esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
###### Abstract  
The performance of Acoustic Emission technique is governed by the measuring efficiency of the piezoelectric sensors usually mounted on the structure surface. In the case of damage of bulk materials or plates, the sensors receive the acoustic waveforms of which the frequency and shape are correlated to the damage mode. This numerical study measures the waveforms received by point, medium and large size sensors and evaluates the effect of sensor size on the acoustic emission signals. Simulations are the only way to quantify the effect of sensor size ensuring that the frequency response of the different sensors is uniform. The cases of horizontal (on the same surface), vertical and diagonal excitation are numerically simulated, and the corresponding elastic wave displacement is measured for different sizes of sensors. It is shown that large size sensors significantly affect the wave magnitude and content in both time and frequency domains and especially in the case of surface wave excitation. The coherence between the original and received waveform is quantified and the numerical findings are experimentally supported. It is concluded that sensors with a size larger than half the size of the excitation wavelength start to seriously influence the accuracy of the AE waveform.  
acoustic emission; concrete; sensor size effect; source orientation; excitation frequency; coherence +
Footnote †: journal: Applied Physics Letters A
## 4 Test Results and Discussion  
### Degree of Surface Roughness Degradation  
According to the angular convention, the shear directions were determined to be 90\({}^{\circ}\) and 270\({}^{\circ}\). The polar plots of the 3D roughness parameter \(A_{0}\theta_{max}^{*}/(C+1)\) (Figure 9) present the calculated degree of the surface roughness degradation for the tested joint samples under different normal stress conditions. Figure 9 quantitatively illustrates the relative differences in the surface roughness along the 36 analyzed directions; the surface roughness decreased in all directions as the number of cycles increased. Taking every five cyclic shear cycles as intervals, the ratio of the variation in the roughness \(A_{0}\theta_{max}^{*}/(C+1)\) at this interval to time was calculated in order to obtain the roughness degradation rate. It could be seen that the roughness degraded the fastest along the cyclic shear direction (90\({}^{\circ}\) and 270\({}^{\circ}\)) during the first five shear cycles (Figure 10). The intuitive prediction was that the roughness polar curve will shrink toward the origin. That is, it will gradually develop from an initial approximate circle to an ellipse, with the 0\({}^{\circ}\) direction as the long axis and the 90\({}^{\circ}\) direction as the short axis. In addition, the roughness degradation rate differed as the number of shear cycles increased under different normal stress conditions.  
Figure 8: (**a**) Surface wear area and scratches of specimen H3 after cyclic shear. (**b**) The image binarization method was used to distinguish the worn area from the unworm area.  
Figure 10: Plots of the roughness parameter, \(A_{0}\theta_{max}^{*}/(C+1)\), degradation rate of the lower joint surface under different normal stresses: (**a**) 0.4 MPa, (**b**) 1.0 MPa, and (**c**) 2.5 Mpa.  
Figure 9: Polar plots of the 3D roughness parameter, \(A_{0}\theta_{max}^{*}/(C+1)\), of the lower joint surface under different normal stresses: (**a**) 0.4 MPa, (**b**) 1.0 MPa, and (**c**) 2.5 Mpa.  
Using the Cartesian coordinate system to plot the roughness parameter curves can more clearly and intuitively show the degradation rate otherness of the joint surface roughness under different normal stress conditions. Figure 11 clearly shows that the roughness parameter \(A_{0}\theta_{max}^{*}/(C+1)\) decayed approximately uniformly as the number of shear cycles increased with 0.4 MPa normal stress. The roughness was seriously attenuated after two shear cycles, and the degradation rate slowed down in the subsequent shearing process under 2.5 MPa normal stress (Figure 11c). The roughness degradation rate under 1.0 MPa normal stress (Figure 11b) was between these results.
## 2 Materials and Methods
### Water Quality Assessment  
#### 2.4.1 Drinking Water Quality Index  
The drinking water quality index (DWQI) is frequently used to determine the suitability of groundwaters. In this study, the determination of DWQI was performed according to Equations (2)-(5) [14; 26; 27].  
\[W_{i}=\frac{w_{i}}{\sum_{i}^{n}w_{i}} \tag{2}\]  
\[Q_{i}=\frac{e_{i}-v_{i}}{b_{i}-v_{i}}*100 \tag{3}\]  
\[SI=W_{i}*Q_{i} \tag{4}\]  
\[DWQI=\sum_{i=1}^{n}SI \tag{5}\]  
where \(W_{i}\) is the relative weight; \(w_{i}\) is the weight assigned to each parameter according to its relative importance for drinking water (the maximum weight of \"5\" has been assigned for the highest importance and the minimum weight of \"2\" for the lowest importance); \"\(n\)\" is the number of groundwater parameters; \(Q_{i}\): is the rating according to the distribution of the \"\(i\)th\" parameter. \(e_{i}\): is the concentration of each parameter; \(v_{i}\): is the optimum value of the parameter (\"0\" is considered as optimum value for all parameters, except pH which is \"7\"); \(b_{i}\) is the guideline value [28] for each parameter; \(Sl\): is the sub-index of \"\(i\)th\" parameter. According to some researchers [14; 29; 30], the optimum values and weights for the parameters of the DWQI are: pH (\(b_{i}\) = 8.5, \(w_{i}\) = 4, \(W_{i}\) = 0.13), TDS (mg/L, \(b_{i}\) = 500, \(w_{i}\) = 4, \(W_{i}\) = 0.13), total hardness (mg/L, \(b_{i}\) = 300, \(w_{i}\) = 3, \(W_{i}\) = 0.10), calcium (mg/L, \(b_{i}\) = 75, \(w_{i}\) = 3, \(W_{i}\) = 0.10), magnesium (mg/L, \(b_{i}\) = 30, \(w_{i}\) = 3, \(W_{i}\) = 0.10), nitrates (mg/L, \(b_{i}\) = 45, \(w_{i}\) = 4, \(W_{i}\) = 0.13), chlorides (mg/L, \(b_{i}\) = 250, \(w_{i}\) = 2, \(W_{i}\) = 0.06), sulfates (mg/L, \(b_{i}\) = 200, \(w_{i}\) = 2, \(W_{i}\) = 0.06), fluorides (mg/L, \(b_{i}\) = 1, \(w_{i}\) = 4, \(W_{i}\) = 0.13), and total alkalinity (mg/L, \(b_{i}\) = 200, \(w_{i}\) = 2, \(W_{i}\) = 0.06). Based on the results of Equation (4), the aquifer water was then classified into different categories: DWQI \(<\) 50 (excellent), DWQI = 50-100 (good), DWQI = 100-150 (moderate), DWQI = 150-200 (poor) and DWQI \(\geq\) 200 (extremely poor).
# Decoupling the Impacts of Climate Change and Human Activities on Terrestrial Vegetation Carbon Sink
## 2 Data and Methodology
### Calculation of NEP  
Net ecosystem productivity (NEP) can capture the dynamics of material cycling and energy flow, serving as a reliable measure of the carbon sink potential in terrestrial vegetation-soil ecosystems [30]. NEP can also help determine whether an ecosystem functions as a carbon source or a carbon sink. A positive NEP denotes that the ecosystem serves as a carbon sink. Conversely, a negative NEP means that it functions as a carbon source [31]. NEP can be estimated by the difference between NPP and soil heterotrophic respiration (\(R_{h}\)) [32; 33]. The formulas are as follows:  
\[NEP=NPP-R_{h}\]  
\[R_{h}=0.6163\times\left(1.55\times e^{0.031T}\times\frac{P}{P+0.68}\times\frac{ SOC}{SOC+2.23}\right)^{0.7918}\times 1000\]  
where NEP is the net ecosystem productivity (gC/m\({}^{2}\)); NPP is the net plant productivity (gC/m\({}^{2}\)); \(T\) is the average temperature (\({}^{\circ}\)C); \(P\) is the annual precipitation (mm); and \(SOC\) is the storage of 0-20 cm topsoil organic carbon (gC/m\({}^{2}\)).
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
## 2 ADC Architecture and Considerations  
The proposed SAR ADC architecture is depicted in Figure 3. During the sampling phase, the high-linearity bootstrap switch [8] is enabled to sample the input signal and store it in the CDAC. The design employs the top plate sampling technique, which means that the sampling signal Vin is directly connected to the input of the comparator. In the sampling phase, the bottom plate voltage of the CDAC is linked to VREF1, VREF2, VREF3, and VREF4 through an inverter and MUX, with VREF1 equaling 2 times VREF2, 4 times VREF3, and 8 times VREF4. After the sampling phase, the SAR ADC enters the successive approximation (SA) phase, during which the dynamic comparator begins to operate. The SAR logic adjusts the control signals SWP<i> and SWN<i> of the CDAC based on the comparator's output and generates the SAR ADC output result Dout. In the SA phase, the bottom plate of the CDAC is sequentially and monotonically switched to VREFN.  
Figure 1: The role of ADC in the receiver.  
Figure 2: Architecture of a conventional SAR ADC.  
The sampling clock signal CLK is generated by a clock generation circuit. The clock generation circuit utilizes a tunable delay unit and a series of digital logic to produce a variable duty cycle sampling clock signal.
# Information, Thermodynamics and Life: A Narrative Review
## 1 Introduction
### The Basic Principles of Information
Alternatively, data could be considered as a set of objective events, observations, or activities that are recorded and stored, but not organized in such a way that they acquire some particular significance. Data can be in various forms. They can be numerical, alphanumeric, shapes, images, sounds, etc. Examples of such records are the temperature changes in one year, the meteorological observations of an area for a certain period of time (date, temperature, humidity, sunshine, etc. ), the altitude of a geographical area, the number of users who wrote the word \"flu\" in social media, etc. Within a system, data can be inserted through paper or electronic form, language, image or audio, stored on various storage media and produce within a system a sufficient bulk able to be converted into information. Although raw data are not particularly important, they have the characteristic that their collection is relatively simple and can be easily transmitted and stored in an electronic database. When data acquire a measuring unit, it is then that they become useful information. Finally, information can be processed in order to become knowledge, which is the level that living organisms use [9]. Information theory focuses on the valuation (quantification), storage and correspondence (communication) of any sort of data. [PERSON] in 1948, in his landmark paper \"A Mathematical Theory of Communication\" [10], proposes a theory about information that includes a transmitter and a receiver for the encoding of every information-message. In this article, the basic elements of communication are described according to the general Shannon diagram and have five key points: (a) an information source, that produces a message; (b) a transmitter, that has the ability to recreate a message as a signal; (c) the signal is sent through the channel; (d) the receiver, that mainly transforms the signal he receives, from the channel into the message that was intended to transfer, and (e) finally the destination which can be a person or a machine for whom the primary message was intended (Figure 1).
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### Developing Artificial Neural Networks
#### 3.4.2 ANN Generated UCS  
This research indicates that using an artificial neural network (ANN) framework consistently predicts untreated and marble powder-stabilized clay soils' unconfined compressive strength (UCS). This prediction is achieved by considering the percentage of waste material, moisture content, and curing time as input variables, as seen in Table 4. The predicted unconfined compressive strength (UCS) values exhibited a robust positive Pearson correlation with the empirical results, with coefficients close to \(1\). The results of this study corroborate those of [PERSON] et al., who found that the predicted UCS values of the ANN model were highly correlated with their input parameters [75].  
Furthermore, this study represents the first application of artificial neural network (ANN) modeling in soil stabilization using marble powder waste. The results demonstrate that artificial neural networks (ANN) can accurately incorporate the various physicochemical effects of the marble powder additive to mimic unconfined compressive strength (UCS).  
Previous studies have used artificial neural networks (ANN) in soil stabilization [75]. However, this research introduces a novel approach by integrating the ANN model with experimental optimization techniques to analyze the components that affect the unconfined compressive strength (UCS). This facilitates the improved prediction of the optimal quanti  
Figure 9: Depicts the UPV of amended and unamended clays influenced by water content and curing time.  
ties of marble powder to be added to achieve the desired engineering targets of unconfined compressive strength (UCS).  
Using an integrated modeling-experimental methodology and validating an artificial neural network (ANN) approach to simulate the significant unconfined compressive strength (UCS) improvements achieved by marble powder stabilization provide a unique tool for designing and optimizing these environmentally friendly soil combinations.
## 4 Material and Methods
### Phenotyping: NIRS (Near-Infrared Spectroscopy) and Chemical Analysis
#### 4.2.6 Predictive Models Based on NIRS  
Modified Partial Least Square (MPLS) [64] was used to model the linear relationship between spectra and chemicals, as implemented in WinISIT 4 software. Raw spectra were pre-processed using a Standard Normal Variate (SNV) coupled with Detrend (DT), and the mathematical treatment \"1,4,4,1\" was applied. Here, the numbers represent the derivative order (1), the gap over which the derivative is calculated (4 data points), the number of data points for smoothing (4), and the smoothing (1).  
The optimal number of latent variables (LVs) was determined via 10-fold cross-validation in WinISIT 4 software. The 10-fold cross-validation splits the data into ten parts--training on nine and testing on one--and is repeated 10 times with each part used as the test set once. Model performance was primarily evaluated using the Pearson correlation coefficient (r\({}_{\rm cv}\)) between predicted and actual chemical values of cross-validated samples.  
Multi-year NIRS models for protein, oil, C18:1, C18:2, and C18:3 used a combined 2021-2022 training set. Year-specific models were developed for the other compounds.
###### Abstract  
This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless data transmission and energy-efficiency in mobile edge devices. Therefore, our proposed dynamic offloading decision algorithm is designed for the joint optimization of delay and energy-efficient communications based on DRL framework. According to the performance evaluation via data-intensive simulations, this paper verifies that the proposed dynamic algorithm achieves desired performance.  
mobile edge computing offloading; real-time; deep reinforcement learning; deep Q-network
# Impact of Plasma Combustion Technology on Micro Gas Turbines Using Biodiesel Fuels
## 2 Materials and Methods
### Micro Gas Turbine (MGT) Engine Fabrication
#### 2.2.3 Fabrication of Oil and Fuel System Unit  
The fabricated parts of the oil system for the MGT engine consist of a 14-L oil tank, a gear oil pump, and a heat exchanger. The fabricated oil system unit comprises an oil filter and a control valve to maintain the system oil pressure at 5 bars for sufficient lubrication of all moving parts. It also serves as a cooling medium, assisting in quick heat dissipation to protect the turbine engine from excessive heating, as displayed in Figure 4a. The oil used in the system is fully synthetic of 5 W-30 grade, in compliance with the Petroleum Quality Institute of America as specified for the smooth operation of turbochargers of any GTE.  
The fuel system consists of two distinct subsystems: one for the fossil kerosene and the other for the biodiesel fuel system, as displayed in Figure 4b. The storage capacity of the fabricated tanks is 20 L in volume, with an 8 bar lifting pressure fuel pump (SKU 456072355_MY-702440838) for each fuel line and a pressure-controlled valve and injector nozzle. The system fuel line has an integrated fuel filter to prevent particle blockage of the injector's nozzles.
## 2 Materials and Methods
### Data Pre-Processing
#### 2.3.1 Motion Signals  
After the subjects performed the experiment, we acquired motion signals from the triaxial accelerometer and gyroscope. Firstly, to describe the spatial variation of acceleration and angular velocity, the Euclidean norm of acceleration (\(a_{norm}\)) and angular velocity (\(\omega_{norm}\)) are defined, as calculated by Equations (1) and (2):  
\[a_{norm}=\sqrt{a_{x}^{2}+a_{y}^{2}+a_{z}^{2}}, \tag{1}\]  
\[\omega_{norm}=\sqrt{\omega_{x}^{2}+\omega_{y}^{2}+\omega_{z}^{2}}, \tag{2}\]  
where \(a_{x}\) is the acceleration of the \(x\)-axis, \(a_{y}\) is the acceleration of the \(y\)-axis, \(a_{z}\) is the acceleration of \(z\)-axis, \(\omega_{x}\) is the angular velocity of the \(x\)-axis, \(\omega_{y}\) is the angular velocity of the \(y\)-axis, and \(\omega_{z}\) is the angular velocity of the \(z\)-axis.  
A fixed-size sliding window technique has been widely utilized in sensor-based activity studies [28]. Hence, the sliding window with 5 s size and 75% overlapping is applied to the continuous sensing data. Then, time-domain features are selected, including mean, variance, standard deviation, maximum, minimum, range, kurtosis, skewness, and correlation coefficient. Therefore, 70 features are extracted for a single sensor, as shown in Table 3, and a total of 210 features (70 features per sensor \(\times\) 3 sensors) are extracted in the experiment.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Feature Vector** & **Description** \\ \hline \(f_{74}^{M}\sim f_{16}^{M}\) & Mean of \(a_{x}\), \(a_{y}\),  
Since data consists of different scales, each of them can potentially have different distributions. However, this variability can affect the performance of machine learning classification. Therefore, it is necessary to scale features proportionally, ensuring all features fall within a specific range, to avoid the impact of these differences on classification outcomes. Normalization is a common approach. In this work, min-max normalization is utilized to scale features, as calculated by Equation (3):  
\[n_{ij}=\frac{x_{ij}-x_{min}^{i}}{x_{max}^{i}-x_{min}^{i}}\,, \tag{3}\]  
where \(\left\{x_{ij}|\ i\in[1,210],j\in[1,\ number\ of\ sliding\ window]\right\}\) is the feature matrix for motion data, \(x_{max}^{i}\) is the maximum at the row \(i\), and \(x_{min}^{i}\) is the minimum at the row \(i\).
# A Framework for Mobile-Assisted Formative Assessment to Promote Students' Self-Determination
## 1 Introduction  
Formative assessment or assessment for learning is defined as the \"assessment for which the first priority in its design and practice is to serve the purpose of promoting students' learning\" (p. 10) [1]. Formative assessment shifts the purpose of assessment from a measurement focus to a learning focus [2]. With the latest developments in mobile technologies, there is an increase in adoption of mobile technologies in educational practice. Research has shown that the integration of mobile technologies in learning has a significant impact on motivation [3, 4]. The use of mobile technologies for formative assessment, when compared with traditional paper-based means, can be beneficial [5, 6]. Features of mobile technologies, such as ubiquity and context-awareness, personalization and adaptivity, interactivity and immediate feedback provision, facilitate the integration of assessment within teaching and learning and therefore have the potential to transformative assessment into a more embedded and pervasive instructional method [7, 8, 9]. However, the integration of mobile technologies in learning and assessment introduces both opportunities and challenges, and therefore necessitates the rethinking of the underlying pedagogical frameworks [7].  
While studies exist that provide evidence for the motivational impact of mobile-based assessment for learning [5, 6], with few exceptions [10], not many frameworks exist to better guide the development of mobile-based assessment [11, 12, 13]. The current study proposes a framework that is grounded on a motivation theory and leverages mobile technologies to guide the design of mobile-assisted formative assessments that can promote motivation.  
The underlying theory is the Self-Determination Theory of Motivation (SDT) [14]. SDT has been already successfully used in mobile-supported learning environments [15]. The importance of developing an SDT-based framework for mobile-learning activities is due to the following reasons. First, mobile-based assessment can be part of a blended learning strategy that often complements and extends other more traditional modes of learning. Second, seamless and ubiquitous formative assessment is connected with the growing need for a continuous evaluation and support of learners' knowledge and skills throughout lifelong learning in the 21 st century [16].
## 2 Related Work
### Deep Learning-Based Methods  
Deep learning methods have significantly advanced the field by learning data-driven mappings between low-light and well-lit image domains. One of the earliest influential works, Learning to See in the Dark (SID), introduced a dataset of raw low-light images and trained a fully supervised model directly in the raw domain [11]. Another seminal work, Retinex-Net, combined Retinex theory with a neural network for decomposing and enhancing images [8].  
Unsupervised and zero-reference methods have gained popularity due to the challenge of obtaining paired datasets. Zero-DCE [9] proposed a zero-reference curve estimation network to enhance images without ground truth, using a set of non-reference quality metrics as loss functions. RetinexDIP [10] extended this concept by integrating the Retinex decomposition into an unsupervised deep image prior framework. More recent efforts also leverage self-attention mechanisms, transformer-based backbones, and foundation models for improved perceptual consistency, particularly in challenging conditions like haze or underwater scenarios [3; 4; 5; 13].  
Recent studies also explored the use of multi-exposure fusion [1], semi-supervised learning [21], and wavelet-domain learning [16] to improve both the visual quality and robustness of enhancement methods. Despite the high performance of these models, most remain too computationally heavy for deployment in time-sensitive or power-constrained environments.