esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Restoration Approach for FSCMR Wall Panel Structure Using the Damage Index  
1. For \(D_{i}\) values below 0.25, where the structure has not yet yielded, the cracks that occur are barely noticeable. Therefore, the structure as a whole does not require substantial functional or exterior restoration to ensure its normal operation.
2. As \(D_{i}\) increases from 0.25 to 0.4, the structure yields. Cracks increase in large quantities, blocks peel off and further exfoliate, and the reinforced steel begins to yield. Structural function can only be guaranteed by necessary restoration. In this study, it is suggested that block replacement can mitigate deterioration in bearing capacity. In the case of visible beam/column cracks, a reinforcement approach for reinforced concrete structures shall be applied.
3. As \(D_{i}\) increases into the range from 0.4 to 0.7, the structure has reached the peak of the failure process, where structural deterioration proceeds but slows down. As block exfoliation continues, the cracking and deformation of beams and columns worsens. However, the structure remains restorable, and the approach applied in stage (2) remains effective.
4. As \(D_{i}\) exceeds 0.7, the structure is gravely damaged. Large-scale exfoliation and sliding at the layer interface mean that the structure, though it has yet to collapse, is no longer serviceable. Technically, the structure can be repaired, but the restoration process is hardly economical.  
Generally, as \(D_{i}\) fluctuates between 0.4 and 0.7, the structure goes through its mid-term, i.e., the stage characterised by the rapid development of accumulative damage. At this point, it is recommended that the force-bearing features of the blocks be leveraged as the first seismic line of defence. Therefore, block replacement can be used to restore the seismic resistance of the wall panel [27]. The move enhances the CMR wall panel structure with energy-efficient blocks, with the features listed below displayed:  
1. The replacement does not generally add to the dead weight or seismic response of the wall panel structure, thereby avoiding problems associated with foundation processing.
2. The restoration requires no support of the beams or to the floor slab, streamlining the operation process and easing construction procedures.
3. The material applied can be aerated concrete block, fly-ash block, clay bricks, hollow bricks, etc. Given the accessibility and the applicability of the materials, restoration stands out for its cost efficiency.
4. Flexibility in the replacement location means that the restoration does not alter the spatial arrangement, which is identical to that existing prior to replacement.  
As \(D_{i}\) increases from 0.25 to 0.7, any crack or deformation of the beams/columns can be repaired in accordance with the general enhancement procedures for a reinforced concrete structure.  
Figure 14: Failure stage of the FSCMR wall panel. (**a**) Convex reinforced steel at the bottom of the FS column Concrete exfoliation; (**b**) Block crushing due to exfoliation. Deformation of the structural columns at both sides of the hole; (**c**) Mortar crack and layer sliding.
## 3 Blockchain
### Public vs. Private Blockchain  
Adhering to the view for open access to everyone, many widely used blockchains (Bitcoin, Ethereum and Dash) are public networks. Public blockchain networks are characterized as permissionless, where anyone can join, read and/or write data, create smart contracts or even run a node within them, ensuring one hundred percent transparency as well as a high level of anonymity. Public networks are recommended for entities involved in crypto-economics. In contrast to public blockchain networks, private networks restrict either participant or validator access as a classic closed ecosystem where all peers are well defined and only preapproved entities can run nodes. Following a business-to-business approach, many companies use private blockchain networks in order to benefit from this technology without sacrificing their autonomy. Private blockchains, in contrast to public ones, typically use a type of consensus other than PoW, and might aim at keeping certain information private from the public. In summary, public blockchain networks enjoy freedom in decentralization, whereas private blockchain networks enjoy freedom in configurational flexibility [77]. Hybrid approaches also exist--these are called permissioned blockchain systems. Different flavors and configurations of permissioned blockchain systems exist, but typically the consensus process is controlled by a pre-predefined list of participants, and users cannot participate without permission. Access to the full information of transactions on the blockchain might be restricted, depending on the user role.  
Table 2 provides an overview of the types of blockchain used in different agriculture traceability systems. The majority of research works have used public blockchain types, in association with either Ethereum or Hyperledger, whereas a smaller percentage prefer the adoption of permissioned or fully private blockchains. Nonetheless, it is notable that many research studies attempt to make a more theoretic contribution and do not explicitly make reference to a specific blockchain implementation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Public vs. Private Blockchain** & **Blockchain Implementation** & **Research Works** \\ \hline \multirow{6}{*}{Public} & Ethereum & [28; 29; 30; 31; 52; 56; 62; 63] \\  & Hyperledger Sawtooth & [54] \\  & Hyperledger (other than & \\  & Sawtooth) & [34; 49] \\  & Ethereum or Hyperledger & \\  & Sawtooth & \\  & Ethereum or Hyperledger (other & \\  & than Sawtooth) & \\  & Not specified & [10; 22; 24; 26; 36; 39; 41; 42; 44; 46; 50; 55, 59; 60; 64; 65; 67; 69; 70; 72; 73; 78; 79] \\  & Ethereum & [51] \\ Private & Ethereum or Hyperledger & \\  & Sawtooth & \\  & Not specified & [26; 32; 47; 50; 66] \\  & Ethereum & [27; 40; 48] \\ Permissioned (Hybrid) & Hyperledger Sawtooth & [37; 45; 57; 58] \\  & Hyperledger Fabric & [35; 53; 68] \\  & Not specified & [25; 61; 71] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Literature survey—use of different blockchain types and implementations in agriculture traceability systems.
## 2 Materials and Methods
### Antibacterial Assay  
Ethanol extracts (leaves and stem bark) and crude saponin extracts of _V. doniana_ and _P. macrophylla_ were assayed for antibacterial activity using the agar well diffusion technique. A stock solution of 100 mg/mL concentration of each extract was prepared, from which varying concentrations (50, 25, 12.5, and 6.25 (mg/ml)) were achieved. About 100 \(\mu\)L of standardized test bacterial suspension (1.5 \(\times\) 108 CFU/mL) was seeded onto sterile Mueller-Hinton agar plates (Conda, Madrid, Spain) and spread evenly to achieve a confluent lawn of microbial growth [3]. The plates were left to dry, and a sterile cork borer 6 mm in diameter was used to bore wells. Afterwards, about 100 \(\mu\)L of each plant extract was introduced into the well. Plates were incubated at 37 \({}^{\circ}\)C for 24 h, and the bacterial inhibition zone diameter was measured on the plates to the nearest millimeter (mm). Ciprofloxacin (Oxoid, Hampshire, UK) served as a positive control, and experimentation was performed in triplicate assays.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation  
This section presents the experimental evaluation, explains the methodology used, and presents the results obtained for each algorithm used in the experiments.  
The experiments used algorithms implemented in Python language chosen for its extensive collection of libraries designed for efficient data processing. The algorithms' execution was on a system running Ubuntu 22.04.3 LTS, equipped with an Intel(R) Core (TM) i7-8565U CPU @ 1.80 GHz and 8 GB of RAM. This setup provided a robust computing environment for the experimental process.  
The categorization of the data reduction algorithms is according to the proposed taxonomy. The selected lossy data reduction algorithms are Random Projection, Quantization, Linear Regression, Principal Component Analysis, K-Means, and Simple Random Sampling. The selected lossless data reduction algorithms are Delta Encoding and BZip2.  
The best results for each algorithm are shown in bold, selected from those that effectively reduced the original size. In the processing time per MB column, datasets that increased in size are shown in _italics_.
# Goal Oriented Time Adaptivity Using Local Error Estimates
## 5 Numerical Results
### Test Problem
#### 5.1.4 Discussion  
Considering the global errors, we see DWR does not yield notably better grids, but it is significantly slower for small tolerances as it needs to solve adjoint problems.  
In the case of \(k=-1\) and \(j(t,\mathbf{u})=u_{2}\), we have comparable timesteps for all local error based methods, since the local error in the second component is dominant. These methods have grids of nearly identical quality and require the same computational effort.  
For \(j(t,\mathbf{u})=u_{1}\), we get larger timesteps using the goal oriented method, particularly for \(k=-100\). Due to the off-diagonal term in \(B\), the flow map shifts errors from the second component, the zero set of \(j\), to the first component. This leads to comparable performance for \(k=-1\) and a performance gain for \(k=-100\). While not immediately evident, for \(k=-1\), we have \(j(t,\mathbf{\phi}_{-}(t,\mathbf{u}(t)))=\frac{1}{2}\mathbf{\mathrm{e}}^{-t}(t-1)\) and do not fulfill Equation (27), which is needed for convergence in the QoI. This does, however, not cause computational problems here, but we see notably worse performance when using only the time-integration error estimate.  
Figure 1: Verification of Theorem 2 using 4-th order schemes for the goal oriented adaptive method on Problem (33) for \(k=-1\) and functionals \(j(t,\mathbf{u})\) as shown in the legend.  
Comparing the computational efficiency with the grid qualities, we see the goal oriented method using only the quadrature estimate has better performance results than other methods with the same grid quality. This is because we reuse values computed for \(\mathcal{J}_{h}(\mathbf{u}_{h})\).  
Figure 2: Performance comparison of the various methods for Problem (33). _Top:_\(k=-1\) and \(j(t,\mathbf{u})=u_{1}\), _center:_\(k=-1\) and \(j(t,\mathbf{u})=u_{2}\), _bottom:_\(k=-100\) and \(j(t,\mathbf{u})=u_{1}\).
## 3 Results and Discussion
### Characterization of Microcapsules
#### 3.2.3 Particle Size  
Based on Figure 3, the particle sizes of spray-dried microcapsules were between 1 and 100 \(\upmu\)m, and most of them were located at 10-30 \(\upmu\)m. The polymer disperse index (PDI) was 0.272, indicating that the particle size distribution was relatively uniform. Colloidal particles in this size range tended to show softness [29] and smoothness [30]. The difference in particle size could be due to the number and volume of the cores in the multicore microcapsules.  
Figure 2: Scanning Electron Microscope (SEM) images of the spray-dried microcapsules.
## 3 Results and Discussion
### EMI Shielding of BP/CFRP Hybrid Composites
At elevated frequencies, instead of flowing uniformly, the current tends to concentrate on the surface of copper, thereby restricting the penetration of electromagnetic waves. The current density decreases exponentially as the wave penetrates the material, and the depth at which it falls to approximately 37% of its surface value is known as the skin depth. The skin depth decreases with increasing frequency. As a result, the effective area available for current flow is reduced, leading to a decrease in EMI SE. Additionally, BP offers a crucial advantage over metallic layers by seamlessly bonding with carbon fiber lamina without compromising mechanical performance, thereby rendering it suitable for high performance in various aspects [17]. Figure 16: Shielding effectiveness in the 50 MHz–12 GHz frequency range for (**a**) buckypaper (BP), (**b**) [90,0], (**c**) [BP90,0], (**d**) [90,BP0], and (**e**) [90,0,BP]. Figure 17: Effect of py orientation on the shielding effectiveness: (**a**) [90,BP,0], (**b**) [45,BP,0], and (**c**) [0,BP,0]. Figure 18: Effect of buckypaper (BP) on the shielding effectiveness of a 12-ply laminate showing the percentage enhancement in SE by incorporating BP.
## 2 Results and Discussion
### Removal of 2,4-DCP
The covalent binding between the lacase and the PIL-NH2 led to the stronger conformation of the lacase, by which the higher temperature was able to make the lacase-activated groups more active, benefiting the diffusion and electron transfer of the substrate in the material channel. In addition, the synergistic mechanism between the lacase and the support enabled the maximum removal of 2,4-DCP by PIL (2)-NH2-GA-Lac at 50 \({}^{\circ}\)C. However, when the temperature continued to increase, this was not conducive to the adsorption and degradation of 2,4-DCP by PIL (2)-NH2-GA-Lac. This was because constant high temperatures will cause protein inactivation and greatly reduce the catalytic oxidation capacity of lacase [57]. The trend of the bar also supports this view. The increase in 2,4-DCP removal decreased with time, reaching its minimum value at 60-70 \({}^{\circ}\)C.  
The cycling properties of the immobilized lacase system are shown in Figure 5d. As expected, the relative removal efficiency of the immobilized lacase system did not decrease significantly as the number of batches increased. After seven cycles, the 2,4-DCP removal was still maintained at more than 80%. Table S1 (in Supplementary Materials) summarizes recent studies on enzyme immobilization and its application in the degradation of phenolic pollutants. Compared with other supports, PILs have higher mechanical properties, more suitable specific surface area and superior circulation performance. They conform with the support selection principle of enzyme immobilization and provide ideas for the further  
Figure 5: Effect of time and concentration of 2,4-DCP (**a**), pH (**b**), and temperature (**c**) on the efficiency of removal of phenolic compounds and reusability of the immobilized lacase (**d**). Conditions: 2,4-DCP (1 mg/L), PIL-NH2-GA-Lac (10 mg/mL), pH = 3 (**b–d**), 20 \({}^{\circ}\)C (**a,c,d**), and 12 h (**d**). development of enzyme immobilization. Compared with the typical reports listed, the PIL (2)-NH\({}_{2}\)-GA-Lac system used in this paper showed comparable or better operational stability for the degradation of phenolic pollutants. This can be attributed to the strong binding between the lacase and the support, which helped avoid leakage [58]. The above results illustrate that the reusability of the current immobilized lacase system showed great potential for the system's large-scale application in the future.
## 1 Introduction
The wind turbines have experienced an improvement from the squirrel-cage asynchronous to the doubly-fed induction generator (DFIG) wind turbine, and then to the direct-driven permanent magnet synchronous generator (D-PMSG) wind turbine. Different types of wind turbine generators have different physical and electromagnetic structures. When establishing a single wind turbine generator, the wind turbine-driven train system, generator, electronic devices, as well as control system should be considered. Meanwhile, the scale of the wind farm, the distribution of wind turbines, the turbine types, and the research target should be considered comprehensively when aggregating the wind farm into a multi-machine aggregation model. Figure 4: The framework of the wind farm modeling with the aggregating method. (3) Modeling of WTG transmission aggregation. The layout of wind farms, the size and type of conductors, and the delivery method (overhead or buried cables) all have an influence when computing the aggregated output of a wind farm at its aggregation bus [20; 21]. As the scale of the wind farm is expanded, the computation complexity increases. The aggregation of the WTG transmission is useful not only for simplifying the equivalent model, but also for wind farm expansion planning. The objective of this paper is to give a understanding of recent aggregation modeling methods for wind farms, including the modeling of wind speed, WTG, and the transmission system inside a wind farm. Different models or modeling methods, as well as their characteristics and applications, are reviewed, which could help researchers in dealing with wind farm aggregation modeling issues. The remaining sections of this paper are structured as follows. In Section 2, different types of wind speed models are introduced. In Section 3, the wind turbine and the generator are considered as the two major parts of the WTG. A single-machine model and multi-machine model of different wind turbine generators are discussed. The WTG transmission system modeling method is introduced in Section 4, and the conclusion is drawn in Section 5.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 3 Experiments Results and Analysis
### Experiments of Step Fault  
#### 3.2.1 The Comparison of Different Parameters  
According to the analysis in Section 2, the parameters of the channel filter are not unique. Therefore, their comparison is made by using the step fault amplitudes of \(5^{\circ}\)/h and \(0.5^{\circ}\)/h in order to verify the correctness of the method proposed in this paper. Two fault amplitudes are added to the laser gyroscope. The parameters selected for the first-order and second-order channel filters are 0.434 and 3.85 (from Table 5) respectively.  
Figure 5 shows that the fault detection function of the original channel filter has never exceeded its threshold value, indicating that no fault has been detected. Figure 6 shows that the fault detection function exceeds the threshold value after 500 s, indicating that the first-order channel filter has detected a fault. Furthermore, its fault isolation function has been separated after 500 s, indicating that this channel  
Figures 8-10 show that the original channel filter and the first-order channel filter cannot detect a fault that has such a small amplitude. Figure 10 show that the fault detection function has exceeded the threshold value before 500 s, indicating that it has detected a fault.  
Figure 6: First-order channel filter (the fault amplitude is \(5^{\circ}/\mathrm{h}\)). (**a**) Fault detection; (**b**) Fault isolation.  
Figure 7: Second-order channel filter (the fault amplitude is \(5^{\circ}/\mathrm{h}\)). (**a**) Fault detection; (**b**) Fault isolation.  
Figure 8: Original channel filter (the fault amplitude is 0.5\({}^{\circ}\)/h). (**a**) Fault detection; (**b**) Fault isolation.  
Figure 9: First-order channel filter (the fault amplitude is 0.5\({}^{\circ}\)/h). (**a**) Fault detection; (**b**) Fault isolation.  
Figures 5-10, using the same sets of parameters of the channel filters, show that the first-order channel filter and the second-order channel filter can detect and isolate the fault that has a large amplitude. But the original channel filter and the first-order channel filter cannot detect the fault that has a small amplitude; only the second-order channel filter can.
# Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM
## 3 Foreign Object Detection Method Based on GhostNet-CBAM-YOLOv4 for Conveyor Belts
### CBAM Attention Mechanism  
In order to cope with the complex and harsh environment under the mine and improve the feature extraction capability, the spatial and channel attention mechanisms were inserted into the three effective feature layers of the improved YOLOv4 model's backbone output, and its network structure is shown in Figure 5.  
The CBAM attention model consists of two parts: channel and space. The channel attention module pools the incoming feature map F for global average pooling and global maximum pooling, respectively. The two feature layers obtained from pooling were summed and activated by the sigmoid function to generate the final features required for channel attention.  
The channel attention mechanism could be expressed as,  
\[M_{C}(F)=\sigma(MLP(Avgpool(F))+MLP(Maxpool(F)))=\sigma(w_{1}\Big{(}w_{0}\ \Big{(}F_{avg}^{c}\Big{)}\Big{)}+w_{1}\ (w_{0}(F_{max}^{c})) \tag{4}\]  
In this equation \(M_{C}\) denotes the channel attention in the model, \(\sigma\) represents the sigmoid activation function, \(MLP\) represents the multilayer perceptron, \(w_{0}\)  and \(w_{1}\) represent the weight matrix, \(F_{avg}^{c}\) and \(F_{max}^{c}\) represent the global average pooling vector and the global maximum pooling vector, and \(Avgpool\) and \(Maxpool\) represent the feature map in the module for average pooling and maximum pooling.  
The feature map F output from the channel attention module was used as the input feature of this module. The spatial attention module performed global average pooling  
Figure 4: GhostNet-CBAM-YOLOv4 structure.  
Figure 5: CBAM attention module.  
and global maximum pooling operations on the input results. The two generated feature layers were stitched onto the channel for the splicing operation, downscaled using convolution and sigmoid activation. Finally, the features required by the spatial attention module were generated.  
The spatial attention mechanism could be expressed as,  
\[M_{-}S(F)=\sigma\big{(}f^{\wedge}(7\times 7)([\text{Avgpool}(F)];(\text{Maxpool}(F)) )=\sigma\big{(}f^{\wedge}(7\times 7)\big{(}[F_{-}\text{avg}^{\wedge}\;S;F_{-}\max \;^{\wedge}S]\big{)} \tag{5}\]  
\(M_{s}\) denotes the spatial attention in the convolutional block attention model and \(f^{\gamma\times 7}\) represents \(7\times 7\) convolutional operations.
## 2 Developed 3-D Profilometry Using Dual Sensing  
In structured fringe projection, the angle between the projection and the sensing detection plays an important role in determining the reflecting direction. Figure 1a shows the fringe image captured from a semiconductor wafer surface detected using a laser-etched microscale grove with the viewing angle set at zero. As can be seen, the groove area is rather clear but the remaining wafer specular area is almost non-detectable. Therefore, the groove area can be seen with an adequate exposure time because diffuse light is generally weak. With larger viewing angle, the wafer surface becomes clearer with a better image contrast (see Figure 1b,c). Illustration shown from Figure 1b-d represents a transition in imaging with increase in viewing angle. When the reflectance relation follows the light reflectance rule, the fringe contrast on the wafer surface reaches its maximum as illustrated in Figure 1e. Figure 1f represents the case when the sensor becomes over saturated owing to excessive exposure time. Figure 1 illustrates the critical reflectance condition of the tested surface under inspection. Figure 2 shows the general reflection model of diffuse and specular components when the light is incident from an angle.  
Figure 1: Images of wafer surface etched with a laser-etched groove and captured at different viewing angles: (**a**) viewing angle \(\pm\) 1\({}^{\circ}\), exposure time 420 ms; (**b**) viewing angle 12 \(\pm\) 1\({}^{\circ}\), exposure time 200 ms; (c) viewing angle 25 \(\pm\) 1\({}^{\circ}\), exposure time 80 ms; (**d**) viewing angle 45 \(\pm\) 1\({}^{\circ}\), exposure time 20 ms; and (e) viewing angle 45 \(\pm\) 1\({}^{\circ}\), exposure time 50 ms.  
Figure 2: Reflection model of diffuse and specular components.  
To resolve the above issue, this article presents a new optical measuring configuration to capture both of specular and diffuse light reflecting from wafer surfaces. A dual sensing strategy is proposed to detect the light reflection from wafer surfaces when structured fringe projection is used to measure and reconstruct 3-D surfaces. The proposed system design and measuring principle are detailed in the following sessions.