Minimum Separation Research Articles

Design of an ATC Tool for Conflict Detection Based on Machine Learning Techniques

Given the ongoing interest in the application of Machine Learning (ML) techniques, the development of new Air Traffic Control (ATC) tools is paramount for the improvement of the management of the air transport system. This article develops an ATC tool based on ML techniques for conflict detection. The methodology develops a data-driven approach that predicts separation infringements between aircraft within airspace. The methodology exploits two different ML algorithms: classification and regression. Classification algorithms denote aircraft pairs as a Situation of Interest (SI), i.e., when two aircraft are predicted to cross with a separation lower than 10 Nautical Miles (NM) and 1000 feet. Regression algorithms predict the minimum separation expected between an aircraft pair. This data-driven approach extracts ADS-B trajectories from the OpenSky Network. In addition, the historical ADS-B trajectories work as 4D trajectory predictions to be used as inputs for the database. Conflict and SI are simulated by performing temporary modifications to ensure that the aircraft pierces into the airspace in the same time period. The methodology is applied to Switzerland’s airspace. The results show that the ML algorithms could perform conflict prediction with high-accuracy metrics: 99% for SI classification and 1.5 NM for RMSE.

Fission: Practical algorithms for computing minimum balanced node separators

Given an undirected graph, a balanced node separator is a set of nodes whose removal splits the graph into connected components of limited size. Balanced node separators are used for graph partitioning, for the construction of graph data structures, and for measuring network reliability. It is NP-hard to decide whether a graph has a balanced node separator of size at most [Formula: see text]. Therefore, practical algorithms typically try to find small separators in a heuristic fashion. In this paper, we present a branching algorithm that for a given value [Formula: see text] either outputs a balanced node separator of size at most [Formula: see text] or certifies that [Formula: see text] is a valid lower bound. Using this algorithm iteratively for growing values of [Formula: see text] allows us to find a minimum balanced node separator. To make this algorithm scalable to real-world (road) networks of considerable size, we first describe pruning rules to reduce the graph size without affecting the minimum balanced separator size. In addition, we prove several structural properties of minimum balanced node separators which are then used to reduce the branching factor and search depth of our algorithm. We experimentally demonstrate the applicability of our algorithm to graphs with thousands of nodes and edges. We showcase the usefulness of having minimum balanced separators for judging the quality of existing heuristics, for improving preprocessing-based route planning techniques on road networks, and for lower bounding important graph parameters. Finally, we discuss how our ideas and methods can also be leveraged to accelerate the heuristic computation of balanced node separators.

Spacing-Assistant for Leipzig and Munich Approach

Before the COVID-19 pandemic and as the ATM industry recovers again, there is a growing demand for airport capacity. A possibility to meet this demand is to introduce revised wake turbulence categories and reduced separation minima according to RECAT. To enable air traffic controllers to memorize and apply these, DFS developed a Spacing-Assistant which shows the optimal turning point from the downwind to the final approach, the minimum separation between aircraft, and their optimal spacing to achieve the separation target. It automatically detects gaps on the final and calculates turning points for aircraft on the downwind to fill these gaps. In mixed mode operations, it can also be actively used to plan gaps for departures between arrivals. Objective and subjective data were collected in a Real Time Simulation with four air traffic controllers from Leipzig and Munich Approach. Results indicate that the Spacing-Assistant supports the controllers’ situational awareness and planning. It shows the potential to save capacity when taking over a working position by helping to quickly familiarize with the traffic situation and to stabilize runway throughput. When working with the Spacing-Assistant the final was shorter which implies a reduced environmental and noise impact.

Graph Learning Over Partially Observed Diffusion Networks: Role of Degree Concentration

This work examines the problem of learning the topology of a network from the samples of a diffusion process evolving at the network nodes, under the restriction that a limited fraction thereof is probed (<i>partial observability</i>). We provide the following main contributions. Given an estimator of the combination matrix (i.e., the matrix that quantifies the pairwise interaction between nodes), we introduce the notion of <i>identifiability gap</i>, a minimum separation between the entries of the estimated matrix that is critical to enable discrimination between connected and unconnected node pairs. Then we focus on the popular Granger estimator. First, we prove that this matrix estimator, followed by a <i>universal</i> clustering algorithm inspired by the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-means algorithm, learns faithfully the probed subgraph as the network size increases. This result is proved for the case where the network topology is obtained through an Erd&#x0151;s-R&#x00E9;nyi <i>random</i> graph under <i>statistical concentration</i> of the node degrees, and the combination matrix is symmetric with nonzero entries bounded in terms of the reciprocal of the maximal and minimal degree. The analysis explores different connectivity regimes, including the <i>dense</i> regime where the probed nodes are influenced by many connections coming from the latent (hidden) part of the graph. Second, we answer a <i>sample complexity</i> question and establish that the number of samples for the Granger estimator scales almost <i>quadratically</i> with the expected graph degree. We also propose three other estimators that are proved to achieve faithful graph learning, and compare them to the Granger estimator, gaining nontrivial insights especially for the case of directed graphs.

Roof-mounted photovoltaic generator temperatue modeling based on common brazil roofing materials

This paper examines the performance of solar photovoltaic generators on roofs of residential buildings. The primary focus is the loss of performance due to temperature increase as function of roof material and the distance from the photovoltaic (PV) generator to the roof. A heat transfer model has been developed to predict PV module temperature, and the equations of the model were solved using the Engineering Equation Solver (EES) software. The research modeling correlates the distance of the solar generator to the roof and the roofing material with the temperature variations in the PV generator. There are many models to predict PV module temperature, but this study refines the prediction by the distance from PV module to roof and the roofing material as variables. Optimal combinations of distance and materials that minimize the heating loss in the solar generator leading to increased electrical power generation. Results show an average error of 3%–4% from the temperature predicted by the model to the temperature measured under experimental conditions in Belo Horizonte, Brazil. The minimum roof-module separation required to ensure minimal PV performance loss from heating from the roof is ∼10 cm for red ceramic and cement fiber roofs. For galvanized steel, the optimal distance is between 20 cm and 30 cm. Cement fiber shows the best predicted and measured characteristics for PV-panel roof mounting among the 3-common commercial roofs evaluated in these studies. These investigations were based on roof installations and local materials in Belo Horizonte, Brazil.

Novel Meteor Observations Using FM Radio Broadcast Over Gadanki, India

A novel GNU radio-based software-defined radio receiver using USRP (universal serial radio peripheral) front end and YAGI antenna is developed to record meteor echoes in passive mode using FM broadcast. Pilot observations are made for four days in August-September 2020 to record FM signals at 103.2MHz, 104MHz, and 107.5 MHz with a varying sample rate of 8 KHz and 200 KHz. The in-phase and quadrature signals are processed to obtain the amplitude and phase of the received signals for identification of the meteor echoes. A new algorithm is developed to automatically detect the echoes using a normalized amplitude threshold and a minimum temporal separation between two echoes. Overdense meteor echoes with a plateau-like amplitude response are found using a ratio test and remaining echoes are rejected. Thus, the system is able to detect almost all types of meteor echoes reported earlier. Notably, signatures of fragmentation and background wind are also observed in form of different phase patterns. Histogram of the meteor counts shows a peak for shorter durations indicating larger populations of the smaller size of meteoroids. The log-log plot of the meteor duration versus count is found to exhibit a characteristic transition of the slope. An empirical estimate of the height of the echoes is obtained. The echoes show micro-meteoroid populations which may depend upon the transmitting-receiving setup and the geometry. Hence, an inexpensive, low-power, portable, passive and automated meteor detection system is demonstrated which can be highly useful for understanding the meteor phenomena and estimation of atmospheric parameters.

Plate lines to enhance wake vortex decay for reduced separations between landing aircraft

To mitigate the risk of wake vortex encounters during final approach, so-called plate lines have been developed. Data collected during a six-month measurement campaign at Vienna International Airport are used to assess the potential for reducing minimum aircraft separations facilitated by plate lines during approach and landing following the re-categorisation (RECAT-EU) methodology for revised wake turbulence categorisation. To ensure that no other parameters controlling wake vortex decay bias the analysis, it is verified that wind speed, atmospheric turbulence, thermal stratification and flight altitude reside in similar ranges with and without the plates. The analysis follows the steps of the RECAT-EU method to generate non-dimensional so-called reasonable worst-case circulation decay curves; one as a reference for nominal operations without plates and one representing the accelerated wake vortex decay brought about by the plate lines. The difference between the two circulation decay curves corresponds to the non-dimensional time-based aircraft separation reduction potential that can be translated into distance-based separation gains. Depending on the particular RECAT-EU category combination, the attained aircraft separation reduction potential ranges between 12% and 15%. Constricting the analysis to wake vortices generated by one aircraft type representing the Upper Heavy RECAT-EU category, the separation reduction potential amounts up to 24%.

Unmanned Aerial Vehicles Swarm-Based Distributed Phased Arrays for Grating Lobe Mitigation and Collision Avoidance

Distributed phased arrays based on unmanned aerial vehicle (UAV) swarm with small antenna elements on each micro-UAV are expected to achieve much better performance than the traditional phased arrays due to a large amount of swarm mobile individuals and deployment flexibility. To avoid the possible collisions of the flying UAVs, a large separation distance between UAVs is required, but grating lobes can appear when the array element spacing is larger than one-half wavelength. To solve this issue, array antennas with aperiodic element placement are normally used to mitigate the grating lobes, while suffering from a high collision risk for UAVs due to the reduced array element separation in the aperiodic array. This paper proposes a novel 3-D aperiodic array design by adding an additional degree of freedom in the optimization of array configuration. The sidelobe levels can be largely reduced from the planar periodic arrays. In addition, the collision risk for UAVs represented by the minimum separation distance between array elements can be largely decreased from the planar aperiodic arrays. This work provides a feasible strategy for the design and optimization of the UAV swarm-based phased arrays for practical applications.

Manifold learning based robust clustering of gene expression data for cancer subtyping

Background:Discovering clinically relevant and statistically significant subtypes of cancer is of paramount importance in precision medicine for customizing the practice of medicine to individual needs. For this purpose gene expression profiles of patients is analysed in an unsupervised manner followed by determining the clinical relevance of the subtypes discovered. However, high dimensional nature of omics data leads to ‘curse of dimensionality’ and results in overlapping subtypes with regard to subtype specific survival curves. None of the methods guarantee separability of patients with respect to subtype specific survival curves. Methods:We propose a robust framework for clustering cancer patients into molecular subtypes by leveraging gene expression data. The framework is based on UMAP and an adaptive to noise robust clustering method OTRIMLE. Survival analysis is performed for the discovered subtypes, and the clustering solution that maximizes the Restricted Life Expectancy difference (RLED) between survival curves for each subtype is chosen. Results:The resulting framework is tested on five datasets of cancer obtained from TCGA, and the results are evaluated by comparing with Neighbourhood based multi-omics (NEMO), Similarity Network Fusion (SNF) and Consensus Clustering (CC). Comparisons show that the proposed framework achieved better separation of subtype specific survival curves. For instance, the minimum separation achieved between any two subtypes for GLIO is 94 days for the proposed framework, while it is about 57, 62 and 38 days respectively for NEMO, SNF and CC. Further, over-representation analysis for each subtype is performed to identify significantly over-represented pathways which can be potential treatment targets. Conclusion:The results advocate that our approach is able to discover subtypes that have better separability in terms of survival, and its significance lies in reducing uncertainty in patient’s expected outcome.

Spatial mapping of soil moisture in an agricultural farm through geostatistical approach

Spatio temporal changes in near-surface soil moisture (NSSM; 0–5 cm) were captured over the research farm (242 ha) at ICAR-Indian Agricultural Research Institute, New Delhi during rabi (winter) season by applying geostatistical techniques. Ranges of NSSM have been different on different days, and was not related to either bulk density or clay content of the surface layer. Even the best-fitting semi-variogram models were different with an average root mean square error of 6.8 over the study period. The minimum separation distance between two sampling points was ~6 m, and nearly 50% variability could be explained by the nugget indicating small scale variations of soil moisture. Spatial variability maps of NSSM were prepared using ordinary kriging wherein a few distinct wetness zones were apparent. Sub-surface soil moisture (SSSM) was correlated with NSSM till 40 cm depth, making plausibility of root-zone soil moisture mapping from surface measurements. The kriged maps will help in scheduling irrigation and deciding sowing dates over a large farm with limited number of in-situ point observations.

Layout optimization for safety evaluation on LNG-fueled ship under an accidental fuel release using mixed-integer nonlinear programming

The Liquefied Natural Gas (LNG)-fueled ship is an alternative solution to deal with the Emission Control Area (ECA) issued by the International Maritime Organization (IMO). LNG fuel has eco-green characteristics since sulfur oxide and particulate matter are eliminated. The equipment sitting on the Fuel Gas Supply System (FGSS) layout needs a good arrangement to prevent the domino effect. This study’s scopes are to establish the feasible FGSS layout based on the equipment position and conduct an accidental LNG release that adopts the Computational Fluid Dynamics (CFD). Mixed-Integer Nonlinear Programming (MINLP) is provided as an optimization tool. The MINLP framework is written in the Python programming language. The minimum separation distance, deck size, and non-overlapping functions of equipment are included as the MINLP constraint. The separation distances are provided based on the Center of Chemical Process Safety (CCPS) recommendation. Also, several options of the separation distance are added that consist of 5.0, 6.0, and 7.0 m to observe the congestion effect. The total pipe length is considered as the MINLP objective that can be used to estimate the pipe cost. The proposed MINLP framework is designated to generate a feasible layout that has an adequate separation distance and minimum cost of the pipe. CFD results show that the increase of separation distance can reduce the gas accumulation effectively which eventually reduces the severity of other consequences. Finally, the feasible layout is proposed based on a series of several consequences under an accidental LNG release, such as gas dispersion, fire, and vapor cloud explosion.

Blind Two-Dimensional Super-Resolution and Its Performance Guarantee

In this work, we study the problem of identifying the parameters of a linear system from its response to multiple unknown input waveforms. We assume that the system response, which is the only given information, is a scaled superposition of time-delayed and frequency-shifted versions of the unknown waveforms. Such kind of problem is severely ill-posed and does not yield a unique solution without introducing further constraints. To fully characterize the linear system, we assume that the unknown waveforms lie in a common known low-dimensional subspace that satisfies certain randomness and concentration properties. Then, we develop a blind two-dimensional (2D) super-resolution framework that applies to a large number of applications such as radar imaging, image restoration, and indoor source localization. In this framework, we show that under a minimum separation condition between the time-frequency shifts, all the unknowns that characterize the linear system can be recovered precisely and with very high probability provided that a lower bound on the total number of the observed samples is satisfied. The proposed framework is based on 2D atomic norm minimization problem which is shown to be reformulated and solved efficiently via semidefinite programming. Simulation results that confirm the theoretical findings of the paper are provided.

A mathematical theory of the computational resolution limit in one dimension

Given an image generated by the convolution of point sources with a band-limited function, the deconvolution problem involves reconstructing the source number, positions, and amplitudes. This problem is related to many important applications in imaging and signal processing. It is well known that it is impossible to resolve the sources when they are sufficiently close in practice. Rayleigh investigated this problem and formulated a resolution limit known as the Rayleigh limit for the case of two sources with identical amplitudes. However, many numerical experiments have demonstrated that stable recovery of the sources is possible even if the sources are separated below the Rayleigh limit. In this study, a mathematical theory for the deconvolution problem in one dimension is developed. The theory addresses the problem when the source number can be recovered exactly from noisy data. The key component is a new concept called the “computational resolution limit,” which is defined as the minimum separation distance between the sources such that exact recovery of the source number is possible. This new resolution limit is determined by the signal-to-noise ratio and the sparsity of sources, as well as the cutoff frequency of the image. Quantitative bounds for this limit are derived, and they demonstrate the importance of the sparsity and signal-to-noise ratio for the recovery problem. The stability of recovering the source positions is also analyzed under a condition on their separation distances. Moreover, a singular value thresholding algorithm is proposed to recover the source number for a cluster of closely spaced point sources and to verify our theoretical results regarding the computational resolution limit. The results are based on a multipole expansion method and a nonlinear approximation theory in Vandermonde space.

Mathematical Foundation of Sparsity-Based Multi-Illumination Super-Resolution∗

It is well-known that the resolution of a traditional optical imaging system is limited by the so-called Rayleigh limit, which is of several hundreds of nanometers. By employing fluorescence techniques, modern microscopic methods can resolve point scatterers separated much lower than the Rayleigh limit. Localization-based fluorescence subwavelength imaging techniques such as PALM and STORM can achieve a spatial resolution of several tens of nanometers. However, these techniques have limited temporal resolution as they require tens of thousands of exposures. Employing sparsity-based models and recovery algorithms is a natural way to reduce the number of exposures and hence obtain high temporal resolution. Recently, a new multi-illumination imaging technique called Brownian Excitation Amplitude Modulation microscopy (BEAM) is introduced. BEAM achieves a threefold resolution improvement by applying a compressive sensing recovery algorithm over only few frames. Motivated by BEAM, our aim in this paper is to pioneer the mathematical foundation for sparsity-based multi-illumination super-resolution. We consider several diffraction-limited images from samples exposed to different illumination patterns and recover the source by considering the sparsest solution. We estimate the minimum separation distance between point scatterers so that they could be stably recovered. By this estimation, we reveal the dependence of the resolution on the cut-off frequency of the imaging system, the SNR, the sparsity of point scatterers, and the incoherence of illumination patterns. Our theory particularly highlights the importance of the high incoherence of illumination patterns in enhancing the resolution. It also demonstrates that super-resolution can be achieved using sparsity-based multi-illumination imaging with very few frames, whereby the spatio-temporal super-resolution becomes possible.

Critical resolution in ghost imaging system with pseudo-thermal light

The resolution limit, which characterizes the resolution power of an optical imaging system, is defined as the resolvable minimum-separation between two points. In classical optical imaging, the resolution limit is given by the Rayleigh diffraction limit. In this paper, we propose a concept of critical resolution, which is used to describe the minimum separation of two adjacent objects that an imaging system can resolve. The critical resolution of pseudo-thermal ghost imaging (PGI) system is analyzed. To quantify the critical resolution, we develop a mathematical formula from which one can evaluate whether a prescribed object can be resolved in a PGI system. The evolution process from critical resolution to resolution limit is presented when the object size towards to zero. The results show that the resolution limit of PGI system is 1.98 times the speckle size. Our results can not only promote the development of GI technology, but also provide theoretical guidance for the design of GI system.

PbBi3O4X3 (X = Cl, Br) with Single/Double Halogen Layers as a Photocatalyst for Visible-Light-Driven Water Splitting: Impact of a Halogen Layer on the Band Structure and Stability

Layered oxyhalides with a Sillén(−Aurivillius) structure have attracted considerable attention as promising photocatalysts for efficient visible-light-induced water splitting due to the elevation of the O-2p valence band maximum (VBM) derived from the strong s–p interaction between Pb/Bi-6s and O-2p electrons and the electrostatic destabilization of oxygen anions in the fluorite layers. However, the role of the halogen layer, consisting of single or double layers, was not known. In this study, we examined a Sillén-type layered oxyhalide PbBi3O4X3 (X = Cl, Br) with alternating stacks of single/double halogen layers and skeletal Bi-based oxide layers. Density functional theory (DFT) calculations of PbBi3O4X3 revealed that the two inequivalent halogen layers contribute differently to the valence band density of states; the halogen p orbitals in double layers are higher in energy and responsible for the VBM, which is understood in terms of Madelung site potentials, where the electrostatic repulsion between halide ions is at play. Another finding from the DFT calculation is the spatial separation of the conduction band minimum and the VBM based on the asymmetry of halogen layers, which may promote the charge separation. While both PbBi3O4Cl3 and PbBi3O4Br3 possess band levels appropriate for visible-light-induced water splitting, only the former showed photocatalytic O2 evolution activity. The stability of PbBi3O4Cl3 is due to the significant contribution of the O-2p orbitals to its VBM, in stark contrast to PbBi3O4Br3, where Br-4p predominately contributes to the VBM. PbBi3O4Cl3 functions as a stable O2-evolving photocatalyst in Z-scheme water splitting under visible light irradiation. These results show that the electrostatic repulsion of halogen layers is a key factor for tuning the valence band structure of oxyhalides while maintaining sufficient stability for water-splitting photocatalysis.

Safety analysis of leakage in a nuclear hydrogen production system

Due to its unique advantages, such as clean and pollution-free, hydrogen energy has gradually improved its energy transition position. Constructing nuclear hydrogen production systems is a necessary means to achieve large-scale hydrogen production, and the study of hydrogen leakage and diffusion behavior is critical to commercializing hydrogen production systems. In engineering practice, the distance between the hydrogen storage device and the nuclear power plant is an important indicator to measure the safety of nuclear hydrogen production. To study the influence of gas storage tank's own conditions and external environmental conditions on leakage diffusion, influencing factors such as wind speed, leakage direction, leakage diameter, leakage height, and leakage angle are discussed in the present study. By calculating severe working conditions combined with the above multiple factors, the longest distance of hydrogen diffusion is determined. Finally, peak overpressure impact generated by hydrogen explosion was evaluated, and the minimum separation distance required to avoid safety risks was predicted. The results demonstrate that when the wind direction is consistent with the leakage direction, and the leakage angle is 0°, the higher the wind speed, the larger the leakage diameter and the lower the leakage height, resulting in a longer diffusion distance. Under more extreme and severe working conditions, the diffusion distance of combustible hydrogen cloud can reach as far as 237 m. Once hydrogen diffusion explodes, the minimum separation distance required is about 338 m. This research provides an effective method for safety risk assessment of a nuclear hydrogen production system.

Study on Calculation of Minimum Separation Distance for Insulation Classification Devices according to Railway Line Condition

In electric railway lines, a dead section is a section in which the power supply is temporarily cut off because of a difference in power-feeding method between two stations. However, owing to the lack of theoretical studies on dead sections and the installation spacing of signals, design changes often occur, such as movements of the signal and various indicators after completion of railway line construction. In this study, the minimum dead section distance between the signal and the coasting indicator according to the tractive car number and track conditions (gradient, slope) was obtained using EL-8500, and the result was verified using TPS based on the tractive force, acceleration, and resistance values of the locomotive. It is expected that a standard can be established with further research through more analysis based on various track conditions and locomotives.

Parametric Characterization and Analysis of Pounding Behavior Adjacent Multi-Storied Buildings of Varying Heights

Abstract: Many past earthquake studies show that during strong vibrations, the adjacent building structures which are closely spaced to each other are vulnerable to severe damage when the adjacent buildings are not at an adequate distance to accommodate their relative displacements. The primary goal of this research is to find out the minimum separation gap between buildings of varying height at the same floor-to-floor height level. SAP 2000 software is used to analyze the structural behavior of building during the earthquake.Three building models are taken for the study, one is six floors (G+6) and another two are nine floors (G+9), and twelve floors (G+12) respectively. Six floors (G+6)&amp; twelve floors (G+12) structures have the same floor to floor height and plan and same beam and column size (equal stiffness) and G+9 buildings have floor to floor height are same but different beam and column sizes (different stiffness). The linear dynamic (RSA) analysis method is used to calculate the response (Displacement, frequency at fundamental time, Base Shear) of the structure at different floors levels. Response (top story displacements) calculated from the response spectrum is compared with the provisions of seismic gap per story height given in IS 4326: 2005.

A Criterion for the Onset of Chaos in Compact, Eccentric Multiplanet Systems

Abstract We derive a semianalytic criterion for the presence of chaos in compact, eccentric multiplanet systems. Beyond a minimum semimajor axis separation, below which the dynamics are chaotic at all eccentricities, we show that (i) the onset of chaos is determined by the overlap of two-body mean motion resonances (MMRs), like it is in two-planet systems; and (ii) secular evolution causes the MMR widths to expand and contract adiabatically, so that the chaotic boundary is established where MMRs overlap at their greatest width. For closely spaced two-planet systems, a near symmetry strongly suppresses this secular modulation, explaining why the chaotic boundaries for two-planet systems are qualitatively different from cases with more than two planets. We use these results to derive an improved angular momentum deficit (AMD) stability criterion, i.e., the critical system AMD below which stability should be guaranteed. This introduces an additional factor to the expression from Laskar and Petit that is exponential in the interplanetary separations, which corrects the AMD threshold toward lower eccentricities by a factor of several for tightly packed configurations. We make routines for evaluating the chaotic boundary available to the community through the open-source SPOCK package.