esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the Risk Preference Characteristics of Members in Supply Chain Emergencies
## 4 Discussion  
In summary, the risk preferences of the supply chain members could be measured regarding the conventional risk and emergency. Compared with the results of [PERSON]'s [10] research, participants showed greater changes in the range of risk preference and risk avoidance in the experiment of the nine major conventional risks and emergency areas of the supply chain. For example, the total number of [PERSON]'s participants was 116 students, of which approximately 18 were risk-averse and another approximately 18 were risk-seeking. The total number of participants in this experiment was 332 individuals from different professions, with a range of 40-70 for risk aversion and 40-60 for risk seeking. The overall risk preference varied more between individuals. Compared with [PERSON]'s experiment, the overall average risk-neutral proportion of participants was slightly lower. In terms of risk-seeking, the overall proportion was slightly higher. In terms of risk aversion, the overall proportion was also slightly higher. From an individual perspective, compared with [PERSON]'s experiment, the significant change was the significant decrease in the number of participants who consistently exhibited risk neutrality.  
Taking the supplies rescue as the scenario, it was found that time pressure weakened the risk aversion degree of subjects. Compared with previous experimental research [25,34], Chinese supply chain members became more risk-averse, and their economic behavior and maturity are gradually improving.  
By introducing the time pressure into four types of supply chain emergency scenarios with a natural disaster, production accident, economic risk, and abnormal accident, it was found that the time pressure only increased the risk aversion degree of decision makers in a natural disaster. Combining experiments 1 and 3, it was found that the risk preference of decision makers in emergencies was consistent with the conventional risk of the supply chain, while the individual risk preferences of the decision makers in the different supply chain risk fields were inconsistent. Decision makers were more risk-averse and individuals were consistent in different emergencies in the natural disaster, production accident, economic risk, and abnormal accident conditions.  
Therefore, hypothesis 1 was not rejected. Regarding the supplies rescue, hypothesis 3 was not rejected. In the natural disaster supply chain emergency, both hypothesis 2 and hypothesis 3 were rejected.
## 6 Discussion
### Potentialities of HCA
#### 6.1.1 Case Study for AMR Parsing  
For AMR parsing, we employed the state-of-the-art AMR parser proposed in [1] to predict the AMR graph of the exemplified sentence in Section 1. As shown in Figure 9, two \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**PLM**} & \multicolumn{3}{c}{**Discourse Parsing**} & \multicolumn{3}{c}{**Clause Parsing**} \\ \cline{3-10}  & & **Span** & **Nuc.** & **Rel.** & **Full** & **Span** & **Nuc.** & **Rel.** & **Full** \\ \hline \multirow{5}{*}{Top-Down} & BERT & \(92.6\pm 0.53\) & \(85.7\pm 0.41\) & \(75.4\pm 0.45\) & \(74.7\pm 0.54\) & \(96.0\pm 0.20\) & \(92.1\pm 0.27\) & \(85.8\pm 0.51\) & \(85.7\pm 0.47\) \\  & RoBERTa & \(94.1\pm 0.46\) & \(88.4\pm 0.46\) & \(79.6\pm 0.17\) & \(78.7\pm 0.11\) & \(96.5\pm 0.02\) & \(93.1\pm 0.09\) & \(87.1\pm 0.23\) & \(87.1\pm 0.22\) \\  & SpanBERT & \(94.1\pm 0.15\) & \(88.8\pm 0.19\) & \(79.4\pm 0.49\) & \(78.5\pm 0.39\) & \(96.4\pm 0.13\) & \(92.6\pm 0.12\) & \(86.1\pm 0.15\) & \(85.9\pm 0.15\) \\  & XLNet & \(\mathbf{94.8\pm 0.39}\) & \(\mathbf{89.5\pm 0.39}\) & \(\mathbf{80.5\pm 0.59}\) & \(\mathbf{79.5\pm 0.53}\) & \(96.6\pm 0.07\) & \(\mathbf{93.5\pm 0.20}\) & \(87.0\pm 0.57\) & \(86.9\pm 0.56\) \\  & DeBERTa & \(94.2\pm 0.33\) & \(89.0\pm 0.16\) & \(80.1\pm 0.43\) & \(79.1\pm 0.32\) & \(\mathbf{96.6\pm 0.02}\) & \(93.3\pm 0.07\) & \(\mathbf{87.2\pm 0.04}\) & \(\mathbf{87.2\pm 0.10}\) \\ \hline \multirow{5}{*}{Bottom-Up} & BERT & \(91.9\pm 0.34\) & \(84.4\pm 0.31\) & \(74.4\pm 0.37\) & \(73.8\pm 0.30\) & \(96.3\pm 0.22\) & \(92.3\pm 0.36\) & \(86.1\pm 0.37\) & \(86.0\pm 0.32\) \\  & RoBERTa & \(94.4\pm 0.12\) & \(89.0\pm 0.34\) & \(80.4\pm 0.47\) & \(79.7\pm 0.51\) & \(96.4\pm 0.16\) & \(92.8\pm 0.20\) & \(86.6\pm 0.56\) & \(86.6\pm 0.58\) \\ \cline{1-1}  & SpanBERT & \(93.9\pm 0.24\) & \(88.2\pm 0.19\) & \(79.3\pm 0.37\) & \(78.4\pm 0.29\) & \(96.5\pm 0.09\) & \(92.6\pm 0.06\) & \(86.4\pm 0.20\) & \(86.2\pm 0.22\) \\ \cline{1-1}  & XLNet & \(\mathbf{94.7\pm 0.31}\) & \(89.4\pm 0.24\) & \(\mathbf{81.2\pm 0.27}\) & \(\mathbf{80.4\pm 0.34}\) & \(96.9\pm 0.10\) & \(93.6\pm 0.09\) & \(87.4\pm 0.33\) & \(87.3\pm 0.31\) \\ \cline{1-1}  & DeBERTa & \(94.6\pm 0.38\) & \(\mathbf{89.8\pm 0.65}\) & \(81.0\pm 0.64\) & \(80.2\pm 0.70\) & \(\mathbf{97.0\pm 0.10}\) & \(\mathbf{94.0\pm 0.17}\) & \(\mathbf{87.8\pm 0.39}\) & \(\mathbf{87.7\pm 0.38}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performances of the top-down and bottom-up parsers with various pretrained language models (PLMs) for the clause parsing and discourse parsing tasks, which were evaluated by four RST-Parseval metrics, i.e., _span_, _nuclearity_ (nuc. ), _relation_ (rel. ), and _full_. Standard deviations for three runs are shown in parentheses. **Bold** numbers are the best scores for each task with each model. Note that we only conducted experiments on the HCA-AMR2.0 and RST-DT datasets for clause parsing and discourse parsing, respectively.
# Haar Wavelet-Based Classification Method for Visual Information Processing Systems
## 3 Case Study
For an ideal classifier, the maximum possible amount of information \(\mathrm{I}_{max}=log_{2}(L)\), and for arbitrary \(P(\Omega_{i})\),  
\[\mathrm{I}_{max}=-\sum_{i=1}^{L}P(\Omega_{i})\mathrm{log}_{2}(P(\Omega_{i})). \tag{7}\]  
To compare different classification procedures, we introduce an indicator of classification efficiency:  
\[\mathrm{I}_{0}=\frac{\mathrm{I}(\Omega,D)}{\mathrm{I}_{max}}. \tag{8}\]  
Then, we estimated the indicator \(\mathrm{I}_{0}\) for two classes with 15 parameter features each. Those two classes were separated using \(\hat{f}(x,c)=-4.0+\Delta_{i}c_{1}x\) at \(\Delta_{1}c_{1}=[1.0,2.36]\) and the <<exact>> value of \(c_{1}=1.438\), determined with an error of 0.001. Note that in determining the parameters (6), the following probabilities were calculated: \(p_{11}\)--the probability of correctly classifying an object into class 1; \(p_{22}\)--the probability of correctly classifying an object of class 1 into class 2; \(p_{21}\)--the probability of correctly classifying an object of class 2 into class 1. Some of the objects fall into the \"range\" because of increasing variance in the classes. As the result, the informativity of indicator \(\mathrm{I}_{0}\) decreases. The results of the investigation are represented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Relative Mean-Square**} & \multicolumn{2}{c}{**Indicator of Efficiency for Classification Procedure, \(\mathrm{I}_{0}\)**} \\ \cline{2-3}  & \(c_{0}\)**=** -4.0, \(c_{1}\)**=1.438** & \(\Delta_{1}c_{1}\)**=[1.0,2.36]** \\ \cline{2-3}  & **1** & **2** \\ \hline
0.050 & 0.9 & 1.0 \\ \hline
0.075 & 0.63 & 0.71 \\ \hline
0.100 & 0.51 & 0.59 \\ \hline
0.125 & 0.43 & 0.50 \\ \hline
0.150 & 0.38 & 0.45 \\ \hline
0.175 & 0.33 The \"widest\" range of \(c_{1}\) coefficient values was selected for the investigation. However, as can be seen from Table 2, changes of indicator I\({}_{0}\) are considerably close. Therefore, if requirements to the system performance are high, then classification in the above example can be made by choosing the coefficient value from the range \(\Delta_{1}c_{1}=[1.0,2.36]\) in order to increase the procedure productivity. For example, when adapting existing IT to new application tasks in a system for visual information processing, it is often necessary to carry out an identification procedure (determination of parameter features). This procedure may require reducing the dimensionality of the feature space. In such a reduction, the classification is performed repeatedly, increasing the set of features consistently in order to ensure the required reliability. To improve the performance of the initial and intermediate stages of classification and enhance the visualization of the result, we can recommend using classification based on the Haar \(WT\) with the definition of the classification efficiency indicator I\({}_{0}\).
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 4 Simulation Results
### Multipath Rayleigh Fading Channels  
As mentioned above, BER performance for the TDCS in multipath fading channels is closely dependent on the channel conditions. Thus, it is assumed that the channel information is perfectly known to the receiver.  
Meanwhile, a cyclic prefix with the length of \(1/4\) is used to combat inter-symbol interference and a one-tap zero-forcing frequency domain equalizer is considered. For Rayleigh channels, the general performance of both systems suffers from multipath fading even with ZF equalization, as shown in Figures 9 and 10. However, this would not change the fact that each pair of simulations and theoretic lines still match rather well for a given \(\alpha\) factor. This proves that the SSM influence has been accurately modeled in our semi-deterministic analysis (providing perfect channel estimation). Meanwhile, in CCSK-based TDCS, the simulation line still tends to get slightly inferior as \(\alpha\) gets larger, due to the destruction of orthogonality in the PCF vector.  
Figure 10: BER performance of TDCS-QPSK in multi-path channels (various mismatch factor \(\alpha\)).  
Figure 9: BER performance of TDCS-CCSK in multi-path channels (various mismatch factor \(\alpha\)).  
Figure 11 shows the BER performances of TDCS for \(a=\{92\%,96\%\}\). We used a CR channel setting with four spectrum holes and 50% available subcarriers. For analysis, we study the \(E_{b}/N_{0}\) loss (compared to that of the perfect spectrum sensing case with \(a=100\%\)) at BER = \(10^{-3}\). One can see that the BER curves for \(a=\{92\%,96\%\}\) are very close to that of the perfect spectrum sensing, with only 0.15 dB and 0.4 dB \(E_{b}/N_{0}\) loss, respectively. As expected, the BER performance is degraded gradually as \(\eta\) decreases. In contrast, when non-continuous OFDM (NC-OFDM) is used, the BER curves under the same \(\eta\) settings show very high error floors at all \(E_{b}/N_{0}\) levels, indicating that it is very sensitive to spectrum sensing mismatch. Therefore, our proposed TDCS system is more robust against modest spectrum sensing mismatch.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Safety Training and Education
#### 4.2.2 Knowledge Management  
Managing and disseminating safety protocols and operational procedures is a critical aspect of maritime safety. LLMs can serve as centralized repositories of knowledge, continuously updated with the latest information [125]. Crew members can interact with the model through conversational interfaces to retrieve information quickly and efficiently.  
By querying the LLM, users can obtain explanations of safety procedures, guidelines, and best practices without navigating through extensive manuals. This ease of access to information promotes adherence to safety protocols and enables crew members to make informed decisions. Additionally, LLMs can assist in updating safety documentation by integrating new regulations and lessons learned from recent incidents.
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Motion Estimation Methods  
As transducer motion is constrained to occur within the imaging plane, it should be visible in the imaging data as a contrary motion of the imaging targets. Although ultrasound is best suited to motion estimation in the beam direction [32; 33], it is possible to estimate motion in the transverse (lateral) direction using speckle tracking [34] or transverse oscillation [35; 36] approaches. An alternative approach is presented below that is well suited to the constrained motion problem. These approaches are shown in Figure 4. In both cases, the same recorded data from plane wave transmissions were used to maintain an approximately constant transmit field and measure the motion of the receiving array (using the diverging wave data only for image formation). While it should be possible to measure displacement from a spatially varying transmit field as well, that would result in additional decorrelation [37] and is outside the scope of this paper.
## 3 Results and Discussion
### Frontier Molecular Orbitals (FMOs) and Ultraviolet Spectral Analysis
The electronic transitions and UV spectra of (2_E_)-HIPC were computed using the TD-DFT/IEFPCM approach at the B3 LYP/6-311++G(d,p) level. The calculations were performed for (2_E_)-HIPC in acetonitrile, DMSO, and the gas phase. The frontier orbital energies, absorption wavelengths (\(\lambda\)), oscillator strengths (_f_), and excitation energies (E) for (2_E_)-HIPC were computed in the gas phase, DMSO, and acetonitrile; the data are presented in Table S5. TD-DFT calculations for (2_E_)-HIPC predicted an intense electronic transition at 279.7 nm (_f_\(=0.4339\)) in acetonitrile, 278.0 nm (_f_\(=0.4700\)) in DMSO, and 284.9 nm (_f_\(=0.5114\)) in the gas phase. The observed differences are due to shifting of the bands in both DMSO and acetonitrile due to their permittivities. The results are better represented in graphical form, as shown in Figure 4. Experimentally, absorption peaks of (2_E_)-HIPC in acetonitrile were observed at 283.1, 234.9, and 199.4 nm. The observed values are shifted downfield compared with the corresponding computed values. The major energy contributions were computed.
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 4 Challenges and Future Work
### Reduce Hardware Costs  
While VBDA technology has been successfully verified in experiments [8; 38; 40; 49; 50; 51; 52; 53; 54; 56; 58; 59; 63; 57; 58; 64; 56; 59; 65; 66; 58; 67; 56; 68], it has not yet been commercialized on a large scale. Therefore, it is necessary to consider the challenges and opportunities associated with scaling up the technology. One critical factor in making this technology more accessible and appealing to potential users is to reduce its cost. Several measures can be employed to achieve this goal.  
1. Modular design: Implement a modular design for the VBDA system, where different components are divided into modules that can be easily replaced when a failure occurs. This can significantly reduce the cost of maintenance and repair, improving the system's overall maintainability. Additionally, by adding new modules, the system's functionality can be easily extended without significantly affecting its overall design. This makes the system more adaptable and able to meet various application scenarios and requirements;
2. Design for Manufacturing (DFM): The design should apply principles that prioritize ease of manufacturing and assembly. It should be simplified to reduce the number of complex parts and manufacturing steps. The use of materials should be optimized to minimize waste and lower production costs. It is important to ensure that the design is compatible with existing manufacturing processes and technologies, thereby avoiding the need for expensive production modifications;
3. Utilize commercially manufactured components: Consider using commercially available components to minimize the number of unique parts needed. This can significantly reduce the costs and time associated with producing custom components. Commercial components are often mass produced and have established quality standards, which ensure the reliability and consistency of the final product.  
It is important to note that reducing the cost of this technology should not come at the expense of its quality and performance. Maintaining a high standard of performance and reliability is essential to ensure that VBDA technology meets the needs and expectations of users. By addressing this challenge, researchers and industry professionals can unlock the potential of this innovative technology and create new opportunities for growth and innovation.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 1 Introduction
### Coupling Failure  
Imbalances, looseness, and misalignments in the twin-spool rotor system of an aero-engine can increase vibration amplitude, which in turn can induce secondary defects like rotor-stator rub impact. [PERSON] [22] developed a model of a twin-spool rotor system with rolling bearings that takes into account the coupling faults caused by pedestal looseness and rub impact. The study examined how the stiffness of the looseness affects the time-frequency characteristics of the HP and LP rotors. It also analyzed the time-frequency response characteristics at various rotational speeds and the impact of reducing stiffness on the time-frequency domain characteristics of rotors. Additionally, the progression of friction at various rotational speeds is examined. [PERSON] [23] developed a model for a twin-spool rotor-bearing system that takes into account the effects of coupling misalignment and rub impact. The model was used to analyze the fault frequency properties of the system when coupling faults occur. The complexity of the twin-spool rotor dynamics and the interplay of rotor failure pose significant challenges in analyzing the coupling fault in the turbofan engine. Furthermore, there is scarce literature on this topic.  
The purpose of this research is to study the dynamic behavior of a twin-spool rotor-bearing system with coupled faults. It specifically aims to investigate how rotor geometry and fault parameters affect the time-frequency response of HP and LP rotors. We will examine the vibration properties of a twin-spool rotor-bearing system in an aero-engine with pedestal looseness and rub impact coupled faults. On the basis of the existing twin-spool rotor-bearing model, we further take into account transient effects and rotational speed control in modeling; the effect of vibration in the acceleration phase on the dynamic behavior in the stable rotation phase is considered in calculating the fault response in order to improve the accuracy and convergence of results.  
The structure of the paper is organized as follows: Firstly, the dynamic modeling of the twin-spool rotor bearing system including multiple rotating shaft, bearing, inter-shaft bearing, pedestal looseness fault and rub impact fault is set up in the Section 2. Next, the effects of pedestal looseness and rub impact on the dynamic behavior of the twin-spool rotor system are analyzed in Section 3. Finally, some conclusions are summarized in Section 4.
## 3 Results
### Selection of One Bacterial Consortium  
The ice recrystallization inhibition (IRI) activity and the ACC deaminase activity were evaluated in strains that composed the bacterial consortia, to select one bacterial consortium for future cold stress analyses. To determine the antifreeze potential of the bacteria, the IRI activity was evaluated, subjecting protein extracts to temperatures below zero degrees Celsius (Figure 8). _Pseudomonas_ sp. strains NUR4a, CcR1d, and TmR5a presented IRI activity, with values of 0.35; 0.32; and 0.24 absorbance units, respectively, which are significantly higher compared to the negative control (0.032) (_E. coli_ JM109).  
To study the ACC deaminase activity, the growth of the four strains belonging to the selected bacterial consortia (BC2 and BC3) was evaluated in DF medium with ACC as sole carbon and nitrogen source (Figure 9a). Only _Pseudomonas_ sp. TmR5a (BC3) showed growth under these conditions. Strain TmR5a presented an ACC deaminase activity of 1.516 umol \(\alpha\)-KB mg protein\({}^{-1}\) h\({}^{-1}\) (Figure 9b).  
Therefore, BC3 (_Pseudomonas_ sp. TmR5a & _Curtobacterium_ sp. BmP22c) was selected because it presented all the evaluated activities together.  
Figure 8: IRI activity of bacterial strains. The protein extracts were exposed to \(-6\)\({}^{\circ}\)C for 48 h to measure their absorbance at 500 nm. Each value is a mean \(\pm\) SD of 6 independent replicates. Significant differences were analyzed by one-way ANOVA followed by the LSD Fisher test. Means with different letters indicate significant differences (\(p<0.05\)). Abbreviations: NC, negative control (_E. coli_ JM109); PC, positive control (Type III AFP); IRI, ice recrystallization inhibition.
## 3 Results  
### Synthesis of TSNP  
Figure 2 shows the normalized UV-Vis spectrum of the seeds and the TSNP prepared using 200 \(\upmu\)L of seeds, and afterwards with the modified method (with 350 \(\upmu\)L seeds) for increased concentration. TSC and PSSS were used as stabilizers for the seed production, and both were reported to aid the formation of the nanoplates. It is postulated that citrate ions and PSSS (being a charged polymer), bind to the surface of the face of the particles, promoting growth of the NPs in a triangular plate shape [5; 50]. When the TSNP synthesis procedure was modified, the amount of silver in the nanoplate solution increased to 21.962 ppm, which led to a narrower LSPR spectrum of ~115 FWHM showing homogeneity of the TSNP with edge lengths around 20-25 nm [38; 50] (Figure 2). The triangular morphology of the TSNP prepared through this method was evaluated using TEM on a previous study performed by our team (Figure 3), where the same synthesis method for the TSNP was used [55].  
Figure 2: (**A**): UV-Vis spectrum of Ag seeds and TSNPs (200 \(\upmu\)L seeds), (**B**): UV-Vis spectrum of Ag seeds and TSNPs prepared with the optimized method (350 \(\upmu\)L seeds).
# Interference Mitigation for Metro Train Location Based on Karhunen-Loeve Decomposition
## 4 Experimental and Analysis of Results
### Single-Tone Interference  
In the first scenario, a single-tone signal with a central frequency of 3575 MHz is added to the CPIS. We conducted Monte Carlo simulations 100 times to study the average value of MSE of different algorithms under different JSNR. In each experiment, the data of different time periods of IF signals collected are selected to ensure the randomness of the experiment. After the phase of performing the interference mitigation, as shown in Figure 4, we calculated the MSE according to Equation (12). It can be seen from Figure 7 that in the single-tone interference scenario, the three algorithms can effectively suppress the single-tone interference. The KL algorithm has an average improvement of 1.8 dB compared with the STFT algorithm and 1.3 dB compared with the WPCT algorithm. The KL algorithm has better performance compared with the other two interference mitigation algorithms, and as the interference becomes stronger, the mitigation effect of the KL algorithm is more obvious. Compared with the other two algorithms, the KL algorithm is less affected by JSRN and is more robust.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Communication Sub-carrier & 1200 \\ \hline The order of modulation of the communication signal & 64-QAM \\ \hline pseudo random noise code (PRN) & 10,230 \\ \hline Code rate & 10.23 MHz \\ \hline IF frequency & 3575 MHz \\ \hline Sampling rate of collector & 50 MHz \\ \hline JSNR & 30\(\sim\)45 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameter configuration.  
Figure 6: Test environment.  
Figure 8: Histogram of eigenvalue distribution.  
Figure 9: MSE of different interference mitigation algorithms under pulse signal interference.