esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery
## 3 Results
The interaction was evaluated using the Short User Experience Questionnaire (UEQ) [26]. According to the results presented in Figure 12, the users considered the system useful and efficient for the job, as evidenced by the Mean Pragmatic Quality (practical and real-world applicability) score of 1.29. It should be mentioned, nevertheless, that users needed multiple repetitions to properly become used to the HoloLens' functionality. The Mean Hedonic Quality (utility) score was 1.45, suggesting that users found the interaction enjoyable and engaging. Finally, the Overall Mean score was 1.32, reflecting an overall positive user experience with the system. Using the long version of UEQ, an overall analysis was performed to evaluate the simulator. The questionnaire was completed by fifteen users: three senior surgeons, four specialists, three residents, and five engineers with competencies in robotic-assisted surgery. All users have previously interacted with surgical simulators, and 12 of them have prior experience with AR technology. The findings highlight both notable areas of performance and opportunities for improvement, as can be seen in Figure 13. Novelty (1.75), perspicuity (1.73), and attractiveness (1.70) had the highest mean values, suggesting that users find the system to be creative, user-friendly, and visually appealing. Additionally, stimulation (1.65)  
Figure 11: Distribution of haptic feedback delay. scored well, indicating that the simulator offers an enjoyable and motivating experience. However, the mean scores for efficiency (1.42) and dependability (1.24) were more modest, suggesting possible issues with task completion speed and system dependability.
# Correlation Analysis of Microbiota and Volatile Flavor Compounds of Caishiji Soybean Paste
## 3 Results  
### VFC Changes during CSP Fermentation  
More than 70 VFCs (including 8 acids, 15 esters, 8 alcohols, 14 aldehydes, 4 ketones, 5 phenols, and 20 miscellaneous VFCs) were detected during the CSP fermentation process (Table 1). As indicated in the Venn diagram in Figure 1a, 17 of the VFCs were common in CSP at all stages, and there were also their own unique VFCs in each stage samples (the quantity and composition of the VFCs are presented in Figures S1 and S2). Among the VFCs identified, esters, aldehydes, and alcohols were the main VFCs in CSP. All eight acids were detected in CG1, and there were only octanoic acid and nonanoic acid in CG6 and CG8. Esters, including amyl acetate, \(\alpha\)-pentyl-\(\gamma\)-butylrolactone, ethyl palmitate, and diethyl phthalate, were the most abundant VFCs throughout the CSP fermentation process, whereas in the late stages of CSP fermentation, the relative abundance of the alcohols and aldehydeswas the highest. Furthermore, the analyzed VFC results revealed that aldehydes of which isovaleraldehyde, 2-methylbutylraldehyde, benzaldehyde, and phenylacetaldehyde were common in all fermentation stages, and they obviously contributed to the flavor of the CSP. In summary, all the VFCs identified in the CSP fermentation process exhibited distinct qualitative and quantitative characteristics, which may lead to diverse flavors of CSP.  
To assess the contribution of characteristic VFCs to the overall flavor of CSP, the OAV analysis was conducted according to the odor threshold values of the VFCs. The results revealed that CSP contains 12 kinds of VFCs: one acid, three esters, one alcohol, and seven aldehydes (OAV \(\geq\) 1, Table 2). Based on the OAV analysis results, the principal component analysis (PCA) was used to investigate the contribution rates of the characteristic VFCs to CSP flavor (Figure 1b). The two-dimension PCA model (F1 and F2) described 77.2% of the total variance, while the first and second principal components (F1 and F2) explained 57.8% and 19.4%, respectively. Isovaleraldehyde, 2-methylbutylraldehyde, and 3-methylhiopropionaldehyde greatly contributed to the flavor of CSP and were, therefore, included in the later correlation analysis.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 3 Materials and Methods
### Method: Comparative Analysis
#### 3.2.1 Inlier Ratio  
Inlier ratio is the ratio of correct matches to the initial matches. This fraction represents the detector-descriptor combination accuracy. A higher inlier ratio value indicates a better detector-descriptor pair. For some image pairs, the value of the inlier ratio may be zero, which indicates that all the initial matches were wrong. In image stitching, at least four correct matched points are required in order to stitch the images. If some combination has less than four correct matched points, we used an inlier ratio equal to zero. The inlier ratio is defined by Equation (2):  
\[\text{InlierRatio}=\frac{\text{numberofcorrectmatches}}{\text{numberofinitialmatches}} \tag{2}\]  
The number of accurate matches has an effect on the quality of the final stitched image since the homography matrix is created using correctly matched points, which are then used in the image alignment stage. As a result, the inlier ratio is one of the most significant parameters to consider when choosing the best detector and descriptor combination.
# Communication Performance Assessment for Advanced Metering Infrastructure
## 4 Simulation and Experimental Results  
### A Simple Six-Meter Case  
Figure 8 shows the communication network configuration of a simple six-meter case used to clarify the basic concepts and implementation of proposed CPI. Table 3 shows the communication paths of the six-meter case. Figures 9 and 10 is the values and Probability Density (PD) of reading success rate and response time for smart meters \(M_{1}\) and \(M_{4}\). Due to limited space, the other data is not shown here. The CPIs calculated by the proposed method is shown in Figure 11. The CPIs of smart meters, communication paths and DCU can be easily observed from Figure 11. The number of each communication path used can be calculated and the results are as shown in Figure 12. From Figures 11 and 12, it can be observed that smart meter \(M_{5}\) with the communication path \"5\(\rightarrow\)4\(\rightarrow\)DCU\" has the lower CPI and the communication path between \(M_{5}\) and DCU has higher CPI. Therefore, if the communication path of smart meter \(M_{5}\) is changed to \"5\(\rightarrow\)DCU\", then the overall CPI may be increased. Figures 13 and 14 show the calculated CPI and the number of each communication path used after the communication paths of smart meter \(M_{3}\) changed to \"3\(\rightarrow\)5\(\rightarrow\)DCU\" and of smart meter \(M_{5}\) changed to \"5\(\rightarrow\)DCU\", respectively. Figure 13 indicates that the CPIs with the changes of communication paths are enhanced; therefore, the proposed CPI and the number of each communication path used can be adopted to find the optimal communication path of each meter. This paper provides an  
Figure 7: An Actual Small-Scale PLC-based AMI.  
Figure 11: CPIs for the Six-Meter Case.  
Figure 10: Measurements and PDs of \(M_{4}\). (**a**) Reading Success Rate; (**b**) PD of Reading Success Rate; (**c**) Response Time; (**d**) PD of Response Time.
## 3 Experiments and Results  
### Experimental Setup  
#### 3.1.1 Parameter Configuration  
The sampling rate of the audio signals is 32 kHz. The frame and hop sizes for the STFT were set to 1024 and 600 samples, respectively. Spectral features composed of log-mel spectrogram, chromagram, and additional spectral information were obtained by librosa [28; 36]. An Adam optimizer [37] was used for the training. The StepLR scheduler [38] that decays the learning rate by a factor every configured epoch was applied to control the learning rate during the training. For our experiments, we decayed the learning rate by a factor of 0.1 every 80 epochs. The initial learning rate was set to 0.0005.
# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 6 Results and Discussion
### Few-Shot Experiment  
To address **RQ2**, we conduct few-shot experiments, and the performance of the model on the two datasets OntoNotes 4.0 (Chinese) and MSRA is shown in Table 6. To more intuitively demonstrate the superiority of the proposed model under the few-shot setting, the best baseline model LEAR is chosen for comparison. The experimental results of LEAR compared in this paper come from the related few-shot exploration experiments in that research work [8]. The experimental results show that whether under the one-shot or five-shot setting, the improvement in the model LGDA proposed in this paper is very significant. Specifically, in the one-shot scenario, LGDA can absolutely improve the F1 metric by 15.48-16.14 compared with the baseline model; in the five-shot scenario, the absolute improvement is 11.33-30.56. When the data augmentation module is removed, taking the 1-shot setting on the OntoNotes 4.0 (Chinese) dataset as an example, the model shows absolute decreases of 9.95, 8.04, and 11.28 in the F1, P, and R metrics, respectively. This indicates that in the few-shot scenario, data augmentation significantly enhances the model's recognition effectiveness.  
* **Conclusion 2:** In the few-shot scenario, the LGDA model still significantly outperforms the baseline models, with the data augmentation module playing the most significant role.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**OntoNotes 4.0 (Chinese)**} & \multicolumn{3}{c}{**MSRA**} \\ \cline{2-7}  & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline BERT + CRF & 72.84 & 59.72 & 65.63 & 90.57 & 83.06 & 86.65 \\ LatticeLSTM & 76.35 & 71.56 & 73.88 & 93.57 & 92.79 & 93.18 \\ BERT-MRC & 82.98 & 81.25 & 82.11 & 96.18 & 95.12 & 95.75 \\ MECT & 77.57 & 76.27 & 76.92 & 94.55 & 94.09 & 94.32 \\ Multi-grained KD & 77.94 & 74.23 & 76.05 & 93.90 & 92.11 & 92.99 \\ LEAR & 81.23 & 83.07 & 82.14 & 96.18 & 95.42 & 95.82 \\ PCBERT & - & - & 81.57 & - & - & 89.72 \\ XLNet-Transformer-R & 79.34 & **83.16** & 81.21 & 92.07 & 94.01 & 93.03 \\ MW-NER & - & - & 81.51 & - & - & 95.47 \\ E-strPron & **84.90** & 77.96 & 81.28 & 96.18 & 95.94 & 96.06 \\ \hline
**LGDA** & 81.72 & 82.67 & **82.19** & **96.52** & **96.21** & **96.36** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model performance comparison on the OntoNotes 4.0 (Chinese) and MSRA datasets, with the best-performing baseline models for each metric indicated by underlines and the best results emphasized in bold.
# Auditory Localization in Low-Bitrate Compressed Ambisonic Scenes
## 3 Methods  
The purpose of the experiment presented in this paper was to subjectively assess the spatial distortion introduced by Ambisonic order truncation and perceptual coding of Ambisonic scenes using different bitrates. The method of adjustment [37; 38] was used for the auditory localization tests. Participants were asked to move an artificially reproduced virtual acoustic pointer to the perceived direction of a reproduced target sound source using a physical controller [39] (see Figure 3). The audio playback of pointer and target scenes was controlled by participants and programmed to ensure that both stimuli were never presented simultaneously. The azimuth and elevation step encoders adjusted the rotation of the rendered acoustic pointer with a single-degree precision. It is important to note that the experiment was designed to examine the perceived differences between the uncompressed and low-bitrate compressed scenes, not the absolute localization error. Both target sound source and acoustic pointer were reproduced using the same rendering method. Assessing the relative localization of virtual sound sources removes the need for real sound sources used as the localization anchor. Therefore, evaluation of binaurally rendered signals reproduced using ear occluding headphones is possible.
## 3 Results and Discussion
### Simulated Waveform Synthesis with cw Harmonic Comb
Normalized electric field strength shown here is color-coded to assist in recognition of the evolution of the field pattern as follows: dark red regions represent the value is from 0.8 to 1.0; orange regions represent the value is from 0.5 to 0.8; green regions represent the value is from 0.253 to 0.5 and blue regions represent the value is from 0.0 to 0.253.
## 5 Experimental Results and Discussion
### Experimental Results of Steel Cord conveyor Belt Defects
#### 5.2.4 Classification of the Defects Signals  
One hundred and sixty-eight sample feature vectors of the normalized signals for the steel cord conveyor belt defects, were obtained. The sample sets were composed by seventy-four sample feature vectors of splices, twenty sample feature vectors of the rope fatigue defects and seventy-four samples feature vectors of the broken rope defects. That is that the samples size is imbalanced. The detailed parameter configuration of the inertia weight PSO algorithm was as follows: The range of \(C\) was 0-100, the range of\(\gamma\)was 0-10, the maximum iteration number was 200, the initial population number was 20 and \(\omega_{\text{max}}=0.9\), \(\omega_{\text{min}}=0.4\) and \(c_{1}=1.5,c_{2}=1.7\). The SVM parametersCand\(\gamma\)were optimized using the inertial weight PSO algorithm under an imbalanced samples condition and the corresponding results of the steel cord conveyor belt defects signals are presented in Figure 16.  
Figure 15: Features distribution for three types of normalized weak magnetic signals.  
Classification results of the steel cord conveyor belt defects using the ISDTSVM algorithm and the conventional SDTSVM algorithm with different kernel functions and five-fold cross-validation are presented in Table 4. As can be observed, the ISDTSVM algorithm improved the classification accuracy of the steel cord conveyor belt defect signals by 4%. The classification accuracy of the radial basis function kernel was higher than the polynomial kernel. The tree structure was obtained in Figure 17 using the ISDTSVM algorithm. In order to verify the classification effect of the ISDTSVM algorithm, the classification results for the three classification orders of the steel cord conveyor belt defect signals are presented in Table 5 using the SDTSVM algorithm with optimized parameters presented in Figure 16 and were validated by a five-fold cross-validation. As can be observed in Table 5, the classification accuracy of the classification order obtained by the ISDTSVM algorithm was the highest in the three classification orders. The ISDTSVM algorithm significantly improved the classification accuracy.
## 5 Implementation of the Adaptive SMC and Detailed Circuital Simulations
### Analog Implementation of the Adaptive SMC
In this case, Figure 6b shows a zoom for the second transient (from \(-2\) A to 0 A), where the steady-state values of the Zeta current and the battery current are different. After the  
Figure 4: Analog implementation of the control algorithm. transient is finished, the Zeta current becomes zero since the supporting device is designed only to provide fast current transients. Therefore, the Zeta converter provides the step-up fast transient from 0 A to 2 A needed to reach the final current of both the battery and the load (0 A). As in the previous case, the waveforms of Figure 6b confirm the stability of the proposed solution and the correct limitation of the switching frequency. Figure 5: Fast load transient to battery discharge. Figure 6: Fast load transient to battery charge. The next simulation, reported in Figure 7a, evaluates the performance of the proposed solution to a sequence of load transients. In this case, the load current changes from 0 A to 14 A with transients of 2 A. As expected, the Zeta converter provides the fast current transients, returning to 0 A for the steady-state condition. Therefore, despite the consistent increment in the battery current, the supporting device (Zeta converter) always provides the same behavior, which prevents the battery from suffering the fast current transients imposed by the load. Since the consecutive transients occur in intervals of 2 ms, which is much shorter than the settling time (\(t_{s,\text{C2}}=100\) ms), the voltage of C2 capacitor is reduced up to 40 V, but the SMC always operates inside the sliding surface, thus ensuring global stability and demonstrating the adaptability and robustness to changes on the operation conditions. Figure 7b presents the fourth simulation, which evaluates the settling time of the C2 voltage. In this test, a single load current perturbation is applied to charge the battery with a load current equal to \(-2\) A. The simulation confirms that C2 voltage is below the designed deviation \(MO_{\text{C2}}\) for that perturbation, and the voltage compensation is achieved in the designed settling time (100 ms). Therefore, both discharging (positive load transients) and charging (negative load transients) times are predictable and equal to the designed settling time. This satisfactory performance confirms that the switching function operates as expected, thus ensuring the capability of providing or absorbing load current transients to protect the battery.
## 2 Material and Methods
### Two ET Models Based on ANN
#### 2.2.2 Medlyn-PM Model  
The Medlyn-PM model uses ANN-derived GPP in conjunction with a theoretical _Gs_ model to estimate surface conductance, and then the computed _Gs_ is used to estimate ET using the PM equation. Firstly, we use the optimal ANN structure selected above to train the GPP model. Secondly, on the pixel scale, the computed GPP, Ca, and air vapor pressure deficit are used for _Gs_ regression analysis to establish the relationship among them and determine the undetermined coefficients \(g_{0}\) and \(g_{1}\). Then, we use the above variables and  
Figure 3: The typical structure of ANN.  
the relationship between them to build the _Gs_ model. Finally, the constructed _Gs_ is input into the PM equation to calculate ET. The relationship is as follows [52]:  
\[G\textsl{s}=1.6*\frac{\text{GPP}}{\text{Ca}}*\left(\frac{\textsl{g}_{1}}{ \sqrt{\text{D}}}+1\right)+\textsl{g}_{0} \tag{2}\]  
where _Gs_ is stomatal conductance, GPP is gross primary production, Ca is CO2 concentration of the air, \(g\)1 and \(g\)0 are undetermined coefficients derived from regression analysis, and D is the vapor pressure deficit of the air. The minimum value of D is fixed to 0.1 KPa.
## 1 Introduction  
Citri reticulatae pericarpium (CRP) is the sun-dried pericarp of mature fruits of _Citrus reticulata_ Blanco (commonly known as tangerine) and its cultivars, and is widely used in drugs and foods in China, involved in more than 10% of all formulae in the Chinese Pharmacopoeia (2015 edition) [1]. _Citrus reticulata_ Blanco grows throughout southern China, and includes various cultivars, such as \"Chachi\", \"Unshiu\", \"Zhuhong\", \"Ponkan\", \"Suavissima\", etc. Generally, the pericarp of Citrus reticulata\"Chachi\" from Xinhui County (Guangdong province, China), called \"Guangchenpi\" (GCP) in Chinese, has always been well-regarded as the best national product with respect to geo-herbalism [2]. Pericarps derived from other cultivars are collectively called \"Chenpi\" (CP) in Chinese, and their origins are diverse in the market [3]. As GCP and CP have quite similar appearances and flavor characteristics, it is difficult to discriminate between them and this could easily lead to economic adulteration. Actually, due to its premium price, GCP is prone to being superseded by cheaper or inferior variants so as to get a higher profit margin in the market. Therefore, there exists significant interest in the development of accurate methods differentiating GCP and CP.  
Chemical profiling has been increasingly applied in the authentication of traditional Chinese medicines (TCMs) [4; 5; 6]. Until now, flavonoids contained in CRP have been analyzed and reportedin many studies [7; 8; 9], while volatile constituents have been rarely focused on. Given that volatile bioactive compounds possess potent efficacy, it is meaningful to compare them in GCP and CP. Extraction is a crucial procedure in the analysis of volatile compounds in CRP, and several methods of doing so (hydro-distillation, carbon dioxide supercritical fluid extraction) have been reported [10; 11; 12]. However, these methods are time- and material- consuming. In recent years, headspace solid-phase microextraction (HS-SPME), which has been proven to be a rapid, simple, and convenient sampling method, has increasingly been used in the analysis of volatile compounds [13; 14].  
In this study, an accurate and robust method based on HS-SPME-GC-MS and multivariate statistical analyses was developed to discriminate between GCP and CP on the basis of potential volatile compounds. Multivariate statistical methods, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least-squares-discrimination analysis (OPLS-DA), were employed to screen potential chemical markers for differentiating GCP and CP. This study facilitated the profiling of volatile constituents of CRP, and provides a practical method for the authentication of CRP varieties.
# Multi-Objective Optimization of Cascade Blade Profile Based on Reinforcement Learning
## 6 Results and Discussion
It can be concluded from the results that the DDPG-based optimizer successfully optimized the cascade blade profile with \(\overline{\omega}\) 3.59% relatively being reduced, and \(\widetilde{A}_{SS}\) 25.4% higher than the initial profile. The large improvement of \(\widetilde{A}_{SS}\) is in line with the information from the PCA that \(\widetilde{A}_{SS}\) contributes more on reducing \(\overline{\omega}\) than \(\widetilde{A}_{PS}\) (cf. Table 4). CFD simulation and ANN prediction on performance characteristics of the initial and optimized cascades are compared in Table 6. Compared to the CFD results, the ANN prediction has an error of about 0.8% on the total pressure loss coefficients and an error of about 3.5% on the laminar flow areas, which proves that the ANN-based model can accurately predict the aerodynamic performance  
Figure 18 shows \(C_{f}\) distributions on suction and pressure surfaces. Compared with the initial profile, the position of the jump of \(C_{f}\) is moved aft at the suction surface, which indicates an obvious delay of transition. The \(C_{f}\) distributions on pressure surfaces are basically the same, consistent with the laminar flow areas obtained with the intermittency factor distributions, as in Table 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\overline{\omega}^{(i)}\) & \(\widetilde{A}_{SS}^{(i)}\) & \(\widetilde{A}_{PS}^{(i)}\) & \(\overline{\omega}^{(o)}\) & \(\widetilde{A}_{SS}^{(o)}\) & \(\widetilde{A}_{PS}^{(o)}\) \\ \hline CFD simulation & 0.01642 & 34.56\% & 29.26\% & 0.01583 & 43.35\% & 30.05\% \\ ANN prediction & 0.01641 & 35.40\% & 29.00\% & 0.01567 & 41.84\% & 29.58\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of CFD simulation and ANN prediction on performance of the initial and optimized cascades. Figure 17: Aerodynamic performance curves of the cascades. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\overline{\omega}\) & \(\widetilde{A}_{SS}\) & \(\widetilde{A}_{PS}\) \\ \hline initial & 0.01642 & 34.56\% & 29.26\% \\ optimized & 0.01583 & 43.35\% & 30.05\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance characteristics of the cascades at the operating point. The absolute Mach number contours of the original and the optimized blades are plotted in Figure 19. Compared with the initial profile, the shock wave at the suction surface is weakened and moved aft. This results in a reduction in shock loss, which is also a contribution to the decrease of the total pressure loss. Figure 19: Absolute Mach number contours of initial and optimized blade profiles. Figure 18: \(C_{f}\) distributions on suction and pressure surfaces.