Coverage Of Space Research Articles

A bootstrap strategy to train, validate and test reduced order models of coupled geomechanical processes

This paper presents an approach for developing Reduced Order Models (ROMs) of complex geomechanical systems from high-fidelity numerical simulations. The approach uses proper orthogonal decomposition of the state variables for the model reduction, combined with long short-term memory neural networks to update the simulations in response to changing boundary conditions. A key challenge in creating these models is the need to efficiently sample the system state during training and testing. Too few (or poorly distributed) training simulations yields inaccurate models, while too many results in wasted effort. However, the exact number required is difficult to determine ahead of time, as it is both context and model dependent. Here, we present a bootstrap strategy to estimate the number of simulations required to develop the reduced order model. The strategy uses an iterative approach in conjunction with a quasi-random sampling technique to ensure consistent coverage of the sample space. We demonstrate the approach by developing a ROM describing the coupled hydro-mechanical response of a section of an open-cut coal mine. Excellent agreement is achieved between the resulting reduced order model and the original high-fidelity simulation for significantly less computational effort.

High-precision retrieval of offshore sea surface temperature: A machine learning framework based on MODIS and in-situ measurements

High-precision sea surface temperature (SST) data is essential for monitoring and management of offshore environment. SST retrievals from thermal infrared remote sensing have the advantages in time frequency and space coverage over measurements from ships and buoys. However, traditional SST retrieval models are not fully applicable for offshore waters where seawater conditions change a lot, due to simple functional forms and empirical fitting of model parameters. Machine learning (ML) is introduced because it doesn’t need to define specific model rules or unknown parameters, which is more intelligent to deal with complex retrieval problems in a data-driven way. But the applicability of ML has not been investigated in the North Yellow Sea of China, and the selection of driving features for model construction has not been fully discussed. Here, we designed a high-precision SST retrieval framework based on random forest (RF) with optimal feature combination using MODIS data and in-situ SST measurements during 2013–2020 in the northern part of the North Yellow Sea. The performances of different feature combinations were assessed based on model testing accuracies and the RF output feature importance. Compared with previous ML-based SST retrieval studies, we added two time-related features (i.e., day of the year and month) and one feature as initial temperature (i.e., SST retrieval from split-window algorithm), which reduced the testing error by around 15 % and 11 %, respectively. The retrieval results were validated with in-situ measurements, showing that the RF model achieved better accuracies (R2 = 0.987, SD = 0.842 °C, MAE = 0.675 °C, RMSE = 0.841 °C) than the improved split-window algorithm (named as ISW) and deep neural network. Compared with ISW, the spatial distribution of RF retrieval map exhibited similar variation across four seasons, mainly differed in the coastal areas or at the formation of temperature fronts. Note that the constructed RF model was still reliable when applied to the independent samples in 2021. This study contributes to improve the accuracy of offshore SST retrieval under different seawater conditions and provides references for SST monitoring.

Design of S-box multi-objective optimization algorithm based on combined chaotic system

S-box is the only nonlinear cryptographic component that determines the security of the block cipher. The higher the security of the S-box, the higher the security of the block cipher. Therefore, this paper proposes an S-box multi-objective optimization algorithm based on the combined chaotic system. Firstly, designing an integrated chaotic system based on a fractional index and its dynamic behavior is studied; it shows incredibly high-performance stability and chaotic parameter range coverage in the entire parameter space. A novel chaotic S-box construction algorithm is proposed based on the combined chaotic system. It introduces a linear congruential pseudo-random number generator to extend the sequence period and scramble the chaotic S-box through Henon mapping to improve the nonlinearity of the s-box. Finally, introducing a teaching and learning multi-objective optimization model and the performance evaluation criteria of the S-box are incorporated into the design of the objective function; this design enables the resulting S-box to perform well under multiple performance indicators, and then the approximate optimal S-box in the population is obtained. Through the performance test of the approximate optimal S-box, the comparative analysis shows that the S-box has good cryptographic performance, can resist linear analysis and differential analysis, and has a good application prospect in lightweight cipher.

A dataset of lake level changes in China between 2002 and 2023 using multi-altimeter data

ABSTRACT Lake water levels are an important indicator of water balance and water cycles, and are essential for climate and environmental change studies and water resource evaluation. Currently, lake level measurements are scarce or inconsistent throughout the country, and traditional gauge measurements of many lakes are not feasible, so satellite altimetry is a vital alternative to gauge lake levels. However, the accuracy and sampling frequency of lake level time series are usually low because of time and space coverage limitations; therefore, it is necessary to utilize multi-altimeter data to monitor lake levels and obtain lake level changes over long time series. In this study, we extracted the water level changes in 988 lakes (>10 km2) in China between 2002 and 2023 based on ICESat/-2, Cryosat-2, Jason-1/2/3, and Sentinel-3A/3B altimetry data using waveform retracking, lake level extraction, lake level time series construction, the fusion of multi-altimeter lake level time series, and outlier removal. A total of 55% of the lakes in this dataset have been monitored for more than 10 years, and 34% have more than 12 times the annual average water level monitoring. At the same time, in situ data from 21 lakes were used for validation, and the average root mean square error (RMSE) for each of the datasets of ICESat/-2, Cryosat-2, Jason-1/2/3, and Sentinel-3A/3B versus the in situ lake levels are 0.223 m, 0.163 m, 0.207 m, 0.596 m, 0.295 m, 0.275 m, 0.243 m, and 0.317 m, respectively, and the mean RMSE of the fused lake levels reaches 0.332 m. During the monitoring period, the water levels in Chinese lakes generally increased. The overall annual average rate of change at the 20 and 10-year scales was 0.123 m/a and 0.151 m/a, respectively, among which the overall water levels in large lakes increased significantly. The lakes with a faster rate of decline in the water level were primarily small. The water storage in each lake region in China shows an upward trend, with the most significant increase in the Tibetan Plateau region, where the average annual water level change rate has remained above 0.15 m/a over the past two decades. This dataset has high spatiotemporal coverage and accuracy and can support the estimation of changes in lake water storage, analysis of lake level trends, plateau flooding, and the relationship between lake ecosystems and water resources.

Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials.

Computational simulation methods based on machine learned potentials (MLPs) promise to revolutionise shape prediction of flexible molecules in solution, but their widespread adoption has been limited by the way in which training data is generated. Here, we present an approach which allows the key conformational degrees of freedom to be properly represented in reference molecular datasets. MLPs trained on these datasets using a global descriptor scheme are generalisable in conformational space, providing quantum chemical accuracy for all conformers. These MLPs are capable of propagating long, stable molecular dynamics trajectories, an attribute that has remained a challenge. We deploy the MLPs in obtaining converged conformational free energy surfaces for flexible molecules via well-tempered metadynamics simulations; this approach provides a hitherto inaccessible route to accurately computing the structural, dynamical and thermodynamical properties of a wide variety of flexible molecular systems. It is further demonstrated that MLPs must be trained on reference datasets with complete coverage of conformational space, including in barrier regions, to achieve stable molecular dynamics trajectories.

Meta-heuristic scheduling auction applying distributed genetic algorithm

Scheduling Auction is a VCG auction, combined with scheduling, utilized where the number of and the kind of auctioned goods have yet to be determined. In its winner determination problem, not only the winners of the goods but also the schedule for providing them should be optimally determined. Due to the NP-hard nature of this problem, it is tempted to apply meta-heuristics to the problem. However, this damages some desirable properties of the VCG mechanism. Thus, we propose the Meta-Heuristic Scheduling Auction (MHSA) to mitigate this issue by using the Distributed Genetic Algorithm. MHSA solves multiple winner determination problems called “islands” in parallel using Genetic Algorithm and correlates the coverage of the solution space in each island by having genes migrate among islands. This study confirmed that the migration operation leads to a better performance.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation.

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

Impacts of local soil and vehicle NOx emissions on ground-level NO2 concentrations on a university campus in the city of Shenyang, China.

Nitrogen dioxide (NO2) is a ubiquitous atmospheric pollutant, and fossil fuel combustion is generally considered its predominant source in and around urban areas. As the total nitrogen deposition is high over here, soil NOx emissions from urban green space might also be an important local source of ground-level NO2. In this study, Willems badge samplers were employed to monitor the spatial and seasonal variations of 2-week mean atmospheric NO2 concentrations at a height of 1.7 m on an urban campus in Northeast China from November 2020 to December 2021. We found considerable small-scale spatial variations of ground-level NO2 concentrations on the campus during the growing season, with local soil NOx emissions as the main driver. According to its linear correlation with green space coverage, the increment in ground-level NO2 concentration was partitioned into two components, with one ascribed to the local soil source (referred to as NO2-Isoil) and the other the local vehicle source (NO2-Ivehicle). NO2-Isoil generally reached a maximum (as high as 25.6 μg/m3) during early spring, while its ratio to the background value generally reached a maximum (could be >1) during late spring and could reach 0.52 to 0.92 during summer. Therefore, soil NOx emissions were an important source of ground-level NO2 on the campus, with the contribution even higher than those of other anthropogenic sources during late spring. Even with light traffic on the campus, NO2-Ivehicle could reach 0.48 times the background value at a site with high frequencies of warm starts.

MARS Plus: An Improved Molecular Design Tool for Complex Compounds Involving Ionic, Stereo, and Cis-Trans Isomeric Structures.

MARS (Molecular Assembling and Representation Suite) (Hsu et al. J. Chem. Inf. Model. 2019, 59, 3703-3713) is a toolbox for the molecular design of organic molecules. MARS uses integer arrays to represent the elements and connectivity between elements of a molecule. It provides a collection of operations to manipulate the elemental composition and connectivity of a molecule (or a pair of molecules), enabling the creation of novel chemical compounds. In this work, the original MARS is extended to handle complex molecular structures, including geometric (cis-trans) isomers, stereo isomers, cyclic compounds, and ionic species. The extended version of MARS, referred to as MARS+, has a more comprehensive coverage of the chemical space and therefore can explore molecules with a greater chemical and physical diversity. Compared to other molecular design tools, MARS+ is designed to perform all possible manipulations on a given molecule or a pair of molecules. Molecular structure manipulation can be conducted in either a controlled or a random fashion. Furthermore, every structure manipulation has a counterpart so that the operation can be reversed. Nearly any possible chemical structure can be generated with MARS+ via a combination of molecular operations. The capabilities of MARS+ are examined by the design of new ionic liquids (ILs). The results show that MARS+ is a useful tool for computer-aided molecular design (CAMD) and molecular structure enumeration.

Inverse-designed metasurfaces for highly saturated transmissive colors

Advances in augmented reality and virtual reality platforms have sparked interest in high-performance metasurface color filters with elevated resolution, saturation, and durability. However, the predominant use of either dielectrics or metals prevents the realization of efficient “transmissive” color filters for displays. Here, we propose a novel, to our knowledge, approach combining dielectric and metallic components, optimizing complex structures using inverse design with height restrictions on the layers of red, green, and blue. The optimized structure achieved full coverage of the sRGB color space and surpassed 70% efficiency. Experimental validation demonstrated the potential of the inverse design for enhancing the performance of complex structures.

Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

ABSTRACT Quantitative structure−activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020–2022) as a successor to the First Project (2014–2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

X-PSI Parameter Recovery for Temperature Map Configurations Inspired by PSR J0030+0451

In the last few years, the NICER collaboration has provided mass and radius inferences, via pulse profile modeling, for two pulsars: PSR J0030+0451 and PSR J0740+6620. Given the importance of these results for constraining the equation of state of dense nuclear matter, it is crucial to validate them and test their robustness. We therefore explore the reliability of these results and their sensitivity to analysis settings and random processes, including noise, focusing on the specific case of PSR J0030+0451. We use X-ray Pulse Simulation and Inference (X-PSI), one of the two main analysis pipelines currently employed by the NICER collaboration for mass and radius inferences. With synthetic data that mimic the PSR J0030+0451 NICER data set, we evaluate the recovery performances of X-PSI under conditions not previously tested, including complex modeling of the thermally emitting neutron star surface. For the test cases explored, our results suggest that X-PSI is capable of recovering the true mass and radius within reasonable credible intervals. This work also reveals the main vulnerabilities of the analysis: a significant dependence on noise and the presence of multimodal structure in the posterior surface. Noise particularly impacts our sensitivity to the analysis settings and widths of the posterior distributions. The multimodal structure in the posterior suggests that biases could be present if the analysis is unable to exhaustively explore the parameter space. Convergence testing, to ensure an adequate coverage of the parameter space and a suitable representation of the posterior distribution, is one possible solution to these challenges.

Latent go-explore with area as unit

The trade-off between exploration and exploitation has been one of the main challenges for ensuring sampling efficiency, optimal solution, and transferability of reinforcement learning. Based on the Go-Explore framework, which is currently the most effective framework for the environments with sparse reward, latent go-explore (LGE) can overcome the complexity of manually designing state features. However, its state feature space is not effective enough for measuring the sampling density, and the exploration mode with a single state as a unit is inefficient. To this end, this paper proposes the LGE with the state area as a unit, named ALGE, which can encode the real environment distance into the state feature space and realize the exploration mode with the state area as a unit to further improve exploring efficiency. The proposed ALGE is verified by a series of experiments in multiple hard-exploration environments including a continuous-maze environment, a robot environment and two Atari environments. The experiment results demonstrate that the ALGE can respectively improve the explored space coverage rate by 12 % and 27 % in the continuous-maze and robot environments and outperformance the state-of-the-art algorithms of go-explore framework in terms of pure exploration in these hard-exploration environments including Montezuma's Revenge and Pitfall.

COOPERATIVE SOURCE DETECTION USING AN OPTIMIZED DISTRIBUTED LEVY FLIGHT ALGORITHM

Source signal detection plays important roles in many real-world target searching problems. Source detection is necessary before a full search process utilizing the detected signal can be performed where minimizing the detection time and maximizing the search space exploration or coverage are the main problems. In this paper, an optimized Levy Flight algorithm known as a Distributed Levy Flight (DLF) for swarm agents is proposed. The DLF algorithm is optimized by means of repulsive artificial potential force to disperse the agents in order to optimize the search space coverage and detection time. Additionally, to integrate cooperative behavior, an artificial attractive force is used to maintain communication among the agents. The results showed that the proposed DLF algorithm successfully improve detection time (113.1s) and area coverage (78.3%) compared to the existing algorithms: Brownian Walk (325.5s, 31.7%), Correlated Random Walk (356.2s, 35.1%), Levy Flight (201.3s, 56.6%), Levy Flight with Artificial Potential Fields (151.9s, 70.2%).

Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data

The ability of deep learning (DL) approaches to learn generalised signal and noise models, coupled with their fast inference on GPUs, holds great promise for enhancing gravitational-wave (GW) searches in terms of speed, parameter space coverage, and search sensitivity. However, the opaque nature of DL models severely harms their reliability. In this work, we meticulously develop a DL model stage-wise and work towards improving its robustness and reliability. First, we address the problems in maintaining the purity of training data by deriving a new metric that better reflects the visual strength of the ‘chirp’ signal features in the data. Using a reduced, smooth representation obtained through a variational auto-encoder (VAE), we build a classifier to search for compact binary coalescence (CBC) signals. Our tests on real LIGO data show an impressive performance of the model. However, upon probing the robustness of the model through adversarial attacks, its simple failure modes were identified, underlining how such models can still be highly fragile. As a first step towards bringing robustness, we retrain the model in a novel framework involving a generative adversarial network (GAN). Over the course of training, the model learns to eliminate the primary modes of failure identified by the adversaries. Although absolute robustness is practically impossible to achieve, we demonstrate some fundamental improvements earned through such training, like sparseness and reduced degeneracy in the extracted features at different layers inside the model. We show that these gains are achieved at practically zero loss in terms of model performance on real LIGO data before and after GAN training. Through a direct search on days of LIGO data, we recover two significant CBC events from GWTC-2.1 (Abbott et al 2022 2108.01045 [gr-qc]), GW190519_153544 and GW190521_074359. We also report the search sensitivity obtained from an injection study.

Global near-earth space coverage by zones of the use of its observation devices: concept and algorithms

The results of the study are presented within the framework of the task of ensuring full coverage of a given area of heights above the Earth's surface (the area of space between two spheres with a common center at the center of the Earth) by instantaneous zones of possible application of orbital-based surveillance devices located on spacecraft in orbital groups of different heights in circular orbits. In the general case, the solution of the problem involves the use of several orbital groupings of different heights on circular quasi-polar orbits, which in the simplified statement of the problem are assumed to be polar. The instantaneous zone of possible application of the surveillance device is simplified in the form of a cone. The cases of using observation devices "up" (above the plane of the instantaneous local horizon of the spacecraft, which is the carrier of the observation device) and observations "down" (below this plane) are considered. The concept of solving the problem is proposed, which is based on the selection (based on the development of methods of applying known algorithms) of such a structure of each orbital grouping, which will ensure continuous coverage of a part of the given observation space (area of guaranteed observation), the boundaries of which are moved away from the location of observation devices, and then - filling the space with these areas. The work is devoted to the space theme, but by generalizing the statement of the prob-lem, varying a number of conditions of this statement and changing the "scale" of the input data, it is possible to arrive at a variety of technical problems where the proposed concept and algorithms used in its implementation will be appropriate and acceptable (in part or in full). In particular, when some surveillance systems or systems of complex application of technical operations devices are created.

Diffuse interface method for solid composite propellant ignition and regression

Solid Composite Propellants (SCPs) are extensively used in the field of propulsion for their chemical and mechanical stability in long-term storage, and for having simple production and operation processes. Computational modeling enables cost reduction, increased efficiency, and greater coverage of the configuration space in the SCP design process. However, accurate and efficient SCP modeling presents a number of numerical challenges. A primary obstacle in modeling these systems is capturing the complex evolving interface. Recently, it has been shown that the phase-field method has a strong ability to model the combustion behavior of SCPs, implicitly capturing the topological evolution at a relatively low computational cost. Initial phase-field methods show promise in their ability to do predictive regression modeling but require a number of approximations and heuristic modeling methods. This work presents a new formulation for the phase field regression model that combines a thermal solver with an Arrhenius rate law to model interface regression using a comprehensive and physics-based approach. This improves the capability of previous methods by increasing the number of kinematic forces that are accounted for, and allowing the study of thermal diffusivity in the system. It also enables the integration of a fully coupled solid-fluid interface. To demonstrate the efficacy of the model, it is applied and calibrated to a homogeneous monopropellant (ammonium perchlorate). The model is validated for a range of temperatures, with a reasonable quantitative match to experimental data. It was also demonstrated that the model recovered the relationship between ignition time and heat flux, with no fitting required. Overall, the method showed great capability of reproducing experimental data by matching temperature, burn rate, and thermal diffusivity profiles. Statement of SignificanceA new diffuse interface method is developed for calculating ignition and regression rates in SCPs. While this builds on previous work in diffuse interface modeling of SCPs, it is novel in its coupling to thermal transport, as well as its development of (and coupling to) a approximation for gas phase heat flux. The model is shown to reproduce experimental regression rate data with reasonable accuracy. Moreover, it quantitatively captures ignition behavior with no additional modifications to the model. The model is implemented in a high performance computational framework that is able to resolve large, three-dimensional mesostuctures.

Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology

IntroductionForest and grassland are the two main carbon-collecting terrestrial ecosystems, and detecting their solar-induced chlorophyll fluorescence (SIF) enables evaluation of their photosynthetic intensity and carbon-collecting capacity. Since SIF that is retrieved directly from satellite observations suffers from low spatial resolution, discontinuity, or low temporal resolution, some vegetation indexes (VIs) and meteorological factors are used as predictors to reconstruct SIF products. Yet, unlike VIs, certain meteorological factors feature a relatively low space resolution and their observations are not always accessible. This study aimed to explore the potential of reconstructing SIF from fewer predictors whose high-resolution observations are easily accessible.MethodsA total of six forest and grassland regions across low, mid, and high latitudes were selected, and the commonly used predictors-normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST)—were compared for their correlation with SIF. Results show that the combination of EVI and LST is more strongly correlated with SIF, but each contributed differently to SIF at differing growth stages of forest and grassland. Accordingly, we proposed the idea of a combined sampling approach that considers both location and phenological phase, to explore the extent to which time and space coverage samples' span could enlarge the disparity of EVI data in particular regions at specific growth stages. To do that, three kinds of sample combination methods were proposed: monthly regression at a global scale, seasonal regression at a regional scale, and monthly regression at a regional scale. Following this, Sentinel-3 EVI and MODIS LST data were used to reconstruct 500 m SIF in the six regions by implementing the proposed methodology.Results and discussionThese results showed that the R2 values were ≥0.90 between the reconstructed SIF and MODIS GPP (gross primary productivity), 0.70 with GOME-2 SIF and 0.77 with GOSIF, thus proving the proposed methodology could produce reliable results for reconstruction of 500 m SIF. This proposed approach, which bypasses dependence of traditional SIF reconstruction model on numerous predictors not easy to obtain, can serve as a better option for more efficient and accurate high-resolution SIF reconstructions in the future.

Degree of Standardisation in Ceramic Gingival Systems.

No gingival shade guide exists that can be used as a 'gold standard' in gingival shade selection. This research, therefore, aimed to determine whether comparable results in subjective gingival shade selection can be achieved using basic gingival colours produced by distinct manufacturers. It also aimed to explore how coverage of the colour space is affected by mixing these basic colours to create additional shades. To achieve these objectives, the basic gingival colours of three ceramic systems (Heraceram, Kulzer, Madrid, Spain; Vita VM9, Vita Zahnfabrik, Bad Säckingen, Germany; IPS Style, Ivoclar, Schaan, Liechtenstein) were analysed. The colour systems were expanded by creating porcelain gingival samples, whose colours were obtained by mixing the basic colours, altering each mixture by increments of 10%, and respecting the numerical order used by manufacturers to identify the colours. The colour coordinates of the basic and additional colours were recorded using spectrophotometry, and the intra- and inter-system colour differences were calculated using the Euclidean (ΔEab) and CIEDE2000 (ΔE00) formulae. None of the basic colours in the three systems, despite their similar nomenclature, were found to be interchangeable (the colour differences exceeded the gingival acceptability threshold: ΔE00 2.9 units). The expanded gingival colour systems, with mixtures altered by 10% increments, notably increased the gingival colour space covered by the original systems. The authors concluded that there are clear differences between the basic gingival colours produced by distinct manufacturers using the same nomenclature. Ceramic samples produced by mixing basic gingival colours are a resource with the potential to improve subjective gingival shade matching.

Implementation of a Transportable Radar Mode S of Monopulse Secondary Surveillance (MSSR-S) for the Peruvian Civil Aviation Surveillance

This article describes the process of implementing a transportable radar MSSR-S for Peruvian civil aviation (ACP) to minimize the operational impact in emergencies that affects air traffic without causing structural damage and restore data from the radar in a short time. In recent years, ACP has shown constant falls in the radars, causing radar data to be lost for long periods of time and putting air safety at risk due to the lack of maintenance and overlapping radar coverage of more than three radars. The deployment of the transportable radar in Mode S of Monopulse Secondary Surveillance (MSSR-S) has allowed for work that involves the prolonged stoppage of the radar to be carried out and provided coverage to eight more radars during maintenance and modernization, covering the areas without coverage in the Peruvian air space (EAP). For the implementation, this was divided into three SPRINTs using the SCRUM methodology; the first sprint refers to the equipment and radar coverage study, the second the implementation and service test phase, and the third the operational analysis phase with the eight modernized radars. As a result of the implementation and integration with the other ACP radar systems, they were able to operate together, providing highly reliable radar data, performing a continuous analysis of radar performance through the PASS software, complying with the thresholds established by ICAO and EuroControl, and guaranteeing that the systems operate under perfect conditions and with full coverage at all time.