esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Condition Monitoring Approach for
## 2 Theoretical Background
### Artificial Neural Network  
An artificial neural network (ANN) is a mathematical model and network system inspired by the biological neural network of the human brain. An ANN is made up of a collection of connected units called artificial neurons, which receive input, change their activation state according to the input, and produce output depending on the input and activation [32]. The weights and thresholds of the neurons can be modified by a process called learning. The backpropagation (BP) algorithm, a supervised gradient-descent learning algorithm, is a learning rule used most frequently in ANNs to calculate a gradient that is needed in the calculation of the weights. The BP network, which is trained through the BP algorithm, is the most widely used neural network at present. Structurally, the BP network is a kind of feedforward ANN consisting of three or more layers (an input layer, one or more hidden layers, and an output layer). Each node in a layer is connected to every node in the next layer with a certain weight. There exists no connection between nodes in the same layer. Except for input nodes, each node is a neuron that uses an activation function. The structure of the BP network is shown in Figure 3.  
Figure 2: Composition of hidden Markov model.  
The basic idea of the BP algorithm is minimizing the mean squared error of the outputs by adjusting the connection weights after each learning iteration. The algorithm mainly include two processes: the forward propagation of the signal and backpropagation of the error. Regarding the forward propagation of the signal, the input sample is transmitted to the output layer by going from a layer to a next layer. If the actual output of the output layer does not match the expected output, the error propagation is shifted to the error backpropagation process. In the other case, the learning algorithm stops. During error backpropagation, the output error is propagated back to the input layer through the hidden layers. The error gradient is reduced to the allowable minimum by adjusting the connection weights and thresholds between the nodes of the former and of latter layer. Adjusting weights and thresholds is the network learning or training process.  
The standard BP algorithms, such as gradient-descent and gradient-descent with momentum, are often too slow because of their inherent defects. In practice, successful training always depends on the appropriate learning rate and momentum constant [39]. To eliminate the disadvantages of BP learning algorithms, faster algorithms, e.g., resilient backpropagation, scaled conjugate gradient, and Levenberg-Marquardt, are included in the training of BP networks.
## 2 Materials and Methods
### Cathepsin Activity Assays  
Cathepsins B and L. Enzyme activity assays were performed as described [18]. Tissue extracts containing 10 \(\upmu\)g of total protein were mixed with 0.1% Brij35 and reactivated for 5 min at 40 \({}^{\circ}\)C with reaction buffer (88 mM KH\({}_{2}\)PO\({}_{4}\), 12 mM Na\({}_{2}\)HPO\({}_{4}\), 1 mM Na\({}_{2}\)EDTA, 2 mM freshly prepared L-cysteine, pH 6.0; all final concentrations). For controls, run in parallel, the reaction buffer included 10 \(\upmu\)M of the cysteine protease inhibitor E64. After reactivation, samples and controls were incubated with 5 \(\upmu\)M cathepsin B substrate N-benzyloxycarbonyl-arginine-7-amido-4-methylcoumarin (Z-Arg-Arg-AMC; Bachem Distribution Services GmbH, Weil am Rhein, Germany) or with 5 \(\upmu\)M cathepsin L substrate N-benzyloxycarbonyl-phenylalanyl-arginine-7-amido-4-methylcoumarin (Z-Phe-Arg-AMC; Bachem Distribution Services GmbH) plus 1.5 \(\upmu\)M CA-074 cathepsin-B-specific inhibitor (Merck Biosciences GmbH, Darmstadt, Germany) at pH 5.5 for 10 min at 40 \({}^{\circ}\)C and 800 rpm. Reactions were terminated by adding stop solution containing 100 mM sodium chloroacetate, 30 mM sodium acetate, and 70 mM acetic acid, pH 4.3, and fluorescence was measured with a Tecan GENios Reader (Tecan Deutschland GmbH, Crailsheim, Germany), using excitation/emission wavelengths of 360/465 nm.  
Cathepsin D. Activity was tested as previously described [18] by incubating tissue extracts with 200 \(\upmu\)M cathepsin D substrate 7-methoxycoumarin-4-yl-acetyl-glycerol-lysyl-pylyl-polyl-isoleucyl-phenylalanyl-phenylalanyl-arginyl-leucyl-lysine (2,4-dinitrophenyl)-D-arginine (MOCAc-Gly-Lys-Pro-Ile-Leu-Phe-Phe-Arg-Leu-Lys (Dnp)-D-Arg-NH\({}_{2}\)); Merck Biosciences GmbH) in 50 mM sodium acetate (pH 4.0) for 10 min at 40 \({}^{\circ}\)C and 800 rpm. Controls were incubated in addition with 1 \(\upmu\)M pepestain A (Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany). Reactions were stopped by adding 5% trichloroacetic acid, and fluorescence was measured using excitation/emission wavelengths of 328/393 nm.  
Analysis. Assays were performed twice generally in duplicate. All assays were performed with \(n=4\) mice for each genotype. Relative fluorescence units (RFU) of controls were subtracted from the values for the corresponding samples. In order to allow averaging over several assays, RFU values were normalized using the averages of young wild-type control measurements within a given assay for each brain region.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 3 Abstract Code Representation
### Defining Semantic Slice  
In order to extract the semantic features of the program and transform it into the input vector of the neural network, we need a reasonable abstract code representation, just as one would with processing natural language. However, there are many differences between program code and natural language. On the one hand, in natural language, the semantics of a word is limited to a small space, whereas in program code, words cannot be directly mapped to a limited semantic space [11]. On the other hand, the syntax of the program code is more flexible than natural language. In a program, two adjacent statements may have no relationship at all, which is rare in natural languages. This means that program code cannot be viewed as a sequence of words just like natural language. Therefore, we propose a _semantic slice_ as an abstract code representation, which is much finer than using words.  
The _semantic slice_ is a kind of program slice, which consists of a number of statements that have semantical dependencies on each other, such as control dependence or data dependence. In order to generate _semantic slice_, we need to introduce the _key function_, which refer to specific API functions in the program. Intuitively, the _key function_ can be seen as an indicator of code snippets implying the existence of a malicious script. For example, malicious code must execute some instructions to achieve their evil intent. This process requires calling specific system API functions, such as the _eval_ function. Therefore, we take the _eval_ function as a _key function_ and get all the statements that are dependent on it. These statements are considered a _semantic slice_. This is inspired by the observation that a large percentage of malicious scripts require specific API function calls to trigger malicious behavior.  
In this paper, we leverage the specific _key functions_ to generate _semantic slices_ of JavaScript programs and demonstrate its effectiveness in malicious JavaScript detection based on the BLSTM neural network.
## 4 The Inversion of HJC Constitutive Parameters for Hard Rock
### Inversion from Experimental Data and Evaluation of Results  
The distribution of Pareto optimal solutions of target variables, obtained through NSGA-II, is depicted by the blue dot in Figure 20. By comparing with the experimental value, the Pareto optimal solution (red dot) closest to the experimental mean value is found. The corresponding parameter values of this solution (perforation depth 32.01 cm, perforation aperture 1.05 cm) are \(A_{1}=0.9\), \(B_{1}=0.8\), \(C_{7}=0.1625\) and \(f_{\rm c}=60\) MPa, respectively, thus indicating that these parameters can be considered optimal HJC constitutive parameters for hard rock in reservoirs.  
Similarly, to verify the reliability of the HJC constitutive parameters of the hard rock obtained by inversion, numerical simulations of the reservoir penetrating the hard rock target with the constitutive parameters obtained by inversion were conducted, and the simulation results are shown in Figure 21b. Compared with the simulation results based on known constitutive parameters (Figure 21a) and experimental results (Figure 21c), it can be found that the perforation depth obtained based on inversion parameters is 32.14 cm, and the perforation aperture is 1.13 cm. The perforation depth is 5.09 cm higher than that of 27.05 cm with known parameters, and the aperture is 0.60 cm smaller than the known parameter of 1.76 cm. Thus, the optimization effect is remarkable. In the experiment, the average perforation depth is 30.00 cm, and the average aperture is 1.08 cm. The error  
Figure 19: Comparison of simulated and predicted values of target variables.  
Figure 20: Pareto optimal solution distribution of target variables.  
between the simulated perforation depth and the experimental results using the inversion parameters is 0.44%, and the error between the perforation aperture is 4.63%, both of which are also within the acceptable 5%. For ease of understanding, we present these results in their entirety in Table 9. This indicates that the inverse parameters can more accurately describe the damage effects of the jet on hard rock than the known parameters in the numerical simulation of perforation, which also proves that the inverse calculation of the HJC constitutive parameters of hard rock is effective and reliable.
## 2 Materials and Methods
### Feature Aggregation Wrapper Module  
Small-sized objects in remote-sensing video sequences pose challenges for effectively capturing their deep features using a backbone network. To address this issue, we proposed an online multi-frame features aggregation module inspired by [44; 45]. The feature aggregation wrapper (FAW) module efficiently aggregates multi-level semantic information, making it suitable for modifying the features of tiny objects with small displacements in remote-sensing video imagery. Additionally, it provides a concise and fast implementation while preserving online processing ability.  
The FAW module takes the deep features of previous T frames, \(\mathrm{f}^{(t-\tau)}\), as inputs, where \(\tau=1,2,\ldots,\mathrm{T}\). Then, a channel-wise feature collection, \(\mathrm{f}_{\mathrm{chan}}\), is extracted from T-consecutive features in all channel dimensions. The feature information from different channel dimensions is aggregated using the deformable convolutional network (DCN). The output's aggregated information is then utilized to enhance the representation of the object features in the current frame. The overall architecture of the FAW is illustrated in Figure 5.
# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
## 4 Results  
Rating maps of the site suitability criteria for PV arrays in Mongolia are shown in Figure 10. Values of 25, 50, 75, or 100 for each of the seven GIS layers were assigned to each cell (spatial resolution: 30 m\({}^{2}\)), using the criteria shown in Table 3. Ten experts participated in the AHP survey--five from Mongolia and five from Korea--all of whom were professors or electricians employed in the energy and electricity sectors. All of the participants have more than 10 years of experience and expertise in PV power plants.  
Figure 10: Criteria rating maps used in this study for Mongolian PV site suitability analysis: (**a**) global horizontal irradiation; (**b**) temperature; (**c**) elevation; (**d**) slope; (**e**) aspect; (**f**) distance from a main road; and (**g**) distance from a major powerline.  
The AHP results are provided in Table 4, indicating that global solar irradiance has the highest weight (43%), followed by slope (12%), distance from a major power line (12%), aspect (11%), and distance from a main road (9%). As the CR was 6%, the results of the pairwise comparisons for this study were acceptable, and the values were regarded as stable.  
The suitability map, with areas rated as having good, fair, low, and poor conditions for installing PV power plants in Mongolia, is shown in Figure 11. The areas rated as having good conditions accounted for 3.27% of the total area, with the majority rated as having either fair (53.06%) or low (42.59%) conditions.  
Areas rated as good should be considered first when planning the construction of a PV power plant in Mongolia.
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
###### Abstract  
The recent progress in blockchain and wireless communication infrastructures has paved the way for creating blockchain-based systems that protect data integrity and enable secure information sharing. Despite these advancements, concerns regarding security and privacy continue to impede the widespread adoption of blockchain technology, especially when sharing sensitive data. Specific security attacks against blockchains, such as data poisoning attacks, privacy leaks, and a single point of failure, must be addressed to develop efficient blockchain-supported IT infrastructures. This study proposes the use of deep learning methods, including Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), to detect phishing attacks in a blockchain transaction network. These methods were evaluated on a dataset comprising malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset, and the results showed an accuracy of 99.72%.  
blockchain; network security; phishing; attack recognition; deep learning Article
## 1 Introduction
Starting from 2013, several studies have been performed to design QTFs with custom geometries optimized for QEPAS sensing [20; 21; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. An extensive investigation correlated the resonance frequency and the quality factor of the fundamental mode of the QTF with the prong sizes and geometry. It has been shown that if some additional mass is added to the free end of the prong, an increase of the global stress field is obtained, enhancing the stress distribution along the internal prong surface and the QTF sound-to-current transduction efficiency [32; 33]. In addition, an enlargement of the prongs spacing of up to 1.5 mm can facilitate the optical alignment and minimize the photothermal noise level, allowing the use of laser beams characterized by a non-optimum spatial intensity distribution, such as high-power laser beams [34], THz quantum cascade lasers (QCLs) [35], arrays of QCLs [36; 37] and dual-antinode configurations [38; 39]. The advantages of low-frequency, high-quality factor QTFs with a large prong spacing have been demonstrated in the mid-infrared spectral range, where improvements in the detection limit have been obtained mainly due a the reduction of the photothermal noise contribution. With near infrared diode lasers, the ultimate noise level, namely the QTF Johnsonnoise, has been reached when a standard 32.7 kHz was employed. However, moving to QTFs with large prong spacing can be advantageous also in the near-infrared range. A large prong spacing can avoid misalignments due to external mechanical vibrations, leading to an increase of the robustness, a crucial intended feature for sensors, for example, when mounted on mobile systems or ultimately on a drone. In this work we report on a comparison between two spectrophotones, one implementing a standard 32.7 kHz QTF (SP1) and a second one, a T-shaped 12.4 kHz custom QTF (SP2). Both spectrophotones were coupled with a pair of optimized resonator tubes, in an \"on beam\" configuration. Subsequently they were tested in a QEPAS sensor employing a DFB diode laser emitting at 1684 nm as a light source in order to target ethane. An investigation on the optimum working conditions as well as an analysis of the ultimate detection limit and long-term stability was also performed.
## 2 Materials and Methods
### Rao-Blackwellized Particle Filter-Based Human Tracking
#### 2.3.2 RBPF-Based Human Tracking  
Our aim is to model the shape of a person as an ellipse and to estimate the state recursively using a state vector and an observation vector. The state vector and observation vector are defined as follows:  
\[x_{t}=(x_{c},y_{c},a,b,v_{x},v_{y},\theta)^{T} \tag{9}\]  
where \((x_{c},y_{c})\) is the human position, \((a,b)\) are half lengths of the major and minor axis of the ellipse, \(\left(v_{x},v_{y}\right)\) is the velocity in 2D Cartesian coordinates, and \(\theta\) is the angle of measure of the ellipse's rotation with respect to the image x-axis. The points on the human contour from the binary image obtained from the detection step are defined as an observation vector: \(y_{k}=\{x_{i},y_{i}\},i=1\cdots p\) where, \(k\) is the time step, \(p\) is the number of points on the contour, and \(x_{i},y_{i}\) are the \(x\) and \(y\) coordinates of each point. In this study, an observation vector is defined as follows using 10 points:  
\[y_{k}=\left\{y_{1}^{T},y_{2}^{T},y_{3}^{T},y_{4}^{T},y_{5}^{T},y_{6}^{T},y_{7} ^{T},y_{8}^{T},y_{9}^{T},y_{10}^{T}\right\}^{T} \tag{10}\]  
The non-linear function \(h\) defines the general parametric ellipse equation given by:  
\[h(x_{t})=\left[\begin{array}{c}X(\theta)=x_{c}+a\cos(\theta)\cos(\varphi)-b \sin(\theta)\sin(\varphi)\\ Y(\theta)=y_{c}+a\cos(\theta)\sin(\varphi)+b\sin(\theta)\cos(\varphi)\end{array}\right] \tag{11}\]  
where \(\theta\in[0,2\pi]\) and \(\phi\) are angles with respect to the x-axis. The Jacobian of \(h\) is given by:\[H(x_{t})=\left[\begin{array}{cc}\frac{\partial\chi(\theta)}{ \partial x_{t}}\\ \frac{\partial\gamma(\theta)}{\partial x_{t}}\end{array}\right] \tag{12}\] \[=\left[\begin{array}{rrrr}1&0&\cos(\theta)\cos(\phi)&-\sin( \theta)\sin(\phi)&-a\cos(\theta)\sin(\phi)&-b\sin(\theta)\cos(\phi)\\ 0&1&\cos(\theta)\sin(\phi)&\sin(\theta)\cos(\phi)&a\cos(\theta)\cos(\phi)&-b \sin(\theta)\sin(\phi)\end{array}\right]\]  
The likelihood function of the observations approximate to Gaussian density as follows [41]:  
\[p(y_{t}|x_{t})\propto N(D_{t};0,\sigma^{2})=\frac{1}{\sqrt{2\pi\sigma^{2}}} exp\left\{-\frac{D_{t}^{2}}{2\sigma^{2}}\right\} \tag{13}\]  
\(D_{t}\) is the Euclidean distance between the predicted observations of the target and true observations \(y_{t}\) obtained from the detection step, specified by the state vector \(x_{t}\). The standard deviation \(\sigma\) of the Gaussian density [42] depends on the designer. Figure 5 represents the human tracking result based on the RBPF algorithm at different locations. The upper images show the normal case during which the subject does not fall throughout the duration of the scene and the lower images indicate the scenario in which the subject falls during the scene.
## 2 EVs Performing Multiple Functions in Cancer Formation
### Angiogenesis  
Angiogenesis is the formation of new blood vessels from an already existing vascular network. Several steps characterize angiogenic sprouting, such as enzymatic degradation of the vessel basement membrane, endothelial cell proliferation, migration, germination, branching, and formation of new vessels [19]. Angiogenesis is regulated by a precise balance between stimulatory and inhibitory signals, which can become altered in cancer pathologies [20]. The potential of EVs to induce angiogenesis is thought to be dependent on their uptake by recipient cells. For example, EVs released from glioblastomas contain a host of pro-angiogenic signals, such as proteoglycans glypican-1 and syndecan-4. These signals increase revascularization through proliferation and formation of endothelial cells and tubules [21].  
EVs released by tumor cells are important mediators of the angiogenic cascade, regardless of their uptake. Tumor angiogenesis is mediated by a repertoire of membrane-bound proteins that could confer selective advantages for tumor growth and metastasis. EVs secreted by cancer cells in the brain and neck contain the ephrin type B receptor 2 (EPHB2), which is associated with tumor angiogenesis by activating the STAT3 signaling pathway via engagement of ephrin-B2 on the surface of endothelial cells [22]. Similarly, ovarian cancer cell-derived EVs contain the soluble form of E-cadherin on their surface, making them capable of stimulating tumor angiogenesis by forming a heterodimer with VE-cadherin on the surface of endothelial cells [23]. In addition, EVs secreted by tumor cells show various cytokines on their vesicular surface, such as IL-8 and VEGF, which can stimulate the growth, migration and/or formation of endothelial tubes [24]. Studies on the role of EVs in angiogenesis show that they are excellent tools for understanding the mechanisms underlying endothelial cell migration, alteration of vessel phenotype, and germination of solid tumors.
# Correlation and Regression Analysis of Spraying Process Quality Indicators
## 4 Discussion  
The present work analyzed the relationships between two quality indicators of the spraying process separately for standard nozzles and air-induction nozzles. Scientists in their studies highlighted the need to select suitable nozzles and optimal spraying parameters in order to achieve the intended biological effect of spraying [50,51]. This aspect concerns both field, orchard, and horticultural crops, regardless of the equipment used, i.e., ground, hand-held, knapsack, or UAV sprayers. The higher values of the degree of coverage and deposition of liquid were obtained for standard nozzles compared to air-induction nozzles. However, this dependence is more visible in the case of horizontal upper surfaces. This confirms the results of similar research, which were published in the state-of-the-art literature.  
The results obtained by [PERSON] et al. [45,46] are consistent with the results obtained in this study. The research was conducted in the orchard using an ATR 80 hollow-cone nozzle and four values of liquid pressure, namely 300, 500, 1000 i 1500 kPa. Based on the results, a significant correlation was revealed at the significance level \(p<\)0.001 and a regression coefficient of R\({}^{2}\) = 0.761 was calculated. Determination coefficients ranging from 0.9949 to 0.9996 were obtained, on the assumption that the values of the mean droplet diameter (VMD) and the coefficient of variation CV are known. Whereas [PERSON] et al. [52] conducted research on the crop of tomatoes. Based on the experimental results, the linear correlation coefficient between the analyzed indicators was calculated (R = 0.7987).  
[PERSON] et al. [53] developed and validated mathematical models to determine the effect of spray performance on droplet size and velocity in the flow field. The authors obtained models of high accuracy (R\({}^{2}>\) 0.86). In the work of [PERSON] et al. [54], the authors presented a comparison between measured and theoretical distribution for different nozzles. Research on the quality of coverage was the subject of many studies. [PERSON] et al. [19] showed that the quality of coverage and the number of droplets per unit area were statistically comparable, irrespective of the nozzles used. [PERSON] et al. [16] carried out studies in apple orchards using different nozzles and adjuvants. Scientists reported that the liquid coverage of plants during coarse spray at high adjuvant concentrations was comparable to the liquid coverage of plants during fine spray with no added adjuvants. [PERSON] et al. [55] carried out tests using adjuvants regarding the deposition of spray liquid. The researchers stated that similar results of the liquid deposition were obtained using the coarse spray, which means they are less prone to variable spraying parameters. Correlation analysis was employed to validate the Computational Fluid Dynamics model. Computational Fluid Dynamic models have allowed the correct prediction of the general behavior of the fluid in the tank [56, 57, 58].