esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study Analysis
### Case Study 5: Subsea Christmas Tree  
This case is about a subsea Christmas tree (XT), including the bodies and valve blocks. The subsea XT is a very reliable system as it is customized to the assigned well and considers the difficulties of intervening with such a critical system in such a remote location. This system is considered a barrier element and safety-critical system that is regulated by several comprehensive testing procedures to ensure its functional integrity and availability on demand. For example, according to the NORSOK D-01 standard [55], the valves shall be tested initially monthly, until three consecutive qualified tests have been performed, then every three months, until three consecutive qualified tests have been performed, then a 6-month interval. These testing events contribute to operating expenses; furthermore, the production is stopped during these events, e.g., four hours. Therefore, the case company has initiated a project to elevate the maintenance concept from inspection and testing to condition-based maintenance.  
Within this exploration project, several emerging practices have been observed in this case, summarized in Table 7. The first emerging practice is a new worksheet created to determine the maintenance concept and the best available technology. The second emerging practice is related to a new worksheet to determine the diagnostic coverage of valve monitoring systems. The third emerging practice is a new predictive model for estimating the probability of passing the test. However, even though the XT is reliable, it is exposed to organic and inorganic deposits that stick, slow the valve performance, or cause erosion or corrosion, increasing the probability of not passing the test. The fourth emerging practice is related to a new guideline that explores the readiness to build model-based maintenance and test concepts for subsea TX.  
The observed impacts of these emerging practices are related to the \"Goals and requirements\" and \"Maintenance Program\" functions in the maintenance management loop. They merge condition-based maintenance into the RBM program. Once this monitoring system is installed and used, maintenance engineers expect that some changes will be observed in the planning, execution, reporting, and analysis functions. The opportunistic maintenance intervals will be better used, and the testing intervals will be adjusted. More condition information (e.g., valve travel, stem torque, solenoid response time) will be gathered, analyzed, and visualized, instead of only \"pass/fail\" data. This information might provide earlier fault detection that enables the use of opportunistic maintenance and keeps the level of damage at a minimum. Therefore, this information is expected to affect maintenance task prioritization and scheduling. It is worth highlighting that failures in such a system are captured by the testing events; therefore, the time between failures is, to some extent, implicitly controlled by the testing intervals. An emerging valve monitoring system with testing intervals adds more variety to the time between failure statistics.
## 4 Results and Discussion
### Real-Time Performance of the Proposed Method  
Although the training time of the improved YOLOv4 model was about 14 h, the time for identifying stem nodes with the improved model was very short. By program statistics, the total execution time spent in the sugarcane stem node spatial location is shown in Table 5. The extraction and matching of feature points and the subsequent elimination of mismatching points occupied most of the time by the proposed method. It should be noted that the other time in Table 5 refers to the time for feature point extraction and matching and the subsequent elimination of mismatching points. Additionally, the before (after) move-1/2/3 refers to the three location experiments before (after) the camera moves D = 100 mm in the verification experiment of stem node location accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Node** & **Coordinate before** & **Coordinate after** & **\(Z\)-Coordinate** & **Actual** & **\(Z\)-Coordinate** \\
**Number** & **Move (mm)** & **Move (mm)** & **D-Value (mm)** & **D-Value (mm)** & **Error (mm)** \\ \hline
1 & (\(-237.98\), \(131.46\), \(370.52\)) & (\(-236.26\), \(133.02\), \(468.25\)) & \(97.73\) & \(100\) & \(-2.27\) \\
2 & (\(-205.83\), \(36.56\), \(382.81\)) & (\(-208.17\), \(36.94\), \(481.17\)) & \(98.36\) & \(100\) & \(-1.64\) \\
3 & (\(-181.95\), \(-500.20\), \(403.56\)) & (\(-179.97\), \(-48.95\), \(502.43\)) & \(98.87\) & \(100\) & \(-1.13\) \\
4 & (\(-157.01\), \(-128.03\), \(422.13\)) & (\(-155.54\), \(-126.08\), \(520.72\)) & \(98.59\) & \(100\) & \(-1.41\) \\
5 & (\(-137.82\), \(-190.23\), \(438.76\)) & (\(-136.56\), \(-189.20\), \(503.67\)) & \(97.91\) & \(100\) & \(-2.09\) \\
6 & (\(131.41\), \(137.18\), \(273.34\)) & (\(133.42\), \(138.83\), \(371.46\)) & \(98.12\) & \(100\) & \(-1.88\) \\
7 & (\(122.20\), \(429.21\), \(288.73\)) & (\(123.62\), \(442.92\), \(386.64\)) & \(97.91\) & \(100\) & \(-2.09\) \\
8 & (\(118.98\), \(-50.49\), \(302.48\)) & (\(119.48\), \(-49.21\), \(401.29\)) & \(98.81\) & \(100\) & \(-1.19\) \\
9 & (\(119.52\), \(-123.13\), \(320.42\)) & (\(120.98\), \(-121.68\), \(418.16\)) & \(97.74\) & \(100\) & \(-2.26\) \\
10 & (\(111.76\), \(-186.15\), \(328.24\)) & (\(113.04\), \(-184.36\), \(425.38\)) & \(97.14\) & \(100\) & \(-2.86\) \\ \hline \end{tabular}
\end{table}
Table 4: The spatial location coordinate and its accuracy of sugarcane nodes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & _AP (\%)_ & _Params_ & _FLOPs_ & **Model Size** & **Speed (s)** \\ \hline SSD [22] & 93.1 & 23,612,246 & 30.44 G & 90.5 M & 0.023 \\ YOLOv3 [23] & 94.1 & 61,523,734 & 32.76 G & 235 M & 0.042 \\ YOLOv4 [24] & 94.5 & 63,937,686 & 29.88 G & 244 M & 0.035 \\ Pruned & 94.4 & 6,973,211 & 3.26 G & 26.7 M & 0.032 \\ YOLOv4 & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of improved YOLOv4 algorithm with SSD, YOLOv3 and YOLOv4 algorithm.
## 4 Conclusions  
This study investigated a methodology to improve the interfacial bonding between aluminum and ferrous alloys, which has previously been difficult. Our results indicated that the application of a nickel coating, combined with an optimized heat treatment process, effectively enhanced the interfacial bonding of the Al/Fe alloys hybrid structure.  
(1) The Al/Fe alloys hybrid structure was fabricated using a dissimilar casting process and utilized a nickel coating on steel. The interface of the Al/Fe alloys hybrid structure with the nickel coating was free from defects and exhibited improved mechanical bonding. However, chemical bonding did not occur at the interface during dissimilar casting.  
Figure 9: Micro-hardness distribution across the Al/Fe alloys hybrid structure’s interface under as-cast and heat treatment conditions; AG: Artificial aging.  
(2) The heat treatment process at 550 \({}^{\circ}\)C, followed by furnace cooling, improved the interfacial bonding through the formation of a continuous and relatively thick intermetallic layer. However, an excessively long heat treatment and a fast cooling rate caused cracks at the interface.  
(3) Depending on the solution temperature, different types of nickel-bearing phases formed at the interface of the Al/Fe alloys hybrid structure and in the Al matrix. The types of these phases were determined by Ni:Cu ratios. This ratio was affected by the heat treatment temperature.  
(4) The nickel coating prevented the diffusion of Fe elements between the steel matrix and the Al matrix during heat treatment. Additionally, the diffused nickel, which acted as an Fe absorber during heat treatment, promoted the formation of T-Al\({}_{9}\)FeNi (called the nickel-bearing phase), instead of \(\beta\)-Al\({}_{4.5}\)FeSi.  
(5) The optimized heat treatment conditions for the chemical bonding of the Al/Fe alloys hybrid structure's interface induced a 40% decrease (from 100 HV to 60 HV) in the hardness of the Al matrix, whereas artificial aging increased the Al matrix hardness by 35% (from 63 HV to 90 HV), through precipitation hardening. Artificial aging had no effect in the Al matrix near the interface, however, due to the diffusion of nickel.  
Conceptualization, G.M. and E.L.; validation, G.M. and E.L.; formal analysis, G.M. and E.L.; investigation, G.M. and E.L.; data curation. G.M.; methodology, G.M.; visualization, G.M.; resources, G.M.; writing--original draft preparation, G.M.; supervision, E.L.; project administration, E.L.; funding acquisition, E.L.; writing--review and editing, E.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) granted by the Korean Government (NRF-2019R1 IIA3A01062863).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 6 Discussion and Prospection  
In our field trial, we demonstrate the feasibility of a real-time 267.8 Tb/s transmission over a 141.52 km field-deployed MCF cable using only C-band 1.2 Tb/s OTUs. Since our L-band 1.2 Tb/s OTU is still under development, we do not investigate the transmission performance of ultra-high-speed 1.2 Tb/s signals over the L-band during this experiment. In addition, the 1.2 Tb/s OTU used in this trial can only be tuned across 32 wavelength channels within the C-band, which is fewer than that of the 400 Gb/s OTU with the same channel spacing in our we previous work [21]. These two limitations have prevented us from fully exploring the capacity potential of the MCF-based transmission system. Therefore, in our future work, we will first accelerate the research and development of C-band and L-band 1.2 Tb/s OTUs, whose central wavelength can be tunable across 40 wavelength channels over the C-band and L-band, respectively. Additionally, we will further investigate the transmission performance of the 1.2 Tb/s 80-wavelength 7-core WDM-CDM ultra-high-speed OTN system over the deployed MCF cable to fully explore its capacity potential and high-value application scenarios. And it can be foreseen that the impact of stimulated Raman scattering (SRS) must be considered in the C + L-band 80-wavelength transmission system [21]. And finally, we need to continuously develop photonic chips with higher modulation rates (e.g., 195-Gaud) to enable the 1.2 Tb/s OTU to adopt noise-tolerant lower-order modulation formats (e.g., PCS-16 QAM), thereby extending the transmission distance of the system.  
Regarding sensing capabilities, since the existing 1.2 Tb/s OTUs lack an integrated sensing functionality, we employ 200 Gb/s ICS OTUs to detect and classify external disturbances to the MCF cable. In future work, we will focus on integrating sensing capabilities into next-generation 1.2 Tb/s OTN systems to further enhance the transmission rate and performance of our ultra-high-speed ICS transmission system. Furthermore, we will re-train the CNN model using disturbed optical signals collected from existing optical fiber networks, which is a crucial step in promoting the field deployment of our ICS OTN system in existing networks. Moreover, unlike OTDR-based sensing systems, the SOP-based  
Figure 7: Sampling SOP curves of the received signals when perceiving **(a)** shaking, **(b)** periodic vibration, and **(c)** striking to the MCF cable.  
sensing system utilized in our field trial cannot currently detect the location of external disturbances. Therefore, multiple extended capabilities, such as disturbance localization methods based on the SOP variations, need to be investigated in future studies. It should be acknowledged that this sensing method based on the SOP of received signals is an efficient way to perceive environmental disturbances and enable early warnings prior to fiber cable interruptions. We believe that it will be applied in high-value scenarios such as financial dedicated networks in the future.
## 4 Results and Discussion
### Selection of Solvents
In Figure 7a, MDEA/DEA and the phase-change solvent DEEA/MAPA formed the Pareto front in the case where \(\zeta\) was equal to \(-0.15\), combining the minimization of both criteria. As in Figure 6b, the optimal solvents in Figure 7b were MEA, AMP, DEA, and MDEA/DEA. It is worth noting that MDEA/DEA was part of the previous Pareto front as well. Figure 7c illustrates that DEEA/MAPA and MDEA/DEA combined flexibility in exogenous variability with a high \(\Delta\alpha\). MDEA/DEA and phase-change solvent DEEA/MAPA formed the Pareto front in Figure 7d. In Figure 7e, DEEA/MAPA continued to be the phase-change solvent that exhibited the lowest net efficiency penalty among all solvents. It is clear now that although there were differences between the Pareto fronts, the mixture with the optimal trade-off between low sensitivity index and process performance was MDEA/DEA, which was part of all Pareto fronts. It is worth noting that DEEA/MAPA exhibited remarkable process performance, especially from the energy efficiency perspective and particularly in the case where \(\zeta^{\prime}=-0.15\). The findings of all the above figures are summarized in Table 8. It is clear that despite some diversification between the Pareto fronts for positive and negative values of \(\zeta\), the solvents with the optimal trade-off between the sensitivity index \(\Omega_{3}\) and the nominal process performance were DEA/MDEA and DEEA/MAPA, as they appear with a very high frequency. It is also worth noting that DEA and AMP also appeared quite frequently in the Pareto fronts in the positive direction, i.e., they were quite resilient under unfavorable conditions. Figure 8 shows the change of the performance indices compared to their value at the nominal point. It is worth noting that despite the small range of \(\zeta\), the respective performance indices changed significantly. In this respect, the performed investigation covered a very wide range of different scenarios and impacts on the performance indicators. \begin{table}
\begin{tabular}{c c c c c c} \hline \(\zeta^{\prime}\) & \(R_{\textit{lost}}\) & \(C_{\textit{sol}}\) & \(\Delta\alpha\) & \(Q_{\textit{regen}}\) & \(\textit{NEP}\) \\ \hline \multirow{4}{*}{0.2} & DEEA/MAPA & \multirow{4}{*}{DEA/MDEA} & \multirow{4}{*}{DEA/MDEA} & DEEA/MAPA & \multirow{4}{*}{DEA/MDEA} \\  & DEA/MDEA & MEA & & DEEA/MAPA & DEA/MDEA \\  & MCA & AMP & & DEA/MDEA & MCA & AMP \\  & AMP & DEA & & MAPA & AMP & DEA \\  & DEA & & & DEA & \\ \hline \multirow{4}{*}{\(-0.15\)} & DEEA/MAPA & \multirow{4}{*}{DEA/MDEA} & DEEA/MAPA & \multirow{4}{*}{DEA/MDEA} \\  & DEEA/MAPA & MEA & & DEEA/MAPA & DEEA/MAPA \\ \cline{1-1}  & DEA/MDEA & AMP & & DEA/MDEA & DEA/MDEA \\ \cline{1-1}  & DEEA & & & DEEA/MDEA & DEEA/MDEA \\ \hline \end{tabular}
\end{table} 
Table 8: Mixtures presenting the optimal trade-off between performance and sensitivity at \(\zeta\ =\ 0.2\) and \(\zeta\prime\ =\ -0.15\).
## 3 Results and Discussion
### Antioxidant Activity  
Certain EOs have been shown to scavenge free radicals that harm the organism and lower the risk of certain diseases brought on by oxidative stress. The peels of GF have been documented to have many phytochemicals, including flavonoids, carotenoids, vitamin C, and other BCs, which have been reported as antioxidant compounds [87]. These BCs promote human health and delay/prevent the incidence of many chronic diseases [13]. To evaluate the antioxidant capacities of GF bioactive extracts, two different methods were used: DPPH and nitric oxide (NO) assays. The antioxidants react with DPPH, transforming it into 1,1-diphenyl-2-picryl hydrazine by swiftly accepting hydrogen, thereby halting the propagation of free radical oxidation chains. This process leads to the creation of stable end products, preventing subsequent lipid oxidation [36]. In the nitric oxide assay, the capacity of GF peel polyphenol-rich extracts to scavenge nitrogen free radicals was evaluated [88]. The antioxidant activities of the GF-EO and polyphenol-rich extracts (GF-CE and GF-PE) were concentration-dependent, which means that the activity increases as the concentration increases (Tables 4 and 5). Vitamin C, used as a positive control, exhibited the same behavior as the extracts. The IC\({}_{50}\) for the GF-EO was 1271 \(\pm\) 0.85 \(\upmu\)g/mL for the DPPH assay and 1656 \(\pm\) 0.71 \(\upmu\)g/mL for the NO assay. Compared to vitamin C, the natural GF-EO showed a lower activity (\(p<\) 0.05); however, compared to the results obtained by [PERSON] et al. [21], the Egyptian GF-EO showed 100 times more activity against DPPH radicals than the GF-EO extracted by cold-pressing. Regarding the polyphenol-rich extracts, the IC\({}_{50}\) values for the GF-CE were 75.69 \(\pm\) 0.81 and 113.45 \(\pm\) 0.71 \(\upmu\)g/mL for the DPPH and NO assays, respectively. The IC\({}_{50}\) values for the GF-PE were much higher for the DPPH (1069 \(\pm\) 0.56 \(\upmu\)g/mL) and for the NO (791 \(\pm\) 0.52 \(\upmu\)g/mL) assays. Meanwhile, the GF-CE showed a higher activity than the EO at the same concentration (1000 \(\upmu\)g/mL) for both assays, while the GF-PE showed a higher activity than the GF-EO only for the NO assay. The variations in antioxidant effectiveness between the polyphenol-rich extracts and the EO could also be related to the levels of ascorbic acid, a water-soluble antioxidant renowned for effectively neutralizing reactive oxygen species [89]. Overall, the polyphenol-rich extracts showed a better antioxidant activity than the EO. Naringenin, the main flavonoid present in both extracts, is a powerful antioxidant that can destroy free radicals and attenuate their formation [90]. However, the GF-EO antioxidant activity could be attributed to the high % of limonene [47]. Citrus polyphenols, which are mainly flavonoids, display exceptional health-promoting protection against oxidative stress-related diseases, including antioxidant, anti-inflammatory, antimicrobial, and other activities [13].
## 5 Discussion
### Resource Management  
Effective resource management is a critical determinant of the performance and QoS satisfaction of OoT systems. IoT devices typically rely on lightweight operating systems, such as TinyOS, FreeRTOS, and Contiki, to manage key resources [42]. Each device requires management across five main components: process management, memory management, energy management, communication management, and file management.  
Focusing on energy management can significantly reduce power consumption in IoT systems. For instance, implementing strategies that leverage CPU sleep modes during inactivity can lead to substantial energy savings. Moreover, understanding the various layers of IoT architecture and their interactions is essential for optimising energy usage and enhancing system efficiency. Inefficient resource management can result in increased power consumption, reduced operational time, and ultimately a shorter network lifespan.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 4 GeoSOT-AS-Based Gridding Modeling Method for Airspace Information  
The management of each element of ATC information is actually the mapping and representation of ATC data information (etc.) across the entire airspace, represented by ADS-B. Each ATC element with spatiotemporal information can be modeled and designed under the GeoSOT-AS model framework, i.e., the moments and their locations corresponding to each information point can be abstracted as attribute information under the GeoSOT-AS framework. The ADS-B data under GeoSOT-AS model not only has its own spatial location attributes, but contains other spatial extension attributes as well, such as its accompanying flight number and landing and takeoff airports. Compared with the 3D raster data model, where the spatial location is identified by the local coordinates of the raster, in the GeoSOT-AS model, the spatio-temporal location attributes can be identified by the coding of the unified transformation of latitude, longitude, height, time, etc. The specific architecture is shown in Figure 7.  
The logical modeling of ADS-B information records the code of the profile element at its spatial location, as well as the entity properties, and the 3D profile modeling of the point can be expressed by Equation (3):  
\[\left\{\begin{array}{l}P_{AS-subdivision}=CODE_{n}\quad\overset{S}{\leftrightarrow} \quad SU\\ CODE_{n}=f(L,B,H,T)\\ A_{p}=A_{su}\end{array}\right. \tag{3}\]  
where \((L,B,H,T)\) are the longitude, latitude, altitude, time and other element information of the location in the airspace space, respectively, and the corresponding value is determined by that of it profile level, where \(SU\) is the profile element of the track point-like entity under the corresponding profile level, and \(CODE_{n}\) is the profile code of the track point data under  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hour** & **Minute** & **Second** \\ \hline Original range & 0–24 & 0–60 & 0–60 \\ Extended range & 0–32 & 0–64 & 0–64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time division scheme.  
this level, indicating that the attribute data of the track point is inherited by the attribute of the profile element at this time. For the ADS-B data with latitude, longitude, elevation and time information, the proposed conversion algorithm of airspace grid (GeoSOT-AS) encoding is used for processing, and the specific calculation steps include: encoding of latitude and longitude information with airspace grid, encoding conversion of altitude information, and conversion encoding of time information.
# A New Approach to Privacy-Preserving Multiple Independent Data Publishing
## 5 Experimental Analysis
### Composition Attack  
We checked the effectiveness of an anonymization algorithm in reducing the privacy risk due to composition attack. Privacy risk is measured by the ratio of true matches to total matches and expressed as [2]:  
\[Privacy\,risk=\frac{True\,Matches\,(P_{T})}{Total\,Matches\,(P_{T}+P_{F})}\times 1 00\%\]  
Composition attacks were measured by calculating privacy risk for all pairs of the extracted dataset with identical overlapping records. In the Merging method, the false matches will be increased because of \(l\) distinct sensitive values linked with the \(QI\) values, and it will decrease the privacy risk.  
Figures 1-4 present the experimental results on the Occupation and Education datasets, respectively. They illustrate the privacy risk resulting from different anonymization techniques. Privacy risk indicates how confidently an adversary can learn sensitive values of a user from the multiple independent datasets. \(\epsilon-DP\)[41] provides the lowest privacy risk for composition attacks among all the compared methods. Privacy risk gets smaller by increasing the false matches in the published datasets. The breaking of cross-column relation increases the probability of false matches in the anonymized datasets by the Merging method. As reported by the privacy risk shown in the result, Merging yields a lower probability of inferring the user's private information than the Probabilistic [23], Composition [24] and Mondrian [44] methods. It has almost identical privacy risk to that of the Hybrid [2] method. Therefore, we can say Merging also reduces the probability of composition attack on published datasets.  
According to the privacy risk shown in Figures 1-4, we see that the \(\epsilon-DP\) method achieves the best result for composition attacks because in an equivalence class it appends Laplacian noise to the count of sensitive values. It thus has the highest probability of yielding false matches. However, it reduces data utility, as discussed in Section 5.3. Conversely, the Merging method preserves more data utility compared with all the other methods.  
Figure 1: Privacy risk on Occupation dataset (\(k=4\), \(l=4\)).  
Figure 3: Privacy risk on Education dataset (\(k=4\), \(l=4\)).  
Figure 2: Privacy risk on Occupation dataset (\(k=6\), \(l=6\)).