Coverage Area Research Articles

Improved gravity field estimation by incorporating the Tiangong Space Station and next-generation CAI satellite gravity mission

The next-generation gravity satellite mission equipped with the Cold Atom Interferometry (CAI) gradiometer has great potential for the Earth's gravity field estimation. Deploying a CAI gradiometer on the Chinese Tiangong Space Station launched for long-term Earth science research not only reduces the cost compared to a dual-satellite constellation but also enhances interdisciplinary collaboration in the Earth's gravity field detection. In this study, we conducted gravity gradient-based simulations to assess the contribution of deploying a CAI gradiometer on the Tiangong Space Station to collaboratively observe the Earth's gravity field with a polar-orbit gravity satellite. The simulation results demonstrate that whether utilizing Vyy component, three diagonal components or full components, the derived gravity field models show significant improvements within 100 degree and above 200 degree after incorporating Tiangong Space Station. In particular, the gravity field solution recovered from three diagonal components achieves the best accuracy. In the case of using diagonal components, the collaboration observation scheme effectively reduced the cumulative geoid height error by approximately 5.3cm (300 d/o). In the spatial domain, the incorporation of the Tiangong Space Station primarily impacts the estimated gravity field within the orbital coverage area of the space station, and this effect is particularly pronounced when just employing Vyy component. However, due to the limitation of angular velocity observation inaccuracy associated with the CAI gradiometer in nadir mode, there is no substantial accuracy improvement observed above 200 degree when adding gradient components.

YOLOX-CS: An Automatic Search Algorithm for Low Surface Brightness Galaxies

The characteristics of Low Surface Brightness Galaxies (LSBGs) are very important for understanding the overall characteristics of galaxies. It is of great significance to search and expand the samples of Low Surface Brightness Galaxies by modern machine learning, especially deep learning algorithm. LSBGs are difficult to discern automatically and accurately with traditional methods because of their obscure features. However, deep learning does have the advantage of automatically identifying complex and effective features. To solve this problem, an algorithm named You Only Look Once version X-CS (YOLOX-CS) is proposed to search LSBG in large sample sky survey. Firstly, five classical target detection algorithms are compared through experiments and the optimal YOLOX algorithm is selected as the basic algorithm. Then, the YOLOX-CS framework is constructed by combining different attention mechanisms and different optimizers. The data set uses images from the Sloan Digital Sky Survey (SDSS), labelled from LSBG in the α.40-SDSS DR7 (the cross coverage area of 40% HI Arecibo Legacy Fast ALFA Survey and SDSS Data Release7) survey. Due to the small number of samples in this data set, Deep Convolutional Generative Adversarial Networks (DCGAN) model is used to expand the experimental test data. After comparing with a series of target detection algorithms, YOLOX-CS has a good test result in searching LSBG recall rate and Average Precision (AP) value in two data sets before and after expansion. The recall rate and AP value in the test set without expansion data set reach 97.75% and 97.83%, respectively. In the expanded data set of DCGAN model, under the same test set, the recall rate reaches 99.10% and the AP value reaches 98.94%, which proves that the algorithm has excellent performance in LSBG search. Finally, the algorithm is applied to SDSS photometric data, and 765 LSBG candidates are obtained.

Digital Fracture Porosity Measurement using Digital Outcrop Modelling (DOM)

Abstract Fracture measurements for porosity calculations often experience problems such as barely accessible locations, limited area coverage, and a long time to carry out field data measurements. Therefore, refined alternative methods are necessary to obtain the most favourable data. This study demonstrates fracture measurements through application of Digital Outcrop Modelling (DOM) using the Structure from Motion (SfM) method to utilize digital photogrammetry techniques. This method was chosen because data collection can be done remotely using Light Detection and Ranging (LiDAR) or digital photogrammetry to obtain point cloud data that is used to perform three-dimensional outcrop modeling. In addition, SfM also produces high-resolution data without the need for experts, is cost-efficient and safer than other methods. Hand-held platform Canon 700D digital single lens reflex (DSLR) camera was used in this study to capture data from an area of a square meter located on the riverbed that requires detailed modeling. DOM using SfM is applied to conduct aperture measurements in fractures inside the scan window, later used as data for calculating basement fracture porosity predictions. Field data collection is also carried out to validate data generated by DOM. Calculations achieved from field data and DOM data will be compared to determine differences of the porosity prediction values. Field data collection was carried out on 3 scan windows, each one is an area of 1 m2 and in totals obtained 117 fracture data, consist of 44 extension joints, 34 release joints, 7 shear joints, and 32 non-systematic joints. In this study, 115 photos were taken from scanwindow 1, 162 photos from scanwindow 2, and 158 photos from scanwindow 3 to construct the DOM using agisoft metashape software. The difference in these values can be used as a correction formula or evaluation for further research. The results of measuring basement fracture porosity using data generated by DOM will remain in the range of less than 1%.

Analysis Placement of Tsunami Vertical Evacuation Shelters Based on Number and Location (Case Study: Painan City)

Painan City is a city whose part of the area is located on the coast, so it is prone to tsunami disasters. This disaster is unpredictable and can occur at any time. Based on interview data with BPBD South Pesisir Regency, Painan City has 10 shelters ranging from; official shelters, buildings that are multifunctional as shelters, and natural shelters. The purpose of this study is to determine the range of services of the tsunami shelter in Painan City today. The research method used is a quantitative descriptive method. In this study, what was discussed was the evaluation of the number and location of existing shelters, as well as determining the location of planned or additional tsunami shelters, as well as to determine the coverage of areas that can be served by these shelters with the help of GIS applications. Based on the results of the analysis, it was found that the entire tsunami danger zone area of Painan City could be served by existing shelters, in a span of 26 minutes with a distance of 1,171.56 meters. This means that people in the tsunami danger zone can evacuate safely before the tsunami disaster comes.

Agricultural Engineering Vocational School in Aceh Besar with Behavior Architecture Approach

Currently, Aceh Besar District has a PP Negeri Saree Vocational School in the agricultural section in Saree Aceh Besar, but this school only studies agricultural information. The condition of Aceh Besar District shows that their land use majority is used for agriculture, animal farming and husbandry, fisheries and paddy fields, farm, and other plantations. Abundant natural resources are not processed into a good business due to a shortage of human resources in the field of agricultural engineering. Therefore, it is necessary to include an Agricultural Engineering in those vocational school with aims to create human resources in the field of agricultural machinery and agribusiness. The object design location is on SIM Airport Street, Gani, Kec. Want Jaya, Aceh Besar. The classification of the Agricultural Engineering Vocational School includes the Department of Agricultural Engineering and Agricultural Product Processing Agribusiness. The approach of behavior architecture into the design is behavior setting in completing spatial design, spatial cognition in completing circulation, and environment perception in completing image or building appearance. The analysis used is functional analysis, site analysis and user analysis. The Agricultural Engineering Vocational School in Aceh Besar is designed on an area of 14,950 m2 with multiple building masses. The ground coverage area (KDB) is 40% which is 5,980 m². The facilities in the building meant to support students in the learning process and as well as supporting facilities, those facilities are practice rooms, namely rice threshing machine engineering rooms, rice processing , rice polisher machine, hand tractor, technology for processing agricultural products, quality testing and quality control practice room.

Distribution Patterns and Accessibility of Health Facilities Analysis Using Geographic Information Systems (GIS) in Badung District, Indonesia

Badung District is located in Bali Island, a famous and most-visited island tourists destination in Indonesia. Tourists generally understand that health is one of the important things in their lives, as well as the need for travel. The problem found is that the public does not fully comprehend the details regarding the location of the health centre, both the address and reliable contact person. In an emergency, people find it difficult to know the location of the nearest health centre so they can get treatment and service quickly. In this research, qualitative descriptive research was used, based on secondary data. The Average Nearest Neighbor method was used as the data analysis method to analyze the distribution pattern of health facilities in Badung Regency, aimed to determine the distribution pattern of health facilities in Badung Regency. The results of the calculation revealed the Nearest Neighbor Ratio value of 1.055839 or T= 1.055839. It showed that the distribution pattern of health facilities in Badung Regency was random. The accessibility of health centre health facilities to settlement areas was highest in the Abiansemal sub-district with a coverage area of 22.33 km2, of which the settlement area was 65.52 km2 or 34%, while the lowest coverage was in the Petang sub-district with 1.75 km2 and an area settlements of 91.98 km2 or 2.67%. The highest accessibility of hospital health facilities was in the Kuta District area with a coverage area of 21.16 km2 with a settlement area of 8.8 km2 or 32.25%.

The timing of local SARS-Cov-2 outbreaks and vaccination coverage during the Delta wave in Melbourne

ObjectiveThis article presents a longitudinal analysis of COVID-19 infection and vaccination coverage in Melbourne metropolitan local government areas (LGAs) during the 2021 Delta wave. MethodsCOVID-19 vaccination and infection data from 12 July to 27 November 2021 were sourced from government websites. Summary statistics and associated 95% confidence intervals (95% CI) were compared by LGA ranked according to socioeconomic status: total “burden” (total infections per thousand), “peak” (highest weekly infection rate), “lag” (interval between peak and 70% double vaccination). ResultsLGAs in the bottom five deciles for social advantage experienced higher infection rates (39.0 per thousand [95% CI: 38.5, 39.5] vs. 14.8 [14.7, 14.9]), and had lower two-dose vaccination coverage (23.8% [23.6, 23.9] vs. 32.7% [32.6, 32.7]) compared with LGAs in the top five deciles. LGAs that achieved 70% coverage two weeks or more after the infection peak experienced nearly twice the total infection burden (27.7 per 1000 [27.3, 28.0] compared with 14.9 [14.7, 15.0]) than LGAs with a shorter lag. ConclusionsExposure and transmission risk factors cluster within disadvantaged LGAs. The potential for large local outbreaks is heightened if vaccination uptake trails in these communities. Implications for Public HealthIn a pandemic, decision-makers must prioritise disease control and harm reduction interventions for at-risk LGAs.

Analysis of Shopee Pay Digital Payment Adoption in Jakarta with Innovation Diffusion Theory

The increase in internet users has significantly impacted people's behaviour patterns in making payment transactions from offline to online. One of the dominating online or digital payment systems with a large number of e-wallet users and frequency is Shopee Pay. The dominance of this application, which quickly outperforms its competitors, is inseparable from its users' good acceptance of something new. Technological diffusion theory explained how an innovation is adopted over time by examining the decision to adopt the process. Therefore, this research aimed to determine the factors affecting Shopee Pay services adoption in Jakarta.Data were collected from 100 Shopee Pay users in Jakarta. The descriptive statistics and statistical data analysis using Smart PLS software were carried out for outer and inner model analysis and hypothesis testing. The results showed that Relative Advantage, Observability and Perceived Risk significantly affect adoption. In contrast, Compatibility, Complexity, and Trialability have an insignificant effect on adoption. Number of respondent, coverage area of the survey and period of research. Future research is expected to be able to use other models besides the Diffusion of Innovation Theory to better identify other additional factors capable of affecting the adoption of Shopee Pay digital payment services and Shopee Pay digital payment is recommended to offer services that are more compatible with various lifestyle needs and beliefs to meet the expectations of current users. This survey has difference with previous research because of adoption level of digital payment specifically focus on Shopee Pay then using Innovation Diffusion Theory as model of research.

Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.

ВАРИАНТЫ РЕКОНСТРУКЦИИ БОЛЬШОЙ СПОРТИВНОЙ АРЕНЫ СТАДИОНА «ЛУЖНИКИ» К КУБКУ МИРА ПО ФУТБОЛУ В 2018 ГОДУ

The article presents the results of comparing the options for choosing the type of console part, which is additionally mounted to increase the coverage area, which is necessary to meet the strict requirements of the International Football Federation (FIFA) for the possibility of holding the opening and final matches of the FIFA World Cup at the stadium. Based on the results of comparing the options, the type of cantilever part and the roof material were selected, the process of determining snow loads obtained by blowing the frame model in a wind tunnel is presented. The paper presents the main results of the calculation, as well as the first values of the natural shapes and frequencies of vibrations. The surface was reconstructed for the 2018 FIFA World Cup in Russia, which made it possible to successfully host the opening and final matches of the championship and increase the prestige of our country in the world.

SPATIAL COVERAGE ANALYSIS OF BUS STOP SET AND ITS RELATIONSHIP TO THE SURROUNDING LAND USES. CASE STUDY, MANIZALES, COLOMBIA

The main objective of the research is to determine the efficiency in population and area coverage of the set of bus stops to existing land uses in the city, allowing the identification of possible shortcomings, which leads to the formulation of restructuring proposals in terms of the location of the stops. The research methodology to be applied includes the use of geostatistical models of accessibility based on GIS-type digital tools, supported by the collection of field information and road inventories. As a main result, it is obtained that the stop-system coverage allows the access of more than 80% of the population in a trip time by walking less than 5 minutes. Likewise, it is possible to identify a robust redundancy concerning the system of stops. Thus, it is concluded that the research process facilitates the evaluation and identification of possible interventions, guaranteeing a better coverage of the stops set regarding the city's existing activities.

Energy efficient scheme for improving network lifetime using BAT algorithm in wireless sensor network

SummaryWireless sensor networks consist of several autonomous nodes that are outfitted with sensors, radio, processors, memory storage, and power sources. These nodes track, sense, and send data using radio. While establishing a network, the two most essential characteristics are coverage and connectivity. For better connectivity and a longer network life, it's important to make the coverage area as big as possible with the fewest number of sensor nodes. The goal of this research is to make a connected sensor network that uses less energy and can be used in situations where the sensors need to be placed in the best way to make the network last as long as possible. The probabilistic sensing model is used, and improved network lifetime is the goal of the research work by using problem‐specific intelligent optimization techniques like BAT, ACO, and JOA to maximize the coverage area with respect to energy and points of interest. This work introduces a novel approach that optimizes both coverage and connectivity. The modified binary bat algorithm overcomes computational complexities and local optima observed in existing methods. Uniquely, it models the three states of each sensor node and includes innovative features like a greedy initialization and a weighted cost function for improving network efficiency. After investigation, it was analyzed that the proposed solution significantly improves network lifetime by over 10% to 12% compared to existing methods like JOA and ACO. The proposed approach converges faster and performs more efficiently.

Edge computing collaborative offloading strategy for space‐air‐ground integrated networks

SummaryDue to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13 and the energy consumption of the edge server by about 35.

A Reinforcement Learning-Based Reverse Auction Enforcing Smart Pricing Policies towards B5G Offloading Strategies

In this paper, we present our work on developing a Smart Pricing Policies module specifically designed for individual users and Mobile Network Operators (MNOs). Our framework will operate in a multi-MNO blockchain radio access network (B-RAN) and is tasked with determining prices for resource sharing among users and MNOs. Our sophisticated adaptive pricing system can adjust to situations where User Equipment (UE) shifts out of the coverage area of their MNO by immediately sealing a contract with a different MNO to cover the users’ needs. This way, we aim to provide financial incentives to MNOs while ensuring continuous network optimization for all parties involved. Our system accomplishes that by utilizing deep reinforcement learning (DLR) to implement a reverse auction model. In our reinforcement learning scenario, the MNOs, acting as agents, enter a competition and try to bid the most appealing price based on the user’s request, and based on the reward system, agents that do not win in the current round will adjust their strategies in an attempt to secure a win in subsequent rounds. The findings indicated that combining DRL with reverse auction theory offers a more appropriate method for addressing the pricing and bid challenges, and additionally, administrators can utilize this strategy to gain a notable edge by dynamically selecting and adjusting their methods according to the individual network conditions and requirements.

Impact of seasonality on the reproductive physiology of diploid and tetraploid eastern oyster, Crassostrea virginica

Aquaculture production of triploid Eastern oyster, Crassostrea virginica, in the Gulf of Mexico (GoM) is reliant on naturally conditioned diploid females and tetraploid males. However, limited knowledge is available on their reproductive biology, including the gametogenic cycle. The aim was to quantify monthly changes in gametogenesis and gamete quality for triploid production in Eastern oyster in the GoM from June 2022 to July 2023. Monthly changes in temperature and salinity were recorded. Shell morphometrics and sex were determined. Sperm density, sperm activity, Spermatogenic Maturity Index (SMI), oocyte diameter, Oogenesis Maturity Index (OMI), and gonad coverage area were quantified. Mean monthly temperature ranged from 15.1 to 31.2 °C and salinity ranged from 12.5 to 27.6 PSU. Shell morphometrics increased over the annual cycle. When gametes were detectable, 48–80% of tetraploids and 45–70% of diploids were males. Hermaphrodites were identified in both ploidies in June; an additional tetraploid hermaphrodite was identified in March. Sperm density exhibited monthly fluctuations with high densities reported in June–July, September, and April–May. Sperm were motile when present, with high motility in June–July. High sperm velocity was also recorded in June–July and March–May. Tetraploids exhibited high SMI from June–September and April–May with increased variability in December–January. Mature sperm cells were predominant in tetraploids in June–July. Gonadal coverage was high from June–September and March–May. OMI was highest from June–September and March–May, while variability was high from August–March. Large oocyte diameters and gonad coverage areas were observed in June–July, September, and April–May. Together, these findings provide novel insights into reproductive dynamics for triploid production of Eastern oyster in GoM.

Multi-resolution remote sensing for flark area detection in boreal aapa mires

ABSTRACT Peatlands have suffered significant degradation globally due to human impacts, which has increased the need to monitor the condition and changes in peatland ecosystems. With remote sensing, point-based in-situ observations can be upscaled to larger areas but there is a need to develop scalable monitoring methods. We predicted wet flark area extent, a key ecological indicator for patterned flark fens, in five sites in central Finland. Our primary aim was to test how the spatial and spectral resolution of different optical satellite image products (Landsat 8–9, Sentinel-2, PlanetScope) affect flark area coverage prediction. We also tested if there were seasonal or site-specific differences in prediction accuracy. Lastly, we upscaled the flark area coverage to entire mire areas. We used unmanned aerial vehicle (UAV)-derived flark area classification as a ground reference data to compare satellite sensors’ prediction accuracies. We predicted flark area coverage using spectral bands and indices as predictor variables using random forest regression. All sensors provided accurate results with some differences between Landsat (pseudo-R2 32−84%, root-mean squared error (RMSE) 10 − 18%), Sentinel-2 (R2 61−92%, RMSE 6−14%), and PlanetScope (R2 46 − 92%, RMSE 6 − 17%). The shortwave infrared bands of Landsat and Sentinel-2 did not increase the prediction accuracy. There were notable site-specific variations in prediction accuracy despite all the sites having typical open aapa mire wet flark – dry string patterns. With single-site models, the prediction accuracies were similar for early and late summer data, but when transferring the models to the other sites, performance significantly decreased, especially with the models using the late-summer imagery. Finally, we successfully upscaled the flark area coverage across entire mire areas. Our results demonstrate that the combination of UAV and satellite imagery can be used successfully to monitor peatland conditions and changes in them.

'Ripple effects' of urban environmental characteristics on cognitive performances in Eurasian red squirrels.

Urban areas are expanding exponentially, leading more species of wildlife living in urban environments. Urban environmental characteristics, such as human disturbance, induce stress for many wildlife and have been shown to affect some cognitive traits, such as innovative problem-solving performance. However, because different cognitive traits have common cognitive processes, it is possible that urban environmental characteristics may directly and indirectly affect related cognitive traits (the ripple effect hypothesis). We tested the ripple effect hypothesis in urban Eurasian red squirrels residing in 11 urban areas that had different urban environmental characteristics (direct human disturbance, indirect human disturbance, areas of green coverage and squirrel population size). These squirrels were innovators who had previously repeatedly solved a food extraction task (the original task). Here, we examined whether and how urban environmental characteristics would directly and indirectly influence performance in two related cognitive traits, generalisation and (long-term) memory. The generalisation task required the innovators to apply the learned successful solutions when solving a similar but novel problem. The memory task required them to recall the learned solution of the original task after an extended period of time. Some of the selected urban environmental characteristics directly influenced the task performance, both at the population level (site) and at individual levels. Urban environmental characteristics, such as increased direct and indirect human disturbance, decreased the proportion of success in solving the generalisation task or the memory task at the population (site) level. Increased direct human disturbance and less green coverage increased the solving efficiency at individual levels. We also found an indirect effect in one of the urban environmental characteristics, indirect human disturbance, in the generalisation task, but not the memory task. Such an effect was only seen at the individual level but not at the population level; indirect human disturbance decreased the first original latency, which then decreased the generalisation latency across successes. Our results partially support the ripple effect hypothesis, suggesting that urban environmental characteristics are stressors for squirrels and have a greater impact on shaping cognitive performance than previously shown. Together, these results provide a better understanding of cognitive traits that support wildlife in adapting to urban environments.

Accessibility to palliative care services in Colombia: an analysis of geographic disparities

ObjectivesDue to the increase in the prevalence of non-communicable diseases and the Colombian demographic transition, the necessity of palliative care has arisen. This study used accessibility and coverage indicators to measure the geographic barriers to palliative care.MethodsPopulation-based observational study focused on urban areas and adult population from Colombia, which uses three measurements of geographic accessibility to services: a) density of palliative care services per 100,000 inhabitants, b) analysis of geographic distribution by territorial nodes of the country, and c) spatial analysis of palliative care services using Voronoi diagrams. ArcGIS Pro software was used to map services’ locations and identify geographic disparities.ResultsA total of 504 palliative care services were identified, of which 77% were primary health care services. The density of palliative care services in Colombia is 1.8 primary care services per 100,000 inhabitants and 0.4 specialized services per 100,000 inhabitants. The average palliative care coverage is 41%, two regions of the country have a coverage below 30%. Twenty-eight percent of the services provide care for a population greater than 50,000 inhabitants within their coverage area, exceeding the acceptable limit by international standards.ConclusionsPalliative care services are concentrated in three main regions (Bogotá D.C., the Center, and the Caribbean) and are limited in the Orinoquia and Amazonia nodes. Density of specialized palliative care services is extremely low and there are regions without palliative services for adults with palliative needs.

Assessing the Expansion of Ground-Motion Sensing Capability in Smart Cities via Internet Fiber-Optic Infrastructure

Abstract Monitoring ground motion in smart cities can improve the public safety by providing critical insights on natural and anthropogenic hazards, for example, earthquakes, landslides, explosions, infrastructure failures, and so forth. Although seismic activity is typically measured using dedicated point sensors (e.g., geophones and accelerometers), techniques such as distributed acoustic sensing have demonstrated the utility of using fiber-optic cable to detect seismic activity over comparable distances. In this article, we present the results of a study that quantifies the expansion in an area monitored for low-amplitude ground-motion events by augmenting existing point sensors with the internet fiber-optic cable infrastructure. We begin by describing our methodology, which utilizes geospatial data on point sensors and internet optical fiber deployed in metropolitan statistical areas (MSAs) in the United States. We extend these data to identify the area that can be monitored by (1) considering the observed seismic noise data in target locations, (2) applying the model from Wilson et al. (2021) to understand the potential coverage area gains using optical fiber sensing, and (3) optimizing the selection of fiber segments to maximize coverage and minimize deployment costs. We implement our methodology in ArcGIS to assess the additional area that can be monitored for low-amplitude ground-motion events (i.e., magnitude >0.5) by utilizing internet fiber-optic cables in the 100 most populous MSAs in the United States. We find that the addition of internet fiber-based sensors in MSAs would increase the area monitored on average by over an order of magnitude from 1% to 12%, if the subset of fiber cable segments that maximize coverage and minimize deployment costs is chosen even if only 20% of all fibers are used.

Leveraging BIM for Enhanced Camera Allocation Planning at Construction Job Sites: A Voxel-Based Site Coverage and Overlapping Analysis

In the construction industry, the imperative for visual surveillance mechanisms is underscored by the need for safety monitoring, resources, and progress tracking, especially with the adoption of vision intelligence technology. Traditional camera installation plans often move toward coverage and cost objectives without considering substantial coverage overlap, inflating processing and storage requirements, and complicating subsequent analyses. To address these issues, this research proposes a voxel-based site coverage and overlapping analysis for camera allocation planning in parametric BIM environments, called the PBA approach. The first step is to collect information from the BIM model, which is the input for the parametric modeling step. After that, the PBA approach simulates the virtual devices and the construction layout by employing visual language programming and then generates a coverage area. Lastly, the performance simulation and evaluation of various placement scenarios against predefined criteria are conducted, including visual coverage and overlapping optimization for eliminating data redundancy purposes. The proposed approach is evaluated through its application to construction projects. The results from these various implementations indicate a marked decrease in data overlap and an overall enhancement in surveillance efficacy. This research contributes a novel, BIM-centric solution to visual information adoption in the construction industry, offering a scalable approach to optimize camera placement while mitigating overlapping areas.