esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrafast Optical Signal Processing with Bragg Structures
## 4 Optical Signal Processing in Chirped Bragg Structures
In this case, the velocity of the pulse is equal to 0.005 c. Further simulations demonstrate that the velocity of the moving soliton increases with a further increase of \(I_{P}\). Since the standing pulse is unstable, a defect is introduced between the chirped and uniform BG segments, as shown in Figure 6a. By introducing such a defect, the stability of pulse trapping can be  
Figure 6: **(a)** The relation between wavenumber detuning and propagation distance in the concatenated BG, which does not include the local defect; **(b)** The evolution of the pulse with peak intensity equal to (d\({}_{1}\)) 0.65 GW/cm\({}^{2}\), (d\({}_{2}\)) 2.26 GW/cm\({}^{2}\), (d\({}_{3}\)) 2.29 GW/cm\({}^{2}\), and (d\({}_{4}\)) 2.30 GW/cm\({}^{2}\)[14]. much improved. Additionally, the intensity range of achieving a standing pulse can be made several times larger than without the defect (Figure 7). To control the trapping position, a periodic set of defects is introduced into the uniform part of the BG structure, as shown in Figure 8, which shows \(\delta_{d}(z)\) as a function of \(z\). Here, \(\varepsilon\) is the strength of each defect and \(d_{w}\) is its width [15]. The results of the simulations show that, at different input-pulse intensities, the pulse can be trapped at different defects. Such a defect array can also trap several pulses at different defects (Figure 9). In addition to that, the trapped pulses can interact with each other. Figure 8: (**a**) A schematic of the system, built of the linearly chirped BG segment (on the left-hand side) followed by the uniform grating with an inserted periodic array of local defects. The system can be described by parameters (\(S\); \(d_{w}\); \(\varepsilon\)), and the definition of \(S\), \(d_{w}\), and \(\varepsilon\) are shown in (**a**); (**b**) Relations between the trapping position and the initial pulse’s intensity \(I_{p}\) for (\(S\); \(d_{w}\); \(\varepsilon\)) = (0.132 cm; 50 \(\upmu\)m; 0.04) and (0.132 cm; 50 \(\upmu\)m; 0.06) (blue and red curves, respectively) [15]. Figure 7: Simulations of the pulse propagation in the concatenated BG, with a defect located at the conjunction of the chirped and uniform segments, for different injected peak intensities: (**a**) 1.94 GW/cm\({}^{2}\); (**b**) 2.14 GW/cm\({}^{2}\); (**c**) 2.33 GW/cm\({}^{2}\); and (**d**) 2.59 GW/cm\({}^{2}\)[14]. An all-optical femtosecond soliton diode can also be designed, using a tailored BG heterojunction. Highly nonreciprocal transmission, with an extinction ratio of up to 120, was produced by simulations of this setting (see Figure 10).
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 2 Background and Overview of WSNs
### Residual Energy
In this protocol, the formation of the cluster is done in circle shapes with equal radius for each, this circle-shape of clusters increase the transmission efficacy. The first cluster is formed and a sensor node that has the maximum energy level becomes the center node of it. Other clusters will be formed around the first one, and the node that is positioned in the center of each cluster becomes the center node of it. The overall structure of a network in this protocol consists of partition clusters, the communication region, and the four partition clusters structure. Each partition cluster has a central nod at the center of it, and four gates nodes to transmit data and monitor the location of mobile sink. If the mobile sink is detected by one of the gate nodes, it sends a message to the others to convert their status to sleep. After that, they will be in a ready state and waiting for a message to wake up in case the mobile sink reaches close to the partition cluster. When they receive a message, they will start sending data to the mobile sink via one of the gate nodes. After finishing the transmission process, and when the mobile sink leaves the partition cluster, nodes will go back to the deep sleep status. Comparable to IB-LEACH, Partition-LEACH increases network lifetime because of the equal split of energy. Furthermore, this protocol is suitable and efficient for large area networks due to the good performance in terms of data transmission. The main drawback of Partition-LEACH is the extra overhead in messages due to the existence of the mobile sink. A new method to reduce fault tolerance in WSNs, called fault tolerance LEACH (FT-LEACH) [95], has also been proposed. Fault tolerance is a problem in LEACH protocols because it affects performance. Different from IB-LEACH and Partition-LEACH, this method concentrates on fault tolerance to increase efficiency. Depending on the energy values, the fault is detected. FT-LEACH considers the cluster formation in two ways; global and local re-clustering. The local re-clustering will be performed to repair the cluster, and global re-clustering is performed to repair the network. If the fault is in a CH, local re-clustering will be performed after deleting the CH. This protocol is better than LEACH based on energy consumption and fault tolerance. A summary of these protocols based on energy efficiency in cluster formation is presented in Table 7. The strengths and weaknesses of each protocol are included.
# Evaluation of the Occurrence of Phthalates in Plastic Materials Used in Food Packaging
## 2 Materials and Methods
### Gas Chromatography Quadrupole Mass Spectrometry (GC-qMS) Conditions  
The SPME fiber with the target analytes was inserted into the injection port of an Agilent Technologies 6890N Network gas chromatograph system (Palo Alto, CA, USA) where the analytes were thermally desorbed. The gas chromatographer was equipped with a 60 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m film thickness HP-5 (SGE, Dortmund, Germany) fused silica capillary column and interfaced with an Agilent 5975 quadrupole inert mass selective detector. The oven temperature program was set as follows: 120 \({}^{\circ}\)C (hold 3 min), increased until 190 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min (hold 4 min after reach 190 \({}^{\circ}\)C), further increased to 240 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, maintained for 20 min. The total GC run time was 50.67 min. The column flow was constant at 1.0 mL/min using He of purity 5.0. The injection port was operated in the splitless mode and held at 250 \({}^{\circ}\)C. For the 5975 MS system, the operating temperatures of the transfer line, quadrupole, and ionization source were 270, 150, and 230 \({}^{\circ}\)C, respectively. Data acquisition was performed in the scan mode (30-300 \(m/z\)) with electron ionization at an energy of 70 eV and the ionization current of 10 \(\upmu\)A. Phthalates identification was accomplished through manual interpretation through comparison of spectra and matching against the Agilent MS ChemStation Software, equipped with an NIST05 mass spectral library with a similarity threshold higher than 90%, and by the standards. The analysis was performed in triplicate, and the results were expressed as mean \(\pm\) standard deviation. In addition, to evaluate the occurrence of phthalates in the GC system, blank runs of the chromatograph and direct injections of ethanol were carried out. None of phthalate targets were detected.
# DEM Analysis of Track Ballast for Track Ballast-Wheel Interaction Simulation
## 4 Case Study: Effects of Contact Model Parameters on Simulation Results  
### Effect of Friction between Gravel Pieces  
In this section, the effect of the friction between the pieces of gravel on the simulation results is described in detail. In the case study, a friction coefficient of 0.4-0.7 was applied in the contact model of the gravel and, then, the motion of the penetrating weight and the friction-energy absorption were numerically compared to investigate the interlocking due to the applied friction coefficients.  
As shown in Figure 12, the friction between the pieces of gravel significantly affects the motion of the weight after the impact. The equivalent geotechnical properties of the gravel layer are determined by the effect of interlocking between the contacted gravel pieces. Based on the simulation results, it can be concluded that the considered friction coefficient between the pieces of gravel governs the geotechnical properties of the gravel ballast, including the rigidity of the layer.  
Figure 13 shows the energy dissipation due to friction in the different gravel layers with different friction coefficients between the gravel pieces. As shown in the figure, the energy dissipation due to friction decreases as the friction coefficient increases. This clearly proves that the layer would be stiffened owing to the considerable interlocking effect induced when a larger friction coefficient between the pieces of gravel is applied. Thus, the friction coefficient between the gravel pieces is one of the most influential factors for the simulation.  
Figure 12: Vertical motion of weights clashing with gravel layers for different friction coefficients between the gravel pieces (friction coefficient between a gravel piece and the weight = 0.6): (**a**) acceleration; (**b**) velocity; (**c**) displacement.  
Figure 13: \(Cont.\)
## 1 Introduction
Firstly, in the convolutional operation of CNN, each kernel slides on the image, focusing on a small region with its receptive field size. This enables CNN to detect similar local features at different positions, such as edges and textures, better capture the differential features between similar characters in Uyghur text and reduce reliance on a large amount of training data. Secondly, the self-attention mechanism of the Transformer establishes associations between different positions, enhancing the model's perceptual ability for contextual information. This contributes to a better understanding of the relationships between Uyghur characters in our research. The contributions of our article are as follows:  
1. We created a real-world Uyghur text recognition dataset comprising 7267 training images and 1815 test images. Additionally, we employed data augmentation techniques to generate two enhanced datasets, Aug1 and Aug2, containing 254,345 and 3,052,140 scene images, respectively. 2. In the context of the low-resource Uyghur language, we propose a Collaborative Encoding Method (CEM) for scene text recognition. The encoding consists of three key modules: a Filter module, a Dual-Branch Feature Extraction (DBFE) module, and a Dynamic Fusion (DF) module. The Filter module aims to obtain multi-level low-level features, emphasizing textual information in the images and reducing irrelevant background noise. The DBFE module enables the model to effectively capture global and local details, thereby improving its ability to discern intricate features. The DFmodule provides the flexibility to choose the most suitable Transformer and CNN encoding weights, allowing optimal performance. 3. Our method achieves state-of-the-art results in Uyghur scene text recognition. 4. This article provides reference examples for other low-resource languages lacking real-world scene text recognition data, such as Mongolian, Kazakh, and Kyrgyz. The paper's structure is outlined as follows: Section 2 discusses the current research status of scene text recognition. Section 3 provides a detailed introduction to the text recognition method proposed in this paper and its various components. Section 4 offers a thorough presentation and analysis of the data and experiments used in this article. Section 5 summarizes the main achievements of this paper.
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 4 Communities from Personalized Centrality
### Results on Static, Synthetic Graphs
The advantage of our centrality approach compared to greedy expansion is greatest when the size of the community is large relative to the total number of vertices. This can be seen in Figure 1b, where we vary the number of communities, while keeping the size of the graph constant at 47,104 vertices with an average degree of 20. It is clear that the speedup of the Katz method is greatest for the graphs with a small number of large communities. This occurs because the personalized Katz centrality computation is global and processes the entire graph, while greedy expansion processes only a local subgraph composed of the community and its one hop neighborhood. If, however, the community to be found is much smaller than the full graph, the greedy expansion method may be preferable. Finally, we consider how the average vertex degree affects relative running times in Figure 1c. The number of vertices is held constant at 1000 with 2 communities. As the average degree increases, the speedup of the Katz method over greedy expansion first increases and then decreases. The increase in speedup occurs because a higher average degree results in larger community neighborhoods and the larger the neighborhood of the community is as it expands, the slower the greedy expansion is. However, once the average degree grows large enough, few or no new vertices are added to the neighborhood and the clustering coefficient of the graph simply increases. These results show that the running time advantage of the personalized Katz method compared to greedy expansion is greatest in graphs in which the community of interest has a large neighborhood and a low clustering coefficient. \begin{table}  
\end{table} 
Table 2: The quality of communities detected with our personalized Katz method and greedy expansion is shown. Test graphs are stochastic block model (SBM) graphs with \(n=1000\) and \(k=2\). (a) The average vertex degree \(d\) is varied, while \(\rho=0.01\). (b) The proportion of inter-community edges \(\rho\) is varied, while \(d=20\). (c) The proportion of inter-community edges \(\rho\) is varied, while \(d=100\). Figure 1: _Cont._
## 2 Distributed Fair Rate Allocation Algorithm
### Convergence to the Optimal Rate Allocation
Since \(TS_{j}\) in Equation (28) is real-valued, the cluster head has to round the allocation of its nodes. We propose to allocate first \(\lfloor TS_{j}\rfloor\) time slots to each node and then successively allocate one extra time slot to the sensors with the biggest value of \(TS_{j}-\lfloor TS_{j}\rfloor\), where \(\lfloor\cdot\rfloor\) represents the floor function. Note that the higher the \(nBI\), the higher the optimality of the time slot allocation. However, if the network is substantially dynamic, we shall fix \(nBI=1\) and implement other rounding strategies that are out the scope of this paper. In any case, we need algorithms that converge fast to the optimal solution if we want to track the changes in the network efficiently. This is achieved with the proposed CDM as shown in the next simulation results.
## 1 Introduction
However, due to the occurrence of large displacements and the incorporation of limiters, TMDs inevitably exhibit nonlinear characteristics. Neglecting these characteristics can have a detrimental effect on the control performance of the TMD [38]. In order to investigate the nonlinear characteristics that arise when using TMDs, as well as the control performance exhibited by nonlinear tuned mass dampers (NTMDs) under different conditions, this paper analyzes a main structure controlled by a nonlinearly tuned mass damper. The expression for the structure's response under 1:1 resonance conditions is derived using the complex variable averaging method. This expression is then further analyzed to obtain the nonlinear design equation for the NTMD frequency. The validity of the complex variable averaging method, as well as the accuracy and correctness of the obtained nonlinear design formulas, is verified by comparison with the numerical method. Based on the derived nonlinear design formulation, a comprehensive analysis of the nonlinear tuned mass damper system under damping, 1:1 resonance and softening stiffness conditions is conducted. The obtained analytical results can provide valuable references for future studies on TMD nonlinear characteristics and potentially be applied in practical engineering to enhance the control performance of TMDs in real-world applications. The previous section provides an introduction to the history of damper development, theoretical research and the current applications. Section 2 presents the system model used in this study, including the equations of motion and their dimensionless form. It also derives an approximate analytical solution for the system's equations of motion. In Section 3, the obtained approximate analytical solution is further analyzed to derive the nonlinear design formulation for the frequency of the nonlinear tuned mass damper. The correctness and accuracy of the complex variable averaging method employed in this paper to analyze the system equations, as well as the validity of the nonlinear design formulation, are verified using numerical methods. Section 4 focuses on the analysis and design of the control performance of the nonlinear tuned mass damper under different conditions, namely a damping condition, a 1:1 resonance condition and a softened stiffness condition. The results demonstrate that employing different design methods for each condition can effectively enhance the control performance of the nonlinear tuned mass damper. In Section 5, the main findings and conclusions of this study are summarized. Additionally, the limitations of the study and suggestions for future research are discussed.
## 5 Discussion
### Retrograde Metamorphism and Anatexis During the Late Triassic
(2013) [70] further studied the Weihai migmatite and concluded that the plagioclase-rich leucosome (228 \(\pm\) 2 Ma) was formed in the early initial melt separation crystallization, while the potassium feldspar-rich leucosome (219 \(\pm\) 2 Ma) was formed in the later evolution of the melt. [PERSON] et al. (2010) [34] identified the UHP metamorphic zircon (227 \(\pm\) 3 Ma, containing coesite inclusions) and the anatectic zircon (212 \(\pm\) 2 Ma) at the leucosome of the Weihai migmatite, which is believed to have occurred in the HP granulite phase during the exhumation process of the UHP eclogitic metamorphism after the peak metamorphism. To sum up, there are two main stages of anatexis during the Triassic. The first stage of anatexis occurred at \(\sim\)222 Ma, probably near the peak stage of the UHP metamorphism (\(\sim\)230 Ma), which belongs to the partial melting record of the initial exhumation under UHP conditions [71; 72]; The second stage of the anatectic event occurred at \(\sim\)215 Ma, which represents the dehydration and melting event of phengite when the subducting slab further exhumated to the depth of the lower crust [34; 73; 74]. The zircon rims of metamorphic origin in the granitic gneiss provided an age of 211.1 \(\pm\) 2.3 Ma, suggesting that the granitic gneiss underwent retrograde metamorphism subsequent to UHP metamorphism during the Triassic period of continental collisional orogenesis. Anatectic zircons of the biotite schist yielded nearly concordant \({}^{206}\)Pb/\({}^{238}\)U ages ranging from 206 Ma to 225 Ma with a weighted mean of 214.6 \(\pm\) 3.6 Ma. It is evident that the Triassic period witnessed simultaneous occurrences of retrograde metamorphism and anatexis. Based on the above research combined with previous studies, we propose a hypothetical model for the metamorphism and anatexis experienced by the biotite schist and granitic gneiss. The model suggests that (1) during the Triassic Subduction-collision stage (\(\sim\)245-215 Ma), the Yangtze Craton continental crust was subducted beneath the North China Craton continental crust (Figure 8A); (2) after break-off of the deeply subducted oceanic slab (\(\sim\)215 Ma), the Yangtze Craton continental crust reached its maximum depth and underwent UHP metamorphism (Figure 8B); (3) subsequently, the deeply subducted and UHP metamorphosed Yangtze Craton continental crust was exhumed (\(\sim\)215-205 Ma), during which it experienced retrograde metamorphism and anatexis, as recorded by zircons in the granitic gneiss and biotite schist (Figure 8C). These multistage metamorphic and anatectic events during the Triassic may indicate that deep subducted continental crust materials underwent significant reconstruction during the continental collision stage and post-collision tectonic evolution.
## 2 Results and Discussion
### Biological Activity  
The compounds were subjected to an initial evaluation for potential cytotoxic activity against different cancer cell lines, namely, HepG2, MCF-7, and HCT-116 cells, at 50 \(\mu\)M. Cell viability was measured using MTT assay. Among the screened 14 compounds, 3 (5h, 5i, and 5j) did not show any cytotoxic activity against HepG2 cells. The concentration of the active compounds that killed 50% of the cells (IC\({}_{50}\)) was evaluated against HepG2 cells. Compound **5g** (IC\({}_{50}\) = 5.00 \(\pm\) 0.66 \(\mu\)M) was the most potent active compound, showing more potent activity than that of the standard chemotherapeutic drug _cis_platin (IC\({}_{50}\) = 9.00 \(\pm\) 0.76 \(\mu\)M) (Table 1). Moderate anticancer activity against HepG2 cells was observed for compounds **5a** and **5m** (IC\({}_{50}\) = 10.00 \(\pm\) 0.47 and 17.00 \(\pm\) 0.68 \(\mu\)M, respectively).  
The same three inactive compounds (5h, 5i, and 5j) did not show activity against MCF-7 or HCT-116 cells (Table 1). The other 11 tested compounds (IC\({}_{50}\) \(\leq\) 9.00 \(\mu\)M) showed superior activity to that of cisplatin (IC\({}_{50}\) = 9.00 \(\pm\) 0.29 \(\mu\)M) against MCF-7 cells (Table 1); only compounds (5c, 5f, 5g, and 5l) (IC\({}_{50}\) < 3.00 \(\mu\)M) were more potent than cisplatin (IC\({}_{50}\) = 3.00 \(\pm\) 0.24 \(\mu\)M) against colon cancer cells (Table 1). The present study showed that compound **5g** retained broad anticancer activity against the three tested cell lines of liver, breast, and colorectal cancers; HepG2, MCF-7, and HCT-116 cells, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Cancer Type/Cell Line**} \\ \cline{3-5} Compound & R & **Liver** & **Breast** & **Colon** \\  & **HepG2 (IC\({}_{\text{50}}\), \(\mu\)M)** & **MCF-7 (IC\({}_{\text{50}}\), \(\mu\)M)** & **HCT-116 (IC\({}_{\text{50}}\), \(\mu\)M)** \\ \hline
4b & 3.57 \(\pm\) 0.50 & NT \({}^{\text{c}}\) & 8.00 \(\pm\) 1.20 \\  
& 2.00 \(\pm\) 0.20 & NA & NA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of anticancer activity against HepG2, MCF-7, and HCT-116 cells.