esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigation of Gravity Segregation by Foam to Enhance Sweep Efficiency
## 6 Conclusions  
This study examines the impact of foam on gravity segregation in reservoirs with varying heterogeneity. Based on our findings, we can conclude that:  
* In the horizontal and homogeneous reservoirs, both with and without foam, the numerical results and analytical solutions exhibit a mutually favorable agreement. The length of segregation is dictated by the vertical permeability and the apparent viscosity of foam, which is contingent upon the total mobility at a constant rate of injection. * The heterogeneity of reservoirs plays a critical role in the regulation of gravity segregation, particularly within two-layer models. The thickness of the uppermost layer exerts a noteworthy influence on the extent of segregation in the underlying layer. A top layer with reduced permeability and thickness results in an elongated segregation  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{\(K_{1}\) \& \(K_{2}\) Combination} & \multicolumn{4}{c}{\(K_{1}\) \& \(K_{3}\) Combination} & \multicolumn{4}{c}{\(K_{1}\) \& \(K_{4}\) Combination} \\ \cline{3-13}
**Thickness Ratio, m/m** & **Ave.** & \(K\)**, **Ave.** & \(\lambda_{tr}\). & \(L_{avg}\). & \(L_{avg}\). & \(L_{avg}\). & **Ave.** & \(K\)**, **Ave.** & \(\lambda_{tr}\). & \(L_{avg}\). length in the underlying layer. Conversely, the introduction of a top layer with greater permeability and reduced thickness serves to diminish the extent of segregation in the underlying layer. However, for a thicker top layer, the influence of the bottom layer on the segregation length is comparatively less significant. * Injection pressure is one key factor that affects the ultimate segregation length in a layered model, further affecting sweep efficiency. With different injection strategies, the ultimate sweep efficiency shows a distinct trend. * A single-layer approximation cannot predict the segregation length in the layered reservoir with a severe permeability contrast and a comparable thickness ratio. **Author Contributions:** M.W. : methodology, simulation and writing; R.S. : supervision and editing, Y.H. : writing and simulation; D.C.: simulation; Y.L. : writing and editing; H.X. : Conceptualization and editing; W.G. : writing and editing; L.C. : supervision and writing. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** MDPI Research Data policies  
**Acknowledgments:** We acknowledge the technical support from [PERSON] at the China University of Petroleum (Beijing). **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
# An Auto-Adjustable Semi-Supervised Self-Training Algorithm
## 4 Experimental Results
### Performance Evaluation of AAST  
Subsequently, we evaluate the performance of the proposed algorithm AAST against Self-training using C4.5, JRip and \(k\)NN as base learners. In the rest of this section, the value of parameter \(k\) in Algorithm AAST is set to 8 which exhibited the highest classification accuracy.  
Figure 2 presents the performance profiles for Self-training and AAST. Obviously, AAST illustrates the highest probability of being the optimal classifier since it corresponds to the top curve, regarding all used labeled ratio. More analytically, AAST reports the best performance, classifying 72.5%, 87.5% and 60% of the test problems with the highest accuracy using 10%, 20% and 30% as labeled ratio, respectively, followed by Self-training (\(k\)NN) reporting 22.5%, 10% and 25%, in the same situations.  
Finally, in order to demonstrate the classification performance of the proposed algorithm, we compare it with other state-of-the-art self-labeled algorithms such as Co-training [12] and Tri-training [22] using C4.5, JRip and \(k\)NN as base learners, Co-Forest [21] and SETRED [15]. Notice that all algorithms were used with the parameters presented in [30].  
Figure 3 presents the performance profiles of some state-of-the-art self-labeled algorithms and AAST, regarding the used labeled ratio. Despite the ratio of instances, AAST algorithm managed to achieve the best overall performance, outperforming all self-labeled algorithms. More specifically, AAST classifies 45%, 52.5% and 35% of the test problems with the highest accuracy, using 10%, 20% and 30% as labeled ratio, respectively. Conclusively, it is worth mentioning that the reported performance  
Figure 2: Log\({}_{10}\) scaled performance profiles for Self-training and AAST.  
profiles illustrate that AAST exhibits better performance on average, outperforming classical SSL methods, but this is not in general the case for a single dataset.
## 3 Results  
Table 3 ranks and summarizes the performance of the 14 optiBET and 140 BET brain extraction pipelines with T1-MPRAGE and T2-FLAIR MRIs in terms of the mean DSC. Referencing the DSC scores of the brain extractions in Figure 1 can be helpful for imagining the degree of inaccuracies in a brain extraction based on the mean DSC scores reported in Table 3. There was significant variability in performance across extraction configurations, intensity processing procedures, and MRI sequences. Pipelines using optiBET generated more accurate extractions than pipelines extracting with a configuration of BET for all intensity processing procedures and both MRI types. Of these top pipelines, the highest-performing pipelines for both image sequences were those employing optiBET with either one iteration of N3 inhomogeneity correction (denoted as N3 iter = 1) or four iterations (denoted as N3 iter = 4). Specifically, optiBET-N3 iter = 1 generated the most accurate extractions for T1-MPRAGE MRIs, with a mean DSC score of 0.9780. Similarly, for the T2-FLAIR images, optiBET-N3 iter = 4 was the highest-performing pipeline (mean DSC = 0.9557). These results are reflected in the first row of Table 3. It should further be noted that for T1-MPRAGE, the optiBET control pipeline was the second-highest-performing pipeline, barely falling short of optiBET-N3 iter = 1 (mean DSC = 0.9779), while, in T2-FLAIRs, optiBET-N3 iter = 4 showed a more substantial increase from the performance of its control pipeline (mean DSC = 0.9471).
## 4 Implementing L2S-M in a Cloud-Native Platform for Intra-Site Connectivity: K8s Case
### L2S-M Implementation as a K8s Operator
In case the Kubernetes API schedules the pod's deletion, the operator will remove the interface from the virtual network in the corresponding node, and the vEth will be returned to the host namespace to be available for future workloads. During all these events, the operator will be modifying its DB depending on their actions. In order to provide the mechanisms to isolate traffic between virtual networks, L2S-M contemplates the deployment of a software-defined networking (SDN) controller in the K8s cluster as seen in Figure 3. The L2S-M operator and the controller interact through a common API, which allows the operator to communicate the interfaces (ports) where each pod is attached since the operator knows which virtual networks the pod belongs to. The SDN controller will use this information to send the appropriate traffic rules to each one of the programmable switches to ensure that the traffic generated in each network will only be sent to the proper ports (either forwarding the information to the corresponding neighbour in the overlay or to one of the ports in the switch). This mechanism ensures that the traffic in each virtual networks becomes isolated between each other since traffic will not be forwarded between workloads unless they belong to the same virtual network (i.e., they are treated as if they were in the same LAN). The current version of L2S-M [45] does not implement the entire isolation mechanism: it is able to isolate most of the traffic within a virtual network since it can interact with an ONOS controller [46] to enable the forwarding only with the appropriate ports depending on the network. However, traffic destined to multiple hosts/pods (e.g., broadcast traffic in ARP Requests) must be forwarded to all elements in the overlay since ONOS does not natively implement a way to isolate this traffic. Future versions of L2S-M will fully isolate traffic in their respective virtual networks, regardless of their nature, using a specific SDN application used with ONOS.
# Assessment and Review of Heavy Metals Pollution in Sediments of the Mediterranean Sea
## 2 Materials and Methods
### Sampling Sites and Sample Collection  
To assess heavy metal contamination in the Mediterranean Sea, we collected data from various sources, including the southwest of Spain (69 samples) [15; 23] Greece (53 samples) [24], South of Italy (10 and 19 = 29 samples) [21; 22]. Additionally, we reviewed manuscripts presenting heavy metal analyses in the Mediterranean Sea [29; 30]. The fieldwork in the southwest of Mallorca Island [2] contributed 96 samples. All examined locations shared significant anthropic pressure from tourism and urban development along the Mediterranean coast. In Mallorca, sampling conducted between 2020 and 2022 was done using dredger blades and autonomous diving equipment, supported by an oceanographic research vessel. Sampling was systematic, considering factors like distribution, bathymetry, distance to the littoral coast, and wastewater discharge points. The study focused on the areas of the coastal areas closest to the points where wastewater is discharged, while the control samples were taken on the furthest margins of the outfalls. The results showed that the highest concentrations were located near the discharge points and decreased towards the open sea. Variations could be attributed to factors such as depth, marine flow patterns, or geological substrates. The analyzed samples revealed concentrations of heavy metals that exceeded the natural values of the regional sediments.
## 3 Results and Discussion
### Costs Comparison
The fixed-bed dryer demanded intense work during loading and unloading plus when moving and turning the product during drying. The workload of the pneumatic dryer did not have those peaks and was rather steady over time. Nevertheless, the processing enterprise employed one person to operate each dryer, working in an 8 h shift. Therefore, despite the different working regimes, no difference in labour costs for each dryer was observed. Regarding maintenance cost, it is known to be low in fixed-bed dryers and pneumatic dryers due to the few numbers of moving parts [11]. The enterprise spending logbook had revealed that the fixed-bed dryer and the pneumatic dryer were inspected at the same intervals and for both, the maintenance included servicing the diesel burner and replacing the bearings that hold the shaft of the fan. Therefore, no difference in maintenance costs for each dryer was observed. Regarding capital cost, based on the equipment manufacturer contacted, the cost of the fixed-bed dryer used in this study was approximately USD 4000. In contrast, the cost of the pneumatic dryer was approximately USD 7300. Fixed-bed dryers are well known for having low capital cost [46]. As a result of the lower operating costs of the pneumatic dryer, the additional investment could be recovered in 194 days of operation. However, the sensitivity analysis, shown in Figure 7, indicates that the payback period is highly sensitive to variations in diesel and electricity prices. It reduces if the diesel prices rise (Figure 7a) and it increases if electricity prices rise (Figure 7b).
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Cryptographic Techniques  
Cryptographic techniques are mathematical algorithms and protocols used to secure communication and protect data from unauthorized access. They are an essential component of modern information security systems. Cryptographic techniques can be used for various purposes such as confidentiality (hiding the content of a message), integrity (detecting changes to the message) and authentication (verifying the identity of the sender). Some common cryptographic techniques include symmetric key encryption, asymmetric key encryption, hash functions, digital signatures and zero-knowledge proofs. These techniques are designed to be computationally infeasible to break, making it difficult for attackers to access or modify sensitive information. Cryptographic techniques are widely used in various applications, such as online banking, e-commerce and secure communication over the internet.  
[PERSON] and [PERSON] [147] argue that strong encryption, when implemented correctly, can provide protection against de-anonymization techniques that use statistical analysis, which can uniquely identify a person from surprisingly small pieces of information. They argue that users must be in control of their data. Combining the traditional cryptographic applications with end-to-end cryptography, it is possible to ensure that breaches reveal nothing about the data protected, while maintaining data integrity and authenticity.  
There are many use cases where it is necessary to encrypt private data. Mutual authentication is useful for many scenarios, and one of the most known for IoT devices is the leakage of sensitive data during service discovery, such as owner name and service type. By using an identity-based encryption, clients can reveal their identity only to authorized clients. In this way, only authorized customers can decrypt the information [148].  
Cryptography can support device-local operations even if the provider has to be assured of their correctness. By supporting the processing of data locally on devices, it is possible to discard raw data [149]. Furthermore, with ZKP for example, it is possible to perform time-of-use billing on smart meters [150] and enforce honesty of vehicles for local processing, e.g., for electronic tolling [151].  
CHORUS is an anonymization technique that uses cryptographic techniques to protect the sensitive information in a database. This method is particularly useful in smart cities when data is collected from multiple sources and it is important to ensure the integrity and authenticity of the data.
## 2 Improved Penzien Model
### Dynamic Response of the Free Field  
#### 2.2.1 Equation of Motion  
In the improved Penzien model, the motion of the pile foundation is caused not only by the inertial force of the structural system but also by the motion of the free field. Before analyzing the seismic response of the structure system, it is necessary to analyze the seismic response of the free field and solve its motion equation. The dynamic equation of the free field is as follows:  
\[M^{G}\ddot{U}^{G}+C^{G}\dot{U}^{G}+K^{G}U^{G}=-M^{G}\ddot{U}_{g} \tag{2}\]  
Figure 2: Improved Penzien model.  
where \(U^{G}\), \(\dot{U}^{G}\), and \(\ddot{U}^{G}\) denote the displacement, velocity, and acceleration vectors of the free-field system, respectively. \(M^{G}\), \(C^{G}\), and \(K^{G}\) are the mass, damping, and stiffness matrixes of the multi-mass system of the free field, respectively. The soil column per unit area can be calculated according to the shear multi-mass system because of the soil deformation caused by the shear deformation, and the shear stiffness and damping of soil can be considered by connecting springs and dampers between the soil layers. The displacement and velocity of the free field calculated by Equation (2) are applied to Equation (1).
## 3 Results
### Characterization of Encapsulated CBG Particles
Figure 2 presents the ATR-FT-IR spectra for CNC-CTAB encapsulating CBG, alongside spectra for pure CBG and CNC-CTAB alone. The CBG spectrum exhibits characteristic peaks at 2960 cm\({}^{-1}\), indicating C-H stretching; 1620 cm\({}^{-1}\) and 1580 cm\({}^{-1}\), associated with C=C stretching, and 1450 cm\({}^{-1}\), related to CH\({}_{2}\) bending. These vibrational bands are also evident in the encapsulation system even though at lower intensities, suggesting the successful incorporation of CBG into the CNC-CTAB matrix [13]. There were no significant shifts in the characteristic peaks of CBG within the loaded system, suggesting that the chemical structure of CBG remained intact after encapsulation and indicating a non-covalent interaction between CBG and the CNC-CTAB matrix. The CTAB modification of CNC introduces hydrophobic domains that can interact with the lipophilic regions of CBG molecules. Additionally, the positively charged quaternary ammonium groups of CTAB may interact with any slightly negatively charged regions of CBG (e.g., phenolic hydroxyl group, ether oxygen, and pi-electron), while the hydroxyl groups present in both CNC and CBG can potentially form hydrogen bonds, further stabilizing the encapsulation. Van der Waals interactions also likely contribute to the overall stability of CBG within the CNC-CTAB matrix, while the surfactant monomers on CNC-CTAB can reorganize and induce hydrophobic interactions between its alkyl chains to form surfactant clusters, further favoring CBG binding. These non-covalent interactions collectively facilitate the encapsulation of CBG without altering its chemical structure, as evidenced by the preservation of its characteristic FTIR peaks. This analysis aligns with previous research on encapsulating lipophilic compounds and demonstrates the effectiveness of CNC-CTAB as a carrier for CBG, potentially enhancing its stability and bioavailability for various applications.
## 3 Results and Discussion
### Radiation Shielding Attributes
The incoming gamma photon energy is considered as the first factor. The received dose rate is directly proportional to the incoming photon energy. For glasses with a thickness of 5 cm, for example, the dose rate received varied in the range between 23.638-51.476 \(\mu\)Sv/h at gamma energies between 0.248-1.406 MeV. The direct correlation between the dose rate and the incoming energy is related to the photons' penetration power. The penetration power increases with an increase in photon energies. Thus, the incoming photons spend a short time in the glass material. The photons lose a small part of their incoming energies before passing through the shielding material, so the passing photons' dose rate will be high. The glass thickness is the second parameter that affected the received dose rate. The dose rate is exponentially decreased with an increase in the thickness. Figure 12 depicts the high dose rate received from thinner glasses, with the thicker glasses providing better protection. The SCNSb samples' effectiveness to absorb fast neutrons was studied through the calculation of \(\sum_{\mathrm{R}}\) (cm\({}^{2}\)/g) (see Figure 13). It is known that materials doped with light elements and compounds such as C, B, and water (H\({}_{2}\)O) absorb fast neutrons better than those doped with heavy compounds such as PbO and Sb\({}_{2}\)O\({}_{3}\). In the present work, the glass with a higher ratio of Sb\({}_{2}\)O\({}_{3}\) possessed a lower fast neutron removal cross-section than the glasses with lower amounts of Sb\({}_{2}\)O\({}_{3}\). Thus, the SCNSb10 glass with 10 mol % Sb\({}_{2}\)O\({}_{3}\) has an \(\sum_{\mathrm{R}}\) = 0.0286 cm\({}^{2}\)/g, which is lower than the \(\sum_{\mathrm{R}}\) = 0.0341 cm\({}^{2}\)/g recorded SCNSb0 glass, which has no Sb\({}_{2}\)O\({}_{3}\) content. The effective removal cross-section affected by the energy of the projected neutrons besides the chemical composition for the studied glass samples. Figure 13 depicts that the \(\sum_{\mathrm{R}}\) (cm\({}^{2}\)/g) is exponentially decreased with the increasing of the Sb\({}_{2}\)O\({}_{3}\) content in the investigated glasses. The partial substitution of the SiO\({}_{2}\) by Sb\({}_{2}\)O\({}_{3}\) causes an increase in the effective atomic weight of the investigated glass. Figure 11: Comparison between the mean free path of the studied samples to those of some previously prepared glasses (Borax 40) and standard shielding glass RS 253-G18. Hence, Equations (16) and (17) showed an inversely relationship between the \(\sum_{\text{R}}\) (cm\({}^{2}\)/g) and the Z of the investigated glass samples, so the \(\sum_{\text{R}}\) (cm\({}^{2}\)/g) decreased exponentially with the increasing of the \(Z_{\text{eff}}\).
## 9 Conclusions  
This work presents a metaheuristic optimisation algorithm technique for fine-tuning a lead lag controller of PSS related to the angle stability of the SMIB system. The design of a vigorous lead lag controller of the AVR system of the SMIB system was formulated with a minimum damping ratio as an indicator by developing its mathematical model to perform eigenvalue analysis and objective function. Five methods based on EP, MFO and SCA computation algorithms for optimising the \(T_{1}\), \(T_{2}\) and \(K_{stb}\) parameters of the controller based on objective function as damping ratio were designed. The use of optimisation methods moved the eigenvalues more to the real axis line and left side of the imaginary axis of the complex s-plane. The results showed that SCA provided better damping of low frequency oscillations compared with EP, MFFO and unoptimised PSS.  
The dynamic performance was validated with respect to damping ratio, the eigenvalue's location in the s-plane and rotor angle deviation response, and ensured system stability. MFO converged with the maximum iterations (i.e., 92-103 iterations), followed by the SCA and EP techniques that converged at 34-46 and 13-23 iterations, respectively. The total iterations utilising the SCA method is insignificant yet adequate. In conclusion, the SCA optimisation algorithm found improved computations of the best possible parameters of the damping controller to boost system stability and matched with those attained with the MFO and EP procedures. In contrast with the optimisation methods built on five cases for the PSS lead lag controller using damping ratio as the objective function, the PSS-SCA-based method demonstrated the best stability performance.  
Conceptualisation, A.W.K. and N.A.M.K.; methodology, A.W.K. and N.A.M.K.; software, A.W.K.; validation, A.W.K. and N.A.M.K.; formal analysis, A.W.K.; investigation, A.W.K.; resources, A.W.K. and N.A.M.K.; data curation, A.W.K.; writing--original draft preparation, A.W.K.; writing--review and editing, A.W.K., N.A.M.K. and M.A.A.M.Z.; visualisation, A.W.K. and N.A.M.K.; supervision, N.A.M.K. and M.A.A.M.Z.; project administration, A.W.K. and N.A.M.K.; funding acquisition, N.A.M.K. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the support given by the Ministry of Higher Education and the Universiti Kebangsan Malaysia for the operational and financial support to this project under Project Code GUP-2020-018.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 15: Rotor angle deviation for loading condition 5 of the SMIB with PSS.