esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dual Chaotic Scrambling to Enhance Security  
Information security can be further strengthened using another hyper-chaotic watermarking scheme so as to be robust against various attacks. Overlaying the initial chaotic logistic watermark and another hyper-chaotic watermark makes the watermarking scheme robust against potential attacks. Dual chaotic watermarking is based on the 4D hyper-chaotic dynamical system, which is formulated as (2).  
\[\begin{split}\dot{x}_{1}&=-x_{1}-x_{2}\\ \dot{x}_{2}&=x_{1}+ax_{2}+x_{4}\\ \dot{x}_{3}&=b+x_{1}x_{3}\\ \dot{x}_{4}&=-cx_{3}+dx_{4}\\ \end{split} \tag{2}\]  
It exhibited hyperchaotic behavior when parameter values were selected as \(a=0.25\), \(b=3\), \(c=0.5\) and \(d=0.05\). In numerical simulations, initial conditions of 4D chaotic systems can be selected as [\(-10\), \(-6\), \(0\), \(10\)]. It is a dynamical system with 4 Lyapunov exponents. Exponents of Lyapunov have highlighted that, due to the hyperchaotic system's potential to lead to strong randomness, the hyperchaotic array may be used for watermarking. To generate the hyperchaotic array, the hyperchaotic map was used for generating four separate 64 bits or 128 bits hyperchaotic sequences (\(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\)) in hexadecimal, where \(X_{1}=\{x_{1}\}\), \(X_{2}=\{x_{2}\}\), \(X_{3}=\{x_{3}\}\), \(X_{4}=\{x_{4}\}\). After simple discretization with small quantization error, the hyperchaotic matrix of dimension 64 \(\times\) 64 or 128 \(\times\) 128 was formulated by (3), where T refers to transpose operation of a vector, which was used as another watermarking system so as to be embedded into the existing initial chaotic watermarked images.  
\[H=(X_{1}X_{2}^{T})(X_{3}X_{4}^{T}) \tag{3}\]  
For each pixel considered in watermarking blocks, once again bitwise scrambling operation was made between the watermarked images and the hyperchaotic matrix using Exclusive OR binary operation (A\(\oplus\) B), which gave rise to dual watermarked images. The advantage of dual chaotic watermarking lies in high confidentiality, easy implementation, and short processing time. Overlaying the two chaotic watermarking systems makes watermarks difficult to be removed and attacked. The same XOR operation was applied to all three color components of each pixel. At the same time,  
Figure 1: Turnaround time vs. parameter modulation of logistic map.  
the characteristics of the RGB components were kept when pixel shuffling and modulo 2 additions were proceeded. Watermarking can be applied to individual RGB intensity levels and positions of each pixel simultaneously. In order to evaluate the variations between the host and watermarked images, some quantitative tests were needed afterwards.
## 4 The Proposed Use Case: Data Model and Scalability Analysis  
As stated in the introduction, to the best of the authors' knowledge, the detailed definition of a use-case, and of the related data model, is missing in the literature. For this reason, the definition of all the information involved in the communication process and the required data throughput in a smart campus scenario is discussed.
## 4 Simulation and Analysis of EKF-GAPF
### Parameter Estimation for Two Multipath Signals
Figure 9: Comparison of estimation results for direct signal and two multipath signals: (**a**) Estimated amplitude of the direct signal; (**b**) estimated amplitude of the first multipath signal; (**c**) estimated amplitude of the second multipath signal; (**d**) estimated delay of the direct signal; (**e**) estimated delay of the first multipath signal; (**f**) estimated delay of the second multipath signal. greater stability in multiple parameter estimation runs. These results demonstrate that the proposed algorithm outperforms others in multipath signal estimation.
## 5 Discussion
### Challenges  
* The limited battery capacity of devices on the network can reduce the time duration that a device can remain connected to the network. As a result, this might reduce the efficiency of the routing protocol.
* Changes in the network environment can affect the efficiency of the routing protocol, such as changes in the number of nodes in the network or the network topology.
* If a link in the network fails, the routing protocol may fail to identify the correct path on which data packets to travel, resulting in decreased energy efficiency. Moreover, link failure can cause congestion and delays in the network, affecting energy efficiency.
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Entity Type**} & \multicolumn{2}{c}{**BAM**} & \multicolumn{4}{c}{**RoPE-BAM**} & \multicolumn{4}{c}{**CBAM-RoPE-BAM**} \\ \cline{2-9}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline GPE & 85.27 & 86.93 & 86.09 & 89.33 & 89.33 & 86.51 & 88.40 & 86.93 & **87.66** \\ ORG & 77.12 & 80.62 & 78.83 & 80.94 & 81.52 & 81.23 & 81.14 & 82.61 & **81.87** \\ PER & 89.61 & 90.39 & 90.00 & 90.82 & 90.52 & **90.67** & 90.48 & 90.12 & 90.30 \\ LOC & 65.49 & 70.48 & 67.89 & 72.73 & 76.19 & **74.42** & 68.91 & 78.10 & 73.21 \\ FAC & 70.93 & 54.46 & 61.62 & 69.07 & 59.82 & 64.11 & 77.08 & 66.07 & **71.15** \\ VEH & 83.33 & 88.24 & 85.71 & 82.35 & 82.35 & 82.35 & 85.00 & 100.0 & **91.89** \\ WEA & 65.52 & 59.38 & 62.30 & 84.21 & 50.00 & 62.72 & 94.44 & 53.12 & **68.00** \\ Total & 84.55 & 85.44 & 84.99 & 87.16 & 85.21 & 86.17 & 86.96 & 86.36 & **86.66** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the ablation experiments on the ACE2004 dataset. The highest score is marked in bold. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Entity Type**} & \multicolumn{2}{c}{**BAM**} & \multicolumn{4}{c}{**RoPE-BAM**} & \multicolumn{4}{c}{**CBAM-RoPE-BAM**} \\ \cline{2-9}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline NAME & 100.0 & 99.12 & 99.56 & 99.11 & 99.11 & 99.11 & 99.12 & 100.0 & **99.56** \\ CONT & 100.0 & 100.0 & **100.0** & 100.0 & 96.43 & 98.18 & 96.43 & 96.43 & 96.43 \\ RACE & 100.0 & 100.0 & 100.0 & 93.33 & 100.0 & 96.55 & 100.0 & 100.0 & **100.0** \\ TITLE & 93.69 & 96.11 & 94.88 & 97.34 & 94.95 & 96.16 & 96.99 & 96.11 & **96.55** \\ EDU & 95.69 & 99.11 & 97.37 & 99.10 & 98.21 & 98.65 & 95.69 & 99.11 & **97.37** \\ ORG & 94.33 & 96.20 & 95.26 & 95.15 & 95.84 & 95.50 & 97.05 & 95.30 & **96.17** \\ PRO & 84.62 & 100.0 & **91.67** & 93.33 & 84.85 & 88.89 & 78.59 & 100.0 & 88.00 \\ LOC & 75.00 & 100.0 & **85.71** & 100.0 & 50.00 & 66.67 & 100.0 & 66.67 & 80.00 \\ Total & 94.27 & 96.81 & 95.52 & 96.77 & 95.46 & 96.11 & 96.62 & 96.32 & **96.47** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the ablation experiments on the Resume dataset. The highest score is marked in bold.
## 3 Results
### Antibacterial Activity of Eugenol and Isoeugenol Against Gram-Negative Bacteria  
Table 3 below and Figure 3 show the results of the experiments testing the antimicrobial activity of eugenol and isoeugenol against the selected Gram-negative bacteria. The zones of growth inhibition, measured in millimeters, indicate the extent to which the growth of these bacteria is inhibited around disks soaked in the respective compounds. Gram-negative bacteria are known for their ability to produce a variety of resistance mechanisms to antibiotics and other bacteriides and are highly virulent in infections, making them important targets for antimicrobial tests. Figure 3: Example images of the results of the disk diffusion method for Gram-negative bacteria. In this study, the antibacterial activity of eugenol and isoeugenol against the selected strains of Gram-negative bacteria was assessed, revealing promising properties for both compounds. The analysis of inhibition zones indicated that eugenol and isoeugenol exhibited varying effectiveness against different bacterial strains, suggesting their potential as antimicrobial agents in clinical applications. For _Klebsiella pneumoniae_, including strains ATCC 700603, NDM, and ESBL, moderate antibacterial activity was observed. The inhibition zones for these strains ranged from 13.4 mm to 15.4 mm for eugenol and from 10 mm to 15.2 mm for isoeugenol. These results are consistent with the data in the literature [17], which indicate that _K. pneumoniae_ is characterized by resistance to many antimicrobial agents. This resistance may be attributed to the bacterium's ability to produce large amounts of mucus, which forms a protective barrier against external agents. Understanding the mechanisms by which eugenol and isoeugenol disrupt this protective barrier could provide valuable insights into their efficacy against resistant strains. In the case of _Escherichia coli_, including strains ATCC 25922, OXA-48, and ESBL, higher inhibition zones were noted, ranging from 15 mm to 20.4 mm. These findings align with the existing literature [15; 26], which reports the effectiveness of eugenol against _E. coli_, with MIC values ranging from 1 to 16 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. The observed activity suggests that eugenol may effectively target critical metabolic processes or structural components necessary for _E. coli_ survival, warranting further exploration of its mechanisms of action. Conversely, _Pseudomonas aeruginosa_ strains, including ATCC 27853 and VIM, showed high resistance to both compounds--eugenol and isoeugenol.
# Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
###### Abstract  
This paper presents an application of topology optimization and response surface method to optimize the geometry of a bicycle crank arm and the experimental validation of it. This is purposely to reduce the crank arm mass and create a preliminary design of a lightweight structure necessary for the high-performance bicycle development. A three-dimensional bike crank arm model was made in the SpaceClaim software followed by a static finite element analysis using ANSYS Workbench 2019 R1. A multiple cycling load was applied simultaneously in seven crank angles of 30, 45, 60, 90, 120, 135, and 150\({}^{\circ}\) relative to the horizontal position to create the multiple loads to the crank. From there, topology optimization was then conducted to investigate the effect of mass constraint, stress constraint, angle of cycling, and crank materials on the topological pattern result. To minimize stress concentration at corners, a shape optimization using the response surface method was conducted and obtained the final geometry. From the result, it is shown that both optimization methods not only successfully reduce the crank arm mass and provide several optimum design options but also are able to reduce the maximum stress in the crank arm up to 20% after the optimization process. The experimental validation using a newly developed wireless measurement system shows a considerable agreement to the numerical results.  
topology optimization; response surface method; multiple loads; bicycle crank arm
# Lower Limb Joint Coordination and Coordination Variability During Landing: A Scoping Review
###### Abstract  
Landing requires precise coordination among lower limb joints to absorb impact forces and maintain dynamic stability. Coordination and its variability during landing are influenced by factors such as injury status, training, sex, age, fatigue, and task complexity. Altered coordination patterns may compromise impact absorption and increase injury risk, highlighting the importance of understanding these movement strategies across populations and conditions. This scoping review aimed to map and synthesize the existing literature on lower limb joint coordination and coordination variability during landing tasks across different populations and task conditions. A comprehensive search was conducted across four databases (PubMed, Web of Science, Scopus, SPORTDiscus) through November 2024, with additional articles identified through reference screening. Peer-reviewed studies were included if they assessed joint or segmental coordination and/or coordination variability using time-series analyses (such as vector coding, continuous relative phase, and discrete relative phase) during landing tasks in human participants. Formal critical appraisal was not performed, consistent with PRISMA-ScR guidelines. Eighteen studies were thematically grouped into five focus areas: injured/at-risk individuals, training/fatigue interventions, gender differences, age differences, and healthy populations under varied landing conditions. Injured individuals exhibited altered coordination patterns, often showing either rigid or erratic strategies with excessive or reduced variability. Training interventions generally improved coordination stability, whereas fatigue increased variability and disrupted control. Females displayed more constrained patterns and lower coordination variability compared to males, particularly at the knee joint. Children demonstrated greater variability and less refined coordination than adults. Healthy individuals typically showed symmetric adaptable variability. Lower limb joint coordination and its variability during landing are shaped by injury status, fatigue, training, sex, age, and task complexity. These findings highlight the need for consistent methodologies and suggest that coordination analysis can inform injury prevention, rehabilitation, and targeted training strategies to optimize landing performance and safety.  
v 2025 15 5118 1
# Automated Construction Method of Knowledge Graphs for Pirate Events
## 5 Experiment and Analysis
### Performance Validation of NER  
For the performance validation of BERT-BiLSTM-CRF-based NER, the event text was labeled automatically based on DeepKE using the BIO format according to Table 2, and a NER corpus was built. It was subsequently divided into training and test sets in a ratio of 8:2. The results of the experiments are shown in Table 3 and Figure 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Label** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & B-Aggressor & 0.9045 & 0.9225 & 0.9134 \\
2 & I-Aggressor & 0.9160 & 0.9047 & 0.9103 \\
3 & B-Victim & 0.9022 & 0.9118 & 0.9070 \\
4 & I-Victim & 0.9239 & 0.9203 & 0.9221 \\
5 & B-Location & 0.9524 & 0.9524 & 0.9524 \\
6 & I-Location & 0.9416 & 0.9484 & 0.9450 \\
7 & B-Data & 0.9875 & 0.9925 & 0.9900 \\
8 & I-Data & 0.9917 & 0.9913 & 0.9915 \\
9 & O & 0.9441 & 0.9287 & 0.9363 \\
10 & Overall & 0.9404 & 0.9414 & 0.9409 \\ \hline \hline \end{tabular}
\end{table}
Table 3: NER results.  
From the results in Table 3 and Figure 2, it is clear that the BERT-BiLSTM-CRF model scores somewhat differently across entities. Compared to the entity pem: Location and pem: Data, the F1-score of entity pem: Aggressor and pem: Victim was lower. However, the F1-score of all types of entities reached more than 0.9, and the average recognition rate reached more than 0.94. Overall, the BERT-BiLSTM-CRF model performed well in NER.  
Furthermore, several methods were used for comparison: (i) Hidden Markov Model (HMM); (ii) Machine Learning CRF model; (iii) Deep Learning BiLSTM model; and (iv) Deep Learning BiLSTM-CRF model. The above four named-entity algorithms were tested on the same dataset, and the test results were compared with the BERT-BiLSTM-CRF model. The experimental results of each model are shown in Table 4 and Figure 3.  
Figure 3: Comparison results of NER of each model.  
Figure 2: NER results.  
As can be seen from Table 4 and Figure 3, the deep learning models outperformed the traditional machine learning models HMM and CRF in all respects. BiLSTM-CRF was obtained by adding sequence labeling CRF to the deep learning model, and it outperformed the BiLSTM model in all respects, suggesting that the CRF improves the performance of the model after taking into account the global label information of the sequences. After adding the BERT model to the deep learning BiLSTM-CRF model, F1-score was improved by 4.05%, which indicates that the proposed BERT-BiLSTM-CRF model has an obvious advantage in the NER of pirate events compared to other models. Thereby, the quality of the knowledge graph construction for pirate events can be improved.
## 3 Active Disturbance Rejection Control Design
### Trajectory Planner Design
\(\hat{x}_{1d}\leq x_{1d}^{+}\), respectively; \(\hat{x}_{r}\) and \(\hat{x}_{r}\) denote the original desired velocity and acceleration, respectively; \(y(k)=x_{1d}(k)-x_{r}(k)\) represents the filter tracking error, and \(\dot{y}(k)=\dot{x}_{1d}(k)-\dot{x}_{r}(k)\) reflexes the filter velocity error; \(\lfloor\bullet\rfloor\) and \(\lceil\bullet\rceil\) correspond to the floor and ceil functions of their arguments. For the sake of conciseness, the theoretical proof of the trajectory planner is omitted in this paper. Refer to [19] for details. **Remark 3**. : _The primary purpose of construction of the trajectory planner is to relax the restriction on the requirement of sufficient smooth reference trajectories of the conventional backstepping framework. In addition, by considering the kinematic constraints of the EHS, the stress on the actual control input is significantly mitigated. Besides, since the filtered trajectory always commences from zero, the initial matching condition is satisfied to avoid the saturation of the control input effectively as a result._
# Comparison of Two Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider
## 2 Modeling the Magnetic Systems
### Baseline Magnetic System Model  
The model of the FCC-hh detector baseline magnetic system shown in Figure 2 includes three components: the main superconducting coil of 10.9 m inner diameter and 18.954 m length with a total current of 69.6 MA-turns, and two superconducting forward coils of 5.6 m inner diameter and 3.3997 m length with a total current of 12.6 MA-turns each.  
The main coil has about the same central magnetic flux density as the Compact Muon Solenoid (CMS) [9], the largest detector coil in the world, but 1.52 times larger inner diameter and length.  
In the model, the radial thickness of the FCC-hh detector main coil is assumed to be 0.5 m, and the radial thickness of each forward coil is 0.23 m. The distance between the main and each forward coil is 2.823 m. Preparing the design of the baseline magnetic system, this distance is chosen as a compromise between the value of the magnetic flux density in the transition region between the coils, and the attractive axial force onto the forward coil that is minimized to 61.8 MN. The compression axial force in the main coil middle plane is 610.7 MN that creates a pressure of 34.1 MPa. The radial pressure is 1.55 MPa in the main coil, and 2.66 MPa in each forward coil. The stored energy of the baseline magnetic system is 13.8 GJ.
## 4 Model Setting  
### Model Geometry and Boundaries  
The numerical model employed in this study, as shown in Figure 2, was built to simulate a typical application of the AGF method in the construction of a cross passage between two tunnels of an urban subway project. In this study, the focus is on the displacement of the ground surface; the groundwater dynamics, as suggested by [PERSON] [13], have little effect on it. Therefore, the effect of groundwater migration and interaction with the construction is not considered in the model. To reduce the model calculation time, only one layer of saturated clay was considered. The model geometry boundaries, as shown in Figure 2a, was set as 80 m \(\times\) 80 m \(\times\) 40 m, and the tunnels were located 20 m under the ground surface. As presented in Figure 2b, the two tunnels were 14 m away from each other with a 0.3 m thick shield tunnel segment. The arch-shaped cross passage, as illustrated in Figure 2c, was 4 m wide and high, and the liner within it was set as 0.1 m  
Figure 1: The schematic diagram of the tetrahedron element used in FLAC3D.  
thick. When considering the differences in the thickness of the liners, the tunnel liners were simulated using volumetric elements, while the liners of the cross passage were modeled using shell elements. In addition, to examine the impact of the thickness of the freezing wall on ground surface deformation, five models with different freezing wall thicknesses, i.e., 1.0 m, 1.5 m, 2.0 m, 2.5 m, and 3.0 m, were developed.  
For the mechanical boundaries, the top of the model was set as a free boundary, the bottom of the model was set as completely constrained, and the normal displacement of the four vertical faces of the model was set as constrained. For thermal conditions, the temperature field was initialized uniformly at 20 \({}^{\circ}\)C. Considering that the upper face of the model would be open to the external environment and exchange heat with it, the constant temperature boundary of 20 \({}^{\circ}\)C was fixed for all faces except for the top face, while the others were considered as adiabatic boundaries.
## 4 Materials and Methods
### GC-MS Analysis of Volatile Compounds  
The volatile compounds were analyzed by QP2010 ultra gas chromatography/mass spectrometry (Shimadzu Co., Tokyo, Japan) according to the reported method [16]. The tea samples were ground and sifted through a 0.6 mm sifter. Our preliminary experiment showed that the contents of total volatiles were quite different among freeze-dried, green, and black tea samples. To achieve comparable total peak areas of volatiles amongdifferent types of teas and high repeatability, different amounts of ground tea samples, viz. green tea 1.00 g, black tea 0.25 g, and freeze-dried tea 0.075 g, were respectively extracted with 50 mL of water at 70 \({}^{\circ}\)C for 10 min. After centrifugation at 2272\(\times\)\(g\) and 25 \({}^{\circ}\)C for 10 min, the supernatants were collected. A total of 10 mL of supernatants were transferred to a 20 mL headspace bottle, followed by the addition of 20 \(\upmu\)g/mL internal standard guaiaicol (20 \(\upmu\)L for green tea, 120 \(\upmu\)L for black tea, and freeze-dried tea, respectively) and 2 g dried NaCl, and then the headspace bottle was sealed immediately. A 50/30 \(\upmu\)m divinylbenzene/expboxen/poly-dimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Bellefonte, PA, USA) was used to adsorb the volatiles at 60 \({}^{\circ}\)C for 1 h above the liquid surface. Then, the SPME fiber was placed in the GC-MS injection port at 250 \({}^{\circ}\)C for 3.5 min for desorption. GC-MS conditions were: capillary column HP-INNOWax (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Palo Alto, CA, USA), carrier gas (helium, 99.999%), flow rate 1.0 mL/min, injector temperature 250 \({}^{\circ}\)C, ion source temperature 200 \({}^{\circ}\)C, ionization style EI, MS scan range of 35-400 \(m/z\), injection mode splitless; column temperature program: keeping at 50 \({}^{\circ}\)C for the first 10 min, increasing to 150 \({}^{\circ}\)C at the speed of 3 \({}^{\circ}\)C/min, keeping at 150 \({}^{\circ}\)C for another 1 min, increasing to 230 \({}^{\circ}\)C at the speed of 15 \({}^{\circ}\)C/min, followed by keeping at 230 \({}^{\circ}\)C for 3 min. The compounds were annotated based on the published retaining index as well as the National Institute of Standards and Technology (NIST) mass spectral database. Guaiaicol was selected as the internal standard for GC-MS analysis because of its suitable repeatability according to the pretest of our previous work [16], with no interference to these tea samples based on our preliminary experiment. The volatile compounds were quantified by guaiaicol according to the following equation:  
\[\text{Content ($\upmu$g guaiaicol equivalent/g dry tea)}=\frac{\text{(Peak area of target/Peak area of IS) \times Amount of guaicol used ($\upmu$g)}}{\text{Amount of weighted sample($g$)}\times\left(\frac{\text{(H$\upmu$ mL)}}{\text{Amount}}\right)} \tag{1}\]