Spurious Data Research Articles

Localization of AE sources in rocks improved by enhanced arrival time localization

Accurate localization of Acoustic Emission (AE) events in rock structures is crucial for investigating rock failure mechanisms. This paper presents a comprehensive analysis of 3D AE localization in basalt-type rock samples, utilizing a novel combination of three powerful techniques: the Simulated Annealing Algorithm (SAA), an innovative AIC-EEMD picking method, and a velocity-free approach to wave propagation. We employed a localized Hsu–Nielsen source to generate elastic waves, which were captured by six AE sensors strategically placed on the rock surface. To address challenges in identifying P-wave arrivals, particularly in noisy or spurious data, we developed an innovative technique that integrates the Akaike Information Criterion (AIC) with Empirical Ensemble Mode Decomposition (EEMD) to enhance signal clarity. The performance of our proposal was validated achieving an average absolute distance error of 3.74 mm. Compared to traditional methods, our technique demonstrates superior accuracy and offers enhanced robustness in the presence of noise.

Advanced welding automation: Intelligent systems for multipass welding in Butt Double V-Groove and Tee Double Bevel configurations

The paper addresses the imperative shift towards automation in welding processes, leveraging advanced technologies such as industrial robotic systems. Focusing on the reconstruction and classification of weld joints, it introduces a methodology for automatic trajectory determination. Utilizing a laser profilometer mounted on the robot, weld joints are reconstructed in three dimensions, and spurious data is filtered out through signal processing. A classification algorithm, integrating signal processing and artificial intelligence, accurately categorizes joint profiles, including V-joints and single bevel T-joints. The proposed intelligent and adaptive system enhances welding automation by analyzing point cloud data from laser scanning to optimize welding trajectories. This study establishes a foundational framework for further refinement and broader application in welding automation.Key Points•Introduction of a methodology for automated trajectory determination in welding processes.•Utilization of laser scanning and signal processing for reconstruction and classification of weld joints.•Implementation of an intelligent and adaptive system to optimize welding trajectories.

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations.

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals' accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6.

Understanding discordance in systematic reviews for treating chemotherapy-induced peripheral neuropathy: A focused umbrella review.

e24111 Background: Despite the abundance of publications on chemotherapy-induced peripheral neuropathy (CIPN), we are still lacking effective management options. CIPN-focused systematic reviews (SRs) are increasingly encountered but have not aided much in clarifying treatment guidance. Additionally, there is a rising concern that SRs are now over-saturating the clinical literature, leading to redundant and misleading publications. Umbrella reviews (URs) are an attempt to appraise SRs but do not yet have firm methodologic frameworks. The aims of our UR were to appraise the discordance in recently published CIPN SRs and use content analysis as a method for identifying risks of discordance. Methods: This is a focused UR as part of a larger study to characterize the number of efficacy trials for established CIPN. Following PRISMA guidelines, we conducted a systematic literature search of databases MEDLINE, Embase, and Cochrane Library for publications in English from 1/1/2023 to 11/21/2023. Inclusion criteria were published systematic reviews attempting a comprehensive effort to synthesize any treatment outcomes for established CIPN. The AMSTAR 2 framework was used to appraise results for bias. Measures of randomized controlled trial (RCT) overlap are presented using a novel measure of time-adjusted CA, which accounts for RCT publication dates and SR search dates. Grounded theory coding was used for content analysis on efficacy. Results: We identified 18 SRs containing CIPN treatment RCTs published in less than 1 year, resulting in 32 unique CIPN RCTs. Seven SRs assessed exercise interventions, covering 6 exercise RCTs (41% time-adjusted CA). Five SRs assessed acupuncture, with 9 RCTs (50% time-adjusted CA). One SR searched for all CIPN treatments for pain but missed 4 RCTs. One SR was concerning for spurious data. Bias was variable and imperfect across SRs. Contextual analysis identified the themes of SRs combining preventative and treatment RCTs, confusing comparative vs. controlled trials, and frequent use of p-value only reporting to conclude treatment efficacy as major risks of SR discordance. Conclusions: UR was useful in identifying 32 CIPN RCTs that were not comprehensively identified by SRs. We presented a time-adjusted CA measure to indicate concordance percentage. A higher percentage indicates more re-citation of the same RCTs across SRs, with likely better chances of more concordant conclusions. This measure showed that SRs for CIPN treatment are discordant across the topics of exercise and acupuncture interventions. Content analysis revealed multiple potential causes of discordance. However, SR redundancy was helpful for identifying more unique RCTs. CIPN SR content standards should be developed to minimize discordancy and improve clinical applicability. Clinicians should be cautious when applying CIPN SR recommendations given the high discordance.

Intercomparisons of Three Gauge-Based Precipitation Datasets over South America during the 1901–2015 Period

Gridded precipitation (PRP) data have been largely used in diagnostic studies on the climate variability in several time scales, as well as to validate model results. The three most used gauge-based PRP datasets are from the Global Precipitation Climatology Centre (GPCC), University of Delaware (UDEL), and Climate Research Unit (CRU). This paper evaluates the performance of these datasets in reproducing spatiotemporal PRP climatological features over the entire South America (SA) for the 1901–2015 period, aiming to identify the differences and similarities among the datasets as well as time intervals and areas with potential uncertainties involved with these datasets. Comparisons of the PRP annual means and variances between the 1901–2015 period and the non-overlapping 30-year subperiods of 1901–1930, 1931–1960, 1961–1990, and the 25-year subperiod of 1991–2015 for each dataset show varying means of the annual PRP over SA depending on the subperiod and dataset. Consistent patterns among datasets are found in most of southeastern SA and southeastern Brazil, where they evolved gradually from less to more rainy conditions from 1901–1930 to the 1991–2015 subperiod. All three datasets present limitations and uncertainties in regions with poor coverage of gauge stations, where the differences among datasets are more pronounced. In particular, the GPCC presents reduced PRP variability in an extensive area west of 50° W and north of 20° S during the 1901–1930 subperiod. In monthly time scale, PRP time series in two areas show differences among the datasets for periods before 1941, which are likely due to spurious or missing data: central Bolivia (CBO), and central Brazil (CBR). The GPCC has less monthly variability before 1940 than the other two datasets in these two areas, and UDEL presents reduced monthly variability before 1940 and spurious monthly values from May to September of the years from 1929 to 1941 in CBO. Thus, studies with these three datasets might lead to different results depending on the study domain and period of analysis, in particular for those including years before 1941. The results here might be relevant for future diagnostic and modelling studies on climate variability from interannual to multidecadal time scales.

Gender Identity in the 2021 Census of England and Wales: How a Flawed Question Created Spurious Data

The 2021 Census of England and Wales was one of the first in the world to ascertain the gender identity of an entire population. This article argues that its results are implausible with regard to geography, language, education, ethnicity, and religion. The results contradict data on referrals to gender clinics and signatures on a pro-transgender petition. The results are also internally inconsistent when the various categories of gender identity are correlated across localities, and when compared with sexual orientation. The spurious results were produced by a flawed question, which originated with a transgender campaigning organization. The question evidently confused a substantial number of respondents who erroneously declared their gender identity to differ from their natal sex. Confusion is manifested in the overrepresentation of people lacking English proficiency in the most suspect gender categories. These findings demonstrate how a faulty question can distort our apprehension of the social world.

Imaging Left-Lateral and Reverse Near-Surface Slip of the 2020 Mw 5.1 Sparta, North Carolina, Earthquake

ABSTRACT Coseismic deformation of the 9 August 2020 Mw 5.1 Sparta, North Carolina, earthquake was captured by an Advanced Land Observing Satellite 2 interferogram, a Sentinel 1A/B interferogram, and differential light detection and ranging (lidar). Owing to the moderate surface deformation compared to the inherent uncertainties of interferometric data, the interferograms suffered from low signal-to-noise ratios, exacerbated by spurious signals arising from the discrepancy in resolution between the interferograms and digital elevation model used during processing. Nevertheless, coseismic slip can be imaged using graph-guided fused LASSO, where LASSO is the least-absolute shrinkage and selection operator—an inverse method that combines sparsity constraints and smoothness regularization—to infer signal from noisy and/or incomplete data. We develop five coseismic slip models, one determined only from the Sentinel 1A/B interferogram, one determined from both interferograms, one determined from all the Interferometric Synthetic Aperture Radar (InSAR) data masking potentially spurious data corresponding to industrial parks, one from lidar 3D displacements only, and one constrained by the masked InSAR and the lidar data. All five models are coseismic slip on a single fault plane, trending northwest–southeast with dips ranging from 41° to 60° to the southwest. Coseismic slip is imaged to be shallow, with predominantly oblique reverse slip extending to about 2 km depth. We find that masking the interferometric data corresponding to the industrial parks yields a coseismic slip model in better concordance with ancillary information about the Sparta earthquake and that the lidar data yield additional constraints on near-surface coseismic slip. We show that our preferred coseismic slip model is consistent with a stress regime that does not depart from an Andersonian thrust regime to a large degree, constraining that the background stress is larger than 20–30 MPa.

Children in All Policies (CAP) 2030 Citizen Science for Climate Change Resilience: a cross-sectional pilot study engaging adolescents to study climate hazards, biodiversity and nutrition in rural Nepal.

Young people will suffer most from climate change yet are rarely engaged in dialogue about it. Citizen science offers a method for collecting policy-relevant data, whilst promoting awareness and capacity building. We tested the feasibility and acceptability of engaging Nepalese adolescents in climate change and health-related citizen science. We purposively selected 33 adolescents from two secondary schools in one remote and one relatively accessible district of Nepal. We contextualised existing apps and developed bespoke apps to survey climate hazards, waste and water management, local biodiversity, nutrition and sociodemographic information. We analysed and presented quantitative data using a descriptive analysis. We captured perceptions and learnings via focus group discussions and analysed qualitative data using thematic analysis. We shared findings with data collectors using tables, graphs, data dashboards and maps. Adolescents collected 1667 biodiversity observations, identified 72 climate-change related hazards, and mapped 644 geolocations. They recorded 286 weights, 248 heights and 340 dietary recalls. Adolescents enjoyed learning how to collect the data and interpret the findings and gained an appreciation of local biodiversity which engendered 'environmental stewardship'. Data highlighted the prevalence of failing crops and landslides, revealed both under- and over-nutrition and demonstrated that children consume more junk foods than adults. Adolescents learnt about the impacts of climate change and the importance of eating a diverse diet of locally grown foods. A lack of a pre-established sampling frame, multiple records of the same observation and spurious nutrition data entries by unsupervised adolescents limited data quality and utility. Lack of internet access severely impacted feasibility, especially of apps which provide online feedback. Citizen science was largely acceptable, educational and empowering for adolescents, although not always feasible without internet access. Future projects could improve data quality and integrate youth leadership training to enable climate-change advocacy with local leaders.

Disclosing the hidden nucleotide sequences: a journey into DNA barcoding of raptor species in public repositories.

In nucleotide public repositories, studies discovered data errors which resulted in incorrect species identification of several accipitrid raptors considered for conservation. Mislabeling, particularly in cases of cryptic species complexes and closely related species, which were identified based on morphological characteristics, was discovered. Prioritizing accurate species labeling, morphological taxonomy, and voucher documentation is crucial to rectify spurious data. Our study aimed to identify an effective DNA barcoding tool that accurately reflects the efficiency status of barcodes in raptor species (Accipitridae). Barcode sequences, including 889 sequences from the mitochondrial cytochrome c oxidase I (COI) gene and 1052 sequences from cytochrome b (Cytb), from 150 raptor species within the Accipitridae family were analyzed. The highest percentage of intraspecific nearest neighbors from the nearest neighbor test was 88.05% for COI and 95.00% for Cytb, suggesting that the Cytb gene is a more suitable marker for accurately identifying raptor species and can serve as a standard region for DNA barcoding. In both datasets, a positive barcoding gap representing the difference between inter-and intra-specific sequence divergences was observed. For COI and Cytb, the cut-off score sequence divergences for species identification were 4.00% and 3.00%, respectively. Greater accuracy was demonstrated for the Cytb gene, making it the preferred primary DNA barcoding marker for raptors.

Note on the start-up of Couette flow for viscoelastic fluids

This paper is concerned with the numerical modeling of viscoelastic fluids in non-steady shear motions. Time-dependent solutions for three-constant differential models are obtained at the start-up of the planar Couette flows. The influences of (i) the Reynolds number, (ii) the value of κ− material parameter (the ratio between the retardation time and relaxation time), and (iii) the initial condition for the normal stress on the velocity and stresses distributions in the gap are investigated using the numerical solutions obtained with Mathematica software. The focus of the study is the analysis of the Jaumann model (characterized by the corotational derivative) in transitory simple shear rheological tests, as a function of initial conditions for stresses. The steady solutions, corroborated with the non-monotonicity of the steady flow curve, confirm the kink presence in the steady velocity distributions and the formation of shear bandings at Re ≥ 1. The analyses of the strain- and stress-controlled simulations performed at different initial and boundary conditions offer possible explanations of some spurious data recorded in shear measurements of complex viscoelastic fluids. The findings have important consequences for performing transient shear experiments; specifically, it is demonstrated that reproducibility and correlations between the tests require the control of initial normal stresses in the sample.

Explainable conformance checking: Understanding patterns of anomalous behavior

Anomaly detection in the execution of business processes in the organizations has a high level of complexity due to the consideration of various process perspectives and their constraints, business rules, privacy policies, and regulations. Furthermore, only detection of anomalies is not sufficient. There is a crucial need for the results of anomaly detection to be explainable and interpretable, enabling users to adapt the decision making process and handle the detected anomalies. The work presented in this paper aims to provide business process owners with human-interpretable explanations for patterns of deviations found between process models and event data recorded during the execution of business processes, while traditional conformance checking methods only report low-level deviations found.First, by introducing an automated approach for multi-perspective conformance checking and anomaly detection in business process executions, we extract expected and unexpected behavior from event logs and identify patterns in deviations. Finally, by focusing on identifying the context involved in patterns of unexpected behavior, our approach facilitates the interpretations of detected patterns. The approach has been implemented in the open source ProM framework and its applicability is evaluated through a real-life dataset from a financial organization. The experiment not only shows that we can automatically detect more complex anomalies such as spurious data processing and misusage of authorizations, but also that we can explain these deviations in context.

Automated walk-over-weigh system to track daily body mass and growth in grazing steers

Body weight is a critical component for monitoring animal body mass, body condition, nutritional status, and health. However, traditional methods of collecting body weight are stressful, costly, and logistically impractical with cattle on extensive landscapes. Remote systems capable of collecting individual animal body weight provide a potential solution, but automatic systems may collect spurious weight measurements. The objectives of this study were to (1) describe the utility of using a remote weight capture system, (2) develop methods for identifying and removing spurious measurements, and (3) cross-validate automatically collected weights with static chute collected weights. Beef steers (n = 10) were tagged with electronic Radio Frequency Identification tags and grazed for 286 days in an improved pasture (±11.54 ha) containing Bermuda (Cynodon dactylon) and Tall Fescue (Festuca arundinacea) and inter-seeded with Annual Ryegrass (Secale cereale). A walk-over-weigh (WOW) system was designed from a platform scale connected to a TruTest® scale head and paired with a computer to save data files. In total, 5,466 raw weights associated with individuals were collected (0 kg < weight < 1,000 kg). We assessed the use of whole herd and individual means ± 1 standard deviation calculated either daily or over the entire trial to identify potential outliers. Additionally, 3 data smoothing methods were tested: a linear quadratic model, cubic splines, and polynomial regression. Each method was evaluated for total weight events recorded, event frequency, measured body mass and average daily gain. Further, accuracy and precision were calculated using cross-validation with paired chute weights (n = 8). Whole herd means ± 1 SD identified the fewest spurious data points and showed a stronger relationship with paired chute weights (R2 = 0.90) whereas individual daily means ± 1 SD identified the most and exhibited a weaker relationship (R2 = 0.68). Repeated quadratic regression models provided the best results, identifying 3,759 clean weight points and providing an R2 = 0.96 between paired chute and WOW measurements. A real-time simulation analysis found that both individual and the herd means ± 1 SD performed similarly with an average of 30% of data classified as spurious and removed from the analysis, resulting in agreement (R2 = 0.93 for both individual and herd means) between chute and WOW weight measurements. These results indicate the utility of using a simple WOW system to collect data for measuring individual animal body mass and associated metrics, which has implications on making management and marketing decisions in near real-time.

Synchronization of scanning probe and pixelated sensor for image-guided diffraction microscopy

A 4-dimensional modality of a scanning transmission electron microscope (4D-STEM) acquires diffraction images formed by a coherent and focused electron beam scanning the specimen. Newly developed ultrafast detectors offer a possibility to acquire high throughput diffraction patterns at each pixel of the scan, enabling rapid tilt series acquisition for 4D-STEM tomography. Here we present a solution to the problem of synchronizing the electron probe scan with the diffraction image acquisition, and demonstrate on a fast hybrid-pixel detector camera (ARINA, DECTRIS). Image-guided tracking and autofocus corrections are handled by the freely-available microscope-control software SerialEM, in conjunction with a high angle annular dark field (HAADF) image acquired simultaneously. The open source SavvyScan system offers a versatile set of scanning patterns, operated by commercially available multi-channel acquisition and signal generator computer cards (Spectrum Instrumentation GmbH). Images are recorded only within a sub-region of the total field, so as to avoid spurious data collection during flyback and/or acceleration periods in the scan. Hence, the trigger of the fast camera follows selected pulses from the scan generator clock gated according to the chosen scan pattern. Software and protocol are provided for gating the trigger pulses via a microcontroller (ST Microelectronics ARM Cortex). We demonstrate the system on a standard replica grating and by diffraction imaging of a ferritin specimen.

Radiogenic heat production estimation towards sustainable energy drive in northeastern Nigeria

An estimate of the radiogenic heat production (RHP) across the different petrologic units of northeastern, Nigeria was previously not performed. Hence, their geothermal potentials are not widely known. However, an airborne radiometric data of equivalent uranium, (eU), equivalent thorium (eTh,) and percentage potassium (% K) acquired by Nigerian geological survey agency (NGSA) in the year 2009 was deployed in the evaluation of the RHP across the major petrologic outcrops of northeastern, Nigeria. The objective of this study is to estimate the quantity of RHP across the 13 petrologic units of the northeastern Nigerian terrain via the use of an empirical equation (RHP=ρ(0.0952Cu+0.0256CTh+0.0348Ck)). The petrologic units studied are; medium-coarse grained biotite-hornblende granites (OGe), porphyritic biotite-hornblende granites (OGp), banded gneiss (bG), charnokytes (Ch), ignimbrites (JYG), migmatites-gneiss (MG), basalts (bb), Gombe sandstones (GS), Pindiga Formation (PS), Yolde Formation (YL), Bima sandstones (BS), Keri-Keri Formation (KK), and alluvium (AL). Basic/preliminary processing such as; signal integration, signal validation, and examination of spurious data were applied prior to the RHP computation. The results of the heat production analysis performed show the range of RHP to be from 1.11μW/m3 to 3.35μW/m3 Hence, the maximum heat production value of 3.35μW/m3 was recorded along porphyritic biotite-hornblende granites (OGp) rock block, while the least value of 1.11μW/m3 was recorded over alluvium (AL) rock outcrops. Furthermore, the spatial distribution of the RHP values over the study location shows a gradual increase from the middle, low heat production (sedimentary zones) to the high heat producing areas (granitic and metamorphic zones) around eastern and western parts. The petrologic units arranged in order of decreasing magnitude of radiogenic heat generation are; OGp > MG > OGe/bG > bb > GS > Ch > JYG > BS > PS/YL > KK > AL. On a general note, the petrologic units studied were classified as low in terms of geothermal character based on comparison with other previous global RHP studies.

Trusted and privacy-preserving sensor data onloading

To personalize their services (e.g., advertisement, navigation, healthcare), mobile apps collect sensor data. Typically, they upload the collected sensor data to the cloud, which returns the inferred user profiles required to personalize mobile services. However, privacy concerns and network connectivity/congestion issues can render cloud-based processing inapplicable. If different apps collect the same type of sensor data, app providers can collaborate by combining their data collections to infer on-device the user profiles required for personalization. Although major mobile platforms provide on-device data sharing mechanisms, these direct data exchanges provide no privacy protection. As an alternative to direct data sharing, we present differentially privatized sensor data onloading for app providers’ collaboration. With our approach, app providers can safely collaborate by using shared sensor data to personalize their mobile services. We realize our approach as a middleware that acts as a trusted intermediary. The middleware aggregates the sensor data contributed by individual apps, which execute statistical queries against the combined datasets. Furthermore, the middleware’s adaptive privacy-preserving scheme (1) computes and adds the required amount of noise to the query results so as to balance utility and privacy; (2) introduces a Trust-Data Theory so as to detect and remove spurious data from the combined collections; (3) rewards active contributing app providers so as to incentivize data contribution; (4) integrates a Trusted Execution Environment (TEE) so as to secure all data processing. Our evaluation shows that it is feasible and useful to personalize mobile services while protecting data privacy: queries’ execution time is within 10 ms; participants’ dissimilar privacy/utility requirements are satisfied; untrustworthy data are effectively detected; mobile services are personalized, and data privacy of both app providers and users are preserved.11This article is a revised and extended version of our prior paper, published in the 12th EAI International Conference on Mobile Computing, Applications and Services (MobiCASE 2021) (Liu et al., 2022).

Critical Data Visualization to Enhance Protection Schemes for State Estimation

Intelligent electrical power grids, widely referred to as smart grids (SGs), rely on digital technology resources, especially communication and measurement devices, becoming a cyber-physical energy system. Massive data flow between grid elements makes smart grids more vulnerable to cyber-attacks. Power system state estimation (SE)—an essential function of energy management systems—is one of these data integrity attacks’ targets. Integrity validation routines can fail when insufficient redundancy levels are reached, and spurious data occur. These levels are associated with critical data, i.e., those whose unavailability makes the grid unobservable. Data redundancy is a metric that gives a precarious indication that SE can run. Alternatively, it is more appropriate to quantify this function strength concerning its results’ reliability, which can be achieved by criticality analysis (CA). This paper proposes a novel approach to visualize the results of an extensive CA through representative graphs; they facilitate understanding the usefulness of CA. Critical sets of measurements are generalized, and several metrics are proposed to reveal measuring system vulnerabilities, assisting the design of protection schemes to resist cyber-attacks. Simulations attained on the IEEE-30bus system evince significant improvements in the interpretation/use of CA.

A Distributionally Robust Fusion Framework for Autonomous Multisensor Spacecraft Navigation during Entry Phase of Mars Entry, Descent, and Landing

A robust multisensor navigation filter design for the entry phase of next-generation Mars entry, descent, and landing (EDL) is presented. The entry phase is the longest and most uncertain portion of a Mars landing sequence. Navigation performance at this stage determines landing precision at the end of the powered descent phase of EDL. In the present work, measurements from a ground-based radio beacon array, an inertial measurement unit (IMU), as well as an array of atmospheric and aerothermal sensors on the body of a Mars entry vehicle are fused using an M-estimation-based iterated extended Kalman filtering (MIEKF) framework. The multisensor approach enables an increased positioning accuracy as well as the estimation of parameters that are otherwise unobservable. Furthermore, owing to the proposed statistically robust filter formulation, states and parameters can be accurately estimated in the presence of non-Gaussian measurement noise. Deviations from normally distributed observation noise correspond to outlier events such as sensor faults or other sources of spurious sensor data such as interference. The proposed framework provides a significant reduction in estimation error at the parachute phase of EDL, thereby increasing the likelihood of a pinpoint landing at a chosen landing site. Six states and three parameters are estimated. The suggested method is compared to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). Detailed simulation results show that the presented fusion architecture is able to meet future pinpoint planetary landing requirements in realistic sensor measurement scenarios.

Applying neural networks-based modelling to the prediction of mineralization: A case-study using the Western Australian Geochemistry (WACHEM) database

Geochemical data collected and stored by government and private sectors are becoming increasingly complex and too large for manual interpretation. The use of automatic methods for identification of potentially spurious data and estimation of missing values in very sparse geochemical datasets can significantly improve our understanding of geological systems. Deep neural networks have recently achieved remarkable success in a wide range of applied problems. These methods do not require manual feature engineering and significantly outperform traditional machine learning algorithms when applied to large datasets. We present a deep learning based method for estimation of unknown sample analytes in geochemical data. This approach is entirely data-driven and, once the network is trained, delivers the results in real time by predicting the distribution of an unknown analyte in a single step. A case study on the Western Australian Geochemistry (WACHEM) database demonstrates the efficiency of the method. Base metals as well as many other metals show good predictive capability (average symmetric mean absolute percentage errors range from 20% to 26.1%). Silver, platinum and especially gold are found to be more difficult to predict. The estimation of rock types from geochemical data allows for validation of existing datasets and prediction of rock types directly from geochemical data when there is no other existing information. The results of this study can benefit mineral explorers by indicating exploration targets and highlighting gaps in existing data.

Climatology and process-oriented analysis of the Adriatic sea level extremes

The strongest episodes of extremely high sea levels in the Mediterranean are regularly observed in the Adriatic Sea, where they can cause substantial damage and loss of human lives. In this study, episodes of positive and negative sea level extremes were extracted from hourly series measured at six tide gauge stations located along the Adriatic coast (Venice, Trieste, Rovinj, Bakar, Split, Dubrovnik) between 1956 and 2019/2020. The time series were first checked for spurious data and then decomposed using tidal analysis, least-squares fitting and filtering procedures into (1) trend; (2) seasonal; (3) tide, (4) longer than 100 d (> 100 d), (5) 10–100 d, (6) 6 h-10 d, and (7) < 6 h components. These components correspond to sea level oscillations dominantly (but not exclusively) forced by (1) climate and isostatic change; (2) seasonal changes in thermohaline properties and circulation patterns, (3) tidal forcing, (4) quasi-stationary atmospheric and ocean circulation and climate variability patterns, (5) planetary atmospheric waves, (6) synoptic, and (7) mesoscale atmospheric processes.Significant differences exist between (1) the northern and middle/southern Adriatic extremes and (2) positive and negative extremes. The heights and return levels of positive (negative) extremes are 50–100% higher (lower) in the northern than in the middle/southern Adriatic. The northern Adriatic positive sea level extremes dominantly occur due to the superposition of the 6 h-10 d component and tide (contributing jointly to ∼70% of the total extreme height), whereas the middle/southern Adriatic positive extremes mostly occur due to the superposition of the 10–100 d component, 6 h-10 d component, and tide (each contributing ∼25% on average). The negative sea level extremes are explained as a combination of the 10–100 d component and tide: in the northern Adriatic tide provides the largest contribution (∼60%), while in the middle/southern Adriatic, the impacts of the two processes are similar (each contributing an average of ∼30%). Over the entire Adriatic, the < 6 h and seasonal components contribute the least to both positive and negative extremes. Sea level trends at all stations are positive; however, the observed sea level rise did not contribute significantly to the total height of extremes. Extreme episodes tend to occur simultaneously over larger parts of the coast and are often clustered within a few days. Both positive and negative extremes have a strong decadal variability, whereas trends of their number, duration and intensity point to shortening of negative extremes and prolonging and strengthening of positive extremes.

Time-varying Metamaterial-enabled Directional Modulation Schemes for Physical Layer Security in Wireless Communication Links

Novel transmission schemes, enabled by recent advances in the fields of metamaterial (MTM), leaky-wave antenna (LWA) and directional modulation (DM), are proposed for enhancing the physical layer (PHY) security. MTM-LWAs, which offer compact, integrated, and cost-effective alternatives to the classic phased-array architectures, are particularly of interest for emerging wireless communication systems including Internet-of-Things. The proposed secure schemes are devised to accomplish the functionalities of directional modulation (DM) transmitters for orthogonal frequency-division multiplexing (OFDM) and non-contiguous OFDM transmissions, while enjoying the implementation benefits of MTM-LWAs. Specifically, transmitter architectures based on the idea of time-modulated MTM-LWA have been put forth as a promising solution for PHY security for the first time. The PHY security for the proposed schemes are investigated from the point of view of both passive and active attacks where an adversary aims to decode secret information and feed spurious data to the legitimate receiver, respectively. Numerical simulations reveal that even when the adversary employs sophisticated state-of-the-art deep learning based attacks, the proposed transmission schemes are resistant to these attacks and reliably guarantee system security.