esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 5 Discussion  
From the literature, it was clear that the methods proposed thus far require special data processing to perform well, where classical ML methods require extensive amounts of manual labor to prepare the data, while the DL-based methods use an image-based approach to understand the audio features. The preprocessing approach used can affect the performance of the method, and thus new research is recommended to develop new solutions that allow the models to understand the audio data as it is. Nevertheless, it was crucial to analyze the statutes of the current AD detection methods based on previous work experiments. Thus, from the experimental results of the cited studies, a quantitative comparison was conducted based on three criteria (EER, t-DCF, and accuracy), as illustrated in Table 3. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Measures**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Detection Method**} & **Results (The Result Is Approximate from the Evaluation Test Published in the Study)** \\ \hline \multirow{8}{*}{EER} & ASV spoof 2015 challenge & DNN-HLLs [29] & 12.24\% \\ \cline{3-4}  & & GMM-LLR [29] & 42.5\% \\ \cline{3-4}  & & Residual CNN [40] & 6.02\% \\ \cline{3-4}  & & SENet-34 [42] & 6.70\% \\ \cline{3-4}  & & CRNN-Spoof [33] & 4.27\% \\ \cline{3-4}  & & ResNet-34 [36] & 5.32\% \\ \cline{3-4}  & & Siamese CNN [25] & 8.75\% \\ \cline{3-4}  & & CNN [25] & 9.61\% \\ \cline{3-4}  & & DBiLSTM [41] (Synthetic Audio) & 0.74\% \\ \cline{3-4}  & & DBiLSTM [41] (Imitation-based) & 33.30\% \\ \cline{3-4}  & & SSAD [43] & 5.31\% \\ \hline \multicolumn{2}{c}{-} & Bidirectional LSTM [35] & 0.43\% \\ \hline \multirow{2}{*}{FoR} & CNN [27] & 11.00\% \\ \cline{3-4}  & Deep-Sonar [31] & 2.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A quantitative comparison between AD detection methods. Starting with the EER and t-DCF, as shown in Figure 5, it can be concluded that there is no clear pattern in performance with respect to the approach or dataset used. Each method performs differently depending on the technique used. For instance, the Bidirectional LSTM method provides the best EER and t-DCF compared to the other methods, but the dataset information was not clarified in the study, and overfitting was a concern. Another example is GMM-LLR, which provides the worst EER even though it used the same features and
## 3 Results
### Validation
#### 3.3.3 Application Scenario-Functional Test of the Airbus A350-1000 HighLift System  
Objective: the goal of this application scenario is to demonstrate the proof of concept of the replacement of the classical I/O cards with standardized components, as well as their syntactic configuration. Specifically, the case aims to show that the I/O module:  
* routes the ARINC 429 bus over the standardized I/O and does not compromise the real-time capability of the SuT and the test bench,
* can be configured via the test bench and the standardized medium,
* can translate the test bench variables into ARINC 429 messages,
* can translate the ARINC 429 messages into test bench variables.  
Figure 11: Test bench interconnection.  
Based on these criteria, the case must provide all coding procedures for evaluating the test bed variables.  
Boundary conditions: since the HighLift test bench operates at cycle rates of 500 us, the real-time conditions must not exceed this time frame. SFCC operates at slower rates. The ARINC 429 minimum interval is at 10 ms, in the selected scenario at a minimum 60 ms.  
System under test description: The HighLift system controls the leading and trailing edges of the wing, according to (i) pilot control (via the flap lever) and (ii) automatic control functions (initiated by other aircraft systems via the ADIRU bus).  
Test scenario: For the evaluation of the implementation of this work, the flap load relief function of the Airbus A350-1000 SFCC is used. It controls the flaps, according to the CAS values received from ADIRS via the ARINC 429 media. The objective of the function is to protect the landing flaps from damage or total loss. The damage would occur when the aircraft is operating at high speeds with the flaps extended. The air load on the flaps may become too high that the flap shaft breaks.  
Figure 12 shows the semi-integrated SuT. The DuT are two redundant SFCCs. They are connected to an electric motor and to the mechanical HighLift system. The input signals, such as the aircraft speed over ground (Calibrated Air Speed--CAS), are provided by ADIRS via two independent ARINC429 buses. The redundant control computers perform a calculation, based on the three values. The resulting CAS selection is fed into the flap load relief function. Depending on the position of the system and the speed of the aircraft, the flaps are automatically retracted or expanded. In addition, other sensor values are evaluated by the SFCC and used to calculate the flap load relief function. These values are additionally provided to other aircraft systems via the ARINC429. It is expected that the landing flaps will remain extended during take-off and will be retracted with the increasing speed delivered via ADIRU.
## 5 Results and Discussion  
After the data were collected, they were randomly split into training and test sets with an 80:20 ratio (80% training and 20% testing; [55]), and the ranges of the independent variables in the training data were normalized by subtracting their means and dividing them by their standard deviations. In machine learning, data normalization in the preprocessing stage replaces the actual values of each independent variable into z-scores with a mean of zero and a unit variance to reduce the variability among the different variables. The normalization method is widely used to improve the convergence of the machine-learning algorithms [56; 57; 58]. After data normalization, cross-validation (CV) techniques are used to choose the best model. Similar to the bootstrap procedure, CV is a resampling method used to validate the performance of a fitted model. In K-fold CV, the data are divided into K subsamples. (K - 1)/K proportion of the data are used to build the model, and the remaining 1/K proportion of the data are used as a test; this procedure is repeated K times.  
In this study, CV was used to compare the performances of the six competing models to identify the best model for BTS prediction. The root mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)) were used to determine the best model for predicting BTS.
## 6 Future Work and Open Challenges
We anticipate that the QoD of NMG control datawould degrade if the experimental scenarios run for longer durations, as the impact of high-variance background traffic increases on the communication network. On the other hand, NMGs are dependent upon communication networks to operate as efficient, smart, and robust small-scale power systems. However, this reliance transforms NMGs into a more complex, bidirectionally peer-to-peer (P2P) power system, making them vulnerable to cyber attacks [80]. These cyber attacks pose various threats and negatively impact various parts of NMGs. For instance, aurora attacks can damage physical NMG components, such as generators, and lead to degraded operational performance [81]. Despite the declining costs of lithium-ion batteries in recent years, they remain an upfront expense and are considered an ongoing challenge in NMGs [82]. Environmental concerns arise regarding the disposal of these storage components [83]. Emerging storage technologies, such as flow batteries, thermal storage, CO\({}_{2}\)-based storage, and other long-duration energy storage (LDES) products, are gaining popularity as cost-effective and environmentally friendly solutions [84; 85]. As an emerging technology, NMGs are constrained by regulatory limitations. This regulatory gap presents a significant challenge, as NMGs directly facilitate DERs into the main grid. The absence of these regulations prevents the effective usage of NMGs [86].
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 2 System Model  
In this chapter, we will first present the overall framework for the reception and transmission of the 5G NR Co-Band communication and positioning fusion system. Then, the transmission model of the Co-Band PRS signal is discussed, and finally, the reception model of the Co-Band PRS signal will be introduced.
## 4 Stock Price Prediction Model Methodology and Research Design
### Data Preparation
#### 4.2.3 Normalisation  
It is considered best practice to normalise data before feeding them into a neural network. If this is not done, features could have different scales, whereby features with higher values may have excessive weights associated with them during training, while those with smaller values would be neglected. To avoid this and ensure faster learning, normalisation was carried out for the above-described order flow and limit order book data.  
Commonly used methods of normalisation were considered to find the one most suitable for the input data used in the experiments:  
* Min-max;
* Decimal precision;
* Z-score.  
Our empirical findings revealed that the dataset employed in the experiments contained a significant number of outliers--values that were exceptionally high or low. As a result, normalisation methods dependent on maximum or minimum values in their formulas would be notably impacted by these outliers.  
To mitigate this, a min-max normalisation method, as its name implies, takes into account both the minimum and maximum values in its calculation. In contrast, the decimal-precision normalisation approach incorporates only the maximum value in the calculation. Simultaneously, the z-score normalisation method relies on the deviation from the average, which is less susceptible to the influence of outliers compared to the two aforementioned approaches.  
Thus, normalisation was carried out for the whole dataset using the standard z-score approach, \(z=\frac{x_{i}-\mu}{\sigma}\), where \(x_{i}\) is the value of the feature at the current timestamp, \(\mu\) is its mean value, and \(\sigma\) is its standard deviation.
# Brief Review of Recent Developments in Fiber Lasers
## 6 Fiber Laser Applications
### Optical Frequency Combs  
The invention of optical frequency combs (OFCs), also known as the rulers of light, has enabled numerous applications in the fields of optical metrology, spectroscopy, astronomy, and quantum and attosecond science [107].  
Over the past two decades, various types of OFC sources have been demonstrated, including solid-state or fiber-based mode-locked combs, electro-optic combs, and microresonator-based combs. At present, mode-locked fiber combs are technically mature and have a deeper degree of commercialization. This is due to their low noise performance and long-term reliability. OFCs can be realized utilizing various mode-locking techniques, such as nonlinear polarization rotation, a real saturable absorber, and a nonlinear amplifying loop mirror. Fiber laser-based OFCs are particularly attractive for free-space time-frequency transfer, low-noise microwave generation, and gas molecule detection.
###### Abstract
These predictive capabilities of musculoskeletal models provide quantitative patient-specific information that can aid surgeons to formulate an optimal treatment plan tailored to individual patients [15]. Earlier studies typically focus on the primary effects of PE insert thickness on the TF joint biomechanics. For instance, researchers have recently examined the effect of varying PE insert thickness on the TF kinematics after TKA, using a dynamic knee simulator of laxity tests [16], or by performing an intraoperative assessment using a computer-assisted surgery navigation system [7]. However, to the best of our knowledge, current scientific literature lacks a proper investigation of the relationship between PE insert thickness and TF kinetics, as well as of the effects on the patellofemoral (PF) joint where the secondary effects may influence the biomechanical parameters, including forces and kinematics, in a clinically relevant way. Therefore, the aim of this study was to first investigate the influence of PE insert thickness, in isolation, on the TF joint in terms of ligament and compressive forces, and secondly, to assess to what extent PE insert thickness variation affects the biomechanical parameters on the PF joint. For this, we used a patient-specific musculoskeletal model with a cruciate-retaining prosthesis. We hypothesized that thicker tibial PE inserts would result in higher ligament forces and, thus, increased TF compressive force, but the effect would remain neutral with regard to the PF articulation.
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
## 3 Proposed Wind Power Curve Model
### Support Vector Regression  
SVR was first proposed by [PERSON] based on the structural risk minimization [38]. The main idea is to find a nonlinear mapping function \(\phi\), which can map low-dimensional data into high-dimensional feature space.  
Given the training data \(\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots,(x_{n},y_{n})\}\), the aim is to get an estimated \(f(x)\) for the real output \(y\), the expression of \(f(x)\) is \(f(x)=w^{T}\phi(x)+b\), in which \(w\), \(b\) are two model parameters. The objective function for SVR is  
\[\min_{w,b}\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{n}l(f(x_{i})-y_{i}) \tag{21}\]  
where \(l(\cdot)\) is the loss function, \(C\) is the trade-off parameter. For the original SVR, the \(\varepsilon\)-insensitive loss function is employed. When introducing the slack variables \(\xi_{i}\), \(\xi_{i}^{*}\), the primal problem for SVR is  
\[\begin{array}{c}\min_{w,b,\xi_{i},\xi_{i}^{*}}\frac{1}{2}\|w\|^{2}+C\sum_{i= 1}^{n}\Big{(}\xi_{i}+\xi_{i}^{*}\Big{)}\\ s.t.\left\{\begin{array}{c}y_{i}-\Big{(}w^{T}\phi(x_{i})+b\Big{)}< \varepsilon+\xi_{i}\\ \Big{(}w^{T}\phi(x_{i})+b\Big{)}-y_{i}<\varepsilon+\xi_{i}^{*}\\ \xi_{i},\xi_{i}^{*}\geq 0\end{array}\right.\end{array} \tag{22}\]  
The dual problem for SVR can be obtained by applying the Karush-Kuhn-Tucker (KKT) conditions. Then, the decision function \(f(x)\) for a new sample \(x\) is  
\[f(x)=\sum_{i}^{n}\Big{(}\alpha_{i}-\alpha_{i}^{*}\Big{)}K(x,x_{i})+b \tag{23}\]where \(K(x,x_{i})=\left\langle\phi(x),\phi(x_{i})\right\rangle\) is the kernel function, \(\alpha_{i},\alpha_{i}^{*}\)\((i=1,\cdots,n)\) denote the Lagrangian multipliers. Usually, Gaussian kernel is used in many applications due to its superior performance.
## 3 Results
### Classifier Performance with Different Sampling Frequencies  
To compare the results with different sampling frequencies, F1-scores were averaged over different window lengths and feature sets (Figure 5). Overall, the classifier had similar average F1-score with 50 Hz (0.92 \(\pm\) 0.02) and 25 Hz (0.91 \(\pm\) 0.02), while the average F1-score with 13 Hz was lower (0.87 \(\pm\) 0.02).  
Statistically significant differences between different sampling frequencies (marked with an asterisk in Figure 5) were found for most activity types with the exceptions of moderate intensity and running.  
Very large differences in classification performance were noted while classifying outdoor cycling, where the F1-score was 0.93 \(\pm\) 0.04 with 50 Hz, 0.90 \(\pm\) 0.07 with 25 Hz and 0.79 \(\pm\) 0.06 with 13 Hz.
###### Abstract  
Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recovery of the hidden secret. In this paper, a new RDH scheme is proposed for performing reversible data hiding in encrypted images using a Fibonacci transform with an ensemble learning method. In the proposed scheme, the data hider encrypts the original image and performs further data hiding. During data hiding, the encrypted image is partitioned into non-overlapping blocks, with each block considered one-by-one. The selected block undergoes a series of Fibonacci transforms during data hiding. The number of Fibonacci transforms required on a selected block is determined by the integer value that the data hider wants to embed. On the receiver side, message extraction and image restoration are performed with the help of the ensemble learning method. The receiver will try to perform all possible Fibonacci transforms and decrypt the blocks. The recovered block is identified with the help of trained machine-learning models. The novelty of the scheme lies in (1) retaining the encrypted pixel intensities unaltered while hiding the data. Almost every RDH scheme described in the literature alters the encrypted pixel intensities to embed the data, which represents a security concern for the encryption algorithm; (2) Introducing an efficient means of recovery through an ensemble model framework. The majority of votes from the different trained models guarantee the correct recovery of the cover image. The proposed scheme enables reduction in the bit error rate during message extraction and contributes to ensuring the suitability of the scheme in areas such as medical image transmission and cloud computing. The results obtained from experiments undertaken show that the proposed RDH scheme was able to attain an improved payload capacity of 0.0625 bits per pixel, outperforming many related RDH schemes with complete reversibility.  
Fibonacci transform; reversible data hiding; data extraction and image recovery; information hiding 202312450. [[https://doi.org/10.3390/electronics12020450](https://doi.org/10.3390/electronics12020450)]([https://doi.org/10.3390/electronics12020450](https://doi.org/10.3390/electronics12020450))  
14 November 2022  
127 December 2022  
14 January 2023  
Published: 15 January 2023
# Leakage Detection Method of Coal Mine Power Supply System Based on 3 AC
## 2 Leakage Protection Method Based on 3 AC Detection Circuit
### Mathematical Model Based on 3 AC Detection Method
#### 2.2.1 Single-Phase Leakage  
When a leakage fault occurs in a particular phase, the insulation resistance of that phase decreases. Therefore, in Equation (2), it can no longer be considered infinite.  
When a single-phase leakage occurs in phase \(A\), the insulation of \(R_{A}\) decreases. Assuming that the insulation resistance after the decrease is \(R_{A}\), the insulation resistance of \(R_{B}\) and \(R_{C}\) can be regarded as infinite. The formula of \(\dot{U}_{0}\) changed as follows:  
\[\dot{U}_{0}=\frac{-\dot{U}_{A}\frac{1}{R_{A}}-\mathrm{j}\omega C_{2}\dot{U}_{A }}{\frac{1}{R_{A}^{\prime}}+\mathrm{j}\omega 3C+\mathrm{j}\omega 4C_{2}} \tag{6}\]Similarly, it can be deduced that the insulation of \(R_{B}\) and \(R_{C}\) decreases when the separate leakage of the \(B\) phase and the \(C\) phase occurs. If the insulation resistance after the decrease is set to \(R_{B}{}^{\prime}\) and \(R_{C}{}^{\prime}\), then \(\dot{U}_{0}\) is as follows, respectively:  
\[\dot{U}_{0}=\frac{-\dot{U}_{B}\frac{1}{R_{B}^{\prime}}-\dot{\mathrm{j}}\omega C _{2}\dot{U}_{A}}{\frac{1}{R_{A}^{\prime}}+\dot{\mathrm{j}}\omega 3C+\dot{\mathrm{j}} \omega 4C_{2}} \tag{7}\]  
\[\dot{U}_{0}=\frac{-\dot{U}_{C}\frac{1}{R_{C}^{\prime}}-\dot{\mathrm{j}}\omega C _{2}\dot{U}_{A}}{\frac{1}{R_{C}^{\prime}}+\dot{\mathrm{j}}\omega 3C+\dot{ \mathrm{j}}\omega 4C_{2}} \tag{8}\]
## 4 Experiments
### Dataset  
This experiment was conducted using the SWaT dataset [39]. The SWaT dataset originates from a downscaled model testbed designed to simulate a real-world water treatment plant, encompassing six critical stages of water treatment. The dataset comprises \(11\) days of operational data, with the first 7 days representing normal operation conditions collected in a physically isolated environment. The subsequent 4 days feature 41 distinct cyber-attack scenarios, which are categorized into three main types:  
* **Single Stage Single Point Attacks:** These attacks target a single point within one stage of the water treatment process.
* **Single Stage Multi-Point Attacks:** These attacks involve multiple points within a single stage, affecting several components or sensors simultaneously.
* **Multi-Stage Single Point Attacks:** These attacks target a single point across multiple stages of the water treatment process.
* **Multi-Stage Multi-Point Attacks:** These attacks span multiple stages of the water treatment process, impacting various components and sensors across different stages.  
Out of the 41 attack scenarios, 36 resulted in tangible physical impacts on the system. Data were recorded at a frequency of one sample per second, encompassing sensor readings from 25 continuous attributes and actuator states from 26 discrete attributes. The architecture of the SWaT testbed is illustrated in Figure 5.  
For this experiment, the training set consisted of the normal operational data from the first seven days, capturing the system's typical operational conditions. The test set was derived from the data of the following four days, featuring a range of cyber-attack scenarios, to evaluate the model's performance and detection accuracy under various attack conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Specification** \\ \hline CPU & Intel(R) Core(TM) 17-9700F @ 3.00 GHz \\  & (Intel, Santa Clara, CA, USA) \\ RAM & 32 GB \\ Operating System & Windows 11 Pro 64-bit \\ Programming Language & Python 3.9 \\ Data Processing Libraries & Pandas 2.1.3 \\  & NumPy 1.26.2 \\ Visualization Library & Matplotlib 3.5.3 \\ Swarm Intelligence Libraries & NiaARM 0.3.5 \\  & NiaPy 2.0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental setup and environment.
# Nanoencapsulation of Essential Oils as Natural Food Antimicrobial Agents: An Overview
## 5 Main Strategies for Nanoencapsulation of Essential Oils
### Biopolymer-Based Nanoencapsulation
#### 5.1.3 Complexation of Biopolymers  
The combined use of various biopolymers provides different functions and promising properties compared to a single biopolymer [98]. Biopolymer nanoparticles can be synthesized from food-grade biopolymers by self-association of individual biopolymers or by phase separation in biopolymer mixtures [99]. The ability to control and modify the interactions involved can help food technicians in designing new molecular structures to develop foods with more desirable structural properties.  
Among the cited techniques, the most widely used to prepare nanocapsule systems is the complex coacervation method, as shown in Figure 3. Two kinds of polyelectrolytes with opposite charges are used as the wall material, and the core material is dispersed in the wall material solution. By changing the pH value, temperature, and concentration of the solution, or by adding an inorganic salt electrolyte to modify the electrostatic interactions in the system, the solubility of the wall material can be reduced to form nano/microcapsules. The commonly used polyelectrolytes are oppositely charged proteins and polysaccharides, such as gelatin, gum Arabic, carboxymethyl cellulose, alginate, chitosan, and polylysine. To obtain stable complexes, it is important to control the environmental conditions to form oppositely charged polymers with sufficient intensity of ionic interactions. The process of the complex coacervation method is relatively mild. [PERSON] et al. [100] successfully prepared nano-sized fish oil capsules (26-114 nm) with high encapsulation efficiency using gum Arabic and gelatin.