esa-sceva/satcom-chunk-collection huggingface dataset:
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
###### Abstract  
Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world's population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.  
soil moisture; smart irrigation; machine learning; deep learning; LSTM; bidirectional LSTM +
Footnote †: journal: Applied
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 2 Proposed Methodology
### Genetic Algorithm  
GA is an optimization and search technique inspired by the principles of natural selection and evolution. It mimics biological processes such as reproduction, mutation, crossover, and selection to efficiently explore and optimize complex solution spaces. By iteratively evolving a population of potential solutions, GA identifies optimal or near-optimal outcomes for challenging problems. This evolutionary approach enables GA to handle non-linear, multi-dimensional, and highly constrained optimization tasks, making it a powerful tool for solving complex engineering and computational problems.  
The idea of \"survival of the fittest\", which comes from [PERSON]'s theory of evolution, is the foundation of the GA function. This strategy makes use of the essential processes of crossover, mutation, and selection, all of which are useful for building robustness and accomplishing global optimization [35].  
The first step in the GA process is to initialize a population of candidate solutions, with each candidate representing a unique subset of features. The GA assesses each candidate's fitness over several iterative generations by measuring its accuracy against a fully linked model that was trained with those characteristics. In this study, the initial population was set to 20, with generations set to 10. The accuracy of the classification observed on the test dataset acts as the benchmark fitness for evaluating performance throughout the optimization process. The algorithm uses the following steps to evolve the population and improve the solutions continually:  
Selection: The GA selects the best-performing candidates from the current population based on their fitness scores. These candidates are more likely to pass on their features to the next generation.  
Crossover: Pairs of selected candidates are combined to produce offspring. This crossover operation involves mixing the feature subsets of two parents to create new feature subsets, promoting diversity in the population.  
Mutation: To maintain genetic diversity and prevent premature convergence, the GA introduces random changes to some feature subsets. This mutation operation ensures that the search space is thoroughly explored and helps in discovering potentially better feature combinations.  
The pipeline health monitoring process is improved by the GA's selection of the most important features, which guarantees that the model is efficient in spotting leaks in the pipeline. The fully connected layers of the model are subsequently trained using the top-performing feature subset from the last generation, improving the precision and dependability of the leak detection procedure.
# Stress-Strain Model for Freezing Silty Clay under Frost Heave Based on Modified [PERSON]'s Equation
###### Abstract  
In analyzing frost heave, researchers often simplify the compressive modulus of freezing soil by considering it as a constant or only as a function of temperature. However, it is a critical parameter characterizing the stress-strain behavior of soil and a variable that is influenced by many other parameters. Hence, herein several one-dimensional freezing experiments are conducted on silty clay in an open system subjected to multistage freezing by considering the compressive modulus as a variable. First, freezing soil under multistage freezing is divided into several layers according to the frozen fringe theory. Then, the correlation between the freezing rate and temperature gradient within each freezing soil layer is investigated. [PERSON]'s equation for frost heave analysis is modified to extend its application conditions by replacing its freezing rate term with a temperature gradient term. A mechanical model for the stress-strain behavior of freezing soil under the action of frost heave is derived within the theoretical framework of nonlinear elasticity, in which a method for determining the compressive modulus of freezing soil with temperature gradient, overburden pressure, and cooling temperature variables is proposed. This study further enhances our understanding of the typical mechanical behavior of saturated freezing silty clay under frost heave action.  
stress-strain model; silty clay; freezing soil; [PERSON]'s equation; frost heave
# Effect of Food Emulsifiers on Aroma Release
## 2 Results and Discussion
### Influence of Different Emulsifier Systems with Added Thickener on Aroma Release
Analysis of variance showed that concentration had a significant effect (\(p<0.05\)) on aroma release, indicating that binding between these aroma compounds and emulsifiers was significant. Xanthan gum and emulsifiers considerably influenced aroma retention and release. Variations in aroma release were found because of the different concentrations of xanthan and emulsifiers. Different emulsifiers and complexes showed different surface properties that tend to interact with different aroma molecules. Figure 7: Effects of sucrose fatty acid ester and xanthan gum on the release of ethyl acetate, (_E_)-2-hexenal, carvone, perillaldehydehyde (**a**) and \(\alpha\)-pinene, 3-carene, limonene (**b**). Significant differences (\(p<0.05\)) between different concentrations are performed by letters on the same polylines. Where the letters on the same polyline are the same, there is no significant difference between different concentrations. Furthermore, the different characteristics of emulsifiers such as molecular weight, viscosity, droplet size, emulsion type can make a difference on aroma release to different degrees [25]. Overall, emulsifiers considerably affected the behavior of aroma release, due to the composition and structure of emulsifiers, specific interactions such as hydrogen bonding, and the formation of an inclusion compound between emulsions and aroma molecules [5; 26]. The binding effect reduced the concentration of free aroma in the aqueous/oil phase and limited the transmission of aroma molecules from the oil to the water phase; this reduced the transmission rate at the emulsion-gas interface [27; 28].
## 3 File Synchronization Framework
CMFileSyncEventHandler is responsible for handling events when the client and server receive them from another party during the file synchronization process. The file synchronization event is defined by inheriting the CMFileSyncEvent class, and the transmission and handling of the event used in this paper will be described in more detail in the file mode change process in the next section. CMFileSyncInfo manages various pieces of information required by the client and server in the file synchronization process. The information required by the client includes a  
Figure 1: CM file synchronization structure. list of files in the synchronization directory, a list of block checksums for each file sent by the server, whether the synchronization has been completed for each file, and a list of files in the online mode. The information managed by the server includes the file information list sent by the client, the file block checksum information for each client, and the server-side synchronization file list for each client. CMWatchServiceTask is a client-specific class for file synchronization that implements the directory-monitoring task of Figure 1. This class is created as a separate thread object when file synchronization starts and monitors file changes in the client directory. When an event such as adding a new file, modifying an existing file, or deleting a file occurs, CMWatchServiceTask starts synchronizing the modified file with the server. When the file synchronization is stopped, the CMWatchServiceTask thread object is terminated, and the monitoring task stops. CMProactiveModeTask is a client-specific class for active file mode change, which implements the active file mode change task of Figure 1. This class is also created as a separate thread object, periodically calculating the directory activation ratio, and determining whether to change the file mode of files in the directory. We describe the details of the active file mode change mechanism in a different section. CMFileSyncGenerator is the class of file synchronization server that implements the block checksum generation task of Figure 1. When a client starts synchronization and sends a list of files, the server creates a CMFileSyncGenerator object for this client in a separate thread. This object compares the file list sent by the client with the files the server contains, selects the files to be synchronized, generates block checksum lists of the files to be synchronized, and transmits this information to the client. When the CMFileSyncGenerator thread sends the block checksum information to the client, it is terminated, and the rest of the synchronization process is performed by the file synchronization common classes.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
###### Abstract  
The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.  
location; IoT; internal location; fingerprint algorithm Article
## 3 Numerical Simulation
### The Vibration Reduction Characteristics of the System
#### 3.3.1 The Effect of Damper Mass on the Vibration Reduction  
When the working speed is constant, the damper mass has a great influence on the normal pressure. The numerical simulation parameters are taken from Table 4.  
The results are as follows:  
In Figure 11, \(E_{dt}\) and \(E_{ds}\) vary with the increase of the damper mass \(m_{3}\); some peaks of \(E_{dt}\) and \(E_{ds}\) are extant. There is a significant reduction of vibrational power with proper damper mass adding to the blade. In Figure 12, \(A_{dt}\) increases with the damper mass's increase, while \(A_{ds}\) fluctuates while the damper mass increases.\(y_{1 tm}\) and \(y_{1 sm}\) reduce significantly with the proper damper mass adding to the blade. From Figures 11 and 12, the laws of \(E_{ds}\) and \(A_{ds}\) varying with \(m_{3}\) are basically the same, while the laws of \(E_{dt}\) and \(A_{dt}\) are obviously different, as the transient response of the blade is complicated. The maximum values of \(E_{ds}\) and \(A_{ds}\) are smaller than those of \(E_{dt}\) and \(A_{dt}\) with the same parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(m_{2}\) & \(m_{3}+0.009\)kg \\ \(m_{3}\) & 0.04 kg\(-0.08\) kg \\ \(k_{2}\) & \(8\times 10^{5}\) N/m \\ \(F_{0}\) & 400 N \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters of the system.
## 2 Materials and Methods
### Catalyst Characterization  
The actual contents of Pd on the catalyst were measured by inductively coupled plasma atomic emission spectrometry (Agilent, Santa Clara, CA, USA). Nitrogen adsorption was performed on an ASAP 2020-M instrument (Micromertics, Norcross, GA, USA). The Brunauer-Emmett-Teller (BET) surface area (S\({}_{\text{BET}}\)) of the catalyst was obtained by the nitrogen adsorption method after degassing at \(-\)196 \({}^{\circ}\)C for 6 h, then the pore volume (Vp) and pore size distribution (Dp) were determined by the Barret-Joyner-Halender (BJH) method using desorption data. Scanning electron microscopy (Hitachi, Tokyo, Japan) was performed to characterize the catalyst surface morphology. To improve the sharpness of the imaging, the platinum plating treatment was carried out in advance. X-ray polycrystalline diffractometry (Bruker, Billerica, MA, USA) was performed using a Cu K\(\alpha\) source to obtain the phases of crystals on the catalysts. X-ray photoelectron spectroscopy (Thermo Fisher Scientific, Waltham, MA, USA) was performed to analyze the surface chemistry of the catalyst, and the sample was corrected for the charge energy shift by the C 1s peak.
## 1 Introduction
[PERSON] et al., having explored the use of phosphonium-based ILs as an ashless alternative to zinc dialkyldithiophosphate (ZDDP) in engine oil, observed an improvement in friction and wear [9]. The enhanced lubricity provided by ILs can be further complemented by the use of nanomaterials as lubricant additives. Nanomaterials have recently emerged as another approach to additive development for enhanced lubrication and heat transfer capability [10; 11; 12; 13; 14]. For example, the tribological performance of a lubricant can be further enhanced by the addition of carbon-based nanoparticles [11; 13]. Many mechanisms have been suggested for the friction/wear reduction induced by nanoparticles, such as the mending effect, rolling effect, ball bearing effect, colloidal effect, protective film and third body material transfer [14]. In the mending mechanism the nanoparticles fill surface grooves and/or adhere to the wear surface. Recent studies have shown ILs and nanoparticle additives to be synergistic, possibly due to aided suspension of the particles [15; 16]. In recent years, nanoscale graphene (NG) platelets have shown promise and studies have shown that small quantities of 0.05 wt% have improved friction and wear in palm-oil ester-based lubricants [17; 18]. [PERSON] et al. observed reduction in friction and wear in canola oil with optimal concentration at 0.07 wt% [19]. [PERSON] et al. looked at NG additive at 0.25-1 wt% in soybean oil and found reduction in normalized wear at the more severe conditions and low concentrations [20]. [PERSON] et al. have found an optimal concentration of NG in synthetic oil was between 0.02% and 0.06% with the lowest coefficient of friction (COF) at 0.02%. [21]. [PERSON] et al. have theorized based on their work that NG provides a protective film with high thermal conductivity, thus preventing high temperatures and surface degradation [22]. In this work, IL and nanoscale graphene (NG) platelets were added to a common rotorcraft gear lubricant conforming to DoD-PRF-85734 [23]. The tribological behavior of the DoD-PRF-85734 oil with and without the IL and NG particles was determined and compared. The potential of the additive package for enhanced tribological performance under loss of lubrication condition was demonstrated.
## 3 Data-Driven Robust Scheduling Method
### Data-Driven PSO Algorithm with Uncertain Parameters
#### 3.2.3 Particle Update Method  
In this study, we design a discrete PSO algorithm to realize the discretization of the position update process through operations such as insertion and exchange. In addition, the Metropolis criterion in the simulated annealing algorithm is used to set the acceptance probability of the location update.  
To ensure the diversity of the initial population, we use a random generation method to generate initial particles. In the PSO algorithm, the particles are updated according to the interaction of particle velocity, the best-found location, and the global best-found position. In each iteration step, the position and the velocity of the \(i\)-th particle are updated in the following formulas:  
\[X_{i}=\left\{\begin{array}{ll}g(w\otimes h(X_{i}),\ pB_{i}),&if\ RAND<c\\ g(w\otimes h(X_{i}),\ gB_{i}),&else\end{array}\right. \tag{17}\]  
where \(pB_{i}\) is the best-found location by particle \(i\) up to this step; \(\text{g}B_{i}\) is the global best-found position among all particles up to this step; \(w\) is the inertia weight, which is used to control whether to execute \(h(\cdot)\); \(RAND\) represents a random number between [0,1], and \(c\) is a preset positive constant. The inertia weight \(w\) is expressed in the form of probability, and \(w\otimes h(X_{i})\) means that the algorithm executes \(h(X_{i})\) with the probability of \(w\). The design of the functions \(h(X)\) and \(g(X,Y)\) refers to the commonly used genetic operations. Specifically, the function \(h(X)\) exchanges the values of two random positions in the particle \(X\), and the function \(g(X,Y)\) is defined as the crossover operation in genetic algorithm.  
In addition, we introduce the Metropolis criterion to set the particle update probability in the following formulas:  
\[\bigtriangleup_{i}=f\big{(}X_{i}^{{}^{\prime}}\big{)}-f(\ pB_{i}) \tag{18}\]  
where \(X_{i}^{{}^{\prime}}\) represents the updated new position of particle \(i\), and \(f(X_{i})\) is the fitness function value corresponding to particle \(i\) at position \(X_{i}\). If \(\bigtriangleup_{i}<0\), update the new position of particle \(i\) to \(X_{i}^{{}^{\prime}}\). Otherwise, update the position of \(i\) with the probability \(exp(-\bigtriangleup_{i}/f(\ pB_{i}))\).  
In order to ensure the feasibility of the generated solutions, DPSO uses the adjacency matrix to identify whether the new solution is feasible after each operator is executed. If the solution is infeasible, the algorithm re-executes the operator until a feasible solution is obtained.
## 4 Queuing Model for Baseline Algorithm
### Continuous-Time Markov Chain  
Let us describe the system model using a corresponding queuing system, as shown in Figure 6. Continuous-time Markov chain (CTMC) \(S(t)\) has states \(\mathbf{s}=(m,n_{1},n_{2})\), where \(m\) represents the maximum number of 1-users jointly transmitting elastic traffic (size of 1-slice), and \(n_{k}\) represents the number of k-users transmitting elastic traffic and waiting in \(k\)-buffer. The state space is presented as follows:  
\[\mathcal{S}=\Big{\{}\mathbf{s}=(m,n_{1},n_{2}):\;\;m=0,\ldots,N,\;\;n_{1}=0, \ldots,m+R_{1},\;\;n_{2}=0,\ldots,N-m+R_{2}\Big{\}}. \tag{1}\]
## 6 Experimental Results
### Experiment Steps
#### 3- Monitoring:  
During the simulation, system performance was continuously monitored, focusing on metrics such as response time, transaction throughput (TPS), latency, and error rates;
# Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis
###### Abstract  
The gradual deterioration of train wheels can increase the risk of failure and lead to a higher rate of track deterioration, resulting in less reliable railway systems with higher maintenance costs. Early detection of potential wheel damages allows railway infrastructure managers to control railway operators, leading to lower infrastructure maintenance costs. This study focuses on identifying the type of sensors that can be adopted in a wayside monitoring system for wheel flat detection, as well as their optimal position. The study relies on a 3D numerical simulation of the train-track dynamic response to the presence of wheel flats. The shear and acceleration measurement points were defined in order to examine the sensitivity of the layout schemes not only to the type of sensors (strain gauge and accelerometer) but also to the position where they are installed. By considering the shear and accelerations evaluated in 19 positions of the track as inputs, the wheel flat was identified by the envelope spectrum approach using spectral kurtosis analysis. The influence of the type of sensors and their location on the accuracy of the wheel flat detection system is analyzed. Two types of trains were considered, namely the Alfa Pendular passenger vehicle and a freight wagon.  
Keywords:railway vehicles; wheel flat detection; spectral kurtosis analysis; waysside condition monitoring systems; multisensor array +
Footnote †: journal: Applied Sciences