esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 3. Experimental Apparatus and Testing Methods
### Judgment Basis for Dust-Mist Coupling Effect  
It is well known that the coupling between dust particles and mist particles will lead to an increase in the particle size of the coupling body [34], as shown in Figure 7. The coupling mechanism involving mist droplets and dust particles encompasses inertia collision, interception, diffusion effects, and electrostatic interactions among the high concentrations of mist particle droplets [35; 36].  
Figure 7 shows that the coupling of the particle size (D1) is larger than the size of the dust particle (d1) and the droplet particle (d2) after the dust-mist particles coupled, the wrapped particle size (D2) is larger than the size of d1 and d2, and the resuspension particle size (D3) is much greater than the size of d1 and d2. Based on the above analysis, any coupling results between dust and mist particles will lead to an increase in particle size of the coupling body. It can be inferred that a larger coupling ratio between dust-mist particles results in a greater amount of coupling entities generated between dust and mist particles. That is to say, the higher the increase rate of the coupling body size, the higher the coupling proportion of dust and mist particles, and consequently, the higher the dust removal efficiency of water mist.  
Figure 6: Data acquisition system for the PDPA device in experimental settings.
# Optics Based Label-Free Techniques and Applications in Brain Monitoring
## 3 DCS Technology Used Currently in Human Brain Imaging
### Animal Studies  
DCS based measurement of CBF is also attractive in animal studies. In 2010, [PERSON] et al. [12] measured CBF in rat stepped during hypercapnia and validated simultaneously arterial spin labeling (ASL) MRI. The result from both methods were consistent. A similar study was done by [PERSON] et al. whose goal was measurement of absolute CBF in piglets using DCS and time-resolved NIR (TR-NIR) techniques, also a flow tracer. TR-NIR method can extract absolute CBF from DSC data. The measured CBF changes by TR-NIR and the measured diffusion coefficient by DCS were strongly correlated [124]. This group also performed this setup to show the absolute CMRO\({}_{2}\) changes. In addition, they determined cerebral venous blood oxygenation (SvO2) in newborn piglets [125].  
In a study on sheep, DCS-DOS setup has been used to discover spinal cord ischemia in ten adult Dorset sheep. The interesting point in this complicated setup is the usage of one source fiber switched between three amplitude modulated (70 MHz) laser diodes (686, 785 and 830 nm) for NIRS and two SPAPD for DCS [126].  
Another work by [PERSON] et al. [127] conducted on piglets simulated the following conditions of traumatic brain injury. They used two different diffuse optical techniques, DCS and diffuse reflectance spectroscopy (DRS) to quantify cerebral blood oxygenation and blood flow continuously and noninvasively before the injury and up to 6 h after the injury. The DCS rCBF results were compared to the fluorescent microsphere method successfully. In 2011, [PERSON] et al. presented a DCS flow-oximeter for monitoring both CBF and cerebral oxygenation in mice simultaneously and validated it with laser Doppler measurement. Their aim was transient forebrain ischemia monitoring. To model the transient forebrain ischemia, some occlusions in bilateral common carotid arteries (bi-CCA) were made. It was observed that the results of the DCS flow-oximeter is highly close to the laser Doppler technique [128]. In 2018, researchers tried to combine DCS for CBF monitoring and broadband NIRS (B-NIRS) for monitoring the oxidation state of cytochrome c oxidase (oxCCO). oxCCO is the main indicator of oxidative metabolism. This study was done in a piglet model of neonatal hypoxia-ischem (HI). The results demonstrate that simultaneous neuromonitoring of perfusion and metabolism could provide vital alerts of brain injury, especially in preterm infants born [129].
## 3 Seizure Detection Performance and Power Consumption Evaluation
### Assessment of Power Consumption  
The value of the power consumption of each data reduction approach was obtained using an open-source cycle-accurate wireless sensor network simulator called Avrora [22]. Avrora provides a virtual operating environment to emulate the execution of an application program for an Atmel AVR(r) microcontroller-based sensor platform, such as Mica2 and MicaZ. Avrora provides detailed monitoring of the different behaviors of a program, such as packet transmission, interrupts and stack usage, which allow users to test and fine-tune the programs to improve the system performance. In particular, the energy monitor provided in Avrora keeps track of the state changes of the main components on the sensor node and estimates the power consumption of each component based on a detailed energy model.  
Each of the data reduction approaches was implemented in Network Embedded Systems C (nesC) for TinyOS [23]. TinyOS is a light-weight embedded operating system (OS), which provides hardware abstractions for common operations, such as packet transmission, storage and input/output (I/O). The code was compiled and simulated for a MicaZ sensor platform using Avrora. MicaZ consists of an ATMega 128 microcontroller, a CC2420 IEEE 802.15.4/ZigBee-compliant radio, 4 KB of random-access memory (RAM) and 128 KB of flash. For simplicity, we did not simulate real-time EEG acquisition, but alternatively preloaded a short segment of EEG data, sampled at 128 Hz with a 16-bit resolution, into the RAM for program simulation use. As the EEG acquisition is common across all considered approaches, such simplification does not affect the results of the power consumption comparison. Furthermore, as shown in [8], the recent advances in low-power and high-performance readout circuit design for physiological signal acquisition make it possible to reduce the power consumption of the data acquisition module (which captures and digitizes the signals) in a wireless EEG system (sampling at 256 Hz per channel) to less than 10% of the total system power consumption. The remaining power is consumed by the microcontroller and the wireless transmitter, which account for approximately 20% and 70% of the total power consumption, respectively. Based on this result, we focus our attention on the power consumption of the microcontroller and the wireless transmitter in this study.
# Drone Arc Routing Problems and Metaheuristic Solution Approach
## 4 Proposed Solution Approach
### Constraint Handling Strategy  
Drone capacities are allowed to be exceeded in the generating neighbor solution process. However, if the drone capacities are exceeded, the objective function value of the current solution is penalized. Therefore, the dynamic penalty function is used as a constraint handling strategy. The dynamic penalty function is developed to direct the search towards the global optimal solution using the information of infeasible solutions. At the same time, the search process uses the information from the recent iterations of the algorithm. The proposed dynamic penalty function is presented below:\(f(s)=\text{Objective function value of the solution}\)  
\(f^{\prime}(s)=\text{Penalized objective function value of the solution}\)  
\(p=\text{The longest distance value in distance matrix}\)  
\(e=\text{Excess capacity value}\)  
\(k=\text{Penalty coefficient}\)  
\(k=\begin{cases}1,\text{ If the drone capacity is exceeded during the last two iterations}\\ \quad 0.5,\text{ If the drone capacity is exceeded only the last iteration}\\ \quad 0,\text{ If the drone capacity is not exceeded only the last iteration}\end{cases}\)  
\[f^{\prime}(s)=f(s)+(k\times p\times e) \tag{1}\]  
The \(f(s)\) presented in Equation (1) represents the total distance traveled by the drones in solution \(s\). On the other hand, \(f^{\prime}(s)\) represents the penalized objective function, which is the penalized version of \(f(s)\). Therefore, \(f^{\prime}(s)\) is calculated by adding the penalty value to the total distance traveled by the drones in solution \(s\). The main idea of the penalty function presented in Equation (1) is to penalize the objective function by adaptively updating the penalty coefficient. Namely, if the drone capacity is not exceeded in the last iteration, there is no penalty, but if it is exceeded 1 or 2 times, there is a gradually increasing penalty. Hence, even if exceeding drone capacities is allowed during the search phase, feasible solutions are eventually obtained since the solutions that exceed vehicle capacity are penalized. Furthermore, especially at high temperatures, diversification is provided, and quality solution regions is reached by using the information of infeasible solutions.
## 3 Theoretical Model of Fluid Flow in Porous Media
The Richards equation, an extension of [PERSON]'s law, is used for unsaturated porous media and couples the dynamics of saturation and pressure fields. At the macroscopic scale, parameters such as permeability and relative permeability can be derived from the analysis of local flow fields. [PERSON] et al. [48] developed a cyclic hydrogen injection and production system and used Darcy's Law to create a mathematical model for investigating hydrogen production and recovery, as shown in Equations (5) and (6):  
\[v=-\frac{k}{\mu}\
abla p \tag{5}\]  
\[\rho_{g}=\frac{T_{sc}Z_{sc}\rho_{gsc}}{p_{sc}}\cdot\frac{p}{TZ} \tag{6}\]  
where \(Tsc\), \(Zsc\), \(p_{gsc}\), and \(p_{sc}\) represent the temperature, compressibility, gas-phase density, and pressure under standard conditions, respectively. \(p\) denotes the reservoir pressure, \(T\) represents the reservoir temperature, and \(Z\) is the actual compressibility factor. To investigate the migration and mass transfer behavior of saturated and unsaturated gas-liquid two-phase systems, researchers [49] developed a mathematical model to describe the flow process on the basis of Darcy's Law as shown in Equations (7) and (8), in which Equation (7) describes the mass balance of the gas and liquid phases:  
\[\varphi\frac{\partial S_{t}}{\partial t}+\
abla\cdot\overrightarrow{v_{i}}=0 \tag{7}\]  
\[\overrightarrow{v_{i}}=-\frac{k_{r,j}}{\mu_{i}}K(\
abla p_{i}-\rho_{i} \overrightarrow{\delta}) \tag{8}\]  
where the flux of the fluid phase (\(\overrightarrow{v_{i}}\)) can be described by [PERSON]'s law extending to two-phase flow; \(\varphi\) is the porosity; \(g\) is the acceleration due to gravity, m/s\({}^{2}\). The mathematical models mentioned above all assume that the gas-water two-phase flow follows [PERSON]'s law. However, in certain cases, such as when the medium has low permeability, extremely small flow channel sizes, and under critical flow velocities, Reynolds numbers, and nonlinear flow conditions, the flow behavior deviates from [PERSON]'s law. In such cases, the high flow velocity and the interaction between the rock matrix and fluids within the pores lead to nonlinear seepage characteristics. As a result, scholars have turned to non-Darcy flow models to describe two-phase flow. Simultaneously, they have extended or modified [PERSON]'s law to account for multiphase flow and both miscible and immiscible conditions [50; 51; 52]. The theoretical model is a mathematical description of the complex pore-scale flow process and serves as the foundation for experimental and numerical simulation research. The continuous improvement of theoretical models is essential for better understanding complex flow processes.
# AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
## 5 Experiments
### Overall Performance
The contrastive information embedded in the contrastive learning approach (specifically, the contrastive loss function) contributes substantially to the enhanced generalization ability of collaborative filtering. This improvement is primarily attributable to the theoretical shortcomings of the BPR (Bayesian Personalized Ranking) pairwise relative ranking loss, which introduces a large number of ineffective negative samples during the training process. Additionally, BPR typically assumes that unlabeled data are all negative samples, which adversely affects the model's performance. * In general, graph convolutional network (GCN) models outperform traditional web embedding models; however, the performance of these models is contingent upon the volume of data. As evidenced by the table, GCNs exhibit significantly better performance on the Gowalla and Yelp2018 datasets, whereas their advantage on the Movielens-1M dataset is less pronounced. GCNs excel at uncovering higher-order collaborative information, making them more suitable for complex and large datasets. Conversely, for simpler and sparser datasets, lightweight models tend to achieve better results more readily.
## 2 Literature Reviews
### Bogie Instability Detection Systems of High-Speed Rail Trains  
As an example, the safety requirements of THSR are consistent with European standards. The operation, in addition to developing and manufacturing 700 train sets based on Japanese technology, has also embedded several additional security systems, including Bogie Instability Detection System (BIDS).  
When running, BIDS is used to detect the shaking degree of the bogie. In the event that the set value is exceeded, the system will immediately send an alarm to the train driver with a description of the action to take to avoid possible train derailments.  
When BIDS send out an alarm, it will cause the following effects:  
1. Deceleration causes train delays:  
To ensure operational safety, trains that share the same rails should maintain a specified distance from each other; this means that when a train decelerates, the following train may also have to decelerate in order to maintain a safe distance. These changes will significantly impact the operating performance of the company.
2. Alarm causes train to be unserviceable: Unserviceable trains must go through several steps of testing at a maintenance base [27], described as follow:  
* Equipment installation status under unserviceable trains
* Bogies and train wheels
* Rail map system
* Onboard recording confirmation  
There are several reasons why the bogie shakes; among them, two main factors can be identified:  
1. Structure/mechanism transmission causes failures.
2. If the set values of other parts are within standard values, it is the failure of the wheel/rail interface that occurs.
# IoT-Aware Architecture to Guarantee Safety of Maintenance Operators in Industrial Plants
## 4 System Requirements
### Running Scenario  
Before entering the details of the designed architecture, we would like to introduce a running scenario to help readers understand the main actions that an operator can perform during a maintenance procedure.  
Let us suppose that an administrator noticed some troubles in the normal operations of a machinery and scheduled a maintenance intervention for the operator named [PERSON]. Therefore, on a normal working day, [PERSON] arrives at the industrial plant and, after wearing all the safety clothes, he takes his smartphone, and he finds a notification saying that a maintenance intervention was assigned to him. Consequently, he opens the dedicated application and accept the assignment. After that, the application starts to guide him in the maintenance procedure. The first step is to reach the machinery and demonstrate his presence near a LOTO point, the one in which he can de-energize the machinery. So, when he is near the LOTO point, the system recognizes his presence through the continuous monitoring performed by dedicated sensors and asks him to move his smartphone near a dedicated sensor. When the presence of [PERSON] is verified, the system communicates with the machinery and, after some verifications, allows and suggests to [PERSON] to de-energize the machinery. Then, [PERSON] performs the suggested actions, and, after some other verification of the system, [PERSON] will be allowed to end the first step through a dedicated button in the app. Afterward, the system will suggest [PERSON] to go to the next step, and by following steps similar to the previous ones, [PERSON] is guided in the exploitation of the second maintenance step. The procedure continues until [PERSON] successfully accomplishes all the steps and presses the button to conclude the procedure (de-energization included).
# Measurement Uncertainty and Compliance Evaluation Applied to Natural Gas Moisture
## 2 Methodology
### Measurement Uncertainty in Compliance Evaluation  
Based on the information about the measurement uncertainty, it is possible to verify whether the results obtained are within the specified value. Consequently, it is essential to have a decision rule that considers the risks associated with an incorrect decision, which is determined by guard bands, \(g\), that define acceptance and rejection regions (Figure 2).  
Based on JCGM 106:2012, the guard band is established to ensure that the probability of a false acceptance or rejection occurring is less than or equal to a previously defined confidence level \(\alpha\) when a measurement occurs in the acceptance region [18].  
Each measurement result's standard uncertainty, \(u\), is added to the established guard band value. According to the Eurachem guide [19], a value of \(1.64\times u\) is determined at a significance level of 5%.  
In this study, we considered the risk of the consumer, which is defined as the probability of accepting a batch that ought to be rejected.  
The histograms illustrated the most likely value, associated uncertainties, guard bands, and upper acceptance limits. To assess the risk to consumers, Monte Carlo simulations were generated from 100,000 pseudorandomous, ensuring a stability of 0.001%, for the moisture content in natural gas based on the mean experimental value and its expanded uncertainty.  
Here, it was calculated using the MS-Excel function \"=NORM.INV(RAND();\(y_{i}\);\(u_{y_{i}}\))\", where \(\mathbf{y_{i}}\) is the mean of the measured value and \(u_{y_{i}}\) is its expanded uncertainty for the \(i\)th parameter, considering a Gaussian or normal distribution.  
In recent decades, several studies have involved calculating uncertainty in the physico-chemical properties of natural gas. Using measurement uncertainty, [PERSON] demonstrated the extent to which the variation in molar fractions obtained from these two methods metrologically influences the higher calorific value of natural gas. Additionally, it affects the compressibility factor of natural gas [20]. Brazilian researchers compared five approaches to assess the uncertainty of natural gas composition by gas chromatography [21].  
However, the use of measurement uncertainty in assessing the conformity of these parameters in natural gas is still very incipient. This is apart from a recent study where acceptance thresholds were established to ensure no risk of incorrect compliance in the carbon isotopic analysis of natural gas based on this uncertainty data [22]. On the other hand, this approach can be highlighted in other areas of knowledge, such as environmental pollution [23], drug and medicine analysis [24], and microbiology [25].
## 3 Experimental Techniques to Check a Possible Degradation of PCM
### Visually (\"vis.\")  
In the case of solid-liquid PCM, a visual observation of possible phase separation is a useful and simple measure to detect a degradation of a PCM during experimental testing. The presence of solid material at a temperature where the PCM should be completely molten is a clear indication of phase separation. Thereby, a visual control of phase separation can be favorable compared to measured temperature-time curves. If the temperature sensor is not in thermal contact with the precipitated solid, the measured temperature-time curves may not be affected by the phase separation. Practically, a visual check of degradation can be carried out by taking pictures at given time intervals or temperature steps followed by an analysis of pictures from different thermal cycles. In addition, simply looking at the sample after the experimental investigations is a reliable measure to visually check degradation.  
In the case of degradation tests performed on organic PCM at elevated temperatures, a visual check also includes a possible change in the PCM color. For example, a browning of sugar alcohols indicates a thermal degradation of the sample [53].
# Experimental and Numerical Predictions of Cryogenic Leakages in Welded Steel Plates
###### Abstract  
This study presented experimental and numerical research to investigate the effect of cryogenic leakage on a plate structure of AH36-grade steel containing welded joints. To simulate the cryogenic leakage conditions, the welded plate was exposed to a temperature of \(-196\)\({}^{\circ}\)C by supplying liquid nitrogen (LN\({}_{2}\)) to the center of the steel plate. The time-dependent temperature history and strain variation were measured by using thermocouples and strain gauges attached to the plate surface. Additionally, the residual stress of the middle surface section before and after the cryogenic leakage process was measured by X-ray diffraction analysis (XRD). A three-dimensional finite element model was created with the use of a commercial finite element analysis (FEA) program to simulate the flux-cored arc welding process and cryogenic leakage process. The steel surface temperature dropped sharply and reached approximately \(-196\)\({}^{\circ}\)C at 160 s after LN\({}_{2}\) supplement. After the first 650 s of the LN\({}_{2}\) leakage experiment, the outside of the trough reached approximately \(-75\)\({}^{\circ}\)C and \(-25\)\({}^{\circ}\)C, depending on the location of the thermal couples. Although there was a relative difference in the results, the experiment and numerical simulation results for temperature and stress distribution showed good agreement. The results could be utilized in the ship design stage adopting welded structures as a basic database.  
cryogenic leakages effect; cryogenic spills; residual stress +
Footnote †: journal: Applied Sciences