esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 1 Introduction
Rust-preventative fluids are widely utilized to provide temporary corrosion protection throughout production operations, storage, and shipment, among the many options available to prevent corrosion of metals [19]. Depending on the types of base fluid, rust-preventative fluids can be categorized as solvent-based, oil-based, and water-based rust preventatives by adding corrosion inhibitors, film-forming agents, and other additives to a base fluid [20]. Most commonly, corrosion can be controlled by implementing specific corrosion \"probing\" techniques or devices in infrastructure, plants, buildings, bridges, and other engineered systems. In order to stop the contact of water with the steel surface, which causes corrosion, film-forming inhibitors are employed in industries to build a molecular layer directly on the top of the steel and an additional layer of the aliphatic tail in hydrocarbons. Hazardous Cis, such as chromate-based and phosphate-based Cis, are no longer permitted due to their toxicity as a result of increased environmental awareness and legal changes. Green chemistry is gaining popularity among researchers due to its inexpensiveness and, moreover, it is safe for living organisms as well [21]. CIs that are favorable to the environment are growing in popularity because they contain a variety of active components that can be employed in organic compounds to attach to metal surfaces and produce a protective coating that stops further corrosion. As such, CIs developed from natural resources are an excellent option [22; 23; 24]. Nowadays, researchers are employing various techniques to develop environmentally friendly corrosion inhibitors (CIs). These CIs are designed to be more environmentally friendly by utilizing less toxic and biomass-derived nanoparticles due to their favorable physicochemical characteristics [25]. The high surface-to-volume ratio of nanomaterials and their additives compared to those of typical macroscopic materials makes them effective corrosion inhibitors. Nano-compounds pro vide hardness, straightness, durability, optical properties, and thermal stability, in addition to stopping surface reactions and reducing the corrosion rate by blocking active sites on metal surfaces [26]. Numerous plant extracts have been studied as secure environmental corrosion inhibitors [27; 28; 29]. However, more research has to be performed on this green CIs area. This review presents a comprehensive analysis of the multi-faceted influence of corrosion across diverse domains, while also addressing distinct categories of corrosion inhibitors (CIs). It further elucidates the practical implementation of these CIs, the methodologies employed for evaluating their performance, and offers insights into the prevailing trends within this research domain.
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 5 Use Cases
### Use Case 3: Impact of Radio Parameters on the Video Metrics and User QoE
Thus, we replace these metrics by the logarithm of the relative error (denoted by \(log(RR)\)) between the estimated and the reference values as reported in Table 14.  
From Table 14, we observe that the logarithm of the relative error is between 0 and 1, which presents credible values. In fact, we confirm that using radio parameters can give acceptable prediction results in the case of launch time with a \(log(RR)\) of 0.02 that represents an average error rate of 246 ms, and a correlation rate of 87%. However, the results are less good and not sufficient for the bitrate KQI with a \(log(RR)\) of 0.33 that represents an average error rate (MAE) of more than 9400 kbits/s. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method/Performance**} & \multicolumn{5}{c}{**Relative Logarithmic Error (\(log(RR)\))**} \\ \cline{2-7}  & \(log(RR)\)**—Launch Time** & \(log(RR)\)**—Video Bitrate** \\ \hline RF & 0.24 & 0.32 \\ \hline DT & 0.96 & 0.34 \\ \hline KNN & 0.79 & 0.59 \\ \hline GTB & 0.02 & 0.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Bitrate and launch time prediction performance results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method/Performance**} & \multicolumn{5}{c}{**Launch Time KQI Prediction**} & \multicolumn{5}{c}{**Video Bitrate KQI Prediction**} \\ \cline{2-7}  & **MAE** & **MAPE** & **r** & **MAE** & **MAPE** & **r** \\ \hline RF & 536 & 4.47 & 0.77 & 11,078 & 1.21 & 0.84 \\ \hline DT & 1257 & 2.75 & 0.13 & 18,894 & 1.62 & 0.32 \\ \hline KNN & 1004 & 4.16 & 0.10 & 19,967 & 7.73 & 0.31 \\ \hline GTB & 246 & 0.36 & 0.86 & 9406 & 1.06 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Bitrate and launch time prediction performance results using 80% for training and 20% for testing with 5-fold for cross validation.
## 2 Methodology
### Multiscale Local Projection Algorithm
The \(e(i)\) can be regarded as an error, and it needs to choose values of \(a_{2}\), \(b_{1}\), \(\beta\), which are attenuated by the operator \(a_{2}J_{d}^{2}+b_{1}J_{\beta d}\). For \(\beta\leq 2\):  
\[(a_{2}J_{d}^{2}+b_{1}J_{\beta d})e(0)=\sum_{l=-\beta d}^{\beta d}\,(a_{2}\frac {2d+1-|l|}{\left(2d+1\right)^{2}}+\frac{b_{1}}{2\beta d+1})\,e(l)+\sum_{r=\beta d +1}^{2d}\,a_{2}\frac{2d+1-r}{\left(2d+1\right)^{2}}(e(r)+e(-r)). \tag{9}\]  
The result holds for \(\beta\), \(d\) such that \(\beta d\in Z\). A limit \(d{\rightarrow}\infty\) obviates this complication. To preserve the geometry of the underlying attractor, it needs (7) to hold for \(0\leq n\leq 3\), resulting in \(a_{2}=A(\beta)=\beta^{2}/(-2+\beta^{2})\), \(b_{1}=B(\beta)=1-\beta^{2}/(-2+\beta^{2})\). Consider the expectation of the square of filtered error at \(j=0\), \(S_{a_{2},b_{1},\beta,d}=((a_{2}J_{d}^{2}+b_{1}J_{\beta d})e(0))^{2}\), with these geometric constraints on \(a_{2}\), \(b_{1}\), \(\beta\). The desirable case of dense embedding corresponds to a large window \(d\), while in the limit, \(d{\rightarrow}\infty\), \(S_{a_{2},b_{1},\beta,d}{\rightarrow}0\). Thus, normalize the expectation \(S_{a_{2},b_{1},\beta,d}\) by that which refers to prototypical local projection proposed by [PERSON] [14]. This gives:  
\[T_{a_{2},b_{1},\beta}=\lim_{d\rightarrow\infty}\frac{S_{a_{2},b_{1},\beta,d}} {S_{1,0,\beta,d}}, \tag{10}\]  
which is called the normalized expected error, and using (9) and the independence of the variables \(e(i)\), it is straightforward to compute that \(T_{a_{2},b_{1},\beta}=2a_{2}{{}^{2}}/3-0.5a_{2}b_{1}(-4+\beta)+{b_{1}}^{2}/\beta\). Since the parameters\(a_{2}\) and \(b_{1}\) can be calculated directly according to \(\beta\), which established the relationship between the scale factor \(\beta\), and the error \(T(\beta)\). When \(\beta\) varies between [0,5], the corresponding error values are shown in Figures 3 and 4. The Figure 4 is the local enlargement of Figure 4, where the error curve has a local minimum at \(\beta=\beta^{*}\approx 0.96215\) (marked red in Figure 4), and further calculates \(a_{2}=a_{2}{}^{*}=A(\beta^{*})\approx-0.86174\), \(b_{1}={b_{1}}^{*}=B(\beta^{*})\approx 1.86174\). These values are suggestively close to the coefficients (\(a_{2}=0\), \(b_{1}=1\), \(\beta=1\)) of the standard local projection algorithm, but are more accurate. The parameter values corresponding to the local minimum point are taken as the new filter coefficients, thus a new multiscale local projection filter is established, which could effectively attenuate error while more precisely preserving geometry.
## 4 Engineering Application and Analysis
### Characteristics of Rock Mass and Lining Deformation  
#### 4.2.1 Development Law of Plastic Zone of Rock  
The presence and growth of the plastic zone within the surrounding rock is a critical indicator of tunnel stability. An increase in the thickness of the plastic zone, denoted as \(H\), typically signifies a reduction in rock mass stability, necessitating additional support measures to supply adequate pressure and prevent further deformation or collapse. To elucidate this relationship, the study assesses the correlation between the plastic zone radius \(R_{\text{p}}\) of the surrounding rock, the plastic zone thickness \(H\), and the excavation radius \(R_{0}\), as depicted in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pile Numbers** & \(BQ\) & \(p_{0}\) & \(D\) & \(E_{0}\) & \(\sigma_{\text{ci}}\) & Surrounding Rock Grade \\ \hline K25 + 826\(\sim\)K25 + 886 & 294 & 2.46 MPa & 0.3 & 24.26 GPa & 60 MPa & IV \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geological characteristics of the fault zone rock mass.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & \(GSI\) & \(m_{\text{i}}\) & \(m_{\text{b}}\) & \(s\) & \(a\) & Maximum Plastic Zone Radius \(R_{\text{p}}\) & Maximum Wall Displacement \(u_{\text{wmax}}\) \\ \hline Value & 24.63 & 18 & 0.76 & 0 & 0.53 & 8.98 m & 26.9 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation results of deformation of surrounding rock in fault section.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lining Parameters** & \(R_{\text{b}}\) & \(R_{\text{a}}\) & \(t\) & \(E_{\text{c}}\) & \(\mu_{\text{c}}\) & Design Radius \(R_{\text{s}}\) \\ \hline Value & 7.18 m & 6.44 m & 1.115 & 31.5 GPa & 0.2 & 6.41 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: S4-3 type lining parameters.  
Figure 9 illustrates the relationship between the supporting pressure \(p_{\rm i}\), the plastic zone radius \(R_{\rm p}\), and thickness \(H\) of the surrounding rock:  
1. Under a constant excavation radius, a decrease in support pressure \(p_{\rm i}\) leads to an increase in the plastic zone radius \(R_{\rm p}\), indicating that lower support pressure results in more extensive plastic deformation of the surrounding rock. The negative correlation between \(p_{\rm i}\) and \(R_{\rm p}\) remains consistent with varying excavation radii \(R_{0}\), with the curves running parallel to each other.
2. The thickness \(H\) of the plastic zone gradually increases with the enlargement of the excavation radius \(R_{0}\), with an average increase of 1.95 mm in maximum plastic zone thickness for each 1 cm increment in \(R_{0}\). This suggests that larger excavation radii require proportionally higher support pressure to maintain the stability of the surrounding rock.
## 2 Materials and Methods
### Data Collection and Procedures
#### 2.2.2 BIA Measurements  
To assess individual body composition, a portable, a single-frequency (50 kHz), eight-point Tanita Analyzer (Tanita BC-418 MA, Tanita Co., Tokyo, Japan) was used, following the procedures for BIA measurement (refraining from vigorous physical activity at least 12 h prior, intake of no caffeine and alcohol for 24 h prior to testing, fast or 4 h after a meal, and empty the bladder 30 min prior to testing). Due to the fact that dehydration is a recognized factor affecting BIA measurements, hydration status was tested in a urine sample using a gravity test (with arometer) and the color for each person. BIA was performed under standardized conditions, according to the manufacturer's protocol. The results of fat mass (FM, kg) and fat-free mass (FFM, kg) were taken to calculate FM/FFM, fat mass index (FMI), and fat free mass index (FFMI):  
* FMI = FM (in kg)/H (in m)2, kg/m2;
* FFMI = FFM (in kg)/H (in m)2, kg/m2.
###### Abstract  
In order to realize real time monitoring of foundation displacement of railway cable bridge based on satellite observation system, reasonable data sources of satellite monitoring and data analysis duration are necessary, and the influence of various errors should be eliminated in the process of satellite signal acquisition. In this paper, the validity of foundation settlement monitoring of bridge tower by satellite is verified through accuracy and stability tests. For eliminating the multipath error and random noise of GNSS signal, a new method is presented by combining stationary wavelet transform and empirical mode decomposition in this study. For improving the observation accuracy of the satellite monitoring system, combing the measured data, the GNSS data are corrected by establishing non-linear mapping between the GNSS data and precise leveling data by BP neural network. Based on that, the accuracy of the presented method is verified by the foundation settlement data of a new railway cable bridge tower.  
GNSS signals; signal denoising; foundation of tower bridge; stationary wavelet transform; BP neural network Article
## 2 Materials and Methods
### Numerical Studies
#### 2.3.2 Load Application  
The used finite element software is based on an implicit approach. Loading was applied uniformly on top of the upper loading platen in terms of prescribed displacements. In horizontal directions, the value of prescribed displacement was selected with zero. The value in vertical direction \(u_{z}\), to be applied within the calculation phase, was determined by an iterative algorithm that was implemented via the _remote scripting interface_ in PLAXIS 3D [40]. This measure was necessary for yielding the desired axial sample stress of 10 MPa, making the numerical results comparable to the analytical ones. A direct assumption for the value of prescribed vertical displacements was not possible due to the difficulties in assessing the influences of the interfaces together with the platen deformation.  
The automated process for selecting the required value for the prescribed vertical displacements \(u_{z}\) included the determination of initial values using the following equation:  
\[u_{z,init}=\frac{\sigma_{z,req}}{E_{\beta}}\cdot l_{s}, \tag{7}\]  
with \(\sigma_{z,req}\) equal to the considered axial stress level of 10 MPa, \(l_{s}\) corresponding to the sample height and \(E_{\beta}\) chosen as \(E^{\prime}\) for \(\beta=0^{\circ}\) and \(E\) for \(\beta=45^{\circ}\) and \(90^{\circ}\).  
After carrying out a first phase calculation, the _structural forces in volume piles_ feature in PLAXIS was invoked to extract the axial force distribution along the sample axis, as shown in Figure 6a. The average axial stress \(\sigma_{z,av}\) was determined by dividing the average axial force \(N_{av}\) by the sample cross section \(A_{cross}\). From the averaged axial stress \(\sigma_{z,av}\), the value for the prescribed vertical displacement \(u_{z}\) within a single loop was computed by multiplying the current value \(u_{z,current}\) by the factor \(\sigma_{z,req}/\sigma_{z,av}\). Using this newly determined value for \(u_{z}\), the iterative procedure was continued until convergence of \(\sigma_{z,av}\) to \(\sigma_{z,req}=10\) MPa, judged based on a small maximum tolerance, was reached and the final model results could be obtained. The whole iterative process is presented in a flow chart in Figure 6b.
# Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomatages
## 2 Methods
### Frame Sorting
#### 2.2.2. Keypoint Matching Based Rigid Registration  
Rigid registration that is based on keypoint matches is first applied to account for large scale differences in the viewing direction between frames. We apply the SURF keypoint detector and descriptor [12] among many possible methods due to its balance of efficiency and robustness. The transform between the subsequent frame with the montage is modeled as a two-dimensional perspective homography and RANSAC (random sample consensus) is applied to disregard the outliers [20].  
Inadequacy in applying a 2-D homography to represent the view transform of a 3-D spherical object and limited number of matched keypoints stemming from small overlap or insufficient texture may cause failures. Figure 5 depicts an example of erroneous registration and subsequent warping, which occurs when feature matching between overlapped images is not done properly. We add a simple validity test to exclude integrating an erroneously registered frame into the mosaic to avoid this. In particular, we measure the difference in pixel area before and after the determined homography transform. If the difference is larger than 10%, we exclude the frame. Here, the threshold value of 10% was empirically set based on the particular dataset in our experiments.
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
## 3 Results
### Normal Operation Plasma Scenario--Local Analysis  
From the CFD simulation, the pressure profile in fluid bodies and the temperature profile in fluid and solid bodies are evaluated (Figure 10).  
The pressure profile and streamline of the bracket are shown in SS3.4. Pressure drop in these components is low at the nominal mass flow rate. The maximal temperature is on the  
Figure 10: (**a**) Pressure at fluid–structure interface in the mirror. (**b**) Streamline velocity inside fluid mirror body. (**c**) Temperature profile in solid bodies.  
Figure 9: (**a**) Equivalent von Mises stress for primary load. (**b**) Equivalent von Mises stress for secondary load.  
bracket's body (114.5 \({}^{\circ}\)C), far from the water channels. The maximum temperature of the mirror is 106.6 \({}^{\circ}\)C (lower than the 200 \({}^{\circ}\)C limit).  
The temperature profiles of both bodies are imported to a thermomechanical study, as well as the pressure at the fluid-structure interface in mirror and bracket bodies.  
Figure 11 shows the total deformation for the mirror and the bracket, as well as the normal deformation of the mirror optical surface.  
The normal displacement of the optical surface is 115.7 \(\upmu\)m (coming from 41.9 + 73.8 \(\upmu\)m, Figure 10(b)). This displacement corresponds to a tilt of 3 arcmin (around the local relative \(X\)-axis in the figure) that is lower than the allowable value of 6 arcmin. Conversely, a good hydraulic design of the channels has allowed for the obtainment of homogeneous pressure and body temperature distributions. This factor avoids the risk of deformation or tilt around other axes.  
As shown in Figure 12, the primary stress is lower than the allowable value of 111 MPa. The secondary stress is higher than the allowable value of 333 MPa.  
Figure 11: (**a**) Total deformation of mirror and bracket; (**b**) normal deformation of the optical surface.  
Figure 12: (**a**) Equivalent von Mises stress for primary load. (**b**) Equivalent von Mises stress for secondary load.  
However, the maximum stress is located in the screw holes and is caused by the boundary condition. A local yielding might occur in the worst case, but it would not affect the behavior of the system.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 4 Materials and Methods
### GA-Based Compiler Overview  
To address the problem described above, in this work, we propose a synthesis technique based on NSGA-II, a GA used for MOO problems. In this section, we describe the high level flow of the proposed technique, shown in Figure 4, and subsequent sections discuss each component of the technique. The compiler begins with an input unitary matrix, \(U_{in}\), and starts the compilation process by generating an initial population of randomly generated chromosomes, each of which encode a random quantum circuit. The population size, \(N_{pop}\), can be specified by the user, but is ideally adjusted to fully utilize available computational resources. The chromosomes are evaluated, using (6), and the equivalent fitness vector assigned to each chromosome. The population members are then ranked and sorted using the NSGA-II technique and the GA is now ready to begin the main loop.  
The first step in the loop is parent selection for producing offspring. In our proposed technique, we keep the population size constant to constrain the design complexity and to reduce the computational burden of having a growing population. Thus, each generation, the compiler produces a fixed number of offspring, also equal to \(N_{pop}\). At the start of a generation, pairs of parents are selected to produce two offspring. Limiting the number of offspring per parent pair to two allows more chromosomes to participate in the crossover process since the total number of offspring in a generation is kept constant. Thus, \(\frac{N_{pop}}{2}\) parent pairs are chosen to produce \(N_{pop}\) offspring.  
After parent selection, the parents are mutated probabilistically and the mutation rate/probability allows the GA to control the diversity of the population. After mutation, the parents are crossed over, the offspring evaluated for their fitness and merged with the initial population. After the merge, the combined temporary population, of size \(2N_{pop}\), is re-ranked and sorted using NSGA-II sorting. If the stopping criteria is met, the best circuit found so far is returned. Otherwise, the bottom half of the population is discarded and the GA moves on to the next generation. However, if runtime parameter adaptation is enabled, the progress statistics and current mutation rates are used to adjust the mutation rate for the next generation using a fuzzy controller.
## 4 Experiments and Analysis
### Dataset  
In the experiments, to evaluate the model, this paper employed a publicly available dataset, SemEval 2010 Task 8. The dataset comprises a training set and a test set, totaling 10,717 samples, with 8000 samples in the training set and 2717 samples in the test set. The dataset includes 9 relationship types and a unique \"other\" class. These 9 relationship types can be further divided into 18 based on the relative positions of the head and tail entities. For example, \"Entity-Origin\" can be split into \"Entity-Origin (e1, e2)\" and \"Entity-Origin (e2, e1)\", which are distinct relationship types. Therefore, there are ultimately 19 relationship types in the dataset. The relationships contained in the dataset and the number of each relationship are shown in Table 4.
###### Abstract  
Understanding the microstructural and phase changes occurring during irradiation and their impact on metallic fuel behavior is integral to research and development of nuclear fuel programs. This paper reports systematic analysis of as-fabricated and irradiated low-enriched U-Mo (uranium-molybdenum metal alloy) fuel using atom probe tomography (APT). This study is carried out on U-7 wt.% Mo fuel particles coated with a ZrN layer contained within an Al matrix during irradiation. The dispersion fuel plates from which the fuel samples were extracted are irradiated at Belgian Nuclear Research Centre (SCK CEN) with burn-up of 52% and 66% in the framework of the SELENIUM (Surface Engineering of Low ENIrched Uranium-Molybdenum) project. The APT studies on U-Mo particles from as-fabricated fuel plates enriched to 19.8% revealed predominantly \(\gamma\)-phase U-Mo, along with a network of the cell boundary decorated with \(\alpha\)-U, \(\gamma^{\prime}\)-U\({}_{2}\)Mo, and UC precipitates along the grain boundaries. The corresponding APT characterization of irradiated fuel samples showed formation of fission gas bubbles enriched with solid fission products. The intermediate burnup sample showed a uniform distribution of the typical bubble superlattice with a radius of 2 nm arranged in a regular lattice, while the high burnup sample showed a non-uniform distribution of bubbles in grain-refined regions. There was no evidence of remnant \(\alpha\)-U, \(\gamma^{\prime}\)-U\({}_{2}\)Mo, and UC phases in the irradiated U-7 wt.% Mo samples.  
atom probe tomography; nuclear fuel; microstructure; burnup; enrichment; uranium; fission products +
Footnote †: journal: Applied Sciences
## 3 Loss Differentiation Feature Selection for LEO Satellite Network  
Utilizing machine learning techniques to identify the causes of packet loss, our first task is to extract relevant features from the time information of packet transmission.