esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### On Subjectivity and Personalization  
The evaluation results show that some features of Four Seasons triggered both positive and negative experiences among the participants. For example, the interactive insects, VR hardware used, and gesture synchronization received both positive and negative responses:  
* Interactive insects:
* Negative experience: \"In the Autumn scene, if this content had been made more like the real world, I would have been able to kill the dragonflies.\"
* Positive experience: \"I feel realism when insects are on my hand.\"
* VR hardware:
* Negative experience: \"I can't adapt to this easily because this way is not popular yet.\"
* Positive experience: \"...this way of control doesn't need an extra learning process as I can play with bare hands right away, so it is convenient.\"
* Gesture synchronization:
* Negative experience: \"My hand movement is slower in VR than actual movement.\"
* Positive experience: \"My hand movement is expressed minutely, and it makes me feel free.\" Potential reasons for these discrepancies include different preferences and experience levels of the participants, as well as irregular problems in the system (e.g., high motion-to-photon latency) that only affected some users. The subjectivity of the results is aligned with the findings of [PERSON] et al. [10], who suggested in their review of presence factors that presence can be improved when a VR experience considers users' characteristics. Thus, for example, in the case of the interactive insects, Four Seasons could be upgraded to allow the user to choose what kind of interaction is possible depending on their preferences. However, personalizing a VR experience for diverse user types requires significant effort. To alleviate this, pre-constructed interaction profiles based on an existing user model could be used, such as [PERSON]'s player types [52] in the case of VR games. Users would then be categorized into one of the types using a simple questionnaire, with an option to later change the type manually or automatically [53].
###### Abstract  
This paper proposes a novel robust thyristor-controlled series compensator (TCSC) controller based on proportional integral derivative with filter (PIDF) and uses a new hybrid optimisation evolutionary programming sine cosine algorithm (EPSCA) to improve the power system's angle stability. The problem of the PIDF-TCSC design is transformed into an optimisation problem based on performance indices, such as damping factor, damping ratio, and eigenvalues, where the multi-objective function is employed to obtain the optimal stabiliser parameters. To examine the robustness of PIDF-TCSC, it was tested on a single-machine infinite-bus power system under different operating conditions. The performance of the system with the PIDF-TCSC controller was compared with the simulation results, and the results obtained with the proposed EPSCA were compared with those obtained with SCA, moth flame optimisation, and EP-based PIDF-TCSC methods. Simulation results showed the effectiveness of EPSCA for the PIDF-TCSC design and the superior robust performance for the enhancement of power system stability compared with other techniques in different cases.  
angle stability; objective function; eigenvalue analysis; TCSC; PIDF; optimisation
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 3 Method--SNR of ED and SD for Line Detection  
It is necessary to select between edge and line detectors when extracting linear features from an image. To make the right decision, it is necessary to know their performance under various conditions such as line width, noise level, and smoothing factor for noise suppression. Although this information is important for making the decision, presumably, very few studies have focused on this issue. To compensate for these limitations, in this section, the performances of ED and SD are compared by investigating their respective SNRs under various conditions.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 4 Discussion  
In order to investigate the reasons for the 609 keV peak, its daily counts were recorded. There were several days in which the peak counts showed a significant increase, and the corresponding dates are marked in Figure 6.  
When it rains, radionuclides in the atmosphere and on land may be carried into the ocean by the rain, which may result in an increase in the energy spectrum counts. The precipitation during the test period is summarized in Table 5. It can be seen that most of the rainfall is accompanied by an increase in 609 keV peak counts. However, there are also some rainy days when no increase in peak counts is observed, and there are some days with no precipitation but an increase in 609 keV peak count. It can be concluded that the changes in 609 keV peak counts are related but not fully synchronized with the changes in precipitation conditions.  
Figure 5: Spectrum at 200 to 1000 keV in April—August 2018.  
Figure 6: Daily counts of the peak at 609 keV (red and black marks are the days with discharge from reactor units 1/2 and units 3/4, respectively).  
To better clarify the source of the measured 609 keV peak, the authors recorded the liquid effluent discharges from the Ningde NPP. During the test period (April to July 2018), a total of seven discharges were conducted at units 1 and 2 and nine discharges at units 3 and 4 of the Ningde NPP. For each discharge, one tank of waste liquid with a volume of 300 m\({}^{3}\) was discharged in about 20 h. The activity levels of \({}^{124}\)Sb were all below the detection limit (about 0.6 Bq/L) in all the discharges. Considering that the detector was deployed at a distance of 100 m from the discharging outlet of the NPP, it can be inferred that due to the dilution of seawater, the radioactivity of \({}^{124}\)Sb at the position of the detector was lower than the MDAC of \({}^{124}\)Sb listed in Table 4 (0.144 Bq/L). Therefore, it is unlikely that the 609 keV peak found in the spectrum was caused by the discharge of waste liquid from the NPP.  
Another possible source of the 609 keV peak is \({}^{214}\)Bi in seawater. According to the data in Table 5, the change in the count rate of the 609 keV peak may be related to, but not entirely due to, the change in precipitation, as the changes in the two are not completely synchronized. Further research is needed to ascertain the cause of the peak count fluctuations.  
Although this study has not identified the specific cause of the 609 keV peak, it is certain that since this peak does not overlap with the energy peaks of the main emissions from a nuclear power plant, the existence of this peak does not affect the regular or emergency monitoring at the NPP.
###### Abstract  
Automatic speech recognition (ASR) aims at understanding naturally spoken human speech to be used as text inputs to machines. In multi-speaker environments, where multiple speakers are talking simultaneously with a large amount of overlap, a significant performance degradation may occur with conventional ASR systems if they are trained by recordings of single talkers. This paper proposes a multi-speaker ASR method that incorporates speaker embedding information as an additional input. The embedding information for each of the speakers in the training set was extracted as numeric vectors, and all of the embedding vectors were stacked to construct a total speaker profile matrix. The speaker profile matrix from the training dataset enables finding embedding vectors that are close to the speakers of the input recordings in the test conditions, and it helps to recognize the individual speakers' voices mixed in the input. Furthermore, the proposed method efficiently reuses the decoder from the existing speaker-independent ASR model, eliminating the need for retraining the entire system. Various speaker embedding methods such as _i_-vector, _d_-vector, and _x_-vector were adopted, and the experimental results show 0.33% and 0.95% absolute (3.9% and 11.5% relative) improvements without and with the speaker profile in the word error rate (WER).  
speech recognition; speaker embedding; speaker-attributed training +
Footnote †: journal: _Applied Sciences_
# Trends and Opportunities in the Dairy Industry: A2 Milk and Processing Methods
## 4 A2 Milk
### \(\beta\)-Caseins A1 and A2--The Differences  
Among the caseins in cow's milk, A1 and A2 \(\beta\)-caseins comprise the most significant portion. These are peptides consisting of 207 amino acids. The difference in structure between A1 and A2 \(\beta\)-caseins lies in an amino acid change at the 67 th position (Figure 2). A1-type casein has the amino acid proline at the 67 th position, while A2 has histidine at the same position. The binding of histidine to the preceding amino acid Isoleucine is easily broken by pancreatic elastase, resulting in the release in the gastrointestinal tract of the biologically active peptide BCM 7, which comprises seven amino acids. This does not occur with A2-type casein with proline at the histidine position, whose binding to Isoleucine is not hydrolyzed by pancreatic elastase. A2-type casein is generally more susceptible to enzymatic hydrolysis by gastric enzymes such as pancreatic elastase, pepsin, and leucine aminopeptidase. As a result of the different binding hydrolysis sites--in the case of A2 casein--a non-bonded BCM-9 is formed [40; 54]. Even before human domestication of cattle, A2 casein was the predominant casein variant. Following a natural mutation 5000-10,000 years ago, individuals emerged whose milk began to be dominated by A1-type casein. Over the following years, as a result of crossbreeding of individuals giving the most milk, which was beneficial from a production point of view, the breeds currently dominant in industrial milk production--Holstein, British Shorthorn, Ayshire, Friesian, and Saihwal--were separated. These are breeds with the A2A2 genotype, i.e., predominantly producing A2-type casein. The reduction in cattle biodiversity due to selective breeding has displaced A1-type milk. The percentage of A1 \(\beta\)-casein is higher in black and white breeds than in yellow and brown breeds, such as Pezzata Rossa and Bruna. A1 \(\beta\)-casein is absent in the milk of pure Asian and African cattle [25; 40; 55]. Figure 1: Scheme of the content of the different protein fractions in milk and casein types [53]. According to the A1/A2 milk hypothesis, there is evidence of limited adverse health effects of A2 milk compared to A1. This is caused by BCM-7's structural similarity to endogenous opioid peptides, which causes it to attach to opioid receptors found in humans' central nervous system and gastrointestinal tract. At the same time, this structural change also causes another potential effect on human health, like the hydrophobicity of \(\beta\)-casein. Similarly, these two forms affect casein's stability and emulsifying and coagulating properties (Table 1) [48; 54; 55; 57].
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 3 Dataset
### Dataset Preprocessing  
Preprocessing is a vital step that enhances data quality and lays a strong foundation for subsequent feature extraction and pattern recognition [42].  
As shown in Figure 2, the raw accelerometer data exhibit significant noise and variability. This noise can be attributed to various factors, including sensor drift, environmental disturbances, and sensor inaccuracies. To address these challenges, we employed a series of preprocessing steps to improve the quality of our sensor data. In this study, we conducted the following preprocessing steps:  
Filtering: Sensor data often contain various sources of noise. To improve reliability, we applied filtering techniques such as low-pass filters and Kalman filters to effectively remove random noise from the raw data. Through systematic parameter sensitivity analysis, we determined that Kalman filtering with process noise \(Q=10^{-5}\) and measurement noise \(R~{}=~{}10^{-2}\) provides optimal noise reduction while preserving essential signal characteristics. Our ablation studies demonstrate that this optimized filtering step alone improves classification accuracy by 6.3% compared to using raw data.  
Normalization: Differences in sensor performance across devices and varying usage environments can lead to inconsistent data. We normalized the data to a common scale,  
Figure 1: Coordinate system used by Sensor API (relative to the mobile device).  
Figure 2: Pedestrian accelerometer data during walking.  
which standardizes the measurements and facilitates accurate comparison and analysis. When combined with our optimized Kalman filtering, normalization contributed to an overall accuracy improvement of 8.2-14.7% compared to using unprocessed data.  
Window Segmentation: For time series analysis, the continuous sensor data were segmented into multiple time windows, each capturing data over a specific duration. This segmentation enables localized analysis, allowing for more precise feature extraction and time series evaluation. Through empirical testing of various window lengths (20-60 samples), we determined that 30-sample windows provide the optimal balance between temporal resolution and feature stability, with shorter windows failing to capture complete activity cycles and longer windows introducing class boundary confusion.  
Feature Engineering: In the final preprocessing stage, we extracted a set of statistical features from each data window. These features include fundamental metrics such as the mean, variance, maximum, and minimum values of the sensor data within the window.  
The careful balance between sensor configuration, efficient data handling, and robust feature engineering underpins the improved performance of our human activity recognition system. The systematic optimization of preprocessing parameters represents a significant methodological contribution, as it establishes reproducible guidelines for effective sensor data processing in IoT environments.
# Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification
## 5 Conclusions  
In this study, we have presented an approach that exploits the \(T_{ro}\) and the ENF to trace an ENF-containing video to its source camera. We adapted an ENF estimation method in which the \(T_{ro}\) is a sensitive parameter and is employed to compute the number of zeros to be inserted in the idle period for the specific camera that captured a video or the specific resolution and frame rate of the video before analyzing it, which is critical in estimating an ENF signal with no distortion. The \(T_{ro}\) value that corresponds to the camera used to record the video will lead to the estimation of an ENF that best matches the reference signal. In essence, our approach is based on the idea that, given an ENF-containing video from an unknown source camera, we can apply different \(T_{ro}\) values to analyze the video and the \(T_{ro}\) value (camera) that leads to the extraction of an ENF signal with the highest correlation and lowest error margin when compared with the reference ENF can be said to have produced the video. We performed experiments using the \(T_{ro}\) values of seven cameras and the results validate our idea. Our approach could prove very useful in a practical scenario where a video obtained from the public, including social media, is being investigated by law enforcement to ascertain if it originated from a suspect's camera. The limitation of our proposed approach is the computation cost required to calculate the \(T_{ro}\) values for the ENF extraction process and the matching of the extracted ENF against a large ENF reference database. Our approach can be further studied using several videos and more camera \(T_{ro}\) values to examine its consistency in achieving useful performance.  
Conceptualization, E.N., L.-M.A., K.P.S. and M.W.; Methodology, E.N. and L.-M.A.; Software, E.N.; Writing--original draft, E.N.; Writing--review & editing, E.N, L.-M.A., K.P.S. and M.W.; Supervision, L.-M.A., K.P.S. and M.W. All authors have read and agreed to the published version of the manuscript.  
Figure 10: MAE of the extracted ENF and the reference ENF.
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 5 Conclusions  
To address the challenge of text detection in natural scenes, the text detection algorithm was improved in this paper. The global features of the input image were obtained using a multi-headed self-attention mechanism, and then fused with the local features extracted by CNN to obtain a fused feature. In addition, a shifting window calculation was used to reduce the computational effort when calculating the self-attention. Finally, the experimental results in terms of the F1-score values on two benchmark datasets, ICDAR2015 and MSRA-TD500, reached 85.9% and 88.4%, respectively. Compared with the F1-score values of 85.4% and 84.9% of the pre-improved method DBNet for these two datasets, the improvement was 0.5% and 3.5%, respectively, which verified the effectiveness of the proposed method in this paper.  
In the future, we are interested in extending our network to improve the feature extraction from irregular text and the generalization of the model by finding simpler and more efficient global feature extraction methods.  
Conceptualization, X.F.; methodology, X.F.; validation, X.F.; writing--original draft preparation, X.F.; writing--review and editing, B.H.; supervision, B.H.; funding acquisition, B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 61962005.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F1-Score** \\ \hline CTPN [28] & 74.2 & 51.6 & 60.9 \\ SSTD [29] & 80.2 & 73.9 & 76.9 \\ WordSup [30] & 79.3 & 77 & 78.2 \\ Corner [31] & 94.1 & 70.7 & 80.7 \\ RRD [32] & 85.6 & 79 & 82.2 \\ TextBoxes++ [14] & 87.2 & 76.7 & 81.7 \\ MCN [33] & 72 & 80 & 76 \\ TextSnake [34] & 84.9 & 80.4 & 82.6 \\ PSENet [18] & 86.9 & 84.5 & 85.7 \\ SAE [35] & 85.1 & 84.5 & 84.8 \\ DBNet [19] & 88.2 & 82.7 & 85.4 \\ PAN++ [36] & 85.9 & 80.4 & 83.1 \\ FAST-B-736 [37] & 88.0 & 81.7 & 84.7 \\ Ours & 90.7 & 81.6 & 85.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of ICDAR2015 [25].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **F1-Score** \\ \hline DeepReg [38] & 77 & 70 & 74 \\ Corner [31] & 87.6 & 76.2 & 81.5 \\ RRD [32] & 87 & 73 & 79 \\ TextSnake [34] & 83.2 & 73.9 & 78.3 \\ CRAFT [39] & 88.2 & 78.2 & 82.9 \\ SAE [35] & 84.2 & 81.7 & 82.9 \\ DBNet [19] & 91.5 & 79.2 & 84.9 \\ PAN++ [36] & 85.3 & 84.0 & 84.7 \\ FAST-B-736 [37] & 92.1 & 83.0 & 87.3 \\ Ours & 91.8 & 85.3 & 88.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of MSRA-TD500 [26].  
Data Availability Statement: The data presented in this study are openly available in reference number [24; 25; 26; 27].
# Longitudinal Seismic Response of Continuously Welded Track on Railway Arch Bridges
## 1 Introduction
Consequently, it is necessary to further analyze the longitudinal seismic response of CWR on arch bridges to quantitatively predict seismic behavior in different scenarios. In addition, in recent mountain railway construction in China, large-span arch bridges have been frequently utilized [17; 18]; the bridge sites are in dangerous areas and across river valleys, where the probability of earthquakes is greater. The seismic responses of CWR track on arch bridge is yet to be demonstrated. Based on the discussion above, the research presented here aims at identifying the longitudinal stress deformation mechanism of CWR tracks on arch bridges under seismic loading. In particular, an integrated track-bridge spatial coupling finite element model was established and a nonlinear spring element was used to simulate track-bridge contact. The study included an investigation of the effect that seismic loading has on rail longitudinal force, pier top displacement, and dynamic characteristics of track resistance. The effect of seismic excitations, thermal changes, bridge span layout, and changes to other parameters were compared.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 4 Simulation Results  
We have tested the statistical performance of the proposed method over 500 quasi-static fading channels. We consider a quasi-static fading model in which the channel is fixed during each packet and is independently random from packet to packet. Each packet includes 10,366 quadrature phase shift keying (QPSK) symbols, and eight received hydrophones are used. A sparse channel with an extended delay (\(L=100\)) is used in the simulation, where \(N_{p}=15\) is active and the other multipath magnitudes are zero. The amplitude of the path is a Rayleigh distribution, and the average power decreases exponentially along with delay, where the first and last paths differ by 20 dB. The number of feedforward and feedback filters are 32 and 32, respectively. The fractionally spaced equalizer (two samples per symbol) is used for the feedforward filter. The RLS forgetting factor \(\lambda\) in the MC-DFE is 0.999. The residual phase offset is corrected by a DPLL embedded in the MC-DFE. Both \(K_{1}\) and \(K_{2}\) in DPLL are 0.005.  
Figure 4 plots the channel impulse responses (CIR) for eight channels in one simulation. Figure 5 illustrates the output SNR improvements using the proposed TR-MC-DFE according to different input SNRs. The output SNR increases as the input SNR increases. For example, when an input SNR equals 10 dB, the output SNR improvement is about 1 dB. When the input SNR equals 15 dB, the output SNR improvement is about 1.8 dB. The output SNR of the MC-DFE is higher than the TR-MC-DFE in our simulation, but its complexity for eight receivers is high.
## 4 Results
### Results of Analyses of Effects of Two Factors
#### 4.2.2 Analysis of the Pair Camera Type and Contrast Ratio  
The second pair to be analyzed was the camera type and the contrast ratio. Similarly to above, the previously formed contrast ratio groups were used. In \"Orthographic, NO_R\", 696 tests were performed; in \"Orthographic, INC_C\", 595 tests; in \"Perspective, NO_C\", 780; and in \"Perspective, INC_C\", 638 tests. The numerical results are presented in Table 10 and the analysis provided by logistic regression analysis is presented in Table 13.  
\"Orthographic, NO_C\", on the basis of Table 13, was found to be significantly worse than the others. There are no significant differences among the other three.  
The results of the additive model allowing interactions are summarized in Table 14.  
As can be seen, both variables have a significant influence on the probability of correct answers, and interaction also exists (\(p\)-value = \(8.91\times 10^{-5}\)). Table 15 shows the results of their interaction.  
Due to \(p\)-value = \(8.895\times 10^{-5}\), it was concluded that the model that takes interactions into account provided a significantly better probability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Camera Type and Contrast Ratio** & **Estimate** & **Standard Error** & \(z\)**Value** & **Pr (\textgreater{}|z|)** \\ \hline Intercept & 0.53467 & 0.02150 & 24.873 & \textless{}2\times 10^{-16}\) \\ Orthographic, INC\_C & 0.19765 & 0.03215 & 6.148 & \(7.85\times 10^{-10}\) \\ Perspective, NO\_C & 0.23707 & 0.03014 & 7.867 & \(3.64\times 10^{-15}\) \\ Perspective, INC\_C & 0.25819 & 0.03181 & 8.117 & \(4.80\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Logistic regression analysis results of the effects by pairing the camera type and contrast ratio.