esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Water Content Change Rule  
The water content of rock samples is calculated by Equation (1).  
\[\omega=(m_{a}-m_{d})/m_{d}\times 100\% \tag{1}\]  
where \(\omega\) is the water content of rock samples, %; \(m_{a}\) is the mass of water-soaked rock samples, g; \(m_{d}\) is the mass of dry rock samples, g.  
The water content of siltstone and coarse-grained sandstone was found to increase negatively and exponentially with water immersion time, as determined by curve fitting. The relationship between water content and time is represented by Equations (2) and (3), with the corresponding fitting curves illustrated in Figure 6. Initially, the water content of the rock samples increased rapidly during stages I and II. As immersion time progressed, the rate of increase in water content gradually declined during stage III, ultimately leveling off in stage IV. Notably, the water absorption of siltstone consistently exceeded that of coarse-grained sandstone. According to Equation (1), the natural water content of siltstone is approximately 4%, while that of coarse-grained sandstone is about 1.25%. Thus, this variable must be considered when designing nondestructive immersion tests for rock samples.  
\[\omega_{ siltstone}=-11.30446e^{-t/16.8895}+11.15202,\,R^{2}=0.99616 \tag{2}\]  
\[\omega_{ coarse-grained\ sandstone}=-4.50601e^{-t/21.83815}+4.855571,\,R^{2}=0.96024 \tag{3}\]  
where \(\omega\) denotes the water content of the rock specimen, %; \(t\) denotes the immersion time of the rock specimen, and h; \(R^{2}\) denotes the goodness of fit of the fitted curve.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
###### Abstract  
Singular spectrum analysis (SSA) is a non-parametric adaptive technique used for time series analysis. It allows solving various problems related to time series without the need to define a model. In this study, we focus on the problem of trend extraction. To extract trends using SSA, a grouping of elementary components is required. However, automating this process is challenging due to the nonparametric nature of SSA. Although there are some known approaches to automated grouping in SSA, they do not work well when the signal components are mixed. In this paper, a novel approach that combines automated identification of trend components with separability improvement is proposed. We also consider a new method called EOSSA for separability improvement, along with other known methods. The automated modifications are numerically compared and applied to real-life time series. The proposed approach demonstrated its advantage in extracting trends when dealing with mixed signal components. The separability-improving method EOSSA proved to be the most accurate when the signal rank is properly detected or slightly exceeded. The automated SSA was very successfully applied to US Unemployment data to separate an annual trend from seasonal effects. The proposed approach has shown its capability to automatically extract trends without the need to determine their parametric form.  
Keywords:singular spectrum analysis; time series decomposition; trend extraction; seasonality; automated SSA +
Footnote †: journal: _Algorithms_
## 4 Results and Discussion
### Geochemistry Features as Indication for Organic Matter Input and Depositional Conditions
#### 4.2.2 Source of Organic Matter  
The characteristics of \(n\)-alkanes, pristanes and phytane in the extracts of source rocks can reflect the source of organic matter. In the lacustrine samples with low maturity, the higher content of \(n\)-alkanes with small carbon numbers may indicate that organic matter comes from lower hydrobiotics, while the higher content of \(n\)-alkanes with large carbon numbers or odd-even predominance may indicate that the organic matter may come from terrestrial higher plants [43].  
It is common practice to identify the origin of organic materials in source rocks using isoprenoids and n-alkanes. [PERSON] et al. (2015) [44] used the Ph/nC\({}_{18}\) versus Pr/nC\({}_{17}\) cross plot to characterize the source of organic matter in source rocks and oil, achieving good result. The relationship between Ph/nC\({}_{18}\) and Pr/nC\({}_{17}\) reflects the fact that higher terrestrial plants and a mixture of terrestrial and algae are the sources of organic matter (Figure 9).  
An important metric used to identify the source of organic matter in source rocks is the abundance ratio of sterane [37; 45]. Previous scholars believe that C\({}_{27}\) steranes mainly come from algae, low C\({}_{28}\) steranes characterize the lacustrine depositional environment, and C\({}_{29}\) steranes mainly come from terrestrial higher plants or cyanobacteria [37; 46]. The relative content of C\({}_{29}\) sterane in the sample is high, which indicates that the source of organic matter is mainly terrestrial higher plants. As shown in the Figure 10, the source of organic matter is higher plants and the mixed source of higher plants and plankton.  
The ratio of C\({}_{24}\) tetracyclic terpene to C\({}_{26}\) tricyclic terpene (C\({}_{24}\)Te/C\({}_{26}\)TT) is correlated with the input of terrestrial organic matter. High terrestrial organic matter contribution is indicated by high C\({}_{24}\)Te/C\({}_{26}\)TT ratios [30]. The C\({}_{24}\)Te/C\({}_{26}\)TT ratio of dark mudstone samples is between 0.34 and 5.77, with an average of 1.40, which is positively correlated with the Pr/Ph value (Figure 11).  
Figure 8: Cross plot of DBT/P ratios vs. Pr/Ph (after [PERSON] et al., 1995 [42]).  
Figure 10: Ternary diagram of regular steranes (C\({}_{27}\), C\({}_{28}\) and C\({}_{29}\)) showing the relationship between sterane compositions and organic matter input (modified after [PERSON] and [PERSON], 1979 [37]).  
Figure 9: Cross plot of Pr/nC\({}_{17}\) vs. Ph/nC\({}_{18}\) for coal-measure source rocks (after [PERSON] et al., 2015 [44]).
## 3 Materials of the Sandwich Structure's Components  
### Materials of the Face Sheets  
In this study, the subpanel of the train floor was analyzed as a sandwich structure consisting of hybrid composite face sheets made of Fiber Metal Laminates (FML) and an Aluminum (Al) honeycomb core. In recent years, Fiber Metal Laminates have gained considerable attention among the families of Fiber-Reinforced Plastic (FRP) composites. FMLs are composites composed of alternating layers of metal sheets or foils and Fiber-Reinforced Plastic layers bonded together by thermal pressing technique [27]. Figure 4 shows the alternating structure of an FML. Classical Lamination Theory (CLT) has been proposed model to calculate the final mechanical properties of FMLs [28]. This approach offers the possibility of adjusting the properties of the laminated face sheets by changing the number of face sheet layers and also the orientation of FRP layers, as will be explained in the following section.  
When using FMLs, it is important to consider that the hybridization of composite face sheets with FML laminates is a powerful technique for developing composite structures that provide the desired cost and/or weight reduction while effectively improving the stiffness properties. In our study, FMLs were composed of (1) Carbon Fiber-Reinforced Plastic (CFRP), (2) Glass Fiber-Reinforced Plastic (GFRP), and (3) Aluminum (Al). These face sheets consist of layers of unidirectional CFRP, GFRP, and AL, which are arranged  
Figure 3: Loading and boundary condition of the investigated sandwich structure.  
Figure 2: Structure of the high-speed train floor and the investigated subpanel.  
in different orders. As depicted in the right side of Figure 4, the possible orientations of the FRP layers can be \(0^{\circ}\), \(\pm 45^{\circ}\), and \(90^{\circ}\) in the laminated face sheet. The final mechanical properties of the laminated face sheets were calculated using Classical Lamination Theory. Table 1 shows the possible constituent materials properties of the Fiber Metal Laminates that were considered in the optimization [29].
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Machine** & \multicolumn{2}{c}{**Total Gene (16,482)**} & \multicolumn{2}{c}{**Functional Gene Module (54)**} & \multicolumn{2}{c}{**Hub Gene Set (3)**} & \multicolumn{2}{c}{**External Validation (3)**} \\ \cline{2-9}
**Learning** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** \\ \hline LR & 0.571 & 0.571 & 0.429 & 0.593 & 0.714 & 0.587 & 0.759 & 0.746 \\ LDA & 0.429 & 0.429 & 0.429 & 0.614 & 0.522 & 0.603 & 0.759 & 0.746 \\ KNN & 0.429 & 0.831 & 0.571 & 0.714 & 0.714 & 0.757 & 0.667 & 0.825 \\ CART & 0.457 & 0.55 & 0.528 & 0.592 & 0.761 & 0.575 & 0.731 & 0.627 \\ NB & 0.571 & 0.571 & 0.571 & 0.582 & 0.571 & 0.566 & 0.655 & 0.642 \\ SVM & 0.841 & 0.841 & 0.788 & 0.661 & 0.571 & 0.571 & 0.746 & 0.746 \\ RF & 0.769 & 0.769 & 0.782 & 0.866 & 0.945 & 0.945 & 0.910 & 0.910 \\ GB & 0.758 & 0.758 & 0.844 & 0.852 & 0.857 & 0.852 & 0.897 & 0.881 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RNA-sequencing data of accuracy of various machine learning techniques in breast cancer result. Test accuracy of each algorithm using total gene, functional gene module, and hub gene set (3). proliferation are active and essential actions during EMT and cancer cells spreading [38]. The identified hub genes (_NDUIFB3_, _NDUIFB5_, and _NDUIFA9_) are mitochondrial genes that have various functions, but are known to be involved in mitochondria metabolism. This function is known to play an important role in metastasis of cancer cells and is reported to be overexpressed when metastasis of breast, rectal, and head and neck cancers occurs [39; 40; 41]. We used a machine learning method to construct a highly accurate predictive model for breast cancer metastasis. Several algorithms could be used for the machine learning process; hence, since it was not known which algorithm was suitable for breast cancer metastasis, we tested various algorithms. Among the algorithms used to estimate the predictive model, RF and GB showed the highest accuracy. These two most suitable algorithms revealed the highest degree of analysis because the number of analyzed samples was small, and a prediction model was constructed by extracting the most suitable few factors for analysis [42; 43]. The sample used to design the predictive model was not general result since it was based on single cell RNA-seq data. Therefore, a large-scale breast cancer metastasis TCGA RNA-seq dataset was used for external validation, which was classified into the M0 or M1 group with reference to the clinical data. The breast cancer metastasis model using RF showed a high accuracy of 94%, and the external validation also showed a high accuracy of 91%.
## 2 Related Works
### Statistical Learning-Based Pedestrian Detection  
Methods based on statistical learning refer to learning a classifier through a series of training data and classified input regions with the learned classifier. Feature extraction and classifier design are core techniques of pedestrian detection based on statistical learning.  
In the feature extraction stage, the key issue is extracting discriminative pedestrian features. A good feature should not only capture information different from other classes, but also maintain the stability of differences within class. Current feature acquisition methods for pedestrian detection include hand-crafted features and learned features. Hand-crafted features commonly include Haar-like features [6], SIFT (Scale-Invariant Feature Transform) features [7], HOG (Histogram of Oriented Gradients) features [8], and variations or combinations of these features. Using HOG is an important milestone in pedestrian detection, which highly promotes the pedestrian detection effect. CSS (color self-similarity) characterizing interrelationships between local block features [9] is combined with HOG to improve the detection performance greatly. Integral Channel Features [10] also achieved good detection results in pedestrian detection. [PERSON] et al. DPM (Deformable Part Model) [11; 12], as a spring deformation model [13], is effective for solving pose changing in pedestrian detection.  
In the classification stage, commonly used classifiers include SVM (Support Vector Machine) [14], Random Forest, Probabilistic Model, and Neural Network [15; 16; 17]. A combination of HOG and SVM is a classic algorithm in the history of pedestrian detection.  
[PERSON] et al. [18] suggested that the classifier has less of an impact on pedestrian detection, and extracted features are more important. Pedestrian detection based on statistical learning adapts to images with simple backgrounds and less occlusion, but its effects need to be improved for images with complex backgrounds and large pose changing [15], so more robust features must be found.
# Random Exploration and Attraction of the Best in Swarm Intelligence Algorithms
## 3 The General Operation of Swarm Algorithms  
Swarm algorithms are useful in optimization problems where the solution is parameterized, that is, a general form of the solution is known but its parameters are unknown. When potential solutions are parameterized, each solution can be represented by a point in a space. The coordinates of each point correspond to particular solution parameters. The set of all possible solutions is called the _search space_; its dimension is the number of parameters.  
Swarm algorithms are iterative, and are constructed from generation \(k\) to generation \(k+1\). Every generation is a list of vectors, thus the swarm at generation \(k\) denoted by \(S^{k}\) is given by  
\[S^{k}=(\chi_{1}^{k},\chi_{2}^{k},\cdots,\chi_{m}^{k}) \tag{14}\]  
where \(\chi_{i}^{k}\), \(i=\bar{m}\) is the \(i\)-th vector of the \(k\)-th generation of the swarm and \(m\) is the number of vectors in the swarm. Generation \(S^{k+1}\) is constructed from \(S^{k}\) according to precise rules; each swarm algorithm is defined by these rules. Since each element of \(S^{k}\) gives rise to an element in \(S^{k+1}\), it can be thought that every element in \(S^{k}\)_moves_ to another point in \(S^{k+1}\); hence, instead of saying that a swarm is composed of vectors, it is said that a swarm is composed of elements that'move'. Each algorithm names its swarm elements differently: particles in the particle swarm optimization (PSO) [7]; wolves in the gray wolf optimizer (GWO) [19], etc. In this work, elements of a swarm will be called search agents.  
When necessary, the position of a search agent in generation \(k\) will be represented by  
\[\chi_{i}^{k}=[\chi_{i1}^{k},\chi_{i2}^{k},\cdots,\chi_{in}^{k}]^{T} \tag{15}\]  
where \(n\) is the number of parameters of each potential solution to the optimization problem.  
Each \(\chi_{i}^{k}\) is associated with the cost function \(J\) at that point. Let \(f_{i}^{k}\) be the cost, that is  
\[f_{i}^{k}=J(\chi_{i}^{k}) \tag{16}\]  
Swarm algorithms start by generating a list of random potential solutions called the initial generation.  
\[S^{0}=\{\chi_{1}^{0},\chi_{2}^{0},\cdots,\chi_{m}^{0}\} \tag{17}\]  
where \(\chi_{i}^{0},i=\bar{m}\) are randomly generated. It is worth noting that  
* For each parameter, it is necessary to know the interval of admissible parameter values, that is, for each \(\chi\) coordinate, there is an interval of possible values. The user of the algorithm must specify such intervals from the a priori knowledge of the problem.
* The number of elements \(m\) must be specified by the user from the knowledge of the problem. The objective of a swarm algorithm is that the cost of each search agent in the sequence, \[S^{0},S^{1},S^{2},\cdots\] (18)  
decreases, such that in the last iteration, there is at least one agent close to the minimum as possible. Note that in metaheuristic algorithms it is not possible to guarantee finding the minimum.
# A Morphing Deployable Mechanism for Re-Entry Capsule Aeroshell
## 3 The Mechanically Deployable Morphing Aeroshell Concept
### System Configuration  
The SPLASH structural skeleton is a multi-hinge assembly based on a set of finger-like articulations having two-modal capabilities:  
* The deployable surface can be modulated by changing the sphere-cone angle in order to provide drag modulation capabilities, control the trajectory, and target the payload into the desired area for landing and recovery.
* Once deployed, the system can also activate eight small movable aerodynamic flaps that can be individually \"morphed\" to guarantee additional precision in landing that enhance the capsule's maneuverability during re-entry trajectory by using exclusively aerodynamic forces.  
An actuated ring enables symmetrical rotation of the morphing ribs during deployment, as shown in Figure 6. Such a moveable ring is connected to the morphing ribs though eight actuation rods that drive the rotation of the main hinges of the ribs during system deployment/retraction. The whole mechanism is controlled by a screw-jack actuator guiding the position of the moveable ring.  
Figure 5: A general description of the aeroshell components.  
Figure 6: The deployment/retraction mechanism of the SPLASH concept for a single rib with morphing capabilities.  
Each morphing rib consists of two consecutive blocks connected by a hinge, so that the resulting mechanism is forced to rotate according to specific gear ratio depending on the position of the inner linking beam connecting the outer block to the fixed ring. The resulting system, usually referred to as the finger-like mechanism [29], is thus of single-degree-of-freedom architecture (SDOF): if the rotation of any of the blocks is prevented, no change in shape can be obtained. On the contrary, if an actuator moves any of the blocks, all the other blocks follow the movement accordingly.  
Additionally, when the deployable mechanism is locked in a given position, a shape memory alloy-based bending mechanism incorporated within the outer segments of the ribs will enable enhanced control capabilities during the re-entry trajectory by using active morphing flaps. The morphing mechanism is detailed in Section 5.  
Shape Memory Alloys are selected as reference smart materials due to their compactness and high power/weight ratio [30]. They are particularly advantageous for space applications since they do not need external electronic sensors and control, as the shape change responsible for the actuation is an inherent material property. In the proposed application, when the SMA element is exposed to an increase in temperature, it undergoes a phase transformation that constitutes an actuator displacement for each SMA rod. Contraction by heating of the SMA actuators will deform the morphing flaps that will restore the initial shapes upon cooling. In the preliminary assessment, the operating range involves strain and stress limits of 8% and 700 MPa, respectively.
## 4 DPI Applied to Honeynet Traffic and Attacks
### Layer 5 to 7 Payload Analysis
#### 4.4.6 Traffic Analysis of Port 123  
_Monlist_ is a remote command that requests from a NTP server a list of the most recent machines the server has interacted with, up to a maximum of 100 UDP datagrams with 440 bytes of payload each [56]. The response provides statistics useful for debugging, but may also be used by attackers as a reconnaissance or reflective DDoS tool.  
Attackers can send small request packets to an NTP server with the IP source address spoofed with the value of the victim's, who receives larger response packets. By these means, the attacker amplifies the consumed bandwidth by a factor that may vary from 556.9 up to 4670.0 [56]. This vulnerability is identified as CVE-2013-5211.  
Analyzing the _NTP private request code_, as in Figure 12(a), two different requests were made to the NTP honeypot. Most of them were MON_GETLIST_1 (code 42), associated with the vulnerability of the possibility of amplification, and just two occurrences of REQUEST_KEY (code 32).  
Filtering out the packets with request code equal to 32, it was noted that several different IP addresses sent the command to the NTP honeypot, of which most had the _NTP transmit timestamp_ field equal to _24 November 2004 10:12:11.444111000_, as presented in Figure 12(b)'s inner chart.  
The appearance of the same outdated timestamp in packets with different sources indicates they were actually transmitted by the same attacker, who was believed to spoof the source IP address, as seen in Figure 12(b), who are supposed to be the victims of NTP attacks. Analysis in this case showed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Username Guessed** & **Number of Packets** & **Password Guessed** & **Number of Packets** \\ \hline anonymous & 58 & anonymous & 23 \\ ftp & 50 & anonymous@ & 17 \\ admin & 37 & ftp & 17 \\ www-data & 23 & www-data & 15 \\ Admin & 19 & admin & 9 \\ root & 16 & I�User@ & 9 \\ support & 6 & ect & 7 \\ login & 4 & 123456 & 6 \\ PlcmSpIp & 3 & 123 qwe & 6 \\ server & 3 & Admin & 6 \\ system & 3 & root & 6 \\ user & 3 & PlcmSpIp & 5 \\ ect & 2 & [EMAIL_ADDRESS] & 5 \\ Alex & 1 & test & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The fifteen most guessed usernames and passwords in the FTP brute-force.  
Figure 12: Users (**a**) and passwords (**b**) guessed in the FTP brute force.  
that the similarity between some of the IP addresses implied that the attacker had targeted hosts in the same subnetwork.  
[PERSON] et al. [76] cite this exploitation as a common means to perform reflective DDoS attack, with great bandwidth exhaustion power. To avoid this exploitation, _Monlist_ command should not be permitted to public hosts, but only internally, for management purposes, and DPI devices should drop packets with NTP request code equal to 42. Other defense mechanisms include upgrading NTP servers to version 4.2.7 p26 or later, removing the _Monlist_ function, and implementing BCP 38 (RFC 2827) on the network [77]. This analysis reinforces, therefore, the importance of having updated systems to prevent malicious activity.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 4 RSS-Based Localization Algorithms
### Proximity Localization  
In a proximity-based localization algorithm, the location of the eNB with the strongest link to a mobile UE is used as the estimated location of that UE. This basic yet efficient RSS-based algorithm can already satisfy the requirements of several localization or tracking applications, such as logistics use cases and asset tracking. However, the accuracy highly depends on the base station density and type of environment.  
Figure 3: Map of the measurement campaign around a single eNB with three cell sectors, used to study the mobility. The 95 blue circles represent the GPS locations of each uplink transmission. The sector boundaries of each cell of the eNB are shown in red.  
Figure 2: Map of the 53 km\({}^{2}\) test environment in the city of Antwerp, Belgium, used to study the localization accuracy. The 1307 blue dots represent the GPS locations of the transmitter where a message was sent, while the 83 red triangles indicate the eNB locations.