Find research datasets worth reusing

opencc-by-4.0Jan 2024View details →

Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics -- Reproducibility Files

This repository contains the files needed to reproduce the data, analysis and visualizations from the paper 'Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics'. <h3>Data collection</h3> For the data collection, we make use of the IOHexperimenter library to access the BBOB functions. In addion, we rely on the set of libraries discussed in the paper. For the full set of libraries used during the benchmarking experiment, please refer to reqs_benchmark.txt (in the Benchmarking folder). The scripts used throughout the data collection are located in the 'benchmarking' directory. We have 1 file for each set of algorithms (expect mealpy and NIAPY, which are combined in the 'bestiary' file). Running these scripts generates a large amount of IOHprofiler-compatible files, which are availalbe in the 'Raw.zip' (Note: some of the libraries have their raw data split between multiple folders, if you want to run the full analysis, please merge these folders into one before processing). When running these scripts, please make sure to set the relevant FOLDER-variables at the top of the script! <h3>Data processing</h3> The raw data can be processed both for anytime performance (AOCC) and fixed-budget performance using the respective 'processing_METHOD.py' scripts. This generates several large csv-files, which are available in 'processed_data.zip' <h3>Data analyis and visualization</h3> The full set of analysis and corresponding visualizations can be reproduced using the 'Vizualization.ipynb' notebook. This notebook can use the processed performance data from the previous step, or the next level of processing with resulted in the remaining csv-files in this repository (this step is explained in the notebook itself).

opencc-by-4.0Dec 2023View details →

Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization

This is the raw data for the manuscript:Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization.A readme file containing all descriptions can be found in the main folder. All data are sorted in a separate subfolder each according to the three different types of datasets used in the main manuscript. Further, the Python scripts used for tomographic reconstruction are to be found in the zip file.Abstract:Compressed sensing is an image reconstruction technique to achieve high-quality results from limited amount of data. In order to achieve this, it utilizes prior knowledge about the samples that shall be reconstructed. Focusing on image reconstruction in nanotomography, this work proposes enhancements by including additional problem-specific knowledge. In more detail, we propose further classes of algebraic inequalities that are added to the compressed sensing model. The first consists in a valid upper bound on the pixel brightness. It only exploits general information about the projections and is thus applicable to a broad range of reconstruction problems. The second class is applicable whenever the sample material is of roughly homogeneous composition. The model favors a constant density and penalizes deviations from it. The resulting mathematical optimization models are algorithmically tractable and can be solved to global optimality by state-of-the-art available implementations of interior point methods. In order to evaluate the novel models, obtained results are compared to existing image reconstruction methods, tested on simulated and experimental data sets. The experimental data comprise one 360° electron tomography tilt series of a macroporous zeolite particle and one absorption contrast nano X-ray computed tomography (nano-CT) data set of a copper microlattice structure. The enriched models are optimized quickly and show improved reconstruction quality, outperforming the existing models. Promisingly, our approach yields superior reconstruction results, particularly when information about the samples is available for a small number of tilt angles only.

opencc-by-4.0Feb 2024View details →

Datasets of synthetic task flow graphs for evaluating a latency/energy optimization task allocation framework

These datasets of synthetic task flow graphs were generated to evaluate the performance and scalability of an optimal task allocation approach for applications of various structures and sizes in an environment following the edge/hub/cloud paradigm. The system under study comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objective was the minimization of either overall latency or overall energy consumption, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device.   We generated 18 task flow graphs of parallel, serial, and mixed (a combination of parallel and serial) structure with 10, 100, and 1000 nodes, and various in/out degrees, utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt and in [3].   Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10654551.   References: [1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245. [2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/. [3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, "An optimization framework for task allocation in the edge/hub/cloud paradigm," Future Generation Computer Systems, vol. 155, pp. 354-366, Jun. 2024, doi: 10.1016/j.future.2024.02.005.

opencc-by-4.0Jan 2024View details →

Research data for "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"

<h1>Data publication for the paper "Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes"</h1> <div>Contains scripts for performing fabrication uncertainty guided design optimization, example scripts, research data (raw data),  cleanup), additional information on models, convergence plots, and field exports.</div> <h2>Funding</h2> <div> <div> <div>We acknowledge funding by the German Federal Ministry of Education and Research (BMBF project siMLopt number 05M20ZAA and BMBF Forschungscampus MODAL number 05M20ZBM) as well as funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689). This project (20FUN05 SEQUME) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. This project is co-financed by the European Regional Development Fund (EFRD, application no. 10184206, QD-Sense).</div> </div> </div>

opencc-by-4.0Mar 2022View details →

Engineering the Compositional Architecture of Core-Shell Upconverting Lanthanide-Doped Nanoparticles for Optimal Luminescent Donor in Resonance Energy Transfer: The Effects of Energy Migration and Storage

Förster Resonance Energy Transfer (FRET) between single molecule donor (D) and acceptor (A) is well understood from fundamental perspective and is widely applied in biology, biotechnology, medical diagnostics and bio-imaging. However, the reliability of molecular FRET measurements can be affected by numerous artefacts which eventually hamper quantitative and reliable analysis, mostly due to issues with the donor and acceptor molecules. Lanthanide doped upconverting nanoparticles (UCNPs) have demonstrated their suitability as alternative donor species. Nevertheless, while they solved most disadvantageous features of organic donor molecules, such as photo-bleaching, spectral cross-excitation and emission bleed-through, the fundamental understanding and practical realizations of bio-assays with UCNP donors remain challenging. Among others, the actual donor ions in individual donor UCNPs are the numerous activator ions randomly distributed in the nanoparticle at various distances to acceptors anchored on the nanoparticle surface. Further, the power dependent, complex energy transfer upconversion and energy migration between sensitizing and activating lanthanide ions within UCNPs complicate the decay based analysis of D-A interaction. In this work, the assessment of designed virtual core-shell nanoparticle (VNP) models led us to the new designs of UCNPs, such as …@Er, Yb@Er, Yb@YbEr, which were experimentally evaluated as donor nanoparticles and compared to the simulations. Moreover, the specific properties of lanthanide-based upconversion motivated us to analyze not only steady-state luminescence and luminescence decay responses of both the UNCP donor and the sensitized acceptor, but also the effects of their luminescence rise kinetics upon RET was discussed in newly proposed disparity measurements. The presented studies help to understand the role of energy-transfer and energy migration between lanthanide ion dopants (due to their concentration and spatial distribution) and how the architecture of core-shell UCNPs affects their performance as FRET donors to organic acceptor dyes.

The VSMC RNA-seq datasets for "Strategies for Arterial Grafts Optimization at Single Cell Level"

This dataset collection contains the human vascular smooth muscle cell (VSMC) bulk RNA-seq data generated in the study Strategies for Arterial Grafts Optimization at Single Cell Level. Primary human VSMCs (CP-H167, Pricella) were cultured in DM/F12 medium supplemented with FBS, EGF, bFGF, insulin, and penicillin (CM-P167, Pricella). The media was replaced per 48 hours. VSMCs were treated with 100ng/ml MIF (HY-P7387, MCE) for 24 hours in the absence or presence of 50 µMol ISO1 (HY-16692, MCE). The VSMCs with the passage number 3 to 5 were used in the study. Total RNA was extracted from cultured VSMC under different treatments using RNeasy Mini Kit (74104, Qiagen). A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. The index of the reference genome was built using Hisat2 v2.0.5. and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. The sample info for the BAM files and their index files are listed as follows: <table> <tbody> <tr> <td>SampleCode</td> <td>Sample Description</td> </tr> <tr> <td>C1</td> <td>VSMC (replicate 1)</td> </tr> <tr> <td>C2</td> <td>VSMC (replicate 2)</td> </tr> <tr> <td>C3</td> <td>VSMC (replicate 3)</td> </tr> <tr> <td>M1</td> <td>VSMC treated with MIF (replicate 1)</td> </tr> <tr> <td>M2</td> <td>VSMC treated with MIF (replicate 2)</td> </tr> <tr> <td>M3</td> <td>VSMC treated with MIF (replicate 3)</td> </tr> <tr> <td>M_I1</td> <td>VSMC treated with MIF and ISO1 (replicate 1)</td> </tr> <tr> <td>M_I2</td> <td>VSMC treated with MIF and ISO1 (replicate 2)</td> </tr> <tr> <td>M_I3</td> <td>VSMC treated with MIF and ISO1 (replicate 3)</td> </tr> </tbody> </table>

NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA

This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model. The dataset also includes 15 radiosounding launched for assessing the retrievals. For further information, please see: Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.

The existence of optimal (v,4,1) optical orthogonal codes achieving the Johnson bound

This is a program for checking Lemma 3.1 of the paper ``The existence of optimal (v,4,1) optical orthogonal codes achieving the Johnson bound'' .

Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials

This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use" <ol> <li> Input Data: This file includes the model input data. </li> <li> Payoff Matrix Solution: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution). </li> <li>Compromise solution: This file contains all decision variables of the compromise solution.</li> <li> Pareto Curve: This file presents the Pareto curve solution.  </li> <li>Pareto Curve Solution: This file contains all decision variables in each of the Pareto curve solutions.  </li> </ol>

opencc-by-4.0Feb 2024View details →

Supplementary material from: Optimizing the Conversion of Bio-Oil from Haematococcus pluvialis to Fatty Acid Methyl Esters

Data obtained in the thermal characterization of bio-oil and biodiesel derived from Haematococcus pluvialis microalgae. This document contains the data used to generate the plots shown in Figure 3 of the paper. Content: <ul> <li>Bio-oil FTIR results</li> <li>Biodiesel FTIR results</li> <li>Bio-oil TGA results</li> <li>Biodiesel TGA results</li> <li>Bio-oil DSC (pour point) results</li> <li>Biodiesel DSC (pour point) results</li> <li>Biodiesel DSC (heat of combustion) results</li> </ul>

opencc-by-4.0Oct 2023View details →

Optimizing Small Business Operations through Information Architecture and Enterprise Engineering

This research paper investigates the critical significance of information architecture and enterprise engineering in streamlining small business operations for continued expansion and competitiveness. ( Carrasco Ramirez, J. G. (2024).  With rapid technological developments and shifting customer demands, small businesses confront mounting challenges to adjust and flourish amid change. Drawing upon academic sources and practical findings, this study reveals how purposeful implementation of information ( Carrasco Ramirez, J. G. (2024) architecture enables effective data handling, knowledge sharing, and decision-making mechanisms within small organizations.Ramírez, J. G. C. (2023) Additionally, it outlines the tenets of enterprise engineering, stressing the need for creating coherent business frameworks, processes, and systems to maximize operational efficacy and resource allocation. By integrating information architecture and enterprise engineering approaches, small businesses can bolster their organizational resiliency and maintain a competitive edge in today's dynamic marketplace.   Central to these strategies is the development and implementation of comprehensive business plans. This abstract explores the significance of business planning in driving effective management strategies, highlighting its role in guiding decision-making, fostering organizational alignment, and facilitating proactive adaptation to changing market conditions. (Carrasco Ramirez, J. G. (2024).

opencc-by-4.0Apr 2024View details →

Fluid and kinetic studies of tokamak disruptions using Bayesian optimization

The codes and the data in this directory corresponds to the code and results used in the paper [I. Ekmark et al (2024) J. Plasma Phys., Fluid and kinetic studies of tokamak disruptions using Bayesian optimization, http://arxiv.org/abs/2402.05843]. References to figures below refer to this publication.  The optimizations have been performed using the Python package by Fernando Nogueira [https://github.com/bayesian-optimization/BayesianOptimization] and the simulations are performed using the disruptions simulation simulation tool DREAM [https://github.com/chalmersplasmatheory/DREAM, git hash: 0d786e859f6228185ef68b6b3639747e8d96172d], for more information on the latter code visit https://ft.nephy.chalmers.se/dream/. The codes:  - BayesianOptimization.py: Runs the optimization, first in fluid and then in isotropic mode. For activated simulations, use the flag "-A".      - BlackBox.py: Contains the functions that are run in the optimizations and sets up the simulations.      - utils.py: Contains the settings for the simulations as well as some other functions needed in BlackBox.py      - ITER.py: Contains all the ITER specific settings.      - Exceptions.py: Contains exceptions needed during the simulations.       - CostFunction.py: Contains the functions used for evaluating the cost function value for specified values of the representative runaway current, final Ohmic current, current quench time and transported heat fraction.  - RunCases.py: Sets up simulations for the cases of table 1 in the paper, as well as for all the optima found. The data:  - Optimization results:       - Data/OptimizationResults/optresult_fluid.json: Contains the optimization data for the non-activated case using the fluid model. Used to produce figure 1.a.      - Data/OptimizationResults/optresult_isotropic.json: Contains the optimization data for the non-activated case using the isotropic model. Used to produce figure 1.b.      - Data/OptimizationResults/optresult_fluid_activated.json: Contains the optimization data for the activated case using the fluid model. Used to produce figure 5.a.     - Data/OptimizationResults/optresult_isotropic_activated.json: Contains the optimization data for the activated case using the isotropic model. Used to produce figure 5.b.          - Data/OptimizationResults/components_fluid.json: Contains the cost function components for each sample from the optimization of the non-activated case using the fluid model. Used to produce figure 2.a.      - Data/OptimizationResults/components_isotropic.json: Contains the cost function components for each sample from the optimization of the non-activated case using the isotropic model. Used to produce figure 2.b.      - Data/OptimizationResults/components_fluid_activated.json: Contains the cost function components for each sample from the optimization of the activated case using the fluid model. Used to produce figure 6.a.     - Data/OptimizationResults/components_isotropic_activated.json: Contains the cost function components for each sample from the optimization of the activated case using the isotropic model. Used to produce figure 6.b.       - Cases:     - Data/Cases/nonActivatedOpts/fluidOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the non-activated scenario using the fluid model.     - Data/Cases/nonActivatedOpts/isoOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the non-activated scenario using the isotropic model.     - Data/Cases/activatedOpts/fluidOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the activated scenario using the fluid model.     - Data/Cases/activatedOpts/isoOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the activated scenario using the isotropic model.     - Data/Cases/[circle, cross, square, triangle]: Contains outputfiles for fluid and isotropic simulations corresponding to the cases presented in table 1.

Strategic Integration of Urban Excess Heat Sources in District Heating: A Spatio-Temporal Optimization Paradigm

This data repository supports the techno-economic and geospatial optimization analysis of district heating systems (DHS) in Stockholm, with a particular focus on integrating Urban Excess Heat (UEH) sources. The dataset includes both input data and results, organized by the key analytical tools employed in the study: GIS, OSeMOSYS, and the Hotmaps Dispatch Model. The folder structure is organized as follows: /Data │ ├── Input_Data/ │   ├── GIS/ │   ├── OSeMOSYS/ │   ├── Hotmaps_Dispatch/ │ └── Results/     ├── GIS/     │     ├── Optimized DHS network extensions for UEH sources     │     ├── Long-term capacity and emissions projections for DHS           ├── Dispatch outputs for different electricity price scenarios <h2>Dataset Details</h2> 1. Input Data Folder: This folder includes all data used as inputs across the three primary tools:    - GIS: Contains geospatial data on the DHS network paths, source locations, and spatial configurations for network optimization.    - OSeMOSYS: Provides techno-economic parameters for heat generation technologies, including capital and operational costs, fuel price projections, and emissions factors.    - Hotmaps Dispatch: Includes detailed demand profiles, projected electricity prices, and the supply temperature requirements essential for dispatch simulations. 2. Results Folder: This folder contains the results generated by each tool after processing the input data:    - GIS: Outputs optimized paths for network expansion and geospatial mapping of UEH sources.    -OSeMOSYS: Contains projections for DHS technology adoption, emissions reduction, and cost optimization over a 23-year model period.    - Hotmaps Dispatch: Stores dispatch model results under various electricity pricing and temperature scenarios, capturing the optimal deployment of DHS resources. <h2>Summary of Tools Used</h2> - GIS (Geospatial Information System): Used to locate UEH sources and map their proximity to the existing DHS network, guiding the optimization of network expansions. - OSeMOSYS (Open Source Energy Modelling System): Performs long-term optimization for the DHS, factoring in the addition of UEH sources, electricity price scenarios, and emissions constraints. - Hotmaps Dispatch Model: Simulates operational dispatch of heat generation technologies in response to varying electricity prices and network temperatures, providing insights into real-time heat supply optimization. This repository thus provides a comprehensive dataset to support the techno-economic and spatial analysis of DHS, facilitating the integration of UEH sources in urban heating systems through scenario-based optimizations.

QUAPOS: A Dataset for Assessing and Optimizing Quadrant Photodiode-based Visible Light Positioning Systems

The QUAPOS (QUAdrant photodiode-based POSitioning system) database provides a comprehensive simulated dataset for Visible Light Positioning (VLP) systems using quadrant photodiodes as receivers. QUAPOS includes data from 73 unique scenarios, covering a variety of receiver-transmitter configurations, making it ideal for testing the influence of geometric arrangements on emerging algorithms. The database, generated using a novel simulation tool, provides both raw and processed data, including the received energy at each receiver for each emitter, estimated emitter position coordinates (calculated using our own positioning algorithm, assuming known receiver positions), and the ground truth emitter locations. The data are available in .csv and .mat formats,  ensuring compatibility and ease of use with common platforms such as Matlab© and Python. This dataset aims to support research on optimising receiver placement, developing noise-resistant algorithms, and comparing different VLP system configurations. In addition, the dataset contains enough data to support a wide range of algorithms, including those based on Machine Learning.

Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production

These datasets were utilized to generate the figures and tables presented in the paper 'Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production': (1) Fig.1.html: Code for generating Fig. 1. (2) Fig.2.xlsx: Data for Fig. 2. (3) Fig.3.xlsx: Data for Fig. 3. (4) Fig.4.xlsx: Data for Fig. 4. (5) Fig.5.xlsx: Data for Fig. 5. (6) Other .html files are Python code corresponding to different tables and figures.

Optimized Analytical Workflow for Single-Nucleus Transcriptomics in Main Metabolic Tissues

Single-nucleus RNA sequencing (snRNA-seq) has emerged as a powerful approach for studying cellular heterogeneity in metabolic tissues. However, snRNA-seq analysis remains challenging due to low gene expression and data complexity. Here, we introduce an optimized analytical workflow for snRNA-seq data from 67 samples across four main metabolic tissues white adipose tissue, hypothalamus, muscle and liver. We emphasized the importance of key steps including ambient RNA removal, doublet identification, normalization and data integration to ensure accurate downstream analysis. This workflow offers a valuable resource for researchers in metabolism, facilitating deeper insights into cellular diversity and metabolic function through rigorous snRNA-seq analysis.

Hysteresis Performance and Design Optimization of Rubber Concrete Anti-collision Layer of Concrete Bridge Piers

Static and hysteresis experiments were implemented firstly to obtain the compressive strength, elastic modulus and energy dissipation factor of rubber concrete. This study explores the static and dynamic properties of rubber concrete.

Spine model and datasets for "Home Energy Optimization using Vehicle-to-Home"

The Spine models are simulated for the publication of "Home Energy Optimization using Vehicle-to-Home" in the journal of Open Reserach Europe. The file lists are explained as follows: "Non-commuter_spine.zip" is the zipped folder of Spine model for non-commuting household. "Spine_commuter.zip" is the zipped folder of Spine model for commuting household. "Recorded Results and Paramater Variation Graphs - ORE.xlsx" describes the figures and outputs applied for the paper. "PV, elec, thermal_non_commuting.zip" denotes the non-commuting model input. "Model_input_commuter (1).zip" denotes the commuting model input. "Model output_commuter (2).xlsx" denotes the commuting model output.

Optimal IoT Sensor Deployment in the WUI: A Comparative Analysis of Strategies

Included here are individual burn maps used for evaluating algorithm results in the paper: Optimal IoT Sensor Deployment in the WUI: A Comparative Analysis of Strategies. This paper was accepted for presentation at IEEE HONET 2024, the 21st IEEE International Conference on Smart Communities. (For financial/travel reasons the paper has since been withdrawn) Also included are maps of fuel load and elevation (geotifs) and the daily weather (in .csv format) for the region of interest used in the burn probability simulator Burn-P3+ to generate the individual burn maps. This paper investigates optimized IoT sensor deployment strategies within the Wildland-Urban Interface (WUI), a key component of smart communities. It presents a comparative analysis of a novel dynamic grid approach against traditional random and greedy algorithms. By employing the Burn-P3+ simulator, detailed burn probability maps are generated for two Canadian geographically distinct areas: the Halifax Regional Municipality and Kelowna. Our analysis reveals that the dynamic grid method significantly enhances fire detection capabilities by strategically distributing IoT sensors in alignment with calculated burn probability. This approach shows a marked improvement of as much as 35% in burn detection over traditional deployment methods. The paper highlights the advantages of structured IoT sensor placement in supporting smart communities, offering more efficient and effective wildfire management strategies in the WUI through real-time data available to both fire mitigation teams and AI. Partial code for the sensor deployment algorithms discussed in the above mentioned paper is <a href="https://github.com/richardjpurcell/sensor-deployment-algorithms">available on GitHub</a>.