esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Binaural Cue-Based Amplitude Panning in Irregular Loudspeaker Configurations
## 4 Experiments
### Subjective Experiments
The perceived angle results for the proposed method show that the median perceived azimuth angles closely aligned with the desired angles, with errors consistently remaining within \(3^{\circ}\), thereby outperforming both the VBAP and DBAP methods. The results obtained with the other three audio content types are qualitatively similar to those observed with the 1.5 kHz low-pass filtered pink noise. For the VBAP method at \(20^{\circ}\), \(25^{\circ}\), and \(30^{\circ}\), the perceived angles deviate by more than \(5^{\circ}\) from the desired angles, except for the conversation at \(20^{\circ}\) and \(25^{\circ}\). In the case of the 9 kHz low-pass filtered pink noise, the largest error occurs at \(20^{\circ}\), with an error of \(11^{\circ}\). The perception of virtual sound sources using DBAP tends to shift toward the two loudspeakers, with a significant variation observed among subjects at \(30^{\circ}\). The proposed method consistently results in perceived azimuth angle errors within \(5^{\circ}\) across all audio content types and azimuth angles, with the largest error being \(5^{\circ}\) at the \(10^{\circ}\) azimuth angle for the song clip. Wilcoxon signed-rank tests are conducted between the proposed method and the other methods. Significant differences are observed between the proposed method and VBAP in the \(10^{\circ}\) to \(30^{\circ}\) range for all four audio content types (\(p\) < 0.05 for the song at \(20^{\circ}\) and \(p\) < 0.01 for the other cases), except for the 9 kHz low-pass filtered pink noise at \(10^{\circ}\). Significant differences are also found between the proposed method and DBAP at all azimuth angles for all four audio content types (\(p\) < 0.01), except at \(35^{\circ}\) and \(40^{\circ}\). These results suggest that the proposed method improves localization accuracy compared with both VBAP and DBAP. The subjective test results for azimuth angles are consistent with the ITD results from the objective experiments. For the VBAP method, the virtual sound sources in the \(20^{\circ}\) to \(30^{\circ}\) azimuth range exhibit both ITDs and perceived azimuth angles that deviate from the desired values. The virtual sound sources generated by the DBAP method are biased toward the left or right loudspeaker, failing to reproduce stable sound images in the region between the loudspeakers, which aligns with the segmented characteristics of the ITD. The proposed method consistently ensures a close correspondence between the perceived and desired angles. This result suggests that, although the ITD is calculated based on the received signals below 1500 Hz, the proposed method still achieves accurate virtual sound source localization for wideband audio. This indicates that the ITD parameters exert a dominant influence on the subjective localization process.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Proposed Merging-Squeeze-Excitation Feature Fusion
#### 6.3.3 MSE Feature Fusion with Global Channel Attention  
Table 13 shows the accuracy scores of the MSE feature fusion with global channel attention proposed in Section 5.2 according to the merging methods, DL models, and datasets. We have the following results:  
* On classifying the PAMAP2 and DaLiAc datasets, the MSE feature fusion with global channel attention achieves the highest accuracy score of 99.17% and 98.08%, respectively, when using the minimum merging and AlexNet.
* On classifying the DSAD dataset, the MSE feature fusion with global channel attention achieves the highest accuracy scores of 97.87% when using the average merging and AlexNet.
* By comparing these accuracy scores to those of the original MSE feature fusion, we see that the original MSE feature fusion outperforms the MSE feature fusion with global channel attention. The feature maps obtained by using the global channel attention do not provide any additional information.
## 5 Problem Description, Formulation, and Solution
The empirical model in [5] is for wireless discharging and charging processes. As stated earlier, the efficiency of WPT depends on the transfer distance (as shown in Figure 5). However, we are not very much concerned with the distance. Our proposed model presupposes a uniform charging for all the sensor-nodes within the coverage area of the mobile MBC. This implies that each node receives a uniform charging as long as it is within the charging vicinity of the mobile charger. From [5], it is known that the relationship between transmit power, received power, and the wireless power transfer is usually associated with [PERSON] free space propagation model given by:  
\[W_{r(d)}=\frac{G_{T}G_{R}\lambda^{2}}{\left(4\pi d\right)^{2}}W_{t} \tag{12}\]  
where \(W_{t(d)}\) is the transmitted signal power of the MBC in Watts, \(W_{r}\) is the received power by the sensor, S, in Watts, \(G_{T}\) and \(G_{R}\) are the gains of the antennas of the MBC and sensor S, respectively, d is the power transfer distance between the sensor and the MBC, and \(\lambda\) denotes the wavelength of signal power in meters [5]. Figure 5 shows the effect of varying the distance on the WPT efficiency. The [PERSON] free space propagation model in Equation (12) is obtained when the factor L known as system loss is equal to 1, implying no losses as shown in Equation (13). The  
Figure 5: Effect of varying distances on WPT efficiency. free-space radio propagation model can be used to predict the received signal strength when the transmitter and the receiver have a clear LOS between them [46; 47]. \[\mathbf{W}_{r(d)}=\frac{\mathbf{G}_{\mathbf{T}}\mathbf{G}_{R}\mathbf{\lambda}^{2}}{(4\pi d)^{2}\mathbf{L }}\mathbf{W}_{\mathbf{t}} \tag{13}\]  
The parameter \(\mathbf{L}\) is called system loss or other losses not associated with the propagation loss ((\(\mathbf{L}\geq 1\)) [46] and may include loss at the antenna, transmission line attenuation, los at various filters, etc. The factor \(\mathbf{L}\) is usually greater than or equal to 1 with \(\mathbf{L}=1\) for no such system losses. When \(\mathbf{L}=1\), we then obtain Equation (12). The Friis free space propagation model is used to model the line-of-sight (LOS) path loss incurred in free space environment. It is valid only in the far-field region of the transmitted antenna [46; 47; 48] and is based on the inverse square law of distance which states that the received power at a particular distance from the transmitter decays by a factor of square of the distance as shown in Figure 5, which shows the variation of distance with WPT efficiency. Figure 5 is the graphical comparison of the theoretical and experimental model of WPT. From the graph, it is evident that the efficiency of a WPT largely depends on the distance between the sensor-nodes and the wireless MBCs.
## 5 Discussion
### Future Research Directions  
To enhance the universality of the model, future research can explore the following aspects: (1) Refine spatial modeling: Due to the two-dimensional space assumption underestimating paths by neglecting vertical movements (e.g., overpasses/underground passages), the homogeneous space assumption overestimates accessibility in complex road networks. Future studies should investigate the impact of heterogeneous spaces and different cargo characteristics on locker selection and how to optimize the model by incorporating urban road network data. At the same time, the model's errors can also be corrected, such as by introducing a \"road network correction coefficient\" (dynamically weighted distance based on road network topological data) during the model output stage to partially offset theoretical errors. Simultaneously, GPS data can also be used to concentrate speed parameters, facilitating the identification of transportation nodes through speed conversion, thereby integrating package pickup with travel arrangements. (2) Multidimensional data fusion: Research should introduce larger-scale pedestrian spatiotemporal data or traffic data (Gaode API), or fuse multi-source positioning data, to increase data volume, improve model robustness, and quantify the impact of road obstacles and other factors on the results by incorporating a multimodal traffic time cost function. (3) Dynamic parameter optimization: Adaptive bandwidth selection methods, such as reinforcement learning [43], should be explored, as well as the logistics demand forecasting model combining variational autoencoders with recurrent neural networks [44], to enhance the robustness and adaptability of the model. (4) Emerging technologies such as big data analysis and artificial intelligence should be utilized to further enhance the intelligence and automation level of PLC selection. (5) Cross-scenario validation: The model should be tested in typical city types (e.g., tourist cities, industrial cities) to analyze its performance boundaries and parameter tuning strategies.
## 2 Materials and Methods
### Statistical Analyses  
Normality was assessed for all variables using the Shapiro-Wilk statistic. All variables were normally distributed (\(p>\) 0.05). Relationships between RSI and sprint times were determined by Pearson product-moment correlations (alpha \(\leq\) 0.05) using SPSS software (version 22.0, IBM Corp, Armonk, NY, USA). The best repetitions based on 30 m sprint times were used for analysis [30]. Correlations were evaluated as small (0.1-0.3), moderate (0.3-0.5), large (0.5-0.7), very large (0.7-0.9), nearly perfect (0.9-1.0), and perfect (1.0) [36]. To determine the relationship between RSI, its derivatives, and step kinematics, two steps were taken from each sprint section and averaged for analysis. The two steps chosen were based on every player taking those two steps within the given section. Therefore, steps 2 and 3 were selected for the 0-5 m section, steps 5 and 6 for the 5-10 m section, steps 10 and 11 for the 10-20 m section and steps 15 and 16 for the 20-30 m section. Relationshipsbetween RSI and step kinematics were determined by Pearson product-moment correlations (alpha \(\leq\) 0.05) using SPSS software (version 22.0, IBM Corp, Armonk, NY, USA). Post hoc comparisons were conducted using the Bonferroni correction to control for Type 1 error [37].
# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 3 Preparation for the Experiments
### Construction of Automatic Seismic Phase Recognition Dataset  
Because the deep learning method requires a large amount of data to train the network model, this paper uses the dataset STEAD (Stanford Earthquake Dataset) as the original dataset, and generates the sample data according to the magnitude, SNR (signal-to-noise ratio), and other conditions [23]. Each seismic waveform datum in this dataset is 60 s of three-component data, including 35 pieces of label information. The data example of the STEAD dataset is shown in Figure 2, and the sample data label information is shown in Table 1. In Figure 2, the blue line represents the first arrival point of the P-wave phase, the red line represents the first arrival point of the S-wave phase, and the green line represents the point where the energy of the seismic signal disappears. In the original dataset, the data are filtered by magnitude based on the information provided by the labels. Magnitude refers to the size of an earthquake, which is a measure of the strength of an earthquake. It is determined by the amount of energy released by each seismic activity measured by seismometers. The magnitude is usually represented by the letter M, and the internationally recognized Richter scale is divided into nine levels. Earthquakes with M < 1 magnitude are generally referred to as super microseismic events; M more than 1 and less than 3 are called weak or microseismic; earthquakes with M more than 3 and less than 4.5 are called felt earthquakes; M more than 4.5 and less than 6 are called moderate strong earthquakes; strong earthquakes are those with M more than 6 and less than 7; earthquakes with M more than 7 and less than 8 are called major earthquakes; Earthquakes with 8 or above are called massive earthquakes. The laboratory has been studying microseismic signals for a long time. Therefore, this article screened the seismic waveform data according to the magnitude distribution between M0.5 and M2.5 and the signal-to-noise ratio range between 20 dB and 30 dB. A total of 43,700 seismic waveform data were obtained, and 10,000 seismic waveform data were randomly selected as sample data. We preprocess the filtered sample data, cut the waveform according to the first arrival sampling point of the P-wave and the S-wave provided by the data label, randomly cut the 3 to 5 s before the arrival of the P-wave as the waveform starting point and select the 10 s after the arrival of the S-wave as the waveform ending point, and cut the waveform data to a length of 30 s. When the length after cutting is less than 30 s, zero values are randomly used before or after the data for supplement. After cutting, all the data are normalized to prevent the model from overfitting and affecting the training effect of the model. The normalization operation is shown in Equation (2), where \(v_{i}\) represents the amplitude value of the seismic waveform data. \[v_{i}=\frac{v_{i}}{\max\mid v_{i}\mid} \tag{2}\]
## 4 Results
### MoveNet and Classification  
Due to the limitations of the CNN models, we explored the use of MoveNet, a posture estimation model, for trunk posture detection. MoveNet provided us with key anatomical points representing body joint positions and angles relevant to trunk postures.  
In the cross-validation phase, we observed the performance metrics of our classification models using stratified 3-fold cross-validation. Following the cross-validation phase, we tested the final trained models on our independent test dataset to evaluate their performance in real-world scenarios. Table 2 presents the accuracy obtained from the cross-validation for each model, as well as the accuracy, precision, recall, and F1 score achieved by each model on the test dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained Models**} & \multicolumn{2}{c}{**Training Results**} & \multicolumn{2}{c}{**Test Results**} & \multirow{2}{*}{**F1-Score**} \\ \cline{2-2} \cline{4-6}  & **Accuracy** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \hline SVM & 82.91\% & 69.05\% & 68.05\% & 67.15\% & 66.64\% \\ \hline \hline DT & 87.18\% & 71.43\% & 72.74\% & 70.19\% & 70.38\% \\ \hline RF & 91.45\% & 71.43\% & 71.88\% & 70.67\% & 70.87\% \\ \hline ANN & 94.02\% & 80.49\% & 80.61\% & 78.93\% & 79.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics of the classification using MoveNet features.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Trained Models**} & \multicolumn{2}{c}{**Training Results**} & \multicolumn{2}{c}{**Test Results**} & \multirow{2}{*}{**F1-Score**} \\ \cline{2-2} \cline{4-6}  & **Accuracy** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \hline \multirow{2}{*}{\begin{tabular}{l} MobileNet \\ ResNet \\ \end{tabular} } & \multirow{2}{*}{99.77\%} & \multirow{2}{*}{65.56\%} & \multirow{2}{*}{67.96\%} & \multirow{2}{*}{65.56\%} & \multirow{2}{*}{63.11\%} \\ \cline{2-2} \cline{4-6}  & & & & & \\ \hline \hline \multirow{2}{*}{\
\begin{tabular}{l} ResNet \\ \end{tabular} } & \multirow{2}{*}{52.50\%} & \multirow{2}{*}{36.67\%} & \multirow{2}{*}{36.67\%} & \multirow{2}{*}{36.67\%} & \multirow{2}{*}{26.49\%} \\ \cline{2-2} \cline{4-6}  & & & & & \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{l} Inception \\ VGG-16 \\ \end{tabular} } & \multirow{2}{*}{94.32\%} & \multirow{2}{*}{62.22\%} & \multirow{2}{*}{63.18\%} & \multirow{2}{*}{62.22\%} & \multirow{2}{*}{62.18\%} \\ \cline{2-2} \cline{4-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics of transfer learning models.  
The results demonstrated that MoveNet, combined with ANN, yielded improved accuracy in trunk posture classification compared to the other models. However, from the results, we can observe that the model has exhibited overfitting, indicating that we still need to find alternative methods to enhance the model's test performance.
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
###### Abstract  
Surface topography is closely related to fatigue strength and mating accuracy of workpieces. The profile method is widely adopted to evaluate surface topography. In the present study, a 2D profile simulation model of five-axis CNC machining with a bull-nose cutter is proposed to predict the surface topography of a machined workpiece. To this end, a simplified scallop model is established by analyzing the geometry and motion of the bull-nose cutter. Then, the principles of the 2D profile simulation model for plane and free-form surfaces are described to provide the basis for building 2D profile simulation models. After that, 2D profiles are obtained directly from CL data, tool parameters, and workpiece design models, and an algorithm is proposed to obtain 2D profiles. Finally, the proposed algorithm is verified by different machining experiments on plane and free-form surfaces. The results show that the simulation and measurement results are in good agreement. The proposed simulation model for five-axis CNC machining with a bull-nose cutter can be effectively applied to simulate 2D profiles of plane and free-form surfaces. The present study is expected to provide a reference for optimizing process parameters.  
five-axis CNC machining; analytical model; surface topography; bull-nose cutter +
Footnote †: journal: Applied Sciences
## 2 Study Assumptions and Study Area
### Study Area  
Consistent with the underlying assumptions above, the cities of interest are all located in economically developed areas along the KTX lines, because: (a) there was not much change in the traffic facilities (e.g., highway), except for KTX development, during the research period, which means that the construction of infrastructures is relatively completed in these areas; (b) the gap among these areas was not too large in terms of infrastructure development and the rate of socio-economic scale change. Consequently, five metropolises (i.e., Seoul, Busan, Daegu, Daejeon and Gwangju) in Korea, along with the two newly built KTX lines--the Gyeongbu Line (Seoul to Busan) and Honam Line (Seoul to Gwangju)--were chosen as the target. These five cities account for 36% of the country's population and 34% of its GDP. Especially, Seoul is the capital of Korea, which is the center of industry, finance, trade, science, culture, and education in the country.  
The KTX Gyeongbu Line from Seoul to Daegu, with the maximum operating speed at 300 km/h, was completed in 2004 as part of the first stage of the Korean HSR network. The travel time between Seoul and Busan was, therefore, reduced from 4 h and 10 min to 2 h and 40 min. In November 2010, the operation of the Daegu-Busan line as part of the second stage of the HSR network further shortened the travel time from Seoul to Busan by about 44 min. In 2015, the Honam KTX line (Seoul to Gwangju) was completed, the time of Seoul to Gwangju being reduced from 2 h and 39 min to 1 h and 33 min, which would generate more travel demand. The regional distribution is illustrated in Figure 1.
## 5 Experimental Tests
### Communication Tests
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Spreading** & **BER** & **Transmission** & **BCH Code** & **Transmission** & **BCH Code** & **Transmission** \\
**Sequence** & **No ECC** & **Rate** & **Rate** & **Rate** & **Rate** & **Rate** \\  & & **No ECC** & **BER \textless{} 0.01** & **BER \textless{} 0.01** & **BER \textless{} 0.001** & **BER \textless{} 0.001** \\ \hline \multicolumn{8}{c}{**Bandwidth = 1 kHz**} \\ \hline m-sequence & \textless{}0.001 & 3.92 bps & — & 3.92 bps & — & 3.92 bps \\ \hline Kasami code & \textless{}0.001 & 3.92 bps & — & 3.92 bps & — & 3.92 bps \\ \hline Gold code & \textless{}0.001 & 3.92 bps & — & 3.92 bps & — & 3.92 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 2 kHz**} \\ \hline m-sequence & \textless{}0.001 & 7.84 bps & — & 7.84 bps & — & 7.84 bps \\ \hline Kasami code & 0.06 & 7.84 bps & 5/15 & 2.61 bps & 6/31 & 1.62 bps \\ \hline Gold code & 0.18 & 7.84 bps & 7/63 & 0.87 bps & 8/127 & 0.49 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 4 kHz**} \\ \hline m-sequence & 0.07 & 15.69 bps & 5/15 & 5.23 bps & 6/31 & 3.04 bps \\ \hline Kasami code & 0.33 & 15.69 bps & 12/2047 & 0.09 bps & \textgreater{}13/4095 & \textless{}0.05 bps \\ \hline Gold code & 0.17 & 15.69 bps & 6/31 & 3.04 bps & 8/127 & 0.99 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 5 kHz**} \\ \hline m-sequence & 0.20 & 19.61 bps & 7/63 & 2.18 bps & 11/1023 & 0.21 bps \\ \hline Kasami code & 0.28 & 19.61 bps & 12/2047 & 0.11 bps & \textgreater{}13/4095 & \textless{}0.06 bps \\ \hline Gold code & 0.25 & 19.61 bps & 11/1023 & 0.21 bps & 12/2047 & 0.11 bps \\ \hline \multicolumn{8}{c}{**Bandwidth = 8 kHz**} \\ \hline m-sequence & 0.08 & 31.37 bps & 5/15 & 10.46 bps & 6/31 & 6.07 bps \\ \hline Kasami code & 0.31 & 31.37 bps & 12/2047 & 0.18 bps & \textgreater{}13/4095 & \textless{}0.1 bps \\ \hline Gold code & 0.17 & 31.37 bps & 6/31 & 6.07 bps & 8/127 & 1.98 bps \\ \hline \end{tabular}
\end{table} 
Table 10: Results of experimental tests.
# Allogeneicic Demineralized Dentin Matrix Graft for Guided Bone Regeneration in Dental Implants
## 1 Introduction
In 2014, successful clinical outcomes were reported with Allo-DDM procured from a biological family member for alveolar ridge and sinus augmentation [19]. Subsequently, [PERSON] et al. reported that the use of Allo-DDM for alveolar bone augmentation in 18 patients did not elicit an immune response or antigenicity and showed clinical results similar to those of Auto-DDM [20]. In 2017, the first randomized, controlled, prospective, a clinical pilot study on comparative alveolar ridge preservation using allogenous tooth graft versus allogenous free-dried bone graft in 15 patients was reported in India [21]. This study showed that the mineralized whole tooth allograft (WTA) consistently indicated superior results, demonstrating the least reduction in the alveolar crest height. Histological analysis also confirmed the new bone formation at the WTA sites. Even though several clinical studies have been published in the 2010s, there was only one randomized controlled clinical study for socket preservation before implant placement. Most of the other studies were case reports, which evaluated the alveolar bone repair capacity by qualitative measurement that is insufficient to confirm the clinical efficacy of Allo-DDM application. So far, few studies have quantitatively analyzed the clinical effects of Allo-DDM in relation to dental implants. Furthermore, evaluation of buccal marginal bone (BMB) resorption around dental implants before and after prosthetic loading has not yet been reported. The purpose of this clinical pilot study was to analyze the BMB resorption between Allo-DDM and Auto-DDM in GBR around dental implants before and after prosthetic loading. We hypothesizedthat Allo-DDM grafted around dental implants would show similar BMB resorption to those of Auto-DDM within the success criterion, which is defined as BMB loss <1.5 mm in the first year after prosthetic loading.