esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Definition of Pin Geometry  
In the scope of this study, three different pin geometries with a height of 1.8 mm were investigated. As a baseline geometry, which aims to create a comparability between the different non-rotational symmetric pins as well as a comparability to sources in the literature, which primarily use cylindrical pin structures, a cylindrical pin with a diameter of 1 mm was chosen. As a pin with a strong directional dependency in means of fiber displacement and behavior under load, an elliptical pin with a small diameter of 1 mm and a large diameter of 2 mm was chosen. As a third geometry with an anisotropic behavior under compressive and tensile loading, a polygonal pin structure was chosen, which is based on a triangular geometry. It has an in-circle diameter of 1 mm; the sides are curved with a radius of 1.18 while the edges are rounded with a radius of 0.1 mm. Figure 1 displays the three investigated geometries.  
The investigated geometries feature different cross sections, volumes and projection areas (for elliptical pins in dependency of the viewing angle) which are summarized in Table 1. These values allow for assessing the efficiency of a pin-geometry in means of used foot-print on the sample and mechanical performance of the created joints.
# A Distributed RF Threat Sensing Architecture
## 3 Proposed Approach
### Sensor Calibration
#### 3.2.2 Environment RF Profiling  
Having corrected the slope of the logarithmic amplifier, we need to profile the local ambient RF environment at the frequency bands of interest. To achieve this, the sensor is deployed in the specific area of interest to record the ambient RF noise level at each RF sensor band. Each RF sensor performs continuous measurements for up to 24 h, recording samples every second. For each hour, the system then registers the minimum, maximum, and average RSS levels as per Figure 3. At the end of each hour, the system automatically generates a recommended Alarm Threshold Level (ATL) using proposed Formulas (1), (2), and (4), which take into consideration a jamming-to-noise ratio (JNR) that is related to the monitored band and the technologies utilizing this specific band. Alternatively, this guard threshold for alarm activation can be set as a percentage of the increased power level (in dBm) above the calculated average.
# Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
###### Abstract  
Gaussian mixture modeling is a generative probabilistic model that assumes that the observed data are generated from a mixture of multiple Gaussian distributions. This mixture model provides a flexible approach to model complex distributions that may not be easily represented by a single Gaussian distribution. The Gaussian mixture model with a noise component refers to a finite mixture that includes an additional noise component to model the background noise or outliers in the data. This additional noise component helps to take into account the presence of anomalies or outliers in the data. This latter aspect is crucial for anomaly detection in situations where a clear, early warning of an abnormal condition is required. This paper proposes a novel entropy-based procedure for initializing the noise component in Gaussian mixture models. Our approach is shown to be easy to implement and effective for anomaly detection. We successfully identify anomalies in both simulated and real-world datasets, even in the presence of significant levels of noise and outliers. We provide a step-by-step description of the proposed data analysis process, along with the corresponding R code, which is publicly available in a GitHub repository.  
Gaussian mixture modeling; cluster analysis; noise component; outliers; entropy of Gaussian mixtures; EM algorithm +
Footnote †: journal: _Algorithms_
## 5 Conclusions  
Based on the digital images of the tunnel face obtained through sectional shooting, this paper obtained complete and clear images of the tunnel face through image stitching and fusion algorithms. Then, the tunnel face joint information was extracted using three methods: traditional image processing, U-Net convolutional neural network, and Mask R-CNN convolutional neural network. The extraction effects were compared, and the main conclusions are as follows:  
1. Using the SURF algorithm and weighted fusion, sectional images of the tunnel face were stitched into complete, high-clarity images suitable for deep learning algorithms.
2. Traditional image processing methods, including grayscale processing, spatial filtering, binarization, morphological processing, and noise removal, produced suboptimal results with a Dice similarity coefficient of 60.59%. These methods are inefficient, involve significant manual intervention, and lose joint information, making them unsuitable for tunnel engineering applications.
3. The U-Net convolutional neural network achieved relatively good segmentation results with a Dice similarity coefficient of 75.36%. However, it lacked precision and lost target details, indicating room for improvement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Dice (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline Traditional Image Processing & 60.59 & 57.31 & 70.03 \\ U-Net & 75.36 & 70.85 & **85.58** \\ Mask R-CNN & **87.48** & **89.74** & 84.73 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of joint segmentation effects. (The bolded portion is the highest value.)* (4) The Mask R-CNN model excelled in both overall and detailed segmentation, achieving a Dice similarity coefficient of 87.48%. This model demonstrated efficient and accurate extraction of tunnel face joints, outperforming traditional and U-Net methods.  
Data curation, Y.L.; formal analysis, H.Q.; funding acquisition, X.Y.; investigation, H.Q.; methodology, H.Q.; project administration, X.Y.; resources, Y.L.; software, H.Q. and, Z.L.; supervision, J.Z.; validation, H.Q.; visualization, Z.G.; writing--original draft, H.Q.; writing--review & editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study did not require ethical approval.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 The Q-Holonic-Based ARchitecture (QHAR)
### General Overview of the Q-Holonic-Based ARchitecture (QHAR)  
The QHAR consists of three hierarchical levels (see Figure 2): Centralized control level, Decentralized control level, and Execution level.  
The hierarchical representation of production systems was adopted for a long time, with the implementation of the IEC 62,264 standard (see Purdue Reference Architecture [26] and CIMOSA Reference Architecture [27] for examples). This hierarchical approach was also adopted in more recent architectures, such as the RAMI 4.0 architecture [13], ADACOR architecture [8], and ARTI architecture [19], even though there was increasing talk of a decentralized architecture for modern production systems (CPPS) [28]. The persistence of the adoption of these hierarchical architectures is due to several reasons. First of all, there are practical reasons: this is, indeed, the dominant way in which production workshops are organized and operated at present. Not all entities in these workshops have the functionalities to operate in a fully decentralized manner. The compromise between autonomy and global optimization and the better performance offered by the oligarchical control architecture, compared to that of other types of control architectures, may also explain the fact that these hierarchical architectures are still being adopted [20; 23; 29; 30]. The number of levels in these hierarchized architectures is very diverse (e.g., 6 levels in the RAMI 4.0 architecture, 5 levels in ADACOR, and 4 levels in the ARTI architecture). For the QHAR, we decided to divide the production system into 3 levels. The lowest level--the execution level--is made up of entities that may be directly involved in carrying out one or more production activities, materials, and finished or semifinished products. As the entities constituting the execution level may not be completely autonomous, other entities are provided for the control and coordination activities that cannot be performed by them. These entities are assigned to the decentralized control level. Finally, the centralized control level is responsible for the overall optimization and interaction with external actors.
# Numerical Analysis of Reinforcing Effect for Scissors-Type Bridge with Strut Members
## 3 Application of Strut Reinforcement
### Numerical Model Based on Experimental Bridge  
An FE numerical model based on the full-scaled MB having two and three scissors units is created by ABAQUS 6.12 as shown in Figure 6. Each nodal point is defined as \(A_{1},\cdots,A_{n}\) at the upper hinge parts, \(B_{1},\cdots,B_{n}\) at bottom hinge parts, and \(C_{1},\cdots,C_{n}\) at pivot parts, respectively. This simulation model uses linear beam elements and considers a simplified one-side model due to the symmetry of the MB. In order to model pivots and hinges, each scissor member is connected by the joint elements which do not transfer any bearing. Similarly, each reinforcing member is also connected with a scissor member by the joint elements without any friction.  
At full extension, the total length of the span is 7.0 m in the two units model and 10.5 m in the three units model, respectively. The height in both models is 2.0 m. Each model is supported by pins in supported boundary conditions because it is assumed that we will apply the reinforcing member after the bridge is deployed. Now, the vertical and horizontal reinforcing members are assumed to have the same specifications for scissor member and deck boards. Detailed sectional and material properties of structural members are provided in our previous paper [22].  
Figure 5: Relationship between the sectional stresses and the ratio of extensional rigidity \(\alpha\): (**a**) model with upper horizontal strut member; (**b**) model with right vertical strut member.
# A New Framework of Human Interaction Recognition Based on Multiple Stage Probability Fusion
## 3 The Global Feature Extraction and Representation  
In order to realize fast and efficient recognition, it is necessary to extract a small amount of raw feature data with a simple and discriminative feature representation. The HOG (Histogram of Oriented Gradient) descriptors was used to represent the ROI of each frame in this paper, since it has been proved that grid-based HOG descriptors significantly outperform existing feature sets for human detection in a previous study [21]. The HOG descriptor [22] reflects the edge gradient information of human motion, does not need a complex edge detection process and is formed by calculating the gradient histogram in local areas. The process of extraction features is shown in Figure 5.  
The extraction process of the HOG feature requires two steps:  
1. Calculation of the pixels gradient: \[T(x_{i},y_{i})=\sqrt{P_{x}(x_{i},y_{i})^{2}+P_{y}(x_{i},y_{i})^{2}}\] (1) \[\theta=\arctan\frac{P_{x}(x_{i},y_{i})}{P_{y}(x_{i},y_{i})}\] (2) \(T(x_{i},y_{i})\) represents the amplitude of the gradient, and \(\theta\) represents the direction of the gradient. \(P_{x}(x_{i},y_{i})=f(x+1,y)-f(x-1,y)\) and \(P_{y}(x_{i},y_{i})=f(x,y+1)-f(x,y-1)\) are used to represent and calculate the horizontal gradient and vertical gradient respectively.  
Figure 4: The example of image pre-processing.  
Figure 5: Histogram of Oriented Gradient (HOG) descriptor extraction of region of interest (ROI) in different stages (**a**) original image; (**b**) gradient distribution; (**c**) HOG feature; (**d**) original image; (**e**) gradient distribution; (**f**) HOG feature.  
2. Count the histogram of gradient: The pixels gradient distribution is shown as Figure 5b,e. The gradient distribution is then divided into \(ns\times ns\) cells. Finally, the gradient over all the pixels within each cell is projected on \(m\) orientations to form a \(m\)-dimensions feature vector \(\mathbf{v}\). The vector \(\mathbf{v}\) should be normalized as Equation (3): \[\mathbf{v}\rightarrow\frac{\mathbf{v}}{\sqrt{\|\mathbf{v}\|_{2}^{2}+\epsilon^{ 2}}}\] (3) \(\epsilon\) is a small constant.  
All the histograms can be concatenated to form an \(ns\times ns\times m\) feature vector. In our experiments, we also experimented with \(ns=4\), 6, 8, 10 and \(m=9\), 12, 15, 18. The results have shown that the feature reaches the best performance when \(ns=4\) and \(m=12\). So the parameters \(ns=4\) and \(m=12\) are chosen for our experiment.
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 4 Experimental Results and Analysis  
### Pixel Response Test of System Polarization  
The polarization pixel performance of the split-focus plane polarization imaging system is related to three physical quantities. For a given polarization pixel, its polarization response parameter is fixed. Therefore, the direction of polarized light can be changed by rotating the polarizer. The polarization image sensor can obtain different polarization pixel digital signals. This section verifies the effectiveness of the above response model through relevant experiments.  
When the linearly polarized light \(I_{in}\) obtained after polarizing the polarizer passes through the polarizer, its outgoing light \(I_{out}\) meets [PERSON] law, as shown in Equation (10).  
\[I_{out}=I_{in}\cos^{2}\chi \tag{10}\]  
In Equation (10), \(\chi\) is the angle between the polarization direction of the incident polarized light \(I_{in}\) and the optical axis of the polarizer. According to Equation (11), when the intensities of \(I_{out}\) and \(I_{in}\) are equal, it means that the polarization direction of the incident polarized light is consistent with that of the polarizer, \(\chi=0^{\circ}\); when the intensity of \(I_{out}\) is zero, it indicates that the polarization directions of the incident polarized light and the polarizer are orthogonal to each other, \(\chi=90^{\circ}\).  
Based on the above theoretical analysis, a polarization pixel response test platform is built, as shown in Figure 7.  
As shown in Figure 7, the devices numbered 1-5 represent the Ethernet switch, polarization camera, linear polarizer, LED light source and upper computer, respectively. An LED uniform light is used as the input light source of the test platform. A calibrated linear polarizer is placed between the light source and the focal plane polarizing camera, which can be used as a polarizer to generate linearly polarized light. The micropolarizer array in the polarization sensor can be regarded as a polarizer. During the test, the polarization angle increment is set to \(10^{\circ}\) and the polarizer is rotated. Under the same exposure time and gain conditions, 19 groups of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\) polarization grayscale images are collected by the split-focus plane polarization camera at \(0^{\circ}\), \(10^{\circ}\), \(20^{\circ}\). The least-squares method is used to fit the gray value of the pixel response of the polarization image. The relationship between the gray value and the rotation angle is shown in Figure 8.  
Figure 7: Response test platform for polarized pixels.  
According to the polarization pixel response fitting curve shown in Figure 8, it can be concluded that the gray value distribution of the theoretical analysis results is consistent with the actual measured values; the corresponding relationship between the response of polarization pixels and the polarization direction is obtained. The validity of the model can be further verified according to the results.
## 4 Empirical Study
### Initial Parameter Setting  
#### 4.3.1 Parameter Setting within the Structure Learning  
Table 5 presents the specific parameter setting in the methodology applied in this empirical study.  
* 'Random restarts' is a configurable number of perturbing operations and a proceeded initial network structure, which can be used to avoid poor local maximization or optimization [31]. We  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rounds** & **Number of Selected Features** & **Ranking Method** & **Classifier** \\ \hline
35 & 17 & GR & Bayesian Network \\
21 & 19 & OneR & Random Forest \\
21 & 17 & IG & Naive Bayesian \\
12 & 15 & GR & Bayesian Network \\
6 & 16 & GR & Random Forest \\
5 & 17 & CFS & OneR \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compositional classification results of all rounds.  
Figure 3: The classification results using the GR ranking method based on the Recall index.  
utilize 'random restarts' by setting the parameters of'restart' and 'perturb', respectively. More specifically,'restart' makes it possible to probe more area of the search space and increase the chance of finding a structure that fits the data better. Meanwhile, we consider the limit of running time and memory and set them as integers.
* We set the parameter'mapx' for continuously adjusting the maximum of father nodes in order to analyze the relationship between the maximum of father nodes and the accuracy of predictive models. This setting is used to prevent the learnt structure from becoming too complicated, so 7 is set as the maximum. In Section 4.4.1,'mapx' is determined as 5, with the lowest training error and cross-validation error during the network structure learning (Figure 4).
* We measure the strength of directed arcs, compare different network structures and further correct the dependencies between nodes, which facilitates a more understandable network structure.
## 2 Overview of WiFi-Direct and Cyber Attacks
### Cyber Attacks
#### 2.2.4 IP Address Configuration  
After the agreement on the pre-shared key, the P2P device acting as GO, starts a DHCP exchange [11] for automatic IP address allocation of all other group members. The DHCP process begins with a discovery requests from the clients. Then, the P2P GO responds to the request offering an IP address for the client followed by another request from the client choosing one of the offered address. Finally, the P2P GO running the DHCP server, answers and acquits the client. Any external communication is insured by the P2P GO interconnecting all P2P Group clients. In addition, the P2P group is deleted if the P2P GO leaves the group. A new P2P group has to be created selecting a new GO using the Standard Group Formation.  
Furthermore, and with the emergence of 5G technology the world became full of connected objects and devices that are always a target of attackers who exploit different wireless vulnerabilities to perform physical [1], configuration and protocol attacks, or even attacks on core network. The insensibility of mobile users can also cause a cyber-attack. In the following, we present some known attacks that can provide data loss, fake identity and channel communication disrupt. Attacks on D2D communication can be classified in 2 types: first, attacks on the D2D system over wireless channel such as Distributed denial of service or Jamming in which the attacker tries to perturb the communication over wireless channel [12] regardless of the D2D users. Second, attacks affecting cellular mobile users or D2D users; such as man in the middle, eavesdropping or even masquerading; the attacker here aims to manipulate legitimate D2D users using multiple methods such as identity theft or social engineering in order to stole users' credentials or even inject false messages to multiple group users.  
In addition, an attacker targeting a D2D communication can vary according to the attacker privileges over the wireless channel and what type of attack it is trying to launch [12]. Indeed, a local attacker is already a P2P group member and can communicate with authenticated users. Whereas, an outsider attacker has less privileges, which forces him to first search for potential vulnerabilities to exploit in order to gain access to a specific P2P group. Furthermore, an active attacker applies modifications on the transmitted data such as creating, deleting or replying the data on the wireless channel or on mobile devices to obtain sensitive information. This type of attacker can be quickly detected through the scan of the modifications it makes. However, a Passive attacker is considered as the evilest type of threaders due to its inactivity over the wireless channel, acts by listening and sniffing transmitted traffic between legitimate mobile devices which generally makes it hard to be detected.
## 3 Experimental Results Analysis
### Effect of Loading Rate and Inclination Angle on the Elastic Modulus
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Inclination Angle (\({}^{\circ}\))**} & \multirow{2}{*}{**Loading Rate**} & \multirow{2}{*}{**Elasticity Modulus**} & \multirow{2}{*}{**Absolute Error (MPa)**} & \multirow{2}{*}{**Relative Error (\%)**} & \multirow{2}{*}{**Hardicity Modulus (MPa)**} & \multirow{2}{*}{**Standard Deviation (MPa)**} & \multirow{2}{*}{**Relative Standard Deviation (\%)**} \\  & & & & & & & \\ \hline \multirow{4}{*}{0} & \multirow{4}{*}{3.5} & 1067.87 & 49.00 & 4.81 & \multirow{4}{*}{46.91} & \multirow{4}{*}{4.60} \\  & & 974.37 & 44.50 & 4.37 & & & \\  & & 1014.38 & 4.50 & 0.44 & & & \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{7.5} & 746.56 & 43.04 & 5.45 & \multirow{4}{*}{789.59} & \multirow{4}{*}{37.36} & \multirow{4}{*}{4.73} \\  & & 813.73 & 24.14 & 3.06 & & & \\  & & 808.49 & 18.90 & 2.39 & & & \\ \hline \multirow{4}{*}{10} & \multirow{4}{*}{0.05} & 558.21 & 30.26 & 5.73 & \multirow{4}{*}{527.95} & \multirow{4}{*}{33.88} & \multirow{4}{*}{6.42} \\  & & 491.35 & 36.60 & 6.93 & & & \\  & & 534.29 & 6.34 & 1.20 & & & \\ \hline \multirow{4}{*}{15} & \multirow{4}{*}{10.0} & 330.67 & 14.96 & 4.74 & \multirow{4}{*}{315.70} & \multirow{4}{*}{13.81} & \multirow{4}{*}{4.37} \\  & & 303.46 & 12.25 & 3.88 & & & \\  & & 312.99 & 2.72 & 0.86 & & & \\ \hline \multirow{4}{*}{20} & \multirow{4}{*}{3.5} & 348.72 & 23.45 & 6.30 & \multirow{4}{*}{372.16} & \multirow{4}{*}{20.73} & \multirow{4}{*}{5.57} \\  & & 388.06 & 15.90 & 4.27 & & & \\ \cline{1-1}  & & 379.71 & 7.55 & 2.03 & & & \\ \hline \multirow{4}{*}{25} & \multirow{4}{*}{0.05} & 362.81 & 23.62 & 6.11 & \multirow{4}{*}{386.43} & \multirow{4}{*}{21.11} & \multirow{4}{*}{5.46} \\  & & 403.45 & 17.01 & 4.40 & & & \\ \cline{1-1}  & & 393.04 & 6.61 & 1.71 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Elastic modulus under various loading rates and inclination angles. Figure 10: Elastic modulus varies with the loading rate and inclination angle: (**a**) Relationship between elastic modulus and inclination angle; (**b**) relationship between elastic modulus and loading rate.
## 2 Materials and Methods
### Interoperability at the Syntax Level  
Interoperability at the syntax level describes the encoding, decoding and representation of the data. The encoding of the data in the HiL test benches is determined by the SuT. In the avionics integration test, it is the interfaces of the control computers. The syntax of data exchange in avionics is stored in Interface Control Documents (ICDs). The ICD format for the development of the A340 and A350 aircraft variants is table-based. The syntax for the interface description is organized by the avionics equipment. The individual data are recorded in comma-separated format (CSV) [30].  
Standards for the ICD in aviation do not exist. The ARINC 653 [31] contains a proposal for the standardization of configuration. However, it does not include the configuration of the interfaces. ASAM XIL API describes the interfaces of the control computers. Its syntax is not applicable to avionics hardware [15]. Two authors published aviation ICD standardization efforts [32; 33]. Both proposals are XML-based and call their result MetaICD. They allow the encoding of signals for all common communication protocols in avionics. Both publications do not mention the progress of the standardization efforts.