution, and molecular data, and the description of a new species
Figure 4. Oscillograms of the calling song of Altihoratosphaga species, showing one element A and one element B from each species. See Figure 3 for the position of this song detail.
Figure 2 in A new genus of African Acrometopini (Tettigoniidae: Phaneropterinae) based on morphology, chromosomes, acoustics, distribution, and molecular data, and the description of a new species
Figure 2. Stridulatory files of Altihoratosphaga species (wing articulation to the right). A, Altihoratosphaga hanangensis sp. nov. (paratype). B, Altihoratosphaga nou (CH7091). C, Altihoratosphaga montivaga (CH6890). D, Altihoratosphaga nomima (Ruaha National Park). Scale bar: 1 mm.
Figure 1. A–C, C in A new genus of African Acrometopini (Tettigoniidae: Phaneropterinae) based on morphology, chromosomes, acoustics, distribution, and molecular data, and the description of a new species
Figure 1. A–C, C-banded cells of males. A, Altihoratosphaga montivaga – mitotic metaphase, arrows indicate interstitial polymorphism in the quantity of C-bands in pairs L3 and M6/7; B, B chromosome; X, X chromosome. B, Horatosphaga parensis – diakinesis with heteromorphic C-band in the L3 bivalent; X, X chromosome. C, Monticolaria kilimandjarica – diakinesis with telomeric C-bands (arrows); B, B chromosome; X, X chromosome.
Figure 7. A in A new genus of African Acrometopini (Tettigoniidae: Phaneropterinae) based on morphology, chromosomes, acoustics, distribution, and molecular data, and the description of a new species
Figure 7. A, male Altihoratosphaga hanangensis sp. nov. B, male Altihoratosphaga nou. C, male Altihoratosphaga montivaga. D, habitat of A. hanangensis sp. nov. on Mt Hanang, Tanzania. E, female A. nou. F, female A. montivaga.
Raw data for Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests
<p>This dataset, utilized in the research paper "<a href="https://doi.org/10.12688/f1000research.141951.1">Evaluating community-wide temporal sampling in passive acoustic monitoring: A comprehensive study of avian vocal patterns in subtropical montane forests</a>", comprises columns such as site_name, longitude (WGS84), latitude (WGS84), altitude (meters above sea level), vegetation types, date, hour, minute, julian_day, scientific_name, and Vocal Activity Rate per minute (VAR_m). It encompasses data gathered from twelve Passive Acoustic Monitoring (PAM) stations positioned within Yushan National Park (YSNP), Taiwan. The collection period spanned from March 1 to June 30, 2021. The dataset documents 8,202,731 vocalizations from twelve bird species, detected using an automated sound identification tool named SILIC (Sound Identification and Labeling Intelligence for Creatures). The vocalization data is aggregated by site, species, and time (down to the minute).</p>
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
Data from: Acoustic and electrical properties of Fe-Ti oxides with application to the deep lunar mantle
<p>The overturn of titanium-rich mantle cumulates has been invoked to explain the structure and dynamics of the Moon. These dense cumulates are stable at the core-mantle boundary (CMB) and could explain field anomalies inferred from geophysical studies. We report the first acoustic and electrical experiments on natural ilmenite-rutile aggregates up to 4.5 GPa and 1920 K. Seismic velocities show a weak pressure and temperature dependence, with Vs ~4.2 (+/-0.2) km/s and Vp ~ 8.0 (+/-0.2) km/s at the CMB conditions. Conductivity increases by a factor > 10<sup>4</sup> over 373-1920 K and is >10<sup>3</sup> S/m above 1573 K. Seismic and electrical mixing models of Fe-Ti oxides - olivine rocks based on our results indicate that field velocity and conductivity estimates are reproduced satisfactorily with 3-16% Fe-Ti oxides and 20% melt. Interactions between this Ti-rich and melt-bearing layer and the adjacent core likely affect the cooling and magnetic history of the Moon.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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