esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 HSI Procedure
### Hyperspectral Wavelength Selection
#### 2.3.2 Principal Component Analysis (PCA)  
PCA is a traditional technique that has shown satisfactory performance in reducing the dimensions of HSI. This algorithm orthogonally transforms the dataset by capturing most of the data variance into fewer dimensions, enabling it to perform data visualization and analysis efficiently. In terms of exploratory data analysis, it has already occupied a leading position, as it normally learns the data structure with no optimization toward any specific prediction tasks [81]. PCA often finds applications in HSI, mainly because it reduces the processing load and simplifies data interpretation by selecting a very small subset of the best features. For example, in PCA, due to minimum loss of information, computational cost and time can be reduced; at the same time, it reveals the natural latent structure present in the data. However, it is unsupervised and hence cannot give specific classes. Thus, PCA might not be suitable for detailed classification problems [9].  
PCA has high potential for use in conjunction with machine learning models that improve classification efficiency in agricultural products. In one novel study, PCA was performed for modeling degradation kinetics and estimating the shelf life of chia seeds. Using scores of the first PC1 that monitored compositional degradation, the shelf life under different storage conditions was predicted. [82]. Likewise, during the assessment of beef moisture and tenderness upon heating with PCA, the result yielded an R\({}^{2}\) value of 0.912 in the prediction of moisture and a value of 0.771 in tenderness. PCA also provided an important understanding in developing a visual of the compliant distribution on cooked meat and its overall quality analysis [83]. Another application of PCA is in identifying the variety of sorghum; based on this, [PERSON] et al. (2024) made use of PCA to reduce the dimensionality of the HSI data. These MVC-reduced data were then fed into a SICNN, which yielded the result of \(>\)98% in classification accuracy [84].
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 5 Attack Description and Explanation
### Explanation of AMQP Brute Force Attack
#### 5.4.2 Analysis  
This section examines the features identified in the brute force attack signature against the AMQP server. Key features of the attack include the following:  
* tcp.dstport=5672.
* tcp.len=0.
* seq.number=509.
* nxtseq.number=509.
* window.size=508.
* tcp.flag=0x014 in RST/ACK packets.  
Repeated experiments confirmed that these values remained unchanged. These patterns occur when an attacker or IoT device attempts to establish a connection with the target machine, following default packet-specific settings for connection and authorization. Each incorrect login attempt results in the target system discarding the previous message, generating an RST/ACK packet with random sequence numbers to disrupt transmission.  
The value of window.size consistently reflects the RST/ACK packet from the previous ACK packet when login details are incorrect. The syslog logs multiple attempts to connect on port 5672, indicating failed and successful login attempts. Additionally, the syslog detects and logs the correct credentials when successfully brute-forced.
# Computational Complexity and ILP Models for Pattern Problems in the Logical Analysis of Data
## 6 Computational Experiments
### Diagonal Instances  
A simple way of creating instances with equivalent pattern sets is as follows. For every \(n\), we consider two equivalent sets of patterns, which generate all strings with the exception of the string \(11\cdots 11\). We call these _diagonal instances_.  
The first set has \(n\) patterns, with a maximum number of gaps \(n-1\), and is the following (exemplified for \(n=6\)):  
\[\begin{bmatrix}0\text{------}\\ -0\text{---}\\ -0\text{---}\\ ---0\text{---}\\ ---0\end{bmatrix}\]  
The second set has \(2n-1\) patterns, with a maximum number of gaps \(n-2\), and is  
\[\begin{bmatrix}0\text{---}\\ 0\text{---}\\ 0\text{---}1\text{---}\\ 0\text{---}1\\ 1\text{---}0\text{---}\\ 1\text{---}0\text{---}\\ 000000\end{bmatrix}\]  
We perform a sequence of tests to compare the ILP approach with the complete enumeration algorithm, for increasing values of \(n\). These diagonal instances turn out to be very easy for the ILP model. They are all solved in less than \(0.1\) s, for \(n\leq 30\) as can be seen from Table 2. The enumerative approach, however, becomes very soon impractical. For \(n=28\) the algorithm takes already more than \(15\) min, while for \(n=30\) the algorithm has not finished after one hour (which we set as a maximum time limit). It is interesting to notice how the ILP approach solves this instance in less than a second also for \(n=100\), while the enumerative approach would have to generate \(2^{100}-1\) strings.
# Activation-Based Pruning of Neural Networks
## 3 Methodology
### Effect of Pruning on Singular Values of Matrices of Hidden Layers  
Every hidden layer of a feedforward neural network can be represented in the form of a matrix. The singular values of a matrix can be computed using singular-value decomposition (SVD). The magnitudes of the singular values capture information about the data. The larger the singular value, the greater the information captured by the corresponding basis vector is. We observed the change in singular values of the weight matrix of each hidden layer before and after pruning. Notably, activation-based pruning leads to a low-rank representation of the hidden layer by selectively pruning the less important singular values.  
Consider a feedforward neural network for the image classification problem formulated as an optimization problem. Given a set of images with corresponding labels, the goal is to learn a function \(f\) that maps each image \(X\) to its correct label \(y\). The optimization problem is to find the optimal set of weights and biases for the neural network to minimize the classification error on the training data. This can be formalized as:  
\[\min_{w,b}\frac{1}{n}\sum_{i=1}^{n}L(f(x_{i};w,b),y_{i}) \tag{1}\]  
where \(w\) and \(b\) are the weights and biases of the neural network, \(n\) is the number of images in the training set, \(x_{i}\) is the \(i\)th image in the set, \(y_{i}\) is the corresponding label, \(f(x_{i};w,b)\) is the predicted label for image \(x_{i}\) with weights \(w\) and biases \(b\), and \(L\) is the loss function that measures the difference between the predicted and true label.  
The nuclear norm \(\|X\|_{*}\) of a matrix \(X\) is defined as the sum of its singular values:  
\[\|X\|_{*}=\sum_{i}|\lambda_{i}(X)|_{1} \tag{2}\]  
where \(\lambda_{i}(X)\) is the \(i\)-th singular value of the matrix \(X\).  
The weighted nuclear norm of a matrix \(X\) is defined as:  
\[\|X\|_{w,*}=\sum_{i}|w_{i}\lambda_{i}(X)|_{1}, \tag{3}\]  
where \(w=[w_{1},\cdot\cdot,w_{n}]\) and \(w_{i}\geq 0\) is a non-negative weight assigned to \(\lambda_{i}(X)\).  
The weighted nuclear norm of a layer of the feedforward network is defined as \(||X_{j}||_{w,*}\) where \(X_{j}\) is the matrix of the \(j\)-th hidden layer of the network.  
We will show that activation-based pruning is equivalent to adding the sum of weighted nuclear norm as a regularization parameter to the image classification problem:  
\[\min_{w,b}\frac{1}{n}\sum_{i=1}^{n}L(f(x_{i};w,b),y_{i})+\sum_{j=1}^{l}\|X_{j} \|_{w,*} \tag{4}\]  
where parameters for the first part of the equation are as described in the explanation followed by (1). Including the sum of the weighted nuclear norm as a regularization parameter encourages each matrix of the neural network to have a low rank.
# New Methods for the Comprehensive Analysis of Bioactive Compounds in _Camabis sativa_ L. (hemp)
## 3 Materials and Methods
### HPLC-UV/DAD Method Validation  
In this study, the validation of the HPLC-UV/DAD method for the determination of hemp flavonoids and related compounds was carried out, since the chromatographic method for the quantification of non-psychoactive cannabinoids was previously fully validated [9]. The validation was performed in agreement with the international guidelines for analytical techniques for the quality control of pharmaceuticals (ICH guidelines) [35].  
As regards linearity, the stock standard solution of non-psychoactive cannabinoids (CBDA, CBGA, CBG and CBD) was prepared as follows: an accurately measured volume of reference solution (100-200 \(\upmu\)L) was placed into a 1 mL volumetric flask; then, MeOH was added and the solution was diluted to volume with the same solvent. As to flavonoids, the stock standard solution of each compound (canniprene and chrysoeriol) was prepared by weighing an accurate amount of reference compound (0.9 mg) into a 1 mL volumetric flask; then, the solution was adjusted to volume with MeOH. The external standard calibration curve was generated by using six data points, covering the concentration ranges: 2.5-200.0 \(\upmu\)g/mL for CBDA, CBGA and CBD; 1.3-100.0 \(\upmu\)g/mL for CBG; 0.3-23.4 \(\upmu\)g/mL for caniprene, and 1.3-43.0 \(\upmu\)g/mL for chrysoeriol. Three \(\upmu\)L aliquots of each standard solution were used for HPLC analysis. Injections were performed in triplicate for each concentration level. The calibration curve was obtained by plotting the peak area of the compound at each level versus the concentration of the sample. The quantification of non-psychoactive cannabinoids and canniprene was performed by using their calibration curves. The amount of cannflavirus A and B in hemp samples was determined by using the calibration curve of chrysoeriol, having the same chromophore, and the content was corrected by using the molecular weight ratio.  
For reference compounds, the limit of detection (LOD) and the limit of quantification (LOQ) were experimentally determined by HPLC analysis of serial dilutions of a standard solution to reach a signal-to-noise (\(S/N\)) ratio of 3 and 10, respectively.  
The precision of the extraction technique was validated by repeating the extraction procedure on the infloresence of the same hemp sample (C6) six times. An aliquot of each extract was then injected and quantified. The precision of the chromatographic system was tested by performing intra- and inter-day multiple injections of one extract from sample C6 and then checking the %RSD of retention times and peak areas. Six injections were performed each day for three consecutive days.
## 2 Notations and Previous Results
### Extremities defined by minimal triangulations  
For arbitrary graphs, extremities have been yielded by algorithms which compute a _minimal triangulation_ of a graph (a chordal graph obtained from this graph by adding an inclusion-minimal set of edges).  
Obviously, one can use the characterization of [PERSON] and [PERSON] to embed a graph into a chordal graph by repeatedly choosing a vertex, adding to its neighborhood every edge whose absence violates the simpliciality condition, and then removing the vertex from the current graph, thus simulating a simplicial elimination scheme and a perfect elimination ordering \(\alpha\) on the vertices. This process (called the _elimination game_) defines a triangulation of \(G\) denoted \(G_{\alpha}^{+}\). [PERSON], [PERSON] and [PERSON] [15] proved that to compute such a triangulation which is minimal, one has to use a special ordering on the vertices, called a _minimal elimination ordering_ (meo for short). [15] showed that an ordering is a meo if and only if at each step of the simplicial elimination game, a special vertex is chosen. Since they did not give these vertices a name, and since the notion is of importance to our work, we call these vertices _OCF-vertices_. Let us restate their characterization using the notations previously defined in this paper:  
**Definition 2.7**: _[_15_]_ _A vertex \(x\) in \(G=(V,E)\) is an OCF-vertex of \(G\) if for every pair \(\{y,z\}\) of distinct non-adjacent neighbors of \(x\) in \(G\), \(y\) and \(z\) belong to some common substar of \(x\)._  
[15] showed that in any non-clique graph, there is an OCF-vertex.  
**Example 2.8**: _In Figure 3, \(a\) is an OCF-vertex: its substars are \(\{b,c\}\) and \(\{c,d\}\), and only edges \(bc\) and \(cd\) are missing in the neighborhood of \(a\)._  
An OCF-vertex \(x\) can be viewed as an extremity of an arbitrary graph: if \(x\) is chosen first in an instance of the elimination game, \(x\) will be a simplicial vertex of the corresponding minimal triangulation. The notion of moplex, (the definition of which is not restricted to chordal graphs), also strengthens the notion of OCF-vertex, since all the vertices of a moplex are OCF-vertices, but some OCF-vertices may not belong to a moplex.  
In Figure 3, \(a\) is an OCF-vertex but does not belong to a moplex. \(\{g,h\}\) is a moplex, and both \(g\) and \(h\) are OCF-vertices.  
_Algorithm LEX M_, introduced in the same seminal paper as LexBFS [4], finds a meo efficiently, so vertex number 1 of LEX M is always an OCF-vertex. LEX M was extended to _Algorithm MCS-M_ by [PERSON], [PERSON], [PERSON] and [PERSON] [16]. MCS-M computes a meo efficiently using the same label simplification that MCS used to improve LexBFS. In an arbitrary graph, LEX M was shown by [17] to end on a vertex of a moplex.  
Figure 3: A non-chordal graph \(G\). The set of minimal separators of \(G\) is: \(\{\{b,f\},\{c,d\},\ \{c,e\},\{a,d,e\},\{a,d,f\},\{a,b,g,h\},\{a,e,g,h\},\{a,f,g,h\}\}\).
# Flying Ad Hoc Networks: A New Domain for Network Communications
## 2 State of the Art
### Mobility Models
Figure 6: Column mobility models: (**a**) Example of a single step in CLMN where each node has its reference point and reference points are placed on a reference line that moves a distance \(d\) and rotates an angle \(\theta\); (**b**) Trajectory simulation of three nodes using CLMN where reference points randomly move through the simulation area and each node moves randomly around its reference point. A similar taxonomy was proposed in Reference [36], but including the topology-control category and not including the hybrid one. We believe that the topology-control category is closer to positioning techniques, therefore we opted to limit our taxonomy to the mentioned classes. Some of the mobility models that we reviewed in this section do not take into account characteristics of the RW-UAV and FW-UAV nodes such as variations in speed during turns, rotation angles, smooth trajectories, mechanisms to avoid collisions, etc. Interestingly, some authors have demonstrated that these characteristics may not have a significant impact on the results [37]. That is, it might not be significant differences between realistic models (which consider a smooth trajectory, implement mechanisms to avoid collisions, and include accelerations and realistic speeds), and unrealistic models. Nevertheless, further research is needed to corroborate this fact. Table 3 presents a summary of all mobility models described in this section, including the most appropriate type of UAV for each model and their use. Figure 8: Column mobility models: (**a**) Example of a single step in PRS with a target node (black) moving a distance \(d\) and it is pursued by five purser nodes (white); (**b**) Trajectory simulation of a target node and three pursuer nodes using PRS. Figure 7: Column mobility models: (**a**) Example of a single step in Nomadic Community (NC) with five nodes where the nodes have a maximum distance \(r_{max}\) to move away from the reference point and the reference point moves through the simulation area a random distance \(d\) following a randomized model; (**b**) Trajectory simulation of three nodes using NC.
# Analysis of the Near-Ground Wind Field Characteristics during Typhoon Soulik
## 4 Quantitative Analysis of Fluctuating Wind Properties
### Variations in the Turbulence Integral Scale  
The turbulence integral scale represents the mean dimensions of each turbulent eddy and is a significant parameter that reveals the attributes of the wind field. This study employs the autocorrelation function method to integrate it and uses the [PERSON] hypothesis to turn the time scale into a length scale. The expression is  
\[L_{i}=\frac{U}{\sigma_{i}^{2}}\int_{0}^{\infty}R_{i}(\tau)d\tau \tag{18}\]  
where \(L_{i}\) denotes the integral scale components of turbulence in the \(u\), \(v\), and \(w\) orientations, and \(R_{i}(\tau)\) denotes the fluctuating wind speed autocorrelation function.  
Figure 15 displays variations in the scales of turbulence in all directions at heights of 15, 53, and 82 m. It can be observed from the graphic that the longitudinal, transverse, and vertical turbulence integral scales exhibit an upward trend with average wind velocity. Additionally, the distribution becomes progressively more discontinuous. The longitudinal turbulence integral scale exhibits more pronounced fluctuations compared to the transverse and vertical components.  
Figure 16 depicts the probability density distribution of the integral scale of turbulence. It can be clearly found that the probability density distribution range of the longitudinal and transverse turbulence integral scales is much larger than that of the vertical direction. At the same time, the probability density distribution at different observation heights is basically the same, and the distribution range becomes wider and wider as the observation  
Figure 14: Probability density distribution of longitudinal fluctuating wind speed at each height: (a) 15 m, (b) 53 m, and (c) 82 m.  
Figure 15: Changes in the integral scale of turbulence with the average wind speed: (a) Longitudinal, (b) Transverse, and (c) Vertical.  
altitude increases. Simultaneously, the integral scale of turbulence increases proportionally with altitude, aligning with the findings of [PERSON] et al. [18].  
This study aims to validate the accuracy of the scale by calculating the ratio of the measured longitudinal, transverse, and vertical turbulence integral scales. The obtained ratios are then compared with the findings of other researchers, as presented in Table 6. The integral scales of turbulence differ between distinct typhoons and strong winds, as seen in the table. The turbulence intensity ratios measured in this study are higher than those that [PERSON] et al. [27], [PERSON] et al. [28], and [PERSON] et al. [29] researched. The ratio closely approximates that of [PERSON] et al. [30], possibly attributed to the roughness of the underlying terrain, the type of the wind field, and the observation height.
## 4 Results and Discussion
### Model Results
Among these models, the Random Forest Regressor stands out as the most effective with an R\({}^{2}\) of 0.97, as indicated by the metrics. In the initial phase of HWTT prediction, as Figure 10 shows, the RAP percentages of 25%, 50%, 75%, and 100% reveal a strong correlation between predicted values and actual outcomes. Notably, utilizing 100% of RAP significantly enhances resistance to cracking, which explains the positive effect that as utilizing a high percentage of RAP increases, so does the precision of the predictions. Figure 10: First stage of HWTT prediction. Figure 9: Performance of the three models. The second stage of prediction, as Figure 11 presents, involved forecasting rut depth after training the model with a 2% increase in RAP and a total of 1000 passes. The results indicated that using 0% to 40% RAP is generally acceptable, as it slightly enhances resistance. In contrast of it, RAP of between 60% and 80% demonstrated a significant improvement in rut depth up under 13,000 passes, after which rut depth increased as the RAP content exceeded 80%. Notably, resistance improved with the use of up to 100% RAP until reaching 11,000 passes, compared to actual rut depth results. This suggests that higher RAP content can enhance rutting resistance under specific conditions, although attention should be given to the 80% to 90% range, which resulted in reduced resistance to cracking. The findings suggest that optimal RAP usage is crucial for maintaining pavement integrity under varying traffic conditions.
## 2 Materials and Methods
### Magnetic Resonance Imaging (MRI)
#### 2.4.1 Structural MRI  
A T\({}_{1}\)-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence was used to obtain anatomic images of the pig brain with a 0.7 mm isotropic voxel size. Three repetitions were acquired and averaged using SPM8 in Matlab 8.3, and brains were manually extracted using FMRIB Software Library (FSL) (FMRIB Centre, Oxford, UK). Manual extraction was initially performed by asingle trained observer and reviewed by a second trained observer blind to experimental treatment. The following sequence specific parameters were used to acquire T\({}_{1}\)-weighted MPRAGE data: repetition time (TR) = 1900 ms; echo time (TE) = 2.49 ms; 224 slices; field of view (FOV) = 180 mm; flip angle = 9\({}^{\circ}\). Methods for MPRAGE averaging and manual brain extraction were previously described [42]. All data generated used a publicly-available population-averaged pig brain atlas ([[http://pigmri.illinois.edu](http://pigmri.illinois.edu)]([http://pigmri.illinois.edu](http://pigmri.illinois.edu))) [43]. For volumetric assessments, individual brains were segmented into 22 different regions of interest (ROI) using the pig brain atlas. Total brain and individual region volume analysis was performed with SPMS in which an inverse warp file for each ROI was generated from the DARTEL-generated warp files for each region. As described previously [42], the SPM 'Segment' tool, along with pig-specific tissue prior probabilities, was used to obtain gray matter, white matter, and CSF tissue segmentations for each pig, and DARTEL was used to align the native space segmentations. The fslstats toolbox was used to determine the voxel volume of the subject-space segmentation for each of the three tissue types. Using fslmaths, the mean overall partial volume map was obtained for each subject-space segmentation. Overall absolute volume for gray matter, white matter, and CSF was determined by multiplying the voxel volume measure by the mean intensity of the partial volume segmentation. In order to account for absolute whole-brain volume, all regions of interest were also expressed as a percent of total brain volume (%TBV).