Input Data Files Research Articles

SSN2: The next generation of spatial stream network modeling in R.

The SSN2 R package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN R package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the R-spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN R package but leverages modern R-spatial tools like sf (Pebesma, 2018). SSN2 also provides many useful features that were not available in the SSN R package, including new modeling and helper functions, enhanced fitting algorithms, and simplified syntax consistent with other R generic functions.

Формування вихідних даних для вирішення задач маршового розрахунку надзвукового обтікання ракет різного компонування

This paper gives a general overview of components and layouts used in supersonic rockets of different purposes. The rocket layout is specified as a structure and a set of components (a wing, a rudder, a stabilizer, a destabilizer, and a superstructure) arranged along the rocket structure. The goal of this work is to develop a unified approach to specifying the shape parameters of rocket layouts regardless of the rocket type. For complex-shape rocket layouts, the paper proposes an approach in which the shapes of the rocket structure and the additional components installed thereon are specified independently. The additional components of the rocket layout are bound to the rocket structure using operation parameters. The use of the operation parameters binds each additional component of the rocket layout to the rocket structure, thus offering a unified method for specifying the geometrical parameters of variously shaped rocket layouts. This approach is developed towards more complex shapes of rocket layout elements arbitrarily placed on rocket structures. The outside shape of each rocket component is specified in a Cartesian system of coordinates rigidly bound thereto. A unified approach to specifying the outside shape of various rocket components is presented. According to the general scheme of specifying the geometrical parameters of rocket layout components, they are specified by three methods: analytically, by plan shape, and by plan shape with board and end chord profiles. To describe the outside shape of a component, the specification method and the number and the values of its key parameters are specified. To specify rocket layout input data, one has to fix the number of additional components to be installed on the rocket structure. For each layout component, the parameters that define its shape, location on the rocket structure, and deflection angle are specified. To each layout component there corresponds an input data set of its own. The set consists of parameters that define the shape of the component and parameters of its operation as a part of the layout. A standard input data file for specifying rocket layout shapes is configured.

Code and Data Repository for Quadratic Optimization Models for Balancing Preferential Access and Fairness: Formulations and Optimality Conditions

This directory provides code used for the computations associated with the quadratic optimization models in the context of undesirable facility location problems for the abovementioned article. src contains scripts for the optimization of the MIQP model and visualization of the results: model.py contains functions for building and optimizing the MIQP model, while greedy_heuristic.py applies a greedy heuristic to achieve a feasible solution. plotting.py and results.py contain functions tasked with visualizing results through various different plots as well as excel tables. They also contain superordinate functions that create the corresponding results first by running functions from model.py and/or greedy_heuristic.py before creating the corresponding visualization. These functions are called by functions in tables_and_figures.py to create the exact same figures and tables that are included in the paper from scratch. Lastly, utils.py contains helper and utility functions. catchment_population presents an efficient algorithm to compute the "catchment population" of each recycling center that was used to estimate the capacities. An implementation of this algorithm is contained in catchment_population.py. Further, this directory contains two corresponding input data files in csv format: bavaria_grid_population.csv is a file containing the latitude and longitude of the centroid of each 100m x 100m grid in Bavaria as well as the residing population. rc_locations.csv contains the latitude and longitude of each recycling center in Bavaria. data contains the two input data files that are used in the MIQP model: users_and_facilities.xlsx contains all ZIP codes and recycling centers related data like the population, centroid and regional spatial type (rural/urban) of each ZIP code as well as the capacity, centroid and regional spatial type of each recycling center. travel_dict.json.pbz2 is a compressed json file that contains the travel probabilities from each ZIP code to each recycling center. results contains the excel tables and figures visualizing the results that are included in the paper.

LandS: Vegetation modeling based on Ellenberg's ecological indicator values

We present LandS, a new version of the Gras Model. The Gras Model was designed to simulate grassland development at local scales based on Ecological Indicator Values (EIVs) for different grassland management practices. In LandS, we complemented the existing set of EIVs with a second set representing several environmental factors: light, moisture, temperature, soil pH and nitrogen, also known as Ellenberg's EIVs. These new EIVs make the model more versatile and applicable to a wide range of sites across Central Europe. For example, it can be used on sites with dry or moist, acidic or calcareous soils in grassland or forest environments. We have also improved the implementation of the model by introducing version control and moving species and site-specific variables to data input files, so that species sets can be easily swapped for application in new study sites. We demonstrate the use and behavior of the model in two simulation experiments exploring interactions mediated by Ellenberg's EIVs, using input files to apply the model to different landscapes. We also provide detailed guidance on species selection and calibration, and discuss model limitations.•LandS is an improved version of the GraS Model for simulating vegetation development at the local scale.•It includes Ellenberg-like indicator values for environmental variables for inverse prediction of species occurrence and composition.•The model is now flexible enough to be used for study sites throughout Central Europe, using data input files for species initialization.

Face Mask Detection using MobilenetV2 of Convolution Neural Network

In light of the fact that the COVID-19 pandemic is spreading swiftly over the earth, it is crucial to create new technologies to study and resist the disease. Face masks and gloves are required for protection against the coronavirus, and scientists and doctors have advised everyone to wear the mask for the whole day. Thus, various procedures are accessible to different people wearing face masks. Masks are advised as a basic barrier to stop respirational beads from receiving into the air and against other people once someone is found to be using cover hacks. Additionally, this is known as source governance. This arti- cle is based on the current understanding of respiratory beads’ function in the escalation of COVID-19 infection. In this problem, the face mask procedure was built using MobileNetV2. Compared to the current system, Mobile Net V2 can be used to identify face masks among individuals with greater accuracy. The input data file contains 500 images taken from the Kaggle face mask Detec- tion Dataset. A scene with a mix of people donning masks and without mask. The output is a segmented picture of the same. Later, this process is improved by using a webcam to capture real-time video. The video is then segmented into the frame and resized as required, and the result is a video-segmented image. The model was then run to determine whether or not individuals were wearing masks after performing the pre-processing function. An accuracy of 80 was used to acquire the results.

A detailed view of the Adaptive-Comfort-Control-Implementation Script (ACCIS): The capabilities of the automation system for adaptive setpoint temperatures in building energy models

Recent research has looked into the potential for energy savings from adopting setpoint temperatures based on adaptive comfort. The recently suggested Adaptive-Comfort-Control-Implementation Script (ACCIS), a computational method that expands the use of air conditioning to adaptive comfort, can be used to accomplish this. The user's setup and an Input Data File (IDF) are used by ACCIS to convert PMV-based EnergyPlus models into adaptive setpoint models. The Adaptive Comfort Control Implemented Model (“accim”) Python module has been used to nest ACCIS, which was originally an Energy Management System (EMS) script. This research focuses on the creation of ACCIS and offers a more in-depth understanding of how the computational method functions based on the connections between the objects that compose it. Since all weather file quantity constraints have been overcome and studies can now be generated based on millions of simulations, ACCIS opens up new opportunities for performing energy simulations on a worldwide scale rather than only on a national or continental level. This potential is increased considering that, until recently only international models ASHRAE Standard 55 and European standard EN16798-1 were considered in ACCIS, but now a recent update allows to select local comfort models developed for specific areas. As a result, this potential is firstly demonstrated by means of the study of the 3 arguments that have the most important impact on the setpoint temperatures: the thermal comfort model, the acceptability levels and the behaviour of setpoint temperatures when adaptive comfort models are not applicable; and secondly by means of an actual example of use of accim.

Application Contingency Analysis of An IEEE 30 Bus System Using Matpower

Contingency analysis is significant for power system protection. Internal component failures or external causes such as lightning and equipment overloading can cause power system contingencies. Multiple outage scenarios are crucial in today’s deregulated environment for how the transmission network is utilized. The transmission network is highly pressured due to rising load demand and the need to operate economically. Contingency analysis of an IEEE 30 bus system is provided in this article utilizing Matpower software, which includes the source code. Matlab and Matpower are suggested as platforms for developing a screening process that examines the severity of each non-islanding branch failure, generator outage, MVA, and voltage violation. The generator terminal voltages will be permitted to fluctuate between 0.95 per unit and 1.05 per unit, as specified in the case30 input data files, to provide a broader practical zone. The performance index (PI) value is generated by adding the apparent performance index and the voltage performance index to rank the severity of the line outages. For the extreme contingency situation, a load flow study was performed. The four most severe scenarios, generator 22, generator 13, generator outage 23, and branch outage 28, should be constantly monitored to keep the electrical system safe.

Development progress of power prediction robot and platform: Its world level very long term prototyping example

Global Power Prediction Systems prototype version 2021 is presented with its system decomposition, scope, geographical/administrative/power grid decompositions, and similar. “Welcome”, “sign-up”, “log-in”, and “non-registered user main” web-interfaces are designed as draft on Quant UX. Map canvas is given as world political map with/without world power grid layers on QGIS 3.16.7-Hannover. Data input file is prepared based on several sources (1971-2018). It includes minimum and maximum values due to source value differences. 70/30 principle is applied for train/test splitting (training/testing sets: 1971-2003/2004-2018). 10 models are prepared on R version 4.1.1 with RStudio 2021.09.0+351. These are R::base(lm), R::base(glm), R::tidymodels::parsnip(engine("lm")), R::tidymodels::parsnip(engine("glmnet")) with lasso regularization, R::tidymodels::parsnip(engine("glmnet")) with ridge regularization, R::forecast(auto.arima) auto autoregressive integrated moving average (ARIMA), R::forecast(arima) ARIMA(1,1,2), and ARIMA(1,1,8). Electricity demand in kilowatt-hours at the World level zone for up to 500-years (2019-2519) prediction period with only 1-year interval is forecasted. The best model is the auto ARIMA (mean absolute percentage error MAPE and symmetric mean absolute percentage error SMAPE for minimum and maximum electricity consumption respectively 1,1652; 6,6471; 1,1622; 6,9043). Ex-post and ex-ante plots with 80%-95% confidence intervals are prepared in R::tidyverse::ggplot2. There are 3 alternative scripts (long, short, RStudio Cloud). Their respective runtimes are 41,45; 25,44; and 43,33 seconds. Ex-ante 500-year period (2019-2519) is indicative and informative.

Initialization of profile and social network analyses robot and platform with a concise systematic review

This paper presents profile and social network analyses on concise systematic review corpora. It suggests two new robots and platforms for profile and social network analyses, that will serve previously proposed data, expert, and event-driven robots and platforms for energy and power industry. The literature is collected and stored in three topic clusters “location”, “investment”, and “DEMATEL” to prepare corpora. Twenty-five publications are selected in each sample corpus. A sample dataset of each corpus is prepared for thirty-one features such as “author’s full name and surname”, “applied methods”, and “publisher”. Afterward, “authors network matrices” are prepared in spreadsheet software. Data input files (*.csv) are prepared for each dataset. Gephi 0.9.2 201709241107 (free open-source software) is used for social network analyses with built-in layout and statistics algorithms on a desktop Windows 10 Pro, Intel(R) Core(TM) i5 CPU 650 @ 3.20 GHz, 6,00 GB RAM personal computer in an offline and active cybersecurity software environment. Force Atlas, Force Atlas 2, Fruchterman–Reingold, OpenOrd, Yifan Hu, and Yifan Hu Proportional layout algorithms with Noverlap layout algorithm are run one by one. Runtimes range 2–120 s. All default statistic algorithms are run for several metrics like average degree, average weighted degree, betweenness centrality, closeness centrality, harmonic closeness centrality, eccentricity, and density. Authors in “location” cluster have a centralized network, but authors in “investment” and “DEMATEL” clusters have distributed networks. General profile analyses are conducted based on authors’ publications in the literature without any data and information on social media sites and platforms. Two new profile analysis metrics are proposed as “researcher’s past research focus index”, and “researcher’s future research focus prediction index”. Detailed profile analysis is performed for only Burak Omer Saracoglu. All analyses and findings are compared and summarized in the end.

FFT-PEEC: A Fast Tool From CAD to Power Electronics Simulations

A fast and general partial element equivalent circuit (PEEC) method based on the fast Fourier transform (FFT) is proposed for the first time. The numerical tool only requires common CAD data input files (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$.\text{stl}$</tex-math></inline-formula> format), then the discretization process is performed automatically by means of a fast voxelization technique based on ray intersection, thus, drastically reducing the human effort required to setup the model. The method allows for considering at the same time inductive and capacitive effects, and is focused on power electronics applications where propagation effects can be neglected, whereas all the other electromagnetic phenomena are considered. Specifically, the proposed method is particularly suited for problems where both electric and magnetic fields are equally important and, therefore, quasistatic approximations do not apply. An ad-hoc preconditioner which significantly speeds-up the solver is also proposed and, thanks to the FFT, both memory and computation time are significantly reduced, without the need of applying data compression. Both linear and nonlinear materials are considered by the proposed FFT-PEEC method. Sample implementation of the method is made publicly available.

On design sensitivities in the structural analysis and optimization of flexible multibody systems

The structural analysis and optimization of flexible multibody systems become more and more popular due to the ability to efficiently compute gradients using sophisticated approaches such as the adjoint variable method and the adoption of powerful methods from static structural optimization. To drive the improvement of the optimization process, this work addresses the computation of design sensitivities for multibody systems with arbitrarily parameterized rigid and flexible bodies that are modeled using the floating frame of reference formulation. It is shown that it is useful to augment the body describing standard input data files by their design derivatives. In this way, a clear separation can be achieved between the body modeling and parameterization and the system simulation and analysis.

Computational approach to extend the air-conditioning usage to adaptive comfort: Adaptive-Comfort-Control-Implementation Script

Recently, the energy saving potential from using setpoint temperatures based on adaptive comfort has been studied. This study proposes a computational approach, the Adaptive-Comfort-Control-Implementation Script (ACCIS), to extend the air-conditioning usage to adaptive comfort. ACCIS transforms PMV-based into adaptive setpoint building energy models according to both an Input Data File (IDF) and the setup specified by the user. Originally, ACCIS was an Energy Management System (EMS) script, but available functions have been extended, and ACCIS has been nested in an ease-to-use Python package called Adaptive Comfort Control Implemented Model (“accim”). A case study has been tested, whose results showed that adaptive setpoint temperatures could achieve an 83% of energy savings. However, its most powerful attribute is that it allows many simulations to be run with no limit because of both its high customisation properties and the fact that it allows the same IDF to be run with various EPW files.

Conventional Approach to Harmonious Coordinated Cadastral Database Weakness

The eKadaster system depended solely on the use of coordinated cadastral database known as the National Digital Cadastral Database (NDCDB) with an expected accuracy of ±10cm. Till date, there is an approximately of 7.8 million land parcels and 21.9 million boundary markers in the NDCDB that covers the total area of 132,183 km2. However, the NDCDB accuracy of ±10cm is still not at a satisfactory acceptance level and the adjustment keeps continuing without carrying out verification to the data sources weakness of varying accuracy and input errors. Thus, the foremost important corrective is to ensure the adjustment input files to have the exact value of the sources by further divided the existing adjustment blocks into smaller blocks to verify the input data line by line. A well distributed cadastral control points and latest NDCDB accessibility are also extensively needed to plan and to strengthen the adjustment network. The comparison result of the randomly picked ground truthing points in the field has shown a significant impact on the displacement accuracy that meet the expected tolerance of ±10cm or better after the data input file is cleaned without input error. And to further strengthen the adjustment network in order to make NDCDB accuracy better, the current cadastral control points shall need to tie to a highest accuracy fundamental network.

Exploitation of network-segregated CPU resources in CMS

CMS is tackling the exploitation of CPU resources at HPC centers where compute nodes do not have network connectivity to the Internet. Pilot agents and payload jobs need to interact with external services from the compute nodes: access to the application software (CernVM-FS) and conditions data (Frontier), management of input and output data files (data management services), and job management (HTCondor). Finding an alternative route to these services is challenging. Seamless integration in the CMS production system without causing any operational overhead is a key goal. The case of the Barcelona Supercomputing Center (BSC), in Spain, is particularly challenging, due to its especially restrictive network setup. We describe in this paper the solutions developed within CMS to overcome these restrictions, and integrate this resource in production. Singularity containers with application software releases are built and pre-placed in the HPC facility shared file system, together with conditions data files. HTCondor has been extended to relay communications between running pilot jobs and HTCondor daemons through the HPC shared file system. This operation mode also allows piping input and output data files through the HPC file system. Results, issues encountered during the integration process, and remaining concerns are discussed.

Your: Your Unified Reader

The advancement in signal processing and GPU based systems has enabled new transient detectors at various telescopes to perform much more sensitive searches than their predecessors. Typically the data output from the telescopes is in one of the two commonly used formats: psrfits and Sigproc filterbank. Software developed for transient searches often only works with one of these two formats, limiting their general applicability. Therefore, researchers have to write custom scripts to read/write the data in their format of choice before they can begin any data analysis relevant for their research. \textsc{Your} (Your Unified Reader) is a python-based library that unifies the data processing across multiple commonly used formats. \textsc{Your} implements a user-friendly interface to read and write in the data format of choice. It also generates unified metadata corresponding to the input data file for a quick understanding of observation parameters and provides utilities to perform common data analysis operations. \textsc{Your} also provides several state-of-the-art radio frequency interference mitigation (RFI) algorithms, which can now be used during any stage of data processing (reading, writing, etc.) to filter out artificial signals.

Dms-view: Interactive visualization tool for deep mutational scanning data.

The high-throughput technique of deep mutational scanning (DMS) has recently made it possible to experimentally measure the effects of all amino-acid mutations to a protein. Over the past five years, this technique has been used to study dozens of different proteins and answer a variety of research questions. For example, DMS has been used for protein engineering, understanding the human immune response to viruses, and interpreting human variation in a clinical setting. Accompanying this proliferation of DMS studies has been the development of software tools and databases for data analysis and sharing. However, for many purposes it is important to also integrate and visualize the DMS data in the context of other information, such as the 3-D protein structure or natural sequence-variation data. Here we describe dms-view (https://dms-view.github.io/), a flexible, web-based, interactive visualization tool for DMS data. dms-view is written in JavaScript and D3, and links site-level and mutation-level DMS data to a 3-D protein structure. The user can interactively select sites of interest to examine the DMS measurements in the context of the protein structure. dms-view tracks the input data and user selections in the URL, making it possible to save specific views of interactively generated visualizations to share with collaborators or to support a published study. Importantly, dms-view takes a flexible input data file so users can easily visualize their own DMS data in the context of protein structures of their choosing, and also incorporate additional information such amino-acid frequencies in natural alignments.

Enhanced EnHub: dynamic simulation of housing stock energy systems

In the UK, heating systems are the most prominent contributor to residential energy demand, with about 80% of the share. Their representation has thus been at the core of all UK-focussed Housing Stock Energy Models (HSEMs). However, these HSEMs estimate heating demand based on monthly or annual energy balances, with correspondingly approximate representations of heating systems and practices (incl. energy conversion, distribution and spatiotemporal control). This paper describes an extension to the dynamic HSEM: EnHub, to rigorously simulate space heating and hot-water components (i.e. heaters, boilers, pumps, radiators, end-point registers, thermostats, taps). Baseline simulations estimate the English housing stock's energy use as 35.9 mtoe. Alternative scenarios in which heating systems are substituted across the board to district heating or ground-source heat pumps predict a reduction in demand to 30 and 18 mtoe respectively; the latter potentially being zero-carbon if the power sector is successfully decarbonised. Abbreviations ASHP: Air Source Heat Pump; BREDEM: Building Research Establishment Domestic Energy Model; CHM: Cambridge Housing Model; DHW: domestic hot water; EHS: English Housing Survey; EnHub: housing stock Energy Hub; GSHP: Ground Source Heat Pump; GUI: Graphical User Interface; HDD: Heating Degree Day; HSEM: Housing Stock Energy Model; idf: EnergyPlus Input Data File; IEA: International Energy Agency; LHS: Latin Hypercube Sampling; NEED: National Energy Efficiency Data-Framework; SAP: Standard Assessment Procedure; SH: space heating; UK: United Kingdom

Modelling of surface river plume using set-up and input data files of Delft-3D model.

This data article presents set-up and input data files to model a surface river plume through curvilinear nested grid in double-way mode. The hydrodynamic modelling in river deltas with intense transport processes and complex bathymetry such as the Magdalena River delta, requires a mesh grid that ease the natural river discharge into the ocean. The aforementioned may be challenging due to the numerical scheme and stability restrictions of the numerical models that difficult having efficient and effective validated simulations. This dataset files are a reference to perform analysis of the hydrodynamic river deltas, meaningful for optimizing time and resources, easing the planning of measurement campaigns what reduce risks of the personnel and instrumentation during equipment deployment and field work .The application of the set-up and input data files of this data article is shown in Rueda-Bayona et al. [1].

Customization, extension and reuse of outdated hydrogeological software

Each scientist is specialized in his or her field of research and in the tools that he or she uses during the research in a specified site. Thus, he or she is the most suitable person for improving the tools by overcoming their limitations to realize faster and higher quality analysis. However, most scientists are not software developers. Hence, it is necessary to provide them with an easy approach that enables non-software developers to improve and customize their tools. This paper presents an approach for easily improving and customizing any hydrogeological software. It is the result of experiences with updating several interdisciplinary case studies. The main insights of this approachhave been demonstrated using four examples: MIX (FORTRAN-based), BrineMIX (C++-based), EasyQuim and EasyBal (both spreadsheet-based). The improved software has been proven to be a better tool for enhanced analysis by substantially reducing the computation time and the tedious processing of the input and output data files.

Calibration and Validation of the SWAT Hydrological Model for the Air Dingin Watershed

Computer technology has revolutionized the study of hydrological systems and management of water resources. Several computer-based hydrological models have been developed for applications in hydrological modeling and water resource studies. Geographic Information Systems (GIS) Interface models help create input data files, analyze and display results in a more efficient way. One interface GIS model available for water resources analysis is the Arc - Soil and Water Assessment Tool (ArcSWAT), a distributed parameter model developed by the United States Department of Agriculture. ArcSWAT has been applied, validated and calibrated for analyzing surface runoff in Air Dingin Watershed in Padang City - West Sumatra Indonesia. Validation was conducted to test the accuracy of the model in representing the real situation of the Air Dingin Watershed. Furthermore, calibration was performed to enhance the accuracy of the model. The results of ArcSWAT validation indicated that the Nash–Sutcliffe Efficiency (NSE) value was 0.12 and Coefficient of Determination (R2) was 0.4. After the calibration was carried out by changing the value of some parameters in the model, the NSE value becomes 0.68 and R2 becomes 0.66. This accepted model furthermore could be used to simulate water balance in the Air Dingin watershed.