Search Results (113)

Quantum sensing based on NV-centers in diamonds has been demonstrated many times in multiple publications. The majority of publications use lasers in free space or lasers with fiber optics, expensive optical components such as dichroic mirrors, or beam splitters with dichroic filters and expensive detectors, such as Avalanche photodiodes or single photon detectors, overall, leading to custom and expensive setups. In order to provide an inexpensive NV-based magnetometer setup for educational use in schools, to teach the three topics, fluorescence, optically detected magnetic resonance, and Zeeman splitting, inexpensive, miniaturized, off-the-shelf components with high reliability have to be used. The cheaper such a setup, the more setups a school can afford. Hence, in this work, we investigated LEDs as light sources, considered different diamonds for our setup, tested different color filters, proposed an inexpensive microwave resonator, and used a cheap photodiode with an appropriate transimpedance amplifier as the basis for our quantum magnetometer. As a result, we identified cheap and functional components and present a setup and show that it can demonstrate the three topics mentioned at a hardware cost <EUR 100. Full article

The estimation of runout distances for long return period avalanches is vital in zoning schemes for mountainous countries. There are two broad methods to estimate snow avalanche runout distance. One involves the use of a physical model to calculate speeds along the incline, with runout distance determined when the speed drops to zero. The second method, which is used here, is based on empirical or statistical models from databases of extreme runout for a given mountain range or area. The second method has been used for more than 40 years with diverse datasets collected from North America and Europe. The primary reason for choosing the method used here is that it is independent of physical models such as avalanche dynamics, which allows comparisons between methods. In this paper, data from diverse datasets are analyzed to explain the relation between them to give an overall view of the meaning of the data. Runout is formulated from nine different datasets and 738 values of extreme runout, mostly with average return periods of about 100 years. Each dataset was initially fit to 65 probability density functions (pdf) using five goodness-of-fit tests. Detailed discussion and analysis are presented for a set of extreme value distributions (Gumbel, Frechet, Weibull). Two distributions had exemplary results in terms of goodness of fit: the generalized logistic (GLO) and the generalized extreme value (GEV) distributions. Considerations included both the goodness-of-fit and the heaviness of the tail, of which the latter is important in engineering decisions. The results showed that, generally, the GLO has a heavier tail. Our paper is the first to compare median extreme runout distances, the first to compare exceedance probability of extreme runout, and the first to analyze many probability distributions for a diverse set of datasets rigorously using five goodness-of-fit tests. Previous papers contained analysis mostly for the Gumbel distribution using only one goodness-of-fit test. Given that climate change is in effect, consideration of stationarity of the distributions is considered. Based on studies of climate change and avalanches, thus far, it has been suggested that stationarity should be a reasonable assumption for the extreme avalanche data considered. Full article

Investigating avalanche hazards is a fundamental preliminary task in avalanche research. This work is critically important for establishing avalanche warning systems and designing mitigation measures. Primary research data originated from field investigations and UAV aerial surveys, with avalanche counts and timing identified through image interpretation. Snowpack properties were primarily acquired via in situ field testing within the study area. Methodologically, statistical modeling and RAMMS::AVALANCHE simulations revealed spatiotemporal and dynamic characteristics of avalanches. Subsequent application of the Certainty Factor (CF) model and sensitivity analysis determined dominant controlling factors and quantified zonal influence intensity for each parameter. This study, utilizing field reconnaissance and drone aerial photography, identified 86 avalanche points in the study area. We used field tests and weather data to run the RAMMS::AVALANCHE model. Then, we categorized and summarized regional avalanche characteristics using both field surveys and simulation results. Furthermore, the Certainty Factor Model (CFM) and the parameter Sensitivity Index (Sa) were applied to assess the influence of elevation, slope gradient, aspect, and maximum snow depth on the severity of avalanche disasters. The results indicate the following: (1) Avalanches exhibit pronounced spatiotemporal concentration: temporally, they cluster between February and March and during 13:00–18:00 daily; spatially, they concentrate within the 2100–3000 m elevation zone. Chute-confined avalanches dominate the region, comprising 73.26% of total events; most chute-confined avalanches feature multiple release areas; therefore the number of release areas exceeds avalanche points; in terms of scale, medium-to-large-scale avalanches dominate, accounting for 86.5% of total avalanches. (2) RAMMS::AVALANCHE simulations yielded the following maximum values for the region: flow height = 15.43 m, flow velocity = 47.6 m/s, flow pressure = 679.79 kPa, and deposition height = 10.3 m. Compared to chute-confined avalanches, unconfined slope avalanches exhibit higher flow velocities and pressures, posing greater hazard potential. (3) The Certainty Factor Model and Sensitivity Index identify elevation, slope gradient, and maximum snow depth as the key drivers of avalanches in the study area. Their relative impact ranks as follows: maximum snow depth > elevation > slope gradient > aspect. The sensitivity index values were 1.536, 1.476, 1.362, and 0.996, respectively. The findings of this study provide a scientific basis for further research on avalanche hazards, the development of avalanche warning systems, and the design of avalanche mitigation projects in the study area. Full article

The S-box is a key component of modern ciphers, determining the quality and performance of the cryptographic algorithms in which it is applied. Many constructions for synthesizing high-quality S-boxes have been established, and those based on Galois fields theory—for example, the Nyberg construction applied in the AES cryptographic algorithm—are particularly important. An integral component of the Nyberg construction is the affine transformation, which is used to improve the avalanche and correlation properties of the S-box. In this paper, a new approach is adopted for synthesizing affine transformations for S-boxes based on the quaternary matrices over the Galois field GF(4). We describe four basic structures that serve as the foundation for synthesizing a complete class of 648 affine transformation matrices of order n = 3 and a class of 7776 matrices of order n = 4 and introduce a recurrent structure to facilitate the synthesis of matrices for higher orders. Using these matrices in combination with the Nyberg construction, it is possible to construct bijective S-boxes that outperform the original Nyberg construction and many other known S-boxes in terms of strict avalanche criterion (SAC) and bit independence criterion strict avalanche criterion (BIC SAC) values, while maintaining a maximal level of nonlinearity and good cryptographic properties. We also propose modified GF(4) affine transformations that can be applied to specialized S-boxes which already satisfy the SAC for both component Boolean and 4-functions, as well as the criterion of minimal correlation between input and output, allowing us to enhance their nonlinearity to the value of N_f = 96. We integrate the synthesized S-boxes into the AES algorithm and evaluate their practical performance. The encryption outputs successfully pass the NIST statistical test suite in 96 out of 100 cases, outperforming both the original AES S-box and other reference constructions, confirming the practical strength of the proposed method. Full article

Avalanche hazard prediction remains a crucial task for mountainous regions worldwide. This study presents the development and field testing of a prototype automated avalanche hazard monitoring system designed for the East Kazakhstan region. The system integrates a snow avalanche station (including temperature, humidity, and pressure sensors; a magnetoelectric wind sensor; a data logger; and devices for autonomous operation), a temperature snow measuring rod, an API (application programming interface) service for storing weather and climate parameters in a database, and a web interface. Powered by autonomous solar energy solutions, the system ensures continuous, high-resolution monitoring of key environmental parameters. Using initial test datasets, we analyzed the specific strengths and weaknesses of the developed monitoring system using the example of one avalanche site. Avalanche prediction was performed using regression analysis (logistic regression). The evaluation of the model showed a high forecasting accuracy, with recognition rates exceeding 98%. The obtained regression coefficients can be used to predict avalanches based on meteorological data collected using the proposed equipment. The developed solution holds significant promise for improving avalanche risk management practices and can be expanded for broader application in both national and international contexts. Full article

Self-organized criticality (SOC) describes natural systems spontaneously tuned at equilibrium yet capable of catastrophic events or avalanches. The cardiovascular system, characterized by homeostasis and vasovagal syncope, is a prime candidate for SOC. Power laws are the cornerstone for demonstrating the presence of SOC. This study aimed to provide evidence of power-law behavior in cardiovascular dynamics. We analyzed beat-by-beat blood pressure and heart rate data from seven healthy subjects in the head-up position over 40 min. Cardiovascular avalanches were quantified by their duration (in beats), and symbolic sequences were identified. Five types of distributions were assessed for power-law behavior: Gutenberg–Richter, classical Zipf, modified Zipf, Zipf of time intervals between avalanches, and Zipf of symbolic sequences. A three-stage approach was used to show power laws: (1) regression coefficient r > 0.95, (2) comparison with randomized data, and (3) Clauset’s statistical test for power law. Numerous avalanches were identified (13.9 ± 0.8 per minute). The classical and modified Zipf distributions met all the criteria (r = 0.99 ± 0.00 and 0.98 ± 0.01, respectively), while the others showed partial agreement, likely due to the limited data duration. These findings reveal that Zipf’s distributions of cardiovascular avalanches strongly support SOC, shedding light on the organization of this complex system. Full article

With an avalanche of market manipulations and unethical tactics in the Australian financial industry, the empowerment levels of female Australian consumers when making financial investment decisions are highly questionable. Through the theoretical lens of a utilitarian perspective, financial investment decisions are often built on the pillars of trust, security, and assurance, which allow consumers to make decisions rationally and gain empowerment when making these decisions. However, due to the widespread manipulations prevailing in Australian financial markets, the role of rationality and its influence on consumer empowerment remain understudied. Based on this context, this paper uncovers the association between how each stage of rational decision-making (RDM) (i.e., demand identification, information search, and the evaluation of alternatives) influences the consumer power (i.e., consumer resistance and consumer influence) of female Australian consumers when making financial investment decisions. In doing so, this study employs a quantitative approach, whereby the proposed conceptual framework is tested among 357 female Australian consumers to understand their decision-making power in the presence of heightened situations of market manipulation in the financial industry. The results show that information search has a significant positive relationship with consumer influence and consumer resistance when making financial investment decisions. Additionally, the findings suggest that female Australian consumers should not only rely on individual-based sources of power but also have exposure to network-based sources of power to gain empowerment when making financial investment decisions. Lastly, it is suggested that government bodies, financial institutions, and regulatory authorities should not only implement financial literacy programs but also promote gender diversity across organisations to encourage women’s empowerment (i.e., Goal 5 (SDGs)—Achieve Gender Equality and Empower all Women and Girls). Full article

In high-energy physics, resistive plate chamber (RPC) detectors operating in avalanche mode make use of a high-performance gas mixture. Its main component, Tetrafluoroethane (C₂H₂F₄), is classified as a fluorinated greenhouse gas. The RPC EcoGas@GIF++ collaboration is pursuing an intensive R&D on new gas mixtures for RPCs to explore eco-friendly alternatives complying with recent European regulations. The performance of different RPC detectors has been evaluated at the CERN Gamma Irradiation Facility with Tetrafluoropropene (C₃H₂F₄)-CO₂-based gas mixtures. A long-term ageing test campaign was launched in 2022, and since 2023, systematic long-term performance studies have been carried out thanks to dedicated beam tests. The results of these studies are discussed together with their future perspectives. Full article

This paper presents an optoelectronic transceiver (OTRx) realized in a 180 nm CMOS technology for applications of short-range LiDAR sensors, in which a modified current-mode single-ended VCSEL driver (m-CMVD) is exploited as a transmitter (Tx) and a voltage-mode fully differential transimpedance amplifier (FD-TIA) is employed as a receiver (Rx). Especially for Tx, a concurrent automatic power control (APC) circuit is incorporated to compensate for the inevitable increase in the threshold current in a VCSEL diode. For Rx, two on-chip spatially modulated P⁺/N- well avalanche photodiodes (APDs) are integrated with the FD-TIA to achieve circuit symmetry. Also, an extra APD is added to facilitate the APC operations in Tx, i.e., concurrently adjusting the bias current of the VCSEL diode by the action of the newly proposed APC path in Rx. Measured results of test chips demonstrate that the proposed OTRx causes the DC bias current to increase from 0.93 mA to 1.42 mA as the input current decreases from 250 µA_pp to 3 µA_pp, highlighting its suitability for short-range sensor applications utilizing a cost-effective CMOS process. Full article

Single-photon avalanche detectors (SPADs) have significant applications in fields such as autonomous driving. However, processing massive amounts of background data requires substantial storage and computational resources. This paper designs a linear SPAD sensor capable of three detection modes: 2D intensity detection, 3D synchronous detection, and 3D asynchronous detection. A configurable coincidence circuit is used to effectively suppress background light. To overcome the significant resource demands for storage and computation, this paper designs a histogram circuit that simultaneously possesses data storage and shifting capabilities. This circuit can not only perform statistical counting on time data but also shift data to quickly complete computational analysis. The chip is fabricated using a 0.13 μm mixed-signal CMOS process, with a pixel scale of 64 elements, a time resolution of 132 ps, and a power consumption of 12.9 mW. Test results indicate that the chip has good detection capabilities and good background light suppression. When the background light intensity is 6000 lux, the maximum background data are suppressed by 95.4%, and the average suppression rate increases to 86% as the coincidence threshold is raised from 0 to 1. Full article

Debris flows propagating in natural environments often encounter irregular terrain features, such as bottom roughness and man-made structures like groundsills, which significantly influence their behavior and dynamics. In practice, groundsills are commonly used as debris flow mitigation structures. This study examines the effects of a beam-type groundsill array on the flow behavior of sediment mixtures in an inclined channel using numerical simulations. The sediment mixtures, modeled as Bingham fluids, were tested as they flowed over groundsill arrays with varying densities, characterized by the spacing-to-height ratio ($d/h$) ranging from 2 to 10. The findings indicate that interaction with the groundsills produces a hydraulic jump-like flow, reaching a height approximately 2.2 times the approach flow depth across different array densities. High-density arrays ($d/h\le 4$) substantially hindered flow propagation, reducing front velocities but leading to sediment buildup upstream of the groundsills. Conversely, low-density arrays ($d/h>4$) facilitated smoother flow with higher velocities. These insights into the relationship between array density, flow behavior, and sediment trapping provide valuable guidance for optimizing groundsill array designs to effectively reduce the mobility of gravity-driven flows of non-Newtonian fluids (such as snow avalanches, debris, lava, or mudflows) and mitigate the associated risks. Full article

Blockchain has several unique features: data integrity, security, privacy, and immutability. For this reason, it is considered one of the most promising new technologies for a wide range of applications. Initially prominent in cryptocurrencies such as Bitcoin, its applications have expanded into areas such as the Internet of Things. However, integrating blockchain into IoT systems is challenging due to the limited computing and storage capabilities of IoT devices. Efficient blockchain mining requires lightweight hash functions that balance computational complexity with resource constraints. In this study, we employed a structured methodology to evaluate hash functions for blockchain–IoT systems. Initially, a survey is conducted to identify the most commonly used hash functions in such environments. Also, this study identifies and evaluates a lightweight hash function, designated as HashLEA, for integration within blockchain-based IoT systems. Subsequently, these functions are implemented and evaluated using software coded in C and Node.js, thereby ensuring compatibility and practical applicability. Performance metrics, including software efficiency, hardware implementation, energy consumption, and security assessments, were conducted and analyzed. Ultimately, the most suitable hash functions, including HashLEA for blockchain–IoT applications, are discussed, striking a balance between computational efficiency and robust cryptographic properties. Also, the HashLEA hash function is implemented on a Raspberry Pi 4 with an ARM processor to assess its performance in a real-world blockchain–IoT environment. HashLEA successfully passes security tests, achieving a near-ideal avalanche effect, uniform hash distribution, and low standard deviation. It has been shown to demonstrate superior execution time performance, processing 100 KB messages in 0.157 ms and 10 MB messages in 15.48 ms, which represents a significant improvement in execution time over other alternatives such as Scrypt, X11, and Skein. Full article

The development and calibration of a measurement system designed for assessing the performance of the avalanche photodiodes (APDs) used in the Compton scattering polarimeter of the CUSP project is discussed in this work. The designed system is able to characterize the APD gain $G_{APD}$ and energy resolution across a wide range of temperatures T (from −20 °C to +60 °C) and bias voltages $V_{bias}$ (from 260 V to 410 V). The primary goal was to experimentally determine the $G_{APD}$ dependence on the T and $V_{bias}$ in order to establish a strategy for stabilizing $G_{APD}$ by compensating for T fluctuations, acting on $V_{bias}$. The results demonstrate the system capability to accurately characterize APD behavior and develop feedback mechanisms to ensure its stable operation. This work provides a robust framework for calibrating APDs for space environments. It is essential for the successful implementation of spaceborne polarimeters such as the Compton scattering polarimeter foreseen aboard the CUbeSat Solar Polarimeter (CUSP) mission under development to perform solar flare X-ray polarimetry. Full article

In light detection and ranging (LiDAR) applications, correct intensities from echo data intuitively contribute to the characterization of target reflectivity. However, the power in raw echo waveforms may be clipped owing to the limited dynamic range of LiDAR sensors, which directly results in false intensity values generated by existing LiDAR systems working in scenarios involving highly reflective objects or short distances. To tackle the problem, an asymmetric Gaussian echo model is proposed in this paper so as to recover echo power–time curves faithfully to its optical physics. Considering the imbalance in temporal length and steepness between rising and falling edges, the echo model features a shared mean and two distinct standard deviations on both sides. The accuracy and effectiveness of the proposed model are demonstrated by correcting the power–time curve from a real LiDAR loaded with avalanche photodiode (APD) sensors and estimating the reflectivities of real targets. As when tested by targets with reflectivities from low to high placed at distances from near to far, the model achieves a maximum of 41.8-fold improvement in relative error for the same target with known reflectivity and a maximum of 36.0-fold improvement in the coefficient of variation for the same target along the whole range of 100 m. Providing accurate and stable characterization of reflectivity in different ranges, the model greatly boosts applications consisting of semantic segmentation and object recognition, such as autonomous driving and environmental monitoring. Full article

Autoencoders are neural networks that have applications in denoising processes. Their use is widely reported in imaging (2D), though 1D series can also benefit from this function. Here, three canonical waveforms are used to train a neural network and achieve a signal-to-noise reduction with curves whose noise energy is above that of the signals. A real-world test is carried out with the same autoencoder subjected to a set of time series corrupted by noise generated by a Zener diode, biased on the avalanche region. Results showed that, observing some guidelines, the autoencoder can indeed denoise 1D waveforms usually observed in electronics, particularly square waves found in digital circuits. Results showed an average of 2.8 dB in the signal-to-noise ratio for square and triangular waveforms. Full article