esa-sceva/satcom-chunk-collection huggingface dataset:
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 4 Experimental Validation
### Comparative Experiments  
To verify the effectiveness of the proposed method, we conducted two types of experiments: single-condition comparison and varying-condition comparison. In the single-condition experiment, the Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) were selected as comparative models; in the varying-condition experiment, the Deep Transfer Learning (DTL) and Domain Adaptation Network (DAN) models were added. To ensure the fairness of the experiments, all models were trained and tested using the same batch and quantity of vibration signal data points. Before being fed into the models, the data were normalized and presented either as one-dimensional vibration signals or two-dimensional grayscale images, ensuring consistency in the processing across models. The experiments mainly evaluated each model's average classification accuracy, training time, and testing time.
## 3 Results  
### Characteristics of Activated Carbon and Electrode  
Table 1 summarizes the physical properties of P60 and YS-2 (for both powder and electrode forms) such as total BET SSA and total pore volume. The total BET SSAs for P60 and YS-2 were similar, being 1621 and 1554 m\({}^{2}\) g\({}^{-1}\), respectively, while they had a similar pore volume of 0.8 cm\({}^{3}\) g\({}^{-1}\). After the electrode fabrication, their physical properties reduced at a similar rate of approximately 20% in both electrodes. The decreased surface area and pore volume can be explained by a probable blockage of AC pores by the conductive material and binder.  
Although both ACs had similar physical properties, the wettability of the two electrodes ('P60 electrode' and 'YS-2 electrode') appeared to have different behaviors, as shown in Figure 1a. After 1 min, the contact angle value of the YS-2 electrode displayed a large decrease from 118\({}^{\circ}\) to 0\({}^{\circ}\), while that of the P60 electrode slightly decreased from 130\({}^{\circ}\) to 115\({}^{\circ}\). The large decrease in the contact angle on the YS-2 electrode indicates a good wettability. To further analyze the excellent wettability of the YS-2 electrode, FT-IR analysis was conducted as shown in Figure 1b. The FT-IR spectra of both the electrodes showed typical ACs: C-O at 900-1300 cm\({}^{-1}\); C-H at 1430, 2850, and 2917 cm\({}^{-1}\); C=O at 1093 and 1631 cm\({}^{-1}\); and -OH at 3000-3700 cm\({}^{-1}\)[36; 37], except for an additional peak for the YS-2 electrode at 1570 and 1190 cm\({}^{-1}\), which corresponds to C=N and C-N, respectively. It is known that there is a possible addition of an N-terminated functional group when activated carbons are treated with gaseous ammonia [36]. This property of the YS-2 electrode resulted in a faster adsorption of water molecules as well as dissolved ions into the electrode [38].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Activated Carbon** & **Total BET Specific Surface Area (m\({}^{2}\) g\({}^{-1}\))** & **Total Pore Volume (cm\({}^{3}\) g\({}^{-1}\))** \\ \hline Powder & P60 & 1621 & 0.82 \\  & YS-2 & 1554 & 0.80 \\ \hline Electrode & P60 & 1256 & 0.65 \\  & YS-2 & 1217 & 0.63 \\ \hline \hline \end{tabular} BET—Brunauer–Emmett–Teller.  
\end{table}
Table 1: Physical properties of P60 and YS-2 as powder and electrode.
## Appendix D Nomenclature  
\begin{tabular}{l l} \(\Phi_{k}\) & dynamic state transition matrix at epoch k \\ \(\Gamma_{uk}\) & input coefficient matrix at epoch k \\ \(\Gamma_{uk}\) & process noise matrix at epoch k \\ \(\overline{u}_{k}\) & control input vector of IMU measurements of the specific force and angular rate \\ \(\overline{w}_{k}\) & process noise vector at epoch k \\ \(K_{k}\) & Kalman gain at epoch k \\ \(\overline{Z}_{k}\) & estimate of the measurement produced by the states estimated by the Kalman filter \\ \(H_{k}\) & measurement matrix at epoch k \\ \(r_{k}\) & EKF innovation vector at epoch k \\ \(\overline{\omega}_{b}\) & attitude vector (rad) \\ \(\overline{v}_{eb}\) & velocity vector (m/s) \\ \(\overline{r}_{eb}\) & position vector (m) \\ \(C_{b}^{e}\) & coordinate transformation matrix from the body frame to the Earth frame \\ \(\overline{\omega}_{ie}\) & Earth rotation rate (rad/s) \\ \(\overline{s}^{e}\) & gravity vector in the Earth-centered Earth-fixed frame (m/s\({}^{2}\)) \\ \([\bullet\times]\) & calculates a skew-symmetric matrix \\ \(\underline{\omega}_{ab}\) & gyroscope fault vector (rad/s) \\ \(\underline{\Delta f}_{ib}\) & accelerometer fault vector (m/s\({}^{2}\)) \\ \(\Delta C_{b}^{e}\) & difference between the true transformation matrix and the faulty transformation matrix \\ \(\Delta\overline{v}_{cb}^{e}\) & difference between the true velocity vector and the faulty velocity vector (m/s) \\ \(\overline{f}_{ib,mass}\) & IMU measurement of the specific force (m/s\({}^{2}\)) \\ \(\underline{\omega}_{ib,mass}^{b}\) & IMU measurement of the angular rate (rad/s) \\ \(\overline{f}_{ib}^{b}\) & true value of the specific force (m/s\({}^{2}\)) \\ \end{tabular}
## 3 Femtosecond Laser Direct Writing of Gradient Index Flat Lens  
### Experimental Details  
_Glass:_ In this work, we choose to use the GeS\({}_{2}\)-based chalcogenide glass \(75(\text{GeS}_{2})-15(\text{In}_{2}\text{S}_{3})-10(\text{CsCl})\). At first, we noted that pure GeS\({}_{2}\) glass has a quite high glass transition temperature T\({}_{\text{g}}\) of 450 \({}^{\circ}\)C and is composed of repeating tetrahedral GeS\({}^{4/2}\) units. These units are predominately bonded at their corners to other GeS\({}^{4/2}\) tetrahedra, often labeled as Ge\({}^{4}\) (the superscript describes the number of bridging sulfurs), making up a fully connected three-dimensional structure. With its high glass transition temperature (T\({}_{\text{g}}\sim 370\)\({}^{\circ}\)C for our glass), we expect a quite long lifetime at room temperature for the photo-induced index changes [37]. Moreover, this glass presents a wide transmission band which can span across visible and LWIR windows. Indeed, this material has a transmission higher than 70% in the spectral band \([0.5;10]\) um for a 1 mm thick sample.  
_Fs-laser Irradiation:_ The glass samples were individually polished on both sides and placed on a three-axis motorized platform to be irradiated. The laser we used is a commercial Yb-doped fiber amplifier fs-laser (Satsuma, Amplitude Systemes Ltd., Pessac, France). The key parameters were fixed as follows: a pulse duration of 800 fs, a central wavelength of 1030 nm and a repetition rate of 100 kHz. The laser polarization was linear and aligned along the \(x\) axis. The laser beam was focused inside the sample at 500 um below its front face using an aspheric lens with a numerical aperture of 0.6, resulting in a spot diameter estimated at around 1.2 um. The scanning speed of the sample, controlled by a motorized translation stage, and the laser energy, were varied. To write homogeneous disks or rings and to avoid any diffraction effects, the displacement of the sample followed a spiral trajectory with a step of 1 um. The writing speed varied from 0.05 mm/s to 25 mm/s and the laser energy from 0.01 ul to 0.75 ul.  
_Following the laser irradiation:_ Each sample was placed on an Olympus BX51 polarized optical microscope. In addition to a typical optical microscope observation, index changes in writing kinetics were investigated using quantitative phase microscopy (QPM, Iatia, Victoria, Australia). This technique measures the phase retardation (\(\Delta\varphi\), in rad) caused by the irradiated object with respect to the surrounding pristine glass. From these measurements, the refractive index modifications can be calculated through the following relationship: \(\Delta\text{n}=\Delta\varphi\lambda/2\pi\text{d}\), where \(\lambda\) is the wavelength of the illuminating microscope light (550 nm) and d is the thickness of the modified region. Additionally, room-temperature optical transmission measurements in the UV-Visible-NIR and the IR ranges were obtained, respectively, using Agilent Cary 5000 (\(\Delta\lambda\in[0.2;\,3.3]\) um) and Bruker Equinox 55 spectrophotometers (\(\Delta\lambda\in[2;\,20]\) um).
# On the Optimal Calculation of the Rice Coding Parameter
## 6 Performance on Different Datasets Using Multi-Parameter Rice Coding
### Heuristic Performance  
Finally, we illustrate how the second heuristic described in Section 5.4--with lower complexity--is capable of finding the optimal solution to the Rice partitioning partitioning problem.  
In Figure 12, the number of batches where the heuristic was able to find the same optimal solution is shown. The maximum number of optimal solutions that are found occurred when \(\Delta=5\). In total, sixty-five percent of the optimal solutions are found when carrying the \(\Delta\) parameter from zero to five.  
Figure 11: Relative improvement of the compression factor when multi-parameter Rice coding is used per data type.  
Figure 12: Number of minimal bit-length solutions found by the multi-parameter heuristic, as the parameter \(\Delta\) varies. For 35% of the tested batches, the heuristic was not able to find the optimal solution.  
For the remaining 35% of the solutions, where the optimum partitioning solution could not be found, we analyse how close the heuristic solutions are to its optimal.  
In Figure 13, we show a histogram where each cell illustrates the ratio of the optimal solution and the heuristic solution as the batch size varies for different datasets. Empty cells imply that all solutions found by the heuristic were optimal for that batch and dataset combination.
## 3 Experimental Results and Discussion
### Practical Sample 1: Forecasting the Revenue of Thailand's Medical Tourism Industry  
The rapid development of the internet, low-cost outboard travelling, the globalization of health care services and the increase in private health care demand are the result of the booming of medical tourism industry around the world, especially in Asia. Over the last decades, promotional campaigns aimed at developing medical tourism as a major industry has been launched in Southeast Asian countries. With the encouragement of government and the efforts of policy-makers, and based on the competitive advantages of low-cost treatments, advanced technologies in medical specialties, flexible treatment procedures, less-stringent government regulations and effective marketing strategies, some countries such as Thailand, Singapore, Malaysia, India, Korea and Taiwan have successfully gained a remarkable position in global outbound medical services. Among these, Thailand, Singapore and India play a driving role in Asia with approximately 90% of Asia's medical tourism markets, followed by Malaysia, Korea and Taiwan [25]. Compared with other providers in Asia, Thailand's medical tourism industry has the highest annual growth rate with more than a million medical tourists per year. Recently, this nation has become a leading destination in the field of cosmetic surgery and sex change operations; therefore, understanding the trends in Thailand's medical tourism industry is important in evaluating the development of this industry in Asia. For this reason, in this work, forecasting the revenue in Thailand's medical tourism industry is used as a sample to test the effectiveness of the DGM(2,1) model and its modified models. To conduct this task, the annual data for the revenue in the period from 2005 to 2013 is used to construct the forecasting model. The empirical results are shown in Table 2 and Figure 1.  
As shown in Table 2, the _RMSE_ value of the DGM(2,1) model is 835.22, while that of the modified models of the MDGM(2,1), FDGM(2,1) and FMDGM(2,1) are 555.27, 169.92 and 196.09, respectively. Compared with the DGM(2,1) model, the _RMSE_ values obtained from MDGM(2,1), FDGM(2,1) and FMDGM(2,1) are smaller. Furthermore, the _MAPE_ values of the DGM(2,1), MDGM(2,1), FDGM(2,1) and FMDGM(2,1) models are 23.8%, 21.53%, 12.48% and 10.41% respectively. The value of _MAPE_ also reveals that the value of _MAPE_ of the DGM(2,1) model is largest in all of the considered models. From the obtained results using _RMSE_ and _MAPE_, the precision rate of each model is calculated. According to these results, the precision of the DGM(2,1), MDGM(2,1) and FDGM(2,1) has not yet significantly improved to a reliable forecasting level with the data for Thailand's medical tourism. However, the forecasting accuracy of the FMDGM(2,1) reached nearly 90%. According to the notation of [23], this value revealed that the FMDGM(2,1) model obtained a good forecast performance compared with other proposed models.
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 2 LT Code Anti-Eavesdropping Method  
### LT Code Encoding  
The source first packs \(k\) symbols into packets, which are then randomly encoded by the robust soliton distribution (RSD) degree distribution function into an infinite number of coded symbols. Then, [PERSON] continuously sends these coded symbols to [PERSON] through the main channel, [PERSON] receives these coded symbols and performs BP decoding until enough coded symbols are received to recover the source information, [PERSON] sends an ACK feedback to [PERSON] to notify [PERSON] of the completion of the decoding, and [PERSON] immediately stops sending coded symbols after receiving the feedback. Additionally, the performance of the encoding method is directly impacted by the selection of the RSD degree distribution. Therefore, it is crucial to carefully consider how to design the degree distribution function.  
Deletion of LT Code Coding Matrix in the channel  
According to [PERSON]'s theory [15], the Ideal Solitary Distribution (ISD) \(\rho(d)\) and the enhancement factor \(\tau(d)\) after normalization make up the Robust Soliton Distribution(RSD) degree distribution function, and their corresponding mathematical formulas are as follows:  
\[\rho(d)=\begin{cases}1/k&d=1\\ \frac{1}{d(d-1)}&d=2,3,\cdots,k^{\prime}\\ \end{cases} \tag{1}\]  
\[\tau(d)=\begin{cases}\frac{s}{k}\cdot\frac{1}{d}&d=1,2,3,\cdots,(k/s)-1\\ \frac{s}{k}\ln(s/\delta)&d=k/s\\ 0&d>k/s\end{cases} \tag{2}\]  
where \(k\) represents the number of original symbols of the source, \(d\) represents the degree of encoded symbols, \(s=c\ln(k/\delta)\sqrt{k}\), \(c\) is a constant (\(c>0\)), and \(\delta\) represents the maximum failure probability of decoding.  
The RSD degree distribution function is normalized as follows:  
\[\mu(d)=\frac{\rho(d)+\tau(d)}{z}\quad d=1,2,\cdots,k \tag{3}\]  
where \(z=\sum_{d}(\rho(d)+\tau(d))\).  
The source symbols are grouped and d symbols from each group of \(k\) symbols are randomly selected for an XOR operation to obtain the LT coded symbols. The LT encoding matrix \(G\) is obtained according to the probability distribution function of Equation (3) as shown below:  
\[G=\left(\begin{array}{cccc}0&1&\cdots&0&\cdots\\ 1&0&\cdots&0&\cdots\\ \cdots&\cdots&\cdots&\cdots&\cdots\\ 1&0&\cdots&1&\cdots\end{array}\right)_{k\times\frac{s}{1-P_{AB}}} \tag{4}\]  
where \"\(1\)\" indicates the location of the corresponding selected source, \(p_{AB}\) denotes the main channel deletion probability, and \(a\) denotes the number of correctly decoded symbols participating in LT. The value of \(a\) is not fixed because LT encoding has a codeless rate, but it satisfies \(a\geq k\).  
\(C=M\times G\) is the LT code encoding symbol. If \(a\) accurate symbols are needed for LT decoding, considering the impact of channel deletion probability, the actual number of encoding symbols received by the destination node is \(m=\frac{a}{1-P_{AB}}\).
## 2 Materials and Methods
### Image Acquisition  
The transplanter is shown in Figure 1. The transplanter used the ejector rod to eject the entire row of seedlings from the tray into the conveyor belt. An automatic image acquisition and processing system captured and analyzed seedling images on the conveyor. The system consisted of three parts: an industrial camera, a pair of broad area linear arrays, and an IPC (industrial personal computer). The cabbage seedling images were obtained using a MER-131-75 GC network camera (Daheng Imaging, Beijing, China) with a network port. The focal length of the camera lens was 8 mm. The camera was mounted on a camera support at 0.3 m above the conveyor belt of the automatic transplanter (see Figure 1A). The exposure time was 0.5 ms. The device used a lamp house to block the incoming light and filled it with a strip light source. The light source (Daheng Imaging) was a pair of white strip light sources. The obtained images were transmitted to the computer through a gigabit network line and were saved as 24 bit color images in the RGB color space with the BMP format. The resolution of the original images was \(1280\times 1024\). Each image contained five cabbage seedlings, and the images of the middle three cabbage seedlings were clipped. The size of the clipped image was \(265\times 1024\). Finally, a dataset of 500 images was generated.
## 4 Fault Diagnosis Results  
### Evaluation Metrics  
Evaluation metrics are employed to evaluate the classification performance of a DL model. These metrics are calculated by using the predicted labels from the DL model and comparing them with the actual labels. Figure 8 shows the relationships and denotations of these metrics, while Equations (6)-(9) provide the formulae for these evaluation metrics. Accuracy is an intuitive metric with which to evaluate the classification performance; it quantifies the proportion of predicted labels that match the actual labels, including both true and false predictions. Precision is the ratio of actual true labels to the labels that the model predicted as true. Sensitivity is the ratio of the labels that the model predicted as true to the actual true labels. The F1 score is the harmonic average of the precision and sensitivity. All metrics reinforce the weakness of others and thus can evaluate the classification performance of the model.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FN}+\text{FP}+ \text{TN}} \tag{6}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
\[\text{F1 score}=2\times\frac{\text{Precision}\times\text{Sensitivity}}{\text{ Precision}+\text{Sensitivity}} \tag{9}\]  
Figure 8: Denotations of the predicted and actual labels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Healthy** & **Delamination 1** & **Delamination 2** \\ \hline Beta 1 & 0.5 & 0.5 & 0.5 \\ Beta 2 & 0.9 & 0.9 & 0.9 \\ Training critic/Training & 3 & 5 & 5 \\ generator & \(3\times 10^{-4}\) & \(3\times 10^{-4}\) & \(3\times 10^{-4}\) \\ Epoch & 2000 & 2000 & 2000 \\ Batch size & 8 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model parameters of the WGAN model for each health state.
# Trends and Recommendations for Enhancing Maturity Models in Supply Chain Management and Logistics
## 1 Introduction
In order for research to advance in a specific area, it is crucial to comprehend the existing state of the literature, recognize prevailing trends and topics of significance, and pinpoint potential opportunities for future enhancements. Therefore, this work aims to fill the former gaps by conducting a bibliographic and content analysis, to present the state of the art of MMs for SCML. It is important to note that the primary objective is to provide an overview of the existing landscape rather than a direct comparative assessment of individual maturity models. More specifically, we aim to answer the following research questions (RQs):  
* What are the main research contributions to MMs for SCML? * How have the topics evolved and related to each other over time? * What are the key decision criteria for defining, designing, and applying MMs for SCML? * What are the potential future developments for MMs for SCML? \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multicolumn{3}{c}{**Research Method**} & \multicolumn{2}{c}{**Content Sample**} & \multirow{2}{*}{**Review Focus**} \\ \cline{2-2} \cline{4-6}  & **Bibliometric** & **Contents** & **Comparative** & & & \\ \cline{2-6}  & **Analysis** & **Analysis** & & & & \\ \hline Kucinska-Landwojtowicz & \multirow{2}{*}{X} & & & 1980–2019 & 597 & Organizational Maturity \\  & et al. (2023) [47] & & X & & 2013–2022 & 19 & Smart Manufacturing \\  & [PERSON] et al. (2023) [38] & & X & & 2013–2021 & 19 & Sustainable Supply Chain \\  & [PERSON] et al. (2022) [46] & & X & & 2013–2021 & 19 & Management \\  & [PERSON] et al. (2022) [1] & & & X & 1989–2021 & 49 & Supply Chain \\  & [PERSON] et al. (2021) [41] & & X & & 2014–2019 & 28 & Digital Supply Chain \\  & [PERSON] et al. (2020) [45] & X & X & & 2011–2019 & 17 & Industry 4.0 for manufacturing \\  & & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent literature reviews on MMs for SCML have been categorized based on their research method, content sample, and research focus. Accordingly, the research has three primary objectives. Firstly, it aims to provide a comprehensive view of MMs, including their evolution, clustering, and relationship within the SCML framework. Secondly, it analyzes the research findings based on a classification that highlights the key stages and criteria of MM scope, design, and application. Lastly, by representing the state of the art, it identifies significant research gaps and proposes future enhancements. As a reminder, the article is structured as follows: Section 2 outlines the materials and methods adopted in this work, including the literature search, bibliometric analysis, and content analysis. Section 3 presents the research results, which are discussed in Section 4, focusing on research gaps and proposing five areas for future enhancements on MMs for SCML. Finally, Section 5 provides the conclusions.