esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Generalities of the Weibull Analysis  
The aim of this section is to present the generalities of the Weibull distribution based on which we can directly determine the \(\beta\) and \(\eta\) parameters of the _Weibull stress_\((\beta_{s},\ \eta_{s})\) and the _Weibull cycles_\((\beta_{t}=\beta_{s},\ \eta_{t})\) families. The Weibull probability density function (_pdf_) and its reliability function \(R(t)\)[23] are given as:  
\[f(t)=\frac{\beta}{\eta}\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta-1}exp\left(- \bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\right) \tag{6}\]  
and  
\[R(t)=exp\left\{-\bigg{(}\frac{t}{\eta}\bigg{)}^{\beta}\right\} \tag{7}\]  
From the addressed \(\sigma_{1}\) and \(\sigma_{2}\) values, the \((\beta_{s},\ \eta_{s})\) parameters are directly estimated [24]. \(\beta_{s}\) is determined as:  
\[\beta_{s}=\frac{-4\mu_{y}}{0.99176*ln\bigg{(}\frac{\sigma_{1}}{\sigma_{2}} \bigg{)}} \tag{8}\]  
where the constant \(0.99176\) is specified for the analyzed case, and the method to determine it for any case, is given in Section 4.1 in [24]. Also, \(\mu_{y}\) is the mean of the used \(Y\) vector, determined based on the median rank approach. Notice that if \(n=21\) elements are tested, then \(\mu_{y}=\ -0.545624\); and \(\eta_{s}\) is determined as:  
\[\eta_{s}=exp(\mu_{x}) \tag{9}\]  
where from [24]\(\mu x\) in Equation (9) is the log-mean of the observed failure-time data which based on the addressed \(\sigma_{1}\) and \(\sigma_{2}\) values is determined as:  
\[\mu_{x}=ln(\sigma_{1}\sigma_{2})^{\frac{1}{2}} \tag{10}\]  
To summarize, note that by using the \(\sigma_{1}\) and \(\sigma_{2}\) values in Equations (8)-(10), the Weibull stress parameters are both completely determined, and that because \(\mu_{y}=\ -0.545624\) is constant, then the efficiency of \(\beta_{s}\) and \(\eta_{s}\) only depends on the efficiency on which the \(\sigma_{1}\) and \(\sigma_{2}\) values were determined. Based on the addressed stress \((\beta_{s},\ \eta_{s})\) parameters, let us present the method to determine the random behavior of the addressed \(\sigma_{1}\) and \(\sigma_{2}\) values, based on which the random behavior of the expected cycles to failure is formulated.
# A Review of the Artificial Neural Network Models for Water Quality Prediction
## 5 Result
### Data Collection  
The data collection process is not easy due to the requirement of costly measuring instruments (e.g., water quality sensors, meteorological stations), laboratory equipment, and good operating conditions. Water quality variables are primarily collected by the sensors. Meteorological variables, such as AT, WS, RF, SR, Precip, and AP, often influence water quality. Therefore, some researchers took the meteorological station to obtain the data. In addition, some parameters, such as BOD, COD, need to be measured by auxiliary laboratory equipment [44]. Location information is essential when researchers want to make a three-dimensional prediction of water quality. In the above case, the required data is obtained through the device (see Figure 10). In some studies [42; 47], the researchers conduct studies based on an open-source dataset.  
Based on the obtained data, researchers can perform three modeling types. The first type of modeling is where the researcher models only historical information about the output variable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Categories** & **Authors (Year)** & **Methods** & **Scenario (s)** & **Time Step** & **Dataset (Samples)** \\ \hline \multirow{8}{*}{Recurrent} & [41] & Elman, GRNN, & \multirow{2}{*}{river} & monthly or & \multirow{2}{*}{61} \\  & BPNN, MLR & & & semi-monthly & \\  [12] & NARX, BPNN, MLR & & & & monthly \\  & [26] & LSTM & & & 12 hours & 460 (14 months) \\  & [16] & LSTM, BPNN & & & monthly & 657 (7 years) \\  & [66] & LSTM, RNN & & & & \\  & [67] & SRU & & & & \\  & [3] & Elman & & & & \\  & [153] & RNN, LSTM & & reservoir & 5 minutes & 1440 (5 days) \\  & [15] & RNN, BPNN & & river & No details & 1448 \\  & [165] & LSTM & & & & \\  & [54] & LSTM, BPNN & & & & \\  & [38] & RESN & WWTP & No details & 5000 \\  & [45] & RNN & & Freshwater & 10 minutes & 5006 (1 year) \\  & [55] & TLRN, RNN, TDNN & & & & 15 minutes & 13,744 (573 days) \\  & [154] & LSTM & & WWTP & hourly & 23,268 (4 years) \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 10: General framework for water quality modeling.  
The second type of modeling is when the output variables are difficult to measure, and the researchers can use easily measured water quality or meteorological data to complete the prediction. In the first two modeling types, the researchers utilized univariate historical information. However, for the third type of modeling, the researchers used multivariable historical information. Overall, the researchers utilized water quality, atmosphere, and other variables such as location data for the prediction task. The above three modeling types are analyzed from the perspective of data. If analyzed from the perspective of studying the temporal and spatial relationship between input and output, the above modeling types can be further divided.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 4 Results and Discussion
### Time Statistics  
Figure 5 shows the training time of the best model of the classifiers used for both POST and DELTA datasets. ELM (1.45 s) and LR (1.85 s) algorithms recorded the shortest training times, followed by SVM (6.2 s) and RF (35.1 s). In line with the hypothesis, ELM neural network training times are shorter than the rest of the classifiers. This is due to the fact that the training algorithm does not optimize all the network parameters and the calculation of the Moore-Penrose pseudoinverse is highly efficient [61].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **DICE** & **Accuracy** & **Omission** & **Commission** \\ \hline RF & 0.92 & 0.93 & 0.07 & 0.08 \\ SVM & 0.87 & 0.89 & 0.16 & 0.08 \\ LR & 0.84 & 0.83 & 0.17 & 0.19 \\ MLP & 0.86 & 0.85 & 0.10 & 0.10 \\ ELM & 0.91 & 0.91 & 0.12 & 0.02 \\ \hline \end{tabular}
\end{table}
Table 6: Best model performance over the DELTA dataset for the test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **DICE** & **Accuracy** & **Omission** & **Commission** \\ \hline RF & 0.94 & 0.94 & 0.08 & 0.06 \\ SVM & 0.89 & 0.89 & 0.16 & 0.08 \\ LR & 0.84 & 0.84 & 0.11 & 0.16 \\ MLP & 0.89 & 0.89 & 0.04 & 0.12 \\ ELM & 0.93 & 0.92 & 0.12 & 0.02 \\ \hline \end{tabular}
\end{table}
Table 5: Best model performance over the DELTA dataset for the validation set.
# Nearshore Benthic Mapping in the Great Lakes:
## 3 Results
### Classification Performance Analysis
#### 3.1.1 Substrate Component Classification Results
Data order was generally not a factor; however, when S2 SDB was input first with S2 DII + S2 MS, it resulted in a slight decrease in overall accuracy and KC, which may suggest that the S2 SDB input data, the algorithms used to derive it, and the resulting outputs should warrant further investigation and evaluation, especially when used as input into benthic classifications or models (see Discussion Section 4 for additional evaluation of the S2 SDB product). For the Tier 2 fine unconsolidated (substrate) classification, overall accuracy (airborne only) was 93.28% for the two data combinations evaluated, while the KC was 0. The CA MNF FU data combination with additional ROIs, though considered one of the top-performing results, was unable to resolve any of the Tier 2 substrate classes. Therefore, the remaining top two data combinations were evaluated: (1) CA MNF RLR + CA MNF FU + AB DE with additional ROIs and (2) CA MNF FU + CA MNF RLR with additional ROIs. Since the CA MNF WLR + CA MNF FU + AB DE with additional ROIs data combination had the higher overall accuracy and KC of the two from Tier 1, the fine unconsolidated substrate area from that classification result was used as a mask to further discriminate more detailed (Tier 2) class types within that area. Although six sand classes with varying patterns (ripple and pockmarked surface patterns) and co-existing materials (woody debris, organic detritus, and mussel shell hash) were attempted with the SVM classifier, only two were able to be identified: (1) sand, and (2) sand with ripple surface pattern, with the majority of the sand with ripple surface pattern area located in the offshore, northeast part of the study area (Figure 15). Given a KC of 0 and no ground truth available for validation in that area, the reliability of the resultant classification is very low with this same classification result possible by chance. It is also worth mentioning that this location is in the same area where CA MNF FU with additional ROIs follows the flight line pattern (Figure 13, panel b). Thus, more detailed ground truth and data inputs would likely be needed to reliably classify the Tier 2 classes identified in this study, including the characterization of modifiers such as surface patterns or co-existing materials. Yet, it is worth noting that the classes listed in Tier 2 reflect the highest level of detail (subgroup level) within the substrate component of the CMECS hierarchy. Figure 14: Tier 1 substrate component classification results using satellite data combinations for the north-central section of the study area: (**a**) S2 MS + S2 DII + S2 SDB and (**b**) S2 MS + S2 DII + S2 SDB with additional ROIs.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.1 Mate  
Mate [58] is a communication-centric middleware layer for WSNs, based on a virtual machine architecture, which runs on TinyOS (Bombilla [59] is an instance of the Mate virtual machine that runs under TinyOS). Code is divided into 24-instruction capsules; larger programs can be supported using subroutine capsules (currently limited to 4). Programs communicate via message passing, in a similar way to TinyOS, using the Trickle protocol [13]. Code capsules are flooded through the network via viral programming-code capsules can be marked as self-forwarding, and such capsules are flooded throughout the network based on 32-bit version numbers. Every node broadcasts a summary of capsule versions to its neighbours based on a random timer: if a node hears an identical summary, it suppresses its own next transmission; if it hears a version summary with older capsules than it has itself, it broadcasts the newer modules. This flooding continues even after the entire network has been updated. (An earlier version of Mate supported an instruction to initiate code propagation, but experiments showed that this could easily lead to network saturation.) There is support for injecting new modules (or new module versions) into the network via a capsule injector.  
The flexibility provided by the support for mobile agents in Mate might prove to be a useful enabling technology for autonomous updates, allowing a self-managing system to inject different behaviours in order to respond to local conditions.
## 4 Discussion
### Effect of RST vs. MRL Position on Muscle Hardness  
In our study, the shear modulus for all tested muscles was found to be significantly higher under MRL than RST position. This finding is in line with previous works [28; 29]; however, our study provides more information on the intrinsic qualities of skeletal muscles. Regardless of the anatomical location of the tested muscle, when skeletal muscle is elongated, there is a measurable resistance even when motor neurons are quiescent [7]. The MRL position caused a lengthening of the muscle fibers within its physiological limits hence showed a measurable resistance. However, a total change produced by lengthening of the muscle-tendon unit under the MRL condition was much smaller for the RF muscle when the knee is set off the table in comparison to lengthening beyond 90\({}^{\circ}\) knee flexion and for the GM muscle when the outside was in touch with the wall in comparison to lengthening beyond 0\({}^{\circ}\) neutral ankle position. Therefore, the detected increase in the stiffness or local hardness, by shear-wave elastography, can mainly be attributed to the mechanical property representing transverse muscle stiffness, which is different from muscular-tendon complex stiffness along the longitudinal axis of the muscle [30].  
It is also assumed that the increase in local muscle hardness by lengthened muscle fibers was due to collagen fibers aligned in the direction of the stress applied by muscle lengthening [31]. Consequently, the transverse muscle stiffness increases along increasing an intramuscular pressure. Another factor attributable to this is several attached cross-bridges changing linearly with the force developed by the lengthening fibers [32]. The composition of the muscle fibers is another known factor contributing to the muscle stiffness where fast contracting fibers are known to have lower stiffness [33]. All the contributors need to interact with each other to produce an active stiffness required to maintain a quiet standing position [34]. According to our results, when muscle hardness was compared between RST and MRL positions for the TA and GM muscles, as expected, the lengthened muscle up to its mid-range or when joint torque was supposedly minimal, the shear modulus was found to be significantly higher in the lengthened than the shortened condition. The RF muscle has also displayed increased local hardness under lengthened condition. If a change in the muscle hardness, revealed as an increase under MRL position without being stretched beyond this level is evident, it would be of interest to find out whether there is any change induced by pathology or related to age, yet detectable at this particular muscle position while the subject remains relaxed with the fibers not overstretched.
# Proteomics in Forensic Analysis: Applications for Human Samples
## 2 Proteomics Methods: Sample Preparation Techniques, Data Acquisition, and Data Analysis
### Sample Preparation
#### 2.1.2 Protein Extraction  
After proteins are solubilized in buffer solutions, they are extracted or purified by concurrent removal of contaminants. Protein precipitation is one of the most commonly used methods. In this method, proteins are precipitated by organic solvents (e.g., acetone, methanol, or ethanol) and their mixtures with acids (e.g., trichloroacetic acid) or sodium deoxycholate. Acetone is the most commonly used solvent because of its ability to dissolve nonpolar contaminants (e.g., lipids). After precipitation, the protein pellets were collected and washed with pre-chilled acetone to remove contaminants prior to proteolytic digestion [74; 75]. Other methods are also used for protein purification, including chromatography, electrophoresis, dialysis, ultrafiltration, lyophilization, and crystallization [76]. When the samples are serum, plasma, or cerebrospinal fluid, immunodeficiencypletion may be required to remove the most abundant proteins [77]. For example, a human 14 multiple affinity removal system (MARS) column can remove albumin, IgG, antitrypsin, IgA, transferrin, haptoglobin, fibrinogen, alpha-2-macroglobulin, alpha-1-acid glycoprotein, IgM, apolipoprotein AI, apolipoprotein AII, complement C3, and transthyretin.  
Prior to further sample preparation, several methods are used to determine the concentration of protein in the samples. Colorimetric dye-based methods, such as the Bradford assay, are straightforward and fast. The binding between proteins and dye results in a color change and is used to determine protein concentrations. The advantages of these methods include compatibility with most solvents, buffers, salts, reducing substances, thiols, and metal-chelating agents. These can be performed at room temperature [78]. Bicinchoninic acid (BCA) assays (biuret methods) are based on protein-copper chelation in an alkaline environment. In this reaction, Cu\({}^{2+}\) is reduced to Cu\({}^{+}\), which subsequently reacts with the BCA reagent to form a purple complex. The purple color can be measured at wavelengths between 550 and 570 nm. This method is compatible with most detergents and causes less protein-protein variation than the Bradford assay. However, it is not suitable for substancesthat reduce copper [79]. In Lowry assays, Cu+ is allowed to react with Folin-Ciocalteu reagent (a mixture of phosphotungstic acid and phosphomolybdic acid) [80]. Fluorescent dye methods are based on protein-dye binding and direct detection of the fluorescence associated with the dye. These methods are highly sensitive and applicable to samples with low protein amount. Typical examples of fluorescent dye methods are the EZQ fluorescent assay and the Qubit protein assay [81].
## 4 Numerical Simulation
### Finite Element Model  
#### 4.1.1 Element and Material  
The solid element C3D8R for section steel and RAC was used in this study, which is an 8-node hexahedron reduction integral element. This element has more accurate results and saves calculation time, and is also suitable for meshing refinement. The linear three-dimensional truss element T3D2 was adopted for steel and stirrups, which has two nodes, each with three degrees of freedom. The steel and stirrups were assigned as truss elements when meshing.  
The experimental materials adopted in this study were described as shown in Table 7. The properties of the steel used in the tests were determined in accordance with the \"Code for Design of Concrete Structures\" [37]. The elastic properties of the second-class coarse aggregate for RAC are shown in Table 8. The plastic damage model of Abaqus was selected for the plastic part of the RAC.
## 4 Discussion
### Limitations  
While this study contributes important findings, several limitations warrant consideration. First, the sample size is relatively small, particularly for EEG-based research, which may limit statistical power and the generalizability of findings. Additionally, demographic factors such as age, gender, occupant status, trust in AVs, and familiarity with AVs could influence results. For example, prior studies have identified age-related differences in MW among pedestrians [63], gender-based variations in eHMI comprehension [64], and nationality-driven differences in eHMI effects on street-crossing decisions [65; 66]. Future research should employ larger, more diverse samples to enhance generalizability and systematically investigate the impact of these demographic factors. Second, given the constraints in manipulating AV platoons in real-world settings, as well as safety concerns, a video-based simulation was adopted. While this method offered useful observations, it likely reduced the sense of actual risk for participants. Third, the use of a fixed camera perspective did not reflect individual differences in height, which could influence one's field of vision. These factors highlight the need for future studies conducted in real-world or immersive VR environments. Fourth, while the uncontrolled traffic scenario without traffic lights or crosswalks is highly relevant, it is imperative to investigate eHMI performance in signalized environments. Such studies would elucidate how infrastructural elements moderate eHMI effects on crossing decisions and MW [37]. Fifth, this study was conducted under clear weather and daylight conditions, excluding adverse weather (e.g., rain, fog) and variable lighting, which may compromise eHMI visibility [19]. This is particularly pertinent for risk-warning eHMIs based on TTA, where low-risk signals (TTA > 5.1 s) may be less discernible in challenging conditions, requiring further investigation. Moreover, adverse weather, such as rain or extreme heat, is associated with elevated injury risks and crash severity [67,68]. Thus, evaluating eHMI effectiveness in enhancing pedestrian safety under such conditions is imperative. Sixth, this research focused on an abstract light-based eHMI [8]. Although such abstract eHMI can be effectively understood, especially after repeated exposures or dedicated training [10,29,37], alternative eHMI designs--such as textual displays, symbolic icons, or human-like representations--should be systematically examined in future investigations. Seventh, the within-subject design may introduce learning or fatigue effects, potentially biasing results. Although we employed familiarization, randomization, and rest periods to mitigate these issues, future studies should further investigate trial-order effects, learning curves, and fatigue to better understand their impact on eHMI effectiveness.
## 4 Experimental Study
### Results Analysis and Discussion
#### 4.4.1 Analysis of the Comparative Study (1) for FDA and dMOP Problems
The one-way ANOVA results in Figure 7 assuming the competitive importance of DNSGA-II, dCOEA, PPS, MOEA/D, and SGEA to solve FDA and dMOPs test functions with 2 and 3 objectives, including the different environmental changes found using the HVD metric. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**DB-CSA-II (with** & & & & & & \\
**Dynamic Process) vs.** & **Prob.** & **QI** & **R\(-\)** & **R\(+\)** & \(p\)-Value** & **Best Method** \\ \hline DNSGA-II & & & & 235 & 65 & 0.015158 & DB-CSA-II \\ dCOEA & & & & 213 & 87 & 0.071861 & \(\cong\) \\ PPS & FDA & & 241 & 59 & 0.009322 & DB-CSA-II \\ MOEA/D & \& & IGD & 231 & 69 & 0.020652 & DB-CSA-II \\ SGEA & dMOP & & & 204 & 96 & 0.122865 & \(\cong\) \\ CSA & & & 300 & 0 & 0.000018 & DB-CSA-II \\ DB-CSA & & & & 275 & 25 & 0.000355 & DB-CSA-II \\ \hline DNSGA-II & & & & 222 & 78 & 0.039672 & DB-CSA-II \\ dCOEA & & & & 206 & 94 & 0.109599 & \(\cong\) \\ PPS & FDA & & & 219 & 81 & 0.048675 & DB-CSA-II \\ MOEA/D & \& & HVD & 223 & 77 & 0.037005 & DB-CSA-II \\ SGEA & dMOP & & & 205 & 95 & 0.116083 & \(\cong\) \\ CSA & & & & 289 & 11 & 0.000071 & DB-CSA-II \\ DB-CSA & & & & 240 & 60 & 0.010128 & DB-CSA-II \\ \hline DNSGA-II & & & & 91 & 0 & 0.001474 & DB-CSA-II \\ dCOEA & & & & 90 & 0 & 0.001871 & DB-CSA-II \\ PPS & UDF & & & 86 & 5 & 0.004649 & DB-CSA-II \\ MOEA/D & \& & & IGD & 91 & 0 & 0.001474 & DB-CSA-II \\ SGEA & F & & & 85 & 6 & 0.005772 & DB-CSA-II \\ CSA & & & & 90 & 1 & 0.001871 & DB-CSA-II \\ DB-CSA & & & & 91 & 0 & 0.001474 & DB-CSA-II \\ \hline DNSGA-II & & & & 36 & 55 & 0.506746 & \(\cong\) \\ dCOEA & & & & 34 & 57 & 0.421579 & \(\cong\) \\ PPS & UDF & & 35 & 56 & 0.463071 & \(\cong\) \\ MOEA/D & \& & HVD & 36 & 55 & 0.506746 & \(\cong\) \\ SGEA & F & & & 35 & 56 & 0.463071 & \(\cong\) \\ CSA & & & & 69 & 22 & 0.100525 & \(\cong\) \\ DB-CSA & & & & 71 & 20 & 0.074735 & \(\cong\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Non-parametric statistical analysis based on a [PERSON] signed rank test of DB-CSA-II vs. five peer MOEAs over IGD, HVD metrics for FDA, dMOP, UDF and F functions.