Once unplanned urban rail disruptions occur, it is essential to evaluate the impacts on public transport passengers since impact estimation results enable transit agencies to verify whether alternative transit services have adequate capacity to evacuate the affected rail passengers and to adopt effective emergency measures in response to the disruptions. This paper focuses on estimating the impacts of unplanned rail line segment disruptions on rail passengers as well as original bus passengers, as the latter are overlooked in existing studies. A method of identifying affected rail passengers based on passenger tap-in time is proposed, which is helpful for evaluating the scale and origin-destination distribution of the affected passengers. Passengers’ response behaviors are analyzed and modeled in a multi-agent simulation system. The system realizes the simulation of the multimodal evacuation process, in which a rule-based logit model is employed to describe passengers’ travel selection behavior and the Monte Carlo method is utilized to address the issue of uncertainty in passengers’ travel selection. In particular, the original bus passengers are integrated into the simulation and interact with rail passengers. Finally, some indicators assessing the impacts on rail passengers and bus passengers are presented, and a case study based on the Ningbo urban rail transit network is conducted. Urban rail transit (URT) shows a trend of large-scale and rapid development for cities worldwide. Due to continuous expansion, high-intensity operation and aging equipment, unplanned service disruptions occur frequently, causing negative impacts on a large number of passengers as well as the transportation system, not limited to the URT system. What exactly is the influence of rail disruption on passengers and the transportation system? It is essential for transit agencies to identify the impacts and adopt appropriate measures to minimize the negative effects of rail disruptions.The robustness  of the URT service partly depends on the topological structure of the URT network, so some studies focused on the effects on the URT system under random disruptions based on graph theory or complex network theory ,3,4,5,6.The previous studies belong to the static impact analysis of the URT service disruptions. The dynamic passenger flow was not taken into consideration. In practice, once a disruption occurs, affected passengers would make travel adjustments, including mode shift, resetting original and terminal points, and travel route selection. The travel adjustment choice of the affected passengers at the micro level is finally reflected in the redistribution of passenger flow on the transportation network at the macro level. Thus, some studies were dedicated to understanding the mechanism of passengers’ behaviors and constructing passenger reassignment models under unplanned rail disruptions ,13,14,15On the basis of survey results, logit models were commonly employed to make travel choice behavior analyses. Teng and Liu  built a Apart from survey-based approaches, some scholars used data-driven methods to explore the influence of unplanned rail disruptions on the URT passenger flows. The historical passenger flow data collected from the automated fare collection (AFC) system under normal states and disrupted states were compared and analyzed, hence capturing the passenger volume changes and estimating the impacts of rail disruptions ,20,21,22Deterministic methods or aggregated methods cannot reflect the heterogeneity of passengers’ behaviors under unplanned rail disruptions, while simulation is a better approach to model the heterogeneous passengers. On one hand, agent-based simulation was widely used ,27,28,29On the other hand, Monte Carlo (MC) is another simulation method to analyze the uncertainty of passengers’ behaviors. Seger and Kisgyörgy  utilizedHowever, there are some limitations in the previous studies. First, the existing works failed to provide a method to accurately identify and estimate the affected passenger flow. Second, concerning passengers’ modal choice behavior, the existing studies only considered passengers’ individual choices based on the utility of each alternative mode, such as cost, travel time, and transfer times. Actually, due to the limited capacity of transit modes, the choices of passengers who access the URT network in advance would affect those of passengers who enter the network later. However, the complex interactions among different passengers and the transit system were neglected in previous research. Third, most previous studies, model-based, or simulation-based, only performed the impact analysis of unplanned rail disruptions on affected rail passengers and rail networks, e.g., passenger volume, delay, travel time, and rail usage. As a matter of fact, the impacts are not limited to the interior of the rail system but extend to the whole transportation system. For instance, buses are a relatively popular choice for affected rail passengers as an alternative transportation mode to complete their travel ,34. WhenTo fill the research gap, this paper proposes a passenger behavior simulation framework under urban rail disruptions based on the Monte Carlo method and logit model. Affected rail passengers along with conventional bus passengers are modeled as agents that interact with each other and with the transport system. The heterogeneity of passengers’ travel choices can be simulated in a multi-modal public transit network, including the urban rail, bus, taxi, shared bikes and walking. On the basis of simulation, the impacts of the disruptions on rail passengers as well as bus passengers can be analyzed. The results can enable transit agencies to evaluate the passenger evacuation capacity of the present public transit system under unplanned rail disruptions. They can further assist transit management departments in taking appropriate measures to minimize the negative effects on commuters.The rest of the paper is organized as follows. This study focuses on unplanned URT disruptions that result in the closure of rail links between two adjacent turnover stations in both directions . Note thThe passengers affected by the unplanned disruption refer to those whose space-time travel path in the URT network coincides with the occurrence place and duration of the disruption simultaneously. Under the scenario mentioned before, the affected passengers can be divided into two types in terms of whether they need to transfer in the interrupted line section. In this section, we analyze the time domain of the two types of affected passengers entering the URT system, respectively. On this basis, the affected passenger volume can be estimated using historical automatic fare collection (AFC) data. To identify the affected passengers by their travel route, it is assumed that passengers follow the shortest path to complete their trip.Affected passengers who need not transfer in the interrupted section can be classified into four categories in terms of the spatial relationship between passengers’ origins and destinations and the interrupted section see . Based oAffected passengers of the first category are those whose origins and destinations are both in the interrupted section The URT network diagramWe define the URT network as a directed graph (2)The URT disruption dataThe data on the rail disruption including the interrupted section and some time-related parameters, i.e., (3)The shortest paths of all the OD in the URT networkPathData. Correspondingly, the travel time of each shortest path is stored into the database. Based on the shortest path of each OD, we can determine its affected category and time domain that are also laid in PathData.The Dijkstra algorithm is applied to acquire the shortest paths of all the OD in the URT network, which constitute the database of (4)The AFC dataOriginalURTData. Each piece of data represents a passenger trip, including the passenger ID, the tap-in station ID, the tap-in time, the tap-out station ID, and the tap-out time.After data cleaning, the AFC data of a certain day are stored in the database of Based on the AFC data and the affected tap-in time domains, affected passengers can be identified. The basic idea is that for a piece of data, we clarify its affected category and compare its tap-in time with the corresponding affected time domain. However, it is tedious and time-consuming to make the comparison for every piece of AFC data, so we simplify the identification process in such a way that the data are sorted in the light of the tap-in time and then some data are excluded using the upper and lower limits of the time domains. The detailed process is listed as follows:PathData. Find out the smallest lower limit Step 1: Compare the affected tap-in time domains of all OD pairs in OriginalURTData according to the tap-in time. Store the data with the tap-in time within URTData.Step 2: Sort the data in URTData into several groups based on the OD. As a result, data with the same OD are included in the same group. Then sort the data in each group according to the tap-in time.Step 3: Divide the data in PathData. Then identify the data within the domain and store them into AffectedURTData.Step 4: For data in each group, find the affected time domain in Under unplanned rail service disruptions, affected passengers are forced to make adjustments to their trip plans. The options for affected passengers of different categories are somewhat different from each other. As shown in For affected passengers of the fourth category, it should be noted that if passengers have already passed (1)the travel time of the path should not exceed a predetermined limit the total transfer times of the path should be no more than a predetermined limit Affected passengers would select Option (1) if feasible detour routes exist in the degraded URT network. This assumption has been proven to be true in reality by Duan et al. . Feasibl(2)Transit agencies can take passenger flow guidance measures to direct passengers with short travel distances to choose slow traffic. With this in mind, we assume that if there is just one station between a passenger’s current located station and his destination station, he will use a shared bike when there are available shared bikes. Additionally, if a passenger’s current located station and his destination station are adjacent stations, passengers will choose walking.(3)The proportion of travelers canceling or delaying their trips is supposed as a given constant.(4)The duration of service disruption is predictable, and it is announced to passengers. Passengers prefer to wait until the system is restored, i.e., select Option (4), if the announced recovery time is less than a predetermined value , (4) and (5) are definite on some specific conditions. However, Options (2) and (3) are uncertain options that heterogeneous passengers may have distinct choices. Comparatively, Option (3) is more complicated in that for different categories of affected passengers, candidate trip chains of Option (3) exhibit differences. Herein, the trip chain means that travel comprises several trip legs, and the transit modes for adjacent legs are different. Especially, Option (2) can be regarded as a trip chain with only one leg.We further define the starting and ending stations of the trip leg in Options (2) and (3) passed via modes other than rail as When When ived see . Trip chNote that “other” in the trip chains means the transit modes other than rail include walking, shared bicycle, bus and taxi, so with distinct transit modes, a trip chain can evolve into various trip chains. Thus, passengers of each category in either Group (i) or (ii) have more than one choice. Here a classical logit model is constructed to describe the trip chain selection for affected passengers  (see Equrth trip chain. As seen in Equation (4), it consists of the generalized cost of the rth trip chain and a random item. The generalized cost is composed of the money cost and the cost of travel time, which can be divided into in-vehicle time, transfer time and waiting time (see Equation (5)).Given an OD pair, Deterministic methods ignore the randomness in passengers’ travel adjustment behavior, which may result in estimation bias . In thisPassengers’ trip chain selection depends on the availability of the transport modes. Specifically, passengers can only choose transport modes with residual capacity. Note that the residual capacity at a certain time is influenced by passengers’ selection behavior before that time. In other words, passengers’ decision-making on trip chain selection interacts with each other. To better reflect this interaction, a multi-agent simulation model is developed to simulate the process of passengers’ travel adjustment behavior in a multimodal transport system with dynamic changes.Considering the complexity and dynamics of the multi-modal transport system, it is preferable to split the system into several independent and interrelated parts . Each paThe basic data, including the infrastructure data, passenger data, and vehicle data, are predetermined as input information for the simulation system. The infrastructure data mainly comprises information on the disrupted rail stations and related bus stations. The passenger data include attributes of affected rail passengers and original bus passengers, e.g., the origin station, the destination station and the tap-in time. The vehicle in the simulation system consists of trains, buses, taxis and shared bikes. The train and bus data mainly involve the capacity, the load factor, the remaining capacity and the timetable, while the taxi and shared bike data mainly contain the number of available vehicles, the turnover rate and the located station.As the fundamental simulation unit, the passenger agent connects and interacts with other agents in the multi-agent simulation model. Passenger agents are classified into two types in this paper: rail passenger agents and bus passenger agents, representing the affected rail passengers and the original bus passengers in the real world, respectively.For the rail passenger agents, the simulation model mainly simulates the process of train matching, the process of trip chain selection, and the process of using alternative modes. The logical flow chart of the rail passenger agent simulation is displayed in Bus passenger agents have the function of waiting for bus, boarding the bus and alighting the bus see . The busAs the carrier for the passenger agent, the vehicle agent is to realize the function of simulating the boarding and alighting process by interacting with the passenger agent and the infrastructure agent. Vehicle agents can be divided into train agents, bus agents, taxi agents and share bike agents. The train agent mainly realizes the departure function and arrival function based on the train timetable. When a train arrives at a station, it will dwell for a while. Subsequently, rail passenger alighting and boarding events will be triggered. In addition, when the scheduled departure time is up, the departure function is called. As depicted in In evaluating the impacts of unplanned rail disruptions, our main concern is the impacts on passengers, including rail passengers and non-rail passengers. Note that the existing parallel bus system is one of the first alternatives for affected rail passengers to resume their trips. The original bus travelers are certain to be influenced when a large number of rail passengers shift to the bus system. Thus, for the impacts on non-rail passengers, we focus on the delay time of bus travelers caused by rail disruptions. As indicated before, our simulation focuses on passengers in the interrupted section, so here only these passengers are taken into the following impact estimation.(1)Affected rail passenger flowAs noted before, affected rail passengers refer to their travel paths intersecting with the rail disrupted section in space and the rail disruption occurrence period in time. The affected rail passenger flow, denoted as (2)The stranded timeith affected rail passenger, denoted as Herein, the stranded time of the The average and maximum stranded time can also be formulated as Equations (7) and (8), respectively.Compared to the delay time commonly defined as the increase in travel time under disruptions, the stranded time indicating the additional waiting time for alternative transit services is more concerned by affected rail passengers. On the other hand, transport authorities are also more interested in the stranded time which suggests the evacuating speed of affected passengers than the travel time increase .(3)Stranded rail passenger flowDue to the limited capacity of available transport services, some affected rail passengers may be stranded outside a rail station until the disrupted rail service is recovered. Let The larger (1)The delay timeith bus passenger at the jth bus stop in the interrupted section is commonly calculated as half of the headway of the bus line that he desires to take. Suppose ith bus passenger can be formulated as the increase in the waiting time (see Equation (10)).With the increased ridership caused by the modal shift of affected rail passengers, bus passengers would be delayed at the bus stops, resulting in a longer wait time for the original bus passengers waiting for an available bus. jth bus stop during the rail disruption.The total delay time can be estimated by the following equation:The average and maximum delay time can be obtained by Equations (12) and (13).(2)Affected bus passenger flowith bus passenger is affected by the rail disruption. On this basis, a binary variable ith bus passenger at the jth bus stop in the interrupted section is affected (see Equation (14)).Suppose bus headway to be Subsequently, the total number of bus passengers in the interrupted section who are affected by the disruption can be estimated by Equation (15).(3)Stranded bus passenger flowith bus passenger at the jth bus stop in the interrupted section is finally stranded. If yes, Since affected rail passengers take up some bus resources, several affected bus passengers would be unable to board buses and are stranded at bus stops until the disrupted rail service is recovered. Let In this section, part of the Ningbo URT network is employed to conduct a case study. As shown in OriginalURTData and applied to make the impact estimation.Two scenarios of unplanned rail disruption are designed here to make a comparison. As seen in As indicated before, this study focuses on the simulation of passengers in the interrupted section. Therefore, three bus lines parallel to the interrupted section are integrated into the simulation system, and the impact analysis on the original bus passengers also targets these three bus lines. Related data on these bus routes, including data on passengers as well as buses, are stored in the database of the simulation system. Referring to the existing research ,22,42, sSince the simulation involves a stochastic trip chain selection process by the Monte Carlo method, the results of each simulation are distinct. We perform the simulation 50 times. It appears that there are small differences among those simulation results. For instance, regarding the average stranded time for rail passengers, the simulation results of Scenario B are between 14.97 and 15.53, with a standard deviation of 0.15. Thus, the average value of 50 simulation results is taken as the estimation result of each indicator. Some proportion-related data are given using a certain simulation result.(1)Estimation of affected rail passenger flowUsing the identification method described in The results of trip plan selection and trip chain selection for affected rail passengers are shown in To further analyze passengers in the interrupted section, the outbound passengers at each interrupted station needing to be evacuated by a certain transportation mode other than rail are described in (2)Estimation of the stranded time and stranded rail passenger flowAs illustrated in Furthermore, the distribution of stranded time is depicted in As anticipated, with a large number of rail passengers shifting to the bus system, the original bus passengers are more or less influenced. As seen in 10. Comparatively, for passengers at more stops, including the transfer station, turnover stations and intermediate stations, wait times for buses exceeds bus headway in Scenario B, especially for passengers at stops in the upstream direction. These most affected bus stops need to be paid more attention by transit agencies. Note that in these bus stops, the passenger waiting time follows the rule that it gradually increases with the time of arrival and then displays a linear decrease after it reaches the maximum value. It is because the continuous accumulation of arriving passengers results in incremental waiting time for passengers. Passengers whose arrival time is prior to the inflection point can board buses before To further investigate the impacts on bus passengers at different bus stops, Evaluating the impacts of unplanned urban rail disruptions on the transportation system enables transit agencies to determine the severity of the disruptions and adopt effective measures to mitigate the negative effects and ensure passengers’ safety. Previous studies paid much attention to evaluating the impacts on the rail system itself, but little attention to the potential effects on the entire public transport system. To fill the gap, this study proposes a simulation-based approach to estimate the impacts on public transport travelers, including rail travelers as well as bus travelers. Firstly, we present a method of identifying affected rail passengers and estimating the scale and origin-destination distribution of affected passengers. Then a multi-agent simulation model is developed to simulate the multimodal transport evacuation process. Original bus passengers are integrated into the simulation system and interact with the rail passengers. On the basis, some indicators evaluating the impacts on rail passengers and bus passengers are presented respectively. Finally, a case study based on the Ningbo URT network is conducted and some practical results are acquired.Whether simulation or impact assessment, this study only focuses on passengers in the interrupted section, since they are influenced by the disruptions most immediately and to the greatest extent. As analyzed in


The increasing demand in the development of autonomous driving systems makes the employment of automotive radars unavoidable. Such a motivation for the demonstration of fully-autonomous vehicles brings the challenge of secure driving under high traffic jam conditions. In this paper, we present the investigation of Advanced Driver Assistance Systems (ADAS) radars from the perspective of electronic warfare (EW). Close to real life, four ADAS jamming scenarios have been defined. Considering these scenarios, the necessary jamming power to jam ADAS radars is calculated. The required jamming Effective Radiated Power (ERP) is −2 dBm to 40 dBm depending on the jamming scenario. These ERP values are very low and easily realizable. Moreover, the effect of the jamming has been investigated on the radar detection at radar Range Doppler Map (RDM) and 2-Dimensional Constant False Alarm Rate (2D-CFAR). Furthermore, the possible jamming system requirements have been investigated. It is noted that the required jamming system will not require high-end technology. It is concluded that for the security of automotive driving, the ADAS radar manufacturer should consider the intentional jamming and related Electronic Counter Countermeasures (ECCM) features in the design of ADAS radars. Radar, which is an acronym for “Radio Detection and Ranging”, is a detection system that uses radio waves to estimate the position and velocity of a target, approximately. The application areas of the state-of-the-art radar systems include military applications, civilian aviation, navigation, mapping, meteorology, radio astronomy, and medicine .One of the most important application areas of radar systems in daily life is automotive radars. The automotive radars, together with the other sensors such as light detection and ranging (LIDAR), ultrasound, and cameras, constitute the fundamentals of the Advanced Driver Assistance Systems (ADAS) and self-driving automobiles . The milFast-chirp/Chirp-sequence Frequency Modulated Continuous Wave (FMCW) waveform with Multiple Input Multiple Output (MIMO) techniques are commonly used in the automotive radars . ADAS raWith the recent developments, the employment of the affordable millimeter-wave sensors that operate at 77 GHz is expected to become more frequent than ever . NeverthIn our study, compared to other studies in ,13,14,15Despite the fact that the effort in IC technologies pushes the limitations to develop low-cost and compact radar systems, the performance of the ADAS systems as a measure of secure driving operation is discussed in the following section of this paper.ADAS sensors and their functions are described in . The recEW has been used extensively in the Defense Industry where there are military standard and military unique requirements are considered. Furthermore, the amount of equipment is limited, and the cost of each equipment is cheaper than the development cost. So, each equipment should be qualified thoroughly. On the other hand, automotive radars are produced in mass numbers and the cost of each item is an important factor for the automotive industry.There are several studies on unintentional jamming, namely interference, of the ADAS radars. One of the important references on this topic is the MOSARIM (More Safety for All by Radar Interference Mitigation) project report . In the The intentional jamming might be considered as an extraordinary case. However, as the use of autonomous automobiles becomes more common in our life, it might also be considered by some malicious people/organization to harm someone.As radars started to be used for military purposes, the counter measure studies to detect and deteriorate radars started too. This field is known as EW and is commonly defined as:Electronic warfare is defined as any military action that protects the use of the electromagnetic (EM) spectrum, which includes the entire radio frequency (RF) spectrum, the infrared (IR) spectrum, visible spectrum, and ultraviolet (UV) spectrum, as well as direct energy for friendly purposes while denying it to the enemy . ElectroRadar Electronic Countermeasures (ECM) and ECCM were investigated in  where thEW has been mainly considered for the military domain. If there would be a possibility of an intentional jamming against ADAS radars, considering the EW while developing an automotive radar would make them more robust. For an automotive radar design, EW is first to be considered in the EP domain to make them robust against intentional and unintentional jammers. Then, the detection of the jamming is the next step to counteract to the jamming.As the ADAS radars apply coherent processing, jamming techniques also need to be coherent. Therefore, DRFM-based Coherent False Target (CFT) and Correlated Range and Velocity Gate Pull Off/In (RVGPO/I) techniques have been investigated on one of the ADAS radars, i.e., Long-Range Radar (LRR). The detail of CFT and RVGPO/I can be found in ,16,17,18The DRFM is the indispensable part of the modern ECM systems as it provides the ability to generate coherent jamming waveforms which is a crucial means to jam advanced radars. DRFMs have been used in jammer systems for several decades now.The DRFM samples incoming radar signals by using a high-speed Analog to Digital Converter (ADC), store-sampled data in the RF memory, applies the desired jamming modulation, and regenerates the digital signal into the analog signal by using a high-speed Digital to Analog Converter (DAC). The DRFM provides the ability to manipulate the incoming and stored signals in the amplitude, frequency, and phase to generate a wide variety of deception signals .Step-1: Define the assumptions and LRR parameters used on the analysis.Step-2: Calculate the necessary jamming power for the desired Jamming-to-Signal Ratio (JSR) on the radar detection.Step-3: Define jamming scenarios.Step-4: Investigate the jamming scenarios to find the necessary jamming power.Step-5: Investigate the effects of jamming on radar detection at radar Range Doppler Map (RDM) and 2-Dimensional Constant False Alarm Rate (2D-CFAR).Step-6: Analyze the possible jamming system.To analyze the possibility of the jamming of the ADAS radars, some scenarios are defined against the LRR whereas the LRR has a longer range than any other radar in the ADAS which increases the jamming engagement time and therefore the possibility of being jammed. For short range radars, the jamming engagement time would be short. Jamming of the ADAS radars have been investigated in the following steps:Important parameters of LRR used in the analysis are defined in The LRR uses linear polarization as referred in FMCW radars have high processing and modulation gains, so it is difficult to jam them with noncoherent jamming methods. The best jamming method is to use a DRFM to achieve a coherent jamming.For effective jamming, the jammer has to monitor radar parameters. In case there is a parameter update on the radar, then the jammer has to update the jamming parameters. Hence, the jammer needs a receiving cycle (look-through) and during that time the jamming could be interrupted which reduces the effects of the jamming. In this study, it is assumed that the jammer can receive and transmit simultaneously without any interruptions so that the jammer can follow radar parameters. For that purpose, the required isolation between jammer receiver and transmitter is calculated and presented below.The jamming power, detected by the victim radar antenna, is analyzed for four different scenarios initially. It is assumed that the jamming signals, which are generated by a DRFM and the jammer signals are up to date, are processed by the radar like real object echo signals. For one scenario, radar detection and processing with RDM and 2D-CFAR are investigated to inspect the expected outcome on the radar detection process.The DRFM memory size is not considered a problem for the jamming, especially for long pulses.The propagation delay of the jammer is not considered. There is a propagation delay in the jamming system. So, the jamming signal arrives later than the real echo signal. If the jammer can follow the radar parameter, with the appropriate timing, this delay can be compensated by the jammer.Because of the spatial diversity of a MIMO radar, it is difficult to jam them . For RVG(a)It is assumed that the stationary jammer stays on the roadside, and they are on the edge of the −3 dB beamwidth. For a mobile jammer case, no loss is considered as they might be on the boresight of the jammer antenna.(b)MIMO radars can generate simultaneously multiple transmit (Tx) beams . Maximum(c)The polarization loss is taken as 3 dB.Compared to the radar signal, which matches with the radar receiver setting, the jammer signal has a certain amount of jamming losses. Jamming losses for a stationary jammer is 12 dB and for a mobile jammer, 9 dB are considered because of the following reasons:Assumptions considered in the analysis are mentioned below:The loss will be implemented in the related formula.JPr, and two-way Friis’ transmission equation is used find the received radar signal power from the object echo RPr.eA is the effective aperture area of the radar, J GJP is the ERP of the jammer, λ is the wavelength of the radar signal, RG is the gain of the radar. JR is the distance of the jammer to the radar.R GRP is the ERP of the radar, OR is the distance of the object to the radar, and σ is the Radar Cross Section (RCS) of the object.To calculate the necessary jamming Effective Radiated Power (ERP) for the related jamming scenarios, one-way and two-way Friis’ transmission equations are used  with theJSR value as defined in Equation (3). To find the necessary JERP, Equation (3) is reorganized in Equation (4). The loss factors mentioned above would be considered in the formula to obtain more realistic results.Equations (1) and (2) are used to find the necessary Although the key factor in effective jamming technique is to keep the JSR as high as possible, the JSR ratio can be reduced to the 3 to 6 dB range for an effective ECM technique by using the coherent jamming waveforms as long as the jamming power is placed in the appropriate tracking gate of the victim radar ,25. DuriFor CFT techniques power calculation, the victim radars Minimum Discernable Signal (MDS) is an important parameter to consider. Whenever there is a signal over the MDS level, this could be considered as an object. MDS values can be predicted as typical noise floor plus Signal-to-Noise Ratio (SNR) of the radar. The SNR is dynamically set by the CFAR algorithm. However, for this study, JSR = 1 is taken to be on the safe side. This approach would require more jamming power.The following five scenarios were investigated for the LRR jamming:In this scenario, a realistic false pedestrian echo is aimed to be generated with applying a CFT jamming technique by a jammer as shown in In this scenario, an RVGPO/I technique is applied by the jammer located in the automobile which is in front of the radar as shown in In this scenario, an RVGPO/I technique is applied by the jammer as shown in In this scenario, an RVGPO/I technique is applied by the jammer as shown in Required jammer ERP values with respect to the jamming range for each scenario are shown in Depending on the jamming scenario conditions, the required jamming ERP is changing from −2 dBm to 40 dBm. These ERP values are very low and easily realizable.Scenario-2 is chosen for further analysis to investigate the effects of jamming on the RDM and 2D-CFAR detection. Parameters of the scenario are defined in Two-dimensional CFAR detector−9)Probability of False Alarm Rate (FAR) is %0.000000001 (1 × 10Guard band size is 3 cellsTraining band size is 10 cellsIn the scenario, parameters of the LRR CFAR are defined as follows:The LRR radar signal waveform used in simulation is shown in The automobile in front of the radar is detected by the LRR RDM and 2D-CFAR are shown in When the jammer on the front automobile starts jamming, the LRR detects both jamming and echo signals. At first, the JSR is considered as 0 dB which means the jamming signal has the same power level of the echo signal. When the jamming power level is increased to JSR = 10 dB, the LRR increases the detection threshold, and the real object could not be detected as the jamming signal has more power. The RDM and the 2D-CFAR processing of the LRR shows the effect of jamming on the radar.ERP: Considering these four scenarios, the required ERP is changing from −2 dBm to 40 dBm depending on the jamming scenario. With high gain antenna of 30 dBi and 10 mW transmitter, 40 dBm could be achieved. This shows that the required power level for the jammer is very low.Sensitivity: The skin paint of the radar signal for different range values is shown in Isolation: The value of isolation between the transmitter and the receiver provides an important information about the interoperability of Tx and Rx. If the isolation is bigger than the maximum Tx power minus the minimum Rx sensitivity, then there would be no interoperability problem. Therefore, Tx and Rx can operate at the same time and same frequency without interfering with each other. So, the ECM system can jam the radar with up-to-date radar signal information.As shown in In this paper we present the investigation of jamming scenarios and their possible effects on the ADAS radar applications. Our goal was to analyse the possibility of ADAS jamming and the required jammer features. In contrast with other works in the literature that investigate the ADAS jamming, we analyse the jamming system requirements and the effect of the jamming on the ADAS radar on the radar detection i.e., RDM and the 2D-CFAR detection. All possible jamming scenarios were investigated with coherent ECM techniques. While defining scenarios, LRR features and close to real-life conditions were taken into account. Scenario analyses are given in We provided an analysis of the ADAS radar jamming phenomena with the EW perspective. It is known that the ADAS radars can be jammed. However, how and with what sort of jamming system the ADAS jamming can be achieved is not clear. We concluded that the ADAS jamming could be easily achieved. As the advancement on EW resulted in the implementation of ECCM features on the military radars, it is expected that the ADAS radars will also implement the ECCM features to make them less vulnerable to intentional jamming. On the other hand, implementation of complex ECCM features make the ADAS radars more expensive. Even the driving factor in the commercial market is costly, and the safety concerns always comes first then the cost. Then, it is expected that the ADAS radar manufacturers will soon consider the intentional jamming and implement radar ECCM features to minimize the vulnerability of the ADAS radars against intentional jamming.


Aimed at improving the navigation accuracy of the fixed-wing UAVs in GNSS-denied environments, this paper proposes an algorithm of nongravitational acceleration estimation based on airspeed and IMU sensors, which use a differential tracker (TD) model to further supplement the effect of linear acceleration for UAVs under dynamic flight. We further establish the mapping relationship between vehicle nongravitational acceleration and the vehicle attitude misalignment angle and transform it into the attitude angle rate deviation through the nonlinear complementary filtering model for real-time compensation. It can improve attitude estimation precision significantly for vehicles in dynamic conditions. Furthermore, a lightweight complementary filter is used to improve the accuracy of vehicle velocity estimation based on airspeed, and a barometer is fused on the height channel to achieve the accurate tracking of height and the lift rate. The algorithm is actually deployed on low-cost fixed-wing UAVs and is compared with ACF, EKF, and NCF by using real flight data. The position error within 30 s (about 600 m flying) in the horizontal channel flight is less than 30 m, the error within 90 s (about 1800 m flying) is less than 50 m, and the average error of the height channel is 0.5 m. The simulation and experimental tests show that this algorithm can provide UAVs with good attitude, speed, and position calculation accuracy under UAV maneuvering environments. Small fixed-wing unmanned aerial vehicles (UAVs) play an essential role in future air battles and civilian fields due to their low cost, small size, full autonomy, and dense formation. The normal autonomous flight of UAVs is inseparable from the accurate ground velocity and absolute position provided by the Global Navigation Satellite System (GNSS), and especially the real-time kinematic (RTK) measurement can provide centimeter-level position using the technique of dispersion assignment. The traditional navigation mode of the Inertial Navigation System (INS) assisted by GNSS is greatly challenged, since the GNSS is easily interfered with by complex external environments or human factors ,2.State estimation of UAVs can be defined as the process of tracking the current attitude, velocity, and position of the vehicle . The desIn recent years, many researchers have been exploring autonomous navigation techniques in GNSS-denied environments to improve positioning and attitude accuracy. Visual-based position does not depend on external equipment support and has high autonomy. Visual and inertial navigation integration technologies have made great strides in recent years. Mourikis et al.  presenteMost commonly used attitude estimation algorithms can be concluded to three kinds: the extended Kalman filter, the gradient descent algorithm, and the complementary filter algorithms. The EKF is more precise in the process of the state error transfer and the bias error, and the process noise of the gyroscope and accelerometer are modeled, thence the error parameters are estimated and compensated by other sensors, but it obviously adds computational complexity . LeuteneCompared with the convenience of rotorcraft to deploy intelligent algorithms related to vision, and lidar for simultaneous localization and mapping (SLAM) due to its low speed and more stable flight performance, there has been less work on the navigation and position of low-cost fixed-wing UAVs in the GNSS-denied environments because of the reasons mentioned above. Most of the previous works simply provide stable attitude output for UAVs in denied environments. The main contribution of this paper is to explore the provision of UAV stable state estimation in denied environments. The main work and innovations are as follows: (1) Based on , the filThe framework of the algorithm is shown as Euston, M. et al.  proposedOnly considering centripetal acceleration in flight is not sufficient to describe the maneuvering process of the vehicle accurately. When the vehicle speeds up or slows down, the effect of linear acceleration also needs to be taken into consideration.Accurate angle-of-attack calculation requires unique sensors. The angle of attack can be estimated by flight dynamics approximation on low-cost UAVs. We just consider the vertical channel of the UAV, which can be described as follows:For small fixed-wing UAVs, the angle of attack and the sideslip angle are difficult to measure directly by sensors because accurate measurements require atmospheric parameter sensors, but they are not suitable for small vehicles. We notice that if the sideslip angle eference , as for Obviously, the field winds are dynamic and inevitable. Due to the fact that small fixed-wing UAVs are lightweight, they are not suitable to fly in high field wind. Moreover, the wind speed is as hard to estimate as the angle of attack or sideslip, so we try to ignore the effect of the wind. The linear acceleration of the vehicle can be calculated from the differentiation of the linear velocity. Random noise inevitably exists in the pitot airspeed measurement, which leads to an additional error in acceleration estimation. The function of the differential tracker (TD) of the Active Disturbance Rejection Control (ADRC) is to extract differential signals properly from those polluted by noise, so the second-order differential tracker  in the AThe second-order differential tracker is described as follows:Based on the above equation, the vehicle acceleration model is:The inertial navigation-specific force equation under local horizontal frame is written as: follows :(9)an=. The For the yaw channel, the magnetometer complementary filter is adopted.Take the misalignment angle into the complementary filter, and the measurement error generated by the gyroscope can be corrected in the next step. The negative feedback model of the complementary filters uses a PI controller. It can be defined as:At high sampling times, the delta angle The three-axis attitude can be solved from the quaternion.UAV velocity estimation can be obtained recursively through a specific force equation:Due to the errors of inertial measurement and attitude calculation accumulation with time, the velocity position estimation will become meaningless over a long time. A complementary filter is used to smooth and correct the horizontal velocity by airspeed measurement.Using the above Equation (21), the airspeed can be converted to the local frame.We use the following function to adjust the gain For the flight control level, the fixed-wing UAVs require a high accuracy of altitude position and lift rate, which affects the climbing, landing, and cruising performance of the UAV. The GNSS-denied environments are limited by the inertial navigation accuracy and the inability to measure the local gravitational acceleration precisely. Using low-precision inertial guidance alone for altitude solving, the altitude error will diverge significantly over time. The barometer is susceptible to high-frequency noise from the atmospheric environment, so fusing the barometer to the inertial navigation for correction is necessary.The inertial sensor, ADIS16488B, has a built-in barometer to measure static atmospheric pressure. The simplified conversion equation of atmospheric pressure to altitude is shown below . The perBarometer measurement error is mainly affected by airflow intensity and temperature, and the changes of temperature make the barometric output drift. Using the temperature control system in the flight control can achieve the heat balance before the data fusion solution. In contrast, the ADIS16488B sensor embedded in the flight control component has been indirectly isolated from airflow, so the error correlation is significantly reduced. The height measurement error and the rate of change error correlation use the first-order Markov (Markov) process.The above Equation (26) is the error transfer model of the inertial navigation in the altitude channel. After ignoring the small error caused by rotation, the error equation of inertial navigation in the altitude channel is established as Equation (27).According to Equations (25) and (28), the state equation of the system is established as follows:The state is The system translation matrix is:The system observation equation is:The system equation is discretized and solved by the Kalman filter equation. The algorithm flow chart of the filter is shown as The small fixed-wing drone was used for experiments to verify the correct functionality in a practical scenario, and the vehicle is shown in Both the flight control algorithm and the multisensor fusion algorithm are arranged into the flight control hardware platform. The flow chart of the experimental tasks of the whole system is shown in The main parameter values of the algorithm are shown in We also compared the offline computing efficiency of the four algorithms. The host computer with the 3.2 GHz AMD Ryzen 7 5800H CPU was used to run the four models with MATLAB 2021A. The total running time was set to 200 s with each step length of 0.005 s. As can be seen from the This paper proposes a robust and universal sensor fusion algorithm, including an IMU, barometer, magnetometer, and airspeed sensor. The contributions of this paper include the following: (1) We use airspeed to improve the estimation accuracy of the nongravitational acceleration of vehicle, subsequently, to optimize the nonlinear complementary filter model of the vehicle’s attitude misalignment angle based on observability derivation, which can adapt to the state estimation accuracy of the UAV under different maneuvers. In the flight test, the MAE and RMSE of roll error are 1.1388° and 1.4195°, the MAE and RMSE of pitch error are 1.1114° and 1.4672°, and the MAE and RMSE of yaw error are 4.7935° and 5.5443°, respectively, and, when compared with the more commonly used EKF algorithm, is improved. (2) At the level of the horizontal velocity fusion, a complementary filtering model using airspeed correction is established to suppress the accumulated errors caused by the calculation speed of INS. (3) At the height level, the Kalman filter model is designed using barometer data so that the vehicle can obtain the accurate solution of the lift rate and altitude without GNSS. The average error of the height channel is 0.5 m, and the maximum error of the lift rate is 0.5 m/s. This design idea uses a cascade fusion strategy that combines the benefits of an individual systems model using a cascade fusion strategy, combining the advantages of a single system. Compared against the other three conventional methods, the proposed method shows superior performance, providing good attitude velocity and position estimation, even in GNSS-denied environments. In addition, the algorithm proposed in this paper consumes lower computing resources and is suitable for common embedded systems. Taken as a whole, the new approach provides a feasible solution for the navigation and positioning of small UAVs, as much as possible in GNSS-denied environments; however, the result is still not very precise. In further works, we will continue to explore multiple fusion navigation technologies, and explore the use of low-cost camera sensors to enhance the robustness and fault tolerance of navigation systems.


Eddy-current sensors are widely used for precise displacement sensing and non-destructive testing. Application of printed-circuit board (PCB) technology for manufacturing sensor coils may reduce the cost of the sensor and enhance the performance by ensuring consistency. However, these prospects depend on the uniformness of the sensor coil. Inductance measurements of sample coils reveal rather considerable variations. In this paper, we investigate the sources of these variations. Through image analysis of cut-away cross-sections of sensor coils, four factors that contribute to the inductance variations are identified: the distance between layers, the distance between tracings, cross-sectional areas, and misalignment among layers. By using and extending existing method of calculating inductance of spiral coils, the inductance distributions are obtained when these factors are randomly varied. A sensitivity analysis shows that the inductance uncertainty is most affected by the uniformness of the spacings between coil traces and the distances between layers. Improvements in PCB manufacturing process can help to reduce the uncertainty in inductance. Eddy current sensors are widely used for precision measurement of displacement without mechanical contact. Similar to proximity and inductive sensors, eddy-current sensors are capable of working in hostile environments where dust or liquids may appear in the gap between the sensor and the target ,2. A coiPrinted-circuit board (PCB) is a mature technology that is extensively used in the industry. Implementation of spiral coils using PCB is attracting interests because it is possible to mass-produce the coil cost-effectively . In addiIn this work, we measure the inductances of spiral coils implemented with PCB technology and have observed fairly large variations in inductance. Defining what factors contribute to the inductance variations and how the manufacturing process of PCB affects the uncertainties are therefore necessary. Through the analyses of images taken from the cutaway sections of PCB containing spiral coils, four geometric factors contributing to inductance variation are identified and their variabilities are quantified. To see how much these geometric variations result in inductance uncertainty, the methods of calculating the inductance of multi-layer PCB coils are investigated. Existing method such as  can handThis paper is organized as follows. Following the Introduction, inductance measurement of the coil samples is presented. To assess the inductance variations among coils, the measurement uncertainty is identified to separate the effect of geometric factors. Next, the results of measurements on cut-away images are described. Four geometric factors are identified that contribute most to the inductance uncertainty. The amount of variations in these factors are described in this section. n), the number of layers (l), trace width (w), and trace spacing (s). The schematic and picture of the sample coil are shown in To study the feasibility of using PCB coils for eddy-current sensors, sample coils are manufactured. The coil is defined by the inner diameter  is approximated asThe coil can be modeled as a series combination of inductance . The range of the excitation frequency is from 100 kHz to 4 MHz. The SRF is typically around 1 MHz. A curve-fitting procedure using least-square-error (LSE) minimization is employed to identify the self-resonant frequency and then the inductance is estimated by . By repeIf the inductance measurements are variable, two sources can contribute to the variability: one being the coil itself and the other the uncertainty of the measurement setup. If the coils are not consistent due to manufacturing processes, the inductances would change from sample to sample (inter-sample variation). To quantify this inter-sample variation, the uncertainty of the measurement setup must be identified first.The inaccuracy of the measurement process can be estimated by repeating the measurements for the same coils. For all 15 sample coils, the measurements are repeated 20 times. The standard deviations of the measurements are calculated. Then, the measurement uncertainty is obtained fromIf the coil is used as a position sensor, the inductance change in the full sensing range is at most 10% of the base value. Therefore, the measurement uncertainty is quite small: 0.04% for the worst case. We can practically disregard the intra-sample variation.The changes in inductances from one coil to another are much greater than the measurement uncertainty. The average inductances of 15 coils range from 53.92 Why is there so much variability? What are the causes of inconsistencies in parameters of the sensing coils? Which stage in the PCB manufacturing process do these inconsistencies originate from? To answer these questions, the sensor board is cut in half, and the cut-away sections are examined. The cross-sections of coil tracings in these cut-away sections are fairly irregular.We have identified four different types of irregularities, which are shown in In this paper, an image processing technique is employed to quantify the variabilities of these three factors from the cut-away images of all 15 coils.As mentioned above, the sensor board is cut in half. The halved board is positioned so that the cut-away sections are directly under a microscope camera  with a magnification of 160. In order to maximize image resolution, the cut-away section is divided into eight subsections. Therefore, a total of 120 images are taken for 15 coils. The photographing process is set up as The goal of image processing is to identify the copper tracings and obtain information regarding the tracings such as the center coordinates, the areas (in terms of width and thickness), and misalignments. The raw images are first converted into binary images (black and white images) by computing the global threshold using Otsu’s method. After the segmentation, the white color represents copper tracings and the background should be black, which identifies the objects to be measured . HoweverThrough the image analysis described in the previous section, the variabilities of coil parameters are measured in terms of four uncertain sources. The first is the trace spacing. The design value of the spacing is 102 While the tracing thicknesses are relatively uniform, the trace widths are not. The average value is 83 The misalignment between layers have significant variability. Ideally, the misalignment must be zero, but the average is 93 a and r, and axially displaced by z, Maxwell derived an expression of mutual inductance as [In order to assess how much the coil inductance varies due to the irregularities described in the previous section, we need to be able to calculate the inductance of the PCB coil. However, an accurate calculation of the PCB coil inductance is not a straightforward task when the coil patterns are irregular. For a coaxial pair of circular filaments having the radii of tance as (4)M=μ0aR is the geometric mean of the inner and outer radii of the coil. If r at a distance A is the geometric mean radius. The mutual inductance between two coils is given byz replaced with the geometric mean distance (GMD).Extending  to the c and r−α ,19:(6)r=ted from  with z rN the number of turns, and If PCB coils are ideal as illustrated in i-th layer and j and m.The mutual inductances between layers can be obtained from the coupling factor obtained from experiments . The totIf the pattern of the PCB coils is irregular, it is not possible to use . For theThe above procedure cannot be applied to the case of layer misalignment , as it aHowever, this method is unable to handle the coils with rectangular cross-sections. In this paper, we extended the method of  to the cAs summarized in For the sensor coil design in Using the aforementioned inductance model, it is possible to assess how much the inductance changes due to the irregularities observed in actual PCB coils. First, a reference design is determined, based on the measurements. The width and the spacing of trace are 83 The procedure to assess the inductance variation is as follows. The parameter of interest is randomly varied within one standard deviation from the average value in From these results, several observations can be made. First, any irregularities increase the inductance. For the four types of irregularities, the average inductance increases to as much as 56.24 The results of uncertainty simulations can be compared with the measured variation. As shown in Using the standard deviations of four types of irregularities, the total uncertainty is 0.074 What are the causes of the PCB irregularities? The inductance of the sensing coil is directly related to the sensing mechanism of eddy current position sensors. Manufacturing sensing coils using printed-circuit board (PCB) technology can be a solution for implementing high-resolution position sensors at reasonable cost. However, it is observed that the coils from the same batch of PCB exhibit variability in inductance. In this paper, we identified the geometric factors of PCB spiral coils, contributing to the uncertainty of coil inductance: trace spacing, cross-sectional areas of trace, distance between layers, and misalignment of layers. Utilizing and extending existing inductance models, inductance distributions while varying these factors randomly are obtained. The results reveal that the trace spacing affects the most while the layer distance is the most sensitive factor. If the standard process is used for manufacturing PCB coils, the variability in the inductance of the coils is inevitable. It is of course possible to carry out a calibration procedure to account for this variability. For example, the coil inductances are measured with several well-defined and stable gaps between the coil and the target. The measurements will then be used to calibrate the inductance-gap relationship with the added cost of tuning. If the PCB manufacturing process is improved to reduce the inductance uncertainty, this calibration procedure can be eliminated.


In this study, a first approach to model drop size distributions in agitated nanoparticle-stabilized liquid/liquid systems with population balance equations is presented. Established coalescence efficiency models fail to predict the effect of steric hindrance of nanoparticles at the liquid/liquid interface during the film drainage process. A novel modified coalescence efficiency is developed for the population balance framework based on the film drainage model. The elaborate submodel considers the desorption energy required to detach a particle from the interface, representing an energy barrier against coalescence. With an additional implemented function in the population balance framework, the interface coverage rate by particles is calculated for each time step. The transient change of the coverage degree of the phase interface by particles is thereby considered in the submodel. Validation of the modified submodel was performed with experimental data of agitated water-in-oil (w/o) dispersions, stabilized by well-defined spherical silica nanoparticles. The nanospheres with a size of 28 nm are positively charged and were hydrophobized by silanization with dimethyloctadecyl[3-(trimethoxysilyl)propyl]ammoniumchloride. This modeling approach is a first step toward predicting time-resolved dynamic drop size distributions of nanoparticle-stabilized liquid/liquid systems. Solid particles ranging in size from nanometers to micrometers can be used to stabilize dispersions against coalescence. The dispersions can be prepared via ultrasonication  or in agThis work aims to describe the coalescence inhibition induced by particles in agitated liquid/liquid systems and predict drop size distributions using population balance equations (PBEs).Despite the higher numerical effort by solving the population balance equations compared to the use of established (semi)empirical Weber correlations , PBEs exß et al. , who stuIn this work, we take a first step towards applying PBEs in particle-stabilized liquid/liquid systems. A coalescence efficiency sub-model is developed that considers the coalescence efficiency reduction due to the adsorption of particles at the liquid/liquid interface. Experimental data of time-resolved drop size distributions of particle-stabilized dispersions are used to elaborate this modified sub-model. In the following, the theoretical PBE framework is introduced, and the desorption energy of particles and the particle coverage degree are defined. Both quantities are linked to the coalescence inhibition by particles and will be considered in the modified coalescence efficiency model.f over time is formulated with a mass balance that includes terms accounting for the fact that drops can be created or destroyed either by coalescence  With the assumption of ideal mixing, spatial dependency of the drop size distribution can be neglected. Birth- and death terms are represented with submodels for coalescence and breakage rates.The PBE for a discontinuously operated stirred liquid/liquid system is defined by :(2)df from C&F. Therefore, the coalescence rate F of two merging drops with the size The product of the coalescence efficiency The collision frequency ξ by C&T  includesξ by C&T  extendedξ by C&T  by a damThe coalescence efficiency nterface  is(5)λ or d and d’ , cf. Equation . The conquations  and 5)  c1,c andg as a function of the mean energy dissipation rate C&T  assume ie theory  with twoThe breakage birth rate depends on the daughter drop size distribution Equation ). Severabreakage . The splbreakage . Maaß etbreakage  reportedFor a binary breakage process, normal distributions , bimodalIn the model of C&T , a DDSD a and b, which are defined based on previous work [a and b must be equal, which is necessary in case of binary breakage [The parameter C, in the definition of the standard deviation ous work  to a=b=2breakage :(8)βbetaNormal and beta-distributed DDSDs are bell-shaped, so the highest probability occurs for breakage into drops of equal volumes .To account for the impact of nanoparticles on drop size distributions, the focus is on modifying the coalescence efficiency submodel. Although the particles may have an impact on breakage processes as well, it is assumed that the decisive influence of particles on the DSD is the significant reduction of the coalescence rate due to a reduction of the coalescence efficiency caused by the adsorption of the particles at the interface and steric hindrance, respectively.Since this work focuses on coalescence efficiency modification, the three main model approaches and a recently published approach for this submodel are presented. In the mechanistic model approach, the film drainage model from C&T, it is assumed that coalescence occurs when the contact time exceeds the time required for the thin film of continuous phase between the drops to be drained. In this approach, drop interactions and film drainage are treated as independent phenomena. Moreover, regarding the contact time and drainage time defined by C&T, different descriptions of these characteristic times were proposed by several authors. An overview can be taken from the review of Kamp et al. . The appThe energy model of Sovova  is basedIn a recent work from Ozan et al. , a new fThe established coalescence efficiency based on the film drainage model of C&T was chosen as a suitable submodel in this work. The definitions of drainage and contact times of C&T were used as a first approach since an experimental validation of the contact and drainage times proposed by other authors through experiments is a major research topic of its own and is not the focus of this work.The basic mechanisms of coalescence inhibition by particles are still not completely understood, and the impact of substance variables, such as particle shape , and sysParticles adsorb nearly irreversibly at the liquid/liquid interface if the free energy needed to detach the particle from the interface is high. In the following, spherical particles are considered, as they were also used in the experiments. The desorption energy depends on the contact angle, the interfacial tension between the liquid phases, and the size of the particles and can be calculated for a spherical particle by :(9)ΔE=π·High energy values are achieved for particles when the particles are wetted by both phases, i.e., have a contact angle close to Arditty et al.  state thIt includes the planar interface of the spherical particle between oil and water Equation  represenExperimental studies from Stock et al.  showed t lattice .If the amount of particles is insufficient to cover the whole generated interface, drops will coalesce until the interface is completely covered; this time-dependent phenomenon is defined as limited coalescence . If limiIf two coalescing drops are entirely covered with particles, a part of those particles would theoretically have to be detached from the interface so that coalescence into a larger drop with a spherical shape can ensue because coalescence reduces the available liquid/liquid interfacial area for the particles. The energy Equation ) requireThus, the energy barrier increases rapidly at high particle coverage rates of the interface, so that in this case, drops are long-term stable against coalescence.Due to particle–particle interactions, the coverage distribution of particles at an interface can be affected. Particles with a neutral or weak charge are more likely to form clusters than particles with a substantial charge and long-range dipole moment, respectively . In calcThe mobility and deformability of the interface are, in the case of particle-stabilized dispersions, significantly influenced by the interface coverage degree by particles. With increasing particle coverage, the mobility and deformability of the interface are reduced. This affects the film outflow velocities and the film stability during the coalescence process and leads to inhibited coalescence with rising interface coverage degrees by particles.Apart from the formation of a steric barrier, the particles also influence parameters such as density and viscosity, which in turn also affect the mobility and deformability of the drop surfaces and the film drainage process during coalescence (see Equation ). Partichis work . Particlhis work .According to Chesters , an incrConsequently, when predicting the DSD of particle-stabilized liquid/liquid dispersions, it is relevant to consider the impact of the particles on physical properties and particle-linked quantities as interface coverage degree by particles or the desorption energy of particles in the PBEs to model the inhibited coalescence in these systems successfully.Therefore, common model approaches need to be adapted for particle-stabilized systems . So far,These approaches from surfactant systems cannot simply be adopted for particle-stabilized systems due to their different stabilization mechanisms. Adsorption energies are in another order of magnitude so that in contrast to surfactants, particles with a proper wettability can not easily de- and adsorb from an interface. Additionally, the stabilization by surfactants is mainly governed by their high impact on interfacial tension .(a)particle/drop collision efficiency, which depends on the film drainage process,(b)initial attachment efficiency, which considers the impact of the three-phase contact line formation,(c)particle attachment efficiency, which describes the ability of the particles to remain attached at the interface,(d)the drop coverage efficiency, which defines the system’s ability to prevent coalescence as a function of particle coverage.For particle-stabilized systems, Tsabet and Fradette  modifiedTsabet and Fradette  showed tIt has to be considered that the phenomenon of limited coalescence that occurs in particle-stabilized systems is time-dependent, as explained at the beginning of this section. The consideration of the time-varying degree of coverage by particles during a stirring process cannot be taken into account with the approach of semi-empirical correlations, as no time-resolved DSDs can be calculated. In addition, the semi-empirical correlation approach only provides the mean drop size and not the total DSD.Standard coalescence efficiency submodels cannot describe the particle-induced reduction of coalescence in agitated liquid/liquid systems. Therefore, a modification of the coalescence efficiency submodel based on film drainage from C&T is performed in this work. Own experimental data of DSDs stabilized by well-defined, monodisperse spherical particles were used. In the developed new coalescence efficiency submodel, the energy barrier of particles against coalescence is considered. This barrier is equal to the desorption energy of the particles and depends on the time-dependent interface coverage rate by particles. With this new approach of a modified coalescence efficiency, the time-resolved influence of particles on complete drop size distributions can be simulated.In this section, the experimental methods, including used substances and particles, as well as reactor set-up and DSD measurement technique, are introduced. Subsequently, the numerical methods used for solving the PBE and modifying a PBE submodel are presented.The experiments were performed in a baffled and tempered stirred tank equipped with a Rushton turbine with a total volume of It was connected to a camera , and a reflective Teflon device was attached to the endoscope tip to improve image quality, with a gap size of 6 mm cf. . The endß et al.  and for ß et al. . Automatl. [20μm .n = 700–900 rpm, which corresponds to mean energy dissipation rates of Decane  was used as the continuous liquid phase, and ultrapure water . In this work, the RRMSD was additionally used to indicate the deviations between the results of simulations and experiments.The optimization algorithm to determine the free model parameters used in ParsivalSimulations were performed with a single zone PBE, with a mean averaged energy input for the whole vessel. The parameter estimation was done in a two-step fit procedure for the four free fitting parameters: This section starts with the experimental results, and building upon this, the modeling approach and simulation results are shown and evaluated.The experimental results concerning the impact of particles on the physical properties such as density, dynamic viscosity, and interfacial tension are presented first. Experimentally determined DSDs for various particle mass fractions are shown subsequently, including their response to a step change in energy input.The densities of the particle-stabilized systems were calculated with the mixing theory for binary systems assuming no excess volume. This assumption was proven with a conical Hubbard pycnometer, the procedure and used equation to calculate the density can be taken from the previous work . DespiteThe change in density at In accordance with previous work , the chaDue to their hydrophobic character, particles do not build stable suspensions in an aqueous environment. The influence of the particles on the rheological flow behavior could, therefore, only be analyzed with decane as the base fluid. The viscosity deviations compared to the pure system are more significant for small shear rates , the moEquation ). This iThe free parameter ults see , the intults see ) for eacFrom the experimental results presented in The calculated interface coverage degree curves for each investigated particle mass fraction are shown in Equation ), is expThe functional empirical relationship between the parameter line cf. b for intAs a next step, to describe the coalescence inhibition of drops that are almost entirely covered with particles, an approach is chosen where the energy barrier associated with the energy needed to desorb a particle is taken into account based on . In SectDuring the coalescence of two drops, the total interfacial area is reduced. If the particle-covered interface of the individual drops is larger than the interface created after coalescence; particles theoretically need to desorb from the interface for a successful coalescence with subsequent shape relaxation into a spherical shape. The energy required to desorb the corresponding number of particles is the energy barrier against coalescence Equation ):(14)EbaA schematic representation of a coalescence process of two drops, where particles have to be desorbed, is depicted in The number of particles that need theoretically to desorb is given by the quotient between the reduced surface area (Equation ) and theIn Equation , Ap,gap The equation to calculate the number of particles which need to be desorbed results inIn the following, two merging drops with equal coverage degrees are considered. The minimum surface area ®, it is possible to calculate the number of particles Implementing the presented equations into the solver ParsivalThis approach depends on the interface coverage degree and is only considered if the interface is completely filled with particles during or before coalescence.The coverage rate determining if the approach needs to be taken into account, considering that the volume of the dispersed water is conserved, can be determined viaThe lowest coverage degree where particles need to desorb for coalescence to occur is reached if the colliding drops have the same size Using the empirical modeling approach for low interface coverage degrees and considering the energy barrier at high interface coverage degrees, the following case distinction can be used to define the parameter ® and via the case discrimination, ®.For case discrimination, the interface coverage degree by particles In this section, drop size distributions (DSDs) and mean diameters in transient and steady-state are modeled and compared with experimental results. The impact of interface coverage degree by particles, energy input, and the choice of daughter drop size distribution (DDSD) is analyzed. A normally distributed DDSD with A comparison of transient experimental and simulated Sauter mean diameter curves is shown in For low interface coverage degrees (The experimental distributions shown in For the systems where coalescence is nearly or completely halted (The impact of energy input on the distribution shape was exemplarily investigated for The impact of DDSD type and width on the density distribution of volume In agreement with the statement of Maaß et al. , the choTransient and steady-state drop size distributions of a particle-stabilized liquid/liquid system were investigated experimentally using an in situ measurement technique in a stirred tank. The experiments show the impact of particle interface coverage degree on coalescence behavior, for example, after stepwise changes in energy input and tracking of the dynamic changes in Sauter mean diameter for different particle mass fractions. Based on the experiments, a modified PBE submodel to describe the impact of particles with precisely determinable dimensions on the coalescence efficiency was developed. The model can be used to calculate the time-dependent behavior of mean diameters and distributions for different particle interface coverage degrees in good agreement with experimental data. The developed model uses a case distinction depending on the interface coverage degree by particles, which is calculated for each time step of the transient DSD. In the calculation of the interface coverage degree, the impact of contact angle, particle size, particle mass fraction, and energy input is considered. A validation between experimental and simulated values by variation of the energy input and particle mass fraction in steady state was performed. It showed that the changed equilibrium between coalescence and breakage leads to slight deviations of the RRMSD at higher energy inputs and higher deviations at lower energy inputs. The model, thus, underestimates the shift with decreasing energy dissipation rate towards coalescence. Moreover, a DDSD was found to simulate the distribution density function well to match the corresponding experimental distribution almost exactly.Since this work provides a first approach toward modeling particle-stabilized dispersions via PBEs, there naturally are various options to optimize and refine the submodels further. Additional experimental data could be used in the future, including experiments with particles of different sizes or contact angles and an exact determination of the DDSD. How strongly viscoelastic phenomena caused by the particles also influence coalescence and whether this should also be taken into account in the PBE submodels is an open question. For this purpose, the complex flow behavior of particle-stabilized systems and their eventual impact on DSD should be analyzed in more detail. In the context of complex rheological phenomena, a suitable approach might be to use a multi-zone PBE that accounts for the different local shear rates and, thus, viscosities and moduli or to use CFD simulations for this specific purpose. For lower interface coverage rates, the empirical approach should be modified to describe the reduced coalescence efficiency via physical quantities, such as the increased interface elasticity by particles.


Due to the mechanical nature of container handling operations, as well as natural factors, container and handling infrastructure suffers various types of damage during use, especially within the tight and enclosed environments of a ship’s hull. In this operational environment, it is critical to detect any sort of physical impacts between the vertical cell guides of the ship’s hull and the container. Currently, an inspection of impacts and evaluation of any consequences is performed manually, via visual inspection processes. This process is time-consuming and relies on the technical expertise of the personnel involved. In this paper, we propose a five-step impact-detection methodology (IDM), intended to detect only the most significant impact events based on acceleration data. We conducted real measurements in a container terminal using a sensory device placed on the spreader of the quay crane. The proposed solution identified an average of 12.8 container impacts with the vertical cell guides during common handling operations. In addition, the results indicate that the presented IDM can be used to recognize repeated impacts in the same space of each bay of the ship, and can be used as a decision support tool for predictive maintenance systems. Port operators and shipping companies constantly face several serious problems related to shipping container handling in the port environment while transporting shipping containers and performing handling operations with port infrastructure . From anSecurity events related to container impacts with the vertical cell guides mostly occur naturally, due to many external factors, yet a small percentage of these events occur due to illegal or unprofessional actions. Containers tend to bend and shake heavily when subjected to such impacts, thus increasing the chances of breaching their integrity and allowing moisture, gases, or biological agents to enter and cause severe damage to cargo. The possibility of a critical impact is mostly random and is very difficult to detect in a real-time manner, even utilizing the smartest and the fastest methods available. Near real-time solutions are constantly introduced to the market and many intrusion scenarios are being tested by engineers worldwide. However, in real-life applications, trained operators still use ISO-regulated methods of visual detection, relying on their experience and supporting their decisions with the help of other personnel onboard the ship. In normal situations, structural damage can occur on a side wall, on the door, on the corners, or over multiple sections see  at any gIt should be noted that in such a harsh environment, where every object has its own dynamic properties, such as the constant movement of the ship near the berth, unpredictable wind gusts, operator actions, etc., it is extremely difficult to control the handling process with optimized coordination and to limit unnecessary contacts with other metal structures surrounding the handled container. Bad stowage can cause damage to multiple containers onboard the ship. Damage also occurs when heavyweight containers find their way into the upper tiers of container stacks on decks. The damage can be minimal or large enough to stop the entire transportation process within a single hold, sometimes destroying goods completely.Containers are secured with twist-locks, which lock them down to the deck of the ship. Lashing rods and turnbuckles are used to provide additional strength to help secure the containers in place. Custom corner fittings, wheels, and mountings are also used occasionally. Vertical placement of containers within the inner parts of the individual holds of the ship is performed using vertical cell guides see . One of At this point, the biggest challenge is the security and efficiency of the container placement procedure. If performed inappropriately and without strict safety regulations, this could damage not only the container but also the guides, endangering the personnel working nearby see .Container movement along the vertical cell guides is performed using long cables, attached to the spreader of the QC. For this reason, the process itself is nonlinear; the observed motion of the spreader and the attached container follows a nonlinear state equation, more similar to a complex sinusoidal dependency, due to the natural swinging of the two attached bodies in free space. From a strict engineering point of view, control and prediction of such heavy machinery movement in real-time is a very difficult and mostly unoptimized task for port scheduling systems. However, simpler and more practical solutions to this problem may prove far more effective than mainstream methods.Damage to the vertical cell guides can take diverse forms, and the actual damage may be of a single type or a multitype combination, depending on whether the deformation of the structure is caused by the sliding of the container or a severe impact. The variety and the severity of the damage can vary for each case. Detecting of specific damage types is challenging when performed using either visual inspection or by means of digitized inspection. The main cause of damage to the vertical cell guides may be a result of physical wear, wrongly performed container handling operations, or other human error factors. However, most of these events are difficult to predict due to the large volumes of containers being handled each day. It is important to accurately identify any damaged areas in due time and to identify the true cause of the damage, whether this occurs during the carriage of the containers by sea or container handling in maritime ports.Static—as the result of the impacts of containers on the guides due to the force of gravity;Dynamic—as the result of accelerations from physical phenomena in the process of container handling within the holds of the ship.Climatic conditions impact the structural integrity of the vertical cell guides, introducing wear due to corrosion. On the other hand, mechanical damage can be classified into two main categories:In this paper, we propose a possible impact-detection methodology based on signal filtering techniques whilst eliminating natural causes from real impact occurrences. The rest of this article is structured as follows: Ports utilize large-scale inspection devices, heavy-machinery and skilled personnel to detect any damage aboard ships and to the containers . The damSignal-processing algorithms applicable to real-world engineering solutions are a hotspot of data science research ,10. AlthThe current research on this topic shows the limitations of automated methods for detecting damage to container and handling infrastructure in the context of container shipping, and suggests trends for future research. Firstly, fully automated and smartly digitized damage detection methods have not been applied on a large scale in the global supply chain, are limited by the operational environment, and lack standardized solutions . SecondlRegarding the safety and security of the containers and the cargo, a few research efforts have been reported in the literature . HoweverThe recent literature contains many technological solutions for minimizing the number of real damage event occurrences. Smart Digital Technologies (SDT) integrated into the logistic processes of the port are becoming increasingly decisive due to the need for policymakers, urban planners, governmental administrators, managers, transport operators, related port authorities, and shipping companies to develop intelligently digitalized and overall sustainable logistic processes for the entire port and beyond ,24,25. TPlanning methodologies, used for strategic/tactical purposes;Control techniques, used for operational purposes.Container handling operations within ports can be generally classified into the following two categories:As for the problem of planning, the main aim is to develop optimized online systems to govern the motion and transportation of containers in terms of routing, sequencing, and scheduling problems . From thRegarding the control operations, Predictive Model Controllers (MPC) are widely used to control heavy machinery and they tend to perform quite well . The appThere is no doubt that controlling these multiple processes is an optimization task too great to be solved by any individual management or control system. If no opportunity exists to install complex smart controllers within the cranes, then a simplified decision support system can be implemented, supporting the control procedures, and suggesting control patterns by detecting impacts, registering relevant events, and calculating possible risks. However, optimized maneuvering of the container inside the holds of the ship is a serious and demanding task even for operators with years of experience, due to the extremely narrow spaces for maneuvering. That is why detection of all “true” impacts is a must for any practical decision support system adapted for managerial decision support and risk assessments ,44.It is also worth mentioning the problems posed by the integration of shipping containers with different transportation modes, the optimization of resources, and the use of specialized modeling tools to ensure the operational performance of intermodal transportation . When coTo support the proposed methodology, an experimental case study was performed using a custom real-time data acquisition IoT sensor developed and tested in a container terminal in Klaipeda port at the LKAB “Smeltė“ container terminal. The data samples were collected every 100 ms via the sensory unit, allowing the system to collect real-time information on movements and impacts. Beacon intervals and data rates were 100 milliseconds and 250 kbit/s, respectively. The previous measurement results were sent to the server and the user, along with a blank alert message in the packet, forming a historical log remotely and in the SD card of the device as a reserve.t−1 measurement, allowing the system to form the packet and send it to the user, thus having a 100 ms delay. Edge computing capability was established to perform inner analysis of the acquired data samples, described in the next section. The end-node device system consisted of:A data transmission unit;A SINDT-232 Digital accelerometer with high-stability 200 Hz MPU6050 3-Axis acceleration, and a 0.05-degree accuracy. Additional parameters were as follows: Current: <40 mA; Voltage: 5–36 V; Data output frequency: 0.2–200 Hz; Baud Rate: 4800-961200 (adjustable); Working Temperature: −40 ℃~+85 ℃; Range: Acceleration (±16 g);An edge computing unit based on Raspberry Pi 4  used to analyze the signals, form alert messages, and write the connected sensors data at high speeds to the provided 128 Gb SD UHS-I card;An inner 6000 mAh battery;A common enclosure for the electronics built with IP65 protection.Each new packet was formed from a Here, each IoT sensor is a combination of inner electronics, wireless communication and data storage protocols, and a specified computational mechanism, the processor, where the sensor data manipulations are performed using introduced software logic–the developed detection methodology—with step-by-step instructions. The person responsible for the IoT is the system deployer, while the administrator manages the data flows within the dedicated software and the locally operated database. Other key members include the operators of the heavy machinery on site, namely the crane operators; the technicians, who are sent to check the situation and to perform the repair works, while also receiving intel about the situation and suggestions on fixing the problems; and, finally, the operational managers, who perform planning tasks for the terminal, managing logistics and handling processes. With the help of the Edge computing paradigm, every single computational unit performs the analysis, and the data are stored locally, eliminating the need for complex wireless or wired communication channels for data acquisition from all sensors. Each IoT sensor computes the acceleration values according to the inner logical rules, saving the data for critical incidents, and sending the filtered results to the operational manager, who in return suggests predictive maintenance of some of the parts of the guides or the containers. This kind of framework minimizes interference from the human factor and increases the chances of getting the real situation data in time. With each new container handling operation, the holds of all the ships are monitored, along with all the containers, improving the rate of impact detection, and increasing the chances of detecting security events.The following Data were collected by the sensors starting from the initial vertical movements of the spreader, up till the last moment. Although this would be inefficient within real-world applications, in this research a combination of several key data transfer technologies must be used , working in synergy and compensating for the lack of network stability. Schematically, the process of detection can be described as in Containers are placed in stacks inside the ship’s hull, one above the other. The Y-axis marks the movement of the container along the ship’s hull. Throughout the entire handling procedure, the main movement of the container occurs along the X-axis. The mounting point of the sensory device is marked by the red dot.The entire process of transporting a single container from the lowest part of the container ship is presented in Each position from 1 to 7 marks the beginning of a new movement trend, either parallel movements along the ground or vertical movements on both sides of the crane. Thus, the operator uses pre-defined movement and control patterns for each case according to their expertise. Movement patterns between 1 and 2 indicate the process of container hooking by the spreader. Movement patterns between 3 and 4, and between 5 and 6 indicate the sudden change of dynamics with an increased degree of container sway, resulting in unnecessary security issues for the cargo inside and the surrounding infrastructure. Movement patterns between 2 and 3 indicate the movement of the container inside the container ship’s hull. Herein, only the movement between 2 and 3 will be discussed, because the majority of security threats occur there.This figure shows the establishment of the coordinate system used in the next section. The movement of the container along the X-axis is critical. The proposed methodology is based on the measurement results of the experiment that was performed under real working conditions in the port area. It involved a typical ship loading/unloading procedure. During these experiments, the data logger was attached to the spreader of the QC as described previously, which registered the acceleration values, indicating the possibility of adapting the system within smart containers. The full handling process was recorded during these experiments, but our methodology included only the part of the data during which the transshipment process took place within the hull of the ship.When a crane transports a container, it oscillates at its natural frequency. Since the amplitude of the oscillation is small compared to the length of the rope holding the container, the oscillation curve in the plane is close to the sinusoid. However, during the loading process, the container is affected by external forces , that complement the container’s spectrum of motion with components of another frequency.The signal processing algorithm is presented in For the whole handling cycle in the spectrum of accelerations along the X-axis of the container see , the peaHowever, to support the methodology in the initial stage of the handling inside the hull of the ship, only data from the vertical movement between The frequency spectrum between 3.5 Hz and 8 Hz captures the oscillations of the container when subjected to external influences, including the so-called safety events, i.e., the impacts of the container on the vertical cell guides. When a container impacts the guides, the direction of its movement changes see .signal filtering;detection of peak values in the filtered signal using statistical analysis principles;threshold value selection;exclusion of potential impact moments (events) using a threshold value;isolation of actual events by grouping potential events.The proposed methodology  of detecAs mentioned, shocks “generate” higher frequency components in the acceleration signal, so using a high-pass filter isolates these oscillations for further analysis. To minimize the distortion of the signal for further analysis, a filter is required here that has a monotonic amplitude response in the passable frequency range, the lowest possible phase distortions, and a constant delay throughout the frequency range. For this purpose, a high-pass FIR filter with the following coefficients was chosen (1):N—filter queue and b—coefficients are obtained by using the GNU Octave function “fir1”, as described below.Here, af—an array of filtered accelerations;S—an array of half-wave start indices;E—an array of half-wave end indices;M—the number of half-waves;p—values of extremums found;pidx—positions of found extremums in an array of filtered accelerations.Arrays of positive and negative ranges are merged into a common range. The following algorithm was used to identify the extremes of half-waves:A fragment of the results produced by the mathematical algorithm is shown in Any peak found in the previous stage may be the result of an impact. The threshold value of the filtered signal is used to select the peaks associated with the impact: peaks with absolute values greater than the threshold indicate potential moments of events. Statistical analysis is used to determine the threshold. It finds the distribution function that best describes the peak values. In its peak area, the most common peak values see , which aUsing the obtained acceleration threshold value, the peaks with amplitudes higher than the threshold value are selected from the sample of peaks, thereby identifying the peaks that indicate potential events see , marked As mentioned, the methodology uses two selectable parameters: the filter corner frequency and the event frequency threshold. Varying these over a wide range generates results that also vary over a wide range  changes as the filter frequency changes: the RMS value does not fall below 0.15 and several local maxima can be seen here. The conclusion is that the filter corner frequency should be chosen according to the local maximum at lower frequencies because, according to the spectral acceleration analysis . As we can see in ysis see , the oscLooking at the number of potential detected events see , it can Looking at the number of detected events see , it can In addition, as the filter frequency increases see , the graa—acceleration value array;N—the number of half-waves of potential events;S—an array of indices of the half-waves;E—an array of half-wave end indices.After calculating the RMS values of the filtered acceleration half-wave associated with each potential event, and then averaging them, a graph is obtained see . The expAs the filter frequency increases, the mean values decrease. This can be explained by the almost exponential decay of the acceleration components as their frequency increases see . LookingThus, a different weighting is needed to select the parameter values. This could be a product of several of the aforementioned quantities.One option is the product of the number of potential events and the number of events see . HoweverUsing the product of the number of potential events and the average RMS of potential events see , we can In The product of the number of events and the average RMS of potential events see  seems toAfter selecting these values for the methodology, the filtered acceleration signal of one container lift during the sample cycle was obtained, and the selected distribution is given in 2 selected according to the distribution function , and providing the personnel of the terminal with more in-depth and reliable information about these critical incidents.Our future research will further examine the following aspects: increased reflection on the security and synchronization of the physical space during the entire process, taking into consideration other berths and transport means; and increased focus on any correlations between historical and real-time sensory data in order to improve the quality of the estimation results and the level of fidelity for each individual handling process. Regarding the IoT device, we intend to further optimize the data sampling technique locally, improving the Edge computing paradigm, while lowering its power consumption and computational strain, and promoting efficient wireless communication innovations. A new real-time monitoring system will be developed based on the existing prototype and methodology, and may be integrated into the Klaipeda city LKAB “Smelte” container terminal management system (as a sub-section of predictive maintenance and repairs).Finally, it is necessary to qualify the degree of detection accuracy with regards to the severity of any impacts with the vertical cell guides in order to support intelligent decision-making for the entire port operation.