esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Aiming at the mechanical response of geomembrane (GEM) in membrane-faced rock-fill dam (MFRD) to different ambient temperatures, the mechanical properties in axial tension of polyvinyl chloride (PVC) GEM were studied by experiment and theoretical analysis. First, fifteen groups of axial tensile tests for longitudinal/transverse specimens were conducted at different temperatures in the temperature environment laboratory, the stress-strain curve and Young's modulus were obtained, and the variation of Young's modulus with temperature was analyzed by Boltzmann function fitting. Second, the glass transition temperature of PVC GEM was obtained by differential scanning calorimetry (DSC), and the difference in mechanical properties between longitudinal and transverse specimens of PVC GEM was analyzed by thermomechanical analyzer (TMA) thermodynamic test. The results showed that the lower the temperature, the greater the Young's modulus, and the smaller the linear interval of stress and strain, while the higher the temperature, the result is opposite. The difference in mechanical properties between the two directions is related to the ambient temperature. The orientation of polymer structure accounts for the difference in mechanical properties by theoretical analysis. The fitting results of Boltzmann function have a certain reference value for numerical simulation. In design of the membrane impervious structure in MFRD, the ambient temperature should be considered fully, and the longitudinal/transverse welding splicing should be avoided as far as possible. The current test specification should test the mechanical performance of GEM at normal operating temperature of reservoir instead of the test and quality evaluation at a single temperature. The temperature should be considered comprehensively in construction to avoid damaging the performance of impervious structure and ensure the service life.  
Y 2021 11 10864. [[https://doi.org/10.3390/app112210864](https://doi.org/10.3390/app112210864)]([https://doi.org/10.3390/app112210864](https://doi.org/10.3390/app112210864))  
17 November 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Key words:** Young's modulus; mechanical properties; PVC GEM; ambient temperature; MFRD
## 1 Introduction
This is due to the differences in water constituents, such as sediment color, size shape and even concentration, that could influence light absorption, scattering and reflectance. That is why regional TSS algorithms must be developed. Table 1 shows examples of a TSS algorithm that was developed by using various remote sensing sensors and bands around the globe. The number of bands used varies from single-band [3; 32], two-band [28; 30; 33; 34] and [1; 29; 35; 36; 37] and [38], three-band [39] to four-band algorithms [23]. Recently, it was found that the difference in water constituents and seasonal dependence does not solely depend on geographical regions [23]. The aim of this study was to monitor the spatial and temporal TSS concentration over Teluk Lipat, Malaysia. The remote sensing technique was used in this study. To fulfil the aim, three objectives were set up. The first objective was to develop and validate the regional TSS algorithm utilizing the Landsat 8 (OLI) dataset. The second objective was to develop the TSS concentration map of the study area. The third objective was to analyze and investigate the TSS concentration in the space and time domains. To achieve the above-mentioned objective, two field trips were conducted in the study area. The water sample was then processed in the lab to obtain the measured TSS. The relation between the measured TSS and the Landsat surface reflectance was determined by using a regression technique. The algorithm was developed empirically by using three Landsat 8 (OLI) bands: band 3 (Green, \(\lambda\) = 0.53-0.59 \(\upmu\)m), band 5 (near Infrared, \(\lambda\) = 0.85-0.88 \(\upmu\)m) and band 6 (short-wave infrared, \(\lambda\) = 1.57-1.65 \(\upmu\)m). A TSS map was then created by applying the developed algorithm. The map was then analyzed to extract the required information. Since the algorithm was only validated with the data that was acquired over the study area, the application of the developed algorithm was limited to Teluk Lipat, Malaysia.
# Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network
## 3 Proposed Model
### Reward  
The proposed model aims to maximize the throughput (\(\tau\)) of an intersection. In addition, the model intends to distribute signals that are not selfish. Therefore, throughput is considered as a reward parameter. In addition, to avoid a selfish signal allocation, the signal is distributed within the intersection in consideration of the standard deviation of the queue length (\(d_{ql}\)). The throughput and standard deviation of the queue length are adjusted with an adaptive weight factor (\(\alpha\)). The reward (\(r_{t}\)) formula is as indicated in (3):  
\[r_{t}=\alpha\cdot\tau-((1-\alpha)\cdot d_{ql}) \tag{3}\]  
Figure 7 is a framework of the proposed model. A framework shows the interaction between the environment and the agent. The environment represents multi-intersections. The perceived state, which is the queue length of incoming lanes at the target intersection and the traffic load of the exit lane from the target intersection, is sent from the environment to the agent. Then, the agent learns over the DNN using the received information and determines action in the next state. The action represents the phase order of traffic lights at the intersection. The determined action is sent to the environment. Therefore, according to the designated phase order, the green signal at the intersection is turned on.  
Figure 6: The possible movement signal phase in a 4-way intersection.  
Figure 7: A framework of the proposed model.
# Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
## 3 Machine-Learning-Based Methodology for AE Testing
### Artificial Neural Network (ANN) Techniques
[59] identified the type of discontinuity and failure mechanisms within fiber-reinforced plastic (FRP) structures using acoustic emission (AE) data as input to be sent to an ANN-based system. The authors developed two types of networks based on back-propagation and probabilistic method with two levels to improve the accuracy of the forecast. [PERSON] et al. [60] monitored the dressing operation of an aluminum oxide wheel: the dressing operation is necessary to restore the normal operation of a worn wheel. The statistics obtained from the measured acoustic emission (AE) signal were used as input from a classification algorithm based on neural networks. Two classes have been identified: sharp and dull wheel. [PERSON] et al. [61] used ANNs to identify damage locations in deep piles using AEs. The authors performed an impact test on a circular-section concrete column of a building. [PERSON] et al. [62] monitored the steel hardening process using AEs and neural networks. Three types of events were detected in this study: high, medium, and low energy. These events allow the monitoring of the decay process of metastable austenite into bainite and martensite. The method made it possible to identify the alterations that occur on a microscopic scale. [PERSON] et al. [63] studied the degree of degradation of fiber cement panels exposed to fire with the AE method and with the use of ANNs. The fiber cement panels were exposed to fire and subsequently subjected to a three-point curvature with the relief of the acoustic emission: The collected signals were used as input for the ANNs. The degradation of the fibers contained in the boards increases with increasing exposure to fire, with a decrease in the number of AE events recognized by the ANNs as identifiers of fiber breakage. [PERSON] et al. [64] used AEs of thermally modified western hemlock wood as an input to an ANN-based model for the classification of the heat treatment level. The authors used a high-sensitivity broadband differential AE sensor to detect the stress wave generated in the wood: this signal was subsequently processed to extract the time, frequency, and wavelet domain features. These signals were sent as input to three types of networks: multilayer perceptron, group method of data handling, and linear vector quantization. [PERSON] et al. [65] identified deviations from normal bearing operating conditions by detecting AE signals and using ANNs. The AE measurements were performed with piezoelectric sensors mounted on bearings placed on a test bench at a speed of 72 rpm with an axial load of 50 kN. Three models have been implemented: ANN, support vector machine, and Gaussian process regression. In Table 2, the crucial aspect of the methodology based on ANNs is summarized.
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 3. Experimental Apparatus and Testing Methods
### PDPA Experimental System  
A PDPA device employs the optical theory of Lorenz-Mie scattering, typically comprising a laser, an incident optical unit, a receiving optical unit, a signal processor, and a data processing system. It obtains velocity information by measuring the frequency difference between the scattered light and the incident light from moving particles [29]. The particle size is determined by analyzing the phase shift caused by the reflection or refraction of the scattered light from spherical particles passing through the laser measurement volume [30]. Three lasers with different wavelengths (532 nm, 553 nm, and 561 nm) are used to irradiate particles in the flow field, and the feedback signal is then transmitted to the receiving sensor and uploaded to the data processing system, enabling non-contact measurement of the three-dimensional velocity and size of the particles [16; 31].  
The experiment explores the phenomenon of dust-mist coupling to evaluate its effectiveness in suppressing airborne dust under controlled experimental conditions. To study the coupling effect, dust particles of a specific size and quality were introduced into an aerosol generator, forming a controlled coupling field within the test box. We measured the particle size of individual suppressant mist droplets within a defined test area inside the test box and analyzed their behavior under varying conditions, including different spray pressures, suppressant solutions, and mass concentrations. The effectiveness of dust capture was assessed by analyzing the data from the PDPA system at various measurement points [11].
# Fuzzy MLKNN in Credit User Portrait +
## 3 Fuzzy MLKNN
### Basic Concepts of Intuitionistic Fuzzy Sets  
Fuzzy set theory was first proposed by Professor [PERSON] in 1965 and was first used in fuzzy control [41]. It is an effective tool for dealing with uncertain information. Later, with the deepening of research, in 1986, the concept of the intuitionistic fuzzy set (IFS) was introduced into the traditional fuzzy sets [42]. Intuitionistic fuzzy sets can more accurately describe the nature of fuzziness in information from the two dimensions of membership and non-membership and have greater advantages and characteristics when dealing with uncertainty and ambiguity. It has attracted more attention in the application fields, such as pattern recognition, intelligent control, natural language processing, machine learning, etc. [43].  
**Definition 1**.: _Assuming that \(X\) is a non-empty set, \(A=\{(x,\mu_{A}(x),v_{A}(x))|x\in X\}\) is called an intuitionistic fuzzy set._  
Among them, \(\mu_{A}(x)\in[0,1]\) and \(v_{A}(x)\in[0,1]\) are the degree of membership and non-membership of element \(x\) belonging to \(A\), respectively, \(0\leq\mu_{A}(x)+v_{A}(x)\leq 1\ \forall x\in X\). Also, \(\pi_{A}(x)=1-\mu_{A}(x)-v_{A}(x)\) is called the degree of hesitation that the element \(x\) belongs to \(A\). Generally, the intuitionistic fuzzy number is denoted as \(\alpha=(\mu_{\alpha},v_{\alpha})\)[44]. The intuitionistic fuzzy set proposed by [PERSON] is an extension of the traditional fuzzy set [32]. The introduction of the non-membership function enables the intuitionistic fuzzy set to express uncertain and fuzzy data more delicately and objectively.  
**Definition 2**.: _The Euclidean distance of any two intuitionistic fuzzy numbers \(\alpha_{1}=(\mu_{a1},v_{a1})\) and \(\alpha_{2}=(\mu_{a2},v_{a2})\) is defined as:_  
\[d(\alpha_{1},\alpha_{2})=\sqrt{\frac{1}{2}(\mu_{a1}-\mu_{a2})^{2}+(v_{a1}-v_{a2 })^{2}} \tag{1}\]
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 2 Related Works
### Our Contribution  
The study contributes to the development of secure hashing algorithms adapted to the specific challenges presented by IoT applications. It offers a scalable and efficient solution for blockchain and other distributed systems. Comprehensive performance evaluations and security assessments have been carried out, benchmarking HashLEA against other hash algorithms from the existing literature. The main contributions of this paper are as follows:  
* A novel lightweight hash function, designated as HashLEA, is proposed for implementation. It utilizes the LEA block cipher for the purpose of enhancing both performance and security.
* A comprehensive survey is conducted on hash functions. Extensive performance evaluations and security tests have been conducted, comparing HashLEA with other lightweight hash algorithms in the literature. A practical testbed simulating blockchain scenarios, along with hardware implementation on Raspberry Pi 4, validates the algorithm's applicability.
* The results of the security testing, which included statistical analysis of avalanche effect, uniformity of distribution, and collision resistance, demonstrate that HashLEA meets the security requirements and exhibits exceptional diffusion and unpredictability.
* Performance benchmarking, conducted on both software and hardware platforms, demonstrates that HashLEA outperforms existing algorithms by achieving superior execution times across various input sizes, establishing it as a suitable choice for blockchain and IoT applications.
## 1 Introduction
The calculations are based on the joint probability distribution associated with the relevant environmental parameters [13]. The concept of environmental contours has been widely used in the field of offshore engineering, e.g., in connection with hydrodynamic loading [11]. Typically, the joint statistics of wave height and peak period are described by a conditional modeling approach. For a given return period, a circle with the desired radius is created in a normalized Gaussian space, and based on an inverse mapping (IFORM) involving, e.g., the Rosenblatt transformation, the circle is mapped into the corresponding environmental contour as a continuous functional relationship between the relevant physical parameters. In addition to the environmental contours based on IFORM, alternative similar approaches are also available [14; 15]. In this work, application of environmental contours is considered in relation to ice ridge and iceberg characteristics. Estimation of the extreme response due to the associated ice loads is also addressed. As a first step, key parameters associated with ice ridges and icebergs for estimation of the resulting ship-ice loads are identified by consideration of the ship-ice interaction process. Statistical models are introduced in order to characterize the variability of these parameters. By application of the inverse cumulative distribution functions of these key parameters, environmental design contours for a given return period are established. The main objective of the present paper is to extend the methodology associated with recent application of environmental contour line methods such that they can also manage to include accidental load conditions. It is aimed at establishing a unified basis for how this can be accomplished. In order to achieve this, relevant structural design and acceptance criteria must also be revisited since these will be different for accidental conditions as compared to ice loads with a more \"regular\" rate of occurrence (i.e., shorter return periods). Accidental scenarios can be widely different, and relevant design criteria are accordingly not well-established. Recent design codes also tend to move towards a more goal-based approach rather than the prescriptive rules of today, which implies less specific and detailed design requirements. It is believed that the contour design approach will lead to increasingly efficient identification of critical design conditions. This in turn also implies great savings in terms of number of complex and time-consuming nonlinear numerical response-analyses that will be required.
## 3 Case Study Results
### Historical Climate, Sea-Level Change, and Hydomorphology
#### 3.1.3 Hydomorphology
The upper and youngest deposit layers, 3 m-thick, comprise a layer of clay and loam (fine-grained bottomset deposited from a surface sediment plume) created during the period from antiquity to the present day. At a depth of 3-10 m, the sandy material formed as a result of slow transport, and the accumulation of suspended material was created in the Holocene, which also had a layer of silty clay at a depth of 8-10 m, which was probably formed in a shorter period as a bottomset deposit. At a depth of 10-50 m, a layer of gravel with sand (created by the variable intensive transport of dragged and suspended sediment [fluvial sediment load]) in the former middle course of the Neretva River was created during the Pliocene and Pleistocene periods. At a depth of 50-120 m, a layer of coarser gravel formed during a period of continuous intensive transport and deposition of dragged sediment through the upper zone of the river (coarse-grained fluvial-deposited topset) from the Paleocene to the Miocene. At depths greater than 120 m, a geological layer of crushed dolomite and limestone exists. This geological section shows the development of the river network from the Ice Age to the present day [28]. Figure 6: Hydrogeological section of the delta; MSL: mean sea level, ASL: above sea level (modified from [4]) (CC BY 4.0)
###### Abstract  
The article discusses the issue of modelling traffic flows and the transport network. Faced with an increase in the number of vehicles in road networks, the problem of congestion and the need to optimise traffic and adapt the transport infrastructure to changing demand are growing, especially in large cities. With this in mind, the authors of this publication developed a model of the road network in the north-eastern part of the Warsaw agglomeration based on the proposed algorithm. Two methods were used to optimise the distribution of traffic flows: the Nash equilibrium and the Stackelberg approach. The Nash equilibrium assumes the aim of achieving equal average times on all roads for each origin-destination (O-D) pair. This describes the state pursued by a decentralised system guided by the individual benefits of the traffic users. On the contrary, the Stackelberg approach aims to achieve optimal travel times for the entire system. The study was carried out for three scenarios that differed in the assumed traffic demand on the road network. The basic scenario assumed the average hourly traffic demand during the morning peak hour based on traffic measurements. On the other hand, the two alternative scenarios were developed as a 10% variation in traffic volumes from the baseline scenario. On the basis of the results, it was concluded that an increase in traffic volumes for all O-D pairs could result in a decrease in traffic volumes on some links of the road network. This means that the transport network is a complex system and any change in parameters can cause significant and difficult to predict changes. Therefore, the proposed approach is useful in terms of traffic forecasting for road networks under conditions of changing traffic flow volumes. Additionally, the total travel time for the entire system differed for each scenario by a percentage difference of 0.67-1.07% between the optimal solution according to the Nash equilibrium and the Stackelberg approach.  
urban road networks; optimization; traffic congestion; traffic flow; Stackelberg approach; Nash equilibrium Article
## 2 System Model
### Cognitive Radio Wireless Sensor Network Energy Consumption Model
#### 3.3.4 Transceiver Unit Energy Consumption
Thus, energy consumption for intra-cluster communication comprises of total energy consumption of all MNs for transmitting \(B_{dt}\) bits of data packet over a distance \(d_{i,j}\) to their respective clusterheads \(CH_{j}\) and total energy consumption for receiving the data by the clusterhead \(CH_{j}\). This is given as:  
\[E_{Intr}(i,j)=\sum_{j=1}^{q}\sum_{i=1}^{m_{n}}\left(\frac{\mathrm{E}_{tM}(B_{ dt},d_{i,j})}{\frac{Data}{Transmitting}}+\frac{\mathrm{E}_{rM}(B_{dt})}{ \frac{Data}{Receiving}}\right) \tag{26}\]  
In inter-cluster data communications, the distance between CHs and BS is presumably long and therefore, the channel between them follows the Friis free space path loss model with signal power attenuation of \(\beta=4\) power loss [30]. Each clusterhead \(CH_{j}\) forwards its aggregated data packets \(B_{pk}\) to the BS either through intermediate clusterhead \(CH_{g}\) (\(if\)\(d_{j,g}>R_{max,j}\)) or direct to the BS without any intermediate clusterhead \(CH_{g}\) (\(if\)\(d_{j,g}\leq R_{max,j}\)). The total energy consumption for inter-cluster data communications is the energy consumption for aggregating the received data and energy consumption for forwarding the data packet to the BS which is given as:  
\[E_{intr}=\sum_{j=1}^{q}\left(\frac{E_{ap}\big{(}B_{ap},N_{cy}\big{)}}{\frac{ Data}{Aggregation}}+\underbrace{\mathrm{E}_{tH}\big{(}B_{pk},d_{j,g} \big{)}}_{Transmitting}\right) \tag{27}\]  
Thus, the total energy consumption for data communications can be expressed as:  
\[E_{total}^{dt}=E_{int}(i,j)+E_{int} \tag{28}\] \[=\sum_{j=1}^{q}\left(\sum_{i=1}^{m_{n}}\left(\underbrace{ \mathrm{E}_{tM}(B_{dt},d_{i,j})}_{Transmitting}+\underbrace{\mathrm{E}_{rM}(B_ {dt})}_{Receiving}\right)+\underbrace{E_{ap}\big{(}B_{ap},N_{cy}\big{)}}_{ \begin{subarray}{c}Data\\ Aggregation\end{subarray} }+\underbrace{\mathrm{E}_{tH}\big{(}B_{pk},d_{j,g} \big{)}}_{Transmitting}\right)\]  
If \(m_{n,j}\) denotes the number of member nodes in cluster \(cl_{j}\) and \(q\) is the number of clusters in the network which is also equal to the number of clusterheads in the network, then the number of cognitive radio sensor nodes in the network \(N=\sum_{j=1}^{q}(m_{n,j}+1)\). Therefore, total energy consumed by member node \(MN_{i}\) in cluster \(cl_{j}\) is given as:\[ \begin{split} E_{MN}(i,j)=\frac{E_{ss}(B_{ss})}{E_{events}}+ \underbrace{E_{log}\big{(}B_{lg}\big{)}}_{Data}+\underbrace{\frac{E_{cs}(n_{z},T_{cs})}{Channel}}_{sensing}+\underbrace{E_{rp}\big{(}B_{ld},d_{i,j}\big{)}}_{ Decision}+\underbrace{E_{rx}(B_{ld})}_{Receiving}\\ +\underbrace{\frac{E_{tM}(B_{dt},d_{i,j})}{Data}}_{Transmitting}\\ =P_{ss}\;x\;T_{ss}\;x\;B_{ss}+B_{lg}(P_{rd}\;+\;P_{wr})+\sum_{m=1 }^{n_{x}}(T_{cs}P_{ed}+E_{sp})+B_{ld}\big{(}2E_{ec}+E_{am}\;d_{i,j}^{\beta}\big{)} \\ +B_{dt}(E_{ec}+E_{am}\;d_{i,j}^{\beta})\end{split}  \tag{29}\]  
Similarly, total energy consumption of clusterhead \(CH_{j}\) in cluster \(cl_{j}\) is given as:  
\[