Advanced Schemes Research Articles

A new approach to crystal habit retrieval from far-infrared spectral radiance measurements

Abstract. To generate reliable climate predictions, global models need accurate estimates of all the energy fluxes contributing to the Earth's radiation budget (ERB). Clouds in general, and more specifically ice clouds, play a key role in the determination of the ERB as they may exert either a feedback or a forcing action, depending on their optical and microphysical properties and physical state (solid/liquid). To date, accurate statistics and climatologies of cloud parameters are not available. Specifically, the ice cloud composition in terms of ice crystal shape (or habit) is one of the parameters with the largest uncertainty. The Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) experiment, foreseen to be the ninth Earth Explorer mission of the European Space Agency, will measure, for the first time spectrally resolved from space, the entire upwelling spectrum emitted by the Earth from 100 to 1600 cm−1. The far-infrared portion of the Earth spectrum, especially from 200 to 600 cm−1, is very sensitive to cloud ice crystal shapes; thus, FORUM measurements could also represent an opportunity to study the ice cloud composition in terms of ice crystal habit mixtures. To investigate this possibility, we developed an accurate and advanced scheme allowing us to model ice cloud optical properties – also in cases of clouds composed of mixed ice crystal habits. This feature is in fact necessary because in situ measurements acquired over the years also point out that the shape of ice cloud crystals varies depending on the crystal size range. In our model, the resulting cloud optical properties are also determined by the input habit fractions. Thus, the retrieval of these fractions from spectral radiance measurements can be attempted. Using 375 different cloudy scenarios, we assess the performance of our retrieval scheme in the determination of crystal habit mixtures starting from FORUM-simulated measurements. The most relevant error components affecting the retrieved cloud parameters are not very large and are of random nature; thus, FORUM measurements will allow us to set up an accurate climatology of cloud parameters. To provide an example of the benefit that one could get from the habit mixture retrievals, we also show the improved accuracy of the thermal outgoing fluxes calculations compared to using assumed mixtures.

Using the Buckingham π Theorem for Multi-System Transfer Learning: A Case-Study with 3 Vehicles Sharing a Database

Many advanced driver assistance schemes or autonomous vehicle controllers are based on a motion model of the vehicle behavior, i.e., a function predicting how the vehicle will react to a given control input. Data-driven models, based on experimental or simulated data, are very useful, especially for vehicles difficult to model analytically, for instance, ground vehicles for which the ground-tire interaction is hard to model from first principles. However, learning schemes are limited by the difficulty of collecting large amounts of experimental data or having to rely on high-fidelity simulations. This paper explores the potential of an approach that uses dimensionless numbers based on Buckingham’s π theorem to improve the efficiency of data for learning models, with the goal of facilitating knowledge sharing between similar systems. A case study using car-like vehicles compares traditional and dimensionless models on simulated and experimental data to validate the benefits of the new dimensionless learning approach. Preliminary results from the case study presented show that this new dimensionless approach could accelerate the learning rate and improve the accuracy of the model prediction when transferring the learned model between various similar vehicles. Prediction accuracy improvements with the dimensionless scheme when using a shared database, that is, predicting the motion of a vehicle based on data from various different vehicles was found to be 480% more accurate for predicting a simple no-slip maneuver based on simulated data and 11% more accurate to predict a highly dynamic braking maneuver based on experimental data. A modified physics-informed learning scheme with hand-crafted dimensionless features was also shown to increase the improvement to precision gains of 917% and 28% respectively. A comparative study also shows that using Buckingham’s π theorem is a much more effective preprocessing step for this task than principal component analysis (PCA) or simply normalizing the data. These results show that the use of dimensionless variables is a promising tool to help in the task of learning a more generalizable motion model for vehicles, and hence potentially taking advantage of the data generated by fleets of vehicles on the road even though they are not identical.

Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression.

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

An improved lightweight and privacy preserving authentication scheme for smart grid communication

Smart grid is an efficient and reliable technical framework for controlling computers and automation equipment, and how to ensure the communication security in smart grid is an important issue. Cryptographic authentication scheme is a feasible solution, and the existing authentication schemes for smart grid seek to ensure better performance. Some existing authentication schemes lack comprehensive security considerations and have security or privacy vulnerabilities, which makes them vulnerable to specific attacks. The paper reviews a recent scheme ISG-SLAS (Yu and Park, 2022) and analyzes its potential insecure aspects in detail, including unable to resist ESL attack, cannot provide un-traceability and etc. To this end, the paper designs an improved authentication scheme for smart grid based on symmetric cryptography. Through informal security analyses and formal security analyses with real-or-random (ROR) model and Scyther platform, the security of the proposed scheme is demonstrated. From the perspective of performance, the proposed scheme is compared with ten advanced authentication schemes for smart grid, and the results show that the proposed scheme excels other recent schemes in computational overhead, communication overhead and storage overhead, reduced by 10.1%, 30.8% and 36.1% of ISG-SLAS and 58.9%, 64.1% and 24.3% of the average value of all alternatives respectively.

Lumbar and Thoracic Vertebrae Segmentation in CT Scans Using a 3D Multi-Object Localization and Segmentation CNN.

Radiation treatment of cancers like prostate or cervix cancer requires considering nearby bone structures like vertebrae. In this work, we present and validate a novel automated method for the 3D segmentation of individual lumbar and thoracic vertebra in computed tomography (CT) scans. It is based on a single, low-complexity convolutional neural network (CNN) architecture which works well even if little application-specific training data are available. It is based on volume patch-based processing, enabling the handling of arbitrary scan sizes. For each patch, it performs segmentation and an estimation of up to three vertebrae center locations in one step, which enables utilizing an advanced post-processing scheme to achieve high segmentation accuracy, as required for clinical use. Overall, 1763 vertebrae were used for the performance assessment. On 26 CT scans acquired for standard radiation treatment planning, a Dice coefficient of 0.921 ± 0.047 (mean ± standard deviation) and a signed distance error of 0.271 ± 0.748 mm was achieved. On the large-sized publicly available VerSe2020 data set with 129 CT scans depicting lumbar and thoracic vertebrae, the overall Dice coefficient was 0.940 ± 0.065 and the signed distance error was 0.109 ± 0.301 mm. A comparison to other methods that have been validated on VerSe data showed that our approach achieved a better overall segmentation performance.

SPONGE+: A NoC-based power gating scheme for overall router column opening and closing control

Power gating, a general-purpose power saving technique, can be used to turn off inactive routers in Networks-on-Chip (NoCs) to reduce static power consumption. SPONGE is an advanced power gating scheme. When the router is dormant, SPONGE maintains the transmission of straight packets, which occupy a large percentage of the network, through a direct bypass design. At the same time, the energized router column’s can ensure the normal transmission of turning packets. However, it suffers from increased latency due to excessive detours, aggravated load imbalance, and violation of the breakeven time (BET) constraint. To address the above problems, we propose an improved energy-efficient router architecture SPONGE+. First, we design a new router bypass architecture that enables powered-off routers to have the ability to transmit straight ahead and turn in the X-to-Y direction to reduce the bypass cost of packet transmission. Secondly, we propose a congestion-aware turning routing algorithm to balance the load among powered-on routers and improve network resource utilization. Finally, we pin improves the power gating mechanism for router columns to satisfy the BET constraint and further reduce the static power expenses. Experimental results indicate that, under real application scenarios, our scheme achieves a 27.3% reduction in static power consumption and a 10.5% decrease in packet latency compared to SPONGE, with commendable scalability.

Domain Transformation of Distortion Costs for Efficient JPEG Steganography with Symmetric Embedding

Nowadays, most image steganographic schemes embed secret messages by minimizing a well-designed distortion cost function for the corresponding domain, i.e., the spatial domain for spatial image steganography or the JPEG (Joint Photographic Experts Group) domain for JPEG image steganography. In this paper, we break the boundary between these two types of schemes by establishing a theoretical link between the distortion costs in the spatial domain and those in the JPEG domain and thus propose a scheme for domain transformations of distortion costs for efficient JPEG steganography with symmetric embedding, which can directly convert the spatial distortion cost into its JPEG counterpart. Specifically, by formulating the distortion cost function for JPEG images in the decompressed spatial domain, a closed-form expression for a distortion cost cross-domain transformation is derived theoretically, which precisely characterizes the conversion from the distortion costs obtained by existing spatial steganographic schemes to those applied in JPEG steganography. Experimental results demonstrate that the proposed method outperforms other advanced JPEG steganographic schemes, e.g., JUNIWARD (JPEG steganography with Universal Wavelet Relative Distortion), JMiPOD (JPEG steganography by Minimizing the Power of the Optimal Detector), and DCDT (Distortion Cost Domain Transformation), in resisting the detection of various advanced steganalyzers.

Investigating wave solutions and impact of nonlinearity: Comprehensive study of the KP-BBM model with bifurcation analysis.

In this paper, we investigate the (2+1)-dimensional Kadomtsev-Petviashvili-Benjamin-Bona Mahony equation using two effective methods: the unified scheme and the advanced auxiliary equation scheme, aiming to derive precise wave solutions. These solutions are expressed as combinations of trigonometric, rational, hyperbolic, and exponential functions. Visual representations, including three-dimensional (3D) and two-dimensional (2D) combined charts, are provided for some of these solutions. The influence of the nonlinear parameter p on the wave type is thoroughly examined through diverse figures, illustrating the profound impact of nonlinearity. Additionally, we briefly investigate the Hamiltonian function and the stability of the model using a planar dynamical system approach. This involves examining trajectories, isoclines, and nullclines to illustrate stable solution paths for the wave variables. Numerical results demonstrate that these methods are reliable, straightforward, and potent tools for analyzing various nonlinear evolution equations found in physics, applied mathematics, and engineering.

Subgrade soil response to rail loading: Instability mechanisms, causative factors, and preventive measures

The demand for more efficient heavy-haul rail networks over soft subgrades poses significant geotechnical challenges and requires a comprehensive understanding of stress conditions as well as the failure potential of subgrade soil under moving wheel loads and increasing rail speeds. Unfavourable stress conditions in the subgrade can result in various types of failures, three of which are identified in this article: (i) excessive plastic settlement, (ii) progressive shear failure, and (iii) subgrade fluidisation (mud pumping). Through a series of advanced testing schemes using cyclic triaxial, hollow cylinder, and an in-house dynamic filtration apparatus, critical stress conditions and soil characteristics prone to subgrade instability are discussed. The results demonstrate that under adverse combinations of loading frequency (f) and cyclic stress ratio (CSR) the continuous application of cyclic loads can lead to an unstable state of soil where excess pore pressure and axial strain increase rapidly. This study also reveals that low to medium plasticity soils (PI < 22) are more vulnerable to subgrade fluidisation, where the rapid internal migration of pore water transforms the upper soil to a fluid-like state with substantial loss in soil stiffness. The layered response of soil through dynamic filtration tests showed larger hydrodynamic forces induced by differential hydraulic gradients in the top layer during cyclic loading causes moisture to move upwards. Various factors that can influence soil instability such as the degree of compaction degree, clay content, soil fabric and stress rotation are also addressed in this paper. Finally, novel solutions for stabilising subgrade such as a vertical drain-composite system and the use of eco-friendly biopolymers are presented.

An Advanced Scheme for Radar Clutter Suppression Scheme Based on Blind Source Separation

In cluttered electromagnetic environments, radar is often disturbed by varied clutter, making target detection challenging. Therefore, achieving effective clutter suppression is crucial for radar target detection. However, traditional clutter suppression methods face three key challenges: (1) significant degradation in target signal detection performance when the clutter’s Doppler spectrum completely masks the target signal; (2) heavy reliance on prior knowledge for optimal performance; and (3) inherent signal energy loss during clutter suppression. To address these challenges, we propose a clutter suppression scheme based on blind source separation (BSS). Initially, the scheme utilizes parallel principal skewness analysis (PPSA) to process the echo signals in the range domain, which helps in identifying the position of moving targets. Subsequently, PPSA is applied once more to process the moving targets in the Doppler domain, allowing for the precise determination of their relative velocities. Subsequently, we evaluate the scheme’s performance with simulated and real data, comparing it with traditional clutter suppression methods and other BSS techniques. The results confirm the effectiveness of the scheme in clutter suppression.

Control of chlorination disinfection by-products in drinking water by combined nanofiltration process: A case study with trihalomethanes and haloacetic acids

Disinfection by-products (DBPs) are prevalent contaminants in drinking water and are primarily linked to issues regarding water quality. These contaminants have been associated with various adverse health effects. Among different treatment processes, nanofiltration (NF) has demonstrated superior performance in effectively reducing the levels of DBPs compared to conventional processes and ozone-biological activated carbon (O3-BAC) processes. In this experiment, we systematically investigated the performance of three advanced membrane filtration treatment schemes, namely "sand filter + nanofiltration" (SF + NF), "sand filter + ozone-biological activated carbon + nanofiltration" (SF + O3-BAC + NF), and "ultrafiltration + nanofiltration" (UF + NF), in terms of their ability to control disinfection by-product (DBP) formation in treated water, analyzed the source and fate of DBP precursors during chlorination, and elucidated the role of precursor molecular weight distribution during membrane filtration in relation to DBP formation potential (DBPFP). The results indicated that each treatment process reduced DBPFP, as measured by trihalomethane formation potential (THMFP) and haloacetic acid formation potential (HAAFP), with the SF + O3-BAC + NF process being the most effective (14.27 μg/L and 14.88 μg/L), followed by the SF + NF process (21.04 μg/L and 16.29 μg/L) and the UF + NF process (26.26 μg/L and 21.75 μg/L). Tyrosine, tryptophan, and soluble microbial products were identified as the major DBP precursors during chlorination, with their fluorescence intensity decreasing gradually as water treatment progressed. Additionally, while large molecular weight organics (60–100,000 KDa) played a minor role in DBPFP, small molecular weight organics (0.2–5 KDa) were highlighted as key contributors to DBPFP, and medium molecular weight organics (5–60 KDa) could adhere to the membrane surface and reduce DBPFP. Based on these findings, the combined NF process can be reasonably selected for controlling DBP formation, with potential long-term benefits for human health.

Control Parameters Design of Spraying Robots Based on Dynamic Feedforward

The positioning and velocity accuracy of spraying robots determine the quality of the coating, and the influence of the robotic dynamic characteristics on control precision is significant. This paper presents a method of linearizing dynamic characteristics into feedforward coefficients and designs a dual-loop control system consisting of an inner velocity loop and an outer position loop. The system is divided into three sections: a cascaded section, a feedback section, and a feedforward section. The cascaded section eliminates the nonlinear characteristics of the system; the feedback section ensures the stability of the system; the feedforward section compensates for the internal errors of the system. The main innovation of this paper lies in proposing an offline parameter tuning method, which avoids online parameter adjustments and significantly enhances the real-time performance of the control system. Additionally, this method does not require specific physical information of the system, thus avoiding the cumbersome process of parameter adjustment. The experimental results demonstrate that when facing different high-speed trajectories, the proposed control system exhibits a significant improvement in control accuracy compared to other advanced control schemes.

Antithetic multilevel particle filters

Abstract In this paper we consider the filtering of partially observed multidimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate-dimensional problems is the multilevel particle filter of Jasra et al. (SIAM J. Numer. Anal.55 (2017), 3068–3096). This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of Giles and Szpruch (Ann. Appl. Prob.24 (2014), 1585–1620). We show empirically for a class of diffusion problems that, for $\epsilon&gt;0$ given, the cost to produce a mean squared error (MSE) of $\mathcal{O}(\epsilon^2)$ in the estimation of the filter is $\mathcal{O}(\epsilon^{-2}\log(\epsilon)^2)$ . In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of Jasra et al. (2017) requires a cost of $\mathcal{O}(\epsilon^{-2.5})$ to achieve the same MSE.

A powered descent trajectory planning method with quantitative consideration of safe distance to obstacle

High scientific value areas on celestial bodies such as the Moon and Mars are often located in hazardous terrains. To achieve safe landing exploration, a novel planning method is proposed, which can ensure that the planned trajectory maintains a user-specified distance from obstacles, thus reducing potential collision risk induced by factors such as the body size and model uncertainties. Firstly, the basic model of the trajectory optimization problem and its convexification version is given. The obstacles are modeled as polynomial functions, based on which the “if-then” obstacle avoidance logic explicitly considering the safe distance, is described as a sum of squares constraint. This constraint formulation applies to any obstacle described by a finite number of polynomials, independent of the specific expression of the polynomials (reflecting the shape of the obstacle). Subsequently, the convexification process for the obstacle avoidance constraint is given. Finally, the sequential sum of squares programming problem for the obstacle avoidance trajectory is established, which boils down to a series of semidefinite programming problems. Simulation results show that the closest distance between the planned trajectory and obstacles strictly satisfies the specified distance constraint, and the trajectory could avoid non-convex obstacles. With the promising convergence properties of underlying convex optimization algorithms, advanced autonomous obstacle avoidance guidance schemes are expected to be formed based on the proposed trajectory planning method.

Procedures from samples to sulfur isotopic data: A review.

Sulfur isotopes have been widely used to solve some key scientific questions, especially in the last two decades with advanced instruments and analytical schemes. Different sulfur speciation and multiple isotopes analyzed in laboratories worldwide and in situ microanalysis have also been reported in many articles. However, methods of sampling to measurements are multifarious, and occasionally some inaccuracies are present in published papers. Vague methods may mislead newcomers to the field, puzzle readers, or lead to incorrect data-based correlations. We have reviewed multiple methods on sulfur isotopic analyses from the perspectives of sampling, laboratory work, and instrumental analysis in order to help reduce operational inhomogeneity and ensure the fidelity of sulfur isotopic data. We do not deem our proposed solutions as the ultimate standard methods but as a lead-in to the overall introduction and summary of the current methods used. It has been shown that external contamination and transformation of different sulfur species should be avoided during the sampling, pretreatment, storage, and chemical treatment processes. Conversion rates and sulfur isotopic fractionations during sulfur extraction, purification, and conversion processes must be verified by researchers using standard or known samples. The unification of absence of isotopic fractionation is needed during all steps, and long-term monitoring of standard samples is recommended. This review compiles more details on different methods in sampling, laboratory operation, and measurement of sulfur isotopes, which is beneficial for researchers' better practice in laboratories. Microanalyses and molecular studies are the frontier techniques that compare the bulk sample with the elemental analysis/continuous flow-gas source stable isotope ratio mass spectrometry method, but the latter is widely used. The development of sulfur isotopic measurements will lead to the innovation in scientific issues with sulfur proxies.

A new approach for drought index adjustment to clay-shrinkage-induced subsidence over France: advantages of the interactive leaf area index

Abstract. Clay shrinkage, which consists of a reduction in the volume of clay soils during dry periods, can affect buildings and cause subsidence damage. In France, losses due to subsidence are estimated at more than EUR 16 billion for the period 1989–2021 (CCR, 2021) and are expected to increase under the effect of climate warming. This work aims to improve the current understanding of the conditions triggering subsidence by proposing an innovative drought index. We use a daily soil wetness index (SWI) to develop a new annual drought index that can be related to subsidence damage. The SWI is derived from simulations of soil moisture profiles from the interactions between soil–biosphere–atmosphere (ISBA) land surface model developed by Météo-France. The ability of the drought index to correlate with insurance claim data is assessed by calculating the Kendall rank correlation over 20 municipalities in France. The insurance data, aggregated by year and municipality, are provided by the Caisse Centrale de Réassurance (CCR). A total of 1200 configurations of the drought index are considered. They are generated by combining different calculation methods, ISBA simulation settings, soil model layers, and drought percentile thresholds. The analysis includes a comparison with the independent claim data of six additional municipalities and with a record of official “CatNat” (the French national natural disaster compensation scheme) decrees, useful for the analysis. The best results are obtained for drought magnitudes based on SWI values of the 0.8-to-1.0 m deep soil layer, an ISBA simulation with interactive leaf area index (LAI), and consideration of low drought SWI percentile thresholds. Comparison with claim data shows that drought magnitude is able to identify subsidence events while being spatially consistent. This drought magnitude index provides more insight into subsidence triggers while benefiting from advanced land surface modeling schemes (interactive LAI, multilayer soil). This work paves the way for more reliable damage estimates.

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

Top-k frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-k items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy. In this paper, we present a novel framework HG-LDP to achieve accurate Top-k item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior "accuracy-privacy-memory efficiency" tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior "accuracy-privacy-memory efficiency" tradeoff, saving 2300× memory over baseline methods when the item domain size is 41,270. Our code is anonymously open-sourced via the link.

An inventory model with preservation technology investments and stock-varying demand under advanced payment scheme

An inventory model with preservation technology investments and stock-varying demand under advanced payment scheme

Building Water Models Compatible with Charge Scaling Molecular Dynamics.

Charge scaling has proven to be an efficient way to account in a mean-field manner for electronic polarization by aqueous ions in force field molecular dynamics simulations. However, commonly used water models with dielectric constants over 50 are not consistent with this approach leading to "overscaling", i.e., generally too weak ion-ion interactions. Here, we build water models fully compatible with charge scaling, i.e., having the correct low-frequency dielectric constant of about 45. To this end, we employ advanced optimization and machine learning schemes in order to explore the vast parameter space of four-site water models efficiently. As an a priori unwarranted positive result, we find a sizable range of force field parameters that satisfy the above dielectric constant constraint providing at the same time accuracy with respect to experimental data comparable with the best existing four-site water models such as TIP4P/2005, TIP4P-FB, or OPC. The present results thus open the way to the development of a consistent charge scaling force field for modeling ions in aqueous solutions.

Influence of the Emitter Shape on the Field-of-View in Atom Probe Tomography.

Atom probe tomography (APT) is a unique analytical technique that offers three-dimensional elemental mapping with a spatial resolution down to the sub-nanometer. When APT is applied on complex heterogenous systems and/or under certain experimental conditions, that is, laser illumination, the specimen shape can deviate from an ideal hemisphere. Insufficient consideration of this aspect can introduce artifacts in the reconstructed dataset, ultimately degrading its spatial accuracy. So far, there has been limited investigation into the detailed evolution of emitter shape and its impact on the field-of-view (FOV). In this study, we numerically and experimentally investigated the FOV for asymmetric emitters and its evolution throughout the analysis depth. Our analysis revealed that, for asymmetric emitters, the ions evaporated from the topmost region of the specimen (summit) project approximately to the detector center. Furthermore, we demonstrated the implications of this finding on the FOV location for asymmetric emitters. Based on our findings, the location of the center of the FOV can deviate from the specimen central axis with an evolution depending on the evolution of the emitter shape. This study highlights the importance of accounting for the specimen shape when developing advanced data reconstruction schemes to enhance spatial resolution and accuracy.