Forward Data Research Articles

A Lightweight Privacy-preserving Path Selection Scheme in VANETs

<p>With the rapid development of edge computing, artificial intelligence and other technologies, intelligent transportation services in the vehicular ad hoc networks (VANETs) such as in-vehicle navigation and distress alert are increasingly being widely used in life. Currently, road navigation is an essential service in the vehicle network. However, when a user employs the road navigation service, his private data maybe exposed to roadside nodes. Meanwhile, when the trusted authorization sends the navigation route data to the user, the user can obtain all the road data. Especially, other unrequested data might be related to the military. Therefore, how to achieve secure and efficient road navigation while protecting privacy is a crucial issue. In this paper, we propose a privacy-preserving path selection protocol that supports a token as the object in the oblivious transfers, which effectively reduces the communication overhead. In addition, a lightweight dual authentication and group key negotiation protocol is provided to support dynamic joining or leaving of group members. Moreover, it can guarantee the security of forward data. After experimental analysis, the proposed protocol has high security and efficiency.</p> <p> </p>

Software defined network‐based multipath state‐aware routing with traffic prediction in satellite network

AbstractUneven traffic demand in satellite networks can lead to link congestion and influence network communication quality. To tackle this problem, we propose a state‐aware routing algorithm based on traffic prediction to disperse network traffic. Reducing the onboard processing requirements, the routing algorithm in this paper introduces a software‐defined network to decouple the control plane and data plane of the satellite network. Also, to better sense the trend of network state, the routing algorithm devises a Spatial‐Temporal Attention Fusion Graph Neural Network (STAFGNN) to predict the future network state by capturing the spatial‐temporal correlation of satellite network traffic. In the control plane, the routing algorithm senses the network state by predicting the future satellite traffic and aggregates the real‐time states of neighboring satellites to identify the load level of the zone. The data plane forwards data based on the control information. Based on the routing algorithm in this paper, multipath routing is designed to reduce the probability of link congestion by dispersing each end‐to‐end traffic. The simulation results demonstrate that the routing algorithm in this paper reduces the number of congestion links and balances the load distribution. The proposed algorithm provides an idea to introduce deep learning to solve the load balancing problem in satellite networks.

Relay Node-Based Routing Algorithm for Reducing Latency in Industrial Mobile Communication Network

Mobile network nodes perform time transfer during data packet performance in an asynchronous manner. Network nodes get packets for transmission from node sensors that reject the demands of node attackers. When packet loss occurs unexpectedly due to a network removal, it is exceedingly difficult to detect assaults and time forwarding. Attack detection accuracy deteriorates, and packet loss rates rise. A strategy for node mobile intermediate that gets over attacks that identify the forward choosing is provided for improved data forwarding. Network performance is completely damaged by specific attacks, such as communication processes that degrade or lose packets. The algorithm’s architecture ensures that the best nodes are chosen for relaying and that transmission packets that do not drop are declined. To enable the node and create an effective path routing, a process is executed. Decreasing the amount of packet loss and increasing attack efficiency are being identified.

Deep-learning-based Intrusion Detection for Software-defined Networking Space Systems

This paper briefly reviews the application of the Software-defined Networking (SDN) architecture to satellite networks. It highlights the prominent cyber threats that SDN-based satellite networks are vulnerable to and proposes relevant defence mechanisms. SDN transforms traditional networking architectures by separating the control plane from the forwarding (data) plane. This separation enhances scalability and centralises management. In comparison, in traditional networks, the control plane and the data plane are usually combined, resulting in complex network management and reduced scalability. Satellite networks can take advantage of these benefits offered by SDN and this supports them as key enablers of critical services, including weather prediction, global broadband Internet coverage, and Internet of Things (IoT) services. Ease of configuration and flexibility are essential for satellites providing critical services to instantly adapt to network changes. These desirable attributes can be realised by applying SDN to satellite networks. Although SDN offers significant benefits to satellite networks, it is vulnerable to cyber-attacks and particularly due to its centralised architecture. A common attack on SDN is the Distributed Denial of Service (DDoS) attack which could render the entire SDN unavailable. To mitigate such threats, an efficient Intrusion Detection System (IDS) is required to monitor the network and detect any suspicious traffic. However, traditional IDSs produce too many false positives and often fail to detect advanced attacks. For their ability to learn feature hierarchies in network traffic data automatically, whether, for network traffic classification or anomaly detection, deep learning (DL) plays an increasingly important role in IDSs. In this paper, we present a brief review of recent developments in cyber security for SDN-based space systems, and we identify vulnerabilities and threats to an SDN-based satellite network. We further discuss the potential of a DL-based IDS for the detection of cyber threats. Finally, we identify further research gaps in the recent literature and propose future research directions.

Proof-of-Concept Real-Time Implementation of Interleavers for Optical Satellite Links

Ground-satellite optical links are a promising technology for increasing capacity and security over radio frequency links, while reducing weight and power consumption. However, atmospheric turbulence generates power fadings at the reception terminal. This effect can be mitigated by combining adaptive optics, forward error correction and data interleaving. While adaptive optics has been extensively studied, forward error correction together with long interleaving (e.g., multiple 10 ms) has not yet been demonstrated in this context. In this article, we present a proof of concept of an optical satellite uplink, precompensated by adaptive optics, which leverages a long interleaver in an external memory and operates at 10 Gbps with SFP+ transceivers. We account for the issues associated with real-time implementation and interfacing to a high-speed memory. To validate our proof of concept, we generate time-series for a typical ground to geostationary satellite communication link, using a field-validated simulator. Using these time series, we calculate the required interleaver time as a function of the launch power. Finally, we show the impact of different transmitter pupil diameters and of different wind speed profiles, on the interleaver duration.

A novel bidirectional DiPLS based LSTM algorithm and its application in industrial process time series prediction

The development of industry has led to the big data revolution in manufacturing industry, which has greatly improved production quality and efficiency. A accuracy prediction model is established by using deep learning method, especially short and long term memory (LSTM). However, the application field of LSTM is usually very limited, and the interpretability of LSTM is weak. In this paper, a novel bidirectional DiPLS based LSTM algorithm (BiDiPLS-LSTM) is proposed to solve this problem. First, DiPLS is used to process the forward time series data to obtain the most predictable dynamic latent variable (DLV) of the target variable. At the same time, DiPLS method is also used to process the reverse time series data and extract the reverse DLV. Then, the forward and reverse dynamic features of the same location will form new input data, and this new input data will be input into the LSTM network to realize the prediction of time series data. In order to verify the effectiveness of the proposed BiDiPLS-LSTM method, the real 660 MW coal-fired boiler process data is used, and the emission of oxides of nitrogen (NOx) is regarded as the output. The results show that the extracted dynamic features can be used to build a very accuracy model, and the results outperform the traditional LSTM method, the PLS-LSTM method, the unidirectional DiPLS-LSTM method, and traditional RNN method in the comparison. For the first time, we used the bidirectional dynamic features of the input data as the real input to predict the output, which will obtain a good prediction effect. What is more, the use of dynamic latent variables made the result more explanatory.

Optimal Computation Offloading in Collaborative LEO-IoT Enabled MEC: A Multiagent Deep Reinforcement Learning Approach

Recently, Low Earth Orbit (LEO) satellite-based Internet of Things (LEO-IoT) becomes attractive for computation offloading in mobile-edge computing (MEC) since it can overcome terrain obstacles, such as in depopulated villages and disaster sites. However, it is extremely hard to allocate bandwidth and power resources jointly with multiple users and satellites. In this paper, we study offloading in collaborative LEO-IoT where satellites forward data from users to the MEC server, with the goal of making offloading fast and energy efficient. To achieve this goal, we first define the data offloading in collaborative LEO-IoT as an optimization problem with resource constraints. Then we formulate the optimization problem as a Partially Observable Markov Decision Processes (POMDP), which differs from the existing Markov Decision Processes (MDP) work for the offloading scenario. We further propose a novel Multi-Agent Information Broadcasting and Judging (MAIBJ) algorithm to allocate resources in a collaborative manner. Finally, extensive experiments are conducted with various configurations and the results show that MAIBJ can shorten at least 33% of transmission latency and save at least 42% of energy consumption compared with several baseline algorithms.

A lane-based advanced forwarding protocol for internet of vehicles

PurposeThe internet of vehicles (IoV) communication has recently become a popular research topic in the automotive industry. The growth in the automotive sector has resulted in significant standards and guidelines that have engaged various researchers and companies. In IoV, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in IoV and inconsistent network coverage in different areas make routing challenging. This paper aims to provide a lane-based advanced forwarding protocol for internet of vehicles (LAFP-IoV) for efficient data distribution in IoV. The proposed protocol’s main feature is that it can identify the destination zone by using position coordinates and broadcasting the packets toward the direction of destination. The novel suppression technique is used in the broadcast method to reduce the network routing overhead.Design/methodology/approachThe proposed protocol considers the interferences between different road segments, and a novel lane-based forwarding model is presented. The greedy forwarding notion, the broadcasting mechanism, and the suppression approach are used in this protocol to reduce the overhead generated by standard beacon forwarding procedures. The SUMO tool and NS-2 simulator are used for the vehicle's movement pattern and to simulate LAFP-IoV.FindingsThe simulation results show that the proposed LAFP-IoV protocol performs better than its peer protocols. It uses a greedy method for forwarding data packets and a carry-and-forward strategy to recover from the local maximum stage. This protocol's low latency and good PDR make it ideal for congested networks.Originality/valueThe proposed paper provides a unique lane-based forwarding for IoV. The proposed work achieves a higher delivery ratio than its peer protocols. The proposed protocol considers the lanes while forwarding the data packets applicable to the highly dense scenarios.

Application research of autoregressive and moving average power spectrum in mine ground‐penetrating radar structure detection data analysis

AbstractThe mine roadway built in the same thick rock stratum will not change the lithologic medium in front of the roadway under the influence of the small structure of the mine, which makes the mine radar face many difficulties in structural interpretation. In the coal measure strata in southwest China, the coal seam roof is the weak aquifer of the Kayitou (T1k) group. The mine structure is the excellent drainage boundary of an aquifer, resulting in the water content of the structural belt being different from that of the surrounding rock. This paper proposes a method to identify hidden structures in mine roadways using the autoregressive and moving average power spectrum energy enveloping medium water content inversion technique. Based on the actual water content of strata in the study area, 48 roadway models are constructed. The finite‐difference time‐domain algorithm was used to obtain the forward data and calculate the volumetric water content of the model. The structure of the roadway model is accurately identified by the water content difference. In the numerical simulation study, the segmentation frequency of high‐frequency and low‐frequency envelope of the autoregressive and moving average spectrum of 100‐MHz radar antenna is determined to be 155 MHz, and the value range of water content calculation regulation parameter k is determined. The high‐ and low‐frequency envelope inversion characteristics of the autoregressive and moving average spectrum of different tunnel media models are summarized and analysed. To solve the calculation problem of energy envelope selection of autoregressive and moving average power spectrum of engineering data, the k‐nearest neighbour machine learning algorithm was used to learn and classify the power spectrum features of different tunnel models. The detection application is analysed through the whole‐rock medium and coal and rock medium roadway in the study area. The results show that the method can effectively identify the location and strike characteristics of the hidden structures in front of the roadway.

A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications

Energy efficiency is an existing research challenge in wireless sensor networks (WSNs); to make WSNs energy efficient, many researchers moved to low-duty cycle WSNs with different data forwarding schemes. Low-duty cycle is considered to be a promising approach to design routing protocols for resource-constrained WSNs. Many applications have real-time constraints, which requires an event must be reported to the sink before deadline. Furthermore, wireless links between low-power radios are highly unreliable, and end-to-end latency requirements are challenging due to multiple transmissions for a single packet delivery. By considering probabilistic delay (i.e., delay bounded data delivery with reliability constraints) and energy efficiency issues, little research has been done using different data forwarding schemes, but still, the problem exists. In this work, two strategies are proposed for data forwarding: an energy-optimal path and a delay-optimal path. The early-arrived packet follows an optimal energy route and a diverse path to achieve a delay guarantee with minimum transmission cost. The proposed scheme improves the performance of low-duty cycle WSNs in terms of delay, reliability, stability, and transmission cost. It is observed that under different low-duty cycles, the proposed scheme achieves up to 25% energy conservation compared with both MinEEC and MinEED. At last, undirected spanning trees are considered to balance communication costs. The simulation results of the proposed scheme are compared with the existing methods.

A cooperative security monitoring mechanism aided by optimal multiple slave cluster heads for UASNs

As each cluster head (CH) sensor node is used to aggregate, fuse, and forward data from different sensor nodes in an underwater acoustic sensor network (UASN), guaranteeing the data security in a CH is very critical. In this paper, a cooperative security monitoring mechanism aided by multiple slave cluster heads (SCHs) is proposed to keep track of the data security of a CH. By designing a low complexity "equilateral triangle algorithm (ETA)", the optimal SCHs (named as ETA-based multiple SCHs) are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve large-scale data security monitoring. In addition, by analyzing the entire monitoring process, the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced. The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes. In addition, the numerical simulation results are consistent with the theoretical analysis results, thus verifying the effectiveness of the proposed scheme.

Reinforcement learning for autonomous vehicle movements in wireless multimedia applications

We develop a Deep Reinforcement Learning (DeepRL)-based, multi-agent algorithm to efficiently control autonomous vehicles that are typically used within the context of Wireless Sensor Networks (WSNs), in order to boost application performance. As an application example, we consider wireless acoustic sensor networks where a group of speakers move inside a room. In a traditional setup, microphones cannot move autonomously and are, e.g., located at fixed positions. We claim that autonomously moving microphones improve the application performance. To control these movements, we compare simple greedy heuristics against a DeepRL solution and show that the latter achieves best application performance.As the range of audio applications is broad and each has its own (subjective) performance metric, we replace those application metrics by two immediately observable ones: First, quality of information (QoI), which is used to measure the quality of sensed data (e.g., audio signal strength). Second, quality of service (QoS), which is used to measure the network’s performance when forwarding data (e.g., delay). In this context, we propose two multi-agent solutions (where one agent controls one microphone) and show that they perform similarly to a single-agent solution (where one agent controls all microphones and has a global knowledge). Moreover, we show via simulations and theoretical analysis how other parameters such as the number of microphones and their speed impacts performance.

Response of small female and midsize male models with active musculature in pre-crash maneuvers and low-speed impacts

Objective The objectives of this study were to evaluate computationally efficient small female (54.1 kg, 149.9 cm) and midsize male (78.4 kg, 174.9 cm) models with active muscles using volunteer sled test data in a frontal-oblique loading direction and check their response in crash mitigating maneuvers using field test data. Methods The Global Human Body Models Consortium small female (F05-OS+Active) and midsize male (M50-OS+Active) simplified occupant models with active musculature were used in this study. The data from a total of 48 previously published sled test experiments were used to simulate a total of 16 simulations. The experimental study recorded occupant responses of six small female and six midsize male volunteers (n = 12 total) in two muscle conditions (relaxed and braced) at two acceleration pulses representing pre-crash braking (1.0 g) and a low-speed impact (2.5 g). Each model’s kinematics and reaction forces were compared with experimental data. Along with sled test simulations, both of these models were simulated in abrupt braking, lane change, and turn and brake events using literature data. A total of 36 field test simulations were carried out. A CORA analysis was carried out using reaction load and displacement time-history data for sled test simulations and head CG displacement time-history was used for field test simulations. Results The occupant peak forward and lateral excursion results of both active models reasonably matched the volunteer data in the low-speed sled test simulations for both pulse severities. The differences between the active and control models were statistically significant (p-value < 0.05) based on the results of Wilcoxon signed-rank tests using peak forward and lateral excursion data. The average CORA scores calculated for the sled test (sled test: M50-OS+Active= 0.543, male control= 0.471, F05-OS+Active= 0.621, female control= 0.505) and field test (M50-OS+Active= 0.836, male control= 0.466, F05-OS+Active= 0.832, female control= 0.787) simulations were higher for active models than control. Conclusions The responses of the F05-OS+Active and M50-OS+Active models were better than control models based on overall CORA scores calculated using both sled and field tests. The results highlight their ability to predict occupant kinematics in crash-mitigating maneuvers and low-speed impacts in the frontal, lateral and frontal-oblique directions.

Automatic Dynamic User Allocation with opportunistic routing over vehicles network for Intelligent Transport System

Dynamic auto node configuration with Adhoc features is an advanced concept for vehicle communication. It is the modern internet-based data transmission in Intelligent Transport Systems. Different node auto-configuration schemas and node address-related approaches were used to improve network infrastructure and robust data communication between vehicles in Transport Intelligence-based wireless vehicle Adhoc networks. To address the problem of vehicle node auto-configuration with reduction of delay in transmission is an emerging research concept in data forwarding between vehicles in Adhoc networks, so this paper proposes an Automatic Dynamic User Allocation based Data Forwarding with Opportunistic Routing (ADUADFOR) approach with the assistance of mobility of vehicle association concerning the forwarding of data between vehicles in Adhoc networks. In this approach, each vehicle node carries a replica of other vehicle information, which decides the route based on this collective dynamic information of vehicles. Extensive simulated results of the proposed approach give better performance to improve Quality of service (QoS) through decreased overhead when compared to traditional mobility-based routing calculation methods in vehicle Adhoc networks.

A Deep Learning Framework to Estimate Elastic Modulus from Ultrasound Measured Displacement Fields.

Ultrasound (US) elastography is a technique that enables non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The displacement field is measured from the US images using image matching algorithms, and then a parameter, often the elastic modulus, is inferred or subsequently measured to identify potential tissue pathologies, such as cancerous tissues. Several traditional inverse problem approaches, loosely grouped as either direct or iterative, have been explored to estimate the elastic modulus. Nevertheless, the iterative techniques are typically slow and computationally intensive, while the direct techniques, although more computationally efficient, are very sensitive to measurement noise and require the full displacement field data (i.e., both vector components). In this work, we propose a deep learning approach to solve the inverse problem and recover the spatial distribution of the elastic modulus from one component of the US measured displacement field. The neural network used here is trained using only simulated data obtained via a forward finite element (FE) model with known variations in the modulus field, thus avoiding the reliance on large measurement data sets that may be challenging to acquire. A U-net based neural network is then used to predict the modulus distribution (i.e., solve the inverse problem) using the simulated forward data as input. We quantitatively evaluated our trained model with a simulated test dataset and observed a 0.0018 mean squared error (MSE) and a 1.14% mean absolute percent error (MAPE) between the reconstructed and ground truth elastic modulus. Moreover, we also qualitatively compared the output of our U-net model to experimentally measured displacement data acquired using a US elastography tissue-mimicking calibration phantom.

Simultaneous Wireless Power and Data Transfer System With Full-Duplex Mode Based on LCC/CLC Resonant Network

Demand for the wireless power transmission system with real-time and full-duplex data transmission, a high-frequency carrier injection full-duplex simultaneous wireless power and data transfer (SWPDT) system is proposed. An <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCC/CLC</i> compensation structure is applied in the power transfer channel to achieve voltage stabilization and reduce the power interference on the data transmission. An <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> parallel branch is utilized in the data receiving circuit to suppress ipsilateral data source crosstalk in full-duplex communication. This article theoretically demonstrates the voltage gain characteristics of the power and data transfer channels. Then, the crosstalk between bidirectional data transmission is discussed, along with the analysis of the power effect on the data transmission. In addition, the parameter design method is presented to optimize the data transmission gains while suppressing interference and crosstalk. Finally, the experimental prototype with 20 W output power, 250 kbps forward data rate, and 170 kbps backward data rate is built to verify the correctness and effectiveness of the proposed full-duplex SWPDT system.

Infant gut microbiome composition correlated with type 1 diabetes acquisition in the general population: the ABIS study.

While autoantibodies are traditional markers for type 1 diabetes development, we identified gut microbial biomarkers in 1-year-old infants associated with future type 1 diabetes up to 20 years before diagnosis. Infants enrolled in the longitudinal general population cohort All Babies In Southeast Sweden (ABIS) provided a stool sample at a mean age of 12.5 months. Samples (future type 1 diabetes, n=16; healthy controls, n=268) were subjected to 16S ribosomal RNA (rRNA) sequencing and quantitative PCR. Microbial differences at the taxonomic and core microbiome levels were assessed. PICRUSt was used to predict functional content from the 16S rRNA amplicons. Sixteen infants, with a future diagnosis of type 1 diabetes at a mean age of 13.3±5.4 years, and one hundred iterations of 32 matched control infants, who remained healthy up to 20 years of age, were analysed. Parasutterella and Eubacterium were more abundant in healthy control infants, while Porphyromonas was differentially more abundant in infants with future type 1 diabetes diagnosis. Ruminococcus was a strong determinant in differentiating both control infants and those with future type 1 diabetes using random forest analysis and had differing trends of abundance when comparing control infants and those with future type 1 diabetes. Flavonifractor and UBA1819 were the strongest factors for differentiating control infants, showing higher abundance in control infants compared with those with future type 1 diabetes. Alternatively, Alistipes (more abundant in control infants) and Fusicatenibacter (mixed abundance patterns when comparing case and control infants) were the strongest factors for differentiating future type 1 diabetes. Predicted gene content regarding butyrate production and pyruvate fermentation was differentially observed to be higher in healthy control infants. This investigation suggests that microbial biomarkers for type 1 diabetes may be present as early as 1 year of age, as reflected in the taxonomic and functional differences of the microbial communities. The possibility of preventing disease onset by altering or promoting a 'healthy' gut microbiome is appealing. The forward and reverse 16S raw sequencing data generated in this study are available through the NCBI Sequence Read Archive under BioProject PRJNA875929. Associated sample metadata used for statistical comparison are available in the source data file. R codes used for statistical comparisons and figure generation are available at: https://github.com/PMilletich/T1D_Pipeline .

Decision-aided evaluation of paths for routing on multi-attribute computer network consisting of encoded paths under uncertainty

The network users need to evaluate the alternative transmission pathways to avoid some routing problems such as possible network attacks, data traffic, etc. in computer networks. In order to provide a mathematical solution to this situation, in this paper the computer network system is considered as a graph, and a random data structure denoted by Bloom filter is used to forward data between computers in the network. The Bloom filter has been used for encoding the routes minimises the query response time, although it has a significant risk of producing errors indicated by false positives (FP). The study aims to assess all paths between two distinct computers on a multi-attribute computer network considering risk and FP values regarding paths. These considerations are inherently vagueness. That being the case, a novel two-stage research methodology is presented to determine the desired pathways during message transmission on a multi-attribute computer network under uncertainty. At this point, the values of the possible risks and the probability of FP of the transmission edges are defined by employing triangular fuzzy numbers to reach the theoretical solutions. The novelty of the study is proposing a research methodology based on fuzzy Multi-Criteria Decision Making (MCDM) techniques to offer alternative routes during data transmission on any multi-attribute computer network under uncertainty for the first time. Further, a sensitivity analysis is implemented. In all computational analyses, the most appropriate and reliable transmission pathways are listed, and some managerial insights and main findings are provided for the network users.

Application of steam explosion treatment on the collagen peptides extraction from cattle bone

In this study, steam explosion (SE) treatment was applied to extract collagen peptides from cattle bone. The SE treatment conditions included steam pressure: 1.5 MPa (200.43 °C), 2.0 MPa (213.85 °C), and 2.5 MPa (224.99 °C), and reaction time: 10 min, 20 min, and 30 min. With the increase of pressure and reaction time, the protein recovery rate improved, and reached to 60.5% at 2.0 MPa–30 min. SE significantly decreased the molecular weight of collagen peptides (P < 0.05) with increasing pressure and reaction time. However, at 2.5 MPa, SE darkened the sample color resulting in dark yellow. Particle size analysis and SEM images indicated that SE decreased the particle size and destroyed microstructure of cattle bone powder. FT-IR analysis showed that SE induced distinct characteristic peaks of hydroxyapatite in cattle bone powder. SE facilitated peptides release from collagen by destroying peptide bonds and enzymatic cross-links. Amino acid analysis indicated SE could not change the amino acid composition of collagen peptides. The calcium-binding ability and osteoblast proliferative activity of extracted collagen peptides were 44.7 μg/mg and 126.7% (200 μg/mL), respectively. The findings put forward data support and a scientific basis for the application and development of SE technology to collagen peptides extraction and high-value utilization of cattle bone.

Outage Probability and Throughput of Mobile Multiantenna UAV-Assisted FD-NOMA Relay System With Imperfect CSI

In this article, we analyze an unmanned aerial vehicle (UAV) equipped with multiple antennas and operating in full-duplex (FD) mode to assist the communication of two source–destination pairs in a power-domain nonorthogonal multiple access (NOMA) system. The UAV acts as a relay to forward data from sources to destinations. For practical purposes, the velocity of the UAV in a horizontal plane is taken into consideration. The impacts of imperfect successive interference cancelation, imperfect channel state information, residual self-interference, and the power allocation coefficient for the users to the system performance are also investigated. We evaluate the performance of the considered mobile multiantenna UAV-assisted FD-NOMA relay system by deriving the outage probability and the throughput’s closed-form expressions for each communication pair in different communication urban environments. Monte Carlo simulation results verify the accuracy of these derived expressions.Numerical results show that the best outage performance can be achieved when the location of the UAV and transmission power of the source and the UAV are properly chosen. Furthermore, increasing the number of antennas at the UAV helps to improve the system performance remarkably.