esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 4 Applications of Molecularly Imprinted Polymers (MIPs)
### Solid-Phase Extraction with Molecularly Imprinted Polymers  
The implementation of MIPs as solid phase extraction (MISPE) adsorbents has been described by numerous reports (MISPE) and the number of papers reveals an exponential increase [30; 36; 37]. The potential merit of MIP-based SPE lies in the ability to selectively bind to a specific target in presence of its structural analogs from a complex matrix. The application of MIP sorbents allows not only preconcentration and cleaning of the sample, but also selective extraction of the target analyte, using a very small amount of imprinted polymer (typically 5 to 200 mg) packed into a cartridge. MISPE is an efficient approach for the isolation and preconcentration of analytes from complex matrices and was successfully reported for environmental waters [38], soils [39], sediments [40], plant extracts [41], and soy [42].  
The current review aims at tracking the fundamentals of molecularly imprinted polymers and their recent applications for the sensing, extraction, and determination of major water pollutant, including heavy metals, dyes, pesticides, endocrine disrupting compounds (EDCs), and pharmaceutical residues in the past five years.
## 2 Dispersion Analysis of the I-Shaped Steel Beam
### Wave Mode Analysis Under the External Load  
From the dispersion curve, it can be observed that multiple modes appear at a single excitation frequency, even in the lower frequency range. The main wave modes are related to the applied external load. Supposing there are P modes at a specific excitation frequency, the mode weight coefficient (\(\alpha_{p}\)) for the pth mode is defined as  
\[\alpha_{p}=-\frac{k_{p}\Phi_{p}f_{ex}}{B_{p}},\ B_{p}=\begin{bmatrix}\Phi_{p}\\ k_{p}\Phi_{p}\end{bmatrix}\mathbf{B}\begin{bmatrix}\Phi_{p}\\ k_{p}\Phi_{p}\end{bmatrix}^{T} \tag{4}\]  
where \(k_{p}\) and \(\Phi_{p}\) are the wave number and mode shape of the pth mode. The location of the external force is \(\mathrm{z}=\mathrm{z}_{s}\), then the displacement \(U^{s}\) at the position of \(\mathrm{z}_{f}\) in the \(s\) direction is  
\[U^{s}=\sum_{p=1}^{p}\alpha_{p}\Phi_{p}^{s}e^{ik_{p}(z_{f}-z_{s})}(se(x,\ y,\ z)) \tag{5}\]  
Two different loads were applied to the web in the x and y directions to simulate Lamb and SH-typed guided waves, respectively, as shown in Figures 3a and 4a. The excitation frequency was determined as 80 kHz, considering the equipment and transducer conditions, and a total of 83 wave modes were identified. The distribution of \(\alpha_{p}\) under the two types of external loads is shown in Figures 3b and 4b, with the mode shapes of the top five main modes depicted in Figures 3c-g and 4c-g. Most of the wave energy is concentrated in the web for two types of external loads, although a portion of the wave energy still transfers to the flange when using the Lamb wave. The phase and group velocities of Mode 2 (the main mode under the Lamb wave) are 2097 m/s and 3142 m/s, respectively, indicating stronger dispersion, which may lead to more serious wave energy attenuation during propagation. In contrast, the phase and group velocities of Mode 39 (main mode under the SH-typed guided wave) are quite close, with values of 3267 m/s and 3191 m/s, respectively. Moreover, the present author has demonstrated that SH-typed guided waves are less affected by different surface media along the propagation path [10]. Hence, the SH-typed guided wave was adopted to detect the loose  
Figure 2: Dispersion curves of the I-shaped steel beam. (**a**) Phase velocity; (**b**) group velocity.
## 4 Discussion
Therefore, the experiment on an underwater plate with three notches is conducted, in which the notches have the smaller spatial interval of \(\sim\)5 mm between them, as shown in Figure 8b. Figure 9b shows the experimental results obtained in the aluminum plate shown in Figure 8b. The energy of the pump laser is increased compared to that in the previous experiment to enhance the transmission of the S\({}_{0}\) mode after multiple notches. Mode conversions take place at three notches, as marked in Figure 9b. In Figure 9b, the mode of 'S\({}_{0}\)-r\({}_{2}\)A\({}_{0}\)' and 'S\({}_{0}\)-r\({}_{2}\)A\({}_{0}\)' is not clear, because of the shallow depth of the second notch. It is also worth noting that in the dotted frames in Figure 9b, the short resonance is not found. This is because the scanning step is approximately equal to the width of notch, while the resonance can only be detected when the probe laser is focused at the notch. However, as shown in Figure 6b, the amplitude decreases at the edges of the notch; this leads to the breakpoints in the dotted frames in Figure 9b. Because of the non-dispersion of the S\({}_{0}\) mode and the difference between the S\({}_{0}\) mode and the A\({}_{0}\) mode in velocity, the three notches with the separations of 5 mm are clearly detected and localized. According to the above results, it can be observed that the non-dispersive S\({}_{0}\) generated by pulsed laser lateral irradiation of the side of the plate is suitable for inspection of underwater plates. The main features are as follows:  
1. By using a laser ultrasonic technique for excitation and detection, the system can realize remote non-contact detection. 2. The S\({}_{0}\) mode generated by lateral generation configuration has good SNR and can propagate a longer distance, while the amplitude of the A\({}_{0}\) mode is significantly restrained. 3. The mode conversion can be an effective tool for defect detection and localization. 4. Because of the non-dispersion and narrow wave packet of the S\({}_{0}\) mode using lateral generation configuration, defect width can be estimated, and multiple defects with short spatial intervals can be detected.
# Smart Manufacturing Application in Precision Manufacturing
###### Abstract  
Industry 4.0 presents an opportunity to gain a competitive advantage through productivity, flexibility, and speed. It also empowers the manufacturing sector to drive the sustainability revolution to achieve net zero carbon by reducing emissions in operations. In this paper, the aim is to demonstrate a practical implementation of a smart manufacturing application using a systematic approach based on conceptual six-gear smart factory roadmap with connectivity, integration and analytics stages to build a smart production management ecosystem using off-the-shelf technologies applied in precision manufacturing. Business benefits from the smart manufacturing application implementation are realized in terms of operational performance, economic benefits, and environmental sustainability over a period of three years (before and after smart manufacturing). The productivity improves as a result of the 47% improvement made to the machines' utilization and the 53% reduction in the total downtime waste. Economic benefits are realized in terms of a cost saving of GBP 420 K that could cost the business and the returns of the financial investment made, which is recovered within a year. An environmental sustainability impact is realized by a reduction in the total greenhouse gas (GHG) emissions by 43%, mostly due to the reduction in the Scope 2 emissions in operations by 50%, which is significantly impacted by the reduction of energy consumption and better power consumption management. The significance of this work is the bridging of the gap between theory and practice by rapidly applying the six-gear smart factory roadmap to start, scale, and sustain the implementation of smart manufacturing applications in the manufacturing industry. This roadmap can serve as a strategic framework tool for smart manufacturing implementations. The technical architecture can serve as a guide for the practical implementation of smart manufacturing applications to reduce the complexity of development. This work also bridges the gap in academia and in industry by showcasing a real-world actual business benefits realized from smart manufacturing, as well as showcasing the practical implementations, limitations, and opportunities of smart manufacturing applications in the precision manufacturing industry, all of which reduce the internal barriers and challenges facing smart manufacturing and industry 4.0 adoption. The value realized in gaining a competitive advantage and driving environmental sustainability from smart manufacturing in this study can serve as a case study for academics and for industry business leaders, digital champions, and digital lighthouses to support value creation and to drive and accelerate smart manufacturing applications, digital transformation initiatives, and industry 4.0 adoption across the value chain.  
s smart manufacturing; industry 4.0; digital transformation; operational performance; environmental sustainability
## 4 Discussion  
### Feature Importance Analysis by SHAP Value Summary Plot  
To investigate the positive and negative relationships between each feature's impact on the model's final output, the SHAP value method is used in this paper. As shown in the figure (Figure 10), each row in the figure corresponds to a feature, with the \(x\)-axis representing its SHAP value and each point signifying a sample. A redder color indicates a higher feature SHAP value, while bluer shades denote lower feature values. A wider range indicates that it contains more samples, a longer area range indicates that the feature has a greater impact on the model, the more left samples have a negative effect on the model, and the more right samples have a positive impact on the model. According to the average absolute value of SHAP, the ranking of features in importance from highest to lowest is descending. For example, we intuitively see that Estimated Mine Life is the most important feature affecting whether it can be put into production. The larger the value, the higher the probability of being put into production, and the amplitude of this feature has the greatest impact on the model, indicating that the life cycle of a gold mine is the most worthy factor when considering gold mine production feasibility; The smaller the LOM Cash Costs value, the greater the likelihood that it will be put into production, that is, the lower the cost of cash invested.
# Building Greibach Normal Form Grammars Using Genetic Algorithms
## 5 Results
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline
**L** & **G** & \multicolumn{4}{c}{**NT**} & \multicolumn{4}{c}{**R**} & \multicolumn{4}{c}{**gen_len**} \\ \hline  & **min** & **mean** & **max** & **min** & **mean** & **max** & **min** & **mean** & **max** & **min** & **mean** & **max** \\ \hline L1 & 12 & 20.6 & 49 & 9 & 9.8 & 10 & 10 & 23.8 & 31 & 34 & 79.0 & 95 \\ L2 & 500 & 500.0 & 500 & 6 & 8.3 & 10 & 20 & 27.3 & 32 & 75 & 88.8 & 100 \\ L3 & 4 & 10.8 & 19 & 9 & 9.9 & 10 & 17 & 21.6 & 27 & 54 & 75.6 & 94 \\ L4 & 500 & 500.0 & 500 & 7 & 8.4 & 10 & 17 & 27.0 & 41 & 48 & 74.0 & 99 \\ L5 & 500 & 500.0 & 500 & 6 & 8.4 & 10 & 15 & 25.0 & 35 & 51 & 74.7 & 98 \\ L6 & 500 & 500.0 & 500 & 8 & 8.9 & 10 & 23 & 28.9 & 34 & 79 & 90.9 & 100 \\ L7 & 17 & 47.6 & 157 & 8 & 9.7 & 10 & 10 & 22.9 & 28 & 32 & 73.2 & 98 \\ L8 & 44 & 214.1 & 500 & 8 & 8.6 & 10 & 25 & 30.9 & 38 & 74 & 87.2 & 100 \\ \hline
**L** & **ACC** & \multicolumn{4}{c}{**TPR**} & \multicolumn{4}{c}{**TNR**} & \multicolumn{4}{c}{**mean_time**} \\ \hline  & **min** & **mean** & **max** & **min** & **mean** & **max** & **min** & **mean** & **max** & & \\ \hline L1 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.150 & \\ L2 & 0.959 & 0.963 & 0.975 & 0.863 & 0.886 & 1.0 & 0.97 & 0.981 & 0.99 & 49.95 & \\ L3 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 0.356 & \\ L4 & 0.954 & 0.974 & 0.990 & 0.821 & 0.907 & 1.0 & 0.975 & 0.997 & 1.0 & 57.85 & \\ L5 & 0.858 & 0.881 & 0.960 & 0.333 & 0.462 & 1.0 & 0.95 & 0.994 & 1.0 & 259.0 & \\ L6 & 0.915 & 0.936 & 0.957 & 0.444 & 0.611 & 0.833 & 0.96 & 0.994 & 1.0 & 39.89 & \\ L7 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 & 1.15 & \\ L8 & 0.987 & 0.995 & 1.0 & 1.0 & 1.0 & 1.0 & 0.98 & 0.993 & 1.0 & 110.7 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results from \(L_{1-8}\). Figure 1: Effect of sub-parse variable on \(L_{9}\). It is clear that the convergence of the algorithm depends on the chromosome size, when target languages are complex and no clear pattern or structure is present.
# A Hybrid SWIM Data Naming Scheme Based on TLC Structure
###### Abstract  
Aiming at the problem of low interconnection efficiency caused by the wide variety of data in SWIM (System-Wide Information Management) and the inconsistent data naming methods, this paper proposes a new TLC (Type-Length-Content) structure hybrid data naming scheme combined with Bloom filters. This solution can meet the uniqueness and durability requirements of SWIM data names, solve the \"suffix loopholes\" encountered in prefix-based route aggregation in hierarchical naming, and realize scalable and effective route state aggregation. Simulation verification results show that the hybrid naming scheme is better than prefix-based aggregation in the probability of route identification errors. In terms of search time, this scheme has increased by 17.8% and 18.2%, respectively, compared with the commonly used hierarchical and flat naming methods. Compared with the other two naming methods, scalability has increased by 19.1% and 18.4%, respectively.  
index terms SWIM; naming method; hybrid; TLC structure; bloom filter +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 142; doi:10.3390/fi12090142
# Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
## 3 Proposed Matrix Completion Based Localization
### Problem Formulation  
Our goal is to reconstruct the complete distance matrix from incomplete and noisy data. The problem of recovering a low rank matrix from a small number of known entries is known as:  
\[\left\{\begin{array}{ccc}\min_{\hat{\mathbf{X}}}&rank(\mathbf{\hat{X}})\\ s.t.\ \hat{x}_{ij}=x_{ij}&where&i,j\in\omega\\ \end{array}\right., \tag{5}\]  
\(\omega\) is the set of known entries. Due to the non convexity and non linearity of the rank matrix [33], the problem in Equation (1) cannot be solved numerically. Inspired by the theory of Compressed Sensing (CS), [PERSON] and [PERSON] proposed to replace the rank function in Equation (1) by the nuclear norm [34]. The model in (1) is reformulated into:\[\left\{\begin{array}{c}\min_{\hat{\mathbf{x}}}||\hat{\mathbf{X}}||_{*}\\ s.t.\ \hat{x}_{ij}=x_{ij}\ \text{where}\ i,j\in\omega\end{array}\right., \tag{6}\]  
\(||\hat{\mathbf{X}}||_{*}\) is the sum of the singular values of \(\hat{\mathbf{X}}\) (i.e., \(||\hat{\mathbf{X}}||_{*}=\sum_{j=1}^{n}s_{j}\). Where \(\hat{\mathbf{X}}=\mathbf{U}\mathbf{S}\mathbf{V}^{T}\)). Considering the assumption of low rank (\(r<<n\)) and taking into account that observations are usually affected by noise, the model of matrix completion can be defined as:  
\[\min_{\hat{\mathbf{x}}}\lambda\times||\hat{\mathbf{X}}||_{*}+||\mathbf{H} \odot(\hat{\mathbf{X}}-\mathbf{X})||_{F}^{2}, \tag{7}\]  
\(\lambda\) is a tunable parameter and \(||.||_{F}\) is the Frobenius norm. \(||\hat{\mathbf{X}}||_{F}=\sqrt{\sum_{i=1}^{n}\sum_{j=1}^{n}|\hat{x}_{ij}|^{2}}\). \(\mathbf{H}\) is the matrix whose entries are:  
\[h_{ij}=h_{ji}=\begin{cases}1&\text{if}\ (i,j)\in\omega\\ 0&\text{otherwise}\end{cases}, \tag{8}\]  
We denote the objective function as:  
\[J(\hat{\mathbf{X}})=f(\hat{\mathbf{X}})+\lambda\times l(\hat{\mathbf{X}}), \tag{9}\]  
where \(f(\hat{\mathbf{X}})=||\mathbf{H}\odot(\hat{\mathbf{X}}-\mathbf{X})||_{F}^{2}\) and \(l(\hat{\mathbf{X}})=||(1-\mathbf{H})\odot\hat{\mathbf{X}}||_{*}\). The defined optimization problem can be solved efficiently by using iterative Gradient Descent method and its variants. The developed algorithm is summarized in Table 1. Where \(\mathbf{V}^{(t)}\) is the matrix used to update the distance matrix, index \(t\) refers to the number of update iteration. Many approaches, detailed in the next section, have been adopted to find the matrix update \(\mathbf{V}^{(t)}\).
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Blockchain  
Blockchain technology is emerging as a foundational component for the realization of a secure, decentralized, and efficient real-time metaverse [205]. In a digital environment where the virtual and physical worlds merge, blockchain offers several critical benefits that ensure the smooth functioning of this complex ecosystem. By enabling decentralized control, enhancing data security, and ensuring trust, blockchain significantly contributes to the metaverse's infrastructure, making it not only more reliable but also scalable for widespread use [5]. One of the primary advantages of blockchain in the metaverse is its ability to provide a decentralized architecture [206]. Unlike traditional systems that rely on centralized servers, blockchain allows data and transactions to be distributed across a network of nodes, reducing the risk of single points of failure. In the metaverse, this decentralization ensures that no single entity has complete control over user data or interactions [207]. This is crucial for fostering a sense of trust and transparency in an environment where users engage in social, economic, and virtual activities that require accountability and fairness.  
Security is another major factor that blockchain brings to the metaverse [208]. Given the vast amount of personal data, assets, and transactions occurring within this space, ensuring the integrity and security of these activities is essential. Blockchain's immutable ledger ensures that once a transaction is recorded, it cannot be altered or tampered with, providing a high level of security [209]. This is particularly important for protecting digital assets like virtual property, NFTs (non-fungible tokens), and cryptocurrencies, which are becoming integral parts of the metaverse economy. Blockchain's encryption and consensus mechanisms prevent fraud, unauthorized access, and cyberattacks, making the metaverse a safer environment for its users. In addition to decentralization and security, blockchain also plays a critical role in establishing trust and ownership within the metaverse. Smart contracts, which are self-executing contracts with the terms directly written into code, allow for transparent, automated transactions between users without the need for intermediaries [55]. This creates a system where users can buy, sell, and trade virtual goods or services with confidence, knowing that the transaction is secure and verifiable. Furthermore, blockchain's ability to record and authenticate digital ownership ensures that users can prove ownership of their virtual assets, from in-game items to virtual real estate, further solidifying the importance of blockchain in creating a functioning digital economy within the metaverse.
## 4 Optimal Exposure Scene Analysis
### Concrete Imaging Mechanism Analysis  
In fifty equivalent exposure scenes, the illuminance at the imaging sensor was different due to exposure time variation, and images of different gray levels were captured, as shown in Figure 7a-h, which includes images captured in the scene with exposures of 5 lx-s, 40 lx-s, 75 lx-s, 110 lx-s, 145 lx-s,180 lx-s, 215 lx-s, and 250 lx-s, respectively. Figure 7a indicates obvious underexposure of the image, and Figure 7e-h indicates overexposure, so images from these exposure scenes did not have enough contrast for crack segmentation.  
To clarify and quantify the gray level distribution law of the concrete images from different exposure scenes, image histograms from the first ten scenes are plotted in Figure 8, from which the mean value \(\mu_{I}\) and standard deviation \(\sigma_{i}\) of each image is calculated. The normal distribution curves are:  
\[f(x)=\frac{1}{\sigma_{i}\sqrt{2\pi}}e^{-\frac{(x-\mu_{i})^{2}}{2\sigma_{i}^{2}}} \leavevmode\
obreak\ (i=1,2,\ldots 10) \tag{8}\]  
where \(i\) is the scene number. The normal distribution curves of the first ten exposure scenes are plotted, in the case that overexposure existed in an image from larger equivalent exposure scenes, compared with the corresponding histograms shown in Figure 9.  
Figure 8: Histogram of images from 50 exposures scenes.  
Figure 7: Images of concrete and crack changes with different equivalent exposures.  
The HMV and HSD variation under the 21 scenes in Section 3 and the 50 equivalent exposure scenes in Table 4 are shown in Figure 10a,b. It can be seen that the two curves of the HMV and HSD in different exposure scenes are closely correlated. In Figure 10, the HMV curve goes up at a nearly constant gradient in the low-exposure interval until leveling off at the cutoff point in the higher exposure interval. After the cutoff exposure point, the HMV remains at the maximum constant value of 65,535. Figure 10 also shows more exposure scenes to help represent the HMV and HSD variation with exposure precisely. For the HMV exposure curve, the cut-off exposure point is 145 k\(\cdot\)s in 50 scenes instead of 154 lx-s, as in Figure 5, and for the HSD exposure curve, the cut-off exposure point is 115 lx-s in 50 scenes instead of 106 lx-s, as in Figure 5. Also, the experiment results show that for the HMV and exposure gradient, the cut-off exposure values are specimen-dependent, depending on the reflective coefficient of the concrete surface according to Equation (6), which is not discussed in this paper.
## 3 Methodology  
In this section, we delineate the methodology employed for temporal evaluation, as depicted in Figure 1. The approach encompasses several stages, each elucidated in detail below. Our objective is to provide a comprehensive explanation of our evaluation process and ensure methodological transparency.  
Four selected frames from the Cross video are showcased in Figure 2 to further explain the methodology. The discernible escalation in noise levels evident in each frame relative to its predecessor is noteworthy. This gradual noise amplification adheres to the previously described methodology, wherein noise intensity systematically escalates as the video progresses. These nuanced alterations in channel conditions allow us to assess the efficacy of various techniques amidst fluctuating noise levels. Ultimately, this facilitates the acquisition of valuable insights into the robustness and adaptability of the video transmission system.
## 3 Classification of Optical Waveguide Sensors
### MZI-Structured Optical Waveguide Sensor
#### 3.2.2 Chalcogenide MZI Optical Waveguide Sensor
In 2020, a ChG/SiO\({}_{2}\) sensor was implemented on a fluorine horizontal trough waveguide runway resonator with a PCF of 44.63% to detect CH\({}_{4}\) at 3.291 um [44]. Chalcogenide glasses exhibit high transmission in the near- and mid-infrared regions, and good properties are obtained in thin films. [PERSON] et al. [104] designed a photochemical sensor based on an anti-resonant reflective optical waveguide (ARROW) structure, as shown in Figure 18, the MZI is composed of an input waveguide and divided into two parallel arms 1 and 2, which can detect gaseous pollutants, and its characteristic vibrational absorption band is located in the mid-infrared region. In this study, Ge\({}_{33}\)As\({}_{12}\)Se\({}_{55}\) and Ge\({}_{28}\)Sb\({}_{12}\)Se\({}_{60}\) materials were selected. At 1.064 um, their bulk refractive indices are approximately 2.586 and 2.707, respectively, their transparent windows are wide (0.8-16 um and 1-14 um, respectively), and they exhibit good thermal stability. Using the 1.55 um waveguide, the total optical loss on the tested sample was 6 dB.