Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space

Air transport poses significant environmental challenges, particularly the contribution of flight contrails to climate change due to their potential global warming impact. Detecting contrails from satellite images has been a long-standing challenge. Traditional computer vision techniques have limitations under varying image conditions, and machine learning approaches using typical convolutional neural networks are hindered by the scarcity of hand-labeled contrail datasets and contrail-tailored learning processes. In this paper, we introduce an innovative model based on augmented transfer learning that accurately detects contrails with minimal data. We also propose a novel loss function, SR Loss, which improves contrail line detection by transforming the image space into Hough space. Our research opens new avenues for machine learning-based contrail detection in aviation research, offering solutions to the lack of large hand-labeled datasets, and significantly enhancing contrail detection models.Comment: Source code available at: https://github.com/junzis/contrail-ne

Genetic algorithms for satellite scheduling problems

Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency) and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.Peer ReviewedPostprint (published version

Fuel inefficiency characterisation and assessment due to early execution of top of descents. A Case Study for Amsterdam-Schiphol Terminal Airspace using ADS-B data

The vertical trajectory plan (altitude and speed) corresponding to the descent phase of a modern airliner is computed by the on-board flight management system while the aircraft is still in cruise. As long as the constraints on the arrival procedure allow, this system plans for an idle descent and the exact location of the (optimal) top of descent (TOD) is determined in this process. In busy terminal airspace, however, air traffic control officers – motivated by the needs to maintain a safe and expeditious flow of aircraft – might require to start the descents before the TOD computed by each particular arriving aircraft. In such situations, most flight guidance systems aim to intercept the original altitude plan from below, by using a shallower descent angle while keeping the speed plan, requiring in this way, additional thrust. This leads, consequently, to higher fuel consumption figures. The objective of this paper is threefold. Firstly, it characterises and quantifies these fuel inefficiencies for an Airbus A320, using accurate aircraft performance data and a trajectory computation software from the manufacturer. Secondly, it proposes a methodology to automatically identify early descents and to extract the key parameters required to compute the fuel inefficiencies by only observing ADS-B (automatic dependent surveillance-broadcast) data. Finally, the method is applied to a case study with 4,139 real ADS-B trajectories in Amsterdam-Schiphol (The Netherlands) terminal airspace; showing that early descents are very frequent and that they increase the fuel consumption by a 5%, in average.Postprint (author's final draft

Mode S Transponder Comm-B Capabilities in Current Operational Aircraft

Mode S surveillance allows air traffic controllers to interrogate certain information from aircraft, such as airspeeds, turn parameters, target altitudes, and meteorological conditions. However, not all aircraft have enabled the same capabilities. Before performing any specific interrogation, the surveillance radar must acquire the transponder capabilities of an aircraft. This is obtained via the common usage Ground-initiated Comm-B (GICB) capabilities report (BDS 1,7). With this report, third-party researchers can further improve the identification accuracy of different Mode S Comm-B message types, as well as study the compliance of surveillance standards. Thanks to the OpenSky network’s large-scale global coverage, a full picture of current Mode S capabilities over the world can be constructed. In this paper, using the OpenSky Impala data interface, we first sample over one month of raw BDS 1,7 messages from around the world. Around 40 million messages are obtained. We then decode and analyze the GICB capability messages. The resulting data contain Comm-B capabilities for all aircraft available to OpenSky during this month. The analyses in this paper focus on exploring statistics of GICB capabilities among all aircraft and within each aircraft type. The resulting GICB capability database is shared as an open dataset

Ground Stations Scheduling with Genetic Algorithm

In this Master Thesis will be conducted a study on the family of scheduling problems from spacecrafts domain. The objective is to identify special cases of problems in this domain and their relevance from a practical perspective. The considered problems will be modeled as optimization problems and their resolution will be tackled using heuristic approaches (Genetic Algorithms). An experimental analysis will be done using both simulation techniques & benchmarking and real data.TThis work presents a Genetic Algorithm (GA) approach to Ground Station (GS) and Spacecraft (SC) Scheduling problem, which is based on the space missions and ground stations from ESA (European Space Agency). Genetic Algorithm has been used for optimization for many years. The first part of the work is to study how GA has been developed and put in position to science and engineering field. A general GA process is been introduced in this section, which describes basic operations of encoding, mutation, crossover, and selection. There are strengths and limitations of Genetic Algorithms for optimization, which are describe in this section too. The GS-SC scheduling problem is a highly resource-constrained. So in section 1.2, the concept of Resource-Constrained Schedule is studied and defined. And the difficulties of this kind of schedule are presented. The second part of the work defines the basic concepts of ground stations and spacecrafts, which is based on ESA examples. A mathematical model of Ground Stations and spacecrafts is built based on the definitions and assumption of the system. It is simplified so that it can be understood and modeled easily. There are three parts of the model: inputs, outputs and intermediate parameters. The system is to take the input data of spacecraft access windows and time requirements, and using an algorithm to generate a valid schedule solution. STK (Satellite Tool Kit) is been selected for data generation of this work. Space mission of selected ones are simulated and executed. The STK generates one of the important input data: Access Window information of GSs to all SCs. Together with defined mission requirement data, they are converted and stored in the schedule system using a pre-defined structure, which is waiting for further GA process. The GA process is the core chapter in this work. It describes the most important part of work that is approaching the solution of the entire problem. It starts from the encoding method, where two encoding methods are invested and tested, binary vector encoding and decimal vector encoding. It has been proved in this work that the decimal encoding has a better performance and computation speed than the other one. There are advantages and weaknesses that are both examined. Also crossover and mutation methods are introduced. The focus of this designated GA is on designing its fitness functions. This task is related with the constraints and objectives for the ground stations and space mission requirements. A technique of Fitness Modules (FM) is been developed to satisfying the varieties of mission objects. Those modules can be sequential or parallel in the fitness evaluation process. The introducing of FM concept gives the answer to add and remove mission objectives without affecting the existing GA fitness functions. Thus the final evaluating fitness is by summarizing all FMs with different weights. In this simplified model four FMs that represent four mission objectives are designed, these are, Fitness for Spacecraft Access Windows, Fitness for Communication Clashes, Fitness for Communication Time Requirement, and Fitness for Maximizing Ground Station Usage. Every GA needs a selection method of choosing chromosomes for population reproduction. There are some traditional selection methods, which are selected, described and studied. Also we have proposed a combinational selection method to accelerate the population fitting value. The last part of the work is to simulate the entire process in computer environment. Matlab is selected because of its excellent mathematical calculations capability. The GA is coded and executed with multiple times, in order to get the average results. Those data are all been illustrated. And one of the best schedules is been generated as the solution of the problem. The designed GA solved defined problem successfully. In the end, the weakness of this GA is mentioned, and future work direction is pointed out

Scheduling for Ground Stations using Genetic Algorithm

This work presents a Genetic Algorithm (GA) approach to Ground Station (GS) and Spacecraft (SC) Scheduling problem, which is based on the space missions and ground stations from ESA (European Space Agency). Genetic Algorithm has been used for optimization for many years. The first part of the work is to study how GA has been developed and put in position to science and engineering field. A general GA process is been introduced in this section, which describes basic operations of encoding, mutation, crossover, and selection. There are strengths and limitations of Genetic Algorithms for optimization, which are describe in this section too. The GS-SC scheduling problem is a highly resource-constrained. So in section 1.2, the concept of Resource-Constrained Schedule is studied and defined. And the difficulties of this kind of schedule are presented. The second part of the work defines the basic concepts of ground stations and spacecrafts, which is based on ESA examples. A mathematical model of Ground Stations and spacecrafts is built based on the definitions and assumption of the system. It is simplified so that it can be understood and modeled easily. There are three parts of the model: inputs, outputs and intermediate parameters. The system is to take the input data of spacecraft access windows and time requirements, and using an algorithm to generate a valid schedule solution. STK (Satellite Tool Kit) is been selected for data generation of this work. Space mission of selected ones are simulated and executed. The STK generates one of the important input data: Access Window information of GSs to all SCs. Together with defined mission requirement data, they are converted and stored in the schedule system using a pre-defined structure, which is waiting for further GA process. The GA process is the core chapter in this work. It describes the most important part of work that is approaching the solution of the entire problem. It starts from the encoding method, where two encoding methods are invested and tested, binary vector encoding and decimal vector encoding. It has been proved in this work that the decimal encoding has a better performance and computation speed than the other one. There are advantages and weaknesses that are both examined. Also crossover and mutation methods are introduced. The focus of this designated GA is on designing its fitness functions. This task is related with the constraints and objectives for the ground stations and space mission requirements. A technique of Fitness Modules (FM) is been developed to satisfying the varieties of mission objects. Those modules can be sequential or parallel in the fitness evaluation process. The introducing of FM concept gives the answer to add and remove mission objectives without affecting the existing GA fitness functions. Thus the final evaluating fitness is by summarizing all FMs with different weights. In this simplified model four FMs that represent four mission objectives are designed, these are, Fitness for Spacecraft Access Windows, Fitness for Communication Clashes, Fitness for Communication Time Requirement, and Fitness for Maximizing Ground Station Usage. Every GA needs a selection method of choosing chromosomes for population reproduction. There are some traditional selection methods, which are selected, described and studied. Also we have proposed a combinational selection method to accelerate the population fitting value. The last part of the work is to simulate the entire process in computer environment. Matlab is selected because of its excellent mathematical calculations capability. The GA is coded and executed with multiple times, in order to get the average results. Those data are all been illustrated. And one of the best schedules is been generated as the solution of the problem. The designed GA solved defined problem successfully. In the end, the weakness of this GA is mentioned, and future work direction is pointed out

The 1090 Megahertz Riddle: A Guide to Decoding Mode S and ADS-B Signals

In the last twenty years, aircraft surveillance has moved from controller-based interrogation to automatic broadcast. The Automatic Dependent Surveillance-Broadcast (ADS-B) is one of the most common methods for aircraft to report their state information like identity, position, and speed. Like other Mode S communications, ADS-B makes use of the 1090 megahertz transponder to transmit data. The protocol for ADS-B is open, and low-cost receivers can easily be used to intercept its signals. Many recent air transportation studies have benefited from this open data source. However, the current literature does not offer a systematic exploration of Mode S and ADS-B data, nor does it explain the decoding process. This book tackles this missing area in the literature. It offers researchers, engineers, and enthusiasts a clear guide to understanding and making use of open ADS-B and Mode S data. The first part of this book presents the knowledge required to get started with decoding these signals. It includes background information on primary radar, secondary radar, Mode A/C, Mode S, and ADS-B, as well as the hardware and software setups necessary to gather radio signals. After that, the 17 core chapters of the book investigate the details of all types of ADS-B signals and commonly used Mode S signals. Throughout these chapters, examples and sample Python code are used extensively to explain and demonstrate the decoding process. Finally, the last chapter of the book offers a summary and a brief overview of research topics that go beyond the decoding of these signals

Scheduling for Ground Stations using Genetic Algorithm

This work presents a Genetic Algorithm (GA) approach to Ground Station (GS) and Spacecraft (SC) Scheduling problem, which is based on the space missions and ground stations from ESA (European Space Agency). Genetic Algorithm has been used for optimization for many years. The first part of the work is to study how GA has been developed and put in position to science and engineering field. A general GA process is been introduced in this section, which describes basic operations of encoding, mutation, crossover, and selection. There are strengths and limitations of Genetic Algorithms for optimization, which are describe in this section too. The GS-SC scheduling problem is a highly resource-constrained. So in section 1.2, the concept of Resource-Constrained Schedule is studied and defined. And the difficulties of this kind of schedule are presented. The second part of the work defines the basic concepts of ground stations and spacecrafts, which is based on ESA examples. A mathematical model of Ground Stations and spacecrafts is built based on the definitions and assumption of the system. It is simplified so that it can be understood and modeled easily. There are three parts of the model: inputs, outputs and intermediate parameters. The system is to take the input data of spacecraft access windows and time requirements, and using an algorithm to generate a valid schedule solution. STK (Satellite Tool Kit) is been selected for data generation of this work. Space mission of selected ones are simulated and executed. The STK generates one of the important input data: Access Window information of GSs to all SCs. Together with defined mission requirement data, they are converted and stored in the schedule system using a pre-defined structure, which is waiting for further GA process. The GA process is the core chapter in this work. It describes the most important part of work that is approaching the solution of the entire problem. It starts from the encoding method, where two encoding methods are invested and tested, binary vector encoding and decimal vector encoding. It has been proved in this work that the decimal encoding has a better performance and computation speed than the other one. There are advantages and weaknesses that are both examined. Also crossover and mutation methods are introduced. The focus of this designated GA is on designing its fitness functions. This task is related with the constraints and objectives for the ground stations and space mission requirements. A technique of Fitness Modules (FM) is been developed to satisfying the varieties of mission objects. Those modules can be sequential or parallel in the fitness evaluation process. The introducing of FM concept gives the answer to add and remove mission objectives without affecting the existing GA fitness functions. Thus the final evaluating fitness is by summarizing all FMs with different weights. In this simplified model four FMs that represent four mission objectives are designed, these are, Fitness for Spacecraft Access Windows, Fitness for Communication Clashes, Fitness for Communication Time Requirement, and Fitness for Maximizing Ground Station Usage. Every GA needs a selection method of choosing chromosomes for population reproduction. There are some traditional selection methods, which are selected, described and studied. Also we have proposed a combinational selection method to accelerate the population fitting value. The last part of the work is to simulate the entire process in computer environment. Matlab is selected because of its excellent mathematical calculations capability. The GA is coded and executed with multiple times, in order to get the average results. Those data are all been illustrated. And one of the best schedules is been generated as the solution of the problem. The designed GA solved defined problem successfully. In the end, the weakness of this GA is mentioned, and future work direction is pointed out