Miracle-dz/UniFAR-FTUs huggingface dataset:
Power-Efficient Trickle Algorithm With Dynamic Adjustment of Idle Interval for Internet-of-Things Application

To address the routing challenges of low-power and lossy networks, the Internet Engineering Task Force (IETF) has proposed the Internet Protocol version 6 (IPv6) Routing Protocol for Low Power and Lossy Networks (RPL). In RPL, control messages are exchanged to construct and maintain the route. Redundant control messages increase the control overhead and consume power. Hence, the trickle algorithm is used in RPL to control the dissemination of the control message—destination information objects (DIOs). Most of the variants of trickle algorithms have focused on reducing convergence time (CT) but have not considered power consumption (PC) and dynamic traffic rates, which are the main requirements of the Internet of Things (IoT). To fill this gap, we propose the power-efficient trickle algorithm (PETA). In the PETA, we conserve power by dynamically adjusting the idle time left after the DIO transmission. The PETA is compared with the Elastic Hop Count Trickle, Elastic Trickle, and Standard Trickle Algorithms for two objective functions (OFs), namely, minimum rank with hysteresis OF (MRHOF) and Objective Function Zero (OF0). Simulation results obtained from the Contiki OS/Cooja Simulator 3.0 show that using MRHOF in a dynamic scenario, the proposed PETA outperforms benchmark protocols by reducing control overhead by 73% and PC by 55% and increasing the packet delivery ratio (PDR) by 11% in 30-node topology. With constant traffic, the PETA gives promising results by reducing control overhead by 61% and PC by 45% in the 30-node network. The proposed PETA is ideal for energy-critical IoT applications, as evidenced by its decreased PC and better PDR.
Structural and performance enhancement focused review of substrate integrated waveguide cavity backed antennas: The perspective of wireless and satellite communication

The increasing demand for efficient data transmission and reception, coupled with high-speed data requirements, is set to drive the extensive global deployment of emerging satellite and terrestrial wireless networks in the next decade. This growth in satellite and terrestrial wireless networks also creates an increased need for reliable and seamless connectivity between ground-based users and satellite communication systems. Substrate integrated waveguide (SIW) antennas offer promising solutions that can serve both terrestrial wireless networks and satellite applications. This review paper presents a comprehensive examination of SIW antennas, exploring their potential applications in both wireless and satellite communication scenarios. An extensive evaluation of various approaches that can be utilized to decrease the size of SIW antennas while also improving their bandwidth and gain are discussed. Also, the design strategies for SIW-based multiband, circularly polarized, dual-polarized, and self-multiplexing antennas designed for satellite communication applications, without relying on rigorous optimization techniques are discussed. Furthermore, the potential future directions and applications of SIW antennas by identifying emerging trends and challenges in the field are also highlighted. Consequently, the review will potentially motivate new research initiatives and directions, while providing valuable guidance for future SIW antenna development and research endeavors.
First steps of a common birds monitoring scheme in the Moscow region, Russia

Morkovin, A. A. , M. V. Kalyakin & O. V. Voltzit 2017: First steps of a common birds monitoring scheme in the Moscow region, Russia. Vogelwelt 137: 89–98. Moscow and the surrounding Moscow region are relatively small (46,929 km2) and the most populated area in the Russian Federation. It is well-studied by ornithologists, but our knowledge about actual species abundance and multi-year dynamics is still scarce. We present the first results of a monitoring project that started in 2011 and meets the requirements of the Pan-European Common Bird Monitoring Scheme (PECBMS). Data are collected by volunteers, annually conducting line-transect censuses on free-chosen plots in Moscow, Moscow region and its surroundings. For five years the censuses were carried out on 30 sampling plots. We calculated population trends for common birds and multi-species trends for two ecological groups (forest species, open-landscape species). In total, 141 species were registered and for 16 common species significant trends were recorded. For 11 species positive trends were obtained: most of them were woodland birds, accordingly the group indicator exhibited an increase of 38 %. The other five species all associated with open habitats, showed negative trends, accordingly the open-landscape group decreased by 13 %. For 11 of 16 species trend signs were consistent with tendencies in the European Union (EU), potentially indicating the influence of some continental-scale factors. Local factors could play a role as well. The increase of some woodland birds probably reflects population recovery after several harsh weather events in 2010-2011. The reasons that determine the dynamics of open-landscape species remain unclear. Further investigations are needed to reach any firm conclusions.
Automated Student's Details Access System Using Face Recognition

The outlook for the development of face recognition technology is quite promising, and there is a significant demand in the market for it. This is due to the introduction of the era of big data in the world, as well as the benefits of face recognition technology. The purpose of our work is to design a real time system to provide student's information using face recognition. Research in the field of facial recognition has been done from time to time and by using different technologies, it is not only limited to recognizing a face but also providing important details about the individual and to some extent, its organization. In the proposed work, we are trying to present vital information which can be acquired by just scanning the face of a particular individual (be it a student or an employee). Proposed work mainly includes four processes, firstly the face of the student is scanned, then a face vector is generated, if the face vector crosses a specific threshold value, then the details of the student will get displayed. The experimental results validate the general viability of the algorithm's design and show that the system is able to produce the desired attendance and informational outcomes using facial recognition technology.
Advances in the Experiments of Leaching in Cement-Based Materials and Dissolution in Rocks

Leaching in cement-based materials and dissolution in rocks are important problems in civil engineering. In the past century, concrete damage caused by leaching have occurred worldwide. And, rock dissolution is usually the main cause of karst rock erosions. This paper provides a review of the causes, influencing factors, and effects on engineering properties of dissolution of rocks and leaching of cement-based materials. The applied experimental methods for leaching and dissolution have been sorted out and discussed. In situ field experiments can be used to study dissolution under natural conditions, while the laboratory experiments can effectively shorten the experiment time length (by changing pH, temperature, pressure or other factors that affect the leaching or dissolution) to quickly investigate the mechanism of dissolution and leaching. Micro tests including XRD, SEM, EDS, and other testing methods can obtain the changes in material properties and microstructures under leaching and dissolution. In addition, with the advances in technologies and updated instruments, more and more new testing methods are being used. The factors affecting the leaching and dissolution include environmental factors, materials, and solvent parameters. The mechanisms and deterioration processes of leaching and dissolution varies according to the types of material and the compositions.
A Joint Saliency Temporal–Spatial–Spectral Information Network for Hyperspectral Image Change Detection

Hyperspectral image change detection (HSI-CD) is a fundamental task in the field of remote sensing (RS) observation, which utilizes the rich spectral and spatial information in bitemporal HSIs to detect subtle changes on the Earth’s surface. However, modern deep learning (DL)-based HSI-CD methods mostly rely on patch-based methods, which leads to spectral band redundancy and spatial information noise in limited receiving domains, thus ignoring the extraction and utilization of saliency information and limiting the improvement of CD performance. To address these issues, this article proposes a joint saliency temporal–spatial–spectral information network (STSS-Net) for HSI-CD. The principal contributions of this article can be summarized: 1) we have designed a spatial saliency information extraction (SSIE) module for denoising based on distance from center pixels and spectral similarity of the substance, which increases the attention to spatial differences between similar spectral substances and different spectral substances; 2) we have designed a compact high-level spectral information tokenizer (CHLSIT) for spectral saliency information, where the high-level conceptual information of changes in spectral interest can be represented by nonlinear combinations of spectral bands, and redundancy can be removed by extracting high-level spectral conceptual features; and 3) utilizing the advantages of CNN and transformer architectures to combine temporal–spatial–spectral information. The experimental results on three real HSI-CD datasets show that STSS-Net can improve the accuracy of CD and has a certain improvement in the detection of edge information and complex information.
P²FRPSI: Privacy-Preserving Feature Retrieved Private Set Intersection

Private Set Intersection (PSI) protocols can securely compute the intersection of the private sets on the server and the client without revealing additional data. This work introduces the concept of Privacy-Preserving Feature Retrieved Private Set Intersection (<inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula>). In <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocols, the client can obtain the intersection that satisfies a given predicate without revealing the predicate and additional data. We formally define the <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol, including its inputs, outputs, functionality, and security. To achieve the privacy guarantee in <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocols, a new two-party protocol is designed, namely Secure Secret Shared Retrieval (<inline-formula> <tex-math notation="LaTeX">$\mathsf {S^{3}R}$ </tex-math></inline-formula>), which can be used to securely determine whether each item on the server satisfies the predicate. We construct an <inline-formula> <tex-math notation="LaTeX">$\mathsf {S^{3}R}$ </tex-math></inline-formula> protocol and prove its security in the semi-honest model. On the basis of this, we design an efficient OT-based <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol and an easy-to-implement DH-based <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol and prove that they are secure in the semi-honest model. Our implementation shows that the OT-based <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol can perform the matching for about 1000K items in 3.8 seconds with a single thread. Moreover, the DH-based <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> can perform the matching for about 7000K items in one hour with four threads, with communication totaling 1456 MB, while the OT-based <inline-formula> <tex-math notation="LaTeX">$\mathsf {P^{2}FRPSI}$ </tex-math></inline-formula> protocol requires 1673 MB.
Development of a security and privacy framework for the internet of things = Desarrollo de un framework de seguridad y privacidad aplicado al internet de las cosas

2.1. Motivation and Goals In recent years, the Internet of Things (IoT) [4] has become a widely used term to describe a global network of interconnected smart objects, whose envisioned scenarios promise to transform our everyday lives. The concept of IoT was mentioned for the first time by Kevin Ashton (co-founder of the Massachusetts Institute of Technology (MIT)’s Auto-ID Center in 1999. However, its realization has been possible in recent years due the confluence of different advances in pervasive computing and wireless communications. These developments are enabling physical devices with capabilities to sense, process and communicate information, becoming Smart Objects [45] that start to compose our surrounding environment. While there are different projections on the IoT impact, this has been identified as one of the major emerging paradigms in the area of Information and Communications Technology (ICT), as indicated by the renowned company Gartner in its last Hype Cycle for Emerging Technologies, which is shown in Figure 2.1. In this sense, different predictions suggest that this trend will continue in the coming years, reaching the interconnection among 50 and 100 billion devices in 2020 [60]. Figure 2.1: Gartner’s 2015 Hype Cycle for Emerging Technologies
Wireless communication network

Providing a wireless communication network (110), comprising: a plurality of communication cells (70), each defined by at least one communication coverage area (54); and a plurality of access points (112), each corresponding to a plurality a cell different communication cells (70). Each access point is configured to (i) at least one communication coverage in a first communication cell (70) in a first frequency region subscriber and (ii) a second communication cell (70) in the subscriber at least one communication. Each access point is further configured to access point communication (70) in communication with the third cell on a second frequency.
Exploiting Hybrid CNN and RF for Detailed Disease Classification of Cotton Leaf Spot: A Trek from Image Pixels to Increased Accuracy

Cotton leaf spot disease can cause severe output losses if not detected and controlled quickly. We propose a hybrid convolutional neural network (CNN) and random forest (RF) model for the multi-classification of cotton leaf spot disease severity levels in this research study. The goal is to appropriately determine severity levels for illness management. The proposed approach uses CNN to extract meaningful features from input photos, including spatial correlations and discriminative patterns. Based on feature significance estimates, the RF algorithm uses these features to create judgments. To test the algorithm, we collected 10,000 cotton leaf spot disease photos in five severity levels. Preprocessing included scaling, normalization, and data supplementation to improve model generalization. The hybrid model was trained to 95.64% accuracy on the test set. This high accuracy shows the model’s accuracy in severity classification. We compared the suggested model to field models. In the case of parameter performance, the hybrid CNN and RF model beat the competing models. The model’s severity level categorization is shown by this superiority and its major ramifications. Correct severity categorization allows quick and targeted disease control, improving crop health and output. Farmers and agronomists may use the model to properly diagnose and treat cotton leaf spot disease.
Study of application of complex signal analysis to predicting karst-fractured ground water with GPR

The precise and immediate forecasting karst-fractured groundwater is a key problem to be studied and resolved currently in tunnel design and construction. Complex signal analysis can be used to extract multiple parameters to make a synthetical judgment. Based on this advantage, it is introduced to the prediction of water with the ground penetrating radar (GPR) to analyze the water response in the radar data from amplitude, frequency and phase, which overcomes disadvantage that the judge criteria is single, and provides a practical method to predict karst-fractured groundwater with GPR. The theory of complex signal analysis and necessity to be introduced to GPR signal processing is first illustrated. Then the feasibility of the karst-fractured groundwater prediction with GPR based on complex signal analysis is discussed; and the criteria to estimate water is obtained. At last, with project example, the GPR data is processed in complex signal analysis and the karst-fractured groundwater is analyzed. It is shown that the application of complex signal analysis improves the accuracy and credibility of prediction greatly in comparison with the excavation result.
Significance of S100B Protein as a Rapid Diagnostic Tool in Emergency Departments for Traumatic Brain Injury Patients

Traumatic brain injuries (TBIs) are not only the leading cause of death among people below 44 years of age, but also one of the biggest diagnostic challenges in the emergency set up. We believe that the use of serum biomarkers in diagnosis can help to improve patient care in TBI. One of them is the S100B protein, which is currently proposed as a promising diagnostic tool for TBI and its consequences. In our study, we analyzed serum biomarker S100B in 136 patients admitted to the Emergency Department of the Regional Specialist Hospital in Olsztyn. Participants were divided into three groups: patients with head trauma and alcohol intoxication, patients with head trauma with no alcohol intoxication and a control group of patients with no trauma or with injury in locations other than the head. In our study, as compared to the control group, patients with TBI had a significantly higher S100B level (both with and without intoxication). Moreover, in both groups, the mean S100B protein level was significantly higher in patients with pathological changes in CT. According to our study results, the S100B protein is a promising diagnostic tool, and we propose including its evaluation in routine regimens in patients with TBI.
$\mu$ PrintGen: Supporting Workflow Logs Analysis Through Visual Microprint

A microprint is a visualization that condenses the contents of a text file into a small space. This is done by using colored scaled -down characters or pixels to represent each character of the text. The colors aim to highlight information relevant to the user. We have created μPrintGen, a framework for creating custom microprints by associating colors to rules that specify what is included or excluded from the line, with plain text or regular expressions. At the same time, the user can customize the look and structure of the created microprint for ease of use. We also created $\mu$ PrintVis, a website for viewing the created microprints that supports various navigation features, like zoom and search. Feedback from the Rust and Julia open-source communities shows that μPrintGen lets users navigate through large log files, while easily highlighting reported errors. Two experts agree on the benefits of $\mu$ PrintGen, compared to pure textual-based searching features commonly offered by text editors. Video URL: https://youtu.beIlJIZHLQdXiO
A Study of Mindfulness Effectiveness among Neurotic Students based on EEG

Anxiety and stress are commonly reported problems related to working life where physical and mental conditions become worse due to environment exhaustion. An objective assessment is conducted to analyze brain signals using electroencephalography (EEG). In this study, Cognitive Ability Modulation Index (CAMI) was measured by calculating the relative band power in the prefrontal lobe to measure cognitive ability. It is observed that CAMI showed higher value in post-MI than pre-MI, especially for the Fp1 channel which relates to the outcome of a positive impact of MI in reducing anxiety and stress while performing the Stroop Colour Task. To validate CAMI as a suitable measure for effectiveness of Mindfulness Intervention (MI) towards reducing anxiety and stress among neurotics, classification of CAMI before and after MI is performed. The classification achieved 80% accuracy using the Quadratic Support Vector Machine classifier.
Guided Acoustic Waves in Polymer Rods with Varying Immersion Depth in Liquid

Monitoring tanks and vessels play an important part in public infrastructure and several industrial processes. The goal of this work is to propose a new kind of guided acoustic wave sensor for measuring immersion depth. Common sensor types such as pressure sensors and airborne ultrasonic sensors are often limited to non-corrosive media, and can fail to distinguish between the media they reflect on or are submerged in. Motivated by this limitation, we developed a guided acoustic wave sensor made from polyethylene using piezoceramics. In contrast to existing sensors, low-frequency Hanning-windowed sine bursts were used to excite the L(0,1) mode within a solid polyethylene rod. The acoustic velocity within these rods changes with the immersion depth in the surrounding fluid. Thus, it is possible to detect changes in the surrounding media by measuring the time shifts of zero crossings through the rod after being reflected on the opposite end. The change in time of zero crossings is monotonically related to the immersion depth. This relative measurement method can be used in different kinds of liquids, including strong acids or bases.
Solving the choice puzzle: Financial and non-financial stakeholders preferences in corporate disclosures

The paper delves into the relationship between accounting conservatism, valued by financial stakeholders, and corporate social performance (CSP), esteemed by non-financial stakeholders. This study assesses the potential impact of financial reporting practices, specifically accounting conservatism, on a firm’s CSP activities, which has significant implications for diverse stakeholders. Employing an accrual-based proxy for accounting conservatism and the social contribution value per share from the Shanghai Stock Exchange as a proxy for CSP, the study utilizes a sample of 25,490 year-company observations of A-share listed companies on China’s Shanghai and Shenzhen stock exchanges spanning from 2008 to 2019. Empirical findings indicate a negative correlation between accounting conservatism and CSP. The study suggests that higher levels of social performance are associated with reduced conservatism in financial reporting, indicating that firms prioritize CSP over the interests of financial stakeholders by adopting less conservative financial reporting policies. Aligned with agency theory, these results underscore that socially responsible firms are less inclined to employ accounting conservatism in reporting earnings. This study establishes a connection between firms’ unconventional and less traditional activities, such as CSP, and conservative financial reporting, offering valuable insights for investors, analysts, and regulators. AcknowledgmentThis paper is co-funded by the European Union through the European Education and Culture Executive Agency (EACEA) within the project “Embracing EU corporate social responsibility: challenges and opportunities of business-society bonds transformation in Ukraine” – 101094100 – EECORE – ERASMUS-JMO-2022-HEI-TCH-RSCH-UA-IBA / ERASMUS-JMO-2022-HEI-TCHRSCH https://eecore.snau. edu.ua/