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This dataset provides satellite-derived chlorophyll-a (Chla) and turbidity (Turb) maps for the Indian River Lagoon system (Florida, USA) at high spatial resolution (20 m), generated from Sentinel-2 Multispectral Imager (MSI) observations for the period 2016–2023. The data were derived using regionally optimized algorithms developed for optically complex waters, with rigorous quality control applied to satellite–in situ matchups and a dynamic optically shallow water mask applied to daily imagery. Monthly and annual products are provided in GeoTIFF format. The geographic extent covers the Indian River Lagoon system (Mosquito Lagoon, Banana River Lagoon, and the Indian River Lagoon) along the east coast of Florida, USA. All products were generated from publicly available Sentinel-2 data provided by the European Space Agency (Copernicus program). Detailed methods are described in a manuscript currently under review. In addition to the datasets archived here, daily Chla_MSI and Turb_MSI imagery can be accessed through https://optics.marine.usf.edu under “Satellite Data Products” > “High Resolution”.

The optical response of a suspension microdroplet is governed not only by the properties of the dispersed phase, but also by the finite size and optical structure of the droplet itself. As a result, the interpretation of scattered-light patterns from such systems constitutes a non-trivial inverse problem. We examined whether laser speckle images recorded from single levitating microdroplets of suspension can be used for data-driven recognition of selected droplet and suspension parameters. Experiments were performed on slowly evaporating microdroplets of monodisperse TiO2 nanoparticle (NP) suspensions in diethylene glycol confined in a linear electrodynamic quadrupole trap. Speckle images were analyzed with a convolutional neural network (CNN) trained to classify droplet diameter, nanoparticle concentration, and nanoparticle diameter, first in separate tasks and then in combined two-parameter and three-parameter classifications. The results suggest that CNN-based analysis of speckle images may provide a viable route toward multi-parameter optical diagnostics of free suspension microdroplets and, potentially, more complex aerosol-like systems. Since the training set size is over 290 GB and the independent data set is over 230 GB, here we provide only sample images (5 of each 5000-image class) used for training and, in a separate folder, from independent data set (5 of 4x1000 - one out of 4 specific microdroplets radii was selected). Each (Matlab) .mat file contains a single frame: a 640x480 array of doubles obtained from the corresponding original image by subtracting the background (a frame with no droplet), Gaussian filtering of width 2 and normalizing from min to max. Names of folders reflect the nanoparticle radius in nm, nanoparticles suspension mass concentration in mg/mL and microdroplet diameter in micrometers. All images were acquired with 20 ms exposure time. We verified that such exposure time is short enough by analysing the autocorrelation function (ACF) obtained from the dynamic light scattering (DLS) experiments performed on the studied microdroplets of suspension. The CNN code is provided in the CNN_4_Speckle_from_droplets_w_tweaks.m Matlab file. The sample ACFs from dynamic light scattering on microdroplets of 6 different diameters, with 20 mg/mL concentration of 100-nm diameter TiO2 NPs are provided in ACFs.csv file. The first line contains the column names: time, 70,60.1,81.7,107.1,131,117, where the numbers give the diameter of the microdroplets. The second line contains the units designation.

This dataset supports the manuscript “Regime-Aware Adaptive Security Policy Management for Smart Environments under Attacker Learning.” The package contains aggregate simulation outputs, experiment configuration files, regime catalogues, transfer and drift catalogues, a run manifest, representative episode-level outputs, and figure files used to support the main manuscript and supplementary material. The study evaluates four defender policy families, namely commitment, Nash, reactive, and static, under a static foundation layer and three attacker-learning conditions: stationary within-regime learning, cross-regime transfer, and drifting-regime adaptation. The attacker is modelled as a discrete, partially observable, recency-weighted softmax Q-learning agent. The evaluation reports security and operational outcomes, including effective risk, defender utility, attack intensity, control intensity, latency burden, energy burden, switching burden, policy stability, attacker reward, attacker abstention rate, attacker entropy, convergence episode, transfer degradation, and drift recovery time. The dataset is intended to support verification of the reported empirical results, inspection of the figure evidence, and reuse of the aggregate simulation outputs. The CSV files can be opened with Python, R, Excel, LibreOffice Calc, or other statistical software. Figures are provided in PNG and PDF formats where available. The current package contains data, configuration files, catalogues, representative outputs, and figures; it does not include a full executable simulation codebase.

Research Hypothesis In semiarid grasslands, long-term annual mowing is expected to alter plant community structure and soil microclimate, thereby affecting soil extracellular enzyme activities and overall soil multifunctionality. We hypothesized that: Mowing increases soil multifunctionality (SMF) by stimulating key carbon- and nitrogen-cycling enzymes, primarily through increased soil temperature and altered substrate availability. The magnitude of mowing effects depends on plant community type (grass-, forb-, or legume-dominated), because different functional groups differ in root architecture, litter quality, and nutrient acquisition strategies. Soil temperature and total nitrogen are the main direct drivers of mowing-induced changes in soil multifunctionality, while plant species richness and cover exert indirect effects. This study tested these hypotheses across six contrasting plant communities on the Mongolian Plateau. 2. Data Gathering Methods In mid-August 2020 (peak growing season), the following variables were measured in each plot: Plant variables: species richness (number of species per 1×1 m quadrat), plant cover (%), litter mass (g·m⁻²). Soil physicochemical properties (0–10 cm depth): ST = soil temperature (°C, measured in situ at 10 cm depth) TN = soil total nitrogen (g·kg⁻¹) DOC = dissolved organic carbon (mg·kg⁻¹) Soil enzyme activities (nmol·g⁻¹·h⁻¹): C-cycle enzymes: BG (β-1,4-glucosidase), CBH (β-cellobiohydrolase), BX (β-xylosidase) N-cycle enzymes: NAG (β-1,4-N-acetylglucosaminidase), LAP (L-leucine aminopeptidase) P-cycle enzyme: AP (acid phosphatase)

Table 1 Clinical characteristics of human infections with avian influenza A H10Nx virus, China, 2013-2025. Table 2 Important mammalian adapting molecular markers from eight human-origin H10Nx isolates. Figure 1. Geographic and temporal distribution of human H10Nx infections in China, 2013-2025. Figure 2. Phylogenetic relationships of the HA and NA genes of human-origin H10Nx viruses. Figure 3. Amino acid conservation and frequency of key PB2 residues in avian H10Nx viruses. Figure 4. Global distribution, host range, and key PB2 residues of mammalian H10 subtype AIVs detected from 1984 to 2025. Supplementary Figure 1. Phylogenetic tree of the PB2 and PB1 genes of human-origin H10Nx avian influenza viruses. Supplementary Figure 2. Phylogenetic tree of the PA and NP genes of human-origin H10Nx avian influenza viruses. Supplementary Figure 3. Phylogenetic tree of the MP and NS genes of human-origin H10Nx avian influenza viruses.

This repository provides a harmonized, station-level daily rainfall dataset and a verified flood event catalog covering the entire province of West Java, Indonesia, from 1 January 2020 to 31 December 2025. Daily rainfall observations were compiled from 460 stations operated by BMKG (synoptic, climatological, and rainfall stations), as well as collaborative networks (BBWS, Pos Hujan Kerjasama). All stations were geolocated using WGS84 coordinates and aligned to the BPS 2023 administrative gazetteer. The flood event catalogue (n = 4,231 events) was assembled from the National Disaster Data and Information (DIBI-BNPB) database and BPBD West Java reports, deduplicated, and cross-validated against media records. A consensus table (n = 3,514 events) links each flood event to the nearest rainfall observation within 25 km and ±1 day, enabling direct event–rainfall analysis. Data have been processed through a four-stage WMO-compliant quality control workflow (format harmonization, range checks, temporal consistency, and spatial consistency). Stations with more than 30% missing daily values are flagged but retained; the published rainfall matrix is left unimputed to preserve raw observational integrity, while the consensus table uses Inverse Distance Weighting (IDW, power = 2) for short gaps (≤2 days). The dataset supports benchmarking of satellite (GSMaP, IMERG) and reanalysis (ERA5, MERRA-2) precipitation products, hydrological modeling, flood-risk assessment, and training of machine-learning forecasting models (e.g., XGBoost, LSTM) over a densely populated tropical region.

The dataset published here supports the article entitled "First Lidar Observations of Ionosphere–Thermosphere–Mesosphere (ITM) Na Transition Layer (~120–95 km): Regular Occurrence Over Boulder (40.13°N, 105.24°W) and Possible Formation Mechanisms", submitted to Geophysical Research Letters (GRL) in 2026. The data are provided in MatLab format along with MatLab plotting code.

This is the source for methods in the article: Modeling Bank Systemic Risk of Emerging Markets under Geopolitical Shocks: Empirical Evidence from BRICS Countries User can test the code with their own private data and their own set of variables, following the instructions below and follow the README file after extract the zip file.

This dataset contains structured information extracted from the nomination affidavits (Holofnama) submitted by candidates contesting in the 13th National Parliamentary Election of Bangladesh, held on 12 February 2026. The source documents were publicly released by the Election Commission of Bangladesh, and the dataset was compiled through a manual extraction process followed by systematic standardization, normalization, and validation to ensure consistency and usability across records. The dataset preserves the information exactly as declared by the candidates in their publicly available affidavits, with no alteration beyond formatting standardization. It includes a broad range of candidate-level attributes such as personal and demographic details, electoral constituency, political party affiliation, profession, educational qualifications, annual income, movable and immovable assets, liabilities, tax-related disclosures, and participation in social activities. The final dataset is organized in both excel and tabular CSV format, where each row represents an individual candidate and each column corresponds to a specific declared attribute. This structure makes the dataset suitable for use in a wide variety of computational and social science workflows. This dataset may be useful for researchers and practitioners working in political science, governance, electoral studies, public policy, socio-economic analysis, statistical modeling, data mining, machine learning, and comparative election studies. It also provides a valuable resource for transparency and accountability research in democratic institutions. No sensitive or hidden personal information is included beyond what is already publicly disclosed in official candidate affidavits, and no private identifiers beyond candidate names have been added.

Este dataset contiene los datos de temperatura y humedad obtenidos mediante un sensor DHT11 conectado a un microcontrolador Arduino en un mini terrario, además, se incluye el código fuente utilizado para la adquisición de los datos y programacion del Arduino, lo que permite la replicación del experimento y el análisis del comportamiento de las variables ambientales en un sistema cerrado.