esa-sceva/satcom-chunk-collection huggingface dataset:
# OSINT-Based LPC-MTD and HS-Decoy for Organizational Defensive Deception
## 3 Proposed Deception Models with Organizational OSINT, LPC-MTD and HS-Decoy
### Organizational Social Engineering Deception in the Proposed Model  
Figure 1 shows the standardized main overview of each applicational deception concept, i.e., organizational OSINT, LPC-MTD, and HS-Decoy, in the proposed deception model. First, the organizational OSINT and related deception knowledge were standardized through the asynchronous parallel chain-based OSINT process, followed by the application of the knowledge to the applicable concepts, such as LPC-MTD and HS-Decoy.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Approach** & **Specific Technique** & **Advantages** & **Disadvantages** \\ \hline Game theory-based MTD [27, 28, 29, 30, 31, 32, 33, 34, 35] & General game theory, Bayesian Stackelberg game and stochastic game theory & Determining the optimal MTD strategy and realizing the decision model. & Requires additional reasonability and reliability of the calculated decision. & Requires additional reasonability and reliability of the calculated decision. \\ \hline Machine learning-based MTD [39, 30, 31, 32, 33, 34, 35] & Classification or clustering, reinforcement learning, and deep neural network. & Calculating specialized defense indicators for each organization through the introduction of advanced learning concepts. & Requires training with large datasets. Complexity due to computational overhead. Limitations in realization of multi-factor-based deception process. \\ \hline Calculation of detailed mutation parameter-based MTD [42, 43] & Before definition of MTD and after definition of MTD. & Realization of visibility and efficacy of practical application of any proposed MTD scheme. & Evaluation dependent on existing simulation models. Limited in performing abstraction in arbitrary legacy nets. Limitations of applicability in real world. \\ \hline \end{tabular}
\end{table}
Table 1: Taxonomy of existing moving target defense (MTD) and decoy-based deception research and proposed scheme.  
Subsequently, the integrated OSINT-based deceptive architecture (IODA) was built and integrated based on the configured LPC-MTD and HS-Decoy. The annotations related to equations and algorithms for each concept that will be presented in the remainder of this section can be viewed in Table A7 in Appendix A.
# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
###### Abstract  
With the development of big data technology, creating the 'Digital Campus' is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on K-Means and clustering by fast search and find of density peaks (K-CFSFDP) is proposed, which improves on the distance and density of data points. This method is used to cluster students from four universities. The experiment shows that K-CFSFDP algorithm has better clustering results and running efficiency than the traditional K-Means clustering algorithm, and it performs well in large scale campus data. Additionally, the results of the cluster analysis show that the students of different categories in four universities had different performances in living habits and learning performance, so the university can learn about the students' behavior of different categories and provide corresponding personalized services, which have certain practical significance.  
Keywords:students' behavior; K-Means; CFSFDP; SSE; density; distance; machine learning; clustering +
Footnote †: journal: Applied sciences
## 4 Optical Methods
### Streamer Variability Calculation  
For each quadrant, the calculation of the binarized area (i.e., the group of pixels related to the discharge) over time was performed. This was repeated over the different discharge events. For each frame, the algorithm evaluated the streamer penetration P, i.e., the maximum distance between the streamer and the corresponding tip from which it was generated. In Figure 7a, referred to as the first quadrant, as an example, the pixel which identifies the streamer penetration is marked with a white cross. This series of operations was repeated for each frame of the discharge sequence, and then for all the 63 consecutive events. In Figure 7b, for example, the trends of four discharges (among the 63) are shown. All the discharges started and ended at the same time, but the trend was found to be potentially different over the 63 events, particularly for the maximum streamer penetration P\({}_{\text{max}}\) over a single discharge (colored circular marker). Note that the uncertainty in identifying the streamer penetration for each frame corresponded to \(\pm 1\) pixel, i.e., 0.125 mm (according to Table 3), which is negligible if compared to the P\({}_{\text{max}}\) values. In Figure 7c, the P\({}_{\text{max}}\) for each discharge is shown. From this series, statistic parameters as the average (red dashed line) and standard deviation from the average (red dotted lines) were computed. Finally, the coefficient of variation (CoV) of the P\({}_{\text{max}}\), i.e., the ratio between standard deviation and average, was found.  
Note that this method did not take into account the branching that potentially occurred; in every frame, only the farthest point in the entire quadrant was considered. Future work will be focused on shape detection, to improve this algorithm and make it robust for branching cases.
## 3 Results and Discussion
### Main Effect Parameter Analysis
#### 3.2.1 Effect of Methanol-to-Oil Molar Ratio  
Experiments were performed after varying the M:O ratio in ranges of 3:1 to 12:1; their impact on biodiesel yield is analyzed, as shown in Figure 3. An increase in the concentration of the M:O ratio tends to increase the biodiesel yield up to the mid-values and thereafter decreases. The desired maximum biodiesel yield was attained approximately nearer to the mid-values of the M:O ratio (see Figure 3). As the M:O ratio increases in the reaction process, biodiesel yield tends to increase up to the mid-values of their respective levels. At lower values of the M:O ratio, the time required to complete the transesterification reaction (KOH reacts with methanol to yield methoxide, which is treated as an actual reactant in the transesterification process, and water, which causes partial hydrolysis of acylglycerides or the methyl ester formed) will be very high with less usage of methanol. After crossing the mid-values of the M:O ratio, the excess quantity of methanol results in a decrease in biodiesel yield. This occurs due to the interference of excess methanol with alkyl ester and glycerol separation by increasing glycerol solubility. This results in the dilution of a portion of the glycerol remaining in the alkyl ester phase, which causes the loss of ester due to soap formation. In addition, the presence of glycerol in the solution shifts the equilibrium back to the left and results in a decreased biodiesel yield. A similar trend corresponding to the effect of methanol-to-oil molar ratio on yield was reported with the transesterification of Musa acuminata peduncle [41] and Pithecellobium dulce seed oil [35].
## 1 Introduction  
Radon is part of the decay chain of the uranium-238 radioactive family and is continuously formed in natural environments during the radioactive decay of its parent isotope, radium-226, the half-life of which is about 1600 years [1, 2, 3, 4, 5]. Being an inert gas with a relatively long half-life (3.82 days), in terms of its physical and chemical properties, it acts as an optimal indicator for studying many processes occurring in the environment [4, 6, 7, 8], including several important geological processes [9, 10]. This is due to the fact that radon gas shows the ability to easily migrate in the geological environment in the gas phase or dissolved in pore waters [3, 11, 12, 13], forming a radon field with the appearance of emanation anomalies in the near-surface horizons of rocks and soils [3, 14, 15]. In this regard, emanation methods began to be widely used in geochemical, geophysical and geodynamic studies to track geological processes [4]. Emanation methods are also used in the search for mineral deposits, including kimberlites [16, 17, 18, 19]. Emanation studies carried out in the territory of the kimberlite fields of the Arkhangelsk diamondiferous province (Russian Federation) showed that, in the soil horizons above the kimberlite bodies, there are increased values of the volumetric activity of radon that are several times higher than the background values [20]. It was shown that the nature of the distribution of radon over kimberlite pipes has a subcircular structure, which is associated with the development of faults and fracturing in the enclosing rocks of the near-pipe space [19, 21]. Previously obtained data have shown high prospects for the application of emanation methods in the search for kimberlite pipes in the territory of the Arkhangelsk diamondiferous province [22].  
However, for a more complete understanding of the specifics of locating explosion pipes in the field of radon volumetric activity and the development of emanation methods to search for kimberlites, it is necessary to conduct detailed studies of the mechanisms and conditions of radon formation in the rocks that compose the kimberlite body. The purpose of this study was to study the emanation properties of rocks that make up the kimberlite pipes of the Arkhangelsk diamondiferous province, so the radiation and physical parameters of the kimberlites that overlap and the host rocks of the Lomonosov diamond deposit of the Arkhangelsk diamondiferous province were studied. The following radiation and physical parameters were studied: activity of radium-226, activity of radon-222, emanation coefficient, radon rate production, average and true density, and porosity. An assessment of the main factors causing the formation of the increased activity of radon over the pipes has been carried out.
# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
## 4 Active Learning
### Maximum Entropy Principle  
The maximum entropy principle is a method based on information theory, aimed at inferring the maximum entropy distribution from a known data distribution in order to minimize bias. In active learning, we used the maximum entropy principle to select the most informative samples for training models and to achieve the best results.  
Entropy is defined as the uncertainty of random variables. For discrete random variable \(X\), if it can take the possible value from \(\{X_{1},X_{2},\ldots,X_{n}\}\), then its entropy is defined as Formula (6).  
\[H(x)=\sum\frac{p(X_{i})\log_{2}l}{p(X_{i})}=-\sum p(X_{i})\log_{2}p(X_{i}) \tag{6}\]  
Figure 5: The active learning principle.  
In this work, we used a grapheme-to-phoneme (G2P) model to predict phonemes for unannotated words, and obtained the predicted phonemes sequence along with the maximum probability of each predicted phoneme. To select the samples for labeling, we calculated the entropy value for each sample and ranked them in descending order. The sample with the highest entropy value, which has the greatest potential to improve the model, is then given to an expert for correction and labeling. By using a small number of high-value samples, our approach can improve model performance, greatly reduce the workload of expert labeling, and lower the construction cost of pronunciation dictionaries.  
Assuming the grapheme sequence corresponding to sample \(k\) is \([x_{1},x_{2},\ldots,x_{n}]\) and the predicted probability vector corresponding to phoneme sequence is \([y_{1},y_{2},\ldots,y_{n}]\), the entropy value \(H_{k}\) of sample \(k\), is calculated by Equation (7).  
\[H_{k}=\max(y_{1})logmax(y_{1})+\max(y_{2})logmax(y_{2})+\ldots+\max(y_{n})logmax (y_{n}) \tag{7}\]  
Calculate the probability vector \(y_{n}\) for the predicted phoneme at location \(n\) by using Equation (8).  
\[y_{n}=softmax(W*h_{n}) \tag{8}\]  
\(W\) is the weight of the current hidden state \(h_{n}\).
## 2 Bayesian Optimization
### Gaussian Process (GP) Regression
When it comes to choosing a kernel function, it is typically necessary to select it based on certain properties. First of all, kernel functions need to be positive semidefinite. Secondly, it is important that points closer in the input space are more strongly correlated, i.e., if \(||\mathbf{x}-\mathbf{x}^{\prime}||<||\mathbf{x}-\mathbf{x}^{\prime\prime}||\), then \(k(\mathbf{x},\mathbf{x}^{\prime})>k(\mathbf{x},\mathbf{x}^{\prime\prime})\). There are many kernel functions commonly used in BO; however, the Matern kernel [56] will be the main focus, since it is a more general form of kernel functions. \[k(\mathbf{x},\mathbf{x}^{\prime})=\alpha_{0}\frac{2^{1-\
u}}{\Gamma(\
u)} \Big{(}\sqrt{2\
u}||\mathbf{x}-\mathbf{x}^{\prime}||\Big{)}^{\
u}K_{\
u}( \sqrt{2\
u}||\mathbf{x}-\mathbf{x}^{\prime}||) \tag{6}\]  
where \(K_{\
u}\) is the modified Bessel function [57], \(\Gamma(\
u)\) is the Gamma function, and \(\alpha_{0}\) is a hyperparamter [58]. Certain values of \(\
u\) in Equation (6) reduce the equation to other popular kernels, including the following:  
* Radial basis function (RBF) kernel [59] for \(\
u\to\inf\);
* Absolute exponential kernel for \(\
u=0.5\);Once-differentiable functions for \(\
u=1.5\);
* Twice-differentiable functions for \(\
u=2.5\). So far, the prior distribution of functions has been constructed, but no observed information has been introduced to the model yet. To do so, the objective function's value has to be measured at some points, which are denoted by \(\mathbf{y}_{1:n}\). These points, commonly referred to as training data, are observed by sampling the black-box objective function. By incorporating this information into our model, the posterior distribution can be defined. First of all, the distribution of \(\mathbf{f}(\mathbf{x}_{1:q},\mathbf{y}_{1:n})\) is now a \((q+n)\)-dimensional MVN. To acquire the posterior distribution on a certain point of \(\mathbf{x}_{1:q}\), conditioning and marginalization have to be applied. By using the Bayes rule, Equation (7) is obtained. \[f(\mathbf{x})|\mathbf{f}(\mathbf{y}_{1:n})\sim N(\mu_{n}(\mathbf{x}),\sigma_{n }^{2}(\mathbf{x})) \tag{7}\]  
where the mean value \(\mu_{n}(\mathbf{x})\) is given by Equation (8) and represents a weighted average between the prior and an estimate based on the training data. \[\mu_{n}(\mathbf{x})=\mathbf{k}(\mathbf{x},\mathbf{y}_{1:n})\mathbf{K}(\mathbf{ y}_{1:n},\mathbf{y}_{1:n})^{-1}(\mathbf{f}(\mathbf{y}_{1:n})-\mu_{0}( \mathbf{y}_{1:n}))+\mu_{0}(\mathbf{x}) \tag{8}\]  
and the posterior variance \(\sigma_{n}^{2}(\mathbf{x})\) seen in Equation (9) is equal to the previous covariance, minus a term that corresponds to the variance removed by observing the training data. \[\sigma_{n}^{2}(\mathbf{x})=k(\mathbf{x},\mathbf{x})-\mathbf{k}(\mathbf{x}, \mathbf{y}_{1:n})\mathbf{K}(\mathbf{y}_{1:n},\mathbf{y}_{1:n})^{-1}\mathbf{k}( \mathbf{y}_{1:n},\mathbf{x}) \tag{9}\]  
This regression process updates the surrogate objective function's values by conditioning on the training data.
## 3 Plugging and Capacity  
The ultimate bearing capacity of the pile (\(Q_{c}\)) is typically calculated by adding the shaft resistance \(R_{s}\), and the toe resistance \(R_{p}\). Open-ended piles experience one of three possible plugging conditions: (i) unplugged, (ii) plugged, or (iii) internal plug. Designers are typically unable to predict which condition will prevail, and thus check all conditions and adopt the minimum capacity as a conservative approach. [PERSON] and [PERSON] [20] presented equations to calculate the pile capacity if the pile is plugged (Equation (1)) or if the pile is partially plugged or unplugged (Equation (2)).  
\[Q_{c}=\sum f_{so}A_{so}+q_{p}A_{pp}-W_{p} \tag{1}\]  
\[Q_{c}=\sum f_{so}A_{so}+\sum f_{si}A_{si}+q_{p}A_{p}-W_{p} \tag{2}\]  
where \(f_{so}\) is the exterior unit shaft resistance, \(A_{so}\) is the pile exterior surface area, \(q_{p}\) is the unit toe resistance, \(A_{pp}\) is the cross-sectional area of the pile and soil plug at pile toe (\(\frac{MD^{2}}{4}\)), \(D\) is pile outer diameter, \(f_{si}\) is the interior unit shaft resistance, \(A_{si}\) is the pile interior surface area, \(A_{p}\) is the pile toe cross-sectional area (\(\frac{\pi(D^{2}-D_{i}^{2})}{4}\)), \(D_{i}\) is pile inner diameter and \(W_{p}\) is the weight of the pile and soil plug.  
In the unplugged condition, the pile is assumed to behave as a \"cookie-cutter\" coring through the soil without exhibiting internal friction. The pile capacity is the summation of exterior skin friction and end bearing of the pile annulus section (i.e.,\(f_{si}\) in Equation (2) is taken as 0). For the plugged condition, the pile is assumed to behave as a full displacement pile and hence the capacity is the summation of exterior skin friction and end bearing of the entire toe area (Equation (1)). Finally, in the internal plug condition, it is theorized that the pile might experience interior skin friction. The capacity is taken as the summation of exterior skin friction and the lesser of: (1) the end bearing of the entire toe area; or (2) the end bearing of the pile annulus section plus the interior skin friction. Interior skin friction (\(f_{si}\)) is typically taken as 40% of the exterior skin friction (\(f_{so}\)) in cohesive soils, and as 100% of the exterior skin friction in cohesionless soil (\(f_{si}=f_{so}\)) [36].
# Performance of Satellite-Based Evapotranspiration
## 3 Methods
### Net Radiation Estimates  
The SEB models used in this work rely on the spatialized net radiation flux (R\({}_{\text{n}}\)) as a key variable for ET\({}_{\text{a}}\) retrievals because R\({}_{\text{n}}\) is partitioned into sensible heat flux (H), latent heat flux (\(\lambda\)E) and soil heat flux (G). R\({}_{\text{n}}\) was obtained using Equation (2):  
\[\text{R}_{\text{n}}=(1-\alpha)\text{R}_{\text{swd}}+\varepsilon_{\lambda} \text{R}_{\text{lwd}}-\varepsilon_{\lambda}\sigma\text{T}_{\text{s}}^{4} \tag{2}\]  
where \(\alpha\) is the surface albedo, R\({}_{\text{swd}}\) (W m\({}^{-2}\)) is the descending short-wave radiation, R\({}_{\text{lwd}}\) (W m\({}^{-2}\)) is the long-wave descending radiation and \(\sigma\) is the Boltzmann constant. The \(\alpha\) calculation was done following the method proposed by [51]:  
\[\alpha_{\text{sw}}=0,356\alpha_{\text{b}}+0,130\alpha_{\uptau}+0,373\alpha_{ \text{mir}}+0,085\alpha_{\text{sw1}}+0,072\alpha_{\text{sw2}}-0.0018 \tag{3}\]where \(\alpha_{\rm sw}\) is the broadband surface albedo for the shortwave spectrum and \(\alpha_{\rm b}\), \(\alpha_{\rm r}\), \(\alpha_{\rm nir}\), \(\alpha_{\rm sw1}\) and \(\alpha_{\rm sw2}\) are the narrowband surface albedo values for the blue, red, near infrared and shortwave infrared spectrum, respectively. Although the albedo estimate requires the Bidirectional Reflectance Distribution Function (BRDF), it has been shown that for evapotranspiration retrievals bi-directional surface reflectance derived from satellite can be used for ET\({}_{\rm a}\) maps [52]. R\({}_{\rm swd}\) was computed following the method of [53], using extraterrestrial radiation (R\({}_{\rm A}\)) and Inverse Distance Weighted (IDW) interpolated near-surface air temperature (T\({}_{\rm a}\)), maximum daily temperature (T\({}_{\rm x}\)) and daily minimum temperature (T\({}_{\rm n}\)):  
\[R_{\rm swd}=R_{\rm A}\left(A\left(1-\exp(-B\Delta T)^{\rm C}\right)\right) \tag{4}\]  
The values of A, B and C were derived from the constant proposed by [53]. Then, R\({}_{\rm lwd}\) was calculated through Stefan-Boltzmann equation. Atmospheric emissivity was estimated with the method proposed by [54] using T\({}_{\rm a}\) and actual vapor pressure (e) as input.  
\[R_{\rm lwd}=1.24\left(\frac{\rm e}{\rm T_{a}}\right)^{\frac{1}{7}}\sigma{\rm T _{a}}^{4} \tag{5}\]  
Finally, R\({}_{\rm n}\) was validated against ground measurements in the Oromo calibration site at the same time of the satellite overpass.
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 3 Methodology
### Compatibility Settings  
To ensure the compatibility of each stage in the SG framework, the vector matrices of the different stages are dimensionally transformed. Initially, in the feature extraction stage, the input data are first extracted into a 300-dimensional feature matrix, whose dimension is restricted by the GloVe pre-training model, and later subsequently transformed into a 768-dimensional feature matrix by BiLSTM, thus preparing for the subsequent label attention extraction with the matched shape. The feature matrix is then fed into the label attention extractor, which contains two paralleled label attention blocks with identical input and output shape to the 768, respectively, and specifically for intent and slot label extraction. Subsequently, the two feature matrices are spliced into a 1536-dimensional vector and fed into 12 dynamic gates parallelly. In order to enable the features to be fused as  
Figure 1: Overview of the proposed semantic guidance framework.  
semantic guides for the textual information in BERT, the vector is transformed into two 768-dimensional feature matrices. Finally, in the feature fusion stage, the two 768-dimensional semantic guide vectors, separately acting as key and value, are deeply fused with the same shaped text information as used in BERT.
## 4 Results and Discussion
### ANOVA Results
The ANOVA results relative to the routing overhead show that the node density (ND) is the factor that produces the most important effect, with percentage contributions greater than 83.0% from the three methods. To conclude, we note that ANOVA analysis based on the F-ratio statistic gave the same results for both full factorial and Plackett-Burman methods, and their results were close to those from the Taguchi method. The differences are observed regarding the percentage contributions and, subsequently, the ranks of the factors in affecting the four responses. In that regard, both the FFM and PBM present closer percentage contributions than with those computed from the Taguchi method. This is mainly due to the lowest percentage contribution of error produced by the Taguchi method, since ANOVA analysis was conducted using the measured experimental responses from both the full factorial and the Plackett-Burman methods and the signal-to-noise ratio (SNR) from the Taguchi method.