For secure transmission of low cost single antenna communication nodes in wireless crowdsensing networks under static channel, a physical layer communication scheme is proposed, where each digital modulated symbol is encrypted by a random key at the transmitter and decrypted with the same key at the receiver. The legal users exploit the synchronized chaotic sequence and the two-stage block interleaver to generate a complex random variable (random key), whereby its envelope obeys the Rayleigh distribution and its phase obeys the uniformly distribution. The modulated symbol is multiplied by the complex random variable (encryption) to imitate the Rayleigh fading of the channel at the transmitting end. The received symbol is divided by the identical complex random variable (decryption) to recover the transmitted message before the digital demodulation at the receiving end. Simulation results show that the bit error ratio (BER) performance of the legitimate users is consistent with the theoretical value of the Rayleigh fading channel, while the corresponding BER of the eavesdropper is too high (about 0.5) to intercept any information. With the rapid development during the past decades, wireless communication networks have been applied in many fields ,2. HowevMaurer firstly put forward the idea that legitimate parties can extract secret key by exploiting channel characteristics in . Subsequm sequence. The eavesdropper cannot demodulate the received signal correctly without knowing the structure of the modulator. However, considering the high standardization of modern communication protocols, when the eavesdropper knows the modulator structure and part of the original text, the m sequence used to control the modulator structure may be deciphered by the eavesdropper. In [Recently, the use of digital modulation encryption as a new physical layer security technology has attracted lots of attention. Because its security does not depend on the channel characteristics, this kind of physical layer encryption technology has inherent advantages in the communication security aspect under the static or quasi-static channel environment. In , Zang etpper. In , Husain pper. In  proposedpper. In . HoweverBecause chaotic signals have impulse-like auto-correlation and low cross-correlation values, many DCSK  modulation technique for secure multi-user communication systems have been studied and evaluated ,28. CoopIn this paper, a physical layer encryption technique based on artificial Rayleigh fading is proposed for the security of digital transmission of low cost single antenna wireless nodes in a static or quasi-static channels. The main idea of the proposed scheme is as follows. For the transmitter, each digitally modulated symbol is multiplied by a complex random variable used to imitate channel fading in wireless communications. For legitimate recipients, the identical complex random variable can be generated synchronously so that the transmitted information can be recovered correctly. For eavesdroppers, if they could not acquire synchronously the complex random variable, no information can be obtained. In this paper, a synchronous chaotic sequence generator is used to generate an uniformly distribution random sequence, and then a complex random variable whose envelope is Rayleigh distribution and whose phase is an uniformly distributed is generated by the transformation of the uniformly distribution.The main contributions of this paper are as follows. (1) A physical layer encryption method is proposed. The legitimate parties in a static or quasi-static channel generate complex random variables (key) with a synchronous chaotic sequence, which is used to simulate the static or quasi-static channel into a Rayleigh fading channel. (2) A two-stage interleaver is introduced to make a synchronous chaotic sequence divided into four groups of irrelevant sequences. (3) The scrambling of chaotic sequences by the introduced two-stage interleaver significantly increases the difficulty of the eavesdropper’s cracking.The rest of this paper is organized as follows. The proposed physical layer encryption scheme, including the structure of the two-stage interleaver and the generation process of complex Gauss random variables, is elaborated in In this paper, we assume that both legitimate parties are equipped with single antenna, and their communication protocols are open and standardized. We consider the secure transmission of the communication between the two parties in a static/quasi-static channel. In wireless communication, the amplitude and phase of the signal sent by the sender are randomly faded or fluctuated due to the random characteristics of the channel. If the channel estimation is not accurate enough, the SNR  of the receiver may deteriorate dramatically and the demodulator cannot work properly, which results in a high BER. The most serious consequence is the inability to communicate, that means the receiver fails to obtain any information. Inspired by this, we propose the following encryption communication scheme. The training sequence used for channel estimation is sent according to the communication protocol, and the legitimate users or eavesdroppers can estimate the CSI accurately. For binary bitstreams that represent confidential information, the legitimate parties need to be encrypted and decrypted to prevent interception by the eavesdroppers. We emphasize here that both encryption process and decryption process are implemented in the physical layer. A synchronous chaotic generator is employed to generate random complex variables  This section mainly introduces the baseband communication system with the secure transmission shown in In wireless communications, Rayleigh  is the messed ashk=hk,R+ıN represents the number of symbols to be transmitted. It is reasonable to assume that both To make it harder for eavesdropper to decipher, a complex random variable, This means that the encryption operation does not introduce additional power consumption.We note that Equation  is compoChaos is a deterministic pseudorandom process that occurs in nonlinear dynamic systems. This process is aperiodic, non-convergent and highly sensitive to initial values . Owing tA.An appropriate parameter Equation  can guarAs shown in fined as (8)rk=ckAbsolute autocorrelation coefficients of the first 41 lags of a sampled chaotic sequence, A new chaotic sequence According to Equations  and 5),,5), we nAs mentioned in The numbers in For each chaotic sequence in Equation (10), the adjacent offset is greater than 20. Hence, as can be seen in To improve the efficiency of generating random keys, two two-stage block interleavers, as shown in The initial seed It can be seen in According to Equation , it is eL, the superscript k is the lag.Furthermore, to verify the independent expression in Equation , a normak, In this simulation, the absolute value of normalized correlation coefficient with lags ented in . SimilarB and the interval of symbol is Assuming that the power spectral density of white noise MQAM channel, the symbol received by the legitimate user isCombining Equation (20) and Table 6.1 in , the BERIn this section, we carry out Matlab (ver. R2014b) simulation on the system shown in ication)  and DMC ication)  technoloThe chaotic generator at the legitimate receiver is assumed to be synchronized perfectly, i.e., the generated complex random variables are identical with that of the transmitter. Based on Equation , the legEquation  producesEquation . MeanwhiP is a fixed transmit power, h of the channel. In this paper, the AWGN channel is stationary and ergodic, and bit SNR is denoted as Considering the case of a complex, flat-fading channel with the receiver having perfect knowledge of the channel state, the ergodic capacity of such a channel is given by :(23)C=ElEquation . TherefoIn Equation , the expis taken .At the same time, we can see from the simulations in m-Nakagami fading. It is well known that Nakagami fading degenerates to Rayleigh fading when m value to take both reliability and security into account.In this paper, we have proposed a scheme for physical layer security transmission of single antenna node in wireless crowdsensing networks under static channel. In our scheme, the chaotic sequence generator with Tent mapping is used to generate random complex variables. The modulated symbol is multiplied by the complex random variable (encryption) to imitate the Rayleigh fading of the channel at the transmitting end. The received symbol is divided by the identical complex random variable (decryption) to recover the transmitted message before the digital demodulation at the receiving end. The eavesdropper is unable to intercept any effective information in the case of the unsynchronized the chaotic sequence and/or the unknown structure of the two-stage block interleaver, while the legitimate user can still transmit in security under a certain BER. With the introduction of artificial fading, the phase random rotation of the complex random variable does not cause the increase of BER. However, the variation of the modulated symbol power caused by the envelope fluctuation of the complex random variable , makes the symbol SNR fluctuate. When the envelope is less than 1, the power of the modulation symbol sent to the channel is reduced, that is, the signal-to-noise ratio is reduced, leading to the increase of the bit error ratio. This means reducing reliability to improve safety. Rayleigh fading is one of the worst fading channels. If we want to reduce BER while keeping security, we can also consider


Information-Centric Networking (ICN) is a new Internet architecture design, which is considered as the global-scale Future Internet (FI) paradigm. Though ICN offers considerable benefits over the existing IP-based Internet architecture, its practical deployment in real life still has many challenges, especially in the case of high congestion and limited power in a sensor enabled-network for the Internet of Things (IoT) era. In this paper, we propose a smart congestion control mechanism to diminish the network congestion rate, reduce sensor power consumptions, and enhance the network performance of ICN at the same time to realize a complete green and efficient ICN-based sensor networking model. The proposed network system uses the chunk-by-chunk aggregated packets according to the content popularity to diminish the number of exchanged packets needed for data transmission. We also design the sensor power-based cache management strategy, and an adaptive Markov-based sensor scheduling policy with selective sensing algorithm to further maximize power savings for the sensors. The evaluation results using ndnSIM (a widely-used ICN simulator) show that the proposed model can provide higher network performance efficiency with lower energy consumption for the future Internet by achieving higher throughput with higher cache hit rate and lower Interest packet drop rate as we increase the number of IoT sensors in ICN. Nowadays, communication represents one of the most important parts of our interconnected world because the Internet is a fundamental technology that provides many beneficial applications for our society and daily lives. From the advantage of the Internet architecture, Internet of Things (IoT) has been developed in which different types of devices or objects things can make decisions, communicate, and exchange data with each other . Thanks To address the ICN congestion issue in sensor networking, in our previous work , we propWe utilize Markov theory to classify the sensor status into four different operating modes: active, inactive, unicast, and broadcast for the proposed adaptive sensor scheduling strategy. This mechanism can identify the optimized power profile of each state to enable maximum power saving, especially in the case of inactive mode. Particularly, we apply our sensor scheduling scheme over a proposed ICN scenario in which the system only generates content requests (Interest packets) to the sensors that are not inactive state;We design a sensor power-based caching mechanism to store the content data from sensors which have their energy value less than the threshold power level value so that those content items can be served by network nodes instead of sending requests to the low-power sensors. In this way, the congestion rate in sensor-enabled ICN system is also considerably diminished due to the reduced number of Interest packets;As forwarding scheme, we use a popularity-based chunk aggregation mechanism to calculate the appropriate number of chunks corresponding to the content popularity class and send these content chunks as an aggregated packet to the edge routers . Together with the proposed caching mechanism, this forwarding scheme aims to minimize the number of the Interest packets needed for data transmission, then reduce the congestion rate in ICN substantially;To resolve the energy efficiency (EE) problem in the sensor-enabled network, we use Markov-based adaptive sensor scheduling and selective sensing to maximize sensor power savings.In this research, we propose a smart congestion control mechanism for ICN-based wireless sensor networks in the context of IoT, given that a sensor network consists a large number of small battery-powered devices which monitor, record, analyze, and process the surrounding environment wireless information for various practical applications ,11,12. PIn short, this paper contributes to the new concept of green networking and congestion control in ICN with innovative ideas of selective sensing mechanism together with customized sensor operating modes according to the energy level of the sensors. This concept is supported by an implementation of a smart sensor power-based caching mechanism and packet aggregation method corresponding to the popularity of the content to prolong the lifetime of sensors and enable the network stability. The reason is that a request can be satisfied by a content node thanks to the proposed caching scheme or an active sensor with respective data while a content producer power is low. Specifically, by supporting multiple available sensors as content producers to serve a content, the system can redirect packet to more capable sensors and content nodes in ICN interconnections to avoid unnecessary communications via constrained producers for the overall significant EE performance improvement over the sensor-enabled ICN system. The proposal with related combined technique is therefore practical and feasible from both research and application perspectives by tackling the EE issue in a sensor-enabled network towards efficient communications in IoT era, given that a sensor has resource constraints, in terms of memory, processing ability, and power.To the best of our knowledge, this proposal is the first work which integrates both green networking and efficient power-based caching scheme as well as content chunk aggregation as a potential hybrid wireless sensor-enabled network access solution which uses practical caching and forwarding schemes to leverage the sensor power saving for efficient potential content-based services in ICN. This proposal then realizes a practical and efficient Green networking approach in ICN architecture corresponding to the sensor status and content popularity to enhance the ICN migration process towards various dynamic content-oriented Next-Generation wireless applications in the Big Data era.The rest of the paper is organized as follows: in In this section, we present the literature review related to our proposal for the goal of realizing a complete green and efficient ICN-based sensor networking framework with minimized network traffic rate and low energy consumption at the same time.ICN is a promising FI design, which focuses on in-network caching and named content for efficient content dissemination. Though several Internet architectures have been proposed for the FI, ICN has been considered as the global-scale FI paradigm, thanks to its benefits over existing IP-based Internet architectures. Typically, ICN can improve major network metrics, e.g., data rate, network utilization, and especially latency, compared to the current host-centric Internet because different from the existing host-to-host Internet design, the requested content data can be served by a replica from a content node in ICN interconnections, instead of only from the content source. The main concepts of ICN are named data, in-network caching, and multicasting. These three ICN fundamentals allow network elements to be aware of the content requests then aggregate multiple requests of the same content for optimizing bandwidth usage . Also, mAlthough naming, caching, and forwarding mechanisms are among the core parts of ICN, which lead the direction of the ICN research, congestion control also takes a critical part as a huge number of content requests from users due to ICN request packet flooding mechanism can make the network congested, especially in the case of sensor networking in the IoT era. The reason is that all sensors in a sensor-enabled network are connected to the Internet so that all the collected data can be transmitted to server/data center for analysis and used in real-life applications ,9. This To reduce the congestion rate of ICN systems network resource management is among the most efficient policies as cache hit rate is the primary performance metric to evaluate ICN efficiency. For this, several cache management schemes have been studied by recent research papers. For instance,  proposedMoreover, the in-network caching capability in ICN also raises energy consumption problems due to additional energy for caching capability compared to an IP-based network system, as analyzed in our previous work ,7,8. AlsFor green networking, a combination of rate-adaptivity and power-aware routing for greening approaches was stated in  to save For EE formulation and evaluation, an energy model is the key to evaluate the EE performance of a networking model. The authors in  proposedTo model different state transitions using Markov-based finite state machine, recently, the authors in  mention Regarding green networking in ICN environment, in our prior work, we applied Adaptive Link Rate (ALR), one of the well-known green networking techniques, to reduce the power consumption of network systems in ICN by dynamically varying link rate to the optimal utilization, which matches the requested content popularity quickly ,7,8. In Moreover, in ICN as multiple content request and data can result in significant delays, high collisions, and packet loss rate, Cheng et al.  proposedThis section states the proposed communication model in a wireless environment to yield the efficient congestion control mechanism with high energy savings in IoT sensor-enabled ICN.We select the tree topology to closely reflect how the content data varies at different levels of the network topology . This tyIn this network system model, as the sensors are used to collect, store and analyze different data, the system checks the sensors’ status using adaptive sensor scheduling policy and performs suitable actions: the server only sends content requests to the appropriate sensors which are not in the low power mode and have the requested content data. The sensors then response the content request by replying corresponding data to the server.This research focuses on IoT sensor-enabled scenario in which sensors are utilized to sense and analyze the surrounding data. Hence, we assume that a wireless sensor device only moves within a specified area managed by an edge router as a Point of Attachment (PoA), given that all ICN edge routers (Wi-Fi access points) have the same transmission radius. We define the core ICN router is at level 1 of the topology and it is connected to the data server (root node). Also note that the proposed system is not limited in the scope of the simple tree topology as we can apply the proposal to various dynamic hierarchical network models where the wireless edge routers (PoAs) connect directly to the sensors, and link to other ICN intermediate routers at upper level of network topology simultaneously.This section discusses a smart sensor scheduling scheme with selective sensing strategy and customized sensor energy policies to enable optimized operating nodes for maximizing the power saving gains.For the adaptive sensor scheduling, we adopted Markov model  to classT1 as the threshold value of the sensor’s inactive state. If the system identifies that the sensor energy level is less than T1 , the server does not send Interest packets to the sensor to minimize the congestion rate due to packet flooding mechanism in ICN. Otherwise, the server sends interest packets to the sensor and performs the selective sensing, caching, routing and forwarding data operation based on context. This mechanism will be stated in the next sections.As shown in T1, the inactive mode is activated for the sensors to maximize the sensor’s power saving. Otherwise, the sensor then performs sensing, routing or forwarding content to intermediate ICN routers as shown in Regarding selective sensing for power control, when the system identifies that a sensor’s power level is less than the threshold value T1. If the sensor energy level not less than T1 value, then the system forwards the content to the edge router. Otherwise, the system defines the group of non-inactive sensors including the same desired content data. Then, the system selects sending the Interest packet to the sensor with the highest energy level and the data will be forwarded to the attached edge Router from this sensor.In particular, in the situation when a sensor is in the inactive mode and the server needs to get data content from it, we consider that the requested data should be served by another sensor with the matched content from the network. We then propose selective sensing mechanism to improve the QoS in ICN . To do tIn this study, we propose a smart congestion control for the green IoT sensor enabled ICN, in which the content producers (sensors) receive the request packets from the ICN server, then send content data with its attached content popularity. We integrate cache management policy and chunk-based aggregated packets for forwarding scheme to realize an efficient ICN communication model with low congestion rate gained from the reduced network traffic.In this section, we discuss a smart forwarding scheme, under the assumption that a chunk is the unit for data transmission . We thenk is a rank of the content, α is the skewness factor characterizing the Zipf distribution, and F is the total number of content items (files). From the return value of f as defined in (1), we can identify the content popularity level of a specific content.To characterize the content priority and content popularity levels, we take Zipf distribution-based model , and itsWhen the content is transmitted through the intermediate routers (from the IoT sensors as the content sources), the system assigns an appropriate number of chunks for data aggregation before forwarding the content to the intermediate routers. In particular, this number is based on content popularity and the content chunks will be aggregated at the sensor in order of chunk number, i.e., start from the foremost chunks, then forwarded to the edge Router.popK be the rank of content popularity, then each sensor’s data aggregation method is identified corresponding to the popK value of the content that the sensor serves. Particularly, each sensor employs a chunk-based aggregated number characterized by a popK value of the content. Based on this value, an adaptive chunk-level forwarding policy will be applied for each arriving content to the attached edge router. Let ratioChunk be the chunk-by-chunk aggregated-ratio of the content item (start from foremost chunk) and kallChun be the total number of chunks of a content. Then, the proposed forwarding aggregated ratio for a specific content from a producer (sensor) can be identified by the following formula:lowK indicates the rank of lowest content among the most popular content class.To do this, let In this way, only the most popular content items are fully-transmitted to the edge router. All the remaining content are then only partially-forwarded to the aggregated router for distribution in ICN interconnections via the in-network caching mechanism.ratioChunk according to content popularity level as defined by (2).The detailed chunk-based aggregation scheme is depicted in T2 as the threshold value of the sensor energy level with low power (where T2 > T1). In detail, a sensor with energy level pe: T1 < pe < T2 means that the sensor is limited access due to low power though it is not in an inactive mode. To realize a complete and efficient content data allocation among ICN caches, we then propose a sensor power-based caching mechanism, which can reduce the number of chunks for caching at ICN router based on its attached sensor’s power level. Particularly, the system uses T2 as the energy threshold value characterized the state of the sensor (where T2> T1) to identify the suitable caching policy: If the sensor energy level is greater than T2 value, the content data will be forwarded and cached at the attached edge router based on our proposed aggregation method  be the possibility that i = xiX. Then, the probability P(i = xiX) and the joint probability P then can be unified as P(x). The condition that X is the MRF of a sensor S in the set of a sensor’s all available states N can be described as:i and j denote the current and next operating power state, respectively, iN is the set of all available upcoming states of a sensor from its current state i, Nix = {jx| ij N}, and 𝕏 = nD. In case that D = ℝ, 𝕏 = nℝ represents the real number space with n dimensions.Let In this proposal, we utilize adaptive Markov-based smart scheduling operating strategy for Greening ICN-Based IoT Sensor Networking. We define the sensor status from four proposed sensor power operating four modes as: active, broadcast, unicast, and inactive respectively. In other words, we define one state for each sensor at a time and schedule the sensor state in the next period, with the constraint that a sensor with higher energy state can only transit to a lower energy state, not in the inversed way. Particularly, we utilize the Markov model to identify the probabilistic finite sensor state transitions in which if a power state has more than one available upcoming state , the probability of the transition will be higher when the power gap of two states is small, and vice versa.S (SS), we adopt the concept of MRF, where SS can be identified as follows:ip and jp denote the power state of i and j, respectively. In this way, the producers (sensors) can stay in in-active mode and extend its lifetime to maximize power saving as the requested content can be served by other content nodes or producers. Note that we have the plan to extend this Markov-based model by allowing an in-active sensor to fully-recharge its power to the active mode  in our upcoming paper.Then, to identify the possibility of the optimized upcoming sensor power status of T1 and T2. Accordingly, from this estimation, the sensor adapts its operation (sensing/caching/routing and forwarding policy) based on the context as defined in Also note that the Markov-based energy state transition is implemented by the system into a sensor as a built-in function for scheduling the sensor energy state. In detail, after the sensors change their state via the proposed MRF-based dynamic scheduling strategy, the system will perform the suitable adaptive operation by sending the content requests to only capable sensors  with corresponding data. At that time, the system will check the current energy level status of each sensor obtained by the Markov-based model and compare the energy value to the threshold value totalE) by the following formula:cT is the total amount of energy consumed by the sensors, and bT denotes the total amount of data successfully delivered to the server from the sensors.This section states the proposed mathematical energy models for energy savings in the proposed sensor-enabled ICN system. Typically, the energy is consumed by forwarding frames per bit during the transmission process to the receiver without error . In our In this paper, we build the energy model to evaluate the proposed green IoT sensor-enabled system in ICN in the same way as we did in our prior work . ParticuIn this section, we verify the efficiency of the proposed smart congestion control for the green IoT sensor-enabled system by simulation using ndnSIM (a widely-used ICN simulator) , a widelFor simplicity, we assume that all the content objects share the same size of 1 MB with a chunk payload size of 1 KB to prevent fragmentation. The simulation link capacity/bandwidth is 100 Mb/s 1 Mb/s for wired and wireless connections, respectively. The key simulation parameters are presented in α = 1) for the content popularity distribution because it is widely-used to model the content objects request frequency in the network. We refer to related studies to collect data of key network power elements and their energy value for calculating the total energy per successfully transfer bit [We adopt the Zipf distribution  .Transfer bit rate over total energy consumptionThe total network transfer bit per total energy consumption, measured in bit per watt (bit/W).Considering average power of the overall network system is total system energy  and better leverage the in-network caching feature in ICN. Thanks to the smart proposed methods, this results in the network system’s reduced traffic load, and in turn energy consumption. In particular, the evaluation results show that our ICN model can provide higher network performance compared to the conventional ICN design and sleep scheduling ICN model, by achieving higher throughput and cache hit rate, with lower Interest packet drop rate and energy consumption, simultaneously. As a result, our proposed ICN can be considered as a feasible and potential solution to solve the network congestion issues in the ICN-based sensor network, especially in the context of green IoT and big data era.Regarding future work, we will consider several content popularity models to improve the overall network efficiency under various network topologies. Regarding the aggregation method, we will design a probabilistic coefficient to aggregate content chunks of content not belong to the most popular class in which the data aggregation probability is a function of sensor residual power and its power capacity. Also, we will implement our work as a practical approach towards sensor-enabled scenario for the health-care system, which aims to get low congestion as well as high network performance in ICN for efficient health-care services.


Manufacturing more efficient low pressure turbines has become a topic of primary importance for aerospace companies. Specifically, wire electrical discharge machining of disc turbine fir-tree slots has attracted increasing interest in recent years. However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. The current work presents a novel approach for tolerance monitoring based on unsupervised machine learning methods using distribution of ionization time as a variable. The need for time-consuming experiments to set-up threshold values of the monitoring signal is avoided by using K-means and hierarchical clustering. The developments have been tested in the WEDM of a generic fir-tree slot under industrial conditions. Results show that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. Further, 100% of the zones classified in Clusters 3 and 5 lie within the tolerance band of ±15 μm. Finally, the 9 regions classified in Cluster 4 correspond to situations in which the wire is moving too far away from the part surface. These results are strongly in accord with tolerance distribution as measured by a coordinate measuring machine. The manufacture of aircraft engines has increased dramatically in recent years. Some figures show that the number of passengers transported by air has seen continuous growth during the last decade, with some forecasts even predicting an exponential increase for the period 2020–2030 . At the Manufacturing more efficient LPTs has become a topic of primary importance for aerospace companies. The efficiency in manufacturing an LPT is closely related to the manufacturing of its components. In fact, the number of high-added value parts in the turbine is very high. These components are characterized by their ability to withstand extremely high temperatures and pressures during their service life. Advanced materials such as nickel-based alloys and other super-alloys  are usedThe term fir-tree slot refers to the shape of the slot that must be machined on the turbine disc. The component and the manufacturing methods have been described and studied in detail by Klocke et al. . In ordeThe fir-tree slot exhibits low fillet radii, extremely tight tolerances  and controlled surface roughness below Ra1μm. Moreover, in many cases the slot shows tapering with respect to the plane of the disc. All these characteristics ensure a perfect match with the root of the turbine blade, thus enabling retention of the blade against the centrifugal forces acting when the disc is rotating at very high peripheral speeds. Taking into account that an aircraft engine may require many stages of discs (in excess of 40 in many instances), and that in some cases the number of blades can be as high as 200, it is unsurprising that so much attention is currently being paid to the manufacturing of this feature, since it accounts for a significant part of the cost of the engine. Broaching of fir-tree slots on turbine discs is currently accepted as an optimum machining process . It is aAt this point, the wire electrical discharge machining (WEDM) process has emerged as a promising alternative to broaching. In the WEDM process, a brass copper wire is fed between two guides in the presence of a dielectric medium, normally deionized water . The wirJet engine manufacturers now regard WEDM as the process that can overcome some of the limitations shown by broaching for the production of fir-tree slots. For instance, the process is economical, flexible, and can be easily automated. Although in the past, due to its thermal nature, WEDM has been disregarded for the manufacturing of critical engine components, many recent studies ,10,11,12However, important issues must be still addressed for optimum application of the WEDM process for fir-tree slot production. Strict tolerance bands throughout the entire profile, the absence of wire marks on the surface of the component, and manufacturing traceability (imposed by aerospace industry) are state-of-the-art research topics. In this regard, the work carried out by Klocke et al.  is partiThe development of machine learning (ML) techniques in recent years has opened up a new perspective on the problems of process monitoring and traceability through the use of large quantities of data incoming from the process. ML and AI techniques have been successfully applied to the modeling of the manufacturing process, mainly due to their ability to deal with complex multivariable and non-linear phenomena . ExampleHowever, the application of unsupervised learning to manufacturing processes, and more specifically, to the manufacturing of turbine components is still scarce. When using unsupervised learning techniques, labeling can be avoided and the amount of data for training can be largely reduced. Undoubtedly, the development of unsupervised ML techniques for clustering to engineering problems shows high potential. In production operations it is difficult to obtain a sufficiently large number of labeled data , which opens up the possibility of successfully applying unsupervised learning techniques to industrial applications.Some examples of unsupervised learning in industrial data can be found in the field of supply chains and production logistics . The K-mClustering is an important task within unsupervised learning . A consiIn this work, a novel approach is proposed for monitoring the production of disc turbine fir-tree slots by WEDM. The approach is based on the use of unsupervised machine learning techniques. The main contribution of this work with respect to previous works is to explore the possibility of automatically monitoring component tolerances, which are very strict in fir-tree slot production. The ability of these ML techniques to classify the different regions of the fir-tree slot geometry as a function of the achieved tolerances eliminates the need for preliminary time-consuming experiments to set up threshold limits. Basic experiments reveal that unsupervised clustering techniques—namely K-means and Hierarchical Clustering—are very efficient in producing clusters as a function of the distance between wire and work-piece, or in other words, as a function of the final deviation of wire path and nominal profile. As a result, the approach becomes general and can be easily automated in machine control.Since the second trim cut does not modify the geometry of the part, local damage on the fir-tree profile previously caused by the roughing and/or the first trim cut will result in modifications of the gap during the second trim cut. If, for instance, during the roughing cut, excessive wire-lag removes too much of the part material at a convex lobe of the fir-tree, during the second trim cut the distance between wire and machined surface at that exact point of wire path will be higher than ideally expected. Most high-quality WEDM machines are equipped with an iso-frequency pulse generator for the second trim cut, which maintains constant both the off-time and pulse time of each pulse. Since the feed rate is constant and is established by machine table look-up, the generator cannot control gap dimension. As a consequence, if an excess or lack of material is present , instabilities, marks, and deleterious surface finish may occur at that point, thus affecting the quality of the part. Taking into account that infeed is a measure of the actual distance between wire and machined surface, it can also be used to define the final tolerance of the profile. This is due to the fact that the second trim cut does not modify the geometry, which is instead dependent on the quality of the roughing and the first trim cut.Fundamental experiments have been carried out on an ONA AV-35 WEDM machine (CuZn37 0.25 mm diameter brass wire), which is the industrial equipment for disc turbine production. Part material is Inconel 718.Electrical parameters for the second trim cut were selected by machine table look-up, and are listed in For every region of the experiment, the voltage signals were directly measured from the WEDM machine generator by using a Tektronix ThDP0200 Differential Probe. Data were collected using a Tektronix 5034B Digital Oscilloscope, which allows for a very high sampling rate of 50 MS/s. Signal resolution is therefore 20 ns, which is sufficiently high to detect process-related events that may appear in the voltage signal. 30 trains of voltage pulses of 0.1 ms were recorded in each region directly from the WEDM process under actual industrial conditions. High frequency voltage signals have been collected as explained above, and thus the hypothesis of a possible correlation between voltage and wire infeed can be addressed. As a result of the experimental tests, surface finish was measured at three heights of each region, thus allowing for detecting the deformation of the wire related to excessive distance to the machined surface. The potential occurrence of short-circuits due to an excess of part material also becomes apparent from surface finish measurements. Surface roughness has been measured using a Leica optical roughness measurement instrument. The voltage signal shows patterns that help to understand the appearance of the different regions of the machined surface. Region 1 is characterized by a relatively large distance between wire and work-piece. As is well-known, the wire loses straightness and cleaning of the gap becomes difficult in the upper and bottom zones. This explains the variation in surface finish for a given set of cutting conditions. In this case, failure discharges occur, in which although ionization has started, no current flow finally occurs during the on time. Regions 2, 3, and 4 exhibit a more uniform pattern. Effective discharges occur, and straightness of the wire is ensured. Finally, in Region 5 the wire is too close to the machined surface, producing short-circuits that increase surface finish.dT plays a fundamental role in the second trim cut. Trains of discharges of “large” ionization time can be observed, which are related to an excessive distance between wire and work-piece. Further, short-circuit situations occur during which contact between wire and machined surface show instantaneous ionization. Therefore in the current work, it is proposed to use the distribution of ionization time  as the main indicator. Since the erosion mode is iso-frequency, it becomes clear that the largest possible ionization time is equal to pulse time, whilst the shortest possible ionization time corresponds to short-circuit situations. Between these two limits, a distribution of discharges as a function of ionization time can be obtained. Inspection of the characteristics of the voltage signal see , recordedT for each single discharge can be extracted. As a result, the curve of distribution of dT , and as dT increases, the possibility of a failure discharge also increases. In this example, which corresponds to Region 1, it can be observed that approximately 42% of the discharges exhibit values of dT smaller than 0.4 μs. In other words, around 60% of discharges correspond to situations during which the wire is too far from the work-piece surface, resulting in failure discharge.From the oscilloscope measurements, values of Therefore, by following this approach, curves for the different regions have been obtained. In the following paragraphs, the feasibility of using this new indicator for tolerance monitoring using unsupervised ML techniques will be addressed.Hierarchical clustering (HC) can be applied on the curves defined in the previous section and obtained from the fundamental experiments. Python 3.6 and Pycharm community are adopted as programming language and software respectively for HC. This technique forms a clustering tree at once, so that that the number of clusters is not fixed a priori. The results of HC on complete voltage sequences are listed in dT distribution to detect variations of wire infeed. Two large clusters have been created. Group 1 includes Regions 4 and 5, characterized by large values of wire infeed (higher than 20 μm). Clearly separated from Group 1, Group 2 includes Regions 1, 2, and 3, in which wire infeed ranges from −8 μm to 12 μm. When compared with dT distribution. The objective is to finally decide the most suitable technique for tolerance monitoring not only in basic experiments, but also in industrial case studies. The selection of the optimum features was carried out by using the Pearson correlation coefficient (PCC), which is a well-known measure of the linear correlation between two variables, and it takes values from +1 to −1. The following features were selected for the analysis from the dT distribution: average (Avg), standard deviation (Std), skewness (Skw), and kurtosis (Krt). dT and wire infeed. In order to confirm this result, the K-means technique has also been applied using features extracted from Thus, using the values of average and skewness, K-means simulations, were run with different numbers of clusters. Numbers of pre-assumed clusters from 2 to 9 have been tried, with the highest scores corresponding to 2 clusters (0.6644), 3 clusters (0.4657), and 4 clusters (0.4546). Clustering has therefore been carried out assuming 2 and 3 clusters. The results are presented in From these preliminary conclusions, the practical validation of the proposed methodology for tolerance monitoring on a generic fir-tree slot geometry subjected to WEDM under actual industrial conditions can be addressed. A photograph of the set-up of the Inconel 718 prototype of the disc turbine mounted on the machine before WEDM, is presented in As explained above, in order to test the accuracy of the clustering technique, tolerance deviations on the machined fir-tree slot geometry were also measured using a Mitutoyo 9106 coordinate measuring machine. A tolerance band of ±15 μm has been represented on the nominal profile obtained from the CAD file, so that the geometry of the deviations can be easily understood. Part thickness is 15 mm, and WEDM electrical parameters are selected by machine table look-up  have shown efficiency and sufficient sensitivity to detect variations in wire infeed , it was finally decided to use hierarchical clustering for simplicity. Since there is no need for feature extraction, and data regarding the Even though the geometry of the error throughout the profile is variable and cannot be characterized only by the maximum value, the efficiency of the technique can be quantified and interesting conclusions can be drawn from Wire electrical discharge machining of fir-tree slots for aerospace applications is currently a hot topic of research. Tolerances in this type of components are within the strongest requirements imposed on the machining processes. A novel approach for tolerance monitoring using unsupervised machine learning techniques has been presented. In order to avoid time-consuming experiments for establishing threshold values for the monitoring variable, the use of unsupervised machine learning techniques  were examined in this study. The possibility of using distribution ionization time was studied through preliminary experiments. Hierarchical clustering of ionization time distribution curves appears to be efficient at classifying the regions as a function of wire infeed. Additional features were extracted from the curves of ionization time. Results from the Pearson correlation coefficient show the highest values for average and kurtosis. Classification of the regions using K-means with these features shows very good agreement with wire infeed. The proposed technique was validated by applying the process of WEDM to an actual fir-tree slot geometry under industrial conditions. The results from the clustering technique  were compared with actual deviations as measured using a CMM. Using 5 clusters allows for a better representation of the various zones in which the fir-tree geometry was divided. It was found that 100% of the zones classified into Clusters 1 and 2 are related to short-circuit situations. The maximum error is positive in all the cases and is always clearly higher (above +20 μm) than the tolerance band (+15 μm). Further, 100% of the zones classified into Clusters 3 and 5 lay within the tolerance band of ±15 μm. Finally, the 9 regions classified into Cluster 4 correspond to situations in which the wire is moving too far away from the part surface .


Throughout the past decade, silicon-based neural probes have become a driving force in neural engineering. Such probes comprise sophisticated, integrated CMOS electronics which provide a large number of recording sites along slender probe shanks. Using such neural probes in a chronic setting often requires them to be mechanically anchored with respect to the skull. However, any relative motion between brain and implant causes recording instabilities and tissue responses such as glial scarring, thereby shielding recordable neurons from the recording sites integrated on the probe and thus decreasing the signal quality. In the current work, we present a comparison of results obtained using mechanically fixed and floating silicon neural probes chronically implanted into the cortex of a non-human primate. We demonstrate that the neural signal quality estimated by the quality of the spiking and local field potential (LFP) recordings over time is initially superior for the floating probe compared to the fixed device. Nonetheless, the skull-fixed probe also allowed long-term recording of multi-unit activity (MUA) and low frequency signals over several months, especially once pulsations of the brain were properly controlled. Chronically stable, extracellular recording of cortical activity is mandatory for brain-computer interfaces used to accurately and reliably control a robotic arm . Long-teIn view of the long-term recording stability of cortical implants a variety of probe parameters promoting a low tissue response are discussed controversially . While sNeuroSeeker  are directly comparable to those in rodents and to which extent glial scarring will prevent the recording of single-unit activity (SUA) from the brain. Recently it has been shown that skull-fixed approaches can be used to record SUA for more than 100 days in mice .To find a suitable trade-off between technological requirements of high-frequency circuitry and biological demands regarding glial response, we fabricated a system allowing to directly compare the electrical recording quality of a floating silicon-based neural probe with a skull-fixed device. Both probes consist of the exact same materials and have the same geometry. We implanted and recorded signals over the course of 65 days. Based on the recorded data, we draw a first conclusion regarding the electrical signal quality of the recordings from a NHP . They are implanted within a few millimeters of each other.In its upper section, the chamber base  is equipThe silicon (Si) probes used in this study comprise an 8-mm-long probe shaft  and carry 16 platinum recording sites. These are equidistantly distributed at a pitch of 250 μm over a length of 3.75 mm of the distal shaft section. The probe shaft terminates in a pointy tip which facilitates probe insertion through both the silastic membrane and the dura mater. While the skull-fixed probe is adhesively attached and wire bonded to a PCB mechanically secured on the holder of the recording chamber, we interface the floating probe via a 10-μm-thin polyimide cable with a length of 30 mm . The mecFor implantation of the recording chamber, a trepanation fitting to the diameter of the chamber base is cut into the skull of the monkey at the desired position. Bone screws are in addition implanted into the skull close to the chamber. They are used to fix the chamber base to the skull using acrylic bone cement. The brain surface with intact dura mater is sealed using the silastic membrane mechanically fixed using the Ti sleeve. The skull-bone cement interface is allowed to mechanically strengthen in a subsequent healing period of 3 weeks before the probes are implanted through the silastic membrane and the dura mater. In this period, the chamber is closed by a flat cap from Gray Matter Research, effectively sealing the chamber.Using a vacuum inserter, the floating probe is inserted under microscope control into the targeted brain area, as previously described in Macaca fascicularis) following the guidelines of the European Community for the care and use of laboratory animals (European Union Directive 86/609/EEC) with approval by the appropriate local committee on animal welfare . During the recordings of this study, the monkey was exclusively in the awake state while receiving rewards for fixating his eyes on a fixation spot at the center of a monitor screen [cf. methods section of The study was conducted with one adult macaque . We sampled broadband activity at 25 kHz which was band-pass filtered between 1 and 300 Hz for local field potentials (LFPs) and between 300 and 3000 Hz for multi-unit activity (MUA). The recorded data were stored offline, and sessions were concatenated before sorting the recorded spiking activity in all the sessions at once using a custom-made spike sorter based on a dynamic template matching algorithm. Statistics have been performed using the ANOVA test, with further Offline spike sorting was performed using a dynamic template matching method implemented in a custom software package (“Smart Spike Sorter”). Initially, up to twelve different clusters were automatically defined by an artificial neural network based on the adaptive resonance theory . VariousAs described in x and y positions) on the monitor screen  or varying spike amplitudes  along different recording sessions are shown in Finally, the ISI CV was close to one in most instances , showingIn the present study we assessed the stability of long-term recordings from chronically implanted silicon-based laminar probes. Stability criteria were the ability to record spiking activity (MUA), the drift in the shape of action potentials analyzed after spike sorting and application of template matching procedures and the signal-to-noise ratio of LFPs. In agreement with previous publications we found that LFP recordings were stable over the whole analysis period  but recoThe comparison between the recording performances of a floating and a skull-fixed silicon-based neural probe, inserted in the same custom-made chamber, demonstrated that both implantation types are suitable to obtain long-term stable recordings of LFP and MUA from the primary visual cortex of awake behaving monkeys. The floating probe yielded slightly more stable recordings with better signal-to-noise ratios right after implantation and during the first weeks. The likely and anticipated reason for the higher recording performance variability of the skull-fixed probe is movements of the brain relative to the fixed probe. Such movements can account for the transient loss of spiking activity from individual recording sites and changing composition of neurons contributing to the recorded MUA. In addition, it is likely that the mechanical irritation caused by the movement induced glial reactions that may have further reduced recording performance . HoweverIf spikes have similar waveform but are recorded from different sites with single electrodes, it must be assumed that they come from different neurons. However, spikes with similar waveforms recorded in the same session from the same electrode are likely to originate from the same cell, in particular if there are indications for a refractory time in the interval distribution. The recording horizon of a single electrode typically includes only up to twenty neurons in cortical layer V and fifty neurons in layer IV, the majority of cells tend to be most of the time silent and numerous cells with small spikes escape sorting. Thus, chances that more than one neuron contributes to a collection of identical spikes are reasonably low. Whether spikes with similar waveforms recorded from the same channel but in different sessions are from the same cell is impossible to decide when recordings are discontinuous as in the present experiment. Thus, we ignore for how long individual cells can be followed.The spontaneous firing rates of the sampled neurons were low as is characteristic for cortical neurons. They increase to variable extents with light stimulation. However, the increase depends critically on the type of neurons and the match between stimulus configuration and receptive field structure. In the present experiments no attempts were made to optimize this match. For the determination of basic receptive field properties  we used simple grating stimuli and light bars, whose orientation and direction of drift were varied systematically. Thus, it is conceivable that we failed to optimally drive some of the recorded cells. However, we had no indications for pathological discharge patterns such as sustained high frequency discharges or high frequency bursts.The gradual increase in recording stability observed for the fixed probe is probably due to several processes that reduced relative movements between the brain and the probe. Insertion of probes is inevitably associated with dimpling of brain tissue due to compression, and the gradual re-expansion of tissue that may last several days leads to relative motion and unstable recordings. The floating probe is less exposed to this effect as it moves with the brain. Another factor for a protracted increase in stability is improved sealing. After craniotomies the exposed dura mater tends to proliferate. This has the dual effect that the leakage of cerebrospinal fluid through the hole caused by the penetrating probe gets sealed and that the stiffness of the dura increases. Both factors reduce movements of the brain and thereby enhance recording stability. Again, the floating probe is less likely to benefit from these proliferative processes which likely account for the time-dependent decrease of the differences in performance between the two probes . As expeThe fact that spikes get smaller across sessions probably results from a deterioration of the interface between the electrode and the tissue . That spikes become wider could mean that electrodes become less sensitive and pick up only cells with large dipoles and these tend to be large pyramids with broad spikes.Numerous studies have shown that long-term recordings can be obtained with skull-fixed, chronically implanted microwires, or matrix electrodes in rodents , NHP Ja and humaIn conclusion, our results suggest a slight advantage in recording stability of floating over fixed probes in early phases after chronic implantation but this advantage levels off after several weeks. Therefore, a number of arguments lead us to suggest to not pursue the floating solution any further, but to rather invest in the perfection of chronic implantation of head fixed high-density laminar probes in NHPs. A major disadvantage of floating probes is that they cannot be adjusted after insertion which limits the possibility to optimize the number of hot spots recording MUA responses. Another problem is their connection with the plug in the chamber. Already with our 16-channel probes, these connections required the development of special highly flexible ribbon cables. When the number of hot spots is scaled up to several hundreds, this solution becomes obsolete because the cables become too rigid. The alternative to realize connections via a PCB is equally incompatible with a floating solution.In the light of this evidence the results obtained in the present study with laminar probes are promising and suggest the following strategy for the chronic implantation of high-density laminar probes. We suggest opting for a skull-fixed configuration and the use of a PCB for connections as described in this study. Leaving the dura mater intact and assuring a hermetic seal of the intracranial space with an additional silastic membrane proved sufficient to prevent loss of cerebrospinal fluid and infections. However, this approach required beveling of the tip of the laminar probe in order to permit penetration of the two barriers. To overcome the initial instability of recording performance and to minimize tissue irritation by movement-related friction it is imperative to further reduce brain movement. In a pilot experiment, we have therefore begun to test a viable and simple solution for the reduction of pulsations. We enlarged the base plate of the inserter, brought it in direct contact with the silastic membrane and advanced the electrode through a small hole in the baseplate. This effectively reduced brain pulsation and assured stable recordings right from the beginning. Furthermore, we suggest to miniaturize the inserter so that it can remain in the chamber and permit repeated adjustment of the probe position. In this case the probe can be moved up and down without further surgical intervention. In this way the distance of the hot spots relative to active neurons can be optimized and neuronal responses recovered over very long periods of time. In addition, the probe can be retracted once laminar recordings have been completed, the inserter can be removed together with the electrode and replaced by other devices that permit insertion of electrode arrays covering larger regions. All these manipulations can be executed without breaking the seal between the chamber and intracranial space and without surgery. This should permit long-term recordings with changing electrode configurations and will substantially enhance the wealth and quality of data obtainable from a single animal. As NHP can learn to perform a great variety of tasks and usually cooperate over many years, numerous different paradigms can be investigated in the same animal following a single surgical intervention for the implantation of the recording chamber.We are currently investigating these options further and hope to be able to report about the results in the near future. If successful, this approach would pave the way for the chronic implantation of various electrode configurations, including high density laminar probes and would substantially scale up the amount of data obtainable per animal, thereby complying with two of the three R’s  which are mandatory for the protection of experimental animals.in vivo recordings. FP realized the modified recording chamber and designed the probe interfaces. AA, TH, and OP participated in the technical aspects of the probe design. LC wrote the first draft of the manuscript. LC, FP, PR, and WS wrote sections of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version.PR and WS developed the concept and design of the study. LC was involved in the feasibility of the study in monkeys, organized the database, analyzed the LFP data, and performed spike-sorting. KG performed the statistical analysis on spike-sorted data. JK-L performed the TH, AA, OP, and PR are co-founders of ATLAS Neuroengineering bvba, Leuven, Belgium. TH and AA are CTO and CEO of ATLAS, respectively. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


NMR spectroscopy is the leading technique to characterize protein internal dynamics at the atomic level and on multiple time scales. However, the structural interpretation of the observables obtained by various measurements is not always straightforward and in many cases dynamics-related parameters are only used to “decorate” static structural models without offering explicit description of conformational heterogeneity. To overcome such limitations, several computational techniques have been developed to generate ensemble-based representations of protein structure and dynamics with the use of NMR-derived data. An important common aspect of the methods is that NMR observables and derived parameters are interpreted as properties of the ensemble instead of individual conformers. The resulting ensembles reflect the experimentally determined internal mobility of proteins at a given time scale and can be used to understand the role of internal motions in biological processes at atomic detail. In this review we provide an overview of the calculation methods currently available and examples of biological insights obtained by the ensemble-based models of the proteins investigated. Nuclear magnetic resonance spectroscopy is the method of choice for structural investigation of proteins and peptides in solution. The diversity of described pulse sequences and the availability of analysis tools and automated structure refinement protocols offer convenient solutions for the most common structure-related problems. However, the true strength of today’s biomolecular NMR is its ability to report on the internal dynamics of the molecules at the atomic level and on multiple time scales . AlthougDespite this dynamics-based view, structural representations of proteins where a single conformer or a number of highly similar ones are shown and used for biological interpretations are still prevalent. The success of solution-state structure determination of proteins is often measured with the similarity to a single X-ray structure . HoweverIntrinsically disordered proteins (IDPs) comprise the most prevalent examples of systems where the single-conformer approach is not feasible. Their extreme flexibility compared to globular proteins pointed to the need of ensemble interpretations of experimental data from the earliest systematic structural studies of such systems . HoweverIn this review we outline these methods with emphasis on those directly reflecting the ensemble-based nature of NMR data. We argue that the resulting ensembles are better models of protein structures than “static” ones and provide examples of conclusions that could be drawn with the use of ensemble representations.i.e., by determining the same structure both by X-ray and NMR, but also different types of NMR observables can provide independent sources of information. Thus, an accurate model should be consistent with a high number of experimental parameters.An important point to make right at the beginning is that all protein structures we encounter are models . This mui.e., represent the same structure. It should be stressed here that structure determination protocols use force field-type parametrization to ensure the adequate geometry of the structures generated, thus, the balance between geometric and restraint terms is a key issue in such protocols. In contrast to the approximation inherent in the SCR methodology, the NMR tube contains on the order of 1016–1017 dynamically fluctuating molecules, thus, each measured parameter represents the time and ensemble average of high number of conformers :A frequent common aspect of ensemble restraining is to turn on the restraint-associated forces relatively gently, which is especially important when the initial state does not correspond well to the restraints to be introduced. One of the solutions to this is to use “half-harmonic” restraining where the restraint potential is adjusted according to how close the system already got the desired values. A progress variable ρ can be introduced, typically separately for each parameter type (Equation (1), ):(1)wh2, are commonly used to describe the extent of motions on the ps-ns time scale and are usually derived from heteronuclear relaxation data [2 parameters for a single structure are always unity). A practical aspect of S2 restraining is that order parameters are characteristic of the motions within the molecular reference frame, meaning that bond orientations should be compared only for superimposed conformers. The exact set of atoms for which this superposition should be done could be a matter of consideration, although most commonly all backbone atoms are used. S2 restraining is available in XPLOR-NIH [General order parameters, denoted Sion data . They arPLOR-NIH  and has PLOR-NIH  and GROMPLOR-NIH .NOE distances are commonly used for conventional structure calculations in protein NMR spectroscopy and are prevalent examples of parameters that are usually not totally fulfilled by a set of highly similar conformers . HoweverResidual dipolar couplings report on the structure and dynamics of proteins up to microseconds . CharactChemical shifts (CS) have been shown to bear sufficient information to obtain correct protein folds . HoweverOther, less frequently used types of parameters used for ensemble calculations include distances obtained from paramagnetic relaxation enhancement (PRE) , scalar For most applications, multiple types of ensemble-averaged restraints are used to obtain a more complete and realistic picture of the internal motions of the protein. There are several protocols, usually identified with an acronym, that have been described in the literature in the past years. 2 restraining and NOE restraining in a pairwise linked manner over the replicas. However, the term “MUMO” in general refers to considering different ensemble sizes for different restraint types rather than being the name of a strictly defined protocol.It should be noted that different NMR-derived parameters average effectively on different time scales and ensemble sizes, the latter of which can also reflected in the name of the protocol. For example, the MUMO protocol, as originally described, uses eight replicas for SAnother issue to be considered is how the MD run itself is set up. To achieve sufficient sampling of available conformations, a standard MD run at a constant temperature might not be the best choice. For example, simulated annealing (SA) cycles can be used and the synchronous ensembles then correspond to the final step of each SA cycle. Additionally, the force constants of the restraints can be adjusted for different phases of the SA cycles . Such a For systems with considerable conformational diversity, such as multidomain or intrinsically disordered proteins, restraining protocols are not always feasible. The other possibility to obtain NMR structural ensembles is the generation of a very large pool of different conformers during the molecular simulation and then applying a conformer selection algorithm to choose the most relevant subset of conformers in accordance with available experiment-derived data.de novo conformer building. For both approaches, reaching maximal diversity while ensuring the geometric feasibility of the structures is a key aspect. As classical MD methods do not provide sufficient sampling, a reasonable choice for globular proteins is accelerated molecular dynamics (AMD) [Possible methods for conformer generation include MD simulations and cs (AMD) . Howevercs (AMD) . Naturalcs (AMD) . This alcs (AMD) . TraDES A special approach of conformer generation is used for multidomain proteins where the domains can be treated as rigid bodies while probing realistic conformations of the flexible linker whilst avoiding steric clashes. Such calculations can be performed with the Pre_bunch algorithm, originally developed for the BUNCH program used to interpret small-angle X-ray scattering (SAXS) data  and usedSome of the early applications that can be mentioned here evaluated the whole ensemble generated against experimental data and no selection step was performed ,49,50,512 relaxation rate, paramagnetic relaxation enhancement (PRE), PRE ratio, NOE, solvent accessibility and hydrodynamic radius (Rh) as well as SAXS data [The program ENSEMBLE uses Monte Carlo optimization to select a sub-ensemble from the “initial soup” corresponding to a set of user-generated conformations . The ideAXS data .The program ASTEROIDS uses a genetic algorithm to produce and ensemble of IDPs consistent with experimental data, measured with a simple quadratic fitness function measuring the deviation of the back-calculated parameters from the measured ones. ASTEROIDS can handle diverse NMR-derived parameters like RDCs , PREs 5 and chemThe MaxOcc algorithm was developed to select a set of domain orientations for multidomain proteins to fit experimental RDCs, pseudocontact shifts and SAXS data. The algorithm requires a pregenerated set of structures sampling possible domain orientations. From this set, a number of conformations will be tested with maximum occurrence (MO) analysis. The selected conformation is put into an ensemble along with others from the pool and the correspondence of the ensemble to the experimental values is checked for different weights of the selected structure. The maximal weight, corresponding to the maximal occurrence is defined as the largest value with which the experimental data are reproducible. The MO value does not mean that the conformation in question is populated to that extent, only that its contribution to the observables can not exceed it. Thus, although MaxOcc analysis reports a number of structures with MO values above a selected threshold, they can not be regarded as equivalents to the ensembles generated by the other method above.The ensembles obtained either by restraining or ensemble selection should be evaluated for correspondence to experimental data. The robustness of the ensemble description is commonly assessed by cross-validation, correspondence to data not used in the generation process. If a large number of data sets are available, e.g., for RDCs, leave-one-out procedures can be systematically performed . In othei.e., pathways of interconversion between the obtained conformers cannot be evaluated. However, such ensembles are suitable to analyze the extent of correlated motions [Dynamic structural ensembles comprise a novel type of models of protein structures and their value is the addition of a fourth, dynamic dimension to our understanding of the relationships between protein structure and function. Such models exhibit properties not readily available from conventional NMR ensembles or X-ray structures. For example, burial of residues can vary greatly from conformer to conformer in such ensembles, with the most deeply buried residues showing the largest variability , adding  motions  and chan motions . It shou motions , most ofDetailed analysis often requires comparison of different ensembles, e.g., those generated with different protocols, as well as dynamic ensembles versus a set of conventionally determined NMR models or different X-ray structures like complexes of the same protein with different partners. Such comparisons can be done using reduction of dimensionality like principal component analysis (PCA) . More soDynamic ensembles break with the common representation of protein structures with single conformers. Although the dynamic nature of proteins is widely acknowledged, the exact inclusion of dynamics in structural representations is not without discrepancies. First and foremost, reproduction of all experimental data becomes easier using ensembles of multiple conformations just because of increasing the degrees of freedom, opening up the path to possible overfitting. Overfitting or under-restraining occurs when the number of replicas is unnecessarily high and the heterogeneity of the resulting ensemble reflects uncertainty rather than the information content of the restraints. On the other hand, in the case of underfitting or over-restraining the number of replicas is not sufficient to consistently account for the dynamics reflected by the experimental information . This prIn ensemble generation-selection approaches the minimal number of structures suitable to reproduce all desired data is also investigated but might vary from case to case. Another related problem stems from the degeneracy of the ensembles as solutions to reproduce experimental data. The fact that we can found a set of conformations that describes our experimental data does not necessarily mean that these structures reflect the actual sampling of the conformational space of the protein . To expli.e., the force field) is reasonably accurate, and does not mean that routinely calculated ensemble-averaged ensembles have this property.The question whether ensemble-restrained conformer sets reflect Boltzmann distributions has been recently addressed in detail ,67,68. TTo put the described methods into wider context, we briefly discuss some other approaches aimed at generating conformer ensembles that correspond to experimental data better than conventional SCR and MD methods.An alternative to ensemble restraining is to use time-averaged restraints. Similarly to ensemble restraining, instantaneous correspondence of a single conformer to all of the restraints is not required, rather, average values calculated from snapshots taken from a single run are expected to match the experimental observations closely. In the course of the simulation, the time-averaged parameter is calculated from the structures at previous steps with the application of an exponential “memory decay” factor. This is necessary to avoid cases where the restraint force would be zero despite an instantaneous violation but when the time-averaged value is still within the restrained boundaries . Due to One of the disadvantages of restraining methodologies is that they do not yield a trajectory that can be used to assess the processes of conformational rearrangements. In addition, reliable experimental data should be available for restraining and parameter adjustment  should be done on a case-by-case basis. Moreover, restraining may introduce perturbations in the dynamics and might lead to ensembles deviating from Boltzmann statistics (but see 4.3). Thus, efforts have been made to reproduce NMR parameters without restraining.First of all, as MD force fields are constantly improved, agreement of “standard” MD calculations with NMR parameters is better with newer force fields than with older ones . This haA different approach is to modify existing force fields in order to get results more concordant with NMR data. Modifications in the dihedral potentials of the amber99ffsb force field  were shoBesides its wide and still increasingly recognized role in regulating biological processes, the 76-residue ubiquitin is the most widely studied globular, folded protein by NMR. Most of the dynamic structural ensemble calculation protocols were tested on this molecule, mostly because of the vast amount of experimental NMR data available for this protein. The structural ensemble generated with the EROS protocol reflects internal motions in the molecule up to the microsecond time scale and covers a larger conformational space than the MUMO ensemble corresponding to ps-ns dynamics ,38. More2 order parameters compared to well-folded globular parts of proteins. Dynamic ensembles of small, 35-residue inhibitors were calculated using a protocol resembling the original MUMO approach [Canonical, standard mechanism serine protease inhibitors are classic examples of the lock-and-key molecular recognition. The textbook view of their efficiency is that the key to their efficacy is their rigidity, and no changes in the structure and dynamics of their protease binding loop occurs upon binding to the target protease. However, NMR dynamics studies from the 1990s suggested that the protease binding loop of practically all of the investigated, evolutionarily unrelated inhibitors exhibit relatively low SThe intrinsically disordered protein ACTR (activator for tyroid hormone and retinoid receptors) acts as a transcriptional activator upon hormone binding by nuclear receptors. It recruits a number of partner proteins to form a complex involved in histone acetylation. ACTR and its complex with the nuclear coactivator binding domain of CREB binding protein is a frequently used model of the folding and binding mechanism characteristic of IDPs. It is likely that the binding interaction requires a preorganized conformation of ACTR in accordance with a conformer selection scenario. A recent study on ACTR combining mutagenesis studies and ensemble description based on PREs, RDCs and chemical shifts suggested the presence of long-range helix-helix interactions in the protein. Ensembles with structures containing helical segments of various length were generated with the flexible-meccano algorithm and selection was performed with the ASTEROIDS method. The significance of the obtained results lies in establishing a connection between short- and long-range interactions in a disordered protein that is not trivially detectable because of the masking effect of stronger interactions contributing dominantly to NMR observables .An ensemble-based dynamics approach using solid-state NMR parameters (termed SSNMR-ED) was developed and applied for a transmembrane helix of the viral protein “u” (VpuTM). The main parameters investigated were the tilt and rotation angles of the helix within the membrane. The combined study of restrained and standard molecular dynamics suggested that the TM helix exhibits a considerably higher orientational variability than previously anticipated. The motion appears to be fast at the NMR time scale. The results shed light on the intramembrane mobility of TM helices which are considered important in defining distinct functional states of transmembrane proteins .In this review we gave a brief overview of presently available approaches to generate structural ensembles of proteins that reflect their internal mobility. Such ensembles give a more realistic description of protein behavior than static structures and can be used to draw conclusions on biologically relevant processes in dynamic systems at atomic resolution using data from NMR experiments. One of the challenges of the near future is the characterization of protein complexes and their formation by detailed atomic-level description of the dynamics of the partners both in free and bound forms. Another task to solve is the generation of ensembles reflecting dynamics on multiple time scales, which can already be successfully addressed by molecular dynamics methods with enhanced sampling . From th


Conventional wavelet transform (WT) filters have less effect on de-noising and correction of a north-seeking gyroscope sensor exposed to vibration, since the optimal wavelet decomposed level for de-noising is difficult to determine. To solve this problem, this paper proposes an optimized WT filter which is suited to the magnetic levitation gyroscope (GAT). The proposed method was tested on an equivalent mock-up network of the tunnels associated with the Hong Kong‒Zhuhai‒Macau Bridge. The gyro-observed signals exposed to vibration were collected in our experiment, and the empirical values of the optimal wavelet decomposed levels (from 6 to 10) for observed signals were constrained and validated by the high-precision Global Navigation Satellite System (GNSS) network. The result shows that the lateral breakthrough error of the tunnel was reduced from 12.1 to 3.8 mm with a ratio of 68.7%, which suggests that the method is able to correct the abnormal signal of a north-seeking gyroscope sensor exposed to vibration. A gyro total station (gyrotheodolite) is a north-seeking instrument that combines a gyroscope sensor and total station (theodolite), which can measure true geographical azimuths in narrow spaces where satellite signals cannot be received ,2. The tDuring orientation measurement in a tunnel, gyro data are not only affected by the constant and random drift errors of the sensor but also the external environmental factors, such as blasting, construction vibration, air draft-induced vibration of passing vehicles ,7, magneThe wavelet transform (WT) proposed by Morlet is a signal analysis and filtering method with high resolution in both the time and frequency domains , which hAn innovative new type of north-seeking gyroscope, the magnetic levitation gyroscope (GAT), developed by the Chang’an University and China Space Age Electronics 16th Research Institute in 2008, adopts a magnetic suspension-supporting and non-contact photoelectric technology to measure the true geographical azimuth  . The GATHowever, there are some problems in the application of the WT filter to GAT data correction and de-noising: (1) The filters applicable for FOG and MEMS have less effect on the application of the GAT since their observed signal distribution characteristics are different . (2) SomTo solve the above problems and improve the robustness and environmental adaptability of the GAT exposed to vibration, based on the WT and time-frequency analysis theory , a filteThe main structure of the manuscript is composed of four sections. The observed signal collected by the GAT is the real-time discrete dynamic electro-optical parameter: the rotor current of the damping torque . The gyrDifferently from attitude gyros, which are susceptible to random drift even in an ideal working environment, the GAT adopts a magnetic suspension-supporting and photoelectric torque feedback technology to restrict the gyro precession, so that the gyro axis does not oscillate about the meridian direction but remains relatively static . In the The wavelet transform is defined as :f is the original observed signal; The Mallat algorithm is used to decompose signals with orthogonal scaling functions and wavelet functions under two-scale coefficients . The scar length . The numsign(x) is defined as:The threshold de-noising is carried out for the high-frequency coefficients by corresponding criteria after wavelet decomposition. To smooth the contaminated signal, a soft threshold function is selected to process the signal of GAT. The criterion of the soft threshold function is :(5)d^j,ksx(k) represents the sort command in program code. The risk curve generated by this threshold is:The Rigrsure adaptive threshold setting method is selected for our study, which can better preserve the smoothness and similarity of the signal . The calRish(k) = min, the Rigrsure adaptive threshold can be defined as:When the risk curve When F(t) is reconstructed [A Mallat inverse operation is performed on the low-frequency wavelet coefficient structed  as:(10)FAccording to the north-seeking algorithm , the gyra, it is necessary to ensure that the frequency range of the Sym10 wavelet function is greater than or equal to that of the observed signal. According to the sampling theorem, the sampling frequency of the signal is at least twice the maximum frequency, and the conversion relationship between the scale parameter and the frequency of the wavelet function is as follows:aF is the frequency of the wavelet function; SF is the signal sampling frequency; and Fc is the central frequency of the wavelet function. For Sym10, cF = 0.6842 Hz. To make sure the frequency range of the wavelet function is , the frequency scale range of the wavelet function should be ; a = 100 for GAT data in our research.For the scale parameter f(i) and F(i) are the original signal and the reconstructed signal, respectively; The optimal decomposed level of a signal is generally determined by the root mean square error (RMSE), signal-to-noise ratio (SNR), correlation coefficient RMSEte index . The aboW is the weight of each index in the composite index; and H can be calculated by:Then the indexes are weighted by the coefficients of variation:H = max, the corresponding optimal decomposed level can be determined.When A field experiment was designed to explore the effect of different wavelet levels for de-noising on improving gyroscope data exposed to vibration . The expThe experimental site  was located on a town road with a subtropical monsoon climate where it is often windy during summer nights. The road lies in an approximate east–west direction and has an occasional vehicle passing along it. More engineering details of the Hong Kong‒Zhuhai‒Macao Bridge can be found in References ,47. A GNAll gyro grid bearings in the experiment needed to be corrected by the gyro calibration, which was measured on the gyro calibration line  Define the absolute differences between the filtered gyro grid bearings and the GNSS grid bearings as FD, which represents the corrective effect of the filtering model for the north-seeking result. When FD= min, the optimal decomposed level can be determined. Two kinds of indexes were used to evaluate and compare the de-noising effect of filtering under different decomposed levels: (1) the normalized composite index TD, which represents the external coincidence accuracy of gyro bearings; (2) the lateral breakthrough error (LBE) at the breakthrough point  lines in the network  steady signal; (b) periodic signal; (c) jitter signal; and (d) jumping signal . H and DH, the better the de-noising effect   . Consequ-noising a and cor-noising  were ach−7) were left in the spectrum , a change of H was no longer obvious   . AlthougFD to approach zero infinitely after filtering. This is because even after correcting for the errors caused by environmental factors, the observed signal may contain some system errors that we cannot eliminate. Fortunately, combined with the above analysis of H and FD results, we can determine the most suitable decomposed level corresponding to different types of environmental factors and optimize the de-noising and corrective effects of wavelet filters  can meet the requirements of the correction of the north-seeking result  mentioned in TD for different filtering schemes and for each type of observed signal are shown in To verify the correctness of the optimal wavelet decomposed level, additional gyro observed signals were collected independently on five gyro lines , so thatTD showed a significant difference compared to filtering with the optimal decomposed level . Compared to the filter with decomposed level = 5, TD also showed a significant difference to the filter with the optimal decomposed level , which suggested that the accuracy of the north-seeking results was improved compared to the empirical decomposed level in previous studies [TD is, the more significant the de-noising effect of the wavelet filter. The result showed that the de-noising effect on jitter and jumping signals is better than on steady and periodic signals, which is consistent with the result of the above experiments in TD with the three kinds of filtering scheme mentioned in In  studies ,41,42. TThe lateral breakthrough error (LBE) is a common precision indicator to check whether the gyro data can correct the grid bearing of the underground network in a tunnel. In our study, the LBE of the breakthrough point  was calcIn our study, the observations were obtained under the influence of known environmental factors, such as wind vibration or vehicle vibration. According to the natural environment, noise sources are uncertain in practical applications. Therefore, the optimal decomposition levels of wavelet filters are not classified according to the magnitude of the observed signal noise, but are mainly determined by the wave patterns of the observations because, although the magnitude of noise is different, the influence of similar error sources on the observations has common characteristics.Therefore, if we do not know clearly which environmental factors affect the observations in the data acquisition process, we can select the appropriate filter decomposition level from The observed signal of a north-seeking gyro sensor is affected by different environmental factors and shows different non-stationary characteristics, so that the selection of the wavelet decomposed level for de-noising is adapted to different types of observed signals.Using the constraints of high-precision external verification conditions (GNSS grid bearings) combined with the prior observed signals will provide empirical values for the wavelet decomposed level and optimize the efficiency of the filtering model. This method of determining the optimal wavelet decomposed level is able to be used not only in GAT, but also in other types of gyroscope sensors.In the application of tunnel surveying engineering, which is vulnerable to complex environmental factors, the optimized model eliminates the influence of external disturbances on the observed signal to a certain extent and enhances the north-seeking accuracy of gyro sensors.From the above experimental results, the following conclusions can be drawn:Due to the limitation of the length of the article and experimental data, we only classified the observed signals into four typical categories. The classification of observed signals under different environmental impacts needs to be further examined by more sophisticated experiments. To further improve the de-noising effect, different thresholding methods will be adopted for high-frequency coefficients of different decomposed levels in our future research. Another filtering method suitable for non-stationary GAT signal analysis, the Hilbert–Huang transform, will also be discussed and compared with the wavelet method in subsequent research.