esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Illustration of the Proposed Approach: Results of Case Studies
### Fatigue Crack Growth Test
What should be noticed in the behaviour of the AE median frequency is that it exhibits a local maximum at the intermediate \(\Delta K\) values. Presumably, this specific behaviour reflects the AE PSD response to the local strain hardening in the growing plastic zone. This process was not visible in the very small plastic zone at the beginning of the crack growth. As the plastic zone increased the contribution of strain hardening became appreciable in the AE PSD evolution. The gradual shift of \(f_{m}\) towards the higher frequency domain was finally overridden by the rapid development of the excess crack tip plasticity beyond the LEFM SSY limit resulting in the rapid expansion of the plastic zone and the reduction in AE PSD. Concluding this section, a brief discussion regarding uncertainty of the measurements is required. Similarly to many commercially available infrared cameras with cooled quantum sensors, the factory-calibrated TELOPS FAST M350 thermograph used in the present work delivers a radiometric temperature accuracy of 1 K (or \(\pm\)1% over the entire range), which is not very impressive, yet typical in the class. The energy calculations are, however, based on the spatio-temporal difference in the temperature. The resolution related to the temperature increment is, fortunately, much better, reducing ideally to 20 mK, as has been discussed above. The greatest uncertainty in the estimates of the thermodynamic quantities, such as a stored energy, is known to be associated with the measurements of the temperature rate and the Laplacian of the temperature field. We have not performed the formal differential error analysis of the propagation of uncertainty in our measurements of quantities entering Equations (10) and (13). However, based on the \"worst case\" analysis, the relative uncertainty in the estimated stored energy does not exceed 15% over the entire range, and this is the most conservative estimate. Furthermore, the calibration procedure applied to the IRT system supports the opinion that 15% can be regarded as the upper boundary in uncertainty, and is likely to be notably smaller. The propagation of error analysis and the details of the calibration procedure, which itself might be of special interest, will be reported elsewhere. In brief, the well-controlled rectangular DC current pulses of 2, 5, 10, 20, and 50 A amplitude and 200 ms duration were passed through the Inconel-625 test specimen (being initially at room temperature), and the corresponding voltage drops and Joule heat dissipation were independently measured. The temperature rise did not exceed 7 K at 20 A, and 55 K at 50 A current. Regardless of the magnitude of the temperature rise, the measured thermal power consistently compared favourably to the measured Joule heat (electric power). This justifies that the obtained estimates of the thermodynamic quantities are reliable that the procedures used to obtain them are adequate.
## 4 Advantages entailed by the proposed Approach and Architecture  
The proposed approach and architecture have the following key advantages which are hereinafter outlined in a qualitative way:  
_Advantages related to efficiency_  
1. The aggregated metadata, which are the inputs to the thinking modules, include both transport related parameters (bit rates, delays, losses, _etc._) and service/content related information, thus potentially allowing _to perform the relevant elaborations availing of information coming from all the layers of the protocol stack and from all underlying access networks_. Oversimplifying, according to the proposed approach, potentially, _all layers_ benefit from information coming from all layers of all networks, thus allowing a full cross-layer, cross-network optimization. For instance, transport layer related decisions can be dynamically related to the kind of content which has to be carried (e.g., valuable contents experience better transport layer performance); for another instance, the way of aggregating services and contents can be dynamically related to transport layer parameters (e.g., following an user query asking for a given content, such content can be provided with an accuracy, completeness and quality consistent with the present performance of the underlying networks presently supporting the user).
2. The decoupling of the Cognitive Middleware layer from the underlying technology transport layers on the one hand, and from the specific service/content layers on the other hand, allows to take _all_ decisions and elaborations in a fully cognitive, _abstract, coordinated and cooperative fashion within a set of strictly cooperative thinking modules.  
3. The more complete information mentioned in the issue (1) together with the decoupling mentioned in the issue (2) _allow the adoption of innovative and advanced closed-loop methodologies for the algorithms and rules embedded in the thinking modules, which are expected to remarkably improve the efficiency of the relevant procedures/algorithms._ So, the thinking modules are expected to work according to approaches based on, for instance, constrained optimization, data mining, adaptive control, robust control, game theory, operation research, as detailed in the thinking module description section 5.
4. The concentration of all control functionalities in a single layer _allows the adoption of multi-object algorithms and procedures_ (based on the approaches mentioned in the issue (3)) _which_ jointly address problems traditionally dealt with in a separate and uncoordinated fashion at different layers, thus allowing in a natural way inter-layer, inter-network optimization which is expected to enhance efficiency. The thinking module section 5 highlights the synergies which will be achieved in the framework of the project.
# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 5 Numerical Evaluation
### Comparative Evaluation
Also, it should be noted that the execution time of the TRUST-ME model was very low (few msec), as discussed in Section 5.1, and TRUST-ME ensured lower latency by effectively balancing the resource allocation and adapting to real-time network conditions and user behaviors. The integration of trust metrics allowed for a more comprehensive assessment of the servers' performance and it led to more informed and efficient resource allocation decisions. This resulted in a consistent reduction in latency, as presented in Figure 8c. Moreover, TRUST-ME enhanced the overall user experience by providing a balanced and fair distribution of resources. Specifically, the users with varying demands could rely on the trust-based allocation to receive sufficient resources and ensure a satisfactory quality of service.
## 6 A Fault Diagnosis Algorithm
### Application Example  
**Example 1**: _Given the network \(BC_{2,2}\) (see Figure 5), first execute the C-UC(u) algorithm. Based on \(|F|\leq(h+1)(k-1)+n\), the algorithm randomly generates four faulty nodes, including 1110, 1100, 0012 and 1011. When the algorithm executes Nth times, the largest connected component is chosen to start from the node 0000. Breadth-first Search is used to put it in the set C. The largest connected component is shown in Figure 6. C = 0000, 0001, 0002, 0010, 0101, 1002, 0011, 0100, 0102, 0101, 0100, 0102, 1000, 1001, 0111, 0110, 1101, 0112, 1112, 1111. Meanwhile, UC = 1110, 1100, 0012, 1011. So, all faulty nodes have been diagnosed. There are no undiagnosed nodes.  
**Example 2**.: _Given the network BC\({}_{5,2}\), we let \(h=4\), at which point \(|F|=(h+1)(k-1)+n=10\). The selected fault nodes are 0000, 0001, 0011, 0002, 0012, 0021, 0031, 0022, 0032, 0040. Figure 7 shows the largest connected component obtained after the breadth-first search. Figure 8 shows the faulty nodes from the breadth-first search. It can be obtained that all the faulty nodes are tested except 0000. At this point, it satisfies \(|F|=(h+1)(k-1)+n-1\) and \(|U|>0\). After calling the UDiag(C,UC,U) algorithm, 0000 is diagnosed as the faulty node (Figure 9 shows the set of fault nodes). So, all faulty nodes have been diagnosed. There are no undiagnosed nodes._
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
###### Abstract  
Grid-forming (GFM) inverters play a critical role in stabilizing future power grids. However, their synchronization is inherently coupled with frequency support, which poses a challenge to prevent overloading while maintaining synchronization. While existing literature has proposed strategies to mitigate the overload of GFM inverters during frequency excursions, these typically focus on limiting primary frequency regulation and overlook their inertial contribution, limiting their effectiveness. The present work addresses this gap by analyzing three overload mitigation strategies that dynamically adjust both primary frequency regulation and inertia. The main contribution of this work is formal analysis of the control structures, providing insight into the tuning process, dynamic behavior, and inherent trade-offs. The performance of these strategies is evaluated under grid frequency excursions and oscillations, focusing on their ability to limit overloads and ensure seamless recovery. Simulation results are validated through experimental testing.  
Keywords:frequency regulation; grid-connected inverter; grid-forming control; grid synchronization; inertia; overload mitigation +
Footnote †: journal: Applied sciences
## 2 System Model
### Beamforming Model  
The mmWave spectrum provides substantial bandwidth, which can facilitate fast data transmission. Nevertheless, mmWave signals are characterized by shorter wavelengths that are highly prone to path loss and attenuation. To address this issue, we leverage beamforming at both the transmitter and receiver ends to enhance signal quality and mitigate noise, path loss, and interference. Considering mmWave path loss effects, highly directive antenna beams are considered to enhance data transmission rate and coverage [33]. In this work, we adopt the IEEE 802.15.3c reference antenna model, characterized by a main lobe of Gaussian form and constant level side lobes [34]. The directional antenna gain is expressed by  
\[G(\theta)=\left\{\begin{array}{ll}G_{m}-3.01\cdot\left(\frac{2\theta}{\omega _{m}}\right)^{2},&0^{\circ}\leq|\theta|\leq\frac{\theta_{m}}{2},\\ G_{s},&\frac{\theta_{m}}{2}\leq\theta\leq 180^{\circ},\end{array}\right. \tag{2}\]  
where \(G_{m}\) and \(G_{s}\) represent the maximum antenna and side lobe gains, respectively. \(\theta\) is an arbitrary angle of the main lobe center having a range of [0\({}^{\circ}\),180\({}^{\circ}\)], \(\omega_{m}\) represents the angle of half-power beamwidth, and \(\theta_{m}\) denotes the angle of fist null beamwidth, \(\theta_{m}=2.6\cdot\omega_{m}\), \(G_{m}=10\log\left(\frac{1.6162}{\sin\left(\frac{500}{2}\right)}\right)^{2}\), and \(G_{s}=-0.4111\cdot\ln(\omega_{m})-10.579\).
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 1 Introduction
Moreover, the data is most susceptible to disruption by intruders and even this data can be subverted by malicious/rogue intermediate nodes when sensed facts, figures, or evidence are communicated by participating member nodes to Base Station (BS) or sink node and then subsequently to fog node or cloud server. Rogue/malicious intermediary nodes perform a number of assaults to block the data from getting to its final destination. Examples of possible internal threats to the IoT include black hole attacks, sinkhole attacks, selective forwarding, Sybil attacks, warm hole attacks, and HELLO flood assaults, among others. The main contribution of this paper aiming at making the entire network safe and secure from malicious internal node attacks, which are based upon trust awareness, is discussed below to detect and expel faulty/malicious nodes from the IoT network. * The comprehensive trust value of each IoT network is computed which is comprised of direct trust, indirect trust and energy trust value. * In order to identify and remove malicious nodes as quickly as possible, the volatilization factor has been included in the selection of Cluster Head (CH) to minimize energy utilization and to manage the densely IoT network. * Aside from this, an enhanced RSA algorithm has also been employed in the proposed paradigm to protect data originality from external attackers. The remainder of this paper is structured as follows: The relevant work is described in Section 2. Section 3 explains the proposed model of trust-aware security, encryption algorithm and cluster head selection technique. Section 4 shows the simulations with results discussion and finally in Section 5 conclusion is given.
## 5 Discussion and Conclusions
However, the single- and multi-fidelity LRA method can estimate the mean and standard deviation values, as well as the skewness and kurtosis values, quite accurately. Despite the outperforming results of the LRA method when compared to another popular polynomial basis approximation, PCE, further investigations are needed for challenging problems to observe the capturing capability for a wide range of benchmarks with diverse mathematical characteristics. Based on the present results, one particular challenge in some implementations may be the ability to capture local behaviours. Therefore, advanced investigations are required to enhance the ability to represent local characteristics. In addition, applications for higher dimensional industrial problems are currently underway. Conceptualisation, S.Y., H.P.S. and M.N. ; methodology, S.Y., H.P.S. and M.N. ; software, S.Y. ; validation, S.Y. and H.P.S. ; formal analysis, S.Y. and H.P.S. ; investigation, S.Y. and H.P.S. ; data curation, S.Y. ; writing--original draft preparation, S.Y. and H.P.S. ; writing--review and editing, S.Y., H.P.S. and M.N. ; visualisation, S.Y. ; supervision M.N. All authors have read and agreed to the published version of the manuscript. First and last authors would like to express their gratitude to TUBITAK for the research grant provided under the 218M471 TUBITAK 1001 project titled as \"Development of Multifidelity and Multidisciplinary Methodologies Integrating Sonic Boom, Aeroelasticity and Propulsion System for Supersonic Aircraft Design\". First and last authors authors would like to acknowledge the graduate thesis support provided by Istanbul Technical University Scientific Research Program for MYL-2019-42352 project titled as \"Application of Multidisciplinary and Multifidelity Optimization Techniques for Supersonic Aircraft\". Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to public release restrictions. **Acknowledgments:** All authors would like to acknowledge the collaboration established among members of the NATO STO AVT-331 Research Task Group titled \"Goal-driven, multi-fidelity approaches for military vehicle system-level design\" co-chaired by [PERSON] and [PERSON] under the framework of NATO Science and Technology Organization Applied Vehicle Technology Panel. All authors would like to thank [PERSON] for his support and suggestions in improving the writing of the article. M.N. would like to thank NASA Langley Research Center for distributing sBOOM sonic boom prediction code internationally for academic research. M.N. would like to acknowledge ITU Scientific Research Projects Unit for \"Stochastic Design Optimization of Military Sea and Air Vehicles\" Project with grant number MGA-2018-41090. **Conflicts of Interest:** The authors declare no conflict of interest. The founders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Methodology
### Data Processing Methods
#### 3.3.2 The STL Method  
STL is used to analyze the fluctuations of the data. Various time series are handled using the STL method [35; 36]. The STL method decomposes time series data into variation components, including seasonality, trends, and residuals, as shown using Equation (1) [37].  
\[Y_{t}=T_{t}+S_{t}+R_{t}, \tag{1}\]  
where \(Y_{t}\), \(S_{t}\), \(T_{t}\) and \(R_{t}\), respectively, represent the time series data, seasonal component, trend component, and residual component. \(t=1\) to \(N\) represents the sequence length.  
The iteration mechanism of STL comprises two recursive procedures, the inner and outer loops. The inner loops are used to calculate the seasonal component and the trend component. The steps of the inner loops are shown in Table 2.  
The outer loops are used to adjust the robust weight \(\rho_{v}\). It is used in Step 2 and Step 6 of the inner loops. In the process of smoothing via loess, the neighborhood weight should be multiplied by a robust weight, and the calculation of \(\rho_{v}\) is as follows:  
\[\rho_{v}=B\bigg{(}\frac{|R_{v}|}{h}\bigg{)} \tag{2}\]  
\[h=6*median(|R_{v}|),v=1,\ldots,N \tag{3}\]  
\[B(u)=\left\{\begin{aligned} &(1-u^{2})^{2},for\ 0\leq u<1\\ & 0,for\ u\geq 1\end{aligned}\right. \tag{4}\]  
where \(R_{v}\) represents the residual of the data point, \(h\) is defined as an intermediate variable, and \(B(u)\) represents the bisquare function.  
Figure 1: Prediction workflow.  
The advantage of the STL method is its robust adaptation to outliers in the data. It can be successfully applied in handling the case of a large number of time series data with great volatility and instability [39].
## 4 Genetic Algorithm Configuration  
This study utilized a GA to optimize satellite constellation parameters for a region between latitudes \(30^{\circ}\) and \(60^{\circ}\). This region was selected because it encompasses densely populated and economically active areas, as evidenced by the four chosen cities (Seoul, Berlin, Oslo, and Houston). These cities represent areas with high demand for IoT connectivity.  
Unlike traditional Delta-Walker constellations, which deploy satellites in multiple planes with a single inclination, our approach divides the constellation into two distinct inclination groups. This dual-inclination strategy was adopted to ensure that both the revisit time and communication duration were evenly secured, thereby providing comprehensive service coverage over the \(30\)-\(60^{\circ}\) range.  
The main objectives of the GA are outlined as follows:  
1. To maintain an average revisit time (\(R_{\mathrm{avg}}\)) of \(60\) min or less, ensuring data are collected at least once per hour;
2. To limit the maximum revisit time (\(R_{\mathrm{max}}\)) to \(90\) min, preventing excessive data accumulation;
3. To ensure sufficient data transmission capability by meeting a target data volume, roughly set at \(15\%\) of the original data (approximately \(1.73\) MB based on an initial value of \(11.52\) MB) considering our IoT scenario;
4. To minimize the total number of satellites (\(S_{\mathrm{total}}\)) deployed, thereby reducing overall system cost.  
These performance metrics are primarily evaluated based on the link margin, which is influenced by power consumption and the data transmission rate.  
The GA simulation was performed using real satellite observation data collected over a \(14\)-day period (\(1\)-\(14\) January \(2025\)). This period was chosen because it reflects recent operational conditions, and daily averages calculated over \(14\) days provide statistically significant results. Furthermore, the minimum daily data transmission volume was set to \(2\) MB to account for variability.
# Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
## 1 Introduction
Hence, this paper changes the logarithmic expression of the information gain of ID3 into the form of the four arithmetic operations by introducing the Taylor formula for developing in real time. Thirdly, this paper introduces coordination degree [28; 29] in rough set theory [30] to control the decision tree size by one step because most current decision tree methods take the pruning strategy to simplify the tree structure, which can complete the whole process in two steps. Finally, this paper proposes a new method to build a more concise and reasonable decision tree model with lower computational complexity. The experiment results finally shows that the proposed algorithm gets better performance than ID3. The rest of this paper is organized as follows: Section 2 describes steps of building a decision tree based on an ID3 algorithm as well as some advantages and disadvantages about the ID3 algorithm. Section 3 addresses some drawbacks of the ID3 method by the proposed algorithm, which combines simplified information entropy with coordination degree in rough set theory. Section 4 presents the assessment algorithm used in this paper. In Section 5, differences between the traditional ID3 algorithm and the proposed algorithm are compared and analyzed through experiments. Final comments and conclusions are provided in Section 6.
## 1 Introduction  
Equipment and devices can fail during their operation in the field and this may occur prematurely. Such a premature failure can render inconveniences and even safety concerns because the failures are unexpected. To prevent the reoccurrence of the failures, a root cause analysis is important. There are differences between failure modes, failure mechanisms, and root causes. An example is an open circuit (failure mode) caused by conductor corrosion (failure mechanism) due to an incomplete protective coating (root cause). Whilst the identification of failure modes can be easy because they can be seen or measured, the finding of failure mechanisms and subsequent root causes are not trivial.  
Printed circuit boards (PCBs) are essential for all electronics. They provide mechanical support and electrical connections to the electronic components of an electronic system. As electronic controls and the internet become essential, many public transport systems contain several PCBs. However, the operating environment in public transport can be harsh due to cyclic temperature and humidity, especially in regions with four different weather seasons.  
PCB failures range from circuit malfunctions to propagating PCB faults. PCB failures that may appear similar can originate from many different root causes. A propagating circuit board fault is usually considered universally to be a high severity thermal event.  
Its initiating mechanism starts when a resistive path forms between two traces or planes in a PCB that are at a different electric potential. This resistive path can form due to an external heat source [1], insulation breakdown [2], arcing [3], or contamination [4]. The power dissipated at the fault region should be sufficient to generate enough heat to further damage the PCB and sustain the thermal event [1]. [PERSON] et al. [1] described various causes of propagating faults in PCBs; namely, resistive heating, interconnect overheating, contamination, electrochemical migration, tin whiskers, insulation failure, and component failure.  
In this work, a propagating fault reported was due to electrochemical migration. We report our steps of tracing from the failure modes to the root cause of a PCB used in public transport for an ethernet connection. After their operation for 1.5 months, the operations of the ethernet device stopped and the burn-out area at the bottom edge of the PCB was found, as shown in Figure 1.  
These PCBs have multilayers that consist of 8 layers with 2 mm of total thickness. Layer 4 is the electrical ground plane and layer 5 is the power plane (V\({}_{\text{IN}}\)) where 12 V is connected to it. Its base material is FR4 (flame retardant). This PCB had superior CAF (conductive anodic filament)-resistance (anti-migration) properties and a high glass transition temperature (T\({}_{\text{g}}\)) (minimum 170 \({}^{\circ}\)C), which makes it a better candidate in the comparison of a standard FR4 for applications where high mechanical and chemical resistances to heat and moisture properties are required.