esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Furthermore, a bit-level permutation process in the confusion stage and a hyperchaos-based ciphering and hierarchical encryption process in the diffusion stage make the current system even more secure. The initial conditions for the confusion and diffusion stages are defined at the beginning of the process. This is to produce large chaos trajectories that eventually become pseudorandom values after the correct iteration of hyperchaotic systems and several chaotic maps. A subsection of the image is chosen by an appropriate automated-selection mechanism to start the encryption process. Then, the process shuffles the image at the bit level via a logistic map in the confusion stage, which not only relocates the bits within the selected subimage, but also changes the pixel values, increasing the permutation efficiency. Finally, through a combination of two hyperchaotic systems and a second logistic map, the diffusion stage becomes a multilevel encryption scheme that makes patterns much harder to identify, and vastly increases the amount of data needed to analyze in order to break the cipher image. Therefore, the system is highly secure and beneficial to many companies looking for a fast, safe, and efficient way to cipher their images, especially those which are high-resolution. The rest of this paper is organized as follows. In Section 2, the interactive encryption algorithm is described step by step. Simulation results and security analysis are discussed in Section 3, and in Section 4, the conclusions are summarized.
## 6 Statistical Analysis
### Multivariate Analysis
#### 6.2.2 Classification Models  
These models are based on the specification of defined data classes by means of a property set, with the objective of evaluating the feasibility of sample assignation to any of these data classes. Some of these models are linear discriminant analysis (LDA), artificial neural networks (ANN), partial least squares discriminant analysis (PLS-DA), and more. The main effective application of classification models is when the number of variables exceeds the number of objects (samples) [213].  
From the latter methods named above, the PLS-DA is one of the most popular in VOC profiling. This method calculates a regression model from a data matrix with instrumental measurements (e.g., chromatographic peaks VOC or concentrations) related to the sample class matrix in codified units (ones and zeros), predicting a corresponding zero or one value for each sample that is subsequently converted to a class marker using an optimized limit [213].
## 3 Simulation Results and Comparison
### Simulation of Phused Array Ultrasonic Sound Field and Detection Signal
The PA transverse wave signal of the defect simulated using the fast FEM code and commercial finite element software is shown in Figure 7. It can also be seen that the waveforms of the two signals were very similar to each other. In the finite element model, the distance between the defect and the center of the PAUT probe was 26.63 mm. Using the arrival time of the defect signals in Figure 6, the velocity of both the longitudinal wave and transverse wave simulated using the fast FEM method and commercial finite element software could be calculated and compared with the theoretical values, as shown in Table 3. It can be seen that ultrasonic wave velocity from the results of both the fast FEM code and commercial finite element software are in good agreement with the theoretical values. After verifying the accuracy of the fast finite element numerical simulation method, the rapidity of this method was verified by comparing the storage and calculation time of the fast finite element simulation program and commercial finite element software. It can be seen from Figure 8 that, under the same grid size and time step, the program based on the fast finite element numerical simulation method occupied less CPU and computer memory and had a higher computational efficiency compared with the commercial finite element software.
## 2 Results and Discussion
### Electron Microscopy  
The FE-SEM images of pure silica WMS and NR/WMS nanocomposites prepared without and with functionalization are compared in Figure 6. The addition of rubber in the NR/WMS synthesis enhanced the agglomeration of silica particles when compared to the WMS. This observation was in accord with their N\({}_{2}\) physisorption isotherms of which the pure silica WMS had a larger hysteresis loop at _P/P\({}_{0}\)_ > 0.8 than the NR/WMS (Figure 5A). From the particle size measurement using ImageJ software, the WMS and NR/WMS particles were 57-72 nm and 26-41 nm in size, respectively. The rubber phase,  
Figure 5: (**A**) N\({}_{2}\) adsorption–desorption isotherms and (**B**) BJH pore size distribution of (a) WMS, (b) WMS-0.10, (c) NR/WMS, (d) NR/WMS-0.05, (e) NR/WMS-0.10 and (f) NR/WMS-0.15.  
with its stiff nature, not only hampered the sol-gel reaction, resulting in a less condensed silicate framework, but also induced the particles' packing [13]. The WMS-NH\({}_{2}\) exhibited a larger particle size and more agglomerate than the WMS. A similar effect of surface functionalization was found for the NR/WMS nanocomposites. The particle size of the WMS-NH\({}_{2}\) was 30-50 nm, while that of the NR/WMS-NH\({}_{2}\) was 200-600 nm. The result confirmed the base-catalyzed hydrolysis and condensation of the silica precursor in the presence of aminopropylsilane.  
As shown in Figure 7, the TEM images of the pristine and amino-functionalized NR/WMS nanocomposites revealed the uniform wormhole-like mesopores conventionally observed in WMS materials [13; 16; 33; 37]. The result indicated that the mesostructured framework of the NR/WMS was preserved after the functionalization.  
Figure 6: FE-SEM images of (**A**) WMS, (**B**) WMS-0.10, (**C**) NR/WMS and (**D**) NR/WMS-0.10 at magnification of 100,000\(\times\).  
Figure 7: TEM images of (**A**) WMS, (**B**) WMS-0.10, (**C**) NR/WMS and (**D**) NR/WMS-0.10 at magnification of 100,000\(\times\).
# Blockchain-Based Secured Access Control in an IoT System
## 2 Background of Access Control System in IoT  
### Access Control in IoT  
Access Control (AC) involves the authentication and authorization of communication rights and resource access with respect to defined security guidelines and policies [4]. AC refers to the granting of permission to authenticated entities so that they can access the resources under predefined conditions.  
Applying an optimal access control model to billions of IoT devices is a challenging task. Although authentication and authorization issues have been extensively studied in the literature, these issues in the IoT environment are still in their early stages. Access control List (ACL), Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) are some of the most widely used access control mechanisms in IT infrastructure but are not fully appropriate for providing scalable, efficient and easily manageable applicability in an IoT environment [1].  
In ACL, access control strategies are implemented in the cloud with better administration and tracking of activities but are constrained by a centralized infrastructure. As the number of IoT devices increases, the complexity of access rules also increases, resulting in confused duty problems. ACL lacks granularity and scalability and is very much prone to a single point of failure due to its centralized infrastructure [5].  
The RBAC model provides a resource access authorization mechanism to users based on roles and defined principles, such as priorities, the separation of duties and administrative function partitioning, in order to define access policies for smart devices by capitalizing on the Web of Things (WoTs) approach [6]. These solutions do not fully meet the requirements of access control mechanisms and inter-device communication in a highly distributed network environment. The drawbacks associated with conventional access control models make them unsuitable for an IoT environment, which leads to the use of Capability-Based Access Control (CapAC) systems. To overcome the role explosion problem in RBAC, the ABAC model directly associates attributes with subjects. Access rights are granted based on the user attribute certificate [5]. As the number of IoT devices increases, the ABAC model's complexity increases and policy management becomes a critical issue [2].  
A capability model is implemented for large-scale IoT-based systems. In this model, the capability list is associated with each subject, which defines its access rights to the target objects [7]. Despite its large-scale successful deployment, CapAC introduces several challenges in relation to the propagation and revocation of access rights. Table 1 shows a comparative list of access control limitations in the IoT environment.
# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
## 5 Conclusions  
Water scarcity is a major problem in the 21 st century. The Internet of Things followed by smart A.I. irrigation algorithms can lead to adaptive precision farming, improving crop yield, reducing water consumption, and further increasing crop productivity and product quality.  
Authors designed and implemented an Open Watering System (OWS) architecture that can communicate and exchange sensory data information with an Application Server connected to the Internet. Focusing on capabilities of existing IoT communication protocols, such as: IEEE 802.15.4, BLE, LoRa, and 3G/4G, the authors proposed their OWS IoT irrigation system infrastructure over LoRa radio. This LoRa radio implementation is targeted on low-cost and low-node energy consumption. The authors also proposed an application protocol called the FITRA application protocol, which can be used both over LoRa OWS and 3G/4G OWS systems and described its states and functionality.  
The authors propose a new fuzzy neural network transition algorithm called FITRA that controls state of the irrigation system actuator. The authors measured the performance of their proposedalgorithm towards MVA algorithm on two nearby tomato crop fields. From the experimental results it is easily proven that the FITRA protocol prevails over the majority MVA algorithm. The FITRA algorithm manages to save 7% of water consumption while maintaining 12.5% more ground moisture and producing 25% more monthly crop yield.  
Furthermore, experiments proposed by the author's FITRA application protocol for the communication between the OWS system and the Application server have shown that LoRa OWS can send the same amount of data up to 1 km away with 80% less node energy consumption thana node that uses a 3G network transponder. For future work, the authors plan to compare the FITRA algorithm with other known irrigation systems and algorithms.  
All authors equally conceived and designed the experiments, performed the experiments, analyzed the data, and wrote the paper.  
The authors declare no conflict of interest.
# Pruning Optimization over Threshold-Based Historical Continuous Query
## 1 Introduction
THC queries can be processed by the classic \"pruning-refinement\" method [4]. In the pruning step, a certain pruning strategy is used to roughly check whether the trajectory data in the moving object database satisfies the constraint condition, and the trajectory data that does not match the condition is eliminated, and finally, a candidate set of trajectory data is obtained. The entire pruning process is usually performed by traversing a spatiotemporal index. In the refinement step, the candidate set is further filtered by the distance calculation, and the results that finally satisfy the query condition are returned to the user. In the above process, the pruning step has a decisive influence on the overall query performance. This is because, first, the time overhead of pruning is an important part of the overall query time. If the pruning strategy can be optimized, and the processing time can be reduced, the overall query performance will undoubtedly be improved effectively. Second, the candidate set of pruning directly affects the performance of refinement. The closer the candidate trajectory data generated by pruning is to the final result, the less the number of trajectory distance calculations required in the refinement step, and the I/O times and the calculation time required for the query can be reduced effectively. In view of this, in order to achieve efficient query performance, we optimized the pruning step of THC  
Figure 1: Example of threshold-based historical continuous (THC) query. queries. First, the pruning strategy based on extended Minimum Bounding Rectangle (MBR) overlap is proposed to reduce the processing overhead of a single pruning check. Secondly, we designed an extended 3 DR-tree [5] structure called E3 DR-tree to the index query object, and based on this, we implemented a best-first traversal algorithm, so that an accurate pruning candidate set was obtained with accessing as few nodes as possible in the traversal of the indexes. Our main contributions can be summarized as follows:  
* We propose a pruning strategy based on extended MBR overlap to optimize the single pruning check, thereby effectively reducing the processing time. * We introduce a best-first traversal algorithm based on E3 DR tree, to obtain a precise pruning candidate set for refinement processing under the premise of effectively controlling the number of accessed nodes when traversing the indexes. * We evaluate our method with the dataset of ship trajectory, the experimental results verify the efficiency. Section 2 introduces the related work. The background is introduced in Section 3. The method of pruning optimization is described in Section 4. Section 5 provides an experimental study of our method. Finally, we give a brief conclusion in Section 6.
# Monolithic 3D Inverter with Interface Charge: Parameter Extraction and Circuit Simulation
###### Abstract  
We have simulated a monolithic three-dimensional inverter (M3 DINV) structure by considering the interfacial trap charges generated thermally during the monolithic three-dimensional integration process. We extracted the SPICE model parameters from M3 DINV structures with two types of inter-layer dielectric thickness \(T_{\text{ILD}}\) (=10,100 nm) using the extracted interface trap charge distribution of the previous study. Logic circuits, such as inverters (INVs), ring oscillators (ROs), a 2 to 1 multiplexer (MUX), and D flip-flop and 6-transistor static random-access memory (6T SRAM) containing M3 DINVs, were simulated using the extracted model parameters, and simulation results both with and without interface trap charges were compared. The extracted model parameters reflected current reduction, threshold voltage increase, and subthreshold swing (SS) degradation due to the interface trap charge. HSPICE simulation results of the fanout-3 (FO3) ring oscillator considering the interface trap charges showed a 20% reduction in frequency and a 30% increase in propagation delay compared to those without the interface trap charges. The propagation delays of the 2 \(\times\) 1 MUX and D flip-flop with the interface trap charges were approximately 78.2 and 39.6% greater, respectively, than those without the interface trap charges. The retention static noise margin (SNM) of the SRAM increased by 16 mV (6.4%) and the read static noise margin (SNM) of SRAM decreased by 43 mV (35.8%) owing to the interface trap charge. The circuit simulation results revealed that the propagation delay increases owing to the interface trap charges. Therefore, it is necessary to fully consider the propagation delay of the logic circuit due to the generated interface trap charges when designing monolithic 3D integrated circuits.  
monolithic 3D integration; inverter; interface trap charge; parameter extraction +
Footnote †: journal: Applied Sciences
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
###### Abstract  
Coastline changes have significant impacts on coastal hydrodynamics. Xiangshan Bay is a semi-closed and long-narrow bay located in Zhejiang Province, China. Its coastline has changed dramatically in recent decades; however, the variations in the small-scale hydrodynamics in the changed coastline areas have not been carefully studied. This study uses the Finite-Volume Community Ocean Model (FVCOM) to design a set of control experiments and five sets of compared experiments targeting the areas with significant coastline changes in Xiangshan Bay over the past 21 years. It was found that the coastline changes at the mouth of the bay, such as areas near Meishan Island and Dasong, have a significant impact on both residual currents and tidal currents, changing the amplitudes and phase distributions of the tides. Coastline changes in the inner bay have lesser impacts on hydrodynamics, mainly affecting the small-scale areas in the vicinity.  
coastline change; hydrodynamic; Xiangshan Bay; FVCOM 2023 13 8071 11 July 2 and a reduction in suspended sediment concentration in Wenzhou Coast, China [13]. Understanding the complex interactions between coastline changes and hydrodynamics is crucial for effective coastal management and conservation efforts.  
The accumulated influences of the coastline changes in Xiangshan Bay have been considered [14]. However, the variations in small-scale hydrodynamics for each area have not been carefully studied, which have a more direct impact on local sediment erosion and deposition as well as the ecological environment.  
In this study, we used the Finite-Volume Community Ocean Model (FVCOM) [15] to set up a group of control experiments and five groups of comparative experiments corresponding to five areas with drastic changes in the coastline of Xiangshan Bay. We built a three-dimensional hydrodynamic model for Xiangshan Bay before and after the changes and examined the effects of coastline changes on the nearby waters' hydrodynamic environment. The materials and methods are described in Section 2. The results are analyzed in Section 3, with conclusions presented in Section 4.
# Reliability-Based Optimization of Impeller Structure Using Exponential Function Approximation
## 4 Reliability-Based Optimization of an Impeller Structure
### Centrifugal Compressor Impeller Structure
#### 4.1.2 Structural Analysis Methodology  
The impeller is a critical component that directly affects the efficiency and stability of rotating machinery such as fans and pumps. Therefore, this study used the proposed  
Figure 3: Flowchart of the proposed decoupling reliability-based optimization algorithm.  
iterative decoupling reliability-based optimization method based on first-order exponential approximation to design a centrifugal compressor impeller blade.  
First, a 3D model of the considered centrifugal compressor impeller was completed using SOLIDWORKS, as shown in Figure 3. The process for analyzing the rotor blade structure without considering the coupling of the fluid-structure interaction is simple. First, the pressure distribution on the blade surface was solved within the fluid domain using computational fluid dynamics. Next, the 3D model was imported into ANSYS 2024 R1 for meshing using a hexahedral-dominated hybrid meshing scheme with local refinements on the blade surfaces and near stress concentrations. A comparative analysis of incrementally denser meshes indicated that the relative change in the maximum stress was less than 1% once the number of mesh elements exceeded 150,000, satisfying the mesh independence requirement. In the structural analysis of the impeller, the boundary conditions are set as follows: A fixed support constraint is applied to the bottom end face of the impeller to simulate its connection to the shaft; a rotational speed of \(-3000\) r/min is applied to the entire structural domain, and the influence of the Earth's gravitational field (9.81 m/s\({}^{2}\)) on the structure is considered. Meanwhile, the pressure loads obtained from the CFD analysis of the fluid domain are mapped onto the blade surfaces to simulate the fluid-structure interaction effects. The
## 6 Results and Discussions
However, the eye diagrams of the sensors have wide openings that result in easier discrimination between the zeros and ones at the receiver. To observe the performance of the sensors for different ranges of the FSO channel while considering different weather conditions, the length of the FSO channel was gradually increased in order to observe its effect on the Q-factor. Figure 5 shows the plots for the FSO range versus the Q-factor for sensor 1 and sensor 5 with different values of atmospheric attenuation. It is clear that a maximum range of around 2.75 km is achieved for sensor 1 and sensor 5 at \(\alpha_{atm}\) = 2 dB/km, while the minimum range of around 1 km is achieved for sensor 1 and sensor 5 at \(\alpha_{atm}\) = 21 dB/km, corresponding to a Q-factor of 1. Moreover, it can be observed that the value of the Q-factor decreases on increasing the FSO range for all values of atmospheric attenuation. To show the effect of variation in the receiver telescope's aperture diameter and beam divergence over the BER and Q-factor, the aperture diameter and beam divergence were gradually increased in order to observe their effects on the BER and Q-factor. Figure 6 shows plots of the BER and Q-factor versus the receiver aperture diameter for sensor 1 and sensor 5 obtained at \(\alpha_{atm}\) = 21 dB/km; the values of \(C_{n}^{2}\) and the beam divergence are \(5\times 10^{-16}\) m\({}^{-2/3}\) and 2 mrad, respectively. It is clearly evident that the BER decreases while the Q-factor increases on increasing the receiver aperture diameter. Similarly, Figure 7 shows plots of the BER and Q-factor versus the beam divergence for sensor 1 and sensor 5 obtained at \(\alpha_{atm}\) = 21 dB/km; the values of \(C_{n}^{2}\) and the receiver aperture diameter were \(5\times 10^{-16}\) m\({}^{-2/3}\) and 20 cm, respectively. It is clearly evident that the BER increases and the Q-factor decreases when increasing the beam divergence. Figure 4: Normalized eye diagrams of sensors for different values of atmospheric attenuation. (**a**,**b**) Sensor-1 and sensor 5 at \(\alpha_{atm}=2\) dB/km. (**c**,**d**) Sensor 1 and sensor 5 at \(\alpha_{atm}=11\) dB/km. (**e**,**f**) Sensor 1 and sensor 5 at \(\alpha_{atm}=21\) dB/km. Figure 5: Q-factor versus FSO range plots for different values of atmospheric attenuation: (**a**) sensor 1 and (**b**) sensor 5. A value of the \(C_{n}^{2}\) parameter equal to 5 \(\times\) 10\({}^{-16}\) m\({}^{-2/3}\) was used.