esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 1 Introduction  
The Industrial Internet of Things (IIoT) comprises internet-connected devices and cutting-edge analytics platforms that process manufacturing data. Generally, IIoT refers to applying Internet of Things (IoT)-related technologies in the manufacturing industry. These associated technologies are concerned with the interconnection of smart objects within cyber-physical systems for industrial applications. With the rapid advancement in IIoT, considerable industrial data are available on the cloud. Although many studies exist on data analytics [1] and IIoT, few studies have investigated their convergence [2]. Recent IIoT applications have aimed to create more value for their services. Further, predictive maintenance (PdM) is a maintenance approach that predicts the future failure of manufacturing process-related problems (e.g., yield, equipment, and product quality). PdM is used to maintain industrial assets such as devices and production quality and quantity.  
IIoT applications have been integrated with the PdM approach, referred to as \"IIoT-based PdM\". The combination of IIoT and PdM has been widely used in the energy industry to improve production, identify potential failures, and detect anomalies. Chevron Corporation (San Ramon, CA, USA), a famous energy company, leverages IIoT applications to identify corrosion and pipeline damages. Jabil Inc. (Saint Petersburg, FL, USA), an electronic manufacturing company, uses cloud analytics to predict product images on the plant floor [3]. IBM (San Francisco, CA, USA), Salesforce Inc. (San Francisco, CA, USA), General Electric (GE; Boston, MA, USA), and Cloudera Inc. (Santa Clara, CA, USA) provide PdM applications in the factory even though each of these companies has a unique manufacturing process [4]. IIoT-based PdM has transferred large amounts of data from theedge-side to the cloud side. The manufacturing industry faces a challenge in using big data and extracting valuable industrial information. For small- or medium-sized manufacturing companies, using data to enable intelligent manufacturing is an economical approach. Data analytics using existing equipment is less expensive than purchasing new advanced equipment. Thus, using big data to enhance industrial manufacturing processes is essential to the manufacturing industry. Machine learning (ML) can extract data in manufacturing processes to obtain useful information applicable to specific manufacturing processes. To enhance the performance of industrial data analytics, herein, we propose a new ML algorithm--an adaptive boosted decision tree that integrates a neural network and boosted decision tree. The proposed algorithm is based on the boosted decision tree, and the neural network is used for retaining a weaker sample. The proposed method uses adaptive learning to dynamically improve the boosted decision tree, making an ML model smarter. This study uses the proposed algorithm to improve production quality in semiconductor manufacturing processes and packaging machining.
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Short-Term Traffic Flow Combination Model Based on CEEMDAN
#### 4.1.2 Construction of the IGWO-SVM Model  
After using CEEMDAN to decompose the traffic flow sequence, the intermediate-frequency component sequence is selected according to the size of the permutation entropy. The volatility, randomness, and nonlinearity of the intermediate-frequency component sequences are not as strong as those of the high-frequency components. Because the support vector machine has a good nonlinear mapping ability, and at the same time, the theory of the support vector machine is mature and the training time is much shorter than that of the neural network, the support vector machine was selected to construct the intermediate-frequency component sequence prediction model.  
In order to build an accurate intermediate-frequency component sequence prediction model, the improved gray wolf algorithm (IGWO) was used to optimize the parameters of the support vector machine (SVM) and establish an IGWO-SVM model. The IGWO-SVM model uses cross-validation to divide the training set into five folds and determine the value range of parameter C and parameter \(\gamma\) from 0.01 to 100. Through the excellent ability of the IGWO to deal with continuous optimization problems, the combination of C and \(\gamma\) with the best performance in the training set is found. The best combination of parameters is applied to the support vector machine prediction so as to improve the prediction accuracy. The flowchart of the model is shown in Figure 2.  
Figure 2: Periodic component prediction flowchart based on IGWO-SVM.
# Investigation of the Effect of Capillary Barrier on Water-Oil Movement in Water Flooding
## 4 Effect of the Capillary Barrier on Oil-Water Two-Phase Flow in Porous Media Model
### Results and Discussion
#### 4.3.1 Water-Wet Condition
The higher injection velocity of the water phase results in a relatively weaker capillary barrier effect, which indicates that the breakthrough of the water phase occurs along the diagonal line of the porous media and the porous media will be quickly filled with the water phase; while the lower injection of the water phase results in the relatively stronger effect of the capillary barrier, which indicates that the breakthrough of the water phase is not along the diagonal line and the porous media will be slowly filled with the water phase, which leads to larger sweep area. Figure 18: The change of oil and water distribution with time at capillary number of \(1.8\times 10^{-4}\) under water-wet conditions (the contact angle is 60\({}^{\circ}\)): (**a**) 0.0265 s; (**b**) 0.0330 s; (**c**) 0.0513 s; (**d**) 0.0670 s. (The italic letters (a–j) in the figure indicate the locations of the oil–water interface.)
# Approximation Ratios of RePair, LongestMatch and Greedy on Unary Strings +
## 2 Preliminaries
In each round \(i\geq 1\), the algorithm selects a maximal string \(\gamma\) of \(\mathbb{A}_{i-1}\) and updates \(\mathbb{A}_{i-1}\) to \(\mathbb{A}_{i}\) by replacing a largest set of pairwise nonoverlapping occurrences of \(\gamma\) in \(\mathbb{A}_{i-1}\) by a new nonterminal \(X\). Additionally, the algorithm introduces the rule \(X\to\gamma\) in \(\mathbb{A}_{i}\). The algorithm stops when no maximal string occurs. Please note that the replacement is not unique, e.g., the word \(a^{5}\) has a unique maximal string \(\gamma=aa\), which yields SLPs with rules \(S\to XXa\), \(X\to aa\) or \(S\to XaX\), \(X\to aa\) or \(S\to aXX\), \(X\to aa\). We assume the first variant here, i.e., maximal strings are replaced from left to right. The compressor Greedy that we study in this work chooses a maximal string in each round \(i\geq 1\) such that the size of \(\mathbb{A}_{i}\) is minimal. **Example 2** (Greedy). : _Let \(w=aaaaabbababbaaabb\). We have_  
* \(\mathbb{A}_{0}\)_:_ \(S\to aaaaabbabbbaaabb\)_,_
* \(\mathbb{A}_{1}\)_:_ \(S\to aaaaXXbXbaX\)_,_\(\mathbb{A}_{2}\): \(S\to YYXabXbYX\), \(X\to abb\), \(Y\to aa\), \(\mathbb{A}_{3}\): \(S\to YYZXbYX\), \(X\to Zb\), \(Y\to aa\), \(Z\to ab\), \(\mathbb{A}_{4}\): \(S\to YAZXbA\), \(X\to Zb\), \(Y\to aa\), \(Z\to ab\), \(A\to YY\). Please note that in the first round, instead of the maximal string \(abb\) the algorithm could also choose the maximal string \(aaabb\), because both choices yield SLPs of minimal size \(15\). In the second round, instead of \(aa\) the algorithm could also choose \(aaX\), because both choices yield SLPs of size \(14\). Finally, the order of the choices \(ab\) (round \(3\)) and \(YX\) (round \(4\)) could be swapped because both choices yield SLPs of unchanged size \(14\). The following lemma from [13] provides a lower bound on the size of an SLP for a word of length \(n\). **Lemma 1** ([13] (Lemma 1)). : _For every word \(w\in\Sigma^{+}\) of length \(n\), we have \(g(w)\geq 3\log_{3}(n)-3\)._
## 4 Discussion
This MnP per se did not alter the collective cell migration and chemotaxis. However, combined with dos, it significantly reduced cell motility and chemotactic migration on both cell lines [24]. When MnTnHex was combined with radiotherapy, it significantly reduced the migration and invasion of mouse mammary carcinoma 4T1 cells compared to control and radiation therapy alone. Similar to the results obtained in this work, MnTnHex as a single drug was the best in reducing migration [30]. Metabolomics is an emerging research field that has been used in recent years to gain further mechanistic data supporting the mode of action of drugs and other xenobiotics. In the area of SODm and other redox-active drugs, scarce information on this topic is available. Regarding the release/uptake of VCCs to/from a culture medium by NSCLC cells, we found higher levels of isobutanal and 3-methylpentanal. The increase in aldehyde levels is likely related to the MnTnHex/H\({}_{2}\)O\({}_{2}\)-driven catalysis of lipid peroxidation and/or oxidation of corresponding alcohols [61]. However, the results were only significant for H1975 cells. Of note, a similar trend in A549 cells was also observed. The treatment with higher concentrations of MnTnHex might result in more pronounced effects in both cell lines. Nevertheless, we aimed to evaluate this aspect of the metabolome at MnTnHex concentrations that were not markedly toxic, avoiding other potential interferences. Performing studies at lower levels of Mn porphyrin is also clinically more relevant; the BMX-001 analog is used in clinical trials at \(\sim\) 0.2 mg/kg (www.clinicaltrials.gov). Regarding the uptake of lower levels of benzaldehyde, other authors showed that this metabolite is uptaken by mammalian cells [62] and may be oxidized by aldehyde dehydrogenases (ALDHs) to the corresponding carboxylic acid [63]. These alterations appear to be a consequence of the mechanism of action of MnTnHex (Figure 6C) since a similar behavior was observed for H1975 cells exposed to MnTnHex alone and combined with cisplatin, whereas no significant changes were observed for cisplatin alone. Finally, it should be mentioned that the analysis of other metabolome alterations, for instance, in the endometabolome, may be important to better characterize the impact of MnTnHex in the context of NSCLC. These studies are currently undertaken using other methodologies, mostly nuclear magnetic resonance (NMR) spectroscopy, which may shed further light on this topic.
## 6 Discussion
### Challenges and Future Directions
#### 6.3.5 Processing Multi-Source Heterogeneous Data  
Traffic prediction involves a variety of data sources, including road sensors, vehicle sensors, video surveillance, GPS devices, social media, and meteorological stations, all of which are large in scale and diverse in form. Initially, these different data sources provide various formats of data; for instance, sensors typically offer real-time traffic flow figures, video surveillance yields image data, and GPS devices provide geographical location and timestamp information. Furthermore, the accuracy and update frequency of data from these sources vary; GPS data may update multiple times per second, while some sensors update less frequently. Additionally, due to potential asynchrony in device clocks, there may be slight discrepancies in the time stamps of data from different sources. Lastly, data may be missing due to various reasons, such as equipment failure or communication disruptions. Facing the massive multi-source heterogeneous data, how to build a suitable deep learning model that can fully tap the value of these data for traffic prediction will become a major challenge in the future.  
Therefore, future development needs to focus on extracting useful features from these data sources and using ensemble learning methods or multi-model fusion techniques to combine the predictive results from these sources, thereby enhancing the accuracy and robustness of predictions. Moreover, developing more intelligent data fusion algorithms that automatically identify and utilize information from each data source is key to improving data utilization and enhancing the adaptive capacity of predictions.
In this direction, the aims of the present work were to evaluate the antimicrobial activity of N-TiO\({}_{2}\) NPs under visible-light irradiation. It should be noted that this research actually extends the findings of a recent publication form our research group [32], in order to explore the capability of such particles to have end-use biomedical applications, along with environmental ones, illustrating that they can work as multifunctional materials. To achieve this, N-TiO\({}_{2}\) nanoparticles were synthesized by the sol-gel method. Full characterization of the particles followed, utilizing instrumental analysis techniques such as Fourier-transform infrared (FTIR) spectroscopy, dynamic light scattering (DLS), Brunauer-Emmett-Teller (BET) analysis of surface area, scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDS), and transmission electron microscopy (TEM). All of the abovementioned techniques were applied to examine the physicochemical properties and morphology of the synthesized NPs, as well as to ensure that the particles were nano-sized. _Salmonella_ biofilms were then conveyed and developed, so that the antimicrobial activity of the NPs could be evaluated under visible-light irradiation. This is a strong advantage in comparison with the established antimicrobial activity of TiO\({}_{2}\), which is evident only under UV irradiation. Consequently, the novelty of this research is that this is the first study to investigate the antimicrobial properties of N-TiO\({}_{2}\) NPs on biofilms--which are the natural state of the microbes found in nature--under visible-light irradiation (i.e., close to real-life conditions). These results can contribute to the problem of bacterial infection in the food industry or in hospitals and other public places.
## 2 Real-Time City-Scale Nonlinear Time-History Analysis
### Real-Time Record Ground Motions  
The ground motion records can fully describe the features of the ground motions with no information loss. The densely distributed seismic stations and communication network make it possible to obtain the real-time ground motion records. After an earthquake, the ground motion record near the epicenter can be quickly obtained through the seismic stations and communication network, and information, such as the station's latitude, longitude, and recording time, can be collected simultaneously. With the development of monitoring and data-transforming technology, the densely distributed strong motion network will cover more regions, and the ground motion data will be easier to access in a timely manner after an earthquake.
## 3 Mechanical Overview
### Series Elastic Actuator (SEA)  
To make _Serpens_ highly-compliant with the environment, a newly designed series elastic actuator (SEA) is embedded in each joint module. This makes it possible to achieve passively-compliant motion and precise torque-control. Each SEA deliberately introduces compliance via a spring between the motor-gearbox and the load [27], therefore achieving intrinsic low impedance. As shown in Figure 6, the design of the elastic gear consists of a housing case, a base, a shaft, and a cogwheel. The intermediate element is connected to the shaft and works as a transmission between the cogwheel and the shaft itself. The cogwheel and the actuator are connected through a gear mechanism, where passive-compliance is provided by placing compression springs on each side of the outset of the base, in the chamber of the cogwheel. Even though the spring stiffness can be considered linear within a certain range, an encoder is employed to precisely monitor the misalignment/deviation of the compliant mechanism.  
The novel design of _Serpens_ is exclusively based on a 3D-printing process with _polylactic acid_ (PLA) through FDM with the exception of a limited number of elements such as springs, nuts, bolts, bearings and electrical components. One of the main benefits of using PLA is that a relatively high strength construction can be achieved compared to the production cost. In addition, 3D-printing with PLA allows obtaining a reliable rapid-prototyping process by offering a wide range of customisation for different printing-parameters, such as print-speed, wall-thickness and layer-height. This makes it possible to tune the printing process according to the different parts and the properties the parts should hold.  
Figure 5: The proposed screw-less assembly mechanism consists of three 3D-printed components (two enclosure parts and a push-button) with two springs orientated in the direction of the pin placed under the enclosure cover.
## 1 Introduction
### Differences between Ad Hoc CRNs, WSNs and CR-WSNs  
This section examines the properties, differences and commonalities of _Ad Hoc_ CRNs, WSNs, and CR-WSNs. Although some of the channel sensing, channel decision, channel access, spectrum management, reliability, network security, and issues in CR-WSNs are similar to the issues in _ad hoc_ CRNs or conventional WSNs, there are some differences in a number of factors. Some issues in CRNs have been addressed well [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]. Table 3 compares several factors among _ad hoc_ CRNs, conventional WSNs and CR-WSNs.
## 2 Numerical Simulation Calculation Methods and Model Validation
### Model Accuracy Verification  
#### 2.2.1 Verification of Aerodynamic Performance  
According to the \"GB/T 1236-2017 Standard Airway Performance Test for Industrial Fans\" [28], the aerodynamic performance of a large axial fan in this study was tested using a C-type air chamber. Figure 4 shows the air performance test device diagram for the test and the photos of the field test device. A comprehensive tester of atmospheric pressure, temperature, and air humidity was arranged at the front end of the inlet collector (air velocity is zero). The corresponding air parameters of the axial fan under different operating conditions were recorded. The performance test was carried out at room temperature and atmospheric pressure. The flow rate is changed during the test from 40,000 m\({}^{3}\)/h to 85,000 m\({}^{3}\)/h by adjusting the inlet throttling device. The pressure difference and the specific value of the inlet and outlet pressure of the Pitot hydrostatic pipe under nine different flow conditions are read. The data for the different operating conditions are shown in Table 1.  
Figure 4: \(Cont.\)To improve the reliability of the experimental data, this paper uses standard errors to analyse the experimental data; the standard measuring error calculation formula is:  
\[\sigma=\sqrt{\frac{\sum\left(p_{i}-\overline{p}\right)^{2}}{n-1}} \tag{6}\]  
where \(\sigma\) is the standard error values, \(n\) is the number of measurements, \(p_{\text{i}}\) is the deviation of the measured values, and \(\overline{p}\) is average values.  
The flow rates at 10 sets of design operating points were tested and the results are shown in Table 2.  
Therefore, the standard error values can be calculated by the formula:  
\[\sigma=\sqrt{\frac{\sum\left(\overline{p}-p_{i}\right)^{2}}{n-1}}=\sqrt{\frac {48.57}{9}}=5.40\]  
The error analysis for the other operating points is similar to it. According to this method, it is known that the measurement error values are within reasonable limits.  
The total pressure-flow performance curve and the efficiency-flow performance curve shown in Figure 5 were obtained by comparing the performance results of the prototype predicted by the numerical calculation model with the experimental values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Group** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(Q\left(\text{m}^{3}/\text{h}\right)\) & 40,516.5 & 57,319.8 & 65,316.5 & 67,684.8 & 70,263.1 & 73,718.2 & 77,661.4 & 81,414.0 & 84,769.2 \\ \(p_{\text{tf}}\left(\text{Pa}\right)\) & 1432.7 & 1747.5 & 1978.4 & 1895.4 & 1781.7 & 1582.1 & 1143.2 & 442.7 & 207.5 \\ \hline \end{tabular}
\end{table}
Table 1: Data for different working conditions.  
Figure 4: Aerodynamic performance test device. 1. Fan under experiment. 2. Pitot tube. 3. Star rectifier. 4. Honeycomb rectifier. 5. Transition. 6. Inlet conical tube. (**a**) Air performance experimental device diagram. (**b**) Field experiment device.
## 4 Discussion
### Association between CMRJ and 20 m Linear Sprint  
Substantiating the findings of previous studies [4; 5; 10; 35], large and statistically significant inverse relationships between JH-1, RSImod, and RSI with the 20 m linear sprint test were observed (\(r=-0.54\) to \(-0.66\), \(p\leq 0.001\)). These findings underscore that athletes demonstrating superior vertical explosive power and reactive strength, as quantified by their CMRJ metrics, also tend to accelerate faster over short distances. The median split analysis further reinforced these associations, revealing that athletes with higher JH-1 and JH-2 values exhibited notably quicker times in the 10 m sprint (\(g\geq-0.75\), \(p<0.05\)) and 20 m sprint (\(g\geq-0.55\), \(p<0.05\)), respectively (Figures 3 and 6). This aligns with the biomechanical notion that the vertical jump and sprint share mechanical similarities, predominantly the flexion and extension patterns of the hip, knee, and ankle joints [36]. The increased propulsive force or impulse in a vertical jump implies a possible augmented force application during ground contact of sprinting, potentially resulting in an extended stride length and a faster leg swing cycle [5; 36]. Despite the insights provided by the median split analysis, the direct correlation between JH-2 and sprint time was moderate and failed to achieve statistical significance. This discrepancy may reflect the heterogeneous nature of our sample (different jump and sprint techniques amongst three sports), suggesting that a potential non-linear relationship existed, where only athletes surpassing a specific JH-2 threshold demonstrated reduced sprint times.  
Delving into the specifics of RSImod and RSI, the median split analysis elucidates their nuanced contribution to sprint performance. Specifically, athletes with higher RSImod and RSI scores exhibited significantly shortened 10 m (\(g=-0.58\), \(p=0.04\) and \(g=-0.64\), \(p=0.04\)) and 10-20 m sprint times (\(g=-0.74\), \(p=0.03\) and \(g=-0.73\), \(p=0.04\)), which also extended to shorten the 20 m sprint times (\(g=-0.75\), \(p=0.04\) and \(g=-0.73\), \(p=0.03\)), as can be seen in Figures 3 and 6. Collectively, these insights advocate that the CMRJ test is integral to sprinting due to its incorporation of both slow and fast SSC movements. Developing explosive force through SSC movements in the CMRJ is particularly beneficial to force generation during the start acceleration and maximum speed phases of sprinting [37]. This is because of the necessity for athletes to rapidly increase their lower limb joint extension velocity and exert substantial force against the ground, which are key factors in effectively building up sprint velocity [17; 35]. Although this study did not directly investigate the training effect, it could be speculated that the training program designed to enhance RSImod and RSI in the CMRJ test may have the potential to improve both acceleration and overall sprint performance [37].