esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 5 Conclusions  
In this paper, a novel chaotic pulse-shaping filter and matched filter from RRC division are proposed. The unequal RRC division method provides the possibility to generate chaotic waveforms while ensuring no ISI. Based on this, a wireless communication system architecture using generated chaotic waveforms is developed, where a chaotic pulse-shaping filter and corresponding matched filter are used to implement encoding at the transmitter and maximize the SNR during decoding at the receiver, respectively. In order to guarantee that the information can be retrieved from the received signal, a continuous-time chaotic waveform is proved to be topologically conjugate to the bi-polar information. Simulation results in a single-path channel and multi-path channel with a time delay validate its effectiveness and superiority. Under an AWGN single-path channel, the proposed chaotic waveform can achieve the same BER performance as an RRC filter, which is very close to the lower bound of the BER. Under a multi-path channel, the proposed method is  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Pulse Shaping** & **Matched Filtering** & **Decoding** \\ \hline Root-raised-cosine (RRC) with minimum mean square error (MMSE) & \(2N_{o}N_{f}\) & \(2N_{o}N_{f}\) & \(24L^{3}\) \\ Chaotic baseband wireless communication system (CBWCS) & \(2N_{o}N_{f}\) & \(2N_{o}N_{f}\) & \(40N_{s}L\) \\ Proposed method & \(2N_{o}N_{f}\) & \(2N_{o}N_{f}\) & \(19N_{s}L\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computational complexity comparison.  
demonstrated to be 2 dB better than CBWCS when the threshold is zero. In the case with optimal and suboptimal thresholds, compared with CBWCS, the proposed scheme reduces computational complexity by 52.5% without effecting the BER performance.  
Conceptualization, X.T.; methodology, X.T.; software, X.T.; validation, X.T. and Z.W.; formal analysis, X.T.; writing--original draft preparation, X.T.; writing--review and editing, Z.W. and Q.H.; funding acquisition, Z.W. and Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under Grant 61971025 and Grant 62071026.  
Not applicable.  
The authors declare no conflict of interest.
# Quadrotor Stabilization and Tracking Using Nonlinear Surface Sliding Mode Control and Observer
## 7 Conclusions  
In this paper, we proposed a nonlinear-sliding-surface-based SMC for tracking and stabilization with an SMO. We set up the conventional quadrotor dynamic model based on Newton-Euler formalism. To improve the performance of the quadrotor system, we remodeled the augmented quadrotor dynamics and estimated the unmeasured states and their derivatives through the SMO. To improve the SMC control performance, we set up a nonlinear sliding surface to reduce the convergence time with a modified deceleration curve. We derived the proper control inputs \((u_{1},u_{2},u_{3},u_{4},u_{x},\) and \(u_{y})\) using SMC and calculated the desired angle \((\phi_{d},\theta_{d})\) from the control input \(u_{x,y}\). Moreover, the augmented quadrotor dynamics with the estimated states and the correction variables using SMO was derived to improve the representation of quadrotor system. We confirmed the improved performance in attitude control by SMO, which estimated the angles of the quadrotor and augmented its dynamics. Moreover, we proved the performance of a tracking system driven by NSMC.  
Conceptualization, K.L. and K.Y.; methodology, K.L. and S.K. ([PERSON]); validation, S.K. ([PERSON]) and K.Y.; investigation, S.K. ([PERSON]) and K.Y.; writing--original draft preparation, K.L. and K.Y.; visualization, K.L. and S.K. ([PERSON]); writing--review and editing, K.L., S.K. ([PERSON]), and K.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2019R1A2C1002343, NRF-2020R11A1A01061632) and the BK21 FOUR Project.  
Data sharing not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
The quality of \({}^{226}\)Ra and \({}^{228}\)Ra data reported by the existing contract laboratory (Lab A) was investigated using an ILC which included analysis of primary samples, a duplicate sample, and field blanks. Groundwater samples were collected from the Site compliance well (Well 176), the Site background well (Well 182), and from an intermediate well located outside of the Site boundary (Well 179). Surface water was collected from the adjacent Compoundment where the U ore was originally mined. Samples were collected for analysis of \({}^{226}\)Ra and \({}^{228}\)Ra, in addition to \({}^{232}\)Th to evaluate the potential role of \({}^{232}\)Th as a source of \({}^{228}\)Ra. Water samples were split and processed accordingly to evaluate their dissolved (filtered, 0.45 \(\upmu\)m pore-size filter) and total (unfiltered) forms. A duplicate was collected from Well 176, and field blanks (filtered and unfiltered) were prepared using deionized H\({}_{2}\)O supplied by each laboratory. All sample collection, preservation, and storage protocols followed standard methods for water sample collection [4]. At each location, the filtered and non-filtered samples were split into four groups and submitted to four laboratories designated as Lab A (existing contract laboratory), Lab B, Lab C, and Lab D. Each laboratory and their corresponding analytical methods are summarized in Table 1.  
Figure 2: \({}^{228}\)Ra/\({}^{226}\)Ra ratios in selected Site monitoring wells and the Impoundment.  
The methods used for \({}^{232}\)Th varied among laboratories but are all based on alpha spectroscopy; the methods used for \({}^{226}\)Ra and \({}^{228}\)Ra have been approved by the U.S. Environmental Protection Agency (USEPA) [5] and also varied among the laboratories (Table 2). For \({}^{226}\)Ra, three of the laboratories (Labs A, B, C) used Method 903.0 (M903.0). Although the results are reported as \({}^{226}\)Ra, M903.0 measures the total alpha emitting isotopes of Ra (\({}^{223}\)Ra, \({}^{224}\)Ra, \({}^{226}\)Ra) and is therefore considered a screening method. Method 903.1 (M903.1), which is specific to \({}^{226}\)Ra, was used by Lab D and can be compared to results from M903.0 to determine if other alpha emitting isotopes contribute to the reported \({}^{226}\)Ra concentrations for the Site. For \({}^{228}\)Ra, two of the laboratories used Method 904.0 (M904.0) (Labs B, C), while Lab A and Lab D used Method Ra-05. Each Ra method in Table 2 has been approved for drinking water compliance analysis by the USEPA and therefore should not produce significantly different results [6].
## 4 Algorithm Verification
Reference [33] gives the definition of difference degree: the calculation formula of the difference degree of \(T\) base classifiers on \(N\) samples is as follows:  
\[Div=\frac{1}{TN}{\sum\limits_{t=1}^{T}{\sum\limits_{i=1}^{N}{d_{t}(\mathbf{x} _{i})}}} \tag{16}\]  
\[d_{t}(\mathbf{x}_{i})=\left\{ \begin{array}{l c c}0&if&h_{t}(\mathbf{x}_{i})=f (\mathbf{x}_{i})\\ 1&if&h_{t}(\mathbf{x}_{i})\
eq f(\mathbf{x}_{i})\end{array} \right. \tag{17}\]  
where \(h_{t}(\mathbf{x}_{i})\) is the prediction label of the \(t\)-th individual classifier on sample \(\mathbf{x}_{i}\), and \(f(\mathbf{x}_{i})\) is the prediction result after integrating all individual classifiers. The difference threshold is not less than 80% of the difference degree of the previous individual SVM classifiers. The larger the difference degree is, the smaller the correlation between the base classifiers is, and the final integration effect will be better. We calculated the average difference of the three ensemble algorithms on different datasets, and the results are shown in Table 2.  
As can be seen from Table 2, the difference degree of the bagging and boosting integration methods is not particularly large. In comparison, the average difference degree of the proposed method is significantly higher than that of the previous two methods. This is because the method in this paper uses heterogeneous integration. Next, we compared the classification accuracy of several ensemble algorithms to investigate their generalization performance. Here, we focused on the SQGA optimization and decision fusion results only by voting. Table 3 shows the classification accuracy of the four methods: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Classification Accuracy (\%)**} \\ \cline{2-6}  & **KNN** & **BN** & **BPNN** & **SVM** & **Proposed** \\ \hline Thyroid & 94.2 & 95.8 & 95.3 & 96.0 & 98.2 \\ Ionosphere & 82.4 & 83.7 & 85.5 & 87.2 & 91.8 \\ Image & 93.6 & 95.4 & 96.3 & 96.9 & 99.2 \\ German & 75.6 & 77.8 & 76.9 & 78.6 & 82.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental accuracy on general datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Average Difference**} \\ \cline{2-4}  & **Bagging** & **Boosting** & **Proposed Method** \\ \hline Thyroid & 0.075 & 0.084 & 0.104 \\ Ionosphere & 0.181 & 0.192 & 0.223 \\ Image & 0.014 & 0.013 & 0.021 \\ German & 0.214 & 0.227 & 0.283 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average difference degree between base classifiers. From the experimental results, we can see that the classification accuracy of ensemble algorithms is improved. The classification accuracy of voting fusion is higher than traditional bagging and boosting, due to the increased differences between base classifiers. Compared with the simple voting method, the SQGA optimization method can obtain the highest classification accuracy, which shows that the method in this paper can further improve the performance of ensemble learning. This also lays a foundation for its application in fatigue detection.
## 2 Problem Definition  
GS deployment is a multivariable problem that is challenging due to the complex relation between variables and objectives. In this section, the variable structure and objectives are defined for the optimization of GS deployment.  
GS deployment optimization has been achieved for the monitoring and tracking of the Walker star constellation. The pattern of the Walker constellation is commonly expressed as i: T/P/F. The parameters i, T, P and F refer to the orbit inclination angle, the total number of satellites, the number of planes and the relative spacing between satellites of two adjacent planes. The relative angular shift between satellites is equal to F\(\times\) (360/T) degrees. The total number of satellites T is evenly distributed among P planes, where T/P satellites are orbiting in each orbit plane.  
The constellation used in this study is defined as 87.3\({}^{\circ}\): 210/15/6, where the orbit inclination is 87.3\({}^{\circ}\), and a total of 210 satellites are uniformly distributed on 15 planes, with relative spacing between satellites in 6 adjacent planes at an altitude of 1176.6 km. The LEO configuration is shown in Figure 1.  
SPDOP [23] is considered as a good indicator of the accuracy of satellite orbit determination [24; 25]. Therefore, to evaluate the performance of GS deployment, this study aims to achieve optimal deployment based on the SPDOP indicator.  
Figure 1: LEO satellite constellation of [PERSON] star pattern 87.3\({}^{\circ}\): 210/15/6.  
Two schemes of deployments are analyzed. In the first scheme, the GSs could be deployed on any land on Earth (including Antarctica and islands). In the second scheme, the GSs are deployed only in a particular region.  
In order to achieve an optimal distribution of SPDOP with the global deployment of GSs, heavy observing coverage [21] with a full observing rate is considered as an objective. For the regional deployment scheme, the objective is obtaining the minimum value of mean SPDOP (of visible satellites). Moreover, establishing the total number of GSs used for global deployment is considered as another objective, while the number of GSs is fixed for the regional deployment scheme.
## 3 Results and Discussion
### Pre-Treatment of NIR Spectra  
The pre-treatment of NIR spectra is important because raw spectral data often contain noise, baseline shifts, and scattering effects, which may arise from the physical characteristics of TMR samples or from instrumental factors.  
These interferences can obscure some peaks and, consequently, chemical information in the spectra, making it difficult to develop accurate predictive models [26; 28]. For this purpose, preprocessing techniques like SNV are employed to mitigate scattering effects [26; 49]. In addition, NIR spectra typically produce low-resolution and poorly defined signals. Therefore, for some chemical models, a mathematical treatment--such as the first derivative using the Savitzky-Golay algorithm--was applied to transform subtle oscillations into discernible peaks. The derivative applied to the preprocessing data was coded with the sequence 1-2-2-1, where the first value corresponds to the derivative order,  
Figure 1: NIR spectra from NIRFlex 500 with the typical band of chemical compound absorption. Shaded areas highlight characteristic absorption bands of specific chemical components: light blue for water, pink for proteins, yellow for fats, and green for fibers.  
the second denotes the interval used for its calculation, the third serves as a smoothing factor representing the number of data points included in a running average, and the fourth indicates the secondary smoothing. Various preprocessing techniques, including the second derivative, were tested; however, they resulted in minimal improvements in cross-validation performance. Therefore, a limited number of preprocessing steps were applied to prevent overfitting.  
The pre-treated spectra were subsequently used with PLSR analysis to develop models that maximised R\({}^{2}\) and minimised the SEC for each analyte studied. The most frequently applied spectral pre-treatments in our data are shown in Figure 2.
## 1 Introduction
### Sampling Methods in FTIR
#### 1.4.1 Transmission Method  
This method is convenient and simple and relied on the idea of light passing through a surface through a process of passing IR radiation through a thin, free-standing film with a thickness of fewer than 50 microns, thereby making it suitable for liquid, solid, and gas applications. The sample preparation by direct transmission could be difficult as it requires mixing the sample with a reference substance for quantitative analysis. To create a pellet, mull, or film, the sample would be combined with KBr in a volume ratio of 1 to 10 [15]. The method was utilized by [PERSON] et al. [26] in 1958 to create a translucent disk before conducting the test. Likewise, [PERSON] et al. [27] also added a sample to KBr blank ratio in one hundredth (weight). [PERSON] et al. [28; 29; 30; 31] studied bond characteristics in alkaline-activated palm oil fuel ash and also employed the KBr to sample ratio of 1:9 (by volume). [PERSON] et al. [32] made use of kaolin and rice husk ash as precursors but reduced the sample/KBr volume ratio to 1:5. The prepared sample is then compressed using a hydraulic press (10 MPa) into a thin clear pellet before being placed into the radiation absorption device. After absorbing some energy, the sample delivers the remaining energy to the detector. The sample surface area, which can be increased by grinding the sample to a fineness below 2-5 microns, determines the increase in transmittance and resolution of the absorption bands [15; 33]. To prevent cracking or shattering of the translucent disc, the sample must be gently inserted into the holder.  
For cementitious material application, the transmission method is commonly used as the samples are well-prepared into a paste and then properly cured for a specific period. The sample should be adequately dried and then ground into a finely divided powder through a mortar and pestle before being pressed into a pellet by using a mold at a pressure of about 10 MPa to make it translucent. Grinding the sample into a finely divided powder reduces distortion of the absorption band and scattering losses, while a wet sample that is not properly dried will produce a broad peak with poor resolution. KBr should be well covered to prevent attracting moisture because it is a hygroscopic substance.
## 2 Related Works  
### VR Use Cases and Effects in Experiments Related to Autonomous Vehicles (AVs)  
Virtual reality (VR) enables a simulated experience that allows users to engage as if they are in a real environment [11]. Based on this feature, various researches have conducted experiments using VR simulators, including observing pedestrian behavior [12], safety education [13], advancement of AV safety [14], and communication between AV and its surroundings [15]. By using VR for AV research, subjects can proceed in a realistic environment without exposure to the physical risks in the real environment. Thus, user behavior observation can be performed in completely controlled scenarios using VR simulations [14].  
[PERSON] et al. evaluated the suggestions made by pedestrians for the external features of fully autonomous vehicles (AVs) [12]. The study assessed the potential external features of a self-driving vehicle and identified the ones that would aid pedestrians in comprehending the actions of the vehicle at a crosswalk, boost their acceptance of self-driving cars, and affect how they cross the street. [PERSON] et al. investigated the training and assessment of driving algorithms in VR environments [14]. The study aimed to comprehend the use of VR in boosting the safety of automated vehicles. [PERSON] et al. has seven publications and preprints that discuss the use of VR in research on AV external communications [15]. Through these studies, it can be confirmed that VR is being used in fields which require controlled scenarios such as self-driving vehicle studies.  
Prior Research on the Degree of Risk and the Reliability of Users According to Information Provision  
Previous studies have investigated how risks affect users' trust before and after interaction with AVs. Experiments were conducted under four critical conditions (Very High Risk, High Risk, Low Risk, and No Risk) based on vehicle safety and integrity levels [9].  
[PERSON] et al. studied the virtual reality-driven simulators' role in trust building and human-machine interface (HMI) design for AVs [10]. Their research proposed a method to confirm the user experience in autonomous vehicles (AVs) using continuous and objective data obtained from physiological signals of the users during their immersion in VR driving simulations, demonstrating the usefulness of HMI design, specifically in the context of HUDs for AVs.
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 3 Strategies Enhancing Remanufacturing
### Product Lifecycle Data Acquisition and Sharing  
One of the challenges at the remanufacturing stage is the lack of product lifecycle information. With inadequate information on a product, it is very challenging to come up with a process plan for that product. Data are stored at different levels of a product's lifecycle by different stakeholders, whether for its manufacturing production planning system or the distribution and usage stages [81]. These data are often not shared by manufacturing companies due to fears of competition from their direct OEM rivals or even from third party remanufacturing companies. However, the synchronization of such data acquired from the entire lifecycle of the product can benefit remanufacturing in so many ways [51]. IoT scheduling can be used to predict when a product can be remanufactured based on lifecycle data [52]. With the emergence of 5G networks and very sophisticated cloud technology, product information can be stored in the cloud and later accessed when needed; this greatly helps in providing access to a product's information at any stage [98]. A demonstration of the flow of information in a supply chain is shown in Figure 7. The figure clearly shows how information flows in both directions from the cloud to different stages in a supply chain and vice versa. This information flow needs to be secured and well defined.  
Due to the security concerns of OEMs and other key members of the supply chain, a solution has to be found to tackle this problem of companies copying data from others with the access they have to other companies' data. One proposed solution is to create a well-secured cloud with limited access only for those who have the green code to access the information [99]. There also has to be an agreement put in place that prohibits them sharing the information with non-authorized personnel or even for their personal interest. It would be in the interests of all the parties involved. This project to create a universal, well-secured cloud, which could be used on a worldwide basis, is still in process and it could be a breakthrough for the full integration of the fourth industrial revolution [100].  
This will have a great impact on the functioning of CLSCs and will benefit remanufacturing as well.
## 4 Experiments
### Experimental Result
#### 4.4.2 Prediction Performance Comparison Based on Helpful Review Filtering  
This session identifies the effectiveness of the framework proposed in this study. Firstly, we have classified whether the new reviews written by users were helpful through the CNN-BiLSTM hybrid model. Then, we have produced a new user profile by filtering only helpful reviews. Comparing the existing recommendation methodology with the proposed RHRM framework in this study with the prediction performance through UBCF, SVD, and NCF techniques, respectively, are shown in Figures 6-8, where \"Existing\" represents a traditional recommendation methodology that produces user profiles, including all reviews. \"Proposal\" represents a proposed recommendation framework that produces user profiles, which includes only helpful reviews. We have set the neighbor sizes from 1 to 100 to evaluate the prediction performance of changing the neighbor size in the UBCF technique. We set the latent factor sizes of SVD and NCF techniques to 8, 16, 32, 64, and 128 and compared the prediction performance. MAE and RMSE metrics are used to measure the prediction performance for the error between predict rating and actual ratings.  
The results of the experiment show that the prediction performance of the proposed recommendation framework has improved regardless of the neighbor size and number of latent factors. When we have applied both MAE and RMSE metrics for the UBCF technique, both metrics showed excellent prediction performance regardless of the neighbor size. It showed the best prediction performance when neighbor size is 10. The SVD and NCF technique indicate excellent prediction performance when the number of latent factors is 8 and 32, respectively. Therefore, we have compared the proposed framework to the existing methodology: when using the MAE metric, the prediction performance improved 14.95% (UBCF), 14.99% (SVD), and 22.08% (NCF), respectively. Similarly, using the RMSE  
Figure 6: Prediction performance of MAE (**a**) and RMSE (**b**) of UBCF model.  
Figure 7: Prediction performance of MAE (**a**) and RMSE (**b**) of SVD model.  
metric, the prediction performance improved 15.38% (UBCF), 16.58% (SVD), and 21.59% (NCF). Experiments show that producing user profiles using only helpful reviews results in a better prediction performance than the existing methodology. Therefore, reflecting review helpfulness information on personalized recommendation services can improve the performance of recommender systems, which we have further conducted two-sample \(t\)-tests as shown in Table 6 to confirm that all improvements were statistically significant for \(p<0.01\).