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378 results for “RF”
Spatially Resolved Infrared Radiofluorescence (SR IR-RF) Image Data
<p>This dataset contains measurement sequences and data output <br> of spatially resolved infrared radiofluorescence (SR IR-RF) measurements<br> on K-feldspar samples carried out at the IRAMAT-CRP2A, UMR 5060, CNRS-Université Bordeaux Montaigne (France)<br> in 2019. The data analysis was performed in 2020. </p> <p>The data may serve as reference data and allow detailed inspection by others to <br> verify or advance the used analysis procedures. </p> <p>Along with the raw image data (TIF-files), the datasets also contain documented R scripts used for data processing and partly treated data as an example. To reproduce the full data analysis, additional software is needed; not part of this repository. </p> <p>Further details can be found in the README.md (README.html), which is part of the dataset.</p>
Madrid Grid Area Buildings + Reachable Endpoints for Given RF Transmitter Location and Parameters, with and without RISs Installation.
<p>1- The obstacles_save folder contains arrays defining the vertices locations (x,y) of buildings in the considered area in Madrid Grid.</p> <p>A transmitter is placed at the center of a square at location [600, 900]. Possible receiver (or relay trasnceivers) locations are defined as the vertices (i.e., corners) of buildings (from previous list). The goal of the simulation is to find how many hops are needed to reach, if possible, each location from the previously mentioned list of vertices, assuming a maximum allowed path loss value of 90 dB between any two consecutive hops. </p> <p>2- The arrays in no_ris specify the vertices reachable within N sucessive hops, when no RIS is installed in the area.</p> <p>3- Similarly, the arrays in double_ris give the coordinates of vertices reachable with N hops when a two RISs are installed in the middle square(as shown in related paper).</p> <p>The RIS beamforming gain is 20 dB (in Table 1 in the paper the gain should be 20 not 15 dB).</p>
Transmission of optical analog signals with 16QAM modulation scheme using a 5GHz RF carrier signal
<p>The specific data sets correspond to the transmission experiments carried out in laboratory settings to assess the performance of an analog optical link. The optical link is based on a commercial InP Mach-Zehnder modulator (MZM) with approximately 25GHz 3-dB bandwidth, which modulates the CW signal of a DFB laser diode at 1560 nm. The electrical signals driving the modulator were generated using a arbitrary waveform generator (AWG) with 20GHz analog bandwidth and 65GSa/s sampling rate (Keysight M8195A). Electrical 16QAM signals at 1GBaud having a 5GHz RF carrier and utilizing Raised Cosine pulse shaping filters were used to feed the MZM. The detection of the back-to-back signals was realized by means of a single 40GHz photodiode. The signals were acquired, sampled and stored using Agilent Infinium DSO-X93304Q 33GHz, 80GSa/s real time oscilloscope.</p> <p>The data sets have the name format of "ModulatorType_ModulationFormat_RFcarierFrequency_SignalBandwidth_FIlterType_Roll-offFactor_OpticalReceivedPower_#of run.bin" . As an example "MZM_16QAM_5GHz_1Gbaud_RC_035_-3dbm_run0.bin".</p> <p>For each experimental set two instances were captured "run0, run1" in a slightly different time.</p>
Illustrative dataset for Ozone radiative forcing calculations using SOCRATES-RF
<p>This dataset provides to the reader/user with two netCDF files which illustrate the structure and properties of the input datasets (used directly by the software SOCRATES-RF) in support of the publication: "<strong>Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database</strong>". It comprises two examples of January (pre-industrial decade, 1850s): one based on CMIP5 ozone concentrations and other based on the recently available CMIP6 ozone dataset. Both were created with the procedure described on the supplementary information of the publication "Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database", Checa-Garcia, R et al.</p> <p>The sources of information for these datasets are the CMIP5 / CMIP6 ozone dataset, the ERA-Interim reanalysis dataset (2000-01 to 2009-12) and the solar irradiance from SORCE and TIM projects. Please see the references:</p> <ul> <li>Cionni, I., Eyring, V., Lamarque, J. F., Randel, W. J., Stevenson, D. S., Wu, F., Bodeker, G. E., Shepherd, T. G., Shindell, D. T., and Waugh, D. W.: Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing, Atmos. Chem. Phys., 11, 11267-11292, https://doi.org/10.5194/acp-11-11267-2011, 2011.</li> <li>Hegglin, M. I., D. Kinnison, D. Plummer, R.Checa-Garcia et al., Historical and future ozone database (1850-2100) in support of CMIP6, GMD, in preparation.</li> <li>Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi: 10.1002/qj.828</li> <li>Kopp G., Heuerman K., Lawrence G. (2005) The Total Irradiance Monitor (TIM): Instrument Calibration. In: Rottman G., Woods T., George V. (eds) The Solar Radiation and Climate Experiment (SORCE). Springer, New York, NY</li> </ul>
Raw data (RF) provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Article</strong> : <a href="https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext">https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext</a></p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>Beamformed dataset</strong> : <a href="../record/6811910#.ZA9dV3bMLid">https://zenodo.org/record/6811910#.ZA9dV3bMLid</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
Temporal SNR optimization through RF coil combination in fMRI: The more, the better? - DATASET
Open the record for dataset details and reuse information.
[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia doi.org/10.3390/s22103610
<p>[DATASET] Design and characterization of an RF applicator for in vitro tests of electromagnetic hyperthermia <a href="https://doi.org/10.3390/s22103610">doi.org/10.3390/s22103610</a></p> <p>The data used in the paper are dived in separate folder for each published figure.</p>
5G Aerial RF Radiation Data
<p>Data is contained in zipped files. Each zipped file represents a measurement campaign. Data includes measurement (.mat, .csv, .xlsx) files, plot (.jpg, .fig) files, and a readme (.txt) file.</p>
→ Fig. 9. Antiarchan fish Bothriolepis leptocheira jeremejevi (Rohon, 1900), Sosnogorsk locality, Sosnogorsk Formation, lowermost Famennian, anterior median dorsal (A–G) and posterior median dorsal (H–M) plates of the trunk armour. A. IG KSC 155/5 in dorsal (A1) and visceral (A2) views. B. IG KSC 155/108 in dorsal (B1) and visceral (B2) views. C. IG KSC 155/97 in dorsal view. D. IG KSC 155/113 in dorsal (D1) and visceral (D2) views. E. IG KSC 155/140 in dorsal (E1) and visceral (E2) views. F. Impression of the dorsal surface of IG KSC 155/42. G. IG KSC 155/44 in dorsal view. H. Fragment of IG KSC 155/7 in dorsal view. I. IG KSC 155/1 in dorsal (I1) and visceral (I2) views. J. IG KSC 155/71 in dorsal view. K. Slightly deformed IG KSC 155/70 in dorsal (K1) and visceral (K2) views. L. IG KSC 155/158 in dorsal view. M. IG KSC 155/157 in dorsal (M1) and visceral (M2) views. Abbreviations: ADL, anterior dorso-lateral plate; alr, postlevator thickening; AMD, anterior median dorsal plate; cf.ADL, cf.AMD, and cf.MxL, area overlapping ADL, AMD or MxL respectively; cr.tp, posterior transversal internal crest; dlg1 and dlg2, anterior and posterior oblique dorsal sensory line groove; dma, tergal angle; dmr, dorsal median ridge; f.retr, levator fossa; grm, ventral median groove; l, lateral corner; mvr, median ventral ridge; MxL, mixilateral plate; npn, postnuchal notch; oa.ADL, oa.MxL and oa.PMD, area overlapped by ADL, MxL or PMD respectively; pa, posterior corner; pma, posterior marginal area; PMD, posterior median dorsal plate; pr.p, posterior process of AMD; pr.pl, external postlevator process; prv2, posterior ventral process of dorsal wall of trunk armour; pt1 and pt2, anterior and posterior ventral pit; pua, posterior unornamented area of PMD; rf, "round fossula"; sna, supranuchal area; tb, ventral tuberosity. in A new assessment of the Late Devonian antiarchan fish Bothriolepis leptocheira from South Timan (Russia) and the biotic crisis near the Frasnian-Famennian boundary
→ Fig. 9. Antiarchan fish Bothriolepis leptocheira jeremejevi (Rohon, 1900), Sosnogorsk locality, Sosnogorsk Formation, lowermost Famennian, anterior median dorsal (A–G) and posterior median dorsal (H–M) plates of the trunk armour. A. IG KSC 155/5 in dorsal (A1) and visceral (A2) views. B. IG KSC 155/108 in dorsal (B1) and visceral (B2) views. C. IG KSC 155/97 in dorsal view. D. IG KSC 155/113 in dorsal (D1) and visceral (D2) views. E. IG KSC 155/140 in dorsal (E1) and visceral (E2) views. F. Impression of the dorsal surface of IG KSC 155/42. G. IG KSC 155/44 in dorsal view. H. Fragment of IG KSC 155/7 in dorsal view. I. IG KSC 155/1 in dorsal (I1) and visceral (I2) views. J. IG KSC 155/71 in dorsal view. K. Slightly deformed IG KSC 155/70 in dorsal (K1) and visceral (K2) views. L. IG KSC 155/158 in dorsal view. M. IG KSC 155/157 in dorsal (M1) and visceral (M2) views. Abbreviations: ADL, anterior dorso-lateral plate; alr, postlevator thickening; AMD, anterior median dorsal plate; cf.ADL, cf.AMD, and cf.MxL, area overlapping ADL, AMD or MxL respectively; cr.tp, posterior transversal internal crest; dlg1 and dlg2, anterior and posterior oblique dorsal sensory line groove; dma, tergal angle; dmr, dorsal median ridge; f.retr, levator fossa; grm, ventral median groove; l, lateral corner; mvr, median ventral ridge; MxL, mixilateral plate; npn, postnuchal notch; oa.ADL, oa.MxL and oa.PMD, area overlapped by ADL, MxL or PMD respectively; pa, posterior corner; pma, posterior marginal area; PMD, posterior median dorsal plate; pr.p, posterior process of AMD; pr.pl, external postlevator process; prv2, posterior ventral process of dorsal wall of trunk armour; pt1 and pt2, anterior and posterior ventral pit; pua, posterior unornamented area of PMD; rf, "round fossula"; sna, supranuchal area; tb, ventral tuberosity.
Dataset: RF Acquisition Corp. (RFAC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: RF Acquisition Corp. (RFACW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: RF Acquisition Corp II (RFAIU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: RF Acquisition Corp. (RFACU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: RF Industries, Ltd. (RFIL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: RF Acquisition Corp. (RFACR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
Calculation of RF sheath properties from surface wave-fields: a post-processing method
<p>The accompanying files contain digital data for figures in the article "Calculation of RF sheath properties from surface wave-fields: a post-processing method" by J.R. Myra and H. Kohno, submitted to the journal Plasma Physics and Controlled Fusion.</p> <p><br> Abstract:</p> <p>In ion cyclotron range of frequency (ICRF) experiments in fusion research devices, radio frequency (RF) sheaths form where plasma, strong RF wave fields and material surfaces coexist. These RF sheaths affect plasma material interactions such as sputtering and localized power deposition, as well as the global RF wave fields themselves. RF sheaths may be modeled by employing a sheath boundary condition (BC) in place of the more customary conducting wall BC; however, there are still many ICRF computer codes that do not implement the sheath BC. In this paper we present a method for post-processing results obtained with the conducting wall BC. The post-processing method produces results that are equivalent to those that would have been obtained with the RF sheath BC, under certain assumptions. The post-processing method is also useful for verification of sheath BC implementations and as a guide to interpretation and understanding of the role of RF sheaths and their interactions with the waves that drive them.</p> <p> </p>
RF recordings of GPS SVN49 broadcasting non-standard codes
<p>This dataset consists of several recordings of the L1 signal broadcasted by GPS<br> SVN49 between 2019-06-26 and 2019-06-28. This satellite broadcasts a<br> non-standard code as its L1 C/A signal, where the usual spread-spectrum PRN is<br> replaced by a PRN which consists of alternating zeros and ones (akin to a square<br> wave subcarrier). This produces strong spectral lines at +/-511.5kHz from the L1<br> centre frequency.</p> <p>The signal is modulated with a navigation message in the usual way using 50baud<br> BPSK.</p> <p>The recordings have been made with a LimeSDR using an external 10MHz GPSDO and a<br> small and inexpensive GPS patch antenna.</p> <p><strong>Included files</strong></p> <p>The .bin files contain waterfall data generated with STRF. This<br> data can be used to identify the satellite producing the signal using Doppler<br> measurements.</p> <p>The mjd58660.dat file contains Doppler measurements extracted with STRF.</p> <p>The .c64 files are IQ recordings of the upper sideband of<br> the modulation (centred at L1 + 511.5kHz). One of them is done at 5ksps and<br> another at 10ksps. The timestamp of the start of each recording is indicated in<br> the filename. The format is complex IQ data as 32-bit floats.</p>
GNSS RF Recordings Dataset from Static Antenna
<p>GNSS RF recordings dataset from the static antenna located on the rooftop of the Tampere Wireless Research Center. The recordings were performed using a NI USRP-2953R and an external clock reference Spectracom GSG-6. The files are provided in binary format. A non-selective gain from the USRP has been applied during the recordings.</p> <p>Novatel_20211130_resampled_10MHz_8bit_IQ_gain25</p> <ul> <li>Date: 2021/11/30 - 8:40 (UTC)</li> <li>Centre frequency: 1575.42 MHz</li> <li>Sampling frequency: 40 MHz</li> <li>Intermediate frequency: 0 Hz (Baseband)</li> <li>Quantization: 8 bits integers, I+Q </li> <li>Gain: 25 dB (non-selective)</li> <li>Note: The In-Phase and Quadraphase measurements are recorded in binary as follow: I_1 Q_1 I_2 Q_2, etc.</li> </ul> <p>Novatel_20240731_142746_40MHz_10MHz_8bit_real_gain15.bin</p> <ul> <li>Date: 2024/07/31 - 11:27 (UTC)</li> <li>Centre frequency: 1575.42 MHz</li> <li>Sampling frequency: 40 MHz</li> <li>Intermediate frequency: 10 MHz</li> <li>Quantization: 8 bits integers, real</li> <li>Gain: 15 dB (non-selective)</li> <li>Note: The real measurements are recorded in binary as follow: R_1 R_2 etc.</li> </ul> <p> </p>
Stable and compact RF-to-optical link using lithium niobate on insulator waveguides
<p>Stable and compact RF-to-optical link using lithium niobate on insulator waveguides</p> <p>Optical frequency combs have become a very powerful tool in metrology and beyond thanks to their ability to link radio frequencies with optical frequencies via a process known as self-referencing. Typical self-referencing is accomplished in two steps: the generation of an octave-spanning supercontinuum spectrum and the frequency-doubling of one part of that spectrum. Traditionally, these two steps have been performed by two separate optical components. With the advent of photonic integrated circuits, the combination of these two steps has become possible in a single small and monolithic chip. One photonic integrated<br> circuit platform very well suited for on-chip self-referencing is lithium niobate on insulator - a platform characterised by high second and third order nonlinearities. Here we show that combining a lithium niobate on insulator waveguide with a silicon photodiode results in a very compact and direct low-noise path towards self-referencing of mode-locked lasers. Using digital servo electronics, the resulting frequency comb is fully stabilized. Its high degree of stability is verifed with an independent out-of-loop measurement and is quantifed to be 6.8 mHz. Furthermore, we show that the spectrum generated inside the lithium niobate waveguide remains stable over many hours.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.