esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 7 Behavior of the Nonlinear Parameters during Fatigue  
Figure 5 shows the evolution of the mechanical parameters \(\mid\!E^{*}\!\mid\), \(I_{2/1}\) and \(I_{3/1}\) as a function of the cycle number for a fatigue test using AL at \(\varepsilon_{0}\) = 0.08%. It can be seen that \(\mid\!E^{*}\!\mid\) is strain hardening in the first few cycles of the test, then decreases until failure. But more importantly, the changes are less than 2% of the initial value for over 90% of the complete fatigue lifetime, before a sudden decrease occurs very close to failure. This lack of sensitivity (below the experimental uncertainty) indicates that \(\mid\!E^{*}\!\mid\) is a poor criterion to predict a part's failure needing high safety factors. On the contrary, the higher harmonics substantially change over the duration of the test. The first odd higher harmonic (\(I_{3/1}\)) slightly decreases at first or stays constant which is associated to regime I. For the test presented in Figure 5 this is valid up to \(N\) = 30,000 cycles which represents 15% of its complete fatigue lifetime. Between \(N\) = 30,000 and \(N\) = 160,000 cycles, the \(I_{3/1}\) intensity increases with a constant slope (\(dI_{3/1}\)/\(dN\) = const.) which is associated to regime II. Beyond \(N\) = 160,000 cycles, a rapid growth is observed leading to failure onset as regime III. This last part of the curve implies a 50% increase from the minimum value in regime I to failure in regime III. For the first even higher harmonic (\(I_{2/1}\)), which represents the level of asymmetry in the force response, its value also changes with ongoing fatigue, but with lower intensity than for \(I_{3/1}\). At the beginning of the test, \(I_{2/1}\) is constant (noise level), but around \(N\) = 75,000 cycles, the value starts to slowly increase before a faster variation is observed, especially getting closer to failure. In this case, the change is by a factor of 20 compared to the initial constant value. In both cases (\(I_{2/1}\)_and_\(I_{3/1}\)), the variation are much higher than \(\mid\!E^{*}\!\mid\), which does not change by more than 5% before failure. These sensitivity differences will be investigated in more details in the next section. In fact, the fast \(I_{2/1}\) increase can be associated to the onset of a macroscopic crack generating internal friction between the fractured surfaces.  
Figure 4: Strain-life curves for CRS (black square), AL (red circle) and brass (blue triangle) revealing the poor reproducibility and prediction precision of this type of representation.
## 3 Results and Discussion
### Hydraulic Characterization  
In this section, the main results obtained in the study are analyzed. The focus is pointed to the description of the hydraulic results, showing the injection rate shape characteristics of each tested fuels. Figure 4 shows the rate of injection (ROI) traces for all the fuels used in the test campaign, respectively. The injection rate, therefore, the fuel injection quantity, as discussed above, is obtained by the pressure signal, the pipes' geometric characteristics, and the fuel chemical-physical characteristics, reported in Table 2[4]. ROI profiles with two different energizing times, which correspond to a pilot and main injections for the \(1500\times 2\) test case at the injection pressures of 640 bar, are shown in Figure 4 for all tested fuels. Both tests were carried out in the engine-like backpressure conditions (p\({}_{\text{back}}\)), at 30, and 50 bar for the pilot and main injection, respectively.  
Regarding the injector performance, there is a hydraulic delay defined as the time between the electric command and the fuel injection; more details are available in the literature [13]. The different viscosity, as well as the compressibility of the blends (due to the presence of LPG) in comparison to diesel fuel, justifies the differences in the dynamic and injection timing. The hydraulic delay is reduced proportionally to the LPG percentage, in the range 0.35-0.4 ms, while for the conventional diesel is higher by around 15%. Similar considerations also for the closing phase, with greater durations in the range 0.1-0.12 ms for the blends, keeping constant the energizing time (ET). Therefore, different amounts of delivered fuel have been registered at the same ET; the values are inserted in Figure 4c,d. The trends observed for this operating point can be extended to the other points tested and are not reported here for brevity. In general, the hydraulic characterization has confirmed the blends do not alter the injector performance except for the different opening and closing delays of the mixture, which can be solved through an appropriate calibration of the engine map.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fuel Name** & **DIESEL** & **DLPG20** & **DLPG35** & **LPG** \\ \hline
**LPG content w/w [\%]** & 0 & 20 & 35 & 100 \\
**Liquid density [kg/m3]** & 836 & 772 & 723 & 514 \\
**Mass LHV [MJ/kg]** & 42.5 & 43.2 & 43.8 & 46.1 \\
**Heat of evaporation [kJ/kg]** & 260 & 294 & 320 & 430 \\
**Stoichiometric/F [-]** & 14.67 & 15.4 & 15.6 & 16.7 \\
**Boiling point [\({}^{\circ}\)C]** & 362 & - & - & \(-\)42 \\
**Autoignition Temperature [\({}^{\circ}\)C]** & 250 & - & - & 365–470 \\
**H/C [-]** & 1.9 & 2.3 & 2.5 & 3.4 \\
**Kinematic viscosity [m\({}^{2}\)/s]** & \(2.5\times 10^{-6}\) & \(5.0\times 10^{-6}\) & \(6.9\times 10^{-6}\) & \(1.5\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuels proprieties of diesel–liquefied petroleum gas (LPG) blends [4].
## 2 Literature Review
### Fruits and Vegetables Detections
Furthermore, the algorithm demonstrates an inference speed of 3.06 Frames Per Second (FPS). It is worth noting that the dataset used in this study was a combination of the fruit360 dataset and a manually collected dataset. Although the achieved accuracy is excellent, it was tested on a set of only 210 images. In comparison, our models achieved a mean Average Precision (mAP) of 0.98 and were validated on 525 images with 1818 instances. Moreover, our work exhibits slightly higher precision and recall. Additionally, the graphical user interface (GUI) we developed does not require a database, unlike the system described in this study. Overall, a comprehensive analysis of the existing literature indicates that the majority of models focusing on the detection and classification of fruits and vegetables heavily rely on the fruit360 dataset or datasets sourced from online resources. However, these studies often suffer from the limitation of utilizing small datasets, resulting in inadequate performance when it comes to detecting unknown orientations and variations. The reported accuracy levels in most of these studies range from 51% to 78%, which falls short of the requirements for real-time applications that demand high precision and recall [34]. This gap often overlooks the crucial aspect of achieving accurate measurements on validation datasets, as the datasets utilized in the aforementioned literature review tend to differ from what the models would encounter during testing. Additionally, there is a notable gap in the literature regarding the development of weighing systems and graphical user interfaces (GUIs) for practical implementation. To facilitate the accurate measurement of variable weights of different fruits and vegetables, it is imperative to have a weighing scale with a precise weighing system. Moreover, in order to enhance usability, an interface that allows customers to utilize the weighing scale for self-service purposes is essential.
# Security and Independence of Process Safety and Control Systems in the Petroleum Industry
## 5 Measures to Resist Cyberattacks
### Properties of Zones and Conduits  
The concept of zones and conduits as illustrated in Figure 1 is often pointed to as the solution to protect against undesirable external influence; however, these concepts do not guarantee independence, partly because the scope of proposed measures will depend on the application, established SL, what requirements are actually implemented, and the fact that all systems/components within a zone cannot necessarily be implemented with the given SL requirements for the zone.  
Requirements for the implementation of zones and conduits are given in different parts of the IEC 62443 standard series. Below are some issues that can be solved if relevant measures have been implemented:  
* A conduit between two zones can be made to protect against undesirable influences, also through the conduit, but this is not part of the requirements that follow directly from the level (SL, Security level) and other requirements for conduits and zones in IEC 62443. Such protection must either be realized in the conduit or at the receiver if one is not to be able to influence SIS from other systems. (IEC 62443-2-4, SP.05.02). This means that requirements of the type stated in NOG 070 are still necessary.
* In IEC 62443, there is a possibility that the safety systems (SIS) may be logically or physically separated in zones that are different from those containing those systems that are not safety systems (PCS). If they cannot be separated, both must be in the same safety-related zone. (IEC 62443-3-2 Chapter 4.4.4). For devices in the same zone, the standard does not provide any protection or independence.  
* Configuring SIS on remote access may prevent configuration. This must also be verified by an independent third party. (IEC 62443-2-4, SP05.09)  
The measures found in 62443-2-4 are described as opportunities (or capabilities) that a supplier should be able to offer a plant owner and are not explicitly linked to the security level (SL). On the other hand, the requirements of IEC 62443-3-3 (for systems) and IEC 62443-4-2 (for components) are linked to a fixed SL, which in turn shall appear as a result of risk assessments. However, it is the plant owner who ultimately decides which requirements should actually be implemented, typically by establishing a \"profile\". It is therefore essential that this profile includes the requirements that one believes are important for providing as much independence as possible.
## 2 Materials and Methods
### Finite Element Modeling
#### 2.2.5 Finite Element Mesh  
The RC beam and epoxy were meshed with solid hexahedral FE with three translational degrees of freedom per node (denoted in the software as C3D8R) [39]. The \"shear locking\" phenomenon was prevented via reduced integration for stiffness matrix determination [39; 43]. The steel reinforcement and GFRP bars have been meshed with truss FE with 2 nodes, having axial stiffness only (denoted in the software as T3D2) [39]. An appropriate cross-section area has been defined according to the diameter of longitudinal reinforcement, stirrus, and GFRP bars. A mesh convergence analysis has been conducted for model B-CON in order to check mesh sensitivity and to determine the optimal FE mesh size. In this analysis, five FE sizes have been varied, namely 12.5, 20, 25, 30, and 50 mm. For a comparative analysis, vertical displacement in the beam midspan at a load of 2500 GaN has been selected. It has been concluded (Figure 10) that the mesh with an FE size of 50 mm was too coarse, and the results have become stable for an FE size lower than or equal to 30 mm. Consequently, for further research, an FE size of 25 mm has been adopted as optimal from the perspective of required computer resources and the accuracy of results. In cases of strengthened beams, the FE size for epoxy modeling was 10 mm, because of its small dimensions compared to the dimensions of the RC beam cross-section and expected stress concentration in the epoxy. The FE mesh of model B-G3 is shown in Figure 11, as a representation.  
Figure 8: Damage parameters in the CDP model of epoxy: (**a**) compression; (**b**) tension.  
Figure 9: Stress–strain diagrams: (**a**) steel reinforcement (bilinear kinematic material model); (**b**) GFRP and CFRP reinforcement (linear elastic material model).  
2.2.6. Modeling of Interaction between Different Materials  
The modeling of the interaction between the epoxy and concrete has been carried out with cohesive contact elements, as recommended in [28]; the maximal normal contact stress has been set to 70 MPa and the maximal contact shear stress to 3 MPa, according to the manufacturer's data [20]. Damage evolution has been described via fracture energy of 15 MPa, based on the available and proven models [28; 44]. Interaction surfaces of epoxy and concrete are presented in Figure 12a, again for model B-G3. The contact between concrete and steel reinforcement, as well as between the epoxy and the GFRP bars, has been ideally modeled without slip, using the built-in embedded region function of the software (Figure 12b).  
Figure 11. FE mesh of model B-G3: (**a**) beam; (**b**) steel and GFRP bars.  
Figure 12. Modeling of interaction between different materials: (**a**) cohesive contact surfaces of concrete and epoxy; (**b**) bond connection between concrete/epoxy and reinforcement/GFRP.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 3 Corrosion Prediction Model
### Statistical Model  
The statistical model is a prediction method that uses statistical analysis to detect and predict corrosion progression using historical data. Information can be obtained from CCTV systems installed in various places such as gas pipelines, sewers, and others. A wide range of statistical methods can be used to develop a prediction model, such as the Markov chain, Bayesian inference, distribution fitting, polynomial regression, linear regression, etc. The accuracy of the statistical model depends on the data quality and sufficient input.  
The corrosion mechanism for marine material is difficult to understand because of the diversity involved with corrosion morphology. As a result, the Markov chain with the gray level co-occurrence method [28] were introduced in the seawater to detect corrosion damages on Q420 steel. The extraction of the corrosion morphology from images was achieved, and it was consistent with the electrochemical results. However, there were discrepancies with regard to pit depths, interconnected corrosion holes, and irregular corrosion morphologies. Despite these issues, they can be overcome with Bayesian optimization (see the image-based corrosion rating in [29]). Bayesian optimization was used to fine-tune the corrosion classifiers. The corroded regions in the images were characterized with the local texture features and high discriminatory power. Consequently, the cross-validation error was minimized after the hyperparameters were fine-tuned. The other statistical models, such as polynomial regression [30] and linear regression [31; 32], can also be used to develop the statistical prediction model for corrosion detection using corrosion images.
## 4 Discussion
Additionally, the lower variance observed in the TD-NMR results suggests that TD-NMR offers greater reproducibility and less variation compared to MRI (Table 1). Although MRI systems may involve higher initial costs, the cost-benefit analysis must also consider their long-term advantages, including rapid analysis, accuracy, and the broad applicability of these techniques in food quality control, which justifies their investment. The time-savingaspect of these methods, especially in high-throughput environments, further strengthens their economic feasibility. Comparing MRI and TD-NMR methods, each presents unique benefits: MRI's spatial encoding allows for the simultaneous analysis of multiple samples, which is advantageous for high-throughput settings. Conversely, TD-NMR offers a cost-effective, rapid solution for direct oil analysis within its commercial packaging, making it ideal for on-site quality control where simplicity and speed are crucial. A key advantage of these methods is their non-invasive nature, allowing for rapid IV analysis (<2 min with TD-NMR) without the need for sample preparation. This is especially advantageous for quality control and authenticity verification, as oils can be analyzed in their commercial packaging without manipulation. Our findings align with other studies that highlight TD-NMR as one of the most efficient and advanced methods for food product analysis [27]. The technique's sensitivity to molecular structures enables rapid and accurate assessments of key properties, such as unsaturation levels, with minimal sample preparation. Given the reproducibility and non-invasiveness of TD-NMR, this method is increasingly valuable for routine quality control in complex food matrices. The speed and simplicity of the approach make it particularly beneficial for high-throughput industrial applications, where rapid screening is crucial for ensuring quality and detecting fraud. Its ability to deliver timely results can enhance process efficiency in industrial settings. To ensure food safety, future analytical methods need to combine rapid screening capabilities with confirmatory analyses. NMR relaxometry techniques should be incorporated as part of this approach, as they not only offer quick and reliable screening but can also complement other detection techniques based on different chemical principles. This combined methodology would allow for efficient detection and thorough verification, improving quality control in the food industry [28]. The relaxation curves obtained can not only be used to determine the IV but also to detect potential adulterations and monitor oxidative stability, as the IV decreases with oxidation due to the loss of unsaturation. This will be a future objective based on previous studies conducted by our group [29]. Although this study provides promising results, it is acknowledged as a preliminary investigation. Further investigations, incorporating more variables such as different oil types, storage conditions, and detailed comparisons with official reference methods, are essential for the full validation and confirmation of the method's robustness for industrial applications.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Computation Overhead  
This section focuses on the total cost incurred for the computation performed by the edge devices and server before sending the messages. These costs include encryption, signing, and message formulation costs. The cost of computing certain mathematical operations is well-defined and can be obtained from these values [29]. Table 4 presents the computational costs of various operations, and Table 5 shows the cost of encryption and decryption as the number of clients or iterators increases. From these, we can deduce the computational cost of our protocol via the following steps:
# Heart Murmur Quality Detection Using Deep Neural Networks with Attention Mechanism
## 6 Discussion
### The Significance of the Model's Ability to Choose the Important Segment  
The difficulty of detecting the murmur quality varies in different auscultation locations and cardiac cycles (i.e., different segments) for a given patient, although the murmur quality remains consistent for that patient. For instance, a \"Harsh\" murmur may be present in patients with pulmonary valve or arterial stenosis [16], and the AV and PV are closer to the aortic and pulmonary valves, respectively, resulting in a stronger and more easily recognizable murmur in the AV and PV heart sounds in these patients. Similarly, both types of murmurs may be present in patients with ventricular septal defect and mitral or tricuspid regurgitation [16], and the MV and TV are closer to the mitral and tricuspid valves, respectively, leading to a stronger and more readily detectable murmur in the MV and TV heart sounds in these patients. In addition to these physiologic reasons, the possible presence of ambient noise contamination in a patient's segments can also lead to variations in the difficulty of recognizing the murmur quality for different segments. The method of \"Feature Weighting\" took this view into account. Differences in the difficulty of segments from a single patient in detecting the murmur quality lead to differences in the importance of the features extracted from the different segments. Features of easily detectable segments outweigh features of segments that are hard to detect. This method used \"Feature Attention\" to assign different weights to the features of different segments, enabling distinction of the importance of features from different segments. The method of \"Max\" considered this view as well. When a segment is easier to detect, the greater the confidence the neural network has, and the higher the likelihood of the correct class; and vice versa, the likelihood is relatively low, although it is correct. This method could select the segment with the highest likelihood value from all segments of a single patient; that is, it considered the likelihood of easily detectable segments as the likelihood of the patient while ignoring the likelihood of segments that were hard to detect. However, both the method of \"Voting\" and the method of \"Average\" failed to consider this view. When using the method of \"Voting\", it is possible that a patient had a greater number of tough segments than easy segments, and the results would favor the tough segments over the easy ones. The \"Average\" method just averaged the likelihood values and did not consider the importance of the different segments. The results in Table 4 confirmed this view and show the significance of the model's ability to choose the important segment. The detection result was better (73.6% and 72.7%) using the method of \"Feature Weighting\" and \"Max\", and it was poorer (69.5% and 68.1%) using the method of \"Voting\" and \"Average\", which was only comparable to the segment-level.
# Throughput and Packet Loss Probability Analysis of Long Range Wide Area Network
## 5 Conclusions  
In this paper, we studied the performance evaluation of uplink transmission in LoRaWAN Class-A mode. We provided an analytical evaluation of the normalized throughput and packet loss probability of LoRaWAN MAC using Markov chain model, and we evaluated the performances of the normalized throughput and the packet loss probability as the number of EDs in a GW. The results showed a reference for deploying the appropriate number of EDs that can be accepted in a GW while maximizing network throughput or guaranteeing the packet loss rate of EDs. For example, suppose that a network mainly consists of \(A_{2}\) nodes. In this case, the number of nodes achieving the maximum throughput is around 7000, while satisfying the packet loss probability of around 0.65. If the network operator wishes to restrict the packet loss rate within 0.3, the number of EDs should be less than 2000 with  
Figure 3: Packet Loss Probability as a function of the number of EDs.  
Figure 2: Normalized throughput as a function of the number of EDs.  
the normalized throughput of 0.09. We expect this study to contribute to understanding LoRaWAN MAC and providing guidance on system performance analysis of LoRaWAN.  
[PERSON] contributed the paper by deriving results of simulation and by making the draft version of the paper. [PERSON] was responsible for the main idea, theoretical analysis, coordination, revision, and proof reading of the paper. Both authors have read and agreed to the published version of the manuscript.  
This work was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC support program (IITP-2021-2018-0-01799) supervised by the IITP (Institute for Information and communications Technology Planning and Evaluation, in part by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea under Grant 2021400000280, and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) under Grant NRF-2020R1A2C1010929.  
Not applicable.  
The authors declare no conflict of interest.