IoT-based wireless seismic quality control

IoT-based wireless seismic quality control Hadi Jamali-Rad1, Xander Campman1, Ian MacKay2, Wim Walk1, Mark Beker3, Jo van den Brand3, Henk Jan Bulten4, and Vincent van Beveren4

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Abstract

At the present, sensors are everywhere across different sectors of the oil and gas industry. Seismic acquisition in upstream, pipeline monitoring in midstream, and asset tracking in downstream are examples of applications in which we need more and more sensors to satisfy a pressing need for accuracy. Sensor data in many cases should be quickly aggregated and coordinated, sometimes from harsh environments where crew intervention and maintenance must be minimized for safety and cost reasons. This mandates data collection/transmission strategies that are power efficient and demand minimal maintenance to operate autonomously. To address this issue, a unified wireless sensing framework is required that consists of the following three components: low-power, long-range wireless sensors with inherent compatibility with the “Internet of Things” (IoT); advanced scalable wireless networking protocols; and data storage/analytics on the cloud for analysis and decision making. These three components combined create a flexible, plug-and-play, scalable network that provides worldwide accessibility to the data and is cost efficient because you pay as you grow for storage and computation. Aiming at materializing such a ubiquitous wireless sensing paradigm, we have studied the feasibility of using a new family of IoT-based wireless technologies: socalled low-power wide-area networks (LPWANs). We have conducted a proof-of-concept field test in which we have employed LoRa, a predominant member of the LPWAN family, for real-time seismic quality control/monitoring. Our field test results corroborate that cheap (less than US$10) subscription-free LoRa wireless modules can be embedded into our seismic recording systems allowing us to transmit more than 6 MB of data per node per day, while the data can be transmitted over distances of a few kilometers with less than a milliwatt of average power consumption. The transmitted data can be monitored in real time on the cloud for further analysis and decision making.

A challenge and an opportunity

Regulators are placing growing pressure on operators to adhere to increasingly strict regulations related to the environment and safety. Hence, operators are required to predict and contain risks related to hydrocarbon production and their infrastructure in order to maintain their licenses to operate. A deeper understanding of production optimization and production-related risks requires strengthened knowledge of reservoir behavior and overburden dynamics. To accomplish this, more sensing to obtain sufficient temporal and spatial resolution is required as well as an integration of various sensor measurements. The challenge is that we not only need more sensors but also flexible, low-maintenance, and remotely controlled sensing; data collection/coordination; and transmission solutions. Shell Global Solutions International B.V. Shell UK Exploration and Production Ltd. 3 Innoseis B.V. 4 National Institute for Subatomic Physics. 2

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Related work

A good example of effort being put in place to address such a challenge in the oil and gas industry is in cable-based land seismic operations. Cables are vulnerable to environmental effects and create interference and destructive induction on neighboring cables. Conversely, wireless sensors are lighter than the wired ones and are easier to transport, install, and retrieve. As a result, eliminating cables decreases the weight of cable-based seismic recording systems, makes transportation less expensive, and removes issues such as tangling and cable-break repairs (Hollis et al., 2005; Savazzi and Spagnolini, 2008). Some interesting recent studies tackle the issues associated with cable-based seismic sensing by designing wireless seismic networks, such as those described in Tran (2007), Barakat (2008), Savazzi and Spagnolini (2008), Savazzi et al. (2009), and Savazzi et al. (2011). Similarly, there are relevant studies on wireless networking for ground motion and landslide monitoring or early-warning systems for volcanic activities, including Husker et al. (2008), Weber (2008), Fischer et al. (2009), Fleming et al. (2009), and Pereira et al. (2014). These studies target different wireless technologies and networking architectures based on what the nature of the scenario demands. In other words, they are typically designed to fit in a specific application or location rather than a general study of the problem. More importantly, most of the existing studies suggest using mature but high-data-rate and power-consuming wireless technologies in the market without appropriate consideration of the imposed practical and cost burden for large-scale network implementation. We target a different category of wireless technologies that trades high data rate with long range and low power consumption and offers an inherent compatibility to a cloud storage/computing framework. https://doi.org/10.1190/tle37030214.1.

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At the same time, tremendous developments are taking place in both sensor and networking technologies. To be more specific, sensors are getting smaller and less expensive while still offering excellent capabilities. Improved battery technologies combined with smart power consumption strategies have prolonged sensor battery lifetimes and have minimized maintenance requirements. New ecosystems of ubiquitous wireless communications allow us to affordably connect to the Internet with minimal effort. Moreover, recent advances in data analytics and cloud computing offer structured, fast, and efficient solutions to handle different types and amounts of data. The link between the challenge and a potential solution is almost obvious. This is clearly a case of action for the energy industry and highlights why we should exploit the capacity of this readily available ecosystem composed of advanced sensing, networking, and analytics components.


IoT-based wireless seismic

An important phenomenon we have observed and incorporated in our recent studies is the advent of low-power, wide-area wireless technologies in the market. These technologies are the response of wireless communications and cellular networks to the upsurge of attention that the Internet of Things (IoT) has received recently. Low-power wide-area networks (LPWANs) are best suited for applications that require a relatively low data rate but that must transmit over a long range in a battery-limited mode. Their low price per module (less than US$10), long operational range, and long battery life make them promising options for several core applications of interest in the oil and gas industry. Our studies in Jamali-Rad and Campman (2017, 2018) show that for a range of seismic applications, including quality control (QC) of large-scale land seismic surveys, we can live with the provided data rates of the LPWANs, especially if the application is delay tolerant. In contrast to the trend in the literature, we have put LPWANs at the core of our networking design, allowing us to exploit the potential of IoT-based wireless networks. We have developed network architectures that are a natural fit into a combined IoT cloud storage/computing framework, which as a result can also benefit from a wide variety of cloud-based data analytics techniques. Our proposed network architectures in Jamali-Rad and Campman (2017, 2018) follow along the lines of the two categories of the LPWAN family (Figure 1). Our main focus in this paper is on establishing IoT-based networks with the long-range (LoRa) technology for seismic QC.

LoRa and LoRaWAN

LoRa offers a compelling mix of long range, low power consumption, and secure data transmission. It plugs into existing infrastructure and offers a solution to serve battery-operated IoT applications (SEMTECH Co., 2016). LoRaWAN is the network protocol built on top of LoRa technology developed by the LoRa Alliance. It uses the unlicensed radio spectrum in the industrial,

scientific, and medical (ISM) band to enable wide-area communication between remote sensors and gateways connected to the backbone network. To use LoRaWAN, a network server must be established and run on the cloud. There are several network server options, such as establishing a private network server, SEMTECH network server, The Things Network (TTN) server, and so on. A schematic view of a LoRa network consisting of the end nodes, gateways (sometimes called concentrators), network server, and application server is illustrated in Figure 2. LoRa is a proprietary technology based on a variation of chirp spread spectrum modulation with forward error correction. In Europe, it operates on the 868 MHz ISM unlicensed band divided in subbands ranging from 863 to 870 MHz. Two main restrictions imposed due to the unlicensed band are the following. First, LoRa cannot exceed a transmit power of 14 dBm (25 mW). Second, for its three subbands g1, g2, and g3 nodes should respectively obey a 1%, 0.1%, and 10% duty cycle. LoRa nodes are allowed to use different subbands independently, therefore increasing the permitted time on air. LoRa gateways should also follow the duty-cycle restrictions on their downlink messages, i.e., messages transmitted toward LoRa nodes. By applying different spreading factors (SFs), LoRa offers below-the-noisefloor detection possibility at the receiver side. The higher the SF, the higher this detection power and the longer the achievable range. On the other hand, increasing the SF results in decreasing the operational bit rate and thus increasing the time on air (delay) for the reception of the packets. In practice, this means that the throughput is higher for lower SF. LoRa offers different data rates ranging from 0.25 to 11 kbps, with the possibility to scale up to 50 kbps. For a large-scale network, LoRaWAN applies an adaptive data rate algorithm to optimize the data rate in order to ensure minimum power consumption. LoRa does not require accurate timing to demodulate symbols at the receivers. The high clock tolerance allows the use of low-cost clock circuitry, simple reception, and bidirectional communication.

Figure 1. Low-power wide-area technology overview (Nokia, 2016). Members of the LPWAN family lie in two categories based on their spectrum usage. Technologies operating in the unlicensed band, such as LoRa, are subscription-free but offer relatively lower data rates and have to cope with interference in such occupied bands. The ones operating in the licensed band, such as NB-LTE-M, offer a more stable communication and a higher data rate by exploiting the existing infrastructure of 3G/4G at the expense of subscription.

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Besides promising a battery life on the order of years and ranges of up to 15 km, LoRa claims to be interference resistant and immune to multipath fading and non-line-of-sight (NLoS) effects. LoRa also claims to be Dopplershift resistant and thus applicable to moving devices. The literature contains different (sometimes conflicting) claims about LoRa’s capabilities. That is why we have conducted extensive tests within and outside the context of the field test to clarify the potential and the shortcomings of LoRa, some of which are reported in the following.

Wireless seismic QC with LoRaenabled nodes

Figure 2. LoRaWAN network composed of the end nodes (any LoRa-enabled sensor node), gateways, network server, and application server (source SEMTECH).

Active seismic surveys can be large undertakings and can run from a few months to a year. The area of an acquisition typically extends to a few tens of kilometers in one direction and a few kilometers in the other direction. Spacing of sensors along the receiver lines (inline direction) is typically 12.5 to 25 m, and the spacing of receiver lines in the other (crossline) direction is about 100 to 200 m. This means there can be tens of thousands to even hundreds of thousands of sensors in place. An acquisition crew needs to control the quality of the acquisition through a few parameters, typically on a daily basis. These parameters include root-mean-squared (rms) noise level per channel, rms seismic noise level, geophone tilt, remaining battery, and so on. It is also of high interest if sensors can report QC alerts, such as theft based on significant change of their locations or unexpected operational issues. For such a monitoring/QC application, the size of data per sensor is only a few bytes in the worst case, whereas the number of sensors is huge. This necessitates a different approach for collecting, coordinating, and transferring the data through a wireless network. Sometimes QC is conducted on a per-shot basis wherein only a subset of nodes should be monitored for each shot.5 Note that seismic surveys are usually conducted in remote areas where a wired Internet connection is nonexistent. This is a good example where establishing scalable and real-time wireless sensing helps minimize maintenance requirements, crew involvement, and, as a result, the costs involved and health and safety exposure of the crew. As a first step toward materializing such a real-time IoT-based wireless seismic QC, in December 2016, Shell in collaboration with third party Innoseis ran a proof-of-concept (PoC) field test in the Netherlands. To this end, LoRa-enabled nodes with optimized sensing technology and packaging were produced. The main goal of the test was to assess the fundamental performance of a LoRa-based wireless seismic network for real-time seismic QC/monitoring. The field test had three main stages: performance assessment of a single transceiver pair of a LoRa-based seismic node and a LoRa gateway (Kerlink Wirnet Station); network-level performance with eight nodes and two gateways; and yet another 5

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Figure 3. (a) LoRa-enabled node with embedded RN2483 LoRaWAN chips inside and a solar panel on top. (b) Kerlink Wirnet Station providing direct 3G/4G connection as well as Ethernet connection and power on Ethernet power supply. All nodes and gateways feature GPS with submeter location and microsecond time accuracies.

network-level test where a mobile node was added to the aforementioned network. Figure 3 illustrates the employed nodes and gateways and provides more details on the hardware. Figure 4 shows the field test’s coverage area of about 13 km 2 as well as the distribution of the nodes and gateways. The first gateway (GW0) was installed in Gasselte on top of a drilling facility at a height of 11 m, and the second (GW1) was installed in Gieten on a pole at a height of 4.5 m. On the figure, the nodes are numbered from 90 to 98 where 94 is assigned to the mobile node, which is only introduced at stage III of the test in the following sections. It is worth highlighting that the coverage area involved a lot of NLoS complications including trees and buildings, and the weather was partly rainy, foggy, and at times freezing

If properly incorporated in the data communication protocol, this inherent per-shot scheduling helps reduce the communication burden.

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during the test. The network was deployed in less than one day, and the whole test was run for three days after which the network was easily retrieved. We have employed a variety of network servers — e.g., SEMTECH and TTN — for the field test, allowing us to keep track of and download detailed information about communicated packets in a real-time fashion on the cloud (Figure 5a). The network server, which was easily accessible through the Internet on authorized personal devices, also acted as our data server where we could easily download and analyze the data itself. Next to that,

our custom-developed applications provided us with visual QC tools for monitoring purposes (Figure 5b).

Stage I: Single-link performance

The first stage of our test was dedicated to assessment of the fundamental performance limits of a single transceiver pair (LoRa node-gateway) within the seismic QC context. We have investigated several factors, including reception quality, power consumption, maximum achievable range, and data rate, to name a few. In the following, we briefly present some of these results. Our main metric to quantify the signal reception (and thus data delivery) quality was packet loss rate (PLR), which counts the rate of dropped transmitted packets at the receiver side. Here, a packet was flagged as correctly received if a polynomial-based error detection code called cyclic redundancy check (CRC) was passed. CRC is part of the LoRa packet and is assigned to both header and payload of the packet to check their reception qualities. The header typically contains IDs, addresses, packet synchronization data, etc., and the payload contains the main data, which in our QC test was rms noise level, remaining battery, and GPS data. In the following, we state the PLR in percentage and refer to (100 – PLR)% as efficiency.6 Figure 6 illustrates field test results on the communication efficiency versus distance for several trials with different node-gateway pairs where in all of which only the PLR for the specific node and corresponding gateway pair Figure 4. Area of the PoC field test in Drenthe, topography of the region, nodes (#90–#98), and gateway (GW0-1) locations. To give the reader an idea of the scales, GW0 and node #96 are placed 6.5 km apart, and the elevation is depicted. Notably, the optimal SFs profile (green shaded area) at the bottom left shows considerable variations between the two. corresponding to the pairwise distances are applied in this test, which is why the rates are reasonably good for a single communication channel over such a long range. As expected in the general trend, the efficiency decreases with distance even though the SFs are accordingly adjusted. On the right-hand side, four transceiver pairs are picked where the pairwise distance is about 3 km. For each pair, the elevation profile is also depicted with the green shade. As can be seen, the top two cases (#1 and #2) with an LoS condition show an efficiency of

Figure 5. (a) The SEMTECH network server illustrated on a tablet, providing real-time information about modulation, signal-to-noise ratio, coding ratio, SF, operating frequency, etc. (b) Application tool running on a cell phone providing visual QC and status information.

6 Note that reception quality assessment can also be applied at one level deeper, namely bit error rate. Due to limited space, we omit those results.

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Figure 6. Packet reception quality assessment test. Linear fit to the samples acquired shows a decreasing trend with distance. The four picked cases, all with pairwise (node-gateway) distance above 3 km, show that increased obstruction in the first Fresnel zone results in higher PLR (lower efficiency), and an NLoS condition can have destructive impact on the packet reception quality. The green shaded areas illustrate the elevation profile between the given transceiver (node-gateway) pair.

KPN and TTN (both in the Netherlands) continued transmitting packets. Interestingly, 123 and 156 LoRa gateways, respectively, from TTN and KPN reported packet receptions from our flying LoRa node. Our records show that packets from a distance of 354 km are received by the TTN network. It is noteworthy that such a long achievable range could also highlight an opportunity for scenarios with drones or balloons hovering above some surveying area for Figure 7. Free LoS maximum achievable range test conducted with a LoRa node mounted on a balloon. The two left- QC and monitoring purposes. An important concern for operamost snapshots demonstrate gateways of KPN and TTN receiving packets from a flying LoRa node. tional purposes when it comes to mingreater than 95%, which is reasonably good for a single link. In imum-maintenance monitoring/QC networks is battery life of the the bottom two (#3 and #4), the LoS conditions are getting worse. nodes. This obviously is a function of two main components: Notice that #3 is still an LoS condition; however, the first Fresnel namely, energy consumed by the sensing module and that of the zone is much more obstructed as compared to #1 and #2, and wireless communication module. While most of the current wireless thus we see a higher PLR = 12%. Finally, #4 depicts an NLoS technologies such as 3G/4G could very well be a dominant comchannel facing a few tens of meters of solid obstruction. As a ponent in this problem as compared to the power consumed by result, efficiency has dropped to 65% conveying an important the sensing modules, LoRa claims to offer a battery life on the message that for single communication channels, LoRa does not order of years. To investigate this, we have focused on power provide an impressive NLoS mitigation performance. On the consumption of the LoRa module for which the drawn current other hand, what is notable in the figure is that in foggy weather, was measured at different SFs and for different payload lengths. at a distance of 6.5 km, in NLoS conditions, along with a consider- We have found that the average power consumption per frequency able amount of first Fresnel zone obstruction, 60% of packets channel is independent of LoRaWAN settings and remains below were correctly received at GW0 from node #96 (see also Figure 4 1 mW. As a result, assuming a 1% duty-cycle restriction, the for the elevation profile). The efficiencies we have reported are 48 Whr battery pack used in our LoRa-enabled node (with 90% promising for field operations wherein long-range, low-power conversion efficiency) would survive for several years. This corroborates the fact that LoRa can be employed for long-lasting communications are required. The considerable impact of the first Fresnel zone obstruction (seismic) operations without imposing a major power consumption on the communication efficiency of our LoRa-enabled seismic load, as opposed to many wireless seismic technologies in the nodes intrigued us to conduct yet another test to assess the maxi- market for which the wireless modules are power consuming. It is worth reporting another result from the first stage of our mum achievable range of LoRa in a fully unobstructed LoS condition. To this end, a separate LoRa chipset equipped with GPS test on the maximum amount of data that could be transmitted was mounted on a balloon, and it was set free at Hilversum. The per LoRa-enabled sensor per day. This parameter specifically balloon ascended to an altitude of about 12 km while traveling becomes important for more data-demanding applications than toward the southeast for about 100 km and finally landed close to our QC test (see Jamali-Rad and Campman, 2018). In short, if Monchengladbach in Germany (Figure 7). In the meantime, the all the frequency subbands of LoRa are optimally used, for node that was subscribed to the existing LoRa infrastructure of SFs = 7, 8, 10, and 12, respectively corresponding to nominal

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bitrates of 6.8, 3.9, 1.2, and 0.36 kbps, a throughput of respectively 6.29, 3.26, 0.76, and 0.19 MB per day is achievable.

reasonably good for a network of this size, especially given the coverage area and only two gateways. This also conveys an important message that once an appropriate network infrastructure Stage II: Small-scale network performance including a reasonable number of optimally located gateways is In the second stage of the field test, the full network shown put in place, even higher efficiencies are expected. in Figure 4 has been put in action, aiming at a network-level Several other functionalities of the deployed LoRa-based performance assessment of our LoRa-based seismic QC network. network have been tested at this stage, some of which are briefly More specifically, at this stage the SEMTECH network server reported in the following. We have assessed the possibility of running on the cloud simultaneously monitored packets forwarded recognizing other LoRa users in the covered area. This is an by both gateways and optimally combined them. important concern from a bandwidth efficiency perspective. ExcesThe impact of such a network with two simultaneous gateways sive activities could potentially saturate LoRa gateways. We have in action is illustrated in Figure 8. The figure focuses on the found out that we can distinguish packets from other LoRa users efficiency of packet reception of our eight static nodes (#94 is not only at the network server but also at the gateways, which if mobile) based on what has been forwarded by each of the two not forwarded to the network server could help avoid gateway gateways separately, their intersection (marked as “both”), and saturation in extreme cases. We have not detected any LoRa activity their optimal combination (marked as “either”) at the network during the field test in Gasselte and Gieten and had to repeat the server. Notice that except for nodes #90 and #92 that only see test at Innoseis’ laboratories at Science Park in Amsterdam where one of the gateways, the rest show improvement in the overall we have detected three outsider LoRa nodes. We have also invesefficiency surpassing a 90% threshold (except #96), which is tigated the possibility of bidirectional communication between node-gateway pairs. By definition, the downlink communication (from gateways to nodes) can only happen in two small reception windows right after a node has transmitted a packet. This is a limiting factor when it comes to implementing more involved communication protocols such as multicasting or clustering because all the nodes cannot be summoned at the same time. Bidirectional communication has been demonstrated in the field test, allowing commands to be transmitted to individual sensors. This is of great value for realFigure 8. Noticeable improvement in packet reception efficiency by increasing only one gateway and optimally time seismic QC applications. combining the forwarded packets by both gateways at the network server side.

Figure 9. (a) Considerable activity around 868 MHz at Science Park. (b) Limited activity at Gieten. In both cases LoRa performs robust against interference. Note that the solid line at 868 MHz is due to our own LoRa activity.

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Stage III: Interference rejection and mobility

The third stage of the field test was devoted to two pivotal investigations: namely, interference rejection and a mobility test. For interference rejection, we have conducted two experiments. The first experiment was conducted during the f ield test by concentrating on our gateway in Gieten, which was surrounded by active devices operating around 868 MHz (LoRa’s central frequency). An Ettus USRPN210 software-defined radio was used for our measurements. Notably, interferers such as GSM, UMTS, and LTE were located close Figure 10. A mobile LoRa-enabled node mounted on a car (top right) was driven around following the path marked with a solid red line. The transmitted locations of the node are compared with the path, confirming a seamless to the gateway in Gieten and to a lesser communication at speeds up to 100 km/h. extent in Gasselte. Figure 9b illustrates limited activity around 868 MHz within a short timespan, as well as a source of interference close establish minimum-maintenance QC/monitoring wireless to our gateway. This, together with our high reception efficiencies networks while the data could be monitored in real time on the presented in the previous sections, confirm that interference is cloud. No external wireless network infrastructure was required, mitigated by LoRa. Figure 9a shows yet another experiment at allowing this technology to be applied in remote areas without Nikhef Science Park in Amsterdam where a gateway was placed any preexisting infrastructure. We envisage that a variety of applications across different 7 m above the ground. The snapshot shows sizable activity around and on 868 MHz. In this case, a packet reception efficiency rate businesses in the oil and gas industry could benefit from such a of 90% was recorded from a distance of 4.3 km, which again network for monitoring, predictive maintenance, and QC purposes. In the coming years, market penetration of other members proves that LoRa stays robust against interference. Our final experiment was an effort to investigate the impact of the LPWAN family such as NB-LTE-M (or NB-IoT) would of mobility on LoRa communication and to assess the performance allow us to establish large-scale IoT networks, especially for of our network with respect to a mobile LoRa-enabled node. To urban areas, by tapping into the existing LTE infrastructure. this end, a node was mounted on a car as shown in Figure 10, and This would also open the doors to a variety of opportunities, the car was driven reaching a maximum speed of about 100 km/h allowing us to materialize more data-demanding or critical as shown on the map in the figure. In the meantime, the node applications in the oil and gas industry within the framework continued transmitting packets containing its own GPS location of IoT-based wireless networks. readings with SFs 7 or 8 every 15 s. The packets received (with a few seconds of delay) at the gateways are used to extract location Acknowledgments The authors thank Shell Global Solutions International B.V. information, and they are plotted on the map. This is compared with the actual driving path of the car (solid red line) and shows and Innoseis B.V. for permission to publish this work. We would a good consistency. A zoom-in snapshot in the figure also shows also like to thank our colleagues at Shell Global Solutions how seamlessly (without requiring a handover process) the packets International B.V., Michael Kaldenbach, Paul Kobylanski, and were received at different gateways, allowing us to track the move- Dirk Smit for their guidance and support. ment of the car at speeds ranging from 50 to 100 km/h. This result highlights the potential of LoRa for asset tracking purposes, as Corresponding author: [email protected] well as for monitoring scenarios requiring mobility.

References

Concluding remarks

Shell and Innoseis have conducted a PoC field test in Drenthe, the Netherlands, to demonstrate the potential of an IoT-based wireless network for seismic QC/monitoring. We have shown that a test network of LoRa-enabled seismic sensor nodes and gateways could be set up and configured within a few hours. Our field test results corroborate that inexpensive subscriptionfree LoRa modules can be embedded into seismic sensors, allowing us to achieve long-range, low-power communications to

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