Dynamic Coverage Research Articles

Three-dimensional physical experimental study on mechanisms and influencing factors of CO2 huff-n-puff and flooding process in shale reservoirs after fracturing

Carbon dioxide (CO2) capture and geological storage technology is a promising strategy for enhancing oil and gas production and mitigating climate change in unconventional formations. In shale reservoirs, production decline is rapid under volumetric fracturing development. Currently, CO2 injection and huff-n-puff techniques are cost-effective methods to boost shale oil recovery. However, traditional huff-n-puff techniques are limited in capturing reservoir heterogeneity and intricate fracture conditions. This study employed a three-dimensional physical experimental apparatus to explore CO₂ huff-n-puff dynamics across six distinct fracture patterns. By implementing a real-time monitoring system with multiple pressure points, the study elucidated the impacts of varying fracture penetration levels and spacing on dynamic pressure wave patterns, mobilization extent, and development outcomes during CO2 huff-n-puff. The results demonstrate that fractures substantially improve CO₂ huff-n-puff efficiency in shale reservoirs compared to a seamless model, with Model 6 achieving the highest recovery rate of 42.38%. As fracture penetration increases, CO₂ dynamic coverage expands. However, full penetration leads to preferential flow through fractures, reducing matrix contact and diminishing huff-n-puff efficiency. Reduced fracture spacing enlarges the well's control domain, amplifies CO2 huff-n-puff dynamic coverage area, and increases oil production and recovery rate. Conversely, decreasing fracture spacing accelerates oil and gas displacement rates, leading to higher economic expenses. The study concludes that 2/3 fracture penetration in five fractures offers the most efficient recovery method. For a 500 m horizontal well, optimal fracture spacing is recommended at 125 m, with a fracture length of 145.8 m for a well spacing of 417 m.

Dynamic low earth orbit multi satellite hopping beam resource allocation considering load and dynamic coverage balance (LDCB-MSHBRA)

A dynamic low earth orbit multi satellite hopping beam resource allocation method based on time slicing is proposed to address the issue of changing coverage and beam interference caused by satellite mobility in multi satellite and multi beam communication scenarios. The method involves six processes: (1) Establishing a low earth orbit satellite system with three identical configuration parameters and motion speed parameters; (2)Assign a value to the time slicing weight coefficient and use the principle of load balancing to minimize the difference in demand for dual star services as the optimization objective; (3) Obtain initial power resource allocation with maximum system capacity as the optimization objective; (4) Considering the mobility of low earth orbit satellites, updating weight coefficients and dynamically allocating power resources, a time resource dynamic allocation matrix and the optimal service cell set are obtained; (5) Update the independent coverage cell set of the third low earth orbit satellite, use the Lagrange equation of the weighted fair objective function to obtain the time resource allocation optimization matrix, and use one-way search to determine the optimal service cell set; (6) With the goal of maximizing system capacity, the optimal solution of the power resource allocation matrix for the third low earth orbit satellite is obtained. The system throughput is introduced to intuitively measure system performance, and adjustments are made based on the overlap of Samsung coverage. The resource allocation method is based on a fairness model, considering load balancing and dynamic coverage range changes. On the basis of improving the overall system throughput, it ensures the fairness of resource allocation and allocates time, frequency, and power resources to multiple satellites with overlapping coverage, which helps to improve the throughput and resource utilization of satellite systems.

Dynamic Coverage Control for Constrained Mobile Sensor Networks

Dynamic Coverage Control for Constrained Mobile Sensor Networks

Reinforcement Learning-Based Dynamic Coverage Control of Multi-Rotor UAVs With Safety Priority

Reinforcement Learning-Based Dynamic Coverage Control of Multi-Rotor UAVs With Safety Priority

DC-RAN dynamic coverage methods to improve its performance

Abstract
 In cellular networks, there are usually times when the density of the network changes at different times of the day, where congested areas attract a large demand for communications and data, which generates peak demand at specific times of the day, and vice versa also happens in the same area but at another time of the day, and in order to meet the needs of this changing demand the network must keep pace with changes in the density of the areas served. This can only be done if the network has a dynamic ability to handle the change in traffic density and demand intensity, which requires turning on/off a certain number of Remote Radio Heads (RRHs) that represent the Radio Frequency (RF) front end of the mobile network. For the purpose of providing a reliable and acceptable telecommunications service to users and financially useful to network operators by rationing energy consumption and meeting the required needs, dynamic 5G networks can do this by relying on a Dynamic Cloud Radio Access Network (DC-RAN) that enables them to respond to demand variables by shifting several types of coverage cells into a single serviced area and stopping them when needed.

LC-MS investigated as a tool to study the metabolomic characteristics of cereal fermentation

Fermentation of cereals to obtain desired food products is an age-old technique. It is often used to enhance the acceptability and shelf-life of the product. Fermentation can be viewed as a process where microbes bring about metabolic changes in the overall metabolic constitution of the product. In recent years, food metabolomics is emerging as a field to decipher these metabolomic changes to develop a more sustainable and robust food industry. With the advent of advanced analytical platforms, it is now possible to analyse cereal fermentation in-depth in a high-throughput manner. One such tool is LC-MS which has now become an indispensable tool for such food-based studies. The dynamic coverage of metabolites along with high qualitative and quantitative reproducibility makes it a method of choice in many metabolomic studies. The present study undertakes an exhaustive survey of the scholarly literature from 2005 to 2023 focusing on key studies that employ the LC-MS platform to study fermentation, particularly in cereals. The findings strongly advocate for the versatility and utility of LC-MS especially when complemented with chemometric approach in unravelling the intricacies of cereal fermentation. By leveraging these insights, the food industry can further enhance the development of functional foods and foster innovation.

Model-domain failing test augmentation with Generative Adversarial Networks

It is undoubtedly that test suites are crucial for fault localization techniques. Through the running of a single test case in the test suite, a fault localization technology is able to obtain the dynamic coverage information of the statements, which is a vector and could be analyzed to identify the corresponding coefficients between statements and program failures. This vector is commonly referred to as a model domain test sample. There are two types of model domain test samples: passing ones and failing ones. However, the number of passing test samples often far exceeds the number of failing ones, which undoubtedly causes the asymmetry phenomenon. Previous studies have proved that the imbalance of model domain test samples may hamper the effectiveness of fault localization techniques. In practice, it is difficult to create failing test cases in input domain. With the rapid development of deep learning techniques, Generative Adversarial Networks (GAN) have achieved promising results in many fields, which may bring a new perspective for augmenting model domain failing test samples. Thus, we try to provide a new method named MAG: Model-domain failing test Augmentation with Generative Adversarial Networks. MAG first constructs a Generative Adversarial Network, and then generates vectors with the abstract representation of execution information as input. At last, MAG utilizes influential global and local contexts to enhance the generated vectors, and to formulate model domain test samples with failing labels. MAG augments model domain failing test samples. Different from traditional methods of generating test cases directly from input domain, MAG seeks to augment failing test samples of model domain, which could improve the effectiveness of existing fault localization techniques. Experimental results illustrate that MAG significantly improves the effectiveness of 13 typical fault localization approaches and is better than two representative data optimization techniques.

Deterministic sampling methods coupled with dynamic coverage factors for uncertainty analyses in MELCOR simulation

This study proposed a modified uncertainty quantification (UQ) approach which couples deterministic sampling (DS) methods with dynamic coverage factors, to overcome the disadvantage of the previous UQ approach (Wang and Ma, 2023b) in obtaining numerically equivalent estimates of 95/95 tolerance limits for MELCOR simulations of postulated severe accidents in a Swedish pressurized water reactor (PWR). In the modified UQ approach two deterministic sampling (DS) methods were used to obtain the first two statistical moments, and dynamic coverage factors calculated from fitted beta distributions are employed to extend the moment information to the 95th percentile. The modified UQ approach was compared with the previous UQ approach (coupling DS methods with a fixed coverage factor) and conventional UQ approach of the first order Wilks’ method. The comparative results showed that the modified UQ approach not only enabled eliminating unrealistic estimates in the previous UQ approach, but also offered estimates that are covered by boxplots from the first order Wilks’ method with a significant reduction of computational cost. In the modified UQ approach 10–18 samples were sufficient, while at least 59 samples were required for the first order Wilks’ method. In the scope of the present study, DS-Standard method was recommended in the modified UQ approach in view of conservatism, while DS-Simplex method was preferred in view of accuracy and computational cost. Since the behavior of a DS method is dependent of time and outputs, in practice different DS methods should be integrated to the modified UQ approach for numerically equivalent estimates of 95/95 tolerance limits in MELCOR severe accident simulations.

Two-Level Control of Multiagent Networks for Dynamic Coverage Problems.

We propose a two-level coverage control framework for a multiagent network whose members have to deploy over a given region in accordance with a (possibly time-varying) coverage density function. Our approach is based on a two-level description of the multiagent network. The first level corresponds to the probability density function of the agents' locations over a given region, in which the multiagent network is treated as one unit (macroscopic description), whereas in the second level, the network is described in terms of the collection of all individual positions of its agents (microscopic description). The goal of the multiagent network is to attain a spatial distribution that (approximately) matches the reference coverage density function (high-level coverage control problem) through local interactions of the agents of the network at the individual level (low-level coverage control problem). We address the high-level control problem by associating it with an interpolation problem in the class of Gaussian mixtures. Furthermore, we address the low-level control problem by utilizing a variation of Lloyd's algorithm with a time-varying coverage density function, which is updated at each step based on the distribution of the agents' locations. Because the high-level and the low-level coverage control problems are inherently coupled to each other, we propose an iterative scheme that combines their solutions in order to address the deployment problem in a holistic way. Finally, a set of simulation results is provided to show the effectiveness of the proposed approach.

Computational Screening of Two-Dimensional Metal-Organic Frameworks as Efficient Single-Atom Catalysts for Oxygen Reduction Reaction.

The development of efficient and inexpensive oxygen reduction reaction (ORR) catalysts is crucial for renewable energy technologies. Herein, using density functional theory (DFT) methods and microkinetic simulations, we systematically investigated the ORR catalytic performance of a series of 2D metal-organic frameworks M3 (HADQ)2 (HADQ=2,3,6,7,10,11-hexaamine dipyrazino quinoxaline). It shows that all 2D M3 (HADQ)2 (M=Cr, Mn, Fe, Co, Ni, Cu, Ru, Rh and Pd) monolayers are metallic, due to π-conjugated crystal orbitals centered on the central metals and ligand N atoms. The catalytic activity of M3 (HADQ)2 depends on the binding strength between ORR intermediates and metal species, and can be tuned via changing the central metals. Among these candidates, Rh3 (HADQ)2 and Co3 (HADQ)2 show superior ORR performance to Pt (111) with high half-wave potentials of 0.99 and 0.93 V, respectively. Moreover, the screened two catalysts have excellent intermediate-tolerance ability for dynamic coverage of oxygenated species on the active sites. Our work provides a new path towards developing efficient ORR electrocatalysts.

Region coverage-aware path planning for unmanned aerial vehicles: A systematic review

The use of unmanned aerial vehicles (UAVs) to carry out remote aerial surveys has become prominent in recent years. The UAV-based survey faces several operational issues, such as complicated terrain, limited UAV resources, obstacles, sensor limitations, and other environmental factors. In addition, the coverage plan includes numerous objectives, such as lowering path length, maximizing coverage, and limiting survey time, necessitating multi-objective optimization. UAVs require effective coverage path planning (CPP) to generate the ideal route. It involves determining the path which encompasses every point inside the required region under different constraints. The process automates the process of route determination for autonomous operation by considering various environmental constraints. In this paper, we explore and analyze the existing research on the various techniques used in coverage route planning for UAVs. It provides an overview of the current state-of-the-art CPP methods for UAVs. The study discusses the key challenges and requirements of CPP for UAVs and presents various approaches proposed in the literature to tackle these challenges. We explore a variety of geometric flight patterns for the area of interest having UAV deployment. It also features multi-UAV and multi-region coverage strategies, providing a new dimension to UAV-based operations. The energy consumption of UAVs during CPP is an essential factor, as it influences their flight length and mission duration. The design of the CPP algorithm is determined by the unique requirements of the UAV application, such as the size and form of the region to be mapped, the existence of obstacles, and the desired coverage resolution. Path planning strategies in a three-dimensional environment and dynamic coverage are also included in the study. Moreover, we compare the existing strategies using different performance metrics to evaluate the success of covering missions. Finally, the problems and unresolved concerns related to UAV coverage path planning are examined to provide valuable insights to the readers.

Multi-path exploration guided by taint and probability against evasive malware

Static analysis is often impeded by malware obfuscation techniques, such as encryption and packing, whereas dynamic analysis tends to be more resistant to obfuscation by leveraging concrete execution information. Unfortunately, malware can employ evasive techniques to detect the analysis environment and alter its behavior accordingly. While known evasive techniques can be explicitly dismantled, the challenge lies in generically dismantling evasions without full knowledge of their conditions or implementations, such as logic bombs that rely on uncertain conditions, let alone unsupported evasive techniques, which contain evasions without corresponding dismantling strategies and those leveraging unknown implementations. In this paper, we present Antitoxin, a prototype for automatically exploring evasive malware. Antitoxin utilizes multi-path exploration guided by taint analysis and probability calculations to effectively dismantle evasive techniques. The probabilities of branch execution are derived from dynamic coverage, while taint analysis helps identify paths associated with evasive techniques that rely on uncertain conditions. Subsequently, Antitoxin prioritizes branches with lower execution probabilities and those influenced by taint analysis for multi-path exploration. This is achieved through forced execution, which forcefully sets the outcomes of branches on selected paths. Additionally, Antitoxin employs active anti-evasion countermeasures to dismantle known evasive techniques, thereby reducing exploration overhead. Furthermore, Antitoxin provides valuable insights into sensitive behaviors, facilitating deeper manual analysis. Our experiments on a set of highly evasive samples demonstrate that Antitoxin can effectively dismantle evasive techniques in a generic manner. The probability calculations guide the multi-path exploration of evasions without requiring prior knowledge of their conditions or implementations, enabling the dismantling of unsupported techniques such as C2 and significantly improving efficiency compared to linear exploration when dealing with complex control flows. Additionally, taint analysis can accurately identify branches related to logic bombs, facilitating preferential exploration.

Sensor-Based Planning and Control for Robotic Systems: Introducing Clarity and Perceivability

In this paper, we first introduce an information measure, termed clarity, motivated by information entropy, and show that it has intuitive properties relevant to dynamic coverage control and informative path planning. Clarity defines on a scale of [0, 1] the quality of the information that we have about a variable of interest in an environment. Clarity lower bounds the expected estimation error of any estimator, and is used as the information metric in the notion of perceivability, which is defined later on and is the primary contribution of the paper. Perceivability captures whether a given robotic (or more generally, sensing and control) system has sufficient sensing and actuation capabilities to gather desired information about an environment. We show that perceivability relates to the reachability of an augmented system, which encompasses the robot dynamics and the clarity about the environment, and we derive the corresponding Hamilton-Jacobi-Bellman equations. Thus, we provide an algorithm to measure an environment’s perceivability, and obtain optimal controllers that maximize information gain. In simulations, we demonstrate how clarity is a useful concept for planning trajectories, how perceivability can be determined using reachability analysis, and how a Control Barrier Function controller can be used to design controllers to maintain a desired level of information.

Dynamic game coverage control algorithms for multiple mobile agents through virtual repulsive force

To improve the coverage efficiency of mobile agents, the related information of the agents is generally required partially or completely during the coverage control process, which may lead to a dramatic control cost and energy consumption increase. How to balance the improvement in coverage rate and the reduction of control cost is an important issue in the coverage control area of mobile agents. This paper addresses the dynamic cooperative game coverage control algorithm design problem of a multi-agent system under an incomplete information condition. First, by separating the moving multi-agent system into several subgroups, where each agent can only obtain the position information of the neighbor agents, the coverage control problem of the moving multi-agent system is transformed into a dynamic cooperative game coverage problem of moving multi-groups under incomplete information conditions. Then, the cooperative game rules of the subgroups are presented, and a virtual repulsive force-based dynamic coverage game decision strategy of the agent system is developed to compute the new candidate positions of the agents. Third, the moving multi-agent system is considered a rigid virtual structure, and the coverage control problem of the system is turned into a new one with the virtual structure as a reference frame. Thus, the displacement path planning design of the coverage control for a multi-agent system is simplified. Simulation results reveal that the dynamic cooperative game coverage algorithm based on the virtual repulsive force can effectively realize the coverage control requirement of the moving agent system under the incomplete information condition and can remarkably reduce the cost of coverage control. In addition, compared with several existing coverage algorithms, using the dynamic cooperative game coverage algorithm proposed in this paper requires fewer moving times of agents while obtaining a higher coverage rate.

Dynamic Coverage Optimization for 5G Ultra-dense Cellular Networks Based on Their User Densities

This paper has proposed a user-density-based coverage optimization technique for ultra-dense cellular networks. Antenna tilting is a promising coverage optimization technique to be used in 5G networks, that significantly improve the signal to interference plus noise ratio (SINR) by choosing the appropriate angle of tilt. In this paper, the cellular coverage has been optimized for scattered user densities/user hotspots using an adaptive antenna tilting mechanism that steers the beams towards the temporal hot spot in the coverage area. The proposed method has the competence to improve the desired SINR level and coverage area for a group of users rather than a single user. In this work, a reinforcement learning (RL) algorithm has been implemented to optimize the tilt angle. The performance of the proposed technique has been evaluated in the simulation platform considering a three-sectored multicellular mobile network where the groups of user clusters are distributed randomly. The result confirms the improvement in RSS and SINR values in the group of users having high density with maximum user satisfaction.

Optical Beams Switching-Based Coverage Enhancement Scheme for Compact Visible Light Communications

Visible light communication (VLC) has been recognized as one promising candidate to satisfy the unprecedented throughput requirement of near-future mobile wireless networks. Due to the directional Lambertian optical beam-based design approach, multiple visible light transmitters are essential to provide sufficient coverage. However, these schemes could not meet spatial compactness requirements and fail to match harsher constrained transmitters or even single transmitter scenarios. To address these challenges, this article proposes a non-Lambertian optical beam switching-based dynamic coverage scheme for VLC mobility enhancement. Unlike the conventional Lambertian beam-based technique paradigm, the proposed scheme tentatively utilizes the commercially available asymmetric non-Lambertian beam to configure the VLC links with azimuth selectivity. Numerical results show that, for the solo visible light transmitter scenario, compared with the approximate 39.22 dB average signal to noise ratio (SNR) of the conventional Lambertian configuration, up to 5.27 dB average SNR gain is derived through the proposed dynamic scheme with four candidate non-Lambertian beams. Moreover, this potential gain will be further enhanced to about 13.24 dB when up to four visible light transmitters are available.

Deep neural network based UAV deployment and dynamic power control for 6G-Envisioned intelligent warehouse logistics system

The intelligent warehouse logistics system (IWLS) is an essential component in the emerging industry 5.0. To well assist the IWLS, advanced network architecture and control policies with the adaptive capability to the variation of traffic should be specially designed. In this paper, we consider an air-and-ground cooperative wireless network that enables dynamic coverage to support the flexible scheduling of the automatic guided vehicles (AGVs) in the IWLS. Jointly considering the dynamic deployment of unmanned aerial vehicles (UAVs) as well as the adaptive power control of AGVs, we formulate a two time-scale network control problem to minimize the transmission power consumption of all AGVs under their individual rate requirement. On large time scales, we first propose a particle swarm optimization based algorithm (PSOA) to obtain the deployment position of the ABS. Then, using the results of the PSOA as training data, we design a deep neural network (DNN) framework aimed at reducing the computational time of the PSOA. On small time scales, we devise an online power control algorithm (OPCA) by using some of stochastic network optimization methods. With current channel conditions, the OPCA can generate the real-time power control policy and ensure the long-term rate requirement. Numerical simulations indicate that the DNN framework enhances the coverage performance of the network only consuming a few milliseconds of computation time. Incorporated with the OPCA, the total transmission power of the AGVs is significantly reduced.

System-Level Performance Analysis of Cooperative Multiple Unmanned Aerial Vehicles for Wildfire Surveillance Using Agent-Based Modeling

In this paper, we propose an agent-based approach for the evaluation of Multiple Unmanned Autonomous Vehicle (MUAV) wildfire monitoring systems for remote and hard-to-reach areas. Emerging environmental factors are causing a higher number of wildfires and keeping these fires in check is becoming a global challenge. MUAV deployment for the monitoring and surveillance of potential fires has already been established. However, most of the scholarly work is still focused on MUAV operations details. In wildfire surveillance and monitoring, evaluations of the system-level performance in terms of the analysis of the effects of individual behavior on system surveillance has yet to be established. Especially in an MUAV system, the individual and cooperative behaviors of the team affect the overall performance of the system. Such systems are dynamic and stochastic because of an ever-changing environment. Quantifying the emergent system behavior and general performance measures of such a system by analytical methods is challenging. In our work, we present an agent-based model for MUAV surveillance missions. This paper focuses on the overall system performance of cooperative UAVs performing forest fire surveillance. The principal theme is to present the effects of three behaviors on overall performance: (1) the area allocation and (2) dynamic coverage, and (3) the effects of forest density on team allocation. For area allocation, three behaviors are simulated: (1) randomized, (2) two-layer barrier sweep coverage, and (3) full sweep coverage. For dynamic coverage, the effects of communication and resource unavailability during the mission are studied by analyzing the agent’s downtime spent on refueling. Last, an extensive simulation is carried out on wildfire models with varying forest density. It is found that cooperative complete sweep coverage strategies perform better than the rest and the performance of the team is greatly affected by the forest density.

Dynamic coverage performance of wind-assisted balloons mesh based on Voronoi partition and energy constraint

Efficiently navigating a fleet of several thousand high-altitude balloons to provide communication network and information system to the entire Earth is a promising technology. Here we proposed the use of balloons with altitude control system to coverage the entire Earth by exploiting the natural time-varying wind field. The energy constraints are deduced based on the energy collecting-consuming model and the energy management strategy of the wind-assisted balloon. The Voronoi partition is introduced to optimize the configuration. Then a dynamic coverage algorithm for multi-balloons system is developed. Three schemes of 1500 balloons in two launch sites are simulated to show the effectiveness of the algorithm. The results demonstrate that Voronoi partition with energy constraints is an effective solution to spread the wind-assisted balloons out over the entire Earth within a short time and then maintain the optimal dynamic coverage configuration for quite a long time. This can serve as a guideline for the design and flight test of the serviceable wind-assisted balloons coverage mesh.

Event-Triggered Dynamic Coverage Control for Multiple Stratospheric Airships.

This article first investigates the dynamic coverage control problem for the multiple stratospheric airships (MSAs) system considering its practical application scenarios. A dynamic coverage control framework is put forward, in which the MSA system can be guided and controlled to fully cover the observation target region. Once a specific target is detected, the coverage target can be switched. First, the location information of the monitored target is predicted by an autoregressive model against processing delay. Second, the coverage control scheme consists of two layers: a novel potential field-based virtual control law to generate the desired velocity and angular velocity and an adaptive tracking controller to track them. In the virtual control law, a dynamic artificial potential field is introduced to adapt to the dynamic scenarios. In the tracking controller, which is combined with the adaptive control technique and the saturation compensator theory, the external disturbances and input saturation are addressed. Third, the event-triggered mechanism is designed to reduce the control frequency to prolong the actuator life. The simulation results are given to substantiate the capability of the proposed dynamic coverage control framework.