esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Our current research thoroughly examined and summarized the challenges related to data security and privacy preservation in EC, along with corresponding countermeasures. We also discussed the advantages and limitations of integrated EC and IoT paradigms. Furthermore, we performed an in-depth analysis of security and privacy issues within EC-assisted IoT networks, including a comprehensive survey of potential security attacks and their countermeasures. We researched how state-of-the-art technologies, including PUFs, AI, IoT, and ML, can mitigate security-related challenges in an EC paradigm. Given that resource-limited edge devices may not support traditional cryptographic security solutions, lightweight security primitives like PUFs are an alternative solution for low-cost key generation. Additionally, we conducted a detailed examination of AI/ ML-based security mechanisms, categorizing them extensively. We also provided insights into commercially available toolkits from leading manufacturers and developers utilized in deploying EC services. Finally, we identified open research directions and gaps in data security and privacy issues within EC, outlining areas for future investigation and development.  
**Author Contributions**: Conceptualization, A.M.S., M.R.I. and M.H.H.; methodology, A.M.S., M.H.H., M.R.I. and A.K.; formal analysis, A.M.S., S.A.Z. and M.H.H.; investigation, A.M.S., S.A.Z. and A.R.B.N.; resources, A.M.S., M.R.I. and A.K.; data curation, A.M.S. and M.H.H.; writing--original draft preparation, A.M.S. and M.H.H.; writing--review and editing, A.M.S., M.R.I, M.H.H., A.K., S.A.Z. and A.R.B.N.; visualization, A.M.S. and M.H.H.; supervision, M.R.I. and M.H.H. and A.K.; project administration, A.M.S.; funding acquisition, A.M.S. and A.K. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This work is part of the A'Sharniyah University, Oman-Internal Research Grant (IRG-16), 2024-26 \"Intrusion detection in an IoT network through Machine Learning (ML) of hardware characteristics\".  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 3 Engineered Water Flooding (EWF)
### Wettability Modification
#### 3.2.1 Mineral Dissolution Reactions  
Another mechanism involved in the alteration of the wettability is the enhanced connectivity between micropores and macropores due to mineral dissolution by EW at the micro level [9; 65; 99; 100]. The study conducted by [PERSON] et al. [101] showed that more calcite was dissolved as the pH of the injected low-salinity brine decreased. The effluent brine pH was higher in this case, leading to crude rock surface charge modification and additional oil recovery. Another study also documented similar results, showing that increases in the effluent pH and Ca\({}^{2+}\) concentration were observed after injecting different dilutions of seawater, which could lead to incremental oil production caused by modification of the wettability or alkali formation [64]. Reductions of the IFT instead of the wettability alteration have also been reported in some cases [80; 102]. Experiments performed by [PERSON] et al. [80] showed a reduction of the IFT of around 42% using seawater that had been diluted 20 times in a low-temperature carbonate reservoir.
## 4 Results and Discussions
### Evolution of Karst on Jintan and M1 Platform  
Karst evolution of the Jintan and M1 platforms was analyzed by comparing the volume of karst features on three selected karst horizons K1, K2 and K3. In this analysis, spectral decomposition by using STFT with frequencies of 46, 54, and 60 Hz was applied on the seismics. Horizon slices of K1, K2, K3 were interpreted on the RGB Blending viewer. K3 horizon falls in between seismic unit (SU) 3 and SU 4, K2 horizon falls on the highly karstified intervals of SU 4 and SU 5 while K3 horizons fall in between SU 5 and SU 6 as interpreted by [12]. Karst features on the three horizons were discussed and quantified in detail to understand the development of karst in the platforms (Figure 15).  
Interpretation of the K1, K2, and K3 time slices in Figure 16 showed that the karst features were more prominent and well-developed on the K2 horizon. K1 horizon karst mostly developed on the Jintan platform and comprised 1-2 km-long dendritic patterns. The dendritic features were mainly observed in the lagoonal area of Jintan and M1 platforms. Along the K2 horizon, karst started to develop more on M1 platform especially closer to the reef rim area. Karst in K2 horizon showed a continuous dendritic pattern between 3-5 km-long on the Jintan platform. Interpretation of the K3 horizon revealed circular features of less than 1 km in diameter which are most likely sinkhole or cavern type karst on the Jintan lagoonal area. Spectral decomposition techniques applied during seismic interpretation has proven to be one of the best techniques to delineate the karst features on carbonate platforms. Table 3 summarizes the quantitative seismic interpretation of karst features on both platforms.
However, these studies are only concerned with the milling process referred to as micro-milling, and hence it is difficult to extrapolate and compare with traditional rough or finishing processes performed in typical industrial-grade production procedures. A significant amount of available research is focused on the analysis of the machining stability of aluminium alloys [27; 28; 29; 30; 31]. On the other hand, difficult-to-machine materials are very popular in scientific research [32; 33]. Research in the field of magnesium alloys is performed much less often and is carried out along standard straight paths [34; 35; 36]. Moreover, publications mainly only concern the analysis of vibration amplitude. It is quite evident that this approach is rather inadequate for describing machining vibration. The number of publications concerning the impact of toolholder types on the occurrence of vibration during magnesium alloy machining is also very limited. This state of knowledge indicates a continuing need to improve and expand expertise on the milling stability of magnesium alloys. The AZ91D magnesium alloy is used in many industries. Most often, it is employed in the automotive and aviation industries, but other industries, such as the sports industry, electronics, and computing, also make use of this alloy. For example, the AZ91D alloy can be used to produce elements such as gear housings, small cockpit elements, car rims, car engine components, laptop and telephone casings, and bicycle or motorcycle frames. A major novelty of the performed study is the application of a trochoidal strategy to the machining of magnesium alloy. Based on the completed literature review, that strategy has not yet been thoroughly analysed in relation to this group of materials. However, it enables a significant increase in the milling process efficiency and a reduction in cutting temperature, which is crucial when it comes to safety for magnesium alloys, which are prone to ignition. This study extensively investigates machining stability based on the different parameters and the tools used for signal analysis. The main goal of this research was to determine how the trochoidal strategy affects the stability of the machining of the AZ91D magnesium alloy. An additional objective was to analyse the influence of the toolholder type on vibrations during trochoidal milling. When using different types of holders, the stability may differ in relation to machining the magnesium alloy along straight paths.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 5 Spectral Clustering
### Similarity Matrix Construction  
Before applying a spectral clustering algorithm, the distance matrix \(\mathbf{D}=\{d_{ij}\}\) should be transformed into a similarity matrix \(\mathbf{S}=\{s_{ij}\}\). A commonly used procedure is to apply a Gaussian kernel,  
\[s_{ij}=\left\{\begin{array}{ll}exp(-\frac{d_{ij}^{2}}{2\sigma^{2}})&\text{ for }i\
eq j\\ 0&\text{ for }i=j\end{array}\right. \tag{10}\]  
where \(\sigma\) is the scaling parameter controlling the kernel width.  
The value of \(\sigma\) is commonly specified manually, or numerous iteration has to be run for a range of \(\sigma\)[33]. However, when the input data includes clusters with different local statistics, a single value of \(\sigma\) may not work well for all the data. Thus, instead of selecting a single scaling parameter, we propose calculating a local scaling \(\sigma_{i}\) for each data point \(d_{i}\)[12]. The similarity between \(Y_{i}\) to \(Y_{j}\) can be revised as \(d_{ij}/\sigma_{i}\) while the converse is \(d_{ji}/\sigma_{j}\). Therefore, \(d_{ij}\) is symmetry, and the Equation (10) can be generalized as:  
\[s_{ij}=\left\{\begin{array}{ll}exp(-\frac{d_{ij}^{2}}{\sigma_{i}\sigma_{j}}) &\text{for }i\
eq j\\ 0&\text{ for }i=j\end{array}\right. \tag{11}\]  
where \(\sigma_{i}=d(Y_{i},Y_{K})\), \(Y_{K}\) is the K'th neighbor of \(Y_{i}\). The selection of \(K\) is independent of scale and is a function of the data dimension of the embedding space.  
Thus, the scaling parameters for each pair of \(Y_{i}\) and \(Y_{j}\) are not fixed; they are determined automatically according to the local statistics of the neighborhoods.
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
## 1 Introduction
### Research Background and Starting Point
For some dishonest PBS and PUs, violations of spectrum access regulations would not bring serious consequences, but acquire extra incompings. These violations may hurt honest users' LSA authorities, leading to the unfairness in the spectrum distribution process. 2. Lack of supervision and audit mechanism. It is difficult for users to defend their rights when the spectrum accessing service provided with MNOs is degraded. To guarantee the fairness between MNOs and spectrum users, there is an urgent need to introduce a transparent supervision and auditing mechanism to help users defend their rights. 3. Existing incentives are inefficient for the operators. Under the present LSA mechanism, users belonging to a specific operator can only passively accept the LSA services provided with the MNOs. And MNOs obtain revenue from the upper tier operators. For them, there is no incentive to provide better service to users. For the PBS and PUs, misbehaviors in spectrum usage would not lead to a disadvantage in subsequent spectrum access. Thus, for the users, there lacks the incentive to maintain good credit. PBS and PUs play key roles in future 6G ultra-dense mobile networks; sufficient spectrum resources are of vital importance for them to serve for the subordinate user nodes. The present licensed spectrum distribution faces the challenges of an unfair status and lack of a supervision and audit mechanism. Therefore, towards 6G-envisioned communications, how to effectively and fairly distribute the licensed spectrum from telecom operators to PBS and PUs is a problem that needs to be solved in the future. Moreover, to protect honest users' interest and encourage MNOs to provide better LSA services, a supervision and auditing mechanism is an urgent need. To summarize, a more fair licensed spectrum distribution or primary-level allocation method is the scientific question we are interested in. Since [PERSON] proposed Bitcoin [4] in 2008, the concept of blockchain has attracted worldwide attention. As an open decentralized ledger system, blockchain effectively combines cryptography and distributed consensus mechanisms to ensure data transparency and tamper resistance. Moreover, blockchain technology is also widely applied to many fields such as the Internet of Things (IoT) [5; 6], secure storage [7; 8] and supply chain management [9; 10]. In recent years, researchers in academia and industries are beginning to explore the use of blockchain technology for spectrum allocation [11; 12; 13; 14].
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Stability of ISBs
#### 3.3.1 Short-Term ISB  
As few visible GPS satellites transmit the L5 signals, we selected BDS-3 as the pivot system. The ISBs of overlapping frequencies of B1C/L1/E1 and B2a/L5/E5a in BDS-3, GPS, and Galileo were all assessed to analyze their stability.  
Figure 4 displays the time series of both phase and code ISBs for the baseline GODN-GODS on DOY 120, 2021. The corresponding statistics of the short-term ISB are summarized in Table 4. Taking BDS-3 as the pivot system, all the ISBs are relative to the B1C or B2a in BDS-3. From the figure, regardless of the phase ISB or the code ISB, they are all stable in time. Specifically, the mean value of phase ISTBs for BDS-3/GPS is approximately \(-0.002\) cycles in both B1C/L1 and B2a/L5, whereas the STD is 0.01 cycles. As for the statistics of the ISBs in BDS-3/Galileo, a zero mean was observed and the STD is lower  
Figure 3: (**a**) Time series of the TF phase combination extracted by the observations from C19, G01, and E01. (**b**) The RMS of TF phase MP vs. elevation angle of BDS-3, GPS, and Galileo computed with the whole constellation that can be tracked of every 2\({}^{\circ}\) of elevation with inter-frequency clock bias (IFCB) corrected by the baseline GODN-GODS.  
than 0.01 cycles in both B1C/L1 and B2a/E5a. Due to the larger amount of noise in code observations, the values of the statistics of code ISBs are much higher than those of phase ISB. However, they are still within the noise of the corresponding code observations. Thus, it is reasonable to take ISB as a constant in the continuous observations. Comparing the differences in the statistics of ISB between BDS-3/GPS and BDS-3/Galileo, we found a lower STD in BDS-3/Galileo for overlapping frequency BIC/L1/E1 because the observation noise level of Galileo E1 is lower than that of GPS L1. For the overlapping frequency B2a/L5/E5a, Galileo transmits the E5a signal in all observable satellites, whereas only 12 BLOCK IIF and 4 III-A satellites in GPS transmit the L5 signal. Therefore, the ISB time-series of B2a/L5 is vacant from about 8:00 to 10:00 UTC mainly because of the relatively fewer tracked satellites that transmit the L5 signal in GPS. The time series of ISBs prove the short-term stability of ISBs, and the zero-mean characteristics reflect that the ISBs between identical types of receivers may be ignored when inter-system DD is conducted.
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 3 Molecular Mechanisms of T3 SS Deployment in Response to Environmental Stimuli
### Regulation of the T3 SS by the CvsSR Two-Component System  
The calcium-induced two-component system CvsSR is a regulator of _P. syringae_ T3 SS gene expression and virulence [149]. The expression of CvsSR is induced in DC3000 treated with apoplastic fluid from tomato leaves, and is also induced by various cations including calcium (Ca\({}^{2+}\)) [149; 150]. Calcium is abundant in the plant apoplast and has been observed to increase in concentration during bacterial infection of host plants [151; 152]. In T3 SS-inducing minimal medium supplemented with calcium, response regulator CvsR binds the _hrpRS_ promoter as well as various sites in or around T3 SS effector genes, and drives the expression of T3 SS-associated genes [149]. Loss-of-function _cvsS\({}^{-}\)_ and _cvsR\({}^{-}\)_ mutations have been observed to attenuate DC3000 growth and disease symptom formation on host plants, indicating a role of CvsSR in overall _P. syringae_ virulence [149]. However, CvsSR was not required for T3 SS deployment by DC3000 in _Nicotiana benthamiana_[149]. As such, it remains unclear whether CvsSR contributes to virulence through the regulation of the T3 SS in the host environment. CvsSR additionally indirectly represses the expression of _algU_, an alternate sigma factor that functions as a positive regulator of the T3 SS in DC3000 by an unknown mechanism (reviewed in Section 4.2), indicating potentially complex effects of CvsSR on T3 SS dynamics [149].
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 8 Validation
### Formation Flying  
We test the formation flight function with two drones first in an indoor test environment (Figure 14a) and at a later stage in an outdoor test near a powerline installation (Figure 14b). The drone prototype for validating the swarming function is built based on the ModalAI VOXL platform and the S500 ARF Frame Kit. The drone hardware platform is based on the VOXL platform and implements the VOXL Flight Deck with onboard computer and Flight Core Flight controllers, stereo and tracking image sensors, and a GPS (outdoor) or Vicon motion capture system markers (indoor) with a body frame, motors, propellers, and ESCs. The ModalAI VOXL platform provides a self-contained low-level flight control allowing design and validation to focus on high-level control functions needed  
Figure 13: State machine diagram of a drone from the perspective of the group’s controller. State changes occur through messages sent by the member (blue) and controller (red) or timeouts.  
for the multi-drone system's cooperative functions. We constructed a mockup of a bridge segment. Two drones estimate their state based on onboard sensors using visual inertial odometry tracking software. The two drones are controlled by their onboard computers while receiving mission commands from the GCS. Communication between the drones is established using WiFi. Formation flying is first demonstrated with vertical movement along the bridge pillars (modeled with vertical metallic bars) followed by horizontal movement along the bridge deck (blue ropes). The second pylon structure is inspected with another vertical movement and drones are hereafter landed safely.  
It is observed that drones are moving synchronously from waypoint to waypoint. Two drones can respond to GCS mission commands in real time based on established communication. In the outdoor experiment (Figure 13(b)), the two drones are conducting a powerline inspection. The communication setup and state estimation method are the same as the indoor experiment.  
The video recording (Video S2) of the formation flying with two drones demonstrates the cooperation of a powerline inspection mission. The two drones fly synchronously along the powerline allowing inspections from different viewpoints while keeping a fixed distance.
## 3 Experiments and Results  
### Property Identification  
The base ABS plastic properties were initially determined from six tensile tests performed at a rate of 5 mm/min. The stress-strain curves from the tensile test were obtained with good repeatability as shown in Figure 7. In addition, most of the ABS material samples were subject to sudden fracture after reaching a yield point corresponding to a 426 N load, and an extension of 2.4 mm, as reported in Table 4.
## 3 Results and Discussion
### In Silico Drug-Likeness Prediction
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Compound**} \\ \cline{2-13}
**Descriptors** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline MW (g/mol) & 296.37 & 312.36 & 312.36 & 326.39 & 326.39 & 326.39 & 324.39 & 356.42 & 330.81 & 339.43 & 341.36 & 341.36 & 396.48 \\ \#1-bond acceptors & 1 & 2 & 2 & 2 & 2 & 2 & 3 & 3 & 1 & 1 & 3 & 3 & 1 \\ \#1-bond donors & 1 & 2 & 2 & 1 & 1 & 1 & 2 & 1 & 1 & 1 & 1 & 1 & 1 \\ TNA (Å\({}^{2}\)) & 28.68 & 48.91 & 48.91 & 37.91 & 37.91 & 37.91 & 58.14 & 47.14 & 28.68 & 31.92 & 74.5 & 74.5 & 28.68 \\ Consensus Log P & 4.59 & 4.26 & 4.17 & 4.56 & 4.55 & 4.52 & 4.27 & 4.54 & 5.15 & 4.6 & 4 & 3.95 & 6.36 \\ ESO Log S & \(-\)5.4 & \(-\)5.24 & \(-\)5.24 & \(-\)5.44 & \(-\)5.44 & \(-\)5.44 & \(-\)5.29 & \(-\)5.49 & \(-\)5.97 & \(-\)5.59 & \(-\)5.42 & \(-\)5.42 & \(-\)5.42 & \(-\)7.6 \\ ESO Class & MS & MS & MS & MS & MS & MS & MS & MS & MS & MS & MS & MS & MS \\ CI absorption & High & High & High & High & High & High & High & High & High & High & High & Low \\ BBB percent & Yes & Yes & Yes & Yes & Yes & Yes & Yes & No & Yes & No & No \\ P-pg substrate & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & No \\ CYPI2 inhibitor & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ CYPI5 inhibitor & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ CYPI20 inhibitor & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & No & No \\ CYP3A inhibitor & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & No & No \\ Lipinski violations & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular} MW = Molecular weight: TPSA = Topological polar surface area: Log P = Logarithm of the partition coefficient; ESO Log S = ES = ISO model logarithm of molar solubility in water; ESO, class = Solubility class in Log S scale, MS = Moderately soluble; PS = Poorly soluble; GI = Gastrointestinal; BBB = Blood-brain barrier; P:gp = Permeability glycoprotein; CYP = Cytochrome.  
\end{table} 
Table 4: Pharmacokinetics and drug-likeness calculations made by SwissADME for the synthesized compounds **1–13**.
## 2 Design of Antenna Element and Feed Networks
Furthermore, the four-way corporate feed networks for each polarization are aligned orthogonally, which is indicated in the yellow box in Figure 5. The Tx-band feed network is composed of T-junctions, microstrip-to-SICL transition, and an SICL-based multi-order quarter-wavelength impedance matcher. Similar to the Tx-band feed network, the Rx-band quarter-parallel feed network comprises T-junctions and a multi-order impedance matcher based on the MS structure. The simulated results of the impedance bandwidth are presented in Figures 6b and 7b. It can be seen that the \(\mid\)S\({}_{11}\)\(\mid\) of less than \(-20\) dB are covering the Tx band and Rx band separately, while the amplitude difference and the phase difference between the output ports are less than 0.06 dB and 0.15\({}^{\circ}\), respectively. Figure 6: Simulated S-parameters of Tx-band feed network: (**a**) Miniaturized ring coupler. (**b**) SICL-based four-way corporate feed network.