Version 1

The counters are in the range 1-20.

  Version 2

The counters are in the range 1-50.

  Version 3

The counters are in the range 1-100.

  Version 4

The counters are in the range 1-200. Histogram.remove_other_colors() added.

  Version 5

I forgot to update the range of the counters when doing comparisons. Now the counters are in the range 1-100.

  Version 6

The counters are in the range 1-200.

  Version 7

The counters are in… See the full description on the dataset page: https://huggingface.co/datasets/neoneye/simon-arc-histogram-v9.

This data release includes the data used to generate histograms that compared total watershed pollutant removal efficiency (TWPRE) in the two study watersheds Crystal Rock (traditional watershed) and Tributary (Trib.) 104 low impact development (LID watershed) to determine if LID BMP design offered an improved water quality benefit. Input/calibrants data used in the model (Monte Carlo) are described in the manuscript as mentioned in the list below: -BMP Name and Type: references in the manuscript -BMP Connectivity: Proprietary (derived from Montgomery County GIS Data) -BMP Drainage Areas: Proprietary (derived from Montgomery County GIS Data) -BMP Efficiency Ranges: referenced in manuscript -Baseline Pollutant Loadings: referenced in manuscript Stormwater runoff and associated pollutants from urban areas in the Chesapeake Bay Watershed represent a serious impairment to local streams and downstream ecosystems, despite urbanized land comprising only 7% of the Bay watershed area. Excess nitrogen, phosphorus, and sediment affect local streams in the Bay watershed by causing problems ranging from eutrophication and toxic algal blooms to reduced oxygen levels and loss of biodiversity. Traditional management of urban stormwater has primarily focused on directing runoff away from developed areas as quickly as possible. More recently, stormwater best management practices (BMPs) have been implemented in a low impact development (LID) manner on the landscape to treat stormwater runoff closer to its source.The objective of this research was to use a modeling approach to compare total watershed pollutant removal efficiency (TWPRE) of two watersheds with differing spatial patterns of SW BMP design (traditional and LID), and determine if LID SW BMP design offered an improved water quality benefit.

Hello all, this dataset involves various factors effecting cancer and based upon those factors, I have created a Histogram of various columns of the table which leads to heart disease. A histogram is a bar graph-like representation of data that buckets a range of outcomes into columns along the x-axis. The y-axis represents the number count or percentage of occurrences in the data for each column and can be used to visualize data distribution. At last I have created combined histogram of entire table which involves all the columns. Giving Titles, X-axis name, Y-axis name, Sizes and Colors is also done in this notebook.

Monte Carlo simulations are the foundational technique for predicting thermodynamic properties of open systems where the process of interest involves the exchange of particles. Thus, they have been used extensively to computationally evaluate the adsorption properties of nanoporous materials and are critical for the in silico identification of promising materials for a variety of gas storage and chemical separation applications. In this work we demonstrate that a well-known biasing technique, known as “flat-histogram” sampling, can be combined with temperature extrapolation of the free energy landscape to efficiently provide significantly more useful thermodynamic information than standard open ensemble MC simulations. Namely, we can accurately compute the isosteric heat of adsorption and number of particles adsorbed for various adsorbates over an extremely wide range of temperatures and pressures from a set of simulations at just one temperature. We extend this derivation of the temperature extrapolation to adsorbates with intramolecular degrees of freedom when Rosenbluth sampling is employed. Consequently, the working capacity and isosteric heat can be computed for any given combined temperature/pressure swing adsorption process for a large range of operating conditions with both rigid and deformable adsorbates. Continuous thermodynamic properties can be computed with this technique at very moderate computational cost, thereby providing a strong case for its application to the in silico identification of promising nanoporous adsorbents.

This dataset contains daily histograms of wind speed at 100m ("WS100"), wind direction at 100 m ("WD100") and an atmospheric stability proxy ("STAB") derived from the ERA5 hourly data on single levels [1] accessed via the Copernicus Climate Change Climate Data Store [2]. The dataset covers six geographical regions (illustrated in regions.png) on a reduced 0.5 x 0.5 degrees regular grid and covers the period 1994 to 2023 (both years included). The dataset is packaged as a zip folder per region which contains a range of monthly zip folders following the convention of zarr ZipStores (more details here: https://zarr.readthedocs.io/en/stable/api/storage.html). Thus, the monthly zip folders are intended to be used in connection with the xarray python package (no unzipping of the monthly files needed).Wind speed and wind direction are derived from the U- and V-components. The stability metric makes use of a 5-class classification scheme [3] based on the Obukhov length whereby the required Obukhov length was computed using [4]. The following bins (left edges) have been used to create the histograms:Wind speed: [0, 40) m/s (bin width 1 m/s)Wind direction: [0,360) deg (bin width 15 deg)Stability: 5 discrete stability classes (1: very unstable, 2: unstable, 3: neutral, 4: stable, 5: very stable)Main Purpose: The dataset serves as minimum input data for the CLIMatological REPresentative PERiods (climrepper) python package (https://gitlab.windenergy.dtu.dk/climrepper/climrepper) in preparation for public release).References:[1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)[2] Copernicus Climate Change Service, Climate Data Store, (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47 (Accessed Nov. 2024)'[3] Holtslag, M. C., Bierbooms, W. A. A. M., & Bussel, G. J. W. van. (2014). Estimating atmospheric stability from observations and correcting wind shear models accordingly. In Journal of Physics: Conference Series (Vol. 555, p. 012052). IOP Publishing. https://doi.org/10.1088/1742-6596/555/1/012052[4] Copernicus Knowledge Base, ERA5: How to calculate Obukhov Length, URL: https://confluence.ecmwf.int/display/CKB/ERA5:+How+to+calculate+Obukhov+Length, last accessed: Nov 2024

Abstract ======== This data set consists of the MESSENGER XRS reduced data record (RDR) footprints which are derived from the navigational meta-data for each calibrated data record (CDR) whose FOV_STATUS is 1 or 3; that is, when the field of view intersects the planet and is either partially or entirely sunlit. Each XRS observation results in four X-ray spectra. When an X-ray interacts with one of the four detectors, a charge or voltage pulse is generated. This signal is converted into one of 2^8 (256) channels, which are correlated to energy. Over a commanded integration time period a histogram of counts as a function of energy (channel number) is recorded. The EDRs are the number of events in each channel of the four detectors accumulated over the integration period. Channels above or below the useful energy range of the detectors are not transmitted. The result is three 244-channel GPC histograms and one 231-channel solar monitor histogram, each of which is designated as a single X-ray spectrum. Each observation is calibrated and processed into the CDR data set. For each CDR whose field of view is contained or partially contained on the planetary surface, a footprint is computed that corresponds to the perimeter of the planetary region within the instrument field of view during the integration time of the observation.

On each field, soil samples were taken. These soil samples are evaluated using the Eurofins protocol, and provide us the amount of the following macro- and micronutrients: N, P, K, Ca, Mg, S, Si, Fe, Zn, Mn, and B. In these histograms, two lines are present as well. The left line represents the lower limit of the advise of Eurofins, and the right line represents the maximum of the range. In addition, some categorical variables are provided. The nutrient content of the field is determined by the farmer’s team, who classifies fields as poor, average or rich. In addition, the field is classified as dry, average or wet by the farmer himself. Potato is a rotation crop; only once per four years, potatoes can be grown on the same field. The crop cultivated before potatoes were grown on the field is the previously cultivated crop. In the “others” category all kinds of crops are captured. Usually, only one or two times, a field is cultivated with that crop. Crops in this category are for example conifers, salsify, or peas. Finally, some fields suffer from nematodes, which can have a negative effect on potato yield. A: N in soil. B: P in soil. C: K in soil. D: Ca in soil. E: Mg in soil. F: Si in soil. G: S in soil. H: Fe in soil. I: Zn in soil. J: Mn in soil. K: B in soil. L: Tuber weight. M: Nutrient content. N: Contains nematodes? O: Year. P: Dryness. Q: Previously cultivated crop. (ZIP)

Abstract ======== This data set consists of the MESSENGER XRS uncalibrated observations, also known as EDRs. Each XRS observation results in four X-ray spectra. When an X-ray interacts with one of the four detectors, a charge or voltage pulse is generated. This signal is converted into one of 2^8 (256) channels, which are correlated to energy. Over a commanded integration time period a histogram of counts as a function of energy (channel number) is recorded. The EDRs are the number of events in each channel of the four detectors accumulated over the integration period. Channels above or below the useful energy range of the detectors are not transmitted. The result is three 244-channel GPC histograms and one 231-channel solar monitor histogram, each of which is designated as a single X-ray spectrum. In addition to the science data, associated instrument parameters are included.

Abstract ======== This data set consists of the MESSENGER XRS reduced data record observations, also known as RDRs, which are derived from the calibrated data records, CDRs. Each XRS observation results in four X-ray spectra. When an X-ray interacts with one of the four detectors, a charge or voltage pulse is generated. This signal is converted into one of 2^8 (256) channels, which are correlated to energy. Over a commanded integration time period a histogram of counts as a function of energy (channel number) is recorded. The EDRs are the number of events in each channel of the four detectors accumulated over the integration period. Channels above or below the useful energy range of the detectors are not transmitted. The result is three 244-channel GPC histograms and one 231-channel solar monitor histogram, each of which is designated as a single X-ray spectrum. Each observation is calibrated and processed into the CDR data set and then further processed to produce a map of elemental ratios, the maps of which compose the RDR data set.

The dataset contains 100 photographic images that are treated as ground-truth images. On each ground truth image, the effects of underwater environment are applied and 150 synthetic underwater images are generated. Hence, the data set contains 100 ground-truth photographic images and 100*150=15000 synthetic underwater images. Four effects of the underwater environment, i.e., color cast, blurring, contrast reduction, and low light, are considered. These effects are applied individually and in combinations. Four effects result in a total of 15 combinations, and the effect of each combination is varied by considering 10 levels. This results in a total of 150 images for a single ground-truth image. In addition to this, 21 focus metrics are evaluated on all these 1,50,100 images. The metrics calculated are Absolute central moment (ACMO), Brenner's focus measure (BREN), Image curvature (CURV), Gray-level variance (GLVA), Gray-level local variance (GLLV), Gray-level variance normalized (GLVN), Squared gradient (GRAS), Helmli's measure (HELM), Histogram entropy (HISE), Histogram range (HISR), Energy of Laplacian (LAPE), Diagonal Laplacian (LAPD), Modified Laplacian (LAPM), Variance of Laplacian (LAPV), Tenengrad variance (TENV), Vollat's correlation (VOLA), Wavelet ratio (WAVR), Wavelet sum (WAVS), and Wavelet variance (WAVV). In addition, 7 statistical measures are also calculated. The statistical measures calculated are Mean Intensity, Standard Deviation, Skewness, Kurtosis, Entropy, Contrast, and Sharpness (Laplacian Variance). The literature categorizes these metrics as gradient-based and non-gradient-based.

Histogram of the (A) coefficient of variation (CV) and (B) range of log-transformed leaf life span (LLS) for local data sets within GLOPNET.

The Excel spreadsheet (with the comma-separated-value: CSV files using the same names) contains the three tables and the raw data used to plot histograms of Fig. 8, Fig. 11(a), and Fig. 11(b). Here, the range is from 0 to 100 μm. Each Tab corresponds to Figure number in the paper. The Feret diameter is obtained by using image analysis software (Fiji 2.3.0: Schindelin et al., 2012). The histogram bin width is calculated using the formula (Scott, 1979).

Abstract ======== This data set consists of the MESSENGER XRS calibrated observations, also known as CDRs. Each XRS observation results in four X-ray spectra. When an X-ray interacts with one of the four detectors, a charge or voltage pulse is generated. This signal is converted into one of 2^8 (256) channels, which are correlated to energy. Over a commanded integration time period a histogram of counts as a function of energy (channel number) is recorded. The EDRs are the number of events in each channel of the four detectors accumulated over the integration period. Channels above or below the useful energy range of the detectors are not transmitted. The result is three 244-channel GPC histograms and one 231-channel solar monitor histogram, each of which is designated as a single X-ray spectrum. In addition to the science data, associated instrument parameters are included.

Time-at-temperature data (to generate histograms) from tagged jumbo squid from R/V R4107, R/V Pacific Storm, Chartered Vessels, R/V BIP XII cruises in the Monterey Bay vicinity and Gulf of California from 2004-2009. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=All data were collected with Mk10-PAT tags (Wildlife Computers, Redmond, WA) attached to living Humboldt squid (Dosidicus gigas) as described elsewhere (Gilly et al. 2006). Tags were programmed to sample at 0.5 Hz or 1 Hz. Tags deployed in Monterey Bay (CCS-1through CCS-6; deployed during OCE-0850839) were programmed to transmit time series data (75 s intervals = 0.01333 Hz) for depth, temperature and light to the Argos satellite system. Tags deployed in the Gulf of California (GOC-1 through GOC-6; deployed during OCE-0526640) were physically recovered, and the data were subsampled to match the 75 s interval of the CCS tags. This procedure was also carried out for tag CCS-6 that was recovered but never reported to Argos.

Mk10 PAT tags measure depth from 0 to 2000 m with a resolution of 0.5 m and temperature from 0 to +40 degrees C with a resolution of 0.05 degree C. The tags were used as supplied by the manufacturer without additional calibration.

References:
Gilly, W.F., Zeidberg, L.D., Booth, J.A.T, Stewart, J.S., Marshall, G., Abernathy, K., and Bell, L.E. 2012. Locomotion and behavior of Humboldt squid, Dosidicus gigas, in relation to natural hypoxia in the Gulf of California, Mexico. The Journal of Experimental Biology, 215, 3175-3190. doi: 10.1242/jeb.072538.
Gilly, W.F., Markaida, U., Baxter, C.H., Block, B.A., Boustany, A., Zeidberg, L., Reisenbichler, K., Robinson, B., Bazzino, G., and Salinas, C. 2006. Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging. Marine Ecology Press Series, 324, 1-17. doi: 10.3354/meps324001.
Stewart, J.S., Field, J.C., Markaida, U., and Gilly, W.F. 2013. Behavioral ecology of jumbo squid (Dosidicus gigas) in relation to oxygen minimum zones. Deep Sea Research Part II: Topical Studies in Oceanography, 95, 197-208. doi:10.1016/j.dsr2.2012.06.005. awards_0_award_nid=55203 awards_0_award_number=OCE-0850839 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0850839 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 awards_1_award_nid=55226 awards_1_award_number=R/OPCFISH-06 awards_1_funder_name=California Sea Grant awards_1_funding_acronym=CASG awards_1_funding_source_nid=402 awards_2_award_nid=471705 awards_2_award_number=OCE-0526640 awards_2_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0526640 awards_2_funder_name=NSF Division of Ocean Sciences awards_2_funding_acronym=NSF OCE awards_2_funding_source_nid=355 awards_2_program_manager=David L. Garrison awards_2_program_manager_nid=50534 cdm_data_type=Other comment=Jumbo squid (Dosidicus gigas) time-at-temperature data from MK10 PAT tags California Current System (CCS) & Gulf of California (GOC) PI: William Gilly (Stanford University) Version: 21 Nov 2013

NOTE: 1 count represents a 75-second interval (in count_night and count_day columns) Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.471922.1 Easternmost_Easting=-111.22 geospatial_lat_max=37.91 geospatial_lat_min=27.34 geospatial_lat_units=degrees_north geospatial_lon_max=-111.22 geospatial_lon_min=-123.48 geospatial_lon_units=degrees_east geospatial_vertical_max=1400.0 geospatial_vertical_min=400.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/471922 institution=BCO-DMO instruments_0_acronym=MK10 PAT instruments_0_dataset_instrument_description=Mk10-PAT tags (Wildlife Computers, Redmond, WA) were programmed to sample at 0.5 Hz or 1 Hz. Tags deployed in Monterey Bay (CCS-1through CCS-6) were programmed to transmit time series data (75 s intervals = 0.01333 Hz) to the Argos satellite system. Tags deployed in the Gulf of California (GOC-1 through GOC-6) were physically recovered. Mk10 PAT tags measure depth from 0 to 2000 m with a resolution of 0.5 m and temperature from 0 to +40 degrees C with a resolution of 0.05 degree C. The tags were used as supplied by the manufacturer without additional calibration. instruments_0_dataset_instrument_nid=471925 instruments_0_description=The Pop-up Archival Transmitting (Mk10-PAT) tag, manufactured by Wildlife Computers, is a combination of archival and Argos satellite technology. It is designed to track the large-scale movements and behavior of fish and other animals which do not spend enough time at the surface to allow the use of real-time Argos satellite tags. The PAT can be configured to transmit time-at-depth and time-at-temperature histograms, depth-temperature profiles, and/or light-level curves. The histogram duration (1 to 24 hours) and bin ranges can also be configured. PAT archives depth, temperature, and light-level data while being towed by the animal. At a user-specified date and time, the PAT actively corrodes the pin to which the tether is attached, thus releasing the PAT from the animal. The PAT then floats to the surface and transmits summarized information via the Argos system. Argos also uses the transmitted messages to provide the position of the tag at the time of release. instruments_0_instrument_name=Wildlife Computers Mk10 Pop-up Archival Tag (PAT) instruments_0_instrument_nid=471924 instruments_0_supplied_name=MK10 PAT metadata_source=https://www.bco-dmo.org/api/dataset/471922 Northernmost_Northing=37.91 param_mapping={'471922': {'lon_start': 'flag - longitude', 'max_depth': 'flag - depth', 'lat_start': 'flag - latitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/471922/parameters people_0_affiliation=Stanford University people_0_person_name=William Gilly people_0_person_nid=51715 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI BCO-DMO people_1_person_name=Shannon Rauch people_1_person_nid=51498 people_1_role=BCO-DMO Data Manager people_1_role_type=related project=Jumbo Squid Physiology,Jumbo Squid Vertical Migration projects_0_acronym=Jumbo Squid Physiology projects_0_description=This project concerns the ecological physiology of Dosidicus gigas, a large squid endemic to the eastern Pacific where it inhabits both open ocean and continental shelf environments. Questions to be addressed include: 1) How does utilization of the OML by D. gigas vary on both a daily and seasonal basis, and how do the vertical distributions of the OML and its associated fauna vary? 2) What behaviors of squid are impaired by conditions found in the OML, and how are impairments compensated to minimize costs of utilizing this environment? and 3) What are the physiological and biochemical processes by which squid maintain swimming activity at such remarkable levels under low oxygen conditions? The investigators will use an integrated approach involving oceanographic, acoustic, electronic tagging, physiological and biochemical methods. D. gigas provides a trophic connection between small, midwater organisms and top vertebrate predators, and daily vertical migrations between near-surface waters and a deep, low-oxygen environment (OML) characterize normal behavior of adult squid. Electronic tagging has shown that this squid can remain active for extended periods in the cold, hypoxic conditions of the upper OML. Laboratory studies have demonstrated suppression of aerobic metabolism during a cold, hypoxic challenge, but anaerobic metabolism does not appear to account for the level of activity maintained. Utilization of the OML in the wild may permit daytime foraging on midwater organisms. Foraging also occurs near the surface at night, and Dosidicus may thus be able to feed continuously. D. gigas is present in different regions of the Guaymas Basin on a predicable year-round basis, allowing changes in squid distribution to be related to changing oceanographic features on a variety time scales. This research is of broad interest because Dosidicus gigas has substantially extended its range over the last decade, and foraging on commercially important finfish in invaded areas off California and Chile has been reported. In addition, the OML has expanded during the last several decades, mostly vertically by shoaling, including in the Gulf of Alaska, the Southern California Bight and several productive regions of tropical oceans, and a variety of ecological impacts will almost certainly accompany changes in the OML. Moreover, D. gigas currently supports the world's largest squid fishery, and this study will provide acoustic methods for reliable biomass estimates, with implications for fisheries management in Mexico and elsewhere. This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). This is a Collaborative Research project encompassing three NSF-OCE awards. Background Publications: Stewart, J.S., Field, J.C., Markaida, U., and Gilly, W.F. 2013. Behavioral ecology of jumbo squid (Dosidicus gigas) in relation to oxygen minimum zones. Deep Sea Research Part II: Topical Studies in Oceanography, 95, 197-208. doi:10.1016/j.dsr2.2012.06.005. Gilly, W.F., Zeidberg, L.D., Booth, J.A.T, Stewart, J.S., Marshall, G., Abernathy, K., and Bell, L.E. 2012. Locomotion and behavior of Humboldt squid, Dosidicus gigas, in relation to natural hypoxia in the Gulf of California, Mexico. The Journal of Experimental Biology, 215, 3175-3190. doi:

Background: To investigate descriptive characteristics and dose metric (DM) parameters associated with development of pleural effusions (PlEf) in non-small cell lung cancer (NSCLC) treated with definitive chemoradiation therapy (CRT). Materials and methods: We retrospectively assessed treatment records and follow-up imaging of 66 NSCLC patients to identify PlEf formation after CRT. PlEf association between mean heart dose (MHD), mean lung dose (MLD), heart V5–V60 (HV), and lung V5–V60 (LV) were evaluated using Cox Proportional Hazard Models. Results: A total of 52% (34 of 66 patients) of our population developed PlEf and the actuarial rates at 6 months, 12 months, and 18 months were 7%, 30%, and 42%, respectively. Median time to diagnosis was five months (range 0.06–27 months). The majority of PlEfs were grade one (67%) and developed at a median of four (0.06–13) months, followed by grade two (15%) at a median 11 (5–12) months, and grade three (18%) at a median of 11 (3–27) months. On multivariate analysis, increasing HV5–HV50, LV5–LV50, MHD, and MLD were associated with greater risk of PlEf. Higher grade PlEf was also associated with higher doses of radiation to the heart, while lung DM parameters were not significantly associated with higher PlEf grades. At five-months post-CRT, MHD of 25 Gy was associated with a 100% chance of grade one PlEf, an 82% risk of grade two PlEf, and a 19% risk of grade three PlEf. Conclusions: Post-CRT PlEf is common in NSCLC with the majority being grade one. Increasing heart and lung irradiation was associated with increased risk of PlEf. Increasing heart irradiation also correlated with development of increasing grades of PlEf. The impact of potential cardiopulmonary toxicity and resultant PlEfs after CRT requires additional study.

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

Measurement and calibration of an analog-to-digital converter (ADC) using a histogram-based method requires a large volume of data and a long test duration, especially for a high resolution ADC. A fast and accurate calibration method for pipelined ADCs is proposed in this research. The proposed calibration method composes histograms through the outputs of each stage and calculates error sources. The digitized outputs of a stage are influenced directly by the operation of the prior stage, so the results of the histogram provide the information of errors in the prior stage. The composed histograms reduce the required samples and thus calibration time being implemented by simple modules. For 14-bit resolution pipelined ADC, the measured maximum integral non-linearity (INL) is improved from 6.78 to 0.52 LSB, and the spurious-free dynamic range (SFDR) and signal-to-noise-and-distortion ratio (SNDR) are improved from 67.0 to 106.2dB and from 65.6 to 84.8dB, respectively.

This dataset comprises sensor readings collected from various sensors deployed in an environment. Each entry in the dataset includes the following information:

1. SensorID: Identifier for the sensor.
2. Timestamp: The date and time when the data was recorded.
3. SensorType: Type of sensor used.
4. X, Y: Coordinates of the sensor's location.
5. SensorData: Recorded data from the sensor.
6. BatteryLife: Remaining battery life of the sensor.
7. Temperature: Temperature reading from the sensor.
8. IsFaulty: Indicates whether the sensor is faulty (binary: 0 for non-faulty, 1 for faulty).
9. Label: A categorical label assigned to the data.
10. Count: The count of data instances falling within specific ranges for each label.

The dataset also includes additional information in the form of histograms and time series data:

• Histograms depict the distribution of certain parameters like temperature, humidity, etc., across different ranges.
• Time series data provides sequential readings of temperature, humidity, and pressure over time, along with associated timestamps.

This dataset is valuable for tasks such as anomaly detection, predictive maintenance, and environmental monitoring.

Context

This dataset consists of 384 features extracted from CT images. The class variable is numeric and denotes the relative location of the CT slice on the axial axis of the human body. The data was retrieved from a set of 53500 CT images from 74 different patients (43 male, 31 female).

Content

Each CT slice is described by two histograms in polar space. The first histogram describes the location of bone structures in the image, the second the location of air inclusions inside of the body. Both histograms are concatenated to form the final feature vector. Bins that are outside of the image are marked with the value -0.25.

The class variable (relative location of an image on the axial axis) was constructed by manually annotating up to 10 different distinct landmarks in each CT Volume with known location. The location of slices in between landmarks was interpolated.

Field Descriptions:

• patientId: Each ID identifies a different patient
• value[1-241]: Histogram describing bone structures
• value[242 - 385]: Histogram describing air inclusions
• 386: Relative location of the image on the axial axis (class value).

Values are in the range [0; 180] where 0 denotes the top of the head and 180 the soles of the feet.

Acknowledgements

Original dataset was downloaded from UCI Machine learning Repository

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Banner image acknowledgement:

• Self Portre on cat scan, 1997
• Title: "Soon I will be there"
• Date: 8 April 1997
• Author: Sérgio Valle Duarte
• License: CC BY 3.0
• Source: Wikipedia

Inspiration

Predict the relative location of CT slices on the axial axis

The process of desertification in the semi-arid climatic zone is considered by many as a catastrophic regime shift, since the positive feedback of vegetation density on growth rates yields a system that admits alternative steady states. Some support to this idea comes from the analysis of static patterns, where peaks of the vegetation density histogram were associated with these alternative states. Here we present a large-scale empirical study of vegetation dynamics, aimed at identifying and quantifying directly the effects of positive feedback. To do that, we have analyzed vegetation density across 2.5 × 106 km2 of the African Sahel region, with spatial resolution of 30 × 30 meters, using three consecutive snapshots. The results are mixed. The local vegetation density (measured at a single pixel) moves towards the average of the corresponding rainfall line, indicating a purely negative feedback. On the other hand, the chance of spatial clusters (of many “green” pixels) to expand in the next census is growing with their size, suggesting some positive feedback. We show that these apparently contradicting results emerge naturally in a model with positive feedback and strong demographic stochasticity, a model that allows for a catastrophic shift only in a certain range of parameters. Static patterns, like the double peak in the histogram of vegetation density, are shown to vary between censuses, with no apparent correlation with the actual dynamical features. Our work emphasizes the importance of dynamic response patterns as indicators of the state of the system, while the usefulness of static modality features appears to be quite limited.