Miracle-dz/UniFAR-FTUs huggingface dataset:
Heavy Metal Contamination in Agricultural Soils: A Case Study in Mohammedia Benslimane Region (Morocco)

This research aimed to determine the physicochemical characteristics and heavy metal concentrations of agricultural soils used for grape and wheat production in Morocco in the Mohammedia Benslimane area. The organic matter (OM) content ranged from 0.6% to 2.93%. The degree of total nitrogen was higher in the wheat plots than in the vine plots in the Mohammedia and Benslimane regions. Total nitrogen average rates ranged from 0.04 to 0.5% and from 0.07 to 0.8% in the vine and wheat plots. These results imply that the soil was silty clay and clay texture, neutral to slightly acidic at all stations. The P 2 O 5 concentrations were 11.15 ppm and 68.14 ppm under the vine and the wheat plots, respectively, while the potassium concentration ranged from 33.1 to 287.9 ppm and from 26.9 to 184.75 ppm under the vine and the wheat plots, respectively. Furthermore, the concentrations of Cd at a few stations exceeded the standard value (2 ppm), reaching 10.375 ppm. The Pb and Zn concentrations were higher in vineyard plots than in wheat plots. The Pb and Zn concentrations were 20.22 ppm and 148.60 ppm, respectively. This study reports updated information on the states of eight stations in Mohammedia and Benslimane. However, further research is necessary to determine the pollution factors in local practice crops and naturally growing plants at these stations to assess their impact on livestock and humans.
The CIE 2016 Colour Appearance Model for Colour Management Systems: CIECAM16

A colour appearance model provides a viewing-condition-specific method for the transformation of the tristimulus values, X, Y, Z, to or from perceptual attribute correlates. This publication describes a specific colour appearance model, CIECAM16, which may be useful for colour management systems, used in the imaging industries, that involve related colours. The main applications of the model are the evaluation of photographic prints and self-luminous displays, where the colours will be perceived as related colours. This model is based on the CAM16 colour appearance model. It consists of a chromatic adaptation transform and equations to calculate a set of perceptual attribute correlates using the CIE 1931 standard colorimetric observer. This report provides revisions to the CIE colour appearance model for colour management systems that involve related colours, CIECAM02. The CIECAM16 model is simpler than the original CIECAM02 model, but it maintains the same prediction performance for visual data as the original model. The evolution and application of this colour appearance model, CIECAM16, are presented, as is additional information about the use of the model in practical applications. The publication is written in English, with a short summary in French and German. It consists of 38 pages with 6 figures and is readily available from the CIE Webshop or from the National Committees of the CIE.
OPTIMISATION OF COMMERCIAL BUILDING MANAGEMENT PROCESSES USING USER BEHAVIOUR ANALYSIS SYSTEMS SUPPORTED BY COMPUTATIONAL INTELLIGENCE AND RTI

The aim of the presented project was to create a comprehensive building management system equipped with a network of wireless and energy-efficient sensors that collect data about users and on their basis control final devices such as lighting, ventilation, air conditioning and heating. In the presented system, end devices can be both products offered by the market (commercial) and proprietary solutions (own). This is to allow the adaptation of commercial radio communication protocols with high integration capabilities and common occurrence. In addition, the system has been enriched with an innovative system of tracking and building navigation and access control, which are supported by a network of radio beacons and radio-tomographic imaging technology (RTI). The whole system is to be supervised by computational intelligence learned from scratch.
Self-Sovereign Identity using Smart Contracts on the Ethereum Blockchain

Centralised identity services that exist today fail to operate transparently and protect the rights of users. Single points of trust present constant operational risks for both companies and individuals. Self-sovereign identity is a solution to address this, which speciﬁes a user-focused approach that gives full control of an identity back to the individual. This paper proposes the blockchain, a secure and decentralised trust-less system, as the platform to achieve this. A proof-of-concept identity system for the Ethereum blockchain is designed and developed in this paper. Smart contracts are used to facilitate the secure storage and open processing of user data. It also presents a novel approach to the secure recovery of encrypted private data. Emphasis is placed on the implementation security, information privacy and data recovery procedures of the system.
Compressive full waveform lidar

To avoid high bandwidth detector, fast speed A/D converter, and large size memory disk, a compressive full waveform LIDAR system, which uses a temporally modulated laser instead of a pulsed laser, is studied in this paper. Full waveform data from NEON (National Ecological Observatory Network) are used. Random binary patterns are used to modulate the source. To achieve 0.15 m ranging resolution, a 100 MSPS A/D converter is assumed to make measurements. SPIRAL algorithm with canonical basis is employed when Poisson noise is considered in the low illuminated condition.
The rate of Index Coding

The problem of Informed Source Coding on Demand, introduced by Birk and Kol [4], describes a setting where a server wishes to transmit an n -bit input word via broadcast to a n receivers; each receiver is interested in a speciﬁc block of the input data and may have side-information on other blocks. The goal of the sender is to use a code of minimal word-length, while allowing every receiver to recover his desired bit. This problem can be formulated as a graph parameter: let G be a directed graph on the vertex set [ n ], where ij is an edge iﬀ the i -th receiver knows the j -th bit, and let ‘ ( G ) denote the length of a minimal index code for G . In this work, we show that an index code for a disjoint union of graphs can be strictly better than a concatenation of the optimal codes for the individual graphs. This justiﬁes a deﬁnition of a “rate” of an index code of a graph, as the (normalized) length of an index code when performing multiple transmissions with a constant side-information graph. The proof is based on a relation between index codes for a disjoint union of graphs and the OR product of certain graphs, and relies on some results in the study of Witsenhausen’s rate and colorings of graph powers.
Self-witnessing coherent imaging for automatic noise filtering and artifact removal in laser process monitoring

High-power lasers are rapidly becoming standard tools in advanced manufacturing, mainly in the form of laser welding, laser cutting, and laser additive manufacturing. Of these applications, laser welding in the electric mobility sector—particularly in the manufacturing of battery packs—presents unique challenges. Weld depth needs to be precisely controlled to ensure joint strength and to ensure the weld does not puncture into the lithium ion cell. These processes often involve highly reflective metals (such as copper), which have material properties that lead to unstable welds; this requires an unprecedented level of control to ensure weld quality and depth. To ensure weld quality, we need in-line monitoring during the process. Inline Coherent Imaging (ICI) is a process monitoring technique that has been demonstrated to measure keyhole depth (down to 15 μm axial resolution) at high camera rates ( 200 kHz), with unparalleled sensitivity and dynamic range. Unfortunately, it suffers from sources of noise such as speckle and imaging artifacts that can pose a significant challenge to quality assurance and closed-loop control. To mitigate these problems, I have integrated a second, automatically synchronized, imaging channel into a standard ICI system, by exploiting a previously unused part of the imaging window. This “witness” image makes it possible to identify real signal based on correlation and filter out the uncorrelated noise. This has allowed feature-identification intensity threshold
A General Hebbian Learning for Nonlinear Neuron with Application to Laterally Interconnected Synergetically Self-organizing Map

In this paper we propose a nonlinear neuron model trained with Least Mean Square Error Reconstruction (LMSER) principle as a general realization of Hebbian mechanisms and apply it to a cortical self-organization model, Laterally Interconnected Synergetically Self-organizing Map (LISSOM), which is a biologically motivated self-organizing model for the simultaneous development of topographic maps and lateral interactions in the visual cortex. In the LISSOM, the simple Hebbian mechanism for afferent connections requires a redundant dimension to be added to the input and normalization is necessary. Another shortcoming of LISSOM is that several parameters must be chosen before the LISSOM can be used as a model of topographic map formation. The Least Mean Square Error Reconstruction (LMSER) learning for a nonlinear neuron can be considered as an effective alternative to the simple Heb-bian rule for the afferent connections. Our preliminary experiments demonstrate the essential topographic map property from the improved LISSOM model.
Exploiting High-Bandwidth Memory for FPGA-Acceleration of Inference on Sum-Product Networks

Due to the memory wall becoming increasingly problematic in high-performance computing, there is a steady push to improve memory architectures, mainly focusing on better bandwidth as well as latency. One of the results of this push is the development of High-Bandwidth Memory (HBM) which is an alternative to the regular DRAM typically used by accelerator-cards. This work adapts an existing accelerator architecture for inference on Sum-Product Networks (SPN) to exploit the HBM present on more recent high-performance FPGA-accelerator cards. The evaluation shows that the use of HBM enables almost linear scaling of the performance due to the embarrassingly parallel nature of batch-wise SPN inference. It is also shown that the only hindrance to this scaling is the limited bandwidth available for data-transfers between host and FPGA. Even with this bottleneck, the prior FPGA-based implementation is outperformed by up to 1.50x (geo.-mean 1.29x). Similarly, the CPU and GPU baselines are outperformed by up to 2.4x (geo.-mean 1.6x) and 8.4x (geo.-mean 6.9x) respectively. Based on the evaluation, the scaling potential of HBM-based FPGA-accelerators is explored to give an outlook on what is to come with future generations of PCIe-based interfaces.
The NOAA Unique CrIS/ATMS Processing System (NUCAPS): first light retrieval results

Launched on board the Joint Polar Satellite System (JPSS) Suomi National Polar-orbiting Partnership (NPP) platform on October 28th 2011, the Cross-track Infrared Sounder and the Advanced Technology Microwave Sounder represent the US next generation of polar-orbiting operational hyper spectral sounders. We present “first light” temperature and water vapor retrieval results from the NOAA Unique CrIS/ATMS Processing Systems. A comparison with respect to collocated ECMWF analysis and AIRS/AMSU retrieval profiles shows good stability in the retrieval performance statistics, already at this early stage of the post launch mission.
Real Time Action Recognition with Instant Characteristics

: In this research work, we proposed a most effective noble approach for Human action recognition in real-time environments. We recognize several distinct dynamic human activity actions using kinect. A 3D skeleton data is processed from real-time video gesture to sequence of frames and getter skeleton joints (Energy Joints), orientation, rotations of joint angles from random selected number of frames. Thus after extracting the set of frames we implements classification techniques Principal Component Analysis (PCA), Artificial Neural Network (ANN) and Deep Neural Network (DNN) respectively with some variants for classify our all different gesture models. However we conclude that use very less number of frame (10-15%) for train our system efficiently from entire set of gesture frames. After successfully completion of our classification methods we got an excellent overall accuracy 94%, 96% and 98% respectively. We finally observe that our proposed system is more useful then comparing to other existing system, therefore this model is best suitable for real-time application such as in video games for player action/gesture recognition.
PERFORMANCE ENHANCEMENT MODEL OF CLUSTERING TECHNIQUES FOR OUTLIER DETECTION

In Data mining there are heaps of strategies are utilized to distinguish the anomaly by causing the bunches of information and afterward to recognize the exception from them. All in all Clustering technique assumes a significant function in information mining. Clustering implies gathering the comparative information protests together dependent on the trademark they have. Exception Detection is a significant issue in Data mining; especially it has been utilized to recognize and wipe out odd information objects from given informational index where anomaly is the information thing whose worth falls outside the limits in the example information may demonstrate abnormal information. In this work we have proposed a grouping-based anomaly identification calculation for powerful information mining which uses upgraded k-implies clustering calculation to group the informational collections and weight-based focus approach. In proposed approach, two procedures are consolidated to effectively discover the anomaly from the informational index. Edge worth can be determined automatically by taking supreme estimation of least and most extreme estimation of a specific group. The test results show that upgraded technique takes least computational time and focuses on decreasing the exception that could improve proficiency of k-implies grouping for accomplishing the better-qualityclusters.
Fractional Fourier transform-based chirp radars for countering self-protection frequency-shifting jammers

Self-protection deceptive jammers create at the radar receiver output multiple-false targets that are impossible to isolate in both time and frequency domains. In this paper, we introduce a novel technique based on fractional Fourier transform (FrFT) to discriminate between the true target echo and those false targets in the case of frequency-shifting jammers. In fact, we exploit the capability of the FrFT to resolve, in a matched manner, spectra that are overlapping in time and frequency. This is a property that cannot be achieved using a standard matched filter. The theoretical analysis of this technique is presented and its effectiveness is verified by simulation.
Barriers affecting export performance of Nigerian SMEs in the leather industry

Despite the large amount of research that has been carried out to investigate the factors that affect export performance, very little research has been conducted for specific industries operating in developing countries like Nigeria. This is the case even though findings from past studies seem to indicate that export barriers are not only country but also industry specific. The specific roles of moderating and mediating variables have also being seemingly neglected in past research. This present work therefore, is a study of the factors that affect export performance of SMEs in the Nigerian leather industry. In so doing, this research evaluates the relationship between nine categories of export barriers grouped into tangible resources, intangible resources and facilitating factors on the one hand and firm export performance on the other. Based on the contingency paradigm and the resource-based view,this study posits that (1) knowledge resources barriers, (2) communication resources barriers,(3) exogenous barriers, (4) export support structure barriers, (5) image resources barriers, (6) operational resources barriers, (7) financial resources barriers, (8) marketing resources barriers and (9) human resources barriers are all strongly related to firm export performance. The study also hypothesizes that firm size moderates the relationship between tangible resources barriers and export performance and that entrepreneurial orientation moderates the relationship between tangible as well as intangible resources and export performance. Perception of export difficulty is hypothesized to mediate the relationship between intangible resources barriers and export performance. The survey type of data collection whereby self-dministered questionnaires were distributed to respondents was used for data collection. Questionnaires were mostly directly distributed and collected by trained enumerators. Data for the study was collected in Nigeria and the target sample was drawn from (1) Manufacturers Association of Nigeria (MAN), (2) the Nigerian Industrial Directory and (3) the Nigerian Exporters directory. In addition, the lists of members of the local tannery councils in the study areas were used to obtain the names of SMEs to include in the sample. Given that this research is centred on firm level activities and their impacts, the unit of analysis is at the firm level. All in 623 questionnaires were distributed and 458 were collected over a period of nine weeks for a response rate of about 74% and out of the 458, 449 usable questionnaires were obtained finally 117 questionaires were used for the analysis on exporting SMEs. Descriptive statistics were used to analyse the relative severity of export barriers and multiple regression analysis was used for hypotheses testing. Findings from the data analysis provide support for all the hypothesized relationships thus suggesting support for the theoretical model of the study.
Quintuple-mode wideband filter based on a single metal cavity

A quintuple-mode wideband filter based on a single cavity is proposed. A pair of shorting pins is inserted into the cuboid cavity along the symmetry plane. The cavity filter is fed by a pair of coaxial cables and the inner conductor is inserted into the cavity with a circular disc at the end of the probe. The pair of inserted probes and the pair of shorting pins excite five resonant modes in the cavity. The proposed filter has the central frequency at 5.8 GHz with a bandwidth of 70%. Meanwhile the wideband filter has transmission zeros at the lower and upper-stopbands to improve the selectivity and out-of-band performance. The measured insertion loss is <0.65 dB, and all measured results have a reasonably good agreement with the simulated results.
Comparison of different cooling systems using compact model

Due to the strong influence of temperature rising on integrated circuits performance and reliability, an adequate thermal analysis of their cooling systems is required during chip packaging in order to prevent the thermal catastrophe. In this paper, we propose an approach based on RC compact model, which enables in one hand an approximation of dynamic thermal behaviour and in other hand the accurate temperature computation at any measurement point of parallel plate fin heat sink with U-shape channels. Farther more, we present the impacts of convection coefficient on heat sink surface temperature using a simplified approach, which enables the temperature computation by using the RC thermal compact model. Due to the convection coefficient change of surrounding environment, the surfaces and mass of parallel plate fin heat sink with U-shape channels were reduced while enhancing heat transfer capability.
Emerging (2+1) D massive graviton in graphene-like systems

Unlike the fundamental forces of the Standard Model the quantum effects of gravity are still experimentally inaccessible. Rather surprisingly quantum aspects of gravity, such as massive gravitons, can emerge in experiments with fractional quantum Hall liquids. These liquids are analytically intractable and thus offer limited insight into the mechanism that gives rise to quantum gravity effects. To thoroughly understand this mechanism we employ a graphene-like system and we modify it appropriately in order to realise simple (2+1) -dimensional massive gravity model. More concretely, we employ (2+1) -dimensional Dirac fermions, emerging in the continuous limit of a fermionic honeycomb lattice, coupled to massive gravitons, simulated by bosonic modes positioned at the links of the lattice. The quantum character of gravity can be determined directly by measuring the correlations on the bosonic atoms or by the interactions they effectively induce on the fermions. The similarity of our approach to current optical lattice configurations suggests that quantum signatures of gravity can be simulated in the laboratory in the near future, thus providing a platform to address question on the unification theories, cosmology or the physics of black holes.
No arbitrage global parametrization for the eSSVI volatility surface

This article describes a global and arbitrage-free parametrization of the eSSVI implied volatility surfaces introduced by Hendriks and Martini [The extended SSVI volatility surface. J. Comput. Finance, 2019, 22, 25–39]. A calibration of such surfaces has already been proposed by the quantitative research team at Zeliade Systems [Cohort et al., Robust calibration and arbitrage-free interpolation of SSVI slices. Decisions Econ. Finance, 2019, 42, 665–677], but the calibration algorithm is sequential in expiries (one maturity is calibrated after the other), lacking a global view on the surface. The alternative calibration suggested in this article targets all maturities at once and always guarantees an arbitrage-free fit of market data.
Physics–Guided Neural Networks for Feedforward Control: From Consistent Identification to Feedforward Controller Design*

Model–based feedforward control improves tracking performance of motion systems if the model describing the inverse dynamics is of sufficient accuracy. Model sets, such as neural networks (NNs) and physics–guided neural networks (PGNNs) are typically used as flexible parametrizations that enable accurate identification of the inverse system dynamics. Currently, these (PG)NNs are used to identify the inverse dynamics directly. However, direct identification of the inverse dynamics is sensitive to noise that is present in the training data, and thereby results in biased parameter estimates which limit the achievable tracking performance. In order to push performance further, it is therefore crucial to account for noise when performing the identification. To address this problem, this paper proposes a forward system identification using (PG)NNs from noisy data. Afterwards, two methods are proposed for inverting PGNNs to design a feedforward controller. The developed methodology is validated on a real–life industrial linear motor, where it showed significant improvements in tracking performance with respect to the direct inverse identification.