esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of The Art
### Gaps in the State of The Art  
The state of the art review has revealed that while current approaches do provide interesting applications, they are limited in utilizing the potential of video while representing the content. For example hypervideos and interactive video navigation systems do allow users to explore video content in a nonlinear and interactive manner and there have been some attempts to allow users to explore content at different levels of detail [36; 37; 57; 58]. Multimodality of video content is still underutilized in the presentation of content to the user. In essence, the state of the art approaches are limited in either one or more of the following aspects.  
* Lack of user control in the configuration of the representation of content
* Solution is either designed to provide an overall synopsis of the video or search for some particular and not a combination of both, which affects the user experience in tasks which have evolving exploration goals  
- overly complex
* Require prior curation by humans i.e., manual effort.  
It is our contention that an exploration approach can utilize multimodal features more widely to enhance the user's exploration experience with video content. The approach should automatically curate content on-demand by representing the content in a configurable manner. By configuration, we mean that content representation may be configurable not just in terms of the amount of detail but also in the choice of combination of different modalities. Automatic curation of extracted features would minimize the dependence on prior human curation and supplementary material, and would allow users to potentially get more value out of video content. While providing the ability to change the configuration of the representation, enable users to go beyond the anticipation of designers and customize the content to their evolving exploration needs.
## 3 Analysis and Results
### Effects of Structural Parameters on Slip Anchoring Performance  
Following the finite element simulation results described above, the block-type slip exhibited superior performance. Under this configuration, the stress distributions across the slip block, casing, and slip base were relatively uniform, and the primary factor influencing the anchoring capacity of the slip was determined to be the structural parameters of the slip block. To further investigate the effects of these parameters on slip anchoring performance, the tooth apex angle and tooth inclination angle of the slip block were selected as variables for finite element simulations. The optimal combination of these angles was sought through systematic parameter variations.  
Finite element software Ansys Workbench 2022 R1 was employed to simulate the slip behavior after individually modifying each parameter. The structural parameters are shown in Figure 10. By comparing the experimental results, the influence of each structural parameter on the anchoring performance was quantified.  
Figure 10: Structural parameters for the slips.  
Figure 9: Anchoring test results of two kinds of structure slip and casing wall. (**a**) Nail-type slip. (**b**) Block-type slip.
## 3 Results
### Simplified Model
#### 3.6.2 Simplified Prediction Model of UVE-SPA-MLR  
After screening variables by SPA, the UVE-SPA-MLR model was established to simplify the prediction. The predicted scatter plot is presented in Figure 9, where \(R_{C}^{2}=0.81\), \(R_{P}^{2}=0.76\), \(\text{RMSEC}=0.56\%\) and \(\text{RMSEP}=0.47\%\). The results indicate that compared with the UVE-PLSR model, the prediction accuracy of the UVE-SPA-MLR model is slightly reduced, but its computational efficiency is significantly improved, and the model structure is more concise and easy to understand and implement. In actual production, when the model accuracy requirements are relatively loose, the UVE-SPA-MLR model can significantly improve the computational efficiency by greatly reducing the number of variables, and has good potential application value in spectrometers with limited wavelengths.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Advanced Metering Infrastructure  
In the smart grid infrastructure, the system that assumes the special role of interconnecting the distribution network with the systems of smart metering, building automation, industrial automation [56], e-mobility, and distributed energy resources is the advanced metering infrastructure (AMI). It includes data collection and analysis systems as well as energy audit systems, and provides interactive communication between customers, suppliers, utilities, and service providers [9; 41]. Among others, AMI includes specifications for network monitoring, power quality monitoring, fraud detection, load leveling, recording of capacity utilization, load/source-shedding, and remote switching procedures.  
All available wired or wireless communication systems can be used for the AMI integration (markets, transmission, bulk generation, non-bulk generation, distribution, customer, service provider, building, industry, e-mobility, and foundational support systems) [53; 54]. The main standards regarding AMI are IEC/TR 62051, IEC 61968 to 9, and AEIC Guidelines v. 3.0, as well as a vast array of parallel and even conflicting standards with many differences in functionality developed in different countries/geographic areas (e.g., China: GB/Z 20965, USA: ANSI/ASHRAE 135-2008/ISO 16484-5 BACnet), without the clear definition of distinct subsets of common semantics. This makes it difficult to define a common set of cross-cutting requirements between standards to facilitate the exchange of classified information [38].  
It should be mentioned that there is a process underway in order to extend IEC 61850 to include the ((DLMS--Device Language Message Specification)/(COSEM--Companion Specification for Energy Metering objects), thus promoting the coexistence of smart application networks. The extension will include a set of interoperability characteristics of processes such as energy measurement, load control, pricing, exchange of information, modeling data management events, etc. [7].
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### DT Application in Agricultural Water Management  
Despite the number of publications, research papers specifically addressing crop water management are very limited, amounting to only 11 papers. Figure 4 presents the crop area-wise distribution of these publications.  
This distribution shows that certain areas, like irrigation, have received moderate interest in comparison to others. Irrigation scheduling using a DT optimizes water usage by creating a virtual model of agricultural systems and employing a predictive model to regulate the plant's nutrient and water requirements. [PERSON] et al. [73] developed and tested a real-time, automated drip irrigation system using DT technology. This DT can be integrated into existing manual irrigation setups, thereby reducing the necessity for a complete overhaul of current systems. Vertical farming, which operates in controlled environments, benefits from DTs in managing soil moisture and humidity levels [74]. Hydroponics is a soil-less method of growing plants using nutrient-rich water solutions, allowing for precise control over growth conditions. This technique often leads to faster plant growth and higher yields compared to traditional soil cultivation [75]. Effective nutrient solution, pathogen, and weed control is crucial for successful hydroponic farming. Nutrient solutions are particularly important, as productivity and yield are closely tied to nutrient consumption and pH regulation [76]. Similarly, electrical conductivity (EC) and Dissolved Oxygen (DO), which are important factors [77], can be managed by DTs. Aquaponics is a combination of aquaculture and hydroponics in a symbiotic environment [78]. Various aquaponics conditions -- total dissolved solids (TDSs) in water, pH, nitrate levels, and plant growth--were modeled using simulation approaches [79]. A DT of a plant refers to a computer model that replicates the life cycle of a plant and synchronizes with the living organism under different soil, water, and weather scenarios [24]. These plant DTs leverage real-time data, mathematical and simulation models, ML algorithms, and various predictive methodologies to forecast plant behavior under diverse conditions [80]. Many studies employed process models and knowledge-based models to present a framework for developing DTs of plants [81]. Despite their efforts, these studies did not achieve predictive capabilities or effectively evaluate different scenarios.  
Except for the above, DT technology has some other applications in crop modeling, such as photosynthesis, root growth simulation, water redistribution by the roots, and canopy  
Figure 4: Breakdown of selected articles.  
estimation. However, no studies have detailed the specific procedures involved in these applications. For instance, [PERSON] et al. [82] developed a 3D model of a plant, but it lacks the incorporation of water application in the model.
# Effect of Soil Reinforcement on Tunnel Deformation as a Result of Stress Relief
## 3 Results and Analysis  
### Effect of Reinforced Soil's Young's Modulus on Tunnel Heave  
Figure 6 shows the comparison of the measured and computed normalized tunnel heave and maximum tunnel heave variation with different soil reinforcement moduli, where \(H_{\mathrm{e}}\) is the final excavation depth. As can be seen in Figure 6a, the computed values were generally in agreement with the measured data reported by [PERSON] et al. (2013) [4]. The difference between the measurements and the simulation results may be due to the chosen constitutive model. The adopted elastic-plasticity model with Mohr-Coulomb criterion could not capture the strain- and path-dependent soil stiffness. The maximum tunnel heave occurred at the tunnel crown underneath the basement center and decreased gradually as the distance away from the center of the basement increased.  
According to the Land Transport Authority of Singapore [25], the maximum tunnel movement should not exceed 15 mm (i.e., 0.17% \(H_{\mathrm{e}}\)). When none of the soil elements were reinforced, the computed maximum tunnel heave was 0.116% \(H_{\mathrm{e}}\), which overestimated the measured tunnel heave by 50%. In this study, both the computed and measured maximum tunnel heave were within the recommended allowable limit. Because the soil area S1 was reinforced with a Young's modulus that varied between \(E_{\mathrm{s}}\) and \(5E_{\mathrm{s}}\), the computed tunnel heave decreased greatly from 0.116% \(H_{\mathrm{e}}\) to 0.095% \(H_{\mathrm{e}}\), which overestimated the tunnel heave by 32%. However, the measured tunnel heave was overestimated by 23% as the reinforced soil's Young's modulus increased to \(10E_{\mathrm{s}}\). Thus, it is obvious that soil reinforcement underneath the basement reduced the tunnel heave. Further increase of the reinforcement's Young's modulus reduced the tunnel heave, but with a decreasing tendency. For comparison, the overestimated maximum tunnel heave is listed in Table 2. The basic value is the result obtained from the centrifuge model test.  
Figure 5: Reinforcement scheme for basement bottom.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cases** & \
\begin{tabular}{c} **Young’s Modulus of** \\ **Reinforced Soil, \(E_{\tau}\)** \\ \end{tabular} & **Max. Tunnel Heave** &
\begin{tabular}{c} **Max. Change in Tunnel** \\ **Diameter** \\ \end{tabular} \\ \hline No reinforcement & \(E_{\tau}=E_{\rm S}\) & \(+50\%\) & \(-35\%\) \\ Reinforcement S1 & \(E_{\tau}=5E_{\rm S}\) & \(+32\%\) & \(-39\%\) \\ Reinforcement S1 & \(E_{\tau}=10E_{\rm S}\) & \(+23\%\) & \(-39\%\) \\ Reinforcement S2 & \(E_{\tau}=5E_{\rm S}\) & \(+31\%\) & \(-39\%\) \\ Reinforcement S3 & \(E_{\tau}=5E_{\rm S}\) & \(+28\%\) & \(-40\%\) \\ \hline \hline \end{tabular} Note: The –\(-\)ve sign indicates an underestimation and the +ve sign indicates an overestimation.  
\end{table}
Table 2: Comparison of computed and measured values.  
Figure 6: Measured and computed (**a**) tunnel heave and (**b**) maximum tunnel heave with different reinforced soil Young’s moduli.
## 4 Evaluation and Implementation
### Discussion  
The representation noise analysis and the proposed adaptive scaling algorithm is based on a general stochastic bit stream and not specialized for the SFIR filter, which makes it possible to extend it to many other stochastic computing-based DSP systems, such as fast Fourier transform (FFT), discrete wavelet transform (DWT), and support vector machine (SVM). The proposed adaptive scaling method shall be considered in these modules in the future.
# Decomposition of Flavonols in the Presence of Saliva
## 3 Results and Discussion
### Stability of Flavonols in Saliva Solution
The results obtained for rutin and quercetin are in good agreement with previous studies, which showed that these two glycosides were hydrolyzed very slowly or were resistant to salivary hydrolysis [12; 31]. On the other hand, lack of spiraeoside hydrolysis was inconsistent with experiments which suggested that glucose conjugates are rapidly hydrolyzed to corresponding aglycones [11; 12]. However, in the same study, a large interindividual variability in hydrolysis rate was also observed. According to the aim of our study, it is interesting to note that sugar moiety attached to flavonoid inhibits C-ring decomposition. The presented studies allow creating the general scheme of flavonoids' degradation in the presence of two oxidation agents: H\({}_{2}\)O\({}_{2}\) and saliva (Figure 5). Table 2 presents the summarized products detected in samples after oxidation with H\({}_{2}\)O\({}_{2}\) and saliva. Figure 4: Changes in amount of quercetin (22.8 min) and protocatechic acid (4.5 min) during incubation of quercetin with saliva. Overall, decomposition of flavonoids is based on the cleavage of heterocyclic C-ring, with no changes in hydroxyl and methoxy substituents in A-ring and B-ring. Decomposition of myricetin and quercetin leads to the formation of gallic and protocatechuic acid, respectively, which exhibit high redox potential due to the presence of adjacent hydroxyl groups attached to the aromatic ring [43]. Contrarily, vanillin and 4-hydroxybenzoic acids were found to be less efficient in radical neutralization reaction [43]. As mentioned above, the human oral cavity contains numerous and diverse microorganisms as commensals. It is known that microbiota can split the flavonoids by opening the heterocyclic ring and releasing simpler aromatic compounds, such as hydroxyphenylacetic acids from flavonoids, which could be further metabolized to derivatives of benzoic acid [44]. Taking that into consideration, we decided to incubate quercetin, as a representative flavonoid, with saliva containing microorganisms (filtration through 0.45 \(\upmu\)m filters) and without microorganisms (filtration through 0.22 \(\upmu\)m sterile filters). In both cases, quercetin was degraded to the same extent during its incubation to protocatechuic acid, as a dead-end product. This indicates that the decomposition of quercetin is independent of the presence of microbiota.
## 4 Experimental Results
### Comparison of Proposed JD-TV with Conventional Video Compression Schemes
As mentioned earlier, the proposed JD-TV make use of keyframes that contains larger set of measurements (higher subrate) to improve the non-key frames with lower measurement set (low subrates). Thus, the key frames having larger set of measurements superimposes the correlated smaller measurements set encompass by non-key frames. This, helps to decrease the non-key frame prediction errors, that arises due to smaller measurements set and predict a better version of non-keyframes. Further, DISCOVER and H.264 (I-P-P) schemes use the feedback channel to improve the reconstruction of the keyframes.
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 3 Proposed Method
### Enhancement of Saturation Component  
In HSV space, the saturation component determines the overall color impression of the image. When enhancing low-light images, the enhancement of brightness will also cause the saturation to change accordingly. Processing only the brightness component will cause the image to be altered and appear desaturated, with color imbalance [25], causing the image to look overexposed and inconsistent with human visual effects. Therefore, an adaptive stretching method is put forward to alter the saturation component. The expression is as follows:  
\[S_{out}=\frac{S_{in}-low_{value}}{high_{value}-low_{value}}, \tag{18}\]  
where \(S_{in}\) is the input saturation component; \(low_{value}\) and \(high_{value}\) are minimum and maximum boundaries of the saturation component stretching, respectively, which are calculated based on the percentile of the saturation distribution. The percentile indicates the percentage of the values in the data that are less than or equal to the initial value and can be customized by selecting the saturation value of the percentile that you want to retain. As can be seen in Figure 7, after adaptive stretching, the saturation range is extended to a wider range, and then the stretched image has richer color variations and light and dark levels.  
Figure 6: Results of different algorithms. (**a**) Original image; (**b**) CLAHE algorithm; (**c**) WWGIF algorithm; (**d**) CLAHE-WWGIF algorithm.
## 2 Principe of SOA-Induced Nonlinearity, FFE-Based LUT and 2 nd-Order VNLE
### FFE-Based LUT  
For each \(2M+1\) consecutive symbols \(x(k-M:k:k+M)\) in the original PAM-\(K\) signals \(x(k)\in\{0,\ 1,\ 2,\ 3,\ \dots,\ K-1\}\), it can be mapped to an LUT index and the mapping relationship can be expressed as:  
\[i=\sum_{m=-M}^{M}K^{m+M}x(k-m) \tag{6}\]  
\(2M+1\) is defined as the memory length of the LUT. The \(2M+1\) symbols having the same form compose a pattern, and the same pattern is mapped to the same index. In addition to initializing an LUT of length \(K^{2M+1}\) to all zero, another table \(C\) with the same length as LUT is also needed to count the occurrence number of the same pattern. The error between the middle symbol \(x(k)\) and the corresponding FFE recovered symbol \(y(k)\) is calculated and can be written as:  
\[e(k)=y(k)-x(k) \tag{7}\]  
The error is then accumulated to the index \(i\) of the LUT, and the index \(i\) of \(C\) is increased by 1:  
\[\text{LUT}(i)=\text{LUT}(i)+e(k) \tag{8}\]  
\[C(i)=C(i)+1 \tag{9}\]  
A sliding window is utilized to slide from the start to the end of \(N\) consecutive symbols in the transmitted sequence to select \(2M+1\) consecutive symbols, and then the above steps repeat. \(N\) is defined as the training sequence length of the LUT. As the sliding window moves to the end, the final LUT can be obtained by the average value of the sum of errors stored in LUT, which is described as:  
\[\text{LUT}(i)=\frac{\text{LUT}(i)}{C(i)} \tag{10}\]Figure 1 shows the brief process of training an LUT. The completed LUT is placed at the transmitter to pre-compensate the transmitted signal. Similarly, an LUT index \(i\) can be mapped from the real original transmitted sequence, and the final transmitted symbol is the difference between the original symbol and the error stored in the index \(i\) of the LUT, which is:  
\[X^{\prime}(k)=X(k)-\text{LUT}(i) \tag{11}\]
###### Abstract  
To improve the reliability of power supply in the case of the fault of distribution system with multiple distributed generators (DGs) and reduce the influence of node voltage fluctuation on the stability of distribution system operation in power restoration, this paper proposes an island partition strategy of the distribution system considering the protection of vulnerable nodes. First of all, the electrical coupling coefficient of neighboring nodes is put forward according to distribution system topology and equivalent electrical impedance, and the power-dependence relationship between neighboring nodes is calculated based on the direction and level of the power flow between nodes. Then, the bidirectional transmission of the coupling features of neighboring nodes is realized through the modified PageRank algorithm, thus identifying the vulnerable nodes that have a large influence on the stability of distribution system operation. Next, combining the index of node vulnerability, an island partition model is constructed with the restoration of important loads as the primary goal. In addition, the mutually exclusive firefly algorithm (MEFA) is also proposed to realize the interaction of learning and competition among fireflies, thus enhancing the globally optimal solution search ability of the algorithm proposed. The proposed island partition method is verified with a Pacific Gas and Electric Company (PG and E) 60-node test system. Comparison with other methods demonstrates that the new method is feasible for the distribution system with multiple types of distributed generations and valid to enhance the stability and safety of the grid with a relatively power restoration ratio.  
distributed generator; distribution system; island partition; vulnerable node; mutually exclusive firefly +
Footnote †: journal: Applied Soft 2017
## 3 Experimental Research and Discussion
### Static Characteristic
#### 3.3.3 Influence of Initial Volume  
The initial volume of the HFAM sleeve chamber is an essential factor affecting the mechanical characteristics of the system. The initial volume can be changed by rotating the liquid replenishment shaft. The influences of different initial volumes on the mechanical characteristics of the diaphragms are shown in Figure 20. The set displacement of the CD is 0, and the rotation angle of the liquid replenishment shaft ranges from 0 to 360 degrees, with an interval of 180 degrees. The control procedure for the VCM is consistent with the static experiments of different diaphragm displacements in Section 3.3.1.  
In the static experiments of different initial volumes, when the displacement of the RD is 6 mm, and the rotation angle is 360 degrees, the maximum force of the RD is 23.4 N, and the maximum force of the CD is 59.5 N.  
The forces of the diaphragms increase with the increase in the RD displacement and increase with the increase in the initial volume. After the least square polynomial fittings, the average slope of the RD is 3.045 N/mm, the maximum root mean square error is 0.731, and the maximum slope deviation is 10.81%. The average slope of the CD is 6.939 N/mm, the maximum root mean square error is 1.822, and the maximum slope deviation is 5.17%. When the displacement of the RD is 6 mm, the liquid replenishment shaft is rotated in 180-degree intervals, the average force of the RD increases by 2.7 N, and the average force of the CD increases by 8.1 N.  
Figure 19: Displacement ranges of diaphragms with different FARs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Relationships Between RD Displacement \(\boldsymbol{x_{1}}\) and CD Displacement \(\boldsymbol{x_{2}}\)** & **FAR \(\boldsymbol{A}\) Ranges** \\ \hline \(x_{2}\geq 0.01885{x_{1}}^{4}-0.3043{x_{1}}^{3}+1.788{x_{1}}^{2}-4.314{x_{1}}+4.021\) & \(A<1\) \\ \(0.008885{x_{1}}^{2}+0.07746{x_{1}}+0.009964\leq x_{2}<0.01885{x_{1}}^{4}-0.304 3{x_{1}}^{3}+\) & \(1\leq A<1.5\) \\ \(1.788{x_{1}}^{2}-4.314{x_{1}}+4.021\) & & \\ \(0.006457{x_{1}}^{3}-0.03382{x_{1}}^{2}+0.0583{x_{1}}-0.03243\leq x_{2}<0.00888 5{x_{1}}^{2}+\) & \(1.5\leq A<2\) \\ \(x_{2}\leq 0.006457{x_{1}}^{3}-0.03382{x_{1}}^{2}+0.0583{x_{1}}-0.03243\) & \(2\leq A\leq 2.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Displacement ranges of diaphragms with different FARs.  
There is hysteresis in the axial reciprocating motion of the diaphragm. The maximum hysteresis of the RD is 2.23 N, 2.09 N, and 2.12 N, respectively, and the maximum hysteresis deviation is 6.28%. The maximum hysteresis of the CD is 6.49 N, 6.59 N, and 6.72 N, respectively, and the maximum hysteresis deviation is 3.42%.