Miracle-dz/UniFAR-FTUs huggingface dataset:
Trustworthy Blockchain Gateways for Resource-Constrained Clients and IoT Devices

Resource-constrained blockchain clients and the Internet of Things (IoT) devices pose limitations in terms of processing and storing the entire blockchain ledger and mining blocks. Such clients and devices rely on a view of the blockchain provided by full nodes acting as gateways. However, gateway nodes sometimes can provide a distorted view of the blockchain that makes lightweight clients vulnerable to the eclipse attack. When under such an attack, a client cannot differentiate between a forked view of the blockchain and the legitimate blockchain ledger leading to fatal consequences and huge losses incurred. In this paper, we propose a data attestation solution that employs full nodes as validators to attest the responses reported by gateways of lightweight nodes. We leverage smart contracts to give lightweight clients confidence in the data reported as they are unable to validate it from the blockchain network themselves. The system governs the attestation process that comprises submitting attestation requests, approving them, recording the response of validators, and manage payments. Clients can, thereafter, provide their feedback about the validator/gateway performance in the form of a reputation score. We present the proposed system architecture and describe its implementation on the Ethereum blockchain network. We evaluate the proposed solution with respect to functionality testing, cost of execution, and security analysis of the developed smart contracts. We make our smart contracts code and testing scripts publicly available.
Ecological Footprint Impact Factors Forecasts using VAR Model: Decision Making Case Study From Ukraine

In this article we investigated main factors affecting ecological footprint in Ukraine. Ecological footprint helps to improve sustainability and well-being, to optimize public project investments and to understand their impact in the planet. Deep analysis of researches for other countries has been adopted. We concluded that such factors as GDP per capita, exports of goods and services and population have an impact at ecological footprint. Further, we simulated data by Vector Autoregression model and used it as a forecasting algorithm. We chose this model because there are various time series influencing each other and EF. We’ve got statistically significant forecast values that can be used by policy and decision makers at the country level.
Optical suppression of energy barriers in single molecule-metal binding

Understanding the interactions between molecules and metal surfaces is of widespread importance in electrochemistry, sensing, medical imaging/targeting, molecular electronics and spintronics. Although many techniques have characterized the molecule-metal transient bonds, conflicting conclusions arise from their buried location and heterogeneity, while single-molecule probes are scarce. Confinement of optical fields to picometre length-scales around adatoms (termed picocavities [1] , Fig. 1a ) has enabled tip-enhanced and surface-enhanced Raman spectroscopies (TERS and SERS, Fig. 1e ) of single-molecules. However how adatoms change with molecule-metal interactions, with light (both key ingredients in photocatalysis), and how they link to picocavity formation in metal nanogaps is not understood.
Effects of Image Preprocessing on the Performance of Convolutional Neural Networks for Pneumonia Detection

Pneumonia is an infection that is typically diagnosed by taking X-Ray images of chest of the subject and it has a very high death rate in children. Recent advancements in deep learning allows for analyzing large amounts of images for detection of such diseases. In this study, effects of three image preprocessing methods on the pneumonia detection performance of deep learning models are analyzed. Contrast limited adaptive histogram equalization (CLAHE), unsharp masking, and wiener filtering are the involved preprocessing methods. For the deep learning models, two different convolutional neural network (CNN) architectures, namely, MobileNetV2 and EfficientNetB0 are trained with and without these preprocessing steps and the corresponding classification results are compared. According to the results, application of Wiener filtering operation improves the classification accuracy for both models. However, the other two preprocessing methods may improve the performance only when used with MobileNetV2. In particular, the highest accuracy, precision, and f1-score are obtained when MobileNetV2 model is trained with images preprocessed with Wiener filtering, and their respective values are 0.9199, 0.9187, and 0.9372.
High-Resolution Measurements of Halogenated Volatile Organic Compounds Using Frequency Comb Fourier Transform Spectroscopy

Halogenated volatile organic compounds (HVOCs) play an important role in the photo-chemistry of the atmosphere, for example in ozone depletion [1] . They are produced naturally in the oceans but are also used in industrial and agricultural applications where they may pose a health-hazard due to their biological effects. Optical detection of these compounds would hence be of great value in, for example, atmospheric monitoring and leak detection in workplaces. Crucial for such detection schemes is access to accurate spectroscopic models, which in turn require high-precision laboratory measurements. Due to the combination of broad spectral coverage and high resolution, optical frequency comb Fourier transform spectroscopy is an excellent tool for providing the necessary spectroscopic data. We use a mid-infrared frequency comb and a Fourier transform spectrometer (FTS) to measure and assign high-resolution spectra of multiple absorption bands of two HVOCs: methyl iodide, CH 3 I [2] , and dibromomethane, CH 2 Br 2 , around 3.3 µm.
Vehicle Speed Measurement Methodology Robust to Playback Speed-Manipulated Video File

Concomitant with the virtual ubiquity of dashcams in vehicles, the instantaneous speed of vehicles during accidents can now be measured using dashcam videos to understand the causes of such accidents. In this study, we estimated vehicle speeds using dashcam videos recorded in various road environments and analyzed them by comparing them to ground truth. We also considered various scenarios yielding retaken and re-encoded videos from additional recording devices, depending on the circumstances, wherein the original video can be manipulated in terms of playback speed and/or frames per second. The experimental results revealed that the actual and measured speeds were similar for most user-specific intervals. Moreover, the results suggest that our estimated speed is adequately valid as evidence, regardless of retake and re-encoding states. Furthermore, we adopted a statistical approach (i.e., Pearson’s correlation coefficient analysis) to verify the proposed vehicle speed estimation method. The results showed that when playback speed was manipulated in re-encoded or retaken video, the proposed method consistently measured speeds at 99.99 % accuracy. This verified method will play an important role in deriving reliable results from various evidential video sources.
Exploring Design Alternatives for Replicated RAMP Transactions Using Maude

Developing correct, scalable, and fault-tolerant distributed databases is hard and labor-intensive. The increasing complexity of such systems under modern cloud infrastructures, e.g., geo-replicated multi-partitioned datacenters, further limits the number of design alternatives that can be explored in practice. There is therefore a need for the formal analysis of both their qualitative properties, e.g., data consistency, and their quantitative properties, e.g., latency, at an early design stage. In this paper we use formal modeling and both standard and statistical model checking techniques to explore the design space of replicated RAMP (Read Atomic Multi-Partition) transactions for geo-replicated databases. Specifically, we develop in Maude formal, executable specifications of three replicated RAMP designs, two by the RAMP developers and one by us, and analyze their data consistency properties. We further transform the Maude models into probabilistic rewrite theories for statistical model checking w.r. t. performance properties. Our results: (i) are consistent with the conjectures made by the RAMP developers; (ii) uncover our promising new design that not only provides all crucial data consistency guarantees but also outperforms the other design alternatives.
Information Technology Model for Customer Relationship Management of Nonbank Lenders: Coupling Profitability and Risk

Nonbank lenders demonstrate upward trends of development. Mainly it is due to fintech growth and a high level of adaptability to customer`s needs and preferences. One of the actual channelings for such development is elaborating on customer relationship management. Significant credit portfolios are the background for this. The analysis of credit portfolios indicates great differences in borrower’s profitability as in borrower`s risk. The idea of this paper lies in constructing a methodological way for borrowers clustering and constructing the design of an information technology model which applies to customer relationship management. The starting point of the proposed way is grouping borrowers based on Stobachoff curves. Following that to create clusters by K-means and fuzzy approaches through adding some characteristic of the borrower. The lending strategy corresponds to each cluster. Considering that changes in borrower status and payments are very dynamic we propose the design of an IT model, which in automatic mode form a lending strategy to concreate borrower. Such an approach is applied to customer relationship management.
Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud

Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data. Some works address these issues using Markov Random Fields and Belief Propagation, but these rely on local geometric features uniquely. This article presents a strategy for ground segmentation in LiDAR point clouds composed by two main steps: (i) First, an initial classification is performed dividing the points in small groups and analyzing geometric features between them. (ii) Then, this initial classification is used to model the surrounding ground height as a Markov Random Field, which is solved using the Loopy Belief Propagation algorithm. Points are finally classified comparing their height with the estimated ground height map. On one hand, using an initial estimation to model the Markov Random Field provides a better description of the scene than local geometric features commonly used alone. On the other hand, using a graph-based approach with message passing achieves better results than simpler filtering or enhancement techniques, since data propagation compensates sparse distributions of LiDAR point clouds. Experiments are conducted with two different sources of information: nuScenes’s public dataset and an autonomous vehicle prototype. The estimation results are analyzed with respect to other methods, showing a good performance in a variety of situations.
Fuzzy Logic Based Scheme for Directional Overcurrent Detection and Classification for Transmission Line

This paper proposes a fuzzy logic-based scheme to detect the direction of the overcurrent faults (phase-phase) in the transmission lines through polarized voltage. The fault detection is crucial to avoid the overlap action for the protection relays in fault conditions which are led to the mal-operation in the transmission grid with unwanted tripping for healthy lines. When the fault occurs, the magnitude of the voltage and the current direction is changed (dip in voltage and rise in current) according to the type of fault in the grid. Three-phase voltage and current are measured to generate the inputs for the proposed fuzzy logic-based scheme. The typical IEEE 9-Bus Bars transmission grid has been used to implement the test for the fuzzy scheme using Simulink and Simpower systems toolbox in MATLAB software. The proposed scheme is applied to six-phase to phase faults cases in forward and reverse direction. Results show the effectiveness of the directional fault detection scheme using the fuzzy logic system.
High energy density deposition inside the bulk of dielectrics via resonance absorption

Ultrafast laser processing of nanochannels and nanovoids in the bulk of dielectrics such as fused silica, glass or sapphire is highly desirable for a number of applications such as stealth dicing, nano-fluidics or nano-photonics. It was demonstrated more than 10 years ago that high angle femtosecond Bessel beams can generate high aspect ratio with only a single laser pulse. However the exact mechanism of energy deposition was still lacking. Indeed, most numerical models were predicting defocussing of the pulse on the plasma, and very weak energy density deposition which was incompatible with the experimental result using an imaging technique developed in [1] .
Design and Calibration of a Joint Torque Sensor for Robot Compliance Control

Compliance control is one way to achieve compliance for rigid robots and is widely used in cooperative robots, lightweight robots, and exoskeleton robots. Real-time accurate collection of joint torque data is essential for compliance control during dynamic physical interactions with humans or the environment. Therefore, it is highly desirable to develop high-frequency joint torque sensor (JTS) that are accurate and sensitive and that can be properly calibrated. Toward this end, we propose herein a multi-objective optimization design method to improve the elastic strain distribution in the strain gauge working area, thereby improving the sensitivity of JTS. An analysis and comparison of a variety of easily manufactured inner hole spoke structures shows that the circle-rhombus type spoke structure is the most sensitive. An amplifier and low-pass filter circuit are designed for signal acquisition. An analysis of the sensitivity, linearity, hysteresis, repeatability, resolution, zero drift, and mechanical response of the sensor shows that the optimal sensor designed with high sensitivity of 30.5 mV/Nm, ±100 Nm capacity, 0.1 Nm resolution and 78.8 g weight, and the sampling frequency is 2 kHz. The experimental results show that the JTS is lightweight with high sensitivity and resolution, low noise, high dynamic response, all of which satisfy the design requirements. These results lead to an efficient and economical method of customizing JTS.
Optical Memory Based on Conterpropagating Light in Microresonators

A consequence of the Kerr effect in ring microresonators is that light can not always circulate simultaneously in both directions. Instead, under the appropriate conditions of input power and detuning, light can couple into the resonator only in one direction while the other continues on the coupling waveguide [1] – [3] . The two resulting light states, clockwise (CW) and counterclockwise (CCW), can be interpreted as the digital states 1 and 0 as shown in Fig. 1 (c) . Microresonators have been theorized to be a suitable solution for on-chip logical processing and switching [4] . Here we present an all-optical bistable device based on the difference of the Kerr effect for co-propagating and counterpropagating light. The device does not require any electrical power, and relies only on a single laser input for its operation. The optical state is set by the dominant input power direction. The system exhibits hysteresis ( Fig. 1 (d) ) hence the optical state is maintained until an significant input in the opposite direction is received. The hysteresis provides resilience to fluctuations in the inputs up to half of the total input power.
Electrical injection-locking dynamics of a frequency-modulated comb

The comb spacing of semiconductor comb lasers can be locked to an external reference by injecting a radio-frequency (RF) signal f inj close to the laser beat note round-trip frequency $f_{rt}^0$ [1] . This allows for controlling $f_{rt}^0$ and reducing its RF line width ∆ f rt . In this work, electrical-injection locking of a frequency-modulated quantum dash comb laser emitting at 1.57 µ m is demonstrated. Its round-trip frequency is $f_{rt}^0 = 20.1{\text{ GHz}}$ (cavity length 2 mm). The locking dynamics, recorded by direct detection of the comb’s output in the RF domain, are depicted in Fig. 1(a) for injection powers P inj from -10 dBm to 8 dBm. From -10 dBm up to 0 dBm, $f_{rt}^0$ and its -3 dB line width ∆ f rt remain unaffected. Within this range, the beat note line width amounts to 80 kHz. By increasing P inj from 0 dBm to 2.5 dBm, a pulling effect towards f inj becomes evident. For P inj > 2.5 dBm, the laser remains locked with ∆ f rt reduced to below 1 Hz, the microwave generator bandwidth. When sweeping f in j across $f_{rt}^0$ at P inj =8 dBm, Fig. 1(b) , two regimes evolve. For f inj outside the locking range of 1.8 MHz, $f_{rt}^0$ remains mostly unaffected with a small pulling effect for f inj close to the locking range. For frequencies inside the locking range ∆ f rt narrows to the bandwidth of the microwave oscillator and f rt follows f inj . Weak mixing side-band signals become apparent outside the locking range. Locking dynamics will be explained in more detail in the presentation. We employ the formalism of injection-locked coupled oscillators [2] and extend it by a noise term to take into account the stochastic origin of timing fluctuations. Results are depicted in Fig. 1(c) and Fig. 1(d) and are in agreement with the experiments. In order to quantify the improvement in phase noise of the comb by electrical injection locking $\left( {{f_{inj}} = f_{rt}^0} \right)$ , we measure phase noise spectra in dependence on P inj . Fig. 1(e) depicts increasing phase noise reduction by up to 20 dB at 100 kHz for increasing electrical injection strength. Simulation results ( Fig. 1(f) ) with a stochastic model [3] , developed for amplitude-modulated semiconductor comb lasers, are in excellent agreement with the experimental findings. Finally, we will show, that by joining both models, the comb laser beat note tuning can be directly linked with the phase noise reduction.
3D-printed core-cladding waveguides and adiabatic splitters for integrated photonic circuits

Additive manufacturing by two-photon polymerization offers the possibility to directly print free-form efficient optical interconnects for integrated photonic systems. Core-cladding 3D-laser printed structures are nowadays the subject of intense study due to its ability to propagate, couple and manipulate single-mode light. Basic photonic tools like single-mode low-loss waveguides [1] or multimode light splitters [2] have recently been demonstrated. However, current fabrication technologies imply multiple processing steps and frequently require utilizing different materials for a waveguide’s core and cladding, which significantly increases process complexity.
A Smart Sterilization Robot System With Chlorine Dioxide for Spray Disinfection

The highly infectious and serious nature of coronavirus disease 2019 (COVID-19) has highlighted the need for hospital space disinfection technology and the prevention of human exposure to pathogenic environments. This research developed novel chlorine dioxide (ClO2) sterilization technology to reduce bacteria and viruses in the air and on surfaces. A smart sterilization robot system was also developed to spray disinfectants in operating theaters or patients’ rooms, designed according to the results of controlled experiments and the requirements for hospital disinfection. The system was built incorporated a semi-automatic remote-controlled module and an automatic intelligent disinfection function; that is, it could operate independently according to specific epidemic prevention strategies, which were implemented using a combination of Internet of Things (IoT) applications and a gesture recognition function. The elimination of Escherichia coli (E. coli) bacteria on sample plates was 99.8 % effective. This paper reviews the evolution of various disinfection technologies and describes a disinfection robot system in detail.
Quality Scoring of the Fengyun 4A Clear Sky Radiance Product

The Clear Sky Radiance (CSR) product has been widely used instead of Level 1 (L1) geostationary imager data in data assimilation for numerical weather prediction due to its many advantages concerning superobservation methodology. In this study, CSR was produced in two water vapor channels (channels 9 and channel 10, with wavelengths at 5.8–6.7 μm and 6.9–7.3 μm) of the Advanced Geostationary Radiation Imager aboard Fengyun 4A. The root mean square error (RMSE) between CSR observations and backgrounds was used as a quality flag and was predicted by cloud cover, standard deviation (STD), surface type, and elevation of a CSR field of view (FOV). Then, a centesimal scoring system based on the predicted RMSE was set to a CSR FOV that indicates its percentile point in the quality distribution of the whole FOV. Validations of the scoring system demonstrated that the biases of the predicted RMSE were small for all FOVs and that the score was consistent with the predicted RMSE, especially for FOVs with high scores. We suggest using this score for quality control (QC) to replace the QC of cloud cover, STD, and elevation of CSR, and we propose 40 points as the QC threshold for the two channels, above which the predicted RMSE of a CSR is superior to the RMSE of averaged clear-sky L1 data.
The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.