esa-sceva/satcom-chunk-collection huggingface dataset:
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 4 Experimental Methods
### Experimental Results  
#### 4.3.1 Results of Spoof Detection  
To demonstrate the effectiveness of the one-class learning and domain-adaptive methods, we compared our proposed OC-Softmax and domain-adaptive methods with the traditional binary classification loss function under the same input features and model settings. They were also compared with the EER and min t-DCF metrics of the baseline model LFCC-GMM, officially provided by the ASVspoof2019 competition, as shown in Table 1. In Table 1, the OC-Softmax and domain adaptive methods obtained the best results, with equal error rate EER metrics of 2.530 and 2.638%, respectively. Under the metric min t-DCF, the OC-Softmax method obtained the best result of 0.0682. This indicated that, based on the strong generalization ability of the one-class loss function in the model inference stage, it could still recognize the truth and falsity of the voice spoofing of unknown attack types in the test set.  
To further analyze the performance of each method, we obtained the distribution of the CM scores in the four sets of experiments, as shown in Figure 6. The comparison shows a considerable overlap between the bona fide and spoof distributions in Figure 6a, which indicates that the AM-Softmax-trained model has low generalization and a high misspecification rate. This overlap is almost invisible in Figure 6b-d. However, as can be seen from the horizontal coordinates, the difference between the mean CM scores of the bona fide and spoof samples in Figure 6b is approximately 1. The difference between the mean CM scores of the bona fide and spoof samples in Figure 6c is approximately 1.5. The difference between the mean CM scores of the bona fide and spoof samples in Figure 6d is approximately 2. The more significant the difference between the mean CM scores of the bona fide and spoof samples, the stronger the model's ability to distinguish between true and spoofing voices and the higher the generalizability. Therefore, we can consider the generalization ability for voice spoof detection: OC-Softmax > domain adaptation > Sigmoid > AM-Softmax.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **EER (\%)** & **Min t-DCF** \\ \hline LFCC-GMM & 8.090 & 0.21160 \\ LFCC-LCNN—AM-Softmax & 4.647 & 0.08278 \\ LFCC-LCNN—Sigmoid & 2.869 & 0.07248 \\ LFCC-LCNN—Domain adaptation & 2.638 & 0.08844 \\ LFCC-LCNN—OC-Softmax & 2.530 & 0.0682 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the evaluation set at ASVspoof2019 LA.
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Comparative Technical Analysis of Machine Learning Methods
In light of the distinct trade-offs between classical and deep learning approaches, hybrid and ensemble methodologies are increasingly being adopted as a pragmatic strategy to leverage the strengths of different machine learning paradigms. Hybrid models, for instance, often combine the automatic feature extraction capabilities of deep learning networks--such as CNNs for processing time-frequency representations--with the classification efficiency and interpretability of classical algorithms like SVM or kNN. This synergistic integration has been shown to yield substantial accuracy gains, particularly when fusing data from multiple sensor types (e.g., vibration, acoustic, current), improving performance by 15-20% over single-sensor, single-algorithm approaches. Similarly, ensemble methods, including Random Forests and Extreme Gradient Boosting (XGBoost), enhance model robustness by aggregating predictions from multiple base learners, thereby mitigating overfitting and providing valuable insights through built-in feature importance metrics. These advanced architectural choices reflect a growing sophistication in model design, aimed at achieving optimal performance, resilience, and practical utility in complex vibroacoustic diagnostic scenarios. The accuracy ranges reported in Table 1 are based on the minimum and maximum values found across the individual studies included in this review. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Feature Engineering** & **Computation** & **Noise Robustness** & **Use Case Example** \\ \hline SVM & 85–95\% & Required & Low & Medium & Fault classification in bearings \\ DT & 80–90\% & Required & Very Low & Low & Brake diagnostics \\ CNN & 90–98\% & Not required & High & High & Gearbox, bridge diagnostics \\ LSTM & 88–96\% & Not required & Very High & High & Time-series strain prediction \\ Autoencoder & — & Not required & Medium & Medium–High & Novelty detection \\ RF/XGBoost & 88–94\% & Required & Medium & High & Acoustic signal classification \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical characteristics of selected ML methods applied to vibroacoustic signal analysis.
# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
## 3 Methods
### Classification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scales** & **16–64** & **20–68** & **24–72** & **28–76** & **32–80** & **36–84** & **40–88** \\ \hline CHF I & \(1.03\pm 0.01\) & \(1.01\pm 0.01\) & \(1.00\pm 0.01\) & \(0.99\pm 0.01\) & \(0.98\pm 0.16\) & \(0.96\pm 0.02\) & \(0.94\pm 0.02\) \\ CHF II & \(0.93\pm 0.07\) & \(0.96\pm 0.06\) & \(1.00\pm 0.04\) & \(1.03\pm 0.03\) & \(1.05\pm 0.03\) & \(1.05\pm 0.03\) & \(1.08\pm 0.38\) \\ CHF III & \(0.97\pm 0.05\) & \(1.00\pm 0.05\) & \(1.02\pm 0.05\) & \(1.03\pm 0.06\) & \(1.04\pm 0.06\) & \(1.05\pm 0.07\) & \(1.05\pm 0.08\) \\ CHF IV & \(0.83\pm 0.08\) & \(0.89\pm 0.08\) & \(0.93\pm 0.08\) & \(0.97\pm 0.08\) & \(1.00\pm 0.09\) & \(1.02\pm 0.09\) & \(1.04\pm 0.09\) \\ \(p\)-value & \(0.34\) & \(0.55\) & \(0.73\) & \(0.87\) & \(0.91\) & \(0.92\) & \(0.85\) \\ \hline
**Scales** & **44–92** & **48–96** & **52–100** & **56–104** & **60–108** & **64–112** & **68–116** \\ \hline CHF I & \(0.91\pm 0.02\) & \(0.92\pm 0.02\) & \(0.90\pm 0.02\) & \(0.88\pm 0.02\) & \(0.87\pm 0.03\) & \(0.96\pm 0.03\) & \(0.93\pm 0.06\) \\ CHF II & \(1.09\pm 0.04\) & \(1.10\pm 0.04\) & \(1.09\pm 0.05\) & \(1.08\pm 0.05\) & \(1.07\pm 0.05\) & \(1.05\pm 0.04\) & \(1.03\pm 0.04\) \\ CHF III & \(1.05\pm 0.08\) & \(1.05\pm 0.09\) & \(1.04\pm 0.93\) & \(1.03\pm 0.09\) & \(1.02\pm 0.09\) & \(1.01\pm 0.09\) & \(0.99\pm 0.09\) \\ CHF IV & \(1.07\pm 0.09\) & \(1.07\pm 0.09\) & \(1.08\pm 0.09\) & \(1.08\pm 0.08\) & \(1.08\pm 0.08\) & \(1.08\pm 0.09\) & \(1.07\pm 0.07\) \\ \(p\)-value & \(0.79\) & \(0.74\) & \(0.67\) & \(0.60\) & \(0.58\) & \(0.54\) & \(0.46\) \\ \hline \hline \end{tabular}  
*: Different parameters (\(m\) and \(r\)) of sample entropy (SampEn) and fuzzy measure entropy (FuzzyMEn) also lead to different quantification power of HRV signals. In order to find the best combinations of parameters, the parameter \(m\) changed from one to three with a step of one, and \(r\) changed from 0.10 to 0.20 with a step of 0.05. For the normal and CHF groups, Table 6 gives the statistical Kolmogorov-Smirnov (KS) test results of SampEn and FuzzyMEn with a different combination of parameters. For CHF diseases with different severity levels, Table 7 gives the statistical F-test results of SampEn and FuzzyMEn with different combinations of parameters.  
\end{table} 
Table 5: The statistical F-test results of the feature \(\alpha_{2}\) in the 14 scale ranges for four severity levels of congestive heart failure (CHF).
## 3 Results and Discussion
### Synthetic Data  
To evaluate the quality of the synthetic data generated, we first try to find the correlation between the synthetic and the original data. We use Pearson's correlation coefficient with values ranging from \(-1\) to \(1\) to measure the relationship between two variables [33]. The representation of the correlation values is shown in Table 2, where a positive value represents a direct relationship, zero represents no relationship, and a negative value indicates an inverse relationship. We can define Pearson's correlation coefficient \((Coef)\) as in Equation (9), where \(Ori\) represents original data and \(Syn\) is generated synthetic data.  
\[Coef=\frac{\sum_{i=1}^{n}(Ori_{i}-Ori)(Syn_{i}-Syn)}{\sqrt{[\sum_{i=1}^{n}(Ori_{ i}-\bar{O}ri)][\sum_{i=1}^{n}(Syn_{i}-\bar{Syn})]}}. \tag{9}\]
# A Review of Oil Spill Remote Sensing
## 5 Detection of Oil on the Sea Bottom
### Cameras  
Cameras operating in the visual spectrum have been used for mapping oil on the bottom of a water body [63]. Extensive ground-truthing may be required if the oil does not contrast with the bottom material.
## 3 Analysis Methods
To improve the utilization of the sampled data and the confidence of the calculation results, overlapping Hadamard variance is applied to calculate frequency stability, with the calculation equation of [28]:  
\[H\sigma_{y}^{2}(\tau)=\frac{1}{6m^{2}(N-3m)}\sum\limits_{i=1}^{N-3m}[x_{i+3m} -3x_{i+2m}+3x_{i+m}-x_{i}]^{2} \tag{8}\]  
where \(\tau=m\tau_{0}\) is the smoothing time, \(x_{i}\) is the satellite clock offset data on the time series, \(N\) is the number of input clock offset data, and \(m\) is the smoothing factor. ### Main Performance Metrics of the Satellite Atomic Clock  
The exact procedures and steps of the evaluation process can be expressed as follows. BDS precise clock offset data are obtained by GFZ and converted into average frequency data; then, the outliers are removed by the MAD-based detection method. On this basis, the stability is obtained by overlapping Hadamard deviation, the frequency accuracy is solved by a linear model, the daily fitting residual and periodic term characteristics are obtained by a quadratic polynomial model, and the daily drift rate is solved by linear fitting of frequency data. Further to this, herein, the performance characterization is analyzed comprehensively. Finally, the necessary classification discussions are given according to clock, track, and system type. The evaluation process of main indicators is shown in Figure 5, and some technical details are described as follows:  
1. During outlier detection, the daily frequency data are detected. The value of N is the empirical value, which is determined as 5 in this study. If the ratio of outliers in the day is lower than 20%, the processed data is used normally; otherwise, the data of the corresponding date will not be analyzed. 2. In terms of frequency drift rate, the drift rate and the daily drift rate are calculated to comprehensively reflect the short-term and long-term drift characteristics of the atomic clock. 3. The calculation of frequency stability requires large volumes of data to obtain high confidence levels. The frequency stability is calculated when the smoothing time is 10,200 s (smoothing factor of 34) and 86,400 s (smoothing factor of 288), representing the stability of 10,000 s and the daily stability, respectively. The stability index is given in different months to reflect its change trend. Figure 5: Evaluation process of main indicators.
###### Abstract  
This study seeks to optimize the shape of a wave absorber with an inclined porous plate using an artificial neural network (ANN) model to improve the operating efficiency and experimental accuracy of a square wave basin. As our numerical tool, we employed the dual boundary element method (DBEM) to avoid the rank deficiency problem occurring at the degenerate plate boundary with zero thickness. A quadratic velocity model incorporating a CFD-based drag coefficient was employed to account for energy dissipation across the porous plate. The developed DBEM tool was validated through comparisons with self-conducted experiments in a two-dimensional wave flume. The input features such as the inclined angle and plate length affect the performance of the wave absorber. These features have been optimized to minimize the averaged reflection coefficient and the installation space (spatial footprint) with the application of a trained ANN model. The dataset used for training the ANN model was created using the DBEM model. The trained model was subsequently utilized to predict the averaged reflection coefficient using a larger dataset, aiding in the determination of the optimal wave absorber design. In the optimization process of minimizing both reflected waves and spatial footprint, the weighting factors are assigned according to their relative importance to each other, using the weighted sum model (WSM) within the multi-criteria decision-making framework. It was found that the optimal design parameters of the non-dimensional plate length (\(l/h\)) and inclined angle (\(\theta\)) are 1.46 and 5.34\({}^{\circ}\) when performing with a weighting factor ratio (80%: 20%) between reflection and spatial footprint.  
wave absorber; inclined porous plate; dual boundary element method; optimal design; artificial neural network model; weighted sum model; reflection coefficient; spatial footprint 2025  
[PERSON], [PERSON] [PERSON] Optimal Design of an Inclined Porous Plate Wave Absorber Using an Artificial Neural Network Model._Appl. Sci._**2025**, 15, 4895.  
[[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))  
Copyright: 6 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results
### FE Model Results  
The results of the initial numerical model highlight three modes: 12.8 Hz, 54.5 Hz and 126.9 Hz, depicted in Figure 3. The deformations of the first mode are located at the top of the trunk and the foot. Experimental mode shapes have been visually compared to the numerical ones: the modes corresponding to the numerical modes were Numbers 3 and 8. Mode 3 matched numerical Mode 2;  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mode** & **Score** & **Frequency (Hz) m \(\pm\) std (CV\%)** & **Damping \% m \(\pm\) std (CV\%)** \\ \hline
1 & 100\% & 36.82 \(\pm\) 0.98 (2.7) & 10.03 \(\pm\) 1.73 (17.2) \\
2 & 60\% & 42.26 \(\pm\) 2.02 (4.8) & 7.5 \(\pm\) 3.29 (43.7) \\
3 & 100\% & 49.48 \(\pm\) 1.23 (2.5) & 7.04 \(\pm\) 1.74 (24.7) \\
4 & 90\% & 62.51 \(\pm\) 2.68 (4.3) & 7.78 \(\pm\) 2.19 (28.2) \\
5 & 30\% & 71.30 \(\pm\) 1.26 (1.8) & 4.81 \(\pm\) 2.40 (49.9) \\
6 & 80\% & 82.14 \(\pm\) 2.60 (3.2) & 7.82 \(\pm\) 1.26 (16.2) \\
7 & 100\% & 98.26 \(\pm\) 3.16 (3.2) & 7.53 \(\pm\) 2.33 (31.0) \\
8 & 90\% & 122.89 \(\pm\) 1.07 (0.9) & 2.58 \(\pm\) 0.76 (29.3) \\ \hline \end{tabular}
\end{table}
Table 4: Identification results: repeatability (Protocol 1). m: mean, std: standart deviation, CV: coefficient of variation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mode** & **Frequency (Hz) m \(\pm\) std (CV\%)** & **Damping \% m \(\pm\) std (CV\%)** \\ \hline
1 & 35.66 \(\pm\) 1.00 (2.8) & 1.39 \(\pm\) 0.97 (69.8) \\
2 & 43.24 \(\pm\) 2.09 (4.8) & 7.28 \(\pm\) 2.36 (32.4) \\
3 & 52.54 \(\pm\) 2.05 (3.9) & 6.41 \(\pm\) 2.35 (36.8) \\
4 & 61.99 \(\pm\) 1.67 (2.7) & 5.16 \(\pm\) 2.28 (44.2) \\
5 & 70.40 \(\pm\) 1.60 (2.3) & 4.73 \(\pm\) 1.57 (33.1) \\
6 & 84.06 \(\pm\) 3.71 (4.4) & 4.46 \(\pm\) 1.34 (30.1) \\
7 & 100.38 \(\pm\) 3.71 (3.7) & 3.52 \(\pm\) 2.12 (60.1) \\
8 & 118.94 \(\pm\) 2.70 (2.3) & 1.60 \(\pm\) 0.76 (47.7) \\ \hline \end{tabular}
\end{table}
Table 5: Results of the modal identification on eleven subjects (Protocol 2).  
Mode 8 matched numerical Mode 3. The frequency of Mode 3 is 52.54 Hz and that of Mode 8 is 118.94 Hz. Given that our study was based on a two-dimensional model, the other modes could not be obtained by the numerical model. The corresponding experimental mode shapes showed however that deformation is mostly oriented out-of-plane, suggesting that the numerical model accurately describes the in-plane dynamic behavior.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
###### Abstract  
The effects of ionizing radiation on field-programmable gate arrays (FPGAs) have been investigated in depth during the last decades. The impact of these effects is typically evaluated on implementations which have a deterministic behavior. In this article, two well-known true-random number generators (TRNGs) based on sampling jitter signals have been exposed to a Co-60 radiation source as in the standard tests for space conditions. The effects of the accumulated dose on these TRNGs, an in particular, its repercussion over their randomness quality (e.g., entropy or linear complexity), have been evaluated by using two National Institute of Standards and Technology (NIST) statistical test suites. The obtained results clearly show how the degradation of the statistical properties of these TRNGs increases with the accumulated dose. It is also notable that the deterioration of the TRNG (non-deterministic component) appears before that the degradation of the deterministic elements in the FPGA, which compromises the integrated circuit lifetime.  
TRNG entropy; ionizing radiation; ring oscillator; self-timed ring; NIST +
Footnote †: journal:
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 3 Standard, Network and Channel Models
### Channel Models
In wireless communication, the loss of signal between the transmitter and the receiver is called path loss, and it is a major factor in wireless communication system design and analysis [56]. Generally, the path loss can be expressed in dB as Equation (9), where \(PL\) is total path loss, \(d\) is distance between transmitter and receiver, \(\beta\) is path loss exponent and \(C\) is other system parameter noise:  
\[PL(d)=PL(d_{0})+10\beta log_{10}(d/d_{0})+C \tag{9}\]  
The value of a path loss exponent varies significantly according to communication environment. The path loss exponent is two for free space during LOS communication between transmitter and receiver and up to six for obstructed in building propagation. It is an important parameter that represents the degree of path loss with respect to the distance. Path loss models are very useful for planning and predicting the loss of signal strength in a specific location of the propagating channel [57]. Furthermore, path loss models can be classified as either bounded or unbounded path loss model and details can be found in [58]. One of the popular path loss model used in WSN is the two slope path loss model that is validated by real measurements as well [59]. As it is difficult to generalize behaviour of WSN channels, many researchers have used empirical channel models and a real channel model (i.e., implementing WSN in a practical setting) to study WSN channel behaviours.
## 4 Results and Discussion
### Experimental Results
#### 4.2.2 Case Study  
We conducted a case study to intuitively visualize the fitting results of our proposed model. We selected the Pukou site in Nanjing and Nannen site in Suzhou, and plotted the fitting results for 500 continuous hours utilizing SVR, GAT-LSTM, and FedDeep, as depicted in Figure 4. Some conclusions can be drawn: (1) SVR could only identify linear dependencies, making it challenging to handle the complex non-linearities in \(PM_{2.5}\) data. It led to the poorest fitting performance. (2) In contrast to SVR, GAT-LSTM could capture spatio-temporal correlations, enhancing its fitting capacity. Unfortunately, GAT-LSTM failed to learn the abrupt change in \(PM_{2.5}\) at these two sites. (3) Compared with GAT-LSTM, FedDeep accomplished the best fitting performance, whether in the abrupt change or the smooth phase of the \(PM_{2.5}\) sequence. The main reason for this was that our proposed method integrated external factors, assisting in learning the patterns in abrupt changes and smooth phases in the \(PM_{2.5}\) sequence. This also reflects the fact that external factors are one of the triggers that lead to mutations in an \(PM_{2.5}\) sequence.
# Effects of the Damping Parameters on the Opening and Closing Characteristics of Vent Valves
## 3 Results
### Different Diameters of Nozzle 2  
Nozzle 2 is located at the back end of the membrane assembly and is the key element of the second stage of pressure control. Figure 15 shows the opening and closing characteristic curves of the nozzle diameters of the middle shell under different conditions. It can be seen from Figure 15a that the variation rule of the middle pressure of the support was not affected by the diameter of nozzle 2. As can be seen from Figure 15b, with the decrease in diameter of nozzle 2, the pressure mutation point of the piston chamber decreased  
Figure 14: Opening and closing characteristics of valve with different middle nozzle diameters: (**a**) The variation law of middle bearing pressure with time; (**b**) Control of the change in cavity pressure with time; (**c**) The variation law of piston displacement with time; (**d**) The variation of piston displacement with pressure in control chamber.  
slightly when the valve was closed. When T\({}_{2}\) was equal to 1.0 mm, there was always a small preloaded pressure in the piston chamber, which was caused by the nozzle being too small. As can be seen from Figure 15c,d, with the increase in the nozzle diameter of the middle shell, the stability of the valve in the closing process gradually decreased, especially when T\({}_{2}\) was 9.0 mm, which seriously affected the performance of the vent valve. Preload pressure in the piston chamber caused the valve to open with less pressure when T\({}_{2}\) was equal to 1.0 mm. Therefore, the recommended diameter of the middle shell nozzle is 2.5-6.0 mm.
## 4 Experiment and Analysis
### Evaluation Indicators  
There are only two types of anomaly detection targets in this paper. The positive examples are normal data, and the negative ones are abnormal. The classification results of the experiments can be divided into the following four categories:  
* True positives (TP): TP represents the proportion of abnormal behavior correctly identified as abnormal behavior;
* False positives (FP): FP represents the proportion of normal behavior incorrectly identified as abnormal behavior;
* False negatives (FN): FN represents the proportion of abnormal behavior incorrectly identified as normal behavior;
* True negatives (TN): TN represents the proportion of normal behavior correctly identified as normal behavior;  
After classifying the results, this paper evaluates the algorithm's performance by Precision, Recall, and F1-score.  
\[Precision =\frac{TP}{TP+FP}; \tag{16}\] \[Recall =\frac{TP}{TP+FN};\] (17) \[F1\text{-}score =2\times\frac{Precision\times Recall}{Precision+Recall} \tag{18}\]  
among them, precision indicates the rate of correct identification of abnormal behavior and normal behavior. Recall describes how many real positive examples in the test set are selected by the binary classifier. The core idea of the F1-score is that, while improving precision and recall as much as possible, we also want the difference between the two to be as small as possible.