The use of multiple spatially distributed microphones allows performing spatial filtering along with conventional temporal filtering, which can better reject the interference signals, leading to an overall improvement of the speech quality. In this paper, we propose a novel dual-microphone generalized sidelobe canceller (GSC) algorithm assisted by a bone-conduction (BC) sensor for speech enhancement, which is named BC-assisted GSC (BCA-GSC) algorithm. The BC sensor is relatively insensitive to the ambient noise compared to the conventional air-conduction (AC) microphone. Hence, BC speech can be analyzed to generate very accurate voice activity detection (VAD), even in a high noise environment. The proposed algorithm incorporates the VAD information obtained by the BC speech into the adaptive blocking matrix (ABM) and adaptive noise canceller (ANC) in GSC. By using VAD to control ABM and combining VAD with signal-to-interference ratio (SIR) to control ANC, the proposed method could suppress interferences and improve the overall performance of GSC significantly. It is verified by experiments that the proposed GSC system not only improves speech quality remarkably but also boosts speech intelligibility. Speech technology plays an important role in speech communication and human-computer interaction. Microphone arrays have been widely studied in speech enhancement because of their great performance in enhancing the quality and intelligibility of the received speech signal . They arDespite the effectiveness of the various proposed methods, the accurate control of the ABM and ANC, especially under highly non-stationary noise and low signal-to-noise ratio (SNR) conditions, is still very challenging. To improve the control accuracy, other information offered by new types of sensors can be a complement to the microphone signals. Various sensors have been widely used, especially in the Internet of Things ,12. AmonThere are two categories of approaches employing BC speech signals for speech enhancement. The first one is to explore the non-linear mapping of BC speech signals to AC speech signals ,15,16,17The dual-microphone array has advantages of low cost, small size, and ultra-low power consumption and has been widely used in wearable devices such as hearing aids, earphones, and smart glasses, in which BC sensors are suitably embedded. Therefore, this paper focuses on the dual-microphone array framework, where we propose a novel robust dual-microphone GSC assisted by BC sensor. With the accurate VAD consistently obtained through the BC signal even in low SNR environments, the successful control of the ABM can be achieved.By further incorporating the VAD information together with the SIR information into the control of ANC, a satisfactory result can also be obtained. The effectiveness of the proposed GSC algorithm is then confirmed by experiments in the presence of the non-stationary and diffuse noises.The rest of the paper is organized as follows: The work in this paper is based on the GSC structure with ABM that was proposed in . As showi = 1, 2) enter the FBF that can steer the main beam to the direction of desired signal. First, the two microphone inputs parators . In FiguThe adaptation of the MC is obtained as:The index Although the idea of the above GSC algorithm is very practical, it still has some drawbacks. First, if the performance of ABM in a certain frame is unsatisfactory, the coefficient update decision of all future frames of ABM and MC will probably be inaccurate, which leads to an overall poor performance. In addition, the estimation of the SIR is inaccurate in a strong noise environment. These problems are addressed using BC speech to control ABM and MC in the next section.The structure of the proposed robust GSC is depicted in a priori SNR and is a fixed value of In the proposed algorithm, the VAD information is obtained via the BC speech signal. The primary task is to estimate the SPP in each frame of the BC speech signal. In , the a pξ)=15 dB  for redua posteriori SPP in (12) will be overestimated. It follows that then the noise power will not be tracked as quickly as expected. In the extreme case, when The noise power estimation is updated with the SPP-based noise estimation algorithm, as follows:a posteriori SPP To avoid a stagnation of the noise power update owing to an underestimated noise power, additional mechanisms are further employed. First, the inter-frame smoothing on In the proposed algorithm, only the specific part of the BC speech signal whose frequency spectrum lies between 70 Hz and 800 Hz is used for VAD because no human voice is below 70 Hz and the power of BC speech is attenuated significantly above 800 Hz. Note that for female voices or general higher pitch voices, the upper limit of the applicable BC speech frequency can be up to 1.5 kHz, and setting the upper frequency limit to 800 Hz can also obtain accurate VAD information. Let Note that VADs for the ABM and ANC are different. This is because the VAD in the ABM should make all the speech presence frames be detected at the expense of some speech absence frames being misjudged. However, the speech absence frames need be detected as much as possible in the ANC. Therefore, a smoothing operation is performed when using VAD to control the ABM. Specifically, The ABM is a spatial rejection filter, where it rejects the desired signal and passes the noise. The FBF output L. In this paper, the recursive least squares (RLS) algorithm [The proposed algorithm utilizes the VAD information lgorithm  is emploI and L and a positive parameter that prevent the RLS algorithm from divergence when the covariance matrix is ill conditioned. Equation (19) can be updated using the QR decomposition technique, when and only when the time dependent control factor, as shown below, is 1:The QRD implementation can be found in 2). Note, we have used italic and bold letters to denote matrices and vectors in From The goal of the ANC is to reject the noise and extract the desired signal. It eliminates the portions in the FBF output The strategy of AJC is that The variable FF can be computed from:Equation (26) with a variable FF can also be implemented by using the QR decomposition as shown in The tanh function in (24) is a monotonically increasing function. When current frame is detected as the speech presence frame, it can be seen from (24) and (27) that the larger The output of the ANC should contain less noise than the output of the FBF. Theoretically, letting ANC output The outputs of the FBF and ABM are still sent to the ANC as same as (23). The control method of coefficients update of the ABM and ANC is the same as above. The experimental results show that iterating only once can lead to a better performance than no iteration while iterating multiple times produces no further performance improvements. In this work, only one iteration is adopted.To validate the usefulness of the proposed BCA-GSC algorithm, we compare its performance with the conventional GSC algorithm  in varioThe noisy AC speech signals were generated by corrupting the clean AC speech with noise under various SNRs . The BC speech signals and clean AC speech signals were recorded simultaneously in the absence of background noise . The clean AC speech signals were at 0°. To demonstrate the robustness of the proposed BCA-GSC algorithm, four types of noise including directional noise and diffuse noise were used, and these noises were recorded indoors separately. Specifically, directional noise was obtained by a loudspeaker playing noise in a certain direction, and diffuse noise was obtained by simultaneously playing noises from four loudspeakers placed respectively in the four corners of the room. To further verify the advantage of the proposed algorithm, the objective test perceptual evaluation of speech quality (PESQ) , which iThe frequency domain segment SNR (FsegSNR)  measure PESQ and FsegSNR are objective measure of the speech quality. For evaluating speech intelligibility performance, short time objective intelligibility (STOI) measure was performed. In this paper, an improved robust GSC algorithm using two AC microphones and a BC sensor is proposed. The special characteristics of the BC sensor are exploited to obtain accurate VAD to control coefficients update of the ABM and ANC. The recursive least squares algorithm is employed to update the adaptive coefficients due to the efficiency in terms of convergence speed. The proposed BCA-GSC algorithm enjoys robustness under various background noise conditions, and provides a good noise suppression while protecting desired signal. The experiments demonstrated the proposed BCA-GSC algorithm improves both speech quality and intelligibility significantly, compared with the traditional GSC.


Temporary safety facilities (TSFs) are an essential support system providing necessary protection to workers during construction activities, which are targeted towards preventing the occurrence of incidents and accidents at the construction site; however, the schedule and location of installation and demolition of TSFs continue to rely on labor experience, and are often omitted from formal drawings or documents. This results in thousands of accidents in the construction industry, especially in construction small and medium-sized enterprises (SMEs) because of their several limiting factors; therefore, this study proposes automatic workspace planning for TSFs based on construction activities, which is a systematized approach for construction SMEs to practice occupational health and safety (OHS). By using building information modeling (BIM) and add-in algorithm, safety facilities can be simulated and visualized to integrate into the designated workspace. The developed system was implemented utilizing 4D-BIM for TSFs installation and validated with a case study on a residential building project. The result revealed that the visualized TSF produces a better understanding of safety measures with regard to project schedule. Additionally, TSFs workspace planning provides an affordable approach that motivates safety practices among the SMEs; consequently, the effectiveness of construction safety measures and their management is enhanced appreciably. The integration of innovative technologies in the field of information and communication technology (ICT) is rapidly transforming conventional construction into smart construction. Nonetheless, the nature of the construction process makes it prone to risks and accidents; moreover, innovations in the construction sector have given rise to new safety issues. According to recent public reports, construction is one of the most hazardous occupations with considerably higher number of work-related injuries and fatal accidents than other industries ,2,3. TheOn the other hand, the planning of occupational safety is determined by the size of the construction site, system of work, and project delivery methods. A comparison among firms of various sizes shows that a high proportion of accidents in terms of number or severity occurred in small firms . The litOn the other hand, these traits are not all negative, they also have positive points. SMEs could have more potential than larger companies in simplifying the incorporation of BIM by converting their drawbacks into opportunities . AlthougThis paper proposes a 4D-BIM-based automatic workspace planning which allocates temporary safety facilities (TSFs) for designated model objects/conditions integrating activity-based hazard evaluation and its corresponding prevention. The assessment data were arranged specifically by the severity rate of accidents related to individual activities and then the correlated protection was calculated. The proposed approach was applied to a residential building project. It was found that 4D-BIM software platforms could automatically place the TSFs at the right time and right place. It is expected that the system can provide general safety planning without the participation of safety experts. Consequently, it decreases the manager workload, improves safety management, saves SME resources, and helps construction SMEs to adopt a closer approach to a smart and safe working environment.The remaining paper is structured as follows: The section below sets the scene by giving an overview of the existing research on accident data analysis, temporary safety facility, and initiatives concerning BIM-based safety planning in the construction industry.Certain research has underlined the importance of identifying the cause of accidents and determining their role to prevent accidents from happening. Betsis  highlighTSFs are essential equipment/facilities used for a limited period, depending on the change in constructing conditions, for the purpose of providing adequate safety and prevention at a construction site. Although extensive research is available on the planning of permanent structures in construction, temporary structures continue to be overlooked or receive minimal consideration . Hence, On the contrary, by comparing the distinct characteristics of the firm size, particularly with regard to safety management and execution, several studies have emphasized that weak occupational health and safety (OHS) practices resulting in high risk of accidents at construction sites were commonly observed in SMEs. Some papers claimed that the biggest restricting factor in construction SMEs was skilled labor, whereas others attributed this to cost ,17. AddiEmerging as a promising visualization technology, the BIM application has paved the way for safety performance by its capability to deliver a rich profusion of digital construction data ,26,27,28With construction rapidly becoming a data-driven industry, digitizing construction information is one of the first steps toward smart management. Accordingly, to enhance the competitive ability in the construction market, SMEs have to transform their conventional method into smart construction. Nevertheless, most of construction SMEs operate under several constraints such as time, cost, and the shortage of qualified personnel, which restricted them to utilize expensive innovative technologies in construction operation , particuHazard investigation module. This module consists of the following two-stage process: data input process and allocating safety contents process. The input data process is a preparation of project 3D model and 2D data such as project schedule and the Fatal Four data collected from the OSHA website. Following this, the allocating safety contents is a procedure of analyzed and assessed Fatal Four data. Concurrently, the designated tools for 2D and 3D data exchange such as a graphical algorithm and BIM were employed for developing the linking of databases for the next module.Hazard Elimination module. The safety performance process was developed by 4D-based safety information modeling (4D-SIM). Relevant safety contents consist of safety regulations and prioritized TSFs are displayed in the visual environment, in which serious hazard notifications and model-linked TSFs automatically shown by construction sequences. The accessible approach was adopted by uploading 4D-SIM data on the company’s server using the BIM cloud. Finally, it can be seen from the beige box in the last process, 4D-SIM data can be integrated with augmented reality (AR) for the purposes of safety monitoring at construction site. In this practical application step, the required TSFs for specific activities were uploaded and inquired from BIM cloud server. Then, the users can approach the WPT application everywhere by accessible devices such as a tablet, mobile phone, monitor, etc. The AR can connect to these devices for supervision purposes. The integration of BIM and AR creates a visual interaction for users to read and update safety facilities information to facilitate AR part for monitoring the installation and removal of TSFs in real site construction. More details of this collaboration process will be developed and clarified in further study.To address the aforementioned problems, this study aimed at developing and validating a concept prototype of 4D-BIM-based automated workspace planning for TSFs (WPT) system by considering the severity of activity-based hazard and its corresponding prevention with regard to specific spatial and temporal conditions. Assessment systems include several steps, which aim to indicate the severity of hazards in each work activity. Then, the highest hazard is addressed along with proper prevention method. To develop an assessment database, this study considers a broad expanse of reports on construction accidents to identify the essential hazards in each working activity. Various methods are available for collecting and analyzing the occurrence of accidents, which aim to clarify the cause of an incident based on subjective aspects  or objective problems such as environmental issues ,43. FataFatal Four Ratio: From OSHA data reports, the percentage of fatality cases corresponding to each Fatal-four type  is determined for each construction activity.Essential Hazards Assessment: The essential hazard is a type of Fatal Four that has high possibility to happen. It is determined by Fatal Four type that has highest percentage from the Fatal Four ratio.Hazard identification and assessment play a crucial role in the OHS program. Failure to recognize hazards is one of the root causes of injuries, accidents, and incidents in the construction site. Therefore, this data analysis step aimed at indicating the main potential hazards in work sequences to prevent future occurrences of accidents. By thoroughly investigating the accidents and fatality reports, we can assess and determine essential hazards that might potentially happens for each activity. The activity-based assessment was conducted by evaluating the severity of the potential hazard outcome and prioritizing the hazards. This process includes two steps:In the database development step, a database that includes the recommended TSFs is developed corresponding to hazard scenarios assessment. After obtaining the essential hazards for each activity from previous step, related TSFs is recommended based on the hazard scenarios according to OSHA regulations . The criThe scenario assessment was conducted by evaluating 430 construction fatality cases which were attributed to the Fatal Four among 1266 fatality cases reporting by OSHA in 2018 . By clasAn FFA system was developed to analyze the Fatal Four data and output the database of the recommended TSFs related to activities with a high rate of accident occurrence. From input data of the Fatal Four collected from OSHA reports, FFA system uses semantic and textual algorithms to analyze and find information related to accidents for each activity . Next, tThe workspace planning for TSFs (WPT) system was designed to introduce activity-based safety facilities that appear in a specific condition during the construction process. The developed WPT system utilized five software applications: (1) Autodesk Revit 2018: a BIM tool to stored, visualized, and managed digital information related to building and construction process; (2) Dynamo 2.0.2: a Visual Programming Language (VPL) that extents the power of Autodesk Revit; (3) Microsoft (MS) Excel 2016; (4) MS Project: a project management software product; (5) Navisworks Manage 2019: a BIM coordinator solutions.The process of data procedure in the WPT system includes three stages: local database preparation, data processing, and data delivery to users. The WPT system architecture is presented in This section focuses on the details process of the development of data exchange procedures which coordinated the allocation of selected TSFs in WPT system . The intIn order to guarantee workplace safety, the participants, including managers and workers, have the responsibility to understand and meet several safety requirements. Navigating the TSFs system to specific work activity in a 4D-SIM framework depends on the relationships between three factors, i.e., the relationship between activity and FFA result, and that between the FFA result and subjects (location and space). The available TSFs that satisfy both these two linking factors are recommended for accident prevention. The field safety case study was performed during the construction of a private residential building project with a total area of 320 sq. m and construction density of 60 percent. The example given in this case study is BIM model of a five-story building developed in Autodesk Revit software. It is a complete model with architectural and structural elements  equipment and furniture), and the project schedule was planned for six months  at the specified Key . The “Element.SetParameterByName” will automatically add the information (masonry work date) to all external walls.In the Revit model, the first step was obtaining the data (masonry work planning) from the project schedule that was made in Excel format (.xls). Accordingly, this information was added to all external walls through visual scripting using Dynamo. The categorical process of schedule integration with the exterior wall as illustrated in According to OSHA regulations see , if the The Dynamo script will depend on the progress of the schedule to automatically calculate and propose safety measures. A safety system that employs 3D BIM related to the activity of block laying was developed. The system allows synchronization of the model data back and forth between Autodesk Revit and the schedule spreadsheets. In this process, the schedule, activity, and the corresponding safety information ID are set up and loaded to the system database. The database contains Fatal-four-related fatality cases, the activity-based essential hazard, hazard scenario, and corresponding safety measures. Combining with the developed parameter model in Revit, the design team employs Dynamo to read available schedule data of exterior walls from MS Excel format. This VPL will automatically retrieve and integrate data to acquire information about the by-floor construction progress of architectural walls . The user can choose any work dates in the schedule to estimate the needed tasks and continue using Dynamo to distinguish between the constructed walls and those under construction.After retrieving all walls that are not yet constructed, a new railing system is constructed at the positions of these walls. The new 3D view was also created to show the TSF system in the Revit model. As illustrated in Understand: How well do the users understand the system? Considering the participant’s ability to understand safety requirements and its implementation through these approaches.Feasibility: Is this system capable to be implemented in your company? Representing the usability and applicability of the proposed system in construction SMEs.Cost: Do you think this system is costly? Considering the long-term impact on the cost of using this system including labor-cost, rework-cost, and other indirect costs.Ease: Is this system easy to use? Considering participants’ senses when using the interactive visualization system and paper-based system. The safety information can be approached easily by flexible access from personal devices.Effectiveness: Does this system improve accessibility to safety practices? Considering the performance of the system by addressing required safety contents in a specific time and place through the visual interaction.Time: How much time you must spend to make safety planning on this system? Considering spending time in comparison of WPT system and conventional method to develop a safety planning based on participants’ experience.Upgradability: Can the system be improved for changes in the market? Focusing on the ability to update and synchronize TSFs information from the changing of supply market and project progress.Following the prototype development of the WPT system, an experiment was conducted to validate the usability of the WPT system. The process commenced with small training, whereby the WPT system functions and features were explained, and a workspace planning for the residential project was delivered, giving participants an overview of this visual safety planning to acquire safety information pertaining specifically to required TSFs in the construction process. After that, the participant took part in the survey through questionnaires and interviews related to the criteria for the usability of the WPT system. These criteria were given to evaluate the characteristics of WPT system that are particularly useful for SMEs’ access. A group of 18 participants including contractors of construction SMEs (three companies in Korea and seven companies in Vietnam), three Vietnamese site supervisors, and five construction managers from Iraq, Pakistan, and Vietnam were asked to experience the WPT trial platform. Trials were conducted with two methods, utilizing the conventional paper-based approach and the WPT system approach, with survey questionnaires. Subsequently, through issued questionnaires, interviewees evaluated the potential of the WPT in terms of the following seven criteria ,28,49,50The evaluation interview ranged from 10 to 15 min per people, and grade on the 10-point scale of satisfaction. The popularization of BIM is an opportunity and also a challenge to its effective application for all construction participants including both large scale and SMEs. This study proves the scalability of BIM and the application of graphical algorithm for automatically allocating schedule-based TSFs in small or medium constructions. The intention of targeting construction SMEs in this research is to motivate them to apply the digital transformation in their everyday work. Multiple steps are performed to transfer schedule data thorough an understanding of Excel and Dynamo.The main function of WPT system is to automatically provide the prioritized TSF recommends for a specific time and workspace. When using this system, cost-saving can be achieved with the reduction of labor resources and deficiencies in safety management. It also saves time and reduces accidents because of the automation, convenience, and accuracy of the system  compared to the conventional method. The spreadsheet used for quantity take-offs of the TSFs can be exported from the BIM environment to the Excel format. When the category and quantity of required TSFs are identified, SMEs will have many options of choosing TSFs with proper materials, styles, and prices on the supply market. This offers efficient data for detailed quantity estimation of TSFs, it also plays a critical role in reducing time and labor’s workload in cost planning process.Despite a lot of potential with the involvement of this Autodesk’s add-in, the integration outlined in this paper has some limitations. Firstly, there is a restriction in mapping safety information for a specific duration with model objects information that requires manual review when safety contents are distributed to these components. Some regulations are designed for general construction activities, and some others are formulated for a distinct activity period; therefore, safety regulations cannot be considered solely based on object information. Sometimes it must be divided by conducted time. It is necessary to define such rules clearly in the 4D-BIM for each planned activity, and then develop an ID system to match the related information accurately. In addition, it is also necessary to provide ideal and accurate schedule information to the 4D-BIM platform to update it with specific safety tasks and conditions. The assessment method proposed in this study focused on the Fatal Four; the remaining accident factors that account for 40 percent of all construction fatalities would be explored in future studies. Arguably, the accuracy of the proposed concept depends on the number of fatality cases evaluated in calculating the accident rates by severity. Further research would be necessary to collect more data on fatalities and analyze the rate of fatalities in construction SMEs.The paper contributes to the investigation of pros and cons of construction SMEs in implementing safety requirements. The research has identified the importance of automated workspace planning for TSFs to SMEs application. Our paper proposed a WPT system for automatically providing the prioritized TSF recommendations for the schedule-based spatial and temporal. By collecting Fatal Four data from OSHA website, categorizing them by construction sequences, investigating OSHA regulations for using activity-based TSFs and getting the advantage of BIM and VLP, a residential building case study was developed to prove the application of WPT system. Consequently, this paper proposed workplace planning focusing on the operational requirements and constraints of construction SMEs, which contribute to facilitate the safety planning process, enhance safety management, reduce manager workload, simplify worker involvement, and increase the information exchange between participants.The proposed framework comprises two main modules: (1) hazard investigation, which identifies serious hazards related to activities and their corresponding safety measures; (2) hazard elimination, which provides visual safety planning with semi-automatic schedule-linked temporary safety measures. The WPT approach has the advantage of saving time and labor resources required by construction SMEs to implement the safety requirements. This results in considerable time and cost savings apart from ensuring safe working conditions for workers. To comprehensively evaluate the effectiveness of the proposed system, future work would conduct an objective comparison between the conventional and WPT approaches with the full-scale WPT systems.On the other hand, the FFA-based computer vision application to remotely monitor and calculate the severity of accidents should be experimentally validated. Additionally, it would also be worthwhile to consider the effect of utilizing artificial intelligence (AI) to automatically complete the FFA process and recommend accurate risk management strategies. We also plan to integrate the system with AR technologies to deliver a real-like experience to users in onsite construction. The proposed approach will be validated further by applying it to real construction sites in Vietnam and South Korea to compare the practical applicability of this system in a developing and developed country.


Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used. Since NASA launched the first satellite (at that time known as the Earth Resources Technology Satellite) of the United States’ Landsat program in 1972 [SAR is an active microwave remote sensing tool  with widInterferometric Synthetic Aperture Radar (InSAR) technology exploits differences in the phase of the waves returning to the satellite of at least two complex SAR images to generate, for instance, surface topography and deformation maps or Digital Elevation Models (DEMs) ,7,8.Among various InSAR techniques, satellite Differential Interferometric Synthetic Aperture Radar (DInSAR) has emerged as a powerful instrument to measure surface deformation associated with ground subsidence on a large scale with centimeter to millimeter accuracy ,10,11.DInSAR exploits the phase information of SAR images to calculate the ground displacements between two different satellite acquisitions . The PerThanks to the increasing availability of a large amount of SAR data from such missions like ALOS-2, COSMO-SkyMed, PAZ, RADARSAT-2, Sentinel-1 or TerraSAR-X ,11 and dDInSAR and PSInSAR are complementary methods and both have essential advantages and some disadvantages . By way Despite the obvious benefits, InSAR technology has some limitations. InSAR measurements are often affected by various artefacts that not only make interpreting them more challenging, but also affect the reliability and accuracy of its outcomes.One of the most significant is the effect of the atmosphere. In general, it results from the phenomenon of electromagnetic waves delay when traveling through the troposphere and accelerate when traveling through the ionosphere . AtmosphAnother type of InSAR data failure described in the literature is a border noise ,25,26,27Contrary to the unwanted artefacts influence on InSAR data, Bouvet  proposedThe primary issue of this paper is one more contamination frequently appearing in SAR images that is called Radio-Frequency Interference (RFI). Since the work is in progress on the development of the automatic monitoring system for high-energy para-seismic events, the urgent matter is to elaborate the effective method supporting the automatic PSInSAR processing workflow by the removal of faulty SAR data (with artefacts) that can lead to misinterpretation of the final results.Our main goal is to find the easiest to implement technique for marking images with RFI artefacts. The solution presented in this paper for the purpose of RFI detection was based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering techniques. As the reference classifier we used a convolutional neural network.After a short introduction on the characteristics of RFI, common forms and sources of this artefact, included in RFI is defined as ’the effect of unwanted energy due to one or a combination of emissions, radiations or inductions upon reception in a radio communication system, manifested by any performance degradation, misinterpretation or loss of information which could be extracted in the absence of such unwanted energy’ according to Article 1.166 of the International Telecommunication Union Radio Regulations .In the SAR images these incoherent electromagnetic interference signals usually appear as various kind of bright linear features ,31 like The reason for RFI contamination for SAR is that many different radiation sources operate with the same frequency band as the SAR system ,30. In gOver the past years, great efforts have been made to better understand RFI effects and to develop robust methods for detecting and mitigating this artefact, e.g., ,35,36,37This section shortly describes the materials we used in our investigation. We depict the source data and datasets used to carry out experiments and tests of our solution.A set of dedicated satellites, the Sentinel families, are developed by the European Space Agency (ESA) for the operational needs of the Copernicus programme . The EurThe Sentinel-1 mission is a polar-orbiting, all-weather, day-and-night C-band synthetic aperture radar imaging mission for land and ocean services. It is based on a constellation of two satellites: Sentinel-1A was launched on 3 April 2014 and Sentinel-1B on 25 April 2016 ,42.Sentinel-1 data are intended to be available systematically and free of charge, without limitations, to all data users including the general public, scientific and commercial users ; howeverLevel-1 Single Look Complex (SLC) products, as other Sentinel-1 data products, are delivered as a package containing, among others, metadata, measurement data sets and previews .The Sentinel-1 SAR instrument mainly operates in the Interferometric Wide (IW) swath mode over land ,40 that In this study, we used quick-look images contained in the preview folder, a part of Sentinel-1 data package. As in our case we have dual polarization products, data are represented by a single composite color image in RGB . BecauseDue to these advantages of Sentinel-1 data products, in summary, high and free of charge availability, wide ground coverage and fine resolution, these data are used in developing the automatic monitoring system for high-energy para-seismic events and its influence on the surface in the study area, the Żelazny Most, with the use of GNSS, seismic and PSInSAR techniques. The Żelazny Most is the largest tailings storage facility in Europe and the second largest in the world ,45. It iRFI characteristic depends strongly on the geographic position of data acquisition . AccordiIn order to carry out experiments and to verify the proposed approach for RFI artefacts detection, we used 3 different quick-look datasets, named RIYADH, MOSCOW and ASMOW, for the needs of our research. This selection was based on above mentioned RFI power map and IEEE GRSS RFI Observations Display System. Each collection includes both correct images and images with various levels of RFI contamination. RIYADH dataset consists of 136 images that were collected between May 2015 and January 2019, and comprise the area of the capital of Saudi Arabia. The MOSCOW set includes 99 images acquired between January and December 2019. These images cover the area surrounding the capital of Russia. The ASMOW collection consists of 53 images covering the study area, located in southwest Poland, in Lower Silesia, east of the town of Polkowice in the municipality of Rudna. These data were acquired from September 2018 to February 2020.In this section, we introduce basic information about the techniques we used in the experimental part. Here we start by discussing an example of how to store an image in digital form.F is the letter of the hexadecimal system alphabet to which a decimal value of 15 is assigned.A digital image can be simply represented by binary numbers of pixel color saturation in the relevant system ,49. GreyIn the pre-processing step, the images are converted to greyscale representation. This conversion makes it very easy to use feature extraction techniques. Next, we discuss a sample conversion.The above formula is a transformation from OpenCV library . An examIn the next section we discuss the technique of image features extraction based on pixel convolution ,52,53.In Other useful techniques that were applied are image filtering techniques by means of thresholding ,52,54. TWe used two types of thresholding . The firThe second method consisted of using a histogram of pixel values and filtering out those which  belong to a fixed frequency range.The last method we tested for an artefact classifier was the use of structural filtering with the nearest pixel neighbors ,52,55,56We also applied the technique of filtering the image structure by replacing the pixel with its closest neighbor in the sense of Manhattan distance ,58. FiltAs a reference classifier defined in the following section, we used the Convolutional Neural Network (CNN) ,60.To verify the effectiveness of our artefacts extraction method, we used a simple deep neural network ,61 definThe Adam optimizer  was usedListing 1A detailed definition of the neural network is given in Listing 2To make it easier to reproduce our research results, here are the versions of the libraries we installed to carry out the experiments see .ListingTo carry out the research we have used the bitmap library.hpp—see  library,Consider the examples of problems with artefacts detection from our experimental groups of quick-look images. The first one is the RIYADH quick-look dataset. In Next, we describe the steps of searching for our method of extracting artefacts on the RIYADH collection.The first step was to convert the image into a greyscale form—according to the formula from In the next step, we tested the detectors of features based on pixel convolution.In this section, we present the selected feature detectors on the basis of an image with artefacts—see the left picture . We treaWe have considered the following feature detectors:We provide more extensive testing of the Gaussian blur on the RIYADH data in Next, we discuss the results of experiments with the application of thresholding techniques from In Summarizing the results obtained, we can state that the most effective technique of detecting artefacts, from among the ones we have studied, is the use of thresholding based on histograms of pixel values and noise filtering using the closest neighbors method. The result of good performance of this combination was predictable, because artefact colors usually have a low frequency in pixel histograms; therefore, it is quite easy to visualize clear artefacts. Then the filtering method allows us to remove single, unstructured pixels. Let us present the results (before and after the application of our method) on the RIYADH dataset using the neural network described in Next, we discuss the results on artefacts collection from the MOSCOW database.0 (no artefacts), I (weak artefacts) and II (strong artefacts)—see 0, We considered three classes: We applied the same steps as for the RIYADH dataset. We used a frequency range of 1000 for thresholding. Samples of data after applying our method on the MOSCOW collection can be seen in Next, we briefly present our results.I) are comparable on the validation set. Despite the fact that the quality of neural network learning on three classes has increased significantly (see 0) and classifying all three classes. In case of the classification of weak artefacts the results are comparatively low with high standard deviation up to 18 percentage points.Detailed test results using the MCCV5 method are shown in ntly see , the clantly see  without Let us move on to test the ASMOW collection, an important one for our project.In this section, we describe the detection of artefacts, which appear in the ASMOW quick-look dataset. Performing referenced deep learning classification was not possible due to the availability of RFI-affected images. From here we simply present the procedure for detecting artefacts on this data—with the position of the artefact marked in the picture.Considering sample data where the middle picture contains an artefact—see We applied a hybrid threshold method based on the histogram , and using the 1 We have applied pixel binarization , settingThen we detected the area whose pixels are arranged in a thick straight line—see The detection procedure is as follows:The experimental results show that the detection of these very little visible artefacts requires the following steps. The first two are analogous to the detection of the artefacts of RIYADH dataset, that is, (1) we use pixel convolutions by means of Gaussian blur, (2) thresholding on the basis of the color occurrence frequency, (3) defrosting using the 1 nn technique, (4) binarization and (5) detection of a plane consisting of pixel blocks. Artefacts can be successfully detected as we can see in In this work, we tested a group of image processing techniques to separate clear RFI artefacts from undamaged images. We reviewed the masks used to select features in the process of convolution. Then we tested methods of thresholding. The first one is based on the selection of pixels in a fixed standardized range. The second one involves filtering pixels that do not meet the established criterion of color frequency (based on color histogram). Then we tested the hybrid solution with thresholding and filtration method based on the nearest neighbor of pixels. We verified the results of our methods of separating artefacts using a convolutional neural network as a reference classifier. The classification was carried out on raw data and on data prepared by our methods—with the Monte Carlo Cross Validation 5 model. In our work, we considered three datasets with artefacts. The first one comes from RIYADH, the second one from MOSCOW and the third one from ASMOW quick-look dataset. In case of RIYADH and MOSCOW datasets we see that for the separation of our RFI artefacts, the best level of their separation is given by the Gaussian blur. The best method, among the tested ones, that gives a clear separation of large artefacts is a hybrid of frequency based on thresholding and filtration with the nearest neighbors method. In case of the ASMOW dataset of RFI artefacts, we applied the same steps as with RIYADH and MOSCOW, additionally applying binarization and detection of the plane arranged horizontally on the image consisting of pixel blocks. The initial goal was achieved, we found an easy to implement method of separating large RFI artefacts—not repairable with image filtering methods. The proposed solution can effectively support the automatic PSInSAR processing workflow by recognizing RFI-affected data, and as a consequence, removing them from the stack of SAR images required to determine the ground displacements. Our method improves (compared to the classification on raw data) the efficiency of artefact detection by up to 27 percentage points depending on the classification context under consideration. The standard deviation of the results after application of our methods is nearly One of the methods of developing our detection system was the use of techniques for the recognition of specific shapes relevant to the appearing RFI artefacts and the use of complex convolutional neural networks—which is the foreground of our future research.


With economic development, the volume of hazardous materials is increasing, and the potential risks to human beings and the natural environment are expanding. Road transportation has become the main mode of transportation for hazardous materials. Because of the specific characteristics of hazardous materials, if an accident occurs in the transportation process, it often causes mass casualties, serious property and socioeconomic damage, and damage to the ecological environment. Hence, transportation is an important part of the life cycle of hazardous materials. This paper designs an optimization platform for multidestination, multiterminal, and multivehicle networks that transport hazardous materials. The logistics module in TransCAD software is used to construct this platform. By identifying the effective transportation routes considering the transportation risk, sensitive target population, and transportation time of each road section, the entropy method can be used to fuse and obtain the comprehensive impedance value of each road section. Finally, the optimal transportation network of hazardous materials was obtained by the transportation network optimization algorithm in TransCAD. The platform can display the optimal transport program with data windows, text, and maps. The research results provide a reference for relevant departments to scientifically manage the transport of hazardous materials. Hazardous materials are substances with explosive, flammable, poisonous, corrosive, and other characteristics ,2. TheseIn recent years, hazardous material transportation accidents in China have occasionally occurred ,8. From The safety management of the road transportation of hazardous materials has attracted extensive attention from society ,7,8. HazTo avoid similar accidents, improving the professional quality and regulatory awareness of hazardous chemical transportation practitioners is important. This approach can reduce the accident rates and number of casualties associated with hazardous material transportation, and the safety risks of the route can be evaluated to select the route with the highest safety level and avoid densely populated areas. Therefore, it is of great practical significance to reasonably plan the road transportation routes of hazardous chemicals transport to reduce the public safety risks along the routes and improve the level of natural environmental protection. Under such circumstances, studies of the optimal design of hazardous material transportation networks can help identify safe and efficient transportation networks for vehicles. This approach will help improve the safety management levels of government regulatory agencies and production and business units and prevent and control the occurrence of road transportation accidents involving hazardous materials.Many experts and scholars have conducted research on the optimal design of hazardous material transportation networks. Batta and Chiu  defined Obviously, there have been numerous achievements in the current optimization design of hazardous material transportation networks, but the network design solutions obtained from these studies have been poorly visualized. Because TransCAD ,36 is a TransCAD software hierarchically stores and manages information with geographic features. That is, different geographic information is stored in different layers, and geographic objects with the same characteristics are stored in a geographic layer. Moreover, each layer requires the support of the database table, and a layer corresponds to a database table. An object in a layer corresponds to a record in the database table. By overlaying multiple layers, area information can be intuitively studied. This article divides the entire network into three geographic layers.(1) Road network layerThe road sections are represented by a line geographic file. All the road sections form a road network layer. The role of this layer is to store the road network, and it is linked to the road-related geographic data.(2) Customer point layerThe customer point layer is a geographical layer for storing customer points, and it is associated with all geographic information data related to logistical facilities and service objects.(3) Facility point layerThe facility point layer is a geographic layer used to store logistical facility points. This layer is associated with all the data related to logistics facility points. The three geographic layers can be superimposed to visualize the layout of the road network and the distributions of facilities and customers.(1) Road network dataThe information attributes of the road network include the length of each road section, the population on both sides of a route, the vehicle speed of a road section, and the travel time along a road section. In TransCAD, the elements of the transportation network are nodes and lines. Nodes are used to represent feature points, such as traffic hubs and road intersections, and lines represent road sections.Length: The length of a road section in TransCAD refers to the route length between two road network nodes. TransCAD can calculate the length of each road section and automatically generate a road network property table.Speed: The road section speed refers to the vehicle speed between two nodes of the road network.Time: The road section travel time refers to the time spent by vehicles using the route to travel between two nodes in the road network. The unit is min. This value can be calculated using the formula Time = Length/Speed in the TransCAD data window.(2) Facility dataThe facility data mainly include the facility ID, facility name, facility service time, facility location, and node ID. The Facility ID is an inherent attribute of the facility layer, which distinguishes different facilities by using numbers. The facility name (NAME), denoted by “F*”, * is a number, or directly uses a Chinese name. The facility location will be given directly and expressed in longitude and latitude coordinates in TransCAD. After importing the AutoCAD road map into TransCAD, latitude and longitude registration will be performed according to the selected area. The facility service time refers to the time at which the facility provides a delivery service. This time definition requires two boundary values, namely the service start time of the facility (OPEN TIME) and the stop service time of the facility (CLOSE TIME). This time interval is the normal service time interval of the facility, and facilities will provide logistical and distribution services to customers. Outside this time interval, the facility will stop providing services to customers. This paper sets all facility service time intervals to 0000−2400 to provide services 24 h a day. The time format is military format, i.e., 1800 is 6:00 PM. The facility node ID (NODE ID) is the ID nearest to the facility point in the road network, and this ID is used to relate the facility point layer and the road network layer. The data table of the facility point layer is shown in (3) Customer dataThese data mainly include the customer ID, customer name, customer location, customer demand, and customer business hours. The customer ID is an inherent attribute of the customer’s point layer and is distinguished by numbers. The customer name (NAME) is denoted by “C*”. The customer location is given directly and is expressed in latitude and longitude coordinates. The customer business hours refer to the time when the customer accepts delivery services. This variable requires two boundary values, namely the customer’s opening hours (OPEN TIME) and the customer’s closing hours (CLOSE TIME). This time period establishes the normal business hour interval for each customer. During this time interval, customers will receive the logistical and distribution services provided by the facilities. The fixed service time (min) refers to the time between when a transportation vehicle arrives at a customer location and must stay for a given amount of time, such as to perform checks or inspections; this time is denoted as the FIXED SERVICE TIME in the attribute field. The unit unloading time (min) refers to the average time required by the unloading personnel at the customer location to unload one unit of goods, which is denoted by TIME PER UNIT in the attribute field. Customer demand volume (DEMAND) refers to the customer’s demand for goods. For different customers, the demand is not the same, so the degree of importance is also different. The customer demand in this paper does not set the unit. It only represents customer demand weight. The node ID (NODE ID) refers to the node ID nearest to the customer point in the road network. This field is used to relate the customer point layer to the road network layer. The data table of the customer point layer is shown in (4) Vehicle dataThe vehicle data mainly include the vehicle type, number of vehicles at various facility points, vehicle capacity, and vehicle operating costs. Depot ID refers to the ID of the facility at which the vehicle is located. This attribute is used to link vehicle information and facility point layer information. The vehicles at facility points are divided into three types, namely, A, B, and C, according to the vehicle capacity, as shown in The risk values of different road sections differ greatly, depending on whether or not there are key protection measures implemented for events, such as hazardous material spills or explosions. If the affected area contains key protected places , then the road section is considered an invalid road section, and hazardous materials vehicles are not allowed to drive on these road sections.The optimization platform treats the road sections that hazardous material vehicles use for travel as the elements and the radius of influence of hazardous material leakage or explosion events as the buffer zone. The attribute screening function in TransCAD is used to determine whether the buffer zone contains key protection areas; if so, the road section is deemed an invalid road section, and the road section impedance is set to infinity. Conversely, if no protection areas are found, the section is considered an effective road section, and the algorithm is used to generate an optimal transportation path along effective road sections.The hazardous material transportation risk, the number of sensitive target populations, and the transportation time are selected as the attribute parameters of the effective road section. The calibration method of each parameter is as follows.(1) Transportation riskTo consider the road section transportation risks of hazardous materials, domestic and foreign scholars have conducted multifaceted measurements in terms of the environmental impact, road conditions, driver factors, and uncertainty factors. For ease of operation, the system in this study uses a traditional transportation risk definition model to quantitatively describe the road section transportation risks.(2) Sensitive target populationThe buffer analysis in TransCAD has powerful functions involving spatial map information retrieval and comprehensive information processing. These functions can accurately measure and visually display the population distributions around the road sections of hazardous material transportation. Taking the hazardous material transportation road sections as the element, the radius of influence of hazardous material spills or explosions can be used as the buffer zone, and the total population and number of coverage facilities  can be used to determine the sensitive target population within a given buffer zone.(3) Transportation timeThe transportation time can be obtained according to the historical time records returned by the GPSs installed on hazardous material transportation vehicles. In the vehicle environment, the traffic information centre collects road network and the vehicle data through the road infrastructure. The relevant data are then processed to obtain traffic flow data for each monitoring point and road section, and transportation time can be obtained by the BPR function that is currently used by the US Highway Bureau.Based on the screening of effective transportation sections and the transportation risk, sensitive target populations, and transportation time, we use the entropy method to obtain the integrated impedance value for each road section. Finally, the multidestination, multiterminal, and multivehicle transportation network optimization algorithm in TransCAD can be used to obtain the optimal transport network of hazardous materials. The algorithm flow chart is shown in Large-scale hazardous material distribution centres are located in Donggang and Xigu, and these centres are responsible for transporting hazardous materials to 26 customers in the surrounding cities and counties. The demand point layer attribute data is shown in As described in With the optimal platform, the specific transportation route and departure time of each vehicle transporting hazardous materials from each distribution centre can be obtained, and the specific driving route plan can be intuitively and clearly displayed on a map.If we optimize the route of dangerous goods transportation vehicles according to the traditional method, which only considers the shortest driving time of vehicles and not the transportation risk and sensitive target population, the route of hazardous materials transportation can be obtained, as shown in From Although this optimal plan comprehensively considers the transportation risks, transportation time, and sensitive target populations, and it effectively avoids densely populated areas, there are fewer sensitive target populations along the selected transportation routes, the transportation risks are low, and the total transportation time is short. These results provide a valuable reference value for hazardous material transportation companies.With the continuous socioeconomic development, the transportation volumes of various hazardous materials will continue to increase. The risk of hazardous material transportation accidents will also increase. Hazardous material accidents are different from ordinary traffic accidents and they are often sudden and destructive with a social impact. To reduce the accident rates of hazardous material transportation and scientifically guide first-line hazardous material enterprises in the transport of hazardous materials, it is necessary to optimize and design hazardous material transportation networks. This paper has designed an optimization platform for hazardous material transportation networks.TransCAD is used to combine a variety of information involved in the optimization of hazardous material transportation networks and identify effective road sections suitable for hazardous material transportation. Then, the road transportation risk, sensitive target population, and transportation time are selected as optimization parameters. A hybrid algorithm is designed to solve the hazardous material optimal transportation problem for a given network. The optimization platform developed in this paper can rapidly generate and visually display the optimal transportation network. The results of repeated tests show that the optimization platform is stable in operation and can be used as an auxiliary decision-making system by the relevant management departments. Thus, this platform provides a certain reference value for improving the existing hazardous material transportation management systems.The optimization platform uses an artificial vectorized map, and the production cost of this map is relatively large. In the future, the Baidu map API or the Gaode map API could be used to access the real-time electronic map. At the same time, an online version of the system could be released to further optimize the platform.


Optimum frequency range of inspection was at 15−25 kHz due to the level of attenuation at higher frequencies and the larger dead zone at lower frequencies due to the pulse length. A minimum of 14 transducers around the circumference of a 3 inch pipe were required to suppress higher order flexural modes at 16 kHz. According to the studied condition, 1.84 m of inspection coverage could be achieved at a single direction for pulse-echo, which could be improved by using a higher number of transducers for excitation and using pitch-catch configuration.The structural integrity assessment of thermoplastic pipes has become an interesting area of research due to its elevated usage in the liquid/gas transportation industry. Ultrasonic guided wave testing has gained higher attention from industry for the inspection of elongated structures due to the reduced inspection time and cost associated with conventional non-destructive testing techniques, e.g., ultrasonic testing, radiography, and visual inspection. Current research addresses the inspection of thermoplastic pipes using ultrasonic guided waves as a low cost and permanently installed structural health-monitoring tool. Laboratory and numerical investigations were conducted to study the potential of using ultrasonic guided waves to assess the structural health of thermoplastic pipe structures in order to define optimum frequency range for inspection, array design, and length of inspection. In order to achieve a better surface contact, flexible Macro-Fiber Composite transducers were used in this investigation, and the Teletest The use of thermoplastic pipe systems, i.e., high density polyethylene (HDPE), are becoming increasingly popular for many applications, such as transporting gas, water, and chemicals. The corrosion and high fouling resistance as well as the lightweight properties of thermoplastic pipes, makes it more attractive for the industry to replace the usage of steel pipes in some applications with safety-critical infrastructures, for example, in nuclear power stations ,2. The material degradation over time is a common problem in pipe networks, as it causes loss of structural integrity, which can lead to severe catastrophic failures. For example, the two largest water utilities in the world, Veolia environment and Suez environment, experienced severe HDPE pipe system failures due to degradation that caused a premature failure by chemically induced embrittlement . Veolia The current state-of-the-art inspection techniques for the inspection of HDPE pipes include localized non-destructive testing (NDT) techniques such as radiography, conventional ultrasonic testing, acoustic emission, visual inspection, thermal imaging, laser scanning, and ground penetrant testing ,7. The cA great deal of research was conducted on the application of UGWs to inspect metallic structures, and it has gained a high level of attention from industries due to their long-range inspection capabilities from a single test location (~100 m of pipelines from single test location) . HoweverFlexible macro-fiber composite (MFC) transducers were used to design the transducer array to achieve a better contact with the structure of interest compared to the brittle piezoelectric transducers used in previous studies . The preThe paper is organized as follows. An understanding of elastic wave propagation within solid media is important for the development of inspection procedures. The wave propagation in elastic infinite media is presented in literature and is briefly outlined . Navier’By substituting the potentials of Helmholtz decomposition (Equations (2) and (3)) into Navier’s equation (Equation (1)) of motion, one receives two separate formulae for the unknown potentials that govern longitudinal waves (Equation (4)) and shear waves (Equation (5)).Based on this, there are two types of elastic waves that can propagate within solids, known as longitudinal waves (Equation (6)) and shear waves (Equation (7)). These two modes can travel in any direction in an infinite solid media. However, UGWs are constrained to propagate along the axis of the structure and therefore result in many possible modes of vibration. Depending on the waveguide geometry, the material properties, and the excitation frequency of the discrete pulse, the potential number of wave modes varies . Due to Thermoplastic pipe inspection using UGW is an arising area of interest. Ghazali et al. used a piezoelectric stack actuator for excitation of UGW to detect cracks in plastic pipes . DiffereZhu et al. showed how a water-coupled focused conventional ultrasonic transducer can be used to study the effect of ground conditions surrounding the pipe. This study contains the interaction with pipe material, soil, fluids, and formation of voids in the soil bedding. Two common plastic pipe materials; polyvinyl chloride (PVC) and HDPE, were tested . DetectiRecent developments on nonlinear ultrasonic behavior such as harmonic generation, nonlinear resonance, and mixed frequency response have shown potential in detecting a crack at its preliminary stage. The nonlinear ultrasonic technique can detect a crack by the harmonics and the modulations created by a nonlinear source i.e., the defect. When an ultrasonic wave at a single input frequency is propagated through the nonlinear source, additional harmonics of the input frequency are generated. Also, when two ultrasonic waves at two distinct frequencies are propagated, the interaction caused between them produces a nonlinear response. An amplitude modulation signal can be produced between two signals by addition of the amplitude of each signal. Hong et al. used piezoelectric transducers for UGW excitation based on nonlinear amplitude modulation to study crack detection in a PVC pipe . Eleven Moreover, research on damage detection in plastic structures has been conducted using various methods. For instance, Cheraghi et al. investigated the detection of damage in adhesively bonded joints in plastic pipes and compared the effectiveness of different methods based on Fourier transform, continuous wavelet transform, and Hilbert Huang transform . NeverthPrior to designing the optimum transducer array for the UGW inspection of HDPE pipes, it is important to study the UGW propagation; therefore, laboratory experiments were conducted to study the UGW propagation on a 0.92 m long, 3 inch diameter, 10 mm thick HDPE pipe section. The test sample rested on two wooden blocks to minimise UGW interaction with its surroundings. The experimental set-up is illustrated in Flexible MFC2814 macro fiber composite transducers were used in the present study to obtain a higher surface contact between the transducer and the pipe surface ,22. Due ® Focus+ system was used to excite the transducers. The data acquisition was conducted in the pulse-echo configuration due to the length limitation of the pipe sample. A frequency sweep was conducted from 10 to 30 kHz in 1 kHz increments. A 5-cycle Hann-windowed excitation pulse was applied for a discrete input excitation signal. Using the velocities of the two longitudinal modes, L and L, obtained from the group velocity dispersion curves, the expected time-of-arrival (ToA) at possible reflections are tabulated in The state-of-the-art commercially available TeletestTo assist in understanding the wave propagation over the HDPE pipe sample, a finite element analysis (FEA) was conducted in COMSOL Multiphysics 5.3. The model consisted of a 0.92 m length HDPE pipe with a 110 mm outer wall diameter and an 88 mm inner wall diameter. The material properties can be found in The transmission/receiving location was placed 0.25 m from the pipe end to replicate the configuration in the laboratory trials refer to . The transducer configuration was represented as point loads in the FEA model. Symmetry was used within the model to reduce computational time by exciting one-point load. The point load was applied parallel to the length of the pipe to generate the longitudinal wave mode. The pressure point was a sine wave modulated using Hann window function as shown below:oh), the following equation was used: c is the velocity, N is the number of elements per wavelength, and 0f is the center frequency.For the dynamic transient simulation to map out the wave propagation throughout the structure, the mesh was required to be calculated to an optimal mesh element length. The wave equation required the meshing to compliment the time stepping within the solver to produce an accurate solution. The mesh sizing required a minimum of eight second order mesh elements per wavelength. To calculate the maximum allowed element size  by utilizing the selected optimal frequency found in the laboratory investigation. The numerical model was used to optimize the number of transducers within the array to cancel any excitation due to higher order modes (axisymmetric wave modes). The selected optimal transducer array was then used to investigate the number of cycles of the discrete pulse  to select the optimal cycle input for inspection to allow separation of the receiving pulse. The separation of the signal is most important at joints and bends, as this needs to be identifiable as a feature during inspection. A point load was used to excite the discrete pulse, and a monitoring point at the excitation location was used to collect the pulse-echo signal for each FEA computation, as shown in bwf is the frequency bandwidth, and ik is the bandwidth of the desired lobe, where ik = 0 is for the main lobe and ik = 1 is the side lobe.The frequency range of the pulse is called the bandwidth, and the following equation is used to define the frequency range of the bandwidth:The frequency range for each investigated cycle input has been calculated and tabulated in When comparing each transducer configuration at the received signal, there was a phase shift in the signals due to the amplitude and the multi-mode response. The 14 transducer case was selected to investigate the number of cycles. In the present study, authors conducted a series of experimental cases and numerical validations to understand the potential of using UGWs for the inspection of HDPE pipes, which are mainly used in the energy sector for gas transportation. The mode of interest in this case was the second order longitudinal UGW mode. An array of 14 transducers was used in this study, which was governed by the size of the transducers and, according to the dispersion curves calculated, it required at least 14 transducers to suppress flexural modes. At the conditions studied, 1.84 m inspection coverage was achieved at pulse configuration. This could be improved by increasing the number of transducers around the circumference and using pitch-catch configuration for data acquisition. A numerical model was built using COMSOL Multiphysics based on the experimental trials, which was then used to conduct a parametric study to optimize the operational parameters, i.e., number of transducers and number of cycles. Such a model helps the current authors and other researchers interested in this topic to conduct their investigations without carrying out time and cost heavy experiments. The next stage for this research is to apply the validated numerical modeling for investigation of defects within the pipeline and its joints, such as delamination and dis-bond, allowing further analysis of signal responses by the presence of a defect. Such a study will provide confidence of defect characterization and sizing for industrial adaptation. Further research will also be conducted on the miniaturization of the UGW flexible transducers to accommodate a higher number of transducers around the circumference, which will increase the inspection resolution.


In the present study we investigate the selected local aspects of the metal-induced gap states (MIGSs) at the disordered metal–insulator interface, that were previously proposed to produce magnetic moments responsible for the magnetic flux noise in some of the superconducting qubit modalities. Our analysis attempts to supplement the available studies and provide new theoretical contribution toward their validation. In particular, we explicitly discuss the behavior of the MIGSs in the momentum space as a function of the onsite energy deviation, that mimics random potential disorder at the interface in the local approximation. It is found, that when the difference between the characteristic electronic potentials in the insulator increases, the corresponding MIGSs become more localized. This effect is associated with the increasing degree of the potential disorder that was earlier observed to produce highly localized MIGSs in the superconducting qubits. At the same time, the presented findings show that the disorder-induced localization of the MIGSs can be related directly to the decay characteristics of these states as well as to the bulk electronic properties of the insulator. As a result, our study reinforces plausibility of the previous corresponding investigations on the origin of the flux noise, but also allows to draw future directions toward their better verification. However, although the superconducting approach satisfies all of the DiVincenzo’s criteria, it still suffers from some important drawbacks3. One of such limitations concerns the universal low-frequency (1/f) magnetic flux noise that occurs in SQUIDs5 and greatly influences all related qubit modalities7 i.e. qubits based on closed superconducting loops such as the tunable phase8 and flux qubits10. As a result, the magnetic flux noise visibly limits dephasing times of the mentioned qubit types10, hampers down their scalability6, and reduces rate of coherently tunneling qubits in terms of the quantum annealers12.At present, the superconducting quantum interference devices (SQUIDs) constitute promising platform for the quantum information processing, that allows to build tunable qubits15. In general, these are the non-tunable qubits that base on different modalities, including relatively new ones such as the quantronium (introduced via processes at noise unsusceptible bias points)13, transmon (based on shunt capacitive effect)14 or xmon 15. However, despite being highly successful in some areas, this approach comes with still noticable trade-offs e.g. the intrinsic qubit anharmonicity or crowding in multi-qubit systems19. At the same time, it does not provide detailed understanding of the discussed magnetic noise, but rather attempts to bypass this issue. On the other hand, the second strategy aims at the in-depth elucidation of the magnetic flux noise in already existing superconducting qubit types, in order to suppress this negative phenomenon and improve performance of the related qubit solutions25. In these terms, hitherto not fully explained origin of the magnetic noise in SQUIDs is still an intriguing and important aspect within the domain of superconducting qubits.Over the years, various attempts to tackle the magnetic flux noise were proposed, that can be grouped into two main research strategies. First scenario concentrates on modifications of the superconducting qubit design, toward archetypes inherently insensitive to the magnetic noise25. According to that, local effects can be argued to play important role in producing the discussed noise. In this regard, several theories were presented to explain the origin of the flux noise at the microscopic level. Specifically, Koch et al. proposed that the noise arises due to the electrons with random magnetic moments, that stochastically hop between defect centers at the surface of the superconductor20. On the other hand, Faoro et al. attempted to explain this noise via dynamics of the spins that are strongly-coupled by the Ruderman–Kittel–Kasuya–Yosida (RKKY) interactions at the superconductor–insulator interface22. In what follows, the RKKY interactions were also adopted in the spin-cluster model by De23. Yet another proposal by Wu and Yu suggested that the noise emerges from the hyperfine interactions of the relaxing surface spins24. To this end, Choi et al. claimed increased role of the metal-induced gap states that become localized at the superconductor-insulator interface by the potential disorder25.Interestingly, the magnitude of the magnetic flux noise is known to weakly depend on the SQUID area, as well as on the superconductor or substrate type25. However, they also independently tackle other aspects of the discussed effect. In details, the model of Koch et al.20 is supported by the recent experimental studies that consider qubit coherence optimization based on SQUID geometry26. Some other models emphasize the interactions between surface spins24, in agreement with the experimental observations provided in27. The model of Choi et al.25, concentrates on describing correctly the areal spin density from the susceptibility measurements28. Finally, the latest theoretical approaches attempt to include the role of an extrinsic effects coming from the surface adsorbents30. As a result, they altogether suggest complex nature of the noise that may arise from the interplay of a various intrinsic and extrinsic effects.In general, the described above theories follow the most important experimental findings on the magnetic flux noise. Specifically, they render the universal and weak scalability of the noise with the overall size of the SQUID25. The importance of the MIGSs steams from the fact that they are considered to inevitably appear whenever metal–insulator junction (MIJ) is created32. Thus, they should be also present at the Josephson junctions that build SQUIDs. Moreover, the MIGSs are likely to localize at the discussed interface, as they constitute direct continuum of the metal states that decay into the first few layers of the insulator33. According to Choi et al.25, the mentioned localization of the MIGSs notably contributes to the flux noise  and should occur due to the presence of the potential disorder at the metal–insulator interface. Such interplay between the interfacial disorder and the MIGSs localization is the central point of the approach presented by Choi et al.25. However, we argue here that the universal and inherent nature of this relation has not been yet explicitly recognized, in order to further reinforce importance of the MIGSs concept in explaining the magnetic flux noise origin.In this context, there is a strong motivation to verify each of the theoretically postulated contributions to the origin of the flux noise. This will allow to account for the most important physical parameters that govern the noise, toward its further reduction. In the present study, of special interest is the role of the metal-induced gap states (MIGSs), as proposed by Choi et al. in25. In particular, contrary to the supercell approach by Choi et al.25, our theoretical analysis is intended to directly examine relation between the potential disorder at the unit cell level and the MIGSs localization, near the metal–insulator interface. To do so, we use the so-called complex band structure method, instead of following the isotropically-averaged (in the momentum space) density of states scenario similar to the Cardona-Christiansen approximation34 and adopted by Choi et al.25. In this manner, we are able to directly investigate the behavior of the MIGSs in the momentum space, when the onsite potentials varies. Hence, the behavior of interest is determined in terms of the insulator band structure , so that the localization effect is explicitly related to the intrinsic properties of the interface. Note that this approach follows in spirit considerations presented by Tersoff for the Fermi level pinning at the metal–semiconductor junctions33. Moreover, since our calculations are employed at the local level they demonstrate importance of the relative energy difference and its fluctuations within unit cells, in correspondence to the fundamental features of the insulating layer. Therefore, the presented here study can be viewed not only as a supplementary analysis to the investigations conducted by Choi et al.25, but also first step in developing fully-anisotropic theoretical approach that explains disorder-induced MIGSs localization at the MIJs, in direct relation to the main characteristics of these states .With respect to the above, in this study we attempt to present new insight into the localization of the MIGSs, as compared to the study already presented in Choi et al.s-type orbital at the ith site, equal to -4 and 2 eV for the undisturbed Cs and Cl atoms, respectively. In what follows, the ith site with the spin j denotes nearest-neighbors (NN) or next-nearest-neighbors (NNN) of i. Hence, the 25, where above values were chosen in order to set the van Hove singularity away from the conduction band edge at the MIJ, therefore keeping the density of states relatively realistic in the vicinity of the insulators band gap. To this end, the U parameter describes the on-site Coulomb repulsion, with 25. Note, that above assumptions allow us to conduct our analysis on the same footing with the discussion provided by Choi et al. (see25 for more details). Moreover, the assumed U value is much greater than the superconducting pairing gap, hence our approach is expect to be valid also in the case when the attached metal is superconducting. Nonetheless, it is instructive to note here that we do not explicitly consider attached metal, by arguing the fact that MIGS should constitute intrinsic property of the insulator, according to the previous similar studies37. This is to say, the interfacial behavior of MIGS is described by the insulator complex band structure (CBS), that can be generalized to the interface32. Such approach is possible, since MIGS constitute direct analytical continuation of the propagating states in a metal38. It also allows to trace, hitherto not considered, canonical aspects of the MIGS localization at the MIJ.To investigate the localization effect of the MIGSs near the MIJ, we concentrate our attention on the insulator region in a benchmark CsCl structure. The electronic properties of such structure are described in the framework of the mean-field Hubbard Hamiltonian given as:39:In particular, the MIGSs are calculated here by adopting the CBS method, that requires us to solve the following generalized eigenvalue problemn and 28. For more technical details on the CBS method we refer to the studies already mentioned above i.e.39.In Eq. , the \doentclass1pt{minima41, the density of MIGSs must be derived from the contributions of the conduction and valence bands of the insulator. In this context, the highest density of MIGSs is expected at the point where the conduction and valence bands contributes equally to the MIGSs . By following Allen43 and Tersoff33, this point can be located within the energy gap by using the following cell-averaged Green’s function:N gives the number of the unit cells in the considered system, n counts all the accessible eigenvalues in the momentum space  and Im[k] axes is presented in the right and left panel of Fig. In Fig. entclass1pt{minimak, composing characteristic unit circles in the complex plane of 44. In what follows, we distinguish two major types of the evanescent states that can be observed within our theoretical model. First, we note the evanescent states that appear at the boundaries of the adopted Brillouin zone and are depicted by the green color . These states exhibit exponentially decaying character inside the unit circles defined by the propagating states. Next, we observe the gap states of interest, that are given in the red color (k is complex). Contrary to the boundary states they decay in the oscillatory manner and compose already mentioned complex band loops, that join maximum of the valence band with the minimum of the conduction band and can be interpreted as the MIGSs. To elucidate the presented results even further we remind that each of the above solutions appears in pairs i.e. the pairs of The purpose of Fig. mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimk-axes (the Re[k] and Im[k] axes), respectively. The first rows of the sub-figures depict solutions for the selected positive values of In Figs. entclass1pt{minima}Δ Figs. A–D, wherentclass1pt{minimaes Figs. E–H. As mk-space changes notably as the k-space. This behavior occurs due to the fact that the onsite energies at the Cs and Cl atoms determine the upper and lower energy limits of the complex band loops. Therefore, when their relative energy difference changes the size of the complex band loops changes accordingly. It can be then qualitatively argued that By inspecting the results given in Figs.  see Eq. . However37. Specifically, by solving the Eq. (d). In correspondence to the behavior of the complex band loops, the decay rate k-space  the technique based on the renormalization group approach45. Such investigations are expected to allow to relate the decay characteristics of the MIGSs, not only to the potential fluctuations, but also the experimentally observed areal density of the paramagnetic spins in SQUIDs. As a result, further verification of the flux noise origin in terms of MIGSs should be feasible. Simultaneously, we note that the described improvement of the theoretical techniques may allow to consider still open problems such as the role of interactions between the paramagnetic spins in producing the flux noise. To this end, the presented results indicate potential experimental routes toward suppression of the noise. In particular, they suggest avenues aimed at the reduction of the potential disorder at the MIGSs  or engineering Fermi level position within insulator toward regions less sensitive against disorder (the band gap edges).In what follows, in our analysis we confirm that the variations of the potential at the interface can cause stronger localization of the MIGSs, that appears as an effect directly related to the inherent electronic properties of the insulator material. At the same time, the obtained results validate, to some extent, the proposal of Choi et al., saying that the MIGSs can play an important role in describing the magnetic flux noise in superconducting qubits. However, it should be noticed that further investigations are desirable, in order to combine our approach with the large scale modeling techniques, toward more comprehensive analysis of the discussed processes. In particular, it should be possible to adapt our theoretical techniques within the calculations that tackle realistic metal–insulator junctions with the random potential disorder. As least two directions in this respect can be listed: (1) the Anderson-derived scenario as considered by the Choi et al.