Large-scale Environment Research Articles

Protostellar disk accretion in turbulent filaments

Context. Recent observations of protostellar cores suggest that most of the material in the protostellar phase is accreted along streamers. Streamers in this context are defined as velocity coherent funnels of denser material potentially connecting the large-scale environment to the small scales of the forming accretion disk. Aims. Using simulations that simultaneously resolve the driving of turbulence on the filament scale as well as the collapse of the core down to protostellar disk scales, we aim to understand the effect of the turbulent velocity field on the formation of overdensities in the accretion flow. Methods. We performed a three-dimensional numerical study on a core collapse within a turbulent filament using the RAMSES code and analysed the properties of overdensities in the accretion flow. Results. We find that overdensities are formed naturally by the initial turbulent velocity field inherited from the filament and subsequent gravitational collimation. This leads to streams that are not really filamentary but show a sheet-like morphology. Moreover, they have the same radial infall velocities as the low density material. As a main consequence of the turbulent initial condition, the mass accretion onto the disk does not follow the predictions for solid body rotation. Instead, most of the mass is funneled by the overdensities to intermediate disk radii.

SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments

Semantic reasoning and dynamic planning capabilities are crucial for an autonomous agent to perform complex navigation tasks in unknown environments. It requires a large amount of common-sense knowledge, that humans possess, to succeed in these tasks. We present SayNav, a new approach that leverages human knowledge from Large Language Models (LLMs) for efficient generalization to complex navigation tasks in unknown large-scale environments. SayNav uses a novel grounding mechanism, that incrementally builds a 3D scene graph of the explored environment as inputs to LLMs, for generating feasible and contextually appropriate high-level plans for navigation. The LLM-generated plan is then executed by a pre-trained low-level planner, that treats each planned step as a short-distance point-goal navigation sub-task. SayNav dynamically generates step-by-step instructions during navigation and continuously refines future steps based on newly perceived information. We evaluate SayNav on multi-object navigation (MultiON) task, that requires the agent to utilize a massive amount of human knowledge to efficiently search multiple different objects in an unknown environment. We also introduce a benchmark dataset for MultiON task employing ProcTHOR framework that provides large photo-realistic indoor environments with variety of objects. SayNav achieves state-of-the-art results and even outperforms an oracle based baseline with strong ground-truth assumptions by more than 8% in terms of success rate, highlighting its ability to generate dynamic plans for successfully locating objects in large-scale new environments. The code, benchmark dataset and demonstration videos are accessible at https://www.sri.com/ics/computer-vision/saynav.

Agile tailoring in distributed large-scale environments using agile frameworks: A Systematic Literature Review

With the increasing adoption of agile methodologies in distributed software development teams, there is a need to adapt these practices for large-scale environments. However, the lack of specific guidance can make this process difficult. To evaluate how large-scale agile distributed teams adapt their practices to meet their specific contexts, this study launches a Systematic Literature Review (SLR). The SLR presents adaptations of agile methodologies in distributed software development teams operating in large-scale environments. With the growing popularity of agile methodologies, there is an increasing need to adapt them to suit the specific needs of distributed teams operating in large-scale contexts. The review identified 96 adapted practices from five agile frameworks (Scrum, Scaled Agile Framework (SAFe), Large Scale Scrum (LeSS), the Spotify model, and Disciplined Agile Delivery (DAD)) used in various case studies between 2007 and 2021. Scrum was the most commonly adapted framework with 32 customized practices, followed by SAFe (25), LeSS (17), the Spotify model (13), and DAD (9). The review provides insights into how these practices have been tailored to meet the needs of distributed teams in large-scale contexts. The findings can guide organizations in adapting agile practices to their specific contexts.

FLsM: Fuzzy Localization of Image Scenes Based on Large Models

This article primarily focuses on the study of image-based localization technology. While traditional methods have made significant advancements in technology and applications, the emerging field of visual image-based localization technology demonstrates tremendous potential for research. Deep learning has exhibited a strong performance in image processing, particularly in developing visual navigation and localization techniques using large-scale visual models. This paper introduces a sophisticated scene image localization technique based on large models in a vast spatial sample environment. The study involved training convolutional neural networks using millions of geographically labeled images, extracting image position information using large model algorithms, and collecting sample data under various conditions in elastic scene space. Through visual computation, the shooting position of photos was inferred to obtain the approximate position information of users. This method utilizes geographic location information to classify images and combines it with landmarks, natural features, and architectural styles to determine their locations. The experimental results show variations in positioning accuracy among different models, with the most optimal model obtained through training on a large-scale dataset. They also indicate that the positioning error in urban street-based images is relatively small, whereas the positioning effect in outdoor and local scenes, especially in large-scale spatial environments, is limited. This suggests that the location information of users can be effectively determined through the utilization of geographic data, to classify images and incorporate landmarks, natural features, and architectural styles. The study’s experimentation indicates the variation in positioning accuracy among different models, highlighting the significance of training on a large-scale dataset for optimal results. Furthermore, it highlights the contrasting impact on urban street-based images versus outdoor and local scenes in large-scale spatial environments.

Research on Path Planning Method based on Graph Search Algorithm

Path planning, crucial in robotics and autonomous vehicles, involves finding efficient routes from start to finish while avoiding obstacles. Graph search algorithms like BFS, DFS, and A* are commonly used for this purpose. This research focuses on analyzing these algorithms' performance and applicability in diverse scenarios. The study provides a comprehensive overview of path planning's definition, application domains, and fundamental principles. It then delves into the core graph search algorithms: BFS explores nodes layer by layer, ensuring the shortest path is found first but suffering from high time complexity. DFS explores nodes in a depth-first manner, potentially leading to shorter paths but risking getting trapped in local optima. In contrast, A* combines the strengths of BFS and DFS by using heuristic functions to guide the search for the cheapest path to the goal. Despite their utility, graph search algorithms have limitations. The high time complexity of A* in large-scale environments and the potential for local optima are key challenges. To address these, research explores improved methods like GBFS and bidirectional A* search. The future direction of research includes developing more efficient heuristic functions, exploring hybrid algorithms, parallelizing path planning, and applying these methods to new domains like autonomous drones and self-driving cars. This research paves the way for more robust and efficient path planning algorithms in various applications.

Distributed Computing Optimization in Unmanned Aerial Vehicle Swarm Cooperative Networks

The Unmanned Aerial Vehicle (UAV) swarm cooperative networks across diverse domains present significant challenges in management and control to maximize collaborative efficiency, particularly in applications for large-scale and dynamic environments where real-time coordination is essential. This challenge has led to the development of a UAV swarm cooperative network flow prediction model, facilitating the strategic anticipation of network dynamics. A novel algorithm for distributed, finite-time cooperative control is introduced, grounded in this model. This algorithm optimizes the operational efficiency and control precision of UAV systems, while simultaneously augmenting the collaborative efficiency and stability of the UAV swarms. It marks a significant advancement in distributed computing strategies, offering a viable solution for the real-time coordination challenges in extensive UAV swarm cooperative networks. The proposed approach leverages multi-agent technology, integrating spatial-temporal constraints, location limitations, and path selection in cooperative behavior for dynamic and effective network management. This study not only enhances the understanding of UAV swarm dynamics but also contributes to the practical application of UAV swarms in areas requiring precise coordination and robust network stability.

Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTVEnvironments

As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

Coverage Planning for UVC Irradiation: Robot Surface Disinfection Based on Swarm Intelligence Algorithm.

Ultraviolet (UV) radiation has been widely utilized as a disinfection strategy to effectively eliminate various pathogens. The disinfection task achieves complete coverage of object surfaces by planning the motion trajectory of autonomous mobile robots and the UVC irradiation strategy. This introduces an additional layer of complexity to path planning, as every point on the surface of the object must receive a certain dose of irradiation. Nevertheless, the considerable dosage required for virus inactivation often leads to substantial energy consumption and dose redundancy in disinfection tasks, presenting challenges for the implementation of robots in large-scale environments. Optimizing energy consumption of light sources has become a primary concern in disinfection planning, particularly in large-scale settings. Addressing the inefficiencies associated with dosage redundancy, this study proposes a dose coverage planning framework, utilizing MOPSO to solve the multi-objective optimization model for planning UVC dose coverage. Diverging from conventional path planning methodologies, our approach prioritizes the intrinsic characteristics of dose accumulation, integrating a UVC light efficiency factor to mitigate dose redundancy with the aim of reducing energy expenditure and enhancing the efficiency of robotic disinfection. Empirical trials conducted with autonomous disinfecting robots in real-world settings have corroborated the efficacy of this model in deactivating viruses.

An automated geometry generation procedure for CWE applications in OpenFOAM: the case study of a thunderstorm downburst moving over Genoa city

AbstractSince a decade, Computational Fluid Dynamics (CFD) simulations for Wind Engineering (WE) on large-scale environments became a norm. Meso-$$\gamma$$ γ scales and microscale can be characterized by a large number of structures as well as complex terrain topographies. CFD simulations over such an area would commonly imply the involvement of a time-consuming geometry preparation in the pre-processing stage. This is also the innovative goal of the present paper for which an automated geometry generation procedure for Computational Wind Engineering (CWE) applications is developed in open-source environments. This paper further expands on the unsteady Reynolds-averaged Navier–Stokes (URANS) simulation of a thunderstorm downburst immersed in a background Atmospheric Boundary Layer (ABL) wind moving over Genoa city, Italy. Recommendations for geometry and grid generation of the present case study are also proposed. The study considers two simultaneous approaching wind conditions, a background ABL wind and a moving downburst (DB), the latter modeled as an impinging jet. The simulated results are compared with available full-scale LiDAR profiler data in terms of the vertical profiles of outflow velocity, showing relatively good agreement in terms of magnitude and profile shape.

Data Structure Profiler Using MERN Technology

Profiler introduces a cutting-edge approach to efficiently manage and track Data Structures and Algorithms (DSA) in software development projects. In the realm of modern software engineering, where complexity is ubiquitous and scalability is critical, effective management of DSAs is essential for ensuring the robustness, maintainability, and performance of software systems. DSA Profiler seamlessly integrates into the development lifecycle, offering developers a centralized platform to meticulously document, organize, and monitor the implementation of various data structures and algorithms within their codebase. Its intuitive interfaces and powerful functionalities facilitate the process of incorporating, modifying, and optimizing DSAs. By providing detailed insights and analytics, DSA Profiler empowers developers to make informed decisions about the most appropriate data structures and algorithms for their specific needs, ultimately streamlining the development process and significantly enhancing code quality. One of the standout features of DSA Profiler is its ability to benchmark data structures and algorithms, allowing developers to evaluate their performance under different conditions. This benchmarking capability ensures that developers can identify and address performance bottlenecks early in the development cycle. Furthermore, This collaborative environment helps maintain consistency and adherence to standards across projects, which is particularly beneficial in large-scale and distributed development environments. Keywords - data structure benchmarking, data structure optimization, algorithm performance tracking, software scalability, development lifecycle integration, codebase management, performance analytics, collaborative development tools, real-time optimization recommendations.

Enhancing Security and Efficiency: A Fine-Grained Searchable Scheme for Encryption of Big Data in Cloud-Based Smart Grids

The smart grid, as a crucial part of modern energy systems, handles extensive and diverse data, including inputs from various sensors, metering devices, and user interactions. Outsourcing data storage to remote cloud servers presents an economical solution for enhancing data management within the smart grid ecosystem. However, ensuring data privacy before transmitting it to the cloud is a critical consideration. Therefore, it is common practice to encrypt the data before uploading them to the cloud. While encryption provides data confidentiality, it may also introduce potential issues such as limiting data owners’ ability to query their data. The searchable attribute-based encryption (SABE) not only enables fine-grained access control in a dynamic large-scale environment but also allows for data searches on the ciphertext domain, making it an effective tool for cloud data sharing. Although SABE has become a research hotspot, existing schemes often have limitations in terms of computing efficiency on the client side, weak security of the ciphertext and the trapdoor. To address these issues, we propose an efficient server-aided ciphertext-policy searchable attribute-based encryption scheme (SA-CP-SABE). In SA-CP-SABE, the user’s data access authority is consistent with the search authority. During the search process, calculations are performed not only to determine whether the ciphertext matches the keyword in the trapdoor, but also to assist subsequent user ciphertext decryption by reducing computational complexity. Our scheme has been proven under the random oracle model to achieve the indistinguishability of the ciphertext and the trapdoor and to resist keyword-guessing attacks. Finally, the performance analysis and simulation of the proposed scheme are provided, and the results show that it performs with high efficiency.

A hierarchical model with hexagon grids for multi-objective route planning in large-scale off-road environments

Off-road route planning plays a vital role across various domains, including civil transportation, earthquake relief and civil production. Off-road route planning requires the consideration of a diverse range of environmental constraints, which is a considerable challenge compared with conventional route planning along existing networks. We aimed to present a multi-objective route planning method for off-road environments. Specifically, we designed a heuristic function incorporating multiple characteristics to generate reliable route planning solutions based on Pareto optimality. We also constructed a hierarchical hexagonal grid model by merging similar grid cells and reconstructing adjacency relationships with other grid cells to achieve high efficiency. The proposed method was compared with conventional route planning methods using single- and multi-objective approaches in traditional and hierarchical hexagonal grid models. The proposed method can effectively plan off-road routes, improve optimal route distance and trafficability, and provide more efficient route solutions. It reduced the planning time by 97.1% compared with multi-objective route planning based on a traditional hexagonal grid model.

Individual tree detection in large-scale urban environments using high-resolution multispectral imagery

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future.

Photometric Objects Around Cosmic Webs (PAC). VI. High Satellite Fraction of Quasars

The Photometric objects Around Cosmic webs (PAC) approach developed in Xu et al. has the advantage of making full use of spectroscopic and deeper photometric surveys. With the merits of PAC, the excess surface density n¯2wp of neighboring galaxies can be measured down to stellar mass 1010.80 M ⊙ around quasars at redshift 0.8 < z s < 1.0, with the data from the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey and the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. We find that n¯2wp generally increases quite steeply with the decrease of the separation. Using the subhalo abundance-matching method, we can accurately model the n¯2wp both on small and large scales. We show that the steep increase of n¯2wp toward the quasars requires that a large fraction fsate=0.29−0.06+0.05 of quasars should be satellites in massive halos, and we find that this fraction measurement is insensitive to the assumptions of our modeling. This high satellite fraction indicates that the subhalos have nearly the same probability of hosting quasars as the halos for the same (infall) halo mass and that the large-scale environment has negligible effect on the quasar activity. We show that even with this high satellite fraction, each massive halo on average does not host more than one satellite quasar, due to the sparsity of quasars.

A Stochastic Statistical Model for U.S. Outbreak-Level Tornado Occurrence Based on the Large-Scale Environment

Abstract Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index. Significance Statement Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that “storm relative helicity” (a proxy for potential rotation in a storm’s updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity.

Engineering 3D microenvironments with femtosecond laser-fabricated microtubes to direct neuronal network formation

The regulation of cell growth characteristics through physical structures on material surfaces plays a crucial role in advancing our understanding of cell behavior. Three-dimensional (3D) microstructures, offering multidimensional growth space and a richer nutrient environment compared to their two-dimensional counterparts, have attracted significant attention from researchers. However, the fabrication of high-precision 3D microstructures, especially those with a high aspect ratio (H-AR), remains challenging. In this study, we employ femtosecond laser to achieve exceptional precision and patterning capabilities in fabricating 3D microstructures. To enhance the manufacturing efficiency of axial 3D structures, we introduce a novel single pulse multi-photon polymerization (SP-MPP) method for efficient fabrication of H-AR micropillar and microtube structures. Our innovative approach involves a dual-aperture optical modulation method that effectively modulates the annular energy distribution, enabling the creation of H-AR microstructures. We can achieve a maximum processing height of up to 130 μm with an aspect ratio of 16 within single pulse exposure of 217 fs. Compared to the layer-by-layer scanning method, our proposed approach enhances the axial manufacturing efficiency of 3D structures by 1–2 orders of magnitude. Through experimentation, we validate the applicability of microtube structures for guiding neuronal orientation by utilizing microtube arrays in hippocampal neuronal cell culture. Our results demonstrate that neurons not only grow along the patterned microtubes but also exhibit preliminary synaptic differentiation. Furthermore, we effectively regulate the 3D patterning of neuronal growth by implementing diverse patterned microtube arrays. This proposed method enables the rapid construction of a large-scale 3D cell growth environment, offering significant advantages in inducing neuronal orientation. Consequently, this technique holds immense potential for diverse applications in biomedicine and tissue engineering, including the establishment of isolated neural networks.

Star Formation and Dust in the Cosmic Web

The large-scale environment of the cosmic web is believed to impact galaxy evolution, but there is still no consensus regarding the mechanisms. We use a semi-analytic model (SAM) galaxy catalog to study the star formation and dust content of local galaxies in different cosmic environments of the cosmic web—namely voids, filaments, walls, and nodes. We find a strong impact of the environment only for galaxies with M stars ≲ 1010.8 M ⊙: the less dense the environment, the larger the star formation rate and dust content at fixed stellar mass. This is attributed to the fact that galaxies in less dense environments typically feature younger stellar populations, a slower evolution of their stellar mass, and delayed star formation compared to galaxies in denser environments. As for galaxies with M stars ≳ 1010.8 M ⊙, the differences among environments are milder, due to the disk-instability-driven supermassive black hole (SMBH) growth implemented in the SAM, which makes SMBH growth, and thus galaxy quenching, environment-insensitive. We qualitatively test our predictions against observations by identifying environments in Sloan Digital Sky Survey Data Release 16 using dust masses derived from the GAMA survey. The agreement is encouraging, particularly at logMstars/M⊙≳10.5–11 , where the specific star formation rates and dust masses appear quite environment-insensitive. This result confirms the importance of in situ growth channels of SMBHs.

Robust point cloud registration for map-based autonomous robot navigation

Robust point cloud registration for map-based autonomous robot navigation

A Design Method for an SVM-Based Humidity Sensor for Grain Storage.

One of the crucial factors in grain storage is appropriate moisture content, which plays a significant role in reducing storage losses and ensuring quality. However, currently available humidity sensors on the market fail to meet the demands of modern large-scale grain storage in China in terms of price, size, and ease of implementation. Therefore, this study aims to develop an economical, efficient, and easily deployable grain humidity sensor suitable for large-scale grain storage environments. Simultaneously, it constructs humidity calibration models applicable to three major grain crops: millet, rice, and wheat. Starting with the probe structure, this study analyzes the ideal probe structure for grain humidity sensors. Experimental validations are conducted using millet, rice, and wheat as experimental subjects to verify the accuracy of the sensor and humidity calibration models. The experimental results indicate that the optimal length of the probe under ideal conditions is 0.67 m. Humidity calibration models for millet, rice, and wheat are constructed using SVM models, with all three models achieving a correlation coefficient R2 greater than 0.9. The measured data and model-calculated data show a linear relationship, closely approximating y = x, with R2 values of all three fitted models above 0.9. In conclusion, this study provides reliable sensor technological support for humidity monitoring in large-scale grain storage and processing, with extensive applications in grain storage and grain safety management.

Differentiated Security Requirements: An Exploration of Microservice Placement Algorithms in Internet of Vehicles

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate.