Timestamp Changes Research Articles

Detecting Looting Activity through Earth Observation Multi-Temporal Analysis over the Archaeological Site of Apamea (Syria) during 2011–2012

This study aims to investigate the potentials of open-access, freely distributed Earth Observation images for detecting large-scale looted areas. The analysis was conducted using medium-resolution Landsat 7 ETM+ images over the archaeological site of Apamea, at Syria. The site has been systematically and extensively looted in the recent past, attracting the interest of scholars. We propose a multi-temporal analysis of cloud-free multispectral Landsat 7 ETM+ images throughout the period between January 2011 to April 2012, just at the beginning of the Syrian war. The analysis was completed through the interpretation of pseudo-color temporal composites, investigation of the multi-temporal spectral profiles, correlations between the spectral bands, and the application of principal component analysis (PCA). The overall analysis was limited within the spectral range of 450–1750 nm. This wavelength range corresponds to the first five spectral bands of the Landsat images. Furthermore, we explored the big data cloud platform Google Earth Engine to detected looted areas. A supervised classification strategy was designed and performed on this cloud platform employing the Random Forest classifier. Finally, a time-stamp change detection approach was implemented. The overall findings were compared with available images from Google Earth Digital Globe and published articles and reports related to the Apamea archaeological site. It was found that the high revisit temporal resolution of the Landsat sensor was able to detect and map the looting activity in the area as a result of the spectral change in the archaeological landscape, despite its 30 m spatial resolution. At the same time, however, the analysis has provided other false-true detections in other areas in the landscape.

A constraint programming model with time uncertainty for cooperative flight departures

The lack of a proper integration of strategic Air Traffic Management decision support tools with tactical Air Traffic Control interventions usually generates a negative impact on the Reference Business Trajectory adherence, and in consequence affects the potential of the Trajectory-Based Operations framework. In this paper, a new mechanism relaying on Reference Business Trajectories as a source of data to reduce the amount of Air Traffic Controller interventions at the tactical level while preserving Air Traffic Flow Management planned operations is presented. Artificial Intelligence can enable Constraint Programming as it is a powerful paradigm for solving complex, combinatorial search problems. The proposed methodology takes advantage of Constraint Programming and fosters adherence of Airspace User’s trajectory preferences by identifying tight interdependencies between trajectories and introducing a new mechanism to improve the aircraft separation at concurrence events considering time uncertainty. The underlying philosophy is to capitalize present degrees of freedom between layered Air Traffic Management planning tools, when sequencing departures at the airports by considering the benefits of small time stamp changes in the assigned Calculated Take-Off Time departures and to enhance Trajectory-Based Operations concepts.

VTWM: An Incremental Data Extraction Model Based on Variable Time-Windows

Continuously extracting and integrating changing data from various heterogeneous systems based on an appropriate data extraction model is the key to data sharing and integration and also the key to building an incremental data warehouse for data analysis. The traditional data capture method based on timestamp changes is plagued with anomalies in the data extraction process, which leads to data extraction failure and affects the efficiency of data extraction. To address the above problems, this paper improves the traditional data capture model based on timestamp increments and proposes VTWM, an incremental data extraction model based on variable time-windows, based on the idea of extracting a small number of duplicate records before removing duplicate values. The model reduces the influence of abnormalities on data extraction, improves the reliability of the traditional data extraction ETL processes, and improves the data extraction efficiency.

Access Control Model Based on Time Synchronization Trust in Wireless Sensor Networks.

Internal reliability and external safety of Wireless Sensor Networks (WSN) data transmission have become increasingly outstanding issues with the wide applications of WSN. This paper proposes a new method for access control and mitigation of interfering noise in time synchronization environments. First, a formal definition is given regarding the impact interference noise has on the clock skew and clock offset of each node. The degree of node interference behavior is estimated dynamically from the perspective of time-stamp changes caused by the interference noise. Secondly, a general access control model is proposed to resist invasion of noise interference. A prediction model is constructed using the Bayesian method for calculating the reliability of neighbor node behavior in the proposed model. Interference noise, which attacks the time synchronization, is regarded as the key factor for probability estimation of the reliability. The result of the calculations determines whether it is necessary to initiate synchronization filtering. Finally, a division of trust levels with bilinear definition is employed to lower interference noise and improve the quality of interference detection. Experimental results show that this model has advantages in system overhead, energy consumption and testing errors, compared to its counterparts. When the disturbance intensity of a WSN increases, the proposed optimized algorithm converges faster with a lower network communication load.

A case of a modelled saturation level for cooperative flight departures

Owing to increasing air traffic operations, the development of advanced decision support tools (DSTs) in air traffic management (ATM) is driven forward to guarantee that sustainable transport logistics balance airspace capacity with user demands. In this paper, the tuning of calculated-take-off times (CTOTs) as a tool for mitigating the propagation of perturbations between trajectories in dense sectors is analysed. The proposed methodology uses a powerful tool for predicting potential spatio-temporal concurrence events between trajectories over the European airspace. In the first place, the aim is to remove the detected concurrence events by considering bounded time stamp adjustments on strategic agreed points of the aircraft trajectory. In the second place, the model is extended to identify route interdependencies of over constraint topologies that could lead to a saturation event. The approach is based on a robust constraint programming model aiming to determine the feasible time stamp changes considering reference based trajectories (RBTs).

A case of a modelled saturation level for cooperative flight departures

Owing to increasing air traffic operations, the development of advanced decision support tools (DSTs) in air traffic management (ATM) is driven forward to guarantee that sustainable transport logistics balance airspace capacity with user demands. In this paper, the tuning of calculated-take-off times (CTOTs) as a tool for mitigating the propagation of perturbations between trajectories in dense sectors is analysed. The proposed methodology uses a powerful tool for predicting potential spatio-temporal concurrence events between trajectories over the European airspace. In the first place, the aim is to remove the detected concurrence events by considering bounded time stamp adjustments on strategic agreed points of the aircraft trajectory. In the second place, the model is extended to identify route interdependencies of over constraint topologies that could lead to a saturation event. The approach is based on a robust constraint programming model aiming to determine the feasible time stamp changes considering reference based trajectories (RBTs).

Causal Analysis of Airline Trajectory Preferences to Improve Airspace Capacity

The problem of fitting the maximum number of aircraft into ATC sectors, keeping in mind aircraft separation and safety standards, area navigation direct routings and other factors, is known as the airspace capacity problem. Above the European airspace, a high density network of air traffic can be found which is determined by the workload of controllers. Constraint Programming (CP) is a powerful powerful paradigm for representing and solving a wide range of combinatorial problems. The PARTAKE project fosters adherence of air space user's trajectory preferences enhancing Trajectory Based Operations (TBO) concepts by identifying tight interdependencies between trajectories and introducing a new mechanism to improve aircraft separation at the hot spots by the mean of CP. The underlying philosophy is to capitalize present freedom degrees between layered ATM planning tools, when sequencing departures at airports by considering the benefits of small time stamp changes in the assigned slot departures.

A New Timestamp Digital Forensic Method Using a Modified Superincreasing Sequence

This paper proposes a new digital forensic method using a modified superincreasing sequence. Timestamp changes by file commands in Windows NTFS file system are used for identifying what commands were executed and are a useful and a logical way for performing digital forensics. A superincreasing sequence is modified for the timestamp change patterns to make each timestamp pattern have a distinct value. The method has two functions; one is a timestamp change check function and the other is a forensic evaluation function. The former checks differences of timestamps between before and after command execution, and the latter produces a characteristic output by applying ten kinds of timestamp change patterns. According to the characteristic output, the kind of command that is executed is identified. By virtue of adopting the modified superincreasing sequence, the evaluation function could produce distinct characteristic output values and thereby provides a way to reconstruct executed file commands.

A computer forensic method for detecting timestamp forgery in NTFS

In this paper, we present a computer forensic method for detecting timestamp forgeries in the Windows NTFS file system. It is difficult to know precisely that the timestamps have been changed by only examining the timestamps of the file itself. If we can find the past timestamps before any changes to the file are made, this can act as evidence of file time forgery. The log records operate on files and leave large amounts of information in the $LogFile that can be used to reconstruct operations on the files and also used as forensic evidence. Log record with 0x07/0x07 opcode in the data part of Redo/Undo attribute has timestamps which contain past-and-present timestamps. The past-and-present timestamps can be decisive evidence to indicate timestamp forgery, as they contain when and how the timestamps were changed. We used file time change tools that can easily be found on Internet sites. The patterns of the timestamp change created by the tools are different compared to those of normal file operations. Seven file operations have ten timestamp change patterns in total by features of timestamp changes in the $STANDARD_INFORMATION attribute and the $FILE_NAME attribute. We made rule sets for detecting timestamp forgery based on using difference comparison between changes in timestamp patterns by the file time change tool and normal file operations. We apply the forensic rule sets for “.txt”, “.docx” and “.pdf” file types, and we show the effectiveness and validity of the proposed method. The importance of this research lies in the fact that we can find the past time in $LogFile, which gives decisive evidence of timestamp forgery. This makes the timestamp active evidence as opposed to simply being passive evidence.