Distance Estimation Research Articles

Development of a real-world simulated instrument for evaluating visuospatial working memory: a preliminary psychometric study on older adults

BackgroundA prevalent challenge in neuropsychological assessment, particularly when utilizing instruments designed for controlled laboratory environments, is that the outcomes may not correspond to an individual’s real-life status. Accordingly, assessments of visuospatial working memory (VSWM) conducted in such settings might fail to capture certain facets of this function, as it operates in real life. On the other hand, entirely ecological assessments may risk compromising internal validity. This study aimed to develop an intermediate mode of assessment that measures VSWM in older adults by employing a setting, a task, and a response format that aligns closely with both laboratory and ecological assessments. Furthermore, a preliminary investigation was carried out to study the variations in spatial cognition among different demographic groups.MethodsIn a two-session study, 77 healthy older adults, eight patients with mild cognitive impairment (MCI), and seven patients with Alzheimer’s disease (AD) were recruited to complete the wayfinding questionnaire (WQ), the Corsi block-tapping task (CBTT), and the Spatial Memory Table (SMT). The SMT is a novel instrument developed specifically for this study, aiming to provide a more accurate measure of VSWM performance in older adults’ everyday life. Test-retest and split-half reliabilities, as well as the face, content, concurrent, convergent, and known-groups validities, were analyzed to investigate the psychometric properties of the SMT.ResultsThe analyses were mainly centered on studying the psychometric properties of the SMT. Test-retest reliability (r = .753, p < .001) and split-half reliability (ρSC = 0.747) were found to be acceptable. Concurrent validity using CBTT (r = .264, p = .021), convergent validity using WQ subscales (navigation and orientation: r = .282, p = .014; distance estimation: r = .261, p = .024), and known-groups validity using the SMT scores among people with MCI and AD (χ2 = 35.194, df = 2, p < .001) were also indicative of the instrument’s good validity. Data analysis also revealed acceptable levels of face validity (U = 4.50; p = .095) and content validity (CVR ≥ 0.60). As a result of comparing VSWM and wayfinding variables across genders and education levels, a significant difference was observed for navigation and orientation and spatial anxiety between women and men (p < .05). None of the variables were different among education levels.ConclusionThe SMT was found to be a reliable and valid tool for measuring VSWM performance in older adults. Given these findings, the SMT can be regarded as a measure that sufficiently approximates both laboratory and real-life demands for VSWM. Additionally, the instrument demonstrated a preliminary acceptable capacity to differentiate between healthy individuals and those with MCI and AD.

CRT-Based Clock Synchronization for Millimeter-Wave Communication with Asymmetric Propagation Delays

The rapid advancement of millimeter-wave communication technology has presented new challenges for time synchronization, driven by the need for high-speed and low-latency data transmission. Ethernet synchronization technologies, such as Precise Time Protocol (PTP), have emerged to overcome the limitations of point-to-point architecture and enable precise synchronization across multiple devices. A key drawback of conventional PTP is its reliance on symmetric packet exchange delays, which may not hold in real-world scenarios where there is relative mobility between master and slave nodes, leading to asymmetric propagation delays and clock offset errors. To address this issue, a novel clock synchronization protocol that integrates Chinese Remainder Theory (CRT) has been proposed. This protocol enhances conventional PTP by incorporating distance estimation based on CRT using multi-carrier phase measurement. By combining a coarse estimation of one-way propagation delay from conventional PTP with a fine estimation of remainder distance from CRT, the protocol accurately determines the distance between master and slave nodes, reducing motion errors and enhancing clock synchronization accuracy. Simulation results indicate that the Root Mean Square Error (RMSE) of distance estimation remains below 10−5 m, with a corresponding motion time error of approximately 0.01 picosecond.

Smart Drill for a Streamlined Estimation of the Drilling Angle and Channel Length in Orthopedic Surgical Procedures.

The estimation of distances and angles is a routine part of an orthopedic surgical procedure. However, despite their prevalence, these steps are most often performed manually, heavily relying on the surgeon's skill and experience. To address these issues, this study presents a sensor-equipped drill system which enables automatic estimation of the drilling angle and channel length. The angular accuracy and precision of the system were tested over a range of inclination angles and proved to be superior to the manual approach, with mean absolute errors ranging from 1.9 to 4.5 degrees for the manual approach, and from 0.6 to 1.3 degrees with the guided approach. When sensors were used for simultaneous estimation of both the inclination and anteversion angles, the obtained mean absolute errors were 0.35 ± 0.25 and 2 ± 1.33 degrees for the inclination and anteversion angles, respectively. Regarding channel length estimation, using measurements obtained with a Vernier caliper as a reference, the mean absolute error was 0.33 mm and the standard deviation of errors was 0.41 mm. The obtained results indicate a high potential of smart drill systems for improvement of accuracy and precision in orthopedic surgical procedures, enabling better patient clinical outcomes.

Distance- and Angle-Based Hybrid Localization Integrated in the IEEE 802.15.4 TSCH Communication Protocol

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space.

Monitoring of a rockfill embankment dam using TLS and sUAS point clouds

Abstract Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.

Revisiting the accretion disc spectra of dwarf novae and nova-like variables: implications for the standard disc model

ABSTRACT Accretion discs are fundamental to much of astronomy. They can occur around stars, both young and old, around compact objects they provide a window into the extremes of physics, and around supermassive black holes in galaxy centres, they generate spectacular luminosities that can outshine the entire galaxy. However, our understanding of the inner workings of accretion discs remains far from complete. Here, we revisit a conundrum in the observations of some of the simplest accreting systems: the cataclysmic variables (CVs). The high-accretion-rate states of (non-magnetic) CVs can be divided into the short-lived outbursts (∼a week), typical of dwarf novae (DNe), and the long-lived (and sometimes perpetual) high states of nova-like (NL) CVs. Since both sorts of high-state occur in approximately steady-state accretion discs with similar properties and accretors, we would expect them to display similar spectral energy distributions. However, previous analyses based on UV spectra from the International Ultraviolet Explorer have shown that their spectral energy distributions are different. We perform a re-analysis of the data using up to date calibrations and distance (and thus dereddening) estimates to test whether this difference persists and whether it is statistically significant over the sample. We find that it does persist, and it is statistically significant. We propose routes to investigating this discrepancy further and discuss the implications this has for other accreting systems, such as X-ray binaries, active galactic nuclei, and protoplanetary discs.

Estimating separable matching models

SummaryMost recent empirical applications of matching with transferable utility have imposed a natural restriction: that the joint surplus be separable in the sources of unobserved heterogeneity. We propose here two simple methods to estimate models in this class. The first method is a minimum distance estimator that relies on the generalized entropy of matching. The second applies to the more special but popular Choo and Siow model, which reformulates as a generalized linear model with two‐way fixed effects. Neither method requires solving for the stable matching. Both methods are easy to apply and perform very well.

Effect of early versus late onset of partial visual loss on judgments of auditory distance.

It is important to know whether early-onset vision loss and late-onset vision loss are associated with differences in the estimation of distances of sound sources within the environment. People with vision loss rely heavily on auditory cues for path planning, safe navigation, avoiding collisions, and activities of daily living. Loss of vision can lead to substantial changes in auditory abilities. It is unclear whether differences in sound distance estimation exist in people with early-onset partial vision loss, late-onset partial vision loss, and normal vision. We investigated distance estimates for a range of sound sources and auditory environments in groups of participants with early- or late-onset partial visual loss and sighted controls. Fifty-two participants heard static sounds with virtual distances ranging from 1.2 to 13.8 m within a simulated room. The room simulated either anechoic (no echoes) or reverberant environments. Stimuli were speech, music, or noise. Single sounds were presented, and participants reported the estimated distance of the sound source. Each participant took part in 480 trials. Analysis of variance showed significant main effects of visual status (p<0.05) environment (reverberant vs. anechoic, p<0.05) and also of the stimulus (p<0.05). Significant differences (p<0.05) were shown in the estimation of distances of sound sources between early-onset visually impaired participants and sighted controls for closer distances for all conditions except the anechoic speech condition and at middle distances for all conditions except the reverberant speech and music conditions. Late-onset visually impaired participants and sighted controls showed similar performance (p>0.05). The findings suggest that early-onset partial vision loss results in significant changes in judged auditory distance in different environments, especially for close and middle distances. Late-onset partial visual loss has less of an impact on the ability to estimate the distance of sound sources. The findings are consistent with a theoretical framework, the perceptual restructuring hypothesis, which was recently proposed to account for the effects of vision loss on audition.

A gravitational constant transition within cepheids as supernovae calibrators can solve the Hubble tension

Local universe measurements of the Hubble constant H 0 using SNe Ia with Cepheids as calibrators yield a value of H 0 = 73.04 ± 1.04 km s-1 Mpc-1, which is in tension with the value of H 0 inferred from the Cosmic Microwave Background and other higher redshift probes.In ref. [1], the authors proposed a rapid transition in the value of the effective Newtonian gravitational constant G in order to alleviate the Hubble tension. The transition point was chosen so as to only affect distance estimates to Hubble flow SNe. However, in this study, the authors made the assumption that SNe Ia peak luminosity L increases with Chandrasekhar mass Mc . This hypothesis contradicts a previous semi-analytic study of SN light curves in the presence of a G-transition [2] which concluded that there is an inverse relationship between L and Mc . Motivated by the results of ref. [1] and [2], we propose a hypothesis of a sudden recent change in the effective Newtonian gravitational constant G at an epoch which corresponds to a smaller look-back distance between ∼ 7–80 Mpc. A transition in G at these distances would affect both our estimates of the distances to Cepheids in calibrator galaxies, as well as to the Hubble flow supernovae. Upon fitting the observational data to this hypothesis, we find three interesting results: (i) we find mild evidence for a G-transition at 22.4 Mpc (73 million years ago) which is preferred (using certain estimators) by the calibrator type Ia SNe data over no G-transition, (ii) the Hubble constant inferred under this hypothesis is in good agreement with the value obtained from the CMB for a 4% larger value of G at earlier times, thus potentially resolving the Hubble tension, (iii) we obtain a fit to the scaling relationship between SN peak luminosity L and Chandrasekhar mass Mc , as L ∝ Mc -1.68 ± 0.68, which is in good agreement with the prediction of the theoretical study of ref. [2]. We also discuss how other probes could be used to verify this transition in the value of G.

HPETC: History Priority Enhanced Tensor Completion for Network Distance Measurement

In network distance measurement, how to estimate the whole network distance data from partially observed samples has attracted lots of attention because of its significance for network performance evaluation. Matrix completion becomes the most effective approach. However, the two-dimension matrix can only capture the spatial features in the network distance data while ignoring the temporal features. To conquer the problem, few recent studies begin to model the network distance data as a three-dimension tensor and propose tensor completion approaches for distance estimation. Although promising, existing tensor completion approaches still suffer the problem of low recovery accuracy and high measurement cost because they ignore the history priority information. To fully utilize both spatial and temporal features hidden in the distance data, this paper formulates a novel History Priority Enhanced Tensor Completion (HPETC) for distance estimation as a weighted tensor nuclear norm minimization problem where the weight is defined based on the history subspaces information. To solve the weighted tensor nuclear norm minimization problem, we firstly transform it into a factorization-based Frobenius norm minimization problem to avoid costly T-SVD computations, and then propose an iterative algorithm to solve the transformed problem. We further derive a theoretical sampling bound that is lower than the existing sampling bound, thus leads a lower measurement cost. We demonstrate the effectiveness of the proposed algorithm by conducting extensive experiments using two real network distance datasets. The result shows that the proposed algorithm can not only improve the estimation accuracy but also reduce the sampling complexity compared to the state-of-the-art approaches.

ЗАСТОСУВАННЯ ФІЛЬТРА ЧАСТОЧОК ДО ЗАДАЧ САМОЛОКАЛІЗАЦІЇ ЛИШЕ ЗА ВІДСТАННЮ ДО ДЖЕРЕЛ РАДІОСИГНАЛІВ

This work is aimed at searching for a relatively accurate and computationally efficient algorithm for range-only self-localization. Key requirement to the data sources is cost effectiveness. The radio signal strength indicator (RSSI)-based distance estimation model was chosen, due to wide availability of hardware and cost effectiveness. Key requirements to the algorithm are robustness towards noise introduced by RSSI-based distance estimation errors and flexibility to introduce additional data sources. Particle filter algorithm was chosen, because it satisfies both requirements, although it has bigger computational costs comparing to Kalman filter. A simulation environment was created to conduct experiments with assumed transmitted signal strength of 1W and square root signal decay law. A Particle filter-based actor was implemented. Actor starts by translating geo-spatial coordinates of beacons into relative planar cartesian coordinates, generates particles around beacons. Particles are placed with uniform distribution within a torus with co-planar circular axis radius equal to distance estimation. In each experiment, simulation software walks actor through a pre-defined path, compares obtained position estimates with expected state and calculates MAE and RMSE. Obtained results were satisfactory, in some cases showing MAE=2.5981 and RMSE=2.8474 after short stabilization period of few iterations. This period exists due to suboptimal initial particles placement. A Kalman filter-based actor was implemented as a comparison. A reference Kalman filter-based implementation showed slightly worse overall results with MAE=12.5199 and RMSE=13.2238. This can be explained by a lack of input from IMU. Further research will be directed towards UAV swarm self-localization and collision avoidance.

Utilisation of A Star Algorithm in Determining Tourism Routes in Palangka Raya City

The Utilization of the A* Algorithm in Determining Tour Routes in Palangka Raya City is a study aimed at providing a solution for determining optimal tourist routes for visitors in Palangka Raya City. By employing the A* algorithm, this research offers an efficient tool to find the shortest route between selected tourist destinations, considering both distance and travel cost. The method involves initializing a graph that represents the relationships between tourist destinations, followed by the implementation of the A* algorithm for finding the best route. The research findings demonstrate that this algorithm can provide optimal routes with accurate estimations of distance and cost. A brief discussion on the research results highlights the strengths and limitations of this algorithm in the context of tourism applications. In conclusion, this research makes a significant contribution to facilitating visitors in planning their tourist trips in Palangka Raya City, with the potential positive impact on the tourism industry and visitor experiences.

Lightweight railroad semantic segmentation network and distance estimation for railroad Unmanned aerial vehicle images

Derailments significantly harm railroads in terms of severity and fatality rates. Manually monitoring railway tracks is a tedious and often insufficient task to prevent derailment accidents. This necessitates the development of an automated monitoring system to oversee the condition of railway tracks. In the foreseeable future, Unmanned Aerial Vehicles (UAVs), artificial intelligence, and computer vision will play a crucial role in periodically assessing the railway environment to ensure passenger safety. This paper proposes a lightweight and efficient railroad semantic segmentation network (Lite-RSNet), which segments real-time aerial railroad images into rails, gauges, and backgrounds. The segmented regions then compute the distance between rails and obstacles, identifying potential derailment hazards in railroad applications. Lite-RSNet incorporates a multi-level feature fusion framework that includes a residual layer, a multi-head attention layer, a Concurrent Spatial and Channel Squeeze Excitation (CSSE) block, and a Convolutional Block Attention Module (CBAM). The designed approach enhances onboard processing efficiency by streamlining the model to use fewer parameters and reduce storage demands. Performance assessment of the model utilized the Rail Segmentation Dataset (RSD), which consists of high-resolution, low-altitude aerial photographs of Indian railroads. The Lite-RSNet model outperforms current leading methods, maintaining a lean structure with only 6 million parameters and achieving notable metrics of 0.971 for the Dice Coefficient (DC) and 0.947 for the Jaccard Index (JI) on the test dataset. Furthermore, distance estimation between an obstacle and rail has been implemented using image processing techniques as an additional work of this proposal to know about the caution of an obstacle.

WolfFuzz: A Dynamic, Adaptive, and Directed Greybox Fuzzer

As the directed greybox fuzzing (DGF) technique advances, it is being extensively utilized in various fields such as defect reproduction, patch testing, and vulnerability identification. Nevertheless, current DGFs waste a significant amount of resources due to their simplistic distance definitions and overly straightforward energy distribution for the seeds. To address these issues, a dynamic distance-weighting-based distance estimation strategy is proposed first, which facilitates strategies for seed distribution that take energy into consideration. Second, to overcome the limitations of current seed energy distribution strategies, the gray wolf optimizer (GWO) is improved by integrating four strategies, leading to the development of the improved gray wolf optimizer (IGWO). Lastly, an adaptive search algorithm is proposed, and the WolfFuzz prototype tool is implemented. In vulnerability recurrence scenarios, WolfFuzz is 3.2× faster on average compared with the baseline and reproduces 76.4% of existing bugs faster. WolfFuzz also discovers nine different types of bugs in seven real-world programs.

Distance Regression Enhanced with Temporal Information Fusion and Adversarial Training for Robot-Assisted Endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) has a role in characterising tissue intraoperatively to guide tumour resection during surgery. To capture good quality pCLE data which is important for diagnosis, the probe-tissue contact needs to be maintained within a working range of micrometre scale. This can be achieved through micro-surgical robotic manipulation which requires the automatic estimation of the probe-tissue distance. In this paper, we propose a novel deep regression framework composed of the Deep Regression Generative Adversarial Network (DR-GAN) and a Sequence Attention (SA) module. The aim of DR-GAN is to train the network using an enhanced image-based supervision approach. It extents the standard generator by using a well-defined function for image generation, instead of a learnable decoder. Also, DR-GAN uses a novel learnable neural perceptual loss which combines for the first time spatial and frequency domain features. This effectively suppresses the adverse effects of noise in the pCLE data. To incorporate temporal information, we've designed the SA module which is a cross-attention module, enhanced with Radial Basis Function based encoding (SA-RBF). Furthermore, to train the regression framework, we designed a multi-step training mechanism. During inference, the trained network is used to generate data representations which are fused along time in the SA-RBF module to boost the regression stability. Our proposed network advances SOTA networks by addressing the challenge of excessive noise in the pCLE data and enhancing regression stability. It outperforms SOTA networks applied on the pCLE Regression dataset (PRD) in terms of accuracy, data quality and stability.

Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring.

This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance.

Test Power Reduction through Reordering Algorithm Implementation and Advancements in BIST Architecture

Due to the reduction in transistor size, test engineers face a severe issue in power minimization. VLSI circuit's power dissipation has become a vital concern due to the switching activity of the Circuit Under Test (CUT). Switching activity is a significant factor in determining power consumption for CMOS VLSI circuits. In this paper, we formulate a distance estimation technique and reordering using the Tversky index. Here, the distance between the test vectors is calculated using the Tversky index with the formation of an adjacency matrix, and reordering is performed. The experimental results of the proposed method have been compared to different distance measures and existing techniques. It shows that the switching activity is reduced from 52.5% to 21.5%, and the average power reduction is from 42% to 14.6% when the distance is estimated on reordering using the Tversky index measure. Thus, the Tversky index distance measure performs better than the Hamming, gray, and Linear feedback shift register (LFSR) methods. Switching activity estimation for complex ISCAS circuits has been performed and compared with existing methods for superior performance. We have also developed a Built in Self-Test (BIST) for the C17 benchmark circuit, which uses the Tversky counter as a test pattern generator using the TANNER EDA tool. Thus, an efficient deterministic BIST for the C17 benchmark circuit was designed to detect all the stuck-at faults, and the same procedure can be used to create BIST for all CUT and precomputed test sets for real-time field testing.

A novel data-driven algorithm for object detection, tracking, distance estimation, and size measurement in stereo vision systems

A novel data-driven algorithm for object detection, tracking, distance estimation, and size measurement in stereo vision systems

Enhanced distance-based protection for high impedance faults considering dynamic load modelling

The load can be represented by different models, including static and dynamic representations; each has different responses when analysing a power system, which also impacts the protection system's performance. For instance, in the distance relay, the estimated impedance can present overreach or underreach, affecting the selectivity of the protection and causing malfunction of triggering schemes. Under fault conditions, the load modelling effect can be magnified by the fault resistance effect, where the combination of both effects has not been addressed in the revisited bibliography. The present paper analyses the effect of load modelling for non-bolted faults in the distance relay impedance estimation. Distance relays are of interest due to their wide use in transmission systems and the emerging applications in distribution systems and microgrids. The fundamental contribution of the present paper is to propose a countermeasure for the effects of four different load types and fault resistance. The results demonstrate that the distance estimation is correctly enhanced, and then a close to the real positive sequence impedance is estimated from the relay point to the fault point. The proposal can be included in distance relays without requiring additional hardware, allowing countermeasure of the load type and fault resistance effects.

CFD modelling of methane dispersion from buried pipeline leaks: Experimental validation and hazard distance estimation

The safe operation of buried pipelines necessitates an understanding of potential leak dynamics and the subsequent formation of flammable clouds defining hazard distances. This paper presents a computational fluid dynamics (CFD) model and its validation against experimental data on the dispersion of methane through a sand layer of 100 mm thickness to the atmosphere. A leak orifice diameter of 4 mm was considered for pipeline gauge pressures in the range of 10–300 kPa. This study describes the methane propagation in time through the sand and the development of the flammable cloud in the atmosphere. The simulations demonstrate a high degree of accuracy in capturing the transient behaviour of methane propagation in the sand and dispersion in the atmosphere when compared with a 60 s experiment. The model was applied to predict the development and maximum spread of the flammable cloud and hazard distances are presented. The simulations provide insight into the development of the flammable cloud. The validated CFD model can serve as a predictive tool for hazard distance estimation in case of buried leaks, inform safer pipeline design and improve emergency response strategies for gas leaks.