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69 results for “SCA”
SJR Dolphin SCA: Degradation Scores, OL Length and Identifications of Otoliths Collected from Bottlenose Dolphin Stomachs
Otoliths were collected from stomach contents of stranded bottlenose dolphins (Tursiops erebennus) in the St. Johns River in Jacksonville, Florida. Otoliths were analyzed by a panel of 3 reviewers to determine the level of otolith degradation that occurred during digestive processes. Otoliths with scores ≤ 3 were measured. Otolith length measurements were used to estimate the size of most species by applying standard regression equations developed from fish species collected from a nearby water system, the Indian River Lagoon, and for one species, equations developed from violet gobies collected from the St. Johns River. These equations enabled estimation of the mass of each prey species in each dolphin’s stomach, then the calculation of their relative proportions of reconstructed mass across all stomachs. For otolith identification purposes, a panel of 4 reviewers assigned each otolith with a family-level and species-level identification. Each identification was given a confidence code ranging from 1 (no confidence) to 4 (certainty). When the average code for all reviewers was < 3, the otolith was considered unidentified. If two of the reviewers agreed with the “weight” reviewer and all gave scores ≥ 3, the score of the outlying reviewer was discarded. Identification was assigned when the average confidence code was ≥ 3. The minimum number of species per dolphin stomach was determined by counting the left and right otoliths for each species separately, using the higher count as the minimum prey number. Unidentified species were counted, and half of their sum was considered the minimum prey number. The frequency of occurrence (%FO, or proportion of stomachs in which a species was detected) and numerical proportion (%N, or proportion of a given species pooled across all stomach samples) of each prey species were then calculated.
Ascon SCA software and hardware databases
<h2><strong>Software and hardware side-channel analysis databases for attack on Ascon AEAD</strong><br> </h2><p>This repository contains datasets collected for side-channel analysis attack on Ascon AEAD.</p><p>The repository is divided into two folders, one for software side-channel analysis and one for hardware side-channel analysis:</p><p> </p><p>- cw.zip</p><p> - ascon_collect.ipynb : Jupyter Notebook used to collect the traces for the unprotected Ascon implementation</p><p> - ascon_protected_collect.ipynb : Jupyter Notebook used to collect the traces for the protected Ascon implementation</p><p> - simpleserial-ascon : Ascon firmwares used to collect the traces</p><p>- hw.zip</p><p> - ascon_g_protected</p><p> - test_ascon.py : Script to test the protected Ascon implementation</p><p> - collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope</p><p> - rtl_src : RTL source files for the protected Ascon implementation</p><p> - ascon_g_unprotected</p><p> - test_ascon.py : Script to test the unprotected Ascon implementation</p><p> - collect_lecroy.py : Script to collect traces for unprotected Ascon implementation with Lecroy oscilloscope</p><p> - rtl_src : RTL source files for the unprotected Ascon implementation</p><p>- helpers.zip</p><p> - ASCON.py : Python implementation of Ascon</p><p> - SASEBO.py : Helper functions to communicate with the SAKURA-G board</p><p> - lecroy3.py : Helper functions for Lecroy oscilloscope</p><p> - ascon_helper.py : Helper functions for Ascon</p><p> - convert_trs_to_h5.py : Script to convert Trsfile traceset to HDF5 database</p><p> </p><p>ascon_cw_protected.h5 : Side-channel database for software protected Ascon implementation</p><p>ascon_cw_unprotected.h5 : Side-channel database for software unprotected Ascon implementation</p><p>ascon_hw_protected.h5 : Side-channel database for hardware protected Ascon implementation</p><p>ascon_hw_unprotected.h5 : Side-channel database for hardware unprotected Ascon implementation</p><p> </p><p>ascon_cw_protected.trs : Traces for software protected Ascon implementation</p><p>ascon_cw_unprotected.trs : Traces for software unprotected Ascon implementation</p><p>ascon_hw_protected.trs : Traces for hardware protected Ascon implementation</p><p>ascon_hw_unprotected.trs : Traces for hardware unprotected Ascon implementation</p><p> </p><h3><strong>Ascon authenticated encryption attack on a Chipwhisperer STM32F4</strong> </h3><p>The dataset was used for side-channel attack on Ascon initialization phase attack of the authenticated encryption mode on a ChipWhisperer STM32F4 target board.</p><p>The power traces are collected with the ChipWhisperer-Lite oscilloscope at a sampling rate of 4x the target clock frequency, and captures the first call of the round function of the Chi function of Ascon permutation.</p><p>The code used to collect the traces is also available in this repository, and the trace collection can be replicated with a ChipWhisperer-Lite and a STM32F4 target board.</p><p> </p><h3><strong>Ascon authenticated encryption attack on a SAKURA-G FPGA</strong></h3><p>The hardware designs of the unprotected and protected Ascon implementations are available in the `hw` folder.</p><p>Both implementations are written in VHDL/Verilog and can be synthesized for Spartan6 (XC6SLX75) with Xilinx ISE.</p><p>Traces are collected with a Lecroy WaveRunner 610Zi oscilloscope at a sampling rate of 500 MS/s.</p><p> </p><h3><strong>Databases description:</strong></h3><p>Database | Ntraces | Traces (samples) | Label* (bytes) | Metadata (bytes) </p><p> | Nf | Nr | | | Key | Nonce | Plaintext | Associated data | Ciphertext | Tag</p><p>ascon_cw_unprotected.h5 | 100,000 | 100,000 | 772 | 64 | 16 | 16 | 4 | 4 | 4 | 16</p><p>ascon_cw_protected.h5 | 500,000 | 500,000 | 1408 | 64 | 32 | 32 | 16 | 16 | 16 | 32</p><p>ascon_hw_unprotected.h5 | 100,000 | 100,000 | 6,000 | 64 | 16 | 16 | 4 | 4 | 4 | 16</p><p>ascon_hw_protected.h5 | 500,000 | 500,000 | 10,000 | 64 | 32 | 32 | 8 | 8 | 8 | 32</p><p> </p><p>*Label computed with the intermediate_value leakage model described in `ascon_helper.py`</p>
SCA-2023: A two-part dataset for benchmarking the methods of image precompensation for users with refractive errors
<p>The recent practices of demonstrating various static and video images to users by means of digital, processor-controlled, often self-luminous devices (computer monitors, smartphone and tablet screens, etc.) have spurred the development of various methods for improving the perception of such images through their computer processing. In particular, this applies to the task of precompensating images shown to users with various anomalies of refraction of the eyes (e.g. myopia or astigmatism) in situations where they are not equipped with glasses or other corrective devices. Researchers have proposed a considerable number of such precompensation methods, but to this day there has been no way to accurately compare their quality. We propose an original dataset, which we called “SCA-2023”, of images specially designed for this purpose. Its most important feature is the fact that it includes not only a set of ground-truth images for implementing the precompensation transform, but also a separate set of images characterizing specific types and degrees of manifestation of the refractive errors. The benchmarking procedure itself includes applying the precompensation transformation to a certain image from the first part of the dataset, computer simulation of the so-called retinal image (distribution of light on the retina of an imaginary observer) based on the selection of the “distorting eye” from the second part of the dataset, and evaluating the similarity of this image to the ground-truth image, using any of the commonly used similarity metrics for this purpose.</p>
Smart Cable Air (SCA) and Smart Cable Water (SCW) public data sets, april 2024
<p>These datasets are supporting material for the publication<br>"An ASIC-based system-in-package MEMS gas sensor with impedance spectroscopy readout and AI-enabled identification capabilities"<br>by CNR-IMM Bologna, Univerisity of Pisa and Sensichips.</p> <p>These are some data sets as acquired to date using the Smart Cable Air and Smart Cable Water devices, for public use by data scientists and AI developers.</p> <p>SCA.zip contains datasets for the "Smart Cable Air" sensor: <a href="https://sensichips.com/air-sensor/">https://sensichips.com/air-sensor/</a></p> <p>SCW.zip contains datasets for the "Smart Cable Water" sensor: <a href="https://sensichips.com/water-sensor/">https://sensichips.com/water-sensor/</a></p> <p>The contents are described in the readme files contained in the subdirectories.</p>
ACSAC_ML_SCA_EVA Artifacts
<p>The artifacts for ACSAC 2024 paper <em><span><span>R+R: Demystifying ML-Assisted Side-Channel Analysis Framework: A Case of Image Reconstruction</span></span></em></p>
Improve SCA Bridge Study
ClinicalTrials.gov study NCT03715790. IPD Sharing: NO. Countries: 16. Publications: 5.
Trial in Adult Participants With Spinocerebellar Ataxia (SCA)
ClinicalTrials.gov study NCT02960893. IPD Sharing: NO. Countries: 1. Publications: 1.
Large-scale Analysis of Infrastructure-leaking DNS Servers - (Dataset)
<p>Dataset of the paper: "Lar­ge-sca­le Ana­ly­sis of In­fra­struc­tu­re-lea­king DNS Ser­vers", published at Con­fe­rence on De­tec­tion of In­tru­si­ons and Mal­wa­re & Vul­nerabi­li­ty As­sess­ment (DIMVA), Go­then­burg, Swe­den, June 2019.</p> <p>Abstract</p> <p>The Do­main Name Sys­tem (DNS) is a fun­da­men­tal back­bone ser­vice of the In­ter­net. In prac­tice, this in­fra­struc­tu­re often shows flaws, which in­di­ca­te that me­a­su­ring the DNS is im­portant to un­der­stand po­ten­ti­al (se­cu­ri­ty) is­su­es. Se­ver­al works deal with the DNS and pre­sent such pro­blems, miti­ga­ti­ons, and at­tack vec­tors. A so far over­look­ed issue is the fact that DNS ser­vers might an­s­wer with in­for­ma­ti­on about in­ter­nal net­work in­for­ma­ti­on (e.g., host­na­mes) to ex­ter­nal que­ries. This be­ha­vi­or re­sults in a ca­pa­bi­li­ty to per­form an ac­tive net­work re­con­nais­sance wi­thout the need for in­di­vi­du­al vul­nerabi­li­ties or ex­ploits. Ana­ly­zing how pu­blic DNS ser­vices might in­vol­un­ta­ri­ly dis­clo­se sen­si­ti­ve in­for­ma­ti­on ties in with the trust we have on In­ter­net ser­vices.</p> <p>To in­ves­ti­ga­te this phe­no­men­on, we con­duc­ted a sys­te­ma­tic me­a­su­re­ment study on this topic. We crawl all pu­blic re­acha­ble DNS ser­vers in 15 scans over a pe­ri­od of al­most six months and ana­ly­ze up to 574,000 DNS ser­vers per run that are con­fi­gu­red in a way that might lead to this kind of in­for­ma­ti­on le­a­ka­ge. With this lar­ge-sca­le eva­lua­ti­on, we show that the amount of this pos­si­ble in­fra­struc­tu­re lea­king DNS ser­vers is on aver­a­ge al­most 4 per­cent over all of our scans on every re­acha­ble DNS ser­vers on the In­ter­net. Based on our ne­west scan, the coun­tri­es with most of these ser­vers are Ro­ma­nia, China, and the US. In these coun­tri­es, the share of such ser­vers among of all re­acha­ble ser­vers is about 15% in Ro­ma­nia, 9% in China, and 2.9% in the US. A de­tai­led ana­ly­sis of the re­s­pon­ses re­veals that not all an­s­wers pro­vi­de use­ful in­for­ma­ti­on for an ad­versa­ry. Howe­ver, we found that up to 158,000 DNS ser­vers pro­vi­de po­ten­ti­al­ly ex­ploi­ta­ble in­for­ma­ti­on in the wild. Hence, this me­a­su­re­ment study de­mons­tra­tes that the con­fi­gu­ra­ti­on of a DNS ser­ver should be exe­cu­ted ca­re­ful­ly; other­wi­se, it may be pos­si­ble to dis­clo­se too much in­for­ma­ti­on.</p>
Albuminuria Reduction With Renin Angiotensin System Inhibitors in SCA Patients
ClinicalTrials.gov study NCT01195818. IPD Sharing: Not stated. Countries: 1. Publications: 24.
Study Evaluating SCA-136 in Subjects With Acute Exacerbations of Schizophrenia
ClinicalTrials.gov study NCT00265551. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Optimizing Hydroxyurea Therapy in Children With SCA In Malaria Endemic Areas
ClinicalTrials.gov study NCT03128515. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluation of Whole Body Examination by MRI Integrating the "Zero Time Eco" Sequence (ZTE, Pseudo-CT) for the Detection of Bone Lesions in Multiple Myeloma: Comparison With Pet / CT and Whole Body Sca
ClinicalTrials.gov study NCT05381077. IPD Sharing: Not stated. Countries: 1. Publications: 2.
DIagnostics, Fatty Acids and Vitamin D in SCA
ClinicalTrials.gov study NCT02886273. IPD Sharing: NO. Countries: 1. Publications: 4.
A Trial to Compare the Incidence of Squamous Cell Carcinoma (SCC) and Other Skin Neoplasia on Skin Areas Treated With Ingenol Disoxate Gel or Vehicle Gel for Actinic Keratosis on Face and Chest or Sca
ClinicalTrials.gov study NCT03115476. IPD Sharing: NO. Countries: 6. Publications: 0.
Cholecystokinin 1 Receptor (Cck1R) Activation Restores Normal mTORC1 signaling and is Protective to Purkinje cells of SCA Mice
GEO Series GSE180969. Mus musculus. 18 samples. Type: Expression profiling by high throughput sequencing.
Gene expression signature of Runx1Δ/Δ lin- sca- kit+ CD105- CD16/32+ CD150+ (XMP) progenitors
GEO Series GSE81421. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
The analysis of gene expression in hematopietic stem/progenitor cells (HSPCs; LSK cells, Lineage-, c-kit+, Sca-1+) from diabetic/nondiabetic mice.
GEO Series GSE117088. Mus musculus. 2 samples. Type: Expression profiling by array.
Comparison of gene expression profiles of Sca-1+ and Sca-1- cells post alveolar injury induced by P. aeruginosa
GEO Series GSE47600. Mus musculus. 12 samples. Type: Expression profiling by array.
Transcriptional profile of PDGFRa+ Sca-1+ and Sca-1- cells at steady state and at 7, 14 and 28 days post LAD ligation
GEO Series GSE141927. Mus musculus. 26 samples. Type: Expression profiling by high throughput sequencing.
Expression data from small intestinal Lin-c-Kit+Sca-1- cells and Lin-c-Kit-Sca-1- cells.
GEO Series GSE40882. Mus musculus. 2 samples. Type: Expression profiling by array.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.