esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, we propose to use a semantic representation model inspired by PV-DM neural network to solve binary clone detection problems. Under this approach, instructions correspond to words, basic blocks correspond to sentences, functions correspond to paragraphs, and binary files correspond to articles. In this paper, we propose GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. To address the differences caused by different architectures, GeneDiff converts binaries from different ISAs into a Valgrind intermediate representation (VEX) implementation representation to mitigate the differences. GeneDiff utilizes a novel semantic-based representation learning model for binaries without any prior knowledge. Inspired by the PV-DM model in NLP, GeneDiff uses a large number of binary files to train a semantic representation learning model. Then, GeneDiff utilizes the representation model to map assembly functions from different ISAs into high-dimensional numeric vectors. To match similar functions, GeneDiff uses cosine distance to evaluate the similarity between high-dimensional vectors of the functions, which makes the clone detection approach scalable. In particular, GeneDiff's matching efficiency is much higher than approaches that use symbolic execution (see Section 5.3). The contributions of this paper are as follows:* We propose and implement GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. This is the first work that converts the assembly instructions into a VEX intermediate representation and uses a semantic representation model to implement clone detection for cross-architectures. * We use real-world IoT firmware across different architectures as targets, therein proving the practicality of GeneDiff in being able to detect known vulnerabilities for IoT devices. In addition, GeneDiff can reduce the burden of manual analysis during reverse engineering, which can be used in many applications, such as detecting code plagiarism or malware. * The results of our experiments show that it is feasible to analyze the intermediate representation to achieve cross-architecture clone detection using NLP techniques. In addition, many prior systems that use assembly code as a feature for binary clone detection [14; 15; 16; 18], could benefit from GeneDiff, which uses the intermediate representation to mitigate the differences between different architectures. The remainder of this paper is organized as follows. In Section 2, we review the related literature. In Section 3, we formally define the problem of this study. In Section 4, we thoroughly describe the design of GeneDiff. Section 5 presents our experiments. Section 6 reports the results of detecting known vulnerabilities using real-world IoT firmware. In Section 7, we discuss why GeneDiff performs well and its limitations. Finally, Section 8 concludes the paper.
## 3 Results
### Relationships of SIF-EVI at a Local Scale  
Greater Khingan Mountains contain cold-temperate coniferous forests dominated by Dahurian larch (_Larix gmelini_). The branch vein of Yilehuli Mountain divides Greater Khingan Mountains into two slope directions, Lingnan (the southern slope of the mountains) and Lingbei (the northern slope of the mountains) (Figure 4). The broadleaf tree ratio in the south of Yilehuli Mountain is higher than that in the north [36], and a broadleaf tree usually has a higher LAI than a needle leaf tree.  
To understand the relationship between vegetation indices and SIF, vegetation index values corresponding to OCO-2 SIF points were extracted. Thanks to the highly efficient data acquisition techniques of OCO-2, there are thousands of SIF data points and density distributions over the course of 16 days. To eliminate the spatial autocorrelation in SIF, we randomly selected 200 points with point distances longer than 5 km (on some Julian days, the random points did not reach 200). Figure 4 shows an example of random point extraction on Julian day 2015193.  
SIF can reflect a part of actual photosynthesis. NDVI and EVI are the vegetation indices that reflect vegetation greenness, and EVI is widely used in forest regions because it is less affected by the saturation effect compared to the NDVI [33]. Significant correlations (P \(<\) 0.05) between SIF and EVI in the growing seasons in 2015 and 2016 were found (Figure 5), which are different from the results of former studies and those at a regional scale. The correlation coefficients of SIF-NDVI, SIF-GPP, SIF-LAI, and SIF-FAPAR were also calculated, but no significant correlations were found.  
Figure 4: Example of random point extraction. Figures on the left and right show the full SIF points and the random points. The red rectangle insets in the top-right corners are the enlarged versions of the small, red rectangles in the figures.  
Figure 5: _Cont._The correlation coefficient between EVI and SIF changed over the course of 16 days; however, it steadily reached a peak value on Julian day 209 and subsequently decreased. This is similar to the pattern observed in Figure 2.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 4 NU-FTG Algorithm
### Algorithm Descriptions
```
1:\(\triangleright\) Initialization
2:Sts \(\leftarrow\) RS
3:myCh \(\leftarrow\) none
4:\(S_{rs}\gets S_{ch}\gets S_{fch}\leftarrow\varnothing\)
5:
6:\(\triangleright\) First part--Role selection
7:while(Sts \(\
eq\) FCH and Sts \(\
eq\) FM)
8:\(S_{rs}\)\(\leftarrow\)[\(v:v\) is RS node in 1-hop]
9:\(S_{ch}\)\(\leftarrow\)[\(v:v\) is available TCH or FCH node in 1-hop]
10:\(\triangleright\) RS nodes
11:if(Sts = RS)
12:if(\(S_{ch}\
eq\varnothing\)) Sts \(\leftarrow\) TM
13:elseif(my _nscore_\(\geq\) max _nscore_ in \(S_{rs}\)) Sts\(\leftarrow\)-TCH
14:\(\triangleright\) TM nodes
15:elseif(Sts = TM)
16: Send _req_join_ to the max _nscore_ node in \(S_{ch}\)
17:if(allowed)
18: myCh \(\leftarrow\)_Id_ of max _nscore_ node in \(S_{ch}\)
19: Sts \(\leftarrow\) FM
20:else Sts \(\leftarrow\) RS
21:\(\triangleright\) TCH nodes
22:elseif(Sts = TCH)
23: Wait for _req_join_ from neighbors
24:if(received any _req_join_ or \(S_{ch}=\varnothing\)) Sts\(\leftarrow\)-FCH
25:else Sts \(\leftarrow\) RS
```  
**Algorithm 1**Non-uniform Fusion Tree Generation (NU-FTG) algorithm  
If the FM node is allowed to join, it migrates to the selected FCH node.
## 5 Conclusions
### Role of Feature Extraction  
The effectiveness of different feature extraction methods varied across models, with combined features (e.g., pre-processed spectrum with its first derivative) generally leading to better performance. This suggests that incorporating multiple levels of spectral information helps in capturing more discriminative features, which is particularly beneficial for complex models like CNNs. The superior performance of the \"prep + der1\" combination in the CNN model highlights the importance of selecting appropriate feature vectors that align with the model's architecture and learning capacity.
## 4 Approaches of Optimizing Geolocation Measurements
### Least Square (LS)  
The Least-Squares (LS) approach is a standard method that may be used to optimize emitter localization measurements. The method depends on minimizing the amount of the squares of the residuals obtained in the results of all individual equations [86]. In [87], the geolocation scenario and performance of the proposed algorithm was validated using Linear Least Squares (LLS). In this scenario, four static sensors were considered to geolocate a moving target using the LLS method based on the TOA/RSS measurement, as shown in Figure 14. The mean square position error (MSPE) against the Signal to the Noise ratio (SNR) was computed in this scenario to present the performance of the proposed algorithm.  
A Non-Linear Least Squares (NLLS) estimator can be utilized to solve non-linear functions that cannot be solved analytically. Consequently, numerical search algorithms such as the Newton and the Gauss-Raphson algorithms are utilized to approximate the NLS estimate. Moreover, the LLS algorithms can be employed in the non-linear expressions to obtain a closed-form solution and avoid the explicit minimization problems [77; 88].
## 3 Results and Discussion
### Validation of the Model and the MUAE Efficiency  
The maximum extraction yield of quercetin at the predicted conditions (10.85%) was achieved at an ultrasound time of 15.49 min, ultrasound temperature of 67.31 \({}^{\circ}\)C, solvent-to-solid ratio of 20.33 mL/g, microwave time of 46.83 s, and ethanol concentration of 69.34% (\(v/v\), water). The predicted condition for the extraction of total flavonoids at an ultrasound time of 16.45 min, ultrasound temperature of 64.86 \({}^{\circ}\)C, solvent-to-solid ratio of 28.00 mL/g, microwave time of 38.34 s, and ethanol concentration of 69.50% (\(v/v\), water) provided the maximum extraction yield of 12.90%. To verify the validity of the model, the specific conditions for the maximum responses were predicted using response surfaces with numerical optimization as follows: ultrasound time 15.02 min, ultrasound temperature 61.56 \({}^{\circ}\)C, solvent-to-solid ratio 39.87 mL/g, microwave time 40.57 s and ethanol concentration 62.43% (\(v/v\), water). The extraction factors were adjusted to be 15.00 min, 60 \({}^{\circ}\)C, 40 mL/g, 40 s, and 60% to consider practical limitations and convenient operation. The predicted values for recovery yields were 10.05% for quercetin and 12.72% for total flavonoids. These predicted points were very close to the experimental results for Y\({}_{1}\) (10.01%) and Y\({}_{2}\) (12.36%), with relative percent deviations of 0.39 and 2.91%, respectively (Figure 4). This strong correlation signifies the suitability of the model to predict the responses at optimal conditions. This study suggests these accurate models and optimal conditions could be significantly helpful for further scale-up of the MUAE process for quercetin and total flavonoid extraction from red onion skin.
## 3 Results and Discussion
### Machine Learning and Feature Importance  
The performance of ML models (Figure 2) suggests the following. The highest performance in terms of coefficient \(R^{2}\) is obtained for Ridge regression (\(R^{2}\) = 0.399), SVR (\(R^{2}\) = 0.38), kNN (\(R^{2}\) = 0.314) and ElasticNet (\(R^{2}\) = 0.313). From the perspective of error distributions, the Ridge-RMSE (0.731), ElasticNet-RMSE (0.791), Lasso-RMSE (0.801), and kNN (0.815) have the lowest reported RMSE. However, MSE and RMSE are sensitive to outliers, especially in this study, where a high deviation in the score, even after normalisation, is observed. Thus, MAE/MAD and MAPE may provide much better insight into the model's performance due to lower sensitivity to outliers. The best-performing algorithms include Ridge, ElasticNet, Lasso, and SVR models.  
The predictive performance of samples considering predicted and observed test values (Figure 3) suggests that the least variability is observed in Lasso, Ridge, ElasticNet, and SVR regressions. Specifically, similar outcomes but with smaller point error estimates in elaborating the performance are observed in Ridge regression. It was a bit surprising seeing that even after performing an in-depth design and modification of a Deep Neural Network topology with GA (Genetic Algorithm) using MLP (Multilayer Perceptron) GLPROP-SLR-GA, the maximum regression obtained was \(R^{2}\) = 0.229. Even so, the complete set of predictors could not be included for a network to converge at the \(10^{5}\) maximum number of training repetitions, which forced us to train the network only with continuous variables.  
To avoid making biased statements, instead of including or selecting the highest-performing model, we visualise the results of feature importance based on the MDL score for extracting an overall average feature importance score (Figure 4). The overall assessment suggests that FAP_Binary, followed by NWEC_Est, HMA_Est, CMS_Coded, MAP_Coded, TTOR_Est, MDS_Coded, FRT_Est, and MPPM, contribute the most amount of variance to the proposed models. Although the literature often reports Machine Age (HMA) as the most contributing factor to the technical performance metrics, such as MTTR and MTBF, it was surprising that maintenance practice performed is the least contributing factor to the overall ML regression. This forced us to conduct additional hypothesis testing to understand the actual contribution of individual variables (predictors) to the overall ML models by estimating whether or not these predictors have a statistically significant contribution.  
Figure 2: Predictive performance scores.
## 6 Conclusions  
The higher the number of iterations in the estimation process, the more accurately the channel estimation will be performed, but the number of iterations in the decoding process will be increased considerably. For example, in the case of turbo decoding, a new estimation of h is made every five iterations. In the case of LDPC codes, after ten iterations, a new estimation of h is done, which proves that iterative LS estimation is time consuming.  
If h was recalculated R times, and let I be the number of iterations performed by the decoder within a decoding process, then the total number of iterations performed by the turbo decoder, for example, is \((\mathrm{R}+1)\cdot\mathrm{I}\). In the simulations realized for the scheme in Figure 9, the recalculation of h was performed only once \(\mathrm{R}=1\), and the number of iterations performed by the turbo decoder within a decoding process was five. It follows that in this case, the minimum number of iterations performed by the turbo decoder in Figure 9 is ten.  
The performance obtained with iterative estimation will never be able to exceed the performance obtained in the case of a perfect estimation of h.  
In this research, equalization was performed before decoding for the three analyzed codes, but in the future, it would be interesting to research what happens if the equalization is performed within the decoding process, such as in turbo equalization.  
Conceptualization, A.-M.C., F.L.M. and C.G.; methodology, A.-M.C. and F.L.M.; software, A.-M.C. and F.L.M.; validation, A.-M.C., F.L.M. and C.G.; formal analysis, C.G.; investigation, A.-M.C.; resources, F.L.M.; data curation, A.-M.C.; writing--original draft preparation, A.-M.C. and F.L.M.; writing--review and editing, A.-M.C. and C.G.; visualization, A.-M.C.; supervision, C.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** 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 authors declare no conflict of interest.
# Failure Prediction of the Rotating Machinery Based on CEEMDAN-ApEn Feature and AR-UKF Model
## 2 Methodology
### Fault Feature Extraction Stage
#### 2.2.2 Maximal Information Coefficient  
As a relationship measurement method, the maximal information coefficient (MIC) is proposed by [PERSON] et al. [31] to reveal the correlation of variables. Therefore, MIC is applied to perform selection of IMFs which achieved by CEEMDAN. The details of MIC are described as follows.  
1. Denote \(p\left(y_{1},y_{2}\right)\) as a joint probability distribution function of two variables \(y_{1}\) and \(y_{2}\), and mutual information of \(y_{1}\) and \(y_{2}\) is defined as: \[I\left(y_{1},y_{2}\right)=\int\limits_{y_{1}}\int\limits_{y_{2}}p\left(y_{1}, y_{2}\right)\log\left(\frac{  
Denote \(I\left(y_{1},y_{2},Y|n_{y_{1}},n_{y_{2}}\right)\) as the mutual information of \(y_{1}\) and \(y_{2}\) on grid \(G\) with \(n_{y_{1}}\) rows and \(n_{y_{2}}\) columns. The feature matrix of \(Y\) is defined as: \[\mathrm{Y}\left(y_{1},y_{2}|Y\right)_{n_{y_{1}},n_{y_{2}}}=\frac{I^{*}\left(y_{ 1},y_{2},Y,n_{y_{1}},n_{y_{2}}\right)}{\log\min\left(n_{y_{1}},n_{y_{2}}\right)},\] (10) where \(I^{*}\left(y_{1},y_{2},Y,n_{y_{1}},n_{y_{2}}\right)=I\left(y_{1},y_{2},Y|G,n_ {y_{1}},n_{y_{2}}\right)\) represents the maximum mutual information of \(y_{1}\) and \(y_{2}\) on grid \(G\).
3. Furthermore, MIC of \(y_{1}\) and \(y_{2}\) in set \(Y\) with \(N\) samples is defines as follows. \[M\left(y_{1},y_{2}|Y\right)=\max_{n_{y_{1}}\times n_{y_{2}}<\theta\left(N\right) }\left\{\mathrm{Y}\left(y_{1},y_{2}|Y\right)_{n_{y_{1}},n_{y_{2}}}\right\},\] (11) where \(\theta\left(N\right)\) is the upper bound of the grid size required to be selected carefully, and its suitable value is \(\theta\left(N\right)=N^{0.6}\).  
By calculating the MIC value between each IMF and the original signals according to above steps, IMF with MIC value larger than a threshold is selected as the eligible one, and it is added into a set of IMF \(Z\). Then, the signals \(U\) with helpful information can be reconstructed with all eligible IMFs in \(Z\).
###### Abstract  
In this paper, we investigate the energy efficiency (EE) maximization in multi-cell multi-carrier non-orthogonal multiple access (MCMC-NOMA) networks. To achieve this goal, an optimization problem is formulated then the solution is divided into two parts. First, we investigate the inter-cell interference mitigation and then we propose an auction-based non-cooperative game for power allocation for base stations. Finally, to guarantee the rate requirements for users, power is allocated fairly to users. The simulation results show that the proposed scheme has the best performance compared with the existing NOMA-based fractional transmit power allocation (FTPA) and the conventional orthogonal frequency division multiple access (OFDMA).  
auction-based game; clustering; energy efficiency; multi-cell and multi-carrier; non-orthogonal multiple access (NOMA) Article
# Addressable Refraction and Curved Soliton Waveguides Using Electric Interfaces
###### Abstract  
A great deal of interest over the years has been directed to the optical space solitons for the possibility of realizing 3D waveguides with very low propagation losses. A great limitation in their use for writing complex circuits is represented by the impossibility of making curved structures. In the past, solitons in nematic liquid crystals, called nematicons, were reflected on electrical interfaces, and more recently photorefractive spatial solitons have been, as well. In the present work, we investigate refraction and total reflection of spatial solitons with saturable electro-optic nonlinearity, such as the photorefractive ones, on an electric wall acting as a reflector. Using a custom FDTD code, the propagation of a self-confined beam was analyzed as a function of the applied electric bias. The electrical reflector was simulated by applying different biases in two adjacent volumes. We observed both smaller and larger angles of refraction, up to the critical \(\pi/2\)-refraction condition, and then the total reflection. The radii of curvature of the associated guides can be varied from centimeters down to hundreds of microns. The straight guides showed losses as low as 0.07 dB/cm as previously observed, while the losses associated with single curves were estimated to be as low as 0.2 dB.  
spatial soliton; soliton waveguiding; photorefractivity; 3D photonics; neuromorphic photonics; addressable circuits; smart circuits; nonlinear optics; photonics +
Footnote †: journal: Applied Physics Letters
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 2 Disassociation and Utility Awareness for Associations  
The dissociation technique [2] ensures the constraint of \(k^{m}\)-anonymity by separating the elements of a record into several chunks within the same group. It thus creates association ambiguity between its separated terms, which causes a reduction of the utility for the association in question. Dissociation, as defined by [PERSON], is based on two assumptions. The first is that no one association is more significant than another. The second is that the data should not be modified, generalized or deleted. In the next section, we provide an algorithm to preserve a better utility for a set of predefined associations, called the utility rules, by reducing the amount of split-ups a utility rule has to endure in order to preserve \(k^{m}\)-anonymity [2].  
Table 1 recalls the basic notations used in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \(\mathcal{D}\) & a set of items \\ \(\mathcal{T}\) & a table containing individuals related records \\ \(\mathcal{T}^{*}\) & a table anonymized using the disassociation technique \\ \(r\) & a record (of \(\mathcal{T}\)) which is set of items associated with a specific individual of a population \\ \(I\) & an itemset included in \(\mathcal{D}\) \\ \(s(I,\mathcal{T})\) & the support of itemset \(I\) i.e., the number of records in \(\mathcal{T}\) that are superset of \(I\) \\ \(C\) & a cluster in a disassociated dataset, formed by the horizontal partitioning of \(\mathcal{T}\) \\ \(C^{*}\) & a vertically partitioned cluster \(C\) that results in record chunks and a term chunk \\ \(R_{C}\) & a record chunk from the vertically partitioned cluster \(C^{*}\) \\ \(T_{C}\) & the term chunk from the vertically partitioned cluster \(C^{*}\) \\ \(\delta\) & maximum number of records allowed in a cluster, also know as the maximum cluster size \\ \hline \end{tabular}
\end{table}
Table 1: Notations used in the paper.  
Figure 1: Synthesis of the anonymization of setvalued datasets based on disassociation optimized by ant colonies and a reconstruction respecting the statistical item appearance frequencies.
## 3 Results and Discussion
### Sem  
The corresponding SEM images of CF, ao-PDA/ACF and ht-PDA/ACF before and after electrolysis are shown in Figure 5 and Figure S4. It can be seen that the surface of raw CF before electrolysis was flat and smooth (Figure 5a), with some shallow grooves along the direction of the carbon fiber. For the ao-PDA/ACF sample, the pDA was covered on the surface of the carbon fiber (Figure 5b), which formed the rough film structure of ao-PDA/ACF (Figure 1) [20]. In general, the rough structure possesses a larger specific surface than the flat surface, which is beneficial for increasing the number of active sites and reactivity of the electrocatalysis, thus leading to higher H\({}_{2}\)O\({}_{2}\) yield at the ao-PDA/ACF cathode than CF (Figure 3a). Differently, there are many spherical particles evenly accumulated on the surface of ht-PDA/ACF prepared by the hydrothermal method (Figures 1 and 5c,d). The particle size distribution shows the particles varied from 200 nm to 550 nm (Figure S5). It has been reported that the spherical structure catalyst could have the largest specific surface area compared to film structures, and thus, could be the ideal shape of catalyst [21]. Furthermore, the SEM images after electrolysis (Figure S4) show that the CF, ao-PDA/ACF and ht-PDA/ACF cathode can maintain their morphology and structure after electrolysis, suggesting that all three samples have good stability to H\({}_{2}\)O\({}_{2}\) production.  
Meantime, compared to the film pDA structure in which many active sites could be buried, the spherical structure pDA could have a better structure for the exposed active sites and accelerate the reaction [21]. By comparing the SEM image of CF, ao-PDA/ACF and ht-PDA/ACF, it is clear that the change of dopamine oxidation process could lead to a different morphology of pDA accumulated on the surface of the CF fiber and further influence the cathode performance. After comparing the above three materials, ht-PDA/ACF could become the most promising cathode material due to its spherical structure providing a larger specific area, and hence, having more exposed active sites compared to ao-PDA/ACF, as a result of H\({}_{2}\)O\({}_{2}\) production (Figure 2).  
Figure 4: The UV-visible spectra of polydopamine solution prepared by a hydrothermal process with different amounts of dopamine from 60 mg to 90 mg.