esa-sceva/satcom-chunk-collection huggingface dataset:
# Using BiLSTM Networks for Context-Aware Deep Sensitivity Labelling on Conversational Data
## 8 Discussion  
Sensitive information labelling is a prominent problem when designing privacy-aware decision systems. Automated sensitivity labelling is especially relevant when considering users as custodians of their own personal data. With this in mind, we developed our model to enhance sensitivity annotation by first offering a taxonomy of four context classes (semantic, agent, temporal and self), and then using these to implement context-aware labelling.  
Our model does not come without limitations, and future work should: involve the full set of context classes; extend the auxiliary features experiments to evaluate tweaked models such as plain BiLSTM (without CRF); incorporate additional word, sentence and character embeddings; and perform testing and validation on more extensive datasets. Yet, the work presented in this paper can essentially serve as a framework for building similar models within alternative well-defined problem domains.  
Figure 5: Comparative evaluation between our system and Google’s Data Loss Prevention (DLP) system.  
The impact of our work is manifold, although we acknowledge potential risks that typically come with advancements in understanding and extracting sensitive information. While our models contribute to more accurate context-aware sensitivity labelling, our choice to adopt P3P sensitivity classes also supports openness and extensibility to third party applications, offering a platform for others to further develop suitable methods. It furthermore demonstrates promising results from using deep learning techniques in text sensitivity annotation, an area that is sparsely addressed in the literature. We believe that further expanding our approach will offer a more concrete future direction for privacy-preserving information exchange.  
This paper was accomplished based on the collaborative work of the authors. [PERSON] performed the experiments and analysed the data. Experiment interpretation and paper authorship were jointly performed by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
BERT Bidirectional Encoder Representations from Transformers BiLSTM Bidirectional Lond-Short Term Memory CNN Convolutional Neural Network CRF Conditional Random Field CWI Complex Word Identification DLP Data Loss Prevention IC Information Context LSTM Long Short Term Memory RNN Recurrent Neural Network NER Named Entity Recognition OOV Out-of-Vocabulary POS Part of Speech SVM Support Vector Machine WS WordShape
## 2 Engineering Background and in Situ Monitoring Methods
### In Situ Monitoring Method of Surrounding Rock Pressure
#### 2.2.2 Basic Theory of in Situ Monitoring of Surrounding Rock Pressure  
By embedding or installing sensors, real-time monitoring of the mechanical interactions between the tunnel surrounding rock and its supporting structure is facilitated. The  
Figure 4: Pressure sensor installation diagram on **(a)** the first group, **(b)** all pressure sensors.  
Figure 3: Three-dimensional layout of the single row pressure sensors.  
surrounding rock pressure is converted into measurable signals, enabling the evaluation of the stability and engineering safety of the surrounding rock through data analysis and processing. As the surrounding rock deforms, it induces a corresponding deformation in the pressure surface of the vibrating wire pressure sensor. This deformation results in strain on the steel wire within the pressure sensor. The theoretical relationship between the vibration frequency of the steel wire and the tensile stress is given by [38]:  
\[f=\frac{1}{2l}\sqrt{\frac{\sigma^{\prime}}{\rho}} \tag{1}\]  
In Formula (1), \(f\) is the natural vibration frequency of the steel string (Hz), \(l\) is the length of the steel string (mm), \(\rho\) is the density of the steel string, and \(\sigma^{\prime}\) is the tensile stress borne by the steel string (MPa).  
According to the calculation theory of axisymmetric bending of elastic thin plate, it can be obtained:  
\[tg\theta=\frac{pl}{128D}(l^{2}-L^{2}) \tag{2}\]  
In Formula (2), \(p\) is the uniform load acting on the bearing plate (MPa), \(l\) is the steel string length (mm), \(L\) is the diameter of the bearing plate (mm), \(D\) is the bending stiffness of the bearing plate, and \(D=E\cdot t^{3}/12(l-\mu^{2})\), where \(E\) is the elastic modulus of the bearing plate (MPa) and \(\mu\) is the Poisson ratio of the bearing plate. \(t\) is the thickness of the bearing plate (mm).  
As the deformation of the bearing plate is very small, \(tg\)\(\theta\approx sin\)\(\theta\). Based on [PERSON]'s law, Equation (2) can be substituted to deduce:  
\[\sigma=E\frac{\Delta l}{l}=E\frac{2 bsin\theta}{l}=E\frac{bp}{64D}(l^{2}-L^{2}) \tag{3}\]  
In Formula (3), \(b\) is the height of the column (mm).  
By combining Formulas (1) and (3) and considering the stress sign in elasticity, it can be obtained:  
\[p=\frac{256l^{2}p\cdot D}{Eb(L^{2}-l^{2})}(f^{2}-f_{0}^{2}) \tag{4}\]  
In Formula (4), \(f\) is the frequency of the steel string under the action of external force \(p\) of the pressure box, and \(f_{0}\) is the initial frequency of the steel string.  
In order to meet the needs of the project, the measured stress of the pressure box can be corrected.
# Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation
###### Abstract  
We address the challenge of determining a valid set of parameters for a dynamic line scan thermography setup. Traditionally, this optimization process is labor- and time-intensive work, even for an expert skilled in the art. Nowadays, simulations in software can reduce some of that burden. However, when faced with many parameters to optimize, all of which cover a large range of values, this is still a time-consuming endeavor. A large number of simulations are needed to adequately capture the underlying physical reality. We propose to emulate the simulator by means of a Gaussian process. This statistical model serves as a surrogate for the simulations. To some extent, this can be thought of as a \"model of the model\". Once trained on a relative low amount of data points, this surrogate model can be queried to answer various engineering design questions. Moreover, the underlying model, a Gaussian process, is stochastic in nature. This allows for uncertainty quantification in the outcomes of the queried model, which plays an important role in decision making or risk assessment. We provide several real-world examples that demonstrate the usefulness of this method.  
active thermography; parameter design; emulation; Gaussian process Article  
Dynamic Line Scan Thermography Parameter Design via Gaussian Process Emulation  
1 Faculty of Applied Engineering, Department Electromechanics, Research Group InViLab, University of Antwerp, Groenenborgerlaan 171, B 2020 Antwerp, Belgium; [EMAIL_ADDRESS] (I.D.B.); [EMAIL_ADDRESS] (R.P.); [EMAIL_ADDRESS] (G.S.)
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 6 The Related Literature
### Communication of 5G and 6G for Industrial Deployment: Impact on Data Processing Requirements  
The introduction of 5G and the upcoming emergence of 6G networking communication technologies is anticipated to revolutionize industrial deployments, attributed to their capabilities in providing the necessary connectivity for massive machine-type communication and ultra-reliable low-latency communication (URLLC) [31]. These advancements can easily enable the real-time control of manufacturing processes by real-time data processing [31]. The unprecedented increase in networking connectivity and data arrival rates adds challenging demands on underlying data processing infrastructures. This is very relevant to our work, because the performance of Apache Spark on ARM-based HPC systems has a clear and direct impact on the ability to leverage the benefits of 5G/6G networking technologies in industrial settings.  
The integration of dynamic reconfigurable antennas is also being considered for enhancing reliability and seamless connectivity in Industry 4.0 application scenarios, particularly in Industrial IoT and smart manufacturing scenarios. Such antennas basically leverage electrical components and AI/ML-based agile beam-scanning techniques. The advanced and improved connectivity provided by such technologies is set to generate data on a rate that is unprecedented, a fact that further emphasizes the need for efficient big data processing solutions, like the deployment of Apache Spark big data processing systems on HPC-enabled deployments [32].  
Furthermore, a flexible hyper-distributed IoTEdgeCloud computing platform, incorporated with a private 5G network, can support real-time digital twins in logistics handling and industrial environments [33]. This platform enhances the constrained capabilities of IoT devices with additional cloud and edge computing functionalities, thus creating an IoT-edge-cloud continuum [33]. Performance evaluations on real data have shown the ability of the platform in supporting the high-throughput communications required for digital twins scenarios [33]. Such a platform is an excellent example that demonstrates the convergence of multiple technologies, and our research on Apache Spark can contribute to optimizing the data processing component of such integrated systems.  
Also, the introduction of 5G-enabled networks is also driving exploration and innovation in IIoT, particularly in relation to cybersecurity [34]. The increased attack surface associated with 5G-connected IIoT devices requires robust security information and event management (SIEM) systems, which directly depend on efficient big data processing for detecting and responding to cybersecurity threats. Our work with Apache Spark is set to potentially contribute to the development of high-performance SIEM solutions for IIoT environments.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
###### Abstract  
Voiceprint recognition systems often face challenges related to limited and diverse datasets, which hinder their performance and generalization capabilities. This study proposes a novel approach that integrates generative adversarial networks (GANs) for data augmentation and convolutional neural networks (CNNs) with mel-frequency cepstral coefficients (MFCCs) for voiceprint classification. Experimental results demonstrate that the proposed methodology improves recognition accuracy by up to 15% in low-resource scenarios. The optimal ratio of real-to-GAN-generated samples was determined to be 3.2, which balanced dataset diversity and model performance. In specific cases, the model achieved an accuracy of 96.6%, showcasing its effectiveness in capturing unique voice characteristics while mitigating overfitting. These results highlight the potential of combining GAN-augmented data and CNN-based classification to enhance voiceprint recognition in diverse and resource-constrained environments.  
Keywords:data augmentation; deep learning; generative adversarial networks; GANs; information security; voiceprint recognition +
Footnote †: journal: _Algorithms_
## 2 Materials and Methods
### Analysis of Laboratory Results  
The pH value of all sampling points was 6-9, and all samples met the environmental quality standards for surface water. At sampling point B, COD \(>\) 45 failed to meet the water quality safety index. The measuring point was surrounded by high-grade residential areas. At D point, COD \(>\) 45. Nearby is a university, with a total construction area of 900,000 m\({}^{2}\), and a five-star city tourist attraction project. As shown in Table 2, the COD value of point H was higher than 45, with small high-rise residential groups, a middle school (Chongqing Binjiang Experimental Middle School, Binjiang, China), and a hospital nearby. The COD at point J was 390, which is much higher than the standard limit for COD concentration. To verify the results, another sampling was conducted and the detection results were similar to the first. The urban functions near point J include high-rise residential areas and commercial centers, with a concentrated population. The other indexes of each measuring point met the environmental quality standards for surface water.  
As shown in Figure 5, the residential group near point B is close to the river, with a plot ratio of less than 2.0, and has sufficient green areas. High-rise buildings and multi-story buildings are adjacent to the river. This kind of layout is more common in riverside areas. The aerial picture shows that there was a large amount of unused land near the river at point D, with few traces of human activity. In addition, there is a basketball court. The shoreline here has not been over-developed. The government has strictly stipulated the floor area ratio and building height of this area.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sampling Point** & **pH** & **COD (mg/L)** & **TN (mg/L)** & **TP (mg/L)** \\ \hline A & 7.5 & 12 & 0.217371 & 0.069283 \\ B & 7.56 & 49 & 0.235464 & 0.031492 \\ C & 7.56 & 5 & 0.243218 & 0.144865 \\ D & 7.57 & 49 & 0.207032 & 0.056686 \\ E & 7.59 & 24 & 0.225125 & 0.107074 \\ F & 7.54 & 19 & 0.22771 & 0.056686 \\ G & 7.49 & 30 & 0.444822 & 0.119671 \\ H & 7.6 & 60 & 0.26648 & 0.119671 \\ I & 7.58 & 25 & 0.214786 & 0.006298 \\ J & 7.56 & 390 & 0.248387 & 0.056686 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of various indicators.  
Figure 5: Typical urban environment and architectural layout near the four measuring points (B, D, H).
## 5 Informative Path Planning for Mobile Network Mapping
### IPP Simulation Results  
The evaluation results for the different travel budgets are shown in Figure 8. The root-mean-square error (RMSE) between the measurement from the test set and the predicted value of the GP with the indicated mean function (\(m_{\text{ZERO}}\), \(m_{\text{CD}}\), \(m_{\text{SIM}}\)) and the tuning parameters of the IPP algorithm (\(\alpha\) and \(\kappa\)) is plotted using a logarithmic color scale. The maximum observed RMSE is 14.41 dBm for the distance-based mean with \(\alpha=0.5\) and \(\kappa=0.0\).  
Similar to the preceding evaluation, the simulation-based mean is mostly independent of both the travel budget and the IPP tuning parameters (\(\alpha\) and \(\kappa\)). The distance-based mean and the zero mean, on the other hand, both show improvement with an increasing travel budget.
## 4 An Energy-Balanced Protocol Based on the Improved CFSFDP
### Algorithm Description
#### 4.3.1 Phase 1: Clustering Set-up Phase  
_Step 1._ Parameter Initialization  
1. Set the number of nodes \(N\) and maximum round \(r_{\max}\) for WSN;
2. Set cutoff distance \(d_{c}\) and threshold \(\eta\);
3. Calculate the distance \(d_{ij}\) between nodes;  
_Step 2._ Determine whether the node is exhausted  
Calculate every node's residual energy through Equation (10). If the energy is exhausted, the node is dead. Only living nodes can go on following steps.  
_Step 3._ Select CHs  
Calculate every node's local density, density-energy and distance through Equations (3), (12) and (13) in the current round \(r\), respectively. Based on these parameters, calculate \(\gamma\) of all the living nodes through Equation (11). If \(\gamma_{i}>\eta\), the node \(i\) will be chosen as cluster head.  
_Step 4._ Allocate CMs.  
Sort all \(\omega\) calculated by Equation (12) in decreasing order. Assign the remaining nodes to the same cluster as its nearest neighbor with higher density-energy.
## 2 Method
### LR Model  
In Equation (4), the sigmoid function is used for predicting the output of LR with the inputs features \(x_{1}\)-\(x_{n}\) in Equation (5) using model parameters \(\theta_{0}\)-\(\theta_{n}\). A conventional run-to-failure LR sets y as a constant to 1 for the initial healthy status, to 0 for failure, and to 0-1 for the rest of the work asset's lifetime. To build the RUL model accurately with a LR, the difference between the predicted \(h_{\theta}(x)\) and \(y\) is minimized. Therefore, one trains the model parameters for each \(\theta_{0}\)-\(\theta_{n}\) based on Equation (4) to predict actual the CV, y, of the machine's health. The model parameters for \(\theta_{0}\)-\(\theta_{n}\) can be derived using the Bernoulli distribution, maximum likelihood estimation (MLE), a loss function, and the method of gradient descent.  
\[h_{\theta}(x)=\frac{1}{1+e^{-\theta^{T}x}} \tag{4}\]  
\[\theta^{T}x=\theta_{0}+\theta_{1}x_{1}+\theta_{2}x_{2}+\theta_{3}x_{3}+\ldots +\theta_{n}x_{n} \tag{5}\]  
The probability by Bernoulli distribution is given by Equation (6).  
\[p(y|x)=h_{\theta}(x)^{y}(1-h_{\theta}(x))^{1-y}=\left\{\begin{array}{cc}h_{ \theta}(x)&\quad\quad\quad if\ y=1\\ 1-h_{\theta}(x)&\quad\quad if\ y=0\end{array}\right. \tag{6}\]  
The MLE function is applied by multiplying each probability in Equation (6) after the most likely real outputs y are obtained from the inputs \(X_{1}\)-\(X_{m}\) and the model parameters \(\theta_{0}\)-\(\theta_{m}\). Thus, Equation (7) provides a likelihood estimation function based on m known samples and the loss function in Equation (8). Iterations of the gradient descent method with the learning rate (\(\alpha\)) to find the value of each model parameter \(\theta_{j}\) are performed according to Equations (9) and (10).  
Figure 1: General Regression Neural Network architecture used for a Wafer-Handling Robot Arm.  
Figure 2: Data processing architecture of the k General Regression Neural Networks by using a Genetic Algorithm.  
\[L(\theta)=\prod_{i=1}^{m}p(y^{(i)}|\chi^{(i)};\theta)=\prod_{i=1}^{m}h_{\theta}(x^ {(i)})^{y^{(i)}}(1-h_{\theta}(x^{(i)}))^{1-y^{(i)}} \tag{7}\]  
\[J(\theta)=-\log L(\theta)=\sum_{i=1}^{m}\left(\log\left(1+exp^{\theta T^{x(i)} }\right)-y^{(i)}\theta^{T}x^{(i)}\right) \tag{8}\]  
\[\frac{\partial J(\theta)}{\partial\theta_{j}}=\frac{\partial\sum_{i=1}^{m} \left(\log\left(1+exp^{\theta T^{x(i)}}\right)-y^{(i)}\theta^{T}x^{(i)}\right) }{\partial\theta_{j}}=\sum_{i=1}^{m}\left(h_{\theta}(x^{(i)})-y^{(i)}\right)x _{j}^{(i)} \tag{9}\]  
\[\theta_{j}=\theta_{j}-\alpha\sum_{i=1}^{m}\left(h_{\theta}(x^{(i)})-y^{(i)} \right)x_{j}^{(i)} \tag{10}\]
## 3 Proposed SLM PAPR Scheme
### Complexity Analysis  
The computational complexity of the proposed PAPR reduction method can be evaluated in terms of the number of complex multiplications (CMs) and complex additions (CAs) required for OFDM systems. For an OFDM system with \(N\) subcarriers and \(G\) times oversampling, each IDFT operation requires \((GN/2)\mathrm{log}_{2}(GN)\) CMs and \(GN\mathrm{log}_{2}(GN)\) CAs, respectively.  
In the conventional SLM PAPR method, the transmitter requires \(U\) IDFT operations to generate \(U\) candidate signals. Hence, the conventional SLM method requires \((UGN/2)\mathrm{log}_{2}(GN)\) CMs and \(UGN\mathrm{log}_{2}(GN)\) CAs.  
At the transmitter, both the proposed method and the SLM Method in [10] use a conversion matrix to generate \((U-1)\) different candidate signals after one IDFT operation, and each conversion matrix only has the number of \(K\) nonzero elements. Thus, the computational complexity of the transmitter is the same for both methods, and the transmitter requires \((GN/2)\mathrm{log}_{2}(GN)\) CMs and \(GN\mathrm{log}_{2}(GN)+(U-1)(K-1)GN\) CAs.  
Table 2 summarizes the computational complexities of the three PAPR schemes at the transmitter. It is seen that the conventional SLM method requires the greatest number of CMs and CAs of the three methods. Moreover, it is observed that the SLM scheme in [10] and the proposed method require the same number of CMs and CAs at the transmitter.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PAPR Schemes** & **Number of CMs** & **Number of CAs** \\ \hline Conventional SLM method & \((UGN/2)\mathrm{log}_{2}(GN)\) & \(UGN\mathrm{log}_{2}(GN)\) \\ SLM method in [10] & \((GN/2)\mathrm{log}_{2}(GN)\) & \(GN\mathrm{log}_{2}(GN)+(U-1)(K-1)GN\) \\ Proposed method & \((GN/2)\mathrm{log}_{2}(GN)\) & \(GN\mathrm{log}_{2}(GN)+(U-1)(K-1)GN\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of computational complexity at the transmitter. PAPR: peak-to-average power ratio; CM: complex multiplications; CA: complex additions.  
The above discussion gives the comparisons of the computational complexity of the three methods at the transmitter. It should be noted that, as with the conventional SLM method, the SLM method in [10] needs to transmit SI to indicate which conversion matrix is chosen. If \(U\) candidate signals are generated, \(\log_{2}(U)\) SI bits are required for the receiver to recover the original transmitter signal. At the receiver, the decision procedure of SI still requires a number of computational complexities for the SLM method in [10]. However, the proposed method without SI transmission does not need additional computational complexity and the effect of the conversion matrix can be removed by the channel equalization operation. Obviously, the receiver of the SLM scheme in [10] requires significantly higher computational complexity than that of the proposed method. Thus, the overall computational complexity of the proposed method is the lowest in the three schemes at the transmitter and the receiver.
## 3 Beamformer Design  
Here, we design a beamformer set \(\mathbf{w}_{A}\) and \(\mathbf{w}_{T,1},\ldots,\mathbf{w}_{T,L}\) with the MRC for \(\mathbf{w}_{R,\ell}\) as defined in the previous section. Note that all these beamformers are assumed to be rank one, for which the following Section 3.1 provides a proof using the result of Reference [21] that such rank one assumption of the beamformers for our system model is sufficient to achieve the optimum points. The decoding conditions based on the SINR expressions in (3) and (4) result in a design method as is discussed in Reference [12] for the conventional NOMA system without the cooperative UT. In Reference [12], it is shown that the AP beamformer for a NOMA system maximizes the minimum channel gain (\(\min_{j}|\mathbf{h}_{j}\mathbf{w}_{A}|\)). This design principle is similar to that for the multicast system [28], which is not so surprising since message signals of a NOMA system should be decoded at most of the UTs under the SIC rule. We formally extend this result to the beamformer set optimization in the cooperative NOMA case.