esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 4 Clinical Dataset
### Structure of Dataset
#### 4.1.1 Demographic Dataset  
This dataset includes demographic information, such as age and gender, for both non-AAS and AAS patient groups, as depicted in Table 3. Specifically, a gender imbalance is evident within the AAS group, with males constituting 65.1% compared to females, at 34.9%, suggesting gender-specific trends in AAS. The presence of missing data underscores the importance of ensuring analysis reliability. Furthermore, Figure 3 and Table 3 highlight that 46.5% of AAS patients fall within the 60-80-year-old age range, emphasizing the prevalence of this condition among older demographics. Meanwhile, the disposition outcomes column offers insights into patients' final healthcare status, encompassing discharge, admission, transfer, and mortality, reflecting the diverse scenarios encountered within the ED setting for both non-AAS and AAS groups. Additionally, the chief complaint column details the primary reasons for seeking medical attention, notably focusing on chest pain symptoms. Further analysis elucidates the prevalence and severity of chest pain within the AAS group, emphasizing the importance of comprehensive symptom assessment in addressing healthcare needs. These combined analyses provide valuable insights into demographic characteristics, disposition outcomes, diagnosis patterns, and symptom profiles within both patient populations.  
The comparison of gender distributions between patients without and with AAS reveals noteworthy findings. In the absence of Acute Aortic Syndrome (AAS) (Figure 4), the gender distribution is relatively balanced, with women at 54.13% and men at 45.87%. However, in the presence of AAS (Figure 5), there is a significant shift, indicating a higher proportion of male patients at 65.12%, with females comprising 34.88%. This gender disparity suggests that AAS may exhibit gender-specific patterns or predispositions. Further investigation into the underlying factors contributing to this gender-related difference could provide valuable insights into the characteristics and risk factors associated with Acute Aortic Syndrome in different demographic groups.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Categories**} & \multirow{2}{*}{**No. of Files**} & \multicolumn{2}{c}{**No. of**} & \multirow{2}{*}{**No. of Rows**} & \multirow{2}{*}{**Unique-UID**} \\  & & **Columns** & & \\ \hline Demographic & 11 & 6 & 335,201 & 146,114 \\ Procedure & 15 & 3 & 308,828 & 109,736 \\ Lab result & 15 & 5 & 4,202,716 & 92,892 \\ Vital & 15 & 4 & 1,048,575 & 57,465 \\ Total & 56 & 18 & 5,895,320 & **148,707** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset overview.
# A Data-Driven Analysis on the Impact of High-Speed Rails on Land Prices in Taiwan
## 4 Data-Driven Analysis
### The Contribution of HSR to Land Prices
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**3-PC Model**} & \multicolumn{3}{c}{**4-PC Model**} & \multicolumn{3}{c}{**5-PC Model**} \\ \hline  & **PC1** & **PC2** & **PC3** & **PC1** & **PC2** & **PC3** & **PC4** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** \\ \hline _HSR_ & 0.722 & 0.256 & 0.101 & 0.830 & 0.081 & 0.051 & \(-\)0.033 & 0.849 & 0.115 & 0.192 & 0.052 & \(-\)0.019 \\ _GenPop_ & 0.918 & \(-\)0.021 & \(-\)0.020 & 0.816 & \(-\)0.110 & \(-\)0.040 & \(-\)0.413 & 0.722 & \(-\)0.101 & 0.448 & \(-\)0.040 & \(-\)0.343 \\ _NumGuests_ & 0.708 & 0.421 & 0.027 & 0.618 & 0.407 & 0.036 & \(-\)0.408 & 0.463 & 0.399 & 0.494 & 0.031 & \(-\)0.318 \\ _FTZ_ & 0.703 & 0.022 & \(-\)0.017 & 0.568 & \(-\)0.002 & \(-\)0.015 & \(-\)0.424 & 0.208 & \(-\)0.060 & 0.834 & \(-\)0.023 & \(-\)0.242 \\ _AirDist_ & \(-\)0.604 & 0.199 & 0.084 & \(-\)0.165 & 0.009 & 0.009 & 0.909 & \(-\)0.170 & \(-\)0.002 & -0.194 & 0.007 & 0.895 \\ _CC_ & 0.653 & 0.239 & 0.047 & 0.735 & 0.091 & 0.005 & \(-\)0.057 & 0.835 & 0.140 & 0.034 & 0.008 & \(-\)0.082 \\ _CPI_ & 0.076 & 0.883 & 0.106 & 0.221 & 0.861 & 0.106 & 0.099 & 0.092 & 0.846 & 0.253 & 0.097 & 0.158 \\ _Forex_ & 0.042 & \(-\)0.673 & 0.124 & 0.047 & \(-\)0.753 & 0.091 & 0.102 & \(-\)0.096 & \(-\)0.788 & 0.244 & 0.092 & 0.170 \\ _Internet_ & 0.454 & 0.725 & 0.100 & 0.659 & 0.549 & 0.050 & 0.200 & 0.443 & 0.521 & 0.569 & 0.040 & 0.326 \\ _UnEmp_ & \(-\)0.022 & \(-\)0.322 & 0.861 & \(-\)0.029 & \(-\)0.329 & 0.858 & 0.020 & 0.050 & \(-\)0.308 & \(-\)0.138 & 0.862 & \(-\)0.013 \\ _IRrf_ & \(-\)0.021 & \(-\)0.341 & \(-\)0.837 & \(-\)0.064 & \(-\)0.360 & \(-\)0.850 & 0.005 & 0.007 & \(-\)0.355 & \(-\)0.138 & \(-\)0.846 & \(-\)0.028 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Variable loadings for 3, 4, and 5 principal component (PC) models. It is noted that amidst the strong correlations among variables within a component, the components or dimensions are independent of one another. The variable loadings on the components reveal that HSR stations, the size of population, and construction cost are correlated positively and closely. A two-way relationship could be inferred such that the existence of HSR stations makes it attractive for people to settle in the city and the bigger population also contributes to the utilization of HSR. The latter justifies the high construction cost. These factors reinforce one another and together they increase land prices.
## 4 Mathematical Implementation of the Problem
### Technical and Economical Constraints
#### GB constraints in H-MG n
When the temperature lies between two minimum and maximum amount, \(\ \bar{T}_{n}^{REF}\leq T_{h,n}^{REF}\leq T_{n}^{REF}\), the refrigerator stays in off mode. Hence, the temperature increases because the compressor of refrigerator is working (Equation (84)). \[\left\{ \begin{array}{ll}m_{h,n}^{REF}=0&;\ \ \bar{T}_{n}^{REF}\leq T_{h,n}^{ REF}\leq T_{n}^{REF};\\ m_{h,n}^{REF}=1&;\ \ \ \text{Otherwise}\end{array} \right. \tag{83}\]  
\[m_{h,n}^{REF}=1\Rightarrow\left\{ \begin{array}{ll}P_{h,\mu,n}^{REF}=\hat{ P}_{n}^{REF}\\ T_{h,n}^{REF}=T_{h-1,n}^{REF}-T_{n}^{REF}\end{array} \right. \tag{84}\]  
\[m_{h,n}^{REF}=0\Rightarrow\left\{ \begin{array}{ll}P_{h,\mu,n}^{REF}=0\\ T_{h,n}^{REF}=T_{h-1,n}^{REF}+T_{n}^{REF}\end{array} \right. \tag{85}\]  
\[CO_{h,\mu,n}^{REF}=\pi_{h,e,n}^{REF}\times P_{h,e,n}^{REF} \tag{86}\]  
\[0\leq\pi_{h,e,n}^{REF}\leq\lambda_{h,e}^{MCP} \tag{87}\]  
**DW constraints**  
The modeling of DW are presented in Equations (88)-(91). In addition, Equations (90) and (91), respectively, show the cost resulting from buying power by DW and the price bid interval for buying power. \[if\ \ m_{h,n}^{\text{DW}}=1\Rightarrow\left\{ \begin{array}{ll}P_{h,e,n}^{ DW}=\hat{P}_{n}^{DW}\\ D_{h,n}^{\text{DW}}=T_{h-1,n}^{\text{DW}}+1\end{array} \right. \tag{88}\]  
\[if\ \ DT_{h,n}^{\text{DW}}=\hat{DT}_{n}^{\text{DW}}\Rightarrow\left\{ \begin{array}{ll}P_{h,e,n}^{\text{DW}}=0\\ m_{h,n}^{\text{DW}}=0\end{array} \right. \tag{89}\]\[CO_{h,ht,n}^{DW}=\pi_{h,e,n}^{DW}\times P_{h,e,n}^{DW} \tag{90}\] \[0\leq\pi_{h,e,n}^{DW}\leq\lambda_{h,e}^{MCP} \tag{91}\]
# Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos
## 3 Methodology
### Model Selection  
After a thorough literature review, we chose state-of-the-art Scaled-YOLOv4 as an object detection model that has proven to be much better than YOLOv4 in terms of FPS and mAP. It should be noted that the model performance varies for different datasets; some perform well on small images while others perform well on large images. Scaled-YOLOv4 can be tuned to achieve the best result. The model used the cross stage partial (CSP) approach to improve the speed and accuracy. The network is scalable in terms of depth, width, resolution, and structure. The YOLOv4 was designed for object detection on general GPU, which is why it lacks speed and accuracy. The redesigning of YOLOv4 with the CSP approach enhances its speed and accuracy. The backbone of YOLOv4 is Darknet, which is replaced with CSPDarknet53 that has lower computations. The number of computations can be estimated by using the following equation:  
\[whb^{2}\bigg{(}\frac{9}{4}+\frac{3}{4}+\frac{5k}{2}\bigg{)} \tag{1}\]  
where \(w\) is the width, \(h\) is the height of the image, \(b\) is the base layer channels, and \(k\) is the number of layers. The neck of YOLOv4 consists of a path aggregation network (PAN) that is further CSP-ized to reduce the computation by 40%.
# Refractive Indices of Ge and Si at Temperatures between 4-296 K in the 4-8 THz Region
## 1 Introduction  
Optical science in the terahertz region (roughly 0.3-30 THz, although definitions vary) has developed rapidly in recent decades [1, 2], with sensitive, coherent, and single-cycle pulse technologies causing a revolution in spectroscopy [3, 4, 5], imaging [6, 7], nonlinear optics [8], quantum technologies [9], and a growing number of industrial applications [10]. However, techniques vary in the wavelength ranges available, and extending the bandwidths of current technologies such as terahertz time-domain spectroscopy (THz-TDS) to higher frequencies is a major topic of research [11, 12, 13]. As part of this endeavour, novel semiconductor materials are of interest [11]. Furthermore, in applications such as solid-state THz lasers and quantum devices (e.g., impurity-implanted silicon cubits), the knowledge of temperature variation of the refractive index of substrate material can be essential for the design of such devices. It is therefore important to find methods to accurately measure refractive index in frequency regions above 3 THz which the most popular technologies (e.g., THz time-domain spectroscopy and vector network analyser-based) find challenging, to perform such measurements as a function of temperature, and to establish relationships between refractive index, frequency, and temperature.  
We have previously reported on a relatively simple measurement technique which obtains the complex refractive index spectrum of a solid from only its power transmission spectrum, taking advantage of the Fabry-Perot interference effect [14]. While this type of method can be applied to almost any measurement of the transmission spectrum of a plane-parallel sample, it is most useful in the terahertz region above 3 THz, where THz-TDS and other terahertz technologies are still limited. We report in this work on measurements using Fourier-transform spectroscopy (FTS), which is especially useful due to its wide bandwidth and high frequency resolution. Since FTS paired with cryogenics provides access to higher frequencies than does THz-TDS [15], it is an obvious choice for the present work. Furthermore, we demonstrate in this work how accurate refractive index values may be extracted from FTS transmission data by combining two calculation approaches, takingadvantage of complementarity between different algorithms to overcome the limitations of the toolbox reported in our previous work, as explained below [14].  
This study was driven by the realisation that there is scarce data available on the temperature dependence of refractive indices of Si and Ge at THz frequencies, and that such data are necessary for a number of applications. In this work, we measure the refractive indices of high purity commercial single-crystal Si and Ge plates in the THz region between 4.2-7.7 THz (140-257 cm\({}^{-1}\), 71-38 \(\upmu\)m) as a function of temperature between 4-296 K.
## 2 Materials and Methods
### Statistical Analysis  
#### 2.4.1 Comparison of Ground Data with Image Pixels  
Ground observation points were acquired from 40 regions of interest (ROI) within the PVNP accounting for burned areas from the ortho-imagery. In addition, these values corresponded to scorched vegetation and soil cover. Historical Google Earth images were used as reference observation data for burn severity. The ground data values were then used to derive correlation between MTMF scores, NBR values, and dNBR values for each endmember using Pearson correlation statistics in SAS (Base SAS(R) 9.4). All correlations were regarded as a statistically significant difference when the \(p\) value < 0.05 [53]. To examine the relationship between ground data and spectral data, linear regressions were computed on MTMF scores. MTMF scores negative or greater than one were rescored as 0 or 1 indicating no data or high score. All the scores were multiplied by 100 to derive the 1:1 correlation with ground data acquired from 2015 aerial imagery.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 3 Results
### Content of Polyphenols and Flavonoids  
Table 4 shows the total polyphenol and flavonoid content of the methanolic extracts obtained from the [PERSON]'s hand exocarp and mesocarp. Both parts possessed a similar content of the two types of compounds. Nevertheless, as expected, EEX was found to be richer in both polyphenols and flavonoids than MEX. Their quantity in the EEX was about 1.5 times higher.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Component 1** & **LRI 2** & **LRI 3** & **EO 4 (\%)** & **Hy 5 (\%)** \\ \hline
1 & \(\alpha\)-thujene & 923 & 925 & 0.2 \(\pm\) 0.02 & - \\
2 & \(\alpha\)-pinene & 941 & 943 & 0.6 \(\pm\) 0.03 & - \\
3 & \(\beta\)-thujene & 966 & 968 & 0.1 \(\pm\) 0.00 & - \\
4 & \(\beta\)-myrcene & 983 & 980 & 0.9 \(\pm\) 0.01 & - \\
5 & \(\beta\)-pinene & 980 & 986 & 0.8 \(\pm\) 0.01 & - \\
6 & \(\alpha\)-phalendrene & 998 & 996 & tr & - \\
7 & (+)-4-carene & 1005 & 1001 & 0.2 \(\pm\) 0.02 & - \\
8 & \(p\)-cymene & 1020 & 1016 & 1.6 \(\pm\) 0.02 & 0.4 \(\pm\) 0.01 \\
9 & limonene & 1024 & 1023 & 66.9 \(\pm\) 0.02 & 3.2 \(\pm\) 0.02 \\
10 & \(\beta\)-terpinene & 1040 & 1036 & - & 1.0 \(\pm\) 0.01 \\
11 & \(\gamma\)-terpinene & 1057 & 1054 & 20.0 \(\pm\) 0.03 & - \\
12 & linalol & 1091 & 1088 & - & 5.7 \(\pm\) 0.03 \\
13 & R-(+)-citronellal & 1158 & 1152 & 0.1 \(\pm\) 0.02 & - \\
14 & terpinen-4-ol & 1186 & 1182 & 0.5 \(\pm\) 0.02 & 21.6 \(\pm\) 0.02 \\
15 & \(\alpha\)-terpineol & 1185 & 1193 & 1.2 \(\pm\) 0.01 & 44.7 \(\pm\) 0.01 \\
16 & \(cis\)-geraniol & 1228 & 1231 & 0.2 \(\pm\) 0.02 & 7.2 \(\pm\) 0.01 \\
17 & \(\beta\)-citral & 1238 & 1242 & 0.9 \(\pm\) 0.05 & 5.8 \(\pm\) 0.03 \\
18 & \(\alpha\)-citral & 1285 & 1287 & 1.3 \(\pm\) 0.01 & 8.0 \(\pm\) 0.02 \\
19 & thymol & 1308 & 1310 & - & 1.9 \(\pm\) 0.02 \\
20 & piperitone & 1316 & 1315 & - & 0.4 \(\pm\) 0.01 \\
21 & nerol acetate & 1362 & 1365 & 0.2 \(\pm\) 0.02 & - \\
22 & geranyl acetate & 1381 & 1380 & 0.1 \(\pm\) 0.02 & - \\
23 & \(\beta\)-caryophyllene & 1455 & 1457 & 1.7 \(\pm\) 0.02 & - \\
24 & humulene & 1470 & 1473 & 0.1 \(\pm\) 0.00 & - \\
25 & germacrene D & 1491 & 1489 & 0.6 \(\pm\) 0.01 & - \\
26 & \(\beta\)-bisabolene & 1505 & 1501 & 1.2 \(\pm\) 0.01 & - \\
27 & bicyclogermacrene & 1510 & * & 0.1 \(\pm\) 0.00 & - \\
28 & \(\delta\)-cadinene & 1532 & 1530 & 0.1 \(\pm\) 0.00 & - \\
29 & \(\alpha\)-bisabolol & 1670 & 1668 & 0.1 \(\pm\) 0.02 & - \\  & SUM & & & 99.7 & 99.9 \\ \hline  & Monoterpene hydrocarbons & & 91.3 & 10.3 \\  & Oxygenated monoterpenes & & 4.5 & 89.6 \\  & Sesquiterpene hydrocarbons & & 3.9 & - \\ \hline \end{tabular}  
* The components are reported according to their elution order on apolar column; 2, Linear Retention Indices measured on apolar column; 3, Linear Retention indices from literature; *, LRI not available; EO 4, percentage mean values of exocarp components (%); Hy 5, percentage mean values of exocarp components (%); *, not detected, tr, traces (mean value \(<\) 0.1%).  
\end{table}
Table 3: Chemical composition (percentage mean value \(\pm\) standard deviation) of EO and Hy.
## 4 Fractional-Order Controller and Fractional-Order Disturbance Observer Design
### Heading Controller Design
 \tag{80}\]  
where \(\dot{\zeta}_{71}\) in (80) can be expressed as \(\dot{\zeta}_{71}=D^{\overline{7}_{1}}\zeta_{72}\), similar to the previous result  
\[\alpha_{7}=D^{\overline{7}_{2}}k_{7}^{2}(b_{71}e_{7}+b_{72}sig^{1/2}(e_{7}))+ \beta_{71}\xi_{71}-\xi_{72}+\lambda_{71}sig^{1/2}(\xi_{71}), \tag{81}\]  
\[\alpha_{8}=-D^{\overline{7}_{1}}\zeta_{72}+D^{\overline{7}_{2}}k_{8}^{2}(b_{8 1}e_{8}+b_{82}sig^{1/2}(e_{8}))+\beta_{82}\xi_{82}+\xi_{81}+\lambda_{82}sig^{ 1/2}(\xi_{82}). \tag{82}\]  
The virtual controls \(\alpha_{7}\) for the recursive input, and control inputs \(u_{8}\) are designed as follows:  
\[\alpha_{7}=-s_{71}\alpha_{7}-e_{8}-s_{72}sig^{1/2}(\sigma_{7})+\dot{x}_{74}- \alpha_{7}, \tag{83}\]  
\[u_{8}=g_{8}^{-1}[-s_{81}\sigma_{8}-\omega_{8}-\dot{\tau}_{48}-s_{82}sig^{1/2} (\sigma_{8})-s_{83}\sigma_{8}/(|\sigma_{8}|+\varepsilon_{\sigma 8})^{-1}], \tag{84}\]  
where \(s_{ij}>0\), \(i=7,8\), \(j=1,2,3\), and \(\varepsilon_{\sigma8}>0\) are constants, and \(\dot{\tau}_{48}\) are estimates of \(\tau_{48}\), respectively. The Lyapunov function candidates are defined as follows:  
\[V_{7}=\frac{1}{2}\sigma_{7}^{2}, \tag{85}\]  
\[V_{8}=\frac{1}{2}\sigma_{8}^{2}. \tag{86}\]  
The fractional-order operations of (85) and (86) along with the results of (79), (80), (83), and (84) yield to the following expression:\[ \begin{array}{l}D^{\overline{7}_{1}}(V_{7}+V_{8})=\sigma_{7}D^{\overline{7}_{1}} \sigma_{7}+\sigma_{8}D^{\overline{7}_{1}}\sigma_{8}\\ =\sigma_{7}(e_{8}-\dot{x}_{8d}+\sigma_{7}+\alpha_{7})+\sigma_{8}(f_{8}+g_{8}u_{4 }+\alpha_{8}+\tau_{48})\\ +\left|\sum\limits_{k=1}^{\infty}\frac{\Gamma(1+\overline{\tau}_{1})}{\Gamma(1 -k+\overline{\tau}_{1})\Gamma(1+k)}D^{k}\sigma_{7}D^{\overline{7}_{1}-k} \sigma_{7}\right|+\left|\sum\limits_{k=1}^{\infty}\frac{\Gamma(1+\overline{ \tau}_{1})}{\Gamma(1-k+\overline{\tau}_{1})\Gamma(1+k)}D^{k}\sigma_{8}D^{ \overline{7}_{1}-k}\sigma_{8}\right|\\ \leq-s_{71}\sigma_{7}^{2}-s_{72}|\sigma_{7}|^{3/2}+\mu_{7}|\sigma_{7}|^{3/2}\\ -s_{81}\sigma_{8}^{2}-s_{82}|\sigma_{8}|^{3/2}-s_{83}\sigma_{8}^{2}/(|\sigma_ {8}|+\varepsilon_{\sigma 8})^{-1}+\mu_{8}|\sigma_{8}|^{3/2}+\widetilde{\tau}_{48}\\ \leq-(s_{72}-\mu_{7})|\sigma_{7}|^{3/2}-(s_{82}-\mu_{8})|\sigma_{8}|^{3/2}\\ -|\sigma_{8}|(\xi_{83}|\sigma_{8}|/(|\sigma_{8}|+\varepsilon_{\sigma 8})^{-1}-\delta_{48}),\end{array}  \tag{87}\]  
where, \(\widetilde{\tau}_{48}=\tau_{48}-\dot{\tau}_{48}\) and \(|\widetilde{\tau}_{48}|\leq\delta_{48}\), \(\delta_{48}\) is an unknown upper bound. If \(s_{83}\) is selected such that the following conditions are satisfied:  
\[s_{83}|\sigma_{8}|/(|\sigma_{8}|+\varepsilon_{\sigma 8})^{-1}\geq\delta_{48}, \tag{88}\]  
we obtain  
\[D^{\overline{7}_{1}}(V_{7}+V_{8})\leq-(s_{72}-\mu_{7})|\sigma_{7}|^{3/2}-(s_{ 82}-\mu_{7})|\sigma_{8}|^{3/2}. \tag{89}\]
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 1 Introduction  
The iris contains crucial biometric characteristics marked by stability, uniqueness, and universality, making it highly valuable for personal identification purposes. However, the acquisition of low-resolution iris images may be attributed to hardware limitations and extended acquisition distances, significantly compromising the recognition performance of the iris recognition system. One feasible approach is enhancing the recognition performance of low-resolution iris images by utilizing a super-resolution model to boost the resolution before the iris recognition processing.  
Compared to general images, there is less structural information revealed in iris patterns. In addition, the randomness, complexity, and significant variability in iris texture make the super-resolution task more complex. Furthermore, beyond the visual effect, the recognition performance of the reconstructed iris image is also an important point to consider.  
The iris super-resolution task requires the recovery of meaningful texture information, which consists mainly of high-frequency components. Our objective is to identify an optimal solution that not only enhances the visual effect but also improves recognition performance. To achieve this, we designed an iris image super-resolution model based on a Swin Transformer and Generative Adversarial Network (GAN). Through experimental validation, it has been confirmed that the method fulfills the requirements of the iris super-resolution task.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 4 Discussion
Especially for the observation removal strategy, the satellite geometry is supposed to be less degraded with more visible satellites from multiple GNSS than using GPS only. Furthermore, with methods such as Bayesian optimization [43], genetic algorithm and machine learning, it is likely that the efficiency and consistency of weight strategy could also be improved. Thus, further research is necessary to evaluate the improvement of strategies through these methods.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 6 Two Illustrative Examples
### Example 1
To enhance the robustness of the monitoring scheme and address the uncertainty in estimating the process mean, the low bound of the bootstrap confidence interval, \(\widehat{\theta}_{0L}=0.853\), was used to recalculate the adjusted parameters \(B_{L}^{-}{}^{\prime},k_{L}^{\prime}\), and \(LCL_{L}^{\prime}\). These adjusted parameters offer a more cautious estimation of the control limits, ensuring that the monitoring scheme remains effective if the true process mean exceeds initial estimates. The recalculated parameters are as follows:  
\[B^{-}{}_{L}^{\prime} = 2\qquad\times 0.853=1.706,\] \[k_{L}^{\prime} = 1.175\times 0.853=1.002,\] \[LCL_{L}^{\prime} = 0.754\times 0.853=0.6432.\]  
The AEWMA statistic \(A_{tL}^{-}\) is then calculated using Equation (9), with the updated parameters (refer to Table 6 for details). Furthermore, to facilitate a comparative analysis with the existing EWMA-type and Shewhart-type schemes in practical applications, we consider the case where \(c=0.8\) and \(MRL_{0}=300\). The optimal parameters for the EWMA-type scheme were obtained from Table 1, which are \((\lambda^{-},LCL^{-})=(0.052,0.688)\). Subsequently, \(B^{-}\) and \(LCL^{-}\) were multiplied by the scaling factor \(\widehat{\theta}_{0}=1.245\), resulting in \(B^{-}{}^{\prime}=2.490\) and \(LCL^{\prime}=0.8566\). The corresponding optimal EWMA scheme statistic \(Q_{t}^{-}\) can be computed using the Equation (6) (see Table 6). For the Shewhart-type scheme, the control limit was set at \(0.0023\) to satisfy \(MRL_{0}=300\). After multiplying this value by the scaling factor \(\widehat{\theta}_{0}=1.245\), the adjusted lower control limit \(LCL^{\prime}\) is calculated as \(LCL^{\prime}=0.0023\times 1.245=0.0029\). Observations from the data indicate that until \(t=4\), \(A_{t}^{-}=Q_{t}^{-}\), suggesting that the prediction error is relatively small during this period, and the performance of the AEWMA scheme closely resembles that of the EWMA scheme. After \(t=4\), the prediction error increases, leading to \(A_{t}^{-}>Q_{t}^{-}\). This suggests that the AEWMA scheme may better accommodate larger shifts in the process mean after this point. Figures 4-7 illustrate the application of these schemes in the OLED production process monitoring, respectively. As shown in these figures, no alarm signals are generated across all three schemes, indicating that the process remains in-control throughout the monitoring period. As depicted in Figure 5, some sample statistics fall within the reflection boundaries \(B_{L}^{-}{}^{\prime}\), suggesting that the probability of the system's estimated parameters reaching \(\widehat{\theta}_{0L}\) is relatively low. Figure 4: Optimal lower-sided exponential AEWMA scheme in the OLED production process. Figure 5: Adjusted optimal lower-sided exponential AEWMA scheme in the OLED production process. Figure 6: Optimal lower-sided exponential EWMA scheme in the OLED production process.