esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 TruMuzic: Deep Learning and Data Provenance-based Plagiarism-Detection
### The Overall Functionality of TruMuzic  
The overall functionality of the model is explained in Figure 3. TruMuzic can be divided into three phases: the preprocessing phase, the processing phase, and the postprocessing phase, as detailed in our previous work [16]. In the preprocessing phase, the model works on feature selection, data normalization, and developing additional quantitative features. In the processing phase, the features of the query song are compared against the features of the data song, which come from legitimate original songs stored in the database. A combination of similarity comparison algorithms is used. Additionally, the weights of the features are adjusted based on the ground truth, which was provided by experts. Moreover, as shown in Algorithm 5, after the similarity comparison, we can see that, in the deep learning-based phase of TruMuzic, the scores are adjusted based on the given weights. In the postprocessing phase, the final score S\({}_{\text{d}}\) is presented as the model's output. We define this solution as follows:  
```
Input: Individual Score; \(S_{n},\ S_{p},\ S_{c},\ \ and\ S_{pr}\) from Algorithm 1 Weights from the DNN; \(w=\left\{w_{n},\ \ w_{p},\ \ w_{c},\ \ w_{pr}\right\}\) Output: Final score S\({}_{\text{d}}\) Function: ScoreAljustment Steps: 1. Read individual scores  2. Read weights provided by DNN  3. \(final_{score}=w_{n}*S_{n}+w_{p}*S_{p}+w_{c}*S_{c}+w_{pr}*S_{pr}\)  4. repeat until \(final_{score}==S_{d}\):  5. \(w_{n}\sim w_{n}\); \(w_{p}\sim w_{p}\); \(w_{c}\sim w_{c}\); \(w_{pr}\sim w_{pr}\)  6. check; if \(final_{score}==S_{d}\) : return \(w_{n}\), \(w_{p}\), \(w_{c}\), \(w_{pr}\)
```  
**Algorithm 5** Adjusting the final score  
Every song S is first represented using a set of feature vectors denoted by \(f(s)=\left\{f_{1},f_{2},f_{3},\ldots,f_{n}\right\}\). Each of the individual f\({}_{\text{i}}\) here represents a class of features. For example, they could be pitch or chord features. These weights are set to zero at the beginning as the default.  
\[S_{d}=W_{0}+(S_{n}\times w_{1})+(S_{p}\times w_{2})+(S_{c}\times w_{3})+(S_{pr }\times w_{4}) \tag{8}\]  
The similarity scores for each respective feature in the two given songs, \(P\) and \(Q\), are sent to the final score calculation function along with their weights, and then the final score is calculated by summing all the individual feature scores. Feature comparison algorithms for each feature type compare every particular feature of the two given songs and then generate a similarity score. The individual similarity scores are sent to the final score calculation function, where these similarity scores are adjusted based on their generatedweights. The individual weights of the features are presented in the experimental results in the next section.
## 5 Conclusions  
In this paper, an electromagnetic-thermal bidirectional coupling model of TPMLG is proposed and electromagnetic-thermal characteristics of TPMLG are investigated. The loss calculated in the electromagnetic field is adopted as the heat source for temperature field calculation, and the temperature field distribution calculated in the thermal-fluid field determines the material properties in an electromagnetic field. The temperature distribution of TPMLG is analyzed, and the following conclusions are drawn.  
The hottest spot of the TPMLG appears at the top of the central position In the windings. Due to the top temperature of the PMs reaching 109.2 \({}^{\circ}\)C, there is a potential risk of demagnetization resulting from an excessive temperature rise due to insufficient heat dissipation. Through calculation, the insulation in the TPMLG is proved to avoid failure under the high temperature of the windings.  
The main loss comes from copper loss. One-way coupling analysis without considering effects of temperature changes on material properties leads to an error of 24.5% in calculating copper loss compared to bidirectional coupling.  
High air flow velocity helps to promote the convective heat transfer in the TPMLG. As the air flow velocity rises from 1 m/s to 3 m/s, the maximum temperature of major components of the TPMLG decreases by over 15%. However, as the air flow velocity continues to rise, the cooling effect increment provided by the increased velocity gradually weakens.  
Conceptualization, W.L., J.L. and B.J.; methodology, W.L., J.L. and B.J.; software, W.L. and J.L.; validation, W.L., J.L. and B.J.; formal analysis, W.L.; investigation, W.L. and J.L.; resources, H.F.; data curation, W.L. and J.L.; writing--original draft preparation, W.L.; writing--review and editing, J.L. and B.J.; visualization, W.L.; supervision, H.F. and B.J.; project administration, H.F. and B.J.; funding acquisition, H.F. and B.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Beijing Institute of Technology Research Fund Program for Young Scholars.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.  
Figure 14: Maximum temperatures of the windings and the PMs with the air flow velocity.
###### Abstract  
Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV's eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.  
selection policy; eavesdropping; jamming; fading channel; UAV +
Footnote †: journal: Accepted: 28 February 2019; Published: 5 March 2019
## 5 Protection Against Vampire Attacks in Routing
### Priority Calculation in Optimal Route Determination  
Assume, on any specific \(i\)-th route that involves a total number of \(J_{i}\) nodes, that the expected total priority \(P_{y}(i)\) of this route can be determined by the following:  
\[P_{y}(i)=P_{y}(i,1)+P_{y}(i,2)+...+P_{y}(i,J_{i}-1)) \tag{20}\]  
where \(P_{y}(i,m)\) refers to the priority from the \(m\)-th node on this route to its next hop (\(1\leq m\leq J_{i}-1\)).  
A \"standard next-hop\" is defined in advance to offer fair judgment to different routes: the next-hop node is 100% not compromised; the distance to the next-hop node is the maximum radio range of the node; and the energy cost after a packet is successfully delivered to its next-hop node is represented by\(E_{st}\).  
Resembling the example previously given in Section 5.4, the \"relative\" priority on each hop compared to that of a pre-defined'standard next hop' is calculated as follows:  
\[P_{y}(i,m)=P_{ey}(i,m)+I_{s}\cdot P_{sy}(i,m) \tag{21}\]  
where \(P_{ey}(i,m)\) is the priority (importance) of energy efficiency and is inversely proportional to the normalised expected transmission cost with respect to \(E_{st}\). For details on how to estimate transmission costs, see Section 3.2.1.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Scale Types** & **\(\text{Route 2}\)** & **\(\text{Route 3}\)** & **\(\text{Route 4}\)** & **\(\text{Route 5}\)** & **\(\text{Route 6}\)** & **\(\text{Route 7}\)** & **\(\text{Route 8}\)** & **\(\text{Route 9}\)** \\ \hline Linear & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ Power & 1 & 4 & 9 & 16 & 25 & 36 & 49 & 64 & 81 \\ Geometric & 1 & 2 & 4 & 8 & 16 & 32 & 64 & 128 & 256 \\ Logarithmic & 1 & 1.58 & 2 & 2.32 & 2.58 & 2.81 & 3 & 3.17 & 3.32 \\ Square Root & 1 & 1.41 & 1.73 & 2 & 2.23 & 2.45 & 2.65 & 2.83 & 3 \\ Asymptotical & 0 & 0.12 & 0.24 & 0.36 & 0.46 & 0.55 & 0.63 & 0.70 & 0.76 \\ Inverse Linear & 1 & 1.13 & 1.29 & 1.5 & 1.8 & 2.25 & 3 & 4.5 & 9 \\ Balanced & 1 & 1.22 & 1.5 & 1.86 & 2.33 & 3 & 4 & 5.67 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different scales of priority setup.  
Analogously, \(P_{sy}(i,m)\) refers to the priority (importance) of security concerns, it is proportional to the possibility that the next hop node is not compromised (for more details, see Equation (15) in Section 5.1). \(I_{s}\) refers to the corresponding scale of security concern priority, meaning that security concern is set to \(I_{s}\) times as important as the energy efficiency concern. The exact number of \(I_{s}\) can be selected among various available options in Table 5.
# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 3 Methodology
### The CMVAE Framework  
To address the above challenges, this paper proposes CMVAE, a deep clustering model for code based on multi-view representations. This method first converts codes into ASTs, enhances their strategy structures, and then transforms them into tree graphs with a fixed number of nodes and extracts the centrality metric vectors of each node to obtain structurally relevant code representations. Meanwhile, the semantically relevant code representations are extracted using the CodeBERT pre-training model, which is combined with the reconstruction loss and contrast loss of the transformer encoder for model pre-training. Under the premise of ensuring that the core features of the input data are not lost, the complementarity between the representations from different perspectives is enhanced, so that the resulting representations are more relevant to the coding strategy. On this basis, joint training is performed by deep clustering methods, which are applied to the downstream task of code strategy clustering. The overall framework of CMVAE is shown in Figure 1. The model contains the following two important parts:  
1. Pre-training learning yields code representations that can embody code strategies. We divide code strategy representations into the understanding of structure and the understanding of semantics. The representations of different views of the code are obtained through the centrality feature of tree diagrams and the code pre-training model CodeBERT, respectively. Representation fusion by dynamically selecting weights for different representations through the attention layer. Then, a pre-training process that jointly trains reconstruction loss and contrast loss is designed to integrate the features  
Figure 1: The framework of CMVAE.  
that utilize different views of the code to learn how to obtain code representations rich in structural and semantic information.
2. The part of clustering student code strategies is implemented through a joint encoder-decoder-based training approach. After obtaining the code representation for clustering, the quality of clustering is improved without losing essential features by minimizing the difference between the pairwise soft assignment probabilities and the target distribution, jointly reconstructing the loss and clustering loss.
## 4 Analysis and Discussion
### PUF-Based Authentication Challenges and Solutions
#### 4.4.3 Challenge 3: Power Efficiency  
Power consumption is a major challenge for PUFs. The authentication process, stages of logical gates, and growth of CRPs consume previous components with too low power. However, some resistors employing PUFs require more power. In addition, using modern encryption and complex authentication lead to increased power consumption. There is a need to design ultra-low-power and aging-tolerant PUFs. In addition to that, performance optimization is a solution to improve power efficiency.
## 3 Establishment of RTSAM
### Random Field of Soil Elastic Modulus  
The spatial variability of soil properties can be reasonably described by the random field method in practical applications [21; 23; 29; 37; 38]. In this study, it is assumed that the soil elastic modulus in the longitudinal direction \(E_{g}(x)\) is characterized by a one-dimensional stationary lognormal random field with mean value \(\mu_{E}\), standard deviation \(\sigma_{E}\), and scale of fluctuation (SOF) \(\theta_{E}\). The coefficient of variation (COV) of soil elastic modulus \(\text{COV}_{E}\) is expressed as \(\sigma_{E}/\mu_{E}\). The use of lognormal distribution aims to avoid the generation of unreal negative values of soil elastic modulus. The following equation can be given for the random field of the soil elastic modulus with lognormal distribution:  
\[G_{E}(x)=\exp(\mu_{\ln E}+\sigma_{\ln E}G(x)) \tag{32}\]  
Figure 4: Comparison between tunnel responses obtained from proposed method and analytical solution: (**a**) Settlement, (**b**) Bending moment; (**c**) Shear force.  
where \(G_{E}(x)\) is the lognormal random field of the soil elastic modulus; \(G(x)\) is the correlated standard normal random field; \(\mu_{\text{InE}}\) and \(\sigma_{\text{InE}}\) are the mean and standard deviation of the correlated random field with normal distribution, respectively, that can be given as  
\[\sigma_{\text{In}E}=\sqrt{\text{ln}\left(1+\frac{\sigma_{E}^{2}}{\mu_{E}^{2}} \right)} \tag{33}\]  
\[\mu_{\text{In}E}=\text{ln}(\mu_{E})-\frac{\sigma_{\text{InE}}^{2}}{2} \tag{34}\]  
A common exponential autocorrelation function is adopted to represent the correlation between the soil elastic modulus of two arbitrary points:  
\[\rho_{E}=\text{exp}\left(-\frac{2|x_{1}-x_{2}|}{\theta_{E}}\right) \tag{35}\]  
where \(\rho_{E}\) is the autocorrelation function; \(x_{1}\) and \(x_{2}\) are the longitudinal locations of the random field.  
The lognormally distributed random field \(G_{E}(x)\) can be discretized through the K-L expansion method, that is, approximately expressed by a series including finite random variables [37; 39; 40] as shown in Equation (36). The K-L expansion method is implemented in this study because of its high efficiency and accuracy.  
\[G_{E}(x)\approx\text{exp}\left(\mu_{\text{In}E}+\sigma_{\text{In}E}\sum\limits _{i=1}^{M_{\text{k}}}\sqrt{\lambda_{i}}\xi_{i}\varphi_{i}(x)\right) \tag{36}\]  
where \(\xi_{i}\) is a set of independent random variables following the standard normal distribution; \(\varphi_{i}(x)\) and \(\lambda_{i}\) are the eigenfunctions and eigenvalues of the autocorrelation function, respectively; \(M_{\text{k}}\) is the number of truncation terms of the K-L expansion that is usually obtained based on truncation error \(\varepsilon_{\text{r}}\)[37; 41]:  
\[\varepsilon_{\text{r}}=1-\frac{\sum\limits_{i=1}^{M_{\text{k}}}\lambda_{i}}{ \sum\limits_{i=1}^{M_{\text{k}}}\lambda_{i}}<\varepsilon_{\text{a}} \tag{37}\]  
where \(M_{\text{a}}\) is the total number of discretized nodes of the random field and \(\varepsilon_{\text{a}}\) is the allowable value of the truncation error.
## 5 Conclusions  
Digital images have emerged as the primary means of disseminating information in the contemporary age of technology. Human social interactions provide a lot of picture data. The social lives of individuals are made much more convenient by clear and greater-quality photos. This is, however, constrained by the state of information technology today. The collection of picture information is directly impacted by the introduction of various disruptions during the operations of image synthesis, communication, preservation and other inter-processing. Studies in the area of objective IQA are a way to solve this issue, because it has practical implications for measuring this attenuation at the algorithm level. The least restrictive requirements were found in the NR-IQA method in particular. Deep neural networks' recent growth has substantially improved technology, making it easier to complete the NR-IQA assignment. In this paper, we proposed a framework based on deep neural networks and visual saliency. Visual saliency helps to extract high-frequency regions in the image which are the most affected regions due to the introduction of noise. The proposed framework was evaluated using the KADID-10K dataset. The achieved re  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **SROCC** & **PLCC** & **KROCC** \\ \hline BRISQUE [30] & 0.519 & 0.554 & 0.368 \\ \hline BIQI [31] & 0.431 & 0.460 & 0.229 \\ \hline CORNIA [32] & 0.541 & 0.580 & 0.384 \\ \hline SSEQ [33] & 0.424 & 0.463 & 0.295 \\ \hline DIVINE [34] & 0.489 & 0.532 & 0.341 \\ \hline BLINDS-II [35] & 0.527 & 0.559 & 0.375 \\ \hline HOSA [36] & 0.609 & 0.653 & 0.438 \\ \hline CNN [19] & 0.603 & & 0.619 & - \\ \hline BosICIP [37] & 0.630 & & 0.628 & - \\ \hline LPIPS [38] & 0.721 & 0.713 & - \\ \hline InceptionResNetV2 [22] & 0.731 & 0.734 & 0.546 \\ \hline Proposed Framework & **0.834** & **0.867** & **0.680** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of proposed framework with existing NR-IQA techniques.  
Figure 3: Scatter plot of the proposed framework.  
sults of the model demonstrate the superiority of the proposed framework. Visual saliency plays a significant role in improving NR-IQA performance. In the future, we intend to use this framework to evaluate the image quality acquired and transferred from drones.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 6 Conclusions  
In this paper, a wavefront decoupling control algorithm based on DM eigenmodes is proposed for the LC-DM AO system. The DM eigenmodes and the eigenmodes' DM correction method were derived. An initial wavefront with a PV = 3.66 \(\upmu\)m and an RMS = 0.52 \(\upmu\)m was corrected through eigenmode correction. After the DM correction, the PV and RMS were minimized to 0.41 and 0.03 \(\upmu\)m, respectively. For a 2 m telescope, the turbulence intensity was \(\pi 0=10\) cm at \(\lambda=550\) nm; for the DM and LC, the number of correction modes can be fixed to 55 eigenmodes and 69 Zernike modes, respectively. After decoupling correction, the RMS of the residual aberration decreased by 0.02 \(\upmu\)m. Finally, a setup of an LC-DM cascade AO system was developed, with a standard resolution plate as the imaging object. The imaging resolution in the 1.5-1.7 and 0.95-1.5 \(\upmu\)m wavebands was 19.7 and 15.6 \(\upmu\)m, respectively. These results indicated that the resolution reaches the diffraction-limited resolution in the infrared waveband. The imaging resolution without and with decoupling in the visible waveband of 0.7-0.95 \(\upmu\)m was 11.04 and 9.84 \(\upmu\)m, respectively, which is approximately 1.1 and 1 times, respectively, the diffraction-limited resolution. Therefore, the decoupling algorithm can achieve the diffraction-limited resolu  
Figure 9: Image captured with the visible camera at 700–950 nm: (**a**) original image without correction; (**b**) image before correction using the \(LC\) and after correction through the \(DM\); (**c**) image after nondecoupling correction with the \(LC\) and \(DM\); (**d**) image after decoupling correction through the LC and DM. (The clearest line pair has been circled in red).  
tion in the visible waveband, which is 1.2 times better than the diffraction-limited resolution of the traditional Zernike mode algorithm. This algorithm can be used to expand the work waveband of AO systems in large aperture telescopes by approximately 250 nm at a low cost.  
The proposed method can realize the cooperative control of two correctors and establish a foundation for the engineering applications of LC-DM cascade AO systems.  
This work was conducted by [PERSON], [PERSON] and [PERSON]. The conceptualization, methodology, and validation were conducted by [PERSON] and [PERSON] conducted the software and formal analysis. [PERSON] completed the investigation, data curation, and original draft preparation. [PERSON] was responsible for project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 12004381.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Context-Aware Systems Architecture in Industry 4.0: A Systematic Literature Review
## 5 Analysis and Discussion
### Requirements of the Architectures Researched
#### 5.1.1 Interoperability  
According to [PERSON] et al. [55], one of the functional requirements listed relates to the need for the architecture to enable interconnection with factory stakeholders and facilitate interoperability. [PERSON] et al. [49] present an architecture that promotes interoperability exclusively with the factory's legacy systems, such as the manufacturing execution system (MES) and enterprise resource planning (ERP), to extract knowledge, perform calculations, or carry out some simulations.  
[PERSON] et al. [85], citing [PERSON] and [PERSON] [95], refer to the fact that interoperability between stakeholders makes it possible to increase the flexibility and adaptability of production systems. They also state that, according to [PERSON] et al. [17], the principle of interoperability associated with I4.0 allows stakeholders to exchange information. With this principle in mind, [PERSON] et al. [85] propose an approach to solving the integration and interoperability challenges of I4.0 using a five-level structure called the 5C stack: connection, communication, coordination, cooperation, and collaboration.  
A physical space incorporates physical and computational artefacts, where machines and humans collaborate to perform various tasks, and, according to [PERSON] et al. [77], an architecture must support the integration of heterogeneous environments. Additionally, [PERSON] et al. [81] suggest that it is necessary to apply specific techniques to coordinate, communicate with, and recognise heterogeneous devices. It is important to remember that the ecosystem incorporates variations in production modules or equipment, which may have different functionalities, sizes, or configurations [74], and the amount of data generated, both structured and unstructured, adds further challenges in promoting interoperability [87]. Furthermore, from the perspective considered by [PERSON] et al. [90], we must consider the heterogeneity of the technologies involved.  
Finally, we have real scenarios described by [PERSON] et al. [83], in which robots and humans coexist and collaborate to carry out tasks, highlighting the need to coordinate the work of these players. In concordance with this topic, the design of the HORSE architecture, presented by [PERSON] et al. [90], and the architecture proposed by [PERSON] et al. [83] support hybrid manufacturing processes.