Households and businesses are generating unprecedented levels of electrical and electronic wastes (e-waste), fueled by modernisation and rapid obsolescence. While the challenges imposed by e-waste are similar everywhere in the world, disparities in progress to deal with it exists; with developing nations such as South Africa lagging. The increase in e-waste generation increases the need to formulate strategies to manage it. This paper presents an overview of e-waste management on a global and South African scenarios with a specific case for Cathode Ray Tube (CRT) waste management practices in South Africa. CRTs present the biggest problem for recyclers and policy makers because they contain hazardous elements such as lead and antimony. Common disposal practices have been either landfilling or incineration. The research into the South African practices with regards to CRT waste management showed that there is still more to be done to effectively manage this waste stream. This is despite clear waste regulatory frameworks in the country. However, recent developments have placed e-waste as a priority waste stream, which should lead to intensified efforts in dealing with it. Overall, these efforts should aim to maximise diversion from landfilling and to create value-addition opportunities, leading to social and environmental benefits.

The continuous nature of space and time is a fundamental tenet of many scientific endeavours. That digital representation imposes granularity is well recognized but whether it is possible to address space completely remains unanswered. Part 1 argues that Hales’ proof of Keppler’s conjecture on the packing of hard spheres suggests the answer to be ‘no’, providing examples of why this matters in GIS generally and spatio-temporal GIS in particular. Part 2 seeks to resolve the dichotomy between continuous and granular space, showing how a continuous space may be emergent over a random graph. However, its projection into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a ‘smooth’ spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation.

The rational design of magnetic carbon composites, encompassing both composition and microstructure, holds significant potential for achieving exceptional electromagnetic wave absorbing materials (EAMs). In this study, FeCo@CM composites was efficiently fabricated through an advanced microwave plasma assisted reduction chemical vapor deposition (MPARCVD) technique, offering high efficiency, low cost, and energy-saving benefits. By depositing graphitized carbon microspheres, the dielectric properties were significantly enhanced, resulting in improved electromagnetic wave absorption performances through optimized impedance matching and a synergistic effect with magnetic loss. A systematic investigation revealed that the laminar-stacked structure of FeCo exhibited superior properties compared to its spherical counterpart, supplying a higher number of exposed edges and enhanced catalytic activity, which facilitated the deposition of uniform and low-defect graphitized carbon microspheres. Consequently, the dielectric loss performance of the FeCo@CM composites was dramatically improved due to increased electrical conductivity and the formation of abundant heterogeneous interfaces. At a 40 wt% filling amount and a frequency of 7.84 GHz, the FeCo@CM composites achieved a minimum reflection loss value of -58.2 dB with an effective absorption bandwidth (fE) of 5.13 GHz. This study presents an effective strategy for developing high-performance EAMs.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

The continuous flowing spacetime forms a spacetime group G, one of its fundamental groups is the Poincare group PO(1,3), and the matter and interaction fields are representations of its intrinsic spacetime group. As a kind of quantized space-time unit, space-time group elements generate both space-time manifolds and matter. Matter and fields are aggregates of the quantum space-time within them and determine their type: the Lorentz group SO(1,3) represents a rotating spacetime corresponding to visible matter and the translation group P^1,3 represents a translational spacetime corresponding to dark matter and dark energy. The aggregation mode of space-time degrees of freedom in matter reveals that charge conjugation symmetry is the inverse symmetry of internal time of charged particles, and the external parity breaking of neutrinos is also due to their internal spatial aggregation mode.

Addressing real-time aircraft target detection in microsatellite-based visible light remote sensing video imaging requires considering the limitations of imaging payload resolution, complex ground backgrounds, and the relative positional changes between the platform and aircraft. These factors lead to multi-scale variations in aircraft targets, making high-precision real-time detection of small targets in complex backgrounds a significant challenge for the detection algorithms. Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the YOLOv7 framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) with a lightweight convolutional SEConv to reduce the computational parameters and accelerate the detection process of small aircraft targets, thus enhancing real-time onboard processing capabilities. Besides, the SEConv-based SPPCSPC (Spatial Pyramid Pooling and Connected Spatial Pyramid Convolution) module extracts image features. It improves detection accuracy while the feature fusion section integrates the CBAM hybrid attention network, forming the CBAMCAT module. Furthermore, it optimizes small aircraft target features in channel and spatial dimensions, improving the model's feature fusion capabilities. Experiments on public remote sensing datasets reveal the proposed SE-CBAM-YOLOv7 improves detection accuracy by 3% and mAP value by 4.2% compared to YOLOv7, significantly enhancing the detection capability for small-sized aircraft targets in satellite remote sensing imaging.

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

In this paper, we use a machine learning method, known as Nonnegative Matrix Factorization, to extract from a given image some new information obtained from a number of color space representations. Such kind of information, termed ``metacolor'', can be considered as a particular color space representation of the image, which can be used to obtain a binary segmentation of the investigated image by distinguishing foreground and background pixels. Some numerical experiments prove that the proposed method shows improved results when compared with common simpler thresholding algorithms.

Particulate organic carbon (POC) is a pivotal component within the marine carbon cycle, actively involved in diverse biogeochemical processes. This study focuses on Mediterranean as the research region, selecting the most influential factors from 47 potential ones influencing surface POC. Using geographic detector for factor identification, ten primary influencers were identified. Preliminarily optimized random forest (BRF), backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Notably BRF exhibited superior performance in estimating the sea surface POC. Further optimization of RF using the tuneRanger R package resulted in R² of 0.868, mean squared error of 1.119 (mg/m³) ², and mean absolute error of 1.041 (mg/m³). Surface POC concentrations in the Mediterranean for May and June 2017 were estimated. Spatial distribution analysis unveiled higher concentrations in the west, lower in the east, higher in the north, lower in the south, with higher levels near the coast and lower far from the coast. Additionally, the study deliberated on the impact of human activities on surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in the area.

Dark energy and dark matter are described as different manifestations of vacuum energy in a new cyclic universe framework of Extra Dimensional Symmetry (EDS) that doubles large spatial dimensions with microscopic partners. In this testable framework, vacuum energy, constrained by a Planck density constraint, exists in a gravitationally dark state where actual gravitational constant G_o = 0G. While a small repulsive component is constrained by an asymptotically evolving asymmetry to the active state where G_o = 2G, some components in the inert state are enabled to gravitationally attract by neutrino induced gravitational constant with light and shear activated heavy components. Observational and experimental tests are briefly discussed.

We show that, as in the case of the principle of minimum action in classical and quantum mechanics, there exists an even more general principle in the very fundamental structure of quantum space-time: This is the principle of minimal group that allows to consistently and simultaneously obtain a natural description of the spacetime dynamics and the physical states admissible in it. The theoretical construction is based on the physical states that are average values of the generators of the Metaplectic group Mp(n) , the double covering of SL(2C) in a vector representation, with respect to the coherent states carrying the spin weight. Our main results here are: (i) There exists a connection between the dynamics given by the Metaplectic group symmetry generators and the physical states (mappings of the generators through bilinear combinations of the basic states). (ii) The ground states are coherent states of the Perelomov-Klauder type defined by the action of the Metaplectic group which divide the Hilbert space into even and odd states mutually orthogonal. They carry a spin weight 1/4 and 3/4 respectively from which, two other basic states can be formed. (iii) The physical states, mapped bilinearly with the basic 1/4 and 3/4 spin weight states, plus their symmetric and antisymmetric combinations, have spin contents s = 0, 1/2, 1, 3/2 and 2. (iv) The generators realized with the bosonic variables of the harmonic oscillator introduce a natural supersymmetry and a superspace whose line element is the geometrical Lagrangian of our model. (v) From the line element as operator level, a coherent physical state of spin 2 can be obtained and naturally related to the metric tensor. (vi) The metric tensor is naturally discretized by taking the discrete series given by the basic states (coherent states) in the n number representation, reaching the classical (continuous) space-time for n going to infinity. (vii) There emerges a relation between the eigenvalue of our coherent state metric solution and the black hole area (entropy) as A_{bh}/4 l_{p}^{2} = I alpha I relating the phase space of the metric found g_{ab} and the black hole area A_{bh} through the Planck length l_{p}^{2} and the eigenvalue I alpha I of the coherent states. As a consequence of the lowest level of the quantum discrete space-time spectrum, eg the ground state associated to n = 0 and its characteristic length, there exists a minimum entropy related to the black hole history.

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

We proposed in this work the introduction of a new vision of stochastic processes through geometry induced by dilation. The dilation matrices of a given process are obtained by a composition of rotation matrices built in with respect to partial correlation coefficients. Particularly interesting is the fact that the obtention of dilation matrices is regardless of the stationarity of the underlying process. When the process is stationary, only one dilation matrix is obtained and it corresponds therefore to Naimark dilation. When the process is nonstationary, a set of dilation matrices is obtained. They correspond to Kolmogorov decomposition. In this work, the nonstationary class of periodically correlated processes was of interest. The underlying periodicity of correlation coefficients is then transmitted to the set of dilation matrices. Because this set lives on the Lie group of rotation matrices, we can see them as points of a closed curve on the Lie group. Geometrical aspects can then be investigated through the shape of the obtained curves, and to give a complete insight into the space of curves, a metric and the derived geodesic equations are provided. The general results are adapted to the more specific case where the base manifold is the Lie group of rotation matrices, and because the metric in the space of curve naturally extends to the space of shapes, this enables a comparison between curves’ shapes and allows then the classification of processes’ measure.

The study of the change in entropy during the formation of stars in collapsing molecular clouds is an important area of research in astrophysics. As the cloud decreases in size, the presence of gases leads to an increase in entropy. However, the extent of this change in entropy is influenced by the amount of space available for the gases. In this scientific investigation, I conduct a detailed analysis of entropy on a small scale within the contracting cloud, following the principles of the second law of thermodynamics. Initially, my focus is on a turbulent cloud composed of N particles with a mass of M, which collapses under the force of gravity. It becomes clear that in order for the collapse to occur, gravitational energy must overcome other pressure forces that oppose it. This process results in the rapid movement of particles within the cloud, ultimately leading to a change in entropy

The wealth of data obtained during the past 20 years using Laser-Induced Breakdown Spectroscopy (LIBS) indicates that the technique is very promising to detect small chemical contents of alloying elements. The potential of LIBS was looked more seriously during the past two decades or so as more data were obtained on Aluminum and steel to study the phenomena in the condition of local thermodynamic equilibrium and time-delay between a Q-switch laser and an intensified CCD camera. Since the past decade, some data on compounds were also shown to be useful in determining small concentrations of harmful elements. This manuscript is intended to show that the technique of LIBS performance is very promising in fields such as micro-machining, in alloying element analysis, surface cleaning and environmental applications even with lightweight spectrometers having a relatively low resolution, which can potentially be air-borne.

Aiming at solving difficulties related to aero-engine classification and identification, the infrared spectra of aero-engine hot jets are measured by telemetry Fourier transform infrared spectrometer, and a six types aero-engine hot jet spectrum data set is generated. The spectrum data set were randomly sampled at a ratio of 8:1:1 to generate training set, validation set and prediction set. In this paper, a Peak Finding Siamese Convolutional Neural Network (PF-SCNN) is designed to match and classify spectrum data. In the first place, the matching pairs of training set and validation set are made independently with the labels of positive and negative samples creating. The spectrum feature extraction and distance similarity calculation are carried out by the Siamese convolutional neural network (SCNN). When entering the prediction phase, the prediction set and training set are integrated to predict in the trained model. The prediction set labels are ultimately determined by matching them with the training set labels having the highest similarities to ensure accurate predictions. To improve the operation efficiency of the SCNN, a peak finding method is introduced to extract the spectrum peaks. Peak positions of high frequency are counted, while the peak data are extracted. The peak data are used to take the place of the original data for training and prediction. The performance measures are defined as Accuracy, Precision, Recall, Confusion matrix and F1-score, and the prediction accuracy was up to 99%. The experimental results indicate that the proposed PF-SCNN is suitable for our spectral dataset and can complete the task of infrared spectrum classification of aero-engine hot jets.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, Random Forest), unsupervised learning models (K-means clustering), and neural networks (Multi-Layer Perceptron) to predict and identify ice formation patterns. Experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real-time, optimizes de-icing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance.

Recurrent sunspot groups were studied by using the Debrecen Photoheliographic Data (DPD) which covers more than 40 years. For this task it is necessary to make unambiguous the identification of the returning solar active regions. It starts with an algorithmic listing of possible returns, then the candidates of the algorithmic identifications are checked by the unique HTML-feature of the DPD. The final check is the application of an asymmetric Gaussian function introduced in previous articles to describe the development of short-lived sunspot groups observed continuously from start to end on the visible solar disc. The data of correctly identified recurrent sunspot groups fit surprisingly well to this function throughout the rotations enabling the reconstruction of the development also on the far side of the sun. The primary goal was to find any relationship between the maximum area and the lifetime deduced from the data of the asymmetric curves. This relationship is more complex than earlier results in the literature for short-lived groups. The lifetimes exhibit weak dependencies on the heliographic latitude and solar cycle phase. An apparently returning active area is rather an active nest if its data do not fit to the asymmetric Gaussian.

We present an analysis of high-resolution, near-infrared (NIR) spectra relative to the solar transit of Venus of 5-6 June 2012, as observed with the Facility Infrared Spectropolarimeter (FIRS) at the Dunn Solar Telescope in New Mexico. These observations offer the unique opportunity to probe the upper layers (between ∼84 and 150 km in altitude) of a thick, CO2-dominated atmosphere with the transmission spectroscopy technique – a proxy for future studies of highly-irradiated atmospheres of Earth-sized exoplanets. We were able to visually identify absorption lines from the two most abundant CO2 isotopologues, and from the main isotopologue of CO in the retrieved spectrum of Venus. Furthermore, we perform a cross-correlation analysis of the transmission spectrum using transmission templates generated with petitRADTRANS. With the cross-correlation technique, it was possible to confirm detections of both CO2 isotopologues and CO. Additionally, we retrieve a cross-correlation signal for O3 on Venus. We demonstrate the feasibility of high-resolution, ground-based observations to study the chemical inventory of planetary atmospheres, employing techniques commonly used in exoplanet characterization.

Recent discoveries of potential ice particles and ice-cemented regolith on celestial bodies like the Moon and Mars have ushered in new opportunities for developing technologies to extract water, facilitating future space missions and activities on these celestial surfaces. This study delves into the potential for water extraction from regolith through an experiment designed to test water recuperation from regolith simulant under varying gravitational conditions. The resultant water vapor extracted from the regolith is re-condensed on a substrate surface and collected in liquid form. Three types of substrates, hydrophobic, hydrophilic, and grooved, are explored. The system’s functionality was assessed during a parabolic flight campaign simulating three distinct gravity levels: microgravity, lunar gravity, and Martian gravity. Our findings reveal that the hydrophobic surface demonstrates the highest efficiency due to drop-wise condensation, and lower gravity levels result in increased water condensation on the substrates. This experiment aims to provide insights into in-situ water recovery, which is pivotal for establishing economically-sustainable water supplies for future missions.

In the context of improving aircraft safety, this study focuses on the development and evaluation of a graphene-based ice detection system using an environmental chamber. The research is driven by the need for more accurate and efficient ice detection methods, crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors’ heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors, tested under varied and controlled atmospheric conditions, exhibit a remarkable potential in enhancing ice detection systems for aircraft. Their lightweight, efficient and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically-active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how the Generative Topographic Mapping (GTM), a Bayesian realization of the Self-organizing Map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply the GTM to visualize the distribution of captured reflectance spectra revealing small-scale spatial variability of water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of near shore algae as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source.

Considering the dynamic stall effects in engineering calculations is essential for correcting the aerodynamic loads acting on wind turbines, both during power production and stand-still cases, and impacts significantly the turbine aeroelastic stability. The employed dynamic stall model needs to be accurate and robust for a wide range of airfoils and range of angle of attack. The present studies are intended to demonstrate the performance of a recently implemented "IAG dynamic stall" model in a wind turbine design tool Bladed. The model is transformed from the indicial type of formulation into a state-space representation. The new model is validated against measurement data and other dynamic stall models in Bladed for various flow conditions and airfoils. It is demonstrated that the new model is able to reproduce the measured dynamic polar accurately without airfoil specific parameter calibration and has a superior performance compared to other models in Bladed.

The successful 2020 launch and 2021 landing of the National Aeronautics and Space Administration’s (NASA) Perseverance Mars rover initiated the first phase of the NASA and European Space Agency (ESA) Mars Sample Return (MSR) campaign. The goal of the MSR campaign is to collect scientifically interesting samples from the Martian surface and return them to Earth for further study in terrestrial laboratories. The MSR campaign consists of three major spacecraft components to accomplish this objective: the Perseverance Mars rover, the Sample Retrieval Lander (SRL) and the Earth Return Orbiter (ERO). Onboard the ERO spacecraft is the Capture, Containment and Return System (CCRS). CCRS will capture, process and return to Earth the samples that have been collected after they are launched into Mars orbit by the Mars Ascent Vehicle (MAV), which is delivered to Mars onboard the SRL. To facilitate the processing of the orbiting sample (OS) by CCRS we have designed and developed a vision system to determine the OS capture orientation. The vision system is comprised of two cameras sensitive to the visible portion of the electromagnetic spectrum and two illumination modules constructed from broadband light emitting diodes (LED). Vision system laboratory tests and physics-based optical simulations predict CCRS ground processing will be able to correctly identify the OS post-capture orientation using only a single vision system image that is transmitted to Earth from Mars orbit.

With the continual global increase of PV power systems and the inherent weather-induced volatility of their power output, understanding the underlying variability of solar radiation in both space and time is important for the planning and reliable operation of future power grids. This paper concisely reviews recent advances in the characterization of irradiance variability with an emphasis on small spatial and temporal scales (respectively less than about 10\,km and 1\,min), for which comprehensive data sets are time consuming and expensive to collect, and although needed have not been available until recently. Special attention is given to studies dealing with the quantification of variability using such unique data, the analysis and modeling of spatial smoothing, and the evaluation of temporal averaging.

Global navigation satellite system (GNSS) array antenna receivers are widely used to suppress wideband interference in navigation countermeasures. However, existing array antenna receivers all adopt digital array structure and digital beamforming technique, and it has limited analog-front-end (AFE) dynamic range. In strong interference scenarios, AFE saturation will occur, which limits the maximum interference suppression ability of the array receiver. Aiming at this issue, this paper proposes a robust wideband interference suppression method for GNSS array antenna receivers based on hybrid beamforming technique. Firstly, a novel fully connected hybrid array receiver structure is proposed; Secondly, the corresponding hybrid beamforming method is proposed at the same time, and it realizes the complete elimination of strong interference by jointly suppression in analog domain and digital domain. After mathematical simulation experiments, it is verified that compared to the digital beamforming-based anti-jamming technique, the proposed method can effectively suppress strong wideband interference, and the maximum interference suppression ability is improved by 36 dB.

This work presents emission line flux and kinematic structure mapping based on IFU data of the planetary nebulae Abell 30 (A30). The IFU has a field of view (FoV) of 16" x 12.3" and was used to partially cover knots J1 and J2, and completely cover knots J3 and J4 of A30. Our emission line flux map shows recombination species C II, C III, He I, O II as well as forbidden line species [O III], [Ne III] and [Ne IV] of high excitation level are all distributed in different parts of the knots. The kinematic structure shows considerable variation in both the velocities and spatial locations of these knots with positive and negative velocity excursions from the systematic value. Forbidden line species [O III], [Ne III] are distributed in both knots J3 and J4 but they are travelling at different speeds in opposite directions. Despite variation in distribution of the species, the velocities of [Ne IV] and [O III] are similar and travelling in the same direction while the other species [Ne III], He I, and O II share similar velocity of 200kms−1 up in the opposite direction. The complexity of distribution and kinematic structure of the knots revealed in this work indicates a strong tendency towards the hypothesis of multiple triggering events in the past history of A30.

Due to its potential harm to space payload, microwave corona breakdown of microstrip circuits has attracted much attention. This work describes an efficient way to suppress corona breakdown. Since corona breakdown threshold is determined by the highest electric field intensity at the surface of microstrip circuits, lacquer coating with thickness of tens of microns is sprayed on the top of microstrip circuits. The applied dielectric coating is used to move the discharge location away from circuit’s surface which is equivalent to reduce the highest electric field intensity on the in-terface of solid/air of the circuit and thus results into higher breakdown threshold. Two designs of classic coupled-line band-pass filter were used for verification. Corona experimental results at 2.5 GHz show that, in the interested low pressure range (100 to 4500 Pa), 5.3 dB improvement of mi-crowave corona breakdown threshold can be achieved for a filter with narrowest gap of 0.2 mm while its electrical performances like insertion loss, Q-factor are still acceptable. A threshold improvement prediction method is also presented and validated.

This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid obtaining precise scales by interpolation and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution template are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change and even viewpoint change.

Relativistic shocks are one of the most plausible sites of the emission of strongly variable, polarized multi-wavelength emission from relativistic jet sources such as blazars, via diffusive shock acceleration (DSA) of relativistic particles. This paper summarizes recent results on a self-consistent coupling of diffusive shock acceleration and radiation transfer in blazar jets. We demonstrate that the observed spectral energy distributions (SEDs) of blazars strongly constrain the nature of hydromagnetic turbulence responsible for pitch-angle scattering by requiring a strongly energy-dependent pitch-angle mean free path. The prominent soft X-ray excess (``Big Blue Bump'') in the SED of the BL Lac object AO 0235+164 can be modelled as the signature of bulk Compton scattering of external radiation fields by the thermal electron population, which places additional constraints on the level of hydromagnetic turbulence. It has further been demonstrated that internal shocks propagating in a jet pervaded by a helical magnetic field naturally produce polarization-angle swings by 180$^o$, in tandem with multi-wavelength flaring activity, without requiring any helical motion paths or other asymmetric jet structures. The specific application of this model to 3C279 presents the first consistent, simultaneous modeling of snap-shot SEDs, multi-wavelength light curves and time-dependent polarization signatures of a blazar during a polarization-angle (PA) rotation. This model has recently been generalized to a lepto-hadronic model, in which the high-energy emission is dominated by proton synchrotron radiation. It is shown that in this case, the high-energy (X-ray and $\gamma$-ray) polarization signatures are expected to be significantly more stable (not showing PA rotations) than the low-energy (electron-synchrotron) signatures.

A new generalized definition of Mersenne numbers is proposed of the form (a^n - (a-1)^n), called Global Generalized Mersenne numbers, or simply Generalized Mersenne numbers and noted GM_{a,n} where a is the base and n is the exponent, both being positive integers. The properties are investigated for prime exponents n and several theorems on Mersenne numbers regarding their congruence properties are generalized and demonstrated. In particular, it is found that, for any base a, Generalized Mersenne numbers are in general such that (GM_{a,n} - 1) are even and divisible by n, a and (a-1) for any odd prime exponent n and by (a(a-1)+1) for any prime exponent n > 5. The remaining factor is a function of triangular numbers of (a-1), specific to each prime exponent n. Four theorems on Mersenne numbers are generalized and four new theorems are demonstrated, allowing to show first, that (GM_{a,n} - 1) are divisible by 6, and more precisely GM_{a,n} are congruent to 1(mod 12) or 7(mod 12) depending on the congruence of the base a(mod 4); second, that (GM_{a,n} - 1) are divisible by 10 if n == 1(mod 4) and, if n == 3(mod 4), GM_{a,n} == 1(mod 10), or 7(mod 10) or 9(mod 10) depending on the congruence of the base a(mod 5); third, that all factors c_{i} of GM_{a,n} are of the form (2nf_{i}+1) with f_{i} natural integers such that c_{i} is prime itself or the product of primes of the form (2nj+1) with j natural integer; fourth, that for odd prime exponents n, all GM_{a,n} are periodically congruent to either +/-1\(mod 8) or +/-3(mod 8) depending on the congruence of the base a(mod 8); and fifth, that the factors of a composite GM_{a,n} is of the form (2nf_{i}+1) with f_{i} == u(mod 4) and u being either 0, 1, 2 or 3 depending on the congruence of the exponent n(mod 4) and on the congruence of the base a(mod 8). The potential use of Generalized Mersenne primes in cryptography is shortly addressed.

A suitable waste management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and requires precise data on the volume of demolition waste (DW) gen-erated. Therefore, we developed an optimal machine learning (ML) model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. A dataset comprising infor-mation on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, and landfill mix waste, and recyclable minerals) was constructed. ML models were developed to predict the quantities of such waste. Artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest (RF), and support vector regression were applied, and optimal models were derived via hyperparameter tuning. The RF model demonstrated superior performance. In both validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies of of 0.987 and 0.972, re-spectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. SHapley Additive exPlanations analysis highlighted floor area as the primary input variable influencing model performance. The type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill wastes generated. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

The comparison of three types of ionosonde data from Europe during an Interplanetary Coronal Mass Ejection (ICME) and a Stream Interaction Regions (SIRs) / Corotating Interaction Regions (CIRs) -driven geomagnetic storm event is detailed in this study. The selected events are 16-20 March 2015 for the ICME-driven and 30 May to 04 June 2013 for the SIR/CIR-driven one. Ionospheric data from three European ionosonde stations, namely Pruhonice (PQ), Sopron (SO) and Rome (RO), are investigated. The ionospheric F2-layer responses to these geomagnetic events are analyzed with the ionospheric foF2 and h’F2 parameter, the calculated deltafoF2 and deltahF2 values, ratio of Total Electron Content (rTEC) and Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) satellite Global Ultraviolet Imager (GUVI) thermospheric [O]/[N2] measurement data. The storm-time and the quiet day mean values are also compared, and it can be concluded that the quiet day curves are similar at all stations while the storm-time ones showed the latitudinal dependence during the development of the storm. As a result of the electron density comparison, during the two events it can be concluded that SSC that characterized the ICME induced a Travelling Atmospheric Disturbance (TAD) seen in the European stations, while this is not in the SIR/CIR-driven ionospheric storm, which showed a stronger and more prolonged negative effect in all stations probably due to the season.

This paper proposes an enhanced Quantum Key Distribution (QKD) protocol by incorporating a Three-Dimensional Angular Momentum Representation (3D AMR) to address vulnerabilities inherent in traditional two-dimensional polarization states-based systems. The 3D AMR method expands the state space, potentially increasing security against sophisticated quantum attacks. We present a theoretical and computational framework that transitions from the conventional two-dimensional polarization to a three-dimensional angular momentum space, which inherently increases the complexity and security of the quantum states used in QKD.

This brief essay explores both the social and biological dimensions of human sexuality in light of the possibility of a substantial commerce in sexually-enabled robots, and concludes with some potentially strategic considerations for those who find themselves involved with their design, production, and/or marketing.

We investigate the behavior of the partial derivative approach to the change of scale formula and prove relationships among the analytic Wiener integral and the analytic Feynman integral of the partial derivative for the function space integral.

Hyperspectral imaging, a rapidly evolving technique, furnishes exhaustive information on Earth's surface reflectance over an extensive spectrum of wavelengths. We have developed a more precise method for analyzing spectral data by meticulously selecting plant leaf pixels and employing a relative reference approach. This enhanced technique correlates camera pixel values with light intensity, bypassing the need for complex equations and thereby providing clearer insights into vegetation vitality. The findings of this study not only enhance the precision of hyperspectral imaging but also bolster our capability for ecological monitoring and understanding environmental health. A novel algorithm based on the red-edge slope was established to classify images, exploiting the spectral reflectance data gleaned from the hyperspectral Liquid Crystal Tunable Filter (LCTF) camera. Compared with conventional SVM and K-means algorithms, our novel algorithm demonstrated commendable performance, achieving an overall accuracy of 90% with a Kappa Coefficient of 0.86. This comparison highlights the novel algorithm's relative strength and potential for large-scale satellite and drone imagery analysis applications. The advancements facilitated by this research are instrumental in ecological assessments, conservation efforts, and resource management strategies, signaling a leap forward in the era of environmental monitoring and analysis.

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection And Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, with each stem further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. The study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees.

The Microwave Humidity and Temperature sounder (MWHTS) on board the Fengyun (FY)-3C satellite measure the outgoing radiance form the Earth surface and atmospheric constituents. MWHTS makes measurements in the isolated oxygen absorption line near 118 GHz and the vicinity of strong water vapor line around 183 GHz, can provide fine vertical distribution structure of both atmospheric humidity and temperature. However, in order to obtain the accurate soundings of humidity and temperature by the physical retrieval method, bias between the observed radiance and those simulated by radiative transfer model from the background or first guess profiles must be correct. In this study, two bias correction methods are developed through the correlation analysis between MWHTS measurements and air mass identified by the first guess profiles of the physical inversion, one is the linear regression correction (LRC) and the other is neural networks correction (NNC), representing the linear and nonlinear nature between MWHTS measurements and air mass, respectively. Both correction methods have been applied to MWHTS observed brightness temperatures over the geographic area (180° W-180° E, 60° S-60° N). The corrected results are evaluated by the probability density function of the difference between corrected observations and simulated values and the root mean square error (RMSE) with respect to simulated observations. The numerical results show that the NNC method perform better, especially in MWHTS channels 1 and 7-9 whose peak weight function heights are close to the surface. In order to assess the effects of bias correction methods proposed in this study on the retrieval accuracy, a one-dimensional variational system was built and applied to the MWHTS uncorrected and corrected brightness temperatures to estimated atmospheric temperature and humidity profiles, The retrieval results show that the NNC has better performance which is to be expected. An indication of the stability and robustness of NNC method is given which suggests that the NNC method has promising application perspectives in the physical retrieval.

Developing short-term forecasting model for global atmospheric temperature and wind in the near space is crucial for understanding atmospheric dynamics and supporting human activities in this region. While numerical models have been extensively developed, deep learning techniques have recently shown promise in improving atmospheric forecasting accuracy. In this study, convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) neural networks were applied to build for short-term global-scale forecasting model of atmospheric temperature and wind in the `near space based on the MERRA-2 reanalysis dataset from 2010-2022. The model results showed that the ConvGRU model outperforms the ConvLSTM model in the short-term forecast results. The ConvGRU model achieved a root mean square error in the first three hours of approximately 1.8 K for temperature predictions, and errors of 4.2 m/s and 3.8 m/s for eastward and northward wind predictions, respectively.

Mathematician and philosopher, Charles Howard Hinton, posited a plausible correlation between of higher-dimensional spaces, also referred to as ‘hyperspaces’, and the allegorical concept artic-ulated by the Ancient Greek philosopher Plato in his work, Republic, known as the 'Cave.' In Plato's allegory, individuals find themselves situated in an underground 'Cave,' constrained by chains on their legs and neck, perceiving shadows and sound reflections from the ‘real’ world cast on the ‘Cave’ wall as their immediate reality. Hinton extended the interpretation of these 'shadows' through the induction method, asserting that, akin to a 3D object casting a 2D shadow, the 'shadow' of a 4D hyper-object would exhibit one dimension less, manifesting as a 3D object. Building upon this conceptual framework, the authors posit a correlation between the perceived acoustic space of the chained individuals within the 'Cave' and the characteristics of a 4D acoustic space, a proposition substantiated mathematically by scientific inquiry. Furthermore, the authors introduce an interactive art application developed as a methodical approach to exploring the hypothetical 4D acoustic space within Plato's 'Cave,' as perceived by the chained individuals and someone liberated from his constraints navigating through the 'Cave.'

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of <1%. To this end, the operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing at the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (-7.4 ppb versus -5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since November 21, 2023, the RFC approach is part of ESA’s operational processing chain. For now the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data is unavailable do we resort to the RFC cloud mask.

The r-process is one of the processes that produce heavy elements in the Universe. One of its possible astrophysical sites is the neutron star - black hole (NS-BH) merger. We first show that the neutrons may be degenerate before and during the r-process in these mergers. Previous studies assumed neutrons were non-degenerate and the related rates were calculated under Maxwell-Boltzmann approximations. Hence, we corrected the related rates with neutron degeneracy and put into the network code and calculated with the trajectories of NS-BH mergers. We show that there are differences in the nuclei distributions and leads to big changes in the heat generation and hence the temperature changes during the r-process. The higher temperatures in the r-process lead to higher luminosity of the light curves of these mergers than the one using non-degenerate rates. However, this has little effect on the final abundances.

The kinase pathway plays a crucial role in blood vessel function. Particular attention is paid to VEGFR-type 2 angiogenesis and vascular morphogenesis as the tyrosin kinase pathway is preferentially activated. In silico studies were performed on several peptides that affect VEGFR2 in both stimulating and inhibitory ways. This investigation aims to examine the molecular properties of VEGFR2, a molecule primarily involved in the processes of vasculogenesis and angiogenesis. These relationships were defined by the interactions between vascular endothelial growth factor receptor 2 (VEGFR2) and the structural features of the systems. The chemical space of the inhibitory peptides and stimulators was described using topological and energetic properties. Furthermore, chimeric models of stimulating and inhibitory proteins (for VEGFR2) were computed using the protein system structures. The interaction between the chimeric proteins and VEGFR was computed. The chemical space was further characterized using complex manifolds and high-dimensional data visualization. The results show that a slightly similar chemical area is shared by VEGFR2 and is shared by stimulating and inhibitory proteins. On the other hand, the stimulator peptides and the inhibitors have distinct chemical spaces.

Novel freeform optical design methods can be classified in two categories, relevant to the generation of a starting point or to the development of new optimization tools. In this paper, we design a freeform TMA optimized with different surface representations using either a differential ray-tracer as a new optimization tool, or commercial ray tracer (ANSYS-ZEMAX OpticStudio) for comparison. For differential ray tracing, we use FORMIDABLE (Freeform Optics Raytracer with Manufacturable Imaging Design cApaBiLitiEs): an optical design library with differential ray tracing and NURBS optimization capabilities under European Software Community License (ESCL). NURBS allow to represent a freeform surface without needing any prior knowledge on the surface, such as the polynomial degree in polynomial descriptions. However, commercial optical design software are designed to optimize polynomial surfaces, but are not well-suited to optimize NURBS surfaces. This requires to use a custom optical design library. To demonstrate the interest of using NURBS representation, freeform telescopes are independently optimized with NURBS using FORMIDABLE, or XY polynomials with OpticStudio over different iteration cycles. We then compare the resulting systems using their RMS Field Maps to assess the optimization quality of each surface representation. We also provide a full-system comparison, including mirror freeform departures. This study shows that NURBS can be a relevant alternative to XY polynomials for freeform optimization of reflective 3-mirror telescopes.

We have recently presented a unified quantum gravity theory [1]. Here we extend on that work and present an even simpler version of that theory. For about hundred years, modern physics has not been able to build a bridge between quantum mechanics and gravity. However, a solution may be found here; we present our quantum gravity theory, which is rooted in indivisible particles where matter and gravity are related to collisions and can be described by collision space-time. In this paper, we also show that we can formulate a quantum wave equation rooted in collision space-time, which is equivalent to mass and energy.The beauty of our theory is that most of the main equations that currently exist in physics are not changed (in terms of predictions), except at the Planck scale. The Planck scale is directly linked to gravity and gravity is, surprisingly, actually a Lorentz symmetry as well as a form of Heisenberg uncertainty break down at the Planck scale. Our theory gives a dramatic simplification of many physics formulas without altering the output predictions. The relativistic wave equation, the relativistic energy momentum relation, and Minkowski space can all be represented by simpler equations when we understand mass at a deeper level. This not attained at a cost, but rather a reflection of the benefit in having gravity and electromagnetism unified under the same theory.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Major (exo)planetary and satellite bodies seem to concentrate at intermediate areas of the radial 1 distributions of all the objects orbiting in each (sub)system. We show that angular-momentum transport during secular evolution of (exo)planets and satellites necessarily results in the observed intermediate accumulation of the massive objects. We quantify the ‘middle’ as the mean of mean motions (orbital angular velocities) when three or more massive objects are involved. Radial evolution of the orbits is expected to be halted when the survivors settle near mean-motion resonances and angular-momentum transfer between them ceases (gravitational Landau damping). This dynamical behavior is opposite in direction to what has been theorized for viscous and magnetized accretion disks, in which gas spreads out and away from either side of any conceivable intermediate area. We present angular momentum transfer simulations in few-body systems, and we calculate the tidal dissipation timescales and the physical properties of the mean tidal field in planetary and satellite (sub)systems.

As part of the Copernicus programme of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This paper provides a description of the calibration activities and the current status of the mission products validation activities. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. Results presented in this paper show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

Global warming leads in general to a reduction in precipitations and non-rainfall water contributions such as dew. The present study, carried out in Gujarat (India) for 17 years between 2005 and 2021, shows however a rare increase of the rain falls and dew condensation, the latter related to an increase in relative humidity and a decrease in wind amplitudes. Rain was obtained from meteo stations. Dew was calculated from an energy model and meteo data from 15 meteo stations (14 in India and 1 in Pakistan) and compared to early and dedicated measurements in 13 sites. Maps of dew, rain and their relative contributions during the period are provided. They show that, while rain mainly occurs during the monsoon (June to September), dew forms during the dry season (October to April). This water resource was mapped over the Gujarat state of India by using a kriging process. According to the site, the dew/rain ratios can represent between 4.6% (Ahmedabad) to 37.2% (Jamnagar) on the whole period of 17 years. This positive evolution, which is observed since 2015-2017, is likely to continue in the future.

This study concerns the transport and retention of polydisperse micron-sized (16 ± 6 µm) of microplastics (MPls) in porous media under varying flow rate conditions. Sorption kinetics were modeled using first-order reversible and irreversible kinetic sorption models, with sensitivity analyses providing insights into each sorption parameter's effect. Both numerical modeling and experimental measurements were employed to assess sand filter retention rates. The impact of flow rate on sorption reveals variations in distribution coefficient (Kd), mass transfer coefficient (β), and irreversible sorption rate (K1). Lower flow rates are correlated with higher Kd and β values, indicating an increase in sorption and diminished mass transfer rates. The findings revealed that an increase in Kd resulted in a more gradual sorption process with a decrease in peak concentration, whereas changes in β influenced the rate of sorption and peak concentration to a lesser extent compared to Kd. Lower K1 values are associated with higher peak concentrations and decreased retention efficiency. Retention rates were evaluated by a numerical model and found as 28 ± 1% at a flow rate of 31 ml min⁻¹ and 17 ± 1% at 65 ml min⁻¹. The introduction of MPls into soil environments has been noted to modify transport dynamics into soil. As a result, these alterations effects hydrological characteristics of soil, thereby impacting quality of groundwater and agricultural output. The mean absolute error (MAE) of 6% between modeled and observed retention rates suggests minor discrepancies. This study highlights the importance of examining retention efficiency and the accuracy of numerical models in porous media during MPl transport.

Distant Retrograde Orbits in the Earth-Moon system are gaining in popularity as stable `parking' orbits for various conceptual missions. To investigate the stability of potential Distant Retrograde Orbits, simulations were executed, with propagation running over a thirty-year period. Initial conditions for the vehicle state were limited such that the position and velocity vectors were in the Earth-Moon orbital plane, with the velocity oriented such that it would produce retrograde motion about Moon. The resulting trajectories were investigated for stability against the eccentric relative orbits of Earth and Moon in an environment that also included gravitational perturbations from Sun, Jupiter, and Venus, and the effects of radiation pressure. The results appear to indicate that stability is enhanced for certain resonant states within the Earth-Moon system.

The report details out programming approach for automating warehouse using Answer Set Programming.

In this study, we adopt a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Air pollution has numerous impacts on human health on a variety of time scales. This study uses biometric observations of the human autonomic response on the second timescale to investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including particulate matter. PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) on small temporal and spatial scales. These pollutants are exemplars of the wider human exposome. We compare two experimental approaches that use a similar methodology, employing a biometric suite to capture the physiological responses of cyclists and sensors to monitor the pollutants in the air surrounding them. We employ machine learning algorithms to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. We observed high precision in predicting PM1, PM2.5 and CO2 using a limited set of biometrics from participants. Although the predictions for NO2 and NO were reliable at lower concentrations, the precision varied throughout the data range. This discrepancy suggests the potential to improve our models with more comprehensive data collection or advanced machine learning techniques.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

Quantum gravity theories rely on a minimal measurable length for their formulations, which clashes with the classical formulation of the uncertainty principle and with Lorentz invariance from general relativity. These incompatibilities led to the development of the generalized uncertainty principle (GUP) from string theories and its various modifications. GUP and covariant formulations of the uncertainty principle are discussed, together with implications for space-time quantization.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

We develop an action principle for producing a single fluid, two-constituent system with dissipation in general relativity. The two constituents in the model are particles and entropy. The particle flux creation rate is taken to be zero, while the entropy creation rate is non-zero. Building on previous work, it is demonstrated that a new term (the proper time derivative of the matter space ``metric'') is required in the Lagrangian in order to produce terms typically associated with bulk and shear viscosity. Equations of motion, entropy creation rate, and energy-momentum-stress tensor are derived. Using an Onsager approach of identifying thermodynamic ``forces'' and ``fluxes'' a model is produced which delivers the same entropy creation rate as the standard, relativistic Navier-Stokes equations. This result is then contrasted with a model generated in the spirit of the action principle, which takes as its starting point a specific Lagrangian and then produces the equations of motion, entropy creation rate, and energy-momentum-stress tensor. Unlike the equations derived from Onsager reasoning, where the analogs of the bulk and shear viscosity coefficients are prescribed ``externally'', we find that the form of the coefficients in the second example are a direct result of the specified Lagrangian. Furthermore, the coefficients are shown to satisfy evolution equations along the fluid worldline; also a product of the specific Lagrangian.

The theory of water transport in trees, according to which the main driving force of water movement is the suction created by the evaporation of water by the meniscus (the curved surface of the capillary liquid column) on the evaporating elements, supported from below by root pressure, is flawed. The capillary effect in nature is around one meter. In the case of open-air gaps, the leaf cannot suck in the water against gravity because, in this case, the plant would not be sucking in water, but air through the open-air gap. If not, how do trees transport water? When evaporation occurs, there is no suction towards the canopy, but pressure is due to a reduction in the cross-section of the water pipe caused by heat loss through evaporation. At night, when evaporation stops, a thermal equilibration process is triggered, which restores the original cross-section of the pipe. This generates suction and draws water from the soil. As the hydrostatic pressure in the pipe is high for tall trees, the pipe is cut off. To prove my theory, I have given several examples. Water transport is not based on physical mechanical laws alone. There may be complex physiological, biochemical, and biophysical processes behind the operation of the pipe system.

We consider spontaneous quark transitions between the Λ0 baryon and its resonant states, and (anti)quark transitions between the neutral kaon K0 and the two heavy ηq-mesons (q = c, b). We use the measured differences in mass deficits to calculate the binding energy levels of valence c and b (anti)quarks in these transitions. In the process, we account for isospin energy release in K0 transitions and for the work done by the strong force in suppressing Coulomb repulsions in the charged Λc+-baryon. We find that the flips s→c and s¯→c¯ both release energy back to the strong field; and that the overall range of quark energy levels above their u-ground is 100-MeV wider than that of antiquark energy levels above their d¯-ground. The wider quark range stems from the flip s→b, which costs 283 MeV more (or 3× more) than the corresponding antiquark flip s¯→b¯. At the same time, transitions from the respective ground states to the s and s¯ states (or the c and c¯ states) point to a clear origin of the elusive charge-parity (CP) violation. The determined binding energy levels of (anti)quarks allow us to analyze in depth the (anti)quark transitions in Λ-baryons and B-mesons.

To analyze the combined response of ocular cyclotorsion and head tilt during body tilt and to interpret the physiologic role of those movements, each of the three subjects completed six different experiments of body tilt not fixating their head movements using a flight simulator, which consisted of combinations of three tilt angles (30, 45, and 60) and two visual conditions. Head tilt and ocular cyclotorsion of the right eye before, during, and after the tilt maneuver were recorded and analyzed by time sequence. In the active tilting phase, the right eye showed a stepladder pattern of multiple repeats of fast and slow cyclotorsion along with head tilt to obtain maximum visual information. During the stationary tilting phase, there occurred a small amount (mean, 2.3) of ocular cyclotorsion which was reciprocal to that of head tilt. The visual cue of body tilt through the central visual field induced a larger amount of head tilt but affected little ocular cyclotorsion. The maximum degree of ocular cyclotorsion during each experiment was 10.2 on average. In conclusion, the combined response of ocular cyclotorsion and head tilt develops during body tilt and it compromises the conflicting needs between binocular vision and gravitational orientation.

Abiotic stress is responsible for significant reduction in crop plant productivity worldwide. Ultraviolet (UV) radiation is a natural component of sunlight and a permanent environmental stimulus. This study investigated the distinct responses of young wheat and einkorn plants to excessive UV-B radiation (180 min at λmax 312 nm) following foliar pretreatment with 1 µM synthetic cytokinin 4PU-30. Results demonstrated that UV radiation significantly amplified hydrogen peroxide levels in both wheat and einkorn, with einkorn exhibiting more pronounced increase compared to wheat. This elevation indicated the induction of oxidative stress by UV radiation in the two genotypes. Intensified antioxidant enzyme activities and increased accumulation of typical stress markers and non-enzyme protectants were evidenced. Transcriptional activity of genes encoding key antioxidant enzymes was also investigated aiming to elucidate the genetic regulation of the antioxidant systems in both wheat and einkorn seedlings. Our results suggested a role for POX1 and POX7 genes in the UV-B tolerance of the two wheat species as well as a cytokinin stimulated UV-B stress response in einkorn mediated through upregulation of the tau subfamily gene GSTU6. Based on these findings it could be concluded that 4PU-30 had the potential of alleviating oxidative stress by attenuating the symptoms of superfluous UV-B illumination in the two examined plant species.

In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Pepper leaf segmentation plays a crucial role in monitoring pepper leaf diseases in various backgrounds and ensuring the healthy growth of peppers. However, existing transformer-based segmentation methods suffer from computational inefficiency, excessive parameterization, and limited utilization of edge information. To tackle these challenges, we propose an adaptive multi-scale MLP framework, named AMS-MLP, which combines the multi-path aggregation module (MPAM) and the multi-scale context relation mask module (MCRD) to refine the object boundaries in pepper leaf segmentation. AMS-MLP consists of an encoder-based network, an adaptive multi-scale MLP (AM-MLP) module, and a decoder network. In the encoder network, the MPAM module effectively fuses five-scale features to generate a single-channel mask, improving the accuracy of pepper leaf boundary extraction. The AM-MLP module divides the input features into two branches: the global multi-scale MLP branch captures long-range dependencies between image information, while the local multi-scale MLP branch focuses on extracting local feature maps. Adaptive attention mechanism is designed to dynamically adjust the weights of global and local features. The decoder network incorporates the MCRD module into the convolutional layer, enhancing the extraction of boundary features. To verify the performance of the proposed method, we conducted extensive experiments on three pepper leaf datasets with different backgrounds. The results demonstrate mIoU scores of 97.39%, 96.91%, and 97.91%, as well as F1 scores of 98.29%, 97.86%, and 98.51%, respectively. Comparative analysis with U-Net and state-of-the-art models reveals that the proposed method dramatically improves the accuracy and efficiency of pepper leaf image segmentation.

The control of the fine particle emission from the coal-fired power plants has been a big challenge in china. Either the conventional electrostatic precipitator or the fabric filter has some shortages. The electrostatic precipitator is hard to keep high efficiency for the submicron particles. The fabric filter could keep high efficiency but with very high pressure drop. One solution is to combine them together. Several types of hybrid particle collectors (HPC) combining the electrostatic precipitator and the fabric filter together have been developed and applied in a number of projects in China. In recent years, numerical simulation technology has been widely used in the design of the collectors. In this paper, an unsteady numerical model is used to investigate the particle deposition characteristics in a scale hybrid collector. The fluid field is modeled by using incompressible Navier-Stokes equations with the RNG turbulence equations. The corona discharge is solved by using a finite volume method. The particle charging is modeled by using a filed-diffusing combined model. And an unsteady cake formation model is used to simulate the filtration process.

A low cost, scalable and robust process has been proposed as innovative method for coating solar mirrors with a self-cleaning, transparent in the full solar range and versatile material based on auxetic aluminium nitrides, previously obtained on the laboratory scale. This work presents the scaling-up of fabrication process from laboratory to prototypal scale and pre-liminary results of outdoor self-cleaning solar mirror prototypes testing in the demonstrative CSP plant ENEASHIP located in Casaccia (Rome) ENEA research centre. Prototypes 50x40cm have shown stability in external conditions: no degradation occurs during the test campaign. Their washing restores the initial reflectance affected by soiling and the self-cleaning performance allows the utilization of a reduced quantity of water for cleaning operations with respect to uncoated glass of back surface mirrors. A similar self-cleaning AlN coating could be utilized on other solar components affected by soiling, such as glass envelopes of heat collecting elements, PV panels and everywhere are required self-cleaning performance combined to optical ones.

This study investigated the efficiency of scale-propellers, typically used on small drones. A scale-propeller is accepted as having a diameter of 7 to 21 inches. The Ukraine War has demonstrated the utility of relatively small, low-cost first-person view (FPV) drones, which are attritable. This investigation outlines the development of a MATLAB optimisation code, based on minimum induced loss propeller theory, which calculates the optimal chord and twist distribution for a chosen propeller operating in known flight conditions. The MATLAB code includes a minimum Reynolds number functionality, which provides the option to alter the chord distribution to ensure the entire propeller is operating above a set threshold value of Reynolds (>100,000), as this has been found to be a transition point between low and high section lift-to-drag ratios. Additional functions allow plotting of torque and thrust distributions along the blade. The results have been validated on experimental data taken from an APC ‘Thin Electric’ 10” x 7” propeller, where it was found that both the chord and twist distributions were accurately modelled. The MATLAB code resulted in a 16% increase in the maximum propulsive efficiency. Further work will investigate a direct interface to SolidWorks to aid rapid propeller manufacturing capability.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

In this paper, we introduce a way of representing a given mapping as the sum of odd and even mappings. Then, by using this representation, we investigate the stability of various forms for the general nonic functional equation.

AI research and implementations are growing, and so are the risks associated with AI (Artificial Intelligence) developments, especially when it’s difficult to understand exactly what they do and how they work, both at a localized level, and at deployment, in particular when distributed and on a large scale. Governments are pouring massive funding to promote AI research and education, yet research results and claims, as well as the effectiveness of educational programmes, can be difficult to evaluate given the limited reproducibility of computations based on ML (machine learning) and poor explainability, which in turn limits the accountability of the systems and can cause cascading systemic problems and challenges including poor reproducibility, reliability, and overall lack of trustworthiness. This paper addresses some of the issues in Knowledge Representation for AI at system level, identifies a number of knowledge gaps and epistemological challenges as root causes of risks and challenges for AI, and proposes that neurosymbolic and hybrid KR approaches can serve as mechanisms to address some of the challenges. The paper concludes with a postulate and points to related and future research

The realm of natural language processing has always been fascinated by the intricacies of sentence semantics. The emergence of context-aware word representations, especially from pre-trained models like ELMO and BERT, has revolutionized several semantic tasks including but not limited to, question answering, text categorization, and sentiment analysis. Despite these advancements, the integration of supplementary knowledge, particularly syntactic, to augment semantic comprehension in models remains an area ripe for exploration. This paper introduces the Syntactic Enhancement Network (SEN), a pioneering method for synergizing syntactic elements with established pre-trained language models. Our approach encompasses a dual-phase evaluation: initially, we delve into the syntactic augmentation of both RNN-based and Transformer-based language models; subsequently, we test our SEN's proficiency in two specific domains: the task of sentence completion and the extraction of biological relationships. The results are striking, with SEN achieving a stellar 91.2% accuracy in sentence completion—surpassing baseline models by a substantial 37.8%. In the context of biological relation extraction, SEN demonstrates competitive prowess with a 75.1% F1 score.

Scientific progress is increasingly dependent on knowledge in computation-ready forms. In the life sciences, among others, many scientists therefore extract and structure knowledge from the literature. In a process called manual curation, they enter knowledge into spreadsheets, or into databases where it serves their and many others' research. Valuable as these curation efforts are, the range and detail of what can practically be captured and shared remains limited, because of the constraints of current curation tools. Many important contextual aspects of observations described in literature simply do not fit in the form defined by these tools, and thus cannot be captured. Here we present the design of an easy-to-use, general-purpose method and interface, that enables the precise semantic capture of virtually unlimited types of information and details, using only a minimal set of building blocks. Scientists from any discipline can use this to convert any complex knowledge into a form that is easily readable and meaningful for both humans and computers. The method VSM forms a universal and high-level language for encoding ideas, and for interacting with digital knowledge.

In this paper we discuss a problem of generalization of binomial distributed triangle, that is sequence A287326 in OEIS. The main property of A287326 that it returns a perfect cube n as sum of n-th row terms over k; 0 ≤ k ≤ n−1 or 1 ≤ k ≤ n, by means of its symmetry. In this paper we have derived a similar triangles in order to receive powers m = 5; 7 as row items sum and generalized obtained results in order to receive every odd-powered monomial n^2m+1; m ≥ 0 as sum of row terms of corresponding triangle. In other words, in this manuscript are found and discussed the polynomials D_m(n,k) and U_m(n,k), such that, when being summed up over k in some range with respect to m and n returns the monomial n^2m+1.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Existing methods of hyperspectral anomaly detection still face several challenges: 1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; 2) The ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; 3) Current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind spot network based on multi-scale blind spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial-spectral joint module and a background feature attention mechanism to enhance the interaction between spatial-spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that integrates pixel shuffle down-sampling (PD) operation with spatial spectral joint screening to address the issue of anomalous identity map-ping and facilitate finite-scale mask convolution to adapt to the detection of more scale targets. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the art methods.

It is of great academic significance to understand how the atomic structure of interfaces and boundaries in materials impacts the magnetic coupling nature across the boundaries and encourage development of state-of-the-art magnetic devices. Here, we carry out a systematic investigation of the atomic and electronic structures of twin boundaries (TBs) in LiFe5O8 thin films and determine their concurrent magnetic couplings using atomic-resolution transmission electron microscopy and atomistic first-principles calculations. The results show ferromagnetic or antiferromagnetic coupling can exist across the different TBs in LiFe5O8 (LFO) thin films and electrical structures within a few atomic layers directly rely on the TB atomic core structures. Uncovering one-to-one relationship between the magnetic properties of individual TB and the local atomic structures will clarify a thorough comprehension of numerous fascinating magnetic properties of commonly utilized magnetic materials, which will undoubtedly encourage the progress of sophisticated magnetic materials and devices.

This research analyzes the phenomenon of the reduction of urban green spaces in the post-socialist city. Belgrade was selected as a post-socialist city of the former Yugoslavia. The article analyses the views of urban planners on the reasons for the reduction of urban green areas in the post-socialist city based on semi-structured interviews with representative samples for urban areas. This result research confirms the hypothesis that post-socialist city planning reduces urban green areas due to the lack of new mechanisms in documents for the planning of urban green space. The concept of sustainability should connect and balance the skills of urban planners and focus on how urban planners develop and apply standards and protect them in market-democratic systems. In other words, a new mechanism of post-socialist city planning implies a balance between the private and public sectors. In the planning document, urban planners should find a common denominator between the public interest, citizens, investors, and spheres determined by the sustainable principles of the development. City institutions should provide opportunities for the application of those standards. With the new approach, planning would imply work in the service of citizens in the concepts of a sustainable and green city.

Weak signal spectral detection is a serious problem in many applications, especially the target possesses certain frequency band and under low signal-to-noise ratio (SNR). A kind of novel technique based on pre-whitening scale-transformation symmetric bistable parallel stochastic resonance (SR) is proposed to solve the problem. Firstly, the pre-whitening can ensure the even distribution of the receiving signal to fit the requirement of SR processing. Secondly, the scale- transformation can help to utilize the weak signal improvement property especially under low frequency band more effectively. Thirdly, the symmetric bistable parallel SR can try to reduce the variance of the clutter and additive noise fluently. Fourthly, by subtracting the steady state re-sponse of the selected symmetric bistable SR system from the parallel SR output, the weak signal spectral detection objective can be realized successfully. Simulation results based on the real sea clutter radar data guarantee the effectiveness and applicability of the proposed symmetric bistable PSR processing approach.

Ecosystem physical structure, defined by the quantity and spatial distribution of biomass, influences a range of ecosystem functions. Remote sensing tools permit the non-destructive characterization of canopy and root features, potentially providing opportunities to link above- and belowground structure at fine spatial resolution in functionally meaningful ways. To test this possibility, we employed ground-based portable canopy lidar (PCL) and ground penetrating radar (GPR) along co-located transects in forested sites spanning multiple stages of ecosystem development and, consequently, of structural complexity. We examined canopy and root structural data for coherence at multiple spatial scales ≤ 10 m within each site using wavelet analysis. Forest sites varied substantially in vertical canopy and root structure, with leaf area index and root mass more evenly distributed by height and depth, respectively, as forests aged. In all sites, above- and belowground structure, characterized as mean maximum canopy height and root mass, exhibited significant coherence at a scale of 3.5-4 meters, and results suggest that the scale of coherence may increase with stand age. Our findings demonstrate that canopy and root structure are linked at characteristic spatial scales, which provides the basis to optimize scales of observation. Our study highlights the potential, and limitations, for fusing lidar and radar technologies to quantitatively couple above- and belowground ecosystem structure.

The impact of the agriculture net carbon effect is crucial for climate change mitigation, yet research on this topic in China is currently insufficient. In this study, the carbon balance ratio (CBR) is used to characterise the net carbon effect of agriculture, and the spatial Durbin model and threshold regression model were used to empirically analyse the CBR with a sample of 30 provincial-level regions in China from 2004 to 2019. The aim was to investigate the mechanism of the influence of the land management scale (SCALE) of farming on the CBR. The results of the study indicate that national farming generally has a net sink effect, with large changes in interannual fluctuations; the expansion of SCALE has an obvious role in increasing sinks and reducing emissions, and has a positive spatial spillover effect on CBR, with significant spatial heterogeneity. Furthermore, as the SCALE expands, the direction of the influence of rural residents’ income and education level on CBR changes in the opposite direction, showing a significant non-linear relationship characteristic. However, in most areas, the SCALE threshold has not yet been reached, suggesting that the improvement in education levels can still offset the negative impact of economic income growth in the short term. The study recommends paying attention to the role of farmers’ human capital, changing production methods, and implementing carbon peaking and carbon neutrality goals to enhance the net sink effect of the farming industry, which would be achieved by increasing land management scale.

Grazing exclusion (GE) and rest grazing (RG) are important management to restore grassland ecosystems. In order to evaluate the effects and mechanisms of different grazing management on species diversity, vegetation community indices and soil variables were determined in the plots of Qilian Mountains in Gansu Province. The results showed that diversity effects and their regulating mechanisms had space-scale dependence under different grazing management. Species richness and species diversity indices of RG in grassland were significantly higher than that of GE at the regional scale. Additionally, three grazing management in mountain meadows had only a significant effect on species richness, but different management in mountain meadows and temperate steppes had a significantly different effect on species diversity indices. Meanwhile, soil variables only influenced species diversity at the regional scale. Most of community and soil variables at each scale had positive effects on species diversity, except that the direction of biodiversity effects was negative for species coverage, mean plant height, soil porosity (SK) and bulk density (BD) under two contrasting grazing management. In conclusion, choosing RG at the regional scale and select grazing management according to different grassland types at the local scale to restore degraded grassland vegetation.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

Pulsed-field gradient (PFG) diffusion experiments can be used to measure anomalous diffusion in many polymer or biological systems. However, it is still complicated to analyze PFG anomalous diffusion, particularly the finite gradient pulse width (FGPW) effect. In practical applications, the FGPW effect may not be neglected such as in clinical diffusion magnetic resonance imaging (MRI). Here, two significantly different methods are proposed to analyze PFG anomalous diffusion: the effective phase shift diffusion equation (EPSDE) method and an observing the signal intensity at the origin method. The EPSDE method describes the phase evolution in virtual phase space, while the method to observe the signal intensity at the origin describes the magnetization evolution in real space. However, these two approaches give the same general PFG signal attenuation including FGPW effect, which can be numerically evaluated by a direct integration method. The direct integration method is fast and without overflow. It is a convenient numerical evaluation method for Mittag-Leffler function type PFG signal attenuation. The methods here provide a clear view of spin evolution under field gradient, and their results will help the analysis of PFG anomalous diffusion.

Molecular-scale density fluctuations at equilibrium produce cavities in a liquid and can be analyzed to shed light on the statistics of the number of molecules occupying observation volumes of increasing radius. An information theory approach led to the conclusion that these probabilities should follow a Gaussian distribution function. This prediction has been confirmed by computer simulations in different liquid models if the size of the observation volume is not large. The reversible work of cavity creation is exactly linked to the probability of finding zero molecules in a given observation volume. The Gaussian formula for the latter probability is scrutinized to arrive at the enthalpy and entropy changes associated with cavity creation. The reversible work of cavity creation has a purely entropic origin due to the solvent-excluded volume effect produced by the inaccessibility of a given region of the configurational space. The consequent structural reorganization of liquid molecules leads to exactly compensating enthalpy and entropy changes. These results are in line with those obtained in a direct statistical mechanical study by Lee.

The main objective of this study was to compare the removal efficiency of nutrients using Lactuca sativa, Medicago sativa and Phragmites australis in subsurface flow constructed wetlands with horizontal flow. In order to test water quality, fabricated reactors designed and the plants cultivated in the soil while their root were inside the wastewater. A long time study carried out from spring till end of autumn (9 months) in order to evaluate the difference in removal rate based on the seasonal changes. The highest removal rate was during summer which followed by spring and autumn. Thus, the effect of plants on the removal efficiency of organic matter (COD, BOD), TSS and nutrient (P and TN) appeared to be dependent on the seasonal growth. Phragmites australis the most sensitive species in order the removal of nutrient from wastewater.

The intentional damages to local Cultural Heritage sites carried out in recent months by the Islamic State have received wide coverage from the media worldwide. Earth Observation data provide important information to assess these damages in such non-accessible areas, and automated image processing techniques would be needed to speed up the analysis if a fast response is desired. This paper shows the first results of applying fast and robust change detection techniques to sensitive areas, based on the extraction of textural information and robust differences of brightness values related to pre- and post-disaster satellite images. A map highlighting potentially damaged buildings is derived, which could help experts at timely assessing the damages to the Cultural Heritage sites of interest. Encouraging results are obtained for two archaeological sites in Syria and Iraq.

Outliers seriously affect the accuracy in geometric model fitting. Previous works in coping with outliers involve threshold selection and scale estimation. However most scale estimators suppose the inlier distribution as a Gaussian model, which usually poorly meets the cases in geometric model fitting. Outliers, considered as the points with big residuals to all the true models, share common items with big values in quantized residual preferences, thus making the outliers gather away from the inliers in quantized residual preference space. In this paper we make use of this outlier consensus in quantized residual preference space by extending the usage of energy minimization to combine the model error and the spatial smoothness in quantized residual preference space for outlier detection. The energy minimization based outlier detection process follows an alternate sampling and labeling framework. After the outlier detection, an ordinary energy minimization method is employed to optimize the inlier labels, which also follows the framework of alternate sampling and labeling. The experimental results in this study show that the energy minimization based outlier detection method can detect most of the outliers in the data. Furthermore, the proposed energy minimization based inlier segmentation process segments the inliers into different models quite accurately. Overall, the performance of the proposed method is better than most of the state-of-the-art methods.

Aiming at the development of the application of massive and heterogeneous LEO constellations, the influence of typical perturbation forces on the orbit prediction of LEO satellites at different altitudes is analyzed. Firstly, the main perturbation force model of LEO satellites is introduced. Then experiments for analyzing the influence of orbit prediction on LEO satellites at different altitudes are designed. Finally, the influence of typical perturbation forces on the orbit prediction under different prediction durations by numerical analysis is assessed systematically, and the orbit prediction of LEO satellites with different non-spherical gravitational field orders, ocean tide orders and atmospheric density models were compared and calculated. The research results show that different orders of non-spherical gravitational fields should be considered when the altitude of satellites is different, the reasonable gravitational field orders for LEO satellites at 500km, 1000km, 1500km and 2000km under 1-hour forecast time are 140x140, 80x80, 60x60 and 40x40 orders, the reasonable orders of ocean tide perturbation for 1500km and 2000km satellites are 20x20 orders. Under the 24-hour forecast time, atmospheric resistance has a great influence on the precision of LEO satellites at orbit altitude below 1,000km. The forecast deviation of the LEO satellites orbit at 500km reaches 8751.576m. The difference of orbit precision between different atmospheric density models is in the meter level. The orbit deviation by the Standard Atmosphere 1976 and NRLMSISE 2000 atmospheric models respectively reaches 25.494m. Relevant conclusions provide support for the optimization and selection of dynamical models in high precision orbit determination and fast orbit prediction of LEO satellites.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human keypoint detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of keypoints. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model's robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to re-weight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded data sets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores.

A model of space-time is presented, whose origin is set before the appearance of any universe. It uses concepts of previous work, a central one being that the information about the laws of nature is stored locally everywhere in space such that all physical processes obey them. Since information storage is bound to matter or energy, space is assumed to consist of dark energy (DE), which thus assumes an importance far beyond its function as a stopgap for an energy deficit in our universe ( U ). The primordial state (PS) is a timeless and relatively large space of 3 dimensions described by a Wheeler DeWitt (WDW) equation, whose solution is interpreted as an arrangement of very many space quanta and possesses properties that remind of the time-honored concept of the unmoved mover. The comparison of WDW solutions belonging to different mass densities of the DE favors one in which the space quanta are mini black holes. This allows the space quanta to be imagined as mass points which are kept at a distance compatible with the quantum volume by the Pauli principle or something equivalent. Triggered by a dramatic increase in probability, the PS is followed by a time-controlled sequence of quantized expansion states (ES i ). The concomitant emergence of time occurs in a lightning huge crash, consuming almost all DE of the PS. The subsequent quantized ES i emerge from a WDW equation, novelly derived from a solution of the modified cosmic momentum equation, which classically describes an exponential space expansion (CS). The huge volme of the ES i is also used as size of the PS. Information is assumed to be geometrically stored in arrangements of space quanta similar to how genetic information is stored in DNA molecules. During the CS information must be transferred from already existing "information quanta" to new ones. This requires time, slows down the expansion and is taken into account by a friction term in the cosmological momentum equation thus modified. Since the Friedmann-Lemaître (FL) equation remains fully valid, the model is widely based on recognized equations, so that some results may be understood as interpretations.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

Color centers in diamond and nanodiamonds can be utilized as quantum sensors for measuring various physical parameters, particularly magnetic and electric fields, as well as temperature. Due to its small size and possible surface functionalization, fluorescent nanodiamonds are attractive systems for biological and medical applications since they can be used for intracellular experiments. This review focuses on fluorescent nanodiamonds for thermometry with nanoscale spatial resolution for living systems. The current state of the art, possible further development, and potential limitations will be discussed here.

Three-dimensional (3D) geological modeling technology offers intricate geological information services and technical support for the research of urban geological conditions and their evolutionary mechanisms. It further facilitates the analysis of environmental geological problems, encompassing their distinguishing features and genetic mechanisms. The precision of such three-dimensional models is predominantly influenced by the caliber of modeling data, the chosen modeling methodologies, and the intricacy of the modeled geological objects. This research, exemplified by the three-dimensional geological structure modeling of the Wuhan metropolitan development area, aims to explore a comprehensive three-dimensional modeling framework for urban geology, particularly in scenarios with large-scale and complex geological conditions. This study holds significant technical value in enhancing the application of 3D geological modeling, as well as practical importance for urban geological investigations and research.

The goal is the optimal synthesis of the parameters of the control law for a nonlinear satellite attitude control system under constraints related to the saturation effect of reaction wheels. The maximum stability degree of the control system is considered as a criterion for the optimality of the parameters of the control law, and the absolute values of the control torques and angular velocities of the reaction wheels, achievable within the limits of the physical characteristics of the drives, are considered as constraints. The solution to the optimal synthesis problem is based on the transformation of the nonlinear control system model into a linear model, within the framework of the problem of global asymptotic stability of the control system is also guaranteed. The problem is decomposed into a subproblems of obtaining the optimal transient process shape in the control system and considering the constraints. A method of synthesizing the optimal transient process shape based on the criterion of maximum stability degree had been developed. An analytical method of determining the optimal values of the control law parameters, based on decomposing the normalized characteristic polynomial in the linear model into three identical factors had been proposed. A method with regards to the constraints of the control torque and angular speed of the reaction wheels, based on transitioning from relative to real time is developed. An algorithm for calculation of the transition scale from relative to real time, ensuring the fulfillment of all constraints in the optimal synthesis problem, is proposed. An example demonstrating the efficiency and simplicity of the proposed methods and algorithm for targeted selection of technical characteristics of the control system, considering the maximum absolute values of angular speeds and control torques of the reaction wheels, is provided.

This article explores the intricate dynamics of complexity and emergence through the lens of a graphical model representing hippocampal functions. Utilizing a series of overlapping curves that intersect at varying degrees and frequencies, we create a visual metaphor for the sophisticated processes to store information observed in the hippocampus. This model not only depicts the spatial and frequency-related interactions of these curves but also illustrates the emergent patterns that arise from these interactions. These patterns exemplify the principles of complexity and emergence, where the collective behavior of simple elements results in intricate and unexpected outcomes. This graphical representation serves as a bridge between the abstract mathematical concepts of complexity theory and the tangible biological processes observed in neuroscience. By drawing parallels to the hippocampus, known for its role in memory formation and spatial navigation, the article sheds light on how complex systems, such as neural networks, exhibit properties that are more than just the sum of their parts. The model thus offers a unique perspective on understanding complex systems, not only in neuroscience but in broader scientific inquiries where complexity and emergent phenomena are pivotal.

The Collatz conjecture, a longstanding mathematical puzzle, posits that, regardless of the starting integer, iteratively applying a specific formula will eventually lead to the value 1. This paper introduces a novel approach to validate the Collatz conjecture by leveraging the binary representation of generated numbers. Each transition in the sequence is predetermined using the Collatz conjecture formula, yet the path of transitions is revealed to be intricate, involving alternating increases and decreases for each initial value. The study delves into the global flow of the sequence, investigating the behavior of the generated numbers as they progress toward the termination value of 1. The analysis utilizes the concept of probability to shed light on the complex dynamics of the Collatz conjecture. By incorporating probabilistic methods, this research aims to unravel the underlying patterns and tendencies that govern the convergence of the sequence. The findings contribute to a deeper understanding of the Collatz conjecture, offering insights into the inherent complexities of its trajectories. This work not only validates the conjecture through binary representation but also provides a probabilistic framework to elucidate the global flow of the sequence, enriching our comprehension of this enduring mathematical mystery.