esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Composition of Telekia speciosa Essential Oils
[3] & tr & 0.1 & 0.1 & 1524 & - \\
99 & \(\alpha\)-Calanceance & 0.6 & 0.2 & 1530 & 1527 \\
100 & (\(\beta\)-Bisolabeabe & 0.1 & 0.6 & 0.2 & 1533 & 1530 \\
101 & Calina-107(11)-diene & 2.4 & 0.6 & tr & 1539 & 1538 \\
102 & \(\beta\)-Caryophyllene epoxide & 0.4 & 0.2 & 0.2 & 1540 & 1544 \\
103 & \(\beta\)-Calacorene & 0.1 & 0.1 & 1544 & 1545 \\
104 & (\(\beta\)-Nerol  
\begin{table}
\begin{tabular}{c c c c c c} \hline No. & Compounds & Leaf & Flower & Root & RI Exp. [1] & RI LiL. [2] \\ \hline  & [\%] & [\%] & [\%] & & \\ \hline
106 & Nery12-methylbutyrate & 1.0 & & 0.4 & 1564 & 1560 \\
107 & Nery1 ovalerate & 0.9 & & 1566 & 1560 \\
108 & Phenopans-8-d & 3.5 & 2.8 & 1569 & 1569 \\
109 & \(\beta\)-caryophylline oxide & 5.1 & 6.7 & 3.2 & 1579 & 1578 \\
110 & \(\beta\)-epi-Cerenode & r & 0.1 & 0.8 & 1582 & 1630 (HPS) \\
111 & 162/147/M206 n.i. [3] & 0.1 & r & 1584 & \\
112 & Thubipans2-e-ol & 0.1 & 0.1 & 1587 & 1589 \\
113 & 162/147/M206 n.i. [3] & 0.2 & 0.6 & 0.1 & 1592 \\
114 & Isomanodendence epoxide & r & 0.8 & 1594 & 1590 \\
115 & 162/147/M206/120/173 n.i [3] & 1.3 & 3.9 & 1600 & – \\
116 & 162/147/M206/120/173 n.i [3] & 0.5 & 1.0 & 0.2 & 1601 & – \\
117 & 162/147/M206/120/91 n.i [3] & 0.7 & tr & 0.2 & 1603 & – \\
118 & bi-Humachler epoxide & 0.2 & 0.3 & 1606 & 1594 \\
119 & Endosomal-e-en-/ol & & 0.1 & 1606 & 1604 \\
120 & 160/145/91/131/M182 n.i [3] & 0.1 & 0.3 & 1609 & – \\
121 & Isospinodendence & 0.1 & 0.4 & 0.4 & 1611 & 1619 \\
122 & allo-Aromadendence epoxide & 0.2 & 0.1 & 1616  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No. ** & & **Compounds** & **Leaf** & **Flower** & **Root** & **RI Exp. **1 ** & **RI Lin. **2 ** \\ \hline  & & **[\%]** & **[\%]** & **[\%]** & \\ \hline
139 & Farnessal (isomer 3) & 0.1 & 0.4 & tr & 1696 & 1707 \\
140 & (E)-Farnessal & 6.7 & 1.4 & 0.4 & 1710 & 1716 \\
141 & cis-2-\(\alpha\)-bisadecene operide & tr & 0.9 & 1718 & 1717 \\
142 & (E,E)-Famessal & 1.3 & tr & tr & 1721 & 1717 \\
143 & 6-lopropyl-4.8\(\alpha\)-dimethyl3.5/6.8/8.8e-hexahdr0-2(1H)-naphthalene & 0.5 & 0.1 & 0.3 & 1731 & 1772 (semi-polar column) \\
144 & Hexohydrodynamics/acetone & 1.3 & 3.8 & 0.3 & 1834 \\
145 & Alantolactone & 0.1 & 0.3 & 1.5 & 1866 & 1873 \\
146 & 9-Isobutylpropyl-hydhydobutyrate & 0.7 & 1.0 & 0.4 & 1882 & 1884 \\
147 & 10-Isobutylrylov-8.9-dehydrothymool isobutyrate & 0.4 & 0.6 & 0.6 & 1885 & 1887 \\
148 & Isoallantolactone & 0.9 & 3.1 & 46.2 & 1907 & 1912 \\
149 & Methyl palmitate & 0.1 & 0.6 & & 1910 & 1915 \\
150 & 7-Isobutylpropyl-hydhydhydhyd & 0.3 & 0.4 & & 1920 & 1922 \\
151 & n+Headacco acid & 0.4 & 0.3 & 0.4 & 1954 & 1956 \\
152 & 9-(2-Methylbutylrylov) thymol isobutyrate & 0.1 & 0.2 & 0.4 & 1969 & 1964 \\
153 & 10-(2-Methylbutyl
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 2 Materials and Methods
### Data Collection and Preprocessing  
The creep rupture dataset of modified 9 Cr-1 Mo steel with different forms was obtained from the National Institute for Material Science (NIMS) creep database [36] and many published studies in the literature [37; 38; 39; 40]. A total of 431 sets of data were collected, including 265 sets of tube data, 158 sets of plate data, and 38 sets of pipe data. Figure 2 plots an instance of the creep life distribution diagram for modified 9 Cr-1 Mo steel with different material forms at 600 \({}^{\circ}\)C. It is obvious that creep data present remarkable dispersion because of different chemical compositions and manufacturing processes from various data sources. In addition, despite the same applied stress level and temperature, the material form also influences the creep rupture life. This shows that it is challenging to account for some factors, such as alloy compositions and other manufacturing features, when predicting creep rupture life using conventional approaches.  
The collected dataset contains eighteen input features and one output feature (creep rupture life), and Table 1 provides a statistical summary of the information gathered. Both the material characteristics and environmental parameters were taken into consideration for the input features. The material characteristics contain eleven features of chemical element content, including C, Mn, P, S, Ni, Cr, Mo, N, Al, V, and Nb (in wt.%), and five processing parameters, namely the normalizing temperature (N Temp), normalizing time (N Time), tempering temperature (T Temp), tempering time (T Time), and material form. In particular, various material forms such as tube, plate, and pipe are represented by 1, 2, and 3 in the dataset, respectively. Each material form contains both short-term data (creep life smaller than 104 h) and long-term data (creep life larger than 104 h). In addition to material qualities, two significant environmental parameters, test stress and temperature, are also regarded as input features.  
The input variables have different levels of change, as indicated in Table 1, and the creep life is distributed in a wide range with an extremely large standard deviation. To minimize issues caused by the significant range variation between various features, all input variables were normalized to a certain range between 0 to 1. Moreover, the logarithmic transformation is applied for the output feature. The applied preprocessing process makes it simpler to examine how the data are distributed and helps to improve the modeling accuracy. The standardized dataset is then divided into a training dataset (80% data) and a testing dataset (20% data) to evaluate the generalization ability of various machine learning models.  
Figure 2: Instance of creep life distribution of modified 9 Cr-1 Mo steel with different forms at 600 \({}^{\circ}\)C.
## 3 Test Results
### Excess Support Pressure
#### 3.1.2 Soil and Pore Pressure  
The variation of measured soil and pore pressure with the increase of support pressure under different buried depths is illustrated in Figure 7. Since the supporting pressure is  
Figure 6: The law of supporting pressure changing with the advancing of the tunnel face.  
much larger than other pressures, it is demonstrated as the x-axis. As it can be seen, all the pore pressures almost change very little indicating that the support pressure has little influence on it. In particular, the pore water pressure at P2 in the buried depth of 1.0 D was missing due to the failure of the sensor while the pore water pressure at P1 increased because the location has been changed due to the squeeze of the soil. The pore water pressure in the case of the tunnel located at cover over diameter (C/D) ratios equal to 0.5 and 1.5 reduced slightly due to the water leakage caused by improper sealing of the excavation surface in the late period when the support pressure is very large. It can also be proved by the leaked water found in the hydraulic cylinder system after the test. In terms of stresses, the advancement of face results in an increase of the soil pressure at most positions. In the buried depth of 0.5 D, the measured lateral soil pressure at S1 and S3 are equal to and half of the support pressure respectively while that in a tunnel located at C/D ratio equals to 1.0 D and 1.5 D was much less than the support pressure. The reason may be that the position of S1 and S3 are in front of the excavation face in the buried depth of 0.5 D while they are above the excavation face in the buried depth of 1.0 D and 1.5 D. This is also why the vertical soil pressure at S2 in the tunnel located at C/D ratio equals to 0.5 D almost keeps stable while in a tunnel located at C/D ratio equals to 1.0 D and 1.5 D it increases with the increase of support pressure and is approximately equal to the lateral soil pressure at S1. In addition, the pressure values of S1 and S3 are very close at the buried depth of 1.5 D, but there is a big difference between them in the tunnel located at C/D ratio equals 0.5 D and 1.0 D. This phenomenon can be explained by the fact that the greater the buried depth, the denser the soil and the better the efficiency of force transmission.  
Figure 7: The variation of soil and pore pressures with the support pressure (excess support pressure): (a) 0.5 D; (b) 1.0 D; (c) 1.5 D.
# A Novel Higher-Order Numerical Scheme for System of Nonlinear Load Flow Equations
###### Abstract
The basic procedure to address the power flow problem is described as follows:  
* Constructing a mathematical model that illustrates the relationship between voltages \(|V|\) and powers in an interconnected system. * Specifying the voltage and power conditions for each network bus. * Calculating the voltage magnitude \(|V|\) and phase angle \(\delta\) at each bus in a power system under some balanced steady-state conditions  
Each power system has one slack bus containing two known quantities, voltage magnitude and phase angle. The power station in a power system refers to the generator bus, sometimes known as the \(PV\) bus or generation bus. In the \(PV\) bus, the known quantities are voltage magnitude \(|V|\) and active power \(P\). The load bus, also known as the \(PQ\) bus, is a type of bus in the network that holds both active and reactive power. The main purpose of this paper is to develop and examine a new Jarratt-type method with higher convergence order in order to solve the systems of nonlinear equations, so that we can achieve higher convergence order as well as better computational efficiency for solving the load flow problem. The outline is as follows. Section 2 consists of the development of an efficient seventh-order method in order to solve the system of nonlinear equations. The structure and various cases of the weight functions are also introducedin this section. The computational cost for various operations is also calculated such as matrix-vector multiplication, matrix-matrix multiplication, and LU decomposition. Then, the total computational cost for our newly developed three-step seventh-order method is calculated and compared with the cost of some existing methods. Furthermore, in Section 3, the power flow equations in an electrical power systems are described and converted into systems of nonlinear equations by using our newly introduced family of seventh-order method. For this, power flow problems are considered and the results are compared with a seventh-order method proposed by [PERSON] et al. [6]. Conclusions are given in Section 4.
## 3 Experiment and Simulation
### Shaking-Table TEST Scheme  
Shaking-table tests on loess subway station models are mainly used to assess the seismic mechanism and soil structure dynamic interaction mechanism of subway station structures in loess sites. Due to the small size of the subway station model used here, it is difficult to eliminate the gravitational distortion effect by the artificial mass model with full counterweight, so an added-mass model was adopted. Based on the Buckingham \(\pi\) theorem, the length, elastic modulus, and acceleration were selected as basic physical quantities, and the table size, dynamic performance, bearing tonnage, and other supporting equipment performance of the test system were fully considered to ascertain the similarity relationship of the model system (Table A3). In the test, the method of sticking lead blocks into the structure was used to realize the additional artificial mass. Taking Feitian Road Station of Xi'an Metro Line 4 as the prototype structure, the subway station model was established by using particulate concrete and galvanized steel wire. The loess was taken from the foundation pit of Feitian Road Station of Xi'an Metro Line 4. When preparing the model foundation, the loess was layered into the model soil box, and the water content and density of the model foundation were controlled according to the natural water content and density of the prototype site soil. The excitation system used in this test was a horizontal one-way high-performance seismic simulation shaking table produced by MTS Company, Eden Prairie, MN, USA. The table measures 3.36 m \(\times\) 4.86 m, the maximum load is 25 t, and the maximum acceleration is 1.0 g. The Taft wave, Songpan wave and Xi'an artificial wave were selected as input ground motions, respectively. Since the subway station was not situated in an active fault zone, only the transverse ground motion was input. The sensor arrangement is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**50-Year Exceedance Probability**} & \multicolumn{2}{c}{**100-Year Exceedance Probability**} \\ \cline{3-6}  & & **63\%** & **10\%** & **2\%** & **10\%** & **2\%** \\ \hline \multirow{2}{*}{Ground} & Tg(s) & 0.38 & 0.43 & 0.68 & 0.59 & 0.83 \\  & Ag(g) & 0.079 & 0.235 & 0.457 & 0.340 & 0.575 \\ \hline \multirow{2}{*}{Floor} & Tg(s) & 0.60 & 0.64 & 0.90 & 0.75 & 1.00 \\  & Ag(g) & 0.051 & 0.151 & 0.358 & 0.247 & 0.441 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ground motion parameters.  
Figure 8: Schematic diagram of a typical cross section (dim.: mm).
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 14 Enhancements
#### 2.2.6 User Equipment Mobility Enhancement  
For the use cases that involve mobility, the temporary loss of radio interface impacts the system to a degree that can degrade link performance in terms of transmission errors. In this regard, 3 GPP Release 14 introduces the possibility of Radio Resource Control (RRC) re-establishment for NB-IoT UE that supports data transfer via the control plane, i.e., the UE will try to re-establish the connection on that cell and resume the data transfer. This new RRC re-establishment feature hides the temporary loss of the radio interface to the upper layers.
# Using Feature Selection with Machine Learning for Generation of Insurance Insights
## 4 Methods and Materials
### Real Insurance Datasets  
For our work, we looked for insurance benchmark datasets in the public machine learning repositories, e.g., UCI [47] and Kaggle [48]. We excluded many insurance datasets because they are either not annotated or lacked meta information (such as feature titles) for interpretation, finally selecting five insurance datasets for this comparison. These datasets are publicly available from the Kaggle machine learning repository [48]. Furthermore, we manually prune the features to remove individual identifiers or constant features. Table 1 shows the characteristics of selected datasets, describing each of the datasets as set out in the repository [48].  
The first dataset, Car Insurance Cold Calls, is a dataset from a bank, which constructs campaigns to engage new clients for car insurance services. The bank needs to predict whether those potential customers will buy car insurance or not based on their data from previous campaigns. It provides general information about clients and specific information about the insurance campaigns. This dataset consists of 18 attributes (after removing the id attribute) in relation to 4000 customers contacted during the last campaign.  
The Medical Insurance Claim Fraud dataset relates to the detection of health insurance fraud claims based on patients' data. It provides general information about insurance claims and their owners such as gender, location, employer, cause, and fee charged. The original datasets contain 15 attributes and 7000 claims. However, we removed 3 noisy attributes: patient name, DOB, and email.  
The Caravan Insurance Challenge dataset was used in the CoIL 2000 challenge [49]. It contains socio-demographic information about clients of an insurance company to predict which potential customer will buy a caravan insurance policy. It consists of 86 attributes and 9822 observations.  
The Health Insurance Lead Prediction dataset was collected by a financial services company. This company built this dataset to predict whether a client is interested in its recommended medical insurance policies based on their profile on the company website. The client information includes demographics information and previous holding polices. The customer is classified as a lead if she/he fills in the policy form. Its training dataset consists of 50,882 rows and 13 attributes after removing the individual identifier attribute for prediction, \"Id\".  
The final dataset is named Insurance Company. It aims to identify customers willing to buy a new insurance product. It contains customer information and their buying behaviour of two other services. It consists of 15 attributes and 14,017 customers. We removed two features, customer-id and contract, because contract contains a constant value which has 0 variance, so it cannot be used for extracting any interesting information.
# Experiment on Flexural Fatigue Performance of Precast Bridge Deck Joints with Loop Connections
## 4 Experiment Results and Analysis
### Variation of Strain Amplitude  
Due to the large amount of strain test data during the test, the analysis results selected are only displayed at some typical measuring points, as shown in Table 2. During the fatigue test, due to the failure of some measuring points in the compression side of the precast segment at 0 \(\times\) 10\({}^{4}\) or 200 \(\times\) 10\({}^{4}\) times, the test results of these measuring points cannot be well compared. Therefore, these test results are no longer analyzed here.  
The strain results of the fatigue specimens SF1 to SF3 under static loading after 0 \(\times\) 10\({}^{4}\) and 200 \(\times\) 10\({}^{4}\) fatigue-loading cycles are compared in Figure 8. It can be learnt from the results that, except for the measuring point C9 of the specimen SF2, the difference between the results of each strain measuring point after 0 \(\times\) 10\({}^{4}\) and 200 \(\times\) 10\({}^{4}\) fatigue cycles is small. It can be considered that there is no obvious degradation of concrete and steel bars at the location of these measuring points.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Strain Type** & **Position** & **SF1** & **SF2** & **SF3** \\ \hline \multirow{2}{*}{Strain of concrete} & Compression side & C2 & C1 & C2 \\  & Tension side & C5 & C9 & C5, C9 \\ \hline \multirow{4}{*}{Strain of longitudinal loop bar} & Compression side of the precast segment & - & - & - \\  & Tension side of the precast segment & R-5-6 & R-5-6 & R-2-6, R-4-6 \\ \cline{1-1}  & Compression side of the joint segment & R-5-3 & R-3-1 & R-1-1 \\ \cline{1-1}  & Tension side of the joint segment & R-5-4, R-4-2 & R-4-2, R-5-4 & R-4-4 \\ \hline \multirow{2}{*}{Strain of double loop bar} & Compression side & / & / & R-5-5 \\ \cline{1-1}  & Tension side & / & / & R-5-4 \\ \hline \end{tabular}
\end{table}
Table 2: Location of typical strain measuring points.  
In addition, the load-strain curve of each specimen is basically in a linear change state, which indicates that the loop connection of each specimen is still in an elastic state after \(200\times 10^{4}\) fatigue cycles.  
While for the measuring point C9 of the specimen SF2, the slope of the load-strain curve is significantly reduced, but it still maintains a linear change state. It can be considered that there are concrete cracks at the measuring point C9, but the crack did not expand further.
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 4 Results
### Ablation Study  
This subsection performed some ablation experiments to verify the necessity of adding each module to the network. Figure 12 shows the comparison of the results with or without SR module for Ours(b). By adding the SR module, the generated image can achieve pixel-level detail representation in the corresponding region, and the overall image is clearer than without adding the SR module. Figure 13 compares the effect of with or without content constraint for Ours(b). Because the underlying information with richer texture information is used instead of global features, the details of the corresponding regions are more elaborated (see the edge information in the top row and the outline of eyes in the bottom row). Tables 4 and 5 present the quantitative evaluation results of the model without SR module and content loss for Ours(a) and Ours(b), respectively, again using the average results of 50 test images. Both tables compare the ablation experiments in terms of three metrics, with the difference that the inputs in Table 5 carry more information about the transmission layer. In general, the values of the metrics in Table 5 are better than those in Table 4. In addition, the results for the model with only the SR module, with only content constraints and completeness, also achieve an improvement in metrics compared to the model without the module and a decrease in SSIM of Table 4. On the one hand, this metric focuses more on the structural similarity between the two images, while the input of the overall network contains only a small amount of transmission layer information, which is incomplete for the guidance of the generated images. On the other hand, with the addition of the SR module and content loss, the overall model is more concerned with the consistency of the generated images with the pixel-level ground truth. In contrast, the input of Table 5 has more complete input information and learns the gradient as well as region block information during the bootstrapping process, so there will be further improvement in the metric. The best results can be achieved by combining the SR module and content loss as can be seen in the table.  
Figure 11: Qualitative comparison of the transmission removal results on synthetic mixture images. (**a**) Mixture image, (**b**) Ground truth reflection, (**c**) [13], (**d**) [14], (**e**) [16], (**f**) [17], (**g**) Ours(a), and (**h**) Ours(b).
## 2 Methods
### Technical Details
#### 2.4.2 Performance Measures for Evaluating Forecasting Methods  
The following classic performance measures are used to evaluate forecasting methods in the perspective of theoretical background: (i) Mean absolute error (MAE) = \(\frac{1}{\overline{n}}\sum_{t=1}^{n}|y_{t}-f_{t}|\), (ii) Mean absolute percentage error (MAPE) = \(\frac{1}{\overline{n}}\sum_{t=1}^{n}\left|\frac{y_{t}-f_{t}}{y_{t}}\right|\), (iii) Root mean squared error (RMSE) =\(\sqrt{\frac{1}{n}\sum_{t=1}^{n}(y_{t}-f_{t})^{2}}\), where \(y_{t}\) represents the actual value and \(f_{t}\) represents the forecasted value at Period \(t\).  
However it is found that the selection based on those performance measures might not lead to the desirable outcome from a company's perspective [46]. To keep the accuracy high, it is common in data-driven approaches to procure a large volume, at least at the beginning of the training data period [47]. Therefore, the following KPIs are also used to judge forecasting accuracy: (iv) Fill Rate (FR): The percentage of demand that can be fulfilled with the stock on-hand at a given point in time. (v) Average Inventory Level (AIL): (vi) Total Holding Cost (THC): The amount of holding cost aggregated for each period, (vii) Stock-outs: How many times the actual demand is above the inventory level.  
The listed KPIs are deliberately selected based on both academic and industrial implications, given that the objective is to measure performance from a mathematical and industrial perspective.