esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of Dual-Motor Speed Synchronization Controller
### Stability Analysis of the Dual-Motor System  
We define the Lyapunov function as follows:  
\[\begin{array}{l}V=\frac{1}{2}\sum\limits_{i=1}^{n}s_{i}^{2}+\frac{1}{2}\tau^ {2}\\ =\frac{1}{2}s_{1}^{2}+\frac{1}{2}s_{2}^{2}+\frac{1}{2}\tau^{2}\end{array} \tag{23}\]  
The time derivative of Equation (23) can be obtained as follows:  
\[\begin{array}{l}\dot{V}=\sum\limits_{i=1}^{n}s_{i}\dot{s}_{i}+\tau\dot{\tau} \\ =s_{1}\dot{s}_{1}+s_{2}\dot{s}_{2}+\tau\dot{\tau}\end{array} \tag{24}\]  
Figure 3: Dual\(-\)motor speed synchronization control structure based on the improved GFTSMC.  
The time derivative of the sliding mode surface \(s_{1}\) can be obtained as follows:  
\[\dot{s}_{1}=\dot{e}_{1}+\alpha e_{1}(t) \tag{25}\] \[+\beta\frac{d(e_{1}(t))^{q/p}}{dt}+\eta\big{(}e_{1}(t)\big{)}^{2}\]  
Substituting Equation (7) into Equation (25), Equation (26) can be written as follows:  
\[\dot{s}_{1}=\dot{\omega}_{r}-au_{1}+b\omega_{1}+d_{1}+\alpha e_{1}(t) \tag{26}\] \[+\beta\frac{d(e_{1}(t))^{q/p}}{dt}+\eta(e_{1}(t))^{2}\]  
Substituting Equation (16) into Equation (26), Equation (27) can be written as follows:  
\[\dot{s}_{1}=-\varphi e_{1}(t)-\gamma e_{1}(t)^{q/p} \tag{27}\]  
\[s_{1}\dot{s}_{1}=-\varphi e_{1}(t)^{2}-\gamma e_{1}(t)^{(p+q)/p} \tag{28}\]  
Similarly, the result is obtained as follows:  
\[s_{2}\dot{s}_{2}=-\varphi e_{2}(t)^{2}-\gamma e_{2}(t)^{(p+q)/p} \tag{29}\]  
Substituting Equation (19) into Equation (20), Equation (30) can be written as follows:  
\[\dot{\tau}=-\varphi\delta-\gamma\delta^{q/p} \tag{30}\]  
\[\dot{\tau}\dot{\tau}=-\varphi\delta^{2}-\gamma\delta^{(p+q)/p} \tag{31}\]  
Substituting Equations (28) and (31) into Equation (24), we obtain the following:  
\[\dot{V}=\sum\limits_{i=1}^{2}s_{i}\dot{s}_{i}+\tau\dot{\tau} \tag{32}\] \[=\sum\limits_{i=1}^{2}\left(-\varphi s_{i}^{2}-\gamma s_{i}^{(p+ q)/p}\right)-\varphi\delta^{2}-\gamma\delta^{(p+q)/p}\]  
Since \(p\) and \(q\) are positive odd numbers, \((p+q)\) is an even positive number and the following result can be obtained:  
\[s_{i}^{(p+q)/p}\geq 0 \tag{33}\]  
\[\tau^{(p+q)/p}\geq 0 \tag{34}\]  
Therefore, \(\dot{v}\leq 0\). According to the Lyapunov stability theorem, the controller for this dual-motor system is asymptotically stable.
###### Abstract  
The Magnetic Barkhausen Noise (MBN) is a non-destructive testing method, which, due to its high sensitivity to changes in the microstructure of the material, is increasingly being applied with success as a tool for evaluation of magnetic material state and properties. However, it is no less difficult to analyze the measurement signals and their correct interpretation due to the complex, non-deterministic and stochastic nature of the Barkhausen phenomenon. Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. Due to the increased computational capabilities of computers, more and more advanced signal analysis methods are being used and artificial intelligence is being involved as well. Recently, the time-frequency (TF) approach for MBN signal analysis was introduced and discussed in several papers, where short-time Fourier Transform (STFT) found frequent application with promising results. Due to the automation of the search for diagnostic patterns, the stage of selecting transformation parameters becomes extremely important in the process of preparing training data for evaluation algorithms. This paper investigates the influence of the STFT computational window size on the material state evaluation results obtained using convolutional neural network (CNN). The studies were performed for MBN signals obtained from grain-oriented electrical steel with anisotropic properties. The carried out work made it possible to draw connections on the importance of the choice of the window during the implementation of CNN network training.  
Magnetic Barkhausen Noise; anisotropy; artificial neural network; time-frequency analysis; grain-oriented steel Article
## 2 Incident Surface Wave and the Scattering Problem
### Scattering Problem  
There are body waves and surface waves in the scattered waves caused by surface cracks. However, it is known to us all that the surface wave energy usually dominates after a certain distance of propagation due to different geometric decays. In a two-dimensional half space, surface waves attenuate slowly, while the attenuation speed of body waves is \(1/\sqrt{r}\), where \(r\) is the travel distance of the body waves. Therefore, only scattered surface waves are considered in this paper. It is worth mentioning that the following analysis is based on the assumption that the two surfaces of the surface-breaking crack do not interact with each other under the action of the ultrasonic field.  
The model is regarded as a two-dimensional homogeneous, isotropic, linear viscoelastic half space with \(x\)-\(z\) as the coordinate axis, where the surface crack is on the boundary of the half space (\(z=0\)); \(h\) is defined as the depth of the crack center, and \(l\) is the length of the crack, as shown in Figure 1.  
As shown in Figure 2, the incident surface wave propagates in the \(x\) positive direction along the free surface and interacts with the surface crack, producing forward- and backscattered surface waves. The total wave field during the interaction of the surface waves with the surface crack can be expressed as:  
\[u_{total}(x)=u_{in}(x)+u_{sc}(x) \tag{12}\]  
Figure 1: Schematic diagram of a surface crack.  
where \(u_{total}\) is the total sound field, \(u_{in}\) is the incident sound field, and \(u_{sc}\) is the scattered sound field. According to reference [23], the surface wave scattered by a crack can be equivalent to the radiated surface wave generated by the application of tractions (\(P^{\prime}\) and \(T^{\prime}\), as shown in Figure 2c) on the crack surface. The direction of the tractions shown in Figure 2c are opposite to the tractions (\(P\) and \(T\) as shown in Figure 2b) that are generated by the incident surface wave at the virtual crack boundary. In addition, the magnitudes of \(P^{\prime}\) and \(P\) and \(T^{\prime}\) and \(T\) are equal, respectively.
## 2 Materials and Methods
### Analysis of Data Correlation  
Because the data did not follow a normal distribution, we analyzed the correlation between variables using Spearman's correlation. While [PERSON]'s correlation is typically used for normally distributed data, [PERSON]'s correlation is more appropriate for non-normally distributed data. From the 109 available variables, we identified the 23 most influential factors affecting CO2 emissions. As shown in Figure 2, the significant input variables selected for this study include main engine (M/E) load, shaft power, fuel flow, M/E scavenge air receiver inlet pressure, shaft torque, M/E turbocharger (T/C) RPM, M/E piston cooling oil temperature, M/E air cooler cooling water outlet temperature, M/E fuel oil inlet temperature, M/E T/C exhaust gas inlet temperature, exhaust gas boiler outlet temperature, average mean indicated pressure, average maximum cylinder pressure, M/E RPM, average cylinder compression pressure, M/E T/C exhaust gas outlet temperature, M/E jacket cooling fresh water inlet temperature, and M/E No. 1-6 cylinder exhaust gas outlet temperatures.  
According to Table 6, fuel flow was identified as the most influential factor affecting CO2 emissions, with a [PERSON]'s correlation coefficient of 0.9850, followed by M/E T/C RPM (0.9791), M/E load (0.9772), and M/E air cooler cooling water outlet temperature  
Figure 2: Heat\(-\)map of [PERSON]’s correlation analysis for the input experimental data.  
(0.8610), which had the weakest correlation among the significant factors. To further ensure the reliability of the model, we also conducted a Pearson correlation analysis, selecting only the variables that directly impact CO2 emissions for use as training data.
## 3 Results and Discussion
### Two Revised DNNs Applied to the IDRC 2016 Data Sets  
The IDRC 2016 \"Shootout\" data sets mainly aimed to provide a calibration transfer challenge. Calibration transfer means to establish a calibration model for one instrument and then apply it to other different instruments. This transfer is usually ineffective because the differences among the instruments will generate sufficiently different spectra even measuring same sample that the calibration fails. However, for a big enterprise that deploys many of the instruments at different locations, calibration transfer is a necessity that cannot be avoided. One can see from the IDRC 2016 report [19], some participants did obtain satisfactory results by combining multiple techniques. However, their success was highly dependent on the experience and skill of these skilled participants and may not be possible for typical users.  
Figures 7 and 8 show the results of applying the two revised DNNs to the data sets. We used the IDRC-2016 A1 in Table 1 as the training set (corresponding to CalSetA1 in Figures 7 and 8) and the IDRC-2016 T (corresponding to Test set in Figures 7 and 8) as the validation set in the training process. The threshold was set as \(R^{2}\geq 0.99\). The obtained calibration model was then applied to calculate other data sets.  
It can be seen that the results are sufficient when the calibration model was applied to the data sets from manufacturer A. However, somewhat poorer results were obtained when the calibration model was applied to the data sets from manufacturer B because the calibration model was established based on the measured data sets on the instrument of manufacturer A. Thus, for a large enterprise, deploying instruments from same manufacturer will be the best choice. Although the results were not fully satisfactory when the calibration model was applied to the unknown samples (Validation set in Figures 7 and 8), these results are understandable because the unknown samples were developed to be the most challenging ones. It may be that there were intentionally designed spectra pretreatments, wavelength selections, calibration transfer techniques, etc. However, we did not apply pretreatment of the spectra. We did not use extra techniques such as the wavelength selection, and we did not use any other calibration transfer techniques. Thus,  
Figure 6: Predicted versus measured quantities of the active substance of the Escitalopramt tablets based on the revised attention mechanism DNN.  
future work could be to develop DNN architectures that possess multiple abilities such as the most effective wavelength selection.  
Figure 7: Predicted versus measured plots of the all data sets based on an acceptable quantitative model established from the NIR spectra of CalSetA1 of manufacturer A using the revised U-Net DNN.
# Nonlinear Vibration and Post-Buckling Behaviors of Metal and FGM Pipes Transporting Heavy Crude Oil
###### Abstract  
Functionally graded materials (FGMs) have the potential to revolutionize the oil and gas transportation sector, due to their increased strengths and efficiencies as pipelines. Conventional pipelines frequently face serious problems such as extreme weather, pressure changes, corrosion, and stress-induced pipe bursts. By analyzing the mechanical and thermal performance of FGM-based pipes under various operating conditions, this study investigates the possibility of using them as a more reliable substitute. In the current study, the post-buckling and nonlinear vibration behaviors of pipes composed of FGMs transporting heavy crude oil were examined using a Timoshenko beam framework. The material properties of the FGM pipe were observed to change gradually across the thickness, following a power-law distribution, and were influenced by temperature variations. In this regard, two types of FGM pipes are considered: one with a metal-rich inner surface and ceramic-rich outer surface, and the other with a reverse configuration featuring metal on the outside and ceramic on the inside. The nonlinear governing equations (NGEs) describing the system's nonlinear dynamic response were formulated by considering nonlinear strain terms through the von [PERSON] assumptions and employing Hamilton's principle. These equations were then discretized using [PERSON]'s method to facilitate the analytical investigation. The Runge-Kutta method was employed to address the nonlinear vibration problem. It is concluded that, compared with pipelines made from conventional materials, those constructed with FGMs exhibit enhanced thermal resistance and improved mechanical strength.  
post-buckling; nonlinear vibration; functionally graded materials; heavy crude oil transportation; Timoshenko beam theory 2025
## 1 Introduction
In addition, convolutional neural network (CNN) models can extract data embedded in color barcodes to perform synchronization of packets and data decoding. DeepCCB-OCC can provide reliable communications, owing to the generalization performance of inherent deep neural network (DNN) properties, and thus, the root cause of the unreliability in the conventional scheme can be solved. Experiments with a commercial display and a smartphone demonstrate that the proposed DeepCCB-OCC system boosts the data rate performance in various D2C scenarios. In summary, the main contributions are as follows:  
* We introduce deep learning-aided color barcode extraction and data decoding methods for CCB-OCC systems. We achieve reliable data transmission by a YOLO-based color barcode extraction mechanism together with CNN classification models for packet synchronization and data decoding. * We design a novel color barcode extraction scheme with the YOLO-based structure and the post-processing process, which accounts for real-time detection and generalization performance, and ensures robust color barcode extraction over the full frame. * In our experiments, we confirm that various types of color barcodes can be produced due to the time offset problem between the display and the camera. By analyzing these color barcodes, the form of color barcodes collected when shooting a display with a 60 Hz refresh rate at 30 fps are defined as four patterns and used for packet synchronization and signal detection. * We design CNN models to classify color barcodes for packet synchronization and data decoding. Through this, the problem of instability and generalization of the existing histogram analysis method is solved, and proposed models achieve higher data rates than the previous work in various distance and alignment angle scenarios. In the remainder of this paper, related research on deep learning-based approaches are introduced in Section 2. The DeepCCB-OCC system model is described in Section 3. The color barcode extraction using deep learning and post-processing methods are presented in Section 4. Then, the various types of color barcode patterns, and deep learning models for packet synchronization and data decoding are given in Section 5 and Section 6, respectively. In Section 7, the models' training results and experimental results are presented to show the feasibility of the proposed method, followed by the conclusion in Section 8.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 4 Protocol Evaluation  
In this section, we evaluate the performance of the proposed extensions and new protocols, and compare it with the original FRESH protocol. The goal is to demonstrate the feasibility of using AoI for meaningful purposes in DTN and quantify the obtained benefits/costs.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Image and Workload Hardening
#### Integrating Hardening with CI/CD Pipelines  
The last hardening step for images would include scanning container images into the whole CI/CD pipeline. That is where GitLab integration becomes very useful. The Trivy tool is the scanner, whereas the GitLab CI/CD operates as the orchestrator. Each time GitLab executes the \"scan\" job, it employs Trivy to assess the image for vulnerabilities. Should any critical issues be identified, the pipeline will fail, thereby upholding security standards within the development lifecycle. Integrating security at each phase of the development and deployment lifecycle is the primary aim of CI/CD and an essential element of the shift-left strategy in DevOps methodologies.  
Integrating image scanning into the CI/CD workflow enables development teams to obtain security assessments immediately upon a developer's code commitment to the repository. New vulnerabilities are continually identified, necessitating the maintenance of an updated vulnerability database. That indicates that an image scan may succeed during the build phase but could subsequently fail at runtime if a newly identified critical vulnerability exists within the image. In such instances, workload deployment must be suspended, and suitable mitigation strategies should be enacted.  
In practical CI/CD processes, a failed Trivy scan, primarily upon identifying high or critical vulnerabilities, usually results in the automatic cessation of the pipeline. That inhibits the deployment of vulnerable images to production registries or clusters. Continuous Integration technologies, such as GitLab CI/CD or Jenkins, can be configured to terminate the build process if certain severity thresholds are reached. For instance, GitLab permits conditional job failure contingent upon Trivy's exit codes or severity filters (e.g., --severity CRITICAL, HIGH). These controls implement security measures into DevSecOps pipelines, facilitating early correction before deploying insecure workloads in production systems.  
Although fortified container images and secure workload configurations diminish the probability of vulnerabilities at the application layer, a secure Kubernetes environment also relies on regulating access to cluster resources. Unauthorized or too-permissive access remains a prevalent root cause of breaches in cloud-native environments. The subsequent part analyzes identity and access management systems within Kubernetes, emphasizing authentication methods, certificate utilization, token-based access, and the implementation of granular permissions via Role-Based Access Control (RBAC) and node authorization policies.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 5 Research Gaps and Future Direction
### Transmission Policy  
Because of its implementation in the ISM license-free frequency band, LoRa deployments are bound by strict legal protocols, especially where no listen-before-talk schemes are utilized. ISM bands are regulated by the TDC to determine the maximum time the band can be occupied per hour. For example, in Europe, the ETSI TR 103,526 documents rule that, for the 868.0-868.8 MHz band, the maximum allowable TDC is one percent. This implies that, IoT devices may not occupy the ISM band for more than thirty-six seconds per hour, prohibiting the transmission of new packets when this limit is attained. Schemes that mitigate against strict restrictions imposed on duty cycle of ISM bands are an open area for research.
# Modeling Intelligent Control Switch IEC 61850 Based Substation Automation Communication
## 1 Introduction
This has resulted in the IEC 61850 being extended to become an inter-substations standard that has the ability to forward all messages to any appliance in the network within 2 to 4 ms [9]. Recently, the extra object models have become widely accepted as it achieves the benefits for industries by vendors. Oil and gas industries, as well as water manufacturing, are good examples of being object-oriented. Based on that, IEC 61850 cannot be just a communication standard; rather, it is an inclusive standard utilized for designing the automation systems. It adds arrangement and structure to information which is generally unformatted. Furthermore, it is a combination of several standards and concepts that, working together, facilitate the design, utilization and operation of devices. Features of IEC 61850, such as the high-speed peer-to-peer communications, are used to replace the conventional method of the hard-wired control signals that exchange among IEDs for protection and control purposes [11]. Figure 1: International Electrotechnical Commission (IEC) 61850 communication profile [10]. In this paper, our research is motivated by the means of providing a simple switch architecture that has the ability to achieve flow packets management in the network with low packets losses. Accordingly, the essential objective of IEC 61850 in this work is to present the control intelligence network communication for managing the transfer information through the network in the substation automation environments, and to operate it on protocols of top standard communication. Furthermore, utilizing the GOOSE protocol with time-critical operations, such as tripping, is one of the main challenges in this study, because of the possibility of failure due to the massive traffic congestion with constant generation packets of multiple events. Thus, it is crucial to provide reliable design rules for the substation automation switch, that has proper modeling and consistent configuration parameters. The rest of the paper is organized as follows: GOOSE protocol background is presented in Section 2. Section 3 goes on to detail traffic management in the communication network. The switch design architecture presented in detail in Section 4. Simulink design of the proposed substation switch is explained in Section 5. Results and discussion of the numerical results are discussed in Section 6. Section 7 presents the conclusion.
## 2 Key Concepts and Enablers for 5G Private Networks
### Resource and Service Orchestration  
Along with the evolution in the 5G capabilities, there is also an industry-wide change towards software defined and cloud technologies, using commercial off-the-shelf (COTS) compute and networking infrastructure to (i) manage costs and expand the supplier eco-system, and (ii) enhance openness, competition, and spur innovation in the RAN and core network. In this context, there is a need for efficient management of these virtualized infrastructures, as well as versatile service orchestration.  
In our proposed approach, there is an E2E orchestration solution suitable for 5G infrastructures composed of heterogeneous components, spanning across multiple domains with various underlying virtualized infrastructure management technologies. The heterogeneous components include virtual network functions, MEC resources, and hardware devices. The orchestration solution includes support for assigning a process to a specific central processing unit (CPU) core in order to provide guaranteed QoS, and for network service migration within the available core or edge infrastructure in cases of service disruptions. In addition, it is aligned with the O-RAN specifications and compatible with the relevant interfaces for managing the O-Cloud infrastructures.