esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of the Models
### Model 3  
Figures 12 and 13 show the overall result and precision of Model 3. Model 3 behaves in a similar way to Model 1. It must be remembered that the zero-inflated model has been designed as a variation of the Poisson model, with a component that introduces a binomial model to account for an excess of zeros. However, the estimate of parameter \(\gamma_{0}\) (see Table 3) with a mean value of 980 and a similar standard deviation, namely 997, indicates that the binomial model does not give a good fit.  
Figure 12: Model 3: predicted values (red) vs. actual values (black), and 2.5% and 97.5% confidence intervals for Model 3.  
Figure 13: Model 3: Residuals, predicted values vs. residuals, and Q–Q plot for Model 3.  
This model contributes very little in addition to Model 1. This is because the difficulty in achieving a good fit is not caused by an excess of zeros but rather due to difficulties in reproducing the high-number SMIs. Table 3 shows that the values of the hyperparameters \(\hat{\theta_{j}}\) (\(\hat{\theta}\), \(\theta_{1}\), \(\theta_{2}\), \(\hat{\theta}_{3}\), and \(\hat{\theta}_{4}\)) are similar to those obtained using Model 1. Similarly, in this model there are eight measurements with errors greater than 50, which correspond to the ANSPs 9, 22, 28, 20, and 12.  
Figure 14 comprises caterpillar plots giving the distributions of each hyperparameter \(K_{js}\) of the model (K, K1, K2, K3, and K4).
###### Abstract  
In this study, six extracts of _Anhys carduchorum_ prepared from solvents of varying polarity (water, methanol, ethyl acetate, dichloromethane, and n-hexane) were investigated for their chemical profiles and total phenolic and flavonoid contents (TPC/TFC) using HPLC-MS/MS and spectrophotometric/colorimetric assays, respectively, along with the assessment of their antioxidant and enzyme inhibitory effects. In general, the polar extracts (methanol, water, and infusion) showed higher TPC/TFC than the other extracts (range: 13.73-26.62 mg GAE/g; 2.66-52.95 mg RE/g, respectively). The total antioxidant capacity of the polar extracts ranged from 0.90-1.85 mmol TE/g in phosphomolybdenum assay, while the free radical scavenging potential ranged from 4.36-32.13 mg TE/g and 12.58-67.73 mg TE/g in DPPH and ABTS assays, respectively. While the water and infuse extract inhibited only butyrylcholinesteraseterase (2.68 and 2.39 mg GALAE/g, respectively), the other extracts were found to inhibit both acetyl- and butyryl-cholinesterasereases (0.96-2.73 mg GALAE/g and 1.86-5.11 mg GALAE/g, respectively). On the contrary, all extracts exhibited anti-tyrosine (40.24-59.22 mg KAE/g) and anti-amylase (0.09-0.63 mmol ACAE/g) activities. Overall, this study highlighted the effect of solvent polarity on the extraction of bioactive compounds and the biological properties of the extracts of _A. carduchorum_; hence, the results obtained could be used to guide optimum extraction in future studies.  
Arnabis carduchorum; phenolics; flavonoids; HPLC-MS/MS; solvent polarity; antioxidant; enzyme inhibition 1
## 1 Introduction
The key findings of this study and comparisons with relative research in the field of wind speed forecasting are as follows:  
1. To achieve accurate and stable forecasting of short-term wind speed and wind power, a robust, novel, combined system based on three modules was developed in this study. This novel hybrid system for forecasting short-term wind speed and wind power includes a data preprocessing module, a forecast optimization module, and an evaluation module. The excellent performances of these algorithms are combined to provide accurate and stable results for multi-step wind speed forecasting. 2. To effectively eliminate fluctuations in the original time series and avoid the limitations of a single algorithm, a new data preprocessing step was proposed. This was shown to decrease uncertainty and irregularity in the wind speed times series. SSA-EEMD, a powerful secondary denoising algorithm, was used to decompose and further denoise the actual wind speed time series. These steps were found to successfully overcome the limitations of single SSA algorithms. 3. To overcome the disadvantages of individual models, multi models were used to forecast the wind speed and wind power. To consider the nonlinear characteristics of wind speed and wind power time series, seven artificial neural networks (ANNs) were employed to forecast two types of time series, and five optimal hybrid models were selected based on the accuracy of data testing by hybrid models to form a combined model and act as sub-models. 4. To further improve forecasting accuracy and stability, the multi-objective dragonfly algorithm was used to determine the optimal weight of the combined model. In the optimization process, the optimized parameters were found to not only have good accuracy, but they also ensured that the output results had a high level of stability. Therefore, this paper used the multi-objective optimization algorithm to optimize the weight of the combined model. 5. A more scientific and comprehensive forecasting evaluation method was conducted to estimate the forecasting performance of the developed forecasting system in the model evolution module. Interval forecasting was used to assess the uncertainty of the combined model, and this indicated that the forecasting results of the proposed combined model were accurate and stabilized in an all-around manner. Additionally, the Diebold-Mariano test and Wilcoxon rank-sum test were implemented to further analyze the forecasting accuracy of each model. The remaining sections of this paper are as follows: The methods involved in the proposed model are introduced in Section 2. Section 3 demonstrates the experiment preparations and the four numerical experiments used, as well as presenting the forecast results. Deeper discussion about the forecasting performance of our developed model is presented in Section 4. Section 5 provides the conclusions.
# On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures
## 1 Introduction
In addition, to make matters worse, available simulation tools do not completely support the random access procedure evaluation [23; 24] and the NB-IoT open-source simulation presented in [25] does not offer a satisfactory implementation of the procedure itself. Based on these premises, this contribution provides a significant step forward in the current state of the art, by overcoming the evident limitations characterizing the scientific literature and presenting key instruments for the evaluation of the NB-IoT random access procedure. For the first time, to the best of the authors' knowledge, the NB-IoT random access procedure is jointly investigated through analytical models and system-level simulations, paving the way for future standard improvements. Specifically, the contribution of this paper is three-fold. 1. Starting from the platform presented in [25], it puts forward an open-source and 3 GPP-compliant implementation of the NB-IoT random access procedure, which handles different coverage classes, global and local transmission attempts counters, backoff times, and transitions from a class to another, as well as other key simulations. 2. It formulates an analytical model describing both collision and success probabilities, given the number of users willing to perform the random access procedure. 3. It deeply cross-validates the analytical model and the simulation tool, by considering realistic NB-IoT deployments enabling periodic reporting in monitoring infrastructures, with different resource configurations. The rest of this work is organized as follows. Section 2 illustrates the NB-IoT technology, describing in details the Random Access Procedure, and the currently available analytical models and network simulators, as well as several real NB-IoT monitoring systems. Section 3 presents the implemented simulation tool and the conceived analytical model. Section 4 discusses the resultsobtained through the cross-validation process. Section 5 concludes the paper and draws future research activities.
## 2 WO\({}_{3}\) and Its Crystal Structure  
Tungsten trioxide (WO\({}_{3}\)) is a very promising metal oxide with diverse properties. It has an n-type (E\({}_{\text{g}}\) = 2.60-3.25 eV) [19; 20] semiconducting nature and unique electrical properties. Further, it is transparent to visible and infrared light. Therefore, it has been used in different applications including smart windows [21], photocatalysts [22], solar cells [23], humidity sensors [24], and gas sensors [25]. Moreover, due to its excellent coloration efficiency [26], WO\({}_{3}\) is the most used chromogenic material in photochromic, electrochromic, and gasochromic applications [27].  
Tungsten oxide has a perovskite-type WO\({}_{6}\) octahedral crystal structure (Figure 1). In its structure, W\({}^{6+}\) ions occupy the corners of the octahedra, and oxygen ions are located at mid-crystal edge. In the ideal form, the octahedra are connected at the corners. The central atom (C) is absent and this defective perovskite configuration is often referred to as the ReO\({}_{3}\) structure [28].  
Similar to the behavior of most perovskites and ceramics, depending on the temperature, WO\({}_{3}\) crystals can structurally transform in the following order: Monoclinic (\(\varepsilon\)-WO\({}_{3}\), \(\lesssim\)-43 \({}^{\circ}\)C), triclinic (6-WO\({}_{3}\), -43 to 17 \({}^{\circ}\)C), monoclinic (\(\gamma\)-WO\({}_{3}\), 17-330 \({}^{\circ}\)C), orthorhombic (\(\beta\)-WO\({}_{3}\), 330-740 \({}^{\circ}\)C), and tetragonal (\(\alpha\)-WO\({}_{3}\)\(>\) 740 \({}^{\circ}\)C) [29; 30]. The monoclinic crystal structure is the most stable at room temperature [30]. The large voids generated in WO\({}_{6}\) octahedral networks in the WO\({}_{3}\) structure induce some variations in the position of W and in the WO\({}_{6}\) octahedron orientation. Thus, displacement of tungsten from the center of the octahedron and tilting of the WO\({}_{6}\) octahedra are two kinds of distortions [26], which lead to 11 different structures of WO\({}_{3}\)[31]. The gasochromic coloration of crystalline WO\({}_{3}\) is associated with changes in its structure from monoclinic to tetragonal and cubic [32]. For example, [PERSON] et al. [33] reported crystal structure transition from monoclinic to tetragonal in RF-sputtered WO\({}_{3}\) films upon exposure to hydrogen gas. However, the structure of hydrated WO\({}_{3}\)-xH\({}_{2}\)O sensors does not change during gasochromic detection of hydrogen gas [32].
# Effect of Drying-Wetting Cycle and Vibration on Strength Properties of Granite Residual Soil
## 2 Experimental Setup
### Test Methods
#### 2.2.1 Sample Preparation  
The GRS samples were prepared according to the specification [32]. Firstly, the retrieved soil samples were gently crushed with a rubber hammer and dried. Subsequently, a suitable amount of distilled water was added to the dried soil to achieve the same water content and dry density as the in situ soil (\(w\) = 17.6%, \(\rho_{d}\) = 1.71 g/cm\({}^{3}\)). The mixture was then placed in a sealed bag for over 24 h to ensure uniform moisture distribution within the soil. Finally, according to the dry density and water content, the required sample soil mass is weighed. The direct shear sample was pressed into a ring cutter using static pressure through a sampler to make a sample with a diameter of 61.8 mm and a height of 20 mm; the triaxial samples were compacted in five equal layers on the bottom plate inside a split mold with a diameter of 39.1 mm and a height of 80 mm. After the samples are prepared, they need to be reweighed to ensure that the difference in quality between the samples does not exceed 1%.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 1 Introduction
### Hardware Identified as Relevant to Manufacturing in Space  
The construction of a lunar base is planned to be carried out in phases categorized as survivability (1 st), sustainability (2 nd) and operational (3 rd), while in the final phase, it is expected that six astronauts will live and work at the lunar outpost [40]. As part of an ESA study [39], it was examined which hardware types are required in the individual phases (Table 5). Moreover, within this study, professionals involved in both human spaceflight and analog site campaigns were asked which objects and utensils are needed (Table 5).  
It can be seen (Table 5) that the highest number of elements are those manufactured on demand, which underpins the mass reduction approach (Section 1.1). In addition, the main material distribution of the identified hardware was also determined [39] (Table 6).  
Hence, it can be concluded that to produce this hardware (Table 5), the materials mentioned (Table 6) must be processed using in-space manufacturing.
## 3 Numerical Simulation and Experimental Analysis
### Crack Location
#### 3.2.2 Experimental Analysis  
Experiments are performed in the rig according to the conditions listed in Table 1. As mentioned before, the uniform shaft possessing the same mass with the non-uniform shaft is also studied here for deducing the propagation characteristics by spectrum comparison. The results of the experiments are shown in Figure 9.  
Figure 9: Cont.  
The time histories of the acceleration signals are truncated to 0.01 s for all cases, and these signals were captured after propagating through the shaft. The real lines stand for the uniform shaft while the dotted ones stand for the non-uniform shaft. All the stopbands shown in Figure 9 are listed in Table 4 for comparison. The case of location 0.3\(L\) has already been studied in Section 3.1 and the result of which is listed in the table too.  
From Table 4, it can be found that the experimental results match well with the numerical ones. The bandwidths of the first stopbands vary slightly with the location, while the bandwidths of the second and third stopbands fluctuate with the location obviously. For the second stopband, the bandwidth of which will reach a peak when the crack locates at 0.5\(L\), while, the bandwidth of the third stopbands will reach a peak at locations 0.3\(L\) and 0.7\(L\). The bandwidths of the three stopbands for each crack location do reflect the location changing of the crack.  
Since only one transverse crack is considered in the paper, it is difficult to locate the transverse crack in one shaft by directly using the above conclusion in a real practice, for the comparison cannot be made within a single shaft. However, if a set of cracked shafts with the same geometries can be measured and then different stopbands can be obtained for each of the shafts. By comparing the differences of the three stopbands for the individual shaft, the above conclusion can be helpful to locate the cracks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Locations** & **Stopbands (Hz)** \\ \hline
0.1\(L\) & [20, 140], [360, 610], [985, 1280] \\
0.2\(L\) & [25, 142], [375, 530], [935, 1200] \\
0.3\(L\) & [30, 120], [335, 520], [810, 1275] \\
0.4\(L\) & [35, 120], [310, 540], [855, 1220] \\
0.5\(L\) & [38, 100], [295, 570], [905, 1145] \\
0.6\(L\) & [40, 100], [278, 540], [845, 1245] \\
0.7\(L\) & [40, 110], [285, 510], [790, 1270] \\
0.8\(L\) & [45, 120], [290, 500], [805, 1195] \\
0.9\(L\) & [50, 130], [295, 550], [830, 1220] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental stopbands derived from different crack locations.  
Figure 9: Experimental Stop Bands derived at different crack locations, where (**a**) is for location 0.1\(L\), (**b**) is for location 0.2\(L\), (**c**) is for location 0.4\(L\), (**d**) is for location 0.5\(L\), (**e**) is for location 0.6\(L\), (**f**) is location 0.7\(L\), (**g**) is for location 0.8\(L\) and (**h**) is for location 0.9\(L\).
# New Hybrid Algorithms for Prediction of Daily Load of Power Network
## 4 Experiments and Results
### Processing of Input Data  
In order to accurately predict the daily load, it should take into account various factors affecting daily load forecasting and select appropriate features. Therefore, the prediction model includes some relevant factors, such as date classification and daily average temperature. When the weather changes, it has a great impact on the power load. For example, in the summer, air conditioning and other related equipment are used more often than in the spring and autumn due to high temperature and the demand for heatstroke prevention and cooling. In such a situation, it inevitably leads to an increase in the power load. On the rest days, large users such as factories and schools use less electricity, while shopping malls and households use more electricity. But working days are the opposite, especially for major festivals, most enterprises are in a state of holiday. They have very little load, while only domestic electricity and some tertiary industries use electricity, and their electricity consumption is relatively low. We use the data from January 1 st to December 30 th of a city in China, including daily power load data, weather data and date types, to predict its daily load from January 2 nd to December 31 st. Because the data has different values and great differences, it is necessary to quantify and normalize the input data to avoid distortion of the model. At the input layer, the daily load is converted to the value of [-1, 1] by the following equation.  
\[x^{\prime}=\frac{x-(x_{max}+x_{min})/2}{(x_{max}+x_{min})/2} \tag{26}\]  
Figure 10: The three-layer neural network structure.  
where \(x\) is the current value; \(x^{\prime}\) is the converted result; \(x_{max}\) and \(x_{min}\) respectively represents the maximum and minimum values of the daily load. In order to improve the accuracy of daily load forecasting, the fitness is redefined as follows:  
\[f=(\sum_{i=1}^{n}|y-\hat{y}|)/n \tag{27}\]  
where \(n\) is the number of prediction data; y indicates the actual result and \(\hat{y}\) presents its corresponding prediction.  
When the temperature is within a certain range, it is a small influence on the electric load, but when the temperature rises or falls to a degree, it is a large impact on the load. Therefore, it is necessary to segment and quantify the temperature, as shown in Table 7. There are also three categories of date types, which are weekdays (Monday-Friday), rest days (Saturday-Sunday), and holidays. 0.4, 0.7 and 1 are the values corresponding to weekdays, rest days and holidays, respectively.
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 3 Framework of the Proposed Scheduling Method
### Performance Prediction and Parameter Optimization  
To choose investigated performances scientifically, we performed a performance correlation analysis in our previous work. Based on this analysis, in this study, we selected six types of performance that belonged to the following three categories: equipment-related performances, fab-wide performances, and wafer-related performances; they are listed in Table 2.  
Figure 3: The flowchart of the PSO-SVM algorithm.  
This research used a PSO-SVM to build the performance prediction model and the parameter optimization model. The workflow is illustrated in Figures 4 and 5, respectively.  
In performance prediction, the input data is the real-time production status, which can be obtained in the production line; the output data is the predicted performance that we expected. Through the performance prediction illustrated in Figure 4, we can obtain the expected performances according to the real-time production status.  
In parameter optimization, the input data includes two parts: the real-time production status and the predicted performances. The output data is the five dispatching parameters \((\alpha_{1},\alpha_{2},\ldots,\alpha_{5})\) Through parameter optimization illustrated in Figure 5, we can obtain the expected dispatching parameters according to the real-time production status and the predicted performances.  
In this section, the above-mentioned real-time production status is filtered by the immune-algorithm-based attribute selection method. The above-mentioned six predicted performances were chosen through performance correlation analysis in our previous work.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Performance** \\ \hline Equipment related & EU\_P (equipment utilization of photolithography area) \\  & EU\_O (equipment utilization of oxidation area) \\ \hline \multirow{2}{*}{Fab-wide related} & MOV (movement of the manufacturing system) \\  & PR (production rate) \\ \hline \multirow{2}{*}{Wafer related} & PCSR (process constraint satisfaction rate) \\  & ODR (on-time delivery rate) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected performance.  
Figure 4: PSO-SVM-based performance prediction.  
Figure 5: PSO-SVM-based parameter optimization.
# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 3 PreMovEst Method for Select Best AIS Data in Prediction Vessel Movements
** Two techniques for prediction are proposed in this paper to show the accuracy of the position of vessel movements prediction for route estimation after selecting all routes that resemble the current one. The first is the use of an Artificial Recurrent Neural Network (ARNN) [25] with Long Short-Term Memory (LSTM) [26; 27] using historical data as continual input streams. The second technique employs Multivariate Imputation by Chained Equations (MICE), a statistical method for handling missing data [28]. The network is made up of three layers, with 64 neurons in the input layer, 32 in the intermediate layer, and two neurons for the output layer. The ANN application is based on time-series forecasting, and it is auto-regressive to forecast multiple steps. MICE offers a significant advantage over other missing data techniques in terms of its flexibility. However, a primary disadvantage is that MICE does not have the same theoretical justification as different imputation approaches. In particular, fitting a series of conditional distributions is done using a series of regression models that are not consistent with proper joint distribution. The purpose of using MICE is to generate the route and an approximation concerning the full path's prediction based on it.