esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Dry eye disease (DED) has been identified as a multifactorial condition of the ocular surface commonly encountered in the ophthalmic practice [1, 2]. The prevalence of DED has been estimated to be between 5 and 50% [3], highlighting the outstanding impact that this disorder has on the quality of life [4, 5]. Currently, DED diagnosis and monitoring are still challenging for clinicians [6] due to its multifactorial nature. According to the Tear Film and Ocular Surface Society's Dry Eye Workshop (TFOS DEWS II, 2017), DED diagnostic protocol should include both symptomatology questionnaires, as a screening tool, and homeostasis markers tests [7]. However, most of the clinical tests used for DED diagnosis have limitations as they are semi-invasive, subjective, and/or have insufficient sensitivity and specificity and low repeatability [8]. In fact, there is a lack of agreement between clinical signs and the severity of DED symptoms, making DED diagnosis difficult [9, 10]. Thus, efforts are made to develop more objective, less invasive, and more repeatable methods and technologies to obtain a more reliable DED diagnosis.  
Thermographic imaging has been recently applied in different disciplines of biomedicine [11, 12, 13] as a potential diagnostic and preventive device to evaluate stress reactions. Infrared (IR) thermography has been used to detect changes in surface temperature across a range of anatomical regions, the eye being where this method fits better [14, 15]. The ocular surface (OST) evaluation through IR thermography is gaining importance as a potential non-invasive method to assess the ocular surface [11, 16]. OST has shown to depend on several extrinsic and intrinsic factors, such as ambient and body temperature, contact time with the vascular palpebral conjunctiva (i.e., lid closure), ocular inflammation, age, and blinking [11; 17; 18].  
In this context, DED is well-known to be related to ocular surface inflammation [19], and its prevalence increases with age [3], both key factors influencing OST [17; 18]. Therefore, it is to be expected that OST will experiences changes in DED eyes compared to healthy eyes [20].  
At this point, the question arises whether thermography can be a potential tool for DED diagnosis. Therefore, it would be expected that eyes with DED had faster TER and, accordingly, cooler OST. However, there are discrepancies between studies in this regard [21; 22; 23]. However, most studies evaluating the association between OST and DED perform the measurements under controlled blinking conditions.  
Knowing that the stability of the tear film decreases with time when the eye is open, and that OST is dependent on the tear film stability [11; 24], maintaining the eyes open for a longer period than the normal blink could influence the results. To the best of our knowledge, no previous research has investigated OST under natural non-controlled blinking conditions in DED eyes. Therefore, this study aims to evaluate OST variations under natural non-controlled blinking conditions in DED eyes and healthy controls.
# Flow Dynamics of Sulfate-Modified Water/Polymer Flooding in Micromodels with Modified Wettability
## Materials and Methods  
### Fluids and Chemicals  
#### 2.1.1 Brines  
Brines were prepared by mixing different salt components in deionized water. Synthetic seawater (SSW) was used as the base brine (Brine 1), while sulfate-modified water was prepared by doubling the amount of sulfate of SSW (Brine 2). A third brine was prepared by diluting the SSW 10 times to achieve TDS \(\approx\) 5 g/L (Brine 3). The fourth injection brine (Brine 4) was prepared by mixing all the salts of SSW except NaCl. Brine 4 was used to investigate the role of potential determining ions (PDI) in the formation brine and the removal of non-PDI from injection brine, as studied previously [32]. The chemical compositions of brines are described in Table 2. The filtration of all brines was performed at room temperature by applying \(\sim\)40 psi (2.76 bar) N\({}_{2}\) gas pressure using a 0.2-m MF-Millipore Membrane Filter, in order to avoid any undissolved solvent or small particles. The pore structure of the micromodels used in this work tend to be sensitive to undissolved components, and this can cause severe pore plugging/injectivity problems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Total Dissolved Solids (g/L)**} \\ \cline{2-6}
**Chemical Formula** & \multicolumn{6}{c}{**Formation Brine**} & \multicolumn{6}{c}{**Injection Brines**} \\ \cline{2-6}  & **Brine 1** & **Brine 1** & **Brine 2** & **Brine 3** & **Brine 4** \\ \cline{2-6}  & **SSW** & **SSW** & **SSW+2 SO42** & **10 DSSW** & **SSW-Na** \\ \hline NaCl & 23.97 & 23.97 & 23.97 & 2.39 & 0.00 \\ KCl & 0.80 & 0.80 & 0.80 & 0.08 & 0.80 \\ CaCl\({}_{2}\)-2H\({}_{2}\)O & 1.11 & 1.11 & 1.11 & 0.11 & 1.11 \\ MgCl\({}_{2}\)-6H\({}_{2}\)O & 11.04 & 11.04 & 11.04 & 1.10 & 11.04 \\ SrCl\({}_{2}\)-6H\({}_{2}\)O & 0.03 & 0.02 & 0.03 & 0.003 & 0.03 \\ Na\({}_{2}\)SO\({}_{4}\) & 3.93 & 3.93 & 7.86 & 0.39 & 3.93 \\ NaHCO\({}_{3}\) & 0.27 & 0.27 & 0.27 & 0.03 & 0.27 \\ TDS & 41.15 & 41.15 & 45.09 & 4.11 & 17.18 \\ Hardness & 0.13 & 0.13 & 0.11 & 0.13 & 0.48 \\ Density (g/cm\({}^{3}\)) at 22 \({}^{\circ}\)C & 1.03 & 1.03 & 1.02 & 0.98 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of formation and injection brines.
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.2 Pseudo-Second-Order Kinetics Model  
The pseudo-second-order equation depends on the assumption of the rate-limiting step due to chemical adsorption containing valence forces by exchange and/or sharing of electrons between dye ions and the carbon nanotube (adsorbent). The pseudo-second-order equation is expressed following [62]. Plots of (\(t/q_{t}\)) against (\(t\)) should offer a linear correlation from which the data of parameters \(q_{e}\) and \(k_{2}\) can be calculated from the slope and intercept, respectively. It can be observed in Figure 18B and Table 4 that this model fits well, with a higher correlation coefficient (R\({}^{2}\) = 0.999). Apart from this, it was obvious that the value of the \(k_{2}\) parameter was greater than the corresponding \(k_{1}\) parameter value. This is because the pseudo-second-order model presumes that the adsorption rate is proportional to the square of a number of unoccupied sites, as reported by [PERSON] and [PERSON] et al. [53].  
Table 4 provides the values of \(K_{1}\), the experimental and calculated values of \(q_{e}\), and the correlation coefficients for the pseudo-first-order kinetics plots. The theoretical values of \(q_{e}\) do not agree with experimental data obtained. This suggests a poor fit between the kinetics data and the pseudo-first-order model. Thus, the pseudo-first-order equation was ruled out as a result of its correlation coefficients (R\({}^{2}\)) for the current experimental results being small for the IV2R dye in MWCNTs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Kinetic Models** & **Parameters** & **Value** \\ \hline \multirow{3}{*}{First-order} & \(q_{e}\) (calc.) (mg g\({}^{-1}\)) & 29.3 \\ \cline{2-3}  & \(k_{1}\times 10^{3}\) (min\({}^{-1}\)) & 6.6787 \\ \cline{2-3}  & \(\mathrm{R}^{2}\) & 0.0797 \\ \hline \multirow{3}{*}{Second-order} & \(q_{e}\) (calc.) (mg g\({}^{-1}\)) & 1.066 \\ \cline{2-3}  & \(k_{2}\times 10^{3}\) (mg g\({}^{-1}\) min\({}^{-1}\)) & 9300.677 \\ \cline{2-3}  & \(\mathrm{R}^{2}\) & 0.9993 \\ \hline \multirow{3}{*}{Intraparticle diffusion} & \(K_{diff}\) (mg g\({}^{-1}\) min\({}^{-0.5}\)) & 13.434 \\ \cline{2-3}  & \(C\) cal (mg g\({}^{-1}\)) & 0.862 \\ \cline{1-1} \cline{2-3}  & \(\mathrm{R}^{2}\) & 0.867 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the different adsorption kinetics models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Isotherm Model** & **APE\%** & \(\chi^{2}\) & **EABS** \\ \hline [PERSON] & 0.031 & 0 & 0.034 \\ \hline [PERSON] & 2.079 & 0.326 & 2.236 \\ \hline Harkins–Jura & 85.602 & 551.664 & 92.065 \\ \hline [PERSON] & 12.38 & 11.539 & 13.315 \\ \hline [PERSON] & 0.022 & 0 & 0.024 \\ \hline [PERSON] & 0.004 & 0 & 0.004 \\ \hline [PERSON] & 0.001 & 0 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The most appropriate isotherm model to investigate the equilibrium data through various errors functions.
## 3 Results and Discussion
### Ignition and Combustion of the Biopellets before and after Torrefaction  
Figure 11 compares the ignition characteristics and combustion duration of biopellets before and after torrefaction. Torrefaction of the biopellets reduced the initial ignition time by 50-59% and prolonged the combustion duration by 15-24%, regardless of the types of woody raw materials. This can be attributed to the significant removal of moisture and volatile compounds during torrefaction. The absence of these components facilitates easier ignition at lower temperatures, as less energy is required to overcome the latent heat of vaporization and decomposition of volatile compounds. Additionally, the increase in fixed carbon content in the biopellets due to torrefaction contributes to a slower and more uniform combustion rate, as fixed carbon serves as a steady source of energy during the combustion process, influencing its kinetics. These improvements in ignition and combustion properties are consistent with the results reported by [PERSON] et al. (2008), who found that torrefied biomass ignites more easily and burns more uniformly than raw biomass [53].
# Study on the Prediction Method of the Ultra-Low-Cycle Fatigue Damage of Steel
## 2 Theoretical Model for ULCF of Structural Steel
### Calibration of Material Parameters for CDM  
The uniaxial plastic strain threshold \(\varepsilon_{th}\) of the Q345 qC base metal, HAZ and weld metal was calibrated by the uniaxial tensile tests [29]. The values of \(\varepsilon_{th}\) for the base metal, HAZ and weld metal are 0.4455, 0.4357 and 0.3673. The value of \(\varepsilon_{th}\) for the weld metal is the lowest, which is 17.56% and 15.70% less than those of the base metal and HAZ, respectively. The dispersion coefficient of weld metal is 34.84%, thereby indicating that the weld metal has the largest material uniformity [29].  
In addition, the distribution of equivalent plastic strain \(\varepsilon^{p}\) and stress triaxiality \(T\) at the root of the notch section is shown in Figure 2. Equivalent plastic strain \(\varepsilon^{p}\) is nearly the same across the notch section in the case of notch radius \(R\) = 4.25 mm in Figure 2a. In the case of notch radii \(R\) = 3.0 mm and \(R\) = 1.5 mm, the maximum equivalent plastic strain appears on the notch surface. Figure 2b shows that the maximum stress triaxiality appears at the centre of the notch section and decreases to the notch surface. In the test, ductile initiation occurs at the centre of the notch section of all the notched specimens, and the equivalent plastic strain is the largest on the notched surface. Therefore, under the state of stress triaxiality, the equivalent plastic strain alone cannot be used for fracture prediction.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \(\sigma\)**\(|\)0/**MPa** & \(Q\)**\(\infty\)/**MPa** & \(b\) & \(C_{k}\)/**MPa** & \(\gamma_{1}\) & \(C_{2}\)/**MPa** & \(\gamma_{2}\) & \(C_{3}\)/**MPa** & \(\gamma_{3}\) \\  & **MPa** & **MPa** & **MPa** & **MPa** & **MPa** & **MPa** & **MPa** & **MPa** \\ \hline Base metal & 354.10 & 13.2 & 0.6 & 44373.7 & 523.8 & 9346.6 & 120.2 & 946.1 & 18.7 \\ Heat-affected zone (HAZ) & 312.57 & 9.8 & 0.7 & 32242.4 & 199.2 & 3858.5 & 43.1 & 329.2 & 0.3 \\ Weld Metal & 428.45 & 17.4 & 0.4 & 12752.3 & 160.0 & 1111.2 & 160.0 & 630.5 & 26.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of mixed hardening model.
## 3 Modification of the _RMR_ and \(Q\) Systems Since Their Establishment
### Development in Terms of Support
#### 3.2.1 Rmr  
System  
The initial guidelines were for drill and blast excavated tunnel support and were based on the rock-mass class in a tabulated form for a tunnel span from 5 to 12 m [8]. The length of fully resin-grouted rock bolts (25 mm in diameter) was purely based on the tunnel width, where the length of a rock bolt is half of the tunnel width. The other supporting materials were shotcrete, steel sets and wire mesh. In the 1976 version of \(RMR\), the support guideline table was updated for a 10-m-wide tunnel with a horseshoe shape [75]. The rock bolts length and the thickness of shotcrete was reduced. In 1979, the length of the 20-mm-diameter fully grouted rock bolts was made a function of the \(RMR\) value [44]. The limit of vertical stresses was specified. Separate guidelines were suggested for support in walls and the crown of the tunnel. In 1989, the support chart remained unchanged and refinement of the system was made in characterization. Support technology upgraded for tunnels with time and thus, the designers used support for different tunnel spans [76; 77; 78]. The foremost update in support using \(RMR\) system was accomplished, after twenty four years of experience, in which the tunnel support is a function of the tunnel size and rock-mass score [46]. In this revision, the shotcrete thickness and rock bolt length (\(L_{b}\)) are functions of rock-mass score and tunnel span. The length of the rock bolt can be calculated using Equation (10).  
\[span\left(m\right)=\frac{\left(L_{b}(m)+2.5\right)^{\frac{RMR_{89}+25}{32}}}{3.6} \tag{10}\]  
Rock-bolt spacing (\(S_{b}\)) is the function of \(RMR\) score only. When \(RMR_{89}>85\), spot bolting is required. In shotcrete design graph, the shotcrete thickness is greatly influenced by the tunnel size at low \(RMR_{89}\) score, whereas shotcrete thickness is greatly influenced by \(RMR_{89}\) for larger tunnel spans (Span > 10 m) [46]. The rock-bolt spacing can be obtained from Equation (11).  
\[S_{b}(m)=0.5+2.5\times\frac{RMR_{89}-20}{65},\;20\;<RMR_{89}\;\leq\;85 \tag{11a}\]  
\[S_{b}(m)=0.25+\frac{(RMR_{89}-10)^{1.5}}{140},\;(10\;<RMR_{89}\;\leq\;20 \tag{11b}\]  
\[S_{b}(m)=0.25,\;RMR_{89}\;\leq\;10 \tag{11c}\]
## 2 Research on the Collaborative Search Efficiency Evaluation Model of Multi-Sonar System
### Efficiency Evaluation Indicators  
We select two indicators, the detection probability and cumulative detection probability, to evaluate the collaborative search efficiency of the multi-sonar system.  
(1) Detection probability  
The detection probability represents the instantaneous detection capability of sonar for surrounding targets at a certain moment and in a certain determined situation, which can be calculated via Formula (2). It is closely related to factors such as sonar performance parameters, target radiation noise, marine environmental noise, self-noise, and the relative situation.  
(2) Cumulative detection probability  
The detection probability represents only the sonar detection result at a certain moment. In practical tasks, we should consider the entire task process in conjunction, that is, the cumulative detection probability. It is defined as the probability that the search platform can effectively detect the target after a period of searching during the search process. The cumulative detection probability is based on the instantaneous detection probability, and is accumulated during the deduction process to evaluate the possibility of the target being detected throughout the entire process, which more comprehensively reflects the performance of the detection system. Owing to the certain correlation between the two detection results with a small time interval, the \((\lambda,\sigma)\) process model considering temporal correlation is used to construct the formula for calculating the cumulative detection probability. The calculation process is as follows [37]:  
\[P_{cd}=1-\frac{1-\max(P_{n})}{1-\alpha\cdot\max(P_{n})}\prod_{n=1}^{N}(1-\alpha \cdot P_{n}) \tag{9}\]  
where, \(P_{n}\) represents the instantaneous detection probability corresponding to \(N\) time points, \(\max(P_{n})\) represents the maximum value of \(P_{n}\), \(\alpha=1-exp(1-\lambda\Delta)\), \(\Delta\) represents the sonar detection time interval, and \(\lambda\) represents the signal jump rate.  
On the basis of the target motion model, the sonar detection model, and the multi-sensor information fusion model, with detection probability and cumulative detection probability as evaluation indicators, combined with task requirements and a target collaborative search scheme, a collaborative search efficiency evaluation model of multi-sonar system based on information fusion and dynamic deduction under uncertain situation conditions was established based on the Monte Carlo method. The model schematic diagram is shown in Figure 5.
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 4 Model Construction  
The current study aims to examine the specific impact of Twitter and news count variables on stock-price prediction, with a primary emphasis on the North American context. To this end, as shown in Figure 1, we first selected a group of stocks for investigation. A subset of the chosen stocks is collectively known as \"FAANG\", an acronym representing Facebook Inc. (FB), Apple Inc. (AAPL), Amazon.com Inc. (AMZN), Netflix Inc. (NFLX), and Alphabet Inc. (GOOG). Coined by former Goldman Sachs fund manager [PERSON] [158], FAANG stocks are particularly significant for North American investors and the overall stock market and are publicly traded on the NASDAQ. As of July 2020, these five companies boasted a combined market capitalization of USD 4.1 trillion, which constituted 16.64% of the total $&P 500 market capitalization. The $&P 500, an index comprised of 500 companies, historically represents 70% to 80% of the total US stock-market capitalization. The substantial contribution of FAANG stocks to the $&P 500 highlights their broader importance in shaping the North American stock market. The movements of FAANG stocks directly influence North American investors' perceptions of the overall market and thereby impact trading decisions.  
The choice of Walmart as a focal point stems from the unique characteristics of the Bloomberg Twitter and news count variables. These variables gauge the frequency of mentions a specific company receives on Twitter or on digital news platforms. Given the recent surge in the popularity of digital and social media, particularly amongst a younger demographic, there is potential variation in the significance of Twitter and News count variables for companies adhering to traditional business models versus those embracing non-traditional models. To investigate this distinction, we opted to conduct a comparative analysis between Walmart and Amazon. Our aim was to discern any disparities in how Twitter and news count variables manifested for a conventional brick-and-mortar retailer like Walmart versus a more contemporary retailer like Amazon. Similarly, the selection of Ford and Tesla was motivated by the desire to contrast a more traditional automobile manufacturer with a purely electric one. Ford adheres to the traditional car dealership model while Tesla's showrooms are commonly situated in malls and have all transactions occurring online [159; 160]. Exploring the potential impact of this dichotomy in business models on both the Twitter and news count variables, as well as their effectiveness as inputs in stock-price prediction models, adds an intriguing dimension to our analysis.
## 3 Results and Discussion
The corresponding ML-predicted results were around a few micrometers, and were approximately an order of magnitude higher than the DL-predicted results. For 30.3 \(\upmu\)m wide trenches, the standard deviation and error were 280 nm and 300 nm, respectively. The corresponding ML-predicted results were 2.4 \(\upmu\)m and 850 nm, which were much higher than the DL-predicted results. For ML depth prediction results, the predicted performance was degraded obviously as the trench depth increased or width decreased. This can be understood by the fact that less light reached the bottom and carried the depth information for narrow or deep trenches. Less depth information collected resulted in a larger standard deviation and greater error. Moreover, the scanning range was much smaller than the depth of the trench. This resulted in incomplete TSOM images, which could lead to poorer performance. However, the DL depth prediction performance did not change obviously as the trench depth or width changed. It was proven that the DL model extracted more information from less input data compared with the ML model. It was also noticed that the standard deviation and error of the depth prediction results were obviously higher than those of the width prediction results. This was mainly because the depth interval of the depth training trench set was much larger than the width interval of the width training trench set, which was only a few  
Figure 8: Standard deviation and error of width results for widths around (a) 2 \(\upmu\)m, (b) 10 \(\upmu\)m, and (c) 30 \(\upmu\)m, predicted by machine learning model (dot line) and deep learning model (solid line). ML, machine learning; DL, deep learning; SD, standard deviation. hundred nanometers. The prediction performance could be further improved the proper adoption of parameter intervals. Previously reported TSOM systems had achieved a nanometer measurement resolution, which was much higher than the result shown in this paper. This was mainly due to the accuracy of the SEM values. The SEM value was taken as the true value, and directly affected the prediction ability of the CNN model. In our study, the SEM results were rounded to the nearest 0.01 \(\upmu\)m for dimensions less than 10 \(\upmu\)m, 0.1 \(\upmu\)m for dimensions from 10 \(\upmu\)m to 100 \(\upmu\)m, and 1 \(\upmu\)m for dimensions greater than 100 \(\upmu\)m, and this was not sufficient for dimensional measurement with nanometer accuracy. Moreover, the training trench set and trench set for SEM were etched in the same wafer, but with different positions, so etching errors also degraded the prediction ability of the CNN model.
## 2 Materials and Methods
### Acid Number and Peroxide Value Measurements  
The acid number (AN) was determined in accordance with PN-EN ISO 660:2009 [17]. The method allows one to measure the content of free fatty acids formed during the hydrolysis of ester bonds. First, 2 g of oil was measured into a conical flask, to which 50 cm\({}^{3}\) of a 1:1 mixture of ether and ethyl alcohol was added, as well as several drops of phenolphthalein. The resulting mixture was then titrated with a KOH solution until a pink colour was obtained. The acid value was calculated from the following formula:  
\[AN=\frac{V\cdot N\cdot 56.1}{m} \tag{5}\]  
_V_: volume of the titrating solution; _N_: concentration of the titrating solution; _m_: mass of the sample; 56.1: conversion factor.  
The peroxide value (PV) was determined in accordance with PN-EN ISO 3960:2017 [26]. It allows one to measure the content of iodine released from potassium iodide under the influence of peroxides present in the oil sample. First, 2 g of oil was measured into a conical flask. Next, 10 cm\({}^{3}\) of chloroform was added, followed by 15 cm\({}^{3}\) of ice-cold acetic acid and 1 cm\({}^{3}\) of a potassium iodide solution. The content of the flask was stirred for 1 min, and then set aside for 5 min away from light. Afterwards, 75 mL of distilled water was added and the mixture was titrated with a 0.002 N solution of sodium thiosulphate in the presence of several drops of starch as an indicator. A parallel, blank test was also performed. The peroxide value was calculated from the following formula:  
\[LOO=\frac{(V-V_{1})\cdot c\cdot 10^{3}}{m} \tag{6}\]  
_V_: volume of the sodium thiosulphate solution used for sample titration; \(V\)1: volume of the sodium thiosulphate solution used for the titration of the reagent blank; _c_: concentration of the sodium thiosulphate solution; _m_: mass of the sample.