esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 NMR Analysis
### Analysis of Aperture Ratio  
There are various forms and sizes of pores in rocks, and changes in stress conditions can affect the internal pore structure of a rock sample. To analyze the pore size distribution of the fractured sandstone with different inclinations under the joint action of freeze-thaw-fatigue loading, the pore size occupancy of fractured sandstone before and after 20 freezing and thawing cycles, as well as after the destruction of the fatigue loading, is plotted as shown in Figure 8. As can be seen from the figure, the pore diameter distribution span of fractured sandstone was large, and the proportions varied significantly. Most pore diameters were distributed between 1 and 100 \(\upmu\)m. After the freeze-thaw cycles, the proportion of small pores increased by 1.45%, 1.56%, 3.91%, and 5.05%, respectively. The proportion of medium pores decreased to 4.14%, 13.76%, 17.65%, and 21.08%, respectively. There was no significant change in the proportion of large pores. The change rate of the proportion of small pores was positively correlated with the fracture inclination angle, and the change rate of the mesopore proportion was negatively correlated with the fracture inclination angle. The change rate increased with the increase of the fracture inclination angle. This shows that the micropores and medium pores inside the rock sample gradually developed into small pores during the process of frost having and melting shrinkage. The small pores increased significantly, while the mesopores showed a decreasing trend. After fatigue loading and damage, large-scale pores appeared inside the rock sample. At the same time, the proportion of micropores increased by 5.16%, 26.92%, 3.54%, and 19.33%, respectively. The proportion of small pores decreased by 2.51%, 4.39%, 1.81%, and 1.29%. This was because under the action of fatigue load, the bonding force between the internal particles of the rock sample decreased, along with the change of its internal structure. At this time, the small pores inside the rock sample gradually changed into micropores. When the pores were connected and expanded to a certain extent, large pores dominated, inducing adjacent medium and small pores to form a macroscopic failure surface, and the rock sample lost its bearing capacity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Spectral Area**} & \multicolumn{3}{c}{**Spectral Area Change Rate/\%**} \\ \cline{2-6}
**Crack Dip Angle**\({}^{\circ}\) & **Before Freezing and Thawing** & **After Freezing and Failure** & **After Freezing and Thawing** & **After Fatigue Failure** \\ \hline
30 & 13,623.27 & 14,327.33 & 16,443.29 & +5.17 & +20.70 \\
45 & 13,207.61 & 13,958.64 & 14,884.98 & +5.69 & +12.70 \\
60 & 14,141.08 & 15,015.77 & 16,290.52 & +6.19 & +15.20 \\
90 & 13,764.38 & 14,872.42 & 15,416.11 & +8.05 & +12.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: NMR spectral area of fissured sandstones with different dip angles.
## 6 Abuse Attack Techniques
### Cryptography
#### 6.3.2 CPLS as Redirector to C&C Domain  
[PERSON] et al. [28] proposed a botnet design that combines QR codes, Twitter search, domain generation algorithms (DGAs), cryptography (AES and RSA), and Tor. In their design, Twitter is utilized as a mapper to the C&C web server. The Twitter search engine is queried for a particular keyword to locate the botmaster's post, which includes a QR code image. Upon scanning the QR code, the encoded combination of three components--the C&C web server address, the hard-coded token, and the RSA public key--is retrieved. The recovered RSA public key is then used to encrypt the data to be sent to the C&C web server.  
The Casbaneiro botnet [73] utilizes YouTube, a legitimate website, to store its C&C server domains. The malware operator of Casbaneiro embeds an encrypted C&C web server address in a false Facebook or Instagram URL within the description of a specific YouTube video. This serves to redirect compromised machines to the threat actor's C&C infrastructure.
# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
## 2 Materials and Methods
The output of this process is a clear and up-to-date picture of the current and predicted state of health of the aircraft, including probable failure modes. This enables maintenance crews and operation managers to make informed decisions, optimizing maintenance schedules and reducing unplanned downtimes. The integration of AIoT in AHMS ensures that these outcomes are not only accurate but also timely. As the models improve over time, the predictive capabilities of the system become more nuanced, potentially foreseeing issues well before they would otherwise be detectable. The AIoT offers a wealth of opportunities for advancing AHMS:  
* AIoT maximizes operational efficiency by allowing real-time monitoring and faster decision-making processes. * By accurately predicting when maintenance is needed, AIoT-driven AHMS can prevent costly repairs and downtime, resulting in significant cost savings. * Enhanced predictive maintenance leads to increased safety margins, as potential issues can be addressed proactively. * The wealth of data collected and analyzed through AIoT enables deeper insights, facilitating continuous improvement in aircraft design and operation. The materials and methods described in this study represent a comprehensive approach to integrating AIoT into aviation health monitoring systems. By leveraging the power of AI and the ubiquity of IoT sensors, this study aims to contribute to the development of more intelligent, efficient, and safer aircraft operations.
## 1 Introduction
[16] investigated the effect of pole head geometry in a multipole dual disc and discovered that hexagonal pole heads had a larger magnetic field area and an increase of 3.88% for braking torque performance. [PERSON] et al. [17] developed a rotor design that protrudes the poles slightly outward on one side, resulting in a decrease in stator pole heights, and this configuration increases the average torque. The analysis also showed that noncircular stator pole faces increase average torque. [PERSON] et al. [18] conducted a study on different shapes of magnetic medium sections and found that toothed plate section shape improves magnetic force at 2.5 A magnetizing current compared to cylindrical sections[PERSON] [PERSON] et al. [19] examined the potential of a magnetic-resistance brake with geometric constraints to achieve the desired design objective and flexible structure, and results showed that the initial dynamic behavior of the magnetic field yields a significant braking torque while maintaining a compact and solid design. Similarly, [PERSON] et al. [20] explored the use of MRB in automotive applications and demonstrated that increasing the number of discs enhances the torque, thereby confirming its viability in real-world braking applications. The primary purpose of the geometrical designing of pole head structure into circular sector-shaped multiple poles that can be radially arranged is to understand the flux distribution in its axial outward direction of a rotating brake disc's overall surface, which can maximize MRF active working areas[PERSON] Changing the shape of the pole head can impact the distribution and intensity of the magnetic flux, which affects the performance characteristics like output braking torque and overall efficiency. The proposed pole head design leads to a consistent and uniform magnetic field to achieve specific torque characteristics. This allows for precise control over the braking force exerted by the MR brake. By enhanced magnetic efficiency, it may be possible to achieve a better braking force in a compact design structure. This paper indicates that proper design considerations can significantly influence magnetic flux density and increased braking torque capacity that impact the brake's performance.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 5 Comparison of The Three Types of Sensor Networks
### Maintenance  
_Ranking:_ VSN \(>\) SSN \(>\) CSN  
_Reasons:_  
In order to guarantee the data quality, maintenance on the sensor nodes like changing dead batteries, replacing malfunction components or calibrating sensors are indispensable. As a matter of fact, all TNGAPMSs require massive deployment of the sensor nodes and the sensors used in these systems need frequent calibration to be efficient [83]. We expect that, in real-life large-scale deployment, the maintenance on the sensor nodes will occupy the vast majority of efforts of the maintenance on the whole system. The feasibility of maintenance on the sensor nodes is critical in this case.  
_Explanation:_  
In CSN systems, the sensor nodes are carried out by the public users who are lack of special knowledge and equipment or even unlikely to explicitly maintain the sensor nodes. Moreover, it is infeasible for the professionals to collect and maintain all the sensor nodes frequently. Hence the sensor nodes in CSN systems are typically not well maintained. In SSN systems, the sensor nodes are well maintained by the professionals. However, the professionals need to visit all locations with sensor node deployment to conduct the maintenance periodically. Tremendous amount of manpower and time are required in this case and the flexibility of maintenance is reduced compared to VSN systems. In VSN systems, the sensor nodes carried by the public transportations can be driven to a specific location on demand for maintenance by the professionals. Manpower and time are saved because the professionals are able to maintain large amount of sensor nodes simultaneously. As a result, the flexibility of maintenance of VSN systems is the highest among these three types of systems.
## 2 Spectral Analysis
For a measurable function \(g:D\subseteq\mathbb{R}^{t}\to\mathbb{C}\), the essential range of \(g\) is defined as \(\mathcal{ER}(g)\doteq\{z\in\mathbb{C}:\,\mu_{t}(\{g\in B(z,\epsilon)\})>0\text{ for all }\epsilon>0\}\), where \(\{g\in B(z,\epsilon)\}\doteq\{x\in D:\,g(x)\in B(z,\epsilon)\}\). \(\mathcal{ER}(g)\) is always closed and, if \(g\) is continuous and \(D\) is contained in the closure of its interior, then \(\mathcal{ER}(g)\) coincides with the closure of the image of \(g\). Hence, if \(\{A_{n}\}_{n}\sim_{\lambda}\psi\) (with \(\{A_{n}\}_{n}\), \(\psi\) as in Definition 1), then, by ([6], Theorem 4.2), \(\{A_{n}\}_{n}\) is weakly clustered at the essential range of \(\psi\), defined as the union of the essential ranges of the eigenvalue functions \(\lambda_{i}(\psi),\ i=1,\ldots,r\): \(\mathcal{ER}(\psi)\doteq\bigcup_{i=1}^{s}\mathcal{ER}(\lambda_{i}(\psi))\): all the considerations above can be translated in the singular value setting as well, with obvious minimal modifications. In addition, the following result holds. Theorem 1: _If \(\mathcal{ER}(\psi)=s\) with \(s\) fixed complex number then we have the subsequent equivalence: \(\{A_{n}\}_{n}\sim_{\lambda}\psi\) iff \(\{A_{n}\}_{n}\) is weakly clustered at \(s\) in the eigenvalue sense. Hence, if \(\mathcal{ER}(|\psi|)=s\) with \(s\) fixed nonnegative number then we have the subsequent equivalence: \(\{A_{n}\}_{n}\sim_{\sigma}\psi\) if \(\{A_{n}\}_{n}\) is weakly clustered at \(s\) in the singular value sense._  
A noteworthy example treated in the previous theorem is that of zero-distributed sequences \(\{A_{n}\}_{n}\) expressed by definition as \(\{A_{n}\}_{n}\sim_{\sigma}0\) (see [3]). We will make use of [Theorem 1] in [4], which we report below and which extends previous results in [6] in the context of the zero distribution of zeros of perturbed orthogonal polynomials. Theorem 2: _Let \(\{X_{n}\}_{n}\) be a matrix-sequence such that each \(X_{n}\) is Hermitian of size \(d_{n}\) and \(\{X_{n}\}_{n}\sim_{\lambda}f\), where \(f\) is a measurable function defined on a subset of \(\mathbb{R}^{q}\) for some \(q\), with finite and positive Lebesgue measure. If \(\|Y_{n}\|_{2}=o(\sqrt{d}_{n})\), with \(\|\cdot\|_{2}\) being the Frobenius norm, then \(\{Y_{n}\}_{n}\sim_{\lambda}0\) and \(\{X_{n}+Y_{n}\}_{n}\sim_{\lambda}f\)._
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 2 Status and Challenges of VLC  
Visible light communication introduces special nonlinearities due to the additional electro-optical conversion, which can significantly impair communication performance. Due to the spontaneous radiation properties of LEDs, visible light signals can only be directly modulated in communication. This means that changes in signal amplitude will directly affect the carrier concentration, and thus the recombination of electrons and holes [18]. In this section, we present the complexity of visible channels in terms of physical channels and modulation formats, and afterward, show the superiority of machine learning and the attendant costs.
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
## 3 Results and Discussion
### Dark Count Rate  
For photon-counting measurements, an external quenching resistor of 47 k\(\Omega\) is used and the parasitic capacitance of the bond pad is estimated to be around 1 pF which disguises the small (\(\sim\)1 fF) capacitance of the p-n junction.  
Figure 5a shows the dark count rate of the device with respect to the excess bias voltage (V\({}_{\text{ex}}\)). As expected from the IV characteristics, the DCR is high. The main contribution to the DCR is possibly band-to-band tunneling, traps in the surface, and the silicide layer present close to the avalanching junction. Therefore, the device should be optimized in future versions to achieve a low DCR.
# A Study of Cavitation Erosion in Artificial Submerged Water Jets
## 2 Numerical Simulation
### Cavitation Model  
The mixture model implemented in ANSYS Fluent is widely employed as a multiphase flow modeling method for simulating complex flows that consist of multiple immiscible fluids. Its applicability extends to various phenomena, including liquid-gas interaction, free surface flow, and dispersed phase flow [30; 31; 32]. The fundamental assumption of the mixing model is that the fluid phases are thoroughly mixed, thereby sharing identical velocity and pressure fields. Each phase is treated as an individual component, and their interactions are quantified through interphase momentum and mass transfer terms. The mass transfer rate is calculated by the vapor transport equation:  
\[\frac{\partial(\rho_{v}\alpha_{v})}{\partial t}+\frac{\partial \left(\rho_{v}\alpha_{v}u_{j}\right)}{\partial x_{j}}=M_{e}-M_{c}, \tag{3}\]  
where \(\rho_{v}\) and \(\alpha_{v}\) represent the vapor phase density and vapor phase volume fraction; \(M_{e}\) and \(M_{c}\) denote evaporation and condensation rates, respectively. The mass transfer rate between phases can be determined using various mass transfer equation models (TEM), such as the Schnerr-Sauer model, ZGB model, and Singhal models. Among these models, employing the ZGB cavitation model for simulating cavitation flow can offer improved calculation accuracy and faster convergence speed [33]. The equations for the ZGB cavitation model are as follows:  
\[M_{\rm e}=F_{\rm vap}\frac{3\alpha_{\rm nuc}(1-\alpha_{v})\rho_{v}}{R_{\rm b}} \sqrt{\frac{2(P_{v}-P)}{3\rho_{1}}},\ \text{if}\ P<P_{\rm V} \tag{4}\]  
\[M_{c}=F_{\rm cond}\frac{3\alpha_{v}\rho_{v}}{R_{\rm b}}\sqrt{\frac{2(P-P_{v}) }{3\rho_{1}}},\ \text{if}\ P\geq P_{\rm V} \tag{5}\]  
where \(\rho_{v}\), \(\rho_{l}\), \(P\), and \(Pv\) denote the vapor phase density, the liquid phase density, the total pressure, and the vapor phase pressure, respectively. The \(F_{\rm vap}\), \(\alpha_{\rm nuc}\), \(R_{\rm b}\), and \(F_{\rm cond}\) represent the evaporation coefficient, the volume of gas nucleus in the liquid, the radius of the bubble, and the condensation coefficient, respectively. The default values for these variables are \(F_{\rm vap}=50\), \(\alpha_{\rm nuc}=5*10^{-4}\), \(R_{\rm b}=10^{-6}\), and \(F_{\rm cond}=0.01\).
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 2 Materials and Methods
### Convolutional Neural Network (CNN) Architectures
#### 2.4.1 1D CNN  
The input of this network corresponds to a given window of the raw audio signal. The network is composed of 3 layers in which a convolution with a ReLU activation function is followed by a max pool stage. A flatten layer is added, followed by a dropout layer for regularization. Then, four consecutive dense layers are implemented, where the first three have 20 neurons each and a ReLU activation function, and the last one has 4 neurons (one per class) and a softmax activation function.
# Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research
## 2 The Microgrid Architecture
### Communication Protocols and Standards of Microgrid
Two effective solutions were used as the basis for the design of the security functionality of DNP3 by [31]: (1) the introduction of security mechanisms to the DNP3 stack through the modification of the original protocol, and (2) the insertion of a security layer between the DNP3 protocol stack and the TCP/IP layer. The first solution offers suitable security solely for DNP3. Nonetheless, the protocol stack needs to be repeatedly modified while the communication systems in the power devices require upgrading. As such, the compatibility of legacy devices with smart grid devices can be more desirably achieved through the insertion of a security layer between the DNP3 and TCP/IP. This security layer aims to specifically assist the DNP3 protocol in attaining the primary security requirements for confidentiality and integrity. This is achieved through the interception of the DNP3 packets distributed to the TCP/IP layer by the security layer. The data are then encrypted, and the encrypted packets are then sent to the TCP/IP layer. All these are performed at the transmitter, the data packets are passed to the application layer (DNP3 layers) once the security layer has decrypted them. The confidentiality and integrity of DNP3 packets can be ensured through symmetric or asymmetric algorithms. In [32], for instance, MAC-based authentication was designed and implemented to function as an extension to the security of DNP3-based communication for distribution automation systems.
## 1 Introduction  
The institutions involved in electric power transmission in Lithuania and Poland have decided to implement the electricity link \"LitPol Link\". This link is designed to connect the power systems of the Baltic States to those in Western Europe and also contributes to the development of the general European electricity market as well as boosting the reliability of the power supply. In pursuit of increasing the capacities of electric power transmission, the decision was made to supplement the LitPol Link by installing an extra 400 kV high-voltage AC double-circuit transmission line.  
Recently, there has been increasingly widespread concern over the influence of the electromagnetic field (50 Hz) on the health of residents. One of the fundamental challenges is ensuring safe conditions for those living in locations where such an electromagnetic field is present.  
The recently adopted Hygiene Standard HN 104:2011 \"Human protection against electromagnetic fields caused by overhead power lines\" is currently valid in the European Union (EU) and states that the effective values of the magnetic field strength must never exceed exceed 32 A/m or 40 \(\mu\)T (magnetic flux density) in residential environments, or 16 A/m or 20 \(\mu\)T (magnetic flux density) within residential and public service buildings. These values should never be exceeded irrespective of the duration of a person's exposure to an electromagnetic field.  
The double-circuit 400 kV power transmission line is one of the most powerful electrical installations in the entire power system; at a frequency of 50 Hz, it generates an intense magnetic field. Therefore, it is necessary to focus on the installation and safe operation of the 400 kV double-circuitoverhead transmission line in order to prevent the magnetic field generated by this power transmission line from exceeding the requirements set by the European Union (EU) Hygiene Standard HN 104:2011.
# A Digital Twin System for Adaptive Aligning of Large Cylindrical Components
###### Abstract  
Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.  
large aerospace cylindrical component; digital twin; adaptive aligning system; aligning trajectory error prediction; adaptive aligning control
# Research on an Improved SOM Model for Damage Identification of Concrete Structures
## 3 Improved SOM Damage Identification Method
### Construction of Damage Identification Model  
In order to establish the damage identification model of concrete structure, the structure and algorithm characteristics of the SOM neural network are analyzed according to the performance requirements of the model. Its core steps include the selection of input samples, the setting of network parameters, the judgment of winning neurons and the analysis of topological graphs. Figure 2 is the overall process diagram of the construction method of the damage identification algorithm model for concrete structures.  
Figure 2 shows the whole process of structural damage identification based on the SOM neural network, which is the core framework system of the research. Analysis of Figure 2 shows that the selection of input samples is the first step in the construction of the network model, which directly determines the network structure and is an important factor affecting the efficiency of network operation. As the last  
Figure 2: The whole process of model building.  
model, the analysis of the topology structure directly determines the specific category of each neuron and is a key factor affecting the accuracy of network recognition. Therefore, in order to improve the recognition performance of the SOM network for concrete structure damage, the research will mainly focus on these two parts to improve the SOM neural network model.